Reset23/the-stack-v2-python · Datasets at Hugging Face

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### Investigacion (Modulos) # 1: Uso de Modulo tkinter: Crear una interfaz grafica (GUI) básica que sea de 600 px de ancho y 400 px de alto, fondo negro y un botton en el centrode la pantalla # 2: Como hacer manejo de archivos: Leer un archivo y mostrarlo en pantalla (consola), posteriormente agregar una linea al final del documento # 3: Como hacer nuestros propios modulos: El ejercicio de la Caja Registradora (la funcion/metodo que se hizo) habrá que ponerlo en un módulo que podamos importar ### EJEMPLO de lectura de linea por linea de un archivo: with open('ejemplo.txt', 'r') as lectura: # Leer la primera linea linea = lectura.readline() while linea != '': # el EOF o fin de caracter es void, si hay un caracter en particular, especificar... # Aqui podemos hacer el procesamiento de nuestra linea linea = lectura.readline() ### Aquipodemos seguir leyendo en el while hasta que se acabe... ### EJEMPLO DE LECTURA completa y generar un array (list) de cada linea: file_open = open('ejemplo.txt') file_open.readlines() # Nos retorna una lista o array de cada linea... file_open.close() ### Ejemplo de escritura en python a un archivo de texto que ya tiene informacion, es decir: APPEND: with open('ejemplo.txt', 'a') as escritura: escritura.write('\nSiguiente linea a insertar')

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import re file=input() fh=open(file) sum=0 for line in fh: y=re.findall('[0-9]+',line) if len(y)==0: continue for x in y: sum=sum+int(x) print(sum)

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tanurag0@gmail.com

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/Projects/Online Workouts/w3resource/Basic - Part-II/program-6.py

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ivenpoker/Python-Projects

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#!/usr/bin/env python3 ############################################################################################ # # # Program purpose: Print a long text, convert the string to a list and print all the # # words and their frequencies. # # Program Author : Happi Yvan <ivensteinpoker@gmail.com> # # Creation Date : September 4, 2019 # # # ############################################################################################ def process_data(text=""): data = {} list_data = text.split(" ") punc = ['?', '.', '!', '\''] for x in range(len(list_data)): word = list_data[x].strip() if len(word) >= 1: if word[-1] in punc: word = word[0:len(word) - 1] if word in data.keys(): data[word] = int(data.get(word)) + 1 else: data[word] = 1 return data if __name__ == "__main__": string_words = '''United States Declaration of Independence From Wikipedia, the free encyclopedia The United States Declaration of Independence is the statement adopted by the Second Continental Congress meeting at the Pennsylvania State House (Independence Hall) in Philadelphia on July 4, 1776, which announced that the thirteen American colonies, then at war with the Kingdom of Great Britain, regarded themselves as thirteen independent sovereign states, no longer under British rule. These states would found a new nation – the United States of America. John Adams was a leader in pushing for independence, which was passed on July 2 with no opposing vote cast. A committee of five had already drafted the formal declaration, to be ready when Congress voted on independence. John Adams persuaded the committee to select Thomas Jefferson to compose the original draft of the document, which Congress would edit to produce the final version. The Declaration was ultimately a formal explanation of why Congress had voted on July 2 to declare independence from Great Britain, more than a year after the outbreak of the American Revolutionary War. The next day, Adams wrote to his wife Abigail: "The Second Day of July 1776, will be the most memorable Epocha, in the History of America." But Independence Day is actually celebrated on July 4, the date that the Declaration of Independence was approved. After ratifying the text on July 4, Congress issued the Declaration of Independence in several forms. It was initially published as the printed Dunlap broadside that was widely distributed and read to the public. The source copy used for this printing has been lost, and may have been a copy in Thomas Jefferson's hand.[5] Jefferson's original draft, complete with changes made by John Adams and Benjamin Franklin, and Jefferson's notes of changes made by Congress, are preserved at the Library of Congress. The best-known version of the Declaration is a signed copy that is displayed at the National Archives in Washington, D.C., and which is popularly regarded as the official document. This engrossed copy was ordered by Congress on July 19 and signed primarily on August 2. The sources and interpretation of the Declaration have been the subject of much scholarly inquiry. The Declaration justified the independence of the United States by listing colonial grievances against King George III, and by asserting certain natural and legal rights, including a right of revolution. Having served its original purpose in announcing independence, references to the text of the Declaration were few in the following years. Abraham Lincoln made it the centerpiece of his rhetoric (as in the Gettysburg Address of 1863) and his policies. Since then, it has become a well-known statement on human rights, particularly its second sentence: We hold these truths to be self-evident, that all men are created equal, that they are endowed by their Creator with certain unalienable Rights, that among these are Life, Liberty and the pursuit of Happiness. This has been called "one of the best-known sentences in the English language", containing "the most potent and consequential words in American history". The passage came to represent a moral standard to which the United States should strive. This view was notably promoted by Abraham Lincoln, who considered the Declaration to be the foundation of his political philosophy and argued that it is a statement of principles through which the United States Constitution should be interpreted. The U.S. Declaration of Independence inspired many other similar documents in other countries, the first being the 1789 Declaration of Flanders issued during the Brabant Revolution in the Austrian Netherlands (modern-day Belgium). It also served as the primary model for numerous declarations of independence across Europe and Latin America, as well as Africa (Liberia) and Oceania (New Zealand) during the first half of the 19th century.''' dict_data = process_data(text=string_words) print(f"Dictionary obtained:\n{dict_data}")

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#Complete Python Masterclass - Tim Buchalka & Jean-Paul Roberts #Challenge - Program Flow #Regina Gates - First efforts with many edits, but without checking, and without correcting for all cases, like the IP # address starting with '.' # Even though this code does not account for every error, including adding a + sign, which signals a segment count, # I'm going to submit to github, before I watch the solution video. -RG name = input("What is your name? ") user_IP_address = input("Hello, {}, what is your IP address? ".format(name)) seg_count = 0 new_string = '' if user_IP_address == '': user_IP_address = input("Please enter a valid IP address? ") elif user_IP_address[len(user_IP_address) - 1] == '.': for char in user_IP_address: if char not in '0123456789': seg_count += 1 if user_IP_address[0] == '.': seg_count -= 1 elif user_IP_address[len(user_IP_address) - 1] != '.': for char in user_IP_address: if char not in '0123456789': seg_count += 1 seg_count += 1 user_IP_address += '.' if user_IP_address[0] =='.': seg_count -= 1 print("This IP address has {} segments.".format(seg_count)) seg_count = 0 for char in user_IP_address: if char in '0123456789': new_string += char elif char == '.': seg_count += 1 print("Segment {0} has {1} digits.".format(seg_count, len(new_string))) new_string = ''

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itsolutionscorp/AutoStyle-Clustering

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def detect_anagrams(word, choices): anagrams = [] lWord = list(word.lower()) lWord.sort() for choice in choices: if choice.lower() == word.lower(): continue lChoice = list(choice.lower()) lChoice.sort() if lChoice == lWord: anagrams.append(choice) return anagrams

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ROLL_PATTERN = '(^\d+d\d+)|([\+\-]\d+d\d+)|([\+\-]\d+(?!d))' BONUS_PATTERN = '(^[+-]?\d+)|((?<=\/)[+-]?\d+)*' def concat_lists(list_of_iters): output = [] for sub in list_of_iters: for item in sub: output.append(item) return output def remove_nulls(origin_list): return [item for item in origin_list if item] def clean_float(f): if f == int(f): return int(f) for i in range(2, 5): if f == round(f, i): return round(f, i-1) return round(f, 4)

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danyglewin@gmail.com

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waffle-iron/eAccount

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""" Django settings for src project. Generated by 'django-admin startproject' using Django 1.9.6. For more information on this file, see https://docs.djangoproject.com/en/1.9/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/1.9/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/1.9/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = '%g38=9v4u55yo_-!8fb#ci=kqcwmwjq9m_$=d$j^r-^%(5gw_9' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'v0', 'rest_framework', 'corsheaders' ] REST_FRAMEWORK_DOCS = { 'HIDE_DOCS': False # Default: False } CORS_ORIGIN_ALLOW_ALL = True MIDDLEWARE_CLASSES = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'corsheaders.middleware.CorsMiddleware', 'django.middleware.common.CommonMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.auth.middleware.SessionAuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'src.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'src.wsgi.application' # Database # https://docs.djangoproject.com/en/1.9/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/1.9/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/1.9/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/1.9/howto/static-files/ STATIC_URL = '/static/' REST_FRAMEWORK = { 'TEST_REQUEST_DEFAULT_FORMAT': 'json', 'DEFAULT_PERMISSION_CLASSES': ( 'rest_framework.permissions.AllowAny', ), }

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# Copyright (c) 2017 crocoite contributors # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. """ Chrome browser interactions. """ import asyncio from base64 import b64decode, b64encode from datetime import datetime, timedelta from http.server import BaseHTTPRequestHandler from yarl import URL from multidict import CIMultiDict from .logger import Level from .devtools import Browser, TabException # These two classes’ only purpose is so we can later tell whether a body was # base64-encoded or a unicode string class Base64Body (bytes): def __new__ (cls, value): return bytes.__new__ (cls, b64decode (value)) @classmethod def fromBytes (cls, b): """ For testing """ return cls (b64encode (b)) class UnicodeBody (bytes): def __new__ (cls, value): if type (value) is not str: raise TypeError ('expecting unicode string') return bytes.__new__ (cls, value.encode ('utf-8')) class Request: __slots__ = ('headers', 'body', 'initiator', 'hasPostData', 'method', 'timestamp') def __init__ (self, method=None, headers=None, body=None): self.headers = headers self.body = body self.hasPostData = False self.initiator = None # HTTP method self.method = method self.timestamp = None def __repr__ (self): return f'Request({self.method!r}, {self.headers!r}, {self.body!r})' def __eq__ (self, b): if b is None: return False if not isinstance (b, Request): raise TypeError ('Can only compare equality with Request.') # do not compare hasPostData (only required to fetch body) and # timestamp (depends on time) return self.headers == b.headers and \ self.body == b.body and \ self.initiator == b.initiator and \ self.method == b.method class Response: __slots__ = ('status', 'statusText', 'headers', 'body', 'bytesReceived', 'timestamp', 'mimeType') def __init__ (self, status=None, statusText=None, headers=None, body=None, mimeType=None): self.status = status self.statusText = statusText self.headers = headers self.body = body # bytes received over the network (not body size!) self.bytesReceived = 0 self.timestamp = None self.mimeType = mimeType def __repr__ (self): return f'Response({self.status!r}, {self.statusText!r}, {self.headers!r}, {self.body!r}, {self.mimeType!r})' def __eq__ (self, b): if b is None: return False if not isinstance (b, Response): raise TypeError ('Can only compare equality with Response.') # do not compare bytesReceived (depends on network), timestamp # (depends on time) and statusText (does not matter) return self.status == b.status and \ self.statusText == b.statusText and \ self.headers == b.headers and \ self.body == b.body and \ self.mimeType == b.mimeType class ReferenceTimestamp: """ Map relative timestamp to absolute timestamp """ def __init__ (self, relative, absolute): self.relative = timedelta (seconds=relative) self.absolute = datetime.utcfromtimestamp (absolute) def __call__ (self, relative): if not isinstance (relative, timedelta): relative = timedelta (seconds=relative) return self.absolute + (relative-self.relative) class RequestResponsePair: __slots__ = ('request', 'response', 'id', 'url', 'remoteIpAddress', 'protocol', 'resourceType', '_time') def __init__ (self, id=None, url=None, request=None, response=None): self.request = request self.response = response self.id = id self.url = url self.remoteIpAddress = None self.protocol = None self.resourceType = None self._time = None def __repr__ (self): return f'RequestResponsePair({self.id!r}, {self.url!r}, {self.request!r}, {self.response!r})' def __eq__ (self, b): if not isinstance (b, RequestResponsePair): raise TypeError (f'Can only compare with {self.__class__.__name__}') # do not compare id and _time. These depend on external factors and do # not influence the request/response *content* return self.request == b.request and \ self.response == b.response and \ self.url == b.url and \ self.remoteIpAddress == b.remoteIpAddress and \ self.protocol == b.protocol and \ self.resourceType == b.resourceType def fromRequestWillBeSent (self, req): """ Set request data from Chrome Network.requestWillBeSent event """ r = req['request'] self.id = req['requestId'] self.url = URL (r['url']) self.resourceType = req.get ('type') self._time = ReferenceTimestamp (req['timestamp'], req['wallTime']) assert self.request is None, req self.request = Request () self.request.initiator = req['initiator'] self.request.headers = CIMultiDict (self._unfoldHeaders (r['headers'])) self.request.hasPostData = r.get ('hasPostData', False) self.request.method = r['method'] self.request.timestamp = self._time (req['timestamp']) if self.request.hasPostData: postData = r.get ('postData') if postData is not None: self.request.body = UnicodeBody (postData) def fromResponse (self, r, timestamp=None, resourceType=None): """ Set response data from Chrome’s Response object. Request must exist. Updates if response was set before. Sometimes fromResponseReceived is triggered twice by Chrome. No idea why. """ assert self.request is not None, (self.request, r) if not timestamp: timestamp = self.request.timestamp self.remoteIpAddress = r.get ('remoteIPAddress') self.protocol = r.get ('protocol') if resourceType: self.resourceType = resourceType # a response may contain updated request headers (i.e. those actually # sent over the wire) if 'requestHeaders' in r: self.request.headers = CIMultiDict (self._unfoldHeaders (r['requestHeaders'])) self.response = Response () self.response.headers = CIMultiDict (self._unfoldHeaders (r['headers'])) self.response.status = r['status'] self.response.statusText = r['statusText'] self.response.timestamp = timestamp self.response.mimeType = r['mimeType'] def fromResponseReceived (self, resp): """ Set response data from Chrome Network.responseReceived """ return self.fromResponse (resp['response'], self._time (resp['timestamp']), resp['type']) def fromLoadingFinished (self, data): self.response.bytesReceived = data['encodedDataLength'] def fromLoadingFailed (self, data): self.response = None @staticmethod def _unfoldHeaders (headers): """ A host may send multiple headers using the same key, which Chrome folds into the same item. Separate those. """ items = [] for k in headers.keys (): for v in headers[k].split ('\n'): items.append ((k, v)) return items async def prefetchRequestBody (self, tab): if self.request.hasPostData and self.request.body is None: try: postData = await tab.Network.getRequestPostData (requestId=self.id) self.request.body = UnicodeBody (postData['postData']) except TabException: self.request.body = None async def prefetchResponseBody (self, tab): """ Fetch response body """ try: body = await tab.Network.getResponseBody (requestId=self.id) if body['base64Encoded']: self.response.body = Base64Body (body['body']) else: self.response.body = UnicodeBody (body['body']) except TabException: self.response.body = None class NavigateError (IOError): pass class PageIdle: """ Page idle event """ __slots__ = ('idle', ) def __init__ (self, idle): self.idle = idle def __bool__ (self): return self.idle class FrameNavigated: __slots__ = ('id', 'url', 'mimeType') def __init__ (self, id, url, mimeType): self.id = id self.url = URL (url) self.mimeType = mimeType class SiteLoader: """ Load site in Chrome and monitor network requests XXX: track popup windows/new tabs and close them """ __slots__ = ('requests', 'browser', 'logger', 'tab', '_iterRunning', '_framesLoading', '_rootFrame') allowedSchemes = {'http', 'https'} def __init__ (self, browser, logger): self.requests = {} self.browser = Browser (url=browser) self.logger = logger.bind (context=type (self).__name__) self._iterRunning = [] self._framesLoading = set () self._rootFrame = None async def __aenter__ (self): tab = self.tab = await self.browser.__aenter__ () # enable events await asyncio.gather (*[ tab.Log.enable (), tab.Network.enable(), tab.Page.enable (), tab.Inspector.enable (), tab.Network.clearBrowserCache (), tab.Network.clearBrowserCookies (), ]) return self async def __aexit__ (self, exc_type, exc_value, traceback): for task in self._iterRunning: # ignore any results from stuff we did not end up using anyway if not task.done (): task.cancel () self._iterRunning = [] await self.browser.__aexit__ (exc_type, exc_value, traceback) self.tab = None return False def __len__ (self): return len (self.requests) async def __aiter__ (self): """ Retrieve network items """ tab = self.tab assert tab is not None handler = { tab.Network.requestWillBeSent: self._requestWillBeSent, tab.Network.responseReceived: self._responseReceived, tab.Network.loadingFinished: self._loadingFinished, tab.Network.loadingFailed: self._loadingFailed, tab.Log.entryAdded: self._entryAdded, tab.Page.javascriptDialogOpening: self._javascriptDialogOpening, tab.Page.frameStartedLoading: self._frameStartedLoading, tab.Page.frameStoppedLoading: self._frameStoppedLoading, tab.Page.frameNavigated: self._frameNavigated, } # The implementation is a little advanced. Why? The goal here is to # process events from the tab as quickly as possible (i.e. # asynchronously). We need to make sure that JavaScript dialogs are # handled immediately for instance. Otherwise they stall every # other request. Also, we don’t want to use an unbounded queue, # since the items yielded can get quite big (response body). Thus # we need to block (yield) for every item completed, but not # handled by the consumer (caller). running = self._iterRunning tabGetTask = asyncio.ensure_future (self.tab.get ()) running.append (tabGetTask) while True: done, pending = await asyncio.wait (running, return_when=asyncio.FIRST_COMPLETED) for t in done: result = t.result () if result is None: pass elif t == tabGetTask: method, data = result f = handler.get (method, None) if f is not None: task = asyncio.ensure_future (f (**data)) pending.add (task) tabGetTask = asyncio.ensure_future (self.tab.get ()) pending.add (tabGetTask) else: yield result running = pending self._iterRunning = running async def navigate (self, url): ret = await self.tab.Page.navigate(url=url) self.logger.debug ('navigate', uuid='9d47ded2-951f-4e09-86ee-fd4151e20666', result=ret) if 'errorText' in ret: raise NavigateError (ret['errorText']) self._rootFrame = ret['frameId'] # internal chrome callbacks async def _requestWillBeSent (self, **kwargs): self.logger.debug ('requestWillBeSent', uuid='b828d75a-650d-42d2-8c66-14f4547512da', args=kwargs) reqId = kwargs['requestId'] req = kwargs['request'] url = URL (req['url']) logger = self.logger.bind (reqId=reqId, reqUrl=url) if url.scheme not in self.allowedSchemes: return ret = None item = self.requests.get (reqId) if item: # redirects never “finish” loading, but yield another requestWillBeSent with this key set redirectResp = kwargs.get ('redirectResponse') if redirectResp: if item.url != url: # this happens for unknown reasons. the docs simply state # it can differ in case of a redirect. Fix it and move on. logger.warning ('redirect url differs', uuid='558a7df7-2258-4fe4-b16d-22b6019cc163', expected=item.url) redirectResp['url'] = str (item.url) item.fromResponse (redirectResp) logger.info ('redirect', uuid='85eaec41-e2a9-49c2-9445-6f19690278b8', target=url) # XXX: queue this? no need to wait for it await item.prefetchRequestBody (self.tab) # cannot fetch response body due to race condition (item id reused) ret = item else: logger.warning ('request exists', uuid='2c989142-ba00-4791-bb03-c2a14e91a56b') item = RequestResponsePair () item.fromRequestWillBeSent (kwargs) self.requests[reqId] = item return ret async def _responseReceived (self, **kwargs): self.logger.debug ('responseReceived', uuid='ecd67e69-401a-41cb-b4ec-eeb1f1ec6abb', args=kwargs) reqId = kwargs['requestId'] item = self.requests.get (reqId) if item is None: return resp = kwargs['response'] url = URL (resp['url']) logger = self.logger.bind (reqId=reqId, respUrl=url) if item.url != url: logger.error ('url mismatch', uuid='7385f45f-0b06-4cbc-81f9-67bcd72ee7d0', respUrl=url) if url.scheme in self.allowedSchemes: item.fromResponseReceived (kwargs) else: logger.warning ('scheme forbidden', uuid='2ea6e5d7-dd3b-4881-b9de-156c1751c666') async def _loadingFinished (self, **kwargs): """ Item was fully loaded. For some items the request body is not available when responseReceived is fired, thus move everything here. """ self.logger.debug ('loadingFinished', uuid='35479405-a5b5-4395-8c33-d3601d1796b9', args=kwargs) reqId = kwargs['requestId'] item = self.requests.pop (reqId, None) if item is None: # we never recorded this request (blacklisted scheme, for example) return if not item.response: # chrome failed to send us a responseReceived event for this item, # so we can’t record it (missing request/response headers) self.logger.error ('response missing', uuid='fac3ab96-3f9b-4c5a-95c7-f83b675cdcb9', requestId=item.id) return req = item.request if item.url.scheme in self.allowedSchemes: item.fromLoadingFinished (kwargs) # XXX queue both await asyncio.gather (item.prefetchRequestBody (self.tab), item.prefetchResponseBody (self.tab)) return item async def _loadingFailed (self, **kwargs): self.logger.info ('loadingFailed', uuid='4a944e85-5fae-4aa6-9e7c-e578b29392e4', args=kwargs) reqId = kwargs['requestId'] logger = self.logger.bind (reqId=reqId) item = self.requests.pop (reqId, None) if item is not None: item.fromLoadingFailed (kwargs) return item async def _entryAdded (self, **kwargs): """ Log entry added """ entry = kwargs['entry'] level = {'verbose': Level.DEBUG, 'info': Level.INFO, 'warning': Level.WARNING, 'error': Level.ERROR}.get (entry.pop ('level'), Level.INFO) entry['uuid'] = 'e62ffb5a-0521-459c-a3d9-1124551934d2' self.logger (level, 'console', **entry) async def _javascriptDialogOpening (self, **kwargs): t = kwargs.get ('type') if t in {'alert', 'confirm', 'prompt'}: self.logger.info ('js dialog', uuid='d6f07ce2-648e-493b-a1df-f353bed27c84', action='cancel', type=t, message=kwargs.get ('message')) await self.tab.Page.handleJavaScriptDialog (accept=False) elif t == 'beforeunload': # we must accept this one, otherwise the page will not unload/close self.logger.info ('js dialog', uuid='96399b99-9834-4c8f-bd93-cb9fa2225abd', action='proceed', type=t, message=kwargs.get ('message')) await self.tab.Page.handleJavaScriptDialog (accept=True) else: # pragma: no cover self.logger.warning ('js dialog unknown', uuid='3ef7292e-8595-4e89-b834-0cc6bc40ee38', **kwargs) async def _frameStartedLoading (self, **kwargs): self.logger.debug ('frameStartedLoading', uuid='bbeb39c0-3304-4221-918e-f26bd443c566', args=kwargs) self._framesLoading.add (kwargs['frameId']) return PageIdle (False) async def _frameStoppedLoading (self, **kwargs): self.logger.debug ('frameStoppedLoading', uuid='fcbe8110-511c-4cbb-ac2b-f61a5782c5a0', args=kwargs) self._framesLoading.remove (kwargs['frameId']) if not self._framesLoading: return PageIdle (True) async def _frameNavigated (self, **kwargs): self.logger.debug ('frameNavigated', uuid='0e876f7d-7129-4612-8632-686f42ac6e1f', args=kwargs) frame = kwargs['frame'] if self._rootFrame == frame['id']: assert frame.get ('parentId', None) is None, "root frame must not have a parent" return FrameNavigated (frame['id'], frame['url'], frame['mimeType'])

[ "lars@6xq.net" ]

lars@6xq.net

7e7ef5d3b401e5fb75250bfbcaee25f66f613d00

8e3486b4a58f13f46d306b0aca8ff57f7de83fb2

/tennisGame/tennis.py

c5429138d195f2ff1ed85a8d86f1bec5265b26a8

[]

no_license

Minato007/python-dev

18f8c5af1da3877a64625f5c95546fd3fe34d752

ccd1904df3dccf959a2b5d7806ffd89e55f5a487

refs/heads/master

2020-04-09T08:43:55.498204

2018-12-10T06:03:06

160,206,127

0

null

UTF-8

Python

false

5,490

py

import pygame import random state = 'gamestart' class Player: def __init__(self, x, y, width, height): self.x = x self.y = y self.width = width self.height = height self.dy = 0 self.score = 0 class Ball: def __init__(self, x, y, size): self.x = x self.y = y self.size = size self.dx = 0 self.dy = 0 while self.dx == 0 or self.dy == 0: self.dx = random.randint(-3, 3) self.dy = random.randint(-3, 3) self.img = pygame.image.load('ball.png') self.img = pygame.transform.scale(self.img, (self.size, self.size)) self.move = False size = (540, 500) player1 = Player(5, 100, 20, 100) player2 = Player(size[0]-5-20, 100, 20, 100) balls = [] ball = Ball(size[0]/2-20, size[1]/2-20, 40) balls.append(ball) pygame.init() pygame.font.init() # you have to call this at the start, # if you want to use this module. myfont = pygame.font.SysFont('Comic Sans MS', 30) myfont1 = pygame.font.SysFont('Calibri', 65, True, False) text_game_over = myfont1.render("Game Over", True, (255,0,0)) screen = pygame.display.set_mode(size) clock = pygame.time.Clock() done = False c = 0 clock = pygame.time.Clock() counter = 5 text = '5' pygame.time.set_timer(pygame.USEREVENT, 1000) font = pygame.font.SysFont('Consolas', 30) while not done: for event in pygame.event.get(): if event.type == pygame.USEREVENT: counter -= 1 if counter > 0: text = str(counter) else: text = 'new ball' ball = Ball(size[0]/2-20, size[1]/2-20, 40) ball.move = True balls.append(ball) counter = 5 text = '5' if event.type == pygame.QUIT: done = True if event.type == pygame.KEYDOWN: if event.key == pygame.K_SPACE: ball.move = True if event.key == pygame.K_s: player1.dy = 5 if event.key == pygame.K_w: player1.dy = -5 if event.key == pygame.K_DOWN: player2.dy = 5 if event.key == pygame.K_UP: player2.dy = -5 if event.type == pygame.KEYUP: if event.key == pygame.K_s: player1.dy = 0 if event.key == pygame.K_w: player1.dy = 0 if event.key == pygame.K_DOWN: player2.dy = 0 if event.key == pygame.K_UP: player2.dy = 0 if state == "gamestart": player1.y += player1.dy player2.y += player2.dy if player1.y < 1: player1.y = 1 if player2.y < 1: player2.y = 1 if player1.y > size[1] - 1 - player1.height: player1.y = size[1] - 1 - player1.height if player2.y > size[1] - 1 - player2.height: player2.y = size[1] - 1 - player2.height for ball in balls: if ball.move: ball.x += ball.dx ball.y += ball.dy # Bounce for ball in balls: if ball.x > size[0] - ball.size - player2.width: if (ball.y + ball.size/2 > player2.y) and (ball.y + ball.size/2 < player2.y + player2.height): ball.dx = -abs(ball.dx) ball.dx = ball.dx * 1.1 ball.dy = ball.dy * 1.1 else: # ball = Ball(size[0] / 2 - 20, size[1] / 2 - 20, 40) # ball.move = True balls.remove(ball) player1.score += 1 c = 0 for ball in balls: if ball.y > size[1] - ball.size: ball.dy = -abs(ball.dy) for ball in balls: if ball.x < player1.x + player1.width: if (ball.y + ball.size/2 > player1.y) and (ball.y + ball.size/2 < player1.y + player1.height): ball.dx = abs(ball.dx) ball.dx = ball.dx * 1.1 ball.dy = ball.dy * 1.1 else: # ball = Ball(size[0] / 2 - 20, size[1] / 2 - 20, 40) # ball.move = True balls.remove(ball) player2.score += 1 c = 0 for ball in balls: if ball.y < 1: ball.dy = abs(ball.dy) c += 10 if c > 255: c = 255 if state == "gameOver": c = 255 screen.fill((255,c,c)) pygame.draw.rect(screen, (0, 128, 0), [ player1.x, player1.y, player1.width, player1.height ], 0) pygame.draw.rect(screen, (0, 128, 0), [ player2.x, player2.y, player2.width, player2.height ], 0) for ball in balls: screen.blit(ball.img, (ball.x, ball.y)) text_surface = myfont.render(str(player1.score),False,(20,20,250)) screen.blit(text_surface, (size[0]/3, 10)) text_surface = myfont.render(str(player2.score),False,(20,20,250)) screen.blit(text_surface, (size[0]*2/3, 10)) screen.blit(font.render(text, True, (0, 0, 0)), (250, 10)) #вывод каунтера на экран if (player1.score == 3) or (player2.score == 3): state = 'gameOver' if state == 'gameOver': screen.blit(text_game_over, [10, 200]) pygame.display.flip() clock.tick(80) pygame.quit()

[ "yellow_sama@mail.ru" ]

yellow_sama@mail.ru

82104f52be98ba50d1033ca4bd55be1cbe90cd11

02e116cd7ae672d3ab999c6f389288c7aa1028ec

/ttt.py

d7da943ae9ce3a15f1f6c5a87697df020c978f6a

[]

no_license

tanxiumei/interfaceWushui

6abcd8ccb8948d2b596b654bac471d8d40abf868

ffa2ee71dbcbfed1f0153943eda9640bfc29e31e

refs/heads/master

2022-12-20T06:31:18.629197

2020-10-13T03:06:23

303,572,596

0

null

UTF-8

Python

false

607

py

import datetime from xlrd import open_workbook from xlutils.copy import copy import os from test_excel import ParamFactory # r_xls = open_workbook('equipmentid_param.xls') # 读取excel文件 # row = r_xls.sheets()[0].nrows # 获取已有的行数 # excel = copy(r_xls) # 将xlrd的对象转化为xlwt的对象 # table = excel.get_sheet(0) # 获取要操作的sheet # # # 对excel表追加一行内容 # table.write(5, 10, '内容1') # 括号内分别为行数、列数、内容 # # excel.save('equipmentid_param.xls') # 保存并覆盖文件 datetime.datetime.strptime(string,'%Y-%m-%d %H:%M:%S')

[ "757560315@qq.com" ]

757560315@qq.com

b1e80f1195383a0d0027d1835ba47e97c66c74ae

97aa853c4e05fdade3938205e4e63b66ebb430b1

/Ex3.4.4.2-2.py

b0fdf4e6a6f6f2bb1e40cef84131016a95093aba

[]

no_license

EllenHoffmann/BasicTrack3

30fd397e5e60837c3e0c0fcf3b93f9e51ffc0a4e

95b57f5dfd0360288449f9eafb2e3cb1d51421a8

refs/heads/master

2022-12-23T17:20:27.656751

2020-09-30T15:58:02

296,393,060

0

null

UTF-8

Python

false

226

py

months = ["January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December"] for element in range(12): print("One of the months of the year is "+str(months[element]))

[ "70917850+EllenHoffmann@users.noreply.github.com" ]

70917850+EllenHoffmann@users.noreply.github.com

15cc7da59b5b7e081c4812327c849244bde141ca

8e6c2c8fc95551416fe17b1f423f8ed80f44bce4

/example_scripts/getusers.py

fae0871815a9bd73f808424ad0e73120bad1a0ff

[ "MIT" ]

permissive

Tethik/eves-ornate-lockbox

b1b512aa0b7c4a32f5597478b7115f2d5cdd01a2

9eb9acbb19b193d0f852c70d6c626f62780f4f4e

refs/heads/master

2021-01-02T23:07:46.725732

2014-03-13T02:08:32

null

0

null

UTF-8

Python

false

486

py

#!/usr/bin/python import httplib import base64 import json addr = "127.0.0.1:5000" endpoint = "/users" auth = "secret:" use_ssl = False headers = {"Content-type": "application/json", "Accept": "text/plain", "Authorization": "Basic " + base64.b64encode(auth)} h1 = httplib.HTTPConnection(addr) if use_ssl: h1 = httplib.HTTPSConnection(addr) h1.request("GET", endpoint, headers=headers) resp = h1.getresponse().read() decoded_resp = json.loads(resp) print decoded_resp h1.close()

[ "joakim@uddholm.com" ]

joakim@uddholm.com

317510238069e3293f597cd045c2e062ec2bbe4a

a4ee3873ccd4b09a26b9febff9cd1a678dd90cc2

/solved/swea5650.py

760e244f3f34133ef82a8671c933fefe7f975c8d

[]

no_license

young2141/PS_Codes

d37d97d9b92931d27cefcef052a7f3f897ef8e1c

856fe7646d133cfb7e107b05ffe8d03ab8901e2d

refs/heads/master

2023-02-25T19:10:41.890785

2023-02-14T04:16:36

191,506,351

0

null

2019-06-14T04:06:42

2019-06-12T05:53:16

C++

UTF-8

Python

false

1,659

py

#sw expert academy 5650 dfs from pprint import pprint dir = {'UP':0,'RIGHT':1,'DOWN':2,'LEFT':3} bounce = [[2,2,1,3,2,2,0,0,0,0,0], [3,3,3,2,0,3,1,1,1,1,1], [0,1,0,0,3,0,2,2,2,2,2], [1,0,2,1,1,1,3,3,3,3,3],] dy = [-1,0,1,0] dx = [0,1,0,-1] def find(w,n,ii,jj,holes,d): count = 0 y,x = ii,jj while True: y,x = y + dy[d], x + dx[d] if y <0 or y >=n or x<0 or x >= n: return count*2 + 1 elif y == ii and x == jj: return count elif w[y][x] == 0: pass elif 1 <= w[y][x] <= 5: d = bounce[d][w[y][x]] count += 1 elif 6 <= w[y][x] <= 10: if holes[w[y][x]][0] == [y,x]: y,x = holes[w[y][x]][1] else : y,x = holes[w[y][x]][0] elif w[y][x] == -1 : return count tc = int(input()) for tc in range(1,tc+1): n = int(input()) w = [[int(x) for x in input().split()] for _ in range(n)] answer = 0 holes = [[] for _ in range(11)] for i in range(n): for j in range(n): if 6 <= w[i][j] <= 10: holes[w[i][j]].append([i,j]) for i in range(n): for j in range(n): if w[i][j] == 0: for d in dir.values(): answer = max(answer,find(w,n,i,j,holes,d)) print('#{} {}'.format(tc, answer)) ''' 1 10 0 1 0 3 0 0 0 0 7 0 0 0 0 0 -1 0 5 0 0 0 0 4 0 0 0 3 0 0 2 2 1 0 0 0 1 0 0 3 0 0 0 0 3 0 0 0 0 0 6 0 3 0 0 0 2 0 0 1 0 0 0 0 0 0 0 1 0 0 4 0 0 5 0 4 1 0 7 0 0 5 0 0 0 0 0 1 0 0 0 0 2 0 6 0 0 4 0 0 0 4 '''

[ "noreply@github.com" ]

young2141.noreply@github.com

840826d546985b8d4f5d68fd09f358fd07b5351b

887f89e6e86b76a6fd128e76e3d198731fc3761d

/class_circle.py

a24894e0651641f8d0f76188d86baf3e67418cf8

[]

no_license

geovanne97/python_learn

5c38a26cf7a81ec28de400acee11d89320dfcfc9

5914d1bc4ee91244f0ef95537031fec25ffc0f97

refs/heads/master

2021-10-24T18:39:19.351875

2019-03-27T17:46:03

null

0

null

UTF-8

Python

false

282

py

class Circle(): pi = 3.14 def __init__(self,radius=1): self.radius = radius def area(self): return self.radius*self.radius * Circle.pi def set_radius(self,new_r): self.radius = new_r myc = Circle(3) myc.set_radius(100) print(myc.area())

[ "geovannessaraiva97@gmail.com" ]

geovannessaraiva97@gmail.com

57f7d154697ab944d89794d7699105e1d49cb04e

165d45b38f681f80c4f98372415835a14befb9dd

/bin/doc-format

3467037e15117c305cdbd5cc091647486e2f09ad

[]

no_license

Mark-Seaman/My-Book-Online

4f9c5c9c2b6bd21ee6e2105f6def29ce744d3366

0a280ef23b31dc26ab1522aac147f887314b4786

refs/heads/master

2016-08-12T17:21:15.336778

2014-08-16T04:04:47

null

0

null

UTF-8

Python

false

97

#!/usr/bin/env python # Wiki text formatter from util.doc import doc_format print doc_format()

[ "mark.seaman@shrinking-world.com" ]

mark.seaman@shrinking-world.com

e3750481e5642f2fbcf23111a5675c34b4bd7ebc

20d651e38f44b89da6bd0e0d0e0bd95d47a9fa59

/Offer_letter_Sender3.py

797743b2d5934d77ad7cf0dcc84c7089c373e6ee

[]

no_license

Lokesh2703/Offer_letter_Sender

1af3a1872781e6955f1476d59cd9d61a8a0b10f7

3d8028502300ffabfa5ffdf78850e1e6d09c3cf8

refs/heads/master

2020-07-02T18:09:51.994535

2019-12-08T17:52:45

201,617,426

0

1

null

UTF-8

Python

false

4,824

py

import docx import pandas as pd from docx.shared import Pt import os.path from os import chdir, getcwd, listdir, path from time import strftime from win32com import client import smtplib from email.mime.multipart import MIMEMultipart from email.mime.text import MIMEText from email.mime.base import MIMEBase from email import encoders from tkinter import * def run_sender(file_nm): print(file_nm) # file_nm = input('Enter the filename(with extension): ') file = pd.read_csv(file_nm) names = file.iloc[:,0] emails = file.iloc[:,1] # print(emails) doc = docx.Document('Offer Letter - Campus Ambassador Program _ Aparoksha.docx') doc.paragraphs[6].text='Dear ' doc.paragraphs[6].add_run() doc.paragraphs[6].runs[1].bold=True name = doc.paragraphs[6].runs[1] font = name.font font.size = Pt(14) for name in names: print(name) doc.paragraphs[6].runs[1].text=name +',' doc.save(os.path.join("E:\\Projects\\OfferLetter_sender\\pdfs", (name+'.docx'))) def count_files(filetype): count_files = 0 for files in listdir(folder): if files.endswith(filetype): count_files += 1 return count_files def check_path(prompt): abs_path = input(prompt) while path.exists(abs_path) != True: print ("\nThe specified path does not exist.\n") abs_path = input(prompt) return abs_path print ("\n") folder = "E:\\Projects\\OfferLetter_sender\\pdfs" chdir(folder) num_docx = count_files(".docx") num_doc = count_files(".doc") if num_docx + num_doc == 0: print ("\nThe specified folder does not contain docx or docs files.\n") exit() else: print ("\nNumber of doc and docx files: ", num_docx + num_doc, "\n") print ("\n\nStarting to convert files ...\n") try: word = client.DispatchEx("Word.Application") for files in listdir(getcwd()): match = 0 if files.endswith(".doc"): s, match = "doc", 1 elif files.endswith(".docx"): s, match = "docx", 1 if match: new_name = files.replace("."+s, r".pdf") in_file = path.abspath(folder + "\\" + files) new_file = path.abspath(folder + "\\" + new_name) doc = word.Documents.Open(in_file) print ('Conversion Completed (from .docx to .pdf) ', path.relpath(new_file)) doc.SaveAs(new_file, FileFormat = 17) doc.Close() except (Exception, e): print (e) finally: word.Quit() print("\n", "Finished converting files to pdf format!!!") print("Starting to send email!!!") # Count the number of pdf files. # num_pdf = count_files(".pdf") # print ("\nNumber of pdf files: ", num_pdf) # Check if the number of docx and doc file is equal to the number of files. # if num_docx + num_doc == num_pdf: # print ("\nNumber of doc and docx files is equal to number of pdf files.") # else: # print ("\nNumber of doc and docx files is not equal to number of pdf files.") os.chdir('E:\\Projects\\OfferLetter_sender\\pdfs') i=3 j=0 for name in names: filename = name + '.pdf' # print(os.getcwd) # print(filename) # print(type(filename)) msg = MIMEMultipart() msg['TO'] = "TO_EMAIL_ADDRESS@gmail.com" msg['Subject']='Hi This is an pdf for '+ name body = "Hello" msg.attach(MIMEText(body,'plain')) image = open(str(filename),'rb') part = MIMEBase('application','octet-stream') part.set_payload(image.read()) encoders.encode_base64(part) part.add_header('Content-Disposition','attachment;filename=' + filename) msg.attach(part) smtp=smtplib.SMTP('smtp.gmail.com',587) smtp.ehlo() smtp.starttls() smtp.ehlo() smtp.login('FROM_EMAIL@gmail.com','PASSWORD') # subject='Hi I am Lokesh' # body='Hello! Welcome to Gmail2.' # message = f'Subject:{subject}\n\n{body}' smtp.sendmail('FROM_EMAIL@gmail.com',emails[j],msg.as_string()) smtp.close() print('DONE ' + 'for ' + name) j+=1 # msg['Subject']='Hi This is an pdf'+ str(i) filename = None run_sender('names.csv') # window = Tk() # file_info_shower = Label(window,text="Filename : ") # file_nm_entered = Entry(window) # file_nm = file_nm_entered.get() # print(file_nm) # btn = Button(window,text= "Submit",command =lambda: '')#run_sender(file_nm)) # file_info_shower.grid(row=0,column = 0) # file_nm_entered.grid(row=0,column = 1) # btn.grid(row = 1,column = 1) # window.mainloop()

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/auctioning_platform/processes/processes/tests/test_repository.py

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2022-07-18T01:05:28.784222

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from datetime import datetime import json from uuid import UUID, uuid4 import pytest from sqlalchemy.engine import Connection, Engine from foundation.value_objects.factories import get_dollars from db_infrastructure import Base from processes.paying_for_won_item import PayingForWonItemSagaData from processes.paying_for_won_item.saga import SagaState from processes.repository import SagaDataRepo, saga_data_table EXAMPLE_DATETIME = datetime(2019, 5, 24, 15, 20, 0, 12) @pytest.fixture(scope="session") def sqlalchemy_connect_url() -> str: return "sqlite:///:memory:" @pytest.fixture(scope="session", autouse=True) def setup_teardown_tables(engine: Engine) -> None: Base.metadata.create_all(engine) @pytest.fixture() def repo(connection: Connection) -> SagaDataRepo: return SagaDataRepo(connection) @pytest.mark.parametrize( "data, json_repr", [ ( PayingForWonItemSagaData(UUID("331831f1-3d7c-48c2-9433-955c1cf8deb6")), { "saga_uuid": "331831f1-3d7c-48c2-9433-955c1cf8deb6", "state": None, "timeout_at": None, "winning_bid": None, "auction_title": None, "auction_id": None, "winner_id": None, }, ), ( PayingForWonItemSagaData( UUID("d1526bb4-cee4-4b63-9029-802abc0f7593"), SagaState.PAYMENT_STARTED, EXAMPLE_DATETIME, get_dollars("15.99"), "Irrelevant", 1, 2, ), { "saga_uuid": "d1526bb4-cee4-4b63-9029-802abc0f7593", "state": SagaState.PAYMENT_STARTED.value, "timeout_at": EXAMPLE_DATETIME.isoformat(), "winning_bid": {"amount": "15.99", "currency": "USD"}, "auction_title": "Irrelevant", "auction_id": 1, "winner_id": 2, }, ), ], ) def test_saving_and_reading( repo: SagaDataRepo, connection: Connection, data: PayingForWonItemSagaData, json_repr: dict ) -> None: saga_uuid = uuid4() connection.execute(saga_data_table.insert(values={"uuid": saga_uuid, "json": json.dumps(json_repr)})) assert repo.get(saga_uuid, type(data)) == data connection.execute(saga_data_table.delete().where(saga_data_table.c.uuid == saga_uuid)) repo.save(saga_uuid, data) row = connection.execute(saga_data_table.select(saga_data_table.c.uuid == saga_uuid)).first() assert json.loads(row.json) == json_repr

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nnplaya@gmail.com

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refs/heads/master

2021-01-16T00:42:40.154663

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from heapq import heappop,heappush from copy import deepcopy import random import time def permutation(lst): if len(lst)==0: return[] if len(lst)==1: return[lst] l=[]#empty list that will store current permutation for i in range(len(lst)): m=lst[i] remlst=lst[:i]+lst[i+1:] for p in permutation(remlst): l.append([m]+p) return l class Fila: def __init__(self): self.fila=[] def obtener(self): return self.fila.pop() def meter(self,e): self.fila.insert(0,e) return len(self.fila) @property def longitud(self): return len(self.fila) class Pila: def __init__(self): self.pila=[] def obtener(self): return self.pila.pop() def meter(self,e): self.pila.append(e) return len(self.pila) @property def longitud(self): return len(self.pila) def flatten(L): while len(L)>0: yield L[0] L=L[1] class Grafo: def __init__(self): self.V = set()#un conjunto self.E = dict()#un mapeo de pesos de aristas self.vecinos = dict()#un mapeo def agrega(self, v): self.V.add(v) if not v in self.vecinos:#vecindad de v self.vecinos[v] = set()#inicialmente no tiene nada def conecta(self, v, u, peso=1): self.agrega(v) self.agrega(u) self.E[(v, u)] = self.E[(u, v)] = peso#en ambos sentidos self.vecinos[v].add(u) self.vecinos[u].add(v) def complemento(self): comp= Grafo() for v in self.V: for w in self.V: if v != w and (v, w) not in self.E: comp.conecta(v, w, 1) return comp def BFS(self,ni): visitados=[] f=Fila() f.meter(ni) while (f.longitud>0): na=f.obtener() visitados.append(na) ln=self.vecinos[na] for nodo in ln: if nodo not in visitados: f.meter(nodo) return visitados def DFS(self,ni): visitados=[] f=Pila() f.meter(ni) while (f.longitud>0): na=f.obtener() visitados.append(na) ln=self.vecinos[na] for nodo in ln: if nodo not in visitados: f.meter(nodo) return visitados def shortests(self,v):#algoritmo de dijkstra q=[(0,v,())]#arreglo q de las tuplas de lo que se va a almacenar donde 0 es la distancia, v el nodo y() el camino hacia el dist=dict()#diccionario de distancias visited=set()#conjunto de visitados while len(q)>0:#mientras exista un nodo pendiente (l,u,p)=heappop(q)#se toma la tupla con la distancia menor if u not in visited:#si no lo hemos visitado visited.add(u)#se agrega a visitados dist[u]=(l,u,list(flatten(p))[::-1]+[u])#agrega el diccionario p=(u,p)#tupla del nodo y el camino for n in self.vecinos[u]:#para cada hijo del nodo actual if n not in visited:#si no lo hemos visitado el=self.E[(u,n)]#se toma la distancia del nodo actual mas la distancia hacia el nodo hijo heappush(q,(l+el,n,p))#se agrega el arreglo q la distancia actual mas la distancia hacia el nodo hijo n hacia donde se va y el camino return dist #regresa el diccionario de distancias def kruskal(self): e=deepcopy(self.E) arbol=Grafo() peso=0 comp=dict() t=sorted(e.keys(),key=lambda k:e[k],reverse=True) nuevo=set() while len(t)>0 and len(nuevo)<len(self.V): #print(len(t)) arista=t.pop() w=e[arista] del e[arista] (u,v)=arista c=comp.get(v,{v}) if u not in c: #print('u',u,'v',v,'c',c) arbol.conecta(u,v,w) peso+=w nuevo=c.union(comp.get(u,{u})) for i in nuevo: comp[i]=nuevo print('MST con peso', peso, ':', nuevo, '\n', arbol.E) return arbol def vecinoMasCercano(self): lv=list(self.V) random.shuffle(lv) ni=lv.pop() le=dict() while len(lv)>=0: ln=self.v[ni] for nv in ln: le[nv]=self.E[(ni,nv)] menor=min(le.values()) lv.append(menor) del lv[menor] return lv g=Grafo() g.conecta('a','b', 381) g.conecta('a','c', 2789) g.conecta('a','d', 2015) g.conecta('a','e', 2733) g.conecta('a','f', 2655) g.conecta('a','g', 1352) g.conecta('a','h', 1377) g.conecta('a','i', 373) g.conecta('a','j', 2071) g.conecta('b','c', 2905) g.conecta('b','d', 2131) g.conecta('b','e', 3113) g.conecta('b','f', 2818) g.conecta('b','g', 1733) g.conecta('b','h', 1758) g.conecta('b','i', 753) g.conecta('b','j', 2275) g.conecta('c','d', 789) g.conecta('c','e', 1284) g.conecta('c','f', 192) g.conecta('c','g', 1823) g.conecta('c','h', 1743) g.conecta('c','i', 2408) g.conecta('c','j', 709) g.conecta('d','e', 1377) g.conecta('d','f', 702) g.conecta('d','g', 1240) g.conecta('d','h', 1161) g.conecta('d','i', 1753) g.conecta('d','j', 181) g.conecta('e','f', 1098) g.conecta('e','g', 1383) g.conecta('e','h', 1352) g.conecta('e','i', 2360) g.conecta('e','j', 1197) g.conecta('f','g', 1640) g.conecta('f','h', 1560) g.conecta('f','i', 2293) g.conecta('f','j', 594) g.conecta('g','h', 79) g.conecta('g','i', 981) g.conecta('g','j', 1172) g.conecta('h','i', 1066) g.conecta('h','j', 1094) g.conecta('i','j', 1703) print(g.kruskal()) print(g.shortests('c')) print(g) k=g.kruskal() print([print(x,k.E[x]) for x in k.E]) for r in range(10): ni=random.choice(list(k.V)) dfs=k.DFS(ni) c=0 #print(dfs) #print(len(dfs)) for f in range(len(dfs)-1): c+=g.E[(dfs[f],dfs[f+1])] print(dfs[f],dfs[f+1],g.E[(dfs[f],dfs[f+1])]) c+=g.E[(dfs[-1],dfs[0])] print(dfs[-1],dfs[0],g.E[(dfs[-1],dfs[0])]) print('costo',c) data=list('abcdefghij') tim=time.clock() per=permutation(data) print(time.clock()-tim)

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import conf from rackspace_monitoring.providers import get_driver from rackspace_monitoring.types import Provider _DRIVER_INSTANCE = None def get_instance(): """Get instance of the rackspace cloud monitoring driver. :returns: Driver instance :rtype: rackspace_monitoring.drivers.rackspace.RackspaceMonitoringDriver """ global _DRIVER_INSTANCE if _DRIVER_INSTANCE is None: driver = get_driver(Provider.RACKSPACE) rax_conf = conf.get_raxrc() _DRIVER_INSTANCE = driver( rax_conf.get('credentials', 'username'), rax_conf.get('credentials', 'api_key'), ex_force_auth_url=rax_conf.get('auth_api', 'url') ) return _DRIVER_INSTANCE

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n = int(input()) # 도시의 개수 m = int(input()) # 버스의 개수 INF = int(1e9) graph = [[INF] * (n+1) for _ in range(n+1)] for i in range(1, n+1): for j in range(1, n+1): if i == j: graph[i][j] = 0 for _ in range(m): a, b, cost = map(int, input().split()) graph[a][b] = min(graph[a][b], cost) for c in range(1, n+1): for a in range(1, n+1): for b in range(1, n+1): graph[a][b] = min(graph[a][b], graph[a][c]+graph[c][b]) for i in range(1, n+1): for j in range(1, n+1): print(graph[i][j], end=' ') print()

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def reverse(test): n=len(test) x="" for i in range(n-1,-1,-1) x +=[i] return x

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from django.urls import path from . import views as v urlpatterns = [ path('', v.startpage, name='base'), path('product.html', v.product, name='product'), path('product/<str:name>', v.product, name='product'), path('tool1.html', v.product, name='index.html') ]

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#!/bin/python from ryu.base import app_manager from ryu.controller import ofp_event from ryu.controller.handler import MAIN_DISPATCHER from ryu.controller.handler import set_ev_cls class L2Switch(app_manager.RyuApp): def __init__(self, *args, **kwargs): super(L2Switch, self).__init__(*args, **kwargs) @set_ev_cls(ofp_event.EventOFPPacketIn, MAIN_DISPATCHER) def packet_in_handler(self, ev): msg = ev.msg dp = msg.datapath ofp = dp.ofproto ofp_parser = dp.ofproto_parser actions = [ofp_parser.OFPActionOutput(ofp.OFPP_FLOOD)] out = ofp_parser.OFPPacketOut( datapath=dp, buffer_id=msg.buffer_id, in_port=msg.in_port, actions=actions) dp.send_msg(out)

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channon@hawk.iit.edu

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2023-01-27T17:08:44.809715

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py

import pynotify import sys title, msg = sys.argv[1:] assert( pynotify.init('MATLAB') ) n = pynotify.Notification(title, msg) n.show()

[ "roman.zeyde@gmail.com" ]

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from django.utils.translation import ugettext_lazy as _ from mayan.apps.permissions import PermissionNamespace namespace = PermissionNamespace(label=_('Redactions'), name='redactions') permission_redaction_create = namespace.add_permission( label=_('Create new redactions'), name='redaction_create' ) permission_redaction_delete = namespace.add_permission( label=_('Delete redactions'), name='redaction_delete' ) permission_redaction_edit = namespace.add_permission( label=_('Edit redactions'), name='redaction_edit' ) permission_redaction_exclude = namespace.add_permission( label=_('Exclude redactions'), name='redaction_exclude' ) permission_redaction_view = namespace.add_permission( label=_('View existing redactions'), name='redaction_view' )

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print("harsh jain")

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from django.utils.translation import (ugettext as _,) from django import forms from crispy_forms.helper import FormHelper #from crispy_forms.layout import Submit class AdminForm(forms.Form): username = forms.CharField( label = _("User Name"), max_length = 100, required = True, widget = forms.TextInput({ "placeholder": _("User Name")}) ) password = forms.CharField( label = _("Password"), max_length = 100, required = True, widget = forms.PasswordInput({ "placeholder": _("Password")}) ) firstname = forms.CharField( label = "%s %s"%(_("First"),_("Name")), max_length = 100, required = True, widget = forms.TextInput({ "placeholder": "%s %s"%(_("First"),_("Name"))}) ) lastname = forms.CharField( label = "%s %s"%(_("last"),_("Name")), max_length = 100, required = True, widget = forms.TextInput({ "placeholder": "%s %s"%(_("Last"),_("Name"))}) ) emailid = forms.CharField( label = _("Email Id"), max_length = 100, required = True, widget = forms.TextInput({ "placeholder": _("Email Id")}) ) image = forms.CharField( label = _("Photo"), max_length = 100, required = True, widget = forms.HiddenInput({ "placeholder": _("Email Id")}) ) def __init__(self, *args, **kwargs): super(AdminForm, self).__init__(*args, **kwargs) self.helper = FormHelper() self.helper.form_id = 'add-admin' self.helper.form_class = 'form-horizontal' self.helper.label_class = 'col-sm-4' self.helper.field_class = 'col-sm-8'

[ "karthik@jega.in" ]

karthik@jega.in

c7a6d5dd8c41683db707f5e807851a5f4cf4e8bd

686810fe4ae24622dcd2dd191ddb97141232f191

/DepositionComponents/deposition_components/generic/ChamberCryoPump.py

9ad392564889361a2d763ccce5198ee3d4aede49

[]

no_license

JPHammonds/DepositionComponents

22f904f7d0c08af21a819affa5ec708771e080a9

600b74b8f2f35662597286af5d39ed462a16669d

refs/heads/master

2020-04-18T04:43:50.216740

2019-04-18T20:56:05

167,250,224

0

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''' Created on Jan 23, 2019 @author: hammonds ''' from deposition_components.DepositionListDevice import DepositionListDevice from ophyd import (Component as Cpt, DynamicDeviceComponent as DDC, FormattedComponent as FC) from ophyd.signal import EpicsSignal class ChamberCryoPump(DepositionListDevice): power_on = FC(EpicsSignal, '{self.prefix}{self.power_on_read_pv_suffix}', write_pv='{self.prefix}{self.power_on_write_pv_suffix}', name='power_on') exhaust_to_vp1 = FC(EpicsSignal, '{self.prefix}{self.exhaust_read_pv_suffix}', write_pv='{self.prefix}{self.exhaust_write_pv_suffix}', name='exhaust_to_vp1') pressure = FC(EpicsSignal, "{self.prefix}{self.pressure_read_pv_suffix}", name='pressure') temperature_status = FC(EpicsSignal, "{self.prefix}{self.temp_status_read_pv_suffix}", name='temperature_status') n2_purge = FC(EpicsSignal, "{self.prefix}{self.n2_purge_read_pv_suffix}", write_pv="{self.prefix}{self.n2_purge_write_pv_suffix}", name='n2_purge') def __init__(self, prefix, power_on_read_pv_suffix, power_on_write_pv_suffix, exhaust_read_pv_suffix, exhaust_write_pv_suffix, pressure_read_pv_suffix, temp_status_read_pv_suffix, n2_purge_read_pv_suffix, n2_purge_write_pv_suffix, **kwargs): self.power_on_read_pv_suffix = power_on_read_pv_suffix self.power_on_write_pv_suffix = power_on_write_pv_suffix self.exhaust_read_pv_suffix = exhaust_read_pv_suffix self.exhaust_write_pv_suffix = exhaust_write_pv_suffix self.pressure_read_pv_suffix = pressure_read_pv_suffix self.temp_status_read_pv_suffix = temp_status_read_pv_suffix self.n2_purge_read_pv_suffix = n2_purge_read_pv_suffix self.n2_purge_write_pv_suffix = n2_purge_write_pv_suffix super(ChamberCryoPump, self).__init__(prefix, **kwargs) def is_cryo_on(self): return self.power_on.get() == 1 def is_cryo_exhausting_to_vp1(self): return self.exhaust_to_vp1.get() == 1 # def set(self): # ''' # Turn the cryo pump on, but make sure that it is ready before turning # it on and make sure that it is on before completion # '''

[ "JPHammonds@anl.gov" ]

JPHammonds@anl.gov

0af37c323dddce4b21f380367cbf84bb87457ff2

38915942f3719baea08396f819707c885b9315ce

/smartsheet/staircase/views/foup_wafer/wafer.py

9418871f86877c85c3bf6ca911d5c8e40e5c834e

[]

no_license

leopardary/smartsheet

a2c2b351dd86b904c110c458ffa587d0458158c4

4b51a672d6b13ec018a80fa0f88b7d22a5bb9d70

refs/heads/python3a

2022-11-28T19:23:32.072813

2019-04-21T23:23:41

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0

1

null

2022-11-19T19:21:30

2017-04-24T07:00:55

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py

from ...models import Group,Foup from django.shortcuts import render from django.http import HttpResponseRedirect def reclaim_wafers(request,foup_name): staircase=Group.objects.filter(group_name='Staircase') foup_list=Foup.objects.filter(owner__group=staircase[0]) foup=Foup.objects.filter(foupname=foup_name)[0] slot_list=foup.foup_slot_set.all() context={ 'foup':foup, 'slot_list':slot_list, 'foup_list':foup_list, } return render(request,'staircase/foup_wafer/foup_details/reclaim_wafers.html',context) def reclaim_execute(request,foup_name): if request.method=='POST': staircase=Group.objects.filter(group_name='Staircase') foup_list=Foup.objects.filter(owner__group=staircase[0]) foup=Foup.objects.filter(foupname=foup_name)[0] slot_list=request.POST.getlist('occupied_slot') #pdb.set_trace() for slot in slot_list: foup_slot=foup.foup_slot_set.filter(slot=int(slot))[0] foup_slot.reclaim_wafer() return HttpResponseRedirect('/staircase/foups/%s'%foup.foupname) return render(request,'/staircase/foups/%s'%foup.foupname) def load_execute(request,foup_name): if request.method=='POST': staircase=Group.objects.filter(group_name='Staircase') foup_list=Foup.objects.filter(owner__group=staircase[0]) foup=Foup.objects.filter(foupname=foup_name)[0] wafer_type=request.POST['wafer_type'] slot_list=request.POST.getlist('available_slot') #pdb.set_trace() for slot in slot_list: foup_slot=foup.foup_slot_set.filter(slot=int(slot))[0] foup_slot.load_new_wafers(str(wafer_type)) return HttpResponseRedirect('/staircase/foups/%s'%foup.foupname) return render(request,'/staircase/foups/%s'%foup.foupname) def load_wafers(request,foup_name): staircase=Group.objects.filter(group_name='Staircase') foup_list=Foup.objects.filter(owner__group=staircase[0]) foup=Foup.objects.filter(foupname=foup_name)[0] slot_list=foup.foup_slot_set.all() context={ 'foup':foup, 'slot_list':slot_list, 'foup_list':foup_list, } return render(request,'staircase/foup_wafer/foup_details/load_wafers.html',context)

[ "wenjiao.wang1@gmail.com" ]

wenjiao.wang1@gmail.com

d0f2be29afccd729b75626d77f3edb71dca4e661

c9c6c77995923d50e93da62b95a21455f812aec1

/src/bumblebot/__main__.py

151bb2def08d243400a1af16196915300644d117

[]

no_license

Salade2chats/PyBumbleBot

cf463a1fda0a02c6c9de25b42648bb31452c88e4

c8db36a4ea1f6eb20dcd5351aede5760dd82506c

refs/heads/master

2020-03-26T13:35:29.019065

2018-08-16T06:32:05

144,946,674

0

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UTF-8

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import os from pathlib import Path import click from aiohttp import web from dotenv import load_dotenv from bumblebot.controllers import Route from bumblebot.services.google import GoogleClient from .__about__ import __version__ from .services.logger import Logger @click.group(context_settings={'help_option_names': ['-h', '--help']}) @click.version_option(__version__) @click.option('-q', '--quiet', is_flag=True, help="no output") @click.option('-v', '--verbose', count=True, help="verbosity level") def main(quiet, verbose): """Bumblebot is ur salve. Slap it. 🐟""" Logger.prepare('main', 1000 if quiet else 50 - verbose * 10) # configure app load_dotenv(dotenv_path=Path('.') / '.env') @main.command() def run(): app = web.Application() routes = Route( google=GoogleClient(api_key=os.getenv('GOOGLE_API_KEY')) ).all() app.add_routes(routes) # app.add_routes([web.get('/', handle), # web.get('/{name}', handle)]) web.run_app(app) if __name__ == '__main__': main()

[ "dams_terdam@hotmail.fr" ]

dams_terdam@hotmail.fr

ff2509fe6271ef698b97954ab63a3aee5c0e98f2

1e7e56dbc226bddf380eec960588a393e98181cc

/CGI/scripts/genTable_F.py

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[]

no_license

pratik-pato/SE01_UI_Database-Programming

a01926c2b915063529838d29c6102059f302b672

1ee16eb53c28acbeaa91e35430b295390915dca1

refs/heads/master

2020-05-20T06:03:11.571129

2016-06-20T16:37:37

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#!/usr/bin/python import genJS, psycopg2, sys, cgi form = cgi.FieldStorage() sDbname = 'SE01' sUser = 'pratik_SE' sHost = 'localhost' sPass = 'easports' sTablename = form["tabName"].value conn = psycopg2.connect("dbname={} user={} host={} password={}".format(sDbname, sUser, sHost, sPass)) conn.autocommit = True cursor = conn.cursor() sql = [] sql.append("select column_name from information_schema.columns where table_name = '%s';"%(sTablename)) sql.append("select count(*) from %s"%(sTablename)) def executeQuery(query): resList = [] try: cursor.execute(query) results = cursor.fetchall() for row in results: resList.append(row[0]) except Exception as e: print e return resList conn.close() ''' try: jsFile = open('phase3.html','w+') except IOError as e: print "I/O error({0}): {1}".format(e.errno, e.strerror) except ValueError: print "Could not convert data to an integer." except: print "Unexpected error:", sys.exc_info()[0] raise ''' print("Content-Type: text/html") print "" #print(""" """) genJS.genOpenTag() genJS.javaScriptType("jquery/jquery.js", "", "") countRows = executeQuery(sql[1]) countRows = map(int, countRows) colList = executeQuery(sql[0]) #print colList colListStr = ','.join(colList) emptyLst = [] for i in range(countRows[0]): emptyLst.append("") print("<script type=\"text/javascript\">\n") print("var count = %d;var table = %s;function getValues(event, n) {if(event.which == \"9\") {var focusedElement = $(\":focus\");var ele = Number(focusedElement.attr(\"id\").slice(4));var val = focusedElement.attr(\"value\");var temp = ele/n;var tempDiff = temp%%1;temp = temp - tempDiff;var temp1 = ele%%n;table[temp][temp1] = val;}else if(event.which == \"13\") {var value = \"\";var i;var rowEntry = [];for(i = (count * n); i < ((count + 1) * n); i++) {var temp;temp = document.getElementById(\"attr\" + i);if(temp != null) {value = temp.value;if(value == \'\') {alert(\"enter attribute \" + (i + 1));break;}else {rowEntry.push(value);}}}table.push(rowEntry);count++;var newTableRow = $(document.createElement(\'tr\')).attr(\"id\", \'TableRow\' + count);newTableRow.after().html("%(int(countRows[0]), str(emptyLst))) genJS.genColumnTag(int(countRows[0]), len(colList)) print(");newTableRow.appendTo(\"#idTable\");document.getElementById(\"attr\" + (count * n)).focus();}else if(event.which == \"37\") {var focusedElement = $(\":focus\");var ele = Number(focusedElement.attr(\"id\").slice(4));if(ele > 0) {var input = document.getElementById(\"attr\" + (ele - 1));input.focus();var val = input.value;input.value = \'\';input.value = val;}else {var input = document.getElementById(\"attr\" + ele);input.focus();var val = input.value;input.value = \'\';input.value = val;}}else if(event.which == \"38\") {var focusedElement = $(\":focus\");var ele = Number(focusedElement.attr(\"id\").slice(4));if(ele >= n) {var input = document.getElementById(\"attr\" + (ele - n));input.focus();var val = input.value;input.value = \'\';input.value = val;}}else if(event.which == \"39\") {var focusedElement = $(\":focus\");var ele = Number(focusedElement.attr(\"id\").slice(4));if(ele < (count * n) + %d) {var input = document.getElementById(\"attr\" + (ele + 1));input.focus();var val = input.value;input.value = \'\';input.value = val;}}else if(event.which == \"40\") {var focusedElement = $(\":focus\");var ele = Number(focusedElement.attr(\"id\").slice(4));if(ele < (count * n)) {var input = document.getElementById(\"attr\" + (ele + n));input.focus();var val = input.value;input.value = \'\';input.value = val;}}}function submitTable(n) {var i;var rowEntry = [];for(i = 0; i < n; i++)rowEntry.push(document.getElementById(\"attr\" + ((count * n) + i)).value);console.log(count);table.push(rowEntry);for(i = 0; i <= count; i++)console.log(table[i]);}</script>"%(len(colList)-1)) print("\n<body>\n<table id=\"idTable\" border=\"1\" style=\"width:100%\">\n<tr id=\"tableHead\">\n") genJS.genAttrNames(colList) print("</tr>") tableDataQuery = "select %s from %s;"%(colListStr, sTablename) tableData = [] for i in colList: tableData.append([]) try: cursor.execute(tableDataQuery) results = cursor.fetchall() for row in results: for i in range(len(row)): tableData[i].append(row[i]) #print "tableData" #print tableData except: print "Error: unable to fecth data" conn.close() genJS.genRow( tableData) print("</table><input type = \"button\" value = \"Submit\" onkeydown=\"submitTable(%d)\" onclick=\"submitTable(%d)\"></body></html>"%(int(countRows[0]), int(countRows[0])))

[ "charwad.pratik@gmail.com" ]

charwad.pratik@gmail.com

c710e670770a317ba2296df1ec8941a218f68280

d1aef0e74af3ed2e4f040ff0812ed13dd754db36

/ScrapyTieba/ScrapyTieba/items.py

660599df0aded2624f713ced087fa7cd2093aa64

[]

no_license

Sora-Shiro/ScrapyLearn

d5533c98c02b0b809051bee942a9580ed2b9a067

74e30f05e4ba1ff550647f09e4f05c9afd9fddb8

refs/heads/master

2021-03-16T05:17:21.005992

2017-10-24T11:58:20

102,423,813

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UTF-8

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py

# -*- coding: utf-8 -*- # Define here the models for your scraped items # # See documentation in: # http://doc.scrapy.org/en/latest/topics/items.html import scrapy class ScrapyTiebaItem(scrapy.Item): # define the fields for your item here like: # name = scrapy.Field() pass class AskScoreToUniversityItem(scrapy.Item): title = scrapy.Field() href = scrapy.Field() province = scrapy.Field() university = scrapy.Field() author = scrapy.Field() date = scrapy.Field() class TiebaPostItem(scrapy.Item): # 用户ID name_user = scrapy.Field() # 贴吧名 name_tieba = scrapy.Field() # 帖子主题名 title = scrapy.Field() # 帖子主题链接 url = scrapy.Field() # 回复内容 content = scrapy.Field() # 回复所在楼层数 level = scrapy.Field() # 回复所在楼中楼层数，在回复内容所在位置非楼中楼时数值为-1 level_in_level = scrapy.Field() # 回复时间 time = scrapy.Field()

[ "sora95shiro@gmail.com" ]

sora95shiro@gmail.com

8c7586d5846fb0da1f4c78ceef07a2846e702539

b3699724907850fd26cbce4509fec83a33b89760

/python/ray/tune/tests/tutorial.py

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[ "Apache-2.0", "MIT" ]

permissive

BonsaiAI/ray

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refs/heads/master

2023-06-12T05:15:29.370188

2021-05-06T07:03:53

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2023-05-27T08:06:37

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# flake8: noqa # Original Code: https://github.com/pytorch/examples/blob/master/mnist/main.py # yapf: disable # __tutorial_imports_begin__ import numpy as np import torch import torch.optim as optim import torch.nn as nn from torchvision import datasets, transforms from torch.utils.data import DataLoader import torch.nn.functional as F from ray import tune from ray.tune.schedulers import ASHAScheduler # __tutorial_imports_end__ # yapf: enable # yapf: disable # __model_def_begin__ class ConvNet(nn.Module): def __init__(self): super(ConvNet, self).__init__() # In this example, we don't change the model architecture # due to simplicity. self.conv1 = nn.Conv2d(1, 3, kernel_size=3) self.fc = nn.Linear(192, 10) def forward(self, x): x = F.relu(F.max_pool2d(self.conv1(x), 3)) x = x.view(-1, 192) x = self.fc(x) return F.log_softmax(x, dim=1) # __model_def_end__ # yapf: enable # yapf: disable # __train_def_begin__ # Change these values if you want the training to run quicker or slower. EPOCH_SIZE = 512 TEST_SIZE = 256 def train(model, optimizer, train_loader): device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.train() for batch_idx, (data, target) in enumerate(train_loader): # We set this just for the example to run quickly. if batch_idx * len(data) > EPOCH_SIZE: return data, target = data.to(device), target.to(device) optimizer.zero_grad() output = model(data) loss = F.nll_loss(output, target) loss.backward() optimizer.step() def test(model, data_loader): device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.eval() correct = 0 total = 0 with torch.no_grad(): for batch_idx, (data, target) in enumerate(data_loader): # We set this just for the example to run quickly. if batch_idx * len(data) > TEST_SIZE: break data, target = data.to(device), target.to(device) outputs = model(data) _, predicted = torch.max(outputs.data, 1) total += target.size(0) correct += (predicted == target).sum().item() return correct / total # __train_def_end__ # __train_func_begin__ def train_mnist(config): # Data Setup mnist_transforms = transforms.Compose( [transforms.ToTensor(), transforms.Normalize((0.1307, ), (0.3081, ))]) train_loader = DataLoader( datasets.MNIST("~/data", train=True, download=True, transform=mnist_transforms), batch_size=64, shuffle=True) test_loader = DataLoader( datasets.MNIST("~/data", train=False, transform=mnist_transforms), batch_size=64, shuffle=True) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = ConvNet() model.to(device) optimizer = optim.SGD( model.parameters(), lr=config["lr"], momentum=config["momentum"]) for i in range(10): train(model, optimizer, train_loader) acc = test(model, test_loader) # Send the current training result back to Tune tune.report(mean_accuracy=acc) if i % 5 == 0: # This saves the model to the trial directory torch.save(model.state_dict(), "./model.pth") # __train_func_end__ # yapf: enable # __eval_func_begin__ search_space = { "lr": tune.sample_from(lambda spec: 10**(-10 * np.random.rand())), "momentum": tune.uniform(0.1, 0.9) } # Uncomment this to enable distributed execution # `ray.init(address="auto")` # Download the dataset first datasets.MNIST("~/data", train=True, download=True) analysis = tune.run(train_mnist, config=search_space) # __eval_func_end__ #__plot_begin__ dfs = analysis.trial_dataframes [d.mean_accuracy.plot() for d in dfs.values()] #__plot_end__ # __run_scheduler_begin__ analysis = tune.run( train_mnist, num_samples=20, scheduler=ASHAScheduler(metric="mean_accuracy", mode="max"), config=search_space) # Obtain a trial dataframe from all run trials of this `tune.run` call. dfs = analysis.trial_dataframes # __run_scheduler_end__ # yapf: disable # __plot_scheduler_begin__ # Plot by epoch ax = None # This plots everything on the same plot for d in dfs.values(): ax = d.mean_accuracy.plot(ax=ax, legend=False) # __plot_scheduler_end__ # yapf: enable # __run_searchalg_begin__ from hyperopt import hp from ray.tune.suggest.hyperopt import HyperOptSearch space = { "lr": hp.loguniform("lr", 1e-10, 0.1), "momentum": hp.uniform("momentum", 0.1, 0.9), } hyperopt_search = HyperOptSearch(space, metric="mean_accuracy", mode="max") analysis = tune.run(train_mnist, num_samples=10, search_alg=hyperopt_search) # To enable GPUs, use this instead: # analysis = tune.run( # train_mnist, config=search_space, resources_per_trial={'gpu': 1}) # __run_searchalg_end__ # __run_analysis_begin__ import os df = analysis.results_df logdir = analysis.get_best_logdir("mean_accuracy", mode="max") state_dict = torch.load(os.path.join(logdir, "model.pth")) model = ConvNet() model.load_state_dict(state_dict) # __run_analysis_end__ from ray.tune.examples.mnist_pytorch_trainable import TrainMNIST # __trainable_run_begin__ search_space = { "lr": tune.sample_from(lambda spec: 10**(-10 * np.random.rand())), "momentum": tune.uniform(0.1, 0.9) } analysis = tune.run( TrainMNIST, config=search_space, stop={"training_iteration": 10}) # __trainable_run_end__

[ "noreply@github.com" ]

BonsaiAI.noreply@github.com

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/novel_site/utils/chapterParser.py

48ed0aa4322faa7e8afed93565bec11b0c6e2d0f

[]

no_license

gzgdouru/novel_site_beta

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refs/heads/master

2020-04-10T04:34:30.438838

2019-03-08T09:36:55

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import requests from scrapy.selector import Selector def biquge(chapter_url): response = requests.get(url=chapter_url, timeout=30) response.encoding = "gbk" selector = Selector(text=response.text) chapter_content = selector.css("#content").extract_first() return chapter_content def dingdian(chapter_url): response = requests.get(url=chapter_url, timeout=30) response.encoding = "gbk" selector = Selector(text=response.text) chapter_content = selector.css("#content").extract_first() return chapter_content

[ "18719091650@163.com" ]

18719091650@163.com

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/all_data/exercism_data/python/bob/bce5dc09fb584ddbbf75a78974fd9b10.py

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[]

no_license

itsolutionscorp/AutoStyle-Clustering

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be0e2f635a7558f56c61bc0b36c6146b01d1e6e6

refs/heads/master

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2016-05-23T05:40:56

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def isyelling(some_string): return some_string.isupper() def issilent(some_string): return not some_string or some_string.isspace() def isquestion(some_string): return some_string.strip().endswith('?') def hey(some_string): if issilent(some_string): return 'Fine. Be that way!' elif isyelling(some_string): return 'Whoa, chill out!' elif isquestion(some_string): return 'Sure.' else: return 'Whatever.'

[ "rrc@berkeley.edu" ]

rrc@berkeley.edu

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[ "MIT" ]

permissive

aredshaw/CodePractice

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test_file = open("list.txt", "w" ) test_file.write("This is a test file. It is also a text file.\nI wonder how it will look altogether.\nWhen I write, I write a lot, \nso there is no telling where I will end up \nwith all the thoughts in my mind emptied out onto \nthe paper, or in this case, the screen.") test_file.close() test_file = open("list.txt", "r") print(test_file.read(20)) #the first 20 characters print(test_file.readlines(1)) #the next line print(test_file.read(30)) #another 30 charaxters. It ends in the middle of a word. print(test_file.read()) #the rest of the text. I added line breaks since my terminal does not wrap lines. test_file.close()

[ "akredshaw@gmail.com" ]

akredshaw@gmail.com

039f6e3ac3e74a9d72e9a33b99502e945ff5acce

fe9b12cb370cc4f1682a04cb36e49132d3831bbe

/main.py

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nellyloh/capstone-dashboard

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refs/heads/master

2023-08-22T09:12:21.715988

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import individual_query import webscraper_confidence_score import sentiment_model def run(name=None, nationality=None, gender=None, dob=None): individual_dict = individual_query.preprocess_input_to_dict(name, nationality=nationality, gender=gender, dob=dob) print(individual_dict) articles = webscraper_confidence_score.search_articles_on_individual(individual_dict, no_of_articles=10, additional_keywords=None) print(articles) model_output = sentiment_model.sentiment_model(articles) print(model_output) return individual_dict, model_output

[ "nellylohhj@gmail.com" ]

nellylohhj@gmail.com

afcf3e94534926410bf355eff01591a124e75f29

86c6012e7ff8b19db7db21606f7747cc6b9f3a01

/2018/11/2.py

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no_license

philipdexter/aoc

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2023-07-21T15:28:32.106390

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grid_serial = 7347 def power_level(x, y): rack_id = x + 10 power_level = rack_id * y power_level += grid_serial power_level *= rack_id power_level = int(str(power_level)[len(str(power_level))-3]) power_level -= 5 return power_level grid = {} for x in range(1,300+1): for y in range(1,300+1): grid[(x,y)] = power_level(x,y) def calc(x, y, size): agg = 0 smaller = group_power_levels.get((x,y,size-1)) if smaller: for a in range(x, x+size): agg += grid[(a,y+size-1)] for b in range(y, y+size-1): agg += grid[(x+size-1, b)] if size == 3 and (x,y) == (243,17): print(smaller) print(agg) for a in range(x, x+size): for b in range(y, y+size): print(grid[(x, y)], end=' ') print() agg += smaller else: for a in range(x, x+size): for b in range(y, y+size): agg += grid[(a,b)] return agg group_power_levels = {} for size in range(1, 300+1): print(f'size {size}') for x in range(1,300+1-size): for y in range(1,300+1-size): group_power_levels[(x,y,size)] = calc(x,y,size) print(max(group_power_levels.items(), key=lambda x: x[1])) print(max(group_power_levels.items(), key=lambda x: x[1]))

[ "philip.dexter@gmail.com" ]

philip.dexter@gmail.com

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2020-02-26T14:20:33.989571

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import re import os from file_base import FileBase class HelperFile(FileBase): def remove_base_directory(self, filename, root_path): return re.sub(os.path.join(root_path, 'app/helpers/'), '', filename)

[ "baenej@gmail.com" ]

baenej@gmail.com

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/pypureclient/flasharray/FA_2_19/models/protection_group_performance_array_response.py

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2023-09-04T10:59:03.009972

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# coding: utf-8 """ FlashArray REST API No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) OpenAPI spec version: 2.19 Generated by: https://github.com/swagger-api/swagger-codegen.git """ import pprint import re import six import typing from ....properties import Property if typing.TYPE_CHECKING: from pypureclient.flasharray.FA_2_19 import models class ProtectionGroupPerformanceArrayResponse(object): """ Attributes: swagger_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ swagger_types = { 'more_items_remaining': 'bool', 'total_item_count': 'int', 'continuation_token': 'str', 'items': 'list[ProtectionGroupPerformanceArray]' } attribute_map = { 'more_items_remaining': 'more_items_remaining', 'total_item_count': 'total_item_count', 'continuation_token': 'continuation_token', 'items': 'items' } required_args = { } def __init__( self, more_items_remaining=None, # type: bool total_item_count=None, # type: int continuation_token=None, # type: str items=None, # type: List[models.ProtectionGroupPerformanceArray] ): """ Keyword args: more_items_remaining (bool): Returns a value of `true` if subsequent items can be retrieved. total_item_count (int): The total number of records after applying all filter query parameters. The `total_item_count` will be calculated if and only if the corresponding query parameter `total_item_count` is set to `true`. If this query parameter is not set or set to `false`, a value of `null` will be returned. continuation_token (str): Continuation token that can be provided in the `continuation_token` query param to get the next page of data. If you use the continuation token to page through data you are guaranteed to get all items exactly once regardless of how items are modified. If an item is added or deleted during the pagination then it may or may not be returned. The continuation token is generated if the limit is less than the remaining number of items, and the default sort is used (no sort is specified). items (list[ProtectionGroupPerformanceArray]): List performance data, broken down by array. """ if more_items_remaining is not None: self.more_items_remaining = more_items_remaining if total_item_count is not None: self.total_item_count = total_item_count if continuation_token is not None: self.continuation_token = continuation_token if items is not None: self.items = items def __setattr__(self, key, value): if key not in self.attribute_map: raise KeyError("Invalid key `{}` for `ProtectionGroupPerformanceArrayResponse`".format(key)) self.__dict__[key] = value def __getattribute__(self, item): value = object.__getattribute__(self, item) if isinstance(value, Property): raise AttributeError else: return value def __getitem__(self, key): if key not in self.attribute_map: raise KeyError("Invalid key `{}` for `ProtectionGroupPerformanceArrayResponse`".format(key)) return object.__getattribute__(self, key) def __setitem__(self, key, value): if key not in self.attribute_map: raise KeyError("Invalid key `{}` for `ProtectionGroupPerformanceArrayResponse`".format(key)) object.__setattr__(self, key, value) def __delitem__(self, key): if key not in self.attribute_map: raise KeyError("Invalid key `{}` for `ProtectionGroupPerformanceArrayResponse`".format(key)) object.__delattr__(self, key) def keys(self): return self.attribute_map.keys() def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.swagger_types): if hasattr(self, attr): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value if issubclass(ProtectionGroupPerformanceArrayResponse, dict): for key, value in self.items(): result[key] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, ProtectionGroupPerformanceArrayResponse): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other

[ "noreply@github.com" ]

PureStorage-OpenConnect.noreply@github.com

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/app/controllers/default.py

d01b1b1a469a9e8bd597e60aaa35da8beef03739

[]

no_license

ThuBitencourtt/Robotica

2dc47b1d298e6b6c96efbd26c6bbee45b4e0e300

a00dac34e388f1f514eb04c67c8beaae0053114f

refs/heads/master

2022-10-16T22:02:02.415991

2019-11-30T15:03:56

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null

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from flask import render_template, flash, redirect, url_for, request from flask_login import login_user, logout_user from app import app, db, lm from app.models.tables import User, Equipe,Pessoa,Robo,Evento from app.models.forms import LoginForm, CadastroForm,EquipeForm,PessoaForm,RoboForm,EventoForm @lm.user_loader def load_user(id): return User.query.filter_by(id=id).first() @app.route("/index") @app.route("/") def index(): return render_template('index.html') @app.route("/login", methods=["GET","POST"]) def login(): form = LoginForm() if form.validate_on_submit(): user = User.query.filter_by(username=form.username.data).first() if user and user.password == form.password.data: login_user(user) flash("Login in") return redirect(url_for("index")) else: flash("Invalid Login") return render_template('login.html', form=form) @app.route("/logout") def logout(): logout_user() flash ("Logged out") return redirect(url_for("login")) @app.route("/cadastro", methods=["GET","POST"]) @app.route("/") def cadastro(): form = CadastroForm() if request.form: user = User(username=request.form.get("username"),password=request.form.get("password")) db.session.add(user) db.session.commit() return render_template('cadastro.html', form=form) @app.route("/cadastro_equipe", methods=["GET","POST"]) @app.route("/") def cadastro_equipe(): form = EquipeForm() if request.form: equipe = Equipe(firstname=request.form.get("firstname"), lastname=request.form.get("lastname"),slogan=request.form.get("slogan"),email=request.form.get("email"), site=request.form.get("site"),país=request.form.get("país"),estado=request.form.get("estado"), cidade=request.form.get("cidade"),instituicao=request.form.get("instituicao"),capitao=request.form.get("capitao")) db.session.add(equipe) db.session.commit() return render_template('cadastro_equipe.html', form=form) @app.route("/cadastro_pessoa", methods=["GET","POST"]) @app.route("/") def cadastro_pessoa(): form = PessoaForm() if request.form: pessoa = Pessoa(name=request.form.get("name"),email=request.form.get("email"), RG=request.form.get("RG"),CPF=request.form.get("CPF"),telefone=request.form.get("telefone"), idade=request.form.get("idade")) db.session.add(pessoa) db.session.commit() return render_template('cadastro_pessoa.html', form=form) @app.route("/cadastro_robo", methods=["GET","POST"]) @app.route("/") def cadastro_robo(): form = RoboForm() if request.form: robo = Robo(name=request.form.get("name"),email=request.form.get("categoria"), RG=request.form.get("peso"),CPF=request.form.get("responsavel")) db.session.add(robo) db.session.commit() return render_template('cadastro_robo.html', form=form) @app.route("/cadastro_evento", methods=["GET","POST"]) @app.route("/") def cadastro_evento(): form = EventoForm() if request.form: evento = Evento(name=request.form.get("name"), endereco=request.form.get("endereco"),email=request.form.get("email"), site=request.form.get("site"),país=request.form.get("país"),estado=request.form.get("estado"), cidade=request.form.get("cidade")) db.session.add(evento) db.session.commit() return render_template('cadastro_evento.html', form=form)

[ "thubittencourt@gmail.com" ]

thubittencourt@gmail.com

5bb26fe6d3ac1cb96a4721f35a25d9b559af030d

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/tests/bugs/core_4342_test.py

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permissive

FirebirdSQL/firebird-qa

89d5b0035071f9f69d1c869997afff60c005fca9

cae18186f8c31511a7f68248b20f03be2f0b97c6

refs/heads/master

2023-08-03T02:14:36.302876

2023-07-31T23:02:56

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#coding:utf-8 """ ID: issue-4664 ISSUE: 4664 TITLE: Non-privileged user can delete records from RDB$SECURITY_CLASSES table DESCRIPTION: JIRA: CORE-4342 FBTEST: bugs.core_4342 """ import pytest from firebird.qa import * db = db_factory() user_boss = user_factory('db', name='boss', password='123') user_mngr = user_factory('db', name='mngr', password='456') test_script = """ -- Add these DDL privileges in order to have some rows in -- rdb$security_classes table for user BOSS: grant create table to boss; grant alter any table to boss; grant drop any table to boss; commit; set list on; select current_user,count(*) acl_count from rdb$security_classes where rdb$acl containing 'boss'; select 1 from rdb$security_classes where rdb$acl containing 'boss' with lock; update rdb$security_classes set rdb$security_class = rdb$security_class where rdb$acl containing 'boss'; delete from rdb$security_classes where rdb$acl containing 'boss'; commit; connect '$(DSN)' user 'MNGR' password '456'; select current_user,count(*) acl_count from rdb$security_classes where rdb$acl containing 'boss'; select 1 from rdb$security_classes where rdb$acl containing 'boss' with lock; update rdb$security_classes set rdb$security_class = rdb$security_class where rdb$acl containing 'boss'; delete from rdb$security_classes where rdb$acl containing 'boss'; commit; """ expected_stdout = """ USER SYSDBA ACL_COUNT 1 USER MNGR ACL_COUNT 1 """ # version: 3.0 act = isql_act('db', test_script) expected_stderr_1 = """ Statement failed, SQLSTATE = HY000 Cannot select system table RDB$SECURITY_CLASSES for update WITH LOCK Statement failed, SQLSTATE = 42000 UPDATE operation is not allowed for system table RDB$SECURITY_CLASSES Statement failed, SQLSTATE = 42000 DELETE operation is not allowed for system table RDB$SECURITY_CLASSES Statement failed, SQLSTATE = HY000 Cannot select system table RDB$SECURITY_CLASSES for update WITH LOCK Statement failed, SQLSTATE = 28000 no permission for UPDATE access to TABLE RDB$SECURITY_CLASSES Statement failed, SQLSTATE = 28000 no permission for DELETE access to TABLE RDB$SECURITY_CLASSES """ @pytest.mark.version('>=3.0,<4.0') def test_1(act: Action, user_boss: User, user_mngr: User): act.expected_stdout = expected_stdout act.expected_stderr = expected_stderr_1 act.execute() assert (act.clean_stderr == act.clean_expected_stderr and act.clean_stdout == act.clean_expected_stdout) # version: 4.0 expected_stderr_2 = """ Statement failed, SQLSTATE = HY000 Cannot select system table RDB$SECURITY_CLASSES for update WITH LOCK Statement failed, SQLSTATE = 42000 UPDATE operation is not allowed for system table RDB$SECURITY_CLASSES Statement failed, SQLSTATE = 42000 DELETE operation is not allowed for system table RDB$SECURITY_CLASSES Statement failed, SQLSTATE = HY000 Cannot select system table RDB$SECURITY_CLASSES for update WITH LOCK Statement failed, SQLSTATE = 28000 no permission for UPDATE access to TABLE RDB$SECURITY_CLASSES -Effective user is MNGR Statement failed, SQLSTATE = 28000 no permission for DELETE access to TABLE RDB$SECURITY_CLASSES -Effective user is MNGR """ @pytest.mark.version('>=4.0') def test_2(act: Action, user_boss: User, user_mngr: User): act.expected_stdout = expected_stdout act.expected_stderr = expected_stderr_2 act.execute() assert (act.clean_stderr == act.clean_expected_stderr and act.clean_stdout == act.clean_expected_stdout)

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pcisar@ibphoenix.cz

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/02_steamer.py

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[]

no_license

williampsena/python-text-mining

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refs/heads/master

2020-07-04T16:35:54.996064

2019-08-21T12:08:57

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py

import pprint from nltk.corpus import stopwords from nltk.tokenize import word_tokenize from nltk.stem import RSLPStemmer pp = pprint.PrettyPrinter(width=41, compact=True) vegeta_quotes = [ ('voce nao e derrotado quando perde, mais sim quando voce desiste.', 'confiante'), ('O melhor guerreiro nao e aquele que sempre ganha, mas o que mantem o seu orgulho mesmo na derrota', 'orgulhoso'), ('Enquanto o inimigo estiver na minha frente, eu lutarei.', 'confiante'), ('Eu sou calmo e tenho o coracao puro... mas e pura maldade.', 'sincero'), ('Meu coracao e puro... pura maldade!', 'sincero'), ('Verme insolente nao entre na frente.', 'bravo'), ('O miseravel e um genio.', 'feliz') ] stop_words = set(stopwords.words('portuguese') + [ ',', 'eu', '!' ]) # Steamming words, derrota or derrotar or derrotei == derrot stemmer = RSLPStemmer() quotes = [(stemmer.stem(q.lower()), f) for (q, f) in vegeta_quotes] filtered = [] for (quote, felling) in quotes: filtered.append( ( [w for w in word_tokenize(quote) if not w in stop_words and w.isalpha()], felling ) ) pp.pprint(filtered) # [..., ['melhor', 'guerreiro', 'nao', 'sempre', 'ganha', 'mantem', 'orgulho', 'derrot'], ... ]

[ "william.sena@skyhub.com.br" ]

william.sena@skyhub.com.br

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/backup/user_154/ch36_2019_03_31_18_21_54_470469.py

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no_license

gabriellaec/desoft-analise-exercicios

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2023-01-31T17:19:42.050628

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py

def eh_primo(n): test = 2 while(test < n): if ((n % test) == 0): return False test = test + 1 return True

[ "you@example.com" ]

you@example.com

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2020-05-21T06:06:11.950552

2017-02-27T16:26:14

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""" Altair Plot Sphinx Extension ============================ This extension provides a means of inserting live-rendered Altair plots within sphinx documentation. There are two directives defined: ``altair-setup`` and ``altiar-plot``. ``altair-setup`` code is used to set-up various options prior to running the plot code. For example:: .. altair-setup:: from altair import * import pandas as pd data = pd.DataFrame({'a': list('CCCDDDEEE'), 'b': [2, 7, 4, 1, 2, 6, 8, 4, 7]}) .. altair-plot:: Chart(data).mark_point().encode( x='a', y='b' ) In the case of the ``altair-plot`` code, the *last statement* of the code-block should contain the chart object you wish to be rendered. Options ------- The directives have the following options:: .. altair-setup:: :show: # if set, then show the setup code as a code block pass .. altair-plot:: :hide-code: # if set, then hide the code and only show the plot :code-below: # if set, then code is below rather than above the figure :alt: text # Alternate text when plot cannot be rendered :links: editor source export # specify one or more of these options Chart() Additionally, this extension introduces a global configuration ``altair_plot_links``, set in your ``conf.py`` which is a dictionary of links that will appear below plots, unless the ``:links:`` option again overrides it. It should look something like this:: # conf.py # ... altair_plot_links = {'editor': True, 'source': True, 'export': True} # ... If this configuration is not specified, all are set to True. """ import os import json import warnings import jinja2 from docutils import nodes from docutils.parsers.rst import Directive from docutils.parsers.rst.directives import flag, unchanged from sphinx.locale import _ from sphinx import addnodes, directives from sphinx.util.nodes import set_source_info from altair.api import TopLevelMixin from .utils import exec_then_eval VGL_TEMPLATE = jinja2.Template(""" <div id="{{ div_id }}"> <script> vg.embed("#{{ div_id }}", "{{ filename }}", function(error, result) {}); </script> </div> """) class altair_plot(nodes.General, nodes.Element): pass class AltairSetupDirective(Directive): has_content = True option_spec = {'show': flag} def run(self): env = self.state.document.settings.env targetid = "altair-plot-{0}".format(env.new_serialno('altair-plot')) targetnode = nodes.target('', '', ids=[targetid]) code = '\n'.join(self.content) # Here we cache the code for use in later setup if not hasattr(env, 'altair_plot_setup'): env.altair_plot_setup = [] env.altair_plot_setup.append({ 'docname': env.docname, 'lineno': self.lineno, 'code': code, 'target': targetnode, }) result = [targetnode] if 'show' in self.options: source_literal = nodes.literal_block(code, code) source_literal['language'] = 'python' result.append(source_literal) return result def purge_altair_plot_setup(app, env, docname): if not hasattr(env, 'altair_plot_setup'): return env.altair_plot_setup = [item for item in env.altair_plot_setup if item['docname'] != docname] DEFAULT_LINKS = {'editor': True, 'source': True, 'export': True} def validate_links(links): if links.strip().lower() == 'none': return {} links = links.strip().split() diff = set(links) - set(DEFAULT_LINKS.keys()) if diff: raise ValueError("Following links are invalid: {0}".format(list(diff))) return dict((link, link in links) for link in DEFAULT_LINKS) class AltairPlotDirective(Directive): has_content = True option_spec = {'hide-code': flag, 'code-below': flag, 'alt': unchanged, 'links': validate_links} def run(self): env = self.state.document.settings.env app = env.app show_code = 'hide-code' not in self.options code_below = 'code-below' in self.options setupcode = '\n'.join(item['code'] for item in getattr(env, 'altair_plot_setup', []) if item['docname'] == env.docname) code = '\n'.join(self.content) if show_code: source_literal = nodes.literal_block(code, code) source_literal['language'] = 'python' #get the name of the source file we are currently processing rst_source = self.state_machine.document['source'] rst_dir = os.path.dirname(rst_source) rst_filename = os.path.basename(rst_source) # use the source file name to construct a friendly target_id serialno = env.new_serialno('altair-plot') rst_base = rst_filename.replace('.', '-') div_id = "{0}-altair-plot-{1}".format(rst_base, serialno) target_id = "{0}-altair-source-{1}".format(rst_base, serialno) target_node = nodes.target('', '', ids=[target_id]) # create the node in which the plot will appear; # this will be processed by html_visit_altair_plot plot_node = altair_plot() plot_node['target_id'] = target_id plot_node['div_id'] = div_id plot_node['code'] = code plot_node['setupcode'] = setupcode plot_node['relpath'] = os.path.relpath(rst_dir, env.srcdir) plot_node['rst_source'] = rst_source plot_node['rst_lineno'] = self.lineno default_links = app.builder.config.altair_plot_links plot_node['links'] = self.options.get('links', default_links) if 'alt' in self.options: plot_node['alt'] = self.options['alt'] result = [target_node] if code_below: result += [plot_node] if show_code: result += [source_literal] if not code_below: result += [plot_node] return result def html_visit_altair_plot(self, node): # Execute the setup code, saving the global & local state namespace = {} if node['setupcode']: exec(node['setupcode'], namespace) # Execute the plot code in this context, evaluating the last line try: chart = exec_then_eval(node['code'], namespace) except Exception as e: warnings.warn("altair-plot: {0}:{1} Code Execution failed:" "{2}: {3}".format(node['rst_source'], node['rst_lineno'], e.__class__.__name__, str(e))) raise nodes.SkipNode if isinstance(chart, TopLevelMixin): # Last line should be a chart; convert to spec dict spec = chart.to_dict() # Create the vega-lite spec to embed embed_spec = json.dumps({'mode': 'vega-lite', 'actions': node['links'], 'spec': spec}) # Prevent http/https request errors by doing this embed_spec = embed_spec.replace('http://', '//') embed_spec = embed_spec.replace('https://', '//') # Write embed_spec to a *.vl.json file dest_dir = os.path.join(self.builder.outdir, node['relpath']) if not os.path.exists(dest_dir): os.makedirs(dest_dir) filename = "{0}.vl.json".format(node['div_id']) dest_path = os.path.join(dest_dir, filename) with open(dest_path, 'w') as f: f.write(embed_spec) # Pass relevant info into the template and append to the output html = VGL_TEMPLATE.render(div_id=node['div_id'], filename=filename) self.body.append(html) else: warnings.warn('altair-plot: {0}:{1} Malformed block. Last line of ' 'code block should define a valid altair Chart object.' ''.format(node['rst_source'], node['rst_lineno'])) raise nodes.SkipNode def generic_visit_altair_plot(self, node): # TODO: generate PNGs and insert them here if 'alt' in node.attributes: self.body.append(_('[ graph: %s ]') % node['alt']) else: self.body.append(_('[ graph ]')) raise nodes.SkipNode def setup(app): setup.app = app setup.config = app.config setup.confdir = app.confdir app.add_stylesheet('altair-plot.css') app.add_javascript("https://d3js.org/d3.v3.min.js") app.add_javascript("https://vega.github.io/vega/vega.js") app.add_javascript("https://vega.github.io/vega-lite/vega-lite.js") app.add_javascript("https://vega.github.io/vega-editor/vendor/vega-embed.js") app.add_config_value('altair_plot_links', DEFAULT_LINKS, 'env') app.add_node(altair_plot, html=(html_visit_altair_plot, None), latex=(generic_visit_altair_plot, None), texinfo=(generic_visit_altair_plot, None), text=(generic_visit_altair_plot, None), man=(generic_visit_altair_plot, None)) app.add_directive('altair-plot', AltairPlotDirective) app.add_directive('altair-setup', AltairSetupDirective) app.connect('env-purge-doc', purge_altair_plot_setup) return {'version': '0.1'}

[ "jakevdp@gmail.com" ]

jakevdp@gmail.com

efaf48f8b7e9fa2ee10924f45b9ae140e3122820

09b8a76c8ae621fc761904823ab2cdc70f347432

/src/user_interface.py

1a5705afc43a1412e026818bef13e3ab4551bd67

[ "MIT" ]

permissive

dat-adi/alarm-clock

d99c94789a0b2f9e25f8deda8fec43d782998e73

2d877506fe518197fe5f36eab10f09843db42a83

refs/heads/master

2022-07-23T17:51:16.538454

2020-05-21T17:58:47

264,455,545

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MIT

2020-05-21T07:01:41

2020-05-16T14:31:11

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from tkinter import Frame, Canvas, Tk, Text, LEFT, INSERT, END, messagebox, Button, X from alarming_service import time_diff def GUI_present(): root = Tk() canvas = Canvas(root) canvas.pack() frame = Frame(canvas) frame.pack() top_text = Text(frame) top_text.insert( INSERT, "Welcome to the Simple Alarming Service" ) top_text.pack() alarm_set = Button(frame, text="Set and Deploy Alarm", command=time_diff) alarm_set.pack(fill=X) root.mainloop() if __name__ == "__main__": GUI_present()

[ "naruita201@gmail.com" ]

naruita201@gmail.com

e091044347fb78ed6d76d5bc87eacd18312f6d61

7f3c7a65d3723d0f48beea963fa9a7477cf6627f

/PSFModel.py

7a9ca0f6ee4be2d770b4e50171b8f389c9b02071

[]

no_license

chrisglass/yapsfm

a123ff453f0d443b12abb222c10a95b8b3f2d148

9f98236e9eed6bfec53b391a5a9b735311556f1a

refs/heads/master

2021-01-14T10:36:57.568234

2015-04-02T23:21:03

33,686,906

0

null

2015-04-09T19:00:26

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#! /usr/bin/env python import glob,os,sys import numpy as np import png import matplotlib.pyplot as plt from scipy.misc import imread # image handling import scipy.ndimage.interpolation # fits file handling and creation import pyfits from datetime import datetime as dt """ PSF modeling script: the pupil function P(x,y) = A(x,y) exp(2 \pi i OPD(x,y) / \lambda) the PSF = | FFT(P(x,y)) |**2 + convolution with a gaussian for jitter and imprecision -------------------- The script can load a grayscale image with imageToAperture(image) or create a circular aperture using aperture(size) Feb 25th: PSF computed as perfect theoretical Airy Disk: OPD=0. Mar 3rd: computing the OPD in cartesian space is problematic because of the polar nature of Zernike modes. Can't get a good representation in cartesian space right away. Decided to compute it in polar coords, interpolate it to a continuous function and map it into cartesian space. Mar 6th: OPD computation is working. Linear combination of Zernike mode done. Mar 9th: PSF scaling to detector space (0.005''/pixel). Mar 10th: Code cleaned and polar2cart: theta fixed. Mar 11th: .fits creation with header information corresponding to Tiny Tim's. Possibility to change pixel scale, defaulted as constant with wavelength. """ #-------------------------------------------------- # Methods definition #-------------------------------------------------- def aperture(size=101,ap=None): """creates the aperture function as a matrix where 0 = obstructed and 1 = unobstructed # for HST: # secondary obstruction: 0.330 # spider width: 0.022 # pads: (v3(x), v2(y), radius) x,y were arbitrarily chosen # 1: 0.8921 0.0000 0.065 # 2: -0.4615 0.7555 0.065 # 3: -0.4564 -0.7606 0.065 # ---------- # if nothing specified: circular aperture is used""" A=np.zeros((size,size)) center=[size/2,size/2] #center of the image secMir=0.330*size/2 spWidth=0.022*size/2 pad1=[0.8921*size/2,0.0000,0.065*size/2] #x,y,radius pad2=[-0.4615*size/2,0.7555*size/2,0.065*size/2] pad3=[-0.4564*size/2,-0.7606*size/2,0.065*size/2] for y in range(size): for x in range(size): # main aperture (including secondary obstruction) radPos=np.sqrt((x-center[0])**2+(y-center[1])**2) if ap=='HST': if radPos<=size/2 and radPos>secMir: A[y][x]=1. #Obstructions: # spiders if center[0]-spWidth/2.<=x<=center[0]+spWidth/2: A[y][x]=0. if center[0]-spWidth/2<=y<=center[1]+spWidth/2: A[y][x]=0. # pads if np.sqrt((x-center[0]-pad1[0])**2+(y-center[1]-pad1[1])**2)<=pad1[2]: A[y][x]=0. if np.sqrt((x-center[0]-pad2[0])**2+(y-center[1]-pad2[1])**2)<=pad2[2]: A[y][x]=0. if np.sqrt((x-center[0]-pad3[0])**2+(y-center[1]-pad3[1])**2)<=pad3[2]: A[y][x]=0. else: if radPos<=size/2: A[y][x]=1. print 'Aperture image size: (%s,%s)'%(len(A), len(A[0])) png.from_array(A,mode='L;1').save('analyticAp.png') print 'Aperture created' return A #-------------------------------------------------- def imageToAperture(image): """transforms a black and white image into an aperture array, where 0 = obstructed, 1 = unobstructed""" im=imread(image).astype(np.float32) im/=255. if len(np.shape(im))>2: #only take the R component of RGB img (supposedly grayscale so R=G=B) image_2d=im[:,:,0] else: # if the image has only 1 plane image_2d=im return image_2d #-------------------------------------------------- def psf(A,L=.76,scaleFactor=5,dist=[0,0,0,0,0,0,0,0]): """fft=complex numbers: amplitude AND phase We take the modulus square --> distrib. of light L is the wavelength, same units as OPD (microns) np.fft.fft2 manages zero-padding on each axis with s[0],s[1] corresponding to both x and y axis. ==================== with a zero-padding factor of 5, the pixel scale of the PSF image is 0.0088''/pixel. Since: 6 pixel @ .76um = 1.22*.76um/(2.4*10**6um) = 0.0797'' , 1 pixel @ .76um = 0.0797'' / 6 pixels = 0.133''/px """ P=pupil(A,L,dist) size=np.shape(P) scaled=[size[i]*scaleFactor for i in range(len(size))] print 'Starting FFT with zero-padding factor of %s...'%(scaleFactor) tmp=np.fft.fft2(P,s=[scaled[0],scaled[1]]) # padding with 0s #switch quadrant to place the origin in the middle of the array tmp=np.fft.fftshift(tmp) #modulus square of the complex matrices print '... done' PSF=np.real(np.multiply(tmp,np.conjugate(tmp))) print '----------\nPSF image size: (%s,%s)'%(np.shape(PSF)[0],np.shape(PSF)[1]) print 'lambda = %s'%(L) print "pixel size = 0.110''/px" #print "Pixel size at .76mu: %.4f''"%(1.22*(7.6*10**(-7))/2.4*206264.81) # for Lambda=1. 1 rad = 206264.81 arcsec #print "PSF size: 5 pixels" print '----------\nPSF computed' return PSF #-------------------------------------------------- def pupil(A,L=.76,dist=[0,0,0,0,0,0,0,0]): """P = A exp(2pi i OPD / L), L=lambda""" print 'Computing pupil...' size=np.shape(A)[0] OPD=pathDiff(size,L,dist) P=np.multiply(A,np.exp(np.divide(2j*np.pi*OPD,L))) print '... done' return P #-------------------------------------------------- def pathDiff(size=101,L=.76,dist=[0.,0.,0.,0.,0.,0.,0.,0.]): """ ================================================== Optical Path Differences for pupil characterization ================================================== from Noll (1976): If phi(r,theta) is an arbitrary function, its expansion over a circle of radius R is: phi(R*rho, theta) = Sum_j a_j Z_j(rho,theta), rho=r/R -------------------- OPD(R*rho, theta) is therefore equals to phi(R*rho, theta) which is, in cartesian space: OPD(sqrt(x**2+y**2), arctan(y/x)) the wavelength dependence is hidden in a_j. -> each Zernike mode has a different coefficient, depending on the wavelength. ================================================== Method: Compute the Zernike mode(s) and multiply each of them by its coefficient (from Zmax file). The matrix of the Zernike values is in polar coordinates. Has to be transformed back to cartesian. ================================================== 1 micron of despace induces 6.1nm of rms wave Zernike coefficient values are given in microns RMS of wave at 547 microns """ # defocus,z5,z6,z7,z8,z9,z10,z11 #Aj=[-0.0068802,0.016,-0.006,-0.003,-0.003,0.011,0.02,-0.0348] #microns #Aj=[-0.001,0.002,-0.002,-0.001,0.0002,0.,0.001,-0.002] #Aj=[0.0026,0.0089,0.0222,-0.0018,0.0113,0.,0.,0.] Znm=[(2,0),(2,-2),(2,2),(3,-1),(3,1),(3,-3),(3,3),(4,0)] # Creation of matrices rhorange= np.linspace(0,1,size) thetarange=np.linspace(0,2*np.pi,size) rho,theta=np.meshgrid(rhorange,thetarange) # OPD = Sum aj Zj Ztot=np.zeros((size,size)) for i in range(len(dist)): #aj=dist[i] aj=dist[i]*.547/L #Zern coef at wavelength L. L and .547 in microns print 'Computing Z%s with aj=%s'%(4+i,aj) n,m=Znm[i][0],Znm[i][1] if m<0.: Z=Zodd(n,-m,rho,theta) else: Z=Zeven(n,m,rho,theta) #Z*=aj Ztot+=np.multiply(Z,aj) print 'Saving Ztot' #print type(Ztot[50][50]) plt.imshow(Ztot) plt.savefig('Ztot.png') cartesian_Z=scipy.ndimage.interpolation.geometric_transform(Ztot,polar2cart,extra_arguments=(size,)) print 'Saving cartesian_Z' plt.imshow(cartesian_Z) plt.savefig('cartesian_Z.png') return cartesian_Z #-------------------------------------------------- def Rnm(n,m,r): """computes the radial part R_n^m(r) for r a meshgrid object""" R = 0 for s in range((n-m)/2+1): R += (((-1)**s*np.math.factorial(n-s))/(np.math.factorial(s)*np.math.factorial((n+m)/2-s)*np.math.factorial((n-m)/2-s)))*r**(n-2*s) return R #-------------------------------------------------- def Zeven(n,m,r,theta): """computes the even Zernike Polynomials r,theta are meshgrid objects""" Z = np.sqrt(n+1)*Rnm(n,m,r)*np.sqrt(2)*np.cos(m*theta) return Z #-------------------------------------------------- def Zodd(n,m,r,theta): """calculates the odd Zernike Polynomials r,theta are meshgrid objects""" Z = np.sqrt(n+1)*Rnm(n,m,r)*np.sqrt(2)*np.sin(m*theta) return Z #-------------------------------------------------- def polar2cart(coords,size=101): """conversion to be used in geometric_transform(input,mapping) as mapping""" #origin back at the center of the image x=(coords[1]-size//2.)/(size//2.) y=(coords[0]-size//2.)/(size//2.) #compute -1<r<1 and 0<theta<2pi r=np.sqrt(x*x+y*y) theta=np.arctan2(y,x) theta=theta<0 and theta+2*np.pi or theta #bin r,theta back to pixel space (101,101) r*=size-1 theta*=(size-1)/(2*np.pi) return (theta,r) #-------------------------------------------------- def jitter(PSF,jitterSize): """Compute the Optical Transfer Function (OTF) and multiply it by the gaussian jitter function WORK IN PROGRESS""" jitter=0. OTF=np.fft.fft2(PSF)*jitter OTF=np.fft.ifft2(OTF) return #-------------------------------------------------- def bin2detector(coords,L,size,detectorScale): """rebin the Optical Transfer Function to the detector's scale. ---Used in resizePSF()--- The OTF can be the PSF, or if jitter is specified, its convolution with a gaussian. """ # scale the PSF to the desired size (0.106 arcsec) scaleFactor=0.0797/6.*(L/0.76) #at 5x zero-padding #PSF=0.0797'' in sky space, has to be 0.106'' in detector #factor of 0.106/0.0797 = 1.32998 #scaleFactor=0.751887*L/.76 #scaleFactor=0.132998*L/.76 # pixel scale taken from Tiny Tim psf.fits' header #detectorScale=0.0251 # ''/pixel x=(coords[1]-size//2.)*detectorScale/scaleFactor+(size//2.) y=(coords[0]-size//2.)*detectorScale/scaleFactor+(size//2.) return (y,x) #-------------------------------------------------- def resizePSF(PSF,L=.76,size=505,scale=0.110): """Resize the PSF to match pixel size and resolution of instrument (0.12'') at .76um""" print "resizing PSF to match detector pixel size of %s''/px..."%(scale) newPSF=scipy.ndimage.interpolation.geometric_transform(PSF,bin2detector,extra_arguments=(L,size,scale)) newPSF=newPSF[size//2.-32:size//2.+32,size//2.-32:size//2.+32] print '... done' return newPSF #-------------------------------------------------- def createFits(PSF,disto=[0,0,0,0,0,0,0,0],pixelScale=0.0251,wavelength=0.76): """Creates a .fits file containing the PSF image with header informations: Created, Instrument, Focus, Astigmatism (0,45), Coma (x,y), Trefoil (x,y), Spherical Pixel scale and Wavelength""" name='psf_%s.fits'%(wavelength) print 'Writting psf to file %s...'%(name) hdu=pyfits.PrimaryHDU(PSF) header=hdu.header now=dt.now() header['CREATED']=('%s %s %s %s %s'%(dt.strftime(now,'%a'),dt.strftime(now,'%b'),dt.strftime(now,'%d'),dt.strftime(now,'%X'),dt.strftime(now,'%Y')),'Time and date file was created') header['INSTRUME']=('WFIRST_WFI','Simulated instrument') header['FOCUS']=(disto[0],'PSF RMS focus (waves @ 547 nm)') header['X_ASTIG']=(disto[1],'PSF RMS 0d astig (waves @ 547 nm)') header['Y_ASTIG']=(disto[2],'PSF RMS 45d astig (waves @ 547 nm)') header['X_COMA']=(disto[3],'PSF RMS X-coma (waves @ 547 nm)') header['Y_COMA']=(disto[4],'PSF RMS Y-coma (waves @ 547 nm)') header['X_CLOVER']=(disto[5],'PSF RMS X-clover (waves @ 547 nm)') header['Y_CLOVER']=(disto[6],'PSF RMS Y-clover (waves @ 547 nm)') header['SPHEICL']=(disto[7],'PSF RMS spherical (waves @ 547 nm)') header['PIXSCALE']=(round(pixelScale,4),'Pixel scale in arcseconds') header['WAVELNTH']=(wavelength,'PSF wavelength in microns') hdu.writeto('%s'%(name),clobber=True) print '... done' return #================================================== def main(): L=float(raw_input('Lambda? (0.76-2.00 microns) ') or .76) A=aperture(101,'HST') # Zernike coefficients for distortions Z4 to Z11 (defocus to spherical) dist=[0.0026,0.0089,0.0222,-0.0018,0.0113,0.,0.,0.] PSF=psf(A,L,5,dist) size=np.shape(PSF)[0] # size of the array pixelScale=0.110 # WFI pixel scale #with constant pixelScale, the size of the PSF will vary with the wavelength #and its sampling too. newPSF=resizePSF(PSF,L,size,pixelScale) #plt.imshow(newPSF,origin='lower',interpolation='nearest') #plt.show() createFits(newPSF,pixelScale=pixelScale,wavelength=L,disto=dist) return #================================================== if __name__=='__main__': main()

[ "glass.florian@gmail.com" ]

glass.florian@gmail.com

8a29d8ec0053b40edbbece96e608f56686f2d9a6

9ba95ea195c81fe30bffb198e79dc4714721a5b8

/mydjangoapp/settings.py

40aaadc3e922116dad4e2e3c69bec4f46a2b3384

[]

no_license

vincedgy/myDjangoApp

11b01d5f153a8b098191c4a88c5f4211c2f70592

e6e7c72eb1e867d18c86cff7d1841f33961a2d9c

refs/heads/master

2020-12-30T15:54:07.179984

2017-05-28T15:49:13

91,178,863

0

null

UTF-8

Python

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""" Django settings for mydjangoapp project. Generated by 'django-admin startproject' using Django 1.11.1. For more information on this file, see https://docs.djangoproject.com/en/1.11/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/1.11/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/1.11/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = '98i(j+en+)c276&q0gaafh1k496zok#@djbah^j!s7$bv+01&_' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True # AWS config for ElasticBeanstalk ALLOWED_HOSTS = [ '127.0.0.1', 'localhost', '.compute-1.amazonaws.com', # allows viewing of instances directly '.elasticbeanstalk.com' ] # Application definition INSTALLED_APPS = [ 'polls.apps.PollsConfig', 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'mydjangoapp.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [os.path.join(BASE_DIR, 'templates')], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'mydjangoapp.wsgi.application' # Database # https://docs.djangoproject.com/en/1.11/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/1.11/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/1.11/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'Europe/Paris' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/1.11/howto/static-files/ STATIC_URL = '/static/' STATIC_ROOT = 'static'

[ "vincent.dagoury@gmail.com" ]

vincent.dagoury@gmail.com

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# -*- coding: utf-8 -*- # ------------------------------------------------------------------------------ # # Copyright 2018-2019 Fetch.AI Limited # # 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. # # ------------------------------------------------------------------------------ """This module contains the implementation of the http echo skill.""" from aea.configurations.base import PublicId PUBLIC_ID = PublicId.from_str("fetchai/http_echo:0.9.0")

[ "david.minarsch@googlemail.com" ]

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import connexion, os from connexion.resolver import RestyResolver from flask import json from flask_sqlalchemy import SQLAlchemy from flask_marshmallow import Marshmallow # Globally accessible libraries db = SQLAlchemy() mm = Marshmallow() def init_app(): """Initialize the Connexion application.""" BASE_DIR = os.path.abspath(os.path.dirname(__file__)) openapi_path = os.path.join(BASE_DIR, "../") conn_app = connexion.FlaskApp( __name__, specification_dir=openapi_path, options={ "swagger_ui": True, "serve_spec": True } ) conn_app.add_api("openapi.yaml", resolver=RestyResolver('run'), strict_validation=True) # Flask app and getting into app_context app = conn_app.app # Load application config app.config.from_object('config.ProdConfig') app.json_encoder = json.JSONEncoder # Initialize Plugins db.init_app(app) mm.init_app(app) with app.app_context(): # Include our Routes/views import run # Register Blueprints # app.register_blueprint(auth.auth_bp) # app.register_blueprint(admin.admin_bp) return app

[ "g.j.werpachowski@gmail.com" ]

g.j.werpachowski@gmail.com

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facs = [] M = 1000000007 def modulo(n): global facs facs = [1]*(n+1) last = 1 for i in range(1,n+1): last = facs[i] = mulmod(last,i,M) def mulmod(x,y,p): return x*y % p def divmod(x,y,p): return mulmod(x,powmod(y,p-2,p),p) def ncr(n,r): if n<r: return 0 if n==r: return 1 res = facs[n] res = divmod(res,facs[r],M) res = divmod(res,facs[n-r],M) return res def powmod(x,y,p): if y==0: return 1 elif y==1: return x % p elif (y%2)==0: return powmod(x,y//2,p)**2 % p else: return powmod(x,y//2,p)**2 * x % p def resolve(): n,m,k = map(int,input().split()) modulo(2*10**5+5) kumi=ncr(n*m-2,k-2) ans = 0 for i in range(1,n): ans = ans + ((i*(n-i)*m*m)%M) for i in range(1,m): ans = ans + ((i*(m-i)*n*n)%M) print(ans*kumi%M) if __name__ == "__main__": resolve()

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#opencv videoreader test import cv2 import os import sys import pdb import glob as glob import matplotlib.pyplot as plt from DataPathclass import * DataPathobj = DataPath(dataSource,VideoIndex) from parameterClass import * Parameterobj = parameter(dataSource,VideoIndex) def readVideo(cap,subSampRate): """when read video in a loop, every subSampRate frames""" status, frame = cap.read() for ii in range(subSampRate-1): status, frameskip = cap.read() return frame, status def readBuffer(startOffset, cap): for ii in range(startOffset): ret, frame = cap.read() return cap cap = cv2.VideoCapture(DataPathobj.video) print 'fps=', np.int(DataPathobj.cap.get(cv2.cv.CV_CAP_PROP_FPS)) ## this is not reliable print 'whole frame count=', np.int(DataPathobj.cap.get(cv2.cv.CV_CAP_PROP_FRAME_COUNT)) ## this is not reliable, either startOffset = 0 cap = readBuffer(startOffset, cap) frameInd = 0 subSampRate = 1 status = True while status: frame,status = readVideo(cap, subSampRate) # cv2.imshow('vis', frame) # cv2.waitKey(5) # print 'current frame loc=', DataPathobj.cap.get(cv2.cv.CV_CAP_PROP_POS_FRAMES) frameInd+=1 print startOffset+(frameInd-1)*subSampRate print 'is the last ', startOffset+(frameInd-1)*subSampRate, '= 54552?'

[ "lichenge0223@gmail.com" ]

lichenge0223@gmail.com

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doubleClick(Pattern("1370982387429.png").similar(0.82)) click("SelectaClinic.png") click(Pattern("open.png").similar(0.74)) find(Pattern("day_week.png").similar(0.63))

[ "brian.lampe@medsphere.com" ]

brian.lampe@medsphere.com

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#exp formulas as given by Bulbapedia GEN5_FORMULA = True def getExpGain(victor, defeated, isTrainer=False): #trainer trainer = 1.5 if isTrainer else 1.0 #traded traded = 1 #base exp baseExp = defeated.baseExp #lucky egg egg = 1 #levels levelVictor = victor.level levelDefeated = defeated.level #exp share share = 1 #exp point power power = 1 if GEN5_FORMULA: first = (trainer*baseExp*levelDefeated)/(5*share) second = (((2*levelDefeated)+10)**2.5)/((levelDefeated+levelVictor+10)**2.5) block = (first*second)+1 return int(block*traded*egg*power) else: return 1

[ "andrew.turner@merton.ox.ac.uk" ]

andrew.turner@merton.ox.ac.uk

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lksshub/deeplearning

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#coding:utf-8 import tensorflow as tf from tensorflow.contrib import slim from tensorflow.contrib.tensorboard.plugins import projector import numpy as np num_keep_radio = 0.7 #define prelu def prelu(inputs): alphas = tf.get_variable("alphas", shape=inputs.get_shape()[-1], dtype=tf.float32, initializer=tf.constant_initializer(0.25)) pos = tf.nn.relu(inputs) neg = alphas * (inputs-abs(inputs))*0.5 return pos + neg def dense_to_one_hot(labels_dense,num_classes): num_labels = labels_dense.shape[0] index_offset = np.arange(num_labels)*num_classes #num_sample*num_classes labels_one_hot = np.zeros((num_labels,num_classes)) labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1 return labels_one_hot #cls_prob:batch*2 #label:batch def cls_ohem(cls_prob, label): zeros = tf.zeros_like(label) #label=-1 --> label=0net_factory #pos -> 1, neg -> 0, others -> 0 label_filter_invalid = tf.where(tf.less(label,0), zeros, label) num_cls_prob = tf.size(cls_prob) cls_prob_reshape = tf.reshape(cls_prob,[num_cls_prob,-1]) label_int = tf.cast(label_filter_invalid,tf.int32) # get the number of rows of class_prob num_row = tf.to_int32(cls_prob.get_shape()[0]) #row = [0,2,4.....] row = tf.range(num_row)*2 indices_ = row + label_int label_prob = tf.squeeze(tf.gather(cls_prob_reshape, indices_)) loss = -tf.log(label_prob+1e-10) zeros = tf.zeros_like(label_prob, dtype=tf.float32) ones = tf.ones_like(label_prob,dtype=tf.float32) # set pos and neg to be 1, rest to be 0 valid_inds = tf.where(label < zeros,zeros,ones) # get the number of POS and NEG examples num_valid = tf.reduce_sum(valid_inds) keep_num = tf.cast(num_valid*num_keep_radio,dtype=tf.int32) #FILTER OUT PART AND LANDMARK DATA loss = loss * valid_inds loss,_ = tf.nn.top_k(loss, k=keep_num) return tf.reduce_mean(loss) def bbox_ohem_smooth_L1_loss(bbox_pred,bbox_target,label): sigma = tf.constant(1.0) threshold = 1.0/(sigma**2) zeros_index = tf.zeros_like(label, dtype=tf.float32) valid_inds = tf.where(label!=zeros_index,tf.ones_like(label,dtype=tf.float32),zeros_index) abs_error = tf.abs(bbox_pred-bbox_target) loss_smaller = 0.5*((abs_error*sigma)**2) loss_larger = abs_error-0.5/(sigma**2) smooth_loss = tf.reduce_sum(tf.where(abs_error<threshold,loss_smaller,loss_larger),axis=1) keep_num = tf.cast(tf.reduce_sum(valid_inds)*num_keep_radio,dtype=tf.int32) smooth_loss = smooth_loss*valid_inds _, k_index = tf.nn.top_k(smooth_loss, k=keep_num) smooth_loss_picked = tf.gather(smooth_loss, k_index) return tf.reduce_mean(smooth_loss_picked) def bbox_ohem_orginal(bbox_pred,bbox_target,label): zeros_index = tf.zeros_like(label, dtype=tf.float32) #pay attention :there is a bug!!!! valid_inds = tf.where(label!=zeros_index,tf.ones_like(label,dtype=tf.float32),zeros_index) #(batch,) square_error = tf.reduce_sum(tf.square(bbox_pred-bbox_target),axis=1) #keep_num scalar keep_num = tf.cast(tf.reduce_sum(valid_inds)*num_keep_radio,dtype=tf.int32) #keep valid index square_error square_error = square_error*valid_inds _, k_index = tf.nn.top_k(square_error, k=keep_num) square_error = tf.gather(square_error, k_index) return tf.reduce_mean(square_error) #label=1 or label=-1 then do regression def bbox_ohem(bbox_pred,bbox_target,label): ''' :param bbox_pred: :param bbox_target: :param label: class label :return: mean euclidean loss for all the pos and part examples ''' zeros_index = tf.zeros_like(label, dtype=tf.float32) ones_index = tf.ones_like(label,dtype=tf.float32) # keep pos and part examples valid_inds = tf.where(tf.equal(tf.abs(label), 1),ones_index,zeros_index) #(batch,) #calculate square sum square_error = tf.square(bbox_pred-bbox_target) square_error = tf.reduce_sum(square_error,axis=1) #keep_num scalar num_valid = tf.reduce_sum(valid_inds) #keep_num = tf.cast(num_valid*num_keep_radio,dtype=tf.int32) # count the number of pos and part examples keep_num = tf.cast(num_valid, dtype=tf.int32) #keep valid index square_error square_error = square_error*valid_inds # keep top k examples, k equals to the number of positive examples _, k_index = tf.nn.top_k(square_error, k=keep_num) square_error = tf.gather(square_error, k_index) return tf.reduce_mean(square_error) def landmark_ohem(landmark_pred,landmark_target,label): ''' :param landmark_pred: :param landmark_target: :param label: :return: mean euclidean loss ''' #keep label =-2 then do landmark detection ones = tf.ones_like(label,dtype=tf.float32) zeros = tf.zeros_like(label,dtype=tf.float32) valid_inds = tf.where(tf.equal(label,-2),ones,zeros) square_error = tf.square(landmark_pred-landmark_target) square_error = tf.reduce_sum(square_error,axis=1) num_valid = tf.reduce_sum(valid_inds) #keep_num = tf.cast(num_valid*num_keep_radio,dtype=tf.int32) keep_num = tf.cast(num_valid, dtype=tf.int32) square_error = square_error*valid_inds _, k_index = tf.nn.top_k(square_error, k=keep_num) square_error = tf.gather(square_error, k_index) return tf.reduce_mean(square_error) def cal_accuracy(cls_prob,label): ''' :param cls_prob: :param label: :return:calculate classification accuracy for pos and neg examples only ''' # get the index of maximum value along axis one from cls_prob # 0 for negative 1 for positive pred = tf.argmax(cls_prob,axis=1) label_int = tf.cast(label,tf.int64) # return the index of pos and neg examples cond = tf.where(tf.greater_equal(label_int,0)) picked = tf.squeeze(cond) # gather the label of pos and neg examples label_picked = tf.gather(label_int,picked) pred_picked = tf.gather(pred,picked) #calculate the mean value of a vector contains 1 and 0, 1 for correct classification, 0 for incorrect # ACC = (TP+FP)/total population accuracy_op = tf.reduce_mean(tf.cast(tf.equal(label_picked,pred_picked),tf.float32)) return accuracy_op def _activation_summary(x): ''' creates a summary provides histogram of activations creates a summary that measures the sparsity of activations :param x: Tensor :return: ''' tensor_name = x.op.name print('load summary for : ',tensor_name) tf.summary.histogram(tensor_name + '/activations',x) #tf.summary.scalar(tensor_name + '/sparsity', tf.nn.zero_fraction(x)) #construct Pnet #label:batch def P_Net(inputs,label=None,bbox_target=None,landmark_target=None,training=True): #define common param with slim.arg_scope([slim.conv2d], activation_fn=prelu, weights_initializer=slim.xavier_initializer(), biases_initializer=tf.zeros_initializer(), weights_regularizer=slim.l2_regularizer(0.0005), padding='valid'): print(inputs.get_shape()) net = slim.conv2d(inputs, 10, 3, stride=1,scope='conv1') _activation_summary(net) print(net.get_shape()) net = slim.max_pool2d(net, kernel_size=[2,2], stride=2, scope='pool1', padding='SAME') _activation_summary(net) print(net.get_shape()) net = slim.conv2d(net,num_outputs=16,kernel_size=[3,3],stride=1,scope='conv2') _activation_summary(net) print(net.get_shape()) # net = slim.conv2d(net,num_outputs=32,kernel_size=[3,3],stride=1,scope='conv3') _activation_summary(net) print(net.get_shape()) #batch*H*W*2 conv4_1 = slim.conv2d(net,num_outputs=2,kernel_size=[1,1],stride=1,scope='conv4_1',activation_fn=tf.nn.softmax) _activation_summary(conv4_1) #conv4_1 = slim.conv2d(net,num_outputs=1,kernel_size=[1,1],stride=1,scope='conv4_1',activation_fn=tf.nn.sigmoid) print (conv4_1.get_shape()) #batch*H*W*4 bbox_pred = slim.conv2d(net,num_outputs=4,kernel_size=[1,1],stride=1,scope='conv4_2',activation_fn=None) _activation_summary(bbox_pred) print (bbox_pred.get_shape()) #batch*H*W*10 landmark_pred = slim.conv2d(net,num_outputs=10,kernel_size=[1,1],stride=1,scope='conv4_3',activation_fn=None) _activation_summary(landmark_pred) print (landmark_pred.get_shape()) # add projectors for visualization #cls_prob_original = conv4_1 #bbox_pred_original = bbox_pred if training: #batch*2 # calculate classification loss cls_prob = tf.squeeze(conv4_1,[1,2],name='cls_prob') cls_loss = cls_ohem(cls_prob,label) #batch # cal bounding box error, squared sum error bbox_pred = tf.squeeze(bbox_pred,[1,2],name='bbox_pred') bbox_loss = bbox_ohem(bbox_pred,bbox_target,label) #batch*10 landmark_pred = tf.squeeze(landmark_pred,[1,2],name="landmark_pred") landmark_loss = landmark_ohem(landmark_pred,landmark_target,label) accuracy = cal_accuracy(cls_prob,label) L2_loss = tf.add_n(slim.losses.get_regularization_losses()) return cls_loss,bbox_loss,landmark_loss,L2_loss,accuracy #test else: #when test,batch_size = 1 cls_pro_test = tf.squeeze(conv4_1, axis=0, name='cls_prob') bbox_pred_test = tf.squeeze(bbox_pred,axis=0, name='bbox_pred') landmark_pred_test = tf.squeeze(landmark_pred,axis=0, name='landmark_pred') return cls_pro_test,bbox_pred_test,landmark_pred_test def R_Net(inputs,label=None,bbox_target=None,landmark_target=None,training=True): with slim.arg_scope([slim.conv2d], activation_fn = prelu, weights_initializer=slim.xavier_initializer(), biases_initializer=tf.zeros_initializer(), weights_regularizer=slim.l2_regularizer(0.0005), padding='valid'): print (inputs.get_shape()) net = slim.conv2d(inputs, num_outputs=28, kernel_size=[3,3], stride=1, scope="conv1") print (net.get_shape()) net = slim.max_pool2d(net, kernel_size=[3, 3], stride=2, scope="pool1", padding='SAME') print(net.get_shape()) net = slim.conv2d(net,num_outputs=48,kernel_size=[3,3],stride=1,scope="conv2") print(net.get_shape()) net = slim.max_pool2d(net,kernel_size=[3,3],stride=2,scope="pool2") print(net.get_shape()) net = slim.conv2d(net,num_outputs=64,kernel_size=[2,2],stride=1,scope="conv3") print(net.get_shape()) fc_flatten = slim.flatten(net) print(fc_flatten.get_shape()) fc1 = slim.fully_connected(fc_flatten, num_outputs=128,scope="fc1") print(fc1.get_shape()) #batch*2 cls_prob = slim.fully_connected(fc1,num_outputs=2,scope="cls_fc",activation_fn=tf.nn.softmax) print(cls_prob.get_shape()) #batch*4 bbox_pred = slim.fully_connected(fc1,num_outputs=4,scope="bbox_fc",activation_fn=None) print(bbox_pred.get_shape()) #batch*10 landmark_pred = slim.fully_connected(fc1,num_outputs=10,scope="landmark_fc",activation_fn=None) print(landmark_pred.get_shape()) #train if training: cls_loss = cls_ohem(cls_prob,label) bbox_loss = bbox_ohem(bbox_pred,bbox_target,label) accuracy = cal_accuracy(cls_prob,label) landmark_loss = landmark_ohem(landmark_pred,landmark_target,label) L2_loss = tf.add_n(slim.losses.get_regularization_losses()) return cls_loss,bbox_loss,landmark_loss,L2_loss,accuracy else: cls_prob = tf.identity(cls_prob, name='cls_prob') bbox_pred = tf.identity(bbox_pred, name='bbox_pred') landmark_pred = tf.identity(landmark_pred, name='landmark_pred') return cls_prob,bbox_pred,landmark_pred def O_Net(inputs,label=None,bbox_target=None,landmark_target=None,training=True): with slim.arg_scope([slim.conv2d], activation_fn = prelu, weights_initializer=slim.xavier_initializer(), biases_initializer=tf.zeros_initializer(), weights_regularizer=slim.l2_regularizer(0.0005), padding='valid'): print(inputs.get_shape()) net = slim.conv2d(inputs, num_outputs=32, kernel_size=[3,3], stride=1, scope="conv1") print(net.get_shape()) net = slim.max_pool2d(net, kernel_size=[3, 3], stride=2, scope="pool1", padding='SAME') print(net.get_shape()) net = slim.conv2d(net,num_outputs=64,kernel_size=[3,3],stride=1,scope="conv2") print(net.get_shape()) net = slim.max_pool2d(net, kernel_size=[3, 3], stride=2, scope="pool2") print(net.get_shape()) net = slim.conv2d(net,num_outputs=64,kernel_size=[3,3],stride=1,scope="conv3") print(net.get_shape()) net = slim.max_pool2d(net, kernel_size=[2, 2], stride=2, scope="pool3", padding='SAME') print(net.get_shape()) net = slim.conv2d(net,num_outputs=128,kernel_size=[2,2],stride=1,scope="conv4") print(net.get_shape()) fc_flatten = slim.flatten(net) print(fc_flatten.get_shape()) fc1 = slim.fully_connected(fc_flatten, num_outputs=256,scope="fc1") print(fc1.get_shape()) #batch*2 cls_prob = slim.fully_connected(fc1,num_outputs=2,scope="cls_fc",activation_fn=tf.nn.softmax) print(cls_prob.get_shape()) #batch*4 bbox_pred = slim.fully_connected(fc1,num_outputs=4,scope="bbox_fc",activation_fn=None) print(bbox_pred.get_shape()) #batch*10 landmark_pred = slim.fully_connected(fc1,num_outputs=10,scope="landmark_fc",activation_fn=None) print(landmark_pred.get_shape()) #train if training: cls_loss = cls_ohem(cls_prob,label) bbox_loss = bbox_ohem(bbox_pred,bbox_target,label) accuracy = cal_accuracy(cls_prob,label) landmark_loss = landmark_ohem(landmark_pred, landmark_target,label) L2_loss = tf.add_n(slim.losses.get_regularization_losses()) return cls_loss,bbox_loss,landmark_loss,L2_loss,accuracy else: cls_prob = tf.identity(cls_prob, name='cls_prob') bbox_pred = tf.identity(bbox_pred, name='bbox_pred') landmark_pred = tf.identity(landmark_pred, name='landmark_pred') return cls_prob,bbox_pred,landmark_pred

[ "980294373@qq.com" ]

980294373@qq.com

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471af9c94770ee1e977f72f9f6bd2164db71096e

/ntuple/cmssw.py

6e7738e20c0318aec1656e2808016d99313737d4

[ "BSD-3-Clause", "MIT" ]

permissive

kreczko/ntuple

670e4625413121cff96fe6577954a2d88b5a04e7

6a2cebe3a416db5d7130056921e0c7c32caa1736

refs/heads/master

2016-08-10T08:37:08.966445

2016-02-16T11:33:55

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''' Converter for the CMS SoftWare (CMSSW). It requires a fully set up CMSSW area ''' class CMSSWConverter(): def __init__(self): # check if CMSSW_BASE is set import os is_cmssw_set_up = 'CMSSW_base' in os.environ if not is_cmssw_set_up: import sys sys.exit('CMSSW does not seem to be set up, aborting...') def convert_file_path(self, file_path): # call edmFileUtil -d file_path pass

[ "lkreczko@googlemail.com" ]

lkreczko@googlemail.com

672f3ed658454c5a1185392cddf2dbd60fabaa86

c6247cdc17bd1d1d88255d21e6f09c838fda2bf4

/tsp_utils.py

2dfe162783aab4fba0d3ceaa8ea4c132e159be9a

[]

no_license

maxgold/gcn

7925f86ff8b37eef9cea4101f5e15036a84bcf45

75f42e616d8fd76b0fea4e00f87b19d041eb6f02

refs/heads/master

2021-04-26T16:52:33.247542

2017-10-13T20:23:45

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## Utility functions to help generate TSP instances import numpy as np import pickle from copy import copy import numpy.linalg as nlg import json import random #from tsp_utils import * class create_adj(object): def __init__(self, tsp_size): self.scale = 1000 self.matrix = np.round(np.random.rand(tsp_size,tsp_size),3)*self.scale np.fill_diagonal(self.matrix, 0) self.matrix = (self.matrix + self.matrix.T)/2 def Distance(self, from_node, to_node): return self.matrix[from_node][to_node] def edges_from_mat(matrix): edges = zip(np.where(matrix>0)[0], np.where(matrix>0)[1]) return(edges) class create_adj_cycle(object): def __init__(self, tsp_size, num_new_edges = None): if num_new_edges == None: num_new_edges = tsp_size self.scale = 1000 cycle_weights = np.random.rand(tsp_size) cycle_cost = np.sum(cycle_weights) self.matrix = np.zeros((tsp_size, tsp_size)) for i in range(tsp_size): self.matrix[i, (i+1)%tsp_size] = cycle_weights[i] self.matrix[(i+1)%tsp_size, i] = cycle_weights[i] cycle_edges = edges_from_mat(self.matrix) t = np.ones((tsp_size, tsp_size)) np.fill_diagonal(t, 0) all_edges = np.array(edges_from_mat(t)) all_edges = all_edges[all_edges[:,0] < all_edges[:,1]] all_edges = list(map(tuple, all_edges)) new_edges = [x for x in all_edges if x not in cycle_edges] random.shuffle(new_edges) new_edges = np.array(new_edges) num_new_edges = min(len(new_edges), num_new_edges) for i in range(num_new_edges): val = np.random.rand(1) self.matrix[new_edges[i,0],new_edges[i,1]] = val self.matrix[new_edges[i,1],new_edges[i,0]] = val self.matrix = np.round(self.matrix, 3) * self.scale self.matrix[self.matrix==0] = 1e6 self.cycle_cost = cycle_cost np.fill_diagonal(self.matrix, 0) def Distance(self, from_node, to_node): return(self.matrix[from_node][to_node]) def distance(x1, y1, x2, y2): dist = np.sqrt((x1 - x2)**2 + (y1 - y2)**2) return dist def construct_cycle_feature_route_cw(num_nodes, start_node, goal_node): pos_mat = np.eye(num_nodes) pos_mat[start_node, :] = 1 pos_vec = np.zeros(num_nodes) pos_vec[start_node] = 1 goal_vec = np.zeros(num_nodes) goal_vec[goal_node] = 1 visited_vec = np.zeros(num_nodes) visited_vec[start_node] = 1 goal_feature = np.zeros([num_nodes, 3]) goal_feature[start_node, 0] = 1 goal_feature[start_node, 1] = 1 goal_feature[:, 2] = 1 #feature_tour = np.c_[pos_mat, goal_vec, visited_vec, goal_feature][:,:,None] feature_tour = np.c_[pos_vec, goal_vec, visited_vec, goal_feature][:,:,None] route0 = np.zeros([num_nodes, 1]) route0[0] = (start_node + 1)%num_nodes for i in range(1, num_nodes): cur_node = (start_node + i) % num_nodes pos_mat = np.eye(num_nodes) pos_mat[cur_node, :] = 1 pos_vec = np.zeros(num_nodes) pos_vec[cur_node] = 1 goal_vec = np.zeros(num_nodes) goal_vec[start_node] = 1 visited_vec[cur_node] = 1 #feature0 = np.c_[pos_mat, goal_vec, visited_vec, goal_feature][:,:,None] feature0 = np.c_[pos_vec, goal_vec, visited_vec, goal_feature][:,:,None] feature_tour = np.concatenate((feature_tour, feature0), axis=2) route0[i] = (cur_node + 1) % num_nodes return(feature_tour, route0) def construct_cycle_feature_route_ccw(num_nodes, start_node, goal_node): pos_mat = np.eye(num_nodes) pos_mat[start_node, :] = 1 pos_vec = np.zeros(num_nodes) pos_vec[start_node] = 1 goal_vec = np.zeros(num_nodes) goal_vec[goal_node] = 1 visited_vec = np.zeros(num_nodes) visited_vec[start_node] = 1 goal_feature = np.zeros([num_nodes, 3]) goal_feature[start_node, 0] = 1 goal_feature[start_node, 1] = 1 goal_feature[:, 2] = 1 #feature_tour = np.c_[pos_mat, goal_vec, visited_vec, goal_feature][:,:,None] feature_tour = np.c_[pos_vec, goal_vec, visited_vec, goal_feature][:,:,None] route0 = np.zeros([num_nodes, 1]) route0[0] = (start_node - 1)%num_nodes for i in range(1, num_nodes): cur_node = (start_node - i) % num_nodes pos_mat = np.eye(num_nodes) pos_mat[cur_node, :] = 1 pos_vec = np.zeros(num_nodes) pos_vec[cur_node] = 1 goal_vec = np.zeros(num_nodes) goal_vec[start_node] = 1 visited_vec[cur_node] = 1 #feature0 = np.c_[pos_mat, goal_vec, visited_vec, goal_feature][:,:,None] feature0 = np.c_[pos_vec, goal_vec, visited_vec, goal_feature][:,:,None] feature_tour = np.concatenate((feature_tour, feature0), axis=2) route0[i] = (cur_node - 1) % num_nodes return(feature_tour, route0) def construct_cycle_adj(num_nodes): adj = np.zeros([num_nodes, num_nodes]) for i in range(num_nodes): adj[i, (i+1)%num_nodes] = 1 adj[(i+1)%num_nodes, i] = 1 return adj def construct_cycle_feature_missing(num_nodes, start_node, goal_node, start_missing, end_missing): visited = np.zeros(num_nodes) visited[start_node] = 1 goal = np.zeros([num_nodes, 3]) goal[goal_node, 0] = 1 goal[goal_node, 1] = 1 goal[:, 2] = 1 if start_missing <= end_missing: if end_missing - num_nodes != -1: goal[(end_missing-num_nodes+1)%num_nodes:,2] = 0 goal[:start_missing, 2] = 0 else: goal[(end_missing+1)%num_nodes:start_missing,2] = 0 features = np.zeros([num_nodes,6,0]) routes = [] #routes.append((start_node - 1)%num_nodes) cur_node = start_node for i in range(0, (start_node - start_missing + num_nodes)%num_nodes): cur_node = (start_node - i)%num_nodes state0 = np.zeros([num_nodes, 3]) state0[cur_node, 0] = 1 state0[goal_node, 1] = 1 visited[cur_node] = 1 state0[:, 2] = visited feature0 = np.c_[state0, goal][:,:,None] features = np.concatenate((features,feature0), axis=2) routes.append((cur_node - 1)%num_nodes) for i in range(0, (end_missing - start_missing + num_nodes)%num_nodes): cur_node = (start_missing + i)%num_nodes state0 = np.zeros([num_nodes, 3]) state0[cur_node, 0] = 1 state0[goal_node, 1] = 1 visited[cur_node] = 1 state0[:, 2] = visited feature0 = np.c_[state0, goal][:,:,None] features = np.concatenate((features,feature0), axis=2) routes.append((cur_node + 1)%num_nodes) return(features, routes) def construct_cycle_adj_missing_fieldsize(num_nodes, field_size, num_layers, start_node, start_missing, end_missing): adj = np.zeros([num_nodes, num_nodes]) for i in range(num_nodes): adj[i, (i+1)%num_nodes] = 1 adj[(i+1)%num_nodes, i] = 1 if end_missing < start_missing: adj[end_missing+1:start_missing] = 0 adj[end_missing, (end_missing+1)%num_nodes] = 0 adj[start_missing, start_missing-1] = 0 else: adj[end_missing+1:] = 0 adj[end_missing, (end_missing+1)%num_nodes] = 0 adj[:start_missing] = 0 adj[start_missing, (start_missing-1)%num_nodes] = 0 res = {} for i in range(num_layers): t = nlg.matrix_power(adj, field_size[i]) #t[t>0] = 1 res[i] = t return res def construct_cycle_adj_missing_fieldsize_beta(num_nodes, field_size, num_layers, start_node, start_missing, end_missing): adj = np.zeros([num_nodes, num_nodes]) for i in range(num_nodes): adj[i, (i+1)%num_nodes] = 1 adj[(i+1)%num_nodes, i] = 1 if end_missing < start_missing: adj[end_missing+1:start_missing, :] = 0 adj[end_missing, (end_missing+1)%num_nodes] = 0 adj[start_missing, start_missing-1] = 0 else: adj[end_missing+1:, :] = 0 adj[end_missing, (end_missing+1)%num_nodes] = 0 adj[:start_missing, :] = 0 adj[start_missing, (start_missing-1)%num_nodes] = 0 ### a lot of choices down here power_tracker = np.zeros([num_nodes, num_nodes]) counter = nlg.matrix_power(adj, field_size[0]) res = {} for i in range(num_layers): t1 = nlg.matrix_power(adj, field_size[i]) #t1[power_tracker > 0] = 0 power_tracker = power_tracker + t1 t2 = np.zeros([num_nodes,num_nodes]) t2[t1 > 0] = field_size[i] mask = (t2 > 0) & (counter == 0) counter[mask] = np.maximum(counter[mask], t2[mask]) t = np.concatenate((t1[:,:,None],counter[:,:,None]), axis=2) #t[t>0] = 1 res[i] = t return res def construct_cycle_adj_missing(num_nodes, start_node, start_missing, end_missing): adj = np.zeros([num_nodes, num_nodes]) for i in range(num_nodes): adj[i, (i+1)%num_nodes] = 1 adj[(i+1)%num_nodes, i] = 1 if end_missing < start_missing: adj[end_missing+1:start_missing] = 0 adj[end_missing, (end_missing+1)%num_nodes] = 0 adj[start_missing, start_missing-1] = 0 else: adj[end_missing+1:] = 0 adj[end_missing, (end_missing+1)%num_nodes] = 0 adj[:start_missing] = 0 adj[start_missing, (start_missing-1)%num_nodes] = 0 return adj def construct_cycle_weight(num_nodes, max_weight = 10): W = np.zeros([num_nodes, num_nodes]) for i in range(num_nodes): weight = np.random.randint(max_weight) W[i, (i+1)%num_nodes] = weight W[(i+1)%num_nodes, i] = weight return W def nn_mats_from_adj(A): num_edges = np.sum(A > 0) num_nodes = A.shape[0] edges = np.zeros([0,2]) R = A.shape[0] C = A.shape[1] for i in range(C): for j in range(R): if A[i,j] > 0: edges = np.r_[edges, np.array([[i, j]])] edges = edges.astype(int) P1 = np.zeros([edges.shape[0], num_nodes]) P2 = np.zeros([edges.shape[0], num_nodes]) P1[np.arange(edges.shape[0]).astype(int), edges[:,0]] = 1 P2[np.arange(edges.shape[0]).astype(int), edges[:,1]] = 1 A_nn = np.zeros([num_nodes, num_edges]) c = 0 for edge in edges: A_nn[edge[0], c] = 1 # this should be set to W_ij c += 1 return(P1, P2, A_nn) def nn_mats_from_adj_fieldsize(adj, field_size, num_layers): P1d = {} P2d = {} A_nnd = {} Fd = {} num_nodes = adj[0].shape[0] for layer in range(num_layers): dist = field_size[layer] A = adj[layer] num_edges = np.sum(A > 0) num_nodes = A.shape[0] edges = np.zeros([0,2]) R = A.shape[0] C = A.shape[1] for i in range(C): for j in range(R): if A[i,j] > 0: edges = np.r_[edges, np.array([[i, j]])] edges = edges.astype(int) P1 = np.zeros([edges.shape[0], num_nodes]) P2 = np.zeros([edges.shape[0], num_nodes]) F = np.zeros([edges.shape[0], 1]) P1[np.arange(edges.shape[0]).astype(int), edges[:,0]] = 1 P2[np.arange(edges.shape[0]).astype(int), edges[:,1]] = 1 A_nn = np.zeros([num_nodes, num_edges]) c = 0 for edge in edges: F[c, 0] = A[edge[0], edge[1]] #F[c, 1] = A[edge[0], edge[1]] #should be field_size[layer] #F[c, 2] = A[edge[0], edge[1], 2] #should be entry of the weight power matrix A_nn[edge[0], c] = 1 # this should be set to W_ij c += 1 F = F/num_nodes P1d[layer] = P1 P2d[layer] = P2 A_nnd[layer] = A_nn Fd[layer] = F return(P1d, P2d, A_nnd, Fd) def nn_mats_from_adj_fieldsize_beta(adj, field_size, num_layers): P1d = {} P2d = {} A_nnd = {} Fd = {} num_nodes = adj[0].shape[0] for layer in range(num_layers): dist = field_size[layer] A = adj[layer] num_edges = np.sum(A[:,:,0] > 0) num_nodes = A.shape[0] edges = np.zeros([0,2]) R = A.shape[0] C = A.shape[1] for i in range(C): for j in range(R): if A[i,j, 0] > 0: edges = np.r_[edges, np.array([[i, j]])] edges = edges.astype(int) P1 = np.zeros([edges.shape[0], num_nodes]) P2 = np.zeros([edges.shape[0], num_nodes]) F = np.zeros([edges.shape[0], 2]) ## TODO try replacing this with the weight P1[np.arange(edges.shape[0]).astype(int), edges[:,0]] = 1 P2[np.arange(edges.shape[0]).astype(int), edges[:,1]] = 1 A_nn = np.zeros([num_nodes, num_edges]) c = 0 for edge in edges: F[c, 0] = A[edge[0], edge[1], 0] F[c, 1] = A[edge[0], edge[1], 1] #should be field_size[layer] #F[c, 2] = A[edge[0], edge[1], 2] #should be entry of the weight power matrix A_nn[edge[0], c] = 1 # this should be set to W_ij c += 1 F = F/num_nodes P1d[layer] = P1 P2d[layer] = P2 A_nnd[layer] = A_nn Fd[layer] = F return(P1d, P2d, A_nnd, Fd) def gen_cycle_data(num_nodes, trials = 100): #features = np.zeros([num_nodes, num_nodes + 5, num_nodes, 0]) features = np.zeros([num_nodes, 6, num_nodes, 0]) weights = np.zeros([num_nodes, num_nodes, 0]) adj = np.zeros([num_nodes, num_nodes, 0]) routes = np.zeros([num_nodes, 0]) P1 = np.zeros([2*num_nodes, num_nodes, 0]) P2 = np.zeros([2*num_nodes, num_nodes, 0]) A_nn = np.zeros([num_nodes, 2*num_nodes, 0]) for i in range(num_nodes): start_node = i goal_node = i feature_tour1, route1 = construct_cycle_feature_route_cw(num_nodes, start_node, goal_node) feature_tour2, route2 = construct_cycle_feature_route_ccw(num_nodes, start_node, goal_node) adj0 = construct_cycle_adj(num_nodes) weight0 = construct_cycle_weight(num_nodes) P1_0, P2_0, A_nn0 = nn_mats_from_adj(adj0) features = np.concatenate((features, feature_tour1[:,:,:,None]), axis=3) features = np.concatenate((features, feature_tour2[:,:,:,None]), axis=3) routes = np.concatenate((routes, route1), axis=1) routes = np.concatenate((routes, route2), axis=1) adj = np.concatenate((adj, adj0[:,:,None]), axis=2) adj = np.concatenate((adj, adj0[:,:,None]), axis=2) weights = np.concatenate((weights, weight0[:,:,None]), axis=2) weights = np.concatenate((weights, weight0[:,:,None]), axis=2) P1 = np.concatenate((P1, P1_0[:,:,None]), axis=2) P2 = np.concatenate((P2, P2_0[:,:,None]), axis=2) A_nn = np.concatenate((A_nn, A_nn0[:,:,None]), axis=2) return(features, weights, adj, routes, P1, P2, A_nn) def gen_cycle_data_missing(num_nodes, field_size, num_layers): features = {} adj = {} routes = {} P1 = {} P2 = {} A_nn = {} F = {} for start_missing in range(num_nodes): for end_missing in range(num_nodes): if (start_missing != end_missing) & (abs((start_missing - end_missing )%num_nodes)!=1): if start_missing < end_missing: start_vals = np.arange(start_missing,end_missing+1) else: start_vals = np.arange(start_missing, end_missing + num_nodes + 1) % num_nodes for i in start_vals: start_node = i goal_node = end_missing feature0, route0 = construct_cycle_feature_missing(num_nodes, start_node, goal_node, start_missing, end_missing) adj0 = construct_cycle_adj_missing_fieldsize_beta(num_nodes, field_size, num_layers, start_node, start_missing, end_missing) P1_0, P2_0, A_nn0, F_0 = nn_mats_from_adj_fieldsize_beta(adj0, field_size, num_layers) features[(start_missing, end_missing, i)] = feature0 routes[(start_missing, end_missing, i)] = route0 adj[(start_missing, end_missing, i)] = adj0 P1[(start_missing, end_missing, i)] = P1_0 P2[(start_missing, end_missing, i)] = P2_0 A_nn[(start_missing, end_missing, i)] = A_nn0 F[(start_missing, end_missing, i)] = F_0 return(features, adj, routes, P1, P2, A_nn, F) def construct_adj_fieldsize(A, W, field_size, num_layers): res = {} for i in range(num_layers): t1 = nlg.matrix_power(W, field_size[i]) t2 = nlg.matrix_power(A, field_size[i]) t = np.concatenate((t1[:,:,None],t2[:,:,None]), axis=2) res[i] = t return res def feature_from_assignment(routing, assignment, num_nodes): route = [] features = np.zeros([num_nodes,6,0]) index = routing.Start(0) cur_node = index % num_nodes start_node = cur_node goal_node = cur_node visited = np.zeros(num_nodes) visited[start_node] = 1 goal = np.zeros([num_nodes, 3]) goal[goal_node, 0] = 1 goal[goal_node, 1] = 1 goal[:, 2] = 1 state0 = np.zeros([num_nodes, 3]) state0[cur_node, 0] = 1 state0[goal_node, 1] = 1 visited[cur_node] = 1 state0[:, 2] = visited feature0 = np.c_[state0, goal][:,:,None] features = np.concatenate((features,feature0), axis=2) while np.sum(visited)!=num_nodes: index = assignment.Value(routing.NextVar(cur_node)) cur_node = index % num_nodes route.append(cur_node) state0 = np.zeros([num_nodes, 3]) state0[cur_node, 0] = 1 state0[goal_node, 1] = 1 visited[cur_node] = 1 state0[:, 2] = visited feature0 = np.c_[state0, goal][:,:,None] features = np.concatenate((features,feature0), axis=2) route.append(start_node) return(features, route) def feature_from_route(route, num_nodes): # this doesn't work for some unknown reason...w features = np.zeros([num_nodes,6,0]) cur_node = route[0] i = 1 start_node = cur_node goal_node = cur_node visited = np.zeros(num_nodes) visited[start_node] = 1 goal = np.zeros([num_nodes, 3]) goal[goal_node, 0] = 1 goal[goal_node, 1] = 1 goal[:, 2] = 1 state0 = np.zeros([num_nodes, 3]) state0[cur_node, 0] = 1 state0[goal_node, 1] = 1 visited[cur_node] = 1 state0[:, 2] = visited feature0 = np.c_[state0, goal][:,:,None] features = np.concatenate((features,feature0), axis=2) while np.sum(visited)!=num_nodes: cur_node = route[i] state0 = np.zeros([num_nodes, 3]) state0[cur_node, 0] = 1 state0[goal_node, 1] = 1 visited[cur_node] = 1 state0[:, 2] = visited feature0 = np.c_[state0, goal][:,:,None] features = np.concatenate((features,feature0), axis=2) i += 1 route = list(route) route.append(start_node) route = route[1:] return(features, route) def np_dic_to_json2(dic): keys1 = dic.keys() new_dic = {} for key1 in keys1: new_key1 = str(key1[0]) + ',' + str(key1[1]) new_dic[new_key1] = {} keys2 = dic[key1].keys() for key2 in keys2: new_key2 = str(key2) arr = dic[key1][key2] if type(arr) == np.ndarray: new_dic[new_key1][new_key2] = arr.tolist() else: new_dic[new_key1][new_key2] = arr return(new_dic) def np_dic_to_json3(dic): keys1 = dic.keys() new_dic = {} for key1 in keys1: new_key1 = str(key1[0]) + ',' + str(key1[1]) new_dic[new_key1] = {} keys2 = dic[key1].keys() for key2 in keys2: new_key2 = str(key2) new_dic[new_key1][new_key2] = {} keys3 = dic[key1][key2].keys() for key3 in keys3: new_key3 = str(key3) arr = dic[key1][key2][key3] if type(arr) == np.ndarray: new_dic[new_key1][new_key2][new_key3] = arr.tolist() else: new_dic[new_key1][new_key2][new_key3] = arr return(new_dic) def json_dic_to_np2(dic): keys1 = dic.keys() new_dic = {} for key1 in keys1: new_key1 = tuple(np.array(key1.split(',')).astype(int)) new_dic[new_key1] = {} keys2 = dic[key1].keys() for key2 in keys2: new_key2 = int(key2) arr = dic[key1][key2] if type(arr) == list: new_dic[new_key1][new_key2] = np.array(arr) else: new_dic[new_key1][new_key2] = arr return(new_dic) def json_dic_to_np3(dic): keys1 = dic.keys() new_dic = {} for key1 in keys1: new_key1 = tuple(np.array(key1.split(',')).astype(int)) new_dic[new_key1] = {} keys2 = dic[key1].keys() for key2 in keys2: new_key2 = int(key2) new_dic[new_key1][new_key2] = {} keys3 = dic[key1][key2].keys() for key3 in keys3: new_key3 = int(key3) arr = dic[key1][key2][key3] if type(arr) == list: new_dic[new_key1][new_key2][new_key3] = np.array(arr) else: new_dic[new_key1][new_key2][new_key3] = arr return(new_dic)

[ "noreply@github.com" ]

maxgold.noreply@github.com

01672a0b179f6d2567c1d6f70a6625e4008e3138

2f7ade102f564759bb55d7dfde2bf25ca78fe574

/Set5/Challenge33/CP5_33.py

f9e0aa941f2eddfdfd77bba48217aee1629f159b

[]

no_license

grandfoosier/Cryptopals

6d60948b148ab6edd433fb413e849a4e623ef9e2

a1f79e240ba9264b31569786ba7d05122e313958

refs/heads/master

2020-04-11T02:06:00.700555

2016-12-13T21:02:52

68,222,057

1

0

null

UTF-8

Python

false

1,404

py

from hashlib import sha256 from random import randint import array ####################################################################### # Bignum given from challenge class Bignum(object): def __init__(self): self.ig = 0xffffffffffffffffc90fdaa22168c234c4c6628b80dc1cd129024e088a67cc74020bbea63b139b22514a08798e3404ddef9519b3cd3a431b302b0a6df25f14374fe1356d6d51c245e485b576625e7ec6f44c42e9a637ed6b0bff5cb6f406b7edee386bfb5a899fa5ae9f24117c4b1fe649286651ece45b3dc2007cb8a163bf0598da48361c55d39a69163fa8fd24cf5f83655d23dca3ad961c62f356208552bb9ed529077096966d670c354e4abc9804f1746c08ca237327ffffffffffffffff ####################################################################### # Create a key given inputs p and g def dh_key(p, g): a = randint(0, p-1); b = randint(0, p-1) A = pow(g, a, p); B = pow(g, b, p) s = pow(B, a, p); assert s == pow(A, b, p) h = hex(s)[2:] if h[-1] == b'L': h = h[: -1] if len(h) % 2: h = "0" + h bh = bytearray.fromhex(h) keyEh = sha256(bh).hexdigest()[: 32] keyMh = sha256(bh).hexdigest()[32: ] print keyEh, keyMh return keyEh, keyMh ####################################################################### # Main routine if __name__ == "__main__": B = Bignum() print "" print "37, 5:" keyE, keyM = dh_key(37, 5) print "" print "big, 2:" keyE, keyM = dh_key(B.ig, 2) print "\n"

[ "noreply@github.com" ]

grandfoosier.noreply@github.com

713733903696af3343825284b53b29db939fa02c

7b971c4465797ef3561c69d7dd819fcd5d2a3736

/support/convexhull.py

09d10549b53809c9137859ed37245488e2207775

[]

no_license

rkdarst/pcd

978d898a3ab1a1fd8fbb48e644234d66fe7ab95f

54dda49ce8a248446b236a42b9d313ce410cf58b

refs/heads/master

2021-01-23T12:38:09.042817

2016-11-01T18:02:25

22,289,583

5

4

null

UTF-8

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py

# See convexhull.txt def convex_hull(points): """Computes the convex hull of a set of 2D points. Input: an iterable sequence of (x, y) pairs representing the points. Output: a list of vertices of the convex hull in counter-clockwise order, starting from the vertex with the lexicographically smallest coordinates. Implements Andrew's monotone chain algorithm. O(n log n) complexity. """ # Sort the points lexicographically (tuples are compared lexicographically). # Remove duplicates to detect the case we have just one unique point. points = sorted(set(points)) # Boring case: no points or a single point, possibly repeated multiple times. if len(points) <= 1: return points # 2D cross product of OA and OB vectors, i.e. z-component of their 3D cross product. # Returns a positive value, if OAB makes a counter-clockwise turn, # negative for clockwise turn, and zero if the points are collinear. def cross(o, a, b): return (a[0] - o[0]) * (b[1] - o[1]) - (a[1] - o[1]) * (b[0] - o[0]) # Build lower hull lower = [] for p in points: while len(lower) >= 2 and cross(lower[-2], lower[-1], p) <= 0: lower.pop() lower.append(p) # Build upper hull upper = [] for p in reversed(points): while len(upper) >= 2 and cross(upper[-2], upper[-1], p) <= 0: upper.pop() upper.append(p) # Concatenation of the lower and upper hulls gives the convex hull. # Last point of each list is omitted because it is repeated at the beginning of the other list. return lower[:-1] + upper[:-1]

[ "rkd@zgib.net" ]

rkd@zgib.net

230ce511489abcd87db47480e34a007726c3f506

c971765ba3c96288f935455e3fdc587ddec3689c

/ir_cdk_stacks/in_clt_01_stack.py

1b12aea8a3c8ccb68582513593d5404b339379f6

[]

no_license

martinpham97/IR-CDK-Stacks

358f5d7e93fb896179b2223dc2eee9faed4824b0

1043f4bf1639b6736b3f7858ed7b2b82975ae7bb

refs/heads/master

2022-07-31T01:13:47.802511

2020-05-07T07:26:14

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from aws_cdk import ( core, aws_cloudwatch as cloudwatch, aws_events as events, aws_lambda as _lambda, aws_iam as iam, aws_events_targets as event_target, aws_sns as sns, aws_sns_subscriptions as subs ) import os import logging logger = logging.getLogger(__name__) class InClt01Stack(core.Stack): def __init__(self, scope: core.Construct, id: str, **kwargs) -> None: super().__init__(scope, id, **kwargs) NOTIFY_EMAIL = self.node.try_get_context("notify_email") SLACK_WEBHOOK_URL = self.node.try_get_context("webhook_url") WHITE_LIST_GROUP = self.node.try_get_context("white_list_group") if ( not NOTIFY_EMAIL or not SLACK_WEBHOOK_URL or not WHITE_LIST_GROUP ): logger.error(f"Required context variables for {id} were not provided!") else: # 1. Create Response Lambda lambda_dir_path = os.path.join(os.getcwd(), "ir_cdk_stacks", "in_clt_01") response_lambda = _lambda.Function( self, "InClt01ResponseFunction", runtime=_lambda.Runtime.PYTHON_3_8, handler="clUnauthAccessResponse.lambda_handler", code=_lambda.Code.from_asset(lambda_dir_path), function_name="InClt01ResponseFunction", environment={ "webhook_url": SLACK_WEBHOOK_URL, "white_list_group": WHITE_LIST_GROUP, } ) ep = { "source": [ "aws.cloudtrail" ] } # 2. Make that rule Track Cloudtrail events rule = events.Rule(self, "cdkRule", description= 'Rule created by CDK for monitoring CloudTrail access', enabled= True, rule_name= "CltAccessRule", event_pattern= ep ) # 3. Add Permissions and role to Lambda action = [ "iam:*", "organizations:DescribeAccount", "organizations:DescribeOrganization", "organizations:DescribeOrganizationalUnit", "organizations:DescribePolicy", "organizations:ListChildren", "organizations:ListParents", "organizations:ListPoliciesForTarget", "organizations:ListRoots", "organizations:ListPolicies", "organizations:ListTargetsForPolicy" ] response_lambda.add_to_role_policy( iam.PolicyStatement( actions=action, effect=iam.Effect.ALLOW, resources=["*"], ) ) # 4. Permission to send SNS notification response_lambda.add_to_role_policy( iam.PolicyStatement( actions=[ "sns:*" ], effect=iam.Effect.ALLOW, resources=["*"], ) ) # 5. Add Lambda as target of Rule rule.add_target(event_target.LambdaFunction(response_lambda)) # 6. Create SNS topic and subscription topic = sns.Topic(self, "CLTAccessCDK", topic_name="CLTAccessCDK") # topic.grant_publish(iam.ServicePrincipal("*")) topic.add_subscription(subs.EmailSubscription(NOTIFY_EMAIL)) # 7. Create IAM allow/deny policy cltDenyAccessPolicy = iam.ManagedPolicy(self, "InCLT01DenyPolicy", managed_policy_name = "CltDenyAccess", statements=[ iam.PolicyStatement( effect=iam.Effect.DENY, actions=["cloudtrail:*"], resources=["*"] ) ]) # 8. Create IAM group cltAccessGroup = iam.Group( self, "cltAccessGroup", group_name = "cltAccessGroup" )

[ "neel64tamakuwala@gmail.com" ]

neel64tamakuwala@gmail.com

e6013f393ec1ffdd449fa0e80b59f1a6ecfe2670

482d7d5770dfc17db5b1a0e780b634d3a9f5572a

/Project3/code/metrics.py

5e5138446ddf6d1d6ee23c6b0936f15c514201be

[]

no_license

fmsilver89/FYS_STK_4155

5b9a878330f06a29ec6416aff92a06ebf0ba8dd8

189b7ef0d18cd9395eeab82702376ae91ad24d17

refs/heads/master

2020-09-11T13:24:15.963157

2019-11-16T10:18:21

222,078,923

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UTF-8

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# Required packages import numpy as np def mean_squared_error(y, yhat): """ y: True values. yhat: Predictions. """ res = y - yhat mse = np.divide(res.T@res, len(yhat)) return mse def r2_score(y, yhat): """ y: True values. yhat: Predictions. """ res = y - yhat ymean = np.mean(y) ssr = y - ymean * np.ones((len(y),)) R2 = 1 - np.divide(res.T@res, ssr.T@ssr) return R2 def bias2(y, yhat): """ y: True values. yhat: Predictions. """ n = len(yhat) bias2 = np.sum((y - (np.mean(yhat)))**2) / n return bias2 def variance_error(yhat): """ yhat: Predictions. """ variance = np.mean(yhat**2) - np.mean(yhat)**2 return variance def accuracy(y, yhat): """ Metrics for binary data. y: True values. yhat: Predictions. """ n = len(y) accuracy = np.sum(y == yhat) / n return accuracy

[ "noreply@github.com" ]

fmsilver89.noreply@github.com

26384a8be39cfdd9cbc222c1b37af013b4f43337

1a330be03318d7402e4525d435ee169e0f796f04

/camera_info_publisher/camera_info_publisher.py

23f5ff979a3f1b82c640d1117abacef7769ce1aa

[]

no_license

purdue-arc/autonomous_car_misc

dd5f5998a86a97776428a4c8796c98c1b9cae9bf

235e2f15550c73a1adb8fb22f4ce25929164ed37

refs/heads/master

2020-05-02T17:26:11.180990

2019-04-14T23:39:16

178,098,194

0

1

null

2019-10-01T00:27:45

2019-03-28T00:53:07

C++

UTF-8

Python

false

2,542

py

""" pointgrey_camera_driver (at least the version installed with apt-get) doesn't properly handle camera info in indigo. This node is a work-around that will read in a camera calibration .yaml file (as created by the cameracalibrator.py in the camera_calibration pkg), convert it to a valid sensor_msgs/CameraInfo message, and publish it on a topic. The yaml parsing is courtesy ROS-user Stephan: http://answers.ros.org/question/33929/camera-calibration-parser-in-python/ This file just extends that parser into a rosnode. """ import rospy import yaml from sensor_msgs.msg import CameraInfo def yaml_to_CameraInfo(yaml_fname): """ Parse a yaml file containing camera calibration data (as produced by rosrun camera_calibration cameracalibrator.py) into a sensor_msgs/CameraInfo msg. Parameters ---------- yaml_fname : str Path to yaml file containing camera calibration data Returns ------- camera_info_msg : sensor_msgs.msg.CameraInfo A sensor_msgs.msg.CameraInfo message containing the camera calibration data """ # Load data from file with open(yaml_fname, "r") as file_handle: calib_data = yaml.load(file_handle) # Parse camera_info_msg = CameraInfo() camera_info_msg.width = calib_data["image_width"] camera_info_msg.height = calib_data["image_height"] camera_info_msg.K = calib_data["camera_matrix"]["data"] camera_info_msg.D = calib_data["distortion_coefficients"]["data"] camera_info_msg.R = calib_data["rectification_matrix"]["data"] camera_info_msg.P = calib_data["projection_matrix"]["data"] camera_info_msg.distortion_model = calib_data["distortion_model"] return camera_info_msg if __name__ == "__main__": # Get fname from command line (cmd line input required) import argparse arg_parser = argparse.ArgumentParser() arg_parser.add_argument("filename", help="Path to yaml file containing " +\ "camera calibration data") args = arg_parser.parse_args() filename = args.filename # Parse yaml file camera_info_msg = yaml_to_CameraInfo(filename) # Initialize publisher node rospy.init_node("camera_info_publisher", anonymous=True) publisher = rospy.Publisher("camera_info", CameraInfo, queue_size=10) rate = rospy.Rate(10) # Run publisher while not rospy.is_shutdown(): publisher.publish(camera_info_msg) rate.sleep()

[ "baxter26@purdue.edu" ]

baxter26@purdue.edu

73487eec553a8ca6ee7d5c5a641a16f04d766f97

b0c071eabc7f51f335892d49b015e28fcc4e9193

/flaskRestFul/venv/bin/easy_install

dc197eb4ac7fa243d1b9ddba65756660efe84d2f

[]

no_license

namelessfintech/FlaskMastery

64627e4e8d361ec2e92794a39935342d356eb6d4

6b98d42f591ee8a0ff023f0f5b55f87e550c3c36

refs/heads/master

2021-11-04T17:26:52.323376

2019-04-28T00:53:11

null

0

null

UTF-8

Python

false

316

#!/Users/MichaelBallard/Documents/2019/Code/Python/April/FlaskMaster/flaskRestFul/venv/bin/python3.7 # -*- coding: utf-8 -*- import re import sys from setuptools.command.easy_install import main if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) sys.exit(main())

[ "MichaelBallard@Michaels-MacBook-Pro-2.local" ]

MichaelBallard@Michaels-MacBook-Pro-2.local

d54f6d7bb54e5d14437247e6cb86fbff66c9e315

e980879b9b96e466fae0093d2aa10c90119f03a9

/splitPng/ToolFunctions.py

c653fc6777ec8c87a45e60b5b2827f831d7d974a

[]

no_license

a77654135/tools

dc05d3e6fba2b59ae8a8fa36b3e6160f15e2908a

c0028c108bf8ea46f6cc09076441f28cae640d63

refs/heads/master

2018-12-25T20:42:37.754086

2018-11-21T05:47:48

120,172,453

0

null

UTF-8

Python

false

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# encoding: utf-8 """ ------------------------------------------------- File Name： ToolFunctions @time: 2018/2/4 21:47 @author: talus @desc: 图片处理工具 ------------------------------------------------- """ import os from PIL import Image class Rectangle(): def __init__(self, l=0, t=0, w=1, h=1): self.left = l self.top = t self.width = w self.height = h @property def right(self): return self.left + self.width - 1 @property def bottom(self): return self.top + self.height - 1 def __str__(self): return "{} {} {} {}".format(self.left,self.top,self.width,self.height) def __unicode__(self): return "{} {} {} {}".format(self.left,self.top,self.width,self.height) def getColors(img): """ 判断所有像素点是否存在非透明像素 :param img: :return: 二维数组，存储像素是否为非透明像素 """ width,height = img.size has = [] for i in range(0,width): has.append([]) for j in range(0,height): has[i].append([]) has[i][j] = img.getpixel((i,j)) != 0 return has def Exist(colors,x,y): """ 判断坐标处是否存在非透明像素值 :param colors: :param x: :param y: :return: """ if x < 0 or y < 0 or x >= len(colors) or y >= len(colors[0]): return False else: return colors[x][y] def L_Exist(colors,rect): """ 判定区域Rect右侧是否存在像素点 :param colors: :param rect: :return: """ if rect.right >= len(colors )or rect.left < 0: return False for i in range(0,rect.height): if Exist(colors,rect.left - 1, rect.top + i): return True return False def R_Exist(colors,rect): """ 判定区域Rect右侧是否存在像素点 :param colors: :param rect: :return: """ if rect.right >= len(colors)or rect.left < 0: return False for i in range(0,rect.height): if Exist(colors,rect.right + 1,rect.top + i): return True return False def D_Exist(colors,rect): """ 判定区域Rect下侧是否存在像素点 :param colors: :param rect: :return: """ if rect.bottom >= len(colors[0]) or rect.top < 0: return False for i in range(0,rect.width): if Exist(colors, rect.left + i,rect.bottom + 1): return True return False def U_Exist(colors,rect): """ 判定区域Rect上侧是否存在像素点 :param colors: :param rect: :return: """ if rect.bottom >= len(colors[0]) or rect.top < 0: return False for i in range(0,rect.width): if Exist(colors, rect.left + i,rect.top - 1): return True return False def clearRect(colors,rect): """ 清空区域内的像素非透明标记 :param colors: :param rect: :return: """ for i in range(rect.left,rect.right+1): for j in range(rect.top,rect.bottom+1): colors[i][j] = False def getRect(colors,x,y): """ 获取坐标所在图块的区域范围 :param colors: :param x: :param y: :return: """ rect = Rectangle(x,y,1,1) flag = True while flag: flag = False while R_Exist(colors,rect): rect.width += 1 flag = True while D_Exist(colors,rect): rect.height += 1 flag = True while L_Exist(colors,rect): rect.width += 1 rect.left -= 1 flag = True while U_Exist(colors,rect): rect.height += 1 rect.top -= 1 flag = True clearRect(colors,rect) return rect def GetRects(img): """ 对图像pic进行图块分割，分割为一个个的矩形子图块区域分割原理：相邻的连续区域构成一个图块，透明区域为分割点 :param img: :return: """ rects = [] colors = getColors(img) width,height = img.size for i in range(0,width): for j in range(0,height): if Exist(colors,i,j): rect = getRect(colors,i,j) rects.append(rect) # if rect.width > 10 and rect.height > 10: # rects.append(rect) return rects if __name__ == "__main__": path = os.path.abspath(r"C:\Users\talus\work\moni\sheep\0\103.png") dirname = os.path.dirname(path) img = Image.open(path) rects = GetRects(img) for idx, rect in enumerate(rects): im = img.crop((rect.left, rect.top, rect.left + rect.width, rect.top + rect.height)) print rect im.save(os.path.join(dirname, "{}.png".format(idx)))

[ "330243319@qq.com" ]

330243319@qq.com

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/run_meta_mnist_test.py

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[]

no_license

jinxu06/hmaml

6ce2e5a6307da69eb848592ac0d351afcf07aa94

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refs/heads/master

2020-03-20T17:56:08.689374

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import numpy as np import tensorflow as tf import argparse from models.classifiers import MNISTClassifier from components.learners import Learner import data.mnist as mnist parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('--load_params', help='', action='store_true', default=False) parser.add_argument('--num_inner_iters', help='', default=10, type=int) args = parser.parse_args() meta_train_set, meta_val_set, meta_test_set = mnist.load(data_dir="~/scikit_learn_data", num_classes=5, batch_size=5, split=[5./7, 1./7, 1./7], return_meta=True) model = MNISTClassifier(num_classes=5, inputs=None, targets=None) update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) with tf.control_dependencies(update_ops): optimizer = tf.train.AdamOptimizer(1e-4).minimize(model.loss) global_init_op = tf.global_variables_initializer() saver = tf.train.Saver() save_dir = "/data/ziz/jxu/hmaml-saved-models" config = tf.ConfigProto() config.gpu_options.allow_growth = True with tf.Session(config=config) as sess: acc_arr = [] for dk in range(20): sess.run(global_init_op) if args.load_params: ckpt_file = save_dir + '/params_' + "mnist" + '.ckpt' print('restoring parameters from', ckpt_file) saver.restore(sess, ckpt_file) print(dk, "resample dataset...") train_set, val_set = meta_train_set.sample_mini_dataset(num_classes=5, num_shots=15, test_shots=5, classes=[0,1,2,3,4]) learner = Learner(session=sess, model=model) accs = [] for epoch in range(args.num_inner_iters): # print(epoch, "......") learner.train(train_set, optimizer) evals = learner.evaluate(val_set) accs.append(evals["accuracy"]) acc_arr.append(accs) m = np.array(acc_arr) print(m.mean(0)) # train_set, test_set = meta_train_set.sample_mini_dataset(num_classes=5, num_shots=15, test_shots=5, classes=[5,6,7,8,9]) # learner = Learner(session=sess, model=model) # for epoch in range(20): # print(epoch, "......") # learner.train(train_set, optimizer) # evals = learner.evaluate(test_set) # print(evals) # saver.save(sess, save_dir + '/params_' + "mnist" + '.ckpt')

[ "aaron.jin.xu@gmail.com" ]

aaron.jin.xu@gmail.com

d129125455e5894af610b0743efe026f7323fd5d

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/dialogue_system/nlu/nlu_baseline.py

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[]

no_license

brunonishimoto/tcc

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refs/heads/master

2021-07-15T19:19:51.704993

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''' Created on Jul 13, 2016 @author: xiul ''' import pickle import copy import numpy as np import dialogue_system.constants as const from .models import lstm, biLSTM class NLUBaseline: def __init__(self, config): self.params = config["nlu"] self.load_model(self.params["model_path"]) def generate_dia_act(self, annot): """ generate the Dia-Act with NLU model """ if len(annot) > 0: tmp_annot = annot.strip('.').strip('?').strip(',').strip('!') rep = self.parse_str_to_vector(tmp_annot) Ys, cache = self.model.fwdPass(rep, self.params, predict_model=True) # default: True maxes = np.amax(Ys, axis=1, keepdims=True) e = np.exp(Ys - maxes) # for numerical stability shift into good numerical range probs = e/np.sum(e, axis=1, keepdims=True) if np.all(np.isnan(probs)): probs = np.zeros(probs.shape) # special handling with intent label for tag_id in self.inverse_tag_dict.keys(): if self.inverse_tag_dict[tag_id].startswith('B-') or self.inverse_tag_dict[tag_id].startswith('I-') or self.inverse_tag_dict[tag_id] == 'O': probs[-1][tag_id] = 0 pred_words_indices = np.nanargmax(probs, axis=1) pred_tags = [self.inverse_tag_dict[index] for index in pred_words_indices] print(pred_tags) diaact = self.parse_nlu_to_diaact(pred_tags, tmp_annot) return diaact else: return None def load_model(self, model_path): """ load the trained NLU model """ model_params = pickle.load(open(model_path, 'rb'), encoding='latin1') hidden_size = model_params['model']['Wd'].shape[0] output_size = model_params['model']['Wd'].shape[1] if model_params['params']['model'] == 'lstm': # lstm_ input_size = model_params['model']['WLSTM'].shape[0] - hidden_size - 1 rnnmodel = lstm(input_size, hidden_size, output_size) elif model_params['params']['model'] == 'bi_lstm': # bi_lstm input_size = model_params['model']['WLSTM'].shape[0] - hidden_size - 1 rnnmodel = biLSTM(input_size, hidden_size, output_size) rnnmodel.model = copy.deepcopy(model_params['model']) self.model = rnnmodel self.word_dict = copy.deepcopy(model_params['word_dict']) self.slot_dict = copy.deepcopy(model_params['slot_dict']) self.act_dict = copy.deepcopy(model_params['act_dict']) self.tag_set = copy.deepcopy(model_params['tag_set']) self.params = copy.deepcopy(model_params['params']) self.inverse_tag_dict = {self.tag_set[k]:k for k in self.tag_set.keys()} def parse_str_to_vector(self, string): """ Parse string into vector representations """ tmp = 'BOS ' + string + ' EOS' words = tmp.lower().split(' ') vecs = np.zeros((len(words), len(self.word_dict))) for w_index, w in enumerate(words): if w.endswith(',') or w.endswith('?'): w = w[0:-1] if w in self.word_dict.keys(): vecs[w_index][self.word_dict[w]] = 1 else: vecs[w_index][self.word_dict['unk']] = 1 rep = {} rep['word_vectors'] = vecs rep['raw_seq'] = string return rep def parse_nlu_to_diaact(self, nlu_vector, string): """ Parse BIO and Intent into Dia-Act """ tmp = 'BOS ' + string + ' EOS' words = tmp.lower().split(' ') print(tmp) print(words) diaact = {} diaact[const.INTENT] = const.INFORM diaact[const.REQUEST_SLOTS] = {} diaact[const.INFORM_SLOTS] = {} intent = nlu_vector[-1] pre_tag = nlu_vector[0] pre_tag_index = 0 index = 1 slot_val_dict = {} while index<(len(nlu_vector)-1): # except last Intent tag cur_tag = nlu_vector[index] if cur_tag == 'O' and pre_tag.startswith('B-'): slot = pre_tag.split('-')[1] slot_val_str = ' '.join(words[pre_tag_index:index]) slot_val_dict[slot] = slot_val_str elif cur_tag.startswith('B-') and pre_tag.startswith('B-'): slot = pre_tag.split('-')[1] slot_val_str = ' '.join(words[pre_tag_index:index]) slot_val_dict[slot] = slot_val_str elif cur_tag.startswith('B-') and pre_tag.startswith('I-'): if cur_tag.split('-')[1] != pre_tag.split('-')[1]: slot = pre_tag.split('-')[1] slot_val_str = ' '.join(words[pre_tag_index:index]) slot_val_dict[slot] = slot_val_str elif cur_tag == 'O' and pre_tag.startswith('I-'): slot = pre_tag.split('-')[1] slot_val_str = ' '.join(words[pre_tag_index:index]) slot_val_dict[slot] = slot_val_str if cur_tag.startswith('B-'): pre_tag_index = index pre_tag = cur_tag index += 1 if cur_tag.startswith('B-') or cur_tag.startswith('I-'): slot = cur_tag.split('-')[1] slot_val_str = ' '.join(words[pre_tag_index:-1]) slot_val_dict[slot] = slot_val_str if intent != 'null': arr = intent.split('+') diaact[const.INTENT] = arr[0] diaact[const.REQUEST_SLOTS] = {} for ele in arr[1:]: diaact[const.REQUEST_SLOTS][ele] = 'UNK' diaact[const.INFORM_SLOTS] = slot_val_dict # add rule here for slot in diaact[const.INFORM_SLOTS].keys(): slot_val = diaact[const.INFORM_SLOTS][slot] if slot_val.startswith('bos'): slot_val = slot_val.replace('bos', '', 1) diaact[const.INFORM_SLOTS][slot] = slot_val.strip(' ') self.refine_diaact_by_rules(diaact) return diaact def refine_diaact_by_rules(self, diaact): """ refine the dia_act by rules """ # rule for taskcomplete if const.REQUEST_SLOTS in diaact.keys(): if const.TASK_COMPLETE_SLOT in diaact[const.REQUEST_SLOTS].keys(): del diaact[const.REQUEST_SLOTS][const.TASK_COMPLETE_SLOT] diaact[const.INFORM_SLOTS][const.TASK_COMPLETE_SLOT] = const.PLACEHOLDER # rule for request if len(diaact[const.REQUEST_SLOTS])>0: diaact[const.INTENT] = const.REQUEST def diaact_penny_string(self, dia_act): """ Convert the Dia-Act into penny string """ penny_str = "" penny_str = dia_act[const.INTENT] + "(" for slot in dia_act[const.REQUEST_SLOTS].keys(): penny_str += slot + ";" for slot in dia_act[const.INFORM_SLOTS].keys(): slot_val_str = slot + "=" if len(dia_act[const.INFORM_SLOTS][slot]) == 1: slot_val_str += dia_act[const.INFORM_SLOTS][slot][0] else: slot_val_str += "{" for slot_val in dia_act[const.INFORM_SLOTS][slot]: slot_val_str += slot_val + "#" slot_val_str = slot_val_str[:-1] slot_val_str += "}" penny_str += slot_val_str + ";" if penny_str[-1] == ";": penny_str = penny_str[:-1] penny_str += ")" return penny_str

[ "bruno_nishimoto@hotmail.com" ]

bruno_nishimoto@hotmail.com

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/Production/python/PrivateSamples/EMJ_2018_mMed-1200_mDark-20_ctau-100_unflavored-down_cff.py

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[]

no_license

cms-svj/TreeMaker

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refs/heads/Run2_2017

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import FWCore.ParameterSet.Config as cms maxEvents = cms.untracked.PSet( input = cms.untracked.int32(-1) ) readFiles = cms.untracked.vstring() secFiles = cms.untracked.vstring() source = cms.Source ("PoolSource",fileNames = readFiles, secondaryFileNames = secFiles) readFiles.extend( [ 'gsiftp://hepcms-gridftp.umd.edu//mnt/hadoop/cms/store/group/EMJRunII/2018/step4_MINIAOD_mMed-1200_mDark-20_ctau-100_unflavored-down_n-500_part-1.root', 'gsiftp://hepcms-gridftp.umd.edu//mnt/hadoop/cms/store/group/EMJRunII/2018/step4_MINIAOD_mMed-1200_mDark-20_ctau-100_unflavored-down_n-500_part-2.root', 'gsiftp://hepcms-gridftp.umd.edu//mnt/hadoop/cms/store/group/EMJRunII/2018/step4_MINIAOD_mMed-1200_mDark-20_ctau-100_unflavored-down_n-500_part-3.root', 'gsiftp://hepcms-gridftp.umd.edu//mnt/hadoop/cms/store/group/EMJRunII/2018/step4_MINIAOD_mMed-1200_mDark-20_ctau-100_unflavored-down_n-500_part-4.root', 'gsiftp://hepcms-gridftp.umd.edu//mnt/hadoop/cms/store/group/EMJRunII/2018/step4_MINIAOD_mMed-1200_mDark-20_ctau-100_unflavored-down_n-500_part-5.root', 'gsiftp://hepcms-gridftp.umd.edu//mnt/hadoop/cms/store/group/EMJRunII/2018/step4_MINIAOD_mMed-1200_mDark-20_ctau-100_unflavored-down_n-500_part-6.root', 'gsiftp://hepcms-gridftp.umd.edu//mnt/hadoop/cms/store/group/EMJRunII/2018/step4_MINIAOD_mMed-1200_mDark-20_ctau-100_unflavored-down_n-500_part-7.root', 'gsiftp://hepcms-gridftp.umd.edu//mnt/hadoop/cms/store/group/EMJRunII/2018/step4_MINIAOD_mMed-1200_mDark-20_ctau-100_unflavored-down_n-500_part-8.root', 'gsiftp://hepcms-gridftp.umd.edu//mnt/hadoop/cms/store/group/EMJRunII/2018/step4_MINIAOD_mMed-1200_mDark-20_ctau-100_unflavored-down_n-500_part-9.root', 'gsiftp://hepcms-gridftp.umd.edu//mnt/hadoop/cms/store/group/EMJRunII/2018/step4_MINIAOD_mMed-1200_mDark-20_ctau-100_unflavored-down_n-500_part-10.root', ] )

[ "enochnotsocool@gmail.com" ]

enochnotsocool@gmail.com

fd92b400aba7975634f692a9bc72e5a32d14007d

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/utils/imessage.py

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permissive

baileyparker/whatwouldisay.py

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refs/heads/master

2016-09-05T16:49:17.854025

2015-07-02T05:05:56

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import sqlite3 from os.path import expanduser from operator import itemgetter MESSAGES_FROM_ME_QUERY = 'SELECT `text` FROM message WHERE `is_from_me` = 1' def get_messages_from_me(): """ Get all "from me" iMessages in the current users's chat db. """ with sqlite3.connect(expanduser('~/Library/Messages/chat.db')) as conn: return map(itemgetter(0), conn.execute(MESSAGES_FROM_ME_QUERY))

[ "b@ileyparker.com" ]

b@ileyparker.com

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/exam_project_15_08_2021/project/drink/tea.py

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[ "MIT" ]

permissive

ivan-yosifov88/python_oop_june_2021

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refs/heads/main

2023-07-04T21:12:07.592730

2021-08-18T15:12:50

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from project.drink.drink import Drink class Tea(Drink): _cost = 2.50 def __init__(self, name, portion, brand): super().__init__(name, portion, self._cost, brand)

[ "ivan.yosifov88@gmail.com" ]

ivan.yosifov88@gmail.com

a5d1257d55107efdb8b87da0580bf3aee73e665c

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/7 - Machine Learning/Decision Tree/Decision_Tree_example.py

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[]

no_license

ilopezgazpio/UD_Intelligent_Systems

80629464baaed39dd06bdeb76ce4b0b662840806

f5c518a08760b70c3b8464b5e8ac84997ff6019f

refs/heads/master

2023-09-02T13:47:29.069765

2021-11-08T15:09:28

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py

#!/usr/bin/python3 #------------------------------------------- # Decision Trees Concepts # Intelligent Systems - University of Deusto # Inigo Lopez-Gazpio #------------------------------------------- #---------------------------------- # PART 1: Environment and libraries #---------------------------------- # We'll use scikit-learn, numpy, pandas and matplotlib libraries for Machine Learning projects # These libraries are amongst the strongest ones for data scientists # All of them can be installed through conda environments, pip or pip3 from math import * epsilon = -1e-100 #--------------------------------------------------------------------------- # PART 2: Formulas #--------------------------------------------------------------------------- # Entropy of a dataset with 50-50 elements of different classes (worst case, maximum entropy) entropy = -0.50 * log2(0.50) - 0.50 * log2(0.50) print(entropy) # Entropy of a dataset with 100-0 elements of different classes (best case, minimum entropy) entropy = epsilon * log2(epsilon) - 1.00 * log2(1.00) print(entropy) # Entropy of intermediate distributions entropy = -0.75*log2(0.75) - 0.25*log2(0.25) print(entropy) entropy = -0.01*log2(0.01) - 0.99*log2(0.99) print(entropy) # Defining a function def entropy (a, b): total = a + b prob_a = a / total prob_b = b / total if prob_a == 0 or prob_b == 0: return 0 else: return -prob_a * log2(prob_a) - prob_b * log2(prob_b) # Imagine we have an initial datase with 10 and 10 elements entropy(10, 10) # We can split into a 7-3 and 3-7 datasets... and start computing information gain # IG = H(class) - H ( class | attributes) gain1 = entropy(10, 10) - ( (10/20) * entropy(3,7) + (10/20) * entropy(7,3) ) print(gain1) # We can split into a 1-9 and 9-1 datasets... gain2 = entropy(10,10) - ( (10/20) * entropy(1,9) + (10/20) * entropy(9,1) ) print(gain2) # We can split into a 9-9 and 1-1 datasets... gain3 = entropy(10,10) - ( (18/20) * entropy(9,9) + (2/20) * entropy(1,1) ) print(gain3) # We can split into a 9-3 and 1-7 datasets... gain4 = entropy(10,10) - ( (12/20) * entropy(9,3) + (8/20) * entropy(1,7) ) print(gain4) # We can split into a 10-0 and 0-10 datasets... gain5 = entropy(10,10) - ( (10/20) * entropy(10,0) + (10/20) * entropy(0,10) ) print(gain5) # Entropy is implemented in scikit-learn... https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.entropy.html #--------------------------------------------------------------------------- # PART 3: (General porpuses) Formulas #--------------------------------------------------------------------------- # Define the entropy as a function able to receive any set of partitions import numpy as np import pandas as pd import requests def entropy (s : np.array): # s is a numpy array with counts per class probs = s / np.sum(s) logprobs = np.log2(probs) logprobs[logprobs == np.inf * -1 ] = 0 return sum(-1 * probs * logprobs) # Defining information gain as a function able to receive any set of partitions def gain (dataframe : pd.DataFrame, attr : str, target : str): values = dataframe.groupby([attr, target]).size().unstack().values values = np.nan_to_num(values) # to compute class entropy H(class) class_variable_counts = np.sum(values, axis = 0) # class given value entropy H(class | attribute) attribute_variable_counts = np.sum(values, axis=1) attribute_variable_probs = attribute_variable_counts / np.sum(values) entropy_given_attribute = np.apply_along_axis(entropy, 1, values) return entropy(class_variable_counts) - np.sum(attribute_variable_probs * entropy_given_attribute) url = "https://raw.githubusercontent.com/lgazpio/UD_Intelligent_Systems/master/Datasets/agaricus-lepiota.csv" #source = requests.get(url).content data = pd.read_csv(url) data.columns = [ "class", "cap.shape", "cap.surface", "cap.color", "bruises", "odor", "gill.attachment", "gill.spacing", "gill.size", "gill.color", "stalk.shape", "stalk.root", "stalk.surface.above.ring", "stalk.surface.below.ring", "stalk.color.above.ring", "stalk.color.below.ring", "veil.type", "veil.color", "ring.number", "ring.type", "spore.print.color", "population", "habitat" ] data.size data.head() data.columns # Which is the attribute with more "gain"? gain(data, "cap.shape", "class") gain(data, "ring.type", "class") gain(data, "cap.color", "class") gain(data, "odor", "class") # For each partition we should keep on with this process recursively... # If data is categorical instead of numerical we need to transform it to numbers by discretizing... # Mutual information classifier from scikit-learn performs similar job # https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.mutual_info_classif.html#sklearn.feature_selection.mutual_info_classif #--------------------------------------------------------------------------- # PART 4: Decision tree is (Fortunately) implemented in Python #--------------------------------------------------------------------------- # https://scikit-learn.org/stable/modules/tree.html from sklearn import tree import matplotlib.pyplot as plt data_onehot = pd.get_dummies( data ) trainingSet = data_onehot.values[:,2:] trainingSet.shape labels = data.values[:,0] labels.shape clf = tree.DecisionTreeClassifier() clf = clf.fit(trainingSet, labels) tree.plot_tree(clf.fit(trainingSet, labels)) plt.show() # export with graphviz import graphviz tree_data = tree.export_graphviz(clf, out_file=None) graph = graphviz.Source(tree_data) graph.render("Decision_tree_example")

[ "inigo.lopezgazpio@deusto.es" ]

inigo.lopezgazpio@deusto.es

e76e287685752e28fa2e8c5f6863ee900fe5fcbb

cbda89443b351bb2047180dad4e300c13dc3df7f

/Crystals/Morpurgo_all_sp_Reorgs/Jobs/Rubrene/Rubrene_cation_neut_inner3_outer0/Rubrene_cation_neut_inner3_outer0.py

ab3611a677adb23520fda61425992f20962063c3

[]

no_license

sheridanfew/pythonpolarisation

080f52979f98d26360a46412a10c8e3f51ee4549

178e2684e9a239a8e60af5f7b1eb414ac5f31e92

refs/heads/master

2021-07-10T01:07:40.978790

2021-03-11T16:56:37

96,101,351

0

null

2017-07-03T13:37:06

2017-07-03T10:54:52

null

UTF-8

Python

false

7,186

py

import sys sys.path.append('../../../../../') from BasicElements import * from BasicElements.Register import GetRegister from BasicElements.MoleculeFactory import ReadMoleculeType from BasicElements.MoleculeFactory import GetMolecule from BasicElements.Crystal import * from Polarizability.GetDipoles import get_dipoles,split_dipoles_onto_atoms from Polarizability import * from Polarizability.GetEnergyFromDips import * from Polarizability.JMatrix import JMatrix import numpy as np from math import * from time import gmtime, strftime import os print strftime("%a, %d %b %Y %X +0000", gmtime()) name='Rubrene_cation_neut_inner3_outer0' #For crystals here, all cubic and centred at centre insize=3 #number of TVs in each dir central mol is from edge of inner region outsize=0 mols_cen=['sp_Rubrene_mola_cation.xyz','sp_Rubrene_molb_neut.xyz'] mols_sur=['sp_Rubrene_mola_neut.xyz','sp_Rubrene_molb_neut.xyz'] mols_outer=['sp_Rubrene_mola_neut.xyz','sp_Rubrene_molb_neut.xyz'] #centres=['Rubrene_mola_anion_aniso_cifstruct_chelpg_edited.xyz','Rubrene_molb_neut_aniso_cifstruct_chelpg_edited.xyz','Rubrene_mola_neut_aniso_cifstruct_chelpg_edited.xyz','Rubrene_molb_neut_aniso_cifstruct_chelpg_edited.xyz'] #surroundings=['Rubrene_mola_neut_aniso_cifstruct_chelpg_edited.xyz','Rubrene_molb_neut_aniso_cifstruct_chelpg_edited.xyz','Rubrene_mola_neut_aniso_cifstruct_chelpg_edited.xyz','Rubrene_molb_neut_aniso_cifstruct_chelpg_edited.xyz'] #From cif: ''' Rubrene _cell_length_a 7.184(1) _cell_length_b 14.433(3) _cell_length_c 26.897(7) _cell_angle_alpha 90 _cell_angle_beta 90 _cell_angle_gamma 90 _cell_volume 2788.86 _cell_formula_units_Z 4 ''' #Get translation vectors: a=7.1841/0.5291772109217 b= 14.4333/0.5291772109217 c= 26.8977/0.5291772109217 alpha=90*(pi/180) beta=90*(pi/180) gamma=90*(pi/180) cif_unit_cell_volume=2788.86/(a*b*c*(0.5291772109217**3)) cell_volume=sqrt(1 - (cos(alpha)**2) - (cos(beta)**2) - (cos(gamma)**2) + (2*cos(alpha)*cos(beta)*cos(gamma))) #Converts frac coords to carts matrix_to_cartesian=np.matrix( [[a, b*cos(gamma), c*cos(beta)], [0, b*sin(gamma), c*(cos(alpha) - cos(beta)*cos(gamma))/sin(gamma)], [0, 0, c*cell_volume/sin(gamma)]]) #carts to frac matrix_to_fractional=matrix_to_cartesian.I #TVs, TV[0,1,2] are the three translation vectors. TV=matrix_to_cartesian.T cut=8.0 totsize=insize+outsize #number of TVs in each dir nearest c inner mol is from edge of outer region cenpos=[totsize,totsize,totsize] length=[2*totsize+1,2*totsize+1,2*totsize+1] maxTVs=insize outer_maxTVs=insize+outsize #for diamond outer, don't specify for cube and will fill to cube edges. print 'name: ',name,'mols_cen: ', mols_cen,' mols_sur: ',mols_sur,' TVs: ', TV # Place Molecules prot_neut_cry=Crystal(name=name,mols_cen=mols_cen,mols_sur=mols_sur,cenpos=cenpos,length=length,TVs=TV,maxTVs=maxTVs,mols_outer=mols_outer,outer_maxTVs=outer_maxTVs) #prot_neut_cry._mols contains all molecules. #mols[0] contains a list of all molecules in position a, mols[1] all mols in pos'n b, etc. #mols[0][x,y,z] contains molecule a in position x,y,z #mols may as such be iterated over in a number of ways to consider different molecules. prot_neut_cry().print_posns() #Calculate Properties: print strftime("%a, %d %b %Y %X +0000", gmtime()) E0 = np.matrix([0.,0.,0.]) print strftime("%a, %d %b %Y %X +0000", gmtime()) print 'Calc jm' jm = JMatrix(cutoff=cut) print strftime("%a, %d %b %Y %X +0000", gmtime()) print 'Calc dips:' d = get_dipoles(E0=E0,jm=jm._m,cutoff=cut) print strftime("%a, %d %b %Y %X +0000", gmtime()) Efield = get_electric_field(E0) potential = get_potential() print strftime("%a, %d %b %Y %X +0000", gmtime()) #print 'dips', d print 'splitting dips onto atoms' split_d = split_dipoles_onto_atoms(d) print strftime("%a, %d %b %Y %X +0000", gmtime()) print 'summing dips:' tot = np.matrix([0.,0.,0.]) for dd in split_d: tot += dd print strftime("%a, %d %b %Y %X +0000", gmtime()) print 'total dip moment', tot Uqq = np.multiply(get_U_qq(potential=potential),27.211) print strftime("%a, %d %b %Y %X +0000", gmtime()) print 'Uqq', Uqq Uqd = np.multiply(get_U_qdip(dips=d,Efield=Efield),27.211) print strftime("%a, %d %b %Y %X +0000", gmtime()) print 'Uqd', Uqd Udd = np.multiply(get_U_dipdip(jm=jm._m,dips=d.T),27.211) print strftime("%a, %d %b %Y %X +0000", gmtime()) print 'Udd', Udd energyev = Udd+Uqd+Uqq print 'energyev', energyev energy=energyev/27.211 print strftime("%a, %d %b %Y %X +0000", gmtime()) print 'Making .dat cross sections for gnuplot' # print TVs if not os.path.exists('Dips_Posns_TVs'): os.makedirs('Dips_Posns_TVs') f = open('Dips_Posns_TVs/%s_TVs.dat' % name, 'w') TVstr=str(str(TV[0,0]) + ' ' + str(TV[0,1]) + ' ' + str(TV[0,2]) + '\n' + str(TV[1,0]) + ' ' + str(TV[1,1]) + ' ' + str(TV[1,2]) + '\n' + str(TV[2,0]) + ' ' + str(TV[2,1]) + ' ' + str(TV[2,2])+ '\n') f.write(TVstr) f.flush() f.close() # print dipoles if not os.path.exists('Dips_Posns_TVs'): os.makedirs('Dips_Posns_TVs') f = open('Dips_Posns_TVs/%s_dipoles.dat' % name, 'w') for dd in split_d: dstr=str(dd) f.write(dstr) f.write('\n') f.flush() f.close() # print properties for charge in centrepos time=strftime("%a, %d %b %Y %X +0000", gmtime()) f = open('%s_properties.csv' % name, 'w') f.write ('time\tname\tmols_cen\tmols_sur\tmols_outer\tinsize\toutsize\tenergyev\tUqq\tUqd\tUdd\tTotdip_x\tTotdip_y\tTotdip_z') f.write ('\n%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s' % (time,name,mols_cen,mols_sur,mols_outer,insize,outsize,energyev,Uqq,Uqd,Udd,tot[0,0],tot[0,1],tot[0,2])) f.flush() f.close() # print header for reorgs f = open('reorg_energies_%s_properties.csv' % name, 'w') f.write ('time\tname\tmols_cen\tmols_sur\tmols_outer\tinsize\toutsize\ta\tb\tc\tmolincell\tReorg(eV)') f.flush() f.close() # REORGANISATION ENERGIES #Note that this assumes a cube, and values for which for dist in range(0,(length[0]/2)+1,1): print '\n\nDIST: ', dist, '\n' for a in range(prot_neut_cry()._cenpos[0]-dist,prot_neut_cry()._cenpos[0]+dist+1,1): for b in range(prot_neut_cry()._cenpos[1]-dist,prot_neut_cry()._cenpos[1]+dist+1,1): for c in range(prot_neut_cry()._cenpos[2]-dist,prot_neut_cry()._cenpos[2]+dist+1,1): print strftime("%a, %d %b %Y %X +0000", gmtime()) print 'a,b,c',a,b,c for molincell in range(0,len(prot_neut_cry()._mols),1): prot_neut_cry().calc_reorg(a1=prot_neut_cry()._cenpos[0],b1=prot_neut_cry()._cenpos[1],c1=prot_neut_cry()._cenpos[2],molincell1=0,a2=a,b2=b,c2=c,molincell2=molincell,dips=d,oldUqd=Uqd) print 'Reorg: ', prot_neut_cry()._reorgs[molincell][a][b][c] f = open('reorg_energies_%s_properties.csv' % name, 'a') f.write ('\n%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s' % (time,name,mols_cen,mols_sur,mols_outer,insize,outsize,a,b,c,molincell,prot_neut_cry()._reorgs[molincell][a][b][c])) f.flush() f.close() # Redo this and overwrite after each set to ensure we have some even if not all reorgs complete prot_neut_cry().print_reorgs() print 'Job Completed Successfully.'

[ "sheridan.few@gmail.com" ]

sheridan.few@gmail.com

1002495073d3e000e8aabd09b9992e5f51966b27

24c489f58213971f23e72a5aa8ba92758f14077d

/notario/tests/test_exceptions.py

a8b3a0092b261b8ddb89c724fff5dca4b6426e1b

[]

no_license

shaunduncan/notario

2ff7287903950d899d1c0bc2fa708221f2142de7

d6ba713f017ff9914aadf28f0694465d8996c223

refs/heads/master

2021-01-22T07:39:19.557961

2014-02-26T12:53:55

null

0

null

UTF-8

Python

false

1,996

py

from notario import exceptions def foo(): return True class Object(object): pass class TestInvalid(object): def test_include_the_key(self): error = exceptions.Invalid('key', ['foo', 'bar', 'key']) assert 'key' in error._format_path() def test_include_the_path_in_str(self): error = exceptions.Invalid('key', ['path']) assert 'path' in error.__str__() def test_include_the_key_in_str(self): error = exceptions.Invalid('key', ['path']) assert 'key' in error.__str__() def test_multiple_keys_in_format_path(self): error = exceptions.Invalid('schema', ['key', 'subkey', 'bar']) assert '-> key -> subkey -> bar' in error._format_path() def test_full_message(self): error = exceptions.Invalid('3', ['foo', 'bar', 'baz']) result = error.__str__() assert "-> foo -> bar -> baz key did not match '3'" == result def test_full_message_for_callable(self): error = exceptions.Invalid(foo, ['foo', 'bar', 'baz']) result = error.__str__() assert "-> foo -> bar -> baz key did not pass validation against callable: foo" == result def test_full_message_for_value(self): error = exceptions.Invalid('3', ['foo', 'bar', 'baz'], pair='value') result = error.__str__() assert "-> foo -> bar -> baz did not match '3'" == result def test_full_message_for_callable_with_value(self): error = exceptions.Invalid(foo, ['foo', 'bar', 'baz'], pair='value') result = error.__str__() assert "-> foo -> bar -> baz did not pass validation against callable: foo" == result class TestSchemaError(object): def test_reason_has_no_args(self): class Foo(object): def __repr__(self): return "some reason" reason = Foo() reason.args = [] error = exceptions.SchemaError(foo, ['foo'], reason=reason, pair='value') assert "some reason" == repr(error.reason)

[ "alfredodeza@gmail.com" ]

alfredodeza@gmail.com

a325d753908629cb45ff4bb92d59c15ade88a68d

11d2c81d0b66f5742159e35442b1867bd44390a1

/production/Algorithm/python_code/line4.py

9016633fcfde697dba59d5e4d8e4d5286a8a36d5

[]

no_license

hyeongseoblim/Algorithm

2c482e240a7278a0fd487769d3b95527fae95e7f

a6c903ab13e3ba418ce4f94d2f20b6fdf8433131

refs/heads/master

2023-02-07T11:04:48.897168

2021-01-02T15:15:24

255,542,563

0

null

UTF-8

Python

false

1,518

py

west = 0 north = 1 east = 2 south = 3 isgo = 4 dy = [-1, 1, 0, 0] dx = [0, 0, -1, 1] def solution(maze): answer = 0 st_x = 0 st_y = 0 n = len(maze) maze[n - 1][n - 1] = 'E' direction = nextDirection(st_x, st_y, maze) while (True): temp_x = st_x + dx[direction] temp_y = st_y + dy[direction] if temp_x == len(maze) - 1 and temp_y == len(maze) - 1 or maze[temp_y - 1][temp_x - 1] == 'E': break if temp_x < len(maze[0]) and temp_x >= 0: if (maze[temp_y][temp_x] == 1): direction = nextDirection(st_x, st_y, maze, direction) continue left = seeLeft(temp_x, temp_y, direction, maze) st_x = temp_x st_y = temp_y if left == '0' or left == "E": direction = (direction + isgo - 1) % 4 answer += 1 return answer def nextDirection(st_x, st_y, maze, cur_dir=east): next_dir = None for j in range(len(dx)): d = (cur_dir + isgo - 1 + j) % 4 ne_x = st_x + dx[d] ne_y = st_y + dy[d] if (ne_x >= len(maze[0]) or ne_x < 0) or (ne_y >= 7 or ne_y < 0): continue if (maze[ne_y][ne_x] == 0): next_dir = d return next_dir def seeLeft(x, y, direction, maze): reverse = (direction + 2) % 4 d = (reverse + 1) % 4 nx = x + dx[d] ny = y + dy[d] return maze[ny][nx] solution([[0, 1, 0, 1], [0, 1, 0, 0], [0, 0, 0, 0], [1, 0, 1, 0]])

[ "iii3@cnu.ac.kr" ]

iii3@cnu.ac.kr

dde06cae367508f8aae19177ac78dd22367a7276

1f76baa1a461a9b3e72deeef1b527a8ae51624bf

/mysite final/mysite/frasim/admin.py

f25cbf15a4dfe1eb0d704bdbc8c8d6d7e77c8285

[]

no_license

nnaser/pibidi

0e1fd222a16fc7baaaab8e6683df72e46b08cda3

b19ced18e08a4a93f898d3955084b44e05f1d086

refs/heads/master

2021-01-13T01:48:53.817906

2013-08-26T07:32:36

null

0

null

UTF-8

Python

false

271

py

from frasim.models import * from django.contrib import admin admin.site.register(Vendedor) admin.site.register(Material) admin.site.register(Proveedor) admin.site.register(Bodega) admin.site.register(Area) admin.site.register(Cotizacion) admin.site.register(Productos)

[ "nicolas.naser@usach.cl" ]

nicolas.naser@usach.cl

daf659e14bd42e00eb25e3082ca4417436c386b3

c25c5f7637dd7e259e9d1e3b47ee013c8b2a2f18

/Filtragem no domínio espacial/smoothing.py

d34bced244f027139ab3e1093db2cedc78c5c4bf

[]

no_license

JuliaOli/DIP

4083b09ffe2727661a66ade31cac5027699fda09

fb3bfccade0951ad42971c0d4cd5793f89d28e2c

refs/heads/master

2018-10-01T04:09:17.462068

2018-06-08T00:15:51

120,705,039

0

null

UTF-8

Python

false

1,753

py

import cv2 import numpy as np def callback(img): pass def createWindow(img): wind = "Smoothing" slider_name = "1-Aver 2-Med 3-Gau 4-Bil" slider_Pos = 0 image = img.copy() #create window cv2.namedWindow(wind) #show image cv2.imshow(wind, image) #add slider (slider_name, window_name, start_value, max_value, callback) cv2.createTrackbar(slider_name, wind, 1, 4, callback) while(cv2.waitKey(1000)): cv2.imshow(wind, image) slider_Pos = cv2.getTrackbarPos(slider_name, wind) if(slider_Pos == 1): image = averageBlur(img) elif(slider_Pos == 2): image = meidanBlur(img) elif(slider_Pos == 3): image = gaussianBlur(img) else: image = bilateralFilt(img) cv2.destroyAllWindows() # Takes the average of all the pixels under kernel area and replace the central element. def averageBlur(img): return cv2.blur(img,(5,5)) # Takes median of all the pixels under kernel area and central element is replaced with this median value. def meidanBlur(img): return cv2.medianBlur(img,5) # Gaussian blurring is highly effective in removing gaussian noise from the image. #This gaussian filter is a function of space alone, that is, nearby pixels are considered while filtering. def gaussianBlur(img): return cv2.GaussianBlur(img,(5,5),0) # Takes a gaussian filter in space, but one more gaussian filter which is a function of pixel difference. def bilateralFilt(img): return cv2.bilateralFilter(img,9,75,75) def main(): kitty = cv2.imread('big_cat.png') createWindow(kitty) if __name__=='__main__': main()

[ "maju.olivi@gmail.com" ]

maju.olivi@gmail.com

326af3c8492ea7522eef273a09e92137b6e039cf

1554e209866ddcfc75519278303229bfdff4c9d3

/tbjcconstants.py

6908d70687a7f62b9cfba799665336607001670a

[]

no_license

tbjc1magic/HELIOStrajectory

215b4da6515351811f08ece1289f48a17460fccf

c095c85f209f6bee5e51f96f028dd6fa67df76f7

refs/heads/master

2016-08-09T03:46:29.945441

2016-01-19T19:25:32

49,976,166

0

null

UTF-8

Python

false

100

py

__constant_u = 1.66e-27 __constant_MeV = 1.6e-13 __constant_e = 1.6e-19 __constant_c = 3.0e8

[ "tbjc1magic@gmail.com" ]

tbjc1magic@gmail.com

27ed96ecec25d22429ab603aebcd2572ce077373

5ab4ecce716fba15f0ee298320fd9ce81593b296

/src/app.py

59b1773e2715b89865968eb1b0cc28ebf7865ff7

[]

no_license

AdleyTales/img2text

c97f3726753cb31ce4db34f1443fc56079388c60

31c91c3aa64262b540e19fc0fb8d1ccde9a52cb7

refs/heads/main

2023-02-14T02:28:02.422838

2021-01-06T01:50:38

326,582,225

0

null

UTF-8

Python

false

797

py

# -*- coding: utf-8 -*- """ pip install baidu-aip """ from aip import AipOcr # 定义常量 APP_ID = '10379743' API_KEY = 'xxx' SECRET_KEY = 'xxx' # 初始化文字识别分类器 aipOcr=AipOcr(APP_ID, API_KEY, SECRET_KEY) # 读取图片 filePath = "b.png" def get_file_content(filePath): with open(filePath, 'rb') as fp: return fp.read() # 定义参数变量 options = { 'detect_direction': 'true', 'language_type': 'CHN_ENG', } # 网络图片文字文字识别接口 result = aipOcr.webImage(get_file_content(filePath),options) # 如果图片是url 调用示例如下 # result = apiOcr.webImage('http://www.xxxxxx.com/img.jpg') # print(result['words_result']) res = result['words_result'] word = '' for item in res: word = word + item['words'] print(word)

[ "adleytales@126.com" ]

adleytales@126.com

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2021-05-01T03:39:49.588523

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# -*- coding: UTF-8 -*- from selenium import webdriver from bs4 import BeautifulSoup from selenium.webdriver.support.ui import Select mykey = u"日本地震" print mykey browser = webdriver.Firefox() browser.get('http://search.gmw.cn/search.do?advType=news') ''' The following statement can be used to fill the keyword form, yet I don't want to use that input = browser.find_element_by_css_selector('input[type="text"]') input.send_keys(mykey) print "done" ''' browser.execute_script("document.getElementById('keyword').value='"+mykey+"'") select = Select(browser.find_element_by_id("time")) select.select_by_visible_text("2011") button = browser.find_element_by_css_selector('button') button.click() #browser.quit()

[ "jp2011212847@qmul.ac.uk" ]

jp2011212847@qmul.ac.uk

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/Space Invader/space_invader_main.py

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omar-zaman10/Space-Invaders-pygame

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2021-07-28T14:51:20

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import pygame import numpy as np from pygame import mixer import random #initialise pygame pygame.init() # Initial screen diplay, title and background music screen_size = (900 ,600) screen = pygame.display.set_mode(screen_size) running = True clock = pygame.time.Clock() fps = 300 pygame.display.set_caption('Space Invaders') icon = pygame.image.load('Images/logo.png') pygame.display.set_icon(icon) background = pygame.image.load('Images/background.jpg') mixer.music.load('Sounds/background_music.mp3') mixer.music.play(-1) title = pygame.image.load('Images/title.png') font = pygame.font.Font('arcade_ya/ARCADE_N.TTF',32) opacity = 0 opacity_change = 0.5 def fading_text(text,opacity,position): text = font.render(text, True, (255,255,255)) surf = pygame.Surface(text.get_size()).convert_alpha() surf.fill((255, 255, 255, opacity)) text.blit(surf, (0, 0), special_flags=pygame.BLEND_RGBA_MULT) screen.blit(text, position) def Title_screen(): global opacity global opacity_change text = 'Ready Player One' fading_text(text,opacity,(225,500)) screen.blit(title,(175,100)) if opacity > 254: opacity_change = -1.0 elif opacity < 1: opacity_change = 0.5 opacity += opacity_change def Ending_screen(): global opacity_change global opacity text = 'Press Space to play again' fading_text(text, opacity,(75,500)) if opacity > 254: opacity_change = -1.0 elif opacity < 1: opacity_change = 0.5 opacity += opacity_change #PLayer player_image = pygame.image.load('Images/space_ship.png') playerX = 450 playerY = 700 player_life = True player_explosion_index = 0 movement_x = 0 movement_y = 1 def player(): global playerX global playerY global player_life global movement_x global fire if player_life: playerX += movement_x playerX = np.clip(playerX,0,850) screen.blit(player_image,(playerX,playerY)) else: fire = False pass # Projectile projectile_image = pygame.image.load('Images/bullet.png') projectileX = 400 projectileY = 500 fire = False movement_y_projectile= 3 bullet_sound = mixer.Sound('Sounds/laser_shot.mp3') bullet_sound.set_volume(0.5) def projectile_fire(projectileX): global projectileY global fire if fire: screen.blit(projectile_image,(projectileX+12,projectileY-20)) projectileY -= movement_y_projectile if projectileY <= 0 : fire = False projectileY = 500 # Alien alien_image = pygame.image.load('Images/green_alien.png') number_of_aliens = 24 collisions = [False for i in range(number_of_aliens)] vel_alien_x = np.zeros(number_of_aliens) vel_alien_y = np.ones(number_of_aliens) * 1.0 alien_x = np.linspace(90,810,number_of_aliens//3) alien_x = np.concatenate([alien_x,alien_x,alien_x]) alien_y = np.ones(number_of_aliens//3) *-50 alien_y = np.concatenate([alien_y,3*alien_y,5*alien_y]) def alien(index): global collisions global alien_x global alien_y if not collisions[index]: screen.blit(alien_image,(alien_x[index],alien_y[index])) if alien_x[index] >= 850: vel_alien_x[index] = -1.5 alien_y[index] += vel_alien_y[index] elif alien_x[index] <= 0: vel_alien_x[index] = 1.5 alien_y[index] += vel_alien_y[index] alien_x[index] += vel_alien_x[index] else: vel_alien_x[index] = 0 vel_alien_y[index] = 0 #Loading screen round_one_sound = mixer.Sound('Sounds/round_one.mp3') def load_aliens(): global number_of_aliens global alien_x global alien_y global vel_alien_x global vel_alien_y global load_state_1 global play_state_1 global movement_y global movement_x global finish_him font = pygame.font.Font('arcade_ya/ARCADE_N.TTF',20) text_string = 'Aliens are coming to invade!' text = font.render(text_string, True, (255,255,255)) screen.blit(text,(200,400)) if finish_him: round_one_sound.play() finish_him = False #Update positions alien_y = alien_y + vel_alien_y for i in range(number_of_aliens): screen.blit(alien_image,(alien_x[i],alien_y[i])) if alien_y[i] >= 100: finish_him = True load_state_1 = False play_state_1 = True vel_alien_x = np.ones(number_of_aliens) * 1.0 vel_alien_y = np.ones(number_of_aliens) * 50.0 movement_y = 0.0 movement_x = 0.0 # Aliens explosion def is_collision(index,x1,y1,x2,y2): global collisions global fire global projectileY distance = np.linalg.norm(np.array([x1,y1])-np.array([x2,y2])) if distance < 25: collisions[index] = True fire = False projectileY = 500 explosion_sheet = pygame.image.load('Images/explosion_sheet.png').convert_alpha() explosion_sound = mixer.Sound('Sounds/explosion1.mp3') explosion_sound.set_volume(0.5) explosion_pass = [True for i in range(number_of_aliens)] explosion_indexs = np.zeros(number_of_aliens) explosion_x = np.zeros(number_of_aliens) explosion_y = np.zeros(number_of_aliens) def get_explosion_image(index,width,height): global explosion_sheet x = index % 5 y = index // 5 x *= 48 y *= 48 image = pygame.Surface((width,height)).convert_alpha() image.blit(explosion_sheet,(0,0),(x,y,width,height)) return image def new_explosion(i): global alien_x global alien_y global collisions global explosion_indexs global explosion_x global explosion_y is_collision(i,projectileX,projectileY,alien_x[i],alien_y[i]) frames = 8 if not collisions[i]: explosion_x[i] = alien_x[i] explosion_y[i] = alien_y[i] else: #off screen alien_x[i] = 300 alien_y[i] = -500 index = explosion_indexs[i] // frames if explosion_indexs[i] < 1.0: explosion_sound.play() image = get_explosion_image(index,48,48) screen.blit(image,(explosion_x[i],explosion_y[i])) explosion_indexs[i] += 1 #Cap out index #Boss boss_image = pygame.image.load('Images/boss.png') boss_health = 10 boss_x = 200 boss_y = -1000 boss_collision = False boss_explosion_sound = mixer.Sound('Sounds/explosion2.mp3') boss_final_explosion = mixer.Sound('Sounds/boss_explosion.mp3') vel_boss_x = 2.0 vel_boss_y = 5.0 boss_index = 0 def boss(): global boss_health global boss_index global boss_x global boss_y global vel_boss_y global vel_boss_x global play_state_1 global wins_state if boss_health > 0.0: if boss_y < -75: boss_y += vel_boss_y if boss_x > 675: vel_boss_x = -2.0 elif boss_x < 0: vel_boss_x = 2.0 boss_x += vel_boss_x screen.blit(boss_image,(boss_x,boss_y)) side_fire() middle_fire() else: #Remove Boss from screen frames = 20 if boss_index == 0: mixer.music.stop() boss_final_explosion.play() toasty_sound.play() enter_index = boss_index // frames if enter_index > 30: play_state_1 = False wins_state = True image = boss_explosion_image(enter_index,300,200) screen.blit(image,(boss_x,boss_y+50)) boss_index +=1 # Boss explosion boss_explosion_sheet = pygame.image.load('Images/boss_explosion.png') def boss_explosion_image(index,width,height): global boss_explosion_sheet x = index % 5 y = index // 5 x *= 300 y *= 200 y += 75 image = pygame.Surface((width,height)).convert_alpha() image.blit(boss_explosion_sheet,(0,0),(x,y,width,height)) return image def is_boss_collision(): global projectileX global projectileY global boss_x global boss_y global boss_collision global boss_health global fire x_dist = boss_x - projectileX y_dist = abs(boss_y - projectileY) if x_dist <5 and x_dist > -200: if y_dist < 150: boss_health -= 1.0 projectileY = 500 fire = False boss_explosion_sound.play() #Boss firing projectiles double_fire = pygame.image.load('Images/double_fire.png') triple_fire = pygame.image.load('Images/triple_fire.png') quadruple_fire_image = pygame.image.load('Images/quadruple_fire.png') special_fire = pygame.image.load('Images/special_fire.png') boss_fire_velovity = 3.0 boss_side_fire = False boss_middle_fire = False special_fire_choice = False middle_fire_x = 0 middle_fire_y = 0 side_fire_x = 0 side_fire_y = 0 middle_firing_frames = 800 side_firing_frames = 700 side_fire_sound = mixer.Sound('Sounds/side_fire.mp3') side_fire_sound.set_volume(0.5) middle_fire_sound = mixer.Sound('Sounds/middle_fire.mp3') middle_fire_sound.set_volume(0.5) def middle_fire(): global boss_middle_fire global boss_x global boss_y global middle_fire_y global middle_fire_x global middle_firing_frames global special_fire_choice if middle_firing_frames < 1: middle_firing_frames = random.randint(250,500) boss_middle_fire = True special_fire_choice = random.choice([True,False]) middle_fire_sound.play() else: middle_firing_frames -= 1 if boss_middle_fire: middle_fire_y += boss_fire_velovity if special_fire_choice: screen.blit(special_fire,(middle_fire_x+65,middle_fire_y+140)) else: screen.blit(quadruple_fire_image,(middle_fire_x+85,middle_fire_y+150)) if middle_fire_y > 600: middle_fire_y = 0 boss_middle_fire = False else: middle_fire_y = boss_y middle_fire_x = boss_x def side_fire(): global boss_side_fire global boss_x global boss_y global side_fire_y global side_fire_x global side_firing_frames if side_firing_frames < 1: side_firing_frames = random.randint(250,500) boss_side_fire = True #triple_fire_choice = random.choice([True,False]) triple_fire_choice = True side_fire_sound.play() else: side_firing_frames -= 1 if boss_side_fire: side_fire_y += boss_fire_velovity screen.blit(triple_fire,(side_fire_x+40,side_fire_y+160)) screen.blit(triple_fire,(side_fire_x+150,side_fire_y+160)) if side_fire_y > 600: side_fire_y = 0 boss_side_fire = False else: side_fire_y = boss_y side_fire_x = boss_x # Player Explosion def laser_collision(): global middle_fire_x global middle_fire_y global side_fire_x global side_fire_y global playerX global playerY global player_life global special_fire_choice mid_dist_y = playerY - middle_fire_y mid_dist_x = playerX - middle_fire_x side_dist_x = playerX - side_fire_x side_dist_y = playerY - side_fire_y #distance = np.linalg.norm(np.array([playerX,playerY])-np.array([x2,y2])) if not special_fire_choice: if mid_dist_y < 200 and mid_dist_y > 165: if mid_dist_x > 50 and mid_dist_x < 155: player_life = False explosion_sound.play() else: if mid_dist_y < 200 and mid_dist_y > 165: if mid_dist_x > 25 and mid_dist_x < 175: player_life = False explosion_sound.play() if side_dist_y < 200 and side_dist_y > 165: if (side_dist_x > 0 and side_dist_x < 90) or (side_dist_x > 110 and side_dist_x < 200) : player_life = False explosion_sound.play() def destruction(): global alien_x global alien_y global playerX global player_life global lose_state global play_state_1 if max(alien_y) > 450: i = list(alien_y).index(max(alien_y)) dist = abs(alien_x[i]-playerX) if dist < 400: if player_life: explosion_sound.play() player_life = False def player_explosion(): global player_life global playerX global playerY global player_explosion_index global lose_state global play_state_1 frames = 15 laser_collision() destruction() if not player_life: index = player_explosion_index // frames image = get_explosion_image(index,48,48) screen.blit(image,(playerX,playerY)) player_explosion_index += 1 if index == 16: lose_state = True play_state_1 = False # State Machine intro_state = True load_state_1 = False play_state_1 = False wins_state = False lose_state = False def play_state(): screen.blit(background,(0,0)) player() boss() is_boss_collision() player_explosion() for i in range(number_of_aliens): new_explosion(i) alien(i) projectile_fire(projectileX) def original_state(): global playerX global playerY global player_life global player_explosion_index global movement_x global movement_y global collisions global vel_alien_x global vel_alien_y global alien_x global alien_y global alien_y global projectileX global projectileY global fire global movement_y_projectile global boss_health global boss_x global boss_y global boss_collision global vel_boss_x global vel_boss_y global boss_index global boss_fire_velovity global boss_side_fire global boss_middle_fire global special_fire_choice global middle_fire_x global middle_fire_y global side_fire_x global side_fire_y global middle_firing_frames global side_firing_frames global explosion_pass global explosion_indexs global explosion_x global explosion_y global finish_him global finish_sound playerX = 450 playerY = 700 player_life = True player_explosion_index = 0 movement_x = 0 movement_y = 1 collisions = [False for i in range(number_of_aliens)] vel_alien_x = np.zeros(number_of_aliens) vel_alien_y = np.ones(number_of_aliens) * 1.0 alien_x = np.linspace(90,810,number_of_aliens//3) alien_x = np.concatenate([alien_x,alien_x,alien_x]) alien_y = np.ones(number_of_aliens//3) *-50 alien_y = np.concatenate([alien_y,3*alien_y,5*alien_y]) projectileX = 400 projectileY = 500 fire = False movement_y_projectile= 3 boss_health = 10 boss_x = 200 boss_y = -1000 boss_collision = False vel_boss_x = 2.0 vel_boss_y = 5.0 boss_index = 0 boss_fire_velovity = 2.5 boss_side_fire = False boss_middle_fire = False special_fire_choice = False middle_fire_x = 0 middle_fire_y = 0 side_fire_x = 0 side_fire_y = 0 middle_firing_frames = 800 side_firing_frames = 700 explosion_pass = [True for i in range(number_of_aliens)] explosion_indexs = np.zeros(number_of_aliens) explosion_x = np.zeros(number_of_aliens) explosion_y = np.zeros(number_of_aliens) finish_him = True finish_sound = True mixer.music.play(-1) # Added sound effects finish_him_sound = mixer.Sound('Sounds/finish_him.mp3') flawless = mixer.Sound('Sounds/flawless.mp3') fatality = mixer.Sound('Sounds/fatality.mp3') fatality_background = mixer.Sound('Sounds/fatality_background.mp3') toasty_sound = mixer.Sound('Sounds/toasty.mp3') finish_him = True finish_sound = True while running: if intro_state: for event in pygame.event.get(): if event.type == pygame.QUIT: running = False if event.type == pygame.KEYDOWN: intro_state = False load_state_1 = True screen.fill((0,0,0)) Title_screen() elif load_state_1: for event in pygame.event.get(): if event.type == pygame.QUIT: running = False screen.blit(background,(0,0)) load_aliens() if playerY > 500: playerY -= movement_y screen.blit(player_image,(playerX,playerY)) elif play_state_1: for event in pygame.event.get(): if event.type == pygame.QUIT: running = False if event.type == pygame.KEYDOWN: if event.key == pygame.K_LEFT: movement_x = -1.5 if event.key == pygame.K_RIGHT: movement_x = 1.5 if event.key == pygame.K_UP: movement_y = -1 if event.key == pygame.K_DOWN: movement_y = 1 if event.key == pygame.K_SPACE: if not fire: projectileX = playerX bullet_sound.play() fire = True if event.type == pygame.KEYUP: if event.key == pygame.K_RIGHT or event.key == pygame.K_LEFT: movement_x = 0.0 if event.key == pygame.K_UP or event.key == pygame.K_DOWN: movement_y = 0.0 #running in the game play_state() if all(collisions): if finish_him: finish_him_sound.play() finish_him = False elif lose_state: mixer.music.stop() screen.fill((0,0,0)) text_string = 'Game Over' text = font.render(text_string, True, (255,255,255)) screen.blit(text,(325,250)) text_string = 'You Lose' text = font.render(text_string, True, (255,255,255)) screen.blit(text,(350,350)) Ending_screen() if finish_sound: fatality_background.play() fatality.play() finish_sound = False for event in pygame.event.get(): if event.type == pygame.QUIT: running = False if event.type == pygame.KEYDOWN: if event.key == pygame.K_SPACE: original_state() intro_state = True load_state_1 = False play_state_1 = False wins_state = False lose_state = False elif wins_state: mixer.music.stop() screen.fill((0,0,0)) text_string = 'Hudaifa is a Whore!!!' text = font.render(text_string, True, (255,255,255)) screen.blit(text,(150,100)) text_string = 'Game Over' text = font.render(text_string, True, (255,255,255)) screen.blit(text,(325,250)) text_string = 'You Win' text = font.render(text_string, True, (255,255,255)) screen.blit(text,(350,350)) Ending_screen() if finish_sound: flawless.play() finish_sound = False for event in pygame.event.get(): if event.type == pygame.QUIT: running = False if event.type == pygame.KEYDOWN: if event.key == pygame.K_SPACE: original_state() intro_state = True load_state_1 = False play_state_1 = False wins_state = False lose_state = False clock.tick(fps) pygame.display.update() pygame.quit()

[ "noreply@github.com" ]

omar-zaman10.noreply@github.com

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/makereport.py

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[]

no_license

fancker1992/UnitTestCase

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# 导包 import unittest import time from Tools.HTMLTestReportCN import HTMLTestRunner if __name__ == '__main__': # 1.组装测试用例case case_dir = './Case/' discover = unittest.defaultTestLoader.discover(case_dir, pattern='test_*.py') # 2.准备报告生成的路径 report_dir = './Report/' # 3.获取当前时间 now_time = time.strftime('%Y-%m-%d %H_%M_%S') # 4.设置报告名称 report_name = report_dir + now_time + 'Report.html' print(report_name) # 打开报告写入文件流 with open(report_name, 'wb') as f: # 初始化报告生成对象 runner = HTMLTestRunner(stream=f, verbosity=2, title='单元测试报告', description='运行环境：macOS，执行人：test04QA') runner.run(discover)

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786087292@qq.com

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/authentication/helper/models.py

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[]

no_license

dachieng/django-custom-user-model

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from django.db import models from django.contrib.auth.models import User class TrackingModel(models.Model): created_at = models.DateTimeField(auto_now_add=True) end_date = models.DateTimeField(auto_now=True) class Meta: abstract = True ordering = ['-created_at']

[ "oloodorcas99@gmail.com" ]

oloodorcas99@gmail.com

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/Exercise3/code/Optimization/Optimizers.py

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[]

no_license

Chengjun-Xie/Deep-Learning

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ac8371b328dd949a3655cf69b72607ca156c9fa5

refs/heads/master

2022-04-05T19:00:19.864485

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217,325,961

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import numpy as np import matplotlib.pyplot as plt class Sgd: def __init__(self, learning_rate): self.learning_rate = learning_rate def calculate_update(self, weight_tensor, gradient_tensor): return weight_tensor - self.learning_rate * gradient_tensor class SgdWithMomentum: def __init__(self, learning_rate, momentum_rate): self.learning_rate = learning_rate self.momentum_rate = momentum_rate self.prevMomentum = 0 def calculate_update(self, weight_tensor, gradient_tensor): curMomentum = self.momentum_rate * self.prevMomentum - self.learning_rate * gradient_tensor weight_tensor = weight_tensor + curMomentum self.prevMomentum = curMomentum return weight_tensor class Adam: def __init__(self, learning_rate, mu, rho): self.learning_rate = learning_rate self.mu = mu self.rho = rho self.preV = 0 self.preR = 0 self.k = 1 def calculate_update(self, weight_tensor, gradient_tensor): g = gradient_tensor v = self.mu * self.preV + (1 - self.mu) * g r = self.rho * self.preR + (1 - self.rho) * g * g v_hat = v / (1 - self.mu ** self.k) r_hat = r / (1 - self.rho ** self.k) eps = np.finfo(float) weight_tensor -= self.learning_rate * ((v_hat + eps.eps) / (np.sqrt(r_hat) + eps.eps)) self.preV = v self.preR = r self.k += 1 return weight_tensor

[ "15863001671@gmail.com" ]

15863001671@gmail.com

ce3cdf2600b9127334c5c2d8af577397f215eb63

9bdbb1a1e8e1a047c1ccbdb2eb1506983de87d5d

/dev.py

b9a201de2d95de3d60f545a852023fd543c23165

[]

no_license

Tepuradesu/AutoReportTemperature

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refs/heads/main

2023-04-11T18:18:10.216335

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null

UTF-8

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py

from line_notify import LineNotify #変数定義 token='' bot = LineNotify(access_token=token) bot.send( message='Your Message', )

[ "tepuracococo@gmail.com" ]

tepuracococo@gmail.com

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/BeautyForMe/myvenv/Lib/site-packages/ebcli/operations/gitops.py

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permissive

YooInKeun/CAU_CSE_Capstone_3

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refs/heads/master

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MIT

2022-11-22T04:52:11

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Python

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# Copyright 2016 Amazon.com, Inc. or its affiliates. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"). You # may not use this file except in compliance with the License. A copy of # the License is located at # # http://aws.amazon.com/apache2.0/ # # or in the "license" file accompanying this file. This file 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 cement.utils.misc import minimal_logger from ebcli.core import fileoperations, io from ebcli.lib import codecommit from ebcli.objects.exceptions import CommandError, ValidationError from ebcli.objects.sourcecontrol import SourceControl from ebcli.operations import commonops LOG = minimal_logger(__name__) def git_management_enabled(): return get_default_branch() and get_default_repository() def get_config_setting_from_current_environment_or_default(key_name): setting = get_setting_from_current_environment(key_name) return setting or fileoperations.get_config_setting('global', key_name) def write_setting_to_current_environment_or_default(keyname, value): env_name = commonops.get_current_branch_environment() if env_name is None: fileoperations.write_config_setting('global', keyname, value) else: fileoperations.write_config_setting('environment-defaults', env_name, {keyname: value}) def get_setting_from_current_environment(keyname): env_name = commonops.get_current_branch_environment() env_dict = fileoperations.get_config_setting('environment-defaults', env_name) if env_dict: return env_dict.get(keyname) def set_branch_default_for_global(branch_name): fileoperations.write_config_setting('global', 'branch', branch_name) def set_repo_default_for_global(repo_name): fileoperations.write_config_setting('global', 'repository', repo_name) def set_branch_default_for_current_environment(branch_name): write_setting_to_current_environment_or_default('branch', branch_name) def set_repo_default_for_current_environment(repo_name): write_setting_to_current_environment_or_default('repository', repo_name) def get_branch_default_for_current_environment(): return get_config_setting_from_current_environment_or_default('branch') def get_repo_default_for_current_environment(): return get_config_setting_from_current_environment_or_default('repository') def get_default_branch(): result = get_branch_default_for_current_environment() if result: return result LOG.debug('Branch not found') def get_default_repository(): result = get_repo_default_for_current_environment() if result: return result LOG.debug('Repository not found') def initialize_codecommit(): source_control = SourceControl.get_source_control() try: source_control_setup = source_control.is_setup() except CommandError: source_control_setup = False if not source_control_setup: io.log_error("Cannot setup CodeCommit because there is no Source Control setup") return if codecommit.region_supported(commonops.get_default_region()): codecommit_setup = print_current_codecommit_settings() if codecommit_setup: try: io.validate_action("Do you wish to continue (y/n)", "y") except ValidationError: return source_control.setup_codecommit_cred_config() from ebcli.controllers import initialize repository = initialize.get_repository_interactive() branch = initialize.get_branch_interactive(repository) set_repo_default_for_current_environment(repository) set_branch_default_for_current_environment(branch) else: io.log_error("The region {0} is not supported by CodeCommit".format(commonops.get_default_region())) def disable_codecommit(): LOG.debug("Denied option to use CodeCommit removing default values") set_repo_default_for_current_environment(None) set_branch_default_for_current_environment(None) fileoperations.write_config_setting('global', 'repository', None) fileoperations.write_config_setting('global', 'branch', None) LOG.debug("Disabled CodeCommit for use with EB CLI") def print_current_codecommit_settings(): default_branch = get_default_branch() default_repo = get_default_repository() codecommit_setup = default_repo or default_branch if codecommit_setup: io.echo("Current CodeCommit setup:") io.echo(" Repository: " + str(default_repo)) io.echo(" Branch: " + str(default_branch)) return codecommit_setup

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# Generated by Django 3.1.6 on 2021-02-05 15:01 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('website', '0006_auto_20210205_1904'), ] operations = [ migrations.RemoveField( model_name='menuitem', name='quantity', ), migrations.AddField( model_name='ordermodel', name='quantity', field=models.IntegerField(default=0, null=True), ), ]

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#! /usr/bin/env python3 """ drop.py Simulation of parachute unraveling and opening during a UGV drop. Author: Jacob Willis Date: 29-Jan-2019 """ import numpy as np import matplotlib.pyplot as plt # when the parachute is opening # times estimated from drop testing with actual parachute paraTrans = (1.3,1.67) # seconds # calculate coefficient of drag for opening the parachute after a certain # time. Piecewise step with a slope between the min and max def Cd(t): minCd = .5 # starting coefficient of drag maxCd = 1.5 # final coefficient of drag if (t < paraTrans[0]): # parachute not deployed return minCd elif (t < paraTrans[1]): # parachute deploying return minCd + (t-paraTrans[0])*(maxCd-minCd)/(paraTrans[1] - paraTrans[0]) else: return maxCd # parachute fully deployed # calculate the parachute area for opening the parachute after a certain # time. Piecewise step with a slope between the min and max def Area(t): minArea = np.pi*.05**2 # area of closed parachute maxArea = np.pi*.5**2 # area of open parachute if (t < paraTrans[0]): # parachute not deployed return minArea elif (t < paraTrans[1]): # parachute deploying return minArea + (t-paraTrans[0])*(maxArea-minArea)/(paraTrans[1] - paraTrans[0]) else: return maxArea # parachute fully deployed g = 9.8 # acceleration due to gravity mass = 450 # grams rho = 1.2 # density of air k = np.array([[0], [0], [1]]) t_start = 0 t_end = 10 Ts = .01 tvec = np.linspace(t_start, t_end, (t_end-t_start)/Ts) v0 = np.array([10, 0, 0]) # initial velocity conditions (i, j, k) v = np.zeros([3, len(tvec)]) v[:,0] = v0 z = np.zeros([3, len(tvec)]) # run the simulation step = 0 while step < len(tvec)-1: t = tvec[step] vs = v[:, [step]] v[:, [step+1]] = vs + Ts*(g*k - (rho*Area(t)*Cd(t)*(np.linalg.norm(vs)*vs))) z[:, [step+1]] = z[:, [step]] + Ts*vs step += 1 # plot the velocity results plt.subplot(2, 1, 1) plt.title("Object Drop with Deploying Parachute") plt.plot(tvec, v[0,:], 'r', label='$v_i$') plt.plot(tvec, v[1, :], 'g', label='$v_j$') plt.plot(tvec, v[2, :], 'b', label='$v_k$') plt.legend(loc=1) plt.xlabel("Time (s)") plt.ylabel("Velocity (m/s)") # draw arrow for parachute deployment arrow_x = paraTrans[0] arrow_y = v[2, int(paraTrans[0]*len(tvec)/(t_end - t_start))] arrow_dx = 1.5 arrow_dy = -.5 plt.arrow(arrow_x, arrow_y, arrow_dx, arrow_dy) text_x = arrow_x + arrow_dx + .1 text_y = arrow_y + arrow_dy - .1 plt.text(text_x, text_y, "Parachute begins to open") # draw arrow for parachute fully open arrow_x = paraTrans[1] arrow_y = v[2, int(paraTrans[1]*len(tvec)/(t_end - t_start))] arrow_dx = 1.5 arrow_dy = .5 plt.arrow(arrow_x, arrow_y, arrow_dx, arrow_dy) text_x = arrow_x + arrow_dx + .1 text_y = arrow_y + arrow_dy + .1 plt.text(text_x, text_y, "Parachute fully open") # plot position results plt.subplot(2, 1, 2) plt.plot(tvec, z[0,:], 'r', label='$z_i$') plt.plot(tvec, z[1, :], 'g',label='$z_j$') plt.plot(tvec, z[2, :], 'b', label='$z_k$') plt.xlabel("Time (s)") plt.ylabel("Position (m)") plt.legend(loc=1) plt.show(block=False) input("Press any key to continue...")

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trcritchfield@gmail.com

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import json numbers = [2,3,5,7,11,13] filename = 'numbers.json' with open(filename,'w') as f_obj: #函数json.dump()将数字列表存储到文件numbers.json中 json.dump(numbers,f_obj)

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# -*- coding: utf-8 -*- """ Created on Wed Jul 27 13:34:13 2016 @author: sebalander """ # %% import cv2 import numpy as np import matplotlib.pyplot as plt # %% # input # 6x9 chessboard #imageFile = "./resources/fishChessboard/Screenshot from fishSeba.mp4 - 12.png" # 8x11 A4 shetts chessboard imageFile = "ptz_(0.850278, -0.014444, 0.0).jpg" cornersIniFile = "PTZgridImageInitialConditions.txt" # output cornersFile = "ptzCorners.npy" patternFile = "ptzGridPattern.npy" imgShapeFile = "ptzImgShape.npy" # load # corners set by hand, read as (n,1,2) size # must format as float32 cornersIni = np.array([[crnr] for crnr in np.loadtxt(cornersIniFile)], dtype='float32') img = cv2.imread(imageFile, cv2.IMREAD_GRAYSCALE) imgCol = cv2.imread(imageFile) # %% BINARIZE IMAGE # see http://docs.opencv.org/3.0.0/d7/d4d/tutorial_py_thresholding.html th = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 501, 0) # haceomos un close para sacar manchas kernel = np.ones((5,5),np.uint8) closed = cv2.morphologyEx(th, cv2.MORPH_CLOSE, kernel) plt.imshow(th) plt.imshow(closed) plt.imshow(imgCol) plt.plot(cornersIni[:,0,0],cornersIni[:,0,1],'ow') # %% refine corners # criterio de finalizacion de cornerSubPix subpixCriteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, # termination criteria type 300, # max number of iterations 0.01) # min accuracy corners = np.copy(cornersIni) cv2.cornerSubPix(closed, corners, (15, 15), (5, 5), subpixCriteria); plt.imshow(imgCol[:,:,[2,1,0]]) plt.plot(cornersIni[:,0,0],cornersIni[:,0,1],'+r', label="Initial") plt.plot(corners[:,0,0],corners[:,0,1],'xb', label="Optimized") plt.legend() # %% DEFINE FIDUCIAL POINTS IN 3D SCENE, by hand # shape must be (1,n,3), float32 nx = 8 ny = 12 xx = range(nx) y0 = 12 yy = range(y0,y0-ny,-1) grid = np.array([[[[x, y, 0] for x in xx] for y in yy]], dtype='float32') grid = grid.reshape((1,nx*ny,3)) toDelete = np.logical_and(grid[0,:,0] < 2, grid[0,:,1] < 2) grid = grid[:,np.logical_not(toDelete),:] # scale to the size of A4 sheet grid[0,:,0] *= 0.21 grid[0,:,1] *= 0.297 # %% PLOT FIDUCIAL POINTS fig = plt.figure() from mpl_toolkits.mplot3d import Axes3D ax = fig.gca(projection='3d') ax.scatter(grid[0,:,0], grid[0,:,1], grid[0,:,2]) plt.show() # %% SAVE DATA POINTS np.save(cornersFile, corners) np.save(patternFile, grid) np.save(imgShapeFile, img.shape)

[ "seba.arroyo7@gmail.com" ]

seba.arroyo7@gmail.com

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import sys array = {} previousWhile = 0 dataPtr = 0 prgPtr = 0 def parse(program): '''Parses a string of brainfuck commands''' global array global dataPtr global prgPtr while(prgPtr < len(program)): def goto(): global array global dataPtr global prgPtr try: if dataPtr < 0: dataPtr = 0 print("Pointer has been reset to zero because it was negative.") if prgPtr < 0: print("Fatal Error: Instruction Pointer less than zero") quit() eval(program[prgPtr], program) except KeyError as e: array[prgPtr] = 0 goto() except ValueError as e: print(e) quit() goto() prgPtr += 1 def increment(x): '''Increments an input by one''' x = x + 1 return x def decrement(x): '''Decrements an input by one''' x = x - 1 return x def beginLoop(program): '''Begins a while loop''' global array global dataPtr global prgPtr global previousWhile if array[dataPtr] == 0: for x, item in enumerate(program): if item == "]": previousWhile = prgPtr prgPtr = x + 1 def closeLoop(program): '''Closes a while loop''' global array global dataPtr global prgPtr global previousWhile if array[dataPtr] != 0: prgPtr = previousWhile; def eval(x, program): '''Evaluates a single brainfuck command''' global array global dataPtr global prgPtr global previousWhile command = { ">" : lambda _: increment(dataPtr), "<" : lambda _: decrement(dataPtr), "+" : lambda _: increment(array[dataPtr]), "-" : lambda _: decrement(array[dataPtr]), "." : lambda _: print("print ", array[dataPtr]), "," : lambda _: sys.stdin.read(1), "[" : lambda _: beginLoop(program), "]" : lambda _: closeLoop(program), }.get(x, ValueError) print(command(0)) return command(0) def main(): """Interpreter debugging""" pass if __name__ == "__main__": main()

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import os from http.server import HTTPServer as BaseHTTPServer, SimpleHTTPRequestHandler class HTTPHandler(SimpleHTTPRequestHandler): """This handler uses server.base_path instead of always using os.getcwd()""" def translate_path(self, path): path = SimpleHTTPRequestHandler.translate_path(self, path) relpath = os.path.relpath(path, os.getcwd()) fullpath = os.path.join(self.server.base_path, relpath) return fullpath class HTTPServer(BaseHTTPServer): """The main server, you pass in base_path which is the path you want to serve requests from""" def __init__(self, base_path, server_address, RequestHandlerClass=HTTPHandler): self.base_path = base_path BaseHTTPServer.__init__(self, server_address, RequestHandlerClass) web_dir = os.path.join(os.path.dirname(__file__), 'data') httpd = HTTPServer(web_dir, ("", 8000)) httpd.serve_forever() # serves files with pathway localhost:8000/file_name

[ "wino6687@colorado.edu" ]

wino6687@colorado.edu

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/算法/情景题/找重的球.py

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RichieSong/algorithm

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# -*- coding: utf-8 -*- """ 8个球有一个重一点,最少称几次能找出来？ 2次能称出来？球分3堆分别3、3、2个 1、先称 3-3 如果一样证明在2哪里，再来一次称就知道那个重就知道结果，如果不一样，将重的一端分别拿出2个放入天平上，如果还一样，那剩下没称的就是重球，如果不一样，那么沉下去的就是重球扩展：100个球找出一个轻的球的最少次数？ """

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import math import random as rnd #!/usr/bin/env python try: import matplotlib.pyplot as plt except: raise import networkx as nx def create_barabasi_albert_graph(en,em): n = en m = em G = nx.nx.barabasi_albert_graph(n,m) node_total = 130 node_weight = node_total/ len(G.nodes(data=True)) edge_total = 96 edge_weight = edge_total/ len(G.edges(data=True)) #G=nx.Graph() for n in G.nodes(): G.add_node(n,weight=node_weight) for ee in G.edges(data=True): G.add_edge(ee[0],ee[1],weight=edge_weight) print G.edges(data=True) print G.nodes(data=True) return G def plot_the_graph(G): elarge=[(u,v) for (u,v,d) in G.edges(data=True) if d['weight'] >0.5] esmall=[(u,v) for (u,v,d) in G.edges(data=True) if d['weight'] <=0.5] pos=nx.spring_layout(G) # positions for all nodes # nodes nx.draw_networkx_nodes(G,pos,node_size=700) # edges nx.draw_networkx_edges(G,pos,edgelist=elarge, width=6) nx.draw_networkx_edges(G,pos,edgelist=esmall, width=6,alpha=0.5,edge_color='b',style='dashed') # labels nx.draw_networkx_labels(G,pos,font_size=20,font_family='sans-serif') plt.axis('off') plt.savefig("weighted_graph2.png") # save as png plt.show() # display def print_graph(G, pr = 'e'): if pr == 'n': for iterator in G.nodes_iter(data=True): print iterator else: for iterator in G.edges_iter(data=True): print iterator def add_node_info(G): d_nodes = {i:{} for i in range(len(G.nodes())) } #initialise cv to node weigths (reserve) for iterator in G.nodes_iter(data=True): d_nodes[iterator[0]]['cv'] = iterator[1]['weight'] d_nodes[iterator[0]]['ev'] = iterator[1]['weight'] d_nodes[iterator[0]]['sv'] = 0 for iterator in G.edges_iter(data=True): d_nodes[iterator[0]]['ev']= d_nodes[iterator[0]]['ev'] - iterator[2]['weight'] d_nodes[iterator[1]]['ev']= d_nodes[iterator[1]]['ev'] + iterator[2]['weight'] return d_nodes def shock_nodes(G, d_nodes, kappa, phi): n_shocked_nodes = int(kappa*(len(G.nodes())) ) print "number of shocked nodes for kappa %f = %d " %(kappa, n_shocked_nodes) shocked_nodes = [] while len(shocked_nodes) < n_shocked_nodes: r = rnd.randint(0, len(G.nodes())-1) if r not in shocked_nodes: shocked_nodes.append(r) print "Nodes to be shocked : ", shocked_nodes for n in shocked_nodes: d_nodes[n]['sv'] = d_nodes[n]['ev']*(phi) d_nodes[n]['cv'] = d_nodes[n]['cv'] - d_nodes[n]['sv'] print "Nodes shocked" def check_shock_result(G, d_nodes): insolvent_nodes = set([]) for iterator in G.edges_iter(data=True): if d_nodes[iterator[1]]['cv'] <=0 : if iterator[1] in insolvent_nodes: continue print "Node %d bankrupt !!!" %iterator[1] d_nodes[iterator[0]]['cv'] = d_nodes[iterator[0]]['cv'] - iterator[2]['weight'] iterator[2]['weight'] = 0 insolvent_nodes.add(iterator[1]) print "Insolvent Nodes : ", insolvent_nodes for n in insolvent_nodes: G.remove_node(n) return insolvent_nodes def iterate_shocks(G, d_nodes, k): insolvent_nodes = [] i = 1 shock_nodes(G, d_nodes, k, 0.8) while(1): print 'iteration %d' %i tmp = check_shock_result(G,d_nodes) if len(tmp) == 0: break insolvent_nodes.extend(tmp) i = i+1 def mainCall(n,m,k): G = create_barabasi_albert_graph(n,m) d_nodes = add_node_info(G) print_graph(G, 'e') print "---------------------" print_graph(G, 'n') iterate_shocks(G,d_nodes,k) solvent_nodes= G.nodes() print "nodes surviving at the end:- ", solvent_nodes return solvent_nodes ''' Created on Feb 28, 2015 @author: Saurav '''

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import logging import torch from tqdm.autonotebook import tqdm from utils.data_logger import find_device, MetricLogger class BaseTrainer: def __init__(self, models, optimizers, lr_schedulers, loss_funcs, train_dataset, batch_size, dataloader_num_workers, mining_funcs=lambda x, y: x, sampler=None ): self.models = models self.optimizers = optimizers self.lr_schedulers = lr_schedulers self.loss_funcs = loss_funcs self.mining_funcs = mining_funcs self.train_dataset = train_dataset self.batch_size = batch_size self.dataloader_num_workers = dataloader_num_workers self.sampler = sampler self.dataloader = None self.metrics = MetricLogger(name="train") self.device = find_device() self.logger = logging.getLogger('metric.learning') self.setup_dataloader() # self.models = self.models.to(self.device) def setup_dataloader(self): self.dataloader = torch.utils.data.DataLoader( self.train_dataset, batch_size=int(self.batch_size), sampler=self.sampler, drop_last=True, num_workers=self.dataloader_num_workers, shuffle=self.sampler is None, pin_memory=True ) def train(self): self.set_to_train() with tqdm(total=len(self.dataloader)) as pbar: for idx, (data, labels) in enumerate(self.dataloader): self.forward_and_backward(data, labels) pbar.set_postfix(loss=self.metrics['loss'].latest(), refresh=False) pbar.update() self.logger.info(f"End Epoch: Loss Mean Value: {self.metrics['loss'].avg(window_size=len(self.dataloader))}") return self.metrics.avg(window_size=len(self.dataloader)) def set_to_train(self): self.models.train() def forward_and_backward(self, data, labels): """ Step of optimization 1. Move data to device 2. Get emmbeddings from data 3. Run Miners for triplet/pair mining. It can be also empty function 4. Run Loss function. Return Loss and logs 5. Update model parameters :param data: Data as Tensor :param labels: Labels as Tensor """ data = data.to(self.device) labels = labels.to(self.device) embeddings = self.models(data) embeddings = self.mining_funcs(embeddings, labels) # triplet sampler and Loss loss, logs = self.loss_funcs(embeddings, labels) self.metrics.update(**logs) self.optimizers.zero_grad() loss.backward() self.optimizers.step()

[ "bartosz.ludwiczuk@intive.com" ]

bartosz.ludwiczuk@intive.com

81845f1d6ef44c5f7e0e38f392a3088e19d63850

e67d38be1fda0d9c09fb169d73d9f3153f3c4610

/events/urls.py

2318893123ab4d4f8713b33ead420f33d8f9bb64

[]

no_license

s-kobets/book_me

965b95e92886baaeb389e4212a7c88e08626a200

d5999ac1cec08adc126b46615ecdd6ba753d2c35

refs/heads/master

2023-06-10T03:12:08.264666

2021-07-05T18:57:22

382,767,972

0

null

UTF-8

Python

false

478

py

from django.contrib import admin from django.urls import path, include from django.views.generic import ListView, DetailView from .models import Event from . import views, api from rest_framework import routers router = routers.DefaultRouter() router.register(r'events', api.EventViewSet, 'event') urlpatterns = [ path('', views.EventsView.as_view(), name='list'), path('<int:pk>/', views.EventView.as_view(), name='detail'), path('api/', include(router.urls)), ]

[ "s.kobets@semrush.com" ]

s.kobets@semrush.com

6afb9cbab6b859d87b8bf06d5ae9afbffa896968

6059b2c07cff534ca54c766a9a628992daf31a15

/Platform/utils/context_processors.py

958991575e23a3d0dbf5212e2a17b64916be47c4

[]

no_license

rac2895/Wishkaro

0ce5e01a0ebd7f54f88332bdd6f4469d85aa8f03

3430302f8f943ce5fe52890ca7dbf5a4cec90a63

refs/heads/master

2021-01-23T14:14:43.144391

2017-06-17T13:31:34

93,248,791

2

1

null

UTF-8

Python

false

317

py

from products.models import Category from django.conf import settings def pycart_store(request): return { 'active_categories': Category.objects.filter(is_active=True), 'site_name': settings.SITE_NAME, 'meta_keywords': settings.META_KEYWORDS, 'meta_description': settings.META_DESCRIPTION, 'request': request }

[ "jainrachit28@gmail.com" ]

jainrachit28@gmail.com

63a64b3a4dc82edd47fdc95d4a457373004013fb

76173fc1c994a854d4e40591c79fcd261ff6f88c

/object_detection/protos/model_pb2.py

2e3b1bf639563eb8d6249d0a2dd1b08ba69164ad

[]

no_license

DongChen06/Icon_detector

4c5c87abf6836bb76dbff4384adff259d7f5dbfb

eac27e81624ce4b8d4c8da53fc9a3b21ca896858

refs/heads/master

2022-12-08T07:54:16.236694

2020-08-18T19:22:20

null

0

null

UTF-8

Python

false

true

6,958

py

# -*- coding: utf-8 -*- # Generated by the protocol buffer compiler. DO NOT EDIT! # source: object_detection/protos/model.proto import sys _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1')) from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() from object_detection.protos import center_net_pb2 as object__detection_dot_protos_dot_center__net__pb2 from object_detection.protos import faster_rcnn_pb2 as object__detection_dot_protos_dot_faster__rcnn__pb2 from object_detection.protos import ssd_pb2 as object__detection_dot_protos_dot_ssd__pb2 DESCRIPTOR = _descriptor.FileDescriptor( name='object_detection/protos/model.proto', package='object_detection.protos', syntax='proto2', serialized_options=None, serialized_pb=_b('\n#object_detection/protos/model.proto\x12\x17object_detection.protos\x1a(object_detection/protos/center_net.proto\x1a)object_detection/protos/faster_rcnn.proto\x1a!object_detection/protos/ssd.proto\"\x86\x02\n\x0e\x44\x65tectionModel\x12:\n\x0b\x66\x61ster_rcnn\x18\x01 \x01(\x0b\x32#.object_detection.protos.FasterRcnnH\x00\x12+\n\x03ssd\x18\x02 \x01(\x0b\x32\x1c.object_detection.protos.SsdH\x00\x12H\n\x12\x65xperimental_model\x18\x03 \x01(\x0b\x32*.object_detection.protos.ExperimentalModelH\x00\x12\x38\n\ncenter_net\x18\x04 \x01(\x0b\x32\".object_detection.protos.CenterNetH\x00\x42\x07\n\x05model\"!\n\x11\x45xperimentalModel\x12\x0c\n\x04name\x18\x01 \x01(\t') , dependencies=[object__detection_dot_protos_dot_center__net__pb2.DESCRIPTOR,object__detection_dot_protos_dot_faster__rcnn__pb2.DESCRIPTOR,object__detection_dot_protos_dot_ssd__pb2.DESCRIPTOR,]) _DETECTIONMODEL = _descriptor.Descriptor( name='DetectionModel', full_name='object_detection.protos.DetectionModel', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='faster_rcnn', full_name='object_detection.protos.DetectionModel.faster_rcnn', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='ssd', full_name='object_detection.protos.DetectionModel.ssd', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='experimental_model', full_name='object_detection.protos.DetectionModel.experimental_model', index=2, number=3, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='center_net', full_name='object_detection.protos.DetectionModel.center_net', index=3, number=4, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[ _descriptor.OneofDescriptor( name='model', full_name='object_detection.protos.DetectionModel.model', index=0, containing_type=None, fields=[]), ], serialized_start=185, serialized_end=447, ) _EXPERIMENTALMODEL = _descriptor.Descriptor( name='ExperimentalModel', full_name='object_detection.protos.ExperimentalModel', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='name', full_name='object_detection.protos.ExperimentalModel.name', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=_b("").decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto2', extension_ranges=[], oneofs=[ ], serialized_start=449, serialized_end=482, ) _DETECTIONMODEL.fields_by_name['faster_rcnn'].message_type = object__detection_dot_protos_dot_faster__rcnn__pb2._FASTERRCNN _DETECTIONMODEL.fields_by_name['ssd'].message_type = object__detection_dot_protos_dot_ssd__pb2._SSD _DETECTIONMODEL.fields_by_name['experimental_model'].message_type = _EXPERIMENTALMODEL _DETECTIONMODEL.fields_by_name['center_net'].message_type = object__detection_dot_protos_dot_center__net__pb2._CENTERNET _DETECTIONMODEL.oneofs_by_name['model'].fields.append( _DETECTIONMODEL.fields_by_name['faster_rcnn']) _DETECTIONMODEL.fields_by_name['faster_rcnn'].containing_oneof = _DETECTIONMODEL.oneofs_by_name['model'] _DETECTIONMODEL.oneofs_by_name['model'].fields.append( _DETECTIONMODEL.fields_by_name['ssd']) _DETECTIONMODEL.fields_by_name['ssd'].containing_oneof = _DETECTIONMODEL.oneofs_by_name['model'] _DETECTIONMODEL.oneofs_by_name['model'].fields.append( _DETECTIONMODEL.fields_by_name['experimental_model']) _DETECTIONMODEL.fields_by_name['experimental_model'].containing_oneof = _DETECTIONMODEL.oneofs_by_name['model'] _DETECTIONMODEL.oneofs_by_name['model'].fields.append( _DETECTIONMODEL.fields_by_name['center_net']) _DETECTIONMODEL.fields_by_name['center_net'].containing_oneof = _DETECTIONMODEL.oneofs_by_name['model'] DESCRIPTOR.message_types_by_name['DetectionModel'] = _DETECTIONMODEL DESCRIPTOR.message_types_by_name['ExperimentalModel'] = _EXPERIMENTALMODEL _sym_db.RegisterFileDescriptor(DESCRIPTOR) DetectionModel = _reflection.GeneratedProtocolMessageType('DetectionModel', (_message.Message,), { 'DESCRIPTOR' : _DETECTIONMODEL, '__module__' : 'object_detection.protos.model_pb2' # @@protoc_insertion_point(class_scope:object_detection.protos.DetectionModel) }) _sym_db.RegisterMessage(DetectionModel) ExperimentalModel = _reflection.GeneratedProtocolMessageType('ExperimentalModel', (_message.Message,), { 'DESCRIPTOR' : _EXPERIMENTALMODEL, '__module__' : 'object_detection.protos.model_pb2' # @@protoc_insertion_point(class_scope:object_detection.protos.ExperimentalModel) }) _sym_db.RegisterMessage(ExperimentalModel) # @@protoc_insertion_point(module_scope)

[ "2452291964@gmail.com" ]

2452291964@gmail.com

b94a889eb6767ea49216906720df55b94298b3bb

76e3a7f9853d731f4ef02d829bf2bae8dc056b4a

/vview/server/launch_server.py

c9044a6b760d05ad0993852e66a4053bad93a5f5

[ "LicenseRef-scancode-public-domain", "MIT", "BSD-3-Clause" ]

permissive

ppixiv/ppixiv

1521178e8b1668c67f9d2b9d76c215a6a8033d3d

d09fd1d6bb42386895270ddde48e57d974fb4f37

refs/heads/master

2023-07-25T09:27:26.087692

2023-07-23T01:44:25

141,506,792

129

10

null

2023-09-11T04:52:19

2018-07-19T01:07:07

JavaScript

UTF-8

Python

false

863

py

# This module allows launching the server in another process if it's not already # running. # # This is separate from server.py, since that file imports a lot and can take some # time just to import (200-300ms), which is a waste of time if we're in a front-end # process that won't be running the server itself. import subprocess, sys from ..util import win32 def fork_server(): """ If the server isn't already running, start it in a new process. This is used when we want to make sure the server is running before doing something that requires it, like opening a file association. Note that this doesn't wait for the server to be ready to receive requests. """ if win32.is_server_running(): return # Run the module in a new process. process = subprocess.Popen([sys.executable, "-m", "vview.server.start_server"])

[ "ppixiv@users.noreply.github.com" ]

ppixiv@users.noreply.github.com

093052f73dc45d04b79f6cea6212ffd7f1906cb4

91c5a1865717e6757bbfec825d411f96dcf2e2e5

/python/10_file/10.4/try/10-13.py

93ea04eae4afc0a8faad8d216f36e21678127699

[]

no_license

ydPro-G/Python_file

6f676d1b66e21548f5fad8a715a1767344c2b0c4

c035e7c344c3c224d55e33efc16ee941bc3d6ff2

refs/heads/master

2023-06-19T06:12:44.550778

2021-07-14T09:15:15

261,126,468

0

null

UTF-8

Python

false

1,369

py

import json def get_stored_username(): # 存储了用户名这个函数就获取并返回它 """如果存储了用户名，就获取它""" filename = 'username.json' try: with open(filename) as f_obj: username = json.load(f_obj) # 使用json.load()来加载存储在json文件中的信息 except FileNotFoundError: # 值不存在返回None return None else: return username # 值存在返回变量 # 这个函数获取用户输入，然后获取输入的变量被写入转储到json文件中，最后返回获取用户输入的变量 def get_new_username(): """提示用户输入名字""" username = input("What is your name? ") filename = 'username.json' with open(filename,'w') as f_obj: json.dump(username,f_obj) return username # 定义两个变量，每一个变量都获取一个函数，输出相应的内容 def greet_user(): """问候用户，指出用户名字""" username = get_stored_username() filename = 'username.json' if username: print(username) ask = input('Your user name is ' + username + 'right(y/n)?') if ask == 'y': print('welcome,' + username) else: username = get_new_username() print(('We`ll remember you when you come back, ' + username + '.')) greet_user()

[ "46178109+ydPro-G@users.noreply.github.com" ]

46178109+ydPro-G@users.noreply.github.com

caab9c4dcd28b1fed838b27677ee92281aae7c0f

d488423a19469e753a7b5c4074670dbae21c3679

/02.multiple_linear/multiple_linear.py

31c8aa8b7c430a539fa6348e634852773481f34f

[]

no_license

hiro9108/linear_regression

19105714db14a36cb4ef50fd24843eb6f0149292

0cb69fc08cff11fc2d9656abb0115c065eaffb90

refs/heads/master

2022-12-21T05:49:46.398774

2020-10-11T16:13:26

null

0

null

UTF-8

Python

false

1,590

py

# import numpy as np import pandas as pd # import matplotlib.pyplot as plt from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split import joblib # import seaborn as sns """ Multiple Linear Regression """ # Read csv file df = pd.read_csv('sample_multiple_liner_data.csv') # Show plot with seaborn # sns.distplot(df['y'], bins=50) # plt.show() # check correlation # print(df.corr()) # check correlation with graph # sns.pairplot(df, height=0.75, aspect=1.8) # plt.show() # Separate Input(x) and Output(y) values X = df.iloc[:, :-1] y = df.iloc[:, -1] """Using sklearn""" # Declare the model model = LinearRegression() """(Not separate data -> Using all data for creating model) # Learning the model model.fit(X, y) # test print("All data (100%):", model.score(X, y)) """ """Separate train and test data""" # test data is 40% X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=1) # Learning the model with train data model.fit(X_train, y_train) # test with test data print("Train data (60%)", model.score(X_test, y_test)) # test with train data (sample) # print("Train data (60%)", model.score(X_train, y_train)) """Predict value""" x = X.iloc[0, :] y_predict = model.predict([x]) # print(X) # print(x) print(y_predict) """Save the model""" joblib.dump(model, 'model.pkl') """load the model""" model_load = joblib.load('model.pkl') print(model_load.predict([x])) # Check parameter # print(model.coef_) # Easy to read # np.set_printoptions(precision=3, suppress=True) # print(model.coef_)

[ "hiroshi.8.egawa@gmail.com" ]

hiroshi.8.egawa@gmail.com

20a220e42cb348b2ec6ce84271f291e56870174b

68f1a939e05009f8ac20fc7347bfbdeff5500535

/Scripts/metaDataService.py

d41ef1bc815df9b2476f499a62b4a30ef55517c8

[]

no_license

ChiniSinha/ASD-DynamoDB

a1c396e3b07654fca6391b76e70e9323632e364c

2f095385107869609b2b86a38b2c8f142c6f5736

refs/heads/master

2021-01-13T06:48:53.378979

2017-02-21T21:17:00

81,150,816

0

null

UTF-8

Python

false

202

py

#!/usr/bin/env python import boto3 import json username = 'Chini' client = boto3.client('dynamodb') response = client.get_item( TableName='MetaData', Key={'UserName': { 'S': username }}) print response

[ "chinisinha@gmail.com" ]

chinisinha@gmail.com