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import sys, os pykin_path = os.path.abspath(os.path.dirname(__file__)+"/../../" ) sys.path.append(pykin_path)
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var x = x < 0 ? -x: x var arr: [Int] = Array<Int>() var result = 0 while true { // 最后一位 let temp = x % 10 arr.append(temp) x = x / 10 if x == 0 { break } } for (index, value) in arr.enumerated() { result += value * Int(pow(10.0, Double(arr.count - index - 1))) } if result > Int(pow(2.0, 31.0)) { return 0; } result = result * sign return result } } var s = Solution() var result = s.reverse(-123) print(result)
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# Copyright 2020 Google LLC and Stanford University # # 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 # #     https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from .laikago import LaikagoBullet from pybullet_utils import bullet_client import pybullet import pybullet_data import time import gym, gym.utils.seeding, gym.spaces import numpy as np from my_pybullet_envs import utils from collections import deque import os import inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
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# -*- encode: utf-8 -*- from typing import List, Tuple, Callable import numpy as np from models import Aircraft, Airport, City, Time from settings import time_limit, time_limit, distance_to_weights # Phantom class if __name__ == '__main__': from data import cities airline = Airline(cities, time_limit, distance_to_weights)
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import setuptools with open("README.md", "r") as fh: long_description = fh.read() setuptools.setup( name='django_sourcerer', version='2.1', author='Cem Arslan, Kadir Kocak, Ugur Dogus Hamarat, Cagatay Em, Farid Murmadov, Burak Karaca', author_email="cmarslan06@gmail.com", description="Django app that import and correlating data from external sources.", long_description=long_description, long_description_content_type="text/markdown", url="https://github.com/nokia-wroclaw/innovativeproject-django-sourcerer", packages=setuptools.find_packages(), install_requires=[ 'django == 3.0.7', 'djangorestframework == 3.11.0', 'requests == 2.23.0', 'pyyaml == 5.3.1', 'pandas == 1.0.3', 'pytest == 5.4.1', 'requests-mock == 1.7.0', ], classifiers=[ "Programming Language :: Python :: 3", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", ], python_requires='>=3.6', )
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from __future__ import division import os import sys import time import threading import re import signal from ..runtime import min_version, runtime_info from ..profiler_scheduler import ProfilerScheduler from ..metric import Metric from ..metric import Breakdown from ..frame import Frame
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#!/usr/bin/env python """ Generalised class: Displays data sources for a class """ import os import sys import lib_util import lib_common try: import lib_wbem wbemOk = True except ImportError: wbemOk = False import lib_wmi from lib_properties import pc # Now, adds the base classes of this one, at least one one level. # Adds the list of base classes. Returns the list of pairs (name node), # so it can be matched againt another inheritance tree. # Adds a WMI node and other stuff, for the class name. # entity_type = "CIM_Process", "Win32_Service" etc... # This might happen at an intermediary level, with inheritance (To be implemented). if __name__ == '__main__': Main()
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import websocket import threading import time import requests from bs4 import BeautifulSoup import pygame import pyperclip import settings new_id = -1 poe_trade_url = None if __name__ == "__main__": poe_trade_url_ini = input("Please enter your poe.trade URL: ") poe_trade_url_split = poe_trade_url_ini.split("search/", 1)[1] poe_trade_url = poe_trade_url_split.split("/live",1)[0] websocket.enableTrace(False) ws = websocket.WebSocketApp(settings.WS_URL + poe_trade_url, on_message = on_message, on_error = on_error, on_close = on_close) ws.run_forever()
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# -*- coding: utf-8 -*- # # Copyright 2021 Google LLC. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Implementation of create command for notifications.""" from __future__ import absolute_import from __future__ import division from __future__ import unicode_literals import textwrap from googlecloudsdk.calliope import base @base.Hidden class Create(base.Command): """Create a notification config on a bucket.""" detailed_help = { 'DESCRIPTION': """ *{command}* creates a notification config on a bucket, establishing a flow of event notifications from Cloud Storage to a Cloud Pub/Sub topic. As part of creating this flow, it also verifies that the destination Cloud Pub/Sub topic exists, creating it if necessary, and verifies that the Cloud Storage bucket has permission to publish events to that topic, granting the permission if necessary. If a destination Cloud Pub/Sub topic is not specified with the `-t` flag, Cloud Storage chooses a topic name in the default project whose ID is the same as the bucket name. For example, if the default project ID specified is `default-project` and the bucket being configured is `gs://example-bucket`, the create command uses the Cloud Pub/Sub topic `projects/default-project/topics/example-bucket`. In order to enable notifications, your project's [Cloud Storage service agent](https://cloud.google.com/storage/docs/projects#service-accounts) must have the IAM permission "pubsub.topics.publish". This command checks to see if the destination Cloud Pub/Sub topic grants the service agent this permission. If not, the create command attempts to grant it. A bucket can have up to 100 total notification configurations and up to 10 notification configurations set to trigger for a specific event. """, 'EXAMPLES': """ Begin sending notifications of all changes to the bucket `example-bucket` to the Cloud Pub/Sub topic `projects/default-project/topics/example-bucket`: $ {command} -f json gs://example-bucket The same as above, but specifies the destination topic ID `files-to-process` in the default project: $ {command} -f json -t files-to-process gs://example-bucket The same as above, but specifies a Cloud Pub/Sub topic belonging to the specific cloud project `example-project`: $ {command} -f json \ -t projects/example-project/topics/files-to-process gs://example-bucket Create a notification config that only sends an event when a new object has been created: $ {command} -f json -e OBJECT_FINALIZE gs://example-bucket Create a topic and notification config that only sends an event when an object beginning with `photos/` is affected: $ {command} -p photos/ gs://example-bucket """, } @staticmethod
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""" Copyright 2020 InfAI (CC SES) 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. """ __all__ = ("getLogger", "initLogger") from .configuration import conf import simple_env_var import logging logging_levels = { 'info': logging.INFO, 'warning': logging.WARNING, 'error': logging.ERROR, 'critical': logging.CRITICAL, 'debug': logging.DEBUG } msg_fmt = '%(asctime)s - %(levelname)s: [%(name)s] %(message)s' date_fmt = '%m.%d.%Y %I:%M:%S %p' handler = logging.StreamHandler() handler.setFormatter(logging.Formatter(fmt=msg_fmt, datefmt=date_fmt)) conf_logger = logging.getLogger("simple-env-var") conf_logger.addHandler(handler) conf_logger.setLevel(logging.INFO) kvs_logger = logging.getLogger("snorkels") kvs_logger.addHandler(handler) kvs_logger.setLevel(logging.INFO) simple_env_var.loadConfig(conf) logger = logging.getLogger("job-manager") logger.propagate = False logger.addHandler(handler)
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from boa3.builtin.interop.contract import GAS from boa3.builtin.type import UInt160
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# -*- coding: utf-8 -*- """ Created on Fri Nov 14 18:53:38 2014 % TESTS : % Y = [ 0 1 0 2 1 0 ; 0 0 1 0 0.01 2 ]; ind_Y = [1 2 3 4 5 6]; % fY = [2 5 1 3 2 6]; % [QZ,RZ,xbase,scale] = bcdfo_build_QR_of_Y( Y, 0, 0, 1, 1, 1e15 ); % Z = QZ*RZ; % [ QZ, RZ, Y, xbase, scale ] = sqpdfo_swap_in_Y( 1, 3, QZ, RZ, Y, ind_Y, ... % fY, [],[],xbase, 0, scale, 0, 1, 1, 1e15 ) % [ QZ, RZ, Y, xbase, scale ] = sqpdfo_swap_in_Y( 1, 3, QZ, RZ, Y, ind_Y, ... % fY, [],[],xbase, 0, scale, 0, 1, 1, 1e15 ) % norm( Z - QZ*RZ) % should give something very small. % % The same holds for the scaled version: % Y = [ 0 1 0 2 1 0 ; 0 0 1 0 0.01 2 ]; ind_Y = [1 2 3 4 5 6]; % fY = [2 5 1 3 2 6]; % [QZ,RZ,xbase,scale] = bcdfo_build_QR_of_Y( Y, 0, 1, 1, 1, 1e15 ); % Z = QZ*RZ; % [ QZ, RZ, Y, xbase, scale ] = sqpdfo_swap_in_Y( 1, 3, QZ, RZ, Y, ind_Y, ... % fY, [], [], xbase, 0, scale, 1, 1, 1, 1e15 ) % [ QZ, RZ, Y, xbase, scale ] = sqpdfo_swap_in_Y( 1, 3, QZ, RZ, Y, ind_Y, ... % fY, [],[],xbase, 0, scale, 1, 1, 1, 1e15 ) % norm( Z - QZ*RZ) @author: jaco_da """ import unittest from sqpdfo.sqpdfo_swap_in_Y import * from sqpdfo.bcdfo_build_QR_of_Y import * from numpy import array class Test_sqpdfo_swap_in_Y(unittest.TestCase): """ Reminder : This class is a test for sqpdfo_swap_in_Y which swaps the position of interpolation points i and j in Y, and updates the factorization of Z(Y) accordingly. """ def test_sqpdfo_swap_in_Y(self): """ Matlab test """ QZ,RZ,xbase,scale = bcdfo_build_QR_of_Y_( self.Y, 0, 0, 1, 1, 1e15 ) Z = QZ.dot(RZ) # ceY = array([]) ciY = array([]) QZ,RZ,Y,ind_Y,fY,ciY,ceY,xbase,scale = sqpdfo_swap_in_Y_( 0, 2, QZ, RZ, self.Y, self.ind_Y, self.fY, ciY, ceY, xbase, 0, scale, 0, 1, 1, 1e15 ) QZ,RZ,Y,ind_Y,fY,ciY,ceY,xbase,scale = sqpdfo_swap_in_Y_( 0, 2, QZ, RZ, Y, ind_Y, fY, ciY, ceY, xbase, 0, scale, 0, 1, 1, 1e15 ) ret = norm_( Z - QZ.dot(RZ)) self.assertTrue(ret < 1e-15) # # #Scaled version : QZ,RZ,xbase,scale = bcdfo_build_QR_of_Y_( self.Y, 0, 1, 1, 1, 1e15 ) Z = QZ.dot(RZ) ceY = array([]) ciY = array([]) QZ,RZ,Y,ind_Y,fY,ciY,ceY,xbase,scale = sqpdfo_swap_in_Y_( 0, 2, QZ, RZ, self.Y, self.ind_Y, self.fY, ciY, ceY, xbase, 0, scale, 1, 1, 1, 1e15 ) QZ,RZ,Y,ind_Y,fY,ciY,ceY,xbase,scale = sqpdfo_swap_in_Y_( 0, 2, QZ, RZ, Y, ind_Y, fY, ciY, ceY, xbase, 0, scale, 1, 1, 1, 1e15 ) ret = norm_( Z - QZ.dot(RZ)) self.assertTrue(ret < 1e-15) if __name__ == '__main__': unittest.main()
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# Generated by Django 2.0.6 on 2018-06-30 15:59 from django.db import migrations
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''' Filename: word_embeding.py Project: models File Created: Sunday, 2nd December 2018 2:38:45 pm Author: xiaofeng (sxf1052566766@163.com) -------------------------- Last Modified: Sunday, 2nd December 2018 2:39:14 pm Modified By: xiaofeng (sxf1052566766@163.com) --------------------------- : 2018.06 - 2018 . ''' import tensorflow as tf def Embedding(name, vocab_embeding, input_sequence): """ Creates an embedding matrix of dimensions n_symbols x embeding_dim upon first use. Looks up embedding vector for each input symbol :parameters: name: name of embedding matrix tensor variable vocab_embeding: full vocabulary embedings input_sequence: input symbols tensor return: word embeding for current input_sequence tensor shape is (batch_size,1+input_sequence_length,embeding_dim) """ with tf.device("/cpu:0"), tf.variable_scope(name): label_embeding = tf.nn.embedding_lookup(vocab_embeding, input_sequence) return label_embeding def embedding_initializer(): """Returns initializer for embeddings""" return _initializer
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################################################################################ # # Originally based on http://djangosnippets.org/snippets/1016/ # with small modifications # ################################################################################ import re from django.contrib import admin from django.conf.urls import patterns, url from django.http import HttpResponseRedirect class ButtonableModelAdmin(admin.ModelAdmin): """ A subclass of this admin will let you add buttons (like history) in the change view of an entry. """ buttons = []
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# -*- coding: utf-8 -*- from django.conf import settings from cms.tests.base import CMSTestCase from cms.utils.plugins import get_placeholders from cms.exceptions import DuplicatePlaceholderWarning import sys import warnings
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#!/usr/bin/env python3 # -*- Coding: utf-8 -*- import subprocess from subprocess import PIPE
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#!/usr/bin/env python # -*- coding: utf-8 -*- from config import database from pymongo import MongoClient from mongoengine import *
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import glob folder = '/data03/shared/lehhou/lym_project/svs_tiles/TCGA-38-4629-01Z-00-DX1.d00cc280-5370-4b9f-9655-6c35deb94647.svs/'; outfile = folder + 'list.txt'; filevar = open(outfile, 'w'); filelist = []; for filename in glob.glob(folder + '/*.png'): name = filename[len(folder):-4]; filevar.write("%s\n" % name); filevar.close();
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import pytube from bs4 import BeautifulSoup import urllib.request import threading from collections.abc import Sequence from spotdl.metadata import StreamsBase from spotdl.metadata import ProviderBase from spotdl.metadata.exceptions import YouTubeMetadataNotFoundError import spotdl.util import spotdl.patch spotdl.patch.youtube.apply_patches() import logging logger = logging.getLogger(__name__) BASE_SEARCH_URL = "https://www.youtube.com/results?sp=EgIQAQ%253D%253D&q={}" HEADERS = [('Range', 'bytes=0-'),]
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#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright (c) 2018, WSO2 Inc. (http://wso2.org) All Rights Reserved. # # WSO2 Inc. licenses this file to you 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. # # ---------------------------------------------------------------------------- # Create AWS CloudFormation template # ---------------------------------------------------------------------------- import argparse import csv import json import os from jinja2 import Environment, FileSystemLoader PATH = os.path.dirname(os.path.abspath(__file__)) TEMPLATE_ENVIRONMENT = Environment( autoescape=False, loader=FileSystemLoader(os.path.join(PATH, 'templates')), trim_blocks=False, keep_trailing_newline=True) if __name__ == "__main__": main()
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# -*- coding: utf-8 -*- from openerp import tools from openerp import models,fields,api from openerp.tools.translate import _ import datetime import pytz
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BASIC_AUTH = "basic" DIGEST_AUTH = "digest" OAUTH1 = "oauth1" BEARER_AUTH = "bearer" PASSWORD_PLACEHOLDER = '*' * 16 CONNECT_TIMEOUT = 60 # If any remote service does not respond within 5 minutes, time out. # (Some OpenMRS reports can take a long time. Cut them a little slack, # but not too much.) READ_TIMEOUT = 5 * 60 REQUEST_TIMEOUT = (CONNECT_TIMEOUT, READ_TIMEOUT) ALGO_AES = 'aes' DATA_TYPE_UNKNOWN = None COMMCARE_DATA_TYPE_TEXT = 'cc_text' COMMCARE_DATA_TYPE_INTEGER = 'cc_integer' COMMCARE_DATA_TYPE_DECIMAL = 'cc_decimal' COMMCARE_DATA_TYPE_BOOLEAN = 'cc_boolean' COMMCARE_DATA_TYPE_DATE = 'cc_date' COMMCARE_DATA_TYPE_DATETIME = 'cc_datetime' COMMCARE_DATA_TYPE_TIME = 'cc_time' COMMCARE_DATA_TYPES = ( COMMCARE_DATA_TYPE_TEXT, COMMCARE_DATA_TYPE_INTEGER, COMMCARE_DATA_TYPE_DECIMAL, COMMCARE_DATA_TYPE_BOOLEAN, COMMCARE_DATA_TYPE_DATE, COMMCARE_DATA_TYPE_DATETIME, COMMCARE_DATA_TYPE_TIME, ) COMMCARE_DATA_TYPES_AND_UNKNOWN = COMMCARE_DATA_TYPES + (DATA_TYPE_UNKNOWN,) DIRECTION_IMPORT = 'in' DIRECTION_EXPORT = 'out' DIRECTION_BOTH = None DIRECTIONS = ( DIRECTION_IMPORT, DIRECTION_EXPORT, DIRECTION_BOTH, )
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from random import randint numeros = (randint(1, 10), randint(1, 10), randint(1, 10), randint(1, 10), randint(1, 10)) print('os numeros sorteados são: ', end='') for n in numeros: print(f'{n}', end=' ') print(f'\no maior numero foi {max(numeros)}') print(f'O menor numero foi {min(numeros)}')
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import numpy as np import pandas as pd import scrape_data import csv team_data = pd.read_csv('data/cbb19.csv') team_data = team_data.drop(columns=['POSTSEASON', 'SEED']) t = list(team_data['TEAM']) all_games = scrape_data.all_games winners = scrape_data.winners name_mappings = {'UNC': 'North Carolina', 'Citadel': 'The Citadel', 'Purdue-Fort Wayne': 'Fort Wayne', 'Arkansas-Pine Bluff': 'Arkansas Pine Bluff', 'UT-Martin': 'Tennessee Martin', 'ETSU': 'East Tennessee St.', 'Gardner-Webb': 'Gardner Webb', 'UIC': 'Illinois Chicago', 'Louisiana-Monroe': 'Louisiana Monroe', 'Ole Miss': 'Mississippi', 'Prairie View': 'Prairie View A&M', "St. John's (NY)": "St. John's", 'Miami (FL)': 'Miami FL', 'Miami (OH)': 'Miami OH', "Loyola (IL)": "Loyola Chicago", "Loyola (MD)": "Loyola MD", "Pitt": "Pittsburgh", "Texas-Arlington": "UT Arlington", "Alabama-Birmingham": "UAB", "St. Joseph's": "Saint Joseph's", 'Louisiana': 'Louisiana Lafayette', 'Omaha': 'Nebraska Omaha', 'California Baptist': 'Cal Baptist', 'Texas-Rio Grande Valley': 'UT Rio Grande Valley', 'Florida International': 'FIU', 'William ': 'William & Mary', 'UMass': 'Massachusetts', 'Saint Francis (PA)': 'St. Francis PA', 'LIU': 'LIU Brooklyn', 'Albany (NY)': 'Albany', 'St. Francis (NY)': 'St. Francis NY', 'Grambling': 'Grambling St.', 'Texas A&M-Corpus': 'Texas A&M Corpus Chris', 'UMass-Lowell': 'UMass Lowell', 'Bethune-Cookman': 'Bethune Cookman', "St. Peter's": "Saint Peter's", "Maryland-Eastern Shore": "Maryland Eastern Shore", "SIU-Edwardsville": "SIU Edwardsville"} for i in range(len(all_games)): for j in range(len(all_games[0])): if('UC' in all_games[i][j]): if(all_games[i][j] == 'UCSB'): all_games[i][j] = 'UC Santa Barbara' elif(all_games[i][j] == "UConn"): all_games[i][j] = 'Connecticut' else: pos = all_games[i][j].find('-') if(pos != -1): all_games[i][j] = all_games[i][j][:pos] + \ ' ' + all_games[i][j][pos+1:] elif('cal state' in all_games[i][j].lower()): pos = all_games[i][j].lower().find('state') all_games[i][j] = all_games[i][j][:pos] + \ 'St. ' + all_games[i][j][pos + 6:] elif('state' in all_games[i][j].lower()): if(all_games[i][j] == 'NC State'): all_games[i][j] = 'North Carolina St.' elif(all_games[i][j] == 'Bowling Green State'): all_games[i][j] = 'Bowling Green' elif(all_games[i][j] != "USC Upstate"): all_games[i][j] = all_games[i][j][:-3] + '.' elif(all_games[i][j] in name_mappings.keys()): all_games[i][j] = name_mappings[all_games[i][j]] for i in range(len(winners)): if('UC' in winners[i]): if(winners[i] == 'UCSB'): winners[i] = 'UC Santa Barbara' elif(winners[i] == "UConn"): winners[i] = 'Connecticut' else: pos = winners[i].find('-') if(pos != -1): winners[i] = winners[i][:pos] + ' ' + winners[i][pos+1:] elif('cal state' in winners[i].lower()): pos = winners[i].lower().find('state') winners[i] = winners[i][:pos] + 'St. ' + winners[i][pos + 6:] elif('state' in winners[i].lower()): if(winners[i] == 'NC State'): winners[i] = 'North Carolina St.' elif(winners[i] == 'Bowling Green State'): winners[i] = 'Bowling Green' elif(winners[i] != "USC Upstate"): winners[i] = winners[i][:-3] + '.' elif(winners[i] in name_mappings.keys()): winners[i] = name_mappings[winners[i]] games = pd.DataFrame(all_games, columns=['Away Team', 'Home Team']) games['Winner'] = winners games_to_drop = [] for i in range(len(games)): if(not(games['Away Team'][i] in t and games['Home Team'][i] in t)): games_to_drop.append(i) games = games.drop(games_to_drop) games = games.reset_index() games = games.drop(columns=['index']) with open('data/all_games_19.csv', 'w', newline='') as csvfile: writer = csv.writer(csvfile, delimiter=',') writer.writerow(['Away Team', 'Home Team', 'Winner']) for i in range(len(games)): writer.writerow( [games['Away Team'][i], games['Home Team'][i], games['Winner'][i]])
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2.003873
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import datetime, pytz def getEventStartEnd(event): '''returns start time & end time as datetime objects from an event struct''' start_string = event['start'].get('dateTime',event['start'].get('date')) start_time = str2time(start_string) end_string = event['end'].get('dateTime',0) whole_day_event = 0 if not end_string: end_time = start_time + datetime.timedelta(days = 1) whole_day_event = 1 else: end_time = str2time(end_string) return start_time,end_time,whole_day_event def daily_insert(service,event,time_begin,duration, effective_days,date_start = datetime.datetime.today(), repeat_on_day_of_week = {0,1,2,3,4,5,6}): """ service: googleCalendar API connection. See connection.py event: event struct. Can be created from eventCreator() ***Note that event does not need to contain start and end dates. If it does, then it is going to overwritten*** repeat_on_day_of_week: an integer list with len 1~7 and values 0-6 each integer value represent a day of week. e.g. 0 is Monday, 2 is Wednesday, etc. This determines on what day of the week the event shall repeat time_start: datetime.time() object. When the event take place on each day even_duartion: datetime.timedelta() object. Duration of the event effective_days: over how many days do you want the event repeated? e.g. 365 days (1 year) date_start (optional) : when do you want the event to start repeating? By default it is going to be the date when this function is executed. Note that if the day is not one of the day specified in repeat_day_of_week, the next closest date meeting the criterial is when the event going to start. """ datetime_begin = datetime.datetime.combine(date_start,time_begin) datetime_end = datetime_begin + duration for days in range(effective_days): datetime_begin = datetime_begin + datetime.timedelta(days = 1) datetime_end = datetime_end + datetime.timedelta(days = 1) #check if right weekday if not datetime_begin.weekday() in repeat_on_day_of_week: continue start_string = time2str(datetime_begin) end_string = time2str(datetime_end) event['start']['dateTime'] = start_string event['end']['dateTime'] = end_string service.events().insert(calendarId = "primary",body = event).execute() def getFreeTime(service,start_datetime = datetime.datetime.now(),check_ndays = 14,min_free_len_mins = 10): """returns a list of free time psuedo-events starting from start_date for n effective_days service: googleCalendar API connection. See connection.py start_date: datetime.datetime obj effective_days: datetime.timedelta obj check_ndays: how many days from the start date to check for min_free_len_mins: minumn lenth of free time. Any free time shorter will be ignored """ calendar_result = service.calendarList().list().execute() calendars = calendar_result.get('items',[]) all_events = [] for calendar in calendars: cal_id = calendar['id'] events = getEvents(service,cal_id,start_datetime,check_ndays) all_events = all_events + events #now we remove all day events remove_list = list() for event in all_events: if not event['start'].get('dateTime',0): remove_list.append(event) for event in remove_list: all_events.remove(event) #now we sort all events by their start time all_events = sorted(all_events, key = lambda t: t['start'].get('dateTime')) #we set the first end time to be start_time end_datetime = start_datetime #create a list holder of free_time events free_times = [] for event in all_events: start_string = event['start']['dateTime'] start_datetime = str2time(start_string) duration_timedelta = start_datetime - end_datetime #we calculate duration by using the start time of the second event minus the end time of the first event duration = divmod(duration_timedelta.total_seconds(),60) duration = duration[0] if duration < min_free_len_mins: # if duration is too short skip end_datetime = str2time(event['end']['dateTime']) continue else: #if duration is good, create free time event free_time = eventCreator(end_datetime,start_datetime,-2,0,'free event',0,0,'Free Time',0) #not the start_datetime field is actually filled by the end_datetime vairable, and vice versa free_times.append(free_time) #update end_datetime for next loop end_datetime = str2time(event['end']['dateTime']) return free_times def getCalendarId(service,calendar_summary): '''returns a calendar_Id from a calendar_summary. Always returns the first one found. If none found, return -1''' calendar_result = service.calendarList().list().execute() calendars = calendar_result.get('items',[]) for calendar in calendars: if calendar['summary'] == calendar_summary: return calendar['id'] return -1 def getEvents(service,calId,start_datetime,search_for_ndays): '''do not use this unless you intend to pull only raw Google Calendar Events from the server. This is intended to be used as a component for methods such as getAllEvents()''' end_datetime = start_datetime.date() + datetime.timedelta(days = int(search_for_ndays) + 1) #+1 here because timeMax is exclusive end_datetime = datetime.datetime.combine(end_datetime, datetime.time(0,0)) #note the end time always at 0:00 start_datetime = utcFormat(start_datetime) end_datetime = utcFormat(end_datetime) events_result = service.events().list(calendarId= calId, timeMin=start_datetime, maxResults= 1000, singleEvents=True, timeMax= end_datetime, orderBy='startTime').execute() events = events_result.get('items', []) return events def createRecurringEvent(event,by_day ='MO,TU,WE,TH,FR,SA,SU',until = 0): '''until needs to be a string in the formta "YYYYMMDD" ''' '''needs more work''' event['recurrence'] = list() if until: until_statement = 'UNTIL=' + until + ';' else: until_statement = '' event['recurrence'].append( 'RRULE:FREQ=WEEKLY;'+ until_statement+'BYDAY=' + by_day) return event
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2.428369
2,827
# Generated by Django 4.0.2 on 2022-02-19 16:35 from django.db import migrations, models
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2.84375
32
# coding: mixt from mixt import Element, NotProvided, Required, html from mixt.contrib.css import css_vars from ... import datatypes from ..doc import DocPart, DocHeader from ..generic import Details from . import Code, DocString, NamedValue, UnnamedValue </Details> </if> </DocPart>
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2.637097
124
from math import sqrt, ceil if __name__ == "__main__": from aocd import get_data data = get_data(year=2021, day=17) print(part1(data)) print(part2(data))
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2.324675
77
# Generated by Django 2.2.6 on 2020-11-14 23:01 from django.conf import settings from django.db import migrations, models import django.db.models.deletion
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3.019231
52
from __future__ import unicode_literals import sys import logging from datetime import datetime from framework.mongo import database from website.app import init_app from scripts import utils as script_utils from framework.transactions.context import TokuTransaction logger = logging.getLogger(__name__) # 2015 Sept 18 CUT_OFF_DATE = (2015, 9, 18) BACKUP_COLLECTION = 'zzz20151009pagecounters' NEW_COLLECTION = '20151009pagecountersmigrated' if __name__ == '__main__': dry = 'dry' in sys.argv if not dry: script_utils.add_file_logger(logger, __file__) # Allow setting the log level just by appending the level to the command if 'debug' in sys.argv: logger.setLevel(logging.DEBUG) elif 'warning' in sys.argv: logger.setLevel(logging.WARNING) elif 'info' in sys.argv: logger.setLevel(logging.INFO) elif 'error' in sys.argv: logger.setLevel(logging.ERROR) main(dry=dry)
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2.648045
358
import json import trustlab.lab.config as config import time from trustlab.consumers.chunk_consumer import ChunkAsyncJsonWebsocketConsumer from trustlab.models import * from trustlab.serializers.scenario_serializer import ScenarioSerializer from trustlab.lab.director import Director class LabConsumer(ChunkAsyncJsonWebsocketConsumer): """ LabConsumer class, with sequential process logic of the Director in its receive_json method. It is therewith the main interface between User Agent and Director. """
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3.737589
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from telegapi.reqs import reqs as reqs from telegapi.telapi import telapi
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2.96
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import bilby import numpy as np from bilby.core.likelihood import ( AnalyticalMultidimensionalBimodalCovariantGaussian, AnalyticalMultidimensionalCovariantGaussian, ) logger = bilby.core.utils.logger cov = [ [ 0.045991865933182365, -0.005489748382557155, -0.01025067223674548, 0.0020087713726603213, -0.0032648855847982987, -0.0034218261781145264, -0.0037173401838545774, -0.007694897715679858, 0.005260905282822458, 0.0013607957548231718, 0.001970785895702776, 0.006708452591621081, -0.005107684668720825, 0.004402554308030673, -0.00334987648531921, ], [ -0.005489748382557152, 0.05478640427684032, -0.004786202916836846, -0.007930397407501268, -0.0005945107515129139, 0.004858466255616657, -0.011667819871670204, 0.003169780190169035, 0.006761345004654851, -0.0037599761532668475, 0.005571796842520162, -0.0071098291510566895, -0.004477773540640284, -0.011250694688474672, 0.007465228985669282, ], [ -0.01025067223674548, -0.004786202916836844, 0.044324704403674524, -0.0010572820723801645, -0.009885693540838514, -0.0048321205972943464, -0.004576186966267275, 0.0025107211483955676, -0.010126911913571181, 0.01153595152487264, 0.005773054728678472, 0.005286558230422045, -0.0055438798694137734, 0.0044772210361854765, -0.00620416958073918, ], [ 0.0020087713726603213, -0.007930397407501268, -0.0010572820723801636, 0.029861342087731065, -0.007803477134405363, -0.0011466944120756021, 0.009925736654597632, -0.0007664415942207051, -0.0057593957402320385, -0.00027248233573270216, 0.003885350572544307, 0.00022362281488693097, 0.006609741178005571, -0.003292722856107429, -0.005873218251875897, ], [ -0.0032648855847982987, -0.0005945107515129156, -0.009885693540838514, -0.007803477134405362, 0.0538403407841302, -0.007446654755103316, -0.0025216534232170153, 0.004499568241334517, 0.009591034277125177, 0.00008612746932654223, 0.003386296829505543, -0.002600737873367083, 0.000621148057330571, -0.006603857049454523, -0.009241221870695395, ], [ -0.0034218261781145264, 0.004858466255616657, -0.004832120597294347, -0.0011466944120756015, -0.007446654755103318, 0.043746559133865104, 0.008962713024625965, -0.011099652042761613, -0.0006620240117921668, -0.0012591530037708058, -0.006899982952117269, 0.0019732354732442878, -0.002445676747004324, -0.006454778807421816, 0.0033303577606412765, ], [ -0.00371734018385458, -0.011667819871670206, -0.004576186966267273, 0.009925736654597632, -0.0025216534232170153, 0.008962713024625965, 0.03664582756831382, -0.009470328827284009, -0.006213741694945105, 0.007118775954484294, -0.0006741237990418526, -0.006003374957986355, 0.005718636997353189, -0.0005191095254772077, -0.008466566781233205, ], [ -0.007694897715679857, 0.0031697801901690347, 0.002510721148395566, -0.0007664415942207059, 0.004499568241334515, -0.011099652042761617, -0.009470328827284016, 0.057734267068088, 0.005521731225009532, -0.017008048805405164, 0.006749693090695894, -0.006348460110898, -0.007879244727681924, -0.005321753837620446, 0.011126783289057604, ], [ 0.005260905282822458, 0.0067613450046548505, -0.010126911913571181, -0.00575939574023204, 0.009591034277125177, -0.0006620240117921668, -0.006213741694945106, 0.005521731225009532, 0.04610670018969681, -0.010427010812879566, -0.0009861561285861987, -0.008896020395949732, -0.0037627528719902485, 0.00033704453138913093, -0.003173552163182467, ], [ 0.0013607957548231744, -0.0037599761532668475, 0.01153595152487264, -0.0002724823357326985, 0.0000861274693265406, -0.0012591530037708062, 0.007118775954484294, -0.01700804880540517, -0.010427010812879568, 0.05909125052583998, 0.002192545816395299, -0.002057672237277737, -0.004801518314458135, -0.014065326026662672, -0.005619012077114913, ], [ 0.0019707858957027763, 0.005571796842520162, 0.005773054728678472, 0.003885350572544309, 0.003386296829505542, -0.006899982952117272, -0.0006741237990418522, 0.006749693090695893, -0.0009861561285862005, 0.0021925458163952988, 0.024417715762416557, -0.003037163447600162, -0.011173674374382736, -0.0008193127407211239, -0.007137012700864866, ], [ 0.006708452591621083, -0.0071098291510566895, 0.005286558230422046, 0.00022362281488693216, -0.0026007378733670806, 0.0019732354732442886, -0.006003374957986352, -0.006348460110897999, -0.008896020395949732, -0.002057672237277737, -0.003037163447600163, 0.04762367868805726, 0.0008818947598625008, -0.0007262691465810616, -0.006482422704208912, ], [ -0.005107684668720825, -0.0044777735406402895, -0.005543879869413772, 0.006609741178005571, 0.0006211480573305693, -0.002445676747004324, 0.0057186369973531905, -0.00787924472768192, -0.003762752871990247, -0.004801518314458137, -0.011173674374382736, 0.0008818947598624995, 0.042639958466440225, 0.0010194948614718209, 0.0033872675386130637, ], [ 0.004402554308030674, -0.011250694688474675, 0.004477221036185477, -0.003292722856107429, -0.006603857049454523, -0.006454778807421815, -0.0005191095254772072, -0.005321753837620446, 0.0003370445313891318, -0.014065326026662679, -0.0008193127407211239, -0.0007262691465810616, 0.0010194948614718226, 0.05244900188599414, -0.000256550861960499, ], [ -0.00334987648531921, 0.007465228985669282, -0.006204169580739178, -0.005873218251875899, -0.009241221870695395, 0.003330357760641278, -0.008466566781233205, 0.011126783289057604, -0.0031735521631824654, -0.005619012077114915, -0.007137012700864866, -0.006482422704208912, 0.0033872675386130632, -0.000256550861960499, 0.05380987317762257, ], ] dim = 15 mean = np.zeros(dim) label = "multidim_gaussian_unimodal" outdir = "outdir" likelihood = AnalyticalMultidimensionalCovariantGaussian(mean, cov) priors = bilby.core.prior.PriorDict() priors.update( { "x{0}".format(i): bilby.core.prior.Uniform(-5, 5, "x{0}".format(i)) for i in range(dim) } ) result = bilby.run_sampler( likelihood=likelihood, priors=priors, sampler="dynesty", outdir=outdir, label=label, check_point_plot=True, resume=True, ) result.plot_corner(parameters={"x{0}".format(i): mean[i] for i in range(dim)}) # The prior is constant and flat, and the likelihood is normalised such that the area under it is one. # The analytical evidence is then given as 1/(prior volume) log_prior_vol = np.sum( np.log([prior.maximum - prior.minimum for key, prior in priors.items()]) ) log_evidence = -log_prior_vol sampled_std = [ np.std(result.posterior[param]) for param in result.search_parameter_keys ] logger.info("Analytic log evidence: " + str(log_evidence)) logger.info( "Sampled log evidence: " + str(result.log_evidence) + " +/- " + str(result.log_evidence_err) ) for i, search_parameter_key in enumerate(result.search_parameter_keys): logger.info(search_parameter_key) logger.info("Expected posterior standard deviation: " + str(likelihood.sigma[i])) logger.info("Sampled posterior standard deviation: " + str(sampled_std[i])) # BIMODAL distribution label = "multidim_gaussian_bimodal" dim = len(cov[0]) mean_1 = 4 * np.sqrt(np.diag(cov)) mean_2 = -4 * np.sqrt(np.diag(cov)) likelihood = AnalyticalMultidimensionalBimodalCovariantGaussian(mean_1, mean_2, cov) priors = bilby.core.prior.PriorDict() priors.update( { "x{0}".format(i): bilby.core.prior.Uniform(-5, 5, "x{0}".format(i)) for i in range(dim) } ) result = bilby.run_sampler( likelihood=likelihood, priors=priors, sampler="dynesty", outdir=outdir, label=label, check_point_plot=True, resume=True, ) result.plot_corner( parameters={"x{0}".format(i): mean_1[i] for i in range(dim)}, filename=outdir + "/multidim_gaussian_bimodal_mode_1", ) result.plot_corner( parameters={"x{0}".format(i): mean_2[i] for i in range(dim)}, filename=outdir + "/multidim_gaussian_bimodal_mode_2", ) log_prior_vol = np.sum( np.log([prior.maximum - prior.minimum for key, prior in priors.items()]) ) log_evidence = -log_prior_vol sampled_std_1 = [] sampled_std_2 = [] for param in result.search_parameter_keys: samples = np.array(result.posterior[param]) samples_1 = samples[np.where(samples < 0)] samples_2 = samples[np.where(samples > 0)] sampled_std_1.append(np.std(samples_1)) sampled_std_2.append(np.std(samples_2)) logger.info("Analytic log evidence: " + str(log_evidence)) logger.info( "Sampled log evidence: " + str(result.log_evidence) + " +/- " + str(result.log_evidence_err) ) for i, search_parameter_key in enumerate(result.search_parameter_keys): logger.info(search_parameter_key) logger.info( "Expected posterior standard deviation both modes: " + str(likelihood.sigma[i]) ) logger.info( "Sampled posterior standard deviation first mode: " + str(sampled_std_1[i]) ) logger.info( "Sampled posterior standard deviation second mode: " + str(sampled_std_2[i]) )
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1.786332
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import re
[ 11748, 302, 628, 198 ]
3
4
from datetime import timedelta import logging from homeassistant.core import callback from homeassistant.components.sensor import SensorEntity from homeassistant.helpers.entity import DeviceInfo from homeassistant.helpers.update_coordinator import ( DataUpdateCoordinator, ) from homeassistant.const import ( CONF_NAME, CONF_UNIT_OF_MEASUREMENT, ) from .const import ( DOMAIN, PROMETHEUS_METRIC_KEY, PROMETHEUS_VALUE_KEY, PROMETHEUS_JOB_KEY, PROMETHEUS_INSTANCE_KEY, ) from . import requestData _LOGGER = logging.getLogger(__name__)
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# coding: utf-8 from __future__ import unicode_literals import pytest @pytest.mark.parametrize('text', ["ca.", "m.a.o.", "Jan.", "Dec."])
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'''This is my first class in python''' p1 = Product() p1.display() # print(p1.name) # print(p1.description) # print(p1.price) p2 = Product() p2.display() # print(p2.name) # print(p2.description) # print(p2.price)
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# Dungeon Crawler import asciiart import level1 import level2 import time # timer import hero #def test(): # print(asciiart.baby_dragon()) # print(asciiart.big_skull()) # print(asciiart.dragon()) # print(asciiart.samurai()) # print(asciiart.skull_cross()) # print(asciiart.warrior()) # print(asciiart.chicken()) global my_hero = hero.Hero("Bob") start_dungeon()
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from distutils.core import setup setup( name='mepfinder', version='0.1', packages=['mepfinder'], url='', license='MIT', author='Theodros Zelleke', author_email='', description='' )
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# Importing the libraries import numpy as np import matplotlib.pyplot as plt import pandas as pd # Importing the dataset dataset = pd.read_csv('pokemonchg.csv') X = dataset.iloc[:, [19,20,21,22,23,25,28,35]].values y = dataset.iloc[:, 40].values # Splitting the dataset into the Training set and Test set from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0) # Feature Scaling from sklearn.preprocessing import StandardScaler sc = StandardScaler() X_train = sc.fit_transform(X_train) X_test = sc.transform(X_test) #accuracy from sklearn.metrics import accuracy_score # Fitting Random Forest Classification to the Training set from sklearn.ensemble import RandomForestClassifier classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0) classifier.fit(X_train, y_train) # Predicting the Test set results y_pred = classifier.predict(X_test) # Making the Confusion Matrix from sklearn.metrics import confusion_matrix cm_randomforest = confusion_matrix(y_test, y_pred) acc_randomforest=accuracy_score(y_test,y_pred)*100 # Fitting Kernel SVM to the Training set from sklearn.svm import SVC classifier = SVC(kernel = 'rbf', random_state = 0) classifier.fit(X_train, y_train) # Predicting the Test set results y_pred = classifier.predict(X_test) # Making the Confusion Matrix from sklearn.metrics import confusion_matrix cm_svmkernel = confusion_matrix(y_test, y_pred) acc_svmkernel=accuracy_score(y_test,y_pred)*100 # Fitting K-NN to the Training set from sklearn.neighbors import KNeighborsClassifier classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'minkowski', p = 2) classifier.fit(X_train, y_train) # Predicting the Test set results y_pred = classifier.predict(X_test) # Making the Confusion Matrix from sklearn.metrics import confusion_matrix cm_knn = confusion_matrix(y_test, y_pred) acc_knn=accuracy_score(y_test,y_pred)*100 # Fitting Naive Bayes to the Training set from sklearn.naive_bayes import GaussianNB classifier = GaussianNB() classifier.fit(X_train, y_train) # Predicting the Test set results y_pred = classifier.predict(X_test) # Making the Confusion Matrix from sklearn.metrics import confusion_matrix cm_naivebayes = confusion_matrix(y_test, y_pred) acc_naivebayes=accuracy_score(y_test,y_pred)*100 # Fitting Decision Tree Classification to the Training set from sklearn.tree import DecisionTreeClassifier classifier = DecisionTreeClassifier(criterion = 'entropy', random_state = 0) classifier.fit(X_train, y_train) # Predicting the Test set results y_pred = classifier.predict(X_test) # Making the Confusion Matrix from sklearn.metrics import confusion_matrix cm_decisiontree = confusion_matrix(y_test, y_pred) acc_decisiontree=accuracy_score(y_test,y_pred)*100 # Fitting SVM to the Training set from sklearn.svm import SVC classifier = SVC(kernel = 'linear', random_state = 0) classifier.fit(X_train, y_train) # Predicting the Test set results y_pred = classifier.predict(X_test) # Making the Confusion Matrix from sklearn.metrics import confusion_matrix cm_svm = confusion_matrix(y_test, y_pred) acc_svm=accuracy_score(y_test,y_pred)*100 # Fitting Logistic Regression to the Training set from sklearn.linear_model import LogisticRegression classifier = LogisticRegression(random_state = 0) classifier.fit(X_train, y_train) # Predicting the Test set results y_pred = classifier.predict(X_test) # Making the Confusion Matrix from sklearn.metrics import confusion_matrix cm_logistic = confusion_matrix(y_test, y_pred) acc_logistic=accuracy_score(y_test,y_pred)*100 # Importing the Keras libraries and packages import keras from keras.models import Sequential from keras.layers import Dense from tensorflow.python.keras import backend as k # Initialising the ANN classifier = Sequential() # Adding the input layer and the first hidden layer classifier.add(Dense(output_dim = 5, init = 'uniform', activation = 'relu', input_dim = 8)) # Adding the second hidden layer classifier.add(Dense(output_dim = 5, init = 'uniform', activation = 'relu')) # Adding the third hidden layer classifier.add(Dense(output_dim = 5, init = 'uniform', activation = 'relu')) # Adding the fourth hidden layer classifier.add(Dense(output_dim = 5, init = 'uniform', activation = 'relu')) # Adding the output layer classifier.add(Dense(output_dim = 1, init = 'uniform', activation = 'sigmoid')) # Compiling the ANN classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy']) # Fitting the ANN to the Training set classifier.fit(X_train, y_train, batch_size = 10, nb_epoch = 100) # Part 3 - Making the predictions and evaluating the model # Predicting the Test set results y_pred = classifier.predict(X_test) y_pred = (y_pred > 0.5) # Making the Confusion Matrix from sklearn.metrics import confusion_matrix cm_ann = confusion_matrix(y_test, y_pred) acc_ann=accuracy_score(y_test,y_pred)*100
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######################################################################## ################# Importing packages ####################### ######################################################################## from flask import Flask from flask_sqlalchemy import SQLAlchemy from flask_login import LoginManager # init SQLAlchemy so we can use it later in our models db = SQLAlchemy()
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os from paddle.utils.cpp_extension import load from .adamw import AdamW from .AdamwOptimizer import AdamwOptimizer from .adamwdl import AdamWDL __all__ = [ '_jit_compile', 'AdamW', 'AdamwOptimizer', 'AdamWDL', ]
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################################################## # Copyright (c) INRIA (France) 2011, 2012, 2013 # # This file is part of inria-mvs. You can redistribute it and/or # modify it under the terms of the GNU General Public License. # # This file is provided AS IS with NO WARRANTY OF ANY KIND, INCLUDING THE # WARRANTY OF DESIGN, MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. # # Author: Jean-Paul CHIEZE <jean-paul.chieze@inria.fr> # ################################################## import sys, os, re, string from scipy import weave import numpy as np class MyException(Exception): """Bad parameter or file. The execution cannot continue """ def draw_point(buf,ix,iy,size,color,cross = False): """ draw a colored square around point (ix,iy) """ code = """ struct pts { int y; int nbx; int x[5]; }; struct pts rpoints[] = { {-1,3,-1,0,1}, {0,2,-1,1,0}, {1,3,-1,0,1}, }; struct pts cpoints[] = { {-2,1,0}, {-1,1,0}, {0,5,-2,-1,0,1,2}, {1,1,0}, {2,1,0}, }; #define NBLR sizeof(rpoints) / sizeof(struct pts) #define NBLC sizeof(cpoints) / sizeof(struct pts) #include <stdio.h> int nbl; struct pts *points; if(cross == 0) { nbl = NBLR; points = rpoints; } else { nbl = NBLC; points = cpoints; } int i, j, k, ky, kx, k0; int rdimx = 3 * dimx; for(j=0;j<nbl;j++) { ky = points[j].y + iy; if(ky < 0 || ky >= dimy) continue; k0 = ky * rdimx; for(i=0;i<points[j].nbx;i++) { kx = points[j].x[i] + ix; //printf("i,j = %d,%d, kx= %d, k0 = %d\\n",i,j,kx,k0); if(kx < 0 || kx >= dimx) continue; k = k0 + 3 * kx; *(buf+k++) = color[0]; *(buf+k++) = color[1]; *(buf+k) = color[2]; } } """ (dimx,dimy) = (size[0],size[1]) weave.inline(code,['buf' ,'ix','iy','dimx','dimy','color','cross']) def draw_color_rect(buf,ix,iy,size,wrect,color): """ draw a square centerd on x,y filled with color """ code = """ int nd = %d; int x, y, i, j; int ny = 1 + 2 * nd; int nx = ny; y = iy - nd; if (y < 0) { ny += y; y = 0; } else if ((y + ny) > dimy) ny -= y + ny - dimy; x = ix - nd; if (x < 0) { nx += x; x = 0; } else if ((x + nx) > dimx) nx -= x + nx - dimx; int k = y * dimx * 3 + 3 * x; int deltak = 3 * (dimx - nx); for (i = 0;i < ny;i++) { for (j = 0;j < nx;j++) { #if 1 *(buf+k++) = color[0]; *(buf+k++) = color[1]; *(buf+k++) = color[2]; #else *(buf+k) = (*(buf+k) / 2) + (color[0] / 2); k++; *(buf+k) = (*(buf+k) / 2) + (color[1] / 2); k++; *(buf+k) = (*(buf+k) / 2) + (color[2] / 2); k++; #endif } k += deltak; } """ %wrect (dimx,dimy) = (size[0],size[1]) #ll lqprint "XX %d %d" %(ix,iy) if(ix < 0 or iy < 0 or ix >= dimx or iy >= dimy): return() weave.inline(code,['buf' ,'ix','iy','dimx','dimy','color']) def c_projection(matr,pts3d,pts2d): """ fills pts2d (2xn array) with projection of pts3d (3xn array) """ code = """ int jj = 0; int i; float X[3]; float x; int ki, kj; for( i =0;i < npts;i++) { double *mat = matr; for(ki = 0;ki < 3; ki++) { x = 0; for(kj = 0;kj < 3;kj++) x += *(mat+kj) * *(pts3d + jj + kj); x += *(mat + 3); X[ki] = x; mat += 4; } x = X[2]; *(pts2d + jj) = X[0] / x; *(pts2d + jj + 1) = X[1] / x; *(pts2d + jj + 2) = x; jj += 3; } """ npts = pts3d.shape[1] weave.inline(code,['matr','pts3d','pts2d','npts']) def decode_line(s,l,func = float): """ split a string and convert each token with <func> and append the result to list <l> """ x = s.split() for v in x: l.append(func(v)) return(l) def get_bundle_header(bundler_file,num = None): """ reads header of <dir>/pmvs/bundle.rd.out output : file handle (pointing to the 1st point), nb of points + if num is given >= 0 : focal length and 3x4 Rotation-translation matrix of camera if num < 0 : array of focals and array of matrices for all cameras """ matPs = [] focals = [] f = open(bundler_file,'r') s = f.readline() s = f.readline() s = s.strip() l = s.split() if len(l) != 2: raise Exception, "Bad 1st line <%s> in bundle file" %s nbcam = int(l[0]) nbpts = int(l[1]) if num != None and num >= nbcam: raise Exception, "Only %d cameras" %nbcam for i in xrange(0,nbcam): (f,focal,m) = get_bundle_cam(f) if num < 0 or i == num: focals.append(focal) matPs.append(m) if num < 0: return(f,nbpts,focals,matPs) else: return(f,nbpts,focals[0],matPs[0]) def read_projmatrx(file,return_coef = False,return_pairs = False): """ return the 3x4 projection matrix read from the given pmvs .txt file """ mat = [] with open(file,'r') as f: lines = f.readlines() s = lines[0].strip() if (s != 'CONTOUR'): raise Exception, "Bad format <%s> for projection file" %s for i in range(1,4): s = lines[i].strip() l = s.split() for j in range(0,4): mat.append(float(l[j])) if return_coef or return_pairs: coef = None pairs = [] if(len(lines) >= 6): if(lines[4].strip() == 'COEF'): coef = float(lines[5].strip()) if return_pairs: if (len(lines) >= 10) and (lines[8].strip() == 'PAIRS'): nbp = int(lines[9].strip()) if (len(lines) >= (10 + nbp)): for i in xrange(0,nbp): xl = map(lambda x : int(x) ,lines[10 + i].strip().split()) pairs.append(xl) return(np.array(mat).reshape(3,4),coef,pairs) return(np.array(mat).reshape(3,4),coef) else: return(np.array(mat).reshape(3,4)) def get_bundle_points(f,nbpts,numcam): """ input : filehandle to bundle file (ready to read 1st point) nb of points, and num of picture output (lists): - 3d coordinates of all points (numcam == None) of points matched in camera numcam - corresponding 2d coordinates if numcam is specified (origine au centre de l'image) - colors of returned points """ pts3d = [] pts2d = [] ptscol = [] for k in xrange(0,nbpts): l = [] s = (f.readline()).strip() decode_line(s,l) l.append(1.) X = np.array(l) s = (f.readline()).strip() cols = [] decode_line(s,cols,int) if (numcam == None): pts3d.append(X) ptscol.append(np.array(cols)) f.readline() else: keys = [] s = (f.readline()).strip() decode_line(s,keys) n = int(keys[0]) i = 1 for j in xrange(0,n): if(int(keys[i]) != numcam): i += 4 continue rx = keys[i+2] ry = keys[i+3] i += 4 pts2d.append((rx,ry)) pts3d.append(X) ptscol.append(np.array(cols)) break return(pts3d,pts2d,ptscol) def get_ply_array(ptsfile): """ read the ply file and return the points coord as a 3xn array """ nbpts = 0 with open(ptsfile,'r') as f: s = f.readline() if s.strip() != 'ply': raise Exception, "Not a ply file!" while True: s = f.readline().strip() l = s.split() if (len(l) == 3 and l[0] == 'element' and l[1] == 'vertex'): nbpts = int(l[2]) continue if(s.strip() == 'end_header'): break if (s == ''): break # start with points kk = 0 pts3d = np.empty((3,nbpts),dtype=np.float,order="F") while True: s = (f.readline()).strip() if (s == ''): break l = s.split() n = len(l) if(n < 3): continue ln = [float(l[i]) for i in range(0,n)] pts3d[0,kk] = ln[0] pts3d[1,kk] = ln[1] pts3d[2,kk] = ln[2] kk += 1 return pts3d def get_ply_points(ptsfile,return_normals = False): """ read the ply file and return the lists of points and colors """ pts3d = [] ptscol = [] ptsnorm = [] with open(ptsfile,'r') as f: s = f.readline() if s.strip() != 'ply': raise Exception, "Not a ply file!" while True: s = f.readline() if(s.strip() == 'end_header'): break if (s == ''): break # start with points while True: s = (f.readline()).strip() if (s == ''): break l = s.split() n = len(l) if(n < 3): continue ln = [float(l[i]) for i in range(0,n)] pts3d.append(np.array([ln[0],ln[1],ln[2],1.])) if (n > 3): ptscol.append(np.array([ln[n-3],ln[n-2],ln[n-1]])) if (n > 6 and return_normals): ptsnorm.append(np.array([ln[3],ln[4],ln[5]])) if return_normals: return(pts3d,ptscol,ptsnorm) else: return(pts3d,ptscol) def projection(matrx,pts3d): """ returns the list of projections of <pts3d> points (origin at the image center). <matrx> is the projection matrix of camera and <size> the image width and height """ ptsxy = [] n = len(pts3d) ## print "MATR %s" %str(matrx) for i in xrange(0,n): X = pts3d[i] Y = np.dot(matrx,X) x = int(0.5 + Y[0] / Y[2]) y = int(0.5 + Y[1] / Y[2]) ptsxy.append((x,y)) return(ptsxy) color = np.array([0,255,0]) color2 = np.array([255,0,0]) color3 = np.array([255,255,0]) def draw_projection(ptsxy,refpts,ptscol,obuf,size,wrect,cross = False): """ input : 2d projections list (ptsxy), original 2d points (refpts) colors of points (ptscol), image buffer (obuf), image size drawing options (wrect,cross) Points are in image coordinates (0,0 = top left corner) writes 2d points (ptsxy) in the image buffer <obuf>; if wrect is None : a litle green square (cross = False) or cross (cross = True) is drawn centered on each point and if refpts is non empty, a red square or croos is drawn on these points If refpts[i] == ptsxy[i] : draw in yellow if wrect is given : at each point, fill a square of (2*wrect +1) size with the point color """ (nx,ny) = size ## nx2 = 0.5 + (float(nx) / 2) ## ny2 = .5 + (float(ny) / 2) n2d = len(refpts) ncol = len(ptscol) n = len(ptsxy) for i in xrange(0,n): (x,y) = ptsxy[i] if(wrect != None): draw_color_rect(obuf,x,y,size,wrect,ptscol[i]) else: draw_point(obuf,x,y,size,color) if n2d > 0: (rx,ry) = refpts[i] ## rx = int(rx + nx2) ## ry = ny - int(ny2 + ry) #print "XXX %d %d (%d %d)" %(x,y,rx,ry) if (rx == x and ry == y): col2 = color3 else: col2 = color2 draw_point(obuf,rx,ry,size,col2,cross) def calc_camparams(m,tzsign,verb=False): """ calculate intrinsics and extrinsics parameter of camera from 3x4 projection matrix m NB : tzsign is ignored and will be removed in a next release """ a1 = m[0,0:3] a2 = m[1,0:3] a3 = m[2,0:3] b = np.array([[m[0,3]],[m[1,3]],[m[2,3]]]) eps = 1 rho=eps / np.linalg.norm(a3) r3 = rho * a3 x0 = rho * rho * np.dot(a1.T, a3) y0 = rho * rho * np.dot(a2.T,a3) b1 = np.cross(a1,a3) b2 = np.cross(a2,a3); theta = np.arccos(- np.dot(b1.T,b2) / (np.linalg.norm(b1)*np.linalg.norm(b2))) # The magnifications are assumed to be positive.. alpha = rho * rho * np.linalg.norm(b1) * np.sin(theta) beta = rho * rho * np.linalg.norm(b2) * np.sin(theta) b23 = np.cross(a2,a3) r1 = (1. / np.linalg.norm(b23)) * b23 r2 = np.cross(r3,r1) K = np.array([[alpha, -alpha * np.cos(theta) / np.sin(theta), x0], [ 0, beta/np.sin(theta), y0], [0, 0, 1]]) t = rho * np.linalg.solve(K,b) # The sign of the solution is unknow : the last element in # original and recalculated projection matrices must be the same R = np.array([r1.T, r2.T, r3.T]) M = m M2 = np.dot(K,np.concatenate((R,t),1)) tzsign = M[0,0] * M2[0,0] * M[2,3] * M2[2,3] if tzsign < 0: r3 = -r3 r2 = -r2 t = -t R = np.array([r1.T, r2.T, r3.T]) M2 = np.dot(K,np.concatenate((R,t),1)) rho =M[0,0] / M2[0,0] M2 = rho * M2 intrins = np.array([alpha,beta,180 * theta / np.pi]) if verb: print "\nrho %f\nM= %s\nM2= %s" %(rho,str(M),str(M2)) print "R= %s\nt= %s\nintrinsic= %s" %(str(R),str(t),str(intrins)) print "x0,y0 %s %s" %(x0,y0) X = np.array([[0.,0.,0.,1.],[t[0,0],t[1,0],-1.,1.]]) pts = projection(m,X) pts2 = projection(M2,X) (x,y) = pts[0];(x1,y1) = pts2[0] print "Proj de 0 : %.3f %.3f, %.3f %.3f" %(x,y,x1,y1) (x,y) = pts[1];(x1,y1) = pts2[1] print "Proj de C : %.3f %.3f, %.3f %.3f\n" %(x,y,x1,y1) return (K,R,t,M2) def calc_transform(pts3d1,pts3d2,cmn_pts): """ renvoie la matrice de transformation pour passer du repere 1 au repere 2 input : pts3d1 pts3d2, liste d'arrays de dim 4 cmn_pts : liste de couples d'indices dans pts3d1 pts3d2 """ nb = len(cmn_pts) if (nb < 6): print "Not enough (%d) common points!" %nb return None # if(nb > 25): # nb = 25 print "-- %d common projections" %nb A = np.zeros((3*nb,12)) zer = np.zeros((4,)) b = np.zeros((3*nb,)) for i in xrange(0,nb): (i1,i2) = cmn_pts[i] A[3 * i,:] = np.concatenate((pts3d1[i1],zer,zer)) A[3 * i + 1,:] = np.concatenate((zer,pts3d1[i1],zer)) A[3 * i + 2,:] = np.concatenate((zer,zer,pts3d1[i1])) b[3*i:3*(i+1)] = pts3d2[i2][0:3] x = np.linalg.lstsq(A,b)[0] x = np.concatenate((x,np.array([0.,0.,0.,1.]))) matr = x.reshape((4,4)) ## print "M= %s" %str(matr) emoy = 0. emax = 0. for i in xrange(0,nb): (i1,i2) = cmn_pts[i] p1 = pts3d1[i1] p2 = pts3d2[i2] y = np.dot(matr,p1) e = np.linalg.norm(y - p2) if e > emax: emax = e emoy += e emoy /= nb print "ERR max %f, moy %f" %(emax,emoy) return matr def print_matrix(M,file= None): """ print matrix m*n as m lines of n floats """ (m,n) = M.shape for i in xrange(0,m): l = map(lambda x : str(x),M[i,:]) s = ' '.join(l) if file == None: print s else: print >>file, s def mk_KRt(mat,focal,w,h): """ makes matrix in pmvs2 format from bundler camera data """ K = np.array([[-focal,0, 0.5 * w - 0.5], [0,focal,0.5 * h - 0.5], [0,0,1]]) M = np.dot(K,mat) return ( - np.concatenate((M,np.array([0,0,0,1],ndmin=2)),0)) def read_metis_graph(file): """ reads the metis graph (1st line = nb-img 2*nb-edges, other lines : neighbours of each image Output : list of lists (list[i] = neighbours of i) """ graph = [] try: f = open(file,'r') s = f.readline().strip() l = s.split() with_weights = False if len(l) == 3: with_weights = True while True: s = f.readline() if s == '': break l = [] if with_weights: x = s.strip().split() for i in xrange(0,len(x),2): l.append(int(x[i]) - 1) else: l = map(lambda x : int(x) - 1,s.strip().split()) graph.append(l) f.close() except Exception: raise MyException("%s open error" %file) return graph def read_adj_matrix(file): """ reads the adjacency matrix (values = nb of matched points) Output : list of lists (list[i] = list of (j, nb-match) where j is a neighbour of i) list[i] is ordered according to nb-match """ adj = [] try: f = open(file,'r') lines = f.readlines() f.close() except Exception: raise MyException("%s open error" %file) for s in lines: l = [] decode_line(s.strip(),l,func = int) neighbours = [] for i in xrange(0,len(l)): if l[i] != 0: neighbours.append((i,l[i])) neighbours = sorted(neighbours,cmp = lambda (i1,nb1),(i2,nb2) : nb1 - nb2) adj.append(neighbours) return adj
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# Managed by bumpversion __version__ = "1.33.0"
[ 2, 1869, 1886, 416, 13852, 9641, 198, 834, 9641, 834, 796, 366, 16, 13, 2091, 13, 15, 1, 198 ]
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import numpy as np inputs = [[1.0, 2.0, 3.0, 2.5], [2.0, 5.0, -1.0, 2.0], [-1.5, 2.7, 3.3, -0.8]] weights = [[0.2, 0.8, -0.5, 1.0], [0.5, -0.91, 0.26, -0.5], [-0.26, -0.27, 0.17, 0.87]] biases = [2.0, 3.0, 0.5] outputs = np.dot(inputs, np.array(weights).T) + biases print(outputs) # https://nnfs.io/crq # https://nnfs.io/gjw # https://nnfs.io/ch2
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from datetime import datetime, timedelta import os import tempfile import torch import logging log = logging.getLogger(__name__) __all__ = ["FlexibleModelCheckpoint"] """ Copied from https://github.com/pytorch/ignite/blob/v0.2.1/ignite/handlers/checkpoint.py due to the change in ignite v0.3.0 """ class ModelCheckpoint(object): """ ModelCheckpoint handler can be used to periodically save objects to disk. This handler expects two arguments: - an :class:`~ignite.engine.Engine` object - a `dict` mapping names (`str`) to objects that should be saved to disk. See Notes and Examples for further details. Args: dirname (str): Directory path where objects will be saved. filename_prefix (str): Prefix for the filenames to which objects will be saved. See Notes for more details. save_interval (int, optional): if not None, objects will be saved to disk every `save_interval` calls to the handler. Exactly one of (`save_interval`, `score_function`) arguments must be provided. score_function (callable, optional): if not None, it should be a function taking a single argument, an :class:`~ignite.engine.Engine` object, and return a score (`float`). Objects with highest scores will be retained. Exactly one of (`save_interval`, `score_function`) arguments must be provided. score_name (str, optional): if `score_function` not None, it is possible to store its absolute value using `score_name`. See Notes for more details. n_saved (int, optional): Number of objects that should be kept on disk. Older files will be removed. atomic (bool, optional): If True, objects are serialized to a temporary file, and then moved to final destination, so that files are guaranteed to not be damaged (for example if exception occures during saving). require_empty (bool, optional): If True, will raise exception if there are any files starting with `filename_prefix` in the directory 'dirname'. create_dir (bool, optional): If True, will create directory 'dirname' if it doesnt exist. save_as_state_dict (bool, optional): If True, will save only the `state_dict` of the objects specified, otherwise the whole object will be saved. Note: This handler expects two arguments: an :class:`~ignite.engine.Engine` object and a `dict` mapping names to objects that should be saved. These names are used to specify filenames for saved objects. Each filename has the following structure: `{filename_prefix}_{name}_{step_number}.pth`. Here, `filename_prefix` is the argument passed to the constructor, `name` is the key in the aforementioned `dict`, and `step_number` is incremented by `1` with every call to the handler. If `score_function` is provided, user can store its absolute value using `score_name` in the filename. Each filename can have the following structure: `{filename_prefix}_{name}_{step_number}_{score_name}={abs(score_function_result)}.pth`. For example, `score_name="val_loss"` and `score_function` that returns `-loss` (as objects with highest scores will be retained), then saved models filenames will be `model_resnet_10_val_loss=0.1234.pth`. Examples: >>> import os >>> from ignite.engine import Engine, Events >>> from ignite.handlers import ModelCheckpoint >>> from torch import nn >>> trainer = Engine(lambda batch: None) >>> handler = ModelCheckpoint('/tmp/models', 'myprefix', save_interval=2, n_saved=2, create_dir=True) >>> model = nn.Linear(3, 3) >>> trainer.add_event_handler(Events.EPOCH_COMPLETED, handler, {'mymodel': model}) >>> trainer.run([0], max_epochs=6) >>> os.listdir('/tmp/models') ['myprefix_mymodel_4.pth', 'myprefix_mymodel_6.pth'] """
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2.690306
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lista = [1, 2, 3] try: numero = int(input(f'Digite um número entre 0 e {len(lista)-1}: ')) print(lista[numero]) except ValueError: print('Não foi digitado um número') except IndexError: print('O número digitado está fora da faixa aceita') #https://pt.stackoverflow.com/q/451936/101
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from urllib import response from django.http import HttpResponse,HttpResponseForbidden, HttpResponseRedirect from teproj.models import TestModel from django.urls import reverse from django.conf import settings from django.core.cache import cache from django.contrib.auth.models import Group
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#!/usr/bin/env python3 # # Licensed under BSD 2-Clause License. # # This is a really crude script. This spits out shell-escaped wget # commands to download the images of the chapters. # # Each image is downloaded into "VOLUME_CHAPTER/XXX.png" where VOLUME # and CHAPTER are the volume and chapter names respectively. If DRY # environmental variable is not empty, then this script does not # create the needed directories beforehand. # # Chapter or series URL may be given as arguments to the script. # # To read the chapter in zathura(1), use the following command # # zip CHAPTER.zip CHAPTER_IMAGES/* import json from os import mkdir, getenv from os.path import splitext, basename from os.path import exists as file_exists_p from shlex import quote as shell_quote from sys import argv import re import urllib.request as req import bs4 DYNASTY_IMAGE_RE = re.compile(r"//<!\[CDATA\[") BATOTO_IMAGE_RE_1 = re.compile(r"(?:const|var) images = \[") BATOTO_IMAGE_RE_2 = re.compile(r"(?:const|var) imgHttpLis = \[") JS_CTXT = None UA = { "User-Agent": "Chrome/96.0.4664.110" } MKDIRP = not getenv("DRY") def request(url): """Request URL.""" return req.urlopen(req.Request(url, headers=UA)) def dynasty_get_chapter_list(url): """Get the list of chapters in URL. Returned value is a dictionary { VOLUME: CHAPTERS } where VOLUME is the name of the volume and CHAPTERS is a tuple (NAME, URL) where NAME is the name of the chapter and URL is the link to the chapter. If the manga is not divided by volumes, then VOLUME is an empty string. """ soup = bs4.BeautifulSoup(request(url), "html.parser") ret = {} vol = "" chs = [] chp = soup.find("dl", class_="chapter-list") for i in chp: if not isinstance(i, bs4.element.Tag): continue if i.name == "dt": if chs: ret[vol] = chs vol = i.string chs = [] elif i.name == "dd": chs.append((i.a.string, "https://dynasty-scans.com" + i.a["href"])) if chs: ret[vol] = chs return ret def dynasty_get_images(ch): """Get list of image URLs for the chapter with URL CH.""" soup = bs4.BeautifulSoup(request(ch), "html.parser") if r := re.search(r"var pages = (\[.*\])", soup.find("script", string=DYNASTY_IMAGE_RE).string): return [ "https://dynasty-scans.com" + i["image"] for i in json.loads(r.group(1)) ] return [] def batoto_get_chapter_list(url): """Get the list of chapters in URL. Returned value is a list of (NAME, URL) where NAME is the name of chapter and URL is the link to the chapter. """ chs = [] # User-Agent is need to be set otherwise cloudfare pops up. soup = bs4.BeautifulSoup(request(url), "html.parser") for i in soup.find_all("a", class_="visited chapt"): # The name of the chapter is surrounded by newlines for some # reason. chs.append((i.text.strip(), "https://bato.to" + i["href"])) chs.reverse() return chs def batoto_get_images(ch): """Get list of image URLs for the chapter CH. I came to know of duktape thanks to the Tachiyomi bato.to extension. """ def js_eval(string): """Evalulate JavaScript code in the string STRING.""" import pyduktape global JS_CTXT if not JS_CTXT: JS_CTXT = pyduktape.DuktapeContext() if not file_exists_p("./crypto.js"): # The same URL tachiyomi uses. cryptojs = request("https://cdnjs.cloudflare.com/ajax/libs/crypto-js/4.0.0/crypto-js.min.js") with open("./crypto.js", "w") as f: f.write(cryptojs.read().decode("utf-8")) JS_CTXT.eval_js_file("./crypto.js") return JS_CTXT.eval_js(string) soup = bs4.BeautifulSoup(request(ch), "html.parser") if js := soup.find("script", text=BATOTO_IMAGE_RE_1): # Most of this magic can be figured out by reading the JavaScript # sources in a chapter page. Alternatively, refer to # https://github.com/tachiyomiorg/tachiyomi-extensions/blob/master/src/all/batoto/src/eu/kanade/tachiyomi/extension/all/batoto/BatoTo.kt js = js.string server = re.search(r"(?:const|var) server = ([^;]+);", js).group(1) batojs = re.search(r"(?:const|var) batojs = ([^;]+);", js).group(1) base = js_eval(f"CryptoJS.AES.decrypt({server} ,{batojs}).toString(CryptoJS.enc.Utf8);").strip("\"") return [ base + i for i in json.loads(re.search(r"(?:const|var) images = (\[.*\]);", js).group(1)) ] elif js := soup.find("script", text=BATOTO_IMAGE_RE_2): # Again, thanks to tachiyomi for showing the light. It is # also easy enough to figure out if you read the JS files. js = js.string img = re.search(r"(?:const|var) imgHttpLis = ([^;]+);", js).group(1) img = json.loads(img) batoword = re.search(r"(?:const|var) batoWord = ([^;]+);", js).group(1) batopass = re.search(r"(?:const|var) batoPass = ([^;]+);", js).group(1) passwd = json.loads(js_eval(f"CryptoJS.AES.decrypt({batoword}, {batopass}).toString(CryptoJS.enc.Utf8);")) if len(passwd) != len(img): return [ ] return [ i + "?" + passwd[n] for n, i in enumerate(img) ] return [] if __name__ == "__main__": for i in argv[1:]: do(i)
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2.309022
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from dataclasses import dataclass from bindings.gmd.abstract_crstype import AbstractCoordinateOperationType __NAMESPACE__ = "http://www.opengis.net/gml" @dataclass
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2.929825
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from .Bassin import Bassin
[ 6738, 764, 33, 44961, 1330, 19829, 259 ]
3.714286
7
# -*- coding: utf-8 -*- # Global variables _HEADER = {"Content-Type": "application/json"} _PAYLOAD = {"stmt": None}
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2.543478
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from django.contrib.auth.models import User from django.shortcuts import render, redirect from zero_ilumi_agenda_core.models import Evento_Agendado from django.contrib.auth.decorators import login_required from django.contrib.auth import authenticate, login, logout from django.contrib import messages from datetime import datetime, timedelta from django.http.response import Http404, JsonResponse # Create your views here. # def index(request): # return redirect('/agenda/') @login_required(login_url='/login/') @login_required(login_url='/login/') @login_required(login_url='/login/') @login_required(login_url='/login/') @login_required(login_url='/login/')
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3.086364
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""" Copyright (c) Contributors to the Open 3D Engine Project. For complete copyright and license terms please see the LICENSE at the root of this distribution. SPDX-License-Identifier: Apache-2.0 OR MIT """ """ C13767841 - All Gradient Modifier nodes can be added to a graph C18055051 - All Gradient Modifier nodes can be removed from a graph """ import os import pytest # Bail on the test if ly_test_tools doesn't exist. pytest.importorskip('ly_test_tools') import ly_test_tools.environment.file_system as file_system import editor_python_test_tools.hydra_test_utils as hydra test_directory = os.path.join(os.path.dirname(__file__), 'EditorScripts') @pytest.mark.parametrize('project', ['AutomatedTesting']) @pytest.mark.parametrize('level', ['tmp_level']) @pytest.mark.usefixtures("automatic_process_killer") @pytest.mark.parametrize("launcher_platform", ['windows_editor'])
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from setuptools import setup from torch.utils.cpp_extension import CUDAExtension, BuildExtension from pathlib import Path # Usage: # python setup.py install (or python setup.py bdist_wheel) # NB: Windows: run from VS2017 x64 Native Tool Command Prompt rootdir = (Path(__file__).parent / '..' / 'op').resolve() setup( name='upfirdn2d', ext_modules=[ CUDAExtension('upfirdn2d_op', [str(rootdir / 'upfirdn2d.cpp'), str(rootdir / 'upfirdn2d_kernel.cu')], ) ], cmdclass={ 'build_ext': BuildExtension } ) setup( name='fused', ext_modules=[ CUDAExtension('fused', [str(rootdir / 'fused_bias_act.cpp'), str(rootdir / 'fused_bias_act_kernel.cu')], ) ], cmdclass={ 'build_ext': BuildExtension } )
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import requests from bs4 import BeautifulSoup import sys import datetime mainPageUrl = 'https://pcgames-download.com/' mainFilePath = './loading/mainPage.html' pagePath = './loading/' def loadGameData(): "Loads computer games data" print ('Loading number of pages') response = requests.get(mainPageUrl) with open(mainFilePath, 'wb') as f: f.write(response.content) soup = BeautifulSoup(open(mainFilePath, encoding="utf8"), 'html.parser') for lastPage in soup.find_all(class_='last'): lastPageText = lastPage.get('href').strip() lastPageNumber = getLastPageNumber(lastPageText) print ('Last available page: ' + lastPageText) continue data = [] for currentPageNumber in range(1, lastPageNumber + 1): print('Loading page ' + str(currentPageNumber)) titleTexts, titleLinks = checkLoadPageData(currentPageNumber) for index in range(len(titleTexts)): data.append(str(titleTexts[index]) + '; ' + str(titleLinks[index]) + '\n') fileName = getCurrentDateTimeFormatted() + '.csv' print('Writing data to csv file: ' + fileName) with open('./savedData/' + fileName, 'w') as f: f.write(''.join(data)) print ("Done") def getLastPageNumber(lastPage): "Gets the last page number of the computer game website" #https://pcgames-download.com/page/271/ --> 271 lastPage = lastPage.replace(mainPageUrl + 'page/', "").replace("/", "") return int(lastPage) def checkLoadPageData(currentPage): "Loads the page data for the given page number" if currentPage == 1: print (mainPageUrl) return loadPageData(mainPageUrl, currentPage) else: pageUrl = mainPageUrl + 'page/' + str(currentPage) print (pageUrl) return loadPageData(pageUrl, currentPage) def loadPageData(pageUrl, currentPage): "Loads the page data for the given page url" response = requests.get(pageUrl) pageFile = pagePath + str(currentPage) + '.html' with open(pageFile, 'wb') as f: f.write(response.content) soup = BeautifulSoup(open(pageFile, encoding="utf8"), 'html.parser') titleTexts = [] titleLinks = [] for posts in soup.find_all(class_='post-title'): for post in posts: titleTexts.append(post.get('title')) titleLinks.append(post.get('href')) return (titleTexts, titleLinks) def getCurrentDateTimeFormatted(): "Returns a string for the json output file with the current date and time" return datetime.datetime.now().isoformat().replace(":", "-").replace(".", "-") loadGameData()
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import sys import os from scipy import stats import tenseal as ts import numpy as np from scipy.stats import t sys.path.append(os.path.abspath('../../tensealstat')) from tensealstat.tools_context import ToolsContext as tc from tensealstat.algebra.algebra_numpy import AlgebraNumpy from tensealstat.algebra.algebra_tenseal import AlgebraTenseal from tensealstat.statistic.student_t_repeated_measures import StudentTRepeatedMeasures statistic_generator = StudentTRepeatedMeasures() # # This test follows Larsen Marc 4Th edition P790 # # 1 done by the key holder context = tc.get_context_default() algebra_tenseal = AlgebraTenseal(context) algebra_numpy = AlgebraNumpy() # 2 done by the data holders sample_0 = np.array([14.6, 17.3, 10.9, 12.8, 16.6, 12.2, 11.2, 15.4, 14.8, 16.2]) sample_1 = np.array([13.8, 15.4, 11.3, 11.6, 16.4, 12.6, 11.8, 15.0, 14.4, 15.0]) list_sample = [sample_0, sample_1] list_sample_encrypted = [algebra_tenseal.encrypt_vector(sample) for sample in list_sample] # 3 done by the agregator statistic_encrypted = statistic_generator.encrypt_statistic(algebra_tenseal, list_sample_encrypted) # 4 done by the key holder t_statistic, degrees_of_freedom = statistic_generator.decrypt_statistic(algebra_tenseal, statistic_encrypted) p_value = t.cdf(t_statistic, degrees_of_freedom) # p value should be between about 0.94 and 0.95 print('via tensealstat') print('t_statistic: ' + str(t_statistic)) print('p_value: ' + str(p_value)) # Test version statistic_encrypted = statistic_generator.encrypt_statistic(algebra_numpy, list_sample) t_statistic, degrees_of_freedom = statistic_generator.decrypt_statistic(algebra_numpy, statistic_encrypted) p_value = t.cdf(t_statistic, degrees_of_freedom) print('') print('via tensealstattest') print('t_statistic: ' + str(t_statistic)) print('p_value: ' + str(p_value)) # Scipy version t_statistic, p_value = stats.ttest_rel(sample_0 ,sample_1) print('') print('via scipy') print('t_statistic: ' + str(t_statistic)) print('p_value: ' + str(1 - (p_value / 2)))
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import os from django.db import models from django.contrib.auth.models import User from easy_thumbnails.fields import ThumbnailerImageField import uuid
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""" @author: magician @file: confirm_subclass.py @date: 2020/1/14 """ class Meta(type): """ Meta """ class MyClass(object, metaclass=Meta): """ MyClass """ stuff = 3 class ValidatePolygon(type): """ ValidatePolygon """ def __new__(meta, name, bases, class_dict): """ Don't validate the abstract Polygon class @param name: @param bases: @param class_dict: """ if bases != (object,): if class_dict['sides'] < 3: raise ValueError('Polygons need 3+ sides') return type.__new__(meta, name, bases, class_dict) class Polygon(object, metaclass=ValidatePolygon): """ Polygon """ # Specified by subclasses sides = None @classmethod class Triangle(Polygon): """ Triangle """ sides = 3 if __name__ == '__main__': print('Before Class') try: class Line(Polygon): """ Line """ print('Before sides') sides = 1 print('After sides') except Exception as e: print(e) print('After Class')
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import FWCore.ParameterSet.Config as cms XMLIdealGeometryESSource = cms.ESSource("XMLIdealGeometryESSource", geomXMLFiles = cms.vstring( 'Geometry/CMSCommonData/data/materials/2021/v3/materials.xml', 'Geometry/CMSCommonData/data/rotations.xml', 'Geometry/CMSCommonData/data/extend/v3/cmsextent.xml', 'Geometry/CMSCommonData/data/cavernData/2021/v1/cavernData.xml', 'Geometry/CMSCommonData/data/cms/2021/v3/cms.xml', 'Geometry/CMSCommonData/data/cmsMother.xml', 'Geometry/CMSCommonData/data/eta3/etaMax.xml', 'Geometry/TrackerCommonData/data/pixfwdCommon.xml', 'Geometry/ForwardCommonData/data/pixfwd/2021/v1/pixfwd.xml', 'Geometry/HcalCommonData/data/average/hcalforwardmaterial.xml', 'Geometry/ForwardCommonData/data/forward/2021/v1/forward.xml', 'Geometry/ForwardCommonData/data/totemt2/2021/v1/totemt2.xml', 'Geometry/ForwardCommonData/data/forwardshield/2021/v1/forwardshield.xml', 'Geometry/ForwardCommonData/data/bhm.xml', 'Geometry/ForwardCommonData/data/pltbcm/2021/v1/pltbcm.xml', 'Geometry/ForwardCommonData/data/bcm1f/2021/v1/bcm1f.xml', 'Geometry/ForwardCommonData/data/plt/2021/v1/plt.xml', 'Geometry/ForwardSimData/data/bcm1fsens.xml', 'Geometry/ForwardCommonData/data/zdcmaterials/2021/v1/zdcmaterials.xml', 'Geometry/ForwardCommonData/data/lumimaterials.xml', 'Geometry/ForwardCommonData/data/zdcrotations.xml', 'Geometry/ForwardCommonData/data/lumirotations.xml', 'Geometry/ForwardCommonData/data/zdc/2021/v1/zdc.xml', 'Geometry/ForwardCommonData/data/zdclumi/2021/v2/zdclumi.xml', 'Geometry/ForwardCommonData/data/cmszdc.xml', 'Geometry/ForwardSimData/data/pltsens.xml', 'Geometry/ForwardSimData/data/bcm1fsens.xml', 'Geometry/ForwardSimData/data/totemsensT2/2021/totemsensT2.xml', 'Geometry/ForwardSimData/data/zdcsens.xml', 'Geometry/ForwardSimData/data/bhmProdCuts/2021/v1/bhmProdCuts.xml', 'Geometry/ForwardSimData/data/zdcProdCuts/2021/v2/zdcProdCuts.xml', 'Geometry/ForwardSimData/data/ForwardShieldProdCuts.xml', 'Geometry/CMSCommonData/data/FieldParameters.xml', ), rootNodeName = cms.string('cms:OCMS') )
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""" MIT License Copyright (c) 2021 TheHamkerCat Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ from wbb import db from typing import Dict, List, Union notesdb = db.notes filtersdb = db.filters warnsdb = db.warns karmadb = db.karma chatsdb = db.chats gbansdb = db.gban coupledb = db.couple captchadb = db.captcha antiservicedb = db.antiservice pmpermitdb = db.pmpermit """ Notes functions """ """ Filters funcions """ """ Warn functions """ """ Karma functions """ """ Chats log functions """ """ Gban functions """ # Couple Chooser # Captcha """Anti Service System""" """ PM PERMIT """
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# -*- coding: utf-8 -*- """ @author: nicolas.posocco """ from sklearn.calibration import calibration_curve import numpy as np
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# coding: utf-8 # # File name "sac" means "SHCNE_ASSOCCHANGED" used at 4xc5k9H. # from __future__ import absolute_import import sys #/ if __name__ == '__main__': sys.exit(main())
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import Grabber gauge_grabber = Grabber.grabb_serial_values() gauge_grabber.init() gauge_grabber.grabb(10) gauge_grabber.convert_grabbed_to_numpy() gauge_grabber.plot_grabbed_data() gauge_grabber.clean_numpy_dict() gauge_grabber.save_cleaned_dict_numpy('test_saving') gauge_grabber.save_cleaned_dict_numpy_csv('test_saving')
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# Copyright (c) 2014, Tomohiro Kusumi # 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 COPYRIGHT HOLDERS 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 COPYRIGHT OWNER 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. import sys from . import filebytes from . import ncurses from . import setting from . import terminal from . import util Error = Exception _count = 0 A_NONE = 0 A_BOLD = 1 A_REVERSE = 2 A_STANDOUT = 4 A_UNDERLINE = 8 A_COLOR_FB = 0 A_COLOR_CURRENT = 0 A_COLOR_ZERO = 0 A_COLOR_FF = 0 A_COLOR_PRINT = 0 A_COLOR_DEFAULT = 0 A_COLOR_VISUAL = 0 A_COLOR_OFFSET = 0 BUTTON1_CLICKED = 0 BUTTON1_PRESSED = 0 BUTTON1_RELEASED = 0 BUTTON1_DOUBLE_CLICKED = 0 BUTTON1_TRIPLE_CLICKED = 0 BUTTON2_CLICKED = 0 BUTTON2_PRESSED = 0 BUTTON2_RELEASED = 0 BUTTON2_DOUBLE_CLICKED = 0 BUTTON2_TRIPLE_CLICKED = 0 BUTTON3_CLICKED = 0 BUTTON3_PRESSED = 0 BUTTON3_RELEASED = 0 BUTTON3_DOUBLE_CLICKED = 0 BUTTON3_TRIPLE_CLICKED = 0 BUTTON4_CLICKED = 0 BUTTON4_PRESSED = 0 BUTTON4_RELEASED = 0 BUTTON4_DOUBLE_CLICKED = 0 BUTTON4_TRIPLE_CLICKED = 0 REPORT_MOUSE_POSITION = 0 # APIs must be compatible with # https://docs.python.org/3/library/curses.html
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import sublime, sublime_plugin import sys import re import os
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#!/usr/bin/env python3 import argparse from glob import glob from skimage import exposure from PIL import Image import colour import numpy as np import pandas as pd import os from colour_demosaicing import ( demosaicing_CFA_Bayer_bilinear, demosaicing_CFA_Bayer_Malvar2004, demosaicing_CFA_Bayer_Menon2007, mosaicing_CFA_Bayer) cctf_encoding = colour.cctf_encoding parser = argparse.ArgumentParser(description='Process quality series of Permacam images and generate error measurements') parser.add_argument('save_dir', help='Optional save detected images to file in dir') args = parser.parse_args() jpegs = glob(args.save_dir + '/image_mono*.jpeg') raws = glob(args.save_dir + '/image_raw*.npy') manifest_fname = args.save_dir + '/manifest.pkl' data = None if not os.path.isfile(manifest_fname) : print('Generating') data = {} data['filename'] = [] data['id'] = [] data['quality'] = [] data['size'] = [] data['raw'] = [] data['time'] = [] data['image_array'] = [] d = None fname = None for f in jpegs: data['filename'].append(f) i = int(f.split('_')[-1].split('.')[0]) data['id'].append(i) q = int(f.split('_')[2]) data['quality'].append(q) data['size'].append(os.path.getsize(f)) data['raw'].append(0) time_fname = args.save_dir + '/time_to_send_' + str(q) + '_' + str(i) + '.txt' time_to_send = 0 with open(time_fname, 'r') as tf: time_to_send = float(tf.read()); data['time'].append(time_to_send) jpeg = Image.open(f) d = np.array(jpeg.getdata()).reshape(jpeg.size[0], jpeg.size[1]) / 0xff d = cctf_encoding(demosaicing_CFA_Bayer_Menon2007(d, 'BGGR')) d = exposure.rescale_intensity(d, (0,1)) d = exposure.adjust_gamma(d, 1.5) #d = (d * 255).astype(np.uint8) fname = f.replace('mono', 'jpeg_demosaiced').replace('.jpeg', '.npy') np.save(fname, d) data['image_array'].append(fname) result = Image.fromarray((d * 255).astype(np.uint8)) result.save(fname.replace('npy','jpeg'), format='JPEG') for f in raws: data['filename'].append(f) i = int(f.split('_')[-1].split('.')[0]) data['id'].append(i) data['quality'].append(100) data['raw'].append(1) data['size'].append(d.shape[0] * d.shape[1]) time_fname = args.save_dir + '/time_to_send_0_' + str(i) + '.txt' time_to_send = 0 with open(time_fname, 'r') as tf: time_to_send = float(tf.read()); data['time'].append(time_to_send) d = np.load(f) / float(0xff) d = cctf_encoding(demosaicing_CFA_Bayer_Menon2007(d, 'BGGR')) d = exposure.rescale_intensity(d, (0,1)) d = exposure.adjust_gamma(d, 1.5) #d = (d * 255).astype(np.uint8) fname = f.replace('raw', 'demosaiced') np.save(fname, d) data['image_array'].append(fname) result = Image.fromarray((d * 255).astype(np.uint8)) result.save(fname.replace('npy','jpeg'), format='JPEG') data = pd.DataFrame(data) qualities = set(np.unique(data['quality']).flatten()) for i in data['id']: avail_qual = set(data[data['id'] == i]['quality']) if (avail_qual != qualities): data = data[data['id'] != i] data.to_pickle(manifest_fname) else: data = pd.read_pickle(manifest_fname) print(data) print(len(np.unique(data['id'])))
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# Generated by Django 2.1.7 on 2020-10-22 12:55 from django.conf import settings from django.db import migrations, models import django.db.models.deletion import django.utils.timezone
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# coding=utf-8 # Copyright 2015 Brave Labs sp. z o.o. # All rights reserved. # # This source code and all resulting intermediate files are CONFIDENTIAL and # PROPRIETY TRADE SECRETS of Brave Labs sp. z o.o. # Use is subject to license terms. See NOTICE file of this project for details. import json import logging from smtplib import SMTPAuthenticationError, SMTPServerDisconnected import html2text from django.conf import settings from django.core.mail import EmailMultiAlternatives from django.core.mail import send_mail as django_send_mail from django.template.exceptions import TemplateDoesNotExist from django.template.loader import render_to_string from sparkpost.exceptions import SparkPostAPIException def send_mail(template_path, ctx, to): """Simplifies message rendering""" ctx.setdefault('BASE_URL', settings.BASE_URL) message = render_to_string(template_path + '.jinja2', ctx) html_message = render_to_string(template_path + '.html', ctx) subject, message = message.split("\n", 1) if not isinstance(to, list): to = [to] django_send_mail( subject=subject, message=message, html_message=html_message, from_email=settings.DEFAULT_FROM_EMAIL, recipient_list=to ) def send_mail_template(template, ctx, subject, to=None, **kwargs): """Simplifies message rendering""" ctx.setdefault('BASE_URL', settings.BASE_URL) html_message = render_to_string(template, ctx) try: text_message = render_to_string(template.replace(".html", ".jinja2"), ctx) except TemplateDoesNotExist: text_message = html2text.html2text(html_message) if not isinstance(to, list): to = [to] kwargs.setdefault('from_email', settings.DEFAULT_FROM_EMAIL) kwargs.setdefault('reply_to', [settings.DEFAULT_REPLY_TO_EMAIL]) message = EmailMultiAlternatives(subject, body=text_message, to=to, **kwargs) if html_message: message.attach_alternative(html_message, 'text/html') logging.debug("to: %s", to) if 'unsubscribe' in ctx: message.extra_headers['List-Unsubscribe'] = "<{}>".format(ctx['unsubscribe']) message.extra_headers['X-MSYS-API'] = json.dumps({'options': {'transactional': True}}) try: return message.send() except SparkPostAPIException as ex: if ex.status == 1902 or not settings.FAIL_ON_EMAIL_SUPPRESSION: logging.error("Email suppression: %s", to, exc_info=ex) else: raise except (SMTPServerDisconnected, SMTPAuthenticationError) as ex: logging.error("", exc_info=ex) # Retry once return message.send()
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import random import matplotlib.pyplot as plt from setting import * from field import Field from qvalue import Hunter if __name__ == "__main__": h0 = Hunter(0) h1 = Hunter(1) epi_itern_l = [] for i in range(EPISODE_N): f = Field() itern = 0 while True: fin = qlearn_step(f, h0, h1) itern += 1 if fin: break epi_itern_l.append(itern) print(i, itern) plt.plot(list(range(EPISODE_N)), epi_itern_l, linewidth=1) plt.xlabel("Number of Trials") plt.ylabel("Steps to Catch") plt.savefig("figure")
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import multiprocessing as mp import time import torch.distributed from rlpyt.runners.minibatch_rl import MinibatchRl from rlpyt.runners.minibatch_rl_eval import MinibatchRlEval from rlpyt.utils.seed import make_seed from rlpyt.utils.collections import AttrDict from rlpyt.utils.quick_args import save__init__args ############################################################################### # Master ############################################################################### ############################################################################### # Worker ############################################################################### # Helpers
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# Copyright 2018 Pants project contributors (see CONTRIBUTORS.md). # Licensed under the Apache License, Version 2.0 (see LICENSE). import os from dataclasses import dataclass from textwrap import dedent from typing import Optional, Tuple from pants.backend.python.rules.pex import Pex from pants.backend.python.rules.pex_from_target_closure import CreatePexFromTargetClosure from pants.backend.python.rules.prepare_chrooted_python_sources import ChrootedPythonSources from pants.backend.python.subsystems.pytest import PyTest from pants.backend.python.subsystems.python_setup import PythonSetup from pants.backend.python.subsystems.subprocess_environment import SubprocessEncodingEnvironment from pants.build_graph.address import Address from pants.engine.addressable import BuildFileAddresses from pants.engine.fs import Digest, DirectoriesToMerge, FileContent, InputFilesContent from pants.engine.interactive_runner import InteractiveProcessRequest from pants.engine.isolated_process import ExecuteProcessRequest, FallibleExecuteProcessResult from pants.engine.legacy.graph import HydratedTargets, TransitiveHydratedTargets from pants.engine.legacy.structs import PythonTestsAdaptor from pants.engine.rules import UnionRule, rule, subsystem_rule from pants.engine.selectors import Get from pants.option.global_options import GlobalOptions from pants.rules.core.strip_source_root import SourceRootStrippedSources from pants.rules.core.test import TestDebugRequest, TestOptions, TestResult, TestTarget DEFAULT_COVERAGE_CONFIG = dedent(f""" [run] branch = True timid = False relative_files = True """) def calculate_timeout_seconds( *, timeouts_enabled: bool, target_timeout: Optional[int], timeout_default: Optional[int], timeout_maximum: Optional[int], ) -> Optional[int]: """Calculate the timeout for a test target. If a target has no timeout configured its timeout will be set to the default timeout. """ if not timeouts_enabled: return None if target_timeout is None: if timeout_default is None: return None target_timeout = timeout_default if timeout_maximum is not None: return min(target_timeout, timeout_maximum) return target_timeout @dataclass(frozen=True) @rule @rule(name="Run pytest") async def run_python_test( test_target: PythonTestsAdaptor, test_setup: TestTargetSetup, python_setup: PythonSetup, subprocess_encoding_environment: SubprocessEncodingEnvironment, global_options: GlobalOptions, test_options: TestOptions, ) -> TestResult: """Runs pytest for one target.""" colors = global_options.colors env = {"PYTEST_ADDOPTS": f"--color={'yes' if colors else 'no'}"} request = test_setup.requirements_pex.create_execute_request( python_setup=python_setup, subprocess_encoding_environment=subprocess_encoding_environment, pex_path=f'./{test_setup.requirements_pex.output_filename}', pex_args=test_setup.args, input_files=test_setup.input_files_digest, output_directories=('.coverage',) if test_options.values.run_coverage else None, description=f'Run Pytest for {test_target.address.reference()}', timeout_seconds=test_setup.timeout_seconds if test_setup.timeout_seconds is not None else 9999, env=env, ) result = await Get[FallibleExecuteProcessResult](ExecuteProcessRequest, request) return TestResult.from_fallible_execute_process_result(result) @rule(name="Run pytest in an interactive process")
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from sciencebeam_utils.utils.string import ( parse_list )
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# coding: utf-8 -*- # TITLE / SUBTITLE / AUTHOR # The title, subtitle, and author of the blog TITLE = "Misa Montes" SUBTITLE = "blog" AUTHOR = "Misa" METADATA_TAGS = ['title', 'author'] # POSTS_PER_PAGE # The number of posts that will appear on each page of index.html POSTS_PER_PAGE = 3 # SUMMARY_DELIMITOR # Used to specify a breaking point from the beginning of the document to the # end of the summary. SUMMARY_DELIMITER = "~~" # USE_HEROKU # Toggled as True/False if the system will or will not be deployed as a Heroku # app, respectively. USE_HEROKU = True # DISQUS_SHORTNAME # Disqus is a free system used to manage comments at the bottom of blog posts. # After signing up, you will be issued a shortname -- put it here. By default # developer mode of the script is enabled. DISQUS_SHORTNAME = "misalabs"
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''' Description: Problem 2 (rearrange the code) Version: 1.0.2.20210118 Author: Arvin Zhao Date: 2021-01-14 22:35:43 Last Editors: Arvin Zhao LastEditTime: 2021-01-18 18:11:03 ''' line = input('Enter a string: ') i = 0 is_palindrome = True while i < len(line) / 2 and is_palindrome: if line[i] != line[len(line) - i - 1]: is_palindrome = False else: is_palindrome = True i += 1 if is_palindrome: print(line, 'is a palindrome.') else: print(line, 'is not a palindrome.')
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""" name - jméno pozition - pozice (x, y) can_train - čísla pro trening jednotky (integer) - pokud je má jednotka alespoň jedno z těchto čísel může budova trénovat tuto jednotku (čím víc společných čísel tím kratší doba tréningu) defend_power - sílá obrany integer - 0 neučastní se obrany lifes - počet životů (integer, integer) - aktuálně, maximalně | 0 v maximalně zničení při zničení města, -1 v maximalně zničení při útoku na město, ostantí kladná čísla v maximalně zničení při dosažení 0 income - vydělá za čas (integer, integer, integer) - vydělá, za, posledně """ from time import time
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## ## Demonstrate some of the operations of the Deck and Card classes ## import cards # Seed the random number generator to a specific value so every execution # of the program uses the same sequence of random numbers (for testing). import random random.seed( 100 ) # Create a deck of cards my_deck = cards.Deck() # Shuffle the deck, then display it in 13 columns my_deck.shuffle() print( "===== shuffled deck =====" ) my_deck.display() # Deal five cards to each player (alternating) print( "Dealt five cards to each player (alternating)" ) print() player1_list=[] player2_list=[] for i in range( 5 ): player1_list.append( my_deck.deal() ) player2_list.append( my_deck.deal() ) # Display each player's cards and the cards still in the deck print( "===== player #1 =====" ) print() print( player1_list ) print() print( "===== player #2 =====" ) print() print( player2_list ) print() print( "===== remaining cards in deck =====" ) my_deck.display() hand(0,'First') hand(0,'Second') hand(-1,'Last')
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from __future__ import absolute_import import os import socket from ur_online_control.ur_direct.mixins.airpick_mixins import AirpickMixins __all__ = [ 'URCommandScript' ] class URCommandScript(AirpickMixins): """Class containing commands for the UR Robot system""" def start(self): """To build the start of the script""" self.add_lines(["def program():", "\ttextmsg(\">> Entering program.\")", "\t# open line for airpick commands"]) def end(self, feedback=None): """To build the end of the script""" if feedback: self.socket_send_line('"Done"') self.add_lines(["\ttextmsg(\"<< Exiting program.\")", "end", "program()\n\n\n"]) def generate(self): """Translate the dictionary to a long string""" self.script = '\n'.join(self.commands_dict.values()) return self.script # Dictionary building def add_line(self, line, i=None): """Add a single line to the script""" if i is None: i = len(self.commands_dict) else: pass self.commands_dict[i] = line def add_lines(self, lines): """Add a multiple lines to the script""" i = len(self.commands_dict) [self.add_line(line, i+line_nr) for (line_nr, line) in zip(range(len(lines)), lines)] # Feedback functionality def get_current_pose_cartesian(self, send=False): """Get the current cartesian pose""" self.get_current_pose("cartesian", send) def get_current_pose_joints(self, send=False): """Get the current joints pose""" self.get_current_pose("joints", send) def get_current_pose(self, get_type, send): """Create get pose code""" pose_type = { "cartesian": "get_forward_kin()", "joints": "get_actual_joint_positions()" } self.add_lines(["\tcurrent_pose = {}".format(pose_type[get_type]), "\ttextmsg(current_pose)"]) if send: self.socket_send_line('current_pose') #self.add_line("\ttextmsg('sending done')") # Connectivity def is_available(self): """Ping the network, to check for availability""" system_call = "ping -r 1 -n 1 {}".format(self.ur_ip) response = os.system(system_call) if response == 0: return True else: return False # Geometric effects def set_tcp(self, tcp): """Set the tcp""" #tcp = [tcp[i]/1000 if i < 3 else tcp[i] for i in range(len(tcp))] tcp = [tcp[i] for i in range(len(tcp))] self.add_line("\tset_tcp(p{})".format(tcp)) def add_move_linear(self, move_command, feedback=None): """Add a move command to the script""" #move = [cmd / 1000 if c not in [3, 4, 5] else cmd for c, cmd in zip(range(len(move_command)), move_command)] move = [cmd for c, cmd in zip(range(len(move_command)), move_command)] [x, y, z, dx, dy, dz, v, r] = move self.add_line("\tmovel(p[{}, {}, {}, {}, {}, {}], v={}, r={})".format(x, y, z, dx, dy, dz, v, r)) if feedback == "Full": self.get_current_pose_cartesian(True) elif feedback == "UR_only": self.get_current_pose_cartesian(False) else: pass
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import os import contextlib filepath = os.path.dirname(os.path.abspath(__file__)) def remove_installed_data_files(): """ Removes some of the files that are downloaded during install. Args: None Returns: None """ # remove the GO file with contextlib.suppress(FileNotFoundError): os.remove(os.path.join(filepath, 'go-basic.obo')) # remove the GO annotations with contextlib.suppress(FileNotFoundError): os.remove(os.path.join(filepath, 'goa_human.gaf.gz')) # remove the housekeeping genes file with contextlib.suppress(FileNotFoundError): os.remove(os.path.join(filepath, 'HK_genes.txt')) # remove the transcription factors genes file with contextlib.suppress(FileNotFoundError): os.remove(os.path.join(filepath, 'TFCheckpoint_download_180515.txt')) # remove the GTEx Rdata file with contextlib.suppress(FileNotFoundError): os.remove(os.path.join(filepath, 'gtex', 'rse_gene.Rdata')) # remove the GTEx expression file with contextlib.suppress(FileNotFoundError): os.remove(os.path.join(filepath, 'gtex', 'expression_data.csv'))
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# if aaa: # ... elif bbb: # ... #
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""" Copyright (c) Microsoft Corporation. Licensed under the MIT license. HERO for Video Question Answering Tasks, shared by: 1. TVQA 2. How2QA 3. VLEP 4. VIOLIN copied/modified from HERO (https://github.com/linjieli222/HERO) """ from collections import defaultdict import copy import torch from torch import nn from torch.nn import functional as F from .model import HeroModel from .layers import MLPLayer from .modeling_utils import mask_logits
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import logging from channels.test import ChannelTestCase, WSClient from .game_logic import add_player_to_game from .models import Player, Game, CardGamePlayer, Card LOGGER = logging.getLogger("cardgame_channels_app")
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# pyt_utilities.py (python3) # utilities for CNN training with pytorch; data preparation, training, evaluation # added saving checkpoint # FEB 2020 / 21 # sources: # https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html#load-data # https://pytorch.org/tutorials/beginner/finetuning_torchvision_models_tutorial.html #updated normalization mean and std: #mean [0.4597244970012637, 0.4887084808460421, 0.46925360649661096] #std [0.20728858675971737, 0.2048932794469992, 0.21645177513430724] #------------------------------------------------------------------------------- import os, sys, time, random, itertools import torch, torchvision from torch.optim import lr_scheduler from torch.autograd import Variable from torchvision import models, datasets, transforms import torch.nn.functional as F from torch import utils import torch.optim as optim from PIL import Image import numpy import array import urllib, glob, shutil from shutil import copyfile, copy from copy import deepcopy import matplotlib.pyplot as plt from plot_helper import * from utilities import * bali26_class_names = ['aroid', 'bamboo', 'banana', 'cacao', 'cinnamon', 'coffeearabica', 'dragonfruit', 'durian', 'frangipani', 'guava', \ 'jackfruit', 'lychee', 'mango', 'mangosteen', 'nilam', 'papaya', 'passiflora', \ 'sawo', 'snakefruit', 'starfruit', 'sugarpalm', 'taro', 'vanilla', 'waterguava', 'whitepepper', 'zodia'] #files on pCloud bali26_alexnet = 'https://filedn.com/lqzjnYhpY3yQ7BdfTulG1yY/models/bali26_alexnet.pth' bali26_resnet152 = 'https://filedn.com/lqzjnYhpY3yQ7BdfTulG1yY/models/bali26_resnet152.pth' bali26_rexnext50 = 'https://filedn.com/lqzjnYhpY3yQ7BdfTulG1yY/models/bali26_resnext50.pth' bali26_samples_zip = 'https://filedn.com/lqzjnYhpY3yQ7BdfTulG1yY/bali26samples.zip' bali3_zip = 'https://filedn.com/lqzjnYhpY3yQ7BdfTulG1yY/bali-3.zip' bali3B_zip = 'https://filedn.com/lqzjnYhpY3yQ7BdfTulG1yY/bali-3B.zip' bali3C_zip = 'https://filedn.com/lqzjnYhpY3yQ7BdfTulG1yY/bali-3C.zip' bali3D_zip = 'https://filedn.com/lqzjnYhpY3yQ7BdfTulG1yY/bali-3D.zip' tests_zip = 'https://filedn.com/lqzjnYhpY3yQ7BdfTulG1yY/tests.zip' bali_mixedplants_zip = 'https://filedn.com/lqzjnYhpY3yQ7BdfTulG1yY/bali_mixedplants.zip' #------------------------------------------------------------------------------- data_transforms = { 'train': transforms.Compose([ transforms.RandomResizedCrop(224), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize([0.4597, 0.4887, 0.4692], [0.2072, 0.2048, 0.2164]) #bali26 #transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]), 'val': transforms.Compose([ transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize([0.4597, 0.4887, 0.4692], [0.2072, 0.2048, 0.2164]) #bali26 #transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]), } predict_transform = transforms.Compose([ transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize([0.4597, 0.4887, 0.4692], [0.2072, 0.2048, 0.2164]) #bali26 #transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]) #------------------------------------------------------------------------------- #simple three layer CNN for 224 x 224 input; reduced alexnet; untrained by default #https://github.com/pytorch/vision/blob/master/torchvision/models/alexnet.py #------------------------------------------------------------------------------- #------------------------------------------------------------------------------- #------------------------------------------------------------------------------- #def train_model(checkpointname, model, criterion, optimizer, scheduler, num_epochs, output): #------------------------------------------------------------------------------- #------------------------------------------------------------------------------- #------------------------------------------------------------------------------- #------------------------------------------------------------------------------- #------------------------------------------------------------------------------- #-------------------------------------------------------------------------------
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import mysql.connector import yaml from os import path, listdir import shutil import datetime db_connection = mysql.connector.connect( host="localhost", port=3308, user="root", passwd="", database="ipldata" ) # Global Variable player_data = {} # Query Team details by name # Add new player to DB. # Get Player details by name # Read data from YAML file # Read files from folders # Upload data into MySQL DB # Parse Innings data ###### Main Program ###### # Read files to load from directory files = readFilesInFolder() # Read Data from the file. for file in files: print("Processing file: " + file) match_data = readDataFromFile(file) #print(match_data['info']) # Upload into DB parseAndUploadDataToDB(match_data) shutil.move(path.dirname(__file__) + "/to_load/" + file, path.dirname(__file__) + "/done/" + file) print("I'm done here !")
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# # # # # # #$Id: groParser.py,v 1.11 2006/12/15 19:53:29 annao Exp $ # """ Module Gromacs Parser. """ import os,types from MolKit.moleculeParser import MoleculeParser from MolKit.protein import Protein, Chain, ChainSet, Residue, ResidueSet, ProteinSet from MolKit.molecule import Atom, AtomSet, Bond, BondSet, HydrogenBond from PyBabel.babelElements import babel_elements if __name__ == '__main__': parser = groParser( filename='/usr/share/gromacs/tutor/methanol/conf.gro' ) print "Reading molecule" mol = parser.parse()
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# Copyright 2012-2013 Eric Ptak - trouch.com # # 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 time import subprocess from webiopi.utils.logger import debug, info BUSLIST = { "I2C": {"enabled": False, "gpio": {0:"SDA", 1:"SCL", 2:"SDA", 3:"SCL"}, "modules": ["i2c-bcm2708", "i2c-dev"]}, "SPI": {"enabled": False, "gpio": {7:"CE1", 8:"CE0", 9:"MISO", 10:"MOSI", 11:"SCLK"}, "modules": ["spi-bcm2708", "spidev"]}, "UART": {"enabled": False, "gpio": {14:"TX", 15:"RX"}}, "ONEWIRE": {"enabled": False, "gpio": {4:"DATA"}, "modules": ["w1-gpio"], "wait": 2} }
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import datetime from clover.common import get_system_info from clover.common import friendly_datetime from clover.report.service import ReportService
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class asyncGwEvent(object): """Basic async event.""" class asyncClEvent(object): """Basic async event."""
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wt1_2_7 = {'192.168.122.112': [6.0527, 8.4551, 7.8479, 7.6681, 7.4932, 6.4745, 6.4198, 8.2644, 10.7811, 10.3783, 9.9412, 9.5791, 9.2604, 9.4337, 9.2044, 8.9888, 8.8576, 8.6938, 8.5687, 8.4276, 8.3159, 8.203, 8.3446, 8.2226, 8.127, 8.0167, 7.9279, 7.8576, 7.9614, 7.9092, 7.8796, 7.8118, 7.747, 7.8486, 7.7813, 7.8853, 7.8253, 7.7668, 7.8695, 7.8173, 7.7653, 7.7128, 7.6701, 7.7426, 7.811, 7.8762, 7.8196, 7.7081, 7.6705, 7.6261, 7.4933, 7.4535, 7.4443, 7.4345, 7.3984, 7.3828, 7.7321, 7.6903, 7.6519, 7.6141, 7.5784, 7.928, 7.9189, 7.9659, 7.944, 7.9437, 7.913, 7.8772, 7.85, 7.8308, 7.8185, 7.792, 7.7742, 7.7612, 7.7356, 7.766, 7.7373, 7.713, 7.6905, 7.6612, 7.6402, 7.6118, 7.6528, 7.6491, 7.6283, 7.6693, 7.6482, 7.6321, 7.6127, 7.6098, 7.591, 7.5769, 7.552, 7.5301, 7.599, 7.5916, 7.5948, 7.5769, 7.5614, 7.5986, 7.6272, 7.6068, 7.5859, 7.5649, 7.5449, 7.5254, 7.5584, 7.5457, 7.5256, 7.507, 7.4923, 7.4765, 7.4653, 7.4491, 7.4337, 7.4291, 7.4144, 7.3997, 7.3822, 7.367, 7.3764, 7.3622, 7.3563, 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1.1, 2.6, 2.6, 2.4, 2.4, 1.0, 2.0, 0.9, 4.9, 0.3, 3.9, 0.6, 0.8, 0.6, 2.1, 2.6, 1.4, 1.7, 1.6, 1.2, 0.9, 0.8, 1.0, 1.4, 0.7, 0.8, 0.9, 1.4, 0.1, 1.5, 0.1, 4.8, 1.6, 2.7, 0.4, 2.4, 2.3, 1.2, 1.0, 2.3, 1.4, 0.8, 0.2, 0.7, 0.3, 0.3, 0.1, 1.8, 5.2, 3.2, 2.8, 1.7, 0.2, 0.0, 1.6, 1.0, 1.6, 2.2, 1.8, 1.9, 0.3, 1.2, 0.0, 0.4, 0.3, 2.2, 1.3, 3.8, 3.5, 1.2, 2.3, 0.9, 1.0, 0.1, 1.5, 0.2, 1.4, 0.4, 0.3, 0.7, 0.9, 0.3, 4.0, 4.5, 0.0, 0.1, 5.9, 5.2, 1.7, 0.9, 0.4, 0.0, 1.3, 0.1, 0.5, 1.6, 0.4, 0.3] off_mec1_2_7 = 284 off_cloud1_2_7 = 392 inward_mec1_2_7 = 222 loc1_2_7 = 635 deadlock1_2_7 = [7] memory1_2_7 = [0.2175, 0.2178, 0.2179, 0.218, 0.2182, 0.2182, 0.2182, 0.2182, 0.2183, 0.2184, 0.2184, 0.2184, 0.2184, 0.2184, 0.2188, 0.2189, 0.2189, 0.219, 0.219, 0.219, 0.219, 0.219, 0.219, 0.2191, 0.2192, 0.2192, 0.2192, 0.2192, 0.2192, 0.2192, 0.2193, 0.2193, 0.2193, 0.2193, 0.2195, 0.2195, 0.2195, 0.2195, 0.2195, 0.2195, 0.2195, 0.2196, 0.2196, 0.2196, 0.2197, 0.2197, 0.2198, 0.2198, 0.2199, 0.2199, 0.2199, 0.2199, 0.2199, 0.2199, 0.22, 0.2201, 0.2201, 0.2201, 0.2201, 0.2201, 0.2201, 0.2201, 0.2201, 0.2202, 0.2202, 0.2203, 0.2204, 0.2204, 0.2205, 0.2205, 0.2205, 0.2205, 0.2205, 0.2205, 0.2205, 0.2206, 0.2206, 0.2207, 0.2207, 0.2208, 0.2209, 0.2209, 0.2209, 0.2209, 0.2209, 0.2209, 0.221, 0.221, 0.221, 0.2211, 0.2211, 0.2211, 0.2213, 0.2213, 0.2213, 0.2214, 0.2214, 0.2215, 0.2215, 0.2215, 0.2215, 0.2215, 0.2215, 0.2215, 0.2215, 0.2215, 0.2215, 0.2215, 0.2216, 0.2217, 0.2217, 0.2217, 0.2217, 0.2218, 0.2218, 0.2218, 0.2218, 0.2218, 0.2218, 0.2219, 0.2219, 0.2219, 0.2219, 0.2219, 0.2219, 0.222, 0.222, 0.222, 0.222, 0.222, 0.2221, 0.2221, 0.2222, 0.2222, 0.2222, 0.2222, 0.2223, 0.2223, 0.2223, 0.2223, 0.2223, 0.2224, 0.2224, 0.2224, 0.2224, 0.2225, 0.2225, 0.2225, 0.2225, 0.2225, 0.2225, 0.2225, 0.2226, 0.2226, 0.2227, 0.2227, 0.2229, 0.2229, 0.223, 0.223, 0.223, 0.223, 0.223, 0.223, 0.223, 0.2231, 0.2232, 0.2232, 0.2232, 0.2232, 0.2232, 0.2232, 0.2232, 0.2232, 0.2232, 0.2233, 0.2233, 0.2233, 0.2234, 0.2234, 0.2234, 0.2234, 0.2234, 0.2235, 0.2235, 0.2235, 0.2235, 0.2235, 0.2236, 0.2236, 0.2237, 0.2237, 0.2237, 0.2238, 0.2238, 0.2238, 0.2239, 0.2239, 0.2239, 0.224, 0.224, 0.224, 0.224, 0.224, 0.224, 0.224, 0.224, 0.224, 0.224, 0.224, 0.224, 0.224, 0.224, 0.224, 0.2241, 0.2241, 0.2241, 0.2241, 0.2242, 0.2242, 0.2242, 0.2242, 0.2245, 0.2245, 0.2245, 0.2246, 0.2246, 0.2246, 0.2246, 0.2246, 0.2246, 0.2246, 0.2246, 0.2246, 0.2247, 0.2247, 0.2247, 0.2247, 0.2248, 0.2248, 0.2248, 0.2248, 0.2249, 0.2249, 0.2249, 0.225, 0.225, 0.225, 0.2251, 0.2251, 0.2251, 0.2251, 0.2252, 0.2252, 0.2253, 0.2253, 0.2253, 0.2253, 0.2253, 0.2253, 0.2253, 0.2253, 0.2254, 0.2255, 0.2255, 0.2255, 0.2255, 0.2258, 0.2258, 0.2259, 0.2259, 0.2259, 0.2259, 0.2259, 0.2259, 0.2259, 0.226, 0.226, 0.226, 0.2261, 0.2261, 0.2261, 0.2261, 0.2262, 0.2262, 0.2262, 0.2262, 0.2262, 0.2262, 0.2262, 0.2262, 0.2262, 0.2262, 0.2262, 0.2263, 0.2263, 0.2263, 0.2263, 0.2264, 0.2264, 0.2264, 0.2264, 0.2265, 0.2265, 0.2267, 0.2268, 0.2268, 0.2269, 0.2269, 0.2269, 0.2272, 0.2272, 0.2272, 0.2272, 0.2272, 0.2272, 0.2272, 0.2272, 0.2273, 0.2274, 0.2274, 0.2274, 0.2274, 0.2274, 0.2274, 0.2274, 0.2274, 0.2274, 0.2274, 0.2274, 0.2274, 0.2275, 0.2275, 0.2275, 0.2275, 0.2275, 0.2275, 0.2275, 0.2275, 0.2275, 0.2275, 0.2275, 0.2275, 0.2275, 0.2275, 0.2276, 0.2277, 0.2277, 0.2277, 0.2277, 0.2277, 0.2277, 0.2277, 0.2278, 0.2278, 0.2278, 0.2279, 0.2279, 0.228, 0.228, 0.228, 0.228, 0.228, 0.228, 0.2283, 0.2283, 0.2283, 0.2283, 0.2284, 0.2284, 0.2284] task_received1_2_7 = 1311 sent_t1_2_7 = {'125': 375, '124': 449, '126': 487} cooperate1_2_7 = {'mec': 284, 'cloud': 392} task_record1_2_7 = {} outward_mec1_2_7 = 244 offload_check1_2_7 = [197, 42] timed_out_tasks1_2_7 = 0
[ 198, 46569, 16, 62, 17, 62, 22, 796, 1391, 6, 17477, 13, 14656, 13, 18376, 13, 14686, 10354, 685, 21, 13, 2713, 1983, 11, 807, 13, 2231, 4349, 11, 767, 13, 23, 31714, 11, 767, 13, 2791, 6659, 11, 767, 13, 2920, 2624, 11, 718, ...
1.616278
214,009
import numpy as np import random import json import hdbscan import numba import sys from sklearn.neighbors import KDTree from pyclustering.cluster.xmeans import xmeans from helpers import distance_matrix as dm from helpers import hparam_functions as hp from helpers import visualization as vis from concurrent.futures import ThreadPoolExecutor
[ 11748, 299, 32152, 355, 45941, 198, 11748, 4738, 198, 11748, 33918, 198, 11748, 289, 9945, 35836, 198, 11748, 997, 7012, 198, 11748, 25064, 198, 198, 6738, 1341, 35720, 13, 710, 394, 32289, 1330, 509, 24544, 631, 198, 6738, 12972, 565, ...
2.902985
134
""" Copyright 2012 Ali Ok (aliokATapacheDOTorg) Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ from __future__ import division from trnltk.morphology.contextful.likelihoodmetrics.hidden.querykeyappender import _word_parse_result_appender, _word_surface_appender
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3.706468
201
import numpy as np
[ 11748, 299, 32152, 355, 45941, 198 ]
3.166667
6
# encoding:utf-8 class Position(object): """ Basic position class. Attributes ---------- symbol : str List of securities. side : str ("Long", "Short"). Positions of different sides will not be merged. cost_price : float Average cost price of current net position. close_pnl : float float_pnl : float trading_pnl : float holding_pnl : float init_size : int Position at the start of the day. enable_size : int Position that can be closed. frozen_size : int Positions to be closed. want_size : int Positions to be opened. pre_size : int Last day's position. today_size : int Today's position. current_size : int Current position. Methods ------- """ ''' __slots__ = ['symbol', 'side', 'cost_price', 'close_pnl', 'float_pnl', 'trading_pnl', 'holding_pnl', 'enable_size', 'frozen_size', 'want_size', 'today_size', 'pre_size', 'current_size', 'init_size', 'commission'] ''' @classmethod @classmethod class GoalPosition(object): """ Used in goal_portfolio function to generate orders. Attributes ---------- symbol : str ref_price : float Reference price, used by risk management, not by order. size : int Target position size. urgency : int The urgency to adjust position, used to determine trading algorithm. """
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# # ------------------------------------------------------------------------- # Copyright (c) 2015-2017 AT&T Intellectual Property # Copyright (C) 2020 Wipro Limited. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # ------------------------------------------------------------------------- # import socket import time import cotyledon import futurist from oslo_config import cfg from oslo_log import log from conductor.common.config_loader import load_config_file from conductor.common import db_backend from conductor.common.music import messaging as music_messaging from conductor.common.utils import conductor_logging_util as log_util from conductor.controller.generic_objective_translator import GenericObjectiveTranslator from conductor.controller.translator import Translator from conductor.controller.translator_utils import TranslatorException from conductor.controller.translator_utils import VERSIONS from conductor.i18n import _LE from conductor.i18n import _LI from conductor import messaging LOG = log.getLogger(__name__) CONF = cfg.CONF CONTROLLER_OPTS = [ cfg.IntOpt('polling_interval', default=1, min=1, help='Time between checking for new plans. ' 'Default value is 1.'), cfg.IntOpt('max_translation_counter', default=1, min=1), cfg.StrOpt('opt_schema_file', default='opt_schema.json', help='json schema file which will be used to validate the ' 'optimization object for the new template version'), ] CONF.register_opts(CONTROLLER_OPTS, group='controller') class TranslatorService(cotyledon.Service): """Template Translator service. This service looks for untranslated templates and preps them for solving by the Solver service. """ # This will appear in 'ps xaf' name = "Template Translator" def __init__(self, worker_id, conf, **kwargs): """Initializer""" LOG.debug("{}".format(self.__class__.__name__)) super(TranslatorService, self).__init__(worker_id) self._init(conf, **kwargs) self.running = True def _gracefully_stop(self): """Gracefully stop working on things""" pass def millisec_to_sec(self, millisec): """Convert milliseconds to seconds""" return millisec / 1000 def _reset_template_status(self): """Reset plans being templated so they are translated again. Use this only when the translator service is not running concurrently. """ plans = self.Plan.query.get_plan_by_col("status", self.Plan.TRANSLATING) for the_plan in plans: the_plan.status = self.Plan.TEMPLATE # Use only in active-passive mode, so don't have to be atomic the_plan.update() def _restart(self): """Prepare to restart the service""" pass def current_time_seconds(self): """Current time in milliseconds.""" return int(round(time.time())) def setup_rpc(self, conf, topic): """Set up the RPC Client""" # TODO(jdandrea): Put this pattern inside music_messaging? transport = messaging.get_transport(conf=conf) target = music_messaging.Target(topic=topic) client = music_messaging.RPCClient(conf=conf, transport=transport, target=target) return client def translate(self, plan): """Translate the plan to a format the solver can use""" # Update the translation field and set status to TRANSLATED. try: LOG.info(_LI("Requesting plan {} translation").format( plan.id)) template_version = plan.template.get("homing_template_version") if template_version in VERSIONS['BASE']: trns = Translator(self.conf, plan.name, plan.id, plan.template) elif template_version in VERSIONS['GENERIC']: trns = GenericObjectiveTranslator(self.conf, plan.name, plan.id, plan.template, self.opt_schema) else: raise TranslatorException( "conductor_template_version must be one " "of: {}".format(', '.join([x for v in VERSIONS.values() for x in v]))) trns.translate() if trns.ok: plan.translation = trns.translation plan.status = self.Plan.TRANSLATED LOG.info(_LI( "Plan {} translated. Ready for solving").format( plan.id)) LOG.info(_LI( "Plan name: {}").format( plan.name)) else: plan.message = trns.error_message plan.status = self.Plan.ERROR LOG.error(_LE( "Plan {} translation error encountered").format( plan.id)) except Exception as ex: template = "An exception of type {0} occurred, arguments:\n{1!r}" plan.message = template.format(type(ex).__name__, ex.args) plan.status = self.Plan.ERROR _is_success = 'FAILURE' while 'FAILURE' in _is_success and (self.current_time_seconds() - self.millisec_to_sec(plan.updated)) \ <= self.conf.messaging_server.timeout: _is_success = plan.update(condition=self.translation_owner_condition) LOG.info(_LI("Changing the template status from translating to {}, " "atomic update response from MUSIC {}").format(plan.status, _is_success)) def __check_for_templates(self): """Wait for the polling interval, then do the real template check.""" # Wait for at least poll_interval sec polling_interval = self.conf.controller.polling_interval time.sleep(polling_interval) # Look for plans with the status set to TEMPLATE # Instead of using the query.all() method, now creating an index for 'status' # field in conductor.plans table, and query plans by status columns template_plans = self.Plan.query.get_plan_by_col("status", self.Plan.TEMPLATE) translating_plans = self.Plan.query.get_plan_by_col("status", self.Plan.TRANSLATING) # combine the plans with status = 'template' and 'translating' together plans = template_plans + translating_plans for plan in plans: # If there's a template to be translated, do it! if plan.status == self.Plan.TEMPLATE: if plan.translation_counter >= self.conf.controller.max_translation_counter: message = _LE("Tried {} times. Plan {} is unable to translate") \ .format(self.conf.controller.max_translation_counter, plan.id) plan.message = message plan.status = self.Plan.ERROR plan.update(condition=self.template_status_condition) LOG.error(message) break else: # change the plan status to "translating" and assign the current machine as translation owner plan.status = self.Plan.TRANSLATING plan.translation_counter += 1 plan.translation_owner = socket.gethostname() plan.translation_begin_timestamp = int(round(time.time() * 1000)) _is_updated = plan.update(condition=self.template_status_condition) log_util.setLoggerFilter(LOG, self.conf.keyspace, plan.id) LOG.info(_LE("Plan {} is trying to update the status from 'template' to 'translating'," " get {} response from MUSIC").format(plan.id, _is_updated)) if not _is_updated: break if _is_updated and 'SUCCESS' in _is_updated: self.translate(plan) break # TODO(larry): sychronized clock among Conducotr VMs, or use an offset elif plan.status == self.Plan.TRANSLATING and (self.current_time_seconds() - self.millisec_to_sec(plan.updated)) \ > self.conf.messaging_server.timeout: plan.status = self.Plan.TEMPLATE plan.update(condition=self.translating_status_condition) break elif plan.timedout: # TODO(jdandrea): How to tell all involved to stop working? # Not enough to just set status. continue def run(self): """Run""" LOG.debug("{}".format(self.__class__.__name__)) # Look for templates to translate from within a thread executor = futurist.ThreadPoolExecutor() while self.running: fut = executor.submit(self.__check_for_templates) fut.result() executor.shutdown() def terminate(self): """Terminate""" LOG.debug("{}".format(self.__class__.__name__)) self.running = False self._gracefully_stop() super(TranslatorService, self).terminate() def reload(self): """Reload""" LOG.debug("{}".format(self.__class__.__name__)) self._restart()
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2.311106
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#!/bin/python3 enum = {} previousValue = None with open("rosecommon/include/epackettype.h", "r") as f: for i in range(46): f.readline() for line in f: if "};" in line: break name, value = parse(line) if value: enum[name] = value while True: value = input("Enter the hex value of the packet: ") for name, v in enum.items(): if int(value, 16) == v: if "PAKCS" in name: print("Sent by client :", name) else: print("Sent by server :", name)
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2.024648
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''' Copyright (C) 2021 CG Cookie http://cgcookie.com hello@cgcookie.com Created by Jonathan Denning, Jonathan Williamson 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/>. ''' import os import re import sys import glob import inspect import operator import itertools import importlib import bpy from mathutils import Vector, Matrix from .blender import get_preferences from .profiler import profiler from .debug import dprint, debugger from .maths import ( Point, Direction, Normal, Frame, Point2D, Vec2D, Direction2D, Ray, XForm, BBox, Plane ) ################################################## # find functions of object that has key attribute # returns list of (attribute value, fn) ################################################## StructRNA = bpy.types.bpy_struct registered_objects = {} ################################################# ######################################################### ######################################################### ######################################################### ################################################# ################################################# def join(sep, iterable, preSep='', postSep='', toStr=str): ''' this function adds features on to sep.join(iterable) if iterable is not empty, preSep is prepended and postSep is appended also, all items of iterable are turned to strings using toStr, which can be customized ex: join(', ', [1,2,3]) => '1, 2, 3' ex: join('.', ['foo', 'bar'], preSep='.') => '.foo.bar' ''' s = sep.join(map(toStr, iterable)) if not s: return '' return f'{preSep}{s}{postSep}' def get_matrices(ob): ''' obtain blender object matrices ''' mx = ob.matrix_world imx = mx.inverted() return [mx, imx] class Dict(): ''' a fancy dictionary object '''
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3.389646
734
import torch import torch.nn as nn import torch.nn.functional as f
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2.740741
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import json from django import template from django.utils.html import mark_safe register = template.Library() @register.simple_tag
[ 11748, 33918, 198, 198, 6738, 42625, 14208, 1330, 11055, 198, 6738, 42625, 14208, 13, 26791, 13, 6494, 1330, 1317, 62, 21230, 198, 198, 30238, 796, 11055, 13, 23377, 3419, 628, 198, 31, 30238, 13, 36439, 62, 12985, 198 ]
3.552632
38
import factory from django.contrib.auth import get_user_model from organisations.models import Organisation, OrganisationProposal, Person
[ 11748, 8860, 198, 6738, 42625, 14208, 13, 3642, 822, 13, 18439, 1330, 651, 62, 7220, 62, 19849, 198, 6738, 16435, 13, 27530, 1330, 30801, 11, 30801, 24331, 40725, 11, 7755, 628, 628, 628 ]
4.333333
33