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

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# Generated by the protocol buffer compiler. DO NOT EDIT! # source: service.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 from google.protobuf import descriptor_pb2 # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() DESCRIPTOR = _descriptor.FileDescriptor( name='service.proto', package='deepwater', syntax='proto3', serialized_pb=_b('\n\rservice.proto\x12\tdeepwater\"\r\n\x0bPingRequest\"\x08\n\x06Status2>\n\x07Service\x12\x33\n\x04Ping\x12\x16.deepwater.PingRequest\x1a\x11.deepwater.Status\"\x00\x42!\n\x10\x61i.h2o.deepwaterB\x0bGRPCServiceP\x01\x62\x06proto3') ) _sym_db.RegisterFileDescriptor(DESCRIPTOR) _PINGREQUEST = _descriptor.Descriptor( name='PingRequest', full_name='deepwater.PingRequest', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=28, serialized_end=41, ) _STATUS = _descriptor.Descriptor( name='Status', full_name='deepwater.Status', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ ], extensions=[ ], nested_types=[], enum_types=[ ], options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=43, serialized_end=51, ) DESCRIPTOR.message_types_by_name['PingRequest'] = _PINGREQUEST DESCRIPTOR.message_types_by_name['Status'] = _STATUS PingRequest = _reflection.GeneratedProtocolMessageType('PingRequest', (_message.Message,), dict( DESCRIPTOR = _PINGREQUEST, __module__ = 'service_pb2' # @@protoc_insertion_point(class_scope:deepwater.PingRequest) )) _sym_db.RegisterMessage(PingRequest) Status = _reflection.GeneratedProtocolMessageType('Status', (_message.Message,), dict( DESCRIPTOR = _STATUS, __module__ = 'service_pb2' # @@protoc_insertion_point(class_scope:deepwater.Status) )) _sym_db.RegisterMessage(Status) DESCRIPTOR.has_options = True DESCRIPTOR._options = _descriptor._ParseOptions(descriptor_pb2.FileOptions(), _b('\n\020ai.h2o.deepwaterB\013GRPCServiceP\001')) import grpc from grpc.beta import implementations as beta_implementations from grpc.beta import interfaces as beta_interfaces from grpc.framework.common import cardinality from grpc.framework.interfaces.face import utilities as face_utilities class ServiceStub(object): def __init__(self, channel): """Constructor. Args: channel: A grpc.Channel. """ self.Ping = channel.unary_unary( '/deepwater.Service/Ping', request_serializer=PingRequest.SerializeToString, response_deserializer=Status.FromString, ) class ServiceServicer(object): def Ping(self, request, context): context.set_code(grpc.StatusCode.UNIMPLEMENTED) context.set_details('Method not implemented!') raise NotImplementedError('Method not implemented!') def add_ServiceServicer_to_server(servicer, server): rpc_method_handlers = { 'Ping': grpc.unary_unary_rpc_method_handler( servicer.Ping, request_deserializer=PingRequest.FromString, response_serializer=Status.SerializeToString, ), } generic_handler = grpc.method_handlers_generic_handler( 'deepwater.Service', rpc_method_handlers) server.add_generic_rpc_handlers((generic_handler,)) class BetaServiceServicer(object): """The Beta API is deprecated for 0.15.0 and later. It is recommended to use the GA API (classes and functions in this file not marked beta) for all further purposes. This class was generated only to ease transition from grpcio<0.15.0 to grpcio>=0.15.0.""" def Ping(self, request, context): context.code(beta_interfaces.StatusCode.UNIMPLEMENTED) class BetaServiceStub(object): """The Beta API is deprecated for 0.15.0 and later. It is recommended to use the GA API (classes and functions in this file not marked beta) for all further purposes. This class was generated only to ease transition from grpcio<0.15.0 to grpcio>=0.15.0.""" def Ping(self, request, timeout, metadata=None, with_call=False, protocol_options=None): raise NotImplementedError() Ping.future = None def beta_create_Service_server(servicer, pool=None, pool_size=None, default_timeout=None, maximum_timeout=None): """The Beta API is deprecated for 0.15.0 and later. It is recommended to use the GA API (classes and functions in this file not marked beta) for all further purposes. This function was generated only to ease transition from grpcio<0.15.0 to grpcio>=0.15.0""" request_deserializers = { ('deepwater.Service', 'Ping'): PingRequest.FromString, } response_serializers = { ('deepwater.Service', 'Ping'): Status.SerializeToString, } method_implementations = { ('deepwater.Service', 'Ping'): face_utilities.unary_unary_inline(servicer.Ping), } server_options = beta_implementations.server_options(request_deserializers=request_deserializers, response_serializers=response_serializers, thread_pool=pool, thread_pool_size=pool_size, default_timeout=default_timeout, maximum_timeout=maximum_timeout) return beta_implementations.server(method_implementations, options=server_options) def beta_create_Service_stub(channel, host=None, metadata_transformer=None, pool=None, pool_size=None): """The Beta API is deprecated for 0.15.0 and later. It is recommended to use the GA API (classes and functions in this file not marked beta) for all further purposes. This function was generated only to ease transition from grpcio<0.15.0 to grpcio>=0.15.0""" request_serializers = { ('deepwater.Service', 'Ping'): PingRequest.SerializeToString, } response_deserializers = { ('deepwater.Service', 'Ping'): Status.FromString, } cardinalities = { 'Ping': cardinality.Cardinality.UNARY_UNARY, } stub_options = beta_implementations.stub_options(host=host, metadata_transformer=metadata_transformer, request_serializers=request_serializers, response_deserializers=response_deserializers, thread_pool=pool, thread_pool_size=pool_size) return beta_implementations.dynamic_stub(channel, 'deepwater.Service', cardinalities, options=stub_options) # @@protoc_insertion_point(module_scope)

[ "mistobaan@gmail.com" ]

mistobaan@gmail.com

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/VCsite/mainapp/models.py

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JungChaeMoon/MyProfile

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from django.db import models from datetime import datetime # Create your models here. class Comment(models.Model): comment_text = models.CharField(max_length=100) pub_date = models.DateTimeField('date published', default=datetime.now()) def __str__(self): return self.comment_text

[ "jungchaemoon@gmail.com" ]

jungchaemoon@gmail.com

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/venv/lib/python3.6/site-packages/app/__main__.py

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tberal/geru

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from waitress import serve from pyramid.config import Configurator from pyramid.session import SignedCookieSessionFactory session_factory = SignedCookieSessionFactory('gerutestkey') def main(**settings): config = Configurator(settings=settings) config.set_session_factory(session_factory) config.include('pyramid_chameleon') config.add_route('random', '/quotes/random') config.add_route('index', '/') config.add_route('quotes', '/quotes') config.add_route('quote', '/quotes/{quote_number}') config.add_route('actions', '/actions') config.add_route('session_actions', '/actions/{session_id}') config.add_route('sessions', '/sessions') config.scan('app.views') return config.make_wsgi_app() if __name__ == '__main__' : app=main() serve(app, host='0.0.0.0', port=5000)

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/flask_app/demo.py

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bkwi/wsgi-mux

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from flask import Flask, jsonify app = Flask(__name__) @app.route("/") def hello(): return jsonify({'hello': 'flask'}) if __name__ == '__main__': app.run()

[ "bartosz@Bartoszs-MacBook-Pro.local" ]

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from django.shortcuts import render, HttpResponse # Create your views here. from django import forms from captcha.fields import CaptchaField from django.contrib.auth.hashers import make_password class CaptchaTestForm(forms.Form): email = forms.EmailField() password = forms.CharField(min_length=6) captcha = CaptchaField(error_messages={ "invalid": "验证码错误" }) def some_view(request): if request.POST: register_form = CaptchaTestForm(request.POST) if register_form.is_valid(): user_name = register_form.cleaned_data.get('email') password = register_form.cleaned_data.get('password') print(user_name, password) password = make_password(password) from users.models import UserProfile UserProfile.objects.create(**{"username": user_name, "email": user_name, "password": password}) return HttpResponse("注册成功") else: register_form = CaptchaTestForm() return render(request, 'register.html', locals())

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/companies/migrations/0047_auto_20190917_1011.py

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dchaplinsky/edrdr

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# Generated by Django 2.2.3 on 2019-09-17 10:11 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('companies', '0046_pepowner_person_type'), ] operations = [ migrations.AddField( model_name='companyrecord', name='charter_capital', field=models.FloatField(default=None, null=True), ), migrations.AddField( model_name='companyrecord', name='reg_date', field=models.DateField(null=True), ), ]

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/app/utils/tests/test_xmlutils.py

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#!/usr/bin/env python # -*- coding:utf-8 -*- from __future__ import absolute_import from __future__ import unicode_literals from test.unit.base import BaseTestCase from utils.xmlutils import XMLUtils class XMLUtilsTestCase(BaseTestCase): """ XMLUtilsTestCase """ def setUp(self): pass def tearDown(self): pass def test_xml2json(self): xml_str = """ <BOSFXII xmlns="http://www.bankofshanghai.com/BOSFX/2010/08" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.bankofshanghai.com/BOSFX/2010/08 BOSFX2.0.xsd"> <XXXRq> <CommonRqHdr> <SPName>CBIB</SPName> <RqUID>20150324800148246569</RqUID> <ClearDate>20160331</ClearDate> <TranDate>20160331</TranDate> <TranTime>094338</TranTime> <ChannelId/> </CommonRqHdr> <SubAcctNo>11022133</SubAcctNo> <ProductCd>zzzzzz</ProductCd> <Amount>11.02</Amount> <Currency>156</Currency> <TheirRef>AAA子账户转第三方（带赎回）</TheirRef> <Purpose>AAA赎回</Purpose> <Attach/> <MemoInfo/> <KoalB64Cert/> <Signature/> </XXXRq> </BOSFXII> """ result = XMLUtils.xml2json(xml_str, need_dict=True) self.assertEquals(result['BOSFXII']['XXXRq']['CommonRqHdr']['SPName'], 'CBIB') def test_json2xml(self): """ json 字典必须有根元素 :return: """ json_data = { "planets": { "planet": [ { "name": "Earth", "radius": "6,371km" }, { "name": "Jupiter", "radius": "69,911km" }, { "name": "Mars", "radius": "3,390km" } ], "@xmlns": "http://www.bankofshanghai.com/BOSFX/2010/08" }, } result = XMLUtils.json2xml(json_data) print result

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# Copyright (c) 2012-2017 The ANTLR Project. All rights reserved. # Use of this file is governed by the BSD 3-clause license that # can be found in the LICENSE.txt file in the project root. #/ # A lexer is recognizer that draws input symbols from a character stream. # lexer grammars result in a subclass of self object. A Lexer object # uses simplified match() and error recovery mechanisms in the interest # of speed. #/ from io import StringIO from typing.io import TextIO import sys from antlr4.CommonTokenFactory import CommonTokenFactory from antlr4.atn.LexerATNSimulator import LexerATNSimulator from antlr4.InputStream import InputStream from antlr4.Recognizer import Recognizer from antlr4.Token import Token from antlr4.error.Errors import IllegalStateException, LexerNoViableAltException, RecognitionException class TokenSource(object): pass class Lexer(Recognizer, TokenSource): DEFAULT_MODE = 0 MORE = -2 SKIP = -3 DEFAULT_TOKEN_CHANNEL = Token.DEFAULT_CHANNEL HIDDEN = Token.HIDDEN_CHANNEL MIN_CHAR_VALUE = 0x0000 MAX_CHAR_VALUE = 0x10FFFF def __init__(self, input:InputStream, output:TextIO = sys.stdout): super().__init__() self._input = input self._output = output self._factory = CommonTokenFactory.DEFAULT self._tokenFactorySourcePair = (self, input) self._interp = None # child classes must populate this # The goal of all lexer rules/methods is to create a token object. # self is an instance variable as multiple rules may collaborate to # create a single token. nextToken will return self object after # matching lexer rule(s). If you subclass to allow multiple token # emissions, then set self to the last token to be matched or # something nonnull so that the auto token emit mechanism will not # emit another token. self._token = None # What character index in the stream did the current token start at? # Needed, for example, to get the text for current token. Set at # the start of nextToken. self._tokenStartCharIndex = -1 # The line on which the first character of the token resides#/ self._tokenStartLine = -1 # The character position of first character within the line#/ self._tokenStartColumn = -1 # Once we see EOF on char stream, next token will be EOF. # If you have DONE : EOF ; then you see DONE EOF. self._hitEOF = False # The channel number for the current token#/ self._channel = Token.DEFAULT_CHANNEL # The token type for the current token#/ self._type = Token.INVALID_TYPE self._modeStack = [] self._mode = self.DEFAULT_MODE # You can set the text for the current token to override what is in # the input char buffer. Use setText() or can set self instance var. #/ self._text = None def reset(self): # wack Lexer state variables if self._input is not None: self._input.seek(0) # rewind the input self._token = None self._type = Token.INVALID_TYPE self._channel = Token.DEFAULT_CHANNEL self._tokenStartCharIndex = -1 self._tokenStartColumn = -1 self._tokenStartLine = -1 self._text = None self._hitEOF = False self._mode = Lexer.DEFAULT_MODE self._modeStack = [] self._interp.reset() # Return a token from self source; i.e., match a token on the char # stream. def nextToken(self): if self._input is None: raise IllegalStateException("nextToken requires a non-null input stream.") # Mark start location in char stream so unbuffered streams are # guaranteed at least have text of current token tokenStartMarker = self._input.mark() try: while True: if self._hitEOF: self.emitEOF() return self._token self._token = None self._channel = Token.DEFAULT_CHANNEL self._tokenStartCharIndex = self._input.index self._tokenStartColumn = self._interp.column self._tokenStartLine = self._interp.line self._text = None continueOuter = False while True: self._type = Token.INVALID_TYPE ttype = self.SKIP try: ttype = self._interp.match(self._input, self._mode) except LexerNoViableAltException as e: self.notifyListeners(e) # report error self.recover(e) if self._input.LA(1)==Token.EOF: self._hitEOF = True if self._type == Token.INVALID_TYPE: self._type = ttype if self._type == self.SKIP: continueOuter = True break if self._type!=self.MORE: break if continueOuter: continue if self._token is None: self.emit() return self._token finally: # make sure we release marker after match or # unbuffered char stream will keep buffering self._input.release(tokenStartMarker) # Instruct the lexer to skip creating a token for current lexer rule # and look for another token. nextToken() knows to keep looking when # a lexer rule finishes with token set to SKIP_TOKEN. Recall that # if token==null at end of any token rule, it creates one for you # and emits it. #/ def skip(self): self._type = self.SKIP def more(self): self._type = self.MORE def mode(self, m:int): self._mode = m def pushMode(self, m:int): if self._interp.debug: print("pushMode " + str(m), file=self._output) self._modeStack.append(self._mode) self.mode(m) def popMode(self): if len(self._modeStack)==0: raise Exception("Empty Stack") if self._interp.debug: print("popMode back to "+ self._modeStack[:-1], file=self._output) self.mode( self._modeStack.pop() ) return self._mode # Set the char stream and reset the lexer#/ @property def inputStream(self): return self._input @inputStream.setter def inputStream(self, input:InputStream): self._input = None self._tokenFactorySourcePair = (self, self._input) self.reset() self._input = input self._tokenFactorySourcePair = (self, self._input) @property def sourceName(self): return self._input.sourceName # By default does not support multiple emits per nextToken invocation # for efficiency reasons. Subclass and override self method, nextToken, # and getToken (to push tokens into a list and pull from that list # rather than a single variable as self implementation does). #/ def emitToken(self, token:Token): self._token = token # The standard method called to automatically emit a token at the # outermost lexical rule. The token object should point into the # char buffer start..stop. If there is a text override in 'text', # use that to set the token's text. Override self method to emit # custom Token objects or provide a new factory. #/ def emit(self): t = self._factory.create(self._tokenFactorySourcePair, self._type, self._text, self._channel, self._tokenStartCharIndex, self.getCharIndex()-1, self._tokenStartLine, self._tokenStartColumn) self.emitToken(t) return t def emitEOF(self): cpos = self.column lpos = self.line eof = self._factory.create(self._tokenFactorySourcePair, Token.EOF, None, Token.DEFAULT_CHANNEL, self._input.index, self._input.index-1, lpos, cpos) self.emitToken(eof) return eof @property def type(self): return self._type @type.setter def type(self, type:int): self._type = type @property def line(self): return self._interp.line @line.setter def line(self, line:int): self._interp.line = line @property def column(self): return self._interp.column @column.setter def column(self, column:int): self._interp.column = column # What is the index of the current character of lookahead?#/ def getCharIndex(self): return self._input.index # Return the text matched so far for the current token or any # text override. @property def text(self): if self._text is not None: return self._text else: return self._interp.getText(self._input) # Set the complete text of self token; it wipes any previous # changes to the text. @text.setter def text(self, txt:str): self._text = txt # Return a list of all Token objects in input char stream. # Forces load of all tokens. Does not include EOF token. #/ def getAllTokens(self): tokens = [] t = self.nextToken() while t.type!=Token.EOF: tokens.append(t) t = self.nextToken() return tokens def notifyListeners(self, e:LexerNoViableAltException): start = self._tokenStartCharIndex stop = self._input.index text = self._input.getText(start, stop) msg = "token recognition error at: '" + self.getErrorDisplay(text) + "'" listener = self.getErrorListenerDispatch() listener.syntaxError(self, None, self._tokenStartLine, self._tokenStartColumn, msg, e) def getErrorDisplay(self, s:str): with StringIO() as buf: for c in s: buf.write(self.getErrorDisplayForChar(c)) return buf.getvalue() def getErrorDisplayForChar(self, c:str): if ord(c[0])==Token.EOF: return "<EOF>" elif c=='\n': return "\\n" elif c=='\t': return "\\t" elif c=='\r': return "\\r" else: return c def getCharErrorDisplay(self, c:str): return "'" + self.getErrorDisplayForChar(c) + "'" # Lexers can normally match any char in it's vocabulary after matching # a token, so do the easy thing and just kill a character and hope # it all works out. You can instead use the rule invocation stack # to do sophisticated error recovery if you are in a fragment rule. #/ def recover(self, re:RecognitionException): if self._input.LA(1) != Token.EOF: if isinstance(re, LexerNoViableAltException): # skip a char and try again self._interp.consume(self._input) else: # TODO: Do we lose character or line position information? self._input.consume()

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felipe.cgr@hotmail.com

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/blog/forms/article.py

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myalcins/django-blog-tutorial

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from django import forms from blog.models import Article class ArticleForm(forms.ModelForm): schedule_time = forms.DateTimeField(required=False ,input_formats=['%Y/%m/%d %H:%M']) class Meta: model = Article fields = ( 'image', 'title', 'content', 'category', 'schedule_time' )

[ "ymehmetsoruklu@gmail.com" ]

ymehmetsoruklu@gmail.com

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# Generated by Django 2.1.15 on 2021-11-05 09:58 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('surveys', '0001_initial'), ] operations = [ migrations.AddField( model_name='surveyquestion', name='freetext_answer_available', field=models.BooleanField(default=True), preserve_default=False, ), ]

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def isPalindromic(number): forward = str(number) for i in range(0,len(forward)/2): if forward[i] != forward[len(forward)-1-i]: return False return True large = 0 for i in range(100,999): for j in range(100,999): if isPalindromic(i * j) and i*j>large: large = i*j print large

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#!/usr/bin/env python """Django's command-line utility for administrative tasks.""" import os import sys def main(): os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'inner_source.settings') try: from django.core.management import execute_from_command_line except ImportError as exc: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) from exc execute_from_command_line(sys.argv) if __name__ == '__main__': main()

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import unittest def digits(x): """Convert an integer into a list of digits. Args: x: The number whose digits we want. Returns: A list of the digits, in order, of ``x``. >>> digits(4586378) [8, 7, 3, 6, 8, 5, 4] """ digs = [] while x != 0: div, mod = divmod(x, 10) digs.append(mod) x = div return digs def is_palindrome(x): """Determine if an integer is a palindrome. Args: x: The number to check for palindromicity Returns: True if the digits of ``x`` are a palindrome, False otherwise. >>> is_palindrome(1234) False >>> is_palindrome(2468642) True """ digs = digits(x) for f, r in zip(digs, reversed(digs)): if f != r: return False return True class Tests(unittest.TestCase): """Tests for the ``is_palindrome()`` function.""" def test_negative(self): "Check that it returns False correctly." self.assertFalse(is_palindrome(1234)) def test_positive(self): "Check that it returns True correctly." self.assertTrue(is_palindrome(1234321)) def test_single_digit(self): "Check that it works for single digit numbers." for i in range(10): self.assertTrue(is_palindrome(i)) if __name__ == '__main__': unittest.main()

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from scrapy import cmdline cmdline.execute("scrapy crawl exhaust_gas".split())

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from app import app from flask import render_template, make_response, jsonify, request, session from app.functions import * import datetime import calendar @app.route('/', methods=['GET', 'POST']) @app.route('/index', methods=['GET', 'POST']) def index(): data = {} try: im = init_mysql() except mysql.connector.errors.ProgrammingError as e: data["errors"] = str(e) return render_template('index.j2', title='Home', data=data, session=session.get("_flashes")) data["db_name"] = "automationdb" data["content"] = "route /" data["method"] = request.args.get("page") if data["method"] == "departments": data["page"] = "type1" else: data["page"] = "type2" return render_template('index.j2', title='Home', data=data, session=session.get("_flashes")) @app.route('/departments') def departments(): data = {} try: im = init_mysql() except mysql.connector.errors.ProgrammingError as e: data["errors"] = str(e) return render_template('index.j2', title='Home', data=data, session=session.get("_flashes")) mycursor = im.mycursor data["db_name"] = "automationdb" sql_query = \ """ SELECT * FROM departments """ mycursor.execute(sql_query) data["departments"] = mycursor.fetchall() return render_template('departments.j2', title='Departments', data=data, ) @app.route('/reports') def reports(): data = {} try: im = init_mysql() except mysql.connector.errors.ProgrammingError as e: data["errors"] = str(e) return render_template('index.j2', title='Home', data=data, session=session.get("_flashes")) mycursor = im.mycursor data["db_name"] = "automationdb" data["time_saved"] = [] sql_query = \ """ SELECT MIN(tasks_executions.timestamp) AS min_date FROM tasks_executions """ mycursor.execute(sql_query) results = mycursor.fetchall() cur_year = datetime.datetime.now().year if results[0]["min_date"] is None: min_year = cur_year else: min_year = results[0]["min_date"].year for year in range(min_year, cur_year + 1): max_month = 12 if year == cur_year: max_month = datetime.datetime.now().month for month in range(1, max_month + 1): first_day = 1 last_day = calendar.monthrange(year,month)[1] sql_query = \ f""" SELECT sum(tasks.time_of_completion) AS time_saved FROM tasks_executions JOIN tasks ON tasks.id=tasks_executions.task_id JOIN connections_tasks_automations ON tasks.id=connections_tasks_automations.task_id WHERE tasks_executions.timestamp >= '{year}-{str(month).zfill(2)}-{str(first_day).zfill(2)}' AND tasks_executions.timestamp <= '{year}-{str(month).zfill(2)}-{str(last_day).zfill(2)}' """ mycursor.execute(sql_query) results = mycursor.fetchall() if results[0]["time_saved"] is None: time_saved = 0 else: time_saved = int(results[0]["time_saved"]) data["time_saved"].append({"year":str(year),"month":str(month).zfill(2), "time_saved":time_saved}) return render_template('reports.j2', title='reports', data=data) @app.route('/workflows') def workflows(): data = {} try: im = init_mysql() except mysql.connector.errors.ProgrammingError as e: data["errors"] = str(e) return render_template('index.j2', title='Home', data=data, session=session.get("_flashes")) mycursor = im.mycursor data["db_name"] = "automationdb" sql_query = \ """ SELECT * FROM departments """ mycursor.execute(sql_query) data["departments"] = mycursor.fetchall() sql_query = \ """ select workflows.id as workflow_id, workflows.name as workflow_name, workflows.description as workflow_description from workflows ORDER BY workflows.name """ mycursor.execute(sql_query) results = mycursor.fetchall() data["workflows"] = {} for item in results: if data["workflows"].get(item["workflow_id"]) is None: data["workflows"][item["workflow_id"]] = { "workflow_name": item["workflow_name"], "workflow_description": item["workflow_description"], } sql_query = \ f""" SELECT departments.id, departments.name FROM connections_workflows_departments JOIN departments ON connections_workflows_departments.department_id = departments.id WHERE connections_workflows_departments.workflow_id = {item['workflow_id']} ORDER BY departments.name """ mycursor.execute(sql_query) results2 = mycursor.fetchall() if data["workflows"][item["workflow_id"]].get("departments") is None: data["workflows"][item["workflow_id"]]["departments"] = {} for task_id in results2: data["workflows"][item["workflow_id"]]["departments"][task_id["id"]] = task_id["name"] data["workflows"][item["workflow_id"]]["tasks"] = {} sql_query = \ f""" SELECT tasks.*, connections_tasks_automations.automation_tool_id, automation_tools.name as automation_tool_name FROM tasks left join connections_tasks_automations on tasks.id= connections_tasks_automations.task_id left join automation_tools on connections_tasks_automations.automation_tool_id = automation_tools.id WHERE tasks.workflow_id = {item['workflow_id']} AND tasks.visibility = 'enabled' ORDER BY tasks.order_number """ mycursor.execute(sql_query) results3 = mycursor.fetchall() for item3 in results3: data["workflows"][item["workflow_id"]]["tasks"][item3["id"]] = { "name": item3["name"], "time_of_completion": item3["time_of_completion"], "order_number": item3["order_number"], "automation_tool_id": item3["automation_tool_id"], "automation_tool_name": item3["automation_tool_name"], } sql_query = \ """ SELECT * FROM automation_tools """ mycursor.execute(sql_query) results = mycursor.fetchall() data["automation_tools"] = results return render_template('workflows.j2', title='workflows', data=data) @app.route('/automation-tools') def automation_tools(): data = {} try: im = init_mysql() except mysql.connector.errors.ProgrammingError as e: data["errors"] = str(e) return render_template('index.j2', title='Home', data=data, session=session.get("_flashes")) mycursor = im.mycursor data["db_name"] = "automationdb" sql_query = \ """ SELECT * FROM automation_tools """ mycursor.execute(sql_query) results = mycursor.fetchall() data["automation-tools"] = [] for item in results: data["automation-tools"].append(item["name"]) return render_template('automation-tools.j2', title='automation-tools', data=data) @app.route('/database/departments', methods=["POST", "PATCH", "DELETE"]) def database_delete_department(): try: im = init_mysql() except mysql.connector.errors.ProgrammingError as e: response = {"message": f"Could not connect to database server.\n{str(e)}", "code": "FAILURE"} return make_response(jsonify(response), 400) mycursor = im.mycursor mydb = im.conn payload = request.get_json() if request.method == "DELETE": if payload.get("department_id") is None: response = {"message": "department_id is not given", "code": "FAILURE"} return make_response(jsonify(response), 400) query = "DELETE FROM departments WHERE id=%s LIMIT 1" try: mycursor.execute(query, (payload['department_id'], )) mydb.commit() except mysql.connector.errors.ProgrammingError: response = {"message": "SQL query resulted in an error.", "code": "FAILURE"} print(f"Query failed: {mycursor.statement}") return make_response(jsonify(response), 400) response = {"message": "", "code": "SUCCESS"} return make_response(jsonify(response), 200) if request.method == "PATCH": if payload.get("department_id") is None or payload.get("department_name") is None: response = {"message": "department_id or department_name is not given", "code": "FAILURE"} return make_response(jsonify(response), 400) query = "UPDATE departments SET name=%s WHERE id=%s LIMIT 1" try: mycursor.execute(query, (payload['department_name'], payload['department_id'], )) mydb.commit() except mysql.connector.errors.ProgrammingError: response = {"message": "SQL query resulted in an error.", "code": "FAILURE"} print(f"Query failed: {mycursor.statement}") return make_response(jsonify(response), 400) response = {"message": "", "code": "SUCCESS"} return make_response(jsonify(response), 200) if request.method == "POST": if payload.get("department") is None: response = {"message": "department is not given", "code": "FAILURE"} return make_response(jsonify(response), 400) query = "SELECT * FROM departments WHERE name=%s" try: mycursor.execute(query, (payload['department'], )) results = mycursor.fetchall() except mysql.connector.errors.ProgrammingError: response = {"message": f"SQL query resulted in an error.", "code": "FAILURE"} print(f"Query failed: {mycursor.statement}") return make_response(jsonify(response), 400) if len(results) != 0: response = {"message": f"Could not insert department {payload['department']} into database because it already exists.", "code": "FAILURE"} return make_response(jsonify(response), 400) query = "INSERT INTO departments(`id`, `name`) VALUES (null, %s)" try: mycursor.execute(query, (payload['department'], )) mydb.commit() except mysql.connector.errors.ProgrammingError: response = {"message": "SQL query resulted in an error.", "code": "FAILURE"} print(f"Query failed: {mycursor.statement}") return make_response(jsonify(response), 400) response = {'message': 'Department inserted into database', 'code': 'SUCCESS'} return make_response(jsonify(response), 201) @app.route('/database/workflows', methods=["POST", "PATCH", "DELETE"]) def database_workflows(): try: im = init_mysql() except mysql.connector.errors.ProgrammingError as e: response = {"message": f"Could not connect to database server.\n{str(e)}", "code": "FAILURE"} return make_response(jsonify(response), 400) mycursor = im.mycursor mydb = im.conn payload = request.get_json() if request.method == "POST": for parameter in ["name"]: if parameter not in payload.keys(): response = {"message": f"'{parameter}' is not given", "code": "FAILURE"} return make_response(jsonify(response), 400) query = "INSERT INTO workflows VALUES(null, %s, '')" try: mycursor.execute(query, (payload['name'], )) mydb.commit() except mysql.connector.errors.ProgrammingError: response = {"message": "SQL query resulted in an error.", "code": "FAILURE"} print(f"Query failed: {mycursor.statement}") return make_response(jsonify(response), 400) response = {"message": "", "code": "SUCCESS"} return make_response(jsonify(response), 200) if request.method == "PATCH": for parameter in ["id", "name"]: if parameter not in payload.keys(): response = {"message": f"'{parameter}' is not given", "code": "FAILURE"} return make_response(jsonify(response), 400) query = "UPDATE workflows SET name=%s WHERE id=%s LIMIT 1" try: mycursor.execute(query, (payload['name'], int(payload['id']), )) mydb.commit() except mysql.connector.errors.ProgrammingError: response = {"message": "SQL query resulted in an error.", "code": "FAILURE"} print(f"Query failed: {mycursor.statement}") return make_response(jsonify(response), 400) response = {"message": "", "code": "SUCCESS"} return make_response(jsonify(response), 200) if request.method == "DELETE": for parameter in ["id"]: if parameter not in payload.keys(): response = {"message": f"'{parameter}' is not given", "code": "FAILURE"} return make_response(jsonify(response), 400) query = "DELETE FROM workflows WHERE id=%s LIMIT 1" try: mycursor.execute(query, (int(payload['id']), )) mydb.commit() except mysql.connector.errors.ProgrammingError: response = {"message": "SQL query resulted in an error.", "code": "FAILURE"} print(f"Query failed: {mycursor.statement}") return make_response(jsonify(response), 400) response = {"message": "", "code": "SUCCESS"} return make_response(jsonify(response), 200) @app.route('/database/tasks', methods=["POST", "PATCH", "DELETE"]) def database_tasks(): try: im = init_mysql() except mysql.connector.errors.ProgrammingError as e: response = {"message": f"Could not connect to database server.\n{str(e)}", "code": "FAILURE"} return make_response(jsonify(response), 400) mycursor = im.mycursor mydb = im.conn payload = request.get_json() if request.method == "DELETE": if payload.get("id") is None: response = {"message": "department_id is not given", "code": "FAILURE"} return make_response(jsonify(response), 400) query = "UPDATE tasks SET visibility='disabled' WHERE id=%s LIMIT 1" try: mycursor.execute(query, (payload['id'], )) mydb.commit() except mysql.connector.errors.ProgrammingError: response = {"message": "SQL query resulted in an error.", "code": "FAILURE"} print(f"Query failed: {mycursor.statement}") return make_response(jsonify(response), 400) response = {"message": "", "code": "SUCCESS"} return make_response(jsonify(response), 200) if request.method == "POST": if payload.get("name") is None: response = {"message": "name is not given", "code": "FAILURE"} return make_response(jsonify(response), 400) if payload.get("workflow_id") is None: response = {"message": "workflow_id is not given", "code": "FAILURE"} return make_response(jsonify(response), 400) # calculate the order_number query = "SELECT max(order_number) AS max_order_number FROM tasks WHERE workflow_id = %s" try: mycursor.execute(query, (payload['workflow_id'],)) except mysql.connector.errors.ProgrammingError: response = {"message": "SQL query resulted in an error.", "code": "FAILURE"} print(f"Query failed: {mycursor.statement}") return make_response(jsonify(response), 400) results = mycursor.fetchall() if results[0]["max_order_number"] is None: order_number = 1 else: order_number = results[0]["max_order_number"] + 1 query = "INSERT INTO tasks VALUES(null, %s, %s, 'enabled', %s, 0)" try: mycursor.execute(query, (int(payload['workflow_id']), payload['name'], order_number)) mydb.commit() except mysql.connector.errors.ProgrammingError: response = {"message": "SQL query resulted in an error.", "code": "FAILURE"} print(f"Query failed: {mycursor.statement}") return make_response(jsonify(response), 400) response = {"message": "", "code": "SUCCESS"} return make_response(jsonify(response), 200) if request.method == "PATCH": for idx,elem in enumerate(payload): for item in ["id", "name", "time_of_completion", "automation_tool_id"]: if item not in elem.keys(): response = {"message": f"'{item}' is not given on row {idx}", "code": "FAILURE"} return make_response(jsonify(response), 400) for idx,elem in enumerate(payload): query = "UPDATE tasks SET name=%s, visibility='enabled', order_number=%s, time_of_completion=%s WHERE id=%s LIMIT 1" try: mycursor.execute(query, (elem['name'], int(idx+1), int(elem['time_of_completion']), int(elem['id']), )) mydb.commit() except mysql.connector.errors.ProgrammingError: response = {"message": "SQL query resulted in an error.", "code": "FAILURE"} print(f"Query failed: {query}\n{mycursor.statement}") return make_response(jsonify(response), 400) query = "SELECT * FROM connections_tasks_automations WHERE task_id=%s" try: mycursor.execute(query, (int(elem['id']), )) except mysql.connector.errors.ProgrammingError: response = {"message": "SQL query resulted in an error.", "code": "FAILURE"} print(f"Query failed: {query}\n{mycursor.statement}") return make_response(jsonify(response), 400) results = mycursor.fetchall() if len(results) != 0 and len(str(elem["automation_tool_id"])) == 0: query = "DELETE FROM connections_tasks_automations WHERE task_id=%s LIMIT 1" try: mycursor.execute(query, (int(elem["id"]), )) mydb.commit() except mysql.connector.errors.ProgrammingError: response = {"message": "SQL query resulted in an error.", "code": "FAILURE"} print(f"Query failed: {query}\n{mycursor.statement}") return make_response(jsonify(response), 400) if len(results) != 0 and elem["automation_tool_id"] is not None and len(str(elem["automation_tool_id"])) == 0: query = "DELETE FROM connections_tasks_automations WHERE task_id=%s LIMIT 1" try: mycursor.execute(query, (int(elem["id"]), )) except mysql.connector.errors.ProgrammingError: response = {"message": "SQL query resulted in an error.", "code": "FAILURE"} print(f"Query failed: {query}\n{mycursor.statement}") return make_response(jsonify(response), 400) if len(results) == 0 and elem["automation_tool_id"] is not None and len(str(elem["automation_tool_id"])) != 0: query = "INSERT INTO connections_tasks_automations VALUES(null, %s, %s)" try: mycursor.execute(query, (int(elem["id"]), elem["automation_tool_id"],)) mydb.commit() except mysql.connector.errors.ProgrammingError: response = {"message": "SQL query resulted in an error.", "code": "FAILURE"} print(f"Query failed: {query}\n{mycursor.statement}") return make_response(jsonify(response), 400) response = {"message": "", "code": "SUCCESS"} return make_response(jsonify(response), 200) @app.route('/database/workflows-associations', methods=["POST", "DELETE"]) def database_workflows_associations(): try: im = init_mysql() except mysql.connector.errors.ProgrammingError as e: response = {"message": f"Could not connect to database server.\n{str(e)}", "code": "FAILURE"} return make_response(jsonify(response), 400) mycursor = im.mycursor mydb = im.conn payload = request.get_json() print(payload) if request.method == "POST": for parameter in ["workflow_id", "department_id"]: if parameter not in payload.keys() or parameter is None: response = {"message": f"'{parameter}' is not given", "code": "FAILURE"} return make_response(jsonify(response), 400) for parameter in ["workflow_id", "department_id"]: if payload[parameter] is None: response = {"message": f"'{parameter}' must be not None", "code": "FAILURE"} return make_response(jsonify(response), 400) query = " INSERT INTO connections_workflows_departments VALUES(null, %s, %s)" try: mycursor.execute(query, (int(payload["workflow_id"]), int(payload["department_id"]),)) mydb.commit() except mysql.connector.errors.ProgrammingError: response = {"message": "SQL query resulted in an error.", "code": "FAILURE"} print(f"Query failed: {query}\n{mycursor.statement}") return make_response(jsonify(response), 400) response = {"message": "", "code": "SUCCESS"} return make_response(jsonify(response), 200) if request.method == "DELETE": for parameter in ["workflow_id", "department_id"]: if parameter not in payload.keys(): response = {"message": f"'{parameter}' is not given", "code": "FAILURE"} return make_response(jsonify(response), 400) query = " DELETE FROM connections_workflows_departments WHERE workflow_id=%s AND department_id=%s LIMIT 1" try: mycursor.execute(query, (int(payload["workflow_id"]), int(payload["department_id"]),)) mydb.commit() except mysql.connector.errors.ProgrammingError: response = {"message": "SQL query resulted in an error.", "code": "FAILURE"} print(f"Query failed: {query}\n{mycursor.statement}") return make_response(jsonify(response), 400) response = {"message": "", "code": "SUCCESS"} return make_response(jsonify(response), 200) @app.route('/database/automation-tools-associations', methods=["DELETE"]) def database_delete_automation_tools_associations(): pass

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""" WSGI config for FreeFishMaster project. It exposes the WSGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/1.9/howto/deployment/wsgi/ """ import os from django.core.wsgi import get_wsgi_application os.environ.setdefault("DJANGO_SETTINGS_MODULE", "FreeFishMaster.settings") application = get_wsgi_application()

[ "xinhai.zeng@careerbuilder.com" ]

xinhai.zeng@careerbuilder.com

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/products/admin.py

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[]

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deone/eqsupply

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from django.contrib import admin from eqsupply.products.models import * admin.site.register(Division) admin.site.register(Category) admin.site.register(Product) admin.site.register(Accessory) admin.site.register(ProductVariant)

[ "alwaysdeone@gmail.com" ]

alwaysdeone@gmail.com

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ashish-bisht/must_do_geeks_for_geeks

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import threading import time start = time.perf_counter() def working_on_something(): print("Sleeping for a sec") time.sleep(1) print("Woke up") working_on_something() working_on_something() finish = time.perf_counter() print("total time taken is ", finish - start)

[ "ashishbisht723@gmail.com" ]

ashishbisht723@gmail.com

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/kaggle_event_recommendation/event_attendees.py

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loyalzc/recommendation-system

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# -*- coding: utf-8 -*- """ @author: Infaraway @time: 2018/5/10 21:07 @Function: event 热度活跃度 """ import scipy.sparse as ss import scipy.io as sio from sklearn.preprocessing import normalize class EventAttendees: """ 统计活动参加和不参加的人数 """ def __init__(self, user_event_entity): num_event = len(user_event_entity.event_index.keys()) self.event_poplarity = ss.dok_matrix((num_event, 1)) with open('data/event_attendees.csv', 'r') as event_att_f: event_att_f.readline() for line in event_att_f.readlines(): cols = line.strip().split(',') event_id = cols[0] if user_event_entity.event_index.__contains__(event_id): event_index = user_event_entity.event_index[event_id] # event 流行度 num_yes - num_no self.event_poplarity[event_index, 0] = len(cols[1].split(' ')) - len(cols[4].split(' ')) self.event_poplarity = normalize(self.event_poplarity, norm='l1', axis=0, copy=False) sio.mmwrite('prep_data/event_popularlity', self.event_poplarity)

[ "loyal.czhong@gmail.com" ]

loyal.czhong@gmail.com

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/twentyThirdSuccessorArray.py

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permissive

MSQFuersti/aoc2020

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puzzleInput = '614752839' # puzzleInput = '389125467' labels = [int(char) for char in puzzleInput] labels.extend(list(range(10, 1000001))) successors = {} for index, label in enumerate(labels): successors[label] = labels[index + 1] if index + 1 < len(labels) else labels[0] currentCup = labels[0] for _ in range(10000000): cupOne = successors[currentCup] cupTwo = successors[cupOne] cupThree = successors[cupTwo] takenCups = [cupOne, cupTwo, cupThree] successors[currentCup] = successors[cupThree] destinationCup = currentCup - 1 while True: if destinationCup < 1: destinationCup = 1000000 continue if destinationCup in takenCups: destinationCup = destinationCup - 1 continue break successors[cupThree] = successors[destinationCup] successors[destinationCup] = cupOne currentCup = successors[currentCup] print(successors[1] * successors[successors[1]])

[ "maximilian.fuerst@tngtech.com" ]

maximilian.fuerst@tngtech.com

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/apps/pinkslips/migrations/0013_auto_20180303_2042.py

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[]

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vincentereyes/phpslips

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2021-01-25T14:55:25.253031

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# -*- coding: utf-8 -*- # Generated by Django 1.10 on 2018-03-03 20:42 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('pinkslips', '0012_auto_20180303_0916'), ] operations = [ migrations.AlterField( model_name='conversation', name='latitude', field=models.FloatField(default=121.031851), ), migrations.AlterField( model_name='conversation', name='latitude2', field=models.FloatField(default=121.057307), ), migrations.AlterField( model_name='conversation', name='longitude', field=models.FloatField(default=14.652723), ), migrations.AlterField( model_name='conversation', name='longitude2', field=models.FloatField(default=14.605568), ), ]

[ "vincentejosereyes@gmail.com" ]

vincentejosereyes@gmail.com

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/rent/apps/authentication/models.py

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permissive

lenileiro/rent-django

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refs/heads/develop

2022-12-12T13:25:44.584374

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import re from datetime import datetime, timedelta from django.conf import settings from django.contrib.auth.models import ( AbstractBaseUser, BaseUserManager, PermissionsMixin ) from django.db import models class Utils: @staticmethod def validate_email(email): check_email = User.objects.filter(email=email) email_regex = r'^([a-zA-Z0-9_\-\.]+)@((\[[0-9]{1,3}\.[0-9]{1,3}\.[0-9]{1,3}\.)|(([a-zA-Z0-9\-]+\.)+))([a-zA-Z]{2,4}|[0-9]{1,3})(\]?)$' if not re.search(email_regex, email): raise TypeError("Incorrect email format please try again") if check_email.exists(): raise TypeError("This email has already been used to create a user") return email # Create your models here. class UserManager(BaseUserManager): def create_user(self, name=None, email=None, password=None): """Create and return a `User` with an email, username and password.""" if name is None: raise TypeError('User must have a name.') if email is None: raise TypeError('User must have an email address.') if password is None: raise TypeError('User Account must have a password.') Utils.validate_email(email) user = self.model(name=name, email=self.normalize_email(email)) user.set_password(password) user.save() return user def create_superuser(self, name, email, password): if password is None: raise TypeError('Superuser must have a password.') user = self.create_user(name, email, password) user.is_superuser = True user.is_staff = True user.save() return user def create_owneruser(self, name, email, password): user = self.create_user(name, email, password) user.is_owner = True user.save() return user def create_vendoruser(self, name, email, password, phone=None,contactperson=None): if phone is None: raise TypeError('Vendor Account must have a phone number.') if contactperson is None: raise TypeError('Vendor Account must have a contactperson.') user = self.create_user(name, email, password) user.is_vendor = True user.phone = phone user.contactperson = contactperson user.save() return user class User(AbstractBaseUser, PermissionsMixin): name = models.CharField(max_length=255) email = models.EmailField(db_index=True, unique=True) is_active = models.BooleanField(default=True) is_vendor = models.BooleanField(default=False) is_owner = models.BooleanField(default=False) is_active = models.BooleanField(default=True) is_staff = models.BooleanField(default=False) created_at = models.DateTimeField(auto_now_add=True) phone = models.CharField(default=False, max_length=255) contactperson = models.CharField(default=False, max_length=255) USERNAME_FIELD = 'email' REQUIRED_FIELDS = ['name'] objects = UserManager() def __str__(self): return self.email @property def is_SuperUser(self): return self.is_superuser @property def is_Owner(self): return self.is_owner @property def is_Vendor(self): return self.is_vendor

[ "lenileiro@gmail.com" ]

lenileiro@gmail.com

b2b49c260bbc149b1e3d30761562a7747b95be52

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/Source/TensorFlowModels/Media-Eval/Regression/preprocess.py

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[]

no_license

aneekroy/mir

b317d5ff49341507ad223016d9488763d4dcc71b

1afdc020acc6732b4dab488d7070951a2ff8eaa6

refs/heads/master

2020-03-21T17:48:10.747151

2018-06-28T11:26:38

138,854,870

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import os import math import numpy as np import ffmpy from python_speech_features import * import scipy.io.wavfile as wav import matplotlib.pyplot as plt import random import Image import csv # This is subject to change if cross-validation is applied. Sticking to this cheap stuff for the time being. TRAIN_PERCENT = 0.8 def normalize(A): M = [[x]*A.shape[1] for x in np.mean(A, axis = 1)] S = [[x]*A.shape[1] for x in np.std(A, axis = 1) if x is not 0] A = (A - M) for i in range(len(A)): for j in range(len(A[i])): if S[i][j] != 0.0: A[i][j] = A[i][j]/S[i][j] return A ''' Converts mp3 files in mp3dir to wav files in wavdir ''' def convert_mp3_to_wav(mp3dir, wavdir): for _,_,files in os.walk(mp3dir): for file in files: filename = file[:file.find(".mp3")] ff = ffmpy.FFmpeg(inputs = {os.path.join(mp3dir, filename + ".mp3"):None}, outputs = {os.path.join(wavdir, filename + ".wav"):None}) ff.run() ''' Converts wav files in wavdir to corresponding spectograms in specdir ''' def convert_wav_to_spec(wavdir, specdir): for _,_, files in os.walk(wavdir): for file in files: filename = file[:file.find(".wav")] (rate, sig) = wav.read(os.path.join(wavdir, file)) sig = sig[:, 0] plt.clf() _,_,_,spec = plt.specgram(sig, NFFT = 256, Fs = rate, noverlap = 16, cmap = plt.cm.jet) image = Image.fromarray(spec) image = image.convert("L") image.save(os.path.join(specdir, filename + ".jpg")) LAB_CLASSES = {"Happy":0, "Sad":1, "Tender":2, "Anger_Fear":3} ''' Extracts mfcc and delta features from the wav files and ''' def extract_features(wavdir, featuredir): f = open("/home/soms/EmotionMusic/MediaEval/new-label-file.txt", "w") infohandle = open("/home/soms/EmotionMusic/MediaEval-2013/annotations/songs_info.csv", "r") labelhandle = open("/home/soms/EmotionMusic/MediaEval-2013/annotations/static_annotations.csv", "r") reader1 = csv.reader(infohandle) train = [] test = [] i = 0 for row in reader1: if i !=0 : if row[-1] == "development": train.append(row[0]) else: test.append(row[0]) i += 1 i = 0 labels = {} reader2 = csv.reader(labelhandle) m_a = 999. M_a = -999. m_v = 999. M_v = -999. for row in reader2: if i != 0: if float(row[1]) > M_a: M_a = float(row[1]) if float(row[1]) < m_a: m_a = float(row[1]) if float(row[3]) > M_v: M_v = float(row[3]) if float(row[3]) < m_v: m_v = float(row[3]) labels[row[0]] = [float(row[1]), float(row[3])] i += 1 for label in labels.keys(): labels[label][0] = float(labels[label][0] - m_a)/(M_a - m_a) - 0.5 labels[label][1] = float(labels[label][1] - m_v)/(M_v - m_v) - 0.5 for _,_, files in os.walk(wavdir): # Min shape 0 is 4458 m = 9999 cnt = 0 for file in files: filename = file[:file.find(".wav")] print(file) (rate, sig) = wav.read(os.path.join(wavdir, file)) log_energy_feat = normalize(logfbank(sig, rate, winlen = 0.025, winstep = 0.01, nfilt = 32, nfft = 1024)) shape = log_energy_feat.shape if shape[0] < 4500: log_energy_feat = np.resize(np.asarray(log_energy_feat), [4500, shape[1]]) log_energy_feat = log_energy_feat[:4500,:] image = np.split(log_energy_feat, 75) print("Image segmented shape", np.asarray(image).shape) index = 1 if filename in train: cnt += 1 for image_segment in image: if image_segment.shape[0] >= 57: image_segment = image_segment[image_segment.shape[0] - 57 : ] else: print("Shape error",image_segment.shape[0]) if image_segment.shape[0] < m: m = image_segment.shape[0] print(image_segment.shape) #plt.imsave(os.path.join(featuredir + "/Train", file + "_" + str(index) + ".png"), image_segment.T, cmap = "gray") f.write(filename + "_" + str(index) + " " + str(labels[filename][0]) + " " + str(labels[filename][1]) + " D\n") index += 1 elif filename in test: cnt += 1 for image_segment in image: image_segment = image_segment[image_segment.shape[0] - 57 : ] if image_segment.shape[0] < m: m = image_segment.shape[0] print(image_segment.shape) #plt.imsave(os.path.join(featuredir + "/Test", file + "_" + str(index) + ".png"), image_segment.T, cmap = "gray") f.write(filename + "_" + str(index) + " " + str(labels[filename][0]) + " " + str(labels[filename][1]) + " E\n") index += 1 else: print("Duplicate") print("Count = ", str(cnt)) print(m) if __name__ == "__main__": mp3dir = "/home/soms/EmotionMusic/MediaEval-2013/clips_45seconds" wavdir = "/home/soms/EmotionMusic/MediaEval/WavFiles" specdir = "/home/soms/EmotionMusic/Spectograms" featuredir = "/home/soms/EmotionMusic/MediaEval/Spec_Feat" #convert_mp3_to_wav(mp3dir, wavdir) #convert_wav_to_spec(wavdir, specdir) extract_features(wavdir, featuredir)

[ "aneek.roy5@gmail.com" ]

aneek.roy5@gmail.com

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Icaruspy/addons

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refs/heads/master

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# -*- encoding: utf-8 -*- ################################################################################# # # # Copyright (C) 2009 Renato Lima - Akretion, Gabriel C. Stabel # # # #This program is free software: you can redistribute it and/or modify # #it under the terms of the GNU General Public License as published by # #the Free Software Foundation, either version 3 of the License, or # #(at your option) any later version. # # # #This program is distributed in the hope that it will be useful, # #but WITHOUT ANY WARRANTY; without even the implied warranty of # #MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # #GNU General Public License for more details. # # # #You should have received a copy of the GNU General Public License # #along with this program. If not, see <http://www.gnu.org/licenses/>. # ################################################################################# from openerp.osv import osv, fields ############################################################################## # Parceiro Personalizado ############################################################################## class res_currency_rate(osv.osv): _inherit = 'res.currency.rate' _columns = { 'tasa': fields.integer('Tasa Gs', digits=(16,2), required=True ), } # funcion para calcular el ratio estandart de trabajo de openerp # en paraguay se usa el valor de tasa en guaranies def on_change_tasa(self, cr, uid, ids, tasa , rate ): rate3 = rate rate3 = 1.000000000 / tasa return {'value': { 'rate': rate3 } } res_currency_rate() # vim:expandtab:smartindent:tabstop=4:softtabstop=4:shiftwidth=4:

[ "camiloramirez90@hotmail.com" ]

camiloramirez90@hotmail.com

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/1-abrindoarquivo.py

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[]

no_license

pablogonzalezz/curso-python

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arquivo = open("carros.txt", "w")

[ "pablojuangonzalez@hotmail.com" ]

pablojuangonzalez@hotmail.com

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/src/gui_kill_creo.py

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permissive

loleven/creo_kill_proc

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refs/heads/master

2020-05-24T00:48:23.222996

2017-03-13T09:34:28

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# -*- coding: utf-8 -*- # Form implementation generated from reading ui file 'C:\Data\Development\PycharmProjects\KillCreo\src\gui_kill_creo.ui' # # Created by: PyQt5 UI code generator 5.5.1 # # WARNING! All changes made in this file will be lost! from PyQt5 import QtCore, QtGui, QtWidgets class Ui_frm_kill_creo(object): def setupUi(self, frm_kill_creo): frm_kill_creo.setObjectName("frm_kill_creo") frm_kill_creo.resize(400, 300) frm_kill_creo.setMinimumSize(QtCore.QSize(400, 300)) frm_kill_creo.setMaximumSize(QtCore.QSize(400, 300)) self.horizontalLayout_2 = QtWidgets.QHBoxLayout(frm_kill_creo) self.horizontalLayout_2.setObjectName("horizontalLayout_2") self.verticalLayout = QtWidgets.QVBoxLayout() self.verticalLayout.setObjectName("verticalLayout") self.list_result = QtWidgets.QListWidget(frm_kill_creo) self.list_result.setObjectName("list_result") self.verticalLayout.addWidget(self.list_result) self.horizontalLayout = QtWidgets.QHBoxLayout() self.horizontalLayout.setObjectName("horizontalLayout") spacerItem = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum) self.horizontalLayout.addItem(spacerItem) self.btn_kill = QtWidgets.QPushButton(frm_kill_creo) self.btn_kill.setObjectName("btn_kill") self.horizontalLayout.addWidget(self.btn_kill) self.verticalLayout.addLayout(self.horizontalLayout) self.horizontalLayout_2.addLayout(self.verticalLayout) self.retranslateUi(frm_kill_creo) QtCore.QMetaObject.connectSlotsByName(frm_kill_creo) def retranslateUi(self, frm_kill_creo): _translate = QtCore.QCoreApplication.translate frm_kill_creo.setWindowTitle(_translate("frm_kill_creo", "Kill Creo")) self.btn_kill.setText(_translate("frm_kill_creo", "Kill Creo"))

[ "loleven@gmail.com" ]

loleven@gmail.com

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/non_semantic_speech_benchmark/distillation/train_keras_test.py

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refs/heads/master

2021-12-02T18:36:03.760434

2021-09-30T20:59:01

2021-09-30T21:07:02

156,725,548

1

0

Apache-2.0

2018-11-08T15:13:53

2018-11-08T15:13:52

null

UTF-8

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py

# coding=utf-8 # Copyright 2021 The Google Research Authors. # # 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. # Lint as: python3 """Tests for non_semantic_speech_benchmark.eval_embedding.keras.train_keras.""" from absl import flags from absl.testing import absltest from absl.testing import flagsaver from absl.testing import parameterized import mock import tensorflow as tf from non_semantic_speech_benchmark.distillation import train_keras def _get_data(*args, **kwargs): del args assert 'samples_key' in kwargs assert 'min_length' in kwargs assert 'batch_size' in kwargs bs = kwargs['batch_size'] samples = tf.zeros((bs, 16000), tf.float32) targets = tf.ones([bs, 10], tf.float32) return tf.data.Dataset.from_tensors((samples, targets)).repeat() class TrainKerasTest(parameterized.TestCase): @parameterized.parameters( {'bottleneck_dimension': 3, 'alpha': 1.0}, {'bottleneck_dimension': 5, 'alpha': 0.5}, ) def test_get_model(self, bottleneck_dimension, alpha): batched_samples = tf.zeros([3, 16000]) output_dimension = 10 targets = tf.ones([3, output_dimension]) model = train_keras.models.get_keras_model( f'mobilenet_debug_{alpha}_False', bottleneck_dimension=bottleneck_dimension, output_dimension=output_dimension) loss_obj = tf.keras.losses.MeanSquaredError() opt = tf.keras.optimizers.Adam() train_loss = tf.keras.metrics.MeanSquaredError() train_mae = tf.keras.metrics.MeanAbsoluteError() summary_writer = tf.summary.create_file_writer( absltest.get_default_test_tmpdir()) train_step = train_keras.get_train_step( model, loss_obj, opt, train_loss, train_mae, summary_writer) gstep = opt.iterations train_step(batched_samples, targets, gstep) self.assertEqual(1, gstep) train_step(batched_samples, targets, gstep) self.assertEqual(2, gstep) @mock.patch.object(train_keras.get_data, 'get_data', new=_get_data) @mock.patch.object(train_keras.hub, 'load') @flagsaver.flagsaver def test_full_flow(self, mock_load): del mock_load flags.FLAGS.file_pattern = 'dummy' flags.FLAGS.teacher_model_hub = 'dummy' flags.FLAGS.output_key = 'dummmy' flags.FLAGS.bottleneck_dimension = 2 flags.FLAGS.output_dimension = 10 flags.FLAGS.shuffle_buffer_size = 4 flags.FLAGS.samples_key = 'audio' flags.FLAGS.logdir = absltest.get_default_test_tmpdir() train_keras.train_and_report(debug=True) if __name__ == '__main__': tf.compat.v2.enable_v2_behavior() assert tf.executing_eagerly() absltest.main()

[ "copybara-worker@google.com" ]

copybara-worker@google.com

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# mecab neko.txt > neko.txt.mecab from p30 import load_mecab_output import collections if __name__ == '__main__': lines = load_mecab_output('neko.txt.mecab') lines = [l['surface'] for line in lines for l in line] counter = collections.Counter(lines) print(counter.most_common())

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N = int(input()) K = int(input()) x = list(map(int, input().split())) A=[] B=[] for i in range(len(x)): a = min(2*(x[i]), 2*abs(K-x[i])) A.append(a) print(sum(A))

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""" CS4243 Lab 4: Tracking Please read accompanying Jupyter notebook (lab4.ipynb) and PDF (lab4.pdf) for instructions. """ import cv2 import numpy as np import random from time import time # Part 1 def meanShift(dst, track_window, max_iter=100,stop_thresh=1): """Use mean shift algorithm to find an object on a back projection image. Args: dst (np.ndarray) : Back projection of the object histogram of shape (H, W). track_window (tuple) : Initial search window. (x,y,w,h) max_iter (int) : Max iteration for mean shift. stop_thresh(float) : Threshold for convergence. Returns: track_window (tuple) : Final tracking result. (x,y,w,h) """ completed_iterations = 0 """ YOUR CODE STARTS HERE """ centroid = np.array([-1,-1]) while (True): old_centroid = centroid x,y,w,h = track_window start_x = 0 if x < 0 else x start_y = 0 if y < 0 else y end_x = dst.shape[1]-1 if x+w >= dst.shape[1] else x+w end_y = dst.shape[0]-1 if y+h >= dst.shape[0] else y+h coord_x = np.array(list(np.arange(start_x,end_x)) * (end_y-start_y)) coord_y = np.array([[i]*(end_x-start_x) for i in range(start_y,end_y)]).flatten() weights = dst[start_y:end_y, start_x:end_x].flatten() centroid_x = coord_x.dot(weights) / weights.sum() centroid_y = coord_y.dot(weights) / weights.sum() centroid = np.array([centroid_y, centroid_x]) track_window = (int(centroid_x) - w//2, int(centroid_y) - h//2,w,h) completed_iterations += 1 if (np.max(np.abs(old_centroid-centroid)) <= stop_thresh or completed_iterations == max_iter): break """ YOUR CODE ENDS HERE """ return track_window def IoU(bbox1, bbox2): """ Compute IoU of two bounding boxes. Args: bbox1 (tuple) : First bounding box position (x, y, w, h) where (x, y) is the top left corner of the bounding box, and (w, h) are width and height of the box. bbox2 (tuple) : Second bounding box position (x, y, w, h) where (x, y) is the top left corner of the bounding box, and (w, h) are width and height of the box. Returns: score (float) : computed IoU score. """ x1, y1, w1, h1 = bbox1 x2, y2, w2, h2 = bbox2 score = 0 """ YOUR CODE STARTS HERE """ box1_x_start, box1_x_end, box1_y_start, box1_y_end = x1, x1+w1, y1, y1+h1 box2_x_start, box2_x_end, box2_y_start, box2_y_end = x2, x2+w2, y2, y2+h2 x_interset = box1_x_end-box2_x_start if box1_x_start < box2_x_start else box2_x_end-box1_x_start y_interset = box1_y_end-box2_y_start if box1_y_start < box2_y_start else box2_y_end-box1_y_start score = (x_interset*y_interset) / (w1*h1 + w2*h2 - x_interset*y_interset) # sumOfAreas = w1*h1 + w2*h2 # x1_prime = x1 + w1 # y1_prime = y1 + h1 # x2_prime = x2 + w2 # y2_prime = y2+ h2 # x_lower_bound = max(x1, x2) # x_upper_bound = min(x1_prime, x2_prime) # y_lower_bound = max(y1, y2) # y_upper_bound = min(y1_prime, y2_prime) # if x_upper_bound < x_lower_bound : # return 0 # if y_upper_bound < y_lower_bound : # return 0 # intersect = (x_upper_bound - x_lower_bound) * (y_upper_bound - y_lower_bound) # score = intersect / (sumOfAreas - intersect) # xA = max(x1, x2) # yA = max(y1, y2) # xB = min(x1+w1, x2+w2) # yB = min(y1+h1, y2+h2) # # compute the area of intersection rectangle # interArea = (xB - xA) * (yB - yA) # # compute the area of both the prediction and ground-truth # # rectangles # boxAArea = w1*h1 # boxBArea = w2*h2 # # compute the intersection over union by taking the intersection # # area and dividing it by the sum of prediction + ground-truth # # areas - the interesection area # score = interArea / float(boxAArea + boxBArea - interArea) # return the intersection over union value # """ YOUR CODE ENDS HERE """ # return score """ YOUR CODE ENDS HERE """ return score # Part 2: def lucas_kanade(img1, img2, keypoints, window_size=9): """ Estimate flow vector at each keypoint using Lucas-Kanade method. Args: img1 (np.ndarray) : Grayscale image of the current frame. Flow vectors are computed with respect to this frame. img2 (np.ndarray) : Grayscale image of the next frame. keypoints (np.ndarray) : Coordinates of keypoints to track of shape (N, 2). window_size (int) : Window size to determine the neighborhood of each keypoint. A window is centered around the current keypoint location. You may assume that window_size is always an odd number. Returns: flow_vectors (np.ndarray) : Estimated flow vectors for keypoints. flow_vectors[i] is the flow vector for keypoint[i]. Array of shape (N, 2). Hints: - You may use np.linalg.inv to compute inverse matrix. """ assert window_size % 2 == 1, "window_size must be an odd number" flow_vectors = [] w = window_size // 2 # Compute partial derivatives Iy, Ix = np.gradient(img1) It = img2 - img1 # For each [y, x] in keypoints, estimate flow vector [vy, vx] # using Lucas-Kanade method and append it to flow_vectors. for y, x in keypoints: # Keypoints can be loacated between integer pixels (subpixel locations). # For simplicity, we round the keypoint coordinates to nearest integer. # In order to achieve more accurate results, image brightness at subpixel # locations can be computed using bilinear interpolation. y, x = int(round(y)), int(round(x)) """ YOUR CODE STARTS HERE """ A = [] b = [] for j in range(y-w, y+w+1): for i in range(x-w, x+w+1): A.append([Ix[j][i], Iy[j][i]]) b.append(It[j][i] * -1) v,_,_,_ = np.linalg.lstsq(A, b, rcond=None) flow_vectors.append(v) """ YOUR CODE ENDS HERE """ flow_vectors = np.array(flow_vectors) return flow_vectors def compute_error(patch1, patch2): """ Compute MSE between patch1 and patch2 - Normalize patch1 and patch2 - Compute mean square error between patch1 and patch2 Args: patch1 (np.ndarray) : Grayscale image patch1 of shape (patch_size, patch_size) patch2 (np.ndarray) : Grayscale image patch2 of shape (patch_size, patch_size) Returns: error (float) : Number representing mismatch between patch1 and patch2. """ assert patch1.shape == patch2.shape, 'Differnt patch shapes' error = 0 """ YOUR CODE STARTS HERE """ std_patch1 = np.std(patch1) mean_patch1 = np.mean(patch1) norm_patch1 = (patch1 - mean_patch1) / std_patch1 std_patch2 = np.std(patch2) mean_patch2 = np.mean(patch2) norm_patch2 = (patch2 - mean_patch2) / std_patch2 error = np.square(np.subtract(norm_patch1, norm_patch2)).mean() """ YOUR CODE ENDS HERE """ return error def iterative_lucas_kanade(img1, img2, keypoints, window_size=9, num_iters=5, g=None): """ Estimate flow vector at each keypoint using iterative Lucas-Kanade method. Args: img1 (np.ndarray) : Grayscale image of the current frame. Flow vectors are computed with respect to this frame. img2 (np.ndarray) : Grayscale image of the next frame. keypoints (np.ndarray) : Coordinates of keypoints to track of shape (N, 2). window_size (int) : Window size to determine the neighborhood of each keypoint. A window is centered around the current keypoint location. You may assume that window_size is always an odd number. num_iters (int) : Number of iterations to update flow vector. g (np.ndarray) : Flow vector guessed from previous pyramid level. Array of shape (N, 2). Returns: flow_vectors (np.ndarray) : Estimated flow vectors for keypoints. flow_vectors[i] is the flow vector for keypoint[i]. Array of shape (N, 2). """ assert window_size % 2 == 1, "window_size must be an odd number" # Initialize g as zero vector if not provided if g is None: g = np.zeros(keypoints.shape) flow_vectors = [] w = window_size // 2 # Compute spatial gradients Iy, Ix = np.gradient(img1) for y, x, gy, gx in np.hstack((keypoints, g)): v = np.zeros(2) # Initialize flow vector as zero vector y1 = int(round(y)); x1 = int(round(x)) """ YOUR CODE STARTS HERE """ G = np.array([[0,0],[0,0]]) # for j in range(y1-w, y1+w+1): # for i in range(x1-w, x1+w+1): for i in range(x1-w, x1+w+1): for j in range(y1-w, y1+w+1): G = G + ([ [(Ix[j][i])**2, Ix[j][i]*Iy[j][i]], [Ix[j][i]*Iy[j][i], (Iy[j][i])**2] ]) for k in range(num_iters): b_k = np.array([0,0]) for i in range(x1-w, x1+w+1): for j in range(y1-w, y1+w+1): v_x = int(round(v[0])) v_y = int(round(v[1])) x_idx = int(round(i + gx + v_x)) y_idx = int(round(j + gy + v_y)) temp_diff = img1[j][i] - (img2[y_idx][x_idx]) to_add = np.array([temp_diff * Ix[j][i], temp_diff * Iy[j][i] ]) b_k = b_k + to_add G_inv = np.linalg.inv(G) v_k = np.matmul(G_inv, b_k) v = v + v_k """ YOUR CODE ENDS HERE """ vx, vy = v flow_vectors.append([vy, vx]) return np.array(flow_vectors) def pyramid_lucas_kanade(img1, img2, keypoints, window_size=9, num_iters=5, level=2, scale=2): """ Pyramidal Lucas Kanade method Args: img1 (np.ndarray) : Grayscale image of the current frame. Flow vectors are computed with respect to this frame. img2 (np.ndarray) : Grayscale image of the next frame. keypoints (np.ndarray) : Coordinates of keypoints to track of shape (N, 2). window_size (int) : Window size to determine the neighborhood of each keypoint. A window is centered around the current keypoint location. You may assume that window_size is always an odd number. num_iters (int) : Number of iterations to run iterative LK method level (int) : Max level in image pyramid. Original image is at level 0 of the pyramid. scale (float) : Scaling factor of image pyramid. Returns: d - final flow vectors """ # Build image pyramids of img1 and img2 pyramid1 = tuple(pyramid_gaussian(img1, max_layer=level, downscale=scale)) pyramid2 = tuple(pyramid_gaussian(img2, max_layer=level, downscale=scale)) # Initialize pyramidal guess g = np.zeros(keypoints.shape) """ YOUR CODE STARTS HERE """ # range from l to 0 for l in range(level, -1, -1): p_L = keypoints / (scale**l) d = iterative_lucas_kanade(pyramid1[l], pyramid2[l], p_L, g=g) if l != 0: g = scale * (g + d) """ YOUR CODE ENDS HERE """ d = g + d return d """Helper functions: You should not have to touch the following functions. """ import os import cv2 import matplotlib.pyplot as plt from matplotlib import animation from matplotlib.patches import Rectangle from skimage import filters, img_as_float from skimage.io import imread from skimage.transform import pyramid_gaussian def load_frames_rgb(imgs_dir): frames = [cv2.cvtColor(cv2.imread(os.path.join(imgs_dir, frame)), cv2.COLOR_BGR2RGB) \ for frame in sorted(os.listdir(imgs_dir))] return frames def load_frames_as_float_gray(imgs_dir): frames = [img_as_float(imread(os.path.join(imgs_dir, frame), as_gray=True)) \ for frame in sorted(os.listdir(imgs_dir))] return frames def load_bboxes(gt_path): bboxes = [] with open(gt_path) as f: for line in f: x, y, w, h = line.split(',') #x, y, w, h = line.split() bboxes.append((int(x), int(y), int(w), int(h))) return bboxes def animated_frames(frames, figsize=(10,8)): fig, ax = plt.subplots(figsize=figsize) ax.axis('off') im = ax.imshow(frames[0]) def animate(i): im.set_array(frames[i]) return [im,] ani = animation.FuncAnimation(fig, animate, frames=len(frames), interval=60, blit=True) return ani def animated_bbox(frames, bboxes, figsize=(10,8)): fig, ax = plt.subplots(figsize=figsize) ax.axis('off') im = ax.imshow(frames[0]) x, y, w, h = bboxes[0] bbox = ax.add_patch(Rectangle((x,y),w,h, linewidth=3, edgecolor='r', facecolor='none')) def animate(i): im.set_array(frames[i]) bbox.set_bounds(*bboxes[i]) return [im, bbox,] ani = animation.FuncAnimation(fig, animate, frames=len(frames), interval=60, blit=True) return ani def animated_scatter(frames, trajs, figsize=(10,8)): fig, ax = plt.subplots(figsize=figsize) ax.axis('off') im = ax.imshow(frames[0]) scat = ax.scatter(trajs[0][:,1], trajs[0][:,0], facecolors='none', edgecolors='r') def animate(i): im.set_array(frames[i]) if len(trajs[i]) > 0: scat.set_offsets(trajs[i][:,[1,0]]) else: # If no trajs to draw scat.set_offsets([]) # clear the scatter plot return [im, scat,] ani = animation.FuncAnimation(fig, animate, frames=len(frames), interval=60, blit=True) return ani def track_features(frames, keypoints, error_thresh=1.5, optflow_fn=pyramid_lucas_kanade, exclude_border=5, **kwargs): """ Track keypoints over multiple frames Args: frames - List of grayscale images with the same shape. keypoints - Keypoints in frames[0] to start tracking. Numpy array of shape (N, 2). error_thresh - Threshold to determine lost tracks. optflow_fn(img1, img2, keypoints, **kwargs) - Optical flow function. kwargs - keyword arguments for optflow_fn. Returns: trajs - A list containing tracked keypoints in each frame. trajs[i] is a numpy array of keypoints in frames[i]. The shape of trajs[i] is (Ni, 2), where Ni is number of tracked points in frames[i]. """ kp_curr = keypoints trajs = [kp_curr] patch_size = 3 # Take 3x3 patches to compute error w = patch_size // 2 # patch_size//2 around a pixel for i in range(len(frames) - 1): I = frames[i] J = frames[i+1] flow_vectors = optflow_fn(I, J, kp_curr, **kwargs) kp_next = kp_curr + flow_vectors new_keypoints = [] for yi, xi, yj, xj in np.hstack((kp_curr, kp_next)): # Declare a keypoint to be 'lost' IF: # 1. the keypoint falls outside the image J # 2. the error between points in I and J is larger than threshold yi = int(round(yi)); xi = int(round(xi)) yj = int(round(yj)); xj = int(round(xj)) # Point falls outside the image if yj > J.shape[0]-exclude_border-1 or yj < exclude_border or\ xj > J.shape[1]-exclude_border-1 or xj < exclude_border: continue # Compute error between patches in image I and J patchI = I[yi-w:yi+w+1, xi-w:xi+w+1] patchJ = J[yj-w:yj+w+1, xj-w:xj+w+1] error = compute_error(patchI, patchJ) if error > error_thresh: continue new_keypoints.append([yj, xj]) kp_curr = np.array(new_keypoints) trajs.append(kp_curr) return trajs

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"""Main module.""" import tempfile from railyard.assembler import readStacks, assembleStack import railyard.builder as builder import time import os def assemble(base_stack, additional_stacks, path): s = readStacks(base_stack, additional_stacks) if not os.path.exists(path): os.mkdir(path) if not os.path.exists(os.path.join(path, s['package_hash'])): os.mkdir(os.path.join(path, s['package_hash'])) assembleStack(s, os.path.join(path, s['package_hash'])) def test(base_stack, additional_stack): s = readStacks(base_stack, additional_stack) tag = 'ktaletsk/polus-notebook:' + s['package_hash'] # Create temporary folder for Dockerfile and additional files # Folder is securely created with `tempfile` and is destroyed afterwards with tempfile.TemporaryDirectory() as tmpdirname: assembleStack(s, tmpdirname) builder.build(tmpdirname, tag)

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#!/usr/bin/env python import sys from launchpadlib.launchpad import Launchpad __DEBUG__ = False DISTRO = 'ubuntu' RELEASE = 'trusty' ARCHITECTURE = 'amd64' DISTRO_ARCH_SERIES_URL = 'https://api.launchpad.net/1.0/' + DISTRO + '/' + RELEASE + '/' + ARCHITECTURE ## TODO: Accept multiple app names and get all their versions line per line def get_ppa_archive(launchpad, ppa_url): (prefix, rest_ppa_url) = str.split(ppa_url, ':') (owner_name, package_name) = str.split(rest_ppa_url, '/') owner = launchpad.people[owner_name] return owner.getPPAByName(name = package_name) def get_distro_archive(launchpad, distro_name = DISTRO): distro = launchpad.distributions[distro_name] return distro.main_archive def get_latest_source(archive, app_name, distro_url = DISTRO_ARCH_SERIES_URL): for series in archive.distribution.series.entries: if series['displayname'].lower() == RELEASE.lower(): distro_series = series['self_link'] sources = archive.getPublishedSources( status = 'Published', exact_match = True, source_name = app_name, distro_series = distro_series ) if len(sources) == 0: print("No entries found for app: " + sys.argv[1] + " in archive: " + str(archive)) sys.exit(1) elif __DEBUG__ == True: print("Found " + str(len(sources)) + " matches") for source in sources: print(str(source.display_name)) return sources[0] def get_latest_package(archive, app_name, distro_url = DISTRO_ARCH_SERIES_URL): binaries = archive.getPublishedBinaries( status = 'Published', exact_match = True, binary_name = app_name, distro_arch_series = distro_url ) if len(binaries) == 0: print("No entries found for app: " + sys.argv[1] + " in archive: " + str(archive)) sys.exit(1) elif __DEBUG__ == True: print("Found " + str(len(binaries)) + " matches") for sources in binaries: print(str(sources.display_name)) return binaries[0] def parse_args(): if len(sys.argv) < 2: print("Usage: " + sys.argv[0] + " <app name> [ppa url]") print("Example: " + sys.argv[0] + " solaar ppa:daniel.pavel/solaar") sys.exit(1) app_name = sys.argv[1] ppa_url = None if len(sys.argv) > 2: ppa_url = sys.argv[2] get_url = None if len(sys.argv) > 3: get_url = sys.argv[3].lower() == "--get_url" return { 'app_name': app_name, 'ppa_url': ppa_url, 'get_url': get_url } def print_api(obj): print('lp_attributes: ' + str(sorted(obj.lp_attributes))) print('lp_operations: ' + str(sorted(obj.lp_operations))) print('lp_entries: ' + str(sorted(obj.lp_entries))) print('lp_collections: ' + str(sorted(obj.lp_collections))) def get_archive(ppa_url): launchpad = Launchpad.login_anonymously('launchpad-helper', 'production') if ppa_url != None: return get_ppa_archive(launchpad, ppa_url) else: return get_distro_archive(launchpad) def get_latest_url(archive, app_name): return get_latest_source(archive, app_name).binaryFileUrls()[0] def get_latest_version(archive, app_name): return get_latest_source(archive, app_name).source_package_version if __name__ == '__main__': args = parse_args() archive = get_archive(args['ppa_url']) if args['get_url'] == True: print(get_latest_url(archive, args['app_name'])) else: print(get_latest_version(archive, args['app_name'])) sys.exit(0)

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import argparse import logging import subprocess import os from test.integration_tests.test_case import IntegrationTestCase, IntegrationTestType from test.integration_tests.config import * logging.basicConfig(level=logging.getLevelName(os.environ.get("TESTS_LOGLEVEL", "INFO"))) def test_is_system_running(result): assert result.strip() == "running" def test_timezone(extract_dir): link = os.readlink(f"{extract_dir}/etc/localtime") assert "Europe/Prague" in link def test_firewall(extract_dir): with open(f"{extract_dir}/etc/firewalld/zones/public.xml") as f: content = f.read() assert 'service name="http"' in content assert 'service name="ftp"' in content assert 'service name="telnet"' not in content assert 'port port="53" protocol="tcp"' in content assert 'port port="88" protocol="udp"' in content if __name__ == '__main__': parser = argparse.ArgumentParser(description='Run integration tests') parser.add_argument('--list', dest='list', action='store_true', help='list test cases') parser.add_argument('--build-pipeline', dest='build_pipeline', metavar='PIPELINE', type=os.path.abspath, help='the build pipeline to run tests in') parser.add_argument('--case', dest='specific_case', metavar='TEST_CASE', help='run single test case') args = parser.parse_args() logging.info(f"Using {OBJECTS} for objects storage.") logging.info(f"Using {OUTPUT_DIR} for output images storage.") logging.info(f"Using {OSBUILD} for building images.") f30_boot = IntegrationTestCase( name="f30-boot", pipeline="f30-boot.json", build_pipeline=args.build_pipeline, output_image="f30-boot.qcow2", test_cases=[test_is_system_running], type=IntegrationTestType.BOOT_WITH_QEMU ) timezone = IntegrationTestCase( name="timezone", pipeline="timezone.json", build_pipeline=args.build_pipeline, output_image="timezone.tar.xz", test_cases=[test_timezone], type=IntegrationTestType.EXTRACT ) firewall = IntegrationTestCase( name="firewall", pipeline="firewall.json", build_pipeline=args.build_pipeline, output_image="firewall.tar.xz", test_cases=[test_firewall], type=IntegrationTestType.EXTRACT ) cases = [f30_boot, timezone, firewall] if args.list: print("Available test cases:") for case in cases: print(f" - {case.name}") else: if not args.specific_case: for case in cases: case.run() else: for case in cases: if case.name == args.specific_case: case.run()

[ "teg@jklm.no" ]

teg@jklm.no

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508e3aa5be4a48354811726cdbf6555013fb431c

/main/services/product.py

e41edf07e5debd435859064954b3ad3c1d066110

[]

no_license

JoaoRicardoSimplicio/buying_cheap

eb32362e0cb40048547366666890b25e631e1616

6561411af72f5a69a22e1474c11f507807f5ca1c

refs/heads/master

2023-06-27T06:11:09.927143

2021-07-31T17:12:52

2021-08-01T23:18:44

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2020-09-11T17:16:53

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from main.crawlers.shop2gether import StoreShop2gether from main.crawlers.kabum import StoreKabum from main.crawlers.netshoes import StoreNetshoes from main.crawlers.mercado_livre import StoreMercadoLivre from main.crawlers.bikepointsc import StoreBikePointSC from main.crawlers.bikeinn import Bikeinn # from main.crawlers.zattini import StoreZattini from main.crawlers.dafiti import StoreDafiti from main.models import Product, Store class ProductTool: def __init__(self): pass def create(self, *args, **kwargs): for item in args: store_name = select_store(item['name']) try: store_crawler = store_name(item['url_product']) store, _ = Store.objects.get_or_create( name=store_crawler.store ) product, _ = Product.objects.update_or_create( url_product=store_crawler.url, defaults={ 'name': store_crawler.name, 'store': store, 'price_product': store_crawler.price, 'description_product': store_crawler.description, 'url_image': store_crawler.image, 'sizes': store_crawler.avaliable_sizes } ) except Exception as Error: raise Error def select_store(name): if name == "Shop2gether": return StoreShop2gether elif name == "Kabum": return StoreKabum elif name == "Netshoes": return StoreNetshoes elif name == "MercadoLivre": return StoreMercadoLivre elif name == "BikePointSC": return StoreBikePointSC elif name == "Zattini": return StoreZattini elif name == "Dafiti": return StoreDafiti elif name == "Bikeinn": return Bikeinn

[ "joaoricardosimplicio16@gmail.com" ]

joaoricardosimplicio16@gmail.com

6789dd297f2257bed4110e83f0000b3c798dea43

303004eb1b84f247d959a2ac75e38f6e7b156715

/baixar.py

3bcd31baf17c38c5ce04a46c8c7a0ee91754fbbb

[]

no_license

DiegoLins10/PyTube

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1787c6fa953bc04c69acc6fa0d28ac80ac9b3e07

refs/heads/master

2023-08-07T00:48:55.701767

2021-09-21T01:09:39

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## Para baixar a lib pytube: pip install pytube ## baixar um video do youtube com pytube ## python youtube.py https://youtu.be/61R6Qq7mmIA ## gerar arquivo exe pyinstaller --onefile -w baixar.py ## baixar pyinstaller pip install pyinstaller from pytube import YouTube from tkinter import * def baixar(): ##link = str(input("Digite o link: ")) link = vlink.get() video = YouTube(link) stream = video.streams.get_highest_resolution() stream.download() print("baixado com sucesso") ##pega o conteudo indo() def indo(): a= "baixado com sucesso" Label(app, text=a,background="#dde", foreground="#009", anchor=W).place(x=10, y=100, width=200, height=20) app = Tk() ##intanciando a classe app.title("Youtube video download") app.geometry("500x300") ## config tamanho janela app.configure(background="#dde") ## mudar cor Label(app, text="Link",background="#dde", foreground="#009", anchor=W).place(x=10, y=10, width=100, height=20) ##declarar label ## posicionando o label vlink = Entry(app) vlink.place(x=10, y=30, width=200, height=20) Button(app, text="Baixar", command=baixar).place(x=10, y=80, width=100, height=20) app.mainloop()

[ "diegofernandeslins@gmail.com" ]

diegofernandeslins@gmail.com

e23788ac80dab20c3cb46f534e00b56e8af74fb5

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/accounts/migrations/0007_auto_20210307_1517.py

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[]

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alishkb/m42.django

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refs/heads/master

2023-05-15T00:51:20.440239

2021-06-10T11:56:14

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# Generated by Django 3.1.5 on 2021-03-07 11:47 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('auth', '0012_alter_user_first_name_max_length'), ('accounts', '0006_auto_20210307_1515'), ] operations = [ migrations.AddField( model_name='user', name='groups', field=models.ManyToManyField(blank=True, help_text='The groups this user belongs to. A user will get all permissions granted to each of their groups.', related_name='user_set', related_query_name='user', to='auth.Group', verbose_name='groups'), ), migrations.AddField( model_name='user', name='is_superuser', field=models.BooleanField(default=False, help_text='Designates that this user has all permissions without explicitly assigning them.', verbose_name='superuser status'), ), migrations.AddField( model_name='user', name='user_permissions', field=models.ManyToManyField(blank=True, help_text='Specific permissions for this user.', related_name='user_set', related_query_name='user', to='auth.Permission', verbose_name='user permissions'), ), ]

[ "ashokouhabdi@gmail.com" ]

ashokouhabdi@gmail.com

61c84d50f2b2a028c625d955f4e469b7c998c883

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/env/lib/python3.8/site-packages/torpedo/clients.py

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permissive

vivekgupta1mg/miniprojectapp

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2584a1696cc847767998c4fb5cf3413b006d6f67

refs/heads/master

2023-08-24T12:57:51.653656

2021-10-12T16:15:25

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import threading from elasticapm import Client, async_capture_span from elasticapm.instrumentation import register from elasticapm.instrumentation.packages.asyncio.aiohttp_client import \ AioHttpClientInstrumentation from elasticapm.instrumentation.packages.asyncio.asyncpg import \ AsyncPGInstrumentation from elasticapm.instrumentation.packages.dbapi2 import extract_signature from elasticapm.instrumentation.register import _instrumentation_singletons from elasticapm.traces import DroppedSpan, execution_context from elasticapm.utils import (default_ports, get_host_from_url, sanitize_url, url_to_destination) from elasticapm.utils.disttracing import TracingOptions from elasticapm.utils.module_import import import_string from .common_utils import CONFIG _lock = threading.Lock() apm_config = CONFIG.config.get('APM') apm_config['SERVICE_NAME'] = CONFIG.config.get('NAME', 'undefined') class CustomAioHttpClientInstrumentation(AioHttpClientInstrumentation): async def call(self, module, method, wrapped, instance, args, kwargs): method = kwargs["method"] if "method" in kwargs else args[0] url = kwargs["url"] if "url" in kwargs else args[1] url = str(url) destination = url_to_destination(url) signature = " ".join([method.upper(), get_host_from_url(url)]) sub_type = get_host_from_url(url) url = sanitize_url(url) transaction = execution_context.get_transaction() async with async_capture_span( signature, span_type="external", span_subtype=sub_type, extra={"http": {"url": url}, "destination": destination}, leaf=True, ) as span: leaf_span = span while isinstance(leaf_span, DroppedSpan): leaf_span = leaf_span.parent parent_id = leaf_span.id if leaf_span else transaction.id trace_parent = transaction.trace_parent.copy_from( span_id=parent_id, trace_options=TracingOptions(recorded=True) ) headers = kwargs.get("headers") or {} self._set_disttracing_headers(headers, trace_parent, transaction) kwargs["headers"] = headers response = await wrapped(*args, **kwargs) if response: if span.context: span.context["http"]["status_code"] = response.status span.set_success() if response.status < 400 else span.set_failure() # pylint: disable=W0106 return response class CustomAsyncPGInstrumentation(AsyncPGInstrumentation): async def call(self, module, method, wrapped, instance, args, kwargs): query = args[0] if len(args) else kwargs["query"] name = extract_signature(query) context = {"db": {"type": "sql", "statement": query}} action = "query" destination_info = { "address": kwargs.get("host", "localhost"), "port": int(kwargs.get("port", default_ports.get("postgresql"))), "service": {"name": "postgres", "resource": "postgres", "type": "db"}, } context['destination'] = destination_info async with async_capture_span( name, leaf=True, span_type="db", span_subtype="postgres", span_action=action, extra=context ): return await wrapped(*args, **kwargs) def instrument(): """ Instruments all registered methods/functions with a wrapper """ with _lock: for obj in register.get_instrumentation_objects(): custom = False if isinstance(obj, AioHttpClientInstrumentation): obj = 'torpedo.clients.CustomAioHttpClientInstrumentation' custom = True elif isinstance(obj, AsyncPGInstrumentation): obj = 'torpedo.clients.CustomAsyncPGInstrumentation' custom = True if custom: cls = import_string(obj) _instrumentation_singletons[obj] = cls() obj = _instrumentation_singletons[obj] obj.instrument() apm_client = Client(config=apm_config) instrument()

[ "cons-vivek.gupta@1mg.com" ]

cons-vivek.gupta@1mg.com

c51438dca74d9ee3958675018e129bb173024552

c82490eb4903a9b6b23bdef7c528def682a2672f

/eightpuzzle.py

61084b4c00b5f6223a01ba7230814872d31e6b42

[]

no_license

zariuq/Misc

5563b3f8c015c305a6023dca42e8e616e8dc146b

e5b5e4229b29a68e4a3d042bed62c200d3af8e2e

refs/heads/master

2020-05-20T23:55:43.067819

2015-08-10T07:48:06

30,136,064

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UTF-8

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import pygame from eightpuzzlesolver import aStar pygame.init() WIDTH = 500 HEIGHT = 400 screen = pygame.display.set_mode((WIDTH, HEIGHT)) done = False clock = pygame.time.Clock() # Switch for human or playthrough HUMAN = False # Define the colors we will use in RGB format BLACK = ( 0, 0, 0) WHITE = (255, 255, 255) BLUE = ( 0, 0, 255) GREEN = ( 0, 255, 0) RED = (255, 0, 0) # Find centers centers = [] box_w = WIDTH // 3 box_h = HEIGHT // 3 for j in range(1,4): # switched i and j to make numbers ordered horizontally. for i in range(1,4): #remember the colon! centers = centers + [( (i * box_w + (i-1) * box_w) // 2 , (j * box_h + (j-1) * box_h) // 2 )] #print (centers) # Render numbers font = pygame.font.Font(None, 36) numbers = [] for i in range(0,9): # Don't forget the colon, damnit! numbers = numbers + [font.render(str(i), True, BLUE)] # Default board position (to be randomized eventually) board2 = [1,5,3,2,8,4,6,7,0] # no solution? board4 = [1,0,2,4,5,3,6,7,8] win = [0,1,2,3,4,5,6,7,8] # Surely there's a better way to do this. It reeks of a succinct pattern... def get_adjacent(index): if index == 0: return [1,3] if index == 1: return [0,2,4] if index == 2: return [1,5] if index == 3: return [0,4,6] if index == 4: return [1,3,5,7] if index == 5: return [2,4,8] if index == 6: return [3,7] if index == 7: return [4,6,8] if index == 8: return [5,7] return [] coordinates = [(0,0),(1,0),(2,0),(0,1),(1,1),(2,1),(0,2),(1,2),(2,2)] # originally (s1,s2), but I'll assume one goal-state :P def man_distance(s1): sum = 0 for i in range(0,9): #Fucking colon! ideal = coordinates[win[i]] current = coordinates[s1[i]] #I had board[i] instead of s1[i] sum += abs(ideal[0] - current[0]) + abs(ideal[1] -current[1]) return sum if HUMAN == True: while not done: nearest_center = -1 for event in pygame.event.get(): if event.type == pygame.QUIT: done = True if event.type == pygame.KEYDOWN: #need to check this or .key produces an error if event.key == pygame.K_ESCAPE: done = True if event.type == pygame.MOUSEBUTTONDOWN: posi = event.pos print (posi) # find nearest center -- should totally be a function of its own :p dis = [] px = posi[0] py = posi[1] for i in range(0,9): dis = dis + [(px-centers[i][0])**2 + (py-centers[i][1])**2] # powers are with **, not ^ nearest_center = dis.index(min(dis)) print ("The position is: " + str(board[nearest_center])) # perform swap if next to 0. if nearest_center != -1: zero_loc = board.index(0) adjacents = get_adjacent(nearest_center) if zero_loc in adjacents: board[zero_loc] = board[nearest_center] board[nearest_center] = 0 print("The Manhattan distance is: " + str(man_distance(board))) else: screen.fill(RED) # red doesn't pygame.time.wait(500) # wait works if win == board: # works, but just closing is ugly done = True #doing anything else without more modular code seems icky :p screen.fill(WHITE) # Draw the grid for i in range(1,3): # range(1,2) only drew one line! pygame.draw.line(screen, BLACK, [i * (WIDTH // 3), 0], [i * (WIDTH // 3),HEIGHT], 5) pygame.draw.line(screen, BLACK, [0, i * (HEIGHT // 3)], [WIDTH, i * (HEIGHT // 3)],5) # Draw 'numbers' for i in range(0,9): p = board[i] if p != 0: screen.blit(numbers[p], (centers[i][0] - numbers[p].get_width() // 2, centers[i][1] - numbers[p].get_height() // 2)) #pygame.draw.circle(screen, BLUE, centers[i], 10) pygame.display.flip() clock.tick(60) else: solution = aStar(board4,win) print("Length = " + str(len(solution))) print("Solution = " + str(solution)) for step in solution: for event in pygame.event.get(): if event.type == pygame.QUIT: done = True if event.type == pygame.KEYDOWN: #need to check this or .key produces an error if event.key == pygame.K_ESCAPE: done = True screen.fill(WHITE) # Draw the grid for i in range(1,3): # range(1,2) only drew one line! pygame.draw.line(screen, BLACK, [i * (WIDTH // 3), 0], [i * (WIDTH // 3),HEIGHT], 5) pygame.draw.line(screen, BLACK, [0, i * (HEIGHT // 3)], [WIDTH, i * (HEIGHT // 3)],5) # Draw 'numbers' for i in range(0,9): p = step[i] if p != 0: screen.blit(numbers[p], (centers[i][0] - numbers[p].get_width() // 2, centers[i][1] - numbers[p].get_height() // 2)) #pygame.draw.circle(screen, BLUE, centers[i], 10) pygame.display.flip() pygame.time.wait(350) clock.tick(60) pygame.time.wait(1500)

[ "zariuq@gmail.com" ]

zariuq@gmail.com

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/codigo/tutorial/tut0C_camara.py

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javacasm/TutorialPyGame

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refs/heads/master

2021-07-25T20:01:04.504958

2021-05-10T12:33:26

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https://www.pygame.org/docs/tut/CameraIntro.html ```python class Capture(object): def __init__(self): self.size = (640,480) # create a display surface. standard pygame stuff self.display = pygame.display.set_mode(self.size, 0) # this is the same as what we saw before self.clist = pygame.camera.list_cameras() if not self.clist: raise ValueError("Sorry, no cameras detected.") self.cam = pygame.camera.Camera(self.clist[0], self.size) self.cam.start() # create a surface to capture to. for performance purposes # bit depth is the same as that of the display surface. self.snapshot = pygame.surface.Surface(self.size, 0, self.display) def get_and_flip(self): # if you don't want to tie the framerate to the camera, you can check # if the camera has an image ready. note that while this works # on most cameras, some will never return true. if self.cam.query_image(): self.snapshot = self.cam.get_image(self.snapshot) # blit it to the display surface. simple! self.display.blit(self.snapshot, (0,0)) pygame.display.flip() def main(self): going = True while going: events = pygame.event.get() for e in events: if e.type == QUIT or (e.type == KEYDOWN and e.key == K_ESCAPE): # close the camera safely self.cam.stop() going = False self.get_and_flip() ```

[ "javacasm@gmail.com" ]

javacasm@gmail.com

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/src/the_tale/the_tale/game/companions/tests/test_abilities_effects.py

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refs/heads/develop

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import smart_imports smart_imports.all() effects = companions_abilities_effects MODIFIERS = heroes_relations.MODIFIERS class BaseEffectsTests(utils_testcase.TestCase): def setUp(self): super(BaseEffectsTests, self).setUp() game_logic.create_test_map() self.account = self.accounts_factory.create_account() self.storage = game_logic_storage.LogicStorage() self.storage.load_account_data(self.account.id) self.hero = self.storage.accounts_to_heroes[self.account.id] self.companion_record = logic.create_companion_record(utg_name=game_names.generator().get_test_name(), description='description', type=tt_beings_relations.TYPE.random(), max_health=10, dedication=relations.DEDICATION.random(), archetype=game_relations.ARCHETYPE.random(), mode=relations.MODE.random(), abilities=companions_abilities_container.Container(), communication_verbal=tt_beings_relations.COMMUNICATION_VERBAL.random(), communication_gestures=tt_beings_relations.COMMUNICATION_GESTURES.random(), communication_telepathic=tt_beings_relations.COMMUNICATION_TELEPATHIC.random(), intellect_level=tt_beings_relations.INTELLECT_LEVEL.random(), structure=tt_beings_relations.STRUCTURE.random(), features=frozenset((tt_beings_relations.FEATURE.random(), tt_beings_relations.FEATURE.random())), movement=tt_beings_relations.MOVEMENT.random(), body=tt_beings_relations.BODY.random(), size=tt_beings_relations.SIZE.random(), orientation=tt_beings_relations.ORIENTATION.random(), weapons=[artifacts_objects.Weapon(weapon=artifacts_relations.STANDARD_WEAPON.random(), material=tt_artifacts_relations.MATERIAL.random(), power_type=artifacts_relations.ARTIFACT_POWER_TYPE.random())], state=relations.STATE.ENABLED) self.hero.set_companion(logic.create_companion(self.companion_record)) def apply_ability(self, ability): container = companions_abilities_container.Container(common=(), start=frozenset((ability,)), coherence=None, honor=None, peacefulness=None) self.companion_record.abilities = container self.hero.reset_accessors_cache() def get_ability(self, *argv): return random.choice([ability for ability in effects.ABILITIES.records if any(isinstance(ability.effect, effect) for effect in argv)]) class CommonTests(BaseEffectsTests): def test_aprox(self): self.assertEqual(effects.aprox(1, 2, 1), 1.2) self.assertEqual(effects.aprox(1, 2, 2), 1.4) self.assertEqual(effects.aprox(1, 2, 3), 1.6) self.assertEqual(effects.aprox(1, 2, 4), 1.8) self.assertEqual(effects.aprox(1, 2, 5), 2) class CoherenceSpeedTests(BaseEffectsTests): def test_effect(self): effect = effects.CoherenceSpeed(0.8) self.assertEqual(effect._modify_attribute({}, MODIFIERS.COHERENCE_EXPERIENCE, 10), 8) self.assertEqual(effect._modify_attribute({}, MODIFIERS.COHERENCE_EXPERIENCE, 11), 8.8) self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.COHERENCE_EXPERIENCE,)), 11), 11) effect = effects.CoherenceSpeed(1.2) self.assertEqual(effect._modify_attribute({}, MODIFIERS.COHERENCE_EXPERIENCE, 10), 12) self.assertEqual(effect._modify_attribute({}, MODIFIERS.COHERENCE_EXPERIENCE, 11), 13.2) self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.COHERENCE_EXPERIENCE,)), 11), 11) def test_in_game(self): ability = self.get_ability(effects.CoherenceSpeed) self.hero.companion.coherence = c.COMPANIONS_MAX_COHERENCE - 1 self.hero.companion.experience = 0 self.hero.companion.add_experience(10) old_delta = self.hero.companion.experience self.hero.companion.experience = 0 self.apply_ability(ability) self.hero.companion.add_experience(10) new_delta = self.hero.companion.experience self.assertEqual(int(round(old_delta * ability.effect.multiplier_left)), new_delta) class ChangeHabitsTests(BaseEffectsTests): def test_effect(self): effect = effects.ChangeHabits(habit_type=game_relations.HABIT_TYPE.HONOR, habit_sources=(heroes_relations.HABIT_CHANGE_SOURCE.COMPANION_HONOR_NEUTRAL_1, heroes_relations.HABIT_CHANGE_SOURCE.COMPANION_HONOR_NEUTRAL_2)) self.assertEqual(effect._modify_attribute({}, MODIFIERS.HABITS_SOURCES, set()), set((heroes_relations.HABIT_CHANGE_SOURCE.COMPANION_HONOR_NEUTRAL_1, heroes_relations.HABIT_CHANGE_SOURCE.COMPANION_HONOR_NEUTRAL_2))) self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.HABITS_SOURCES,)), set()), set()) def check_habits_changed(self, honor, peacefulness, honor_check, peacefulness_check): self.hero.habit_honor.set_habit(honor) self.hero.habit_peacefulness.set_habit(peacefulness) for habit_source in self.hero.companion.modify_attribute(heroes_relations.MODIFIERS.HABITS_SOURCES, set()): self.hero.update_habits(habit_source) self.assertTrue(honor_check(self.hero.habit_honor.raw_value)) self.assertTrue(peacefulness_check(self.hero.habit_peacefulness.raw_value)) def test_in_game__aggressive(self): self.apply_ability(effects.ABILITIES.AGGRESSIVE) self.check_habits_changed(honor=-c.HABITS_BORDER, peacefulness=0, honor_check=lambda v: v == -c.HABITS_BORDER, peacefulness_check=lambda v: v < 0) self.check_habits_changed(honor=0, peacefulness=c.HABITS_BORDER, honor_check=lambda v: v == 0, peacefulness_check=lambda v: v < c.HABITS_BORDER) self.check_habits_changed(honor=c.HABITS_BORDER, peacefulness=-c.HABITS_BORDER, honor_check=lambda v: v == c.HABITS_BORDER, peacefulness_check=lambda v: v == -c.HABITS_BORDER) def test_in_game__peaceful(self): self.apply_ability(effects.ABILITIES.PEACEFUL) self.check_habits_changed(honor=-c.HABITS_BORDER, peacefulness=0, honor_check=lambda v: v == -c.HABITS_BORDER, peacefulness_check=lambda v: v > 0) self.check_habits_changed(honor=0, peacefulness=c.HABITS_BORDER, honor_check=lambda v: v == 0, peacefulness_check=lambda v: v == c.HABITS_BORDER) self.check_habits_changed(honor=c.HABITS_BORDER, peacefulness=-c.HABITS_BORDER, honor_check=lambda v: v == c.HABITS_BORDER, peacefulness_check=lambda v: v > -c.HABITS_BORDER) def test_in_game__reserved(self): self.apply_ability(effects.ABILITIES.RESERVED) self.check_habits_changed(honor=-c.HABITS_BORDER, peacefulness=0, honor_check=lambda v: v == -c.HABITS_BORDER, peacefulness_check=lambda v: v == 0) self.check_habits_changed(honor=0, peacefulness=c.HABITS_BORDER, honor_check=lambda v: v == 0, peacefulness_check=lambda v: v < c.HABITS_BORDER) self.check_habits_changed(honor=c.HABITS_BORDER, peacefulness=-c.HABITS_BORDER, honor_check=lambda v: v == c.HABITS_BORDER, peacefulness_check=lambda v: v > -c.HABITS_BORDER) def test_in_game__canny(self): self.apply_ability(effects.ABILITIES.CANNY) self.check_habits_changed(honor=-c.HABITS_BORDER, peacefulness=0, honor_check=lambda v: v > -c.HABITS_BORDER, peacefulness_check=lambda v: v == 0) self.check_habits_changed(honor=0, peacefulness=c.HABITS_BORDER, honor_check=lambda v: v == 0, peacefulness_check=lambda v: v == c.HABITS_BORDER) self.check_habits_changed(honor=c.HABITS_BORDER, peacefulness=-c.HABITS_BORDER, honor_check=lambda v: v < c.HABITS_BORDER, peacefulness_check=lambda v: v == -c.HABITS_BORDER) def test_in_game__honest(self): self.apply_ability(effects.ABILITIES.HONEST) self.check_habits_changed(honor=-c.HABITS_BORDER, peacefulness=0, honor_check=lambda v: v > -c.HABITS_BORDER, peacefulness_check=lambda v: v == 0) self.check_habits_changed(honor=0, peacefulness=c.HABITS_BORDER, honor_check=lambda v: v > 0, peacefulness_check=lambda v: v == c.HABITS_BORDER) self.check_habits_changed(honor=c.HABITS_BORDER, peacefulness=-c.HABITS_BORDER, honor_check=lambda v: v == c.HABITS_BORDER, peacefulness_check=lambda v: v == -c.HABITS_BORDER) def test_in_game__sneaky(self): self.apply_ability(effects.ABILITIES.SNEAKY) self.check_habits_changed(honor=-c.HABITS_BORDER, peacefulness=0, honor_check=lambda v: v == -c.HABITS_BORDER, peacefulness_check=lambda v: v == 0) self.check_habits_changed(honor=0, peacefulness=c.HABITS_BORDER, honor_check=lambda v: v < 0, peacefulness_check=lambda v: v == c.HABITS_BORDER) self.check_habits_changed(honor=c.HABITS_BORDER, peacefulness=-c.HABITS_BORDER, honor_check=lambda v: v < c.HABITS_BORDER, peacefulness_check=lambda v: v == -c.HABITS_BORDER) class QuestMoneyRewardTests(BaseEffectsTests): def test_effect(self): effect = effects.QuestMoneyReward(0.5) self.assertEqual(effect._modify_attribute({}, MODIFIERS.QUEST_MONEY_REWARD, 10), 10.5) self.assertEqual(effect._modify_attribute({}, MODIFIERS.QUEST_MONEY_REWARD, 11), 11.5) self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.QUEST_MONEY_REWARD,)), 11), 11) effect = effects.QuestMoneyReward(2.0) self.assertEqual(effect._modify_attribute({}, MODIFIERS.QUEST_MONEY_REWARD, 10), 12) self.assertEqual(effect._modify_attribute({}, MODIFIERS.QUEST_MONEY_REWARD, 11), 13) self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.QUEST_MONEY_REWARD,)), 11), 11) def test_in_game(self): ability = self.get_ability(effects.QuestMoneyReward) with self.check_changed(lambda: self.hero.quest_money_reward_multiplier()): self.apply_ability(ability) class MaxBagSizeTests(BaseEffectsTests): def test_effect(self): effect = effects.MaxBagSize(666) self.assertEqual(effect._modify_attribute({}, MODIFIERS.MAX_BAG_SIZE, 10), 676) self.assertEqual(effect._modify_attribute({}, MODIFIERS.MAX_BAG_SIZE, 11), 677) self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.MAX_BAG_SIZE,)), 11), 11) def test_in_game(self): ability = self.get_ability(effects.MaxBagSize) with self.check_changed(lambda: self.hero.max_bag_size): self.apply_ability(ability) class PoliticsPowerTests(BaseEffectsTests): def test_effect(self): effect = effects.PoliticsPower(3) self.assertEqual(effect._modify_attribute({}, MODIFIERS.POWER, 11), 14.0) self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.POWER, )), 11), 11) def test_in_game(self): ability = self.get_ability(effects.PoliticsPower) with self.check_changed(lambda: self.hero.politics_power_modifier): self.apply_ability(ability) class MagicDamageBonusTests(BaseEffectsTests): def test_effect(self): effect = effects.MagicDamageBonus(2) self.assertEqual(effect._modify_attribute({}, MODIFIERS.MAGIC_DAMAGE, 10), 20) self.assertEqual(effect._modify_attribute({}, MODIFIERS.PHYSIC_DAMAGE, 10), 10) self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.MAGIC_DAMAGE,)), 11), 11) def test_in_game(self): ability = self.get_ability(effects.MagicDamageBonus) with self.check_changed(lambda: self.hero.magic_damage_modifier): self.apply_ability(ability) class PhysicDamageBonusTests(BaseEffectsTests): def test_effect(self): effect = effects.PhysicDamageBonus(2) self.assertEqual(effect._modify_attribute({}, MODIFIERS.MAGIC_DAMAGE, 10), 10) self.assertEqual(effect._modify_attribute({}, MODIFIERS.PHYSIC_DAMAGE, 10), 20) self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.PHYSIC_DAMAGE,)), 11), 11) def test_in_game(self): ability = self.get_ability(effects.PhysicDamageBonus) with self.check_changed(lambda: self.hero.physic_damage_modifier): self.apply_ability(ability) class SpeedTests(BaseEffectsTests): def test_effect(self): effect = effects.Speed(2) self.assertEqual(effect._modify_attribute({}, MODIFIERS.SPEED, 10), 20) self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.SPEED,)), 11), 11) def test_in_game(self): ability = self.get_ability(effects.Speed) with self.check_changed(lambda: self.hero.move_speed): self.apply_ability(ability) class BattleAbilityTests(BaseEffectsTests): def test_effect(self): effect = effects.BattleAbilityFireball() self.assertEqual(effect._modify_attribute({}, MODIFIERS.INITIATIVE, 10), 10.25) self.assertEqual(effect._modify_attribute({}, MODIFIERS.ADDITIONAL_ABILITIES, []), [effect.ABILITY]) self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.INITIATIVE, MODIFIERS.ADDITIONAL_ABILITIES)), 11), 11) def test_in_game(self): ability = self.get_ability(effects.BattleAbilityHit, effects.BattleAbilityStrongHit, effects.BattleAbilityRunUpPush, effects.BattleAbilityFireball, effects.BattleAbilityPoisonCloud, effects.BattleAbilityFreezing) with self.check_changed(lambda: self.hero.initiative): with self.check_changed(lambda: len(self.hero.companion.modify_attribute(heroes_relations.MODIFIERS.ADDITIONAL_ABILITIES, heroes_relations.MODIFIERS.ADDITIONAL_ABILITIES.default()))): self.apply_ability(ability) class InitiativeTests(BaseEffectsTests): def test_effect(self): effect = effects.Initiative(2) self.assertEqual(effect._modify_attribute({}, MODIFIERS.INITIATIVE, 10), 20) self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.INITIATIVE,)), 11), 11) def test_in_game(self): ability = self.get_ability(effects.Initiative) with self.check_changed(lambda: self.hero.initiative): self.apply_ability(ability) class BattleProbabilityTests(BaseEffectsTests): def test_effect(self): effect = effects.BattleProbability(1.5) self.assertEqual(effect._modify_attribute({}, MODIFIERS.BATTLES_PER_TURN, 10), 11.5) self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.BATTLES_PER_TURN,)), 11), 11) def test_in_game(self): ability = self.get_ability(effects.BattleProbability) with self.check_changed(lambda: self.hero.battles_per_turn_summand): self.apply_ability(ability) class LootProbabilityTests(BaseEffectsTests): def test_effect(self): effect = effects.LootProbability(2) self.assertEqual(effect._modify_attribute({}, MODIFIERS.LOOT_PROBABILITY, 10), 20) self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.LOOT_PROBABILITY,)), 11), 11) def test_in_game(self): ability = self.get_ability(effects.LootProbability) with self.check_changed(lambda: self.hero.loot_probability(mobs_storage.mobs.all()[0])): self.apply_ability(ability) class CompanionDamageTests(BaseEffectsTests): def test_effect(self): effect = effects.CompanionDamage(3) self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_DAMAGE, 10), 13) self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.COMPANION_DAMAGE,)), 11), 11) def test_in_game(self): ability = self.get_ability(effects.CompanionDamage) with mock.patch('the_tale.game.balance.constants.COMPANIONS_BONUS_DAMAGE_PROBABILITY', 6666666666): with self.check_changed(lambda: self.hero.companion_damage): self.apply_ability(ability) class CompanionDamageProbabilityTests(BaseEffectsTests): def test_effect(self): effect = effects.CompanionDamageProbability(3) self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_DAMAGE_PROBABILITY, 10), 30) self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.COMPANION_DAMAGE_PROBABILITY,)), 11), 11) def test_in_game(self): ability = self.get_ability(effects.CompanionDamageProbability) with self.check_changed(lambda: self.hero.companion_damage_probability): self.apply_ability(ability) class CompanionStealMoneyTests(BaseEffectsTests): def test_effect(self): effect = effects.CompanionStealMoney(3) self.assertTrue(effect._check_attribute(MODIFIERS.COMPANION_STEAL_MONEY)) self.assertFalse(effect._check_attribute(MODIFIERS.COMPANION_STEAL_MONEY_MULTIPLIER)) self.assertFalse(effect._check_attribute(MODIFIERS.random(exclude=(MODIFIERS.COMPANION_STEAL_MONEY, MODIFIERS.COMPANION_STEAL_MONEY_MULTIPLIER)))) self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_STEAL_MONEY, 10), 10) self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_STEAL_MONEY_MULTIPLIER, 10), 30) self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.COMPANION_STEAL_MONEY, MODIFIERS.COMPANION_STEAL_MONEY_MULTIPLIER)), 11), 11) def test_in_game(self): ability = self.get_ability(effects.CompanionStealMoney) with self.check_changed(lambda: self.hero.can_companion_steal_money()): with self.check_changed(lambda: self.hero.companion_steal_money_modifier): self.apply_ability(ability) class CompanionStealItemTests(BaseEffectsTests): def test_effect(self): effect = effects.CompanionStealItem(3) self.assertTrue(effect._check_attribute(MODIFIERS.COMPANION_STEAL_ITEM)) self.assertFalse(effect._check_attribute(MODIFIERS.COMPANION_STEAL_ITEM_MULTIPLIER)) self.assertFalse(effect._check_attribute(MODIFIERS.random(exclude=(MODIFIERS.COMPANION_STEAL_ITEM, MODIFIERS.COMPANION_STEAL_ITEM)))) self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_STEAL_ITEM, 10), 10) self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_STEAL_ITEM_MULTIPLIER, 10), 30) self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.COMPANION_STEAL_ITEM, MODIFIERS.COMPANION_STEAL_ITEM_MULTIPLIER)), 11), 11) def test_in_game(self): ability = self.get_ability(effects.CompanionStealItem) with self.check_changed(lambda: self.hero.can_companion_steal_item()): with self.check_changed(lambda: self.hero.companion_steal_artifact_probability_multiplier): self.apply_ability(ability) class CompanionSparePartsTests(BaseEffectsTests): def test_effect(self): effect = effects.CompanionSpareParts() self.assertTrue(effect._check_attribute(MODIFIERS.COMPANION_SPARE_PARTS)) self.assertFalse(effect._check_attribute(MODIFIERS.random(exclude=(MODIFIERS.COMPANION_SPARE_PARTS,)))) def test_in_game(self): ability = self.get_ability(effects.CompanionSpareParts) with self.check_changed(lambda: self.hero.can_companion_broke_to_spare_parts()): self.apply_ability(ability) class CompanionSayWisdomTests(BaseEffectsTests): def test_effect(self): effect = effects.CompanionSayWisdom(3) self.assertTrue(effect._check_attribute(MODIFIERS.COMPANION_SAY_WISDOM)) self.assertFalse(effect._check_attribute(MODIFIERS.COMPANION_SAY_WISDOM_PROBABILITY)) self.assertFalse(effect._check_attribute(MODIFIERS.random(exclude=(MODIFIERS.COMPANION_SAY_WISDOM, MODIFIERS.COMPANION_SAY_WISDOM_PROBABILITY)))) self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_SAY_WISDOM, 10), 10) self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_SAY_WISDOM_PROBABILITY, 10), 30) self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.COMPANION_SAY_WISDOM, MODIFIERS.COMPANION_SAY_WISDOM_PROBABILITY)), 11), 11) def test_in_game(self): ability = self.get_ability(effects.CompanionSayWisdom) with self.check_changed(lambda: self.hero.can_companion_say_wisdom()): with self.check_changed(lambda: self.hero.companion_say_wisdom_probability): self.apply_ability(ability) class CompanionExpPerHealTests(BaseEffectsTests): def test_effect(self): effect = effects.CompanionExpPerHeal(2) self.assertTrue(effect._check_attribute(MODIFIERS.COMPANION_EXP_PER_HEAL)) self.assertFalse(effect._check_attribute(MODIFIERS.COMPANION_EXP_PER_HEAL_PROBABILITY)) self.assertFalse(effect._check_attribute(MODIFIERS.random(exclude=(MODIFIERS.COMPANION_EXP_PER_HEAL, MODIFIERS.COMPANION_EXP_PER_HEAL_PROBABILITY)))) self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_EXP_PER_HEAL, 10), 10) self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_EXP_PER_HEAL_PROBABILITY, 10), 20) self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.COMPANION_EXP_PER_HEAL, MODIFIERS.COMPANION_EXP_PER_HEAL_PROBABILITY)), 11), 11) def test_in_game(self): ability = self.get_ability(effects.CompanionExpPerHeal) with self.check_changed(lambda: self.hero.can_companion_exp_per_heal()): with self.check_changed(lambda: self.hero.companion_exp_per_heal_probability): self.apply_ability(ability) class DoubleReligionProfitTests(BaseEffectsTests): def test_effect(self): effect = effects.DoubleReligionProfit(0.1) self.assertEqual(effect._modify_attribute({}, MODIFIERS.DOUBLE_RELIGION_PROFIT, 0), 0.1) self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.DOUBLE_RELIGION_PROFIT,)), 11), 11) def test_in_game(self): ability = self.get_ability(effects.DoubleReligionProfit) with self.check_changed(lambda: self.hero.double_religion_profit_probability): self.apply_ability(ability) class CompanionEatCorpsesTests(BaseEffectsTests): def test_effect(self): effect = effects.CompanionEatCorpses(3) self.assertTrue(effect._check_attribute(MODIFIERS.COMPANION_EAT_CORPSES)) self.assertFalse(effect._check_attribute(MODIFIERS.COMPANION_EAT_CORPSES_PROBABILITY)) self.assertFalse(effect._check_attribute(MODIFIERS.random(exclude=(MODIFIERS.COMPANION_EAT_CORPSES, MODIFIERS.COMPANION_EAT_CORPSES_PROBABILITY)))) self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_EAT_CORPSES, 1), 1) self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_EAT_CORPSES_PROBABILITY, 1), 3) self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.COMPANION_EAT_CORPSES, MODIFIERS.COMPANION_EAT_CORPSES_PROBABILITY)), 11), 11) def test_in_game(self): ability = self.get_ability(effects.CompanionEatCorpses) with self.check_changed(lambda: self.hero.can_companion_eat_corpses()): with self.check_changed(lambda: self.hero.companion_eat_corpses_probability): self.apply_ability(ability) class CompanionRegenerateTests(BaseEffectsTests): def test_effect(self): effect = effects.CompanionRegenerate(2) self.assertTrue(effect._check_attribute(MODIFIERS.COMPANION_REGENERATE)) self.assertFalse(effect._check_attribute(MODIFIERS.COMPANION_REGENERATE_PROBABILITY)) self.assertFalse(effect._check_attribute(MODIFIERS.random(exclude=(MODIFIERS.COMPANION_REGENERATE, MODIFIERS.COMPANION_REGENERATE_PROBABILITY)))) self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_REGENERATE, 10), 10) self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_REGENERATE_PROBABILITY, 10), 20) self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.COMPANION_REGENERATE, MODIFIERS.COMPANION_REGENERATE_PROBABILITY)), 11), 11) def test_in_game(self): ability = self.get_ability(effects.CompanionRegenerate) with self.check_changed(lambda: self.hero.can_companion_regenerate()): with self.check_changed(lambda: self.hero.companion_regenerate_probability): self.apply_ability(ability) class CompanionEatAndDiscountTest(BaseEffectsTests): def test_effect(self): effect = effects.CompanionEat(0.5) self.assertTrue(effect._check_attribute(MODIFIERS.COMPANION_MONEY_FOR_FOOD)) self.assertFalse(effect._check_attribute(MODIFIERS.random(exclude=(MODIFIERS.COMPANION_MONEY_FOR_FOOD,)))) self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_MONEY_FOR_FOOD, 2), 1.0) self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.COMPANION_MONEY_FOR_FOOD,)), 11), 11) def test_in_game(self): ability = self.get_ability(effects.CompanionEat) with self.check_changed(lambda: self.hero.can_companion_eat()): with self.check_changed(lambda: self.hero.companion_money_for_food_multiplier): self.apply_ability(ability) class CompanionDrinkArtifactTests(BaseEffectsTests): def test_effect(self): effect = effects.CompanionDrinkArtifact(0.5) self.assertTrue(effect._check_attribute(MODIFIERS.COMPANION_DRINK_ARTIFACT)) self.assertFalse(effect._check_attribute(MODIFIERS.COMPANION_DRINK_ARTIFACT_PROBABILITY)) self.assertFalse(effect._check_attribute(MODIFIERS.random(exclude=(MODIFIERS.COMPANION_DRINK_ARTIFACT, MODIFIERS.COMPANION_DRINK_ARTIFACT_PROBABILITY)))) self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_DRINK_ARTIFACT, 2), 2) self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_DRINK_ARTIFACT_PROBABILITY, 2), 1.0) self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.COMPANION_DRINK_ARTIFACT, MODIFIERS.COMPANION_DRINK_ARTIFACT_PROBABILITY,)), 11), 11) def test_in_game(self): ability = self.get_ability(effects.CompanionDrinkArtifact) with self.check_changed(lambda: self.hero.can_companion_drink_artifact()): with self.check_changed(lambda: self.hero.companion_drink_artifact_probability): self.apply_ability(ability) class CompanionExorcistTests(BaseEffectsTests): def test_effect(self): effect = effects.CompanionExorcist(0.5) self.assertTrue(effect._check_attribute(MODIFIERS.COMPANION_EXORCIST)) self.assertFalse(effect._check_attribute(MODIFIERS.COMPANION_EXORCIST_PROBABILITY)) self.assertFalse(effect._check_attribute(MODIFIERS.random(exclude=(MODIFIERS.COMPANION_EXORCIST, MODIFIERS.COMPANION_EXORCIST_PROBABILITY)))) self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_EXORCIST, 2), 2) self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_EXORCIST_PROBABILITY, 2), 1.0) self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.COMPANION_EXORCIST, MODIFIERS.COMPANION_EXORCIST_PROBABILITY,)), 11), 11) def test_in_game(self): ability = self.get_ability(effects.CompanionExorcist) with self.check_changed(lambda: self.hero.can_companion_do_exorcism()): with self.check_changed(lambda: self.hero.companion_do_exorcism_probability): self.apply_ability(ability) class RestLenghtTests(BaseEffectsTests): def test_effect(self): effect = effects.RestLenght(3) self.assertEqual(effect._modify_attribute({}, MODIFIERS.REST_LENGTH, 12), 36) self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.REST_LENGTH,)), 11), 11) def test_in_game(self): ability = self.get_ability(effects.RestLenght) with self.check_changed(lambda: self.hero.rest_length): self.apply_ability(ability) class IDLELenghtTests(BaseEffectsTests): def test_effect(self): effect = effects.IDLELenght(3) self.assertEqual(effect._modify_attribute({}, MODIFIERS.IDLE_LENGTH, 12), 36) self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.IDLE_LENGTH,)), 11), 11) def test_in_game(self): ability = self.get_ability(effects.IDLELenght) with self.check_changed(lambda: self.hero.idle_length): self.apply_ability(ability) class CompanionBlockProbabilityTests(BaseEffectsTests): def test_effect(self): effect = effects.CompanionBlockProbability(3) self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_BLOCK_PROBABILITY, 12), 36) self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.COMPANION_BLOCK_PROBABILITY, )), 11), 11) def test_in_game(self): ability = self.get_ability(effects.CompanionBlockProbability) with self.check_changed(lambda: self.hero.companion_block_probability_multiplier): self.apply_ability(ability) class HucksterTests(BaseEffectsTests): def test_effect(self): effect = effects.Huckster(buy_bonus_left=3, buy_bonus_right=3, sell_bonus_left=2, sell_bonus_right=2) self.assertEqual(effect._modify_attribute({}, MODIFIERS.BUY_PRICE, 12), 15) self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.BUY_PRICE, MODIFIERS.SELL_PRICE)), 11), 11) self.assertEqual(effect._modify_attribute({}, MODIFIERS.SELL_PRICE, 130), 132) self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.BUY_PRICE, MODIFIERS.SELL_PRICE)), 11), 11) def test_in_game(self): ability = self.get_ability(effects.Huckster) with self.check_changed(self.hero.buy_price): with self.check_changed(self.hero.sell_price): self.apply_ability(ability) class EtherealMagnetTests(BaseEffectsTests): def test_effect(self): effect = effects.EtherealMagnet(0.1) self.assertEqual(effect._modify_attribute({}, MODIFIERS.CHARACTER_QUEST_PRIORITY, 0), 0.1) self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.CHARACTER_QUEST_PRIORITY,)), 11), 11) def test_in_game(self): ability = self.get_ability(effects.EtherealMagnet) with self.check_changed(lambda: self.hero.attribute_modifier(heroes_relations.MODIFIERS.CHARACTER_QUEST_PRIORITY)): self.apply_ability(ability) class CompanionTeleportTests(BaseEffectsTests): def test_effect(self): effect = effects.CompanionTeleport(0.1) self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_TELEPORTATOR, 0), 0.1) self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.COMPANION_TELEPORTATOR,)), 11), 11) def test_in_game(self): ability = self.get_ability(effects.CompanionTeleport) with self.check_changed(lambda: self.hero.companion_teleport_probability): self.apply_ability(ability) class CompanionFly(BaseEffectsTests): def test_effect(self): effect = effects.CompanionFly(0.1) self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_FLYER, 0), 0.1) self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.COMPANION_FLYER,)), 11), 11) def test_in_game(self): ability = self.get_ability(effects.CompanionFly) with self.check_changed(lambda: self.hero.companion_fly_probability): self.apply_ability(ability) class UnsociableTests(BaseEffectsTests): def test_effect(self): effect = effects.Unsociable(0.1) self.assertEqual(effect._modify_attribute({}, MODIFIERS.COMPANION_LEAVE_IN_PLACE, 0), 0.1) self.assertEqual(effect._modify_attribute({}, MODIFIERS.random(exclude=(MODIFIERS.COMPANION_LEAVE_IN_PLACE,)), 11), 11) def test_in_game(self): ability = self.get_ability(effects.Unsociable) with self.check_changed(lambda: self.hero.companion_leave_in_place_probability): self.apply_ability(ability)

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import hashlib import sqlite3 def adduser(user_id, role, login, password): sqlite_file = 'waste_management.db' conn = sqlite3.connect(sqlite_file) c = conn.cursor() hash_name = 'sha256' salt = 'ssdirf993lksiqb4' iterations = 100000 dk = hashlib.pbkdf2_hmac(hash_name, bytearray(password, 'ascii'), bytearray(salt, 'ascii'), iterations) c.execute('''INSERT INTO users(user_id, role, login, password) VALUES(?,?,?,?)''', (user_id, role, login, str(dk))) conn.commit() conn.close() return 0

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from sqlalchemy import inspect, and_, func from sqlalchemy.orm import sessionmaker, scoped_session import logging logger = logging.getLogger(__name__) class DAO: """ This is the base data access object class """ def __init__(self, table, engine=None): self.engine = engine self.session = scoped_session(sessionmaker(bind=self.engine))() self.table = table def get(self, id): return self.session.query(self.table).get(id) def update(self, entity): self.session.merge(entity) return entity def bulk_update(self, entities): for entity in entities: self.session.merge(entity)

[ "sampoorna.biswas01@gmail.com" ]

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freedream520/django-ozgweb

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import os import sys import random import io from django.shortcuts import render from django.http import JsonResponse from django.http import HttpResponse from PIL import Image, ImageDraw, ImageFont from math import ceil from . import cfg #验证码部分 #修改自https://github.com/tianyu0915/DjangoCaptcha，以支持python3 current_path = os.path.normpath(os.path.dirname(__file__)) class Captcha(object): def __init__(self,request): """ 初始化,设置各种属性 """ self.django_request = request self.session_key = '_django_captcha_key' self.words = self._get_words() # 验证码图片尺寸 self.img_width = 150 self.img_height = 30 self.type = 'number' def _get_font_size(self): """ 将图片高度的80%作为字体大小 """ s1 = int(self.img_height * 0.8) s2 = int(self.img_width // len(self.code)) return int(min((s1,s2)) + max((s1, s2)) * 0.05) def _get_words(self): """ 读取默认的单词表 """ #TODO 扩充单词表 file_path = os.path.join(current_path, 'words.list') f = open(file_path, 'r') return [line.replace('\n', '') for line in f.readlines()] def _set_answer(self,answer): """ 设置答案 """ self.django_request.session[self.session_key] = str(answer) def _yield_code(self): """ 生成验证码文字,以及答案 """ # 英文单词验证码 def word(): code = random.sample(self.words,1)[0] self._set_answer(code) return code # 数字公式验证码 def number(): m, n = 1, 50 x = random.randrange(m, n) y = random.randrange(m, n) r = random.randrange(0 ,2) if r == 0: code = "%s - %s = ?" % (x, y) z = x - y else: code = "%s + %s = ?" % (x, y) z = x + y self._set_answer(z) return code fun = eval(self.type.lower()) return fun() def display(self): """ 生成验证码图片 """ # font color self.font_color = ['black', 'darkblue', 'darkred'] # background color self.background = (random.randrange(230, 255), random.randrange(230, 255), random.randrange(230, 255)) # font path self.font_path = os.path.join(current_path, 'timesbi.ttf') #self.font_path = os.path.join(current_path, 'Menlo.ttc') # clean self.django_request.session[self.session_key] = '' # creat a image im = Image.new('RGB', (self.img_width, self.img_height), self.background) self.code = self._yield_code() # set font size automaticly self.font_size = self._get_font_size() # creat a pen draw = ImageDraw.Draw(im) # draw noisy point/line if self.type == 'word': c = int(8 // len(self.code) * 3) or 3 elif self.type == 'number': c = 4 for i in range(random.randrange(c - 2, c)): line_color = (random.randrange(0, 255), random.randrange(0, 255),random.randrange(0, 255)) xy = ( random.randrange(0, int(self.img_width * 0.2)), random.randrange(0, self.img_height), random.randrange(3 * self.img_width // 4, self.img_width), random.randrange(0, self.img_height) ) draw.line(xy, fill = line_color, width = int(self.font_size * 0.1)) #draw.arc(xy,fill = line_color, width = int(self.font_size * 0.1)) #draw.arc(xy, 0, 1400, fill = line_color) # draw code j = int(self.font_size * 0.3) k = int(self.font_size * 0.5) x = random.randrange(j, k) #starts point for i in self.code: # 上下抖动量,字数越多,上下抖动越大 m = int(len(self.code)) y = random.randrange(1, 3) if i in ('+', '=', '?'): # 对计算符号等特殊字符放大处理 m = ceil(self.font_size * 0.8) else: # 字体大小变化量,字数越少,字体大小变化越多 m = random.randrange(0, int( 45 // self.font_size) + int(self.font_size // 5)) self.font = ImageFont.truetype(self.font_path.replace('\\', '/'),self.font_size + int(ceil(m))) draw.text((x, y), i, font = self.font, fill = random.choice(self.font_color)) x += self.font_size * 0.9 del x del draw buf = io.BytesIO() im.save(buf, 'gif') buf.closed return HttpResponse(buf.getvalue(), 'image/gif') def check(self, code): """ 检查用户输入的验证码是否正确 """ _code = self.django_request.session.get(self.session_key) or '' self.django_request.session[self.session_key] = '' return _code.lower() == str(code).lower() #验证码部分 end #公用的render函数，主要加入一些公用变量 def render_template(request, templates, res_data = None): response_data = { "cfg_jquery": cfg.jquery, "cfg_title": cfg.web_name } if(res_data != None): response_data["res_data"] = res_data return render(request, templates, response_data) #仅在这个模块用到 def res(res_code, desc, data): res_data = { "code": res_code, "desc": desc, } if data: res_data["data"] = data response = JsonResponse(res_data) return response #回应请求成功 def res_success(desc, data = None): return res(0, desc, data) #回应请求失败 def res_fail(res_code, desc, data = None): return res(res_code, desc, data) #计算总页数 def page_count(count, page_size): if(count % page_size == 0): return (count // page_size) else: return (count // page_size) + 1;

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import torch.nn as nn from textclf.config import ResRNNClassifierConfig from .base import Classifier from .components import RNN, AttentionLayer class ResRNNClassifier(Classifier): def __init__(self, config: ResRNNClassifierConfig): super(ResRNNClassifier, self).__init__(config) rnn_config = config.rnn_config rnn_config.input_size = config.input_size self.rnn = RNN(rnn_config) if rnn_config.bidirectional: hidden_size = rnn_config.hidden_size*2 else: hidden_size = rnn_config.hidden_size self.dropout = nn.Dropout(p=config.dropout) self.output_layer = nn.Linear(hidden_size, config.output_size) def forward(self, embedding, seq_lens): outputs, last_hidden = self.rnn(embedding, seq_lens) if self.use_attention: # attention mechanism context = self.attention_layer(outputs, seq_lens) else: context = last_hidden logits = self.output_layer(self.dropout(context)) return logits

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""" Django settings for composeexample project. Generated by 'django-admin startproject' using Django 1.11.6. 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 = 'qxji#2zzj%$s$*wj8ww54x#bg$gc889eogo%oz!70vgmz8v5gw' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = ['0.0.0.0'] # 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 = 'composeexample.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 = 'composeexample.wsgi.application' # Database # https://docs.djangoproject.com/en/1.11/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.postgresql', 'NAME': 'postgres', 'USER': 'postgres', 'HOST': 'db', 'PORT': 5432, } } # 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 = 'UTC' 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/'

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""" Implements basic structures used throughout """ import math class matrix: """ Class implementing all matrix functionality """ # Commonly used matrices as classmethods @classmethod def identity(cls, n=4): # Identity matrix of degree n result = matrix(n, n) for i in range(n): result.insert(i, i, 1) return result def __init__(self, rows=4, coloumns=4): # Matrix is internally stored as an array of size rows (first index) x coloumns (second index) self._rows = rows self._coloumns = coloumns self._matrix = [[0 for i in range(coloumns)] for i in range(rows)] def getSize(self): return (self._rows, self._coloumns) def get(self, row, coloumn): return self._matrix[row][coloumn] def insert(self, row, coloumn, element): self._matrix[row][coloumn] = element return self def __mul__(self, other): if isinstance(other, vector): # matrix * vector, returns vector result = vector() for i in range(4): value = 0.0 for k in range(4): value += self.get(i, k) * other.get(k) result.insert(i, value) return result else: # matrix * matrix, returns matrix sizes = (self.getSize(), other.getSize()) if sizes[0][1] != sizes[1][0]: raise TypeError r = sizes[0][0] c = sizes[1][1] iterations = sizes[0][1] result = matrix(r, c) for i in range(r): for j in range(c): value = 0.0 for k in range(iterations): value += self.get(i, k) * other.get(k, j) result.insert(i, j, value) return result def __str__(self): # Pretty formatting string = "" for i in range(self._rows): for j in range(self._coloumns): string += str(self.get(i, j)) + "\t" string += "\n" return string class vector: """ Class implementing all vector functionality """ # Commonly used vectors as classmethods @classmethod def direction(cls, n): # Vector pointing along a particular axis (0 for x-axis, 1 for y-axis, 2 for z-axis) result = vector() result.insert(n, 1) return result @classmethod def one(cls): # Vector with all components as 1.0 return vector(1.0, 1.0, 1.0) def __init__(self, x=0.0, y=0.0, z=0.0): # Vector is internally stored as a 4x1 matrix (but does not inherit from it) self._vector = matrix(4, 1) self._vector.insert(0, 0, x) self._vector.insert(1, 0, y) self._vector.insert(2, 0, z) self._vector.insert(3, 0, 1.0) def get(self, index): return self._vector.get(index, 0) def getMagnitude(self): return math.sqrt(sum(math.pow(self._vector.get(i), 2) for i in range(3))) def normalized(self): return self / self.getMagnitude() def insert(self, index, element): self._vector.insert(index, 0, element) return self # Vector transformations (commit is used to commit the change to self) def translate(self, positionVector, commit=True): result = self + positionVector if commit: self._vector = result.asMatrix() return self else: return result def rotate(self, rotationVector, commit=True): result = self for i in range(3): # Separately for x, y, z axes r = matrix.identity(4) r.insert((i + 1) % 3, (i + 1) % 3, math.cos(math.radians(rotationVector.get(i)))) r.insert((i + 1) % 3, (i + 2) % 3, - math.sin(math.radians(rotationVector.get(i)))) r.insert((i + 2) % 3, (i + 1) % 3, math.sin(math.radians(rotationVector.get(i)))) r.insert((i + 2) % 3, (i + 2) % 3, math.cos(math.radians(rotationVector.get(i)))) result = r * result if commit: self._vector = result.asMatrix() return self else: return result def scale(self, scaleVector, commit=True): s = matrix.identity(4) s.insert(0, 0, scaleVector.get(0)) s.insert(1, 1, scaleVector.get(1)) s.insert(2, 2, scaleVector.get(2)) if commit: self._vector = (s * self).asMatrix() return self else: return s * self # Converting vector in another form def asMatrix(self): m = matrix(4, 1) for i in range(4): m.insert(i, 0, self.get(i)) return m def asList(self): return [self.get(0), self.get(1), self.get(2)] def __add__(self, other): # Vector addition result = vector() for i in range(3): result.insert(i, self.get(i) + other.get(i)) return result def __mul__(self, num): # Scalar multiplication result = vector() for i in range(3): result.insert(i, self.get(i) * num) return result def __truediv__(self, num): # Scalar division result = vector() for i in range(3): result.insert(i, self.get(i) / num) return result def __str__(self): # Pretty formatting return "({}, {}, {})".format(self.get(0), self.get(1), self.get(2))

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s = input().split('-') sum = 0 result = [] for i in s: if '+' in i: a = i.split('+') for j in a: sum += int(j) result.append(sum) else: result.append(int(i)) minus = result[0] for i in range(1, len(result)): minus -= result[i] print(minus)

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__version__ = '1.0.3-alpha' __author__ = "Olamilekan Wahab" __license__ = 'MIT' __copyright__ = 'Copyright 2017. Olamilekan Wahab' from .payment import Payment from .transaction import Transaction from .misc import Misc from .preauth import Preauth from .encryption import RaveEncryption

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#---LESSON 5: Dictionaries - Working #with Key-Value Pairs--- student = {'name': 'Jose', 'age': 22, 'courses': ['Math', 'Communications']} print(student) print(student['name']) print(student['courses']) ''' Dictionaries can contain any immutable data type as key or value. Accessing a key that doesn't exist results in a KeyError ''' #---Overiding KeyErrors (using the get() function)--- print(student.get('name')) print(student.get('phone')) print(student.get('phone', 'Not Found')) #--Updating values using direct assignment--- student['phone'] = '555-5555' #adds a new Key-Value pair student['name'] = 'Jane' #changes the existing value for the given key print(student) #---Updating values using update() function--- student.update({'name': 'Val', 'age': 26, 'phone': '555-5555'}) print(student) #print(dir(student)) #--- deleting values from a dictionary--- del student['age'] phone = student.pop('phone') print(student) print(phone) #shows the poppped value saved in 'phone' variable #---Want to know the number of keys in a dict?--- print(len(student)) print(student.keys()) #shows the keys print(student.values()) #shows the values print(student.items()) #shows the keys and values #--Looping through dictionaries-- for key in student: print(key) for key,value in student.items(): #"for key,value in student:" will return a ValueError print(key, value) #---LESSON 6: Conditionals and Boolean - If, Else #and Elif Statements--- if True: print('Conditional was True') if False: print('Conditional was True') #doesn't evaluate language = 'Python' if language == 'Python': print('Conditional was True') ''' Comparisons: Equal:----------------== Not Equal:------------!= Greater Than:---------> Less Than:------------< Greater or Equal:----->= Less or Equal:--------<= Object Identity:------is 'Object identity' is used for keyword check ie to check if values have the same id False Values: False None Zero of any numeric type Any Empty sequence, e.g., '', (), []. Any empty mapping, e.g., {} Boolean Operators: and or not ''' user = 'Admin' logged_in = False if user == 'Admin' and logged_in: print('Admin Page') else: print('Bad Creds') if user == 'Admin' or logged_in: print('Admin Page') else: print('Bad Creds') if not logged_in: print('Please Log In') else: print('Welcome') a = [1, 2, 3] b = [1, 2, 3] c = a print(a==b) print(a is b) #evaluates to False since they do not have same memory address as can ba seen below print(a is c) #---Print memory locations of the objects--- print(id(a)) print(id(c)) print(id(b)) ''' 'is' operator like in (a is b) is synonymous to id(a) == id(b) check behind the scene '''

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import basicprogs as b import boardstate_class as bsc from inspect import signature # use this to check the "condition" argument class rule: def __init__(self, condition, rule_type, name='rule'): if not callable(condition): print('Error: condition argument must be a callable function') raise(ValueError) sig = signature(condition) if len(sig.parameters) != 2: print('Error: condition argument can only take two arguments') raise(ValueError) if not isinstance(rule_type, str): print('Error: rule_type argument must be a string') raise(ValueError) if rule_type.lower() not in ['lead', 'follow', 'call']: print('Error: rule_type argument can only be "lead", "follow", or "call"') raise(ValueError) self.type = rule_type self.condition = condition self.name = name def is_satisfied(self, board, pos): if not isinstance(board, bsc.boardstate): print('Error: improper board argument') raise(ValueError) if not isinstance(pos, str): print('Error: position argument not a string') raise(ValueError) if pos.lower() not in ['o1', 'o2', 'p', 'd']: print('Error: invalid position choice given') raise(ValueError) c, have = self.condition(board, pos) if have: return(c) else: return(None)

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n = int(input()) while n: print(n % 10, end="") n //= 10 print()

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# # Copyright 2018 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. # __pip_package_name__ = 'ask-sdk-dynamodb-persistence-adapter' __description__ = ( 'The ASK SDK DynamoDB Persistence Adapter package provides DynamoDB ' 'Adapter, that can be used with ASK SDK Core, for persistence management') __url__ = 'http://developer.amazon.com/ask' __version__ = '0.1' __author__ = 'Alexa Skills Kit' __author_email__ = 'ask-sdk-dynamic@amazon.com' __license__ = 'Apache 2.0' __keywords__ = ['ASK SDK', 'Alexa Skills Kit', 'Alexa', 'ASK SDK Core', 'Persistence', 'DynamoDB'] __install_requires__ = ["boto3", "ask-sdk-core"]

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''' 两进程进行通信一个输出Ping，一个输出Pong，两个进程输出的Ping和Pong加起来一共10个 ''' from multiprocessing import Process from time import sleep counter=0 def sub_task(string): global counter while counter<10: print(string,end=' ',flush=True) counter+=1 sleep(0.01) def main(): Process(target=sub_task,args=('ping',)).start() Process(target=sub_task,args=('pong',)).start() if __name__ == '__main__': main()

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import itertools import os import pytest from pathlib import Path from typing import Iterable import datacube import digitalearthau import digitalearthau.system from datacube.config import LocalConfig from . import factories # These are unavoidable in pytests due to fixtures # pylint: disable=redefined-outer-name,protected-access,invalid-name try: from yaml import CSafeLoader as SafeLoader except ImportError: from yaml import SafeLoader # The default test config options. # The user overrides these by creating their own file in ~/.datacube_integration.conf INTEGRATION_DEFAULT_CONFIG_PATH = Path(__file__).parent.joinpath('testing-default.conf') def pytest_report_header(config): if config.getoption('verbose') > 0: return ( f"digitaleathau {digitalearthau.__version__}, " f"opendatacube {datacube.__version__}" ) return None @pytest.fixture(scope='session') def integration_config_paths(): if not INTEGRATION_DEFAULT_CONFIG_PATH.exists(): # Safety check. We never want it falling back to the default config, # as it will alter/wipe the user's own datacube to run tests raise RuntimeError( 'Integration default file not found. This should be built-in?') return ( str(INTEGRATION_DEFAULT_CONFIG_PATH), os.path.expanduser('~/.datacube_integration.conf') ) @pytest.fixture(scope='session') def global_integration_cli_args(integration_config_paths: Iterable[str]): """ The first arguments to pass to a cli command for integration test configuration. """ # List of a config files in order. return list( itertools.chain(*(('--config_file', f) for f in integration_config_paths))) @pytest.fixture(scope='session') def local_config(integration_config_paths): return LocalConfig.find(integration_config_paths) # Default fixtures which will drop/create on every individual test function. db = factories.db_fixture('local_config') index = factories.index_fixture('db') dea_index = factories.dea_index_fixture('index')

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#!/usr/bin/env python3 # -*- coding: utf8 -*- from __future__ import print_function # new print() on python2 from datetime import datetime import sys import numpy as np from mnist import MNIST # Display full arrays np.set_printoptions(threshold=np.inf) mndata = MNIST('./data') images_full, labels_full = mndata.load_training() images = [] labels = [] # dynamic arguments batch_size = int(sys.argv[1]) size_1 = int(sys.argv[2]) size_2 = int(sys.argv[3]) batch_training_size = int(sys.argv[4]) data_part = 5 # only one fifth of the whole dataset to speed up training for i in range(len(labels_full) // batch_size // data_part): images.append(images_full[i*batch_size : (i+1)*batch_size]) labels.append(labels_full[i*batch_size : (i+1)*batch_size]) def sigmoid_prime(x): return np.exp(-x) / ((np.exp(-x) + 1) ** 2) def sigmoid(x): return 1 / (1 + np.exp(-x)) # nowe, przyda się? def relu(x): return np.maximum(x, x * 0.01) def relu_prime(x): if x >= 0: return 1 # ej nie jest tak xd # a jak xd? type(x) == no.ndarray # no x to macierz xd # np.exp jest przeładowane ale jakakoleiwk funkcja to chyba nie # to co foreach ? :( # właśnie nie wiem, a co z gpu? # to miało być szybsze a nie xd # mamy duzo mozliwosci zmian ale nie na raz trzeba ustalic jakos # hm TODO gpu TODO wincyj procent TODO gui gotowe # xd # tamto myliło hah # to co najpierw? :p # ssh daje wglad do basha tylko tak ? # nie, to jest taki fajny programik, byobu # i ten pasek na dole też jest z byobu # on udostepnia tylko basha ? # tak, ale basha multiplayer xd # szkoda że 2 kursorow nie ma # hm return 0.01 # chyba tak xd nikt nie widzial xd # ale x to macierz :p # ale to jest przeciazone i jak jest funkcja od macierzy to bierze po kolei kazdy element # w sumie # zobacze na drugiej karcie xd #X = np.array([[0, 0], # [0, 1], # [1, 0], # [1, 1]]) #X = np.array(images) y = [] for batch in labels: y.append([]) for label in batch: y[-1].append([1.0 if i == label else 0.0 for i in range(10)]) y = np.array(y) #y = np.array([[0], # [1], # [1], # [0]]) np.random.seed(1) LEN = len(labels) SIZES = [ 784, size_1, size_2, 10 ] syn0 = 2 * np.random.random((SIZES[0], SIZES[1])) - 1 syn1 = 2 * np.random.random((SIZES[1], SIZES[2])) - 1 syn2 = 2 * np.random.random((SIZES[2], SIZES[3])) - 1 # biases for respective layers b0 = 2 * np.random.random((1, SIZES[1])) - 1 b1 = 2 * np.random.random((1, SIZES[2])) - 1 b2 = 2 * np.random.random((1, SIZES[3])) - 1 for i, batch in list(enumerate(images)): X = np.array(batch) print("x:") print(np.shape(X)) print("======================= BATCH {} =======================".format(i)) error = 1 j = 0 while j < batch_training_size: l0 = X l1 = sigmoid(np.dot(l0, syn0) + b0) l2 = sigmoid(np.dot(l1, syn1) + b1) l3 = sigmoid(np.dot(l2, syn2) + b2) l3_error = (y[i] - l3)#** 2 error = np.mean(np.abs(l3_error)) j += 1 if j % 20 == 0: print(("[%d] error: " % j) + str(error)) l3_delta = l3_error * sigmoid_prime(l3) l2_error = l3_delta.dot(syn2.T) l2_delta = l2_error * sigmoid_prime(l2) l1_error = l2_delta.dot(syn1.T) l1_delta = l1_error * sigmoid_prime(l1) syn2 += l2.T.dot(l3_delta) syn1 += l1.T.dot(l2_delta) syn0 += l0.T.dot(l1_delta) b0 += l1_delta.mean(axis=0) b1 += l2_delta.mean(axis=0) b2 += l3_delta.mean(axis=0) def predict(data): l0 = [data] l1 = sigmoid(np.dot(l0, syn0) + b0) l2 = sigmoid(np.dot(l1, syn1) + b1) l3 = sigmoid(np.dot(l2, syn2) + b2) return np.argmax(l3) print("Output after training: ") print(l3) for i, el in enumerate(l3): print(labels[0][i], "=", np.argmax(el), " predictions: ", el) testing_images, testing_labels = mndata.load_testing() correct = 0.0 for i, (image, label) in enumerate(zip(testing_images, testing_labels)): prediction = predict(image) if label == prediction: correct += 1.0 correct_rate = correct / (i + 1.0) print("{} = {} (correct {}%)".format(label, prediction, 100 * correct_rate)) with open('log/' + str(datetime.now()), 'a') as f: with open(__file__, 'r') as myself: print(myself.read(), file=f) print("", file=f) print("#### answers:", file=f) print("argv =", sys.argv, file=f) print("correct_rate =", correct_rate, file=f) print("SIZES =", SIZES, file=f) print("syn0 =", syn0, file=f) print("syn1 =", syn1, file=f) print("syn2 =", syn2, file=f) print("b0 =", b0, file=f) print("b1 =", b1, file=f) print("b2 =", b2, file=f) #### answers: argv = ['./main.py', '59', '36', '34', '25'] correct_rate = 0.594 SIZES = [784, 36, 34, 10] syn0 = [[-1.65955991e-01 4.40648987e-01 -9.99771250e-01 -3.95334855e-01 -7.06488218e-01 -8.15322810e-01 -6.27479577e-01 -3.08878546e-01 -2.06465052e-01 7.76334680e-02 -1.61610971e-01 3.70439001e-01 -5.91095501e-01 7.56234873e-01 -9.45224814e-01 3.40935020e-01 -1.65390395e-01 1.17379657e-01 -7.19226123e-01 -6.03797022e-01 6.01489137e-01 9.36523151e-01 -3.73151644e-01 3.84645231e-01 7.52778305e-01 7.89213327e-01 -8.29911577e-01 -9.21890434e-01 -6.60339161e-01 7.56285007e-01 -8.03306332e-01 -1.57784750e-01 9.15779060e-01 6.63305699e-02 3.83754228e-01 -3.68968738e-01] [ 3.73001855e-01 6.69251344e-01 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[-2.51686524e+00 -8.27406926e-01 -3.93807549e+00 7.17862265e-01 -5.52424423e-01 1.78066048e+00 -4.17260083e+00 4.63216439e+00 -3.43925439e+00 6.77243178e-01] [-1.65359357e-01 -2.18030892e+00 -1.76545898e+00 -3.96749333e-01 2.22075536e+00 2.59673099e+00 1.48400204e-01 -3.91178321e-01 -6.71836591e-01 1.02435614e+00] [-2.96126101e+00 -5.66150537e-01 -3.03042301e+00 3.60454753e+00 -3.74489853e+00 8.86472593e-01 -2.68186065e+00 -3.90179658e+00 1.97754003e+00 -2.11475778e+00] [-4.19420977e-01 -6.64976468e+00 -2.80863799e+00 4.17531061e+00 -4.52969619e-01 -2.53312500e+00 -2.04243429e+00 -1.93778209e+00 -1.59148232e-02 5.02316842e+00] [ 7.77756034e-01 -2.87151110e+00 2.43779910e+00 1.08941564e+00 -2.65841603e+00 1.89653658e+00 -7.38960685e-01 -7.36362648e+00 1.12415557e-01 -1.41625224e+00] [ 1.33079760e+00 -6.60172572e+00 -2.26338025e+00 -5.07170852e+00 2.19034334e+00 1.40784744e-01 -4.04383085e-01 -6.57130084e-01 8.20751426e-01 1.98176397e-01] [-4.72883332e-01 3.57740762e-01 -4.65194027e+00 -4.01463192e+00 -3.47407602e+00 -1.41359965e+00 2.89756373e+00 2.80812333e-01 -2.63416377e+00 1.82721019e+00] [ 5.64979039e-01 1.57499196e+00 -2.02698719e+00 2.53095086e+00 1.40221816e+00 -8.30099075e-01 -2.02834490e+00 -3.27276885e+00 -3.24942111e+00 1.33428065e+00] [-2.93535955e+00 2.13567448e+00 -7.52365913e-01 2.31130072e+00 -1.56005211e+00 -5.26342415e+00 -3.08157013e+00 2.72945538e-01 -2.84029347e+00 1.78330794e+00] [-2.56831877e+00 -1.68549930e+00 -2.93974885e+00 -2.87900416e+00 -1.73070613e+00 3.59006710e+00 -4.01741500e+00 -3.75313207e+00 -2.20974683e+00 4.10056117e+00]] b0 = [[ -875.73663618 -813.85866971 -840.27174486 -1066.0344334 -826.36406162 -877.04031066 -916.60081508 -515.31463157 -856.81938453 -934.05288301 -882.58779013 -884.11254226 -344.67593241 -503.62217279 -882.00032229 -899.51823195 -921.02182561 -1127.18384338 -628.77626535 -831.6744478 -846.43811066 -941.3049701 -301.58188636 -890.1993107 -862.5599114 -661.3040105 -254.89316881 -871.50264689 -869.4125715 -820.86846255 -840.98990615 -850.6850811 -861.05567862 -914.68797341 -250.68963748 -257.9943676 ]] b1 = [[-1.66278210e+00 -3.65324540e+00 -4.51958146e+00 -2.57870597e+00 -4.87897223e+00 5.29131753e-01 -6.82521641e-01 -3.82414882e+00 -3.59395758e+00 -4.49837233e+00 -2.93700807e+00 -2.09688231e+00 -2.71914216e+00 -7.11979726e-01 -4.03584819e+00 3.47651147e-03 1.68643496e-01 -2.81502351e+00 -4.15120256e+00 -5.23985551e+00 -4.01459467e+00 -4.10819489e+00 -3.10466809e+00 -4.72698550e+00 -5.82501507e-01 -3.52325194e+00 -2.77943189e+00 -4.12160271e+00 -3.03927975e+00 -2.62742330e+00 -3.49018083e+00 -2.38325761e+00 -5.95594166e+00 -1.73942359e+00]] b2 = [[-1.29065252 -1.07410532 -1.12565259 -1.91341438 -0.90875645 -1.56695869 -1.52767328 -0.36236739 -1.04558348 -0.45456078]]

[ "246992@student.pwr.edu.pl" ]

246992@student.pwr.edu.pl

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/simulation_main.py

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Jarvis-X/pioneer_free_run

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refs/heads/master

2022-07-31T01:17:43.708467

2020-05-15T22:52:35

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# Make sure to have the server side running in CoppeliaSim: # in a child script of a CoppeliaSim scene, add following command # to be executed just once, at simulation start: # # simRemoteApi.start(19999) # # then start simulation, and run this program. # # created by: Jiawei Xu try: import sim import numpy as np import cv2 import time except: print ('--------------------------------------------------------------') print ('Library loading failed!') print ('') def filter_red(img): red1 = cv2.inRange(img, (0, 220, 100), (5, 255, 255)) red2 = cv2.inRange(img, (175, 220, 100), (180, 255, 255)) return red1+red2 def filter_green(img): green = cv2.inRange(img, (55, 220, 100), (65, 255, 255)) return green def sensor_color(img): sensor_array = np.sum(img, 0, dtype=np.uint16) sensor_array_shrunken = [0.0]*16 for i in range(16): sensor_array_shrunken[i] = np.sum(sensor_array[i*16:(i+1)*16], dtype=np.uint32)*1.0 if sensor_array_shrunken[i] > 300000: sensor_array_shrunken[i] = 1.0 elif sensor_array_shrunken[i] < 60000: sensor_array_shrunken[i] = 0.0 else: sensor_array_shrunken[i] = (sensor_array_shrunken[i]-60000)/(300000.0-60000.0) # print sensor_array_shrunken return sensor_array_shrunken def free_running(sonar_readings, img, ID, left_motor, right_motor): red_threshold = filter_red(img) green_threshold = filter_green(img) green_sensor_array = sensor_color(green_threshold) red_sensor_array = sensor_color(red_threshold) # cv2.imshow("red", red_threshold) # cv2.imshow("green", green_threshold) # cv2.waitKey(1) v_left = 1.0 v_right = 1.0 for i in range(len(sonar_readings)): v_left += sonar_readings[i]*braitenberg_sonar_L[i] v_right += sonar_readings[i]*braitenberg_sonar_R[i] print v_left, " ", v_right # TODO: add red cube avoidance terms for i in range(len(red_sensor_array)): v_left += red_sensor_array[i] * braitenberg_red_L[i] v_right += red_sensor_array[i] * braitenberg_red_R[i] for i in range(len(green_sensor_array)): v_left += green_sensor_array[i] * braitenberg_green_L[i] v_right += green_sensor_array[i] * braitenberg_green_R[i] print v_left, " ", v_right # print v_left, " ", v_right sim.simxSetJointTargetVelocity(ID, left_motor, v_left, sim.simx_opmode_oneshot) sim.simxSetJointTargetVelocity(ID, right_motor, v_right, sim.simx_opmode_oneshot) def read_image(image_ready, ID, handler): res, resolution, image = sim.simxGetVisionSensorImage(ID, handler, 0, sim.simx_opmode_buffer) if res == sim.simx_return_ok: if not image_ready: print "image OK!!!" image_ready = True img = np.array(image, dtype=np.uint8) img.resize([resolution[1], resolution[0], 3]) img = cv2.cvtColor(img, cv2.COLOR_RGB2HSV) return img, image_ready elif res == sim.simx_return_novalue_flag: if image_ready: print "no image" image_ready = False return np.array([], dtype=np.uint8), image_ready else: print "error: " + str(res) return np.array([], dtype=np.uint8), image_ready def read_sonar(ID, handlers, sonar_ready): points = [None]*8 states = [False]*8 for i in range(8): res, states[i], points[i], _, normal_vec = sim.simxReadProximitySensor(ID, handlers[i], sim.simx_opmode_buffer) dists = [i[2] for i in points] if sonar_ready: for i in range(len(dists)): if states[i] and dists[i] < 0.5: if dists[i] < 0.2: dists[i] = 0.2 # map how close an obstacle is to the robot to [0, 1] dists[i] = 1.0 - (dists[i] - 0.2) / (0.5 - 0.2) else: dists[i] = 0.0 return dists, sonar_ready else: flag = True for i in range(len(dists)): if dists[i] == 0.0: flag = False break if flag: sonar_ready = True return None, sonar_ready if __name__ == "__main__": print ('Program started') sim.simxFinish(-1) # just in case, close all opened connections clientID = sim.simxStart('127.0.0.1', 19999, True, True, 5000, 5) # Connect to CoppeliaSim if clientID != -1: print ('Connected to remote API server') # Now try to retrieve data in a blocking fashion (i.e. a service call): res, objs = sim.simxGetObjects(clientID, sim.sim_handle_all, sim.simx_opmode_blocking) if res == sim.simx_return_ok: print ('Number of objects in the scene: ', len(objs)) else: print ('Remote API function call returned with error code: ', res) time.sleep(2) # get vision sensor handler print 'Vision Sensor object handling' res, veh_camera = sim.simxGetObjectHandle(clientID, 'veh_camera', sim.simx_opmode_oneshot_wait) # get sonor handler print 'Sonar object handling' veh_sonar = [None]*8 for i in range(8): res, veh_sonar[i] = sim.simxGetObjectHandle(clientID, 'Pioneer_p3dx_ultrasonicSensor'+'{}'.format(i+1), sim.simx_opmode_oneshot_wait) # print res == sim.simx_return_ok # get left motor handler res, veh_left_motor = sim.simxGetObjectHandle(clientID, 'Pioneer_p3dx_leftMotor', sim.simx_opmode_oneshot_wait) # print res == sim.simx_return_ok # get right motor handler res, veh_right_motor = sim.simxGetObjectHandle(clientID, 'Pioneer_p3dx_rightMotor', sim.simx_opmode_oneshot_wait) # print res == sim.simx_return_ok # let the server prepare the first image print 'Getting first image' res, resolution, image = sim.simxGetVisionSensorImage(clientID, veh_camera, 0, sim.simx_opmode_streaming) image_ready_flag = False # let the server prepare the first sonar reading points = [None] * 8 for i in range(8): res, state, points[i], _, normal_vec = sim.simxReadProximitySensor(clientID, veh_sonar[i], sim.simx_opmode_streaming) braitenberg_sonar_L = [0.2, 0.0, -0.2, -0.4, -0.6, -0.8, -1.0, -1.2] braitenberg_sonar_R = [-1.2, -1.0, -0.8, -0.6, -0.4, -0.2, 0.0, 0.2] # TODO: fix the red cube avoidance braitenberg_red_L = [-0.65, -0.6, -0.55, -0.5, -0.45, -0.4, -0.35, -0.3, -0.25, -0.2, -0.15, -0.1, -0.05, 0.0, 0.05, 0.10] braitenberg_red_R = [0.10, 0.05, 0.0, -0.05, -0.1, -0.15, -0.2, -0.25, -0.3, -0.35, -0.4, -0.45, -0.5, -0.55, -0.6, -0.65] braitenberg_green_L = [0.8, 0.75, 0.7, 0.65, 0.6, 0.55, 0.5, 0.45, 0.4, 0.35, 0.3, 0.25, 0.2, 0.15, 0.1, 0.05] braitenberg_green_R = [0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8] # braitenberg_sonar_L = [0.6, 0.8, 0.6, 0.4, -0.4, -0.6, -0.8, -0.6] # braitenberg_sonar_R = [-0.6, -0.8, -0.6, -0.4, 0.4, 0.6, 0.8, 0.6] # print [j[2] for j in points] sonar_ready_flag = False # keep running until the server shuts down while sim.simxGetConnectionId(clientID) != -1: image, image_ready_flag = read_image(image_ready_flag, clientID, veh_camera) detections, sonar_ready_flag = read_sonar(clientID, veh_sonar, sonar_ready_flag) # print detections # if image_ready_flag: # cv2.imshow("image", image) # cv2.waitKey(1) if image_ready_flag and sonar_ready_flag and not detections is None: free_running(detections, image, clientID, veh_left_motor, veh_right_motor) cv2.destroyAllWindows() # Now send some data to CoppeliaSim in a non-blocking fashion: # sim.simxAddStatusbarMessage(clientID, 'Hello CoppeliaSim!', sim.simx_opmode_oneshot) # Before closing the connection to CoppeliaSim, make sure that the last command sent out had time to arrive. You # can guarantee this with (for example): sim.simxGetPingTime(clientID) # Now close the connection to CoppeliaSim: sim.simxFinish(clientID) else: print 'Failed connecting to remote API server' print 'Program ended'

[ "noreply@github.com" ]

Jarvis-X.noreply@github.com

c762cc47d5dd64dfe9ce9a6360e92b0bfa00928f

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/pyupbit/__init__.py

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[]

no_license

lsjhome/pyupbit

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68c4541bd04532bcfdf46fa2004b5db4d11b9be9

refs/heads/master

2020-04-28T09:39:28.944916

2019-10-26T20:16:51

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py

from pyupbit.pyupbit import PyUpbit __version__ = '1.0.0'

[ "lsjhome007@gmail.com" ]

lsjhome007@gmail.com

1c7d4bc21df35e1245c6c10e7f05a4b42cec7807

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/training/forms.py

d1451fd8bfefd183677dad3546419a9ee1ec7904

[]

no_license

alex2702/iotrec-backend

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e9c66baf789d3efc20cb109b7d69fff6d658760c

refs/heads/master

2022-12-03T08:01:21.950701

2019-12-23T07:12:14

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null

2022-11-22T04:38:09

2019-12-16T06:19:51

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py

import random from django import forms from .models import Sample, ReferenceThing, ContextFactor # form to collect five training samples for a reference thing class SampleForm(forms.Form): # thing, CF and CFV fields are all hidden thing = forms.IntegerField(widget=forms.HiddenInput()) context_factor_1 = forms.IntegerField(widget=forms.HiddenInput()) context_factor_2 = forms.IntegerField(widget=forms.HiddenInput()) context_factor_3 = forms.IntegerField(widget=forms.HiddenInput()) context_factor_4 = forms.IntegerField(widget=forms.HiddenInput()) context_factor_5 = forms.IntegerField(widget=forms.HiddenInput()) context_factor_value_1 = forms.IntegerField(widget=forms.HiddenInput()) context_factor_value_2 = forms.IntegerField(widget=forms.HiddenInput()) context_factor_value_3 = forms.IntegerField(widget=forms.HiddenInput()) context_factor_value_4 = forms.IntegerField(widget=forms.HiddenInput()) context_factor_value_5 = forms.IntegerField(widget=forms.HiddenInput()) CHOICES = [ ('-1', 'negative effect'), ('0', 'no effect'), ('1', 'positive effect') ] value_1 = forms.ChoiceField(choices=CHOICES, widget=forms.RadioSelect) value_2 = forms.ChoiceField(choices=CHOICES, widget=forms.RadioSelect) value_3 = forms.ChoiceField(choices=CHOICES, widget=forms.RadioSelect) value_4 = forms.ChoiceField(choices=CHOICES, widget=forms.RadioSelect) value_5 = forms.ChoiceField(choices=CHOICES, widget=forms.RadioSelect) user = forms.CharField(widget=forms.HiddenInput()) class Meta: model = Sample fields = ['thing', 'context_factor_1', 'context_factor_2', 'context_factor_3', 'context_factor_4', 'context_factor_5', 'context_factor_value_1', 'context_factor_value_2', 'context_factor_value_3', 'context_factor_value_4', 'context_factor_value_5','value_1', 'value_2', 'value_3', 'value_4', 'value_5', 'user']

[ "alex.ikas@tum.de" ]

alex.ikas@tum.de

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/__manifest__.py

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[]

no_license

ValeroMateo/RecuperacionOdoo

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2020-12-07T17:56:19.724969

2020-01-28T10:40:05

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py

# -*- coding: utf-8 -*- { 'name': "Remedial exam Odoo", 'summary': """Hoping for the best""", 'description': """ Remedial exam module with the purpose of making our professor feel better about his students """, 'author': "Valero Mateo", 'website': "http://www.mybigassdisaster.com", # Categories can be used to filter modules in modules listing # Check https://github.com/odoo/odoo/blob/12.0/odoo/addons/base/data/ir_module_category_data.xml # for the full list 'category': 'Test', 'version': '0.1', # any module necessary for this one to work correctly 'depends': ['base', 'baseModule'], # always loaded 'data': [ 'security/ir.model.access.csv', 'views.xml', 'reports.xml' ], }

[ "valero.mateo98@gmail.com" ]

valero.mateo98@gmail.com

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/tests/front_office/not_sorted/example_suite.py

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[]

no_license

Maksim1988/test

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refs/heads/master

2021-01-10T17:03:36.871180

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__author__ = 'm.senchuk' import itertools from nose.loader import TestLoader from nose import run from nose.suite import LazySuite #paths = ("C:\Users\\m.senchuk\\PycharmProjects\\FF4F\\api-tests\\tests\\front_office\\test_search.py:TestSearchSeller", # ) # # #def run_my_tests(): # all_tests = () # for path in paths: # all_tests = itertools.chain(all_tests, TestLoader().loadTestsFromName(path)) # suite = LazySuite(all_tests) # result = run(suite=suite) # assert result is True, "One or more tests FAILED. See console log." # #if __name__ == '__main__': # run_my_tests()

[ "m.senchuk@yandex.ru" ]

m.senchuk@yandex.ru

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/hello-django/question/migrations/0005_auto_20190108_1023.py

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[]

no_license

mixkungz/django-for-deploy

923ff536fd8a2506bb53cb77bdd315513e40ae9c

187fe18ad85ca8bd04ee2e053248cdeff030cc85

refs/heads/master

2020-05-03T13:37:10.190004

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2019-03-31T07:40:20

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# Generated by Django 2.0.9 on 2019-01-08 03:23 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('question', '0004_auto_20190108_1010'), ] operations = [ migrations.RemoveField( model_name='answer', name='created', ), migrations.RemoveField( model_name='answer', name='modified', ), ]

[ "supawit.ruen@mail.kmutt.ac.th" ]

supawit.ruen@mail.kmutt.ac.th

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/script/label_image.runfiles/org_tensorflow/tensorflow/contrib/resampler/python/ops/resampler_ops.py

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permissive

harshit-jain-git/ImageNET

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refs/heads/master

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/home/co/Documents/ImageClassifier/tensorflow/tensorflow/contrib/resampler/python/ops/resampler_ops.py

[ "harshitjain1371999@gmail.com" ]

harshitjain1371999@gmail.com

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/lib/python2.7/site-packages/tflearn/helpers/trainer.py

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[]

no_license

ZachPhillipsGary/CS200-NLP-ANNsProject

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refs/heads/master

2022-12-22T07:42:52.513314

2016-05-06T13:52:40

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2016-05-02T04:22:19

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# -*- coding: utf-8 -*- from __future__ import division, print_function, absolute_import import numpy as np import tensorflow as tf from tensorflow.python.training import optimizer as tf_optimizer import tflearn from .. import callbacks from ..config import init_training_mode from ..utils import to_list, id_generator, check_dir_name, standarize_dict, \ get_dict_first_element, make_batches, slice_array, check_scope_path from .summarizer import summaries, summarize, summarize_gradients, \ summarize_variables, summarize_activations class Trainer(object): """ Trainer. Generic class to handle any TensorFlow graph training. It requires the use of `TrainOp` to specify all optimization parameters. Arguments: train_ops: list of `TrainOp`. A list of a network training operations for performing optimizations. graph: `tf.Graph`. The TensorFlow graph to use. Default: default tf graph. clip_gradients: `float`. Clip gradient. Default: 5.0. tensorboard_dir: `str`. Tensorboard log directory. Default: "/tmp/tflearn_logs/". tensorboard_verbose: `int`. Verbose level. It supports: ```python 0 - Loss, Accuracy. (Best Speed) 1 - Loss, Accuracy, Gradients. 2 - Loss, Accuracy, Gradients, Weights. 3 - Loss, Accuracy, Gradients, Weights, Activations, Sparsity. (Best Visualization) ``` checkpoint_path: `str`. Path to store model checkpoints. If None, no model checkpoint will be saved. Default: None. max_checkpoints: `int` or None. Maximum amount of checkpoints. If None, no limit. Default: None. keep_checkpoint_every_n_hours: `float`. Number of hours between each model checkpoints. random_seed: `int`. Random seed, for test reproductivity. Default: None. session: `Session`. A session for running ops. If None, a new one will be created. Note: When providing a session, variables must have been initialized already, otherwise an error will be raised. """ def __init__(self, train_ops, graph=None, clip_gradients=5.0, tensorboard_dir="/tmp/tflearn_logs/", tensorboard_verbose=0, checkpoint_path=None, max_checkpoints=None, keep_checkpoint_every_n_hours=10000.0, random_seed=None, session=None): self.graph = tf.get_default_graph() if graph: self.graph = graph with self.graph.as_default(): init_training_mode() train_ops = to_list(train_ops) duplicate_identical_ops(train_ops) if random_seed: tf.set_random_seed(random_seed) self.restored = False self.tensorboard_dir = check_dir_name(tensorboard_dir) self.training_step = 0 self.train_ops = to_list(train_ops) self.validate_trainop_names() self.global_loss = None self.global_step = tf.Variable(0., name='Global_Step', trainable=False) self.incr_global_step = tf.assign(self.global_step, tf.add(self.global_step, 1)) config = None tflearn_conf = tf.get_collection(tf.GraphKeys.GRAPH_CONFIG) if tflearn_conf: config = tflearn_conf[0] if not session: self.session = tf.Session(config=config) else: self.session = session self.restored = True for i, train_op in enumerate(self.train_ops): # For display simplicity in Tensorboard, if only one optmizer, # we don't display its name if len(train_ops) == 1: train_op.scope_name = "" train_op.initialize_training_ops(i, self.session, tensorboard_verbose, clip_gradients) # Saver for saving a model self.saver = tf.train.Saver( max_to_keep=max_checkpoints, keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours) # Saver for restoring a model (With exclude variable list) all_vars = tf.get_collection(tf.GraphKeys.VARIABLES) excl_vars = tf.get_collection(tf.GraphKeys.EXCL_RESTORE_VARS) to_restore = [item for item in all_vars if item not in excl_vars] self.restorer = tf.train.Saver( var_list=to_restore, max_to_keep=max_checkpoints, keep_checkpoint_every_n_hours=keep_checkpoint_every_n_hours) self.checkpoint_path = checkpoint_path if not self.restored: init = tf.initialize_all_variables() self.session.run(init) def fit(self, feed_dicts, n_epoch=10, val_feed_dicts=None, show_metric=False, snapshot_step=None, snapshot_epoch=True, shuffle_all=None, run_id=None): """ fit. Train network with feeded data dicts. Examples: ```python # 1 Optimizer trainer.fit(feed_dicts={input1: X, output1: Y}, val_feed_dicts={input1: X, output1: Y}) trainer.fit(feed_dicts={input1: X1, input2: X2, output1: Y}, val_feed_dicts=0.1) # 10% of data used for validation # 2 Optimizers trainer.fit(feed_dicts=[{in1: X1, out1:Y}, {in2: X2, out2:Y2}], val_feed_dicts=[{in1: X1, out1:Y}, {in2: X2, out2:Y2}]) ``` Arguments: feed_dicts: `dict` or list of `dict`. The dictionary to feed data to the network. It follows Tensorflow feed dict specifications: '{placeholder: data}'. In case of multiple optimizers, a list of dict is expected, that will respectively feed optimizers. n_epoch: `int`. Number of epoch to runs. val_feed_dicts: `dict`, list of `dict`, `float` or list of `float`. The data used for validation. Feed dict are following the same specification as `feed_dicts` above. It is also possible to provide a `float` for splitting training data for validation. show_metric: `bool`. If True, accuracy will be calculated and displayed at every step. Might give slower training. snapshot_step: `int`. If not None, the network will be snapshot every provided step (calculate validation loss/accuracy and save model, if a `checkpoint_path` is specified in `Trainer`). snapshot_epoch: `bool`. If True, snapshot the network at the end of every epoch. shuffle_all: `bool`. If True, shuffle all data batches (overrides `TrainOp` shuffle parameter behavior). run_id: `str`. A name for the current run. Used for Tensorboard display. If no name provided, a random one will be generated. """ if not run_id: run_id = id_generator(6) print("---------------------------------") print("Run id: " + run_id) print("Log directory: " + self.tensorboard_dir) # shuffle is an override for simplicty, it will overrides every # training op batch shuffling if isinstance(shuffle_all, bool): for t in self.train_ops: t.shuffle = shuffle_all with self.graph.as_default(): self.summ_writer = tf.train.SummaryWriter( self.tensorboard_dir + run_id, self.session.graph_def) # TODO: Add a check that all keys in feed dict match val feed dict feed_dicts = to_list(feed_dicts) for d in feed_dicts: standarize_dict(d) val_feed_dicts = to_list(val_feed_dicts) if val_feed_dicts: [standarize_dict(d) for d in val_feed_dicts] # Handle validation split validation_split(val_feed_dicts, feed_dicts) termlogger = callbacks.TermLogger(self.training_step) modelsaver = callbacks.ModelSaver(self.save, self.training_step, self.checkpoint_path, snapshot_epoch) for i, train_op in enumerate(self.train_ops): vd = val_feed_dicts[i] if val_feed_dicts else None # Prepare all train_ops for fitting train_op.initialize_fit(feed_dicts[i], vd, show_metric, self.summ_writer) # Prepare TermLogger for training diplay metric_term_name = None if train_op.metric is not None: if hasattr(train_op.metric, 'm_name'): metric_term_name = train_op.metric.m_name else: metric_term_name = train_op.metric.name.split(':')[0] termlogger.add(train_op.n_train_samples, val_size=train_op.n_val_samples, metric_name=metric_term_name, name=train_op.name) max_batches_len = np.max([t.n_batches for t in self.train_ops]) termlogger.on_train_begin() modelsaver.on_epoch_begin() try: for epoch in range(n_epoch): termlogger.on_epoch_begin() modelsaver.on_epoch_begin() # Global epoch are defined as loop over all data (whatever # which data input), so one epoch loop in a multi-inputs # model is equal to max(data_input) size. for batch_step in range(max_batches_len): self.training_step += 1 termlogger.on_batch_begin() modelsaver.on_batch_begin() global_loss, global_acc = 0., 0. for i, train_op in enumerate(self.train_ops): termlogger.on_sub_epoch_begin() modelsaver.on_sub_batch_begin() snapshot = train_op._train(self.training_step, snapshot_epoch, snapshot_step, show_metric) global_loss += train_op.loss_value if train_op.acc_value and global_acc: global_acc += train_op.acc_value / len( self.train_ops) else: global_acc = None # Optimizer batch end termlogger.on_sub_batch_end(i, train_op.epoch, train_op.step, train_op.loss_value, train_op.acc_value, train_op.val_loss, train_op.val_acc) modelsaver.on_sub_batch_end() # All optimizers batch end self.session.run(self.incr_global_step) termlogger.on_batch_end(global_loss, global_acc, snapshot) modelsaver.on_batch_end(snapshot) # Epoch end termlogger.on_epoch_end() modelsaver.on_epoch_end() finally: termlogger.on_train_end() modelsaver.on_train_end() def save(self, model_file, global_step=None): """ save. Save a Tensorflow model Arguments: model_file: `str`. Saving path of tensorflow model global_step: `float`. The training step to append to the model file name (optional). """ # Temp workaround for tensorflow 0.7.0 dict proto serialization issue try: # Try latest api l = tf.get_collection_ref("summary_tags") except Exception: l = tf.get_collection("summary_tags") l_stags = list(l) del l[:] # Temp workaround for tensorflow 0.7.0 relative path issue if model_file[0] not in ['/', '~']: model_file = './' + model_file self.saver.save(self.session, model_file, global_step=global_step) # 0.7 workaround, restore values for t in l_stags: tf.add_to_collection("summary_tags", t) def restore(self, model_file): """ restore. Restore a Tensorflow model Arguments: model_file: path of tensorflow model to restore """ self.close_session() self.session = tf.Session() self.session.run(tf.initialize_all_variables()) self.restorer.restore(self.session, model_file) for o in self.train_ops: o.session = self.session self.restored = True self.training_step = int(self.global_step.eval(self.session)) def close_session(self): """ Close session """ self.session.close() def validate_trainop_names(self): """ Give names to all TrainOp, handle no names and duplicated names """ t_len = len(self.train_ops) # Rename optimizers without name for i in range(t_len): if not self.train_ops[i].name: self.train_ops[i].name = 'Optimizer' self.train_ops[i].scope_name = 'Optimizer' # Handle duplicate names for i in range(t_len): dupl = 0 for j in range(i+1, t_len): if not self.train_ops[i].name: break if self.train_ops[i].name == self.train_ops[j].name: if dupl == 0: self.train_ops[i].name += '_' + str(dupl) self.train_ops[i].scope_name = self.train_ops[i].name dupl += 1 self.train_ops[j].name += '_' + str(dupl) self.train_ops[j].scope_name = self.train_ops[j].name class TrainOp(object): """ TrainOp. TrainOp represents a set of operation used for optimizing a network. A TrainOp is meant to hold all training parameters of an optimizer. `Trainer` class will then instantiate them all specifically considering all optimizers of the network (set names, scopes... set optimization ops...). Arguments: loss: `Tensor`. Loss operation to evaluate network cost. Optimizer will use this cost function to train network. optimizer: `Optimizer`. Tensorflow Optimizer. The optimizer to use to train network. metric: `Tensor`. The metric tensor to be used for evaluation. batch_size: `int`. Batch size for data feeded to this optimizer. Default: 64. ema: `float`. Exponential moving averages. trainable_vars: list of `tf.Variable`. List of trainable variables to use for training. Default: all trainable variables. shuffle: `bool`. Shuffle data. step_tensor: `tf.Tensor`. A variable holding training step. If not provided, it will be created. Early defining the step tensor might be useful for network creation, such as for learning rate decay. name: `str`. A name for this class (optional). graph: `tf.Graph`. Tensorflow Graph to use for training. Default: default tf graph. """ def __init__(self, loss, optimizer, metric=None, batch_size=64, ema=0., trainable_vars=None, shuffle=True, step_tensor=None, name=None, graph=None): self.graph = tf.get_default_graph() if graph: self.graph = graph self.name = name self.scope_name = name # Ops self.loss = loss self.optimizer = optimizer self.metric = metric self.metric_summ_name = "" if metric is not None: self.metric_summ_name = metric.name.split('/')[0] self.grad = None self.apply_grad = None self.summ_op = None self.val_summary_op = None self.train_vars = trainable_vars self.shuffle = shuffle # Train utils self.epoch = 0 self.step = 0 self.batches = None self.batch_index = 0 self.batch_start = 0 self.batch_end = 0 self.batch_size = batch_size self.data_size = 0 self.n_batches = 0 self.ema = ema self.feed_dict = None self.val_feed_dict = None self.loss_value = None self.val_loss = None self.acc_value = None self.val_acc = None if step_tensor is None: with self.graph.as_default(): self.training_steps = tf.Variable(0., name="Training_step", trainable=False) else: self.training_steps = step_tensor # Building if not isinstance(self.loss, tf.Tensor): raise ValueError("Unknown Loss type") if not isinstance(self.optimizer, tf_optimizer.Optimizer): raise ValueError("Unknown Optimizer") if self.train_vars is None: self.train_vars = tf.trainable_variables() else: self.train_var = to_list(self.train_vars) self.train = None def initialize_training_ops(self, i, session, tensorboard_verbose, clip_gradients): """ initialize_training_ops. Initialize all ops used for training. Because a network can have multiple optimizers, an id 'i' is allocated to differentiate them. This is meant to be used by `Trainer` when initializing all train ops. Arguments: i: `int`. This optimizer training process ID. session: `tf.Session`. The session used to train the network. tensorboard_verbose: `int`. Logs verbose. Supports: ``` 0 - Loss, Accuracy. 1 - Loss, Accuracy, Gradients. 2 - Loss, Accuracy, Gradients, Weights. 3 - Loss, Accuracy, Gradients, Weights, Activations, Sparsity.. ``` clip_gradients: `float`. Option for clipping gradients. """ self.session = session # Variables holding mean validation loss and accuracy, assigned after # each model evaluation (by batch). For visualization in Tensorboard. self.val_loss_T = tf.Variable(0., name='val_loss', trainable=False) self.val_acc_T = tf.Variable(0., name='val_acc', trainable=False) # Creating the accuracy moving average, for better visualization. if self.metric is not None: self.acc_averages = \ tf.train.ExponentialMovingAverage(0.9, self.training_steps, name='moving_avg') acc_avg_op = self.acc_averages.apply([self.metric]) else: acc_avg_op = tf.no_op() # Compute total loss, which is the loss of all optimizers plus the # loss of all regularizers. Then, we summarize those losses for # visualization in Tensorboard. with tf.name_scope(self.name): lss = [self.loss] + tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES) total_loss = tf.add_n(lss, name="Total_Loss") loss_avg_op = summaries.add_loss_summaries( total_loss, self.loss, regul_losses_collection_key=tf.GraphKeys.REGULARIZATION_LOSSES, name_prefix=self.scope_name, summaries_collection_key=self.name + "_training_summaries", exp_moving_avg=0.9, ema_num_updates=self.training_steps) # Compute gradients operations with tf.control_dependencies([loss_avg_op, acc_avg_op]): self.grad = tf.gradients(total_loss, self.train_vars) if clip_gradients > 0.0: self.grad, self.grad_norm = \ tf.clip_by_global_norm(self.grad, clip_gradients) self.grad = list(zip(self.grad, self.train_vars)) self.apply_grad = self.optimizer.apply_gradients( grads_and_vars=self.grad, global_step=self.training_steps, name="apply_grad_op_" + str(i)) # Create other useful summary (weights, grads, activations...) # according to 'tensorboard_verbose' level. self.create_summaries(tensorboard_verbose) # Track the moving averages of trainable variables if self.ema > 0.: var_averages = tf.train.ExponentialMovingAverage( self.ema, self.training_steps) var_averages_op = var_averages.apply(self.train_vars) with tf.control_dependencies([var_averages_op]): with tf.control_dependencies([self.apply_grad]): self.train = tf.no_op(name="train_op_" + str(i)) else: with tf.control_dependencies([self.apply_grad]): self.train = tf.no_op(name="train_op_" + str(i)) def initialize_fit(self, feed_dict, val_feed_dict, show_metric, summ_writer): """ initialize_fit. Initialize data for feeding the training process. It is meant to be used by `Trainer` before starting to fit data. Arguments: feed_dict: `dict`. The data dictionary to feed. val_feed_dict: `dict`. The validation data dictionary to feed. show_metric: `bool`. If True, display accuracy at every step. summ_writer: `SummaryWriter`. The summary writer to use for Tensorboard logging. """ self.summary_writer = summ_writer self.feed_dict = feed_dict self.val_feed_dict = val_feed_dict self.n_train_samples = len(get_dict_first_element(feed_dict)) self.n_val_samples = 0 if val_feed_dict: self.n_val_samples = len(get_dict_first_element(val_feed_dict)) self.index_array = np.arange(self.n_train_samples) self.create_testing_summaries(show_metric, self.metric_summ_name, val_feed_dict) if self.shuffle: np.random.shuffle(self.index_array) self.set_batches(make_batches(self.n_train_samples, self.batch_size)) def set_batches(self, batches): self.batches = batches self.n_batches = len(batches) self.batch_size = int(batches[0][1] - batches[0][0]) self.data_size = self.batch_size * (self.n_batches - 1) + \ int(batches[-1][1] - batches[-1][0]) self.batch_start, self.batch_end = self.batches[self.batch_index] def next_batch(self): """ Return True if a next batch is available """ self.batch_index += 1 self.step = min(self.batch_index*self.batch_size, self.data_size) if self.batch_index == self.n_batches: self.batch_index = 0 self.epoch += 1 self.step = 0 return False self.batch_start, self.batch_end = self.batches[self.batch_index] return True def _train(self, training_step, snapshot_epoch, snapshot_step, show_metric): """ Training process for this optimizer. Arguments: training_step: `int`. The global step. snapshot_epoch: `bool`. If True, snapshot network at each epoch. snapshot_step: `int`. If not None, snapshot network given 'step'. show_metric: `bool`. If True, display accuracy at every step. """ tflearn.is_training(True, self.session) self.loss_value, self.acc_value = None, None self.val_loss, self.val_acc = None, None train_summ_str, test_summ_str = None, None snapshot = False batch_ids = self.index_array[self.batch_start:self.batch_end] feed_batch = {} for key in self.feed_dict: # Make batch for multi-dimensional data if np.ndim(self.feed_dict[key]) > 0: feed_batch[key] = slice_array(self.feed_dict[key], batch_ids) else: feed_batch[key] = self.feed_dict[key] tflearn.is_training(True, self.session) self.session.run([self.train], feed_batch) tflearn.is_training(False, self.session) if self.summ_op is not None: train_summ_str = self.session.run(self.summ_op, feed_batch) # Retrieve loss value from summary string sname = "- Loss/" + self.scope_name self.loss_value = summaries.get_value_from_summary_string( sname, train_summ_str) if show_metric and self.metric is not None: # Retrieve accuracy value from summary string sname = "- " + self.metric_summ_name + "/" + self.scope_name self.acc_value = summaries.get_value_from_summary_string( sname, train_summ_str) # Check if data reached an epoch if not self.next_batch(): if self.shuffle: np.random.shuffle(self.index_array) batches = make_batches(self.n_train_samples, self.batch_size) self.set_batches(batches) if snapshot_epoch: snapshot = True # Check if step reached snapshot step if snapshot_step: if training_step % snapshot_step == 0: snapshot = True # Calculate validation if snapshot and self.val_feed_dict: # Evaluation returns the mean over all batches. self.val_loss = evaluate(self.session, self.loss, self.val_feed_dict, self.batch_size) if show_metric and self.metric is not None: self.val_acc = evaluate(self.session, self.metric, self.val_feed_dict, self.batch_size) # Set evaluation results to variables, to be summarized. if show_metric: update_val_op = [tf.assign(self.val_loss_T, self.val_loss), tf.assign(self.val_acc_T, self.val_acc)] else: update_val_op = tf.assign(self.val_loss_T, self.val_loss) self.session.run(update_val_op) # Run summary operation. test_summ_str = self.session.run(self.val_summary_op, self.val_feed_dict) # Write to Tensorboard n_step = self.training_steps.eval(session=self.session) if n_step > 1: if train_summ_str: self.summary_writer.add_summary( train_summ_str, n_step) if test_summ_str: self.summary_writer.add_summary( test_summ_str, n_step) return snapshot def duplicate(self): """ Returns a duplicated `TrainOp` """ return TrainOp(self.loss, optimizer=self.optimizer, batch_size=self.batch_size, ema=self.ema, metric=self.metric, trainable_vars=self.train_vars, shuffle=self.shuffle) def create_summaries(self, verbose=2): """ Create summaries with `verbose` level """ summ_collection = self.name + "_training_summaries" if verbose in [3]: # Summarize activations activations = tf.get_collection(tf.GraphKeys.ACTIVATIONS) summarize_activations(activations, summ_collection) if verbose in [2, 3]: # Summarize variable weights summarize_variables(self.train_vars, summ_collection) if verbose in [1, 2, 3]: # Summarize gradients summarize_gradients(self.grad, summ_collection) self.summ_op = tf.merge_summary(tf.get_collection(summ_collection)) def create_testing_summaries(self, show_metric=False, metric_name="Accuracy", validation_set=None): """ Create accuracy and validation summaries """ tr_summ_collection = self.name + "_training_summaries" te_summ_collection = self.name + "_testing_summaries" mn = metric_name.replace('/Mean:0/', '') if show_metric and self.metric is not None: # Summarize Raw Accuracy sname = "- " + mn + "/" + self.scope_name + " (raw)" summarize(self.metric, "scalar", sname, tr_summ_collection) # Summarize Accuracy's moving averages sname = "- " + mn + "/" + self.scope_name self.summ_op = summarize(self.acc_averages.average(self.metric), "scalar", sname, tr_summ_collection) if validation_set is not None: # Summarive Validation Loss loss_val_name = "- Loss/" + self.scope_name + "/Validation" loss_val_name = check_scope_path(loss_val_name) self.val_summary_op = summarize(self.val_loss_T, "scalar", loss_val_name, te_summ_collection) if show_metric and self.metric is not None: # Summarize Validation Accuracy acc_val_name = "- " + mn + "/" + self.scope_name + "/Validation" acc_val_name = check_scope_path(acc_val_name) self.val_summary_op = summarize(self.val_acc_T, "scalar", acc_val_name, te_summ_collection) def duplicate_identical_ops(ops): """ Duplicate identical `TrainOp` """ for i in range(len(ops)): for j in range(i+1, len(ops)): if ops[i] == ops[j]: ops[j] = ops[i].duplicate() def validation_split(val_feed_dicts, feed_dicts): """ validation_split. Handles validation split; build validation data based on a percentage of training data. It checks all val_feed_dicts keys values for a float, if found, it retrieves the exact same key in feed_dict and split its data according to `float` value and move it to val_feed_dict. Args: val_feed_dicts: `dict` of arrays or float. validation dictionary. feed_dicts: `dict` of arrays. training data dictionary. """ if val_feed_dicts: for i, val_dict in enumerate(val_feed_dicts): for key, val in val_dict.items(): if isinstance(val, float): split = val if type(feed_dicts[i][key]) in [list, np.ndarray]: split_at = int(len(feed_dicts[i][key]) * (1 - split)) feed_dicts[i][key], val_feed_dicts[i][key] = \ (slice_array(feed_dicts[i][key], 0, split_at), slice_array(feed_dicts[i][key], split_at)) else: # If parameter is not an array, we duplicate value val_feed_dicts[i][key] = feed_dicts[i][key] def evaluate(session, op_to_evaluate, feed_dict, batch_size): """ evaluate. Evaluate an operation with provided data dict using a batch size to save GPU memory. Args: session: `tf.Session`. Session for running operations. op_to_evaluate: `tf.Op`. Operation to be evaluated. feed_dict: `dict`. Data dictionary to feed op_to_evaluate. batch_size: `int`. Batch size to be used for evaluation. Ret: `float`. op_to_evaluate mean over all batches. """ tflearn.is_training(False, session) n_test_samples = len(get_dict_first_element(feed_dict)) batches = make_batches(n_test_samples, batch_size) index_array = np.arange(n_test_samples) avg = 0.0 for i, (batch_start, batch_end) in enumerate(batches): batch_ids = index_array[batch_start:batch_end] feed_batch = {} for key in feed_dict: # Make batch for multi-dimensional data if np.ndim(feed_dict[key]) > 0: feed_batch[key] = slice_array(feed_dict[key], batch_ids) else: feed_batch[key] = feed_dict[key] avg += session.run(op_to_evaluate, feed_batch) / len(batches) return avg

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import time import threading import multiprocessing from multiprocessing import Pool from PIL import Image nrOfThreads = multiprocessing.cpu_count() class myThread (threading.Thread): def __init__(self, threadID, kernel, img, originalImg): threading.Thread.__init__(self) self.threadID = threadID self.kernel = kernel self.img = img self.originalImg = originalImg self.pixels = img.load() self.originalPixels = originalImg.load() def run(self): applyFilterOnPart(self.threadID, self.kernel, self.img, self.originalImg) def applyFilterOnPart(threadNr, kernel, img, originalImg): pixels = img.load() originalPixels = originalImg.load() fromI = int(round(threadNr*(img.size[0]-2*(len(kernel)//2))/nrOfThreads)) + len(kernel)//2 toI = int(round((1+threadNr)*(img.size[0]-2*(len(kernel)//2))/nrOfThreads)) + len(kernel)//2 for i in range(fromI, toI): for j in range(len(kernel)//2, img.size[1] - len(kernel)//2): accumulatorR = 0 accumulatorG = 0 accumulatorB = 0 for kerRow in range(len(kernel)): for kerCol in range(len(kernel[kerRow])): accumulatorR += kernel[kerRow][kerCol] * originalPixels[i+len(kernel)//2 - kerRow,j+len(kernel)//2 - kerCol][0] accumulatorG += kernel[kerRow][kerCol] * originalPixels[i+len(kernel)//2 - kerRow,j+len(kernel)//2 - kerCol][1] accumulatorB += kernel[kerRow][kerCol] * originalPixels[i+len(kernel)//2 - kerRow,j+len(kernel)//2 - kerCol][2] pixels[i,j] = (int(round(accumulatorR)), int(round(accumulatorG)), int(round(accumulatorB))) return img def generateParameters(kernel, img, originalImg): params = [] for k in range(nrOfThreads): params.append((k, kernel, img, originalImg)) return params def main(imageName, kernel, showResult): print("Regular Solution With Threads Output:") start = time.time() originalImg = Image.open(imageName) img = Image.open(imageName) pixels = img.load() with Pool(processes=nrOfThreads) as pool: results = pool.starmap(applyFilterOnPart, generateParameters(kernel,img,originalImg)) for k in range(nrOfThreads): fromI = int(round(k*(img.size[0]-2*(len(kernel)//2))/nrOfThreads)) + len(kernel)//2 toI = int(round((1+k)*(img.size[0]-2*(len(kernel)//2))/nrOfThreads)) + len(kernel)//2 for i in range(fromI, toI): for j in range(len(kernel)//2, img.size[1] - len(kernel)//2): pixels[i,j] = results[k].load()[i,j] ''' threads = [] for i in range (nrOfThreads): thread = myThread(i, kernel, img, originalImg) threads.append(thread) thread.start() for i in range (nrOfThreads): threads[i].join() ''' width, height = img.size img = img.crop((len(kernel)//2, len(kernel)//2, width - len(kernel)//2, height - len(kernel)//2)) end = time.time() print(end - start,"\n") if showResult: img.show()

[ "tudor_orha@yahoo.com" ]

tudor_orha@yahoo.com

beb4cb8c5fb98b584d7ccafc8ad7673035e22014

5c3cca4ea09b1cad4be3b5973f1c9028661a92b4

/flup/resolver/nopathinfo.py

93063e28daae4b8ec73476e411d81e94ee16ed2f

[]

no_license

cwallenpoole/blog_m3

4cfec16a7cb1f504c8ba579476c18a783725a9aa

1de1e68ae959c092cf1c486d92a25d511881281d

refs/heads/master

2016-09-06T18:55:28.087462

2013-01-28T02:02:03

null

0

null

UTF-8

Python

false

2,521

py

# Copyright (c) 2005 Allan Saddi <allan@saddi.com> # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions # are met: # 1. Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # 2. Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # THIS SOFTWARE IS PROVIDED BY THE AUTHOR AND CONTRIBUTORS ``AS IS'' AND # ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR OR CONTRIBUTORS BE LIABLE # FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS # OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) # HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT # LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY # OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF # SUCH DAMAGE. # # $Id: nopathinfo.py 1755 2005-04-15 03:35:59Z asaddi $ __author__ = 'Allan Saddi <allan@saddi.com>' __version__ = '$Revision: 1755 $' from .resolver import * __all__ = ['NoPathInfoResolver'] class NoPathInfoResolver(Resolver): """ Another meta-resolver. Disallows the existence of PATH_INFO (beyond what's needed to resolve the function). Optionally allows a trailing slash. """ def __init__(self, resolver, allowTrailingSlash=False): self._resolver = resolver self._allowTrailingSlash = allowTrailingSlash def resolve(self, request, redirect=False): orig_script_name, orig_path_info = request.scriptName, request.pathInfo func = self._resolver.resolve(request, redirect) try: if func is not None: path_info = request.pathInfo.split(';')[0] if path_info and \ (not self._allowTrailingSlash or path_info != '/'): func = None return func finally: if func is None: request.environ['SCRIPT_NAME'] = orig_script_name request.environ['PATH_INFO'] = orig_path_info

[ "cw@allen-poole.com" ]

cw@allen-poole.com

c3ccdce9e5846ff35e01721dd3820ee452c05378

e2cdf33fed0f2ea82ca3933ea0f57affb2bb203a

/events.py

4b5b148ce2f959d75029b3f4c61254b6cb90f35f

[]

no_license

shaikhul/scoring_engine

67109c569bfa7b425fee8fdf4367d92adc0bd433

e48693be39cfeb849c245b5bc9b5a67270d7859a

HEAD

2016-09-06T04:30:40.571150

2015-09-01T02:49:11

41,713,070

0

null

UTF-8

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false

496

py

class Event(object): def __init__(self, score=0): self.score = score def get_weighted_score(self): return self.weight * self.score def get_event_type(self): return self.event_type class WebEvent(Event): weight = 1.0 event_type = 'web' class EmailEvent(Event): weight = 1.2 event_type = 'email' class SocialEvent(Event): weight = 1.5 event_type = 'social' class WebinarEvent(Event): weight = 2.0 event_type = 'webinar'

[ "shaikhul.net@gmail.com" ]

shaikhul.net@gmail.com

4e8d14003c2e112ef076b89c4c8a3ad6613f9a2c

8da91c26d423bacbeee1163ac7e969904c7e4338

/pyvisdk/do/customization_failed.py

b63b14e03d5fddb6d06ae4f32d77239d433f8930

[]

no_license

pexip/os-python-infi-pyvisdk

5d8f3a3858cdd61fb76485574e74ae525cdc7e25

1aadea0afbc306d09f6ecb9af0e683dbbf961d20

refs/heads/master

2023-08-28T02:40:28.789786

2020-07-16T04:00:53

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py

import logging from pyvisdk.exceptions import InvalidArgumentError ######################################## # Automatically generated, do not edit. ######################################## log = logging.getLogger(__name__) def CustomizationFailed(vim, *args, **kwargs): '''The customization sequence in the guest failed.''' obj = vim.client.factory.create('{urn:vim25}CustomizationFailed') # do some validation checking... if (len(args) + len(kwargs)) < 5: raise IndexError('Expected at least 6 arguments got: %d' % len(args)) required = [ 'template', 'chainId', 'createdTime', 'key', 'userName' ] optional = [ 'logLocation', 'changeTag', 'computeResource', 'datacenter', 'ds', 'dvs', 'fullFormattedMessage', 'host', 'net', 'vm', 'dynamicProperty', 'dynamicType' ] for name, arg in zip(required+optional, args): setattr(obj, name, arg) for name, value in kwargs.items(): if name in required + optional: setattr(obj, name, value) else: raise InvalidArgumentError("Invalid argument: %s. Expected one of %s" % (name, ", ".join(required + optional))) return obj

[ "jmb@pexip.com" ]

jmb@pexip.com

b8e02cce761e458f0b3ef4e1dd2c7a741cb9b2ad

5131d61e51a227444717abf8180d00db95179b3b

/lessons/point.py

1ffd4a12965568ee3f8a4242ef1cffcf8805dfcf

[]

no_license

yutika01/comp110-21f-workspace

fdfa9894c2d99a1a43c1c12bee3663632e772865

a575deba7283c1cbd3b5c5f52cff37af8de3fa89

refs/heads/main

2023-09-04T13:23:26.508125

2021-11-21T02:27:43

422,988,649

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null

2021-10-30T20:54:08

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UTF-8

Python

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774

py

"""Example of a Point class.""" from __future__ import annotations class Point: x: float y: float def __init__(self, x: float, y: float): """Initialize a Point with its x, y components.""" self.x = x self.y = y def scale_by(self, factor: float) -> None: """Mutates multiplies components by factor.""" self.x *= factor self.y *= factor def scale(self, factor: float) -> Point: """Immutable: mutliplies components by factor without mutation.""" x: float = self.x * factor y: float = self.y * factor scaled_point: Point = Point(x, y) return scaled_point p0: Point = Point(1.0, 2.0) p1: Point = p0.scale(2.0) print(f"({p0.x}, {p0.y})") print(f"({p1.x}, {p1.y})")

[ "yaggarwal@unc.edu" ]

yaggarwal@unc.edu

231b25ba70c9a57c6b57cd97838fe94e8da086cc

6140cd676c3c6f0e65d3be4515789e7c0f255ffd

/numeric-tsne-plotly/settings.py

2c6bcb14653175b3b8fc3e8c7a36707d9890c928

[]

no_license

RezaKakooee/tsne-collection

1efa1ab109d00588ff749bbd5d8f6563c841ce2b

93e58071671e2dfbd9b7e395608c4b2b8dca98ac

refs/heads/master

2022-04-10T08:08:27.427761

2020-03-28T22:53:25

241,490,880

0

null

UTF-8

Python

false

814

py

# -*- coding: utf-8 -*- """ Created on Tue Feb 18 23:53:36 2020 @author: rkako """ import os class Params(): def __init__(self): # Directories ans Pathes self.current_dir = os.getcwd() # self.dataset_folder_name = 'imageset' self.dataset_dir = 'C:/Users/reza/gdrive-redu/hslu/HSLU-Secude/large-data' self.data_file_name = 'pca_output_vectors.pickle' self.data_path = os.path.join(self.dataset_dir, self.data_file_name) self.log_dir_name = 'logs' self.log_dir = os.path.join(self.current_dir, self.log_dir_name) self.similarity_metric = 'cosine' # clucters self.num_clusters = 10 # TSNE self.tsne_n_components = 3 self.tsne_perplexity = 5.0 #params = Params()

[ "rkakooee@gmail.com" ]

rkakooee@gmail.com

626e284b40ec0447bfcba31a165d86827eb7df2a

6fa7f99d3d3d9b177ef01ebf9a9da4982813b7d4

/gHrMmA7emP6CFAMnb_6.py

35eeb43f5be552b55e650249bf1ff464b8e37754

[]

no_license

daniel-reich/ubiquitous-fiesta

26e80f0082f8589e51d359ce7953117a3da7d38c

9af2700dbe59284f5697e612491499841a6c126f

refs/heads/master

2023-04-05T06:40:37.328213

2021-04-06T20:17:44

355,318,759

0

null

UTF-8

Python

false

200

py

def is_apocalyptic(n): L=str(2**n).split('666') if len(L)==1: return "Safe" elif len(L)==2: return "Single" elif len(L)==3: return "Double" elif len(L)==4: return "Triple"

[ "daniel.reich@danielreichs-MacBook-Pro.local" ]

daniel.reich@danielreichs-MacBook-Pro.local

4b77de8df4c09f39015218c28428886bf20eb704

e0df57a2301e9b85fdd95645f5ec7c63e6929cfc

/my_app/currency/migrations/0001_initial.py

044fdfd08fb04514b3a11142463bde7f4e4a3e78

[]

no_license

rajdip34/python-online-compiler

1eb5a8c68d2e019b061f01c91a0788a8d2d2229f

62fa21daf73a3816c8e0d8da38e7f45af28e2424

refs/heads/master

2020-05-04T08:17:05.594349

2019-04-02T13:21:31

179,043,248

0

null

UTF-8

Python

false

1,122

py

# -*- coding: utf-8 -*- # Generated by Django 1.11 on 2019-03-30 04:51 from __future__ import unicode_literals from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ] operations = [ migrations.CreateModel( name='Currency', fields=[ ('id', models.BigAutoField(db_column='Id', primary_key=True, serialize=False)), ('name', models.CharField(db_column='Name', max_length=3)), ('description', models.CharField(max_length=50)), ('modifydatetime', models.DateTimeField(db_column='ModifyDateTime')), ('modifyuserid', models.ForeignKey(blank=True, db_column='ModifyUserid', null=True, on_delete=django.db.models.deletion.DO_NOTHING, to=settings.AUTH_USER_MODEL)), ], options={ 'db_table': 'currency', 'managed': True, }, ), ]

[ "rajdipmondal34@gmail.com" ]

rajdipmondal34@gmail.com

e1ec61ece7c4b94c6d91bb0b115a6a0aefd76e07

bc39253e56a81d65b30975a775ae18e59db75c47

/pygame_basic/game.py

a8a3257ad370d554bb98cdc99e55a5d1b8aae8a5

[]

no_license

skysee6126/py_game

96ed06c93d7566907af61a2df0e199325a1d6191

5213692541817e63b32a4b33af46c2c940836961

refs/heads/master

2023-04-21T15:07:53.074074

2021-04-28T13:22:15

360,445,007

0

null

UTF-8

Python

false

2,917

py

import pygame pygame.init() screen_width = 640 screen_height = 480 screen = pygame.display.set_mode((screen_width, screen_height)) pygame.display.set_caption("Test game") #FPS clock = pygame.time.Clock() # background = pygame.image.load("C:\Users\케이지케이\Documents\practice\game\pygame_basic\background.jpg") character = pygame.image.load("https://www.flaticon.com/svg/vstatic/svg/3885/3885025.svg?token=exp=1619081421~hmac=9f4bd262dddec45be649b11af322333e") character_size = character.get_rect().size character_width = character_size[0] character_height = character_size[1] character_x_pos = (screen_width/2) - (character_width/2) character_y_pos = screen_height - character_height #Move to_x = 0 to_y = 0 character_speed = 0.6 enemy = pygame.image.load("enemy.jpg") enemy_size = enemy.get_rect().size enemy_width = enemy_size[0] enemy_height = enemy_size[1] enemy_x_pos = (screen_width/2) - (enemy_width/2) enemy_y_pos = screen_height - enemy_height game_font = pygame.font.Font(None, 40) total_time = 10 start_ticks = pygame.time.get_ticks() #Even roof running = True while running: dt = clock.tick(30) for event in pygame.event.get(): if event.type == pygame.QUIT: running = False if event.type == pygame.KEYDOWN: if event.key == pygame.K_LEFT: to_x -= character_speed elif event.key == pygame.K_RIGHT: to_x += character_speed elif event.key == pygame.K_UP: to_y -= character_speed elif event.key == pygame.K_DOWN: to_y += character_speed if event.type == pygame.KEYUP: if event.key == pygame.K_LEFT or event.key == pygame.K_RIGHT: to_x = 0 elif event.key == pygame.K_UP or event.key == pygame.K_DOWN: to_y = 0 character_x_pos += to_x *dt character_y_pos += to_y *dt #가로 if character_x_pos < 0: character_x_pos = 0 elif character_x_pos > screen_width - character_width: character_x_pos = screen_width - character_width #세로 if character_y_pos < 0: character_y_pos = 0 elif character_y_pos > screen_height - character_height: character_y_pos = screen_height - character_height #collision character_rect = character.get_rect() character_rect.left = character_x_pos character_rect.right = character_y_pos enemy_rect = enemy.get_rect() enemy_rect.left = enemy_x_pos enemy_rect.right = enemy_y_pos if character_rect.colliderect(enemy_rect): print("Bump!") running = False screen.fill((0,0,130)) # screen.blit(background, (0,0)) screen.blit(character, (character_x_pos,character_y_pos)) screen.blit(enemy, (enemy_x_pos,enemy_y_pos)) elapsed_time = (pygame.time.get_ticks() - start_ticks)/ 1000 timer = game_font.render(str(int(total_time - elapsed_time)), True, (255, 255, 255)) screen.blit(timer, (10,10)) if total_time - elapsed_time <= 0: print("Time out") running = False pygame.display.update() pygame.time.delay(2000) pygame.quit()

[ "chooshinhye@gmail.com" ]

chooshinhye@gmail.com

da7a23f4a851db24c0e9eb29f459bc642aaa7923

a2567270e79ff3f8b7b9a9e28f91b46c2315313a

/unidad_2/ejercicio_2_8.py

5739b6c289dc09a410a352d9b02db209ce324660

[]

no_license

b3nkos/learning_python

b87c421918ff3ad9ae480d351cbabf20d18fbb36

64920a70ad14b94d0abfa690e7c70e209014c5e7

refs/heads/master

2016-09-03T07:34:49.190664

2015-07-13T05:31:27

38,992,888

0

null

UTF-8

Python

false

429

py

#!/usr/bin/python # -*- coding: utf-8 -*- """ Escribir un programa que use un ciclo definido con rango numérico, que averigue a cuántos amigos quieren saludar, les pregunte los nombres de esos amigos/as, y los salude. """ number_of_friends = input("Digite el número de amigos que desea saludar: ") for x in range(number_of_friends): best_fiend = raw_input("Nombre del mejor amigo: ") print "Hola", best_fiend + "!"

[ "cristianandres92@gmail.com" ]

cristianandres92@gmail.com

2505a5e7e60a90a9d8a8586e237cbc32f6195255

0b024b99b7f79f299b9f63ffe08d6ab25c92c560

/rolePermission/wsgi.py

7a5ca7aca64ff5c22e1ddbb8a5511a208563fbce

[]

no_license

Bakhodirov-Jakhongir/django-role-permission-based-app

924afebd9abeb494db89bbb17efc77b0ed3ed623

4f6e698a7bf0988ac1b72a577a83d2f84c3d5767

refs/heads/main

2023-08-25T07:17:06.292622

2021-11-02T11:21:04

423,815,562

0

null

UTF-8

Python

false

405

py

""" WSGI config for rolePermission project. It exposes the WSGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/3.1/howto/deployment/wsgi/ """ import os from django.core.wsgi import get_wsgi_application os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'rolePermission.settings') application = get_wsgi_application()

[ "bahodirovjahongirxoja@gmail.com" ]

bahodirovjahongirxoja@gmail.com

d1ddaf333839d2b4c77c8c4265b2240ac9836035

8d6fa96da4220ba886ef8e858f1925b6dca34e58

/examples/wtf/wtf/config.py

7cf539ff078f59cb14f772090950734c0d091acb

[]

no_license

FZambia/cyclone-wtforms

6ee26c920171685e027529e8f1fbb99c765edc30

c266b5f3bfff77e3a721b3335b74a294966f7daf

refs/heads/master

2016-09-05T15:23:08.336180

2012-10-05T18:55:00

null

0

null

UTF-8

Python

false

3,041

py

# coding: utf-8 # # Copyright 2010 Alexandre Fiori # based on the original Tornado by Facebook # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import os import ConfigParser from cyclone.util import ObjectDict def xget(func, section, option, default=None): try: return func(section, option) except: return default def parse_config(filename): cfg = ConfigParser.RawConfigParser() with open(filename) as fp: cfg.readfp(fp) fp.close() settings = {'raw': cfg} # web server settings settings["debug"] = xget(cfg.getboolean, "server", "debug", False) settings["xheaders"] = xget(cfg.getboolean, "server", "xheaders", False) settings["cookie_secret"] = cfg.get("server", "cookie_secret") settings["xsrf_cookies"] = xget(cfg.getboolean, "server", "xsrf_cookies", False) # get project's absolute path root = os.path.abspath(os.path.join(os.path.dirname(__file__), "..")) getpath = lambda k, v: os.path.join(root, xget(cfg.get, k, v)) # locale, template and static directories' path settings["locale_path"] = getpath("frontend", "locale_path") settings["static_path"] = getpath("frontend", "static_path") settings["template_path"] = getpath("frontend", "template_path") # sqlite support if xget(cfg.getboolean, "sqlite", "enabled", False): settings["sqlite_settings"] = ObjectDict(database=cfg.get("sqlite", "database")) else: settings["sqlite_settings"] = None # redis support if xget(cfg.getboolean, "redis", "enabled", False): settings["redis_settings"] = ObjectDict( host=cfg.get("redis", "host"), port=cfg.getint("redis", "port"), dbid=cfg.getint("redis", "dbid"), poolsize=cfg.getint("redis", "poolsize")) else: settings["redis_settings"] = None # mysql support if xget(cfg.getboolean, "mysql", "enabled", False): settings["mysql_settings"] = ObjectDict( host=cfg.get("mysql", "host"), port=cfg.getint("mysql", "port"), username=xget(cfg.get, "mysql", "username"), password=xget(cfg.get, "mysql", "password"), database=xget(cfg.get, "mysql", "database"), poolsize=xget(cfg.getint, "mysql", "poolsize", 10), debug=xget(cfg.getboolean, "mysql", "debug", False)) else: settings["mysql_settings"] = None return settings

[ "frvzmb@gmail.com" ]

frvzmb@gmail.com

3054352bdfefed54c1ca9176eeaada9868f2f651

97637aa398dd498a4f3598a061329ee0a8919726

/robots/maps.py

41e5db884a562f71bbb89930ad58643e2a6cfabb

[]

no_license

guitaoliu/multi_robot_xplore

1d09a328779484ec482714a0438da75469342eaf

2a006216aaacf4e9e5ae6ca753e390897812f055

refs/heads/master

2022-10-02T21:54:02.535024

2020-06-03T07:30:03

269,020,061

1

0

null

2020-06-03T07:31:11

null

UTF-8

Python

false

3,125

py

import numpy as np from abc import ABC from typing import List from robots.setting import ( MAP_SIZE, BARRIER_PERCENTAGE, PHE_VOLATILIZE_CAP, MAP_EXPLORE_PERCENT, ) class Node: def __init__(self, pos: tuple): self.x = pos[0] self.y = pos[1] def loc(self) -> tuple: return self.x, self.y def __eq__(self, other): return self.x == other.x and self.y == other.y class Map(ABC): def __init__(self): self.map = np.zeros(MAP_SIZE, dtype=int) def __getitem__(self, indices): return self.map[indices[0], indices[1]] class BarrierMap(Map): def __init__(self): super(BarrierMap, self).__init__() self.barrier_num = int(MAP_SIZE[0] * MAP_SIZE[1] * BARRIER_PERCENTAGE) self.load_barrier() def load_barrier(self): choices = np.random.choice(np.arange(MAP_SIZE[0] * MAP_SIZE[1]), size=self.barrier_num, replace=False) map_flatten = self.map.flatten() for choice in choices: map_flatten[choice] = 1 self.map = map_flatten.reshape(MAP_SIZE) def __call__(self, node: Node) -> bool: return self.map[node.x, node.y] == 1 def get_random_node(self): x, y = np.random.choice(range(self.map.shape[0])), np.random.choice(range(self.map.shape[1])) while self.map[x, y] == 1: x, y = np.random.choice(range(self.map.shape[0])), np.random.choice(range(self.map.shape[1])) return Node((x, y)) class ExploreMap(Map): def __init__(self): super(ExploreMap, self).__init__() def update(self, node: Node): self.map[node.x, node.y] = 1 def is_finished(self) -> bool: if self.map.sum() >= MAP_EXPLORE_PERCENT * self.map.shape[0] * self.map.shape[1]: return True else: return False def status(self, node: Node) -> int: return self.map[node.loc()] def get_neighbours(self, node: Node) -> List: node_list = [] for i, j in [(-1, 0), (0, -1), (1, 0), (0, 1)]: if node.x - i < 0 or node.x + i >= MAP_SIZE[0] \ or node.y - j < 0 or node.y + j >= MAP_SIZE[1]: continue if not self.status(Node((node.x + i, node.y + j))): node_list.append(Node((node.x + i, node.y + j))) return node_list class PheMap(Map): def __init__(self): super(PheMap, self).__init__() self.map = np.zeros_like(self.map, dtype=float) def update_phe(self, node: Node): self.map[node.x, node.y] += 1 def phe_volatilize(self): map(self.volatilize, self.map) @staticmethod def volatilize(phe_level: float) -> float: return (1 - PHE_VOLATILIZE_CAP) * phe_level def get_phe(self, node1: Node, node2: Node) -> float: phe = 0 x_start, x_end = min(node1.x, node2.x), max(node1.x, node2.x) y_start, y_end = min(node1.y, node2.y), max(node1.y, node2.y) for i in range(x_start, x_end): for j in range(y_start, y_end): phe += self.map[i, j] return phe

[ "guitao.liu@outlook.com" ]

guitao.liu@outlook.com

128efb9b492a29c2e87a97b932e626a724b6af9f

52b9016932aa426eeaaade5d856af6a1a771683f

/tests/testapp/serializers.py

3c4be81a47c21da377120bda5b7ee7eb6deb647d

[ "MIT" ]

permissive

marlncpe/django-rest-pandas

33033627d88c6467a9677133402fb519d5ea5a75

89a93c3ce8d30688f9137f5a9beacc7d63f621e0

refs/heads/master

2021-01-23T11:55:02.722962

2017-09-01T20:47:46

null

0

null

UTF-8

Python

false

1,844

py

from rest_framework.serializers import ModelSerializer from rest_framework import serializers from rest_pandas import PandasUnstackedSerializer from .models import TimeSeries, MultiTimeSeries, ComplexTimeSeries class TimeSeriesSerializer(ModelSerializer): date = serializers.DateField(format=None) class Meta: model = TimeSeries fields = '__all__' class TimeSeriesNoIdSerializer(TimeSeriesSerializer): class Meta: model = TimeSeries exclude = ['id'] class MultiTimeSeriesSerializer(ModelSerializer): class Meta: model = MultiTimeSeries exclude = ['id'] pandas_index = ['date'] pandas_unstacked_header = ['series'] pandas_scatter_coord = ['series'] pandas_boxplot_group = 'series' pandas_boxplot_date = 'date' class ComplexTimeSeriesSerializer(ModelSerializer): class Meta: model = ComplexTimeSeries exclude = ['id'] pandas_index = ['date', 'type'] pandas_unstacked_header = ['site', 'parameter', 'units'] class ComplexScatterSerializer(ComplexTimeSeriesSerializer): class Meta(ComplexTimeSeriesSerializer.Meta): exclude = ['id', 'flag'] pandas_scatter_coord = ['units', 'parameter'] pandas_scatter_header = ['site'] class ComplexBoxplotSerializer(ComplexTimeSeriesSerializer): class Meta(ComplexTimeSeriesSerializer.Meta): exclude = ['id', 'flag', 'type'] pandas_boxplot_group = 'site' pandas_boxplot_date = 'date' pandas_boxplot_header = ['units', 'parameter'] class NotUnstackableSerializer(ModelSerializer): class Meta: model = MultiTimeSeries fields = '__all__' list_serializer_class = PandasUnstackedSerializer # pandas_unstacked_header = Missing pandas_index = ['series']

[ "andrew@wq.io" ]

andrew@wq.io

e54034ad6c7d99686d24d85630c58338ce0e63ee

6f753a851d5ffc7263160642422ee654e5a75a4e

/blog/templatetags/blog_tags.py

94aa21fe6d3d95bbea80c9dd8e46c0ff3b4876d9

[]

no_license

iteegi/myBlog

1f5dda1f4a615a4e6a8a107838ab4b8db72c283f

b6f5dec8c5dbe258e0e25f2b300c968ace0d8350

refs/heads/master

2021-02-16T08:17:46.222872

2020-03-04T19:27:17

244,984,455

0

null

UTF-8

Python

false

968

py

"""Custom tags for the blog.""" from django.db.models import Count from django import template from ..models import Post from django.utils.safestring import mark_safe import markdown register = template.Library() @register.simple_tag def total_posts(): """Return the number of published articles.""" return Post.published.count() @register.inclusion_tag('blog/post/latest_posts.html') def show_latest_posts(count=5): """Return the last few posts.""" latest_posts = Post.published.order_by('-publish')[:count] return {'latest_posts': latest_posts} @register.simple_tag def get_most_commented_posts(count=5): """Get the article with the most comments.""" return Post.published.annotate(total_comments=Count('comments'))\ .order_by('-total_comments')[:count] @register.filter(name='markdown') def markdown_format(text): """Populate article body with Markdown formatting.""" return mark_safe(markdown.markdown(text))

[ "jd@gmail.com" ]

jd@gmail.com

23cb6c73db0e3711ff0ecbd0b6aa7165e94b3584

a01fb7bb8e8738a3170083d84bc3fcfd40e7e44f

/python3/module/pandas/df/sql/join.py

540fb2077f46a30f47e810c2b98ebc2c0a79da73

[]

no_license

jk983294/CommonScript

f07acf603611b4691b176aa4a02791ef7d4d9370

774bcbbae9c146f37312c771c9e867fb93a0c452

refs/heads/master

2023-08-21T17:50:19.036159

2023-08-16T00:22:03

42,732,160

5

0

null

UTF-8

Python

false

739

py

import pandas as pd import numpy as np df1 = pd.DataFrame({'key': ['A', 'B', 'C', 'D'], 'value': np.random.randn(4)}) df2 = pd.DataFrame({'key': ['B', 'D', 'D', 'E'], 'value': np.random.randn(4)}) print(df1) print(df2) # SELECT * FROM df1 INNER JOIN df2 ON df1.key = df2.key; print(pd.merge(df1, df2, on='key')) # in case join key is different print(pd.merge(df1, df2, left_on='key', right_on='key')) # SELECT * FROM df1 LEFT OUTER JOIN df2 ON df1.key = df2.key; print(pd.merge(df1, df2, on='key', how='left')) # SELECT * FROM df1 RIGHT OUTER JOIN df2 ON df1.key = df2.key; print(pd.merge(df1, df2, on='key', how='right')) # SELECT * FROM df1 FULL OUTER JOIN df2 ON df1.key = df2.key; print(pd.merge(df1, df2, on='key', how='outer'))

[ "jk983294@gmail.com" ]

jk983294@gmail.com

c98cf8d841b31199e65e5296805d025a9c933399

fbea032896db7e9f17687ab1e09cee580a66d179

/utils.py

10af90c3c81b1277d7ffa8227263bb65cfa174ec

[]

no_license

jmadni/ingage

4be888a6afe9cbef7b3c6adda0d27553fa35395c

c4a95bb01c63d12e1128a6a541edab39292c9b03

refs/heads/main

2023-02-13T14:02:42.554191

2021-01-12T07:03:33

328,564,822

0

null

UTF-8

Python

false

312

py

import os import shutil from moviepy.video.io.VideoFileClip import VideoFileClip def output_directory(dir): if os.path.exists(dir): shutil.rmtree(dir) os.mkdir(dir) def check_input_file_valid(video_file): try: VideoFileClip(video_file) except FileNotFoundError: pass

[ "t.vinaykumar98@gmail.com" ]

t.vinaykumar98@gmail.com

3974a3b8483800aaf3ad9f189e529ec6e246d184

f6c5aa5931b71a31246168c9049da50797e5ce57

/djangoapp/src/config/urls.py

80f6238a15460d9c83ea408eb5d3f925bf0bed62

[]

no_license

AlexLoar/musicallity

7b724b8350cab3de516228636268b79ba51f5b4c

5dc09e8b7fed8c666181a4cb87e8d8328481f1d5

refs/heads/master

2023-01-02T12:20:38.648476

2020-10-06T16:00:49

301,781,216

0

null

UTF-8

Python

false

1,250

py

from django.contrib import admin from django.conf import settings from django.urls import path, include from django.conf.urls.static import static from django.utils.translation import ugettext_lazy as _ from django.views import defaults as default_views from config.router import urlpatterns as api_urlpatterns app_name = 'main_music' # Admin URLs admin.site.site_header = _('MUSIC Project') urlpatterns = [ path(r'admin/', admin.site.urls), ] # API URLs # Create a router and register our resources with it. urlpatterns += [ path('api/v1/', include(api_urlpatterns)), ] if settings.DEBUG: # This allows the error pages to be debugged during development, just visit # these url in browser to see how these error pages look like. urlpatterns += [ path(r'400/', default_views.bad_request, kwargs={'exception': Exception('Bad Request!')}), path(r'403/', default_views.permission_denied, kwargs={'exception': Exception('Permission Denied')}), path(r'404/', default_views.page_not_found, kwargs={'exception': Exception('Page not Found')}), path(r'500/', default_views.server_error), ] # Media URLs on debug urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)

[ "alexloar87@gmail.com" ]

alexloar87@gmail.com

a6cc13163a16574087d481e50d5730f46c476e01

ef4a2e4aadc67a1d5e929f5c0c3e902b34f663dc

/examples/psychotria.lagrange.py

767424584fc3bc922133c667c347f6c9b73538e6

[]

no_license

zxf-art/lagrange-python

326b23cc69b1eb81db9127a2ec8fc91bcc0b6dbc

e67f1e671d313319bdc374a3371fa3717a8e7091

refs/heads/master

2020-06-12T06:18:20.917164

2018-01-22T19:50:43

null

0

null

UTF-8

Python

false

3,929

py

#!/usr/bin/env python import os import lagrange data = """\ ### begin data {'area_adjacency': [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]], 'area_dispersal': [[[1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0]]], 'area_labels': ['K', 'O', 'M', 'H'], 'base_rates': '__estimate__', 'dispersal_durations': [10.0], 'dm_symmetric_entry': True, 'excluded_ranges': [], 'lagrange_version': '20120508', 'max_range_size': 2, 'model_name': 'psychotria', 'newick_trees': [{'included': '__all__', 'name': 'Tree0', 'newick': '((((((((P_hawaiiensis_WaikamoiL1:0.010853, P_mauiensis_Eke:0.010853)N2:0.007964, (P_fauriei2:0.013826, P_hathewayi_1:0.013826)N5:0.004991)N6:0.001986, (P_kaduana_PuuKukuiAS:0.020803, P_mauiensis_PepeAS:0.020803)N9:1e-05)N10:0.003762, P_kaduana_HawaiiLoa:0.024565)N12:0.003398, (P_greenwelliae07:0.012715, P_greenwelliae907:0.012715)N15:0.015248)N16:0.018984, ((((P_mariniana_MauiNui:0.02241, P_hawaiiensis_Makaopuhi:0.02241)N19:0.008236, P_mariniana_Oahu:0.030646)N21:0.002893, P_mariniana_Kokee2:0.033539)N23:0.005171, P_wawraeDL7428:0.03871)N25:0.008237)N26:0.008255, (P_grandiflora_Kal2:0.027864, P_hobdyi_Kuia:0.027864)N29:0.027338)N30:0.003229, ((P_hexandra_K1:0.026568, P_hexandra_M:0.026568)N33:0.005204, P_hexandra_Oahu:0.031771)N35:0.026659)N36;', 'root_age': 5.2}], 'ranges': [(), (0,), (0, 1), (0, 2), (0, 3), (1,), (1, 2), (1, 3), (2,), (2, 3), (3,)], 'taxa': ['P_mariniana_Kokee2', 'P_mariniana_Oahu', 'P_mariniana_MauiNui', 'P_hawaiiensis_Makaopuhi', 'P_wawraeDL7428', 'P_kaduana_PuuKukuiAS', 'P_mauiensis_PepeAS', 'P_hawaiiensis_WaikamoiL1', 'P_mauiensis_Eke', 'P_fauriei2', 'P_hathewayi_1', 'P_kaduana_HawaiiLoa', 'P_greenwelliae07', 'P_greenwelliae907', 'P_grandiflora_Kal2', 'P_hobdyi_Kuia', 'P_hexandra_K1', 'P_hexandra_M', 'P_hexandra_Oahu'], 'taxon_range_data': {'P_fauriei2': (1,), 'P_grandiflora_Kal2': (0,), 'P_greenwelliae07': (0,), 'P_greenwelliae907': (0,), 'P_hathewayi_1': (1,), 'P_hawaiiensis_Makaopuhi': (3,), 'P_hawaiiensis_WaikamoiL1': (2,), 'P_hexandra_K1': (0,), 'P_hexandra_M': (0,), 'P_hexandra_Oahu': (1,), 'P_hobdyi_Kuia': (0,), 'P_kaduana_HawaiiLoa': (1,), 'P_kaduana_PuuKukuiAS': (2,), 'P_mariniana_Kokee2': (0,), 'P_mariniana_MauiNui': (2,), 'P_mariniana_Oahu': (1,), 'P_mauiensis_Eke': (2,), 'P_mauiensis_PepeAS': (2,), 'P_wawraeDL7428': (0,)}} ### end data """ i = 0 while 1: if not i: outfname = "psychotria.results.txt" else: outfname = "psychotria.results-"+str(i)+".txt" if not os.path.exists(outfname): break i += 1 outfile = open(outfname, "w") lagrange.output.log(lagrange.msg, outfile, tee=True) model, tree, data, nodelabels, base_rates = lagrange.input.eval_decmodel(data) lagrange.output.ascii_tree(outfile, tree, model, data, tee=True) if base_rates != "__estimate__": d, e = base_rates else: d, e = lagrange.output.optimize_dispersal_extinction(outfile, tree, model, tee=True) if nodelabels: if nodelabels == "__all__": nodelabels = None lagrange.output.ancsplits(outfile, tree, model, d, e, nodelabels=nodelabels, tee=True)

[ "github@rree.fastmail.fm" ]

github@rree.fastmail.fm

4037c0492bcce6dde2154f61c014bed9328f5868

ed36a77bf31a5e2d99f80fe4c13224976ea274fd

/blog/models.py

c1d564824fd2f82a1cb2709ca4bab96d404d49a3

[]

no_license

lovelove0618/django_swu_website

b2752623112bf85a5789b42f6569156293ddd01a

b33c2f267bfcf00490226d8797c7f0190c7466b2

refs/heads/master

2022-11-29T10:06:26.382813

2020-08-17T16:39:13

287,696,266

0

null

UTF-8

Python

false

272

py

from django.db import models from django.contrib.auth.models import User class Post(models.Model): title = models.CharField(max_length=30) content = models.TextField() created = models.DateTimeField() author = models.ForeignKey(User, on_delete=True)

[ "lovelove0618@naver.com" ]

lovelove0618@naver.com

478b126ab280b9343347c1ee8bc9238dd9f45703

86da8c4d616a78afc7cd09711b0151e5f852a8b8

/pythonprograms/LanguageFundamentals/Logicaloperator.py

98dae1b5e2d38ecb15661dfb77541e77356b7768

[]

no_license

sharijamusthafa/luminarpython

d1d3274d23d93af2c5e4db7d2652e8cb46b133aa

8ebd75ea5f734e5061a7138153a2c6b1cd43a738

refs/heads/master

2022-12-23T22:45:40.194242

2020-10-07T16:40:09

290,109,565

0

null

UTF-8

Python

false

32

py

num1=2 num2=4 print(num1&num2)

[ "sharijamusthafa@gmail.com" ]

sharijamusthafa@gmail.com

7d3341862b6c5f086993c701914ada358509663c

d27c668c98b83904c830141f143125c4eca54406

/Compiler.py

d1e042d8eaee3e7225397dcecd832c4bb8d157d7

[]

no_license

VladasZ/build_tools

d38dd7c1b237ec0c7f5d2c0610d85ee94539c923

926b1c4b2467e7bb5c58d217cc39cb8d8e9dd8d0

refs/heads/master

2022-02-25T04:33:07.360975

2022-02-09T21:29:16

144,903,216

0

null

UTF-8

Python

false

634

py

import Args import System import Compilers.GCC import Compilers.Clang import Compilers.VisualStudio gcc = Compilers.GCC.get() clang = Compilers.Clang.get() visual_studio = Compilers.VisualStudio.get() def get_ide(): if System.is_windows: return visual_studio if System.is_mac: return clang return gcc def get(): if Args.android: return clang if Args.ide: return get_ide() if Args.clang: return clang if Args.gcc: return gcc if System.is_mac: return clang return gcc def print_info(): print(clang) print(gcc)

[ "146100@gmail.com" ]

146100@gmail.com

ecb2abc4963884e24944a06ebcf5ef842448978d

95bebfc06f69425706cf18b717e6b4341373bc30

/anagram.py

6212e6c237f6e41af84b6696ebcd2bd95e9231d6

[]

no_license

vinodrajendran001/python-interview-prep

9200a8f3076ab1e0a3325f4a3814f811f3c0757c

80143a4b2b2957b6aff15b0b225f192c04dd1461

refs/heads/master

2021-01-12T06:52:32.266606

2016-12-23T04:01:02

76,851,795

0

null

UTF-8

Python

false

1,513

py

# O(n^2) def getstr1(a, b): c = "" d = "" if len(a) == len(b): for i in range(len(a)): for j in range(len(b)): if a[i] == b[j]: c = c + b[j] if a == c: print "given 2 text is anagram" else: print "not anagram" else: print "length is not same" # It is O(nlogn) because we use sort() def getstr2(a, b): s1 = list(a) s2 = list(b) s1.sort() s2.sort() pos = 0 matches = True while pos < len(s1) and matches: if s1[pos] == s2[pos]: matches = True pos = pos + 1 else: matches = False return matches # O(n) def getstr3(a, b): total_a = 0 for i in range(len(a)): total_a = total_a + ord(a[i]) total_b = 0 for j in range(len(b)): total_b = total_b + ord(b[j]) if total_b == total_a: print "it is anagram" else: print "not anagram" ''' Given two strings, a and b, that may or may not be of the same length, determine the minimum number of character deletions required to make them anagrams. Any characters can be deleted from either of the strings e.g. str1 : cde str2 : abc result : 4 ''' def minanagram(a, b): c1 = [0]*26 c2 = [0]*26 for i in range(len(a)): pos = ord(a[i]) - ord('a') c1[pos] = c1[pos] + 1 for i in range(len(b)): pos = ord(b[i]) - ord('a') c2[pos] = c2[pos] + 1 j=0 count = 0 print c1 print c2 while j<26: count = count + abs(c1[j]-c2[j]) j = j + 1 return count print minanagram('cdef','abc') getstr1("python","typhon") print(getstr2("python","typhon")) getstr3("typhon","python")

[ "vinodr.msec@gmail.com" ]

vinodr.msec@gmail.com

633864555ecc66eee48aee8dc62a911061bc4b80

931fafb77d3d1e00d25a4ab658f75d2cd9f5abb8

/Fundamentals/Session1/YoB.py

5ee44632a7db87bb8911a50eb0952ebb32a409d0

[]

no_license

andynguyendk/andynguyen_fundamentals_C4E15

c72e74d13fcd59dc0d6c7072d55d092418cabfa7

845da653307dc72ad68f55d756c36ff17802c6aa

refs/heads/master

2021-09-09T02:54:30.863797

2018-03-13T12:17:11

117,338,525

0

2

null

UTF-8

Python

false

88

py

n = int(input("What's your year of birth? ")) a = 2018 - n print ("Now your age is", a)

[ "andynguyendk@gmail.com" ]

andynguyendk@gmail.com

a5196fdf75c378ca343c8727c8f7e946e3cccf00

b8b8722787ee6ee0ebe3eefb6d9e9e0db11a445f

/CIFAR10_MLP/main_5.py

93f889cdfcaa742288a3b9dc41dfd230478abf24

[]

no_license

gaow1423/Machine-Learning-Problems

846061975efd63eddcd1e33e401a3aa2b699b875

7a00352cdc213234c23bc215cdb326c032bedd05

refs/heads/master

2020-03-17T12:22:46.607558

2018-06-17T07:07:55

null

0

null

UTF-8

Python

false

6,735

py

import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torchvision import datasets, transforms from torch.autograd import Variable import numpy as np import matplotlib import math import matplotlib.pyplot as plt cuda = torch.cuda.is_available() print('Using PyTorch version:', torch.__version__, 'CUDA:', cuda) ##data preparation batch_size = 40 kwargs = {'num_workers': 2, 'pin_memory': True} if cuda else {} trainset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transforms.Compose([transforms.ToTensor()])) train_loader = torch.utils.data.DataLoader(trainset, batch_size, shuffle=True, **kwargs) validationset = datasets.CIFAR10(root='./data', train=False, download=True, transform=transforms.Compose([transforms.ToTensor()])) validation_loader = torch.utils.data.DataLoader(validationset, batch_size, shuffle=False, **kwargs) classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck') class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.fc1 = nn.Linear(3*32*32, 100) self.fc1_drop = nn.Dropout(0.2) self.fc2 = nn.Linear(100,10) def forward(self, x): x = x.view(-1, 3*32*32) x = F.sigmoid(self.fc1(x)) x = self.fc1_drop(x) return F.log_softmax(self.fc2(x), 1) #model = Net() #if cuda: # model.cuda() #learningrate = [0.1, 0.01, 0.001, 0.0001] #for k in learningrate: # optimizer = optim.SGD(model.parameters(), lr = k, momentum = 0.5) #print(model) def train(epoch, k, model, log_interval = 100): # print (k) model.train() for batch_idx, (data, target) in enumerate(train_loader): if (batch_idx <= 999): if cuda: data, target = data.cuda(), target.cuda() data, target = Variable(data), Variable(target) optimizer = optim.SGD(model.parameters(), lr = 0.1, momentum = 0.5, weight_decay = k) optimizer.zero_grad() output = model(data) loss = F.nll_loss(output, target) loss.backward() optimizer.step() # if batch_idx % log_interval == 0: # print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(epoch, (batch_idx) * len(data), len(train_loader.dataset), 100. * batch_idx / len(train_loader), loss.data[0])) def validate(loss_vector, accuracy_vector, epochs, model): model.eval() val_loss, correct = 0, 0 for batch_idx, (data, target) in enumerate(train_loader): if (batch_idx > 999): if cuda: data, target = data.cuda(), target.cuda() data, target = Variable(data), Variable(target) output = model(data) val_loss += F.nll_loss(output, target).data[0] pred = output.data.max(1)[1] # get the index of the max log-probability correct += pred.eq(target.data).cpu().sum() val_loss /= (10000/batch_size) loss_vector.append(val_loss) accuracy = 100. * correct / len(validation_loader.dataset) accuracy_vector.append(accuracy) print('\nEpoch {}: Validation set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(epochs, val_loss, correct, len(validation_loader.dataset), accuracy)) def test(loss_vector, accuracy_vector, epochs, model): model.eval() val_loss, correct = 0, 0 for (data, target) in validation_loader: if cuda: data, target = data.cuda(), target.cuda() data, target = Variable(data), Variable(target) output = model(data) val_loss += F.nll_loss(output, target).data[0] pred = output.data.max(1)[1] # get the index of the max log-probability correct += pred.eq(target.data).cpu().sum() val_loss /= len(validation_loader) loss_vector.append(val_loss) accuracy = 100. * correct / len(validation_loader.dataset) accuracy_vector.append(accuracy) print('\nEpoch {}: Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(epochs, val_loss, correct, len(validation_loader.dataset), accuracy)) def main(): # learningrate = [0.2, 0.3, 0.4, 0.5] # for k in learningrate: # momentum = [0.5, 0.7, 0.9, 1.1] # for k in momentum: weightdecay = [0.001, 0.01, 0.1, 1] for k in weightdecay: # class Net(nn.Module): # def __init__(self): # super(Net, self).__init__() # self.fc1 = nn.Linear(3*32*32, 100) # self.fc1_drop = nn.Dropout(k) # self.fc2 = nn.Linear(100,10) # def forward(self, x): # x = x.view(-1, 3*32*32) # x = F.sigmoid(self.fc1(x)) # x = self.fc1_drop(x) # return F.log_softmax(self.fc2(x), 1) model = Net() if cuda: model.cuda() epochs = 150 lossv, accv = [], [] lossv_t, accv_t = [], [] for epochs in range(1, epochs + 1): train(epochs, k, model) validate(lossv, accv, epochs, model) test(lossv_t, accv_t, epochs, model) l = range(1, epochs + 1) plt.subplot(1, 3, 1) plt.plot(l, accv, label = "Training Accuracy with weight decay = %f"%(k)) plt.title('Training Accuracy') plt.xlabel('x-axis: the Number of Epochs') plt.ylabel('y-axis: Accuracy of Validation set (%)') plt.subplot(1, 3, 2) plt.plot(l, lossv, label = "Training Loss with weight decay = %f"%(k)) plt.title('Training loss curve') plt.xlabel('x-axis: the Number of Epochs') plt.ylabel('y-axis: Training loss') plt.subplot(1, 3, 3) plt.plot(l, accv_t, label = "Test Accuracy with weight decay = %f"%(k)) plt.title('Testing Accuracy') plt.xlabel('x-axis: the Number of Epochs') plt.ylabel('y-axis: Accuracy of Testing set (%)') # f.plot(l, accv, label = "Training Accuracy with drop out = %f"%(k)) # g.plot(l, lossv, label = "Training Loss with drop out = %f"%(k)) # t.plot(l, accv_t, label = "Test Accuracy with drop out = %f"%(k)) # # f.title('Training Accuracy') # f.xlabel('x-axis: the Number of Epochs') # f.ylabel('y-axis: Accuracy of Validation set (%)') # f.legend() # f.show() # # g.title('Training loss curve') # g.xlabel('x-axis: the Number of Epochs') # g.ylabel('y-axis: Training loss') # g.legend() # g.show() # # t.title('Testing Accuracy') # t.xlabel('x-axis: the Number of Epochs') # t.ylabel('y-axis: Accuracy of Testing set (%)') # t.legend() # t.show() # raw_input() plt.legend() plt.show() if __name__ == '__main__': main()

[ "weigao@WEIs-MBP.lan" ]

weigao@WEIs-MBP.lan

5b8dec57487f5b0f362fcf9ff61241d3a643f9ec

b8454cadc306140b7140bd4a5040da9b9c18c980

/base/migrations/0001_initial.py

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[]

no_license

amar3142/mywebsite3

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db45c03af908bc5df68154e3227f4dee0db28bc6

refs/heads/master

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2020-12-17T10:36:50

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# Generated by Django 3.1.4 on 2020-12-16 13:20 from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Task', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('title', models.CharField(max_length=200)), ('complete', models.BooleanField(default=False)), ('created', models.DateTimeField(auto_now_add=True)), ], ), ]

[ "amarsagat@gmail.com" ]

amarsagat@gmail.com

3825b458baa098aaab3dae1806870377a5cbf625

e16d41602b9887c814ae3ea5615f2734f15cdf50

/unitTest/myfun.py

d667bdc2da29531c20b24441a912f9dc26e5ebed

[]

no_license

tomreddle/vip3test

32183d652c8847b5fded7c4f1b4740f09b5fc850

ecc2e6be0503235a1fcfd7447d5722816ffcfe8c

refs/heads/master

2020-07-30T21:38:23.636803

2019-11-20T13:08:11

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py

import unittest def add(a, b): return a + b def plus(a, b): return a - b def mul(a, b): return a * b def div(a, b): return a / b

[ "844626764@qq.com" ]

844626764@qq.com

10061e9d1ee1fd83d00916d518364042992e220e

784ce7c5c3f08602cf0d4b3a80ec74d8e07990a1

/web/models.py

358465843bd033dd80eaab90527c19011b4fe5ce

[]

no_license

lcdevelop/shareditor

1a05e558a2cbd76c5272f7d0d43222b5a47bc139

318d8b2c77b8a8065399aeeab7e827f74c6abec0

refs/heads/master

2022-03-30T07:40:32.876433

2020-01-20T14:36:20

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# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import models from ckeditor_uploader.fields import RichTextUploadingField class Subject(models.Model): name = models.CharField(max_length=255, verbose_name='类别名称') introduce = models.CharField(max_length=255, verbose_name='类别简介') image = models.ImageField(max_length=255, verbose_name='类别图片', null=True) class Meta: verbose_name_plural = '类别' def __unicode__(self): return self.name class Tag(models.Model): name = models.CharField(max_length=255, verbose_name='标签名称') image = models.ImageField(max_length=255, verbose_name='标签图片', null=True) sort = models.IntegerField(verbose_name='排序越大越靠前', default=0) show = models.IntegerField(verbose_name='是否展示在首页', default=1) def get_latest_blogpost(self, count=5): return self.blogpost_set.filter(verify=True).order_by('id').reverse()[0:count] class Meta: verbose_name_plural = '标签' def __unicode__(self): return self.name class BlogPost(models.Model): id = models.AutoField(primary_key=True) title = models.CharField(max_length=255, verbose_name='文章标题') image = models.ImageField(max_length=255, verbose_name='文章图片', null=True) abstract = models.CharField(max_length=255, verbose_name='文章摘要', null=True) body = RichTextUploadingField(config_name='default', verbose_name='文章内容') create_time = models.DateTimeField(verbose_name='创建时间') subject = models.ForeignKey(Subject, verbose_name='类别', null=True) tags = models.ManyToManyField(Tag, verbose_name='标签', null=True) pv = models.IntegerField(verbose_name='pv', default=0) verify = models.BooleanField(verbose_name='是否生效', default=False) def get_simple_title(self): return self.title.replace(self.tags.first().name, '') class Meta: verbose_name_plural = '文章' def __unicode__(self): return self.title class Chat(models.Model): client_ip = models.CharField(max_length=16, verbose_name='用户ip') message = models.TextField(verbose_name='说的话') talker = models.IntegerField(verbose_name='说话者:0-机器人;1-用户') create_time = models.DateTimeField(verbose_name='创建时间', auto_now_add=True) class CorpusQuestion(models.Model): text = models.CharField(max_length=512, verbose_name='问') bad = models.IntegerField(verbose_name='踩', default=0) is_del = models.IntegerField(verbose_name='1-删除；0-正常', default=0) def __unicode__(self): return self.text class CorpusAnswer(models.Model): text = models.CharField(max_length=512, verbose_name='答') like = models.IntegerField(verbose_name='点赞量', default=0) is_del = models.IntegerField(verbose_name='1-删除；0-正常', default=0) question = models.ForeignKey(CorpusQuestion, verbose_name='问题') def __unicode__(self): return self.text

[ "lichuang@baijiahulian.com" ]

lichuang@baijiahulian.com

c98aeb07e64d3799b2f504ba1c4037f3fe5d4773

0561347e739592204aa0711ff54c03ad5d484f25

/app_v1.0.py

6304529efec43df5bc416b5cae6712df5dddfa47

[]

no_license

AikoChou/cht2017-demo

fd13aaec9294c30e937c2b61113ee4abb0daff03

6412f0675fd194caf7adc59e4844a0a7b7ed4396

refs/heads/master

2021-01-02T08:17:22.522379

2017-09-19T08:30:04

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import dash from dash.dependencies import Input, Output, State, Event import dash_core_components as dcc import dash_html_components as html import plotly.plotly as py ### from plotly import graph_objs as go from plotly.graph_objs import * from flask import Flask import pandas as pd import numpy as np import os import copy import functools32 server = Flask('my app') server.secret_key = os.environ.get('secret_key', 'secret') app = dash.Dash('CHTApp', server=server, url_base_pathname='/cht2017-demo/v1.0', csrf_protect=False) if 'DYNO' in os.environ: app.scripts.append_script({ 'external_url': 'https://cdn.rawgit.com/chriddyp/ca0d8f02a1659981a0ea7f013a378bbd/raw/e79f3f789517deec58f41251f7dbb6bee72c44ab/plotly_ga.js' }) external_css = ["https://cdnjs.cloudflare.com/ajax/libs/skeleton/2.0.4/skeleton.min.css", "//fonts.googleapis.com/css?family=Raleway:400,300,600", "//fonts.googleapis.com/css?family=Dosis:Medium", "https://fonts.googleapis.com/css?family=Overpass:300,300i", "https://cdn.rawgit.com/plotly/dash-app-stylesheets/dab6f937fd5548cebf4c6dc7e93a10ac438f5efb/dash-technical-charting.css" "https://cdn.rawgit.com/plotly/dash-app-stylesheets/62f0eb4f1fadbefea64b2404493079bf848974e8/dash-uber-ride-demo.css", "https://maxcdn.bootstrapcdn.com/font-awesome/4.7.0/css/font-awesome.min.css", "https://cdnjs.cloudflare.com/ajax/libs/normalize/7.0.0/normalize.min.css", ] #"https://cdn.rawgit.com/plotly/dash-app-stylesheets/5047eb29e4afe01b45b27b1d2f7deda2a942311a/goldman-sachs-report.css" for css in external_css: app.css.append_css({"external_url": css}) external_js = [ "https://code.jquery.com/jquery-3.2.1.min.js", "https://cdn.rawgit.com/plotly/dash-app-stylesheets/a3401de132a6d0b652ba11548736b1d1e80aa10d/dash-goldman-sachs-report-js.js" ] for js in external_js: app.scripts.append_script({ "external_url": js }) mapbox_access_token = 'pk.eyJ1IjoiYWlrb2Nob3UiLCJhIjoiY2o1bWF2emI4M2ZoYjJxbnFmbXFrdHQ0ZCJ9.w0_1-IC0JCPukFL7Bpa92w' # Global map layout layout = dict( showlegend = True, legend={'x': 0, 'y': 1}, height=700, margin=Margin(l=0, r=0, t=0, b=0), autosize=True, hovermode='closest', mapbox=dict( accesstoken=mapbox_access_token, bearing=0, center=dict( lat=25.032969, lon=121.565418 ), pitch=0, zoom=9, style='streets' ), ) def initialize(): uid = "u_466924201064380" file_list = ['20161021','20161123','20161220','20170116','20170213','20170217','20170222','20170303','20170317','20170512'] raw_dfs = {} prepro_dfs = {} for selectedData in ["raw", "prepro"]: for f in file_list: filename = f + '_' + selectedData + '.csv' if selectedData == 'raw': df = pd.read_csv(os.path.join('data', uid, selectedData, filename), dtype={'lon': str, 'lat': str}) raw_dfs[f] = df else: df = pd.read_csv(os.path.join('data', uid, selectedData, filename), dtype=str) prepro_dfs[f] = df cellular_dfs = {} for f in file_list: filename = f + '.csv' df = pd.read_csv(os.path.join('data', uid, filename), dtype=str) cellular_dfs[f] = df result_dfs = pd.read_csv(os.path.join('data', uid, 'result.csv'), dtype={'uid':str, 'date':str}) return raw_dfs, prepro_dfs, cellular_dfs, result_dfs app.layout = html.Div([ html.Div([ html.Div([ html.H2("Transportation Mode Detection", style={'font-family': 'Dosis', 'float': 'left', 'position': 'relative', 'top': '30px'}), html.P("Public transportation mode detection with cellular data.\ Select different users and days using the dropdowns below.\ ", className="explanationParagraph twelve columns", style={'float': 'left', 'position': 'relative', 'top': '20px', 'fontSize': 20}), ], className='row'), html.Hr(style={'margin': '0', 'margin-bottom': '5'}), html.Div([ html.Div([ html.P('Select user:', style={'fontSize': 17, 'marginBottom': 1}), dcc.Dropdown( id='user-dropdown', options=[ {'label': 'WCPeng :)', 'value': 'u_466924201064380'}, ], value="u_466924201064380", placeholder="Please choose an user", className="user-picker" ), html.Div([ dcc.RadioItems( id='radio-selector', options=[ {'label': 'Raw Cellular Data ', 'value': 'raw'}, {'label': 'Preprocessed Data ', 'value': 'prepro'}, {'label': 'Mode Detection ', 'value': 'mode'} ], value='raw', labelStyle={'display': 'inline-block'} ), ],style={'marginTop': '10', 'marginLeft': '7'}), ],className='six columns'), html.Div([ html.P('Select day:', style={'fontSize': 17, 'marginBottom': 1}), dcc.Dropdown( id='day-dropdown', placeholder="Please choose a day", value='20161123', ), html.Div([ dcc.Checklist( id='lock-selector', options=[ {'label': 'Lock camera', 'value': 'lock'} ], values=[], inputStyle={"z-index": "3"} ), ],style={'marginTop': '10', 'marginLeft': '7'}) ], className='six columns'), ], className='row'), html.Div([ dcc.Graph(id='map-graph'), ]), html.P("", id="popupAnnotation", className="popupAnnotation", style={'color': 'black', 'fontSize': 20, 'font-family': 'Dosis'}), ], style={'margin': 'auto auto'}), html.Hr(style={'margin': '0', 'margin-bottom': '5'}), dcc.Markdown("[NCTU-ADSL/cht2017-demo](https://github.com/NCTU-ADSL-public/cht2017-demo)", className="source"), ], className='ten columns offset-by-one') def fetch_raw_prepro_dataframe(uid, date, selectedData): if selectedData == 'raw': df = raw_dfs[date] else: df = prepro_dfs[date] return df def fetch_mode_dataframe(uid, date): result_df = result_dfs[result_dfs.date == date] ntrips = result_df.shape[0] trip_dfs = [] trip_mode = [] for i in range(ntrips): trip_start_t = result_df.iloc[i]['start_t'] trip_end_t = result_df.iloc[i]['end_t'] cellular_df = cellular_dfs[date] cellular_df.ctimestamp = pd.to_datetime(cellular_df.ctimestamp) trip_df = cellular_df[(cellular_df.ctimestamp >= pd.to_datetime(trip_start_t))&(cellular_df.ctimestamp <= pd.to_datetime(trip_end_t))] trip_df = trip_df.sort_values(by='ctimestamp') trip_dfs.append(trip_df) trip_mode.append(result_df.iloc[i]['mode']) return result_df, trip_dfs, trip_mode @app.callback(Output('day-dropdown','options'),[ Input("user-dropdown", "value"), Input('radio-selector', 'value')]) def set_day_options(uid, selectedData): if uid == "u_466924201064380": options=[ {'label': '2016-10-21', 'value': '20161021'}, {'label': '2016-11-23', 'value': '20161123'}, {'label': '2016-12-20', 'value': '20161220'}, {'label': '2017-01-16', 'value': '20170116'}, {'label': '2017-02-13', 'value': '20170213'}, {'label': '2017-02-17', 'value': '20170217'}, {'label': '2017-02-22', 'value': '20170222'}, {'label': '2017-03-03', 'value': '20170303'}, {'label': '2017-03-17', 'value': '20170317'}, {'label': '2017-05-12', 'value': '20170512'},] return options @app.callback(Output("popupAnnotation", "children"), [Input("user-dropdown", "value"), Input("day-dropdown", "value"), Input("radio-selector", "value")]) def set_pop_annotation(uid, date, selectedData): if date in ['20170217','20170303','20170317'] and selectedData == 'mode': return "No transportation mode detected!" else: return "" @app.callback(Output("map-graph", "figure"),[ Input("user-dropdown", "value"), Input("day-dropdown", "value"), Input("radio-selector", "value")],[ State('lock-selector', 'values'), State('map-graph', 'relayoutData')]) def update_graph(uid, date, selectedData, lockSelector, prevLayout): if selectedData == 'raw': df = fetch_raw_prepro_dataframe(uid, date, selectedData) total = df['pop'].sum() df['text'] = 'Occurrence ' + df['pop'].astype(str) + ' / ' + str(total) data = Data([ Scattermapbox( lon=df['lon'], lat=df['lat'], mode='markers', marker=dict( size=df['pop']*21, sizemode='area', opacity=0.3, color='black', ), hoverinfo='skip', ), Scattermapbox( lat=df['lat'], lon=df['lon'], text=df['text'], mode='markers', marker=dict( size=df['pop']*20, sizemode = 'area', color='mediumvioletred', opacity=0.8, ), hoverinfo = "text", name = "Cellular Points", ), ]) layout['mapbox']['center']['lon'] = df['lon'].astype(float).mean() layout['mapbox']['center']['lat'] = df['lat'].astype(float).mean() layout['showlegend'] = False elif selectedData == 'prepro' or (date in ['20170217','20170303','20170317'] and selectedData == 'mode'): df = fetch_raw_prepro_dataframe(uid, date, selectedData) endpt_size=20 scale=30 data = Data([ Scattermapbox( lat=[df.lat.loc[i] for i in df.index], lon=[df.lon.loc[i] for i in df.index], text=[df.start_t.loc[i]+' - '+df.end_t.loc[i]+'<br>Stayed '+df.stay_t.loc[i]+'s' for i in df.index], mode='markers+lines', marker=Marker( color="dimgray", size=15, ), hoverinfo = "text", name = "Cellular Trajectory" ) ]) layout['mapbox']['center']['lon'] = np.mean([float(df.lon.loc[i]) for i in df.index]) layout['mapbox']['center']['lat'] = np.mean([float(df.lat.loc[i]) for i in df.index]) layout['showlegend'] = True elif selectedData == 'mode': result_df, trip_dfs, trip_mode = fetch_mode_dataframe(uid, date) colors = {"hsr": "rgb(0,116,217)", "mrt": "rgb(255,65,54)", "bus": "rgb(133,20,75)", "train": "rgb(255,133,27)"} names = {"hsr": "HSR trip", "mrt": "MRT trip", "bus": "BUS trip", "train": "TRA trip"} endpt_size = 25 trips = [] for k in range(len(trip_dfs)): trip_df = trip_dfs[k] labels = [trip_df.ctime.loc[i].split(',')[0] for i in trip_df.index] labels[0] = result_df.iloc[k].s_id+' : '+result_df.iloc[k].s_name+'<br>'+labels[0] labels[-1]= result_df.iloc[k].e_id+' : '+result_df.iloc[k].e_name+'<br>'+labels[-1] trip = Scattermapbox( lat = [trip_df.y.loc[i] for i in trip_df.index], lon = [trip_df.x.loc[i] for i in trip_df.index], text= labels, mode= 'markers+lines', marker=Marker( size=[endpt_size] + [10 for j in range(len(trip_df.index) - 2)] + [endpt_size], color=colors[trip_mode[k]] ), name = names[trip_mode[k]]+': '+result_df.iloc[k].s_name+' --> ' + result_df.iloc[k].e_name, hoverinfo = "text", ) trips.append(trip) data = Data(trips) layout['showlegend'] = True if (prevLayout is not None and lockSelector is not None and 'lock' in lockSelector): layout['mapbox']['center']['lon'] = float(prevLayout['mapbox']['center']['lon']) layout['mapbox']['center']['lat'] = float(prevLayout['mapbox']['center']['lat']) layout['mapbox']['zoom'] = float(prevLayout['mapbox']['zoom']) fig = dict(data=data, layout=layout) return fig @app.server.before_first_request def defineTotalDF(): global raw_dfs, prepro_dfs, cellular_dfs, result_dfs raw_dfs, prepro_dfs, cellular_dfs, result_dfs = initialize() if __name__ == '__main__': #app.run_server(debug=True) #localhost app.run_server(host='0.0.0.0', port=8050, debug=False)

[ "qwanqwanro@gmail.com" ]

qwanqwanro@gmail.com

c2bad406da73b4a740249a716af90952cf0bb2c0

3a3f4477a517c9757432042ceb4939f37762c2a4

/12.IntegertoRoman/int_to_roman.py

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[]

no_license

marathohoho/leetcode-progress

f351ad89905c8e61fd5b5adff62320ce8ba9645d

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refs/heads/master

2020-12-24T01:39:15.117334

2020-04-06T20:20:16

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""" Roman numerals are represented by seven different symbols: I, V, X, L, C, D and M. Symbol Value I 1 V 5 X 10 L 50 C 100 D 500 M 1000 For example, two is written as II in Roman numeral, just two one's added together. Twelve is written as, XII, which is simply X + II. The number twenty seven is written as XXVII, which is XX + V + II. Roman numerals are usually written largest to smallest from left to right. However, the numeral for four is not IIII. Instead, the number four is written as IV. Because the one is before the five we subtract it making four. The same principle applies to the number nine, which is written as IX. There are six instances where subtraction is used: I can be placed before V (5) and X (10) to make 4 and 9. X can be placed before L (50) and C (100) to make 40 and 90. C can be placed before D (500) and M (1000) to make 400 and 900. Given an integer, convert it to a roman numeral. Input is guaranteed to be within the range from 1 to 3999. Example 1: Input: 3 Output: "III" Example 2: Input: 4 Output: "IV" Example 3: Input: 9 Output: "IX" Example 4: Input: 58 Output: "LVIII" Explanation: L = 50, V = 5, III = 3. Example 5: Input: 1994 Output: "MCMXCIV" Explanation: M = 1000, CM = 900, XC = 90 and IV = 4. """ def solution(num) : values = [1000, 900, 500, 400, 100, 90, 50, 40, 10, 9, 5, 4, 1] numerals = ['M', 'CM', 'D', 'CD', 'C', 'XC', 'L', 'XL', 'X', 'IX', 'V', 'IV', 'I'] res = "" for i, v in enumerate(values) : res += (num // v) * numerals[i] num %= v return res if __name__ == "__main__": print(solution(14)) print(solution(73)) print(solution(122))

[ "codmarat@gmail.com" ]

codmarat@gmail.com

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/MLlab02_logistic_classification/02_1-softmax_test.py

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[]

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yurimkoo/dl_lab

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3967c6112a24a130bc54da774929f58c5f290b97

refs/heads/master

2021-01-21T20:38:54.666029

2017-05-24T07:37:18

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import tensorflow as tf import numpy as np #'soft_max.txt' uses 'one-hot encoding' xy = np.loadtxt('softmax_train.txt', unpack=1, dtype='float32') x_data = np.transpose(xy[0:3]) #transpose를 사용하는 것은 거울처럼 반대로 뒤집어달라는 뜻 y_data = np.transpose(xy[3:]) X = tf.placeholder('float', [None, 3]) #x1, x2, bias(x0) total 3 (None인 이유는 총 데이터 개수가 몇 개인지 모르기 때문) Y = tf.placeholder('float', [None, 3]) #A, B, C total 3 W = tf.Variable(tf.zeros([3, 3])) #3x3 matrix (첫번째 값: x가 3개, 두번째 값: y가 3개) // tf.zeros 는 행렬 만드는 함수 hypothesis = tf.nn.softmax(tf.matmul(X, W)) #뒤집는 이유는 계산상의 편의를 위해해 learning_rate = 0.001 cost = tf.reduce_mean(-tf.reduce_sum(Y * tf.log(hypothesis), reduction_indices=1)) optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost) init = tf.global_variables_initializer() sess = tf.Session() sess.run(init) for step in range(2001): sess.run(optimizer, feed_dict={X: x_data, Y: y_data}) if step % 20 == 0: print(step, sess.run(cost, feed_dict={X: x_data, Y: y_data}), sess.run(W)) print('-----------------') #test & one-hot encoding #0: A, 1: B, 2: C a = sess.run(hypothesis, feed_dict={X: [[1, 11, 7]]}) print(a, sess.run(tf.arg_max(a, 1))) b = sess.run(hypothesis, feed_dict={X: [[1, 3, 4]]}) print(b, sess.run(tf.arg_max(b, 1))) c = sess.run(hypothesis, feed_dict={X: [[1, 1, 0]]}) print(c, sess.run(tf.arg_max(c, 1))) all = sess.run(hypothesis, feed_dict={X: [[1, 11, 7], [1, 3, 4], [1, 1, 0]]}) print(all, sess.run(tf.arg_max(all, 1)))

[ "yurimzzang51@gmail.com" ]

yurimzzang51@gmail.com

c2c9f862af8d90b56e812982f7aebae9015306e5

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/serene_load/serene_load/merge_trees.py

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NICTA/serene-etl

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refs/heads/master

2021-01-22T13:48:03.297688

2017-10-09T01:30:44

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import logging import argparse from serene_load.meta_select import load_meta def create_arguments(): parser = argparse.ArgumentParser(description='take all json data from one directory and merge into another tree') parser.add_argument('--meta', type=unicode, help='Directory containing primary metadata', required=True) parser.add_argument('--source', type=unicode, help='Directory containing secondary metadata to merge into primary', required=True) parser.add_argument('--verbose', type=bool) def setup_logging(LEVEL): logger = logging.getLogger() logger.setLevel(LEVEL) stream_handler = logging.StreamHandler() stream_handler.setLevel(LEVEL) formatter = logging.Formatter('%(asctime)s %(levelname)s: %(message)s') stream_handler.setFormatter(formatter) logger.addHandler(stream_handler) return logger def main(): parser = create_arguments() args = parser.parse_args() log = setup_logging(LEVEL=logging.DEBUG if args.debug is True else logging.INFO) primary = load_meta(args.meta) secondary = load_meta(args.source) print secondary if __name__ == '__main__': main()

[ "alex.collins@nicta.com.au" ]

alex.collins@nicta.com.au

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/instagram/tests.py

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Joseph-Odhiambo/Insta-clone

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refs/heads/master

2023-01-01T22:56:22.902366

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from django.test import TestCase from .models import Profile, Post from django.contrib.auth.models import User # Create your tests here. class TestProfile(TestCase): def setUp(self): self.user = User(username='Joseph') self.user.save() self.profile_test = Profile(id=1, name='image', profile_picture='default.jpg', bio='this is a test profile', user=self.user) def test_instance(self): self.assertTrue(isinstance(self.profile_test, Profile)) def test_save_profile(self): self.profile_test.save_profile() after = Profile.objects.all() self.assertTrue(len(after) > 0) class TestPost(TestCase): def setUp(self): self.profile_test = Profile(name='Joseph', user=User(username='Joseph')) self.profile_test.save() self.image_test = Post(image='default.png', name='test', caption='default test', user=self.profile_test) def test_insatance(self): self.assertTrue(isinstance(self.image_test, Post)) def test_save_image(self): self.image_test.save_image() images = Post.objects.all() self.assertTrue(len(images) > 0) def test_delete_image(self): self.image_test.delete_image() after = Profile.objects.all() self.assertTrue(len(after) < 1)

[ "josephkdo@gmail.com" ]

josephkdo@gmail.com

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/data_wordcloud02.py

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[]

no_license

haorenxwx/WordCloudLearning1

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2021-01-23T06:02:31.898291

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#-*- coding: utf-8 -*- #test change in github #from wordcloud import wordcloud import wordcloud as wc import codecs import jieba #import jieba.analyse as analyse from scipy.misc import imread import os from os import path #import matplotlib.pyplot as plt import matplotlib.pylab as plt #from PIL import Image, ImageDraw, ImageFont import pandas as pda from PIL import Image from numpy import array #path="E:/电子书/《那些回不去的少年时光》.txt" path="E:/电子书/BL Novel/柴鸡蛋/《你丫上瘾了？》BY：柴鸡蛋.txt" data=open(path,"r",encoding="gbk").read() #data=open(path,"r",encoding="UTF-8").read() #h=pda.read_table("E:/电子书/《那些回不去的少年时光》.txt") #comment_text = open('E:/电子书/《那些回不去的少年时光》.txt',encoding='UTF-8','r').read() #print(comment_text) #cut_text = " ".join(jieba.cut(h)) #d=path.dirname(_file_)#当前文件所在文件夹目录 cutdata=jieba.cut(data) alldata="" for i in cutdata: alldata=alldata+" "+str(i)#通过迭代器把词语连起来并用空格隔开 color_mask = imread ("E:/study/python/onepiece.png")#读取背景图片 #font=r"E:/study/python/HYQiHei-25J.ttf" font=r"E:/study/python/simhei.ttf" cloud=wc.WordCloud( collocations=False, #font_path ="HYQiHei-25J.ttf", font_path=font, #指定字体 #font_path =path.join(d,"HYQiHei-25J.ttf"), background_color='white', #设置背景色 mask=color_mask, #词云形状 max_words=500, max_font_size=40 ) word_cloud =cloud.generate(alldata) #word_cloud =cloud.generate(h) #word_cloud =cloud.generate(cut_text) word_cloud.to_file("losttime.jpg") plt.imshow(word_cloud) plt.axis('off') plt.show()

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

haorenxwx@users.noreply.github.com

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/experiments/__init__.py

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satyam-cyc/MASS-Learning

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2022-01-10T02:23:06.670225

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from .LogModelParameters import LogModelParameters from .MASSLossTerms import MASSLossTerms from .ModelLossAndAccuracy import ModelLossAndAccuracy from .OODDetection import OODDetection from .SaveModelParameters import SaveModelParameters from .UncertaintyQuantification import UncertaintyQuantification

[ "mwcvitkovic@gmail.com" ]

mwcvitkovic@gmail.com

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/ML_AI_TechWithTim/K-Means/K_Means.py

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[]

no_license

snehilk1312/ML_1

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8e3b081b1037ab999ca78fa282ce7041730d082a

refs/heads/master

2020-09-07T20:01:45.509060

2020-03-15T15:44:54

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2

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# Importing modules import numpy as np import sklearn from sklearn.preprocessing import scale from sklearn.datasets import load_digits from sklearn.cluster import KMeans from sklearn import metrics # Loading Data sets digits = load_digits() data = scale(digits.data) y = digits.target k = len(np.unique(y)) # or here k=10 samples, features = data.shape def bench_k_means(estimator, name, data): estimator.fit(data) print('%-9s\t%i\t%.3f\t%.3f\t%.3f\t%.3f\t%.3f\t%.3f' % (name, estimator.inertia_, metrics.homogeneity_score(y, estimator.labels_), metrics.completeness_score(y, estimator.labels_), metrics.v_measure_score(y, estimator.labels_), metrics.adjusted_rand_score(y, estimator.labels_), metrics.adjusted_mutual_info_score(y, estimator.labels_), metrics.silhouette_score(data, estimator.labels_, metric='euclidean'))) clf = KMeans(n_clusters=k, init="random", n_init=10) bench_k_means(clf, "1", data)

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snehilk1312.noreply@github.com

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/leopi/projects/tweeter/leonorrepitweeter.py

bee7022c3feac5a2737c4d4bf4deb2be3e7e6f95

[]

no_license

efrister/LeoNorrePi

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2a25eba405fb22ddc3faf38cceaa29f955996ba2

refs/heads/master

2021-01-01T15:19:00.653023

2014-02-22T15:15:08

null

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null

UTF-8

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py

from twython import * import configparser # Read configuration configuration = {} try: config = configparser.ConfigParser() config.read('config.ini') # Read all options into a dictionary for section in config.sections(): configuration[section] = {} for option in config.options(section): configuration[section][option] = config.get(section, option) except configparser.MissingSectionHeaderError as e: print("Could not read configuration. Exiting") exit() # Instantiate Twitter API with credentials oauth = configuration['OAuth'] twitter = Twython(oauth['app_key'], oauth['app_secret'], oauth['oauth_token'], oauth['oauth_token_secret']) # Check if we want to tweet something tweet = input("Please enter a message to tweet, or leave empty if you don\'t want to tweet at this time:") if 0 < len(tweet): try: twitter.update_status(status=tweet) print("Successfully tweeted", tweet) except TwythonError as Error: print("Could not authenticate with the Twitter API. Your API is probably set to Read-Only.") print(format(Error)) else: print("Not tweeting anything.") # Cleanup del tweet # Delete last tweet delete = int(input("Enter the number of last tweets you wish to delete:")) if 0 < delete: timeline = twitter.get_home_timeline() counter = len(timeline) if delete > counter: print("Cannot delete more items than are in the timeline. Not deleting anything.") else: counter = 0 while delete > 0: # Get tweet id tweet = timeline[counter] tweetId = tweet['id_str'] tweetText = tweet['text'] # Perform delete action twitter.destroy_status(id=tweetId) # Notify print("Deleted the tweet with the text '", tweetText, "'", sep="") # Increment counter += 1 delete -= 1 else: print("Not deleting anything.") # Tweet uptime from datetime import timedelta try: with open('/proc/uptime', 'r') as f: uptime_seconds = float(f.readline().split()[0]) uptime_string = str(timedelta(seconds=uptime_seconds)) except IOError: print("Cannot tweet uptime, not on a Pi.")

[ "erik.frister@aijko.com" ]

erik.frister@aijko.com