Reset23/the-stack-v2-blamed2 · Datasets at Hugging Face

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# Generated by Django 3.0.5 on 2020-06-07 12:19 import datetime from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('catalog', '0015_auto_20200607_2018'), ] operations = [ migrations.AlterField( model_name='noticing', name='create_time', field=models.DateTimeField(default=datetime.datetime(2020, 6, 7, 20, 19, 20, 812109)), ), migrations.AlterField( model_name='training', name='create_time', field=models.DateTimeField(default=datetime.datetime(2020, 6, 7, 20, 19, 20, 808117)), ), migrations.AlterField( model_name='voting', name='create_time', field=models.DateTimeField(default=datetime.datetime(2020, 6, 7, 20, 19, 20, 810108)), ), migrations.AlterField( model_name='voting', name='expire_time', field=models.DateTimeField(blank=True, null=True), ), ]

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morriscodez/critters-and-croquettes

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class SlitherInn: def __init__(self, name): self.attraction_name = name self.description = "safe place to fall asleep" self.animals = []

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huanpython/mysite

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from django.apps import AppConfig class TradeConfig(AppConfig): name = 'app.trade' verbose_name = "交易管理"

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""" Parser for asr training options """ # Copyright (c) 2021 Idiap Research Institute, http://www.idiap.ch/ # Written by Apoorv Vyas <apoorv.vyas@idiap.ch> import argparse import sys def add_downstream_options(parser): parser.add_argument( '--ckpt', default='', type=str, help='Path to upstream pre-trained checkpoint, required if using other than baseline', required=True ) parser.add_argument( '--config', default='config/asr-downstream.yaml', type=str, help='Path to downstream experiment config.', required=True ) parser.add_argument( '--upconfig', default='default', type=str, help='Path to the option upstream config. Pass default to use from checkpoint', ) parser.add_argument( '--cpu', action='store_true', help='Disable GPU training.' ) return parser def print_downstream_options(args): sys.stderr.write(""" Downstream Config: Checkpoint: {ckpt} ASR Config: {config} Upconfig: {upconfig} CPU Training: {cpu} """.format(**vars(args)))

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/Bolivian_Lowlands/Scenarios/Scenario_2_new.py

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# -*- coding: utf-8 -*- """ Created on Tue Sep 21 10:25:06 2021 @author: Clau """ ''' Paper: Energy sufficiency, lowlands. SCENARIO 2 ''' from core import User, np User_list = [] #Defining users H1 = User("low income", 73) User_list.append(H1) H2 = User("high income", 53) User_list.append(H2) Public_lighting = User("Public lighting ", 2) User_list.append(Public_lighting) HP = User("Health post", 1) User_list.append(HP) SA = User("School type A", 1) User_list.append(SA) Church = User("Church", 1) User_list.append(Church) WSS = User("water supply system", 1) User_list.append(WSS) Coliseum = User("Coliseum", 1) User_list.append(Coliseum) R = User("Restaurant", 1) User_list.append(R) GS = User("Grocery Store 1", 2) User_list.append(GS) EB = User("Entertainment Business", 3) User_list.append(EB) WS = User("Workshop", 2) User_list.append(WS) LAU = User("Lowlands agro-productive unit", 1) User_list.append(LAU) IW = User("Irrigation Water", 7) User_list.append(IW) #Appliances #Low Income Households H1_indoor_bulb = H1.Appliance(H1,3,7,2,120,0.2,10) H1_indoor_bulb.windows([1082,1440],[0,30],0.35) H1_outdoor_bulb = H1.Appliance(H1,1,13,2,600,0.2,10) H1_outdoor_bulb.windows([0,330],[1082,1440],0.35) H1_TV = H1.Appliance(H1,1,60,3,90,0.1,5) H1_TV.windows([750,840],[1082,1440],0.35,[0,30]) H1_Antenna = H1.Appliance(H1,1,8,3,90,0.1,5) H1_Antenna.windows([750,840],[1082,1440],0.35,[0,30]) H1_Phone_charger = H1.Appliance(H1,2,2,1,300,0.2,5) H1_Phone_charger.windows([1080,1440],[0,0],0.35) #High income households H2_indoor_bulb = H2.Appliance(H2,4,7,2,120,0.2,10) H2_indoor_bulb.windows([1082,1440],[0,30],0.35) H2_outdoor_bulb = H2.Appliance(H2,2,13,2,600,0.2,10) H2_outdoor_bulb.windows([0,330],[1082,1440],0.35) H2_TV = H2.Appliance(H2,2,60,2,120,0.1,5) H2_TV.windows([1082,1440],[0,60],0.35) H2_DVD = H2.Appliance(H2,1,8,2,40,0.1,5) H2_DVD.windows([1082,1440],[0,60],0.35) H2_Antenna = H2.Appliance(H2,1,8,2,80,0.1,5) H2_Antenna.windows([1082,1440],[0,60],0.35) H2_Radio = H2.Appliance(H2,1,36,2,60,0.1,5) H2_Radio.windows([390,450],[1082,1260],0.35) H2_Phone_charger = H2.Appliance(H2,4,2,2,300,0.2,5) H2_Phone_charger.windows([1110,1440],[0,30],0.35) H2_Freezer = H2.Appliance(H2,1,200,1,1440,0,30, 'yes',2) H2_Freezer.windows([0,1440],[0,0]) H2_Freezer.specific_cycle_1(5,15,200,15) H2_Freezer.specific_cycle_2(200,10,5,20) H2_Freezer.cycle_behaviour([480,1200],[0,0],[0,479],[1201,1440]) H2_Mixer = H2.Appliance(H2,1,50,3,30,0.1,1, occasional_use = 0.33) H2_Mixer.windows([420,450],[660,750],0.35,[1020,1170]) H2_Fan = H2.Appliance(H2,1,171,1,220,0.27,60) H2_Fan.windows([720,1080],[0,0]) H2_Laptop = H2.Appliance(H2,1,70,1,90,0.3,30) H2_Laptop.windows([960,1200],[0,0]) #Health post HP_indoor_bulb = HP.Appliance(HP,12,7,2,690,0.2,10) HP_indoor_bulb.windows([480,720],[870,1440],0.35) HP_outdoor_bulb = HP.Appliance(HP,1,13,2,690,0.2,10) HP_outdoor_bulb.windows([0,342],[1037,1440],0.35) HP_Phone_charger = HP.Appliance(HP,5,2,2,300,0.2,5) HP_Phone_charger.windows([480,720],[900,1440],0.35) HP_TV = HP.Appliance(HP,1,150,2,360,0.1,60) HP_TV.windows([480,720],[780,1020],0.2) HP_radio = HP.Appliance(HP,1,40,2,360,0.3,60) HP_radio.windows([480,720],[780,1020],0.35) HP_PC = HP.Appliance(HP,1,200,2,300,0.1,10) HP_PC.windows([480,720],[1050,1440],0.35) HP_printer = HP.Appliance(HP,1,100,1,60,0.3,10) HP_printer.windows([540,1020],[0,0],0.35) HP_fan = HP.Appliance(HP,2,60,1,240,0.2,60) HP_fan.windows([660,960],[0,0],0.35) HP_sterilizer_stove = HP.Appliance(HP,1,600,2,120,0.3,30) HP_sterilizer_stove.windows([540,600],[900,960],0.35) HP_needle_destroyer = HP.Appliance(HP,1,70,1,60,0.2,10) HP_needle_destroyer.windows([540,600],[0,0],0.35) HP_water_pump = HP.Appliance(HP,1,400,1,30,0.2,10) HP_water_pump.windows([480,510],[0,0],0.35) HP_Fridge = HP.Appliance(HP,3,150,1,1440,0,30, 'yes',3) HP_Fridge.windows([0,1440],[0,0]) HP_Fridge.specific_cycle_1(150,20,5,10) HP_Fridge.specific_cycle_2(150,15,5,15) HP_Fridge.specific_cycle_3(150,10,5,20) HP_Fridge.cycle_behaviour([580,1200],[0,0],[420,579],[0,0],[0,419],[1201,1440]) #School A SA_indoor_bulb = SA.Appliance(SA,6,7,2,120,0.25,30) SA_indoor_bulb.windows([480,780],[840,1140],0.2) SA_outdoor_bulb = SA.Appliance(SA,1,13,1,60,0.2,10) SA_outdoor_bulb.windows([1007,1080],[0,0],0.35) SA_TV = SA.Appliance(SA,1,60,2,120,0.1,5, occasional_use = 0.5) SA_TV.windows([480,780],[840,1140],0.2) SA_radio = SA.Appliance(SA,3,4,2,120,0.1,5, occasional_use = 0.5) SA_radio.windows([480,780],[840,1140],0.2) SA_DVD = SA.Appliance(SA,1,8,2,120,0.1,5, occasional_use = 0.5) SA_DVD.windows([480,780],[840,1140],0.2) #Public lighting Public_lighting_lamp_post = Public_lighting.Appliance(Public_lighting,12,40,2,310,0,300, 'yes', flat = 'yes') Public_lighting_lamp_post.windows([0,362],[1082,1440],0.1) #Church Ch_indoor_bulb = Church.Appliance(Church,10,26,1,210,0.2,60,'yes', flat = 'yes') Ch_indoor_bulb.windows([1200,1440],[0,0],0.1) Ch_outdoor_bulb = Church.Appliance(Church,7,26,1,240,0.2,60, 'yes', flat = 'yes') Ch_outdoor_bulb.windows([1200,1440],[0,0],0.1) Ch_speaker = Church.Appliance(Church,1,100,1,240,0.2,60) Ch_speaker.windows([1200,1350],[0,0],0.1) #Water supply system WSS_water_pump = WSS.Appliance(WSS,1,1700,2,60,0.2,10,occasional_use = 0.33) WSS_water_pump.windows([420,720],[840,1020],0.35) #Coliseum Lights = Coliseum.Appliance(Coliseum,25,150,2,310,0.1,300, 'yes', flat = 'yes') Lights.windows([0,336],[1110,1440],0.2) #Grocery Store GS_indoor_bulb = GS.Appliance(GS,2,7,2,120,0.2,10) GS_indoor_bulb.windows([1107,1440],[0,30],0.35) GS_outdoor_bulb = GS.Appliance(GS,1,13,2,600,0.2,10) GS_outdoor_bulb.windows([0,330],[1107,1440],0.35) GS_freezer = GS.Appliance(GS,1,200,1,1440,0,30,'yes',3) GS_freezer.windows([0,1440],[0,0]) GS_freezer.specific_cycle_1(200,20,5,10) GS_freezer.specific_cycle_2(200,15,5,15) GS_freezer.specific_cycle_3(200,10,5,20) GS_freezer.cycle_behaviour([480,1200],[0,0],[300,479],[0,0],[0,299],[1201,1440]) GS_Radio = GS.Appliance(GS,1,36,2,60,0.1,5) GS_Radio.windows([390,450],[1140,1260],0.35) #Restaurant R_indoor_bulb = R.Appliance(R,2,7,2,120,0.2,10) R_indoor_bulb.windows([1107,1440],[0,30],0.35) R_Blender = R.Appliance(R,1,350,2,20,0.375,5) R_Blender.windows([420,480],[720,780],0.5) R_freezer = R.Appliance(R,1,200,1,1440,0,30,'yes',3) R_freezer.windows([0,1440],[0,0]) R_freezer.specific_cycle_1(200,20,5,10) R_freezer.specific_cycle_2(200,15,5,15) R_freezer.specific_cycle_3(200,10,5,20) R_freezer.cycle_behaviour([480,1200],[0,0],[300,479],[0,0],[0,299],[1201,1440]) #Entertainment Business EB_indoor_bulb = EB.Appliance(EB,2,7,2,120,0.2,10) EB_indoor_bulb.windows([1107,1440],[0,30],0.35) EB_outdoor_bulb = EB.Appliance(EB,1,13,2,600,0.2,10) EB_outdoor_bulb.windows([0,330],[1107,1440],0.35) EB_Stereo = EB.Appliance(EB,1,150,2,90,0.1,5, occasional_use = 0.33) EB_Stereo.windows([480,780],[0,0],0.35) EB_TV = EB.Appliance(EB,1,60,2,120,0.1,5, occasional_use = 0.5) EB_TV.windows([480,780],[840,1140],0.2) EB_PC = EB.Appliance(EB,1,50,2,210,0.1,10) EB_PC.windows([480,780],[840,1140],0.35) EB_freezer = EB.Appliance(EB,1,200,1,1440,0,30,'yes',3) EB_freezer.windows([0,1440],[0,0]) EB_freezer.specific_cycle_1(200,20,5,10) EB_freezer.specific_cycle_2(200,15,5,15) EB_freezer.specific_cycle_3(200,10,5,20) EB_freezer.cycle_behaviour([480,1200],[0,0],[300,479],[0,0],[0,299],[1201,1440]) #Workshop WS_indoor_bulb = WS.Appliance(WS,2,7,2,120,0.2,10) WS_indoor_bulb.windows([1107,1440],[0,30],0.35) WS_welding_machine = WS.Appliance(WS,1,5500,1,60,0.5,30,occasional_use = 0.3) WS_welding_machine.windows([0,1440],[0,0],0.35) WS_grinding_machine = WS.Appliance(WS,1,750,1,480,0.125,60,occasional_use = 0.3) WS_grinding_machine.windows([0,1440],[0,0],0.35) WS_Radio = WS.Appliance(WS,1,36,2,60,0.1,5) WS_Radio.windows([390,450],[1140,1260],0.35) #trans LAU_GD = LAU.Appliance(LAU,1,9360,1,180,0.2,30,occasional_use = 0.33) LAU_GD.windows([420,1080],[0,0],0.35) LAU_VW = LAU.Appliance(LAU,1,1170,1,480,0.2,15,occasional_use = 0.82) LAU_VW.windows([420,1140],[0,0],0.35) LAU_BT = LAU.Appliance(LAU,1,370,2,900,0.2,180) LAU_BT.windows([360,930],[1080,1440],0.35) #Irrigation IW_water_pump = IW.Appliance(IW,1,1700,2,60,0.2,10,occasional_use = 0.33) IW_water_pump.windows([420,720],[840,1020],0.35)

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# coding: utf-8 # In[2]: import datetime import json import warnings import pandas as pd import requests from pandas.io.json import json_normalize warnings.filterwarnings('ignore') import sys reload(sys) sys.setdefaultencoding('utf8') sys.path.append('./utils') from google_sheet_update import * # In[3]: # utility function, http request def getjson(url, auth=0): if auth == 0: with open('./utils/secret.json') as json_file: secret = json.load(json_file) auth = (str(secret['github']['username']), str(secret['github']['password'])) else: pass header = {'x-requested-with': 'XMLHttpRequest'} mainPage = requests.get(url, auth=auth) data = mainPage.json() return data # In[4]: # ingest coin github org def coin_github_org_ingestion(): sheet_key = '1tpOAiuRo9RNKnyPCVTGjc3H9S1miIJD1AimFLg8sv4E' tab = 'organization' data = get_googlesheet_data(sheet_key, tab) return data # ingest coin github exclude data def coin_github_exclusion_ingestion(): sheet_key = '1tpOAiuRo9RNKnyPCVTGjc3H9S1miIJD1AimFLg8sv4E' tab = 'excluding_repos' data = get_googlesheet_data(sheet_key, tab) return data # In[5]: # ingest coinmarketcap data def coin_marketcap_ingestion(limit=200): data = json_normalize(getjson("https://api.coinmarketcap.com/v1/ticker/?limit=" + str(limit))) return data # In[6]: # ingest github repo data def github_repo_ingestion(github_org_data, trunc_date=datetime.date(2017, 1, 1)): start_time = datetime.datetime.today() data = pd.DataFrame() for symbol in github_org_data.symbol.unique(): for github_org in list(github_org_data[github_org_data.symbol == symbol].github_org): try: data_repo = getjson("https://api.github.com/users/" + str( github_org) + "/repos?sort=updated&direction=desc&per_page=100") repo_dict = json_normalize(data_repo).set_index('name') repo_dict['updated_at'] = pd.to_datetime(repo_dict['updated_at']) repo_dict['symbol'] = symbol repo_list = repo_dict[repo_dict.updated_at >= trunc_date].index data = data.append(repo_dict) print str(github_org) + ' completed!' except: print str(github_org) + ' failed!' pass # pd.DataFrame.to_csv(token_repo_df,'./data/token_repo_dictionary_'+str(today)+'.csv') minutes_passed = (datetime.datetime.today() - start_time).seconds / 60 data.pushed_at = pd.to_datetime(data.pushed_at) print 'finished ingesting coin github repo data! used ' + str(minutes_passed) + ' minutes!' return data # In[7]: # write github org google sheet with coins that needs to be updated with github orgs def update_no_org_coins(coin_github_org_data, coin_marketcap_data): coin_org_list = coin_github_org_data.symbol.unique() coin_total_list = coin_marketcap_data.symbol.unique() coin_gap_list = list(set(coin_total_list) - set(coin_org_list)) coin_gap_list_df = coin_marketcap_data[coin_marketcap_data.symbol.isin(coin_gap_list)][['symbol', 'id']] sheet_key = '1tpOAiuRo9RNKnyPCVTGjc3H9S1miIJD1AimFLg8sv4E' tab = 'undocumented_top_200_coins' cell_col = 'A' cell_row = 1 write_cells(coin_gap_list_df, sheet_key, tab, cell_col, cell_row, transpose=0) return coin_gap_list # In[8]: # full contribution list per repo def get_full_contribution_history(coin_github_repo_data): start_time = datetime.datetime.today() data_contributions_entry = pd.DataFrame() for repo_name in coin_github_repo_data.full_name.unique(): try: data_repo_contributors = json_normalize(getjson( "https://api.github.com/repos/" + repo_name + "/stats/contributors?sort=total&direction=desc&per_page=100")) data_repo_contributors['repo_full_name'] = repo_name data_repo_contributors = \ data_repo_contributors.dropna(subset=['author.login']).set_index(['repo_full_name', 'author.login'])[ ['weeks']] data_repo_contributors = data_repo_contributors.weeks.apply(pd.Series) data_repo_contributors = pd.DataFrame(data_repo_contributors.stack())[0].apply(pd.Series) data_repo_contributors = data_repo_contributors[data_repo_contributors.c > 0] data_contributions_entry = data_contributions_entry.append(data_repo_contributors) memory = (data_contributions_entry.memory_usage()).sum() / (1024 ** 2) minutes_passed = (datetime.datetime.today() - start_time).seconds / 60 print 'repo ' + repo_name + ' flattern completed! used ' + str( minutes_passed) + ' minutes! ' + 'memory used ' + str(memory) + 'MB' del data_repo_contributors except: print 'repo ' + repo_name + ' flattern failed! used ' + str( minutes_passed) + ' minutes! ' + 'memory used ' + str(memory) + 'MB' pass minutes_passed = (datetime.datetime.today() - start_time).seconds / 60 print 'finished ingesting coin contribution data! used ' + str(minutes_passed) + ' minutes!' data_contributions_entry['w'] = pd.to_datetime(data_contributions_entry.w, unit='s') data_contributions_entry = data_contributions_entry.reset_index().drop(['level_2'], axis=1) data_contributions_entry = data_contributions_entry.rename( columns={'w': 'week', 'c': 'commits', 'a': 'additions', 'd': 'deletions', 'author.login': 'login'}) return data_contributions_entry # In[52]: # pulling repo lists that need to be updated def generate_update_repo_list(data_contributions_entry_existing, coin_github_repo_data): # dropping empty rows data_contributions_entry_existing = data_contributions_entry_existing[data_contributions_entry_existing.commits > 0] # formatting dates data_contributions_entry_existing.week = pd.to_datetime(data_contributions_entry_existing.week) coin_github_repo_data.pushed_at = pd.to_datetime(coin_github_repo_data.pushed_at) # contribution update_time contribution_update_time = data_contributions_entry_existing.week.max() # existing records for last commit week repo_last_commit_week = pd.DataFrame( data_contributions_entry_existing.groupby('repo_full_name').week.max()).reset_index() # latest last commit timestamp from github repo repo_latest_record_week = coin_github_repo_data[['full_name', 'pushed_at']].rename( columns={'full_name': 'repo_full_name'}) # merge to generate list of repo lists that have a new push repo_compare = repo_last_commit_week.merge(repo_latest_record_week, how='right') repo_compare.week = pd.to_datetime(repo_compare.week).fillna(datetime.datetime(1900, 1, 1)) repo_update_list = repo_compare[((repo_compare.pushed_at - repo_compare.week).dt.days > 7) & (repo_compare.pushed_at > contribution_update_time - datetime.timedelta( 7))].repo_full_name return repo_update_list # In[ ]: # In[23]: # full contribution list per repo def update_contribution_history(data_contributions_entry_existing, coin_github_repo_data): # generate repo lists that needs to be updated repo_update_list = generate_update_repo_list(data_contributions_entry_existing, coin_github_repo_data) print 'number of repos needed to be updated: ' + str(len(repo_update_list)) start_time = datetime.datetime.today() data_contributions_entry = pd.DataFrame() for repo_name in repo_update_list: try: data_repo_contributors = json_normalize(getjson( "https://api.github.com/repos/" + repo_name + "/stats/contributors?sort=total&direction=desc&per_page=100")) data_repo_contributors['repo_full_name'] = repo_name data_repo_contributors = \ data_repo_contributors.dropna(subset=['author.login']).set_index(['repo_full_name', 'author.login'])[ ['weeks']] data_repo_contributors = data_repo_contributors.weeks.apply(pd.Series) data_repo_contributors = pd.DataFrame(data_repo_contributors.stack())[0].apply(pd.Series) data_repo_contributors = data_repo_contributors[data_repo_contributors.c > 0] data_contributions_entry = data_contributions_entry.append(data_repo_contributors) memory = (data_contributions_entry.memory_usage()).sum() / (1024 ** 2) minutes_passed = (datetime.datetime.today() - start_time).seconds / 60 print 'repo ' + repo_name + ' flattern completed! used ' + str( minutes_passed) + ' minutes! ' + 'memory used ' + str(memory) + 'MB' del data_repo_contributors except: print 'repo ' + repo_name + ' flattern failed! used ' + str( minutes_passed) + ' minutes! ' + 'memory used ' + str(memory) + 'MB' pass minutes_passed = (datetime.datetime.today() - start_time).seconds / 60 print 'finished ingesting coin contribution data! used ' + str(minutes_passed) + ' minutes!' data_contributions_entry['w'] = pd.to_datetime(data_contributions_entry.w, unit='s') data_contributions_entry = data_contributions_entry.reset_index().drop(['level_2'], axis=1) data_contributions_entry = data_contributions_entry.rename( columns={'w': 'week', 'c': 'commits', 'a': 'additions', 'd': 'deletions', 'author.login': 'login'}) data_contributions_entry_updated = data_contributions_entry_existing[ (~data_contributions_entry_existing.repo_full_name.isin(repo_update_list)) & (data_contributions_entry_existing.commits > 0)].append(data_contributions_entry) data_contributions_entry_updated.week = pd.to_datetime(data_contributions_entry_updated.week) data_contributions_entry_updated = data_contributions_entry_updated[ data_contributions_entry_updated.week >= datetime.date(2009, 1, 1)] return data_contributions_entry_updated # In[11]: # main function, update print 'start github_data_pipeline! UTC time: '+str(datetime.datetime.today()) coin_github_org_data = coin_github_org_ingestion() coin_marketcap_data = coin_marketcap_ingestion() coin_github_repo_data = github_repo_ingestion(coin_github_org_data) coin_github_exclude_data = coin_github_exclusion_ingestion() coin_gap_list = update_no_org_coins(coin_github_org_data, coin_marketcap_data) # update contribution data from existing file data_contributions_entry_existing = pd.DataFrame.from_csv('./data/latest_data/top_coin_repo_contributions_entry.csv') data_contributions_entry = update_contribution_history(data_contributions_entry_existing, coin_github_repo_data) data_contributions_entry = data_contributions_entry[~data_contributions_entry.repo_full_name.isin(coin_github_exclude_data.repo_full_name)] # pull from scratch # data_contributions_entry = get_full_contribution_history(coin_github_repo_data) # In[69]: # saving to csv today = datetime.date.today() pd.DataFrame.to_csv(coin_marketcap_data, './data/latest_data/coin_marketcap_data.csv') pd.DataFrame.to_csv(coin_github_repo_data, './data/latest_data//top_coin_repo_list.csv') pd.DataFrame.to_csv(data_contributions_entry, './data/latest_data/top_coin_repo_contributions_entry.csv') # archiving just token contribution data pd.DataFrame.to_csv(data_contributions_entry, './data/archive_data/top_coin_repo_contributions_entry_' + str(today) + '.csv') print 'finished github_data_pipeline! UTC time: '+str(datetime.datetime.today())

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/expected_move.py

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import models from database import SessionLocal, engine from models import Stock, Option from sqlalchemy.orm import Session from sqlalchemy import and_, or_, desc, asc import math import tools import numpy as np def get_expected_move(symbol, underlying_price, dte): two_atm_call_iv = None strikes = tools.strike_increments(symbol, dte) # print('strikes') # print(strikes) # print('underlying_price') # print(underlying_price) if strikes[0] < underlying_price and underlying_price < strikes[len(strikes) - 1]: two_atm_strikes = two_ntm_strikes(strikes, underlying_price) two_atm_call_iv = tools.get_option_prop(symbol, two_atm_strikes, 'CALL', 'impliedVolatility', dte) two_atm_put_iv = tools.get_option_prop(symbol, two_atm_strikes, 'PUT', 'impliedVolatility', dte) print('dte: ' + str(dte)) print('two_atm_strikes') print(two_atm_strikes) # print('two_atm_call_iv') # print(two_atm_call_iv) expected_move_iv = calc_expected_move_iv(underlying_price, two_atm_call_iv, two_atm_put_iv, dte) return expected_move_iv else: return None def calc_expected_move_iv(underlying_price, call_iv, put_iv, dte): iv_sum = 0 for val in call_iv: iv_sum = iv_sum + val # print('iv_sum: ' + str(iv_sum)) for val in put_iv: iv_sum = iv_sum + val # print('iv_sum: ' + str(iv_sum)) avg_iv = iv_sum / 4 expected_move = float(underlying_price) * (float(avg_iv) / 100) * (math.sqrt(int(dte)) / math.sqrt(365)) # print('iv: ' + str(avg_iv)) return expected_move def two_ntm_strikes(strikes, underlying_price): # find 2 near-the-money strikes # First find the atm_strike strike_1 = tools.find_atm_strike_index(strikes, underlying_price) # If the underlying_price is less than the initial strike price if (underlying_price < strikes[strike_1]): strike_2 = strike_1 - 1 else: strike_2 = strike_1 + 1 return sorted([strikes[strike_1], strikes[strike_2]], key=float) def get_expected_move_premium(symbol, underlying_price, dte): strikes = tools.strike_increments(symbol, dte) if strikes[0] < underlying_price and underlying_price < strikes[len(strikes) - 1]: if len(strikes) > 1: two_atm_strikes = two_ntm_strikes(strikes, underlying_price) two_premium_calls_bids = tools.get_option_prop(symbol, two_atm_strikes, 'CALL', 'bid', dte) two_premium_calls_asks = tools.get_option_prop(symbol, two_atm_strikes, 'CALL', 'ask', dte) two_premium_puts_bids = tools.get_option_prop(symbol, two_atm_strikes, 'PUT', 'bid', dte) two_premium_puts_asks = tools.get_option_prop(symbol, two_atm_strikes, 'PUT', 'ask', dte) # Since the underlying price won't be exactly on a strike, calculate the weighted difference between the nearest strikes strike_diff = abs(two_atm_strikes[1] - two_atm_strikes[0]) price_distance = abs(underlying_price - two_atm_strikes[1]) price_distance_percent = price_distance / strike_diff two_premium_calls_mid = (np.array(two_premium_calls_bids) + np.array(two_premium_calls_asks)) / 2.0 two_premium_puts_mid = (np.array(two_premium_puts_bids) + np.array(two_premium_puts_asks)) / 2.0 two_premium_calls_mid_diff = abs(two_premium_calls_mid[1] - two_premium_calls_mid[0]) two_premium_puts_mid_diff = abs(two_premium_puts_mid[1] - two_premium_puts_mid[0]) premium_call = two_premium_calls_mid[1] + (two_premium_calls_mid_diff * price_distance_percent) premium_put = two_premium_puts_mid[1] - (two_premium_puts_mid_diff * price_distance_percent) # print('premium_put') # print(premium_put) expected_move_premium = calc_expected_move_premium(underlying_price, premium_call, premium_put, dte) return expected_move_premium else: return None def calc_expected_move_premium(underlying_price, prem_call, prem_put, dte): # average the two calls and puts premiums total_prem = prem_call + prem_put expected_move_premium_percent = total_prem * 85 / underlying_price expected_move_calc = expected_move_premium_percent / 100 * underlying_price return expected_move_calc

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"""All In One Block Layer.""" # Copyright (c) https://github.com/vislearn/FrEIA # SPDX-License-Identifier: MIT # Copyright (C) 2022 Intel Corporation # SPDX-License-Identifier: Apache-2.0 from __future__ import annotations import warnings from typing import Callable import torch import torch.nn.functional as F from FrEIA.modules import InvertibleModule from scipy.stats import special_ortho_group from torch import Tensor, nn def _global_scale_sigmoid_activation(input: Tensor) -> Tensor: """Global scale sigmoid activation. Args: input (Tensor): Input tensor Returns: Tensor: Sigmoid activation """ return 10 * torch.sigmoid(input - 2.0) def _global_scale_softplus_activation(input: Tensor) -> Tensor: """Global scale softplus activation. Args: input (Tensor): Input tensor Returns: Tensor: Softplus activation """ softplus = nn.Softplus(beta=0.5) return 0.1 * softplus(input) def _global_scale_exp_activation(input: Tensor) -> Tensor: """Global scale exponential activation. Args: input (Tensor): Input tensor Returns: Tensor: Exponential activation """ return torch.exp(input) class AllInOneBlock(InvertibleModule): """Module combining the most common operations in a normalizing flow or similar model. It combines affine coupling, permutation, and global affine transformation ('ActNorm'). It can also be used as GIN coupling block, perform learned householder permutations, and use an inverted pre-permutation. The affine transformation includes a soft clamping mechanism, first used in Real-NVP. The block as a whole performs the following computation: .. math:: y = V\\,R \\; \\Psi(s_\\mathrm{global}) \\odot \\mathrm{Coupling}\\Big(R^{-1} V^{-1} x\\Big)+ t_\\mathrm{global} - The inverse pre-permutation of x (i.e. :math:`R^{-1} V^{-1}`) is optional (see ``reverse_permutation`` below). - The learned householder reflection matrix :math:`V` is also optional all together (see ``learned_householder_permutation`` below). - For the coupling, the input is split into :math:`x_1, x_2` along the channel dimension. Then the output of the coupling operation is the two halves :math:`u = \\mathrm{concat}(u_1, u_2)`. .. math:: u_1 &= x_1 \\odot \\exp \\Big( \\alpha \\; \\mathrm{tanh}\\big( s(x_2) \\big)\\Big) + t(x_2) \\\\ u_2 &= x_2 Because :math:`\\mathrm{tanh}(s) \\in [-1, 1]`, this clamping mechanism prevents exploding values in the exponential. The hyperparameter :math:`\\alpha` can be adjusted. """ def __init__( self, dims_in, dims_c=[], subnet_constructor: Callable | None = None, affine_clamping: float = 2.0, gin_block: bool = False, global_affine_init: float = 1.0, global_affine_type: str = "SOFTPLUS", permute_soft: bool = False, learned_householder_permutation: int = 0, reverse_permutation: bool = False, ): """ Args: subnet_constructor: class or callable ``f``, called as ``f(channels_in, channels_out)`` and should return a torch.nn.Module. Predicts coupling coefficients :math:`s, t`. affine_clamping: clamp the output of the multiplicative coefficients before exponentiation to +/- ``affine_clamping`` (see :math:`\\alpha` above). gin_block: Turn the block into a GIN block from Sorrenson et al, 2019. Makes it so that the coupling operations as a whole is volume preserving. global_affine_init: Initial value for the global affine scaling :math:`s_\mathrm{global}`. global_affine_init: ``'SIGMOID'``, ``'SOFTPLUS'``, or ``'EXP'``. Defines the activation to be used on the beta for the global affine scaling (:math:`\\Psi` above). permute_soft: bool, whether to sample the permutation matrix :math:`R` from :math:`SO(N)`, or to use hard permutations instead. Note, ``permute_soft=True`` is very slow when working with >512 dimensions. learned_householder_permutation: Int, if >0, turn on the matrix :math:`V` above, that represents multiple learned householder reflections. Slow if large number. Dubious whether it actually helps network performance. reverse_permutation: Reverse the permutation before the block, as introduced by Putzky et al, 2019. Turns on the :math:`R^{-1} V^{-1}` pre-multiplication above. """ super().__init__(dims_in, dims_c) channels = dims_in[0][0] # rank of the tensors means 1d, 2d, 3d tensor etc. self.input_rank = len(dims_in[0]) - 1 # tuple containing all dims except for batch-dim (used at various points) self.sum_dims = tuple(range(1, 2 + self.input_rank)) if len(dims_c) == 0: self.conditional = False self.condition_channels = 0 else: assert tuple(dims_c[0][1:]) == tuple( dims_in[0][1:] ), f"Dimensions of input and condition don't agree: {dims_c} vs {dims_in}." self.conditional = True self.condition_channels = sum(dc[0] for dc in dims_c) split_len1 = channels - channels // 2 split_len2 = channels // 2 self.splits = [split_len1, split_len2] try: self.permute_function = {0: F.linear, 1: F.conv1d, 2: F.conv2d, 3: F.conv3d}[self.input_rank] except KeyError: raise ValueError(f"Data is {1 + self.input_rank}D. Must be 1D-4D.") self.in_channels = channels self.clamp = affine_clamping self.GIN = gin_block self.reverse_pre_permute = reverse_permutation self.householder = learned_householder_permutation if permute_soft and channels > 512: warnings.warn( ( "Soft permutation will take a very long time to initialize " f"with {channels} feature channels. Consider using hard permutation instead." ) ) # global_scale is used as the initial value for the global affine scale # (pre-activation). It is computed such that # global_scale_activation(global_scale) = global_affine_init # the 'magic numbers' (specifically for sigmoid) scale the activation to # a sensible range. if global_affine_type == "SIGMOID": global_scale = 2.0 - torch.log(torch.tensor([10.0 / global_affine_init - 1.0])) self.global_scale_activation = _global_scale_sigmoid_activation elif global_affine_type == "SOFTPLUS": global_scale = 2.0 * torch.log(torch.exp(torch.tensor(0.5 * 10.0 * global_affine_init)) - 1) self.global_scale_activation = _global_scale_softplus_activation elif global_affine_type == "EXP": global_scale = torch.log(torch.tensor(global_affine_init)) self.global_scale_activation = _global_scale_exp_activation else: raise ValueError('Global affine activation must be "SIGMOID", "SOFTPLUS" or "EXP"') self.global_scale = nn.Parameter(torch.ones(1, self.in_channels, *([1] * self.input_rank)) * global_scale) self.global_offset = nn.Parameter(torch.zeros(1, self.in_channels, *([1] * self.input_rank))) if permute_soft: w = special_ortho_group.rvs(channels) else: indices = torch.randperm(channels) w = torch.zeros((channels, channels)) w[torch.arange(channels), indices] = 1.0 if self.householder: # instead of just the permutation matrix w, the learned housholder # permutation keeps track of reflection vectors vk, in addition to a # random initial permutation w_0. self.vk_householder = nn.Parameter(0.2 * torch.randn(self.householder, channels), requires_grad=True) self.w_perm = None self.w_perm_inv = None self.w_0 = nn.Parameter(torch.FloatTensor(w), requires_grad=False) else: self.w_perm = nn.Parameter( torch.FloatTensor(w).view(channels, channels, *([1] * self.input_rank)), requires_grad=False ) self.w_perm_inv = nn.Parameter( torch.FloatTensor(w.T).view(channels, channels, *([1] * self.input_rank)), requires_grad=False ) if subnet_constructor is None: raise ValueError("Please supply a callable subnet_constructor" "function or object (see docstring)") self.subnet = subnet_constructor(self.splits[0] + self.condition_channels, 2 * self.splits[1]) self.last_jac = None def _construct_householder_permutation(self): """Computes a permutation matrix from the reflection vectors that are learned internally as nn.Parameters.""" w = self.w_0 for vk in self.vk_householder: w = torch.mm(w, torch.eye(self.in_channels).to(w.device) - 2 * torch.ger(vk, vk) / torch.dot(vk, vk)) for i in range(self.input_rank): w = w.unsqueeze(-1) return w def _permute(self, x, rev=False): """Performs the permutation and scaling after the coupling operation. Returns transformed outputs and the LogJacDet of the scaling operation.""" if self.GIN: scale = 1.0 perm_log_jac = 0.0 else: scale = self.global_scale_activation(self.global_scale) perm_log_jac = torch.sum(torch.log(scale)) if rev: return ((self.permute_function(x, self.w_perm_inv) - self.global_offset) / scale, perm_log_jac) else: return (self.permute_function(x * scale + self.global_offset, self.w_perm), perm_log_jac) def _pre_permute(self, x, rev=False): """Permutes before the coupling block, only used if reverse_permutation is set""" if rev: return self.permute_function(x, self.w_perm) else: return self.permute_function(x, self.w_perm_inv) def _affine(self, x, a, rev=False): """Given the passive half, and the pre-activation outputs of the coupling subnetwork, perform the affine coupling operation. Returns both the transformed inputs and the LogJacDet.""" # the entire coupling coefficient tensor is scaled down by a # factor of ten for stability and easier initialization. a *= 0.1 ch = x.shape[1] sub_jac = self.clamp * torch.tanh(a[:, :ch]) if self.GIN: sub_jac -= torch.mean(sub_jac, dim=self.sum_dims, keepdim=True) if not rev: return (x * torch.exp(sub_jac) + a[:, ch:], torch.sum(sub_jac, dim=self.sum_dims)) else: return ((x - a[:, ch:]) * torch.exp(-sub_jac), -torch.sum(sub_jac, dim=self.sum_dims)) def forward(self, x, c=[], rev=False, jac=True): """See base class docstring""" if self.householder: self.w_perm = self._construct_householder_permutation() if rev or self.reverse_pre_permute: self.w_perm_inv = self.w_perm.transpose(0, 1).contiguous() if rev: x, global_scaling_jac = self._permute(x[0], rev=True) x = (x,) elif self.reverse_pre_permute: x = (self._pre_permute(x[0], rev=False),) x1, x2 = torch.split(x[0], self.splits, dim=1) if self.conditional: x1c = torch.cat([x1, *c], 1) else: x1c = x1 if not rev: a1 = self.subnet(x1c) x2, j2 = self._affine(x2, a1) else: a1 = self.subnet(x1c) x2, j2 = self._affine(x2, a1, rev=True) log_jac_det = j2 x_out = torch.cat((x1, x2), 1) if not rev: x_out, global_scaling_jac = self._permute(x_out, rev=False) elif self.reverse_pre_permute: x_out = self._pre_permute(x_out, rev=True) # add the global scaling Jacobian to the total. # trick to get the total number of non-channel dimensions: # number of elements of the first channel of the first batch member n_pixels = x_out[0, :1].numel() log_jac_det += (-1) ** rev * n_pixels * global_scaling_jac return (x_out,), log_jac_det def output_dims(self, input_dims): return input_dims

[ "noreply@github.com" ]

noreply@github.com

3ad493b7dfecdacdc6ea5bd86467d39b3b95ef44

8b9dfacd464558d1aacdef387ec3078a03d59158

/aruco_detect_master/aruco_detect.py

f4ac1f0f3472cc04ad87f0aa78aeb49df4970415

[]

no_license

Taospirit/HITsz_Course_Code

56bb4a4327b39d9c45405e367dafc4211b74930b

b2f33bb3e5ce31894b12bfbf2f42cbf482933ed0

refs/heads/master

2020-04-10T20:30:07.952396

2019-07-10T13:53:38

161,269,766

2

0

null

UTF-8

Python

false

4,294

py

import cv2 as cv import cv2.aruco as aruco import numpy as np import copy # IMG_WIDTH = camera_matrix = np.array(([693.2, 0, 666.8], # 内参矩阵 [0, 693.4, 347.7], [0, 0, 1]), dtype=np.double) dist_coefs = np.array([-0.050791, 0.217163, 0.0000878, -0.000388, -0.246122], dtype=np.double) # k1 k2 p1 p2 k3 VIDEO_WIDTH, VIDEO_HEIGHT = 640, 480 SHOW_WIDTH = 550 def drawPolyLines(img, raw_point_list): point_list = [[elem[0], elem[1]] for elem in raw_point_list] pts = np.array(point_list, np.int32) pts = pts.reshape((-1, 1, 2)) cv.polylines(img, [pts], True, (0, 255, 255)) def saveVideo(cap_save, num): fourcc = cv.VideoWriter_fourcc(*'XVID') out = cv.VideoWriter('./aurco_test'+str(num)+'.avi', fourcc, 20.0, (VIDEO_WIDTH, VIDEO_HEIGHT)) while cap_save.isOpened(): ret, frame = cap_save.read() if ret: out.write(frame) cv.imshow('frame', frame) if cv.waitKey(1) & 0xFF == ord('s'): print ('End record video!') break else: print ('ret is False...break out!') break out.release() def detectMarkersOrigin(img_origin): frame = copy.deepcopy(img_origin) gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY) aruco_dict = aruco.Dictionary_get(aruco.DICT_6X6_250) corners, ids, rejectedImgPoints = aruco.detectMarkers(gray, aruco_dict) if ids is not None: id_show = [[ids[i][0], corners[i][0][0][0], corners[i][0][0][1]] for i in range(len(corners))] # print (len(ids), type(ids), ids) rvec, tvec, _ = aruco.estimatePoseSingleMarkers(corners, 0.05, camera_matrix, dist_coefs) for i in range(rvec.shape[0]): aruco.drawAxis(frame, camera_matrix, dist_coefs, rvec[i, :, :], tvec[i, :, :], 0.03) aruco.drawDetectedMarkers(frame, corners, ids) for elem in id_show: cv.putText(frame, 'id='+str(elem[0]), (elem[1], elem[2]), cv.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2, cv.LINE_AA) else: cv.putText(frame, "No Aruco_Markers in sight!", (50, 50), cv.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2, cv.LINE_AA) cv.namedWindow('Marker_Detect', cv.WINDOW_NORMAL) cv.resizeWindow('Marker_Detect', (SHOW_WIDTH, int(SHOW_WIDTH*480/640))) cv.moveWindow('Marker_Detect', 50, 50) cv.imshow('Marker_Detect', frame) def detectMarkersMaster(img_origin): img = copy.deepcopy(img_origin) cv.namedWindow('Origin_Img', cv.WINDOW_NORMAL) cv.moveWindow('Origin_Img', 650, 50) cv.resizeWindow('Origin_Img', (SHOW_WIDTH, int(SHOW_WIDTH*480/640))) cv.imshow('Origin_Img', img) cv.namedWindow('Canny_Img', cv.WINDOW_NORMAL) cv.moveWindow('Canny_Img', 1250, 50) cv.resizeWindow('Canny_Img', (SHOW_WIDTH, int(SHOW_WIDTH*480/640))) gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY) edges = cv.Canny(gray, 100, 200) cv.imshow('Canny_Img', edges) drawing = np.zeros(img.shape[:], dtype=np.uint8) #TODO: lines_p = cv.HoughLinesP(edges, 0.5, np.pi / 180, 90, minLineLength=10, maxLineGap=15) if lines_p is not None: for line in lines_p: x1, y1, x2, y2 = line[0] cv.line(img, (x1, y1), (x2, y2), (0, 255, 0), 3, lineType=cv.LINE_AA) # cv.imshow('Hough_p', img) #寻找Harris角点 gray = np.float32(gray) dst = cv.cornerHarris(gray, 2, 3, 0.04) dst = cv.dilate(dst,None) img[dst > 0.01*dst.max()]=[0, 0, 255] cv.imshow('dst', img) # ret, dst = cv.threshold(dst,0.01*dst.max(),255,0) # dst = np.uint8(dst) # #找到重心 # ret, labels, stats, centroids = cv.connectedComponentsWithStats(dst) # #定义迭代次数 # criteria = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_MAX_ITER, 100, 0.001) # corners = cv.cornerSubPix(gray, np.float32(centroids),(5,5),(-1,-1),criteria) # #返回角点 # #绘制 # res = np.hstack((centroids,corners)) # res = np.int0(res) # img[res[:,1],res[:,0]]=[0,0,255] # img[res[:,3],res[:,2]] = [0,255,0] # cv.imwrite('./subpixel5.png',img) def main(): cap, num = cv.VideoCapture(1), 1 if not cap.isOpened(): print ('Failed to open the camera...') return -1 while cap.isOpened(): ret, img = cap.read() detectMarkersOrigin(img) detectMarkersMaster(img) key = cv.waitKey(1) & 0xff if key == 27: print ("close window for keyboard break") break if key == ord('s'): print ('Start to record video...') saveVideo(cap, num) num += 1 cap.release() cv.destroyAllWindows() if __name__ == "__main__": main()

[ "lintao209@outlook.com" ]

lintao209@outlook.com

65c961193678438ef37cfc7bff2d0c2383ddd805

d767da4400de5b0d17bab56eeb678b4ff1052913

/harifile.py

1b506a1cd942ed997fb8479f420f87147594750d

[]

no_license

HARI5KRISHNAN/newproject

9b792f8960bb0656b8e82e9d4a15284f0e72aff1

d3dec2ce5b025e730157cf83d9df82278d9dda93

refs/heads/master

2021-01-05T05:00:00.639829

2020-02-16T12:34:21

240,888,234

0

null

UTF-8

Python

false

28

py

print("welocme to eduerka")

[ "root@ip-172-31-25-170.ap-southeast-1.compute.internal" ]

root@ip-172-31-25-170.ap-southeast-1.compute.internal

106c381e4786484cc282efd00c668dcd74a0a99b

420b062ab05f6dcbe718acfbb7fa92e380b03d25

/runit_sfdc.py

de72019a86fd4cd675992b4640518d513d99dc89

[]

no_license

fkoncz/sfdc---PoC-code

e130e5f8addfbd7dcbca55b5a93a3657baa694b6

076f27cddac17a7be65a04469cd509373ae62156

refs/heads/master

2022-04-03T10:23:24.234233

2020-02-14T03:10:00

null

0

null

UTF-8

Python

false

28,884

py

import requests import xml.dom.minidom import collections import time import os import zipfile import base64 import beatbox from runit_sfdc_ui import * from random import choice from string import ascii_lowercase from Config.config_sfdc import * from simple_salesforce import Salesforce sf = Salesforce(username=ADMIN1_USERNAME, password=ADMIN1_PASSWORD, security_token=ADMIN1_TOKEN) def main(): # -----Admin 1--Getting global Administrator Session ID. admin_sid = get_user_sid(ADMIN1_USERNAME, ADMIN1_PTK) # Admin 1--Making sure we will be able to manipulate without any identification set_ip_range(sysadmin_profile_name, admin_sid) # -----Super-Admin----- # -----Admin 1--Because of weak lockout policy, it triggers # Security Control: Lockout effective period -super-admin change_lockout_period(admin_sid) # -----Admin 1--Disable clickjack protection for customer Visualforce pages with standard headers disable_clickjack_with_standard_headers(admin_sid) # -----Admin 1--Creating 4 users - due to license limitations, # the other 2 will be Force.com Free users. create_user(LSL_USER1_USERNAME, LSL_USER1_ALIAS, LSL_USER1_USERNAME, LSL_USER1_USERNAME, 'Standard Platform User') create_user(LSL_USER2_USERNAME, LSL_USER2_ALIAS, LSL_USER2_USERNAME, LSL_USER2_USERNAME, 'Force.com - Free User') create_user(LSL_USER3_USERNAME, LSL_USER3_ALIAS, LSL_USER3_USERNAME, LSL_USER3_USERNAME, 'Force.com - Free User') create_user(LSL_USER4_USERNAME, LSL_USER4_ALIAS, LSL_USER4_USERNAME, LSL_USER4_USERNAME, 'Force.com - App' 'Subscription User') # -----Admin 1--set IP range (for admin profile) - making sure we # will be able to manipulate without any identification set_ip_range(sysadmin_profile_name, admin_sid) # Path 1: Account compromise -- User1 # -----User 1--brute force login, Attacker brute forced account successfully, # triggers Threat: Failed login(e.g. 5 average, 2x) switch_user_profile_or_role(LSL_USER1_USERNAME, 'System Administrator') # failUserLogins(SFDC_TEST_USER1, "X", num_failed_attempts) # -----User 1--Login from remote triggers UBA Risk User: High, activity from unseen browser, # device, OS, unseen location(including unseen IPs v2) (score approx: 45-50) # failUserLogins(SFDC_TEST_USER1, SFDC_TEST_USER1_PASSWORD, num_failed_attempts, tor_proxy_ip, # tor_proxy_port, "Mozilla/1.0 (Windows CE 0.1; Win63; x63; rv:1.1) GeckoX/20100101 Firebug/0.1") # -----User 1-----UBA Risk User: 10x High, Data export --- Instead of this, # Attacker set Trusted IP Range to enable backdoor access, triggers Policy alert. # To verify, in the UI this is at "Network Access" set_trusted_ip_range(howmany_trusted_ip_range_sets, 'lsl-TrustRange-' + random_string_generator(4), '192.168.0.11', '192.168.0.200', LSL_USER1_USERNAME, default_user_password) switch_user_profile_or_role(LSL_USER1_USERNAME, 'Standard Platform User') # Path 2: Data exfiltration -- User2 # -----User 2--Grant Admin permissions switch_user_profile_or_role(LSL_USER2_USERNAME, 'System Administrator') # -----User 2--60+(configurable) Mass Transfer to another account, # triggers UBA Risk User: Medium, Mass Transfer+After-hr. # Creating given numbers of mockup account data to have something to transfer. LSL_USER2_FULLNAME = get_user_full_name(LSL_USER2_USERNAME) admin1_full_name = get_user_full_name(ADMIN1_USERNAME) create_mockup_account(howManyMockupAccounts, ADMIN1_USERNAME) mass_transfer(LSL_USER2_USERNAME, default_user_password, admin1_full_name, LSL_USER2_FULLNAME, how_many_mass_transfers) switch_user_profile_or_role(LSL_USER2_USERNAME, 'Force.com - Free User') # Path#3: Insider Threat--User3 # -----User 3--Admin grant excessive permissions to insider user, triggers Policy alert: # Profile/Change user permissions switch_user_profile_or_role(LSL_USER3_USERNAME, 'System Administrator') # -----User 3--We deploy new Sharing Rules as an insider threat. # We have some static XML content and if we want to add multiple rules, # don't want to add the header all the time. # create some mockup sharing rules. create_zip_objects() add_lead_sharing_rule(how_many_sharing_rules, "Read") close_rules() deploy_zipfile(LSL_USER3_USERNAME, default_user_password) # -----User 3--3-Insider user is corrupted by a vendor, he helped vendor to extend # contract term, triggers Policy alert: Contract Create+Update response = create_mockup_contract(LSL_USER3_USERNAME, "lsl-Account-firstMockup", "3", "2016-03-01") update_contract(response['id']) # -----User 3--4-Before termination, insider user also Mass deleting data, # triggers UBA Risk User: High, Mass Delete for x in range(0, howManyMassDelete): create_mockup_account(howManyMockupAccounts, LSL_USER3_USERNAME) mass_delete(LSL_USER3_USERNAME, default_user_password) print("Mass Delete iteration nr.: " + str(x)) # -----User 3--Policy alert: Change user profile switch_user_profile_or_role(LSL_USER3_USERNAME, 'Force.com - Free User') # Path 4: Insider Threat--User4 # -----User 4--UBA Risk User: 20x Medium, Reports export, Report Run # 2 - The 3rd party has the permission to access sensitive data and function, # he run and export the reports, sale to competitor, triggers UBA Risk User: Medium, # Reports exported, Report Run # 3 - The 3rd party also export data, triggers UBA Risk User: High, Data Export # 4 - For all report activities by the 3rd party, stand out in KSI: # Top customer report run and Top customer report exported switch_user_profile_or_role(LSL_USER4_USERNAME, 'System Administrator') report_name = create_report(howManyReportsCreate, LSL_USER4_USERNAME, default_user_password, "Accounts") export_report(how_many_export_reports, report_name, LSL_USER4_USERNAME, default_user_password) switch_user_profile_or_role(LSL_USER4_USERNAME, 'Force.com - App Subscription User') # Creating a user def create_user(username, alias, email, last_name, profile_name): """ :param username: :param alias: :param email: :param last_name: :param profile_name: :return: """ profile_id = get_profile_id(profile_name) try: sf.User.create({'userName': username, 'Alias': alias, 'Email': email, 'lastName': last_name, 'EmailEncodingKey': 'UTF-8', 'TimeZoneSidKey': 'America/New_York', 'LocaleSidKey': 'en_US', 'profile_id': profile_id, 'LanguageLocaleKey': 'en_US'}) set_password(username, default_user_password) except Exception as e: try: activate_user(username) set_password(username, default_user_password) except Exception as e: set_password(username, default_user_password) def get_user_full_name(username): """ :param username: :return: """ userinfo = sf.query("SELECT FirstName, LastName FROM User WHERE username = '" + username + "'") dict = collections.OrderedDict(userinfo) dictitems = list(dict.values())[2] firstname = list(collections.OrderedDict(dictitems.pop()).values())[1] lastname = list(collections.OrderedDict(dictitems.pop()).values())[2] if firstname is None: fullname = lastname else: fullname = firstname + " " + lastname return fullname # Resetting a user's password def set_password(username, default_user_password): """ :param username: :param default_user_password: :return: """ uid = get_user_id(username) print("\nDefaulting Password for user with UID: " + uid + "\n") sf2 = beatbox.PythonClient() sf2.login(ADMIN1_USERNAME, ADMIN1_PASSWORD) try: sf2.setPassword(uid, default_user_password) except Exception as e: pass # Login for all users, keep session Ids def get_user_sid(username, password): """ :param username: :param password: :return: """ login_headers = { 'content-type': 'text/xml', 'charset': 'UTF-8', 'SOAPAction': 'login' } login_envelope = """ <soapenv:Envelope xmlns:soapenv="http://schemas.xmlsoap.org/soap/envelope/" xmlns:urn="urn:enterprise.soap.sforce.com"> <soapenv:Header> </soapenv:Header> <soapenv:Body> <urn:login> <urn:username>""" + '' + username + '' + """</urn:username> <urn:password>""" + '' + password + '' + """</urn:password> </urn:login> </soapenv:Body> </soapenv:Envelope> """ login_response = requests.post(partnerURL, login_envelope, headers=login_headers) dom = xml.dom.minidom.parseString(login_response.text) user_sid_result = dom.getElementsByTagName('sessionId') if user_sid_result[0].firstChild.nodeValue is None: print("\nI wasn't successful. Error was:\n") print(login_response.text + '\n') else: user_sid = user_sid_result[0].firstChild.nodeValue return user_sid # This is useful in general to manipulate any user's details def get_user_id(username): """ :param username: :return: """ # Userinfo is an OrderedDict that contains a list that contains another OrderedDict so we need to dig in a bit. userinfo = sf.query("SELECT Id FROM User WHERE username = '" + username + "'") dict = collections.OrderedDict(userinfo) dictitems = list(dict.values())[2] itemlist = (dictitems.pop()) dict2 = collections.OrderedDict(itemlist) uid = list(dict2.values())[1] return uid def get_user_profile_id(which_user): """ :param which_user: :return: """ query = sf.query("SELECT ProfileId FROM User where username = '" + which_user + "'") dictitems = list(collections.OrderedDict(query).values())[2] if len(dictitems) == 0: print("Could not get System Administrator Profile Id. Continuing...\n") return None else: prof_id = list(collections.OrderedDict(dictitems.pop()).values())[1] return prof_id def get_profile_id(profile_name): """ :param profile_name: :return: """ query = sf.query("SELECT Id FROM Profile WHERE name = '" + profile_name + "'") dictitems = list(collections.OrderedDict(query).values())[2] if len(dictitems) == 0: print("Could not get System Administrator Profile Id. Continuing...\n") return None else: prof_id = list(collections.OrderedDict(dictitems.pop()).values())[1] return prof_id def switch_user_profile_or_role(user1, user1_profile, user2_profile=None, how_many_times=None): """ :param user1: :param user1_profile: :param user2_profile: :param how_many_times: :return: """ if how_many_times is None: user_id = get_user_id(user1) switch_to_profile_id = get_profile_id(user1_profile) sf.User.update(user_id, {'ProfileId': '' + switch_to_profile_id + ''}) else: while how_many_times > 0: user_id = get_user_id(user1) get_user_profile_id(user1) switch_between1 = get_profile_id(user1_profile) switch_between2 = get_profile_id(user2_profile) sf.User.update(user_id, {'ProfileId': '' + switch_between2 + ''}) print("The " + user1 + "'s profile switched from " + switch_between1 + " to " + switch_between2 + " Profile Id.") get_user_profile_id(user1) sf.User.update(user_id, {'ProfileId': '' + switch_between1 + ''}) print("The " + user1 + "'s profile switched from " + switch_between2 + " to " + switch_between1 + " Profile Id.") print("UserProfile switches left: " + str(how_many_times - 1)) how_many_times -= 1 # Reactivate a user if existing def activate_user(username): """ :param username: :return: """ userinfo = sf.query("SELECT IsActive FROM User WHERE username = '" + username + "'") itemlist = (userinfo.values())[2] dictitems = list(collections.OrderedDict(userinfo).values())[2] is_active = list(collections.OrderedDict(dictitems.pop()).values())[1] if not is_active: print("User exists, but is not active. Activating.") sf.User.update(get_user_id(username), {'IsActive': 'true'}) else: print("User is active, no need to re-enable.") def create_mockup_account(how_many, owner): """ :param how_many: :param owner: :return: """ owner_id = get_user_id(owner) sf.Account.create({'type': 'Account', 'Name': 'lsl-Account-firstMockup', 'Website': 'http://www.IamJustAtestWebSite.com', 'owner_id': '' + owner_id + ''}) acc_list = ['lsl-Account-firstMockup'] how_many -= 1 while how_many > 0: test_data = "lsl-Account-" + random_string_generator(8) owner_id = get_user_id(owner) sf.Account.create({'type': 'Account', 'Name': '' + test_data + '', 'Website': 'http://www.IamJustAtestWebSite.com', 'owner_id': '' + owner_id + ''}) print("Some mockup Account " + test_data + " for user: " + owner + " created.") acc_list.append(test_data) how_many -= 1 print("Following mockup Accounts have been created: " + str(acc_list)) return acc_list def get_account_id(account_name): """ :param account_name: :return: """ userinfo = sf.query("SELECT Id FROM Account WHERE Name = '" + account_name + "'") acc_id = list(collections.OrderedDict(list(collections.OrderedDict(userinfo).values())[2].pop()).values())[1] return acc_id def create_mockup_contract(owner, account_name, contract_term, start_date): """ :param owner: :param account_name: :param contract_term: :param start_date: :return: """ account_id = get_account_id(account_name) data1 = sf.Contract.create({'AccountId': account_id, 'ContractTerm': contract_term, 'StartDate': start_date, 'owner_id': get_user_id(owner)}) print("Mockup contract for Account " + account_id + " created.") return data1 def update_contract(user_id): """ :param user_id: :return: """ sf.Contract.update(user_id, {'ContractTerm': '75'}) def set_ip_range(profile_name, admin_sid): """ :param profile_name: :param admin_sid: :return: """ update_metadata_envelope = """ <env:Envelope xmlns:env="http://schemas.xmlsoap.org/soap/envelope/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> <env:Header> <urn:SessionHeader xmlns:urn="http://soap.sforce.com/2006/04/metadata"> <urn:sessionId>""" + admin_sid + """</urn:sessionId> </urn:SessionHeader> </env:Header> <env:Body> <updateMetadata xmlns="http://soap.sforce.com/2006/04/metadata"> <metadata xsi:type="Profile"> <fullName>""" + profile_name + """</fullName> <loginIpRanges> <endAddress>255.255.255.255</endAddress> <startAddress>0.0.0.0</startAddress> </loginIpRanges> </metadata> </updateMetadata> </env:Body> </env:Envelope> """ soap_response = requests.post(metadata_url, update_metadata_envelope, headers=updateMetadataHeader) dom = xml.dom.minidom.parseString(soap_response.text) result_element = dom.getElementsByTagName('success') result_value = result_element[0].firstChild.nodeValue if len(result_value) == 0: print("I've encountered an issue. Request response:\n") print(soap_response.text + "\n") return None else: if result_element[0].firstChild.nodeValue: print("Login IP range successfully set.") else: print("I've encountered an issue. Request response:\n") print(soap_response.text + "\n") return None def change_lockout_period(admin_sid): """ :param admin_sid: :return: """ soap_body = """ <env:Envelope xmlns:env="http://schemas.xmlsoap.org/soap/envelope/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> <env:Header> <urn:SessionHeader xmlns:urn="http://soap.sforce.com/2006/04/metadata"> <urn:sessionId>""" + admin_sid + """</urn:sessionId> </urn:SessionHeader> </env:Header> <env:Body> <updateMetadata xmlns="http://soap.sforce.com/2006/04/metadata"> <metadata xsi:type="SecuritySettings"> <fullName>*</fullName> <passwordPolicies> <lockoutInterval>""" + lockout_interval + """</lockoutInterval> </passwordPolicies> </metadata> </updateMetadata> </env:Body> </env:Envelope> """ soap_result = requests.post(metadata_url, soap_body, headers=updateMetadataHeader) dom = xml.dom.minidom.parseString(soap_result.text) result_element = dom.getElementsByTagName('success') result_value = result_element[0].firstChild.nodeValue if len(result_value) == 0: print("I've encountered an issue. Request response:\n") print(soap_result.text + "\n") return None else: if result_element[0].firstChild.nodeValue: print("New Lockout time successfully set.") else: print("I've encountered an issue. Request response:\n") print(soap_result.text + "\n") return None def disable_clickjack_with_standard_headers(admin_sid): """ :param admin_sid: :return: """ soap_body = """ <env:Envelope xmlns:env="http://schemas.xmlsoap.org/soap/envelope/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> <env:Header> <urn:SessionHeader xmlns:urn="http://soap.sforce.com/2006/04/metadata"> <urn:sessionId>""" + admin_sid + """</urn:sessionId> </urn:SessionHeader> </env:Header> <env:Body> <updateMetadata xmlns="http://soap.sforce.com/2006/04/metadata"> <metadata xsi:type="SecuritySettings"> <fullName>*</fullName> <sessionSettings> <enableClickjackNonsetupUser>false</enableClickjackNonsetupUser> </sessionSettings> </metadata> </updateMetadata> </env:Body> </env:Envelope> """ soap_result = requests.post(metadata_url, soap_body, headers=updateMetadataHeader) dom = xml.dom.minidom.parseString(soap_result.text) result_element = dom.getElementsByTagName('success') result_value = result_element[0].firstChild.nodeValue if len(result_value) == 0: print("I've encountered an issue. Request response:\n") print(soap_result.text + "\n") return None else: if result_element[0].firstChild.nodeValue: print("Successfully disabled clickjack protection for customer Visualforce pages with standard headers.") else: print("I've encountered an issue. Request response:\n") print(soap_result.text + "\n") return None def random_string_generator(nr): """ :param nr: :return: """ rand_string = (''.join(choice(ascii_lowercase) for i in range(nr))) return rand_string def create_zip_objects(): """ :return: """ if not os.path.exists(os.path.dirname(rulefile)): try: os.makedirs(os.path.dirname(rulefile)) except Exception as e: pass with open(rulefile, "w") as f: f.write("""<?xml version="1.0" encoding="UTF-8"?> <SharingRules xmlns="http://soap.sforce.com/2006/04/metadata">""" + "\n") with open('./tmp/unpackaged/package.xml', "w") as f: f.write("""<?xml version="1.0" encoding="UTF-8"?> <Package xmlns="http://soap.sforce.com/2006/04/metadata"> <types> <members>*</members> <name>SharingRules</name> </types> <version>35.0</version> </Package>""" + "\n") def add_lead_sharing_rule(how_many, access_level): """ :param how_many: :param access_level: :return: """ while how_many > 0: full_name = "lsl_" + random_string_generator(4) label = "lsl-" + random_string_generator(5) with open(rulefile, "a") as f: f.write(""" <sharingOwnerRules> <full_name>""" + full_name + """</full_name> <accessLevel>""" + access_level + """</accessLevel> <label>""" + label + """</label> <sharedTo> <allInternalUsers></allInternalUsers> </sharedTo> <sharedFrom> <allInternalUsers></allInternalUsers> </sharedFrom> </sharingOwnerRules>""" + "\n") print("Lead sharing rule with label: " + label + " successfully created.") how_many -= 1 def close_rules(): with open(rulefile, "a+") as f: f.write("""</SharingRules>""" + "\n") def get_report_id(report_name, as_user, as_password): """ :param report_name: :param as_user: :param as_password: :return: """ user_sid = get_user_sid(as_user, as_password) sf2 = Salesforce(instance_url=instanceURL, session_id=user_sid) query = sf2.query("SELECT Id FROM Report WHERE Name = '" + report_name + "'") dictitems = list(collections.OrderedDict(query).values())[2] report_id = list(collections.OrderedDict(dictitems.pop()).values())[1] if len(collections.OrderedDict(dictitems.pop())) == 0: print("Could not get report_id.\n") return None else: return report_id, user_sid def export_report(how_many, report_name, as_user, as_password): """ :param how_many: :param report_name: :param as_user: :param as_password: :return: """ (report_id, user_sid) = get_report_id(report_name, as_user, as_password) while how_many > 0: response = requests.get(instanceURL + "/" + report_id + "?view=d&snip&export=1&enc=UTF-8&excel=1", headers=sf.headers, cookies={'sid': user_sid}) f = open("lsl-report-" + random_string_generator(4) + ".csv", 'w') f.write(response.text) f.close() how_many -= 1 def deploy_zipfile(as_user, as_password): """ :param as_user: :param as_password: :return: """ user_sid = get_user_sid(as_user, as_password) new_zip = zipfile.ZipFile(packageZipFile, "w") dir_path = './tmp' len_dir_path = len(dir_path) for root, _, files in os.walk(dir_path): for file in files: file_path = os.path.join(root, file) new_zip.write(file_path, file_path[len_dir_path:]) new_zip.close() with open(packageZipFile, "rb") as f: bytes_read = f.read() encoded = base64.b64encode(bytes_read) b64code = encoded.decode("utf-8") deploy_headers = { 'content-type': 'text/xml', 'charset': 'UTF-8', 'SOAPAction': 'deploy' } deploy_body = """<soapenv:Envelope xmlns:soapenv="http://schemas.xmlsoap.org/soap/envelope/" xmlns:met="http://soap.sforce.com/2006/04/metadata"> <soapenv:Header> <met:SessionHeader> <sessionId>""" + user_sid + """</sessionId> </met:SessionHeader> </soapenv:Header> <soapenv:Body> <deploy xmlns="http://soap.sforce.com/2006/04/metadata"> <ZipFile>""" + b64code + """</ZipFile> <DeployOptions> <allowMissingFiles>false</allowMissingFiles> <autoUpdatePackage>true</autoUpdatePackage> <checkOnly>false</checkOnly> <ignoreWarnings>false</ignoreWarnings> <performRetrieve>false</performRetrieve> <rollbackOnError>true</rollbackOnError> <runAllTests>false</runAllTests> <singlePackage>false</singlePackage> </DeployOptions> </deploy> </soapenv:Body> </soapenv:Envelope>""" soap_result = requests.post(metadata_url, deploy_body, headers=deploy_headers) dom = xml.dom.minidom.parseString(soap_result.text) result_element = dom.getElementsByTagName('id') result_value = result_element[0].firstChild.nodeValue if len(result_value) == 0: print("I've encountered an issue. Request response:\n") print(soap_result.text + "\n") return None else: if result_element[0].firstChild.nodeValue: print("Got deployment ID.") did = result_element[0].firstChild.nodeValue else: print("I've encountered an issue. Request response:\n") print(soap_result.text + "\n") return None time.sleep(2) check_deploy_headers = { 'content-type': 'text/xml', 'charset': 'UTF-8', 'SOAPAction': 'checkDeployStatus' } check_deploy_status = """<soapenv:Envelope xmlns:soapenv="http://schemas.xmlsoap.org/soap/envelope/" xmlns:met="http://soap.sforce.com/2006/04/metadata"> <soapenv:Header> <met:SessionHeader> <sessionId>""" + user_sid + """</sessionId> </met:SessionHeader> </soapenv:Header> <soapenv:Body> <met:checkDeployStatus> <met:asyncProcessId>""" + did + """</met:asyncProcessId> <met:includeDetails>true</met:includeDetails> </met:checkDeployStatus> </soapenv:Body> </soapenv:Envelope>""" soap_result = requests.post(metadata_url, check_deploy_status, headers=check_deploy_headers) dom = xml.dom.minidom.parseString(soap_result.text) result_element = dom.getElementsByTagName('status') result_value = result_element[0].firstChild.nodeValue if len(result_value) == 0: print("I've encountered an issue. Request response:\n") print(soap_result.text + "\n") return None else: if result_element[0].firstChild.nodeValue == 'Succeeded': print("Deployment succeeded.") else: print("I've encountered an issue. Request response:\n") print(soap_result.text + "\n") return None # UBA Risk User: 10x High, Set Trusted IP range. def set_trusted_ip_range(count, description, start_ip, end_ip, owner, password): """ :param count: :param description: :param start_ip: :param end_ip: :param owner: :param password: :return: """ user_sid = get_user_sid(owner, password) soap_body_part1 = """ <env:Envelope xmlns:env="http://schemas.xmlsoap.org/soap/envelope/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> <env:Header> <urn:SessionHeader xmlns:urn="http://soap.sforce.com/2006/04/metadata"> <urn:sessionId>""" + user_sid + """</urn:sessionId> </urn:SessionHeader> </env:Header> <env:Body> <updateMetadata xmlns="http://soap.sforce.com/2006/04/metadata"> <metadata xsi:type="SecuritySettings"> <fullName>*</fullName> <networkAccess>""" soap_body_part2 = """ </networkAccess> </metadata> </updateMetadata> </env:Body> </env:Envelope> """ while count > 0: ip_range = """ <ipRanges> <description>""" + description + """</description> <start>""" + start_ip + """</start> <end>""" + end_ip + """</end> </ipRanges>""" requests.post(metadata_url, soap_body_part1 + ip_range + soap_body_part2, headers=updateMetadataHeader) print("Added trusted IP Range " + str(count) + " time(s).") requests.post(metadata_url, soap_body_part1 + soap_body_part2, headers=updateMetadataHeader) print("Deleted trusted IP Ranges " + str(count) + " times.") count -= 1 if __name__ == "__main__": main()

[ "fkoncz@outlook.com" ]

fkoncz@outlook.com

61bb250a081ee773aa1aa16082aa203ec3a1eefd

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/KYLIN_USB/sources/spark/load/sources/020-mds_elt_niveau.py

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bmwalid/Controlla

85718a94e47acdc2a90ee06b951282954dc1ed11

c63febd9aeb1ae268b79ade1880d867a888554d0

refs/heads/master

2020-04-29T16:15:27.646599

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# import Libraries from pyspark.conf import SparkConf from pyspark.sql import SparkSession from pyspark.sql.types import * # init sparkConf conf = SparkConf() conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer") \ .set("spark.executor.cores", "1") \ .set("spark.executor.memory", "1G") # Initialize Spark Session spark = SparkSession.builder.appName("020-mds_elt_niveau").config(conf=SparkConf()).enableHiveSupport().getOrCreate() # Data path path = "s3://decathlon-ods/ods/mds_elt_niveau/*.gz" # From gz files on S3 to Spark Dataframe df = spark.read.option("header", "false").option("delimiter", "|").csv(path) # write to hdfs df.select("_c0", "_c1", "_c2", "_c6", "_c7", "_c3", "_c4", "_c5") \ .withColumn("org_num_organisation_niv", df["_c0"].cast(IntegerType())) \ .withColumn("niv_num_niveau_niv", df["_c1"].cast(StringType())) \ .withColumn("eln_num_elt_niveau", df["_c2"].cast(IntegerType())) \ .withColumn("tlb_typ_libelle_lib", df["_c6"].cast(StringType())) \ .withColumn("lib_num_libelle_lib", df["_c7"].cast(IntegerType())) \ .withColumn("org_num_organisation_sup", df["_c3"].cast(IntegerType())) \ .withColumn("niv_num_niveau_sup", df["_c4"].cast(IntegerType())) \ .withColumn("eln_num_elt_niveau_sup", df["_c5"].cast(IntegerType())) \ .select("org_num_organisation_niv", "niv_num_niveau_niv", "eln_num_elt_niveau", "tlb_typ_libelle_lib", "lib_num_libelle_lib", "org_num_organisation_sup", "niv_num_niveau_sup", "eln_num_elt_niveau_sup") \ .repartition(80).write.option("compression", "snappy").mode("overwrite").format("parquet").saveAsTable( "kylin_usb_mqb.mds_elt_niveau") # stopping session spark.sparkContext.stop()

[ "bmwalide@gmail.com" ]

bmwalide@gmail.com

413fe0cf74f78a5479abcffb6ba6f1b944f65717

59b87e4892a583e1eafaeca8582320b3db6e4435

/.c9/metadata/environment/products/views.py

756c77246def161c889dabccb347e037f5dd2284

[]

no_license

cgaynor91/E-Commerce

65e112b4a2c66725d27a65847686c497574d1f58

f7e3e81358f494cd16768e4aba73b19bc16a29ab

refs/heads/master

2021-07-11T23:26:22.895787

2020-03-11T21:47:28

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2021-06-10T22:39:00

2020-03-10T21:32:01

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[ "ubuntu@ip-172-31-36-97.ec2.internal" ]

ubuntu@ip-172-31-36-97.ec2.internal

d9cb619d465d33a6f910d50958beb4ca360e904b

47a15501446aa286f89e9ac0f751945d6b86829b

/bqdc.py

261399d44024c3c85083a7f55f72e7373b1f8f09

[ "MIT" ]

permissive

karlo0/bqdc

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1aca9dcd2c519e9ade1988bdee47cf98d51c10f8

refs/heads/master

2023-08-24T00:42:19.044385

2023-08-14T23:43:35

2023-08-17T22:18:04

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MIT

2023-08-17T22:18:05

2019-08-21T08:39:04

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r""" bqdc.py Python Module to download, upload metadata (Datacatalog Tags, BigQuery table, field descriptions and schema) from Bigquery Tables and its attached tags in DataCatalog. It can synchronize table descriptions and field descriptions from within Bigquery and constructs tags in Datacatalog for this metadata. The main funcions are the following: - The download function stores metadata in an Excel .xlsx file - The upload function uses the metadata from an Excel .xlsx file (e.g. obtained from a previous download which has since then been updated) and uploads it to BigQuery and DataCatalog - The synchronize function downloads the metadata and uploads it again to BigQuery and DataCatalog. This can be used to synchronize table and field description metadata that is found in one of the two GCP apps to upload it to the other too, if it has not been there before Please check the jupyter notebooks for more detailed information. The module can only be used when the following conditions are met: - 2 tag templates in DataCatalog are specified: - A tag template that is used to attach tags to whole BigQuery tables, in the following referred to as table tag template - A tag template that is used to attach tags to fields of BigQuery tables, in the following referred to as field tag template - The table tag template is required to have an attribute with key name 'table_description', that is intended to store table descriptions similar to the attribute 'description' of the BigQuery 'Table' class - The field tag template is required to have an attribute with key name 'field_description', that is intended to store field/column descriptions similar to the attribute 'description' of the BigQuery 'SchemaField' class Version: 0.1 Author: Karsten Leonhardt Date: 21.08.2019 """ # Connect to the Google Data Catalog python modul from google.cloud import datacatalog_v1beta1 # Connect to the Google BigQuery python modul from google.cloud import bigquery # Connect to the Google Authentification python modul from google.oauth2 import service_account import pandas as pd from collections import OrderedDict import os import shutil import re import glob from functools import reduce class clients: type_table_ref_bq = bigquery.TableReference # BigQuery maximum string length bq_max_string_length = 1024 # DataCatalog maximum string length dc_max_string_length = 2000 # the current path CDIR = os.path.dirname(os.path.realpath(__file__)) def __init__(self, PROJECT_ID, PATH_SERVICE_ACCOUNT_KEY): self.P_ID = PROJECT_ID self.DS_ID = None """get credentials through service account file""" self.credentials = service_account.Credentials.from_service_account_file(PATH_SERVICE_ACCOUNT_KEY) """establish a datacatalog client""" self.dc_client = datacatalog_v1beta1.DataCatalogClient(credentials=self.credentials) """establish a BigQuery client""" self.bq_client = bigquery.Client(project=PROJECT_ID, credentials = self.credentials) class toolbox(clients): pattern_table_descr_bq_pure = re.compile(r"(?P<descr>^[\s\S]*?)\s*Table attributes") # max length of sheet names in Excel excel_max_sheet_name_length = 31 overview_sheet_name = 'metadata_of_tables' def __init__(self, PROJECT_ID, PATH_SERVICE_ACCOUNT_KEY = None, prefer_bq_for_downlad_update = True, logfile = '', do_print_log = False): """ This class establishes a connection to both Bigquery and Datacatalog clients and allows for the manipulation and creation of tags in Datacatalog attached to Bigquery tables and the manipulation of BigQuery table schemas. Parameters: ----------- PROJECT_ID: String Specifies the GCP Project ID of which resources in BigQuery and Datacatalog are requested. PATH_SERVICE_ACCOUNT_KEY: String, None (Default) The full path to the Json file containing the service account key. If no string is provided, it searches for a .json file in the current directory and tries to connect to the BQ and DC clients with this file. prefer_bq_for_download_update: False, True (Default) When set to true, the table description of BQ is prefered over the DC table description when it exists. logfile: String, '' (Default) When the specified string is not empty it will created in the current directory a logfile with the specified string as name. If not provided, no logfile is written do_print_log: True, False (Default) if 'True' print log status messages to the stdout aka the screen Return: ------- Instance of class 'toolbox' """ assert isinstance(PROJECT_ID, str), "The 'PROJECT_ID' argument requires a string to specify the project ID to the GCP project for which BigQuery and DataCatalog resources are requested." if PATH_SERVICE_ACCOUNT_KEY is None: service_key_list = glob.glob('*.json') if len(service_key_list) == 1: PATH_SERVICE_ACCOUNT_KEY = os.path.join(self.CDIR, service_key_list[0]) elif len(service_key_list) == 0: raise Exception("No service account key found in the current folder. Please initialise the object with the 'PATH_SERVICE_ACCOUNT_KEY' argument set to the full path (including the json filename with .json extension) of the service account key") else: raise Exception("There are more than one .json files in the current folder. Please initialise the object with the 'PATH_SERVICE_ACCOUNT_KEY' argument set to the full path (including the json filename with .json extension) of the service account key") super().__init__(PROJECT_ID, PATH_SERVICE_ACCOUNT_KEY) self.sheet = None self.ds_table_tags_df = None self.ds_field_tags_dicts = None self.table_instance_dc = None self.table_instance_bq = None self.__table_id = None self.__table_id_dc = '' self.__prefer_bq_for_downlad_update = prefer_bq_for_downlad_update self.__update = False self.__do_print_log = do_print_log if len(logfile) > 0: self.__do_log = True self.__log = '' self.__logfile = logfile else: self.__do_log = False def init_tag_templates(self, table_tag_template_str = None, field_tag_template_str = None, LOCATION_TAGS = 'us-central1', table_tag_fields_keys_ordered = [], field_tag_fields_keys_ordered = []): """ Initializes tag templates. The whole class requires 2 tag templates: - a tag template whose id is specified by the 'table_tag_template_str' argument and which is used to attach tags to tables themselves - a tag template whose id is specified by the 'field_tag_template_str' argument and which is used to attach tags to fields of tables table_tag_template_str: String, None (Default) specifies an ID of a tag template that is used to attach tags to tables. The default is None, however the initialisation fails when no string is provided field_tag_template_str: String, None (Default) specifies an ID of a tag template that is used to attach tags to fields of tables. The default is None, however the initialisation fails when no string is provided LOCATION_TAGS: String, 'us-central1' (Default) The location of the tags. At the moment only 'us-central1' is supported table_tag_fields_keys_ordered: List of Strings, Empty lis (Default) A list of the table tag template attribute keys ordered in a list. If this is not provided the internal ordering of the attribute keys is used to set up DataFrame columns field_tag_fields_keys_ordered: List of Strings, Empty lis (Default) A list of the field tag template attribute keys ordered in a list. If this is not provided the internal ordering of the attribute keys is used to set up DataFrame columns """ assert isinstance(table_tag_template_str, str), "A string must be passed for the 'table_tag_template_str' argument to specify an ID of a tag template that is used to attach tags to tables" assert isinstance(field_tag_template_str, str), "A string must be passed for the 'field_tag_template_str' argument to specify an ID of a tag template that is used to attach tags to fields of tables" self.TABLE_TAG_TEMPLATE_STR = table_tag_template_str self.FIELD_TAG_TEMPLATE_STR = field_tag_template_str TABLE_TAG_TEMPLATE_PATH=self.dc_client.tag_template_path(self.P_ID, LOCATION_TAGS, self.TABLE_TAG_TEMPLATE_STR) FIELD_TAG_TEMPLATE_PATH=self.dc_client.tag_template_path(self.P_ID, LOCATION_TAGS, self.FIELD_TAG_TEMPLATE_STR) try: self.table_tag_template = self.dc_client.get_tag_template(TABLE_TAG_TEMPLATE_PATH) except: msg = "Referencing the tag template used for attaching tags to whole tables failed" raise Exception(msg) try: self.field_tag_template = self.dc_client.get_tag_template(FIELD_TAG_TEMPLATE_PATH) except: msg = "Referencing the tag template used for attaching tags to fields of tables failed" raise Exception(msg) assert 'table_description' in self.table_tag_template.fields.keys(), "The tag template used for attaching tags to whole tables must contain an attribute with key ID = 'table_description'" assert 'field_description' in self.field_tag_template.fields.keys(), "The tag template used for attaching tags to fields of tables must contain an attribute with key ID = 'field_description'" self.__table_tag_fields_keys_ordered = self.__check_complete_ordered_list_of_keys(table_tag_fields_keys_ordered, self.table_tag_template.fields.keys()) self.__field_tag_fields_keys_ordered = self.__check_complete_ordered_list_of_keys(field_tag_fields_keys_ordered, self.field_tag_template.fields.keys()) self.__field_keys_to_ndx = {field_keys: k for k, field_keys in enumerate(self.__field_tag_fields_keys_ordered)} self.__upload_table_description_bq_init() pass def __check_complete_ordered_list_of_keys(self, ordered_keys_in, keys): set_keys_intersect = set(ordered_keys_in).intersection(set(keys)) set_remaining_keys = set(keys) - set_keys_intersect ordered_keys = [key for key in ordered_keys_in if key in set_keys_intersect] for key in set_remaining_keys: ordered_keys.append(key) return ordered_keys def set_dataset(self, DS_ID): self.DS_ID = DS_ID pass def get_table_instance_dc(self, table_id, return_instance = False): resource_name = "//bigquery.googleapis.com/projects/{}/datasets/{}/tables/{}".format(self.P_ID, self.DS_ID, table_id) self.table_instance_dc = self.dc_client.lookup_entry(linked_resource=resource_name) if self.__table_id != table_id: self.__table_id = table_id if return_instance: return self.table_instance_dc else: pass def get_table_instance_bq(self, table_x, return_instance = False): if(isinstance(table_x, self.type_table_ref_bq)): self.table_instance_bq = self.bq_client.get_table(table_x) elif(isinstance(table_x, str)): try: self.table_instance_bq = self.bq_client.get_table(self.P_ID+'.'+self.DS_ID+'.'+table_x) except: raise Exception('The table can not be found under the specified PROJECT_ID/DATASET_ID') pass else: raise Exception('String or table_reference required as argument') if self.__table_id != self.table_instance_bq.table_id: self.__table_id = self.table_instance_bq.table_id self.get_bq_schema_metadata() if return_instance: return self.table_instance_bq else: pass def list_all_tags_entry(self, entry = None): """ Prints all the tags attached to an entry (here an entry is a table instance) """ if entry is None: entry = self.table_instance_dc if entry is not None: for tag in self.dc_client.list_tags(entry.name): print(tag) else: raise Exception('\nNo datacatalog entry instance provided. Call method again as ''list_all_tags_entry(entry)'' with entry a datacatalog entry instance') pass def get_all_tags_table(self, entry = None, delete_tags_not_in_bq_schema = False, make_field_sheet_df = False): if entry is None: entry = self.table_instance_dc if entry is not None: tags = self.dc_client.list_tags(entry.name) update_table_instance_bq = False try: if self.__table_id != self.table_instance_bq.table_id: update_table_instance_bq = True except: update_table_instance_bq = True if update_table_instance_bq: self.get_table_instance_bq(self.__table_id) tag_columns = [] tag_list = [] if make_field_sheet_df: field_vals = [[] for i in range(len(self.__field_keys_to_ndx))] field_names = [] for tag in tags: if tag.template == self.field_tag_template.name: tag_column_lower = tag.column.lower() if tag_column_lower in self.schema_bq_df.index: tag_columns.append(tag_column_lower) tag_list.append(tag) if make_field_sheet_df: field_names.append(tag_column_lower) for attr in self.__field_keys_to_ndx.keys(): if attr in tag.fields.keys(): field_vals[self.__field_keys_to_ndx[attr]].append(tag.fields[attr].string_value) else: field_vals[self.__field_keys_to_ndx[attr]].append('') else: if delete_tags_not_in_bq_schema: self.dc_client.delete_tag(tag.name) else: tag_columns.append(tag.column) tag_list.append(tag) if make_field_sheet_df: field_tags_df = pd.DataFrame.from_dict(dict(zip(self.__field_tag_fields_keys_ordered, field_vals))).set_index(pd.Index(field_names)).applymap(lambda x: '' if x is None else x).astype(str).fillna('') self.sheet = self.schema_bq_df.join(field_tags_df,lsuffix='_bq', rsuffix='_dc').fillna('') n_cols = len(self.sheet.columns) self.sheet.insert(n_cols - 1,'field_description', [ row['field_description_dc'] if ( row['field_description_bq'] is None or len(row['field_description_bq']) == 0 ) else row['field_description_bq'] if ( len(row['field_description_dc']) == 0 or len(row['field_description_dc']) < len(row['field_description_bq']) ) else row['field_description_bq']+row['field_description_dc'][self.bq_max_string_length:] if len(row['field_description_bq']) == self.bq_max_string_length else row['field_description_dc'] for index, row in self.sheet.iterrows() ]) self.sheet = self.sheet.drop(columns=['field_description_dc', 'field_description_bq']).astype(str).fillna('').set_index('field_name') self.tags = dict(zip(tag_columns, tag_list)) if len(self.tags) == 0: self.tags = None else: raise Exception('\nNo datacatalog entry instance provided. Call method again as ''list_all_tags_entry(entry)'' with entry a datacatalog entry instance') pass def lookup_and_list_all_tags_entry(self, table_id): self.list_all_tags_entry(self.get_table_instance_dc(table_id)) pass def delete_all_tags_entry(self, entry = None): """ Deletes all the tags attached to an entry (here an entry is a table instance) """ if entry is None: entry = self.table_instance_dc if entry is not None: for tag in self.dc_client.list_tags(entry.name): self.dc_client.delete_tag(tag.name) else: raise Exception('\nNo datacatalog entry instance provided. Call method again as ''delete_all_tags_entry(entry)'' with entry a datacatalog entry instance') pass def get_bq_schema_metadata(self, table_instance_bq = None): if table_instance_bq is None: table_instance_bq = self.table_instance_bq if table_instance_bq is not None: self.schema_bq = table_instance_bq.schema[:] self.schema_bq_df = pd.DataFrame.from_records((schemafield._key()[0:4] for schemafield in self.schema_bq), columns = ['field_name', 'field_type', 'field_mode', 'field_description']).applymap(lambda x: '' if x is None else x).astype('str').assign(field_name_lower=lambda x: x.field_name.apply(lambda y: y.lower())).set_index('field_name_lower').fillna('') else: raise Exception('\nNo BigQuery table instance provided. Call method again as ''get_bq_schema_metadata(entry)'' with entry a BigQuery table instance') pass def update_field_tag(self, field_entry_dict, table_instance_dc = None, dict_tags = None): """ This function tries to find a field tag with a column field (which is the actual table field name \ and in the code below accessed by tag.column) equals the requested field name as specified with \ field_entry_dict['field_name']. If such a tag can be found in the DataCatalog for the table instance, then it checks whether the \ field attributes ( specified as the field values of the tag.fields[key] below, where key is a specific \ tag field attribute name (field_format, field_description, field_example)) of the DataCatalog tag have \ different values as the requested/new tag field attributes (which are specified as the values of \ field_entry_dict['field_attributes'][key] where key is again a tag field attribute name) Only if the new field attribute values differ from the ones in the tag already on Datacatalog, the tag will be updated. The function returns: - True: when the tag has either been updated or does not need to be updated - False: when the requested tag has not been found, indictating the tag needs to be newly created """ if table_instance_dc is None: table_instance_dc = self.table_instance_dc if dict_tags is None: dict_tags = self.tags found_tag = False if dict_tags is not None: field_name = field_entry_dict['field_name'] try: tag = dict_tags[field_name] found_tag = True except KeyError: pass update_tag = False if found_tag: for key, value in field_entry_dict['field_attributes'].items(): if len(value) > 0: if key in self.field_tag_template.fields.keys(): if tag.fields[key].string_value != value: tag.fields[key].string_value = value update_tag = True if update_tag: self.dc_client.update_tag(tag) return found_tag def create_field_tag(self, field_entry_dict, table_instance_dc = None, dict_tags = None): """ This function creates a field tag for a table instance (which is not the table name! \ An instance object is return by the datacatalog.lookup_entry function and the name member of that instance is used as the parent when creating the tag with datacatalog.create_tag. Input: - table_instance_dc: an instance of a table (we get the instance via the lookup_entry method\ of the datacatalog_v1beta1.DataCatalogClient class) - field_entry_dict: a dictionary containg the field attributes and corresponding values of the sadc_fieldstored as a dic """ if table_instance_dc is None: table_instance_dc = self.table_instance_dc if dict_tags is None: dict_tags = self.tags if(not self.update_field_tag(field_entry_dict, table_instance_dc = table_instance_dc, dict_tags = dict_tags)): new_field_tag = datacatalog_v1beta1.types.Tag() new_field_tag.template = self.field_tag_template.name create_tag = False field_name = field_entry_dict['field_name'] if(field_name != ''): for key, value in field_entry_dict['field_attributes'].items(): if len(value) > 0: if key in self.field_tag_template.fields.keys(): new_field_tag.fields[key].string_value = value create_tag = True if(create_tag): new_field_tag.column = field_name if create_tag: try: self.dc_client.create_tag(parent=table_instance_dc.name,tag=new_field_tag) except: self.to_log('\t\tProblem to write tag to field {} of table {}\n'.format(field_name, self.__table_id)) pass def update_table_tag(self, table_entry_dict, table_instance_dc = None, dict_tags = None): """ This function tries to find a table tag for the table instance. If such a tag can be found in the DataCatalog, then it checks whether the field attributes \ ( specified as the field values of the tag.fields[key] below, where key is a specific \ tag field attribute name (table_description, table_data_source)) of the DataCatalog tag have \ different values as the requested/new field attributes (which are specified as the values of \ table_entry_dict[key] where key is again a tag field attribute) Only if the new tag field attribute values differ from the ones in the tag already on Datacatalog, the tag will be updated. The function returns: - True: when the tag has either been updated or does not need to be updated - False: when the requested tag has not been found, indictating the tag needs to be newly created """ if table_instance_dc is None: table_instance_dc = self.table_instance_dc if dict_tags is None: dict_tags = self.tags found_tag = False if dict_tags is not None: try: tag = dict_tags[''] if tag.template == self.table_tag_template.name: found_tag = True except KeyError: pass update_tag = False if found_tag: for key, value in table_entry_dict.items(): if len(value) > 0: if key in self.table_tag_template.fields.keys(): if tag.fields[key].string_value != value: tag.fields[key].string_value = value update_tag = True if update_tag: self.dc_client.update_tag(tag) return found_tag def create_table_tag(self, table_entry_dict, table_instance_dc = None, dict_tags = None): if table_instance_dc is None: table_instance_dc = self.table_instance_dc if dict_tags is None: dict_tags = self.tags if(not self.update_table_tag(table_entry_dict, table_instance_dc, dict_tags)): new_table_tag = datacatalog_v1beta1.types.Tag() new_table_tag.template = self.table_tag_template.name create_tag = False for key, value in table_entry_dict.items(): if len(value) > 0: if key in self.table_tag_template.fields.keys(): new_table_tag.fields[key].string_value = value create_tag = True if create_tag: self.dc_client.create_tag(parent=table_instance_dc.name,tag=new_table_tag) pass def download(self, tables = None, DS_ID = None, PATH=None): """ Downloads metadata of tables in a dataset specified by DS_ID. - By default metadata for all tables in the dataset is downloaded in an Excel .xlsx file in a folder that has the name of the dataset. For each table a separate sheet of that .xlsx file is created containing the field_names, field_descriptions and more. - Specifying the parameter 'tables' allows to download metadata for a single or a list of tables. - For all tables in the dataset table tags metadata is written to a sheet with the name 'metadata_of_tables' - The PATH specifies the path where the metadata shall be written. Parameters ---------- tables: String, List of Strings, None (default) A String or List of Strings specifying the table_ids for which metadata should be downloaded. If not provided, metadata for all tables in the dataset is downloaded DS_ID: String, None (default) dataset_id for which metadata shall be downloaded. If no dataset_id is provided via DS_ID, the one specified by the member attribute .DS_ID is used which is by default 'sadc_generated'. PATH: String, None (default) The PATH where the metadata shall be written. """ assert isinstance(tables, list) or isinstance(tables, str) or tables is None, "'Tables' parameter must be String, List or None" assert isinstance(DS_ID, str) or DS_ID is None, "'DS_ID' parameter must be String or None" assert isinstance(PATH, str) or PATH is None, "'PATH' parameter must be String or None" DS_ID_old = self.DS_ID if DS_ID is None: DS_ID = self.DS_ID else: self.set_dataset(DS_ID) if DS_ID is not None: if not self.__update: self.to_log('# Download\n') if PATH is not None: PATH_OUT = os.path.join(PATH, DS_ID) else: PATH_OUT = os.path.join(self.CDIR, DS_ID) if not os.path.exists(PATH_OUT): os.makedirs(PATH_OUT) else: self.to_log('\n\t# Download\n') self.overview_sheet = construct_overview_sheet(self.table_tag_template, attributes = self.__table_tag_fields_keys_ordered) table_sheets = construct_table_sheets() if tables is None: tables = self.bq_client.list_tables("{}.{}".format(self.P_ID, self.DS_ID)) elif isinstance(tables, str): tables = [tables] for table in tables: try: self.__table_id = table.table_id except: self.__table_id = table self.to_log('\t{}'.format("Table '{}'".format(self.__table_id))) self.to_log('\t\t{}'.format('get BigQuery table instance')) self.get_table_instance_bq(self.__table_id) self.to_log('\t\t{}'.format('get DataCatalog table instance')) self.get_table_instance_dc(self.__table_id) self.to_log('\t\t{}'.format('get all tags and create dataframe with out of field tags and BigQuery schema')) self.get_all_tags_table(make_field_sheet_df = True) self.to_log('\t\t{}'.format('append fields dataframe to dict')) table_sheets.append(self.__table_id, self.sheet) self.to_log('\t\t{}'.format('append table tag to overview sheet variable')) self.append_to_overview_sheet() self.to_log('\n\t{}'.format('make Dictionary out of field metadata dataframes for all specified tables')) self.ds_field_tags_dicts = table_sheets.get_dict() self.to_log('\t{}'.format('make Dataframe out of table tag metadata for all specified tables')) self.ds_table_tags_df = self.overview_sheet.get_dataframe() if not self.__update: FULLPATH = os.path.join(PATH_OUT, DS_ID+'.xlsx') self.to_log('\twrite to {}\n'.format(FULLPATH)) with pd.ExcelWriter(FULLPATH) as writer: self.ds_table_tags_df.to_excel(writer, sheet_name=self.overview_sheet_name, header=True, index=True) for table_id, table_df in self.ds_field_tags_dicts.items(): table_df.to_excel(writer, sheet_name=self.shorten_string(table_id, self.excel_max_sheet_name_length), header=True, index=True) self.set_dataset(DS_ID_old) else: raise Exception("No Dataset specified. Please call the function as 'download(DS_ID=dataset_id)' again with dataset_id a string specifying a dataset ID") pass def append_to_overview_sheet(self): table_description_bq = self.table_instance_bq.description table_description_bq = self.clean_sentence_string(self.pure_table_description_bq(table_description_bq)) dict_table_descr_bq = None if len(table_description_bq) > 0: dict_table_descr_bq = {'table_description': table_description_bq} try: table_tag = self.tags[''] if self.__prefer_bq_for_downlad_update: if len(table_description_bq) > 0: self.overview_sheet.append(self.__table_id, table_tag, dict_table_descr_bq) else: self.overview_sheet.append(self.__table_id, table_tag) else: self.overview_sheet.append(self.__table_id, table_tag) except: self.overview_sheet.append(self.__table_id, alt_tag_vals = dict_table_descr_bq) pass def upload(self, tables = None, DS_ID = None, PATH = None, delete_old_tags_before_upload = False, delete_sheet_after_upload = True, upload_from_backup = False): """ uploads metadata of tables in a dataset specified by DS_ID. - By default metadata for all tables in the dataset is uploaded from an Excel .xlsx file in a folder that has the name of the dataset. For each table a separate sheet of that .xlsx file is created containing the field_names, field_descriptions and more. - Specifying the parameter 'tables' allows to download metadata for a single or a list of tables. - For all tables in the dataset table tags metadata is in a sheet with the name 'metadata_of_tables' - The PATH specifies the path where the Excel .xlsx file is contained. Parameters ---------- tables: String, List of Strings, None (default) A String or List of Strings specifying the table_ids for which metadata should be downloaded. If not provided, metadata for all tables in the dataset is downloaded DS_ID: String, None (default) dataset_id for which metadata shall be downloaded. If no dataset_id is provided via DS_ID, the one specified by the member attribute .DS_ID is used which is by default 'sadc_generated'. PATH: String, None (default) The PATH where the metadata shall be read from. delete_old_tags_before_upload: True, False (Default) If set to True it deletes all tags in the datacatalog for a table instance before writing new ones. If set False the tags in datacalog are updated with the new information but not deleted. delete_sheet_after_upload: False, True (Default) If True, the folder including the sheet that has been uploaded will be deleted. upload_from_backup: True, False (Default) if True, use the backup Excel sheets for upload """ assert isinstance(tables, list) or isinstance(tables, str) or tables is None, "'Tables' parameter must be String, List or None" assert isinstance(DS_ID, str) or DS_ID is None, "'DS_ID' parameter must be String or None" assert isinstance(PATH, str) or PATH is None, "'PATH' parameter must be String or None" DS_ID_old = self.DS_ID if DS_ID is None: DS_ID = self.DS_ID else: self.set_dataset(DS_ID) self.delete_old_tags_before_upload = delete_old_tags_before_upload if DS_ID is not None: if not self.__update: self.to_log('\n# Upload\n') if PATH is None: PATH = os.path.join(self.CDIR, DS_ID) if upload_from_backup: PATH = os.path.join(os.path.join(self.CDIR, 'backup_sheets'), DS_ID) excel_files = glob.glob(os.path.join(PATH, r"*.xlsx")) assert len(excel_files) > 0, "No .xlsx files under the path {}".format(PATH) FULLPATH = os.path.join(PATH, DS_ID+'.xlsx') try: self.ds_table_tags_df = pd.read_excel(FULLPATH, sheet_name=self.overview_sheet_name, index_col = 0, dtype = str).fillna('').astype(str).applymap(lambda x: x.strip()) except: msg = 'Reading {} was not successful. Check path and existence of file.'.format(FULLPATH) self.to_log('\t\n{}\n'.format(msg)) raise Exception(msg) if tables is None: tables = self.ds_table_tags_df.index.to_list() else: diff_keys_set = set(tables) - set(self.ds_table_tags_df.index) assert len(diff_keys_set) == 0, "The tables {} are not contained in the spreadsheet.".format(diff_keys_set) table_to_ndx = {table_id: k+1 for k, table_id in enumerate(self.ds_table_tags_df.index) if table_id in tables} self.ds_field_tags_dicts = pd.read_excel(FULLPATH, sheet_name=list(table_to_ndx.values()), index_col = 0, dtype = str) else: if tables is None: tables = self.ds_table_tags_df.index.to_list() else: diff_keys_set = set(tables) - set(self.ds_table_tags_df.index) assert len(diff_keys_set) == 0, "The tables {} are not contained in the spreadsheet.".format(diff_keys_set) self.to_log('\n\t# Upload\n') table_to_ndx = {table_id: table_id for table_id in self.ds_table_tags_df.index if table_id in tables} for table_id, k in table_to_ndx.items(): self.to_log('\t{}'.format("Table '{}'".format(table_id))) self.__table_id = table_id self.to_log('\t\t{}'.format('get BigQuery table instance')) self.get_table_instance_bq(table_id) self.to_log('\t\t{}'.format('get DataCatalog table instance')) self.get_table_instance_dc(table_id) self.to_log('\t\t{}'.format('get all tags')) self.get_all_tags_table(delete_tags_not_in_bq_schema=True) self.to_log('\t\t{}'.format('create table tag dictionary')) self.table_tag_dict = dict(self.ds_table_tags_df.loc[table_id]) self.to_log('\t\t{}'.format('upload table tag')) self.upload_table_tag() self.to_log('\t\t{}'.format('upload BigQuery table description')) self.__upload_table_description_bq() self.sheet = self.ds_field_tags_dicts[k].fillna('').astype(str).applymap(lambda x: x.strip()) self.to_log('\t\t{}'.format('upload BigQuery and DataCatalog field information')) self.upload_fields_sheet() if not self.__update and delete_sheet_after_upload and not upload_from_backup: shutil.rmtree(PATH) self.set_dataset(DS_ID_old) self.write_log() else: raise Exception("No Dataset specified. Please call the function as 'upload(DS_ID=dataset_id)' again with dataset_id a string specifying a dataset ID") pass def synchronize(self, tables = None, DS_ID = None): """ Synchronizes metadata between Bigquery and Datacatalog of tables in a dataset specified by DS_ID. - By default metadata for all tables in the dataset is downloaded in an Excel .xlsx file in a folder that has the name of the dataset. For each table a separate sheet of that .xlsx file is created containing the field_names, field_descriptions and more. - Specifying the parameter 'tables' allows to download metadata for a single or a list of tables. - For all tables in the dataset table tags metadata is written to a sheet with the name 'metadata_of_tables' Parameters ---------- tables: String, List of Strings, None (default) A String or List of Strings specifying the table_ids for which metadata should be downloaded. If not provided, metadata for all tables in the dataset is downloaded DS_ID: String, None (default) dataset_id for which metadata shall be downloaded. If no dataset_id is provided via DS_ID, the one specified by the member attribute .DS_ID is used which is by default 'sadc_generated'. """ assert isinstance(tables, list) or isinstance(tables, str) or tables is None, "'Tables' parameter must be String, List or None" assert isinstance(DS_ID, str) or DS_ID is None, "'DS_ID' parameter must be String or None" DS_ID_old = self.DS_ID if DS_ID is None: DS_ID = self.DS_ID else: self.set_dataset(DS_ID) if DS_ID is not None: self.to_log('\n# Synchronize\n') self.__update = True self.download(tables=tables, DS_ID = DS_ID) self.upload(tables=tables, DS_ID = DS_ID, delete_sheet_after_upload = False) self.__update = False else: raise Exception("No Dataset specified. Please call the function as 'synchronize(DS_ID=dataset_id)' again with dataset_id a string specifying a dataset ID") pass def upload_fields_sheet(self): for column_name, row in self.sheet.iterrows(): if len(column_name) > 0: try: # this tries to get a numeric key value for the column name by checking first whether\ # the column name is in the table schema of BQ # if it is not found means that this column field is no longer part of the schema # and skips over that entry num_index = self.schema_bq_df.index.get_loc(column_name.lower()) has_descr = False if 'field_description' in row.keys(): has_descr = True field_description = self.clean_sentence_string(row['field_description']) field_attributes_dc = {**{key: self.clean_string(row[key]) for key in row.keys() if key not in ['field_description']}, 'field_description': self.clean_sentence_string(row['field_description'])} else: field_attributes_dc = {key: self.clean_string(row[key]) for key in row.keys()} field_entry_dict = {'field_name': column_name.lower(), 'field_attributes': field_attributes_dc} self.create_field_tag(field_entry_dict) field_bq = self.schema_bq[num_index] field_bq_name = field_bq.name field_bq_field_type = field_bq.field_type field_bq_mode = field_bq.mode if has_descr: field_description_bq = self.shorten_string(field_description, self.bq_max_string_length) self.schema_bq[num_index] = bigquery.SchemaField(name=field_bq_name, field_type=field_bq_field_type, mode=field_bq_mode, description=field_description_bq) except KeyError: pass else: break self.check_non_matching_columns_bq_excel() self.table_instance_bq.schema = self.schema_bq num_trials = 1 update_schema = False while num_trials < 11 and not update_schema: try: self.table_instance_bq = self.bq_client.update_table(self.table_instance_bq, ["schema"]) update_schema = True except Exception as e: if hasattr(e, 'message'): err = e.message else: err = e num_trials = num_trials + 1 if num_trials == 11: self.to_log("\t\t\terror while trying to write schema to BigQuery:") self.to_log(err) self.to_log("\t\t\terror occured, this was the last attempt\n") else: self.to_log("\t\t\terror while trying to write schema to BigQuery:\n") self.to_log(err) self.to_log("\t\t\terror occured, start {}. attempt\n".format(num_trials)) pass def upload_table_tag(self): diff_keys_set = set(self.table_tag_dict.keys()) - set(self.table_tag_template.fields.keys()) assert len(diff_keys_set) == 0, "The attribute names {} are no attribute names of the tag template {}".format(diff_keys_set, self.table_tag_template.name) self.create_table_tag(self.table_tag_dict) def __upload_table_description_bq_init(self): """ This function is only executed during initialisation of the class instance to set parameter for the function upload_table_description_bq """ self.__table_attrs = [attr for attr in self.table_tag_template.fields.keys() if attr not in ['table_description']] max_str_len_extra_metadata_keys = reduce((lambda x,y: max(x,y)), map( lambda x: len(x) , self.table_tag_template.fields.keys()) ) self.__n_int_tab = 5 self.__max_n_tabs = (max_str_len_extra_metadata_keys+1)//self.__n_int_tab def __upload_table_description_bq(self): table_description = self.clean_sentence_string(self.table_tag_dict['table_description']) extra_metadata_string = '\n\nTable attributes:\n\n' has_extra_metadata = False for column in self.__table_attrs: if len(self.table_tag_dict[column]) > 0: has_extra_metadata = True column_first_part = column[6:9] if column[6:9] == 'gcp': column_first_part = 'GCP' else: column_first_part = column[6].upper() + column[7:9] n_tabs = self.__max_n_tabs - ((len(column)+1)//self.__n_int_tab) + 1 extra_metadata_string = extra_metadata_string + column_first_part \ + re.sub(r'_+',' ', column[9:]) + ":" + "\t"*n_tabs \ + self.table_tag_dict[column] if extra_metadata_string[-1] != '\n': extra_metadata_string = extra_metadata_string + "\n" if has_extra_metadata: self.table_instance_bq.description = table_description + extra_metadata_string else: self.table_instance_bq.description = table_description self.table_instance_bq = self.bq_client.update_table(self.table_instance_bq, ["description"]) pass def check_non_matching_columns_bq_excel(self, table_instance_dc = None, excel_column_names = None, bq_column_names = None): if table_instance_dc is None: table_instance_dc = self.table_instance_dc if excel_column_names is None: excel_column_names = self.sheet.index if bq_column_names is None: bq_column_names = self.schema_bq_df.index set_excel_column_fields = set(excel_column_names.map(lambda x: x.lower())) set_bq_column_fields = set(bq_column_names) set_not_in_bq = set_excel_column_fields.difference(set_bq_column_fields) set_not_in_excel = set_bq_column_fields.difference(set_excel_column_fields) if bool(set_not_in_bq) or bool(set_not_in_excel): self.to_log('\t\t\tFor the table at the BigQuery path\n \'{}\''.format(table_instance_dc.linked_resource)) self.to_log('\t\t\tIn the following list, entries prefixed with:') self.to_log('\t\t\t \'<\':\tare contained in the Excel spreadsheet but not in the BigQuery table schema (anymore).\n\t\t\tPlease delete them in the Excel spreadsheet!') self.to_log('\t\t\t \'>\':\tare contained in the BigQuery table schema but not in the Excel spreadsheet.\n\t\t\t\tPlease add them in the Excel spreadsheet!\n') if bool(set_not_in_bq): for column_name in set_not_in_bq: self.to_log('\t\t\t\t< {}'.format(column_name)) if bool(set_not_in_excel): if bool(set_not_in_bq): self.to_log('\n') for column_name in set_not_in_excel: self.to_log('\t\t\t\t> {}'.format(column_name)) def to_log(self, message = None): if isinstance(message, str): if self.__do_log: self.__log = self.__log + message if self.__do_print_log: print(message) pass def write_log(self): if self.__do_log: F = open(self.__logfile, "w") F.write(self.__log) self.__log = '' F.close() @staticmethod def clean_string(string): string = string.strip() if len(string) > 0: string = re.sub(r'\s+',' ', string) return string @classmethod def clean_sentence_string(cls, string): string = cls.clean_string(string) if len(string) > 0: string = string[0].upper() + string[1:] if string[-1] != r"." and string[-1] != r"]": string = string + r"." return string @staticmethod def shorten_string(string, n): if len(string) < n: return string else: return string[:n] @classmethod def pure_table_description_bq(cls, table_description_bq): if table_description_bq is not None: try: table_description_bq_pure = cls.pattern_table_descr_bq_pure.search(table_description_bq).group('descr') except: table_description_bq_pure = table_description_bq return table_description_bq_pure else: return '' class construct_overview_sheet: def __init__(self, tag_template, attributes = None): self.__dict_attributes = {item[0]: k for k, item in enumerate(tag_template.fields.items())} self.__num_el = len(self.__dict_attributes) self.__list_attributes = [[] for i in range(self.__num_el)] self.__list_table_id = [] if attributes is None: self.__attributes_ordered = list(tag_template.fields.keys()) else: assert isinstance(attributes, list), "'attributes' parameter must be a list" assert len(set(tag_template.fields.keys()) - set(attributes)) == 0, "The provided attributes are no permutation of the field keys of the provided tag_template" self.__attributes_ordered = attributes def append(self, table_id, tag = None, alt_tag_vals = None): assert isinstance(alt_tag_vals, dict) or alt_tag_vals is None, "'alt_tag_vals' must be of type dict or None" if alt_tag_vals is None: alt_tag_vals = {} self.__list_table_id.append(table_id) if tag is not None: for attr, index in self.__dict_attributes.items(): alt_val_not_avail = True if attr in alt_tag_vals.keys(): self.__list_attributes[index].append(alt_tag_vals[attr]) alt_val_not_avail = False if alt_val_not_avail: try: if(attr == 'table_description'): self.__list_attributes[index].append(toolbox.clean_sentence_string(tag.fields[attr].string_value)) else: self.__list_attributes[index].append(tag.fields[attr].string_value) except: self.__list_attributes[index].append('') else: for attr, index in self.__dict_attributes.items(): if attr in alt_tag_vals.keys(): self.__list_attributes[index].append(alt_tag_vals[attr]) else: self.__list_attributes[index].append('') def get_dataframe(self): return pd.DataFrame.from_dict({'table_id': self.__list_table_id, **{attr: self.__list_attributes[index] for attr, index in self.__dict_attributes.items()}}).fillna('').astype(str).applymap(lambda x: x.strip()).set_index('table_id')[self.__attributes_ordered] def set_datframe(self, return_df = False): self.df = self.get_dataframe() if return_df: return self.df else: pass class construct_table_sheets: def __init__(self): self.__list_table_id = [] self.__list_of_sheet_df = [] def append(self, table_id, sheet): self.__list_table_id.append(table_id) self.__list_of_sheet_df.append(sheet) def get_dict(self): return OrderedDict(zip(self.__list_table_id, self.__list_of_sheet_df)) def set_dict(self, return_dict = False): self.dict_sheets = self.get_dict() if return_dict: return self.dict_sheets else: pass

[ "karlo1986@gmx.de" ]

karlo1986@gmx.de

6747e33efcd4f93c3dbf79fe12368de440154955

b45e649b4580692dd1b8bf63ad29befb3daad95a

/spark/src/main/python/preprocBinning.py

6c21866ee6f9e294698dfe7cff5be5841bf1c7fa

[]

no_license

xu-hao/FHIR-PIT

21ea0e5b8796d86f3a931b99e3e7a3f1e58b04a2

db2fb04e2cc0d9fce2f8043f594f60fdb8f5a8e8

refs/heads/master

2021-05-25T09:49:48.084629

2021-05-19T20:17:11

127,015,534

2

0

null

UTF-8

Python

false

660

py

import os import sys import json from preprocPatient import * from preprocVisit import * year_start, year_end, config_file, input_dir, output_dir = sys.argv[1:] for year in range(int(year_start), int(year_end) + 1): print(year) input_file_p = f"{input_dir}/{year}/all_patient" output_file_p = f"{output_dir}/{year}patient" preproc_patient(config_file, input_file_p, output_file_p) input_file_v = f"{input_dir}/{year}/all_visit" output_file_v = f"{output_dir}/{year}visit" preproc_visit(config_file, input_file_v, output_file_v)

[ "xuh@cs.unc.edu" ]

xuh@cs.unc.edu

9125851b1d61361d8d141a4dca0d69cccfebbf50

b62d6c292e573dca91d33dfbe61a417e18330d50

/app/KS/image/helpers.py

b773d4bef9998935d10af95089338174274ed702

[]

no_license

MCapallera/PatternRecognition_JungleSpeed

7dd2e7854df8c4585612e5455505fcfb6f443972

d8254abc26152af449fc17881283da46359b712d

refs/heads/master

2020-05-01T17:54:37.985790

2019-05-26T17:22:27

177,611,998

1

null

UTF-8

Python

false

167

py

import numpy from skimage.filters import threshold_yen def get_background_color(img): threshold = threshold_yen(img) return numpy.mean(img[img > threshold])

[ "melania.grotti@unifr.ch" ]

melania.grotti@unifr.ch

89390f2b4c8bd7d25ec8c7791c9b3502343ed13a

ebe20199181927f3eb36aedfe66d5d179f28628f

/concierge/event/models.py

d66ac24619b9e9b28680c89204f8dfd3e80b9f4c

[]

no_license

rajat404/concierge

8f1c1d826334f8a0364d4b64efebc722b6f6f061

60290e70741060c78c860779a19bf81a90058675

refs/heads/master

2021-07-01T08:28:27.020737

2017-09-03T22:23:12

103,437,813

0

null

UTF-8

Python

false

5,103

py

# Third Party Imports from django.db import models from django.utils import timezone from django.utils.translation import ugettext_lazy as _ from simple_history.models import HistoricalRecords # Concierge Imports from concierge.base.models import SlugModel, TimeStampedModel, UUIDModel from concierge.quiz.models import Quiz class Speaker(UUIDModel, TimeStampedModel): history = HistoricalRecords(table_name='event_speaker_history') first_name = models.CharField(max_length=120) last_name = models.CharField(max_length=120) email = models.EmailField(unique=True, db_index=True) about = models.TextField(blank=True) class Meta: db_table = 'event_speaker' verbose_name = _('Speaker') verbose_name_plural = _('Speakers') def __str__(self): return '{} {}'.format(self.first_name, self.last_name) class Event(UUIDModel, TimeStampedModel, SlugModel): EVENT_CHOICES = ( ('EVENT', 'EVENT'), ('SESSION', 'SESSION'), ('MEETUP', 'MEETUP'), ('CONFERENCE', 'CONFERENCE'), ('TALK', 'TALK'), ('WORKSHOP', 'WORKSHOP'), ('DEV_SPRINT', 'DEV SPRINT'), ('PANEL_DISCUSSION', 'PANEL DISCUSSION'), # TODO: BOF & Open Spaces ) VISIBILITY_CHOICES = ( ('PUBLIC', 'PUBLIC'), ('PRIVATE', 'PRIVATE'), ) # Need to be nullable, as the value will be populated after creation of the `Event` instance registration_quiz = models.ForeignKey(Quiz, related_name='event_registration', null=True) feedback_quiz = models.ForeignKey(Quiz, related_name='event_feedback', null=True) history = HistoricalRecords(table_name='event_event_history') kind = models.CharField(max_length=15, choices=EVENT_CHOICES) happening = models.ForeignKey('self', blank=True, null=True) speaker = models.ForeignKey(Speaker, related_name='events', null=True, blank=True) venue = models.CharField(max_length=100, null=True, blank=True) description = models.TextField(blank=True) start = models.DateTimeField() end = models.DateTimeField() participation_open = models.BooleanField(default=False, help_text='can a user participate in this event') participation_start = models.DateTimeField(null=True, blank=True) participation_end = models.DateTimeField(null=True, blank=True) is_offline = models.BooleanField(default=True) class Meta: db_table = 'event_event' verbose_name = _('Event') verbose_name_plural = _('Events') def __str__(self): return self.slug def can_participate(self): # Valiation for `participation_start` & `participation_end` is handled by the serializer # These value cannot be None return bool(self.participation_open and (self.participation_start <= timezone.now() < self.participation_end)) class OfflineEvent(UUIDModel, TimeStampedModel): history = HistoricalRecords(table_name='event_offline_event_history') event = models.OneToOneField(Event, related_name='offline') longitude = models.DecimalField(max_digits=9, decimal_places=6, null=True, blank=True) latitude = models.DecimalField(max_digits=9, decimal_places=6, null=True, blank=True) address = models.TextField() address_guidelines = models.TextField() rsvp_open = models.BooleanField(default=False, help_text='can a participant RSVP for this event') rsvp_start = models.DateTimeField(null=True, blank=True) rsvp_end = models.DateTimeField(null=True, blank=True) class Meta: db_table = 'event_offline_event' verbose_name = _('Offline Event') verbose_name_plural = _('Offline Events') def __str__(self): return self.event.slug class Organisation(UUIDModel, TimeStampedModel, SlugModel): ORG_CHOICES = ( ('HOST', 'HOST'), ('SPONSOR', 'SPONSOR'), ('OTHER', 'OTHER'), ) history = HistoricalRecords(table_name='organisation_organisation_history') kind = models.CharField(max_length=15, choices=ORG_CHOICES) class Meta: db_table = 'organisation_organisation' verbose_name = _('Organisation') verbose_name_plural = _('Organisations') def __str__(self): return self.slug class SponsorCategory(models.Model): """To be added via Admin Panel(or Fixture), prior to adding Sponsors""" name = models.CharField(max_length=50, unique=True) class Meta: db_table = 'event_sponsor_category' verbose_name = _('Sponsor Category') verbose_name_plural = _('Sponsor Categories') def __str__(self): return self.name class Sponsor(TimeStampedModel): history = HistoricalRecords(table_name='event_sponsor_history') event = models.ForeignKey(Event) organisation = models.ForeignKey(Organisation) category = models.ForeignKey(SponsorCategory, to_field='name') class Meta: db_table = 'event_sponsor' verbose_name = _('Sponsor') verbose_name_plural = _('Sponsors') def __str__(self): return '{}--{}'.format(self.organisation, self.event)

[ "404rajat@gmail.com" ]

404rajat@gmail.com

cf8828a7f7d1d13855bca279de6c79655a778dcd

97825c904d512fdfd6a4418d6820eb8fd3cdd202

/largest_number.py

cf7f5a702a9e0821a28b1fd3e4074a3fe1484bf5

[]

no_license

charliedmiller/coding_challenges

97426741778d0498321cb2fec2e7d3c3d3c0b26e

ea6aa0cffb55b2d88f71f108f16c34e9779da9c7

refs/heads/master

2023-02-21T21:54:26.500399

2021-01-28T23:50:42

287,142,628

0

null

UTF-8

Python

false

1,186

py

# Charlie Miller # Leetcode - 179. Largest Number # https://leetcode.com/problems/largest-number/ """ I had the right idea, though needed to look at the sln to get there https://leetcode.com/problems/largest-number/solution/ Create a comparator that compares the numbers created by ordering them differently (a then b or b then a) Sort using this """ from functools import cmp_to_key class Solution: #see what the numbers would look like using each order def cmp(self,a,b): a_first = int(a+b) b_first = int(b+a) #return the LARGER number as less than (before) return b_first - a_first #for edge cases where there are leading zeros def remove_leading_zeros(self,string): for start in range(len(string)): if string[start] != "0": return string[start:] return string[len(string)-1:] def largestNumber(self, nums: List[int]) -> str: stringified = [str(num) for num in nums] stringified.sort(key=cmp_to_key(self.cmp)) whole = ''.join(stringified) return self.remove_leading_zeros(whole)

[ "charliedmiller@gmail.com" ]

charliedmiller@gmail.com

69b0fa5230cadb504175821f7cb8097e99df18c4

901658f002f0d996fe17b9f1a241ccf95bdb82e3

/home/migrations/0002_auto_20200801_1224.py

76aa831a18ba7eb063bd34d75b1394c39ac08103

[]

no_license

OnurBoynuegri/RentHome

2d0fb308664d1095eaddafd5838982751a497c37

c20c934f9edbc8d9c7215d0d3f462769b5d048c7

refs/heads/master

2022-11-28T04:42:45.158688

2020-08-11T15:09:38

280,904,461

0

null

UTF-8

Python

false

1,729

py

# Generated by Django 3.0.8 on 2020-08-01 09:24 import ckeditor_uploader.fields from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('home', '0001_initial'), ] operations = [ migrations.CreateModel( name='ContactFormMessage', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(blank=True, max_length=20)), ('email', models.CharField(blank=True, max_length=50)), ('subject', models.CharField(blank=True, max_length=50)), ('message', models.CharField(blank=True, max_length=255)), ('status', models.CharField(choices=[('New', 'New'), ('Read', 'Read')], default='New', max_length=10)), ('ip', models.CharField(blank=True, max_length=20)), ('note', models.CharField(blank=True, max_length=100)), ('create_at', models.DateTimeField(auto_now_add=True)), ('update_at', models.DateTimeField(auto_now=True)), ], ), migrations.AlterField( model_name='setting', name='aboutus', field=ckeditor_uploader.fields.RichTextUploadingField(blank=True), ), migrations.AlterField( model_name='setting', name='contact', field=ckeditor_uploader.fields.RichTextUploadingField(blank=True), ), migrations.AlterField( model_name='setting', name='references', field=ckeditor_uploader.fields.RichTextUploadingField(blank=True), ), ]

[ "onurboynueğri@gmail.com" ]

onurboynueğri@gmail.com

1ed5d148e48f6eaac83bf81e500e00be6515e921

e9534ef4fbaea3cfee556e5da32927ba3860834c

/autoScale/main.py

0f5a11984e217850e3df919739199b47384e4c69

[]

no_license

joaoleite/marathon-autoscaling-app

91791b831428105f4c4a778ffcacecad3edefe99

ce45b562f006958dc14b88af6611092604cf4cfb

refs/heads/master

2021-01-19T19:05:17.509686

2017-05-09T00:35:50

88,398,742

1

0

null

UTF-8

Python

false

1,288

py

from marathonAPI import MarathonAPI from rabbitMQ import rabbitMQ from settings import VAR_MARATHON_PORT, VAR_MARATHON_USE_HTTPS, VAR_MARATHON_PASSWORD, VAR_MARATHON_USER, \ VAR_RABBITMQ_WEB_PORT, VAR_RABBITMQ_PASSWORD, VAR_RABBITMQ_USER, VAR_RABBITMQ_HOST from settings import MARATHON_INTERVAL_REFRESH_APP from settings import logger, VAR_MARATHON_HOST logger.info('Configurating MarathonAPI...') host = MarathonAPI(host=VAR_MARATHON_HOST, port=VAR_MARATHON_PORT, use_https=VAR_MARATHON_USE_HTTPS, user=VAR_MARATHON_USER, password=VAR_MARATHON_PASSWORD) logger.info('Configurating RabbitMQ...') target = rabbitMQ(host=VAR_RABBITMQ_HOST, user=VAR_RABBITMQ_USER, password=VAR_RABBITMQ_PASSWORD, web_port=VAR_RABBITMQ_WEB_PORT) import asyncio def callback(n, loop): try: host.findAppsWithAutoscaleLabels() host.scaleApps(target) except Exception as e: logger.error(e) finally: now = loop.time() loop.call_at(now + n, callback, n, loop) async def main(loop): delta_time = MARATHON_INTERVAL_REFRESH_APP loop.call_soon(callback, delta_time, loop) while True: await asyncio.sleep(1) event_loop = asyncio.get_event_loop() try: event_loop.run_until_complete(main(event_loop)) finally: event_loop.close()

[ "joaoleite@gmail.com" ]

joaoleite@gmail.com

f748579f1a20a23d901c31b88322c26f451af433

44ce370130c7cd9a6a763be8dcc62362f57724e7

/vagrant/data/Math2/cont2_8/run

eb3597973d86ef0dc6f0ed2817058872d9b99942

[]

no_license

akemery/cnp3-docker

c122aac493868ec4fa2b7795b1fd7110998f93dc

3f24649ab81e828cf6babcfac7965251cb4dc7e2

refs/heads/main

2023-06-21T10:06:05.668090

2021-07-26T07:39:42

363,279,299

0

null

UTF-8

Python

false

666

#! /usr/bin/python3 # -*- coding: utf-8 -*- import sys sys.path.insert(1, '/course/common') from inginious import input, feedback, rst from parsingDomain import compareDomains correct = "{}" # à modifier /!\ answer = input.get_input("q1") grade = 0 result = compareDomains(answer, correct) if result[0]: feedback.set_problem_result("success","q1") feedback.set_problem_feedback("Bravo!","q1") grade += 100 else: feedback.set_problem_result("failed","q1") feedback.set_problem_feedback(result[1],"q1") feedback.set_grade(grade) if grade == 100 : feedback.set_global_result("success") else : feedback.set_global_result("failed")

[ "assogba.emery@gmail.com" ]

assogba.emery@gmail.com

bed6c7b8a9b18521ccb6830724efd339b5523cb9

a8cb99c512946691b6c53cf14538b44c39c62e88

/models.py

a8f9314eba0b89e27e3eaf4139a8ce19fb2b8f63

[]

no_license

Louise-LuLin/debias-gcn

3d3f0c57cd22ed6506841c463820eac980ae4769

82ee00cce6f5672c8823cf31a2fe9e5b54eed56e

refs/heads/main

2023-05-03T05:24:45.506722

2021-05-17T18:30:15

360,814,170

0

null

UTF-8

Python

false

3,250

py

import dgl from dgl.nn import SAGEConv # Define a GraphSAGE model from dgl.nn import GATConv # Define a GAT model import torch import torch.nn as nn import torch.nn.functional as F ###################################################################### # build a two-layer GraphSAGE model class GraphSAGE(nn.Module): def __init__(self, graph, in_dim, hidden_dim, out_dim): super(GraphSAGE, self).__init__() self.conv1 = SAGEConv(in_feats=in_dim, out_feats=hidden_dim, aggregator_type='mean') self.conv2 = SAGEConv(in_feats=hidden_dim, out_feats=out_dim, aggregator_type='mean') self.graph = graph def forward(self, in_feat): h = self.conv1(self.graph, in_feat) h = F.relu(h) h = self.conv2(self.graph, h) return h ###################################################################### # build a two-layer GAT model class GATLayer(nn.Module): def __init__(self, graph, in_dim, out_dim): super(GATLayer, self).__init__() self.graph = graph self.fc = nn.Linear(in_dim, out_dim, bias=False) self.attn_fc = nn.Linear(2 * out_dim, 1, bias=False) self.reset_parameters() def reset_parameters(self): """Reinitialize learnable parameters.""" gain = nn.init.calculate_gain('relu') nn.init.xavier_normal_(self.fc.weight, gain=gain) nn.init.xavier_normal_(self.attn_fc.weight, gain=gain) def edge_attention(self, edges): # attention z2 = torch.cat([edges.src['z'], edges.dst['z']], dim=1) a = self.attn_fc(z2) return {'e': F.leaky_relu(a)} def message_func(self, edges): return {'z': edges.src['z'], 'e': edges.data['e']} def reduce_func(self, nodes): alpha = F.softmax(nodes.mailbox['e'], dim=1) h = torch.sum(alpha * nodes.mailbox['z'], dim=1) return {'h': h} def forward(self, in_feat): z = self.fc(in_feat) self.graph.ndata['z'] = z self.graph.apply_edges(self.edge_attention) self.graph.update_all(self.message_func, self.reduce_func) return self.graph.ndata.pop('h') class MultiHeadGATLayer(nn.Module): def __init__(self, graph, in_dim, out_dim, num_heads, merge='cat'): super(MultiHeadGATLayer, self).__init__() self.heads = nn.ModuleList() for i in range(num_heads): self.heads.append(GATLayer(graph, in_dim, out_dim)) self.merge = merge def forward(self, h): head_outs = [attn_head(h) for attn_head in self.heads] if self.merge == 'cat': return torch.cat(head_outs, dim=1) else: return torch.mean(torch.stack(head_outs)) class GAT(nn.Module): def __init__(self, graph, in_dim, hidden_dim, out_dim, num_heads): super(GAT, self).__init__() self.layer1 = MultiHeadGATLayer(graph, in_dim, hidden_dim, num_heads) self.layer2 = MultiHeadGATLayer(graph, hidden_dim * num_heads, out_dim, 1) def forward(self, in_feat): h = self.layer1(in_feat) h = F.elu(h) h = self.layer2(h) return h

[ "lulin199209@gmail.com" ]

lulin199209@gmail.com

da6084b7a1b5f3aa319565d6778d11e1160946a3

d7b403a8e4b124e42feb0b72c502df438f5552f6

/speech/TextToSpeech.py

afda438ef79dc3cb346c9c3df8dea77969416e10

[]

no_license

Mallington/Hacker-The-Hardware-bear

ea3b7b7bd1a908ff613cce414d623b2d263c0955

4f21318879bedac726c00a26f9c80095f73c1881

refs/heads/master

2020-09-16T04:10:39.319071

2019-11-24T11:01:43

223,648,970

0

null

UTF-8

Python

false

273

py

from gtts import gTTS import os from datetime import date class tts(): def __init__(self): pass def say(self, message): tts = gTTS(text=message, lang='en') now = date.today() tts.save("{}.mp3".format(now)) os.system("mpg321 {}.mp3".format(now))

[ "funkymallington@gmail.com" ]

funkymallington@gmail.com

f93a39f3c7ce5dc35b811f46c70586ec4a00c270

4d93acd63ce2835fcd7ea610fcd412b727a4f03e

/08-Markov/decay.py

aa454eea1ad7fb4d3765d62e0e5f8e83dfc8525a

[]

no_license

jsbarbosa/JuanBarbosa_MCA

41ebcc27bb7dd8a886c9b4c1b416bd7e3cad2e57

4f49d17282679ae1fa81d7cc892b6560edf93828

refs/heads/master

2021-01-11T17:53:44.115810

2017-04-24T17:58:09

79,863,469

0

null

UTF-8

Python

false

1,590

py

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Mon Mar 13 18:21:00 2017 @author: juan """ import numpy as np import matplotlib.pyplot as plt obs = np.array([1.5, 1.7, 2]) def rand(): return 2*np.random.random() - 1 def integral(a, b, lm): return -lm*(func(b, lm) - func(a, lm)) def func(x, lm): return np.exp(-x/lm) def probability(x, lm): p = 1 z = integral(1, 20, lm) for x_ in x: p *= func(x_, lm)/z return p def bayesian(x, lm): return probability(x, lm) def hastings(N, dx = 1): lambdas = np.ones(N+1) lambdas[0] = np.random.random()*10.0 for i in range(N): second = lambdas[i] + dx*rand() q = bayesian(obs, second)/bayesian(obs, lambdas[i]) alpha = min(q, 1.0) u = np.random.random() if u <= alpha and second > 0: lambdas[i+1] = second else: lambdas[i+1] = lambdas[i] return lambdas def rubin(N, M, dl): avs = np.zeros(M) vas = np.zeros(M) R = np.zeros(N-2) chains = np.array([hastings(N, dl) for i in range(M)]) for j in range(2, N): for i in range(M): avs[i] = np.mean(chains[i, :j]) vas[i] = np.std(chains[i, :j])**2 total = np.mean(avs) B = j/(M-1)*np.sum((avs-total)**2) W = vas.mean() R[j-2] = (j-1)/j + (B/W)*(M+1)/(j*M) return R N = 10000 lm = np.logspace(-3, 3, 5) for l in lm: R = rubin(N, 5, l) plt.plot(R, label="%f"%l) plt.xscale('log') plt.yscale('log') plt.legend() plt.show()

[ "js.barbosa10@uniandes.edu.co" ]

js.barbosa10@uniandes.edu.co

b892725d3b4dcb01c4b54bf97ca0a1f759db0640

87a7e63bf86dad9ca5dbe812ea8caaa013ab1856

/Step6RectangleDivision.py

31a102235257fba937444e2cf0323933e2ceefe3

[]

no_license

sugoigroup/CodeTestPython

884ee5aa7abec588eb04df930b32c824b1a37a93

ba08dc8c2a7c8f385055b4e21a6de10e73f7e8fd

refs/heads/master

2023-03-18T12:01:30.055923

2021-03-15T13:32:58

347,641,273

0

null

UTF-8

Python

false

609

py

# 긴변의 길이가 1000이하에서 만들어질수 있는 정사각형 개수가 딱 20개인 직사각형의 가로세로 길이 쌍이 몇 쌍인지 구하시요? 응? # 단 직사각형의 가로세로 길ㄹ이를 바꾼 경우는 하나로 취급됨. # 뭔개소리냐 W, N = 1000, 20 def cut(w, h, n): if w==h: return n==0 if w>h: w, h = h, w q, r = divmod(h, w) if (n-q<0) or (r==0): return (n-q==0) else: return cut(w,r,n-q) cnt = 0 for i in range(1, W+1): for j in range(i, W+1): if cut(i, j, N): cnt += 1 print(cnt)

[ "" ]

ecca9f66970644f8ebd7962b370b64e54427a5c2

339ec05910ea45e91222a33ef926d8f108f87486

/server.py

24f5afea45ca70ff80b5c4b8f1bc8566d864ad53

[]

no_license

ajeebbasheer/chat-server

199eda1b67a2449e300ba693a1f735521c89d236

9ffdd57a617bed06f256e3fe2fd1926c34064cc9

refs/heads/master

2021-09-13T17:45:04.167524

2018-05-02T15:44:19

null

0

null

UTF-8

Python

false

1,247

py

#/usr/bin/env python import socket import sys import select CONNECTION_LIST=[] RECV_BUFFER=4096 PORT=5000 def broadcast(sock,message): for s in CONNECTION_LIST: if s != server_socket and socket!=sock: try: s.send(message) except: s.close() CONNECTION_LIST.remove(socket) server_socket=socket.socket(socket.AF_INET,socket.SOCK_STREAM) server_socket.setsockopt(socket.SOL_SOCKET,socket.SO_REUSEADDR,1) server_socket.bind(("localhost", PORT)) server_socket.listen(10) CONNECTION_LIST.append(server_socket) print "Chat server started on port " + str(PORT) while 1: read_sockets,write_sockets,error_sockets = select.select(CONNECTION_LIST,[],[]) for sock in read_sockets: if sock==server_socket: sockfd, addr = server_socket.accept() CONNECTION_LIST.append(sockfd) print "client (%s,%s) is connected" %addr broadcast(sockfd,"[%s:%s] entered room\n" %addr) else: try: data=sock.recv(RECV_BUFFER) if data: broadcast(sock, "\r" + 'machan::<' + str(sock.getpeername()) + '> ::' + data) except: broadcast(sock, "client(%s,%s) is offline" %addr) print "client(%s,%s) is offline " %addr server_socket.close() CONNECTION_LIST.remove(sock) continue server_socket.close()

[ "to.ajeeb@gmail.com" ]

to.ajeeb@gmail.com

96522b5e865f8371b780903ffd0f11fcf1ca2dfa

6a18c479a83e238484fd481c97fdca776091d4aa

/deepracer/RL_Model_Training/benchmark_dr.py

4d10f2d5ad61ca5e66c85c7cf425a6a26321f5ae

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

permissive

Currycurrycurry/Time-in-State-RL

f07e91fc3f6111869dfa380de87970086895e046

f106d92c3556b955c108bf3e147bb0b12e60259c

refs/heads/main

2023-03-20T16:13:53.414126

2021-03-09T02:24:31

345,659,435

0

BSD-3-Clause

2021-03-08T13:07:45

null

UTF-8

Python

false

24,399

py

# Code credits: The model loading code is taken from open AI baselines with modifications done to allow # variable timing characteristics during evaluation. The deepracer environment is taken from the aws deepracer github # code with modifications for the variable timing characteristics. # Changing the sampling and latency input to the model #multiple models and multiple paths can be added path1 = 'Deepracer-checkpoints/Latency_138.pb' #Path of saved model #path2 = 'dr_tf_frozen_model.pb' #Path of saved model #path3 = 'dr_tf_frozen_model.pb' #Path of saved model #paths = [path1, path2, path3] paths = [path1] #the folder to save the data data_folder = 'data_dr/' #the data saved in in folder with this name #experiments = ['dr_1', 'dr_2', 'dr_3'] experiments = ['dr_1'] latencies = [20, 20, 40, 40, 60, 60, 80, 80, 100, 100, 120, 120] sampling_sleeps = [0.033, 0.033, 0.040, 0.040, 0.060, 0.060, 0.080, 0.080, 0.100, 0.100, 0.120, 0.120] directions = [ 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2] #number of continuous steps to run the car steps = 5000 # Changing the sampling and latency input to the model import time import gym import queue import numpy as np from gym import spaces from PIL import Image import os import math from rotation import Rotation from collections import OrderedDict import random import bisect import json import math # Type of worker SIMULATION_WORKER = "SIMULATION_WORKER" SAGEMAKER_TRAINING_WORKER = "SAGEMAKER_TRAINING_WORKER" node_type = os.environ.get("NODE_TYPE", SIMULATION_WORKER) #saving the debug data import pickle if node_type == SIMULATION_WORKER: import rospy from ackermann_msgs.msg import AckermannDriveStamped from gazebo_msgs.msg import ModelState from gazebo_msgs.srv import GetLinkState, GetModelState, JointRequest from gazebo_msgs.srv import SetModelState from std_msgs.msg import Float64 from sensor_msgs.msg import Image as sensor_image from deepracer_msgs.msg import Progress from shapely.geometry import Point, Polygon from shapely.geometry.polygon import LinearRing, LineString TRAINING_IMAGE_SIZE = (160, 120) FINISH_LINE = 1000 # REWARD ENUM CRASHED = -30.0 NO_PROGRESS = -1 FINISHED = 10000000.0 MAX_STEPS = 100000000 # WORLD NAME EASY_TRACK_WORLD = 'easy_track' MEDIUM_TRACK_WORLD = 'medium_track' HARD_TRACK_WORLD = 'hard_track' # Normalized track distance to move with each reset ROUND_ROBIN_ADVANCE_DIST = 0.02#0.02 #0.01 # List of required velocity topics, one topic per wheel VELOCITY_TOPICS = ['/racecar/left_rear_wheel_velocity_controller/command', '/racecar/right_rear_wheel_velocity_controller/command', '/racecar/left_front_wheel_velocity_controller/command', '/racecar/right_front_wheel_velocity_controller/command'] # List of required steering hinges STEERING_TOPICS = ['/racecar/left_steering_hinge_position_controller/command', '/racecar/right_steering_hinge_position_controller/command'] # List of all effort joints EFFORT_JOINTS = ['/racecar/left_rear_wheel_joint', '/racecar/right_rear_wheel_joint', '/racecar/left_front_wheel_joint','/racecar/right_front_wheel_joint', '/racecar/left_steering_hinge_joint','/racecar/right_steering_hinge_joint'] # Radius of the wheels of the car in meters WHEEL_RADIUS = 0.1 # Size of the image queue buffer, we want this to be one so that we consume 1 image # at a time, but may want to change this as we add more algorithms IMG_QUEUE_BUF_SIZE = 1 #print(delays_array) ### Gym Env ### class DeepRacerEnv(gym.Env): def __init__(self): self.sampling_rate = 30.0 self.sampling_sleep = (1.0/self.sampling_rate) #self.sampling_rates = [15.0, 30.0] self.sampling_rates = [30.0, 30.0] self.sampling_rate_index = 0 self.latencies = [10.0, 20.0, 40.0, 60.0, 80.0, 100.0, 120.0] self.latency_index = 0 self.latency_max_num_steps = 500 # for these steps latency will be fixed or change on reset or done after 500. self.latency_steps = 0 self.latency = 10.0 #10 is the starting latency self.model_running_time = (2.0/1000.0) #model runtime screen_height = TRAINING_IMAGE_SIZE[1] screen_width = TRAINING_IMAGE_SIZE[0] self.on_track = 0 self.progress = 0 self.yaw = 0 self.x = 0 self.y = 0 self.z = 0 self.distance_from_center = 0 self.distance_from_border_1 = 0 self.distance_from_border_2 = 0 self.steps = 0 self.progress_at_beginning_of_race = 0 self.reverse_dir = False self.start_ndist = 0.0 # actions -> steering angle, throttle self.action_space = spaces.Box(low=np.array([-1, 0]), high=np.array([+1, +1]), dtype=np.float32) # given image from simulator self.observation_space = spaces.Box(low=0, high=255, shape=(screen_height, screen_width, 1), dtype=np.uint8) self.allow_servo_step_signals = True #stores the time when camera images are received self.cam_update_time=[] #stores the time when consequetive actions are send self.cons_action_send_time=[] #stores the time when progress updates are received self.progress_update_time = [] #folder location to store the debug data self.debug_data_folder = [] self.debug_index = 0 if node_type == SIMULATION_WORKER: # ROS initialization rospy.init_node('rl_coach', anonymous=True) self.ack_publisher = rospy.Publisher('/vesc/low_level/ackermann_cmd_mux/output', AckermannDriveStamped, queue_size=100) self.racecar_service = rospy.ServiceProxy('/gazebo/set_model_state', SetModelState) self.clear_forces_client = rospy.ServiceProxy('/gazebo/clear_joint_forces', JointRequest) # Subscribe to ROS topics and register callbacks rospy.Subscriber('/progress', Progress, self.callback_progress) rospy.Subscriber('/camera/zed/rgb/image_rect_color', sensor_image, self.callback_image) self.world_name = 'hard_track'#rospy.get_param('WORLD_NAME') self.set_waypoints() waypoints = self.waypoints is_loop = np.all(waypoints[0,:] == waypoints[-1,:]) if is_loop: self.center_line = LinearRing(waypoints[:,0:2]) else: self.center_line = LineString(waypoints[:,0:2]) self.center_dists = [self.center_line.project(Point(p), normalized=True) for p in self.center_line.coords[:-1]] + [1.0] self.track_length = self.center_line.length self.reward_in_episode = 0 self.prev_progress = 0 self.steps = 0 # Create the publishers for sending speed and steering info to the car self.velocity_pub_dict = OrderedDict() self.steering_pub_dict = OrderedDict() for topic in VELOCITY_TOPICS: self.velocity_pub_dict[topic] = rospy.Publisher(topic, Float64, queue_size=1) for topic in STEERING_TOPICS: self.steering_pub_dict[topic] = rospy.Publisher(topic, Float64, queue_size=1) def get_data_debug(self): print("center_line",self.center_line) print("track_length",self.track_length) def reset(self,inp_x=1.75,inp_y=0.6): if node_type == SAGEMAKER_TRAINING_WORKER: return self.observation_space.sample() # print('Total Reward Reward=%.2f' % self.reward_in_episode, # 'Total Steps=%.2f' % self.steps) #self.send_reward_to_cloudwatch(self.reward_in_episode) self.reward_in_episode = 0 self.reward = None self.done = False self.next_state = None self.image = None self.steps = 0 self.prev_progress = 0 # Reset car in Gazebo self.send_action(0, 0) # set the throttle to 0 self.racecar_reset(0, 0) self.infer_reward_state(0, 0) self.cam_update_time = [] self.cons_action_send_time = [] self.progress_update_time = [] self.debug_index= self.debug_index+1 return self.next_state def add_latency_to_image(self,observation): observation = observation.reshape(observation.shape[0],observation.shape[1],1) #print('Set latency is:',self.latency*self.latency_max) #observation[119, 159, 0] = int(self.latency) #setting the sampling rate #observation[119, 158, 0] = int(self.sampling_rate) #print(observation[119, 159, 0],observation[119, 158, 0] ) return observation def convert_rgb_to_gray(self, observation): r, g, b = observation[:, :, 0], observation[:, :, 1], observation[:, :, 2] observation = 0.2989 * r + 0.5870 * g + 0.1140 * b return observation def set_next_state(self): if(self.image!=None): #t1 = time.time() image_data = self.image # Read the image and resize to get the state #print(image_data.width, image_data.height) image = Image.frombytes('RGB', (image_data.width, image_data.height), image_data.data, 'raw', 'RGB', 0, 1) image = image.resize(TRAINING_IMAGE_SIZE, resample=2) image = np.array(image) #image = do_randomization(image) image = self.convert_rgb_to_gray(image) image = self.add_latency_to_image(image) self.next_state = image def racecar_reset(self, ndist, next_index): rospy.wait_for_service('gazebo/set_model_state') #random_start = random.random() prev_index, next_index = self.find_prev_next_waypoints(self.start_ndist) # Compute the starting position and heading #start_point = self.center_line.interpolate(ndist, normalized=True) start_point = self.center_line.interpolate(self.start_ndist, normalized=True) start_yaw = math.atan2(self.center_line.coords[next_index][1] - start_point.y, self.center_line.coords[next_index][0] - start_point.x) start_quaternion = Rotation.from_euler('zyx', [start_yaw, 0, 0]).as_quat() # Construct the model state and send to Gazebo model_state = ModelState() model_state.model_name = 'racecar' model_state.pose.position.x = start_point.x model_state.pose.position.y = start_point.y model_state.pose.position.z = 0 model_state.pose.orientation.x = start_quaternion[0] model_state.pose.orientation.y = start_quaternion[1] model_state.pose.orientation.z = start_quaternion[2] model_state.pose.orientation.w = start_quaternion[3] model_state.twist.linear.x = 0 model_state.twist.linear.y = 0 model_state.twist.linear.z = 0 model_state.twist.angular.x = 0 model_state.twist.angular.y = 0 model_state.twist.angular.z = 0 self.racecar_service(model_state) for joint in EFFORT_JOINTS: self.clear_forces_client(joint) #keeping track where to start the car self.reverse_dir = not self.reverse_dir self.start_ndist = (self.start_ndist + ROUND_ROBIN_ADVANCE_DIST) % 1.0 self.progress_at_beginning_of_race = self.progress def find_prev_next_waypoints(self, ndist): if self.reverse_dir: next_index = bisect.bisect_left(self.center_dists, ndist) - 1 prev_index = next_index + 1 if next_index == -1: next_index = len(self.center_dists) - 1 else: next_index = bisect.bisect_right(self.center_dists, ndist) prev_index = next_index - 1 if next_index == len(self.center_dists): next_index = 0 return prev_index, next_index def step(self, action): self.latency_steps = self.latency_steps+1 #print('latency set in env:',self.latency) #bookeeping when the action was send #self.cons_action_send_time.append([self.steps,time.time()]) latency = (self.latency-2.0)/1000.0 #10ms latency is substracted, because that is the avg default latency observed on the training machine if latency>0.001: time.sleep(latency) else: latency = 0.0 # Initialize next state, reward, done flag self.next_state = None self.reward = None self.done = False # Send this action to Gazebo and increment the step count self.steering_angle = float(action[0]) self.speed = float(action[1]) self.send_action(self.steering_angle, self.speed) self.steps += 1 #sleep to control sampling rate to_sleep = (self.sampling_sleep - self.model_running_time - latency) if to_sleep>0.001: time.sleep(to_sleep) # if self.latency_steps == self.latency_max_num_steps: # #update the latency # self.latency_index = (self.latency_index+1) % (len(self.latencies)) # self.latency = self.latencies[self.latency_index] # #update the sampling rate # self.sampling_rate_index = random.randint(0,1) # self.sampling_rate = self.sampling_rates[self.sampling_rate_index] # self.sampling_sleep = (1.0/self.sampling_rate) # if (self.latency/1000.0)> self.sampling_sleep: # match sampling input to the model and latency # self.sampling_rate = 1000.0/self.latency # self.latency_steps = 0 # Compute the next state and reward self.infer_reward_state(self.steering_angle, self.speed) return self.next_state, self.reward, self.done, {} def send_action(self, steering_angle, speed): # Simple v/r to computes the desired rpm wheel_rpm = speed/WHEEL_RADIUS for _, pub in self.velocity_pub_dict.items(): pub.publish(wheel_rpm) for _, pub in self.steering_pub_dict.items(): pub.publish(steering_angle) def callback_image(self, data): self.image = data #bookeeping when the image was received #self.cam_update_time.append([self.steps,time.time()]) def callback_progress(self, data): self.on_track = not (data.off_track) self.progress = data.progress self.yaw = data.yaw self.x = data.x self.y = data.y self.z = data.z self.distance_from_center = data.distance_from_center self.distance_from_border_1 = data.distance_from_border_1 self.distance_from_border_2 = data.distance_from_border_2 #bookeeping when the progress was received #self.progress_update_time.append([self.steps,time.time()]) def reward_function (self, on_track, x, y, distance_from_center, throttle, steering, track_width): marker_1 = 0.1 * track_width marker_2 = 0.15 * track_width marker_3 = 0.20 * track_width reward = (track_width - distance_from_center) #max reward = 0.44 if distance_from_center >= 0.0 and distance_from_center <= marker_1: reward = reward * 2.5 #0.90, 0.44 max is scaled to 1.0 elif distance_from_center <= marker_2: reward = reward * 1.33 #0.85, 0.375 max is scaled to 0.5 elif distance_from_center <= marker_3: reward = reward * 0.71 #0.80, 0.352 max is scaled to 0.25 else: reward = 0.001 # may go close to off track # penalize reward for the car taking slow actions if throttle < 1.6 and reward>0: reward *= 0.95 if throttle < 1.4 and reward>0: reward *= 0.95 return float(reward) def infer_reward_state(self, steering_angle, throttle): #state has to be set first, because we need most accurate reward signal self.set_next_state() on_track = self.on_track done = False if on_track != 1: reward = CRASHED done = True else: reward = self.reward_function(on_track, self.x, self.y, self.distance_from_center, throttle, steering_angle, self.road_width) #after 500 steps in episode we want to restart it if self.steps==500: done = True if reward > 0: #car is not crashed reward = reward *5.0 #bonus on completing 500 steps self.reward_in_episode += reward self.reward = reward self.done = done def set_waypoints(self): if self.world_name.startswith(MEDIUM_TRACK_WORLD): self.waypoints = vertices = np.zeros((8, 2)) self.road_width = 0.50 vertices[0][0] = -0.99; vertices[0][1] = 2.25; vertices[1][0] = 0.69; vertices[1][1] = 2.26; vertices[2][0] = 1.37; vertices[2][1] = 1.67; vertices[3][0] = 1.48; vertices[3][1] = -1.54; vertices[4][0] = 0.81; vertices[4][1] = -2.44; vertices[5][0] = -1.25; vertices[5][1] = -2.30; vertices[6][0] = -1.67; vertices[6][1] = -1.64; vertices[7][0] = -1.73; vertices[7][1] = 1.63; elif self.world_name.startswith(EASY_TRACK_WORLD): self.waypoints = vertices = np.zeros((2, 2)) self.road_width = 0.90 vertices[0][0] = -1.08; vertices[0][1] = -0.05; vertices[1][0] = 1.08; vertices[1][1] = -0.05; else: self.waypoints = vertices = np.zeros((30, 2)) self.road_width = 0.44 vertices[0][0] = 1.5; vertices[0][1] = 0.58; vertices[1][0] = 5.5; vertices[1][1] = 0.58; vertices[2][0] = 5.6; vertices[2][1] = 0.6; vertices[3][0] = 5.7; vertices[3][1] = 0.65; vertices[4][0] = 5.8; vertices[4][1] = 0.7; vertices[5][0] = 5.9; vertices[5][1] = 0.8; vertices[6][0] = 6.0; vertices[6][1] = 0.9; vertices[7][0] = 6.08; vertices[7][1] = 1.1; vertices[8][0] = 6.1; vertices[8][1] = 1.2; vertices[9][0] = 6.1; vertices[9][1] = 1.3; vertices[10][0] = 6.1; vertices[10][1] = 1.4; vertices[11][0] = 6.07; vertices[11][1] = 1.5; vertices[12][0] = 6.05; vertices[12][1] = 1.6; vertices[13][0] = 6; vertices[13][1] = 1.7; vertices[14][0] = 5.9; vertices[14][1] = 1.8; vertices[15][0] = 5.75; vertices[15][1] = 1.9; vertices[16][0] = 5.6; vertices[16][1] = 2.0; vertices[17][0] = 4.2; vertices[17][1] = 2.02; vertices[18][0] = 4; vertices[18][1] = 2.1; vertices[19][0] = 2.6; vertices[19][1] = 3.92; vertices[20][0] = 2.4; vertices[20][1] = 4; vertices[21][0] = 1.2; vertices[21][1] = 3.95; vertices[22][0] = 1.1; vertices[22][1] = 3.92; vertices[23][0] = 1; vertices[23][1] = 3.88; vertices[24][0] = 0.8; vertices[24][1] = 3.72; vertices[25][0] = 0.6; vertices[25][1] = 3.4; vertices[26][0] = 0.58; vertices[26][1] = 3.3; vertices[27][0] = 0.57; vertices[27][1] = 3.2; vertices[28][0] = 1; vertices[28][1] = 1; vertices[29][0] = 1.25; vertices[29][1] = 0.7; def get_closest_waypoint(self): res = 0 index = 0 x = self.x y = self.y minDistance = float('inf') for row in self.waypoints: distance = math.sqrt((row[0] - x) * (row[0] - x) + (row[1] - y) * (row[1] - y)) if distance < minDistance: minDistance = distance res = index index = index + 1 return res class DeepRacerDiscreteEnv(DeepRacerEnv): def __init__(self): DeepRacerEnv.__init__(self) with open('custom_files/model_metadata.json', 'r') as f: model_metadata = json.load(f) self.json_actions = model_metadata['action_space'] self.action_space = spaces.Discrete(len(self.json_actions)) print("Intialized action space") print(self.json_actions) print("num of actions",self.action_space ) def step(self, action): action = int(action) # Convert discrete to continuous steering_angle = float(self.json_actions[action]['steering_angle']) * math.pi / 180.0 throttle = float(self.json_actions[action]['speed']) continous_action = [steering_angle, throttle] return super().step(continous_action) import tensorflow as tf config = tf.ConfigProto() config.gpu_options.allow_growth=True import json with open('custom_files/model_metadata.json', 'r') as f: model_metadata = json.load(f) json_actions = model_metadata['action_space'] def get_session(frozen_graph): with tf.gfile.GFile(frozen_graph, "rb") as f: restored_graph_def = tf.GraphDef() restored_graph_def.ParseFromString(f.read()) with tf.Graph().as_default() as graph: tf.import_graph_def(restored_graph_def, name="",input_map=None) x = graph.get_tensor_by_name('ppo2_model/Ob:0') y = graph.get_tensor_by_name('ppo2_model/pi_1/add:0') sess = tf.Session(graph=graph, config=config) return sess, x,y # Automate the testing of the models #Runs a simple setting def test_in_simulator(sess, latency, sampling_sleep, total_steps, direction, x, y): time_taken = [] Actions_Taken = [] #stores speed and steering of the actions taken total_rewards = [] env = DeepRacerDiscreteEnv() env.sampling_sleep = sampling_sleep env.sampling_rate = 1.0/(env.sampling_sleep) #print(env.sampling_sleep, env.sampling_rate) env.latency = latency env.dist_and_speed = [] if direction==2: # when 2, we want to reverse the direction env.reverse_dir = not env.reverse_dir steps_done = 0 local_steps = 0 obs = env.reset() #warmup obs = obs.reshape(1,120,160,1) action = sess.run(y, feed_dict={x: obs}) while local_steps<=total_steps: done = False obs = env.reset() while not done and local_steps<=total_steps: t1 = time.time() obs = obs.reshape(1,120,160,1) action = sess.run(y, feed_dict={x: obs}) action = np.argmax(action) steering_angle = json_actions[action]['steering_angle'] throttle = json_actions[action]['speed'] Actions_Taken.append([steering_angle,throttle]) #updating the exact model runtime env.model_running_time = (time.time() - t1) obs, rew, done, _ = env.step(action) total_rewards.append(rew) t2 = time.time() time_taken.append(t2-t1) local_steps = local_steps + 1 if done: obs = env.reset() dist_and_speed = env.dist_and_speed del env return total_rewards, Actions_Taken, dist_and_speed, time_taken # save the data import pickle def save_data(path, total_rewards, dist_and_speed, Actions_Taken): with open(path, 'wb') as f: print("Saving the data", path) data = [total_rewards, dist_and_speed, Actions_Taken] pickle.dump(data, f) def do_testing(sess, x, y, exp_name): for i in range(len(latencies)): latency = latencies[i] sampling_sleep = sampling_sleeps[i] direction = directions[i] total_rewards, Actions_Taken, dist_and_speed, time_taken = test_in_simulator(sess, latency, sampling_sleep, steps, direction, x, y) path = data_folder + exp_name+'_'+str(direction)+'_'+str(latency) save_data(path, total_rewards, dist_and_speed, Actions_Taken) del total_rewards, Actions_Taken, dist_and_speed, time_taken for i in range(len(paths)): frozen_graph = paths[i] exp_name = experiments[i] sess, x,y = get_session(frozen_graph) do_testing(sess,x,y, exp_name) del sess, x, y print('done')

[ "sandha.iitr@gmail.com" ]

sandha.iitr@gmail.com

d39c8a61833fc2f4123d6803bf8dce614ed0a12a

cfc9a8831e5946d738329fad2763d643dec8566f

/src/encoded/tests/test_create_mapping.py

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[ "MIT" ]

permissive

emi80/encoded

8e244a66b0d36610dcf8d9a47d385640dfa7987d

2fe2c2afbd3be21b65b10a189a3bd623ecdaee37

refs/heads/master

2021-01-18T12:34:56.060690

2015-03-05T21:56:05

null

0

null

UTF-8

Python

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299

py

import pytest from ..loadxl import ORDER @pytest.mark.parametrize('item_type', ORDER) def test_create_mapping(registry, item_type): from ..commands.create_mapping import type_mapping from ..contentbase import TYPES mapping = type_mapping(registry[TYPES], item_type) assert mapping

[ "laurence@lrowe.co.uk" ]

laurence@lrowe.co.uk

9c93902848978428f5ced5f6c21786783cea6d85

a4623b72797d87baf17ca48406e36da8af78e7eb

/backend/settings.py

b4dcb752ef5348911c4f31d20ef9747037555f2c

[]

no_license

janefwp/task

f410965a381769864f51d485f72e5c4a0738ebad

f7366c9f8e77a3927acaff3758916779e670cf53

refs/heads/main

2023-04-13T19:47:36.614907

2021-04-25T09:02:43

null

0

null

UTF-8

Python

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py

""" Django settings for backend project. Generated by 'django-admin startproject' using Django 3.1.6. For more information on this file, see https://docs.djangoproject.com/en/3.1/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/3.1/ref/settings/ """ import os from datetime import timedelta from pathlib import Path # Build paths inside the project like this: BASE_DIR / 'subdir'. BASE_DIR = Path(__file__).resolve().parent.parent # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/3.1/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = '#js5i*nzoc5w(4a2v@+m@i0j8z^7()6+ne#^@q^%iur06fdg&7' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = ['127.0.0.1', 'localhost', '.herokuapp.com', 'https://hospitalbooksystem.herokuapp.com/'] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'rest_framework', 'corsheaders', 'storages', 'base.apps.BaseConfig', ] REST_FRAMEWORK = { 'DEFAULT_AUTHENTICATION_CLASSES': ( 'rest_framework_simplejwt.authentication.JWTAuthentication', ) } MIDDLEWARE = [ 'corsheaders.middleware.CorsMiddleware', 'whitenoise.middleware.WhiteNoiseMiddleware', 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'backend.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [ os.path.join(BASE_DIR, 'frontend/build') ], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'backend.wsgi.application' # Database # https://docs.djangoproject.com/en/3.1/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': BASE_DIR / 'db.sqlite3', } } # Password validation # https://docs.djangoproject.com/en/3.1/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/3.1/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.1/howto/static-files/ STATIC_URL = '/static/' MEDIA_URL = '/images/' STATICFILES_DIRS = [ BASE_DIR / 'static', BASE_DIR / 'frontend/build/static' ] MEDIA_ROOT = BASE_DIR / 'static/images' STATIC_ROOT = BASE_DIR / 'staticfiles' CORS_ALLOW_ALL_ORIGINS = True if os.getcwd() == '/app': # SECURE_PROXY_SSL_HEADER = ('HTTP_X_FORWARDED_PROTO', 'https') # SECURE_SSL_REDIRECT = True DEBUG = False

[ "cuij1012@gmail.com" ]

cuij1012@gmail.com

7dc54bea595127ad1357734ba33347c1f4cb9188

c26e98676846ab321065e416ee8b3e2f5b1d4b43

/PyPoll_Challenge.py

6fa47eaa8b51b4dfb0beb98ed3f51a144c38e5f7

[]

no_license

jaredcclarke/Election_Analysis

d510f401555515fdf8e601bfdab5b401b9ef9de9

512c2ee41ed8aec440e6e3b7f3459e58211a4e51

refs/heads/main

2022-12-26T15:11:30.816855

2020-10-12T06:03:51

301,816,774

1

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# -*- coding: UTF-8 -*- # """PyPoll Homework Challenge Solution.""" # Add our dependencies. import csv import os # Add a variable to load a file from a path. file_to_load = os.path.join("Resources", "election_results.csv") # Add a variable to save the file to a path. file_to_save = os.path.join("analysis", "election_analysis.txt") # Initialize a total vote counter. total_votes = 0 total_county_votes = 0 # Candidate Options and candidate votes. candidate_options = [] candidate_votes = {} # 1: Create a county list and county votes dictionary. county_list = [] county_votes = {} # Track the winning candidate, vote count and percentage winning_candidate = "" winning_count = 0 winning_percentage = 0 # 2: Track the largest county and county voter turnout. county_largest_turnout = "" largest_turnout_count = 0 largest_county_percentage = 0 # Read the csv and convert it into a list of dictionaries with open(file_to_load) as election_data: reader = csv.reader(election_data) # Read the header header = next(reader) # For each row in the CSV file. for row in reader: # Add to the total vote count total_votes = total_votes + 1 # Get the candidate name from each row. candidate_name = row[2] # If the candidate does not match any existing candidate add it to # the candidate list if candidate_name not in candidate_options: # Add the candidate name to the candidate list. candidate_options.append(candidate_name) # And begin tracking that candidate's voter count. candidate_votes[candidate_name] = 0 # Add a vote to that candidate's count candidate_votes[candidate_name] += 1 # 4a: Write a decision statement that checks that the total_county_votes = total_votes + 1 # 3: Extract the county name from each row. county_name = row[1] # county does not match any existing county in the county list. if county_name not in county_list: # 4b: Add the existing county to the list of counties. county_list.append(county_name) # 4c: Begin tracking the county's vote count. county_votes[county_name] = 0 # 5: Add a vote to that county's vote count. county_votes[county_name] += 1 # Save the results to our text file. with open(file_to_save, "w") as txt_file: # Print the final vote count (to terminal) election_results = ( f"\nElection Results\n" f"-------------------------\n" f"Total Votes: {total_votes:,}\n" f"-------------------------\n\n" f"County Votes:\n") print(election_results, end="") txt_file.write(election_results) # 6a: Write a repetition statement to get the county from the county dictionary. for county_name in county_votes: # 6b: Retrieve the county vote count. votes_county = county_votes[county_name] # 6c: Calculate the percent of total votes for the county. county_vote_percentage = float(votes_county) / float(total_votes) * 100 # 6d: Print the county results to the terminal. county_results = ( f"{county_name}: {county_vote_percentage:.1f}% ({votes_county:,})\n") print(county_results) # 6e: Save the county votes to a text file. txt_file.write(county_results) # 6f: Write a decision statement to determine the winning county and get its vote count. if (votes_county > largest_turnout_count) and (county_vote_percentage > largest_county_percentage): largest_turnout_count = votes_county largest_county_percentage = county_vote_percentage county_largest_turnout = county_name # 7: Print the county with the largest turnout to the terminal. largest_turnout_summary = ( f"\n" f"-------------------------\n" f"Largest County Turnout: {county_largest_turnout}\n" f"-------------------------\n") print(largest_turnout_summary) # 8: Save the county with the largest turnout to a text file. txt_file.write(largest_turnout_summary) # Save the final candidate vote count to the text file. for candidate_name in candidate_votes: # Retrieve vote count and percentage votes = candidate_votes.get(candidate_name) vote_percentage = float(votes) / float(total_votes) * 100 candidate_results = ( f"{candidate_name}: {vote_percentage:.1f}% ({votes:,})\n") # Print each candidate's voter count and percentage to the # terminal. print(candidate_results) # Save the candidate results to our text file. txt_file.write(candidate_results) # Determine winning vote count, winning percentage, and candidate. if (votes > winning_count) and (vote_percentage > winning_percentage): winning_count = votes winning_candidate = candidate_name winning_percentage = vote_percentage # Print the winning candidate (to terminal) winning_candidate_summary = ( f"-------------------------\n" f"Winner: {winning_candidate}\n" f"Winning Vote Count: {winning_count:,}\n" f"Winning Percentage: {winning_percentage:.1f}%\n" f"-------------------------\n") print(winning_candidate_summary) # Save the winning candidate's name to the text file txt_file.write(winning_candidate_summary)

[ "jared@Jareds-Air.fios-router.home" ]

jared@Jareds-Air.fios-router.home

007d263722520390990a2b56a70c7cb4328ec8b9

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/Session info to table/macOS app/Tracks to Table/tracks_to_table.py

f7d4b2789f5e0e70306dadea74eacbeb13a6aed0

[ "MIT" ]

permissive

fantopop/post-production-scripts

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refs/heads/master

2022-04-30T15:44:37.249831

2022-04-19T15:32:34

91,468,867

24

1

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UTF-8

Python

false

17,676

py

#!/usr/bin/python """ tracks_to_table.py Author: Ilya Putilin https://github.com/fantopop/post-production-scripts Special thanks to Philippe Lagadec for HTML.py module for generating HTML tables. http://www.decalage.info/python/html This scripts converts .txt file, that could be exported from Pro Tools using "Export Session Info as Text" command into .csv file. This CSV file can be easily opened with Number app. There are two formats available: - with TRACK_NAME column as one table. - grouped by TRACK NAME with [--tracks] option. """ import sys, csv, argparse from os import path # Separator. sep = '\t' header = ['#', 'EVENT', 'START', 'END', 'DURATION'] footer = [''] * 5 # TABLE_STYLE_THINBORDER = "border: 1px solid #000000; border-collapse: collapse;" TABLE_STYLE_THINBORDER = "" table_style = 'table {border-collapse: collapse;} th, td {border: 1px solid #ccc;padding: 8px;}' #--- CONSTANTS ----------------------------------------------------------------- # Table style to get thin black lines in Mozilla/Firefox instead of 3D borders TABLE_STYLE_THINBORDER = "border: 1px solid #000000; border-collapse: collapse;" #TABLE_STYLE_THINBORDER = "border: 1px solid #000000;" #=== CLASSES =================================================================== class TableCell (object): """ a TableCell object is used to create a cell in a HTML table. (TD or TH) Attributes: - text: text in the cell (may contain HTML tags). May be any object which can be converted to a string using str(). - header: bool, false for a normal data cell (TD), true for a header cell (TH) - bgcolor: str, background color - width: str, width - align: str, horizontal alignement (left, center, right, justify or char) - char: str, alignment character, decimal point if not specified - charoff: str, see HTML specs - valign: str, vertical alignment (top|middle|bottom|baseline) - style: str, CSS style - attribs: dict, additional attributes for the TD/TH tag Reference: http://www.w3.org/TR/html4/struct/tables.html#h-11.2.6 """ def __init__(self, text="", bgcolor=None, header=False, width=None, align=None, char=None, charoff=None, valign=None, style=None, attribs=None): """TableCell constructor""" self.text = text self.bgcolor = bgcolor self.header = header self.width = width self.align = align self.char = char self.charoff = charoff self.valign = valign self.style = style self.attribs = attribs if attribs==None: self.attribs = {} def __str__(self): """return the HTML code for the table cell as a string""" attribs_str = "" if self.bgcolor: self.attribs['bgcolor'] = self.bgcolor if self.width: self.attribs['width'] = self.width if self.align: self.attribs['align'] = self.align if self.char: self.attribs['char'] = self.char if self.charoff: self.attribs['charoff'] = self.charoff if self.valign: self.attribs['valign'] = self.valign if self.style: self.attribs['style'] = self.style for attr in self.attribs: attribs_str += ' %s="%s"' % (attr, self.attribs[attr]) if self.text: text = str(self.text) else: # An empty cell should at least contain a non-breaking space text = ' ' if self.header: return ' <TH%s>%s</TH>\n' % (attribs_str, text) else: return ' <TD%s>%s</TD>\n' % (attribs_str, text) #------------------------------------------------------------------------------- class TableRow (object): """ a TableRow object is used to create a row in a HTML table. (TR tag) Attributes: - cells: list, tuple or any iterable, containing one string or TableCell object for each cell - header: bool, true for a header row (TH), false for a normal data row (TD) - bgcolor: str, background color - col_align, col_valign, col_char, col_charoff, col_styles: see Table class - attribs: dict, additional attributes for the TR tag Reference: http://www.w3.org/TR/html4/struct/tables.html#h-11.2.5 """ def __init__(self, cells=None, bgcolor=None, header=False, attribs=None, col_align=None, col_valign=None, col_char=None, col_charoff=None, col_styles=None): """TableCell constructor""" self.bgcolor = bgcolor self.cells = cells self.header = header self.col_align = col_align self.col_valign = col_valign self.col_char = col_char self.col_charoff = col_charoff self.col_styles = col_styles self.attribs = attribs if attribs==None: self.attribs = {} def __str__(self): """return the HTML code for the table row as a string""" attribs_str = "" if self.bgcolor: self.attribs['bgcolor'] = self.bgcolor for attr in self.attribs: attribs_str += ' %s="%s"' % (attr, self.attribs[attr]) if self.header: result = '<THEAD>' else: result = '' result += ' <TR%s>\n' % attribs_str for cell in self.cells: col = self.cells.index(cell) # cell column index if not isinstance(cell, TableCell): cell = TableCell(cell, header=self.header) # apply column alignment if specified: if self.col_align and cell.align==None: cell.align = self.col_align[col] if self.col_char and cell.char==None: cell.char = self.col_char[col] if self.col_charoff and cell.charoff==None: cell.charoff = self.col_charoff[col] if self.col_valign and cell.valign==None: cell.valign = self.col_valign[col] # apply column style if specified: if self.col_styles and cell.style==None: cell.style = self.col_styles[col] result += str(cell) result += ' </TR>\n' if self.header: result += '</THEAD>' return result #------------------------------------------------------------------------------- class Table (object): """ a Table object is used to create a HTML table. (TABLE tag) Attributes: - rows: list, tuple or any iterable, containing one iterable or TableRow object for each row - header_row: list, tuple or any iterable, containing the header row (optional) - border: str or int, border width - style: str, table style in CSS syntax (thin black borders by default) - width: str, width of the table on the page - attribs: dict, additional attributes for the TABLE tag - col_width: list or tuple defining width for each column - col_align: list or tuple defining horizontal alignment for each column - col_char: list or tuple defining alignment character for each column - col_charoff: list or tuple defining charoff attribute for each column - col_valign: list or tuple defining vertical alignment for each column - col_styles: list or tuple of HTML styles for each column Reference: http://www.w3.org/TR/html4/struct/tables.html#h-11.2.1 """ def __init__(self, rows=None, border='1', style=None, width=None, cellspacing=None, cellpadding=4, attribs=None, header_row=None, col_width=None, col_align=None, col_valign=None, col_char=None, col_charoff=None, col_styles=None): """TableCell constructor""" self.border = border self.style = style # style for thin borders by default if style == None: self.style = TABLE_STYLE_THINBORDER self.width = width self.cellspacing = cellspacing self.cellpadding = cellpadding self.header_row = header_row self.rows = rows if not rows: self.rows = [] self.attribs = attribs if not attribs: self.attribs = {} self.col_width = col_width self.col_align = col_align self.col_char = col_char self.col_charoff = col_charoff self.col_valign = col_valign self.col_styles = col_styles def __str__(self): """return the HTML code for the table as a string""" attribs_str = "" if self.border: self.attribs['border'] = self.border if self.style: self.attribs['style'] = self.style if self.width: self.attribs['width'] = self.width if self.cellspacing: self.attribs['cellspacing'] = self.cellspacing if self.cellpadding: self.attribs['cellpadding'] = self.cellpadding for attr in self.attribs: attribs_str += ' %s="%s"' % (attr, self.attribs[attr]) result = '<TABLE%s>\n' % attribs_str # insert column tags and attributes if specified: if self.col_width: for width in self.col_width: result += ' <COL width="%s">\n' % width # First insert a header row if specified: if self.header_row: if not isinstance(self.header_row, TableRow): result += str(TableRow(self.header_row, header=True)) else: result += str(self.header_row) # Then all data rows: for row in self.rows: if not isinstance(row, TableRow): row = TableRow(row) # apply column alignments and styles to each row if specified: # (Mozilla bug workaround) if self.col_align and not row.col_align: row.col_align = self.col_align if self.col_char and not row.col_char: row.col_char = self.col_char if self.col_charoff and not row.col_charoff: row.col_charoff = self.col_charoff if self.col_valign and not row.col_valign: row.col_valign = self.col_valign if self.col_styles and not row.col_styles: row.col_styles = self.col_styles result += str(row) result += '</TABLE>' return result def table(*args, **kwargs): 'return HTML code for a table as a string. See Table class for parameters.' return str(Table(*args, **kwargs)) #------------------------------------------------------------------------------- tab = ' ' class Tag(): ''' A class to provide correct opening and closing tags, with intendation support via HTML class instance. Implies usage of the "with" statement: with Tag('tag', HTML-instance): <code> ''' def __init__(self, name, HTML): self.name = name self.HTML = HTML def __enter__(self): self.HTML.content += tab * self.HTML.indent + '<' + self.name + '>\n' self.HTML.indent += 1 def __exit__(self, exc_type, exc_value, traceback): self.HTML.indent -= 1 self.HTML.content += tab * self.HTML.indent + '</' + self.name + '>\n' class HTML(): ''' HTML() class instance accumulates generated HTML code, handles indentation and provides several html-tags as methods, returning Tag() class instances. Common usage pattern: h = HTML() with h.html(): with h.head(): with h.title() h.add('Hello world page') with h.body(): with h.h1(): h.add('Hello World!') with h.p(): h.add('This is the HTML code') print(str(h)) ''' def __init__(self): self.indent = 0 self.content = '<!DOCTYPE html>\n' def __str__(self): return self.content def add(self, text): for line in text.split('\n'): self.content += tab * self.indent + line + '\n' def html(self): return Tag('html', self) def body(self): return Tag('body', self) def head(self): return Tag('head', self) def title(self): return Tag('title', self) def h1(self): return Tag('h1', self) def h2(self): return Tag('h2', self) def style(self): return Tag('style', self) def p(self): return Tag('p', self) #------------------------------------------------------------------------------- class Track(): ''' Stores track name and list of track events: [NUMBER, CLIP_NAME, START TC, END TC, DURATION TC] ''' def __init__(self, name): self.name = name self.events = [] class Session(): ''' Session() instance reads .txt file, exported from Pro Tools and stores every tracks EDL as list of Track() instances. Supports export to .csv and .html formats. ''' def __init__(self, filename): # Open session info file for reading csv_reader = csv.reader(filename, dialect='excel-tab') # Create array for Track() objects self.tracks = [] for raw_row in csv_reader: # Check, whether the row is not empty. if raw_row: # Remove all whitespaces from start and end of the cells. row = [cell.strip() for cell in raw_row] # Get track name. if row[0].startswith('TRACK NAME:'): track = Track(name=row[1]) self.tracks.append(track) continue # Skip original header lines. if row[0].startswith('CHANNEL'): continue if len(row) > 6: track.events.append(row[1:6]) def to_csv(self, filename): with open(filename, 'w') as outputfile: csv_writer = csv.writer(outputfile, dialect='excel-tab') for track in self.tracks: csv_writer.writerow([''] + [track.name] + ['']*3) csv_writer.writerow(header) for line in track.events: csv_writer.writerow(line) csv_writer.writerow(footer) def to_html(self, filename): h = HTML() with h.html(): with h.head(): h.add('<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">') with h.title(): # Add document title h.add(filename.split('.')[-2].split('/')[-1]) with h.style(): h.add('@media print {') h.indent += 1 # Add page break after each track's table when printing h.add('TABLE { page-break-after: always}') # Configure correct display of table over multiple printing pages h.add('TR { page-break-inside:avoid; page-break-after:auto }') h.add('TD { page-break-inside:avoid; page-break-after:auto }') h.add('THEAD { display:table-header-group }') h.add('TFOOT { display:table-footer-group }') # Set default landscape orientation when printing h.add('@page {size: landscape;}}') h.indent -= 1 h.add(table_style) with h.body(): for track in self.tracks: # Add track name as header with h.h2(): h.add(track.name) # Add track's EDL table h.add(table(track.events, header_row=header, width='100%', border=None, cellpadding=None, col_width=['2.5%', '', '5%', '5%', '5%'], col_align=['center', 'left', 'center', 'center', 'center'], style=TABLE_STYLE_THINBORDER )) with open(filename, 'w') as outputfile: outputfile.write(str(h)) def export(self, filename, to): outputfile = outname(filename, to) if to == 'csv': self.to_csv(outputfile) else: self.to_html(outputfile) print('Source: ' + filename) print('Result: ' + outputfile) def outname(filename, ext='csv'): """ Constructs output filename from input file, replacing extension with '.csv'. Example: input.txt >>> input.csv """ split = (path.basename(filename)).split('.') l = len(split) if l > 1: output = '.'.join(split[0:l-1] + [ext]) else: output = filename + '.' + ext return path.join(path.dirname(filename), output) def main(): parser = argparse.ArgumentParser( description="Converts '.txt' file from Pro Tools 'Export Session Info as Text' command to '.csv' or '.html' file") parser.add_argument( 'txt', metavar='textfile', type=argparse.FileType(mode='rU'), help='session info text file from Pro Tools') parser.add_argument( '--to', choices=['csv', 'html'], required=True, help='export format: "csv" or "html"') args = parser.parse_args() # Read session info to Session() object session = Session(args.txt) args.txt.close() # Export to the file of choses format. session.export(filename=args.txt.name, to=args.to) if __name__ == '__main__': main()

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""" WSGI config for Lab1 project. It exposes the WSGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/1.10/howto/deployment/wsgi/ """ import os from django.core.wsgi import get_wsgi_application os.environ.setdefault("DJANGO_SETTINGS_MODULE", "Source.settings") application = get_wsgi_application()

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# https://www.reddit.com/r/dailyprogrammer/comments/784fgr/20171023_challenge_337_easy_minimize_maximize/ # Area of circle = (pi)r^2 # Circumference of circle = 2(pi)r # Area of sector covering x of circumference = rx/2 # x - angle subtended by arc

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# class Phone : # def __init__ (self,brand,model,color): # self.brand = brand # self.model = model # self.color = color # def show (self): # print(f"{self.brand},{self.model},{self.color}") # phone = Phone("Apple", "XS", "black") # phone.show() # class Monkey: # max_age = 12 # loves_bananas = True # def climb(self): # print('I am climbing the tree') # abc = Monkey() # abc.climb() # print(abc.max_age) # abc.climb() # print(abc.loves_bananas) # Это через input---------------------------------------------------------------- # class Person: # def __init__(self,name,age,gender): # self.name = name # self.age = age # self.gender = gender # def calculate_age(self): # self.number = int(input('enter year:')) # print(self.age + self.number) # p = Person('John', 23, 'male') # p.calculate_age() # #Это через self----------------------------------------------------------------------- # class Person: # def __init__(self,name,age,gender): # self.name = name # self.age = age # self.gender = gender # def calculate_age(self,year): # self.year = year # print(self.age + self.year) # p = Person('John', 23, 'male') # p.calculate_age(10) #

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from django.conf import settings from django.contrib.contenttypes.fields import GenericForeignKey from django.contrib.contenttypes.models import ContentType from django.db import models from django.utils.functional import cached_property from django.utils.translation import gettext_lazy as _ from ephios.modellogging.json import LogJSONDecoder, LogJSONEncoder from ephios.modellogging.recorders import ( InstanceActionType, capitalize_first, recorder_types_by_slug, ) # pylint: disable=protected-access class LogEntry(models.Model): content_type = models.ForeignKey( ContentType, on_delete=models.CASCADE, related_name="logentries", ) content_object_id = models.PositiveIntegerField(db_index=True) content_object = GenericForeignKey("content_type", "content_object_id") attached_to_object_type = models.ForeignKey( ContentType, on_delete=models.CASCADE, related_name="associated_logentries" ) attached_to_object_id = models.PositiveIntegerField(db_index=True) attached_to_object = GenericForeignKey("attached_to_object_type", "attached_to_object_id") datetime = models.DateTimeField(auto_now_add=True) user = models.ForeignKey( settings.AUTH_USER_MODEL, null=True, blank=True, on_delete=models.PROTECT, related_name="logging_entries", ) action_type = models.CharField( max_length=255, choices=[(value, value) for value in InstanceActionType] ) request_id = models.CharField(max_length=36, null=True, blank=True) data = models.JSONField(default=dict, encoder=LogJSONEncoder, decoder=LogJSONDecoder) class Meta: ordering = ("-datetime", "-id") verbose_name = _("Log entry") verbose_name_plural = _("Log entries") @cached_property def records(self): recorder_types = recorder_types_by_slug(self.content_type.model_class()) for recorder in self.data.values(): if not isinstance(recorder, dict) or "slug" not in recorder: continue yield recorder_types[recorder["slug"]].deserialize( recorder["data"], self.content_type.model_class(), self.action_type ) @property def content_object_classname(self): return capitalize_first(self.content_type.model_class()._meta.verbose_name) @property def content_object_or_str(self): return self.content_object or self.data.get("__str__") def __str__(self): if self.content_object: return f"{self.action_type} {type(self.content_object)._meta.verbose_name} {str(self.content_object)}" return f"{self.action_type} {self.content_type.model} {self.content_object_or_str}"

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# Copyright 2014 The Chromium Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. import urllib ################################################################################ # Gerrit API ################################################################################ class QueryBuilder(object): """Class to iteratively construct a Gerrit query string. This functions as a helper class to simplify explicit versus implicit quoting and nesting of Gerrit query strings. Gerrit query semantics are documented here: https://gerrit-review.googlesource.com/Documentation/user-search.html """ def __init__(self, terms, operator): """ Initializes a Gerrit query object. This should not be used directly; instead, one of the supplied constructors (New, NewOR, NewAND) should be used to create a new builder. Args: terms: (list) A list of explicit query parameters to start with. If 'terms' is an existing Query instance, the current instance will be initialized as a clone. operator: (str) If not 'None', this term will be implicitly added after each explicit query term. Suggested values are 'AND' and 'OR'. """ self._terms = tuple(terms) self._operator = operator @classmethod def _New(cls, terms, operator=None): return cls( [cls._prepareTerm(t) for t in terms], operator) @classmethod def New(cls, *terms): return cls._New(terms) @classmethod def NewOR(cls, *terms): return cls._New(terms, operator='OR') @classmethod def NewAND(cls, *terms): return cls._New(terms, operator='AND') @classmethod def _prepareTerm(cls, value): """Analyze the type of 'value' and generates a term from it (see 'add()')""" if isinstance(value, basestring): parts = value.split(':', 1) if len(parts) == 2: return cls._prepareSelector(parts[0], parts[1]) else: return cls._prepareString(value, quoted=True) if isinstance(value, QueryBuilder): # Return its query verbatim, enclosed in parenthesis return list(value.termiter()) # Try iterator it = None try: it = iter(value) except TypeError: pass if it is not None: return tuple(cls._prepareTerm(x) for x in it) # Default to stringify return cls._prepareString(str(value), quoted=True) @classmethod def _prepareString(cls, value, quoted=False): """Constructs a string term.""" if quoted: value = urllib.quote(value) return value @classmethod def _prepareSelector(cls, key, value): """Constructs a selector (e.g., 'label:Code-Review+1') term""" # Quote key/value individually; the colon does not get quoted return '%s:%s' % ( cls._prepareString(key, quoted=True), cls._prepareString(value, quoted=True)) def _cloneWithTerms(self, *terms): """Creates a new 'QueryBuilder' with an augmented term set.""" new_terms = self._terms + terms return self.__class__(new_terms, self._operator) def add(self, *values): """Adds a new query term to the Query. This is a generic 'add' function that infers how to add 'value' based on its type and contents. For more specific control, use the specialised 'add*' functions. The query term ('value') may be any of the following: - A key:value term, in which case the key and value are quoted but the colon is left unquoted. - A single term string, in which case the entire term is quoted - A QueryBuilder instance, in which case it is embedded as a single term bounded by parenthesis. - An iterable of query terms, in which case each term will be formatted recursively and placed inside parenthesis. Args: values: The query term to add (see above). Returns: (Query) this Query object """ terms = [] for value in values: term = self._prepareTerm(value) if term is not None: terms.append(term) if len(terms) == 0: return self return self._cloneWithTerms(*terms) def addSelector(self, key, value): """Adds a 'key:value' term to the query. The 'key' and 'value' terms will be URL quoted. Args: key: (str) the key value: (str) the value Returns: (Query) this Query object """ return self._cloneWithTerms(self._prepareSelector(key, value)) def addQuoted(self, value): """Adds a URL-quoted term to the query. Args: value: (str) the value to quote and add Returns: (Query) this Query object """ return self._cloneWithTerms(self._prepareString(value, quoted=True)) def addUnquoted(self, value): """Directly adds a term to the query. Args: value: (str) the value to add Returns: (Query) this Query object """ return self._cloneWithTerms(self._prepareString(value, quoted=False)) @classmethod def _formatQuery(cls, terms): """Recursive method to convert internal nested string/list to a query""" formatted_terms = [] for term in terms: if isinstance(term, (list, tuple)): if len(term) == 0: continue term = '(%s)' % (cls._formatQuery(term)) formatted_terms.append(term) return '+'.join(formatted_terms) def termiter(self): """Iterator overload to iterate over individual query terms""" first = True for param in self._terms: if first: first = False elif self._operator is not None: yield self._operator yield param def __len__(self): """Returns: (int) the number of explicit query terms""" return len(self._terms) def __str__(self): """Constructs a URL-quoted query string from this query's terms""" return self._formatQuery(self.termiter())

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# Input n strings in a list and print all the strings ending with a specific character provided by the user def seive(string): index = (len(string)-1) - len(match) if string[index + 1: ] == match: return string strnum = int(input("Enter num of strings: ")) strs = [] for i in range(0, strnum): strs.append(input(f"Enter string {i+1}: ")) global match matchstr: str = input("Enter the matching character at end of string: ") match = matchstr output = list(filter(seive, strs)) print(f"Strings ending with {matchstr}:", output)

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# n = input() # # swaped_n = n.swapcase() # # print(swaped_n) def swap_case(s): return s.swapcase() if __name__ == '__main__': s = input() result = swap_case(s) print(result)

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#!/Users/novan/Desktop/Github/pythonprac/pythonprac/bin/python3 # -*- coding: utf-8 -*- import re import sys from setuptools.command.easy_install import main if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) sys.exit(main())

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import datetime class index(): SQLALCHEMY_DATABASE_URI = 'sqlite:///service.db' SQLALCHEMY_TRACK_MODIFICATIONS = True PERMANENT_SESSION_LIFETIME = datetime.timedelta(hours=2,minutes=30) SEND_FILE_MAX_AGE_DEFAULT = datetime.timedelta(days=7) #PERMANENT_SESSION_LIFETIME = datetime.timedelta(seconds=10) DEBUG = True

[ "zhenglinfeng43@gmail.com" ]

zhenglinfeng43@gmail.com

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BINDS-LAB-UMASS/bindsnet_experiments

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import os import sys import torch import argparse import numpy as np import matplotlib.pyplot as plt from time import time as t sys.path.append('..') from utils import print_results, update_curves from bindsnet.datasets import CIFAR10 from bindsnet.network import Network from bindsnet.learning import Hebbian from bindsnet.encoding import bernoulli from bindsnet.network.monitors import Monitor from bindsnet.network.nodes import Input, DiehlAndCookNodes from bindsnet.evaluation import update_ngram_scores, assign_labels from bindsnet.network.topology import Conv2dConnection, SparseConnection from bindsnet.analysis.plotting import plot_input, plot_spikes, plot_conv2d_weights print() parser = argparse.ArgumentParser() parser.add_argument('--seed', type=int, default=0) parser.add_argument('--n_train', type=int, default=60000) parser.add_argument('--n_test', type=int, default=10000) parser.add_argument('--kernel_size', type=int, nargs='+', default=[16]) parser.add_argument('--stride', type=int, nargs='+', default=[4]) parser.add_argument('--n_filters', type=int, default=25) parser.add_argument('--padding', type=int, default=0) parser.add_argument('--inhib', type=float, default=100.0) parser.add_argument('--time', type=int, default=100) parser.add_argument('--dt', type=float, default=1.0) parser.add_argument('--intensity', type=float, default=0.5) parser.add_argument('--progress_interval', type=int, default=10) parser.add_argument('--update_interval', type=int, default=250) parser.add_argument('--train', dest='train', action='store_true') parser.add_argument('--test', dest='train', action='store_false') parser.add_argument('--plot', dest='plot', action='store_true') parser.add_argument('--gpu', dest='gpu', action='store_true') parser.set_defaults(plot=False, gpu=False, train=True) args = parser.parse_args() seed = args.seed n_train = args.n_train n_test = args.n_test kernel_size = args.kernel_size stride = args.stride n_filters = args.n_filters padding = args.padding inhib = args.inhib time = args.time dt = args.dt intensity = args.intensity progress_interval = args.progress_interval update_interval = args.update_interval train = args.train plot = args.plot gpu = args.gpu if len(kernel_size) == 1: kernel_size = [kernel_size[0], kernel_size[0]] if len(stride) == 1: stride = [stride[0], stride[0]] args = vars(args) print('\nCommand-line argument values:') for key, value in args.items(): print('-', key, ':', value) print() model = 'conv' data = 'cifar10' assert n_train % update_interval == 0 and n_test % update_interval == 0, \ 'No. examples must be divisible by update_interval' params = [seed, n_train, kernel_size, stride, n_filters, padding, inhib, time, dt, intensity, update_interval] model_name = '_'.join([str(x) for x in params]) if not train: test_params = [seed, n_train, n_test, kernel_size, stride, n_filters, padding, inhib, time, dt, intensity, update_interval] np.random.seed(seed) if gpu: torch.set_default_tensor_type('torch.cuda.FloatTensor') torch.cuda.manual_seed_all(seed) else: torch.manual_seed(seed) n_examples = n_train if train else n_test input_shape = [32, 32, 3] if kernel_size == input_shape: conv_size = [1, 1] else: conv_size = (int((input_shape[0] - kernel_size[0]) / stride[0]) + 1, int((input_shape[1] - kernel_size[1]) / stride[1]) + 1) n_classes = 10 n_neurons = n_filters * np.prod(conv_size) per_class = int(n_neurons / n_classes) total_kernel_size = int(np.prod(kernel_size)) total_conv_size = int(np.prod(conv_size)) # Build network. network = Network() input_layer = Input(n=32*32*3, shape=(1, 3, 32, 32), traces=True) conv_layer = DiehlAndCookNodes(n=n_filters * total_conv_size, shape=(1, n_filters, *conv_size), thresh=-64.0, traces=True, theta_plus=0.05, refrac=0) conv_layer2 = DiehlAndCookNodes(n=n_filters * total_conv_size, shape=(1, n_filters, *conv_size), refrac=0) conv_conn = Conv2dConnection(input_layer, conv_layer, kernel_size=kernel_size, stride=stride, update_rule=Hebbian, norm=0.5 * int(np.sqrt(total_kernel_size)), nu=(1e-3, 1e-3), wmax=2.0) conv_conn2 = Conv2dConnection(input_layer, conv_layer2, w=conv_conn.w, kernel_size=kernel_size, stride=stride, update_rule=None, nu=(0, 1e-3), wmax=2.0) w = torch.ones(1, n_filters, conv_size[0], conv_size[1], 1, n_filters, conv_size[0], conv_size[1]) for f in range(n_filters): for i in range(conv_size[0]): for j in range(conv_size[1]): w[0, f, i, j, 0, f, i, j] = 0 w = w.view(conv_layer.n, conv_layer.n) i = w.nonzero() v = -inhib * torch.ones(i.shape[0]) w = torch.sparse.FloatTensor(i.t(), v, w.size()) # for fltr1 in range(n_filters): # for fltr2 in range(n_filters): # for i1 in range(conv_size): # for j1 in range(conv_size): # for i2 in range(conv_size): # for j2 in range(conv_size): # if not (i1 == i2 and j1 == j2): # w[0, fltr1, i1, j1, 0, fltr2, i2, j2] = -inhib # if fltr1 != fltr2: # for i in range(conv_size): # for j in range(conv_size): # w[0, fltr1, i, j, 0, fltr2, i, j] = -inhib # for i1 in range(conv_size[0]): # for j1 in range(conv_size[1]): # for i2 in range(conv_size[0]): # for j2 in range(conv_size[1]): # if not (fltr1 == fltr2 and i1 == i2 and j1 == j2): # w[0, fltr1, i1, j1, 0, fltr2, i2, j2] = -inhib # if fltr1 != fltr2: # for i1 in range(conv_size): # for j1 in range(conv_size): # for i2 in range(conv_size): # for j2 in range(conv_size): # w[0, fltr1, i1, j1, 0, fltr2, i2, j2] = -inhib recurrent_conn = SparseConnection(conv_layer, conv_layer, w=w) network.add_layer(input_layer, name='X') network.add_layer(conv_layer, name='Y') network.add_layer(conv_layer2, name='Y_') network.add_connection(conv_conn, source='X', target='Y') network.add_connection(conv_conn2, source='X', target='Y_') network.add_connection(recurrent_conn, source='Y', target='Y') # Voltage recording for excitatory and inhibitory layers. voltage_monitor = Monitor(network.layers['Y'], ['v'], time=time) network.add_monitor(voltage_monitor, name='output_voltage') # Load CIFAR-10 data. dataset = CIFAR10(path=os.path.join('..', '..', 'data', 'CIFAR10'), download=True) if train: images, labels = dataset.get_train() else: images, labels = dataset.get_test() images *= intensity # Record spikes during the simulation. spike_record = torch.zeros(update_interval, time, n_neurons) # Neuron assignments and spike proportions. if train: assignments = -torch.ones_like(torch.Tensor(n_neurons)) proportions = torch.zeros_like(torch.Tensor(n_neurons, n_classes)) rates = torch.zeros_like(torch.Tensor(n_neurons, n_classes)) ngram_scores = {} else: path = os.path.join('..', '..', 'params', data, model) path = os.path.join(path, '_'.join(['auxiliary', model_name]) + '.pt') assignments, proportions, rates, ngram_scores = torch.load(open(path, 'rb')) # Sequence of accuracy estimates. curves = {'all': [], 'proportion': [], 'ngram': []} if train: best_accuracy = 0 spikes = {} for layer in set(network.layers): spikes[layer] = Monitor(network.layers[layer], state_vars=['s'], time=time) network.add_monitor(spikes[layer], name='%s_spikes' % layer) # Train the network. if train: print('\nBegin training.\n') else: print('\nBegin test.\n') inpt_ims = None inpt_axes = None spike_ims = None spike_axes = None weights_im = None start = t() for i in range(n_examples): if i % progress_interval == 0: print('Progress: %d / %d (%.4f seconds)' % (i, n_train, t() - start)) start = t() if i % update_interval == 0 and i > 0: if i % len(labels) == 0: current_labels = labels[-update_interval:] else: current_labels = labels[i % len(images) - update_interval:i % len(images)] # Update and print accuracy evaluations. curves, predictions = update_curves( curves, current_labels, n_classes, spike_record=spike_record, assignments=assignments, proportions=proportions, ngram_scores=ngram_scores, n=2 ) print_results(curves) if train: if any([x[-1] > best_accuracy for x in curves.values()]): print('New best accuracy! Saving network parameters to disk.') # Save network to disk. path = os.path.join('..', '..', 'params', data, model) if not os.path.isdir(path): os.makedirs(path) network.save(os.path.join(path, model_name + '.pt')) path = os.path.join(path, '_'.join(['auxiliary', model_name]) + '.pt') torch.save((assignments, proportions, rates, ngram_scores), open(path, 'wb')) best_accuracy = max([x[-1] for x in curves.values()]) # Assign labels to excitatory layer neurons. assignments, proportions, rates = assign_labels(spike_record, current_labels, n_classes, rates) # Compute ngram scores. ngram_scores = update_ngram_scores(spike_record, current_labels, n_classes, 2, ngram_scores) print() # Get next input sample. image = images[i].permute(2, 0, 1) sample = bernoulli(datum=image, time=time, dt=dt, max_prob=1.0).unsqueeze(1) inpts = {'X': sample} # Run the network on the input. network.run(inpts=inpts, time=time) retries = 0 while spikes['Y_'].get('s').sum() < 5 and retries < 3: retries += 1 sample = bernoulli(datum=image, time=time, dt=dt, max_prob=1.0).unsqueeze(1) inpts = {'X': sample} network.run(inpts=inpts, time=time) # Add to spikes recording. spike_record[i % update_interval] = spikes['Y_'].get('s').view(time, -1) # Optionally plot various simulation information. if plot: _input = image.permute(1, 2, 0).float() _input /= _input.max() reconstruction = inpts['X'].sum(0).view(3, 32, 32).permute(1, 2, 0).float() reconstruction /= reconstruction.max() w = conv_conn.w _spikes = {'X': spikes['X'].get('s').view(32*32*3, time), 'Y': spikes['Y'].get('s').view(n_filters * total_conv_size, time), 'Y_': spikes['Y_'].get('s').view(n_filters * total_conv_size, time)} inpt_axes, inpt_ims = plot_input( images[i].view(32, 32, 3), reconstruction, label=labels[i], ims=inpt_ims, axes=inpt_axes ) spike_ims, spike_axes = plot_spikes(spikes=_spikes, ims=spike_ims, axes=spike_axes) weights_im = plot_conv2d_weights(w, im=weights_im, wmax=0.1) plt.pause(1e-8) network.reset_() # Reset state variables. print(f'Progress: {n_examples} / {n_examples} ({t() - start:.4f} seconds)') i += 1 if i % len(labels) == 0: current_labels = labels[-update_interval:] else: current_labels = labels[i % len(images) - update_interval:i % len(images)] # Update and print accuracy evaluations. curves, predictions = update_curves( curves, current_labels, n_classes, spike_record=spike_record, assignments=assignments, proportions=proportions, ngram_scores=ngram_scores, n=2 ) print_results(curves) if train: if any([x[-1] > best_accuracy for x in curves.values()]): print('New best accuracy! Saving network parameters to disk.') # Save network to disk. path = os.path.join('..', '..', 'params', data, model) if not os.path.isdir(path): os.makedirs(path) network.save(os.path.join(path, model_name + '.pt')) path = os.path.join(path, '_'.join(['auxiliary', model_name]) + '.pt') torch.save((assignments, proportions, rates, ngram_scores), open(path, 'wb')) best_accuracy = max([x[-1] for x in curves.values()]) if train: print('\nTraining complete.\n') else: print('\nTest complete.\n') print('Average accuracies:\n') for scheme in curves.keys(): print('\t%s: %.2f' % (scheme, np.mean(curves[scheme]))) # Save accuracy curves to disk. path = os.path.join('..', '..', 'curves', data, model) if not os.path.isdir(path): os.makedirs(path) if train: to_write = ['train'] + params else: to_write = ['test'] + params to_write = [str(x) for x in to_write] f = '_'.join(to_write) + '.pt' torch.save((curves, update_interval, n_examples), open(os.path.join(path, f), 'wb')) # Save results to disk. path = os.path.join('..', '..', 'results', data, model) if not os.path.isdir(path): os.makedirs(path) results = [ np.mean(curves['all']), np.mean(curves['proportion']), np.mean(curves['ngram']), np.max(curves['all']), np.max(curves['proportion']), np.max(curves['ngram']) ] if train: to_write = params + results else: to_write = test_params + results to_write = [str(x) for x in to_write] name = 'train.csv' if train else 'test.csv' if not os.path.isfile(os.path.join(path, name)): with open(os.path.join(path, name), 'w') as f: if train: columns = [ 'seed', 'n_train', 'kernel_size', 'stride', 'n_filters', 'padding', 'inhib', 'time', 'dt', 'intensity', 'update_interval', 'mean_all_activity', 'mean_proportion_weighting', 'mean_ngram', 'max_all_activity', 'max_proportion_weighting', 'max_ngram' ] header = ','.join(columns) + '\n' f.write(header) else: columns = [ 'seed', 'n_train', 'n_test', 'kernel_size', 'stride', 'n_filters', 'padding', 'inhib', 'time', 'dt', 'intensity', 'update_interval', 'mean_all_activity', 'mean_proportion_weighting', 'mean_ngram', 'max_all_activity', 'max_proportion_weighting', 'max_ngram' ] header = ','.join(columns) + '\n' f.write(header) with open(os.path.join(path, name), 'a') as f: f.write(','.join(to_write) + '\n') print()

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import time # Global variable to calculate the complexity for algorithm and store the cutoff value to insertion sort cutoff = 6 complexity_count = 0 # Function to read the dataset def readDataset(): filename = "data0.32768" # Dataset file name file = "/Users/learning/Documents/Pratik Mistry/Sem2/Data Structures and Algorithms/dataset-problem2-hw2/" + filename # Complete File Path file_object = open(file,"r") lines = file_object.readlines() # Reading all the lines from the file opened dataset = [] for line in lines: line = line.strip() dataset.append(int(line)) # Casting to int as numbers are read as strings while reading file return dataset # Return the dataset # Function to sort the dataset using insertion sort def insertionSort(arr,low,high): global complexity_count # Referring global scope variable for counting complexity for i in range(low+1,high+1,1): # Traversing each array element temp = arr[i] index = i complexity_count+=1 # Increment the count complexity while index > 0 and arr[index-1] > temp: # Sort the left subarray of the current index complexity_count+=1 # Increment the count complexity arr[index] = arr[index-1] index-=1 arr[index] = temp # Function to calculate median of the array def medianOf3(arr,low,mid,high): if arr[low] > arr [high]: if arr[high] > arr[mid]: return high elif arr[mid]> arr[low]: return low else: return mid else: if arr[low] > arr[mid]: return low elif arr[mid] > arr[high]: return high else: return mid # Function to quick sort the array with median of 3 and Cutoff to insertion method def medianQuickSort_CutOff(data_list,low,high): if (high - low + 1)<= cutoff: # Base condition to stop resursion while sorting the elements using insertion sort insertionSort(data_list,low,high) return mid = int((low+high)/2) median = medianOf3(data_list,low,mid,high) # Calculate the median of array swap(data_list,low,median) # Swap median with lowest index of the array pivot_partition = partition(data_list,low,high) # Find the pivot/partition medianQuickSort_CutOff(data_list,low,pivot_partition-1) # Apply quick sort to left subarray medianQuickSort_CutOff(data_list,pivot_partition+1,high) # Apply quick sort to right subarray # Function to partition the array and returning the pivot element def partition(arr,low,high): global complexity_count # Referring global scope variable for counting complexity pivot = arr[low] # Selecting lowest element as pivot left = low right = high while left < right: while arr[right] >= pivot and left < right: # Move from right towards left and check for element less than pivot complexity_count +=1 # Increment the count complexity right-=1 if right!=left: arr[left] = arr[right] # Swap the smaller element at the right to the left of pivot left+=1 while arr[left] <= pivot and left < right: # Move from left towards right and check for element greater than pivot complexity_count +=1 # Increment the count complexity left += 1 if right!=left: arr[right] = arr[left] # Swap the greater element at the left to the right of pivot right-=1 arr[left] = pivot return left # Function to swap the median and lowest index of the subarray def swap(data_list,low,median): temp = data_list[median] data_list[median] = data_list[low] data_list[low] = temp # Driver/Main program to read dataset, and call quick sort with median of 3 and cutoff to insertion printing output if __name__ == "__main__": data_list = readDataset() # Reading the dataset start = time.time() medianQuickSort_CutOff(data_list,0,len(data_list)-1) # Calling Quick Sort: cutoff to insertion sort end = time.time() total_time = end-start # Calculating physical clock time # Printing the outputs print("\nThe sorted list using quick sort with cutoff to insertion sort is: ") print(data_list) print("\nThe total time taken for quick sort with cutoff to insertion sort is:",total_time*1000 , " ms") print("\nThe complexity count for quick sort with cutoff to insertion sort is:",complexity_count)

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import cassiopeia as cass import arrow import pandas as pd import time import requests ''' Go to https://developer.riotgames.com/ and create a LOGIN. After that, you'll be taken to a screen with the API key. There are 3 types of API keys in Riot Games: - Development API (which is the default once you create a developer account): it's a key that needs to be refreshed every 24h - Personal API: after registering a product (I didn't do it, so the API I've been using is Development), you don't need to refreseh your api key. There are some restrcitions in the access (such as how many calls per minute/hour etc) - Production API: this is for a real product, deployed, etc. I didn't even read details about it because it's way out of the scope of this project. You can get reference for them in https://developer.riotgames.com/docs/portal#product-registration_application-process ''' API_KEY = "RGAPI-xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxx" REGION = 'NA' # can be any region (NA, BR, TR, etc) def get_curr_data(pentakiller, kill, start_time,): ''' This function returns the requested info from the pentakiller (items, position, timestamp, etc) ''' curr_data = { "summoner": pentakiller['summoner'], "match id": pentakiller['match'], "champion": pentakiller['champion'], "region": REGION, "x_pos": tuple(kill.get('position').values())[0], "y_pos": tuple(kill.get('position').values())[1], "item_1": list(map(lambda x: x if x else "empty slot", pentakiller.get("items")))[0], "item_2": list(map(lambda x: x if x else "empty slot", pentakiller.get("items")))[1], "item_3": list(map(lambda x: x if x else "empty slot", pentakiller.get("items")))[2], "item_4": list(map(lambda x: x if x else "empty slot", pentakiller.get("items")))[3], "item_5": list(map(lambda x: x if x else "empty slot", pentakiller.get("items")))[4], "item_6": list(map(lambda x: x if x else "empty slot", pentakiller.get("items")))[5], "timestamp": start_time } return curr_data def new_kills_heatmap(self): ''' I am MonkeyPatching the cassiopedia.core.match.Match.kills_heatmap method (because originally it didn't return the FIG image) Now that it is being returned, I can save to a file. That method was already written by the developers of the cassiopedia module, and I'm simply updating it for our needs. ''' if self.map.name == "Summoner's Rift": rx0, ry0, rx1, ry1 = 0, 0, 14820, 14881 elif self.map.name == "Howling Abyss": rx0, ry0, rx1, ry1 = -28, -19, 12849, 12858 else: raise NotImplemented imx0, imy0, imx1, imy1 = self.map.image.image.getbbox() def position_to_map_image_coords(position): x, y = position.x, position.y x -= rx0 x /= (rx1 - rx0) x *= (imx1 - imx0) y -= ry0 y /= (ry1 - ry0) y *= (imy1 - imy0) return x, y import matplotlib.pyplot as plt size = 8 fig = plt.figure(figsize=(size, size)) # updated this line plt.imshow(self.map.image.image.rotate(-90)) for p in self.participants: for kill in p.timeline.champion_kills: x, y = position_to_map_image_coords(kill.position) if p.team.side == cass.data.Side.blue: plt.scatter([x], [y], c="b", s=size * 10) else: plt.scatter([x], [y], c="r", s=size * 10) plt.axis('off') plt.show() return fig # added this line cass.core.match.Match.kills_heatmap = new_kills_heatmap # updating the method def setup(key, region): ''' Basic setups for the cassiopedia module - logging, API_KEY and REGION ''' cass.apply_settings({"logging": { "print_calls": False, "print_riot_api_key": False, "default": "WARNING", "core": "WARNING" }}) cass.set_riot_api_key(API_KEY) cass.set_default_region(REGION) def get_week_matches(summoner): ''' This function takes the 'summoner' object and returns the match history for the period of 7 days that the summoner played ''' now = arrow.utcnow() last_week = now.shift(days=-7) since = last_week.floor('day') until = now.floor('day') matches = cass.get_match_history(summoner, begin_time=since, end_time=until) return matches def get_uri_region(region=REGION): mapping = { 'BR':'BR1', 'EUNE':'EUN1', 'EUW':'EUW1', 'JP':'JP1', 'KR':'KR', 'LAN':'LA1', 'LAS':'LA2', 'NA':'NA1', 'OCE':'OC1', 'TR':'TR1', 'RU':'RU' } return mapping.get(region) def get_diamonds(page, tier): ''' Generator for diamond players. Since there's no implementation in the module Cass for diamond (and the # of players is vast), I created this function. Handle with care not overload the server with thousands of requests. ''' headers_dict = {"X-Riot-Token": API_KEY} region_api = str.lower(get_uri_region(REGION)) URL = f"https://{region_api}.api.riotgames.com/lol/league/v4/entries/RANKED_SOLO_5x5/DIAMOND/{tier}?page={page}" response = requests.get(URL, headers=headers_dict) players_list = map(lambda x: x.get('summonerId'), response.json()) for player in players_list: yield player def get_masters(): ''' Generator for all masters in 'master league' ''' masters = cass.get_master_league(queue=cass.Queue.ranked_solo_fives) for master in masters: yield master def get_grandmasters(): ''' Generator for all grandmasters in 'grandmaster league' ''' grandmasters = cass.get_grandmaster_league(queue=cass.Queue.ranked_solo_fives) for gm in grandmasters: yield gm def get_challengers(): ''' Generator for all challengers in 'challenger league' ''' challengers = cass.get_challenger_league(queue=cass.Queue.ranked_solo_fives) for challenger in challengers: yield challenger def get_participant_info(match): ''' This function generates a dictionary with the required data from a match if it had a pentakill ''' pentakiller = None for participant in match.participants: if participant.stats.largest_multi_kill >= 5: pentakiller = { 'summoner':participant.summoner.name, 'match':match.id, 'region':match.region.value, 'champion':participant.champion.name, 'participant':participant, 'participant_id':participant.id, 'items':list(map(lambda x: x.name if x is not None else None, participant.stats.items)), } return pentakiller def get_kills_dict(participant_no, match_id): ''' This function takes the match that had the kill and the participant that had the pentakill. It then access the 'frames' of that match's timeline and creates a list of dictionaries of frames events (kills, drops, items built, etc) Then I only keep the events that had the property 'killerId' (which means it's a kill that a player did, and not a NPC) and filter only CHAMPION_KILLS (so PvP, and not PvE, for instance). Then I save into kills_list and return that information ''' kills_list = [] events = [] match = cass.get_match(match_id) for frame in match.timeline.frames: events.extend([x.to_dict() for x in frame.events]) kill_events = [x for x in events if 'killerId' in x] kills = filter(lambda x: x['killerId']==participant_no and x['type']=='CHAMPION_KILL', kill_events) kills_list += kills return kills_list def get_pentakill(kills_list): ''' According to LoL wiki, the kills interval must be under 10 seconds until the 4th kill and then 30s (max) in the 5th kill. That way, I'm looping through all kills and checking if the next 1, 2, 3 and 4 kills are in the time range in relation to the 0, 1, 2 and 3 kill. The timestamp comes in miliseconds, so I have to multiply by 1000. When it finds a group of 5 kills that fits the restrictions, breaks out of the loop and returns the first kill. ''' for i, kill in enumerate(kills_dict): if all([(kills_dict[i+4]['timestamp'] - kills_dict[i+3]['timestamp'] <= 1000 * 30), (kills_dict[i+3]['timestamp'] - kills_dict[i+2]['timestamp'] <= 1000 * 10), (kills_dict[i+2]['timestamp'] - kills_dict[i+1]['timestamp'] <= 1000 * 10), (kills_dict[i+1]['timestamp'] - kills_dict[i]['timestamp'] <= 1000 * 10)]): break return kill def generate_heatmap(match_id): ''' Simple function that takes the match_id and saves the heatmap with the match_id in the filename. ''' match = cass.get_match(match_id) fig = match.kills_heatmap() fig.savefig(f"{match_id}_heatmap.png") setup(API_KEY, REGION) print('Fetching data for Challengers League:\n') counter = 0 # I added a counter so we could stop early if we wanted MATCH_LIST = [] # this match_list is a list where I append all matches that are processed. That way, we can avoid repeated calls for similar matches PENTAKILLERS_LIST = [] # a list with data from matches that happened to have pentakills players = get_challengers() # assigned the challengers generator to the variable 'players' player = next(players, None) # tried to retrieve the next challenger. if the generator is exhausted, this will return None while player: # loops until the challengers generator is exhausted counter += 1 print(f"\n{counter}. Evaluating Player: {player.summoner.name}") matches = get_week_matches(player.summoner) if not matches: print(f"No matches in the last 7 days for {player.summoner.name}") player = next(players, None) continue for i, match in enumerate(matches): print(f"Fetching data for Match {i+1}/{len(matches)}") if MATCH_LIST.count(match.id): print("Already fetched this Match") continue MATCH_LIST.append(match.id) pentakillers = get_participant_info(match) if not pentakillers: print(f"Match {match.id} did not have any pentakillers...") continue print(f"Pentakillers on Match {match.id}: {pentakillers}") PENTAKILLERS_LIST.append(pentakillers) print(f"Finished fetching data for Player: {player.summoner.name}") print('\n--- Waiting 5 seconds to start next Player ---\n') # this is to try to avoig making too many requests and being interrupted time.sleep(5) player = next(players, None) if counter == 50: break print("Finished fetching data for Challenger League.\n") print('Fetching data for GrandMasters League:\n') counter = 0 players = get_grandmasters() # assigned the grandmasters generator to the variable 'players' player = next(players, None) # tried to retrieve the next grandmaster. if the generator is exhausted, this will return None while player: # loops until the challengers generator is exhausted counter += 1 print(f"\n{counter}. Evaluating Player: {player.summoner.name}") matches = get_week_matches(player.summoner) if not matches: print(f"No matches in the last 7 days for {player.summoner.name}") player = next(players, None) continue for i, match in enumerate(matches): print(f"Fetching data for Match {i+1}/{len(matches)}") if MATCH_LIST.count(match.id): print("Already fetched this Match") continue MATCH_LIST.append(match.id) pentakillers = get_participant_info(match) if not pentakillers: print(f"Match {match.id} did not have any pentakillers...") continue print(f"Pentakillers on Match {match.id}: {pentakillers}") PENTAKILLERS_LIST.append(pentakillers) print(f"Finished fetching data for Player: {player.summoner.name}") print('\n--- Waiting 5 seconds to start next Player ---\n') # this is to try to avoig making too many requests and being interrupted time.sleep(5) player = next(players, None) if counter == 50: break print("Finished fetching data for GrandMaster League.\n") print('Fetching data for Masters League:\n') counter = 0 players = get_masters() # assigned the challengers generator to the variable 'players' player = next(players, None) # tried to retrieve the next master. if the generator is exhausted, this will return None while player: # loops until the challengers generator is exhausted counter += 1 print(f"\n{counter}. Evaluating Player: {player.summoner.name}") matches = get_week_matches(player.summoner) if not matches: print(f"No matches in the last 7 days for {player.summoner.name}") player = next(players, None) continue for i, match in enumerate(matches): print(f"Fetching data for Match {i+1}/{len(matches)}") if MATCH_LIST.count(match.id): print("Already fetched this Match") continue MATCH_LIST.append(match.id) pentakillers = get_participant_info(match) if not pentakillers: print(f"Match {match.id} did not have any pentakillers...") continue print(f"Pentakillers on Match {match.id}: {pentakillers}") PENTAKILLERS_LIST.append(pentakillers) print(f"Finished fetching data for Player: {player.summoner.name}") print('\n--- Waiting 5 seconds to start next Player ---\n') # this is to try to avoig making too many requests and being interrupted time.sleep(5) player = next(players, None) if counter == 50: break print("Finished fetching data for Master League.\n") print('Fetching data for Diamond League:\n') counter = 0 players = get_diamonds(page=1, tier='I') # assigned the challengers generator to the variable 'players' player = next(players, None) # tried to retrieve the next diamond. if the generator is exhausted, this will return None while player: # loops until the challengers generator is exhausted counter += 1 summoner = cass.get_summoner(id=player) print(f"\n{counter}. Evaluating Player: {summoner.name}") matches = get_week_matches(summoner) if not matches: print(f"No matches in the last 7 days for {summoner.name}") player = next(players, None) continue for i, match in enumerate(matches): print(f"Fetching data for Match {i+1}/{len(matches)}") if MATCH_LIST.count(match.id): print("Already fetched this Match") continue MATCH_LIST.append(match.id) pentakillers = get_participant_info(match) if not pentakillers: print(f"Match {match.id} did not have any pentakillers...") continue print(f"Pentakillers on Match {match.id}: {pentakillers}") PENTAKILLERS_LIST.append(pentakillers) print(f"Finished fetching data for Player: {summoner.name}") print('\n--- Waiting 5 seconds to start next Player ---\n') # this is to try to avoig making too many requests and being interrupted time.sleep(5) player = next(players, None) if counter == 50: break print("Finished fetching data for Diamond League.\n") data = [] ''' general printing and returning images for the pentakills ''' for pentakiller in PENTAKILLERS_LIST: print(f"Fetching data for Pentakiller '{pentakiller['summoner']}' in Match {pentakiller['match']}:") print("Generating kills heatmap...",end=' ') generate_heatmap(pentakiller['match']) print("Done!") kills_dict = get_kills_dict(pentakiller['participant_id'], pentakiller['match']) kill = get_pentakill(kills_dict) minutes = kill['timestamp']//60000 seconds = int(60*(kill['timestamp']/60000 - minutes)) start_time = f"{minutes:02}:{seconds:02}" print(f"The Pentakill started at the {start_time} mark, with coordinates {tuple(kill.get('position').values())}.") print(f"The player finished the game with the following items:\n{pentakiller.get('items')}") data.append(get_curr_data(pentakiller, kill, start_time)) print('\n') # exporting datat to a csv file. pd.DataFrame(data).to_csv('pentakills.csv', index=False, header=True, encoding='utf-8')

[ "noreply@github.com" ]

noreply@github.com

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mollinaca/ac

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#!/usr/bin/env python3 # -*- coding: utf-8 -*- print("ABC"+input())

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morisyot@square-enix.com

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/Tutorial 2/Code/Lambda/lambda2.py

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#!/usr/bin/env python3 OPERATION_COUNT = 4 simple_collection_of_lambdas = [None] * OPERATION_COUNT #set up the lambdas for calculator def setup_lambdas(): #add simple_collection_of_lambdas[0] = lambda x, y: x + y #subtract simple_collection_of_lambdas[1] = lambda x, y: x - y #multiply simple_collection_of_lambdas[2] = lambda x, y: x * y #divide simple_collection_of_lambdas[3] = divide def divide(x, y): return (x / y) if __name__ == '__main__': setup_lambdas() number_one = int(input("Enter the first number: ")) number_two = int(input("Enter the second number: ")) for x in range(0, OPERATION_COUNT): print(simple_collection_of_lambdas[x](number_one, number_two))

[ "adijnair@gmail.com" ]

adijnair@gmail.com

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/main/migrations/0013_auto_20200527_2144.py

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rezendi/scanvine

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# Generated by Django 3.0.5 on 2020-05-27 21:44 from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('main', '0012_auto_20200525_2338'), ] operations = [ migrations.AlterField( model_name='sharer', name='category', field=models.IntegerField(choices=[(-2, 'Personal'), (-1, 'None'), (0, 'Health'), (1, 'Science'), (2, 'Tech'), (3, 'Business'), (4, 'Media')], db_index=True), ), migrations.CreateModel( name='FeedShare', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created_at', models.DateTimeField(auto_now_add=True)), ('share', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='main.Share')), ('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], ), ]

[ "jon@rezendi.com" ]

jon@rezendi.com

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/examples_save/examples_save/medusaCsvProcess.py

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nanjunda/fc_iscsi_scripts

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import csv with open('x.csv') as csvfile: readCSV = csv.reader(csvfile, delimiter=',') next(readCSV) count = 0 sum4=0.0 sum8=0.0 sum11=0.0 for row in readCSV: s = str(row[4]) if "Avg" not in s: count += 1 print(row[4],row[8],row[11]) sum4 += float(row[4]) sum8 += float(row[8]) sum11 += float(row[11]) avg4=sum4/count avg8=sum8/count avg11=sum11/count print (avg4, avg8, avg11)

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noreply@github.com

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/tensforflow_mnist.py

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[]

no_license

kpodlaski/NeuralNetworks2018

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2020-04-01T13:39:57.231298

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import tensorflow as tf import numpy as np net = tf.keras.models.Sequential([ tf.keras.layers.Flatten(input_shape=(28,28)), tf.keras.layers.Dense(30,activation=tf.nn.sigmoid),#tf.nn.relu tf.keras.layers.Dense(10, activation=tf.nn.softmax) ]) net.compile(optimizer=tf.train.GradientDescentOptimizer(0.5), loss='sparse_categorical_crossentropy',#tf.keras.losses.mean_absolute_error , # metrics=['accuracy']#[tf.keras.metrics.mean_squared_error] ) mnist = tf.keras.datasets.mnist (train_inputs, train_labels), (test_inputs, test_labels) = mnist.load_data() train_inputs, test_inputs = train_inputs / 255.0, test_inputs / 255.0 net.fit(train_inputs, train_labels, epochs=10, batch_size=100) test_loss, test_acc = net.evaluate(test_inputs, test_labels) print('Test accuracy:', test_acc) print('Test loss:', test_loss) predictions = net.predict(test_inputs) print("Result : ") good_response = 0 for elem in range(0,len(test_inputs)): if np.argmax(predictions[elem]) == test_labels[elem]: good_response+=1 print(predictions[len(test_inputs)-1]) print(np.argmax(predictions[len(test_inputs)-1])) print(test_labels[len(test_inputs)-1]) print(good_response/len(test_inputs)*100.0)

[ "podlaski@uni.lodz.pl" ]

podlaski@uni.lodz.pl

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/run_grain_facet_from_params.py

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[]

no_license

gregtucker/mountain_front_model

b836641521c4627e2c6f2267cb7b4449a088d87f

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refs/heads/master

2021-01-17T14:46:53.591850

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# -*- coding: utf-8 -*- """ run_grain_facet_from_params.py: demonstrates how to instantiate and run a GrainFacetSimulator from another Python script, passing parameters via a dictionary rather than using a separate input file. A note on time scales, in seconds: Duration (sec) (equiv) -------------- 1 s ~ 1 s 10 s ~ 1 min 100 s ~ 1 min 1000 s ~ 1 hr 10,000 s ~ 1 hr 10^5 s ~ 1 day (28 hrs) 10^6 s ~ 1 week (12 days) 10^7 s ~ 3 months 10^8 s ~ 3 years Created on Sun Jun 26 09:13:46 2016 @author: gtucker """ import time from grain_facet_model import GrainFacetSimulator params = { 'number_of_node_rows' : 20, 'number_of_node_columns' : 31, 'report_interval' : 5.0, 'run_duration' : 150.0, 'output_interval' : 1000.0, 'plot_interval' : 10.0, 'uplift_interval' : 10.0, 'disturbance_rate' : 0.01, 'weathering_rate' : 0.002, 'friction_coef' : 1.0, 'fault_x' : 8.0, 'cell_width' : 1.0 } start = time.time() gridsize = (params['number_of_node_rows'], params['number_of_node_columns']) gfs = GrainFacetSimulator(gridsize, **params) gfs.run() print('Run complete. Run time (sec):') print(time.time() - start)

[ "gtucker@colorado.edu" ]

gtucker@colorado.edu

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/extractors.py

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[]

no_license

ilay32/wals-project

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refs/heads/master

2021-04-06T10:59:34.236027

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import re,logging ### globals #### """ match first numeric sequence as group(1), and make sure there are no other numbers after it. """ numerizer = re.compile("(^\d+)([^\d]*$)") """ (tmp?) fallback for failed numerization. """ simplenumerizer = re.compile("(^\d+)") ### extractors ### def natural(c): """ just get the numeric value of the cell """ return numerize(c) def mult2bin(target_value,value): """ binarize a multi-valued feature, returning -1 if the value is n, and 1 otherwise, returns the function that does that """ def which(c): return value if numerize(c) == target_value else -1*value return which ### helpers ### def numerize(txt): """ if there's no match, it means there is more than one numeric sequence in the cell, in which case, print the cell contents so, we can see what's what """ m = numerizer.match(txt) if m: return int(m.group(1)) else: logging.warning("can't numerize cell contents: %s",txt) return int(simplenumerizer.match(txt).group(1))

[ "silayster@gmail.com" ]

silayster@gmail.com

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/users/views.py

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no_license

ritiksingh8/Buy-It-An-Ecommerce-WebApplication

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refs/heads/master

2020-12-01T18:13:46.229547

2020-02-16T12:34:15

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from django.shortcuts import render, redirect from django.contrib import messages from .forms import UserRegisterForm from shop.models import Products from django.contrib.auth.models import User from .models import Cart from django.contrib.auth.decorators import login_required def register(request): if request.method == 'POST': form = UserRegisterForm(request.POST) if form.is_valid(): form.save() username = form.cleaned_data.get('username') messages.success(request, f'Account created for {username}!') return redirect('index') else: form = UserRegisterForm() return render(request, 'users/register.html', {'form': form}) @login_required def new_cart(request,param1,param2): user=request.user product=Products.objects.filter(title=param2).first() cart_row=Cart.objects.filter(user=user).filter(product=product).first() if param1=='add': if cart_row is None: new_cart_row=Cart(user=user,product=product) new_cart_row.save() else: cart_row.quantity=cart_row.quantity+1 cart_row.save() print("in the else") elif param1=='remove': cart_row.quantity=cart_row.quantity-1 cart_row.save() if cart_row.quantity==0: cart_row.delete() if len(Cart.objects.filter(user=user))==0: empty=True else: empty=False return render(request,'users/cart.html',{'cart_items':Cart.objects.filter(user=user),'add':'add','remove':'remove','empty':empty})

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num_rows, num_cols = [int(data) for data in input().split()] num_paths = [[0] * num_cols for row in range(num_rows)] num_paths[0][0] = 1 num_cats = int(input()) for cat in range(num_cats): row, col = input().split() row, col = int(row) - 1, int(col) - 1 num_paths[row][col] = -1 for row in range(num_rows): for col in range(num_cols): if num_paths[row][col] != -1: left = num_paths[row][col - 1] if num_paths[row][col - 1] != -1 else 0 up = num_paths[row - 1][col] if num_paths[row - 1][col] != -1 else 0 cur = num_paths[row][col] num_paths[row][col] = max(left + up, cur) print(num_paths[-1][-1])

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from pathlib import Path if __name__ == '__main__': content = Path('../input/input_2020_06.txt').read_text().split('\n\n') dataset = [] for group in range(0, len(content)): dataset.append(content[group].split('\n')) questions = [chr(value) for value in range(97, 123)] solution = [0, 0] for group in range(0, len(dataset)): yes = [0 for letter in range(0, len(questions))] for person in range(0, len(dataset[group])): for letter in range(0, len(questions)): if questions[letter] in dataset[group][person]: yes[letter] += 1 for letter in range(0, len(questions)): solution[0] += (yes[letter] > 0) solution[1] += (yes[letter] == len(dataset[group])) print('Answer part 1 = {:d} '.format(solution[0])) print('Answer part 2 = {:d} '.format(solution[1]))

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2023-08-04T17:00:02.200085

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from django.apps import AppConfig class AppOrderConfig(AppConfig): default_auto_field = 'django.db.models.BigAutoField' name = 'App_Order'

[ "shamimmsas7@gmail.com" ]

shamimmsas7@gmail.com

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K = int(input()) A, B = 1, 0 for k in range(K): A, B = B, A + B print(A, B)

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m.cheney95@outlook.com

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# Looks good. Cleared up a few caps and what not.

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drewmullen/personal

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import random secret_num = random.randint(1,10) def main(): counter = 0 while counter < 6: guess = take_input() input_eval(guess) counter+=1 print("Too many guesses, you lose!") exit() def take_input(): try: guess = int(input("Guess a number between 1 and 10: ")) # counter+=1 except ValueError: print("That's not a number! Try again...") take_input() if guess > 10: print("Thats too high! Try again...") take_input() elif guess <= 0: print("Thats too low! Try again...") take_input() else: return guess def input_eval(guess): if guess == secret_num: print("You got it! The number was {}.".format(secret_num)) exit() #It took you {} guesses".format(secret_num, counter)) else: print("That's not it! Try again...") main()

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import csv def carregar_acessos(arquivo_nome): dados = [] marcacoes = [] arquivo = open(arquivo_nome, 'rb') leitor = csv.reader(arquivo) leitor.next() for P1,P2,P3,P4,P5,P6,P7,P8,P9,P10,P11,P12,P13,Origem in leitor: dados.append([float(P1), float(P2), float(P3), float(P4), float(P5), float(P6), float(P7), float(P8), float(P9), float(P10), float(P11), float(P12), float(P13)]) marcacoes.append(Origem) return dados, marcacoes def taxa_acerto(resultado, gabarito): i=0 acertos=0 for r in resultado: if r == gabarito[i]: acertos=acertos+1 taxa = 100.0*acertos/len(resultado) return taxa dados, marcacoes = carregar_acessos('dados_tratados.csv') teste, marcacoes_teste = carregar_acessos('dados_teste.csv') from sklearn.naive_bayes import MultinomialNB modelo = MultinomialNB() modelo.fit(dados, marcacoes) resultado1 = modelo.predict(teste) taxa_final = taxa_acerto(resultado1, marcacoes_teste) print("Taxa de acerto em % :") print(taxa_final)

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edmlover/badouai-tujiban

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import torch import torch.nn as nn num_embeddings = 6#字符集的大小 embedding_dim = 3#每个字符向量化后的维度 embedding_layer = nn.Embedding(num_embeddings, embedding_dim)#指定生成的随机初始化的矩阵的行列 print(embedding_layer.weight,"随机初始化矩阵") x = torch.LongTensor([1,2,5])#给定字符编号,也就是输入 embedding_out = embedding_layer(x) print(embedding_out)

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#!/usr/bin/env python3 """ get_data_gov_sg_met.py: Download meteorological station data (and/or air quality data) for a specific month via the data.gov.sg APIs. API key requirement: In order to use this script, an API key needs to be obtained via https://developers.data.gov.sg. Usage: To download a specific month and variable, specify the month (e.g. 2017_02) and variable, e.g.: ./get_data_gov_sg_met.py 2017_02 rainfall To download data for all variables in a specific month, specify just the month: ./get_data_gov_sg_met.py 2017_02 To download data for all variables from last month, just run the script with no command-line arguments: ./get_data_gov_sg_met.py Output files: Gzipped CSV files, corresponding to different variables, will be saved in data_gov_sg_met_v1/ The file naming convention is as follows: data_gov_sg_met_v1/<variable>_<yyyy-mm>_c<today>.csv.gz where <today> is the date on which the file was created. For example, data_gov_sg_met_v1/wind-speed_2017-02_c20170526.csv.gz Information about input data: For information about the input data used to derive the output CSV files, please see https://developers.data.gov.sg, https://data.gov.sg/open-data-licence, and https://data.gov.sg/api-terms. Author: Benjamin S. Grandey, 2017 """ import calendar import os import pandas as pd import requests import sys import time # Get my API keys from my_api_keys import my_api_dict # Note: this module, containing my API keys, will not be shared via GitHub # You can obtain your own API key(s) by registering at https://developers.data.gov.sg my_key = my_api_dict['data.gov.sg'] # API key for data.gov.sg # Output directory here = os.path.abspath(os.path.dirname(__file__)) data_dir = os.path.join(here, 'data_gov_sg_met_v1') # If directory does not exist, create it if not os.path.exists(data_dir): os.makedirs(data_dir) print('Created {}'.format(data_dir)) def retrieve_data_via_api(variable, dt, n_attempts=10): """ Function to attempt to retrieve data for a specific datetime. Args: variable: string of variable name used by API (e.g. 'rainfall') dt: pd.datetime, corresponding to 'date_time' in the API n_attempts: number of attempts to retry if API connection fails Returns: pd.DataFrame containing data (if successful), or None """ try: # Try to connect to API r = requests.get('https://api.data.gov.sg/v1/environment/{}'.format(variable), headers={'api-key': my_key}, params={'date_time': dt.strftime('%Y-%m-%dT%H:%M:%S')}, timeout=30) if r.status_code == 200: # If API connection was successful, load data into DataFrame, unless no data present if len(r.json()['items'][0]['readings']) >= 1: result = pd.DataFrame(r.json()['items'][0]['readings']) if variable == 'pm25': # necessary due to diff in pm25 API return format result = result.reset_index() result = result.rename(columns={'index': 'region'}) result['timestamp_sgt'] = pd.to_datetime(r.json()['items'][0]['timestamp'] .split('+')[0]) else: result = None else: # If API query failed, sleep 10s, then retry recursively (up to n_attempts) if n_attempts > 1: print(' dt = {}, r.status_code = {}, (n_attempts-1) = {}. ' 'Retrying in 10s.'.format(dt, r.status_code, (n_attempts-1))) time.sleep(10) result = retrieve_data_via_api(variable, dt, n_attempts=(n_attempts-1)) else: print(' dt = {}, r.status_code = {}, (n_attempts-1) = {}. ' 'FAILED TO RETRIEVE DATA.'.format(dt, r.status_code, (n_attempts-1))) result = None r.close() except (requests.exceptions.SSLError, requests.exceptions.ConnectionError, requests.exceptions.ConnectTimeout, requests.exceptions.ReadTimeout): # If connection failed, sleep 10s, then retry recursively (up to n_attempts) if n_attempts > 1: print(' dt = {}, error = {}, (n_attempts-1) = {}. ' 'Retrying in 10s.'.format(dt, sys.exc_info()[0], (n_attempts-1))) time.sleep(10) result = retrieve_data_via_api(variable, dt, n_attempts=(n_attempts-1)) else: print(' dt = {}, error = {}, (n_attempts-1) = {}. ' 'FAILED TO CONNECT.'.format(dt, sys.exc_info()[0], (n_attempts-1))) result = None except KeyError: # KeyError is encountered, sleep 10s, then retry once only if n_attempts > 1: print(' dt = {}, error = {}, (n_attempts-1) = {}. ' 'Retrying ONCE in 10s.'.format(dt, sys.exc_info()[0], (n_attempts-1))) time.sleep(10) result = retrieve_data_via_api(variable, dt, n_attempts=1) else: print(' dt = {}, error = {}, (n_attempts-1) = {}. ' 'FAILED TO RETRIEVE DATA.'.format(dt, sys.exc_info()[0], (n_attempts - 1))) result = None return result def download_month(variable, yyyy, mm): """ Function to attempt to retrieve data for a specific month. Args: variable: string of variable name used by API (e.g. 'rainfall') yyyy: string containing year (e.g. '2017') mm: string containing month (e.g. '05') Output file: CSV file: data_gov_sg_met_v1/<variable>_<yyyy-mm>_c<today>.csv where <today> is today's date. """ print('variable = {}, yyyy = {}, mm = {}'.format(variable, yyyy, mm)) # Number of days in month ndays = calendar.monthrange(int(yyyy), int(mm))[1] # supports leap years # Time interval dependent on variable if variable == 'rainfall': freq = '5 min' periods = (ndays * 24 * 12) + 1 elif variable == 'pm25': freq = '60 min' periods = (ndays * 24 * 1) + 1 else: freq = '1 min' periods = (ndays * 24 * 60) + 1 # Datetime range to search through datetime_range = pd.date_range('{}-{}-01 00:00:00'.format(yyyy, mm), periods=periods, freq=freq) # Loop over datetimes for dt, i in zip(datetime_range, range(len(datetime_range))): # Attempt to retrieve data via API temp_df = retrieve_data_via_api(variable, dt) # If data available and timestamp indicates correct month, then append to DataFrame df if temp_df is not None: if temp_df['timestamp_sgt'].loc[0].month == int(mm): # querying 00:00 on 1st day may try: # may return 23:59 on prev. day df = df.append(temp_df, ignore_index=True) except UnboundLocalError: # 1st time, initialise df df = temp_df # Indicate progress perc = i / periods * 100 # percentage progress print(' {:000.1f}%'.format(perc), end='\r', flush=True) print() # start new line # Print summary of number of records print(' {} records'.format(len(df))) # Remove duplicates df = df.drop_duplicates() print(' {} records after removing duplicates'.format(len(df))) # Save DataFrame to CSV file out_filename = '{}/{}_{}_{}_c{}.csv.gz'.format(data_dir, variable, yyyy, mm, pd.datetime.today().strftime('%Y%m%d')) df.to_csv(out_filename, index=False, compression='gzip') print(' Written {}'.format(out_filename)) return 0 if __name__ == '__main__': # Year and month to get data for try: yyyy, mm = sys.argv[1].split('_') # if specified via command-line except IndexError: # otherwise get data for last month month_ago = (pd.datetime.today() - pd.Timedelta(1, 'M')) # ~1 month ago (not exact) yyyy, mm = month_ago.strftime('%Y_%m').split('_') # Variable(s) to get data for try: variables = [sys.argv[2], ] # if specified via command-line except IndexError: # otherwise get data for all variables variables = ['rainfall', 'wind-speed', 'wind-direction', 'air-temperature', 'relative-humidity', 'pm25'] # Loop over variables for variable in variables: download_month(variable, yyyy, mm)

[ "benjamin.grandey@gmail.com" ]

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yvonnebutselaar/data-driven-design

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import json with open("movies.json") as f: movies = json.load(f) ayear = input("What year do you want to see?") for items in movies: if str(items["year"]) == ayear: print(f'{items["title"]} is a movie from {items["year"]}') f.close()

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# -*- coding: utf-8 -*- # This module is responsible for communicating with the outside of the yolo package. # Outside the package, someone can use yolo detector accessing with this module. import os import numpy as np from yolo.backend.decoder import YoloDecoder from yolo.backend.loss import YoloLoss from yolo.backend.network import create_yolo_network from yolo.backend.batch_gen import create_batch_generator from yolo.backend.utils.fit import train from yolo.backend.utils.annotation import get_train_annotations, get_unique_labels from yolo.backend.utils.box import to_minmax def get_object_labels(ann_directory): files = os.listdir(ann_directory) files = [os.path.join(ann_directory, fname) for fname in files] return get_unique_labels(files) def create_yolo(architecture, labels, input_size = 416, anchors = [0.57273, 0.677385, 1.87446, 2.06253, 3.33843, 5.47434, 7.88282, 3.52778, 9.77052, 9.16828], coord_scale=1.0, class_scale=1.0, object_scale=5.0, no_object_scale=1.0): n_classes = len(labels) n_boxes = int(len(anchors)/2) yolo_network = create_yolo_network(architecture, input_size, n_classes, n_boxes) yolo_loss = YoloLoss(yolo_network.get_grid_size(), n_classes, anchors, coord_scale, class_scale, object_scale, no_object_scale) yolo_decoder = YoloDecoder(anchors) yolo = YOLO(yolo_network, yolo_loss, yolo_decoder, labels, input_size) return yolo class YOLO(object): def __init__(self, yolo_network, yolo_loss, yolo_decoder, labels, input_size = 416): """ # Args feature_extractor : BaseFeatureExtractor instance """ self._yolo_network = yolo_network self._yolo_loss = yolo_loss self._yolo_decoder = yolo_decoder self._labels = labels # Batch를 생성할 때만 사용한다. self._input_size = input_size def load_weights(self, weight_path, by_name=False): if os.path.exists(weight_path): print("Loading pre-trained weights in", weight_path) self._yolo_network.load_weights(weight_path, by_name=by_name) else: print("Fail to load pre-trained weights. Make sure weight file path.") def predict(self, image, threshold=0.3): """ # Args image : 3d-array (BGR ordered) # Returns boxes : array, shape of (N, 4) probs : array, shape of (N, nb_classes) """ def _to_original_scale(boxes): height, width = image.shape[:2] minmax_boxes = to_minmax(boxes) minmax_boxes[:,0] *= width minmax_boxes[:,2] *= width minmax_boxes[:,1] *= height minmax_boxes[:,3] *= height return minmax_boxes.astype(np.int) netout = self._yolo_network.forward(image) boxes, probs = self._yolo_decoder.run(netout, threshold) if len(boxes) > 0: boxes = _to_original_scale(boxes) return boxes, probs else: return [], [] def train(self, img_folder, ann_folder, nb_epoch, saved_weights_name, batch_size=8, jitter=True, learning_rate=1e-4, train_times=1, valid_times=1, valid_img_folder="", valid_ann_folder="", first_trainable_layer=None, is_only_detect=False): # 1. get annotations train_annotations, valid_annotations = get_train_annotations(self._labels, img_folder, ann_folder, valid_img_folder, valid_ann_folder, is_only_detect) # 1. get batch generator train_batch_generator = self._get_batch_generator(train_annotations, batch_size, train_times, jitter=jitter) valid_batch_generator = self._get_batch_generator(valid_annotations, batch_size, valid_times, jitter=False) # 2. To train model get keras model instance & loss fucntion model = self._yolo_network.get_model(first_trainable_layer) loss = self._get_loss_func(batch_size) # 3. Run training loop train(model, loss, train_batch_generator, valid_batch_generator, learning_rate = learning_rate, nb_epoch = nb_epoch, saved_weights_name = saved_weights_name) def _get_loss_func(self, batch_size): return self._yolo_loss.custom_loss(batch_size) def _get_batch_generator(self, annotations, batch_size, repeat_times=1, jitter=True): """ # Args annotations : Annotations instance batch_size : int jitter : bool # Returns batch_generator : BatchGenerator instance """ batch_generator = create_batch_generator(annotations, self._input_size, self._yolo_network.get_grid_size(), batch_size, self._yolo_loss.anchors, repeat_times, jitter=jitter, norm=self._yolo_network.get_normalize_func()) return batch_generator

[ "xinyug2@uw.edu" ]

xinyug2@uw.edu

d5c8cacfbea754c24822fed7168c9ea69133b51e

56fe5ad50f01e347182f75b984e97f5b7ac4d647

/security_in_memory_db.py

b3c8dd1f3784fda121e1d4c45d15b0505afd46e2

[]

no_license

leenjiru/Sonia_books

e6a18033155e808339b6786018c3bdaca99fcf72

3cb752160eb789d9155482701fd581eb4aa8d170

refs/heads/master

2023-03-24T11:37:41.607157

2020-07-22T23:38:17

null

0

null

UTF-8

Python

false

778

py

# this applies when using an in memory db from models.users import UserModel from werkzeug.security import safe_str_cmp # users = [ # { # 'id': 1, # 'username': 'Nick', # 'Password': 'password' # } # ] users = [ UserModel(1, 'Nicky', 'password1') ] username_mapping = {u.username: u for u in users} user_id_mapping = {u.id: u for u in users} # user_id_mapping = { # 1: { # 'id': 1, # 'username': 'Nick', # 'Password': 'password' # } # } def authenticate(username, password): user = username_mapping.get(username, None) if user and safe_str_cmp(user.password, password): return user def identity(payload): user_id = payload['identity'] return user_id_mapping.get(user_id, None)

[ "www.leenick@gmail.com" ]

www.leenick@gmail.com

adef9bc871758731eed912cc5563b6f355987ed3

ec03d9949eb202c9de6c163566d7edffd583db93

/dags/hello_world.py

515b4e25dbc49b07eb81865d92302d7164e7deff

[]

no_license

tomatohope/airflow

770ba89dd68259566969259c08bb63071d24248a

8cbb9f94edf10fb66bddcd522fa71c8a186b4e6d

refs/heads/master

2021-07-21T03:26:34.251577

2021-01-08T09:31:44

231,941,543

1

0

null

UTF-8

Python

false

2,614

py

# coding: utf-8 import os import airflow from airflow import DAG from airflow.operators.python_operator import PythonOperator from datetime import datetime as datetime1, timedelta import datetime as datetime2 # interval time: 与 schedule_interval=timedelta(days=1), 一致 dt = datetime1.now() - datetime2.timedelta(days=1) airflow_home = '/root/airflow' os.environ['airflow_home'] = str(airflow_home) # default_args default_args = { 'owner': 'user1', 'depends_on_past': False, # start time: year month day hour minutes seconds 'start_date': datetime1(dt.year, dt.month, dt.day, 10, 2, 0), 'email': ['user1@xxx.com', 'user2@xxx.com'], 'email_on_failure': True, 'email_on_retry': True, 'retries': 0, 'retry_delay': timedelta(seconds=5) } # define DAG dag = DAG( # display DAG name dag_id='test', default_args=default_args, # interval time: 与 dt = datetime1.now() - datetime2.timedelta(days=1) 一致 schedule_interval=timedelta(days=1), # 禁用回补禁止执行过期任务 catchup=False ) def hello_world_1(ds, **kwargs): print("hello_world1") with open("/tmp/a", "at") as f: f.write("hello----word" + "\n") # 可以在函数中使用assert断言来判断执行是否正常，也可以直接抛出异常 assert 1 == 1 def hello_world_2(ds, **kwargs): ret = os.system("python $airflow_home/python_file/print.py") # 执行状态返回值判断 if ret != 0: os._exit(-1) print("Continued....") # task 1 t1 = PythonOperator( task_id='hello_world_1', # 指定要执行的函数 python_callable=hello_world_1, # 指定归属的dag provide_context=True, dag=dag, retries=0, ) # task 2 t2 = PythonOperator( task_id='hello_world_2', python_callable=hello_world_2, provide_context=True, dag=dag, ) # task plan #t2.set_upstream(t1) # t2依赖于t1; 等价于 t1.set_downstream(t2);同时等价于 dag.set_dependency('hello_world_1', 'hello_world_2') # 表示t2这个任务只有在t1这个任务执行成功时才执行 # t1 ##only t1 # t1 >> t2 ## t1 first success && t2 t1 >> t2 # airflow.note # http://note.youdao.com/noteshare?id=bb4888b561b3468e732361de74c7794e&sub=FD605AE047F04575A92C1DF2BCF9E7EA # exec time ############################################### # start_date + schedule_interval # https://www.jianshu.com/p/5aa4447f48ea # # # start_date # # if now time ago: # real # start # time: now # time + schedule_interval # # # schedule_interval # if cron: # not now # time: now # time + schedule_interval

[ "hope.gong@jingdigital.com" ]

hope.gong@jingdigital.com

c4a735ef7e38d09ec514e43a3557b1d581e8db67

b3e39afef4ddac88f1391964995c2eefd02818e7

/CantUseFour.py

e114529ae5c0e7df55d188f30bf5f8355f1ac7c3

[]

no_license

ommmishra/randomPrograms

44628e3c4b381e14b8d908342dcb9b7a854284c2

f52f3b0b485b310b9d5429667fba9422b6af045c

refs/heads/master

2022-02-25T08:12:25.656096

2022-01-19T15:08:14

179,322,921

0

null

UTF-8

Python

false

90

py

n = "44523432342343243234237" x = n.replace("4","3") z = int(n) - int(x) print(x) print(z)

[ "ommmishra830@gmail.com" ]

ommmishra830@gmail.com

4e8a125a7458dd004507e648e9417922ad85affe

14d7f5f83b6f84871ff6ebfa0af4c17b7115a33f

/eco_models/mpb/integration_stop.py

f391a20c2a14bae90e14d4ebe8bd5777a3fa7d32

[]

no_license

tonychangmsu/Python_Scripts

8ca7bc841c94dcab36743bce190357ac2b1698a5

036f498b1fc68953d90aac15f0a5ea2f2f72423b

refs/heads/master

2016-09-11T14:32:17.133399

2016-03-28T16:34:40

10,370,475

2

0

null

UTF-8

Python

false

736

py

# Title: integration_stop.py # Author: Tony Chang # Date: 10.26.2015 # Abstract: Attempt to find a solution to determining where the cumulative sum (numerical integration), of a array of 2D matricies sum up to one (find the index) import numpy as np #first suppose we have a 3D matrix of values under 1 G = np.random.uniform(0,.05, (365,500,400)) #now develop a cumulative sum for each step integral_G = np.cumsum(G, axis =0) #now find out the index of the first axis where the value is equal to one. index = np.argmax(integral_G>1, axis = 0) #if any of these equals to 0 then we have a development that didn't complete, and we have a problem #need more time to finish (i.e. more years to inspect). #done!

[ "tony.chang@msu.montana.edu" ]

tony.chang@msu.montana.edu

986d5bf4bc52b34aa41124711f6c80c7e1957253

4538728d33a8700e5bec08ec0d7b05480e042902

/utils/send_email.py

7f3a23ccb391e13913a08307e567cd922bcea390

[]

no_license

Faouzizi/Alerting-System

5b841a7374da0012bc0a9a8111d8925139ce89a7

98fdf263efb0f3e007bf666fb355ec3cede44076

refs/heads/main

2023-01-19T02:00:19.773848

2020-11-26T01:11:01

314,676,909

5

0

null

UTF-8

Python

false

1,533

py

############################################################################# ########### Import python packages ############################################################################# import smtplib import config from utils.get_templates import get_html_template from email.mime.multipart import MIMEMultipart from email.mime.text import MIMEText ############################################################################# ########### Send the email ############################################################################# def send_alerting_email(alert_message,type_alert): # get the email message_template = get_html_template(type_alert) #connect to the SMTP server s = smtplib.SMTP(host='smtp.gmail.com', port=587) s.starttls() s.login(config.smtp_email, config.smtp_password) # Send the email for each email on the recipient list for email in config.recipient_list: msg = MIMEMultipart() # create a message # add in the actual person name to the message template message = message_template.substitute() # setup the parameters of the message msg['From']=config.smtp_email msg['To']=email msg['Subject'] = alert_message # add in the message body msg.attach(MIMEText(message, 'html')) # send the message via the server set up earlier. s.send_message(msg) del msg # Terminate the SMTP session and close the connection s.quit() return('email sent :)')

[ "noreply@github.com" ]

noreply@github.com

25dd87758892b414426ec0e9c48e05fb4ac4a527

a4a44ad46cd1306e2da72ff89483b0102fc9787d

/SamplePython/Developer Tool/fab_fabric/pengenalan_dr_web/11_settings.py

8a018fc9d73ccd0692dcf6cf3d3a40dad2777d35

[]

no_license

okipriyadi/NewSamplePython

640eb3754de98e6276f0aa1dcf849ecea22d26b1

e12aeb37e88ffbd16881a20a3c37cd835b7387d0

refs/heads/master

2020-05-22T01:15:17.427350

2017-02-21T04:47:08

30,009,299

1

0

null

UTF-8

Python

false

333

py

""" settings (fabric.context_managers.settings) When you need to temporarily (i.e. for a certain command chain), you can use the settings statement (i.e. override env values). Usage examples: """ from fabric.api import settings, sudo # Perform actions using a different *user* with settings(user="avionics"): sudo("cmd")

[ "oki.priyadi@pacificavionics.net" ]

oki.priyadi@pacificavionics.net

ad5ff59ea22ba3812b95850738f8c40ca4e9036d

d19a463c154da8e66330450d8462d1c4d6de3cc1

/server/data/sp500.py

c725d6384d1d1d94739c2f624948942607e13ade

[]

no_license

nhatduy227/StockApp

7209ede008205de1f8b2997ed2d8d8677bad43ea

5a2dc2153d92eb8afba1dfd4b61b6a849f237b9b

refs/heads/master

2023-07-14T06:48:02.937346

2021-09-02T18:22:13

390,561,887

3

1

null

2021-08-29T23:35:36

2021-07-29T01:33:13

JavaScript

UTF-8

Python

false

1,728

py

'''S&P 500 Stock Reader The script reads in all companines in the Standard & Poor Index, provided by Wikipedia. It then grabs the historical stock price of each ticker and saves them in PyStore. Then, continuously adds in end-of-day stocks daily. ''' import pandas as pd import yfinance as yf from sqlalchemy import create_engine import psycopg2 import io def read_in_sp500( URL: str = 'https://en.wikipedia.org/wiki/List_of_S%26P_500_companies', ) -> pd.DataFrame: '''Read in all of Standard & Poor's 500 largest companies Args: URL (str): a Wikipedia url holding a data table of all S&P 500 companies Returns: pd.DataFrame: a data frame with info on all S&P 500 companies ''' sp500_df = pd.read_html(URL)[0] sp500_df['Symbol'] = sp500_df['Symbol'].str.replace('.', '-', regex=True) return sp500_df # ----------------- Run only once ------------------- def get_stock_data( interval: str = '1d', ) -> pd.DataFrame: '''Retrieves all previous day-by-day stock prices in the S&P500 Note: This function should only run once ''' sp500_tickers = read_in_sp500()['Symbol'] # Batch calls all stock tickers sp500_df = yf.download( tickers=sp500_tickers.to_list(), interval=interval, ) sp500_df = sp500_df.stack().reset_index().rename( columns={'level_1': 'Ticker'}) sp500_df.columns = sp500_df.columns.str.lower().str.replace(' ', '_') return sp500_df # --------------------------------------------------- if __name__ == '__main__': db_url = 'postgresql://postgres:password@localhost:5432/stocks' engine = create_engine(db_url) df = get_stock_data() df.to_sql('stock_data', engine)

[ "aowangphilly@gmail.com" ]

aowangphilly@gmail.com

e8a67b91c6b02523eb9741b4118764bca75190c1

cbb29f7a30b5de0dc2cb421edc259d6ce2586279

/tests/fixtures/readme_book.py

a9e759336c311c41d1228ce0baafcb0cb304adf2

[]

no_license

Madoshakalaka/python-typing-to-typescript

a4ebd58ff22f85fe1d2affcd32390ae5e8f19104

d5fd19b7f804a5e95f8b62a4d208c98d5b830593

refs/heads/master

2023-02-02T05:55:23.095977

2020-12-22T22:38:23

321,219,843

1

0

null

UTF-8

Python

false

291

py

from typing import TypedDict class Book(TypedDict): pages: int chapters: List[Chapter] Authors: List[str] class Chapter(TypedDict): title: str # short chapters only has one paragraph content: Union[str, List[Paragraph]] class Paragraph(TypedDict): content: str

[ "syan4@ualberta.ca" ]

syan4@ualberta.ca

64940b59557a57f6050239f90c6e4c8057f3ca09

7becb767c5536f450d9aa83821c2a62a0b66175a

/admintools/round_lib/get_round_id.py

90fc6b42835245bf12461b4b1a8a8f3cf9a678a8

[]

no_license

urirot/planetWarsRunner

af69837cc8f83902505bbe1f4628aaee7476348a

76e0458115ebbe581c104d569ad17899dae78fb3

refs/heads/master

2021-01-01T17:55:33.012912

2018-07-04T19:06:04

98,202,326

0

null

UTF-8

Python

false

582

py

#! /usr/bin/python from urllib import urlopen import json import sys from config import * if len(sys.argv) != 3: print "Usage: ./get_round_id.py <tournament id> <round number>" sys.exit(2) tourn_id = sys.argv[1] round_number = sys.argv[2] url = HOST + "tournaments/%s/round_by_name?name=%s" % (tourn_id, round_number) result = urlopen(url).read() if not result: sys.stderr.write("Can't find this round (round number = %s). Are you sure you created it?\n" % round_number) sys.exit(1) round_id = str(json.loads(result)["id"]) print round_id

[ "gazolin@gmail.com" ]

gazolin@gmail.com

9ae067e5cd4eccc2e3a324cc2e07669caccf8637

6630694f401f6f475dd81bb01ff9368db844ccff

/configs/_base_/models/hrnet/hrnet-w48.py

f0604958481ba2af277e3a0f9515dc1423def6c6

[ "Apache-2.0" ]

permissive

open-mmlab/mmpretrain

98a4d6b3bb747efc3d50decebf84fc3ffa41076a

d2ccc44a2c8e5d49bb26187aff42f2abc90aee28

refs/heads/main

2023-08-30T19:11:24.771498

2023-08-23T02:45:18

278,415,292

652

186

Apache-2.0

2023-09-08T08:01:40

2020-07-09T16:25:04

Python

UTF-8

Python

false

418

py

# model settings model = dict( type='ImageClassifier', backbone=dict(type='HRNet', arch='w48'), neck=[ dict(type='HRFuseScales', in_channels=(48, 96, 192, 384)), dict(type='GlobalAveragePooling'), ], head=dict( type='LinearClsHead', in_channels=2048, num_classes=1000, loss=dict(type='CrossEntropyLoss', loss_weight=1.0), topk=(1, 5), ))

[ "noreply@github.com" ]

noreply@github.com

170f4291b543e014fadf954a0e8b37173c22f52f

965e1e205bf053d93b32be0dab4d45455b42b3a2

/NLP/PartsSpeech.py

29aa35ab37a1d1ca416e2d528400a686da8f4ba4

[]

no_license

panditdandgule/DataScience

9e58867dd960ec554e0bbb8e4ce93baa226ab927

3eb59c129d81a6ba6b45e24113e25e63d19c60cb

refs/heads/master

2021-07-22T21:44:12.700518

2020-05-14T12:01:05

166,497,260

2

0

null

UTF-8

Python

false

669

py

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Sat Dec 15 19:59:50 2018 @author: pandit """ import nltk from nltk.corpus import state_union from nltk.tokenize import PunktSentenceTokenizer train_text=state_union.raw("2005-GWBush.txt") sample_text=state_union.raw("2005-GWBush.txt") custom_sent_tokenizer=PunktSentenceTokenizer(train_text) tokenized =custom_sent_tokenizer.tokenize(sample_text) def process_content(): try: for i in tokenized: words=nltk.word_tokenize(i) tagged=nltk.pos_tag(words) print(tagged) except Exception as e: print(str(e)) process_content()

[ "panditdandgule777@gmail.com" ]

panditdandgule777@gmail.com

d4a278c814384d490f690a077bab77a109b60b57

0ad79e7104500b5988f07e9f19f17a540f07605a

/Python算法指南/动态规划/67_最小和子数组_灵活运用动态规划.py

3273d8c3606054f4d873463057975d507015c93a

[]

no_license

tonyyo/PycharmProjects

f0ce458ed662e33e75ddffbfcf28b0d1ed638743

a28620923336c352103858e0ccfc4117d1c4ea01

refs/heads/master

2022-09-19T02:02:15.919201

2020-06-03T12:57:38

263,204,131

0

null

UTF-8

Python

false

551

py

class Solution: def minSubArray(self, nums): MIN = 65536 SUM = nums[0] for i in range(1, len(nums)): SUM = SUM + nums[i] if SUM < 0 else nums[i] # SUM > 0有害于最小和 MIN = min(MIN, SUM) return MIN if __name__ == '__main__': temp = Solution() List1 = [1, -1, -2, 1] List2 = [3, -2, 2, 1] print("输入：" + str(List1)) print(("输出：" + str(temp.minSubArray(List1)))) print("输入：" + str(List2)) print(("输出：" + str(temp.minSubArray(List2))))

[ "1325338208@qq.com" ]

1325338208@qq.com

5d9a526f41762caa210b4add3fe922444501227d

1546f47a68577c55371d61d9805d890fddf843d5

/Arthur/events/filters.py

4c0aeb65c0fcf2f8dd463090bb5dc37135f85a58

[]

no_license

tadgh/Arthur

b24299fc4edc5eba424e3007389e2a469a38e1a0

6ff839904973b8c3ad3eecb677cb9f3e3bbc1934

refs/heads/master

2022-12-14T13:34:44.610442

2020-04-28T04:37:07

189,875,591

0

null

UTF-8

Python

false

160

py

from django_filters import rest_framework as filters class LeaderboardFilter(filters.FilterSet): posted = filters.DateFromToRangeFilter(field_name='date')

[ "garygrantgraham@gmail.com" ]

garygrantgraham@gmail.com

455eb5dcfc9b2227454cfd2a1ff46a485409a8ad

3b7d8027e34f2338517d9c34f82bb02eb88de201

/Vote.py

b8027b299ae5b4a3e556f3b8b20d8304c2ad44ca

[]

no_license

amishofficial/decentralizeVoting

44dd2dd0fb4f4779a59503ff87e8cbfeea9d0028

e957e317958173b4ba006518c8b87dfbb765593a

refs/heads/main

2023-03-08T13:03:45.835971

2021-02-24T11:27:40

341,876,887

0

null

UTF-8

Python

false

681

py

from collections import OrderedDict from utility.printable import Printable class Vote(Printable): """A transaction which can be added to a vote_block in the votechain. Attributes: :voter: The voter of the coins. :vote_to: The vote_to of the coins. :signature: The signature of the transaction. :amount: """ def __init__(self, voter, vote_to, signature): self.voter = voter self.vote_to = vote_to self.signature = signature def to_ordered_dict(self): """Converts this transaction into a (hashable) OrderedDict.""" return OrderedDict([('voter', self.voter), ('vote_to', self.vote_to)])

[ "noreply@github.com" ]

noreply@github.com

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syurskyi/Python_Topics

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py

def sum(a, b): return a + b def contains_numbers(alpha_num_str): for char in alpha_num_str: if char.isdigit(): return False return False def div(a, b): return a / b

[ "sergejyurskyj@yahoo.com" ]

sergejyurskyj@yahoo.com

d88adc7061a3c48c6d2afe7420444e5c70762261

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/memword/api/lessons.py

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[]

no_license

scdekov/memword

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2022-12-11T09:13:27.507111

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from django.contrib.auth import get_user_model from django.shortcuts import get_object_or_404 from django.utils import timezone from rest_framework import serializers, viewsets, decorators, status from rest_framework.response import Response from memword.api.serializers import TargetSerializer from memword.models.lesson import Lesson, Question from memword.logic.target_picker import TargetPicker from memword.logic.learning_intervals_manager import LearningIntervalsManager User = get_user_model() class SubmitQuestionSerializer(serializers.Serializer): confidence_level = serializers.IntegerField() question_id = serializers.IntegerField() answer = serializers.CharField(allow_blank=True, required=False) def validate(self, data): if self.context['lesson'].lesson_type == 'exam' and not data.get('answer'): raise serializers.ValidationError('answer is required when submitting exam question') return data def validate_confidence_level(self, confidence_level): if confidence_level not in range(1, 11): raise serializers.ValidationError('confidence_level should be between 1 and 10') return confidence_level class QuestionSerializer(serializers.ModelSerializer): class Meta: model = Question fields = ('id', 'target', 'passed', 'correct', 'confidence_level') target = TargetSerializer() class LessonSerializer(serializers.ModelSerializer): class Meta: model = Lesson fields = ('id', 'student_id', 'questions', 'lesson_type', 'start_time', 'end_time', 'expected_duration', 'title', 'target_ids', 'planned_start_time') questions = QuestionSerializer(many=True, read_only=True) lesson_type = serializers.ChoiceField(allow_blank=True, default=Lesson.TYPE_LECTURE, choices=Lesson.TYPES) target_ids = serializers.ListField(child=serializers.IntegerField(), write_only=True) planned_start_time = serializers.DateTimeField(default=timezone.now) expected_duration = serializers.DurationField(default='60') def save(self): # target_ids may need to be validated if they belongs to the current user target_ids = self.validated_data.pop('target_ids', []) student_id = self.context['request'].user.id lesson = super().save(student_id=student_id) Question.objects.bulk_create([Question(lesson=lesson, target_id=target_id) for target_id in target_ids]) return lesson class TopTargetsQuerySerializer(serializers.Serializer): targets_count = serializers.IntegerField(required=False, default=10) class LessonsViewSet(viewsets.ModelViewSet): queryset = Lesson.objects.all().order_by('-id') serializer_class = LessonSerializer def filter_queryset(self, queryset): queryset = super().filter_queryset(queryset) return queryset.filter(student=self.request.user) @decorators.action(detail=True, methods=['POST'], url_path='@submit-answer') def submit_answer(self, request, pk): lesson = self.get_object() serializer = SubmitQuestionSerializer(data=request.data, context={'request': request, 'lesson': lesson}) serializer.is_valid(raise_exception=True) question = get_object_or_404(Question, lesson_id=pk, id=serializer.validated_data['question_id']) question.confidence_level = serializer.validated_data['confidence_level'] question.passed = True question.pass_time = timezone.now() if lesson.lesson_type == 'exam': question.correct = serializer.validated_data['answer'] == question.target.description question.save() if question.lesson.should_finish(): question.lesson.finalize() LearningIntervalsManager.handle_submitted_question(question) return Response({'question': QuestionSerializer(question).data}) @decorators.action(detail=True, methods=['POST'], url_path='@start') def start(self, request, **kwargs): lesson = self.get_object() lesson.start_time = timezone.now() lesson.save() return Response({'lesson': LessonSerializer(lesson).data}) @decorators.action(detail=True, methods=['POST'], url_path='@duplicate') def duplicate(self, request, **kwargs): original_lesson = self.get_object() # this is suposed to be in atomic transactions new_lesson = Lesson.objects.create(student_id=request.user.id, lesson_type=original_lesson.lesson_type, expected_duration=original_lesson.expected_duration, planned_start_time=timezone.now()) # start time should be calculated somehow Question.objects.bulk_create([Question(target_id=question.target_id, lesson_id=new_lesson.id)\ for question in original_lesson.questions.all()]) return Response({'lesson': LessonSerializer(new_lesson).data}, status=status.HTTP_201_CREATED) @decorators.action(detail=False, url_path='@get-top-targets') def get_top_targets(self, request): serializer = TopTargetsQuerySerializer(data=request.GET) serializer.is_valid(raise_exception=True) top_targets = TargetPicker.pick_top(request.user, serializer.validated_data['targets_count']) return Response({'targets': TargetSerializer(top_targets, many=True).data})

[ "svetlio1994@gmail.com" ]

svetlio1994@gmail.com

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/toMap.py

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haegray/Python-and-Java-Files

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2021-01-13T14:39:06.230924

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#toNumbers.py def toMap(strList): for i in strList: return (map(float,i)) toMap()

[ "anaisgray.ag@gmail.com" ]

anaisgray.ag@gmail.com

b593fcc836a48a7354d9240bfcd92cf6de664747

992f080016e7de4e3de9ff875a9778404c6c8fdc

/app.py

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[]

no_license

Garasuno/lineBot2

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refs/heads/master

2020-06-10T13:37:15.646984

2019-09-03T07:09:11

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from flask import Flask, jsonify, request import os import json import requests app = Flask(__name__) @app.route('/') def index(): a=os.environ['Authorization'] return "นางสาวนฤภร สัมมา เลขที่ 10 ชั้น ม.4/9" @app.route("/webhook", methods=['POST']) def webhook(): if request.method == 'POST': return "OK" @app.route('/callback', methods=['POST']) def callback(): json_line = request.get_json() json_line = json.dumps(json_line) decoded = json.loads(json_line) #user = decoded["events"][0]['replyToken'] user = decoded['originalDetectIntentRequest']['playload']['data']['replyToken'] #usertext = decoded["events"][0]['message']['text'] userText = decoded['queryResult']['intent']['displayname'] #sendText(user,userText) if (usertext == 'สวัสดี'): senttext(user , 'เอ่อดีด้วย') elif (usertext == 'บายจ้า'): senttext(user , 'บาย') else : senttext(user , 'กาว') return '',200 def sendText(user, text): LINE_API = 'https://api.line.me/v2/bot/message/reply' headers = { 'Content-Type': 'application/json; charset=UTF-8', 'Authorization': os.environ['Authorization'] # ตั้ง Config vars ใน heroku พร้อมค่า Access token } data = json.dumps({ "replyToken":user, "messages":[{"type":"text","text":text}] }) r = requests.post(LINE_API, headers=headers, data=data) # ส่งข้อมูล if __name__ == '__main__': app.run()

[ "noreply@github.com" ]

noreply@github.com

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/python/NaverMovieCrawling.py

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[]

no_license

slomeer/sparta

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refs/heads/master

2021-03-15T04:44:54.479873

2020-06-15T06:44:46

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py

import requests from bs4 import BeautifulSoup # 타겟 URL을 읽어서 HTML를 받아오고, headers = {'User-Agent' : 'Mozilla/5.0 (Windows NT 10.0; Win64; x64)AppleWebKit/537.36 (KHTML, like Gecko) Chrome/73.0.3683.86 Safari/537.36'} data = requests.get('https://movie.naver.com/movie/sdb/rank/rmovie.nhn?sel=pnt&date=20200303',headers=headers) # HTML을 BeautifulSoup이라는 라이브러리를 활용해 검색하기 용이한 상태로 만듦 # soup이라는 변수에 "파싱 용이해진 html"이 담긴 상태가 됨 # 이제 코딩을 통해 필요한 부분을 추출하면 된다. soup = BeautifulSoup(data.text, 'html.parser') # data.txt는 웹사이트에서 우리가 받는 html # 분석된 html 파일이 soup에 들어가 있는 상태 movies = soup.select('#old_content > table > tbody > tr') for i, movie in enumerate(movies): # movie 안에 a 가 있으면, a_tag = movie.select_one('td.title > div > a') rate = movie.select_one('td.point') if a_tag is not None: # a의 text를 찍어본다. print(i, end=' ') print(a_tag.text, end=' ') print(rate.text)

[ "61731412+slomeer@users.noreply.github.com" ]

61731412+slomeer@users.noreply.github.com

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/Lesson23/manage.py

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IslamRaslambekov/HomeWork

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#!/usr/bin/env python """Django's command-line utility for administrative tasks.""" import os import sys def main(): """Run administrative tasks.""" os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'Lesson24.settings') try: from django.core.management import execute_from_command_line except ImportError as exc: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) from exc execute_from_command_line(sys.argv) if __name__ == '__main__': main()

[ "rodoslav12345@gmail.com" ]

rodoslav12345@gmail.com

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/page/showreview.py

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[ "Apache-2.0", "MIT" ]

permissive

ctesting/critic

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refs/heads/master

2021-01-17T13:43:41.205293

2013-03-15T20:34:47

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# -*- mode: python; encoding: utf-8 -*- # # Copyright 2012 Jens Lindström, Opera Software ASA # # 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 time import re import datetime import calendar import traceback import dbutils import gitutils import htmlutils import page.utils import log.html import review.utils as review_utils import review.html as review_html import review.comment as review_comment import configuration import diff import profiling import linkify import extensions try: from customization.paths import getModuleFromFile except: def getModuleFromFile(repository, filename): try: base, rest = filename.split("/", 1) return base + "/" except: return None class SummaryColumn(log.html.SummaryColumn): def __init__(self, review, linkToCommit): log.html.SummaryColumn.__init__(self, linkToCommit) self.__review = review self.__cache = {} def fillCache(self, db, review): cursor = db.cursor() cursor.execute("""SELECT DISTINCT assignee, child FROM fullreviewuserfiles JOIN changesets ON (changesets.id=changeset) WHERE review=%s AND state='pending'""", (review.id,)) for user_id, commit_id in cursor: self.__cache.setdefault(commit_id, set()).add(user_id) def render(self, db, commit, target): user_ids = self.__cache.get(commit.getId(db)) if user_ids: users = ["%s:%s" % (user.fullname, user.status) for user in dbutils.User.fromIds(db, [user_id for user_id in user_ids])] target.setAttribute("critic-reviewers", ",".join(sorted(users))) log.html.SummaryColumn.render(self, db, commit, target) class ApprovalColumn: APPROVED = 1 TOTAL = 2 def __init__(self, user, review, type, cache): self.__user = user self.__review = review self.__type = type self.__cache = cache @staticmethod def fillCache(db, user, review, cache, profiler): cursor = db.cursor() profiler.check("fillCache") cursor.execute("""SELECT child, state, COUNT(*), SUM(deleted), SUM(inserted) FROM changesets JOIN reviewfiles ON (changeset=changesets.id) WHERE review=%s GROUP BY child, state""", (review.id,)) for commit_id, state, nfiles, deleted, inserted in cursor: data = cache.get(commit_id) if not data: data = cache[commit_id] = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] if state == 'reviewed': data[3] += nfiles data[4] += deleted data[5] += inserted data[0] += nfiles data[1] += deleted data[2] += inserted profiler.check("fillCache: total") cursor.execute("""SELECT child, COALESCE(reviewfilechanges.to, reviewfiles.state) AS effective_state, COUNT(*), SUM(deleted), SUM(inserted) FROM changesets JOIN reviewfiles ON (changeset=changesets.id) JOIN reviewuserfiles ON (reviewuserfiles.file=reviewfiles.id) LEFT OUTER JOIN reviewfilechanges ON (reviewfilechanges.file=reviewfiles.id AND reviewfilechanges.uid=reviewuserfiles.uid AND reviewfilechanges.state='draft') WHERE review=%s AND reviewuserfiles.uid=%s GROUP BY child, effective_state""", (review.id, user.id)) for commit_id, state, nfiles, deleted, inserted in cursor: data = cache.get(commit_id) if state == 'reviewed': data[9] += nfiles data[10] += deleted data[11] += inserted data[6] += nfiles data[7] += deleted data[8] += inserted profiler.check("fillCache: user") def __calculate(self, db, commit): return self.__cache.get(commit.id, [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) def className(self, db, commit): if commit: (total_nfiles, total_deleted, total_inserted, approved_nfiles, approved_deleted, approved_inserted, user_total_nfiles, user_total_deleted, user_total_inserted, user_approved_nfiles, user_approved_deleted, user_approved_inserted) = self.__calculate(db, commit) if user_approved_nfiles == user_total_nfiles: category = "" else: category = " user" else: category = "" if self.__type == ApprovalColumn.APPROVED: return "approval" + category else: return "total" + category def heading(self, target): if self.__type == ApprovalColumn.APPROVED: target.text("Pending") else: target.text("Total") def render(self, db, commit, target): (total_nfiles, total_deleted, total_inserted, approved_nfiles, approved_deleted, approved_inserted, user_total_nfiles, user_total_deleted, user_total_inserted, user_approved_nfiles, user_approved_deleted, user_approved_inserted) = self.__calculate(db, commit) if self.__type == ApprovalColumn.APPROVED: if user_approved_nfiles == user_total_nfiles: if approved_nfiles == total_nfiles: target.text() elif approved_deleted == total_deleted and approved_inserted == total_inserted: target.span().text("?? %") else: target.span().text("%d %%" % int(100.0 * ((total_deleted + total_inserted) - (approved_deleted + approved_inserted)) / (total_deleted + total_inserted))) elif user_approved_deleted == user_total_deleted and user_approved_inserted == user_total_inserted: target.span().text("?? %") else: target.span().text("%d %%" % int(100.0 * ((user_total_deleted + user_total_inserted) - (user_approved_deleted + user_approved_inserted)) / (user_total_deleted + user_total_inserted))) else: if user_approved_deleted == user_total_deleted and user_approved_inserted == user_total_inserted: target.span().text("-%d/+%d" % (total_deleted, total_inserted)) else: target.span().text("-%d/+%d" % (user_total_deleted, user_total_inserted)) def notModified(req, db, user, review): value = req.getRequestHeader("If-None-Match") return review.getETag(db, user) == value def usesExperimentalFeature(req, db, review): return False def renderShowReview(req, db, user): profiler = profiling.Profiler() cursor = db.cursor() if user.getPreference(db, "commit.diff.compactMode"): default_compact = "yes" else: default_compact = "no" compact = req.getParameter("compact", default_compact) == "yes" highlight = req.getParameter("highlight", None) review_id = req.getParameter("id", filter=int) review = dbutils.Review.fromId(db, review_id, load_commits=False, profiler=profiler) profiler.check("create review") if not review: raise page.utils.DisplayMessage, ("Invalid Review ID", "%d is not a valid review ID." % review_id) if review.getETag(db, user) == req.getRequestHeader("If-None-Match"): raise page.utils.NotModified profiler.check("ETag") # if usesExperimentalFeature(req, db, review): # def renderMessage(target): # url = "%s/r/%d" % (configuration.URL_PER_TYPE['development'], review.id) # p = target.p(style="padding-top: 1em") # p.text("Sorry, this review uses experimental features currently only available in the development version of Critic. Because of that, it can only be displayed there.") # p = target.p(style="padding-top: 1em") # p.b().a(href=url).text(url) # yield page.utils.displayMessage(db, req, user, "Experimental Feature Alert!", message=renderMessage) # return repository = review.repository prefetch_commits = {} cursor.execute("""SELECT sha1, child FROM changesets JOIN reviewchangesets ON (reviewchangesets.changeset=changesets.id) JOIN commits ON (commits.id=changesets.child) WHERE review=%s""", (review.id,)) prefetch_commits.update(dict(cursor)) profiler.check("commits (query)") cursor.execute("""SELECT old_head, commits1.sha1, new_head, commits2.sha1, new_upstream, commits3.sha1 FROM reviewrebases LEFT OUTER JOIN commits AS commits1 ON (commits1.id=old_head) LEFT OUTER JOIN commits AS commits2 ON (commits2.id=new_head) LEFT OUTER JOIN commits AS commits3 ON (commits3.id=new_upstream) WHERE review=%s""", (review.id,)) rebases = cursor.fetchall() if rebases: has_finished_rebases = False for old_head_id, old_head_sha1, new_head_id, new_head_sha1, new_upstream_id, new_upstream_sha1 in rebases: if old_head_id: prefetch_commits[old_head_sha1] = old_head_id if new_head_id: prefetch_commits[new_head_sha1] = new_head_id has_finished_rebases = True if new_upstream_id: prefetch_commits[new_upstream_sha1] = new_upstream_id profiler.check("auxiliary commits (query)") if has_finished_rebases: cursor.execute("""SELECT commits.sha1, commits.id FROM commits JOIN reachable ON (reachable.commit=commits.id) WHERE branch=%s""", (review.branch.id,)) prefetch_commits.update(dict(cursor)) profiler.check("actual commits (query)") prefetch_commits = gitutils.FetchCommits(repository, prefetch_commits) document = htmlutils.Document(req) html = document.html() head = html.head() body = html.body(onunload="void(0);") def flush(target=None): return document.render(stop=target, pretty=not compact) def renderHeaderItems(target): has_draft_items = review_utils.renderDraftItems(db, user, review, target) target = target.div("buttons") if not has_draft_items: if review.state == "open": if review.accepted(db): target.button(id="closeReview", onclick="closeReview();").text("Close Review") else: if user in review.owners or user.getPreference(db, "review.pingAnyReview"): target.button(id="pingReview", onclick="pingReview();").text("Ping Review") if user in review.owners or user.getPreference(db, "review.dropAnyReview"): target.button(id="dropReview", onclick="dropReview();").text("Drop Review") if user in review.owners and not review.description: target.button(id="writeDescription", onclick="editDescription();").text("Write Description") else: target.button(id="reopenReview", onclick="reopenReview();").text("Reopen Review") target.span("buttonscope buttonscope-global") profiler.check("prologue") page.utils.generateHeader(body, db, user, renderHeaderItems) cursor.execute("SELECT 1 FROM fullreviewuserfiles WHERE review=%s AND state='pending' AND assignee=%s", (review.id, user.id)) hasPendingChanges = bool(cursor.fetchone()) if hasPendingChanges: head.setLink("next", "showcommit?review=%d&filter=pending" % review.id) profiler.check("header") document.addExternalStylesheet("resource/showreview.css") document.addExternalStylesheet("resource/review.css") document.addExternalStylesheet("resource/comment.css") document.addExternalScript("resource/showreview.js") document.addExternalScript("resource/review.js") document.addExternalScript("resource/comment.js") document.addExternalScript("resource/autocomplete.js") document.addInternalScript(user.getJS()) document.addInternalScript("var owners = [ %s ];" % ", ".join(owner.getJSConstructor() for owner in review.owners)) document.addInternalScript("var updateCheckInterval = %d;" % user.getPreference(db, "review.updateCheckInterval")); log.html.addResources(document) document.addInternalScript(review.getJS()) target = body.div("main") basic = target.table('paleyellow basic', align='center') basic.col(width='10%') basic.col(width='60%') basic.col(width='30%') h1 = basic.tr().td('h1', colspan=3).h1() h1.text("r/%d: " % review.id) h1.span(id="summary").text("%s" % review.summary, linkify=linkify.Context(db=db, review=review)) h1.a("edit", href="javascript:editSummary();").text("[edit]") def linkToCommit(commit): cursor.execute("SELECT 1 FROM commits JOIN changesets ON (child=commits.id) JOIN reviewchangesets ON (changeset=changesets.id) WHERE sha1=%s AND review=%s", (commit.sha1, review.id)) if cursor.fetchone(): return "%s/%s?review=%d" % (review.repository.name, commit.sha1, review.id) return "%s/%s" % (review.repository.name, commit.sha1) def row(heading, value, help, right=None, linkify=False, cellId=None): main_row = basic.tr('line') main_row.td('heading').text("%s:" % heading) if right is False: colspan = 2 else: colspan = None if callable(value): value(main_row.td('value', id=cellId, colspan=colspan).preformatted()) else: main_row.td('value', id=cellId, colspan=colspan).preformatted().text(value, linkify=linkify, repository=review.repository) if right is False: pass elif callable(right): right(main_row.td('right', valign='bottom')) else: main_row.td('right').text() if help: basic.tr('help').td('help', colspan=3).text(help) def renderBranchName(target): target.code("branch").text(review.branch.name, linkify=linkify.Context()) if repository.name != user.getPreference(db, "defaultRepository"): target.text(" in ") target.code("repository").text("%s:%s" % (configuration.base.HOSTNAME, repository.path)) cursor.execute("""SELECT id, remote, remote_name, disabled, previous FROM trackedbranches WHERE repository=%s AND local_name=%s""", (repository.id, review.branch.name)) row = cursor.fetchone() if row: trackedbranch_id, remote, remote_name, disabled, previous = row target.p("tracking disabled" if disabled else "tracking").text("tracking") target.code("branch").text(remote_name, linkify=linkify.Context(remote=remote)) target.text(" in ") target.code("repository").text(remote, linkify=linkify.Context()) if previous: target.span("lastupdate").script(type="text/javascript").text("document.write('(last fetched: ' + shortDate(new Date(%d)) + ')');" % (calendar.timegm(previous.utctimetuple()) * 1000)) if user in review.owners: buttons = target.div("buttons") if disabled: buttons.button("enabletracking", onclick="enableTracking(%d);" % trackedbranch_id).text("Enable Tracking") else: buttons.button("disabletracking", onclick="triggerUpdate(%d);" % trackedbranch_id).text("Update Now") buttons.button("disabletracking", onclick="disableTracking(%d);" % trackedbranch_id).text("Disable Tracking") def renderReviewers(target): if review.reviewers: for index, reviewer in enumerate(review.reviewers): if index != 0: target.text(", ") span = target.span("user %s" % reviewer.status) span.span("name").text(reviewer.fullname) if reviewer.status == 'absent': span.span("status").text(" (%s)" % reviewer.getAbsence(db)) elif reviewer.status == 'retired': span.span("status").text(" (retired)") else: target.i().text("No reviewers.") cursor.execute("""SELECT reviewfilters.id, reviewfilters.uid, reviewfilters.directory, reviewfilters.file FROM reviewfilters JOIN users ON (reviewfilters.uid=users.id) WHERE reviewfilters.review=%s AND reviewfilters.type='reviewer' AND users.status!='retired'""", (review.id,)) rows = cursor.fetchall() reviewer_filters_hidden = [] if rows: table = target.table("reviewfilters reviewers") row = table.thead().tr("h1") row.th("h1", colspan=4).text("Custom filters:") filter_data = {} reviewfilters = {} for filter_id, user_id, directory_id, file_id in rows: filter_user = dbutils.User.fromId(db, user_id) if file_id: path = dbutils.describe_file(db, file_id) else: path = dbutils.describe_directory(db, directory_id) + "/" reviewfilters.setdefault(filter_user.fullname, []).append(path) filter_data[(filter_user.fullname, path)] = (filter_id, filter_user) count = 0 tbody = table.tbody() for fullname in sorted(reviewfilters.keys()): original_paths = sorted(reviewfilters[fullname]) trimmed_paths = diff.File.eliminateCommonPrefixes(original_paths[:]) first = True for original_path, trimmed_path in zip(original_paths, trimmed_paths): row = tbody.tr("filter") if first: row.td("username", rowspan=len(original_paths)).text(fullname) row.td("reviews", rowspan=len(original_paths)).text("reviews") first = False row.td("path").span().innerHTML(trimmed_path) filter_id, filter_user = filter_data[(fullname, original_path)] href = "javascript:removeReviewFilter(%d, %s, 'reviewer', %s, %s);" % (filter_id, filter_user.getJSConstructor(), htmlutils.jsify(original_path), "true" if filter_user != user else "false") row.td("remove").a(href=href).text("[remove]") count += 1 tfoot = table.tfoot() tfoot.tr().td(colspan=4).text("%d line%s hidden" % (count, "s" if count > 1 else "")) if count > 10: tbody.setAttribute("class", "hidden") reviewer_filters_hidden.append(True) else: tfoot.setAttribute("class", "hidden") reviewer_filters_hidden.append(False) buttons = target.div("buttons") if reviewer_filters_hidden: buttons.button("showfilters", onclick="toggleReviewFilters('reviewers', $(this));").text("%s Custom Filters" % ("Show" if reviewer_filters_hidden[0] else "Hide")) if review.applyfilters and review.repository.parent and not review.applyparentfilters: buttons.button("applyparentfilters", onclick="applyParentFilters();").text("Apply Upstream Filters") buttons.button("addreviewer", onclick="addReviewer();").text("Add Reviewer") buttons.button("manage", onclick="location.href='managereviewers?review=%d';" % review.id).text("Manage Assignments") def renderWatchers(target): if review.watchers: for index, watcher in enumerate(review.watchers): if index != 0: target.text(", ") span = target.span("user %s" % watcher.status) span.span("name").text(watcher.fullname) if watcher.status == 'absent': span.span("status").text(" (%s)" % watcher.getAbsence(db)) elif watcher.status == 'retired': span.span("status").text(" (retired)") else: target.i().text("No watchers.") cursor.execute("""SELECT reviewfilters.id, reviewfilters.uid, reviewfilters.directory, reviewfilters.file FROM reviewfilters JOIN users ON (reviewfilters.uid=users.id) WHERE reviewfilters.review=%s AND reviewfilters.type='watcher' AND users.status!='retired'""", (review.id,)) rows = cursor.fetchall() watcher_filters_hidden = [] if rows: table = target.table("reviewfilters watchers") row = table.thead().tr("h1") row.th("h1", colspan=4).text("Custom filters:") filter_data = {} reviewfilters = {} for filter_id, user_id, directory_id, file_id in rows: filter_user = dbutils.User.fromId(db, user_id) if file_id: path = dbutils.describe_file(db, file_id) else: path = dbutils.describe_directory(db, directory_id) + "/" reviewfilters.setdefault(filter_user.fullname, []).append(path) filter_data[(filter_user.fullname, path)] = (filter_id, filter_user) count = 0 tbody = table.tbody() for fullname in sorted(reviewfilters.keys()): original_paths = sorted(reviewfilters[fullname]) trimmed_paths = diff.File.eliminateCommonPrefixes(original_paths[:]) first = True for original_path, trimmed_path in zip(original_paths, trimmed_paths): row = tbody.tr("filter") if first: row.td("username", rowspan=len(original_paths)).text(fullname) row.td("reviews", rowspan=len(original_paths)).text("watches") first = False row.td("path").span().innerHTML(trimmed_path) filter_id, filter_user = filter_data[(fullname, original_path)] href = "javascript:removeReviewFilter(%d, %s, 'watcher', %s, %s);" % (filter_id, filter_user.getJSConstructor(), htmlutils.jsify(original_path), "true" if filter_user != user else "false") row.td("remove").a(href=href).text("[remove]") count += 1 tfoot = table.tfoot() tfoot.tr().td(colspan=4).text("%d line%s hidden" % (count, "s" if count > 1 else "")) if count > 10: tbody.setAttribute("class", "hidden") watcher_filters_hidden.append(True) else: tfoot.setAttribute("class", "hidden") watcher_filters_hidden.append(False) buttons = target.div("buttons") if watcher_filters_hidden: buttons.button("showfilters", onclick="toggleReviewFilters('watchers', $(this));").text("%s Custom Filters" % ("Show" if watcher_filters_hidden[0] else "Hide")) buttons.button("addwatcher", onclick="addWatcher();").text("Add Watcher") if user not in review.reviewers and user not in review.owners: if user not in review.watchers: buttons.button("watch", onclick="watchReview();").text("Watch Review") elif review.watchers[user] == "manual": buttons.button("watch", onclick="unwatchReview();").text("Stop Watching Review") def renderEditOwners(target): target.button("description", onclick="editOwners();").text("Edit Owners") def renderEditDescription(target): target.button("description", onclick="editDescription();").text("Edit Description") def renderRecipientList(target): cursor.execute("SELECT uid, fullname, include FROM reviewrecipientfilters JOIN users ON (uid=id) WHERE review=%s", (review.id,)) default_include = True included = dict((owner.fullname, owner.id) for owner in review.owners) excluded = {} for user_id, fullname, include in cursor: if user_id == 0: default_include = include elif include: included[fullname] = user_id elif user_id not in review.owners: excluded[fullname] = user_id mode = None users = None buttons = [] opt_in_button = False opt_out_button = False if default_include: if excluded: mode = "Everyone except " users = excluded opt_out_button = user.fullname not in excluded opt_in_button = not opt_out_button else: mode = "Everyone." opt_out_button = True else: if included: mode = "No-one except " users = included opt_in_button = user.fullname not in included opt_out_button = not opt_in_button else: mode = "No-one at all." opt_in_button = True if user in review.owners or user in review.reviewers or user in review.watchers: if opt_in_button: buttons.append(("Include me, please!", "includeRecipient(%d);" % user.id)) if opt_out_button: buttons.append(("Exclude me, please!", "excludeRecipient(%d);" % user.id)) target.span("mode").text(mode) if users: container = target.span("users") first = True for fullname in sorted(users.keys()): if first: first = False else: container.text(", ") container.span("user", critic_user_id=users[fullname]).text(fullname) container.text(".") if buttons: container = target.div("buttons") for label, onclick in buttons: container.button(onclick=onclick).text(label) row("Branch", renderBranchName, "The branch containing the commits to review.", right=False) row("Owner%s" % ("s" if len(review.owners) > 1 else ""), ", ".join(owner.fullname for owner in review.owners), "The users who created and/or owns the review.", right=renderEditOwners) if review.description: row("Description", review.description, "A longer description of the changes to be reviewed.", linkify=linkToCommit, cellId="description", right=renderEditDescription) row("Reviewers", renderReviewers, "Users responsible for reviewing the changes in this review.", right=False) row("Watchers", renderWatchers, "Additional users who receive e-mails about updates to this review.", right=False) row("Recipient List", renderRecipientList, "Users (among the reviewers and watchers) who will receive any e-mails about the review.", right=False) profiler.check("basic") review_state = review.getReviewState(db) profiler.check("review state") progress = target.table('paleyellow progress', align='center') progress_header = progress.tr().td('h1', colspan=3).h1() progress_header.text("Review Progress") progress_header_right = progress_header.span("right") progress_header_right.text("Display log: ") progress_header_right.a(href="showreviewlog?review=%d&granularity=module" % review.id).text("[per module]") progress_header_right.text() progress_header_right.a(href="showreviewlog?review=%d&granularity=file" % review.id).text("[per file]") progress_h1 = progress.tr().td('percent', colspan=3).h1() title_data = { 'id': 'r/%d' % review.id, 'summary': review.summary, 'progress': str(review_state) } if review.state == "closed": progress_h1.img(src=htmlutils.getStaticResourceURI("seal-of-approval-left.png"), style="position: absolute; margin-left: -80px; margin-top: -100px") progress_h1.text("Finished!") elif review.state == "dropped": progress_h1.text("Dropped...") elif review.state == "open" and review_state.accepted: progress_h1.img(src=htmlutils.getStaticResourceURI("seal-of-approval-left.png"), style="position: absolute; margin-left: -80px; margin-top: -100px") progress_h1.text("Accepted!") progress_h1.div().span("remark").text("Hurry up and close it before anyone has a change of heart.") else: progress_h1.text(review_state.getProgress()) if review_state.issues: progress_h1.span("comments").text(" and ") progress_h1.text("%d" % review_state.issues) progress_h1.span("comments").text(" issue%s" % (review_state.issues > 1 and "s" or "")) if review_state.getPercentReviewed() != 100.0: cursor = db.cursor() cursor.execute("""SELECT 1 FROM reviewfiles LEFT OUTER JOIN reviewuserfiles ON (reviewuserfiles.file=reviewfiles.id) WHERE reviewfiles.review=%s AND reviewfiles.state='pending' AND reviewuserfiles.uid IS NULL""", (review.id,)) if cursor.fetchone(): progress.tr().td('stuck', colspan=3).a(href="showreviewlog?review=%d&granularity=file&unassigned=yes" % review.id).text("Not all changes have a reviewer assigned!") cursor.execute("""SELECT uid, MIN(reviewuserfiles.time) FROM reviewfiles JOIN reviewuserfiles ON (reviewuserfiles.file=reviewfiles.id) WHERE reviewfiles.review=%s AND reviewfiles.state='pending' GROUP BY reviewuserfiles.uid""", (review.id,)) def total_seconds(delta): return delta.days * 60 * 60 * 24 + delta.seconds now = datetime.datetime.now() pending_reviewers = [(dbutils.User.fromId(db, user_id), total_seconds(now - timestamp)) for (user_id, timestamp) in cursor.fetchall() if total_seconds(now - timestamp) > 60 * 60 * 8] if pending_reviewers: progress.tr().td('stragglers', colspan=3).text("Needs review from") for reviewer, seconds in pending_reviewers: if reviewer.status == 'retired': continue elif reviewer.status == 'absent': warning = " absent" elif not reviewer.getPreference(db, "email.activated"): warning = " no-email" else: warning = "" if seconds < 60 * 60 * 24: hours = seconds / (60 * 60) duration = " (%d hour%s)" % (hours, "s" if hours > 1 else "") elif seconds < 60 * 60 * 24 * 7: days = seconds / (60 * 60 * 24) duration = " (%d day%s)" % (days, "s" if days > 1 else "") elif seconds < 60 * 60 * 24 * 30: weeks = seconds / (60 * 60 * 24 * 7) duration = " (%d week%s)" % (weeks, "s" if weeks > 1 else "") else: duration = " (wake up!)" progress.tr().td('straggler' + warning, colspan=3).text("%s%s" % (reviewer.fullname, duration)) if user in review.owners: progress.tr().td('pinging', colspan=3).span().text("Send a message to these users by pinging the review.") title_format = user.getPreference(db, 'ui.title.showReview') try: document.setTitle(title_format % title_data) except Exception, exc: document.setTitle(traceback.format_exception_only(type(exc), exc)[0].strip()) profiler.check("progress") check = profiler.start("ApprovalColumn.fillCache") def linkToCommit(commit): return "%s?review=%d" % (commit.sha1[:8], review.id) approval_cache = {} ApprovalColumn.fillCache(db, user, review, approval_cache, profiler) check.stop() summary_column = SummaryColumn(review, linkToCommit) summary_column.fillCache(db, review) profiler.check("SummaryColumn.fillCache") columns = [(10, log.html.WhenColumn()), (60, summary_column), (16, log.html.AuthorColumn()), (7, ApprovalColumn(user, review, ApprovalColumn.APPROVED, approval_cache)), (7, ApprovalColumn(user, review, ApprovalColumn.TOTAL, approval_cache))] def renderReviewPending(db, target): if not user.isAnonymous(): target.text("Filter: ") if hasPendingChanges: target.a(href="showcommit?review=%d&filter=pending" % review.id, title="All changes you need to review.").text("[pending]") target.text() if user in review.reviewers: target.a(href="showcommit?review=%d&filter=reviewable" % review.id, title="All changes you can review, including what you've already reviewed.").text("[reviewable]") target.text() target.a(href="showcommit?review=%d&filter=relevant" % review.id, title="All changes that match your filters.").text("[relevant]") target.text() target.text("Manual: ") target.a(href="filterchanges?review=%d" % review.id, title="Manually select what files to display of the changes from all commits.").text("[full]") target.text() target.a(href="javascript:void(filterPartialChanges());", title="Manually select what what files to display of the changes in a selection of commits.").text("[partial]") req.addResponseHeader("ETag", review.getETag(db, user)) if user.getPreference(db, "review.useMustRevalidate"): req.addResponseHeader("Cache-Control", "must-revalidate") yield flush(target) try: try: prefetch_commits.getCommits(db) except AttributeError: raise Exception, prefetch_commits.error profiler.check("FetchCommits.getCommits()") cursor.execute("""SELECT child FROM changesets JOIN reviewchangesets ON (reviewchangesets.changeset=changesets.id) JOIN commits ON (commits.id=changesets.child) WHERE review=%s""", (review.id,)) commits = [gitutils.Commit.fromId(db, repository, commit_id) for (commit_id,) in cursor] cursor.execute("""SELECT id, old_head, new_head, new_upstream, uid, branch FROM reviewrebases WHERE review=%s""", (review.id,)) all_rebases = [(rebase_id, gitutils.Commit.fromId(db, repository, old_head), gitutils.Commit.fromId(db, repository, new_head) if new_head else None, dbutils.User.fromId(db, user_id), gitutils.Commit.fromId(db, repository, new_upstream) if new_upstream is not None else None, branch_name) for rebase_id, old_head, new_head, new_upstream, user_id, branch_name in cursor] bottom_right = None finished_rebases = filter(lambda item: item[2] is not None, all_rebases) current_rebases = filter(lambda item: item[2] is None, all_rebases) if current_rebases: assert len(current_rebases) == 1 def renderCancelRebase(db, target): target.button("cancelrebase").text("Cancel Rebase") if user == current_rebases[0][3]: bottom_right = renderCancelRebase else: def renderPrepareRebase(db, target): target.button("preparerebase").text("Prepare Rebase") bottom_right = renderPrepareRebase if finished_rebases: cursor.execute("""SELECT commit FROM reachable WHERE branch=%s""", (review.branch.id,)) actual_commits = [gitutils.Commit.fromId(db, repository, commit_id) for (commit_id,) in cursor] else: actual_commits = [] log.html.render(db, target, "Commits (%d)", commits=commits, columns=columns, title_right=renderReviewPending, rebases=finished_rebases, branch_name=review.branch.name, bottom_right=bottom_right, review=review, highlight=highlight, profiler=profiler, user=user, extra_commits=actual_commits) yield flush(target) profiler.check("log") except gitutils.GitError, error: div = target.div("error") div.h1().text("Error!") if error.sha1: div.text("The commit %s is missing from the repository." % error.sha1) else: div.text("Failed to read commits from the repository: %s" % error.message) all_chains = review.getCommentChains(db, user, skip=set(['commits', 'lines'])) profiler.check("chains (load)") if all_chains: issue_chains = filter(lambda chain: chain.type == "issue", all_chains) draft_issues = filter(lambda chain: chain.state == "draft", issue_chains) open_issues = filter(lambda chain: chain.state == "open", issue_chains) addressed_issues = filter(lambda chain: chain.state == "addressed", issue_chains) closed_issues = filter(lambda chain: chain.state == "closed", issue_chains) note_chains = filter(lambda chain: chain.type == "note", all_chains) draft_notes = filter(lambda chain: chain.state == "draft", note_chains) open_notes = filter(lambda chain: chain.state != "draft" and chain.state != "empty", note_chains) else: open_issues = [] open_notes = [] chains = target.table("paleyellow comments", align="center", cellspacing=0) h1 = chains.tr("h1").td("h1", colspan=3).h1().text("Comments") links = h1.span("links") if all_chains: links.a(href="showcomments?review=%d&filter=all" % review.id).text("[display all]") if not user.isAnonymous(): links.a(href="showcomments?review=%d&filter=all&blame=%s" % (review.id, user.name)).text("[in my commits]") cursor.execute("""SELECT count(commentstoread.comment) > 0 FROM commentchains JOIN comments ON (comments.chain=commentchains.id) JOIN commentstoread ON (commentstoread.comment=comments.id) WHERE commentchains.review=%s AND commentstoread.uid=%s""", [review.id, user.id]) if cursor.fetchone()[0]: links.a(href="showcomments?review=%d&filter=toread" % review.id).text("[display unread]") def renderChains(target, chains): for chain in chains: row = target.tr("comment %s %s" % (chain.type, chain.state)) row.td("author").text(chain.user.fullname) row.td("title").a(href="showcomment?chain=%d" % chain.id).innerHTML(chain.leader()) ncomments = chain.countComments() nunread = chain.countUnread() cell = row.td("when") if ncomments == 1: if nunread: cell.b().text("Unread") else: cell.text("No replies") else: if nunread: cell.b().text("%d of %d unread" % (nunread, ncomments)) else: cell.text("%d repl%s" % (ncomments - 1, "ies" if ncomments > 2 else "y")) if draft_issues: h2 = chains.tr("h2", id="draft-issues").td("h2", colspan=3).h2().text("Draft Issues") h2.a(href="showcomments?review=%d&filter=draft-issues" % review.id).text("[display all]") h2.a(href="showcomments?review=%d&filter=draft-issues&blame=%s" % (review.id, user.name)).text("[in my commits]") renderChains(chains, draft_issues) if open_issues: h2 = chains.tr("h2", id="open-issues").td("h2", colspan=3).h2().text("Open Issues") h2.a(href="showcomments?review=%d&filter=open-issues" % review.id).text("[display all]") h2.a(href="showcomments?review=%d&filter=open-issues&blame=%s" % (review.id, user.name)).text("[in my commits]") renderChains(chains, open_issues) if addressed_issues: h2 = chains.tr("h2", id="addressed-issues").td("h2", colspan=3).h2().text("Addressed Issues") h2.a(href="showcomments?review=%d&filter=addressed-issues" % review.id).text("[display all]") h2.a(href="showcomments?review=%d&filter=addressed-issues&blame=%s" % (review.id, user.name)).text("[in my commits]") renderChains(chains, addressed_issues) if closed_issues: h2 = chains.tr("h2", id="closed-issues").td("h2", colspan=3).h2().text("Resolved Issues") h2.a(href="showcomments?review=%d&filter=closed-issues" % review.id).text("[display all]") h2.a(href="showcomments?review=%d&filter=closed-issues&blame=%s" % (review.id, user.name)).text("[in my commits]") renderChains(chains, closed_issues) if draft_notes: h2 = chains.tr("h2", id="draft-notes").td("h2", colspan=3).h2().text("Draft Notes") h2.a(href="showcomments?review=%d&filter=draft-notes" % review.id).text("[display all]") h2.a(href="showcomments?review=%d&filter=draft-notes&blame=%s" % (review.id, user.name)).text("[in my commits]") renderChains(chains, draft_notes) if open_notes: h2 = chains.tr("h2", id="notes").td("h2", colspan=3).h2().text("Notes") h2.a(href="showcomments?review=%d&filter=open-notes" % review.id).text("[display all]") h2.a(href="showcomments?review=%d&filter=open-notes&blame=%s" % (review.id, user.name)).text("[in my commits]") renderChains(chains, open_notes) buttons = chains.tr("buttons").td("buttons", colspan=3) buttons.button(onclick="CommentChain.create('issue');").text("Raise Issue") buttons.button(onclick="CommentChain.create('note');").text("Write Note") profiler.check("chains (render)") yield flush(target) cursor.execute("""SELECT DISTINCT reviewfiles.file, theirs.uid FROM reviewfiles JOIN reviewuserfiles AS yours ON (yours.file=reviewfiles.id) JOIN reviewuserfiles AS theirs ON (theirs.file=yours.file AND theirs.uid!=yours.uid) WHERE reviewfiles.review=%s AND yours.uid=%s""", (review.id, user.id)) rows = cursor.fetchall() profiler.check("shared assignments (query)") if rows: reviewers = {} for file_id, user_id in rows: reviewers.setdefault(file_id, {})[user_id] = set() shared = target.table('paleyellow shared', align='center', cellspacing=0) row = shared.tr('h1') shared_header = row.td('h1', colspan=2).h1() shared_header.text("Shared Assignments") shared_buttons = row.td('buttons', colspan=2).span(style="display: none") shared_buttons.button("confirm").text("Confirm") shared_buttons.button("cancel").text("Cancel") granularity = "module" def moduleFromFile(file_id): filename = dbutils.describe_file(db, file_id) return getModuleFromFile(repository, filename) or filename def formatFiles(files): paths = sorted([dbutils.describe_file(db, file_id) for file_id in files]) if granularity == "file": return diff.File.eliminateCommonPrefixes(paths) else: modules = set() files = [] for path in paths: module = getModuleFromFile(path) if module: modules.add(module) else: files.append(path) return sorted(modules) + diff.File.eliminateCommonPrefixes(files) files_per_team = review_utils.collectReviewTeams(reviewers) teams_per_modules = {} profiler.check("shared assignments (collect teams)") for team, files in files_per_team.items(): modules = set() for file_id in files: modules.add(moduleFromFile(file_id)) teams_per_modules.setdefault(frozenset(modules), set()).update(team) for modules, team in teams_per_modules.items(): row = shared.tr("reviewers") cell = row.td("reviewers") members = sorted([dbutils.User.fromId(db, user_id).fullname for user_id in team]) for member in members: cell.text(member).br() row.td("willreview").innerHTML("<span class='also'>also</span> review changes in") cell = row.td("files") for path in diff.File.eliminateCommonPrefixes(sorted(modules)): cell.span("file").innerHTML(path).br() directory_ids = "[ %s ]" % ", ".join([str(dbutils.find_directory(db, path=path[:-1])) for path in modules if path.endswith("/")]) file_ids = "[ %s ]" % ", ".join([str(dbutils.find_file(db, path=path)) for path in modules if not path.endswith("/")]) user_ids = "[ %s ]" % ", ".join(map(str, team)) cell = row.td("buttons") cell.button("accept", critic_directory_ids=directory_ids, critic_file_ids=file_ids, critic_user_ids=user_ids).text("I will review this!") cell.button("deny", critic_directory_ids=directory_ids, critic_file_ids=file_ids, critic_user_ids=user_ids).text("They will review this!") yield flush(target) profiler.check("shared assignments") cursor.execute("SELECT batches.id, users.fullname, batches.comment, batches.time FROM batches JOIN users ON (users.id=batches.uid) WHERE batches.review=%s ORDER BY batches.id DESC", [review.id]) rows = cursor.fetchall() if rows: notes = dict([(chain.id, chain) for chain in open_notes]) batches = target.table("paleyellow batches", align="center", cellspacing=0) batches.tr().td("h1", colspan=3).h1().text("Work Log") for batch_id, user_fullname, chain_id, when in rows: row = batches.tr("batch") row.td("author").text(user_fullname) title = "<i>No comment</i>" if chain_id: if chain_id in notes: title = notes[chain_id].leader() else: for chain in all_chains: if chain.id == chain_id: title = chain.leader() break row.td("title").a(href="showbatch?batch=%d" % batch_id).innerHTML(title) row.td("when").text(time.strftime("%Y-%m-%d %H:%M", when.timetuple())) profiler.check("batches") profiler.output(db, user, target) yield flush() if review.branch.head: try: head_according_to_git = repository.revparse(review.branch.name) except: head_according_to_git = None head_according_to_us = review.branch.head.sha1 if head_according_to_git != head_according_to_us: # The git repository disagrees with us. Potentially harmful updates # to the branch will be rejected by the git hook while this is the # case, but this means that "our" head might not be referenced at # all and thus that it might be GC:ed by the git repository at some # point. To avoid that, add a keepalive reference. repository.keepalive(head_according_to_us) yield "\n" % (head_according_to_git[:8] if head_according_to_git else "N/A", head_according_to_us[:8])

[ "jl@opera.com" ]

jl@opera.com

a15574410724ba4ab74e9a614969967bd761fc75

242fc8beff7e1e51b5cdd9bfa3d118267f56861d

/shunting_yard/parser.py

dd42d72177d0963e445e9387f19599b0eaaefeaf

[]

no_license

gmkvaal/shunting_yard

dac0dd780cf2c779f410edad54f72618c5379f80

d84b0c4aa3c36aff435ede5252f143182f656fc0

refs/heads/master

2021-07-23T12:14:48.666536

2017-11-03T13:08:13

null

0

null

UTF-8

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py

from typing import List from collections import namedtuple from enum import Enum, auto from .tokenizer import tokenizer StateRet = namedtuple('StateRet', ['next_state', 'increment']) class StateType(Enum): NUMBER = auto() def classify_token(token: dict, operator_stack: List[str], output_queue: List[str]) -> StateRet: """Classifies tokens Args: token: Currently read token. operator_stack: Stack of operators output_queue: Tokens in RPN order Returns: Tuple of: Next state, if increment """ print(token['name'], [operator['name'] for operator in output_queue], [operator['name'] for operator in operator_stack]) if token['type'] == StateType.NUMBER: output_queue.append(token) return StateRet(classify_token, True) if token['type'] == 'OPERATOR': return StateRet(operator, False) if token['type'] == 'FUNCTION': operator_stack.append(token) return StateRet(classify_token, True) if token['type'] == 'LEFT_PARENTHESIS': operator_stack.append(token) return StateRet(classify_token, True) if token['type'] == 'RIGHT_PARENTHESIS': return StateRet(right_parenthesis, False) if token['type'] == 'SKIP': return StateRet(classify_token, True) def operator(token: dict, operator_stack: List[str], output_queue: List[str]) -> StateRet: """Called when a token is classified as an operator Appends to stack of the operator stack is empty, if the last token in the stack is a function, or if the token is right associative. Else, pops operators from the stack Args: token: Currently read token. operator_stack: Stack of operators output_queue: Tokens in RPN order Returns: Tuple of: Next state, if increment """ del output_queue # Not used in this state if len(operator_stack) == 0 or operator_stack[-1]['precedence'] is None: operator_stack.append(token) return StateRet(classify_token, True) elif token['associativity'] == 'RIGHT': operator_stack.append(token) return StateRet(classify_token, True) else: return StateRet(pop_operators, False) def pop_operators(token: dict, operator_stack: List[str], output_queue: List[str]) -> StateRet: """Pops operators from the stack Operators are popped from the operator stack to the output queue until reaching an operator with lower precedence or the stack is empty Args: token: Currently read token. operator_stack: Stack of operators output_queue: Tokens in RPN order Returns: Tuple of: Next state, if increment """ if (len(operator_stack) > 0 and operator_stack[-1]['precedence'] is not None and operator_stack[-1]['precedence'] >= token['precedence'] and operator_stack[-1]['associativity'] == 'LEFT'): output_queue.append(operator_stack.pop()) return StateRet(pop_operators, False) else: operator_stack.append(token) return StateRet(classify_token, True) def right_parenthesis(token: dict, operator_stack: List[str], output_queue: List[str]) -> StateRet: """Called when a token is classified as a right parenthesis Operators are popped from the operator stack to the output queue until reaching a left parenthesis Args: token: Currently read token. operator_stack: Stack of operators output_queue: Tokens in RPN order Returns: Tuple of: Next state, if increment """ del token # Not used in this state if operator_stack == []: raise Exception('Mismatching parentheses') elif operator_stack[-1]['type'] != 'LEFT_PARENTHESIS': output_queue.append(operator_stack.pop()) return StateRet(right_parenthesis, False) else: operator_stack.pop() return StateRet(post_right_parenthesis, False) def post_right_parenthesis(token: dict, operator_stack: List[str], output_queue: List[str]) -> StateRet: """Called after brackets are matched If a function is atop of the stack it is poped to the output queue Args: token: Currently read token. operator_stack: Stack of operators output_queue: Tokens in RPN order Returns: Tuple of: Next state, if increment """ if len(operator_stack) > 0 and operator_stack[-1]['type'] == 'FUNCTION': output_queue.append(operator_stack.pop()) return StateRet(classify_token, True) def empty_operator_stack(operator_stack: List[str], output_queue: List[str]) -> None: """ Pops remaining operators from the operator stack to the output queue Args: token: Currently read token. operator_stack: Stack of operators output_queue: Tokens in RPN order """ while len(operator_stack) > 0: output_queue.append(operator_stack.pop()) def shunting_yard(input_string: str) -> List[str]: """ Engine of shunting yard parser finite state machine algorithm Args: input_string: A mathematical expression Returns: A list of tokens ordered in Reverse Polish Notation """ operator_stack = [] output_queue = [] token_list = tokenizer(input_string) state = classify_token idx = 0 while True: token = token_list[idx] #print(token['name'], state.__name__, operator_stack) return_state = state(token, operator_stack, output_queue) if return_state.increment: idx += 1 state = return_state.next_state if idx == len(token_list): empty_operator_stack(operator_stack, output_queue) break return output_queue

[ "guttorm.kvaal@gmail.com" ]

guttorm.kvaal@gmail.com

3f2d06c3d1274caa259fdb14604ed63acc54c4a3

e950cafc9feeeacba9d40b18477dab43bb8737bf

/src/lab1/tfs_client.py

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permissive

aws-samples/aws-inf1-gcr-workshop

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refs/heads/master

2022-04-23T20:34:53.914422

2020-04-28T02:36:42

254,085,220

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1

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1,791

py

''' Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance with the License. A copy of the License is located at http://www.apache.org/licenses/LICENSE-2.0 or in the "license" file accompanying this file. This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ''' import numpy as np import grpc import tensorflow as tf from tensorflow.keras.preprocessing import image from tensorflow.keras.applications.resnet50 import preprocess_input from tensorflow.keras.applications.resnet50 import decode_predictions from tensorflow_serving.apis import predict_pb2 from tensorflow_serving.apis import prediction_service_pb2_grpc tf.keras.backend.set_image_data_format('channels_last') if __name__ == '__main__': channel = grpc.insecure_channel('localhost:8500') stub = prediction_service_pb2_grpc.PredictionServiceStub(channel) img_file = tf.keras.utils.get_file( "./kitten_small.jpg", "https://raw.githubusercontent.com/awslabs/mxnet-model-server/master/docs/images/kitten_small.jpg") img = image.load_img(img_file, target_size=(224, 224)) img_array = preprocess_input(image.img_to_array(img)[None, ...]) request = predict_pb2.PredictRequest() request.model_spec.name = 'resnet50_inf1_serve' request.inputs['input'].CopyFrom( tf.contrib.util.make_tensor_proto(img_array, shape=img_array.shape)) result = stub.Predict(request) prediction = tf.make_ndarray(result.outputs['output']) print(decode_predictions(prediction))

[ "zhazhn@amazon.com" ]

zhazhn@amazon.com

716139a42c9d3176dc33f024f607d86df864e7be

b5cd2a7828acdaebd49964ac93be7085bea0d011

/carte.py

c4e4e9aa68d71d16ec5026c7e3144cfd0e940827

[]

no_license

stevchen99/pythonRoboc

e5576930cd9295c08336ad3754c7a4e326e5a8a5

5301be8e918ce6b3c36b491e02c8aaddd520eb4e

refs/heads/master

2020-12-08T16:44:46.439760

2020-01-17T18:03:39

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0

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# -*-coding:Utf-8 -* """Ce module contient la classe Carte.""" class Carte: """Objet de transition entre un fichier et un labyrinthe.""" def __init__(self, nom, chaine): self.nom = nom self.labyrinthe = creer_labyrinthe_depuis_chaine(chaine) def __repr__(self): return "<Carte {}>".format(self.nom)

[ "stevchen99@gmail.com" ]

stevchen99@gmail.com

962ad189b3695ad55e5db43027b6e869b2817147

fb408595c1edee0be293302c6d7bfc0c77d37c46

/python/DP/DP_2096.py

a5753e0e8dda2057310f4dee0f056e7940fbb74d

[]

no_license

as950118/Algorithm

39ad25519fd0e42b90ddf3797a61239862ad79b5

739a7d4b569057cdb6b6faa74254512b83d02bb1

refs/heads/master

2023-07-21T12:38:00.653579

2023-07-19T06:57:17

125,176,176

1

0

null

UTF-8

Python

false

610

py

n = int(input()) arr = [0]*(3) dp_max = [0]*(3) dp_min = [0]*(3) arr = list(map(int, input().split())) temp = arr[:] dp_max = temp[:] dp_min = temp[:] for i in range(1, n): arr = list(map(int, input().split())) temp[0] = max(dp_max[0], dp_max[1]) + arr[0] temp[1] = max(dp_max[0], dp_max[1], dp_max[2]) + arr[1] temp[2] = max(dp_max[1], dp_max[2]) + arr[2] dp_max = temp[:] temp[0] = min(dp_min[0], dp_min[1]) + arr[0] temp[1] = min(dp_min[0], dp_min[1], dp_min[2]) + arr[1] temp[2] = min(dp_min[1], dp_min[2]) + arr[2] dp_min = temp[:] print(max(dp_max), min(dp_min))

[ "na_qa@icloud.com" ]

na_qa@icloud.com

f443e27275903b151314c40311f6464aafca1b44

72784799e5436e8a96462bdbcb29baeb644dcc7f

/utilities/animate.py

2c562e41c8ec2e736db293f0f772a55ff0091345

[]

no_license

simonsben/undergrad_thesis

31dd205cb734f7c876b5053040e5ab0bf8fbd5cb

8458d00ae6525602b944279c2c280149a5957cb1

refs/heads/master

2020-04-02T10:46:55.255322

2019-04-08T06:01:48

154,354,775

1

0

null

UTF-8

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false

979

py

from matplotlib.pylab import figure, show, savefig, title, axis, draw from networkx import spring_layout, draw_networkx_edges, draw_networkx_nodes from matplotlib.animation import FuncAnimation def add_node(graph, i, plot_layout): # draw_networkx_edges(graph, plot_layout, alpha=.3) # draw_networkx_nodes(node, plot_layout, node_size=100, edgecolors='k', node_color='w') i += 1 draw() def animate_creation(network, blocking=True, save_plot=True): _title = 'Free-Scale Network' fig = figure(_title) axis('off') graph = network.network_plot plot_layout = spring_layout(graph) init_nodes = graph.nodes[:3] init_edges = graph.edges[:2] draw_networkx_nodes(graph, plot_layout, nodelist=init_nodes, node_size=100, edgecolors='k', node_color='w') draw_networkx_edges(graph, plot_layout, edgelist=init_edges, alpha=.3) draw() show() i = 3 animation = FuncAnimation(fig, add_node, fargs=(graph, i, plot_layout))

[ "simons.ben0@gmail.com" ]

simons.ben0@gmail.com

52a608c85aa5b18e530c6cb0cae1d8d2f58b7ec4

14d8418ca5990217be67aee89fdaa310db03fbba

/test_stats_d_graph_display.py

fffe014750a15f323e8f39408530e03c6133cae4

[ "Apache-2.0" ]

permissive

sachanta/lm-sdk-python

3a16457bd2d5b880a0d238a88a9d1d5b8d9675f0

e476d415c7279457f79b5d032a73d950af2fe96b

refs/heads/master

2023-08-03T08:39:42.842790

2021-09-13T07:20:56

null

0

null

UTF-8

Python

false

1,445

py

# coding: utf-8 """ LogicMonitor REST API LogicMonitor is a SaaS-based performance monitoring platform that provides full visibility into complex, hybrid infrastructures, offering granular performance monitoring and actionable data and insights. logicmonitor_sdk enables you to manage your LogicMonitor account programmatically. Note: For Python SDKs, the REQUEST parameters can contain camelCase or an underscore. However, the RESPONSE parameters will always contain an underscore. For example, the REQUEST parameter can be testLocation or test_location. The RESPONSE parameter will be test_location. # noqa: E501 OpenAPI spec version: 2.0.0 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import unittest import logicmonitor_sdk from logicmonitor_sdk.models.stats_d_graph_display import StatsDGraphDisplay # noqa: E501 from logicmonitor_sdk.rest import ApiException class TestStatsDGraphDisplay(unittest.TestCase): """StatsDGraphDisplay unit test stubs""" def setUp(self): pass def tearDown(self): pass def testStatsDGraphDisplay(self): """Test StatsDGraphDisplay""" # FIXME: construct object with mandatory attributes with example values # model = logicmonitor_sdk.models.stats_d_graph_display.StatsDGraphDisplay() # noqa: E501 pass if __name__ == '__main__': unittest.main()

[ "bamboo@build01.us-west-1.logicmonitor.net" ]

bamboo@build01.us-west-1.logicmonitor.net

d5816bf17fecd71e306ba321ee4fd6bda1913e63

2c4f00e65671467ed14b33f4f6b574bd9944eaea

/test.py

eceeb7bd27460d1c67779648392af96cb4ff1456

[ "MIT" ]

permissive

ashafer01/chain

20a459a7a4f0bc43668cfe71f58d568689c61f23

e7ea9b3fb2b92459c581dfc0ebc424300cd333c0

refs/heads/master

2020-07-06T14:18:42.242072

2019-08-18T20:37:19

203,046,524

4

0

null

UTF-8

Python

false

1,107

py

import unittest from chain import chain, args class TestChain(unittest.TestCase): def test_chain(self): def _test_x(x): return x + 'x' def _test_y(x): return x + 'y' def _test_z(x): return x + 'z' def _test_2(a,b): return a+b def _test_3(a,b,c): return a+b+c chain_res = chain(args('w') | _test_x | _test_y | args('2'), _test_2, _test_z, args('3', '4'), _test_3) native_res = _test_3(_test_z(_test_2(_test_y(_test_x('w')), '2')), '3', '4') self.assertEqual(chain_res, native_res) def test_return_args(self): def _test_ret_args(x): res = args('hello', world=x) return res def _test_accept_args(hello, world=''): return hello + ' ' + world def _test_1(x): return '1 ' + x def _test_2(x): return x + ' 2' res = chain(args('test') | _test_1 | _test_2 | _test_ret_args | _test_accept_args) expected = 'hello 1 test 2' self.assertEqual(res, expected)

[ "ashafer@pm.me" ]

ashafer@pm.me

7bc0cb59175906afc9c0c195273cb6c3843800f3

f213d968b6d43ca27c52d8d61311c054be0e204c

/tutorial2.py

84a2831dee2e41157438f719de429b18aaf113bf

[]

no_license

datasqr/OpenCV

f9a8c161735fd3b31f6a65b73b41e5ed7fda783c

3fe8d52e1ea133f3c402ae7fd2606d1f716e0215

refs/heads/master

2021-01-25T04:57:50.930615

2015-01-07T19:25:34

28,535,548

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null

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# http://docs.opencv.org/trunk/doc/py_tutorials/py_gui/py_drawing_functions/py_drawing_functions.html #Drawing Functions import numpy as np import cv2 # Create a black image img = np.zeros((250,250,3), np.uint8) print img # Draw a diagonal blue line with thickness of 5 px #cv2.line(img,(0,0),(511,511),(255,0,0),5) #scv2.rectangle(img,(384,0),(510,128),(0,255,0),3) #cv2.circle(img,(447,63), 63, (0,0,255), -1) #cv2.ellipse(img,(256,256),(100,50),0,0,180,255,-1) pts = np.array([[10,5],[20,30],[50,10]], np.int32) pts = pts.reshape((-1,1,2)) cv2.polylines(img,[pts],True,(0,255,255)) cv2.imshow('image',img) cv2.waitKey(0) cv2.destroyAllWindows()

[ "mateusz.zbikowski@gmail.com" ]

mateusz.zbikowski@gmail.com

5e85990864fdcde2e416a6a7d1e2c645b29cd5de

93b866284ca1ac29c5005555f2cb30454a0fb5cf

/Problems/59-Problem/Problem 59.py

7f38e437f006d4d25166047a46688def172fbf69

[]

no_license

FrancoisdeFouchecour/Projet-Euler

c2b17d1e35fbd10a708ba3221825a62a17818382

0cf70457c0418264c2eff7cdd0e92a07b61ecb07

refs/heads/master

2021-12-25T05:44:08.054648

2021-11-27T21:47:42

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import time import numpy as np problem_number = 59 def decimal_to_binar(n): if n == 0: return 0 return n%2 + 10*decimal_to_binar(n//2) def binar_to_decimal(n): if n == 1 or n == 0: return n else: return int(str(n)[-1]) + 2* binar_to_decimal(int(str(n)[:-1])) def XOR(A, B): a = str(decimal_to_binar(A)) b = str(decimal_to_binar(B)) while len(a) < len(b): a = '0'+a while len(a) > len(b): b = '0'+b c = '' for i in range(len(a)): if a[i] == b[i]: c += '0' else: c += '1' return binar_to_decimal(c) def decipher(text, key): plain_text = "" for i in range(len(text)): plain_text += chr(XOR(text[i], key[i%3])) return plain_text #read data number_file = open("data.txt", "r") raw_data = number_file.read() number_file.close() list_name = [] word = "" for char in raw_data: if char == ',': list_name.append(int(word)) word = "" elif char == '\n': list_name.append(int(word)) elif char != '"': word += char #Solution def solution(input_list): result = 0 length = len(input_list) normal_frequency = [11.682, 4.434, 5.238, 3.174, 2.799, 4.027, 1.642, 4.200, 7.294, 0.511, 0.456, 2.415, 3.826, 2.284, 7.631, 4.319, 0.222, 2.826, 6.686, 15.978, 1.183, 0.824, 5.497, 0.045, 0.763, 0.045] score = np.infty for a in range(97, 123): for b in range(97, 123): for c in range(97, 123): key = [a, b, c] new_text = [0 for i in range(length)] for i in range(len(new_text)): new_text[i] = XOR(input_list[i], key[i%3]) letter_frequency = [[0 for j in range(26)] for i in range(3)] for i in range(len(new_text)): if 65 <= new_text[i] and new_text[i] <= 90: letter_frequency[i%3][new_text[i] - 65] += 1 elif 97 <= new_text[i] and new_text[i] <= 122: letter_frequency[i%3][new_text[i] - 97] += 1 new_score = 0 for i in range(3): for j in range(26): letter_frequency[i][j] = letter_frequency[i][j]/(length//3) new_score += abs(letter_frequency[i][j] - normal_frequency[j]) if new_score < score: score = new_score result = sum(new_text) return result #Test & Result fichier = open("Solution "+str(problem_number)+".txt", "w") string = "" begin_problem = time.time() problem_value = solution(list_name) end_problem = time.time() problem_time = end_problem - begin_problem string += "RESULT PROBLEM #"+str(problem_number)+"\n\n" string += "Output: "+str(problem_value)+"\n" string += "Computation time: "+str(problem_time)+" sec\n" string += "\n\n\nCurrent date & time: " + time.strftime("%c") fichier.write(string) fichier.close()

[ "francois.de-salivet-de-fouchecour@polytechnique.edu" ]

francois.de-salivet-de-fouchecour@polytechnique.edu

85f5b6db2f09ac1feae49616d3363ce62a7724e1

bab70d19d523e616ebaa1f74c114e88fd5d2ad83

/CAhw2/RunMeForTest.py

af2bd375ebc9796f9d89d82792c8c2ae4b9716d8

[]

no_license

FuHsinyu/Cellular-Automata-model

4ef3351735aee8a4d268f67d3d04b36809bb33d2

2a0bdf505fd0b793caee5b1b708fb9d591532ebb

refs/heads/master

2021-05-06T19:49:16.148410

2018-06-13T01:55:14

112,194,748

1

0

null

UTF-8

Python

false

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from pycx_gui import GUI from myModel import CAmodel import matplotlib.pyplot as plt import numpy as np import random #change digit in line 18 and run to get a FAST TESTING RESULT initRow = [0] * 100 for i in range(100): initRow[i] = random.randrange(2) r = 1 #range systemSize = r * 2 + 1 k = 2 #base rule = 0 #decimal rule initilized cycleLenList = [] #final cycle length recording list cycleReachedBool = False #booleanB resultDict = dict() #hash results with respect to rules while rule < 6: #By changing these for FAST TEST model = CAmodel(r, k, rule, initRow, systemSize) #def __init__(self, range, base, rule, initRow, systemSize): runTime = 0 #run times while runTime < 10e4: currentRow = tuple(model.currentRow) if currentRow not in resultDict: resultDict[currentRow] = runTime #print(resultDict) else: cycleLenList.append(runTime - resultDict[currentRow]) cycleReachedBool = True break runTime += 1 model.step() if not cycleReachedBool: cycleLenList.append(-1) rule += 1 # cycleReachedBool = False resultDict.clear() model.draw(cycleLenList)

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247047.niuitmo.ru

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yamaguchi-milkcocholate/GoogleBrain-VentilatorPressurePrediction

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from random import seed import numpy as np import pandas as pd import json import os import sys import gc import shutil from pprint import pprint from pathlib import Path from typing import * import tensorflow as tf from tensorflow import keras from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint from sklearn.metrics import mean_absolute_error as mae from sklearn.preprocessing import RobustScaler, PowerTransformer, MinMaxScaler from sklearn.model_selection import KFold import sys print(str(Path(__file__).resolve().parent.parent.parent)) sys.path.append(str(Path(__file__).resolve().parent.parent.parent)) from src.utils import ( seed_every_thing, fetch_data, Config, plot_metric, reduce_tf_gpu_memory, reduce_mem_usage, fetch_custom_data, CustomL1Loss ) def build_model(config: Config, n_features) -> keras.models.Sequential: model = keras.models.Sequential([keras.layers.Input(shape=(config.cut, n_features))]) for n_unit in config.n_units: model.add( keras.layers.Bidirectional( keras.layers.LSTM( n_unit, return_sequences=True, ) ) ) for n_unit in config.n_dense_units: model.add(keras.layers.Dense(n_unit, activation="selu")) model.add(keras.layers.Dense(1)) model.compile( optimizer=keras.optimizers.Adam(learning_rate=config.lr), loss='mae') return model def main(config: Dict[str, Any]): config = Config().update(config) seed_every_thing(seed=config.seed) reduce_tf_gpu_memory(gpu_id=config.gpu_id) basedir = Path(__file__).resolve().parent datadir = basedir / ".." / ".." / "data" logdir = basedir / ".." / ".." / "logs" / config.dirname cachedir = basedir / ".." / ".." / "cache" os.makedirs(logdir, exist_ok=True) config.to_json(logdir / "config.json") _, test_df, submission_df = fetch_custom_data(datadir=datadir, n_splits=config.n_splits) test_df["count"] = (np.arange(test_df.shape[0]) % 80).astype(int) test_preds_idx = test_df["count"] < config.cut test_df = test_df[test_preds_idx].reset_index(drop=True) test_df["pressure"] = 0 train_df = reduce_mem_usage(pd.read_csv(cachedir / f"train-10fold-debug{config.debug}.csv")) test_df = reduce_mem_usage(pd.read_csv(cachedir / f"test-10fold-debug{config.debug}.csv")) kfolds = train_df.iloc[0::config.cut]['kfold'].values features = list(train_df.drop(["kfold", "pressure"], axis=1).columns) pprint(features) print(len(features)) cont_features = [f for f in features if ("RC_" not in f) and ("R_" not in f) and ("C_" not in f) and ("u_out" not in f)] pprint(cont_features) RS = RobustScaler() train_df[cont_features] = RS.fit_transform(train_df[cont_features]) test_df[cont_features] = RS.transform(test_df[cont_features]) train_data, test_data = train_df[features].values, test_df[features].values train_data = train_data.reshape(-1, config.cut, train_data.shape[-1]) targets = train_df[["pressure"]].to_numpy().reshape(-1, config.cut) test_data = test_data.reshape(-1, config.cut, test_data.shape[-1]) with tf.device(f"/GPU:{config.gpu_id}"): valid_preds = np.empty_like(targets) test_preds = [] for fold in range(config.n_splits): train_idx, test_idx = (kfolds != fold), (kfolds == fold) print("-" * 15, ">", f"Fold {fold+1}", "<", "-" * 15) savedir = logdir / f"fold{fold}" os.makedirs(savedir, exist_ok=True) X_train, X_valid = train_data[train_idx], train_data[test_idx] y_train, y_valid = targets[train_idx], targets[test_idx] model = build_model(config=config, n_features=len(features)) # es = EarlyStopping( # monitor="val_loss", # patience=config.es_patience, # verbose=1, # mode="min", # restore_best_weights=True, # ) customL1 = CustomL1Loss( X_valid=X_valid, y_valid=y_valid, u_outs=X_valid[:, :, features.index("u_out")], filepath=savedir / "weights_custom_best.h5" ) check_point = ModelCheckpoint( filepath=savedir / "weights_best.h5", monitor="val_loss", verbose=1, save_best_only=True, mode="min", save_weights_only=True, ) schedular = ReduceLROnPlateau( mode="min", **config.schedular ) history = model.fit( X_train, y_train, validation_data=(X_valid, y_valid), epochs=config.epochs, batch_size=config.batch_size, callbacks=[check_point, schedular, customL1] ) model.save_weights(savedir / "weights_final.h5") model.load_weights(savedir / "weights_custom_best.h5") pd.DataFrame(history.history).to_csv(savedir / "log.csv") plot_metric(filepath=savedir / "log.csv", metric="loss") valid_preds[test_idx, :] = model.predict(X_valid).squeeze() test_preds.append(model.predict(test_data).squeeze().reshape(-1, 1).squeeze()) del model, X_train, X_valid, y_train, y_valid keras.backend.clear_session() gc.collect() pd.DataFrame(valid_preds).to_csv(logdir / "valid_preds.csv") if not config.debug: submission_df.loc[test_preds_idx, "pressure"] = np.median(test_preds, axis=0) submission_df.to_csv(logdir / "submission.csv", index=False) shutil.copyfile(Path(__file__), logdir / "script.py") if __name__ == "__main__": cnf_file = sys.argv[1] cfg_file_path = Path(__file__).resolve().parent / cnf_file with open(cfg_file_path, "rb") as f: config = json.load(f) main(config=config)

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zuuuubo.tetsu@outlook.jp

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/dataxHWSp2021/Practice Homework/Numpy_Pandas/student/tests/q1d.py

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2023-03-03T09:51:35.255111

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test = { 'name': 'q1d', 'points': 1, 'suites': [ { 'cases': [ { 'code': '>>> isinstance(d, float)\nTrue', 'hidden': False, 'locked': False}], 'scored': True, 'setup': '', 'teardown': '', 'type': 'doctest'}]}

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2023-08-12T04:07:37.381429

2021-09-12T21:18:18

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import base64 import logging from pathlib import Path from requests.models import Response from typing import NamedTuple, Tuple from urllib.parse import unquote # noinspection PyPep8Naming import xml.etree.ElementTree as ET __all__ = ["Header", "Merchant", "Buyer", "Item", "Disbursement", "SzamlazzResponse", "PdfDataMissingError", "EmailDetails", ] # "WayBill" logger = logging.getLogger(__name__) class PdfDataMissingError(Exception): pass class Header(NamedTuple): """<fejlec>""" creating_date: str = "" # <keltDatum>2020-01-20</keltDatum> payment_date: str = "" # <teljesitesDatum>2020-01-20</teljesitesDatum> due_date: str = "" # <fizetesiHataridoDatum>2020-01-20</fizetesiHataridoDatum> payment_type: str = "Átutalás" # <fizmod>Átutalás</fizmod> currency: str = "HUF" # <penznem>HUF</penznem> invoice_language: str = "hu" # <szamlaNyelve>hu</szamlaNyelve> // can be: de, en, it, hu, fr, ro, sk, hr invoice_comment: str = "" # <megjegyzes>Invoice comment</megjegyzes> name_of_bank: str = "MNB" # <arfolyamBank>MNB</arfolyamBank> exchange_rate: float = 0.0 # <arfolyam>0.0</arfolyam> order_number: str = "" # <rendelesSzam></rendelesSzam> pro_forma_number_ref: str = "" # <dijbekeroSzamlaszam></dijbekeroSzamlaszam> deposit_invoice: bool = False # <elolegszamla>false</elolegszamla> invoice_after_deposit_invoice: bool = False # <vegszamla>false</vegszamla> correction_invoice: bool = False # <helyesbitoszamla>false</helyesbitoszamla> number_of_corrected_invoice: str = "" # <helyesbitettSzamlaszam></helyesbitettSzamlaszam> proforma_invoice: bool = False # <dijbekero>false</dijbekero> invoice_prefix: str = "" # <szamlaszamElotag></szamlaszamElotag> invoice_number: str = "" # <szamlaszam>E-TST-2011-1</szamlaszam> // needed for reverse_invoice|storno only invoice_template: str = "" #  class Merchant(NamedTuple): """<elado>""" bank_name: str = "" # <bank>BB</bank> bank_account_number: str = "" # <bankszamlaszam>11111111-22222222-33333333</bankszamlaszam> reply_email_address: str = "" # <emailReplyto> </emailReplyto> email_subject: str = "" # <emailTargy>Invoice notification</emailTargy> email_text: str = "" # <emailSzoveg>mail text</emailSzoveg> class Buyer(NamedTuple): """<vevo>""" name: str = "" # <nev>Kovacs Bt.</nev> zip_code: str = "" # <irsz>2030</irsz> city: str = "" # <telepules>Érd</telepules> address: str = "" # <cim>Tárnoki út 23.</cim> email: str = "" # <email>buyer@example.com</email> send_email: bool = False # <sendEmail>false</sendEmail> tax_number: str = "" # <adoszam>12345678-1-42</adoszam> tax_number_eu: str = "" # <adoszamEU>HU55555555</adoszamEU> // needed for reverse_invoice|storno only delivery_name: str = "" # <postazasiNev>Kovács Bt. mailing name</postazasiNev> delivery_zip: str = "" # <postazasiIrsz>2040</postazasiIrsz> delivery_city: str = "" # <postazasiTelepules>Budaörs</postazasiTelepules> delivery_address: str = "" # <postazasiCim>Szivárvány utca 8.</postazasiCim> identification: str = "" # <azonosito>1234</azonosito> phone_number: str = "" # <telefonszam>Tel:+3630-555-55-55, Fax:+3623-555-555</telefonszam> comment: str = "" # <megjegyzes>Call extension 214 from the reception</megjegyzes> # class WayBill(NamedTuple): # """<fuvarlevel>""" #  # uticel: str = "" # # futarSzolgalat: str = "" # class ItemLedger(NamedTuple): # language=XML """ <sequence> <element name="gazdasagiEsem" type="string" maxOccurs="1" minOccurs="0"></element> <element name="gazdasagiEsemAfa" type="string" maxOccurs="1" minOccurs="0"></element> <element name="arbevetelFokonyviSzam" type="string" maxOccurs="1" minOccurs="0"></element> <element name="afaFokonyviSzam" type="string" maxOccurs="1" minOccurs="0"></element> <element name="elszDatumTol" type="date" maxOccurs="1" minOccurs="0"></element> <element name="elszDatumIg" type="date" maxOccurs="1" minOccurs="0"></element> </sequence> """ economic_event: str = "" # <gazdasagiesemeny></gazdasagiesemeny> economic_event_tax: str = "" # <gazdasagiesemenyafa></gazdasagiesemenyafa> sales_ledger_number: str = "" vat_ledger_number: str = "" settlement_date_from: str = "" settlement_date_to: str = "" class Item(NamedTuple): name: str = "" # <megnevezes>Elado izé</megnevezes> identifier: str = "" # <azonosito>ASD-123</azonosito> quantity: str = "" # <mennyiseg>1.0</mennyiseg> quantity_unit: str = "" # <mennyisegiEgyseg>db</mennyisegiEgyseg> unit_price: str = "" # <nettoEgysegar>10000</nettoEgysegar> vat_rate: str = "" # <afakulcs>27</afakulcs> margin_tax_base: float = "" # <arresAfaAlap>10.25</arresAfaAlap> net_price: str = "" # <nettoErtek>10000.0</nettoErtek> vat_amount: str = "" # <afaErtek>2700.0</afaErtek> gross_amount: str = "" # <bruttoErtek>12700.0</bruttoErtek> comment_for_item: str = "" # <megjegyzes>lorem ipsum</megjegyzes> item_ledger: ItemLedger = "" # <element name="tetelFokonyv" type="tns:tetelFokonyvTipus" maxOccurs="1" minOccurs="0"></element> class Disbursement(NamedTuple): date: str title: str amount: float description: str = "" class EmailDetails(NamedTuple): addresses: str reply_to_address: str subject: str body_text: str = "" class SzamlazzResponse: def __init__(self, response: Response, xml_namespace: str, ): self.xml_namespace = xml_namespace self.__response = response self.__action_success: bool = False content_type = response.headers.get("Content-Type") if content_type == "application/octet-stream": # Parse XML and map into class members root = ET.fromstring(self.__response.text) self.__pdf: str = self.__get_tag_text(root, "pdf") self.__pdf_bytes: bytes = b"" self.__action_success: bool = True if (self.__get_tag_text(root, "sikeres") == "true") else False else: self.__pdf_bytes: bytes = response.content self.__pdf: str = base64.b64encode(self.__pdf_bytes).decode("ascii") # Error Handling self.error_code: str = response.headers.get("szlahu_error_code") self.error_message: str = response.headers.get("szlahu_error") if self.error_message: self.error_message = unquote(self.error_message) self.http_request_success: str = "false" if self.error_code else "true" # Extract Details self.invoice_number: str = response.headers.get("szlahu_szamlaszam") self.invoice_net_price: str = response.headers.get("szlahu_nettovegosszeg") self.invoice_gross_price: str = response.headers.get("szlahu_bruttovegosszeg") self.receivables: str = response.headers.get("szlahu_kintlevoseg") self.buyer_account_url: str = response.headers.get("szlahu_vevoifiokurl") if self.buyer_account_url: self.buyer_account_url = unquote(response.headers.get("szlahu_vevoifiokurl")) self.payment_method: str = response.headers.get("szlahu_fizetesmod") self.__has_errors = self.error_code or self.error_message if self.has_errors: logger.error(f"Error Code: {self.error_code}") logger.error(f"Error Message: {self.error_message}") @property def action_success(self) -> bool: return self.__action_success @property def has_errors(self): return self.__has_errors @property def ok(self): """ Shortcut to the original response's attribute with the same name """ return self.__response.ok @property def response(self) -> Response: """ Original HTTP Response object returned by the requests package :return: requests.models.Response """ return self.__response @property def text(self) -> str: """ Shortcut to the original response's attribute with the same name """ return self.__response.text def get_pdf_base64(self) -> str: """ Get PDF from response in Base64 format :return: PDF (in Base64 format) :rtype: str """ if (not self.__pdf) and (not self.__pdf_bytes): raise PdfDataMissingError("No PDF was returned. Check the value of szamlaLetoltes|invoice_download") return self.__pdf def get_pdf_bytes(self) -> bytes: pdf_base64 = self.get_pdf_base64() return base64.b64decode(pdf_base64) if pdf_base64 else self.__pdf_bytes def write_pdf_to_disk(self, pdf_output_path: Path): if not pdf_output_path.parent.exists(): raise FileNotFoundError(f"Output file's parent folder is missing: {pdf_output_path.parent.as_posix()}") data = self.get_pdf_bytes() with open(pdf_output_path, "wb+") as f: f.write(data) def print_errors(self) -> Tuple[str, str]: """ Prints the returned error_code and error_message :return: Tuple[error_code, error_message] """ if self.has_errors: print("error_code:", self.error_code) print("error_message:", self.error_message) return self.error_code, self.error_message def __get_tag_text(self, root: ET.Element, tag_name): tag = root.find(f"{self.xml_namespace}{tag_name}") return tag.text if tag is not None else None

[ "kristof.daja@semcon.com" ]

kristof.daja@semcon.com

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/app/campaign_rec.py

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tb16/fundraising-campaign

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refs/heads/master

2021-01-11T18:17:27.329103

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import pandas as pd import numpy as np import requests import bs4 from string import punctuation import cPickle as pickle from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics.pairwise import linear_kernel from tokenizer import tokenize ''' campaign recommendation using cosine similarity of vectorised stories. ''' df = pd.read_csv('../data/featured_data1.csv') def bag_of_words(df): ''' Applies Tfidf vectorizer to descriptions in the dataframe. Returns the vectorizer instance and sparse matrix. ''' vectorizer = TfidfVectorizer(max_features = 4000, decode_error='ignore', max_df = 0.90, min_df= 2, stop_words = 'english', tokenizer = tokenize) vectorizer.fit(df.story) sparse = vectorizer.fit_transform(df.story) return vectorizer, sparse def pickle_vec(vectorizer, sparse): ''' Pickle the vectorizer instance and sparse matrix ''' v = open('../data/vectorizer.pkl', 'w') pickle.dump(vectorizer, v) v.close() s = open('../data/sparse_mat.pkl', 'w') pickle.dump(sparse, s) s.close() def get_success_index(df): ''' returns the indices of successsful campaigns from the dataframe ''' indices = df[df.percentage>=0.5].index.tolist() return np.array(indices) def download(url, *a, **kw): ''' download and returns the html parsed beautifulsoup ''' _user_agent = ('Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5) AppleWebKit/537.36 ' '(KHTML, like Gecko) Chrome/53.0.2785.116 Safari/537.36') kw.setdefault('headers', {})['User-Agent'] = _user_agent return bs4.BeautifulSoup(requests.get(url, *a, **kw).text, 'html.parser') def search_url(title): ''' url search for gofund me website given a title ''' search_url = 'https://www.gofundme.com/mvc.php?' soup = download(search_url, params={'term' : title, 'route': 'search'}) for tile in soup.select('.search_tile'): try: return 'https:'+tile.select('.name')[0]['href'] except: continue return 'link not found' def similar_campaign(vector, vectorizer, sparse_mat): ''' Finds the similar success story to the given campaign. returns top 3 campaigns and keywords. similarity from cosine similarity with tfidf vectors. top words from tfidf values of a story ''' feature_names = np.array(vectorizer.get_feature_names()) similarity = linear_kernel(vector, sparse_mat) top_indices_story = np.argsort(similarity.flatten())[-1::-1] success_indices = [] for top_index in top_indices_story: if df.percentage[top_index] >= 0.5: success_indices.append(top_index) keywords = [] for idx in success_indices[:3]: keywords_indices = np.argsort(sparse_mat[idx].toarray()).flatten()[-1:-11:-1] keywords.append(' '.join(feature_names[keywords_indices])) print success_indices[:3] output_df = df.iloc[success_indices[:3]] output_df['keywords'] = keywords output_df['url'] = map(search_url, output_df.title) output_df.reset_index(inplace = True) return output_df[['category', 'days','title', 'story', 'friends','shares', 'goal', 'percentage', 'keywords', 'url']] if __name__ == '__main__': # df = df[df['percentage'] >= 0.50] # df.to_csv('../data/featured_data_success.csv', index = False) vectorizer, sparse = bag_of_words(df) pickle_vec(vectorizer, sparse)

[ "Komal@Teks-MacBook-Pro.local" ]

Komal@Teks-MacBook-Pro.local

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/Ex09_4_uses_only.py

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ishwarjindal/Think-Python

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#Author : Ishwar Jindal #Created On : 26-May-2019 05:19 PM IST #Purpose : Find words that uses only the specificed letters def has_no_char(word, char_to_be_missing): for char in word: if char == char_to_be_missing: return False return True def uses_only(word, letters_to_use): for letter in word: if has_no_char(letters_to_use, letter) == False: continue else: return False return True print("main started") total = 0 matched = 0 letters_to_use = input("Enter the superset of letters to be used in word : ") fin = open("words.txt") for line in fin: total = total + 1 word = line.strip() if uses_only(word, letters_to_use): matched = matched + 1 print(str.format("{0} does have all letters in {1}", word, letters_to_use)) print(str.format("{0} words out of {1} i.e. {2}% have all their letters in {3}", matched, total, round(matched*100/total, 2), letters_to_use)) print("main ended")

[ "ishwarjindal@MacBook-Pro-4.local" ]

ishwarjindal@MacBook-Pro-4.local

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/project2/rl_federated_nas/glace_search_svhn.py

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no_license

dixiyao/CS385

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import os import sys import time import glob import numpy as np import torch import utils import logging import argparse import torch.nn as nn import torch.utils import torch.nn.functional as F import torchvision.datasets as dset import torch.backends.cudnn as cudnn import random import copy from torch.autograd import Variable from model_search import Network from model_search_local import MaskedNetwork from architect import Architect from federated import sample_mask, client_update, fuse_weight_gradient, init_gradient, client_weight_param, extract_index from data_distribution import _data_transforms_cifar10, even_split, none_iid_split from noniid_svhn import client_data parser = argparse.ArgumentParser("cifar") parser.add_argument('--data', type=str, default='../data', help='location of the data corpus') parser.add_argument('--client_batch_size', type=int, default=256, help='batch size') parser.add_argument('--learning_rate', type=float, default=0.025, help='init learning rate') parser.add_argument('--learning_rate_min', type=float, default=0.001, help='min learning rate') parser.add_argument('--momentum', type=float, default=0.9, help='momentum') parser.add_argument('--weight_decay', type=float, default=3e-4, help='weight decay') parser.add_argument('--report_freq', type=float, default=1000, help='report frequency') parser.add_argument('--gpu', type=int, default=0, help='gpu device id') parser.add_argument('--epochs', type=int, default=10000, help='num of training epochs') parser.add_argument('--init_channels', type=int, default=16, help='num of init channels') parser.add_argument('--layers', type=int, default=8, help='total number of layers') parser.add_argument('--model_path', type=str, default='saved_models', help='path to save the model') parser.add_argument('--cutout', action='store_true', default=False, help='use cutout') parser.add_argument('--cutout_length', type=int, default=16, help='cutout length') parser.add_argument('--drop_path_prob', type=float, default=0.3, help='drop path probability') parser.add_argument('--save', type=str, default='EXP', help='experiment name') parser.add_argument('--seed', type=int, default=2, help='random seed') parser.add_argument('--grad_clip', type=float, default=5, help='gradient clipping') parser.add_argument('--train_portion', type=float, default=0.9, help='portion of training data') parser.add_argument('--unrolled', action='store_true', default=False, help='use one-step unrolled validation loss') parser.add_argument('--arch_learning_rate', type=float, default=3e-3, help='learning rate for arch encoding') parser.add_argument('--arch_weight_decay', type=float, default=1e-3, help='weight decay for arch encoding') parser.add_argument('--arch_baseline_decay', type=float, default=0.99, help='weight decay for reward baseline') parser.add_argument('--client', type=int, default=10, help='number of clients') parser.add_argument('--glace_epoch', type=int, default=10000, help='number of epoch for freezing alpha') parser.add_argument('--non_iid', action='store_true', default=False, help='use non iid dataset') parser.add_argument('--fed_non_iid',action='store_true',default=False,help='use non iid distribution in FedNAS(CVPR20)') parser.add_argument('--fed_selection',default=None,help='prepared distribution') args = parser.parse_args() args.save = 'search-{}-{}'.format(args.save, time.strftime("%Y%m%d-%H%M%S")) utils.create_exp_dir(args.save, scripts_to_save=glob.glob('*.py')) log_format = '%(asctime)s %(message)s' logging.basicConfig(stream=sys.stdout, level=logging.INFO, format=log_format, datefmt='%m/%d %I:%M:%S %p') fh = logging.FileHandler(os.path.join(args.save, 'log.txt')) fh.setFormatter(logging.Formatter(log_format)) logging.getLogger().addHandler(fh) CIFAR_CLASSES = 10 def main(): if not torch.cuda.is_available(): logging.info('no gpu device available') sys.exit(1) np.random.seed(args.seed) torch.cuda.set_device(args.gpu) cudnn.benchmark = True torch.manual_seed(args.seed) cudnn.enabled=True torch.cuda.manual_seed(args.seed) logging.info('gpu device = %d' % args.gpu) logging.info("args = %s", args) criterion = nn.CrossEntropyLoss() # criterion = criterion.cuda() global_model = Network(args.init_channels, CIFAR_CLASSES, args.layers, criterion) # global_model = global_model.cuda() # logging.info("param size = %fMB", utils.count_parameters_in_MB(global_model)) global_optimizer = torch.optim.SGD( global_model.parameters(), args.learning_rate, momentum=args.momentum, weight_decay=args.weight_decay) #train_transform, valid_transform = _data_transforms_cifar10() #dataset = dset.SVHN(root='../data', download=True, transform=train_transform) ''' testds=[] for (img, label) in dataset: if label in [0,2,3,5,9]: testds.append((img,label)) random.shuffle(testds) dataset=copy.deepcopy(testds) ''' train_queues = [] if args.fed_non_iid: train_queues=client_data(args.data,args.client,args.client_batch_size) else: if args.non_iid: user_split = none_iid_split(dataset, num_user=args.client) else: user_split = even_split(dataset, args.client) for i in range(args.client): train_data = user_split[i] num_train = len(train_data) indices = list(range(num_train)) train_queue = torch.utils.data.DataLoader( train_data, batch_size=args.client_batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(indices), pin_memory=True, num_workers=2) train_queues.append(train_queue) # valid_data = user_split[-1] # num_train = len(valid_data) # indices = list(range(num_train)) # valid_queue = torch.utils.data.DataLoader( # valid_data, batch_size=32, # sampler=torch.utils.data.sampler.SubsetRandomSampler(indices), # pin_memory=True, num_workers=2) scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( global_optimizer, int(args.epochs), eta_min=args.learning_rate_min) global_architect = Architect(global_model, args) init_gradient(global_model) global_accuracy = [] client_accuracy = [] total_loss = [] for epoch in range(args.epochs): scheduler.step() lr = scheduler.get_lr()[0] logging.info('epoch %d lr %e', epoch, lr) # genotype = global_model.genotype() # logging.info('genotype = %s', genotype) # client_models = [] epoch_acc = [] epoch_loss = [] epoch_index_normal = [] epoch_index_reduce = [] for client_idx in range(args.client): mask_normal = sample_mask(global_model.alphas_normal) mask_reduce = sample_mask(global_model.alphas_reduce) index_normal = extract_index(mask_normal) index_reduce = extract_index(mask_reduce) client_model = MaskedNetwork(args.init_channels, CIFAR_CLASSES, args.layers, criterion, mask_normal, mask_reduce) client_models.append(client_model) epoch_index_normal.append(index_normal) epoch_index_reduce.append(index_reduce) # copy weight of global model to client models # alphas in client models are actually gates, and equal to 1 forever client_weight_param(global_model, client_models) for client_idx in range(args.client): client_model = client_models[client_idx] client_models[client_idx], acc, loss = client_update(train_queues[client_idx], client_model, criterion) epoch_acc.append(acc) epoch_loss.append(loss) avg_acc = float(torch.mean(torch.Tensor(epoch_acc))) avg_loss = float(torch.mean(torch.Tensor(epoch_loss))) logging.info("client accuracy: " + str(epoch_acc)) logging.info("client loss: " + str(epoch_loss)) logging.info("client accuracy: "+str(avg_acc)+" , loss: "+str(avg_loss)) client_accuracy.append(avg_acc) total_loss.append(avg_loss) fuse_weight_gradient(global_model,client_models) global_optimizer.step() global_optimizer.zero_grad() # if epoch > args.glace_epoch: # global_architect.step(epoch_acc,epoch_index_normal,epoch_index_reduce) if (epoch+1) % args.report_freq == 0: # valid_acc, valid_obj = infer(valid_queue,global_model,criterion) # logging.info('valid_acc %f', valid_acc) # global_accuracy.append(valid_acc) logging.info("alphas normal") logging.info(F.softmax(global_model.alphas_normal, dim=-1)) logging.info("alphas reduce") logging.info(F.softmax(global_model.alphas_reduce, dim=-1)) utils.save(global_model, os.path.join(args.save, 'weights_epoch'+str(epoch)+'.pt')) logging.info("*** final log ***") logging.info("loss") logging.info(total_loss) logging.info("client accuracy") logging.info(client_accuracy) logging.info("global accuracy") logging.info(global_accuracy) def infer(valid_queue, model, criterion): objs = utils.AvgrageMeter() top1 = utils.AvgrageMeter() top5 = utils.AvgrageMeter() model = model.cuda() model.eval() for step, (input, target) in enumerate(valid_queue): input = Variable(input, volatile=True).cuda() target = Variable(target, volatile=True).cuda(async=True) logits = model(input) loss = criterion(logits, target) prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5)) n = input.size(0) objs.update(loss.data.item(), n) top1.update(prec1.data.item(), n) top5.update(prec5.data.item(), n) # if step % args.report_freq == 0: # logging.info('valid %03d %e %f %f', step, objs.avg, top1.avg, top5.avg) model = model.cpu() return top1.avg, objs.avg if __name__ == '__main__': main()

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alexander@schrepfer.us