xszheng2020/the_stack_dedup_python · Datasets at Hugging Face

c18f09b8f5369b2d98ea558dbc26227c15367da2

131

py

Python

2344.py

heltonricardo/URI

160cca22d94aa667177c9ebf2a1c9864c5e55b41

[ "MIT" ]

6

2021-04-13T00:33:43.000Z

2022-02-10T10:23:59.000Z

2344.py

heltonricardo/URI

160cca22d94aa667177c9ebf2a1c9864c5e55b41

[ "MIT" ]

null

2344.py

heltonricardo/URI

160cca22d94aa667177c9ebf2a1c9864c5e55b41

[ "MIT" ]

3

2021-03-23T18:42:24.000Z

2022-02-10T10:24:07.000Z

n = int(input()) if n == 0: print('E') elif n <= 35: print('D') elif n <= 60: print('C') elif n <= 85: print('B') else: print('A')

18.714286

24

0.519084

26

131

2.615385

0.615385

0.220588

0

0.066038

0.19084

131

6

25

21.833333

0.575472

0

0.038168

0

1

0

false

0

0.833333

1

0

null

1

0

1

0

null

0

1

0

4

c18f1ada21fe45a22c35f613a302bd0bd45fb736

871

py

Python

tests/test_Sk.py

sambit-giri/DMemu

1827832efdab2dfcb735035b6956854e34d17202

[ "MIT" ]

3

2021-08-23T14:50:38.000Z

2022-02-25T11:06:05.000Z

tests/test_Sk.py

sambit-giri/BCMemu

3b2afb1fb3be09f98a7730e7a5ff18baafd8660e

[ "MIT" ]

null

tests/test_Sk.py

sambit-giri/BCMemu

3b2afb1fb3be09f98a7730e7a5ff18baafd8660e

[ "MIT" ]

null

import numpy as np import pickle from BCMemu import * ### Cosmology Ob, Om = 0.0463, 0.2793 bcmdict = {'log10Mc': 13.32, 'mu' : 0.93, 'thej' : 4.235, 'gamma' : 2.25, 'delta' : 6.40, 'eta' : 0.15, 'deta' : 0.14, } k_eval = 10**np.linspace(-1,1.08,50) def test_BCM_7param(): ''' With this test, the 7 parameter baryonic power suppression is tested. ''' bfcemu = BCM_7param(Ob=Ob, Om=Om) p0 = bfcemu.get_boost(0.0, bcmdict, k_eval) p0p5 = bfcemu.get_boost(0.5, bcmdict, k_eval) p1 = bfcemu.get_boost(1.0, bcmdict, k_eval) p1p5 = bfcemu.get_boost(1.5, bcmdict, k_eval) p2 = bfcemu.get_boost(2.0, bcmdict, k_eval) assert np.abs(p0[0]-0.999129)<0.00001 and np.abs(p0p5[0]-0.998741)<0.00001 and np.abs(p1[0]-0.998928)<0.00001 and np.abs(p1p5[0]-0.999030)<0.00001 and np.abs(p2[0]-0.999575)<0.00001

31.107143

182

0.619977

159

871

3.308176

0.446541

0.057034

0.13308

0.08365

0.106464

0

0.201729

0.203215

871

28

182

31.107143

0.556196

0.091848

0

0.038462

0

0.047619

1

0.047619

false

0

0.142857

0

0.190476

0

null

0

1

0

null

0

1

c18ff04ae0b27c497f2250200b6017afaacf3341

271

py

Python

main/forms.py

levertco/stactistics

a62ba5e43b6ffb22f8b22d974aa4942d133bf7d1

[ "MIT" ]

null

main/forms.py

levertco/stactistics

a62ba5e43b6ffb22f8b22d974aa4942d133bf7d1

[ "MIT" ]

3

2021-03-19T03:26:02.000Z

2021-06-10T21:55:35.000Z

main/forms.py

levertco/stactistics

a62ba5e43b6ffb22f8b22d974aa4942d133bf7d1

[ "MIT" ]

null

from django import forms from .models import Review,Website class WebsiteForm(forms.ModelForm): class Meta: model= Website exclude= ['reviews','owner'] class ReviewForm(forms.ModelForm): class Meta: model= Review fields='__all__'

22.583333

36

0.671587

30

271

5.933333

0.6

0.157303

0.213483

0.258427

0.314607

0

0.236162

271

12

37

22.583333

0.859903

0

0.2

0

0.069853

0

1

0

false

0

0.2

0

0.6

0

null

0

1

0

null

0

2

c1911e4814c500c6d717e67d052d56f8aad603a6

1,693

py

Python

controle_financeiro/autenticacao/forms.py

douglaspands/controle-financeiro

1f8f44dca6132b730e92ccf62447dede4119b28e

[ "MIT" ]

null

controle_financeiro/autenticacao/forms.py

douglaspands/controle-financeiro

1f8f44dca6132b730e92ccf62447dede4119b28e

[ "MIT" ]

null

controle_financeiro/autenticacao/forms.py

douglaspands/controle-financeiro

1f8f44dca6132b730e92ccf62447dede4119b28e

[ "MIT" ]

1

2021-06-15T22:14:22.000Z

import re from django import forms from django.utils.translation import gettext_lazy as _ from usuarios.models import Usuario class RegistroForm(forms.Form): REGEX_USERNAME = r'^[a-zA-Z0-9]([._-](?![._-])|[a-zA-Z0-9]){3,18}[a-zA-Z0-9]$' username = forms.CharField( max_length=100, label=_('Usuário'), required=True, ) password1 = forms.CharField( widget=forms.PasswordInput(render_value=False), label=_('Senha'), required=True, ) password2 = forms.CharField( widget=forms.PasswordInput(render_value=False), label=_('Confirme a senha'), required=True, ) first_name = forms.CharField( max_length=100, label=_('Nome'), required=True, ) last_name = forms.CharField( max_length=100, label=_('Sobrenome'), required=True, ) email = forms.EmailField( label=_('Email'), required=True, ) def clean_username(self): if not re.search(self.REGEX_USERNAME, self.cleaned_data['username']): raise forms.ValidationError( _('Favor use somente letras, números e períodos.') ) if Usuario.objects.filter( username__exact=self.cleaned_data['username'] ).exists(): raise forms.ValidationError(_('Já existe um usuário com o mesmo nome.')) else: return self.cleaned_data['username'] def clean(self): if self.cleaned_data.get('password1') != self.cleaned_data.get('password2'): raise forms.ValidationError(_('Os dois campos de senha não coincidem.')) return self.cleaned_data

26.873016

84

0.604843

188

1,693

5.281915

0.452128

0.072508

0.090634

0.018127

0.220544

0.189325

0.118832

0

0.017785

0.269344

1,693

62

85

27.306452

0.784964

0

0.22

0

0.02

0.157708

0.034259

0

1

0.04

false

0.1

0.08

0

0.32

0

null

0

null

0

1

0

1

c191569683e9fab2fbe1691bb0fddbc792e5b431

1,586

py

Python

main-bme280-thread.py

obviateio/micropython-esp32

9b10e1fd96a0ef80ddd6ced79d0ab1a941da4870

[ "MIT" ]

null

main-bme280-thread.py

obviateio/micropython-esp32

9b10e1fd96a0ef80ddd6ced79d0ab1a941da4870

[ "MIT" ]

null

main-bme280-thread.py

obviateio/micropython-esp32

9b10e1fd96a0ef80ddd6ced79d0ab1a941da4870

[ "MIT" ]

null

# Originally from: # https://github.com/loboris/MicroPython_ESP32_psRAM_LoBo/blob/master/MicroPython_BUILD/components/micropython/esp32/modules_examples/bme280.py import machine, _thread, time import micropython, gc import bme280 i2c=machine.I2C(scl=machine.Pin(22),sda=machine.Pin(21),speed=400000) bme=bme280.BME280(i2c=i2c) def bmevalues(): t, p, h = bme.read_compensated_data() p = p // 256 pi = p // 100 pd = p - pi * 100 hi = h // 1024 hd = h * 100 // 1024 - hi * 100 return "[{}] T={}C ".format(time.strftime("%H:%M:%S",time.localtime()), t / 100) + "P={}.{:02d}hPa ".format(pi, pd) + "H={}.{:02d}%".format(hi, hd) def bmerun(interval=10): _thread.allowsuspend(True) sendmsg = True send_time = time.time() + interval while True: while time.time() < send_time: notif = _thread.getnotification() if notif == 10002: _thread.sendmsg(_thread.getReplID(), bmevalues()) elif notif == 10004: sendmsg = False elif notif == 10006: sendmsg = True elif (notif <= 3600) and (notif >= 10): interval = notif send_time = time.time() + interval _thread.sendmsg(_thread.getReplID(), "Interval set to {} seconds".format(interval)) time.sleep_ms(100) send_time = send_time + interval if sendmsg: _thread.sendmsg(_thread.getReplID(), bmevalues()) _thread.stack_size(3*1024) bmeth=_thread.start_new_thread("BME280", bmerun, (10,))

36.883721

153

0.594578

194

1,586

4.721649

0.443299

0.043668

0.062227

0.091703

0.133188

0

0.082051

0.262295

1,586

42

154

37.761905

0.700855

0.099622

0

0.166667

0

0.05614

0

1

0.055556

false

0

0.083333

0

0.166667

0

null

0

null

0

1

0

c191d5b851aa17930934dfcfc06e7f9a47195619

1,644

py

Python

preprocess_package/test_pdf2crops.py

Hupengyu/Paddle_learning

0ac1e2ad32e41ac87bbb19e4535a4bc253ca9b0f

[ "Apache-2.0" ]

1

2021-08-02T01:51:35.000Z

preprocess_package/test_pdf2crops.py

Hupengyu/Paddle_learning

0ac1e2ad32e41ac87bbb19e4535a4bc253ca9b0f

[ "Apache-2.0" ]

1

2021-11-03T08:58:30.000Z

preprocess_package/test_pdf2crops.py

Hupengyu/Paddle_learning

0ac1e2ad32e41ac87bbb19e4535a4bc253ca9b0f

[ "Apache-2.0" ]

null

from preprocess_package.pdf2pages import pdf2pages from preprocess_package.cut_images import cut_images_save import os import cv2 pwd = os.getcwd() image_index = 1 if __name__ == '__main__': imgs_file_path = './pictures/发票扫描图片' failed_imgs_file_path = './pictures/failed_images' single_img_file_path = './pictures/single_image/' pdf_file_path = './pictures/pdf/山东宏瑞达开票4.28.pdf' img_path = './pictures/image/Image_00096.jpg' crops_save_path = './results/crops/' path_now = pdf_file_path # # # ------pdf转images------ if path_now[-3:] == 'pdf': imgs_list = pdf2pages(path_now) for img in imgs_list: # pdf文件夹 cut_images_save(img=img, if_show_pre=False, if_show=False, img_name='', save_path='./results/crops/') else: # 单张图片 for img_name in os.listdir(path_now): img = path_now + "/" + img_name img = cv2.imread(img) cut_images_save(img=img, if_show_pre=False, if_show=False, img_name='', save_path='./results/crops/') # # # -----------单张图片---------- # if type(imgs_list) == numpy.ndarray: # invoices_num = detect_image_counts(imgs_list) # if invoices_num > 1: # cut_images_save(imgs_list, crops_save_path) # -----------多张图片---------- # else: # imgs_list = cv2.imread(imgs_file_path) # path = imgs_file_path # # global if_show_pre # if_show_pre = False # for img_name in os.listdir(crops_save_path): # 图片文件夹 # img = crops_save_path + "/" + img_name # img = cv2.imread(img) # cut_images_save(img, if_show_pre, img_name, crops_save_path)

33.55102

113

0.631995

223

1,644

4.264574

0.255605

0.058885

0.068349

0.063091

0.256572

0.212408

0

0.014073

0.222019

1,644

48

114

34.25

0.729476

0.327859

0

0.086957

0

0.173469

0.102041

0

1

0

false

0

0.173913

0

0.173913

0

null

0

null

0

1

0

c19246725b954f2f43ba88f9e9c66c4d573ae9a3

1,238

py

Python

bot.py

oumike/rachmaninoff-bot

b8a44468a70020342b483f4de8980ca3859c806e

[ "Apache-2.0" ]

null

bot.py

oumike/rachmaninoff-bot

b8a44468a70020342b483f4de8980ca3859c806e

[ "Apache-2.0" ]

null

bot.py

oumike/rachmaninoff-bot

b8a44468a70020342b483f4de8980ca3859c806e

[ "Apache-2.0" ]

null

# bot.py import os from discord.ext import commands from dotenv import load_dotenv from rachmaninoff_bot import RachmaninoffBot from cogs.general_cog import GeneralCog from cogs.weather_cog import WeatherCog from cogs.traffic_cog import TrafficCog from cogs.covid_cog import CovidCog from cogs.stock_cog import StockCog load_dotenv() TOKEN = os.getenv('DISCORD_TOKEN') GUILD = os.getenv('DISCORD_GUILD') MONGODB_CONNECTION = os.getenv('MONGODB_CONNECTION') ALLOWED_USERS = os.getenv('ALLOWED_USERS') OPENWEATHERMAP_APIKEY = os.getenv('OPENWEATHERMAP_APIKEY') bot = RachmaninoffBot(command_prefix='!') bot.add_cog(TrafficCog(bot=bot, mongodb_connection=MONGODB_CONNECTION, allowed_users=ALLOWED_USERS)) bot.add_cog(GeneralCog(bot=bot, allowed_users=ALLOWED_USERS)) bot.add_cog(WeatherCog(bot=bot, allowed_users=ALLOWED_USERS, openweathermap_apikey=OPENWEATHERMAP_APIKEY, mongodb_connection=MONGODB_CONNECTION)) bot.add_cog(CovidCog(bot=bot, allowed_users=ALLOWED_USERS)) bot.add_cog(StockCog(bot=bot, allowed_users=ALLOWED_USERS)) bot.run(TOKEN)

32.578947

80

0.710824

148

1,238

5.689189

0.25

0.171021

0.053444

0.142518

0.206651

0.171021

0.092637

0

0.211632

1,238

37

81

33.459459

0.862705

0.004847

0

0.06428

0.017087

0

1

0

false

0

0.333333

0

0.333333

0

null

0

null

0

1

0

1

c192bf77330b23f5e695e3815dc856ee252d5e96

5,305

py

Python

geeker/mylog/log_config.py

4379711/functools_lyl

61b6cdbf304d3eacbffcbf85a27ecf72d3d275d8

[ "MIT" ]

1

2019-07-23T09:35:35.000Z

geeker/mylog/log_config.py

4379711/functools_lyl

61b6cdbf304d3eacbffcbf85a27ecf72d3d275d8

[ "MIT" ]

null

geeker/mylog/log_config.py

4379711/functools_lyl

61b6cdbf304d3eacbffcbf85a27ecf72d3d275d8

[ "MIT" ]

null

# -*- coding: utf-8 -*- import logging import logging.handlers from logging.handlers import TimedRotatingFileHandler import gzip import os import time from geeker.functions import Singleton class GzTimedRotatingFileHandler(TimedRotatingFileHandler): def __init__(self, filename, when, interval, **kwargs): super(GzTimedRotatingFileHandler, self).__init__(filename, when, interval, **kwargs) @staticmethod def do_gzip(old_log): with open(old_log, 'rb') as old: with gzip.open(old_log.replace('.log', '', 1) + '.gz', 'wb') as comp_log: comp_log.writelines(old) os.remove(old_log) # overwrite def doRollover(self): if self.stream: self.stream.close() self.stream = None current_time = int(time.time()) dst_now = time.localtime(current_time)[-1] t = self.rolloverAt - self.interval if self.utc: time_tuple = time.gmtime(t) else: time_tuple = time.localtime(t) dst_then = time_tuple[-1] if dst_now != dst_then: if dst_now: addend = 3600 else: addend = -3600 time_tuple = time.localtime(t + addend) dfn = self.baseFilename + "." + time.strftime(self.suffix, time_tuple) if os.path.exists(dfn): os.remove(dfn) if os.path.exists(self.baseFilename): os.rename(self.baseFilename, dfn) self.do_gzip(dfn) if self.backupCount > 0: for s in self.getFilesToDelete(): os.remove(s) if not self.delay: self.stream = self._open() new_rollover_at = self.computeRollover(current_time) while new_rollover_at <= current_time: new_rollover_at = new_rollover_at + self.interval if (self.when == 'MIDNIGHT' or self.when.startswith('W')) and not self.utc: ds_att_rollover = time.localtime(new_rollover_at)[-1] if dst_now != ds_att_rollover: if not dst_now: # DST kicks in before next rollover, so we need to deduct an hour addend = -3600 else: # DST bows out before next rollover, so we need to add an hour addend = 3600 new_rollover_at += addend self.rolloverAt = new_rollover_at class LogBase(Singleton): def __init__(self, dir_path='./logs/', logger_name='special_log_name', info_name='info.log', error_name='error.log', warning_name='warning.log', debug_name='debug.log', interval=7, detail=False, debug=False, info=True, error=True, warning=True, ): self.info_name = info_name self.error_name = error_name self.warning_name = warning_name self.debug_name = debug_name self.path = dir_path self.interval = interval self._logger = logging.getLogger(logger_name) self._debug = debug self._warning = warning self._error = error self._info = info self._detail = detail def __handler(self, log_name): handler = GzTimedRotatingFileHandler(self.path + log_name, when='D', interval=self.interval, backupCount=3, encoding='utf-8') return handler def __filter_message(self, handler, log_level): """ 过滤不同等级日志的其他信息,只保留当前日志等级的信息 :param handler: handler :param log_level: 字符串 :return: handler """ if self._detail: formatter = logging.Formatter("%(asctime)s - %(filename)s - %(funcName)s - %(lineno)d - %(message)s", "%Y%m%d %H:%M:%S") else: formatter = logging.Formatter("%(asctime)s - %(message)s", "%Y%m%d %H:%M:%S") _filter = logging.Filter() handler.suffix = "%Y%m%d.log" handler.setFormatter(formatter) handler.setLevel(log_level) _filter.filter = lambda record: record.levelno == log_level handler.addFilter(_filter) return handler def _get_logger(self): # 添加此行，防止日志重复记录 if not self._logger.handlers: # 设置日志等级,默认是 DEBUG self._logger.setLevel(logging.DEBUG) levels = [self._debug, self._info, self._warning, self._error] log_names = [self.debug_name, self.info_name, self.warning_name, self.error_name] levels_ = [10, 20, 30, 40] for i, lev in enumerate(levels): if lev: _handler = self.__handler(log_names[i]) _handler = self.__filter_message(_handler, levels_[i]) # handler添加给日志对象 self._logger.addHandler(_handler) return self._logger

37.359155

114

0.532328

561

5,305

4.818182

0.276292

0.023677

0.033666

0.019238

0.072512

0.031077

0.010359

0

0.009925

0.373233

5,305

141

115

37.624113

0.803008

0.055231

0

0.086957

0

0.008696

0.045672

0

1

0.06087

false

0

0.06087

0

0.165217

0

null

0

null

0

1

0

c1957a877ea69974035f42a8db38ff5234fcd531

13,245

py

Python

src/Ikarus/strategies/StrategyBase.py

bilkosem/Icarus

16d66600f1764a43dccd1b19153b906452cdef5a

[ "Apache-2.0" ]

null

src/Ikarus/strategies/StrategyBase.py

bilkosem/Icarus

16d66600f1764a43dccd1b19153b906452cdef5a

[ "Apache-2.0" ]

2

2022-01-23T20:27:16.000Z

2022-01-30T15:51:35.000Z

src/Ikarus/strategies/StrategyBase.py

bilkosem/Icarus

16d66600f1764a43dccd1b19153b906452cdef5a

[ "Apache-2.0" ]

1

2022-01-23T22:16:07.000Z

import json import logging from binance.helpers import round_step_size from sqlalchemy import false from ..enums import * import bson import abc import itertools from ..objects import EState, EOrderType, ECommand, EnhancedJSONEncoder from ..utils import safe_sum, round_step_downward, truncate, safe_multiply, safe_substract from .. import binance_filters as filters from ..exceptions import NotImplementedException logger = logging.getLogger('app') class StrategyBase(metaclass=abc.ABCMeta): # NOTE: fee can stay here until a better place is found fee = 0 def __init__(self, _name, _config, _symbol_info): self.name = _name self.alloc_ratio = 0 self.logger = logging.getLogger('app.{}'.format(__name__)) self.config = _config['strategy'][self.name] self.max_lto = self.config.get('max_lto',1) # NOTE: Assigning the fee multiple times is not the most optimal solution StrategyBase.fee = _config['broker'].get('fee', 0) # TODO: Rename this config as strategy config etc. because some modules means the whole config dict some are just a portion self.quote_currency = _config['broker']['quote_currency'] # TODO: Make proper handling for symbol_info self.symbol_info = _symbol_info # NOTE: Hardcoded time-scales list (scales should be in ascending order) self.min_period = self.config['time_scales'][0] self.meta_do = list(itertools.product(self.config['time_scales'], self.config['pairs'])) # It seems possible to have this on_STAT_EXIT_EXP() like approach. Surely needs to be tried again. # Since it facilitates so much new strategy creation and modular implementation # NOTE: strategywise_alloc_rate determines the available rate of use from the main capital # If self.strategywise_alloc_rate is 0.25 then this strategy can use max %25 of the main capital self.strategywise_alloc_rate = 0 # Will be filled by the strategy manager # NOTE: pairwise_alloc_rate determines the available rate of use from the strategywise allocated capital # If self.strategywise_alloc_rate is 0.25 then this strategy can use max %25 of the main capital pass @staticmethod def is_lto_dead(trade): if trade.command == ECommand.CANCEL or trade.status == EState.CLOSED: return True # Trade is dead else: return False # Trade is alive # Skip evaluation if non of this is true (LTO will be alive until the next cycle) @staticmethod async def run_logic(self, analysis_dict, trade_list, ikarus_time, total_qc, free_qc): """[summary] Args: analysis_dict ([type]): [description] lto_list ([type]): [description] df_balance ([type]): [description] ikarus_time ([type]): [description] total_qc ([type]): [description] Returns: [type]: [description] """ # Preliminary condition: all of the config['pairs'] exist in analysis_dict if not set(self.config['pairs']).issubset(analysis_dict.keys()): self.logger.warn(f"Configured pair \"{self.config['pairs']}\" does not exist in analysis_dict. Skipping {self.name}.run") return [] # Initialize trade_dict to be filled trade_objects = [] # Handle LTOs separately before the new evaluation # Create a mapping between the pair and lto such as {'BTCUSDT':{...}, ...} pair_grouped_ltos = {} alive_lto_counter = 0 in_trade_capital = 0 dead_lto_capital = 0 for lto_idx in range(len(trade_list)): # If handle_lto_logic fails then it means that the trade_list[lto_idx] is unchanged. if not await StrategyBase.handle_lto_logic(self, analysis_dict, trade_list[lto_idx], ikarus_time): self.logger.warn(f"Function failed: 'handle_lto_logic'. Trade info: '{trade_list[lto_idx]._id}', '{trade_list[lto_idx].strategy}'") pair_grouped_ltos[trade_list[lto_idx].pair] = trade_list[lto_idx] # It is needed to know how many of LTOs are dead or will be dead if not StrategyBase.is_lto_dead(trade_list[lto_idx]): # NOTE: in_trade_capital is only calcualted for LTOs that will last until at least next candle #in_trade_capital += lto_list[lto_idx][PHASE_ENTER][TYPE_LIMIT]['amount'] # NOTE: For the enter_expire, PHASE_ENTER can be directly reflected to balance # market_exit is not considered as dead lto # The result of the OCO orders is unknown in_trade_capital = safe_sum(in_trade_capital, trade_list[lto_idx].enter.amount) alive_lto_counter += 1 # NOTE: TYPE_MARKET PHASE:_EXIT LTOs are considered as alive right here. Not sure if it is a good approach else: # Dead capital dead_lto_capital = safe_sum(dead_lto_capital, trade_list[lto_idx].enter.amount) # NOTE: Only iterate for the configured pairs. Do not run the strategy if any of them is missing in analysis_dict total_lto_slot = min(self.max_lto, len(self.config['pairs'])) empty_lto_slot = total_lto_slot - alive_lto_counter if empty_lto_slot < 1: return [] # TODO Debug this ansync LTO issue buy doing debugging around here # Evaluate pairwise_alloc_share strategy_capital = safe_multiply(total_qc, self.strategywise_alloc_rate) #for lto in lto_list: # in_trade_capital += lto[PHASE_ENTER][TYPE_LIMIT]['amount'] free_strategy_capital = safe_substract(strategy_capital, in_trade_capital) available_capital = min(free_strategy_capital, safe_sum(free_qc, dead_lto_capital)) # TODO: This can be updated to use some kind of precision from the symbol info instead of hardcoded 8 pairwise_alloc_share = truncate(available_capital/empty_lto_slot, 8) #available_lto_capital = min(pairwise_alloc_share, free_qc+dead_lto_capital) # Iterate over pairs and make decisions about them for ao_pair in self.config['pairs']: # Break if there is no empty_lto_slot left if empty_lto_slot < 1: break # Continue if the LTO of the pair is not dead if ao_pair in pair_grouped_ltos.keys(): if not StrategyBase.is_lto_dead(pair_grouped_ltos[ao_pair]): continue # Perform evaluation if trade:= await self.make_decision(analysis_dict, ao_pair, ikarus_time, pairwise_alloc_share): # Apply exchange filters if not StrategyBase.apply_exchange_filters(trade.enter, self.symbol_info[ao_pair]): continue trade_objects.append(trade) empty_lto_slot -= 1 return trade_objects @staticmethod async def handle_lto_logic(self, analysis_dict, trade, ikarus_time): """ This function decides what to do for the LTOs based on their 'status' """ is_success = False if trade.status == EState.ENTER_EXP: if self.config['action_mapping'][EState.ENTER_EXP] == ECommand.CANCEL: is_success = await self.on_cancel(trade) elif trade.status == EState.EXIT_EXP: if self.config['action_mapping'][EState.EXIT_EXP] == ECommand.UPDATE: is_success = await self.on_update(trade, ikarus_time, analysis_dict=analysis_dict) elif self.config['action_mapping'][EState.EXIT_EXP] == ECommand.MARKET_EXIT: # NOTE: Market exit requires the exit prices to be known, thus provide the analysis_dict to that is_success = await StrategyBase.on_market_exit(self, trade, analysis_dict) elif trade.status == EState.WAITING_EXIT: # LTO is entered succesfully, so exit order should be executed # NOTE: expire of the exit_module can be calculated after the trade entered is_success = await self.on_waiting_exit(trade, analysis_dict) else: is_success = True return is_success @abc.abstractclassmethod async def on_update(self): pass @staticmethod async def on_market_exit(self, trade, analysis_dict): # TODO: Create market exit logic raise NotImplementedException() ''' #lto = await StrategyBase._config_market_exit(lto, self.config['exit']['type']) lto['exit'] = await StrategyBase._create_exit_module( TYPE_MARKET, 0, lto['result'][PHASE_ENTER]['quantity'], analysis_dict[lto['pair']][self.min_period]['close'], 0) lto['exit'][TYPE_MARKET] = await StrategyBase.apply_exchange_filters(lto, self.symbol_info[lto['pair']]) trade.exi trade.command = ECommand.MARKET_EXIT self.logger.info(f'LTO: market exit configured') # TODO: Add orderId ''' return trade @abc.abstractclassmethod async def on_waiting_exit(self): pass @abc.abstractclassmethod async def on_closed(self): pass @classmethod def __subclasshook__(cls, subclass): return (hasattr(subclass, 'run') and callable(subclass.run) and hasattr(subclass, 'dump_to') and callable(subclass.dump_to) or NotImplemented) @staticmethod def _eval_future_candle_time(start_time, count, minute): return bson.Int64(start_time + count*minute*60*1000) @staticmethod async def _config_market_exit(lto, type): # TODO: NEXT NEXT Integrate fee to market order # Continue here # TODO: Integrate price to market order, even if it has no use # For now, it works and I am not gonna touch it for a rework lto['action'] = ACTN_MARKET_EXIT lto['exit'][TYPE_MARKET] = { 'amount': lto['exit'][type]['amount'], 'quantity': lto['exit'][type]['quantity'], 'orderId': '', } return lto @staticmethod def apply_exchange_filters(trade_order, symbol_info): # TODO: Make the function orer specific using trade_order instead of trade """ - Call this method prior to any order placement - Apply the filter of exchange pair - This methhod does not check if the current conditiones are good to go. If a filter is not satisfied then it would create an exception. Validation costs time. Maybe in future - Separating enter and exit does not make any sense since the filters are valid for both side. Returns: Order: enter or exit module """ # LOT_SIZE # https://github.com/binance/binance-spot-api-docs/blob/master/rest-api.md#lot_size if result := filters.lot_size(trade_order.quantity, symbol_info): trade_order.quantity = result else: #logger.error(f"Filter failure: LOT_SIZE. {trade.strategy} in phase {phase} with quantity {str(trade.enter.quantity)}") return False # PRICE_FILTER # https://github.com/binance/binance-spot-api-docs/blob/master/rest-api.md#price_filter if type(trade_order).__name__ == EOrderType.MARKET: pass elif type(trade_order).__name__ == EOrderType.LIMIT: trade_order.set_price(round_step_downward(trade_order.price, float(symbol_info['filters'][0]['tickSize']))) # Fixing PRICE_FILTER: tickSize if trade_order.price > float(symbol_info['filters'][0]['maxPrice']): pass # TODO: BUG: NEXT: Add proper error handling or check for the prices elif type(trade_order).__name__ == EOrderType.OCO: trade_order.set_price(round_step_downward(trade_order.price, float(symbol_info['filters'][0]['tickSize']))) # Fixing PRICE_FILTER: tickSize trade_order.stopPrice = round_step_downward(trade_order.stopPrice, float(symbol_info['filters'][0]['tickSize'])) trade_order.stopLimitPrice = round_step_downward(trade_order.stopLimitPrice, float(symbol_info['filters'][0]['tickSize'])) if not filters.min_notional(trade_order.stopPrice, trade_order.quantity, symbol_info): logger.warn(f"Trade object skipped due to MIN_NOTIONAL filter for {symbol_info['symbol']}. NTO: {json.dumps(trade_order, cls=EnhancedJSONEncoder)}") return False # MIN_NOTIONAL # https://github.com/binance/binance-spot-api-docs/blob/master/rest-api.md#min_notional if not filters.min_notional(trade_order.price, trade_order.quantity, symbol_info): logger.warn(f"Trade object skipped due to MIN_NOTIONAL filter for {symbol_info['symbol']}. NTO: {json.dumps(trade_order, cls=EnhancedJSONEncoder)}") return False return True

44.003322

174

0.649

1,697

13,245

4.856217

0.226871

0.026696

0.012134

0.016382

0.243417

0.193544

0.167819

0.130567

0.115277

0

0.004123

0.267573

13,245

300

175

44.15

0.845377

0.302907

0

0.251748

0

0.020979

0.090604

0.025603

0

0.02

0

1

0.034965

false

0.041958

0.083916

0.013986

0.223776

0

null

0

null

0

1

0

1

c198314254036559bc40906b82bd6eec9b2cf13a

4,150

py

Python

ohos/ndk/archive_ndk.py

ShadowCCY/build

5c88ebad21093ef816087c9160bda8e5e9035008

[ "Apache-2.0" ]

null

ohos/ndk/archive_ndk.py

ShadowCCY/build

5c88ebad21093ef816087c9160bda8e5e9035008

[ "Apache-2.0" ]

14

2021-09-07T08:39:43.000Z

2021-09-17T08:50:23.000Z

ohos/ndk/archive_ndk.py

ShadowCCY/build

5c88ebad21093ef816087c9160bda8e5e9035008

[ "Apache-2.0" ]

1

2021-09-07T06:19:48.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright (c) 2021 Huawei Device Co., Ltd. # 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 optparse import os import sys import zipfile sys.path.append(os.path.join(os.path.dirname(__file__), os.pardir, os.pardir)) from scripts.util import build_utils # noqa: E402 def parse_args(args): args = build_utils.expand_file_args(args) parser = optparse.OptionParser() build_utils.add_depfile_option(parser) parser.add_option('--output', help='generated ndk stub file') parser.add_option('--os-irrelevant-dir', help='base directory of ndk common files') parser.add_option('--os-specific-dir', help='base directory of os specific stuff') parser.add_option('--prefix', help='prefix string of directory in archive zipfile') parser.add_option('--notice-file', help='path to notice file') parser.add_option('--record-path', help='path to md5.stamp file') options, _ = parser.parse_args(args) return options def do_archive(output, directory, prefix, compress_fn): files = [] for root, _, filenames in os.walk(directory): for f in filenames: files.extend([os.path.join(root, f)]) with zipfile.ZipFile(output, 'a') as outfile: for f in files: compress = compress_fn(f) if compress_fn else None if prefix: zip_path = os.path.join(prefix, os.path.relpath(f, directory)) else: zip_path = os.path.relpath(f, directory) build_utils.add_to_zip_hermetic(outfile, zip_path, src_path=f, compress=compress) def archive_ndk(output, os_irrelevant_dir, os_specific_dir, prefix, compress_fn, notice): # Create an empty zipfile first, then add stuff to it. with zipfile.ZipFile(output, 'w') as outfile: pass for directory in [os_irrelevant_dir, os_specific_dir]: do_archive(output, directory, prefix, compress_fn) with zipfile.ZipFile(output, 'a') as zip_file: compress = compress_fn(notice) if compress_fn else None if prefix: zip_path = os.path.join(prefix, os.path.basename(notice)) else: zip_path = os.path.basename(notice) build_utils.add_to_zip_hermetic(zip_file, zip_path, src_path=notice, compress=compress) def main(args): options = parse_args(args) os_irrelevant_dir = options.os_irrelevant_dir os_specific_dir = options.os_specific_dir depfile_deps = set( build_utils.get_all_files(os_irrelevant_dir) + build_utils.get_all_files(os_specific_dir)) depfile_deps.add(options.notice_file) build_utils.call_and_write_depfile_if_stale(lambda: archive_ndk( options.output, os_irrelevant_dir, os_specific_dir, options.prefix, lambda _: True, options.notice_file), options, depfile_deps=depfile_deps, input_paths=depfile_deps, output_paths=([options.output]), record_path=options.record_path, force=False, add_pydeps=False) if __name__ == '__main__': sys.exit(main(sys.argv[1:]))

39.150943

78

0.604819

510

4,150

4.709804

0.319608

0.022481

0.043714

0.021649

0.267277

0.201499

0.126561

0.04746

0

0.004871

0.30747

4,150

105

79

39.52381

0.830898

0.161687

0

0.109589

0

0.077101

0

1

0.054795

false

0.013699

0.068493

0

0.136986

0

null

0

null

0

1

0

c1983d2c626217431ca80bafdd345d71048cc22d

587

py

Python

lib/utils/utils.py

PaperCodeSubmission/ICML2020-697

00f7732c236b9c6234e76a47dfebe5de314d5c01

[ "MIT" ]

12

2019-09-26T01:55:25.000Z

2020-01-21T06:53:04.000Z

lib/utils/utils.py

PaperCodeSubmission/ICML2020-697

00f7732c236b9c6234e76a47dfebe5de314d5c01

[ "MIT" ]

2

2021-08-09T03:53:26.000Z

2021-08-18T10:16:25.000Z

lib/utils/utils.py

PaperCodeSubmission/ICML2020-697

00f7732c236b9c6234e76a47dfebe5de314d5c01

[ "MIT" ]

4

2021-06-09T06:02:15.000Z

2021-10-05T13:33:15.000Z

import numpy as np """ some really utils functions """ def get_score_label_array_from_dict(score_dict, label_dict): """ :param score_dict: defaultdict(list) :param label_dict: defaultdict(list) :return: np array with score and label """ assert len(score_dict) == len(label_dict), "The score_dict and label_dict don't match" score = np.ones(len(score_dict)) label = np.ones(len(label_dict)) for idx, (key, score_l) in enumerate(score_dict.items()): label[idx] = max(label_dict[key]) score[idx] = max(score_l) return score, label

27.952381

90

0.678024

88

587

4.306818

0.409091

0.14248

0.073879

0

0.201022

587

21

91

27.952381

0.808102

0.190801

0

0.098321

0

0.111111

1

0.111111

false

0

0.111111

0

0.333333

0

null

0

null

0

1

0

c19b573ce46ab404e883f0fd6d1da1c94abd41bc

512

py

Python

algorithms/Python/implementation/bon appetit.py

Kumbong/hackerrank

36125f3a17c3e0f1fa889495e8ad33b0aa424552

[ "MIT" ]

8

2019-09-19T19:38:09.000Z

2022-02-14T13:59:37.000Z

algorithms/Python/implementation/bon appetit.py

Kumbong/hacker-rank

36125f3a17c3e0f1fa889495e8ad33b0aa424552

[ "MIT" ]

null

algorithms/Python/implementation/bon appetit.py

Kumbong/hacker-rank

36125f3a17c3e0f1fa889495e8ad33b0aa424552

[ "MIT" ]

7

2019-09-23T13:17:27.000Z

2022-01-27T18:02:16.000Z

#!/bin/python3 import math import os import random import re import sys # Complete the bonAppetit function below. def bonAppetit(bill, k, b): sum = 0 for i in bill: sum+=i sum = sum - bill[k] if sum//2 == b: print('Bon Appetit') else: print(b-(sum//2)) if __name__ == '__main__': nk = input().rstrip().split() n = int(nk[0]) k = int(nk[1]) bill = list(map(int, input().rstrip().split())) b = int(input().strip()) bonAppetit(bill, k, b)

16

51

0.556641

76

512

3.644737

0.526316

0.054152

0.108303

0.115523

0

0.016173

0.275391

512

31

52

16.516129

0.730458

0.103516

0

0.041575

0

1

0.047619

false

0

0.238095

0

0.285714

0.095238

0

null

0

null

0

1

0

c19b66b11e8920b6ef16169525e7de5c6d536e86

3,675

py

Python

tests/test_expr_where.py

alexkyllo/kusto-tool

c7689dfa363a1c8d40532cc7929570154fe82307

[ "MIT" ]

null

tests/test_expr_where.py

alexkyllo/kusto-tool

c7689dfa363a1c8d40532cc7929570154fe82307

[ "MIT" ]

null

tests/test_expr_where.py

alexkyllo/kusto-tool

c7689dfa363a1c8d40532cc7929570154fe82307

[ "MIT" ]

null

from kusto_tool import expression as exp from pytest import fixture from .fake_database import FakeDatabase @fixture def db(): return FakeDatabase("test", "testdb") @fixture def tbl(db): return exp.TableExpr("tbl", database=db, columns={"foo": str, "bar": int}) def test_where_eq_str(): actual = str(exp.Where(exp.Infix(exp.OP.EQ, exp.Column("foo", str), "a"))) expected = "| where foo == 'a'" assert actual == expected def test_where_eq_int(): actual = str(exp.Where(exp.Infix(exp.OP.EQ, exp.Column("foo", str), 2))) expected = "| where foo == 2" assert actual == expected def test_where_ne_int(): actual = str(exp.Where(exp.Infix(exp.OP.NE, exp.Column("foo", str), 2))) expected = "| where foo != 2" assert actual == expected def test_where_lt_int(): actual = str(exp.Where(exp.Infix(exp.OP.LT, exp.Column("foo", str), 2))) expected = "| where foo < 2" assert actual == expected def test_where_lte_int(): actual = str(exp.Where(exp.Infix(exp.OP.LE, exp.Column("foo", str), 2))) expected = "| where foo <= 2" assert actual == expected def test_where_gt_int(): actual = str(exp.Where(exp.Infix(exp.OP.GT, exp.Column("foo", str), 2))) expected = "| where foo > 2" assert actual == expected def test_where_gte_int(): actual = str(exp.Where(exp.Infix(exp.OP.GE, exp.Column("foo", str), 2))) expected = "| where foo >= 2" assert actual == expected def test_table_where_eq(tbl): q = str(tbl.where(tbl.bar == "foo")) ex = "cluster('test').database('testdb').['tbl']\n| where bar == 'foo'\n" assert q == ex def test_table_where_ne(tbl): q = str(tbl.where(tbl.bar != "foo")) ex = "cluster('test').database('testdb').['tbl']\n| where bar != 'foo'\n" assert q == ex def test_table_where_lt(tbl): q = str(tbl.where(tbl.bar < "foo")) ex = "cluster('test').database('testdb').['tbl']\n| where bar < 'foo'\n" assert q == ex def test_table_where_le(tbl): q = str(tbl.where(tbl.bar <= "foo")) ex = "cluster('test').database('testdb').['tbl']\n| where bar <= 'foo'\n" assert q == ex def test_table_where_gt(tbl): q = str(tbl.where(tbl.bar > "foo")) ex = "cluster('test').database('testdb').['tbl']\n| where bar > 'foo'\n" assert q == ex def test_table_where_ge(tbl): q = str(tbl.where(tbl.bar >= "foo")) ex = "cluster('test').database('testdb').['tbl']\n| where bar >= 'foo'\n" assert q == ex def test_where_repr(): where = exp.Where(exp.Infix(exp.OP.EQ, exp.Column("foo", str), 2)) assert repr(where) == "Where(Column(\"foo\", <class 'str'>) == 2)" def test_where_and(): foo = exp.Column("foo", str) bar = exp.Column("bar", str) where = exp.Where((foo == "a") & (bar == "b")) assert str(where) == "| where (foo == 'a') and (bar == 'b')" def test_where_or(): foo = exp.Column("foo", str) bar = exp.Column("bar", str) where = exp.Where((foo == "a") | (bar == "b")) assert str(where) == "| where (foo == 'a') or (bar == 'b')" def test_not(): foo = exp.Column("foo", bool) where = exp.Where(~(foo == "a")) assert str(where) == "| where not(foo == 'a')" def test_where_cols(): foo = exp.Column("foo", str) bar = exp.Column("bar", str) where = exp.Where(foo == bar) assert str(where) == "| where foo == bar" def test_where_isin(): foo = exp.Column("foo", str) where = exp.Where(foo.isin("bar", "baz")) assert str(where) == "| where foo in ('bar', 'baz')" def test_where_isin_int(): foo = exp.Column("foo", str) where = exp.Where(foo.isin(1, 2, 3)) assert str(where) == "| where foo in (1, 2, 3)"

26.824818

78

0.593741

564

3,675

3.771277

0.10461

0.06582

0.078984

0.091678

0.764457

0.746121

0.70851

0.629525

0

0.006796

0.199184

3,675

136

79

27.022059

0.715936

0

0.252747

0

0.220136

0.073469

0

0.21978

1

0.241758

false

0

0.032967

0.021978

0.296703

0

null

0

1

0

null

0

1

0

6

c19ba5d245f195cdb6eb4605b71e37758440a2a2

4,991

py

Python

twitchtv/datadog_checks/twitchtv/twitchtv.py

johananlai/integrations-extras

e3cf6f17f9f2ebf4f997b67aa5b757be90d31ec4

[ "BSD-3-Clause" ]

null

twitchtv/datadog_checks/twitchtv/twitchtv.py

johananlai/integrations-extras

e3cf6f17f9f2ebf4f997b67aa5b757be90d31ec4

[ "BSD-3-Clause" ]

null

twitchtv/datadog_checks/twitchtv/twitchtv.py

johananlai/integrations-extras

e3cf6f17f9f2ebf4f997b67aa5b757be90d31ec4

[ "BSD-3-Clause" ]

null

import requests import simplejson as json from six.moves.urllib.parse import urljoin from datadog_checks.checks import AgentCheck class TwitchtvCheck(AgentCheck): CHECK_NAME = 'twitchtv' def __init__(self, name, init_config, agentConfig, instances=None): super(TwitchtvCheck, self).__init__(name, init_config, agentConfig, instances) def check(self, instance): # parse config fields self._validate_instance(instance) api_url = instance['api_url'] client_id = instance['client_id'] channels = instance.get("channels", []) # get channel metrics from API payload = {} tags = {} try: payload = self._get_channel_data(instance, api_url, client_id, channels) tags = self._get_game_tags(instance, api_url, client_id, payload) except Exception, e: self.log.error("Failed to get metrics with error: {}".format(e)) # send to DD try: self._report_channel_metrics(instance, payload, tags) except Exception, e: self.log.error("Failed to report channel metrics with error: {}".format(e)) # get follower metrics from API users_payload = {} follows = {} try: users_payload = self._get_user_data(instance, api_url, client_id, channels) follows = self._get_all_follows(instance, api_url, client_id, users_payload) except Exception, e: self.log.error("Failed to get user follows with error: {}".format(e)) # send to DD try: self._report_follows_metrics(instance, follows) except Exception, e: self.log.error("Failed to report follows metrics with error: {}".format(e)) def _validate_instance(self, instance): if any([x for x in ['api_url', 'client_id', 'channels'] if x not in instance]): raise Exception("Missing 'api_url', 'client_id', or 'channels' in config") def _report_channel_metrics(self, instance, payload, tags): metric_name = 'twitchtv.live.viewers' for ch in payload['data']: self.gauge(metric_name, ch['viewer_count'], tags=instance.get('tags', []) + ['channel:' + ch['user_name']] + ['language:' + ch['language']] + ['game:' + tags[ch['user_name']]]) def _report_follows_metrics(self, instance, follows): metric_name = 'twitchtv.followers' for ch, total in follows.items(): self.gauge(metric_name, total, tags=instance.get('tags', []) + ['channel:' + ch]) def _get_channel_data(self, instance, api_url, client_id, channels): path = "streams" headers = {'Client-ID': client_id} params = [('user_login', ch) for ch in channels] r = requests.get(urljoin(api_url, path), headers=headers, params=params, timeout=60) r.raise_for_status() return json.loads(r.text) def _get_game_data(self, instance, api_url, client_id, game_id): path = "games" headers = {'Client-ID': client_id} params = {'id': game_id} r = requests.get(urljoin(api_url, path), headers=headers, params=params, timeout=60) r.raise_for_status() return json.loads(r.text) def _get_game_tags(self, instance, api_url, client_id, payload): tags = {} for ch in payload['data']: try: game_payload = self._get_game_data(instance, api_url, client_id, ch['game_id']) tags[ch['user_name']] = game_payload['data'][0]['name'] except Exception, e: self.log.error("Failed to get game name with error: {}".format(e)) return tags def _get_user_data(self, instance, api_url, client_id, channels): path = "users" headers = {'Client-ID': client_id} params = [('login', ch) for ch in channels] r = requests.get(urljoin(api_url, path), headers=headers, params=params, timeout=60) r.raise_for_status() return json.loads(r.text) def _get_follow_data(self, instance, api_url, client_id, user_id): path = "users/follows" headers = {'Client-ID': client_id} params = {'to_id': user_id} r = requests.get(urljoin(api_url, path), headers=headers, params=params, timeout=60) r.raise_for_status() return json.loads(r.text) def _get_all_follows(self, instance, api_url, client_id, payload): follows = {} for ch in payload['data']: try: follow_payload = self._get_follow_data(instance, api_url, client_id, ch['id']) follows[ch['login']] = follow_payload['total'] except Exception, e: self.log.error("Failed to get user follows with error: {}".format(e)) return follows

36.97037

95

0.598077

611

4,991

4.666121

0.155483

0.067345

0.063136

0.073658

0.576991

0.495265

0.412487

0.324798

0.252192

0

0.002518

0.283911

4,991

134

96

37.246269

0.795187

0.020036

0

0.397959

0

0.125307

0.0043

0

null

0

0.040816

null

0

null

0

null

0

1

0

1

c19c3f1d1283d225db3d7d7ddef5d1c3d3346d48

393

py

Python

plugins/amazon/__init__.py

nim4/pantea

29bee731157b1a643bcfeed37133c7575bc9340f

[ "MIT" ]

3

2016-09-26T06:47:57.000Z

2017-10-03T17:05:16.000Z

plugins/amazon/__init__.py

nim4/pantea

29bee731157b1a643bcfeed37133c7575bc9340f

[ "MIT" ]

null

plugins/amazon/__init__.py

nim4/pantea

29bee731157b1a643bcfeed37133c7575bc9340f

[ "MIT" ]

null

from lib.util import get_info_by_url, insert_to_db __host = ".amazon." __sess = "x-main=" def parse(headers): name = get_info_by_url('http://' + headers["Host"] + '/gp/history/', headers, [("id='nav-signin-text' class='nav-button-em'>", "<")])[0] if name is None: return False insert_to_db("Amazon", headers, name, "http://" + headers["Host"] + "/") return True

28.071429

76

0.613232

56

393

4.053571

0.660714

0.061674

0.079295

0.105727

0

0.003106

0.180662

393

13

77

30.230769

0.701863

0

0.254453

0.05598

0

1

0.1

false

0

0.1

0

0.4

0

null

0

null

0

1

0

c19e5fe5435a4cf4b75d4a2f8a46480d0ba1db04

2,842

py

Python

tests/test_config.py

drdavella/tox-conda

8cb9d2f4fed1f7b3e851a2460bbd7756fad7d19c

[ "MIT" ]

2

2018-12-05T18:37:46.000Z

2018-12-29T02:41:23.000Z

tests/test_config.py

drdavella/tox-conda

8cb9d2f4fed1f7b3e851a2460bbd7756fad7d19c

[ "MIT" ]

7

2018-11-03T14:55:23.000Z

2019-03-27T20:26:07.000Z

tests/test_config.py

drdavella/tox-conda

8cb9d2f4fed1f7b3e851a2460bbd7756fad7d19c

[ "MIT" ]

1

2018-12-28T16:00:19.000Z

import pytest def test_conda_deps(tmpdir, newconfig): config = newconfig( [], """ [tox] toxworkdir = {} [testenv:py1] deps= hello conda_deps= world something """.format( tmpdir ), ) assert len(config.envconfigs) == 1 assert hasattr(config.envconfigs['py1'], 'deps') assert hasattr(config.envconfigs['py1'], 'conda_deps') assert len(config.envconfigs['py1'].conda_deps) == 2 # For now, as a workaround, we temporarily add all conda dependencies to # deps as well. This allows tox to know whether an environment needs to be # updated or not. Eventually there may be a cleaner solution. assert len(config.envconfigs['py1'].deps) == 3 assert 'world' == config.envconfigs['py1'].conda_deps[0].name assert 'something' == config.envconfigs['py1'].conda_deps[1].name def test_no_conda_deps(tmpdir, newconfig): config = newconfig( [], """ [tox] toxworkdir = {} [testenv:py1] deps= hello """.format( tmpdir ), ) assert len(config.envconfigs) == 1 assert hasattr(config.envconfigs['py1'], 'deps') assert hasattr(config.envconfigs['py1'], 'conda_deps') assert hasattr(config.envconfigs['py1'], 'conda_channels') assert len(config.envconfigs['py1'].conda_deps) == 0 assert len(config.envconfigs['py1'].conda_channels) == 0 assert len(config.envconfigs['py1'].deps) == 1 def test_conda_channels(tmpdir, newconfig): config = newconfig( [], """ [tox] toxworkdir = {} [testenv:py1] deps= hello conda_deps= something else conda_channels= conda-forge """.format( tmpdir ), ) assert len(config.envconfigs) == 1 assert hasattr(config.envconfigs['py1'], 'deps') assert hasattr(config.envconfigs['py1'], 'conda_deps') assert hasattr(config.envconfigs['py1'], 'conda_channels') assert len(config.envconfigs['py1'].conda_channels) == 1 assert 'conda-forge' in config.envconfigs['py1'].conda_channels def test_conda_force_deps(tmpdir, newconfig): config = newconfig( ['--force-dep=something<42.1'], """ [tox] toxworkdir = {} [testenv:py1] deps= hello conda_deps= something else conda_channels= conda-forge """.format( tmpdir ), ) assert len(config.envconfigs) == 1 assert hasattr(config.envconfigs['py1'], 'conda_deps') assert len(config.envconfigs['py1'].conda_deps) == 2 assert 'something<42.1' == config.envconfigs['py1'].conda_deps[0].name

26.811321

78

0.574243

301

2,842

5.322259

0.20598

0.2397

0.237204

0.224719

0.792759

0.734082

0.692884

0.618602

0

0.021989

0.295918

2,842

105

79

27.066667

0.778611

0.071429

0

0.641509

0

0.105182

0.01334

0

0.45283

1

0.075472

false

0

0.018868

0

0.09434

0

null

1

0

1

0

1

0

null

0

1

0

6

c19ec7fa6563be347aaab52b5eafbb49e3825d49

20,933

py

Python

site/_build/jupyter_execute/notebooks/book/19_training_and_deploying_at_scale.py

rpi-techfundamentals/spring2020_website

b4b208ce7555f5574054ff5ff5d79b9e0e825499

[ "MIT" ]

1

2021-07-01T13:00:30.000Z

site/_build/jupyter_execute/notebooks/book/19_training_and_deploying_at_scale.py

rpi-techfundamentals/spring2020_website

b4b208ce7555f5574054ff5ff5d79b9e0e825499

[ "MIT" ]

2

2020-12-31T14:33:02.000Z

2020-12-31T14:38:26.000Z

site/_build/jupyter_execute/notebooks/book/19_training_and_deploying_at_scale.py

rpi-techfundamentals/spring2020_website

b4b208ce7555f5574054ff5ff5d79b9e0e825499

[ "MIT" ]

3

2021-01-05T20:26:15.000Z

2021-02-15T14:54:44.000Z

**Chapter 19 – Training and Deploying TensorFlow Models at Scale** _This notebook contains all the sample code in chapter 19._ <table align="left"> <td> <a target="_blank" href="https://colab.research.google.com/github/ageron/handson-ml2/blob/master/19_training_and_deploying_at_scale.ipynb"><img src="https://www.tensorflow.org/images/colab_logo_32px.png" />Run in Google Colab</a> </td> </table> # Setup First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20 and TensorFlow ≥2.0. # Python ≥3.5 is required import sys assert sys.version_info >= (3, 5) # Scikit-Learn ≥0.20 is required import sklearn assert sklearn.__version__ >= "0.20" try: # %tensorflow_version only exists in Colab. %tensorflow_version 2.x !echo "deb http://storage.googleapis.com/tensorflow-serving-apt stable tensorflow-model-server tensorflow-model-server-universal" > /etc/apt/sources.list.d/tensorflow-serving.list !curl https://storage.googleapis.com/tensorflow-serving-apt/tensorflow-serving.release.pub.gpg | apt-key add - !apt update && apt-get install -y tensorflow-model-server !pip install -q -U tensorflow-serving-api IS_COLAB = True except Exception: IS_COLAB = False # TensorFlow ≥2.0 is required import tensorflow as tf from tensorflow import keras assert tf.__version__ >= "2.0" if not tf.config.list_physical_devices('GPU'): print("No GPU was detected. CNNs can be very slow without a GPU.") if IS_COLAB: print("Go to Runtime > Change runtime and select a GPU hardware accelerator.") # Common imports import numpy as np import os # to make this notebook's output stable across runs np.random.seed(42) tf.random.set_seed(42) # To plot pretty figures %matplotlib inline import matplotlib as mpl import matplotlib.pyplot as plt mpl.rc('axes', labelsize=14) mpl.rc('xtick', labelsize=12) mpl.rc('ytick', labelsize=12) # Where to save the figures PROJECT_ROOT_DIR = "." CHAPTER_ID = "deploy" IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, "images", CHAPTER_ID) os.makedirs(IMAGES_PATH, exist_ok=True) def save_fig(fig_id, tight_layout=True, fig_extension="png", resolution=300): path = os.path.join(IMAGES_PATH, fig_id + "." + fig_extension) print("Saving figure", fig_id) if tight_layout: plt.tight_layout() plt.savefig(path, format=fig_extension, dpi=resolution) # Deploying TensorFlow models to TensorFlow Serving (TFS) We will use the REST API or the gRPC API. ## Save/Load a `SavedModel` (X_train_full, y_train_full), (X_test, y_test) = keras.datasets.mnist.load_data() X_train_full = X_train_full[..., np.newaxis].astype(np.float32) / 255. X_test = X_test[..., np.newaxis].astype(np.float32) / 255. X_valid, X_train = X_train_full[:5000], X_train_full[5000:] y_valid, y_train = y_train_full[:5000], y_train_full[5000:] X_new = X_test[:3] np.random.seed(42) tf.random.set_seed(42) model = keras.models.Sequential([ keras.layers.Flatten(input_shape=[28, 28, 1]), keras.layers.Dense(100, activation="relu"), keras.layers.Dense(10, activation="softmax") ]) model.compile(loss="sparse_categorical_crossentropy", optimizer=keras.optimizers.SGD(lr=1e-2), metrics=["accuracy"]) model.fit(X_train, y_train, epochs=10, validation_data=(X_valid, y_valid)) np.round(model.predict(X_new), 2) model_version = "0001" model_name = "my_mnist_model" model_path = os.path.join(model_name, model_version) model_path !rm -rf {model_name} tf.saved_model.save(model, model_path) for root, dirs, files in os.walk(model_name): indent = ' ' * root.count(os.sep) print('{}{}/'.format(indent, os.path.basename(root))) for filename in files: print('{}{}'.format(indent + ' ', filename)) !saved_model_cli show --dir {model_path} !saved_model_cli show --dir {model_path} --tag_set serve !saved_model_cli show --dir {model_path} --tag_set serve \ --signature_def serving_default !saved_model_cli show --dir {model_path} --all Let's write the new instances to a `npy` file so we can pass them easily to our model: np.save("my_mnist_tests.npy", X_new) input_name = model.input_names[0] input_name And now let's use `saved_model_cli` to make predictions for the instances we just saved: !saved_model_cli run --dir {model_path} --tag_set serve \ --signature_def serving_default \ --inputs {input_name}=my_mnist_tests.npy np.round([[1.1739199e-04, 1.1239604e-07, 6.0210604e-04, 2.0804715e-03, 2.5779348e-06, 6.4079795e-05, 2.7411186e-08, 9.9669880e-01, 3.9654213e-05, 3.9471846e-04], [1.2294615e-03, 2.9207937e-05, 9.8599273e-01, 9.6755642e-03, 8.8930705e-08, 2.9156188e-04, 1.5831805e-03, 1.1311053e-09, 1.1980456e-03, 1.1113169e-07], [6.4066830e-05, 9.6359509e-01, 9.0598064e-03, 2.9872139e-03, 5.9552520e-04, 3.7478798e-03, 2.5074568e-03, 1.1462728e-02, 5.5553433e-03, 4.2495009e-04]], 2) ## TensorFlow Serving Install [Docker](https://docs.docker.com/install/) if you don't have it already. Then run: ```bash docker pull tensorflow/serving export ML_PATH=$HOME/ml # or wherever this project is docker run -it --rm -p 8500:8500 -p 8501:8501 \ -v "$ML_PATH/my_mnist_model:/models/my_mnist_model" \ -e MODEL_NAME=my_mnist_model \ tensorflow/serving ``` Once you are finished using it, press Ctrl-C to shut down the server. Alternatively, if `tensorflow_model_server` is installed (e.g., if you are running this notebook in Colab), then the following 3 cells will start the server: os.environ["MODEL_DIR"] = os.path.split(os.path.abspath(model_path))[0] %%bash --bg nohup tensorflow_model_server \ --rest_api_port=8501 \ --model_name=my_mnist_model \ --model_base_path="${MODEL_DIR}" >server.log 2>&1 !tail server.log import json input_data_json = json.dumps({ "signature_name": "serving_default", "instances": X_new.tolist(), }) repr(input_data_json)[:1500] + "..." Now let's use TensorFlow Serving's REST API to make predictions: import requests SERVER_URL = 'http://localhost:8501/v1/models/my_mnist_model:predict' response = requests.post(SERVER_URL, data=input_data_json) response.raise_for_status() # raise an exception in case of error response = response.json() response.keys() y_proba = np.array(response["predictions"]) y_proba.round(2) ### Using the gRPC API from tensorflow_serving.apis.predict_pb2 import PredictRequest request = PredictRequest() request.model_spec.name = model_name request.model_spec.signature_name = "serving_default" input_name = model.input_names[0] request.inputs[input_name].CopyFrom(tf.make_tensor_proto(X_new)) import grpc from tensorflow_serving.apis import prediction_service_pb2_grpc channel = grpc.insecure_channel('localhost:8500') predict_service = prediction_service_pb2_grpc.PredictionServiceStub(channel) response = predict_service.Predict(request, timeout=10.0) response Convert the response to a tensor: output_name = model.output_names[0] outputs_proto = response.outputs[output_name] y_proba = tf.make_ndarray(outputs_proto) y_proba.round(2) Or to a NumPy array if your client does not include the TensorFlow library: output_name = model.output_names[0] outputs_proto = response.outputs[output_name] shape = [dim.size for dim in outputs_proto.tensor_shape.dim] y_proba = np.array(outputs_proto.float_val).reshape(shape) y_proba.round(2) ## Deploying a new model version np.random.seed(42) tf.random.set_seed(42) model = keras.models.Sequential([ keras.layers.Flatten(input_shape=[28, 28, 1]), keras.layers.Dense(50, activation="relu"), keras.layers.Dense(50, activation="relu"), keras.layers.Dense(10, activation="softmax") ]) model.compile(loss="sparse_categorical_crossentropy", optimizer=keras.optimizers.SGD(lr=1e-2), metrics=["accuracy"]) history = model.fit(X_train, y_train, epochs=10, validation_data=(X_valid, y_valid)) model_version = "0002" model_name = "my_mnist_model" model_path = os.path.join(model_name, model_version) model_path tf.saved_model.save(model, model_path) for root, dirs, files in os.walk(model_name): indent = ' ' * root.count(os.sep) print('{}{}/'.format(indent, os.path.basename(root))) for filename in files: print('{}{}'.format(indent + ' ', filename)) **Warning**: You may need to wait a minute before the new model is loaded by TensorFlow Serving. import requests SERVER_URL = 'http://localhost:8501/v1/models/my_mnist_model:predict' response = requests.post(SERVER_URL, data=input_data_json) response.raise_for_status() response = response.json() response.keys() y_proba = np.array(response["predictions"]) y_proba.round(2) # Deploy the model to Google Cloud AI Platform Follow the instructions in the book to deploy the model to Google Cloud AI Platform, download the service account's private key and save it to the `my_service_account_private_key.json` in the project directory. Also, update the `project_id`: project_id = "onyx-smoke-242003" import googleapiclient.discovery os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "my_service_account_private_key.json" model_id = "my_mnist_model" model_path = "projects/{}/models/{}".format(project_id, model_id) model_path += "/versions/v0001/" # if you want to run a specific version ml_resource = googleapiclient.discovery.build("ml", "v1").projects() def predict(X): input_data_json = {"signature_name": "serving_default", "instances": X.tolist()} request = ml_resource.predict(name=model_path, body=input_data_json) response = request.execute() if "error" in response: raise RuntimeError(response["error"]) return np.array([pred[output_name] for pred in response["predictions"]]) Y_probas = predict(X_new) np.round(Y_probas, 2) # Using GPUs tf.test.is_gpu_available() tf.test.gpu_device_name() tf.test.is_built_with_cuda() from tensorflow.python.client.device_lib import list_local_devices devices = list_local_devices() devices # Distributed Training keras.backend.clear_session() tf.random.set_seed(42) np.random.seed(42) def create_model(): return keras.models.Sequential([ keras.layers.Conv2D(filters=64, kernel_size=7, activation="relu", padding="same", input_shape=[28, 28, 1]), keras.layers.MaxPooling2D(pool_size=2), keras.layers.Conv2D(filters=128, kernel_size=3, activation="relu", padding="same"), keras.layers.Conv2D(filters=128, kernel_size=3, activation="relu", padding="same"), keras.layers.MaxPooling2D(pool_size=2), keras.layers.Flatten(), keras.layers.Dense(units=64, activation='relu'), keras.layers.Dropout(0.5), keras.layers.Dense(units=10, activation='softmax'), ]) batch_size = 100 model = create_model() model.compile(loss="sparse_categorical_crossentropy", optimizer=keras.optimizers.SGD(lr=1e-2), metrics=["accuracy"]) model.fit(X_train, y_train, epochs=10, validation_data=(X_valid, y_valid), batch_size=batch_size) keras.backend.clear_session() tf.random.set_seed(42) np.random.seed(42) distribution = tf.distribute.MirroredStrategy() # Change the default all-reduce algorithm: #distribution = tf.distribute.MirroredStrategy( # cross_device_ops=tf.distribute.HierarchicalCopyAllReduce()) # Specify the list of GPUs to use: #distribution = tf.distribute.MirroredStrategy(devices=["/gpu:0", "/gpu:1"]) # Use the central storage strategy instead: #distribution = tf.distribute.experimental.CentralStorageStrategy() #resolver = tf.distribute.cluster_resolver.TPUClusterResolver() #tf.tpu.experimental.initialize_tpu_system(resolver) #distribution = tf.distribute.experimental.TPUStrategy(resolver) with distribution.scope(): model = create_model() model.compile(loss="sparse_categorical_crossentropy", optimizer=keras.optimizers.SGD(lr=1e-2), metrics=["accuracy"]) batch_size = 100 # must be divisible by the number of workers model.fit(X_train, y_train, epochs=10, validation_data=(X_valid, y_valid), batch_size=batch_size) model.predict(X_new) Custom training loop: keras.backend.clear_session() tf.random.set_seed(42) np.random.seed(42) K = keras.backend distribution = tf.distribute.MirroredStrategy() with distribution.scope(): model = create_model() optimizer = keras.optimizers.SGD() with distribution.scope(): dataset = tf.data.Dataset.from_tensor_slices((X_train, y_train)).repeat().batch(batch_size) input_iterator = distribution.make_dataset_iterator(dataset) @tf.function def train_step(): def step_fn(inputs): X, y = inputs with tf.GradientTape() as tape: Y_proba = model(X) loss = K.sum(keras.losses.sparse_categorical_crossentropy(y, Y_proba)) / batch_size grads = tape.gradient(loss, model.trainable_variables) optimizer.apply_gradients(zip(grads, model.trainable_variables)) return loss per_replica_losses = distribution.experimental_run(step_fn, input_iterator) mean_loss = distribution.reduce(tf.distribute.ReduceOp.SUM, per_replica_losses, axis=None) return mean_loss n_epochs = 10 with distribution.scope(): input_iterator.initialize() for epoch in range(n_epochs): print("Epoch {}/{}".format(epoch + 1, n_epochs)) for iteration in range(len(X_train) // batch_size): print("\rLoss: {:.3f}".format(train_step().numpy()), end="") print() batch_size = 100 # must be divisible by the number of workers model.fit(X_train, y_train, epochs=10, validation_data=(X_valid, y_valid), batch_size=batch_size) ## Training across multiple servers A TensorFlow cluster is a group of TensorFlow processes running in parallel, usually on different machines, and talking to each other to complete some work, for example training or executing a neural network. Each TF process in the cluster is called a "task" (or a "TF server"). It has an IP address, a port, and a type (also called its role or its job). The type can be `"worker"`, `"chief"`, `"ps"` (parameter server) or `"evaluator"`: * Each **worker** performs computations, usually on a machine with one or more GPUs. * The **chief** performs computations as well, but it also handles extra work such as writing TensorBoard logs or saving checkpoints. There is a single chief in a cluster. If no chief is specified, then the first worker is the chief. * A **parameter server** (ps) only keeps track of variable values, it is usually on a CPU-only machine. * The **evaluator** obviously takes care of evaluation. There is usually a single evaluator in a cluster. The set of tasks that share the same type is often called a "job". For example, the "worker" job is the set of all workers. To start a TensorFlow cluster, you must first specify it. This means defining all the tasks (IP address, TCP port, and type). For example, the following cluster specification defines a cluster with 3 tasks (2 workers and 1 parameter server). It's a dictionary with one key per job, and the values are lists of task addresses: ``` { "worker": ["my-worker0.example.com:9876", "my-worker1.example.com:9876"], "ps": ["my-ps0.example.com:9876"] } ``` Every task in the cluster may communicate with every other task in the server, so make sure to configure your firewall to authorize all communications between these machines on these ports (it's usually simpler if you use the same port on every machine). When a task is started, it needs to be told which one it is: its type and index (the task index is also called the task id). A common way to specify everything at once (both the cluster spec and the current task's type and id) is to set the `TF_CONFIG` environment variable before starting the program. It must be a JSON-encoded dictionary containing a cluster specification (under the `"cluster"` key), and the type and index of the task to start (under the `"task"` key). For example, the following `TF_CONFIG` environment variable defines a simple cluster with 2 workers and 1 parameter server, and specifies that the task to start is the first worker: import os import json os.environ["TF_CONFIG"] = json.dumps({ "cluster": { "worker": ["my-work0.example.com:9876", "my-work1.example.com:9876"], "ps": ["my-ps0.example.com:9876"] }, "task": {"type": "worker", "index": 0} }) print("TF_CONFIG='{}'".format(os.environ["TF_CONFIG"])) Some platforms (e.g., Google Cloud ML Engine) automatically set this environment variable for you. Then you would write a short Python script to start a task. The same script can be used on every machine, since it will load the `TF_CONFIG` variable, which will tell it which task to start: import tensorflow as tf resolver = tf.distribute.cluster_resolver.TFConfigClusterResolver() worker0 = tf.distribute.Server(resolver.cluster_spec(), job_name=resolver.task_type, task_index=resolver.task_id) Another way to specify the cluster specification is directly in Python, rather than through an environment variable: cluster_spec = tf.train.ClusterSpec({ "worker": ["127.0.0.1:9901", "127.0.0.1:9902"], "ps": ["127.0.0.1:9903"] }) You can then start a server simply by passing it the cluster spec and indicating its type and index. Let's start the two remaining tasks (remember that in general you would only start a single task per machine; we are starting 3 tasks on the localhost just for the purpose of this code example): #worker1 = tf.distribute.Server(cluster_spec, job_name="worker", task_index=1) ps0 = tf.distribute.Server(cluster_spec, job_name="ps", task_index=0) os.environ["TF_CONFIG"] = json.dumps({ "cluster": { "worker": ["127.0.0.1:9901", "127.0.0.1:9902"], "ps": ["127.0.0.1:9903"] }, "task": {"type": "worker", "index": 1} }) print(repr(os.environ["TF_CONFIG"])) distribution = tf.distribute.experimental.MultiWorkerMirroredStrategy() keras.backend.clear_session() tf.random.set_seed(42) np.random.seed(42) os.environ["TF_CONFIG"] = json.dumps({ "cluster": { "worker": ["127.0.0.1:9901", "127.0.0.1:9902"], "ps": ["127.0.0.1:9903"] }, "task": {"type": "worker", "index": 1} }) #CUDA_VISIBLE_DEVICES=0 with distribution.scope(): model = create_model() model.compile(loss="sparse_categorical_crossentropy", optimizer=keras.optimizers.SGD(lr=1e-2), metrics=["accuracy"]) import tensorflow as tf from tensorflow import keras import numpy as np # At the beginning of the program (restart the kernel before running this cell) distribution = tf.distribute.experimental.MultiWorkerMirroredStrategy() (X_train_full, y_train_full), (X_test, y_test) = keras.datasets.mnist.load_data() X_train_full = X_train_full[..., np.newaxis] / 255. X_test = X_test[..., np.newaxis] / 255. X_valid, X_train = X_train_full[:5000], X_train_full[5000:] y_valid, y_train = y_train_full[:5000], y_train_full[5000:] X_new = X_test[:3] n_workers = 2 batch_size = 32 * n_workers dataset = tf.data.Dataset.from_tensor_slices((X_train[..., np.newaxis], y_train)).repeat().batch(batch_size) def create_model(): return keras.models.Sequential([ keras.layers.Conv2D(filters=64, kernel_size=7, activation="relu", padding="same", input_shape=[28, 28, 1]), keras.layers.MaxPooling2D(pool_size=2), keras.layers.Conv2D(filters=128, kernel_size=3, activation="relu", padding="same"), keras.layers.Conv2D(filters=128, kernel_size=3, activation="relu", padding="same"), keras.layers.MaxPooling2D(pool_size=2), keras.layers.Flatten(), keras.layers.Dense(units=64, activation='relu'), keras.layers.Dropout(0.5), keras.layers.Dense(units=10, activation='softmax'), ]) with distribution.scope(): model = create_model() model.compile(loss="sparse_categorical_crossentropy", optimizer=keras.optimizers.SGD(lr=1e-2), metrics=["accuracy"]) model.fit(dataset, steps_per_epoch=len(X_train)//batch_size, epochs=10) # Hyperparameter tuning # Only talk to ps server config_proto = tf.ConfigProto(device_filters=['/job:ps', '/job:worker/task:%d' % tf_config['task']['index']]) config = tf.estimator.RunConfig(session_config=config_proto) # default since 1.10 strategy.num_replicas_in_sync

37.514337

655

0.716572

3,108

20,933

4.682111

0.224582

0.018898

0.006872

0.003711

0.383933

0.363043

0.333288

0.319269

0.305319

0.290269

0

0.043198

0.162853

20,933

558

656

37.514337

0.786864

0.070463

0

0.483029

0

0.057441

0.1005

0.026992

0

0.007833

0

null

0.005222

0.060052

null

0.031332

0

null

0

null

0

1

0

1

c19f8556fffeaba80b71b3e0bca33cbbc4243288

2,291

py

Python

test/test_testrun_api.py

rcbops/qtest-swagger-client

28220aa95d878922ca4b35c325706932adabea4e

[ "Apache-2.0" ]

1

2019-09-10T17:55:53.000Z

test/test_testrun_api.py

rcbops/qtest-swagger-client

28220aa95d878922ca4b35c325706932adabea4e

[ "Apache-2.0" ]

null

test/test_testrun_api.py

rcbops/qtest-swagger-client

28220aa95d878922ca4b35c325706932adabea4e

[ "Apache-2.0" ]

2

2019-02-12T23:15:10.000Z

2022-03-11T20:08:28.000Z

# coding: utf-8 """ qTest Manager API Version 8.6 - 9.1 qTest Manager API Version 8.6 - 9.1 OpenAPI spec version: 8.6 - 9.1 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import os import sys import unittest import swagger_client from swagger_client.rest import ApiException from swagger_client.apis.testrun_api import TestrunApi class TestTestrunApi(unittest.TestCase): """ TestrunApi unit test stubs """ def setUp(self): self.api = swagger_client.apis.testrun_api.TestrunApi() def tearDown(self): pass def test_add_comment(self): """ Test case for add_comment Adds a Comment to a Test Run """ pass def test_create(self): """ Test case for create Creates a Test Run """ pass def test_delete(self): """ Test case for delete Deletes a Test Run """ pass def test_delete_comment(self): """ Test case for delete_comment Deletes a Comment of a Test Run """ pass def test_get(self): """ Test case for get Gets a Test Run """ pass def test_get_comment(self): """ Test case for get_comment Gets a Comment from a Test Run """ pass def test_get_comments(self): """ Test case for get_comments Gets all Comments of a Test Run """ pass def test_get_of(self): """ Test case for get_of Gets multiple Test Runs """ pass def test_get_status_valuable(self): """ Test case for get_status_valuable Gets Test Run statuses """ pass def test_update(self): """ Test case for update Updates a Test Run """ pass def test_update_comment(self): """ Test case for update_comment Updates a Comment of a Test Run """ pass if __name__ == '__main__': unittest.main()

18.328

69

0.526408

266

2,291

4.360902

0.266917

0.066379

0.10431

0.142241

0.468966

0.225862

0.193103

0.086207

0

0.009503

0.402881

2,291

124

70

18.475806

0.83845

0.337844

0

0.333333

1

0

0.007859

0

1

0.361111

false

0.333333

0.194444

0

0.583333

0

null

0

1

0

null

0

1

0

1

0

1

0

4

c1a047f305103c02462a6d290c41963621ea947a

3,731

py

Python

scripts/evaluations/create_pruned_corpus.py

mikiec84/delphi

2e517f21e76e334c7dfb14325d25879ddf26d10d

[ "Apache-2.0" ]

25

2018-03-03T11:57:57.000Z

2022-01-16T21:19:54.000Z

scripts/evaluations/create_pruned_corpus.py

mikiec84/delphi

2e517f21e76e334c7dfb14325d25879ddf26d10d

[ "Apache-2.0" ]

385

2018-02-21T16:52:06.000Z

2022-02-17T07:44:56.000Z

scripts/evaluations/create_pruned_corpus.py

mikiec84/delphi

2e517f21e76e334c7dfb14325d25879ddf26d10d

[ "Apache-2.0" ]

19

2018-03-20T01:08:11.000Z

2021-09-29T01:04:49.000Z

""" Prune the preassembled corpus json file """ import json import sys from tqdm import tqdm def isInfluenceStatement(s): return s["type"] == "Influence" if __name__ == "__main__": with open(sys.argv[1], "r") as f: sts = json.load(f) filtered_sts = [] hasWMKey = lambda x: x.get("WM") is not None hasNonZeroWMGroundingList = lambda x: len(x["WM"]) != 0 isNotGroundedToCausalFactor = lambda x: x["WM"][0][0] not in ( "wm/concept/causal_factor", "wm/concept/causal_factor/condition", "wm/concept/causal_factor/condition/trend", "wm/concept/causal_factor/access", "wm/concept/causal_factor/intervention", "wm/concept/causal_factor/movement/movement", "wm/concept/causal_factor/social_and_political", "wm/concept/entity/artifact", "wm/concept/entity/geo-location", "wm/concept/entity/government_entity", "wm/concept/entity/organization", "wm/concept/entity/person_and_group/community", "wm/concept/causal_factor/economic_and_commerce/economic_activity/market", "wm/concept/indicator_and_reported_property/weather" ) for s in filter( lambda s: isInfluenceStatement(s) and all( map( lambda x: hasWMKey(x) and hasNonZeroWMGroundingList(x) and isNotGroundedToCausalFactor(x), map(lambda x: s[x]["concept"]["db_refs"], ("subj", "obj")), ) ), sts, ): for c in (s["subj"], s["obj"]): for k in list(c["concept"]["db_refs"].keys()): if k != "WM": del c["concept"]["db_refs"][k] c["concept"]["db_refs"]["WM"] = c["concept"]["db_refs"]["WM"][ 0:1 ] c["concept"]["db_refs"]["WM"][0][0] = ( c["concept"]["db_refs"]["WM"][0][0] .replace(" ", "_") .replace( "wm/concept/causal_factor/economic_and_commerce/economic_activity/market/price/food_price", "wm/concept/causal_factor/economic_and_commerce/economic_activity/market/price_or_cost/food_price", ) .replace( "wm/concept/causal_factor/economic_and_commerce/economic_activity/market/price/oil_price", "wm/concept/causal_factor/economic_and_commerce/economic_activity/market/price_or_cost/oil_price", ) .replace( "wm/concept/causal_factor/intervention/provision_of_goods_and_services/provide_stationary", "wm/concept/causal_factor/intervention/provision_of_goods_and_services/provide_stationery", ) .replace( "wm/concept/causal_factor/intervention/provision_of_goods_and_services/provide_moving_of_houseHolds", "wm/concept/causal_factor/intervention/provision_of_goods_and_services/provide_moving_of_households", ) .replace( "wm/concept/causal_factor/social_and_political/crime", "wm/concept/causal_factor/social_and_political/crime/crime", ) .replace( "wm/concept/causal_factor/social_and_political/education", "wm/concept/causal_factor/social_and_political/education/education", ) ) filtered_sts.append(s) with open(sys.argv[2], "w") as f: f.write(json.dumps(filtered_sts, indent=2))

42.397727

125

0.564996

395

3,731

5.078481

0.268354

0.11665

0.149551

0.209372

0.544865

0.490528

0.477069

0.439681

0.340977

0

0.004693

0.314661

3,731

87

126

42.885057

0.77982

0.010453

0

0.077922

0

0.450054

0.408523

0

1

0.012987

false

0

0.038961

0.012987

0.064935

0

null

0

1

0

1

null

0

2

c1a0698046ea5c15fed9403075b6dadc726dcd15

1,562

py

Python

ax/exceptions/data_provider.py

sparks-baird/Ax

57ba8714902ac218eb87dc2f90090678aa307a43

[ "MIT" ]

null

ax/exceptions/data_provider.py

sparks-baird/Ax

57ba8714902ac218eb87dc2f90090678aa307a43

[ "MIT" ]

null

ax/exceptions/data_provider.py

sparks-baird/Ax

57ba8714902ac218eb87dc2f90090678aa307a43

[ "MIT" ]

null

#!/usr/bin/env python3 # Copyright (c) Meta Platforms, Inc. and affiliates. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. from typing import Any, Iterable class DataProviderError(Exception): """Base Exception for Ax DataProviders. The type of the data provider must be included. The raw error is stored in the data_provider_error section, and an Ax-friendly message is stored as the actual error message. """ def __init__( self, message: str, data_provider: str, data_provider_error: Any ) -> None: self.message = message self.data_provider = data_provider self.data_provider_error = data_provider_error def __str__(self) -> str: return ( "{message}. \n Error thrown by: {dp} data provider \n" + "Native {dp} data provider error: {dp_error}" ).format( dp=self.data_provider, message=self.message, dp_error=self.data_provider_error, ) class MissingDataError(Exception): def __init__(self, missing_trial_indexes: Iterable[int]) -> None: missing_trial_str = ", ".join([str(index) for index in missing_trial_indexes]) self.message: str = ( f"Unable to find data for the following trials: {missing_trial_str} " "consider updating the data fetching kwargs or manually fetching " "data via `refetch_data()`" ) def __str__(self) -> str: return self.message

33.234043

86

0.65493

200

1,562

4.91

0.435

0.14664

0.10387

0.04277

0.038697

0

0.000867

0.261204

1,562

46

87

33.956522

0.850087

0.258643

0

0.074074

0

0.222812

0

1

0.148148

false

0

0.037037

0.074074

0.333333

0

null

0

null

0

1

0

c1a329ce0d56913c2a3d217d2eff794cd4ce53ce

156

py

Python

zfit/core/__init__.py

olantwin/zfit

dae89fd95fc2158c0e7530664d8ca999db4802c5

[ "BSD-3-Clause" ]

1

2022-01-15T13:38:12.000Z

zfit/core/__init__.py

kailiu77/zfit

00eed81fb34e0eb2e4bae5ddc9ebf38699e107ca

[ "BSD-3-Clause" ]

null

zfit/core/__init__.py

kailiu77/zfit

00eed81fb34e0eb2e4bae5ddc9ebf38699e107ca

[ "BSD-3-Clause" ]

null

from . import (baseobject, basepdf, basemodel, basefunc, data, interfaces, integration, math, loss, sample, limits, operations, parameter, )

52

99

0.692308

15

156

7.2

1

0

0.205128

156

2

100

78

0.870968

0

1

0

true

0

0.5

0

0.5

0

1

0

null

0

1

0

null

0

1

0

1

0

4

c1a365128f0c36be71cba9ee9f85b9c58b5f1207

29,113

py

Python

groupdocs_comparison_cloud/models/settings.py

groupdocs-comparison-cloud/groupdocs-comparison-cloud-python

f970b22fae7a791d07b756c2d418217fd368c289

[ "MIT" ]

null

groupdocs_comparison_cloud/models/settings.py

groupdocs-comparison-cloud/groupdocs-comparison-cloud-python

f970b22fae7a791d07b756c2d418217fd368c289

[ "MIT" ]

null

groupdocs_comparison_cloud/models/settings.py

groupdocs-comparison-cloud/groupdocs-comparison-cloud-python

f970b22fae7a791d07b756c2d418217fd368c289

[ "MIT" ]

1

2021-02-02T18:41:48.000Z

# coding: utf-8 # ----------------------------------------------------------------------------------- # <copyright company="Aspose Pty Ltd" file="Settings.py"> # Copyright (c) 2003-2021 Aspose Pty Ltd # </copyright> # <summary> # 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. # </summary> # ----------------------------------------------------------------------------------- import pprint import re # noqa: F401 import six class Settings(object): """ Defines comparison process additional settings """ """ Attributes: swagger_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ swagger_types = { 'generate_summary_page': 'bool', 'show_deleted_content': 'bool', 'show_inserted_content': 'bool', 'style_change_detection': 'bool', 'inserted_items_style': 'ItemsStyle', 'deleted_items_style': 'ItemsStyle', 'changed_items_style': 'ItemsStyle', 'words_separator_chars': 'list[str]', 'details_level': 'str', 'use_frames_for_del_ins_elements': 'bool', 'calculate_component_coordinates': 'bool', 'mark_changed_content': 'bool', 'mark_nested_content': 'bool', 'clone_metadata': 'str', 'meta_data': 'Metadata', 'password_save_option': 'str', 'password': 'str', 'diagram_master_setting': 'DiagramMasterSetting', 'original_size': 'Size', 'header_footers_comparison': 'bool', 'paper_size': 'str', 'sensitivity_of_comparison': 'int' } attribute_map = { 'generate_summary_page': 'GenerateSummaryPage', 'show_deleted_content': 'ShowDeletedContent', 'show_inserted_content': 'ShowInsertedContent', 'style_change_detection': 'StyleChangeDetection', 'inserted_items_style': 'InsertedItemsStyle', 'deleted_items_style': 'DeletedItemsStyle', 'changed_items_style': 'ChangedItemsStyle', 'words_separator_chars': 'WordsSeparatorChars', 'details_level': 'DetailsLevel', 'use_frames_for_del_ins_elements': 'UseFramesForDelInsElements', 'calculate_component_coordinates': 'CalculateComponentCoordinates', 'mark_changed_content': 'MarkChangedContent', 'mark_nested_content': 'MarkNestedContent', 'clone_metadata': 'CloneMetadata', 'meta_data': 'MetaData', 'password_save_option': 'PasswordSaveOption', 'password': 'Password', 'diagram_master_setting': 'DiagramMasterSetting', 'original_size': 'OriginalSize', 'header_footers_comparison': 'HeaderFootersComparison', 'paper_size': 'PaperSize', 'sensitivity_of_comparison': 'SensitivityOfComparison' } def __init__(self, generate_summary_page=None, show_deleted_content=None, show_inserted_content=None, style_change_detection=None, inserted_items_style=None, deleted_items_style=None, changed_items_style=None, words_separator_chars=None, details_level=None, use_frames_for_del_ins_elements=None, calculate_component_coordinates=None, mark_changed_content=None, mark_nested_content=None, clone_metadata=None, meta_data=None, password_save_option=None, password=None, diagram_master_setting=None, original_size=None, header_footers_comparison=None, paper_size=None, sensitivity_of_comparison=None, **kwargs): # noqa: E501 """Initializes new instance of Settings""" # noqa: E501 self._generate_summary_page = None self._show_deleted_content = None self._show_inserted_content = None self._style_change_detection = None self._inserted_items_style = None self._deleted_items_style = None self._changed_items_style = None self._words_separator_chars = None self._details_level = None self._use_frames_for_del_ins_elements = None self._calculate_component_coordinates = None self._mark_changed_content = None self._mark_nested_content = None self._clone_metadata = None self._meta_data = None self._password_save_option = None self._password = None self._diagram_master_setting = None self._original_size = None self._header_footers_comparison = None self._paper_size = None self._sensitivity_of_comparison = None if generate_summary_page is not None: self.generate_summary_page = generate_summary_page if show_deleted_content is not None: self.show_deleted_content = show_deleted_content if show_inserted_content is not None: self.show_inserted_content = show_inserted_content if style_change_detection is not None: self.style_change_detection = style_change_detection if inserted_items_style is not None: self.inserted_items_style = inserted_items_style if deleted_items_style is not None: self.deleted_items_style = deleted_items_style if changed_items_style is not None: self.changed_items_style = changed_items_style if words_separator_chars is not None: self.words_separator_chars = words_separator_chars if details_level is not None: self.details_level = details_level if use_frames_for_del_ins_elements is not None: self.use_frames_for_del_ins_elements = use_frames_for_del_ins_elements if calculate_component_coordinates is not None: self.calculate_component_coordinates = calculate_component_coordinates if mark_changed_content is not None: self.mark_changed_content = mark_changed_content if mark_nested_content is not None: self.mark_nested_content = mark_nested_content if clone_metadata is not None: self.clone_metadata = clone_metadata if meta_data is not None: self.meta_data = meta_data if password_save_option is not None: self.password_save_option = password_save_option if password is not None: self.password = password if diagram_master_setting is not None: self.diagram_master_setting = diagram_master_setting if original_size is not None: self.original_size = original_size if header_footers_comparison is not None: self.header_footers_comparison = header_footers_comparison if paper_size is not None: self.paper_size = paper_size if sensitivity_of_comparison is not None: self.sensitivity_of_comparison = sensitivity_of_comparison @property def generate_summary_page(self): """ Gets the generate_summary_page. # noqa: E501 Indicates whether to add summary page to resultant document or not # noqa: E501 :return: The generate_summary_page. # noqa: E501 :rtype: bool """ return self._generate_summary_page @generate_summary_page.setter def generate_summary_page(self, generate_summary_page): """ Sets the generate_summary_page. Indicates whether to add summary page to resultant document or not # noqa: E501 :param generate_summary_page: The generate_summary_page. # noqa: E501 :type: bool """ if generate_summary_page is None: raise ValueError("Invalid value for `generate_summary_page`, must not be `None`") # noqa: E501 self._generate_summary_page = generate_summary_page @property def show_deleted_content(self): """ Gets the show_deleted_content. # noqa: E501 Indicates whether to show deleted components in resultant document or not # noqa: E501 :return: The show_deleted_content. # noqa: E501 :rtype: bool """ return self._show_deleted_content @show_deleted_content.setter def show_deleted_content(self, show_deleted_content): """ Sets the show_deleted_content. Indicates whether to show deleted components in resultant document or not # noqa: E501 :param show_deleted_content: The show_deleted_content. # noqa: E501 :type: bool """ if show_deleted_content is None: raise ValueError("Invalid value for `show_deleted_content`, must not be `None`") # noqa: E501 self._show_deleted_content = show_deleted_content @property def show_inserted_content(self): """ Gets the show_inserted_content. # noqa: E501 Indicates whether to show inserted components in resultant document or not # noqa: E501 :return: The show_inserted_content. # noqa: E501 :rtype: bool """ return self._show_inserted_content @show_inserted_content.setter def show_inserted_content(self, show_inserted_content): """ Sets the show_inserted_content. Indicates whether to show inserted components in resultant document or not # noqa: E501 :param show_inserted_content: The show_inserted_content. # noqa: E501 :type: bool """ if show_inserted_content is None: raise ValueError("Invalid value for `show_inserted_content`, must not be `None`") # noqa: E501 self._show_inserted_content = show_inserted_content @property def style_change_detection(self): """ Gets the style_change_detection. # noqa: E501 Indicates whether to detect style changes or not # noqa: E501 :return: The style_change_detection. # noqa: E501 :rtype: bool """ return self._style_change_detection @style_change_detection.setter def style_change_detection(self, style_change_detection): """ Sets the style_change_detection. Indicates whether to detect style changes or not # noqa: E501 :param style_change_detection: The style_change_detection. # noqa: E501 :type: bool """ if style_change_detection is None: raise ValueError("Invalid value for `style_change_detection`, must not be `None`") # noqa: E501 self._style_change_detection = style_change_detection @property def inserted_items_style(self): """ Gets the inserted_items_style. # noqa: E501 Style for inserted components # noqa: E501 :return: The inserted_items_style. # noqa: E501 :rtype: ItemsStyle """ return self._inserted_items_style @inserted_items_style.setter def inserted_items_style(self, inserted_items_style): """ Sets the inserted_items_style. Style for inserted components # noqa: E501 :param inserted_items_style: The inserted_items_style. # noqa: E501 :type: ItemsStyle """ self._inserted_items_style = inserted_items_style @property def deleted_items_style(self): """ Gets the deleted_items_style. # noqa: E501 Style for deleted components # noqa: E501 :return: The deleted_items_style. # noqa: E501 :rtype: ItemsStyle """ return self._deleted_items_style @deleted_items_style.setter def deleted_items_style(self, deleted_items_style): """ Sets the deleted_items_style. Style for deleted components # noqa: E501 :param deleted_items_style: The deleted_items_style. # noqa: E501 :type: ItemsStyle """ self._deleted_items_style = deleted_items_style @property def changed_items_style(self): """ Gets the changed_items_style. # noqa: E501 Style for components with changed style # noqa: E501 :return: The changed_items_style. # noqa: E501 :rtype: ItemsStyle """ return self._changed_items_style @changed_items_style.setter def changed_items_style(self, changed_items_style): """ Sets the changed_items_style. Style for components with changed style # noqa: E501 :param changed_items_style: The changed_items_style. # noqa: E501 :type: ItemsStyle """ self._changed_items_style = changed_items_style @property def words_separator_chars(self): """ Gets the words_separator_chars. # noqa: E501 An array of delimiters to split text into words # noqa: E501 :return: The words_separator_chars. # noqa: E501 :rtype: list[str] """ return self._words_separator_chars @words_separator_chars.setter def words_separator_chars(self, words_separator_chars): """ Sets the words_separator_chars. An array of delimiters to split text into words # noqa: E501 :param words_separator_chars: The words_separator_chars. # noqa: E501 :type: list[str] """ self._words_separator_chars = words_separator_chars @property def details_level(self): """ Gets the details_level. # noqa: E501 Gets of sets the comparison details level # noqa: E501 :return: The details_level. # noqa: E501 :rtype: str """ return self._details_level @details_level.setter def details_level(self, details_level): """ Sets the details_level. Gets of sets the comparison details level # noqa: E501 :param details_level: The details_level. # noqa: E501 :type: str """ if details_level is None: raise ValueError("Invalid value for `details_level`, must not be `None`") # noqa: E501 allowed_values = ["Low", "Middle", "High"] # noqa: E501 if not details_level.isdigit(): if details_level not in allowed_values: raise ValueError( "Invalid value for `details_level` ({0}), must be one of {1}" # noqa: E501 .format(details_level, allowed_values)) self._details_level = details_level else: self._details_level = allowed_values[int(details_level) if six.PY3 else long(details_level)] @property def use_frames_for_del_ins_elements(self): """ Gets the use_frames_for_del_ins_elements. # noqa: E501 Indicates whether to use frames for shapes in Word Processing and for rectangles in Image documents # noqa: E501 :return: The use_frames_for_del_ins_elements. # noqa: E501 :rtype: bool """ return self._use_frames_for_del_ins_elements @use_frames_for_del_ins_elements.setter def use_frames_for_del_ins_elements(self, use_frames_for_del_ins_elements): """ Sets the use_frames_for_del_ins_elements. Indicates whether to use frames for shapes in Word Processing and for rectangles in Image documents # noqa: E501 :param use_frames_for_del_ins_elements: The use_frames_for_del_ins_elements. # noqa: E501 :type: bool """ if use_frames_for_del_ins_elements is None: raise ValueError("Invalid value for `use_frames_for_del_ins_elements`, must not be `None`") # noqa: E501 self._use_frames_for_del_ins_elements = use_frames_for_del_ins_elements @property def calculate_component_coordinates(self): """ Gets the calculate_component_coordinates. # noqa: E501 Indicates whether to calculate coordinates for changed components # noqa: E501 :return: The calculate_component_coordinates. # noqa: E501 :rtype: bool """ return self._calculate_component_coordinates @calculate_component_coordinates.setter def calculate_component_coordinates(self, calculate_component_coordinates): """ Sets the calculate_component_coordinates. Indicates whether to calculate coordinates for changed components # noqa: E501 :param calculate_component_coordinates: The calculate_component_coordinates. # noqa: E501 :type: bool """ if calculate_component_coordinates is None: raise ValueError("Invalid value for `calculate_component_coordinates`, must not be `None`") # noqa: E501 self._calculate_component_coordinates = calculate_component_coordinates @property def mark_changed_content(self): """ Gets the mark_changed_content. # noqa: E501 Indicates whether to use frames for shapes in Word Processing and for rectangles in Image documents # noqa: E501 :return: The mark_changed_content. # noqa: E501 :rtype: bool """ return self._mark_changed_content @mark_changed_content.setter def mark_changed_content(self, mark_changed_content): """ Sets the mark_changed_content. Indicates whether to use frames for shapes in Word Processing and for rectangles in Image documents # noqa: E501 :param mark_changed_content: The mark_changed_content. # noqa: E501 :type: bool """ if mark_changed_content is None: raise ValueError("Invalid value for `mark_changed_content`, must not be `None`") # noqa: E501 self._mark_changed_content = mark_changed_content @property def mark_nested_content(self): """ Gets the mark_nested_content. # noqa: E501 Gets or sets a value indicating whether to mark the children of the deleted or inserted element as deleted or inserted # noqa: E501 :return: The mark_nested_content. # noqa: E501 :rtype: bool """ return self._mark_nested_content @mark_nested_content.setter def mark_nested_content(self, mark_nested_content): """ Sets the mark_nested_content. Gets or sets a value indicating whether to mark the children of the deleted or inserted element as deleted or inserted # noqa: E501 :param mark_nested_content: The mark_nested_content. # noqa: E501 :type: bool """ if mark_nested_content is None: raise ValueError("Invalid value for `mark_nested_content`, must not be `None`") # noqa: E501 self._mark_nested_content = mark_nested_content @property def clone_metadata(self): """ Gets the clone_metadata. # noqa: E501 Gets or sets type of metadata to clone # noqa: E501 :return: The clone_metadata. # noqa: E501 :rtype: str """ return self._clone_metadata @clone_metadata.setter def clone_metadata(self, clone_metadata): """ Sets the clone_metadata. Gets or sets type of metadata to clone # noqa: E501 :param clone_metadata: The clone_metadata. # noqa: E501 :type: str """ if clone_metadata is None: raise ValueError("Invalid value for `clone_metadata`, must not be `None`") # noqa: E501 allowed_values = ["Default", "Source", "Target", "FileAuthor"] # noqa: E501 if not clone_metadata.isdigit(): if clone_metadata not in allowed_values: raise ValueError( "Invalid value for `clone_metadata` ({0}), must be one of {1}" # noqa: E501 .format(clone_metadata, allowed_values)) self._clone_metadata = clone_metadata else: self._clone_metadata = allowed_values[int(clone_metadata) if six.PY3 else long(clone_metadata)] @property def meta_data(self): """ Gets the meta_data. # noqa: E501 Gets or sets user metadata # noqa: E501 :return: The meta_data. # noqa: E501 :rtype: Metadata """ return self._meta_data @meta_data.setter def meta_data(self, meta_data): """ Sets the meta_data. Gets or sets user metadata # noqa: E501 :param meta_data: The meta_data. # noqa: E501 :type: Metadata """ self._meta_data = meta_data @property def password_save_option(self): """ Gets the password_save_option. # noqa: E501 Gets or sets type of password saving # noqa: E501 :return: The password_save_option. # noqa: E501 :rtype: str """ return self._password_save_option @password_save_option.setter def password_save_option(self, password_save_option): """ Sets the password_save_option. Gets or sets type of password saving # noqa: E501 :param password_save_option: The password_save_option. # noqa: E501 :type: str """ if password_save_option is None: raise ValueError("Invalid value for `password_save_option`, must not be `None`") # noqa: E501 allowed_values = ["None", "Source", "Target", "User"] # noqa: E501 if not password_save_option.isdigit(): if password_save_option not in allowed_values: raise ValueError( "Invalid value for `password_save_option` ({0}), must be one of {1}" # noqa: E501 .format(password_save_option, allowed_values)) self._password_save_option = password_save_option else: self._password_save_option = allowed_values[int(password_save_option) if six.PY3 else long(password_save_option)] @property def password(self): """ Gets the password. # noqa: E501 Gets or sets user password to resultant document # noqa: E501 :return: The password. # noqa: E501 :rtype: str """ return self._password @password.setter def password(self, password): """ Sets the password. Gets or sets user password to resultant document # noqa: E501 :param password: The password. # noqa: E501 :type: str """ self._password = password @property def diagram_master_setting(self): """ Gets the diagram_master_setting. # noqa: E501 Gets or sets master for Diagram document # noqa: E501 :return: The diagram_master_setting. # noqa: E501 :rtype: DiagramMasterSetting """ return self._diagram_master_setting @diagram_master_setting.setter def diagram_master_setting(self, diagram_master_setting): """ Sets the diagram_master_setting. Gets or sets master for Diagram document # noqa: E501 :param diagram_master_setting: The diagram_master_setting. # noqa: E501 :type: DiagramMasterSetting """ self._diagram_master_setting = diagram_master_setting @property def original_size(self): """ Gets the original_size. # noqa: E501 Gets or sets original document size when picture is compared with other different formats # noqa: E501 :return: The original_size. # noqa: E501 :rtype: Size """ return self._original_size @original_size.setter def original_size(self, original_size): """ Sets the original_size. Gets or sets original document size when picture is compared with other different formats # noqa: E501 :param original_size: The original_size. # noqa: E501 :type: Size """ self._original_size = original_size @property def header_footers_comparison(self): """ Gets the header_footers_comparison. # noqa: E501 Control to turn on comparison of header/footer contents # noqa: E501 :return: The header_footers_comparison. # noqa: E501 :rtype: bool """ return self._header_footers_comparison @header_footers_comparison.setter def header_footers_comparison(self, header_footers_comparison): """ Sets the header_footers_comparison. Control to turn on comparison of header/footer contents # noqa: E501 :param header_footers_comparison: The header_footers_comparison. # noqa: E501 :type: bool """ if header_footers_comparison is None: raise ValueError("Invalid value for `header_footers_comparison`, must not be `None`") # noqa: E501 self._header_footers_comparison = header_footers_comparison @property def paper_size(self): """ Gets the paper_size. # noqa: E501 Gets or sets the result document paper size # noqa: E501 :return: The paper_size. # noqa: E501 :rtype: str """ return self._paper_size @paper_size.setter def paper_size(self, paper_size): """ Sets the paper_size. Gets or sets the result document paper size # noqa: E501 :param paper_size: The paper_size. # noqa: E501 :type: str """ if paper_size is None: raise ValueError("Invalid value for `paper_size`, must not be `None`") # noqa: E501 allowed_values = ["Default", "A0", "A1", "A2", "A3", "A4", "A5", "A6", "A7", "A8"] # noqa: E501 if not paper_size.isdigit(): if paper_size not in allowed_values: raise ValueError( "Invalid value for `paper_size` ({0}), must be one of {1}" # noqa: E501 .format(paper_size, allowed_values)) self._paper_size = paper_size else: self._paper_size = allowed_values[int(paper_size) if six.PY3 else long(paper_size)] @property def sensitivity_of_comparison(self): """ Gets the sensitivity_of_comparison. # noqa: E501 Gets or sets a sensitivity of comparison. Default is 75 # noqa: E501 :return: The sensitivity_of_comparison. # noqa: E501 :rtype: int """ return self._sensitivity_of_comparison @sensitivity_of_comparison.setter def sensitivity_of_comparison(self, sensitivity_of_comparison): """ Sets the sensitivity_of_comparison. Gets or sets a sensitivity of comparison. Default is 75 # noqa: E501 :param sensitivity_of_comparison: The sensitivity_of_comparison. # noqa: E501 :type: int """ if sensitivity_of_comparison is None: raise ValueError("Invalid value for `sensitivity_of_comparison`, must not be `None`") # noqa: E501 self._sensitivity_of_comparison = sensitivity_of_comparison def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, Settings): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other

36.574121

624

0.645828

3,417

29,113

5.216564

0.088382

0.06014

0.028275

0.016045

0.641515

0.496213

0.393043

0.239215

0.188387

0.12892

0

0.020956

0.275513

29,113

795

625

36.620126

0.824151

0.342527

0

0.102639

0

0.152875

0.050799

0

1

0.146628

false

0.082111

0.008798

0

0.246334

0.005865

0

null

0

null

0

1

0

1

c1a36f901ada2ecff8f592caff250af3e4962c6a

6,713

py

Python

gui/backend/tests/unit/test_Sqleditor.py

mike-lischke/mysql-shell-plugins

d7d15591dd8e70f7f5ef8ea579e0797eff30fa0a

[ "Apache-2.0", "CC0-1.0" ]

11

2022-03-02T11:04:16.000Z

2022-03-29T05:28:23.000Z

gui/backend/tests/unit/test_Sqleditor.py

mike-lischke/mysql-shell-plugins

d7d15591dd8e70f7f5ef8ea579e0797eff30fa0a

[ "Apache-2.0", "CC0-1.0" ]

1

2022-03-25T15:12:16.000Z

2022-03-31T18:59:22.000Z

gui/backend/tests/unit/test_Sqleditor.py

mike-lischke/mysql-shell-plugins

d7d15591dd8e70f7f5ef8ea579e0797eff30fa0a

[ "Apache-2.0", "CC0-1.0" ]

3

2022-03-24T11:32:12.000Z

2022-03-25T20:40:14.000Z

# Copyright (c) 2020, 2022, Oracle and/or its affiliates. # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License, version 2.0, # as published by the Free Software Foundation. # # This program is also distributed with certain software (including # but not limited to OpenSSL) that is licensed under separate terms, as # designated in a particular file or component or in included license # documentation. The authors of MySQL hereby grant you an additional # permission to link the program and your derivative works with the # separately licensed software that they have included with MySQL. # 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, version 2.0, for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software Foundation, Inc., # 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA import pytest import uuid import config from gui_plugin import sqleditor from gui_plugin import dbconnections from gui_plugin.core.Error import MSGException from tests.conftest import backend_callback from .MockWebSession import MockWebSession from tests import backend_callback_with_pending import time import gui_plugin.core.Logger as logger class Parameters: _connection_id = None _web_session = None _module_session = None _module_session_id = None _db_connection_id = None @pytest.fixture(scope="module") def params(): parameters = Parameters() parameters._connection_id = None parameters._web_session = MockWebSession() @backend_callback(1) def open_connection_cb(msg_type, msg, request_id, values): if values['request_state']['type'] != "OK": raise Exception('Failed opening connection.') parameters._web_session.register_callback( open_connection_cb.request_id, open_connection_cb) result = sqleditor.start_session(parameters._web_session) parameters._module_session_id = result['module_session_id'] parameters._module_session = parameters._web_session.module_sessions[ parameters._module_session_id] connection_options = config.Config.get_instance( ).database_connections[0]['options'].copy() del connection_options['portStr'] result = dbconnections.add_db_connection(1, { "db_type": "MySQL", "caption": "This is a test MySQL database", "description": "This is a test MySQL database description", "options": connection_options }, '', parameters._web_session) parameters._db_connection_id = result['result']['db_connection_id'] sqleditor.open_connection( parameters._db_connection_id, parameters._module_session, open_connection_cb.request_id) open_connection_cb.join_and_validate() yield parameters parameters._web_session.db.close() result = sqleditor.close_session(parameters._module_session) # del parameters._web_session.module_sessions[parameters._module_session_id] class TestSqleditor: def test_service_connection(self, params): @backend_callback_with_pending() def callback_request1(msg_type, msg, request_id, values): logger.debug("callback_request1") @backend_callback_with_pending() def callback_schemas(msg_type, msg, request_id, values): logger.debug("callback_schemas") params._web_session.register_callback( callback_request1.request_id, callback_request1) params._web_session.register_callback( callback_schemas.request_id, callback_schemas) sqleditor.execute(sql="SELECT SLEEP(3)", module_session=params._module_session, request_id=callback_request1.request_id) sqleditor.get_current_schema(module_session=params._module_session, request_id=callback_schemas.request_id) callback_schemas.join_and_validate() callback_request1.join_and_validate() def test_close_session(self, params): request_id1 = str(uuid.uuid1()) sqleditor.close_session(params._module_session) with pytest.raises(MSGException) as e: sqleditor.execute("SELECT SLEEP(1)", params._module_session, request_id1) assert e.value.args[0] == "Error[MSG-1200]: The database session needs to be opened before SQL can be executed." @backend_callback(1) def open_connection_cb(msg_type, msg, request_id, values): if values['request_state']['type'] != "OK": raise Exception('Failed opening connection.') params._web_session.register_callback( open_connection_cb.request_id, open_connection_cb) sqleditor.open_connection( params._db_connection_id, params._module_session, open_connection_cb.request_id) open_connection_cb.join_and_validate() def test_kill_query(self, params): @backend_callback_with_pending() def callback_sleep(msg_type, msg, request_id=None, values=None): assert 'request_state' in values assert 'type' in values['request_state'] assert 'msg' in values['request_state'] assert values['request_state']['type'] == "ERROR" assert values['request_state']['msg'] == "Query killed" params._web_session.register_callback( callback_sleep.request_id, callback_sleep) sqleditor.execute("SELECT SLEEP(3)", params._module_session, callback_sleep.request_id) # since kill works in a different session (service session) # it might happen that we try to kill a query that is still not running. # so avoid that, just wait a bit. there's plenty of time to kill it. time.sleep(1) sqleditor.kill_query(params._module_session) callback_sleep.join_and_validate() def test_execute_query_with_params(self, params): @backend_callback_with_pending() def callback_execute(msg_type, msg, request_id=None, values=None): assert 'done' in values assert 'columns' in values assert 'rows' in values assert values['done'] == True params._web_session.register_callback( callback_execute.request_id, callback_execute) result = sqleditor.execute( "SHOW DATABASES LIKE ?", params._module_session, callback_execute.request_id, ['mysql']) callback_execute.join_and_validate()

39.958333

120

0.714584

839

6,713

5.444577

0.261025

0.039405

0.035026

0.022329

0.359676

0.321804

0.258538

0.243214

0.175569

0.113398

0

0.008457

0.207359

6,713

167

121

40.197605

0.850028

0.202443

0

0.212963

0

0.104898

0

0.092593

1

0.101852

false

0

0.101852

0

0.268519

0

null

0

null

0

1

0

c1a48eb1c0e309496f0be9a970ec65fc62f63f3c

981

py

Python

tests/losses/test_lamb_gan_loss.py

limberc/HyperGAN

b074e74abf0ed9b81bd52084706e3707a47e0fe2

[ "MIT" ]

889

2016-08-27T01:37:35.000Z

2018-10-07T19:47:56.000Z

tests/losses/test_lamb_gan_loss.py

limberc/HyperGAN

b074e74abf0ed9b81bd52084706e3707a47e0fe2

[ "MIT" ]

218

2021-05-25T01:46:15.000Z

2022-02-11T01:08:52.000Z

tests/losses/test_lamb_gan_loss.py

limberc/HyperGAN

b074e74abf0ed9b81bd52084706e3707a47e0fe2

[ "MIT" ]

145

2016-09-27T06:56:24.000Z

2018-09-25T16:09:28.000Z

import tensorflow as tf import hyperchamber as hc import hypergan as hg import numpy as np from hypergan.losses.lamb_gan_loss import LambGanLoss from hypergan.ops import TensorflowOps from unittest.mock import MagicMock from tests.mocks import mock_gan loss_config = {'test': True, 'reduce':'reduce_mean', 'labels': [0,1,0], 'label_smooth': 0.3, 'alpha': 0.2, 'beta': 0.1} class LambGanLossTest(tf.test.TestCase): def test_config(self): with self.test_session(): loss = LambGanLoss(mock_gan(), loss_config) self.assertTrue(loss.config.test) def test_create(self): with self.test_session(): gan = mock_gan() loss = LambGanLoss(gan, loss_config) d_loss, g_loss = loss.create() d_shape = gan.ops.shape(d_loss) g_shape = gan.ops.shape(g_loss) self.assertEqual(d_shape, []) self.assertEqual(g_shape, []) if __name__ == "__main__": tf.test.main()

32.7

119

0.654434

135

981

4.533333

0.392593

0.05719

0.053922

0.055556

0.075163

0

0.011921

0.230377

981

29

120

33.827586

0.798676

0

0.08

0

0.057085

0

0.12

1

0.08

false

0

0.32

0

0.44

0

null

0

null

0

1

0

1

c1a639306302ef557c6752a6a01fb763ef49ebd4

4,846

py

Python

examples/poly_model/poly_dimension_adaptive.py

wedeling/FabUQCampaign

f89ee1a7b72ec1c41d6bf662f1b42acd8065cb32

[ "BSD-3-Clause" ]

1

2020-06-26T10:37:56.000Z

examples/poly_model/poly_dimension_adaptive.py

wedeling/FabUQCampaign

f89ee1a7b72ec1c41d6bf662f1b42acd8065cb32

[ "BSD-3-Clause" ]

null

examples/poly_model/poly_dimension_adaptive.py

wedeling/FabUQCampaign

f89ee1a7b72ec1c41d6bf662f1b42acd8065cb32

[ "BSD-3-Clause" ]

2

2020-04-20T12:50:11.000Z

2020-04-24T10:35:13.000Z

import chaospy as cp import numpy as np import easyvvuq as uq import os # import fabsim3_cmd_api as fab import matplotlib.pyplot as plt plt.close('all') # author: Wouter Edeling __license__ = "LGPL" HOME = os.path.abspath(os.path.dirname(__file__)) # Set up a fresh campaign called "sc" my_campaign = uq.Campaign(name='sc', work_dir='/tmp') #number of uncertain parameters d = 5 # Define parameter space params = {} for i in range(45): params["x%d" % (i + 1)] = {"type": "float", "min": 0.0, "max": 1.0, "default": 0.5} params["d"] = {"type": "integer", "default": d} params["out_file"] = {"type": "string", "default": "output.csv"} output_filename = params["out_file"]["default"] output_columns = ["f"] # Create an encoder, decoder and collation element encoder = uq.encoders.GenericEncoder( template_fname=HOME + '/sc/poly.template', delimiter='$', target_filename='poly_in.json') decoder = uq.decoders.SimpleCSV(target_filename=output_filename, output_columns=output_columns, header=0) collater = uq.collate.AggregateSamples() # Add the SC app (automatically set as current app) my_campaign.add_app(name="sc", params=params, encoder=encoder, decoder=decoder, collater=collater) #uncertain variables vary = {} for i in range(d): vary["x%d" % (i + 1)] = cp.Uniform(0, 1) #================================= #create dimension-adaptive sampler #================================= #sparse = use a sparse grid (required) #growth = use a nested quadrature rule (not required) #dimension_adaptive = use a dimension adaptive sampler (required) my_sampler = uq.sampling.SCSampler(vary=vary, polynomial_order=1, quadrature_rule="C", sparse=True, growth=True, dimension_adaptive=True) # Associate the sampler with the campaign my_campaign.set_sampler(my_sampler) # Will draw all (of the finite set of samples) my_campaign.draw_samples() my_campaign.populate_runs_dir() ## Use this instead to run the samples using EasyVVUQ on the localhost my_campaign.apply_for_each_run_dir(uq.actions.ExecuteLocal( "./sc/poly_model.py poly_in.json")) # fab.run_uq_ensemble(my_campaign.campaign_dir, script_name='poly_model', # machine='localhost') # fab.get_uq_samples(my_campaign.campaign_dir, machine='localhost') my_campaign.collate() data_frame = my_campaign.get_collation_result() # Post-processing analysis analysis = uq.analysis.SCAnalysis(sampler=my_sampler, qoi_cols=output_columns) my_campaign.apply_analysis(analysis) # how many adaptation to make number_of_adaptations = 2 for i in range(number_of_adaptations): #required parameter in the case of a Fabsim run skip = my_sampler.count print('Adaptation %d' % (i+1)) #look-ahead step (compute the code at admissible forward points) my_sampler.look_ahead(analysis.l_norm) #proceed as usual my_campaign.draw_samples() my_campaign.populate_runs_dir() my_campaign.apply_for_each_run_dir(uq.actions.ExecuteLocal( "./sc/poly_model.py poly_in.json")) # fab.run_uq_ensemble(my_campaign.campaign_dir, script_name='poly_model', # machine='localhost', skip = skip) # fab.get_uq_samples(my_campaign.campaign_dir, machine='localhost') my_campaign.collate() #compute the error at all admissible points, select direction with #highest error and add that direction to the grid data_frame = my_campaign.get_collation_result() analysis.adapt_dimension('f', data_frame, method='var') #proceed as usual with analysis my_campaign.apply_analysis(analysis) results = my_campaign.get_last_analysis() #some post-processing #analytic mean and standard deviation a = np.array([1/(2*(i+1)) for i in range(d)]) ref_mean = np.prod(a+1)/2**d ref_std = np.sqrt(np.prod(9*a[0:d]**2/5 + 2*a[0:d] + 1)/2**(2*d) - ref_mean**2) print("======================================") print("Number of samples = %d" % my_sampler._number_of_samples) print("--------------------------------------") print("Analytic mean = %.4e" % ref_mean) print("Computed mean = %.4e" % results['statistical_moments']['f']['mean']) print("--------------------------------------") print("Analytic standard deviation = %.4e" % ref_std) print("Computed standard deviation = %.4e" % results['statistical_moments']['f']['std']) print("--------------------------------------") print("First order Sobol indices =", results['sobols_first']['f']) print("--------------------------------------") analysis.plot_grid() analysis.plot_stat_convergence() analysis.adaptation_table()

35.372263

88

0.639909

626

4,846

4.757189

0.333866

0.067159

0.028543

0.014775

0.246474

0.198791

0.173942

0.143049

0

0.009422

0.189641

4,846

137

89

35.372263

0.748918

0.291993

0

0.225

0

0.185458

0.055932

0

1

0

false

0

0.0625

0

0.0625

0.15

0

null

0

null

0

1

0

c1a725906e88f1b2357a9d34b387186061ce645e

280

py

Python

tests/data/test_regression_file_path_fixture/tests-inputs/case/test_fixture.py

sdinot/pytest-executable

45e5b2df8e81213973c6d8d70ec6e0fb0c4b8db6

[ "Apache-2.0" ]

8

2020-04-22T06:26:58.000Z

2022-01-22T05:14:45.000Z

tests/data/test_regression_file_path_fixture/tests-inputs/case/test_fixture.py

sdinot/pytest-executable

45e5b2df8e81213973c6d8d70ec6e0fb0c4b8db6

[ "Apache-2.0" ]

6

2020-08-08T19:56:15.000Z

2021-11-11T09:05:43.000Z

tests/data/test_regression_file_path_fixture/tests-inputs/case/test_fixture.py

sdinot/pytest-executable

45e5b2df8e81213973c6d8d70ec6e0fb0c4b8db6

[ "Apache-2.0" ]

2

2020-04-23T06:37:42.000Z

2020-04-29T21:59:25.000Z

from fnmatch import fnmatch def test_fixture(regression_file_path): assert fnmatch(str(regression_file_path.relative), "[01].xmf") assert fnmatch( str(regression_file_path.absolute), "*/test_regression_file_path_fixture0/references/case/[01].xmf", )

28

72

0.732143

35

280

5.542857

0.514286

0.28866

0.371134

0.268041

0.350515

0

0.021186

0.157143

280

9

73

31.111111

0.800847

0

0.246429

0.217857

0

0.285714

1

0.142857

false

0

0.142857

0

0.285714

0

null

1

0

1

0

null

0

4

c1a87ca84277a3dd76ad935ca0c45a7628e65f14

3,996

py

Python

preprocess_data/preprocess_data.py

s-diaco/DRL4Trading

8f6042d7ce0381dc1fc809558e529a512c4c4e01

[ "MIT" ]

4

2021-07-05T12:18:34.000Z

2022-02-07T19:41:45.000Z

preprocess_data/preprocess_data.py

s-diaco/DRL4Trading

8f6042d7ce0381dc1fc809558e529a512c4c4e01

[ "MIT" ]

5

2021-04-04T09:44:59.000Z

2021-06-26T09:38:53.000Z

preprocess_data/preprocess_data.py

s-diaco/DRL4Trading

8f6042d7ce0381dc1fc809558e529a512c4c4e01

[ "MIT" ]

2

2021-07-05T12:18:34.000Z

2021-08-04T08:01:11.000Z

"""preprocess data before using it""" import logging import pandas as pd from preprocess_data import csv_data, custom_col_base, custom_columns def col_from_cls(client_class, data): ''' Create "series" from a given function and dataframe Parameters: client_func (callable): function used to create the column data (pd.DataFrame): data used to calculate new columns Returns: column (pd.Series): calculated column Raises: TypeError: if the column type is not pd.Series ''' # TODO check if there are any Nan or inf values in new column column_cls = client_class(data) col_name, col_data = column_cls.add_column() if isinstance(col_data, pd.Series): return col_name, col_data else: raise TypeError('Method "add_column()" has to return "pd.Series"') def add_user_defined_features(data: pd.DataFrame, user_cols) -> pd.DataFrame: ''' Add data from functions in 'user_calculated_columns.py'. Parameters: data (pd.DataFrame): data used to calculate new columns user_cols (list): user class names to use Returns: data (pd.DataFrame): the updated dataframe ''' logging.info('Adding custom columns') for col_cls in custom_col_base.CustomColumn.__subclasses__(): cls_name = col_cls.__name__ if(col_cls.__module__ == custom_columns.__name__): if cls_name in user_cols: try: # add new column to dataframe new_col_name, new_col = col_from_cls(col_cls, data) data[new_col_name] = new_col logging.info(f'Add column "{new_col_name}" ✅') except Exception as e: logging.info(f'Add column from user class "{cls_name}" ' f'❌: {str(e)}') return data def preprocess_data(tic_list, start_date, end_date, field_mappings, baseline_filed_mappings, csv_file_info, user_columns) -> pd.DataFrame: """preprocess data before using""" logging.info(f'Train start date: {start_date}') logging.info(f'Train end date: {end_date}') logging.info(f'Tickers: {tic_list}') logging.info(f'Fetching data from csv files') data_loader = csv_data.CSVData( start_date=start_date, end_date=end_date, ticker_list=tic_list, csv_dirs=csv_file_info["dir_list"], baseline_file_name=csv_file_info["baseline_file_name"], has_daily_trading_limit=csv_file_info["has_daily_trading_limit"], use_baseline_data=csv_file_info["use_baseline_data"], baseline_filed_mappings=baseline_filed_mappings, baseline_date_column_name=csv_file_info["baseline_date_column_name"] ) processed_data = data_loader.fetch_data( field_mappings = field_mappings, date_column=csv_file_info["date_column_name"]) # Preprocess Data processed_data = add_user_defined_features( processed_data, user_columns ) logging.info(f'Preprocessed data (tail): \n{processed_data.tail()}') logging.info(f'Sample size: {len(processed_data)}') logging.info(f'Columns after preprocess: {processed_data.columns}') return processed_data def get_baseline_df( start_date, end_date, baseline_filed_mappings, csv_file_info ): """ return a dataframe for baseline data """ data_loader = csv_data.CSVData( start_date=start_date, end_date=end_date, baseline_file_name=csv_file_info["baseline_file_name"], use_baseline_data=csv_file_info["use_baseline_data"], baseline_filed_mappings=baseline_filed_mappings, baseline_date_column_name=csv_file_info["baseline_date_column_name"] ) baseline_df = data_loader.baseline_df return baseline_df

35.362832

78

0.651401

512

3,996

4.732422

0.230469

0.031779

0.049938

0.026414

0.288898

0.258357

0.231944

0.160132

0

0.265766

3,996

112

79

35.678571

0.825153

0.214965

0

0.205882

0

0.182508

0.047195

0

0.008929

0

1

0.058824

false

0

0.044118

0

0.161765

0

null

0

null

0

1

0

c1ab4ab02c0597d4e6630967716526281f28d594

17,930

py

Python

constants.py

datarobot-community/soccer_match_prediction

80aea8e7e14d772178909656496e05122ebff0a8

[ "Apache-2.0" ]

2

2020-06-16T14:34:13.000Z

2020-07-15T18:13:41.000Z

constants.py

datarobot-community/soccer_match_prediction

80aea8e7e14d772178909656496e05122ebff0a8

[ "Apache-2.0" ]

null

constants.py

datarobot-community/soccer_match_prediction

80aea8e7e14d772178909656496e05122ebff0a8

[ "Apache-2.0" ]

2

2021-07-23T06:40:49.000Z

2022-02-11T17:52:26.000Z

LEAGUES = [ 'Scottish Premiership', 'Italy Serie A', 'French Ligue 1', 'Spanish Segunda Division', 'Australian A-League', 'Italy Serie B', 'Dutch Eredivisie', 'Mexican Primera Division Torneo Clausura', 'Russian Premier Liga', 'Spanish Primera Division', 'English League One', 'UEFA Europa League', 'Mexican Primera Division Torneo Apertura', 'German Bundesliga', 'South African ABSA Premier League', 'Austrian T-Mobile Bundesliga', 'Barclays Premier League', 'English League Two', 'Greek Super League', 'German 2. Bundesliga', 'United Soccer League', 'Chinese Super League', 'UEFA Champions League', 'Portuguese Liga', 'English League Championship', 'Belgian Jupiler League', 'Norwegian Tippeligaen', 'Turkish Turkcell Super Lig', 'Danish SAS-Ligaen', 'Japanese J League', 'Swedish Allsvenskan', 'Swiss Raiffeisen Super League', 'Brasileiro Série A', 'Major League Soccer', 'Argentina Primera Division', 'French Ligue 2' ] TEAMS = { 'Manchester City': 94.25, 'Bayern Munich': 93.96, 'Liverpool': 92.92, 'Barcelona': 91.22, 'Paris Saint-Germain': 87.79, 'Chelsea': 85.96, 'Real Madrid': 85.23, 'Tottenham Hotspur': 85.23, 'Juventus': 84.07, 'Borussia Dortmund': 83.63, 'Atletico Madrid': 83.11, 'Bayer Leverkusen': 82.44, 'Ajax': 82.17, 'RB Leipzig': 81.72, 'Internazionale': 81.52, 'Napoli': 80.98, 'Manchester United': 79.79, 'Arsenal': 79.22, 'Everton': 78.53, 'FC Salzburg': 78.51, 'Atalanta': 78.14, 'FC Porto': 78.03, 'Valencia': 77.81, 'Benfica': 76.86, 'TSG Hoffenheim': 76.23, 'Leicester City': 75.82, 'Olympiacos': 75.73, 'AC Milan': 75.49, 'Sevilla FC': 74.86, 'Lyon': 74.57, 'Wolverhampton': 73.87, 'AS Roma': 73.69, 'Getafe': 73.57, 'Real Sociedad': 73.55, 'Athletic Bilbao': 73.38, 'Eibar': 73.31, 'Eintracht Frankfurt': 72.83, 'FC Krasnodar': 71.74, 'Real Betis': 71.51, 'Young Boys': 71.5, 'Villarreal': 71.3, 'Palmeiras': 71.27, 'Borussia Monchengladbach': 71.24, 'Zenit St Petersburg': 71.18, 'Lazio': 71.13, 'Crystal Palace': 71.09, 'VfL Wolfsburg': 70.72, 'Espanyol': 70.42, 'Leganes': 70.32, 'PSV': 70.14, 'Werder Bremen': 70.1, 'PAOK Salonika': 70.07, 'Newcastle': 69.88, 'West Ham United': 69.71, 'AFC Bournemouth': 69.62, 'Lille': 69.47, 'CSKA Moscow': 69.44, 'Galatasaray': 69.39, 'Fulham': 69.3, 'Sporting CP': 68.75, 'Southampton': 68.7, 'Flamengo': 68.34, 'Shakhtar Donetsk': 68.14, 'Schalke 04': 68.02, 'Watford': 67.92, 'Celta Vigo': 67.77, 'Burnley': 67.76, 'Torino': 67.76, 'Mainz': 67.75, 'Genk': 67.46, 'FC Copenhagen': 67.37, 'Fiorentina': 67.14, 'Marseille': 66.56, 'Sampdoria': 66.48, 'Hertha Berlin': 66.08, 'Alavés': 65.93, 'Club Brugge': 65.76, 'River Plate': 65.61, 'Boca Juniors': 65.43, 'Basel': 65.38, 'Lokomotiv Moscow': 65.26, 'Levante': 64.99, 'Dynamo Kyiv': 64.82, 'Bologna': 64.78, 'Aston Villa': 64.76, 'Besiktas': 64.76, 'Viktoria Plzen': 64.72, 'Santos': 64.41, 'Real Valladolid': 64.29, 'St Etienne': 64.25, 'AS Monaco': 64.14, 'Osasuna': 64.11, 'Fortuna Düsseldorf': 64.04, 'Montpellier': 63.95, 'Granada': 63.78, 'Mallorca': 63.77, 'Genoa': 63.48, 'Celtic': 63.39, 'Ludogorets': 63.34, 'Brighton and Hove Albion': 62.79, 'Norwich City': 62.76, 'Leeds United': 62.74, 'Nantes': 62.67, 'Guangzhou Evergrande': 62.64, 'Red Star Belgrade': 62.4, 'Grêmio': 62.21, 'Atlético Paranaense': 61.84, 'Sheffield United': 61.76, 'Club América': 61.7, 'FC Cologne': 61.4, 'Sassuolo': 61.3, 'Dinamo Zagreb': 61.22, 'SC Freiburg': 60.9, 'Nice': 60.42, 'Angers': 60.15, 'Istanbul Basaksehir': 59.94, 'Los Angeles FC': 59.87, 'Stade Rennes': 59.76, 'Trabzonspor': 59.59, 'FC Augsburg': 59.56, 'Feyenoord': 59.49, 'Spal': 59.18, 'Monterrey': 58.88, 'Beijing Guoan': 58.81, 'Tigres UANL': 58.78, 'Cagliari': 58.67, '1. FC Union Berlin': 58.66, 'Strasbourg': 58.39, 'Huddersfield Town': 58.38, 'Rangers': 58.36, 'FC Astana': 58.29, 'Braga': 58.28, 'Nimes': 58.22, 'LASK Linz': 58.2, 'Slavia Prague': 58.2, 'SC Paderborn': 58.12, 'Internacional': 57.99, 'Udinese': 57.93, 'Jablonec': 57.83, 'Empoli': 57.74, 'Shanghai SIPG': 57.73, 'FC Midtjylland': 57.18, 'Spartak Moscow': 57.05, 'São Paulo': 56.84, 'Bordeaux': 56.83, 'Corinthians': 56.28, 'Reims': 56.26, 'Cruz Azul': 56.08, 'Atletico Mineiro': 55.95, 'Toulouse': 55.55, 'Stoke City': 55.52, 'Brentford': 55.4, 'Kawasaki Frontale': 55.29, 'West Bromwich Albion': 55.12, 'Amiens': 54.82, 'AEK Athens': 54.46, 'Fenerbahce': 54.28, 'Racing Club': 54.18, 'Cardiff City': 54.00, 'Malmo FF': 53.82, 'FC Arsenal Tula': 53.55, 'Metz': 53.48, 'Frosinone': 53.36, 'Swansea City': 53.3, 'Brondby': 53.26, 'Vitesse': 53.01, 'Parma': 53.00, 'Molde': 52.92, 'Brest': 52.77, 'Dijon FCO': 52.75, 'Santos Laguna': 52.58, 'Bahía': 52.53, 'VfB Stuttgart': 52.47, 'Middlesbrough': 52.45, 'León': 52.45, 'Anderlecht': 52.41, 'CA Independiente': 52.38, 'Girona FC': 52.34, 'Standard Liege': 51.98, 'Bristol City': 51.95, 'Kashima Antlers': 51.94, 'Derby County': 51.83, 'Pachuca': 51.59, 'AZ': 51.32, 'Guimaraes': 51.04, 'Guingamp': 50.72, 'KAA Gent': 50.44, 'Terek Grozny': 50.03, 'FK Qarabag': 49.91, 'Dinamo Moscow': 49.76, 'Gazovik Orenburg': 49.68, 'Shandong Luneng': 49.46, 'New York City FC': 49.34, 'Chievo Verona': 49.3, 'Vasco da Gama': 49.27, 'Caykur Rizespor': 49.26, 'Apollon Limassol': 49.22, 'Cruzeiro': 49.03, 'Rostov': 48.88, 'AIK': 48.83, 'Velez Sarsfield': 48.74, 'Nottingham Forest': 48.59, 'Fluminense': 48.22, 'Rapid Vienna': 48.13, 'Defensa y Justicia': 48.07, 'Atlanta United FC': 48.06, 'ADO Den Haag': 47.8, 'Rayo Vallecano': 47.69, 'BATE Borisov': 47.66, 'Blackburn': 47.63, 'Sheffield Wednesday': 47.59, 'Ceará': 47.18, 'Arizona United': 46.91, 'Hull City': 46.74, 'FC Utrecht': 46.5, 'Rio Ave': 46.41, 'Hammarby': 46.39, 'Jiangsu Suning FC': 46.38, 'Millwall': 46.37, 'FK Austria Vienna': 46.36, 'Desportivo Aves': 46.34, 'Birmingham': 46.25, 'Toluca': 46.21, 'Konyaspor': 46.04, 'Botafogo': 46.01, 'Portimonense': 45.95, 'Djurgardens IF': 45.8, 'FC Nordsjaelland': 45.73, 'Tijuana': 45.61, 'BK Hacken': 45.6, 'Hannover 96': 45.41, 'AEK Larnaca': 45.4, 'FC Luzern': 45.22, 'IFK Norrkoping': 45.14, 'FC Ufa': 45.09, 'Queens Park Rangers': 44.99, 'St. Truidense': 44.82, 'Boavista': 44.8, 'Lanus': 44.75, 'Pumas Unam': 44.64, 'Rubin Kazan': 44.62, 'Moreirense': 44.44, 'Aris Salonika': 44.4, 'SK Sturm Graz': 44.36, 'Ural Sverdlovsk Oblast': 44.21, 'Portland Timbers': 44.21, 'Sivasspor': 44.2, 'Guadalajara': 43.99, 'Hamburg SV': 43.91, 'Goztepe': 43.83, 'Preston North End': 43.78, 'Mouscron-Peruwelz': 43.73, 'Málaga': 43.71, 'KV Kortrijk': 43.71, 'Philadelphia Union': 43.65, 'Sporting Kansas City': 43.52, 'San Lorenzo': 43.5, 'Santa Clara': 43.46, 'Cerezo Osaka': 43.34, 'Perth Glory': 43.32, 'Troyes': 43.27, 'Wigan': 43.27, 'Tigre': 43.21, 'Alanyaspor': 43.08, 'Wolfsberger AC': 42.98, 'Sunderland': 42.97, 'Banfield': 42.9, 'Videoton FC': 42.86, 'FC Groningen': 42.82, 'Union Santa Fe': 42.76, 'Talleres de Córdoba': 42.61, 'Vitoria Setubal': 42.61, 'FC Lugano': 42.58, 'Chapecoense AF': 42.54, 'Rosenborg': 42.42, 'Antwerp': 42.37, 'Cashpoint SC Rheindorf Altach': 42.26, 'Yokohama F. Marinos': 42.21, 'Seattle Sounders FC': 42.2, 'Dalian Aerbin': 42.17, 'Sporting de Charleroi': 42.09, 'New York Red Bulls': 42.07, 'Goiás': 42.06, 'Atromitos': 41.89, 'Sanfrecce Hiroshima': 41.86, 'Vorskla': 41.83, "Newell's Old Boys": 41.61, 'Necaxa': 41.59, 'Yeni Malatyaspor': 41.39, 'Deportivo La Coruña': 41.33, 'Erzurumspor': 41.29, 'Odense BK': 41.26, 'FC Tokyo': 41.03, 'AGF Aarhus': 41.01, 'Caen': 40.93, 'Fortaleza': 40.83, 'Belenenses': 40.7, 'Chaves': 40.45, 'SV Zulte Waregem': 40.42, 'St Gallen': 40.4, 'Los Angeles Galaxy': 40.4, 'Colon Santa Fe': 40.26, 'FC Sion': 40.1, 'Esbjerg': 40.03, 'Mamelodi Sundowns': 39.98, 'Tondela': 39.98, 'Real Salt Lake': 39.97, 'Huracán': 39.85, 'Chicago Fire': 39.84, 'Atlas': 39.75, 'Morelia': 39.72, 'FC Dallas': 39.56, 'Argentinos Juniors': 39.55, 'Querétaro': 39.45, 'San Jose Earthquakes': 39.43, 'FC Zurich': 39.42, 'Estudiantes': 39.42, 'Atlético Tucumán': 39.31, 'Hebei China Fortune FC': 39.31, 'Reading': 39.26, 'Shanghai Greenland': 39.21, 'Avaí': 38.95, 'Rosario Central': 38.9, 'Kasimpasa': 38.86, 'Lorient': 38.86, 'Toronto FC': 38.86, 'Guangzhou RF': 38.74, 'Lens': 38.63, 'Sydney FC': 38.56, 'Valerenga': 38.53, 'Heracles': 38.4, 'Maritimo': 38.33, 'Heerenveen': 38.33, 'Godoy Cruz': 38.24, 'Minnesota United FC': 38.22, 'Krylia Sovetov': 38.2, '1. FC Nürnberg': 38.04, 'Tianjin Teda': 38.03, 'Puebla': 37.85, 'Panathinaikos': 37.79, 'Ankaragucu': 37.73, 'PEC Zwolle': 37.54, 'Chongqing Lifan': 37.53, 'New England Revolution': 37.44, 'Bursaspor': 37.3, 'Aldosivi': 37.3, 'Barnsley': 37.3, 'Willem II': 37.21, 'Thun': 37.2, 'Holstein Kiel': 37.00, 'Luton Town': 36.99, 'AaB': 36.96, 'Cadiz': 36.86, 'SV Mattersburg': 36.85, 'Montreal Impact': 36.82, 'Patronato': 36.77, 'Belgrano Cordoba': 36.75, 'FC Ingolstadt 04': 36.73, 'Orlando Pirates': 36.72, 'Kilmarnock': 36.62, 'Columbus Crew': 36.61, 'San Martin San Juan': 36.48, 'Randers FC': 36.42, 'FC Spartak Trnava': 36.38, 'Charlton Athletic': 36.34, 'Aberdeen': 36.33, 'Kayserispor': 36.16, 'C.D. Nacional': 36.16, 'Almeria': 36.02, 'VVV Venlo': 35.95, 'Waasland-Beveren': 35.86, 'Arminia Bielefeld': 35.84, 'FC Trenkwalder Admira': 35.73, 'Sporting Gijón': 35.67, 'Henan Jianye': 35.55, 'Gimnasia La Plata': 35.53, 'Wuhan Zall': 35.52, 'Bodo/Glimt': 35.39, 'San Martin de Tucuman': 35.27, 'DC United': 35.25, 'Consadole Sapporo': 35.14, 'Antalyaspor': 34.92, 'Real Zaragoza': 34.82, 'Houston Dynamo': 34.79, 'Jahn Regensburg': 34.67, 'Akhisar Belediye': 34.59, 'Melbourne City': 34.57, 'F91 Dudelange': 34.49, 'Emmen': 34.46, '1. FC Heidenheim 1846': 34.41, 'Hartberg': 34.3, 'Paris FC': 34.23, 'Albacete': 34.09, 'IFK Goteborg': 34.07, 'FC Wacker Innsbruck': 34.04, 'Orlando City SC': 33.94, 'OFI Crete': 33.93, 'Haugesund': 33.88, 'Asteras Tripolis': 33.84, 'Newcastle Jets': 33.78, 'Bidvest Wits': 33.7, 'Tianjin Quanujian': 33.64, 'Urawa Red Diamonds': 33.57, 'Neuchatel Xamax': 33.54, 'Las Palmas': 33.52, 'Feirense': 33.38, 'Hibernian': 33.28, 'SD Huesca': 33.26, 'Metallurg Krasnoyarsk': 33.25, 'Eupen': 33.19, 'SK Brann': 33.18, 'Ipswich Town': 33.13, 'Excelsior': 33.07, 'Sonderjyske': 32.99, 'KV Oostende': 32.85, 'Vissel Kobe': 32.81, 'Kristiansund BK': 32.72, 'Lamia': 32.57, 'VfL Bochum': 32.34, 'Nagoya Grampus Eight': 32.21, 'Larissa': 32.08, 'SV Sandhausen': 31.97, 'Atlético San Luis': 31.67, 'Palermo': 31.44, 'Cercle Brugge': 31.44, 'Dynamo Dresden': 31.43, 'Tenerife': 31.38, 'CSA': 31.38, 'SV Darmstadt 98': 31.35, 'Melbourne Victory': 31.32, 'Nashville SC': 31.29, 'Clermont Foot': 31, 'KSC Lokeren': 31, 'Le Havre': 30.95, 'Extremadura UD': 30.94, 'Elche': 30.89, 'Odd BK': 30.89, 'Real Oviedo': 30.87, 'Gamba Osaka': 30.76, 'Reno 1868 FC': 30.57, '1. FC Magdeburg': 30.45, 'Lobos de la BUAP': 30.3, 'St. Pölten': 30.28, 'New York Red Bulls II': 30.21, 'Hobro IK': 30.13, 'Oita Trinita': 30.06, 'Benevento': 29.83, 'Veracruz': 29.73, 'Shenzhen FC': 29.66, 'Vendsyssel': 29.55, 'Östersunds FK': 29.53, 'FC Juárez': 29.48, 'Chateauroux': 29.36, 'Adelaide United': 29.25, 'De Graafschap': 29.25, 'Black Aces': 29.18, 'Lugo': 29.18, 'Cittadella': 29.16, 'FC St. Pauli': 29.04, 'Vejle': 29.03, 'Numancia': 28.98, 'Tampa Bay Rowdies': 28.68, 'AS Nancy Lorraine': 28.55, 'FC Xanthi': 28.54, 'Fortuna Sittard': 28.47, 'Kaizer Chiefs': 28.47, 'Orléans': 28.4, 'Panetolikos': 28.33, 'Rayo Majadahonda': 28.2, 'Colorado Rapids': 28.18, 'Orebro SK': 28.18, 'Vegalta Sendai': 27.98, 'Auxerre': 27.98, 'Erzgebirge Aue': 27.94, 'Portsmouth': 27.65, 'Hearts': 27.65, 'Western Sydney FC': 27.57, 'SpVgg Greuther Fürth': 27.42, 'Sarpsborg': 27.21, 'Panionios': 27.09, 'Crotone': 27.07, 'Bolton': 27.05, 'Valenciennes': 27.03, 'Pittsburgh Riverhounds': 27.02, 'Milton Keynes Dons': 26.96, 'Perugia': 26.91, 'Shimizu S-Pulse': 26.87, 'Spezia': 26.33, 'AC Horsens': 26.27, 'Shonan Bellmare': 26.25, 'Giannina': 26.12, 'Guizhou Renhe': 26.02, 'Burton Albion': 25.86, 'Grasshoppers Zürich': 25.61, 'Motherwell': 25.53, 'GIF Sundsvall': 25.32, 'Indy Eleven': 24.83, 'Anzhi Makhachkala': 24.82, 'Cosenza': 24.61, 'FC Cincinnati': 24.46, 'Grenoble': 24.43, 'North Carolina FC': 24.33, 'Wellington Phoenix': 24.27, 'AD Alcorcon': 24.26, 'MSV Duisburg': 24.25, 'Doncaster Rovers': 24.13, 'Real Monarchs SLC': 24.09, 'Vancouver Whitecaps': 23.95, 'Scunthorpe': 23.88, 'Peterborough United': 23.86, 'IK Sirius': 23.78, 'Fresno FC': 23.66, 'Niort': 23.62, 'Rotherham United': 23.58, 'Cordoba': 23.39, 'IF Elfsborg': 23.35, 'Stabaek': 23.05, 'Louisville City FC': 22.79, 'Livingston': 22.74, 'Foggia': 22.69, 'Sochaux': 22.69, 'Viking FK': 22.65, 'Cremonese': 22.61, 'Sagan Tosu': 22.51, 'Mjondalen': 22.5, 'St Johnstone': 22.45, 'Lillestrom': 22.3, 'Reus Deportiu': 22.2, 'San Antonio FC': 22.16, 'Fleetwood Town': 21.97, 'Ottawa Fury FC': 21.68, 'AC Ajaccio': 21.65, 'Coventry City': 21.61, 'SuperSport United': 21.58, 'St Mirren': 21.54, 'Bradford City': 21.53, 'Oxford United': 21.38, 'Beziers AS': 21.15, 'Lincoln City': 20.97, 'Highlands Park FC': 20.97, 'Polokwane City FC': 20.91, 'Helsingborgs IF': 20.78, 'NAC': 20.75, 'Bloem Celtic': 20.58, 'Ranheim': 20.56, 'Jubilo Iwata': 20.46, 'Brescia': 20.45, 'Stromsgodset': 20.41, 'Maritzburg Utd': 20.38, 'Orange County SC': 20.31, 'US Pescara': 20.15, 'Sacramento Republic FC': 20.03, 'New Mexico United': 19.99, 'Golden Arrows': 19.68, 'Gimnástic Tarragona': 19.51, 'Bristol Rovers': 19.43, 'AmaZulu': 19.28, 'Gillingham': 19.2, 'Shrewsbury Town': 18.97, 'F.B.C Unione Venezia': 18.89, 'Livorno': 18.84, 'Salernitana': 18.81, 'Portland Timbers 2': 18.71, 'Kalmar FF': 18.71, 'Charleston Battery': 18.69, 'Lecce': 18.66, 'Verona': 18.53, 'Tromso': 18.14, 'Austin Bold FC': 17.99, 'Chippa United': 17.97, 'Brisbane Roar': 17.89, 'Padova': 17.85, 'Oklahoma City Energy FC': 17.6, 'Mansfield Town': 17.08, 'Blackpool': 17.06, 'Ascoli': 17.06, 'Free State Stars': 17.00, 'Southend United': 16.98, 'Levadiakos': 16.8, 'Exeter City': 16.76, 'Saint Louis FC': 16.71, 'Red Star FC 93': 16.37, 'Hamilton Academical': 16.25, 'Baroka FC': 16.09, 'GFC Ajaccio': 15.91, 'Accrington Stanley': 15.69, 'Falkenbergs FF': 15.6, 'Tranmere Rovers': 15.57, 'Carpi': 15.5, 'Vasby United': 15.27, 'Matsumoto Yamaga FC': 14.98, 'Central Coast Mariners': 14.88, 'Bury': 14.87, 'Charlotte Independence': 14.84, 'Wycombe Wanderers': 14.69, 'LA Galaxy II': 14.67, 'Oldham Athletic': 14.62, 'Swindon Town': 14.56, 'Black Leopards': 14.41, 'Plymouth Argyle': 14.2, 'AFC Wimbledon': 14.07, 'El Paso Locomotive FC': 13.7, 'Las Vegas Lights FC': 13.18, 'Bethlehem Steel FC': 13.11, 'Apollon Smyrni': 13.08, 'Forest Green Rovers': 12.88, 'Rochdale': 12.68, 'Dundee': 12.52, 'Rio Grande Valley FC Toros': 12.48, 'Northampton Town': 12.36, 'Loudoun United FC': 11.49, 'Crewe Alexandra': 11.05, 'Colchester United': 10.65, 'Carlisle United': 10.37, 'Stevenage': 10.24, 'Cheltenham Town': 10.22, 'Memphis 901 FC': 10.15, 'Walsall': 9.95, 'Swope Park Rangers': 9.9, 'Hartford Athletic': 9.63, 'Birmingham Legion FC': 9.29, 'Morecambe': 8.8, 'Newport County': 8.19, 'Grimsby Town': 7.39, 'Crawley Town': 7.18, 'Port Vale': 6.94, 'Tulsa Roughnecks': 6.63, 'Colorado Springs Switchbacks FC': 6.45, 'Cambridge United': 6.41, 'Macclesfield': 6.28, 'Atlanta United 2': 4.73, 'Notts County': 4.52, 'Yeovil Town': 4.35, 'Tacoma Defiance': 4.14 }

26.562963

47

0.56068

2,533

17,930

3.968812

0.409791

0.005968

0.004377

0.00557

0

0.186672

0.255159

17,930

674

48

26.602374

0.56608

0

0.456497

0

1

0

false

0

null

0

null

0

1

0

c1ae4861c8eb6db57eabd223609f74170cb8381d

474

py

Python

string-list.py

leaen/Codeeval-solutions

fa83cb4fba3e56f79c0a6b00361c18cd3092c3f0

[ "MIT" ]

null

string-list.py

leaen/Codeeval-solutions

fa83cb4fba3e56f79c0a6b00361c18cd3092c3f0

[ "MIT" ]

null

string-list.py

leaen/Codeeval-solutions

fa83cb4fba3e56f79c0a6b00361c18cd3092c3f0

[ "MIT" ]

null

import sys import itertools def main(): with open(sys.argv[1]) as input_file: for line in input_file: length, letters = line.strip().split(',') length = int(length) letters = letters.strip() result = set(''.join(e) for e in itertools.product(letters, repeat=length)) result = sorted(result) print(','.join(result)) if __name__ == '__main__': main()

24.947368

87

0.525316

52

474

4.596154

0.576923

0.075314

0

0.003247

0.350211

474

18

88

26.333333

0.772727

0

0.021097

0

1

0.076923

false

0

0.153846

0

0.230769

0.076923

0

null

0

null

0

1

0

c1ae52c695e3f0b50fa41e0e39c71498b5115833

403

py

Python

src/chitty/web/meta.py

zgoda/chitty-server

8257ab39a9a6c0ae70d6e39e595925905c444850

[ "BSD-3-Clause" ]

null

src/chitty/web/meta.py

zgoda/chitty-server

8257ab39a9a6c0ae70d6e39e595925905c444850

[ "BSD-3-Clause" ]

null

src/chitty/web/meta.py

zgoda/chitty-server

8257ab39a9a6c0ae70d6e39e595925905c444850

[ "BSD-3-Clause" ]

null

import os from falcon import Request, Response class ServerMetadataResource: def on_get(self, req: Request, resp: Response) -> None: resp.cache_control = ['public', 'max-age=604800'] resp.media = { 'chat': { 'host': os.getenv('CHITTY_CHAT_HOST', '127.0.0.1'), 'port': int(os.getenv('CHITTY_CHAT_PORT', '5000')) } }

25.1875

67

0.550868

46

403

4.695652

0.695652

0.074074

0.12963

0.166667

0

0.05694

0.30273

403

15

68

26.866667

0.711744

0

0.191067

0

1

0.090909

false

0

0.181818

0

0.363636

0

null

0

1

0

null

0

1

c1aeb6b648385e5dd2afb1d49db366eb4bba7f45

1,295

py

Python

tests/compiler_2.py

louisyoungx/zpycli

87deb188d1fd7782c60912edf1eeedb719b649a6

[ "MIT" ]

null

tests/compiler_2.py

louisyoungx/zpycli

87deb188d1fd7782c60912edf1eeedb719b649a6

[ "MIT" ]

null

tests/compiler_2.py

louisyoungx/zpycli

87deb188d1fd7782c60912edf1eeedb719b649a6

[ "MIT" ]

null

import sys if ".." not in sys.path: sys.path.insert(0,"..") import zpylib.ast.lexer as lex from zpylib.grammar import * # Test it out data = """ # TEST Apple = 3 + 4 * 10 + -20 *2 def Print(what): if True: go(what + 10) 如果错: x = 'yoo what's up' '''what fuck''' {:.2f}.format(name) """ class Compiler(): def __init__(self, data): # Build the lexer self.lexer = lex.lex() # Give the lexer some input self.lexer.input(data) self.data = data self.positionOffset = 0 self.tokenize() def tokenize(self): print(self.data) # Tokenize while True: tok = self.lexer.token() if tok: self.update(tok) else: break # No more input print(self.data) def update(self, tok): #print(tok.type, tok.value, tok.lexpos, tok.lineno) if tok.type == 'NAME': self.subData(tok.value, tok.value+'_', tok.lexpos) def subData(self, oldStr, newStr, index): start = index + self.positionOffset end = start + len(oldStr) self.data = self.data[:start] + newStr + self.data[end:] self.positionOffset = self.positionOffset - (len(oldStr) - len(newStr)) Compiler(data)

24.433962

79

0.550579

166

1,295

4.26506

0.421687

0.079096

0.04661

0.048023

0

0.013514

0.314286

1,295

52

80

24.903846

0.783784

0.097297

0

0.05

0

0.143718

0

1

0.1

false

0

0.075

0

0.2

0.05

0

null

0

null

0

1

0

c1aeb773120f6e8b25cc706f47a68a22cbbc7e59

989

py

Python

setup.py

wasilak/yamllint-junit

0861484dc38220f772a4563974eebeae71ee6fb2

[ "MIT" ]

4

2017-11-28T22:04:01.000Z

2021-08-18T16:09:02.000Z

setup.py

wasilak/yamllint-junit

0861484dc38220f772a4563974eebeae71ee6fb2

[ "MIT" ]

5

2020-08-03T15:44:28.000Z

2021-11-05T10:47:51.000Z

setup.py

wasilak/yamllint-junit

0861484dc38220f772a4563974eebeae71ee6fb2

[ "MIT" ]

1

2020-08-28T15:05:51.000Z

#! /usr/bin/env python # -*- coding: utf-8 -*- from setuptools import setup from yamllint_junit_bootstrap import bootstrap version = bootstrap.__version__ with open("README.md", "r") as fh: long_description = fh.read() setup( name='yamllint-junit', packages=['yamllint_junit_bootstrap'], version=version, description='yamllint to JUnit converter.', long_description=long_description, long_description_content_type="text/markdown", author='wasil', author_email='piotr.m.boruc@gmail.com', url='https://github.com/wasilak/yamllint-junit', download_url='https://github.com/wasilak/yamllint-junit/archive/%s.tar.gz' % version, keywords=['yaml', 'junit'], classifiers=[], entry_points={ "console_scripts": ['yamllint-junit = yamllint_junit_bootstrap.bootstrap:main'] }, install_requires=[ 'yamllint', ], tests_require=[ 'pytest', 'flake8', 'coverage', 'mock', ], )

25.358974

89

0.65723

109

989

5.761468

0.605505

0.144904

0.105096

0.095541

0.117834

0

0.002509

0.194135

989

38

90

26.026316

0.785445

0.044489

0

0.064516

0

0.348887

0.091198

0

1

0

false

0

0.064516

0

0.064516

0

null

0

null

0

1

0

c1aeb94e32529b20eed44693cf5cf6e8db8c7b4a

199

py

Python

pyot/utils/tft/routing.py

FabrizioCoder/Pyot

2ca5114e97fa9785b030c60892838f3605664c21

[ "MIT" ]

1

2022-02-04T03:12:09.000Z

pyot/utils/tft/routing.py

FabrizioCoder/Pyot

2ca5114e97fa9785b030c60892838f3605664c21

[ "MIT" ]

null

pyot/utils/tft/routing.py

FabrizioCoder/Pyot

2ca5114e97fa9785b030c60892838f3605664c21

[ "MIT" ]

null

from pyot.models.tft import base def platform_to_region(platform: str) -> str: '''Return the region correspondent to a given platform''' return base.PyotRouting._platform2regions[platform]

28.428571

61

0.763819

26

199

5.730769

0.692308

0

0.005882

0.145729

199

6

62

33.166667

0.870588

0.256281

0

1

0.333333

false

0

0.333333

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

5

c1af19eb5f9a129022353a3807101d1e028dd303

242

py

Python

problems/biclique/solver/main.py

Benezivas/algobattle-problems

b00b85413893bd1618001a4cdaa0dd7442f4e481

[ "MIT" ]

null

problems/biclique/solver/main.py

Benezivas/algobattle-problems

b00b85413893bd1618001a4cdaa0dd7442f4e481

[ "MIT" ]

null

problems/biclique/solver/main.py

Benezivas/algobattle-problems

b00b85413893bd1618001a4cdaa0dd7442f4e481

[ "MIT" ]

null

"""Simple dummy solver for the BiClique problem, outputting a static solution.""" with open("output", "w") as output: output.write("s set1 1\n") output.write("s set2 2\n") output.write("s set2 3\n") output.write("s set2 4\n")

34.571429

81

0.652893

41

242

3.853659

0.609756

0.278481

0.303797

0.246835

0.322785

0

0.040404

0.181818

242

6

82

40.333333

0.757576

0.309917

0

0.291925

0

1

0

true

0

null

1

0

1

0

null

0

1

0

5

c1affedd9b9ad2746552cd2f0960a1b9537db67c

6,151

py

Python

pyalfred/server/database.py

tingiskhan/pyalfred

c9fac2672af92906bcc4294e14844e904423c2e6

[ "MIT" ]

null

pyalfred/server/database.py

tingiskhan/pyalfred

c9fac2672af92906bcc4294e14844e904423c2e6

[ "MIT" ]

null

pyalfred/server/database.py

tingiskhan/pyalfred

c9fac2672af92906bcc4294e14844e904423c2e6

[ "MIT" ]

null

from typing import Union, List, Type from sqlalchemy.orm import scoped_session, sessionmaker from logging import Logger from starlette.endpoints import HTTPEndpoint from starlette.requests import Request from starlette.responses import JSONResponse from starlette.status import HTTP_500_INTERNAL_SERVER_ERROR, HTTP_200_OK from pyalfred.contract.utils import chunk, serialize from auto_schema import AutoMarshmallowSchema from query_serializer import QueryBuilder from pyalfred.contract.utils import get_columns_in_base_mixin from pyalfred.server.utils import make_base_logger, apply_filter_from_string from pyalfred.constants import CHUNK_SIZE def get_bool_from_string(x: str): return x.lower() == "true" class DatabaseResource(HTTPEndpoint): schema = None session_factory = None logger = None _create_ignore = None @classmethod def make_endpoint( cls, schema: Type[AutoMarshmallowSchema], session_factory: Union[scoped_session, sessionmaker], logger: Logger = None, mixin_ignore: Type[object] = None, create_ignore: List[str] = None, ): """ Implements a base resources for exposing database models. :param schema: The schema to use, must be marshmallow.Schema :param session_factory: The sqlalchemy scoped_session object to use :param logger: The logger to use :param mixin_ignore: If all of your models inherit from a single mixin that defines server side generated columns, you may pass that here. """ _create_ignore = [] if mixin_ignore is not None: _create_ignore += get_columns_in_base_mixin(mixin_ignore) elif create_ignore is not None: _create_ignore += create_ignore state_dict = { "schema": schema, "session_factory": session_factory, "logger": logger or make_base_logger(schema.__name__), "_create_ignore": _create_ignore, } return type(f"DatabaseResource_{schema.__name__}", (DatabaseResource,), state_dict) @property def model(self): return self.schema.Meta.model @property def fields_to_skip_on_create(self): schema_fields_to_load = list(getattr(self.schema, "load_only_fields", [])) return self._create_ignore + schema_fields_to_load async def get(self, req: Request): session = self.session_factory() try: query = session.query(self.model).with_for_update() filter_ = req.query_params.get("filter", None) if filter_: query_builder = QueryBuilder(self.model) query = query_builder.from_string(query, filter_) ops = req.query_params.get("ops", "") result = apply_filter_from_string(self.model, query, ops.split(",")) if result is None: result = list() elif not isinstance(result, list): result = [result] media = serialize(result, self.schema, many=True) status = HTTP_200_OK except Exception as e: self.logger.exception(e) status = HTTP_500_INTERNAL_SERVER_ERROR media = f"{e.__class__.__name__}: {e}" self.session_factory.remove() return JSONResponse(media, status) async def put(self, req: Request): batched = get_bool_from_string(req.query_params.get("batched", "false")) schema = self.schema(dump_only=self.fields_to_skip_on_create, many=True) objs = schema.load_instance(await req.json()) self.logger.info(f"Now trying to create {len(objs):n} objects") session = self.session_factory() try: for c in chunk(objs, CHUNK_SIZE): session.add_all(c) session.flush() session.commit() self.logger.info(f"Successfully created {len(objs):n} objects, now trying to serialize") media = serialize(objs, self.schema, many=True) if not batched else [] status = HTTP_200_OK except Exception as e: self.logger.exception(e) status = HTTP_500_INTERNAL_SERVER_ERROR media = f"{e.__class__.__name__}: {e}" session.rollback() self.session_factory.remove() return JSONResponse(media, status) async def delete(self, req: Request): session = self.session_factory() try: nums = session.query(self.model).filter(self.model.id == req.query_params["id"]).delete("fetch") self.logger.info(f"Now trying to delete {nums:n} objects") session.commit() self.logger.info(f"Successfully deleted {nums:n} objects") media = {"deleted": nums} status = HTTP_200_OK except Exception as e: self.logger.exception(e) session.rollback() media = f"{e.__class__.__name__}: {e}" status = HTTP_500_INTERNAL_SERVER_ERROR self.session_factory.remove() return JSONResponse(media, status) async def patch(self, req: Request): batched = get_bool_from_string(req.query_params.get("batched", "false")) schema = self.schema(many=True) objs = schema.load_instance(await req.json()) session = self.session_factory() self.logger.info(f"Now trying to update {len(objs):n} objects") try: for c in chunk(objs, CHUNK_SIZE): for obj in c: session.merge(obj) session.flush() session.commit() self.logger.info(f"Successfully updated {len(objs):n} objects, now trying to serialize") media = serialize(objs, self.schema, many=True) if not batched else [] status = HTTP_200_OK except Exception as e: self.logger.exception(e) session.rollback() media = f"{e.__class__.__name__}: {e}" status = HTTP_500_INTERNAL_SERVER_ERROR self.session_factory.remove() return JSONResponse(media, status)

34.172222

113

0.634368

734

6,151

5.076294

0.220708

0.048846

0.038647

0.024155

0.458132

0.405797

0.391304

0.359635

0.322598

0.272142

0

0.006733

0.275565

6,151

179

114

34.363128

0.829443

0.058202

0

0.418605

0

0.094781

0.021993

0

1

0.031008

false

0

0.100775

0.015504

0.232558

0

null

0

null

0

1

0

c1b0a13dd61f692f08ec2294ecce99fedfad9c35

4,628

py

Python

hdv/redfish/http_handler.py

opnfv/cirv-hdv

2d145d4f1fd231def2c9d52a71267031b938c0ac

[ "Apache-2.0" ]

null

hdv/redfish/http_handler.py

opnfv/cirv-hdv

2d145d4f1fd231def2c9d52a71267031b938c0ac

[ "Apache-2.0" ]

null

hdv/redfish/http_handler.py

opnfv/cirv-hdv

2d145d4f1fd231def2c9d52a71267031b938c0ac

[ "Apache-2.0" ]

null

############################################################################## # Copyright (c) 2020 China Mobile Co.,Ltd and others. # # All rights reserved. This program and the accompanying materials # are made available under the terms of the Apache License, Version 2.0 # which accompanies this distribution, and is available at # http://www.apache.org/licenses/LICENSE-2.0 ############################################################################## ''' a common http_handler ''' import urllib.request import json import ssl from http.client import HTTPException from urllib.error import HTTPError, URLError # pylint: disable=E0611 from log_utils import LOGGER from errors import ERROR_CODE # pylint: disable=W0212 ssl._create_default_https_context = ssl._create_unverified_context HEADERS = { 'Connection': 'keep-alive', 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 \ (KHTML, like Gecko) Chrome/67.0.3396.62 Safari/537.36', } TIME_OUT = 3000 class UrllibHttpHandler: """ http handler based on urllib of python2.7 """ def __init__(self): self.__header = HEADERS def get(self, url): """ run the get request """ try: req = urllib.request.Request(url, headers=self.__header) res = urllib.request.urlopen(req, timeout=TIME_OUT) except HTTPException as http_exp: LOGGER.error(http_exp) LOGGER.error(u"%s %s", ERROR_CODE['E100001'], url) except HTTPError as http_err: LOGGER.error(http_err) LOGGER.error(u"%s %s", ERROR_CODE['E100001'], url) LOGGER.error(u"%s %s", ERROR_CODE['E600001'], url) else: return res def post(self, url, parameter=None): """ run the post request, parameter must to encode to bytes """ try: data = json.dumps(parameter).encode(encoding="utf-8") LOGGER.debug("data is %s", data) req = urllib.request.Request(url, data=data, headers=self.__header) req.add_header("Content-Type", "application/json") res = urllib.request.urlopen(req, timeout=TIME_OUT) except HTTPException as http_exp: LOGGER.error(http_exp) LOGGER.error(u"%s %s", ERROR_CODE['E100001'], url) except TimeoutError as timeout_error: LOGGER.error(timeout_error) LOGGER.error(u"%s", ERROR_CODE['E100003']) except HTTPError as http_err: LOGGER.error(http_err) LOGGER.error(u"%s %s", ERROR_CODE['E100001'], url) LOGGER.error(u"%s %s", ERROR_CODE['E600001'], url) except URLError as url_err: LOGGER.error(url_err) LOGGER.error(u"%s %s", ERROR_CODE['E100001'], url) else: return res def put(self, url, parameter=None): """ run the put request, parameter must to encode to bytes """ # parameter_data = urllib.parse.urlencode(parameter) #?? data = json.dumps(parameter).encode(encoding="utf-8") LOGGER.debug("data is %s", data) req = urllib.request.Request(url, data=data, headers=self.__header) req.get_method = lambda: 'PUT' res = urllib.request.urlopen(req) return res def patch(self, url, parameter=None, etag=None): """ run the patch request, parameter must to encode to bytes """ data = json.dumps(parameter).encode(encoding="utf-8") LOGGER.debug("data is %s", data) req = urllib.request.Request(url, data=data, headers=self.__header) req.add_header("Content-Type", "application/json") req.add_header("If-Match", etag) req.get_method = lambda: 'PATCH' res = None try: res = urllib.request.urlopen(req, timeout=TIME_OUT) except HTTPException as http_exp: LOGGER.error(http_exp) LOGGER.error(u"%s %s", ERROR_CODE['E100001'], url) except HTTPError as http_err: LOGGER.error(http_err) LOGGER.error(u"%s %s", ERROR_CODE['E100001'], url) LOGGER.error(u"%s %s", ERROR_CODE['E600001'], url) except TypeError as type_err: LOGGER.error(type_err) LOGGER.error(u"%s %s", ERROR_CODE['E100001'], url) return res def delete(self, url): ''' run the delete request, ''' req = urllib.request.Request(url, headers=self.__header) req.get_method = lambda: 'DELETE' res = urllib.request.urlopen(req) return res

35.6

79

0.590104

574

4,628

4.641115

0.259582

0.086712

0.054054

0.058559

0.588213

0.576201

0.556682

0.478604

0.446321

0

0.035975

0.261236

4,628

129

80

35.875969

0.7432

0.144555

0

0.593023

0

0.080416

0

1

0.069767

false

0

0.081395

0

0.22093

0

null

0

null

0

1

0

c1b2c130162bc0b7988dfc259c05389cac64465c

3,006

py

Python

appserver/migrations/versions/a6ca510027a5_seed_annotationstyle_and_domainurlsmap_.py

SBRG/lifelike

a7b715f38b389a585c10e6d0d067345937455c13

[ "MIT" ]

8

2022-01-28T08:43:07.000Z

2022-03-23T11:18:10.000Z

appserver/migrations/versions/a6ca510027a5_seed_annotationstyle_and_domainurlsmap_.py

SBRG/lifelike

a7b715f38b389a585c10e6d0d067345937455c13

[ "MIT" ]

23

2022-02-14T15:25:00.000Z

2022-03-28T15:30:45.000Z

appserver/migrations/versions/a6ca510027a5_seed_annotationstyle_and_domainurlsmap_.py

SBRG/lifelike

a7b715f38b389a585c10e6d0d067345937455c13

[ "MIT" ]

5

2022-01-28T15:45:44.000Z

2022-03-14T11:36:49.000Z

"""Seed AnnotationStyle and DomainURLsMap tables Revision ID: a6ca510027a5 Revises: fb1654973fbd Create Date: 2020-08-19 23:27:53.132930 """ import json from alembic import context from alembic import op import sqlalchemy as sa from sqlalchemy.orm.session import Session from sqlalchemy.sql import table, column from neo4japp.models import AnnotationStyle, DomainURLsMap # revision identifiers, used by Alembic. revision = 'a6ca510027a5' down_revision = 'fb1654973fbd' branch_labels = None depends_on = None t_annotation_style = table( 'annotation_style', column('id', sa.Integer), column('label', sa.String), column('color', sa.String), column('icon_code', sa.String), column('font_color', sa.String), column('border_color', sa.String), column('background_color', sa.String), ) t_domain_urls_map = table( 'domain_urls_map', column('id', sa.Integer), column('domain', sa.String), column('base_URL', sa.String), ) def upgrade(): if context.get_x_argument(as_dictionary=True).get('data_migrate', None): data_upgrades() def downgrade(): pass # ### end Alembic commands ### # NOTE: In practice perfect downgrades are difficult and in some cases # impossible! It is more practical to use database backups/snapshots to # "downgrade" the database. Changes to the database that we intend to # push to production should always be added to a NEW migration. # (i.e. "downgrade forward"!) def data_upgrades(): """Add optional data upgrade migrations here""" session = Session(op.get_bind()) domain_urls_map_json = {} annotation_style_json = {} with open("fixtures/seed.json", "r") as f: data = json.load(f) for model in data: if model['model'] == 'neo4japp.models.DomainURLsMap': domain_urls_map_json = model['records'] continue if model['model'] == 'neo4japp.models.AnnotationStyle': annotation_style_json = model['records'] continue domain_urls_map_data = [] for row in domain_urls_map_json: domain_urls_map_data.append( { 'domain': row['domain'], 'base_URL': row['base_URL'] } ) session.execute(t_domain_urls_map.insert(), domain_urls_map_data) annotation_style_data = [] for row in annotation_style_json: annotation_style_data.append( { 'label': row['label'], 'color': row['color'], 'border_color': row.get('border_color', None), 'background_color': row.get('background_color', None), 'font_color': row.get('font_color', None), 'icon_code': row.get('icon_code', None), } ) session.execute(t_annotation_style.insert(), annotation_style_data) session.commit() def data_downgrades(): """Add optional data downgrade migrations here""" pass

28.093458

76

0.641717

361

3,006

5.152355

0.365651

0.072581

0.062903

0.030645

0.052688

0

0.023348

0.244844

3,006

106

77

28.358491

0.796035

0.192947

0

0.085714

0

0.161519

0.025042

0

1

0.057143

false

0.028571

0.1

0

0.157143

0

null

0

null

0

1

0

c1b48598f2501dd038d05132ab6a96a3689973e1

18,205

py

Python

trace.py

rawoul/apitrace

e9fcdcf14a99f5cb4729abb7bbf7817d7aa59d18

[ "MIT" ]

1

2017-07-25T20:22:08.000Z

trace.py

rawoul/apitrace

e9fcdcf14a99f5cb4729abb7bbf7817d7aa59d18

[ "MIT" ]

null

trace.py

rawoul/apitrace

e9fcdcf14a99f5cb4729abb7bbf7817d7aa59d18

[ "MIT" ]

null

########################################################################## # # Copyright 2008-2010 VMware, Inc. # All Rights Reserved. # # 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. # ##########################################################################/ """Common trace code generation.""" import specs.stdapi as stdapi from dispatch import Dispatcher def interface_wrap_name(interface): return "Wrap" + interface.expr class DumpDeclarator(stdapi.OnceVisitor): '''Declare helper functions to dump complex types.''' def visit_void(self, literal): pass def visit_literal(self, literal): pass def visit_string(self, string): pass def visit_const(self, const): self.visit(const.type) def visit_struct(self, struct): for type, name in struct.members: self.visit(type) print 'static void _write__%s(const %s &value) {' % (struct.tag, struct.expr) print ' static const char * members[%u] = {' % (len(struct.members),) for type, name, in struct.members: print ' "%s",' % (name,) print ' };' print ' static const trace::StructSig sig = {' print ' %u, "%s", %u, members' % (struct.id, struct.name, len(struct.members)) print ' };' print ' trace::localWriter.beginStruct(&sig);' for type, name in struct.members: dump_instance(type, 'value.%s' % (name,)) print ' trace::localWriter.endStruct();' print '}' print def visit_array(self, array): self.visit(array.type) def visit_blob(self, array): pass __enum_id = 0 def visit_enum(self, enum): print 'static const trace::EnumValue __enum%s_values[] = {' % (enum.tag) for value in enum.values: print ' {"%s", %s},' % (value, value) print '};' print print 'static const trace::EnumSig __enum%s_sig = {' % (enum.tag) print ' %u, %u, __enum%s_values' % (enum.id, len(enum.values), enum.tag) print '};' print def visit_bitmask(self, bitmask): print 'static const trace::BitmaskFlag __bitmask%s_flags[] = {' % (bitmask.tag) for value in bitmask.values: print ' {"%s", %s},' % (value, value) print '};' print print 'static const trace::BitmaskSig __bitmask%s_sig = {' % (bitmask.tag) print ' %u, %u, __bitmask%s_flags' % (bitmask.id, len(bitmask.values), bitmask.tag) print '};' print def visit_pointer(self, pointer): self.visit(pointer.type) def visit_handle(self, handle): self.visit(handle.type) def visit_alias(self, alias): self.visit(alias.type) def visit_opaque(self, opaque): pass def visit_interface(self, interface): print "class %s : public %s " % (interface_wrap_name(interface), interface.name) print "{" print "public:" print " %s(%s * pInstance);" % (interface_wrap_name(interface), interface.name) print " virtual ~%s();" % interface_wrap_name(interface) print for method in interface.itermethods(): print " " + method.prototype() + ";" print #print "private:" print " %s * m_pInstance;" % (interface.name,) print "};" print def visit_polymorphic(self, polymorphic): print 'static void _write__%s(int selector, const %s & value) {' % (polymorphic.tag, polymorphic.expr) print ' switch (selector) {' for cases, type in polymorphic.iterswitch(): for case in cases: print ' %s:' % case dump_instance(type, 'static_cast<%s>(value)' % (type,)) print ' break;' print ' }' print '}' print class DumpImplementer(stdapi.Visitor): '''Dump an instance.''' def visit_literal(self, literal, instance): print ' trace::localWriter.write%s(%s);' % (literal.kind, instance) def visit_string(self, string, instance): if string.length is not None: print ' trace::localWriter.writeString((const char *)%s, %s);' % (instance, string.length) else: print ' trace::localWriter.writeString((const char *)%s);' % instance def visit_const(self, const, instance): self.visit(const.type, instance) def visit_struct(self, struct, instance): print ' _write__%s(%s);' % (struct.tag, instance) def visit_array(self, array, instance): length = '__c' + array.type.tag index = '__i' + array.type.tag print ' if (%s) {' % instance print ' size_t %s = %s;' % (length, array.length) print ' trace::localWriter.beginArray(%s);' % length print ' for (size_t %s = 0; %s < %s; ++%s) {' % (index, index, length, index) print ' trace::localWriter.beginElement();' self.visit(array.type, '(%s)[%s]' % (instance, index)) print ' trace::localWriter.endElement();' print ' }' print ' trace::localWriter.endArray();' print ' } else {' print ' trace::localWriter.writeNull();' print ' }' def visit_blob(self, blob, instance): print ' trace::localWriter.writeBlob(%s, %s);' % (instance, blob.size) def visit_enum(self, enum, instance): print ' trace::localWriter.writeEnum(&__enum%s_sig, %s);' % (enum.tag, instance) def visit_bitmask(self, bitmask, instance): print ' trace::localWriter.writeBitmask(&__bitmask%s_sig, %s);' % (bitmask.tag, instance) def visit_pointer(self, pointer, instance): print ' if (%s) {' % instance print ' trace::localWriter.beginArray(1);' print ' trace::localWriter.beginElement();' dump_instance(pointer.type, "*" + instance) print ' trace::localWriter.endElement();' print ' trace::localWriter.endArray();' print ' } else {' print ' trace::localWriter.writeNull();' print ' }' def visit_handle(self, handle, instance): self.visit(handle.type, instance) def visit_alias(self, alias, instance): self.visit(alias.type, instance) def visit_opaque(self, opaque, instance): print ' trace::localWriter.writeOpaque((const void *)%s);' % instance def visit_interface(self, interface, instance): print ' trace::localWriter.writeOpaque((const void *)&%s);' % instance def visit_polymorphic(self, polymorphic, instance): print ' _write__%s(%s, %s);' % (polymorphic.tag, polymorphic.switch_expr, instance) dump_instance = DumpImplementer().visit class Wrapper(stdapi.Visitor): '''Wrap an instance.''' def visit_void(self, type, instance): raise NotImplementedError def visit_literal(self, type, instance): pass def visit_string(self, type, instance): pass def visit_const(self, type, instance): pass def visit_struct(self, struct, instance): for type, name in struct.members: self.visit(type, "(%s).%s" % (instance, name)) def visit_array(self, array, instance): # XXX: actually it is possible to return an array of pointers pass def visit_blob(self, blob, instance): pass def visit_enum(self, enum, instance): pass def visit_bitmask(self, bitmask, instance): pass def visit_pointer(self, pointer, instance): print " if (%s) {" % instance self.visit(pointer.type, "*" + instance) print " }" def visit_handle(self, handle, instance): self.visit(handle.type, instance) def visit_alias(self, alias, instance): self.visit(alias.type, instance) def visit_opaque(self, opaque, instance): pass def visit_interface(self, interface, instance): assert instance.startswith('*') instance = instance[1:] print " if (%s) {" % instance print " %s = new %s(%s);" % (instance, interface_wrap_name(interface), instance) print " }" def visit_polymorphic(self, type, instance): # XXX: There might be polymorphic values that need wrapping in the future pass class Unwrapper(Wrapper): def visit_interface(self, interface, instance): assert instance.startswith('*') instance = instance[1:] print " if (%s) {" % instance print " %s = static_cast<%s *>(%s)->m_pInstance;" % (instance, interface_wrap_name(interface), instance) print " }" wrap_instance = Wrapper().visit unwrap_instance = Unwrapper().visit class Tracer: def __init__(self): self.api = None def trace_api(self, api): self.api = api self.header(api) # Includes for header in api.headers: print header print # Type dumpers types = api.all_types() visitor = DumpDeclarator() map(visitor.visit, types) print # Interfaces wrapers interfaces = [type for type in types if isinstance(type, stdapi.Interface)] map(self.interface_wrap_impl, interfaces) print # Function wrappers map(self.trace_function_decl, api.functions) map(self.trace_function_impl, api.functions) print self.footer(api) def header(self, api): pass def footer(self, api): pass def trace_function_decl(self, function): # Per-function declarations if function.args: print 'static const char * __%s_args[%u] = {%s};' % (function.name, len(function.args), ', '.join(['"%s"' % arg.name for arg in function.args])) else: print 'static const char ** __%s_args = NULL;' % (function.name,) print 'static const trace::FunctionSig __%s_sig = {%u, "%s", %u, __%s_args};' % (function.name, function.id, function.name, len(function.args), function.name) print def is_public_function(self, function): return True def trace_function_impl(self, function): if self.is_public_function(function): print 'extern "C" PUBLIC' else: print 'extern "C" PRIVATE' print function.prototype() + ' {' if function.type is not stdapi.Void: print ' %s __result;' % function.type self.trace_function_impl_body(function) if function.type is not stdapi.Void: self.wrap_ret(function, "__result") print ' return __result;' print '}' print def trace_function_impl_body(self, function): print ' unsigned __call = trace::localWriter.beginEnter(&__%s_sig);' % (function.name,) for arg in function.args: if not arg.output: self.unwrap_arg(function, arg) self.dump_arg(function, arg) print ' trace::localWriter.endEnter();' self.dispatch_function(function) print ' trace::localWriter.beginLeave(__call);' for arg in function.args: if arg.output: self.dump_arg(function, arg) self.wrap_arg(function, arg) if function.type is not stdapi.Void: self.dump_ret(function, "__result") print ' trace::localWriter.endLeave();' def dispatch_function(self, function, prefix='__', suffix=''): if function.type is stdapi.Void: result = '' else: result = '__result = ' dispatch = prefix + function.name + suffix print ' %s%s(%s);' % (result, dispatch, ', '.join([str(arg.name) for arg in function.args])) def dump_arg(self, function, arg): print ' trace::localWriter.beginArg(%u);' % (arg.index,) self.dump_arg_instance(function, arg) print ' trace::localWriter.endArg();' def dump_arg_instance(self, function, arg): dump_instance(arg.type, arg.name) def wrap_arg(self, function, arg): wrap_instance(arg.type, arg.name) def unwrap_arg(self, function, arg): unwrap_instance(arg.type, arg.name) def dump_ret(self, function, instance): print ' trace::localWriter.beginReturn();' dump_instance(function.type, instance) print ' trace::localWriter.endReturn();' def wrap_ret(self, function, instance): wrap_instance(function.type, instance) def unwrap_ret(self, function, instance): unwrap_instance(function.type, instance) def interface_wrap_impl(self, interface): print '%s::%s(%s * pInstance) {' % (interface_wrap_name(interface), interface_wrap_name(interface), interface.name) print ' m_pInstance = pInstance;' print '}' print print '%s::~%s() {' % (interface_wrap_name(interface), interface_wrap_name(interface)) print '}' print for method in interface.itermethods(): self.trace_method(interface, method) print def trace_method(self, interface, method): print method.prototype(interface_wrap_name(interface) + '::' + method.name) + ' {' print ' static const char * __args[%u] = {%s};' % (len(method.args) + 1, ', '.join(['"this"'] + ['"%s"' % arg.name for arg in method.args])) print ' static const trace::FunctionSig __sig = {%u, "%s", %u, __args};' % (method.id, interface.name + '::' + method.name, len(method.args) + 1) print ' unsigned __call = trace::localWriter.beginEnter(&__sig);' print ' trace::localWriter.beginArg(0);' print ' trace::localWriter.writeOpaque((const void *)m_pInstance);' print ' trace::localWriter.endArg();' for arg in method.args: if not arg.output: self.unwrap_arg(method, arg) self.dump_arg(method, arg) if method.type is stdapi.Void: result = '' else: print ' %s __result;' % method.type result = '__result = ' print ' trace::localWriter.endEnter();' print ' %sm_pInstance->%s(%s);' % (result, method.name, ', '.join([str(arg.name) for arg in method.args])) print ' trace::localWriter.beginLeave(__call);' for arg in method.args: if arg.output: self.dump_arg(method, arg) self.wrap_arg(method, arg) if method.type is not stdapi.Void: print ' trace::localWriter.beginReturn();' dump_instance(method.type, "__result") print ' trace::localWriter.endReturn();' wrap_instance(method.type, '__result') print ' trace::localWriter.endLeave();' if method.name == 'QueryInterface': print ' if (ppvObj && *ppvObj) {' print ' if (*ppvObj == m_pInstance) {' print ' *ppvObj = this;' print ' }' for iface in self.api.interfaces: print r' else if (riid == IID_%s) {' % iface.name print r' *ppvObj = new Wrap%s((%s *) *ppvObj);' % (iface.name, iface.name) print r' }' print r' else {' print r' os::log("apitrace: warning: unknown REFIID {0x%08lX,0x%04X,0x%04X,{0x%02X,0x%02X,0x%02X,0x%02X,0x%02X,0x%02X,0x%02X,0x%02X}}\n",' print r' riid.Data1, riid.Data2, riid.Data3,' print r' riid.Data4[0],' print r' riid.Data4[1],' print r' riid.Data4[2],' print r' riid.Data4[3],' print r' riid.Data4[4],' print r' riid.Data4[5],' print r' riid.Data4[6],' print r' riid.Data4[7]);' print r' }' print ' }' if method.name == 'Release': assert method.type is not stdapi.Void print ' if (!__result)' print ' delete this;' if method.type is not stdapi.Void: print ' return __result;' print '}' print class DllTracer(Tracer): def __init__(self, dllname): self.dllname = dllname def header(self, api): print ''' static HINSTANCE g_hDll = NULL; static PROC __getPublicProcAddress(LPCSTR lpProcName) { if (!g_hDll) { char szDll[MAX_PATH] = {0}; if (!GetSystemDirectoryA(szDll, MAX_PATH)) { return NULL; } strcat(szDll, "\\\\%s"); g_hDll = LoadLibraryA(szDll); if (!g_hDll) { return NULL; } } return GetProcAddress(g_hDll, lpProcName); } ''' % self.dllname dispatcher = Dispatcher() dispatcher.dispatch_api(api) Tracer.header(self, api)

35.418288

166

0.572535

2,031

18,205

5.005416

0.151157

0.035412

0.0723

0.028133

0.42229

0.308971

0.211981

0.157289

0.108696

0.101023

0

0.005383

0.295853

18,205

513

167

35.487329

0.787659

0.071848

0

0.41555

0

0.005362

0.26304

0.087982

0

0.008043

0

null

0.042895

0.005362

null

0.41555

0

null

0

null

0

1

0

1

0

2

c1b62424e13951dc15b2f1d8f1ebe1749e5c2d11

3,334

py

Python

prepare_dataset.py

TheElderMindseeker/pytorch-domain-adaptation

70ca862708bd6e59b5eee5d7c8bd808ef3457dc8

[ "MIT" ]

null

prepare_dataset.py

TheElderMindseeker/pytorch-domain-adaptation

70ca862708bd6e59b5eee5d7c8bd808ef3457dc8

[ "MIT" ]

null

prepare_dataset.py

TheElderMindseeker/pytorch-domain-adaptation

70ca862708bd6e59b5eee5d7c8bd808ef3457dc8

[ "MIT" ]

null

# pylint: disable=invalid-name,missing-docstring import os import subprocess import urllib.parse import uuid def read_temporal_data(temporal_path: str): temporal_data = dict() for line in open(temporal_path, 'r'): name, v_cls, s_frm_1, f_frm_1, s_frm_2, f_frm_2 = line.strip().split() temporal_data[name] = { 'class': v_cls, 'action_1': (int(s_frm_1), int(f_frm_1)), 'action_2': (int(s_frm_2), int(f_frm_2)), } return temporal_data source_path = '/home/daniil/Downloads/UCF-Crime/Videos' result_path = '/home/daniil/Documents/Projects/University/Thesis/frames2' if not os.path.exists(result_path): os.mkdir(result_path) normal_frames = os.path.join(result_path, 'Normal') if not os.path.exists(normal_frames): os.mkdir(normal_frames) temporal_data = read_temporal_data('./temporal_data.txt') err_log = open('./err.log', 'w') for video_class in os.listdir(source_path): frames_dir = os.path.join(result_path, video_class) class_path = os.path.join(source_path, video_class) if not os.path.exists(frames_dir): os.mkdir(frames_dir) for idx, video in enumerate(os.listdir(class_path)): video_path = os.path.join(class_path, video).strip() print(f'Start working on {os.path.abspath(video_path)}') video_name, video_ext = os.path.splitext(os.path.basename(video_path)) video_name = urllib.parse.unquote(video_name) command = ('ffmpeg', '-i', os.path.abspath(video_path), '-vf', 'select=not(mod(n\\,120))', '-vsync', 'vfr', '-hide_banner', '-threads', '16', os.path.join(frames_dir, f'{idx:03d}-%06d.jpg')) try: subprocess.check_call(command, stdout=subprocess.DEVNULL, stderr=err_log) except subprocess.CalledProcessError as exc: print(f'ffmpeg failed with return code {exc.returncode}') video_frames = sorted([ frame for frame in os.listdir(frames_dir) if frame.startswith(f'{idx:03d}-') ]) if video in temporal_data.keys() and temporal_data[video] != 'Normal': frames_data = temporal_data[video] frames_to_move = set() for action in ['action_1', 'action_2']: if frames_data[action][0] == -1: continue start_frame, stop_frame = frames_data[action] for frm_idx, frame in enumerate(video_frames): frm_num = frm_idx * 120 if not start_frame < frm_num < stop_frame: frames_to_move.add(os.path.join(frames_dir, frame)) for frame_path in frames_to_move: uuid_idx = str(uuid.uuid4()) os.rename( frame_path, os.path.join(result_path, 'Normal', f'moved-{uuid_idx}.jpg')) frames_to_rename = list(os.listdir(frames_dir)) for frame in frames_to_rename: frame_idx = frame.split('-')[0] frame_path = os.path.join(frames_dir, frame) uuid_idx = str(uuid.uuid4()) os.rename(frame_path, os.path.join(frames_dir, f'{frame_idx}-{uuid_idx}.jpg'))

38.767442

79

0.596581

442

3,334

4.255656

0.278281

0.051037

0.047847

0.037214

0.178628

0.117491

0.068581

0.04891

0

0.013344

0.280744

3,334

85

80

39.223529

0.771059

0.013797

0

0.028169

0

0.126293

0.053256

0

1

0.014085

false

0

0.056338

0

0.084507

0.028169

0

null

0

null

0

1

0

c1b820b4d47994172a5b13534ae44af8e3d7d6e0

228

py

Python

10.变态跳台阶/10.变态跳台阶(DP).py

shenweichen/coding_interviews

990cc54a62b8fa277b743289e8d6f6e96a95225d

[ "MIT" ]

483

2020-01-05T12:58:59.000Z

2022-03-19T05:44:00.000Z

10.变态跳台阶/10.变态跳台阶(DP).py

moshilangzi/coding_interviews

990cc54a62b8fa277b743289e8d6f6e96a95225d

[ "MIT" ]

1

2020-01-20T08:47:15.000Z

2020-01-27T13:24:15.000Z

10.变态跳台阶/10.变态跳台阶(DP).py

moshilangzi/coding_interviews

990cc54a62b8fa277b743289e8d6f6e96a95225d

[ "MIT" ]

122

2020-01-05T14:10:04.000Z

2022-03-19T05:24:42.000Z

# -*- coding:utf-8 -*- class Solution: def jumpFloorII(self, number): # write code here dp = [0,1,2] for i in range(3,number+1): dp.append(sum(dp)+1) return dp[number]

25.333333

35

0.491228

31

228

3.612903

0.774194

0

0.048276

0.364035

228

9

36

25.333333

0.724138

0.157895

0

1

0.166667

false

0

0.5

0

null

0

1

0

null

0

1

c1b891e7d61a9e3e64538ab34a986ee96aa12260

2,866

py

Python

corehq/ex-submodules/phonelog/migrations/0006_auto__chg_field_devicereportentry_app_version__chg_field_devicereporte.py

dslowikowski/commcare-hq

ad8885cf8dab69dc85cb64f37aeaf06106124797

[ "BSD-3-Clause" ]

1

2015-02-10T23:26:39.000Z

corehq/ex-submodules/phonelog/migrations/0006_auto__chg_field_devicereportentry_app_version__chg_field_devicereporte.py

SEL-Columbia/commcare-hq

992ee34a679c37f063f86200e6df5a197d5e3ff6

[ "BSD-3-Clause" ]

null

corehq/ex-submodules/phonelog/migrations/0006_auto__chg_field_devicereportentry_app_version__chg_field_devicereporte.py

SEL-Columbia/commcare-hq

992ee34a679c37f063f86200e6df5a197d5e3ff6

[ "BSD-3-Clause" ]

null

# encoding: utf-8 import datetime from south.db import db from south.v2 import SchemaMigration from django.db import models class Migration(SchemaMigration): def forwards(self, orm): # Changing field 'DeviceReportEntry.app_version' db.alter_column(u'phonelog_devicereportentry', 'app_version', self.gf('django.db.models.fields.TextField')(null=True)) # Changing field 'DeviceReportEntry.device_id' db.alter_column(u'phonelog_devicereportentry', 'device_id', self.gf('django.db.models.fields.CharField')(max_length=50, null=True)) def backwards(self, orm): # Changing field 'DeviceReportEntry.app_version' db.alter_column(u'phonelog_devicereportentry', 'app_version', self.gf('django.db.models.fields.TextField')(default='')) # Changing field 'DeviceReportEntry.device_id' db.alter_column(u'phonelog_devicereportentry', 'device_id', self.gf('django.db.models.fields.CharField')(default='', max_length=50)) models = { u'phonelog.devicereportentry': { 'Meta': {'unique_together': "[('xform_id', 'i')]", 'object_name': 'DeviceReportEntry'}, 'app_version': ('django.db.models.fields.TextField', [], {'null': 'True'}), 'date': ('django.db.models.fields.DateTimeField', [], {'db_index': 'True'}), 'device_id': ('django.db.models.fields.CharField', [], {'max_length': '50', 'null': 'True', 'db_index': 'True'}), 'domain': ('django.db.models.fields.CharField', [], {'max_length': '100', 'db_index': 'True'}), 'i': ('django.db.models.fields.IntegerField', [], {}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'msg': ('django.db.models.fields.TextField', [], {}), 'type': ('django.db.models.fields.CharField', [], {'max_length': '32', 'db_index': 'True'}), 'username': ('django.db.models.fields.CharField', [], {'max_length': '100', 'null': 'True', 'db_index': 'True'}), 'xform_id': ('django.db.models.fields.CharField', [], {'max_length': '50', 'db_index': 'True'}) }, u'phonelog.userentry': { 'Meta': {'unique_together': "[('xform_id', 'i')]", 'object_name': 'UserEntry'}, 'i': ('django.db.models.fields.IntegerField', [], {}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'sync_token': ('django.db.models.fields.CharField', [], {'max_length': '50'}), 'user_id': ('django.db.models.fields.CharField', [], {'max_length': '50'}), 'username': ('django.db.models.fields.CharField', [], {'max_length': '100', 'db_index': 'True'}), 'xform_id': ('django.db.models.fields.CharField', [], {'max_length': '50', 'db_index': 'True'}) } } complete_apps = ['phonelog']

54.075472

140

0.60328

318

2,866

5.279874

0.207547

0.10006

0.166766

0.238237

0.775462

0.746873

0.697439

0.630733

0.570578

0

0.011578

0.186322

2,866

52

141

55.115385

0.708405

0.069435

0

0.166667

0

0.504697

0.300639

0

1

0.055556

false

0

0.111111

0

0.25

0

null

0

1

0

1

0

null

0

3

c1b8b6e21410622622aa640ba1d082574eb412ec

8,201

py

Python

lib/providers/theaudiodb.py

DaVukovic/script.artwork.beef

cb6e54cd2b3bccc82660ad277c13618746f36e08

[ "MIT" ]

40

2017-10-29T22:43:43.000Z

2022-03-12T05:59:05.000Z

lib/providers/theaudiodb.py

supmagc/script.artwork.beef

afd76b290d13b2e9ea00d7f9772961353f4640b8

[ "MIT" ]

47

2017-01-31T21:28:20.000Z

2021-03-23T06:53:51.000Z

lib/providers/theaudiodb.py

supmagc/script.artwork.beef

afd76b290d13b2e9ea00d7f9772961353f4640b8

[ "MIT" ]

31

2017-10-16T05:28:53.000Z

2022-02-24T19:50:24.000Z

import xbmc from lib.libs import mediatypes from lib.libs.addonsettings import settings from lib.libs.pykodi import json, UTF8JSONDecoder from lib.libs.utils import SortedDisplay from lib.providers.base import AbstractProvider, AbstractImageProvider, cache, build_key_error class TheAudioDBAbstractProvider(AbstractImageProvider): name = SortedDisplay('theaudiodb.com', 'TheAudioDB.com') contenttype = 'application/json' def __init__(self): super(TheAudioDBAbstractProvider, self).__init__() # url param i=MB track/album/artist ID self.artmap = {'mbtrack': {'datakey':'track', 'artmap': {'strTrackThumb': 'thumb'}, 'url': 'https://www.theaudiodb.com/api/v1/json/{0}/track-mb.php'}, 'mbgroup': {'datakey':'album', 'artmap': {'strAlbumThumb': 'thumb', 'strAlbumCDart': 'discart', 'strAlbumThumbBack': 'back', 'strAlbumSpine': 'spine'}, 'url': 'https://www.theaudiodb.com/api/v1/json/{0}/album-mb.php'}, 'mbartist': {'datakey':'artists', 'artmap': {'strArtistThumb': 'thumb', 'strArtistLogo': 'clearlogo', 'strArtistBanner': 'banner', 'strArtistFanart': 'fanart', 'strArtistFanart2': 'fanart', 'strArtistFanart3': 'fanart', 'strArtistClearart': 'clearart', 'strArtistWideThumb': 'landscape'}, 'url': 'https://www.theaudiodb.com/api/v1/json/{0}/artist-mb.php'} } self.provtypes = set(x for data in self.artmap.values() for x in data['artmap'].values()) def get_data(self, url, params): result = cache.cacheFunction(self._get_data, url.format(settings.get_apikey('tadb')), params) return result if result != 'Empty' else None def _get_data(self, url, params): apikey = settings.get_apikey('tadb') if not apikey: raise build_key_error('tadb') self.log('uncached', xbmc.LOGINFO) response = self.doget(url, params=params) if response is None: raise build_key_error('tadb') return 'Empty' if response is None else json.loads(response.text, cls=UTF8JSONDecoder) def _build_image(self, url, size, title=None): result = {'provider': self.name, 'url': url, 'preview': url + '/preview', 'size': size, 'language': None, 'rating': SortedDisplay(5.1 if title == 'track' else 5.0, '')} if title: result['title'] = title return result class TheAudioDBMusicVideoProvider(TheAudioDBAbstractProvider): mediatype = mediatypes.MUSICVIDEO def provides(self, types): if 'artistthumb' in types: return True return bool(set(types) & self.provtypes) def get_images(self, uniqueids, types=None): if not settings.get_apienabled('tadb'): return {} if types is not None and not self.provides(types) or not (uniqueids.get('mbtrack') or uniqueids.get('mbgroup') or uniqueids.get('mbartist')): return {} images = {} for idsource, artdata in self.artmap.iteritems(): if idsource not in uniqueids or types is not None and not \ any(x in types for x in artdata['artmap'].itervalues()): continue data = self.get_data(artdata['url'], {'i': uniqueids[idsource]}) if not data or not data.get(artdata['datakey']): continue data = data[artdata['datakey']][0] for originaltype, finaltype in artdata['artmap'].iteritems(): if (originaltype in ('strAlbumThumbBack', 'strAlbumSpine')): continue if originaltype == 'strArtistThumb': finaltype = 'artistthumb' elif originaltype in ('strTrackThumb', 'strAlbumThumb'): finaltype = 'poster' if data.get(originaltype): _insertart(images, finaltype, self._build_image(data[originaltype], _get_imagesize(originaltype), artdata['datakey'])) return images class TheAudioDBAbstractMusicProvider(TheAudioDBAbstractProvider): def _inner_get_images(self, uniqueids, idsource, types): if not settings.get_apienabled('tadb'): return {} if not uniqueids.get(idsource): return {} artdata = self.artmap[idsource] if types and not any(x in types for x in artdata['artmap'].itervalues()): return {} images = {} data = self.get_data(artdata['url'], {'i': uniqueids[idsource]}) if not data or not data.get(artdata['datakey']): return {} data = data[artdata['datakey']][0] for originaltype, finaltype in artdata['artmap'].iteritems(): if data.get(originaltype): _insertart(images, finaltype, self._build_image(data[originaltype], _get_imagesize(originaltype), artdata['datakey'])) return images class TheAudioDBAlbumProvider(TheAudioDBAbstractMusicProvider): mediatype = mediatypes.ALBUM def get_images(self, uniqueids, types=None): return self._inner_get_images(uniqueids, 'mbgroup', types) class TheAudioDBArtistProvider(TheAudioDBAbstractMusicProvider): mediatype = mediatypes.ARTIST def get_images(self, uniqueids, types=None): return self._inner_get_images(uniqueids, 'mbartist', types) class TheAudioDBSongProvider(TheAudioDBAbstractMusicProvider): mediatype = mediatypes.SONG def get_images(self, uniqueids, types=None): return self._inner_get_images(uniqueids, 'mbtrack', types) def _get_imagesize(arttype): if arttype in ('strTrackThumb', 'strAlbumThumb', 'strArtistThumb', 'strAlbumThumbBack'): return SortedDisplay(500, '500-800') if arttype in ('strAlbumCDart',): return SortedDisplay(500, '500 or 1000') if arttype in ('strArtistLogo',): return SortedDisplay(400, '400x155 or 800x310') if arttype in ('strArtistBanner',): return SortedDisplay(1000, '1000x185') if arttype in ('strArtistClearart', 'strArtistWideThumb'): return SortedDisplay(1000, '1000x562') if arttype in ('strArtistFanart', 'strArtistFanart2', 'strArtistFanart3'): return SortedDisplay(1280, '1280x720 or 1920x1080') if arttype in ('strAlbumSpine',): return (SortedDisplay(700, '700x35')) return SortedDisplay(0, '') def _insertart(images, arttype, image): if arttype not in images: images[arttype] = [] images[arttype].append(image) class TheAudioDBSearch(AbstractProvider): name = SortedDisplay('theaudiodb.com:search', 'TheAudioDB.com search') contenttype = 'application/json' def __init__(self): super(TheAudioDBSearch, self).__init__() # s=[artist], t=[track title] self.url_trackby_artistandtrack = 'https://www.theaudiodb.com/api/v1/json/{0}/searchtrack.php' def get_data(self, url, params=None): apikey = settings.get_apikey('tadb') if not apikey: raise build_key_error('tadb') result = cache.cacheFunction(self._get_data, url.format(settings.get_apikey('tadb')), params) return result if result != 'Empty' else None def _get_data(self, url, params=None): self.log('uncached', xbmc.LOGINFO) if params is None: params = {} response = self.doget(url, params=params) if response is None: raise build_key_error('tadb') return 'Empty' if response is None else response.json() def search(self, query, mediatype): if mediatype != mediatypes.MUSICVIDEO: return [] query = query.split(' - ', 1) if len(query) != 2: return [] data = self.get_data(self.url_trackby_artistandtrack, {'s': query[0], 't': query[1]}) if not data or not data.get('track'): return [] return [{'label': item['strArtist'] + ' - ' + item['strTrack'], 'uniqueids': {'mbtrack': item['strMusicBrainzID'], 'mbartist': item['strMusicBrainzArtistID'], 'mbgroup': item['strMusicBrainzAlbumID']}} for item in data['track']]

44.32973

118

0.631386

864

8,201

5.900463

0.204861

0.010592

0.015104

0.013731

0.37603

0.36583

0.354649

0.321302

0.300314

0.280306

0

0.017363

0.241556

8,201

184

119

44.570652

0.802251

0.007804

0

0.374194

0

0.019355

0.177158

0.007868

0

1

0.103226

false

0

0.03871

0.019355

0.432258

0

null

0

null

0

1

0

c1b9ee59be99f775c2f552e13c66ff23db053369

1,025

py

Python

osp/test/corpus/models/document/test_format_counts.py

davidmcclure/open-syllabus-project

078cfd4c5a257fbfb0901d43bfbc6350824eed4e

[ "Apache-2.0" ]

220

2016-01-22T21:19:02.000Z

2022-01-25T04:33:55.000Z

osp/test/corpus/models/document/test_format_counts.py

davidmcclure/open-syllabus-project

078cfd4c5a257fbfb0901d43bfbc6350824eed4e

[ "Apache-2.0" ]

14

2016-01-23T14:34:39.000Z

2016-09-19T19:58:37.000Z

osp/test/corpus/models/document/test_format_counts.py

davidmcclure/open-syllabus-project

078cfd4c5a257fbfb0901d43bfbc6350824eed4e

[ "Apache-2.0" ]

14

2016-02-03T13:47:48.000Z

2019-03-27T13:09:05.000Z

import pytest from osp.corpus.models import Document from osp.corpus.models import Document_Format pytestmark = pytest.mark.usefixtures('db') def test_format_counts(): """ Document.format_counts() """ d1 = Document.create(path='1') d2 = Document.create(path='2') d3 = Document.create(path='3') d4 = Document.create(path='4') d5 = Document.create(path='5') d6 = Document.create(path='6') # 1 doc with 'format1'. f1 = Document_Format.create(document=d1, format='format1') # 2 docs with 'format2'. f2 = Document_Format.create(document=d2, format='format2') f3 = Document_Format.create(document=d3, format='format2') # 3 docs with 'format3'. f4 = Document_Format.create(document=d4, format='format3') f5 = Document_Format.create(document=d5, format='format3') f6 = Document_Format.create(document=d6, format='format3') assert Document_Format.format_counts() == [ ('format3', 3), ('format2', 2), ('format1', 1) ]

24.404762

62

0.650732

128

1,025

5.117188

0.328125

0.192366

0.164886

0.256489

0.100763

0

0.051095

0.198049

1,025

41

63

25

0.745742

0.090732

0

0.077851

0

0.045455

1

0.045455

false

0

0.136364

0

0.181818

0

null

0

1

0

null

0

1

c1ba667085462dd4a1725b7706f902a599a1b8e8

3,696

py

Python

polyaxon/dockerizer/builders/jobs.py

vfdev-5/polyaxon

3e1511a993dc1a03e0a0827de0357f4adcc0015f

[ "MIT" ]

null

polyaxon/dockerizer/builders/jobs.py

vfdev-5/polyaxon

3e1511a993dc1a03e0a0827de0357f4adcc0015f

[ "MIT" ]

null

polyaxon/dockerizer/builders/jobs.py

vfdev-5/polyaxon

3e1511a993dc1a03e0a0827de0357f4adcc0015f

[ "MIT" ]

null

from django.conf import settings from django.core.exceptions import ObjectDoesNotExist from constants.jobs import JobLifeCycle from db.models.repos import Repo from dockerizer.builders.base import BaseDockerBuilder class BaseJobDockerBuilder(BaseDockerBuilder): CHECK_INTERVAL = 10 def __init__(self, project_id, project_name, repo_path, from_image, image_name, image_tag, copy_code=True, in_tmp_repo=True, build_steps=None, env_vars=None, dockerfile_name='Dockerfile'): self.project_id = project_id self.project_name = project_name super().__init__( repo_path=repo_path, from_image=from_image, image_name=image_name, image_tag=image_tag, copy_code=copy_code, in_tmp_repo=in_tmp_repo, build_steps=build_steps, env_vars=env_vars, dockerfile_name=dockerfile_name) def _handle_logs(self, log_line): pass def _check_pulse(self, check_pulse): pass def get_job_repo_info(project, job): project_name = project.name # job_spec = job.specification # if job_spec.build.git: # We need to fetch the repo first # # repo, is_created = ExternalRepo.objects.get_or_create(project=project, # git_url=job_spec.build.git) # if not is_created: # # If the repo already exist, we just need to refetch it # git.fetch(git_url=repo.git_url, repo_path=repo.path) # # repo_path = repo.path # repo_name = repo.name # last_commit = repo.last_commit # else: repo_path = project.repo.path last_commit = project.repo.last_commit repo_name = project_name image_name = '{}/{}'.format(settings.REGISTRY_HOST, repo_name) if not last_commit: raise Repo.DoesNotExist image_tag = last_commit[0] return { 'repo_path': repo_path, 'image_name': image_name, 'image_tag': image_tag } def build_job(project, job, job_builder, image_tag=None): """Build necessary code for a job to run""" job_spec = job.specification build_info = get_job_repo_info(project, job) # Build the image docker_builder = job_builder(project_id=project.id, project_name=project.unique_name, repo_path=build_info['repo_path'], from_image=job_spec.build.image, image_name=build_info['image_name'], image_tag=image_tag or build_info['image_tag'], build_steps=job_spec.build.build_steps, env_vars=job_spec.build.env_vars) docker_builder.login(registry_user=settings.REGISTRY_USER, registry_password=settings.REGISTRY_PASSWORD, registry_host=settings.REGISTRY_HOST) if docker_builder.check_image(): # Image already built docker_builder.clean() return True if not docker_builder.build(): docker_builder.clean() return False if not docker_builder.push(): docker_builder.clean() try: job.set_status(JobLifeCycle.FAILED, message='The docker image could not be pushed.') except ObjectDoesNotExist: pass return False docker_builder.clean() return True

34.222222

91

0.588474

420

3,696

4.859524

0.261905

0.050955

0.041156

0.039196

0.129838

0.086722

0.050955

0.033317

0

0.00123

0.340097

3,696

107

92

34.542056

0.835588

0.151786

0

0.139241

0

0.034693

0

1

0.063291

false

0.050633

0.063291

0

0.21519

0

null

0

null

0

1

0

1

c1bb44a49533620993e5fb48385ae2720ad43392

2,786

py

Python

code/attr2vec_func.py

xuliang09/JAPE

961a0cf9d10abf81cc24a71ac33274b42bc2fbc1

[ "MIT" ]

91

2018-03-13T03:56:15.000Z

2022-03-26T13:47:22.000Z

code/attr2vec_func.py

codeinging/JAPE

b4b3617a7c61df5f7093921553dd3b0a7497506d

[ "MIT" ]

10

2018-04-02T15:47:08.000Z

2022-03-01T09:28:10.000Z

code/attr2vec_func.py

codeinging/JAPE

b4b3617a7c61df5f7093921553dd3b0a7497506d

[ "MIT" ]

23

2018-05-30T07:18:38.000Z

2021-08-15T06:13:29.000Z

import math import collections import random import numpy as np import tensorflow as tf import itertools import time def sum_rows(x): """Returns a vector summing up each row of the matrix x.""" cols = tf.shape(x)[1] ones_shape = tf.stack([cols, 1]) ones = tf.ones(ones_shape, x.dtype) return tf.reshape(tf.matmul(x, ones), [-1]) def compute_sampled_logits(weights, biases, labels, inputs, num_sampled, num_classes, num_true=1): if not isinstance(weights, list): weights = [weights] if labels.dtype != tf.int64: labels = tf.cast(labels, tf.int64) labels_flat = tf.reshape(labels, [-1]) sampled_ids, true_expected_count, sampled_expected_count = tf.nn.log_uniform_candidate_sampler( true_classes=labels, num_true=num_true, num_sampled=num_sampled, unique=True, range_max=num_classes) true_w = tf.nn.embedding_lookup(weights, labels_flat) true_b = tf.nn.embedding_lookup(biases, labels_flat) sampled_w = tf.nn.embedding_lookup(weights, sampled_ids) sampled_b = tf.nn.embedding_lookup(biases, sampled_ids) dim = tf.shape(true_w)[1:2] new_true_w_shape = tf.concat([[-1, num_true], dim], 0) row_wise_dots = tf.multiply(tf.expand_dims(inputs, 1), tf.reshape(true_w, new_true_w_shape)) dots_as_matrix = tf.reshape(row_wise_dots, tf.concat([[-1], dim], 0)) true_logits = tf.reshape(sum_rows(dots_as_matrix), [-1, num_true]) true_b = tf.reshape(true_b, [-1, num_true]) true_logits += true_b sampled_b_vec = tf.reshape(sampled_b, [num_sampled]) sampled_logits = tf.matmul(inputs, sampled_w, transpose_b=True) + sampled_b_vec return true_logits, sampled_logits def nce_loss(weights, biases, labels, inputs, num_sampled, num_classes, num_true=1, v=None): batch_size = int(labels.get_shape()[0]) if v is None: v = tf.ones([batch_size, 1]) true_logits, sampled_logits = compute_sampled_logits( weights=weights, biases=biases, labels=labels, inputs=inputs, num_sampled=num_sampled, num_classes=num_classes, num_true=num_true) true_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.ones_like(true_logits), logits=true_logits) sampled_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.zeros_like(sampled_logits), logits=sampled_logits) true_loss = tf.multiply(true_loss, v) return tf.div(tf.reduce_sum(true_loss) + tf.reduce_sum(sampled_loss), tf.constant(batch_size, dtype=tf.float32))

35.265823

119

0.645011

389

2,786

4.329049

0.249357

0.037411

0.038599

0.045131

0.192993

0.179335

0.11639

0

0.011021

0.250897

2,786

78

120

35.717949

0.795879

0.019024

0

0.179104

0

1

0.044776

false

0

0.104478

0

0.19403

0

null

0

null

0

1

0

c1bb5795f5cf4b1f0f656850cbb021970ffc7c82

5,218

py

Python

src/installer/src/tortuga/db/componentDbApi.py

sutasu/tortuga

48d7cde4fa652346600b217043b4a734fa2ba455

[ "Apache-2.0" ]

33

2018-03-02T17:07:39.000Z

2021-05-21T18:02:51.000Z

src/installer/src/tortuga/db/componentDbApi.py

sutasu/tortuga

48d7cde4fa652346600b217043b4a734fa2ba455

[ "Apache-2.0" ]

201

2018-03-05T14:28:24.000Z

2020-11-23T19:58:27.000Z

src/installer/src/tortuga/db/componentDbApi.py

sutasu/tortuga

48d7cde4fa652346600b217043b4a734fa2ba455

[ "Apache-2.0" ]

23

2018-03-02T17:21:59.000Z

2020-11-18T14:52:38.000Z

# Copyright 2008-2018 Univa Corporation # # 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 typing import Optional, Union from sqlalchemy.orm.session import Session from tortuga.db.componentsDbHandler import ComponentsDbHandler from tortuga.db.softwareProfilesDbHandler import SoftwareProfilesDbHandler from tortuga.db.tortugaDbApi import TortugaDbApi from tortuga.exceptions.tortugaException import TortugaException from tortuga.objects.component import Component from tortuga.objects.osInfo import OsInfo class ComponentDbApi(TortugaDbApi): """ Component DB API class. """ def __init__(self): TortugaDbApi.__init__(self) self._softwareProfilesDbHandler = SoftwareProfilesDbHandler() self._componentsDbHandler = ComponentsDbHandler() def getComponent(self, session: Session, name: str, version: str, osInfo: OsInfo, optionDict: Optional[Union[dict, None]] = None) \ -> Component: """ Get component from the db. Returns: component Throws: ComponentNotFound DbError """ try: dbComponent = self._componentsDbHandler.getComponentByOsInfo( session, name, version, osInfo) self.loadRelations(dbComponent, optionDict) return Component.getFromDbDict(dbComponent.__dict__) except TortugaException: raise except Exception as ex: self._logger.exception(str(ex)) raise def getBestMatchComponent(self, session: Session, name, version, osInfo, kitId): """ Get component from the db. Returns: component Throws: ComponentNotFound DbError """ try: dbComponent = self._componentsDbHandler.getBestMatchComponent( session, name, version, osInfo, kitId) self.loadRelations(dbComponent, { 'os': True, 'family': True, 'kit': True, 'os_components': True, 'osfamily_components': True, }) return Component.getFromDbDict(dbComponent.__dict__) except TortugaException: raise except Exception as ex: self._logger.exception(str(ex)) raise def addComponentToSoftwareProfile(self, session: Session, componentId, softwareProfileId): """ Add component to softwareProfile. Returns: None Throws: SoftwareProfileNotFound ComponentNotFound SoftwareProfileComponentAlreadyExists DbError """ try: self._softwareProfilesDbHandler.addComponentToSoftwareProfile( session, componentId, softwareProfileId) session.commit() except TortugaException: session.rollback() raise except Exception as ex: session.rollback() self._logger.exception(str(ex)) raise def deleteComponentFromSoftwareProfile(self, session: Session, componentId, softwareProfileId): """ Delete component to software profile. Returns: None Throws: SoftwareProfileNotFound ComponentNotFound SoftwareProfileComponentNotFound DbError """ try: self._softwareProfilesDbHandler.\ deleteComponentFromSoftwareProfile( session, componentId, softwareProfileId) session.commit() except TortugaException: session.rollback() raise except Exception as ex: session.rollback() self._logger.exception(str(ex)) raise def getComponentList(self, session: Session, softwareProfile=None): try: if softwareProfile: return self._softwareProfilesDbHandler.getSoftwareProfile( session, softwareProfile).components # List all components self._logger.debug('Retrieving component list') dbComps = self._componentsDbHandler.getComponentList(session) return self.getTortugaObjectList(Component, dbComps) except Exception as ex: self._logger.exception(str(ex)) raise

31.624242

76

0.590264

413

5,218

7.380145

0.336562

0.019685

0.029528

0.031168

0.352362

0.265748

0

0.003531

0.348793

5,218

164

77

31.817073

0.893467

0.232656

0

0.47619

0

0.018423

0

1

0.071429

false

0

0.095238

0

0.22619

0

null

0

null

0

1

0

c1bd3526605a6b1d3a41d10be0e5bafa8ed8f574

312

py

Python

python_data_structures/stack.py

chandlerbrtek/python-data-structures

7fb8a4c4879fb148e607a627a98d0111e5a942a3

[ "MIT" ]

null

python_data_structures/stack.py

chandlerbrtek/python-data-structures

7fb8a4c4879fb148e607a627a98d0111e5a942a3

[ "MIT" ]

null

python_data_structures/stack.py

chandlerbrtek/python-data-structures

7fb8a4c4879fb148e607a627a98d0111e5a942a3

[ "MIT" ]

null

"""Main module.""" class Stack: stack = [] def isEmpty(self) -> bool: return len(self.stack) == 0 def push(self, value: any) -> None: self.stack.append(value) def peek(self) -> any: return self.stack[-1] def pop(self) -> any: return self.stack.pop()

20.8

39

0.532051

40

312

4.15

0.475

0.216867

0.156627

0.204819

0.26506

0

0.009259

0.307692

312

15

40

20.8

0.759259

0.038462

0

1

0.4

false

0

0.3

0.9

0

null

1

0

1

0

null

0

1

0

1

0

5

c1c255b1f03413b3ba902b4ef59492fdc92fec91

2,667

py

Python

network/embedding.py

cherry979988/OpenNRE

740e6abd3b8b30625cb88a643a2acb65d5e923dd

[ "MIT" ]

1

2019-10-08T02:53:28.000Z

network/embedding.py

flyounger/OpenNRE

1f30a0b3109e9b3c3284fa72a1562f81cdb70fdf

[ "MIT" ]

null

network/embedding.py

flyounger/OpenNRE

1f30a0b3109e9b3c3284fa72a1562f81cdb70fdf

[ "MIT" ]

1

2020-07-22T08:39:09.000Z

import tensorflow as tf import numpy as np FLAGS = tf.app.flags.FLAGS class Embedding(object): def __init__(self, is_training, word_vec, word, pos1, pos2): temp_word_embedding = tf.get_variable(initializer=word_vec, name = 'temp_word_embedding', dtype=tf.float32) unk_word_embedding = tf.get_variable('unk_embedding', [FLAGS.word_size], dtype=tf.float32, initializer=tf.contrib.layers.xavier_initializer()) self.word_vec = tf.concat([temp_word_embedding, tf.reshape(unk_word_embedding, [1, FLAGS.word_size]), tf.reshape(tf.constant(np.zeros([FLAGS.word_size], dtype=np.float32)), [1, FLAGS.word_size])], 0) self.word = word self.pos1 = pos1 self.pos2 = pos2 self.is_training = is_training def word_embedding(self, var_scope = None, reuse = False): with tf.variable_scope(var_scope or 'word_embedding', reuse = reuse): x = tf.nn.embedding_lookup(self.word_vec, self.word) return x def pos_embedding(self, simple_pos=False): with tf.name_scope("pos_embedding"): if simple_pos: temp_pos_array = np.zeros((FLAGS.pos_num + 1, FLAGS.pos_size), dtype=np.float32) temp_pos_array[(FLAGS.pos_num - 1) / 2] = np.ones(FLAGS.pos_size, dtype=np.float32) pos1_embedding = tf.constant(temp_pos_array) pos2_embedding = tf.constant(temp_pos_array) else: temp_pos1_embedding = tf.get_variable('temp_pos1_embedding', [FLAGS.pos_num, FLAGS.pos_size], dtype=tf.float32, initializer=tf.contrib.layers.xavier_initializer()) pos1_embedding = tf.concat([temp_pos1_embedding, tf.reshape(tf.constant(np.zeros(FLAGS.pos_size, dtype=np.float32)), [1, FLAGS.pos_size])], 0) temp_pos2_embedding = tf.get_variable('temp_pos2_embedding', [FLAGS.pos_num, FLAGS.pos_size], dtype=tf.float32, initializer=tf.contrib.layers.xavier_initializer()) pos2_embedding = tf.concat([temp_pos2_embedding, tf.reshape(tf.constant(np.zeros(FLAGS.pos_size, dtype=np.float32)), [1, FLAGS.pos_size])], 0) input_pos1 = tf.nn.embedding_lookup(pos1_embedding, self.pos1) input_pos2 = tf.nn.embedding_lookup(pos2_embedding, self.pos2) x = tf.concat(values = [input_pos1, input_pos2], axis = 2) return x def concat_embedding(self, word_embedding, pos_embedding): if pos_embedding is None: return word_embedding else: return tf.concat(values = [word_embedding, pos_embedding], axis = 2)

56.744681

179

0.656543

359

2,667

4.612813

0.167131

0.057971

0.061594

0.399758

0.336957

0.253623

0.234903

0

0.026316

0.230596

2,667

46

180

57.978261

0.780702

0

0.102564

0

0.03637

0

1

0.102564

false

0

0.051282

0

0.282051

0

null

0

null

0

1

0

c1c3368fb90c0a4c1c5f3d9670d74ff773fbc850

768

py

Python

experiments/mapModel.py

PerceptronV/denoising-text-autoencoders

8ef59e2531a4fc7531002cffd3546f27eadb8ec9

[ "Apache-2.0" ]

null

experiments/mapModel.py

PerceptronV/denoising-text-autoencoders

8ef59e2531a4fc7531002cffd3546f27eadb8ec9

[ "Apache-2.0" ]

null

experiments/mapModel.py

PerceptronV/denoising-text-autoencoders

8ef59e2531a4fc7531002cffd3546f27eadb8ec9

[ "Apache-2.0" ]

1

2022-03-04T05:57:52.000Z

import torch.nn as nn class MappingModel(nn.Module): def __init__(self, dims, nlayers=1, units=128, activation=nn.ReLU): super(MappingModel, self).__init__() print(nlayers) if nlayers == 1: self.linmap = nn.Linear(dims, dims) elif nlayers == 2: self.linmap = nn.Sequential( nn.Linear(dims, units), activation(), nn.Linear(units, dims) ) else: stack = ( [nn.Linear(dims, units), activation()] + [nn.Linear(units, units), activation()] * (nlayers - 2) + [nn.Linear(units, dims)] ) self.linmap = nn.Sequential(*stack) def forward(self, x): x = self.linmap(x) return x

30.72

76

0.518229

84

768

4.642857

0.357143

0.123077

0.092308

0.112821

0.205128

0

0.014028

0.35026

768

24

77

32

0.767535

0

1

0.095238

false

0

0.047619

0

0.238095

0.047619

0

null

0

null

0

1

0

c1c4c548cee3344d6a0288f1062e34cef764d45e

1,166

py

Python

clafiyy/new/sum.py

cy486/DomRead

852289588b902dedba5fd3f2a57b39d2a2b027ba

[ "Apache-2.0" ]

null

clafiyy/new/sum.py

cy486/DomRead

852289588b902dedba5fd3f2a57b39d2a2b027ba

[ "Apache-2.0" ]

null

clafiyy/new/sum.py

cy486/DomRead

852289588b902dedba5fd3f2a57b39d2a2b027ba

[ "Apache-2.0" ]

null

# @Time : 2019/5/22 11:26 # @Author : shakespere # @FileName: sum.py import pandas as pd submission_1 = pd.read_csv("./data/merge_0.8550913438849271_predictions.csv") submission_2 = pd.read_csv("./data/merge_0.8551243481873769_predictions.csv") submission_3 = pd.read_csv("./data/merge_0.8571411176454415_predictions.csv") submission_4 = pd.read_csv("./data/merge_0.8582128855527719_predictions.csv") submission_5 = pd.read_csv("./data/merge_0.8585647873963975_predictions.csv") submission_6 = pd.read_csv("./data/merge_0.8599225290804536_predictions.csv") submission_7 = pd.read_csv("./data/merge_0.860564284049377_predictions.csv") submission_8 = pd.read_csv("./data/merge_0.8606908440533374_predictions.csv") submission = pd.DataFrame.from_dict({ 'ID': submission_1['ID'], 'Pred': (submission_1.Pred.values * 0.125) + (submission_2.Pred.values * 0.125) + (submission_3.Pred.values * 0.125) + (submission_4.Pred.values * 0.125)+ (submission_5.Pred.values * 0.125)+ (submission_6.Pred.values * 0.125)+ (submission_7.Pred.values * 0.125)+ (submission_8.Pred.values * 0.125) }) submission.to_csv('./data/submission.csv', index=False)

58.3

302

0.747856

169

1,166

4.905325

0.278107

0.075995

0.086852

0.125452

0.414958

0.183353

0

0.184484

0.093482

1,166

20

303

58.3

0.599811

0.056604

0

0.374768

0.367347

0

1

0

false

0

0.071429

0

0.071429

0

null

0

null

0

1

0

c1c554676d59b1bbb3e5980c3277d57ab792911e

4,216

py

Python

lexos/receivers/matrix_receiver.py

WheatonCS/Lexos

994be4e403053ebbef18e5758a100af616195706

[ "MIT" ]

107

2015-03-19T09:10:31.000Z

2022-01-29T01:33:48.000Z

lexos/receivers/matrix_receiver.py

WheatonCS/Lexos

994be4e403053ebbef18e5758a100af616195706

[ "MIT" ]

864

2015-05-19T19:27:00.000Z

2022-01-28T18:48:52.000Z

lexos/receivers/matrix_receiver.py

WheatonCS/Lexos

994be4e403053ebbef18e5758a100af616195706

[ "MIT" ]

25

2015-06-02T23:03:06.000Z

2020-08-06T04:27:49.000Z

"""This is the receiver for the matrix model.""" from typing import NamedTuple, Optional, Dict from lexos.receivers.base_receiver import BaseReceiver DocumentLabelMap = Dict[int, str] class TokenOption(NamedTuple): """A typed tuple to represent token option.""" # the size of each token n_gram_size: int # the token type to send to CountVerctorizer # available options are 'word', 'char_wb', and 'char' token_type: str class NormOption(NamedTuple): """A typed tuple to keep the normalize option.""" # True if we are using proportional count, False if we are using raw count use_freq: bool # True if we are using proportional count, False if we are using raw count use_tf_idf: bool # the normalize option in TF-IDF # available options are 'l1' and 'l2'. nice naming, SciPy! tf_idf_norm_option: str class CullingOption(NamedTuple): """A typed tuple to represent all the culling options.""" # the lowest word rank to keep in DTM # if none, then don't apply most frequent word mfw_lowest_rank: Optional[int] # the least number of passage that the word needs to be in # if none, then don't apply culling cull_least_seg: Optional[int] class MatrixFrontEndOption(NamedTuple): """A typed tuple to represent all the matrix options.""" # the token options token_option: TokenOption # the normalize options norm_option: NormOption # the culling options culling_option: CullingOption class MatrixReceiver(BaseReceiver): """This class receives the front end options.""" def __init__(self): """Get all the matrix options using the receiver.""" super().__init__() def _get_token_option_from_front_end(self) -> TokenOption: """Get the token option from front end. :return: a token option struct """ token_type_is_word = self._front_end_data['token_type'] == 'Tokens' token_type_is_char = self._front_end_data['token_type'] == 'Characters' char_within_word = False # get the token type if token_type_is_word: token_type = 'word' elif token_type_is_char and char_within_word: token_type = 'char_wb' elif token_type_is_char and not char_within_word: token_type = 'char' else: raise ValueError('invalid token type from front end') # get the n_gram_size n_gram_size = int(self._front_end_data['token_size']) return TokenOption(token_type=token_type, n_gram_size=n_gram_size) def _get_normalize_option_from_front_end(self) -> NormOption: """Get the normalize option from front end. :return: a normalize option struct """ use_freq = self._front_end_data['normalization_method'] == \ 'Proportional' # if use TF/IDF use_tfidf = self._front_end_data['normalization_method'] == 'TF-IDF' return NormOption(use_freq=use_freq, use_tf_idf=use_tfidf, tf_idf_norm_option='l2') def _get_culling_option_from_front_end(self) -> CullingOption: """Get the culling option from the front end. :return: a culling option struct """ if 'enable_most_frequent_words' in self._front_end_data: lower_rank_bound = int(self._front_end_data['most_frequent_words']) else: lower_rank_bound = None if 'enable_minimum_occurrences' in self._front_end_data: least_num_seg = int(self._front_end_data['minimum_occurrences']) else: least_num_seg = None return CullingOption(cull_least_seg=least_num_seg, mfw_lowest_rank=lower_rank_bound) def options_from_front_end(self) -> MatrixFrontEndOption: """Get all the matrix options from front end. :return: all the options packed together into a matrix option class """ return MatrixFrontEndOption( token_option=self._get_token_option_from_front_end(), norm_option=self._get_normalize_option_from_front_end(), culling_option=self._get_culling_option_from_front_end() )

31.699248

79

0.671252

561

4,216

4.743316

0.221034

0.066141

0.049605

0.054115

0.361894

0.278091

0.074408

0.045847

0

0.000952

0.252609

4,216

132

80

31.939394

0.843542

0.306689

0

0.052632

0

0.087675

0.018685

0

1

0.087719

false

0

0.035088

0

0.45614

0

null

0

null

0

1

0

c1c5a2754fda11c89b443f7e02da5e10a6ab2f74

3,791

py

Python

src/train_process/build_cnn_model/Train_model.py

rober5566a/NTUT_109-2_MVA_Final-Project

d18494760750efae1ff0810dcaa281a03d0827c0

[ "MIT" ]

null

src/train_process/build_cnn_model/Train_model.py

rober5566a/NTUT_109-2_MVA_Final-Project

d18494760750efae1ff0810dcaa281a03d0827c0

[ "MIT" ]

null

src/train_process/build_cnn_model/Train_model.py

rober5566a/NTUT_109-2_MVA_Final-Project

d18494760750efae1ff0810dcaa281a03d0827c0

[ "MIT" ]

null

import torch import torch.nn as nn import torch.nn.functional as F from torch.autograd import Variable from Model.CNN_1 import CNN def test_performance(model, device, test_loader, loss, pred_train_labels, train_labels, printHistory=True): test_num_right = 0 for step, (datas, labels) in enumerate(test_loader): b_test_datas = datas.clone().detach().type(torch.float32) b_test_labels = labels.clone().detach().type(torch.int64) b_test_datas = Variable(b_test_datas, requires_grad=False).to(device) # b_test_labels don't need to calculate loss b_test_labels = Variable(b_test_labels, requires_grad=False) b_test_output = model(b_test_datas) pred_test_labels = torch.argmax( b_test_output, dim=1).detach().cpu() # pred_test_labels = F.log_softmax(test_output, dim=1) b_test_num_right = int(sum(pred_test_labels == b_test_labels)) test_num_right += b_test_num_right # train_acc train_num_right = int(sum(pred_train_labels == train_labels)) train_acc = train_num_right / train_labels.size(0) # test_acc test_acc = test_num_right / len(test_loader.dataset.labels) if printHistory is True: print('train_acc: {:5f} | train_loss: {:5f} | test_acc: {:5f}'.format( train_acc, loss, test_acc)) return train_acc, test_acc def train_model(device, EPOCH, train_loader, test_loader, model, loss_func, optimizer, printHistory=True): train_loss_ls = [] train_acc_ls = [] test_acc_ls = [] pred_train_labels = torch.tensor([], dtype=torch.int64).cpu() train_labels = torch.tensor([], dtype=torch.int64).cpu() for epoch in range(1, EPOCH+1): # print("EPOCH: ", epoch) # total = 0 train_acc = 0 for step, (b_datas, b_labels) in enumerate(train_loader): b_datas = b_datas.clone().detach().type(torch.float32) b_labels = b_labels.clone().detach().type(torch.int64) b_datas = Variable(b_datas).to(device) b_labels = Variable(b_labels).to(device) output = model(b_datas) loss = loss_func(output, b_labels) optimizer.zero_grad() loss.backward() optimizer.step() pred_b_train_labels = torch.argmax( output, dim=1).detach().cpu() pred_train_labels = torch.cat( (pred_train_labels, pred_b_train_labels), dim=0) train_labels = torch.cat((train_labels, b_labels.cpu()), dim=0) # pred_train_labels = F.log_softmax(output, dim=1) # total += b_labels.size(0) # if step % 10 == 0: # train_acc, test_acc = test_performance(model, loss, test_datas, # pred_train_labels, b_labels, test_labels, printHistory=False) # if printHistory is True: # print('Step: {} | train_acc: {:5f} | train_loss: {:5f} | test_acc: {:5f}'.format( # step, train_acc, loss, test_acc)) train_acc, test_acc = test_performance(model, device, test_loader, loss.cpu(), pred_train_labels, train_labels, printHistory=False) if printHistory is True: print('EPOCH: {} | train_acc: {:5f} | train_loss: {:5f} | test_acc: {:5f}'.format( epoch, train_acc, loss, test_acc)) if loss > 1: loss = 1 try: train_loss_ls.append(float(loss.cpu().detach().numpy())) except AttributeError: train_loss_ls.append(float(loss)) train_acc_ls.append(train_acc) test_acc_ls.append(test_acc) return (train_loss_ls, train_acc_ls, test_acc_ls)

37.534653

118

0.613558

502

3,791

4.318725

0.161355

0.086255

0.055351

0.0369

0.430812

0.350554

0.278598

0.158672

0.077491

0.049816

0

0.01454

0.274334

3,791

100

119

37.91

0.773537

0.152994

0

0.032787

0

0.016393

0.037547

0

1

0.032787

false

0

0.081967

0

0.147541

0.114754

0

null

0

null

0

1

0

c1c7615a1093ffe7d7e900aa21ffc5ae797e37b3

374

py

Python

tests/mocks.py

lgatellier/webhook-router

7e68e469b54c1f605d03e10744774cb9f1f094f3

[ "Apache-2.0" ]

1

2022-03-06T19:01:23.000Z

tests/mocks.py

lgatellier/webhook-router

7e68e469b54c1f605d03e10744774cb9f1f094f3

[ "Apache-2.0" ]

8

2021-11-18T22:49:49.000Z

2022-03-30T09:29:43.000Z

tests/mocks.py

lgatellier/webhook-gateway

975b7d380b17fd3b534d9c1eca51b6dd2cc44d00

[ "Apache-2.0" ]

null

from fastapi import Request class MockedHTTPRequest(Request): def __init__(self, body: dict = {}, headers: dict = {}): super().__init__({"type": "http"}) self.__body = body self.__headers = headers @property def body(self) -> dict: return self.__body @property def headers(self) -> dict: return self.__headers

22

60

0.604278

40

374

5.25

0.425

0.114286

0.133333

0.171429

0

0.272727

374

16

61

23.375

0.772059

0

0.166667

0

0.02139

0

1

0.25

false

0

0.083333

0.166667

0.583333

0

null

0

1

0

null

0

1

0

1

0

4

c1c7c2c4ce7d208b7884ed9d436d4aa0dfa66353

2,821

py

Python

src/csi_rover_vision/nodes/depth_image_processor.py

BhargavRE25/Rover-Machine-Learning

af48811ceb08acae1dda76473d294f362178dcbe

[ "MIT" ]

3

2020-09-21T17:15:08.000Z

2020-09-25T01:08:19.000Z

src/csi_rover_vision/nodes/depth_image_processor.py

columbia-university-robotics/vehicle-machine-learning

af48811ceb08acae1dda76473d294f362178dcbe

[ "MIT" ]

null

src/csi_rover_vision/nodes/depth_image_processor.py

columbia-university-robotics/vehicle-machine-learning

af48811ceb08acae1dda76473d294f362178dcbe

[ "MIT" ]

null

#!/usr/bin/env python import rospy from std_msgs.msg import Header from geometry_msgs.msg import PoseStamped from nav_msgs.msg import Odometry from sensor_msgs.msg import Image from cv_bridge import CvBridge, CvBridgeError import cv2 import sys import numpy as np import message_filters import tf class DepthImageProcessor: def __init__(self): self.HOME_BASE_KEY = 1 rospy.init_node('depth_image_processor') self.bridge = CvBridge() self.listener = tf.TransformListener() # subscribe to depth image and segmentation result self.base_pose_pub = rospy.Publisher("/base_station/pose", PoseStamped, queue_size=3) rospy.Subscriber("/stereo/depth_image", Image, callback=self.depth_image_callback) rospy.Subscriber("/segmented_image", Image, callback=self.segmentat_image_callback) self.odom_frame = "odom" self.camera_frame = "scout_1_tf/camera_link" self.depth_image = None print("started node") rospy.spin() def depth_image_callback(self, depth_image): try: cv_mat_depth = self.bridge.imgmsg_to_cv2(depth_image, desired_encoding="passthrough") except CvBridgeError, e: raise e self.depth_image = cv_mat_depth # # Convert the depth image to a Numpy array # self.depth_image = np.array(cv_mat_depth, dtype=np.float32) def segmentat_image_callback(self, segmentation_image): try: cv_mat_seg = self.bridge.imgmsg_to_cv2(segmentation_image, desired_encoding="mono8") except CvBridgeError, e: raise e if self.depth_image is not None: cv_mat_seg = np.array(cv_mat_seg) cv_mat_seg[cv_mat_seg != self.HOME_BASE_KEY] = 0 cv_mat_seg[cv_mat_seg > 0] = 1 masked_depth = cv2.bitwise_and(self.depth_image, self.depth_image, mask=cv_mat_seg) # convert depth mask to numpy and clean np_array = np.array(masked_depth).flatten() where_are_NaNs = np.isnan(np_array) np_array[where_are_NaNs] = 0 count = np.count_nonzero(np_array) sum = np_array.sum() dist = sum / count obj_pose = PoseStamped() obj_pose.header = Header() obj_pose.header.frame_id = self.camera_frame obj_pose.pose.position.x = dist obj_pose.pose.position.y = 0 obj_pose.pose.position.z = 0 final_pose = self.listener.transformPose(self.odom_frame, obj_pose) self.base_pose_pub.publish(final_pose) # print(obj_pose) # print(final_pose) # cv2.imshow("masked_data", masked_depth) # cv2.waitKey(0) if __name__ == "__main__": DepthImageProcessor()

30.333333

97

0.651542

369

2,821

4.674797

0.317073

0.081159

0.064928

0.017391

0.108986

0.023188

0

0.009165

0.265154

2,821

92

98

30.663043

0.822962

0.105636

0

0.105263

0

0.054162

0.017125

0

null

0.017544

0.192982

null

0.017544

0

null

0

null

0

1

0

1

c1c86881d74c59dcc1055e4123f85ff985b2a7b8

14,313

py

Python

src/ecs/packing_utils_v2.py

luojie1024/Huawei_CodeCraft_2018

f7fc6db09c65d9b19c773d3a8933109084ec0489

[ "Apache-2.0" ]

3

2019-03-01T12:16:02.000Z

2019-12-19T07:59:07.000Z

src/ecs/packing_utils_v2.py

luojie1024/Huawei_CodeCraft_2018

f7fc6db09c65d9b19c773d3a8933109084ec0489

[ "Apache-2.0" ]

null

src/ecs/packing_utils_v2.py

luojie1024/Huawei_CodeCraft_2018

f7fc6db09c65d9b19c773d3a8933109084ec0489

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- import const_map import math import packing_method # 选择在packing_model 中的装配方案 pack_function = packing_method.used_func def pack_api(dataObj, predict_result,target_c_m): ''' 装配接口 :param dataObj: 数据对象 :param predict_result: 预测结果 ''' picker = VmWorker(predict_result) group = ServerObj(dataObj, picker.origin_cpu_mem_sum()) pack_function(picker, group,target_c_m) vm_size, vm = picker.to_origin_desc() pm_size, pm, pm_name = group.to_description() res_use = group.get_res_used_pro() pm_free = group.get_pm_free() print(group.to_usage_status()) return vm_size, vm, pm_size, pm, pm_name, res_use, pm_free class ServerObj(): # 计数 empty = 0 # 集群中物理机参数 server_info = {'CPU': 0, # u数 'MEM': 0, # m数 'HDD': 0} # h数 # 物理机计数量 pm_size = 0 PM_status = [] # 当前集群中虚拟机计数 vm_size = 0 # 虚拟机存储状态，对应的存储为 VM = {} # 物理机存储状态，对应存储值 PM = [] PM_name = [] # 剩余资源表 PM_Free = [] # 当前指向的物理机id 索引从0开始 PM_ID = 0 # 目标比例 direction = [] # 剩余的cpu数量 lave_cpu_sum = 0 # 剩余的mem数量 lave_mem_sum = 0 # vm总共需要cpu数量 need_cpu_sum = 0 # vm总共需要mem数量 need_mem_sum = 0 # 物理机cpu总数 pm_cpu_sum = 0 # 物理机内存总算 pm_mem_sum = 0 def __init__(self, dataObj, vm_res): ''' 初始化 ''' self.vm_size = 0 self.PM = [] self.VM = {} self.PM_status = [] self.pm_size = 0 self.PM_ID = -1 self.empty = 0 self.PM_Free = [] self.server_info = {} self.server_info = dataObj.pm_type_list self.direction = [0.25, 0.5, 1] self.lave_mem_sum = vm_res[1] self.lave_cpu_sum = vm_res[0] self.need_mem_sum = vm_res[1] self.need_cpu_sum = vm_res[0] self.pm_cpu_sum = 0 self.pm_mem_sum = 0 self.PM_name = [] def new_physic_machine(self, pm_type): ''' 创建物理机 :param pm_type:虚拟机类型 :return: ''' C_M = const_map.PM_TYPE[pm_type]['CPU'] / float(const_map.PM_TYPE[pm_type]['MEM']) re_cpu = const_map.PM_TYPE[pm_type]['CPU'] re_mem = const_map.PM_TYPE[pm_type]['MEM'] temp = { 'pm_type': pm_type, 'C_M': C_M, 're_cpu': re_cpu, 're_mem': re_mem, 'vm_size': 0 } self.PM_status.append(temp) self.PM.append({}) self.pm_size += 1 self.PM_ID += 1 # 保存现在总的物理资源开辟数量 self.pm_cpu_sum += re_cpu self.pm_mem_sum += re_mem # 存储物理机名字 self.PM_name.append(pm_type) print 'apply pm：%s , C/M=%.2f\n' % (pm_type, C_M) return self.PM_ID def get_nearest_distance(self, c_m): ''' 获取最接近c_m的优化目标 :param c_m: :return: ''' min_distance_target = 1 distance = 1 for i in range(len(self.direction)): # 距离更接近 if abs(c_m - self.direction[i]) < distance: distance = abs(c_m - self.direction[i]) min_distance_target = self.direction[i] return min_distance_target def get_pm_c_m(self, pm_id): ''' 返回指定物理机的c/m :param pm_id: :return: ''' c_m = self.PM_status[pm_id]['C_M'] return c_m def get_lave_cpu_mem_sum(self): ''' 获取当前cpu mem的数量 :return: ''' return self.lave_cpu_sum, self.lave_mem_sum def get_sum_C_M(self): return self.lave_cpu_sum * 1.0 / self.lave_mem_sum def is_free(self, pm_id): ''' 判断是否还没放满 :param pm: 物理机编号 :return: 状态 ''' re_cpu = self.PM_status[pm_id]['re_cpu'] re_mem = self.PM_status[pm_id]['re_mem'] if re_cpu > 0 and re_mem > 0: return True else: return False def get_pm_cpu_mem(self, pm_id): ''' 返回指定物理机的cpu 内存剩余空间 :param pm_id: :return: ''' re_cpu = self.PM_status[pm_id]['re_cpu'] re_mem = self.PM_status[pm_id]['re_mem'] return re_cpu, re_mem def test_put_vm(self, pm_id, vm_type): ''' 测试能否放置虚拟机 :param pm_id: 物理机id :param vm_type: 虚拟机类型 :return: 剩余资源数 ''' # 数据异常 if pm_id is None or \ pm_id < 0 or pm_id >= self.pm_size: raise ValueError('error pm_id=', pm_id) vm_cpu, vm_mem = const_map.VM_PARAM[vm_type][:2] # 从物理机状态表中获取参数 pmstatus = self.PM_status[pm_id] re_cpu = pmstatus['re_cpu'] - vm_cpu re_mem = pmstatus['re_mem'] - vm_mem if re_cpu == 0 or re_mem == 0: c_m = 0 else: c_m = re_cpu * 1.0 / re_mem # 返回能否放置,并返回放置后的剩余空间大小 if re_cpu >= 0 and re_mem >= 0: return (True, [re_cpu, re_mem, c_m]) else: return (False, [re_cpu, re_mem, c_m]) def put_vm(self, pm_id, vm_type): ''' :param pm_id:物理机id :param vm_type: 虚拟机类型 :return: ''' if pm_id is None or \ pm_id < 0 or pm_id >= self.pm_size: raise ValueError('error pm_id=', pm_id) # 获取资源数 vm_cpu, vm_mem = const_map.VM_PARAM[vm_type][:2] # 获取参数状态 pmstatus = self.PM_status[pm_id] re_cpu = pmstatus['re_cpu'] - vm_cpu re_mem = pmstatus['re_mem'] - vm_mem # 剩余总数计算 self.lave_cpu_sum -= vm_cpu self.lave_mem_sum -= vm_mem # 资源充足,分配 if re_cpu >= 0 and re_mem >= 0: self.empty += 1 pmstatus['re_cpu'] = re_cpu pmstatus['re_mem'] = re_mem # 计算c/m比例 if re_cpu == 0 or re_mem == 0: c_m = 0 else: c_m = re_cpu * 1.0 / re_mem pmstatus['C_M'] = c_m pmstatus['vm_size'] += 1 self.vm_size += 1 # 记录虚拟机种类数量 if vm_type not in self.VM.keys(): self.VM[vm_type] = 0 self.VM[vm_type] += 1 pm = self.PM[pm_id] # 记录物理机种类数量 if vm_type not in pm.keys(): pm[vm_type] = 0 pm[vm_type] += 1 return (re_cpu, re_mem) return None # 超分返回 def to_description(self): if self.empty != 0: return self.pm_size, self.PM, self.PM_name else: return 0, self.PM, self.PM_name def get_res_used_pro(self): ''' :return: 返回资源使用率 ''' cpu_use = self.need_cpu_sum * 1.0 / self.pm_cpu_sum mem_use = self.need_mem_sum * 1.0 / self.pm_mem_sum use = cpu_use * 0.5 + mem_use * 0.5 # 返回物理机的资源使用率 # return cpu_use, mem_use, use return use def to_usage_status(self): ''' 生成当前集群中各个物理机的使用状态 ''' result = '' usage = self.PM_status # result = 'CPU:%d MEM:%d\n' % (cpu_max, mem_max) for i in range(self.pm_size): pm_type = usage[i]['pm_type'] cpu_max = self.server_info[pm_type]['CPU'] mem_max = self.server_info[pm_type]['MEM'] cpu_used = cpu_max - usage[i]['re_cpu'] mem_used = mem_max - usage[i]['re_mem'] cpu_usage_rate = cpu_used * 100.0 / cpu_max mem_usage_rate = mem_used * 100.0 / mem_max total_usage_rate = cpu_usage_rate * 0.5 + mem_usage_rate * 0.5 vm_cot = usage[i]['vm_size'] string = 'pm_id:%d \t cpu_used:%d(%.2f%%)\t' % (i, cpu_used, cpu_usage_rate) string += 'mem_used:%d(%.2f%%)\t' % (mem_used, mem_usage_rate) string += 'total_used:(%.2f%%)\tvm_cot:%d\n' % (total_usage_rate, vm_cot) # 保存剩余空间情况表 self.PM_Free.append([cpu_max - cpu_used, mem_max - mem_used]) result += string return result def get_pm_free(self): return self.PM_Free def is_packing(self): if self.lave_cpu_sum == 0 or self.lave_mem_sum == 0: return False else: return True ################## end class Server #################### class VmWorker(): # 预测输入的原始数据 origin_data = None # 原始输入描述 origin_desc_table = {} origin_vm_size = 0 # 虚拟机总数，非零虚拟机总数 vm_size = 0 # 虚拟机中cpu总数 vm_cpu_size = 0 # 虚拟机中mem总数 vm_mem_size = 0 # 预测虚拟机的在M/U权重与核心数级别 # 上展开 shape=[3,6] # CPU=1,2,4,8,16,32 VM = [[-1, -1, -1, -1, -1, -1], # weight_1.0 [-1, -1, -1, -1, -1, -1], # weight_2.0 [-1, -1, -1, -1, -1, -1] # weight_4.0 ] # 虚拟机类型名数组 vm_types = const_map.VM_TYPE_DIRT def __init__(self, predict_result): # 保存原始数据 self.origin_data = predict_result # 初始化分拣对象 self.init_worker(predict_result) self.vm_size, self.origin_desc_table = self.set_data_info() self.origin_vm_size = self.vm_size # 初始化实时的cpu mem数量 self.cpu_sum = self.vm_cpu_size self.mem_sum = self.vm_mem_size pass def init_worker(self, predict_result): ''' 初始化分拣对象 :param predict_result:预测结果 ''' types = predict_result.keys() # 遍历计算总共需要cpu mem 的数量 for vmtype in types: vm_sum = 0 pre_temp = predict_result[vmtype] vm_cpu, vm_mem, _ = const_map.VM_PARAM[vmtype] for i in range(len(pre_temp)): vm_sum += pre_temp[i] self.vm_cpu_size += vm_cpu * vm_sum self.vm_mem_size += vm_mem * vm_sum # 添加到数量列表 row, col = self.type2index(vmtype) self.VM[row][col] = vm_sum def type2index(self, vm_type): tindex = self.vm_types.index(vm_type) windex = tindex % 3 cindex = int(tindex / 3) return windex, cindex def index2type(self, windex, cindex): if windex < 0 or cindex < 0: raise ValueError('Error ', (windex, cindex)) return self.vm_types[cindex * 3 + windex] def get_vm_by_index(self, windex, cindex): ''' :param windex: :param cindex: :return: ''' re_vm = self.VM[windex][cindex] if self.vm_size == -1 or re_vm == -1: return None elif self.vm_size == 0 or re_vm == 0: return -1 else: re_vm -= 1 self.vm_size -= 1 self.VM[windex][cindex] = re_vm return re_vm pass def get_vm_by_wc(self, weight, cpu): ''' :param weight: :param cpu: :return: ''' windex = int(math.log(weight, 2)) cindex = int(math.log(cpu, 2)) return self.get_vm_by_index(windex, cindex) pass def get_vm_by_type(self, vm_type): windex, cindex = self.type2index(vm_type) return self.get_vm_by_index(windex, cindex) def get_vm_by_mu_weight(self, mu_weight, order=0): result = [[], # vm_type []] # cot windex = int(math.log(mu_weight, 2)) start = 0 end = 5 step = 1 if order == 1: start = 4 end = -1 step = -1 for cindex in range(start, end, step): tmp = self.VM[windex][cindex] if tmp > 0: result[0].append(self.index2type(windex, cindex)) result[1].append(tmp) self.VM[windex][cindex] = 0 self.vm_size -= tmp if len(result[0]) == 0: return None return result def get_vm_order(self, cpu): ''' :param cpu:CPU :return: 返回该cpu类型下所有比例队列 ''' result = {} col = int(math.log(cpu, 2)) start = col end = -1 step = -1 temp_1 = [[], []] temp_2 = [[], []] temp_4 = [[], []] for col in range(start, end, step): if self.VM[0][col] != -1: temp_1[0].append(self.index2type(0, col)) temp_1[1].append(self.VM[0][col]) if self.VM[1][col] != -1: temp_2[0].append(self.index2type(1, col)) temp_2[1].append(self.VM[1][col]) if self.VM[2][col] != -1: temp_4[0].append(self.index2type(2, col)) temp_4[1].append(self.VM[2][col]) # 如果都为空,则无需放置 if len(temp_1[0]) == 0 and len(temp_2[0]) == 0 and len(temp_4[0]) == 0: return result else: result['1.0'] = temp_1 result['2.0'] = temp_2 result['4.0'] = temp_4 return result def get_vm_by_cpu(self, cpu, order=0): ''' 获得队列顺序 :param cpu: :param order: :return: ''' result = [[], # vm_type []] # cot # 计算CPU所在列 col = int(math.log(cpu, 2)) start = 0 end = 3 step = 1 # 从下往上取 1：4->1：1 if order == 1: start = 2 end = -1 step = -1 # 从上往下取 1：1->1：4 for row in range(start, end, step): tmp = self.VM[row][col] if tmp > 0: result[0].append(self.index2type(row, col)) result[1].append(tmp) self.VM[row][col] = 0 self.vm_size -= tmp # 没有vm 返回None if len(result[0]) == 0: return None return result def origin_cpu_mem_sum(self): return self.vm_cpu_size, self.vm_mem_size def to_origin_desc(self): return self.origin_vm_size, self.origin_desc_table def set_data_info(self): ''' 设置虚拟机数量表计算虚拟机总数 ''' info_table = {} vm_sum = 0 flag = True for i in range(len(self.VM)): # 行 for j in range(len(self.VM[2])): # 列 tmp = self.VM[i][j] if tmp != -1: flag = False vm_sum += tmp info_table[self.index2type(i, j)] = tmp if flag: vm_sum = -1 return vm_sum, info_table

27.107955

90

0.505485

1,942

14,313

3.45829

0.123584

0.038416

0.005807

0.00536

0.324598

0.216796

0.171828

0.144133

0.105718

0.094997

0

0.027532

0.375742

14,313

527

91

27.159393

0.724119

0.052051

0

0.25

0

0.024894

0.00606

0

null

0.009146

null

0.006098

0

null

0

null

0

1

0

1

c1c892c83086b1d88c5c729a393446e22f1c34cf

1,653

py

Python

edgedb/lang/schema/basetypes/uuid.py

jonathanslenders/edgedb

35ad66c4bd525cd9966f8029e5b385e888323f82

[ "Apache-2.0" ]

1

2021-12-15T09:34:48.000Z

edgedb/lang/schema/basetypes/uuid.py

jonathanslenders/edgedb

35ad66c4bd525cd9966f8029e5b385e888323f82

[ "Apache-2.0" ]

null

edgedb/lang/schema/basetypes/uuid.py

jonathanslenders/edgedb

35ad66c4bd525cd9966f8029e5b385e888323f82

[ "Apache-2.0" ]

null

# # This source file is part of the EdgeDB open source project. # # Copyright 2008-present MagicStack Inc. and the EdgeDB authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import uuid from edgedb.lang.common import exceptions as edgedb_error from edgedb.lang.common.persistent_hash import persistent_hash from . import base as s_types _add_impl = s_types.BaseTypeMeta.add_implementation _add_map = s_types.BaseTypeMeta.add_mapping class UUID(uuid.UUID): def __init__(self, value, *, hex=None, bytes=None, bytes_le=None, fields=None, int=None, version=None): try: if isinstance(value, uuid.UUID): int = value.int super().__init__(hex, bytes, bytes_le, fields, int, version) else: hex = value super().__init__(hex, bytes, bytes_le, fields, int, version) except ValueError as e: raise edgedb_error.ScalarTypeValueError(e.args[0]) from e def persistent_hash(self): return persistent_hash(self.int) _add_impl('std::uuid', UUID) _add_map(UUID, 'std::uuid') _add_map(uuid.UUID, 'std::uuid')

30.611111

76

0.696915

235

1,653

4.748936

0.47234

0.053763

0.023297

0.028674

0.071685

0

0.006944

0.215971

1,653

53

77

31.188679

0.854167

0.389595

0

0.086957

0

0.027245

0

1

0.086957

false

0

0.173913

0.043478

0.347826

0

null

0

null

0

1

0

c1c9379695253067028b6d9dd41b3f7776b4991c

304

py

Python

experimental/python_q_wrapper/Q/validate.py

subramon/qlu

2fb8a2b3636dd11e2dfeae2a6477bd130316da47

[ "MIT" ]

null

experimental/python_q_wrapper/Q/validate.py

subramon/qlu

2fb8a2b3636dd11e2dfeae2a6477bd130316da47

[ "MIT" ]

7

2020-07-29T16:48:25.000Z

2020-09-26T23:47:22.000Z

experimental/python_q_wrapper/Q/validate.py

subramon/qlu

2fb8a2b3636dd11e2dfeae2a6477bd130316da47

[ "MIT" ]

1

2015-05-14T22:34:13.000Z

def is_p_vector(val): """checks whether given value is of type P_Vector""" from Q.p_vector import PVector return isinstance(val, PVector) def is_p_scalar(val): """checks whether given vlaue is of type P_Scalar""" from Q.p_scalar import PScalar return isinstance(val, PScalar)

23.384615

56

0.710526

48

304

4.333333

0.416667

0.100962

0.057692

0.201923

0

0.203947

304

13

57

23.384615

0.859504

0.305921

0

1

0.333333

false

0

0.333333

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

4

c1cbab332513a23ee2e04a851cca5c5b17c6cd1c

9,348

py

Python

GraphOfDocs_Representation/create.py

imis-lab/book-chapter

8260a60ec91dd29616eeed80f34bdea00fb73cd7

[ "MIT" ]

2

2020-09-29T11:40:56.000Z

2020-09-29T11:41:04.000Z

GraphOfDocs_Representation/create.py

imis-lab/personnel-selection

f04d78c8211f21ab53db4ecdddfc9c78ffc26a36

[ "MIT" ]

null

GraphOfDocs_Representation/create.py

imis-lab/personnel-selection

f04d78c8211f21ab53db4ecdddfc9c78ffc26a36

[ "MIT" ]

null

""" This script contains functions that create data in the Neo4j database. """ import json import platform from pathlib import Path from gensim.models import Word2Vec from GraphOfDocs_Representation.utils import ( clear_screen, generate_words ) # Initialize an empty set of edges. edges = {} # Initialize an empty list of unique terms. # We are using a list to preserver order of appearance. nodes = [] def create_graph_of_words(words, database, filename, relationship, window_size = 4): """ Function that creates a Graph of Words that contains all nodes from each document for easy comparison, inside the neo4j database, using the appropriate cypher queries. """ # Files that have word length < window size, are skipped. # Window size ranges from 2 to 6. length = len(words) if (length < window_size): # Early exit, we return the skipped filename return filename # We are using a global set of edges to avoid creating duplicate edges between different graph of words. # Basically the co-occurences will be merged. global edges # We are using a global set of edges to avoid creating duplicate nodes between different graph of words. # A list is being used to respect the order of appearance. global nodes # We are getting the unique terms for the current graph of words. terms = [] creation_list = [] for word in words: if word not in terms: terms.append(word) # Remove end-of-sentence token, so it doesn't get created. if 'e5c' in terms: terms.remove('e5c') # If the word doesn't exist as a node, then add it to the creation list. for word in terms: if word not in nodes: creation_list.append(word) # Append word to the global node graph, to avoid duplicate creation. nodes.append(word) # Create all unique nodes, from the creation list. database.execute(f'UNWIND {creation_list} as key ' 'CREATE (word:Word {key: key})', 'w') # Create unique connections between existing nodes of the graph. for i, current in enumerate(words): # If there are leftover items smaller than the window size, reduce it. if i + window_size > length: window_size = window_size - 1 # If the current word is the end of sentence string, # we need to skip it, in order to go to the words of the next sentence, # without connecting words of different sentences, in the database. if current == 'e5c': continue # Connect the current element with the next elements of the window size. for j in range(1, window_size): next = words[i + j] # Reached the end of sentence string. # We can't connect words of different sentences, # therefore we need to pick a new current word, # by going back out to the outer loop. if next == 'e5c': break edge = (current, next) if edge in edges: # If the edge, exists just update its weight. edges[edge] = edges[edge] + 1 query = (f'MATCH (w1:Word {{key: "{current}"}})-[r:connects]-(w2:Word {{key: "{next}"}}) ' f'SET r.weight = {edges[edge]}') else: # Else, create it, with a starting weight of 1 meaning first co-occurence. edges[edge] = 1 query = (f'MATCH (w1:Word {{key: "{current}"}}) ' f'MATCH (w2:Word {{key: "{next}"}}) ' f'MERGE (w1)-[r:connects {{weight: {edges[edge]}}}]-(w2)') # This line of code, is meant to be executed, in both cases of the if...else statement. database.execute(query, 'w') # Connect the paper, with all of its words. query = (f'MATCH (w:Word) WHERE w.key IN {terms} ' 'WITH collect(w) as words ' f'MATCH (i:Issue {{key: "{filename}"}}) ' 'UNWIND words as word ' f'CREATE (i)-[:{relationship}]->(word)') database.execute(query, 'w') return def create_unique_constraints(database): """ Wrapper function that gathers all CREATE CONSTRAINT queries, in one place. """ database.execute('CREATE CONSTRAINT ON (word:Word) ' 'ASSERT word.key IS UNIQUE', 'w') database.execute('CREATE CONSTRAINT ON (issue:Issue) ' 'ASSERT issue.key IS UNIQUE', 'w') database.execute('CREATE CONSTRAINT ON (person:Person) ' 'ASSERT person.uname IS UNIQUE', 'w') return def create_issues_from_json(database, dirpath): """ Function that creates the nodes representing issues, persons assigned to them, sets the properties of the first ones, and create the correspending graph of docs by using the title and description of the issue, based on the supplied json file. """ current_system = platform.system() # Read json in memory. with open(dirpath, encoding = 'utf-8-sig', errors = 'ignore') as f: issues = json.load(f)['issues'] skip_count = 0 count = 1 total_count = len(issues) # Process all issues. for issue in issues: # Print the number of the currently processed issue. print(f'Processing {count + skip_count} out of {total_count} issues...' ) # Extract the title and description from the issue. title = '' if issue.get('title') is None else issue['title'] description = '' if issue.get('description') is None else issue['description'] # If the issue has no title and description, continue. if title == '' and description == '': skip_count += 1 continue # Create the issue, using its fields. query = ( f'CREATE (i:Issue {{key: "{issue["key"]}", ' f'type: "{issue["type"]}", ' f'priority: "{issue["priority"]}", ' f'status: "{issue["status"]}"}})' ) database.execute(query, 'w') # Create the assignee. query = (f'CREATE (p:Person {{uname: "{issue["assignee"]}"}})') database.execute(query, 'w') # Create the connection between the assignee and the issue. query = ( f'MATCH (p:Person {{uname: "{issue["assignee"]}"}}) ' f'MATCH (i:Issue {{key: "{issue["key"]}"}}) ' f'CREATE (p)-[r:is_assigned_to]->(i)' ) database.execute(query, 'w') # Join the text of the title and description. text = ' '.join((title, description)) # Create the graph of words representation from the text of the issue. create_graph_of_words(generate_words(text), database, issue['key'], 'includes') # Update the progress counter. count = count + 1 # Save the last accessed issue in a file. with open('last_accessed_issue.txt', 'w') as f: f.write(issue['key']) # Clear the screen to output the update the progress counter. clear_screen(current_system) print(f'Created {total_count - skip_count}, skipped {skip_count} issues.') return def train_word2vec(dirpath, model_name, size): # Read json in memory. with open(dirpath, encoding = 'utf-8-sig', errors = 'ignore') as f: issues = json.load(f)['issues'] # Generate a list of lists, where each inner list # contains the tokens of each text. texts = [ generate_words(' '.join(( str(issue.get('title', '')), str(issue.get('description', '')) ))) for issue in issues ] # Train the Word2Vec model on the texts of jira issues. model = Word2Vec(texts, size = size, window = 5, min_count = 1, workers = 8) model.save(f'{model_name}') def create_word2vec_similarity_graph(database, dirpath, model_name, size = 100): # If the file doesn't exist, train the word2vec model. if not Path(model_name).is_file(): train_word2vec(dirpath, model_name, size) current_system = platform.system() # Load the word2vec model model = Word2Vec.load(model_name) # Initialize variables. count = 0 total_count = len(model.wv.vocab) # Find all tokens in the vocabulary and their most similar terms. for token in model.wv.vocab: print(f'Processing {count} out of {total_count} tokens...' ) for term, score in model.wv.most_similar(token, topn = 10): # Create the similarity relationship between # the token and each of its terms, # while setting the score property. query = ( f'MATCH (token:Word {{key: "{token}"}}) ' f'MATCH (term:Word {{key: "{term}"}}) ' f'CREATE (token)-[r:similar_w2v{{score: {score}}}]->(term)' ) database.execute(query, 'w') # Clear the screen to output the update the progress counter. clear_screen(current_system) count += 1

39.443038

109

0.586543

1,187

9,348

4.56866

0.240101

0.01844

0.01549

0.023234

0.194726

0.144938

0.106214

0.089618

0

0.007151

0.311831

9,348

236

110

39.610169

0.835846

0.350877

0

0.170543

0

0.007752

0.246756

0.045247

0

0.023256

1

0.03876

false

0

0.03876

0

0.108527

0.023256

0

null

0

null

0

1

0

c1ce58fcb7e82f1a80b448eadcae66a5b6f084aa

199

py

Python

backend/reception/admin.py

singsaker/intern

9376732c6d537f46443ad43afa51e82df2005da8

[ "MIT" ]

4

2021-10-06T19:09:12.000Z

2022-03-28T12:14:42.000Z

backend/reception/admin.py

singsaker/intern

9376732c6d537f46443ad43afa51e82df2005da8

[ "MIT" ]

2

2021-11-30T16:07:05.000Z

2022-02-17T23:57:00.000Z

backend/reception/admin.py

singsaker/intern

9376732c6d537f46443ad43afa51e82df2005da8

[ "MIT" ]

null

from django.contrib import admin from .models import Semester, Shift, ShiftDate # Register your models here. admin.site.register(Semester) admin.site.register(Shift) admin.site.register(ShiftDate)

22.111111

46

0.809045

27

199

5.962963

0.481481

0.167702

0.31677

0

0.100503

199

8

47

24.875

0.899441

0.130653

0

1

0

true

0

0.4

0

0.4

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

5

c1ce88fe488c659942b6230df65f6d5d692a9484

3,720

py

Python

tests/test_validator.py

MoseleyBioinformaticsLab/mwtab

1bc1e3715538348b29a5760a9c3184fe04f568a6

[ "BSD-3-Clause-Clear" ]

7

2018-02-02T07:50:20.000Z

2021-03-14T22:46:58.000Z

tests/test_validator.py

MoseleyBioinformaticsLab/mwtab

1bc1e3715538348b29a5760a9c3184fe04f568a6

[ "BSD-3-Clause-Clear" ]

2

2019-02-14T08:38:54.000Z

2020-02-19T08:08:02.000Z

tests/test_validator.py

MoseleyBioinformaticsLab/mwtab

1bc1e3715538348b29a5760a9c3184fe04f568a6

[ "BSD-3-Clause-Clear" ]

1

2019-10-12T23:38:44.000Z

import pytest import mwtab @pytest.mark.parametrize("files_source", [ "tests/example_data/mwtab_files/ST000122_AN000204.json", "tests/example_data/mwtab_files/ST000122_AN000204.txt" ]) def test_validate(files_source): """Test method for validating passing mwTab and JSON files from Metabolomics Workbench. :param files_source: File path to Metabolomics Workbench file to be validated. :type files_source: :py:class:`str` or """ mwfile = next(mwtab.read_files(files_source)) _, validation_log = mwtab.validate_file(mwfile, metabolites=False) assert len(validation_log.split('\n')) == 9 @pytest.mark.parametrize("file_source", [ "tests/example_data/validation_files/ST000122_AN000204_error_1.txt", "tests/example_data/validation_files/ST000122_AN000204_error_1.json" ]) def test_validate_subject_sample_factors(file_source): mwfile = next(mwtab.read_files(file_source)) _, validation_log = mwtab.validate_file(mwfile, metabolites=False) assert "missing Subject ID" in validation_log assert "missing Sample ID" in validation_log assert "missing value for Factor" in validation_log @pytest.mark.parametrize("file_source", [ "tests/example_data/validation_files/ST000122_AN000204_error_2.txt", "tests/example_data/validation_files/ST000122_AN000204_error_2.json" ]) def test_validate_subject_sample_factors(file_source): mwfile = next(mwtab.read_files(file_source)) _, validation_log = mwtab.validate_file(mwfile, metabolites=False) assert "Section missing data entry for sample(s):" in validation_log assert "SUBJECT_SAMPLE_FACTORS: Section missing sample ID(s)" in validation_log @pytest.mark.parametrize("file_source", [ "tests/example_data/validation_files/ST000122_AN000204_error_3.txt", "tests/example_data/validation_files/ST000122_AN000204_error_3.json" ]) def test_validate_metabolites(file_source): mwfile = next(mwtab.read_files(file_source)) _, validation_log = mwtab.validate_file(mwfile) assert "which matches a commonly used field name" in validation_log @pytest.mark.parametrize("file_source", [ "tests/example_data/validation_files/ST000122_AN000204_error_4.txt", "tests/example_data/validation_files/ST000122_AN000204_error_4.json" ]) def test_validate_schema(file_source): mwfile = next(mwtab.read_files(file_source)) _, validation_log = mwtab.validate_file(mwfile) assert "does not match the allowed schema" in validation_log @pytest.mark.parametrize("file_source", [ "tests/example_data/mwtab_files/ST000122_AN000204.json" ]) def test_validation_log_local(file_source): mwfile = next(mwtab.read_files(file_source)) _, validation_log = mwtab.validate_file(mwfile) # assert "mwtab version: {}".format(mwtab.__version__) in validation_log assert "Source: {}".format(file_source) in validation_log assert "Study ID: {}".format("ST000122") in validation_log assert "Analysis ID: {}".format("AN000204") in validation_log assert "File format: {}".format("json") in validation_log @pytest.mark.parametrize("file_source", [ "2" ]) def test_validation_log_web(file_source): mwfile = next(mwtab.read_files(file_source)) _, validation_log = mwtab.validate_file(mwfile, metabolites=False) # assert "mwtab version: {}".format(mwtab.__version__) in validation_log assert "Source: {}".format("https://www.metabolomicsworkbench.org/rest/study/analysis_id/AN000002/mwtab/txt")\ in validation_log assert "Study ID: {}".format("ST000002") in validation_log assert "Analysis ID: {}".format("AN000002") in validation_log assert "File format: {}".format("txt") in validation_log

43.255814

121

0.75672

485

3,720

5.496907

0.185567

0.131658

0.095649

0.086647

0.73856

0.729557

0.707052

0.610278

0.593023

0.476744

0

0.0534

0.13414

3,720

86

122

43.255814

0.774294

0.092742

0

0.424242

0

0.370083

0.210072

0

0.242424

1

0.106061

false

0

0.030303

0

0.136364

0

null

0

1

0

null

0

2

c1cf6fd8b07f9923a803e8e52a473b551c59d7c1

4,653

py

Python

bin/star_imports.py

HansPinckaers/impsort.vim

56e367e3e0ce5d7ea5c800492270282c3a53eda2

[ "MIT" ]

40

2016-05-31T22:19:42.000Z

2022-01-08T15:24:23.000Z

bin/star_imports.py

HansPinckaers/impsort.vim

56e367e3e0ce5d7ea5c800492270282c3a53eda2

[ "MIT" ]

35

2016-06-06T16:41:49.000Z

2022-03-16T13:40:56.000Z

bin/star_imports.py

HansPinckaers/impsort.vim

56e367e3e0ce5d7ea5c800492270282c3a53eda2

[ "MIT" ]

10

2017-04-14T07:42:06.000Z

2022-02-24T08:45:39.000Z

#!/usr/bin/env python """Very simple AST parser to get star imports. Nothing more. """ import os import ast import imp import sys from importlib import import_module try: str_ = unicode # noqa F821 except: str_ = str modules_seen = set() import_names = set() class NodeVisitor(ast.NodeVisitor): using_all = False names = set() imports = [] def iterable_values(self, node): if not hasattr(node, 'elts'): return [] values = [] types = (ast.Str,) if hasattr(ast, 'Bytes'): types += (ast.Bytes,) for item in node.elts: if isinstance(item, types): values.append(str_(item.s)) return values def add_name(self, name): if name and not self.using_all and name[0] != '_': self.names.add(name) def visit_Import(self, node): for n in node.names: self.add_name(n.asname or n.name) self.generic_visit(node) def visit_ImportFrom(self, node): module = '%s%s' % ('.' * node.level, str_(node.module or '')) for n in node.names: if n.name == '*': if module not in self.imports: self.imports.append(module) else: self.add_name(n.asname or n.name) self.generic_visit(node) def visit_Assign(self, node): for t in node.targets: if not isinstance(t.ctx, ast.Store): continue if isinstance(t, ast.Name): if t.id == '__all__': self.names.clear() self.using_all = True self.names.update(self.iterable_values(node.value)) else: self.add_name(t.id) elif isinstance(t, ast.Tuple): for item in t.elts: self.add_name(item.id) self.generic_visit(node) def visit_AugAssign(self, node): if isinstance(node.op, ast.Add) and node.target.id == '__all__': self.names.update(self.iterable_values(node.value)) def visit_FunctionDef(self, node): # Don't visit the function body self.add_name(node.name) def visit_ClassDef(self, node): # Don't visit the class body self.add_name(node.name) def visit_Try(self, node): for item in node.body: self.visit(item) for item in node.finalbody: self.visit(item) for handler in node.handlers: if handler.type.id == 'ImportError': # Only care about collecting names that would be imported for item in handler.body: self.visit(item) def simple_parse(source_file, module): if module.split('.')[0] in sys.builtin_module_names: try: imported = import_module(module) if hasattr(imported, '__all__'): import_names.update(imported.__all__) else: import_names.update(x for x in dir(imported) if x[0] != '_') except ImportError: pass return if module in modules_seen: return modules_seen.add(module) visitor = NodeVisitor() try: file = None last_path = None if module[0] == '.': module_tmp = module.lstrip('.') p = source_file for _ in range(len(module) - len(module_tmp)): p = os.path.dirname(p) last_path = [p] module = module_tmp for module in module.split('.'): if file is not None: file.close() file, path, desc = imp.find_module(module, last_path) if path: last_path = [path] if desc[2] == imp.PKG_DIRECTORY: for suffix, _, _ in imp.get_suffixes(): init_path = os.path.join(path, '__init__%s' % suffix) if os.path.exists(init_path): file = open(init_path, 'rb') path = init_path break if not file: return except ImportError: return try: root = ast.parse(file.read()) visitor.visit(root) except (SyntaxError, IndentationError): return finally: import_names.update(visitor.names) for module in visitor.imports: simple_parse(path, module) if __name__ == "__main__": if len(sys.argv) > 2: for arg in sys.argv[2:]: simple_parse(sys.argv[1], arg) for name in sorted(import_names): print(name)

28.2

76

0.537073

564

4,653

4.269504

0.257092

0.026578

0.027409

0.016196

0.145349

0.13289

0.104651

0.04402

0

0.003695

0.360198

4,653

164

77

28.371951

0.805173

0.043413

0

0.215385

0

0.016441

0

1

0.076923

false

0.007692

0.161538

0

0.323077

0.007692

0

null

0

null

0

1

0

c1d03ba215616beda33d94352aa9954fa9534d44

3,666

py

Python

ledger/migrations/0001_initial.py

joatuapp/joatu-django

5626d03ba89c55650ff5bff2e706ca0883ae3b9c

[ "MIT" ]

10

2018-05-13T18:01:57.000Z

2018-12-23T17:11:14.000Z

ledger/migrations/0001_initial.py

moileretour/joatu

9d18cb58b4280235688e269be6fd2d34b77ccead

[ "MIT" ]

88

2018-05-04T15:33:46.000Z

2022-03-08T21:09:21.000Z

ledger/migrations/0001_initial.py

joatuapp/joatu-django

5626d03ba89c55650ff5bff2e706ca0883ae3b9c

[ "MIT" ]

7

2018-05-08T16:05:06.000Z

2018-09-13T05:49:05.000Z

# Generated by Django 2.0.3 on 2018-03-24 03:02 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ ('demands', '0001_initial'), ('projects', '0004_auto_20180323_1346'), ('profiles', '0009_profilewallet'), ('offers', '0001_initial'), ] operations = [ migrations.CreateModel( name='Operations', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('date', models.DateTimeField(auto_now_add=True)), ('debit', models.PositiveIntegerField(blank=True, null=True)), ('credit', models.PositiveIntegerField(blank=True, null=True)), ('balance', models.PositiveIntegerField(blank=True, null=True)), ('profile', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to='profiles.Profile')), ], ), migrations.CreateModel( name='Transaction', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('amount', models.DecimalField(decimal_places=2, max_digits=15)), ('date', models.DateTimeField(auto_now_add=True)), ('transaction_type', models.CharField(choices=[('OF', 'Offer'), ('DE', 'Demand'), ('CR', 'Creation')], max_length=2)), ('transaction_id', models.PositiveIntegerField()), ('profile_from', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='from_user', to='profiles.Profile')), ('profile_to', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='to_user', to='profiles.Profile')), ], ), migrations.CreateModel( name='Transaction_isCreation', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('project', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to='projects.Project')), ('transaction', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to='ledger.Transaction')), ], ), migrations.CreateModel( name='Transaction_isDemand', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('demand', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to='demands.Demand')), ('transaction', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to='ledger.Transaction')), ], ), migrations.CreateModel( name='Transaction_isOffer', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('offer', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to='offers.Offer')), ('transaction', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to='ledger.Transaction')), ], ), migrations.AddField( model_name='operations', name='transaction', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, to='ledger.Transaction'), ), ]

50.916667

158

0.611839

382

3,666

5.71466

0.230366

0.047641

0.070545

0.110857

0.702245

0.643152

0.561612

0

0.016864

0.239771

3,666

71

159

51.633803

0.766416

0.012275

0

0.484375

1

0

0.155292

0.012434

0

1

0

false

0

0.03125

0

0.09375

0

null

0

1

0

1

0

null

0

2

c1d2a8d963e6ebb39bdd05cfd3af6f753ebfe074

821

py

Python

cookbook/c09/p06_optarg_decorator.py

Xiao-jiuguan/python3-cookbook

95d5a1d5cb59b5d88e816f6f10eb1e5befc25b05

[ "Apache-2.0" ]

3

2018-05-10T01:13:08.000Z

2018-06-17T12:34:07.000Z

cookbook/c09/p06_optarg_decorator.py

Xiao-jiuguan/python3-cookbook

95d5a1d5cb59b5d88e816f6f10eb1e5befc25b05

[ "Apache-2.0" ]

2

2020-09-19T17:10:23.000Z

2020-10-17T16:43:52.000Z

cookbook/c09/p06_optarg_decorator.py

Xiao-jiuguan/python3-cookbook

95d5a1d5cb59b5d88e816f6f10eb1e5befc25b05

[ "Apache-2.0" ]

1

2020-07-20T22:10:31.000Z

#!/usr/bin/env python # -*- encoding: utf-8 -*- """ Topic: 带可选参数的装饰器 Desc : """ from functools import wraps, partial import logging def logged(func=None, *, level=logging.DEBUG, name=None, message=None): if func is None: return partial(logged, level=level, name=name, message=message) logname = name if name else func.__module__ log = logging.getLogger(logname) logmsg = message if message else func.__name__ @wraps(func) def wrapper(*args, **kwargs): log.log(level, logmsg) return func(*args, **kwargs) return wrapper # Example use @logged def add(x, y): return x + y @logged(level=logging.CRITICAL, name='example') def spam(): print('Spam!') spam() def aa(kk=None, *, a=1,b=2,c=3): print(kk, a, b, c) bbb = partial(aa, a=1,b=2,c=3) bbb('333')

19.093023

71

0.637028

122

821

4.221311

0.442623

0.046602

0.01165

0.015534

0.023301

0

0.015337

0.205847

821

43

72

19.093023

0.77454

0.09866

0

0.02052

0

1

0.208333

false

0

0.083333

0.041667

0.458333

0.083333

0

null

0

null

0

1

0

1

c1d364f1b1140a439139d1301d9e7d4e7d3275ad

482

py

Python

loader.py

afsara-rahman/Quadruple

5cc5e0007f16eb75b0368427652f671cbf78f15f

[ "MIT" ]

null

loader.py

afsara-rahman/Quadruple

5cc5e0007f16eb75b0368427652f671cbf78f15f

[ "MIT" ]

null

loader.py

afsara-rahman/Quadruple

5cc5e0007f16eb75b0368427652f671cbf78f15f

[ "MIT" ]

null

#Media loader class. #Loads images. import os, sys, pygame from pygame.locals import * #Load an image. :) def load_image(file, transparent = True): print("Loading " + file + " ..") fullname = os.path.join('media', file) image = pygame.image.load(fullname) if transparent == True: image = image.convert() colorkey = image.get_at((0,0)) image.set_colorkey(colorkey, RLEACCEL) else: image = image.convert_alpha() return image

24.1

46

0.634855

60

482

5.033333

0.566667

0.099338

0.112583

0

0.00542

0.23444

482

19

47

25.368421

0.813008

0.10166

0

0.037296

0

1

0.076923

false

0

0.153846

0

0.307692

0.076923

0

null

0

null

0

1

0

c1d522ea9886ba08db71b2edc7bf38ab91008a5b

26,793

py

Python

vespa/common/pulse_funcs/bloch_multi.py

vespa-mrs/vespa

6d3e84a206ec427ac1304e70c7fadf817432956b

[ "BSD-3-Clause" ]

null

vespa/common/pulse_funcs/bloch_multi.py

vespa-mrs/vespa

6d3e84a206ec427ac1304e70c7fadf817432956b

[ "BSD-3-Clause" ]

4

2021-04-17T13:58:31.000Z

2022-01-20T14:19:57.000Z

vespa/common/pulse_funcs/bloch_multi.py

vespa-mrs/vespa

6d3e84a206ec427ac1304e70c7fadf817432956b

[ "BSD-3-Clause" ]

3

2021-06-05T16:34:57.000Z

2022-01-19T16:13:22.000Z

# Python modules import multiprocessing import math # 3rd party modules import numpy as np # Our modules from pylab import * # GAMMA = 26753.0 - replaced with user defined value TWOPI = 6.283185 def isiterable(p_object): try: it = iter(p_object) except TypeError: return False return True """ Code for calculating bloch simulations. This code is independent of any GUI and can be called from the command line. There's only one public function which is bloch_multi(). This code iterates over each position and frequency offset in the simulation. For each, it returns the magnetization values (Mx,My,Mz). It uses multiprocessing. """ # The naive algorithm for this code would be to process one simulation at # a time. We found that experiments are processed maybe 10% faster if we # combine the bloch simulation's frequency offset dimension into chunks # prior to processing. # # MAX_CHUNK_SIZE is the largest # of freq offsets that will be grouped together # in one chunk. This is a hard limit. # # Choosing MAX_CHUNK_SIZE was the result of a lot of experimentation. # Obviously we can't test all possibilities but 4-10 gave good average # performance. # # Because the x,y,z position dimension is always fully included in each chunk # the time saved by adding more than 1 freq offset line to the chunk is much # less than expected. Thus for a 1000x200 nf x npos array, we went from 55 sec # on 12 processors to 45 seconds on 12 processors when we set MAX_CHUNK_SIZE # from 1 to 10 respectively. At 50 or 100 per chunk, the total time approached # 55 seconds again. So, nothing too simple to figure out here, just empirical # mysto-crud. # # To force this code to use the naive algorithm (each simulation = one chunk), # set MAX_CHUNK_SIZE = 1. MAX_CHUNK_SIZE = 4 class _Chunk(object): """ A chunk of frequency offset values to process. len(df) is always < MAX_CHUNK_SIZE. b1real, b1imag, xgrad, ygrad, zgrad, tsteps, e1, e2, df, dx, dy, dz, mx, my, mz, mode, gamma """ def __init__(self, b1real, b1imag, xgrad, ygrad, zgrad, tsteps, e1, e2, dx, dy, dz, mx, my, mz, mode, gamma): # Each chunk has a bunch of static info that is used in all # calculations. And a small set of frequency offset values and their # indices for storage back into the results arrays on finish. self.b1real = b1real self.b1imag = b1imag self.xgrad = xgrad self.ygrad = ygrad self.zgrad = zgrad self.tsteps = tsteps self.e1 = e1 self.e2 = e2 self.df = [] self.ifreq = [] self.dx = dx self.dy = dy self.dz = dz self.mx = mx self.my = my self.mz = mz self.mode = mode self.gamma = gamma @property def nlines(self): return len(self.df) def __str__(self): # __str__ is useful for debugging lines = [ ] lines.append("---- chunk ----") lines.append("b1real: %d" % self.b1real.size) lines.append("b1imag: %d" % self.b1imag.size) lines.append("xgrad: %d" % self.xgrad.size) lines.append("ygrad: %d" % self.ygrad.size) lines.append("zgrad: %d" % self.zgrad.size) lines.append("tsteps: %d" % self.tsteps.size) lines.append("e1: %d" % self.e1.size) lines.append("e2: %d" % self.e2.size) lines.append("df: %d" % self.df.size) lines.append("dx: %d" % self.dx.size) lines.append("dy: %d" % self.dy.size) lines.append("dz: %d" % self.dz.size) lines.append("mx: %d" % self.mx.size) lines.append("my: %d" % self.my.size) lines.append("mz: %d" % self.mz.size) lines.append("mode: %d" % self.mode) lines.append("gamma: %f" % self.gamma) return "\n".join(lines) def blochsim(b1real, b1imag, xgrad, ygrad, zgrad, tsteps, ntime, e1, e2, df, dx, dy, dz, mx, my, mz, mode, cpu_count=1, gamma=26751.3 ): """ Builds a set of Magnetization values for spatial locations (dx,dy,dz) and frequency offset values (df) given a B1 pulse and set of x,y,z gradient values. It runs a bloch simulation at each of nf x npos frequencies and locations. It uses multiprocessing. Returns a numpy array appropriate for the magnetization vector at each location and frequency offset. """ mxout = mx.copy() myout = my.copy() mzout = mz.copy() # PS - If you want to run this code without using multiprocessing (e.g. # in order to profile execution), use the 3 lines below in place of # the use of multiprocessing.Pool. # _initializer() # chunks = _build_chunks(b1real, b1imag, xgrad, ygrad, zgrad, tsteps, e1, e2, df, dx, dy, dz, mx, my, mz, mode, gamma ) # results = [_process_chunk(chunk) for chunk in chunks] pool = multiprocessing.Pool(cpu_count, _initializer, []) # The 3rd param to imap_unordered() is a chunksize. These chunks are not # to be confused with the chunks returned by _build_chunks()! chunksize # just determines how many values will be grabbed from the iterator # at once. Using a chunksize > 1 gives slightly better performance, but # only slightly. The advantage of using a chunksize == 1 is that # _build_chunks() is called every time a worker needs new work, so we # can use it as a cheap callback/progress indicator. results = pool.imap_unordered(_process_chunk, _build_chunks(b1real, b1imag, xgrad, ygrad, zgrad, tsteps, e1, e2, df, dx, dy, dz, mx, my, mz, mode, gamma ), 1) pool.close() pool.join() # The lines from each bloch simulation are combined into one array that # has results from all nf x npos frequency offsets and spatial positions for result in results: ifreqs = result[0][0] mxs = result[0][1] mys = result[0][2] mzs = result[0][3] for i, ifreq in enumerate(ifreqs): mxout[ifreq,:,:] = mxs[i,:,:] myout[ifreq,:,:] = mys[i,:,:] mzout[ifreq,:,:] = mzs[i,:,:] return mxout, myout, mzout ############### Internal use only below this line ############### def _build_chunks(b1real, b1imag, xgrad, ygrad, zgrad, tsteps, e1, e2, df, dx, dy, dz, mx, my, mz, mode, gamma ): """ A generator function. Given an experiment, iterates over the bloch simulation's frequency offset dimension and returns a set of offsets chunked according to MAX_CHUNK_SIZE. See here for more info on generators: http://docs.python.org/tutorial/classes.html#generators """ current = _Chunk(b1real, b1imag, xgrad, ygrad, zgrad, tsteps, e1, e2, dx, dy, dz, mx, my, mz, mode, gamma) nlines_processed = 0 for ifreq, dfreq in enumerate(df): nlines = 1 if current.nlines and ((current.nlines + nlines) > MAX_CHUNK_SIZE): # Chunk has enough in it, adding more would exceed the max. nlines_processed += current.nlines yield current current = _Chunk(b1real, b1imag, xgrad, ygrad, zgrad, tsteps, e1, e2, dx, dy, dz, mx, my, mz, mode, gamma) #else: # The current chunk is empty or there's still room in the current # chunk for the next collection of lines. # Append the contents of this simulation to the current chunk. current.df.append(dfreq) current.ifreq.append(ifreq) # Return the final set of lines. yield current def _initializer(): # This function is subtle...it's called by each worker process, and is # passed the values of the global constants that I need in # _process_chunk(). Under *nix, I can just declare them global and # (thanks to the magic of fork()) the variables and their values will be # copied to the worker processes'. Under Windows, this module is # re-imported once for each worker, and as a result these globals are # recreated and re-initialized to 0 in each worker. This function sets # them back to the values they need to be, and that's the only reason # it exists. pass def _process_chunk(chunk): # This is what each worker executes b1real = chunk.b1real b1imag = chunk.b1imag xgrad = chunk.xgrad ygrad = chunk.ygrad zgrad = chunk.zgrad tsteps = chunk.tsteps e1 = chunk.e1 e2 = chunk.e2 df = np.array(chunk.df) ifreq = chunk.ifreq dx = chunk.dx dy = chunk.dy dz = chunk.dz mx = chunk.mx my = chunk.my mz = chunk.mz mode = chunk.mode gamma = chunk.gamma mxout = np.zeros((len(df),mx.shape[1],mx.shape[2]), 'float') myout = np.zeros((len(df),mx.shape[1],mx.shape[2]), 'float') mzout = np.zeros((len(df),mx.shape[1],mx.shape[2]), 'float') npos = len(dx) nf = len(df) bvecs = [np.zeros((3,)) for i in range(nf*npos)] decmat = np.zeros((3,3)) # Decay matrix for each time step. decvec = np.zeros((3,)) # Recovery vector for each time step. amats = [np.eye(3) for i in range(nf*npos)] # A is the identity matrix. imats = [np.eye(3) for i in range(nf*npos)] # I is the identity matrix. mcurr0s = [np.array([mx[j,i,0],my[j,i,0],mz[j,i,0]]) for j in range(nf) for i in range(npos)] # Set starting x,y,z magnetizations for t in range(len(tsteps)): # Rotation df_array = np.repeat(df*TWOPI*tsteps[t], npos) rotz = -(xgrad[t] * dx + ygrad[t] * dy + zgrad[t] * dz) * tsteps[t] rotz = np.tile(rotz, nf) - df_array rotx = (- b1real[t] * gamma * tsteps[t]) roty = (- b1imag[t] * gamma * tsteps[t]) # based on Hao Sun's UMich blochCim code rotmats = calcrotmat(rotx, roty, rotz) if (mode == 1): arots = [np.dot(rotmat, amat) for rotmat, amat in zip(rotmats,amats)] brots = [np.dot(rotmat, bvec) for rotmat, bvec in zip(rotmats,bvecs)] else: mcurr1s = [np.dot(rotmat, mcurr0) for rotmat, mcurr0 in zip(rotmats,mcurr0s)] # Decay decvec[2] = 1-e1[t] decmat[0,0] = e2[t] decmat[1,1] = e2[t] decmat[2,2] = e1[t] if (mode == 1): amats = [np.dot(decmat, arot) for arot in arots] bvecs = [np.dot(decmat, brot) for brot in brots] bvecs = [bvec+decvec for bvec in bvecs] else: mcurr0s = [np.dot(decmat, mcurr1) for mcurr1 in mcurr1s] mcurr0s = [mcurr0+decvec for mcurr0 in mcurr0s] if mode == 2: # Sample output at times. Only do this if transient! mcurr0 = np.array(mcurr0s) mcurr0.shape = nf, npos, 3 mxout[:,:,t] = mcurr0[:,:,0] myout[:,:,t] = mcurr0[:,:,1] mzout[:,:,t] = mcurr0[:,:,2] # If only recording the endpoint, either store the last # point, or calculate the steady-state endpoint. if mode == 0: # Indicates start at given m, or m0. mcurr0 = np.array(mcurr0s) mcurr0.shape = nf, npos, 3 mxout[:,:,0] = mcurr0[:,:,0] myout[:,:,0] = mcurr0[:,:,1] mzout[:,:,0] = mcurr0[:,:,2] elif mode == 1: # Indicates to find steady-state magnetization amats = [imat-amat for imat,amat in zip(imats,amats)] # Now amat = (I-A) imats = [np.linalg.inv(amat) for amat in amats] # Reuse imat as inv(I-A) mvec = [np.dot(imat,bvec) for imat,bvec in zip(imats,bvecs)] # Now M = inv(I-A)*B mvec = np.array(mvec) mvec.shape = nf, npos, 3 mxout[:,:,0] = mvec[:,:,0] myout[:,:,0] = mvec[:,:,1] mzout[:,:,0] = mvec[:,:,2] # The results are a list of 2-tuples (index, Mx, My, Mz). index is an index # into the frequency offset dimension of the magnetization array -- it's # where these mx, my, mz values will reside in the overall results array. result = [ (ifreq, mxout, myout, mzout) ] return result #============================================================================== def times2intervals( endtimes ): """ Function takes the given endtimes of intervals, and returns the interval lengths in an array, assuming that the first interval starts at 0. If the intervals are all greater than 0, then this returns True, otherwise it returns False. """ allpos = True lasttime = 0.0 intervals = [] for endtime in endtimes: intervals.append(endtime-lasttime) lasttime = endtime if intervals[-1] <= 0: allpos = False return allpos, np.array(intervals) def calcrotmat(nx, ny, nz): """ Find the rotation matrix that rotates |n| radians about the vector given by nx,ny,nz From: https://code.google.com/p/robotics-toolbox-python/source/browse/trunk/robotics-toolbox-python/robot/transform.py Approach: Uses Matrices from numpy Turns out to be 1.8 times slower than for-loop with original straight math Found on: http://mail.scipy.org/pipermail/numpy-discussion/2009-March/040806.html Approach: Uses numpy array manipulations and skew array. Turns out to be 2.5 times slower than than for-loop with original straight math This final version takes advantage of ufunc speed of cos, sin, *, etc acting on an array of numbers. This was ~ 3-4 times faster than for-loop with math. """ phi_array = np.sqrt(nx*nx+ny*ny+nz*nz) rmat = [] # First define Cayley-Klein parameters hp = phi_array/2.0 cp = np.cos(hp) sp = np.sin(hp)/phi_array # /phi because n is unit length in defs. ar = cp ai = -nz*sp br = ny*sp bi = -nx*sp # Make auxiliary variables to speed this up arar = ar*ar aiai = ai*ai arai2 = 2*ar*ai brbr = br*br bibi = bi*bi brbi2 = 2*br*bi arbi2 = 2*ar*bi aibr2 = 2*ai*br arbr2 = 2*ar*br aibi2 = 2*ai*bi # Make rotation matrix. rmat = np.array([[arar-aiai-brbr+bibi, arai2-brbi2, arbr2+aibi2], [-arai2-brbi2, arar-aiai+brbr-bibi, arbi2-aibr2], [-arbr2+aibi2, -aibr2-arbi2, arar+aiai-brbr-bibi]]) rmat = rmat.transpose([2,0,1]) for i, phi in enumerate(phi_array): if phi == 0.0: rmat[i,:,:] = np.array( [[1.0,0.0,0.0], [0.0,1.0,0.0], [0.0,0.0,1.0]] ) return rmat def blochsimfz(b1real, b1imag, xgrad, ygrad, zgrad, tsteps, ntime, t1, t2, dfreq, nfreq, dxpos, dypos, dzpos, npos, mx, my, mz, mode, cpu_count=0, gamma=26751.3): """ Comment? """ if mode & 2: ntout = ntime else: ntout = 1 mxout = mx.copy() myout = my.copy() mzout = mz.copy() # First calculate the e1 and e2 values at each time step. e1 = np.exp(-tsteps/t1) e2 = np.exp(-tsteps/t2) gammadx = dxpos.copy()*gamma # Convert to Hz/cm gammady = dypos.copy()*gamma # Convert to Hz/cm gammadz = dzpos.copy()*gamma # Convert to Hz/cm #for ifreq in range(nfreq): if mode == 3: # Steady state AND record all time points. # First go through and find steady state, then # repeat as if transient starting at steady st. mxx, myy, mzz = blochsim(b1real, b1imag, xgrad, ygrad, zgrad, tsteps, ntime, e1, e2, dfreq, gammadx, gammady, gammadz, mx, my, mz, 1, cpu_count, gamma); mxx, myy, mzz = blochsim(b1real, b1imag, xgrad, ygrad, zgrad, tsteps, ntime, e1, e2, dfreq, gammadx, gammady, gammadz, mxx, myy, mzz, 2, cpu_count, gamma); else: mxx, myy, mzz = blochsim(b1real, b1imag, xgrad, ygrad, zgrad, tsteps, ntime, e1, e2, dfreq, gammadx, gammady, gammadz, mx, my, mz, mode, cpu_count, gamma); mxout[:,:,:] = mxx myout[:,:,:] = myy mzout[:,:,:] = mzz return mxout, myout, mzout def bloch_multi(b1,gr,tp,t1,t2,df,dp,mode=0,mx=[],my=[],mz=[], cpu_count=0, gamma=26751.3): """ Calling format [mx,my,mz] = bloch(b1,gr,tp,t1,t2,df,dp,mode=0,mx=[],my=[],mz=[]) blochsimfz(b1r,b1i,gx,gy,gz,tp,ntime,t1,t2,df,nf,dx,dy,dz,npos,mx,my,mz,md); Bloch simulation of rotations due to B1, gradient and off-resonance, including relaxation effects. At each time point, the rotation matrix and decay matrix are calculated. Simulation can simulate the steady-state if the sequence is applied repeatedly, or the magnetization starting at m0. INPUT: b1 = (Mx1) RF pulse in G. Can be complex. gr = (Mx1,2,or 3) 1,2 or 3-dimensional gradient in G/cm. tp = (Mx1) time duration of each b1 and gr point, in seconds, or 1x1 time step if constant for all points or monotonically INCREASING endtime of each interval.. t1 = T1 relaxation time in seconds. t2 = T2 relaxation time in seconds. df = (Nx1) Array of off-resonance frequencies (Hz) dp = (Px1,2,or 3) Array of spatial positions (cm). Width should match width of gr. mode= Bitmask mode: Bit 0: 0-Simulate from start or M0, 1-Steady State Bit 1: 0-Just end time, 1-Record m at time points. (optional) - NB. bjs, swapped N anp P parameters here versus Matlab code mx,my,mz (NxP) arrays of starting magnetization, where N is the number of frequencies and P is the number of spatial positions. OUTPUT: mx,my,mz = NxP arrays of the resulting magnetization components at each position and frequency. B. Hargreaves. Nov 2003. """ # cpu_count is the number of processing cores (virtual CPUs) available on # this machine. We ask multiprocessing.Pool() to create CPU_COUNT workers. # cpu_count can be determined from a variety of sources. We accept any # int > 0. if not cpu_count: # OK, the user didn't specify so we ask multiprocessing. Note that # multiprocessing.cpu_count() isn't implemented on all platforms. # Where it's not implemented we default to 2 for no really strong reasons. try: cpu_count = multiprocessing.cpu_count() except NotImplementedError: cpu_count = 2 print("---------------------------------------") print("3D-position + frequency Bloch Simulator") print("---------------------------------------") ntime = len(b1) # Number of Time, RF, and Grad points # ====================== RF (B1) ========================= # : If complex, split up. If real, allocate an imaginary part. b1 = np.array(b1) if np.iscomplexobj(b1): b1r = b1.real.copy() b1i = b1.imag.copy() else: b1r = b1.real.copy() b1i = b1r.copy() * 0 # ======================= Gradients ========================= gr = np.array(gr) ngrad = gr.size # Number of Time, RF, and Grad points gx = np.zeros((ntime,),'float') gy = np.zeros((ntime,),'float') gz = np.zeros((ntime,),'float') gx[0:ntime] = gr[0:ntime] # X-gradient is first N points. if ngrad >= 2*ntime: # Need to allocate Y-gradient. gy[0:ntime] = gr[ntime:ntime*2] # Assign from Nx3 input array. Assuming (at least) 2-Dimensional Gradient if (ngrad >= 3*ntime): # Need to allocate Z-gradient. gz[0:ntime] = gr[ntime*2:ntime*3] # Assign from Nx3 input array. Assuming 3-Dimensional Gradient # Warning if Gradient length is not 1x, 2x, or 3x RF length. if (ngrad != ntime) and (ngrad != 2*ntime) and (ngrad != 3*ntime): print("Gradient length differs from B1 length") # === Time points ===== # # THREE Cases: # 1) Single value given -> this is the interval length for all. # 2) List of intervals given. # 3) Monotonically INCREASING list of end times given. # # For all cases, the goal is for tp to have the intervals. # if isinstance(tp,float): # === Case 1 === tstep = tp tp = np.zeros((ntime,),'float') + tstep elif len(tp) == 1: # === Case 1 === tstep = tp tp = np.zeros((ntime,),'float') + tstep elif len(tp) != ntime: print("Time-point length differs from B1 length") else: tp = np.array(tp) posflag, tp = times2intervals( tp ) if posflag: print("Times are monotonically increasing. ") # === Relaxation Times ===== t1 = float(t1) t2 = float(t2) # === Frequency Points ===== if isiterable(df): df = np.array(df) else: df = np.array([df]) nf = len(df) # === Position Points ===== if isiterable(dp): dp = np.array(dp) else: dp = np.array([dp]) if len(dp.shape) == 1: dp.shape = 1,dp.shape[0] nposN, nposM = dp.shape npos = nposM dx = np.zeros((npos,), 'float') dy = np.zeros((npos,), 'float') dz = np.zeros((npos,), 'float') if (nposN==3): # Assume 3 position dimensions given dx[0:npos] = dp[0] dy[0:npos] = dp[1] dz[0:npos] = dp[2] elif (nposN==2): # Assume only 2 position dimensions given dx[0:npos] = dp[0] dy[0:npos] = dp[1] else: dx[0:npos] = dp[0] nfnpos = nf*npos; # Just used to speed things up below. # ===== Mode, defaults to 0 (simulate single endpoint, transient). ==== md = int(mode) if (md & 2): ntout = ntime # Include time points. else: ntout = 1 ntnfnpos = ntout*nfnpos; if (md & 1)==0: print("Simulation from Initial Condition.") else: print("Simulation of Steady-State.") if (md & 2)==0: print("Simulation to Endpoint. ") else: print("Simulation over Time.") # ===== Allocate Output Magnetization vectors arrays. mxin = np.zeros((nf, npos, ntout), 'float') myin = np.zeros((nf, npos, ntout), 'float') mzin = np.zeros((nf, npos, ntout), 'float') # ===== If Initial Magnetization is given... if mx and my and mz and len(mx)==nfnpos and len(my)==nfnpos and len(mz)==nfnpos: # Set output magnetization to that passed. # If multiple time points, then just the first is set. print("Using Specified Initial Magnetization.") for ipos in range(npos): for ifreq in range(nf): mxin[ifreq,ipos,0] = mx[ifreq,ipos] myin[ifreq,ipos,0] = my[ifreq,ipos] mzin[ifreq,ipos,0] = mz[ifreq,ipos] else: if mx and my and mz: # Magnetization given, but wrong size! print("Initial magnetization passed, but not Npositions x Nfreq. ") print(" --> Using [0; 0; 1] for initial magnetization. ") for ipos in range(npos): for ifreq in range(nf): mxin[ifreq,ipos,0] = 0 myin[ifreq,ipos,0] = 0 mzin[ifreq,ipos,0] = 1 # ======= Do The Simulation! ====== print("Calling blochsimfz_par() function.") mxout, myout, mzout = blochsimfz(b1r, b1i, x, gy, gz, tp, ntime, t1, t2, df, nf, dx, dy, dz, npos, mxin, myin, mzin, md, cpu_count, gamma=gamma) # ======= Reshape Output Matrices ====== if (ntout > 1) and (nf > 1) and (npos > 1): outsize = nf, npos, ntout else: # Basically "squeeze" the matrix. if nf <= 1: outsize = npos, ntout else: if npos <= 1: outsize = nf, ntout else: outsize = nf, npos, ntout mxout.shape = outsize myout.shape = outsize mzout.shape = outsize return mxout, myout, mzout #------------------------------------------------------------------------------ # Testing def hsinc(npts, ncycles, filter='hamming'): """ Returns a sinc function of length npts, with ncycles sinc-cycles. This yields a time-bandwidth value of 4 * ncycles """ t = np.arange(npts) - (npts/2.0) t = t / (npts/2.0) val = 2*np.pi*ncycles*t + 0.00001 res = np.sin(val) / val if filter == 'hamming': res = res * 4 * ncycles * (0.54 + 0.46*np.cos(np.pi*t)) / npts return res def main(): """ Blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah blah """ gamma = 26751.3 # 1H Hz/gauss my_sinc = hsinc(120,6) T = 0.00002 b1 = np.concatenate((np.zeros(4), my_sinc, np.zeros(4))) g = np.concatenate((np.zeros(4), 1*np.ones(120), np.zeros(4))) b1 = 0.5*b1/np.max(b1); x = np.arange(-5,5,0.05) f = np.arange(-1000.0,2000.0,200.0) t = np.arange(1,len(b1)+1)*T; mx, my, mz = bloch_multi(b1,g,t,1,.2,f,x,mode=0, cpu_count=0, gamma=gamma) mxy = mx + 1j*my ioff = int(len(f)/2)-1 subplot(3,1,1) xlabel('Time [ms]') plot(t*1000,b1) subplot(3,1,2) plot(x, abs(mxy[ioff,:]), x, real(mxy[ioff,:]), x, imag(mxy[ioff,:]) ) xlabel('Position [cm]') ylabel('Magnetization |Mxy|') subplot(3,1,3) plot(x, mz[ioff,:]) xlabel('Position [cm]') ylabel('Magnetization Mz') show() bob = 10 bob = 2*bob if __name__ == "__main__": main() #import cProfile #cProfile.run('main()')

33.324627

134

0.556265

3,653

26,793

4.053928

0.197646

0.005942

0.008508

0.017287

0.166655

0.156054

0.127287

0.109325

0.101762

0

0.030462

0.317546

26,793

803

135

33.366127

0.779437

0.374389

0

0.183824

0

0.051914

0.004926

0

1

0.034314

false

0.004902

0.009804

0.002451

0.073529

0.034314

0

null

0

null

0

1

0

c1d8533dc5b50c1147107c9c121f06d5e5bfcd69

1,318

py

Python

spectator/gauge.py

brharrington/spectator-py

08001c29be458e3b0de24292738441c04c2b3fbc

[ "Apache-2.0" ]

46

2019-02-06T13:20:54.000Z

2021-12-05T02:33:35.000Z

spectator/gauge.py

brharrington/spectator-py

08001c29be458e3b0de24292738441c04c2b3fbc

[ "Apache-2.0" ]

9

2018-04-26T22:07:19.000Z

2022-01-20T00:45:42.000Z

spectator/gauge.py

brharrington/spectator-py

08001c29be458e3b0de24292738441c04c2b3fbc

[ "Apache-2.0" ]

18

2018-04-26T22:00:00.000Z

2021-10-21T04:40:42.000Z

from abc import ABCMeta, abstractmethod from future.utils import with_metaclass from spectator.atomicnumber import AtomicNumber from spectator.clock import SystemClock class AbstractGauge(with_metaclass(ABCMeta)): @abstractmethod def get(self): pass @abstractmethod def set(self, value): pass @abstractmethod def _measure(self): pass class NoopGauge(AbstractGauge): def get(self): return 0 def set(self, value): pass def _measure(self): return {} class Gauge(AbstractGauge): ttl = 15 * 60 def __init__(self, meterId, clock=SystemClock()): self.meterId = meterId self._clock = clock self._last_update = AtomicNumber(float('nan')) self._value = AtomicNumber(float('nan')) def get(self): return self._value.get() def set(self, value): self._last_update.set(self._clock.wall_time()) self._value.set(value) def _has_expired(self): return (self._clock.wall_time() - self._last_update.get()) > self.ttl def _measure(self): id = self.meterId.with_default_stat('gauge') if self._has_expired(): v = self._value.get_and_set(float('nan')) else: v = self._value.get() return {id: v}

20.920635

77

0.626707

156

1,318

5.083333

0.294872

0.090794

0.037831

0.056747

0.100883

0

0.005171

0.266313

1,318

62

78

21.258065

0.814891

0

0.380952

0

0.010622

0

1

0.261905

false

0.095238

0.571429

0

null

0

null

0

1

0

1

0

1

0

3

c1d995325cdcad39ba73751aa9937c73ab171ea8

2,763

py

Python

lib/rucio/client/accountlimitclient.py

balrampariyarath/rucio

8a68017af6b44485a9620566f1afc013838413c1

[ "Apache-2.0" ]

1

2017-08-07T13:34:55.000Z

lib/rucio/client/accountlimitclient.py

balrampariyarath/rucio

8a68017af6b44485a9620566f1afc013838413c1

[ "Apache-2.0" ]

null

lib/rucio/client/accountlimitclient.py

balrampariyarath/rucio

8a68017af6b44485a9620566f1afc013838413c1

[ "Apache-2.0" ]

null

# Copyright European Organization for Nuclear Research (CERN) # # 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 # # Authors: # - Mario Lassnig, <mario.lassnig@cern.ch>, 2013 # - Cedric Serfon, <cedric.serfon@cern.ch>, 2014 # - Martin Barisits, <martin.barisits@cern.ch>, 2014 # - Ralph Vigne, <ralph.vigne@cern.ch>, 2015 # - Vincent Garonne, <vincent.garonne@cern.ch>, 2012-2015 from json import dumps from requests.status_codes import codes from rucio.client.baseclient import BaseClient from rucio.client.baseclient import choice from rucio.common.utils import build_url class AccountLimitClient(BaseClient): """Account limit client class for working with account limits""" ACCOUNTLIMIT_BASEURL = 'accountlimits' def __init__(self, rucio_host=None, auth_host=None, account=None, ca_cert=None, auth_type=None, creds=None, timeout=None, user_agent='rucio-clients'): super(AccountLimitClient, self).__init__(rucio_host, auth_host, account, ca_cert, auth_type, creds, timeout, user_agent) def set_account_limit(self, account, rse, bytes): """ Sends the request to set an account limit for an account. :param account: The name of the account. :param rse: The rse name. :param bytes: An integer with the limit in bytes. :return: True if quota was created successfully else False. """ data = dumps({'bytes': bytes}) path = '/'.join([self.ACCOUNTLIMIT_BASEURL, account, rse]) url = build_url(choice(self.list_hosts), path=path) r = self._send_request(url, type='POST', data=data) if r.status_code == codes.created: return True else: exc_cls, exc_msg = self._get_exception(headers=r.headers, status_code=r.status_code, data=r.content) raise exc_cls(exc_msg) def delete_account_limit(self, account, rse): """ Sends the request to remove an account limit. :param account: The name of the account. :param rse: The rse name. :return: True if quota was removed successfully. False otherwise. :raises AccountNotFound: if account doesn't exist. """ path = '/'.join([self.ACCOUNTLIMIT_BASEURL, account, rse]) url = build_url(choice(self.list_hosts), path=path) r = self._send_request(url, type='DEL') if r.status_code == codes.ok: return True else: exc_cls, exc_msg = self._get_exception(headers=r.headers, status_code=r.status_code, data=r.content) raise exc_cls(exc_msg)

37.337838

154

0.674267

377

2,763

4.801061

0.360743

0.033149

0.024309

0.026519

0.390055

0.285083

0

0.013029

0.222222

2,763

73

155

37.849315

0.829223

0.386898

0

0.428571

0

0.025526

0

1

0.107143

false

0

0.178571

0

0.428571

0

null

0

null

0

1

0

c1dae57fa4e3464ff7fae0220eed527133197673

5,146

py

Python

functions.py

Project3-Group10/stocker-eyes

73fd410091348a4a358681a554b80e7c1c47b1ab

[ "FTL" ]

1

2021-04-17T07:13:39.000Z

functions.py

Project3-Group10/stocker-eyes

73fd410091348a4a358681a554b80e7c1c47b1ab

[ "FTL" ]

33

2021-04-14T13:56:07.000Z

2021-05-05T04:43:10.000Z

functions.py

Project3-Group10/stocker-eyes

73fd410091348a4a358681a554b80e7c1c47b1ab

[ "FTL" ]

1

2021-10-14T00:44:00.000Z

import os from dotenv import load_dotenv, find_dotenv import requests import smtplib, ssl from email.mime.text import MIMEText from email.mime.multipart import MIMEMultipart import requests_cache load_dotenv(find_dotenv()) ALPHA_API_KEY = os.getenv('ALPHA_API_KEY') NEWS_API = os.getenv('GET_NEWS_KEY') EMAIL_PASSWORD = os.getenv('EMAIL_ACCOUNT_PASSWORD') SMTP_SERVER = os.getenv('SMTP_SERVER') def myStockInfo(StockSymbol_1, StockSymbol_2, StockSymbol_3): URL_1 = 'https://www.alphavantage.co/query?function=TIME_SERIES_DAILY_ADJUSTED&symbol=' + StockSymbol_1 + '&apikey=' + ALPHA_API_KEY URL_2 = 'https://www.alphavantage.co/query?function=TIME_SERIES_DAILY_ADJUSTED&symbol=' + StockSymbol_2 + '&apikey=' + ALPHA_API_KEY URL_3 = 'https://www.alphavantage.co/query?function=TIME_SERIES_DAILY_ADJUSTED&symbol=' + StockSymbol_3 + '&apikey=' + ALPHA_API_KEY requests_cache.install_cache('myStock1', expire_after=86400) response_1 = requests.get(URL_1) requests_cache.install_cache('myStock2', expire_after=86400) response_2 = requests.get(URL_2) requests_cache.install_cache('myStock3', expire_after=86400) response_3 = requests.get(URL_3) response_1 = response_1.json() response_2 = response_2.json() response_3 = response_3.json() return [response_1, response_2, response_3] def myStockNewsInfo (StockSymbol_1, StockSymbol_2, StockSymbol_3): URL_1 = 'https://newsapi.org/v2/everything?q=' + StockSymbol_1 + '&apiKey=' + NEWS_API URL_2 = 'https://newsapi.org/v2/everything?q=' + StockSymbol_2 + '&apiKey=' + NEWS_API URL_3 = 'https://newsapi.org/v2/everything?q=' + StockSymbol_3 + '&apiKey=' + NEWS_API requests_cache.install_cache('myStockNews1', expire_after=86400) response_1 = requests.get(URL_1) requests_cache.install_cache('myStockNews2', expire_after=86400) response_2 = requests.get(URL_2) requests_cache.install_cache('myStockNews3', expire_after=86400) response_3 = requests.get(URL_3) response_1 = response_1.json() response_2 = response_2.json() response_3 = response_3.json() return {StockSymbol_1: response_1, StockSymbol_2: response_2, StockSymbol_3: response_3} def searchStock(symbol): URL = 'https://www.alphavantage.co/query?function=TIME_SERIES_DAILY_ADJUSTED&symbol=' + symbol + '&apikey=' + ALPHA_API_KEY cacheName = "{}StockDataFromSearch".format(symbol) requests_cache.install_cache(cacheName, expire_after=86400) response = requests.get(URL) response = response.json() return response def fetchNews(symbol): URL = 'https://newsapi.org/v2/everything?q=' + symbol + '&apiKey=' + NEWS_API cacheName = "{}NewsData".format(symbol) requests_cache.install_cache(cacheName, expire_after=86400) r = requests.get(URL) r = r.json() return r # this is to use in order to send emails notification def send_email_SSL(): print("Send_Email_SSL") port = 465 # this is SSL smtp_server = SMTP_SERVER # smtp server address sender_email = "caballoscuba@gmail.com" # Enter your email receiver_email = "osky.op@gmail.com" password = EMAIL_PASSWORD message = """\ Subject: Stocker Eyes New Notification. A user just logged in""" context = ssl.create_default_context() with smtplib.SMTP_SSL(smtp_server, port, context=context) as server: server.login(sender_email, password) server.sendmail(sender_email, receiver_email, message) print("Send_Email_SSL2") def send_email_starttls(email, textEmail, html1): print("Send_Email_starttls") port = 587 # this is SSL smtp_server = SMTP_SERVER # smtp server address sender_email = "caballoscuba@gmail.com" # Enter your email password = EMAIL_PASSWORD receiver_email = email #message = """\ #Subject: Stocker Eyes # New Notification. A user just logged in""" message = MIMEMultipart("alternative") message["Subject"] = "Stocker-Eyes Notification" message["From"] = sender_email message["To"] = receiver_email message["Cc"] = "oo89@njit.edu" # Create the plain-text and HTML version of your message text = textEmail html = html1 # Turn these into plain/html MIMEText objects part1 = MIMEText(text, "plain") part2 = MIMEText(html, "html") # Add HTML/plain-text parts to MIMEMultipart message # The email client will try to render the last part first message.attach(part1) message.attach(part2) # Create a secure SSL context context = ssl.create_default_context() # Try to log in to server and send email try: print("Send_Email_starttls2") server = smtplib.SMTP(smtp_server,port) server.ehlo() # Can be omitted server.starttls(context=context) # Secure the connection server.ehlo() # Can be omitted server.login(sender_email, password) #Send email here server.sendmail(sender_email, receiver_email, message.as_string()) except Exception as e: print("ERROR1") print(e) finally: server.quit()

37.838235

136

0.706374

671

5,146

5.187779

0.233979

0.028727

0.045964

0.057455

0.493536

0.449296

0.425165

0.367136

0.342718

0

0.028189

0.186553

5,146

136

137

37.838235

0.803392

0.110766

0

0.257426

0

0.208297

0.019096

0

1

0.059406

false

0.049505

0.069307

0

0.168317

0.059406

0

null

0

null

0

1

0

c1dca4278a150c965a6fa445d1242b2411276737

47,762

py

Python

ice/parser/parser.py

i2y/ice

db9abf04fbd0b256f13507900a4e5129e65b71a0

[ "MIT" ]

null

ice/parser/parser.py

i2y/ice

db9abf04fbd0b256f13507900a4e5129e65b71a0

[ "MIT" ]

null

ice/parser/parser.py

i2y/ice

db9abf04fbd0b256f13507900a4e5129e65b71a0

[ "MIT" ]

null

import warnings from collections import Sequence import rply from rply import ParserGenerator from ice import __version__ from . import lexer name_seq = 0 def get_temp_name(): global name_seq name_seq += 1 name_symbol = Symbol('_gs%s' % name_seq) return name_symbol class ParsingError(Exception): def __init__(self, file_path, lineno=1, colno=1): self.file_path = file_path self.lineno = lineno self.colno = colno def __str__(self): return 'ParsingError: file=' \ + self.file_path\ + ' lineno='\ + str(self.lineno)\ + ' colno='\ + str(self.colno) def parse(lexer, filename="<string>"): try: with warnings.catch_warnings(): warnings.simplefilter('ignore') return pg.build().parse(lexer) except rply.errors.ParsingError as e: source_pos = e.getsourcepos() if source_pos is None: raise ParsingError(filename) else: raise ParsingError(filename, source_pos.lineno, source_pos.colno) class Symbol(object): def __init__(self, name, lineno=0, col_offset=0): self.name = name self.outer_name = name self.lineno = lineno self.col_offset = col_offset def eval(self, env): pass def __repr__(self): return self.outer_name def __str__(self): return self.outer_name def __eq__(self, other): if type(other) is not Symbol: return False if self.name == other.name: return True else: return False def __hash__(self): return (self.name.__hash__() << 16) + self.outer_name.__hash__() class Keyword(object): def __init__(self, name, lineno=0, col_offset=0): self.name = name self.lineno = lineno self.col_offset = col_offset self.repr = ':' + self.name def __repr__(self): return self.repr def __str__(self): return self.name def __call__(self, table): return table[self.name] def __eq__(self, other): if type(other) is not Keyword: return False if self.name == other.name: return True else: return False def __hash__(self): return self.name.__hash__() pg = ParserGenerator(['NUMBER', 'OPPLUS', 'OPMINUS', 'OPTIMES', 'OPDIV', 'OPLEQ', 'OPGEQ', 'OPEQ', 'OPNEQ', 'OPLT', 'OPGT', 'OPBITOR', 'OPPOW', 'MACRO_NAME', 'LET', # 'UNION', 'PREDICATE', 'INFIX_MACRO_NAME', 'INFIX_1_MACRO_NAME', 'INFIX_2_MACRO_NAME', 'INFIX', 'INFIX_1', 'INFIX_2', 'OPRSHIFT', 'OPLSHIFT', 'OPFLOORDIV', 'OPBITAND', 'OPBITXOR', 'USER_DEFINED_KEYWORD', 'OPAND', 'OPOR', 'OPIS', 'NOT', 'PERCENT', 'EXPORT', 'ASSERT', 'LPAREN', 'RPAREN', 'TRUE', 'FALSE', 'TQUOTE_STR', 'DQUOTE_STR', 'SQUOTE_STR', 'NAME_LPAREN', 'AT', 'DOT_NAME', 'DOT_NAME_LPAREN', 'TQUOTE_RAW_STR', 'DQUOTE_RAW_STR', 'SQUOTE_RAW_STR', 'NAME', 'EQUALS', 'IF', 'ELSEIF', 'ELSE', 'COLON', 'SEMI', 'DATA', 'IMPORT', 'INCLUDE', 'LBRACK', 'RBRACK', 'COMMA', 'FUNC', 'DOC', 'CALET', 'PIPELINE', 'PIPELINE_BIND', 'PIPELINE_FIRST', 'PIPELINE_FIRST_BIND', 'RETURN', 'CALL', 'DO', 'LBRACE', 'RBRACE', 'MATCH', 'CASE', 'DEFM', 'RECORD', 'AMP', 'FATARROW', 'THINARROW', 'YIELD', 'FROM', 'USE', 'FOR', 'IN', 'TRY', 'FINALLY', 'EXCEPT', 'AS', 'RAISE', 'WITH', 'MACRO', 'QUOTE', 'QUASI_QUOTE', 'UNQUOTE', 'UNQUOTE_SPLICING', 'QUOTE_LPAREN', 'QUASI_QUOTE_LPAREN', 'UNQUOTE_LPAREN', 'UNQUOTE_SPLICING_LPAREN'], precedence=[('left', ['EQUALS']), ('left', ['NOT']), ('left', ['OPIS']), ('left', ['IN']), ('left', ['AS', 'OPEQ', 'OPLEQ', 'OPGEQ', 'OPNEQ', 'OPLT', 'OPGT', 'OPAND', 'OPOR', 'PIPELINE', 'PIPELINE_BIND', 'PIPELINE_FIRST', 'PIPELINE_FIRST_BIND', 'INFIX_MACRO_NAME']), ('left', ['OPPLUS', 'OPMINUS', 'INFIX_1_MACRO_NAME']), ('left', ['LBRACK', 'RBRACK']), ('left', ['OPTIMES', 'OPDIV', 'OPFLOORDIV', 'PERCENT', 'OPBITAND', 'OPBITOR', 'OPBITXOR', 'OPPOW', 'OPRSHIFT', 'OPLSHIFT', 'INFIX_2_MACRO_NAME']), ('left', ['IF'])], cache_id='ice_' + __version__) @pg.production('program : block') def program(p): return p[0] @pg.production('block : stmts') def block(p): return p[0] @pg.production('stmts : stmts stmt') def stmts_b(p): if p[1] is None: return p[0] else: return p[0] + [p[1]] @pg.production('stmts : stmt') def stmts_stmt(p): if p[0] is None: return [] else: return [p[0]] @pg.production('stmt : SEMI') def stmt_semi(p): pass @pg.production('stmt : binop_expr') @pg.production('stmt : let_expr') @pg.production('stmt : as_expr') @pg.production('stmt : deco_expr') @pg.production('stmt : func_expr') @pg.production('stmt : funcm_expr') # @pg.production('stmt : record_expr') @pg.production('stmt : data_expr') @pg.production('stmt : import_expr') @pg.production('stmt : from_expr') @pg.production('stmt : macros_stmt') @pg.production('stmt : try_stmt') @pg.production('stmt : with_stmt') @pg.production('stmt : raise_stmt') @pg.production('stmt : return_stmt') @pg.production('stmt : macro_stmt') @pg.production('stmt : infix_macro_stmt') @pg.production('stmt : q_stmt') @pg.production('stmt : qq_stmt') @pg.production('stmt : assert_stmt') def stmt(p): return p[0] # # TODO fukkatu? # @pg.production('call_macro_stmt : id_expr COLON do_suite') # def call_macro_stmt(p): # head = [] # body = p[2] # rest = [] # return process_calling_macro(p[0], head, body, rest) # @pg.production('call_macro_stmt : id_expr COLON do_suite rest') # def call_macro_expr(p): # head = [] # body = p[2] # rest = p[3] # return process_calling_macro(p[0], head, body, rest) @pg.production('import_expr : IMPORT names_list') def import_expr(p): return [Symbol('import')] + p[1] @pg.production('names_list : names_list COMMA names') def names(p): return p[0] + [p[2]] @pg.production('names_list : names') def names_single(p): return [p[0]] @pg.production('names : _names') def names(p): return Symbol('.'.join(p[0])) @pg.production('_names : NAME') def _names_one(p): return [p[0].getstr()] @pg.production('_names : _names DOT_NAME') def _names(p): return p[0] + [p[1].getstr()[1:]] @pg.production('names_lparen : _names_lparen') def names(p): return Symbol('.'.join(p[0])) @pg.production('_names_lparen : NAME_LPAREN') def _names_one(p): return [p[0].getstr()[:-1]] @pg.production('_names_lparen : _names DOT_NAME_LPAREN') def _names(p): return p[0] + [p[1].getstr()[1:-1]] # @pg.production('include_expr : INCLUDE string') # def include_expr(p): # return [Symbol('require'), p[1]] @pg.production('lbrace : LBRACE') def lbrace(p): pass @pg.production('rbrace : RBRACE') def rbrace(p): pass @pg.production('namelist : namelist COMMA name') def names(p): return p[0] + [p[2]] @pg.production('namelist : name') def names_single(p): return [p[0]] @pg.production('name : NAME') def name(p): return token_to_symbol(p[0]) @pg.production('tuple_elt : binop_expr COMMA') def tuple_elt(p): return p[0] @pg.production('from_expr : FROM names IMPORT namelist') def from_expr(p): return [Symbol('from_import'), p[1], p[3]] @pg.production('macros_stmt : USE lbrace use_expr_list rbrace') def macros_stmt(p): return [Symbol('do')] + p[2] @pg.production('use_expr_list : use_expr') def macros_stmt(p): return [p[0]] @pg.production('use_expr_list : use_expr_list use_expr') def macros_stmt(p): return p[0] + p[1] @pg.production('use_expr : namelist FROM names') def use_expr(p): filename = p[1].filename module_name = p[2].name import importlib mod = importlib.import_module(module_name) if hasattr(mod, 'get_keywords'): keywords = mod.get_keywords() else: keywords = {} for macro_name in p[0]: macro_name_str = str(macro_name) lexer.add_macro_name((filename, macro_name_str)) if macro_name_str in keywords: for user_defined_keyword in keywords[macro_name_str]: print(user_defined_keyword) lexer.add_user_defined_keyword((filename, user_defined_keyword)) # else: # raise SyntaxError(macro_name_str) return [Symbol('use'), p[0], p[2]] #@pg.production('use_infix_expr : USE INFIX namelist FROM names') #def use_expr(p): # for macro_name in p[1]: # lexer.add_infix_macro_name(str(macro_name)) # return [Symbol('use'), p[1], p[3]] @pg.production('suite : binop_expr') def suite_expr(p): return p[0] @pg.production('suite : lbrace stmts rbrace') def suite_stmts(p): return [Symbol('do')] + p[1] # @pg.production('suite : NEWLINE INDENT stmts DEDENT END') # def suite_stmts(p): # return [Symbol('do')] + p[2] @pg.production('suite2 : lbrace stmts rbrace') def suite2_stmts(p): return p[1] # @pg.production('suite2 : NEWLINE INDENT stmts DEDENT END') # def suite2_stmts(p): # return p[2] @pg.production('try_stmt : TRY suite2 finally_cls') def try_finally_stmt(p): return [Symbol('try')] + p[1] + [p[2]] @pg.production('try_stmt : TRY suite2 except_cls_list') def try_except_stmt(p): return [Symbol('try')] + p[1] + p[2] @pg.production('try_stmt : TRY suite2 except_cls_list finally_cls') def try_excepts_finally_stmt(p): return [Symbol('try')] + p[1] + p[2] + [p[3]] @pg.production('except_cls_list : except_cls_list except_cls') def except_cls_list(p): return p[0] + [p[1]] @pg.production('except_cls_list : except_cls') def except_cls_list(p): return [p[0]] @pg.production('except_cls : EXCEPT binop_expr AS NAME suite2') def except_cls(p): return [Symbol('except'), p[1], token_to_symbol(p[3])] + p[4] @pg.production('finally_cls : FINALLY suite2') def finally_cls(p): return [Symbol('finally')] + p[1] @pg.production('raise_stmt : RAISE binop_expr') def raise_stmt(p): return [Symbol('raise'), p[1]] #@pg.production('do_suite : binop_expr') #def suite_expr(p): # return [Symbol('do'), p[0]] @pg.production('do_suite : lbrace stmts rbrace') def suite_stmts(p): return [Symbol('do')] + p[1] # @pg.production('do_suite : NEWLINE INDENT stmts DEDENT END') # def suite_stmts(p): # return [Symbol('do')] + p[2] #@pg.production('labeled_expr : binop_expr COLON binop_expr') #def labeled_expr(p): # # return [Symbol('label'), p[0], p[2]] # return [Symbol('call_macro'), p[0], [], [Symbol('do'), p[2]], []] class Label: def __init__(self, label): self.label = label # @pg.production('labeled_expr : binop_expr do_suite') # def labeled_expr(p): # name = Label(p[0]) # head = [] # body = p[1] # rest = None # return [Symbol('call_macro'), name, [head, body, rest]] #@pg.production('cont_labeled_expr : id_expr COLON do_suite') #def labeled_expr(p): # name = p[0] # head = [] # body = p[2] # rest = None # return [Symbol('call_macro'), name, [head, body, rest]] #@pg.production('cont_labeled_expr : id_expr COLON do_suite cont_labeled_expr') #def labeled_expr(p): # name = p[0] # head = [] # body = p[2] # rest = p[3][1:] # return [Symbol('call_macro'), name, [head, body, rest]] #@pg.production('labeled_expr : binop_expr COLON do_suite cont_labeled_expr') #def labeled_expr(p): # name = p[0] # head = [] # body = p[2] # rest = p[3][1:] # return [Symbol('call_macro'), name, [head, body, rest]] # @pg.production('labeled_expr : binop_expr do_suite labeled_expr') # def labeled_expr(p): # name = Label(p[0]) # head = [] # body = p[1] # rest = p[2][1:] # return [Symbol('call_macro'), name, [head, body, rest]] # @pg.production('labeled_expr : binop_expr do_suite call_macro_expr') # def labeled_expr(p): # name = Label(p[0]) # head = [] # body = p[1] # rest = p[2][1:] # return [Symbol('call_macro'), name, [head, body, rest]] # @pg.production('user_defined_stmt : NAME NAME COLON trailing_dict') # def user_defined_stmt(p): # return [Symbol('val'), # token_to_symbol(p[1]), # [Symbol(p[0].getstr() + '.define'), p[1].getstr(), p[3]]] # @pg.production('user_defined_stmt : NAME id_expr app_args COLON trailing_dict') # def user_defined_stmt(p): # return [Symbol(p[0].getstr() + '.define'), p[1]] + p[2] + [p[4]] # @pg.production('user_defined_stmt : NAME NAME DOT_NAME app_args COLON trailing_dict') # def user_defined_stmt(p): # return [Symbol(p[0].getstr() + '.define'), Symbol(p[1].getstr() + '.' + p[2].getstr()[1:])] + p[3] + [p[5]] @pg.production('macro_stmt : MACRO fun_header suite2') def macro_stmt(p): fun_name, fun_args = p[1] lexer.add_macro_name((p[0].filename, str(fun_name))) return [Symbol('mac'), fun_name, fun_args] + p[2] @pg.production('infix_macro_stmt : INFIX MACRO fun_header suite2') def macro_stmt(p): fun_name, fun_args = p[2] lexer.add_infix_macro_name((p[0].filename, str(fun_name))) return [Symbol('mac'), fun_name, fun_args] + p[3] @pg.production('infix_macro_stmt : INFIX_1 MACRO fun_header suite2') def macro_stmt(p): fun_name, fun_args = p[2] lexer.add_infix_1_macro_name((p[0].filename, str(fun_name))) return [Symbol('mac'), fun_name, fun_args] + p[3] @pg.production('infix_macro_stmt : INFIX_2 MACRO fun_header suite2') def macro_stmt(p): fun_name, fun_args = p[2] lexer.add_infix_2_macro_name((p[0].filename, str(fun_name))) return [Symbol('mac'), fun_name, fun_args] + p[3] @pg.production('q_stmt : QUOTE suite') def q_stmt(p): return [Symbol('quote'), p[1]] #@pg.production('quote_expr : QUOTE_LPAREN binop_expr RPAREN') #@pg.production('quote_expr : QUOTE binop_expr') @pg.production('quote_expr : QUOTE stmt') def quote_expr(p): return [Symbol('quote'), p[1]] @pg.production('qq_stmt : QUASI_QUOTE suite') def qq_stmt(p): return [Symbol('quasiquote'), p[1]] #@pg.production('quasi_quote_expr : QUASI_QUOTE_LPAREN binop_expr RPAREN') #@pg.production('quasi_quote_expr : QUASI_QUOTE binop_expr') @pg.production('quasi_quote_expr : QUASI_QUOTE stmt') def quasi_quote_expr(p): return [Symbol('quasiquote'), p[1]] #@pg.production('uq_expr : UNQUOTE_LPAREN binop_expr RPAREN') @pg.production('uq_expr : UNQUOTE binop_expr') def uq_expr(p): return [Symbol('unquote'), p[1]] #@pg.production('uqs_expr : UNQUOTE_SPLICING_LPAREN binop_expr RPAREN') @pg.production('uqs_expr : UNQUOTE_SPLICING binop_expr') def uqs_expr(p): return [Symbol('unquote_splicing'), p[1]] @pg.production('assert_stmt : ASSERT binop_expr') def assert_stmt(p): return [Symbol('assert'), p[1]] @pg.production('with_stmt : WITH with_contexts suite2') def with_stmt(p): return [Symbol('with'), p[1]] + p[2] @pg.production('with_contexts : with_contexts COMMA with_context') def with_contexts(p): return p[0] + [p[2]] @pg.production('with_contexts : with_context') def with_contexts_one(p): return [p[0]] @pg.production('with_context : binop_expr AS NAME') def with_context(p): return [p[0], token_to_symbol(p[2])] @pg.production('return_stmt : RETURN binop_expr') def raise_stmt(p): return [Symbol('return'), p[1]] def token_to_symbol(token): return Symbol(token.getstr(), token.getsourcepos().lineno, token.getsourcepos().colno) def token_to_keyword(token): return Keyword(token.getstr(), token.getsourcepos().lineno, token.getsourcepos().colno) @pg.production('let_expr : LET pattern EQUALS binop_expr') def let_expr(p): return [Symbol('match'), p[3], p[1], Symbol('True')] @pg.production('binding : NAME') def binding(p): return token_to_symbol(p[0]) @pg.production('as_expr : binop_expr AS id_expr') def let_expr(p): return [Symbol('val', 0, 0), p[2], p[0]] @pg.production('expr : record_expr') # @pg.production('expr : func_expr') # @pg.production('expr : union_expr') # @pg.production('expr : predicate_expr') @pg.production('expr : fn_expr') @pg.production('expr : paren_expr') @pg.production('expr : if_expr') @pg.production('expr : prim_expr') @pg.production('expr : uq_expr') @pg.production('expr : uqs_expr') @pg.production('expr : app_expr') @pg.production('expr : left_app_expr') @pg.production('expr : dict_expr') @pg.production('expr : tuple_expr') @pg.production('expr : match_expr') @pg.production('expr : yield_expr') @pg.production('expr : yield_from_expr') @pg.production('expr : for_expr') @pg.production('expr : block_expr') @pg.production('expr : dot_expr') @pg.production('expr : get_expr') @pg.production('expr : quote_expr') @pg.production('expr : quasi_quote_expr') @pg.production('expr : id_expr') @pg.production('expr : call_macro_expr') @pg.production('expr : call_func_expr') @pg.production('expr : call_method_expr') def expr(p): return p[0] @pg.production('paren_expr : LPAREN binop_expr RPAREN') def paren_expr(p): return p[1] @pg.production('prim_expr : NUMBER') def expr_num(p): num_repr = p[0].getstr() try: return int(num_repr) except ValueError as _: return float(num_repr) @pg.production('prim_expr : string') def expr_string(p): return p[0] @pg.production('string : DQUOTE_STR') @pg.production('string : SQUOTE_STR') def expr_quote_str(p): return quote_str(p[0].getstr()[1:-1]) @pg.production('string : TQUOTE_STR') def expr_triple_quote_str(p): return quote_str(p[0].getstr()[3:-3]) def quote_str(string): new_string = '' string_enumerator = enumerate(string) for index, char in string_enumerator: if char == '\\': index, char = next(string_enumerator) if char == 'n': char = '\n' elif char == 't': char = '\t' elif char == 'r': char = '\r' elif char in {'\\', "'", '"'}: pass else: char = '\\' + char new_string = new_string + char return new_string @pg.production('string : DQUOTE_RAW_STR') @pg.production('string : SQUOTE_RAW_STR') def expr_quote_raw_str(p): return p[0].getstr()[2:-1] @pg.production('string : TQUOTE_RAW_STR') def expr_triple_quote_raw_str(p): return p[0].getstr()[4:-3] @pg.production('prim_expr : bool_expr') def expr_false(p): return p[0] @pg.production('bool_expr : TRUE') def expr_true(p): return Symbol('True') @pg.production('bool_expr : FALSE') def expr_false(p): return Symbol('False') @pg.production('id_expr : NAME') def id_expr(p): return token_to_symbol(p[0]) @pg.production('id_expr : AMP') def id_expr(p): return Symbol('&') @pg.production('if_expr : IF binop_expr suite elseif_exprs ELSE suite') def if_else_expr(p): return [Symbol('if'), p[1], p[2]] + p[3] + [p[5]] @pg.production('if_expr : IF binop_expr suite ELSE suite') def if_else_expr(p): return [Symbol('if'), p[1], p[2], p[4]] @pg.production('elseif_exprs : elseif_exprs elseif_expr') def elseif_exprs(p): return p[0] + p[1] @pg.production('elseif_exprs : elseif_expr') def elseif_exprs_expr(p): return p[0] @pg.production('elseif_expr : ELSEIF binop_expr suite') def elseif_expr(p): return [p[1], p[2]] # @pg.production('elseif_expr :') # def elseif_expr_empty(p): # return None #@pg.production('trailing_if_expr : binop_expr IF binop_expr ELSE binop_expr') def trailing_if_expr(p): return [Symbol('if'), p[2], p[0], p[4]] @pg.production('yield_expr : YIELD binop_expr') def yield_expr(p): return [Symbol('yield'), p[1]] @pg.production('yield_from_expr : YIELD FROM binop_expr') def yield_from_expr(p): return [Symbol('yield_from'), p[1]] def issequence(obj): return isinstance(obj, Sequence) def issequence_except_str(obj): if isinstance(obj, str): return False return isinstance(obj, Sequence) def _compute_underscore_max_num(exps): max_num = 0 if not issequence_except_str(exps): exps = (exps,) for exp in exps: if isinstance(exp, Symbol) and exp.name.startswith('$'): try: n = int(exp.name[1:]) except: n = 1 elif issequence_except_str(exp): n = _compute_underscore_max_num(exp) else: n = 0 if n > max_num: max_num = n return max_num @pg.production('dot_expr : expr DOT_NAME') def dot_expr(p): return [Symbol('getattr'), p[0], p[1].getstr()[1:]] @pg.production('get_expr : binop_expr LBRACK binop_expr RBRACK') def get_expr(p): return [Symbol('get'), p[0], p[2]] @pg.production('get_expr : binop_expr LBRACK binop_expr COMMA binop_expr RBRACK') def get_expr(p): return [Symbol('get'), p[0], [Symbol('v'), p[2], p[4]]] @pg.production('get_expr : binop_expr LBRACK binop_expr COMMA binop_expr COMMA binop_expr RBRACK') def get_expr(p): return [Symbol('get'), p[0], [Symbol('v'), p[2], p[4], p[6]]] @pg.production('get_expr : binop_expr LBRACK range_start COLON range_end RBRACK') def get_slice_expr(p): return [Symbol('get'), p[0], p[2], p[4]] @pg.production('get_expr : binop_expr LBRACK range_start COLON range_end COLON range_interval RBRACK') def get_slice_expr(p): return [Symbol('get'), p[0], p[2], p[4], p[6]] @pg.production('range_start : ') @pg.production('range_end : ') @pg.production('range_interval : ') def range_start_none(p): return Symbol('None') @pg.production('range_start : binop_expr') @pg.production('range_end : binop_expr') @pg.production('range_interval : binop_expr') def range_start_none(p): return p[0] @pg.production('for_expr : LBRACK binop_expr FOR pattern IN binop_expr RBRACK') def for_expr(p): pattern = p[3] items = p[5] body = p[1] return [Symbol('tuple_of'), body, [pattern, items]] @pg.production('for_expr : LBRACK binop_expr FOR pattern IN binop_expr IF binop_expr RBRACK') def for_expr_if(p): pattern = p[3] items = p[5] body = p[1] when = p[7] return [Symbol('tuple_of'), body, [pattern, items, Keyword('when'), when]] @pg.production('tuple_expr : LBRACK tuple_elts binop_expr RBRACK') def tuple_expr(p): return [Symbol('make_tuple')] + p[1] + [p[2]] @pg.production('tuple_expr : LBRACK binop_expr RBRACK') def tuple_expr_one(p): return [Symbol('make_tuple'), p[1]] @pg.production('tuple_expr : LBRACK tuple_elts binop_expr RBRACK') def tuple_expr(p): return [Symbol('make_tuple')] + p[1] + [p[2]] @pg.production('tuple_expr : LBRACK binop_expr RBRACK') def tuple_expr_one(p): return [Symbol('make_tuple'), p[1]] @pg.production('tuple_expr : LBRACK RBRACK') def tuple_expr_empty(p): return [Symbol('make_tuple')] @pg.production('tuple_elts : tuple_elts tuple_elt') def tuple_elts(p): return p[0] + [p[1]] @pg.production('tuple_elts : tuple_elt') def tuple_elts_elt(p): return [p[0]] @pg.production('tuple_elt : binop_expr COMMA') def tuple_elt(p): return p[0] #@pg.production('deco_expr : decorators binop_expr') @pg.production('deco_expr : decorators func_expr') def deco_expr(p): # return p[1][:2] + p[0] + p[1][2:] return [Symbol('with_decorator')] + p[0] + [p[1]] @pg.production('decorators : decorators decorator') def decorators(p): return p[0] + [p[1]] @pg.production('decorators : decorator') def decorators_single(p): return [p[0]] @pg.production('decorator : AT binop_expr') def decorator(p): return p[1] @pg.production('func_expr : FUNC fun_header doc_string suite') def fun_expr(p): fun_name, fun_args = p[1] return [Symbol('def'), fun_name, fun_args, p[3]] @pg.production('funcm_expr : FUNC NAME doc_string lbrace defm_case_branches rbrace') def fun_expr(p): return [Symbol('defm'), token_to_symbol(p[1])] + p[4] @pg.production('defm_case_branches : defm_case_branches defm_case_branch') def case_branches(p): return p[0] + p[1] @pg.production('defm_case_branches : defm_case_branch') def case_branches_branch(p): return p[0] @pg.production('defm_case_branch : CASE defm_pattern THINARROW lbrace stmts rbrace') def case_branch(p): return [p[1], [Symbol('do')] + p[4]] @pg.production('defm_case_branch : CASE defm_pattern THINARROW binop_expr') def case_branch(p): return [p[1], p[3]] # @pg.production('defm_case_branch : CASE defm_pattern COLON binop_expr NEWLINE') # def case_branch(p): # return [p[1], p[3]] # @pg.production('defm_case_branch : CASE defm_pattern COLON binop_expr SEMI') # def case_branch(p): # return [p[1], p[3]] @pg.production('defm_pattern : app_nc_args') def pattern(p): return p[0] @pg.production('defm_pattern : pattern') def app_args(p): return [p[0]] @pg.production('defm_pattern : pattern COMMA defm_pattern') def app_args(p): return [p[0]] + p[2] @pg.production('fun_header : NAME_LPAREN list_arg_elts id_expr RPAREN') def fun_header(p): return [namelparen_to_symbol(p[0]), p[1] + [p[2]]] @pg.production('fun_header : NAME_LPAREN id_expr RPAREN') def fun_header(p): return [namelparen_to_symbol(p[0]), [p[1]]] @pg.production('fun_header : NAME_LPAREN RPAREN') def fun_header(p): return [namelparen_to_symbol(p[0]), []] @pg.production('fn_expr : id_expr FATARROW suite') def fun_expr(p): return [Symbol('fn'), [p[0]], p[2]] @pg.production('fn_expr : args FATARROW suite') def fun_expr(p): return [Symbol('fn'), p[0], p[2]] @pg.production('args : LPAREN list_arg_elts id_expr RPAREN') def args(p): return p[1] + [p[2]] @pg.production('args : LPAREN id_expr RPAREN') def args_one(p): return [p[1]] @pg.production('args : LPAREN RPAREN') def args_empty(p): return [] @pg.production('nc_args : list_arg_elts id_expr') def args(p): return p[0] + [p[1]] @pg.production('nc_args : id_expr') def args_one(p): return [p[0]] @pg.production('list_arg_elts : list_arg_elts list_arg_elt') def list_arg_elts(p): return p[0] + [p[1]] @pg.production('list_arg_elts : list_arg_elt') def list_arg_elts_elt(p): return [p[0]] @pg.production('list_arg_elt : id_expr COMMA') def list_arg_elt(p): return p[0] def _create_underscore_args(exps): max_num = _compute_underscore_max_num(exps) if max_num == 1: return [Symbol('$1')] else: return [Symbol('$' + str(n)) for n in range(1, max_num + 1)] @pg.production('block_expr : FATARROW suite') def block_expr(p): block = p[1] return [Symbol('fn'), _create_underscore_args(block), block] @pg.production('doc_string : DOC string') @pg.production('doc_string : ') def doc_string(p): pass from collections import Iterable def flatten_list(lis): i = 0 while i < len(lis): while isinstance(lis[i], Iterable): if not lis[i]: lis.pop(i) i -= 1 break else: lis[i:i + 1] = lis[i] i += 1 return lis @pg.production('call_macro_expr : MACRO_NAME head') def call_macro_expr(p): head = p[1] body = None rest = [] return process_calling_macro(token_to_symbol(p[0]), head, body, rest) @pg.production('call_macro_expr : MACRO_NAME head rest') def call_macro_expr(p): head = p[1] body = None rest = p[2] return process_calling_macro(token_to_symbol(p[0]), head, body, rest) @pg.production('call_macro_expr : MACRO_NAME do_suite') def call_macro_expr(p): head = [] body = p[1] rest = [] return process_calling_macro(token_to_symbol(p[0]), head, body, rest) @pg.production('call_macro_expr : MACRO_NAME head do_suite') def call_macro_expr(p): head = p[1] body = p[2] rest = [] return process_calling_macro(token_to_symbol(p[0]), head, body, rest) @pg.production('call_macro_expr : MACRO_NAME head do_suite rest') def call_macro_expr(p): head = p[1] body = p[2] rest = p[3] return process_calling_macro(token_to_symbol(p[0]), head, body, rest) def process_calling_macro(name, head, body, rest): # macro_name = name.name # if macro_name == 'macro': # call_func, *rest = head # _, fun_name, *fun_args = call_func # return [Symbol('mac'), fun_name, fun_args, body] # elif macro_name == 'def': # call_func, *rest = head # _, fun_name, *fun_args = call_func # return [Symbol('def'), fun_name, fun_args, body] # elif macro_name == 'if': # clauses = [head[0], body] # for rest_clause in rest: # label, head, body = rest_clause # if label == Symbol('elif'): # clauses.append(head[0]) # clauses.append(body) # elif label == Symbol('else'): # clauses.append(body) # return [Symbol('if'), *clauses] # else: # error = SyntaxError(label) # error.filename = '<string>' # error.lineno = name.lineno # error.offset = name.col_offset # raise error # return [Symbol('if'), *clauses] # elif macro_name == 'return': # return [Symbol('return'), head[0]] # elif macro_name == 'raise': # return [Symbol('raise'), head[0]] # else: # if rest is None or len(rest) == 0: # return [Symbol('call_macro'), name, head, body] # else: return [Symbol('call_macro'), name, head, body, rest] @pg.production('rest : ') def rest(p): return [] @pg.production('rest : rest_item') def rest(p): return [p[0]] @pg.production('rest : rest rest_item') def rest(p): return p[0] + [p[1]] @pg.production('rest_item : sub_keyword head do_suite') def rest_item(p): head = p[1] body = p[2] return [p[0], head, body] @pg.production('rest_item : sub_keyword do_suite') def rest_item(p): head = [] body = p[1] return [p[0], head, body] @pg.production('sub_keyword : ELSE') @pg.production('sub_keyword : ELSEIF') @pg.production('sub_keyword : EXCEPT') @pg.production('sub_keyword : USER_DEFINED_KEYWORD') def sub_keyword(p): return token_to_symbol(p[0]) @pg.production('head : app_nc_args') def head(p): return p[0] @pg.production('if_expr : IF binop_expr suite') def if_expr(p): return [Symbol('if'), p[1], p[2]] @pg.production('if_expr : IF binop_expr suite elseif_exprs') def if_expr(p): return [Symbol('if'), p[1], p[2]] + p[3] def namelparen_to_symbol(token): return Symbol(token.getstr()[:-1], token.getsourcepos().lineno, token.getsourcepos().colno) @pg.production('call_func_expr : NAME_LPAREN RPAREN') def call_func_expr(p): return [Symbol('call_func'), namelparen_to_symbol(p[0])] # @pg.production('call_func_expr : NAME_LPAREN RPAREN fn_expr') # @pg.production('call_func_expr : NAME_LPAREN RPAREN block_expr') def call_func_expr(p): return [Symbol('call_func'), namelparen_to_symbol(p[0]), p[2]] @pg.production('call_func_expr : NAME_LPAREN app_args_elts RPAREN') def call_func_expr(p): return [Symbol('call_func'), namelparen_to_symbol(p[0])] + p[1] @pg.production('app_expr : binop_expr app_args') def call_func_expr(p): return [p[0]] + p[1] #@pg.production('call_func_expr : NAME_LPAREN app_args_elts RPAREN fn_expr') #@pg.production('call_func_expr : NAME_LPAREN app_args_elts RPAREN block_expr') #def call_func_expr(p): # return [Symbol('call_func'), # namelparen_to_symbol(p[0])] + [p[3]] + p[1] @pg.production('call_func_expr : paren_expr LPAREN RPAREN') def call_func_expr(p): return [Symbol('call_func'), p[0]] @pg.production('call_func_expr : paren_expr LPAREN app_args_elts RPAREN') def call_func_expr(p): return [Symbol('call_func'), p[0]] + p[2] #@pg.production('call_func_expr : call_func_expr LPAREN RPAREN') #def call_func_expr(p): # return [Symbol('call_func'), p[0]] #@pg.production('call_func_expr : call_func_expr LPAREN app_args_elts RPAREN') #def call_func_expr(p): # return [Symbol('call_func'), p[0]] + p[2] @pg.production('call_method_expr : expr DOT_NAME_LPAREN RPAREN') def call_method_expr(p): return [Symbol('call_func'), [Symbol('getattr'), p[0], p[1].getstr()[1:-1]]] @pg.production('call_method_expr : expr DOT_NAME_LPAREN app_args_elts RPAREN') def call_method_expr(p): return [Symbol('call_func'), [Symbol('getattr'), p[0], p[1].getstr()[1:-1]]] + p[2] @pg.production('app_args : LPAREN app_args_elts RPAREN') def app_args(p): return p[1] @pg.production('app_args : LPAREN RPAREN') def app_args(p): return [] @pg.production('app_args_elts : app_args_elts COMMA app_args_elt') def app_args_elts(p): return p[0] + p[2] @pg.production('app_args_elts : app_args_elt') def app_args_elts(p): return p[0] @pg.production('app_args_elt : NAME EQUALS binop_expr') def app_args_elt(p): return [token_to_keyword(p[0]), p[2]] @pg.production('app_args_elt : EQUALS NAME') def app_args_elt_short(p): return [token_to_keyword(p[1]), token_to_symbol(p[1])] @pg.production('app_args_elt : binop_expr') def app_args_elt(p): return [p[0]] # TODO #@pg.production('app_expr : expr app_args app_args') #@pg.production('app_expr : expr app_args app_args') def trailing_closure_expr(p): return [[p[0]] + p[1]] + p[2] #@pg.production('app_expr : expr app_args AT fn_expr') #@pg.production('app_expr : expr app_args AT block_expr') #def trailing_closure_expr(p): # return [p[0]] + p[1] + [p[3]] @pg.production('app_nc_expr : expr app_nc_args') def app_expr(p): return [p[0]] + p[1] @pg.production('app_nc_args : app_nc_arg') def app_nc_args(p): return [p[0]] @pg.production('app_nc_args : app_nc_arg COMMA app_nc_args') def app_nc_args(p): return [p[0]] + p[2] # @pg.production('app_nc_args : app_nc_arg app_nc_args') # def app_nc_args(p): # return [p[0]] + p[1] # @pg.production('app_nc_args : app_nc_arg labeled_blocks') # def app_nc_args(p): # return [p[0]] + p[1] # # # @pg.production('labeled_blocks : labeled_block labeled_blocks') # def labeled_blocks(p): # return [p[0]] + p[1] # # # @pg.production('labeled_blocks : labeled_block') # def labeled_blocks(p): # return [p[0]] @pg.production('app_nc_arg : binop_expr') def app_nc_arg(p): return p[0] @pg.production('left_app_expr : expr CALET left_app_fun_expr app_args') def left_app_expr(p): expr, _, left_app_fun_expr, app_args = p return [left_app_fun_expr, expr] + app_args @pg.production('left_app_fun_expr : id_expr') def left_app_fun_expr(p): return p[0] @pg.production('dict_expr : lbrace rbrace') def dict_expr_empty(p): return [Symbol('table')] @pg.production('dict_expr : lbrace fields rbrace') def dict_expr(p): return [Symbol('table')] + p[1] @pg.production('fields : field') def fields_one(p): return p[0] @pg.production('fields : list_fields field') def fields(p): return p[0] + p[1] @pg.production('list_fields : list_field') def list_fields_one(p): return p[0] @pg.production('list_fields : list_fields list_field') def list_fields(p): return p[0] + p[1] @pg.production('list_field : field COMMA') def list_field(p): return p[0] @pg.production('field : key COLON binop_expr') def field(p): return [p[0], p[2]] @pg.production('field : EQUALS NAME') def field(p): s = token_to_symbol(p[1]) return [s.name, s] @pg.production('key : prim_expr') @pg.production('key : id_expr') @pg.production('key : call_func_expr') def key(p): return p[0] @pg.production('match_expr : MATCH binop_expr lbrace case_branches rbrace') def case(p): return [Symbol('match'), p[1]] + p[3] @pg.production('case_branches : case_branches case_branch') def case_branches(p): return p[0] + p[1] @pg.production('case_branches : case_branch') def case_branches_branch(p): return p[0] @pg.production('case_branch : CASE pattern THINARROW lbrace stmts rbrace') def case_branch(p): return [p[1], [Symbol('do')] + p[4]] @pg.production('case_branch : CASE pattern THINARROW binop_expr') def case_branch(p): return [p[1], p[3]] # @pg.production('case_branch : CASE pattern COLON binop_expr SEMI') # def case_branch(p): # return [p[1], p[3]] # @pg.production('pattern : fn_expr') @pg.production('pattern : prim_pattern') @pg.production('pattern : dict_pattern') @pg.production('pattern : sequence_pattern') @pg.production('pattern : sequence_type_pattern') @pg.production('pattern : type_pattern') @pg.production('pattern : id_pattern') @pg.production('pattern : ref_pattern') # @pg.production('pattern : and_pattern') # @pg.production('pattern : or_pattern') @pg.production('pattern : quote_pattern') # TODO @pg.production('defm_pattern : app_nc_args') def pattern(p): return p[0] @pg.production('prim_pattern : NUMBER') def pattern_num(p): num_repr = p[0].getstr() try: return int(num_repr) except ValueError as _: return float(num_repr) @pg.production('prim_pattern : string') def pattern_string(p): return p[0] @pg.production('prim_pattern : bool_expr') def pattern_bool(p): return p[0] @pg.production('dict_pattern : lbrace rbrace') def dict_pattern_empty(p): return [Symbol('table')] @pg.production('dict_pattern : lbrace dict_pattern_fields rbrace') def dict_pattern(p): return [Symbol('table')] + p[1] @pg.production('dict_pattern_fields : dict_pattern_field') def fields_one(p): return p[0] @pg.production('dict_pattern_fields : dict_pattern_list_fields dict_pattern_field') def fields(p): return p[0] + p[1] @pg.production('dict_pattern_list_fields : dict_pattern_list_field') def list_fields_one(p): return p[0] @pg.production('dict_pattern_list_fields : dict_pattern_list_fields dict_pattern_list_field') def list_fields(p): return p[0] + p[1] @pg.production('dict_pattern_list_field : dict_pattern_field COMMA') def list_field(p): return p[0] @pg.production('dict_pattern_field : dict_pattern_key COLON pattern') def field(p): return [p[0], p[2]] @pg.production('dict_pattern_field : EQUALS NAME') def field(p): s = token_to_symbol(p[1]) return [s.name, s] @pg.production('dict_pattern_key : binop_expr') def key(p): return p[0] @pg.production('id_pattern : NAME') def id_pattern(p): return token_to_symbol(p[0]) @pg.production('id_pattern : AMP') def id_pattern(p): return Symbol('&') @pg.production('sequence_pattern : LBRACK sequence_pattern_elts pattern RBRACK') def sequence_pattern(p): return [Symbol('make_tuple')] + p[1] + [p[2]] @pg.production('sequence_pattern : LBRACK pattern RBRACK') def sequence_pattern_one(p): return [Symbol('make_tuple'), p[1]] @pg.production('sequence_pattern : LBRACK RBRACK') def sequence_pattern_empty(p): return [Symbol('make_tuple')] @pg.production('sequence_pattern_elts : sequence_pattern_elts sequence_pattern_elt') def sequence_pattern_elts(p): return p[0] + [p[1]] @pg.production('sequence_pattern_elts : sequence_pattern_elt') def sequence_pattern_elts_elt(p): return [p[0]] @pg.production('sequence_pattern_elt : pattern COMMA') def sequence_pattern_elt(p): return p[0] @pg.production('sequence_pattern_named_elts : sequence_pattern_named_elts sequence_pattern_named_elt') def sequence_pattern_named_elts(p): return p[0] + p[1] @pg.production('sequence_pattern_named_elts : sequence_pattern_named_elt') def sequence_pattern_named_elts_elt(p): return p[0] @pg.production('sequence_pattern_named_elt : named_pattern COMMA') def sequence_pattern_named_elt(p): return p[0] @pg.production('named_pattern : NAME EQUALS pattern') def sequence_pattern_named_pattern(p): s = token_to_symbol(p[0]) return [s.name, p[2]] @pg.production('sequence_type_pattern : names_lparen sequence_pattern_elts pattern RPAREN') def sequence_type_pattern(p): return [Symbol('sequence_type'), p[0]] + p[1] + [p[2]] @pg.production('sequence_type_pattern : names_lparen sequence_pattern_named_elts named_pattern RPAREN') def sequence_type_pattern(p): return [Symbol('sequence_type_with_named_member'), p[0]] + p[1] + p[2] @pg.production('sequence_type_pattern : names_lparen pattern RPAREN') def sequence_type_pattern_one(p): return [Symbol('sequence_type'), p[0], p[1]] @pg.production('sequence_type_pattern : names_lparen named_pattern RPAREN') def sequence_type_pattern_one(p): return [Symbol('sequence_type_with_named_member'), p[0], p[1]] @pg.production('and_pattern : pattern OPAND pattern') def and_pattern(p): return [token_to_symbol(p[1]), p[0], p[2]] @pg.production('or_pattern : pattern OPOR pattern') def or_pattern(p): return [token_to_symbol(p[1]), p[0], p[2]] #@pg.production('type_pattern : pattern COLON NAME') #def type_pattern(p): # return [Symbol('type'), token_to_symbol(p[2]), p[0]] @pg.production('type_pattern : pattern COLON binop_expr') def type_pattern(p): return [Symbol('type'), p[2], p[0]] @pg.production('ref_pattern : CALET NAME') def ref_pattern(p): return [Symbol('ref'), token_to_symbol(p[1])] # @pg.production('quote_pattern : QUOTE LPAREN pattern RPAREN') @pg.production('quote_pattern : QUOTE pattern') def quote_pattern(p): return [Symbol('quote'), p[1]] @pg.production('record_expr : RECORD NAME') def record_expr(p): return [Symbol('record'), token_to_symbol(p[1]), []] # @pg.production('record_expr : RECORD NAME OPLT NAME') # def record_expr(p): # return [Symbol('record'), token_to_symbol(p[1]), token_to_symbol(p[3]), []] @pg.production('record_expr : RECORD NAME_LPAREN record_fields RPAREN') def record_expr(p): return [Symbol('record'), namelparen_to_symbol(p[1]), p[2]] @pg.production('record_expr : RECORD NAME_LPAREN record_fields RPAREN OPLT NAME') def record_expr(p): return [Symbol('record'), namelparen_to_symbol(p[1]), token_to_symbol(p[5]), p[2]] @pg.production('record_expr : RECORD NAME lbrace record_body rbrace') def record_expr(p): return [Symbol('record'), token_to_symbol(p[1]), []] + p[3] @pg.production('record_expr : RECORD NAME OPLT NAME lbrace record_body rbrace') def record_expr(p): return [Symbol('record'), token_to_symbol(p[1]), token_to_symbol(p[3]), []] + p[5] @pg.production('record_expr : RECORD NAME_LPAREN record_fields RPAREN lbrace record_body rbrace') def record_expr(p): return [Symbol('record'), namelparen_to_symbol(p[1]), p[2]] + p[5] @pg.production('record_expr : RECORD NAME_LPAREN record_fields RPAREN OPLT NAME lbrace record_body rbrace') def record_expr(p): return [Symbol('record'), namelparen_to_symbol(p[1]), token_to_symbol(p[5]), p[2]] + p[7] # @pg.production('union_expr : UNION suite2') # def union_expr(p): # return [Symbol('union')] + p[1] # @pg.production('predicate_expr : PREDICATE binop_expr') # def union_expr(p): # return [Symbol('predicate'), p[1]] @pg.production('record_body : func_expr') def record_body(p): return [p[0]] @pg.production('record_body : record_body func_expr') def record_body(p): return p[0] + [p[1]] @pg.production('record_fields : record_field') def record_expr(p): return [p[0]] @pg.production('record_fields : record_field COMMA record_fields') def record_expr(p): return [p[0]] + p[2] @pg.production('record_field : id_expr') def record_expr(p): return p[0] @pg.production('record_field : id_expr COLON binop_expr') def record_expr(p): return [p[0], p[2]] @pg.production('data_expr : DATA NAME lbrace data_record_expr_list rbrace') def data_expr(p): return [Symbol('data'), token_to_symbol(p[1])] + p[3] @pg.production('data_record_expr_list : data_record_expr') def record_expr(p): return [p[0]] @pg.production('data_record_expr_list : data_record_expr data_record_expr_list') def record_expr(p): return [p[0]] + p[1] @pg.production('data_record_expr : NAME_LPAREN record_fields RPAREN') def record_expr(p): return [namelparen_to_symbol(p[0])] + p[1] @pg.production('binop_expr : NOT binop_expr') def binop_expr(p): return [token_to_symbol(p[0]), p[1]] @pg.production('binop_expr : binop_expr OPPLUS binop_expr') @pg.production('binop_expr : binop_expr OPMINUS binop_expr') @pg.production('binop_expr : binop_expr OPTIMES binop_expr') @pg.production('binop_expr : binop_expr PERCENT binop_expr') @pg.production('binop_expr : binop_expr OPDIV binop_expr') @pg.production('binop_expr : binop_expr OPLEQ binop_expr') @pg.production('binop_expr : binop_expr OPGEQ binop_expr') @pg.production('binop_expr : binop_expr OPEQ binop_expr') @pg.production('binop_expr : binop_expr OPNEQ binop_expr') @pg.production('binop_expr : binop_expr OPLT binop_expr') @pg.production('binop_expr : binop_expr OPGT binop_expr') @pg.production('binop_expr : binop_expr OPBITOR binop_expr') @pg.production('binop_expr : binop_expr OPBITXOR binop_expr') @pg.production('binop_expr : binop_expr OPBITAND binop_expr') @pg.production('binop_expr : binop_expr OPFLOORDIV binop_expr') @pg.production('binop_expr : binop_expr OPPOW binop_expr') @pg.production('binop_expr : binop_expr OPRSHIFT binop_expr') @pg.production('binop_expr : binop_expr OPLSHIFT binop_expr') @pg.production('binop_expr : binop_expr OPAND binop_expr') @pg.production('binop_expr : binop_expr OPOR binop_expr') @pg.production('binop_expr : binop_expr OPIS binop_expr') @pg.production('binop_expr : binop_expr IN binop_expr') @pg.production('binop_expr : binop_expr AS id_expr') def binop_expr(p): return [token_to_symbol(p[1]), p[0], p[2]] @pg.production('binop_expr : binop_expr INFIX_MACRO_NAME binop_expr') def binop_expr(p): return [Symbol('call_macro'), token_to_symbol(p[1]), p[0], p[2]] @pg.production('binop_expr : binop_expr INFIX_1_MACRO_NAME binop_expr') def binop_expr(p): return [Symbol('call_macro'), token_to_symbol(p[1]), p[0], p[2]] @pg.production('binop_expr : binop_expr INFIX_2_MACRO_NAME binop_expr') def binop_expr(p): return [Symbol('call_macro'), token_to_symbol(p[1]), p[0], p[2]] @pg.production('binop_expr : binop_expr NOT IN binop_expr') def binop_expr(p): return [Symbol('not_in'), p[0], p[3]] @pg.production('binop_expr : binop_expr PIPELINE binop_expr') def binop_expr(p): return [Symbol('|>'), p[0], p[2]] @pg.production('binop_expr : binop_expr PIPELINE_BIND binop_expr') def binop_expr(p): left, _, right = p input_sym = get_temp_name() return [Symbol('|>'), p[0], [Symbol('bind'), [Symbol('fn'), [input_sym], p[2] + [input_sym]]]] @pg.production('binop_expr : binop_expr PIPELINE_FIRST binop_expr') def binop_expr(p): return [Symbol('|>1'), p[0], p[2]] @pg.production('binop_expr : binop_expr PIPELINE_FIRST_BIND binop_expr') def binop_expr(p): left, _, right = p input_sym = get_temp_name() return [Symbol('|>'), p[0], [Symbol('bind'), [Symbol('fn'), [input_sym], [p[2][0], input_sym] + p[2][(1 if len(p[2]) > 1 else len(p[2])):]]]] @pg.production('binop_expr : expr') def binop_expr(p): return p[0]

25.859231

116

0.653825

7,100

47,762

4.152535

0.042535

0.152223

0.030119

0.029

0.706475

0.638063

0.592172

0.531459

0.420683

0.385307

0

0.014188

0.186885

47,762

1,847

117

25.859231

0.744979

0.158264

0

0.371075

0

0.30473

0.018252

0

0.000541

0.004757

1

0.252141

false

0.005709

0.015224

0.210276

0.524263

0.000951

0

null

0

null

0

1

0

1

0

3

c1dd6874deb6cbc28514ef2ec2c7c714f60a5adc

1,282

py

Python

poetry/apps/corpus/spiders/strofa.py

IlyaGusev/PoetryCorpus

7a5f70e6a46b4717f7c903671f9a6a917aee6162

[ "Apache-2.0" ]

45

2016-10-24T13:13:55.000Z

2022-01-21T05:39:06.000Z

poetry/apps/corpus/spiders/strofa.py

IlyaGusev/PoetryCorpus

7a5f70e6a46b4717f7c903671f9a6a917aee6162

[ "Apache-2.0" ]

23

2017-01-18T17:34:25.000Z

2017-11-01T17:39:02.000Z

poetry/apps/corpus/spiders/strofa.py

IlyaGusev/PoetryCorpus

7a5f70e6a46b4717f7c903671f9a6a917aee6162

[ "Apache-2.0" ]

7

2017-08-25T03:08:08.000Z

2020-05-22T22:55:58.000Z

import scrapy import re class StrofaSpider(scrapy.Spider): name = 'poems_strofa' start_urls = ['http://strofa.su/vse-poety/'] custom_settings = {} def parse(self, response): for href in response.css('.poemlinks a::attr(href)'): poet_url = response.urljoin(href.extract()) yield scrapy.Request(poet_url, callback=self.parse_poet) def parse_poet(self, response): for href in response.css('.poemlinks a::attr(href)'): poem_url = response.urljoin(href.extract()) yield scrapy.Request(poem_url, callback=self.parse_poem) def parse_poem(self, response): name = response.css('.poem h1::text').extract_first() text = "\n".join(response.css('.poem .related::text').extract()) meta = response.css('.poem .related p::text').extract_first().split(',') author = meta[0] dates = re.findall(r"1[0-9]{3}", meta[1]) if len(meta) >= 2 else [] result = { 'author': author.strip(), 'text': text } if " ".join(name.strip().split()) != "* * *": result['name'] = name.strip() if len(dates) != 0: result['date_from'] = dates[0] result['date_to'] = dates[-1] yield result

33.736842

80

0.569423

158

1,282

4.525316

0.392405

0.076923

0.062937

0.053147

0.271329

0.13986

0

0.01164

0.262871

1,282

37

81

34.648649

0.744974

0

0.066667

0

0.149219

0

1

0.1

false

0

0.066667

0

0.3

0

null

0

null

0

1

0

c1e179fd88680caa02ec8f42737513b72ccb61e5

1,167

py

Python

FindImmo/admin.py

sdaouda/immoniger

85b4f23fed6e73d8df8700103723725f267e31d8

[ "Apache-2.0" ]

null

FindImmo/admin.py

sdaouda/immoniger

85b4f23fed6e73d8df8700103723725f267e31d8

[ "Apache-2.0" ]

6

2021-03-19T00:41:08.000Z

2022-03-11T23:46:42.000Z

FindImmo/admin.py

sdaouda/immoniger

85b4f23fed6e73d8df8700103723725f267e31d8

[ "Apache-2.0" ]

null

from django.contrib import admin from .models import Category, Product,Project, Vendor,Zone,Localite,TypeProject,Service,TypeService,Commodite class CategoryAdmin(admin.ModelAdmin): list_display = ['name', 'slug'] prepopulated_fields = {'slug': ('name',)} admin.site.register(Category, CategoryAdmin) class CommoditeAdmin(admin.ModelAdmin): list_display = ['name', 'slug'] prepopulated_fields = {'slug': ('name',)} admin.site.register(Commodite, CommoditeAdmin) class ProductAdmin(admin.ModelAdmin): list_display = ['name', 'localite', 'category', 'price','available', 'created'] list_editable = ['price', 'available'] prepopulated_fields = {'slug': ('name',)} admin.site.register(Product, ProductAdmin) class VendorAdmin(admin.ModelAdmin): fieldsets = [ ('Personal', {'fields': ['name','email','phone1','phone2','phone3']}), ('Physical', {'fields': ['address','ville','localization']}), ] admin.site.register(Vendor, VendorAdmin) admin.site.register(Service) admin.site.register(TypeService) admin.site.register(TypeProject) admin.site.register(Zone) admin.site.register(Localite) admin.site.register(Project)

36.46875

109

0.718937

123

1,167

6.764228

0.357724

0.108173

0.204327

0.09375

0.272837

0.236779

0.185096

0

0.00289

0.11054

1,167

31

110

37.645161

0.798651

0

0.185185

0

0.1491

0

1

0

false

0

0.074074

0

0.518519

0

null

0

1

0

null

0

1

0

2

c1e24833c06f938f20d6edff45d2cb02a8374976

145

py

Python

real_graph_select.py

Beaconsyh08/Real_Graph_Select

a164e76102ecd5aa78763050fd05029acb0b4993

[ "MIT" ]

null

real_graph_select.py

Beaconsyh08/Real_Graph_Select

a164e76102ecd5aa78763050fd05029acb0b4993

[ "MIT" ]

null

real_graph_select.py

Beaconsyh08/Real_Graph_Select

a164e76102ecd5aa78763050fd05029acb0b4993

[ "MIT" ]

null

from app import app, db from app.models import Poem @app.shell_context_processor def make_shell_context(): return {'db': db, 'Poem': Poem}

18.125

35

0.731034

23

145

4.434783

0.521739

0.137255

0

0.158621

145

7

36

20.714286

0.836066

0

0.041379

0

1

0.2

true

0

0.4

0.2

0.8

0

1

0

null

0

1

0

null

0

1

0

1

0

6

c1e31e1fa25e6136930d59b414288f8412c884ca

3,194

py

Python

04 - Classes-inheritance-oops/40-classes-numeric-unary-magic-methods.py

python-demo-codes/basics

2a151bbff4b528cefd52978829c632fd087c8f20

[ "DOC" ]

2

2019-08-23T06:05:55.000Z

2019-08-26T03:56:07.000Z

04 - Classes-inheritance-oops/40-classes-numeric-unary-magic-methods.py

python-lang-codes/basics

2a151bbff4b528cefd52978829c632fd087c8f20

[ "DOC" ]

null

04 - Classes-inheritance-oops/40-classes-numeric-unary-magic-methods.py

python-lang-codes/basics

2a151bbff4b528cefd52978829c632fd087c8f20

[ "DOC" ]

4

2020-10-01T07:16:07.000Z

2021-07-17T07:55:08.000Z

# HEAD # Classes - Magic Methods - Unary Numeric Magic Methods # DESCRIPTION # Describes the magic methods of classes # pos, neg, abs, invert # round, floor, ceil, trunc # RESOURCES # # https://rszalski.github.io/magicmethods/ # Now would be a good time to note that you don't have to define # every comparison magic method to get rich comparisons. # The standard library has kindly provided us with a class # decorator in the module functools that will define all # rich comparison methods if you only define __eq__ # and one other (e.g. __gt__, __lt__, etc.) # This feature is only available in Python 2.7, but when # you get a chance it saves a great deal of time and effort. # You can use it by placing @total_ordering above your class definition. # NUMERIC MAGIC METHODS # Just like you can create ways for instances of your class to be compared with comparison operators, you can define behavior for numeric operators. Buckle your seat belts, folks...there's a lot of these. For organization's sake, I've split the numeric magic methods into 5 categories: unary operators, normal arithmetic operators, reflected arithmetic operators (more on this later), augmented assignment, and type conversions. # Unary operators and functions # UNARY OPERATORS and functions only have one operand, e.g. negation, absolute value, etc. # __pos__(self) # Implements behavior for unary positive (e.g. +some_object) # __neg__(self) # Implements behavior for negation (e.g. -some_object) # __abs__(self) # Implements behavior for the built in abs() function. # __invert__(self) # Implements behavior for inversion using the ~ operator. For an explanation on what this does, see the Wikipedia article on bitwise operations. # __round__(self, n) # Implements behavior for the built in round() function. n is the number of decimal places to round to. # __floor__(self) # Implements behavior for math.floor(), i.e., rounding down to the nearest integer. # __ceil__(self) # Implements behavior for math.ceil(), i.e., rounding up to the nearest integer. # __trunc__(self) # Implements behavior for math.trunc(), i.e., truncating to an integral. class Unary(str): def __pos__(self): # Implements behavior for unary positive (e.g. +some_object) def __neg__(self): # Implements behavior for negation (e.g. -some_object) def __abs__(self): # Implements behavior for the built in abs() function. def __invert__(self): # Implements behavior for inversion using the ~ operator. For an explanation on what this does, see the Wikipedia article on bitwise operations. def __round__(self, n): # Implements behavior for the built in round() function. n is the number of decimal places to round to. def __floor__(self): # Implements behavior for math.floor(), i.e., rounding down to the nearest integer. def __ceil__(self): # Implements behavior for math.ceil(), i.e., rounding up to the nearest integer. def __trunc__(self): # Implements behavior for math.trunc(), i.e., truncating to an integral. u = Unary(" Tes ")

47.671642

428

0.715404

460

3,194

4.791304

0.365217

0.084846

0.15245

0.158802

0.522686

0.517241

0

0.001188

0.209455

3,194

66

429

48.393939

0.871683

0.874452

0

0.014164

0

null

0

null

0

null

0

1

0

null

0

1

0

2

c1e580496376c92672392fa607ae6aa0fdbdf110

2,446

py

Python

data/local_news_data/crawler/spmiddlewares/fake404.py

SSK-14/Covid19-Search-Engine

2a9e0066e766d8a356a2c4a1ebd51c0aeb3cd4b6

[ "Apache-2.0" ]

1

2020-06-14T16:52:55.000Z

data/local_news_data/crawler/spmiddlewares/fake404.py

SSK-14/Covid19-Search-Engine

2a9e0066e766d8a356a2c4a1ebd51c0aeb3cd4b6

[ "Apache-2.0" ]

1

2020-05-06T14:28:10.000Z

data/local_news_data/crawler/spmiddlewares/fake404.py

SSK-14/Covid19-Search-Engine

2a9e0066e766d8a356a2c4a1ebd51c0aeb3cd4b6

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- """ This script was borrowed from the RISJbot repository (https://github.com/pmyteh/RISJbot) All credit goes to original author """ # Define here the models for your spider middleware # # See documentation in: # http://doc.scrapy.org/en/latest/topics/spider-middleware.html # from scrapy_deltafetch.middleware import DeltaFetch import logging import re from scrapy.exceptions import IgnoreRequest, NotConfigured logger = logging.getLogger(__name__) class Fake404Error(IgnoreRequest): """A fake 404 page response was found and filtered""" def __init__(self, response, *args, **kwargs): self.response = response super(Fake404Error, self).__init__(*args, **kwargs) class Fake404(object): """Spider middleware to drop pages iff they are that annoyance on the web: the 404 'not found' response returned as a branded page with HTTP code 200 (which should indicate success). This should not be necessary, both because such behaviour is improper on behalf of webservers, and because we are literally crawling the sites' OWN LIST OF VALID PAGES. Nevertheless, foxnews.com does it and others might. """ def __init__(self, settings): if not settings.getbool('FAKE404_ENABLED'): raise NotConfigured # List of ( url re object, matching xpath ) tuples detsigs = settings.get('FAKE404_DETECTIONSIGS') self.detectionsigs = [(re.compile(x), y) for x, y in detsigs] @classmethod def from_crawler(cls, crawler): return cls(crawler.settings) def process_spider_input(self, response, spider): for regex, xp in self.detectionsigs: if regex.match(response.url): if response.xpath(xp): raise Fake404Error(response, 'Ignoring "not found" response ' 'with success HTTP code') return None # Success def process_spider_exception(self, response, exception, spider): if isinstance(exception, Fake404Error): spider.crawler.stats.inc_value('fake404/response_ignored_count') logger.info( 'Ignoring response from %(response)r: Ignoring "not found" ' 'response with success HTTP code', {'response': response}, extra={'spider': spider}, ) return []

34.450704

88

0.647588

284

2,446

5.482394

0.517606

0.030829

0.055234

0

0.018931

0.26574

2,446

70

89

34.942857

0.847996

0.343009

0

0.143974

0.033225

0

1

0.147059

false

0

0.088235

0.029412

0.382353

0

null

0

null

0

1

0

c1e6cca7b165fead74a5bacc1f44b552bb2b6d04

954

py

Python

tests/test_import.py

sandeep937/dvc

b3df249c9f9ae0ff2b54f58845d7c2479be86232

[ "Apache-2.0" ]

null

tests/test_import.py

sandeep937/dvc

b3df249c9f9ae0ff2b54f58845d7c2479be86232

[ "Apache-2.0" ]

null

tests/test_import.py

sandeep937/dvc

b3df249c9f9ae0ff2b54f58845d7c2479be86232

[ "Apache-2.0" ]

null

import os from uuid import uuid4 from dvc.main import main from tests.basic_env import TestDvc class TestCmdImport(TestDvc): def test(self): ret = main(['import', self.FOO, 'import']) self.assertEqual(ret, 0) self.assertTrue(os.path.exists('import.dvc')) ret = main(['import', 'non-existing-file', 'import']) self.assertNotEqual(ret, 0) def test_unsupported(self): ret = main(['import', 'unsupported://path', 'import_unsupported']) self.assertNotEqual(ret, 0) class TestDefaultOutput(TestDvc): def test(self): tmpdir = self.mkdtemp() filename = str(uuid4()) tmpfile = os.path.join(tmpdir, filename) with open(tmpfile, 'w') as fd: fd.write('content') ret = main(['import', tmpfile]) self.assertEqual(ret, 0) self.assertTrue(os.path.exists(filename)) self.assertEqual(open(filename).read(), 'content')

26.5

74

0.619497

113

954

5.20354

0.380531

0.085034

0.088435

0.061224

0.153061

0

0.00823

0.235849

954

35

75

27.257143

0.798354

0

0.24

0

0.119497

0

0.28

1

0.12

false

0

0.4

0

0.6

0

null

0

null

0

1

0

1

c1e77797557582d8cf0f019777241dcacc17a322

1,620

py

Python

wapps/factories/identity.py

apihackers/wapps

e8158747aa3d77246d41142580faf9a5f2b0d968

[ "MIT" ]

7

2018-01-17T20:26:59.000Z

2022-03-23T08:12:00.000Z

wapps/factories/identity.py

apihackers/wapps

e8158747aa3d77246d41142580faf9a5f2b0d968

[ "MIT" ]

511

2017-10-21T17:59:50.000Z

2022-03-28T18:49:21.000Z

wapps/factories/identity.py

apihackers/wapps

e8158747aa3d77246d41142580faf9a5f2b0d968

[ "MIT" ]

2

2018-05-02T08:27:42.000Z

2020-08-17T18:42:49.000Z

import factory from wapps.models import IdentitySettings from .image import ImageFactory, SvgFileField from .site import SiteFactory from .tag import TagFactory class IdentityFactory(factory.DjangoModelFactory): site = factory.SubFactory(SiteFactory) name = factory.Faker('word') description = factory.Faker('paragraph') @factory.post_generation def tags(self, create, extracted, **kwargs): if not create: # pragma: nocover # Simple build, do nothing. return if extracted: # A list of tags were passed in, use them. if isinstance(extracted, int): tags = TagFactory.create_batch(extracted) else: tags = extracted for tag in tags: self.tags.add(tag) class Meta: model = IdentitySettings django_get_or_create = ['site'] class FullIdentityFactory(IdentityFactory): tags = 3 logo = factory.SubFactory(ImageFactory) svg_logo = SvgFileField() favicon = factory.SubFactory(ImageFactory) amp_logo = factory.SubFactory(ImageFactory) email = factory.Faker('email') telephone = factory.Faker('phone_number') address_1 = factory.Faker('street_address') post_code = factory.Faker('postalcode') city = factory.Faker('city') country = factory.Faker('country') facebook = factory.Faker('user_name') twitter = factory.Faker('user_name') linkedin = factory.Faker('uri') instagram = factory.Faker('user_name') pinterest = factory.Faker('user_name') youtube = factory.Faker('user_name')

30.566038

57

0.661728

173

1,620

6.104046

0.479769

0.159091

0.075758

0.094697

0

0.001626

0.240741

1,620

52

58

31.153846

0.856911

0.050617

0

0.076271

0

1

0.02439

false

0

0.121951

0

0.731707

0

null

0

null

0

1

0

1

c1e80041ebc0d75ee3fd1571a9a3316026d87a13

4,405

py

Python

app/views/recommend.py

HunterLC/FARSystem

a8b91fcd1914e84dd2ec2b8321c51627779bb89b

[ "Apache-2.0" ]

null

app/views/recommend.py

HunterLC/FARSystem

a8b91fcd1914e84dd2ec2b8321c51627779bb89b

[ "Apache-2.0" ]

null

app/views/recommend.py

HunterLC/FARSystem

a8b91fcd1914e84dd2ec2b8321c51627779bb89b

[ "Apache-2.0" ]

null

from flask import Blueprint, redirect, url_for from flask import request from flask import render_template from flask import session from sqlalchemy import and_ from .. import db from app.models import Actors, Users, Likes from ..recommend import Recommend recommend_blue = Blueprint('recommend', __name__) @recommend_blue.route('/', methods=['GET', 'POST']) def start(): if request.method == "GET": likes = db.session.query(Likes).all() user_item = Recommend(sim_algorithm=0, top_k_user=3, top_k_actor=3, user_id=session['userid'], users_like=likes).run_collaborative_filtering() # 猜你喜欢 guess_actor = [] for key, value in user_item.items(): actor = db.session.query(Actors).filter(Actors.actor_id == int(key)).all()[0] guess_actor.append(actor) db.session.close() for guess in guess_actor: guess.actor_c_name = guess.actor_c_name.split(' ')[0] guess.actor_img = guess.actor_img.split('/')[-1] return render_template("recommend.html", Guess=guess_actor) @recommend_blue.route('/recommendActor', methods=['GET', 'POST']) def recommend_actor(): if request.method == "GET": # 存储用户输入的数据 feature_dict = {} # 电影类型 film_type_select = request.args.get('filmTypeSelect') feature_dict[film_type_select] = 1 # 演员性别 actor_gender = request.args.get('genderSelect') if actor_gender != "": feature_dict['actor_gender'] = actor_gender # 演员年龄段 actor_age_group = request.args.get('ageSelect') feature_dict['actor_age_group'] = actor_age_group # 演员所属地域 actor_birthplace_faction = request.args.get('areaSelect') if actor_birthplace_faction != "": feature_dict['actor_birthplace_faction'] = actor_birthplace_faction # 是否国际化 actor_international = request.args.get('internationalSelect') if actor_international != "": feature_dict['actor_international'] = actor_international # 是否多职业 actor_multi_career = request.args.get('multiCareerSelect') if actor_multi_career != "": feature_dict['actor_multi_career'] = actor_multi_career # 演员星座 actor_horoscope_code = request.args.get('horoscopeSelect') if actor_horoscope_code != "": feature_dict['actor_horoscope_code'] = actor_horoscope_code # 关心星率 star_rate_select = request.args.get('starRateSelect') if star_rate_select != "": feature_dict[star_rate_select] = 1 # 平均评分 actor_avg_films_score = request.args.get('range_avg_score') feature_dict['actor_avg_films_score'] = actor_avg_films_score # 电影总数 actor_film_sum = request.args.get('range_total_films') feature_dict['actor_film_sum'] = actor_film_sum # 获奖总数 actor_award_sum = request.args.get('range_total_awards') feature_dict['actor_award_sum'] = actor_award_sum # 平均评论数 actor_avg_comments_sum = request.args.get('range_avg_comments') feature_dict['actor_avg_comments_sum'] = actor_avg_comments_sum result = Recommend(r'E:\PythonCode\FARSystem\static\data\actor_similarity_data.csv', current_actor=1314124, like_actors=[1314124], input_dict=feature_dict).run() # 猜你喜欢 guess_actor = [] for key, value in result.items(): actor = db.session.query(Actors).filter(Actors.actor_id == int(key)).all()[0] guess_actor.append(actor) db.session.close() result_list = {} i = 1 for guess in guess_actor: result_dict = {} result_dict[str(guess.actor_id)] = {} result_dict[str(guess.actor_id)]['img'] = guess.actor_img.split('/')[-1] result_dict[str(guess.actor_id)]['name'] = guess.actor_c_name.split(' ')[0] result_list[str(i)] = result_dict print(result_dict) i += 1 print(result_list) return result_list @recommend_blue.route('/a', methods=['GET', 'POST']) def a(): likes = db.session.query(Likes).all() db.session.close() user_item = Recommend(sim_algorithm=0, top_k_user=3, top_k_actor=3, user_id=session['userid'], users_like=likes).run() return user_item

40.787037

115

0.638138

535

4,405

4.938318

0.241122

0.06056

0.063588

0.028766

0.293717

0.260787

0.174868

0.135503

0

0.009023

0.245176

4,405

107

116

41.168224

0.785564

0.019296

0

0.202381

0

0.118494

0.02974

0

1

0.035714

false

0

0.095238

0

0.166667

0.047619

0

null

0

null

0

1

0

c1ea027732ae7e7ee9cb06e19892037f93c8cab4

1,946

py

Python

neutron/tests/unit/agent/ovsdb/native/test_connection.py

SUSE-Cloud/neutron

879665d3041e74df4b287b4c18b88288850cf11c

[ "Apache-2.0" ]

null

neutron/tests/unit/agent/ovsdb/native/test_connection.py

SUSE-Cloud/neutron

879665d3041e74df4b287b4c18b88288850cf11c

[ "Apache-2.0" ]

null

neutron/tests/unit/agent/ovsdb/native/test_connection.py

SUSE-Cloud/neutron

879665d3041e74df4b287b4c18b88288850cf11c

[ "Apache-2.0" ]

null

# Copyright 2015, Red Hat, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import mock from ovsdbapp.backend.ovs_idl import connection from ovsdbapp.backend.ovs_idl import idlutils from neutron.agent.ovsdb.native import connection as native_conn from neutron.agent.ovsdb.native import helpers from neutron.tests import base class TestOVSNativeConnection(base.BaseTestCase): @mock.patch.object(connection, 'threading') @mock.patch.object(idlutils, 'wait_for_change') @mock.patch.object(native_conn, 'idl') @mock.patch.object(helpers, 'enable_connection_uri') @mock.patch.object(idlutils, 'get_schema_helper') def test_do_get_schema_helper_retry(self, mock_get_schema_helper, mock_enable_conn, mock_idl, mock_wait_for_change, mock_threading): mock_helper = mock.Mock() # raise until 3rd retry attempt mock_get_schema_helper.side_effect = [Exception(), Exception(), mock_helper] conn = connection.Connection(idl_factory=native_conn.idl_factory, timeout=mock.Mock()) conn.start() self.assertEqual(3, len(mock_get_schema_helper.mock_calls)) mock_helper.register_all.assert_called_once_with()

43.244444

78

0.658787

239

1,946

5.1841

0.493724

0.048426

0.060533

0.046005

0.140436

0.103309

0

0.007018

0.267729

1,946

44

79

44.227273

0.862456

0.31295

0

0.049168

0.015885

0

0.08

1

0.04

false

0

0.24

0

0.32

0

null

0

null

0

1

0

c1ed06289f200a78cf33c1b5fc0ea50a0834297a

663

py

Python

ml-agents-envs1/mlagents/envs/communicator_objects/__init__.py

MikeWise2718/ml-agents-mod

c8393b7973be329b79cda70a7140d734205013f9

[ "Apache-2.0" ]

null

ml-agents-envs1/mlagents/envs/communicator_objects/__init__.py

MikeWise2718/ml-agents-mod

c8393b7973be329b79cda70a7140d734205013f9

[ "Apache-2.0" ]

null

ml-agents-envs1/mlagents/envs/communicator_objects/__init__.py

MikeWise2718/ml-agents-mod

c8393b7973be329b79cda70a7140d734205013f9

[ "Apache-2.0" ]

null

from .agent_action_pb2 import * from .agent_info_pb2 import * from .brain_parameters_pb2 import * from .command_pb2 import * from .compressed_observation_pb2 import * from .custom_action_pb2 import * from .custom_observation_pb2 import * from .custom_reset_parameters_pb2 import * from .demonstration_meta_pb2 import * from .engine_configuration_pb2 import * from .environment_parameters_pb2 import * from .header_pb2 import * from .space_type_pb2 import * from .unity_input_pb2 import * from .unity_rl_initialization_input_pb2 import * from .unity_rl_initialization_output_pb2 import * from .unity_rl_input_pb2 import * from .__init__ import *

34.894737

50

0.809955

92

663

5.380435

0.293478

0.309091

0.446465

0.145455

0.319192

0.157576

0

0.029668

0.135747

663

18

51

36.833333

0.834206

0

1

0

true

0

1

0

1

0

null

1

0

null

0

1

0

1

0

1

0

5

c1ed087977b1a3a244c379b67768158ed7b31bba

3,031

py

Python

setup.py

satvidh/batch-scoring

13da21e813da3e757526b9c51f7dd1fe2b224603

[ "BSD-3-Clause" ]

null

setup.py

satvidh/batch-scoring

13da21e813da3e757526b9c51f7dd1fe2b224603

[ "BSD-3-Clause" ]

null

setup.py

satvidh/batch-scoring

13da21e813da3e757526b9c51f7dd1fe2b224603

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python import sys import codecs import os.path import re from setuptools import setup, find_packages extra = {} def read_requirements_file(file): fname = os.path.join(os.path.abspath(os.path.dirname(__file__)), file) with open(fname, 'r') as r: return r.readlines() install_requires = read_requirements_file('requirements-base.txt') # Since futures 3.2 [1], the package enforces to be installed only in Python 2 # environments because it's basically a backport of Python 3's built-in # package. So in order to support both Python 2 and Python 3 environments, we # have to skip installation of futures package in case of Python 3. # # It might look natural to use environment markers [2] to achieve this goal but # they are new and were introduced in setuptools in mid of 2017. FWIW, # setuptools on both Ubuntu Trusty and Ubuntu Xenial do not support them and # batch scoring script may be installed in pretty outdated envs. So let's do it # old-fashioned way by adding condition here. # # The above is implemented by splitting dependencies into 2 files: # `requirements-base.txt` - common deps for Py3 and Py2 # `requirements-py27.txt` - for Python 2 only # # [1] https://github.com/agronholm/pythonfutures/commit/d0393ad626d25622927bb0ed47d35ddb2f6cd321 # noqa: E501 # [2] https://www.python.org/dev/peps/pep-0508/#environment-markers if sys.version_info[0] < 3: install_requires.extend( read_requirements_file('requirements-py27.txt') ) extra['entry_points'] = { 'console_scripts': [ 'batch_scoring = datarobot_batch_scoring.main:main', 'batch_scoring_sse = datarobot_batch_scoring.main:main_standalone', 'batch_scoring_deployment_aware = datarobot_batch_scoring.main:main_deployment_aware' ]} extra['install_requires'] = install_requires this_directory = os.path.abspath(os.path.dirname(__file__)) init_fname = os.path.join(this_directory, 'datarobot_batch_scoring', '__init__.py') with codecs.open(init_fname, 'r', 'latin1') as fp: try: version = re.findall(r"^__version__ = '([^']+)'\r?$", fp.read(), re.M)[0] except IndexError: raise RuntimeError('Unable to determine version.') readme_fname = os.path.join(this_directory, 'README.rst') with codecs.open(readme_fname, 'r', 'utf-8') as f: long_description = f.read() setup( name='datarobot_batch_scoring', version=version, description=("A script to score CSV files via DataRobot's prediction API"), long_description=long_description, author='DataRobot', author_email='support@datarobot.com', maintainer='DataRobot', maintainer_email='support@datarobot.com', license='BSD', url='http://www.datarobot.com/', packages=find_packages(), classifiers=[ 'License :: OSI Approved :: Apache Software License', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', ], **extra )

34.83908

109

0.709997

412

3,031

5.072816

0.468447

0.051675

0.050239

0.021531

0.097129

0.055502

0.028708

0

0.026432

0.176179

3,031

86

110

35.244186

0.810573

0.325965

0

0.358379

0.141869

0

1

0.018868

false

0

0.09434

0

0.132075

0

null

0

null

0

1

0

c1ed569418f14a7b151160b01f80064045a30cb9

407

py

Python

evap/results/urls.py

PFischbeck/EvaP

33c4f07186966e0ef2636174aec1e6f330a91226

[ "MIT" ]

null

evap/results/urls.py

PFischbeck/EvaP

33c4f07186966e0ef2636174aec1e6f330a91226

[ "MIT" ]

null

evap/results/urls.py

PFischbeck/EvaP

33c4f07186966e0ef2636174aec1e6f330a91226

[ "MIT" ]

null

from django.urls import path from evap.results import views app_name = "results" urlpatterns = [ path("", views.index, name="index"), path("semester/<int:semester_id>/evaluation/<int:evaluation_id>", views.evaluation_detail, name="evaluation_detail"), path("evaluation/<int:evaluation_id>/text_answers_export", views.evaluation_text_answers_export, name="evaluation_text_answers_export"), ]

31.307692

140

0.766585

52

407

5.730769

0.384615

0.110738

0.171141

0.167785

0

0.09828

407

12

141

33.916667

0.811989

0

0.407862

0.336609

0

1

0

false

0

0.25

0

0.25

0

null

0

1

0

null

0

1

c1eef54b8313d4e6d9e3bc6391a17c5d907262af

2,513

py

Python

Backend/home/home.py

davematias/PortfolioBackendV2

8fedb0cae038dda4d5c06cd2b24e17dcfee614ce

[ "MIT" ]

null

Backend/home/home.py

davematias/PortfolioBackendV2

8fedb0cae038dda4d5c06cd2b24e17dcfee614ce

[ "MIT" ]

9

2020-09-07T07:14:00.000Z

2022-02-18T09:55:16.000Z

Backend/home/home.py

davematias/PortfolioV2

8fedb0cae038dda4d5c06cd2b24e17dcfee614ce

[ "MIT" ]

null

import os import smtplib, ssl from email.message import EmailMessage from typing import Tuple from flask import Blueprint, jsonify, request from flask_jwt_extended import jwt_required from utils import dynamo site_blueprint = Blueprint('site', __name__,) @site_blueprint.route('/profile', methods=['GET']) def profile(): user = __getProfile() if not user: return jsonify({'error': 'User does not exist'}), 404 return jsonify(user) @site_blueprint.route('/contact', methods=['POST']) def contact(): req_data = request.get_json() if not req_data: return jsonify({'error': 'Invalid form data'}), 400 isValid, err = __validateMessage(req_data) if not isValid: return jsonify({'error': err}), 400 try: emailServer = os.getenv('EMAIL_SERVER') emailPort = os.getenv('EMAIL_PORT') emailSender = os.getenv('EMAIL_SENDER') emailSenderPassword = os.getenv('EMAIL_SENDER_PASSWORD') emailReceiver = os.getenv('EMAIL_RECEIVER') msg = EmailMessage() msg.set_content(__createMessage(req_data)) msg['Subject'] = "Contact From Personal Website" msg['From'] = emailSender msg['To'] = emailReceiver context = ssl.create_default_context() with smtplib.SMTP_SSL(emailServer, int(emailPort), timeout=10, context=context) as server: server.login(emailSender, emailSenderPassword) server.send_message(msg) server.close() return jsonify(success=True) except Exception as e: print(e) return jsonify({'error': 'Unable to send the email'}), 500 def __getProfile() -> dict: table = dynamo.getTable(os.getenv('PROFILE_TABLE')) result = table.scan(Limit=1) item = None if 'Items' in result.keys() and len(result['Items']) > 0: item = result['Items'][0] return item def __validateMessage(emailData: dict) -> Tuple[bool, str]: if 'name' not in emailData.keys(): return False, 'Name is required' if 'email' not in emailData.keys(): return False, 'Email is required' if 'subject' not in emailData.keys(): return False, 'Subject is required' if 'message' not in emailData.keys(): return False, 'Message is required' return True, '' def __createMessage(emailData: dict) -> str: return 'Name: {}\nEmail: {} \nSubject: {} \nMessage: {}'.format(emailData['name'], emailData['email'], emailData['subject'], emailData['message'])

28.556818

150

0.653004

296

2,513

5.412162

0.375

0.048689

0.040574

0.044944

0.072409

0

0.008682

0.220852

2,513

88

150

28.556818

0.809499

0

0.163087

0.008353

0

1

0.081967

false

0.032787

0.114754

0.016393

0.409836

0.081967

0

null

0

null

0

1

0

c1ef6cd665d8c946e334fa3d830567b1386f5e61

312

py

Python

src/helper/helper.py

JosephSalomon/GN-Core

9baf2c49bb176a4c9ffc76ab9feb823f7ca7bd20

[ "MIT" ]

null

src/helper/helper.py

JosephSalomon/GN-Core

9baf2c49bb176a4c9ffc76ab9feb823f7ca7bd20

[ "MIT" ]

null

src/helper/helper.py

JosephSalomon/GN-Core

9baf2c49bb176a4c9ffc76ab9feb823f7ca7bd20

[ "MIT" ]

null

###***********************************### ''' Grade Notifier File: helper.py Author: Ehud Adler Core Maintainers: Ehud Adler, Akiva Sherman, Yehuda Moskovits Copyright: Copyright 2019, Ehud Adler License: MIT ''' ###***********************************### def print_to_screen(text): print("RENDER::" + text)

20.8

44

0.541667

31

312

5.387097

0.774194

0.161677

0

0.014493

0.115385

312

14

45

22.285714

0.59058

0.746795

0

0.133333

0

1

0.5

false

0

0.5

1

0

null

0

null

0

1

0

1

0

2

c1eff3a6449550c9247cbee5b8aedcd969ea87e9

1,845

py

Python

src/nmrezman/phase01/train/train_findings.py

mozzilab/NM_Radiology_AI

8df83c14e88534142f43411e33913682eab26582

[ "MIT" ]

1

2022-03-17T12:28:12.000Z

src/nmrezman/phase01/train/train_findings.py

mozzilab/NM_Radiology_AI

8df83c14e88534142f43411e33913682eab26582

[ "MIT" ]

null

src/nmrezman/phase01/train/train_findings.py

mozzilab/NM_Radiology_AI

8df83c14e88534142f43411e33913682eab26582

[ "MIT" ]

null

# %% # nmrezman from .general import train_findings_model # Misc import argparse # %% desc_str = "Train Phase 01 Findings vs No Findings Model" def get_args_parser(): parser = argparse.ArgumentParser(description=desc_str, add_help=False) # Paths parser.add_argument( "--data_path", type=str, required=True, help="Path to dataframe file (e.g., \"/path/to/data/reports_df.gz\")." ) parser.add_argument( "--glove_embedding_path", type=str, required=True, help="Path to GloVe word vector glove.6B.300d file (e.g., \"/path/to/data/glove.6B.300d.txt\")." ) # Output file names parser.add_argument( "--model_checkpoint_name", type=str, required=True, help="Path / filename to save model checkpoints (e.g., \"/path/to/results/phase01/findings/findings_best_model.h5\")." ) parser.add_argument( "--result_fname", type=str, required=True, help="Path / filename to save model evaluation metrics (e.g., \"/path/to/results/phase01/findings/findings_best_result.log\")." ) parser.add_argument( "--tokenizer_fname", type=str, required=True, help="Path / filename to save tokenizer (e.g., \"/path/to/results/phase01/findings/tokenizer.gz\")." ) return parser if __name__ == "__main__": # Parse the arguments parser = argparse.ArgumentParser(desc_str, parents=[get_args_parser()]) args = parser.parse_args() # Train the findings vs no findings model train_findings_model( data_path=args.data_path, glove_embedding_path=args.glove_embedding_path, model_checkpoint_name=args.model_checkpoint_name, result_fname=args.result_fname, tokenizer_fname=args.tokenizer_fname, )

26.73913

135

0.645528

228

1,845

4.995614

0.307018

0.036874

0.074627

0.083407

0.355575

0.311677

0.283582

0.257243

0.199298

0.125549

0

0.012006

0.23252

1,845

68

136

27.132353

0.792373

0.055827

0

0.326087

0

0.220554

0.025982

0

1

0.021739

false

0

0.043478

0

0.086957

0

null

0

null

0

1

0

c1f0856e892c8089aea911b5d6e0ad351d8e20d8

1,474

py

Python

setup.py

olricson/remotefreebox

16e2a42ed7cfcfd1ab303184280564eeace77919

[ "BSD-2-Clause" ]

14

2015-01-04T22:14:07.000Z

2020-11-11T18:53:20.000Z

setup.py

olricson/remotefreebox

16e2a42ed7cfcfd1ab303184280564eeace77919

[ "BSD-2-Clause" ]

3

2017-11-08T14:28:32.000Z

2021-08-30T21:58:04.000Z

setup.py

olricson/remotefreebox

16e2a42ed7cfcfd1ab303184280564eeace77919

[ "BSD-2-Clause" ]

7

2015-03-17T12:43:09.000Z

2020-05-10T23:47:35.000Z

# Always prefer setuptools over distutils from setuptools import setup, find_packages # To use a consistent encoding from codecs import open from os import path here = path.abspath(path.dirname(__file__)) # Get the long description from the README file with open(path.join(here, 'README.md'), encoding='utf-8') as f: long_description = f.read() setup( name='remotefreebox', version='0.3.1', description='A Python module to control a Freebox v6 remotely', long_description=long_description, # The project's main homepage. url='https://github.com/MaximeCheramy/remotefreebox', # Author details author='Maxime Chéramy and Francois Guibert', author_email='maxime.cheramy@gmail.com', # Choose your license license='BSD', # See https://pypi.python.org/pypi?%3Aaction=list_classifiers classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience :: Developers', 'License :: OSI Approved :: BSD License', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.2', 'Programming Language :: Python :: 3.3', 'Programming Language :: Python :: 3.4', ], # What does your project relate to? keywords='freebox remote control rudp hid', # You can just specify the packages manually here if your project is # simple. Or you can use find_packages(). packages=find_packages(), install_requires=['zeroconf>=0.17'] )

29.48

72

0.677069

185

1,474

5.324324

0.589189

0.060914

0.101523

0.105584

0.054822

0

0.014617

0.210991

1,474

49

73

30.081633

0.83233

0.257123

0

0.440959

0.02214

0

1

0

false

0

0.107143

0

0.107143

0

null

0

null

0

1

0

c1f1080c356b5d730e19c3e67bf76fb079c62c16

2,237

py

Python

model_main.py

yuxuan1995liu/darkflowyolo_detection

a7807e9b85833e3f877d46bb60e8fa7d0596a10b

[ "MIT" ]

null

model_main.py

yuxuan1995liu/darkflowyolo_detection

a7807e9b85833e3f877d46bb60e8fa7d0596a10b

[ "MIT" ]

null

model_main.py

yuxuan1995liu/darkflowyolo_detection

a7807e9b85833e3f877d46bb60e8fa7d0596a10b

[ "MIT" ]

null

import matplotlib.pyplot as plt import numpy as np from darkflow.net.build import TFNet import cv2 import pprint as pp import os # options = {"model": "cfg/custum_yolo.cfg", # "batch": 8, # "load": "bin/yolo.weights", # "epoch": 3, # "trainer":"adam", # "gpu": 1.0, # "train": True, # "annotation": "train/train_anno/", # "dataset": "train/train_img/"} # tfnet = TFNet(options) #tfnet.load_from_ckpt() options = {"model": "cfg/custum_yolo.cfg", "load": -1, "batch": 16, "epoch": 4, "gpu": 1.0, "train": True, "annotation": "train/train_anno/", "dataset": "train/train_img/"} tfnet = TFNet(options) # #tfnet.load_from_ckpt() tfnet.train() tfnet.savepb() #prediction img_names = os.listdir('test/test_img/') cnt_valid = 0 for names in img_names: if names[-1] =='g': original_img = cv2.imread("test/test_img/"+names) original_img = cv2.cvtColor(original_img, cv2.COLOR_BGR2RGB) results = tfnet.return_predict(original_img) print(results) if results !=[]: cnt_valid+=1 print(cnt_valid) # original_img = cv2.imread("test/test_img/00029843_001.png") # original_img = cv2.cvtColor(original_img, cv2.COLOR_BGR2RGB) # results = tfnet.return_predict(original_img) def boxing(original_img , predictions): newImage = np.copy(original_img) for result in predictions: print(result) top_x = result['topleft']['x'] top_y = result['topleft']['y'] print(top_x,top_y) btm_x = result['bottomright']['x'] btm_y = result['bottomright']['y'] print(btm_x,btm_y) confidence = result['confidence'] print(confidence) label = result['label'] + " " + str(round(confidence, 3)) if confidence > 0.1: newImage = cv2.rectangle(newImage, (top_x, top_y), (btm_x, btm_y), (255,0,0), 3) newImage = cv2.putText(newImage, label, (top_x, top_y-5), cv2.FONT_HERSHEY_COMPLEX_SMALL , 0.8, (0, 230, 0), 1, cv2.LINE_AA) return newImage # fig, ax = plt.subplots(figsize=(20, 10)) # ax.imshow(boxing(original_img, results)) # plt.show()

28.679487

136

0.599911

292

2,237

4.417808

0.342466

0.093798

0.065116

0.018605

0.382946

0.32093

0.272868

0

0.034911

0.244524

2,237

78

137

28.679487

0.728402

0.281627

0

0.114052

0

1

0.022222

false

0

0.133333

0

0.177778

0.155556

0

null

0

null

0

1

0

c1f245590fbe23acc3fd6f32e6491551e7d43e6a

7,275

py

Python

train.py

angelorodem/tensorflow2-char-rnn

f28503c61de62eade9b477bf13573988fb3807de

[ "MIT" ]

null

train.py

angelorodem/tensorflow2-char-rnn

f28503c61de62eade9b477bf13573988fb3807de

[ "MIT" ]

null

train.py

angelorodem/tensorflow2-char-rnn

f28503c61de62eade9b477bf13573988fb3807de

[ "MIT" ]

null

from __future__ import absolute_import, division, print_function, unicode_literals import argparse import pickle from colorama import init, Fore init(autoreset=True) def str2bool(v): if isinstance(v, bool): return v if v.lower() in ('yes', 'true', 't', 'y', '1'): return True elif v.lower() in ('no', 'false', 'f', 'n', '0'): return False else: raise argparse.ArgumentTypeError('Boolean value expected.') parser = argparse.ArgumentParser(description='List of avaible commands.') parser.add_argument('--data_path',dest="path", type=str, nargs='?', help='Path to learning data') parser.add_argument('--save_path',dest="save", type=str, nargs='?', help='Path to checkpoint storage') parser.add_argument('--epochs',dest="epochs",metavar="100", type=int, nargs='?', help='Number of training epochs', default=100) parser.add_argument('--n_batch',dest="batch",metavar="64", type=int, nargs='?', help='Batch size', default=64) parser.add_argument('--n_units',dest="units",metavar="512", type=int, nargs='?', help='Number of LSTM Units', default=512) parser.add_argument('--n_layers',dest="layers",metavar="3", type=int, nargs='?', help='Number of LSTM Layers', default=3) parser.add_argument('--n_sequence',dest="seq",metavar="100", type=int, nargs='?', help='The maximum length sentence for a single input in characters', default=100) parser.add_argument('--n_embedding',dest="embedding",metavar="128", type=int, nargs='?', help='The embedding dimension size', default=128) parser.add_argument("--continue",dest="cont",metavar="False", type=str2bool, nargs='?',const=True, default=False,help="Continue from last save.") args = parser.parse_args() import tensorflow as tf import numpy as np import os import time def save_model_configs(directory, params): path = os.path.join(directory, "parameters.bin") dumped = pickle.dumps(params) f = open(path, 'wb+') f.write(dumped) def load_model_configs(directory): path = os.path.join(directory, "parameters.bin") return pickle.loads(open(path,'rb').read()) def split_input_target(chunk): input_text = chunk[:-1] target_text = chunk[1:] return input_text, target_text def build_model(vocab_size, embedding_dim, rnn_units, batch_size, nl): layers = [] layers.append(tf.keras.layers.Embedding(vocab_size, embedding_dim, batch_input_shape=[batch_size, None])) for n in range(nl): layers.append(tf.keras.layers.LSTM(rnn_units, return_sequences=True, stateful=True, recurrent_initializer='glorot_uniform')) layers.append(tf.keras.layers.Dense(vocab_size)) model = tf.keras.Sequential(layers) return model @tf.function def train_step(inp, target): with tf.GradientTape() as tape: predictions = model(inp) loss = tf.reduce_mean( tf.keras.losses.sparse_categorical_crossentropy( target, predictions, from_logits=True)) grads = tape.gradient(loss, model.trainable_variables) optimizer.apply_gradients(zip(grads, model.trainable_variables)) return loss text = open(args.path, 'rb').read().decode(encoding='utf-8') vocab = sorted(set(text)) checkpoint_dir = args.save checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt_{epoch}") confs = None if os.path.exists(args.save) and args.cont: print(Fore.LIGHTGREEN_EX + '[Loading existent configurations]') try: confs = load_model_configs(args.save) embedding_dim = confs['embedding'] rnn_units = confs['units'] n_layers = confs['layers'] except Exception as e: print(Fore.RED + 'Error loading checkpoint ' + str(e)) confs = None elif args.cont: if not os.path.exists(args.save): os.mkdir(args.save) print(Fore.RED + '[Directory created]') print(Fore.RED + '[No configurations to load]') if confs is None: embedding_dim = args.embedding rnn_units = args.units n_layers = args.layers # Creating a mapping from unique characters to indices char2idx = {u:i for i, u in enumerate(vocab)} idx2char = np.array(vocab) text_as_int = np.array([char2idx[c] for c in text]) # The maximum length sentence we want for a single input in characters seq_length = args.seq examples_per_epoch = len(text)//seq_length # Create training examples / targets char_dataset = tf.data.Dataset.from_tensor_slices(text_as_int) sequences = char_dataset.batch(seq_length+1, drop_remainder=True) dataset = sequences.map(split_input_target) # Batch size BATCH_SIZE = args.batch steps_per_epoch = examples_per_epoch//BATCH_SIZE # Buffer size to shuffle the dataset # (TF data is designed to work with possibly infinite sequences, # so it doesn't attempt to shuffle the entire sequence in memory. Instead, # it maintains a buffer in which it shuffles elements). BUFFER_SIZE = 10000 dataset = dataset.shuffle(BUFFER_SIZE).batch(BATCH_SIZE, drop_remainder=True) # Length of the vocabulary in chars vocab_size = len(vocab) model = build_model( vocab_size = len(vocab), embedding_dim=embedding_dim, rnn_units=rnn_units, batch_size=BATCH_SIZE, nl=n_layers) if os.path.exists(args.save) and args.cont: print(Fore.LIGHTBLUE_EX + '[Loading existent checkpoint]') try: model.load_weights(tf.train.latest_checkpoint(checkpoint_dir)) except Exception as e: print(Fore.RED + 'Error loading checkpoint ' + str(e)) elif args.cont: print(Fore.RED + '[No checkpoints to load]') if confs is None: embedding_dim = args.embedding rnn_units = args.units n_layers = args.layers EPOCHS=args.epochs if confs is None: confs = { 'units': args.units, 'embedding': args.embedding, 'layers': args.layers, 'vocab_size': vocab_size, 'char2idx': char2idx, 'idx2char': idx2char, } save_model_configs(args.save, confs) model.summary() print (Fore.CYAN + 'Length of text: {} characters'.format(len(text))) print (Fore.CYAN + '{} unique characters'.format(len(vocab))) optimizer = tf.keras.optimizers.Adam() train_start = time.time() for epoch in range(EPOCHS): start = time.time() # initializing the hidden state at the start of every epoch # initally hidden is None hidden = model.reset_states() for (batch_n, (inp, target)) in enumerate(dataset): loss = train_step(inp, target) if batch_n % 100 == 0: template = Fore.LIGHTYELLOW_EX + 'Epoch [{}/{}] Batch [{}/{}] Loss {}' print(template.format(epoch+1,EPOCHS, batch_n, steps_per_epoch, loss)) # saving (checkpoint) the model every 5 epochs if (epoch + 1) % 5 == 0: model.save_weights(checkpoint_prefix.format(epoch=epoch)) print (Fore.LIGHTYELLOW_EX + '[Model saved]\n') print (Fore.LIGHTWHITE_EX + '\n[Epoch {} Loss {:.4f}]'.format(epoch+1, loss)) print (Fore.GREEN + '[Time taken for 1 epoch {} sec]\n'.format(time.time() - start)) print (Fore.LIGHTGREEN_EX + '\n[Total time {} mins]\n'.format((time.time() - train_start)/60)) model.save_weights(checkpoint_prefix.format(epoch=epoch))

33.680556

101

0.677938

992

7,275

4.836694

0.272177

0.024385

0.031888

0.020008

0.189037

0.164652

0.12005

0.093372

0.073364

0

0.010859

0.189828

7,275

215

102

33.837209

0.80319

0.076014

0

0.160256

0

0.143858

0

1

0.038462

false

0

0.051282

0

0.134615

0.096154

0

null

0

null

0

1

0

c1f29d841c4f62a411df976675d972c35cfa098c

35

py

Python

python/testData/completion/fStringLikeCompletionNotAvailableInByteLiterals.py

alexey-anufriev/intellij-community

ffcd46f14e630acdefcc76e2bfc7c43d2449013a

[ "Apache-2.0" ]

2

2019-04-28T07:48:50.000Z

2020-12-11T14:18:08.000Z

python/testData/completion/fStringLikeCompletionNotAvailableInByteLiterals.py

alexey-anufriev/intellij-community

ffcd46f14e630acdefcc76e2bfc7c43d2449013a

[ "Apache-2.0" ]

1

2020-07-30T19:04:47.000Z

python/testData/completion/fStringLikeCompletionNotAvailableInByteLiterals.py

bradleesand/intellij-community

750ff9c10333c9c1278c00dbe8d88c877b1b9749

[ "Apache-2.0" ]

1

2020-10-15T05:56:42.000Z

my_expr = 42 s = b'foo{my_e<caret>'

17.5

22

0.657143

9

35

2.333333

0.888889

0

0.066667

0.142857

35

2

22

17.5

0.633333

0

0.416667

0

1

0

false

0

1

0

null

0

1

0

null

0

3

c1f2c8a367fb5a9ac50a001ff98f24d08a3663e9

1,023

py

Python

spark-ml-workshop/SparkCustomMLExample/src/main/python/train.py

Code360In/spark-code-examples

181c9906d32571ba6138e63040edfcb4c74ef4bf

[ "MIT" ]

null

spark-ml-workshop/SparkCustomMLExample/src/main/python/train.py

Code360In/spark-code-examples

181c9906d32571ba6138e63040edfcb4c74ef4bf

[ "MIT" ]

null

spark-ml-workshop/SparkCustomMLExample/src/main/python/train.py

Code360In/spark-code-examples

181c9906d32571ba6138e63040edfcb4c74ef4bf

[ "MIT" ]

null

#!/usr/bin/python3 import pandas as pd import pickle import sys import base64 import re from sklearn.linear_model import LinearRegression # Here we keep input data to Dataframe constructor rows = [] for line in sys.stdin: line = line.replace('[', '') line = line.replace(']', '') line = line.replace('\n', '') line = re.split('[,]', line) line_dict = {} for i, value in enumerate(line): if i != len(line) - 1: name = "feature_" + str(i) else: name = "label" line_dict[name] = value rows.append(line_dict) # Initialize a dataframe from the list df = pd.DataFrame(rows) feature_columns = [] for i in range(0, len(df.columns) - 1): feature_columns.append("feature_" + str(i)) label_column = "label" model = LinearRegression() model.fit(df[feature_columns], df[label_column]) model_string = base64.b64encode(pickle.dumps(model)).decode('utf-8') # Output to stdin, so that rdd.pipe() can return the string to pipedRdd. print(model_string)

23.790698

72

0.654936

143

1,023

4.594406

0.48951

0.048706

0.068493

0.057839

0.068493

0

0.01358

0.208211

1,023

42

73

24.357143

0.797531

0.170088

0

0.04497

0

1

0

false

0

0.206897

0

0.206897

0.034483

0

null

0

null

0

1

0

c1f2d237f1126654af0b97b1caec7c54058b92fe

15,956

py

Python

models.py

Eudorajab1/jasmin_smsc_gui

c0a240e61791e167f23c39eb4f2663e27294af0d

[ "MIT" ]

9

2021-04-29T19:37:34.000Z

2022-03-28T23:44:54.000Z

models.py

Eudorajab1/jasmin_smsc_gui

c0a240e61791e167f23c39eb4f2663e27294af0d

[ "MIT" ]

1

2022-03-28T23:46:35.000Z

models.py

Eudorajab1/jasmin_smsc_gui

c0a240e61791e167f23c39eb4f2663e27294af0d

[ "MIT" ]

2

2021-11-02T08:57:09.000Z

2022-02-01T14:04:57.000Z

""" This file defines the database models """ from .common import db, Field from pydal.validators import * from yatl.helpers import * from py4web.utils.form import Form, FormStyleBulma MTROUTE_TYPES = ('DefaultRoute', 'StaticMTRoute', 'RandomRoundrobinMTRoute','FailoverMTRoute') MOROUTE_TYPES = ('DefaultRoute', 'StaticMORoute', 'RandomRoundrobinMORoute','FailoverMORoute') HTTP_CON_TYPE =('GET', 'POST') MT_CON_TYPES =('smppc', 'httpc') MT_FILTER_TYPES=('DestinationAddrFilter','UserFilter','GroupFilter','SourceAddrFilter','ShortMessageFilter','DateIntervalFilter','TimeIntervalFilter','TagFilter','TransparentFilter') MO_FILTER_TYPES=('DestinationAddrFilter','SourceAddrFilter','ConnectorFilter','ShortMessageFilter','DateIntervalFilter','TimeIntervalFilter','TagFilter','EvalPyFilter','TransparentFilter') IMO_TYPES=('DefaultInterceptor', 'StaticMOInterceptor') IMT_TYPES=('DefaultInterceptor', 'StaticMTInterceptor') db.define_table('mt_filter', Field('fid', 'string', length=15, label='FID', comment='Filter ID must be unique'), Field('filter_type', requires=IS_IN_SET(MT_FILTER_TYPES), comment='Select from list of available types'), Field('filter_route'), Field('f_value', 'string', length = 50, label='Filter Value', comment='Values must correspond to filter type'), format='%(fid)s') db.define_table('j_imo', Field('motype', label='Type',requires=IS_IN_SET(IMO_TYPES),comment='Type of interceptor'), Field('moorder',label='Order',comment='Interceptor will evaluate in descending order'), Field('mofilters', 'list:reference mt_filter', requires=IS_IN_DB(db,'mt_filter.id','mt_filter.fid',multiple=True),label='Filter(s)', comment='Filters need to be added prior to adding routes. Please see filter management'), Field('moscript', label='Script',comment='Path and script must exist. Only python 3 scripts allowed now')) db.define_table('j_imt', Field('mttype', requires=IS_IN_SET(IMT_TYPES), label='Type', comment='Type of interceptor'), Field('mtorder', label='Order', comment='Interceptor will evaluate in descending order'), Field('mtfilters', 'list:reference mt_filter',requires=IS_IN_DB(db,db.mt_filter._id, db.mt_filter.fid ,multiple=True),label='Filter(s)', comment='Filters need to be added prior to adding routes. Please see filter management'), Field('mtscript', label='Script', comment='Path and script must exist. Only python 3 scripts allowed now')) db.define_table('j_group', Field('name','string',length = 10, comment='Must be a string with no spaces or special characters'), format='%(name)s') db.define_table('j_user', Field('username', 'string', length=10, comment="Jasmin User Name for HTTP and SMPP connecting. Must not include any spaces and can not be longer than 10 characters"), Field('password', 'string', length=10, comment='Jasmin Password for HTTP and SMPP connecting. Must not include any spaces and can not be longer than 10 characters'), Field('j_uid','string',label='Jasmin UID',length=12, comment='Jasmin UID cannot be longer than 12 characters and reccoment all in UPPER case. No spaces allowed. Suggest USER_1 etc.'), Field('j_group','reference j_group',label = 'Jasim GID', comment='Select a Group', requires=IS_IN_DB(db,'j_group.id','j_group.name')), format='%(name)s') db.define_table('j_user_cred', Field('juser', 'string',label='Jasmin UID', length = 10), Field('default_src_addr', default='None', comment='Default source address of SMS-MT'), Field('quota_http_throughput',default='ND', comment='Max. number of messages per second to accept through HTTP API'), Field('quota_balance',default = 'ND', comment='c.f. 1. Balance quota'), Field('quota_smpps_throughput',default = 'ND', comment='Max. number of messages per second to accept through SMPP Server'), Field('quota_sms_count', default='ND', comment='c.f. 2. sms_count quota'), Field('quota_early_percent', default='ND', comment='c.f. Asynchronous billing'), Field('value_priority',default='^[0-3]$', comment='Regex pattern to validate priority of SMS-MT'), Field('value_content',default='.*', comment='Regex pattern to validate content of SMS-MT'), Field('value_src_addr', default='.*', comment='Regex pattern to validate source address of SMS-MT'), Field('value_dst_addr', default='.*', comment='Regex pattern to validate destination address of SMS-MT'), Field('value_validity_period', default='^\d+$', comment='Regex pattern to validate validity_period of SMS-MT'), Field('author_http_send',default=True, comment='Privilege to send SMS through Sending SMS-MT'), Field('author_http_dlr_method', default=True, comment='Privilege to set dlr-method HTTP parameter (default is GET)'), Field('author_http_balance', default= True, comment='Privilege to check balance through Checking account balance'), Field('author_smpps_send',default= True, comment='Privilege to send SMS through SMPP Server API'), Field('author_priority', default= True, comment='Privilege to defined priority of SMS-MT (default is 0)'), Field('author_http_long_content', default= True, comment='Privilege to send long content SMS through Sending SMS-MT'), Field('author_src_addr', default= True, comment='Privilege to defined source address of SMS-MT'), Field('author_dlr_level', default= True, comment='Privilege to set dlr-level parameter (default is 1)'), Field('author_http_rate', default =True, comment='Privilege to check a message rate through Checking rate price'), Field('author_validity_period', default=True, comment='Privilege to defined validity_period of SMS-MT (default is NOT SET)'), Field('author_http_bulk', default= False, comment='Privilege to send bulks through http api (Not implemented yet)'), format = '%(juser)s') db.define_table('mo_filter', Field('fid', 'string', length=15, unique=True), Field('filter_type', requires=IS_IN_SET(MO_FILTER_TYPES)), Field('f_value', 'string', length = 50), format='%(name)s') db.define_table('connector', Field('name','string',length=15, label='Connector ID',comment='Connector ID must be unique on each gateway', requires=[IS_LENGTH(minsize=1,maxsize=15),IS_NOT_IN_DB(db, 'connector.name')]), Field('c_logfile', label = 'Logfile',default='/var/log/jasmin/default-<cid>.log'), Field('c_logrotate', label = 'Log Rotate', default='midnight', comment='When to rotate the log file, possible values: S=Seconds, M=Minutes, H=Hours, D=Days, W0-W6=Weekday (0=Monday) and midnight=Roll over at midnight'), Field('c_loglevel', label = 'Log Level',default='20', comment='Logging numeric level: 10=DEBUG, 20=INFO, 30=WARNING, 40=ERROR, 50=CRITICCAL'), Field('c_host', label = 'Host',default='127.0.0.1', comment='Server that runs SMSC'), Field('c_port', label = 'Port',default='2775', comment='The port number for the connection to the SMSC'), Field('c_ssl', label = 'SSL', default='no', comment='Activate ssl connection'), Field('c_username', 'string', label = 'User name',length=15, comment='User name max 12 characters with no spaces'), Field('c_password', 'string', length=15, label = 'Password', comment='Password max 12 characters with no spaces'), Field('c_bind', label = 'Bind Type', requires=IS_IN_SET(('transceiver', 'transmitter', 'receiver')), default='transceiver', comment='Bind type: transceiver, receiver or transmitter'), Field('c_bind_to', label = 'Bind To', default='30', comment='Timeout for response to bind request'), Field('c_trx_to', label = 'Transmit Timeout',default='300', comment='Maximum time lapse allowed between transactions, after which, the connection is considered as inactive and will reconnect'), Field('c_res_to', label = 'Response Timeout',default='60', comment='Timeout for responses to any request PDU'), Field('c_pdu_red_to', label = 'PDU Read Timeout',default='10', comment='Timeout for reading a single PDU, this is the maximum lapse of time between receiving PDU’s header and its complete read, if the PDU reading timed out, the connection is considered as ‘corrupt’ and will reconnect'), Field('c_con_loss_retry', label = 'Coonection Loss Retry', default='yes', comment='Reconnect on connection loss ? (yes, no)'), Field('c_con_loss_delay', label = 'Connection Loss Delay',default='10', comment='Reconnect delay on connection loss (seconds)'), Field('c_con_fail_retry', label = 'Connection Fail Retry',default='yes', comment='Reconnect on connection failure ? (yes, no)'), Field('c_con_fail_delay', label = 'Connection Fail Delay',default='10', comment='Reconnect delay on connection failure (seconds)'), Field('c_src_addr', label = 'Default Source Address',default='Not defined', comment='Default source adress of each SMS-MT if not set while sending it, can be numeric or alphanumeric, when not defined it will take SMSC default'), Field('c_src_ton', label = 'Source TON',default='2', comment='Source address TON setting for the link: 0=Unknown, 1=International, 2=National, 3=Network specific, 4=Subscriber number, 5=Alphanumeric, 6=Abbreviated'), Field('c_src_npi', label = 'Source NPI',default='1', comment='Source address NPI setting for the link: 0=Unknown, 1=ISDN, 3=Data, 4=Telex, 6=Land mobile, 8=National, 9=Private, 10=Ermes, 14=Internet, 18=WAP Client ID'), Field('c_dst_ton', label = 'Destination TON',default='1', comment='Destination address TON setting for the link: 0=Unknown, 1=International, 2=National, 3=Network specific, 4=Subscriber number, 5=Alphanumeric, 6=Abbreviated'), Field('c_dst_npi', label = 'Destination NPI',default='1', comment='Destination address NPI setting for the link: 0=Unknown, 1=ISDN, 3=Data, 4=Telex, 6=Land mobile, 8=National, 9=Private, 10=Ermes, 14=Internet, 18=WAP Client ID'), Field('c_bind_ton', label = 'Bind TON',default='0', comment='Bind address TON setting for the link: 0=Unknown, 1=International, 2=National, 3=Network specific, 4=Subscriber number, 5=Alphanumeric, 6=Abbreviated'), Field('c_bind_npi', label = 'Bind NPI',default='1', comment='Bind address NPI setting for the link: 0=Unknown, 1=ISDN, 3=Data, 4=Telex, 6=Land mobile, 8=National, 9=Private, 10=Ermes, 14=Internet, 18=WAP Client ID'), Field('c_validity', label = 'Validtiy',default='Not defined', comment='Default validity period of each SMS-MT if not set while sending it, when not defined it will take SMSC default (seconds)'), Field('c_priority', label = 'Priority',default='0', comment='SMS-MT default priority if not set while sending it: 0, 1, 2 or 3'), Field('c_requeue_delay', label = 'Requeue Delay',default='120', comment='Delay to be considered when requeuing a rejected message'), Field('c_addr_range', label = 'Address Range',default='Not defined', comment='Indicates which MS’s can send messages to this connector, seems to be an informative value'), Field('c_systype', label = 'System Type',default='Not defined', comment='The system_type parameter is used to categorize the type of ESME that is binding to the SMSC. Examples include “VMS” (voice mail system) and “OTA” (over-the-air activation system)'), Field('c_dlr_expiry', label = 'DLR Expiry',default='86400', comment='When a SMS-MT is not acked, it will remain waiting in memory for expiry seconds, after this period, any received ACK will be ignored'), Field('c_submit_throughput', label = 'Submit Throughput',default='1', comment='Active SMS-MT throttling in MPS (Messages per second), set to 0 (zero) for unlimited throughput'), Field('c_proto_id', label = 'Protocol',default='0', comment='Used to indicate protocol id in SMS-MT and SMS-MO'), Field('c_coding',label = 'Coding',default='0', comment='Default coding of each SMS-MT if not set while sending it: 0=SMSC Default, 1=IA5 ASCII, 2=Octet unspecified, 3=Latin1, 4=Octet unspecified common, 5=JIS, 6=Cyrillic, 7=ISO-8859-8, 8=UCS2, 9=Pictogram, 10=ISO-2022-JP, 13=Extended Kanji Jis, 14=KS C 5601'), Field('c_elink_interval',label = 'Elink',default='30', comment='Enquire link interval (seconds)'), Field('c_def_msg_id',label = 'Default Msg ID',default='0', comment='Specifies the SMSC index of a pre-defined (‘canned’) message'), Field('c_ripf',label = 'Replace If Present',default='0', comment='Replace if present flag: 0=Do not replace, 1=Replace'), Field('c_dlr_msgid',label = 'DLR MsgID',default='0', comment='Indicates how to read msg id when receiving a receipt: 0=msg id is identical in submit_sm_resp and deliver_sm, 1=submit_sm_resp msg-id is in hexadecimal base, deliver_sm msg-id is in decimal base, 2=submit_sm_resp msg-id is in decimal base'), format='%(name)s') db.define_table('http_cons', Field('hcon_cid','string',length=10,label='Connector ID', comment= 'Must be unique'), Field('hcon_method', label='Method', comment='GET/POST',requires = IS_IN_SET(HTTP_CON_TYPE)), Field('hcon_url',label='Base URL', comment='URL for MO messages e.g http://10.10.20.125/receive-sms/mo.php'), format='%(hcon_cid)s') db.define_table('mtroute', Field('mt_order', 'string', length=10, label='Route order', requires=IS_NOT_EMPTY(), comment='Routes will be assesd in descending order based on filters and matches'), Field('mt_type', requires = IS_IN_SET(MTROUTE_TYPES), label='Route type'), Field('mt_connectors', 'list:reference connector', label='SMPP Connector(s)', comment='SMPP connector needs to be available'), Field('mt_filters', 'list:reference mt_filter',label='Filter(s)', comment='Filters need to be added prior to adding routes. Please see filter management'), Field('mt_rate','string',length = 10, label='Rate', comment='Decimal rate value for the connector. All messages going over this connector will be charged at the rate specified'), format='%(mt_order)s') db.define_table('moroute', Field('mo_order', 'string', length=10, label='Route order',comment='Routes will be assesd in descending order based on filters and matches'), Field('mo_type', requires = IS_IN_SET(MOROUTE_TYPES), label='Route type'), Field('mo_connectors', 'list:reference connector', requires=IS_IN_DB(db,'connector.id','connector.name',multiple=True), label='SMPP Connector(s)', comment='SMPP connector needs to be available'), Field('mo_http_cons', 'list:reference http_cons', requires=IS_IN_DB(db,'http_cons.id','http_hcons-hcons_cid', multiple=True), label='HTTP Connector(s)', comment='HTTP connector needs to be available'), Field('mo_filters', 'list:reference mt_filter', requires=IS_IN_DB(db,'mt_filter.id','mt_filter.fid',multiple=True), label='Filter(s)', comment='Filters need to be added prior to adding routes. Please see filter management'), format='%(mo_order)s') db.commit()

109.287671

327

0.675859

2,187

15,956

4.821674

0.204847

0.021053

0.015932

0.025605

0.429872

0.346799

0.296159

0.246657

0.219346

0.206923

0

0.017591

0.191276

15,956

146

328

109.287671

0.799597

0.002319

0

0.031496

0

0.15748

0.57598

0.0159

0

1

0

false

0.015748

0.031496

0

0.031496

0

null

0

1

0

null

0

1

c1f4638cedf017f2f1d8ccd8e4707001e8f8c9ff

27

py

Python

Python POO/main.py

lucasjlgc/Python-POO

56c98e7abb47a0268396f1981e58a0a2441db4fe

[ "MIT" ]

null

Python POO/main.py

lucasjlgc/Python-POO

56c98e7abb47a0268396f1981e58a0a2441db4fe

[ "MIT" ]

null

Python POO/main.py

lucasjlgc/Python-POO

56c98e7abb47a0268396f1981e58a0a2441db4fe

[ "MIT" ]

null

from pessoa import Pessoa

9

25

0.814815

4

27

5.5

0.75

0

0.185185

27

2

26

13.5

1

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

c1f4b67816e30d74cbc32d55dadcfb14a56793f0

1,043

py

Python

concurrent/futures/config.py

mikhtonyuk/rxpython

cfdd38225a3b7960bd475c6a4e380f3dd6a6a0fe

[ "MIT" ]

2

2015-11-25T15:56:04.000Z

2018-11-19T13:31:49.000Z

concurrent/futures/config.py

sergiimk/rxpython

cfdd38225a3b7960bd475c6a4e380f3dd6a6a0fe

[ "MIT" ]

null

concurrent/futures/config.py

sergiimk/rxpython

cfdd38225a3b7960bd475c6a4e380f3dd6a6a0fe

[ "MIT" ]

null

import traceback import logging logger = logging.getLogger(__package__) def log_error_handler(cls, tb): try: logger.error('Future/Task exception was never retrieved:\n%s', ''.join(tb)) except: pass class Default(object): # Called when failure of the future was not handled by any callback # This includes exceptions in on_success and on_failure callbacks UNHANDLED_FAILURE_CALLBACK = staticmethod(log_error_handler) # Default executor for future callbacks CALLBACK_EXECUTOR = None @staticmethod def get_callback_executor(): if not Default.CALLBACK_EXECUTOR: from .cooperative.synchronous_executor import Synchronous Default.CALLBACK_EXECUTOR = Synchronous return Default.CALLBACK_EXECUTOR @staticmethod def on_unhandled_error(exc): tb = traceback.format_exception(exc.__class__, exc, exc.__traceback__) Default.UNHANDLED_FAILURE_CALLBACK(exc.__class__, tb)

28.972222

71

0.684564

115

1,043

5.895652

0.504348

0.117994

0.10177

0

0.255034

1,043

35

72

29.8

0.872587

0.160115

0

0.086957

0

0.052752

0

1

0.130435

false

0.043478

0.130435

0

0.434783

0

null

0

null

0

1

0

c1f564366e13f51ec035c82bf8d6c69e97064976

292

py

Python

mytest.py

chalendony/duden

bf455452ed68b4a7f39b45fec05c7236afef36e1

[ "MIT" ]

null

mytest.py

chalendony/duden

bf455452ed68b4a7f39b45fec05c7236afef36e1

[ "MIT" ]

null

mytest.py

chalendony/duden

bf455452ed68b4a7f39b45fec05c7236afef36e1

[ "MIT" ]

null

import duden def main(): # find the correct url # get definition and examples w1 = duden.get('einfach_einmal_simpel') # remove beispiel code to get the meanings??? print(w1.meaning_example) # change the depth, include code if __name__ == '__main__': main()

16.222222

49

0.660959

38

292

4.789474

0.763158

0

0.009091

0.246575

292

17

50

17.176471

0.818182

0.421233

0

0.176829

0.128049

0

1

0.166667

false

0

0.166667

0

0.333333

0.166667

0

null

0

null

0

1

0

c1f5fa22f45bcd7c67412cd5116711d5de10540b

1,275

py

Python

setup.py

gocept/gocept.download

205fc9d2f6c9dabc3081897ebb7cbaac31737f29

[ "ZPL-2.1" ]

1

2020-07-17T10:05:23.000Z

setup.py

gocept/gocept.download

205fc9d2f6c9dabc3081897ebb7cbaac31737f29

[ "ZPL-2.1" ]

null

setup.py

gocept/gocept.download

205fc9d2f6c9dabc3081897ebb7cbaac31737f29

[ "ZPL-2.1" ]

null

"""zc.buildout recipe for downloading and extracting an archive.""" from setuptools import setup, find_packages name = "gocept.download" classifiers = [ "Environment :: Console", "Environment :: Plugins", "Framework :: Buildout", "Intended Audience :: Developers", "Intended Audience :: System Administrators", "License :: OSI Approved :: Zope Public License", "Operating System :: OS Independent", "Programming Language :: Python", "Topic :: Software Development :: Build Tools", "Topic :: System :: Software Distribution", ] setup( name = name, version = '1.0dev', author = "Christian Theune", author_email = "ct@gocept.com", description = __doc__.strip(), long_description = open("README.txt").read(), license = "ZPL 2.1", keywords = "buildout zc.buildout recipe download extract archive", classifiers = classifiers, url = "https://bitbucket.org/gocept/%s/" % name, packages = find_packages("src"), include_package_data = True, package_dir = {"": "src"}, namespace_packages = ["gocept"], install_requires = ["zc.buildout", "setuptools"], extras_require = {"test": ["zope.testing"]}, entry_points = {"zc.buildout": ["default = %s:Recipe" % name,],}, )

31.875

70

0.644706

130

1,275

6.207692

0.669231

0.049566

0.039653

0

0.003937

0.203137

1,275

39

71

32.692308

0.790354

0.047843

0

0.465232

0

1

0

false

0

0.030303

0

0.030303

0

null

0

null

0

1

0

c1f67abeeac600fa8d3d5268d75de3463965e9bb

144

py

Python

500.py

TebbaaX/DownTime-Score

204f1f69e6ad46c36cc7d3a56483f4e51bf6c29b

[ "MIT" ]

4

2021-05-08T00:13:47.000Z

2021-05-10T00:47:00.000Z

500.py

TebbaaX/DownTime-Score

204f1f69e6ad46c36cc7d3a56483f4e51bf6c29b

[ "MIT" ]

1,449

2021-10-12T20:20:57.000Z

2022-03-31T11:40:41.000Z

500.py

TebbaaX/DownTime-Score

204f1f69e6ad46c36cc7d3a56483f4e51bf6c29b

[ "MIT" ]

1

2021-09-15T21:41:54.000Z

import time print ("this is a 500 years python dummy file") print ("see you after 500 years") time.sleep(15768000000) print ("Time Travel!")

28.8

48

0.722222

23

144

4.521739

0.73913

0.153846

0

0.141667

0.166667

144

5

49

28.8

0.725

0

0.510638

0

1

0

true

0

0.2

0

0.2

0.6

1

0

null

0

1

0

null

0

1

0

1

0

4

c1f7f53e370043972065de6da7367e0c0230a78e

489

py

Python

pyleecan/Methods/Slot/Slot/comp_radius_mid_active.py

IrakozeFD/pyleecan

5a93bd98755d880176c1ce8ac90f36ca1b907055

[ "Apache-2.0" ]

95

2019-01-23T04:19:45.000Z

2022-03-17T18:22:10.000Z

pyleecan/Methods/Slot/Slot/comp_radius_mid_active.py

IrakozeFD/pyleecan

5a93bd98755d880176c1ce8ac90f36ca1b907055

[ "Apache-2.0" ]

366

2019-02-20T07:15:08.000Z

2022-03-31T13:37:23.000Z

pyleecan/Methods/Slot/Slot/comp_radius_mid_active.py

IrakozeFD/pyleecan

5a93bd98755d880176c1ce8ac90f36ca1b907055

[ "Apache-2.0" ]

74

2019-01-24T01:47:31.000Z

2022-02-25T05:44:42.000Z

# -*- coding: utf-8 -*- def comp_radius_mid_active(self): """Compute the radius at the middle of the active part of the slot Parameters ---------- self : Slot A Slot object Returns ------- Rmw: float Mid active radius [m] """ Rbo = self.get_Rbo() Hslot = self.comp_height() Hwind = self.comp_height_active() if self.is_outwards(): return Rbo + Hslot - Hwind / 2 else: return Rbo - Hslot + Hwind / 2

18.807692

70

0.556237

63

489

4.190476

0.539683

0.090909

0.106061

0.143939

0.151515

0

0.008982

0.316973

489

25

71

19.56

0.781437

0.394683

0

1

0.125

false

0

0.375

0

null

0

null

0

1

0

c1f7fea5aff1bf06616d7084eb6453de5655b0d4

9,044

py

Python

lib/roi_data_rel/minibatch_rel.py

champon1020/TRACE

8ed0aed87e153af66f02502887a4de0d39867209

[ "MIT" ]

34

2021-08-19T05:59:58.000Z

2022-03-26T09:26:54.000Z

lib/roi_data_rel/minibatch_rel.py

champon1020/TRACE

8ed0aed87e153af66f02502887a4de0d39867209

[ "MIT" ]

8

2021-09-15T05:27:23.000Z

2022-02-27T12:38:03.000Z

lib/roi_data_rel/minibatch_rel.py

champon1020/TRACE

8ed0aed87e153af66f02502887a4de0d39867209

[ "MIT" ]

6

2021-09-16T10:51:38.000Z

2022-03-05T22:48:54.000Z

# Adapted by Ji Zhang in 2019 # # Based on Detectron.pytorch/lib/roi_data/minibatch.py written by Roy Tseng import numpy as np import cv2 import os from core.config import cfg import utils.blob as blob_utils import roi_data.rpn def get_minibatch_blob_names(is_training=True): """Return blob names in the order in which they are read by the data loader. """ # data blob: holds a batch of N images, each with 3 channels blob_names = ['data', 'all_frames', 'bf_cur_len', 'f_scale'] if cfg.RPN.RPN_ON: # RPN-only or end-to-end Faster R-CNN blob_names += roi_data.rpn.get_rpn_blob_names(is_training=is_training) elif cfg.RETINANET.RETINANET_ON: raise NotImplementedError else: # Fast R-CNN like models trained on precomputed proposals blob_names += roi_data.fast_rcnn.get_fast_rcnn_blob_names( is_training=is_training ) return blob_names def get_minibatch(roidb): """Given a roidb, construct a minibatch sampled from it.""" # We collect blobs from each image onto a list and then concat them into a # single tensor, hence we initialize each blob to an empty list blobs = {k: [] for k in get_minibatch_blob_names()} # Get the input image blob im_blob, im_scales, all_frames_blob, bf_cur_len, f_scale = _get_image_blob(roidb) blobs['data'] = im_blob blobs['all_frames'] = all_frames_blob blobs['bf_cur_len'] = bf_cur_len blobs['f_scale'] = f_scale if cfg.RPN.RPN_ON: # RPN-only or end-to-end Faster/Mask R-CNN valid = roi_data.rpn.add_rpn_blobs(blobs, im_scales, roidb) elif cfg.RETINANET.RETINANET_ON: raise NotImplementedError else: # Fast R-CNN like models trained on precomputed proposals valid = roi_data.fast_rcnn.add_fast_rcnn_blobs(blobs, im_scales, roidb) # add relpn blobs add_relpn_blobs(blobs, im_scales, roidb) return blobs, valid def add_relpn_blobs(blobs, im_scales, roidb): assert 'roidb' in blobs valid_keys = ['dataset_name', 'sbj_gt_boxes', 'sbj_gt_classes', 'obj_gt_boxes', 'obj_gt_classes', 'prd_gt_classes', 'sbj_gt_overlaps', 'obj_gt_overlaps', 'prd_gt_overlaps', 'pair_to_gt_ind_map', 'width', 'height', 'file_name', 'pre_processed_temporal_roi', 'pre_processed_frames_rpn_ret'] ###!!! for i, e in enumerate(roidb): for k in valid_keys: if k in e: blobs['roidb'][i][k] = e[k] # Always return valid=True, since RPN minibatches are valid by design return True def _get_image_blob(roidb): """Builds an input blob from the images in the roidb at the specified scales. """ num_images = len(roidb) # Sample random scales to use for each image in this batch scale_inds = np.random.randint( 0, high=len(cfg.TRAIN.SCALES), size=num_images) processed_ims = [] im_scales = [] #roidb_file_name = [] for i in range(num_images): #im = cv2.imread(roidb[i]['image']) im = cv2.imread(roidb[i]['image'], cv2.IMREAD_COLOR) #print(roidb[i]['image'], im.shape) #roidb_file_name.append(int(roidb[i]['file_name'].split('.')[0])) assert im is not None, \ 'Failed to read image \'{}\''.format(roidb[i]['image']) # If NOT using opencv to read in images, uncomment following lines # if len(im.shape) == 2: # im = im[:, :, np.newaxis] # im = np.concatenate((im, im, im), axis=2) # # flip the channel, since the original one using cv2 # # rgb -> bgr # im = im[:, :, ::-1] if roidb[i]['flipped']: im = im[:, ::-1, :] target_size = cfg.TRAIN.SCALES[scale_inds[i]] im, im_scale = blob_utils.prep_im_for_blob( im, cfg.PIXEL_MEANS, [target_size], cfg.TRAIN.MAX_SIZE) im_scales.append(im_scale[0]) processed_ims.append(im[0]) # Create a blob to hold the input images [n, c, h, w] blob = blob_utils.im_list_to_blob(processed_ims) if (len(cfg.TRAIN.DATASETS) > 0 and \ cfg.TRAIN.DATASETS[0].find('vidvrd') >= 0) or \ (len(cfg.TEST.DATASETS) > 0 and \ cfg.TEST.DATASETS[0].find('vidvrd') >= 0): im_id_st = 0 elif (len(cfg.TRAIN.DATASETS) > 0 and \ cfg.TRAIN.DATASETS[0].find('ag') >= 0) or \ (len(cfg.TEST.DATASETS) > 0 and \ cfg.TEST.DATASETS[0].find('ag') >= 0): im_id_st = 1 else: im_id_st = 1 all_frames_blob, bf_cur_len, f_scale = get_frames_blob(roidb, \ num_images, scale_inds, im_id_st=im_id_st, half_frame_relative_path=cfg.HALF_FRAME_RELATIVE_PATH) ###! #print(blob.shape, all_frames_blob.shape) return blob, im_scales, all_frames_blob, bf_cur_len, f_scale def get_frames_blob(roidb, num_images, scale_inds, im_id_st=1, half_frame_relative_path=''): all_frames_blob = [] bf_cur_len = [] f_scale = [] if half_frame_relative_path == 'sampled_frames': sufix_class = '.jpg' else: sufix_class = '.png' for i in range(num_images): frame_full_name = roidb[i]['image'].split('/')[-1] frame_id = int(frame_full_name.split('.')[0]) tot_video_path_list = roidb[i]['image'].split('/') video_path_list = tot_video_path_list[:-3] video_path = '/' for j in video_path_list: video_path = os.path.join(video_path, j) #video_path = os.path.join(video_path, 'all_frames') video_path = os.path.join(video_path, half_frame_relative_path) ###!!! video_path = os.path.join(video_path, tot_video_path_list[-2]) processed_frames = [] start_f_id = frame_id - (cfg.HALF_NUMBER_OF_FRAMES + 1) * cfg.FRAMES_INTERVAL end_f_id = frame_id + (cfg.HALF_NUMBER_OF_FRAMES + 1) * cfg.FRAMES_INTERVAL process_frames_id = [] for j in range(frame_id, start_f_id, -cfg.FRAMES_INTERVAL): if j < im_id_st: break process_frames_id.append(j) process_frames_id = process_frames_id[::-1] process_frames_id = process_frames_id[:-1] for j in range(frame_id, end_f_id, cfg.FRAMES_INTERVAL): process_frames_id.append(j) off_set_f = 0 off_set_b = cfg.HALF_NUMBER_OF_FRAMES k = 0 for cnt, j in enumerate(process_frames_id): if j < im_id_st: continue frame_path = os.path.join(video_path, '{:06d}'.format(j)+sufix_class) if j == frame_id: off_set_f = k k = 0 #k = k+1 # #continue # frame_path = roidb[i]['image'] if os.path.exists(frame_path): im = cv2.imread(frame_path, cv2.IMREAD_COLOR) if roidb[i]['flipped']: im = im[:, ::-1, :] #target_size = cfg.TRAIN.SCALES[scale_inds[i]] target_size = cfg.TEMPORAL_SCALES im, f_scale_i = blob_utils.prep_im_for_blob( im, cfg.PIXEL_MEANS, [target_size], 1000) processed_frames.append(im[0]) k = k + 1 else: off_set_b = k - 1 break st = cfg.HALF_NUMBER_OF_FRAMES - off_set_f ed = cfg.HALF_NUMBER_OF_FRAMES + off_set_b if cfg.FPN.REL_FPN_ON: frames_blob = blob_utils.im_list_to_blob(processed_frames) else: #frames_blob = np.stack(processed_frames) frames_blob = np.array(processed_frames, dtype=np.float32) channel_swap = (0, 3, 1, 2) frames_blob = frames_blob.transpose(channel_swap) ##got_frames = np.zeros((2*cfg.HALF_NUMBER_OF_FRAMES+1, frames_blob.shape[1], frames_blob.shape[2], frames_blob.shape[3]), dtype=np.float32) ##got_frames[st:ed] = frames_blob.astype(np.float32) pad_st = max(0, st) #pad_ed = max(0, 2*cfg.HALF_NUMBER_OF_FRAMES + 1 - ed) pad_ed = max(0, 2*cfg.HALF_NUMBER_OF_FRAMES - ed) # f_scale.append(f_scale_i[0]) if (pad_st == 0 and pad_ed == 0) or num_images == 1: got_frames = frames_blob elif num_images != 1: got_frames = np.pad(frames_blob, ((pad_st,pad_ed), (0,0), (0,0), (0,0)),'constant',constant_values=0) if num_images != 1: bf_cur_len.append(cfg.HALF_NUMBER_OF_FRAMES) all_frames_blob.append(got_frames) else: bf_cur_len.append(off_set_f) all_frames_blob = np.expand_dims(got_frames, axis=0) if num_images != 1: all_frames_blob = np.stack(all_frames_blob) bf_cur_len = np.array(bf_cur_len, dtype=np.int32) f_scale = np.array(f_scale, dtype=np.float32) return all_frames_blob, bf_cur_len, f_scale

39.321739

148

0.604821

1,322

9,044

3.844175

0.186082

0.047226

0.01889

0.026564

0.389807

0.341401

0.286501

0.224715

0.200708

0.194805

0

0.0151

0.282397

9,044

230

149

39.321739

0.767951

0.194162

0

0.20915

0

0.056002

0.007485

0

0.013072

1

0.03268

false

0

0.039216

0

0.104575

0

null

0

null

0

1

0

c1f8caf0b7bccc8a2071b61868b5c37f051d87b7

1,825

py

Python

src/enamlnative/android/android_adapter.py

codelv/enaml-native

04c3a015bcd649f374c5ecd98fcddba5e4fbdbdc

[ "MIT" ]

237

2017-09-15T19:31:45.000Z

2022-03-17T04:22:20.000Z

src/enamlnative/android/android_adapter.py

codelv/enaml-native

04c3a015bcd649f374c5ecd98fcddba5e4fbdbdc

[ "MIT" ]

74

2017-09-06T20:16:41.000Z

2022-03-05T13:34:35.000Z

src/enamlnative/android/android_adapter.py

codelv/enaml-native

04c3a015bcd649f374c5ecd98fcddba5e4fbdbdc

[ "MIT" ]

22

2017-09-15T19:32:11.000Z

2022-03-17T18:33:39.000Z

""" Copyright (c) 2017, Jairus Martin. Distributed under the terms of the MIT License. The full license is in the file LICENSE, distributed with this software. Created on May 20, 2017 @author: jrm """ from atom.api import Typed, set_default from .android_view_group import AndroidViewGroup, ViewGroup from .bridge import JavaBridgeObject, JavaMethod, JavaCallback class ArrayAdapter(JavaBridgeObject): __nativeclass__ = set_default('android.widget.ArrayAdapter') __signature__ = set_default(('android.content.Context', 'android.R')) add = JavaMethod('java.lang.Object') addAll = JavaMethod('[Ljava.lang.Object;') remove = JavaMethod('java.lang.Object') clear = JavaMethod() class AdapterView(ViewGroup): __nativeclass__ = set_default('android.widget.AdapterView') setEmptyView = JavaMethod('android.view.View') setFocusableInTouchMode = JavaMethod('boolean') setOnItemClickListener = JavaMethod( 'android.widget.AdapterView$OnItemClickListener') setOnItemLongClickListener = JavaMethod( 'android.widget.AdapterView$OnItemLongClickListener') setOnItemSelectedListener = JavaMethod( 'android.widget.AdapterView$OnItemSelectedListener') setSelection = JavaMethod('int') onItemClick = JavaCallback('android.widget.AdapterView', 'android.view.View', 'int', 'long') onItemLongClick = JavaCallback('android.widget.AdapterView', 'android.view.View', 'int', 'long') onItemSelected = JavaCallback('android.widget.AdapterView', 'android.view.View', 'int', 'long') onNothingSelected = JavaCallback('android.widget.AdapterView') class AndroidAdapterView(AndroidViewGroup): #: Adapter reference adapter = Typed(ArrayAdapter)

34.433962

73

0.708493

167

1,825

7.634731

0.461078

0.091765

0.150588

0.112941

0.189804

0.136471

0

0.006734

0.186301

1,825

52

74

35.096154

0.851852

0.118356

0

0.1

0

0.3025

0.203125

0

1

0

false

0

0.1

0

0.8

0

null

0

null

0

1

0

c1f984744cc0b49bb53cce60881571379454c3b2

693

py

Python

instagram_api/interfaces/media/constraints.py

Yuego/instagram_api

b53f72db36c505a2eb24ebac1ba8267a0cc295bb

[ "MIT" ]

13

2019-08-07T21:24:34.000Z

2020-12-12T12:23:50.000Z

instagram_api/interfaces/media/constraints.py

Yuego/instagram_api

b53f72db36c505a2eb24ebac1ba8267a0cc295bb

[ "MIT" ]

null

instagram_api/interfaces/media/constraints.py

Yuego/instagram_api

b53f72db36c505a2eb24ebac1ba8267a0cc295bb

[ "MIT" ]

null

from abc import ABCMeta, abstractmethod, abstractproperty __all__ = ['ConstraintsInterface'] class ConstraintsInterface(metaclass=ABCMeta): @abstractproperty def title(self) -> str: ... @abstractproperty def min_aspect_ratio(self) -> float: ... @abstractproperty def max_aspect_ratio(self) -> float: ... @abstractproperty def recommended_ratio(self) -> float: ... @abstractproperty def recommended_ratio_deviation(self) -> float: ... @abstractproperty def use_recommended_ratio_by_default(self) -> bool: ... @abstractproperty def min_duration(self) -> float: ... @abstractproperty def max_duration(self) -> float: ...

22.354839

59

0.686869

65

693

7.061538

0.4

0.331155

0.272331

0.305011

0.374728

0.300654

0.202614

0

0.193362

693

30

60

23.1

0.821109

0

0.421053

0

0.02886

0

1

0.421053

false

0

0.052632

0

0.526316

0

null

1

0

null

0

1

0

1

0

5

c1f9b7556a7c50bccced5f81f9d0073842096cd3

21,532

py

Python

forms/models.py

CodeForAfrica/gmmp

d7ffe2dac16bd57e81bb3555ddea9df1fe7e9ebf

[ "Apache-2.0" ]

4

2020-01-05T09:14:19.000Z

2022-02-17T03:22:09.000Z

forms/models.py

CodeForAfrica/gmmp

d7ffe2dac16bd57e81bb3555ddea9df1fe7e9ebf

[ "Apache-2.0" ]

68

2019-12-23T02:19:55.000Z

2021-04-23T06:13:36.000Z

forms/models.py

CodeForAfrica/gmmp

d7ffe2dac16bd57e81bb3555ddea9df1fe7e9ebf

[ "Apache-2.0" ]

2

2020-11-07T12:23:21.000Z

2021-11-07T18:21:31.000Z

from collections import OrderedDict from django.db import models from django.utils.translation import gettext_lazy as _ from forms.modelutils import * # The following necessitated for some of the channges # https://code.djangoproject.com/ticket/19539 necessitated removal of __metaclass__ # get_fields_with_model was deprected and thus the move to fields def prepend_verbose(mydict, field_name, num, verbose_name=None): field = mydict[field_name] # Some field_name differ from the expected verbose_name. e.g for age, the desired verbose_name is AGE (PERSON APPEARS). # Instead of passing AGE (PERSON APPEARS) as the field name, we can instead pass it as the expected verbose_name field.verbose_name = f'({num}) {verbose_name if verbose_name else field_name}' # ---------------------------- # Newspaper # ---------------------------- def newspaper_journalist_meta (name, bases, mydict): dct = { 'sex' : bases[0]._meta.fields[0], 'age' : bases[0]._meta.fields[1], } prepend_verbose(dct, 'sex', '9') return type(name, bases, mydict) class NewspaperJournalist(Journalist, metaclass=newspaper_journalist_meta): newspaper_sheet = models.ForeignKey('NewspaperSheet', on_delete=models.CASCADE) class NewspaperPerson(Person): sex = field_sex(_('(10) Sex')) age = field_age(_('(11) Age (person appears)')) occupation = field_occupation(_('(12) Occupation or Position')) function = field_function(_('(13) Function in the news story')) family_role = field_family_role(_('(14) Family Role Given?')) victim_or_survivor = field_victim_or_survivor(_('(15) Does the story identify the person as either a victim or survivor?')) victim_of = field_victim_of(_('(16) The story identifies the person as a victim of:')) survivor_of = field_survivor_of(_('(17) The story identifies the person as a survivor of:')) is_quoted = field_is_quoted(_('(18) Is the person directly quoted')) is_photograph = field_is_photograph(_('(19) Is there a photograph of the person in the story?')) special_qn_1 = field_special_qn(_('(20) Special question (1)')) special_qn_2 = field_special_qn(_('(21) Special question (2)')) special_qn_3 = field_special_qn(_('(22) Special question (3)')) newspaper_sheet = models.ForeignKey('NewspaperSheet', on_delete=models.CASCADE) class NewspaperSheet(SheetModel): class Meta: verbose_name = _('Newspaper') created_at = models.DateTimeField(auto_now_add=True) updated_at = models.DateTimeField(auto_now=True) newspaper_name = models.CharField(max_length=255, verbose_name=_('Newspaper'), help_text=_('''Be as specific as possible. If the paper has different regional editions, write in the name of the edition you are monitoring - e.g. 'The Hindu - Delhi edition'.''')) page_number = models.PositiveIntegerField(verbose_name=_('(1) Page Number'), help_text=_('Write in the number of the page on which the story begins. Story appears on first page = 1, Seventh page = 7, etc.'), null=True, blank=True) covid19 = field_covid19(_('(z) Is this story related to coronavirus Covid-19?')) topic = field_topic(_('(2) Topic')) scope = field_scope(_('(3) Scope')) space = models.PositiveIntegerField(choices=SPACE, verbose_name=_('(4) Space'), null=True, blank=True) equality_rights = field_equality_rights(_('(5) Reference to gender equality / human rights legislation/ policy')) about_women = field_about_women(_('(6) Is the story about a particular woman or group of women?')) inequality_women = field_inequality_women(_('(7) This story clearly highlights issues of inequality between women and men')) stereotypes = field_stereotypes(_('(8) This story clearly challenges gender stereotypes')) further_analysis = field_further_analysis(_('(24) Does this story warrant further analysis?'), _(''' A story warrants further analysis if it clearly perpetuates or clearly challenges gender stereotypes, if it includes women's opinions in a remarkable way, if it contributes to an understanding of inequalities between women and men, if it mentions or calls attention to women's human rights, etc. Consult the guide for further explanation''')) comments = field_comments(_('(23) Describe any photographs included in the story and the conclusions you draw from them.')) def __str__(self): created_at = self.created_at.strftime("%Y-%m-%d") space = SPACE[self.space - 1][1].split(')')[1] if self.space else "" # Extract space title from SPACE tuple page = f" page {self.page_number}" if self.page_number else "" return f"{self.newspaper_name} {created_at}{page} {space}" # ---------------------------- # Radio # ---------------------------- class RadioPerson(Person): sex = field_sex(_('(10) Sex')) occupation = field_occupation(_('(11) Occupation or Position')) function = field_function(_('(12) Function in the news story')) family_role = field_family_role(_('(13) Family Role Given?')) victim_or_survivor = field_victim_or_survivor(_('(14) Does the story identify the person as either a victim or survivor?')) victim_of = field_victim_of(_('(15) The story identifies the person as a victim of:')) survivor_of = field_survivor_of(_('(16) The story identifies the person as a survivor of:')) special_qn_1 = field_special_qn(_('(17) Special question (1)')) special_qn_2 = field_special_qn(_('(18) Special question (2)')) special_qn_3 = field_special_qn(_('(19) Special question (3)')) radio_sheet = models.ForeignKey('RadioSheet', on_delete=models.CASCADE) def radio_journalist_meta(name, bases, mydict): dct = { 'sex' : bases[0]._meta.fields[0], 'role' : bases[0]._meta.fields[2], } prepend_verbose(dct, 'role', '8') prepend_verbose(dct, 'sex', '9') return type(name, bases, mydict) class RadioJournalist(BroadcastJournalist, metaclass=radio_journalist_meta): radio_sheet = models.ForeignKey('RadioSheet', on_delete=models.CASCADE) class RadioSheet(SheetModel): created_at = models.DateTimeField(auto_now_add=True) updated_at = models.DateTimeField(auto_now=True) channel = models.CharField(max_length=255, verbose_name=_('Channel'), help_text=_('''Be as specific as possible. E.g. if the radio company is called RRI, and if the newscast is broadcast on its third channel, write in 'RRI-3'.''')) start_time = models.TimeField(verbose_name=_('Time of Broadcast')) num_female_anchors = field_num_anchors(_('Number of female anchors')) num_male_anchors = field_num_anchors(_('Number of male anchors')) item_number = field_item_number(_('(1) Item Number')) covid19 = field_covid19(_('(z) Is this story related to coronavirus Covid-19?')) topic = field_topic(_('(2) Topic')) scope = field_scope(_('(3) Scope')) equality_rights = field_equality_rights(_('(4) Reference to gender equality / human rights legislation/ policy')) about_women = field_about_women(_('(5) Is the story about a particular woman or group of women?')) inequality_women = field_inequality_women(_('(6) This story clearly highlights issues of inequality between women and men')) stereotypes = field_stereotypes(_('(7) This story clearly challenges gender stereotypes')) further_analysis = field_further_analysis(_('(20) Does this story warrant further analysis?'), _(''' A story warrants further analysis if it clearly perpetuates or clearly challenges gender stereotypes, if it includes women's opinions in a remarkable way, if it contributes to an understanding of inequalities between women and men, if it mentions or calls attention to women's human rights, etc. Consult the guide for further explanation''')) comments = field_comments(_('(N/A) Describe any photographs included in the story and the conclusions you draw from them.')) def __str__(self): item_number = f" story {str(self.item_number)}" if self.item_number else "" return f"{self.channel} {str(self.start_time)}{item_number}" class Meta: verbose_name = _('Radio') # ---------------------------- # Television # ---------------------------- class TelevisionPerson(Person): sex = field_sex(_('(11) Sex')) age = field_age(_('(12) Age (person appears)')) occupation = field_occupation(_('(13) Occupation or Position')) function = field_function(_('(14) Function in the news story')) family_role = field_family_role(_('(15) Family Role Given?')) victim_or_survivor = field_victim_or_survivor(_('(16) Does the story identify the person as either a victim or survivor?')) victim_of = field_victim_of(_('(17) The story identifies the person as a victim of:')) survivor_of = field_survivor_of(_('(18) The story identifies the person as a survivor of:')) special_qn_1 = field_special_qn(_('(19) Special question (1)')) special_qn_2 = field_special_qn(_('(20) Special question (2)')) special_qn_3 = field_special_qn(_('(21) Special question (3)')) television_sheet = models.ForeignKey('TelevisionSheet', on_delete=models.CASCADE) def television_journalist_meta(name, bases, mydict): dct = { 'sex' : bases[0]._meta.fields[0], 'age' : bases[0]._meta.fields[1], 'role' : bases[0]._meta.fields[2], } prepend_verbose(dct, 'role', '8') prepend_verbose(dct, 'sex', '9') prepend_verbose(dct, 'age', '10', 'Age (Person Appears)') return type(name, bases, mydict) class TelevisionJournalist(BroadcastJournalist, metaclass=television_journalist_meta): television_sheet = models.ForeignKey('TelevisionSheet', on_delete=models.CASCADE) class TelevisionSheet(SheetModel): created_at = models.DateTimeField(auto_now_add=True) updated_at = models.DateTimeField(auto_now=True) channel = models.CharField(max_length=255, verbose_name=_('Channel'), help_text=_('''Be as specific as possible. E.g. if the television company is called RTV, and if the newscast is broadcast on its second channel, write in 'RTV-2' ''')) start_time = models.TimeField(verbose_name=_('Time of Broadcast')) num_female_anchors = field_num_anchors(_('Number of female anchors')) num_male_anchors = field_num_anchors(_('Number of male anchors')) item_number = field_item_number(_('(1) Item Number')) covid19 = field_covid19(_('(z) Is this story related to coronavirus Covid-19?')) topic = field_topic(_('(2) Topic')) scope = field_scope(_('(3) Scope')) equality_rights = field_equality_rights(_('(4) Reference to gender equality / human rights legislation/ policy')) about_women = field_about_women(_('(5) Is the story about a particular woman or group of women?')) inequality_women = field_inequality_women(_('(6) This story clearly highlights issues of inequality between women and men')) stereotypes = field_stereotypes(_('(7) This story clearly challenges gender stereotypes')) further_analysis = field_further_analysis(_('(22) Does this story warrant further analysis?'), _(''' A story warrants further analysis if it clearly perpetuates or clearly challenges gender stereotypes, if it includes women's opinions in a remarkable way, if it contributes to an understanding of inequalities between women and men, if it mentions or calls attention to women's human rights, etc. Consult the guide for further explanation''')) comments = field_comments(_('(N/A) Describe any photographs included in the story and the conclusions you draw from them.')) def __str__(self): item_number = f" story {str(self.item_number)}" if self.item_number else "" return f"{self.channel} {str(self.start_time)}{item_number}" class Meta: verbose_name = _('Television') # ---------------------------- # Internet News # ---------------------------- def internet_journalist_meta(name, bases, mydict): dct = { 'sex' : bases[0]._meta.fields[0], 'age' : bases[0]._meta.fields[1], } prepend_verbose(dct, 'sex', '10') prepend_verbose(dct, 'age', '11', 'Age (Person Appears)') return type(name, bases, mydict) class InternetNewsJournalist(Journalist, metaclass=internet_journalist_meta): internetnews_sheet = models.ForeignKey('InternetNewsSheet', on_delete=models.CASCADE) class InternetNewsPerson(Person): sex = field_sex(_('(12) Sex')) age = field_age(_('(13) Age (person appears)')) occupation = field_occupation(_('(14) Occupation or Position')) function = field_function(_('(15) Function in the news story')) family_role = field_family_role(_('(16) Family Role Given?')) victim_or_survivor = field_victim_or_survivor(_('(17) Does the story identify the person as either a victim or survivor?')) victim_of = field_victim_of(_('(18) The story identifies the person as a victim of:')) survivor_of = field_survivor_of(_('(19) The story identifies the person as a survivor of:')) is_quoted = field_is_quoted(_('(20) Is the person directly quoted')) is_photograph = field_is_photograph(_('(21) Is there a photograph of the person in the story?')) special_qn_1 = field_special_qn(_('(22) Special question (1)')) special_qn_2 = field_special_qn(_('(23) Special question (2)')) special_qn_3 = field_special_qn(_('(24) Special question (3)')) internetnews_sheet = models.ForeignKey('InternetNewsSheet', on_delete=models.CASCADE) class InternetNewsSheet(SheetModel): def __init__(self, *args, **kwargs): super(InternetNewsSheet, self).__init__(*args, **kwargs) created_at = models.DateTimeField(auto_now_add=True) updated_at = models.DateTimeField(auto_now=True) # Story website_name = models.CharField(max_length=255, verbose_name=_('Website Name')) website_url = models.CharField(max_length=255, verbose_name=_('URL')) time_accessed = models.DateTimeField(verbose_name=_('Date and Time Accessed')) offline_presence = models.CharField(max_length=1, choices=YESNO, verbose_name=_('Offline presence?')) webpage_layer_no = models.PositiveIntegerField(help_text=_('Webpage Layer Number. Homepage=1, One click away=2, Five clicks away= 5, etc. Note that if a story appears on the front page, code with 1'), verbose_name=_('(1) Webpage Layer Number'), blank=True, null=True) covid19 = field_covid19(_('(z) Is this story related to coronavirus Covid-19?')) topic = field_topic(_('(2) Topic')) scope = field_scope(_('(3) Scope')) shared_via_twitter = models.CharField(max_length=1, verbose_name=_('(4) Shared via twitter?'), choices=YESNO, help_text=_('''Has this story been shared by the media house via Twitter? Enter the exact URL of the story into <a href="https://twitter.com" target="_blank">https://twitter.com</a> - answer yes if the media house's name appears in the search results.''')) shared_on_facebook = models.CharField(max_length=1, choices=YESNO, verbose_name=_('(5) Shared on Facebook'), help_text=_('''Has this story been shared by the media house on its Facebook Page? Scroll down the media house's Facebook page to check.''')) # Analysis equality_rights = field_equality_rights(_('(6) Reference to gender equality / human rights legislation/ policy')) about_women = field_about_women(_('(7) Is the story about a particular woman or group of women?')) inequality_women = field_inequality_women(_('(8) This story clearly highlights issues of inequality between women and men')) stereotypes = field_stereotypes(_('(9) This story clearly challenges gender stereotypes')) further_analysis = field_further_analysis(_('(26) Does this story warrant further analysis?'), _(''' A story warrants further analysis if it clearly perpetuates or clearly challenges gender stereotypes, if it includes women's opinions in a remarkable way, if it contributes to an understanding of inequalities between women and men, if it mentions or calls attention to women's human rights, etc. Consult the guide for further explanation''')) url_and_multimedia = field_url_and_multimedia(_('(25) Copy and paste the URL of the story. Describe any photographs, images, other multimedia features included in the story. Note down the conclusions you draw from the images, audio and video.')) def __str__(self): time_accessed = self.time_accessed.strftime("%Y-%m-%d %H:%M:%S") website_url = f" {self.website_url}" return f"{self.website_name} {time_accessed}{website_url}" class Meta: verbose_name = _('Internet') def twitter_journalist_meta(name, bases, mydict): dct = { 'sex' : bases[0]._meta.fields[0], 'age' : bases[0]._meta.fields[1], } prepend_verbose(dct, 'sex', '7') prepend_verbose(dct, 'age', '8', 'Age (Person Appears)') return type(name, bases, mydict) # ---------------------------- # Twitter # ---------------------------- class TwitterJournalist(Journalist, metaclass=twitter_journalist_meta): twitter_sheet = models.ForeignKey('TwitterSheet', on_delete=models.CASCADE) class TwitterPerson(Person): sex = field_sex(_('(9) Sex')) age = field_age(_('(10) Age (person appears)')) occupation = field_occupation(_('(11) Occupation or Position')) function = field_function(_('(12) Function in the news story')) is_photograph = field_is_photograph(_('(13) Is there a photograph of the person in the story?')) special_qn_1 = field_special_qn(_('(14) Special question (1)')) special_qn_2 = field_special_qn(_('(15) Special question (2)')) special_qn_3 = field_special_qn(_('(16) Special question (3)')) twitter_sheet = models.ForeignKey('TwitterSheet', on_delete=models.CASCADE) class TwitterSheet(SheetModel): class Meta: verbose_name = _('Twitter') created_at = models.DateTimeField(auto_now_add=True) updated_at = models.DateTimeField(auto_now=True) media_name = models.CharField(max_length=255, verbose_name=_('Media Name'), help_text=_('''For example. 'CNN Breaking News' ''')) twitter_handle = models.CharField(max_length=255, verbose_name=_('Twitter Handle'), help_text=_('e.g. @cnnbrk')) # Story retweet = models.PositiveIntegerField(choices=RETWEET, verbose_name=_('(1) Tweet or Retweet'), help_text=_('Only retweets from the same media house can be coded. Do not code retweets from other news providers') ) covid19 = field_covid19(_('(z) Is this story related to coronavirus Covid-19?')) topic = field_topic(_('(2) Topic')) # Analysis equality_rights = field_equality_rights(_('(3) Reference to gender equality / human rights legislation/ policy')) about_women = field_about_women(_('(4) Is the story about a particular woman or group of women?')) inequality_women = field_inequality_women(_('(5) This story clearly highlights issues of inequality between women and men')) stereotypes = field_stereotypes(_('(6) This story clearly challenges gender stereotypes')) further_analysis = field_further_analysis(_('(18) Does this tweet warrant further analysis?'), _(''' A tweet warrants further analysis if it clearly perpetuates or clearly challenges gender stereotypes, if it includes women's opinions in a remarkable way, if it contributes to an understanding of inequalities between women and men, if it mentions or calls attention to women's human rights, etc. Consult the guide for further explanation''')) url_and_multimedia = field_url_and_multimedia(_('(17) Copy and paste the URL of the tweet. Describe any photographs, images, other multimedia features included in the tweet. Note down the conclusions you draw from the images, audio and video.')) def __str__(self): created_at = self.created_at.strftime("%Y-%m-%d %H:%M:%S") twitter_handle = f" {self.twitter_handle}" return f"{self.media_name} {created_at}{twitter_handle}" sheet_models = OrderedDict([ ('Print', NewspaperSheet), ('Radio', RadioSheet), ('Television', TelevisionSheet), ('Internet', InternetNewsSheet), ('Twitter', TwitterSheet) ]) tm_sheet_models = OrderedDict([ ('Print', NewspaperSheet), ('Radio', RadioSheet), ('Television', TelevisionSheet) ]) dm_sheet_models = OrderedDict([ ('Internet', InternetNewsSheet), ('Twitter', TwitterSheet) ]) person_models = OrderedDict([ ('Print', NewspaperPerson), ('Radio', RadioPerson), ('Television', TelevisionPerson), ('Internet', InternetNewsPerson), ('Twitter', TwitterPerson)] ) tm_person_models = OrderedDict([ ('Print', NewspaperPerson), ('Radio', RadioPerson), ('Television', TelevisionPerson), ]) dm_person_models = OrderedDict([ ('Internet', InternetNewsPerson), ('Twitter', TwitterPerson) ]) journalist_models = OrderedDict([ ('Print', NewspaperJournalist), ('Radio', RadioJournalist), ('Television', TelevisionJournalist), ('Internet', InternetNewsJournalist), ('Twitter', TwitterJournalist) ]) tm_journalist_models = OrderedDict([ ('Print', NewspaperJournalist), ('Radio', RadioJournalist), ('Television', TelevisionJournalist), ]) broadcast_journalist_models = OrderedDict([ ('Radio', RadioJournalist), ('Television', TelevisionJournalist), ]) dm_journalist_models = OrderedDict([ ('Internet', InternetNewsJournalist), ('Twitter', TwitterJournalist) ]) all_models = OrderedDict([ ('Sheets', sheet_models), ('Sources', person_models), ('Reporters', journalist_models) ])

51.635492

454

0.711081

2,817

21,532

5.204118

0.124246

0.018417

0.014325

0.012005

0.741678

0.727763

0.695293

0.683765

0.668008

0.573738

0

0.016401

0.158973

21,532

416

455

51.759615

0.793142

0.038826

0

0.456376

0

0.043624

0.419158

0.010353

0

1

0.040268

false

0

0.013423

0

0.630872

0

null

0

1

0

null

0

1

0

2