ytzi/the-stack-dedup-python-filtered-all · Datasets at Hugging Face

5108e36e1b24e0722a49cfe5996dd84987d18722

1,337

py

Python

ex105.py

ArthurCorrea/python-exercises

0c2ac46b8c40dd9868b132e847cfa42e025095e3

[ "MIT" ]

null

ex105.py

ArthurCorrea/python-exercises

0c2ac46b8c40dd9868b132e847cfa42e025095e3

[ "MIT" ]

null

ex105.py

ArthurCorrea/python-exercises

0c2ac46b8c40dd9868b132e847cfa42e025095e3

[ "MIT" ]

null

# Faa um programa que tenha um funo notas() que pode receber vrias # notas de alunos e vai retornar um dicionrio com as seguintes informaes: # - Quantidade de notas; # - A maior nota; # - A menor nota; # - A mdia da turma; # - A situao(opcional); # Adicione tambm as docstrings da funo. def notas(show=False): """ :param show: mostra a situao da turma de acordo com o escolhido: True ou False :return: sem retorno """ somanotas = 0 d = dict() lista = list() qtdvalores = 0 while True: n1 = float(input(f'Nota do aluno {qtdvalores}: ')) somanotas += n1 lista.append(n1) qtdvalores += 1 d['Qtd notas'] = qtdvalores resp = str(input('Quer continuar: [S/N] ')).upper().strip()[0] while resp != 'S' and resp != 'N': resp = str(input('Quer continuar? [S/N] ')).upper().strip()[0] if resp == 'N': break d['Maior nota'] = max(lista) d['Menor nota'] = min(lista) d['Mdia da turma'] = somanotas / qtdvalores if show: if d['Mdia da turma'] < 5: d['Situao'] = 'Ruim' elif 5 <= d['Mdia da turma'] < 7: d['Situao'] = 'Razovel' else: d['Situao'] = 'Boa' print(d) else: print(d) notas() notas(show=True)

27.854167

84

0.554226

# Faça um programa que tenha um função notas() que pode receber várias # notas de alunos e vai retornar um dicionário com as seguintes informações: # - Quantidade de notas; # - A maior nota; # - A menor nota; # - A média da turma; # - A situação(opcional); # Adicione também as docstrings da função. def notas(show=False): """ :param show: mostra a situação da turma de acordo com o escolhido: True ou False :return: sem retorno """ somanotas = 0 d = dict() lista = list() qtdvalores = 0 while True: n1 = float(input(f'Nota do aluno {qtdvalores}: ')) somanotas += n1 lista.append(n1) qtdvalores += 1 d['Qtd notas'] = qtdvalores resp = str(input('Quer continuar: [S/N] ')).upper().strip()[0] while resp != 'S' and resp != 'N': resp = str(input('Quer continuar? [S/N] ')).upper().strip()[0] if resp == 'N': break d['Maior nota'] = max(lista) d['Menor nota'] = min(lista) d['Média da turma'] = somanotas / qtdvalores if show: if d['Média da turma'] < 5: d['Situação'] = 'Ruim' elif 5 <= d['Média da turma'] < 7: d['Situação'] = 'Razoável' else: d['Situação'] = 'Boa' print(d) else: print(d) notas() notas(show=True)

50

0

78baa060484aee5b5791c697ecb50180a17dcae1

15,613

py

Python

core/models/group.py

agolibroda/PyTorWiki

678a2ae13d0027c61af36e61b72e4e54493a29ac

[ "Apache-2.0" ]

null

core/models/group.py

agolibroda/PyTorWiki

678a2ae13d0027c61af36e61b72e4e54493a29ac

[ "Apache-2.0" ]

null

core/models/group.py

agolibroda/PyTorWiki

678a2ae13d0027c61af36e61b72e4e54493a29ac

[ "Apache-2.0" ]

null

#!/usr/bin/env python3.6 # -*- coding: utf-8 -*- # # Copyright 2016 Alec Goliboda # # group.py from __future__ import print_function # import markdown # import pymysql # from _overlapped import NULL ############## from .. import WikiException # from core.models.template import Template

40.343669

193

0.547172

#!/usr/bin/env python3.6 # -*- coding: utf-8 -*- # # Copyright 2016 Alec Goliboda # # group.py from __future__ import print_function import logging import json import zlib # import markdown from datetime import datetime import tornado.options # import pymysql import hashlib import bcrypt import base64 # from _overlapped import NULL ############## import config from . import Model, CipherWrapper from .. import WikiException from core.models.author import Author from ..constants.data_base import * # from core.models.template import Template from ..constants.data_base import * class Group(Model): """ модель - для Группы внутри будут: - список участников - библиотека Просмотр: - список всех групп - одну группу Описание - список участников группы - список статей (библиотека) - создавать группы - "удалять группы" - о... нужен флаг - "группа удалена"??? - добавлять (удаять) участников в группу - по приглашению - нужен список приглашений - соотв, у каждого автора может быть список приглашений "вступить в группу" - нужен список заявок на вступление - это инструмент админа группы "список заявок на вступление" - добавлять (удалять) статьи в библиотеку - статья моет ажодится в библиотеке и иметь флаг "pbl" - для всеобщего доступа "grp" - только для группы, такие стаьи будут ЗАКРЫТЫМИ!!!! Видимость групп (group_status) - публичная - 'pbl' - любой посетитель может читать публичные материалы группы - закрытая - 'shut' ??? - что - то я пока не знаю.... может, в закрытых группах не может быть "публичных статей"?? Процедура создания новой группы: При создании новой группы, Создатель группы становится ее Администратором. Запись о создании группы размещается в таблицах "dt_headers" и "groups" Запись о вступлении в группу Администратора добавляется в таблицу "members"; Процедура работы с Ключами: Создается уникальная пара RSA-ключей, Публичный ключ помещается в заголовок группы, персональный - размещается в списке "members", Приватный ключ группы закрывается Публичным ключем Создателя группы, и добавляется в соответствующее поле таблицы "members" Когда Участник Группы открывает страницу группы (переходит на рабочий стол группы) в профиль Участника добавляется значение его копии приватного ключа группы; После этого пользователь сможет читать и редактировать все статьи из групповой библиотеки, имеющие флаг "grp" """ def __init__(self, group_title = '', group_annotation = '', group_status = 'pbl'): Model.__init__(self) self.dt_header_id = 0 # self.author_id = 0 self.group_title = group_title self.group_annotation = group_annotation self.group_status = group_status self.public_key = '' self.private_key = '' self.private_key_hash = '' # self.group_create_date = datetime.now() self.setDataStruct(Model.TableDef( tabName='groups', idFieldName=None, mainPrimaryList =['dt_header_id'], listAttrNames=['dt_header_id', 'group_title', 'group_annotation', 'group_status'])) self.setHeadStruct(Model.TableDef( tabName='dt_headers', idFieldName='dt_header_id', mainPrimaryList =['dt_header_id'], listAttrNames=['dt_header_type', 'public_key'])) class Member(Model): def __init__(self): Model.__init__(self) self.group_id = 0 self.author_id = 0 self.member_role_type = 'M' self.setDataStruct(Model.TableDef( tabName='members', idFieldName=None, mainPrimaryList =None, listAttrNames=['group_id', 'author_id', 'member_role_type', 'private_key'])) def save(self, authorId ): operationFlag = 'I' revisions_sha_hash_sou = str(self.group_id) + str(self.author_id) + self.member_role_type logging.info(' Member save:: self = ' + str(self)) Model.save(self, authorId, operationFlag, revisions_sha_hash_sou) def getGroupMembersleList(self, groupId): """ Получить список всех соучастников одной группы """ getRez = self.select( 'dt_headers.dt_header_id, author_name, author_surname, author_role, author_phon, author_email, author_create, dt_headers.public_key ', 'authors, dt_headers', { 'whereStr': " members.group_id = authors.dt_header_id AND dt_headers.dt_header_id = authors.dt_header_id AND " +\ " members.actual_flag = 'A' AND authors.actual_flag = 'A' AND " " members.group_id = " + str(groupId) , # строка набор условий для выбора строк 'orderStr': ' author_name, author_surname ', # строка порядок строк } ) # 'whereStr': " groups.author_id = authors.author_id AND groups.group_id = " + str(group_id) # logging.info( 'getGroupMembersleList:: getRez = ' + str(getRez)) if len(getRez) == 0: # raise WikiException( ARTICLE_NOT_FOUND ) return [] authorList = [] author = Author() for autorStruct in getRez: authorList.append(author.parsingAuthor(self, autorStruct)) return authorList class Library(Model): def __init__(self, groupId = 0, articleId=0, libraryPermissionType = 'W' ): Model.__init__(self) self.group_id = groupId self.article_id = articleId self.library_permission_type = libraryPermissionType self.setDataStruct(Model.TableDef( tabName='librarys', idFieldName=None, mainPrimaryList =['group_id','article_id' ], listAttrNames=['group_id', 'author_id', 'library_permission_type'])) def save(self, autorId): operationFlag = 'I' revisionsShaHashSou = str(self.group_id) + str(self.article_id) + self.library_permission_type # logging.info(' Library save:: self = ' + str(self)) Model.save(self, autorId, operationFlag, revisionsShaHashSou) # self.dt_header_id = Model.save(self, self.dt_header_id, operationFlag, sha_hash_sou) def getGroupArticleList(self, groupId): """ Получить список всех статей одной группы """ getRez = self.select( ' articles.article_id, articles.article_title, articles.article_link, ' + ' articles.article_annotation, articles.article_category_id, ' + ' articles.article_template_id, ' + ' null AS group_title, null AS group_annotation, librarys AS group_id, librarys.library_permission_type ', 'articles', { 'whereStr': " librarys.article_id = articles.article_id AND " +\ " articles.actual_flag = 'A' AND librarys.actual_flag = 'A' AND " +\ " librarys.group_id = " + str(groupId) , # строка набор условий для выбора строк 'orderStr': ' articles.article_id ', # строка порядок строк } ) # 'whereStr': " groups.dt_header_id = authors.dt_header_id AND groups.group_id = " + str(group_id) # for item in getRez: # logging.info( 'getGroupArticleList:: getRez = ' + str(item)) if len(getRez) == 0: # raise WikiException( ARTICLE_NOT_FOUND ) return [] return getRez def get(self, groupId): """ загрузить ОДНО значение - по ИД группы """ resList = self.select( 'dt_headers.dt_header_id, group_title, group_annotation ' , # строка - чего хотим получить из селекта 'dt_headers', #'authors', # строка - список таблиц { 'whereStr': " groups.actual_flag = 'A' AND groups.dt_header_id = dt_headers.dt_header_id AND dt_headers.dt_header_id = " + str(groupId) } # все остальные секции селекта ) # for item in resList: # logging.info('Author:: get:: resList = ' + str(item)) if len(resList) == 1: # return resList[0] objValuesNameList = list(resList[0].__dict__.keys()) for objValue in objValuesNameList: if objValue.find('_') != 0: self.__setattr__(objValue,resList[0].__getattribute__(objValue) ) return self else: raise WikiException(LOAD_ONE_VALUE_ERROR) def list(self): """ загрузить список всех групп """ resList = self.select( 'dt_headers.dt_header_id, group_title, group_annotation, group_status ' , # строка - чего хотим получить из селекта 'dt_headers', #'authors', # строка - список таблиц { 'whereStr': " groups.actual_flag = 'A' AND groups.dt_header_id = dt_headers.dt_header_id " } # все остальные секции селекта ) # logging.info('Author:: get:: resList = ') # logging.info(resList) return resList def grouplistForAutor(self, authorId): """ Получить список групп для одного автора - все руппы, которые АВТОР создал, и в которых АВТОР является участником вот тут возможно, надо будет все поправить - и показывать только ПАБЛИК группы, и/или приватные группы, в которых участвуют оба - и зритель, и автор """ try: resList = self.select( ' DISTINCT dt_headers.dt_header_id, groups.group_title, groups.group_annotation, groups.group_status, ' + ' members.member_role_type ' , # строка - чего хотим получить из селекта ' members, dt_headers ', #'authors', # строка - список таблиц { 'whereStr': " groups.actual_flag = 'A' AND groups.dt_header_id = dt_headers.dt_header_id AND " + " members.author_id = " + str(authorId) + " AND members.group_id = groups.dt_header_id ", 'orderStr': ' groups.group_title ' } # все остальные секции селекта ) # logging.info( 'grouplistForAutor:: resList = ' + str(resList)) return resList except Exception as e: # except WikiException as e: # WikiException( ARTICLE_NOT_FOUND ) logging.info( 'grouplistForAutor::Have ERROR!!! ' + str(e)) if not article: raise tornado.web.HTTPError(404) else: return (article, []) def getGroupArticleList(self, groupId): """ Получить список всех статей одной группы """ libControl = self.Library () return libControl.getGroupArticleList( groupId) def getGroupMembersleList(self, groupId): """ Получить список всех Участников одной группы """ memberControl = self.Member () return memberControl.getGroupMembersleList( groupId) def save(self, authorId ): """ сохранить группу, пользователя, который создал группу надо воткнуть не только в авторы группы, но, и в "members" да еще и АДМИНОМ!!! """ bbsalt = config.options.salt.encode() cip = CipherWrapper() logging.info(' save:: before SAVE = ' + str(self)) if self.dt_header_id == 0: # self.group_create_date = datetime.now() operationFlag = 'I' autotControl = Author() creator = autotControl.get(authorId) cip.rsaInit() # сделать пару ключей self.public_key = cip.rsaPubSerialiation(cip.getPublicKey()) pKey = cip.getPrivateKey() # поучить незакрытый приватный ключ # self.private_key_hash = bcrypt.hashpw(cip.rsaPrivateSerialiation(pKey), bbsalt).decode('utf-8') # получим ХЕш приватного ключа - для последуюей проверки при восстановлении пароля # logging.info(' save:: before SAVE creator.publicKey() = ' + str(creator.publicKey())) pkTmp = cip.rsaEncrypt(creator.publicKey(), cip.rsaPrivateSerialiation(pKey)) # logging.info(' save:: before SAVE pkTmp = ' + str(pkTmp)) self.private_key = pkTmp else: operationFlag = 'U' self.begin() revisions_sha_hash_sou = str(self.group_title) + str(self.group_annotation) + str(self.group_status) # self.dt_header_id = Model.save(self, authorId, operationFlag, revisions_sha_hash_sou ) # теперь сохранить автора группы как ее админа. # logging.info(' SAVE:: GROUPPPPP authorId = ' + str(authorId)) # logging.info(' SAVE:: GROUPPPPP 2 = ' + str(self)) if operationFlag == 'I': memberControl = self.Member() memberControl.author_id = authorId memberControl.group_id = self.dt_header_id memberControl.member_role_type = 'A' memberControl.private_key = self.private_key # bbWrk = (bytePass+bbsalt)[0:32] # cipher_aes = AES.new(bbWrk, AES.MODE_EAX) # закроем приватный ключ на пароль пользователя. # ciphertext = cipher_aes.encrypt(pKey) # self.private_key = pickle.dumps({'cipherKey': ciphertext, 'nonce': cipher_aes.nonce}) memberControl.save(authorId) self.commit() return True def librarySave(self, authorId = 0, groupId = 0, article_id=0, library_permission_type = 'W'): """ Добавить статью к группе """ libControl = self.Library(groupId, authorId, library_permission_type) libControl.save(authorId)

4,704

0

12,575

0

14

341

70b6c80341def36320aeb56eea498bea8fda840e

4,327

py

Python

spug_api/libs/parser.py

atompi/spug

88ebd46e47c88731b40cb82a6c7a360511b703fa

[ "MIT" ]

null

spug_api/libs/parser.py

atompi/spug

88ebd46e47c88731b40cb82a6c7a360511b703fa

[ "MIT" ]

null

spug_api/libs/parser.py

atompi/spug

88ebd46e47c88731b40cb82a6c7a360511b703fa

[ "MIT" ]

null

# Copyright: (c) OpenSpug Organization. https://github.com/openspug/spug # Copyright: (c) <[email protected]> # Released under the AGPL-3.0 License. # # # # Json

33.030534

105

0.554657

# Copyright: (c) OpenSpug Organization. https://github.com/openspug/spug # Copyright: (c) <[email protected]> # Released under the AGPL-3.0 License. import json from .utils import AttrDict # 自定义的解析异常 class ParseError(BaseException): def __init__(self, message): self.message = message # 需要校验的参数对象 class Argument(object): """ :param name: name of option :param default: default value if the argument if absent :param bool required: is required """ def __init__(self, name, default=None, handler=None, required=True, type=str, filter=None, help=None, nullable=False): self.name = name self.default = default self.type = type self.required = required self.nullable = nullable self.filter = filter self.help = help self.handler = handler if not isinstance(self.name, str): raise TypeError('Argument name must be string') if filter and not callable(self.filter): raise TypeError('Argument filter is not callable') def parse(self, has_key, value): if not has_key: if self.required and self.default is None: raise ParseError( self.help or 'Required Error: %s is required' % self.name) else: return self.default elif value in [u'', '', None]: if self.default is not None: return self.default elif not self.nullable and self.required: raise ParseError( self.help or 'Value Error: %s must not be null' % self.name) else: return None try: if self.type: if self.type in (list, dict) and isinstance(value, str): value = json.loads(value) assert isinstance(value, self.type) elif self.type == bool and isinstance(value, str): assert value.lower() in ['true', 'false'] value = value.lower() == 'true' elif not isinstance(value, self.type): value = self.type(value) except (TypeError, ValueError, AssertionError): raise ParseError(self.help or 'Type Error: %s type must be %s' % ( self.name, self.type)) if self.filter: if not self.filter(value): raise ParseError( self.help or 'Value Error: %s filter check failed' % self.name) if self.handler: value = self.handler(value) return value # 解析器基类 class BaseParser(object): def __init__(self, *args): self.args = [] for e in args: if isinstance(e, str): e = Argument(e) elif not isinstance(e, Argument): raise TypeError('%r is not instance of Argument' % e) self.args.append(e) def _get(self, key): raise NotImplementedError def _init(self, data): raise NotImplementedError def add_argument(self, **kwargs): self.args.append(Argument(**kwargs)) def parse(self, data=None, clear=False): rst = AttrDict() try: self._init(data) for e in self.args: has_key, value = self._get(e.name) if clear and has_key is False and e.required is False: continue rst[e.name] = e.parse(has_key, value) except ParseError as err: return None, err.message return rst, None # Json解析器 class JsonParser(BaseParser): def __init__(self, *args): self.__data = None super(JsonParser, self).__init__(*args) def _get(self, key): return key in self.__data, self.__data.get(key) def _init(self, data): try: if isinstance(data, (str, bytes)): data = data.decode('utf-8') self.__data = json.loads(data) if data else {} else: assert hasattr(data, '__contains__') assert hasattr(data, 'get') assert callable(data.get) self.__data = data except (ValueError, AssertionError): raise ParseError('Invalid data type for parse')

75

0

3,999

0

-4

133

ca3c214980bb966e02bee0584e6a700a068fc2b7

3,954

py

Python

mBugTranslations/Chrome.py

SkyLined/mBugId

781bfe9a120e55630a91ce1e86b39ad0dee031ec

[ "CC-BY-4.0" ]

22

2016-08-11T14:50:55.000Z

2021-06-06T09:39:26.000Z

mBugTranslations/Chrome.py

SkyLined/mBugId

781bfe9a120e55630a91ce1e86b39ad0dee031ec

[ "CC-BY-4.0" ]

19

2016-09-07T05:54:40.000Z

2020-07-02T07:46:38.000Z

mBugTranslations/Chrome.py

SkyLined/mBugId

781bfe9a120e55630a91ce1e86b39ad0dee031ec

[ "CC-BY-4.0" ]

11

2016-09-03T22:42:50.000Z

2018-10-01T18:28:59.000Z

from .cBugTranslation import cBugTranslation; aoBugTranslations = [ # ASan build related -> Ignored cBugTranslation( azs0rbAdditionalIrrelevantStackFrameSymbols = [ rb".*!`anonymous namespace'::Create", # Part of skia rb".*!base::debug::BreakDebugger", rb".*!base::debug::CollectGDIUsageAndDie", rb".*!blink::ReportFatalErrorInMainThread", rb".*!blink::V8ScriptRunner::CallExtraOrCrash(<.+>)?", rb".*!crash_reporter::internal::CrashForExceptionInNonABICompliantCodeRange", rb".*!CrashForException_ExportThunk", rb".*!crashpad::`anonymous namespace'::UnhandledExceptionHandler", rb".*!crashpad::CrashpadClient::DumpAndCrash", rb".*!raise", rb".*!sk_abort_no_print", rb".*!SkMallocPixelRef::MakeUsing", rb".*!v8::Utils::ApiCheck", rb".*!WTF::Deque<.+>::ExpandCapacity(IfNeeded)", rb".*!WTF::Deque<.+>::push_back", ], ), # Breakpoint -> Ignored cBugTranslation( srzOriginalBugTypeId = r"Breakpoint", azs0rbAppliesOnlyToTopStackFrame = [ rb".*!__sanitizer_cov", ], s0zTranslatedBugTypeId = None, # This is apparently triggered by ASAN builds to determine EIP/RIP. s0zTranslatedBugDescription = None, s0zTranslatedSecurityImpact = None, ), # Breakpoint -> OOM cBugTranslation( srzOriginalBugTypeId = r"Breakpoint", azs0rbAppliesOnlyToTopStackFrame = [ rb".*!base::`anonymous namespace'::OnNoMemory", rb".*!base::internal::SchedulerWorkerPoolImpl::Start", # CHECK() on thread start rb".*!base::PartitionRecommitSystemPages", rb".*!blink::MemoryRegion::Commit", rb".*!content::`anonymous namespace'::CrashOnMapFailure", rb".*!skia::CreateHBitmap", rb".*!ui::ClientGpuMemoryBufferManager::ClientGpuMemoryBufferManager", # std::vector throws breakpoint ], s0zTranslatedBugTypeId = "OOM", s0zTranslatedBugDescription = "The application triggered a breakpoint to indicate it was unable to allocate enough memory.", s0zTranslatedSecurityImpact = None, ), # Breakpoint -> Assert cBugTranslation( srzOriginalBugTypeId = r"Breakpoint", azs0rbAppliesOnlyToTopStackFrame = [ rb".*!blink::reportFatalErrorInMainThread", rb".*!v8::Utils::ReportApiFailure", rb".*!logging::LogMessage::~LogMessage", ], s0zTranslatedBugTypeId = "Assert", s0zTranslatedBugDescription = "The application triggered an exception to indicate an assertion failed.", s0zTranslatedSecurityImpact = None, ), # AVW@NULL -> Assert cBugTranslation( srzOriginalBugTypeId = r"AVW@NULL", azs0rbAppliesOnlyToTopStackFrame = [ rb".*!base::win::`anonymous namespace'::ForceCrashOnSigAbort", ], s0zTranslatedBugTypeId = "Assert", s0zTranslatedBugDescription = "The application triggered a NULL pointer access violation to indicate an assertion failed.", s0zTranslatedSecurityImpact = None, ), # Various -> OOM cBugTranslation( srzOriginalBugTypeId = r"0xE0000008|Assert|AVW@NULL", # 0xE0000008 (win::kOomExceptionCode) -> OOM azs0rbAppliesOnlyToTopStackFrame = [ rb".*!base::`anonymous namespace'::OnNoMemory", rb".*!(?:base|WTF)::[Pp]artitions?(?:ExcessiveAllocationSize|OutOfMemory(Using\w+)?)", rb".*!blink::(?:BlinkGCOutOfMemory|ReportOOMErrorInMainThread)", rb".*!FX_OutOfMemoryTerminate", rb".*!SkBitmap::allocPixels", ], s0zTranslatedBugTypeId = "OOM", s0zTranslatedBugDescription = "The application caused an access violation by writing to NULL to indicate it was unable to allocate enough memory.", s0zTranslatedSecurityImpact = None, ), # OOM -> hide irrelevant frames cBugTranslation( srzOriginalBugTypeId = r"OOM", azs0rbAdditionalIrrelevantStackFrameSymbols = [ rb".+!(.+::)?(Win)?CallNewHandler", rb".+!(.+::)?\w+_malloc(_\w+)?", rb".+!(.+::)?\w*(Alloc|alloc|OutOfMemory)\w*(<.+>)?", ], ), ];

40.762887

151

0.686899

import re; from .cBugTranslation import cBugTranslation; aoBugTranslations = [ # ASan build related -> Ignored cBugTranslation( azs0rbAdditionalIrrelevantStackFrameSymbols = [ rb".*!`anonymous namespace'::Create", # Part of skia rb".*!base::debug::BreakDebugger", rb".*!base::debug::CollectGDIUsageAndDie", rb".*!blink::ReportFatalErrorInMainThread", rb".*!blink::V8ScriptRunner::CallExtraOrCrash(<.+>)?", rb".*!crash_reporter::internal::CrashForExceptionInNonABICompliantCodeRange", rb".*!CrashForException_ExportThunk", rb".*!crashpad::`anonymous namespace'::UnhandledExceptionHandler", rb".*!crashpad::CrashpadClient::DumpAndCrash", rb".*!raise", rb".*!sk_abort_no_print", rb".*!SkMallocPixelRef::MakeUsing", rb".*!v8::Utils::ApiCheck", rb".*!WTF::Deque<.+>::ExpandCapacity(IfNeeded)", rb".*!WTF::Deque<.+>::push_back", ], ), # Breakpoint -> Ignored cBugTranslation( srzOriginalBugTypeId = r"Breakpoint", azs0rbAppliesOnlyToTopStackFrame = [ rb".*!__sanitizer_cov", ], s0zTranslatedBugTypeId = None, # This is apparently triggered by ASAN builds to determine EIP/RIP. s0zTranslatedBugDescription = None, s0zTranslatedSecurityImpact = None, ), # Breakpoint -> OOM cBugTranslation( srzOriginalBugTypeId = r"Breakpoint", azs0rbAppliesOnlyToTopStackFrame = [ rb".*!base::`anonymous namespace'::OnNoMemory", rb".*!base::internal::SchedulerWorkerPoolImpl::Start", # CHECK() on thread start rb".*!base::PartitionRecommitSystemPages", rb".*!blink::MemoryRegion::Commit", rb".*!content::`anonymous namespace'::CrashOnMapFailure", rb".*!skia::CreateHBitmap", rb".*!ui::ClientGpuMemoryBufferManager::ClientGpuMemoryBufferManager", # std::vector throws breakpoint ], s0zTranslatedBugTypeId = "OOM", s0zTranslatedBugDescription = "The application triggered a breakpoint to indicate it was unable to allocate enough memory.", s0zTranslatedSecurityImpact = None, ), # Breakpoint -> Assert cBugTranslation( srzOriginalBugTypeId = r"Breakpoint", azs0rbAppliesOnlyToTopStackFrame = [ rb".*!blink::reportFatalErrorInMainThread", rb".*!v8::Utils::ReportApiFailure", rb".*!logging::LogMessage::~LogMessage", ], s0zTranslatedBugTypeId = "Assert", s0zTranslatedBugDescription = "The application triggered an exception to indicate an assertion failed.", s0zTranslatedSecurityImpact = None, ), # AVW@NULL -> Assert cBugTranslation( srzOriginalBugTypeId = r"AVW@NULL", azs0rbAppliesOnlyToTopStackFrame = [ rb".*!base::win::`anonymous namespace'::ForceCrashOnSigAbort", ], s0zTranslatedBugTypeId = "Assert", s0zTranslatedBugDescription = "The application triggered a NULL pointer access violation to indicate an assertion failed.", s0zTranslatedSecurityImpact = None, ), # Various -> OOM cBugTranslation( srzOriginalBugTypeId = r"0xE0000008|Assert|AVW@NULL", # 0xE0000008 (win::kOomExceptionCode) -> OOM azs0rbAppliesOnlyToTopStackFrame = [ rb".*!base::`anonymous namespace'::OnNoMemory", rb".*!(?:base|WTF)::[Pp]artitions?(?:ExcessiveAllocationSize|OutOfMemory(Using\w+)?)", rb".*!blink::(?:BlinkGCOutOfMemory|ReportOOMErrorInMainThread)", rb".*!FX_OutOfMemoryTerminate", rb".*!SkBitmap::allocPixels", ], s0zTranslatedBugTypeId = "OOM", s0zTranslatedBugDescription = "The application caused an access violation by writing to NULL to indicate it was unable to allocate enough memory.", s0zTranslatedSecurityImpact = None, ), # OOM -> hide irrelevant frames cBugTranslation( srzOriginalBugTypeId = r"OOM", azs0rbAdditionalIrrelevantStackFrameSymbols = [ rb".+!(.+::)?(Win)?CallNewHandler", rb".+!(.+::)?\w+_malloc(_\w+)?", rb".+!(.+::)?\w*(Alloc|alloc|OutOfMemory)\w*(<.+>)?", ], ), ];

0

-12

23

f7b84e0119a5bb7d3e69b3fc77fd9952daf83b18

2,975

py

Python

ProjetoMercado/mercado/models.py

LucasRodriguesDaPaixao/ProjetoMercado

7a086ab0af800b15ef090520c9c81a0cd83dd650

[ "MIT" ]

null

ProjetoMercado/mercado/models.py

LucasRodriguesDaPaixao/ProjetoMercado

7a086ab0af800b15ef090520c9c81a0cd83dd650

[ "MIT" ]

null

ProjetoMercado/mercado/models.py

LucasRodriguesDaPaixao/ProjetoMercado

7a086ab0af800b15ef090520c9c81a0cd83dd650

[ "MIT" ]

null

# Create your models here.

33.806818

103

0.736807

from django.db import models # Create your models here. class Cliente(models.Model): ID_cliente = models.AutoField(primary_key=True) nome_cliente = models.CharField(max_length=100, verbose_name="Nome:") cpf = models.CharField(max_length=14, verbose_name="CPF:") def __str__(self): return self.nome_cliente class Fornecedor(models.Model): ID_fornecedor = models.AutoField(primary_key=True) nome_fornecedor = models.CharField(max_length=100, verbose_name="Nome:") email_fornecedor = models.CharField(max_length=100, verbose_name="Email:") cnpj= models.CharField(max_length=18, verbose_name="CNPJ:") telefone = models.CharField(max_length=13, verbose_name="Telefone:") def __str__(self): return self.nome_fornecedor class Meta: verbose_name_plural="Fornecedores" class Categoria(models.Model): ID_categoria = models.AutoField(primary_key=True) nome_categoria = models.CharField(max_length=45, verbose_name="Nome Categoria:") def __str__(self): return self.nome_categoria class Produto(models.Model): ID_produto = models.AutoField(primary_key=True) nome_produto = models.CharField(max_length=100, verbose_name="Nome:") data_validade = models.DateField(verbose_name="Data de validade:") preco = models.DecimalField(max_digits=5, decimal_places=2, verbose_name="Preço:") quantidade_produto = models.IntegerField(verbose_name="Quantidade de produtos:") FK_categoria = models.ForeignKey(Categoria, on_delete=models.CASCADE, verbose_name="Categoria:") FK_fornecedor = models.ForeignKey(Fornecedor, on_delete=models.CASCADE, verbose_name="Fornecedor:") def __str__(self): return self.nome_produto class Setor(models.Model): ID_setor = models.AutoField(primary_key=True) nome_setor = models.CharField(max_length=45, verbose_name="Setor:") FK_categoria = models.ForeignKey(Categoria, on_delete=models.CASCADE, verbose_name="Categoria:") def __str__(self): return self.nome_setor class Meta: verbose_name_plural="Setores" class Funcionario(models.Model): ID_funcionario = models.AutoField(primary_key=True) nome_funcionario = models.CharField(max_length=45, verbose_name="Nome:") rg = models.CharField(max_length=12, verbose_name="RG:") cpf = models.CharField(max_length=14, verbose_name="CPF:") FK_setor = models.ForeignKey(Setor, on_delete=models.CASCADE, verbose_name="Setor:") def __str__(self): return self.nome_funcionario class Compra(models.Model): ID_compra = models.AutoField(primary_key=True) valor_total = models.DecimalField(max_digits=5, decimal_places=2, verbose_name="Valor total:") FK_cliente = models.ForeignKey(Cliente, on_delete=models.CASCADE, verbose_name="Cliente:") compra_produto = models.ManyToManyField(Produto) def __str__(self): return "Compra: {} <--> {}".format(self.ID_compra, self.FK_cliente)

2

0

2,744

0

7

182

e682d03323f99fc860ddd405e81e02079d38b903

2,979

py

Python

macrokit/_validator.py

hanjinliu/macro-kit

61ebc38ea1086337d5a7477c6e896af0220f8a71

[ "BSD-3-Clause" ]

2

2021-11-02T09:53:49.000Z

2021-11-10T10:33:05.000Z

macrokit/_validator.py

hanjinliu/macro-kit

61ebc38ea1086337d5a7477c6e896af0220f8a71

[ "BSD-3-Clause" ]

null

macrokit/_validator.py

hanjinliu/macro-kit

61ebc38ea1086337d5a7477c6e896af0220f8a71

[ "BSD-3-Clause" ]

null

from typing import Hashable, TypeVar _T = TypeVar("_T", bound=Hashable) _A = TypeVar("_A") validator = Validator()

22.568182

75

0.627727

from typing import Callable, Hashable, TypeVar, Iterable, Union from ._symbol import Symbol from .head import Head _T = TypeVar("_T", bound=Hashable) _A = TypeVar("_A") class Validator: """A validator class that will be used for Expr argument validation.""" def __init__(self): self._map: dict[_T, Callable[[_A], _A]] = {} def register(self, value: _T): """Register value for validation.""" def wrapper(func): self._map[value] = func return func return wrapper def __call__(self, arg: _T, *args: _A) -> Union[_A, Iterable[_A]]: """Run validation.""" try: func = self._map[arg] except KeyError: return args try: out = func(*args) except ValidationError as e: e.args = (f"{args} is incompatible with {arg}",) raise e return out class ValidationError(ValueError): """Raised when validation failed.""" validator = Validator() @validator.register(Head.empty) def _no_arg(args): if len(args) != 0: raise ValidationError() return args @validator.register(Head.del_) @validator.register(Head.raise_) def _single_arg(args): if len(args) != 1: raise ValidationError() return args @validator.register(Head.comment) def _single_str(args): if len(args) != 1: raise ValidationError() k = args[0] if isinstance(k, Symbol): k.name = k.name.strip("'") return args @validator.register(Head.assert_) @validator.register(Head.getitem) @validator.register(Head.unop) def _two_args(args): if len(args) != 2: raise ValidationError() return args @validator.register(Head.getattr) def _getattr(args): if len(args) != 2: raise ValidationError() k = args[1] if isinstance(k, Symbol): k.name = k.name.strip("'") return args @validator.register(Head.assign) @validator.register(Head.kw) @validator.register(Head.annotate) def _symbol_and_any(args): if len(args) != 2: raise ValidationError() k, v = args if isinstance(k, str): k = Symbol.var(k) elif isinstance(k, Symbol) and k.constant: k = Symbol.var(k.name) return [k, v] @validator.register(Head.binop) @validator.register(Head.aug) def _three_args(args): if len(args) != 3: raise ValidationError() return args @validator.register(Head.function) @validator.register(Head.for_) @validator.register(Head.while_) def _an_arg_and_a_block(args): if len(args) != 2: raise ValidationError() b = args[1] if getattr(b, "head", None) != Head.block: raise ValidationError() return args @validator.register(Head.if_) @validator.register(Head.elif_) def _two_args_and_a_block(args): if len(args) != 3: raise ValidationError() b = args[2] if getattr(b, "head", None) != Head.block: raise ValidationError() return args

0

1,745

0

774

0

34

297

2dcc5057b0af83ae887869fbadf0b60476028183

7,579

py

Python

cubi_tk/archive/readme.py

eudesbarbosa/cubi-tk

80c3ef9387f2399f796b2cc445b99781d541f222

[ "MIT" ]

null

cubi_tk/archive/readme.py

eudesbarbosa/cubi-tk

80c3ef9387f2399f796b2cc445b99781d541f222

[ "MIT" ]

null

cubi_tk/archive/readme.py

eudesbarbosa/cubi-tk

80c3ef9387f2399f796b2cc445b99781d541f222

[ "MIT" ]

null

"""``cubi-tk archive prepare``: Prepare a project for archival""" import os import re from ..isa_tpl import IsaTabTemplate from ..isa_tpl import load_variables _BASE_DIR = os.path.dirname(__file__) TEMPLATE = IsaTabTemplate( name="archive", path=os.path.join(os.path.dirname(_BASE_DIR), "isa_tpl", "archive"), description="Prepare project for archival", configuration=load_variables("archive"), ) DU = re.compile("^ *([0-9]+)[ \t]+[^ \t]+.*$") DATE = re.compile("^(20[0-9][0-9]-[01][0-9]-[0-3][0-9])[_-].+$") MAIL = ( "(?:[a-z0-9!#$%&'*+/=?^_`{|}~-]+(?:\\.[a-z0-9!#$%&'*+/=?^_`{|}~-]+)*" '|"(?:[\x01-\x08\x0b\x0c\x0e-\x1f\x21\x23-\x5b\x5d-\x7f]' '|\\\\[\x01-\x09\x0b\x0c\x0e-\x7f])*")' "@(?:(?:[a-z0-9](?:[a-z0-9-]*[a-z0-9])?\\.)+[a-z0-9](?:[a-z0-9-]*[a-z0-9])?" "|\\[(?:(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\\.){3}" "(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?|[a-z0-9-]*[a-z0-9]:" "(?:[\x01-\x08\x0b\x0c\x0e-\x1f\x21-\x5a\x53-\x7f]" "|\\\\[\x01-\x09\x0b\x0c\x0e-\x7f])+)" "\\])" ) PATTERNS = { "project_name": re.compile("^ *- *Project name: *.+$"), "date": re.compile("^ *- *Start date: *20[0-9]{2}-[01][0-9]-[0-3][0-9].*$"), "status": re.compile("^ *- *Current status: *(Active|Inactive|Finished|Archived) *$"), "PI": re.compile("^ *- P.I.: \\[([A-z '-]+)\\]\$mailto:(" + MAIL + ")\$ *$"), "client": re.compile("^ *- *Client contact: \\[([A-z '-]+)\\]\$mailto:(" + MAIL + ")\$ *$"), "archiver": re.compile("^ *- *CUBI contact: \\[([A-z '-]+)\\]\$mailto:(" + MAIL + ")\$ *$"), "CUBI": re.compile("^ *- *CUBI project leader: ([A-z '-]+) *$"), } COMMANDS = { "size": ["du", "--bytes", "--max-depth=0"], "inodes": ["du", "--inodes", "--max-depth=0"], "size_follow": ["du", "--dereference", "--bytes", "--max-depth=0"], "inodes_follow": ["du", "--dereference", "--inodes", "--max-depth=0"], } MSG = "**Contents of original `README.md` file**"

34.766055

98

0.577517

"""``cubi-tk archive prepare``: Prepare a project for archival""" import errno import os import re import shutil import sys import tempfile from cookiecutter.main import cookiecutter from logzero import logger from ..common import execute_shell_commands from ..isa_tpl import IsaTabTemplate from ..isa_tpl import load_variables _BASE_DIR = os.path.dirname(__file__) TEMPLATE = IsaTabTemplate( name="archive", path=os.path.join(os.path.dirname(_BASE_DIR), "isa_tpl", "archive"), description="Prepare project for archival", configuration=load_variables("archive"), ) DU = re.compile("^ *([0-9]+)[ \t]+[^ \t]+.*$") DATE = re.compile("^(20[0-9][0-9]-[01][0-9]-[0-3][0-9])[_-].+$") MAIL = ( "(?:[a-z0-9!#$%&'*+/=?^_`{|}~-]+(?:\\.[a-z0-9!#$%&'*+/=?^_`{|}~-]+)*" '|"(?:[\x01-\x08\x0b\x0c\x0e-\x1f\x21\x23-\x5b\x5d-\x7f]' '|\\\\[\x01-\x09\x0b\x0c\x0e-\x7f])*")' "@(?:(?:[a-z0-9](?:[a-z0-9-]*[a-z0-9])?\\.)+[a-z0-9](?:[a-z0-9-]*[a-z0-9])?" "|\\[(?:(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\\.){3}" "(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?|[a-z0-9-]*[a-z0-9]:" "(?:[\x01-\x08\x0b\x0c\x0e-\x1f\x21-\x5a\x53-\x7f]" "|\\\\[\x01-\x09\x0b\x0c\x0e-\x7f])+)" "\\])" ) PATTERNS = { "project_name": re.compile("^ *- *Project name: *.+$"), "date": re.compile("^ *- *Start date: *20[0-9]{2}-[01][0-9]-[0-3][0-9].*$"), "status": re.compile("^ *- *Current status: *(Active|Inactive|Finished|Archived) *$"), "PI": re.compile("^ *- P.I.: \\[([A-z '-]+)\\]\$mailto:(" + MAIL + ")\$ *$"), "client": re.compile("^ *- *Client contact: \\[([A-z '-]+)\\]\$mailto:(" + MAIL + ")\$ *$"), "archiver": re.compile("^ *- *CUBI contact: \\[([A-z '-]+)\\]\$mailto:(" + MAIL + ")\$ *$"), "CUBI": re.compile("^ *- *CUBI project leader: ([A-z '-]+) *$"), } COMMANDS = { "size": ["du", "--bytes", "--max-depth=0"], "inodes": ["du", "--inodes", "--max-depth=0"], "size_follow": ["du", "--dereference", "--bytes", "--max-depth=0"], "inodes_follow": ["du", "--dereference", "--inodes", "--max-depth=0"], } MSG = "**Contents of original `README.md` file**" def _extra_context_from_config(config=None): extra_context = {} if config: for name in TEMPLATE.configuration: if getattr(config, "var_%s" % name, None) is not None: extra_context[name] = getattr(config, "var_%s" % name) return extra_context def _get_snakemake_nb(project_dir): cmds = [ [ "find", project_dir, "-type", "d", "-name", ".snakemake", "-exec", "du", "--inodes", "--max-depth=0", "{}", ";", ], ["cut", "-f", "1"], ["paste", "-sd+"], ["bc"], ] return execute_shell_commands(cmds, check=False, verbose=False) def _get_archiver_name(): cmds = [ ["pinky", "-l", os.getenv("USER")], ["grep", "In real life:"], ["sed", "-e", "s/.*In real life: *//"], ] output = execute_shell_commands(cmds, check=False, verbose=False) return output.rstrip() def _create_extra_context(project_dir, config=None): extra_context = _extra_context_from_config(config) logger.info("Collecting size & inodes numbers") for (context_name, cmd) in COMMANDS.items(): if context_name not in extra_context.keys(): cmd.append(project_dir) extra_context[context_name] = DU.match( execute_shell_commands([cmd], check=False, verbose=False) ).group(1) if "snakemake_nb" not in extra_context.keys(): extra_context["snakemake_nb"] = _get_snakemake_nb(project_dir) if "archiver_name" not in extra_context.keys(): extra_context["archiver_name"] = _get_archiver_name() if "archiver_email" not in extra_context.keys(): extra_context["archiver_email"] = ( "{}@bih-charite.de".format(extra_context["archiver_name"]).lower().replace(" ", ".") ) if "CUBI_name" not in extra_context.keys(): extra_context["CUBI_name"] = extra_context["archiver_name"] if "PI_name" in extra_context.keys() and "PI_email" not in extra_context.keys(): extra_context["PI_email"] = ( "{}@charite.de".format(extra_context["PI_name"]).lower().replace(" ", ".") ) if "client_name" in extra_context.keys() and "client_email" not in extra_context.keys(): extra_context["client_email"] = ( "{}@charite.de".format(extra_context["client_name"]).lower().replace(" ", ".") ) if "SODAR_UUID" in extra_context.keys() and "SODAR_URL" not in extra_context.keys(): extra_context["SODAR_URL"] = "{}/projects/{}".format( config.sodar_server_url, extra_context["SODAR_UUID"] ) if "directory" not in extra_context.keys(): extra_context["directory"] = project_dir if "project_name" not in extra_context.keys(): extra_context["project_name"] = os.path.basename(project_dir) if "start_date" not in extra_context.keys() and DATE.match(extra_context["project_name"]): extra_context["start_date"] = DATE.match(extra_context["project_name"]).group(1) if "current_status" not in extra_context.keys(): extra_context["current_status"] = "Finished" return extra_context def _copy_readme(src, target): os.makedirs(os.path.realpath(os.path.dirname(target)), mode=488, exist_ok=True) with open(src, "rt") as f: lines = [x.rstrip() for x in f.readlines()] if os.path.exists(target): lines.extend(["", "", "-" * 80, "", "", MSG, "", "", "-" * 80, "", ""]) with open(target, "rt") as f: lines.extend([x.rstrip() for x in f.readlines()]) os.remove(target) with open(os.path.realpath(target), "wt") as f: f.write("\n".join(lines)) def is_readme_valid(filename=None): if filename is None: f = sys.stdin else: if not os.path.exists(filename): return False f = open(filename, "rt") matching = set() for line in f: line = line.rstrip() for (name, pattern) in PATTERNS.items(): if pattern.match(line): matching.add(name) f.close() return set(PATTERNS.keys()).issubset(matching) def create_readme(filename, project_dir, config=None, no_input=False): # If a valid README.md file already exists in the project, do nothing if os.path.exists(filename) and is_readme_valid(filename): logger.info("Using existing file, variables ignored : '{}'".format(filename)) return # Fill defaults (emails, size, inodes, ...) extra_context = _create_extra_context(project_dir, config) try: tmp = tempfile.mkdtemp() # Create the readme file in temp directory cookiecutter( template=TEMPLATE.path, extra_context=extra_context, output_dir=tmp, no_input=no_input ) # Copy it back to destination, including contents of former incomplete README.md _copy_readme(os.path.join(tmp, extra_context["project_name"], "README.md"), filename) finally: try: shutil.rmtree(tmp) except OSError as e: if e.errno != errno.ENOENT: raise def add_readme_parameters(parser): for name in TEMPLATE.configuration: key = name.replace("_", "-") parser.add_argument( "--var-%s" % key, help="template variables %s" % repr(name), default=None )

0

5,266

0

14

341

769fc816a6040cc61dab6376c20fd5c6bf0ebaa0

989

py

Python

sigmod2021-exdra-p523/experiments/archive/submitted_results/code/other/pca.py

damslab/reproducibility

f7804b2513859f7e6f14fa7842d81003d0758bf8

[ "Apache-2.0" ]

4

2021-12-10T17:20:26.000Z

2021-12-27T14:38:40.000Z

sigmod2021-exdra-p523/experiments/code/other/pca.py

damslab/reproducibility

f7804b2513859f7e6f14fa7842d81003d0758bf8

[ "Apache-2.0" ]

null

sigmod2021-exdra-p523/experiments/code/other/pca.py

damslab/reproducibility

f7804b2513859f7e6f14fa7842d81003d0758bf8

[ "Apache-2.0" ]

null

import numpy as np import argparse from sklearn.decomposition import PCA from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler parser = argparse.ArgumentParser() parser.add_argument('-x', '--datapath', type=str, required=True) parser.add_argument('-y', '--labels', type=str, required=True) parser.add_argument('-v', '--verbose', type=bool, default=False) parser.add_argument('-o', '--outputpath', type=str, required=True) args = parser.parse_args() X = np.load(args.datapath, allow_pickle=True) # https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline # https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html # https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA pca = make_pipeline(StandardScaler(), PCA(n_components=10,svd_solver="full")).fit(X) np.savetxt(args.outputpath, pca.steps[1][1].components_, delimiter=",")

43

118

0.781598

import numpy as np import argparse from sklearn.decomposition import PCA from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler parser = argparse.ArgumentParser() parser.add_argument('-x', '--datapath', type=str, required=True) parser.add_argument('-y', '--labels', type=str, required=True) parser.add_argument('-v', '--verbose', type=bool, default=False) parser.add_argument('-o', '--outputpath', type=str, required=True) args = parser.parse_args() X = np.load(args.datapath, allow_pickle=True) # https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html#sklearn.pipeline.make_pipeline # https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html # https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA pca = make_pipeline(StandardScaler(), PCA(n_components=10,svd_solver="full")).fit(X) np.savetxt(args.outputpath, pca.steps[1][1].components_, delimiter=",")

0

ac1fa224c6f4611660c583d6666d2a339221dfa7

8,868

py

Python

prototypes/learn_weigths.py

pantelisantonoudiou/Logic_szrDetect

3267cabc78905c189a97e06ea2731b6f9e7b2def

[ "Apache-2.0" ]

1

2020-11-19T19:26:34.000Z

prototypes/learn_weigths.py

pantelisantonoudiou/Logic_szrDetect

3267cabc78905c189a97e06ea2731b6f9e7b2def

[ "Apache-2.0" ]

null

prototypes/learn_weigths.py

pantelisantonoudiou/Logic_szrDetect

3267cabc78905c189a97e06ea2731b6f9e7b2def

[ "Apache-2.0" ]

1

2021-04-07T11:41:39.000Z

# -*- coding: utf-8 -*- """ Created on Thu Aug 13 09:14:32 2020 @author: Pante """ import features import numpy as np from sklearn.preprocessing import StandardScaler from array_helper import find_szr_idx, match_szrs, merge_close from build_feature_data import get_data, get_features_allch ####### consider isolation forest for outlier detection!!!!!! def user_cost(y_true, y_pred): """ user_cost(y_true, y_pred) Parameters ---------- y_true : 1ndarray bool, ground truth values y_pred : 1ndarray bool, predicted values Returns ------- cost : float """ detected = 0 # number of detected seizures # get bounds of sezures bounds_true = find_szr_idx(y_true, np.array([0,1])) # total predicted bounds_pred = find_szr_idx(y_pred, np.array([0,1])) # total predicted bounds_pred = merge_close(bounds_pred, merge_margin = 5) # merge seizures close together if bounds_pred.shape[0]>0: # find matching seizures detected = match_szrs(bounds_true, bounds_pred, err_margin = 10) # calculate cost a = 1 - (detected/bounds_true.shape[0]) # get detected ratio b = (bounds_pred.shape[0] - detected) # get false positives cost = a + np.log10(b+1) # cost function return cost def create_cost(bounds_true, bounds_pred): """ create_cost(bounds_true, bounds_pred) Parameters ---------- bounds_true : 2d ndarray (rows = seizrs, columns = start,stop), ground truth bounds_pred : 2d ndarray (rows = seizrs, columns = start,stop), predicted Returns ------- cost : Float, """ # find matching seizurs detected = 0 a = 100 if bounds_pred.shape[0]>0: detected = match_szrs(bounds_true, bounds_pred, err_margin = 10) if bounds_true.shape[0]>0: # get detected ratio a = (1 - (detected/bounds_true.shape[0]))*20 # get false positives b = (bounds_pred.shape[0] - detected) # cost function # L = 1 # learning rate cost = a + np.log10(b+1) return cost def szr_cost(bounds_true, bounds_pred): """ create_cost(bounds_true, bounds_pred) Parameters ---------- bounds_true : 2d ndarray (rows = seizrs, columns = start,stop), ground truth bounds_pred : 2d ndarray (rows = seizrs, columns = start,stop), predicted Returns ------- cost : Float, """ # find matching seizurs detected = 0 if bounds_pred.shape[0]>0: detected = match_szrs(bounds_true, bounds_pred, err_margin = 10) if bounds_true.shape[0]>0: # get detected ratio a = 1 - (detected/bounds_true.shape[0]) if (a > 0 and a <= 1): a = 20 # get false positives b = (bounds_pred.shape[0] - detected) # cost function cost = a + np.log10(b+1) return cost def get_min_cost(feature, y_true): """ get_min_cost(feature, y_true) Parameters ---------- feature : 1D ndarray, extracted feature y_true : 1D ndarray, bool grund truth labels Returns ------- TYPE: Float, threshold value that gves minimum cost """ n_loop = 100 # loop number and separation thresh_array = np.linspace(1, 20, n_loop) # thresholds to test cost_array = np.zeros(n_loop) for i in range(n_loop): # thresh_array[i] = thresh y_pred = feature> (np.mean(feature) + thresh_array[i]*np.std(feature)) # get number of seizures bounds_true = find_szr_idx(y_true, np.array([0,1])) # true bounds_pred = find_szr_idx(y_pred, np.array([0,1])) # predicted # merge seizures close together if bounds_pred.shape[0]>1: bounds_pred = merge_close(bounds_pred, merge_margin = 5) cost = szr_cost(bounds_true, bounds_pred) # get cost # pass to array cost_array[i] = cost return thresh_array[np.argmin(cost_array)] # define parameter list param_list = (features.autocorr, features.line_length, features.rms, features.mad, features.var, features.std, features.psd, features.energy, features.get_envelope_max_diff,) cross_ch_param_list = (features.cross_corr, features.signal_covar, features.signal_abs_covar,) # get data and true labels exp_path = r'C:\Users\Pante\Desktop\seizure_data_tb\Train_data\3642_3641_3560_3514' # 071919_3514 071719_3560 data, y_true = get_data(exp_path, '072519_3642',ch_num = [0,1], inner_path={'data_path':'filt_data', 'pred_path':'verified_predictions_pantelis'} , load_y = True) # # get file list # main_path = r'C:\Users\Pante\Desktop\seizure_data_tb\Train_data' # folder_path = '3514_3553_3639_3640' # ver_path = os.path.join(main_path,folder_path, 'verified_predictions_pantelis') # filelist = list(filter(lambda k: '.csv' in k, os.listdir(ver_path))) # get only files with predictions # filelist = [os.path.splitext(x)[0] for x in filelist] # remove csv ending # # data, y_true = get_data(r'W:\Maguire Lab\Trina\2019\07-July\3514_3553_3639_3640, '071819_3553a',ch_num = [0,1], # # inner_path={'data_path':'reorganized_data', 'pred_path':'verified_predictions_pantelis'} , load_y = True) # for i in range(1): # # 071919_3514 071719_3560 # data, y_true = get_data(os.path.join(main_path, folder_path), filelist[i],ch_num = [0,1], # inner_path={'data_path':'filt_data', 'pred_path':'verified_predictions_pantelis'} , load_y = True) # if sum(y_true) == 0: # continue # get features x_data, labels = get_features_allch(data,param_list,cross_ch_param_list) # Normalize data x_data = StandardScaler().fit_transform(x_data) # get cost plot cost_array,thresh_array = find_threshold(x_data, y_true)

27.974763

141

0.614005

# -*- coding: utf-8 -*- """ Created on Thu Aug 13 09:14:32 2020 @author: Pante """ import os, features, time import numpy as np from sklearn.preprocessing import StandardScaler from array_helper import find_szr_idx, match_szrs, merge_close from build_feature_data import get_data, get_features_allch from sklearn.metrics import log_loss,recall_score import matplotlib.pyplot as plt ####### consider isolation forest for outlier detection!!!!!! def user_cost(y_true, y_pred): """ user_cost(y_true, y_pred) Parameters ---------- y_true : 1ndarray bool, ground truth values y_pred : 1ndarray bool, predicted values Returns ------- cost : float """ detected = 0 # number of detected seizures # get bounds of sezures bounds_true = find_szr_idx(y_true, np.array([0,1])) # total predicted bounds_pred = find_szr_idx(y_pred, np.array([0,1])) # total predicted bounds_pred = merge_close(bounds_pred, merge_margin = 5) # merge seizures close together if bounds_pred.shape[0]>0: # find matching seizures detected = match_szrs(bounds_true, bounds_pred, err_margin = 10) # calculate cost a = 1 - (detected/bounds_true.shape[0]) # get detected ratio b = (bounds_pred.shape[0] - detected) # get false positives cost = a + np.log10(b+1) # cost function return cost def create_cost(bounds_true, bounds_pred): """ create_cost(bounds_true, bounds_pred) Parameters ---------- bounds_true : 2d ndarray (rows = seizrs, columns = start,stop), ground truth bounds_pred : 2d ndarray (rows = seizrs, columns = start,stop), predicted Returns ------- cost : Float, """ # find matching seizurs detected = 0 a = 100 if bounds_pred.shape[0]>0: detected = match_szrs(bounds_true, bounds_pred, err_margin = 10) if bounds_true.shape[0]>0: # get detected ratio a = (1 - (detected/bounds_true.shape[0]))*20 # get false positives b = (bounds_pred.shape[0] - detected) # cost function # L = 1 # learning rate cost = a + np.log10(b+1) return cost def szr_cost(bounds_true, bounds_pred): """ create_cost(bounds_true, bounds_pred) Parameters ---------- bounds_true : 2d ndarray (rows = seizrs, columns = start,stop), ground truth bounds_pred : 2d ndarray (rows = seizrs, columns = start,stop), predicted Returns ------- cost : Float, """ # find matching seizurs detected = 0 if bounds_pred.shape[0]>0: detected = match_szrs(bounds_true, bounds_pred, err_margin = 10) if bounds_true.shape[0]>0: # get detected ratio a = 1 - (detected/bounds_true.shape[0]) if (a > 0 and a <= 1): a = 20 # get false positives b = (bounds_pred.shape[0] - detected) # cost function cost = a + np.log10(b+1) return cost def get_min_cost(feature, y_true): """ get_min_cost(feature, y_true) Parameters ---------- feature : 1D ndarray, extracted feature y_true : 1D ndarray, bool grund truth labels Returns ------- TYPE: Float, threshold value that gves minimum cost """ n_loop = 100 # loop number and separation thresh_array = np.linspace(1, 20, n_loop) # thresholds to test cost_array = np.zeros(n_loop) for i in range(n_loop): # thresh_array[i] = thresh y_pred = feature> (np.mean(feature) + thresh_array[i]*np.std(feature)) # get number of seizures bounds_true = find_szr_idx(y_true, np.array([0,1])) # true bounds_pred = find_szr_idx(y_pred, np.array([0,1])) # predicted # merge seizures close together if bounds_pred.shape[0]>1: bounds_pred = merge_close(bounds_pred, merge_margin = 5) cost = szr_cost(bounds_true, bounds_pred) # get cost # pass to array cost_array[i] = cost return thresh_array[np.argmin(cost_array)] def find_threshold(x_data, y_true): # thresh = 1; ftr = 8 x = x_data[:,ftr] # fig = plt.figure() # ax = fig.add_subplot(111) # t = np.ones(x.shape[0]) * (np.mean(x) + thresh*np.std(x)) # line1 = ax.plot(x) # line2 = ax.plot(t) n_loop = 100 cost_array = np.zeros(n_loop) thresh_array = np.zeros(n_loop) thresh_array = np.linspace(1, 20, n_loop) for i in range(n_loop): # thresh_array[i] = thresh y_pred = x> (np.mean(x) + thresh_array[i]*np.std(x)) # get number of seizures bounds_true = find_szr_idx(y_true, np.array([0,1])) # true bounds_pred = find_szr_idx(y_pred, np.array([0,2])) # predicted # merge seizures close together if bounds_pred.shape[0]>1: bounds_pred = merge_close(bounds_pred, merge_margin = 5) cost = create_cost(bounds_true, bounds_pred) # get cost # cost = log_loss(y_true, y_pred ,labels =[True,False]) cost_array[i] = cost # if cost == 0: # print('cost has reached zero, stopping') # return cost_array,thresh_array # thresh += cost # update cost # ax.plot(np.ones(x.shape[0]) * (np.mean(x) + thresh*np.std(x))) # line2[0].set_ydata(np.ones(x.shape[0]) * (np.mean(x) + thresh*np.std(x))) # fig.canvas.draw() plt.figure() plt.plot(thresh_array, cost_array) plt.ylabel('cost') plt.xlabel('thresh') print('seizures = ', bounds_true.shape[0]) return cost_array,thresh_array def find_threshold_all(x_data, y_true): thresh = 1; ftr = 1 x = x_data[:,ftr] fig = plt.figure() ax = fig.add_subplot(111) t = np.ones(x.shape[0]) * (np.mean(x) + thresh*np.std(x)) line1 = ax.plot(x) line2 = ax.plot(t) n_loop = 100 cost_array = np.zeros(n_loop) thresh_array = np.zeros(n_loop) # thresh_array = np.linspace(10, 0, n_loop) for i in range(n_loop): thresh_array[i] = thresh y_pred = x> (np.mean(x) + thresh_array[i]*np.std(x)) # get number of seizures bounds_true = find_szr_idx(y_true, np.array([0,1])) # true bounds_pred = find_szr_idx(y_pred, np.array([0,1])) # predicted # merge seizures close together if bounds_pred.shape[0]>1: bounds_pred = merge_close(bounds_pred, merge_margin = 5) cost = create_cost(bounds_true, bounds_pred) # get cost # cost = log_loss(y_true, y_pred ,labels =[True,False]) cost_array[i] = cost if cost == 0: print('cost has reached zero, stopping') return cost_array,thresh_array return cost_array,thresh_array # define parameter list param_list = (features.autocorr, features.line_length, features.rms, features.mad, features.var, features.std, features.psd, features.energy, features.get_envelope_max_diff,) cross_ch_param_list = (features.cross_corr, features.signal_covar, features.signal_abs_covar,) # get data and true labels exp_path = r'C:\Users\Pante\Desktop\seizure_data_tb\Train_data\3642_3641_3560_3514' # 071919_3514 071719_3560 data, y_true = get_data(exp_path, '072519_3642',ch_num = [0,1], inner_path={'data_path':'filt_data', 'pred_path':'verified_predictions_pantelis'} , load_y = True) # # get file list # main_path = r'C:\Users\Pante\Desktop\seizure_data_tb\Train_data' # folder_path = '3514_3553_3639_3640' # ver_path = os.path.join(main_path,folder_path, 'verified_predictions_pantelis') # filelist = list(filter(lambda k: '.csv' in k, os.listdir(ver_path))) # get only files with predictions # filelist = [os.path.splitext(x)[0] for x in filelist] # remove csv ending # # data, y_true = get_data(r'W:\Maguire Lab\Trina\2019\07-July\3514_3553_3639_3640, '071819_3553a',ch_num = [0,1], # # inner_path={'data_path':'reorganized_data', 'pred_path':'verified_predictions_pantelis'} , load_y = True) # for i in range(1): # # 071919_3514 071719_3560 # data, y_true = get_data(os.path.join(main_path, folder_path), filelist[i],ch_num = [0,1], # inner_path={'data_path':'filt_data', 'pred_path':'verified_predictions_pantelis'} , load_y = True) # if sum(y_true) == 0: # continue # get features x_data, labels = get_features_allch(data,param_list,cross_ch_param_list) # Normalize data x_data = StandardScaler().fit_transform(x_data) # get cost plot cost_array,thresh_array = find_threshold(x_data, y_true)

0

2,799

0

48

98

276b5d3d63f7139687164c5d10374d92ac764ed2

1,016

py

Python

qcloudsdkcmem/DescribeCmemRequest.py

f3n9/qcloudcli

b965a4f0e6cdd79c1245c1d0cd2ca9c460a56f19

[ "Apache-2.0" ]

null

qcloudsdkcmem/DescribeCmemRequest.py

f3n9/qcloudcli

b965a4f0e6cdd79c1245c1d0cd2ca9c460a56f19

[ "Apache-2.0" ]

null

qcloudsdkcmem/DescribeCmemRequest.py

f3n9/qcloudcli

b965a4f0e6cdd79c1245c1d0cd2ca9c460a56f19

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*-

25.4

73

0.643701

# -*- coding: utf-8 -*- from qcloudsdkcore.request import Request class DescribeCmemRequest(Request): def __init__(self): super(DescribeCmemRequest, self).__init__( 'cmem', 'qcloudcliV1', 'DescribeCmem', 'cmem.api.qcloud.com') def get_limit(self): return self.get_params().get('limit') def set_limit(self, limit): self.add_param('limit', limit) def get_offset(self): return self.get_params().get('offset') def set_offset(self, offset): self.add_param('offset', offset) def get_sizeInfo(self): return self.get_params().get('sizeInfo') def set_sizeInfo(self, sizeInfo): self.add_param('sizeInfo', sizeInfo) def get_subnetId(self): return self.get_params().get('subnetId') def set_subnetId(self, subnetId): self.add_param('subnetId', subnetId) def get_vpcId(self): return self.get_params().get('vpcId') def set_vpcId(self, vpcId): self.add_param('vpcId', vpcId)

0

926

0

20

46

a06cceb6d9e57c9f8d1381b5bcfd1fa628bd0789

2,314

py

Python

rssnewsbot/spiders/rssspider.py

hijoe320/RSSBot

cbc0bc24d980ede3419111d51384abbc2c93f70c

[ "MIT" ]

null

rssnewsbot/spiders/rssspider.py

hijoe320/RSSBot

cbc0bc24d980ede3419111d51384abbc2c93f70c

[ "MIT" ]

null

rssnewsbot/spiders/rssspider.py

hijoe320/RSSBot

cbc0bc24d980ede3419111d51384abbc2c93f70c

[ "MIT" ]

null

from time import mktime import xxhash def hs(s): """ hash function to convert url to fixed length hash code """ return xxhash.xxh32(s).hexdigest() def time2ts(time_struct): """ convert time_struct to epoch """ return mktime(time_struct)

32.138889

103

0.617978

from time import sleep, gmtime, mktime from datetime import datetime import logging import scrapy import redis import msgpack import xxhash import pymongo as pm import feedparser as fp from colorama import Back, Fore, Style from ..settings import MONGODB_URI, REDIS_HOST, REDIS_PORT, REDIS_PWD, REDIS_PENDING_QUEUE def hs(s): """ hash function to convert url to fixed length hash code """ return xxhash.xxh32(s).hexdigest() def time2ts(time_struct): """ convert time_struct to epoch """ return mktime(time_struct) class RSSSpider(scrapy.Spider): name = "rssspider" def __init__(self, *args, **kwargs): super(RSSSpider, self).__init__(*args, **kwargs) self.rc = redis.Redis(host=REDIS_HOST, port=REDIS_PORT, password=REDIS_PWD) self.df = redis.Redis(host=REDIS_HOST, port=REDIS_PORT, password=REDIS_PWD, db=REDIS_DUPFLT_DB) self.mc = pm.MongoClient(host=MONGODB_URI, connect=False) def start_requests(self): with self.mc.rssnews.feed.find() as cursor: logging.info("number of rss feeds = %d", cursor.count()) for item in cursor: logging.debug("rss=%(url)s", item) yield scrapy.Request(url=item["url"], callback=self.parse, meta=item) def parse(self, res): logging.debug("%sparsing %s%s", Fore.GREEN, res.url, Style.RESET_ALL) rss = fp.parse(res.body) symbol = res.meta["symbol"] for e in rss.entries: if self.check_exist(e.link): continue if '*' in e.link: url = "http" + e.link.split("*http")[-1] self.append_task(e, url) elif e.link.startswith("http://finance.yahoo.com/r/"): yield scrapy.Request(url=e.link, callback=self.extract_url, meta=e) else: self.append_task(e, e.link) def extract_url(self, res): if res.body.startswith("<script src="): url = res.body.split("URL=\'")[-1].split("\'")[0] self.append_task(res.meta, url) else: pass def check_exist(self, url): return self.df.get(url) def append_task(self, entry, url): self.df.set(url, True, ex=3600) self.rc.append(PENDING_QUEUE, msgpack.packb(task))

0

1,740

0

79

221

0a29357a3fcb65eb38130117fd1af6fb06bc1c40

11,992

py

Python

data/transforms/data_preprocessing.py

zyxwvu321/Classifer_SSL_Longtail

e6c09414c49e695b0f4221a3c6245ae3929a1788

[ "MIT" ]

null

data/transforms/data_preprocessing.py

zyxwvu321/Classifer_SSL_Longtail

e6c09414c49e695b0f4221a3c6245ae3929a1788

[ "MIT" ]

null

data/transforms/data_preprocessing.py

zyxwvu321/Classifer_SSL_Longtail

e6c09414c49e695b0f4221a3c6245ae3929a1788

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Wed Jan 8 15:36:15 2020 dataset transform @author: minjie """ # imgs = [] # pts = [] # # hh,ww,_ = img.shape # for _ in range(self.n_aug): # #points = [ww/2.0,hh/2.0,1.0] # points = [[0.0,0.0,1.0], [0.0,hh,1.0], [ww,0.0,1.0],[ww,hh,1.0]] # augmented = self.augment(image = img,keypoints=points,category_id = ['0']) # imgs.append(augmented['image']) # pts.append(augmented['keypoints']) # # NOTE: use bbox will have prob that box is outside crop region. # bboxes= [[0.45, 0.45, 0.55, 0.55]] # # augmented = self.T_aug(image = img,bboxes = bboxes,category_id = ['0']) # hh,ww,_ = img.shape # points = [[ww/2.0,hh/2.0,1.0]] # augmented = self.augment(image = img,keypoints=points,category_id = ['0']) #return augmented['image']

38.935065

162

0.488075

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Wed Jan 8 15:36:15 2020 dataset transform @author: minjie """ import albumentations as A from albumentations.pytorch import ToTensor as ToTensor_albu import cv2 import torch from multiprocessing import Pool from utils.parse_meta import parse_kpds import numpy as np def get_aug(aug, min_area=0., min_visibility=0.): return A.Compose(aug, bbox_params={'format': 'pascal_voc', 'min_area': min_area, 'min_visibility': min_visibility, 'label_fields': ['category_id']}) class TrainAugmentation_albu: def __init__(self, sz_hw = (384,384),mean=0, std=1.0, crp_scale=(0.08, 1.0),crp_ratio = (0.75, 1.3333), weak_aug = False,n_aug = 1,out_augpos = False): """ Args: weak_aug, week aug for fixmatch """ if isinstance(sz_hw, int): sz_hw = (sz_hw,sz_hw) self.mean = mean self.std = std self.sz_hw = sz_hw self.crp_scale = crp_scale self.crp_ratio = crp_ratio self.n_aug = n_aug # number of repeated augmentation self.out_augpos = out_augpos if self.sz_hw[0] == self.sz_hw[1]: self.T_aug = A.Compose([A.Rotate(p=0.5), A.RandomResizedCrop(height = self.sz_hw[0], width = self.sz_hw[1], scale=self.crp_scale, ratio=self.crp_ratio, interpolation = cv2.INTER_CUBIC,p = 1.0), A.Flip(p = 0.5), A.RandomRotate90(p = 0.5)]) else: self.T_aug = A.Compose([A.Rotate(p=0.5), A.RandomResizedCrop(height = self.sz_hw[0], width = self.sz_hw[1], scale=self.crp_scale, ratio=self.crp_ratio, interpolation = cv2.INTER_CUBIC,p = 1.0), A.Flip(p = 0.5)]) self.I_aug = A.Compose([ A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2, p=0.5), A.HueSaturationValue(hue_shift_limit=2, sat_shift_limit=15, val_shift_limit=20,p = 0.5), A.OneOf([A.Blur(blur_limit=5, p=0.3), A.GaussNoise(var_limit=(5.0, 10.0), p=0.3), A.IAASharpen(alpha=(0.1, 0.3), lightness=(0.5, 1.0), p=0.4)],p=0.5)]) self.N_aug = A.Compose([A.Normalize(mean=mean, std=std, p=1.0), ToTensor_albu()]) if weak_aug is False: self.augment = A.Compose([ self.T_aug, self.I_aug,self.N_aug]) # self.augment = A.Compose([ self.T_aug, self.I_aug]) # self.augment = A.Compose(self.augment, bbox_params={'format': 'albumentations', 'min_area': 0, 'min_visibility': 0, 'label_fields': ['category_id']}) if self.out_augpos is True: self.augment = A.Compose(self.augment,\ keypoint_params = A.KeypointParams(format= 'xys', \ remove_invisible=False, angle_in_degrees=True))#label_fields=['category_id'], \ else: #weak augment self.T_aug = A.RandomResizedCrop(height = self.sz_hw[0], width = self.sz_hw[1], scale=self.crp_scale, ratio=self.crp_ratio, interpolation = cv2.INTER_CUBIC,p = 1.0) self.augment = A.Compose([ self.T_aug, self.N_aug]) def __call__(self, img): """ Args: img: the output of cv.imread in RGB layout. labels: labels of boxes. """ if self.n_aug==1: #augmented = self.augment(image = img) if self.out_augpos is False: augmented = self.augment(image = img) return augmented['image'] else: hh,ww,_ = img.shape points = [[ww/2.0,hh/2.0,1.0],[0.0,0.0,1.0]] hw_in = img.shape[:2] augmented = self.augment(image = img,keypoints=points) image_aug = augmented['image'] hw_out = image_aug.shape[1:] feat_kpds = torch.tensor(parse_kpds(augmented['keypoints'],hw_in,hw_out)) return (image_aug,feat_kpds) else: # test multi-aug if self.out_augpos is False: return torch.stack([self.augment(image = img)['image'] for _ in range(self.n_aug)]) else: img_out = [] feat_out = [] trans_out = [] hh,ww,_ = img.shape #points = [[ww/2.0,hh/2.0,1.0],[0.0,0.0,1.0]] points = [[ww/2.0,hh/2.0,1.0],[0.0,0.0,1.0],[ww,0.0, 1.0]] # add one point for cv2.getAffineTransform hw_in = img.shape[:2] for _ in range(self.n_aug): augmented = self.augment(image = img,keypoints=points) image_aug = augmented['image'] hw_out = image_aug.shape[1:] #feat_kpds = torch.tensor(parse_kpds(augmented['keypoints'],hw_in,hw_out)) feat_kpds = torch.tensor(parse_kpds(augmented['keypoints'][:2],hw_in,hw_out)) pts2 = augmented['keypoints'] pts1 = np.float32([pt[:2] for pt in points]) pts2 = np.float32([pt[:2] for pt in pts2]) trans = cv2.getAffineTransform(pts2,pts1) trans_out.append(trans) img_out.append(image_aug) feat_out.append(feat_kpds) return (torch.stack(img_out), {'feat_out':torch.stack(feat_out), 'trans_out': np.stack(trans_out)}) #return torch.stack([self.augment(image = img)['image'] for _ in range(self.n_aug)]) # imgs = [] # pts = [] # # hh,ww,_ = img.shape # for _ in range(self.n_aug): # #points = [ww/2.0,hh/2.0,1.0] # points = [[0.0,0.0,1.0], [0.0,hh,1.0], [ww,0.0,1.0],[ww,hh,1.0]] # augmented = self.augment(image = img,keypoints=points,category_id = ['0']) # imgs.append(augmented['image']) # pts.append(augmented['keypoints']) # # NOTE: use bbox will have prob that box is outside crop region. # bboxes= [[0.45, 0.45, 0.55, 0.55]] # # augmented = self.T_aug(image = img,bboxes = bboxes,category_id = ['0']) # hh,ww,_ = img.shape # points = [[ww/2.0,hh/2.0,1.0]] # augmented = self.augment(image = img,keypoints=points,category_id = ['0']) #return augmented['image'] class TestAugmentation_albu: def __init__(self, size, mean=0, std=1.0,out_augpos = False): """ Args: size: the size the of final image. mean: mean pixel value per channel. """ if isinstance(size, int): size = (size,size) self.mean = mean self.size = size self.out_augpos = out_augpos self.augment = A.Compose([A.Resize( size[0], size[1], interpolation=cv2.INTER_CUBIC, p=1), A.Normalize(mean=mean, std=std, p=1.0), ToTensor_albu() ]) if self.out_augpos is True: self.augment = A.Compose(self.augment,\ keypoint_params = A.KeypointParams(format= 'xys', \ remove_invisible=False, angle_in_degrees=True)) def __call__(self, img): """ Args: img: the output of cv.imread in RGB layout. labels: labels of boxes. """ if self.out_augpos is False: augmented = self.augment(image = img) return augmented['image'] else: hh,ww,_ = img.shape points = [[ww/2.0,hh/2.0,1.0],[0.0,0.0,1.0]] hw_in = img.shape[:2] augmented = self.augment(image = img,keypoints=points) image_aug = augmented['image'] hw_out = image_aug.shape[1:] feat_kpds = torch.tensor(parse_kpds(augmented['keypoints'],hw_in,hw_out)) return (image_aug,feat_kpds) class TrainAugmentation_bone: def __init__(self, sz_in_hw = (512,512), sz_out_hw = (448,448),mean=0, std=1.0, minmax_h = (0,128), w2h_ratio = 1.0): """ Args: size: the size the of final image. mean: mean pixel value per channel. """ if isinstance(sz_in_hw, int): sz_in_hw = (sz_in_hw,sz_in_hw) if isinstance(sz_out_hw, int): sz_out_hw = (sz_out_hw,sz_out_hw) self.mean = mean self.sz_in_hw = sz_in_hw self.sz_out_hw = sz_out_hw #self.crp_scale = crp_scale #self.crp_ratio = crp_ratio self.minmax_h = minmax_h self.w2h_ratio = w2h_ratio self.I_aug = A.Compose([A.Resize( sz_in_hw[0], sz_in_hw[1], interpolation=1, p=1), A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2, p=0.5), A.OneOf([A.Blur(blur_limit=5, p=0.3), A.GaussNoise(var_limit=(5.0, 10.0), p=0.3), A.IAASharpen(alpha=(0.1, 0.3), lightness=(0.5, 1.0), p=0.4)],p=0.5)]) self.T_aug = A.RandomSizedCrop(min_max_height = (self.minmax_h[0],self.minmax_h[1]),height = self.sz_out_hw[0], width = self.sz_out_hw[1],\ w2h_ratio = self.w2h_ratio,p = 1.0) self.N_aug = A.Compose([A.Normalize(mean=mean, std=std, p=1.0), ToTensor_albu()]) self.augment = A.Compose([self.I_aug, self.T_aug,self.N_aug]) def __call__(self, img): """ Args: img: the output of cv.imread in RGB layout. labels: labels of boxes. """ augmented = self.augment(image = img) return augmented['image'] class TestAugmentation_bone: #def __init__(self, size, mean=0, std=1.0, ext_p =(-0.125,0.25)): def __init__(self, sz_in_hw = (512,512), sz_out_hw = (448,448), mean=0, std=1.0): """ Args: size: the size the of final image. mean: mean pixel value per channel. """ if isinstance(sz_in_hw, int): sz_in_hw = (sz_in_hw,sz_in_hw) if isinstance(sz_out_hw, int): sz_out_hw = (sz_out_hw,sz_out_hw) self.augment = A.Compose([A.Resize( sz_in_hw[0], sz_in_hw[1], interpolation=1, p=1), A.CenterCrop(sz_out_hw[0], sz_out_hw[1], p=1.0), A.Normalize(mean=mean, std=std, max_pixel_value=255.0, p=1.0), ToTensor_albu() ]) # def __call__(self, img): """ Args: img: the output of cv.imread in RGB layout. labels: labels of boxes. """ augmented = self.augment(image = img) return augmented['image']

0

10,462

0

181

0

50

271

22a8e0eda2fca9bf48bd5895ab01712afaaf9054

265

py

Python

Python/leetcode/HIndexIi.py

darrencheng0817/AlgorithmLearning

aec1ddd0c51b619c1bae1e05f940d9ed587aa82f

[ "MIT" ]

2

2015-12-02T06:44:01.000Z

2016-05-04T21:40:54.000Z

Python/leetcode/HIndexIi.py

darrencheng0817/AlgorithmLearning

aec1ddd0c51b619c1bae1e05f940d9ed587aa82f

[ "MIT" ]

null

Python/leetcode/HIndexIi.py

darrencheng0817/AlgorithmLearning

aec1ddd0c51b619c1bae1e05f940d9ed587aa82f

[ "MIT" ]

null

''' Created on 1.12.2016 @author: Darren '''''' Follow up for H-Index: What if the citations array is sorted in ascending order? Could you optimize your algorithm? Expected runtime complexity is in O(log n) and the input is sorted. " '''

18.928571

117

0.656604

''' Created on 1.12.2016 @author: Darren '''''' Follow up for H-Index: What if the citations array is sorted in ascending order? Could you optimize your algorithm? Expected runtime complexity is in O(log n) and the input is sorted. " '''

0

c6d3edd93fcde8345da8ce9f04c85393e6bb98d8

5,516

py

Python

HW1/hw1/code/visual_recog.py

jiansfoggy/16-720B

6395555449fa297f19efb42970e480f1b382e38a

[ "Unlicense" ]

2

2020-03-31T15:54:49.000Z

2022-01-07T13:43:46.000Z

HW1/hw1/code/visual_recog.py

jiansfoggy/16-720B

6395555449fa297f19efb42970e480f1b382e38a

[ "Unlicense" ]

null

HW1/hw1/code/visual_recog.py

jiansfoggy/16-720B

6395555449fa297f19efb42970e480f1b382e38a

[ "Unlicense" ]

4

2019-09-10T00:48:11.000Z

2022-01-07T13:43:50.000Z

import numpy as np import imageio import visual_words import multiprocessing as mp def build_recognition_system(num_workers=2): ''' Creates a trained recognition system by generating training features from all training images. [input] * num_workers: number of workers to process in parallel [saved] * features: numpy.ndarray of shape (N,M) * labels: numpy.ndarray of shape (N) * dictionary: numpy.ndarray of shape (K,3F) * SPM_layer_num: number of spatial pyramid layers ''' train_data = np.load("../data/train_data.npz") dictionary = np.load("../outputs/dictionary.npy") data = train_data['image_names'] SPM_layer_num = 2 K = 100 size_Feature = int(K*(4**(SPM_layer_num+1) -1)/3) pool = mp.Pool(num_workers) results = [] for i in range(0, len(data)): print (i) args = [data[i][0], dictionary, SPM_layer_num, K] results.append(pool.apply_async(get_image_feature, args)) features = [] for result in results: features.append(result.get()) final_features = np.reshape(features, (len(data), size_Feature)) labels = np.asarray(train_data['labels']) np.savez('../outputs/trained_system.npz', features = final_features, labels = labels, SPM_layer_num = SPM_layer_num, dictionary = dictionary) def evaluate_recognition_system(num_workers=2): ''' Evaluates the recognition system for all test images and returns the confusion matrix. [input] * num_workers: number of workers to process in parallel [output] * conf: numpy.ndarray of shape (8,8) * accuracy: accuracy of the evaluated system ''' test_data = np.load("../data/test_data.npz") trained_system = np.load("../outputs/trained_system.npz") features = trained_system['features'] dictionary = trained_system['dictionary'] SPM_layer_num = trained_system['SPM_layer_num'] labels = trained_system['labels'] K = dictionary.shape[0] data = test_data['image_names'] pool = mp.Pool(num_workers) features_test = [] for i in range(0, len(data)): args = [(data[i][0], dictionary, SPM_layer_num, K, features, labels)] features_test.append(pool.apply_async(test_label, args)) test_labels = [] for feature in features_test: test_labels.append(feature.get()) testActualLabels = test_data['labels'] size_confusion = len(np.unique(testActualLabels)) C = np.zeros((size_confusion, size_confusion)) for a,p in zip(testActualLabels, test_labels): C[a][p] += 1 accuracy = np.diag(C).sum()/C.sum() return C, accuracy def get_image_feature(file_path,dictionary,layer_num,K): ''' Extracts the spatial pyramid matching feature. [input] * file_path: path of image file to read * dictionary: numpy.ndarray of shape (K,3F) * layer_num: number of spatial pyramid layers * K: number of clusters for the word maps [output] * feature: numpy.ndarray of shape (K*(4^layer_num-1)/3) ''' image = imageio.imread('../data/' + file_path) wordmap = visual_words.get_visual_words(image, dictionary) hist_all = get_feature_from_wordmap_SPM(wordmap, layer_num, K) return hist_all def distance_to_set(word_hist,histograms): ''' Compute similarity between a histogram of visual words with all training image histograms. [input] * word_hist: numpy.ndarray of shape (K) * histograms: numpy.ndarray of shape (N,K) [output] * sim: numpy.ndarray of shape (N) ''' min_compare = np.minimum(histograms, word_hist) return np.sum(min_compare, axis=1) def get_feature_from_wordmap(wordmap,dict_size): ''' Compute histogram of visual words. [input] * wordmap: numpy.ndarray of shape (H,W) * dict_size: dictionary size K [output] * hist: numpy.ndarray of shape (K) ''' flatten_wordmap = wordmap.flatten() hist = np.histogram(flatten_wordmap, bins = dict_size, range = (0,dict_size)) hist = hist[0]/np.linalg.norm(hist[0], ord = 1) return np.asarray(hist) def get_feature_from_wordmap_SPM(wordmap,layer_num,dict_size): ''' Compute histogram of visual words using spatial pyramid matching. [input] * wordmap: numpy.ndarray of shape (H,W) * layer_num: number of spatial pyramid layers * dict_size: dictionary size K [output] * hist_all: numpy.ndarray of shape (K*(4^layer_num-1)/3) ''' i_h, i_w = wordmap.shape hist_all = [] for layer in range(0, layer_num+1): D = 2**layer if layer == 0 or layer == 1: weight = 1/(2**(layer_num)) else: weight = 1/(2**(layer_num+1-layer)) height_indices = np.round(np.arange(0, i_h+1, i_h/D)).astype('int') width_indices = np.round(np.arange(0, i_w+1, i_w/D)).astype('int') divisions = height_indices.shape[0]-1 for i in range(0, divisions): for j in range (0, divisions): s_h, s_w = height_indices[i], width_indices[j] e_h, e_w = height_indices[i+1], width_indices[j+1] imageSection = wordmap[s_h:e_h, s_w:e_w] imageDictionary = get_feature_from_wordmap(imageSection, dict_size) imageDictionary = imageDictionary*weight hist_all.append(imageDictionary) hist_all = np.asarray(hist_all) hist_all = hist_all.flatten() hist_all = hist_all/np.linalg.norm(hist_all, ord = 1) return hist_all

28.729167

143

0.701051

import numpy as np import threading import queue import imageio import os,time import math import visual_words import multiprocessing as mp def build_recognition_system(num_workers=2): ''' Creates a trained recognition system by generating training features from all training images. [input] * num_workers: number of workers to process in parallel [saved] * features: numpy.ndarray of shape (N,M) * labels: numpy.ndarray of shape (N) * dictionary: numpy.ndarray of shape (K,3F) * SPM_layer_num: number of spatial pyramid layers ''' train_data = np.load("../data/train_data.npz") dictionary = np.load("../outputs/dictionary.npy") data = train_data['image_names'] SPM_layer_num = 2 K = 100 size_Feature = int(K*(4**(SPM_layer_num+1) -1)/3) pool = mp.Pool(num_workers) results = [] for i in range(0, len(data)): print (i) args = [data[i][0], dictionary, SPM_layer_num, K] results.append(pool.apply_async(get_image_feature, args)) features = [] for result in results: features.append(result.get()) final_features = np.reshape(features, (len(data), size_Feature)) labels = np.asarray(train_data['labels']) np.savez('../outputs/trained_system.npz', features = final_features, labels = labels, SPM_layer_num = SPM_layer_num, dictionary = dictionary) def test_label(args): file_path,dictionary,layer_num,K, features, labels = args feature = get_image_feature(file_path, dictionary, layer_num, K) distance = distance_to_set(feature, features) i = np.argmax(distance) label = labels[i] return label def evaluate_recognition_system(num_workers=2): ''' Evaluates the recognition system for all test images and returns the confusion matrix. [input] * num_workers: number of workers to process in parallel [output] * conf: numpy.ndarray of shape (8,8) * accuracy: accuracy of the evaluated system ''' test_data = np.load("../data/test_data.npz") trained_system = np.load("../outputs/trained_system.npz") features = trained_system['features'] dictionary = trained_system['dictionary'] SPM_layer_num = trained_system['SPM_layer_num'] labels = trained_system['labels'] K = dictionary.shape[0] data = test_data['image_names'] pool = mp.Pool(num_workers) features_test = [] for i in range(0, len(data)): args = [(data[i][0], dictionary, SPM_layer_num, K, features, labels)] features_test.append(pool.apply_async(test_label, args)) test_labels = [] for feature in features_test: test_labels.append(feature.get()) testActualLabels = test_data['labels'] size_confusion = len(np.unique(testActualLabels)) C = np.zeros((size_confusion, size_confusion)) for a,p in zip(testActualLabels, test_labels): C[a][p] += 1 accuracy = np.diag(C).sum()/C.sum() return C, accuracy def get_image_feature(file_path,dictionary,layer_num,K): ''' Extracts the spatial pyramid matching feature. [input] * file_path: path of image file to read * dictionary: numpy.ndarray of shape (K,3F) * layer_num: number of spatial pyramid layers * K: number of clusters for the word maps [output] * feature: numpy.ndarray of shape (K*(4^layer_num-1)/3) ''' image = imageio.imread('../data/' + file_path) wordmap = visual_words.get_visual_words(image, dictionary) hist_all = get_feature_from_wordmap_SPM(wordmap, layer_num, K) return hist_all def distance_to_set(word_hist,histograms): ''' Compute similarity between a histogram of visual words with all training image histograms. [input] * word_hist: numpy.ndarray of shape (K) * histograms: numpy.ndarray of shape (N,K) [output] * sim: numpy.ndarray of shape (N) ''' min_compare = np.minimum(histograms, word_hist) return np.sum(min_compare, axis=1) def get_feature_from_wordmap(wordmap,dict_size): ''' Compute histogram of visual words. [input] * wordmap: numpy.ndarray of shape (H,W) * dict_size: dictionary size K [output] * hist: numpy.ndarray of shape (K) ''' flatten_wordmap = wordmap.flatten() hist = np.histogram(flatten_wordmap, bins = dict_size, range = (0,dict_size)) hist = hist[0]/np.linalg.norm(hist[0], ord = 1) return np.asarray(hist) def get_feature_from_wordmap_SPM(wordmap,layer_num,dict_size): ''' Compute histogram of visual words using spatial pyramid matching. [input] * wordmap: numpy.ndarray of shape (H,W) * layer_num: number of spatial pyramid layers * dict_size: dictionary size K [output] * hist_all: numpy.ndarray of shape (K*(4^layer_num-1)/3) ''' i_h, i_w = wordmap.shape hist_all = [] for layer in range(0, layer_num+1): D = 2**layer if layer == 0 or layer == 1: weight = 1/(2**(layer_num)) else: weight = 1/(2**(layer_num+1-layer)) height_indices = np.round(np.arange(0, i_h+1, i_h/D)).astype('int') width_indices = np.round(np.arange(0, i_w+1, i_w/D)).astype('int') divisions = height_indices.shape[0]-1 for i in range(0, divisions): for j in range (0, divisions): s_h, s_w = height_indices[i], width_indices[j] e_h, e_w = height_indices[i+1], width_indices[j+1] imageSection = wordmap[s_h:e_h, s_w:e_w] imageDictionary = get_feature_from_wordmap(imageSection, dict_size) imageDictionary = imageDictionary*weight hist_all.append(imageDictionary) hist_all = np.asarray(hist_all) hist_all = hist_all.flatten() hist_all = hist_all/np.linalg.norm(hist_all, ord = 1) return hist_all

0

236

0

-31

117

076663290b2821e6423b989415d2957ab3b21b81

441

py

Python

app/__init__.py

SalesAppi/JPSSM_topics

6be32fca31e5e15f51753101a222a08fd2013f9b

[ "MIT" ]

1

2022-03-01T08:15:28.000Z

app/__init__.py

SalesAppi/JPSSM_topics

6be32fca31e5e15f51753101a222a08fd2013f9b

[ "MIT" ]

null

app/__init__.py

SalesAppi/JPSSM_topics

6be32fca31e5e15f51753101a222a08fd2013f9b

[ "MIT" ]

1

2020-12-14T05:00:28.000Z

from flask import Flask #from nltk.tokenize import RegexpTokenizer #from nltk import stem #from nltk.stem import WordNetLemmatizer app = Flask(__name__)

19.173913

42

0.825397

from flask import Flask import gensim import re from gensim.models import LdaModel from gensim.test.utils import datapath from gensim import corpora, models from gensim.corpora import Dictionary from re import sub import os import string import codecs import nltk #from nltk.tokenize import RegexpTokenizer #from nltk import stem #from nltk.stem import WordNetLemmatizer app = Flask(__name__) from app import views from app import model

0

-2

290

f6c6564e121ddf8a7df728a398f2d1498dea1117

7,942

py

Python

tables.py

arunext/greffy

001a0b94428629b9cdfaa8966f3cf6cd6f349e8a

[ "Apache-2.0" ]

null

tables.py

arunext/greffy

001a0b94428629b9cdfaa8966f3cf6cd6f349e8a

[ "Apache-2.0" ]

null

tables.py

arunext/greffy

001a0b94428629b9cdfaa8966f3cf6cd6f349e8a

[ "Apache-2.0" ]

null

import psycopg2 from config import config import datetime from textblob import TextBlob import nltk from nltk.corpus import stopwords def create_tables(): """ create tables in the PostgreSQL database""" params = config() # connect to the PostgreSQL server conn = psycopg2.connect(**params) cur = conn.cursor() cur.execute('''CREATE TABLE IF NOT EXISTS POSTS (POST_ID INT PRIMARY KEY NOT NULL, DATA TEXT NOT NULL, CREATED TIMESTAMP NOT NULL, COMMENTS INT, COUNT INT);''') cur.execute('''CREATE TABLE IF NOT EXISTS COMMENTS (POST_ID INT NOT NULL, COMMENT_ID INT PRIMARY KEY NOT NULL, DATA TEXT NOT NULL, CREATED TIMESTAMP NOT NULL, UPVOTES INT, DOWNVOTES INT);''') conn.commit() conn.close() def create_post(postid, text): """ insert a new post into the vendors table """ print("inside create post") # read database configuration params = config() # connect to the PostgreSQL database conn = psycopg2.connect(**params) cur = conn.cursor() count = 0 comments = 0 time = datetime.datetime.utcnow(); cur.execute("INSERT INTO POSTS (POST_ID, DATA, CREATED, COMMENTS, COUNT) VALUES (%s, %s, %s, %s, %s)",(postid,text,time,comments,count)); conn.commit() print("Records created successfully") conn.close() def create_comment(postid, commentid, text): """ insert a new comment into the post table """ print("inside create comments") # read database configuration params = config() # connect to the PostgreSQL database conn = psycopg2.connect(**params) cur = conn.cursor() count = 0 time = datetime.datetime.utcnow(); cur.execute("INSERT INTO COMMENTS (POST_ID, COMMENT_ID, DATA, CREATED, UPVOTES, DOWNVOTES) VALUES (%s, %s, %s, %s, 0, 0)",(postid,commentid,text, time)); # Get Corresponding post cur.execute("SELECT POST_ID, COMMENTS from POSTS where POST_ID = {0} ORDER BY COUNT DESC".format(postid)); rows = cur.fetchall() for row in rows: comments = row[1] break comments = comments+1 # Update Comments count of post cur.execute("UPDATE POSTS set COMMENTS = {0} where POST_ID = {1}".format(comments,postid)); conn.commit() print("Records created successfully") conn.close() def lookup_table(text): """ insert a new post into the vendors table """ print("inside lookup to tables") # read database configuration params = config() # connect to the PostgreSQL database conn = psycopg2.connect(**params) cur = conn.cursor() #initialize id and count to null values postid = 0 count = 0 #Select post cur.execute("SELECT POST_ID, DATA, COUNT from POSTS where DATA = '{0}' ORDER BY COUNT DESC".format(text)); rows = cur.fetchall() for row in rows: postid = row[0] count = row[2] break print "Lookup operation done successfully. Id = {0}".format(id); conn.close() return postid, count def update_table_count(postid, count): """ update post with count """ print("inside lookup to tables") # read database configuration params = config() # connect to the PostgreSQL database conn = psycopg2.connect(**params) cur = conn.cursor() cur.execute("UPDATE POSTS set COUNT = {0} where POST_ID = {1}".format(count,postid)); conn.commit() print "Update operation done successfully for POST_ID {0} and count {1}".format(postid,count) conn.close() def comment_upvote(comment_id): """ update post with count """ print("inside upvote comment") # read database configuration params = config() # connect to the PostgreSQL database conn = psycopg2.connect(**params) cur = conn.cursor() # Get Corresponding comment cur.execute("SELECT COMMENT_ID, UPVOTES, POST_ID from COMMENTS where COMMENT_ID = {0} ORDER BY UPVOTES DESC".format(comment_id)); rows = cur.fetchall() for row in rows: upvotes = row[1] break upvotes = upvotes+1 # Update Comments count of post cur.execute("UPDATE COMMENTS set UPVOTES = {0} where COMMENT_ID = {1}".format(upvotes,comment_id)); conn.commit() print ("Comment upvote completed") conn.close() #return post ID so that redirect can use it return (row[2]) def comment_downvote(comment_id): """ update comment with dwnvote """ print("inside downvote comment") # read database configuration params = config() # connect to the PostgreSQL database conn = psycopg2.connect(**params) cur = conn.cursor() # Get Corresponding comment cur.execute("SELECT COMMENT_ID, DOWNVOTES, POST_ID from COMMENTS where COMMENT_ID = {0} ORDER BY DOWNVOTES DESC".format(comment_id)); rows = cur.fetchall() for row in rows: downvotes = row[1] break downvotes = downvotes+1 # Update Comments count of post cur.execute("UPDATE COMMENTS set DOWNVOTES = {0} where COMMENT_ID = {1}".format(downvotes,comment_id)); conn.commit() print ("Comment upvote completed") conn.close() #return post ID so that redirect can use it return (row[2])

27.013605

157

0.629816

import psycopg2 from config import config import datetime from textblob import TextBlob import nltk from nltk.corpus import stopwords def create_tables(): """ create tables in the PostgreSQL database""" params = config() # connect to the PostgreSQL server conn = psycopg2.connect(**params) cur = conn.cursor() cur.execute('''CREATE TABLE IF NOT EXISTS POSTS (POST_ID INT PRIMARY KEY NOT NULL, DATA TEXT NOT NULL, CREATED TIMESTAMP NOT NULL, COMMENTS INT, COUNT INT);''') cur.execute('''CREATE TABLE IF NOT EXISTS COMMENTS (POST_ID INT NOT NULL, COMMENT_ID INT PRIMARY KEY NOT NULL, DATA TEXT NOT NULL, CREATED TIMESTAMP NOT NULL, UPVOTES INT, DOWNVOTES INT);''') conn.commit() conn.close() def show_table(): print("creating tables with") create_tables() #creating table, later check if table exists. print("Inside show tables") """ show tables from the PostgreSQL database""" params = config() # connect to the PostgreSQL server conn = psycopg2.connect(**params) print ("Opened database successfully") cur = conn.cursor() cur.execute("SELECT POST_ID, DATA, COUNT, COMMENTS from POSTS ORDER BY COUNT DESC") rows = cur.fetchall() #table_text = "" #for row in rows: # table_text += "Post ID = " + str(row[0]) # table_text += "Text = " + row[1] #table_text += "Count = " + str(row[2]) + "\n" conn.close() return rows def show_post(postid): print("Inside show post") """ show tables from the PostgreSQL database""" params = config() # connect to the PostgreSQL server conn = psycopg2.connect(**params) print ("Opened database successfully") cur = conn.cursor() cur.execute("SELECT POST_ID, COMMENT_ID, DATA, CREATED, UPVOTES, DOWNVOTES from COMMENTS where POST_ID = {0} ORDER BY UPVOTES DESC".format(postid)); rows = cur.fetchall() conn.close() return rows def create_post(postid, text): """ insert a new post into the vendors table """ print("inside create post") # read database configuration params = config() # connect to the PostgreSQL database conn = psycopg2.connect(**params) cur = conn.cursor() count = 0 comments = 0 time = datetime.datetime.utcnow(); cur.execute("INSERT INTO POSTS (POST_ID, DATA, CREATED, COMMENTS, COUNT) VALUES (%s, %s, %s, %s, %s)",(postid,text,time,comments,count)); conn.commit() print("Records created successfully") conn.close() def create_comment(postid, commentid, text): """ insert a new comment into the post table """ print("inside create comments") # read database configuration params = config() # connect to the PostgreSQL database conn = psycopg2.connect(**params) cur = conn.cursor() count = 0 time = datetime.datetime.utcnow(); cur.execute("INSERT INTO COMMENTS (POST_ID, COMMENT_ID, DATA, CREATED, UPVOTES, DOWNVOTES) VALUES (%s, %s, %s, %s, 0, 0)",(postid,commentid,text, time)); # Get Corresponding post cur.execute("SELECT POST_ID, COMMENTS from POSTS where POST_ID = {0} ORDER BY COUNT DESC".format(postid)); rows = cur.fetchall() for row in rows: comments = row[1] break comments = comments+1 # Update Comments count of post cur.execute("UPDATE POSTS set COMMENTS = {0} where POST_ID = {1}".format(comments,postid)); conn.commit() print("Records created successfully") conn.close() def lookup_table(text): """ insert a new post into the vendors table """ print("inside lookup to tables") # read database configuration params = config() # connect to the PostgreSQL database conn = psycopg2.connect(**params) cur = conn.cursor() #initialize id and count to null values postid = 0 count = 0 #Select post cur.execute("SELECT POST_ID, DATA, COUNT from POSTS where DATA = '{0}' ORDER BY COUNT DESC".format(text)); rows = cur.fetchall() for row in rows: postid = row[0] count = row[2] break print "Lookup operation done successfully. Id = {0}".format(id); conn.close() return postid, count def get_post_summary(postid): #currently send the top comment, latet this is the key logic to send response print("inside get post summary") # read database configuration params = config() # connect to the PostgreSQL database conn = psycopg2.connect(**params) cur = conn.cursor() cur.execute("SELECT POST_ID, COMMENT_ID, DATA, CREATED, UPVOTES, DOWNVOTES from COMMENTS where POST_ID = {0} ORDER BY UPVOTES DESC".format(postid)); rows = cur.fetchall() count = 0 catcomments = "" for row in rows: count = count + 1 if count == 1: topcomment = row[2] catcomments = catcomments + row[2] if count == 0: #no comments, ask user to comment topcomment = "Sorry, we don't have any comments, be the first one to comment: http://greffy.herokuapp.com/post/" + str(postid) polarity = 0 subjectivity = 0 else: blob = TextBlob(catcomments) # TODO add overall positive, neutral negative instead of polarity blob.sentences words = b polarity =round(blob.sentiment.polarity,2) subjectivity = round(blob.sentiment.subjectivity,2) print(topcomment,polarity) return topcomment,polarity def update_table_count(postid, count): """ update post with count """ print("inside lookup to tables") # read database configuration params = config() # connect to the PostgreSQL database conn = psycopg2.connect(**params) cur = conn.cursor() cur.execute("UPDATE POSTS set COUNT = {0} where POST_ID = {1}".format(count,postid)); conn.commit() print "Update operation done successfully for POST_ID {0} and count {1}".format(postid,count) conn.close() def comment_upvote(comment_id): """ update post with count """ print("inside upvote comment") # read database configuration params = config() # connect to the PostgreSQL database conn = psycopg2.connect(**params) cur = conn.cursor() # Get Corresponding comment cur.execute("SELECT COMMENT_ID, UPVOTES, POST_ID from COMMENTS where COMMENT_ID = {0} ORDER BY UPVOTES DESC".format(comment_id)); rows = cur.fetchall() for row in rows: upvotes = row[1] break upvotes = upvotes+1 # Update Comments count of post cur.execute("UPDATE COMMENTS set UPVOTES = {0} where COMMENT_ID = {1}".format(upvotes,comment_id)); conn.commit() print ("Comment upvote completed") conn.close() #return post ID so that redirect can use it return (row[2]) def comment_downvote(comment_id): """ update comment with dwnvote """ print("inside downvote comment") # read database configuration params = config() # connect to the PostgreSQL database conn = psycopg2.connect(**params) cur = conn.cursor() # Get Corresponding comment cur.execute("SELECT COMMENT_ID, DOWNVOTES, POST_ID from COMMENTS where COMMENT_ID = {0} ORDER BY DOWNVOTES DESC".format(comment_id)); rows = cur.fetchall() for row in rows: downvotes = row[1] break downvotes = downvotes+1 # Update Comments count of post cur.execute("UPDATE COMMENTS set DOWNVOTES = {0} where COMMENT_ID = {1}".format(downvotes,comment_id)); conn.commit() print ("Comment upvote completed") conn.close() #return post ID so that redirect can use it return (row[2])

0

2,434

0

69

2f1c4753ac08df358bf6226a60a7c9bda64e76e2

950

py

Python

weltgeist/units.py

samgeen/Weltgeist

c7d52e879bb3473cecbb06651b5e76dac3020da6

[ "MIT" ]

null

weltgeist/units.py

samgeen/Weltgeist

c7d52e879bb3473cecbb06651b5e76dac3020da6

[ "MIT" ]

null

weltgeist/units.py

samgeen/Weltgeist

c7d52e879bb3473cecbb06651b5e76dac3020da6

[ "MIT" ]

null

""" Defined code units and physical quantities The Python parts of Weltgeist use cgs VH1 uses units defined below Sam Geen, February 2018 """ import numpy as np # Physical quantities (base units in cgs) pc = 3.086e+18 mH = 1.66e-24 year = 3.154e+7 Myr = 1e6*year kB = 1.3806485279e-16 # in cgs G = 6.67428e-8 X = 0.74 mp = mH / X c = 2.998e+10 eV = 1.60217662e-12 # in ergs Msun = 1.9891e33 # g # Code units # Used by VH1 - the Python parts of Weltgeist use cgs distance = pc # in cm density = mH # 1 g/cm^3 time = 1.0 / np.sqrt(G*density) # sets G=1 in VH1 (not super important here, though) # Derived units velocity = distance / time mass = density*distance**3.0 pressure = density * velocity**2.0 energy = mass*velocity**2.0 # Note: this is acceleration! In the code (e.g. forces.f90), grav = v*v/r # e.g. 2*GM/r = v_esc^2, so g=GM/r^2=0.5*v_esc^2/r gravity = G*mass/distance**2 # velocity*velocity/distance

26.388889

85

0.663158

""" Defined code units and physical quantities The Python parts of Weltgeist use cgs VH1 uses units defined below Sam Geen, February 2018 """ import numpy as np # Physical quantities (base units in cgs) pc = 3.086e+18 mH = 1.66e-24 year = 3.154e+7 Myr = 1e6*year kB = 1.3806485279e-16 # in cgs G = 6.67428e-8 X = 0.74 mp = mH / X c = 2.998e+10 eV = 1.60217662e-12 # in ergs Msun = 1.9891e33 # g # Code units # Used by VH1 - the Python parts of Weltgeist use cgs distance = pc # in cm density = mH # 1 g/cm^3 time = 1.0 / np.sqrt(G*density) # sets G=1 in VH1 (not super important here, though) # Derived units velocity = distance / time mass = density*distance**3.0 pressure = density * velocity**2.0 energy = mass*velocity**2.0 # Note: this is acceleration! In the code (e.g. forces.f90), grav = v*v/r # e.g. 2*GM/r = v_esc^2, so g=GM/r^2=0.5*v_esc^2/r gravity = G*mass/distance**2 # velocity*velocity/distance

0

ca8b1afb26f13038161c24aead09569f01b99768

9,456

py

Python

olwidget/widgets.py

jj0hns0n/mednet

efb6681292e7ac8f870ee5967a5a2b352853ae35

[ "BSD-3-Clause" ]

2

2016-02-18T01:06:04.000Z

2016-02-18T03:53:37.000Z

olwidget/widgets.py

jj0hns0n/mednet

efb6681292e7ac8f870ee5967a5a2b352853ae35

[ "BSD-3-Clause" ]

null

olwidget/widgets.py

jj0hns0n/mednet

efb6681292e7ac8f870ee5967a5a2b352853ae35

[ "BSD-3-Clause" ]

null

import re from django.contrib.gis.gdal import OGRGeometry from django.contrib.gis.geos import GEOSGeometry from django.conf import settings # Default settings for paths and API URLs. These can all be overridden by # specifying a value in settings.py api_defaults = { 'GOOGLE_API_KEY': "", 'YAHOO_APP_ID': "", 'OLWIDGET_MEDIA_URL': url_join(settings.MEDIA_URL, "olwidget"), 'GOOGLE_API': "http://maps.google.com/maps?file=api&v=2", 'YAHOO_API': "http://api.maps.yahoo.com/ajaxymap?v=3.0", 'OSM_API': "http://openstreetmap.org/openlayers/OpenStreetMap.js", 'OL_API': "http://openlayers.org/api/2.8/OpenLayers.js", 'MS_VE_API' : "http://dev.virtualearth.net/mapcontrol/mapcontrol.ashx?v=6.1", } for key, default in api_defaults.iteritems(): if not hasattr(settings, key): setattr(settings, key, default) OLWIDGET_JS = url_join(settings.OLWIDGET_MEDIA_URL, "js/olwidget.js") OLWIDGET_CSS = url_join(settings.OLWIDGET_MEDIA_URL, "css/olwidget.css") DEFAULT_PROJ = "4326" ewkt_re = re.compile("^SRID=(?P<srid>\d+);(?P<wkt>.+)$", re.I) def get_wkt(value, srid=DEFAULT_PROJ): """ `value` is either a WKT string or a geometry field. Returns WKT in the projection for the given SRID. """ ogr = None if value: if isinstance(value, OGRGeometry): ogr = value elif isinstance(value, GEOSGeometry): ogr = value.ogr elif isinstance(value, basestring): match = ewkt_re.match(value) if match: ogr = OGRGeometry(match.group('wkt'), match.group('srid')) else: ogr = OGRGeometry(value) wkt = '' if ogr: # Workaround for Django bug #12312. GEOSGeometry types don't support 3D wkt; # OGRGeometry types output 3D for linestrings even if they should do 2D, causing # IntegrityError's. if ogr.dimension == 2: geos = ogr.geos geos.transform(srid) wkt = geos.wkt else: ogr.transform(srid) wkt = ogr.wkt return wkt def collection_wkt(fields): """ Returns WKT for the given list of geometry fields. """ if not fields: return "" if len(fields) == 1: return get_wkt(fields[0]) return "GEOMETRYCOLLECTION(%s)" % \ ",".join(get_wkt(field) for field in fields) def add_srid(wkt, srid=DEFAULT_PROJ): """ Returns EWKT (WKT with a specified SRID) for the given wkt and SRID (default 4326). """ if wkt: return "SRID=%s;%s" % (srid, wkt) return ""

32.273038

88

0.602792

import re from django.contrib.gis.gdal import OGRException, OGRGeometry from django.contrib.gis.geos import GEOSGeometry from django.forms.widgets import Textarea from django.template.loader import render_to_string from django.utils import simplejson from django.conf import settings from django import forms def reduce_url_parts(a, b): if a[-1] == "/": return a + b return a + "/" + b def url_join(*args): return reduce(reduce_url_parts, args) # Default settings for paths and API URLs. These can all be overridden by # specifying a value in settings.py api_defaults = { 'GOOGLE_API_KEY': "", 'YAHOO_APP_ID': "", 'OLWIDGET_MEDIA_URL': url_join(settings.MEDIA_URL, "olwidget"), 'GOOGLE_API': "http://maps.google.com/maps?file=api&v=2", 'YAHOO_API': "http://api.maps.yahoo.com/ajaxymap?v=3.0", 'OSM_API': "http://openstreetmap.org/openlayers/OpenStreetMap.js", 'OL_API': "http://openlayers.org/api/2.8/OpenLayers.js", 'MS_VE_API' : "http://dev.virtualearth.net/mapcontrol/mapcontrol.ashx?v=6.1", } for key, default in api_defaults.iteritems(): if not hasattr(settings, key): setattr(settings, key, default) OLWIDGET_JS = url_join(settings.OLWIDGET_MEDIA_URL, "js/olwidget.js") OLWIDGET_CSS = url_join(settings.OLWIDGET_MEDIA_URL, "css/olwidget.css") DEFAULT_PROJ = "4326" def separated_lowercase_to_lower_camelcase(input): return re.sub('_\w', lambda match: match.group(0)[-1].upper(), input) def translate_options(options): translated = {} for key, value in options.iteritems(): new_key = separated_lowercase_to_lower_camelcase(key) # recurse if isinstance(value, dict): translated[new_key] = translate_options(value) else: translated[new_key] = value return translated class MapMixin(object): def set_options(self, options, template): self.options = options or {} # Though this is the olwidget.js default, it must be explicitly set so # form.media knows to include osm. self.options['layers'] = self.options.get('layers', ['osm.mapnik']) self.template = template or self.default_template def _media(self): js = set() # collect scripts necessary for various layers for layer in self.options['layers']: if layer.startswith("osm."): js.add(settings.OSM_API) elif layer.startswith("google."): js.add(settings.GOOGLE_API + "&key=%s" % settings.GOOGLE_API_KEY) elif layer.startswith("yahoo."): js.add(settings.YAHOO_API + "&appid=%s" % settings.YAHOO_APP_ID) elif layer.startswith("ve."): js.add(settings.MS_VE_API) js = [settings.OL_API, OLWIDGET_JS] + list(js) return forms.Media(css={'all': (OLWIDGET_CSS,)}, js=js) media = property(_media) class EditableMap(forms.Textarea, MapMixin): """ An OpenLayers mapping widget for geographic data. Example:: from django import forms from olwidget.widgets import OLWidget class MyForm(forms.Form): location = forms.CharField(widget=EditableMap( options={'geometry': 'point'})) """ default_template = 'olwidget/editable_map.html' def __init__(self, options=None, template=None): self.set_options(options, template) super(EditableMap, self).__init__() def render(self, name, value, attrs=None): if not attrs: attrs = {} # without an id, javascript fails if attrs.has_key('id'): element_id = attrs['id'] else: element_id = "id_%s" % id(self) # Allow passing of wkt for MapDisplay subclass if attrs.has_key('wkt'): wkt = attrs['wkt'] else: # Use the default SRID's wkt = add_srid(get_wkt(value)) if name and not self.options.has_key('name'): self.options['name'] = name context = { 'id': element_id, 'name': name, 'wkt': wkt, 'map_opts': simplejson.dumps( translate_options(self.options) ), } return render_to_string(self.template, context) class MapDisplay(EditableMap): """ Object for display of geometries on an OpenLayers map. Arguments (all are optional): * ``fields`` - a list of geometric fields or WKT strings to display on the map. If none are given, the map will have no overlay. * ``name`` - a name to use for display of the field data layer. * ``options`` - a dict of options for map display. A complete list of options is in the documentation for olwidget.js. Example:: from olwidget.widgets import MapDisplay map = MapDisplay(fields=[my_model.start_point, my_model.destination]) To use in a template, first display the media (URLs for javascript and CSS needed for map display) and then print the MapDisplay object, as in the following:: <html> <head> {{ map.media }} </head> <body> {{ map }} </body> </html> By default, maps rendered by MapDisplay objects are not editable, but this can be overriden by setting "options['editable'] = True". """ def __init__(self, fields=None, options=None, template=None): self.fields = fields options = options or {} if not options.has_key('editable'): options['editable'] = False if (self.fields and len(self.fields) > 1) or \ (fields[0].geom_type.upper() == 'GEOMETRYCOLLECTION'): options['isCollection'] = True super(MapDisplay, self).__init__(options, template) def __unicode__(self): wkt = add_srid(collection_wkt(self.fields)) name = self.options.get('name', 'data') return self.render(name, None, attrs={'wkt': wkt}) class InfoMap(forms.Widget, MapMixin): """ Widget for displaying maps with pop-up info boxes over geometries. Arguments: * ``info``: an array of [geometry, HTML] pairs that specify geometries, and the popup contents associated with them. Geometries can be expressed as geometry fields, or as WKT strings. Example:: [ [geomodel1.geofield, "<p>Model One</p>"], [geomodel2.geofield, "<p>Model Two</p>"], ... ] * ``options``: an optional dict of options for map display. In templates, InfoMap.media must be displayed in addition to InfoMap for the map to function properly. """ default_template = 'olwidget/info_map.html' def __init__(self, info=None, options=None, template=None): self.info = info self.set_options(options, template) super(InfoMap, self).__init__() def render(self, name, value, attrs=None): if not self.info: info_json = '[]' else: # convert fields to wkt and translate options if needed wkt_array = [] for geom, attr in self.info: wkt = add_srid(get_wkt(geom)) if isinstance(attr, dict): wkt_array.append([wkt, translate_options(attr)]) else: wkt_array.append([wkt, attr]) info_json = simplejson.dumps(wkt_array) # arbitrary unique id div_id = "id_%s" % id(self) context = { 'id': div_id, 'info_array': info_json, 'map_opts': simplejson.dumps( translate_options(self.options) ), } return render_to_string(self.template, context) def __unicode__(self): return self.render(None, None) ewkt_re = re.compile("^SRID=(?P<srid>\d+);(?P<wkt>.+)$", re.I) def get_wkt(value, srid=DEFAULT_PROJ): """ `value` is either a WKT string or a geometry field. Returns WKT in the projection for the given SRID. """ ogr = None if value: if isinstance(value, OGRGeometry): ogr = value elif isinstance(value, GEOSGeometry): ogr = value.ogr elif isinstance(value, basestring): match = ewkt_re.match(value) if match: ogr = OGRGeometry(match.group('wkt'), match.group('srid')) else: ogr = OGRGeometry(value) wkt = '' if ogr: # Workaround for Django bug #12312. GEOSGeometry types don't support 3D wkt; # OGRGeometry types output 3D for linestrings even if they should do 2D, causing # IntegrityError's. if ogr.dimension == 2: geos = ogr.geos geos.transform(srid) wkt = geos.wkt else: ogr.transform(srid) wkt = ogr.wkt return wkt def collection_wkt(fields): """ Returns WKT for the given list of geometry fields. """ if not fields: return "" if len(fields) == 1: return get_wkt(fields[0]) return "GEOMETRYCOLLECTION(%s)" % \ ",".join(get_wkt(field) for field in fields) def add_srid(wkt, srid=DEFAULT_PROJ): """ Returns EWKT (WKT with a specified SRID) for the given wkt and SRID (default 4326). """ if wkt: return "SRID=%s;%s" % (srid, wkt) return ""

0

5,961

0

539

0

81

272

a777c1f7cbe7e6ff795a3c5c9391e45397c000e0

921

py

Python

advent/year2021/day1.py

davweb/advent-of-code

6d9ac52092f4aad26a84d7cfd2fcd8420f1ea612

[ "Unlicense" ]

null

advent/year2021/day1.py

davweb/advent-of-code

6d9ac52092f4aad26a84d7cfd2fcd8420f1ea612

[ "Unlicense" ]

null

advent/year2021/day1.py

davweb/advent-of-code

6d9ac52092f4aad26a84d7cfd2fcd8420f1ea612

[ "Unlicense" ]

null

#!/usr/local/bin/python3 def part1(data): """ >>> part1([199, 200, 208, 210, 200, 207, 240, 269, 260, 263]) 7 >>> part1(read_input()) 1581 """ previous = data[0] count = 0 for value in data[1:]: if value > previous: count += 1 previous = value return count def part2(data): """ >>> part2([199, 200, 208, 210, 200, 207, 240, 269, 260, 263]) 5 >>> part2(read_input()) 1618 """ count = 0 for i in range(1, len(data) - 2): previous = sum(data[i - 1:i + 2]) value = sum(data[i:i + 3]) if value > previous: count += 1 return count if __name__ == "__main__": main()

16.745455

65

0.512486

#!/usr/local/bin/python3 def read_input(): file = open('input/2021/day1-input.txt', 'r') return [int(line) for line in file.readlines()] def part1(data): """ >>> part1([199, 200, 208, 210, 200, 207, 240, 269, 260, 263]) 7 >>> part1(read_input()) 1581 """ previous = data[0] count = 0 for value in data[1:]: if value > previous: count += 1 previous = value return count def part2(data): """ >>> part2([199, 200, 208, 210, 200, 207, 240, 269, 260, 263]) 5 >>> part2(read_input()) 1618 """ count = 0 for i in range(1, len(data) - 2): previous = sum(data[i - 1:i + 2]) value = sum(data[i:i + 3]) if value > previous: count += 1 return count def main(): data = read_input() print(part1(data)) print(part2(data)) if __name__ == "__main__": main()

0

158

0

46

ac3978d7b01ad6d6e0f32900633722a103fd5b2e

4,738

py

Python

Example/Gutenkunst2007/Lee_2003/LeeNet.py

bcdaniels/SloppyCell

17e68127a6aba19056a5067748a2d18241cc4d76

[ "BSD-3-Clause" ]

2

2020-05-26T19:29:39.000Z

2020-08-26T20:54:52.000Z

Example/Gutenkunst2007/Lee_2003/LeeNet.py

bcdaniels/SloppyCell

17e68127a6aba19056a5067748a2d18241cc4d76

[ "BSD-3-Clause" ]

1

2019-04-15T21:08:12.000Z

Example/Gutenkunst2007/Lee_2003/LeeNet.py

jurquiza/SloppyCellUrquiza2019

a9f64d9d4172c82735813f09e48f36777a714e9c

[ "BSD-3-Clause" ]

3

2017-09-12T03:12:01.000Z

2018-10-19T11:08:09.000Z

net = Network('Lee2003') net.add_compartment('extract') net.add_parameter('Dsh0', 100, name = r'Dsh^0') net.add_parameter('APC0', 100, name = r'APC^0') net.add_parameter('TCF0', 15, name = r'TCF^0') net.add_parameter('GSK0', 50, name = r'GSK^0') net.add_species('X2', 'extract', 0)#, name=r'Dsh_a') net.add_species('X3', 'extract', 0)#, name=r'APC^*/axin^*/GSK3') net.add_species('X4', 'extract', 0)#, name=r'APC/axin/GSK3') net.add_species('X9', 'extract', 0)#, name=r'\beta-catenin^*/APC^*/axin^*/GSK3') net.add_species('X10', 'extract', 0)#, name=r'\beta-catenin^*') net.add_species('X11', 'extract', 0)#, name=r'\beta-catenin') net.add_species('X12', 'extract', 0)#, name=r'Axin') #net.add_species('X5', 'extract', 'GSK0', is_constant=True)#, name=r'GSK3') net.add_species('X5', 'extract', 'GSK0', is_constant=True)#, name=r'GSK3') net.add_species('X1', 'extract')#, name=r'Dsh_i') net.add_species('X6', 'extract')#, name=r'APC/axin') net.add_species('X7', 'extract')#, name=r'APC') net.add_species('X8', 'extract')#, name=r'\beta-catenin/APC^*/axin^*/GSK3') net.add_species('X13', 'extract')#, name=r'TCF') net.add_species('X14', 'extract')#, name=r'\beta-catenin/TCF') net.add_species('X15', 'extract')#, name=r'\beta-catenin/APC') net.add_parameter('K7', 50, name = r'K_7') net.add_parameter('K8', 120, name = r'K_8') net.add_parameter('K16', 30, name = r'K_16') net.add_parameter('K17', 1200, name = r'K_17') net.add_parameter('k1', 0.182, name = r'k_{1}') net.add_parameter('k2', 1.82e-2, name = r'k_{2}') net.add_parameter('k3', 5e-2, name = r'k_{3}') net.add_parameter('k4', 0.267, name = r'k_{4}') net.add_parameter('k5', 0.133, name = r'k_{5}') net.add_parameter('k6', 9.09e-2, name = r'k_{6}') net.add_parameter('km6', 0.909, name = 'k_{-6}') net.add_parameter('k9', 206, name = r'k_{9}') net.add_parameter('k10', 206, name = r'k_{10}') net.add_parameter('k11', 0.417, name = r'k_{11}') net.add_parameter('k13', 2.57e-4, name = r'k_{13}') net.add_parameter('k15', 0.167, name = r'k_{15}') net.add_parameter('v12', 0.423, name = r'v_{12}') net.add_parameter('v14', 8.22e-5, name = r'v_{14}') #net.add_parameter('k1', 0.18, name = r'k_{1}') #net.add_parameter('k2', 1.8e-2, name = r'k_{2}') #net.add_parameter('k3', 5e-2, name = r'k_{3}') #net.add_parameter('k4', 0.27, name = r'k_{4}') #net.add_parameter('k5', 0.13, name = r'k_{5}') #net.add_parameter('k6', 9.1e-2, name = r'k_{6}') #net.add_parameter('km6', 0.91, name = 'k_{-6}') #net.add_parameter('k9', 210, name = r'k_{9}') #net.add_parameter('k10', 210, name = r'k_{10}') #net.add_parameter('k11', 0.42, name = r'k_{11}') #net.add_parameter('k13', 2.6e-4, name = r'k_{13}') #net.add_parameter('k15', 0.17, name = r'k_{15}') # #net.add_parameter('v12', 0.42, name = r'v_{12}') #net.add_parameter('v14', 8.2e-5, name = r'v_{14}') net.add_parameter('W', 0, is_optimizable=False) net.add_rate_rule('X2', 'k1*W*(Dsh0-X2)-k2*X2') net.add_rate_rule('X9', 'k9 * X8 - k10*X9') net.add_rate_rule('X10', 'k10*X9-k11*X10') net.add_rate_rule('X4', '-k3*X2*X4 - k4*X4 + k5*X3 + k6*X5*X6 - km6*X4') net.add_parameter('a') net.add_assignment_rule('a', '1+APC0*K17/(K7*(K17+X11))') net.add_parameter('b') net.add_assignment_rule('b', 'APC0*K17*X12/(K7*(K17+X11)**2)') net.add_parameter('c') net.add_assignment_rule('c', 'k3*X2*X4 - k6 * GSK0*APC0*K17*X12/(K7*(K17+X11)) + km6*X4 + v14 - k15*X12') net.add_parameter('d') net.add_assignment_rule('d', '1+X11/K8') net.add_parameter('e') net.add_assignment_rule('e', 'X3/K8') net.add_parameter('f') net.add_assignment_rule('f', 'k4*X4 - k5*X3 - k9*X3*X11/K8 + k10*X9') net.add_parameter('g') net.add_assignment_rule('g', '1+X3/K8+TCF0*K16/(K16+X11)**2 + APC0*K17/(K17+X11)**2') net.add_parameter('h') net.add_assignment_rule('h', 'X11/K8') net.add_parameter('i') net.add_assignment_rule('i', 'v12 - (k9*X3/K8 + k13)*X11') net.add_parameter('rhsX11', name = 'rhs_{X11}') net.add_assignment_rule('rhsX11', '(d*i - f*h)/(d*g - e*h)') net.add_rate_rule('X11', 'rhsX11') net.add_rate_rule('X12', '(c + rhsX11*b)/a') net.add_rate_rule('X3', '(e*i - f*g)/(e*h - d*g)') net.add_assignment_rule('X1', 'Dsh0 - X2') net.add_assignment_rule('X7', 'K17*APC0/(K17+X11)') net.add_assignment_rule('X15', 'X11*APC0/(K17+X11)') net.add_assignment_rule('X13', 'K16*TCF0/(K16+X11)') net.add_assignment_rule('X14', 'X11*TCF0/(K16+X11)') net.add_assignment_rule('X8', 'X3*X11/K8') net.add_assignment_rule('X6', 'K17*X12*APC0/(K7*(K17+X11))') # These are just for my own monitoring purposes net.add_parameter('BCatenin', name = r'\beta-catenin') net.add_assignment_rule('BCatenin', 'X8+X9+X10+X11+X14+X15') net.add_parameter('Axin', name = r'Axin') net.add_assignment_rule('Axin', 'X3+X4+X6+X8+X9+X12')

41.561404

105

0.657239

from SloppyCell.ReactionNetworks import * net = Network('Lee2003') net.add_compartment('extract') net.add_parameter('Dsh0', 100, name = r'Dsh^0') net.add_parameter('APC0', 100, name = r'APC^0') net.add_parameter('TCF0', 15, name = r'TCF^0') net.add_parameter('GSK0', 50, name = r'GSK^0') net.add_species('X2', 'extract', 0)#, name=r'Dsh_a') net.add_species('X3', 'extract', 0)#, name=r'APC^*/axin^*/GSK3') net.add_species('X4', 'extract', 0)#, name=r'APC/axin/GSK3') net.add_species('X9', 'extract', 0)#, name=r'\beta-catenin^*/APC^*/axin^*/GSK3') net.add_species('X10', 'extract', 0)#, name=r'\beta-catenin^*') net.add_species('X11', 'extract', 0)#, name=r'\beta-catenin') net.add_species('X12', 'extract', 0)#, name=r'Axin') #net.add_species('X5', 'extract', 'GSK0', is_constant=True)#, name=r'GSK3') net.add_species('X5', 'extract', 'GSK0', is_constant=True)#, name=r'GSK3') net.add_species('X1', 'extract')#, name=r'Dsh_i') net.add_species('X6', 'extract')#, name=r'APC/axin') net.add_species('X7', 'extract')#, name=r'APC') net.add_species('X8', 'extract')#, name=r'\beta-catenin/APC^*/axin^*/GSK3') net.add_species('X13', 'extract')#, name=r'TCF') net.add_species('X14', 'extract')#, name=r'\beta-catenin/TCF') net.add_species('X15', 'extract')#, name=r'\beta-catenin/APC') net.add_parameter('K7', 50, name = r'K_7') net.add_parameter('K8', 120, name = r'K_8') net.add_parameter('K16', 30, name = r'K_16') net.add_parameter('K17', 1200, name = r'K_17') net.add_parameter('k1', 0.182, name = r'k_{1}') net.add_parameter('k2', 1.82e-2, name = r'k_{2}') net.add_parameter('k3', 5e-2, name = r'k_{3}') net.add_parameter('k4', 0.267, name = r'k_{4}') net.add_parameter('k5', 0.133, name = r'k_{5}') net.add_parameter('k6', 9.09e-2, name = r'k_{6}') net.add_parameter('km6', 0.909, name = 'k_{-6}') net.add_parameter('k9', 206, name = r'k_{9}') net.add_parameter('k10', 206, name = r'k_{10}') net.add_parameter('k11', 0.417, name = r'k_{11}') net.add_parameter('k13', 2.57e-4, name = r'k_{13}') net.add_parameter('k15', 0.167, name = r'k_{15}') net.add_parameter('v12', 0.423, name = r'v_{12}') net.add_parameter('v14', 8.22e-5, name = r'v_{14}') #net.add_parameter('k1', 0.18, name = r'k_{1}') #net.add_parameter('k2', 1.8e-2, name = r'k_{2}') #net.add_parameter('k3', 5e-2, name = r'k_{3}') #net.add_parameter('k4', 0.27, name = r'k_{4}') #net.add_parameter('k5', 0.13, name = r'k_{5}') #net.add_parameter('k6', 9.1e-2, name = r'k_{6}') #net.add_parameter('km6', 0.91, name = 'k_{-6}') #net.add_parameter('k9', 210, name = r'k_{9}') #net.add_parameter('k10', 210, name = r'k_{10}') #net.add_parameter('k11', 0.42, name = r'k_{11}') #net.add_parameter('k13', 2.6e-4, name = r'k_{13}') #net.add_parameter('k15', 0.17, name = r'k_{15}') # #net.add_parameter('v12', 0.42, name = r'v_{12}') #net.add_parameter('v14', 8.2e-5, name = r'v_{14}') net.add_parameter('W', 0, is_optimizable=False) net.add_rate_rule('X2', 'k1*W*(Dsh0-X2)-k2*X2') net.add_rate_rule('X9', 'k9 * X8 - k10*X9') net.add_rate_rule('X10', 'k10*X9-k11*X10') net.add_rate_rule('X4', '-k3*X2*X4 - k4*X4 + k5*X3 + k6*X5*X6 - km6*X4') net.add_parameter('a') net.add_assignment_rule('a', '1+APC0*K17/(K7*(K17+X11))') net.add_parameter('b') net.add_assignment_rule('b', 'APC0*K17*X12/(K7*(K17+X11)**2)') net.add_parameter('c') net.add_assignment_rule('c', 'k3*X2*X4 - k6 * GSK0*APC0*K17*X12/(K7*(K17+X11)) + km6*X4 + v14 - k15*X12') net.add_parameter('d') net.add_assignment_rule('d', '1+X11/K8') net.add_parameter('e') net.add_assignment_rule('e', 'X3/K8') net.add_parameter('f') net.add_assignment_rule('f', 'k4*X4 - k5*X3 - k9*X3*X11/K8 + k10*X9') net.add_parameter('g') net.add_assignment_rule('g', '1+X3/K8+TCF0*K16/(K16+X11)**2 + APC0*K17/(K17+X11)**2') net.add_parameter('h') net.add_assignment_rule('h', 'X11/K8') net.add_parameter('i') net.add_assignment_rule('i', 'v12 - (k9*X3/K8 + k13)*X11') net.add_parameter('rhsX11', name = 'rhs_{X11}') net.add_assignment_rule('rhsX11', '(d*i - f*h)/(d*g - e*h)') net.add_rate_rule('X11', 'rhsX11') net.add_rate_rule('X12', '(c + rhsX11*b)/a') net.add_rate_rule('X3', '(e*i - f*g)/(e*h - d*g)') net.add_assignment_rule('X1', 'Dsh0 - X2') net.add_assignment_rule('X7', 'K17*APC0/(K17+X11)') net.add_assignment_rule('X15', 'X11*APC0/(K17+X11)') net.add_assignment_rule('X13', 'K16*TCF0/(K16+X11)') net.add_assignment_rule('X14', 'X11*TCF0/(K16+X11)') net.add_assignment_rule('X8', 'X3*X11/K8') net.add_assignment_rule('X6', 'K17*X12*APC0/(K7*(K17+X11))') # These are just for my own monitoring purposes net.add_parameter('BCatenin', name = r'\beta-catenin') net.add_assignment_rule('BCatenin', 'X8+X9+X10+X11+X14+X15') net.add_parameter('Axin', name = r'Axin') net.add_assignment_rule('Axin', 'X3+X4+X6+X8+X9+X12')

0

20

22

07f3349c9a417036cdc8776d53fcfa52d2e1af80

5,082

py

Python

PWGJE/EMCALJetTasks/Tracks/analysis/util/Interpolator.py

maroozm/AliPhysics

22ec256928cfdf8f800e05bfc1a6e124d90b6eaf

[ "BSD-3-Clause" ]

114

2017-03-03T09:12:23.000Z

2022-03-03T20:29:42.000Z

PWGJE/EMCALJetTasks/Tracks/analysis/util/Interpolator.py

maroozm/AliPhysics

22ec256928cfdf8f800e05bfc1a6e124d90b6eaf

[ "BSD-3-Clause" ]

19,637

2017-01-16T12:34:41.000Z

2022-03-31T22:02:40.000Z

PWGJE/EMCALJetTasks/Tracks/analysis/util/Interpolator.py

maroozm/AliPhysics

22ec256928cfdf8f800e05bfc1a6e124d90b6eaf

[ "BSD-3-Clause" ]

1,021

2016-07-14T22:41:16.000Z

2022-03-31T05:15:51.000Z

#************************************************************************** #* Copyright(c) 1998-2014, ALICE Experiment at CERN, All rights reserved. * #* * #* Author: The ALICE Off-line Project. * #* Contributors are mentioned in the code where appropriate. * #* * #* Permission to use, copy, modify and distribute this software and its * #* documentation strictly for non-commercial purposes is hereby granted * #* without fee, provided that the above copyright notice appears in all * #* copies and that both the copyright notice and this permission notice * #* appear in the supporting documentation. The authors make no claims * #* about the suitability of this software for any purpose. It is * #* provided "as is" without express or implied warranty. * #************************************************************************** """ Interpolation module @author: Jacek Otwinowski @organization: ALICE Collaboration Translated into PYTHON by Markus Fasel <[email protected]>, Lawrence Berkeley National Laboratory """

37.925373

123

0.538764

#************************************************************************** #* Copyright(c) 1998-2014, ALICE Experiment at CERN, All rights reserved. * #* * #* Author: The ALICE Off-line Project. * #* Contributors are mentioned in the code where appropriate. * #* * #* Permission to use, copy, modify and distribute this software and its * #* documentation strictly for non-commercial purposes is hereby granted * #* without fee, provided that the above copyright notice appears in all * #* copies and that both the copyright notice and this permission notice * #* appear in the supporting documentation. The authors make no claims * #* about the suitability of this software for any purpose. It is * #* provided "as is" without express or implied warranty. * #************************************************************************** """ Interpolation module @author: Jacek Otwinowski @organization: ALICE Collaboration Translated into PYTHON by Markus Fasel <[email protected]>, Lawrence Berkeley National Laboratory """ import math class Interpolator(object): def __init__(self): """ Constructor """ pass def Interpolate(self, x, x1, y1, x2, y2, integrate = False, r = 0, method="lin"): """ Interpolation handler: forwards methods to the different interpolation functions @param x: x at which to evaluate the interpolation @param x1: lower x step @param y1: function value at x1 @param x2: upper x step @param y2: function value at x2 @param integrate: if true we evaluate the integral @param r: """ if method == "lin": return self.__InterpolateLinear(x, x1, y1, x2, y2, integrate, r) elif method == "pow": return self.__InterpolatePowerLaw(x, x1, y1, x2, y2, integrate, r) elif method == "exp": return self.__InterpolateExponential(x, x1, y1, x2, y2) elif method == "hag": return self.__InterpolateSimpleHagedorn(x, x1, y1, x2, y2) def __InterpolateLinear(self, x, x1, y1, x2, y2, integrate = False, r = 0): """ Linear interpolation method @param x: x at which to evaluate the interpolation @param x1: lower x step @param y1: function value at x1 @param x2: upper x step @param y2: function value at x2 @param integrate: if true we evaluate the integral @param r: """ if x1-x2 == 0: return 0 if integrate: return 2*r*(y1+((x-x1)*(y1-y2))/(x1-x2)) else: return (y1 + (((y2-y1)/(x2-x1))*(x-x1))) def __InterpolatePowerLaw(self, x, x1, y1, x2, y2, integrate = False, r = 0): """ Power law interpolation method @param x: x at which to evaluate the interpolation @param x1: lower x step @param y1: function value at x1 @param x2: upper x step @param y2: function value at x2 @param integrate: if true we evaluate the integral @param r: """ #assume functional form y=a*x^n if not self.__AssurePositive(x, x1, x2, y1, y2): return 0. n = (math.log(y1)-math.log(y2))/(math.log(x1)-math.log(x2)); a = y1*pow(x1,-n) print "y: %f" %(a*pow(x,n)) print "n: %f" %(n) print "a: %f" %(a) if integrate: return ((a/(n+1.))*(math.pow(x+r,n+1.)-math.pow(x-r,n+1.))/(2.*r)) else: return (a*math.pow(x,n)) def __InterpolateExponential(self, x, x1, y1, x2, y2): """ Exponential interpolation method @param x: x at which to evaluate the interpolation @param x1: lower x step @param y1: function value at x1 @param x2: upper x step @param y2: function value at x2 """ if not self.__AssurePositive(x, x1, x2, y1, y2): return 0. return math.exp(self.__InterpolateLinear(x,x1,math.log(y1),x2,math.log(y2))) def __InterpolateSimpleHagedorn(self, x, x1, y1, x2, y2): """ Hagedorn interpolation method @param x: x at which to evaluate the interpolation @param x1: lower x step @param y1: function value at x1 @param x2: upper x step @param y2: function value at x2 """ if not self.__AssurePositive(x, x1, x2, y1, y2): return 0. return math.exp(self.__InterpolateLinear(math.log(1.+x),math.log(1.+x1),math.log(y1),math.log(1.+x2),math.log(y2))) def __AssurePositive(self, x, x1, x2, y1, y2): """ Check if all values are positive """ if x <= 0. or x1 <= 0. or x2 <= 0. or y1 <= 0. or y2 <= 0.: return False return True

0

3,791

0

-10

45

0b9bc42aab3a61dc776c20fde1b7be088ba0e2b2

2,276

py

Python

creator/schema.py

kids-first/kf-api-study-creator

93a79b108b6474f9b4135ace06c89ddcf63dd257

[ "Apache-2.0" ]

3

2019-05-04T02:07:28.000Z

2020-10-16T17:47:44.000Z

creator/schema.py

kids-first/kf-api-study-creator

93a79b108b6474f9b4135ace06c89ddcf63dd257

[ "Apache-2.0" ]

604

2019-02-21T18:14:51.000Z

2022-02-10T08:13:54.000Z

creator/schema.py

kids-first/kf-api-study-creator

93a79b108b6474f9b4135ace06c89ddcf63dd257

[ "Apache-2.0" ]

null

""" This is the root schema definition that combines individual applications' schemas into one. Each application that has queries or mutations exports them as either Query or Mutation from the application's schema module. No resolvers or type definitions should be included here. """ import graphene import creator.analyses.schema import creator.buckets.schema import creator.files.schema import creator.studies.schema import creator.projects.schema import creator.users.schema import creator.referral_tokens.schema import creator.status.schema import creator.jobs.schema import creator.releases.schema import creator.data_reviews.schema import creator.ingest_runs.schema import creator.organizations.schema schema = graphene.Schema(query=Query, mutation=Mutation)

29.947368

75

0.773726

""" This is the root schema definition that combines individual applications' schemas into one. Each application that has queries or mutations exports them as either Query or Mutation from the application's schema module. No resolvers or type definitions should be included here. """ import graphene from django.conf import settings import creator.analyses.schema import creator.buckets.schema import creator.files.schema import creator.studies.schema import creator.projects.schema import creator.users.schema import creator.referral_tokens.schema import creator.status.schema import creator.jobs.schema import creator.releases.schema import creator.data_reviews.schema import creator.ingest_runs.schema import creator.organizations.schema import creator.data_templates.schema class Query( creator.analyses.schema.Query, creator.files.schema.Query, creator.studies.schema.Query, creator.users.schema.Query, creator.events.schema.Query, creator.projects.schema.Query, creator.buckets.schema.Query, creator.referral_tokens.schema.Query, creator.status.schema.Query, creator.jobs.schema.Query, creator.releases.schema.Query, creator.data_reviews.schema.Query, creator.ingest_runs.schema.Query, creator.organizations.schema.Query, creator.data_templates.schema.Query, graphene.ObjectType, ): """ Root query schema combining all apps' schemas """ node = graphene.relay.Node.Field() if settings.DEBUG: from graphene_django.debug import DjangoDebug debug = graphene.Field(DjangoDebug, name="_debug") class Mutation( creator.analyses.schema.Mutation, creator.buckets.schema.Mutation, creator.projects.schema.Mutation, creator.studies.schema.Mutation, creator.files.schema.Mutation, creator.users.schema.Mutation, creator.referral_tokens.schema.Mutation, creator.status.schema.Mutation, creator.releases.schema.Mutation, creator.data_reviews.schema.Mutation, creator.ingest_runs.schema.Mutation, creator.organizations.schema.Mutation, creator.data_templates.schema.Mutation, graphene.ObjectType, ): """ Root mutation schema combining all apps' schemas """ pass schema = graphene.Schema(query=Query, mutation=Mutation)

0

1,390

0

26

90

eebaa5aaa5d495d9ab50fc4d5c37d590c86b3096

9,624

py

Python

simulation/simulation.py

bopopescu/sparrow-mod

56c601ee3dd852a9f053bffffc2a52ff3da8d2bd

[ "Apache-2.0" ]

200

2015-01-05T07:37:20.000Z

2022-03-30T03:28:21.000Z

simulation/simulation.py

bopopescu/sparrow-mod

56c601ee3dd852a9f053bffffc2a52ff3da8d2bd

[ "Apache-2.0" ]

1

2016-05-13T10:46:32.000Z

simulation/simulation.py

bopopescu/sparrow-mod

56c601ee3dd852a9f053bffffc2a52ff3da8d2bd

[ "Apache-2.0" ]

73

2015-01-06T02:00:17.000Z

2021-11-22T10:04:03.000Z

# # Copyright 2013 The Regents of The University California # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # MEDIAN_TASK_DURATION = 100 NETWORK_DELAY = 0 TASKS_PER_JOB = 500 SLOTS_PER_WORKER = 4 TOTAL_WORKERS = 10000 PROBE_RATIO = 2 if __name__ == "__main__": main()

40.779661

115

0.641209

# # Copyright 2013 The Regents of The University California # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import logging import math import numpy import random from util import Job, TaskDistributions import Queue MEDIAN_TASK_DURATION = 100 NETWORK_DELAY = 0 TASKS_PER_JOB = 500 SLOTS_PER_WORKER = 4 TOTAL_WORKERS = 10000 PROBE_RATIO = 2 def get_percentile(N, percent, key=lambda x:x): if not N: return 0 k = (len(N) - 1) * percent f = math.floor(k) c = math.ceil(k) if f == c: return key(N[int(k)]) d0 = key(N[int(f)]) * (c-k) d1 = key(N[int(c)]) * (k-f) return d0 + d1 def plot_cdf(values, filename): values.sort() f = open(filename, "w") for percent in range(100): fraction = percent / 100. f.write("%s\t%s\n" % (fraction, get_percentile(values, fraction))) f.close() class Event(object): """ Abstract class representing events. """ def __init__(self): raise NotImplementedError("Event is an abstract class and cannot be " "instantiated directly") def run(self, current_time): """ Returns any events that should be added to the queue. """ raise NotImplementedError("The run() method must be implemented by " "each class subclassing Event") class JobArrival(Event): """ Event to signify a job arriving at a scheduler. """ def __init__(self, simulation, interarrival_delay, task_distribution): self.simulation = simulation self.interarrival_delay = interarrival_delay self.task_distribution= task_distribution def run(self, current_time): job = Job(TASKS_PER_JOB, current_time, self.task_distribution, MEDIAN_TASK_DURATION) logging.getLogger("sim").debug("Job %s arrived at %s" % (job.id, current_time)) # Schedule job. new_events = self.simulation.send_probes(job, current_time) # Add new Job Arrival event, for the next job to arrive after this one. arrival_delay = random.expovariate(1.0 / self.interarrival_delay) new_events.append((current_time + arrival_delay, self)) logging.getLogger("sim").debug("Retuning %s events" % len(new_events)) return new_events class ProbeEvent(Event): """ Event to signify a probe arriving at a worker. """ def __init__(self, worker, job_id): self.worker = worker self.job_id = job_id def run(self, current_time): logging.getLogger("sim").debug("Probe for job %s arrived at worker %s at %s" % (self.job_id, self.worker.id, current_time)) return self.worker.add_probe(self.job_id, current_time) class NoopGetTaskResponseEvent(Event): """ Signifies when a getTask() RPC response arrives at a worker, with a noop response. """ def __init__(self, worker): self.worker = worker def run(self, current_time): logging.getLogger("sim").debug("getTask() request for worker %s returned no task at %s" % (self.worker.id, current_time)) return self.worker.free_slot(current_time) class TaskEndEvent(): def __init__(self, worker): self.worker = worker def run(self, current_time): return self.worker.free_slot(current_time) class Worker(object): def __init__(self, simulation, num_slots, id): self.simulation = simulation self.free_slots = num_slots # Just a list of job ids! self.queued_probes = Queue.Queue() self.id = id self.probes_replied_to_immediately = 0 def add_probe(self, job_id, current_time): self.queued_probes.put(job_id) new_events = self.maybe_get_task(current_time) self.probes_replied_to_immediately += len(new_events) logging.getLogger("sim").debug("Worker %s: %s" % (self.id, self.probes_replied_to_immediately)) return new_events def free_slot(self, current_time): """ Frees a slot on the worker and attempts to launch another task in that slot. """ self.free_slots += 1 get_task_events = self.maybe_get_task(current_time) return get_task_events def maybe_get_task(self, current_time): if not self.queued_probes.empty() and self.free_slots > 0: # Account for "running" task self.free_slots -= 1 job_id = self.queued_probes.get() task_duration = self.simulation.get_task(job_id) probe_response_time = current_time + 2*NETWORK_DELAY if task_duration > 0: task_end_time = probe_response_time + task_duration logging.getLogger("sim").debug(("Task for job %s running on worker %s (get task at: %s, duration: " "%s, end: %s)") % (job_id, self.id, current_time, task_duration, task_end_time)) self.simulation.add_task_completion_time(job_id, task_end_time) new_event = TaskEndEvent(self) return [(task_end_time, new_event)] else: # There was no task left for the job, so send another probe # after 1RTT. logging.getLogger("sim").debug("Noop returning on worker %s at %s" % (self.id, probe_response_time)) return [(probe_response_time, NoopGetTaskResponseEvent(self))] return [] class Simulation(object): def __init__(self, num_jobs, file_prefix, load, task_distribution): avg_used_slots = load * SLOTS_PER_WORKER * TOTAL_WORKERS self.interarrival_delay = (1.0 * MEDIAN_TASK_DURATION * TASKS_PER_JOB / avg_used_slots) print ("Interarrival delay: %s (avg slots in use: %s)" % (self.interarrival_delay, avg_used_slots)) self.jobs = {} self.remaining_jobs = num_jobs self.event_queue = Queue.PriorityQueue() self.workers = [] self.file_prefix = file_prefix while len(self.workers) < TOTAL_WORKERS: self.workers.append(Worker(self, SLOTS_PER_WORKER, len(self.workers))) self.worker_indices = range(TOTAL_WORKERS) self.task_distribution = task_distribution def send_probes(self, job, current_time): """ Send probes to acquire load information, in order to schedule a job. """ self.jobs[job.id] = job random.shuffle(self.worker_indices) probe_events = [] num_probes = PROBE_RATIO * len(job.unscheduled_tasks) for worker_index in self.worker_indices[:num_probes]: probe_events.append((current_time + NETWORK_DELAY, ProbeEvent(self.workers[worker_index], job.id))) return probe_events def get_task(self, job_id): job = self.jobs[job_id] if len(job.unscheduled_tasks) > 0: task_duration = job.unscheduled_tasks[0] job.unscheduled_tasks = job.unscheduled_tasks[1:] return task_duration return -1 def add_task_completion_time(self, job_id, completion_time): job_complete = self.jobs[job_id].task_completed(completion_time) if job_complete: self.remaining_jobs -= 1 logging.getLogger("sim").debug("Job %s completed in %s" % (job_id, self.jobs[job_id].end_time - self.jobs[job_id].start_time)) def run(self): self.event_queue.put((0, JobArrival(self, self.interarrival_delay, self.task_distribution))) last_time = 0 while self.remaining_jobs > 0: current_time, event = self.event_queue.get() assert current_time >= last_time last_time = current_time new_events = event.run(current_time) for new_event in new_events: self.event_queue.put(new_event) print ("Simulation ended after %s milliseconds (%s jobs started)" % (last_time, len(self.jobs))) complete_jobs = [j for j in self.jobs.values() if j.completed_tasks_count == j.num_tasks] print "%s complete jobs" % len(complete_jobs) response_times = [job.end_time - job.start_time for job in complete_jobs if job.start_time > 500] print "Included %s jobs" % len(response_times) plot_cdf(response_times, "%s_response_times.data" % self.file_prefix) print "Average response time: ", numpy.mean(response_times) longest_tasks = [job.longest_task for job in complete_jobs] plot_cdf(longest_tasks, "%s_ideal_response_time.data" % self.file_prefix) tasks_replied_to_immediately = sum([w.probes_replied_to_immediately for w in self.workers]) print "Tasks replied to immeiately: ", tasks_replied_to_immediately return response_times def main(): random.seed(1) logging.basicConfig(level=logging.INFO) sim = Simulation(1000, "sparrow", 0.95, TaskDistributions.CONSTANT) sim.run() if __name__ == "__main__": main()

0

7,905

0

608

0

-25

363

eb0cc8b93b8223d65f24aaccba78c888502d04df

892

py

Python

2015/MAC0327/Desafios 2/p11.py

andredalton/bcc

188190e436615e2344d87b722856fa02e6eec9cc

[ "Apache-2.0" ]

1

2018-08-02T14:09:26.000Z

2015/MAC0327/Desafios 2/p11.py

andredalton/bcc

188190e436615e2344d87b722856fa02e6eec9cc

[ "Apache-2.0" ]

null

2015/MAC0327/Desafios 2/p11.py

andredalton/bcc

188190e436615e2344d87b722856fa02e6eec9cc

[ "Apache-2.0" ]

1

2020-07-13T04:27:02.000Z

# coding=utf-8 __author__ = 'Andr Meneghelli' """ /******************************************************************************* * Aluno: Andr Meneghelli Vale, Nm. USP: 4898948 * Curso: Bacharelado em Cincias da Computao * Aula 13 - Stone Pile * MAC0327 -- IME/USP, -- Prof. Cristina Gomes Fernandes ******************************************************************************/ """ pedras = [] if __name__ == '__main__': main()

22.3

80

0.48991

# coding=utf-8 __author__ = 'André Meneghelli' """ /******************************************************************************* * Aluno: André Meneghelli Vale, Núm. USP: 4898948 * Curso: Bacharelado em Ciências da Computação * Aula 13 - Stone Pile * MAC0327 -- IME/USP, -- Prof. Cristina Gomes Fernandes ******************************************************************************/ """ pedras = [] def procura(s1, s2, index): global pedras if index == -1: return abs(s1-s2) sa = procura(s1 + pedras[index], s2, index-1) sb = procura(s1, s2 + pedras[index], index-1) if sa < sb: return sa return sb def main(): global pedras s1 = 0 raw_input() pedras = map(int, raw_input().split()) pedras.sort() s2 = pedras[len(pedras)-1] print procura(s1, s2, len(pedras)-2) if __name__ == '__main__': main()

12

0

389

0

46

cd67696b0ec1ee40fb689af2c3c02ad3ecc6be4e

5,014

py

Python

model.py

abhitrip/Behavioral-Cloning

9930dc7fc2e6623954f84859b7d011905cd48d30

[ "MIT" ]

null

model.py

abhitrip/Behavioral-Cloning

9930dc7fc2e6623954f84859b7d011905cd48d30

[ "MIT" ]

null

model.py

abhitrip/Behavioral-Cloning

9930dc7fc2e6623954f84859b7d011905cd48d30

[ "MIT" ]

null

import matplotlib.image as mpimg """ To show the preprocessing for final model """ batch_size = 128 # define model """ model = nvidia_model() model.compile(optimizer='adam', loss='mse', metrics=['accuracy']) model.summary() # train model model.fit_generator(read_data_gen(batch_size), samples_per_epoch=8000*2, nb_epoch=5) model.save('model.h5') """ if __name__=="__main__": train_model()

25.451777

89

0.632429

import csv import matplotlib.image as mpimg import pickle import numpy as np from keras.models import Sequential from keras.layers.core import Flatten,Lambda,Dense from keras.layers.convolutional import Cropping2D,Conv2D from keras import backend as K from keras.layers.core import Activation import matplotlib.image as mpimg import matplotlib.pyplot as plt import cv2 def resize(image): import tensorflow as tf resized = tf.image.resize_images(image,(32,32)) return resized def resize_nvidia(image): import tensorflow as tf resized = tf.image.resize_images(image,(66,200)) return resized """ To show the preprocessing for final model """ def process_image(file_name,nvidia_or_final): if nvidia_or_final=='nvidia': crop_top, crop_bot = 70, 25 new_shape = (66,200) elif nvidia_or_final=='final': crop_top, crop_bot = 80, 48 new_shape = (32,32) img = mpimg.imread(file_name) h = img.shape[0] cropped_img = img[crop_top:h-crop_bot,:,:] plt.imshow(cropped_img) plt.savefig("cropped_img") resized_image = cv2.resize(cropped_img,new_shape) plt.imshow(resized_image) plt.savefig("resized_img") plt.imshow(np.fliplr(resized_image)) plt.savefig("flipped_img") def read_data_gen(batch_size): """ Generator function to load driving logs and input images. """ while 1: with open('data/driving_log.csv') as driving_log_file: reader = csv.DictReader(driving_log_file) count = 0 inputs, targets = [], [] try: for row in reader: center_img = mpimg.imread('data/'+ row['center'].strip()) flipped_center_img = np.fliplr(center_img) center_steering = float(row['steering']) if count < batch_size//2: inputs += [center_img, flipped_center_img] targets += [center_steering, -center_steering] count += 1 else: yield np.array(inputs, dtype=center_img.dtype), np.array(targets) count = 0 inputs, targets= [], [] except StopIteration: pass batch_size = 128 # define model def final_model(): # define model model = Sequential() # crop top and bottom parts of the image model.add(Cropping2D(cropping=((80, 48), (0, 0)), input_shape=(160, 320, 3))) # resize image to 32x32 model.add(Lambda(resize,output_shape=(32, 32, 3))) # normalize layer values model.add(Lambda(lambda x: (x / 255.0) - 0.5)) # Model colour information model.add(Conv2D(3, 1, 1, border_mode='valid', subsample=(1, 1), activation='elu')) # Conv filter 1 model.add(Conv2D(3, 3, 3, border_mode='valid', activation='elu')) # Conv filter 2 model.add(Conv2D(6, 5, 5, border_mode='valid', subsample=(2, 2), activation='elu')) # conv filter 3 model.add(Conv2D(16, 5, 5, border_mode='valid', subsample=(2, 2), activation='elu')) # flatten model.add(Flatten()) # Dense layer 1 model.add(Dense(100, activation='elu')) # Dense layer 2 model.add(Dense(25, activation='elu')) # Final Dense for prediction of steering model.add(Dense(1)) return model def nvidia_model(): model = Sequential() # Preprocessing model.add(Lambda(lambda x: x/127.5 -1.0,input_shape=(160,320,3))) model.add(Cropping2D(cropping=((70,25),(0,0)))) #model.add(Lambda(resize_nvidia,output_shape=(32, 32, 3))) # 1st Conv Layer model.add(Conv2D(24,5,5,subsample=(2,2))) model.add(Activation('elu')) # 2nd Conv Layer model.add(Conv2D(36,5,5,subsample=(2,2))) model.add(Activation('elu')) # 3rd Conv Layer model.add(Conv2D(48,5,5,subsample=(2,2))) model.add(Activation('elu')) # 4th Conv Layer model.add(Conv2D(64,3,3)) model.add(Activation('elu')) # 5th Conv Layer model.add(Conv2D(64,3,3)) model.add(Activation('elu')) # Flatten model.add(Flatten()) model.add(Dense(100)) model.add(Activation('elu')) model.add(Dense(50)) model.add(Activation('elu')) model.add(Dense(10)) model.add(Activation('elu')) model.add(Dense(1)) return model """ model = nvidia_model() model.compile(optimizer='adam', loss='mse', metrics=['accuracy']) model.summary() # train model model.fit_generator(read_data_gen(batch_size), samples_per_epoch=8000*2, nb_epoch=5) model.save('model.h5') """ def gen_preprocess_images(): image = 'data/IMG/center_2016_12_01_13_31_13_177.jpg' process_image(image,'final') def train_model(): model = final_model() model.compile(optimizer='adam', loss='mse', metrics=['accuracy']) model.summary() model.fit_generator(read_data_gen(batch_size), samples_per_epoch=8000*2, nb_epoch=5) model.save('model.h5') if __name__=="__main__": train_model()

0

1,002

3,085

0

94

425

43ba3750ab55b89ed9e0505f5404d4b28171dd33

1,647

py

Python

src/downward/experiments/issue739/v5-translate.py

ScarfZapdos/conan-bge-questgen

4d184c5bf0ae4b768b8043cec586395df9ce1451

[ "MIT" ]

1

2021-09-09T13:03:02.000Z

src/downward/experiments/issue739/v5-translate.py

ScarfZapdos/conan-bge-questgen

4d184c5bf0ae4b768b8043cec586395df9ce1451

[ "MIT" ]

null

src/downward/experiments/issue739/v5-translate.py

ScarfZapdos/conan-bge-questgen

4d184c5bf0ae4b768b8043cec586395df9ce1451

[ "MIT" ]

null

#! /usr/bin/env python # -*- coding: utf-8 -*- import os from lab.environments import LocalEnvironment, BaselSlurmEnvironment import common_setup from common_setup import IssueConfig, IssueExperiment DIR = os.path.dirname(os.path.abspath(__file__)) BENCHMARKS_DIR = os.environ["DOWNWARD_BENCHMARKS"] REVISIONS = ["issue739-v5"] CONFIGS = [ IssueConfig('translate', [], driver_options=['--translate']), IssueConfig('translate-with-options', ['--translate-options', '--keep-unreachable-facts', '--keep-unimportant-variables', '--full-encoding'], driver_options=['--translate']), IssueConfig('translate-time-limit', [], driver_options=['--translate-time-limit', '5s', '--translate']), IssueConfig('translate-memory-limit', [], driver_options=['--translate-memory-limit', '100M', '--translate']), ] SUITE = common_setup.DEFAULT_OPTIMAL_SUITE ENVIRONMENT = BaselSlurmEnvironment(email="[email protected]", export=["PATH", "DOWNWARD_BENCHMARKS"]) if common_setup.is_test_run(): SUITE = ['gripper:prob10.pddl','mystery:prob07.pddl'] ENVIRONMENT = LocalEnvironment(processes=4) exp = IssueExperiment( revisions=REVISIONS, configs=CONFIGS, environment=ENVIRONMENT, ) exp.add_suite(BENCHMARKS_DIR, SUITE) exp.add_parser(exp.LAB_STATIC_PROPERTIES_PARSER) exp.add_parser(exp.LAB_DRIVER_PARSER) exp.add_parser(exp.EXITCODE_PARSER) exp.add_parser(exp.TRANSLATOR_PARSER) exp.add_step('build', exp.build) exp.add_step('start', exp.start_runs) exp.add_fetcher(name='fetch') exp.add_absolute_report_step(attributes=['translator_*', 'error']) exp.run_steps()

35.042553

178

0.756527

#! /usr/bin/env python # -*- coding: utf-8 -*- import os from lab.environments import LocalEnvironment, BaselSlurmEnvironment import common_setup from common_setup import IssueConfig, IssueExperiment from relativescatter import RelativeScatterPlotReport DIR = os.path.dirname(os.path.abspath(__file__)) BENCHMARKS_DIR = os.environ["DOWNWARD_BENCHMARKS"] REVISIONS = ["issue739-v5"] CONFIGS = [ IssueConfig('translate', [], driver_options=['--translate']), IssueConfig('translate-with-options', ['--translate-options', '--keep-unreachable-facts', '--keep-unimportant-variables', '--full-encoding'], driver_options=['--translate']), IssueConfig('translate-time-limit', [], driver_options=['--translate-time-limit', '5s', '--translate']), IssueConfig('translate-memory-limit', [], driver_options=['--translate-memory-limit', '100M', '--translate']), ] SUITE = common_setup.DEFAULT_OPTIMAL_SUITE ENVIRONMENT = BaselSlurmEnvironment(email="[email protected]", export=["PATH", "DOWNWARD_BENCHMARKS"]) if common_setup.is_test_run(): SUITE = ['gripper:prob10.pddl','mystery:prob07.pddl'] ENVIRONMENT = LocalEnvironment(processes=4) exp = IssueExperiment( revisions=REVISIONS, configs=CONFIGS, environment=ENVIRONMENT, ) exp.add_suite(BENCHMARKS_DIR, SUITE) exp.add_parser(exp.LAB_STATIC_PROPERTIES_PARSER) exp.add_parser(exp.LAB_DRIVER_PARSER) exp.add_parser(exp.EXITCODE_PARSER) exp.add_parser(exp.TRANSLATOR_PARSER) exp.add_step('build', exp.build) exp.add_step('start', exp.start_runs) exp.add_fetcher(name='fetch') exp.add_absolute_report_step(attributes=['translator_*', 'error']) exp.run_steps()

0

32

22

6ba0b5003d4c97df676dbfc10dff603b15cd48d9

506

py

Python

mplscience/__init__.py

adamgayoso/mpscience

0401ded920a4d09314e9a747cf4da07d17a60a05

[ "MIT" ]

4

2021-07-15T16:55:24.000Z

2022-03-04T23:10:02.000Z

mplscience/__init__.py

adamgayoso/mpscience

0401ded920a4d09314e9a747cf4da07d17a60a05

[ "MIT" ]

null

mplscience/__init__.py

adamgayoso/mpscience

0401ded920a4d09314e9a747cf4da07d17a60a05

[ "MIT" ]

null

"""Matplotlib science style""" from .core import available_styles, set_style, style_context # https://github.com/python-poetry/poetry/pull/2366#issuecomment-652418094 # https://github.com/python-poetry/poetry/issues/144#issuecomment-623927302 try: import importlib.metadata as importlib_metadata except ModuleNotFoundError: import importlib_metadata package_name = "mplscience" __version__ = importlib_metadata.version(package_name) __all__ = ["available_styles", "set_style", "style_context"]

31.625

75

0.804348

"""Matplotlib science style""" from .core import available_styles, set_style, style_context # https://github.com/python-poetry/poetry/pull/2366#issuecomment-652418094 # https://github.com/python-poetry/poetry/issues/144#issuecomment-623927302 try: import importlib.metadata as importlib_metadata except ModuleNotFoundError: import importlib_metadata package_name = "mplscience" __version__ = importlib_metadata.version(package_name) __all__ = ["available_styles", "set_style", "style_context"]

0

3a06da5ff6c0053e4dc72e9a222d828921a7534c

4,153

py

Python

template/templates.py

dkratzert/FinalCif

07ca23dbb4e7439b108a906521a118cdb876d97e

[ "Beerware" ]

13

2020-01-14T16:23:48.000Z

2022-02-16T18:02:08.000Z

template/templates.py

dkratzert/FinalCif

07ca23dbb4e7439b108a906521a118cdb876d97e

[ "Beerware" ]

24

2021-04-21T05:30:42.000Z

2022-03-31T20:07:29.000Z

template/templates.py

dkratzert/FinalCif

07ca23dbb4e7439b108a906521a118cdb876d97e

[ "Beerware" ]

1

2021-08-09T16:48:33.000Z

from contextlib import suppress with suppress(ImportError):

44.180851

120

0.666265

from contextlib import suppress from pathlib import Path from typing import List from PyQt5.QtCore import Qt from PyQt5.QtGui import QColor from PyQt5.QtWidgets import QFileDialog, QListWidgetItem with suppress(ImportError): from appwindow import AppWindow from tools.settings import FinalCifSettings class ReportTemplates: """ Displays the list of report templates in the options menu. """ def __init__(self, app: 'AppWindow', settings: FinalCifSettings): self.app = app self.settings = settings self.lw = self.app.ui.TemplatesListWidget self.load_templates_list() self.app.ui.AddNewTemplPushButton.clicked.connect(self.add_new_template) self.app.ui.RemoveTemplPushButton.clicked.connect(self.remove_current_template) self.app.ui.TemplatesListWidget.currentItemChanged.connect(self.template_changed) self.app.ui.TemplatesListWidget.itemChanged.connect(self.template_changed) self.app.ui.TemplatesListWidget.setCurrentItem( self.app.ui.TemplatesListWidget.item(self.app.options.current_template)) def add_new_template(self, templ_path: str = '') -> None: if not templ_path: templ_path, _ = QFileDialog.getOpenFileName(filter="DOCX file (*.docx)", initialFilter="DOCX file (*.docx)", caption='Open a Report Template File') itemslist = self.get_templates_list_from_widget() self.app.status_bar.show_message('') if templ_path in itemslist: self.app.status_bar.show_message('This templates is already in the list.', 10) print('This templates is already in the list.') return if not Path(templ_path).exists() or not Path(templ_path).is_file() \ or not Path(templ_path).name.endswith('.docx'): self.app.status_bar.show_message('This template does not exist or is unreadable.', 10) print('This template does not exist or is unreadable.', Path(templ_path).resolve()) return item = QListWidgetItem(templ_path) item.setCheckState(Qt.Unchecked) self.app.ui.TemplatesListWidget.addItem(item) self.settings.save_template_list('report_templates_list', self.get_templates_list_from_widget()) def load_templates_list(self): templates = self.settings.load_template('report_templates_list') if not templates: return for text in templates: if text.startswith('Use'): continue with suppress(Exception): if not Path(text).exists(): item = QListWidgetItem(text) item.setForeground(QColor(220, 12, 34)) else: item = QListWidgetItem(str(Path(text).resolve(strict=True))) self.app.ui.TemplatesListWidget.addItem(item) item.setCheckState(Qt.Unchecked) def get_templates_list_from_widget(self) -> List: itemslist = [] for num in range(self.lw.count()): itemtext = self.lw.item(num).text() if not itemtext in itemslist: itemslist.append(itemtext) return itemslist def remove_current_template(self) -> None: if self.lw.currentRow() == 0: return self.lw.takeItem(self.lw.row(self.lw.currentItem())) self.settings.save_template_list('report_templates_list', self.get_templates_list_from_widget()) def template_changed(self, current_item: QListWidgetItem): # Blocking signal in order to avoid infinitive recursion: self.app.ui.TemplatesListWidget.blockSignals(True) options = self.settings.load_options() options.update({'current_report_template': self.lw.row(current_item)}) self.uncheck_all_templates() current_item.setCheckState(Qt.Checked) self.settings.save_options(options) self.app.ui.TemplatesListWidget.blockSignals(False) def uncheck_all_templates(self): for num in range(self.lw.count()): self.lw.item(num).setCheckState(Qt.Unchecked)

0

3,822

0

87

182

1815a1cfdf441bab8f5c07943254b362f00a655f

163

py

Python

celery/settings.py

alculquicondor/AmigoCloud-IGP-Sync

56de7e9137340054159289ef9c6534bb1b5872fc

[ "MIT" ]

null

celery/settings.py

alculquicondor/AmigoCloud-IGP-Sync

56de7e9137340054159289ef9c6534bb1b5872fc

[ "MIT" ]

null

celery/settings.py

alculquicondor/AmigoCloud-IGP-Sync

56de7e9137340054159289ef9c6534bb1b5872fc

[ "MIT" ]

null

from os import environ TOKEN = environ.get('AMIGOCLOUD_TOKEN') BROKER_URL = environ.get('BROKER_URL') PROJECT_URL = 'users/475/projects/13608' DATASET_ID = 79746

23.285714

40

0.779141

from os import environ TOKEN = environ.get('AMIGOCLOUD_TOKEN') BROKER_URL = environ.get('BROKER_URL') PROJECT_URL = 'users/475/projects/13608' DATASET_ID = 79746

0

855c72651aff3902ac92bec1942941cff9cf4170

342

py

Python

scripts/twist_remapper.py

tamago117/kcctsim

0cd72c79ade6be48ad59fb9cfb202dcbe8de69cf

[ "Apache-2.0" ]

1

2021-11-25T07:53:53.000Z

scripts/twist_remapper.py

tamago117/kcctsim

0cd72c79ade6be48ad59fb9cfb202dcbe8de69cf

[ "Apache-2.0" ]

1

2021-09-09T06:34:32.000Z

2021-11-02T11:49:00.000Z

scripts/twist_remapper.py

tamago117/kcctsim

0cd72c79ade6be48ad59fb9cfb202dcbe8de69cf

[ "Apache-2.0" ]

2

2021-10-01T13:43:58.000Z

2021-11-25T07:53:54.000Z

#!/usr/bin/env python import rospy from geometry_msgs.msg import Twist pub = rospy.Publisher("/diff_drive_controller/cmd_vel", Twist, queue_size = 10) if __name__ == '__main__': rospy.init_node('twist_remapper', anonymous=True) rospy.Subscriber("/cmd_vel", Twist, callback) rospy.spin()

28.5

79

0.733918

#!/usr/bin/env python import rospy from geometry_msgs.msg import Twist pub = rospy.Publisher("/diff_drive_controller/cmd_vel", Twist, queue_size = 10) def callback(data): pub.publish(data) if __name__ == '__main__': rospy.init_node('twist_remapper', anonymous=True) rospy.Subscriber("/cmd_vel", Twist, callback) rospy.spin()

0

20

0

22

860f3238dfabe5abdc4b560671b0f41979c23fa1

48,472

py

Python

qiskit/visualization/matplotlib.py

quantumjim/qiskit-terra

5292f487eaa980986a1e5affae8c4fc50c743e71

[ "Apache-2.0" ]

1

2019-12-09T08:25:14.000Z

qiskit/visualization/matplotlib.py

quantumjim/qiskit-terra

5292f487eaa980986a1e5affae8c4fc50c743e71

[ "Apache-2.0" ]

1

2020-03-29T19:57:14.000Z

2020-03-29T21:49:25.000Z

qiskit/visualization/matplotlib.py

quantumjim/qiskit-terra

5292f487eaa980986a1e5affae8c4fc50c743e71

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # This code is part of Qiskit. # # (C) Copyright IBM 2017, 2018. # # This code is licensed under the Apache License, Version 2.0. You may # obtain a copy of this license in the LICENSE.txt file in the root directory # of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. # # Any modifications or derivative works of this code must retain this # copyright notice, and modified files need to carry a notice indicating # that they have been altered from the originals. # pylint: disable=invalid-name,missing-docstring,inconsistent-return-statements """mpl circuit visualization backend.""" import logging try: HAS_MATPLOTLIB = True except ImportError: HAS_MATPLOTLIB = False logger = logging.getLogger(__name__) WID = 0.65 HIG = 0.65 DEFAULT_SCALE = 4.3 PORDER_GATE = 5 PORDER_LINE = 3 PORDER_REGLINE = 2 PORDER_GRAY = 3 PORDER_TEXT = 6 PORDER_SUBP = 4

41.894555

100

0.444875

# -*- coding: utf-8 -*- # This code is part of Qiskit. # # (C) Copyright IBM 2017, 2018. # # This code is licensed under the Apache License, Version 2.0. You may # obtain a copy of this license in the LICENSE.txt file in the root directory # of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. # # Any modifications or derivative works of this code must retain this # copyright notice, and modified files need to carry a notice indicating # that they have been altered from the originals. # pylint: disable=invalid-name,missing-docstring,inconsistent-return-statements """mpl circuit visualization backend.""" import collections import fractions import itertools import json import logging import math import numpy as np try: from matplotlib import get_backend from matplotlib import patches from matplotlib import pyplot as plt HAS_MATPLOTLIB = True except ImportError: HAS_MATPLOTLIB = False from qiskit.circuit import ControlledGate from qiskit.visualization import exceptions from qiskit.visualization.qcstyle import DefaultStyle, BWStyle from qiskit import user_config from qiskit.circuit.tools.pi_check import pi_check logger = logging.getLogger(__name__) WID = 0.65 HIG = 0.65 DEFAULT_SCALE = 4.3 PORDER_GATE = 5 PORDER_LINE = 3 PORDER_REGLINE = 2 PORDER_GRAY = 3 PORDER_TEXT = 6 PORDER_SUBP = 4 class Anchor: def __init__(self, reg_num, yind, fold): self.__yind = yind self.__fold = fold self.__reg_num = reg_num self.__gate_placed = [] self.gate_anchor = 0 def plot_coord(self, index, gate_width, x_offset): h_pos = index % self.__fold + 1 # check folding if self.__fold > 0: if h_pos + (gate_width - 1) > self.__fold: index += self.__fold - (h_pos - 1) x_pos = index % self.__fold + 1 + 0.5 * (gate_width - 1) y_pos = self.__yind - (index // self.__fold) * (self.__reg_num + 1) else: x_pos = index + 1 + 0.5 * (gate_width - 1) y_pos = self.__yind # could have been updated, so need to store self.gate_anchor = index return x_pos + x_offset, y_pos def is_locatable(self, index, gate_width): hold = [index + i for i in range(gate_width)] for p in hold: if p in self.__gate_placed: return False return True def set_index(self, index, gate_width): h_pos = index % self.__fold + 1 if h_pos + (gate_width - 1) > self.__fold: _index = index + self.__fold - (h_pos - 1) else: _index = index for ii in range(gate_width): if _index + ii not in self.__gate_placed: self.__gate_placed.append(_index + ii) self.__gate_placed.sort() def get_index(self): if self.__gate_placed: return self.__gate_placed[-1] + 1 return 0 class MatplotlibDrawer: def __init__(self, qregs, cregs, ops, scale=1.0, style=None, plot_barriers=True, reverse_bits=False, layout=None, fold=25, ax=None): if not HAS_MATPLOTLIB: raise ImportError('The class MatplotlibDrawer needs matplotlib. ' 'To install, run "pip install matplotlib".') self._ast = None self._scale = DEFAULT_SCALE * scale self._creg = [] self._qreg = [] self._registers(cregs, qregs) self._ops = ops self._qreg_dict = collections.OrderedDict() self._creg_dict = collections.OrderedDict() self._cond = { 'n_lines': 0, 'xmax': 0, 'ymax': 0, } config = user_config.get_config() if config and (style is None): config_style = config.get('circuit_mpl_style', 'default') if config_style == 'default': self._style = DefaultStyle() elif config_style == 'bw': self._style = BWStyle() elif style is False: self._style = BWStyle() else: self._style = DefaultStyle() self.plot_barriers = plot_barriers self.reverse_bits = reverse_bits self.layout = layout if style: if isinstance(style, dict): self._style.set_style(style) elif isinstance(style, str): with open(style, 'r') as infile: dic = json.load(infile) self._style.set_style(dic) if ax is None: self.return_fig = True self.figure = plt.figure() self.figure.patch.set_facecolor(color=self._style.bg) self.ax = self.figure.add_subplot(111) else: self.return_fig = False self.ax = ax self.figure = ax.get_figure() self.fold = fold if self.fold < 2: self.fold = -1 self.ax.axis('off') self.ax.set_aspect('equal') self.ax.tick_params(labelbottom=False, labeltop=False, labelleft=False, labelright=False) self.x_offset = 0 def _registers(self, creg, qreg): self._creg = [] for r in creg: self._creg.append(r) self._qreg = [] for r in qreg: self._qreg.append(r) @property def ast(self): return self._ast def _custom_multiqubit_gate(self, xy, cxy=None, fc=None, wide=True, text=None, subtext=None): xpos = min([x[0] for x in xy]) ypos = min([y[1] for y in xy]) ypos_max = max([y[1] for y in xy]) if cxy: ypos = min([y[1] for y in cxy]) if wide: if subtext: boxes_length = round(max([len(text), len(subtext)]) / 7) or 1 else: boxes_length = math.ceil(len(text) / 7) or 1 wid = WID * 2.5 * boxes_length else: wid = WID if fc: _fc = fc else: if self._style.name != 'bw': if self._style.gc != DefaultStyle().gc: _fc = self._style.gc else: _fc = self._style.dispcol['multi'] _ec = self._style.dispcol['multi'] else: _fc = self._style.gc qubit_span = abs(ypos) - abs(ypos_max) + 1 height = HIG + (qubit_span - 1) box = patches.Rectangle( xy=(xpos - 0.5 * wid, ypos - .5 * HIG), width=wid, height=height, fc=_fc, ec=self._style.dispcol['multi'], linewidth=1.5, zorder=PORDER_GATE) self.ax.add_patch(box) # Annotate inputs for bit, y in enumerate([x[1] for x in xy]): self.ax.text(xpos - 0.45 * wid, y, str(bit), ha='left', va='center', fontsize=self._style.fs, color=self._style.gt, clip_on=True, zorder=PORDER_TEXT) if text: disp_text = text if subtext: self.ax.text(xpos, ypos + 0.5 * height, disp_text, ha='center', va='center', fontsize=self._style.fs, color=self._style.gt, clip_on=True, zorder=PORDER_TEXT) self.ax.text(xpos, ypos + 0.3 * height, subtext, ha='center', va='center', fontsize=self._style.sfs, color=self._style.sc, clip_on=True, zorder=PORDER_TEXT) else: self.ax.text(xpos, ypos + .5 * (qubit_span - 1), disp_text, ha='center', va='center', fontsize=self._style.fs, color=self._style.gt, clip_on=True, zorder=PORDER_TEXT, wrap=True) def _gate(self, xy, fc=None, wide=False, text=None, subtext=None): xpos, ypos = xy if wide: if subtext: subtext_len = len(subtext) if '$\\pi$' in subtext: pi_count = subtext.count('pi') subtext_len = subtext_len - (4 * pi_count) boxes_wide = round(max(subtext_len, len(text)) / 10, 1) or 1 wid = WID * 1.5 * boxes_wide else: boxes_wide = round(len(text) / 10) or 1 wid = WID * 2.2 * boxes_wide if wid < WID: wid = WID else: wid = WID if fc: _fc = fc elif self._style.gc != DefaultStyle().gc: _fc = self._style.gc elif text and text in self._style.dispcol: _fc = self._style.dispcol[text] else: _fc = self._style.gc box = patches.Rectangle( xy=(xpos - 0.5 * wid, ypos - 0.5 * HIG), width=wid, height=HIG, fc=_fc, ec=self._style.edge_color, linewidth=1.5, zorder=PORDER_GATE) self.ax.add_patch(box) if text: font_size = self._style.fs sub_font_size = self._style.sfs # check if gate is not unitary if text in ['reset']: disp_color = self._style.not_gate_lc sub_color = self._style.not_gate_lc font_size = self._style.math_fs else: disp_color = self._style.gt sub_color = self._style.sc if text in self._style.dispcol: disp_text = "${}$".format(self._style.disptex[text]) else: disp_text = text if subtext: self.ax.text(xpos, ypos + 0.15 * HIG, disp_text, ha='center', va='center', fontsize=font_size, color=disp_color, clip_on=True, zorder=PORDER_TEXT) self.ax.text(xpos, ypos - 0.3 * HIG, subtext, ha='center', va='center', fontsize=sub_font_size, color=sub_color, clip_on=True, zorder=PORDER_TEXT) else: self.ax.text(xpos, ypos, disp_text, ha='center', va='center', fontsize=font_size, color=disp_color, clip_on=True, zorder=PORDER_TEXT) def _subtext(self, xy, text): xpos, ypos = xy self.ax.text(xpos, ypos - 0.3 * HIG, text, ha='center', va='top', fontsize=self._style.sfs, color=self._style.tc, clip_on=True, zorder=PORDER_TEXT) def _sidetext(self, xy, text): xpos, ypos = xy # 0.15 = the initial gap, each char means it needs to move # another 0.0375 over xp = xpos + 0.15 + (0.0375 * len(text)) self.ax.text(xp, ypos + HIG, text, ha='center', va='top', fontsize=self._style.sfs, color=self._style.tc, clip_on=True, zorder=PORDER_TEXT) def _line(self, xy0, xy1, lc=None, ls=None, zorder=PORDER_LINE): x0, y0 = xy0 x1, y1 = xy1 if lc is None: linecolor = self._style.lc else: linecolor = lc if ls is None: linestyle = 'solid' else: linestyle = ls if linestyle == 'doublet': theta = np.arctan2(np.abs(x1 - x0), np.abs(y1 - y0)) dx = 0.05 * WID * np.cos(theta) dy = 0.05 * WID * np.sin(theta) self.ax.plot([x0 + dx, x1 + dx], [y0 + dy, y1 + dy], color=linecolor, linewidth=2, linestyle='solid', zorder=zorder) self.ax.plot([x0 - dx, x1 - dx], [y0 - dy, y1 - dy], color=linecolor, linewidth=2, linestyle='solid', zorder=zorder) else: self.ax.plot([x0, x1], [y0, y1], color=linecolor, linewidth=2, linestyle=linestyle, zorder=zorder) def _measure(self, qxy, cxy, cid, basis='z'): qx, qy = qxy cx, cy = cxy self._gate(qxy, fc=self._style.dispcol['meas']) # add measure symbol arc = patches.Arc(xy=(qx, qy - 0.15 * HIG), width=WID * 0.7, height=HIG * 0.7, theta1=0, theta2=180, fill=False, ec=self._style.not_gate_lc, linewidth=2, zorder=PORDER_GATE) self.ax.add_patch(arc) self.ax.plot([qx, qx + 0.35 * WID], [qy - 0.15 * HIG, qy + 0.20 * HIG], color=self._style.not_gate_lc, linewidth=2, zorder=PORDER_GATE) # arrow self._line(qxy, [cx, cy + 0.35 * WID], lc=self._style.cc, ls=self._style.cline) arrowhead = patches.Polygon(((cx - 0.20 * WID, cy + 0.35 * WID), (cx + 0.20 * WID, cy + 0.35 * WID), (cx, cy)), fc=self._style.cc, ec=None) self.ax.add_artist(arrowhead) # target if self._style.bundle: self.ax.text(cx + .25, cy + .1, str(cid), ha='left', va='bottom', fontsize=0.8 * self._style.fs, color=self._style.tc, clip_on=True, zorder=PORDER_TEXT) # measurement basis label if basis != 'z': self.ax.text(qx - 0.4 * WID, qy + 0.25 * HIG, basis.upper(), color=self._style.not_gate_lc, clip_on=True, zorder=PORDER_TEXT, fontsize=0.5 * self._style.fs, fontweight='bold') def _conds(self, xy, istrue=False): xpos, ypos = xy if istrue: _fc = self._style.lc else: _fc = self._style.gc box = patches.Circle(xy=(xpos, ypos), radius=WID * 0.15, fc=_fc, ec=self._style.lc, linewidth=1.5, zorder=PORDER_GATE) self.ax.add_patch(box) def _ctrl_qubit(self, xy, fc=None, ec=None): if self._style.gc != DefaultStyle().gc: fc = self._style.gc ec = self._style.gc if fc is None: fc = self._style.lc if ec is None: ec = self._style.lc xpos, ypos = xy box = patches.Circle(xy=(xpos, ypos), radius=WID * 0.15, fc=fc, ec=ec, linewidth=1.5, zorder=PORDER_GATE) self.ax.add_patch(box) def set_multi_ctrl_bits(self, ctrl_state, num_ctrl_qubits, qbit, color_str): # convert op.ctrl_state to bit string and reverse cstate = "{0:b}".format(ctrl_state).rjust(num_ctrl_qubits, '0')[::-1] for i in range(num_ctrl_qubits): # Make facecolor of ctrl bit the box color if closed and bkgrnd if open fc_open_close = (self._style.dispcol[color_str] if cstate[i] == '1' else self._style.bg) self._ctrl_qubit(qbit[i], fc=fc_open_close, ec=self._style.dispcol[color_str]) def _tgt_qubit(self, xy, fc=None, ec=None, ac=None, add_width=None): if self._style.gc != DefaultStyle().gc: fc = self._style.gc ec = self._style.gc if fc is None: fc = self._style.dispcol['target'] if ec is None: ec = self._style.lc if ac is None: ac = self._style.lc if add_width is None: add_width = 0.35 linewidth = 2 if self._style.dispcol['target'] == '#ffffff': add_width = self._style.colored_add_width xpos, ypos = xy box = patches.Circle(xy=(xpos, ypos), radius=HIG * 0.35, fc=fc, ec=ec, linewidth=linewidth, zorder=PORDER_GATE) self.ax.add_patch(box) # add '+' symbol self.ax.plot([xpos, xpos], [ypos - add_width * HIG, ypos + add_width * HIG], color=ac, linewidth=linewidth, zorder=PORDER_GATE + 1) self.ax.plot([xpos - add_width * HIG, xpos + add_width * HIG], [ypos, ypos], color=ac, linewidth=linewidth, zorder=PORDER_GATE + 1) def _swap(self, xy, color): xpos, ypos = xy self.ax.plot([xpos - 0.20 * WID, xpos + 0.20 * WID], [ypos - 0.20 * WID, ypos + 0.20 * WID], color=color, linewidth=2, zorder=PORDER_LINE + 1) self.ax.plot([xpos - 0.20 * WID, xpos + 0.20 * WID], [ypos + 0.20 * WID, ypos - 0.20 * WID], color=color, linewidth=2, zorder=PORDER_LINE + 1) def _barrier(self, config): xys = config['coord'] group = config['group'] y_reg = [] for qreg in self._qreg_dict.values(): if qreg['group'] in group: y_reg.append(qreg['y']) for xy in xys: xpos, ypos = xy self.ax.plot([xpos, xpos], [ypos + 0.5, ypos - 0.5], linewidth=1, linestyle="dashed", color=self._style.lc, zorder=PORDER_TEXT) box = patches.Rectangle(xy=(xpos - (0.3 * WID), ypos - 0.5), width=0.6 * WID, height=1, fc=self._style.bc, ec=None, alpha=0.6, linewidth=1.5, zorder=PORDER_GRAY) self.ax.add_patch(box) def _linefeed_mark(self, xy): xpos, ypos = xy self.ax.plot([xpos - .1, xpos - .1], [ypos, ypos - self._cond['n_lines'] + 1], color=self._style.lc, zorder=PORDER_LINE) self.ax.plot([xpos + .1, xpos + .1], [ypos, ypos - self._cond['n_lines'] + 1], color=self._style.lc, zorder=PORDER_LINE) def draw(self, filename=None, verbose=False): self._draw_regs() self._draw_ops(verbose) _xl = - self._style.margin[0] _xr = self._cond['xmax'] + self._style.margin[1] _yb = - self._cond['ymax'] - self._style.margin[2] + 1 - 0.5 _yt = self._style.margin[3] + 0.5 self.ax.set_xlim(_xl, _xr) self.ax.set_ylim(_yb, _yt) # update figure size fig_w = _xr - _xl fig_h = _yt - _yb if self._style.figwidth < 0.0: self._style.figwidth = fig_w * self._scale * self._style.fs / 72 / WID self.figure.set_size_inches(self._style.figwidth, self._style.figwidth * fig_h / fig_w) if filename: self.figure.savefig(filename, dpi=self._style.dpi, bbox_inches='tight') if self.return_fig: if get_backend() in ['module://ipykernel.pylab.backend_inline', 'nbAgg']: plt.close(self.figure) return self.figure def _draw_regs(self): def _fix_double_script(label): words = label.split(' ') words = [word.replace('_', r'\_') if word.count('_') > 1 else word for word in words] words = [word.replace('^', r'\^{\ }') if word.count('^') > 1 else word for word in words] return ' '.join(words) len_longest_label = 0 # quantum register for ii, reg in enumerate(self._qreg): if len(self._qreg) > 1: if self.layout is None: label = '${{{name}}}_{{{index}}}$'.format(name=reg.register.name, index=reg.index) else: label = '${{{name}}}_{{{index}}} \\mapsto {{{physical}}}$'.format( name=self.layout[reg.index].register.name, index=self.layout[reg.index].index, physical=reg.index) else: label = '${name}$'.format(name=reg.register.name) label = _fix_double_script(label) if len(label) > len_longest_label: len_longest_label = len(label) pos = -ii self._qreg_dict[ii] = { 'y': pos, 'label': label, 'index': reg.index, 'group': reg.register } self._cond['n_lines'] += 1 # classical register if self._creg: n_creg = self._creg.copy() n_creg.pop(0) idx = 0 y_off = -len(self._qreg) for ii, (reg, nreg) in enumerate(itertools.zip_longest( self._creg, n_creg)): pos = y_off - idx if self._style.bundle: label = '${}$'.format(reg.register.name) label = _fix_double_script(label) self._creg_dict[ii] = { 'y': pos, 'label': label, 'index': reg.index, 'group': reg.register } if not (not nreg or reg.register != nreg.register): continue else: label = '${}_{{{}}}$'.format(reg.register.name, reg.index) label = _fix_double_script(label) self._creg_dict[ii] = { 'y': pos, 'label': label, 'index': reg.index, 'group': reg.register } if len(label) > len_longest_label: len_longest_label = len(label) self._cond['n_lines'] += 1 idx += 1 # 7 is the length of the smallest possible label self.x_offset = -.5 + 0.18 * (len_longest_label - 7) def _draw_regs_sub(self, n_fold, feedline_l=False, feedline_r=False): # quantum register for qreg in self._qreg_dict.values(): if n_fold == 0: label = qreg['label'] else: label = qreg['label'] y = qreg['y'] - n_fold * (self._cond['n_lines'] + 1) self.ax.text(self.x_offset - 0.2, y, label, ha='right', va='center', fontsize=1.25 * self._style.fs, color=self._style.tc, clip_on=True, zorder=PORDER_TEXT) self._line([self.x_offset + 0.2, y], [self._cond['xmax'], y], zorder=PORDER_REGLINE) # classical register this_creg_dict = {} for creg in self._creg_dict.values(): if n_fold == 0: label = creg['label'] else: label = creg['label'] y = creg['y'] - n_fold * (self._cond['n_lines'] + 1) if y not in this_creg_dict.keys(): this_creg_dict[y] = {'val': 1, 'label': label} else: this_creg_dict[y]['val'] += 1 for y, this_creg in this_creg_dict.items(): # bundle if this_creg['val'] > 1: self.ax.plot([self.x_offset + 1.1, self.x_offset + 1.2], [y - .1, y + .1], color=self._style.cc, zorder=PORDER_LINE) self.ax.text(self.x_offset + 1.0, y + .1, str(this_creg['val']), ha='left', va='bottom', fontsize=0.8 * self._style.fs, color=self._style.tc, clip_on=True, zorder=PORDER_TEXT) self.ax.text(self.x_offset - 0.2, y, this_creg['label'], ha='right', va='center', fontsize=1.5 * self._style.fs, color=self._style.tc, clip_on=True, zorder=PORDER_TEXT) self._line([self.x_offset + 0.2, y], [self._cond['xmax'], y], lc=self._style.cc, ls=self._style.cline, zorder=PORDER_REGLINE) # lf line if feedline_r: self._linefeed_mark((self.fold + self.x_offset + 1 - 0.1, - n_fold * (self._cond['n_lines'] + 1))) if feedline_l: self._linefeed_mark((self.x_offset + 0.3, - n_fold * (self._cond['n_lines'] + 1))) def _draw_ops(self, verbose=False): _wide_gate = ['u2', 'u3', 'cu3', 'unitary', 'r', 'cu1', 'rzz'] _barriers = {'coord': [], 'group': []} # # generate coordinate manager # q_anchors = {} for key, qreg in self._qreg_dict.items(): q_anchors[key] = Anchor(reg_num=self._cond['n_lines'], yind=qreg['y'], fold=self.fold) c_anchors = {} for key, creg in self._creg_dict.items(): c_anchors[key] = Anchor(reg_num=self._cond['n_lines'], yind=creg['y'], fold=self.fold) # # draw gates # prev_anc = -1 for layer in self._ops: layer_width = 1 for op in layer: # If one of the standard wide gates if op.name in _wide_gate: if layer_width < 2: layer_width = 2 if op.type == 'op' and hasattr(op.op, 'params'): param = self.param_parse(op.op.params) if '$\\pi$' in param: pi_count = param.count('pi') len_param = len(param) - (4 * pi_count) else: len_param = len(param) if len_param > len(op.name): box_width = math.floor(len(param) / 10) if op.name == 'unitary': box_width = 2 # If more than 4 characters min width is 2 if box_width <= 1: box_width = 2 if layer_width < box_width: if box_width > 2: layer_width = box_width else: layer_width = 2 continue # If custom ControlledGate elif isinstance(op.op, ControlledGate) and op.name not in [ 'ccx', 'cx', 'c3x', 'c4x', 'cy', 'cz', 'ch', 'cu1', 'cu3', 'crz', 'cswap']: if op.type == 'op' and hasattr(op.op, 'params'): param = self.param_parse(op.op.params) if '$\\pi$' in param: pi_count = param.count('pi') len_param = len(param) - (4 * pi_count) else: len_param = len(param) if len_param > len(op.name): box_width = math.floor(len_param / 5.5) layer_width = box_width continue # if custom gate with a longer than standard name determine # width elif op.name not in ['barrier', 'snapshot', 'load', 'save', 'noise', 'cswap', 'swap', 'measure', 'measure_x', 'measure_y', 'measure_z'] and len(op.name) >= 4: box_width = math.ceil(len(op.name) / 6) # handle params/subtext longer than op names if op.type == 'op' and hasattr(op.op, 'params'): param = self.param_parse(op.op.params) if '$\\pi$' in param: pi_count = param.count('pi') len_param = len(param) - (4 * pi_count) else: len_param = len(param) if len_param > len(op.name): box_width = math.floor(len(param) / 8) # If more than 4 characters min width is 2 if box_width <= 1: box_width = 2 if layer_width < box_width: if box_width > 2: layer_width = box_width * 2 else: layer_width = 2 continue # If more than 4 characters min width is 2 layer_width = math.ceil(box_width * WID * 2.5) this_anc = prev_anc + 1 for op in layer: _iswide = op.name in _wide_gate if op.name not in ['barrier', 'snapshot', 'load', 'save', 'noise', 'cswap', 'swap', 'measure', 'measure_x', 'measure_y', 'measure_z', 'reset'] and len(op.name) >= 4: _iswide = True # get qreg index q_idxs = [] for qarg in op.qargs: for index, reg in self._qreg_dict.items(): if (reg['group'] == qarg.register and reg['index'] == qarg.index): q_idxs.append(index) break # get creg index c_idxs = [] for carg in op.cargs: for index, reg in self._creg_dict.items(): if (reg['group'] == carg.register and reg['index'] == carg.index): c_idxs.append(index) break # Only add the gate to the anchors if it is going to be plotted. # This prevents additional blank wires at the end of the line if # the last instruction is a barrier type if self.plot_barriers or \ op.name not in ['barrier', 'snapshot', 'load', 'save', 'noise']: for ii in q_idxs: q_anchors[ii].set_index(this_anc, layer_width) # qreg coordinate q_xy = [q_anchors[ii].plot_coord(this_anc, layer_width, self.x_offset) for ii in q_idxs] # creg coordinate c_xy = [c_anchors[ii].plot_coord(this_anc, layer_width, self.x_offset) for ii in c_idxs] # bottom and top point of qreg qreg_b = min(q_xy, key=lambda xy: xy[1]) qreg_t = max(q_xy, key=lambda xy: xy[1]) # update index based on the value from plotting this_anc = q_anchors[q_idxs[0]].gate_anchor if verbose: print(op) if op.type == 'op' and hasattr(op.op, 'params'): param = self.param_parse(op.op.params) else: param = None # conditional gate if op.condition: c_xy = [c_anchors[ii].plot_coord(this_anc, layer_width, self.x_offset) for ii in self._creg_dict] mask = 0 for index, cbit in enumerate(self._creg): if cbit.register == op.condition[0]: mask |= (1 << index) val = op.condition[1] # cbit list to consider fmt_c = '{{:0{}b}}'.format(len(c_xy)) cmask = list(fmt_c.format(mask))[::-1] # value fmt_v = '{{:0{}b}}'.format(cmask.count('1')) vlist = list(fmt_v.format(val))[::-1] # plot conditionals v_ind = 0 xy_plot = [] for xy, m in zip(c_xy, cmask): if m == '1': if xy not in xy_plot: if vlist[v_ind] == '1' or self._style.bundle: self._conds(xy, istrue=True) else: self._conds(xy, istrue=False) xy_plot.append(xy) v_ind += 1 creg_b = sorted(xy_plot, key=lambda xy: xy[1])[0] self._subtext(creg_b, hex(val)) self._line(qreg_t, creg_b, lc=self._style.cc, ls=self._style.cline) # # draw special gates # if op.name[:7] == 'measure': vv = self._creg_dict[c_idxs[0]]['index'] if len(op.name) == 9: basis = op.name[-1] else: basis = 'z' self._measure(q_xy[0], c_xy[0], vv, basis) elif op.name in ['barrier', 'snapshot', 'load', 'save', 'noise']: _barriers = {'coord': [], 'group': []} for index, qbit in enumerate(q_idxs): q_group = self._qreg_dict[qbit]['group'] if q_group not in _barriers['group']: _barriers['group'].append(q_group) _barriers['coord'].append(q_xy[index]) if self.plot_barriers: self._barrier(_barriers) elif op.name == 'initialize': vec = '[%s]' % param self._custom_multiqubit_gate(q_xy, wide=_iswide, text=op.op.label or "|psi>", subtext=vec) elif op.name == 'unitary': # TODO(mtreinish): Look into adding the unitary to the # subtext self._custom_multiqubit_gate(q_xy, wide=_iswide, text=op.op.label or "Unitary") elif isinstance(op.op, ControlledGate) and op.name not in [ 'ccx', 'cx', 'c3x', 'c4x', 'cy', 'cz', 'ch', 'cu1', 'cu3', 'crz', 'cswap']: disp = op.op.base_gate.name num_ctrl_qubits = op.op.num_ctrl_qubits num_qargs = len(q_xy) - num_ctrl_qubits # set the ctrl qbits to open or closed self.set_multi_ctrl_bits(op.op.ctrl_state, num_ctrl_qubits, q_xy, 'multi') # add qubit-qubit wiring self._line(qreg_b, qreg_t, lc=self._style.dispcol['multi']) if num_qargs == 1: if param: self._gate(q_xy[num_ctrl_qubits], wide=_iswide, text=disp, fc=self._style.dispcol['multi'], subtext='{}'.format(param)) else: fcx = op.name if op.name in self._style.dispcol else 'multi' self._gate(q_xy[num_ctrl_qubits], wide=_iswide, text=disp, fc=self._style.dispcol[fcx]) else: self._custom_multiqubit_gate( q_xy[num_ctrl_qubits:], wide=_iswide, fc=self._style.dispcol['multi'], text=disp) # # draw single qubit gates # elif len(q_xy) == 1: disp = op.name if param: self._gate(q_xy[0], wide=_iswide, text=disp, subtext=str(param)) else: self._gate(q_xy[0], wide=_iswide, text=disp) # # draw multi-qubit gates (n=2) # elif len(q_xy) == 2: # cx if op.name == 'cx': if self._style.dispcol['cx'] != '#ffffff': add_width = self._style.colored_add_width else: add_width = None num_ctrl_qubits = op.op.num_ctrl_qubits # set the ctrl qbits to open or closed self.set_multi_ctrl_bits(op.op.ctrl_state, num_ctrl_qubits, q_xy, 'cx') if self._style.name != 'bw': self._tgt_qubit(q_xy[1], fc=self._style.dispcol['cx'], ec=self._style.dispcol['cx'], ac=self._style.dispcol['target'], add_width=add_width) else: self._tgt_qubit(q_xy[1], fc=self._style.dispcol['target'], ec=self._style.dispcol['cx'], ac=self._style.dispcol['cx'], add_width=add_width) # add qubit-qubit wiring self._line(qreg_b, qreg_t, lc=self._style.dispcol['cx']) # cz for latexmode elif op.name == 'cz': disp = op.name.replace('c', '') if self._style.name != 'bw': color = self._style.dispcol['cz'] self._ctrl_qubit(q_xy[0], fc=color, ec=color) self._ctrl_qubit(q_xy[1], fc=color, ec=color) else: self._ctrl_qubit(q_xy[0]) self._ctrl_qubit(q_xy[1]) # add qubit-qubit wiring if self._style.name != 'bw': self._line(qreg_b, qreg_t, lc=color) else: self._line(qreg_b, qreg_t, zorder=PORDER_LINE + 1) # control gate elif op.name in ['cy', 'ch', 'cu3', 'crz']: disp = op.name.replace('c', '') color = None if self._style.name != 'bw': if op.name == 'cy': color = self._style.dispcol['cy'] else: color = self._style.dispcol['multi'] self._ctrl_qubit(q_xy[0], fc=color, ec=color) if param: self._gate(q_xy[1], wide=_iswide, text=disp, fc=color, subtext='{}'.format(param)) else: self._gate(q_xy[1], wide=_iswide, text=disp, fc=color) # add qubit-qubit wiring self._line(qreg_b, qreg_t, lc=color) # rzz gate elif op.name == 'rzz': color = self._style.dispcol['multi'] self._ctrl_qubit(q_xy[0], fc=color, ec=color) self._ctrl_qubit(q_xy[1], fc=color, ec=color) self._sidetext(qreg_b, text='zz({})'.format(param)) # add qubit-qubit wiring self._line(qreg_b, qreg_t, lc=color) # cu1 gate elif op.name == 'cu1': color = self._style.dispcol['multi'] self._ctrl_qubit(q_xy[0], fc=color, ec=color) self._ctrl_qubit(q_xy[1], fc=color, ec=color) self._sidetext(qreg_b, text='U1 ({})'.format(param)) # add qubit-qubit wiring fc = self._style self._line(qreg_b, qreg_t, lc=color) # swap gate elif op.name == 'swap': self._swap(q_xy[0], self._style.dispcol['swap']) self._swap(q_xy[1], self._style.dispcol['swap']) # add qubit-qubit wiring self._line(qreg_b, qreg_t, lc=self._style.dispcol['swap']) # dcx and iswap gate elif op.name in ['dcx', 'iswap']: self._custom_multiqubit_gate(q_xy, c_xy, wide=_iswide, fc=self._style.dispcol[op.name], text=op.op.label or op.name) # Custom gate else: self._custom_multiqubit_gate(q_xy, c_xy, wide=_iswide, text=op.op.label or op.name) # # draw multi-qubit gates (n=3) # elif len(q_xy) in range(3, 6): # cswap gate if op.name == 'cswap': self._ctrl_qubit(q_xy[0], fc=self._style.dispcol['multi'], ec=self._style.dispcol['multi']) self._swap(q_xy[1], self._style.dispcol['multi']) self._swap(q_xy[2], self._style.dispcol['multi']) # add qubit-qubit wiring self._line(qreg_b, qreg_t, lc=self._style.dispcol['multi']) # ccx gate elif op.name == 'ccx' or op.name == 'c3x' or op.name == 'c4x': num_ctrl_qubits = op.op.num_ctrl_qubits # set the ctrl qbits to open or closed self.set_multi_ctrl_bits(op.op.ctrl_state, num_ctrl_qubits, q_xy, 'multi') if self._style.name != 'bw': self._tgt_qubit(q_xy[num_ctrl_qubits], fc=self._style.dispcol['multi'], ec=self._style.dispcol['multi'], ac=self._style.dispcol['target']) else: self._tgt_qubit(q_xy[num_ctrl_qubits], fc=self._style.dispcol['target'], ec=self._style.dispcol['multi'], ac=self._style.dispcol['multi']) # add qubit-qubit wiring self._line(qreg_b, qreg_t, lc=self._style.dispcol['multi']) # custom gate else: self._custom_multiqubit_gate(q_xy, c_xy, wide=_iswide, text=getattr(op.op, 'label', None) or op.name) # draw custom multi-qubit gate elif len(q_xy) > 5: self._custom_multiqubit_gate(q_xy, c_xy, wide=_iswide, text=op.op.label or op.name) else: logger.critical('Invalid gate %s', op) raise exceptions.VisualizationError('invalid gate {}'.format(op)) # adjust the column if there have been barriers encountered, but not plotted barrier_offset = 0 if not self.plot_barriers: # only adjust if everything in the layer wasn't plotted barrier_offset = -1 if all([op.name in ['barrier', 'snapshot', 'load', 'save', 'noise'] for op in layer]) else 0 prev_anc = this_anc + layer_width + barrier_offset - 1 # # adjust window size and draw horizontal lines # anchors = [q_anchors[ii].get_index() for ii in self._qreg_dict] if anchors: max_anc = max(anchors) else: max_anc = 0 n_fold = max(0, max_anc - 1) // self.fold # window size if max_anc > self.fold > 0: self._cond['xmax'] = self.fold + 1 + self.x_offset self._cond['ymax'] = (n_fold + 1) * (self._cond['n_lines'] + 1) - 1 else: self._cond['xmax'] = max_anc + 1 + self.x_offset self._cond['ymax'] = self._cond['n_lines'] # add horizontal lines for ii in range(n_fold + 1): feedline_r = (n_fold > 0 and n_fold > ii) feedline_l = (ii > 0) self._draw_regs_sub(ii, feedline_l, feedline_r) # draw gate number if self._style.index: for ii in range(max_anc): if self.fold > 0: x_coord = ii % self.fold + 1 y_coord = - (ii // self.fold) * (self._cond['n_lines'] + 1) + 0.7 else: x_coord = ii + 1 y_coord = 0.7 self.ax.text(x_coord, y_coord, str(ii + 1), ha='center', va='center', fontsize=self._style.sfs, color=self._style.tc, clip_on=True, zorder=PORDER_TEXT) @staticmethod def param_parse(v): # create an empty list to store the parameters in param_parts = [None] * len(v) for i, e in enumerate(v): try: param_parts[i] = pi_check(e, output='mpl', ndigits=3) except TypeError: param_parts[i] = str(e) if param_parts[i].startswith('-'): param_parts[i] = '$-$' + param_parts[i][1:] param_parts = ', '.join(param_parts) return param_parts @staticmethod def format_numeric(val, tol=1e-5): if isinstance(val, complex): return str(val) elif complex(val).imag != 0: val = complex(val) abs_val = abs(val) if math.isclose(abs_val, 0.0, abs_tol=1e-100): return '0' if math.isclose(math.fmod(abs_val, 1.0), 0.0, abs_tol=tol) and 0.5 < abs_val < 9999.5: return str(int(val)) if 0.1 <= abs_val < 100.0: return '{:.2f}'.format(val) return '{:.1e}'.format(val) @staticmethod def fraction(val, base=np.pi, n=100, tol=1e-5): abs_val = abs(val) for i in range(1, n): for j in range(1, n): if math.isclose(abs_val, i / j * base, rel_tol=tol): if val < 0: i *= -1 return fractions.Fraction(i, j) return None

0

1,388

0

45,685

0

122

369

a7cf222e3f96762239244a7b076603c3ca2e33f3

946

py

Python

sjpClass.py

alkamid/wiktionary

ce242da609a1001ae7462b07da2f6e83f1a7281b

[ "MIT" ]

3

2015-01-06T22:00:22.000Z

2016-08-14T08:07:32.000Z

sjpClass.py

alkamid/wiktionary

ce242da609a1001ae7462b07da2f6e83f1a7281b

[ "MIT" ]

56

2015-07-12T10:21:38.000Z

2020-02-23T18:51:01.000Z

sjpClass.py

alkamid/wiktionary

ce242da609a1001ae7462b07da2f6e83f1a7281b

[ "MIT" ]

2

2015-01-06T21:25:06.000Z

2018-01-17T12:03:17.000Z

#!/usr/bin/python # -*- coding: utf-8 -*-

27.823529

136

0.609937

#!/usr/bin/python # -*- coding: utf-8 -*- import pywikibot class kategoriaSlowa(): def __init__(self, name, counter, pages, tabelka, outputFile): self.name = name self.counter = counter self.pages = 'Wikipedysta:AlkamidBot/sjp/' + pages self.buffer = '' self.tabelka = tabelka self.outputFile = 'output/' + outputFile self.limit = 0 def addLimit(self, limit): self.limit = limit def checkHistory(pagename): #returns 1, if AlkamidBot or Olafbot were the last authors, 0 if someone is verifying the page (=it was last edited by someone else) bots = ('AlkamidBot', 'Olafbot', 'PBbot') site = pywikibot.Site() page = pywikibot.Page(site, pagename) try: page.get() except pywikibot.NoPage: return 1 else: history = page.getVersionHistory() if history[0][2] in bots: return 1 else: return 0

0

370

0

470

0

-5

69

8b6ebb32e27f26c072b135c85ff8fb1b572ad23d

2,446

py

Python

tests/test_filters.py

mobius-medical/flask-genshi

68cba6c9cb604272a25f5e4c74e5a127e3ac7854

[ "BSD-3-Clause" ]

null

tests/test_filters.py

mobius-medical/flask-genshi

68cba6c9cb604272a25f5e4c74e5a127e3ac7854

[ "BSD-3-Clause" ]

null

tests/test_filters.py

mobius-medical/flask-genshi

68cba6c9cb604272a25f5e4c74e5a127e3ac7854

[ "BSD-3-Clause" ]

null

from __future__ import unicode_literals from inspect import cleandoc from flask_genshi import render_template def test_applies_method_filters(app): """Method filters are applied for generated and rendered templates""" with app.test_request_context(): genshi = app.extensions["genshi"] rendered = render_template("filter.html") # Remove leading indentation, for cleaner multi-line string expected = cleandoc( """ <!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01//EN" "http://www.w3.org/TR/html4/strict.dtd"> <html><head><title>Flask-Genshi - Hi!</title></head></html> """ ) assert rendered == expected def test_filters_per_render(app): """Filters can be applied per rendering""" with app.test_request_context(): rendered = render_template("filter.html", filter=prepend_title) # Remove leading indentation, for cleaner multi-line string expected = cleandoc( """ <!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01//EN" "http://www.w3.org/TR/html4/strict.dtd"> <html><head><title>Hi! - Flask-Genshi</title></head></html> """ ) assert rendered == expected def test_works_with_flatland(app): """Filters can take the context and support flatland""" with app.test_request_context(): genshi = app.extensions["genshi"] context = dict(form=FlatlandForm({"username": "dag"})) rendered = render_template("flatland.html", context) # Remove leading indentation, for cleaner multi-line string expected = cleandoc( """ <!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01//EN" "http://www.w3.org/TR/html4/strict.dtd"> <input type="text" name="username" value="dag"> """ ) assert rendered == expected

31.766234

102

0.629599

from __future__ import unicode_literals from inspect import cleandoc from genshi.filters import Transformer from flask_genshi import render_template from flatland.out.genshi import setup as flatland_setup from flatland import Form, String class FlatlandForm(Form): username = String def test_applies_method_filters(app): """Method filters are applied for generated and rendered templates""" with app.test_request_context(): genshi = app.extensions["genshi"] @genshi.filter("html") def prepend_title(template): return template | Transformer("head/title").prepend("Flask-Genshi - ") rendered = render_template("filter.html") # Remove leading indentation, for cleaner multi-line string expected = cleandoc( """ <!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01//EN" "http://www.w3.org/TR/html4/strict.dtd"> <html><head><title>Flask-Genshi - Hi!</title></head></html> """ ) assert rendered == expected def test_filters_per_render(app): """Filters can be applied per rendering""" with app.test_request_context(): def prepend_title(template): return template | Transformer("head/title").append(" - Flask-Genshi") rendered = render_template("filter.html", filter=prepend_title) # Remove leading indentation, for cleaner multi-line string expected = cleandoc( """ <!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01//EN" "http://www.w3.org/TR/html4/strict.dtd"> <html><head><title>Hi! - Flask-Genshi</title></head></html> """ ) assert rendered == expected def test_works_with_flatland(app): """Filters can take the context and support flatland""" with app.test_request_context(): genshi = app.extensions["genshi"] @genshi.template_parsed def callback(template): flatland_setup(template) context = dict(form=FlatlandForm({"username": "dag"})) rendered = render_template("flatland.html", context) # Remove leading indentation, for cleaner multi-line string expected = cleandoc( """ <!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01//EN" "http://www.w3.org/TR/html4/strict.dtd"> <input type="text" name="username" value="dag"> """ ) assert rendered == expected

0

192

0

27

0

89

0

63

183

978ff0be8e3774dfa21908c9b4b49bc92d1eeb4e

3,159

py

Python

forms/models.py

ditttu/gymkhana-Nominations

2a0e993c1b8362c456a9369b0b549d1c809a21df

[ "MIT" ]

3

2018-02-27T13:48:28.000Z

2018-03-03T21:57:50.000Z

forms/models.py

ditttu/gymkhana-Nominations

2a0e993c1b8362c456a9369b0b549d1c809a21df

[ "MIT" ]

6

2020-02-12T00:07:46.000Z

2022-03-11T23:25:59.000Z

forms/models.py

ditttu/gymkhana-Nominations

2a0e993c1b8362c456a9369b0b549d1c809a21df

[ "MIT" ]

1

2019-03-26T20:19:57.000Z

from django import forms FIELD_TYPES = ( ('Short_answer', forms.CharField), ('Paragraph', forms.CharField), ('Integer', forms.IntegerField), ('ChoiceField', forms.ChoiceField), ('MultipleChoiceField', forms.MultipleChoiceField), # ('Date', forms.DateField), ) QUES_TYPES = ( ('Short_answer', 'One Line Answer'), ('Paragraph', 'Multiple Line Answer'), ('Integer', 'Integer Answer'), ('ChoiceField', 'Choice'), ('MultipleChoiceField', 'Multiple-choice'), # ('Date', 'date'), )

28.981651

120

0.660336

from django.db import models from django import forms from django.contrib.auth.models import User from .form_dynamic import NominationForm import json class Questionnaire(models.Model): name = models.CharField(max_length=100, null=True) def __unicode__(self): return self.name def __str__(self): return self.name def get_form(self, *args, **kwargs): fields = [] for question in self.question_set.all(): field = question._get_formfield_class() label = question.question if question.required: label = question.question + " *" field_args = question._get_field_args() ques_id = question.id fields.append((label, field, field_args, ques_id)) return NominationForm(*args, extra=fields, **kwargs) def add_answer(self, applicant, answer_data): json_data = json.dumps(answer_data) answerform = FilledForm(questionnaire=self, applicant=applicant,data=json_data) answerform.save() return answerform FIELD_TYPES = ( ('Short_answer', forms.CharField), ('Paragraph', forms.CharField), ('Integer', forms.IntegerField), ('ChoiceField', forms.ChoiceField), ('MultipleChoiceField', forms.MultipleChoiceField), # ('Date', forms.DateField), ) QUES_TYPES = ( ('Short_answer', 'One Line Answer'), ('Paragraph', 'Multiple Line Answer'), ('Integer', 'Integer Answer'), ('ChoiceField', 'Choice'), ('MultipleChoiceField', 'Multiple-choice'), # ('Date', 'date'), ) class Question(models.Model): questionnaire = models.ForeignKey(Questionnaire,on_delete=models.CASCADE, null=True) question_type = models.CharField(max_length=50, choices=QUES_TYPES, null=True) question = models.CharField(max_length=1000, null=True) question_choices = models.TextField(max_length=600, null=True, blank=True, help_text='make new line for new option') required = models.BooleanField(default=True) def __unicode__(self): return self.question def __str__(self): return self.question def _get_formfield_class(self): for index, field_class in FIELD_TYPES: if self.question_type == index: return field_class def _get_field_args(self): args = {} if self.question_type == 'ChoiceField' or self.question_type == 'MultipleChoiceField': args['choices'] = enumerate(self.question_choices.split('\n')) if self.question_type == 'MultipleChoiceField': args['widget']=forms.CheckboxSelectMultiple if self.question_type == 'Paragraph': args['widget'] =forms.Textarea if self.required: args['label_suffix'] = " *" args.update({'required': self.required}) return args class FilledForm(models.Model): questionnaire = models.ForeignKey(Questionnaire,on_delete=models.CASCADE, null=True) applicant = models.ForeignKey(User, null=True) data = models.CharField(max_length=30000, null=True, blank=True) def __str__(self): return self.questionnaire.name

0

2,430

0

38

157

c910c09445a0e65ba2545dbe1c4a46731ae345b6

4,099

py

Python

degmo/data/datasets.py

IcarusWizard/Deep-Generative-Models

4117c11ad944bdeff106a80adbb3642a076af64e

[ "MIT" ]

2

2019-11-21T15:50:59.000Z

2019-12-17T02:44:19.000Z

degmo/data/datasets.py

IcarusWizard/Deep-Generative-Models

4117c11ad944bdeff106a80adbb3642a076af64e

[ "MIT" ]

null

degmo/data/datasets.py

IcarusWizard/Deep-Generative-Models

4117c11ad944bdeff106a80adbb3642a076af64e

[ "MIT" ]

1

2021-07-02T05:49:29.000Z

import PIL.Image as Image from functools import partial DATADIR = 'dataset/' load_celeba32 = partial(load_celeba, image_size=32) load_celeba64 = partial(load_celeba, image_size=64) load_celeba128 = partial(load_celeba, image_size=128)

37.263636

112

0.68041

import numpy as np import torch, torchvision, math from torch.functional import F import os import PIL.Image as Image from functools import partial DATADIR = 'dataset/' class ImageDataset(torch.utils.data.Dataset): def __init__(self, root, transform=None): super().__init__() self.root = root self.image_list = [os.path.join(root, filename) for filename in os.listdir(root)] self.transform = transform def __len__(self): return len(self.image_list) def __getitem__(self, index): img = Image.open(self.image_list[index]) if self.transform: img = self.transform(img) return (img, ) def load_mnist(normalize=False): config = { "c" : 1, "h" : 28, "w" : 28, } transform = [torchvision.transforms.ToTensor()] if normalize: transform.append(torchvision.transforms.Normalize([0.5], [0.5])) transform = torchvision.transforms.Compose(transform) train_dataset = torchvision.datasets.MNIST(DATADIR, train=True, download=True, transform=transform) test_dataset = torchvision.datasets.MNIST(DATADIR, train=False, download=True, transform=transform) train_dataset, val_dataset = torch.utils.data.random_split(train_dataset, (55000, 5000)) return (train_dataset, val_dataset, test_dataset, config) def load_bmnist(normalize=False): config = { "c" : 1, "h" : 28, "w" : 28, } assert not normalize, "bmnist do not support normalize operation" transform = torchvision.transforms.Compose([ torchvision.transforms.ToTensor(), torchvision.transforms.Lambda(lambda x: (x > 0).float()), ]) train_dataset = torchvision.datasets.MNIST(DATADIR, train=True, download=True, transform=transform) test_dataset = torchvision.datasets.MNIST(DATADIR, train=False, download=True, transform=transform) train_dataset, val_dataset = torch.utils.data.random_split(train_dataset, (55000, 5000)) return (train_dataset, val_dataset, test_dataset, config) def load_svhn(normalize=False): config = { "c" : 3, "h" : 32, "w" : 32, } transform = [torchvision.transforms.ToTensor()] if normalize: transform.append(torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))) transform = torchvision.transforms.Compose(transform) train_dataset = torchvision.datasets.SVHN(DATADIR, split='train', download=True, transform=transform) test_dataset = torchvision.datasets.SVHN(DATADIR, split='test', download=True, transform=transform) train_dataset, val_dataset = torch.utils.data.random_split(train_dataset, (len(train_dataset) - 5000, 5000)) return (train_dataset, val_dataset, test_dataset, config) def load_cifar(normalize=False): config = { "c" : 3, "h" : 32, "w" : 32, } transform = [torchvision.transforms.ToTensor()] if normalize: transform.append(torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))) transform = torchvision.transforms.Compose(transform) dataset = torchvision.datasets.CIFAR10(DATADIR, download=True, transform=transform) return torch.utils.data.random_split(dataset, (40000, 5000, 5000)) + [config] def load_celeba(image_size=128, normalize=False): config = { "c" : 3, "h" : image_size, "w" : image_size, } transform = [ torchvision.transforms.Resize(image_size), torchvision.transforms.CenterCrop(image_size), torchvision.transforms.ToTensor(), ] if normalize: transform.append(torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))) transform = torchvision.transforms.Compose(transform) dataset = torchvision.datasets.CelebA(DATADIR, download=True, transform=transform) return torch.utils.data.random_split(dataset, (len(dataset) - 2000, 1000, 1000)) + [config] load_celeba32 = partial(load_celeba, image_size=32) load_celeba64 = partial(load_celeba, image_size=64) load_celeba128 = partial(load_celeba, image_size=128)

0

479

0

3,150

0

4

230

1a3578a56a4bccb214d3e2c35a83b6e6b51851e2

57,483

py

Python

basistheory/api/tenants_api.py

Basis-Theory/basistheory-python

5fd0f3d20fd07e8de45d6d5919e092c696049df1

[ "Apache-2.0" ]

null

basistheory/api/tenants_api.py

Basis-Theory/basistheory-python

5fd0f3d20fd07e8de45d6d5919e092c696049df1

[ "Apache-2.0" ]

null

basistheory/api/tenants_api.py

Basis-Theory/basistheory-python

5fd0f3d20fd07e8de45d6d5919e092c696049df1

[ "Apache-2.0" ]

null

""" Basis Theory API ## Getting Started * Sign-in to [Basis Theory](https://basistheory.com) and go to [Applications](https://portal.basistheory.com/applications) * Create a Basis Theory Server to Server Application * All permissions should be selected * Paste the API Key into the `BT-API-KEY` variable # noqa: E501 The version of the OpenAPI document: v1 Generated by: https://openapi-generator.tech """

37.768068

300

0.517736

""" Basis Theory API ## Getting Started * Sign-in to [Basis Theory](https://basistheory.com) and go to [Applications](https://portal.basistheory.com/applications) * Create a Basis Theory Server to Server Application * All permissions should be selected * Paste the API Key into the `BT-API-KEY` variable # noqa: E501 The version of the OpenAPI document: v1 Generated by: https://openapi-generator.tech """ import re # noqa: F401 import sys # noqa: F401 from basistheory.api_client import ApiClient, Endpoint as _Endpoint from basistheory.model_utils import ( # noqa: F401 check_allowed_values, check_validations, date, datetime, file_type, none_type, validate_and_convert_types, set_request_options ) from basistheory.model.create_tenant_invitation_request import CreateTenantInvitationRequest from basistheory.model.problem_details import ProblemDetails from basistheory.model.tenant import Tenant from basistheory.model.tenant_invitation_response import TenantInvitationResponse from basistheory.model.tenant_invitation_response_paginated_list import TenantInvitationResponsePaginatedList from basistheory.model.tenant_invitation_status import TenantInvitationStatus from basistheory.model.tenant_member_response_paginated_list import TenantMemberResponsePaginatedList from basistheory.model.tenant_usage_report import TenantUsageReport from basistheory.model.update_tenant_request import UpdateTenantRequest from basistheory.model.validation_problem_details import ValidationProblemDetails class TenantsApi(object): """NOTE: This class is auto generated by OpenAPI Generator Ref: https://openapi-generator.tech Do not edit the class manually. """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client self.create_invitation_endpoint = _Endpoint( settings={ 'response_type': (TenantInvitationResponse,), 'auth': [ 'apiKey' ], 'endpoint_path': '/tenants/self/invitations', 'operation_id': 'create_invitation', 'http_method': 'POST', 'servers': None, }, params_map={ 'all': [ 'create_tenant_invitation_request', 'request_options' ], 'required': [], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { 'create_tenant_invitation_request': (CreateTenantInvitationRequest,), }, 'attribute_map': { }, 'location_map': { 'create_tenant_invitation_request': 'body', }, 'collection_format_map': { } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [ 'application/json' ] }, api_client=api_client ) self.delete_endpoint = _Endpoint( settings={ 'response_type': None, 'auth': [ 'apiKey' ], 'endpoint_path': '/tenants/self', 'operation_id': 'delete', 'http_method': 'DELETE', 'servers': None, }, params_map={ 'all': [ 'request_options' ], 'required': [], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { }, 'attribute_map': { }, 'location_map': { }, 'collection_format_map': { } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [], }, api_client=api_client ) self.delete_invitation_endpoint = _Endpoint( settings={ 'response_type': None, 'auth': [ 'apiKey' ], 'endpoint_path': '/tenants/self/invitations/{invitationId}', 'operation_id': 'delete_invitation', 'http_method': 'DELETE', 'servers': None, }, params_map={ 'all': [ 'invitation_id', 'request_options' ], 'required': [ 'invitation_id', ], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { 'invitation_id': (str,), }, 'attribute_map': { 'invitation_id': 'invitationId', }, 'location_map': { 'invitation_id': 'path', }, 'collection_format_map': { } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [], }, api_client=api_client ) self.delete_member_endpoint = _Endpoint( settings={ 'response_type': None, 'auth': [ 'apiKey' ], 'endpoint_path': '/tenants/self/members/{memberId}', 'operation_id': 'delete_member', 'http_method': 'DELETE', 'servers': None, }, params_map={ 'all': [ 'member_id', 'request_options' ], 'required': [ 'member_id', ], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { 'member_id': (str,), }, 'attribute_map': { 'member_id': 'memberId', }, 'location_map': { 'member_id': 'path', }, 'collection_format_map': { } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [], }, api_client=api_client ) self.get_endpoint = _Endpoint( settings={ 'response_type': (Tenant,), 'auth': [ 'apiKey' ], 'endpoint_path': '/tenants/self', 'operation_id': 'get', 'http_method': 'GET', 'servers': None, }, params_map={ 'all': [ 'request_options' ], 'required': [], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { }, 'attribute_map': { }, 'location_map': { }, 'collection_format_map': { } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [], }, api_client=api_client ) self.get_invitations_endpoint = _Endpoint( settings={ 'response_type': (TenantInvitationResponsePaginatedList,), 'auth': [ 'apiKey' ], 'endpoint_path': '/tenants/self/invitations', 'operation_id': 'get_invitations', 'http_method': 'GET', 'servers': None, }, params_map={ 'all': [ 'status', 'page', 'size', 'request_options' ], 'required': [], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { 'status': (TenantInvitationStatus,), 'page': (int,), 'size': (int,), }, 'attribute_map': { 'status': 'status', 'page': 'page', 'size': 'size', }, 'location_map': { 'status': 'query', 'page': 'query', 'size': 'query', }, 'collection_format_map': { } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [], }, api_client=api_client ) self.get_members_endpoint = _Endpoint( settings={ 'response_type': (TenantMemberResponsePaginatedList,), 'auth': [ 'apiKey' ], 'endpoint_path': '/tenants/self/members', 'operation_id': 'get_members', 'http_method': 'GET', 'servers': None, }, params_map={ 'all': [ 'user_id', 'page', 'size', 'request_options' ], 'required': [], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { 'user_id': ([str],), 'page': (int,), 'size': (int,), }, 'attribute_map': { 'user_id': 'user_id', 'page': 'page', 'size': 'size', }, 'location_map': { 'user_id': 'query', 'page': 'query', 'size': 'query', }, 'collection_format_map': { 'user_id': 'multi', } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [], }, api_client=api_client ) self.get_tenant_operation_report_endpoint = _Endpoint( settings={ 'response_type': (TenantUsageReport,), 'auth': [ 'apiKey' ], 'endpoint_path': '/tenants/self/reports/operations', 'operation_id': 'get_tenant_operation_report', 'http_method': 'GET', 'servers': None, }, params_map={ 'all': [ 'request_options' ], 'required': [], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { }, 'attribute_map': { }, 'location_map': { }, 'collection_format_map': { } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [], }, api_client=api_client ) self.get_tenant_usage_report_endpoint = _Endpoint( settings={ 'response_type': (TenantUsageReport,), 'auth': [ 'apiKey' ], 'endpoint_path': '/tenants/self/reports/usage', 'operation_id': 'get_tenant_usage_report', 'http_method': 'GET', 'servers': None, }, params_map={ 'all': [ 'request_options' ], 'required': [], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { }, 'attribute_map': { }, 'location_map': { }, 'collection_format_map': { } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [], }, api_client=api_client ) self.resend_invitation_endpoint = _Endpoint( settings={ 'response_type': (TenantInvitationResponse,), 'auth': [ 'apiKey' ], 'endpoint_path': '/tenants/self/invitations/{invitationId}/resend', 'operation_id': 'resend_invitation', 'http_method': 'POST', 'servers': None, }, params_map={ 'all': [ 'invitation_id', 'request_options' ], 'required': [ 'invitation_id', ], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { 'invitation_id': (str,), }, 'attribute_map': { 'invitation_id': 'invitationId', }, 'location_map': { 'invitation_id': 'path', }, 'collection_format_map': { } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [], }, api_client=api_client ) self.update_endpoint = _Endpoint( settings={ 'response_type': (Tenant,), 'auth': [ 'apiKey' ], 'endpoint_path': '/tenants/self', 'operation_id': 'update', 'http_method': 'PUT', 'servers': None, }, params_map={ 'all': [ 'update_tenant_request', 'request_options' ], 'required': [], 'nullable': [ ], 'enum': [ ], 'validation': [ ] }, root_map={ 'validations': { }, 'allowed_values': { }, 'openapi_types': { 'update_tenant_request': (UpdateTenantRequest,), }, 'attribute_map': { }, 'location_map': { 'update_tenant_request': 'body', }, 'collection_format_map': { } }, headers_map={ 'accept': [ 'application/json' ], 'content_type': [ 'application/json' ] }, api_client=api_client ) def create_invitation( self, **kwargs ): """create_invitation # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.create_invitation(async_req=True) >>> result = thread.get() Keyword Args: create_tenant_invitation_request (CreateTenantInvitationRequest): [optional] request_options(RequestOptions): [optional] _return_http_data_only (bool): response data without head status code and headers. Default is True. _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. _request_auths (list): set to override the auth_settings for an a single request; this effectively ignores the authentication in the spec for a single request. Default is None async_req (bool): execute request asynchronously Returns: TenantInvitationResponse If the method is called asynchronously, returns the request thread. """ if kwargs.get('request_options'): set_request_options(kwargs.pop('request_options'), self) kwargs['async_req'] = kwargs.get( 'async_req', False ) kwargs['_return_http_data_only'] = kwargs.get( '_return_http_data_only', True ) kwargs['_preload_content'] = kwargs.get( '_preload_content', True ) kwargs['_request_timeout'] = kwargs.get( '_request_timeout', None ) kwargs['_check_input_type'] = kwargs.get( '_check_input_type', True ) kwargs['_check_return_type'] = kwargs.get( '_check_return_type', True ) kwargs['_spec_property_naming'] = kwargs.get( '_spec_property_naming', False ) kwargs['_content_type'] = kwargs.get( '_content_type') kwargs['_host_index'] = kwargs.get('_host_index') kwargs['_request_auths'] = kwargs.get('_request_auths', None) return self.create_invitation_endpoint.call_with_http_info(**kwargs) def delete( self, **kwargs ): """delete # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete(async_req=True) >>> result = thread.get() Keyword Args: request_options(RequestOptions): [optional] _return_http_data_only (bool): response data without head status code and headers. Default is True. _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. _request_auths (list): set to override the auth_settings for an a single request; this effectively ignores the authentication in the spec for a single request. Default is None async_req (bool): execute request asynchronously Returns: None If the method is called asynchronously, returns the request thread. """ if kwargs.get('request_options'): set_request_options(kwargs.pop('request_options'), self) kwargs['async_req'] = kwargs.get( 'async_req', False ) kwargs['_return_http_data_only'] = kwargs.get( '_return_http_data_only', True ) kwargs['_preload_content'] = kwargs.get( '_preload_content', True ) kwargs['_request_timeout'] = kwargs.get( '_request_timeout', None ) kwargs['_check_input_type'] = kwargs.get( '_check_input_type', True ) kwargs['_check_return_type'] = kwargs.get( '_check_return_type', True ) kwargs['_spec_property_naming'] = kwargs.get( '_spec_property_naming', False ) kwargs['_content_type'] = kwargs.get( '_content_type') kwargs['_host_index'] = kwargs.get('_host_index') kwargs['_request_auths'] = kwargs.get('_request_auths', None) return self.delete_endpoint.call_with_http_info(**kwargs) def delete_invitation( self, invitation_id, **kwargs ): """delete_invitation # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_invitation(invitation_id, async_req=True) >>> result = thread.get() Args: invitation_id (str): Keyword Args: request_options(RequestOptions): [optional] _return_http_data_only (bool): response data without head status code and headers. Default is True. _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. _request_auths (list): set to override the auth_settings for an a single request; this effectively ignores the authentication in the spec for a single request. Default is None async_req (bool): execute request asynchronously Returns: None If the method is called asynchronously, returns the request thread. """ if kwargs.get('request_options'): set_request_options(kwargs.pop('request_options'), self) kwargs['async_req'] = kwargs.get( 'async_req', False ) kwargs['_return_http_data_only'] = kwargs.get( '_return_http_data_only', True ) kwargs['_preload_content'] = kwargs.get( '_preload_content', True ) kwargs['_request_timeout'] = kwargs.get( '_request_timeout', None ) kwargs['_check_input_type'] = kwargs.get( '_check_input_type', True ) kwargs['_check_return_type'] = kwargs.get( '_check_return_type', True ) kwargs['_spec_property_naming'] = kwargs.get( '_spec_property_naming', False ) kwargs['_content_type'] = kwargs.get( '_content_type') kwargs['_host_index'] = kwargs.get('_host_index') kwargs['_request_auths'] = kwargs.get('_request_auths', None) kwargs['invitation_id'] = \ invitation_id return self.delete_invitation_endpoint.call_with_http_info(**kwargs) def delete_member( self, member_id, **kwargs ): """delete_member # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.delete_member(member_id, async_req=True) >>> result = thread.get() Args: member_id (str): Keyword Args: request_options(RequestOptions): [optional] _return_http_data_only (bool): response data without head status code and headers. Default is True. _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. _request_auths (list): set to override the auth_settings for an a single request; this effectively ignores the authentication in the spec for a single request. Default is None async_req (bool): execute request asynchronously Returns: None If the method is called asynchronously, returns the request thread. """ if kwargs.get('request_options'): set_request_options(kwargs.pop('request_options'), self) kwargs['async_req'] = kwargs.get( 'async_req', False ) kwargs['_return_http_data_only'] = kwargs.get( '_return_http_data_only', True ) kwargs['_preload_content'] = kwargs.get( '_preload_content', True ) kwargs['_request_timeout'] = kwargs.get( '_request_timeout', None ) kwargs['_check_input_type'] = kwargs.get( '_check_input_type', True ) kwargs['_check_return_type'] = kwargs.get( '_check_return_type', True ) kwargs['_spec_property_naming'] = kwargs.get( '_spec_property_naming', False ) kwargs['_content_type'] = kwargs.get( '_content_type') kwargs['_host_index'] = kwargs.get('_host_index') kwargs['_request_auths'] = kwargs.get('_request_auths', None) kwargs['member_id'] = \ member_id return self.delete_member_endpoint.call_with_http_info(**kwargs) def get( self, **kwargs ): """get # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get(async_req=True) >>> result = thread.get() Keyword Args: request_options(RequestOptions): [optional] _return_http_data_only (bool): response data without head status code and headers. Default is True. _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. _request_auths (list): set to override the auth_settings for an a single request; this effectively ignores the authentication in the spec for a single request. Default is None async_req (bool): execute request asynchronously Returns: Tenant If the method is called asynchronously, returns the request thread. """ if kwargs.get('request_options'): set_request_options(kwargs.pop('request_options'), self) kwargs['async_req'] = kwargs.get( 'async_req', False ) kwargs['_return_http_data_only'] = kwargs.get( '_return_http_data_only', True ) kwargs['_preload_content'] = kwargs.get( '_preload_content', True ) kwargs['_request_timeout'] = kwargs.get( '_request_timeout', None ) kwargs['_check_input_type'] = kwargs.get( '_check_input_type', True ) kwargs['_check_return_type'] = kwargs.get( '_check_return_type', True ) kwargs['_spec_property_naming'] = kwargs.get( '_spec_property_naming', False ) kwargs['_content_type'] = kwargs.get( '_content_type') kwargs['_host_index'] = kwargs.get('_host_index') kwargs['_request_auths'] = kwargs.get('_request_auths', None) return self.get_endpoint.call_with_http_info(**kwargs) def get_invitations( self, **kwargs ): """get_invitations # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_invitations(async_req=True) >>> result = thread.get() Keyword Args: status (TenantInvitationStatus): [optional] page (int): [optional] size (int): [optional] request_options(RequestOptions): [optional] _return_http_data_only (bool): response data without head status code and headers. Default is True. _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. _request_auths (list): set to override the auth_settings for an a single request; this effectively ignores the authentication in the spec for a single request. Default is None async_req (bool): execute request asynchronously Returns: TenantInvitationResponsePaginatedList If the method is called asynchronously, returns the request thread. """ if kwargs.get('request_options'): set_request_options(kwargs.pop('request_options'), self) kwargs['async_req'] = kwargs.get( 'async_req', False ) kwargs['_return_http_data_only'] = kwargs.get( '_return_http_data_only', True ) kwargs['_preload_content'] = kwargs.get( '_preload_content', True ) kwargs['_request_timeout'] = kwargs.get( '_request_timeout', None ) kwargs['_check_input_type'] = kwargs.get( '_check_input_type', True ) kwargs['_check_return_type'] = kwargs.get( '_check_return_type', True ) kwargs['_spec_property_naming'] = kwargs.get( '_spec_property_naming', False ) kwargs['_content_type'] = kwargs.get( '_content_type') kwargs['_host_index'] = kwargs.get('_host_index') kwargs['_request_auths'] = kwargs.get('_request_auths', None) return self.get_invitations_endpoint.call_with_http_info(**kwargs) def get_members( self, **kwargs ): """get_members # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_members(async_req=True) >>> result = thread.get() Keyword Args: user_id ([str]): [optional] page (int): [optional] size (int): [optional] request_options(RequestOptions): [optional] _return_http_data_only (bool): response data without head status code and headers. Default is True. _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. _request_auths (list): set to override the auth_settings for an a single request; this effectively ignores the authentication in the spec for a single request. Default is None async_req (bool): execute request asynchronously Returns: TenantMemberResponsePaginatedList If the method is called asynchronously, returns the request thread. """ if kwargs.get('request_options'): set_request_options(kwargs.pop('request_options'), self) kwargs['async_req'] = kwargs.get( 'async_req', False ) kwargs['_return_http_data_only'] = kwargs.get( '_return_http_data_only', True ) kwargs['_preload_content'] = kwargs.get( '_preload_content', True ) kwargs['_request_timeout'] = kwargs.get( '_request_timeout', None ) kwargs['_check_input_type'] = kwargs.get( '_check_input_type', True ) kwargs['_check_return_type'] = kwargs.get( '_check_return_type', True ) kwargs['_spec_property_naming'] = kwargs.get( '_spec_property_naming', False ) kwargs['_content_type'] = kwargs.get( '_content_type') kwargs['_host_index'] = kwargs.get('_host_index') kwargs['_request_auths'] = kwargs.get('_request_auths', None) return self.get_members_endpoint.call_with_http_info(**kwargs) def get_tenant_operation_report( self, **kwargs ): """get_tenant_operation_report # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_tenant_operation_report(async_req=True) >>> result = thread.get() Keyword Args: request_options(RequestOptions): [optional] _return_http_data_only (bool): response data without head status code and headers. Default is True. _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. _request_auths (list): set to override the auth_settings for an a single request; this effectively ignores the authentication in the spec for a single request. Default is None async_req (bool): execute request asynchronously Returns: TenantUsageReport If the method is called asynchronously, returns the request thread. """ if kwargs.get('request_options'): set_request_options(kwargs.pop('request_options'), self) kwargs['async_req'] = kwargs.get( 'async_req', False ) kwargs['_return_http_data_only'] = kwargs.get( '_return_http_data_only', True ) kwargs['_preload_content'] = kwargs.get( '_preload_content', True ) kwargs['_request_timeout'] = kwargs.get( '_request_timeout', None ) kwargs['_check_input_type'] = kwargs.get( '_check_input_type', True ) kwargs['_check_return_type'] = kwargs.get( '_check_return_type', True ) kwargs['_spec_property_naming'] = kwargs.get( '_spec_property_naming', False ) kwargs['_content_type'] = kwargs.get( '_content_type') kwargs['_host_index'] = kwargs.get('_host_index') kwargs['_request_auths'] = kwargs.get('_request_auths', None) return self.get_tenant_operation_report_endpoint.call_with_http_info(**kwargs) def get_tenant_usage_report( self, **kwargs ): """get_tenant_usage_report # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.get_tenant_usage_report(async_req=True) >>> result = thread.get() Keyword Args: request_options(RequestOptions): [optional] _return_http_data_only (bool): response data without head status code and headers. Default is True. _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. _request_auths (list): set to override the auth_settings for an a single request; this effectively ignores the authentication in the spec for a single request. Default is None async_req (bool): execute request asynchronously Returns: TenantUsageReport If the method is called asynchronously, returns the request thread. """ if kwargs.get('request_options'): set_request_options(kwargs.pop('request_options'), self) kwargs['async_req'] = kwargs.get( 'async_req', False ) kwargs['_return_http_data_only'] = kwargs.get( '_return_http_data_only', True ) kwargs['_preload_content'] = kwargs.get( '_preload_content', True ) kwargs['_request_timeout'] = kwargs.get( '_request_timeout', None ) kwargs['_check_input_type'] = kwargs.get( '_check_input_type', True ) kwargs['_check_return_type'] = kwargs.get( '_check_return_type', True ) kwargs['_spec_property_naming'] = kwargs.get( '_spec_property_naming', False ) kwargs['_content_type'] = kwargs.get( '_content_type') kwargs['_host_index'] = kwargs.get('_host_index') kwargs['_request_auths'] = kwargs.get('_request_auths', None) return self.get_tenant_usage_report_endpoint.call_with_http_info(**kwargs) def resend_invitation( self, invitation_id, **kwargs ): """resend_invitation # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.resend_invitation(invitation_id, async_req=True) >>> result = thread.get() Args: invitation_id (str): Keyword Args: request_options(RequestOptions): [optional] _return_http_data_only (bool): response data without head status code and headers. Default is True. _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. _request_auths (list): set to override the auth_settings for an a single request; this effectively ignores the authentication in the spec for a single request. Default is None async_req (bool): execute request asynchronously Returns: TenantInvitationResponse If the method is called asynchronously, returns the request thread. """ if kwargs.get('request_options'): set_request_options(kwargs.pop('request_options'), self) kwargs['async_req'] = kwargs.get( 'async_req', False ) kwargs['_return_http_data_only'] = kwargs.get( '_return_http_data_only', True ) kwargs['_preload_content'] = kwargs.get( '_preload_content', True ) kwargs['_request_timeout'] = kwargs.get( '_request_timeout', None ) kwargs['_check_input_type'] = kwargs.get( '_check_input_type', True ) kwargs['_check_return_type'] = kwargs.get( '_check_return_type', True ) kwargs['_spec_property_naming'] = kwargs.get( '_spec_property_naming', False ) kwargs['_content_type'] = kwargs.get( '_content_type') kwargs['_host_index'] = kwargs.get('_host_index') kwargs['_request_auths'] = kwargs.get('_request_auths', None) kwargs['invitation_id'] = \ invitation_id return self.resend_invitation_endpoint.call_with_http_info(**kwargs) def update( self, **kwargs ): """update # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.update(async_req=True) >>> result = thread.get() Keyword Args: update_tenant_request (UpdateTenantRequest): [optional] request_options(RequestOptions): [optional] _return_http_data_only (bool): response data without head status code and headers. Default is True. _preload_content (bool): if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. _request_timeout (int/float/tuple): timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. Default is None. _check_input_type (bool): specifies if type checking should be done one the data sent to the server. Default is True. _check_return_type (bool): specifies if type checking should be done one the data received from the server. Default is True. _spec_property_naming (bool): True if the variable names in the input data are serialized names, as specified in the OpenAPI document. False if the variable names in the input data are pythonic names, e.g. snake case (default) _content_type (str/None): force body content-type. Default is None and content-type will be predicted by allowed content-types and body. _host_index (int/None): specifies the index of the server that we want to use. Default is read from the configuration. _request_auths (list): set to override the auth_settings for an a single request; this effectively ignores the authentication in the spec for a single request. Default is None async_req (bool): execute request asynchronously Returns: Tenant If the method is called asynchronously, returns the request thread. """ if kwargs.get('request_options'): set_request_options(kwargs.pop('request_options'), self) kwargs['async_req'] = kwargs.get( 'async_req', False ) kwargs['_return_http_data_only'] = kwargs.get( '_return_http_data_only', True ) kwargs['_preload_content'] = kwargs.get( '_preload_content', True ) kwargs['_request_timeout'] = kwargs.get( '_request_timeout', None ) kwargs['_check_input_type'] = kwargs.get( '_check_input_type', True ) kwargs['_check_return_type'] = kwargs.get( '_check_return_type', True ) kwargs['_spec_property_naming'] = kwargs.get( '_spec_property_naming', False ) kwargs['_content_type'] = kwargs.get( '_content_type') kwargs['_host_index'] = kwargs.get('_host_index') kwargs['_request_auths'] = kwargs.get('_request_auths', None) return self.update_endpoint.call_with_http_info(**kwargs)

0

55,908

0

786

361

2b5c325a1726de056d5d1198acc3940aef23c363

3,454

py

Python

custom_components/nwsradar/config_flow.py

MatthewFlamm/ha_nws_radar

f039bf1abb94a48232599746f80d4c7e4af35de7

[ "MIT" ]

21

2019-07-18T23:38:22.000Z

2021-01-08T01:14:44.000Z

custom_components/nwsradar/config_flow.py

MatthewFlamm/ha_nws_radar

f039bf1abb94a48232599746f80d4c7e4af35de7

[ "MIT" ]

7

2019-09-06T13:14:49.000Z

2020-12-18T17:49:34.000Z

custom_components/nwsradar/config_flow.py

MatthewFlamm/ha_nws_radar

f039bf1abb94a48232599746f80d4c7e4af35de7

[ "MIT" ]

2

2019-07-26T21:23:59.000Z

2020-01-14T23:03:12.000Z

"""Config flow for National Weather Service (NWS) integration.""" import logging # pylint: disable=unused-import _LOGGER = logging.getLogger(__name__)

32.895238

87

0.621888

"""Config flow for National Weather Service (NWS) integration.""" import logging import voluptuous as vol from nws_radar.nws_radar_mosaic import REGIONS from homeassistant import config_entries from . import unique_id # pylint: disable=unused-import from .const import ( CONF_LOOP, CONF_STATION, CONF_STYLE, STYLES, CONF_TYPE, RADAR_TYPES, DEFAULT_RADAR_TYPE, CONF_NAME, DOMAIN, ) _LOGGER = logging.getLogger(__name__) class ConfigFlow(config_entries.ConfigFlow, domain=DOMAIN): """Handle a config flow for National Weather Service (NWS).""" VERSION = 1 CONNECTION_CLASS = config_entries.CONN_CLASS_CLOUD_POLL async def async_step_user(self, user_input=None): """Handle the initial step.""" errors = {} if user_input is not None: self._config = user_input # pylint: disable=attribute-defined-outside-init if user_input[CONF_STYLE] in {"Standard", "Enhanced"}: return await self.async_step_standard_enhanced() # Mosaic return await self.async_step_mosaic() data_schema = vol.Schema( { vol.Required(CONF_STYLE): vol.In(STYLES), } ) return self.async_show_form( step_id="user", data_schema=data_schema, errors=errors ) async def async_step_standard_enhanced(self, user_input=None): """Standard or enhanced step.""" errors = {} if user_input is not None: self._config.update(user_input) self._config[CONF_STATION] = self._config[CONF_STATION].upper() title = unique_id(self._config) self._config[CONF_NAME] = None await self.async_set_unique_id(unique_id(self._config)) self._abort_if_unique_id_configured() return self.async_create_entry(title=title, data=self._config) data_schema = vol.Schema( { vol.Required(CONF_STATION): str, vol.Required(CONF_LOOP, default=True): bool, vol.Required(CONF_TYPE, default=DEFAULT_RADAR_TYPE): vol.In( RADAR_TYPES.keys() ), } ) return self.async_show_form( step_id="standard_enhanced", data_schema=data_schema, errors=errors ) async def async_step_mosaic(self, user_input=None): """Mosaic step.""" errors = {} if user_input is not None: self._config.update(user_input) self._config[CONF_TYPE] = "" self._config[CONF_NAME] = None title = unique_id(self._config) await self.async_set_unique_id(title) self._abort_if_unique_id_configured() return self.async_create_entry(title=title, data=self._config) data_schema = vol.Schema( { vol.Required(CONF_STATION): vol.In(REGIONS), vol.Required(CONF_LOOP, default=True): bool, } ) return self.async_show_form( step_id="mosaic", data_schema=data_schema, errors=errors ) async def async_step_import(self, user_input=None): """Import an entry from yaml.""" title = unique_id(user_input) await self.async_set_unique_id(title) self._abort_if_unique_id_configured() return self.async_create_entry(title=title, data=user_input)

0

2,680

290

0

194

136

e288a19792ad0dab33c86bbab35d030926f6a073

2,944

py

Python

xinci/model.py

Lapis-Hong/xinci

9234ef6e426dfa282c334ff79f4f76b475eb10f3

[ "MIT" ]

23

2018-06-18T15:35:47.000Z

2021-07-28T02:19:16.000Z

xinci/model.py

Lapis-Hong/xinci

9234ef6e426dfa282c334ff79f4f76b475eb10f3

[ "MIT" ]

null

xinci/model.py

Lapis-Hong/xinci

9234ef6e426dfa282c334ff79f4f76b475eb10f3

[ "MIT" ]

10

2018-06-20T07:01:17.000Z

2020-08-31T15:56:24.000Z

#!/usr/bin/env python # coding: utf-8 # @Author: lapis-hong # @Date : 2018/6/17 """This module contains the main algorithm for chinese word extraction. criterion 1: solid rate criterion 2: character entropy """ from __future__ import unicode_literals from __future__ import division

37.74359

108

0.66712

#!/usr/bin/env python # coding: utf-8 # @Author: lapis-hong # @Date : 2018/6/17 """This module contains the main algorithm for chinese word extraction. criterion 1: solid rate criterion 2: character entropy """ from __future__ import unicode_literals from __future__ import division import math from indexer import CnTextIndexer from utils import WordCountDict class EntropyJudger: """Use entropy and solid rate to judge whether a candidate is a chinese word or not.""" def __init__(self, document, least_cnt_threshold=5, solid_rate_threshold=0.018, entropy_threshold=1.92): """ Args: least_cnt_threshold: a word least appeared count, can not pass judge if less than this value. solid_rate_threshold: p(candidate)/p(candidate[0]) * p(candidate)/p(candidate[1]) * ... entropy_threshold: min(left_char_entropy, right_char_entropy), The smaller this values is, more new words you will get, but with less accuracy. """ self._least_cnt_threshold = least_cnt_threshold self._solid_rate_threshold = solid_rate_threshold self._entropy_threshold = entropy_threshold self._indexer = CnTextIndexer(document) def judge(self, candidate): solid_rate = self._get_solid_rate(candidate) entropy = self._get_entropy(candidate) if solid_rate < self._solid_rate_threshold or entropy < self._entropy_threshold: return False return True def _get_solid_rate(self, candidate): if len(candidate) < 2: return 1.0 cnt = self._indexer.count(candidate) # candidate count in document if cnt < self._least_cnt_threshold: # least count to be a word return 0.0 rate = 1.0 for c in candidate: rate *= cnt / self._indexer.char_cnt_map[c] # candidate character count in document return math.pow(rate, 1/float(len(candidate))) * math.sqrt(len(candidate)) # interesting def _get_entropy(self, candidate): left_char_dic = WordCountDict() right_char_dic = WordCountDict() candidate_pos_generator = self._indexer.find(candidate) for pos in candidate_pos_generator: c = self._indexer[pos-1] left_char_dic.add(c) c = self._indexer[pos+len(candidate)] right_char_dic.add(c) previous_total_char_cnt = left_char_dic.count() next_total_char_cnt = right_char_dic.count() previous_entropy = 0.0 next_entropy = 0.0 for char, count in left_char_dic.items(): # efficient prob = count / previous_total_char_cnt previous_entropy -= prob * math.log(prob) for char, count in right_char_dic.items(): prob = count / next_total_char_cnt next_entropy -= prob * math.log(prob) return min(previous_entropy, next_entropy) # 返回前后信息熵中较小的一个

39

0

2,531

0

12

90

86c4428f80dd80644e84963f60d1a11c38e4a4c2

561

py

Python

Machine_Learning/ZCSNumpy.py

ZuoCaiSong/Python

137d1c4c79f9594b9bc2c7dc7728246e697f1329

[ "MIT" ]

null

Machine_Learning/ZCSNumpy.py

ZuoCaiSong/Python

137d1c4c79f9594b9bc2c7dc7728246e697f1329

[ "MIT" ]

null

Machine_Learning/ZCSNumpy.py

ZuoCaiSong/Python

137d1c4c79f9594b9bc2c7dc7728246e697f1329

[ "MIT" ]

null

#! usr/bin/env python # -*- coding:utf-8 -*- """ """ ''' NumPy () ''' ''' NumPy(dimensions)(axes)(rank) ''' #eg: ''' 3D [1,2,3] 13 ''' #ndarraylist arr2 = [[1,0,0], [0,1,0]] ''' arr2 2 () ''' ''' NumPy ndarray numpy.arraypythonarray.array ''' # numpy a = arange(15).reshape(3, 5) print a # print "a", a.ndim help(ndim)

13.357143

44

0.682709

#! usr/bin/env python # -*- coding:utf-8 -*- """ 基础篇 """ from numpy import * ''' NumPy的主要对象是同种元素的多维数组。这是一个所有的元素都是一种类型、通过一个正整数元组索引的元素表格(通常是元素是数字) ''' ''' NumPy中维度(dimensions)叫做轴(axes)，轴的个数叫做秩(rank)。 ''' #eg: ''' 在3D空间一个点的坐标 [1,2,3] 是一个秩为1的数组，它只有一个轴，轴的长度为3 ''' #注意直接写，他的类型不是一个ndarray，是一个list，此处只是用于举例说明秩 arr2 = [[1,0,0], [0,1,0]] ''' arr2 的秩为2 (它有两个维度) ''' ''' NumPy的数组类被称作 ndarray 。通常被称作数组。注意numpy.array和标准python库类array.array并不相同，后者只处理一维数组和提供少量功能 ''' # 创建一个numpy的对象 a = arange(15).reshape(3, 5) print a # 数组轴的个数（秩），行数 print "a的秩为", a.ndim help(ndim)

627

0

-1

22

077630693a28af4ea5bf434f4de1bcb506757b3e

1,696

py

Python

tests/unit/test_fileclient.py

jubrad/salt

7960334fb726cfde45e6409da79a65535c626685

[ "Apache-2.0" ]

1

2020-01-02T09:03:21.000Z

tests/unit/test_fileclient.py

jubrad/salt

7960334fb726cfde45e6409da79a65535c626685

[ "Apache-2.0" ]

null

tests/unit/test_fileclient.py

jubrad/salt

7960334fb726cfde45e6409da79a65535c626685

[ "Apache-2.0" ]

1

2020-01-02T09:03:24.000Z

# -*- coding: utf-8 -*- ''' :codeauthor: :email: `Bo Maryniuk <[email protected]>` ''' # Import Python libs from __future__ import absolute_import # Import Salt Testing libs # Import Salt libs

32.615385

82

0.628538

# -*- coding: utf-8 -*- ''' :codeauthor: :email: `Bo Maryniuk <[email protected]>` ''' # Import Python libs from __future__ import absolute_import import errno # Import Salt Testing libs from tests.support.mock import patch, Mock from tests.support.unit import TestCase # Import Salt libs from salt.ext.six.moves import range from salt.fileclient import Client class FileclientTestCase(TestCase): ''' Fileclient test ''' opts = { 'extension_modules': '', 'cachedir': '/__test__', } def _fake_makedir(self, num=errno.EEXIST): def _side_effect(*args, **kwargs): raise OSError(num, 'Errno {0}'.format(num)) return Mock(side_effect=_side_effect) def test_cache_skips_makedirs_on_race_condition(self): ''' If cache contains already a directory, do not raise an exception. ''' with patch('os.path.isfile', lambda prm: False): for exists in range(2): with patch('os.makedirs', self._fake_makedir()): with Client(self.opts)._cache_loc('testfile') as c_ref_itr: assert c_ref_itr == '/__test__/files/base/testfile' def test_cache_raises_exception_on_non_eexist_ioerror(self): ''' If makedirs raises other than EEXIST errno, an exception should be raised. ''' with patch('os.path.isfile', lambda prm: False): with patch('os.makedirs', self._fake_makedir(num=errno.EROFS)): with self.assertRaises(OSError): with Client(self.opts)._cache_loc('testfile') as c_ref_itr: assert c_ref_itr == '/__test__/files/base/testfile'

0

1,311

0

58

133

9e5a009aa9aeb584ea41f3acb660d59e05af5898

11,072

py

Python

prepare_datasets.py

Jakob-Bach/Meta-Learning-Feature-Importance

089e5c7a5be91307f747e00b38b1567386fbee16

[ "MIT" ]

null

prepare_datasets.py

Jakob-Bach/Meta-Learning-Feature-Importance

089e5c7a5be91307f747e00b38b1567386fbee16

[ "MIT" ]

null

prepare_datasets.py

Jakob-Bach/Meta-Learning-Feature-Importance

089e5c7a5be91307f747e00b38b1567386fbee16

[ "MIT" ]

null

"""Prepare datasets Script that: - downloads, pre-processes, and saves base datasets from OpenML - computes meta-features - computes meta-targets (combining feature-importance measures and base models) - saves the meta-datasets Usage: python -m prepare_datasets --help """ import argparse import pathlib # Download one base dataset with the given "data_id" from OpenML and store it in X, y format in # "base_data_dir", all columns made numeric. Note that the method might throw an exception if # OpenML is not able to retrieve the dataset. # Download OpenML datasets and store them in "base_data_dir". Either retrieve base datasets by # "data_ids" or search according to fixed dataset characteristics. The latter was done for the # paper, but the datasets matching the characteristics can change in future. # Compute all meta-features for one base dataset with "base_dataset_name", located in # "base_data_dir", and store the resulting meta-data in "meta_data_dir" # For each base dataset from "base_data_dir", compute all meta-features. Save the resulting # meta-data into "meta_data_dir". # Compute one meta-target, i.e., apply one importance measure and one base model to one base # dataset. Return the actual meta-target (numeric feature importances) and some information # identifying it. # For each base dataset from "base_data_dir", compute all meta-targets, i.e., all # feature-importance measures for all base models. Save the resulting meta-data into # "meta_data_dir". # Parse command-line arguments and prepare base + meta datasets. if __name__ == '__main__': parser = argparse.ArgumentParser( description='Retrieves base datasets from OpenML, creates meta-datasets ' + 'and stores all these data.', formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('-b', '--base_data_dir', type=pathlib.Path, default='data/base_datasets/', help='Directory to store base datasets. Will be created if necessary.') parser.add_argument('-i', '--data_ids', type=int, default=[], nargs='*', help='Ids of OpenML datasets. If none provided, will search for datasets.') parser.add_argument('-m', '--meta_data_dir', type=pathlib.Path, default='data/meta_datasets/', help='Directory to store meta-datasets. Will be created if necessary.') parser.add_argument('-p', '--n_processes', type=int, default=None, help='Number of processes for multi-processing (default: all cores).') args = parser.parse_args() prepare_base_datasets(base_data_dir=args.base_data_dir, data_ids=args.data_ids) prepare_meta_features(base_data_dir=args.base_data_dir, meta_data_dir=args.meta_data_dir, n_processes=args.n_processes) prepare_meta_targets(base_data_dir=args.base_data_dir, meta_data_dir=args.meta_data_dir, n_processes=args.n_processes)

58.582011

110

0.694906

"""Prepare datasets Script that: - downloads, pre-processes, and saves base datasets from OpenML - computes meta-features - computes meta-targets (combining feature-importance measures and base models) - saves the meta-datasets Usage: python -m prepare_datasets --help """ import argparse import multiprocessing import pathlib from typing import Collection, Dict, Optional, Sequence, Union import warnings import numpy as np import openml import pandas as pd import sklearn.preprocessing import tqdm import data_utility import meta_features import meta_targets # Download one base dataset with the given "data_id" from OpenML and store it in X, y format in # "base_data_dir", all columns made numeric. Note that the method might throw an exception if # OpenML is not able to retrieve the dataset. def download_base_dataset(data_id: int, base_data_dir: pathlib.Path) -> None: dataset = openml.datasets.get_dataset(dataset_id=data_id, download_data=True) X, y, _, _ = dataset.get_data(target=dataset.default_target_attribute) non_numeric_features = [x.name for x in dataset.features.values() if (x.name in X.columns) and (x.data_type != 'numeric')] X[non_numeric_features] = sklearn.preprocessing.OrdinalEncoder(dtype=int).fit_transform( X=X[non_numeric_features]) assert all(np.issubdtype(X[feature].dtype, np.number) for feature in X.columns) y = pd.Series(sklearn.preprocessing.LabelEncoder().fit_transform(y=y), name=y.name) data_utility.save_dataset(X=X, y=y, dataset_name=dataset.name, directory=base_data_dir) # Download OpenML datasets and store them in "base_data_dir". Either retrieve base datasets by # "data_ids" or search according to fixed dataset characteristics. The latter was done for the # paper, but the datasets matching the characteristics can change in future. def prepare_base_datasets(base_data_dir: pathlib.Path, data_ids: Optional[Sequence[int]] = None) -> None: print('Base dataset preparation started.') if not base_data_dir.is_dir(): print('Base-dataset directory does not exist. We create it.') base_data_dir.mkdir(parents=True) if any(base_data_dir.iterdir()): print('Base-dataset directory is not empty. Files might be overwritten, but not deleted.') dataset_overview = openml.datasets.list_datasets(status='active', output_format='dataframe') if (data_ids is None) or (len(data_ids) == 0): dataset_overview = dataset_overview[ (dataset_overview['NumberOfClasses'] == 2) & # binary classification (dataset_overview['NumberOfInstances'] >= 1000) & (dataset_overview['NumberOfInstances'] <= 10000) & (dataset_overview['NumberOfMissingValues'] == 0) ] # Pick latest version of each dataset: dataset_overview = dataset_overview.sort_values(by='version').groupby('name').last().reset_index() # Pick the same amount of datasets from different categories regarding number of features: feature_number_groups = [(6, 11), (12, 26), (27, 51)] # list of (lower, upper); count includes target num_datasets_per_group = 20 data_ids = [] with tqdm.tqdm(total=(len(feature_number_groups) * num_datasets_per_group), desc='Downloading datasets') as progress_bar: for lower, upper in feature_number_groups: current_datasets = dataset_overview[(dataset_overview['NumberOfFeatures'] >= lower) & (dataset_overview['NumberOfFeatures'] <= upper)] successful_downloads = 0 current_position = 0 # ... in the table of datasets while successful_downloads < num_datasets_per_group: data_id = int(current_datasets['did'].iloc[current_position]) try: download_base_dataset(data_id=data_id, base_data_dir=base_data_dir) data_ids.append(data_id) successful_downloads += 1 progress_bar.update() except Exception: # OpenML does not specify exception type for get_dataset() pass finally: # in any case, move on to next dataset current_position += 1 else: print('Using given dataset ids.') for data_id in tqdm.tqdm(data_ids, desc='Downloading datasets'): try: download_base_dataset(data_id=data_id, base_data_dir=base_data_dir) except Exception: # OpenML does not specify exception type for get_dataset() warnings.warn(f'Download of dataset {data_id} failed.') dataset_overview[dataset_overview['did'].isin(data_ids)].to_csv( base_data_dir / '_dataset_overview.csv', index=False) print('Base datasets prepared and saved.') # Compute all meta-features for one base dataset with "base_dataset_name", located in # "base_data_dir", and store the resulting meta-data in "meta_data_dir" def compute_and_save_meta_features(base_data_dir: pathlib.Path, base_dataset_name: str, meta_data_dir: pathlib.Path) -> None: X, y = data_utility.load_dataset(dataset_name=base_dataset_name, directory=base_data_dir) result = meta_features.compute_meta_features(X=X, y=y) data_utility.save_dataset(dataset_name=base_dataset_name, directory=meta_data_dir, X=result) # For each base dataset from "base_data_dir", compute all meta-features. Save the resulting # meta-data into "meta_data_dir". def prepare_meta_features(base_data_dir: pathlib.Path, meta_data_dir: pathlib.Path, n_processes: Optional[int] = None) -> None: print('Meta-feature preparation started.') base_datasets = data_utility.list_datasets(directory=base_data_dir) with tqdm.tqdm(total=(len(base_datasets)), desc='Computing meta-features') as progress_bar: with multiprocessing.Pool(processes=n_processes) as process_pool: results = [process_pool.apply_async(compute_and_save_meta_features, kwds={ 'base_data_dir': base_data_dir, 'base_dataset_name': base_dataset_name, 'meta_data_dir': meta_data_dir}, callback=lambda x: progress_bar.update()) for base_dataset_name in base_datasets] [x.wait() for x in results] # don't need to return value here, just wait till finished print('Meta-features prepared and saved.') # Compute one meta-target, i.e., apply one importance measure and one base model to one base # dataset. Return the actual meta-target (numeric feature importances) and some information # identifying it. def compute_meta_target(base_data_dir: pathlib.Path, base_dataset_name: str, base_model_name: str, importance_measure_name: str) -> Dict[str, Union[str, Collection[float]]]: result = {'base_dataset': base_dataset_name, 'base_model': base_model_name, 'importance_measure': importance_measure_name} X, y = data_utility.load_dataset(dataset_name=base_dataset_name, directory=base_data_dir) importance_type = meta_targets.IMPORTANCE_MEASURES[importance_measure_name] base_model_func = meta_targets.BASE_MODELS[base_model_name]['func'] base_model_args = meta_targets.BASE_MODELS[base_model_name]['args'] result['values'] = importance_type.compute_importance(X=X, y=y, model_func=base_model_func, model_args=base_model_args) return result # For each base dataset from "base_data_dir", compute all meta-targets, i.e., all # feature-importance measures for all base models. Save the resulting meta-data into # "meta_data_dir". def prepare_meta_targets(base_data_dir: pathlib.Path, meta_data_dir: pathlib.Path, n_processes: Optional[int] = None) -> None: print('Meta-target preparation started.') base_datasets = data_utility.list_datasets(directory=base_data_dir) with tqdm.tqdm(total=(len(base_datasets) * len(meta_targets.IMPORTANCE_MEASURES) * len(meta_targets.BASE_MODELS)), desc='Computing meta-targets') as progress_bar: with multiprocessing.Pool(processes=n_processes) as process_pool: results = [process_pool.apply_async(compute_meta_target, kwds={ 'base_data_dir': base_data_dir, 'base_dataset_name': base_dataset_name, 'base_model_name': base_model_name, 'importance_measure_name': importance_measure_name }, callback=lambda x: progress_bar.update()) for base_dataset_name in base_datasets for base_model_name in meta_targets.BASE_MODELS.keys() for importance_measure_name in meta_targets.IMPORTANCE_MEASURES.keys()] results = [x.get() for x in results] # Combine individual meta-targets to one data frame per base dataset: meta_target_data = {base_dataset_name: pd.DataFrame() for base_dataset_name in base_datasets} for result in results: column_name = data_utility.name_meta_target( importance_measure_name=result['importance_measure'], base_model_name=result['base_model']) meta_target_data[result['base_dataset']][column_name] = result['values'] for base_dataset_name, data_frame in meta_target_data.items(): data_utility.save_dataset(dataset_name=base_dataset_name, directory=meta_data_dir, y=data_frame) print('Meta-targets prepared and saved.') # Parse command-line arguments and prepare base + meta datasets. if __name__ == '__main__': parser = argparse.ArgumentParser( description='Retrieves base datasets from OpenML, creates meta-datasets ' + 'and stores all these data.', formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('-b', '--base_data_dir', type=pathlib.Path, default='data/base_datasets/', help='Directory to store base datasets. Will be created if necessary.') parser.add_argument('-i', '--data_ids', type=int, default=[], nargs='*', help='Ids of OpenML datasets. If none provided, will search for datasets.') parser.add_argument('-m', '--meta_data_dir', type=pathlib.Path, default='data/meta_datasets/', help='Directory to store meta-datasets. Will be created if necessary.') parser.add_argument('-p', '--n_processes', type=int, default=None, help='Number of processes for multi-processing (default: all cores).') args = parser.parse_args() prepare_base_datasets(base_data_dir=args.base_data_dir, data_ids=args.data_ids) prepare_meta_features(base_data_dir=args.base_data_dir, meta_data_dir=args.meta_data_dir, n_processes=args.n_processes) prepare_meta_targets(base_data_dir=args.base_data_dir, meta_data_dir=args.meta_data_dir, n_processes=args.n_processes)

0

7,730

0

15

376

3f79f678ffc367e4156a50c0372bba0efcd118d0

4,749

py

Python

mywebsocket.py

malengelajosue/app_flask

ea656abb859d8941e9a4761736f2a6ce4b91f7aa

[ "MIT" ]

null

mywebsocket.py

malengelajosue/app_flask

ea656abb859d8941e9a4761736f2a6ce4b91f7aa

[ "MIT" ]

null

mywebsocket.py

malengelajosue/app_flask

ea656abb859d8941e9a4761736f2a6ce4b91f7aa

[ "MIT" ]

null

#!/usr/bin/env python import signal import tornado.ioloop import tornado.web import tornado.websocket import tornado.wsgi as myapp_wsgi # Javascript Usage: # var ws = new WebSocket('ws://localhost:8000/ws'); # ws.onopen = function(event){ console.log('socket open'); } # ws.onclose = function(event){ console.log('socket closed'); } # ws.onerror = function(error){ console.log('error:', err); } # ws.onmessage = function(event){ console.log('message:', event.data); } # # ... wait for connection to open # ws.send('hello world') application = tornado.web.Application([ (r'/ws', MyAppWebSocket), (r'/(.*)', tornado.web.FallbackHandler, dict( fallback=tornado.wsgi.WSGIContainer(myapp_wsgi) )), ], debug=True) if __name__ == '__main__': application.listen(8001) instance=tornado.ioloop.IOLoop.instance() instance.start() signal.signal(signal.SIGINT, signal_handler) signal.pause()

29.314815

169

0.620131

#!/usr/bin/env python import signal import sys import tornado.ioloop import tornado.web import tornado.websocket import tornado.wsgi as myapp_wsgi from datetime import datetime import time import ast import random from datetime import date from models.model import Sites from models.model import Coordonnates from models.db_connection import Session,engine,Base from myclasses.gpsaccess import Gpsaccess as Gps # Javascript Usage: # var ws = new WebSocket('ws://localhost:8000/ws'); # ws.onopen = function(event){ console.log('socket open'); } # ws.onclose = function(event){ console.log('socket closed'); } # ws.onerror = function(error){ console.log('error:', err); } # ws.onmessage = function(event){ console.log('message:', event.data); } # # ... wait for connection to open # ws.send('hello world') class MyAppWebSocket(tornado.websocket.WebSocketHandler): # Simple Websocket echo handler. This could be extended to # use Redis PubSub to broadcast updates to clients. def getPosition(self): self.connected = False if self.connected == False: self.gpsDevice = Gps() self.myCoord = '' self.connected = True time.sleep(0.5) coordonnates = self.gpsDevice.readCoordonates() self.myCoord = coordonnates if coordonnates != {}: self.lat = float(coordonnates['latitude']) self.long = float(coordonnates['longitude']) self.alt = coordonnates['altitude'] self.speed = coordonnates['speed'] self.course = coordonnates['course'] self.satellite = coordonnates['satellite'] self.moment = datetime.now().strftime('%Y-%m-%d %H:%M:%S') coordonnates = {'Lat': self.lat, 'Long': self.long, 'Alt': self.alt, 'Moment': self.moment, 'Sat': self.satellite,'Course': self.course, 'Speed': self.speed} self.write_message(coordonnates) if self.persit==True: self.saveCoordonates() else: self.write_message({'status':0}) return coordonnates def open(self): self.persit=False self.mysite = '' def on_message(self, message): message=ast.literal_eval(message) print(message) coordonates={} if message.get('action')=='get_position': coordonates=self.getPosition() elif message.get('action')=='start_persiste': print("start persisting....") self.site_name=str(message.get('site_name')) self.capture_type=str(message.get('type')) self.description=str(message.get('description')) _name=self.site_name _description=self.description _type=self.capture_type mySite=Sites(name=_name,description=_description,type_prelevement=_type) self.mysite=mySite self.persit = True elif message.get('action')=='stop_persiste': self.persit=False session=Session() session.add(self.mysite) session.commit() session.close() elif message.get('action')=='gps_test': self.getPosition() print('gps test') elif message.get('action') == 'multiwii_test': self.getPosition() print('Multiwii test') elif message.get('action') == 'arm_test': self.getPosition() print('Arm test') def run(self): time.sleep(1) return def on_close(self): try: print 'connection closed' except tornado.websocket.WebSocketClosedError: print('connection fermee de maniere inatendu!') self.close() def check_origin(self, origin): return True def saveCoordonates(self): _lat=str(self.lat) _long=str(self.long) _alt=str(self.alt) _moment=datetime.now() _vitesse=str(self.speed) _course=str(self.course) _satellite=str(self.satellite) coord=Coordonnates(lat=_lat,long=_long,alt=_alt,moment=_moment,speed=_vitesse,course=_course,satellite=_satellite) self.mysite.coordonnates.append(coord) application = tornado.web.Application([ (r'/ws', MyAppWebSocket), (r'/(.*)', tornado.web.FallbackHandler, dict( fallback=tornado.wsgi.WSGIContainer(myapp_wsgi) )), ], debug=True) if __name__ == '__main__': application.listen(8001) instance=tornado.ioloop.IOLoop.instance() instance.start() def signal_handler(signal, frame): print('You pressed Ctrl+C!') instance.stop() sys.exit(0) signal.signal(signal.SIGINT, signal_handler) signal.pause()

0

3,391

0

94

0

58

271

ef071130d8e688b2bf7d1480cb9a43266fc55e27

4,576

py

Python

compile.py

KValexander/compile-java-project

62aab5ca9ec53705daa25a21875fc5c97e71db97

[ "MIT" ]

null

compile.py

KValexander/compile-java-project

62aab5ca9ec53705daa25a21875fc5c97e71db97

[ "MIT" ]

null

compile.py

KValexander/compile-java-project

62aab5ca9ec53705daa25a21875fc5c97e71db97

[ "MIT" ]

null

import os # Storage concat = "" assets = [] startclass = "" # Static config config = { "javapath": "java", "classpath": "class", "sourcetxt": "source.txt", "compilebat": "compile.bat", "startclass": "Main.class", "runbat": "run.bat", "copyassets": "true" } # Getting configurations from a file if os.path.exists("compile_config.txt"): f = open("compile_config.txt", "r") for line in f: line = line.replace(" ", "").split("="); config[line[0]] = line[1].rstrip(); f.close() # Entries entries = { "javapath": "Java dir: ", "classpath": "Class dir: ", "sourcetxt": "Source txt: ", "compilebat": "Compile bat: ", "startclass": "Start class: ", "runbat": "Run bat: ", "copyassets": "Copy assets: " } # Setting configurations # GUI # Create field # Concatenating paths to java files # Getting the path to the starting class # Copy assets # File creation # Start programm # Call GUI tkinter_interface()

26

138

0.674825

import shutil import os import re from tkinter import * # Storage concat = "" assets = [] startclass = "" # Static config config = { "javapath": "java", "classpath": "class", "sourcetxt": "source.txt", "compilebat": "compile.bat", "startclass": "Main.class", "runbat": "run.bat", "copyassets": "true" } # Getting configurations from a file if os.path.exists("compile_config.txt"): f = open("compile_config.txt", "r") for line in f: line = line.replace(" ", "").split("="); config[line[0]] = line[1].rstrip(); f.close() # Entries entries = { "javapath": "Java dir: ", "classpath": "Class dir: ", "sourcetxt": "Source txt: ", "compilebat": "Compile bat: ", "startclass": "Start class: ", "runbat": "Run bat: ", "copyassets": "Copy assets: " } # Setting configurations def setting_configurations(): for key, val in entries.items(): if(entries[key].get() != ""): config[key] = entries[key].get() # Overwrite config file f = open("compile_config.txt", "w+") for key, val in config.items(): f.write(key + " = " + val + "\n") f.close() # Call start processing start_processing() # GUI def tkinter_interface(): global entries # Window window = Tk() window.title("Java compilation automator") window.resizable(width=False, height=False) window.geometry("400x300") # Labels and Entries i = 0 for key, val in entries.items(): entries[key] = create_field(window, val, 30, 0, i) entries[key].insert(0, config[key]) i += 2 # Button button = Button(window, text="Run", background="#888", foreground="#eee", padx="20", pady="0", font="20", command=setting_configurations) button.grid(column=2,row=0, padx=20) # Mainloop window.mainloop() # Create field def create_field(win, text, width, c, r): label = Label(win, text=text) label.grid(column=c, row=r, pady=10, padx=10) txt = Entry(win, width=width) txt.grid(column=c+1, row=r) return txt # Concatenating paths to java files def java_dir_processing(path): global concat, assets ld = os.listdir(path) for file in ld: if re.search(r"\.java", file): concat += "./" + path + "/" + file + "\n" elif os.path.isdir(path + "/" + file): java_dir_processing(path + "/" + file) else: assets.append(path + "/" + file) # Getting the path to the starting class def class_dir_processing(path): global startclass if(not os.path.exists(path)): return False; ld = os.listdir(path) for file in ld: if re.search(config["startclass"], file): startclass = path + "/" + re.split(r"\.", file)[0] startclass = re.sub(r"/", ".", startclass.replace(config["classpath"]+"/", "")) return; elif os.path.isdir(path + "/" + file): class_dir_processing(path + "/" + file) # Copy assets def assets_processing(): global assets for asset in assets: topath = re.sub(r"\/[\w\-]*\.\w*", "/", asset.replace(config["javapath"], config["classpath"], 1)) if not os.path.exists(topath): shutil.copytree(topath.replace(config["classpath"], config["javapath"]),topath) for filename in os.listdir(topath): fullpath = topath + filename if os.path.isfile(fullpath): os.unlink(fullpath) elif os.path.isdir(fullpath): shutil.rmtree(fullpath) shutil.copy(asset, topath) # File creation def create_file(name, content): f = open(name, "w+") f.write(content) f.close() # Start programm def start_processing(): global concat, assets # Call jdp java_dir_processing(config["javapath"]) # Create file with paths create_file(config["sourcetxt"], concat) concat = "" # Delete class folder if it exists if os.path.exists(config["classpath"]): shutil.rmtree(config["classpath"]) # Create file with compilation command create_file(config["compilebat"], "javac -d " + config["classpath"] + " @" + config["sourcetxt"] + "\n") # Compilation activation os.system(config["compilebat"]) # Removing intermediate files os.remove(config["compilebat"]) os.remove(config["sourcetxt"]) # Checking for compilation success # and getting the path to the starting class if(class_dir_processing(config["classpath"]) == False): return print("\nJCA message: Compilation error") if(not startclass): return print("\nJCA message: Startup error") else: print("JCA message: Compilation is successful") # Call ap if(config["copyassets"] == "true"): assets_processing() assets.clear() # Creating an interpretation file create_file(config["runbat"], "java -classpath ./" + config["classpath"] + " " + startclass) # Running the code os.system(config["runbat"]) # Removing intermediate files os.remove(config["runbat"]) # Call GUI tkinter_interface()

0

3,423

0

-20

243

6d79c61a4cd03cad002390bea3fef1d83f0bef83

601

py

Python

multiprocessing_module/multiprocessing_test2.py

kenwaldek/python

e6aaf5616a456a4fb91889c0617bd6511f1a223e

[ "MIT" ]

1

2019-02-24T09:57:16.000Z

multiprocessing_module/multiprocessing_test2.py

kenwaldek/python

e6aaf5616a456a4fb91889c0617bd6511f1a223e

[ "MIT" ]

null

multiprocessing_module/multiprocessing_test2.py

kenwaldek/python

e6aaf5616a456a4fb91889c0617bd6511f1a223e

[ "MIT" ]

4

2017-05-21T15:34:53.000Z

2018-09-25T06:56:15.000Z

#! /usr/bin/env python3 # -*- coding:utf-8 -*- ############################################################### # kenwaldek MIT-license # # Title: multiprocessing 2 Version: 1.0 # Date: 30-12-16 Language: python3 # Description: multiprocessing dus met meerdere cores te samen # ############################################################### from multiprocessing import Pool if __name__ == '__main__': p = Pool(processes=20) data = p.map(job, range(20)) p.close() print(data)

25.041667

63

0.44426

#! /usr/bin/env python3 # -*- coding:utf-8 -*- ############################################################### # © kenwaldek MIT-license # # Title: multiprocessing 2 Version: 1.0 # Date: 30-12-16 Language: python3 # Description: multiprocessing dus met meerdere cores te samen # ############################################################### from multiprocessing import Pool def job(num): return num * 2 if __name__ == '__main__': p = Pool(processes=20) data = p.map(job, range(20)) p.close() print(data)

2

0

11

0

23

d0f18eb34b3ac7f1fbda4ccdac297a8ef889417b

1,088

py

Python

leetcode-python/num002.py

shuaizi/leetcode

c943410575f380a00335bf5ac8d361af53a92d78

[ "Apache-2.0" ]

null

leetcode-python/num002.py

shuaizi/leetcode

c943410575f380a00335bf5ac8d361af53a92d78

[ "Apache-2.0" ]

null

leetcode-python/num002.py

shuaizi/leetcode

c943410575f380a00335bf5ac8d361af53a92d78

[ "Apache-2.0" ]

null

__author__ = 'shuai' l1 = ListNode(2) l1.next = ListNode(4) l1.next.next = ListNode(3) l2 = ListNode(5) l2.next = ListNode(6) l2.next.next = ListNode(4) sol = Solution() sol.addTwoNumbers(l1, l2)

23.148936

45

0.465074

__author__ = 'shuai' class ListNode(object): def __init__(self, x): self.val = x self.next = None class Solution(object): def addTwoNumbers(self, l1, l2): """ :type l1: ListNode :type l2: ListNode :rtype: ListNode """ ret = ListNode(0) tmp = 0 tmpNode = ret while l1 or l2: if not l1: sum = l2.val l2 = l2.next elif not l2: sum = l1.val l1 = l1.next else: sum = l1.val + l2.val l1 = l1.next l2 = l2.next tmpN = ListNode((sum + tmp) % 10) tmp = (sum + tmp) / 10 tmpNode.next = tmpN tmpNode = tmpNode.next if tmp != 0: tmpN = ListNode(tmp) tmpNode.next = tmpN return ret.next l1 = ListNode(2) l1.next = ListNode(4) l1.next.next = ListNode(3) l2 = ListNode(5) l2.next = ListNode(6) l2.next.next = ListNode(4) sol = Solution() sol.addTwoNumbers(l1, l2)

0

844

0

46

9d16548fc6a8b1b86bb49107b9c13023f78ef594

3,051

py

Python

publish/tests/models.py

nacady/django-publish

a9b0b0b0ce0a2cd664d256edc4c819180dc882df

[ "BSD-3-Clause" ]

null

publish/tests/models.py

nacady/django-publish

a9b0b0b0ce0a2cd664d256edc4c819180dc882df

[ "BSD-3-Clause" ]

null

publish/tests/models.py

nacady/django-publish

a9b0b0b0ce0a2cd664d256edc4c819180dc882df

[ "BSD-3-Clause" ]

1

2021-06-28T03:59:45.000Z

from datetime import datetime # publishable model with a reverse relation to # page (as a child) # non-publishable reverse relation to page (as a child) update_pub_date.pub_date = datetime.now()

29.621359

74

0.715831

from django.db import models from datetime import datetime from publish.models import Publishable class Site(models.Model): title = models.CharField(max_length=100) domain = models.CharField(max_length=100) class FlatPage(Publishable): url = models.CharField(max_length=100, db_index=True) title = models.CharField(max_length=200) content = models.TextField(blank=True) enable_comments = models.BooleanField() template_name = models.CharField(max_length=70, blank=True) registration_required = models.BooleanField() sites = models.ManyToManyField(Site) class Meta: ordering = ['url'] def get_absolute_url(self): if self.is_public: return self.url return '%s*' % self.url class Author(Publishable): name = models.CharField(max_length=100) profile = models.TextField(blank=True) class PublishMeta(Publishable.PublishMeta): publish_reverse_fields = ['authorprofile'] class AuthorProfile(Publishable): author = models.OneToOneField(Author) extra_profile = models.TextField(blank=True) class ChangeLog(models.Model): changed = models.DateTimeField(db_index=True, auto_now_add=True) message = models.CharField(max_length=200) class Tag(models.Model): title = models.CharField(max_length=100, unique=True) slug = models.CharField(max_length=100) # publishable model with a reverse relation to # page (as a child) class PageBlock(Publishable): page = models.ForeignKey('Page') content = models.TextField(blank=True) # non-publishable reverse relation to page (as a child) class Comment(models.Model): page = models.ForeignKey('Page') comment = models.TextField() def update_pub_date(page, field_name, value): # ignore value entirely and replace with now setattr(page, field_name, update_pub_date.pub_date) update_pub_date.pub_date = datetime.now() class Page(Publishable): slug = models.CharField(max_length=100, db_index=True) title = models.CharField(max_length=200) content = models.TextField(blank=True) pub_date = models.DateTimeField(default=datetime.now) parent = models.ForeignKey('self', blank=True, null=True) authors = models.ManyToManyField(Author, blank=True) log = models.ManyToManyField(ChangeLog, blank=True) tags = models.ManyToManyField(Tag, through='PageTagOrder', blank=True) class Meta: ordering = ['slug'] class PublishMeta(Publishable.PublishMeta): publish_exclude_fields = ['log'] publish_reverse_fields = ['pageblock_set'] publish_functions = {'pub_date': update_pub_date} def get_absolute_url(self): if not self.parent: return u'/%s/' % self.slug return '%s%s/' % (self.parent.get_absolute_url(), self.slug) class PageTagOrder(Publishable): # note these are named in non-standard way to # ensure we are getting correct names tagged_page = models.ForeignKey(Page) page_tag = models.ForeignKey(Tag) tag_order = models.IntegerField()

0

2,395

0

129

0

24

295

c4bcfd12173f327f06cebc80aa483d7df62edc93

3,151

py

Python

tests/test_ddg_global_var_dependencies.py

Kyle-Kyle/angr

345b2131a7a67e3a6ffc7d9fd475146a3e12f837

[ "BSD-2-Clause" ]

6,132

2015-08-06T23:24:47.000Z

2022-03-31T21:49:34.000Z

tests/test_ddg_global_var_dependencies.py

Kyle-Kyle/angr

345b2131a7a67e3a6ffc7d9fd475146a3e12f837

[ "BSD-2-Clause" ]

2,272

2015-08-10T08:40:07.000Z

2022-03-31T23:46:44.000Z

tests/test_ddg_global_var_dependencies.py

Kyle-Kyle/angr

345b2131a7a67e3a6ffc7d9fd475146a3e12f837

[ "BSD-2-Clause" ]

1,155

2015-08-06T23:37:39.000Z

2022-03-31T05:54:11.000Z

import os test_location = str(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../binaries/tests')) arches = {'x86_64'} if __name__ == "__main__": main()

43.164384

171

0.720406

import os import angr import nose test_location = str(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../binaries/tests')) arches = {'x86_64'} def main(): test_ddg_global_var_dependencies() def test_ddg_global_var_dependencies(): for arch in arches: run_ddg_global_var_dependencies(arch) def run_ddg_global_var_dependencies(arch): test_file = os.path.join(test_location, arch, 'ddg_global_var_dependencies') proj = angr.Project(test_file, auto_load_libs=False) cfg = proj.analyses.CFGEmulated(context_sensitivity_level=2, keep_state=True, state_add_options=angr.sim_options.refs) ddg = proj.analyses.DDG(cfg) main_func = cfg.functions.function(name='main') target_block_addr = main_func.ret_sites[0].addr target_block = proj.factory.block(addr=target_block_addr) tgt_stmt_idx, tgt_stmt = get_target_stmt(proj, target_block) assert tgt_stmt_idx is not None buf_addr = tgt_stmt.data.addr.con.value tgt_ddg_node = get_ddg_node(ddg, target_block_addr, tgt_stmt_idx) assert tgt_ddg_node is not None # Whether the target depends on the statement assigning 'b' to the global variable has_correct_dependency = False for pred in ddg.get_predecessors(tgt_ddg_node): pred_block = proj.factory.block(addr=pred.block_addr) stmt = pred_block.vex.statements[pred.stmt_idx] has_correct_dependency |= check_dependency(stmt, buf_addr, ord('b')) # If the target depends on the statement assigning 'a' to the global variable, it is underconstrained (this assignment should be overwritten by the 'b' assignment) nose.tools.assert_false(check_dependency(stmt, buf_addr, ord('a')), msg="Target statement has incorrect dependency (DDG is underconstrained)") nose.tools.assert_true(has_correct_dependency, msg='Target statement does not have correct dependency (DDG is overconstrained)') def check_dependency(stmt, addr, const): # Check if we are storing a constant to a variable with constant address if stmt.tag == 'Ist_Store' and stmt.addr.tag == 'Iex_Const' and stmt.data.tag == 'Iex_Const': # Check if we are storing the specified constant to the specified variable address if stmt.addr.con.value == addr and stmt.data.con.value == const: return True return False def get_ddg_node(ddg, block_addr, stmt_idx): for node in ddg.graph.nodes: if node.block_addr == block_addr and node.stmt_idx == stmt_idx: return node return None def get_target_stmt(proj, block): for i, stmt in enumerate(block.vex.statements): # We're looking for the instruction that loads a constant memory address into a temporary variable if stmt.tag == 'Ist_WrTmp' and stmt.data.tag == 'Iex_Load' and stmt.data.addr.tag == 'Iex_Const': addr = stmt.data.addr.con.value section = proj.loader.main_object.find_section_containing(addr) # Confirm the memory address is in the uninitialized data section if section.name == '.bss': return i, stmt return None, None if __name__ == "__main__": main()

0

2,812

0

-20

182

83b0710d125addf1a454b4ea6976092a23001346

930

py

Python

src/IO.py

Rahoo11/Jarvis

6fac03e6f7bb963d0632ec781323210b3379603b

[ "MIT" ]

null

src/IO.py

Rahoo11/Jarvis

6fac03e6f7bb963d0632ec781323210b3379603b

[ "MIT" ]

null

src/IO.py

Rahoo11/Jarvis

6fac03e6f7bb963d0632ec781323210b3379603b

[ "MIT" ]

null

import logging # LOGGING SETTINGS # Save detailed information to log file handler_file = logging.FileHandler("jarvis.log") handler_file.setFormatter(logging.Formatter( "%(asctime)s %(levelname)s %(filename)s:%(lineno)d - %(message)s", "%Y-%m-%d %H:%M:%S" )) # Output simple information to stderr handler_stderr = logging.StreamHandler() handler_stderr.setFormatter(logging.Formatter("%(levelname)s: %(message)s")) # Log everything of level INFO or higher (everything apart from DEBUG) logging.basicConfig( level=logging.INFO, handlers=[ handler_file, handler_stderr ] ) # END LOGGING SETTINGS def stdin() -> str: """ Use this to input commands for Jarvis if the desired way fails """ return input("Command: ") def stdout(response: str): """ Use this to output Jarvis's response if the desired way fails """ print(response)

22.682927

76

0.691398

from datetime import datetime import logging # LOGGING SETTINGS # Save detailed information to log file handler_file = logging.FileHandler("jarvis.log") handler_file.setFormatter(logging.Formatter( "%(asctime)s %(levelname)s %(filename)s:%(lineno)d - %(message)s", "%Y-%m-%d %H:%M:%S" )) # Output simple information to stderr handler_stderr = logging.StreamHandler() handler_stderr.setFormatter(logging.Formatter("%(levelname)s: %(message)s")) # Log everything of level INFO or higher (everything apart from DEBUG) logging.basicConfig( level=logging.INFO, handlers=[ handler_file, handler_stderr ] ) # END LOGGING SETTINGS def stdin() -> str: """ Use this to input commands for Jarvis if the desired way fails """ return input("Command: ") def stdout(response: str): """ Use this to output Jarvis's response if the desired way fails """ print(response)

0

8

22

6bf254e4d47110abc5fa56df01806709a669c1dd

8,744

py

Python

sfo.py

ayassinsayed/py.dataformat.sfo

99b2ad11b162318f7e5251a760bd5b53e1cf826d

[ "MIT" ]

1

2021-09-06T04:27:13.000Z

sfo.py

Jasily/py.dataformat.sfo

99b2ad11b162318f7e5251a760bd5b53e1cf826d

[ "MIT" ]

null

sfo.py

Jasily/py.dataformat.sfo

99b2ad11b162318f7e5251a760bd5b53e1cf826d

[ "MIT" ]

4

2017-10-28T18:31:00.000Z

2021-01-26T00:24:18.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # # Copyright (c) 2016 - cologler <[email protected]> # ---------- # # ---------- __all__ = [ 'FormatError', 'SfoFile', 'PSVGameSfo', 'PSPGameSfo', ] _BYTE_ORDER = 'little' if __name__ == '__main__': for i in range(0, 1): test(r'test_res\param_%s.sfo' % str(i).rjust(2, '0'))

28.763158

98

0.589776

#!/usr/bin/env python # -*- coding: utf-8 -*- # # Copyright (c) 2016 - cologler <[email protected]> # ---------- # # ---------- import io __all__ = [ 'FormatError', 'SfoFile', 'PSVGameSfo', 'PSPGameSfo', ] class FormatError(Exception): pass _BYTE_ORDER = 'little' class Header: def __init__(self): # uint32_t magic; Always PSF # uint32_t version; Usually 1.1 # uint32_t key_table_start; Start offset of key_table # uint32_t data_table_start; Start offset of data_table # uint32_t tables_entries; Number of entries in all tables self._magic = None self._version = None self._key_table_start = None self._data_table_start = None self._tables_entries = None @property def key_table_start(self): return self._key_table_start @property def data_table_start(self): return self._data_table_start @property def tables_entries(self): return self._tables_entries def fix_data(self, sfo): self._tables_entries = len(sfo) raise NotImplementedError def from_reader(self, reader): self._magic = reader.read(4) self._version = reader.read(4) self._key_table_start = int.from_bytes(reader.read(4), _BYTE_ORDER) self._data_table_start = int.from_bytes(reader.read(4), _BYTE_ORDER) self._tables_entries = int.from_bytes(reader.read(4), _BYTE_ORDER) if self._magic != b'\x00PSF': raise FormatError return self class IndexTableEntry: FORMAT_UTF8S = b'\x04\x00' '''utf8 character string, NULL terminated''' FORMAT_UTF8 = b'\x04\x02' ''' Allways has a length of 4 bytes in len and max_len (even in the case some bytes are not used, all them are marked as used) ''' FORMAT_INT32 = b'\x04\x04' def __init__(self): # uint16_t key_offset; param_key offset (relative to start offset of key_table) */ # uint16_t data_fmt; param_data data type */ # uint32_t data_len; param_data used bytes */ # uint32_t data_max_len; param_data total bytes */ # uint32_t data_offset; param_data offset (relative to start offset of data_table) */ self._key_offset = None self._data_fmt = None self._data_len = None self._data_max_len = None self._data_offset = None @property def key_offset(self): return self._key_offset @property def data_fmt(self): return self._data_fmt @property def data_len(self): return self._data_len @property def data_offset(self): return self._data_offset @property def data_max_len(self): return self._data_max_len def fix_data(self, data): raise NotImplementedError def from_reader(self, reader): self._key_offset = int.from_bytes(reader.read(2), _BYTE_ORDER) self._data_fmt = reader.read(2) self._data_len = int.from_bytes(reader.read(4), _BYTE_ORDER) self._data_max_len = int.from_bytes(reader.read(4), _BYTE_ORDER) self._data_offset = int.from_bytes(reader.read(4), _BYTE_ORDER) if self._data_fmt != self.FORMAT_UTF8 and\ self._data_fmt != self.FORMAT_INT32 and\ self._data_fmt != self.FORMAT_UTF8S: print(self._data_fmt) raise FormatError class Data: def __init__(self): self._index_table_entry = IndexTableEntry() self._key = None self._value = None @property def index_table_entry(self): return self._index_table_entry @property def key(self): return self._key @property def value(self): return self._value def fix_data(self): self._index_table_entry.fix_data(self) raise NotImplementedError def __seek(self, reader, offset): pos = reader.tell() if pos != offset: reader.seek(offset) def key_from_reader(self, reader, header): offset = header.key_table_start + self._index_table_entry.key_offset self.__seek(reader, offset) buffer = b'' while True: b = reader.read(1) if b == b'\x00': break buffer += b self._key = buffer.decode('utf8') def value_from_reader(self, reader, header): offset = header.data_table_start + self._index_table_entry.data_offset self.__seek(reader, offset) buffer = reader.read(self._index_table_entry.data_max_len) if self._index_table_entry.data_fmt == IndexTableEntry.FORMAT_UTF8: i = buffer.find(b'\x00') assert i >= 0 buffer = buffer[:i] self._value = buffer.decode('utf8') elif self._index_table_entry.data_fmt == IndexTableEntry.FORMAT_INT32: assert len(buffer) == 4 self._value = int.from_bytes(buffer, _BYTE_ORDER) else: raise NotImplementedError class SfoFile: def __init__(self, header, data): assert isinstance(header, Header) self._header = header self._data = {} for d in data: self._data[d.key] = d def __contains__(self, key): return key in self._data def __getitem__(self, key): return self._data[key].value def __setitem__(self, key, value): raise NotImplementedError def __delitem__(self, key): raise NotImplementedError def __len__(self): return len(self._data) def keys(self): return self._data.keys() def values(self): return self._data.values() def get_or_None(self, key): r = self._data.get(key, None) return None if r == None else r.value def _fix_data(self): for v in self.values(): v.fix_data() self._header.fix_data(self) raise NotImplementedError @staticmethod def from_reader(reader): header = Header().from_reader(reader) datas = [Data() for _ in range(0, header.tables_entries)] for d in datas: d.index_table_entry.from_reader(reader) for d in datas: d.key_from_reader(reader, header) for d in datas: d.value_from_reader(reader, header) sfo = SfoFile(header, datas) return sfo @staticmethod def from_bytes(buffer): return SfoFile.from_reader(io.BytesIO(buffer)) class _Loader: def __init__(self, sfo: SfoFile, key): self._sfo = sfo self._key = key self._value = None self._is_loaded = False def refresh(self): self._is_loaded = False @property def value(self): if not self._is_loaded: self._value = self._sfo.get_or_None(self._key) self._is_loaded = True return self._value class SfoInfoWrapper: def __init__(self, sfo): self._sfo = sfo self._cache = {} @classmethod def from_bytes(cls, buffer): return cls(SfoFile.from_reader(io.BytesIO(buffer))) def refresh(self): for value in self._cache.values(): value.refresh() def _get_value(self, key): loader = self._cache.get(key) if loader == None: loader = _Loader(self._sfo, key) self._cache[key] = loader return loader.value @property def app_ver(self): return self._get_value('APP_VER') @property def category(self): return self._get_value('CATEGORY') @property def title(self): return self._get_value('TITLE') class PSVGameSfo(SfoInfoWrapper): @property def content_id(self): return self._get_value('CONTENT_ID') @property def title_id(self): return self._get_value('TITLE_ID') class PSPGameSfo(SfoInfoWrapper): @property def disc_id(self): return self._get_value('DISC_ID') @property def category(self): return self._get_value('CATEGORY') def test(path): with open(path, mode='rb') as reader: sfo = SfoFile.from_reader(reader) for k in sfo._data: v = sfo._data[k] print('%s: "%s"' % (v._key, v._value)) if __name__ == '__main__': for i in range(0, 1): test(r'test_res\param_%s.sfo' % str(i).rjust(2, '0'))

0

1,655

0

6,146

0

191

0

-12

384

2f90e72ab2ad376594d32a0c909e3065372a297e

1,066

py

Python

motelsAPI/settings/dev.py

amartinez1/5letrasAPI

670b638a8254a0809c9f953350cd1a3264b61bf7

[ "MIT" ]

2

2015-05-02T12:30:22.000Z

2015-05-08T18:13:43.000Z

motelsAPI/settings/dev.py

amartinez1/5letrasAPI

670b638a8254a0809c9f953350cd1a3264b61bf7

[ "MIT" ]

null

motelsAPI/settings/dev.py

amartinez1/5letrasAPI

670b638a8254a0809c9f953350cd1a3264b61bf7

[ "MIT" ]

null

DATABASES = { 'default': { 'ENGINE': 'django.contrib.gis.db.backends.postgis', 'NAME': 'motels_db', } } ALLOWED_HOSTS = [] CORS_ORIGIN_ALLOW_ALL = True DEBUG = True SECRET_KEY = 'test' INSTALLED_APPS += ( 'autofixture', 'debug_toolbar', 'django_extensions', ) MIDDLEWARE_CLASSES += ( 'debug_toolbar.middleware.DebugToolbarMiddleware', ) REST_FRAMEWORK = { 'DEFAULT_FILTER_BACKENDS': ('rest_framework.filters.DjangoFilterBackend',), 'DEFAULT_PERMISSION_CLASSES': ( 'rest_framework.permissions.AllowAny', ), 'DEFAULT_RENDERER_CLASSES': ( 'rest_framework.renderers.JSONRenderer', 'rest_framework.renderers.BrowsableAPIRenderer', ), 'DEFAULT_AUTHENTICATION_CLASSES': ( 'rest_framework.authentication.SessionAuthentication', 'rest_framework.authentication.TokenAuthentication', ), 'DEFAULT_PAGINATION_CLASS': 'rest_framework.pagination.LimitOffsetPagination', 'PAGE_SIZE': 10, }

23.688889

80

0.661351

from .base import * DATABASES = { 'default': { 'ENGINE': 'django.contrib.gis.db.backends.postgis', 'NAME': 'motels_db', } } ALLOWED_HOSTS = [] CORS_ORIGIN_ALLOW_ALL = True DEBUG = True SECRET_KEY = 'test' INSTALLED_APPS += ( 'autofixture', 'debug_toolbar', 'django_extensions', ) MIDDLEWARE_CLASSES += ( 'debug_toolbar.middleware.DebugToolbarMiddleware', ) REST_FRAMEWORK = { 'DEFAULT_FILTER_BACKENDS': ('rest_framework.filters.DjangoFilterBackend',), 'DEFAULT_PERMISSION_CLASSES': ( 'rest_framework.permissions.AllowAny', ), 'DEFAULT_RENDERER_CLASSES': ( 'rest_framework.renderers.JSONRenderer', 'rest_framework.renderers.BrowsableAPIRenderer', ), 'DEFAULT_AUTHENTICATION_CLASSES': ( 'rest_framework.authentication.SessionAuthentication', 'rest_framework.authentication.TokenAuthentication', ), 'DEFAULT_PAGINATION_CLASS': 'rest_framework.pagination.LimitOffsetPagination', 'PAGE_SIZE': 10, }

0

-2

23

a1adb53a7219e0575c94c4f8e32bc32af0a24a42

955

py

Python

snooper.py

boztalay/SuperconCubeCmd

9cbd685a75dbf9fdf7a04e7a240b07117b1fbe82

[ "MIT" ]

null

snooper.py

boztalay/SuperconCubeCmd

9cbd685a75dbf9fdf7a04e7a240b07117b1fbe82

[ "MIT" ]

null

snooper.py

boztalay/SuperconCubeCmd

9cbd685a75dbf9fdf7a04e7a240b07117b1fbe82

[ "MIT" ]

null

import sys import cubey if __name__ == "__main__": if len(sys.argv) != 2: print "Gimme a serial port!" sys.exit(1) serialPort = sys.argv[1] main(serialPort)

23.292683

93

0.536126

import sys import cubey def main(serialPort): cube = cubey.Cube(serialPort) print "Listening, Ctrl-C to stop..." try: while True: rawMessage = cube.sendCommand("m n u") printMessage(rawMessage) except KeyboardInterrupt: print cube.breakOut() print "Done!" def printMessage(rawMessage): print print "Got a message!" print "==============" print contents = map(int, rawMessage.split()) rowFormat = "% 4X |" + (" %02X" * 16) print " 0 1 2 3 4 5 6 7 8 9 A B C D E F" print " ------------------------------------------------" for rowStartIndex in range(0, 512, 16): print rowFormat % tuple([rowStartIndex] + contents[rowStartIndex:rowStartIndex + 16]) if __name__ == "__main__": if len(sys.argv) != 2: print "Gimme a serial port!" sys.exit(1) serialPort = sys.argv[1] main(serialPort)

0

721

0

46

9e5764903cdf85638ab62747d681b0695238c4e3

1,411

py

Python

day-9&10/main.py

a18antsv/Python-Two-Week-Challenge

cfdefe5e2643d1c1ee66d08a16a7ffc175ba1a3a

[ "MIT" ]

null

day-9&10/main.py

a18antsv/Python-Two-Week-Challenge

cfdefe5e2643d1c1ee66d08a16a7ffc175ba1a3a

[ "MIT" ]

null

day-9&10/main.py

a18antsv/Python-Two-Week-Challenge

cfdefe5e2643d1c1ee66d08a16a7ffc175ba1a3a

[ "MIT" ]

null

from flask import Flask base_url = "http://hn.algolia.com/api/v1" # This URL gets the newest stories. new = f"{base_url}/search_by_date?tags=story" # This URL gets the most popular stories popular = f"{base_url}/search?tags=story" # This function makes the URL to get the detail of a storie by id. # Heres the documentation: https://hn.algolia.com/api db = {} app = Flask("DayNine") app.run(host="0.0.0.0")

24.754386

70

0.690291

import requests from flask import Flask, render_template, request, redirect base_url = "http://hn.algolia.com/api/v1" # This URL gets the newest stories. new = f"{base_url}/search_by_date?tags=story" # This URL gets the most popular stories popular = f"{base_url}/search?tags=story" # This function makes the URL to get the detail of a storie by id. # Heres the documentation: https://hn.algolia.com/api def make_detail_url(id): return f"{base_url}/items/{id}" db = {} app = Flask("DayNine") @app.route("/") def index(): allowed_orders = ("popular", "new") order_by = request.args.get("order_by") if order_by: order_by = order_by.lower() if order_by not in allowed_orders: order_by = allowed_orders[0] posts_from_db = db.get(order_by) if posts_from_db: posts = posts_from_db else: posts = requests.get(globals()[order_by]).json()["hits"] db[order_by] = posts return render_template("index.html", order_by=order_by, posts=posts) @app.route("/<id>") def detail(id): try: request = requests.get(make_detail_url(id)) request.raise_for_status() except requests.exceptions.HTTPError: return redirect("/") post = request.json() return render_template( "detail.html", title=post.get("title"), url=post.get("url"), points=post.get("points"), author=post.get("author"), comments=post.get("children") ) app.run(host="0.0.0.0")

0

841

0

37

0

30

90

d32135b6fdf1615d5e0b4352267bf443c9e38704

2,651

py

Python

feewaiver/urls.py

dbca-wa/feewaiver

7938a0e9d18924c12b27c0a411b6d7eccb40166b

[ "Apache-2.0" ]

null

feewaiver/urls.py

dbca-wa/feewaiver

7938a0e9d18924c12b27c0a411b6d7eccb40166b

[ "Apache-2.0" ]

12

2021-02-24T02:33:01.000Z

2022-01-25T02:37:39.000Z

feewaiver/urls.py

mintcoding/feewaiver

47d69db91386f760dd36d87cbb565a9bb72a27d5

[ "Apache-2.0" ]

1

2021-01-08T02:15:27.000Z

from django.conf import settings from django.contrib import admin from django.conf.urls import url, include from django.conf.urls.static import static from rest_framework import routers #from feewaiver import views, users_api, api from feewaiver import views, api from ledger.urls import urlpatterns as ledger_patterns # API patterns router = routers.DefaultRouter() router.register(r'feewaivers',api.FeeWaiverViewSet) router.register(r'feewaivers_paginated',api.FeeWaiverPaginatedViewSet) router.register(r'participants',api.ParticipantsViewSet) router.register(r'parks',api.ParkViewSet) router.register(r'campgrounds',api.CampGroundViewSet) router.register(r'temporary_document', api.TemporaryDocumentCollectionViewSet) api_patterns = [ #url(r'^api/profile$', users_api.GetProfile.as_view(), name='get-profile'), #url(r'^api/department_users$', users_api.DepartmentUserList.as_view(), name='department-users-list'), #url(r'^api/filtered_users$', users_api.UserListFilterView.as_view(), name='filtered_users'), url(r'^api/',include(router.urls)), ] # URL Patterns urlpatterns = [ url(r'^ledger/admin/', admin.site.urls, name='ledger_admin'), url(r'', include(api_patterns)), url(r'^$', views.FeeWaiverRoutingView.as_view(), name='ds_home'), url(r'^contact/', views.FeeWaiverContactView.as_view(), name='ds_contact'), url(r'^admin_data/', views.FeeWaiverAdminDataView.as_view(), name='admin_data'), url(r'^further_info/', views.FeeWaiverFurtherInformationView.as_view(), name='ds_further_info'), url(r'^internal/', views.InternalView.as_view(), name='internal'), url(r'^external/', views.ExternalView.as_view(), name='external'), url(r'^account/$', views.ExternalView.as_view(), name='manage-account'), url(r'^profiles/', views.ExternalView.as_view(), name='manage-profiles'), url(r'^help/(?P<application_type>[^/]+)/(?P<help_type>[^/]+)/$', views.HelpView.as_view(), name='help'), url(r'^mgt-commands/$', views.ManagementCommandsView.as_view(), name='mgt-commands'), url(r'^internal/fee_waiver/(?P<feewaiver_pk>\d+)/$', views.InternalFeeWaiverView.as_view(), name='internal-feewaiver-detail'), url(r'^history/fee_waiver/(?P<pk>\d+)/$', views.FeeWaiverHistoryCompareView.as_view(), name='feewaiver_history'), ] + ledger_patterns if settings.DEBUG: # Serve media locally in development. urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT) if settings.SHOW_DEBUG_TOOLBAR: import debug_toolbar urlpatterns = [ url('__debug__/', include(debug_toolbar.urls)), ] + urlpatterns

48.2

130

0.744247

from django.conf import settings from django.contrib import admin from django.conf.urls import url, include from django.conf.urls.static import static from rest_framework import routers #from feewaiver import views, users_api, api from feewaiver import views, api from ledger.urls import urlpatterns as ledger_patterns from feewaiver.utils import are_migrations_running # API patterns router = routers.DefaultRouter() router.register(r'feewaivers',api.FeeWaiverViewSet) router.register(r'feewaivers_paginated',api.FeeWaiverPaginatedViewSet) router.register(r'participants',api.ParticipantsViewSet) router.register(r'parks',api.ParkViewSet) router.register(r'campgrounds',api.CampGroundViewSet) router.register(r'temporary_document', api.TemporaryDocumentCollectionViewSet) api_patterns = [ #url(r'^api/profile$', users_api.GetProfile.as_view(), name='get-profile'), #url(r'^api/department_users$', users_api.DepartmentUserList.as_view(), name='department-users-list'), #url(r'^api/filtered_users$', users_api.UserListFilterView.as_view(), name='filtered_users'), url(r'^api/',include(router.urls)), ] # URL Patterns urlpatterns = [ url(r'^ledger/admin/', admin.site.urls, name='ledger_admin'), url(r'', include(api_patterns)), url(r'^$', views.FeeWaiverRoutingView.as_view(), name='ds_home'), url(r'^contact/', views.FeeWaiverContactView.as_view(), name='ds_contact'), url(r'^admin_data/', views.FeeWaiverAdminDataView.as_view(), name='admin_data'), url(r'^further_info/', views.FeeWaiverFurtherInformationView.as_view(), name='ds_further_info'), url(r'^internal/', views.InternalView.as_view(), name='internal'), url(r'^external/', views.ExternalView.as_view(), name='external'), url(r'^account/$', views.ExternalView.as_view(), name='manage-account'), url(r'^profiles/', views.ExternalView.as_view(), name='manage-profiles'), url(r'^help/(?P<application_type>[^/]+)/(?P<help_type>[^/]+)/$', views.HelpView.as_view(), name='help'), url(r'^mgt-commands/$', views.ManagementCommandsView.as_view(), name='mgt-commands'), url(r'^internal/fee_waiver/(?P<feewaiver_pk>\d+)/$', views.InternalFeeWaiverView.as_view(), name='internal-feewaiver-detail'), url(r'^history/fee_waiver/(?P<pk>\d+)/$', views.FeeWaiverHistoryCompareView.as_view(), name='feewaiver_history'), ] + ledger_patterns if settings.DEBUG: # Serve media locally in development. urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT) if settings.SHOW_DEBUG_TOOLBAR: import debug_toolbar urlpatterns = [ url('__debug__/', include(debug_toolbar.urls)), ] + urlpatterns

0

29

22

54f82229c0438a79d9123d69c7d0467d0c47c179

1,758

py

Python

ros/src/twist_controller/twist_controller.py

Acharya-Kiran/CarND-Capstone

bc5f59ea20271e2e46e156fff86cd2482b52c5f2

[ "MIT" ]

null

ros/src/twist_controller/twist_controller.py

Acharya-Kiran/CarND-Capstone

bc5f59ea20271e2e46e156fff86cd2482b52c5f2

[ "MIT" ]

null

ros/src/twist_controller/twist_controller.py

Acharya-Kiran/CarND-Capstone

bc5f59ea20271e2e46e156fff86cd2482b52c5f2

[ "MIT" ]

null

GAS_DENSITY = 2.858 ONE_MPH = 0.44704

27.904762

101

0.755973

from pid import PID from lowpass import LowPassFilter from yaw_controller import YawController import rospy GAS_DENSITY = 2.858 ONE_MPH = 0.44704 class Controller(object): def __init__(self,vehicle_mass,fuel_capacity,brake_deadband,decel_limit, accel_limit,wheel_radius,wheel_base,steer_ratio,max_lat_accel,max_steer_angle): # TODO: Implement self.yaw_controller = YawController(wheel_base,steer_ratio,0.1,max_lat_accel,max_steer_angle) kp=0.3 ki=0.1 kd=0. mn=0. mx=0.2 self.throttle_controller=PID(kp,ki,kd,mn,mx) tau=0.5 ts=.02 self.vel_lpf = LowPassFilter(tau,ts) self.vehicle_mass = vehicle_mass self.fuel_capacity=fuel_capacity self.brake_deadband=brake_deadband self.decel_limit=decel_limit self.accel_limit=accel_limit self.wheel_radius=wheel_radius self.last_time = rospy.get_time() def control(self, current_vel,dbw_enabled,linear_vel,angular_vel): # TODO: Change the arg, kwarg list to suit your needs # Return throttle, brake, steer if not dbw_enabled: self.throttle_controller.reset() return 0., 0., 0. current_vel = self.vel_lpf.filt(current_vel) steering = self.yaw_controller.get_steering(linear_vel,angular_vel,current_vel) vel_error = linear_vel - current_vel self.last_vel = current_vel current_time = rospy.get_time() sample_time = current_time - self.last_time self.last_time = current_time throttle = self.throttle_controller.step(vel_error,sample_time) brake = 0 if linear_vel==0 and current_vel<0.1: throttle=0 brake=400 elif throttle<.1 and vel_error<0: throttle=0 decel = max(vel_error,self.decel_limit) brake = abs(decel)*self.vehicle_mass*self.wheel_radius return throttle,brake,steering

0

1,587

0

20

111

9ecb3b223a203a77d74b6711d0796c6b4e890962

27,213

py

Python

others/Pytorch/utilis_rnn.py

jhuebotter/CartpoleSNNdemo

d18a85cbc45bff48295c46c9cd8c9fc00192318c

[ "MIT" ]

null

others/Pytorch/utilis_rnn.py

jhuebotter/CartpoleSNNdemo

d18a85cbc45bff48295c46c9cd8c9fc00192318c

[ "MIT" ]

null

others/Pytorch/utilis_rnn.py

jhuebotter/CartpoleSNNdemo

d18a85cbc45bff48295c46c9cd8c9fc00192318c

[ "MIT" ]

null

import torch import torch.nn as nn from datetime import datetime import collections import os import copy from SI_Toolkit.load_and_normalize import load_normalization_info, load_data, normalize_df, denormalize_df def get_device(): """ Small function to correctly send data to GPU or CPU depending what is available """ if torch.cuda.is_available(): device = torch.device('cuda:0') else: device = torch.device('cpu') return device # Set seeds everywhere required to make results reproducible # Print parameter count # https://stackoverflow.com/questions/49201236/check-the-total-number-of-parameters-in-a-pytorch-model def load_pretrained_rnn(net, pt_path, device): """ A function loading parameters (weights and biases) from a previous training to a net RNN instance :param net: An instance of RNN :param pt_path: path to .pt file storing weights and biases :return: No return. Modifies net in place. """ pre_trained_model = torch.load(pt_path, map_location=device) print("Loading Model: ", pt_path) print('') pre_trained_model = list(pre_trained_model.items()) new_state_dict = collections.OrderedDict() count = 0 num_param_key = len(pre_trained_model) for key, value in net.state_dict().items(): if count >= num_param_key: break layer_name, weights = pre_trained_model[count] new_state_dict[key] = weights # print("Pre-trained Layer: %s - Loaded into new layer: %s" % (layer_name, key)) count += 1 print('') net.load_state_dict(new_state_dict) # Initialize weights and biases - should be only applied if no pretrained net loaded # FIXME: To tailor this sequence class according to the commands and state_variables of cartpole import pandas as pd # # def load_data(a, filepath=None, columns_list=None, norm_inf=False, rnn_full_name=None, downsample=1): # if filepath is None: # filepath = a.val_file_name # # if columns_list is None: # columns_list = list(set(a.inputs_list).union(set(a.outputs_list))) # # if type(filepath) == list: # filepaths = filepath # else: # filepaths = [filepath] # # all_dfs = [] # saved separately to get normalization # all_time_axes = [] # # for one_filepath in filepaths: # # Load dataframe # print('loading data from ' + str(one_filepath)) # print('') # df = pd.read_csv(one_filepath, comment='#') # df=df.iloc[::downsample].reset_index() # # # You can shift dt by one time step to know "now" the timestep till the next row # if a.cheat_dt: # if 'dt' in df: # df['dt'] = df['dt'].shift(-1) # df = df[:-1] # # # FIXME: Make calculation of dt compatible with downsampling # # Get time axis as separate Dataframe # if 'time' in df.columns: # t = df['time'] # elif 'dt' in df.columns: # dt = df['dt'] # t = dt.cumsum() # t.rename('time', inplace=True) # else: # t = pd.Series([]) # t.rename('time', inplace=True) # # time_axis = t # all_time_axes.append(time_axis) # # # Get only relevant subset of columns # if columns_list == 'all': # pass # else: # df = df[columns_list] # # all_dfs.append(df) # # # return all_dfs, all_time_axes # # # This way of doing normalization is fine for long data sets and (relatively) short sequence lengths # # The points from the edges of the datasets count too little # def calculate_normalization_info(df, PATH_TO_EXPERIMENT_RECORDINGS, rnn_full_name): # if type(df) is list: # df_total = pd.concat(df) # else: # df_total = df # # if 'time' in df_total.columns: # df_total.drop('time', # axis='columns', inplace=True) # # df_mean = df_total.mean(axis=0) # df_std = df_total.std(axis=0) # df_max = df_total.max(axis=0) # df_min = df_total.min(axis=0) # frame = {'mean': df_mean, 'std': df_std, 'max': df_max, 'min': df_min} # df_norm_info = pd.DataFrame(frame).transpose() # # df_norm_info.to_csv(PATH_TO_EXPERIMENT_RECORDINGS + rnn_full_name + '-norm' + '.csv') # # # Plot historgrams to make the firs check about gaussian assumption # # for feature in df_total.columns: # # plt.hist(df_total[feature].to_numpy(), 50, density=True, facecolor='g', alpha=0.75) # # plt.title(feature) # # plt.show() # # return df_norm_info # # # def load_normalization_info(PATH_TO_EXPERIMENT_RECORDINGS, rnn_full_name): # return pd.read_csv(PATH_TO_EXPERIMENT_RECORDINGS + rnn_full_name + '-norm' + '.csv', index_col=0) # # # def normalize_df(dfs, normalization_info, normalization_type='minmax_sym'): # if normalization_type == 'gaussian': # def normalize_feature(col): # col_mean = normalization_info.loc['mean', col.name] # col_std = normalization_info.loc['std', col.name] # return (col - col_mean) / col_std # elif normalization_type == 'minmax_pos': # def normalize_feature(col): # col_min = normalization_info.loc['min', col.name] # col_max = normalization_info.loc['max', col.name] # return (col - col_min) / (col_max - col_min) # elif normalization_type == 'minmax_sym': # def normalize_feature(col): # col_min = normalization_info.loc['min', col.name] # col_max = normalization_info.loc['max', col.name] # return -1.0 + 2.0 * (col - col_min) / (col_max - col_min) # # if type(dfs) is list: # for i in range(len(dfs)): # dfs[i] = dfs[i].apply(normalize_feature, axis=0) # else: # dfs = dfs.apply(normalize_feature, axis=0) # # return dfs # # # def denormalize_df(dfs, normalization_info, normalization_type='minmax_sym'): # if normalization_type == 'gaussian': # def denormalize_feature(col): # col_mean = normalization_info.loc['mean', col.name] # col_std = normalization_info.loc['std', col.name] # return col * col_std + col_mean # elif normalization_type == 'minmax_pos': # def denormalize_feature(col): # col_min = normalization_info.loc['min', col.name] # col_max = normalization_info.loc['max', col.name] # return col * (col_max - col_min) + col_min # elif normalization_type == 'minmax_sym': # def denormalize_feature(col): # col_min = normalization_info.loc['min', col.name] # col_max = normalization_info.loc['max', col.name] # return ((col + 1.0) / 2.0) * (col_max - col_min) + col_min # # if type(dfs) is list: # for i in range(len(dfs)): # dfs[i] = dfs[i].apply(denormalize_feature, axis=0) # else: # dfs = dfs.apply(denormalize_feature, axis=0) # # return dfs def plot_results(net, args, dataset=None, normalization_info = None, time_axes=None, filepath=None, inputs_list=None, outputs_list=None, closed_loop_list=None, seq_len=None, warm_up_len=None, closed_loop_enabled=False, comment='', rnn_full_name=None, save=False, close_loop_idx=512): """ This function accepts RNN instance, arguments and CartPole instance. It runs one random experiment with CartPole, inputs the data into RNN and check how well RNN predicts CartPole state one time step ahead of time """ rnn_full_name = net.rnn_full_name if filepath is None: filepath = args.val_file_name if type(filepath) == list: filepath = filepath[0] if warm_up_len is None: warm_up_len = args.warm_up_len if seq_len is None: seq_len = args.seq_len if inputs_list is None: inputs_list = args.inputs_list if inputs_list is None: raise ValueError('RNN inputs not provided!') if outputs_list is None: outputs_list = args.outputs_list if outputs_list is None: raise ValueError('RNN outputs not provided!') if closed_loop_enabled and (closed_loop_list is None): closed_loop_list = args.close_loop_for if closed_loop_list is None: raise ValueError('RNN closed-loop-inputs not provided!') net.reset() net.eval() device = get_device() if normalization_info is None: normalization_info = load_normalization_info(args.PATH_TO_EXPERIMENT_RECORDINGS, rnn_full_name) if dataset is None or time_axes is None: test_dfs, time_axes = load_data(args, filepath) test_dfs_norm = normalize_df(test_dfs, normalization_info) test_set = Dataset(test_dfs_norm, args, time_axes=time_axes, seq_len=seq_len) del test_dfs else: test_set = copy.deepcopy(dataset) test_set.reset_seq_len(seq_len=seq_len) # Format the experiment data features, targets, time_axis = test_set.get_experiment(1) # Put number in brackets to get the same idx at every run features_pd = pd.DataFrame(data=features, columns=inputs_list) targets_pd = pd.DataFrame(data=targets, columns=outputs_list) rnn_outputs = pd.DataFrame(columns=outputs_list) warm_up_idx = 0 rnn_input_0 = copy.deepcopy(features_pd.iloc[0]) # Does not bring anything. Why? 0-state shouldn't have zero internal state due to biases... while warm_up_idx < warm_up_len: rnn_input = rnn_input_0 rnn_input = np.squeeze(rnn_input.to_numpy()) rnn_input = torch.from_numpy(rnn_input).float().unsqueeze(0).unsqueeze(0).to(device) net(rnn_input=rnn_input) warm_up_idx += 1 net.outputs = [] net.sample_counter = 0 idx_cl = 0 close_the_loop = False for index, row in features_pd.iterrows(): rnn_input = pd.DataFrame(copy.deepcopy(row)).transpose().reset_index(drop=True) if idx_cl == close_loop_idx: close_the_loop = True if closed_loop_enabled and close_the_loop and (normalized_rnn_output is not None): rnn_input[closed_loop_list] = normalized_rnn_output[closed_loop_list] rnn_input = np.squeeze(rnn_input.to_numpy()) rnn_input = torch.from_numpy(rnn_input).float().unsqueeze(0).unsqueeze(0).to(device) normalized_rnn_output = net(rnn_input=rnn_input) normalized_rnn_output = np.squeeze(normalized_rnn_output.detach().cpu().numpy()).tolist() normalized_rnn_output = copy.deepcopy(pd.DataFrame(data=[normalized_rnn_output], columns=outputs_list)) rnn_outputs = rnn_outputs.append(copy.deepcopy(normalized_rnn_output), ignore_index=True) idx_cl += 1 targets_pd_denorm = denormalize_df(targets_pd, normalization_info) rnn_outputs_denorm = denormalize_df(rnn_outputs, normalization_info) fig, axs = plot_results_specific(targets_pd_denorm, rnn_outputs_denorm, time_axis, comment, closed_loop_enabled, close_loop_idx) plt.show() if save: # Make folders if not yet exist try: os.makedirs('save_plots') except FileExistsError: pass dateTimeObj = datetime.now() timestampStr = dateTimeObj.strftime("-%d%b%Y_%H%M%S") if rnn_full_name is not None: fig.savefig('./save_plots/' + rnn_full_name + timestampStr + '.png') else: fig.savefig('./save_plots/' + timestampStr + '.png')

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import torch import torch.nn as nn from torch.utils import data from datetime import datetime import collections import os import random as rnd import copy from Modeling.Pytorch.utilis_rnn_specific import * from SI_Toolkit.load_and_normalize import load_normalization_info, load_data, normalize_df, denormalize_df def get_device(): """ Small function to correctly send data to GPU or CPU depending what is available """ if torch.cuda.is_available(): device = torch.device('cuda:0') else: device = torch.device('cpu') return device # Set seeds everywhere required to make results reproducible def set_seed(args): seed = args.seed rnd.seed(seed) np.random.seed(seed) # Print parameter count # https://stackoverflow.com/questions/49201236/check-the-total-number-of-parameters-in-a-pytorch-model def print_parameter_count(net): pytorch_total_params = sum(p.numel() for p in net.parameters()) pytorch_trainable_params = sum(p.numel() for p in net.parameters() if p.requires_grad) print('::: # network all parameters: ' + str(pytorch_total_params)) print('::: # network trainable parameters: ' + str(pytorch_trainable_params)) print('') def load_pretrained_rnn(net, pt_path, device): """ A function loading parameters (weights and biases) from a previous training to a net RNN instance :param net: An instance of RNN :param pt_path: path to .pt file storing weights and biases :return: No return. Modifies net in place. """ pre_trained_model = torch.load(pt_path, map_location=device) print("Loading Model: ", pt_path) print('') pre_trained_model = list(pre_trained_model.items()) new_state_dict = collections.OrderedDict() count = 0 num_param_key = len(pre_trained_model) for key, value in net.state_dict().items(): if count >= num_param_key: break layer_name, weights = pre_trained_model[count] new_state_dict[key] = weights # print("Pre-trained Layer: %s - Loaded into new layer: %s" % (layer_name, key)) count += 1 print('') net.load_state_dict(new_state_dict) # Initialize weights and biases - should be only applied if no pretrained net loaded def initialize_weights_and_biases(net): print('Initialize weights and biases') for name, param in net.named_parameters(): print('Initialize {}'.format(name)) if 'gru' in name: if 'weight' in name: nn.init.orthogonal_(param) if 'linear' in name: if 'weight' in name: nn.init.orthogonal_(param) # nn.init.xavier_uniform_(param) if 'bias' in name: # all biases nn.init.constant_(param, 0) print('') def create_rnn_instance(rnn_name=None, inputs_list=None, outputs_list=None, load_rnn=None, path_save=None, device=None): if load_rnn is not None and load_rnn != 'last': # 1) Find csv with this name if exists load name, inputs and outputs list # if it does not exist raise error # 2) Create corresponding net # 3) Load parameters from corresponding pt file filename = load_rnn print('Loading a pretrained RNN with the full name: {}'.format(filename)) print('') txt_filename = filename + '.txt' pt_filename = filename + '.pt' txt_path = path_save + txt_filename pt_path = path_save + pt_filename if not os.path.isfile(txt_path): raise ValueError( 'The corresponding .txt file is missing (information about inputs and outputs) at the location {}'.format( txt_path)) if not os.path.isfile(pt_path): raise ValueError( 'The corresponding .pt file is missing (information about weights and biases) at the location {}'.format( pt_path)) f = open(txt_path, 'r') lines = f.readlines() rnn_name = lines[1].rstrip("\n") inputs_list = lines[7].rstrip("\n").split(sep=', ') outputs_list = lines[10].rstrip("\n").split(sep=', ') f.close() print('Inputs to the loaded RNN: {}'.format(', '.join(map(str, inputs_list)))) print('Outputs from the loaded RNN: {}'.format(', '.join(map(str, outputs_list)))) print('') # Construct the requested RNN net = Sequence(rnn_name=rnn_name, inputs_list=inputs_list, outputs_list=outputs_list) net.rnn_full_name = load_rnn # Load the parameters load_pretrained_rnn(net, pt_path, device) elif load_rnn == 'last': files_found = False while (not files_found): try: import glob list_of_files = glob.glob(path_save + '/*.txt') txt_path = max(list_of_files, key=os.path.getctime) except FileNotFoundError: raise ValueError('No information about any pretrained network found at {}'.format(path_save)) f = open(txt_path, 'r') lines = f.readlines() rnn_name = lines[1].rstrip("\n") pre_rnn_full_name = lines[4].rstrip("\n") inputs_list = lines[7].rstrip("\n").split(sep=', ') outputs_list = lines[10].rstrip("\n").split(sep=', ') f.close() pt_path = path_save + pre_rnn_full_name + '.pt' if not os.path.isfile(pt_path): print('The .pt file is missing (information about weights and biases) at the location {}'.format( pt_path)) print('I delete the corresponding .txt file and try to search again') print('') os.remove(txt_path) else: files_found = True print('Full name of the loaded RNN is {}'.format(pre_rnn_full_name)) print('Inputs to the loaded RNN: {}'.format(', '.join(map(str, inputs_list)))) print('Outputs from the loaded RNN: {}'.format(', '.join(map(str, outputs_list)))) print('') # Construct the requested RNN net = Sequence(rnn_name=rnn_name, inputs_list=inputs_list, outputs_list=outputs_list) net.rnn_full_name = pre_rnn_full_name # Load the parameters load_pretrained_rnn(net, pt_path, device) else: # a.load_rnn is None print('No pretrained network specified. I will train a network from scratch.') print('') # Construct the requested RNN net = Sequence(rnn_name=rnn_name, inputs_list=inputs_list, outputs_list=outputs_list) initialize_weights_and_biases(net) return net, rnn_name, inputs_list, outputs_list def create_log_file(rnn_name, inputs_list, outputs_list, path_save): rnn_full_name = rnn_name[:4] + str(len(inputs_list)) + 'IN-' + rnn_name[4:] + '-' + str(len(outputs_list)) + 'OUT' net_index = 0 while True: txt_path = path_save + rnn_full_name + '-' + str(net_index) + '.txt' if os.path.isfile(txt_path): pass else: rnn_full_name += '-' + str(net_index) f = open(txt_path, 'w') f.write('RNN NAME: \n' + rnn_name + '\n\n') f.write('RNN FULL NAME: \n' + rnn_full_name + '\n\n') f.write('INPUTS: \n' + ', '.join(map(str, inputs_list)) + '\n\n') f.write('OUTPUTS: \n' + ', '.join(map(str, outputs_list)) + '\n\n') f.close() break net_index += 1 print('Full name given to the currently trained network is {}.'.format(rnn_full_name)) print('') return rnn_full_name # FIXME: To tailor this sequence class according to the commands and state_variables of cartpole class Sequence(nn.Module): """" Our RNN class. """ def __init__(self, rnn_name, inputs_list, outputs_list): super(Sequence, self).__init__() """Initialization of an RNN instance We assume that inputs may be both commands and state variables, whereas outputs are always state variables """ # Check if GPU is available. If yes device='cuda:0' if not device='cpu' self.device = get_device() self.rnn_name = rnn_name self.rnn_full_name = None # Get the information about network architecture from the network name # Split the names into "LSTM/GRU", "128H1", "64H2" etc. names = rnn_name.split('-') layers = ['H1', 'H2', 'H3', 'H4', 'H5'] self.h_size = [] # Hidden layers sizes for name in names: for index, layer in enumerate(layers): if layer in name: # assign the variable with name obtained from list layers. self.h_size.append(int(name[:-2])) if not self.h_size: raise ValueError('You have to provide the size of at least one hidden layer in rnn name') if 'GRU' in names: self.rnn_type = 'GRU' elif 'LSTM' in names: self.rnn_type = 'LSTM' else: self.rnn_type = 'RNN-Basic' # Construct network if self.rnn_type == 'GRU': self.rnn_cell = [nn.GRUCell(len(inputs_list), self.h_size[0]).to(get_device())] for i in range(len(self.h_size) - 1): self.rnn_cell.append(nn.GRUCell(self.h_size[i], self.h_size[i + 1]).to(get_device())) elif self.rnn_type == 'LSTM': self.rnn_cell = [nn.LSTMCell(len(inputs_list), self.h_size[0]).to(get_device())] for i in range(len(self.h_size) - 1): self.rnn_cell.append(nn.LSTMCell(self.h_size[i], self.h_size[i + 1]).to(get_device())) else: self.rnn_cell = [nn.RNNCell(len(inputs_list), self.h_size[0]).to(get_device())] for i in range(len(self.h_size) - 1): self.rnn_cell.append(nn.RNNCell(self.h_size[i], self.h_size[i + 1]).to(get_device())) self.linear = nn.Linear(self.h_size[-1], len(outputs_list)) # RNN out self.layers = nn.ModuleList([]) for cell in self.rnn_cell: self.layers.append(cell) self.layers.append(self.linear) # Count data samples (=time steps) self.sample_counter = 0 # Declaration of the variables keeping internal state of GRU hidden layers self.h = [None] * len(self.h_size) self.c = [None] * len(self.h_size) # Internal state cell - only matters for LSTM # Variable keeping the most recent output of RNN self.output = None # List storing the history of RNN outputs self.outputs = [] # Send the whole RNN to GPU if available, otherwise send it to CPU self.to(self.device) print('Constructed a neural network of type {}, with {} hidden layers with sizes {} respectively.' .format(self.rnn_type, len(self.h_size), ', '.join(map(str, self.h_size)))) print('The inputs are (in this order): {}'.format(', '.join(map(str, inputs_list)))) print('The outputs are (in this order): {}'.format(', '.join(map(str, outputs_list)))) def reset(self): """ Reset the network (not the weights!) """ self.sample_counter = 0 self.h = [None] * len(self.h_size) self.c = [None] * len(self.h_size) self.output = None self.outputs = [] def forward(self, rnn_input): """ Predicts future CartPole states IN "OPEN LOOP" (at every time step prediction for the next time step is done based on the true CartPole state) """ # Initialize hidden layers - this change at every call as the batch size may vary for i in range(len(self.h_size)): self.h[i] = torch.zeros(rnn_input.size(1), self.h_size[i], dtype=torch.float).to(self.device) self.c[i] = torch.zeros(rnn_input.size(1), self.h_size[i], dtype=torch.float).to(self.device) # The for loop takes the consecutive time steps from input plugs them into RNN and save the outputs into a list # THE NETWORK GETS ALWAYS THE GROUND TRUTH, THE REAL STATE OF THE CARTPOLE, AS ITS INPUT # IT PREDICTS THE STATE OF THE CARTPOLE ONE TIME STEP AHEAD BASED ON TRUE STATE NOW for iteration, input_t in enumerate(rnn_input.chunk(rnn_input.size(0), dim=0)): # Propagate input through RNN layers if self.rnn_type == 'LSTM': self.h[0], self.c[0] = self.layers[0](input_t.squeeze(0), (self.h[0], self.c[0])) for i in range(len(self.h_size) - 1): self.h[i + 1], self.c[i + 1] = self.layers[i + 1](self.h[i], (self.h[i + 1], self.c[i + 1])) else: self.h[0] = self.layers[0](input_t.squeeze(0), self.h[0]) for i in range(len(self.h_size) - 1): self.h[i + 1] = self.layers[i + 1](self.h[i], self.h[i + 1]) self.output = self.layers[-1](self.h[-1]) self.outputs += [self.output] self.sample_counter = self.sample_counter + 1 # In the train mode we want to continue appending the outputs by calling forward function # The outputs will be saved internally in the network instance as a list # Otherwise we want to transform outputs list to a tensor and return it return self.output def return_outputs_history(self): return torch.stack(self.outputs, 1) import pandas as pd # # def load_data(a, filepath=None, columns_list=None, norm_inf=False, rnn_full_name=None, downsample=1): # if filepath is None: # filepath = a.val_file_name # # if columns_list is None: # columns_list = list(set(a.inputs_list).union(set(a.outputs_list))) # # if type(filepath) == list: # filepaths = filepath # else: # filepaths = [filepath] # # all_dfs = [] # saved separately to get normalization # all_time_axes = [] # # for one_filepath in filepaths: # # Load dataframe # print('loading data from ' + str(one_filepath)) # print('') # df = pd.read_csv(one_filepath, comment='#') # df=df.iloc[::downsample].reset_index() # # # You can shift dt by one time step to know "now" the timestep till the next row # if a.cheat_dt: # if 'dt' in df: # df['dt'] = df['dt'].shift(-1) # df = df[:-1] # # # FIXME: Make calculation of dt compatible with downsampling # # Get time axis as separate Dataframe # if 'time' in df.columns: # t = df['time'] # elif 'dt' in df.columns: # dt = df['dt'] # t = dt.cumsum() # t.rename('time', inplace=True) # else: # t = pd.Series([]) # t.rename('time', inplace=True) # # time_axis = t # all_time_axes.append(time_axis) # # # Get only relevant subset of columns # if columns_list == 'all': # pass # else: # df = df[columns_list] # # all_dfs.append(df) # # # return all_dfs, all_time_axes # # # This way of doing normalization is fine for long data sets and (relatively) short sequence lengths # # The points from the edges of the datasets count too little # def calculate_normalization_info(df, PATH_TO_EXPERIMENT_RECORDINGS, rnn_full_name): # if type(df) is list: # df_total = pd.concat(df) # else: # df_total = df # # if 'time' in df_total.columns: # df_total.drop('time', # axis='columns', inplace=True) # # df_mean = df_total.mean(axis=0) # df_std = df_total.std(axis=0) # df_max = df_total.max(axis=0) # df_min = df_total.min(axis=0) # frame = {'mean': df_mean, 'std': df_std, 'max': df_max, 'min': df_min} # df_norm_info = pd.DataFrame(frame).transpose() # # df_norm_info.to_csv(PATH_TO_EXPERIMENT_RECORDINGS + rnn_full_name + '-norm' + '.csv') # # # Plot historgrams to make the firs check about gaussian assumption # # for feature in df_total.columns: # # plt.hist(df_total[feature].to_numpy(), 50, density=True, facecolor='g', alpha=0.75) # # plt.title(feature) # # plt.show() # # return df_norm_info # # # def load_normalization_info(PATH_TO_EXPERIMENT_RECORDINGS, rnn_full_name): # return pd.read_csv(PATH_TO_EXPERIMENT_RECORDINGS + rnn_full_name + '-norm' + '.csv', index_col=0) # # # def normalize_df(dfs, normalization_info, normalization_type='minmax_sym'): # if normalization_type == 'gaussian': # def normalize_feature(col): # col_mean = normalization_info.loc['mean', col.name] # col_std = normalization_info.loc['std', col.name] # return (col - col_mean) / col_std # elif normalization_type == 'minmax_pos': # def normalize_feature(col): # col_min = normalization_info.loc['min', col.name] # col_max = normalization_info.loc['max', col.name] # return (col - col_min) / (col_max - col_min) # elif normalization_type == 'minmax_sym': # def normalize_feature(col): # col_min = normalization_info.loc['min', col.name] # col_max = normalization_info.loc['max', col.name] # return -1.0 + 2.0 * (col - col_min) / (col_max - col_min) # # if type(dfs) is list: # for i in range(len(dfs)): # dfs[i] = dfs[i].apply(normalize_feature, axis=0) # else: # dfs = dfs.apply(normalize_feature, axis=0) # # return dfs # # # def denormalize_df(dfs, normalization_info, normalization_type='minmax_sym'): # if normalization_type == 'gaussian': # def denormalize_feature(col): # col_mean = normalization_info.loc['mean', col.name] # col_std = normalization_info.loc['std', col.name] # return col * col_std + col_mean # elif normalization_type == 'minmax_pos': # def denormalize_feature(col): # col_min = normalization_info.loc['min', col.name] # col_max = normalization_info.loc['max', col.name] # return col * (col_max - col_min) + col_min # elif normalization_type == 'minmax_sym': # def denormalize_feature(col): # col_min = normalization_info.loc['min', col.name] # col_max = normalization_info.loc['max', col.name] # return ((col + 1.0) / 2.0) * (col_max - col_min) + col_min # # if type(dfs) is list: # for i in range(len(dfs)): # dfs[i] = dfs[i].apply(denormalize_feature, axis=0) # else: # dfs = dfs.apply(denormalize_feature, axis=0) # # return dfs class Dataset(data.Dataset): def __init__(self, dfs, args, time_axes=None, seq_len=None): 'Initialization - divide data in features and labels' self.data = [] self.labels = [] for df in dfs: # Get Raw Data features = copy.deepcopy(df) targets = copy.deepcopy(df) features.drop(features.tail(1).index, inplace=True) # Drop last row targets.drop(targets.head(1).index, inplace=True) features.reset_index(inplace=True) # Reset index targets.reset_index(inplace=True) features = features[args.inputs_list] targets = targets[args.outputs_list] self.data.append(features) self.labels.append(targets) self.args = args self.seq_len = None self.df_lengths = [] self.df_lengths_cs = [] self.number_of_samples = 0 self.time_axes = time_axes self.reset_seq_len(seq_len=seq_len) def reset_seq_len(self, seq_len=None): """ This method should be used if the user wants to change the seq_len without creating new Dataset Please remember that one can reset it again to come back to old configuration :param seq_len: Gives new user defined seq_len. Call empty to come back to default. """ if seq_len is None: self.seq_len = self.args.seq_len # Sequence length else: self.seq_len = seq_len self.df_lengths = [] self.df_lengths_cs = [] if type(self.data) == list: for data_set in self.data: self.df_lengths.append(data_set.shape[0] - self.seq_len) if not self.df_lengths_cs: self.df_lengths_cs.append(self.df_lengths[0]) else: self.df_lengths_cs.append(self.df_lengths_cs[-1] + self.df_lengths[-1]) self.number_of_samples = self.df_lengths_cs[-1] else: self.number_of_samples = self.data.shape[0] - self.seq_len def __len__(self): 'Total number of samples' return self.number_of_samples def __getitem__(self, idx, get_time_axis=False): """ Requires the self.data to be a list of pandas dataframes """ # Find index of the dataset in self.data and index of the starting point in this dataset idx_data_set = next(i for i, v in enumerate(self.df_lengths_cs) if v > idx) if idx_data_set == 0: pass else: idx -= self.df_lengths_cs[idx_data_set - 1] # Get data features = self.data[idx_data_set].to_numpy()[idx:idx + self.seq_len, :] # Every point in features has its target value corresponding to the next time step: targets = self.labels[idx_data_set].to_numpy()[idx:idx + self.seq_len] # After feeding the whole sequence we just compare the final output of the RNN with the state following afterwards # targets = self.labels[idx_data_set].to_numpy()[idx + self.seq_len-1] # If get_time_axis try to obtain a vector of time data for the chosen sample if get_time_axis: try: time_axis = self.time_axes[idx_data_set].to_numpy()[idx:idx + self.seq_len + 1] except IndexError: time_axis = [] # Return results if get_time_axis: return features, targets, time_axis else: return features, targets def get_experiment(self, idx=None): if self.time_axes is None: raise Exception('No time information available!') if idx is None: idx = np.random.randint(0, self.number_of_samples) return self.__getitem__(idx, get_time_axis=True) def plot_results(net, args, dataset=None, normalization_info = None, time_axes=None, filepath=None, inputs_list=None, outputs_list=None, closed_loop_list=None, seq_len=None, warm_up_len=None, closed_loop_enabled=False, comment='', rnn_full_name=None, save=False, close_loop_idx=512): """ This function accepts RNN instance, arguments and CartPole instance. It runs one random experiment with CartPole, inputs the data into RNN and check how well RNN predicts CartPole state one time step ahead of time """ rnn_full_name = net.rnn_full_name if filepath is None: filepath = args.val_file_name if type(filepath) == list: filepath = filepath[0] if warm_up_len is None: warm_up_len = args.warm_up_len if seq_len is None: seq_len = args.seq_len if inputs_list is None: inputs_list = args.inputs_list if inputs_list is None: raise ValueError('RNN inputs not provided!') if outputs_list is None: outputs_list = args.outputs_list if outputs_list is None: raise ValueError('RNN outputs not provided!') if closed_loop_enabled and (closed_loop_list is None): closed_loop_list = args.close_loop_for if closed_loop_list is None: raise ValueError('RNN closed-loop-inputs not provided!') net.reset() net.eval() device = get_device() if normalization_info is None: normalization_info = load_normalization_info(args.PATH_TO_EXPERIMENT_RECORDINGS, rnn_full_name) if dataset is None or time_axes is None: test_dfs, time_axes = load_data(args, filepath) test_dfs_norm = normalize_df(test_dfs, normalization_info) test_set = Dataset(test_dfs_norm, args, time_axes=time_axes, seq_len=seq_len) del test_dfs else: test_set = copy.deepcopy(dataset) test_set.reset_seq_len(seq_len=seq_len) # Format the experiment data features, targets, time_axis = test_set.get_experiment(1) # Put number in brackets to get the same idx at every run features_pd = pd.DataFrame(data=features, columns=inputs_list) targets_pd = pd.DataFrame(data=targets, columns=outputs_list) rnn_outputs = pd.DataFrame(columns=outputs_list) warm_up_idx = 0 rnn_input_0 = copy.deepcopy(features_pd.iloc[0]) # Does not bring anything. Why? 0-state shouldn't have zero internal state due to biases... while warm_up_idx < warm_up_len: rnn_input = rnn_input_0 rnn_input = np.squeeze(rnn_input.to_numpy()) rnn_input = torch.from_numpy(rnn_input).float().unsqueeze(0).unsqueeze(0).to(device) net(rnn_input=rnn_input) warm_up_idx += 1 net.outputs = [] net.sample_counter = 0 idx_cl = 0 close_the_loop = False for index, row in features_pd.iterrows(): rnn_input = pd.DataFrame(copy.deepcopy(row)).transpose().reset_index(drop=True) if idx_cl == close_loop_idx: close_the_loop = True if closed_loop_enabled and close_the_loop and (normalized_rnn_output is not None): rnn_input[closed_loop_list] = normalized_rnn_output[closed_loop_list] rnn_input = np.squeeze(rnn_input.to_numpy()) rnn_input = torch.from_numpy(rnn_input).float().unsqueeze(0).unsqueeze(0).to(device) normalized_rnn_output = net(rnn_input=rnn_input) normalized_rnn_output = np.squeeze(normalized_rnn_output.detach().cpu().numpy()).tolist() normalized_rnn_output = copy.deepcopy(pd.DataFrame(data=[normalized_rnn_output], columns=outputs_list)) rnn_outputs = rnn_outputs.append(copy.deepcopy(normalized_rnn_output), ignore_index=True) idx_cl += 1 targets_pd_denorm = denormalize_df(targets_pd, normalization_info) rnn_outputs_denorm = denormalize_df(rnn_outputs, normalization_info) fig, axs = plot_results_specific(targets_pd_denorm, rnn_outputs_denorm, time_axis, comment, closed_loop_enabled, close_loop_idx) plt.show() if save: # Make folders if not yet exist try: os.makedirs('save_plots') except FileExistsError: pass dateTimeObj = datetime.now() timestampStr = dateTimeObj.strftime("-%d%b%Y_%H%M%S") if rnn_full_name is not None: fig.savefig('./save_plots/' + rnn_full_name + timestampStr + '.png') else: fig.savefig('./save_plots/' + timestampStr + '.png')

0

9,472

0

5,711

0

35

225

a14001fe338c11a2de9e1cb5a8130727cb1dcd35

7,654

py

Python

resto_client/cli/parser/parser_configure_server.py

CNES/resto_client

7048bd79c739e33882ebd664790dcf0528e81aa4

[ "Apache-2.0" ]

6

2019-12-20T09:12:30.000Z

2021-07-08T11:44:55.000Z

resto_client/cli/parser/parser_configure_server.py

CNES/resto_client

7048bd79c739e33882ebd664790dcf0528e81aa4

[ "Apache-2.0" ]

null

resto_client/cli/parser/parser_configure_server.py

CNES/resto_client

7048bd79c739e33882ebd664790dcf0528e81aa4

[ "Apache-2.0" ]

1

2019-12-17T20:16:39.000Z

# -*- coding: utf-8 -*- """ .. admonition:: License Copyright 2019 CNES Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import argparse from resto_client.base_exceptions import RestoClientDesignError from resto_client.services.service_access import (AuthenticationServiceAccess, RestoServiceAccess) from resto_client.settings.resto_client_config import resto_client_print from resto_client.settings.servers_database import DB_SERVERS from .parser_common import CliFunctionReturnType from .parser_settings import (SERVER_ARGNAME, RESTO_URL_ARGNAME, RESTO_PROTOCOL_ARGNAME, AUTH_URL_ARGNAME, AUTH_PROTOCOL_ARGNAME) def cli_create_server(args: argparse.Namespace) -> CliFunctionReturnType: """ CLI adapter to create a server definition :param args: arguments parsed by the CLI parser :returns: the resto client parameters and the resto server possibly built by this command. """ # TODO: Modify ServiceAcces such that lower is implemented in them resto_access = RestoServiceAccess(getattr(args, RESTO_URL_ARGNAME), getattr(args, RESTO_PROTOCOL_ARGNAME).lower()) auth_access = AuthenticationServiceAccess(getattr(args, AUTH_URL_ARGNAME), getattr(args, AUTH_PROTOCOL_ARGNAME).lower()) DB_SERVERS.create_server(getattr(args, SERVER_ARGNAME), resto_access, auth_access) return None, None def cli_delete_server(args: argparse.Namespace) -> CliFunctionReturnType: """ CLI adapter to delete a server definition :param args: arguments parsed by the CLI parser :returns: the resto client parameters and the resto server possibly built by this command. """ DB_SERVERS.delete(getattr(args, SERVER_ARGNAME)) return None, None def cli_edit_server(args: argparse.Namespace) -> CliFunctionReturnType: """ CLI adapter to edit the server characteristics :param args: arguments parsed by the CLI parser :raises RestoClientDesignError: unconditionally, as this function is not implemented yet """ raise RestoClientDesignError('Edit server unimplemented') def cli_show_servers(args: argparse.Namespace) -> CliFunctionReturnType: """ CLI adapter to show the servers database :param args: arguments parsed by the CLI parser :returns: the resto client parameters and the resto server possibly built by this command. """ _ = args # to avoid pylint warning resto_client_print(DB_SERVERS) return None, None # We need to specify argparse._SubParsersAction for mypy to run. Thus pylint squeals. # pylint: disable=protected-access def add_configure_server_subparser(sub_parsers: argparse._SubParsersAction) -> None: """ Add the 'configure_server' subparser :param sub_parsers: argparse object used to add a parser for that subcommand. """ parser_configure_server = sub_parsers.add_parser( 'configure_server', help='configure servers known by resto_client.', description='Allows to create, modify or delete servers characteristics: url, type, etc.', epilog='Servers definition is stored in a configuration file and can be edited using this' ' command.') help_msg = 'For more help: {} <parameter> -h'.format(parser_configure_server.prog) sub_parsers_configure_server = parser_configure_server.add_subparsers(description=help_msg) add_config_server_create_parser(sub_parsers_configure_server) add_config_server_delete_parser(sub_parsers_configure_server) add_config_server_edit_parser(sub_parsers_configure_server) add_config_server_show_parser(sub_parsers_configure_server) def add_config_server_create_parser( sub_parsers_configure_server: argparse._SubParsersAction) -> None: """ Update the 'configure_server' command subparser with options for 'configure_server create' :param sub_parsers_configure_server: argparse object used to add a parser for that subcommand. """ subparser = sub_parsers_configure_server.add_parser( 'create', help='create a new server', description='Create a new server in the servers configuration database.') _add_positional_args_parser(subparser) subparser.set_defaults(func=cli_create_server) def add_config_server_delete_parser( sub_parsers_configure_server: argparse._SubParsersAction) -> None: """ Update the 'configure_server' command subparser with options for 'configure_server delete' :param sub_parsers_configure_server: argparse object used to add a parser for that subcommand. """ subparser = sub_parsers_configure_server.add_parser( 'delete', help='delete an existing server', description='Delete a server from the configuration database.') subparser.add_argument(SERVER_ARGNAME, help='name of the server to delete') subparser.set_defaults(func=cli_delete_server) def add_config_server_edit_parser( sub_parsers_configure_server: argparse._SubParsersAction) -> None: """ Update the 'configure_server' command subparser with options for 'configure_server edit' :param sub_parsers_configure_server: argparse object used to add a parser for that subcommand. """ subparser = sub_parsers_configure_server.add_parser( 'edit', help='edit server characteristics', description='Edit the characteristics of a server existing in the configuration database.') _add_positional_args_parser(subparser) subparser.set_defaults(func=cli_edit_server) def add_config_server_show_parser( sub_parsers_configure_server: argparse._SubParsersAction) -> None: """ Update the 'configure_server' command subparser with options for 'configure_server show' :param sub_parsers_configure_server: argparse object used to add a parser for that subcommand. """ subparser = sub_parsers_configure_server.add_parser( 'show', help='show servers database', description='Show all the servers defined in the database with their configuration.') subparser.set_defaults(func=cli_show_servers) def _add_positional_args_parser(subparser: argparse.ArgumentParser) -> None: """ Add the positional arguments parsing rules for configure_server subcommands :param subparser: parser to be supplemented with positional arguments. """ subparser.add_argument(SERVER_ARGNAME, help='name of the server') group_resto = subparser.add_argument_group('resto service') group_resto.add_argument(RESTO_URL_ARGNAME, help='URL of the resto server') group_resto.add_argument(RESTO_PROTOCOL_ARGNAME, choices=RestoServiceAccess.supported_protocols(), help='Protocol of the resto server') group_auth = subparser.add_argument_group('authentication service') group_auth.add_argument(AUTH_URL_ARGNAME, nargs='?', help='URL of the authentication server') group_auth.add_argument(AUTH_PROTOCOL_ARGNAME, choices=AuthenticationServiceAccess.supported_protocols(), help='Protocol of the authentication server')

44.5

100

0.74902

# -*- coding: utf-8 -*- """ .. admonition:: License Copyright 2019 CNES Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import argparse from resto_client.base_exceptions import RestoClientDesignError from resto_client.services.service_access import (AuthenticationServiceAccess, RestoServiceAccess) from resto_client.settings.resto_client_config import resto_client_print from resto_client.settings.servers_database import DB_SERVERS from .parser_common import CliFunctionReturnType from .parser_settings import (SERVER_ARGNAME, RESTO_URL_ARGNAME, RESTO_PROTOCOL_ARGNAME, AUTH_URL_ARGNAME, AUTH_PROTOCOL_ARGNAME) def cli_create_server(args: argparse.Namespace) -> CliFunctionReturnType: """ CLI adapter to create a server definition :param args: arguments parsed by the CLI parser :returns: the resto client parameters and the resto server possibly built by this command. """ # TODO: Modify ServiceAcces such that lower is implemented in them resto_access = RestoServiceAccess(getattr(args, RESTO_URL_ARGNAME), getattr(args, RESTO_PROTOCOL_ARGNAME).lower()) auth_access = AuthenticationServiceAccess(getattr(args, AUTH_URL_ARGNAME), getattr(args, AUTH_PROTOCOL_ARGNAME).lower()) DB_SERVERS.create_server(getattr(args, SERVER_ARGNAME), resto_access, auth_access) return None, None def cli_delete_server(args: argparse.Namespace) -> CliFunctionReturnType: """ CLI adapter to delete a server definition :param args: arguments parsed by the CLI parser :returns: the resto client parameters and the resto server possibly built by this command. """ DB_SERVERS.delete(getattr(args, SERVER_ARGNAME)) return None, None def cli_edit_server(args: argparse.Namespace) -> CliFunctionReturnType: """ CLI adapter to edit the server characteristics :param args: arguments parsed by the CLI parser :raises RestoClientDesignError: unconditionally, as this function is not implemented yet """ raise RestoClientDesignError('Edit server unimplemented') def cli_show_servers(args: argparse.Namespace) -> CliFunctionReturnType: """ CLI adapter to show the servers database :param args: arguments parsed by the CLI parser :returns: the resto client parameters and the resto server possibly built by this command. """ _ = args # to avoid pylint warning resto_client_print(DB_SERVERS) return None, None # We need to specify argparse._SubParsersAction for mypy to run. Thus pylint squeals. # pylint: disable=protected-access def add_configure_server_subparser(sub_parsers: argparse._SubParsersAction) -> None: """ Add the 'configure_server' subparser :param sub_parsers: argparse object used to add a parser for that subcommand. """ parser_configure_server = sub_parsers.add_parser( 'configure_server', help='configure servers known by resto_client.', description='Allows to create, modify or delete servers characteristics: url, type, etc.', epilog='Servers definition is stored in a configuration file and can be edited using this' ' command.') help_msg = 'For more help: {} <parameter> -h'.format(parser_configure_server.prog) sub_parsers_configure_server = parser_configure_server.add_subparsers(description=help_msg) add_config_server_create_parser(sub_parsers_configure_server) add_config_server_delete_parser(sub_parsers_configure_server) add_config_server_edit_parser(sub_parsers_configure_server) add_config_server_show_parser(sub_parsers_configure_server) def add_config_server_create_parser( sub_parsers_configure_server: argparse._SubParsersAction) -> None: """ Update the 'configure_server' command subparser with options for 'configure_server create' :param sub_parsers_configure_server: argparse object used to add a parser for that subcommand. """ subparser = sub_parsers_configure_server.add_parser( 'create', help='create a new server', description='Create a new server in the servers configuration database.') _add_positional_args_parser(subparser) subparser.set_defaults(func=cli_create_server) def add_config_server_delete_parser( sub_parsers_configure_server: argparse._SubParsersAction) -> None: """ Update the 'configure_server' command subparser with options for 'configure_server delete' :param sub_parsers_configure_server: argparse object used to add a parser for that subcommand. """ subparser = sub_parsers_configure_server.add_parser( 'delete', help='delete an existing server', description='Delete a server from the configuration database.') subparser.add_argument(SERVER_ARGNAME, help='name of the server to delete') subparser.set_defaults(func=cli_delete_server) def add_config_server_edit_parser( sub_parsers_configure_server: argparse._SubParsersAction) -> None: """ Update the 'configure_server' command subparser with options for 'configure_server edit' :param sub_parsers_configure_server: argparse object used to add a parser for that subcommand. """ subparser = sub_parsers_configure_server.add_parser( 'edit', help='edit server characteristics', description='Edit the characteristics of a server existing in the configuration database.') _add_positional_args_parser(subparser) subparser.set_defaults(func=cli_edit_server) def add_config_server_show_parser( sub_parsers_configure_server: argparse._SubParsersAction) -> None: """ Update the 'configure_server' command subparser with options for 'configure_server show' :param sub_parsers_configure_server: argparse object used to add a parser for that subcommand. """ subparser = sub_parsers_configure_server.add_parser( 'show', help='show servers database', description='Show all the servers defined in the database with their configuration.') subparser.set_defaults(func=cli_show_servers) def _add_positional_args_parser(subparser: argparse.ArgumentParser) -> None: """ Add the positional arguments parsing rules for configure_server subcommands :param subparser: parser to be supplemented with positional arguments. """ subparser.add_argument(SERVER_ARGNAME, help='name of the server') group_resto = subparser.add_argument_group('resto service') group_resto.add_argument(RESTO_URL_ARGNAME, help='URL of the resto server') group_resto.add_argument(RESTO_PROTOCOL_ARGNAME, choices=RestoServiceAccess.supported_protocols(), help='Protocol of the resto server') group_auth = subparser.add_argument_group('authentication service') group_auth.add_argument(AUTH_URL_ARGNAME, nargs='?', help='URL of the authentication server') group_auth.add_argument(AUTH_PROTOCOL_ARGNAME, choices=AuthenticationServiceAccess.supported_protocols(), help='Protocol of the authentication server')

0

30

0

4498832be13a9415d6ca76fd5ad2398b9e886b1d

1,059

py

Python

src/push_button.py

albang/arisa

9b7ea5e7befc92d1febb038476d03e858a622153

[ "MIT" ]

null

src/push_button.py

albang/arisa

9b7ea5e7befc92d1febb038476d03e858a622153

[ "MIT" ]

null

src/push_button.py

albang/arisa

9b7ea5e7befc92d1febb038476d03e858a622153

[ "MIT" ]

null

#!/usr/bin/env python3 import RPi.GPIO as GPIO # Import Raspberry Pi GPIO library import os, time os.system('mpg123 -g100 /home/pi/paw_patrol_courte.mp3 &') GPIO.setwarnings(False) # Ignore warning for now GPIO.setmode(GPIO.BOARD) # Use physical pin numbering GPIO.setup(10, GPIO.IN, pull_up_down=GPIO.PUD_DOWN) # Set pin 10 to be an input pin and set initial value to be pulled low (off) GPIO.add_event_detect(10,GPIO.RISING,callback=button_callback,bouncetime=4000) # Setup event on pin 10 rising edge GPIO.setup(13, GPIO.IN, pull_up_down=GPIO.PUD_DOWN) # Set pin 10 to be an input pin and set initial value to be pulled low (off) GPIO.add_event_detect(13,GPIO.RISING,callback=button_callback2,bouncetime=4000) # Setup event on pin 10 rising edge while True: time.sleep(100000) GPIO.cleanup() # Clean up

40.730769

128

0.756374

#!/usr/bin/env python3 import RPi.GPIO as GPIO # Import Raspberry Pi GPIO library import os, time def button_callback(channel): print("Button was pushed!") os.system('mpg123 /home/pi/minute_courte.mp3 &') def button_callback2(channel): print("Button was pushed!") os.system('mpg123 -g100 /home/pi/paw_patrol_courte.mp3 &') os.system('mpg123 -g100 /home/pi/paw_patrol_courte.mp3 &') GPIO.setwarnings(False) # Ignore warning for now GPIO.setmode(GPIO.BOARD) # Use physical pin numbering GPIO.setup(10, GPIO.IN, pull_up_down=GPIO.PUD_DOWN) # Set pin 10 to be an input pin and set initial value to be pulled low (off) GPIO.add_event_detect(10,GPIO.RISING,callback=button_callback,bouncetime=4000) # Setup event on pin 10 rising edge GPIO.setup(13, GPIO.IN, pull_up_down=GPIO.PUD_DOWN) # Set pin 10 to be an input pin and set initial value to be pulled low (off) GPIO.add_event_detect(13,GPIO.RISING,callback=button_callback2,bouncetime=4000) # Setup event on pin 10 rising edge while True: time.sleep(100000) GPIO.cleanup() # Clean up

0

198

0

46

4da98b7e4cedd701321a8df23f73f41ffd79cf6e

1,054

py

Python

src/utils.py

michaellas/streaming-vid-to-gifs

ee5df22c820d4d631f0437c98a53989ecb76dca3

[ "MIT" ]

null

src/utils.py

michaellas/streaming-vid-to-gifs

ee5df22c820d4d631f0437c98a53989ecb76dca3

[ "MIT" ]

1

2015-04-07T12:24:26.000Z

2015-04-07T12:28:30.000Z

src/utils.py

michaellas/streaming-vid-to-gifs

ee5df22c820d4d631f0437c98a53989ecb76dca3

[ "MIT" ]

null

import time import sys if __name__ == '__main__': ''' @log_called_times_decorator def ff(): print 'f' while True: ff() time.sleep(1) ''' print_progress(45) print '' print_progress(x=20,max=200)

26.35

107

0.578748

import time import sys def log_called_times_decorator(func): def wrapper(*args): wrapper.count += 1 # print "The function I modify has been called {0} times(s).".format(wrapper.count) now = time.time() if now - wrapper.last_log > wrapper.dt: print '[DEBUG] In last %ds %s() was called %d times' % (wrapper.dt,func.__name__,wrapper.count) wrapper.count = 0 wrapper.last_log = now return func(*args) wrapper.count = 0 wrapper.last_log = time.time() wrapper.dt = 5 return wrapper def print_progress( percent=None, x=0, max=100): if not percent: percent = x*100.0/max sys.stdout.write('\r') bars = int(percent / 5) sys.stdout.write("[%-20s] %d%% " % ('='*bars, int(percent))) sys.stdout.flush() if __name__ == '__main__': ''' @log_called_times_decorator def ff(): print 'f' while True: ff() time.sleep(1) ''' print_progress(45) print '' print_progress(x=20,max=200)

0

748

0

46

4495fdf8627af041231ecfd1e216c9c24557ea8c

847

py

Python

monte_carlo.py

yandexdataschool/pyretina

300d3cd460ded071d75d3729e9b5dc1489d86d73

[ "Apache-2.0" ]

2

2016-05-28T15:59:47.000Z

2018-07-30T21:05:18.000Z

monte_carlo.py

yandexdataschool/pyretina

300d3cd460ded071d75d3729e9b5dc1489d86d73

[ "Apache-2.0" ]

null

monte_carlo.py

yandexdataschool/pyretina

300d3cd460ded071d75d3729e9b5dc1489d86d73

[ "Apache-2.0" ]

null

number_of_events = 10 if __name__ == "__main__": main("config/mc.json")

21.175

82

0.641086

from pyretina.mc import monte_carlo import numpy as np import json import os import os.path as osp import shutil number_of_events = 10 def main(conf): with open(conf, 'r') as f: config = json.load(f) for N in np.arange(20, 520, 20): config['scattering']['number_of_particles'] = { 'type' : 'randint', 'low' : N, 'high' : N + 1 } plot_dir = osp.join('./events_img', '%d_particles' % N) try: shutil.rmtree(plot_dir) except: pass os.mkdir(plot_dir) events = monte_carlo(number_of_events, config, plot_dir=plot_dir, plot_each=2) import cPickle as pickle with open('data/mini_velo_sim_%d.pickled' % N, 'w') as f: pickle.dump(events, f) print 'Generated %d events with %d particles' % (number_of_events, N) if __name__ == "__main__": main("config/mc.json")

0

634

0

-19

157

18ed809f9eec9232085b1804143efe6ca93e3a6e

5,950

py

Python

miner.py

OwlEyes33/crypto-alpha

dc3b39ecf38f3f445ecd94057775220b651633fc

[ "Apache-2.0" ]

null

miner.py

OwlEyes33/crypto-alpha

dc3b39ecf38f3f445ecd94057775220b651633fc

[ "Apache-2.0" ]

null

miner.py

OwlEyes33/crypto-alpha

dc3b39ecf38f3f445ecd94057775220b651633fc

[ "Apache-2.0" ]

null

import logging logging.basicConfig(level=logging.DEBUG) if __name__ == "__main__": miner = Miner() miner.routine()

37.421384

86

0.557479

import logging import os import time from math import inf from os import environ from threading import Thread import requests from redis import Redis from block import Block from blockchain import Blockchain from peer2peer import PeerToPeerMessage from transaction import Transaction logging.basicConfig(level=logging.DEBUG) class Miner(object): def __init__(self, *args, **kwargs): self.transactions = kwargs.get('transactions', {}) self.block_size = 64 self.miner = list() self.peers = environ.get('PEERS', 'http://localhost:8000').split(',') assert len(self.peers) self.cached_p2p_messages = dict() self.blockchain = Blockchain() self.redis_cli = Redis(host='redis') self.sync_to_redis() def get_peers_blockchain(self): try: blockchains = dict() _max = -inf best_peer = None with open("blockchain.dat", "rb") as f: blockchain_size = len(f.read()) for peer in self.peers: r = requests.get("http://{}/api/blockchain".format(peer)) if r.json().get('size'): size = int(r.json().get('size')) if size > _max: _max = size best_peer = peer blockchains[peer] = r.json().get('size') if _max > blockchain_size: logging.debug("Downloading new blockchain from: {}".format(best_peer)) os.rename('blockchain.dat', 'blockchain.backup') r = requests.get("http://{}/api/sync".format(best_peer)) with open('blockchain.dat', 'wb') as f: f.write(r.content) if self.blockchain.verify_blockchain(): os.remove('blockchain.backup') else: os.remove('blockchain.dat') os.rename('blockchain.backup', 'blockchain.dat') except requests.exceptions.ConnectionError: pass def sync_to_redis(self): for _, key in enumerate(self.transactions): self.redis_cli[key] = str(self.transactions[key]) self.transactions = {} def broadcast_new_block(self, block): p2p = PeerToPeerMessage(block=block) for peer in self.peers: r = requests.post("http://{}/api/block".format(peer), data=p2p.to_json()) assert r.status_code <= 299 @staticmethod def ping_peer_transactions(peer, p2p_message): logging.debug("Forwarding transactions to nearest peer {}".format(peer)) payload = p2p_message.to_json() try: requests.post("http://{}/api/transactions".format(peer), data=payload) except requests.exceptions.ConnectionError as e: logging.warning("Connection error {}".format(str(e))) @staticmethod def ping_peer_block(peer, p2p_message): logging.debug("Forwarding block to nearest peer {}".format(peer)) payload = p2p_message.to_json() try: requests.post("http://{}/api/block".format(peer), data=payload) except requests.exceptions.ConnectionError as e: logging.warning("Connection error {}".format(str(e))) def forward(self, p2p, target): for peer in self.peers: t = Thread(target=target, args=(peer, p2p)) t.start() # Todo: Transactions should be sorted by timestamp def compile_block(self): data = str() i = 0 for transaction_id in self.redis_cli.keys(): if i < 64: try: transaction = self.redis_cli[transaction_id] t = Transaction() transaction = t.from_string(transaction.decode('utf-8')) if not transaction.verify_signature(): logging.warning("Transaction signature not valid") continue data = data + str(transaction) + '\n' self.redis_cli.delete(transaction.id) i = i + 1 except IndexError: return False block = Block(data=data) return block def do_proof_of_work(self, block, first=False): if block: magic_number = 0 while True: block.magic_number = magic_number if not first: block.blockchain_snapshot = self.blockchain.get_sha512hash() else: block.blockchain_snapshot = 'None' sha512hash = block.generate_hash() block.sha512hash = sha512hash if block.check_proof_of_work(): block.magic_number = magic_number block.sha512hash = sha512hash return block magic_number = magic_number + 1 def routine(self): # Check if there is a new blockchain version while True: logging.debug("Requesting new blockchain info from P2P network") self.get_peers_blockchain() time.sleep(1) # Check if we have transactions if len(list(self.redis_cli.keys())): # Compile a block logging.debug("Building a new block") block = self.compile_block() # Do proof of work logging.debug("Doing proof of work on block") block = self.do_proof_of_work(block) # Verify a block logging.debug("Verifying the block") if self.blockchain.verify_blockchain(new_block=block): # Write the block logging.debug("Writing a new block") self.blockchain.write_new_block(block) if __name__ == "__main__": miner = Miner() miner.routine()

0

745

0

4,784

0

27

267

1486c16002e2c1f7f36eced992718519ad8c6db1

959

py

Python

web2py-appliances-master/MyForum/models/db.py

wantsomechocolate/WantsomeBeanstalk

8c8a0a80490d04ea52661a3114fd3db8de65a01e

[ "BSD-3-Clause" ]

null

web2py-appliances-master/MyForum/models/db.py

wantsomechocolate/WantsomeBeanstalk

8c8a0a80490d04ea52661a3114fd3db8de65a01e

[ "BSD-3-Clause" ]

null

web2py-appliances-master/MyForum/models/db.py

wantsomechocolate/WantsomeBeanstalk

8c8a0a80490d04ea52661a3114fd3db8de65a01e

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- DEBUG = True db = DAL('sqlite://storage.sqlite',pool_size=1,check_reserved=['all']) response.generic_patterns = ['*'] if request.is_local else [] from gluon.tools import Auth, Service auth = Auth(db) auth.define_tables(username=False, signature=False) service = Service() ## configure email mail = auth.settings.mailer mail.settings.server = 'logging' or 'smtp.gmail.com:587' mail.settings.sender = '[email protected]' mail.settings.login = 'username:password' ## configure auth policy auth.settings.registration_requires_verification = False auth.settings.registration_requires_approval = False auth.settings.reset_password_requires_verification = True ## if you need to use OpenID, Facebook, MySpace, Twitter, Linkedin, etc. ## register with janrain.com, write your domain:api_key in private/janrain.key from gluon.contrib.login_methods.rpx_account import use_janrain use_janrain(auth, filename='private/janrain.key')

33.068966

78

0.777894

# -*- coding: utf-8 -*- DEBUG = True db = DAL('sqlite://storage.sqlite',pool_size=1,check_reserved=['all']) response.generic_patterns = ['*'] if request.is_local else [] from gluon.tools import Auth, Service, prettydate auth = Auth(db) auth.define_tables(username=False, signature=False) service = Service() ## configure email mail = auth.settings.mailer mail.settings.server = 'logging' or 'smtp.gmail.com:587' mail.settings.sender = '[email protected]' mail.settings.login = 'username:password' ## configure auth policy auth.settings.registration_requires_verification = False auth.settings.registration_requires_approval = False auth.settings.reset_password_requires_verification = True ## if you need to use OpenID, Facebook, MySpace, Twitter, Linkedin, etc. ## register with janrain.com, write your domain:api_key in private/janrain.key from gluon.contrib.login_methods.rpx_account import use_janrain use_janrain(auth, filename='private/janrain.key')

0

12

0

9ded2fcc8e677e149baf4d0a230b66939619b9e9

8,353

py

Python

conceptnet5/vectors/retrofit.py

MattCurryCom/conceptnet5

a16d94e635aee3d35a22aa04fcad7bb87ce927d8

[ "Apache-2.0" ]

1

2018-11-27T17:00:57.000Z

conceptnet5/vectors/retrofit.py

MattCurryCom/conceptnet5

a16d94e635aee3d35a22aa04fcad7bb87ce927d8

[ "Apache-2.0" ]

null

conceptnet5/vectors/retrofit.py

MattCurryCom/conceptnet5

a16d94e635aee3d35a22aa04fcad7bb87ce927d8

[ "Apache-2.0" ]

null

import pandas as pd import numpy as np def retrofit(row_labels, dense_frame, sparse_csr, iterations=5, verbosity=0, max_cleanup_iters=20, orig_vec_weight=0.15): """ Retrofitting is a process of combining information from a machine-learned space of term vectors with further structured information about those terms. It was originally presented in this 2015 NAACL paper by Manaal Faruqui, Jesse Dodge, Sujay Jauhar, Chris Dyer, Eduard Hovy, and Noah Smith, "Retrofitting Word Vectors to Semantic Lexicons": https://www.cs.cmu.edu/~hovy/papers/15HLT-retrofitting-word-vectors.pdf This function implements a variant that I've been calling "wide retrofitting", which extends the process to learn vectors for terms that were outside the original space. `row_labels` is the list of terms that we want to have vectors for. `dense_frame` is a DataFrame assigning vectors to some of these terms. `sparse_csr` is a SciPy sparse square matrix, whose rows and columns are implicitly labeled with `row_labels`. The entries of this matrix are positive for terms that we know are related from our structured data. (This is an awkward form of input, but unfortunately there is no good way to represent sparse labeled data in Pandas.) `sharded_retrofit` is responsible for building `row_labels` and `sparse_csr` appropriately. """ # Initialize a DataFrame with rows that we know retroframe = pd.DataFrame( index=row_labels, columns=dense_frame.columns, dtype='f' ) retroframe.update(dense_frame) # orig_weights = 1 for known vectors, 0 for unknown vectors orig_weights = 1 - retroframe.iloc[:, 0].isnull() orig_vec_indicators = (orig_weights.values != 0) orig_vecs = retroframe.fillna(0).values # Subtract the mean so that vectors don't just clump around common # hypernyms orig_vecs[orig_vec_indicators] -= orig_vecs[orig_vec_indicators].mean(0) # Delete the frame we built, we won't need its indices again until the end del retroframe vecs = orig_vecs for iteration in range(iterations): if verbosity >= 1: print('Retrofitting: Iteration %s of %s' % (iteration+1, iterations)) # Since the sparse weight matrix is row-stochastic and has self-loops, # pre-multiplication by it replaces each vector by a weighted average # of itself and its neighbors. We really want to take the average # of (itself and) the nonzero neighbors, which we can do by dividing # the average with all the neighbors by the total of the weights of the # nonzero neighbors. This avoids unduly shrinking vectors assigned to # terms with lots of zero neighbors. # Find, for every term, the total weight of its nonzero neighbors. nonzero_indicators = (np.abs(vecs).sum(1) != 0) total_neighbor_weights = sparse_csr.dot(nonzero_indicators) # Now average with all the neighbors. vecs = sparse_csr.dot(vecs) # Now divide each vector (row) by the associated total weight. # Some of the total weights could be zero, but only for rows that, # before averaging, were zero and had all neighbors zero, whence # after averaging will be zero. So only do the division for rows # that are nonzero now, after averaging. Also, we reshape the total # weights into a column vector so that numpy will broadcast the # division by weights across the columns of the embedding matrix. nonzero_indicators = (np.abs(vecs).sum(1) != 0) total_neighbor_weights = total_neighbor_weights[nonzero_indicators] total_neighbor_weights = total_neighbor_weights.reshape((len(total_neighbor_weights), 1)) vecs[nonzero_indicators] /= total_neighbor_weights # Re-center the (new) non-zero vectors. vecs[nonzero_indicators] -= vecs[nonzero_indicators].mean(0) # Average known rows with original vectors vecs[orig_vec_indicators, :] = \ (1.0 - orig_vec_weight) * vecs[orig_vec_indicators, :] + orig_vec_weight * orig_vecs[orig_vec_indicators, :] # Clean up as many all-zero vectors as possible. Zero vectors # can either come from components of the conceptnet graph that # don't contain any terms from the embedding we are currently # retrofitting (and there is nothing we can do about those here, # but when retrofitting is done on that embedding they should be # taken care of then) or from terms whose distance in the graph is # larger than the number of retrofitting iterations used above; we # propagate non-zero values to those terms by averaging over their # non-zero neighbors. Note that this propagation can never reach # the first class of terms, so we can't necessarily expect the # number of zero vectors to go to zero at any one invocation of # this code. n_zero_indicators_old = -1 for iteration in range(max_cleanup_iters): zero_indicators = (np.abs(vecs).sum(1) == 0) n_zero_indicators = np.sum(zero_indicators) if n_zero_indicators == 0 or n_zero_indicators == n_zero_indicators_old: break n_zero_indicators_old = n_zero_indicators # First replace each zero vector (row) by the weighted average of all its # neighbors. vecs[zero_indicators, :] = sparse_csr[zero_indicators, :].dot(vecs) # Now divide each newly nonzero vector (row) by the total weight of its # old nonzero neighbors. new_nonzero_indicators = np.logical_and(zero_indicators, np.abs(vecs).sum(1) != 0) total_neighbor_weights = sparse_csr[new_nonzero_indicators, :].dot(np.logical_not(zero_indicators)) total_neighbor_weights = total_neighbor_weights.reshape((len(total_neighbor_weights), 1)) vecs[new_nonzero_indicators, :] /= total_neighbor_weights else: print('Warning: cleanup iteration limit exceeded.') retroframe = pd.DataFrame(data=vecs, index=row_labels, columns=dense_frame.columns) return retroframe

48.005747

130

0.704058

import pandas as pd import numpy as np from sklearn.preprocessing import normalize from .sparse_matrix_builder import build_from_conceptnet_table from .formats import load_hdf, save_hdf def sharded_retrofit(dense_hdf_filename, conceptnet_filename, output_filename, iterations=5, nshards=6, verbosity=0, max_cleanup_iters=20, orig_vec_weight=0.15): # frame_box is basically a reference to a single large DataFrame. The # DataFrame will at times be present or absent. When it's present, the list # contains one item, which is the DataFrame. When it's absent, the list # is empty. frame_box = [load_hdf(dense_hdf_filename)] sparse_csr, combined_index = build_from_conceptnet_table(conceptnet_filename, orig_index=frame_box[0].index) shard_width = frame_box[0].shape[1] // nshards for i in range(nshards): temp_filename = output_filename + '.shard%d' % i shard_from = shard_width * i shard_to = shard_from + shard_width if len(frame_box) == 0: frame_box.append(load_hdf(dense_hdf_filename)) dense_frame = pd.DataFrame(frame_box[0].iloc[:, shard_from:shard_to]) # Delete full_dense_frame while running retrofitting, because it takes # up a lot of memory and we can reload it from disk later. frame_box.clear() retrofitted = retrofit(combined_index, dense_frame, sparse_csr, iterations, verbosity, max_cleanup_iters, orig_vec_weight) save_hdf(retrofitted, temp_filename) del retrofitted def join_shards(output_filename, nshards=6, sort=False): joined_matrix = None joined_labels = None for i in range(nshards): shard = load_hdf(output_filename + '.shard%d' % i) nrows, ncols = shard.shape if joined_matrix is None: joined_matrix = np.zeros((nrows, ncols * nshards), dtype='f') joined_labels = shard.index joined_matrix[:, (ncols * i):(ncols * (i + 1))] = shard.values del shard normalize(joined_matrix, axis=1, norm='l2', copy=False) dframe = pd.DataFrame(joined_matrix, index=joined_labels) if sort: dframe.sort_index(inplace=True) save_hdf(dframe, output_filename) def retrofit(row_labels, dense_frame, sparse_csr, iterations=5, verbosity=0, max_cleanup_iters=20, orig_vec_weight=0.15): """ Retrofitting is a process of combining information from a machine-learned space of term vectors with further structured information about those terms. It was originally presented in this 2015 NAACL paper by Manaal Faruqui, Jesse Dodge, Sujay Jauhar, Chris Dyer, Eduard Hovy, and Noah Smith, "Retrofitting Word Vectors to Semantic Lexicons": https://www.cs.cmu.edu/~hovy/papers/15HLT-retrofitting-word-vectors.pdf This function implements a variant that I've been calling "wide retrofitting", which extends the process to learn vectors for terms that were outside the original space. `row_labels` is the list of terms that we want to have vectors for. `dense_frame` is a DataFrame assigning vectors to some of these terms. `sparse_csr` is a SciPy sparse square matrix, whose rows and columns are implicitly labeled with `row_labels`. The entries of this matrix are positive for terms that we know are related from our structured data. (This is an awkward form of input, but unfortunately there is no good way to represent sparse labeled data in Pandas.) `sharded_retrofit` is responsible for building `row_labels` and `sparse_csr` appropriately. """ # Initialize a DataFrame with rows that we know retroframe = pd.DataFrame( index=row_labels, columns=dense_frame.columns, dtype='f' ) retroframe.update(dense_frame) # orig_weights = 1 for known vectors, 0 for unknown vectors orig_weights = 1 - retroframe.iloc[:, 0].isnull() orig_vec_indicators = (orig_weights.values != 0) orig_vecs = retroframe.fillna(0).values # Subtract the mean so that vectors don't just clump around common # hypernyms orig_vecs[orig_vec_indicators] -= orig_vecs[orig_vec_indicators].mean(0) # Delete the frame we built, we won't need its indices again until the end del retroframe vecs = orig_vecs for iteration in range(iterations): if verbosity >= 1: print('Retrofitting: Iteration %s of %s' % (iteration+1, iterations)) # Since the sparse weight matrix is row-stochastic and has self-loops, # pre-multiplication by it replaces each vector by a weighted average # of itself and its neighbors. We really want to take the average # of (itself and) the nonzero neighbors, which we can do by dividing # the average with all the neighbors by the total of the weights of the # nonzero neighbors. This avoids unduly shrinking vectors assigned to # terms with lots of zero neighbors. # Find, for every term, the total weight of its nonzero neighbors. nonzero_indicators = (np.abs(vecs).sum(1) != 0) total_neighbor_weights = sparse_csr.dot(nonzero_indicators) # Now average with all the neighbors. vecs = sparse_csr.dot(vecs) # Now divide each vector (row) by the associated total weight. # Some of the total weights could be zero, but only for rows that, # before averaging, were zero and had all neighbors zero, whence # after averaging will be zero. So only do the division for rows # that are nonzero now, after averaging. Also, we reshape the total # weights into a column vector so that numpy will broadcast the # division by weights across the columns of the embedding matrix. nonzero_indicators = (np.abs(vecs).sum(1) != 0) total_neighbor_weights = total_neighbor_weights[nonzero_indicators] total_neighbor_weights = total_neighbor_weights.reshape((len(total_neighbor_weights), 1)) vecs[nonzero_indicators] /= total_neighbor_weights # Re-center the (new) non-zero vectors. vecs[nonzero_indicators] -= vecs[nonzero_indicators].mean(0) # Average known rows with original vectors vecs[orig_vec_indicators, :] = \ (1.0 - orig_vec_weight) * vecs[orig_vec_indicators, :] + orig_vec_weight * orig_vecs[orig_vec_indicators, :] # Clean up as many all-zero vectors as possible. Zero vectors # can either come from components of the conceptnet graph that # don't contain any terms from the embedding we are currently # retrofitting (and there is nothing we can do about those here, # but when retrofitting is done on that embedding they should be # taken care of then) or from terms whose distance in the graph is # larger than the number of retrofitting iterations used above; we # propagate non-zero values to those terms by averaging over their # non-zero neighbors. Note that this propagation can never reach # the first class of terms, so we can't necessarily expect the # number of zero vectors to go to zero at any one invocation of # this code. n_zero_indicators_old = -1 for iteration in range(max_cleanup_iters): zero_indicators = (np.abs(vecs).sum(1) == 0) n_zero_indicators = np.sum(zero_indicators) if n_zero_indicators == 0 or n_zero_indicators == n_zero_indicators_old: break n_zero_indicators_old = n_zero_indicators # First replace each zero vector (row) by the weighted average of all its # neighbors. vecs[zero_indicators, :] = sparse_csr[zero_indicators, :].dot(vecs) # Now divide each newly nonzero vector (row) by the total weight of its # old nonzero neighbors. new_nonzero_indicators = np.logical_and(zero_indicators, np.abs(vecs).sum(1) != 0) total_neighbor_weights = sparse_csr[new_nonzero_indicators, :].dot(np.logical_not(zero_indicators)) total_neighbor_weights = total_neighbor_weights.reshape((len(total_neighbor_weights), 1)) vecs[new_nonzero_indicators, :] /= total_neighbor_weights else: print('Warning: cleanup iteration limit exceeded.') retroframe = pd.DataFrame(data=vecs, index=row_labels, columns=dense_frame.columns) return retroframe

0

2,009

0

81

112

fcd076838a13b16b0181931dfa476968f0b03f64

11,297

py

Python

Stock_Analysis/auto_value_stock.py

parmarsuraj99/Finance

d9f012e33a99b959fdde575feedeb5922b379fe2

[ "MIT" ]

1

2022-02-25T01:25:21.000Z

Stock_Analysis/auto_value_stock.py

StockScripts/Finance

330bb46ea8e4c7ad5f3150cfa6d25e356178b189

[ "MIT" ]

null

Stock_Analysis/auto_value_stock.py

StockScripts/Finance

330bb46ea8e4c7ad5f3150cfa6d25e356178b189

[ "MIT" ]

2

2021-01-28T21:52:30.000Z

2021-02-16T13:26:35.000Z

# Code from https://medium.com/datadriveninvestor/use-python-to-value-a-stock-automatically-3b520422ab6 by Bohmian # Importing required modules import pandas as pd import numpy as np import matplotlib.pyplot as plt import numpy as np import time from config import financial_model_prep pd.set_option('display.max_columns', None) # Settings to produce nice plots in a Jupyter notebook plt.style.use('fivethirtyeight') plt.rcParams['figure.figsize'] = [15, 10] # To extract and parse fundamental data from finviz website import warnings warnings.filterwarnings('ignore') # For parsing financial statements data from financialmodelingprep api # inputs base_url = "https://financialmodelingprep.com/api/v3/" tickers = ['AAL'] apiKey = financial_model_prep() cash_flows = [] total_debts = [] cash_and_ST_investments_list = [] betas = [] discount_rates = [] EPS_growth_5Ys = [] EPS_growth_6Y_to_10Ys = [] EPS_growth_11Y_to_20Ys = [] shares_outstandings = [] intrinsic_values = [] current_prices = [] margins_safety = [] valid_tickers = [] for ticker in tickers: try: q_cash_flow_statement = pd.DataFrame(get_jsonparsed_data(base_url+'cash-flow-statement/' + ticker + '?period=quarter' + '&apikey=' + apiKey)) q_cash_flow_statement = q_cash_flow_statement.set_index('date').iloc[:4] # extract for last 4 quarters q_cash_flow_statement = q_cash_flow_statement.apply(pd.to_numeric, errors='coerce') cash_flow_statement = pd.DataFrame(get_jsonparsed_data(base_url+'cash-flow-statement/' + ticker + '?apikey=' + apiKey)) cash_flow_statement = cash_flow_statement.set_index('date') cash_flow_statement = cash_flow_statement.apply(pd.to_numeric, errors='coerce') ttm_cash_flow_statement = q_cash_flow_statement.sum() # sum up last 4 quarters to get TTM cash flow cash_flow_statement = cash_flow_statement[::-1].append(ttm_cash_flow_statement.rename('TTM')).drop(['netIncome'], axis=1) final_cash_flow_statement = cash_flow_statement[::-1] # reverse list to show most recent ones first # final_cash_flow_statement[['freeCashFlow']].iloc[::-1].iloc[-15:].plot(kind='bar', title=ticker + ' Cash Flows') # plt.show() q_balance_statement = pd.DataFrame(get_jsonparsed_data(base_url+'balance-sheet-statement/' + ticker + '?period=quarter' + '&apikey=' + apiKey)) q_balance_statement = q_balance_statement.set_index('date') q_balance_statement = q_balance_statement.apply(pd.to_numeric, errors='coerce') cash_flow = final_cash_flow_statement.iloc[0]['freeCashFlow'] total_debt = q_balance_statement.iloc[0]['totalDebt'] cash_and_ST_investments = q_balance_statement.iloc[0]['cashAndShortTermInvestments'] # print("Free Cash Flow: ", cash_flow) # print("Total Debt: ", total_debt) # print("Cash and ST Investments: ", cash_and_ST_investments) # List of data we want to extract from Finviz Table metric = ['Price', 'EPS next 5Y', 'Beta', 'Shs Outstand'] finviz_data = get_finviz_data(ticker) # print('\nFinViz Data:\n' + str(finviz_data)) Beta = finviz_data['Beta'] discount_rate = 7 if(Beta<0.80): discount_rate = 5 elif(Beta>=0.80 and Beta<1): discount_rate = 6 elif(Beta>=1 and Beta<1.1): discount_rate = 6.5 elif(Beta>=1.1 and Beta<1.2): discount_rate = 7 elif(Beta>=1.2 and Beta<1.3): discount_rate =7.5 elif(Beta>=1.3 and Beta<1.4): discount_rate = 8 elif(Beta>=1.4 and Beta<1.6): discount_rate = 8.5 elif(Beta>=1.61): discount_rate = 9 # print("\nDiscount Rate: ", discount_rate) EPS_growth_5Y = finviz_data['EPS next 5Y'] EPS_growth_6Y_to_10Y = EPS_growth_5Y/2 # Half the previous growth rate, conservative estimate EPS_growth_11Y_to_20Y = np.minimum(EPS_growth_6Y_to_10Y, 4) # Slightly higher than long term inflation rate, conservative estimate shares_outstanding = round(finviz_data['Shs Outstand']) # print("Free Cash Flow: ", cash_flow) # print("Total Debt: ", total_debt) # print("Cash and ST Investments: ", cash_and_ST_investments) # print("EPS Growth 5Y: ", EPS_growth_5Y) # print("EPS Growth 6Y to 10Y: ", EPS_growth_6Y_to_10Y) # print("EPS Growth 11Y to 20Y: ", EPS_growth_11Y_to_20Y) # print("Discount Rate: ", discount_rate) # print("Shares Outstanding: ", shares_outstanding) intrinsic_value = round(calculate_intrinsic_value(cash_flow, total_debt, cash_and_ST_investments, EPS_growth_5Y, EPS_growth_6Y_to_10Y, EPS_growth_11Y_to_20Y, shares_outstanding, discount_rate), 2) # print("\nIntrinsic Value: ", intrinsic_value) current_price = finviz_data['Price'] # print("Current Price: ", current_price) change = round(((intrinsic_value-current_price)/current_price)*100, 2) # print("Margin of Safety: ", margin_safety) cash_flows.append(cash_flow) total_debts.append(total_debt) cash_and_ST_investments_list.append(cash_and_ST_investments) betas.append(Beta) discount_rates.append(discount_rate) EPS_growth_5Ys.append(EPS_growth_5Y) EPS_growth_6Y_to_10Ys.append(EPS_growth_6Y_to_10Y) EPS_growth_11Y_to_20Ys.append(EPS_growth_11Y_to_20Y) shares_outstandings.append(shares_outstanding) intrinsic_values.append(intrinsic_value) current_prices.append(current_price) margins_safety.append(change) valid_tickers.append(ticker) except: pass df = pd.DataFrame(np.column_stack([valid_tickers, cash_flows, total_debts, cash_and_ST_investments_list, betas, discount_rates, EPS_growth_5Ys, EPS_growth_6Y_to_10Ys, EPS_growth_11Y_to_20Ys, shares_outstandings, intrinsic_values, current_prices, margins_safety]), columns=['Ticker', 'Cash Flow', 'Total Debt', 'Cash and ST investment', 'Beta', 'Discount Rate', 'EPS Growth 5 Y', 'EPS Growth 6-10 Y', 'EPS Growth 11-20 Y', 'Shares Outstanding', 'Intrinsic Value', 'Current Price', 'Margin Safety']).set_index('Ticker') df = df.sort_values(['Margin Safety'], ascending=True) df.to_csv(f'{time.time()}.csv') print (df)

46.681818

284

0.615208

# Code from https://medium.com/datadriveninvestor/use-python-to-value-a-stock-automatically-3b520422ab6 by Bohmian # Importing required modules import pandas as pd import numpy as np import matplotlib.pyplot as plt import numpy as np import time from config import financial_model_prep pd.set_option('display.max_columns', None) # Settings to produce nice plots in a Jupyter notebook plt.style.use('fivethirtyeight') plt.rcParams['figure.figsize'] = [15, 10] import seaborn as sns # To extract and parse fundamental data from finviz website import requests from bs4 import BeautifulSoup as bs import warnings warnings.filterwarnings('ignore') # For parsing financial statements data from financialmodelingprep api from urllib.request import urlopen import json def get_jsonparsed_data(url): response = urlopen(url) data = response.read().decode("utf-8") return json.loads(data) # inputs base_url = "https://financialmodelingprep.com/api/v3/" tickers = ['AAL'] apiKey = financial_model_prep() cash_flows = [] total_debts = [] cash_and_ST_investments_list = [] betas = [] discount_rates = [] EPS_growth_5Ys = [] EPS_growth_6Y_to_10Ys = [] EPS_growth_11Y_to_20Ys = [] shares_outstandings = [] intrinsic_values = [] current_prices = [] margins_safety = [] valid_tickers = [] for ticker in tickers: try: q_cash_flow_statement = pd.DataFrame(get_jsonparsed_data(base_url+'cash-flow-statement/' + ticker + '?period=quarter' + '&apikey=' + apiKey)) q_cash_flow_statement = q_cash_flow_statement.set_index('date').iloc[:4] # extract for last 4 quarters q_cash_flow_statement = q_cash_flow_statement.apply(pd.to_numeric, errors='coerce') cash_flow_statement = pd.DataFrame(get_jsonparsed_data(base_url+'cash-flow-statement/' + ticker + '?apikey=' + apiKey)) cash_flow_statement = cash_flow_statement.set_index('date') cash_flow_statement = cash_flow_statement.apply(pd.to_numeric, errors='coerce') ttm_cash_flow_statement = q_cash_flow_statement.sum() # sum up last 4 quarters to get TTM cash flow cash_flow_statement = cash_flow_statement[::-1].append(ttm_cash_flow_statement.rename('TTM')).drop(['netIncome'], axis=1) final_cash_flow_statement = cash_flow_statement[::-1] # reverse list to show most recent ones first # final_cash_flow_statement[['freeCashFlow']].iloc[::-1].iloc[-15:].plot(kind='bar', title=ticker + ' Cash Flows') # plt.show() q_balance_statement = pd.DataFrame(get_jsonparsed_data(base_url+'balance-sheet-statement/' + ticker + '?period=quarter' + '&apikey=' + apiKey)) q_balance_statement = q_balance_statement.set_index('date') q_balance_statement = q_balance_statement.apply(pd.to_numeric, errors='coerce') cash_flow = final_cash_flow_statement.iloc[0]['freeCashFlow'] total_debt = q_balance_statement.iloc[0]['totalDebt'] cash_and_ST_investments = q_balance_statement.iloc[0]['cashAndShortTermInvestments'] # print("Free Cash Flow: ", cash_flow) # print("Total Debt: ", total_debt) # print("Cash and ST Investments: ", cash_and_ST_investments) # List of data we want to extract from Finviz Table metric = ['Price', 'EPS next 5Y', 'Beta', 'Shs Outstand'] def fundamental_metric(soup, metric): # the table which stores the data in Finviz has html table attribute class of 'snapshot-td2' return soup.find(text = metric).find_next(class_='snapshot-td2').text def get_finviz_data(ticker): try: url = ("http://finviz.com/quote.ashx?t=" + ticker.lower()) soup = bs(requests.get(url,headers={'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:20.0) Gecko/20100101 Firefox/20.0'}).content) dict_finviz = {} for m in metric: dict_finviz[m] = fundamental_metric(soup,m) for key, value in dict_finviz.items(): # replace percentages if (value[-1]=='%'): dict_finviz[key] = value[:-1] dict_finviz[key] = float(dict_finviz[key]) # billion if (value[-1]=='B'): dict_finviz[key] = value[:-1] dict_finviz[key] = float(dict_finviz[key])*1000000000 # million if (value[-1]=='M'): dict_finviz[key] = value[:-1] dict_finviz[key] = float(dict_finviz[key])*1000000 try: dict_finviz[key] = float(dict_finviz[key]) except: pass except Exception as e: print (e) print ('Not successful parsing ' + ticker + ' data.') return dict_finviz finviz_data = get_finviz_data(ticker) # print('\nFinViz Data:\n' + str(finviz_data)) Beta = finviz_data['Beta'] discount_rate = 7 if(Beta<0.80): discount_rate = 5 elif(Beta>=0.80 and Beta<1): discount_rate = 6 elif(Beta>=1 and Beta<1.1): discount_rate = 6.5 elif(Beta>=1.1 and Beta<1.2): discount_rate = 7 elif(Beta>=1.2 and Beta<1.3): discount_rate =7.5 elif(Beta>=1.3 and Beta<1.4): discount_rate = 8 elif(Beta>=1.4 and Beta<1.6): discount_rate = 8.5 elif(Beta>=1.61): discount_rate = 9 # print("\nDiscount Rate: ", discount_rate) EPS_growth_5Y = finviz_data['EPS next 5Y'] EPS_growth_6Y_to_10Y = EPS_growth_5Y/2 # Half the previous growth rate, conservative estimate EPS_growth_11Y_to_20Y = np.minimum(EPS_growth_6Y_to_10Y, 4) # Slightly higher than long term inflation rate, conservative estimate shares_outstanding = round(finviz_data['Shs Outstand']) # print("Free Cash Flow: ", cash_flow) # print("Total Debt: ", total_debt) # print("Cash and ST Investments: ", cash_and_ST_investments) # print("EPS Growth 5Y: ", EPS_growth_5Y) # print("EPS Growth 6Y to 10Y: ", EPS_growth_6Y_to_10Y) # print("EPS Growth 11Y to 20Y: ", EPS_growth_11Y_to_20Y) # print("Discount Rate: ", discount_rate) # print("Shares Outstanding: ", shares_outstanding) def calculate_intrinsic_value(cash_flow, total_debt, cash_and_ST_investments, EPS_growth_5Y, EPS_growth_6Y_to_10Y, EPS_growth_11Y_to_20Y, shares_outstanding, discount_rate): # Convert all percentages to decmials EPS_growth_5Y_d = EPS_growth_5Y/100 EPS_growth_6Y_to_10Y_d = EPS_growth_6Y_to_10Y/100 EPS_growth_11Y_to_20Y_d = EPS_growth_11Y_to_20Y/100 discount_rate_d = discount_rate/100 # print("\nDiscounted Cash Flows") # Lists of projected cash flows from year 1 to year 20 cash_flow_list = [] cash_flow_discounted_list = [] year_list = [] # Years 1 to 5 for year in range(1, 6): year_list.append(year) cash_flow*=(1 + EPS_growth_5Y_d) cash_flow_list.append(cash_flow) cash_flow_discounted = cash_flow/((1 + discount_rate_d)**year) cash_flow_discounted_list.append(cash_flow_discounted) # print("Year " + str(year) + ": $" + str(cash_flow_discounted)) ## Print out the projected discounted cash flows # Years 6 to 10 for year in range(6, 11): year_list.append(year) cash_flow*=(1 + EPS_growth_6Y_to_10Y_d) cash_flow_list.append(cash_flow) cash_flow_discounted = cash_flow/((1 + discount_rate_d)**year) cash_flow_discounted_list.append(cash_flow_discounted) # print("Year " + str(year) + ": $" + str(cash_flow_discounted)) ## Print out the projected discounted cash flows # Years 11 to 20 for year in range(11, 21): year_list.append(year) cash_flow*=(1 + EPS_growth_11Y_to_20Y_d) cash_flow_list.append(cash_flow) cash_flow_discounted = cash_flow/((1 + discount_rate_d)**year) cash_flow_discounted_list.append(cash_flow_discounted) # print("Year " + str(year) + ": $" + str(cash_flow_discounted)) ## Print out the projected discounted cash flows intrinsic_value = (sum(cash_flow_discounted_list) - total_debt + cash_and_ST_investments)/shares_outstanding df = pd.DataFrame.from_dict({'Year': year_list, 'Cash Flow': cash_flow_list, 'Discounted Cash Flow': cash_flow_discounted_list}) df.index = df.Year # df.plot(kind='bar', title = 'Projected Cash Flows of ' + ticker) # plt.show() return intrinsic_value intrinsic_value = round(calculate_intrinsic_value(cash_flow, total_debt, cash_and_ST_investments, EPS_growth_5Y, EPS_growth_6Y_to_10Y, EPS_growth_11Y_to_20Y, shares_outstanding, discount_rate), 2) # print("\nIntrinsic Value: ", intrinsic_value) current_price = finviz_data['Price'] # print("Current Price: ", current_price) change = round(((intrinsic_value-current_price)/current_price)*100, 2) # print("Margin of Safety: ", margin_safety) cash_flows.append(cash_flow) total_debts.append(total_debt) cash_and_ST_investments_list.append(cash_and_ST_investments) betas.append(Beta) discount_rates.append(discount_rate) EPS_growth_5Ys.append(EPS_growth_5Y) EPS_growth_6Y_to_10Ys.append(EPS_growth_6Y_to_10Y) EPS_growth_11Y_to_20Ys.append(EPS_growth_11Y_to_20Y) shares_outstandings.append(shares_outstanding) intrinsic_values.append(intrinsic_value) current_prices.append(current_price) margins_safety.append(change) valid_tickers.append(ticker) except: pass df = pd.DataFrame(np.column_stack([valid_tickers, cash_flows, total_debts, cash_and_ST_investments_list, betas, discount_rates, EPS_growth_5Ys, EPS_growth_6Y_to_10Ys, EPS_growth_11Y_to_20Ys, shares_outstandings, intrinsic_values, current_prices, margins_safety]), columns=['Ticker', 'Cash Flow', 'Total Debt', 'Cash and ST investment', 'Beta', 'Discount Rate', 'EPS Growth 5 Y', 'EPS Growth 6-10 Y', 'EPS Growth 11-20 Y', 'Shares Outstanding', 'Intrinsic Value', 'Current Price', 'Margin Safety']).set_index('Ticker') df = df.sort_values(['Margin Safety'], ascending=True) df.to_csv(f'{time.time()}.csv') print (df)

0

4,418

0

11

253

901b7a71198943a53f223f18bbc124edf656a124

2,580

py

Python

src/100_simple_aggregation.py

j20232/kaggle_earthquake

47fac5f2e8d2ad4fab82426a0b6af18b71e4b57b

[ "MIT" ]

null

src/100_simple_aggregation.py

j20232/kaggle_earthquake

47fac5f2e8d2ad4fab82426a0b6af18b71e4b57b

[ "MIT" ]

null

src/100_simple_aggregation.py

j20232/kaggle_earthquake

47fac5f2e8d2ad4fab82426a0b6af18b71e4b57b

[ "MIT" ]

null

"""Extract simple aggregation features Reference: https://www.kaggle.com/gpreda/lanl-earthquake-eda-and-prediction """ import sys import competition as cc TRAIN_CSV_DIRECTORY_PATH = cc.INPUT_PATH / sys.argv[1] TRAIN_CSV_LIST = list(TRAIN_CSV_DIRECTORY_PATH.glob('**/*.csv')) if __name__ == "__main__": train_csv_path = cc.FEATURE_PATH / "{}".format(sys.argv[1]) train_csv_l = [str(item) for item in TRAIN_CSV_LIST] extract_features(train_csv_l, train_csv_path) test_csv_path = cc.FEATURE_PATH / "test" test_csv_l = [str(item) for item in cc.TEST_CSV_LIST] extract_features(test_csv_l, test_csv_path)

38.507463

91

0.622481

"""Extract simple aggregation features Reference: https://www.kaggle.com/gpreda/lanl-earthquake-eda-and-prediction """ import sys import numpy as np import pandas as pd from pathlib import Path from tqdm import tqdm import competition as cc from common import stop_watch TRAIN_CSV_DIRECTORY_PATH = cc.INPUT_PATH / sys.argv[1] TRAIN_CSV_LIST = list(TRAIN_CSV_DIRECTORY_PATH.glob('**/*.csv')) @stop_watch def extract_features(csv_list, feature_dir_path): df = pd.DataFrame() Path.mkdir(feature_dir_path, exist_ok=True, parents=True) for index, each_csv in enumerate(tqdm(sorted(csv_list))): seg = pd.read_csv(each_csv, dtype=cc.DTYPES) seg_id = each_csv.split("/")[-1].split(".")[0] df.loc[index, "seg_id"] = seg_id xc = pd.Series(seg['acoustic_data'].values) # basic aggregation df.loc[index, "mean"] = xc.mean() df.loc[index, "std"] = xc.std() df.loc[index, "max"] = xc.max() df.loc[index, "min"] = xc.min() df.loc[index, 'sum'] = xc.sum() df.loc[index, 'mad'] = xc.mad() df.loc[index, 'kurtosis'] = xc.kurtosis() df.loc[index, 'skew'] = xc.skew() df.loc[index, 'median'] = xc.median() df.loc[index, 'mean_change_rate'] = np.mean(np.nonzero((np.diff(xc) / xc[:-1]))[0]) # abs aggregation df.loc[index, 'abs_mean'] = np.abs(xc).mean() df.loc[index, 'abs_std'] = np.abs(xc).std() df.loc[index, 'abs_max'] = np.abs(xc).max() df.loc[index, 'abs_min'] = np.abs(xc).min() df.loc[index, 'abs_sum'] = np.abs(xc).sum() df.loc[index, 'abs_mad'] = np.abs(xc).mad() df.loc[index, 'abs_kurtosis'] = np.abs(xc).kurtosis() df.loc[index, 'abs_skew'] = np.abs(xc).skew() df.loc[index, 'abs_median'] = np.abs(xc).median() df.loc[index, 'mean_change_abs'] = np.mean(np.diff(xc)) df.loc[index, 'max_to_min'] = xc.max() / np.abs(xc.min()) df.loc[index, 'max_to_min_diff'] = xc.max() - np.abs(xc.min()) df.loc[index, 'count_big'] = len(xc[np.abs(xc) > 500]) print("Aggregation output is belows:") print(df.head(3)) df.to_csv(feature_dir_path / "{}.csv".format(cc.PREF), index=False) if __name__ == "__main__": train_csv_path = cc.FEATURE_PATH / "{}".format(sys.argv[1]) train_csv_l = [str(item) for item in TRAIN_CSV_LIST] extract_features(train_csv_l, train_csv_path) test_csv_path = cc.FEATURE_PATH / "test" test_csv_l = [str(item) for item in cc.TEST_CSV_LIST] extract_features(test_csv_l, test_csv_path)

0

1,807

0

6

133

0b3eba4af37debbbb40bec37c6e9b379c1156729

8,817

py

Python

segment.py

neelsj/syndata-generation

df73cc9a146c34870c3d80acce0ca04b314ec1b0

[ "MIT" ]

null

segment.py

neelsj/syndata-generation

df73cc9a146c34870c3d80acce0ca04b314ec1b0

[ "MIT" ]

null

segment.py

neelsj/syndata-generation

df73cc9a146c34870c3d80acce0ca04b314ec1b0

[ "MIT" ]

null

from datetime import datetime import torch #model = torch.hub.load('pytorch/vision:v0.10.0', 'deeplabv3_resnet50', pretrained=True) model = torch.hub.load('pytorch/vision:v0.10.0', 'deeplabv3_resnet101', pretrained=True) # model = torch.hub.load('pytorch/vision:v0.10.0', 'deeplabv3_mobilenet_v3_large', pretrained=True) model.eval() COCO_INFO = { "description": "", "url": "", "version": "1", "year": 2022, "contributor": "MSR CV Group", "date_created": datetime.now().strftime("%m/%d/%Y") } COCO_LICENSES = [{ "url": "", "id": 0, "name": "License" }] if __name__ == "__main__": data_dir = "E:/Research/Images/FineGrained/StanfordCars/train_bing/"

31.830325

132

0.565612

import os from datetime import datetime import json import matplotlib.pyplot as plt from tqdm import tqdm import torch import numpy as np from skimage import measure from shapely.geometry import Polygon, MultiPolygon from PIL import Image import cv2 #model = torch.hub.load('pytorch/vision:v0.10.0', 'deeplabv3_resnet50', pretrained=True) model = torch.hub.load('pytorch/vision:v0.10.0', 'deeplabv3_resnet101', pretrained=True) # model = torch.hub.load('pytorch/vision:v0.10.0', 'deeplabv3_mobilenet_v3_large', pretrained=True) model.eval() from torchvision import transforms COCO_INFO = { "description": "", "url": "", "version": "1", "year": 2022, "contributor": "MSR CV Group", "date_created": datetime.now().strftime("%m/%d/%Y") } COCO_LICENSES = [{ "url": "", "id": 0, "name": "License" }] def create_mask(input_image): input_image = input_image.convert("RGB") preprocess = transforms.Compose([ transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) input_tensor = preprocess(input_image) input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model # move the input and model to GPU for speed if available if torch.cuda.is_available(): input_batch = input_batch.to('cuda') model.to('cuda') with torch.no_grad(): output = model(input_batch)['out'][0] output_predictions = output.argmax(0) # plot the semantic segmentation predictions of 21 classes in each color mask = np.uint8(255*(output_predictions.cpu().numpy() > 0)) #mask = output_predictions.byte().cpu().numpy() return mask def create_sub_mask_annotation(sub_mask, image_id, category_id, annotation_id, is_crowd, bbox=None): # Find contours (boundary lines) around each sub-mask # Note: there could be multiple contours if the object # is partially occluded. (E.g. an elephant behind a tree) #contours = measure.find_contours(sub_mask, 0.5, positive_orientation='low') padded_binary_mask = np.pad(sub_mask, pad_width=1, mode='constant', constant_values=0) contours = measure.find_contours(padded_binary_mask, 0.5, positive_orientation='low') segmentations = [] polygons = [] for contour in contours: # Flip from (row, col) representation to (x, y) # and subtract the padding pixel for i in range(len(contour)): row, col = contour[i] contour[i] = (col - 1, row - 1) # Make a polygon and simplify it poly = Polygon(contour) poly = poly.simplify(1.0, preserve_topology=False) polygons.append(poly) segmentation = np.array(poly.exterior.coords).ravel().tolist() segmentations.append(segmentation) # Combine the polygons to calculate the bounding box and area multi_poly = MultiPolygon(polygons) x, y, max_x, max_y = multi_poly.bounds width = max_x - x height = max_y - y bbox = bbox if (bbox) else (x, y, width, height) area = multi_poly.area annotation = { 'segmentation': segmentations, 'iscrowd': is_crowd, 'image_id': image_id, 'category_id': category_id, 'id': annotation_id, 'bbox': bbox, 'area': area } return annotation def generate_masks(data_dir, background=False): dirs = os.listdir(data_dir) # create a color pallette, selecting a color for each class palette = torch.tensor([2 ** 25 - 1, 2 ** 15 - 1, 2 ** 21 - 1]) colors = torch.as_tensor([i for i in range(21)])[:, None] * palette colors = (colors % 255).numpy().astype("uint8") prcThresh = 3 images = [] annotations = [] image_id = 1 category_id = 1 annotation_id = 1 categories = [] for dir in tqdm(dirs): files_dir = os.path.join(data_dir, dir) if (not os.path.isdir(files_dir)): continue files = os.listdir(files_dir) files = [file for file in files if "_mask" not in file] category = {"supercategory": "object", "id": category_id, "name": dir} categories.append(category) for file in tqdm(files): filename = os.path.join(data_dir, dir, file) #print(filename) image = Image.open(filename) new_img={} new_img["license"] = 0 new_img["file_name"] = os.path.join(dir, file) new_img["width"] = int(image.size[0]) new_img["height"] = int(image.size[1]) new_img["id"] = image_id images.append(new_img) mask = create_mask(image) if (background): maskname = os.path.splitext(filename)[0] + "_mask.jpg" maskObj = np.uint8(255*(mask==0)) Image.fromarray(maskObj).save(maskname) #plt.imshow(np.array(image)[:,:,0]*mask) #plt.show() else: nb_components, output, boxes, centroids = cv2.connectedComponentsWithStats(mask, connectivity=8) box_sizes = [box[4] for box in boxes[1:]] for id in range(1, nb_components): box = [int(b) for b in boxes[id][0:4]] sub_mask = np.reshape(output==id, mask.shape).astype(np.double) #plt.imshow(sub_mask) #plt.show() prc = 100*box_sizes[id-1]/(mask.shape[0]*mask.shape[1]) if (prc >= prcThresh): try: annotation = create_sub_mask_annotation(sub_mask, image_id, category_id, annotation_id, False, bbox=box) annotations.append(annotation) annotation_id += 1 except Exception as e: print(e) pass #print(nb_components) #print(output) #print(stats) #print(centroids) # save mask for dominant big object if (box_sizes): max_ind = np.argmax(box_sizes) #print(max_ind) prc = 100*box_sizes[max_ind]/(mask.shape[0]*mask.shape[1]) #print(prc) if (prc >= prcThresh): maskname = os.path.splitext(filename)[0] + "_mask.jpg" #print(maskname) maskObj = np.uint8(255*np.reshape(1-(output==max_ind+1), mask.shape)) #maskObjN = 255-maskObj #edgeSum = np.sum(maskObjN[:,0]) + np.sum(maskObjN[:,-1]) + np.sum(maskObjN[0,:]) + np.sum(maskObjN[-1,:]) #if (edgeSum == 0): Image.fromarray(maskObj).save(maskname) ##mask.putpalette(colors) #plt.subplot(121) #plt.imshow(image) #plt.subplot(122) #plt.imshow(maskObj) #plt.show() image_id += 1 #if (image_id > 3): # break category_id += 1 #if (category_id > 3): # break print("saving annotations to coco as json ") ### create COCO JSON annotations coco = {} coco["info"] = COCO_INFO coco["licenses"] = COCO_LICENSES coco["images"] = images coco["categories"] = categories coco["annotations"] = annotations # TODO: specify coco file locaiton output_file_path = os.path.join(data_dir,"../", "coco_instances.json") with open(output_file_path, 'w+') as json_file: json_file.write(json.dumps(coco)) print(">> complete. find coco json here: ", output_file_path) print("last annotation id: ", annotation_id) print("last image_id: ", image_id) #from pycocotools.coco import COCO ## Initialize the COCO api for instance annotations #coco = COCO(output_file_path) ## Load the categories in a variable #imgIds = coco.getImgIds() #print("Number of images:", len(imgIds)) ## load and display a random image #for i in range(len(imgIds)): # img = coco.loadImgs(imgIds[i])[0] # I = Image.open(data_dir + "/" + img['file_name']) # plt.clf() # plt.imshow(I) # plt.axis('off') # annIds = coco.getAnnIds(imgIds=img['id']) # anns = coco.loadAnns(annIds) # coco.showAnns(anns, True) # plt.waitforbuttonpress() if __name__ == "__main__": data_dir = "E:/Research/Images/FineGrained/StanfordCars/train_bing/"

0

7,808

0

22

293

1abc147f5b65fc34db7ff312e43a5af4e6f6fb0a

21,660

py

Python

analysis/graveyard/study_definition.py

opensafely/antibody-and-antiviral-deployment

27cd171870fdd161468d1cabd1eaee76f1943593

[ "MIT" ]

null

analysis/graveyard/study_definition.py

opensafely/antibody-and-antiviral-deployment

27cd171870fdd161468d1cabd1eaee76f1943593

[ "MIT" ]

1

2022-03-18T16:20:19.000Z

analysis/graveyard/study_definition.py

opensafely/antibody-and-antiviral-deployment

27cd171870fdd161468d1cabd1eaee76f1943593

[ "MIT" ]

null

################################################################################ # # Description: This script provides the formal specification of the study data # that will be extracted from the OpenSAFELY database. # # Output: output/data/input_*.csv.gz # # Author(s): M Green (edited by H Curtis) # Date last updated: 03/02/2022 # ################################################################################ # IMPORT STATEMENTS ---- ## Import code building blocks from cohort extractor package from cohortextractor import (StudyDefinition, patients, combine_codelists) ## Import codelists from codelist.py (which pulls them from the codelist folder) # DEFINE STUDY POPULATION ---- ## Define study time variables from datetime import date campaign_start = "2021-12-16" end_date = date.today().isoformat() ## Define study population and variables study = StudyDefinition( # PRELIMINARIES ---- ## Configure the expectations framework default_expectations = { "date": {"earliest": "2021-11-01", "latest": "today"}, "rate": "uniform", "incidence": 0.4, }, ## Define index date index_date = campaign_start, # POPULATION ---- population = patients.satisfying( """ (registered_eligible OR registered_treated) AND NOT has_died AND (sotrovimab_covid_therapeutics OR molnupiravir_covid_therapeutics OR casirivimab_covid_therapeutics OR covid_test_positive ) """, has_died = patients.died_from_any_cause( on_or_before = "index_date - 1 day", returning = "binary_flag", ), ), # TREATMENT - NEUTRALISING MONOCLONAL ANTIBODIES OR ANTIVIRALS ---- ## Sotrovimab sotrovimab_covid_therapeutics = patients.with_covid_therapeutics( #with_these_statuses = ["Approved", "Treatment Complete"], with_these_therapeutics = "Sotrovimab", with_these_indications = "non_hospitalised", on_or_after = "index_date", find_first_match_in_period = True, returning = "date", date_format = "YYYY-MM-DD", return_expectations = { "date": {"earliest": "2021-12-20"}, "incidence": 0.4 }, ), ### Molnupiravir molnupiravir_covid_therapeutics = patients.with_covid_therapeutics( #with_these_statuses = ["Approved", "Treatment Complete"], with_these_therapeutics = "Molnupiravir", with_these_indications = "non_hospitalised", on_or_after = "index_date", find_first_match_in_period = True, returning = "date", date_format = "YYYY-MM-DD", return_expectations = { "date": {"earliest": "2021-12-20"}, "incidence": 0.4 }, ), ### Casirivimab and imdevimab casirivimab_covid_therapeutics = patients.with_covid_therapeutics( #with_these_statuses = ["Approved", "Treatment Complete"], with_these_therapeutics = "Casirivimab and imdevimab", with_these_indications = "non_hospitalised", on_or_after = "index_date", find_first_match_in_period = True, returning = "date", date_format = "YYYY-MM-DD", return_expectations = { "date": {"earliest": "2021-12-20"}, "incidence": 0.4 }, ), date_treated = patients.minimum_of( "sotrovimab_covid_therapeutics", "molnupiravir_covid_therapeutics", "casirivimab_covid_therapeutics", ), # ELIGIBILITY CRITERIA VARIABLES ---- ## Inclusion criteria variables ### SARS-CoV-2 test # Note patients are eligible for treatment if diagnosed <=5d ago # in the latest 5 days there may be patients identified as eligible who have not yet been treated covid_test_positive = patients.with_test_result_in_sgss( pathogen = "SARS-CoV-2", test_result = "positive", returning = "binary_flag", on_or_after = "index_date - 5 days", find_first_match_in_period = True, restrict_to_earliest_specimen_date = False, return_expectations = { "incidence": 0.2 }, ), covid_test_date = patients.with_test_result_in_sgss( pathogen = "SARS-CoV-2", test_result = "positive", find_first_match_in_period = True, restrict_to_earliest_specimen_date = False, returning = "date", date_format = "YYYY-MM-DD", on_or_after = "index_date - 5 days", return_expectations = { "date": {"earliest": "2021-12-20", "latest": "index_date"}, "incidence": 0.9 }, ), covid_positive_test_type = patients.with_test_result_in_sgss( pathogen = "SARS-CoV-2", test_result = "positive", returning = "case_category", on_or_after = "index_date - 5 days", restrict_to_earliest_specimen_date = True, return_expectations = { "category": {"ratios": {"LFT_Only": 0.4, "PCR_Only": 0.4, "LFT_WithPCR": 0.2}}, "incidence": 0.2, }, ), covid_positive_previous_30_days = patients.with_test_result_in_sgss( pathogen = "SARS-CoV-2", test_result = "positive", returning = "binary_flag", between = ["covid_test_date - 31 days", "covid_test_date - 1 day"], find_last_match_in_period = True, restrict_to_earliest_specimen_date = False, return_expectations = { "incidence": 0.05 }, ), ### Onset of symptoms of COVID-19 symptomatic_covid_test = patients.with_test_result_in_sgss( pathogen = "SARS-CoV-2", test_result = "any", returning = "symptomatic", on_or_after = "index_date - 5 days", find_first_match_in_period = True, restrict_to_earliest_specimen_date = False, return_expectations={ "incidence": 0.1, "category": { "ratios": { "": 0.2, "N": 0.2, "Y": 0.6, } }, }, ), covid_symptoms_snomed = patients.with_these_clinical_events( covid_symptoms_snomed_codes, returning = "date", date_format = "YYYY-MM-DD", find_first_match_in_period = True, on_or_after = "index_date - 5 days", ), # CENSORING ---- registered_eligible = patients.registered_as_of("covid_test_date"), registered_treated = patients.registered_as_of("date_treated"), ## Death of any cause death_date = patients.died_from_any_cause( returning = "date_of_death", date_format = "YYYY-MM-DD", on_or_after = "covid_test_date", return_expectations = { "date": {"earliest": "2021-12-20", "latest": "index_date"}, "incidence": 0.1 }, ), ## De-registration dereg_date = patients.date_deregistered_from_all_supported_practices( on_or_after = "covid_test_date", date_format = "YYYY-MM-DD", return_expectations = { "date": {"earliest": "2021-12-20", "latest": "index_date"}, "incidence": 0.1 }, ), ### Blueteq high risk cohort high_risk_cohort_covid_therapeutics = patients.with_covid_therapeutics( with_these_statuses = ["Approved", "Treatment Complete"], with_these_therapeutics = ["Sotrovimab", "Molnupiravir","Casirivimab and imdevimab"], with_these_indications = "non_hospitalised", on_or_after = "index_date", find_first_match_in_period = True, returning = "risk_group", date_format = "YYYY-MM-DD", return_expectations = { "rate": "universal", "category": { "ratios": { "Down's syndrome": 0.1, "Sickle cell disease": 0.1, "solid cancer": 0.1, "haematological diseases, stem cell transplant recipients": 0.1, "renal disease": 0.1, "liver disease": 0.1, "immune-mediated inflammatory disorders (IMID)": 0.2, "Primary immune deficiencies": 0.1, "HIV/AIDS": 0.1,},}, }, ), ### NHSD high risk cohort (codelist to be defined if/when data avaliable) # high_risk_cohort_nhsd = patients.with_these_clinical_events( # high_risk_cohort_nhsd_codes, # between = [campaign_start, index_date], # returning = "date", # date_format = "YYYY-MM-DD", # find_first_match_in_period = True, # ), ## Exclusion criteria ### Pattern of clinical presentation indicates that there is recovery rather than risk of deterioration from infection # (not currently possible to define/code) ### Require hospitalisation for COVID-19 ## NB this data lags behind the therapeutics/testing data so may be missing covid_hospital_admission_date = patients.admitted_to_hospital( returning = "date_admitted", with_these_diagnoses = covid_icd10_codes, on_or_after = "index_date - 5 days", date_format = "YYYY-MM-DD", find_first_match_in_period = True, return_expectations = { "date": {"earliest": "index_date - 5 days", "latest": "index_date"}, "rate": "uniform", "incidence": 0.05 }, ), ### New supplemental oxygen requirement specifically for the management of COVID-19 symptoms # (not currently possible to define/code) ### Children weighing less than 40kg # (not currently possible to define/code) ### Children aged under 12 years age = patients.age_as_of( "index_date", return_expectations = { "rate": "universal", "int": {"distribution": "population_ages"}, "incidence" : 0.9 }, ), ### Known hypersensitivity reaction to the active substances or to any of the excipients of sotrovimab # (not currently possible to define/code) # HIGH RISK GROUPS ---- ## Down's syndrome downs_syndrome_nhsd_snomed = patients.with_these_clinical_events( downs_syndrome_nhsd_snomed_codes, returning = "date", date_format = "YYYY-MM-DD", find_first_match_in_period = True, ), downs_syndrome_nhsd_icd10 = patients.admitted_to_hospital( returning = "date_admitted", with_these_diagnoses = downs_syndrome_nhsd_icd10_codes, find_first_match_in_period = True, date_format = "YYYY-MM-DD", ), downs_syndrome_nhsd = patients.minimum_of("downs_syndrome_nhsd_snomed", "downs_syndrome_nhsd_icd10"), ## Sickle cell disease sickle_cell_disease_nhsd_snomed = patients.with_these_clinical_events( sickle_cell_disease_nhsd_snomed_codes, returning = "date", date_format = "YYYY-MM-DD", find_first_match_in_period = True, ), sickle_cell_disease_nhsd_icd10 = patients.admitted_to_hospital( returning = "date_admitted", with_these_diagnoses = sickle_cell_disease_nhsd_icd10_codes, find_first_match_in_period = True, date_format = "YYYY-MM-DD", ), sickle_cell_disease_nhsd = patients.minimum_of("sickle_cell_disease_nhsd_snomed", "sickle_cell_disease_nhsd_icd10"), ## Solid cancer cancer_opensafely_snomed = patients.with_these_clinical_events( combine_codelists( non_haematological_cancer_opensafely_snomed_codes, lung_cancer_opensafely_snomed_codes, chemotherapy_radiotherapy_opensafely_snomed_codes ), returning = "date", date_format = "YYYY-MM-DD", find_first_match_in_period = True, ), ## Haematological diseases haematopoietic_stem_cell_transplant_nhsd_snomed = patients.with_these_clinical_events( haematopoietic_stem_cell_transplant_nhsd_snomed_codes, returning = "date", date_format = "YYYY-MM-DD", find_first_match_in_period = True, ), haematopoietic_stem_cell_transplant_nhsd_icd10 = patients.admitted_to_hospital( returning = "date_admitted", with_these_diagnoses = haematopoietic_stem_cell_transplant_nhsd_icd10_codes, find_first_match_in_period = True, date_format = "YYYY-MM-DD", ), haematopoietic_stem_cell_transplant_nhsd_opcs4 = patients.admitted_to_hospital( returning = "date_admitted", with_these_procedures = haematopoietic_stem_cell_transplant_nhsd_opcs4_codes, date_format = "YYYY-MM-DD", find_first_match_in_period = True, return_expectations = { "date": {"earliest": "2020-02-01"}, "rate": "exponential_increase", "incidence": 0.01, }, ), # haematological_malignancies_nhsd_snomed = patients.with_these_clinical_events( # haematological_malignancies_nhsd_snomed_codes, # returning = "date", # date_format = "YYYY-MM-DD", # find_first_match_in_period = True, # #on_or_before = "end_date", # ), haematological_malignancies_nhsd_icd10 = patients.admitted_to_hospital( returning = "date_admitted", with_these_diagnoses = haematological_malignancies_nhsd_icd10_codes, find_first_match_in_period = True, date_format = "YYYY-MM-DD", ), haematological_disease_nhsd = patients.minimum_of("haematopoietic_stem_cell_transplant_nhsd_snomed", "haematopoietic_stem_cell_transplant_nhsd_icd10", "haematopoietic_stem_cell_transplant_nhsd_opcs4", #"haematological_malignancies_nhsd_snomed", "haematological_malignancies_nhsd_icd10"), ## Renal disease ckd_stage_5_nhsd_snomed = patients.with_these_clinical_events( ckd_stage_5_nhsd_snomed_codes, returning = "date", date_format = "YYYY-MM-DD", find_first_match_in_period = True, ), ckd_stage_5_nhsd_icd10 = patients.admitted_to_hospital( returning = "date_admitted", with_these_diagnoses = ckd_stage_5_nhsd_icd10_codes, find_first_match_in_period = True, date_format = "YYYY-MM-DD", ), ckd_stage_5_nhsd = patients.minimum_of("ckd_stage_5_nhsd_snomed", "ckd_stage_5_nhsd_icd10"), ## Liver disease liver_disease_nhsd_snomed = patients.with_these_clinical_events( ckd_stage_5_nhsd_snomed_codes, returning = "date", date_format = "YYYY-MM-DD", find_first_match_in_period = True, ), liver_disease_nhsd_icd10 = patients.admitted_to_hospital( returning = "date_admitted", with_these_diagnoses = ckd_stage_5_nhsd_icd10_codes, find_first_match_in_period = True, date_format = "YYYY-MM-DD", ), liver_disease_nhsd = patients.minimum_of("liver_disease_nhsd_snomed", "liver_disease_nhsd_icd10"), ## Immune-mediated inflammatory disorders (IMID) imid_nhsd = patients.with_these_clinical_events( codelist = combine_codelists(immunosuppresant_drugs_dmd_codes, immunosuppresant_drugs_snomed_codes, oral_steroid_drugs_dmd_codes, oral_steroid_drugs_snomed_codes), returning = "date", find_last_match_in_period = True, date_format = "YYYY-MM-DD", ), ## Primary immune deficiencies immunosupression_nhsd = patients.with_these_clinical_events( immunosupression_nhsd_codes, returning = "date", find_last_match_in_period = True, date_format = "YYYY-MM-DD", ), ## HIV/AIDs hiv_aids_nhsd_snomed = patients.with_these_clinical_events( hiv_aids_nhsd_snomed_codes, returning = "date", date_format = "YYYY-MM-DD", find_first_match_in_period = True, ), hiv_aids_nhsd_icd10 = patients.admitted_to_hospital( returning = "date_admitted", with_these_diagnoses = hiv_aids_nhsd_icd10_codes, find_first_match_in_period = True, date_format = "YYYY-MM-DD", ), hiv_aids_nhsd = patients.minimum_of("hiv_aids_nhsd_snomed", "hiv_aids_nhsd_icd10"), ## Solid organ transplant solid_organ_transplant_nhsd_snomed = patients.with_these_clinical_events( solid_organ_transplant_nhsd_snomed_codes, returning = "date", date_format = "YYYY-MM-DD", find_first_match_in_period = True, ), solid_organ_transplant_nhsd_opcs4 = patients.admitted_to_hospital( returning = "date_admitted", with_these_procedures = solid_organ_transplant_nhsd_opcs4_codes, date_format = "YYYY-MM-DD", find_first_match_in_period = True, return_expectations = { "date": {"earliest": "2020-02-01"}, "rate": "exponential_increase", "incidence": 0.01, }, ), solid_organ_transplant_nhsd = patients.minimum_of("solid_organ_transplant_nhsd_snomed", "solid_organ_transplant_nhsd_opcs4"), ## Rare neurological conditions ### Multiple sclerosis multiple_sclerosis_nhsd_snomed = patients.with_these_clinical_events( multiple_sclerosis_nhsd_snomed_codes, returning = "date", date_format = "YYYY-MM-DD", find_first_match_in_period = True, ), multiple_sclerosis_nhsd_icd10 = patients.admitted_to_hospital( returning = "date_admitted", with_these_diagnoses = multiple_sclerosis_nhsd_icd10_codes, find_first_match_in_period = True, date_format = "YYYY-MM-DD", ), multiple_sclerosis_nhsd = patients.minimum_of("multiple_sclerosis_nhsd_snomed", "multiple_sclerosis_nhsd_icd10"), ### Motor neurone disease motor_neurone_disease_nhsd_snomed = patients.with_these_clinical_events( motor_neurone_disease_nhsd_snomed_codes, returning = "date", date_format = "YYYY-MM-DD", find_first_match_in_period = True, ), motor_neurone_disease_nhsd_icd10 = patients.admitted_to_hospital( returning = "date_admitted", with_these_diagnoses = motor_neurone_disease_nhsd_icd10_codes, find_first_match_in_period = True, date_format = "YYYY-MM-DD", ), motor_neurone_disease_nhsd = patients.minimum_of("motor_neurone_disease_nhsd_snomed", "motor_neurone_disease_nhsd_icd10"), ### Myasthenia gravis myasthenia_gravis_nhsd_snomed = patients.with_these_clinical_events( myasthenia_gravis_nhsd_snomed_codes, returning = "date", date_format = "YYYY-MM-DD", find_first_match_in_period = True, ), myasthenia_gravis_nhsd_icd10 = patients.admitted_to_hospital( returning = "date_admitted", with_these_diagnoses = myasthenia_gravis_nhsd_icd10_codes, find_first_match_in_period = True, date_format = "YYYY-MM-DD", ), myasthenia_gravis_nhsd = patients.minimum_of("myasthenia_gravis_nhsd_snomed", "myasthenia_gravis_nhsd_icd10"), ### Huntingtons disease huntingtons_disease_nhsd_snomed = patients.with_these_clinical_events( huntingtons_disease_nhsd_snomed_codes, returning = "date", date_format = "YYYY-MM-DD", find_first_match_in_period = True, ), huntingtons_disease_nhsd_icd10 = patients.admitted_to_hospital( returning = "date_admitted", with_these_diagnoses = huntingtons_disease_nhsd_icd10_codes, find_first_match_in_period = True, date_format = "YYYY-MM-DD", ), huntingtons_disease_nhsd = patients.minimum_of("huntingtons_disease_nhsd_snomed", "huntingtons_disease_nhsd_icd10"), # CLINICAL/DEMOGRAPHIC COVARIATES ---- ## Sex sex = patients.sex( return_expectations = { "rate": "universal", "category": {"ratios": {"M": 0.49, "F": 0.51}}, } ), ## Ethnicity ethnicity_primis = patients.with_these_clinical_events( ethnicity_primis_codes, returning = "category", find_last_match_in_period = True, include_date_of_match = False, return_expectations = { "category": {"ratios": {"1": 0.2, "2": 0.2, "3": 0.2, "4": 0.2, "5": 0.2}}, "incidence": 0.75, }, ), ethnicity_sus = patients.with_ethnicity_from_sus( returning = "group_6", use_most_frequent_code = True, return_expectations = { "category": {"ratios": {"1": 0.2, "2": 0.2, "3": 0.2, "4": 0.2, "5": 0.2}}, "incidence": 0.8, }, ), ## Index of multiple deprivation imd = patients.categorised_as( {"0": "DEFAULT", "1": """index_of_multiple_deprivation >=1 AND index_of_multiple_deprivation < 32844*1/5""", "2": """index_of_multiple_deprivation >= 32844*1/5 AND index_of_multiple_deprivation < 32844*2/5""", "3": """index_of_multiple_deprivation >= 32844*2/5 AND index_of_multiple_deprivation < 32844*3/5""", "4": """index_of_multiple_deprivation >= 32844*3/5 AND index_of_multiple_deprivation < 32844*4/5""", "5": """index_of_multiple_deprivation >= 32844*4/5 """, }, index_of_multiple_deprivation = patients.address_as_of( "index_date", returning = "index_of_multiple_deprivation", round_to_nearest = 100, ), return_expectations = { "rate": "universal", "category": { "ratios": { "0": 0.01, "1": 0.20, "2": 0.20, "3": 0.20, "4": 0.20, "5": 0.19, }}, }, ), ## Region - NHS England 9 regions region_nhs = patients.registered_practice_as_of( "index_date", returning = "nuts1_region_name", return_expectations = { "rate": "universal", "category": { "ratios": { "North East": 0.1, "North West": 0.1, "Yorkshire and The Humber": 0.1, "East Midlands": 0.1, "West Midlands": 0.1, "East": 0.1, "London": 0.2, "South West": 0.1, "South East": 0.1,},}, }, ), region_covid_therapeutics = patients.with_covid_therapeutics( #with_these_statuses = ["Approved", "Treatment Complete"], with_these_therapeutics = ["Sotrovimab", "Molnupiravir", "Casirivimab and imdevimab"], with_these_indications = "non_hospitalised", on_or_after = "index_date", find_first_match_in_period = True, returning = "region", return_expectations = { "rate": "universal", "category": { "ratios": { "North East": 0.1, "North West": 0.1, "Yorkshire and The Humber": 0.1, "East Midlands": 0.1, "West Midlands": 0.1, "East": 0.1, "London": 0.2, "South West": 0.1, "South East": 0.1,},}, }, ), ## CMDUs/ICS )

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################################################################################ # # Description: This script provides the formal specification of the study data # that will be extracted from the OpenSAFELY database. # # Output: output/data/input_*.csv.gz # # Author(s): M Green (edited by H Curtis) # Date last updated: 03/02/2022 # ################################################################################ # IMPORT STATEMENTS ---- ## Import code building blocks from cohort extractor package from cohortextractor import ( StudyDefinition, patients, codelist_from_csv, codelist, filter_codes_by_category, combine_codelists, Measure ) ## Import codelists from codelist.py (which pulls them from the codelist folder) from codelists import * # DEFINE STUDY POPULATION ---- ## Define study time variables from datetime import date campaign_start = "2021-12-16" end_date = date.today().isoformat() ## Define study population and variables study = StudyDefinition( # PRELIMINARIES ---- ## Configure the expectations framework default_expectations = { "date": {"earliest": "2021-11-01", "latest": "today"}, "rate": "uniform", "incidence": 0.4, }, ## Define index date index_date = campaign_start, # POPULATION ---- population = patients.satisfying( """ (registered_eligible OR registered_treated) AND NOT has_died AND (sotrovimab_covid_therapeutics OR molnupiravir_covid_therapeutics OR casirivimab_covid_therapeutics OR covid_test_positive ) """, has_died = patients.died_from_any_cause( on_or_before = "index_date - 1 day", returning = "binary_flag", ), ), # TREATMENT - NEUTRALISING MONOCLONAL ANTIBODIES OR ANTIVIRALS ---- ## Sotrovimab sotrovimab_covid_therapeutics = patients.with_covid_therapeutics( #with_these_statuses = ["Approved", "Treatment Complete"], with_these_therapeutics = "Sotrovimab", with_these_indications = "non_hospitalised", on_or_after = "index_date", find_first_match_in_period = True, returning = "date", date_format = "YYYY-MM-DD", return_expectations = { "date": {"earliest": "2021-12-20"}, "incidence": 0.4 }, ), ### Molnupiravir molnupiravir_covid_therapeutics = patients.with_covid_therapeutics( #with_these_statuses = ["Approved", "Treatment Complete"], with_these_therapeutics = "Molnupiravir", with_these_indications = "non_hospitalised", on_or_after = "index_date", find_first_match_in_period = True, returning = "date", date_format = "YYYY-MM-DD", return_expectations = { "date": {"earliest": "2021-12-20"}, "incidence": 0.4 }, ), ### Casirivimab and imdevimab casirivimab_covid_therapeutics = patients.with_covid_therapeutics( #with_these_statuses = ["Approved", "Treatment Complete"], with_these_therapeutics = "Casirivimab and imdevimab", with_these_indications = "non_hospitalised", on_or_after = "index_date", find_first_match_in_period = True, returning = "date", date_format = "YYYY-MM-DD", return_expectations = { "date": {"earliest": "2021-12-20"}, "incidence": 0.4 }, ), date_treated = patients.minimum_of( "sotrovimab_covid_therapeutics", "molnupiravir_covid_therapeutics", "casirivimab_covid_therapeutics", ), # ELIGIBILITY CRITERIA VARIABLES ---- ## Inclusion criteria variables ### SARS-CoV-2 test # Note patients are eligible for treatment if diagnosed <=5d ago # in the latest 5 days there may be patients identified as eligible who have not yet been treated covid_test_positive = patients.with_test_result_in_sgss( pathogen = "SARS-CoV-2", test_result = "positive", returning = "binary_flag", on_or_after = "index_date - 5 days", find_first_match_in_period = True, restrict_to_earliest_specimen_date = False, return_expectations = { "incidence": 0.2 }, ), covid_test_date = patients.with_test_result_in_sgss( pathogen = "SARS-CoV-2", test_result = "positive", find_first_match_in_period = True, restrict_to_earliest_specimen_date = False, returning = "date", date_format = "YYYY-MM-DD", on_or_after = "index_date - 5 days", return_expectations = { "date": {"earliest": "2021-12-20", "latest": "index_date"}, "incidence": 0.9 }, ), covid_positive_test_type = patients.with_test_result_in_sgss( pathogen = "SARS-CoV-2", test_result = "positive", returning = "case_category", on_or_after = "index_date - 5 days", restrict_to_earliest_specimen_date = True, return_expectations = { "category": {"ratios": {"LFT_Only": 0.4, "PCR_Only": 0.4, "LFT_WithPCR": 0.2}}, "incidence": 0.2, }, ), covid_positive_previous_30_days = patients.with_test_result_in_sgss( pathogen = "SARS-CoV-2", test_result = "positive", returning = "binary_flag", between = ["covid_test_date - 31 days", "covid_test_date - 1 day"], find_last_match_in_period = True, restrict_to_earliest_specimen_date = False, return_expectations = { "incidence": 0.05 }, ), ### Onset of symptoms of COVID-19 symptomatic_covid_test = patients.with_test_result_in_sgss( pathogen = "SARS-CoV-2", test_result = "any", returning = "symptomatic", on_or_after = "index_date - 5 days", find_first_match_in_period = True, restrict_to_earliest_specimen_date = False, return_expectations={ "incidence": 0.1, "category": { "ratios": { "": 0.2, "N": 0.2, "Y": 0.6, } }, }, ), covid_symptoms_snomed = patients.with_these_clinical_events( covid_symptoms_snomed_codes, returning = "date", date_format = "YYYY-MM-DD", find_first_match_in_period = True, on_or_after = "index_date - 5 days", ), # CENSORING ---- registered_eligible = patients.registered_as_of("covid_test_date"), registered_treated = patients.registered_as_of("date_treated"), ## Death of any cause death_date = patients.died_from_any_cause( returning = "date_of_death", date_format = "YYYY-MM-DD", on_or_after = "covid_test_date", return_expectations = { "date": {"earliest": "2021-12-20", "latest": "index_date"}, "incidence": 0.1 }, ), ## De-registration dereg_date = patients.date_deregistered_from_all_supported_practices( on_or_after = "covid_test_date", date_format = "YYYY-MM-DD", return_expectations = { "date": {"earliest": "2021-12-20", "latest": "index_date"}, "incidence": 0.1 }, ), ### Blueteq ‘high risk’ cohort high_risk_cohort_covid_therapeutics = patients.with_covid_therapeutics( with_these_statuses = ["Approved", "Treatment Complete"], with_these_therapeutics = ["Sotrovimab", "Molnupiravir","Casirivimab and imdevimab"], with_these_indications = "non_hospitalised", on_or_after = "index_date", find_first_match_in_period = True, returning = "risk_group", date_format = "YYYY-MM-DD", return_expectations = { "rate": "universal", "category": { "ratios": { "Down's syndrome": 0.1, "Sickle cell disease": 0.1, "solid cancer": 0.1, "haematological diseases, stem cell transplant recipients": 0.1, "renal disease": 0.1, "liver disease": 0.1, "immune-mediated inflammatory disorders (IMID)": 0.2, "Primary immune deficiencies": 0.1, "HIV/AIDS": 0.1,},}, }, ), ### NHSD ‘high risk’ cohort (codelist to be defined if/when data avaliable) # high_risk_cohort_nhsd = patients.with_these_clinical_events( # high_risk_cohort_nhsd_codes, # between = [campaign_start, index_date], # returning = "date", # date_format = "YYYY-MM-DD", # find_first_match_in_period = True, # ), ## Exclusion criteria ### Pattern of clinical presentation indicates that there is recovery rather than risk of deterioration from infection # (not currently possible to define/code) ### Require hospitalisation for COVID-19 ## NB this data lags behind the therapeutics/testing data so may be missing covid_hospital_admission_date = patients.admitted_to_hospital( returning = "date_admitted", with_these_diagnoses = covid_icd10_codes, on_or_after = "index_date - 5 days", date_format = "YYYY-MM-DD", find_first_match_in_period = True, return_expectations = { "date": {"earliest": "index_date - 5 days", "latest": "index_date"}, "rate": "uniform", "incidence": 0.05 }, ), ### New supplemental oxygen requirement specifically for the management of COVID-19 symptoms # (not currently possible to define/code) ### Children weighing less than 40kg # (not currently possible to define/code) ### Children aged under 12 years age = patients.age_as_of( "index_date", return_expectations = { "rate": "universal", "int": {"distribution": "population_ages"}, "incidence" : 0.9 }, ), ### Known hypersensitivity reaction to the active substances or to any of the excipients of sotrovimab # (not currently possible to define/code) # HIGH RISK GROUPS ---- ## Down's syndrome downs_syndrome_nhsd_snomed = patients.with_these_clinical_events( downs_syndrome_nhsd_snomed_codes, returning = "date", date_format = "YYYY-MM-DD", find_first_match_in_period = True, ), downs_syndrome_nhsd_icd10 = patients.admitted_to_hospital( returning = "date_admitted", with_these_diagnoses = downs_syndrome_nhsd_icd10_codes, find_first_match_in_period = True, date_format = "YYYY-MM-DD", ), downs_syndrome_nhsd = patients.minimum_of("downs_syndrome_nhsd_snomed", "downs_syndrome_nhsd_icd10"), ## Sickle cell disease sickle_cell_disease_nhsd_snomed = patients.with_these_clinical_events( sickle_cell_disease_nhsd_snomed_codes, returning = "date", date_format = "YYYY-MM-DD", find_first_match_in_period = True, ), sickle_cell_disease_nhsd_icd10 = patients.admitted_to_hospital( returning = "date_admitted", with_these_diagnoses = sickle_cell_disease_nhsd_icd10_codes, find_first_match_in_period = True, date_format = "YYYY-MM-DD", ), sickle_cell_disease_nhsd = patients.minimum_of("sickle_cell_disease_nhsd_snomed", "sickle_cell_disease_nhsd_icd10"), ## Solid cancer cancer_opensafely_snomed = patients.with_these_clinical_events( combine_codelists( non_haematological_cancer_opensafely_snomed_codes, lung_cancer_opensafely_snomed_codes, chemotherapy_radiotherapy_opensafely_snomed_codes ), returning = "date", date_format = "YYYY-MM-DD", find_first_match_in_period = True, ), ## Haematological diseases haematopoietic_stem_cell_transplant_nhsd_snomed = patients.with_these_clinical_events( haematopoietic_stem_cell_transplant_nhsd_snomed_codes, returning = "date", date_format = "YYYY-MM-DD", find_first_match_in_period = True, ), haematopoietic_stem_cell_transplant_nhsd_icd10 = patients.admitted_to_hospital( returning = "date_admitted", with_these_diagnoses = haematopoietic_stem_cell_transplant_nhsd_icd10_codes, find_first_match_in_period = True, date_format = "YYYY-MM-DD", ), haematopoietic_stem_cell_transplant_nhsd_opcs4 = patients.admitted_to_hospital( returning = "date_admitted", with_these_procedures = haematopoietic_stem_cell_transplant_nhsd_opcs4_codes, date_format = "YYYY-MM-DD", find_first_match_in_period = True, return_expectations = { "date": {"earliest": "2020-02-01"}, "rate": "exponential_increase", "incidence": 0.01, }, ), # haematological_malignancies_nhsd_snomed = patients.with_these_clinical_events( # haematological_malignancies_nhsd_snomed_codes, # returning = "date", # date_format = "YYYY-MM-DD", # find_first_match_in_period = True, # #on_or_before = "end_date", # ), haematological_malignancies_nhsd_icd10 = patients.admitted_to_hospital( returning = "date_admitted", with_these_diagnoses = haematological_malignancies_nhsd_icd10_codes, find_first_match_in_period = True, date_format = "YYYY-MM-DD", ), haematological_disease_nhsd = patients.minimum_of("haematopoietic_stem_cell_transplant_nhsd_snomed", "haematopoietic_stem_cell_transplant_nhsd_icd10", "haematopoietic_stem_cell_transplant_nhsd_opcs4", #"haematological_malignancies_nhsd_snomed", "haematological_malignancies_nhsd_icd10"), ## Renal disease ckd_stage_5_nhsd_snomed = patients.with_these_clinical_events( ckd_stage_5_nhsd_snomed_codes, returning = "date", date_format = "YYYY-MM-DD", find_first_match_in_period = True, ), ckd_stage_5_nhsd_icd10 = patients.admitted_to_hospital( returning = "date_admitted", with_these_diagnoses = ckd_stage_5_nhsd_icd10_codes, find_first_match_in_period = True, date_format = "YYYY-MM-DD", ), ckd_stage_5_nhsd = patients.minimum_of("ckd_stage_5_nhsd_snomed", "ckd_stage_5_nhsd_icd10"), ## Liver disease liver_disease_nhsd_snomed = patients.with_these_clinical_events( ckd_stage_5_nhsd_snomed_codes, returning = "date", date_format = "YYYY-MM-DD", find_first_match_in_period = True, ), liver_disease_nhsd_icd10 = patients.admitted_to_hospital( returning = "date_admitted", with_these_diagnoses = ckd_stage_5_nhsd_icd10_codes, find_first_match_in_period = True, date_format = "YYYY-MM-DD", ), liver_disease_nhsd = patients.minimum_of("liver_disease_nhsd_snomed", "liver_disease_nhsd_icd10"), ## Immune-mediated inflammatory disorders (IMID) imid_nhsd = patients.with_these_clinical_events( codelist = combine_codelists(immunosuppresant_drugs_dmd_codes, immunosuppresant_drugs_snomed_codes, oral_steroid_drugs_dmd_codes, oral_steroid_drugs_snomed_codes), returning = "date", find_last_match_in_period = True, date_format = "YYYY-MM-DD", ), ## Primary immune deficiencies immunosupression_nhsd = patients.with_these_clinical_events( immunosupression_nhsd_codes, returning = "date", find_last_match_in_period = True, date_format = "YYYY-MM-DD", ), ## HIV/AIDs hiv_aids_nhsd_snomed = patients.with_these_clinical_events( hiv_aids_nhsd_snomed_codes, returning = "date", date_format = "YYYY-MM-DD", find_first_match_in_period = True, ), hiv_aids_nhsd_icd10 = patients.admitted_to_hospital( returning = "date_admitted", with_these_diagnoses = hiv_aids_nhsd_icd10_codes, find_first_match_in_period = True, date_format = "YYYY-MM-DD", ), hiv_aids_nhsd = patients.minimum_of("hiv_aids_nhsd_snomed", "hiv_aids_nhsd_icd10"), ## Solid organ transplant solid_organ_transplant_nhsd_snomed = patients.with_these_clinical_events( solid_organ_transplant_nhsd_snomed_codes, returning = "date", date_format = "YYYY-MM-DD", find_first_match_in_period = True, ), solid_organ_transplant_nhsd_opcs4 = patients.admitted_to_hospital( returning = "date_admitted", with_these_procedures = solid_organ_transplant_nhsd_opcs4_codes, date_format = "YYYY-MM-DD", find_first_match_in_period = True, return_expectations = { "date": {"earliest": "2020-02-01"}, "rate": "exponential_increase", "incidence": 0.01, }, ), solid_organ_transplant_nhsd = patients.minimum_of("solid_organ_transplant_nhsd_snomed", "solid_organ_transplant_nhsd_opcs4"), ## Rare neurological conditions ### Multiple sclerosis multiple_sclerosis_nhsd_snomed = patients.with_these_clinical_events( multiple_sclerosis_nhsd_snomed_codes, returning = "date", date_format = "YYYY-MM-DD", find_first_match_in_period = True, ), multiple_sclerosis_nhsd_icd10 = patients.admitted_to_hospital( returning = "date_admitted", with_these_diagnoses = multiple_sclerosis_nhsd_icd10_codes, find_first_match_in_period = True, date_format = "YYYY-MM-DD", ), multiple_sclerosis_nhsd = patients.minimum_of("multiple_sclerosis_nhsd_snomed", "multiple_sclerosis_nhsd_icd10"), ### Motor neurone disease motor_neurone_disease_nhsd_snomed = patients.with_these_clinical_events( motor_neurone_disease_nhsd_snomed_codes, returning = "date", date_format = "YYYY-MM-DD", find_first_match_in_period = True, ), motor_neurone_disease_nhsd_icd10 = patients.admitted_to_hospital( returning = "date_admitted", with_these_diagnoses = motor_neurone_disease_nhsd_icd10_codes, find_first_match_in_period = True, date_format = "YYYY-MM-DD", ), motor_neurone_disease_nhsd = patients.minimum_of("motor_neurone_disease_nhsd_snomed", "motor_neurone_disease_nhsd_icd10"), ### Myasthenia gravis myasthenia_gravis_nhsd_snomed = patients.with_these_clinical_events( myasthenia_gravis_nhsd_snomed_codes, returning = "date", date_format = "YYYY-MM-DD", find_first_match_in_period = True, ), myasthenia_gravis_nhsd_icd10 = patients.admitted_to_hospital( returning = "date_admitted", with_these_diagnoses = myasthenia_gravis_nhsd_icd10_codes, find_first_match_in_period = True, date_format = "YYYY-MM-DD", ), myasthenia_gravis_nhsd = patients.minimum_of("myasthenia_gravis_nhsd_snomed", "myasthenia_gravis_nhsd_icd10"), ### Huntington’s disease huntingtons_disease_nhsd_snomed = patients.with_these_clinical_events( huntingtons_disease_nhsd_snomed_codes, returning = "date", date_format = "YYYY-MM-DD", find_first_match_in_period = True, ), huntingtons_disease_nhsd_icd10 = patients.admitted_to_hospital( returning = "date_admitted", with_these_diagnoses = huntingtons_disease_nhsd_icd10_codes, find_first_match_in_period = True, date_format = "YYYY-MM-DD", ), huntingtons_disease_nhsd = patients.minimum_of("huntingtons_disease_nhsd_snomed", "huntingtons_disease_nhsd_icd10"), # CLINICAL/DEMOGRAPHIC COVARIATES ---- ## Sex sex = patients.sex( return_expectations = { "rate": "universal", "category": {"ratios": {"M": 0.49, "F": 0.51}}, } ), ## Ethnicity ethnicity_primis = patients.with_these_clinical_events( ethnicity_primis_codes, returning = "category", find_last_match_in_period = True, include_date_of_match = False, return_expectations = { "category": {"ratios": {"1": 0.2, "2": 0.2, "3": 0.2, "4": 0.2, "5": 0.2}}, "incidence": 0.75, }, ), ethnicity_sus = patients.with_ethnicity_from_sus( returning = "group_6", use_most_frequent_code = True, return_expectations = { "category": {"ratios": {"1": 0.2, "2": 0.2, "3": 0.2, "4": 0.2, "5": 0.2}}, "incidence": 0.8, }, ), ## Index of multiple deprivation imd = patients.categorised_as( {"0": "DEFAULT", "1": """index_of_multiple_deprivation >=1 AND index_of_multiple_deprivation < 32844*1/5""", "2": """index_of_multiple_deprivation >= 32844*1/5 AND index_of_multiple_deprivation < 32844*2/5""", "3": """index_of_multiple_deprivation >= 32844*2/5 AND index_of_multiple_deprivation < 32844*3/5""", "4": """index_of_multiple_deprivation >= 32844*3/5 AND index_of_multiple_deprivation < 32844*4/5""", "5": """index_of_multiple_deprivation >= 32844*4/5 """, }, index_of_multiple_deprivation = patients.address_as_of( "index_date", returning = "index_of_multiple_deprivation", round_to_nearest = 100, ), return_expectations = { "rate": "universal", "category": { "ratios": { "0": 0.01, "1": 0.20, "2": 0.20, "3": 0.20, "4": 0.20, "5": 0.19, }}, }, ), ## Region - NHS England 9 regions region_nhs = patients.registered_practice_as_of( "index_date", returning = "nuts1_region_name", return_expectations = { "rate": "universal", "category": { "ratios": { "North East": 0.1, "North West": 0.1, "Yorkshire and The Humber": 0.1, "East Midlands": 0.1, "West Midlands": 0.1, "East": 0.1, "London": 0.2, "South West": 0.1, "South East": 0.1,},}, }, ), region_covid_therapeutics = patients.with_covid_therapeutics( #with_these_statuses = ["Approved", "Treatment Complete"], with_these_therapeutics = ["Sotrovimab", "Molnupiravir", "Casirivimab and imdevimab"], with_these_indications = "non_hospitalised", on_or_after = "index_date", find_first_match_in_period = True, returning = "region", return_expectations = { "rate": "universal", "category": { "ratios": { "North East": 0.1, "North West": 0.1, "Yorkshire and The Humber": 0.1, "East Midlands": 0.1, "West Midlands": 0.1, "East": 0.1, "London": 0.2, "South West": 0.1, "South East": 0.1,},}, }, ), ## CMDUs/ICS )

15

0

82

22

9c633934769dee6380c21948f3259c49e26608fa

5,146

py

Python

records_mover/db/bigquery/unloader.py

cwegrzyn/records-mover

e3b71d6c09d99d0bcd6a956b9d09d20f8abe98d2

[ "Apache-2.0" ]

36

2020-03-17T11:56:51.000Z

2022-01-19T16:03:32.000Z

records_mover/db/bigquery/unloader.py

cwegrzyn/records-mover

e3b71d6c09d99d0bcd6a956b9d09d20f8abe98d2

[ "Apache-2.0" ]

60

2020-03-02T23:13:29.000Z

2021-05-19T15:05:42.000Z

records_mover/db/bigquery/unloader.py

cwegrzyn/records-mover

e3b71d6c09d99d0bcd6a956b9d09d20f8abe98d2

[ "Apache-2.0" ]

4

2020-08-11T13:17:37.000Z

2021-11-05T21:11:52.000Z

import logging logger = logging.getLogger(__name__)

45.539823

115

0.666148

import sqlalchemy from contextlib import contextmanager from typing import List, Iterator, Optional, Union, Tuple import logging from google.cloud.bigquery.dbapi.connection import Connection from google.cloud.bigquery.client import Client from google.cloud.bigquery.job import ExtractJobConfig from records_mover.db.unloader import Unloader from records_mover.records.records_format import BaseRecordsFormat, AvroRecordsFormat from records_mover.url.base import BaseDirectoryUrl from records_mover.url.resolver import UrlResolver from records_mover.records.unload_plan import RecordsUnloadPlan from records_mover.records.records_directory import RecordsDirectory from records_mover.db.errors import NoTemporaryBucketConfiguration logger = logging.getLogger(__name__) class BigQueryUnloader(Unloader): def __init__(self, db: Union[sqlalchemy.engine.Connection, sqlalchemy.engine.Engine], url_resolver: UrlResolver, gcs_temp_base_loc: Optional[BaseDirectoryUrl])\ -> None: self.db = db self.url_resolver = url_resolver self.gcs_temp_base_loc = gcs_temp_base_loc super().__init__(db=db) def can_unload_format(self, target_records_format: BaseRecordsFormat) -> bool: if isinstance(target_records_format, AvroRecordsFormat): return True return False def can_unload_to_scheme(self, scheme: str) -> bool: if scheme == 'gs': return True # Otherwise we'll need a temporary bucket configured for # BigQuery to unload into return self.gcs_temp_base_loc is not None def known_supported_records_formats_for_unload(self) -> List[BaseRecordsFormat]: return [AvroRecordsFormat()] @contextmanager def temporary_unloadable_directory_loc(self) -> Iterator[BaseDirectoryUrl]: if self.gcs_temp_base_loc is None: raise NoTemporaryBucketConfiguration('Please provide a scratch GCS URL in your config ' '(e.g., set SCRATCH_GCS_URL to a gs:// URL)') else: with self.gcs_temp_base_loc.temporary_directory() as temp_loc: yield temp_loc def _parse_bigquery_schema_name(self, schema: str) -> Tuple[Optional[str], str]: # https://github.com/mxmzdlv/pybigquery/blob/master/pybigquery/sqlalchemy_bigquery.py#L320 dataset = None project = None schema_split = schema.split('.') if len(schema_split) == 1: dataset, = schema_split elif len(schema_split) == 2: project, dataset = schema_split else: raise ValueError(f"Could not understand schema name {schema}") return (project, dataset) def _extract_job_config(self, unload_plan: RecordsUnloadPlan) -> ExtractJobConfig: config = ExtractJobConfig() if isinstance(unload_plan.records_format, AvroRecordsFormat): config.destination_format = 'AVRO' # https://cloud.google.com/bigquery/docs/loading-data-cloud-storage-avro#logical_types config.use_avro_logical_types = True else: raise NotImplementedError(f'Please add support for {unload_plan.records_format}') return config def unload(self, schema: str, table: str, unload_plan: RecordsUnloadPlan, directory: RecordsDirectory) -> Optional[int]: if directory.scheme != 'gs': with self.temporary_unloadable_directory_loc() as temp_gcs_loc: temp_directory = RecordsDirectory(temp_gcs_loc) out = self.unload(schema=schema, table=table, unload_plan=unload_plan, directory=temp_directory) temp_directory.copy_to(directory.loc) return out logger.info("Loading from records directory into BigQuery") # https://googleapis.github.io/google-cloud-python/latest/bigquery/usage/tables.html#creating-a-table connection: Connection =\ self.db.engine.raw_connection().connection # https://google-cloud.readthedocs.io/en/latest/bigquery/generated/google.cloud.bigquery.client.Client.html client: Client = connection._client project_id, dataset_id = self._parse_bigquery_schema_name(schema) job_config = self._extract_job_config(unload_plan) records_format = unload_plan.records_format filename = records_format.generate_filename('output') destination_uri = directory.loc.file_in_this_directory(filename) job = client.extract_table(f"{schema}.{table}", destination_uri.url, # Must match the destination dataset location. job_config=job_config) job.result() # Waits for table load to complete. logger.info(f"Unloaded from {dataset_id}:{table} into {filename}") directory.save_preliminary_manifest() return None

0

432

0

3,922

0

429

309

82eca7e21b92148d602ade08730e4aef0f573478

1,219

py

Python

depth_completion/config/resnet18_Baseline_config.py

tsunghan-mama/Depth-Completion

d73328d1d704470a6fd3859e2e1810bc311b1dc3

[ "MIT" ]

67

2020-07-11T09:44:10.000Z

2022-03-30T07:38:46.000Z

depth_completion/config/resnet18_Baseline_config.py

tsunghan-mama/Depth-Completion

d73328d1d704470a6fd3859e2e1810bc311b1dc3

[ "MIT" ]

8

2020-07-14T05:50:03.000Z

2022-01-19T09:07:46.000Z

depth_completion/config/resnet18_Baseline_config.py

patrickwu2/Depth-Completion

e9c52e2cb2dce558d6787e246bbc51c1670c16ca

[ "MIT" ]

9

2019-10-12T01:09:51.000Z

2020-05-26T21:35:28.000Z

common_config = { } train_config = { "dataset_name": "matterport", "model_name": "ResNet18SkipConnection", "in_channel": 9, "device_ids": [0], "seed": 7122, "num_workers": 8, "mode": "train", "train_path": "/tmp2/tsunghan/new_matterport/v1", "lr": 1e-4, "batch_size": 8, "loss_func": {('depth(L2)', 'depth_L2_loss', 1.)}, "load_model_path": None, "param_only": False, "validation": True, "valid_path": "/tmp2/tsunghan/new_matterport/v1", "epoches": 100, "save_prefix": "", } test_config = { "dataset_name": "matterport", "model_name": "ResNet18SkipConnection", "in_channel": 9, "device_ids": [0, 1, 2, 3], "seed": 7122, "num_workers": 8, "mode": "test", "test_path": "/tmp2/tsunghan/new_matterport/v1", "lr": 1e-4, "batch_size": 1, "loss_func": {('depth(L2)', 'depth_L2_loss', 1.), ('img_grad', 'img_grad_loss', 1e-3)}, "load_model_path": "/tmp2/tsunghan/twcc_data/twcc_experience_resnet/matterport_ResNet18SkipConnection_b10_lr0.0001_/epoch_13.pt", "param_only": True, "epoches": 100, "save_prefix": "resnet", "output":"/tmp2/tsunghan/experiment_result/mat_npy/r18sc_epo13", }

27.088889

133

0.61854

common_config = { } train_config = { "dataset_name": "matterport", "model_name": "ResNet18SkipConnection", "in_channel": 9, "device_ids": [0], "seed": 7122, "num_workers": 8, "mode": "train", "train_path": "/tmp2/tsunghan/new_matterport/v1", "lr": 1e-4, "batch_size": 8, "loss_func": {('depth(L2)', 'depth_L2_loss', 1.)}, "load_model_path": None, "param_only": False, "validation": True, "valid_path": "/tmp2/tsunghan/new_matterport/v1", "epoches": 100, "save_prefix": "", } test_config = { "dataset_name": "matterport", "model_name": "ResNet18SkipConnection", "in_channel": 9, "device_ids": [0, 1, 2, 3], "seed": 7122, "num_workers": 8, "mode": "test", "test_path": "/tmp2/tsunghan/new_matterport/v1", "lr": 1e-4, "batch_size": 1, "loss_func": {('depth(L2)', 'depth_L2_loss', 1.), ('img_grad', 'img_grad_loss', 1e-3)}, "load_model_path": "/tmp2/tsunghan/twcc_data/twcc_experience_resnet/matterport_ResNet18SkipConnection_b10_lr0.0001_/epoch_13.pt", "param_only": True, "epoches": 100, "save_prefix": "resnet", "output":"/tmp2/tsunghan/experiment_result/mat_npy/r18sc_epo13", }

0

38b4f6b2219146f62a43cb5525a1f50ceb4102df

660

py

Python

scheduler_task/study_apscheduler/examples/demo.py

2581676612/python

b309564a05838b23044bb8112fd4ef71307266b6

[ "MIT" ]

112

2017-09-19T17:38:38.000Z

2020-05-27T18:00:27.000Z

scheduler_task/study_apscheduler/examples/demo.py

tomoncle/Python-notes

ce675486290c3d1c7c2e4890b57e3d0c8a1228cc

[ "MIT" ]

null

scheduler_task/study_apscheduler/examples/demo.py

tomoncle/Python-notes

ce675486290c3d1c7c2e4890b57e3d0c8a1228cc

[ "MIT" ]

56

2017-09-20T01:24:12.000Z

2020-04-16T06:19:31.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # @Time : 17-8-13 11:33 # @Author : Tom.Lee # @CopyRight : 2016-2017 OpenBridge by yihecloud # @File : demo.py # @Product : PyCharm # @Docs : # @Source : import os from apscheduler.schedulers.blocking import BlockingScheduler if __name__ == '__main__': scheduler = BlockingScheduler() scheduler.add_job('sys:stdout.write', 'interval', seconds=3, args=['tick ...\n']) print('Press Ctrl+{0} to exit'.format('Break' if os.name == 'nt' else 'C')) try: scheduler.start() except (KeyboardInterrupt, SystemExit): pass

26.4

85

0.587879

#!/usr/bin/env python # -*- coding: utf-8 -*- # @Time : 17-8-13 上午11:33 # @Author : Tom.Lee # @CopyRight : 2016-2017 OpenBridge by yihecloud # @File : demo.py # @Product : PyCharm # @Docs : # @Source : import os from apscheduler.schedulers.blocking import BlockingScheduler if __name__ == '__main__': scheduler = BlockingScheduler() scheduler.add_job('sys:stdout.write', 'interval', seconds=3, args=['tick ...\n']) print('Press Ctrl+{0} to exit'.format('Break' if os.name == 'nt' else 'C')) try: scheduler.start() except (KeyboardInterrupt, SystemExit): pass

6

0

0fc246feb45369af60c1a8007ad889850bd24825

4,829

py

Python

clearblade/ClearBladeCore.py

sraman0302/ClearBlade-Python-SDK

bde192ef86969c8d1c592f7697ca104bc2362408

[ "Apache-2.0" ]

2

2018-05-10T18:38:04.000Z

2020-12-19T08:14:21.000Z

clearblade/ClearBladeCore.py

sraman0302/ClearBlade-Python-SDK

bde192ef86969c8d1c592f7697ca104bc2362408

[ "Apache-2.0" ]

6

2018-01-13T17:05:51.000Z

2021-09-01T18:25:41.000Z

clearblade/ClearBladeCore.py

sraman0302/ClearBlade-Python-SDK

bde192ef86969c8d1c592f7697ca104bc2362408

[ "Apache-2.0" ]

4

2018-11-08T21:18:08.000Z

2021-05-10T01:07:14.000Z

from __future__ import absolute_import

31.154839

168

0.600745

from __future__ import absolute_import import atexit from . import Users from . import Devices from . import Collections from . import Messaging from . import Code from .Developers import * # allows you to import Developer from ClearBladeCore from . import cbLogs class System: def __exitcode(self): # forces all users to log out on system close. # I did this to prevent possible token reuse # after client code exits, even if they don't # log their users out themselves. while self.users: self.users.pop(0).logout() def __init__(self, systemKey, systemSecret, url="https://platform.clearblade.com", safe=True, sslVerify=True): self.systemKey = systemKey self.systemSecret = systemSecret self.url = url self.users = [] self.collections = [] self.messagingClients = [] self.devices = [] self.sslVerify = sslVerify if not sslVerify: cbLogs.warn("You have disabled SSL verification, this should only be done if your ClearBlade Platform instance is leveraging self signed SSL certificates.") if safe: atexit.register(self.__exitcode) ############# # USERS # ############# def User(self, email, password="", authToken=""): user = Users.User(self, email, password=password, authToken=authToken) if authToken == "": user.authenticate() return user elif user.checkAuth(): return user else: cbLogs.error("Invalid User authToken") exit(-1) def AnonUser(self): anon = Users.AnonUser(self) anon.authenticate() return anon def registerUser(self, authenticatedUser, email, password): n00b = Users.registerUser(self, authenticatedUser, email, password) self.users.append(n00b) return n00b def ServiceUser(self, email, token): user = Users.ServiceUser(self, email, token) if user.checkAuth(): return user else: cbLogs.error("Service User ", email, "failed to Auth") exit(-1) ############### # DEVICES # ############### def getDevices(self, authenticatedUser, query=None): self.devices = Devices.getDevices(self, authenticatedUser, query) return self.devices def getDevice(self, authenticatedUser, name): dev = Devices.getDevice(self, authenticatedUser, name) return dev def Device(self, name, key="", authToken=""): dev = Devices.Device(system=self, name=name, key=key, authToken=authToken) # check if dev in self.devices? return dev ############ # DATA # ############ def Collection(self, authenticatedUser, collectionID="", collectionName=""): if not collectionID and not collectionName: cbLogs.error("beep") exit(-1) col = Collections.Collection(self, authenticatedUser, collectionID, collectionName) self.collections.append(col) return col ############ # MQTT # ############ def Messaging(self, user, port=1883, keepalive=30, url="", client_id="", use_tls=False): msg = Messaging.Messaging(user, port, keepalive, url, client_id=client_id, use_tls=use_tls) self.messagingClients.append(msg) return msg ############ # CODE # ############ def Service(self, name): return Code.Service(self, name) class Query: def __init__(self): self.sorting = [] # only used in fetches. also, not implemented yet. TODO self.filters = [] def Or(self, query): # NOTE: you can't add filters after # you Or two queries together. # This function has to be the last step. q = Query() for filter in self.filters: q.filters.append(filter) for filter in query.filters: q.filters.append(filter) return q def __addFilter(self, column, value, operator): if len(self.filters) == 0: self.filters.append([]) self.filters[0].append({operator: [{column: value}]}) def equalTo(self, column, value): self.__addFilter(column, value, "EQ") def greaterThan(self, column, value): self.__addFilter(column, value, "GT") def lessThan(self, column, value): self.__addFilter(column, value, "LT") def greaterThanEqualTo(self, column, value): self.__addFilter(column, value, "GTE") def lessThanEqualTo(self, column, value): self.__addFilter(column, value, "LTE") def notEqualTo(self, column, value): self.__addFilter(column, value, "NEQ") def matches(self, column, value): self.__addFilter(column, value, "RE")

0

4,516

0

-4

276

78df92a0ac52515a71841949cff2f4cccb3a01f0

698

py

Python

GoogleCodeJam2017/Round0/TidyNumbers/TidyNumbers.py

Jspsun/CompetitiveCoding

a815bbcdab1fb30bd83730a7abd3505bff8bfb78

[ "MIT" ]

null

GoogleCodeJam2017/Round0/TidyNumbers/TidyNumbers.py

Jspsun/CompetitiveCoding

a815bbcdab1fb30bd83730a7abd3505bff8bfb78

[ "MIT" ]

null

GoogleCodeJam2017/Round0/TidyNumbers/TidyNumbers.py

Jspsun/CompetitiveCoding

a815bbcdab1fb30bd83730a7abd3505bff8bfb78

[ "MIT" ]

null

if __name__ == '__main__': __main__()

21.151515

71

0.465616

def __main__(): f = open("in.txt", 'r') o = open("out.txt", 'w') noOfCases = int(f.readline()) for testNo in range(noOfCases): counter = 0 data = f.readline() output = solver(data[:-1]) output = int(output) o.write("Case #" + str(testNo + 1) + ": " + str(output) + "\n") def solver(n): n = list(n) dex = inOrder(n) while dex != -1: n[dex] = str(int(n[dex]) - 1) n = n[:dex + 1] + ['9'] * (len(n) - dex - 1) dex = inOrder(n) return ''.join(n) def inOrder(n): for i in range(len(n) - 1): if n[i] > n[i + 1]: return i return -1 if __name__ == '__main__': __main__()

0

585

0

68

9d9072a0352d441e7a4e2e3e0c976746c5e8f9af

986

py

Python

project_dashboard/projects/crud.py

KruizerChick/project-dashboard

aa1d3fa713e49049ac7184dbe44a1f915ff56906

[ "MIT" ]

null

project_dashboard/projects/crud.py

KruizerChick/project-dashboard

aa1d3fa713e49049ac7184dbe44a1f915ff56906

[ "MIT" ]

null

project_dashboard/projects/crud.py

KruizerChick/project-dashboard

aa1d3fa713e49049ac7184dbe44a1f915ff56906

[ "MIT" ]

null

""" CRUD class for Projects app """

29

62

0.703854

""" CRUD class for Projects app """ from crudbuilder.abstract import BaseCrudBuilder from .models.project import Project from .models.stakeholder import Stakeholder class ProjectCrud(BaseCrudBuilder): """ CRUD class for Project model """ model = Project search_fields = ["id", "name", "description"] tables2_fields = ("name", "description", 'is_closed') tables2_css_class = "table table-bordered table-condensed" login_required = True permission_required = True # tables2_pagination = 20 # default is 10 modelform_excludes = ['created'] # permissions = {} # custom_templates = {} class StakeholderCrud(BaseCrudBuilder): """ CRUD class for Stakeholder model """ model = Stakeholder search_fields = ["full_name", ] tables2_fields = ("full_name", "organization") tables2_css_class = "table table-bordered table-condensed" login_required = True permission_required = True modelform_excludes = ['created']

0

771

0

63

113

db476ed9048fe8a87e8164fd5dd10cfe61c7b0bf

486

py

Python

L1Trigger/L1TMuonOverlap/python/fakeOmtfFwVersion_cff.py

PKUfudawei/cmssw

8fbb5ce74398269c8a32956d7c7943766770c093

[ "Apache-2.0" ]

2

2020-10-26T18:40:32.000Z

2021-04-10T16:33:25.000Z

L1Trigger/L1TMuonOverlap/python/fakeOmtfFwVersion_cff.py

PKUfudawei/cmssw

8fbb5ce74398269c8a32956d7c7943766770c093

[ "Apache-2.0" ]

30

2015-11-04T11:42:27.000Z

2021-12-01T07:56:34.000Z

L1Trigger/L1TMuonOverlap/python/fakeOmtfFwVersion_cff.py

PKUfudawei/cmssw

8fbb5ce74398269c8a32956d7c7943766770c093

[ "Apache-2.0" ]

8

2016-03-25T07:17:43.000Z

2021-07-08T17:11:21.000Z

import FWCore.ParameterSet.Config as cms omtfFwVersionSource = cms.ESSource( "EmptyESSource", recordName = cms.string('L1TMuonOverlapFwVersionRcd'), iovIsRunNotTime = cms.bool(True), firstValid = cms.vuint32(1) ) ###OMTF FW ESProducer. omtfFwVersion = cms.ESProducer( "L1TMuonOverlapFwVersionESProducer", algoVersion = cms.uint32(0x110), layersVersion = cms.uint32(6), patternsVersion = cms.uint32(3), synthDate = cms.string("2001-01-01 00:00") )

25.578947

58

0.716049

import FWCore.ParameterSet.Config as cms omtfFwVersionSource = cms.ESSource( "EmptyESSource", recordName = cms.string('L1TMuonOverlapFwVersionRcd'), iovIsRunNotTime = cms.bool(True), firstValid = cms.vuint32(1) ) ###OMTF FW ESProducer. omtfFwVersion = cms.ESProducer( "L1TMuonOverlapFwVersionESProducer", algoVersion = cms.uint32(0x110), layersVersion = cms.uint32(6), patternsVersion = cms.uint32(3), synthDate = cms.string("2001-01-01 00:00") )

0

bccbd46e4500f876a02aadf6e0c1065d389cdf38

4,603

py

Python

planning/planning/page/check_in_out/check_in_out.py

nishta/planning

5be1574111b9b94ec75c74960ace4314985b0014

[ "MIT" ]

null

planning/planning/page/check_in_out/check_in_out.py

nishta/planning

5be1574111b9b94ec75c74960ace4314985b0014

[ "MIT" ]

null

planning/planning/page/check_in_out/check_in_out.py

nishta/planning

5be1574111b9b94ec75c74960ace4314985b0014

[ "MIT" ]

null

from __future__ import unicode_literals

39.681034

291

0.74169

from __future__ import unicode_literals import frappe from frappe.utils import getdate, validate_email_add, today import datetime from planning.planning.myfunction import mail_format_pms,actual_date_update,close_task_update @frappe.whitelist() def checking_checkout(task=None,check_status=None,name=None): cur_date_time=frappe.utils.data.now () user_name=frappe.session.user if(task): if(check_status=="0"): doctype="NNTask"; #select parent,members,employee_name,parenttype from `tabNNAssign` where parenttype=%s and employee_name=%s",(doctype,user_name) count=frappe.db.sql("select task from `tabNNTask Check In Out` where status=1 and emp_name=%s",user_name); if(count): task=count[0][0] frappe.msgprint("Please Checkout <b>"+ task+"</b> Task") return "Not Valid" else: frappe.get_doc({ "doctype":"NNTask Check In Out", "task":task, "check_in":cur_date_time, "status":1, "emp_name":user_name }).insert(ignore_permissions=True) actual_date_update(task) else: hourly_rate=frappe.db.sql("""select hourly_rate from tabEmployee where employee_name=%s""",(user_name)) if(hourly_rate): hourly_cost=hourly_rate[0][0] else: hourly_cost=0; checkin_time=frappe.db.sql("""select check_in from `tabNNTask Check In Out` where name=%s""",name) if(checkin_time): checked_intime=checkin_time[0][0]; else: checked_intime=0 time_diff_in_seconds=frappe.utils.data.time_diff_in_seconds(cur_date_time,checked_intime); #frappe.msgprint(time_diff_in_seconds); cost_for_seound=float(hourly_cost)/float(3600); rate=(time_diff_in_seconds)*(cost_for_seound) #frappe.msgprint(str(rate),raise_exception=1) frappe.db.sql("""update `tabNNTask Check In Out` set check_out=%s,status=2,hourly_cost=%s,rate=%s where name=%s""",(cur_date_time,hourly_rate,rate,name)) else: return "not" @frappe.whitelist() def getTask(doctype): data=[] user_name=frappe.session.user select_task=frappe.db.sql("select name,parent,members,employee_name,parenttype from `tabNNAssign` where close_status=0 and parenttype=%s and employee_name=%s",(doctype,user_name)) if(select_task): i=1; values=""; for select_task_list in select_task: sno=i; assign_name=select_task_list[0]; task_name=select_task_list[1]; employee_id=select_task_list[2]; employee_name=select_task_list[3]; select_task_list=frappe.db.sql("""select task_list.project as project ,task_list.milestone as milestone,task_list.tasklist as task_list_name,task.duration as duration from `tabNNTasklist` task_list ,`tabNNTask` task where task.name=%s and task_list.tasklist=task.tasklist""",(task_name)) if(select_task_list): project_name=select_task_list[0][0]; milestone=select_task_list[0][1]; task_list_name=select_task_list[0][2]; duration=select_task_list[0][3]; else: project_name=""; milestone=""; status="Status"; close="Status"; status_che=1 checkin_status=frappe.db.sql("""select * from `tabNNTask Check In Out` where status=%s and task=%s and emp_name=%s order by creation desc""",(status_che,task_name,user_name)) if(checkin_status): check_status=1; check_status_name=checkin_status[0][0] else: check_status=0; check_status_name=""; #worked_cocuation: total_seconds=0; working_hours=frappe.db.sql("""select check_in,check_out from `tabNNTask Check In Out` where status=2 and task=%s and emp_name=%s order by creation desc""",(task_name,user_name)) for working_hours_list in working_hours: checkin_times=working_hours_list[0]; checkout_times=working_hours_list[1]; seconds=frappe.utils.data.time_diff_in_seconds(checkout_times,checkin_times); #frappe.msgprint(seconds); total_seconds=int(seconds)+int(total_seconds); #frappe.msgprint(total_seconds); worked_time=str(datetime.timedelta(seconds=total_seconds)) rows=[project_name]+[milestone]+[task_list_name]+[task_name]+[employee_name]+[check_status]+[check_status_name]+[duration]+[worked_time]+[assign_name] data.append(rows) i=i+1; return data @frappe.whitelist() def close_task(assign_name=None,): frappe.db.sql("""Update `tabNNAssign` set close_status=1 where name=%s""",(assign_name)) task=frappe.db.sql("""select parent from tabNNAssign where name=%s""",(assign_name)) mode=1; task_name=task if task: doctype="NNTask"; count=frappe.db.sql("""select *from tabNNAssign where close_status=0 and parent=%s and parenttype=%s""",(task_name,doctype)) if not count: close_task_update(task) mail_format_pms(task_name,mode)

0

4,303

0

96

158

b88cc6b6407fec4332c3df0cdd6f4c0dc8c904b3

4,290

py

Python

packages/girder/plugins/oauth/girder_oauth/providers/google.py

ShenQianwithC/HistomicsTK

4ad7e72a7ebdabbdfc879254fad04ce7ca47e320

[ "Apache-2.0" ]

1

2019-11-14T18:13:26.000Z

packages/girder/plugins/oauth/girder_oauth/providers/google.py

ShenQianwithC/HistomicsTK

4ad7e72a7ebdabbdfc879254fad04ce7ca47e320

[ "Apache-2.0" ]

3

2018-11-15T19:52:40.000Z

2022-02-14T21:56:22.000Z

packages/girder/plugins/oauth/girder_oauth/providers/google.py

ShenQianwithC/HistomicsTK

4ad7e72a7ebdabbdfc879254fad04ce7ca47e320

[ "Apache-2.0" ]

3

2018-05-21T19:45:19.000Z

2019-04-08T19:53:07.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- ############################################################################### # Copyright Kitware Inc. # # Licensed under the Apache License, Version 2.0 ( the "License" ); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ############################################################################### from .. import constants

35.75

79

0.571329

#!/usr/bin/env python # -*- coding: utf-8 -*- ############################################################################### # Copyright Kitware Inc. # # Licensed under the Apache License, Version 2.0 ( the "License" ); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. ############################################################################### from six.moves import urllib from girder.api.rest import getApiUrl from girder.exceptions import RestException from girder.models.setting import Setting from .base import ProviderBase from .. import constants class Google(ProviderBase): _AUTH_URL = 'https://accounts.google.com/o/oauth2/auth' _AUTH_SCOPES = ['profile', 'email'] _TOKEN_URL = 'https://accounts.google.com/o/oauth2/token' _API_USER_URL = 'https://www.googleapis.com/plus/v1/people/me' _API_USER_FIELDS = ('id', 'emails', 'name') def getClientIdSetting(self): return Setting().get(constants.PluginSettings.GOOGLE_CLIENT_ID) def getClientSecretSetting(self): return Setting().get(constants.PluginSettings.GOOGLE_CLIENT_SECRET) @classmethod def getUrl(cls, state): clientId = Setting().get(constants.PluginSettings.GOOGLE_CLIENT_ID) if clientId is None: raise Exception('No Google client ID setting is present.') callbackUrl = '/'.join((getApiUrl(), 'oauth', 'google', 'callback')) query = urllib.parse.urlencode({ 'response_type': 'code', 'access_type': 'online', 'client_id': clientId, 'redirect_uri': callbackUrl, 'state': state, 'scope': ' '.join(cls._AUTH_SCOPES) }) return '%s?%s' % (cls._AUTH_URL, query) def getToken(self, code): params = { 'grant_type': 'authorization_code', 'code': code, 'client_id': self.clientId, 'client_secret': self.clientSecret, 'redirect_uri': self.redirectUri } resp = self._getJson(method='POST', url=self._TOKEN_URL, data=params) return resp def getUser(self, token): headers = { 'Authorization': ' '.join(( token['token_type'], token['access_token'])) } # For privacy and efficiency, fetch only the specific needed fields # https://developers.google.com/+/web/api/rest/#partial-response query = urllib.parse.urlencode({ 'fields': ','.join(self._API_USER_FIELDS) }) resp = self._getJson(method='GET', url='%s?%s' % (self._API_USER_URL, query), headers=headers) # Get user's OAuth2 ID oauthId = resp.get('id') if not oauthId: raise RestException( 'Google Plus did not return a user ID.', code=502) # Get user's email address # Prefer email address with 'account' type emails = [ email.get('value') for email in resp.get('emails', []) if email.get('type') == 'account' ] if not emails: # If an 'account' email can't be found, consider them all emails = [ email.get('value') for email in resp.get('emails', []) ] if emails: # Even if there are multiple emails, just use the first one email = emails[0] else: raise RestException( 'This Google Plus user has no available email address.', code=502) # Get user's name firstName = resp.get('name', {}).get('givenName', '') lastName = resp.get('name', {}).get('familyName', '') user = self._createOrReuseUser(oauthId, email, firstName, lastName) return user

0

601

0

2,666

0

74

135

f00f0283a00861b00d8ace96a341aa1af6392dc8

177

py

Python

todoapp/todos/urls.py

dhavall13/REST-API-TodoCRUD

5d7179d12c4436e38658d9a7483497c8db99f4be

[ "MIT" ]

null

todoapp/todos/urls.py

dhavall13/REST-API-TodoCRUD

5d7179d12c4436e38658d9a7483497c8db99f4be

[ "MIT" ]

null

todoapp/todos/urls.py

dhavall13/REST-API-TodoCRUD

5d7179d12c4436e38658d9a7483497c8db99f4be

[ "MIT" ]

null

from rest_framework import routers from .api import TodoViewSet router = routers.DefaultRouter() router.register('api/todos', TodoViewSet, 'todos') urlpatterns = router.urls

19.666667

50

0.79096

from rest_framework import routers from .api import TodoViewSet router = routers.DefaultRouter() router.register('api/todos', TodoViewSet, 'todos') urlpatterns = router.urls

0

8e57bc0091c782bab46c7958d378a4ddf117035a

378

py

Python

test.py

xiaoweiChen/OpenVINO_Model_Convert_Website

ce8b0d225d1e0228aace772e3017ad3154543688

[ "Apache-2.0" ]

1

2019-11-12T07:11:39.000Z

test.py

xiaoweiChen/OpenVINO_Model_Convert_Website

ce8b0d225d1e0228aace772e3017ad3154543688

[ "Apache-2.0" ]

null

test.py

xiaoweiChen/OpenVINO_Model_Convert_Website

ce8b0d225d1e0228aace772e3017ad3154543688

[ "Apache-2.0" ]

null

import sys from converter import processPreTrainModels if __name__ == '__main__': if len(sys.argv) < 4: print("usage: {} proto caffemodel output_dir".format(sys.argv[0])) exit(0) proto = sys.argv[1] model = sys.argv[2] output = sys.argv[3] file_path = processPreTrainModels( proto, model, output) print("file_path is", file_path)

19.894737

70

0.648148

import sys from converter import processPreTrainModels if __name__ == '__main__': if len(sys.argv) < 4: print("usage: {} proto caffemodel output_dir".format(sys.argv[0])) exit(0) proto = sys.argv[1] model = sys.argv[2] output = sys.argv[3] file_path = processPreTrainModels( proto, model, output) print("file_path is", file_path)

0

e837781e421b78fc059079fdefb0bdc32efc4414

3,229

py

Python

scripts/eval.py

zsinsense/demosaicnet

bbe8151cab86dbe46b76806cf9ec353994b389ff

[ "MIT" ]

null

scripts/eval.py

zsinsense/demosaicnet

bbe8151cab86dbe46b76806cf9ec353994b389ff

[ "MIT" ]

null

scripts/eval.py

zsinsense/demosaicnet

bbe8151cab86dbe46b76806cf9ec353994b389ff

[ "MIT" ]

null

#!/bin/env python """Evaluate a demosaicking model.""" import argparse import torch as th from torch.utils.data import DataLoader import ttools from ttools.modules.image_operators import crop_like import demosaicnet LOG = ttools.get_logger(__name__) def main(args): """Entrypoint to the training.""" # Load model parameters from checkpoint, if any # meta = ttools.Checkpointer.load_meta(args.checkpoint_dir) # if meta is None: # LOG.warning("No checkpoint found at %s, aborting.", args.checkpoint_dir) # return meta = { 'mode': 'bayer', 'depth': 15, 'width': 64 } data = demosaicnet.Dataset(args.data, download=False, mode=meta["mode"], subset=demosaicnet.TEST_SUBSET) dataloader = DataLoader( data, batch_size=1, num_workers=4, pin_memory=True, shuffle=False) if meta["mode"] == demosaicnet.BAYER_MODE: model = demosaicnet.BayerDemosaick(depth=meta["depth"], width=meta["width"], pretrained=True, pad=False) elif meta["mode"] == demosaicnet.XTRANS_MODE: model = demosaicnet.XTransDemosaick(depth=meta["depth"], width=meta["width"], pretrained=True, pad=False) # checkpointer = ttools.Checkpointer(args.checkpoint_dir, model, meta=meta) # checkpointer.load_latest() # Resume from checkpoint, if any. state_dict = th.load(args.checkpoint_dir) model.load_state_dict(state_dict) # No need for gradients for p in model.parameters(): p.requires_grad = False mse_fn = th.nn.MSELoss() psnr_fn = PSNR() device = "cpu" if th.cuda.is_available(): device = "cuda" LOG.info("Using CUDA") count = 0 mse = 0.0 psnr = 0.0 for idx, batch in enumerate(dataloader): mosaic = batch[0].to(device) target = batch[1].to(device) output = model(mosaic) target = crop_like(target, output) output = th.clamp(output, 0, 1) psnr_ = psnr_fn(output, target).item() mse_ = mse_fn(output, target).item() psnr += psnr_ mse += mse_ count += 1 LOG.info("Image %04d, PSNR = %.1f dB, MSE = %.5f", idx, psnr_, mse_) mse /= count psnr /= count LOG.info("-----------------------------------") LOG.info("Average, PSNR = %.1f dB, MSE = %.5f", psnr, mse) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("data", help="root directory for the demosaicnet dataset.") parser.add_argument("checkpoint_dir", help="directory with the model checkpoints.") args = parser.parse_args() ttools.set_logger(False) main(args)

29.354545

87

0.569836

#!/bin/env python """Evaluate a demosaicking model.""" import argparse import os import time import torch as th from torch.utils.data import DataLoader import numpy as np import ttools from ttools.modules.image_operators import crop_like import demosaicnet LOG = ttools.get_logger(__name__) class PSNR(th.nn.Module): def __init__(self): super(PSNR, self).__init__() self.mse = th.nn.MSELoss() def forward(self, out, ref): mse = self.mse(out, ref) return -10*th.log10(mse+1e-12) def main(args): """Entrypoint to the training.""" # Load model parameters from checkpoint, if any # meta = ttools.Checkpointer.load_meta(args.checkpoint_dir) # if meta is None: # LOG.warning("No checkpoint found at %s, aborting.", args.checkpoint_dir) # return meta = { 'mode': 'bayer', 'depth': 15, 'width': 64 } data = demosaicnet.Dataset(args.data, download=False, mode=meta["mode"], subset=demosaicnet.TEST_SUBSET) dataloader = DataLoader( data, batch_size=1, num_workers=4, pin_memory=True, shuffle=False) if meta["mode"] == demosaicnet.BAYER_MODE: model = demosaicnet.BayerDemosaick(depth=meta["depth"], width=meta["width"], pretrained=True, pad=False) elif meta["mode"] == demosaicnet.XTRANS_MODE: model = demosaicnet.XTransDemosaick(depth=meta["depth"], width=meta["width"], pretrained=True, pad=False) # checkpointer = ttools.Checkpointer(args.checkpoint_dir, model, meta=meta) # checkpointer.load_latest() # Resume from checkpoint, if any. state_dict = th.load(args.checkpoint_dir) model.load_state_dict(state_dict) # No need for gradients for p in model.parameters(): p.requires_grad = False mse_fn = th.nn.MSELoss() psnr_fn = PSNR() device = "cpu" if th.cuda.is_available(): device = "cuda" LOG.info("Using CUDA") count = 0 mse = 0.0 psnr = 0.0 for idx, batch in enumerate(dataloader): mosaic = batch[0].to(device) target = batch[1].to(device) output = model(mosaic) target = crop_like(target, output) output = th.clamp(output, 0, 1) psnr_ = psnr_fn(output, target).item() mse_ = mse_fn(output, target).item() psnr += psnr_ mse += mse_ count += 1 LOG.info("Image %04d, PSNR = %.1f dB, MSE = %.5f", idx, psnr_, mse_) mse /= count psnr /= count LOG.info("-----------------------------------") LOG.info("Average, PSNR = %.1f dB, MSE = %.5f", psnr, mse) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("data", help="root directory for the demosaicnet dataset.") parser.add_argument("checkpoint_dir", help="directory with the model checkpoints.") args = parser.parse_args() ttools.set_logger(False) main(args)

0

205

0

-25

89

ace7c9af9eb249c27faf798e56fca31751c8a6ad

1,030

py

Python

lrp_toolbox/training_test.py

KushDen/deepimportance_code_release

5d16f1f95568dc402be6dfed4ad993ec0dbaa356

[ "MIT" ]

18

2020-07-11T01:58:02.000Z

2021-09-17T07:08:34.000Z

lrp_toolbox/training_test.py

KushDen/deepimportance_code_release

5d16f1f95568dc402be6dfed4ad993ec0dbaa356

[ "MIT" ]

13

2021-01-13T14:41:26.000Z

2021-12-29T02:15:10.000Z

lrp_toolbox/training_test.py

KushDen/deepimportance_code_release

5d16f1f95568dc402be6dfed4ad993ec0dbaa356

[ "MIT" ]

8

2020-02-19T21:30:30.000Z

2022-03-11T01:34:33.000Z

''' @author: Sebastian Lapuschkin @maintainer: Sebastian Lapuschkin @contact: [email protected], [email protected] @date: 30.09.2015 @version: 1.0 @copyright: Copyright (c) 2015-2017, Sebastian Lapuschkin, Alexander Binder, Gregoire Montavon, Klaus-Robert Mueller, Wojciech Samek @license : BSD-2-Clause ''' import modules import model_io import numpy as np ; na = np.newaxis D,N = 2,200000 #this is the XOR problem. X = np.random.rand(N,D) #we want [NxD] data X = (X > 0.5)*1.0 Y = X[:,0] == X[:,1] Y = (np.vstack((Y, np.invert(Y)))*1.0).T # and [NxC] labels X += np.random.randn(N,D)*0.1 # add some noise to the data. #build a network nn = modules.Sequential([modules.Linear(2,3), modules.Tanh(),modules.Linear(3,15), modules.Tanh(), modules.Linear(15,15), modules.Tanh(), modules.Linear(15,3), modules.Tanh() ,modules.Linear(3,2), modules.SoftMax()]) #train the network. nn.train(X,Y,Xval=X,Yval=Y, batchsize = 5) #save the network model_io.write(nn, '../xor_net_small_1000.txt')

28.611111

216

0.703883

''' @author: Sebastian Lapuschkin @maintainer: Sebastian Lapuschkin @contact: [email protected], [email protected] @date: 30.09.2015 @version: 1.0 @copyright: Copyright (c) 2015-2017, Sebastian Lapuschkin, Alexander Binder, Gregoire Montavon, Klaus-Robert Mueller, Wojciech Samek @license : BSD-2-Clause ''' import modules import model_io import numpy as np ; na = np.newaxis D,N = 2,200000 #this is the XOR problem. X = np.random.rand(N,D) #we want [NxD] data X = (X > 0.5)*1.0 Y = X[:,0] == X[:,1] Y = (np.vstack((Y, np.invert(Y)))*1.0).T # and [NxC] labels X += np.random.randn(N,D)*0.1 # add some noise to the data. #build a network nn = modules.Sequential([modules.Linear(2,3), modules.Tanh(),modules.Linear(3,15), modules.Tanh(), modules.Linear(15,15), modules.Tanh(), modules.Linear(15,3), modules.Tanh() ,modules.Linear(3,2), modules.SoftMax()]) #train the network. nn.train(X,Y,Xval=X,Yval=Y, batchsize = 5) #save the network model_io.write(nn, '../xor_net_small_1000.txt')

0

c16cdfe67a57a720e41f4d1f6a82111d663200a5

149

py

Python

tests/iac_integration/cdk/testdata/cdk_v2/python/app.py

zhuhaow/aws-sam-cli

59d82ec6848b5a0cdd544d8ada838d4d34052971

[ "Apache-2.0" ]

2,959

2018-05-08T21:48:56.000Z

2020-08-24T14:35:39.000Z

tests/iac_integration/cdk/testdata/cdk_v2/python/app.py

zhuhaow/aws-sam-cli

59d82ec6848b5a0cdd544d8ada838d4d34052971

[ "Apache-2.0" ]

1,469

2018-05-08T22:44:28.000Z

2020-08-24T20:19:24.000Z

tests/iac_integration/cdk/testdata/cdk_v2/python/app.py

zhuhaow/aws-sam-cli

59d82ec6848b5a0cdd544d8ada838d4d34052971

[ "Apache-2.0" ]

642

2018-05-08T22:09:19.000Z

2020-08-17T09:04:37.000Z

#!/usr/bin/env python3 from aws_cdk import App from python.python_stack import PythonStack app = App() PythonStack(app, "TestStack") app.synth()

13.545455

43

0.751678

#!/usr/bin/env python3 from aws_cdk import App from python.python_stack import PythonStack app = App() PythonStack(app, "TestStack") app.synth()

0

9eeb1c341a09b93233cbe624f89cddfd33fcd2f2

940

py

Python

part4c.py

ddlatumalea/signal_analysis

9e62e553f56e4c60c7e0963187e01c262d8d820e

[ "MIT" ]

null

part4c.py

ddlatumalea/signal_analysis

9e62e553f56e4c60c7e0963187e01c262d8d820e

[ "MIT" ]

null

part4c.py

ddlatumalea/signal_analysis

9e62e553f56e4c60c7e0963187e01c262d8d820e

[ "MIT" ]

1

2022-03-03T13:31:23.000Z

def fourier_transform(yi): """a, b = fourier_transform(yi). Real-valued Fourier transform that determines the coefficients of the Fourier series for a given signal y. The coefficients of the cosine terms are returned in the array a; those of the sine terms in the array b. Frequencies start at zero and do not exceed the Nyquist frequency. yi = {y1,y2,...,xn} """ xi = np.arange(yi.size) length = yi.size // 2 + 1 a, b = np.empty(length), np.empty(length) # Compute zero and Nyquist frequency cases a[0] = np.mean(yi) a[-1] = yi @ np.cos(np.pi * xi) / yi.size b[0] = 0.0 b[-1] = 0.0 # Compute ordinary cases (overwrite Nyquist if odd length) for index in range(1, length + yi.size % 2 - 1): arg = 2.0 * np.pi * xi * index / yi.size a[index] = 2.0 / yi.size * yi @ np.cos(arg) b[index] = 2.0 / yi.size * yi @ np.sin(arg) return a, b

39.166667

62

0.601064

def fourier_transform(yi): """a, b = fourier_transform(yi). Real-valued Fourier transform that determines the coefficients of the Fourier series for a given signal y. The coefficients of the cosine terms are returned in the array a; those of the sine terms in the array b. Frequencies start at zero and do not exceed the Nyquist frequency. yi = {y1,y2,...,xn} """ xi = np.arange(yi.size) length = yi.size // 2 + 1 a, b = np.empty(length), np.empty(length) # Compute zero and Nyquist frequency cases a[0] = np.mean(yi) a[-1] = yi @ np.cos(np.pi * xi) / yi.size b[0] = 0.0 b[-1] = 0.0 # Compute ordinary cases (overwrite Nyquist if odd length) for index in range(1, length + yi.size % 2 - 1): arg = 2.0 * np.pi * xi * index / yi.size a[index] = 2.0 / yi.size * yi @ np.cos(arg) b[index] = 2.0 / yi.size * yi @ np.sin(arg) return a, b

0

686add8ace25e333d96d69d7abbb938d46abc531

1,453

py

Python

distance-betweeen-obj/main.py

CrispenGari/opencv-python

cfa862fbf3b8b2c8899b76cee2774d6fb72ba00e

[ "MIT" ]

1

2021-11-08T07:37:05.000Z

distance-betweeen-obj/main.py

CrispenGari/opencv-python

cfa862fbf3b8b2c8899b76cee2774d6fb72ba00e

[ "MIT" ]

null

distance-betweeen-obj/main.py

CrispenGari/opencv-python

cfa862fbf3b8b2c8899b76cee2774d6fb72ba00e

[ "MIT" ]

null

import cv2 import numpy as np points = [] letters = list("ABCDEFGHIJKLMNOPQRSTUVWXYZ") image = np.zeros((512, 512, 3), np.uint8) while True: cv2.putText(image, f'TO CLEAR THE POINTS PRESS (c)', (20, 20), cv2.FONT_HERSHEY_PLAIN, 1, (255, 255, 255), 1) cv2.imshow("DISTANCE BETWEEN TWO POINTS", image) cv2.setMouseCallback("DISTANCE BETWEEN TWO POINTS", mouseEvent, None) key = cv2.waitKey(1) if key & 0xFF == 27: cv2.destroyAllWindows() break elif key & 0xFF == ord('c'): image = np.zeros((512, 512, 3), np.uint8) points = [] # cm = pixels / 96 * 2.54

37.25641

126

0.604267

import cv2 import numpy as np from math import pow, sqrt points = [] letters = list("ABCDEFGHIJKLMNOPQRSTUVWXYZ") image = np.zeros((512, 512, 3), np.uint8) def mouseEvent(event, x, y, params, flags): if event == cv2.EVENT_LBUTTONDOWN: cv2.circle(image, (x, y), 5, (0, 0, 255), -1) cv2.putText(image, letters[len(points) if len(points) < 26 else 0], (x, y), cv2.FONT_HERSHEY_PLAIN, 2, (255, 0, 0), 2) points.append((x, y)) if len(points) > 1: last_two_points = points[-2:] d, midpoint = findDistance(last_two_points) cv2.putText(image, f'{round(d)} (px)', midpoint, cv2.FONT_HERSHEY_PLAIN, 1, (255, 255, 255), 1) cv2.line(image, tuple(last_two_points[0]), tuple(last_two_points[1]),(0, 255, 0), 2) return def findDistance(points): x1, y1 = points[0] x2, y2 = points[1] d = sqrt(pow((x1 - x2), 2) + pow((y1 - y2), 2)) midpoint = tuple(([(x1 + x2)//2, (y1 + y2)//2])) return d, midpoint while True: cv2.putText(image, f'TO CLEAR THE POINTS PRESS (c)', (20, 20), cv2.FONT_HERSHEY_PLAIN, 1, (255, 255, 255), 1) cv2.imshow("DISTANCE BETWEEN TWO POINTS", image) cv2.setMouseCallback("DISTANCE BETWEEN TWO POINTS", mouseEvent, None) key = cv2.waitKey(1) if key & 0xFF == 27: cv2.destroyAllWindows() break elif key & 0xFF == ord('c'): image = np.zeros((512, 512, 3), np.uint8) points = [] # cm = pixels / 96 * 2.54

0

772

0

5

67

8e8c991f6293082c8cec862c8abc181e7ff19a46

1,948

py

Python

Learning/python_data_analysis8.py

VictoriaGuXY/MCO-Menu-Checker-Online

706e2e1bf7395cc344f382ea2ac53d964d459f86

[ "MIT" ]

null

Learning/python_data_analysis8.py

VictoriaGuXY/MCO-Menu-Checker-Online

706e2e1bf7395cc344f382ea2ac53d964d459f86

[ "MIT" ]

null

Learning/python_data_analysis8.py

VictoriaGuXY/MCO-Menu-Checker-Online

706e2e1bf7395cc344f382ea2ac53d964d459f86

[ "MIT" ]

null

import pandas as pd """ output """ # Note: some output is shortened to save spaces. # This file introduces methods to group data. # Data from https://github.com/mwaskom/seaborn-data df = pd.read_csv('E:\\tips.csv') """ total_bill tip sex smoker day time size 0 16.99 1.01 Female No Sun Dinner 2 1 10.34 1.66 Male No Sun Dinner 3 2 21.01 3.50 Male No Sun Dinner 3 3 23.68 3.31 Male No Sun Dinner 2 4 24.59 3.61 Female No Sun Dinner 4 5 25.29 4.71 Male No Sun Dinner 4 .. ... ... ... ... ... ... ... 240 27.18 2.00 Female Yes Sat Dinner 2 241 22.67 2.00 Male Yes Sat Dinner 2 242 17.82 1.75 Male No Sat Dinner 2 243 18.78 3.00 Female No Thur Dinner 2 [244 rows x 7 columns] """ # ------------------------------------------------------------------------------ # if we want to form group based on 'day' column group = df.groupby('day') # print out the first value (first line) in each group print (group.first()) """ total_bill tip sex smoker time size day Fri 28.97 3.00 Male Yes Dinner 2 Sat 20.65 3.35 Male No Dinner 3 Sun 16.99 1.01 Female No Dinner 2 Thur 27.20 4.00 Male No Lunch 4 """ # print out the last value (last line) in each group print (group.first()) """ total_bill tip sex smoker time size day Fri 10.09 2.00 Female Yes Lunch 2 Sat 17.82 1.75 Male No Dinner 2 Sun 15.69 1.50 Male Yes Dinner 2 Thur 18.78 3.00 Female No Dinner 2 """

32.466667

80

0.479466

import json import pandas as pd import numpy as np from pandas import DataFrame """ output """ # Note: some output is shortened to save spaces. # This file introduces methods to group data. # Data from https://github.com/mwaskom/seaborn-data df = pd.read_csv('E:\\tips.csv') """ total_bill tip sex smoker day time size 0 16.99 1.01 Female No Sun Dinner 2 1 10.34 1.66 Male No Sun Dinner 3 2 21.01 3.50 Male No Sun Dinner 3 3 23.68 3.31 Male No Sun Dinner 2 4 24.59 3.61 Female No Sun Dinner 4 5 25.29 4.71 Male No Sun Dinner 4 .. ... ... ... ... ... ... ... 240 27.18 2.00 Female Yes Sat Dinner 2 241 22.67 2.00 Male Yes Sat Dinner 2 242 17.82 1.75 Male No Sat Dinner 2 243 18.78 3.00 Female No Thur Dinner 2 [244 rows x 7 columns] """ # ------------------------------------------------------------------------------ # if we want to form group based on 'day' column group = df.groupby('day') # print out the first value (first line) in each group print (group.first()) """ total_bill tip sex smoker time size day Fri 28.97 3.00 Male Yes Dinner 2 Sat 20.65 3.35 Male No Dinner 3 Sun 16.99 1.01 Female No Dinner 2 Thur 27.20 4.00 Male No Lunch 4 """ # print out the last value (last line) in each group print (group.first()) """ total_bill tip sex smoker time size day Fri 10.09 2.00 Female Yes Lunch 2 Sat 17.82 1.75 Male No Dinner 2 Sun 15.69 1.50 Male Yes Dinner 2 Thur 18.78 3.00 Female No Dinner 2 """

0

-6

66

948080e247360f7be9e2aa7cdc3fd4bb0c67bdac

438

py

Python

functions/reportIssue.py

chiluf/visvis.dev

373846ea25044b7ca50f44c63dab4248e14deacd

[ "BSD-3-Clause" ]

null

functions/reportIssue.py

chiluf/visvis.dev

373846ea25044b7ca50f44c63dab4248e14deacd

[ "BSD-3-Clause" ]

null

functions/reportIssue.py

chiluf/visvis.dev

373846ea25044b7ca50f44c63dab4248e14deacd

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- # Copyright (C) 2012, Almar Klein # # Visvis is distributed under the terms of the (new) BSD License. # The full license can be found in 'license.txt'. def reportIssue(): """ help() Open a webbrowser with the visvis website at the issue list. """ import webbrowser webbrowser.open("http://code.google.com/p/visvis/issues/list") if __name__ == '__main__': reportIssue()

23.052632

66

0.639269

# -*- coding: utf-8 -*- # Copyright (C) 2012, Almar Klein # # Visvis is distributed under the terms of the (new) BSD License. # The full license can be found in 'license.txt'. def reportIssue(): """ help() Open a webbrowser with the visvis website at the issue list. """ import webbrowser webbrowser.open("http://code.google.com/p/visvis/issues/list") if __name__ == '__main__': reportIssue()

0

405b1e05e30665caf1b56d799edb993551a9f5b1

217

py

Python

thirdfile.py

1frenchfrog1/testgithub

7191e44d75ba50438d9c2fe8f0fcf9fcf3a2a991

[ "MIT" ]

null

thirdfile.py

1frenchfrog1/testgithub

7191e44d75ba50438d9c2fe8f0fcf9fcf3a2a991

[ "MIT" ]

null

thirdfile.py

1frenchfrog1/testgithub

7191e44d75ba50438d9c2fe8f0fcf9fcf3a2a991

[ "MIT" ]

null

#!/usr/bin/python def printme3( str ): "This prints a passed string into this function" print(str) return def printme3too( str ): "This prints a passed string into this function" print(str) return

18.083333

51

0.686636

#!/usr/bin/python def printme3( str ): "This prints a passed string into this function" print(str) return def printme3too( str ): "This prints a passed string into this function" print(str) return

0

52c36ddcbbbc1ea0125baf76215d709418864b64

642

py

Python

lec7.py

uni-student234/ISAT252

4c0942919c432456fe26900c23f076161b4cc266

[ "MIT" ]

null

lec7.py

uni-student234/ISAT252

4c0942919c432456fe26900c23f076161b4cc266

[ "MIT" ]

null

lec7.py

uni-student234/ISAT252

4c0942919c432456fe26900c23f076161b4cc266

[ "MIT" ]

null

""" Week 2, day 7, lec 7 """ # i = 5 # while i >= 0: # i = i - 1 # if i == 3: # # break #breaks the smallest loop # # continue #skips the current iteration and moves on # # pass #does nothing, but is placehold if you need something for syntax # print(i) # for word in 'hello world'.split(): # print(word) # for str_item in word: # if str_item == '1': # break # print(str_item) # try: # print(1/0) # except ZeroDivisionError: # print('error') i = 5 while i >= 0: try: print(1/(i-3)) except: pass i = i - 1

20.0625

90

0.489097

""" Week 2, day 7, lec 7 """ # i = 5 # while i >= 0: # i = i - 1 # if i == 3: # # break #breaks the smallest loop # # continue #skips the current iteration and moves on # # pass #does nothing, but is placehold if you need something for syntax # print(i) # for word in 'hello world'.split(): # print(word) # for str_item in word: # if str_item == '1': # break # print(str_item) # try: # print(1/0) # except ZeroDivisionError: # print('error') i = 5 while i >= 0: try: print(1/(i-3)) except: pass i = i - 1

0

6446ebc359e3c3467ceb30fabeaa007c3100a7f7

11,447

py

Python

scripts/survivor_analysis/utils/annotate.py

a-paxton/oss-community-health

93ff4d266b5390b53d8ed59f71616de68bcfdda7

[ "MIT" ]

null

scripts/survivor_analysis/utils/annotate.py

a-paxton/oss-community-health

93ff4d266b5390b53d8ed59f71616de68bcfdda7

[ "MIT" ]

1

2022-03-22T19:32:27.000Z

2022-03-23T12:43:08.000Z

scripts/survivor_analysis/utils/annotate.py

a-paxton/oss-community-health

93ff4d266b5390b53d8ed59f71616de68bcfdda7

[ "MIT" ]

null

import pandas as pd import numpy as np from collections import Counter from datetime import datetime from nltk.tokenize import RegexpTokenizer from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer import re def annotate_logs(comments, tickets): """ Annotates comments and tickets with additional information: 1. whether the body was updated (Boolean) 2. the number of PRs and issues opened by the comment author at the time of the comment posting 3. comment order (comment dataframe only) 4. identify whether ticket is closed (Boolean; ticket dataframe only) 5. identify whether a comment is associated to an issue or a PR Requires: pandas Parameters ---------- comments : pd.DataFrame tickets : pd.DataFrame Returns ------- The same dataframe, but with additional columns Examples -------- >> import pandas as pd >> import utils >> tickets = pd.read_csv("data/numpy/issues.tsv", sep="\t") >> comments = pd.read_csv("data/numpy/comments.tsv", sep="\t") >> comments, tickets = utils.annotate_logs(comments, tickets) """ # identify whether the body of comments or tickets were updated comments["was_updated"] = comments["created_at"] != comments["updated_at"] tickets["was_updated"] = tickets["created_at"] != tickets["updated_at"] # comments df: add number of PRs created by author to date num_PR_per_pers = [ sum((tickets["created_at"] < created_at) & (tickets["type"] == "pull_request") & (tickets["author_id"] == author_id)) for created_at, author_id in zip(comments["created_at"], comments["author_id"])] comments["num_PR_created"] = num_PR_per_pers # issues df: add number of PRs created by author to date num_PR_per_pers = [ sum((tickets["created_at"] < created_at) & (tickets["type"] == "pull_request") & (tickets["author_id"] == author_id)) for created_at, author_id in zip(tickets["created_at"], tickets["author_id"])] tickets["num_PR_created"] = num_PR_per_pers # comments df: add number of issues created by author to date num_issue_per_pers = [ sum((tickets["created_at"] < created_at) & (tickets["type"] == "issue") & (tickets["author_id"] == author_id)) for created_at, author_id in zip(comments["created_at"], comments["author_id"])] comments["num_issue_created"] = num_issue_per_pers # tickets df: add number of issues created by author to date num_issue_per_pers = [ sum((tickets["created_at"] < created_at) & (tickets["type"] == "issue") & (tickets["author_id"] == author_id)) for created_at, author_id in zip(tickets["created_at"], tickets["author_id"])] tickets["num_issue_created"] = num_issue_per_pers # track the comment order comments['comment_order'] = comments.sort_values(by=['created_at']) \ .groupby(by=['ticket_id']) \ .cumcount() # identify whether the PR is closed tickets['is_closed'] = pd.notnull(tickets['closed_at']) mask = tickets["closed_at"].isnull() tickets.loc[mask, "closed_at"] = pd.to_datetime(datetime.now()) open_duration = ( pd.to_datetime(tickets["closed_at"]) - pd.to_datetime(tickets["created_at"])) tickets["open_duration"] = open_duration.apply( lambda x: x.total_seconds()) # Now we want to remove this estimate for anything created before 1970 m = [True if c.startswith("1970") else False for c in tickets["created_at"]] tickets.loc[m, "open_duration"] = np.nan # For each comment, get the information on when the corresponding ticket # has been opened when it is available (comments can also be added to # commits) tickets.set_index("ticket_id", inplace=True, drop=False) # We're using the reindex function to tacket the case where we don't have # the ticket associated to a particular comment. comments["ticket_created_at"] = tickets.reindex( comments["ticket_id"])["created_at"].values comments["type"] = tickets.reindex( comments["ticket_id"])["type"].values # Reset the old index tickets.set_index("id", inplace=True, drop=False) # return the dataframes return comments, tickets def body_cleanup(comments, grateful_list, bot_list): """ Prepare comment or issue dataframe for text analysis: 1. Count number of times gratitude words appear in HTML comments (i.e., auto-generated templates for PRs and issues provided by projects) 2. Remove HTML comments 3. Remove quoted text 4. Strip newlines 5. Count and remove code blocks 6. Identify other users referenced in body 7. Flag whether the author was a bot Requires: pandas , nltk , collections , re Parameters ---------- comments : pd.DataFrame, ideally annotated with `annotate_logs()`; can be run with either comments df or issues/tickets df grateful_list : list or pd.Series of gratitude words to identify; currently works only with grateful unigrams bot_list : list or pd.Series of bot usernames to be ignored Returns ------- The same dataframe, but with cleaned body text and new columns (code_blocks , referenced_users , bot_flag) Examples -------- >> import pandas as pd >> import utils >> comments = pd.read_csv("data/numpy/comments.tsv", sep="\t") >> comments, tickets = utils.annotate.annotate_logs(comments, tickets) >> comments = utils.annotate.body_cleanup(comments, bot_list_df) """ # replace all NaN with empty strings comments['body'] = comments['body'].replace(np.nan, '', regex=True) # count thanks in HTML comments comments['html_comments'] = comments['body'].str.findall('(\<\!--.*?--\>)').apply(' '.join) # tokenize and count words tokenizer = RegexpTokenizer(r'\w+') comments['html_tokenized'] = comments['html_comments'].apply(str.lower).apply(tokenizer.tokenize) comments['html_word_count'] = comments['html_tokenized'].apply(lambda x: Counter(x)) # count words if they're in our grateful list comments['automatic_grateful_count'] = ( comments['html_word_count'].apply( lambda x: np.sum([v for k, v in x.items() if k in grateful_list]))) # let us know which ones were used comments['automatic_grateful_list'] = ( comments['html_word_count'].apply( lambda x: [k for k in x if k in grateful_list])) # remove the columns we don't need anymore comments = comments.drop(columns=['html_tokenized', 'html_word_count']) # remove the HTML comments from the body comments['body'] = (comments['body'].str.replace( "()", " ", regex=True, flags=re.DOTALL)) # remove text quotes comments['body'] = (comments['body'].replace( "(^|\n|\r)+\>.*(?=\n|$)", " ", regex=True)) # remove newlines comments['body'] = (comments['body'].replace( "[\n\r]+", " ", regex=True)) # count and then remove code blocks comments['code_blocks'] = comments['body'].str.count("\`{3}")/2 comments['body'] = (comments['body'].replace( "\`{3}.*\`{3}", " ", regex=True)) # identify other humans comments['referenced_users'] = comments['body'].str.findall('@\w{1,}') # identify bots comments['bot_flag'] = comments['author_name'].isin(bot_list) # return our dataframe return comments def add_sentiment(comments): """ Add sentiment analysis scores to comments dataframe: * negative emotion * positive emotion * neutral emotion * compound emotion Requires: pandas , vaderSentiment For more on vaderSentiment, see https://github.com/cjhutto/vaderSentiment Parameters ---------- comments : pd.DataFrame ideally after `annotate_logs()` and `body_cleanup()`; can be run with either comments df or issues/tickets df Returns ------- The same dataframe but with new sentiment columns Examples -------- >> import pandas as pd >> import utils >> comments = pd.read_csv("data/numpy/comments.tsv", sep="\t") >> comments, tickets = utils.annotate.annotate_logs(comments, tickets) >> comments = utils.annotate.body_cleanup(comments, bot_list_df) >> comments = utils.annotate.add_sentiment(comments) """ # initialize sentiment analyzer analyser = SentimentIntensityAnalyzer() # remove NaNs comments['body'] = comments['body'].replace(np.nan, ' ', regex=True) # run sentiment analyzer over each comment body sentiment_df = ( comments['body'] .apply(analyser.polarity_scores) .astype(str) .str.strip('{}') .str.split(', ', expand=True)) # split the emotion output dictionary into new columns # (thanks to https://stackoverflow.com/a/13053267 for partial solution) comments['negative_emotion'] = sentiment_df[0].str.split( ': ').str[-1].astype(float) comments['neutral_emotion'] = sentiment_df[1].str.split( ': ').str[-1].astype(float) comments['positive_emotion'] = sentiment_df[2].str.split( ': ').str[-1].astype(float) comments['compound_emotion'] = sentiment_df[3].str.split( ': ').str[-1].astype(float) # return our dataframe return comments def add_gratitude(comments, grateful_list): """ Track expressions of gratitude: * overall counts * specific words Thanks to https://stackoverflow.com/a/47686394 Requires: pandas , nltk , collections Parameters ---------- comments : pd.DataFrame ideally after `annotate_logs()` and `body_cleanup()`; can be run with either comments df or issues/tickets df grateful_list : list or pd.Series of gratitude words to identify; currently works only with grateful unigrams Returns ------- The same dataframe but with new gratitude columns Examples -------- >> import pandas as pd >> import utils >> comments = pd.read_csv("data/numpy/comments.tsv", sep="\t") >> comments, tickets = utils.annotate.annotate_logs(comments, tickets) >> comments = utils.annotate.body_cleanup(comments, bot_list_df) >> comments = utils.annotate.add_gratitude(comments) """ # tokenize and count words tokenizer = RegexpTokenizer(r'\w+') comments['tokenized'] = comments['body'].apply( str.lower).apply(tokenizer.tokenize) comments['word_count'] = comments['tokenized'].apply(lambda x: Counter(x)) # count words if they're in our grateful list comments['grateful_count'] = ( comments['word_count'].apply( lambda x: np.sum([v for k, v in x.items() if k in grateful_list]))) # let us know which ones were used comments['grateful_list'] = ( comments['word_count'].apply( lambda x: [k for k in x if k in grateful_list])) # remove the columns we don't need anymore comments = comments.drop(columns=['tokenized', 'word_count']) # spit back our dataframe now return comments

34.478916

101

0.638857

import pandas as pd import numpy as np from collections import Counter from datetime import datetime from nltk.tokenize import RegexpTokenizer from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer import re def annotate_logs(comments, tickets): """ Annotates comments and tickets with additional information: 1. whether the body was updated (Boolean) 2. the number of PRs and issues opened by the comment author at the time of the comment posting 3. comment order (comment dataframe only) 4. identify whether ticket is closed (Boolean; ticket dataframe only) 5. identify whether a comment is associated to an issue or a PR Requires: pandas Parameters ---------- comments : pd.DataFrame tickets : pd.DataFrame Returns ------- The same dataframe, but with additional columns Examples -------- >> import pandas as pd >> import utils >> tickets = pd.read_csv("data/numpy/issues.tsv", sep="\t") >> comments = pd.read_csv("data/numpy/comments.tsv", sep="\t") >> comments, tickets = utils.annotate_logs(comments, tickets) """ # identify whether the body of comments or tickets were updated comments["was_updated"] = comments["created_at"] != comments["updated_at"] tickets["was_updated"] = tickets["created_at"] != tickets["updated_at"] # comments df: add number of PRs created by author to date num_PR_per_pers = [ sum((tickets["created_at"] < created_at) & (tickets["type"] == "pull_request") & (tickets["author_id"] == author_id)) for created_at, author_id in zip(comments["created_at"], comments["author_id"])] comments["num_PR_created"] = num_PR_per_pers # issues df: add number of PRs created by author to date num_PR_per_pers = [ sum((tickets["created_at"] < created_at) & (tickets["type"] == "pull_request") & (tickets["author_id"] == author_id)) for created_at, author_id in zip(tickets["created_at"], tickets["author_id"])] tickets["num_PR_created"] = num_PR_per_pers # comments df: add number of issues created by author to date num_issue_per_pers = [ sum((tickets["created_at"] < created_at) & (tickets["type"] == "issue") & (tickets["author_id"] == author_id)) for created_at, author_id in zip(comments["created_at"], comments["author_id"])] comments["num_issue_created"] = num_issue_per_pers # tickets df: add number of issues created by author to date num_issue_per_pers = [ sum((tickets["created_at"] < created_at) & (tickets["type"] == "issue") & (tickets["author_id"] == author_id)) for created_at, author_id in zip(tickets["created_at"], tickets["author_id"])] tickets["num_issue_created"] = num_issue_per_pers # track the comment order comments['comment_order'] = comments.sort_values(by=['created_at']) \ .groupby(by=['ticket_id']) \ .cumcount() # identify whether the PR is closed tickets['is_closed'] = pd.notnull(tickets['closed_at']) mask = tickets["closed_at"].isnull() tickets.loc[mask, "closed_at"] = pd.to_datetime(datetime.now()) open_duration = ( pd.to_datetime(tickets["closed_at"]) - pd.to_datetime(tickets["created_at"])) tickets["open_duration"] = open_duration.apply( lambda x: x.total_seconds()) # Now we want to remove this estimate for anything created before 1970 m = [True if c.startswith("1970") else False for c in tickets["created_at"]] tickets.loc[m, "open_duration"] = np.nan # For each comment, get the information on when the corresponding ticket # has been opened when it is available (comments can also be added to # commits) tickets.set_index("ticket_id", inplace=True, drop=False) # We're using the reindex function to tacket the case where we don't have # the ticket associated to a particular comment. comments["ticket_created_at"] = tickets.reindex( comments["ticket_id"])["created_at"].values comments["type"] = tickets.reindex( comments["ticket_id"])["type"].values # Reset the old index tickets.set_index("id", inplace=True, drop=False) # return the dataframes return comments, tickets def body_cleanup(comments, grateful_list, bot_list): """ Prepare comment or issue dataframe for text analysis: 1. Count number of times gratitude words appear in HTML comments (i.e., auto-generated templates for PRs and issues provided by projects) 2. Remove HTML comments 3. Remove quoted text 4. Strip newlines 5. Count and remove code blocks 6. Identify other users referenced in body 7. Flag whether the author was a bot Requires: pandas , nltk , collections , re Parameters ---------- comments : pd.DataFrame, ideally annotated with `annotate_logs()`; can be run with either comments df or issues/tickets df grateful_list : list or pd.Series of gratitude words to identify; currently works only with grateful unigrams bot_list : list or pd.Series of bot usernames to be ignored Returns ------- The same dataframe, but with cleaned body text and new columns (code_blocks , referenced_users , bot_flag) Examples -------- >> import pandas as pd >> import utils >> comments = pd.read_csv("data/numpy/comments.tsv", sep="\t") >> comments, tickets = utils.annotate.annotate_logs(comments, tickets) >> comments = utils.annotate.body_cleanup(comments, bot_list_df) """ # replace all NaN with empty strings comments['body'] = comments['body'].replace(np.nan, '', regex=True) # count thanks in HTML comments comments['html_comments'] = comments['body'].str.findall('(\<\!--.*?--\>)').apply(' '.join) # tokenize and count words tokenizer = RegexpTokenizer(r'\w+') comments['html_tokenized'] = comments['html_comments'].apply(str.lower).apply(tokenizer.tokenize) comments['html_word_count'] = comments['html_tokenized'].apply(lambda x: Counter(x)) # count words if they're in our grateful list comments['automatic_grateful_count'] = ( comments['html_word_count'].apply( lambda x: np.sum([v for k, v in x.items() if k in grateful_list]))) # let us know which ones were used comments['automatic_grateful_list'] = ( comments['html_word_count'].apply( lambda x: [k for k in x if k in grateful_list])) # remove the columns we don't need anymore comments = comments.drop(columns=['html_tokenized', 'html_word_count']) # remove the HTML comments from the body comments['body'] = (comments['body'].str.replace( "()", " ", regex=True, flags=re.DOTALL)) # remove text quotes comments['body'] = (comments['body'].replace( "(^|\n|\r)+\>.*(?=\n|$)", " ", regex=True)) # remove newlines comments['body'] = (comments['body'].replace( "[\n\r]+", " ", regex=True)) # count and then remove code blocks comments['code_blocks'] = comments['body'].str.count("\`{3}")/2 comments['body'] = (comments['body'].replace( "\`{3}.*\`{3}", " ", regex=True)) # identify other humans comments['referenced_users'] = comments['body'].str.findall('@\w{1,}') # identify bots comments['bot_flag'] = comments['author_name'].isin(bot_list) # return our dataframe return comments def add_sentiment(comments): """ Add sentiment analysis scores to comments dataframe: * negative emotion * positive emotion * neutral emotion * compound emotion Requires: pandas , vaderSentiment For more on vaderSentiment, see https://github.com/cjhutto/vaderSentiment Parameters ---------- comments : pd.DataFrame ideally after `annotate_logs()` and `body_cleanup()`; can be run with either comments df or issues/tickets df Returns ------- The same dataframe but with new sentiment columns Examples -------- >> import pandas as pd >> import utils >> comments = pd.read_csv("data/numpy/comments.tsv", sep="\t") >> comments, tickets = utils.annotate.annotate_logs(comments, tickets) >> comments = utils.annotate.body_cleanup(comments, bot_list_df) >> comments = utils.annotate.add_sentiment(comments) """ # initialize sentiment analyzer analyser = SentimentIntensityAnalyzer() # remove NaNs comments['body'] = comments['body'].replace(np.nan, ' ', regex=True) # run sentiment analyzer over each comment body sentiment_df = ( comments['body'] .apply(analyser.polarity_scores) .astype(str) .str.strip('{}') .str.split(', ', expand=True)) # split the emotion output dictionary into new columns # (thanks to https://stackoverflow.com/a/13053267 for partial solution) comments['negative_emotion'] = sentiment_df[0].str.split( ': ').str[-1].astype(float) comments['neutral_emotion'] = sentiment_df[1].str.split( ': ').str[-1].astype(float) comments['positive_emotion'] = sentiment_df[2].str.split( ': ').str[-1].astype(float) comments['compound_emotion'] = sentiment_df[3].str.split( ': ').str[-1].astype(float) # return our dataframe return comments def add_gratitude(comments, grateful_list): """ Track expressions of gratitude: * overall counts * specific words Thanks to https://stackoverflow.com/a/47686394 Requires: pandas , nltk , collections Parameters ---------- comments : pd.DataFrame ideally after `annotate_logs()` and `body_cleanup()`; can be run with either comments df or issues/tickets df grateful_list : list or pd.Series of gratitude words to identify; currently works only with grateful unigrams Returns ------- The same dataframe but with new gratitude columns Examples -------- >> import pandas as pd >> import utils >> comments = pd.read_csv("data/numpy/comments.tsv", sep="\t") >> comments, tickets = utils.annotate.annotate_logs(comments, tickets) >> comments = utils.annotate.body_cleanup(comments, bot_list_df) >> comments = utils.annotate.add_gratitude(comments) """ # tokenize and count words tokenizer = RegexpTokenizer(r'\w+') comments['tokenized'] = comments['body'].apply( str.lower).apply(tokenizer.tokenize) comments['word_count'] = comments['tokenized'].apply(lambda x: Counter(x)) # count words if they're in our grateful list comments['grateful_count'] = ( comments['word_count'].apply( lambda x: np.sum([v for k, v in x.items() if k in grateful_list]))) # let us know which ones were used comments['grateful_list'] = ( comments['word_count'].apply( lambda x: [k for k in x if k in grateful_list])) # remove the columns we don't need anymore comments = comments.drop(columns=['tokenized', 'word_count']) # spit back our dataframe now return comments

0

46a90fe428c07ac7366934d1e4ee7724a8b4f434

352

py

Python

packages/Python/lldbsuite/test/python_api/sbtype_typeclass/TestSBTypeTypeClass.py

nathawes/swift-lldb

3cbf7470e0f9191ec1fc1c69ce8048c1dc64ec77

[ "Apache-2.0" ]

427

2018-05-29T14:21:02.000Z

2022-03-16T03:17:54.000Z

packages/Python/lldbsuite/test/python_api/sbtype_typeclass/TestSBTypeTypeClass.py

DalavanCloud/lldb

e913eaf2468290fb94c767d474d611b41a84dd69

[ "Apache-2.0" ]

25

2018-07-23T08:34:15.000Z

2021-11-05T07:13:36.000Z

packages/Python/lldbsuite/test/python_api/sbtype_typeclass/TestSBTypeTypeClass.py

DalavanCloud/lldb

e913eaf2468290fb94c767d474d611b41a84dd69

[ "Apache-2.0" ]

52

2018-07-19T19:57:32.000Z

2022-03-11T16:05:38.000Z

from lldbsuite.test import decorators from lldbsuite.test import lldbinline lldbinline.MakeInlineTest( __file__, globals(), [ decorators.skipIfFreeBSD, decorators.skipIfLinux, decorators.skipIfWindows, decorators.expectedFailureAll( oslist=['macosx'], archs=['i386'], bugnumber='rdar://28656677')])

32

57

0.6875

from lldbsuite.test import decorators from lldbsuite.test import lldbinline lldbinline.MakeInlineTest( __file__, globals(), [ decorators.skipIfFreeBSD, decorators.skipIfLinux, decorators.skipIfWindows, decorators.expectedFailureAll( oslist=['macosx'], archs=['i386'], bugnumber='rdar://28656677')])

0

af4dceb229fa3c43802c126ad350cbf15950b67e

1,585

bzl

Python

js/extensions.bzl

stoiky/rules_js

e61b61b98c2f5c733bf804f78db9f55b1fb2d599

[ "Apache-2.0" ]

null

js/extensions.bzl

stoiky/rules_js

e61b61b98c2f5c733bf804f78db9f55b1fb2d599

[ "Apache-2.0" ]

null

js/extensions.bzl

stoiky/rules_js

e61b61b98c2f5c733bf804f78db9f55b1fb2d599

[ "Apache-2.0" ]

null

"""Adapt repository rules in npm_import.bzl to be called from MODULE.bazel See https://bazel.build/docs/bzlmod#extension-definition """ load("//js/private:pnpm_utils.bzl", "pnpm_utils") load("//js/private:translate_pnpm_lock.bzl", translate_pnpm_lock_lib = "translate_pnpm_lock") load("//js:npm_import.bzl", "npm_import", "translate_pnpm_lock") load("//js/private:transitive_closure.bzl", "translate_to_transitive_closure") npm = module_extension( implementation = _extension_impl, tag_classes = { "translate_pnpm_lock": tag_class(attrs = dict({"name": attr.string()}, **translate_pnpm_lock_lib.attrs)), # todo: support individual packages as well # "package": tag_class(attrs = dict({"name": attr.string()}, **_npm_import.attrs)), }, )

42.837838

113

0.637855

"""Adapt repository rules in npm_import.bzl to be called from MODULE.bazel See https://bazel.build/docs/bzlmod#extension-definition """ load("//js/private:pnpm_utils.bzl", "pnpm_utils") load("//js/private:translate_pnpm_lock.bzl", translate_pnpm_lock_lib = "translate_pnpm_lock") load("//js:npm_import.bzl", "npm_import", "translate_pnpm_lock") load("//js/private:transitive_closure.bzl", "translate_to_transitive_closure") def _extension_impl(module_ctx): for mod in module_ctx.modules: for attr in mod.tags.translate_pnpm_lock: lockfile = pnpm_utils.parse_pnpm_lock(module_ctx.read(attr.pnpm_lock)) trans = translate_to_transitive_closure(lockfile, attr.prod, attr.dev, attr.no_optional) imports = translate_pnpm_lock_lib.gen_npm_imports(trans, attr) for i in imports: # fixme: pass the rest of the kwargs from i npm_import( name = i.name, package = i.package, version = i.pnpm_version, link_packages = i.link_packages, ) translate_pnpm_lock( name = "npm", pnpm_lock = attr.pnpm_lock, ) npm = module_extension( implementation = _extension_impl, tag_classes = { "translate_pnpm_lock": tag_class(attrs = dict({"name": attr.string()}, **translate_pnpm_lock_lib.attrs)), # todo: support individual packages as well # "package": tag_class(attrs = dict({"name": attr.string()}, **_npm_import.attrs)), }, )

0

787

0

23

c7b09eb689ac8f721c4645e55ec33f8b5d1f82bf

32,780

py

Python

paasta_tools/tron_tools.py

zhaoyanh1202/paasta

b0c148786f44476fe351fe410f0b81f0c941f3b6

[ "Apache-2.0" ]

null

paasta_tools/tron_tools.py

zhaoyanh1202/paasta

b0c148786f44476fe351fe410f0b81f0c941f3b6

[ "Apache-2.0" ]

null

paasta_tools/tron_tools.py

zhaoyanh1202/paasta

b0c148786f44476fe351fe410f0b81f0c941f3b6

[ "Apache-2.0" ]

null

# Copyright 2015-2018 Yelp Inc. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import datetime import glob import json import logging import os import pkgutil import re from typing import List from typing import Tuple import yaml from service_configuration_lib import read_extra_service_information try: from yaml.cyaml import CSafeDumper as Dumper except ImportError: # pragma: no cover (no libyaml-dev / pypy) Dumper = yaml.SafeDumper # type: ignore from paasta_tools.clusterman import get_clusterman_metrics from paasta_tools.tron import tron_command_context from paasta_tools.utils import DEFAULT_SOA_DIR from paasta_tools.utils import InvalidInstanceConfig from paasta_tools.utils import filter_templates_from_config log = logging.getLogger(__name__) logging.getLogger("tron").setLevel(logging.WARNING) MASTER_NAMESPACE = "MASTER" SPACER = "." VALID_MONITORING_KEYS = set( json.loads( pkgutil.get_data("paasta_tools.cli", "schemas/tron_schema.json").decode() )["definitions"]["job"]["properties"]["monitoring"]["properties"].keys() ) MESOS_EXECUTOR_NAMES = ("paasta", "spark") DEFAULT_AWS_REGION = "us-west-2" clusterman_metrics, _ = get_clusterman_metrics() def decompose_instance(instance): """Get (job_name, action_name) from an instance.""" decomposed = instance.split(SPACER) if len(decomposed) != 2: raise InvalidInstanceConfig("Invalid instance name: %s" % instance) return (decomposed[0], decomposed[1]) def decompose_executor_id(executor_id) -> Tuple[str, str, int, str]: """(service, job, run_number, action)""" service, job, str_run_number, action, _ = executor_id.split(SPACER) return (service, job, int(str_run_number), action) def parse_time_variables(command: str, parse_time: datetime.datetime = None) -> str: """Parses an input string and uses the Tron-style dateparsing to replace time variables. Currently supports only the date/time variables listed in the tron documentation: http://tron.readthedocs.io/en/latest/command_context.html#built-in-cc :param input_string: input string to be parsed :param parse_time: Reference Datetime object to parse the date and time strings, defaults to now. :returns: A string with the date and time variables replaced """ if parse_time is None: parse_time = datetime.datetime.now() # We build up a tron context object that has the right # methods to parse tron-style time syntax job_context = tron_command_context.JobRunContext( tron_command_context.CommandContext() ) # The tron context object needs the run_time attribute set so it knows # how to interpret the date strings job_context.job_run.run_time = parse_time return StringFormatter(job_context).format(command) def format_tron_action_dict(action_config): """Generate a dict of tronfig for an action, from the TronActionConfig. :param job_config: TronActionConfig """ executor = action_config.get_executor() result = { "command": action_config.get_cmd(), "executor": executor, "requires": action_config.get_requires(), "node": action_config.get_node(), "retries": action_config.get_retries(), "retries_delay": action_config.get_retries_delay(), "expected_runtime": action_config.get_expected_runtime(), "trigger_downstreams": action_config.get_trigger_downstreams(), "triggered_by": action_config.get_triggered_by(), "on_upstream_rerun": action_config.get_on_upstream_rerun(), "trigger_timeout": action_config.get_trigger_timeout(), } if executor in MESOS_EXECUTOR_NAMES: result["executor"] = "mesos" result["cpus"] = action_config.get_cpus() result["mem"] = action_config.get_mem() result["disk"] = action_config.get_disk() result["env"] = action_config.get_env() result["extra_volumes"] = format_volumes(action_config.get_extra_volumes()) result["docker_parameters"] = [ {"key": param["key"], "value": param["value"]} for param in action_config.format_docker_parameters() ] constraint_labels = ["attribute", "operator", "value"] result["constraints"] = [ dict(zip(constraint_labels, constraint)) for constraint in action_config.get_calculated_constraints() ] result["docker_image"] = action_config.get_docker_url() # Only pass non-None values, so Tron will use defaults for others return {key: val for key, val in result.items() if val is not None} def format_tron_job_dict(job_config): """Generate a dict of tronfig for a job, from the TronJobConfig. :param job_config: TronJobConfig """ action_dict = { action_config.get_action_name(): format_tron_action_dict(action_config) for action_config in job_config.get_actions() } result = { "node": job_config.get_node(), "schedule": job_config.get_schedule(), "actions": action_dict, "monitoring": job_config.get_monitoring(), "queueing": job_config.get_queueing(), "run_limit": job_config.get_run_limit(), "all_nodes": job_config.get_all_nodes(), "enabled": job_config.get_enabled(), "allow_overlap": job_config.get_allow_overlap(), "max_runtime": job_config.get_max_runtime(), "time_zone": job_config.get_time_zone(), "expected_runtime": job_config.get_expected_runtime(), } cleanup_config = job_config.get_cleanup_action() if cleanup_config: cleanup_action = format_tron_action_dict(cleanup_config) result["cleanup_action"] = cleanup_action # Only pass non-None values, so Tron will use defaults for others return {key: val for key, val in result.items() if val is not None} def load_tron_service_config_no_cache( service, cluster, load_deployments=True, soa_dir=DEFAULT_SOA_DIR, for_validation=False, ): """Load all configured jobs for a service, and any additional config values.""" config = read_extra_service_information( service_name=service, extra_info=f"tron-{cluster}", soa_dir=soa_dir ) jobs = filter_templates_from_config(config) job_configs = [ TronJobConfig( name=name, service=service, cluster=cluster, config_dict=job, load_deployments=load_deployments, soa_dir=soa_dir, for_validation=for_validation, ) for name, job in jobs.items() ] return job_configs def create_complete_config(service, cluster, soa_dir=DEFAULT_SOA_DIR): """Generate a namespace configuration file for Tron, for a service.""" job_configs = load_tron_service_config( service=service, cluster=cluster, load_deployments=True, soa_dir=soa_dir ) preproccessed_config = {} preproccessed_config["jobs"] = { job_config.get_name(): format_tron_job_dict(job_config) for job_config in job_configs } return yaml.dump(preproccessed_config, Dumper=Dumper, default_flow_style=False) def list_tron_clusters(service: str, soa_dir: str = DEFAULT_SOA_DIR) -> List[str]: """Returns the Tron clusters a service is configured to deploy to.""" search_re = r"/tron-([0-9a-z-_]*)\.yaml$" service_dir = os.path.join(soa_dir, service) clusters = [] for filename in glob.glob(f"{service_dir}/*.yaml"): cluster_re_match = re.search(search_re, filename) if cluster_re_match is not None: clusters.append(cluster_re_match.group(1)) return clusters def parse_service_instance_from_executor_id(task_id: str) -> Tuple[str, str]: """Parses tron mesos task ids, like schematizer.traffic_generator.28414.turnstyle.46da87d7-6092-4ed4-b926-ffa7b21c7785""" try: service, job, job_run, action, uuid = task_id.split(".") except Exception as e: log.warning( f"Couldn't parse the mesos task id into a valid tron job: {task_id}: {e}" ) service, job, action = "unknown_service", "unknown_job", "unknown_action" return service, f"{job}.{action}"

35.864333

125

0.652013

# Copyright 2015-2018 Yelp Inc. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import datetime import difflib import glob import hashlib import json import logging import os import pkgutil import re import subprocess import traceback from string import Formatter from typing import List from typing import Tuple import yaml from service_configuration_lib import read_extra_service_information from service_configuration_lib import read_yaml_file from service_configuration_lib.spark_config import generate_clusterman_metrics_entries from service_configuration_lib.spark_config import get_aws_credentials from service_configuration_lib.spark_config import get_resources_requested from service_configuration_lib.spark_config import get_spark_conf from service_configuration_lib.spark_config import K8S_AUTH_FOLDER from service_configuration_lib.spark_config import stringify_spark_env from paasta_tools.mesos_tools import mesos_services_running_here try: from yaml.cyaml import CSafeDumper as Dumper except ImportError: # pragma: no cover (no libyaml-dev / pypy) Dumper = yaml.SafeDumper # type: ignore from paasta_tools.clusterman import get_clusterman_metrics from paasta_tools.tron.client import TronClient from paasta_tools.tron import tron_command_context from paasta_tools.utils import DEFAULT_SOA_DIR from paasta_tools.utils import DockerParameter from paasta_tools.utils import DockerVolume from paasta_tools.utils import InstanceConfig from paasta_tools.utils import InvalidInstanceConfig from paasta_tools.utils import load_system_paasta_config from paasta_tools.utils import SystemPaastaConfig from paasta_tools.utils import load_v2_deployments_json from paasta_tools.utils import NoConfigurationForServiceError from paasta_tools.utils import NoDeploymentsAvailable from paasta_tools.utils import time_cache from paasta_tools.utils import filter_templates_from_config from paasta_tools.spark_tools import get_webui_url from paasta_tools.spark_tools import inject_spark_conf_str from paasta_tools import monitoring_tools from paasta_tools.monitoring_tools import list_teams from typing import Optional from typing import Dict from typing import Any log = logging.getLogger(__name__) logging.getLogger("tron").setLevel(logging.WARNING) MASTER_NAMESPACE = "MASTER" SPACER = "." VALID_MONITORING_KEYS = set( json.loads( pkgutil.get_data("paasta_tools.cli", "schemas/tron_schema.json").decode() )["definitions"]["job"]["properties"]["monitoring"]["properties"].keys() ) MESOS_EXECUTOR_NAMES = ("paasta", "spark") DEFAULT_AWS_REGION = "us-west-2" clusterman_metrics, _ = get_clusterman_metrics() class TronNotConfigured(Exception): pass class InvalidTronConfig(Exception): pass class TronConfig(dict): """System-level configuration for Tron.""" def __init__(self, config): super().__init__(config) def get_cluster_name(self): """:returns The name of the Tron cluster""" try: return self["cluster_name"] except KeyError: raise TronNotConfigured( "Could not find name of Tron cluster in system Tron config" ) def get_url(self): """:returns The URL for the Tron master's API""" try: return self["url"] except KeyError: raise TronNotConfigured( "Could not find URL of Tron master in system Tron config" ) def get_tronfig_folder(cluster, soa_dir): return os.path.join(soa_dir, "tron", cluster) def load_tron_config(): return TronConfig(load_system_paasta_config().get_tron_config()) def get_tron_client(): return TronClient(load_tron_config().get_url()) def compose_instance(job, action): return f"{job}{SPACER}{action}" def decompose_instance(instance): """Get (job_name, action_name) from an instance.""" decomposed = instance.split(SPACER) if len(decomposed) != 2: raise InvalidInstanceConfig("Invalid instance name: %s" % instance) return (decomposed[0], decomposed[1]) def decompose_executor_id(executor_id) -> Tuple[str, str, int, str]: """(service, job, run_number, action)""" service, job, str_run_number, action, _ = executor_id.split(SPACER) return (service, job, int(str_run_number), action) class StringFormatter(Formatter): def __init__(self, context=None): Formatter.__init__(self) self.context = context def get_value(self, key, args, kwds): if isinstance(key, str): try: return kwds[key] except KeyError: return self.context[key] else: return Formatter.get_value(key, args, kwds) def parse_time_variables(command: str, parse_time: datetime.datetime = None) -> str: """Parses an input string and uses the Tron-style dateparsing to replace time variables. Currently supports only the date/time variables listed in the tron documentation: http://tron.readthedocs.io/en/latest/command_context.html#built-in-cc :param input_string: input string to be parsed :param parse_time: Reference Datetime object to parse the date and time strings, defaults to now. :returns: A string with the date and time variables replaced """ if parse_time is None: parse_time = datetime.datetime.now() # We build up a tron context object that has the right # methods to parse tron-style time syntax job_context = tron_command_context.JobRunContext( tron_command_context.CommandContext() ) # The tron context object needs the run_time attribute set so it knows # how to interpret the date strings job_context.job_run.run_time = parse_time return StringFormatter(job_context).format(command) def pick_spark_ui_port(service, instance): # We don't know what ports will be available on the agent that the driver # will be scheduled on, so we just try to make them unique per service / instance. hash_key = f"{service} {instance}".encode() hash_number = int(hashlib.sha1(hash_key).hexdigest(), 16) preferred_port = 33000 + (hash_number % 25000) return preferred_port class TronActionConfig(InstanceConfig): config_filename_prefix = "tron" def __init__( self, service, instance, cluster, config_dict, branch_dict, soa_dir=DEFAULT_SOA_DIR, for_validation=False, ): super().__init__( cluster=cluster, instance=instance, service=service, config_dict=config_dict, branch_dict=branch_dict, soa_dir=soa_dir, ) self.job, self.action = decompose_instance(instance) # Indicate whether this config object is created for validation self.for_validation = for_validation def get_spark_config_dict(self): spark_config_dict = getattr(self, "_spark_config_dict", None) # cached the created dict, so that we don't need to process it multiple # times, and having inconsistent result if spark_config_dict is not None: return spark_config_dict if self.get_spark_cluster_manager() == "mesos": mesos_leader = ( f"zk://{load_system_paasta_config().get_zk_hosts()}" if not self.for_validation else "N/A" ) else: mesos_leader = None aws_creds = get_aws_credentials( aws_credentials_yaml=self.config_dict.get("aws_credentials_yaml") ) self._spark_config_dict = get_spark_conf( cluster_manager=self.get_spark_cluster_manager(), spark_app_base_name=f"tron_spark_{self.get_service()}_{self.get_instance()}", user_spark_opts=self.config_dict.get("spark_args", {}), paasta_cluster=self.get_spark_paasta_cluster(), paasta_pool=self.get_spark_paasta_pool(), paasta_service=self.get_service(), paasta_instance=self.get_instance(), docker_img=self.get_docker_url(), aws_creds=aws_creds, extra_volumes=self.get_volumes(load_system_paasta_config().get_volumes()), # tron is using environment variable to load the required creds with_secret=False, mesos_leader=mesos_leader, # load_system_paasta already load the default volumes load_paasta_default_volumes=False, ) return self._spark_config_dict def get_job_name(self): return self.job def get_action_name(self): return self.action def get_deploy_group(self) -> Optional[str]: return self.config_dict.get("deploy_group", None) def get_docker_url( self, system_paasta_config: Optional[SystemPaastaConfig] = None ) -> str: # It's okay for tronfig to contain things that aren't deployed yet - it's normal for developers to # push tronfig well before the job is scheduled to run, and either they'll deploy the service before # or get notified when the job fails. # # This logic ensures that we can still pass validation and run setup_tron_namespace even if # there's nothing in deployments.json yet. return ( "" if not self.get_docker_image() else super().get_docker_url(system_paasta_config=system_paasta_config) ) def get_cmd(self): command = self.config_dict.get("command") if self.get_executor() == "spark": # Spark expects to be able to write to MESOS_SANDBOX if it is set # but the default value (/mnt/mesos/sandbox) doesn't get mounted in # our Docker containers, so we unset it here. (Un-setting is fine, # since Spark will just write to /tmp instead). command = "unset MESOS_DIRECTORY MESOS_SANDBOX; " + inject_spark_conf_str( command, stringify_spark_env(self.get_spark_config_dict()) ) return command def get_spark_paasta_cluster(self): return self.config_dict.get("spark_paasta_cluster", self.get_cluster()) def get_spark_paasta_pool(self): return self.config_dict.get("spark_paasta_pool", "batch") def get_spark_cluster_manager(self): return self.config_dict.get("spark_cluster_manager", "mesos") def get_env(self): env = super().get_env() if self.get_executor() == "spark": spark_config_dict = self.get_spark_config_dict() env["EXECUTOR_CLUSTER"] = self.get_spark_paasta_cluster() env["EXECUTOR_POOL"] = self.get_spark_paasta_pool() env["SPARK_OPTS"] = stringify_spark_env(spark_config_dict) # The actual mesos secret will be decrypted and injected on mesos master when assigning # tasks. env["SPARK_MESOS_SECRET"] = "SHARED_SECRET(SPARK_MESOS_SECRET)" if clusterman_metrics: env["CLUSTERMAN_RESOURCES"] = json.dumps( generate_clusterman_metrics_entries( clusterman_metrics, get_resources_requested(spark_config_dict), spark_config_dict["spark.app.name"], get_webui_url(spark_config_dict["spark.ui.port"]), ) ) else: env["CLUSTERMAN_RESOURCES"] = "{}" if "AWS_ACCESS_KEY_ID" not in env or "AWS_SECRET_ACCESS_KEY" not in env: try: access_key, secret_key, session_token = get_aws_credentials( service=self.get_service(), aws_credentials_yaml=self.config_dict.get( "aws_credentials_yaml" ), ) env["AWS_ACCESS_KEY_ID"] = access_key env["AWS_SECRET_ACCESS_KEY"] = secret_key except Exception: log.warning( f"Cannot set AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY environment " f"variables for tron action {self.get_instance()} of service " f"{self.get_service()} via credentail file. Traceback:\n" f"{traceback.format_exc()}" ) if "AWS_DEFAULT_REGION" not in env: env["AWS_DEFAULT_REGION"] = DEFAULT_AWS_REGION return env def get_extra_volumes(self): extra_volumes = super().get_extra_volumes() if ( self.get_executor() == "spark" and self.get_spark_cluster_manager() == "kubernetes" ): extra_volumes.append( DockerVolume( { "hostPath": "/etc/pki/spark", "containerPath": K8S_AUTH_FOLDER, "mode": "RO", } ) ) return extra_volumes def get_cpu_burst_add(self) -> float: """ For Tron jobs, we don't let them burst by default, because they don't represent "real-time" workloads, and should not impact neighbors """ return self.config_dict.get("cpu_burst_add", 0) def get_executor(self): return self.config_dict.get("executor", "paasta") def get_healthcheck_mode(self, _) -> None: return None def get_node(self): return self.config_dict.get("node") def get_retries(self): return self.config_dict.get("retries") def get_retries_delay(self): return self.config_dict.get("retries_delay") def get_requires(self): return self.config_dict.get("requires") def get_expected_runtime(self): return self.config_dict.get("expected_runtime") def get_triggered_by(self): return self.config_dict.get("triggered_by", None) def get_trigger_downstreams(self): return self.config_dict.get("trigger_downstreams", None) def get_on_upstream_rerun(self): return self.config_dict.get("on_upstream_rerun", None) def get_trigger_timeout(self): return self.config_dict.get("trigger_timeout", None) def get_calculated_constraints(self): """Combine all configured Mesos constraints.""" constraints = self.get_constraints() if constraints is not None: return constraints else: constraints = self.get_extra_constraints() constraints.extend( self.get_deploy_constraints( blacklist=self.get_deploy_blacklist(), whitelist=self.get_deploy_whitelist(), # Don't have configs for the paasta cluster system_deploy_blacklist=[], system_deploy_whitelist=None, ) ) constraints.extend(self.get_pool_constraints()) return constraints def get_nerve_namespace(self) -> None: return None def validate(self): error_msgs = [] error_msgs.extend(super().validate()) # Tron is a little special, because it can *not* have a deploy group # But only if an action is running via ssh and not via paasta if ( self.get_deploy_group() is None and self.get_executor() in MESOS_EXECUTOR_NAMES ): error_msgs.append( f"{self.get_job_name()}.{self.get_action_name()} must have a deploy_group set" ) return error_msgs def format_docker_parameters( self, with_labels: bool = True, system_paasta_config: Optional[SystemPaastaConfig] = None, ) -> List[DockerParameter]: """Formats extra flags for running docker. Will be added in the format `["--%s=%s" % (e['key'], e['value']) for e in list]` to the `docker run` command Note: values must be strings""" parameters = super().format_docker_parameters( with_labels=with_labels, system_paasta_config=system_paasta_config ) if self.get_executor() == "spark": parameters.append({"key": "net", "value": "host"}) return parameters class TronJobConfig: """Represents a job in Tron, consisting of action(s) and job-level configuration values.""" def __init__( self, name: str, config_dict: Dict[str, Any], cluster: str, service: Optional[str] = None, load_deployments: bool = True, soa_dir: str = DEFAULT_SOA_DIR, for_validation: bool = False, ) -> None: self.name = name self.config_dict = config_dict self.cluster = cluster self.service = service self.load_deployments = load_deployments self.soa_dir = soa_dir # Indicate whether this config object is created for validation self.for_validation = for_validation def get_name(self): return self.name def get_node(self): return self.config_dict.get("node", "paasta") def get_schedule(self): return self.config_dict.get("schedule") def get_monitoring(self): srv_monitoring = dict( monitoring_tools.read_monitoring_config(self.service, soa_dir=self.soa_dir) ) tron_monitoring = self.config_dict.get("monitoring", {}) srv_monitoring.update(tron_monitoring) # filter out non-tron monitoring keys srv_monitoring = { k: v for k, v in srv_monitoring.items() if k in VALID_MONITORING_KEYS } return srv_monitoring def get_queueing(self): return self.config_dict.get("queueing") def get_run_limit(self): return self.config_dict.get("run_limit") def get_all_nodes(self): return self.config_dict.get("all_nodes") def get_enabled(self): return self.config_dict.get("enabled") def get_allow_overlap(self): return self.config_dict.get("allow_overlap") def get_max_runtime(self): return self.config_dict.get("max_runtime") def get_time_zone(self): return self.config_dict.get("time_zone") def get_service(self) -> Optional[str]: return self.service or self.config_dict.get("service") def get_deploy_group(self) -> Optional[str]: return self.config_dict.get("deploy_group", None) def get_cluster(self): return self.cluster def get_expected_runtime(self): return self.config_dict.get("expected_runtime") def _get_action_config(self, action_name, action_dict): action_service = action_dict.setdefault("service", self.get_service()) action_deploy_group = action_dict.setdefault( "deploy_group", self.get_deploy_group() ) if action_service and action_deploy_group and self.load_deployments: try: deployments_json = load_v2_deployments_json( service=action_service, soa_dir=self.soa_dir ) branch_dict = { "docker_image": deployments_json.get_docker_image_for_deploy_group( action_deploy_group ), "git_sha": deployments_json.get_git_sha_for_deploy_group( action_deploy_group ), # TODO: add Tron instances when generating deployments json "desired_state": "start", "force_bounce": None, } except NoDeploymentsAvailable: log.warning( f'Docker image unavailable for {action_service}.{self.get_name()}.{action_dict.get("name")}' " is it deployed yet?" ) branch_dict = None else: branch_dict = None action_dict["monitoring"] = self.get_monitoring() return TronActionConfig( service=action_service, instance=compose_instance(self.get_name(), action_name), cluster=self.get_cluster(), config_dict=action_dict, branch_dict=branch_dict, soa_dir=self.soa_dir, for_validation=self.for_validation, ) def get_actions(self): actions = self.config_dict.get("actions") return [ self._get_action_config(name, action_dict) for name, action_dict in actions.items() ] def get_cleanup_action(self): action_dict = self.config_dict.get("cleanup_action") if not action_dict: return None # TODO: we should keep this trickery outside paasta repo return self._get_action_config("cleanup", action_dict) def check_monitoring(self) -> Tuple[bool, str]: monitoring = self.get_monitoring() valid_teams = list_teams() if monitoring is not None: team_name = monitoring.get("team", None) if team_name is None: return False, "Team name is required for monitoring" elif team_name not in valid_teams: suggest_teams = difflib.get_close_matches( word=team_name, possibilities=valid_teams ) return ( False, f"Invalid team name: {team_name}. Do you mean one of these: {suggest_teams}", ) return True, "" def check_actions(self) -> Tuple[bool, List[str]]: actions = self.get_actions() cleanup_action = self.get_cleanup_action() if cleanup_action: actions.append(cleanup_action) checks_passed = True msgs: List[str] = [] for action in actions: action_msgs = action.validate() if action_msgs: checks_passed = False msgs.extend(action_msgs) return checks_passed, msgs def validate(self) -> List[str]: _, error_msgs = self.check_actions() checks = ["check_monitoring"] for check in checks: check_passed, check_msg = getattr(self, check)() if not check_passed: error_msgs.append(check_msg) return error_msgs def __eq__(self, other): if isinstance(other, type(self)): return self.config_dict == other.config_dict return False def format_volumes(paasta_volume_list): return [ { "container_path": v["containerPath"], "host_path": v["hostPath"], "mode": v["mode"], } for v in paasta_volume_list ] def format_master_config(master_config, default_volumes, dockercfg_location): mesos_options = master_config.get("mesos_options", {}) mesos_options.update( { "default_volumes": format_volumes(default_volumes), "dockercfg_location": dockercfg_location, } ) master_config["mesos_options"] = mesos_options return master_config def format_tron_action_dict(action_config): """Generate a dict of tronfig for an action, from the TronActionConfig. :param job_config: TronActionConfig """ executor = action_config.get_executor() result = { "command": action_config.get_cmd(), "executor": executor, "requires": action_config.get_requires(), "node": action_config.get_node(), "retries": action_config.get_retries(), "retries_delay": action_config.get_retries_delay(), "expected_runtime": action_config.get_expected_runtime(), "trigger_downstreams": action_config.get_trigger_downstreams(), "triggered_by": action_config.get_triggered_by(), "on_upstream_rerun": action_config.get_on_upstream_rerun(), "trigger_timeout": action_config.get_trigger_timeout(), } if executor in MESOS_EXECUTOR_NAMES: result["executor"] = "mesos" result["cpus"] = action_config.get_cpus() result["mem"] = action_config.get_mem() result["disk"] = action_config.get_disk() result["env"] = action_config.get_env() result["extra_volumes"] = format_volumes(action_config.get_extra_volumes()) result["docker_parameters"] = [ {"key": param["key"], "value": param["value"]} for param in action_config.format_docker_parameters() ] constraint_labels = ["attribute", "operator", "value"] result["constraints"] = [ dict(zip(constraint_labels, constraint)) for constraint in action_config.get_calculated_constraints() ] result["docker_image"] = action_config.get_docker_url() # Only pass non-None values, so Tron will use defaults for others return {key: val for key, val in result.items() if val is not None} def format_tron_job_dict(job_config): """Generate a dict of tronfig for a job, from the TronJobConfig. :param job_config: TronJobConfig """ action_dict = { action_config.get_action_name(): format_tron_action_dict(action_config) for action_config in job_config.get_actions() } result = { "node": job_config.get_node(), "schedule": job_config.get_schedule(), "actions": action_dict, "monitoring": job_config.get_monitoring(), "queueing": job_config.get_queueing(), "run_limit": job_config.get_run_limit(), "all_nodes": job_config.get_all_nodes(), "enabled": job_config.get_enabled(), "allow_overlap": job_config.get_allow_overlap(), "max_runtime": job_config.get_max_runtime(), "time_zone": job_config.get_time_zone(), "expected_runtime": job_config.get_expected_runtime(), } cleanup_config = job_config.get_cleanup_action() if cleanup_config: cleanup_action = format_tron_action_dict(cleanup_config) result["cleanup_action"] = cleanup_action # Only pass non-None values, so Tron will use defaults for others return {key: val for key, val in result.items() if val is not None} def load_tron_instance_config( service: str, instance: str, cluster: str, load_deployments: bool = True, soa_dir: str = DEFAULT_SOA_DIR, ) -> TronActionConfig: jobs = load_tron_service_config( service=service, cluster=cluster, load_deployments=load_deployments, soa_dir=soa_dir, ) requested_job, requested_action = instance.split(".") for job in jobs: if job.get_name() == requested_job: for action in job.get_actions(): if action.get_action_name() == requested_action: return action raise NoConfigurationForServiceError( f"No tron configuration found for {service} {instance}" ) @time_cache(ttl=5) def load_tron_service_config( service, cluster, load_deployments=True, soa_dir=DEFAULT_SOA_DIR, for_validation=False, ): return load_tron_service_config_no_cache( service, cluster, load_deployments, soa_dir, for_validation, ) def load_tron_service_config_no_cache( service, cluster, load_deployments=True, soa_dir=DEFAULT_SOA_DIR, for_validation=False, ): """Load all configured jobs for a service, and any additional config values.""" config = read_extra_service_information( service_name=service, extra_info=f"tron-{cluster}", soa_dir=soa_dir ) jobs = filter_templates_from_config(config) job_configs = [ TronJobConfig( name=name, service=service, cluster=cluster, config_dict=job, load_deployments=load_deployments, soa_dir=soa_dir, for_validation=for_validation, ) for name, job in jobs.items() ] return job_configs def create_complete_master_config(cluster, soa_dir=DEFAULT_SOA_DIR): system_paasta_config = load_system_paasta_config() tronfig_folder = get_tronfig_folder(soa_dir=soa_dir, cluster=cluster) config = read_yaml_file(os.path.join(tronfig_folder, f"MASTER.yaml")) master_config = format_master_config( config, system_paasta_config.get_volumes(), system_paasta_config.get_dockercfg_location(), ) return yaml.dump(master_config, Dumper=Dumper, default_flow_style=False) def create_complete_config(service, cluster, soa_dir=DEFAULT_SOA_DIR): """Generate a namespace configuration file for Tron, for a service.""" job_configs = load_tron_service_config( service=service, cluster=cluster, load_deployments=True, soa_dir=soa_dir ) preproccessed_config = {} preproccessed_config["jobs"] = { job_config.get_name(): format_tron_job_dict(job_config) for job_config in job_configs } return yaml.dump(preproccessed_config, Dumper=Dumper, default_flow_style=False) def validate_complete_config( service: str, cluster: str, soa_dir: str = DEFAULT_SOA_DIR ) -> List[str]: job_configs = load_tron_service_config( service=service, cluster=cluster, load_deployments=False, soa_dir=soa_dir, for_validation=True, ) # PaaSTA-specific validation for job_config in job_configs: check_msgs = job_config.validate() if check_msgs: return check_msgs master_config_path = os.path.join( os.path.abspath(soa_dir), "tron", cluster, MASTER_NAMESPACE + ".yaml" ) preproccessed_config = {} # Use Tronfig on generated config from PaaSTA to validate the rest preproccessed_config["jobs"] = { job_config.get_name(): format_tron_job_dict(job_config) for job_config in job_configs } complete_config = yaml.dump(preproccessed_config, Dumper=Dumper) proc = subprocess.run( ["tronfig", "-", "-V", "-n", service, "-m", master_config_path], input=complete_config, stdout=subprocess.PIPE, stderr=subprocess.PIPE, encoding="utf-8", ) if proc.returncode != 0: process_errors = proc.stderr.strip() if process_errors: # Error running tronfig print(proc.stderr) return [proc.stdout.strip()] return [] def get_tron_namespaces(cluster, soa_dir): tron_config_file = f"tron-{cluster}.yaml" config_dirs = [ _dir[0] for _dir in os.walk(os.path.abspath(soa_dir)) if tron_config_file in _dir[2] ] namespaces = [os.path.split(config_dir)[1] for config_dir in config_dirs] return namespaces def list_tron_clusters(service: str, soa_dir: str = DEFAULT_SOA_DIR) -> List[str]: """Returns the Tron clusters a service is configured to deploy to.""" search_re = r"/tron-([0-9a-z-_]*)\.yaml$" service_dir = os.path.join(soa_dir, service) clusters = [] for filename in glob.glob(f"{service_dir}/*.yaml"): cluster_re_match = re.search(search_re, filename) if cluster_re_match is not None: clusters.append(cluster_re_match.group(1)) return clusters def get_tron_dashboard_for_cluster(cluster: str): dashboards = load_system_paasta_config().get_dashboard_links()[cluster] if "Tron" not in dashboards: raise Exception(f"tron api endpoint is not defined for cluster {cluster}") return dashboards["Tron"] def tron_jobs_running_here() -> List[Tuple[str, str, int]]: return mesos_services_running_here( framework_filter=lambda fw: fw["name"].startswith("tron"), parse_service_instance_from_executor_id=parse_service_instance_from_executor_id, ) def parse_service_instance_from_executor_id(task_id: str) -> Tuple[str, str]: """Parses tron mesos task ids, like schematizer.traffic_generator.28414.turnstyle.46da87d7-6092-4ed4-b926-ffa7b21c7785""" try: service, job, job_run, action, uuid = task_id.split(".") except Exception as e: log.warning( f"Couldn't parse the mesos task id into a valid tron job: {task_id}: {e}" ) service, job, action = "unknown_service", "unknown_job", "unknown_action" return service, f"{job}.{action}"

0

259

0

17,423

0

4,488

0

775

1,122

6f6564a4b79638714786a730792e5cd34d3f9e05

1,755

py

Python

invenio_records_presentation/workflows/presentation.py

CESNET/invenio-records-presentation

547a2652a97feb1c6cd50e1ea917c2b5decb9286

[ "MIT" ]

null

invenio_records_presentation/workflows/presentation.py

CESNET/invenio-records-presentation

547a2652a97feb1c6cd50e1ea917c2b5decb9286

[ "MIT" ]

4

2019-03-19T16:18:22.000Z

2021-06-28T12:33:14.000Z

invenio_records_presentation/workflows/presentation.py

CESNET/invenio-records-presentation

547a2652a97feb1c6cd50e1ea917c2b5decb9286

[ "MIT" ]

null

# -*- coding: utf-8 -*- # # Copyright (C) 2019 CESNET. # # Invenio Records Presentation is free software; you can redistribute it and/or modify it # under the terms of the MIT License; see LICENSE file for more details. """ Example Presentation workflow.""" from invenio_records_presentation.workflows import presentation_workflow_factory example = presentation_workflow_factory(task_list=[ print_extra_data, create_example_file, print_data, transform_example_file, output_example_file, ])

27

89

0.688889

# -*- coding: utf-8 -*- # # Copyright (C) 2019 CESNET. # # Invenio Records Presentation is free software; you can redistribute it and/or modify it # under the terms of the MIT License; see LICENSE file for more details. """ Example Presentation workflow.""" from invenio_workflows import WorkflowEngine from invenio_records_presentation.api import PresentationOutputFile from invenio_records_presentation.workflows import presentation_workflow_factory def print_extra_data(obj, eng: WorkflowEngine): print(obj.extra_data) return obj def print_data(obj, eng: WorkflowEngine): print(obj.data) return obj def create_example_file(obj, eng: WorkflowEngine): # creates an example input file and passes a path to it input = obj.scratch.create_file(task_name='example_input') with open(input, 'w') as tf: tf.write("example file\n") obj.data = input return obj def transform_example_file(obj, eng: WorkflowEngine): input_data = '' try: with open(obj.data, 'r') as input: input_data = input.read() except OSError: eng.abort() # Cannot read input data, abort workflow execution output = obj.scratch.create_file(task_name='example_output') with open(output, 'w') as tf: tf.write(input_data.title()) obj.data = output return obj def output_example_file(obj, eng: WorkflowEngine): obj.data = PresentationOutputFile(path=obj.data, mimetype='text/plain', filename='example.txt') return obj example = presentation_workflow_factory(task_list=[ print_extra_data, create_example_file, print_data, transform_example_file, output_example_file, ])

0

1,008

0

69

160

af18231ed684c46a269b36519eb707e9ab6b7d6a

34,191

py

Python

twit_analytics.py

nikb999/Twitter-analytics

35074503be495e62fad282b9c723756df87119a7

[ "MIT" ]

null

twit_analytics.py

nikb999/Twitter-analytics

35074503be495e62fad282b9c723756df87119a7

[ "MIT" ]

null

twit_analytics.py

nikb999/Twitter-analytics

35074503be495e62fad282b9c723756df87119a7

[ "MIT" ]

null

#!/usr/bin/python # -*- coding: utf-8 -*- #add the path of the twitter egg import sys egg_path = '/home/users/web/........./cgi-bin/PyPkg/twitter-1.14.3-py2.7.egg' sys.path.append(egg_path) # Import the CGI, string, sys, and md5crypt modules import json, urllib2, re, time, datetime, sys, cgi, os import sqlite3 import MySQLdb as mdb import string, random from urlparse import urlparse from tempfile import TemporaryFile def lex_anal(incomingTweetList): ''' routine to do lexical analysis ''' #final_tweet_list --- date / sender full name / tweet #read the tweets and create a list of sender-htag and sender-@ #incoming TweetList has two layer lists sender_htag = [] sender_at = [] h_tags_all = [] at_items_all = [] ts_all = [] for lex2 in incomingTweetList: for lex22 in lex2: td = lex22[0] #this is the tweet date try: ts = text_sanitize(lex22[1]) #this is the tweet sender except: print 'something wrong with ',lex22[1] ts = '---' ts_all.append(ts) h_tags = re.findall('[#]\w+',lex22[2]) #these are the h-tags at_items = re.findall('[@]\w+',lex22[2]) #these are the other users h_tags = [hti.lower() for hti in h_tags] at_items = [ati.lower() for ati in at_items] for h2 in h_tags: sender_htag.append([td,ts.lower()+'-'+h2]) h_tags_all.append(h2) for at2 in at_items: sender_at.append([td,ts.lower()+'-'+at2]) at_items_all.append(at2) #summarize the two new lists #following lists don't have dates sender_htag2 = [xx[1] for xx in sender_htag] sender_at2 = [yy[1] for yy in sender_at] #make a list of the tweet senders only ts_all = list(set(ts_all)) #print ts_all #get the top 10 htags #py2.6 ht_col = collections.Counter(h_tags_all) htag_data4heatmap = [] at_data4heatmap = [] #print '<ul>Top 10 Hashtags' #py2.6 for h_item in ht_col.most_common(10): for h_item in top_list(h_tags_all,10): #print '<li>', h_item, '</li>' #count the number of times each of the hastag was referenced by each tweet sender try: for tsitem in ts_all: try: itemtocount = str(tsitem+'-'+h_item[1]) htag_data4heatmap.append([tsitem,h_item[1], sender_htag2.count(itemtocount)]) except: print 'Problem here: ',h_item,tsitem except: print 'Problem here',h_item print '</ul>' #get the top 10 user references #py2.6 at_col = collections.Counter(at_items_all) #print '<ul>Top 10 Users' #py2.6 for a_item in at_col.most_common(10): for a_item in top_list(at_items_all,10): #print '<li>', a_item, '</li>' #count the number of times each of the hastag was referenced by each tweet sender try: for tsitem in ts_all: itemtocount = str(tsitem+'-'+a_item[1]) at_data4heatmap.append([tsitem,a_item[1], sender_at2.count(itemtocount)]) except: print 'Problem here 2',a_item print '</ul>' #draw the table with the heatmap tcols = len(ts_all) #number of tweet senders - rows trows = len(htag_data4heatmap) / tcols #number of hastags - cols #print trows, tcols if trows>0: print '<br><br>' print '<h3>Most Popular Hashtags</h3>' heatmap_table(trows,tcols,htag_data4heatmap) tcols = len(ts_all) #number of tweet senders - rows trows = len(at_data4heatmap) / tcols #number of hastags - cols #print trows, tcols if trows>0: print '<br><br>' print '<h3>Most Referenced Users</h3>' heatmap_table(trows,tcols,at_data4heatmap) # Define main function. main()

40.800716

197

0.534176

#!/usr/bin/python # -*- coding: utf-8 -*- #add the path of the twitter egg import sys egg_path = '/home/users/web/........./cgi-bin/PyPkg/twitter-1.14.3-py2.7.egg' sys.path.append(egg_path) # Import the CGI, string, sys, and md5crypt modules import json, urllib2, re, time, datetime, sys, cgi, os import sqlite3 import MySQLdb as mdb import string, random from urlparse import urlparse from twitter import * from tempfile import TemporaryFile from collections import * from py_site_header import * def thisPYfile(): return 'twit_analytics.py' def define_keys(): CONSUMER_KEY="......................" CONSUMER_SECRET="...................." ACCESS_TOKEN="..........................." ACCESS_TOKEN_SECRET="...................................." return CONSUMER_KEY, CONSUMER_SECRET, ACCESS_TOKEN, ACCESS_TOKEN_SECRET def start_database_to_store_tweets(): dbhost="......................" # Host name dbuser="......." # Mysql username dbpswd="......." # Mysql password dbname = '........' # MySql db try: conn = mdb.connect(host=dbhost,user=dbuser,passwd=dbpswd,db=dbname) c = conn.cursor() return c, True, conn except mdb.Error, e: return e, False def site_header(st=''): site_start() print '</div>' site_title(st) def site_start(): print ''' Content-type:text/html\r\n\r\n <html> <div class="wrap" id="wrap_id"> <head> <meta http-equiv="content-type" content="text/html;charset=utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1"> <title>Financial Models</title> <script src="https://ajax.googleapis.com/ajax/libs/jquery/1.11.3/jquery.min.js"></script> <script type="text/javascript" src="../js/js_functions.js"></script> <link rel="stylesheet" href="http://www.w3schools.com/lib/w3.css"> <link rel="stylesheet" href="http://www.w3schools.com/lib/w3-theme-indigo.css"> <link href='http://code.ionicframework.com/ionicons/2.0.1/css/ionicons.min.css' rel='stylesheet' type='text/css'> <link rel="stylesheet" href="http://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.4.0/css/font-awesome.min.css"> <style> a:link { text-decoration: none; } a:visited { text-decoration: none; } a:hover { text-decoration: none; } a:active { text-decoration: none; } </style> </head> <body> ''' def site_title(s_title): print ''' <div id="site_title" class="w3-container w3-theme-d4 w3-center w3-padding-jumbo"> <p> </p> <div class="w3-row w3-jumbo"> ''' print s_title print ''' <br> </div> </div> ''' def site_footer(): import datetime curr_year = datetime.datetime.now().strftime("%Y") print '<div class="w3-container w3-border-top" style="text-align:center">' print '<p> © 2013-'+curr_year+' | ' print '<a>Contact Us</a> </p>' print '<p><a href="./termsofuse.py">Terms of Use</a> |', print '<a href="./home.py#aboutus">About Us</a> </p>' print '</div>' print '</form>' print ' </body>' print ' </div>' #for the div id = wrap print ' </html>' def html_start(): # Start the HLML Block site_header('Twitter Analytics') def html_end(): site_footer() def top_list(in_l,topx): #function to get the top xx items in a list # Need this because v2.6 of python does not have Counter in collections counter = {} for i in in_l: counter[i] = counter.get(i, 0) + 1 final_dict = sorted([ (freq,word) for word, freq in counter.items() ], reverse=True)[:topx] return final_dict def text_sanitize(in_text): out_text = in_text.replace("'","") out_text = out_text.replace("\""," ").replace("\\"," ").replace("="," ").replace("''",'\"').replace("' '",'\"') return out_text def generate_form(): html_start() print '<div id="body_sty">' print '<p>Explore the world of Twitter and discover information about twitter users, their friends and followers as well as lexical analysis of the tweets.</p>' print '<TABLE style="display: block;" BORDER = 0>' print "<FORM METHOD = post ACTION=\'"+thisPYfile()+"\'>" print "<TR><TH align=\"left\">Screen Name:</TH><TD><INPUT type = text name=\"scn_name\"></TD><TR>" print "</TABLE>" print "<INPUT TYPE = hidden NAME = \"action\" VALUE = \"display\">" print "<INPUT TYPE = submit VALUE = \"Enter\">" print "</FORM>" print '</div>' html_end() def user_public_info(find_id_for): #html_start() #this line gets the public info for the user print '<h2>'+'\nUsers Public Info'+'</h2>' do_rest_of_module = 0 try: t = Twitter(auth=OAuth(define_keys()[2],define_keys()[3],define_keys()[0],define_keys()[1])) response = t.users.lookup(screen_name=find_id_for) do_rest_of_module = 1 except: print '<p>', 'Error getting public data' ,'</p>' if do_rest_of_module == 1: print '<h3>'+'\nBasic Info for: ', find_id_for+'</h3>' print '<p>', '\tKey Data' ,'</p>' print '<ul>' print '<li>ID:',response[0]['id'],'</li>' print '<li>Screen Name:',response[0]['screen_name'],'</li>' print '<li>Name:',response[0]['name'] ,'</li>' print '<li>Location:',response[0]['location'] ,'</li>' print '<li>Friends:',response[0]['friends_count'] ,'</li>' print '<li>Followers:',response[0]['followers_count'] ,'</li>' print '<li>Messages posted:',response[0]['statuses_count'] ,'</li>' print '</ul>' def get_last200_tweets(in_user): #this method will get the last 200 tweets of the user #rate limit is 180 requests per 15 min window #print '<h2>'+'\nAnalysis of Past Tweets for',in_user,'</h2>' do_rest_of_module = 0 try: t = Twitter(auth=OAuth(define_keys()[2],define_keys()[3],define_keys()[0],define_keys()[1])) response=t.statuses.user_timeline(screen_name=in_user,count=200) #print '<p>', '\tResponses left:', response.headers['x-rate-limit-remaining'] ,'</p>' #print '<p>Line 201. Response length: ',len(response),'</p>' if len(response) > 0: do_rest_of_module = 1 else: print '<p>', 'No info found for: ',in_user ,'</p>' except: print '<p>', 'Error getting tweets info for: ',in_user ,'</p>' if do_rest_of_module == 1: base_twit_list = [] data_for_plots = [] x = response #x = [element.lower() for element in response] #x is list - LOWER CASE hashtag_list = [] #start an empty list of hashtags at_list = [] #start an empty list of twitter IDs re_twt_list = [] #start a list of retweets #get the start and end dates sdf = x[0]['created_at'] #get the full date of last tweet start_date = datetime.date(int(sdf[26:30]), int(time.strptime(sdf[4:7],'%b').tm_mon), int(sdf[8:10])) edf = x[len(x)-1]['created_at'] #get the full date of first tweet end_date = datetime.date(int(edf[26:30]), int(time.strptime(edf[4:7],'%b').tm_mon), int(edf[8:10])) #end_date = str(edf[8:10])+'-'+str(edf[4:7])+'-'+str(edf[26:30]) twit_day_range = (start_date-end_date).days avg_twit_day = (1.0*len(x)/max(1,twit_day_range)) print >> t2, '<h4>'+'Tweet Stats for ', in_user+'</h4>' #print x[0] #print '\tStats for last',len(x), 'tweets by',in_user fix_nm = x[0]['user']['screen_name'] try: if str(x[0]['user']['name']).decode('ascii'): fix_nm = str(x[0]['user']['name']) except: #print 'something wrong with the name for ', x[0]['user']['name'] fix_nm = x[0]['user']['screen_name'] print >> t2, '<ul>' print >> t2, '<li>Key Personal Data</li>' print >> t2, '<ul>' print >> t2, '<li>ID:',x[0]['user']['id'],'</li>' print >> t2, '<li>Screen Name:',x[0]['user']['screen_name'],'</li>' print >> t2, '<li>Name:',fix_nm,'</li>' #print '<li>Location:',x[0]['user']['location'],'</li>' print >> t2, '<li>Friends:',x[0]['user']['friends_count'] ,'</li>' print >> t2, '<li>Followers:',x[0]['user']['followers_count'] ,'</li>' print >> t2, '<li>Messages posted:',x[0]['user']['statuses_count'] ,'</li>' foll_frnd_rat = 1.0*x[0]['user']['followers_count'] / max(1,x[0]['user']['friends_count']) print >> t2, '<li>Follower to Friend Ratio:', '%.1f' %(foll_frnd_rat),'</li>' print >> t2, '</ul>' print >> t2, '</ul>' print >> t2, '<ul>' print >> t2, '<li>',len(x),'tweets in past',twit_day_range,'days', print >> t2, '(',end_date,'to',start_date,')' ,'</li>' print >> t2, '<li>', 'Avg of ','%.1f' %(avg_twit_day),'tweets per day' ,'</li>' #add info to the data for charts list data_for_plots.extend([x[0]['user']['screen_name']]) data_for_plots.extend([x[0]['user']['friends_count']]) data_for_plots.extend([x[0]['user']['followers_count']]) data_for_plots.extend([x[0]['user']['statuses_count']]) data_for_plots.extend([twit_day_range]) data_for_plots.extend([len(x)]) for item in x: #the encode(ascii,ignore) will convert text to ascii and ignore other td = item['created_at'] twt_date = datetime.date(int(td[26:30]), int(time.strptime(td[4:7],'%b').tm_mon), int(td[8:10])) fix_nm = item['user']['screen_name'] try: if str(item['user']['name']).encode('utf8','ignore'): fix_nm = str(item['user']['name']) except: fix_nm = item['user']['screen_name'] try: fix_text = text_sanitize(item['text'].encode('utf8','ignore')) except: #print 'something wrong with the text in tweet for: ',in_user fix_text = 'Did not process' #print fix_text,'\t',type(item['text']),'\t',len(item['text']),'\t',item['text'], twt_list_data = [twt_date] + [fix_nm.lower()] + [fix_text] try: base_twit_list.append(twt_list_data) except: print '<p>Unknown Error:', type(twt_list_data), twt_list_data, '</p>' textitem = fix_text newhastags = re.findall('[#]\w+',textitem) newatitems = re.findall('[@]\w+',textitem) re_tweets = re.findall('RT',textitem) #before adding to the final lists, convert the hashtags and atitems #to lower case. This will avoid issues of double counting same names newhastags = [hti.lower() for hti in newhastags] newatitems = [ati.lower() for ati in newatitems] #Now add to the list. #Use EXTEND function that adds elements to the list rahter than another list. hashtag_list.extend(newhastags) at_list.extend(newatitems) re_twt_list.extend(re_tweets) #now try to find some patterns in the last 200 tweets #print 'use the collections library to find out the top 5' #Version 2.6 of python does not support Counters within collections #py2.6 hashcollect = collections.Counter(hashtag_list) #py2.6 atcollect = collections.Counter(at_list) totalretweets = len(re_twt_list) retwpercent = (1.0 * totalretweets / max(1,len(x)) ) * 100 top10users = [] #print '\n.............................' ,'</p>' print >> t2, '<li>', '\t',"%.2f%%" % retwpercent, 'are retweets (',totalretweets,'of a total of',len(x),'tweets)' ,'</li>' print >> t2, '<ul>' print >> t2, '<li>',(len(x)-totalretweets), 'tweets in ',twit_day_range,' days (without retweets)</li>' print >> t2, '<li>','Avg of ','%.1f' %( 1.0*(len(x)-totalretweets)/max(twit_day_range,1) ),'tweets per day (without retweets)</li>' print >> t2, '</ul></ul>' data_for_plots.extend([totalretweets]) print >> t2, '<ul>' print >> t2, '<li>', '\tHastags referenced over past',len(x),'tweets = ',len(hashtag_list) ,'</li>' print >> t2, '<li>', '\t10 Most referenced hashtags' ,'</li>' print >> t2, '<ul>' #py2.6 for h_item in hashcollect.most_common(10): #can't use in python 2.6 for h_item in top_list(hashtag_list,10): print >> t2, '<li>',text_sanitize(h_item[1]),'|',h_item[0] ,'</li>' print >> t2, '</ul></ul>' print >> t2, '<ul>' print >> t2, '<li>', '\tTwitter IDs referenced over past',len(x),'tweets = ',len(at_list) ,'</li>' print >> t2, '<li>', '\t10 Most referenced Tweeter IDs' ,'</li>' print >> t2, '<ul>' #py2.6 for at_item in atcollect.most_common(10): for at_item in top_list(at_list,10): print >> t2, '<li>', '\t\t',text_sanitize(at_item[1]),'|',at_item[0],'</li>' #add the list of users to the top10user list top10users.append(at_item[1].replace('@','')) print >> t2, '</ul></ul>' #print '<p>Twit list:',type(base_twit_list),'\t',len(base_twit_list),'</p>' return top10users, base_twit_list, data_for_plots def display_data(scn_name): html_start() print '<div id="body_sty">' print '<h4>Data shown for '+scn_name.upper()+' and 10 other users most referenced in '+scn_name.upper()+'\'s tweets.</h4><hr>' user_to_check = scn_name if user_to_check[0] == '@': user_raw = user_to_check user_to_check = user_raw.replace('@','') # the following lines get the user info # -- this is response limited to 180 #user_public_info(user_to_check) max_items_to_show = 200 max_tweets_to_get = 200 #if temp file exists, close it global t2 try: t2.close() except: print '' #open the temp file t2=TemporaryFile() print >> t2, ''' <a href="#" onclick="show_hideStuff('detailed_data'); return false;"> <br><br><hr><br> <h3>Detailed Data (click to see or hide)</h3></a><br> <div id="detailed_data" style="display:none"> ''' # last xx tweets is response limited to 180 res_last200_tweets = get_last200_tweets(user_to_check.lower()) #print '<p>', type(res_last200_tweets), len(res_last200_tweets), '</p>' final_tweet_list = [] final_data_for_plots = [] do_rest_of_display_data = 0 try: user_reference = res_last200_tweets[0] tweet_last200_tweets = res_last200_tweets[1] final_tweet_list.append(tweet_last200_tweets) final_data_for_plots.append(res_last200_tweets[2]) do_rest_of_display_data = 1 except: print '<p>Something wrong to get the list of twitter IDs</p>' if (do_rest_of_display_data == 1): print >> t2, '<br>' try: if len(user_reference) > 0: for newuser in user_reference: if newuser != user_to_check: res_last200_tweets = get_last200_tweets(newuser.lower()) tweets_from_res_last200 = res_last200_tweets[1] final_tweet_list.append(tweets_from_res_last200) final_data_for_plots.append(res_last200_tweets[2]) else: print >>t2, '<p>', 'Did not find any instance of other users referenced in your tweets.' ,'</p>' except: print >>t2, '<p>', 'No info found.' ,'</p>' #Add the data to the temp file also print >> t2, '<br><br><hr><h4>List of Tweets Analyzed</h4>' print >> t2, '<table id="table1" class="pure-table" width=100% style="display: block;">' print >> t2, '<thead><tr bgcolor=#def><td>Date</td><td>Sender</td><td>Text</td></tr></thead>' row_even = True for i1 in final_tweet_list: for i2 in i1: #database fields: current date, username, screen name, twt_date, twt_writer, twt_text twts = [datetime.date.today(),scn_name,user_to_check,i2[0],text_sanitize(i2[1]),text_sanitize(i2[2])] try: if row_even == True: print >> t2, '<tr><td><sm>', twts[3] ,'</sm></td><td><sm>', str(twts[4]),'</sm></td><td><sm>', str(twts[5]),'</sm></td></tr>' row_even = False else: print >> t2, '<tr class="pure-table-odd"><td><sm>', twts[3] ,'</sm></td><td><sm>', str(twts[4]),'</sm></td><td><sm>', str(twts[5]),'</sm></td></tr>' row_even = True except: print '', print >> t2, '</table>' #print out the chart data #data fields: screen_name, friends, followers, msgs, daterange, tweets, retweets #print json.dumps(final_data_for_plots,indent=2) #try doing a chart #draw a chart showing friends and followers print '<h3>Friends and Followers</h3>' x_fdfp = [] y1_fdfp = [] y2_fdfp = [] #print '<p>Before adding data:',x_fdfp, y_fdfp, '</p>' x_fdfp.append( 'Screen Name' ) y1_fdfp.append( 'Friends' ) y2_fdfp.append( 'Followers' ) for xy1 in range(len(final_data_for_plots)): x_fdfp.append( final_data_for_plots[xy1][0] ) y1_fdfp.append( final_data_for_plots[xy1][1] ) y2_fdfp.append( final_data_for_plots[xy1][2] ) two_bar_chart_data("Friends and Followers", x_fdfp, y1_fdfp, y2_fdfp) print '<h3>Followers to Friends Ratio</h3>' #Draw a bar chart to show followers to friends ratio x_fdfp = [] y_fdfp = [] #print '<p>Before adding data:',x_fdfp, y_fdfp, '</p>' for xy1 in range(len(final_data_for_plots)): x_fdfp.append( final_data_for_plots[xy1][0] ) y_fdfp.append( round( 1.0 * final_data_for_plots[xy1][2] / max(final_data_for_plots[xy1][1],1),1) ) #print '<p>',x_fdfp, y_fdfp, '</p>' bar_chart_data("Followers to Friends Ratio", x_fdfp, y_fdfp) print '<h3>Tweets sent per day</h3>' x_fdfp = [] y1_fdfp = [] y2_fdfp = [] #print '<p>Before adding data:',x_fdfp, y_fdfp, '</p>' x_fdfp.append( 'Screen Name' ) y1_fdfp.append( 'Tweets per day - with retweets' ) y2_fdfp.append( 'Tweets per day - without retweets' ) for xy1 in range(len(final_data_for_plots)): x_fdfp.append( final_data_for_plots[xy1][0] ) y1_fdfp.append( final_data_for_plots[xy1][5] / max(final_data_for_plots[xy1][4],1) ) y2_fdfp.append( (final_data_for_plots[xy1][5]-final_data_for_plots[xy1][6]) / max(final_data_for_plots[xy1][4],1) ) two_bar_chart_data("Tweets sent per day", x_fdfp, y1_fdfp, y2_fdfp) print '<h3>Tweet range (tweets seen per day)</h3>' x_fdfp = [] y_fdfp = [] #print '<p>Before adding data:',x_fdfp, y_fdfp, '</p>' for xy1 in range(len(final_data_for_plots)): x_fdfp.append( final_data_for_plots[xy1][0] ) y_fdfp.append( round( 1.0 * final_data_for_plots[xy1][2] * final_data_for_plots[xy1][5] / max(final_data_for_plots[xy1][4],1) ) ) #print '<p>',x_fdfp, y_fdfp, '</p>' bar_chart_data("Tweet Range", x_fdfp, y_fdfp) lex_anal(final_tweet_list) #print out the detailed data # go to the first record of the temp file first print >> t2, ' </div> ' t2.seek(0) print t2.read() t2.close() #if this works - can delete below this. else: print '<p>Not able to process this user. Please try another.</p>' print '</div>' #close the body_sty div html_end() def lex_anal(incomingTweetList): ''' routine to do lexical analysis ''' #final_tweet_list --- date / sender full name / tweet #read the tweets and create a list of sender-htag and sender-@ #incoming TweetList has two layer lists sender_htag = [] sender_at = [] h_tags_all = [] at_items_all = [] ts_all = [] for lex2 in incomingTweetList: for lex22 in lex2: td = lex22[0] #this is the tweet date try: ts = text_sanitize(lex22[1]) #this is the tweet sender except: print 'something wrong with ',lex22[1] ts = '---' ts_all.append(ts) h_tags = re.findall('[#]\w+',lex22[2]) #these are the h-tags at_items = re.findall('[@]\w+',lex22[2]) #these are the other users h_tags = [hti.lower() for hti in h_tags] at_items = [ati.lower() for ati in at_items] for h2 in h_tags: sender_htag.append([td,ts.lower()+'-'+h2]) h_tags_all.append(h2) for at2 in at_items: sender_at.append([td,ts.lower()+'-'+at2]) at_items_all.append(at2) #summarize the two new lists #following lists don't have dates sender_htag2 = [xx[1] for xx in sender_htag] sender_at2 = [yy[1] for yy in sender_at] #make a list of the tweet senders only ts_all = list(set(ts_all)) #print ts_all #get the top 10 htags #py2.6 ht_col = collections.Counter(h_tags_all) htag_data4heatmap = [] at_data4heatmap = [] #print '<ul>Top 10 Hashtags' #py2.6 for h_item in ht_col.most_common(10): for h_item in top_list(h_tags_all,10): #print '<li>', h_item, '</li>' #count the number of times each of the hastag was referenced by each tweet sender try: for tsitem in ts_all: try: itemtocount = str(tsitem+'-'+h_item[1]) htag_data4heatmap.append([tsitem,h_item[1], sender_htag2.count(itemtocount)]) except: print 'Problem here: ',h_item,tsitem except: print 'Problem here',h_item print '</ul>' #get the top 10 user references #py2.6 at_col = collections.Counter(at_items_all) #print '<ul>Top 10 Users' #py2.6 for a_item in at_col.most_common(10): for a_item in top_list(at_items_all,10): #print '<li>', a_item, '</li>' #count the number of times each of the hastag was referenced by each tweet sender try: for tsitem in ts_all: itemtocount = str(tsitem+'-'+a_item[1]) at_data4heatmap.append([tsitem,a_item[1], sender_at2.count(itemtocount)]) except: print 'Problem here 2',a_item print '</ul>' #draw the table with the heatmap tcols = len(ts_all) #number of tweet senders - rows trows = len(htag_data4heatmap) / tcols #number of hastags - cols #print trows, tcols if trows>0: print '<br><br>' print '<h3>Most Popular Hashtags</h3>' heatmap_table(trows,tcols,htag_data4heatmap) tcols = len(ts_all) #number of tweet senders - rows trows = len(at_data4heatmap) / tcols #number of hastags - cols #print trows, tcols if trows>0: print '<br><br>' print '<h3>Most Referenced Users</h3>' heatmap_table(trows,tcols,at_data4heatmap) def heatmap_table(trows,tcols,hm): #calculate the max and min of the references #and create a normalized color scale mx = max(i[2] for i in hm) mn = min(i[2] for i in hm) itv = mx - mn #COLOR pallete from http://colorbrewer2.org/ for arow in hm: rval = 1.0*arow[2]/itv if rval<0.1: arow[2]='#FFF5F0' elif rval>=0.1 and rval<0.25: arow[2]='#FEE0D2' elif rval>=0.25 and rval<0.4: arow[2]='#FCBBA1' elif rval>=0.4 and rval<0.5: arow[2]='#FC9272' elif rval>=0.5 and rval<0.6: arow[2]='#FB6A4A' elif rval>=0.6 and rval<0.7: arow[2]='#EF3B2C' elif rval>=0.7 and rval<0.8: arow[2]='#CB181D' elif rval>=0.8 and rval<0.9: arow[2]='#A50F15' elif rval>=0.9: arow[2]='#67000D' print '<table width=100% style="display: block;"> ' for i in range(trows+1): print '<tr>', for j in range(tcols+1): if (i==0 and j==0): print '<td width="15%">','','</td>', elif i==0 and j>0 and j<(tcols): print '<td width="8.5%"><sm>',hm[j-1][0][:10],'</sm></td>', elif i==0 and j==(tcols): print '<td width="8.5%"><sm>',hm[j-1][0][:10],'</sm></td></tr>' elif i>0 and j==0: print '<td><sm>',hm[(i-1)*tcols+j+1-1][1],'</sm></td>', elif i>0 and j>0 and j<tcols: print '<td bgcolor=',hm[(i-1)*tcols+j-1][2],'></td>', elif i>0 and j==tcols: print '<td bgcolor=',hm[(i-1)*tcols+j-1][2],'></td></tr>' print '</table> ' def print_detailed_tweets(in_usertocheck): html_start() check_another_user_button() #print '<h3>Listing of tweets analyzed:</h3>' sd2st = start_database_to_store_tweets() if sd2st[1] == True: c2 = sd2st[0] conn2 = sd2st[2] #read all the tweets for the username and screen name read_text = "SELECT * FROM tweetlist WHERE (username =\'"+in_usertocheck+"\')" #print '<p>Select tweet command:',read_text,'</p>' try: c2.execute(read_text) for crow in c2: print crow[1] conn2.close() #print '<h2>Finished with the tweet list</h2>' except conn2.Error, e: print "E Error %d: %s" % (e.args[0], e.args[1]) else: print "F Error %d: %s" % (sd2st[0].args[0],sd2st[0].args[1]) html_end() def bar_chart_data(cht_title,xdata,ydata): #this routine will draw a bar chart #print '<p>DO NOT PRINT anaything inside chart modules except needed items</p>' print '' print '<script type=\"text/javascript\" src=\"https://www.google.com/jsapi\"></script>' print '<script type=\"text/javascript\">' # Load the Visualization API and the piechart package. print ' google.load(\'visualization\', \'1.0\', {\'packages\':[\'corechart\']}); ' # Set a callback to run when the Google Visualization API is loaded. print ' google.setOnLoadCallback(drawChart);' # Callback that creates and populates a data table, # instantiates the pie chart, passes in the data and # draws it. print ' function drawChart() { ' # Create the data table. print ' var data = new google.visualization.arrayToDataTable([ ' print ' [ \'Screen Name\', \' ' , cht_title, ' \', {role:\'style\'} ], ' for cdi in range(len(xdata)): if cdi == 0: print " [ \'", xdata[cdi], "\',", ydata[cdi], ", \'orange\' ], " else: print " [ \'", xdata[cdi], "\',", ydata[cdi], ", \'blue\' ], " print ' ]); ' #Set chart options print " var options = {\'title\':\'",cht_title,"\', " print ' \'width\':600, ' print ' \'height\':400, ' print ' \'hAxis\' : {\'logScale\' : true} , ' print ' legend :\'none\' , \'backgroundColor\': { fill: \"none\" } ' print ' }; ' # chart_bottom(): # Instantiate and draw our chart, passing in some options. print ' var chart = new google.visualization.BarChart(document.getElementById(\"',cht_title+'DIV','\")); ' print ' function selectHandler() { ' print ' var selectedItem = chart.getSelection()[0]; ' print ' if (selectedItem) { ' print ' var topping = data.getValue(selectedItem.row, 0); ' print ' alert(\'The user selected \' + topping); ' print ' } ' print ' } ' print ' google.visualization.events.addListener(chart, \'select\', selectHandler); ' print ' chart.draw(data, options); ' print ' } ' print '</script> ' print ' ' print '<div id=\"',cht_title+'DIV','\" style=\"width:600; height:400\"></div> ' def two_bar_chart_data(cht_title,xdata,ydata1,ydata2): #this routine will draw a bar chart with two bara #print '<p>DO NOT PRINT anaything inside chart modules except needed items</p>' print '' print '<script type=\"text/javascript\" src=\"https://www.google.com/jsapi\"></script>' print '<script type=\"text/javascript\">' # Load the Visualization API and the piechart package. print ' google.load(\'visualization\', \'1.0\', {\'packages\':[\'corechart\']}); ' # Set a callback to run when the Google Visualization API is loaded. print ' google.setOnLoadCallback(drawChart);' print ' function drawChart() { ' print ' var data = new google.visualization.arrayToDataTable([ ' print " [ \'Screen Name\', \' ",ydata1[0], "\' ,{role:\'style\'}, \'" ,ydata2[0], "\' , {role:\'style\'} ], " for cdi in range(len(xdata)): if cdi>0: print " [ \'", xdata[cdi], "\',", ydata1[cdi],",\'blue\',", ydata2[cdi], ", \'red\' ], " print ' ]); ' #Set chart options print " var options = {\'title\':\'",cht_title,"\', " print ' \'width\':600, ' print ' \'height\':400, ' print ' \'hAxis\' : {\'logScale\' : false} , ' print ' legend :\'top\' , \'backgroundColor\': { fill: \"none\" } ' print ' }; ' # chart_bottom(): # Instantiate and draw our chart, passing in some options. print ' var chart = new google.visualization.BarChart(document.getElementById(\"',cht_title+'DIV','\")); ' print ' function selectHandler() { ' print ' var selectedItem = chart.getSelection()[0]; ' print ' if (selectedItem) { ' print ' var topping = data.getValue(selectedItem.row, 0); ' print ' alert(\'The user selected \' + topping); ' print ' } ' print ' } ' print ' google.visualization.events.addListener(chart, \'select\', selectHandler); ' print ' chart.draw(data, options); ' print ' } ' print '</script> ' print ' ' print '<div id=\"',cht_title+'DIV','\" style=\"width:600; height:400\"></div> ' def test3(): #Test some random twitter functions on stream data html_start() testname = "concession,privatization,public private" #testname = "mining,mines,metal,oil,gas,petroleum" try: ts = TwitterStream(auth=OAuth(define_keys()[2],define_keys()[3],define_keys()[0],define_keys()[1])) #response = ts.statuses.sample() response = ts.statuses.filter(track=testname) showcount = 0 maxshow = 50 for tweet in response: showcount += 1 if showcount>= maxshow: break # You must test that your tweet has text. It might be a delete # or data message. if tweet is None: print_para("-- None --") elif tweet.get('text'): print_para(tweet['user']['name']+'.....'+str(twit_date(tweet['created_at']))+'---'+tweet['text']) else: print_para(str(showcount)+'...') #print_para(json.dumps(tweet,indent=2)) except TwitterHTTPError, e: print '<p>Error getting tweets info for:',e['details'],'</p>' html_end() def print_para(instr): print '<p>',instr,'</p>' def twit_date(in_created_at): out_date = datetime.date(int(in_created_at[26:30]), int(time.strptime(in_created_at[4:7],'%b').tm_mon), int(in_created_at[8:10])) return out_date # Define main function. def main(): form = cgi.FieldStorage() if (form.has_key("action") and form.has_key("scn_name")): if (form["action"].value == "display"): display_data(text_sanitize(form["scn_name"].value)) else: generate_form() main()

0

29,208

0

11

682

47aeba5f5a974bde56729cafe676435b3057e324

3,765

py

Python

sonde/qaqc_viewer.py

wilsaj/pint

a2b2a6ea9ff480a168358af642cf36c7f3c5d0e4

[ "BSD-3-Clause" ]

1

2017-12-06T04:28:59.000Z

sonde/qaqc_viewer.py

wilsaj/pint

a2b2a6ea9ff480a168358af642cf36c7f3c5d0e4

[ "BSD-3-Clause" ]

null

sonde/qaqc_viewer.py

wilsaj/pint

a2b2a6ea9ff480a168358af642cf36c7f3c5d0e4

[ "BSD-3-Clause" ]

null

""" QAQC Viewer based on Chaco & Traits """ #from enthought.chaco.example_support import COLOR_PALETTE #from enthought.enable.example_support import DemoFrame, demo_main # Enthought library imports # Chaco imports #============================================================================== # Attributes to use for the plot view. #size=(800,600) #title="Salinity plot example" if __name__ == "__main__": viewer = BaseViewer() viewer.configure_traits()

41.833333

110

0.601594

""" QAQC Viewer based on Chaco & Traits """ #from enthought.chaco.example_support import COLOR_PALETTE #from enthought.enable.example_support import DemoFrame, demo_main # Enthought library imports from enthought.enable.api import Window, Component, ComponentEditor from enthought.traits.api import HasTraits, Instance from enthought.traits.ui.api import Item, Group, View # Chaco imports from enthought.chaco.api import Plot, ArrayDataSource, ArrayPlotData, \ BarPlot, DataRange1D, LabelAxis, LinearMapper, VPlotContainer, \ PlotAxis, PlotGrid, LinePlot, add_default_grids, PlotLabel from enthought.chaco.tools.api import PanTool, ZoomTool from enthought.chaco.scales.api import CalendarScaleSystem from enthought.chaco.scales_tick_generator import ScalesTickGenerator from sonde import Sonde import time import numpy as np class BaseViewer(HasTraits): main_tab = Instance(Component) traits_view = View(Item('main_tab', editor=ComponentEditor), width=500, height=500, resizable=True, title="Salinity Plot") def __init__(self, **kwargs): HasTraits.__init__(self, **kwargs) self.init_data() def init_data(self): file_name = '/home/dpothina/work/apps/pysonde/tests/ysi_test_files/BAYT_20070323_CDT_YS1772AA_000.dat' sonde = Sonde(file_name) sal_ds = np.array([1, 2, 3, 4, 5, 6, 7, 8]) # sonde.data['seawater_salinity'] time_ds = sal_ds**2 # [time.mktime(date.utctimetuple()) for date in sonde.dates] #time_ds = ArrayDataSource(dt) #sal_ds = ArrayDataSource(salinity, sort_order="none") self.plot_data = ArrayPlotData(sal_ds=sal_ds, time_ds=time_ds) def _main_tab_default(self): self.sal_plot = Plot(self.plot_data) self.sal_plot.plot(('time_ds', 'sal_ds'), type='line') #sal_plot.overlays.append(PlotAxis(sal_plot, orientation='left')) #bottom_axis = PlotAxis(sal_plot, orientation="bottom",# mapper=xmapper, # tick_generator=ScalesTickGenerator(scale=CalendarScaleSystem())) #sal_plot.overlays.append(bottom_axis) #hgrid, vgrid = add_default_grids(sal_plot) #vgrid.tick_generator = bottom_axis.tick_generator #sal_plot.tools.append(PanTool(sal_plot, constrain=True, # constrain_direction="x")) #sal_plot.overlays.append(ZoomTool(sal_plot, drag_button="right", # always_on=True, # tool_mode="range", # axis="index", # max_zoom_out_factor=10.0, # )) container = VPlotContainer(bgcolor="lightblue", spacing=40, padding=50, fill_padding=False) container.add(sal_plot) #container.add(price_plot) #container.overlays.append(PlotLabel("Salinity Plot with Date Axis", # component=container, # #font="Times New Roman 24")) # font="Arial 24")) return container #def default_traits_view(self): # return View(Group(Item('main_tab', editor=ComponentEditor)), # width=500, height=500, resizable=True, title="Salinity Plot") #============================================================================== # Attributes to use for the plot view. #size=(800,600) #title="Salinity plot example" if __name__ == "__main__": viewer = BaseViewer() viewer.configure_traits()

0

2,650

0

401

244

120fa0d15479ccd5b4653c3adf9354e51e55b55c

573

py

Python

ComicPub/comics/admin.py

Xonshiz/ComicPub

d332ee1b62d6c28347954280696c86898de6d125

[ "MIT" ]

8

2017-09-02T07:04:59.000Z

2020-12-17T17:30:34.000Z

ComicPub/comics/admin.py

Xonshiz/ComicPub

d332ee1b62d6c28347954280696c86898de6d125

[ "MIT" ]

1

2017-10-24T12:49:57.000Z

2017-10-24T15:04:44.000Z

ComicPub/comics/admin.py

Xonshiz/ComicPub

d332ee1b62d6c28347954280696c86898de6d125

[ "MIT" ]

4

2017-10-24T14:13:13.000Z

2021-12-15T17:09:23.000Z

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.contrib import admin from comics.models import Comic, ComicChapter # class PageFileInline(admin.TabularInline): # model = ComicChapter # # # class PageAdmin(admin.ModelAdmin): # inlines = [PageFileInline, ] # class ChapterInline(admin.TabularInline): # model = ComicChapterFiles # # class ComicAdmin(admin.ModelAdmin): # inlines = [ # ChapterInline, # ] # admin.site.register(ComicChapter, ComicAdmin) admin.site.register(Comic) admin.site.register(ComicChapter)

21.222222

47

0.724258

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.contrib import admin from comics.models import Comic, ComicChapter # class PageFileInline(admin.TabularInline): # model = ComicChapter # # # class PageAdmin(admin.ModelAdmin): # inlines = [PageFileInline, ] # class ChapterInline(admin.TabularInline): # model = ComicChapterFiles # # class ComicAdmin(admin.ModelAdmin): # inlines = [ # ChapterInline, # ] # admin.site.register(ComicChapter, ComicAdmin) admin.site.register(Comic) admin.site.register(ComicChapter)

0

d7e5e4980b5718dcaa9192759e6b4c3e5d658b97

2,457

py

Python

chpt6/Generate_random_characters.py

GDG-Buea/learn-python

9dfe8caa4b57489cf4249bf7e64856062a0b93c2

[ "Apache-2.0" ]

null

chpt6/Generate_random_characters.py

GDG-Buea/learn-python

9dfe8caa4b57489cf4249bf7e64856062a0b93c2

[ "Apache-2.0" ]

2

2018-05-21T09:39:00.000Z

2018-05-27T15:59:15.000Z

chpt6/Generate_random_characters.py

GDG-Buea/learn-python

9dfe8caa4b57489cf4249bf7e64856062a0b93c2

[ "Apache-2.0" ]

2

2018-05-19T14:59:56.000Z

2018-05-19T15:25:48.000Z

# This program displays 100 lowercase letters, fifteen per line main() print() # Draw a line from (x1, y1) to (x2, y2) # def drawLine(x1, y1, x2, y2): # turtle.penup() # turtle.goto(x1, y1) # turtle.pendown() # turtle.goto(x2, y2) # def writeText(s, x, y): # turtle.penup() # Pull the pen up # turtle.goto(x, y) # turtle.pendown() # Pull the pen down # turtle.write(s) # Write a string # # Draw a point at the specified location (x, y) # def drawPoint(x, y): # turtle.penup() # Pull the pen up # turtle.goto(x, y) # turtle.pendown() # Pull the pen down # turtle.begin_fill() # Begin to fill color in a shape # turtle.circle(3) # turtle.end_fill() # Fill the shape # # Draw a circle centered at (x, y) with the specified radius # def drawCircle(x = 0, y = 0, radius = 10): # turtle.penup() # Pull the pen up # turtle.goto(x, y - radius) # turtle.pendown() # Pull the pen down # turtle.circle(radius) # # Draw a rectangle at (x, y) with the specified width and height # def drawRectangle(x = 0, y = 0, width = 10, height = 10): # turtle.penup() # Pull the pen up # turtle.goto(x + width / 2, y + height / 2) # turtle.pendown() # Pull the pen down # turtle.right(90) # turtle.forward(height) # turtle.right(90) # turtle.forward(width) # turtle.right(90) # turtle.forward(height) # turtle.right(90) # turtle.forward(width) # Generate a random uppercase letter # def getRandomUpperCaseLetter() : # return getRandomCharacter('A', 'Z') # # Generate a random digit character # def getRandomDigitCharacter() : # return getRandomCharacter('0', '9') # # Generate a random character # def getRandomASCIICharacter() : # return chr(randint(0, 127)) # # # Generate a random character between ch1 and ch2 # def getRandomCharacter(ch1, ch2) : # return chr(randint(ord(ch1), ord(ch2))) #

23.179245

66

0.659341

# This program displays 100 lowercase letters, fifteen per line import turtle from random import randint def get_random_lower_case_letter(): return get_random_character('a', 'z') def get_random_character(ch1, ch2): return chr(randint(ord(ch1), ord(ch2))) def write_text(s, x, y): turtle.penup() turtle.goto(x, y) turtle.pendown() turtle.write(s) turtle.goto(x, y) turtle.done() def main(): count = 0 number_of_characters = 100 characters_per_line = 15 print("\n") for i in range(number_of_characters): print("\t", get_random_lower_case_letter(), end=' ') count += 1 if count % characters_per_line == 0: print() main() print() # Draw a line from (x1, y1) to (x2, y2) # def drawLine(x1, y1, x2, y2): # turtle.penup() # turtle.goto(x1, y1) # turtle.pendown() # turtle.goto(x2, y2) # def writeText(s, x, y): # turtle.penup() # Pull the pen up # turtle.goto(x, y) # turtle.pendown() # Pull the pen down # turtle.write(s) # Write a string # # Draw a point at the specified location (x, y) # def drawPoint(x, y): # turtle.penup() # Pull the pen up # turtle.goto(x, y) # turtle.pendown() # Pull the pen down # turtle.begin_fill() # Begin to fill color in a shape # turtle.circle(3) # turtle.end_fill() # Fill the shape # # Draw a circle centered at (x, y) with the specified radius # def drawCircle(x = 0, y = 0, radius = 10): # turtle.penup() # Pull the pen up # turtle.goto(x, y - radius) # turtle.pendown() # Pull the pen down # turtle.circle(radius) # # Draw a rectangle at (x, y) with the specified width and height # def drawRectangle(x = 0, y = 0, width = 10, height = 10): # turtle.penup() # Pull the pen up # turtle.goto(x + width / 2, y + height / 2) # turtle.pendown() # Pull the pen down # turtle.right(90) # turtle.forward(height) # turtle.right(90) # turtle.forward(width) # turtle.right(90) # turtle.forward(height) # turtle.right(90) # turtle.forward(width) # Generate a random uppercase letter # def getRandomUpperCaseLetter() : # return getRandomCharacter('A', 'Z') # # Generate a random digit character # def getRandomDigitCharacter() : # return getRandomCharacter('0', '9') # # Generate a random character # def getRandomASCIICharacter() : # return chr(randint(0, 127)) # # # Generate a random character between ch1 and ch2 # def getRandomCharacter(ch1, ch2) : # return chr(randint(ord(ch1), ord(ch2))) #

0

533

0

-3

137

b4b58aa4d7d83f1298f775781fc1a78f79bf902f

531

py

Python

miniProject/miniApp/urls.py

cs-fullstack-2019-spring/django-mini-project5-gkg901

35af15000480a104f46adb62ba9ceebd4d0ad7a1

[ "Apache-2.0" ]

null

miniProject/miniApp/urls.py

cs-fullstack-2019-spring/django-mini-project5-gkg901

35af15000480a104f46adb62ba9ceebd4d0ad7a1

[ "Apache-2.0" ]

null

miniProject/miniApp/urls.py

cs-fullstack-2019-spring/django-mini-project5-gkg901

35af15000480a104f46adb62ba9ceebd4d0ad7a1

[ "Apache-2.0" ]

null

from django.urls import path from . import views urlpatterns = [ path('', views.index, name='index'), path('allrecipes/', views.allrecipes, name='allrecipes'), path('newrecipe/', views.newrecipe, name='newrecipe'), path('profile/', views.profile, name='profile'), path('newuser/', views.newuser, name='newuser'), path('details/<int:ID>', views.details, name='details'), path('edituser/<int:ID>', views.edituser, name='edituser'), path('editrecipe/<int:ID>', views.editrecipe, name='editrecipe'), ]

37.928571

69

0.664783

from django.urls import path from . import views urlpatterns = [ path('', views.index, name='index'), path('allrecipes/', views.allrecipes, name='allrecipes'), path('newrecipe/', views.newrecipe, name='newrecipe'), path('profile/', views.profile, name='profile'), path('newuser/', views.newuser, name='newuser'), path('details/<int:ID>', views.details, name='details'), path('edituser/<int:ID>', views.edituser, name='edituser'), path('editrecipe/<int:ID>', views.editrecipe, name='editrecipe'), ]

0

e63a707a6d1aecf82dd0e657d12e6dcba8e4283c

3,996

py

Python

hash_code.py

Arpan-206/EncryptoCLI

26a7718ef387d46bfcf2d167e17a494de0165858

[ "MIT" ]

2

2021-10-20T13:38:45.000Z

2022-01-11T12:36:49.000Z

hash_code.py

Arpan-206/EncryptoCLI

26a7718ef387d46bfcf2d167e17a494de0165858

[ "MIT" ]

null

hash_code.py

Arpan-206/EncryptoCLI

26a7718ef387d46bfcf2d167e17a494de0165858

[ "MIT" ]

null

# Importing the hashing library # Importing the visual libraries # Defining the hash function.

27.75

129

0.508008

# Importing the hashing library import hashlib # Importing the visual libraries from PyInquirer import Separator, prompt from termcolor import colored # Defining the hash function. def hash_func(): # Asking the user for further data regarding algoritms hash_info = prompt([ { 'type': 'list', 'qmark': '>', 'name': 'algorithm', 'message': 'Which algorithm do you want to use?', 'choices': [ Separator(), { 'name': 'MD5', }, { 'name': 'SHA256', }, { 'name': 'SHA512', }, { 'name': 'BLAKE2', }, { 'name': 'BLAKE2b', }, ], }, { 'type': 'list', 'qmark': '>', 'name': 'type_of_data', 'message': 'What do you want to hash?', 'choices': [ Separator(), { 'name': 'Text', }, { 'name': 'File', }, ], }, ]) # Storing the data into seperate variables algorithm = hash_info['algorithm'] type_of_data = hash_info['type_of_data'] # Determining the type of data to hash and calling the appropriate functions if type_of_data == 'File': handle_file_hashing(algorithm) else: handle_text_hashing(algorithm) def handle_text_hashing(algorithm): # Asking the user for the data data_info = prompt([ { 'type': 'input', 'qmark': '>', 'name': 'hash_data', 'message': 'Enter data to hash.', }, ]) # Defining the hash_out variable according to the algorithm selected by user if algorithm == 'MD5': hash_out = hashlib.md5() elif algorithm == 'SHA256': hash_out = hashlib.sha256() elif algorithm == 'SHA512': hash_out = hashlib.sha512() elif algorithm == 'BLAKE2': hash_out = hashlib.blake2s() else: hash_out = hashlib.blake2b() # Populating it the data after converting it to binary hash_out.update(data_info['hash_data'].encode()) # Calculating the actual hash hash_out = hash_out.hexdigest() # Printing out the hash print(colored('Your hash is: ', 'white') + colored(hash_out, 'green')) return None def handle_file_hashing(algorithm): # Asking the user for the path to the file file_info = prompt([ { 'type': 'input', 'qmark': '>', 'name': 'file_name', 'message': 'Enter the path to the file.', }, ]) try: # Again, Defining the hash_out variable according to the algorithm selected by user if algorithm == 'MD5': hash_out = hashlib.md5() elif algorithm == 'SHA256': hash_out = hashlib.sha256() elif algorithm == 'SHA512': hash_out = hashlib.sha512() elif algorithm == 'BLAKE2': hash_out = hashlib.blake2s() else: hash_out = hashlib.blake2b() # Populating it the data after converting it to binary but this time in chunks so as to not put too much strain on memory with open(file_info['file_name'], 'rb') as file_path: chunk = 0 while chunk != b'': chunk = file_path.read(1024) hash_out.update(chunk) # Calculating the actual hash hash_out = hash_out.hexdigest() # Printing out the hash print(colored('Your hash is: ', 'white') + colored(hash_out, 'green')) except Exception as e: print(colored( 'Can\'t find the file please check the name and make sure the extension is also present.', 'red'))

0

3,741

0

20

135

e155cdbdf8a6a6a7a4d4cc1a43c09c3a16b32d5c

3,800

py

Python

examples/plugins/single_project/sample_project/data/plugin/ui_service.py

janvonrickenbach/Envisage_wxPhoenix_py3

cf79e5b2a0c3b46898a60b5fe5a2fb580604808b

[ "BSD-3-Clause" ]

null

examples/plugins/single_project/sample_project/data/plugin/ui_service.py

janvonrickenbach/Envisage_wxPhoenix_py3

cf79e5b2a0c3b46898a60b5fe5a2fb580604808b

[ "BSD-3-Clause" ]

1

2017-05-22T21:15:22.000Z

examples/plugins/single_project/sample_project/data/plugin/ui_service.py

janvonrickenbach/Envisage_wxPhoenix_py3

cf79e5b2a0c3b46898a60b5fe5a2fb580604808b

[ "BSD-3-Clause" ]

1

2019-10-01T07:03:58.000Z

#----------------------------------------------------------------------------- # # Copyright (c) 2007 by Enthought, Inc. # All rights reserved. # #----------------------------------------------------------------------------- """ The UI service for the Data plugin. """ # Standard library imports. import logging # Enthought library imports. # Data library imports. # Local imports. # Setup a logger for this module logger = logging.getLogger(__name__) #### EOF #####################################################################

29.007634

78

0.460789

#----------------------------------------------------------------------------- # # Copyright (c) 2007 by Enthought, Inc. # All rights reserved. # #----------------------------------------------------------------------------- """ The UI service for the Data plugin. """ # Standard library imports. import logging # Enthought library imports. from envisage.api import ApplicationObject, UOL from pyface.api import confirm, error, FileDialog, information, YES # Data library imports. # Local imports. from services import IDATA_MODEL # Setup a logger for this module logger = logging.getLogger(__name__) class UiService(ApplicationObject): """ The UI service for the Data plugin. """ ########################################################################## # Attributes ########################################################################## #### public 'UiService' interface ######################################## # A reference to the Data plugin's model service. model_service = UOL ########################################################################## # 'Object' interface ########################################################################## #### operator methods #################################################### def __init__(self, **kws): """ Constructor. Extended to ensure our UOL properties are set. """ super(UiService, self).__init__(**kws) # Ensure we have a default model-service if one wasn't specified. if self.model_service is None: self.model_service = 'service://%s' % IDATA_MODEL return ########################################################################## # 'UIService' interface ########################################################################## #### public methods ###################################################### #TODO cgalvan: to be implemented # def delete_data(self, context, data_name, parent_window): # """ # Delete a Data. # # """ # # # Open confirmation-dialog to confirm deletion # message = 'Are you sure you want to delete %s?' % data_name # if confirm(parent_window, message) == YES: # self.model_service.delete_context_item(context, data_name) # # return def edit_data(self, window, data): """ Edit the data parameters of the specified data. """ data_parameters = data.data_parameters edit_ui = data_parameters.edit_traits( view='data_view', kind='livemodal', # handler=handler, parent=window) return edit_ui.result def display_message(self, msg, title=None, is_error=False): """ Display the specified message to the user. """ # Ensure we record any reasons this method doesn't work. Especially # since it's critical in displaying errors to users! try: # Attempt to identify the current application window. parent_window = None workbench = self.application.get_service('envisage.' 'workbench.IWorkbench') if workbench is not None: parent_window = workbench.active_window.control # Display the requested message if is_error: error(parent_window, msg, title=title) else: information(parent_window, msg, title=title) except: logger.exception('Unable to display pop-up message') return #### EOF #####################################################################

0

3,086

0

83

89

20dc02eb654f867beadeef8c295396bcf7913d05

8,460

py

Python

metecho/tests/consumers.py

almostolmos/Metecho

7f58eca163faafea1ce07ffb6f4de2449fa0b8df

[ "BSD-3-Clause" ]

21

2020-04-02T21:39:58.000Z

2022-01-31T19:43:47.000Z

metecho/tests/consumers.py

almostolmos/Metecho

7f58eca163faafea1ce07ffb6f4de2449fa0b8df

[ "BSD-3-Clause" ]

1,613

2020-03-26T16:39:57.000Z

2022-03-07T14:54:16.000Z

metecho/tests/consumers.py

almostolmos/Metecho

7f58eca163faafea1ce07ffb6f4de2449fa0b8df

[ "BSD-3-Clause" ]

21

2020-07-21T11:58:47.000Z

2021-11-25T00:48:21.000Z

import pytest pytestmark = pytest.mark.asyncio # These tests need to go last, after any tests that start up a Communicator:

33.307087

88

0.711348

import pytest from channels.db import database_sync_to_async from channels.testing import WebsocketCommunicator from ..api.model_mixins import Request from ..api.push import push_message_about_instance, report_error from ..api.serializers import ( EpicSerializer, ProjectSerializer, ScratchOrgSerializer, TaskSerializer, ) from ..consumers import PushNotificationConsumer from ..routing import websockets pytestmark = pytest.mark.asyncio @database_sync_to_async def serialize_model(serializer_model, instance, user): serializer = serializer_model(instance, context={"request": Request(user)}) return serializer.data @pytest.mark.django_db async def test_push_notification_consumer__project(user_factory, project_factory): user = await database_sync_to_async(user_factory)() project = await database_sync_to_async(project_factory)() communicator = WebsocketCommunicator(websockets, "/ws/notifications/") communicator.scope["user"] = user connected, _ = await communicator.connect() assert connected await communicator.send_json_to( {"model": "project", "id": str(project.id), "action": "SUBSCRIBE"} ) response = await communicator.receive_json_from() assert "ok" in response await push_message_about_instance( project, {"type": "TEST_MESSAGE", "payload": {"originating_user_id": "abc"}} ) response = await communicator.receive_json_from() model = await serialize_model(ProjectSerializer, project, user) assert response == { "type": "TEST_MESSAGE", "payload": {"originating_user_id": "abc", "model": model}, } await communicator.disconnect() @pytest.mark.django_db async def test_push_notification_consumer__scratch_org__list( user_factory, scratch_org_factory ): user = await database_sync_to_async(user_factory)() scratch_org = await database_sync_to_async(scratch_org_factory)() communicator = WebsocketCommunicator(websockets, "/ws/notifications/") communicator.scope["user"] = user connected, _ = await communicator.connect() assert connected await communicator.send_json_to( {"model": "scratch_org", "id": "list", "action": "SUBSCRIBE"} ) response = await communicator.receive_json_from() assert "ok" in response await push_message_about_instance( scratch_org, {"type": "SCRATCH_ORG_RECREATE", "payload": {"originating_user_id": "abc"}}, for_list=True, ) response = await communicator.receive_json_from() model = await serialize_model(ScratchOrgSerializer, scratch_org, user) assert response == { "type": "SCRATCH_ORG_RECREATE", "payload": {"originating_user_id": "abc", "model": model}, } await communicator.disconnect() @pytest.mark.django_db async def test_push_notification_consumer__epic(user_factory, epic_factory): user = await database_sync_to_async(user_factory)() epic = await database_sync_to_async(epic_factory)(project__repo_id=1234) communicator = WebsocketCommunicator(websockets, "/ws/notifications/") communicator.scope["user"] = user connected, _ = await communicator.connect() assert connected await communicator.send_json_to( {"model": "epic", "id": str(epic.id), "action": "SUBSCRIBE"} ) response = await communicator.receive_json_from() assert "ok" in response await push_message_about_instance( epic, {"type": "TEST_MESSAGE", "payload": {"originating_user_id": "abc"}} ) response = await communicator.receive_json_from() model = await serialize_model(EpicSerializer, epic, user) assert response == { "type": "TEST_MESSAGE", "payload": {"originating_user_id": "abc", "model": model}, } await communicator.disconnect() @pytest.mark.django_db async def test_push_notification_consumer__task(user_factory, task_factory): user = await database_sync_to_async(user_factory)() task = await database_sync_to_async(task_factory)(epic__project__repo_id=4321) communicator = WebsocketCommunicator(websockets, "/ws/notifications/") communicator.scope["user"] = user connected, _ = await communicator.connect() assert connected await communicator.send_json_to( {"model": "task", "id": str(task.id), "action": "SUBSCRIBE"} ) response = await communicator.receive_json_from() assert "ok" in response await push_message_about_instance( task, {"type": "TEST_MESSAGE", "payload": {"originating_user_id": "abc"}} ) response = await communicator.receive_json_from() model = await serialize_model(TaskSerializer, task, user) assert response == { "type": "TEST_MESSAGE", "payload": {"originating_user_id": "abc", "model": model}, } await communicator.disconnect() @pytest.mark.django_db async def test_push_notification_consumer__scratch_org( user_factory, scratch_org_factory ): user = await database_sync_to_async(user_factory)() scratch_org = await database_sync_to_async(scratch_org_factory)( task__epic__project__repo_id=2468 ) communicator = WebsocketCommunicator(websockets, "/ws/notifications/") communicator.scope["user"] = user connected, _ = await communicator.connect() assert connected await communicator.send_json_to( {"model": "scratch_org", "id": str(scratch_org.id), "action": "SUBSCRIBE"} ) response = await communicator.receive_json_from() assert "ok" in response await push_message_about_instance( scratch_org, {"type": "TEST_MESSAGE", "payload": {"originating_user_id": "abc"}} ) response = await communicator.receive_json_from() model = await serialize_model(ScratchOrgSerializer, scratch_org, user) assert response == { "type": "TEST_MESSAGE", "payload": {"originating_user_id": "abc", "model": model}, } await communicator.disconnect() @pytest.mark.django_db async def test_push_notification_consumer__report_error(user_factory): user = await database_sync_to_async(user_factory)() communicator = WebsocketCommunicator(websockets, "/ws/notifications/") communicator.scope["user"] = user connected, _ = await communicator.connect() assert connected await communicator.send_json_to( {"model": "user", "id": str(user.id), "action": "SUBSCRIBE"} ) response = await communicator.receive_json_from() assert "ok" in response await report_error(user) response = await communicator.receive_json_from() assert response == { "type": "BACKEND_ERROR", "payload": {"message": "There was an error"}, } await communicator.disconnect() @pytest.mark.django_db async def test_push_notification_consumer__unsubscribe(user_factory): user = await database_sync_to_async(user_factory)() communicator = WebsocketCommunicator(websockets, "/ws/notifications/") communicator.scope["user"] = user connected, _ = await communicator.connect() assert connected await communicator.send_json_to( {"model": "user", "id": str(user.id), "action": "SUBSCRIBE"} ) response = await communicator.receive_json_from() assert "ok" in response await communicator.send_json_to( {"model": "user", "id": str(user.id), "action": "UNSUBSCRIBE"} ) response = await communicator.receive_json_from() assert "ok" in response await communicator.disconnect() @pytest.mark.django_db async def test_push_notification_consumer__invalid_subscription(user_factory): user = await database_sync_to_async(user_factory)() communicator = WebsocketCommunicator(websockets, "/ws/notifications/") communicator.scope["user"] = user connected, _ = await communicator.connect() assert connected await communicator.send_json_to({"model": "foobar", "id": "buzbaz"}) response = await communicator.receive_json_from() assert "error" in response await communicator.disconnect() # These tests need to go last, after any tests that start up a Communicator: @pytest.mark.django_db async def test_push_notification_consumer__missing_instance(): content = { "model_name": "scratchorg", "id": "bet this is an invalid ID", "payload": {}, } consumer = PushNotificationConsumer() new_content = await consumer.hydrate_message(content) assert new_content == {"payload": {}}

0

7,687

0

253

384

f3976e2ec215dc1bd2bd45dd144b13e71688e6f1

6,227

py

Python

cajitos_site/users/routes.py

OlgaKuratkina/cajitos

0bc13f71281a1a67c8bcd1a3ae343ad0b14d9bad

[ "MIT" ]

null

cajitos_site/users/routes.py

OlgaKuratkina/cajitos

0bc13f71281a1a67c8bcd1a3ae343ad0b14d9bad

[ "MIT" ]

7

2020-05-08T19:51:22.000Z

2022-03-11T23:37:57.000Z

cajitos_site/users/routes.py

OlgaKuratkina/cajitos

0bc13f71281a1a67c8bcd1a3ae343ad0b14d9bad

[ "MIT" ]

null

# Disbaled temporarily or forever # @users.route("/register", methods=['GET', 'POST'])

40.967105

120

0.696965

import markdown from flask import redirect, url_for, flash, render_template, session, request, current_app, abort from flask_login import current_user, login_user, logout_user, login_required from cajitos_site import bcrypt from cajitos_site.users import users from cajitos_site.users.forms import RegistrationForm, LoginForm, UpdateAccountForm, RequestResetForm, ResetPasswordForm from cajitos_site.models import User, load_user from cajitos_site.utils.email import send_service_email from cajitos_site.utils.utils import ( get_redirect_target, save_picture ) from cajitos_site.utils.auth_utils import generate_google_auth_request, get_google_user_info # Disbaled temporarily or forever # @users.route("/register", methods=['GET', 'POST']) def register(): if current_user.is_authenticated: return redirect(url_for('blog.posts')) form = RegistrationForm() if form.validate_on_submit(): user = User.create(username=form.username.data, email=form.email.data) flash(f'Account created for {form.username.data}!', 'success') flash(f'Check your email to confirm your new account', 'success') token = user.get_validation_token() reset_link = f"{url_for('users.validate_token', token=token, _external=True)}" send_service_email(user, reset_link) return redirect(url_for('blog.posts')) return render_template('user/register.html', title='Register', form=form) @users.route("/login", methods=['GET', 'POST']) def login(): form = LoginForm() if form.validate_on_submit(): user = User.select().where(User.email == form.email.data).first() if user and user.status != 'Confirmed': flash('You need to confirm your account to proceed!', 'info') elif user and bcrypt.check_password_hash(user.password, form.password.data): flash('You have been logged in!', 'success') login_user(user, remember=form.remember.data) next_page = get_redirect_target() return redirect(next_page) if next_page else redirect(url_for('blog.posts')) else: flash('Login Unsuccessful. Please check email and password', 'danger') return render_template('user/login.html', title='Login', form=form) @users.route('/google_login') def google_login(): request_uri = generate_google_auth_request() return redirect(request_uri) @users.route('/google_login/callback') def callback(): userinfo_response = get_google_user_info(request) if userinfo_response.get('email_verified'): google_id = userinfo_response['sub'] email = userinfo_response['email'] profile_picture = userinfo_response['picture'] username = userinfo_response['given_name'] else: return 'User email not available or not verified by Google.', 400 user = User.get_user_by_email(email) if not user: user = User.create( google_id=google_id, username=username, email=email, password='', profile_picture=profile_picture, status='Confirmed' ) else: user.google_id = google_id user.username = username if profile_picture: user.profile_picture = profile_picture user.status = 'Confirmed' user.save() login_user(user) return redirect(url_for('blog.posts')) @users.route('/logout') def logout(): logout_user() return redirect(url_for('blog.posts')) @users.route('/account/<int:user_id>') def account(user_id): user = load_user(user_id) return render_template('user/account.html', title='Account', user=user) @users.route('/account/<int:user_id>/update', methods=['GET', 'POST']) @login_required def account_update(user_id): form = UpdateAccountForm() if request.method == 'GET': form.username.data = current_user.username form.email.data = current_user.email form.about_me.data = current_user.about_me if form.validate_on_submit() and current_user.id == user_id: if form.picture.data: picture_file = save_picture(form.picture.data) current_user.profile_picture = picture_file current_user.username = form.username.data current_user.email = form.email.data current_user.about_me = markdown.markdown(form.about_me.data) current_user.save() flash('Your account has been updated!', 'success') return redirect(url_for('users.account', user_id=user_id)) elif current_user.id != user_id: abort(403) return render_template('create_entry.html', title='Account', form=form) @users.route("/reset_password", methods=['GET', 'POST']) def reset_request(): if current_user.is_authenticated: return redirect(url_for('blog.posts')) form = RequestResetForm() if form.validate_on_submit(): user = User.select().where(User.email == form.email.data).first() token = user.get_validation_token() reset_link = f"{url_for('users.validate_token', token=token, _external=True)}" send_service_email(user, reset_link, confirm_account=False) flash('An email has been sent with instructions to complete operation.', 'info') return redirect(url_for('users.login')) return render_template('user/reset_request.html', title='Reset Password', form=form) @users.route("/reset_password/<token>", methods=['GET', 'POST']) def validate_token(token): if current_user.is_authenticated: return redirect(url_for('blog.posts')) user = User.verify_token(token) if user is None: flash('That is an invalid or expired token', 'warning') return redirect(url_for('users.reset_request')) form = ResetPasswordForm() if form.validate_on_submit(): hashed_password = bcrypt.generate_password_hash(form.password.data).decode('utf-8') user.password = hashed_password # Instead of default implementation with user.is_active user.status = 'Confirmed' user.save() flash('Your password has been updated! You are now able to log in', 'success') return redirect(url_for('users.login')) return render_template('user/validate_token.html', title='Reset Password', form=form)

0

4,597

0

668

0

438

427