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ml6team

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the-stack-smol-python

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ML6 Team 85

# encoding: utf-8 """ This module defines the things that are used in setup.py for building JupyterLab This includes: * Functions for finding things like packages, package data, etc. * A function for checking dependencies. """ # Copyright (c) Jupyter Development Team. # Distributed under the terms of the Modified BSD License. import io import json import os import pipes import sys import shutil import tempfile import os.path as osp from os.path import join as pjoin from distutils import log from distutils.cmd import Command from distutils.version import LooseVersion from setuptools.command.egg_info import egg_info from setuptools.command.bdist_egg import bdist_egg from subprocess import check_call if sys.platform == 'win32': from subprocess import list2cmdline else: def list2cmdline(cmd_list): return ' '.join(map(pipes.quote, cmd_list)) # the name of the project name = 'jupyterlab' here = osp.dirname(osp.abspath(__file__)) is_repo = osp.exists(pjoin(here, '.git')) version_ns = {} with io.open(pjoin(here, name, '_version.py'), encoding="utf8") as f: exec(f.read(), {}, version_ns) def run(cmd, *args, **kwargs): """Echo a command before running it""" log.info('> ' + list2cmdline(cmd)) kwargs['shell'] = (sys.platform == 'win32') return check_call(cmd, *args, **kwargs) #--------------------------------------------------------------------------- # Find packages #--------------------------------------------------------------------------- def find_packages(): """ Find all of the packages. """ packages = [] for dir, subdirs, files in os.walk('jupyterlab'): if 'node_modules' in subdirs: subdirs.remove('node_modules') package = dir.replace(osp.sep, '.') if '__init__.py' not in files: # not a package continue packages.append(package) return packages #--------------------------------------------------------------------------- # Find package data #--------------------------------------------------------------------------- def find_package_data(): """ Find package_data. """ theme_dirs = [] for dir, subdirs, files in os.walk(pjoin('jupyterlab', 'themes')): slice_len = len('jupyterlab' + os.sep) theme_dirs.append(pjoin(dir[slice_len:], '*')) schema_dirs = [] for dir, subdirs, files in os.walk(pjoin('jupyterlab', 'schemas')): slice_len = len('jupyterlab' + os.sep) schema_dirs.append(pjoin(dir[slice_len:], '*')) return { 'jupyterlab': ['build/*', '*.js', 'package.app.json', 'yarn.lock', 'yarn.app.lock', '.yarnrc' ] + theme_dirs + schema_dirs } def find_data_files(): """ Find data_files. """ if not os.path.exists(pjoin('jupyterlab', 'build')): return [] files = [] static_files = os.listdir(pjoin('jupyterlab', 'build')) files.append(('share/jupyter/lab/static', ['jupyterlab/build/%s' % f for f in static_files])) for dir, subdirs, fnames in os.walk(pjoin('jupyterlab', 'schemas')): dir = dir.replace(os.sep, '/') schema_files = [] for fname in fnames: schema_files.append('%s/%s' % (dir, fname)) slice_len = len('jupyterlab/') files.append(('share/jupyter/lab/%s' % dir[slice_len:], schema_files)) for dir, subdirs, fnames in os.walk(pjoin('jupyterlab', 'themes')): dir = dir.replace(os.sep, '/') themes_files = [] for fname in fnames: themes_files.append('%s/%s' % (dir, fname)) slice_len = len('jupyterlab/') files.append(('share/jupyter/lab/%s' % dir[slice_len:], themes_files)) return files def js_prerelease(command, strict=False): """decorator for building minified js/css prior to another command""" class DecoratedCommand(command): def run(self): jsdeps = self.distribution.get_command_obj('jsdeps') if not is_repo and all(osp.exists(t) for t in jsdeps.targets): # sdist, nothing to do command.run(self) return try: self.distribution.run_command('jsdeps') except Exception as e: missing = [t for t in jsdeps.targets if not osp.exists(t)] if strict or missing: log.warn('js check failed') if missing: log.error('missing files: %s' % missing) raise e else: log.warn('js check failed (not a problem)') log.warn(str(e)) command.run(self) return DecoratedCommand def update_package_data(distribution): """update build_py options to get package_data changes""" build_py = distribution.get_command_obj('build_py') build_py.finalize_options() class CheckAssets(Command): description = 'check for required assets' user_options = [] # Representative files that should exist after a successful build targets = [ pjoin(here, 'jupyterlab', 'build', 'release_data.json'), pjoin(here, 'jupyterlab', 'build', 'main.bundle.js'), pjoin(here, 'jupyterlab', 'schemas', '@jupyterlab', 'shortcuts-extension', 'plugin.json'), pjoin(here, 'jupyterlab', 'themes', '@jupyterlab', 'theme-light-extension', 'images', 'jupyterlab.svg') ] def initialize_options(self): pass def finalize_options(self): pass def run(self): for t in self.targets: if not osp.exists(t): msg = 'Missing file: %s' % t raise ValueError(msg) target = pjoin(here, 'jupyterlab', 'build', 'release_data.json') with open(target) as fid: data = json.load(fid) if (LooseVersion(data['version']) != LooseVersion(version_ns['__version__'])): msg = 'Release assets version mismatch, please run npm publish' raise ValueError(msg) # update package data in case this created new files update_package_data(self.distribution) class bdist_egg_disabled(bdist_egg): """Disabled version of bdist_egg Prevents setup.py install performing setuptools' default easy_install, which it should never ever do. """ def run(self): sys.exit("Aborting implicit building of eggs. Use `pip install .` to install from source.") class custom_egg_info(egg_info): """Prune JavaScript folders from egg_info to avoid locking up pip. """ def run(self): folders = ['examples', 'packages', 'test', 'node_modules'] folders = [f for f in folders if os.path.exists(pjoin(here, f))] tempdir = tempfile.mkdtemp() for folder in folders: shutil.move(pjoin(here, folder), tempdir) value = egg_info.run(self) for folder in folders: shutil.move(pjoin(tempdir, folder), here) shutil.rmtree(tempdir) return value

bualpha/jupyterlab

setupbase.py

Python

# # SPDX-License-Identifier: MIT # from augur.augurplugin import AugurPlugin # from augur.application import Application # class HousekeeperPlugin(AugurPlugin): # """ # This plugin serves as an example as to how to load plugins into Augur # """ # def __init__(self, augur_app): # super().__init__(augur_app) # self.__housekeeper = self.__call__() # def __call__(self): # from .housekeeper import Housekeeper # return Housekeeper( # user=self._augur.read_config('Database', 'user', 'AUGUR_DB_USER', 'root'), # password=self._augur.read_config('Database', 'password', 'AUGUR_DB_PASS', 'password'), # host=self._augur.read_config('Database', 'host', 'AUGUR_DB_HOST', '127.0.0.1'), # port=self._augur.read_config('Database', 'port', 'AUGUR_DB_PORT', '3306'), # dbname=self._augur.read_config('Database', 'database', 'AUGUR_DB_NAME', 'msr14') # ) # HousekeeperPlugin.augur_plugin_meta = { # 'name': 'housekeeper', # 'datasource': True # } # Application.register_plugin(HousekeeperPlugin) # __all__ = ['HousekeeperPlugin']

0WeiyuFeng0/augur

augur/housekeeper/__init__.py

Python

""" Video Link: https://youtu.be/1s-Tj65AKZA """ from seleniumbase import __version__ from seleniumbase import BaseCase class HackTests(BaseCase): def test_all_your_base_are_belong_to_us(self): # First make sure that seleniumbase 1.65.0 or newer is installed version = __version__.split(".") if version[0] == "1" and int(version[1]) < 65: raise Exception( "This test requires minimum seleniumbase version: 1.65.0" ) self.set_window_size(1220, 740) ayb = "ALL YOUR BASE" abtu = "ARE BELONG TO US" aybabtu = "%s %s" % (ayb, abtu) sb_banner_logo = "//seleniumbase.io/cdn/img/sb_logo_10.png" sb_dashboard_logo = "//seleniumbase.io/img/dash_pie_3.png" yt_chip = "#chips yt-chip-cloud-chip-renderer:nth-of-type" wiki = "https://en.wikipedia.org/wiki/All_your_base_are_belong_to_us" self.open(wiki) self.click_if_visible('button[aria-label="Close"]') self.set_text_content("h1#firstHeading", aybabtu) self.set_text_content("#ca-history a", aybabtu) self.set_text_content('#n-mainpage-description a', "ALL") self.set_text_content('#n-contents a', "YOUR") self.set_text_content('#n-currentevents a', "BASE") self.set_text_content('#n-randompage a', "ARE") self.set_text_content('#n-aboutsite a', "BELONG") self.set_text_content('#n-contactpage a', "TO") self.set_text_content('#n-sitesupport a', "US") self.set_text_content('.tocsection-1 span.toctext', "ALL") self.set_text_content('.tocsection-2 span.toctext', "YOUR") self.set_text_content('.tocsection-3 span.toctext', "BASE") self.set_text_content('.tocsection-4 span.toctext', "ARE") self.set_text_content('.tocsection-5 span.toctext', "BELONG") self.set_text_content('.tocsection-6 span.toctext', "TO") self.set_text_content('.tocsection-7 span.toctext', "US") self.highlight("h1#firstHeading", loops=2, scroll=False) self.highlight("#ca-history a", loops=2, scroll=False) self.highlight("nav#p-navigation", loops=2, scroll=False) self.highlight("div#toc", loops=2, scroll=False) self.highlight('.tocsection-1 span.toctext', loops=1, scroll=False) self.highlight('.tocsection-2 span.toctext', loops=1, scroll=False) self.highlight('.tocsection-3 span.toctext', loops=2, scroll=False) self.highlight('.tocsection-4 span.toctext', loops=1, scroll=False) self.highlight('.tocsection-5 span.toctext', loops=1, scroll=False) self.highlight('.tocsection-6 span.toctext', loops=1, scroll=False) self.highlight('.tocsection-7 span.toctext', loops=2, scroll=False) zoom_in = 'div.thumbinner{zoom: 1.4;-moz-transform: scale(1.4);}' self.add_css_style(zoom_in) self.highlight("div.thumbinner", loops=8, scroll=False) self.open("https://www.apple.com/store") self.set_text_content("div.rs-shop-subheader", aybabtu) self.set_text_content('#shelf-1 a[href*="mac"]', "ALL") self.set_text_content('#shelf-1 a[href*="iphone"]', "YOUR") self.set_text_content('#shelf-1 a[href*="ipad"]', "BASE") self.set_text_content('#shelf-1 a[href*="watch"]', "ARE") self.set_text_content('#shelf-1 a[href*="airpods"]', "BELONG") self.set_text_content('#shelf-1 a[href*="airtag"]', "TO") self.set_text_content('#shelf-1 a[href*="tv"]', "US") self.set_text_content('#shelf-1 a[href*="homepod"]', ".") self.set_text_content("h2", aybabtu + ". ") self.highlight("div.rs-shop-subheader", loops=6, scroll=False) self.highlight("#shelf-1", loops=2, scroll=False) self.highlight('#shelf-1 a[href*="mac"]', loops=1, scroll=False) self.highlight('#shelf-1 a[href*="iphone"]', loops=1, scroll=False) self.highlight('#shelf-1 a[href*="ipad"]', loops=3, scroll=False) self.highlight('#shelf-1 a[href*="watch"]', loops=1, scroll=False) self.highlight('#shelf-1 a[href*="airpods"]', loops=1, scroll=False) self.highlight('#shelf-1 a[href*="airtag"]', loops=1, scroll=False) self.highlight('#shelf-1 a[href*="tv"]', loops=3, scroll=False) self.highlight("h2", loops=9, scroll=False) self.open("https://google.com/ncr") self.set_text_content('a[href*="about.google"]', ayb) self.set_text_content('a[href*="store.google"]', abtu) self.set_text_content('a[href*="mail.google.com"]', ayb) self.set_text_content('a[href*="google.com/img"]', abtu) self.set_attributes('[value="Google Search"]', "value", ayb) self.set_attributes('[value="I\'m Feeling Lucky"]', "value", abtu) zoom_in = 'a{zoom: 1.2;-moz-transform: scale(1.2);}' self.add_css_style(zoom_in) zoom_in = ( '[value="ALL YOUR BASE"]{zoom: 1.3;-moz-transform: scale(1.3);}' '[value="ARE BELONG TO US"]{zoom: 1.3;-moz-transform: scale(1.3);}' ) self.add_css_style(zoom_in) self.highlight('a[href*="about.google"]', loops=3) self.highlight('a[href*="store.google"]', loops=3) self.highlight('a[href*="mail.google.com"]', loops=3) self.highlight('a[href*="google.com/img"]', loops=3) self.highlight('form[role="search"]', loops=8) self.open("https://twitter.com/") if not self.is_element_visible('a[href*="w/signup"] span'): self.refresh() if self.is_element_visible('a[href*="w/signup"] span'): self.set_text_content('a[href*="w/signup"] span', aybabtu) self.highlight('a[href*="w/signup"] span', loops=6, scroll=False) self.highlight('a[href*="w/signup"]', loops=6, scroll=False) self.open("https://www.youtube.com/") self.set_text_content('%s(1)' % yt_chip, "ALL") self.set_text_content('%s(2)' % yt_chip, "YOUR") self.set_text_content('%s(3)' % yt_chip, "BASE") self.set_text_content('%s(4)' % yt_chip, "ARE") self.set_text_content('%s(5)' % yt_chip, "BELONG") self.set_text_content('%s(6)' % yt_chip, "TO") self.set_text_content('%s(7)' % yt_chip, "US") self.set_text_content('%s(8)' % yt_chip, "!") self.set_text_content('%s(9)' % yt_chip, "!") self.set_text_content('%s(10)' % yt_chip, "!") self.click_if_visible("#dismiss-button") self.click_if_visible('button[aria-label="Close"]') self.highlight("#scroll-container", loops=5, scroll=False) self.highlight('%s(1)' % yt_chip, loops=1, scroll=False) self.highlight('%s(2)' % yt_chip, loops=1, scroll=False) self.highlight('%s(3)' % yt_chip, loops=3, scroll=False) self.highlight('%s(4)' % yt_chip, loops=1, scroll=False) self.highlight('%s(5)' % yt_chip, loops=1, scroll=False) self.highlight('%s(6)' % yt_chip, loops=1, scroll=False) self.highlight('%s(7)' % yt_chip, loops=3, scroll=False) self.highlight("#scroll-container", loops=7, scroll=False) self.open("https://github.com/features/actions") self.set_text_content('a[href="/team"]', ayb) self.set_text_content('a[href="/enterprise"]', abtu) self.set_text_content('h1 span:nth-child(1)', ayb) self.set_text_content('h1 span:nth-of-type(2)', "ARE") self.set_text_content('h1 span:nth-of-type(3)', "BELONG") self.set_text_content('h1 span:nth-of-type(4)', "TO") self.set_text_content('h1 span:nth-of-type(5)', "US") self.type('input[name="q"]', aybabtu.lower()) self.click("h1", scroll=False) self.highlight("nav", loops=5, scroll=False) self.highlight('input[name="q"]', loops=5, scroll=False) self.highlight("h1", loops=8, scroll=False) self.open("https://dev.to/top/infinity") self.click_if_visible('button[aria-label="Close campaign banner"]') self.set_text_content('nav a[data-text="Relevant"]', "ALL") self.set_text_content('nav a[data-text="Latest"]', "YOUR") self.set_text_content('nav a[data-text="Top"]', "BASE") self.set_text_content('nav a[data-text="Week"]', "ARE") self.set_text_content('nav a[data-text="Month"]', "BELONG") self.set_text_content('nav a[data-text="Year"]', "TO") self.set_text_content('nav a[data-text="Infinity"]', "US") self.set_text_content('aside a[class*="tful"]', aybabtu) self.set_text_content('aside a[aria-label="Create new account"]', ayb) self.set_text_content('aside a[aria-label="Log in"]', abtu) self.set_text_content('aside a[class*="tful"]:nth-child(2)', aybabtu) self.set_text_content('aside a[class*="tful"]:nth-child(3)', aybabtu) self.set_text_content('aside a[class*="tful"]:nth-child(4)', aybabtu) self.set_text_content('aside a[class*="tful"]:nth-child(5)', aybabtu) self.set_attribute("a.crayons-avatar img", "src", sb_dashboard_logo) self.set_text_content('.profile-preview-card button', "SeleniumBase") self.set_text_content('h2.crayons-story__title a', aybabtu) self.type('input[name="q"]', aybabtu) self.highlight('input[name="q"]', loops=4, scroll=False) self.highlight('[aria-label="Primary sidebar"] div div', scroll=False) self.highlight('nav a[data-text="Relevant"]', loops=1, scroll=False) self.highlight('nav a[data-text="Latest"]', loops=1, scroll=False) self.highlight('nav a[data-text="Top"]', loops=2, scroll=False) self.highlight('nav a[data-text="Week"]', loops=1, scroll=False) self.highlight('nav a[data-text="Month"]', loops=1, scroll=False) self.highlight('nav a[data-text="Year"]', loops=1, scroll=False) self.highlight('nav a[data-text="Infinity"]', loops=2, scroll=False) self.highlight('aside[id*="sidebar"] section', loops=5, scroll=False) self.highlight("div.crayons-story__body", loops=7, scroll=False) self.open("https://azure.microsoft.com/en-us/services/playfab/") self.set_text_content("h1", aybabtu) self.set_text_content('a[aria-label*="Try PlayF"]', ayb) self.set_text_content('a[aria-label*="Sign in to"]', abtu) self.set_text_content('span:contains("Chat with Sales")', aybabtu) self.highlight("h1", loops=6, scroll=False) self.highlight('a[aria-label*="Try PlayF"]', loops=4, scroll=False) self.highlight('a[aria-label*="Sign in to"]', loops=4, scroll=False) self.highlight('button#live-engage-btn', loops=6, scroll=False) self.open("https://www.snapchat.com/") self.set_text_content("h1", ayb) self.set_text_content("form .button-large span span", abtu) zoom_in = 'a.button-large span{zoom: 1.2;-moz-transform: scale(1.2);}' self.add_css_style(zoom_in) self.highlight("h1", loops=6, scroll=False) self.highlight("form .button-large span span", loops=8, scroll=False) self.open("https://store.steampowered.com/") self.set_text_content('div.content a[href*="/about/"]', " ") self.set_text_content('div.content a[href*="help.steam"]', aybabtu) self.set_text_content("#foryou_tab a", "ALL") self.set_text_content("#noteworthy_tab a", "YOUR BASE") self.set_text_content("#genre_tab a", "ARE") self.set_text_content('span:contains("Points Shop")', "BELONG") self.set_text_content('span:contains("News")', "TO") self.set_text_content('span:contains("Labs")', "US") self.set_value("input#store_nav_search_term", ayb + " . . . .") self.highlight('div.content a[href*="help.steam"]', loops=6) self.highlight('#store_nav_area', loops=2, scroll=False) self.highlight("#foryou_tab a", loops=1, scroll=False) self.highlight("#noteworthy_tab a", loops=3, scroll=False) self.highlight("#genre_tab a", loops=1, scroll=False) self.highlight('span:contains("BELONG")', loops=1, scroll=False) self.highlight('span:contains("TO")', loops=1, scroll=False) self.highlight('span:contains("US")', loops=2, scroll=False) self.js_click('input[id*="nav_search"]') self.highlight('input[id*="nav_search"]', loops=6, scroll=False) self.open("https://xkcd.com/286/") self.set_text_content('a[href="/archive"]', "ALL") self.set_text_content('a[href*="what-if"]', "YOUR") self.set_text_content('a[href*="//blag."]', "BASE") self.set_text_content('a[href*="/about"]', abtu) self.remove_element('li:contains("Feed")') self.remove_element('li:contains("TW")') self.remove_element('li:contains("Books")') self.remove_element('li:contains("What")') self.remove_element('li:contains("WI")') self.set_attributes("#news img", "src", sb_banner_logo) self.set_text_content('#ctitle', aybabtu) self.set_text_content('a[rel="prev"]', "All") self.set_text_content('a[href*="random"]', "Your") self.set_text_content('a[rel="next"]', "Base") self.highlight("#topLeft ul", loops=5, scroll=False) self.highlight('a[href="/archive"]', loops=1, scroll=False) self.highlight('a[href*="what-if"]', loops=1, scroll=False) self.highlight('a[href*="//blag."]', loops=2, scroll=False) self.highlight('a[href*="/about"]', loops=5, scroll=False) self.highlight('a[rel="prev"]', loops=1, scroll=False) self.highlight('a[href*="random"]', loops=1, scroll=False) self.highlight('a[rel="next"]', loops=3, scroll=False) self.highlight("#ctitle", loops=7, scroll=False) self.open("https://www.nintendo.com/whatsnew/") self.set_text_content('button[aria-label="Search"]', aybabtu) self.set_text_content('button[data-section="newsevents"]', aybabtu) self.set_text_content("h2", aybabtu) self.highlight('div.search-flex', loops=4, scroll=False) self.highlight('button[data-section*="news"]', loops=4, scroll=False) self.highlight("h2", loops=6, scroll=False) self.open("https://support.gog.com/hc/en-us?product=gog") self.set_text_content("div.intro-title", aybabtu) self.set_text_content("h4", aybabtu) self.highlight("div.intro-title", loops=8, scroll=False) self.highlight("h4", loops=8, scroll=False) self.open("https://slack.com/help/articles/204714258-Giphy-for-Slack") self.set_text_content("h1", aybabtu) self.set_text_content('a[prettyslug="getting-started"]', "ALL") self.set_text_content('a[prettyslug="using-slack"]', "YOUR") self.set_text_content('a[prettyslug="your-profile"]', "BASE") self.set_text_content('a[prettyslug="connect-tools"]', "ARE") self.set_text_content('a[prettyslug="administration"]', "BELONG") self.set_text_content('a[prettyslug="tutorials"]', "TO US") self.highlight("h1", loops=4, scroll=False) self.highlight("div#global_menu", loops=2, scroll=False) self.highlight('a[prettyslug*="g-started"]', loops=1, scroll=False) self.highlight('a[prettyslug="using-slack"]', loops=1, scroll=False) self.highlight('a[prettyslug="your-profile"]', loops=2, scroll=False) self.highlight('a[prettyslug="connect-tools"]', loops=1, scroll=False) self.highlight('a[prettyslug="administration"]', loops=1, scroll=False) self.highlight('a[prettyslug="tutorials"]', loops=2, scroll=False) self.open("https://kubernetes.io/") self.set_text_content('nav a[href="/docs/"]', "ALL") self.set_text_content('nav a[href="/blog/"]', "YOUR") self.set_text_content('nav a[href="/training/"]', "BASE") self.set_text_content('nav a[href="/partners/"]', "ARE") self.set_text_content('nav a[href="/community/"]', "BELONG") self.set_text_content('nav a[href="/case-studies/"]', "TO") self.set_text_content('nav #navbarDropdown', "US") self.set_text_content('nav #navbarDropdownMenuLink', ".") if self.is_element_visible("h1"): self.set_text_content("h1", aybabtu) self.highlight("nav ul.navbar-nav", loops=3, scroll=False) self.highlight('nav a[href="/docs/"]', loops=1, scroll=False) self.highlight('nav a[href="/blog/"]', loops=1, scroll=False) self.highlight('nav a[href="/training/"]', loops=2, scroll=False) self.highlight('nav a[href="/partners/"]', loops=1, scroll=False) self.highlight('nav a[href="/community/"]', loops=1, scroll=False) self.highlight('nav a[href="/case-studies/"]', loops=1, scroll=False) self.highlight('nav #navbarDropdown', loops=2, scroll=False) if self.is_element_visible("h1"): self.highlight('h1', loops=6, scroll=False) self.open("https://www.selenium.dev/") self.set_attributes("a.dropdown-toggle", "class", "nav-link") self.set_text_content('li a:contains("About")', "ALL") self.set_text_content('li a:contains("Downloads")', "YOUR") self.set_text_content('li a:contains("Documentation")', "BASE") self.set_text_content('li a:contains("Projects")', "ARE") self.set_text_content('li a:contains("Support")', "BELONG") self.set_text_content('li a:contains("Blog")', "TO") self.set_text_content('li a:contains("English")', "US") self.set_text_content("div.lead", aybabtu) self.set_text_content("h2", aybabtu) zoom_in = 'div.lead{zoom: 1.25;-moz-transform: scale(1.25);}' self.add_css_style(zoom_in) self.highlight("div#main_navbar", loops=1, scroll=False) self.highlight('li a:contains("ALL")', loops=1, scroll=False) self.highlight('li a:contains("YOUR")', loops=1, scroll=False) self.highlight('li a:contains("BASE")', loops=2, scroll=False) self.highlight('li a:contains("ARE")', loops=1, scroll=False) self.highlight('li a:contains("BELONG")', loops=1, scroll=False) self.highlight('li a:contains("TO")', loops=1, scroll=False) self.highlight('li a:contains("US")', loops=2, scroll=False) self.highlight("div.lead", loops=6, scroll=False) self.highlight("h2", loops=8, scroll=False) self.open("https://www.python.org/") self.set_text_content('a[class="donate-button"]', ayb) self.set_text_content("#about a", "ALL") self.set_text_content("#downloads a", "YOUR") self.set_text_content("#documentation a", "BASE") self.set_text_content("#community a", "ARE") self.set_text_content("#success-stories a", "BELONG") self.set_text_content("#news a", "TO") self.set_text_content("#events a", "US") self.highlight('a[class="donate-button"]', loops=4, scroll=False) self.highlight("nav#mainnav", loops=5, scroll=False) self.highlight("#about a", loops=1, scroll=False) self.highlight("#downloads a", loops=1, scroll=False) self.highlight("#documentation a", loops=2, scroll=False) self.highlight("#community a", loops=1, scroll=False) self.highlight("#success-stories a", loops=1, scroll=False) self.highlight("#news a", loops=1, scroll=False) self.highlight("#events a", loops=2, scroll=False) self.open("https://docs.pytest.org/") self.set_text_content("h1", "pytest: " + aybabtu) self.highlight("h1", loops=10, scroll=False) self.open("https://wordpress.com/") self.set_text_content('a[title="Plans & Pricing"]', aybabtu) self.set_text_content('a[title="Get Started"]', ayb) self.set_text_content("p.no-widows", aybabtu) self.set_text_content("a#lpc-button", "Automate with SeleniumBase") self.highlight('a[title="Plans & Pricing"]', loops=6, scroll=False) self.highlight('a[title="Get Started"]', loops=4, scroll=False) self.highlight("p.no-widows", loops=8, scroll=False) self.highlight("a#lpc-button", loops=4, scroll=False) self.open("https://seleniumbase.com/") self.set_text_content("h1", aybabtu) self.highlight("h1", loops=10, scroll=False) self.open("https://pypi.org/") self.set_text_content('a[href="/sponsors/"]', aybabtu) self.set_text_content("h1", aybabtu) self.set_value("input#search", aybabtu, scroll=False) self.highlight('a[href="/sponsors/"]', loops=6, scroll=False) self.highlight("h1", loops=6, scroll=False) self.highlight("input#search", loops=8, scroll=False) self.open("https://www.atlassian.com/software/jira") self.set_text_content('a[href*="jira/pricing"]', ayb) self.set_text_content('a[href*="jira/enterprise"]', abtu) self.set_text_content('a[href="/software/jira/features"]', "") self.set_text_content('a[href="/software/jira/guides"]', "") self.set_text_content("h1", ayb) self.set_text_content('div.xs-none-bottom a[href*="free"]', abtu) self.highlight("ul.imkt-navbar__link-list", loops=2, scroll=False) self.highlight('a[href*="jira/pricing"]', loops=3, scroll=False) self.highlight('a[href*="jira/enterprise"]', loops=3, scroll=False) self.highlight("h1", loops=3, scroll=False) self.highlight('div.xs-none-bottom a[href*="free"]', scroll=False) self.open("https://status.iboss.com/ibcloud/app/cloudStatus.html") self.set_text_content('div[translate*="cloudStatus"]', ayb) self.set_text_content('div[translate*="maintenance"]', "ARE") self.set_text_content('div[translate*="advisory"]', "BELONG") self.set_text_content('div[translate*="incident"]', "TO US") self.set_text_content("h1", "Cloud Status - " + aybabtu) self.highlight("nav div.ibcloud-header-contents", loops=3) self.highlight('div[translate*="cloudStatus"]', loops=4) self.highlight('div[translate*="maintenance"]', loops=1) self.highlight('div[translate*="advisory"]', loops=1) self.highlight('div[translate*="incident"]', loops=3) self.highlight("h1", loops=9, scroll=False) self.open("https://git-scm.com/") self.set_text_content("span#tagline", aybabtu) self.set_text_content("#nav-about h3", ayb) self.set_text_content("#nav-documentation h3", abtu) self.highlight("span#tagline", loops=8, scroll=False) self.highlight("#nav-about h3", loops=5, scroll=False) self.highlight("#nav-documentation h3", loops=6, scroll=False) self.open("https://teamtreehouse.com/") self.set_text_content("li.nav-item-free-trial", aybabtu) self.set_text_content("h1", aybabtu) self.set_text_content("h2", aybabtu) self.set_text_content("p.homepage-signup-form-banner", aybabtu) self.highlight("li.nav-item-free-trial", loops=6, scroll=False) self.highlight("h1", loops=6, scroll=False) self.highlight('p[class*="signup-form"]', loops=8, scroll=False) self.open("https://pragprog.com/") self.set_text_content("header p", aybabtu) zoom_in = 'header p{zoom: 1.35;-moz-transform: scale(1.35);}' self.add_css_style(zoom_in) self.highlight("header p", loops=10, scroll=False) self.open("https://seleniumbase.io/") self.set_text_content("h1", aybabtu) self.highlight("h1", loops=10, scroll=False)

GoVanguard/SeleniumBase

examples/hack_the_planet.py

Python

from os.path import join as pjoin from scrapy.spiders import ( Rule, CrawlSpider, ) from scrapy import exceptions from scrapy.linkextractors import LinkExtractor from django.conf import settings from django.core.cache import caches import tldextract from core.extractors import ck0tp from crawler import items lockin = caches['lock_in_task'] EOAIENT = settings.ENDPOINTS['ck0tp'] ENDPOINT = EOAIENT['ENDPOINT'] ENDPATH = EOAIENT['ENDPATH'] DIRECTIVE = EOAIENT['DIRECTIVE'] DIRECTIVES = EOAIENT['DIRECTIVES'] class Ck0tp(CrawlSpider): name = 'ck0tp' allowed_domains = [ tldextract.extract(ENDPOINT).registered_domain ] start_urls = [ pjoin(ENDPOINT, DIRECTIVE), ENDPOINT, ] + [ pjoin(ENDPOINT, d) for d in DIRECTIVES ] rules = ( Rule( LinkExtractor(allow=(r'{}/\d+/?$'.format(ENDPATH), )), callback='parse_video', follow=True ), ) def __init__(self, *args, **kwargs): super(Ck0tp, self).__init__(*args, **kwargs) # unduplicate lock if not lockin.add(self.__class__.__name__, 'true', 60 * 60 * 24 * 5): raise exceptions.CloseSpider('already launched spider') def closed(self, *args, **kwargs): lockin.delete(self.__class__.__name__) def parse_video(self, response): vid = ck0tp.Video(response.url) return items.Entry(vid.info())

ikeikeikeike/scrape-django-app

scrape/crawler/crawler/spiders/ck0tp.py

Python

import numpy as np import pandas as pd from sklearn.tree import DecisionTreeRegressor from sklearn.model_selection import KFold import plotly.express as px from plotly.subplots import make_subplots import plotly.graph_objects as go # import data and preprocess it def preprocessing(file_name: str): # data import fish_df = pd.read_csv(file_name) fish_df = pd.get_dummies(fish_df, columns=['Species'], prefix='Species') return fish_df # train-test split by a percentage. # input: dataframe, label column name, split ration, and random state # returns: x_train, x_test, y_train, y_test def split_df(user_df: pd.DataFrame, label_name: str, split_ratio=0.8, random_value=42): x_train = user_df.sample(frac=split_ratio, random_state=random_value) x_test = user_df.drop(x_train.index) return x_train.drop(label_name, axis=1), x_test.drop(label_name, axis=1), pd.DataFrame( x_train[label_name]), pd.DataFrame(x_test[label_name]) # Create as arrays of trees in a given size and depth def create_random_forest(forest_size: int, max_depth: int, random_state_local: int): random_forest = [] for i in range(0, forest_size, 1): random_forest.append(DecisionTreeRegressor(criterion='friedman_mse', max_depth=max_depth, random_state=random_state_local)) return random_forest # train trees in a forest by fitting each tree to the previous tree's error # input: forest of trees, initial training guess, x and y databases, alpha coefficient. # returns: trained forest, initial average value, r_matrix of solutions and mse_list of the results (mean square error) def train_forest(random_forest: list, initial_average_weight: float, x_df: pd.DataFrame, y_df: pd.DataFrame, alpha: float = 0.1): # initial average weight and residuals to be used in the 1st tree predictions = np.ones(len(y_df))*initial_average_weight residuals = np.array(y_df['Weight'])-predictions residuals_matrix = [residuals] # calculates the first mse value mse_list = [(np.square(residuals)).sum()/len(predictions)] for tree in random_forest: # train the current stump tree.fit(x_df, residuals) # predict results based on its training error residuals = tree.predict(x_df) # record residuals and calculate mse residuals_matrix.append(residuals) mse_list.append((np.square(residuals)).sum()/len(predictions)) # update predictions and calculate new residuals predictions = predictions + alpha * residuals residuals = np.array(y_df['Weight']) - predictions return random_forest, predictions, residuals_matrix, mse_list # predict test database by the trained random forest # input: forest of trees, initial training guess, x and y databases. # returns: mse_list of the forest (mean square error) def test_forest(random_forest: list, initial_average_weight: float, x_df: pd.DataFrame, y_df: pd.DataFrame, alpha: float = 0.1): predictions = np.ones(len(y_df))*initial_average_weight mse_list = [(np.square(np.array(y_df['Weight']) - predictions)).sum()/len(predictions)] for tree in random_forest: predictions = predictions + alpha * tree.predict(x_df) mse_list.append((np.square(np.array(y_df['Weight']) - predictions)).sum()//len(predictions)) return predictions, mse_list def main(): # data import and preprocessing fish_df = preprocessing("Fish.csv") # splitting of the data x_train, x_test, y_train, y_test = split_df(fish_df, 'Weight', 0.8, 42) # setting up a random forest: #forest_size_list = [4, 5, 6, 7, 8] # variable calibrated by KFold train-validate forest_size = 20 # max_depth_list = [1, 2, 3, 4, 5] # variable calibrated by KFold train-validate max_depth = 3 random_state_local = 42 random_forest = create_random_forest(forest_size, max_depth, random_state_local) #%% Train #alpha_list = [0.1, 0.3, 0.5, 0.7, 0.9] # variable calibrated by KFold train-validate alpha = 0.5 # gradiant coefficient kf = KFold(n_splits=2, shuffle=True, random_state=42) for train_index, test_index in kf.split(x_train, y_train): X_train, X_validate = x_train.iloc[train_index], x_train.iloc[test_index] Y_train, Y_validate = y_train.iloc[train_index], y_train.iloc[test_index] # first guess initial_average_weight = np.average(Y_train['Weight'].tolist()) # train forest random_forest, predictions_train, r_matrix, mse_list_train = train_forest(random_forest, initial_average_weight, X_train, Y_train, alpha) # validate predictions_validate, mse_list_validate = test_forest(random_forest, initial_average_weight, X_validate, Y_validate, alpha) results = pd.DataFrame(data=np.arange(0, forest_size+1, 1), columns=['tree_intervals']) results['Train'] = mse_list_train results['Validation'] = mse_list_validate fig = px.scatter(results, x='tree_intervals', y=['Train', 'Validation'], size='tree_intervals') fig.update_layout(xaxis_title="Amount of Intervals (num.)", yaxis_title="mean square error") fig.show() #%% Test predictions_test, mse_list_test = test_forest(random_forest, initial_average_weight, x_test, y_test, alpha) # %% plot success rate vs tree intervals fig = make_subplots(rows=1, cols=3, subplot_titles=('Train', 'Validation', 'Test'), x_title='Amount of Intervals (num.)', y_title='mean square error') results = pd.DataFrame(data=np.arange(0, forest_size+1, 1), columns=['tree_intervals']) results['Train'] = mse_list_train fig.add_trace(go.Scatter(x=results['tree_intervals'], y=results['Train'], name='Train'), row=1, col=1) results = pd.DataFrame(data=np.arange(0, forest_size + 1, 1), columns=['tree_intervals']) results['Validation'] = mse_list_validate fig.add_trace(go.Scatter(x=results['tree_intervals'], y=results['Validation'], name='Validation'), row=1, col=2) results = pd.DataFrame(data=np.arange(0, forest_size + 1, 1), columns=['tree_intervals']) results['Test'] = mse_list_test fig.add_trace(go.Scatter(x=results['tree_intervals'], y=results['Test'], name='Test'), row=1, col=3) fig.update_layout(title_text="Random Forest Gradient Boosting") fig.show() if __name__ == '__main__': main()

ofir-frd/Machine-Learning-Bootcamp

gradient-boosting/main.py

Python

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you 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 os DJANGO_APPS = [ "kafka" ] REQUIRES_HADOOP = False MENU_INDEX = 100 NICE_NAME = "Kafka" ICON = "kafka/art/icon_kafka_24.png" IS_URL_NAMESPACED = True PROJECT_ROOT = os.path.dirname(os.path.realpath(__file__)) METRICS_INI = os.path.join(PROJECT_ROOT, 'metrics.ini')

Code-distancing/kafka-hue

kafka/src/kafka/settings.py

Python

# -*- coding: utf-8 -*- SUCCESSFUL_TERMINAL_STATUSES = ('complete', ) UNSUCCESSFUL_TERMINAL_STATUSES = ('cancelled', 'unsuccessful') CONTRACT_REQUIRED_FIELDS = [ 'awardID', 'contractID', 'items', 'suppliers', 'value', 'dateSigned', #'documents' ] CONTRACT_NOT_REQUIRED_FIELDS = [ 'contractNumber', 'title', 'title_en', 'title_ru', 'description', 'description_en', 'description_ru' ]

Scandie/openregistry.convoy

openregistry/convoy/loki/constants.py

Python

""" Plugin for Czech TV (Ceska televize). Following channels are working: * CT1 - https://www.ceskatelevize.cz/porady/ct1/ * CT2 - https://www.ceskatelevize.cz/porady/ct2/ * CT24 - https://ct24.ceskatelevize.cz/#live * CT sport - https://www.ceskatelevize.cz/sport/zive-vysilani/ * CT Decko - https://decko.ceskatelevize.cz/zive * CT Art - https://www.ceskatelevize.cz/porady/art/ Additionally, videos from iVysilani archive should work as well. """ import json import logging import re from html import unescape as html_unescape from urllib.parse import quote from streamlink.plugin import Plugin, PluginError, pluginmatcher from streamlink.plugin.api import useragents, validate from streamlink.stream import DASHStream, HLSStream log = logging.getLogger(__name__) @pluginmatcher(re.compile( r'https?://([\w-]+\.)*ceskatelevize\.cz' )) class Ceskatelevize(Plugin): ajax_url = 'https://www.ceskatelevize.cz/ivysilani/ajax/get-client-playlist' _player_re = re.compile( r'ivysilani/embed/iFramePlayer[^"]+' ) _hash_re = re.compile( r'hash:"([0-9a-z]+)"' ) _playlist_info_re = re.compile( r'{"type":"([a-z]+)","id":"([0-9]+)"' ) _playlist_url_schema = validate.Schema({ validate.optional("streamingProtocol"): validate.text, "url": validate.any( validate.url(), "Error", "error_region" ) }) _playlist_schema = validate.Schema({ "playlist": [{ validate.optional("type"): validate.text, "streamUrls": { "main": validate.url(), } }] }) def _get_streams(self): self.session.http.headers.update({'User-Agent': useragents.IPAD}) self.session.http.verify = False log.warning('SSL certificate verification is disabled.') # fetch requested url and find playlist info response = self.session.http.get(self.url) info = self._find_playlist_info(response) if not info: # do next try with new API def _fallback_api(*args, **kwargs): self.api2 = CeskatelevizeAPI2(self.session, self.url, *args, **kwargs) return self.api2._get_streams() # playlist info not found, let's try to find player url player_url = self._find_player_url(response) if not player_url: log.debug('Cannot find playlist info or player url, do next try with new API') return _fallback_api(res=response) # get player url and try to find playlist info in it response = self.session.http.get(player_url) info = self._find_playlist_info(response) if not info: log.debug('Cannot find playlist info in the player url, do next try with new API') return _fallback_api() log.trace('{0!r}'.format(info)) data = { 'playlist[0][type]': info['type'], 'playlist[0][id]': info['id'], 'requestUrl': '/ivysilani/embed/iFramePlayer.php', 'requestSource': 'iVysilani', 'type': 'html' } headers = { 'x-addr': '127.0.0.1', } # fetch playlist url response = self.session.http.post( self.ajax_url, data=data, headers=headers ) json_data = self.session.http.json(response, schema=self._playlist_url_schema) log.trace('{0!r}'.format(json_data)) if json_data['url'] in ['Error', 'error_region']: log.error('This stream is not available') return # fetch playlist response = self.session.http.post(json_data['url']) json_data = self.session.http.json(response, schema=self._playlist_schema) log.trace('{0!r}'.format(json_data)) playlist = json_data['playlist'][0]['streamUrls']['main'] return HLSStream.parse_variant_playlist(self.session, playlist) @classmethod def _find_playlist_info(cls, response): """ Finds playlist info (type, id) in HTTP response. :param response: Response object. :returns: Dictionary with type and id. """ values = {} matches = cls._playlist_info_re.search(response.text) if matches: values['type'] = matches.group(1) values['id'] = matches.group(2) return values @classmethod def _find_player_url(cls, response): """ Finds embedded player url in HTTP response. :param response: Response object. :returns: Player url (str). """ url = '' matches = cls._player_re.search(response.text) if matches: tmp_url = matches.group(0).replace('&amp;', '&') if 'hash' not in tmp_url: # there's no hash in the URL, try to find it matches = cls._hash_re.search(response.text) if matches: url = tmp_url + '&hash=' + matches.group(1) else: url = tmp_url return 'http://ceskatelevize.cz/' + url class CeskatelevizeAPI2: _player_api = 'https://playlist.ceskatelevize.cz/' _url_re = re.compile(r'http(s)?://([^.]*.)?ceskatelevize.cz') _playlist_info_re = re.compile(r'{\s*"type":\s*"([a-z]+)",\s*"id":\s*"(\w+)"') _playlist_schema = validate.Schema({ "CODE": validate.contains("OK"), "RESULT": { "playlist": [{ "streamUrls": { "main": validate.url(), } }] } }) _ctcomp_re = re.compile(r'data-ctcomp="Video"\sdata-video-id="(?P<val1>[^"]*)"\sdata-ctcomp-data="(?P<val2>[^"]+)">') _ctcomp_schema = validate.Schema( validate.text, validate.transform(_ctcomp_re.findall), validate.transform(lambda vl: [{"video-id": v[0], "ctcomp-data": json.loads(html_unescape(v[1]))} for v in vl]) ) _playlist_info_schema = validate.Schema({ "type": validate.text, "id": validate.any(validate.text, int), "key": validate.text, "date": validate.text, "requestSource": validate.text, "drm": int, validate.optional("canBePlay"): int, validate.optional("assetId"): validate.text, "quality": validate.text, validate.optional("region"): int }) def __init__(self, session, url, res=None): self.session = session self.url = url self.response = res def _get_streams(self): if self.response is None: infos = self.session.http.get(self.url, schema=self._ctcomp_schema) else: infos = self.session.http.json(self.response, schema=self._ctcomp_schema) if not infos: # playlist infos not found raise PluginError('Cannot find playlist infos!') vod_prio = len(infos) == 2 for info in infos: try: pl = info['ctcomp-data']['source']['playlist'][0] except KeyError: raise PluginError('Cannot find playlist info!') pl = self._playlist_info_schema.validate(pl) if vod_prio and pl['type'] != 'VOD': continue log.trace('{0!r}'.format(info)) if pl['type'] == 'LIVE': data = { "contentType": "live", "items": [{ "id": pl["id"], "assetId": pl["assetId"], "key": pl["key"], "playerType": "dash", "date": pl["date"], "requestSource": pl["requestSource"], "drm": pl["drm"], "quality": pl["quality"], }] } elif pl['type'] == 'VOD': data = { "contentType": "vod", "items": [{ "id": pl["id"], "key": pl["key"], "playerType": "dash", "date": pl["date"], "requestSource": pl["requestSource"], "drm": pl["drm"], "canBePlay": pl["canBePlay"], "quality": pl["quality"], "region": pl["region"] }] } headers = { "Content-Type": "application/x-www-form-urlencoded; charset=UTF-8", } data = json.dumps(data) response = self.session.http.post( self._player_api, data="data={}".format(quote(data)), headers=headers ) json_data = self.session.http.json(response, schema=self._playlist_schema) log.trace('{0!r}'.format(json_data)) playlist = json_data['RESULT']['playlist'][0]['streamUrls']['main'] yield from DASHStream.parse_manifest(self.session, playlist).items() __plugin__ = Ceskatelevize

Erk-/streamlink

src/streamlink/plugins/ceskatelevize.py

Python

from __future__ import absolute_import from sentry.testutils.cases import RuleTestCase from sentry.rules.conditions.event_attribute import (EventAttributeCondition, MatchType) class EventAttributeConditionTest(RuleTestCase): rule_cls = EventAttributeCondition def get_event(self): event = self.create_event( message='hello world', platform='php', data={ 'type': 'error', 'sentry.interfaces.Http': { 'method': 'GET', 'url': 'http://example.com', }, 'sentry.interfaces.User': { 'id': '1', 'ip_address': '127.0.0.1', 'email': 'foo@example.com', 'username': 'foo', }, 'sentry.interfaces.Exception': { 'values': [ { 'type': 'SyntaxError', 'value': 'hello world', 'stacktrace': { 'frames': [ { 'filename': 'example.php', 'module': 'example', 'context_line': 'echo "hello";', } ] } }, ], }, 'tags': [('environment', 'production')], 'extra': { 'foo': { 'bar': 'baz', }, 'biz': ['baz'], 'bar': 'foo', } }, ) return event def test_render_label(self): rule = self.get_rule(data={ 'match': MatchType.EQUAL, 'attribute': u'\xc3', 'value': u'\xc4', }) assert rule.render_label() == u'An event\'s \xc3 value equals \xc4' def test_equals(self): event = self.get_event() rule = self.get_rule(data={ 'match': MatchType.EQUAL, 'attribute': 'platform', 'value': 'php', }) self.assertPasses(rule, event) rule = self.get_rule(data={ 'match': MatchType.EQUAL, 'attribute': 'platform', 'value': 'python', }) self.assertDoesNotPass(rule, event) def test_does_not_equal(self): event = self.get_event() rule = self.get_rule(data={ 'match': MatchType.NOT_EQUAL, 'attribute': 'platform', 'value': 'php', }) self.assertDoesNotPass(rule, event) rule = self.get_rule(data={ 'match': MatchType.NOT_EQUAL, 'attribute': 'platform', 'value': 'python', }) self.assertPasses(rule, event) def test_starts_with(self): event = self.get_event() rule = self.get_rule(data={ 'match': MatchType.STARTS_WITH, 'attribute': 'platform', 'value': 'ph', }) self.assertPasses(rule, event) rule = self.get_rule(data={ 'match': MatchType.STARTS_WITH, 'attribute': 'platform', 'value': 'py', }) self.assertDoesNotPass(rule, event) def test_ends_with(self): event = self.get_event() rule = self.get_rule(data={ 'match': MatchType.ENDS_WITH, 'attribute': 'platform', 'value': 'hp', }) self.assertPasses(rule, event) rule = self.get_rule(data={ 'match': MatchType.ENDS_WITH, 'attribute': 'platform', 'value': 'thon', }) self.assertDoesNotPass(rule, event) def test_contains(self): event = self.get_event() rule = self.get_rule(data={ 'match': MatchType.CONTAINS, 'attribute': 'platform', 'value': 'p', }) self.assertPasses(rule, event) rule = self.get_rule(data={ 'match': MatchType.CONTAINS, 'attribute': 'platform', 'value': 'z', }) self.assertDoesNotPass(rule, event) def test_does_not_contain(self): event = self.get_event() rule = self.get_rule(data={ 'match': MatchType.NOT_CONTAINS, 'attribute': 'platform', 'value': 'p', }) self.assertDoesNotPass(rule, event) rule = self.get_rule(data={ 'match': MatchType.NOT_CONTAINS, 'attribute': 'platform', 'value': 'z', }) self.assertPasses(rule, event) def test_message(self): event = self.get_event() rule = self.get_rule(data={ 'match': MatchType.EQUAL, 'attribute': 'message', 'value': 'hello world', }) self.assertPasses(rule, event) rule = self.get_rule(data={ 'match': MatchType.EQUAL, 'attribute': 'message', 'value': 'php', }) self.assertDoesNotPass(rule, event) def test_environment(self): event = self.get_event() rule = self.get_rule(data={ 'match': MatchType.EQUAL, 'attribute': 'environment', 'value': 'production', }) self.assertPasses(rule, event) rule = self.get_rule(data={ 'match': MatchType.EQUAL, 'attribute': 'environment', 'value': 'staging', }) self.assertDoesNotPass(rule, event) def test_http_method(self): event = self.get_event() rule = self.get_rule(data={ 'match': MatchType.EQUAL, 'attribute': 'http.method', 'value': 'get', }) self.assertPasses(rule, event) rule = self.get_rule(data={ 'match': MatchType.EQUAL, 'attribute': 'http.method', 'value': 'post', }) self.assertDoesNotPass(rule, event) def test_http_url(self): event = self.get_event() rule = self.get_rule(data={ 'match': MatchType.EQUAL, 'attribute': 'http.url', 'value': 'http://example.com', }) self.assertPasses(rule, event) rule = self.get_rule(data={ 'match': MatchType.EQUAL, 'attribute': 'http.url', 'value': 'http://foo.com', }) self.assertDoesNotPass(rule, event) def test_user_id(self): event = self.get_event() rule = self.get_rule(data={ 'match': MatchType.EQUAL, 'attribute': 'user.id', 'value': '1', }) self.assertPasses(rule, event) rule = self.get_rule(data={ 'match': MatchType.EQUAL, 'attribute': 'user.id', 'value': '2', }) self.assertDoesNotPass(rule, event) def test_user_ip_address(self): event = self.get_event() rule = self.get_rule(data={ 'match': MatchType.EQUAL, 'attribute': 'user.ip_address', 'value': '127.0.0.1', }) self.assertPasses(rule, event) rule = self.get_rule(data={ 'match': MatchType.EQUAL, 'attribute': 'user.ip_address', 'value': '2', }) self.assertDoesNotPass(rule, event) def test_user_email(self): event = self.get_event() rule = self.get_rule(data={ 'match': MatchType.EQUAL, 'attribute': 'user.email', 'value': 'foo@example.com', }) self.assertPasses(rule, event) rule = self.get_rule(data={ 'match': MatchType.EQUAL, 'attribute': 'user.email', 'value': '2', }) self.assertDoesNotPass(rule, event) def test_user_username(self): event = self.get_event() rule = self.get_rule(data={ 'match': MatchType.EQUAL, 'attribute': 'user.username', 'value': 'foo', }) self.assertPasses(rule, event) rule = self.get_rule(data={ 'match': MatchType.EQUAL, 'attribute': 'user.username', 'value': '2', }) self.assertDoesNotPass(rule, event) def test_exception_type(self): event = self.get_event() rule = self.get_rule(data={ 'match': MatchType.EQUAL, 'attribute': 'exception.type', 'value': 'SyntaxError', }) self.assertPasses(rule, event) rule = self.get_rule(data={ 'match': MatchType.EQUAL, 'attribute': 'exception.type', 'value': 'TypeError', }) self.assertDoesNotPass(rule, event) def test_exception_value(self): event = self.get_event() rule = self.get_rule(data={ 'match': MatchType.EQUAL, 'attribute': 'exception.value', 'value': 'hello world', }) self.assertPasses(rule, event) rule = self.get_rule(data={ 'match': MatchType.EQUAL, 'attribute': 'exception.value', 'value': 'foo bar', }) self.assertDoesNotPass(rule, event) def test_stacktrace_filename(self): event = self.get_event() rule = self.get_rule(data={ 'match': MatchType.EQUAL, 'attribute': 'stacktrace.filename', 'value': 'example.php', }) self.assertPasses(rule, event) rule = self.get_rule(data={ 'match': MatchType.EQUAL, 'attribute': 'stacktrace.filename', 'value': 'foo.php', }) self.assertDoesNotPass(rule, event) def test_stacktrace_module(self): event = self.get_event() rule = self.get_rule(data={ 'match': MatchType.EQUAL, 'attribute': 'stacktrace.module', 'value': 'example', }) self.assertPasses(rule, event) rule = self.get_rule(data={ 'match': MatchType.EQUAL, 'attribute': 'stacktrace.module', 'value': 'foo', }) self.assertDoesNotPass(rule, event) def test_stacktrace_code(self): event = self.get_event() rule = self.get_rule(data={ 'match': MatchType.EQUAL, 'attribute': 'stacktrace.code', 'value': 'echo "hello";', }) self.assertPasses(rule, event) rule = self.get_rule(data={ 'match': MatchType.EQUAL, 'attribute': 'stacktrace.code', 'value': 'foo', }) self.assertDoesNotPass(rule, event) def test_extra_simple_value(self): event = self.get_event() rule = self.get_rule(data={ 'match': MatchType.EQUAL, 'attribute': 'extra.bar', 'value': 'foo', }) self.assertPasses(rule, event) rule = self.get_rule(data={ 'match': MatchType.EQUAL, 'attribute': 'extra.bar', 'value': 'bar', }) self.assertDoesNotPass(rule, event) def test_extra_nested_value(self): event = self.get_event() rule = self.get_rule(data={ 'match': MatchType.EQUAL, 'attribute': 'extra.foo.bar', 'value': 'baz', }) self.assertPasses(rule, event) rule = self.get_rule(data={ 'match': MatchType.EQUAL, 'attribute': 'extra.foo.bar', 'value': 'bar', }) self.assertDoesNotPass(rule, event) def test_extra_nested_list(self): event = self.get_event() rule = self.get_rule(data={ 'match': MatchType.EQUAL, 'attribute': 'extra.biz', 'value': 'baz', }) self.assertPasses(rule, event) rule = self.get_rule(data={ 'match': MatchType.EQUAL, 'attribute': 'extra.biz', 'value': 'bar', }) self.assertDoesNotPass(rule, event) def test_event_type(self): event = self.get_event() rule = self.get_rule(data={ 'match': MatchType.EQUAL, 'attribute': 'type', 'value': 'error', }) self.assertPasses(rule, event) rule = self.get_rule(data={ 'match': MatchType.EQUAL, 'attribute': 'type', 'value': 'csp', }) self.assertDoesNotPass(rule, event)

AlexWayfer/sentry

tests/sentry/rules/conditions/test_event_attribute.py

Python

import re class HeadersFormat(object): @staticmethod def call(header): return HeadersFormat.format(re.sub(r'^HTTP(?:_|-)', '', header, flags=re.I)) @staticmethod def format(header): return '-'.join([v.capitalize() for v in re.split(r'_|-', header)])

castle/castle-python

castle/headers/format.py

Python

a = input() b = input() something = a > b if something: print(a) c = input() <caret>

06needhamt/intellij-community

python/testData/codeInsight/mlcompletion/isAfterIfWithoutElseAfterSameLevelLine.py

Python

#basic example of dict synat my_dict = {'key1':'value1','key2':'value2','key3':'value3'} print(my_dict) print(my_dict['key3']) #xmpl 2 prices = {'apple':100,'banana':60,'gavava':90,'rice':50} print(prices['rice'])

alok-techqware/basic_python_practicse

python_basics/Dictionary/dict.py

Python

import numpy as np from time import sleep import struct import matplotlib.pyplot as plt # input raw samples from MCU # in_data = 'out/data_raw.txt' in_data = 'out/8bit.txt' fs = 5000 in_bits = 8 # load file raw = np.loadtxt(in_data) # Stats print("Max=%d Min=%d Mean=%d swing=%d %.1fbits" % \ (np.max(raw), np.min(raw), np.mean(raw), np.max(raw) - np.min(raw), np.log2(np.max(raw) - np.min(raw)))) # generate different bit audio data_depth = {} print(raw) data_depth['16bit'] = 2**(in_bits-16)*(raw / (2**(in_bits-16))).astype('int') print(data_depth['16bit']) data_depth['10bit'] = 2**(in_bits-10)*(raw / (2**(in_bits-10))).astype('int') data_depth['8bit'] = 2**(in_bits-8)*(raw / (2**(in_bits-8))).astype('int') data_depth['7bit'] = 2**(in_bits-7)*(raw / (2**(in_bits-7))).astype('int') data_depth['6bit'] = 2**(in_bits-6)*(raw / (2**(in_bits-6))).astype('int') data_depth['2bit'] = 2**(in_bits-2)*(raw / (2**(in_bits-2))).astype('int') # normalize and zero mean all for key in data_depth: data_depth[key] = data_depth[key] - np.mean(data_depth[key]) data_depth[key] = data_depth[key] / np.max(np.abs(data_depth[key])) # write audio files from scipy.io.wavfile import write for key in data_depth: write('out/test'+key+'.wav', fs, data_depth[key]) # plot some t = np.arange(0, len(raw)/fs, 1/fs) fig, axs = plt.subplots(1, 1) axs.step(t, data_depth['16bit'], label='16bit') axs.step(t, data_depth['8bit'], label='8bit') axs.step(t, data_depth['7bit'], label='7bit') axs.step(t, data_depth['6bit'], label='6bit') axs.step(t, data_depth['2bit'], label='2bit') # axs.set_xlim(0, 6e-3) # axs.set_ylim(-1, 1) axs.set_xlabel('time [s]') axs.set_ylabel('mic data') axs.grid(True) axs.legend() fig.tight_layout() plt.show()

noah95/edison

audio/edison/audio/bit_depth_analyze.py

Python

#!/home/pi/Documents/Codigos/API_Estacao/bin/python3 """Simple FTDI EEPROM configurator. """ # Copyright (c) 2019-2020, Emmanuel Blot <emmanuel.blot@free.fr> # All rights reserved. # # SPDX-License-Identifier: BSD-3-Clause from argparse import ArgumentParser, FileType from io import StringIO from logging import Formatter, StreamHandler, DEBUG, ERROR from sys import modules, stderr from textwrap import fill from traceback import format_exc from pyftdi import FtdiLogger from pyftdi.eeprom import FtdiEeprom from pyftdi.ftdi import Ftdi from pyftdi.misc import add_custom_devices, hexdump #pylint: disable-msg=too-many-locals #pylint: disable-msg=too-many-branches #pylint: disable-msg=too-many-statements def main(): """Main routine""" debug = False try: argparser = ArgumentParser(description=modules[__name__].__doc__) argparser.add_argument('device', nargs='?', default='ftdi:///?', help='serial port device name') argparser.add_argument('-x', '--hexdump', action='store_true', help='dump EEPROM content as ASCII') argparser.add_argument('-X', '--hexblock', type=int, help='dump EEPROM as indented hexa blocks') argparser.add_argument('-i', '--input', type=FileType('rt'), help='input ini file to load EEPROM content') argparser.add_argument('-l', '--load', default='all', choices=('all', 'raw', 'values'), help='section(s) to load from input file') argparser.add_argument('-o', '--output', type=FileType('wt'), help='output ini file to save EEPROM content') argparser.add_argument('-s', '--serial-number', help='set serial number') argparser.add_argument('-m', '--manufacturer', help='set manufacturer name') argparser.add_argument('-p', '--product', help='set product name') argparser.add_argument('-c', '--config', action='append', help='change/configure a property ' 'as key=value pair') argparser.add_argument('-e', '--erase', action='store_true', help='erase the whole EEPROM content') argparser.add_argument('-u', '--update', action='store_true', help='perform actual update, use w/ care') argparser.add_argument('-P', '--vidpid', action='append', help='specify a custom VID:PID device ID, ' 'may be repeated') argparser.add_argument('-V', '--virtual', type=FileType('r'), help='use a virtual device, specified as YaML') argparser.add_argument('-v', '--verbose', action='count', default=0, help='increase verbosity') argparser.add_argument('-d', '--debug', action='store_true', help='enable debug mode') args = argparser.parse_args() debug = args.debug if not args.device: argparser.error('Serial device not specified') loglevel = max(DEBUG, ERROR - (10 * args.verbose)) loglevel = min(ERROR, loglevel) if debug: formatter = Formatter('%(asctime)s.%(msecs)03d %(name)-20s ' '%(message)s', '%H:%M:%S') else: formatter = Formatter('%(message)s') FtdiLogger.set_formatter(formatter) FtdiLogger.set_level(loglevel) FtdiLogger.log.addHandler(StreamHandler(stderr)) if args.virtual: #pylint: disable-msg=import-outside-toplevel from pyftdi.usbtools import UsbTools # Force PyUSB to use PyFtdi test framework for USB backends UsbTools.BACKENDS = ('pyftdi.tests.backend.usbvirt', ) # Ensure the virtual backend can be found and is loaded backend = UsbTools.find_backend() loader = backend.create_loader()() loader.load(args.virtual) try: add_custom_devices(Ftdi, args.vidpid, force_hex=True) except ValueError as exc: argparser.error(str(exc)) eeprom = FtdiEeprom() eeprom.open(args.device) if args.erase: eeprom.erase() if args.input: eeprom.load_config(args.input, args.load) if args.serial_number: eeprom.set_serial_number(args.serial_number) if args.manufacturer: eeprom.set_manufacturer_name(args.manufacturer) if args.product: eeprom.set_product_name(args.product) for conf in args.config or []: if conf == '?': helpstr = ', '.join(sorted(eeprom.properties)) print(fill(helpstr, initial_indent=' ', subsequent_indent=' ')) exit(1) for sep in ':=': if sep in conf: name, value = conf.split(sep, 1) if not value: argparser.error('Configuration %s without value' % conf) helpio = StringIO() eeprom.set_property(name, value, helpio) helpstr = helpio.getvalue() if helpstr: print(fill(helpstr, initial_indent=' ', subsequent_indent=' ')) exit(1) break else: argparser.error('Missing name:value separator in %s' % conf) if args.hexdump: print(hexdump(eeprom.data)) if args.hexblock is not None: indent = ' ' * args.hexblock for pos in range(0, len(eeprom.data), 16): hexa = ' '.join(['%02x' % x for x in eeprom.data[pos:pos+16]]) print(indent, hexa, sep='') if args.update: if eeprom.commit(False): eeprom.reset_device() if args.verbose > 0: eeprom.dump_config() if args.output: eeprom.save_config(args.output) except (ImportError, IOError, NotImplementedError, ValueError) as exc: print('\nError: %s' % exc, file=stderr) if debug: print(format_exc(chain=False), file=stderr) exit(1) except KeyboardInterrupt: exit(2) if __name__ == '__main__': main()

andrario/API_Estacao

bin/ftconf.py

Python

#!/usr/bin/env python3 ###################################################### ## Calibrating the extrinsics between T265 and D4xx ## ## Based on this example: https://github.com/IntelRealSense/librealsense/pull/4355 ## with changes and modifications. ###################################################### ###################################################### # # General steps: # 1. Mount the two cameras rigidly # 2. Print any one of the checkerboards from: https://markhedleyjones.com/projects/calibration-checkerboard-collection # - The default settings in this script are for: https://markhedleyjones.com/storage/checkerboards/Checkerboard-A4-25mm-8x6.pdf # - Measure the actual printed grid size of the squares and modify size. # 3. Modify the script: # - Change grid_H, grid_W and size according to the actual printed checkerboard. # - Change the path and file_name if necessary (ex: use this script as standalone). # 4. Run the script online: # - python calibrate_extrinsics.py # 5. The results include intrinsics (save file) and extrinsics (terminal output) # ###################################################### from __future__ import print_function import pyrealsense2 as rs import numpy as np np.set_printoptions(suppress=True,precision=5) import cv2 assert cv2.__version__[0] >= '3', 'The fisheye module requires opencv version >= 3.0.0' import os import shutil import json import argparse import glob from collections import OrderedDict parser = argparse.ArgumentParser() parser.add_argument('--SN_T265', help='serial number of T265') parser.add_argument('--SN_D4xx', help='serial number of D4xx') parser.add_argument('--path', default="calibration_results", help='image path') parser.add_argument('--file_name', default="/intrinsics.json", help='intrinsics calibration file name') parser.add_argument('--save_tmp', default=False, help='save the temporary files of this program, useful for debugging purposes') parser.add_argument('--grid_H', default=8, help='grid height (inner corners)') parser.add_argument('--grid_W', default=6, help='grid width (inner corners)') parser.add_argument('--size', default=0.0282, help='grid side length') parser.add_argument('--calibrate', default=False, help='run calibration (only)', action='store_true') parser.add_argument('--visualize', default=True, help='with GUI', action='store_true') args = parser.parse_args() CHECKERBOARD = (args.grid_H, args.grid_W) SIDE_LENGTH = args.size tmp_folder = args.path + "/tmp" def add_camera_calibration(intrinsics, streams = None): cam = {} cam['center_px'] = [intrinsics.ppx, intrinsics.ppy] cam['focal_length_px'] = [intrinsics.fx, intrinsics.fy] cam['distortion'] = {} cam['distortion']['type'] = 'kannalabrandt4' cam['distortion']['k'] = intrinsics.coeffs[:4] if streams: ext = streams["cam1"].get_extrinsics_to(streams["pose"]) # w.r.t. #print(ext) cam["extrinsics"] = {} cam["extrinsics"]["T"] = ext.translation #print(ext.rotation) cam["extrinsics"]["R"] = ext.rotation return cam def save_intrinsics(directory, file_name, intrinsics, streams): D = OrderedDict() # in order (cam1,cam2) D['cameras'] = [] D['cameras'].append(add_camera_calibration(intrinsics["cam1"], streams)) D['cameras'].append(add_camera_calibration(intrinsics["cam2"])) if not os.path.exists(directory): os.mkdir(directory) with open(directory + file_name, 'w') as f: json.dump(D, f, indent=4) print("Intrinsics output written to " + directory + file_name) def read_calibration(cam, extrinsics = False): #print("read_calibration") # intrinsics K = np.array([[cam['focal_length_px'][0], 0, cam['center_px'][0]], [ 0, cam['focal_length_px'][1], cam['center_px'][1]], [ 0, 0, 1]]) D = np.array(cam['distortion']['k']) if extrinsics: H = np.eye(4) H[:3,:3] = np.reshape(cam["extrinsics"]["R"],(3,3)) H[:3,3] = cam["extrinsics"]["T"] #print(H) return (K, D, H) return (K, D) def load_calibration(directory, file_name): with open(directory + file_name, 'r') as f: D = json.load(f) (K1, D1, H1) = read_calibration(D['cameras'][0], True) (K2, D2) = read_calibration(D['cameras'][1]) return (K1, D1, K2, D2, H1) def find_realsense_serial_no(type): camera_name = ['Intel RealSense T265', 'Intel RealSense D435'] # Get realsense pipeline handle pipe = rs.pipeline() # Find the T265 devices = rs.context().devices for i in range(len(devices)): if (devices[i].get_info(rs.camera_info.name) == camera_name[type]): print('Found one connected ' + camera_name[type] + ' with serial no:', devices[i].get_info(rs.camera_info.serial_number)) return devices[i].get_info(rs.camera_info.serial_number) print('No ' + camera_name[type] + ' found, please check connection or input serial manually') return None if not args.calibrate: # Obtain the serial number of the cameras, either automatically or from user's input print("Trying to connect devices...") serial_t265 = None serial_d4xx = None if (not args.SN_T265): serial_t265 = find_realsense_serial_no(0) else: serial_t265 = args.SN_T265 if (not args.SN_D4xx): serial_d4xx = find_realsense_serial_no(1) else: serial_d4xx = args.SN_D4xx if (not serial_t265) or (not serial_d4xx): print("Specify serial numbers --SN_T265 and --SN_D4xx (for online calibration, or --calibrate for prerecorded images with --path path to folder)") exit() # cam 1 pipe1 = rs.pipeline() cfg1 = rs.config() cfg1.enable_device(serial_t265) pipe1.start(cfg1) # cam 2 pipe2 = rs.pipeline() cfg2 = rs.config() cfg2.enable_device(serial_d4xx) cfg2.enable_all_streams() pipe2_profile = pipe2.start(cfg2) sensor_depth = pipe2_profile.get_device().first_depth_sensor() sensor_depth.set_option(rs.option.emitter_enabled, 0) # turn OFF projector try: # Retreive the stream and intrinsic properties for both cameras profile1 = pipe1.get_active_profile() profile2 = pipe2.get_active_profile() # future improvements: make both stream configureable streams = {"cam1" : profile1.get_stream(rs.stream.fisheye, 1).as_video_stream_profile(), "pose" : profile1.get_stream(rs.stream.pose), "cam2" : profile2.get_stream(rs.stream.infrared, 1).as_video_stream_profile()} # IR1 #"cam2" : profile1.get_stream(rs.stream.fisheye, 2).as_video_stream_profile()} # test intrinsics = {"cam1" : streams["cam1"].get_intrinsics(), "cam2" : streams["cam2"].get_intrinsics()} #print("cam1:", intrinsics["cam1"]) #print("cam2:", intrinsics["right"]) save_intrinsics(args.path, args.file_name, intrinsics, streams) # capture images i = 0 print("Press 's' to save image.\nPress 'q' or 'c' to quit recording and start the calibration.") while True: # cam 1 frames1 = pipe1.wait_for_frames() f_fe1 = frames1.get_fisheye_frame(1) # left fisheye f_fe2 = frames1.get_fisheye_frame(2) # right fisheye if not f_fe1 or not f_fe2: continue img_fe1 = np.asanyarray(f_fe1.get_data()) img_fe2 = np.asanyarray(f_fe2.get_data()) # cam 2 frames2 = pipe2.wait_for_frames() f_ir1 = frames2.get_infrared_frame(1) # left infrared f_ir2 = frames2.get_infrared_frame(2) # right infrared f_color = frames2.get_color_frame() if not f_ir1 or not f_ir2 or not f_color: continue img_ir1 = np.asanyarray(f_ir1.get_data()) img_ir2 = np.asanyarray(f_ir2.get_data()) img_color = np.asanyarray(f_color.get_data()) # TODO: configure streams img1 = img_fe1 img2 = img_ir1 # display cv2.imshow('cam1', img1) cv2.imshow('cam2', img2) # save or quit k = cv2.waitKey(1) if k == ord('s'): print("'s' key pressed. Saving temp images..") if not os.path.exists(tmp_folder): os.mkdir(tmp_folder) cv2.imwrite(tmp_folder + '/fe1_' + str(i) + '.png', img_fe1) cv2.imwrite(tmp_folder + '/fe2_' + str(i) + '.png', img_fe2) cv2.imwrite(tmp_folder + '/ir1_' + str(i) + '.png', img_ir1) # cv2.imwrite(tmp_folder+ '/ir2_' + str(i) + '.png', img_ir2) cv2.imwrite(tmp_folder + '/color_' + str(i) + '.png', img_color) print("Saved temp images in temp folder " + tmp_folder) i = i+1 if k == ord('q') or k == ord('c'): break finally: pipe1.stop() pipe2.stop() # calibrate print("Calibrate extrinsics now...") # arrays to store detections P3 = [] # w.r.t. target frame P2_1 = [] # in image #1 P2_2 = [] # in image #2 # TODO: configure streams images1 = glob.glob(tmp_folder + '/fe1_*') #images2 = glob.glob(tmp_folder + '/fe2_*') # test images2 = glob.glob(tmp_folder + '/ir1_*') images1.sort() images2.sort() #print(images1) #print(images2) if len(images1) == len(images2) == 0: print("No images found. Exit.") exit(0) try: for i, fname in enumerate(images1): img1 = cv2.imread(images1[i]) img2 = cv2.imread(images2[i]) gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY) gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY) # detect ret1, corners1 = cv2.findChessboardCorners(gray1, CHECKERBOARD, None) ret2, corners2 = cv2.findChessboardCorners(gray2, CHECKERBOARD, None) if ret1 and ret2: # subpixel refinement criteria_sub = (cv2.TermCriteria_COUNT + cv2.TERM_CRITERIA_EPS, 10, 1e-1) rt = cv2.cornerSubPix(gray1, corners1, (7, 7), (-1, -1), criteria_sub) P2_1.append(corners1) if args.visualize: ret1 = cv2.drawChessboardCorners(img1, CHECKERBOARD, corners1, ret1) cv2.imshow("img1", img1) cv2.waitKey(200) rt = cv2.cornerSubPix(gray2, corners2, (7, 7), (-1, -1), criteria_sub) P2_2.append(corners2) if args.visualize: ret2 = cv2.drawChessboardCorners(img2, CHECKERBOARD, corners2, ret2) cv2.imshow("img2", img2) cv2.waitKey(200) except cv2.error as e: print("Error: ", e) # calibration (stereo extrinsics) R = np.zeros((1, 1, 3), dtype=np.float64) T = np.zeros((1, 1, 3), dtype=np.float64) N = len(P2_1) # number of successful detections p3d = np.zeros( (CHECKERBOARD[0]*CHECKERBOARD[1], 1, 3) , np.float64) p3d[:,0, :2] = np.mgrid[0:CHECKERBOARD[0], 0:CHECKERBOARD[1]].T.reshape(-1, 2) # fisheye.stereoCalibrate needs different data structures/dimensions than cv2.stereoCalibrate, i.e. (N, 1, CHECKERBOARD[0]*CHECKERBOARD[1], 2/3)! P3 = np.array([p3d]*N, dtype=np.float64) P2_1 = np.asarray(P2_1, dtype=np.float64) P2_2 = np.asarray(P2_2, dtype=np.float64) P3 = np.reshape(P3, (N, 1, CHECKERBOARD[0]*CHECKERBOARD[1], 3))*SIDE_LENGTH P2_1 = np.reshape(P2_1, (N, 1, CHECKERBOARD[0]*CHECKERBOARD[1], 2)) P2_2 = np.reshape(P2_2, (N, 1, CHECKERBOARD[0]*CHECKERBOARD[1], 2)) (K1, D1, K2, D2, H1) = load_calibration(args.path, args.file_name) try: (rms, _, _, _, _, R, T) = \ cv2.fisheye.stereoCalibrate( P3, P2_1, P2_2, K1, D1, K2, D2, (0,0), # only used to initialize intrinsics when no intrinsics provided R, T, cv2.fisheye.CALIB_FIX_INTRINSIC # extrinsics only ) except cv2.error as e: print("Error: ", e) print("Please make sure that the checkerboard exists in the images. See tmp images in " + tmp_folder + " to debug.") exit() print("RMS:", rms) H_cam2_cam1 = np.eye(4) H_cam2_cam1[:3,:3] = R H_cam2_cam1[:3,3] = T.flatten() # w.r.t. pose H_ir1_fe1 = H_cam2_cam1 # TODO: configure H_pose_fe1 = H1 H_pose_ir1 = H_pose_fe1.dot( np.linalg.inv(H_ir1_fe1) ) print("H (ir1 wrt pose) =", H_pose_ir1) fn = args.path + "/H.txt" np.savetxt(fn, H_pose_ir1, fmt='%.9f') print("Extrinsic output written to", fn) if not args.save_tmp: if os.path.isdir(tmp_folder): shutil.rmtree(tmp_folder, ignore_errors=True) print("Temporary files deleted. If you wish to keep the tmp files, use --save_tmp True.")

mikobski/Critbot

robot/src/vision_to_mavros/scripts/calibrate_extrinsics.py

Python

"""Implementation of Rule L044.""" from typing import Optional from sqlfluff.core.rules.analysis.select_crawler import Query, SelectCrawler from sqlfluff.core.parser import BaseSegment from sqlfluff.core.rules.base import BaseRule, LintResult, RuleContext from sqlfluff.core.rules.doc_decorators import document_groups from sqlfluff.core.rules.functional import sp class RuleFailure(Exception): """Exception class for reporting lint failure inside deeply nested code.""" def __init__(self, anchor: BaseSegment): self.anchor: BaseSegment = anchor @document_groups class Rule_L044(BaseRule): """Query produces an unknown number of result columns. **Anti-pattern** Querying all columns using ``*`` produces a query result where the number or ordering of columns changes if the upstream table's schema changes. This should generally be avoided because it can cause slow performance, cause important schema changes to go undetected, or break production code. For example: * If a query does ``SELECT t.*`` and is expected to return columns ``a``, ``b``, and ``c``, the actual columns returned will be wrong/different if columns are added to or deleted from the input table. * ``UNION`` and ``DIFFERENCE`` clauses require the inputs have the same number of columns (and compatible types). * ``JOIN`` queries may break due to new column name conflicts, e.g. the query references a column ``c`` which initially existed in only one input table but a column of the same name is added to another table. * ``CREATE TABLE (<<column schema>>) AS SELECT *`` .. code-block:: sql WITH cte AS ( SELECT * FROM foo ) SELECT * FROM cte UNION SELECT a, b FROM t **Best practice** Somewhere along the "path" to the source data, specify columns explicitly. .. code-block:: sql WITH cte AS ( SELECT * FROM foo ) SELECT a, b FROM cte UNION SELECT a, b FROM t """ groups = ("all",) _works_on_unparsable = False def _handle_alias(self, selectable, alias_info, query): select_info_target = SelectCrawler.get( query, alias_info.from_expression_element )[0] if isinstance(select_info_target, str): # It's an alias to an external table whose # number of columns could vary without our # knowledge. Thus, warn. self.logger.debug( f"Query target {select_info_target} is external. Generating warning." ) raise RuleFailure(selectable.selectable) else: # Handle nested SELECT. self._analyze_result_columns(select_info_target) def _analyze_result_columns(self, query: Query): """Given info on a list of SELECTs, determine whether to warn.""" # Recursively walk from the given query (select_info_list) to any # wildcard columns in the select targets. If every wildcard evdentually # resolves to a query without wildcards, all is well. Otherwise, warn. if not query.selectables: return # pragma: no cover for selectable in query.selectables: self.logger.debug(f"Analyzing query: {selectable.selectable.raw}") for wildcard in selectable.get_wildcard_info(): if wildcard.tables: for wildcard_table in wildcard.tables: self.logger.debug( f"Wildcard: {wildcard.segment.raw} has target " "{wildcard_table}" ) # Is it an alias? alias_info = selectable.find_alias(wildcard_table) if alias_info: # Found the alias matching the wildcard. Recurse, # analyzing the query associated with that alias. self._handle_alias(selectable, alias_info, query) else: # Not an alias. Is it a CTE? cte = query.lookup_cte(wildcard_table) if cte: # Wildcard refers to a CTE. Analyze it. self._analyze_result_columns(cte) else: # Not CTE, not table alias. Presumably an # external table. Warn. self.logger.debug( f"Query target {wildcard_table} is external. " "Generating warning." ) raise RuleFailure(selectable.selectable) else: # No table was specified with the wildcard. Assume we're # querying from a nested select in FROM. query_list = SelectCrawler.get( query, query.selectables[0].selectable ) for o in query_list: if isinstance(o, Query): self._analyze_result_columns(o) return self.logger.debug( f'Query target "{query.selectables[0].selectable.raw}" has no ' "targets. Generating warning." ) raise RuleFailure(query.selectables[0].selectable) def _eval(self, context: RuleContext) -> Optional[LintResult]: """Outermost query should produce known number of columns.""" start_types = ["select_statement", "set_expression", "with_compound_statement"] if context.segment.is_type( *start_types ) and not context.functional.parent_stack.any(sp.is_type(*start_types)): crawler = SelectCrawler(context.segment, context.dialect) # Begin analysis at the outer query. if crawler.query_tree: try: return self._analyze_result_columns(crawler.query_tree) except RuleFailure as e: return LintResult(anchor=e.anchor) return None

R7L208/sqlfluff

src/sqlfluff/rules/L044.py

Python

import os, paramiko, time, schedule, smtplib, ssl from datetime import datetime from email.message import EmailMessage host='localhost' port='5432' user='postgres' password='admin' database='testdb' #chemin de sauvegarde locale local_dir = 'C:\\Users\\Kamla\\projets\\auto-backup-sqldb\\backup\\' #local_dir = 'Chemin vers le dossier de la base de donnees a sauvegarder\\' #chemin de sauvegarde distant remote_dir = '/C:/Users/vmwin10/Documents/ftpfile/' def job(): print("Backup working...") filestamp = time.strftime('%Y-%m-%dT%H-%M-%S.%z') #nom pour le fichier sql qui serra genere par pg_dump database_remote = database+"_"+filestamp+".bak.sql" PASS="set PGPASSWORD=%s" % (password) #lancement de la commande mysqldump qui va faire une sauvegarde en local #les fichiers sont sauvegarder dans le respertoire 'backup' os.system("(cd backup) && ("+PASS+") && (pg_dump -h %s -p %s -U %s -f %s -C -d %s)" % (host, port, user, database_remote, database)) print("Database dumped to "+database_remote) # debut du SFTP ssh_client=paramiko.SSHClient() ssh_client.set_missing_host_key_policy(paramiko.AutoAddPolicy()) #on se connecte a la machine dans laquelle serra sauvegarde le le fichier backup ssh_client.connect(hostname='192.168.126.2',username='vmwin10',password='vmwin10') ftp_client=ssh_client.open_sftp() #envoie du fichier local vers le remote ftp_client.put(local_dir+database_remote,remote_dir+database_remote) ftp_client.close() print("Successfull Backup") # A chaque backup un email est envoye msg = EmailMessage() msg.set_content("Un backup vient d'etre effectue") msg["Subject"] = "Email de Backup" msg["From"] = "ksb.cmr@gmail.com" msg["To"] = "test@mail.com" context=ssl.create_default_context() with smtplib.SMTP("smtp.gmail.com", port=587) as smtp: smtp.starttls(context=context) smtp.login(msg["From"], "password") smtp.send_message(msg) # le backup se fait chaque 1h schedule.every(3).seconds.do(job) #schedule.every(15).minutes.do(job) #schedule.every().hour.do(job) #schedule.every().day.at("10:30").do(job) #schedule.every(10).to(10).minutes.do(job) #schedule.every().monday.do(job) #schedule.every().wednesday.at("15:00").do(job) #schedule.every().minute.at(":15").do(job) while True: schedule.run_pending() time.sleep(1)

mykamla/auto-backup-sqldb

pgsqlbackup.py

Python

# Configuration file for the Sphinx documentation builder. # # This file only contains a selection of the most common options. For a full # list see the documentation: # https://www.sphinx-doc.org/en/master/usage/configuration.html # -- Path setup -------------------------------------------------------------- # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. from datetime import date from pathlib import Path ROOT_DIR = Path(__file__).resolve(strict=True).parent.parent PACKAGE_DIR = ROOT_DIR / "email_service" DOCS_DIR = ROOT_DIR / "email_service" version_file_path = PACKAGE_DIR / "version.py" code_obj = compile(version_file_path.read_text(), version_file_path, "exec") __version__ = dict() exec(code_obj, __version__) version = __version__["__version__"] # -- Project information ----------------------------------------------------- project = "Email Service" copyright = """2021, Aditya Raman""" author = "Aditya Raman" # The full version, including alpha/beta/rc tags version = release = f"v{version}" today = str(date.today()) language = "en" # -- General configuration --------------------------------------------------- # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ "sphinx_rtd_theme", "sphinx.ext.autodoc", "sphinx.ext.napoleon", "sphinx.ext.viewcode", ] # Add any paths that contain templates here, relative to this directory. templates_path = ["_templates"] # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path. exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] # -- Options for HTML output ------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # html_theme = "sphinx_rtd_theme" # alternate: "alabaster" # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ["_static"] # These paths are either relative to html_static_path # or fully qualified paths (eg. https://...) # html_css_files = [] # # html_style = "" master_doc = "index" latex_elements = { # The paper size ('letterpaper' or 'a4paper'). # "papersize": "a4paper", # The font size ('10pt', '11pt' or '12pt'). # # 'pointsize': '10pt', # Additional stuff for the LaTeX preamble. # "preamble": "\\addto\\captionsenglish{\\renewcommand{\\contentsname}{Table of contents}}", # Latex figure (float) alignment # # 'figure_align': 'htbp', } latex_show_urls = "footnote" # If true, the current module name will be prepended to all description # unit titles (such as .. function::). add_module_names = False add_function_parentheses = False show_authors = True

ramanaditya/email-service

docs/conf.py

Python

# NOTE - Still seems to be a leak here somewhere # gateway count doesnt hit zero. Hence the print statements! import sys sys.coinit_flags = 0 # Must be free-threaded! import win32api, pythoncom, time import pywintypes import os import winerror import win32com import win32com.client.connect from win32com.test.util import CheckClean from win32com.client import constants, DispatchBaseClass, CastTo, VARIANT from win32com.test.util import RegisterPythonServer from pywin32_testutil import str2memory import datetime import decimal import win32timezone importMsg = "**** PyCOMTest is not installed ***\n PyCOMTest is a Python test specific COM client and server.\n It is likely this server is not installed on this machine\n To install the server, you must get the win32com sources\n and build it using MS Visual C++" error = Exception # This test uses a Python implemented COM server - ensure correctly registered. RegisterPythonServer( os.path.join(os.path.dirname(__file__), "..", "servers", "test_pycomtest.py"), "Python.Test.PyCOMTest", ) from win32com.client import gencache try: gencache.EnsureModule("{6BCDCB60-5605-11D0-AE5F-CADD4C000000}", 0, 1, 1) except pythoncom.com_error: print("The PyCOMTest module can not be located or generated.") print(importMsg) raise RuntimeError(importMsg) # We had a bg where RegisterInterfaces would fail if gencache had # already been run - exercise that here from win32com import universal universal.RegisterInterfaces("{6BCDCB60-5605-11D0-AE5F-CADD4C000000}", 0, 1, 1) verbose = 0 # convert a normal int to a long int - used to avoid, eg, '1L' for py3k # friendliness def ensure_long(int_val): if sys.version_info > (3,): # py3k - no such thing as a 'long' return int_val # on py2x, we just use an expression that results in a long return 0x100000000 - 0x100000000 + int_val def check_get_set(func, arg): got = func(arg) if got != arg: raise error("%s failed - expected %r, got %r" % (func, arg, got)) def check_get_set_raises(exc, func, arg): try: got = func(arg) except exc as e: pass # what we expect! else: raise error( "%s with arg %r didn't raise %s - returned %r" % (func, arg, exc, got) ) def progress(*args): if verbose: for arg in args: print(arg, end=" ") print() def TestApplyResult(fn, args, result): try: fnName = str(fn).split()[1] except: fnName = str(fn) progress("Testing ", fnName) pref = "function " + fnName rc = fn(*args) if rc != result: raise error("%s failed - result not %r but %r" % (pref, result, rc)) def TestConstant(constName, pyConst): try: comConst = getattr(constants, constName) except: raise error("Constant %s missing" % (constName,)) if comConst != pyConst: raise error( "Constant value wrong for %s - got %s, wanted %s" % (constName, comConst, pyConst) ) # Simple handler class. This demo only fires one event. class RandomEventHandler: def _Init(self): self.fireds = {} def OnFire(self, no): try: self.fireds[no] = self.fireds[no] + 1 except KeyError: self.fireds[no] = 0 def OnFireWithNamedParams(self, no, a_bool, out1, out2): # This test exists mainly to help with an old bug, where named # params would come in reverse. Missing = pythoncom.Missing if no is not Missing: # We know our impl called 'OnFire' with the same ID assert no in self.fireds assert no + 1 == out1, "expecting 'out1' param to be ID+1" assert no + 2 == out2, "expecting 'out2' param to be ID+2" # The middle must be a boolean. assert a_bool is Missing or type(a_bool) == bool, "middle param not a bool" return out1 + 2, out2 + 2 def _DumpFireds(self): if not self.fireds: print("ERROR: Nothing was received!") for firedId, no in self.fireds.items(): progress("ID %d fired %d times" % (firedId, no)) # A simple handler class that derives from object (ie, a "new style class") - # only relevant for Python 2.x (ie, the 2 classes should be identical in 3.x) class NewStyleRandomEventHandler(object): def _Init(self): self.fireds = {} def OnFire(self, no): try: self.fireds[no] = self.fireds[no] + 1 except KeyError: self.fireds[no] = 0 def OnFireWithNamedParams(self, no, a_bool, out1, out2): # This test exists mainly to help with an old bug, where named # params would come in reverse. Missing = pythoncom.Missing if no is not Missing: # We know our impl called 'OnFire' with the same ID assert no in self.fireds assert no + 1 == out1, "expecting 'out1' param to be ID+1" assert no + 2 == out2, "expecting 'out2' param to be ID+2" # The middle must be a boolean. assert a_bool is Missing or type(a_bool) == bool, "middle param not a bool" return out1 + 2, out2 + 2 def _DumpFireds(self): if not self.fireds: print("ERROR: Nothing was received!") for firedId, no in self.fireds.items(): progress("ID %d fired %d times" % (firedId, no)) # Test everything which can be tested using both the "dynamic" and "generated" # COM objects (or when there are very subtle differences) def TestCommon(o, is_generated): progress("Getting counter") counter = o.GetSimpleCounter() TestCounter(counter, is_generated) progress("Checking default args") rc = o.TestOptionals() if rc[:-1] != ("def", 0, 1) or abs(rc[-1] - 3.14) > 0.01: print(rc) raise error("Did not get the optional values correctly") rc = o.TestOptionals("Hi", 2, 3, 1.1) if rc[:-1] != ("Hi", 2, 3) or abs(rc[-1] - 1.1) > 0.01: print(rc) raise error("Did not get the specified optional values correctly") rc = o.TestOptionals2(0) if rc != (0, "", 1): print(rc) raise error("Did not get the optional2 values correctly") rc = o.TestOptionals2(1.1, "Hi", 2) if rc[1:] != ("Hi", 2) or abs(rc[0] - 1.1) > 0.01: print(rc) raise error("Did not get the specified optional2 values correctly") progress("Checking getting/passing IUnknown") check_get_set(o.GetSetUnknown, o) progress("Checking getting/passing IDispatch") # This might be called with either the interface or the CoClass - but these # functions always return from the interface. expected_class = o.__class__ # CoClass instances have `default_interface` expected_class = getattr(expected_class, "default_interface", expected_class) if not isinstance(o.GetSetDispatch(o), expected_class): raise error("GetSetDispatch failed: %r" % (o.GetSetDispatch(o),)) progress("Checking getting/passing IDispatch of known type") expected_class = o.__class__ expected_class = getattr(expected_class, "default_interface", expected_class) if o.GetSetInterface(o).__class__ != expected_class: raise error("GetSetDispatch failed") progress("Checking misc args") check_get_set(o.GetSetVariant, 4) check_get_set(o.GetSetVariant, "foo") check_get_set(o.GetSetVariant, o) # signed/unsigned. check_get_set(o.GetSetInt, 0) check_get_set(o.GetSetInt, -1) check_get_set(o.GetSetInt, 1) check_get_set(o.GetSetUnsignedInt, 0) check_get_set(o.GetSetUnsignedInt, 1) check_get_set(o.GetSetUnsignedInt, 0x80000000) if o.GetSetUnsignedInt(-1) != 0xFFFFFFFF: # -1 is a special case - we accept a negative int (silently converting to # unsigned) but when getting it back we convert it to a long. raise error("unsigned -1 failed") check_get_set(o.GetSetLong, 0) check_get_set(o.GetSetLong, -1) check_get_set(o.GetSetLong, 1) check_get_set(o.GetSetUnsignedLong, 0) check_get_set(o.GetSetUnsignedLong, 1) check_get_set(o.GetSetUnsignedLong, 0x80000000) # -1 is a special case - see above. if o.GetSetUnsignedLong(-1) != 0xFFFFFFFF: raise error("unsigned -1 failed") # We want to explicitly test > 32 bits. py3k has no 'maxint' and # 'maxsize+1' is no good on 64bit platforms as its 65 bits! big = 2147483647 # sys.maxint on py2k for l in big, big + 1, 1 << 65: check_get_set(o.GetSetVariant, l) progress("Checking structs") r = o.GetStruct() assert r.int_value == 99 and str(r.str_value) == "Hello from C++" assert o.DoubleString("foo") == "foofoo" progress("Checking var args") o.SetVarArgs("Hi", "There", "From", "Python", 1) if o.GetLastVarArgs() != ("Hi", "There", "From", "Python", 1): raise error("VarArgs failed -" + str(o.GetLastVarArgs())) progress("Checking arrays") l = [] TestApplyResult(o.SetVariantSafeArray, (l,), len(l)) l = [1, 2, 3, 4] TestApplyResult(o.SetVariantSafeArray, (l,), len(l)) TestApplyResult( o.CheckVariantSafeArray, ( ( 1, 2, 3, 4, ), ), 1, ) # and binary TestApplyResult(o.SetBinSafeArray, (str2memory("foo\0bar"),), 7) progress("Checking properties") o.LongProp = 3 if o.LongProp != 3 or o.IntProp != 3: raise error("Property value wrong - got %d/%d" % (o.LongProp, o.IntProp)) o.LongProp = o.IntProp = -3 if o.LongProp != -3 or o.IntProp != -3: raise error("Property value wrong - got %d/%d" % (o.LongProp, o.IntProp)) # This number fits in an unsigned long. Attempting to set it to a normal # long will involve overflow, which is to be expected. But we do # expect it to work in a property explicitly a VT_UI4. check = 3 * 10 ** 9 o.ULongProp = check if o.ULongProp != check: raise error( "Property value wrong - got %d (expected %d)" % (o.ULongProp, check) ) TestApplyResult(o.Test, ("Unused", 99), 1) # A bool function TestApplyResult(o.Test, ("Unused", -1), 1) # A bool function TestApplyResult(o.Test, ("Unused", 1 == 1), 1) # A bool function TestApplyResult(o.Test, ("Unused", 0), 0) TestApplyResult(o.Test, ("Unused", 1 == 0), 0) assert o.DoubleString("foo") == "foofoo" TestConstant("ULongTest1", ensure_long(0xFFFFFFFF)) TestConstant("ULongTest2", ensure_long(0x7FFFFFFF)) TestConstant("LongTest1", ensure_long(-0x7FFFFFFF)) TestConstant("LongTest2", ensure_long(0x7FFFFFFF)) TestConstant("UCharTest", 255) TestConstant("CharTest", -1) # 'Hello World', but the 'r' is the "Registered" sign (\xae) TestConstant("StringTest", "Hello Wo\xaeld") progress("Checking dates and times") # For now *all* times passed must be tz-aware. now = win32timezone.now() # but conversion to and from a VARIANT loses sub-second... now = now.replace(microsecond=0) later = now + datetime.timedelta(seconds=1) TestApplyResult(o.EarliestDate, (now, later), now) # The below used to fail with `ValueError: microsecond must be in 0..999999` - see #1655 # https://planetcalc.com/7027/ says that float is: Sun, 25 Mar 1951 7:23:49 am assert o.MakeDate(18712.308206013888) == datetime.datetime.fromisoformat( "1951-03-25 07:23:49+00:00" ) progress("Checking currency") # currency. pythoncom.__future_currency__ = 1 if o.CurrencyProp != 0: raise error("Expecting 0, got %r" % (o.CurrencyProp,)) for val in ("1234.5678", "1234.56", "1234"): o.CurrencyProp = decimal.Decimal(val) if o.CurrencyProp != decimal.Decimal(val): raise error("%s got %r" % (val, o.CurrencyProp)) v1 = decimal.Decimal("1234.5678") TestApplyResult(o.DoubleCurrency, (v1,), v1 * 2) v2 = decimal.Decimal("9012.3456") TestApplyResult(o.AddCurrencies, (v1, v2), v1 + v2) TestTrickyTypesWithVariants(o, is_generated) progress("Checking win32com.client.VARIANT") TestPyVariant(o, is_generated) def TestTrickyTypesWithVariants(o, is_generated): # Test tricky stuff with type handling and generally only works with # "generated" support but can be worked around using VARIANT. if is_generated: got = o.TestByRefVariant(2) else: v = VARIANT(pythoncom.VT_BYREF | pythoncom.VT_VARIANT, 2) o.TestByRefVariant(v) got = v.value if got != 4: raise error("TestByRefVariant failed") if is_generated: got = o.TestByRefString("Foo") else: v = VARIANT(pythoncom.VT_BYREF | pythoncom.VT_BSTR, "Foo") o.TestByRefString(v) got = v.value if got != "FooFoo": raise error("TestByRefString failed") # check we can pass ints as a VT_UI1 vals = [1, 2, 3, 4] if is_generated: arg = vals else: arg = VARIANT(pythoncom.VT_ARRAY | pythoncom.VT_UI1, vals) TestApplyResult(o.SetBinSafeArray, (arg,), len(vals)) # safearrays of doubles and floats vals = [0, 1.1, 2.2, 3.3] if is_generated: arg = vals else: arg = VARIANT(pythoncom.VT_ARRAY | pythoncom.VT_R8, vals) TestApplyResult(o.SetDoubleSafeArray, (arg,), len(vals)) if is_generated: arg = vals else: arg = VARIANT(pythoncom.VT_ARRAY | pythoncom.VT_R4, vals) TestApplyResult(o.SetFloatSafeArray, (arg,), len(vals)) vals = [1.1, 2.2, 3.3, 4.4] expected = (1.1 * 2, 2.2 * 2, 3.3 * 2, 4.4 * 2) if is_generated: TestApplyResult(o.ChangeDoubleSafeArray, (vals,), expected) else: arg = VARIANT(pythoncom.VT_BYREF | pythoncom.VT_ARRAY | pythoncom.VT_R8, vals) o.ChangeDoubleSafeArray(arg) if arg.value != expected: raise error("ChangeDoubleSafeArray got the wrong value") if is_generated: got = o.DoubleInOutString("foo") else: v = VARIANT(pythoncom.VT_BYREF | pythoncom.VT_BSTR, "foo") o.DoubleInOutString(v) got = v.value assert got == "foofoo", got val = decimal.Decimal("1234.5678") if is_generated: got = o.DoubleCurrencyByVal(val) else: v = VARIANT(pythoncom.VT_BYREF | pythoncom.VT_CY, val) o.DoubleCurrencyByVal(v) got = v.value assert got == val * 2 def TestDynamic(): progress("Testing Dynamic") import win32com.client.dynamic o = win32com.client.dynamic.DumbDispatch("PyCOMTest.PyCOMTest") TestCommon(o, False) counter = win32com.client.dynamic.DumbDispatch("PyCOMTest.SimpleCounter") TestCounter(counter, False) # Dynamic doesn't know this should be an int, so we get a COM # TypeMismatch error. try: check_get_set_raises(ValueError, o.GetSetInt, "foo") raise error("no exception raised") except pythoncom.com_error as exc: if exc.hresult != winerror.DISP_E_TYPEMISMATCH: raise arg1 = VARIANT(pythoncom.VT_R4 | pythoncom.VT_BYREF, 2.0) arg2 = VARIANT(pythoncom.VT_BOOL | pythoncom.VT_BYREF, True) arg3 = VARIANT(pythoncom.VT_I4 | pythoncom.VT_BYREF, 4) o.TestInOut(arg1, arg2, arg3) assert arg1.value == 4.0, arg1 assert arg2.value == False assert arg3.value == 8 # damn - props with params don't work for dynamic objects :( # o.SetParamProp(0, 1) # if o.ParamProp(0) != 1: # raise RuntimeError, o.paramProp(0) def TestGenerated(): # Create an instance of the server. from win32com.client.gencache import EnsureDispatch o = EnsureDispatch("PyCOMTest.PyCOMTest") TestCommon(o, True) counter = EnsureDispatch("PyCOMTest.SimpleCounter") TestCounter(counter, True) # This dance lets us get a CoClass even though it's not explicitly registered. # This is `CoPyComTest` from win32com.client.CLSIDToClass import GetClass coclass_o = GetClass("{8EE0C520-5605-11D0-AE5F-CADD4C000000}")() TestCommon(coclass_o, True) # Test the regression reported in #1753 assert bool(coclass_o) # This is `CoSimpleCounter` and the counter tests should work. coclass = GetClass("{B88DD310-BAE8-11D0-AE86-76F2C1000000}")() TestCounter(coclass, True) # XXX - this is failing in dynamic tests, but should work fine. i1, i2 = o.GetMultipleInterfaces() if not isinstance(i1, DispatchBaseClass) or not isinstance(i2, DispatchBaseClass): # Yay - is now an instance returned! raise error( "GetMultipleInterfaces did not return instances - got '%s', '%s'" % (i1, i2) ) del i1 del i2 # Generated knows to only pass a 32bit int, so should fail. check_get_set_raises(OverflowError, o.GetSetInt, 0x80000000) check_get_set_raises(OverflowError, o.GetSetLong, 0x80000000) # Generated knows this should be an int, so raises ValueError check_get_set_raises(ValueError, o.GetSetInt, "foo") check_get_set_raises(ValueError, o.GetSetLong, "foo") # Pass some non-sequence objects to our array decoder, and watch it fail. try: o.SetVariantSafeArray("foo") raise error("Expected a type error") except TypeError: pass try: o.SetVariantSafeArray(666) raise error("Expected a type error") except TypeError: pass o.GetSimpleSafeArray(None) TestApplyResult(o.GetSimpleSafeArray, (None,), tuple(range(10))) resultCheck = tuple(range(5)), tuple(range(10)), tuple(range(20)) TestApplyResult(o.GetSafeArrays, (None, None, None), resultCheck) l = [] TestApplyResult(o.SetIntSafeArray, (l,), len(l)) l = [1, 2, 3, 4] TestApplyResult(o.SetIntSafeArray, (l,), len(l)) ll = [1, 2, 3, 0x100000000] TestApplyResult(o.SetLongLongSafeArray, (ll,), len(ll)) TestApplyResult(o.SetULongLongSafeArray, (ll,), len(ll)) # Tell the server to do what it does! TestApplyResult(o.Test2, (constants.Attr2,), constants.Attr2) TestApplyResult(o.Test3, (constants.Attr2,), constants.Attr2) TestApplyResult(o.Test4, (constants.Attr2,), constants.Attr2) TestApplyResult(o.Test5, (constants.Attr2,), constants.Attr2) TestApplyResult(o.Test6, (constants.WideAttr1,), constants.WideAttr1) TestApplyResult(o.Test6, (constants.WideAttr2,), constants.WideAttr2) TestApplyResult(o.Test6, (constants.WideAttr3,), constants.WideAttr3) TestApplyResult(o.Test6, (constants.WideAttr4,), constants.WideAttr4) TestApplyResult(o.Test6, (constants.WideAttr5,), constants.WideAttr5) TestApplyResult(o.TestInOut, (2.0, True, 4), (4.0, False, 8)) o.SetParamProp(0, 1) if o.ParamProp(0) != 1: raise RuntimeError(o.paramProp(0)) # Make sure CastTo works - even though it is only casting it to itself! o2 = CastTo(o, "IPyCOMTest") if o != o2: raise error("CastTo should have returned the same object") # Do the connection point thing... # Create a connection object. progress("Testing connection points") o2 = win32com.client.DispatchWithEvents(o, RandomEventHandler) TestEvents(o2, o2) o2 = win32com.client.DispatchWithEvents(o, NewStyleRandomEventHandler) TestEvents(o2, o2) # and a plain "WithEvents". handler = win32com.client.WithEvents(o, RandomEventHandler) TestEvents(o, handler) handler = win32com.client.WithEvents(o, NewStyleRandomEventHandler) TestEvents(o, handler) progress("Finished generated .py test.") def TestEvents(o, handler): sessions = [] handler._Init() try: for i in range(3): session = o.Start() sessions.append(session) time.sleep(0.5) finally: # Stop the servers for session in sessions: o.Stop(session) handler._DumpFireds() handler.close() def _TestPyVariant(o, is_generated, val, checker=None): if is_generated: vt, got = o.GetVariantAndType(val) else: # Gotta supply all 3 args with the last 2 being explicit variants to # get the byref behaviour. var_vt = VARIANT(pythoncom.VT_UI2 | pythoncom.VT_BYREF, 0) var_result = VARIANT(pythoncom.VT_VARIANT | pythoncom.VT_BYREF, 0) o.GetVariantAndType(val, var_vt, var_result) vt = var_vt.value got = var_result.value if checker is not None: checker(got) return # default checking. assert vt == val.varianttype, (vt, val.varianttype) # Handle our safe-array test - if the passed value is a list of variants, # compare against the actual values. if type(val.value) in (tuple, list): check = [v.value if isinstance(v, VARIANT) else v for v in val.value] # pythoncom always returns arrays as tuples. got = list(got) else: check = val.value assert type(check) == type(got), (type(check), type(got)) assert check == got, (check, got) def _TestPyVariantFails(o, is_generated, val, exc): try: _TestPyVariant(o, is_generated, val) raise error("Setting %r didn't raise %s" % (val, exc)) except exc: pass def TestPyVariant(o, is_generated): _TestPyVariant(o, is_generated, VARIANT(pythoncom.VT_UI1, 1)) _TestPyVariant( o, is_generated, VARIANT(pythoncom.VT_ARRAY | pythoncom.VT_UI4, [1, 2, 3]) ) _TestPyVariant(o, is_generated, VARIANT(pythoncom.VT_BSTR, "hello")) _TestPyVariant( o, is_generated, VARIANT(pythoncom.VT_ARRAY | pythoncom.VT_BSTR, ["hello", "there"]), ) def check_dispatch(got): assert isinstance(got._oleobj_, pythoncom.TypeIIDs[pythoncom.IID_IDispatch]) _TestPyVariant(o, is_generated, VARIANT(pythoncom.VT_DISPATCH, o), check_dispatch) _TestPyVariant( o, is_generated, VARIANT(pythoncom.VT_ARRAY | pythoncom.VT_DISPATCH, [o]) ) # an array of variants each with a specific type. v = VARIANT( pythoncom.VT_ARRAY | pythoncom.VT_VARIANT, [ VARIANT(pythoncom.VT_UI4, 1), VARIANT(pythoncom.VT_UI4, 2), VARIANT(pythoncom.VT_UI4, 3), ], ) _TestPyVariant(o, is_generated, v) # and failures _TestPyVariantFails(o, is_generated, VARIANT(pythoncom.VT_UI1, "foo"), ValueError) def TestCounter(counter, bIsGenerated): # Test random access into container progress("Testing counter", repr(counter)) import random for i in range(50): num = int(random.random() * len(counter)) try: # XXX - this appears broken by commit 08a14d4deb374eaa06378509cf44078ad467b9dc - # We shouldn't need to do generated differently than dynamic. if bIsGenerated: ret = counter.Item(num + 1) else: ret = counter[num] if ret != num + 1: raise error( "Random access into element %d failed - return was %s" % (num, repr(ret)) ) except IndexError: raise error("** IndexError accessing collection element %d" % num) num = 0 if bIsGenerated: counter.SetTestProperty(1) counter.TestProperty = 1 # Note this has a second, default arg. counter.SetTestProperty(1, 2) if counter.TestPropertyWithDef != 0: raise error("Unexpected property set value!") if counter.TestPropertyNoDef(1) != 1: raise error("Unexpected property set value!") else: pass # counter.TestProperty = 1 counter.LBound = 1 counter.UBound = 10 if counter.LBound != 1 or counter.UBound != 10: print("** Error - counter did not keep its properties") if bIsGenerated: bounds = counter.GetBounds() if bounds[0] != 1 or bounds[1] != 10: raise error("** Error - counter did not give the same properties back") counter.SetBounds(bounds[0], bounds[1]) for item in counter: num = num + 1 if num != len(counter): raise error("*** Length of counter and loop iterations dont match ***") if num != 10: raise error("*** Unexpected number of loop iterations ***") try: counter = iter(counter)._iter_.Clone() # Test Clone() and enum directly except AttributeError: # *sob* - sometimes this is a real iterator and sometimes not :/ progress("Finished testing counter (but skipped the iterator stuff") return counter.Reset() num = 0 for item in counter: num = num + 1 if num != 10: raise error("*** Unexpected number of loop iterations - got %d ***" % num) progress("Finished testing counter") def TestLocalVTable(ob): # Python doesn't fully implement this interface. if ob.DoubleString("foo") != "foofoo": raise error("couldn't foofoo") ############################### ## ## Some vtable tests of the interface ## def TestVTable(clsctx=pythoncom.CLSCTX_ALL): # Any vtable interfaces marked as dual *should* be able to be # correctly implemented as IDispatch. ob = win32com.client.Dispatch("Python.Test.PyCOMTest") TestLocalVTable(ob) # Now test it via vtable - use some C++ code to help here as Python can't do it directly yet. tester = win32com.client.Dispatch("PyCOMTest.PyCOMTest") testee = pythoncom.CoCreateInstance( "Python.Test.PyCOMTest", None, clsctx, pythoncom.IID_IUnknown ) # check we fail gracefully with None passed. try: tester.TestMyInterface(None) except pythoncom.com_error as details: pass # and a real object. tester.TestMyInterface(testee) def TestVTable2(): # We once crashed creating our object with the native interface as # the first IID specified. We must do it _after_ the tests, so that # Python has already had the gateway registered from last run. ob = win32com.client.Dispatch("Python.Test.PyCOMTest") iid = pythoncom.InterfaceNames["IPyCOMTest"] clsid = "Python.Test.PyCOMTest" clsctx = pythoncom.CLSCTX_SERVER try: testee = pythoncom.CoCreateInstance(clsid, None, clsctx, iid) except TypeError: # Python can't actually _use_ this interface yet, so this is # "expected". Any COM error is not. pass def TestVTableMI(): clsctx = pythoncom.CLSCTX_SERVER ob = pythoncom.CoCreateInstance( "Python.Test.PyCOMTestMI", None, clsctx, pythoncom.IID_IUnknown ) # This inherits from IStream. ob.QueryInterface(pythoncom.IID_IStream) # This implements IStorage, specifying the IID as a string ob.QueryInterface(pythoncom.IID_IStorage) # IDispatch should always work ob.QueryInterface(pythoncom.IID_IDispatch) iid = pythoncom.InterfaceNames["IPyCOMTest"] try: ob.QueryInterface(iid) except TypeError: # Python can't actually _use_ this interface yet, so this is # "expected". Any COM error is not. pass def TestQueryInterface(long_lived_server=0, iterations=5): tester = win32com.client.Dispatch("PyCOMTest.PyCOMTest") if long_lived_server: # Create a local server t0 = win32com.client.Dispatch( "Python.Test.PyCOMTest", clsctx=pythoncom.CLSCTX_LOCAL_SERVER ) # Request custom interfaces a number of times prompt = [ "Testing QueryInterface without long-lived local-server #%d of %d...", "Testing QueryInterface with long-lived local-server #%d of %d...", ] for i in range(iterations): progress(prompt[long_lived_server != 0] % (i + 1, iterations)) tester.TestQueryInterface() class Tester(win32com.test.util.TestCase): def testVTableInProc(self): # We used to crash running this the second time - do it a few times for i in range(3): progress("Testing VTables in-process #%d..." % (i + 1)) TestVTable(pythoncom.CLSCTX_INPROC_SERVER) def testVTableLocalServer(self): for i in range(3): progress("Testing VTables out-of-process #%d..." % (i + 1)) TestVTable(pythoncom.CLSCTX_LOCAL_SERVER) def testVTable2(self): for i in range(3): TestVTable2() def testVTableMI(self): for i in range(3): TestVTableMI() def testMultiQueryInterface(self): TestQueryInterface(0, 6) # When we use the custom interface in the presence of a long-lived # local server, i.e. a local server that is already running when # we request an instance of our COM object, and remains afterwards, # then after repeated requests to create an instance of our object # the custom interface disappears -- i.e. QueryInterface fails with # E_NOINTERFACE. Set the upper range of the following test to 2 to # pass this test, i.e. TestQueryInterface(1,2) TestQueryInterface(1, 6) def testDynamic(self): TestDynamic() def testGenerated(self): TestGenerated() if __name__ == "__main__": # XXX - todo - Complete hack to crank threading support. # Should NOT be necessary def NullThreadFunc(): pass import _thread _thread.start_new(NullThreadFunc, ()) if "-v" in sys.argv: verbose = 1 win32com.test.util.testmain()

AndresFPerezG/jarvisProject

env/Lib/site-packages/win32com/test/testPyComTest.py

Python

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you 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. # pylint: disable=no-member, too-many-lines, redefined-builtin, protected-access, unused-import, invalid-name # pylint: disable=too-many-arguments, too-many-locals, no-name-in-module, too-many-branches, too-many-statements """Read individual image files and perform augmentations.""" from __future__ import absolute_import, print_function import os import random import logging import json import warnings import numpy as np try: import cv2 except ImportError: cv2 = None from ..base import numeric_types from .. import ndarray as nd from ..ndarray import _internal from ..ndarray._internal import _cvimresize as imresize from ..ndarray._internal import _cvcopyMakeBorder as copyMakeBorder from .. import io from .. import recordio def imread(filename, *args, **kwargs): """Read and decode an image to an NDArray. Note: `imread` uses OpenCV (not the CV2 Python library). MXNet must have been built with USE_OPENCV=1 for `imdecode` to work. Parameters ---------- filename : str Name of the image file to be loaded. flag : {0, 1}, default 1 1 for three channel color output. 0 for grayscale output. to_rgb : bool, default True True for RGB formatted output (MXNet default). False for BGR formatted output (OpenCV default). out : NDArray, optional Output buffer. Use `None` for automatic allocation. Returns ------- NDArray An `NDArray` containing the image. Example ------- >>> mx.img.imread("flower.jpg") <NDArray 224x224x3 @cpu(0)> Set `flag` parameter to 0 to get grayscale output >>> mx.img.imread("flower.jpg", flag=0) <NDArray 224x224x1 @cpu(0)> Set `to_rgb` parameter to 0 to get output in OpenCV format (BGR) >>> mx.img.imread("flower.jpg", to_rgb=0) <NDArray 224x224x3 @cpu(0)> """ return _internal._cvimread(filename, *args, **kwargs) def imdecode(buf, *args, **kwargs): """Decode an image to an NDArray. Note: `imdecode` uses OpenCV (not the CV2 Python library). MXNet must have been built with USE_OPENCV=1 for `imdecode` to work. Parameters ---------- buf : str/bytes or numpy.ndarray Binary image data as string or numpy ndarray. flag : int, optional, default=1 1 for three channel color output. 0 for grayscale output. to_rgb : int, optional, default=1 1 for RGB formatted output (MXNet default). 0 for BGR formatted output (OpenCV default). out : NDArray, optional Output buffer. Use `None` for automatic allocation. Returns ------- NDArray An `NDArray` containing the image. Example ------- >>> with open("flower.jpg", 'rb') as fp: ... str_image = fp.read() ... >>> image = mx.img.imdecode(str_image) >>> image <NDArray 224x224x3 @cpu(0)> Set `flag` parameter to 0 to get grayscale output >>> with open("flower.jpg", 'rb') as fp: ... str_image = fp.read() ... >>> image = mx.img.imdecode(str_image, flag=0) >>> image <NDArray 224x224x1 @cpu(0)> Set `to_rgb` parameter to 0 to get output in OpenCV format (BGR) >>> with open("flower.jpg", 'rb') as fp: ... str_image = fp.read() ... >>> image = mx.img.imdecode(str_image, to_rgb=0) >>> image <NDArray 224x224x3 @cpu(0)> """ if not isinstance(buf, nd.NDArray): buf = nd.array(np.frombuffer(buf, dtype=np.uint8), dtype=np.uint8) return _internal._cvimdecode(buf, *args, **kwargs) def scale_down(src_size, size): """Scales down crop size if it's larger than image size. If width/height of the crop is larger than the width/height of the image, sets the width/height to the width/height of the image. Parameters ---------- src_size : tuple of int Size of the image in (width, height) format. size : tuple of int Size of the crop in (width, height) format. Returns ------- tuple of int A tuple containing the scaled crop size in (width, height) format. Example -------- >>> src_size = (640,480) >>> size = (720,120) >>> new_size = mx.img.scale_down(src_size, size) >>> new_size (640,106) """ w, h = size sw, sh = src_size if sh < h: w, h = float(w * sh) / h, sh if sw < w: w, h = sw, float(h * sw) / w return int(w), int(h) def _get_interp_method(interp, sizes=()): """Get the interpolation method for resize functions. The major purpose of this function is to wrap a random interp method selection and a auto-estimation method. Parameters ---------- interp : int interpolation method for all resizing operations Possible values: 0: Nearest Neighbors Interpolation. 1: Bilinear interpolation. 2: Area-based (resampling using pixel area relation). It may be a preferred method for image decimation, as it gives moire-free results. But when the image is zoomed, it is similar to the Nearest Neighbors method. (used by default). 3: Bicubic interpolation over 4x4 pixel neighborhood. 4: Lanczos interpolation over 8x8 pixel neighborhood. 9: Cubic for enlarge, area for shrink, bilinear for others 10: Random select from interpolation method metioned above. Note: When shrinking an image, it will generally look best with AREA-based interpolation, whereas, when enlarging an image, it will generally look best with Bicubic (slow) or Bilinear (faster but still looks OK). More details can be found in the documentation of OpenCV, please refer to http://docs.opencv.org/master/da/d54/group__imgproc__transform.html. sizes : tuple of int (old_height, old_width, new_height, new_width), if None provided, auto(9) will return Area(2) anyway. Returns ------- int interp method from 0 to 4 """ if interp == 9: if sizes: assert len(sizes) == 4 oh, ow, nh, nw = sizes if nh > oh and nw > ow: return 2 elif nh < oh and nw < ow: return 3 else: return 1 else: return 2 if interp == 10: return random.randint(0, 4) if interp not in (0, 1, 2, 3, 4): raise ValueError('Unknown interp method %d' % interp) return interp def resize_short(src, size, interp=2): """Resizes shorter edge to size. Note: `resize_short` uses OpenCV (not the CV2 Python library). MXNet must have been built with OpenCV for `resize_short` to work. Resizes the original image by setting the shorter edge to size and setting the longer edge accordingly. Resizing function is called from OpenCV. Parameters ---------- src : NDArray The original image. size : int The length to be set for the shorter edge. interp : int, optional, default=2 Interpolation method used for resizing the image. Possible values: 0: Nearest Neighbors Interpolation. 1: Bilinear interpolation. 2: Area-based (resampling using pixel area relation). It may be a preferred method for image decimation, as it gives moire-free results. But when the image is zoomed, it is similar to the Nearest Neighbors method. (used by default). 3: Bicubic interpolation over 4x4 pixel neighborhood. 4: Lanczos interpolation over 8x8 pixel neighborhood. 9: Cubic for enlarge, area for shrink, bilinear for others 10: Random select from interpolation method metioned above. Note: When shrinking an image, it will generally look best with AREA-based interpolation, whereas, when enlarging an image, it will generally look best with Bicubic (slow) or Bilinear (faster but still looks OK). More details can be found in the documentation of OpenCV, please refer to http://docs.opencv.org/master/da/d54/group__imgproc__transform.html. Returns ------- NDArray An 'NDArray' containing the resized image. Example ------- >>> with open("flower.jpeg", 'rb') as fp: ... str_image = fp.read() ... >>> image = mx.img.imdecode(str_image) >>> image <NDArray 2321x3482x3 @cpu(0)> >>> size = 640 >>> new_image = mx.img.resize_short(image, size) >>> new_image <NDArray 2321x3482x3 @cpu(0)> """ h, w, _ = src.shape if h > w: new_h, new_w = size * h // w, size else: new_h, new_w = size, size * w // h return imresize(src, new_w, new_h, interp=_get_interp_method(interp, (h, w, new_h, new_w))) def fixed_crop(src, x0, y0, w, h, size=None, interp=2): """Crop src at fixed location, and (optionally) resize it to size. Parameters ---------- src : NDArray Input image x0 : int Left boundary of the cropping area y0 : int Top boundary of the cropping area w : int Width of the cropping area h : int Height of the cropping area size : tuple of (w, h) Optional, resize to new size after cropping interp : int, optional, default=2 Interpolation method. See resize_short for details. Returns ------- NDArray An `NDArray` containing the cropped image. """ out = nd.crop(src, begin=(y0, x0, 0), end=(y0 + h, x0 + w, int(src.shape[2]))) if size is not None and (w, h) != size: sizes = (h, w, size[1], size[0]) out = imresize(out, *size, interp=_get_interp_method(interp, sizes)) return out def random_crop(src, size, interp=2): """Randomly crop `src` with `size` (width, height). Upsample result if `src` is smaller than `size`. Parameters ---------- src: Source image `NDArray` size: Size of the crop formatted as (width, height). If the `size` is larger than the image, then the source image is upsampled to `size` and returned. interp: int, optional, default=2 Interpolation method. See resize_short for details. Returns ------- NDArray An `NDArray` containing the cropped image. Tuple A tuple (x, y, width, height) where (x, y) is top-left position of the crop in the original image and (width, height) are the dimensions of the cropped image. Example ------- >>> im = mx.nd.array(cv2.imread("flower.jpg")) >>> cropped_im, rect = mx.image.random_crop(im, (100, 100)) >>> print cropped_im <NDArray 100x100x1 @cpu(0)> >>> print rect (20, 21, 100, 100) """ h, w, _ = src.shape new_w, new_h = scale_down((w, h), size) x0 = random.randint(0, w - new_w) y0 = random.randint(0, h - new_h) out = fixed_crop(src, x0, y0, new_w, new_h, size, interp) return out, (x0, y0, new_w, new_h) def center_crop(src, size, interp=2): """Crops the image `src` to the given `size` by trimming on all four sides and preserving the center of the image. Upsamples if `src` is smaller than `size`. .. note:: This requires MXNet to be compiled with USE_OPENCV. Parameters ---------- src : NDArray Binary source image data. size : list or tuple of int The desired output image size. interp : int, optional, default=2 Interpolation method. See resize_short for details. Returns ------- NDArray The cropped image. Tuple (x, y, width, height) where x, y are the positions of the crop in the original image and width, height the dimensions of the crop. Example ------- >>> with open("flower.jpg", 'rb') as fp: ... str_image = fp.read() ... >>> image = mx.image.imdecode(str_image) >>> image <NDArray 2321x3482x3 @cpu(0)> >>> cropped_image, (x, y, width, height) = mx.image.center_crop(image, (1000, 500)) >>> cropped_image <NDArray 500x1000x3 @cpu(0)> >>> x, y, width, height (1241, 910, 1000, 500) """ h, w, _ = src.shape new_w, new_h = scale_down((w, h), size) x0 = int((w - new_w) / 2) y0 = int((h - new_h) / 2) out = fixed_crop(src, x0, y0, new_w, new_h, size, interp) return out, (x0, y0, new_w, new_h) def color_normalize(src, mean, std=None): """Normalize src with mean and std. Parameters ---------- src : NDArray Input image mean : NDArray RGB mean to be subtracted std : NDArray RGB standard deviation to be divided Returns ------- NDArray An `NDArray` containing the normalized image. """ if mean is not None: src -= mean if std is not None: src /= std return src def random_size_crop(src, size, area, ratio, interp=2, **kwargs): """Randomly crop src with size. Randomize area and aspect ratio. Parameters ---------- src : NDArray Input image size : tuple of (int, int) Size of the crop formatted as (width, height). area : float in (0, 1] or tuple of (float, float) If tuple, minimum area and maximum area to be maintained after cropping If float, minimum area to be maintained after cropping, maximum area is set to 1.0 ratio : tuple of (float, float) Aspect ratio range as (min_aspect_ratio, max_aspect_ratio) interp: int, optional, default=2 Interpolation method. See resize_short for details. Returns ------- NDArray An `NDArray` containing the cropped image. Tuple A tuple (x, y, width, height) where (x, y) is top-left position of the crop in the original image and (width, height) are the dimensions of the cropped image. """ h, w, _ = src.shape src_area = h * w if 'min_area' in kwargs: warnings.warn('`min_area` is deprecated. Please use `area` instead.', DeprecationWarning) area = kwargs.pop('min_area') assert not kwargs, "unexpected keyword arguments for `random_size_crop`." if isinstance(area, numeric_types): area = (area, 1.0) for _ in range(10): target_area = random.uniform(area[0], area[1]) * src_area new_ratio = random.uniform(*ratio) new_w = int(round(np.sqrt(target_area * new_ratio))) new_h = int(round(np.sqrt(target_area / new_ratio))) if random.random() < 0.5: new_h, new_w = new_w, new_h if new_w <= w and new_h <= h: x0 = random.randint(0, w - new_w) y0 = random.randint(0, h - new_h) out = fixed_crop(src, x0, y0, new_w, new_h, size, interp) return out, (x0, y0, new_w, new_h) # fall back to center_crop return center_crop(src, size, interp) class Augmenter(object): """Image Augmenter base class""" def __init__(self, **kwargs): self._kwargs = kwargs for k, v in self._kwargs.items(): if isinstance(v, nd.NDArray): v = v.asnumpy() if isinstance(v, np.ndarray): v = v.tolist() self._kwargs[k] = v def dumps(self): """Saves the Augmenter to string Returns ------- str JSON formatted string that describes the Augmenter. """ return json.dumps([self.__class__.__name__.lower(), self._kwargs]) def __call__(self, src): """Abstract implementation body""" raise NotImplementedError("Must override implementation.") class SequentialAug(Augmenter): """Composing a sequential augmenter list. Parameters ---------- ts : list of augmenters A series of augmenters to be applied in sequential order. """ def __init__(self, ts): super(SequentialAug, self).__init__() self.ts = ts def dumps(self): """Override the default to avoid duplicate dump.""" return [self.__class__.__name__.lower(), [x.dumps() for x in self.ts]] def __call__(self, src): """Augmenter body""" for aug in self.ts: src = aug(src) return src class ResizeAug(Augmenter): """Make resize shorter edge to size augmenter. Parameters ---------- size : int The length to be set for the shorter edge. interp : int, optional, default=2 Interpolation method. See resize_short for details. """ def __init__(self, size, interp=2): super(ResizeAug, self).__init__(size=size, interp=interp) self.size = size self.interp = interp def __call__(self, src): """Augmenter body""" return resize_short(src, self.size, self.interp) class ForceResizeAug(Augmenter): """Force resize to size regardless of aspect ratio Parameters ---------- size : tuple of (int, int) The desired size as in (width, height) interp : int, optional, default=2 Interpolation method. See resize_short for details. """ def __init__(self, size, interp=2): super(ForceResizeAug, self).__init__(size=size, interp=interp) self.size = size self.interp = interp def __call__(self, src): """Augmenter body""" sizes = (src.shape[0], src.shape[1], self.size[1], self.size[0]) return imresize(src, *self.size, interp=_get_interp_method(self.interp, sizes)) class RandomCropAug(Augmenter): """Make random crop augmenter Parameters ---------- size : int The length to be set for the shorter edge. interp : int, optional, default=2 Interpolation method. See resize_short for details. """ def __init__(self, size, interp=2): super(RandomCropAug, self).__init__(size=size, interp=interp) self.size = size self.interp = interp def __call__(self, src): """Augmenter body""" return random_crop(src, self.size, self.interp)[0] class RandomSizedCropAug(Augmenter): """Make random crop with random resizing and random aspect ratio jitter augmenter. Parameters ---------- size : tuple of (int, int) Size of the crop formatted as (width, height). area : float in (0, 1] or tuple of (float, float) If tuple, minimum area and maximum area to be maintained after cropping If float, minimum area to be maintained after cropping, maximum area is set to 1.0 ratio : tuple of (float, float) Aspect ratio range as (min_aspect_ratio, max_aspect_ratio) interp: int, optional, default=2 Interpolation method. See resize_short for details. """ def __init__(self, size, area, ratio, interp=2, **kwargs): super(RandomSizedCropAug, self).__init__(size=size, area=area, ratio=ratio, interp=interp) self.size = size if 'min_area' in kwargs: warnings.warn('`min_area` is deprecated. Please use `area` instead.', DeprecationWarning) self.area = kwargs.pop('min_area') else: self.area = area self.ratio = ratio self.interp = interp assert not kwargs, "unexpected keyword arguments for `RandomSizedCropAug`." def __call__(self, src): """Augmenter body""" return random_size_crop(src, self.size, self.area, self.ratio, self.interp)[0] class CenterCropAug(Augmenter): """Make center crop augmenter. Parameters ---------- size : list or tuple of int The desired output image size. interp : int, optional, default=2 Interpolation method. See resize_short for details. """ def __init__(self, size, interp=2): super(CenterCropAug, self).__init__(size=size, interp=interp) self.size = size self.interp = interp def __call__(self, src): """Augmenter body""" return center_crop(src, self.size, self.interp)[0] class RandomOrderAug(Augmenter): """Apply list of augmenters in random order Parameters ---------- ts : list of augmenters A series of augmenters to be applied in random order """ def __init__(self, ts): super(RandomOrderAug, self).__init__() self.ts = ts def dumps(self): """Override the default to avoid duplicate dump.""" return [self.__class__.__name__.lower(), [x.dumps() for x in self.ts]] def __call__(self, src): """Augmenter body""" random.shuffle(self.ts) for t in self.ts: src = t(src) return src class BrightnessJitterAug(Augmenter): """Random brightness jitter augmentation. Parameters ---------- brightness : float The brightness jitter ratio range, [0, 1] """ def __init__(self, brightness): super(BrightnessJitterAug, self).__init__(brightness=brightness) self.brightness = brightness def __call__(self, src): """Augmenter body""" alpha = 1.0 + random.uniform(-self.brightness, self.brightness) src *= alpha return src class ContrastJitterAug(Augmenter): """Random contrast jitter augmentation. Parameters ---------- contrast : float The contrast jitter ratio range, [0, 1] """ def __init__(self, contrast): super(ContrastJitterAug, self).__init__(contrast=contrast) self.contrast = contrast self.coef = nd.array([[[0.299, 0.587, 0.114]]]) def __call__(self, src): """Augmenter body""" alpha = 1.0 + random.uniform(-self.contrast, self.contrast) gray = src * self.coef gray = (3.0 * (1.0 - alpha) / gray.size) * nd.sum(gray) src *= alpha src += gray return src class SaturationJitterAug(Augmenter): """Random saturation jitter augmentation. Parameters ---------- saturation : float The saturation jitter ratio range, [0, 1] """ def __init__(self, saturation): super(SaturationJitterAug, self).__init__(saturation=saturation) self.saturation = saturation self.coef = nd.array([[[0.299, 0.587, 0.114]]]) def __call__(self, src): """Augmenter body""" alpha = 1.0 + random.uniform(-self.saturation, self.saturation) gray = src * self.coef gray = nd.sum(gray, axis=2, keepdims=True) gray *= (1.0 - alpha) src *= alpha src += gray return src class HueJitterAug(Augmenter): """Random hue jitter augmentation. Parameters ---------- hue : float The hue jitter ratio range, [0, 1] """ def __init__(self, hue): super(HueJitterAug, self).__init__(hue=hue) self.hue = hue self.tyiq = np.array([[0.299, 0.587, 0.114], [0.596, -0.274, -0.321], [0.211, -0.523, 0.311]]) self.ityiq = np.array([[1.0, 0.956, 0.621], [1.0, -0.272, -0.647], [1.0, -1.107, 1.705]]) def __call__(self, src): """Augmenter body. Using approximate linear transfomation described in: https://beesbuzz.biz/code/hsv_color_transforms.php """ alpha = random.uniform(-self.hue, self.hue) u = np.cos(alpha * np.pi) w = np.sin(alpha * np.pi) bt = np.array([[1.0, 0.0, 0.0], [0.0, u, -w], [0.0, w, u]]) t = np.dot(np.dot(self.ityiq, bt), self.tyiq).T src = nd.dot(src, nd.array(t)) return src class ColorJitterAug(RandomOrderAug): """Apply random brightness, contrast and saturation jitter in random order. Parameters ---------- brightness : float The brightness jitter ratio range, [0, 1] contrast : float The contrast jitter ratio range, [0, 1] saturation : float The saturation jitter ratio range, [0, 1] """ def __init__(self, brightness, contrast, saturation): ts = [] if brightness > 0: ts.append(BrightnessJitterAug(brightness)) if contrast > 0: ts.append(ContrastJitterAug(contrast)) if saturation > 0: ts.append(SaturationJitterAug(saturation)) super(ColorJitterAug, self).__init__(ts) class LightingAug(Augmenter): """Add PCA based noise. Parameters ---------- alphastd : float Noise level eigval : 3x1 np.array Eigen values eigvec : 3x3 np.array Eigen vectors """ def __init__(self, alphastd, eigval, eigvec): super(LightingAug, self).__init__(alphastd=alphastd, eigval=eigval, eigvec=eigvec) self.alphastd = alphastd self.eigval = eigval self.eigvec = eigvec def __call__(self, src): """Augmenter body""" alpha = np.random.normal(0, self.alphastd, size=(3,)) rgb = np.dot(self.eigvec * alpha, self.eigval) src += nd.array(rgb) return src class ColorNormalizeAug(Augmenter): """Mean and std normalization. Parameters ---------- mean : NDArray RGB mean to be subtracted std : NDArray RGB standard deviation to be divided """ def __init__(self, mean, std): super(ColorNormalizeAug, self).__init__(mean=mean, std=std) self.mean = mean if mean is None or isinstance(mean, nd.NDArray) else nd.array(mean) self.std = std if std is None or isinstance(std, nd.NDArray) else nd.array(std) def __call__(self, src): """Augmenter body""" return color_normalize(src, self.mean, self.std) class RandomGrayAug(Augmenter): """Randomly convert to gray image. Parameters ---------- p : float Probability to convert to grayscale """ def __init__(self, p): super(RandomGrayAug, self).__init__(p=p) self.p = p self.mat = nd.array([[0.21, 0.21, 0.21], [0.72, 0.72, 0.72], [0.07, 0.07, 0.07]]) def __call__(self, src): """Augmenter body""" if random.random() < self.p: src = nd.dot(src, self.mat) return src class HorizontalFlipAug(Augmenter): """Random horizontal flip. Parameters ---------- p : float Probability to flip image horizontally """ def __init__(self, p): super(HorizontalFlipAug, self).__init__(p=p) self.p = p def __call__(self, src): """Augmenter body""" if random.random() < self.p: src = nd.flip(src, axis=1) return src class CastAug(Augmenter): """Cast to float32""" def __init__(self, typ='float32'): super(CastAug, self).__init__(type=typ) self.typ = typ def __call__(self, src): """Augmenter body""" src = src.astype(self.typ) return src def CreateAugmenter(data_shape, resize=0, rand_crop=False, rand_resize=False, rand_mirror=False, mean=None, std=None, brightness=0, contrast=0, saturation=0, hue=0, pca_noise=0, rand_gray=0, inter_method=2): """Creates an augmenter list. Parameters ---------- data_shape : tuple of int Shape for output data resize : int Resize shorter edge if larger than 0 at the begining rand_crop : bool Whether to enable random cropping other than center crop rand_resize : bool Whether to enable random sized cropping, require rand_crop to be enabled rand_gray : float [0, 1], probability to convert to grayscale for all channels, the number of channels will not be reduced to 1 rand_mirror : bool Whether to apply horizontal flip to image with probability 0.5 mean : np.ndarray or None Mean pixel values for [r, g, b] std : np.ndarray or None Standard deviations for [r, g, b] brightness : float Brightness jittering range (percent) contrast : float Contrast jittering range (percent) saturation : float Saturation jittering range (percent) hue : float Hue jittering range (percent) pca_noise : float Pca noise level (percent) inter_method : int, default=2(Area-based) Interpolation method for all resizing operations Possible values: 0: Nearest Neighbors Interpolation. 1: Bilinear interpolation. 2: Area-based (resampling using pixel area relation). It may be a preferred method for image decimation, as it gives moire-free results. But when the image is zoomed, it is similar to the Nearest Neighbors method. (used by default). 3: Bicubic interpolation over 4x4 pixel neighborhood. 4: Lanczos interpolation over 8x8 pixel neighborhood. 9: Cubic for enlarge, area for shrink, bilinear for others 10: Random select from interpolation method metioned above. Note: When shrinking an image, it will generally look best with AREA-based interpolation, whereas, when enlarging an image, it will generally look best with Bicubic (slow) or Bilinear (faster but still looks OK). Examples -------- >>> # An example of creating multiple augmenters >>> augs = mx.image.CreateAugmenter(data_shape=(3, 300, 300), rand_mirror=True, ... mean=True, brightness=0.125, contrast=0.125, rand_gray=0.05, ... saturation=0.125, pca_noise=0.05, inter_method=10) >>> # dump the details >>> for aug in augs: ... aug.dumps() """ auglist = [] if resize > 0: auglist.append(ResizeAug(resize, inter_method)) crop_size = (data_shape[2], data_shape[1]) if rand_resize: assert rand_crop auglist.append(RandomSizedCropAug(crop_size, 0.08, (3.0 / 4.0, 4.0 / 3.0), inter_method)) elif rand_crop: auglist.append(RandomCropAug(crop_size, inter_method)) else: auglist.append(CenterCropAug(crop_size, inter_method)) if rand_mirror: auglist.append(HorizontalFlipAug(0.5)) auglist.append(CastAug()) if brightness or contrast or saturation: auglist.append(ColorJitterAug(brightness, contrast, saturation)) if hue: auglist.append(HueJitterAug(hue)) if pca_noise > 0: eigval = np.array([55.46, 4.794, 1.148]) eigvec = np.array([[-0.5675, 0.7192, 0.4009], [-0.5808, -0.0045, -0.8140], [-0.5836, -0.6948, 0.4203]]) auglist.append(LightingAug(pca_noise, eigval, eigvec)) if rand_gray > 0: auglist.append(RandomGrayAug(rand_gray)) if mean is True: mean = nd.array([123.68, 116.28, 103.53]) elif mean is not None: assert isinstance(mean, (np.ndarray, nd.NDArray)) and mean.shape[0] in [1, 3] if std is True: std = nd.array([58.395, 57.12, 57.375]) elif std is not None: assert isinstance(std, (np.ndarray, nd.NDArray)) and std.shape[0] in [1, 3] if mean is not None or std is not None: auglist.append(ColorNormalizeAug(mean, std)) return auglist class ImageIter(io.DataIter): """Image data iterator with a large number of augmentation choices. This iterator supports reading from both .rec files and raw image files. To load input images from .rec files, use `path_imgrec` parameter and to load from raw image files, use `path_imglist` and `path_root` parameters. To use data partition (for distributed training) or shuffling, specify `path_imgidx` parameter. Parameters ---------- batch_size : int Number of examples per batch. data_shape : tuple Data shape in (channels, height, width) format. For now, only RGB image with 3 channels is supported. label_width : int, optional Number of labels per example. The default label width is 1. path_imgrec : str Path to image record file (.rec). Created with tools/im2rec.py or bin/im2rec. path_imglist : str Path to image list (.lst). Created with tools/im2rec.py or with custom script. Format: Tab separated record of index, one or more labels and relative_path_from_root. imglist: list A list of images with the label(s). Each item is a list [imagelabel: float or list of float, imgpath]. path_root : str Root folder of image files. path_imgidx : str Path to image index file. Needed for partition and shuffling when using .rec source. shuffle : bool Whether to shuffle all images at the start of each iteration or not. Can be slow for HDD. part_index : int Partition index. num_parts : int Total number of partitions. data_name : str Data name for provided symbols. label_name : str Label name for provided symbols. dtype : str Label data type. Default: float32. Other options: int32, int64, float64 last_batch_handle : str, optional How to handle the last batch. This parameter can be 'pad'(default), 'discard' or 'roll_over'. If 'pad', the last batch will be padded with data starting from the begining If 'discard', the last batch will be discarded If 'roll_over', the remaining elements will be rolled over to the next iteration kwargs : ... More arguments for creating augmenter. See mx.image.CreateAugmenter. """ def __init__(self, batch_size, data_shape, label_width=1, path_imgrec=None, path_imglist=None, path_root=None, path_imgidx=None, shuffle=False, part_index=0, num_parts=1, aug_list=None, imglist=None, data_name='data', label_name='softmax_label', dtype='float32', last_batch_handle='pad', **kwargs): super(ImageIter, self).__init__() assert path_imgrec or path_imglist or (isinstance(imglist, list)) assert dtype in ['int32', 'float32', 'int64', 'float64'], dtype + ' label not supported' num_threads = os.environ.get('MXNET_CPU_WORKER_NTHREADS', 1) logging.info('Using %s threads for decoding...', str(num_threads)) logging.info('Set enviroment variable MXNET_CPU_WORKER_NTHREADS to a' ' larger number to use more threads.') class_name = self.__class__.__name__ if path_imgrec: logging.info('%s: loading recordio %s...', class_name, path_imgrec) if path_imgidx: self.imgrec = recordio.MXIndexedRecordIO(path_imgidx, path_imgrec, 'r') # pylint: disable=redefined-variable-type self.imgidx = list(self.imgrec.keys) else: self.imgrec = recordio.MXRecordIO(path_imgrec, 'r') # pylint: disable=redefined-variable-type self.imgidx = None else: self.imgrec = None if path_imglist: logging.info('%s: loading image list %s...', class_name, path_imglist) with open(path_imglist) as fin: imglist = {} imgkeys = [] for line in iter(fin.readline, ''): line = line.strip().split('\t') label = nd.array(line[1:-1], dtype=dtype) key = int(line[0]) imglist[key] = (label, line[-1]) imgkeys.append(key) self.imglist = imglist elif isinstance(imglist, list): logging.info('%s: loading image list...', class_name) result = {} imgkeys = [] index = 1 for img in imglist: key = str(index) # pylint: disable=redefined-variable-type index += 1 if len(img) > 2: label = nd.array(img[:-1], dtype=dtype) elif isinstance(img[0], numeric_types): label = nd.array([img[0]], dtype=dtype) else: label = nd.array(img[0], dtype=dtype) result[key] = (label, img[-1]) imgkeys.append(str(key)) self.imglist = result else: self.imglist = None self.path_root = path_root self.check_data_shape(data_shape) self.provide_data = [(data_name, (batch_size,) + data_shape)] if label_width > 1: self.provide_label = [(label_name, (batch_size, label_width))] else: self.provide_label = [(label_name, (batch_size,))] self.batch_size = batch_size self.data_shape = data_shape self.label_width = label_width self.shuffle = shuffle if self.imgrec is None: self.seq = imgkeys elif shuffle or num_parts > 1: assert self.imgidx is not None self.seq = self.imgidx else: self.seq = None if num_parts > 1: assert part_index < num_parts N = len(self.seq) C = N // num_parts self.seq = self.seq[part_index * C:(part_index + 1) * C] if aug_list is None: self.auglist = CreateAugmenter(data_shape, **kwargs) else: self.auglist = aug_list self.cur = 0 self._allow_read = True self.last_batch_handle = last_batch_handle self.num_image = len(self.seq) if self.seq is not None else None self._cache_data = None self._cache_label = None self._cache_idx = None self.reset() def reset(self): """Resets the iterator to the beginning of the data.""" if self.seq is not None and self.shuffle: random.shuffle(self.seq) if self.last_batch_handle != 'roll_over' or \ self._cache_data is None: if self.imgrec is not None: self.imgrec.reset() self.cur = 0 if self._allow_read is False: self._allow_read = True def hard_reset(self): """Resets the iterator and ignore roll over data""" if self.seq is not None and self.shuffle: random.shuffle(self.seq) if self.imgrec is not None: self.imgrec.reset() self.cur = 0 self._allow_read = True self._cache_data = None self._cache_label = None self._cache_idx = None def next_sample(self): """Helper function for reading in next sample.""" if self._allow_read is False: raise StopIteration if self.seq is not None: if self.cur < self.num_image: idx = self.seq[self.cur] else: if self.last_batch_handle != 'discard': self.cur = 0 raise StopIteration self.cur += 1 if self.imgrec is not None: s = self.imgrec.read_idx(idx) header, img = recordio.unpack(s) if self.imglist is None: return header.label, img else: return self.imglist[idx][0], img else: label, fname = self.imglist[idx] return label, self.read_image(fname) else: s = self.imgrec.read() if s is None: if self.last_batch_handle != 'discard': self.imgrec.reset() raise StopIteration header, img = recordio.unpack(s) return header.label, img def _batchify(self, batch_data, batch_label, start=0): """Helper function for batchifying data""" i = start batch_size = self.batch_size try: while i < batch_size: label, s = self.next_sample() data = self.imdecode(s) try: self.check_valid_image(data) except RuntimeError as e: logging.debug('Invalid image, skipping: %s', str(e)) continue data = self.augmentation_transform(data) assert i < batch_size, 'Batch size must be multiples of augmenter output length' batch_data[i] = self.postprocess_data(data) batch_label[i] = label i += 1 except StopIteration: if not i: raise StopIteration return i def next(self): """Returns the next batch of data.""" batch_size = self.batch_size c, h, w = self.data_shape # if last batch data is rolled over if self._cache_data is not None: # check both the data and label have values assert self._cache_label is not None, "_cache_label didn't have values" assert self._cache_idx is not None, "_cache_idx didn't have values" batch_data = self._cache_data batch_label = self._cache_label i = self._cache_idx # clear the cache data else: batch_data = nd.empty((batch_size, c, h, w)) batch_label = nd.empty(self.provide_label[0][1]) i = self._batchify(batch_data, batch_label) # calculate the padding pad = batch_size - i # handle padding for the last batch if pad != 0: if self.last_batch_handle == 'discard': raise StopIteration # if the option is 'roll_over', throw StopIteration and cache the data elif self.last_batch_handle == 'roll_over' and \ self._cache_data is None: self._cache_data = batch_data self._cache_label = batch_label self._cache_idx = i raise StopIteration else: _ = self._batchify(batch_data, batch_label, i) if self.last_batch_handle == 'pad': self._allow_read = False else: self._cache_data = None self._cache_label = None self._cache_idx = None return io.DataBatch([batch_data], [batch_label], pad=pad) def check_data_shape(self, data_shape): """Checks if the input data shape is valid""" if not len(data_shape) == 3: raise ValueError('data_shape should have length 3, with dimensions CxHxW') if not data_shape[0] == 3: raise ValueError('This iterator expects inputs to have 3 channels.') def check_valid_image(self, data): """Checks if the input data is valid""" if len(data[0].shape) == 0: raise RuntimeError('Data shape is wrong') def imdecode(self, s): """Decodes a string or byte string to an NDArray. See mx.img.imdecode for more details.""" def locate(): """Locate the image file/index if decode fails.""" if self.seq is not None: idx = self.seq[(self.cur % self.num_image) - 1] else: idx = (self.cur % self.num_image) - 1 if self.imglist is not None: _, fname = self.imglist[idx] msg = "filename: {}".format(fname) else: msg = "index: {}".format(idx) return "Broken image " + msg try: img = imdecode(s) except Exception as e: raise RuntimeError("{}, {}".format(locate(), e)) return img def read_image(self, fname): """Reads an input image `fname` and returns the decoded raw bytes. Example usage: ---------- >>> dataIter.read_image('Face.jpg') # returns decoded raw bytes. """ with open(os.path.join(self.path_root, fname), 'rb') as fin: img = fin.read() return img def augmentation_transform(self, data): """Transforms input data with specified augmentation.""" for aug in self.auglist: data = aug(data) return data def postprocess_data(self, datum): """Final postprocessing step before image is loaded into the batch.""" return nd.transpose(datum, axes=(2, 0, 1))

Vikas89/private-mxnet

python/mxnet/image/image.py

Python

# coding: utf-8 """ OpenShift API (with Kubernetes) OpenShift provides builds, application lifecycle, image content management, and administrative policy on top of Kubernetes. The API allows consistent management of those objects. All API operations are authenticated via an Authorization bearer token that is provided for service accounts as a generated secret (in JWT form) or via the native OAuth endpoint located at /oauth/authorize. Core infrastructure components may use openshift.client certificates that require no authentication. All API operations return a 'resourceVersion' string that represents the version of the object in the underlying storage. The standard LIST operation performs a snapshot read of the underlying objects, returning a resourceVersion representing a consistent version of the listed objects. The WATCH operation allows all updates to a set of objects after the provided resourceVersion to be observed by a openshift.client. By listing and beginning a watch from the returned resourceVersion, openshift.clients may observe a consistent view of the state of one or more objects. Note that WATCH always returns the update after the provided resourceVersion. Watch may be extended a limited time in the past - using etcd 2 the watch window is 1000 events (which on a large cluster may only be a few tens of seconds) so openshift.clients must explicitly handle the \"watch to old error\" by re-listing. Objects are divided into two rough categories - those that have a lifecycle and must reflect the state of the cluster, and those that have no state. Objects with lifecycle typically have three main sections: * 'metadata' common to all objects * a 'spec' that represents the desired state * a 'status' that represents how much of the desired state is reflected on the cluster at the current time Objects that have no state have 'metadata' but may lack a 'spec' or 'status' section. Objects are divided into those that are namespace scoped (only exist inside of a namespace) and those that are cluster scoped (exist outside of a namespace). A namespace scoped resource will be deleted when the namespace is deleted and cannot be created if the namespace has not yet been created or is in the process of deletion. Cluster scoped resources are typically only accessible to admins - resources like nodes, persistent volumes, and cluster policy. All objects have a schema that is a combination of the 'kind' and 'apiVersion' fields. This schema is additive only for any given version - no backwards incompatible changes are allowed without incrementing the apiVersion. The server will return and accept a number of standard responses that share a common schema - for instance, the common error type is 'unversioned.Status' (described below) and will be returned on any error from the API server. The API is available in multiple serialization formats - the default is JSON (Accept: application/json and Content-Type: application/json) but openshift.clients may also use YAML (application/yaml) or the native Protobuf schema (application/vnd.kubernetes.protobuf). Note that the format of the WATCH API call is slightly different - for JSON it returns newline delimited objects while for Protobuf it returns length-delimited frames (4 bytes in network-order) that contain a 'versioned.Watch' Protobuf object. See the OpenShift documentation at https://docs.openshift.org for more information. OpenAPI spec version: v3.6.0-alpha.0 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import os import sys import unittest import openshift.client from kubernetes.client.rest import ApiException from openshift.client.models.v1beta1_cpu_target_utilization import V1beta1CPUTargetUtilization class TestV1beta1CPUTargetUtilization(unittest.TestCase): """ V1beta1CPUTargetUtilization unit test stubs """ def setUp(self): pass def tearDown(self): pass def testV1beta1CPUTargetUtilization(self): """ Test V1beta1CPUTargetUtilization """ model = openshift.client.models.v1beta1_cpu_target_utilization.V1beta1CPUTargetUtilization() if __name__ == '__main__': unittest.main()

flaper87/openshift-restclient-python

openshift/test/test_v1beta1_cpu_target_utilization.py

Python

import warnings import mmcv import numpy as np import torch from torch.nn.modules.utils import _pair from mmdet.core.anchor.builder import ANCHOR_GENERATORS from mmdet.core.anchor import AnchorGenerator @ANCHOR_GENERATORS.register_module(force=True) class SSDAnchorGenerator(AnchorGenerator): """Anchor generator for SSD Args: strides (list[int] | list[tuple[int, int]]): Strides of anchors in multiple feature levels. ratios (list[float]): The list of ratios between the height and width of anchors in a single level. basesize_ratio_range (tuple(float)): Ratio range of anchors. input_size (int): Size of feature map, 300 for SSD300, 512 for SSD512. scale_major (bool): Whether to multiply scales first when generating base anchors. If true, the anchors in the same row will have the same scales. It is always set to be False in SSD. """ def __init__(self, strides, ratios, basesize_ratio_range, input_size=300, scale_major=True): assert len(strides) == len(ratios) assert mmcv.is_tuple_of(basesize_ratio_range, float) self.strides = [_pair(stride) for stride in strides] self.input_size = max(input_size) if isinstance(input_size, (list,tuple)) else input_size self.centers = [(stride[0] / 2., stride[1] / 2.) for stride in self.strides] self.basesize_ratio_range = basesize_ratio_range # calculate anchor ratios and sizes min_ratio, max_ratio = basesize_ratio_range min_ratio = int(min_ratio * 100) max_ratio = int(max_ratio * 100) step = int(np.floor(max_ratio - min_ratio) / (self.num_levels - 2)) min_sizes = [] max_sizes = [] for ratio in range(int(min_ratio), int(max_ratio) + 1, step): min_sizes.append(int(self.input_size * ratio / 100)) max_sizes.append(int(self.input_size * (ratio + step) / 100)) if self.input_size == 300: if basesize_ratio_range[0] == 0.15: # SSD300 COCO min_sizes.insert(0, int(self.input_size * 7 / 100)) max_sizes.insert(0, int(self.input_size * 15 / 100)) elif basesize_ratio_range[0] == 0.2: # SSD300 VOC min_sizes.insert(0, int(self.input_size * 10 / 100)) max_sizes.insert(0, int(self.input_size * 20 / 100)) else: min_sizes.insert(0, int(self.input_size * basesize_ratio_range[0] * 0.4)) max_sizes.insert(0, int(self.input_size * basesize_ratio_range[0])) warnings.warn( 'according to original SSD, basesize_ratio_range[0] should be either 0.15' 'or 0.2 when input_size is 300, got ' f'{basesize_ratio_range[0]}.') elif self.input_size == 512: if basesize_ratio_range[0] == 0.1: # SSD512 COCO min_sizes.insert(0, int(self.input_size * 4 / 100)) max_sizes.insert(0, int(self.input_size * 10 / 100)) elif basesize_ratio_range[0] == 0.15: # SSD512 VOC min_sizes.insert(0, int(self.input_size * 7 / 100)) max_sizes.insert(0, int(self.input_size * 15 / 100)) else: min_sizes.insert(0, int(self.input_size * basesize_ratio_range[0] * 0.4)) max_sizes.insert(0, int(self.input_size * basesize_ratio_range[0])) warnings.warn('according to original SSD, basesize_ratio_range[0] should be either 0.1' 'or 0.15 when input_size is 512, got' f' {basesize_ratio_range[0]}.') else: if basesize_ratio_range[0] == 0.1: # SSD512 COCO min_sizes.insert(0, int(self.input_size * 4 / 100)) max_sizes.insert(0, int(self.input_size * 10 / 100)) else: min_sizes.insert(0, int(self.input_size * basesize_ratio_range[0] * 0.4)) max_sizes.insert(0, int(self.input_size * basesize_ratio_range[0])) anchor_ratios = [] anchor_scales = [] for k in range(len(self.strides)): scales = [1., np.sqrt(max_sizes[k] / min_sizes[k])] anchor_ratio = [1.] for r in ratios[k]: anchor_ratio += [1 / r, r] # 4 or 6 ratio anchor_ratios.append(torch.Tensor(anchor_ratio)) anchor_scales.append(torch.Tensor(scales)) self.base_sizes = min_sizes self.scales = anchor_scales self.ratios = anchor_ratios self.scale_major = scale_major self.center_offset = 0 self.base_anchors = self.gen_base_anchors() # added for proto export self.min_sizes = min_sizes self.max_sizes = max_sizes def gen_base_anchors(self): """Generate base anchors. Returns: list(torch.Tensor): Base anchors of a feature grid in multiple \ feature levels. """ multi_level_base_anchors = [] for i, base_size in enumerate(self.base_sizes): base_anchors = self.gen_single_level_base_anchors( base_size, scales=self.scales[i], ratios=self.ratios[i], center=self.centers[i]) indices = list(range(len(self.ratios[i]))) indices.insert(1, len(indices)) base_anchors = torch.index_select(base_anchors, 0, torch.LongTensor(indices)) multi_level_base_anchors.append(base_anchors) return multi_level_base_anchors def __repr__(self): """str: a string that describes the module""" indent_str = ' ' repr_str = self.__class__.__name__ + '(\n' repr_str += f'{indent_str}strides={self.strides},\n' repr_str += f'{indent_str}scales={self.scales},\n' repr_str += f'{indent_str}scale_major={self.scale_major},\n' repr_str += f'{indent_str}input_size={self.input_size},\n' repr_str += f'{indent_str}scales={self.scales},\n' repr_str += f'{indent_str}ratios={self.ratios},\n' repr_str += f'{indent_str}num_levels={self.num_levels},\n' repr_str += f'{indent_str}base_sizes={self.base_sizes},\n' repr_str += f'{indent_str}basesize_ratio_range=' repr_str += f'{self.basesize_ratio_range})' return repr_str

www516717402/edgeai-mmdetection

xmmdet/core/anchor/anchor_generator.py

Python

from __future__ import absolute_import """This module offers a display and interaction frontend with Qt. It will try importing PySide first, and if that fails PyQt. The code will constantly be tested with both bindings.""" from .displaywidgets import DisplayWidget, NewDisplayWidget from .control import ControlWidget #from .mainwin import ZasimMainWindow display_objects = [] class ZasimDisplay(object): simulator = None """The `Simulator` object for this display.""" display = None """The `BaseDisplayWidget` in use.""" window = None """The `ZasimMainWindow` instance in use.""" control = None """The `ControlWidget` in use.""" def __init__(self, simulator): """Instantiate a Display (thas is: a window with a display widget and simulation controls) from a simulator. :param simulator: The simulator to use.""" self.simulator = simulator if not self.display: if 'tiles' in self.simulator.palette_info: self.display = NewDisplayWidget(self.simulator) else: self.display = DisplayWidget(self.simulator) if self.control is None: self.control = ControlWidget(self.simulator) from .mainwin import ZasimMainWindow self.window = ZasimMainWindow(self.simulator, self.display, self.control) display_objects.append(self.window) self.window.show() def set_scale(self, scale): """Sets the scale of the display component.""" self.display.set_scale(scale)

timo/zasim

zasim/gui/display.py

Python

import json import pytest from typing import ClassVar, Dict, List, Sequence, Tuple, Union from kat.harness import sanitize, variants, Query, Runner from abstract_tests import AmbassadorTest, HTTP, AHTTP from abstract_tests import MappingTest, OptionTest, ServiceType, Node, Test class LogServiceTest(AmbassadorTest): def init(self): self.extra_ports = [25565] self.target = HTTP() def manifests(self) -> str: return self.format(""" --- apiVersion: v1 kind: Service metadata: name: stenography spec: selector: app: stenography ports: - port: 25565 name: http targetPort: http type: ClusterIP --- apiVersion: apps/v1 kind: Deployment metadata: name: stenography spec: selector: matchLabels: app: stenography replicas: 1 strategy: type: RollingUpdate template: metadata: labels: app: stenography spec: containers: - name: stenography image: securityinsanity/stenography:latest env: - name: PORT value: "25565" ports: - name: http containerPort: 25565 """) + super().manifests() def config(self): yield self, self.format(""" --- apiVersion: getambassador.io/v3alpha1 kind: LogService name: custom-http-logging service: stenography:25565 driver: http driver_config: additional_log_headers: - header_name: "included-on-all" - header_name: "not-included-on-trailer" during_trailer: false - header_name: "not-included on resp-trail" during_trailer: false during_response: false - header_name: "not-anywhere" during_trailer: false during_response: false during_request: false flush_interval_time: 1 flush_interval_byte_size: 1 """) yield self, self.format(""" --- apiVersion: getambassador.io/v3alpha1 kind: Mapping name: config__dump hostname: "*" prefix: /config_dump rewrite: /config_dump service: http://127.0.0.1:8001 """) def requirements(self): yield from super().requirements() yield ("url", Query(self.url("config_dump"))) def queries(self): yield Query(self.url("config_dump"), phase=2) def check(self): found_bootstrap_dump = False found_clusters_dump = False found_listeners_dump = False body = json.loads(self.results[0].body) for config_obj in body.get('configs'): if config_obj.get('@type') == 'type.googleapis.com/envoy.admin.v3.BootstrapConfigDump': found_bootstrap_dump = True clusters = config_obj.get('bootstrap').get('static_resources').get('clusters') found_stenography = False assert len(clusters) > 0, "No clusters found" for cluster in clusters: if cluster.get('name') == 'cluster_logging_stenography_25565_default': found_stenography = True break assert found_stenography if config_obj.get('@type') == 'type.googleapis.com/envoy.admin.v3.ClustersConfigDump': found_clusters_dump = True clusters = config_obj.get('static_clusters') found_stenography = False assert len(clusters) > 0, "No clusters found" for cluster in clusters: if cluster.get('cluster').get('name') == 'cluster_logging_stenography_25565_default': found_stenography = True break assert found_stenography if config_obj.get('@type') == 'type.googleapis.com/envoy.admin.v3.ListenersConfigDump': found_listeners_dump = True for listener in config_obj.get('dynamic_listeners'): for filter_chain in listener.get('active_state').get('listener').get('filter_chains'): for filter_obj in filter_chain.get('filters'): access_logs = filter_obj.get('typed_config').get('access_log') found_configured_access_log = False assert len( access_logs) > 0, "No access log configurations found in any listeners filter chains" for access_log in access_logs: if access_log.get('name') == 'envoy.access_loggers.http_grpc' and access_log.get( 'typed_config').get('common_config').get('grpc_service').get('envoy_grpc').get( 'cluster_name') == 'cluster_logging_stenography_25565_default': found_configured_access_log = True break assert found_configured_access_log assert found_listeners_dump, "Could not find listeners config dump. Did the config dump endpoint work? Did we change Envoy API versions?" assert found_clusters_dump, "Could not find clusters config dump. Did the config dump endpoint work? Did we change Envoy API versions?" assert found_bootstrap_dump, "Could not find bootstrap config dump. Did the config dump endpoint work? Did we change Envoy API versions?" class LogServiceTestLongServiceName(AmbassadorTest): def init(self): self.extra_ports = [25565] self.target = HTTP() def manifests(self) -> str: return self.format(""" --- apiVersion: v1 kind: Service metadata: name: stenographylongservicenamewithnearly60characterss spec: selector: app: stenography-longservicename ports: - port: 25565 name: http targetPort: http type: ClusterIP --- apiVersion: apps/v1 kind: Deployment metadata: name: stenography-longservicename spec: selector: matchLabels: app: stenography-longservicename replicas: 1 strategy: type: RollingUpdate template: metadata: labels: app: stenography-longservicename spec: containers: - name: stenography image: securityinsanity/stenography:latest env: - name: PORT value: "25565" ports: - name: http containerPort: 25565 """) + super().manifests() def config(self): yield self, self.format(""" --- apiVersion: getambassador.io/v3alpha1 kind: LogService name: custom-http-logging service: stenographylongservicenamewithnearly60characterss:25565 driver: http driver_config: additional_log_headers: - header_name: "included-on-all" - header_name: "not-included-on-trailer" during_trailer: false - header_name: "not-included on resp-trail" during_trailer: false during_response: false - header_name: "not-anywhere" during_trailer: false during_response: false during_request: false flush_interval_time: 1 flush_interval_byte_size: 1 """) yield self, self.format(""" --- apiVersion: getambassador.io/v3alpha1 kind: Mapping name: config__dump-longservicename hostname: "*" prefix: /config_dump rewrite: /config_dump service: http://127.0.0.1:8001 """) def requirements(self): yield from super().requirements() yield ("url", Query(self.url("config_dump"))) def queries(self): yield Query(self.url("config_dump"), phase=2) def check(self): found_bootstrap_dump = False found_clusters_dump = False found_listeners_dump = False body = json.loads(self.results[0].body) for config_obj in body.get('configs'): if config_obj.get('@type') == 'type.googleapis.com/envoy.admin.v3.BootstrapConfigDump': found_bootstrap_dump = True clusters = config_obj.get('bootstrap').get('static_resources').get('clusters') found_stenography = False assert len(clusters) > 0, "No clusters found" for cluster in clusters: if cluster.get('name') == 'cluster_logging_stenographylongservicena-0': found_stenography = True break assert found_stenography if config_obj.get('@type') == 'type.googleapis.com/envoy.admin.v3.ClustersConfigDump': found_clusters_dump = True clusters = config_obj.get('static_clusters') found_stenography = False assert len(clusters) > 0, "No clusters found" for cluster in clusters: if cluster.get('cluster').get('name') == 'cluster_logging_stenographylongservicena-0': found_stenography = True break assert found_stenography if config_obj.get('@type') == 'type.googleapis.com/envoy.admin.v3.ListenersConfigDump': found_listeners_dump = True for listener in config_obj.get('dynamic_listeners'): for filter_chain in listener.get('active_state').get('listener').get('filter_chains'): for filter_obj in filter_chain.get('filters'): access_logs = filter_obj.get('typed_config').get('access_log') found_configured_access_log = False assert len( access_logs) > 0, "No access log configurations found in any listeners filter chains" for access_log in access_logs: if access_log.get('name') == 'envoy.access_loggers.http_grpc' and access_log.get( 'typed_config').get('common_config').get('grpc_service').get('envoy_grpc').get( 'cluster_name') == 'cluster_logging_stenographylongservicena-0': found_configured_access_log = True break assert found_configured_access_log assert found_listeners_dump, "Could not find listeners config dump. Did the config dump endpoint work? Did we change Envoy API versions?" assert found_clusters_dump, "Could not find clusters config dump. Did the config dump endpoint work? Did we change Envoy API versions?" assert found_bootstrap_dump, "Could not find bootstrap config dump. Did the config dump endpoint work? Did we change Envoy API versions?"

DoodleScheduling/emissary

python/tests/kat/t_logservice.py

Python

import typing from typing import Dict, Union, Tuple, Iterator, Any from typing import Optional import numpy as np import torch from gym.utils import seeding from advisor_losses import AlphaScheduler, AdvisorWeightedStage from allenact.algorithms.offpolicy_sync.losses.abstract_offpolicy_loss import ( AbstractOffPolicyLoss, ) from allenact.algorithms.onpolicy_sync.policy import ActorCriticModel from allenact.base_abstractions.misc import Memory _DATASET_CACHE: Dict[str, Any] = {} class PoisonedDoorsOffPolicyExpertCELoss(AbstractOffPolicyLoss[ActorCriticModel]): def __init__(self, total_episodes_in_epoch: Optional[int] = None): super().__init__() self.total_episodes_in_epoch = total_episodes_in_epoch def loss( self, model: ActorCriticModel, batch: Dict[str, Union[torch.Tensor, Dict[str, torch.Tensor]]], memory: Memory, *args, **kwargs ) -> Tuple[torch.FloatTensor, Dict[str, float], Memory, int]: rollout_len, nrollouts, _, = batch["poisoned_door_state"].shape observations = {} for k in ["poisoned_door_state"]: if k in batch: observations[k] = batch[k].view( rollout_len, nrollouts, *batch[k].shape[2:] ) ac_out, memory = model.forward( observations=observations, memory=memory, prev_actions=None, masks=batch["masks"], ) expert_ce_loss = -ac_out.distributions.log_prob( batch["expert_action"].view(rollout_len, nrollouts, 1) ).mean() info = {"expert_ce": expert_ce_loss.item()} if self.total_episodes_in_epoch is not None: if "completed_episode_count" not in memory: memory["completed_episode_count"] = 0 memory["completed_episode_count"] += ( int(np.prod(batch["masks"].shape)) - batch["masks"].sum().item() ) info["epoch_progress"] = ( memory["completed_episode_count"] / self.total_episodes_in_epoch ) return expert_ce_loss, info, memory, rollout_len * nrollouts class PoisonedDoorsOffPolicyAdvisorLoss(AbstractOffPolicyLoss[ActorCriticModel]): def __init__( self, total_episodes_in_epoch: Optional[int] = None, fixed_alpha: Optional[float] = 1, fixed_bound: Optional[float] = 0.0, alpha_scheduler: AlphaScheduler = None, smooth_expert_weight_decay: Optional[float] = None, *args, **kwargs ): super().__init__() self.advisor_loss = AdvisorWeightedStage( rl_loss=None, fixed_alpha=fixed_alpha, fixed_bound=fixed_bound, alpha_scheduler=alpha_scheduler, smooth_expert_weight_decay=smooth_expert_weight_decay, *args, **kwargs ) self.total_episodes_in_epoch = total_episodes_in_epoch def loss( self, step_count: int, model: ActorCriticModel, batch: Dict[str, Union[torch.Tensor, Dict[str, torch.Tensor]]], memory: Memory, **kwargs ) -> Tuple[torch.FloatTensor, Dict[str, float], Memory, int]: rollout_len, nrollouts, _ = batch["poisoned_door_state"].shape observations = {"poisoned_door_state": batch["poisoned_door_state"]} ac_out, memory = model.forward( observations=observations, memory=memory, prev_actions=None, masks=batch["masks"].view(rollout_len, nrollouts, -1), ) total_loss, losses_dict = self.advisor_loss.loss( step_count=step_count, batch={ "observations": { "expert_action": torch.cat( ( batch["expert_action"].view(rollout_len, nrollouts, 1), torch.ones(rollout_len, nrollouts, 1, dtype=torch.int64).to( batch["expert_action"].device ), ), dim=-1, ) } }, actor_critic_output=ac_out, ) info = {"offpolicy_" + key: val for key, val in losses_dict.items()} if self.total_episodes_in_epoch is not None: if "completed_episode_count" not in memory: memory["completed_episode_count"] = 0 memory["completed_episode_count"] += ( int(np.prod(batch["masks"].shape)) - batch["masks"].sum().item() ) info["epoch_progress"] = ( memory["completed_episode_count"] / self.total_episodes_in_epoch ) return total_loss, info, memory, rollout_len * nrollouts class PoisonedDoorsExpertTrajectoryIterator(Iterator): def __init__( self, num_doors: int, nrollouts: int, rollout_len: int, dataset_size: int, ): super(PoisonedDoorsExpertTrajectoryIterator, self).__init__() self.np_seeded_random_gen, _ = typing.cast( Tuple[np.random.RandomState, Any], seeding.np_random(0) ) self.ndoors = num_doors self.nrollouts = nrollouts self.rollout_len = rollout_len self.dataset_size = dataset_size self.initial_observations = np.zeros( (rollout_len, nrollouts, 1), dtype=np.int64 ) self.mask = np.zeros((rollout_len, nrollouts, 1), dtype=np.float32) self.expert_actions = np.random.randint( 4, 3 + num_doors, size=(self.dataset_size, 1) ) self.current_ind = 0 def __next__(self) -> Dict[str, torch.Tensor]: start = self.current_ind end = self.current_ind + self.nrollouts * self.rollout_len if end > self.dataset_size: raise StopIteration() self.current_ind = end return { "masks": torch.from_numpy(self.mask), "poisoned_door_state": torch.from_numpy(self.initial_observations), "expert_action": torch.from_numpy( self.expert_actions[start:end].reshape( (self.rollout_len, self.nrollouts) ) ), } def create_poisoneddoors_offpolicy_data_iterator( num_doors: int, nrollouts: int, rollout_len: int, dataset_size: int, ) -> PoisonedDoorsExpertTrajectoryIterator: return PoisonedDoorsExpertTrajectoryIterator( num_doors=num_doors, nrollouts=nrollouts, rollout_len=rollout_len, dataset_size=dataset_size, )

allenai/advisor

poisoneddoors_plugin/poisoneddoors_offpolicy.py

Python

from django.contrib import admin from django.db.models import get_model ConditionalOffer = get_model('offer', 'ConditionalOffer') Condition = get_model('offer', 'Condition') Benefit = get_model('offer', 'Benefit') Range = get_model('offer', 'Range') class ConditionAdmin(admin.ModelAdmin): list_display = ('type', 'value', 'range') class BenefitAdmin(admin.ModelAdmin): list_display = ('__unicode__', 'type', 'value', 'range') class ConditionalOfferAdmin(admin.ModelAdmin): list_display = ('name', 'offer_type', 'start_date', 'end_date', 'condition', 'benefit', 'total_discount') list_filter = ('offer_type',) readonly_fields = ('total_discount', 'num_orders') fieldsets = ( (None, { 'fields': ('name', 'description', 'offer_type', 'condition', 'benefit', 'start_date', 'end_date', 'priority') }), ('Usage', { 'fields': ('total_discount', 'num_orders') }), ) admin.site.register(ConditionalOffer, ConditionalOfferAdmin) admin.site.register(Condition, ConditionAdmin) admin.site.register(Benefit, BenefitAdmin) admin.site.register(Range)

endgame/django-oscar

oscar/apps/offer/admin.py

Python

import numpy from chainer.backends import cuda from chainer import function_node from chainer.utils import type_check class ResizeImages3D(function_node.FunctionNode): def __init__(self, output_shape): self.out_H = output_shape[0] self.out_W = output_shape[1] self.out_D = output_shape[2] def check_type_forward(self, in_types): n_in = in_types.size() type_check.expect(n_in == 1) x_type = in_types[0] type_check.expect( x_type.dtype.char == 'f', x_type.ndim == 5 ) def forward(self, inputs): x, = inputs xp = cuda.get_array_module(x) B, C, H, W, D = x.shape u_1d = xp.linspace(0, W - 1, num=self.out_W) v_1d = xp.linspace(0, H - 1, num=self.out_H) t_1d = xp.linspace(0, D - 1, num=self.out_D) grid = xp.meshgrid(u_1d, v_1d, t_1d) u = grid[0].ravel() v = grid[1].ravel() t = grid[2].ravel() u0 = xp.floor(u).astype(numpy.int32) u0 = u0.clip(0, W - 2) u1 = u0 + 1 v0 = xp.floor(v).astype(numpy.int32) v0 = v0.clip(0, H - 2) v1 = v0 + 1 t0 = xp.floor(t).astype(numpy.int32) t0 = t0.clip(0, D - 2) t1 = t0 + 1 # weights w1 = (u1 - u) * (v1 - v) * (t1 - t) w2 = (u - u0) * (v1 - v) * (t1 - t) w3 = (u1 - u) * (v - v0) * (t1 - t) w4 = (u - u0) * (v - v0) * (t1 - t) w5 = (u1 - u) * (v1 - v) * (t - t0) w6 = (u - u0) * (v1 - v) * (t - t0) w7 = (u1 - u) * (v - v0) * (t - t0) w8 = (u - u0) * (v - v0) * (t - t0) w1 = w1.astype(x.dtype) w2 = w2.astype(x.dtype) w3 = w3.astype(x.dtype) w4 = w4.astype(x.dtype) w5 = w5.astype(x.dtype) w6 = w6.astype(x.dtype) w7 = w7.astype(x.dtype) w8 = w8.astype(x.dtype) y = (w1[None, None, :] * x[:, :, v0, u0, t0] + w2[None, None, :] * x[:, :, v0, u1, t0] + w3[None, None, :] * x[:, :, v1, u0, t0] + w4[None, None, :] * x[:, :, v1, u1, t0] + w5[None, None, :] * x[:, :, v0, u0, t1] + w6[None, None, :] * x[:, :, v0, u1, t1] + w7[None, None, :] * x[:, :, v1, u0, t1] + w8[None, None, :] * x[:, :, v1, u1, t1]) y = y.reshape(B, C, self.out_H, self.out_W, self.out_D) return y, def backward(self, indexes, grad_outputs): return ResizeImagesGrad3D( self.inputs[0].shape, (self.out_H, self.out_W, self.out_D)).apply(grad_outputs) class ResizeImagesGrad3D(function_node.FunctionNode): def __init__(self, input_shape, output_shape): self.out_H = output_shape[0] self.out_W = output_shape[1] self.out_D = output_shape[2] self.input_shape = input_shape def check_type_forward(self, in_types): n_in = in_types.size() type_check.expect(n_in == 1) x_type = in_types[0] type_check.expect( x_type.dtype.char == 'f', x_type.ndim == 5 ) def forward(self, inputs): xp = cuda.get_array_module(*inputs) gy, = inputs B, C, H, W, D = self.input_shape u_1d = xp.linspace(0, W - 1, num=self.out_W) v_1d = xp.linspace(0, H - 1, num=self.out_H) t_1d = xp.linspace(0, D - 1, num=self.out_D) grid = xp.meshgrid(u_1d, v_1d, t_1d) u = grid[0].ravel() v = grid[1].ravel() t = grid[2].ravel() u0 = xp.floor(u).astype(numpy.int32) u0 = u0.clip(0, W - 2) u1 = u0 + 1 v0 = xp.floor(v).astype(numpy.int32) v0 = v0.clip(0, H - 2) v1 = v0 + 1 t0 = xp.floor(t).astype(numpy.int32) t0 = t0.clip(0, D - 2) t1 = t0 + 1 # weights wu0 = u - u0 wu1 = u1 - u wv0 = v - v0 wv1 = v1 - v wt0 = t - t0 wt1 = t1 - t wu0 = wu0.astype(gy.dtype) wu1 = wu1.astype(gy.dtype) wv0 = wv0.astype(gy.dtype) wv1 = wv1.astype(gy.dtype) wt0 = wt0.astype(gy.dtype) wt1 = wt1.astype(gy.dtype) # --- gx if xp is numpy: scatter_add = numpy.add.at else: scatter_add = cuda.cupyx.scatter_add gx = xp.zeros(self.input_shape, dtype=gy.dtype) gy = gy.reshape(B, C, -1) scatter_add(gx, (slice(None), slice(None), v0, u0, t0), gy * wu1 * wv1 * wt1) scatter_add(gx, (slice(None), slice(None), v0, u1, t0), gy * wu0 * wv1 * wt1) scatter_add(gx, (slice(None), slice(None), v1, u0, t0), gy * wu1 * wv0 * wt1) scatter_add(gx, (slice(None), slice(None), v1, u1, t0), gy * wu0 * wv0 * wt1) scatter_add(gx, (slice(None), slice(None), v0, u0, t1), gy * wu1 * wv1 * wt0) scatter_add(gx, (slice(None), slice(None), v0, u1, t1), gy * wu0 * wv1 * wt0) scatter_add(gx, (slice(None), slice(None), v1, u0, t1), gy * wu1 * wv0 * wt0) scatter_add(gx, (slice(None), slice(None), v1, u1, t1), gy * wu0 * wv0 * wt0) return gx, def backward(self, indexes, grad_outputs): return ResizeImages3D( (self.out_H, self.out_W, self.out_D)).apply(grad_outputs) def resize_images_3d(x, output_shape): """Resize images to the given shape. This function resizes 3D data to :obj:`output_shape`. Currently, only bilinear interpolation is supported as the sampling method. Notatition: here is a notation for dimensionalities. - :math:`n` is the batch size. - :math:`c_I` is the number of the input channels. - :math:`h`, :math:`w` and :math:`d` are the height, width and depth of the input image, respectively. - :math:`h_O`, :math:`w_O` and :math:`d_0` are the height, width and depth of the output image. Args: x (~chainer.Variable): Input variable of shape :math:`(n, c_I, h, w, d)`. output_shape (tuple): This is a tuple of length 3 whose values are :obj:`(h_O, w_O, d_O)`. Returns: ~chainer.Variable: Resized image whose shape is \ :math:`(n, c_I, h_O, w_O, d_O)`. """ return ResizeImages3D(output_shape).apply((x,))[0]

pfnet-research/label-efficient-brain-tumor-segmentation

src/links/model/resize_images_3d.py

Python

# coding=utf-8 ''' author: ShiLei Miao analyses and build model about NBA ''' import numpy as np from numpy import * import pandas as pd from pandas import * import os from sklearn.ensemble import RandomForestClassifier from sklearn.cross_validation import KFold from sklearn import metrics os.chdir(r'E:\PycharmProjects\Rong360\dta') def loadDataSetT(path): data = pd.read_csv(path) dataSet = data.values[0:,2:] dataLabel = data.values[0:,1:2] return dataSet,dataLabel def transLabel(Mat_Labels): labels = [] for item in Mat_Labels: labels.append(item[0]) labels = array(labels) return labels def P_YYYY(N_train, target_train, N_test, target_test): clf = RandomForestClassifier(n_estimators=300, random_state=520341, max_depth=9,\ min_samples_split=3, class_weight='balanced_subsample') clf = clf.fit(N_train, target_train) pred = clf.predict_proba(N_test) pred = DataFrame(pred)[0].values N_auc = metrics.roc_auc_score(target_test, 1 - pred) print N_auc print '\n' return N_auc, clf def preds_calculate(Mat_Train,Mat_Labels): kf = KFold(len(Mat_Train), n_folds=10) NN_auc = [] for train_index, test_index in kf: X_train, X_test = Mat_Train[train_index], Mat_Train[test_index] y_train, y_test = Mat_Labels[train_index], Mat_Labels[test_index] N_auc, clf = P_YYYY(X_train, y_train, X_test, y_test) NN_auc.append(N_auc) mean_auc = mean(NN_auc) print 'AUC均值：',mean_auc return mean_auc, clf # 训练集 S_train_user_info = pd.read_csv(r'Generate_dta\S_train_user_info.csv') N_train_user_info = pd.read_csv(r'Generate_dta\N_train_user_info.csv').drop(['lable'],axis=1) relation1_train = pd.read_csv(r'Generate_dta\0909relation1_train.csv') relation2_train = pd.read_csv(r'Generate_dta\0909relation2_train.csv') N_train_consumption1 = pd.read_csv(r'Generate_dta\N_train_consumption1.csv').drop(['lable'],axis=1) t_consumption = pd.read_csv(r'Generate_dta\t_consumption.csv') #rong_tag 没有使用 【下面的数据是one-hot后的特征】 rong_tag_train = pd.read_csv(r'Generate_dta\N_rong_tag_train.csv').drop(['lable'],axis=1) N_rong_tag_train_var = pd.read_excel(r'Stat_importance_var.xls') N_rong_tag_train_var = N_rong_tag_train_var[N_rong_tag_train_var['Importance']>10] N_rong_tag_train = rong_tag_train.reindex(columns = N_rong_tag_train_var['Feature'].values) N_rong_tag_train['user_id'] = rong_tag_train['user_id'] N_rong_tag_train = N_rong_tag_train.replace([None], [-1]) train = merge(S_train_user_info,N_train_user_info,how="left", left_on='user_id', right_on='user_id') train = merge(train,relation1_train,how="left", left_on='user_id', right_on='user_id') train = merge(train,relation2_train,how="left", left_on='user_id', right_on='user_id') train = merge(train,N_train_consumption1,how="left", left_on='user_id', right_on='user_id') train = merge(train,t_consumption,how="left", left_on='user_id', right_on='user_id') train = train.replace([None], [-1]) train['category_null'] = (train<0).sum(axis=1) ## 在统计的train跟test缺失的情况后，选择剔除用户的特征缺失个数为187的【基本都是product_id=2】 train = train[train['category_null'] < 187] train = DataFrame(train.values,columns=train.columns) train = merge(train,N_rong_tag_train,how="left", left_on='user_id', right_on='user_id') Mat_Train = train.drop(['user_id','lable','category_null'],axis=1) Mat_Train = array(Mat_Train) Mat_Label = train['lable'].astype(int) mean_auc, clf = preds_calculate(Mat_Train,Mat_Label)

finlay-liu/rong360-dataanalysis2016

Procedure/2_M1/train/m2-cv-rf.py

Python

__author__ = 'miguel.freitas@checkmarx.com' import os import sys import argparse import pyodbc import json import array DB = "CxDB" def is_str(string): return string is not None and isinstance(string, str) and len(string) > 0 def is_int(integer): return not isinstance(integer, bool) and isinstance(integer, int) def is_conn(conn): return conn is not None and isinstance(conn, pyodbc.Connection) def read_file(filename): if is_str(filename): if filename.endswith(".json"): try: filename = os.path.basename(filename) if os.path.isfile(filename): if os.access(filename, os.R_OK): with open(filename, 'rb') as f: return json.load(f) else: raise PermissionError("You don't have \ permissions to access this file") else: raise FileNotFoundError("File Not Found") except FileNotFoundError: raise FileNotFoundError("File Not Found") else: raise AttributeError("File should have \".json\" extension") else: raise AttributeError("No Filename provided") def connect_to_db(driver, server, user, password, database): if is_str(driver) and \ is_str(server) and \ is_str(user) and \ is_str(password) and \ is_str(database): try: conn = pyodbc.connect( 'DRIVER={' + driver + '};SERVER=' + server + ';DATABASE=' + database + ';UID=' + user + ';PWD=' + password, timeout=3) print("Connection to", database, "success") return conn except pyodbc.OperationalError or \ pyodbc.InterfaceError or \ pyodbc.Error as error: raise ConnectionError(error) else: raise AttributeError( "server | user | password | database were not provided") def get_category_type_id_by_name(conn, category_type_name): if is_conn(conn) and is_str(category_type_name): cursor = conn.cursor() category_type_id = -1 cursor.execute( "SELECT id,Typename FROM dbo.CategoriesTypes WHERE TypeName=?", category_type_name) rows = cursor.fetchall() if len(rows) > 0: for row in rows: category_type_id = row[0] return category_type_id else: return category_type_id else: raise AttributeError( "Connection object or Category Name \ was not provided") def add_category_type_by_name(conn, category_type_name): if is_conn(conn) and is_str(category_type_name): cursor = conn.cursor() cursor.execute("SET IDENTITY_INSERT dbo.CategoriesTypes ON") conn.commit() cursor.execute( "INSERT INTO dbo.CategoriesTypes (Id, Typename) \ VALUES((SELECT max(Id)+1 FROM dbo.CategoriesTypes), ?)", category_type_name) conn.commit() cursor.execute("SET IDENTITY_INSERT dbo.CategoriesTypes OFF") conn.commit() return True else: raise AttributeError( "Connection object or Category Name \ was not provided") def check_category_type_by_name(conn, category_type_name): if is_conn(conn) and is_str(category_type_name): category_type_id = get_category_type_id_by_name( conn, category_type_name) if category_type_id == -1: print("Category Type ", category_type_name, " does not exist.") add_category_type_by_name(conn, category_type_name) category_type_id = get_category_type_id_by_name( conn, category_type_name) print("Creating category type :", category_type_name, "- ID:", category_type_id) else: print("Category already exists :", category_type_name, "- ID:", category_type_id) return category_type_id else: raise AttributeError( "Connection object or Category Name \ was not provided") def delete_categories_by_category_type_id(conn, category_type_id): if is_conn(conn) and is_int(category_type_id): cursor = conn.cursor() cursor.execute( "DELETE FROM dbo.Categories WHERE CategoryType=?", category_type_id) conn.commit() else: raise AttributeError( "Connection object or Category Type ID \ was not provided") def delete_categories_for_queries_by_category_type_id(conn, category_type_id): if is_conn(conn) and is_int(category_type_id): cursor = conn.cursor() cursor.execute( "DELETE FROM dbo.CategoryForQuery WHERE CategoryId \ IN (SELECT id FROM dbo.Categories WHERE CategoryType=?)", category_type_id) conn.commit() else: raise AttributeError( "Connection object or Category Type ID \ was not provided") def clean_old_data(conn, category_type_id): if is_conn(conn) and is_int(category_type_id): delete_categories_for_queries_by_category_type_id( conn, category_type_id) delete_categories_by_category_type_id(conn, category_type_id) print("Clearing old data...") else: raise AttributeError( "Connection object or Category Type ID \ was not provided") def add_category(conn, category_name, category_type_id): if is_conn(conn) and is_str(category_name) and is_int(category_type_id): cursor = conn.cursor() cursor.execute("SET IDENTITY_INSERT dbo.Categories ON") conn.commit() cursor.execute("INSERT INTO dbo.Categories (Id, CategoryName,CategoryType) \ VALUES((SELECT max(Id)+1 FROM dbo.Categories),?,?)", (category_name, category_type_id)) conn.commit() cursor.execute("SET IDENTITY_INSERT dbo.Categories OFF") conn.commit() return True else: raise AttributeError( "Connection object or Category Name or Category Type ID \ was not provided") def get_category_id(conn, category_name, category_type_id): if is_conn(conn) and is_str(category_name) and is_int(category_type_id): cursor = conn.cursor() cursor.execute("SELECT Id FROM dbo.Categories WHERE \ CategoryName=? AND CategoryType=?", (category_name, category_type_id)) return cursor.fetchall()[0][0] else: raise AttributeError( "Connection object or Category Name or Category Type ID \ was not provided") def add_category_for_query(conn, category_id, query_id): if is_conn(conn) and is_int(category_id) and is_int(query_id): cursor = conn.cursor() cursor.execute("SET IDENTITY_INSERT dbo.CategoryForQuery ON") conn.commit() cursor.execute( "INSERT INTO dbo.CategoryForQuery (Id,QueryId,CategoryId) \ VALUES((SELECT max(Id)+1 FROM dbo.CategoryForQuery),?,?)", (query_id, category_id)) conn.commit() cursor.execute("SET IDENTITY_INSERT dbo.CategoryForQuery OFF") conn.commit() return True else: raise AttributeError( "Connection object or Category ID or Query ID \ was not provided") def get_categories_by_category_type_id_and_name(conn, category_name, category_type_id): if is_conn(conn) and is_int(category_type_id) and is_str(category_name): cursor = conn.cursor() cursor.execute("SELECT * FROM dbo.Categories WHERE \ CategoryName=? AND CategoryType=?", category_name, category_type_id) rows = cursor.fetchall() return rows else: raise AttributeError( "Connection object or Category ID or Query ID \ was not provided") def insert_new_categories(conn, category_type_id, group): if is_conn(conn) and is_int(category_type_id): if "name" in group: category_name = group["name"] add_category(conn, category_name, category_type_id) category = get_categories_by_category_type_id_and_name( conn, category_name, category_type_id) print("\nNew Category Inserted : ", category[0]) category_id = category[0][0] return category_id else: raise AttributeError( "Connection object or Category Type ID \ was not provided") def get_queries(conn, query_ids_list): if is_conn(conn) and len(query_ids_list) > 0: sanitized_list = [] for queryId in query_ids_list: if is_int(queryId): sanitized_list.append(queryId) query_ids = str(sanitized_list).strip('[]') if len(query_ids) > 0: cursor = conn.cursor() cursor.execute( "SELECT * FROM dbo.Query WHERE QueryId IN (" + query_ids + ")") return cursor.fetchall() else: raise AttributeError("Connection object or Query List \ was not provided") def get_categories_ids_by_category_type(conn, category_type_id): if is_conn(conn) and is_int(category_type_id): cursor = conn.cursor() cursor.execute( "SELECT [Id] FROM dbo.Categories where CategoryType=?", category_type_id) rows = cursor.fetchall() arr = array.array('i') for row in rows: category_id = row[0] arr.append(category_id) return arr else: raise AttributeError( "Connection object or Category Type ID \ was not provided") def insert_queries(conn, category_id, severity_id, queries): if is_conn(conn) and is_int(category_id) and \ is_int(severity_id) and len(queries) > 0: cursor = conn.cursor() cursor.execute("SET IDENTITY_INSERT dbo.CategoryForQuery ON") conn.commit() i = 0 for query in queries: query_id = query[0] percentage = round((i * 100) / len(queries), 0) print("Inserting Query", query_id, "...", percentage, "%") cursor.execute("INSERT INTO dbo.CategoryForQuery \ (Id, QueryId,CategoryId) VALUES\ ((SELECT max(Id)+1 FROM dbo.CategoryForQuery), ?,?)", (query_id, category_id)) conn.commit() update_customized_queries(conn, category_id, severity_id, query_id) i = i + 1 cursor.execute("SET IDENTITY_INSERT dbo.CategoryForQuery OFF") conn.commit() else: raise AttributeError( "Connection object or Category ID \ was not provided") def get_args(args): if isinstance(args, list) and len(args) > 0: args_parser = argparse.ArgumentParser( description='Add Custom Category to CxDB') args_parser.add_argument( '-dbd', '--dbdriver', help='Checkmarx MSSQL DB Driver', required=False, default="SQL Server") args_parser.add_argument( '-dbu', '--dbuser', help='Checkmarx MSSQL DB Username', required=True) args_parser.add_argument('-dbp', '--dbpassword', help='Checkmarx MSSQL DB Password', required=True) args_parser.add_argument('-dbs', '--dbserver', help='Checkmarx MSSQL DB Server URL', required=True) args_parser.add_argument('-fg', '--file_groups', help='Categories and Queries Mapping File', required=True) return args_parser.parse_args(args) else: raise AttributeError("args should be a non-empty array") def update_category_severity_mapping(conn, severity_id, category_name, group_name): if is_conn(conn) and is_int(severity_id) and is_str(category_name) and \ is_str(group_name): cursor = conn.cursor() cursor.execute("UPDATE Queries \ SET Queries.Severity = ? \ FROM dbo.Categories Categories \ JOIN dbo.CategoryForQuery CategoriesForQuery \ ON Categories.Id=CategoriesForQuery.CategoryId \ JOIN dbo.Query Queries \ ON CategoriesForQuery.QueryId=Queries.QueryId \ JOIN dbo.CategoriesTypes CategoriesTypes \ ON Categories.CategoryType = CategoriesTypes.Id \ WHERE Categories.CategoryName = ? \ AND CategoriesTypes.TypeName = ?", (severity_id, group_name, category_name)) conn.commit() cursor.execute("UPDATE QueryVersions \ SET QueryVersions.Severity = ? \ FROM dbo.Categories Categories \ JOIN dbo.CategoryForQuery CategoriesForQuery \ ON Categories.Id=CategoriesForQuery.CategoryId \ JOIN dbo.QueryVersion QueryVersions \ ON CategoriesForQuery.QueryId=QueryVersions.QueryId \ JOIN dbo.CategoriesTypes CategoriesTypes \ ON Categories.CategoryType = CategoriesTypes.Id \ WHERE Categories.CategoryName = ? \ AND CategoriesTypes.TypeName = ?", (severity_id, group_name, category_name)) conn.commit() print("Updating Severity Mapping for Severity", severity_id, "-", group_name, "-", category_name) else: raise AttributeError( "Connection object was not provided") def update_customized_queries(conn, category_id, severity_id, query_id): if is_conn(conn) and is_int(category_id) and is_int(severity_id) \ and is_int(query_id): cursor = conn.cursor() cursor.execute("SELECT QueryId FROM dbo.Query \ WHERE PackageId IN (\ SELECT DISTINCT PackageId FROM dbo.QueryGroup \ WHERE Name = (\ SELECT Name FROM dbo.QueryGroup \ WHERE PackageId = (\ SELECT DISTINCT PackageId FROM dbo.Query \ WHERE QueryId = ?)\ ) and PackageId > 100000\ ) and Name = (\ SELECT DISTINCT Name FROM dbo.Query \ WHERE QueryId = ?)", (query_id, query_id)) customized_queries_list = cursor.fetchall() if len(customized_queries_list) > 0: for customized_query in customized_queries_list: cursor.execute("INSERT INTO dbo.CategoryForQuery \ (Id, QueryId,CategoryId) VALUES\ ((SELECT max(Id)+1 FROM dbo.CategoryForQuery), ?,?)", (customized_query[0], category_id)) conn.commit() cursor.execute("UPDATE dbo.QueryVersion SET Severity = ? \ WHERE QueryId IN (\ SELECT QueryId FROM dbo.QueryVersion \ WHERE PackageId IN (\ SELECT DISTINCT PackageId FROM dbo.QueryGroup \ WHERE Name = (\ SELECT Name FROM dbo.QueryGroup \ WHERE PackageId = (\ SELECT DISTINCT PackageId FROM dbo.QueryVersion \ WHERE QueryId = ?)\ ) and PackageId > 100000\ ) and Name = (\ SELECT DISTINCT Name FROM dbo.QueryVersion \ WHERE QueryId = ?)\ )", (severity_id, query_id, query_id)) conn.commit() cursor.execute("UPDATE dbo.Query SET Severity = ? \ WHERE QueryId IN (\ SELECT QueryId FROM dbo.Query \ WHERE PackageId IN (\ SELECT DISTINCT PackageId FROM dbo.QueryGroup \ WHERE Name = (\ SELECT Name FROM dbo.QueryGroup \ WHERE PackageId = (\ SELECT DISTINCT PackageId FROM dbo.Query \ WHERE QueryId = ?)\ ) and PackageId > 100000\ ) and Name = (\ SELECT DISTINCT Name FROM dbo.Query \ WHERE QueryId = ?)\ )", (severity_id, query_id, query_id)) conn.commit() print("Updating Customized Queries Severity", severity_id, "- Query ID -", query_id) else: print("No existing customized queries for", query_id) return True else: raise AttributeError( "Connection object bwas not provided") def main(args): if args is not None and hasattr(args, "file_groups"): file_groups = args.file_groups if is_str(file_groups): file_content = read_file(file_groups) category = file_content["category"] category_name = category["name"] groups = category["groups"] if hasattr(args, "dbdriver") and \ hasattr(args, "dbserver") and \ hasattr(args, "dbuser") and \ hasattr(args, "dbpassword"): db_server = args.dbserver db_user = args.dbuser db_pwd = args.dbpassword db_driver = args.dbdriver if is_str(db_driver) and \ is_str(db_server) and \ is_str(db_user) and \ is_str(db_pwd): conn = connect_to_db( db_driver, db_server, db_user, db_pwd, DB) if is_conn(conn): category_type_id = check_category_type_by_name( conn, category_name) clean_old_data(conn, category_type_id) for group in groups: category_id = insert_new_categories( conn, category_type_id, group) if "query_ids" in group and "name" in group and \ "severity_id" in group: severity_id = group["severity_id"] group_name = group["name"] queries = get_queries(conn, group["query_ids"]) print(group_name, ":", len(queries), "queries to change") insert_queries(conn, category_id, severity_id, queries) update_category_severity_mapping( conn, severity_id, category_name, group_name) else: print("Group has 1 missing attribute: name\ query_ids or severity_id") else: raise Exception("Cannot connect to Database") else: raise Exception( "db_server | db_user | db_pwd \ are not valid strings") else: raise Exception( "db_server | db_user | db_pwd \ was not provided as an argument") else: raise TypeError("file_groups is not a string") else: raise AttributeError("args does not has file_groups as attribute") if __name__ == "__main__": main(get_args(sys.argv[1:]))

cxpsemea/CxAddCustomCategory

add_custom_category.py

Python

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.contrib.auth.models import User from django.db import models class Post(models.Model): status_ITEMS = ( (1, '上线'), (2, '草稿'), (3, '删除'), ) title = models.CharField(max_length=50, verbose_name='标题') desc = models.CharField(max_length=255, blank=True, verbose_name='摘要') category = models.ForeignKey('Category', verbose_name='分类') tags = models.ManyToManyField('Tag', related_name="posts", verbose_name='标签') content = models.TextField(verbose_name='内容', help_text='注:目前仅支持Markdown格式') status = models.PositiveIntegerField(default=1, choices=status_ITEMS, verbose_name='状态') owner = models.ForeignKey(User, verbose_name='作者') created_time = models.DateTimeField(auto_now_add=True, verbose_name='创建时间') lasted_update_time = models.DateTimeField(auto_now=True, verbose_name='更新时间') def status_show(self): return '当前状态:%s'%(self.status) status_show.short_description = '展示站台' def __unicode__(self): return self.title class Meta: verbose_name = verbose_name_plural = '文章' class Category(models.Model): status_ITEMS = ( (1, '可用'), (2, '删除'), ) name = models.CharField(max_length=50,verbose_name='名称') status = models.PositiveIntegerField(default=1, choices=status_ITEMS, verbose_name='状态') owner = models.ForeignKey(User, verbose_name='作者') is_nav = models.BooleanField(default=False, verbose_name="是否为导航") created_time = models.DateTimeField(auto_now_add=True, verbose_name='创建时间') # parent = models.ForeignKey('Category', verbose_name='分类') def __unicode__(self): return self.name class Meta: verbose_name = verbose_name_plural = '分类' class Tag(models.Model): status_ITEMS= ( (1, '正常'), (2, '删除'), ) name = models.CharField(max_length=50,verbose_name='名称') status = models.PositiveIntegerField(default=1, choices=status_ITEMS, verbose_name='状态') owner = models.ForeignKey(User, verbose_name='作者') created_time = models.DateTimeField(auto_now_add=True, verbose_name='创建时间') def __unicode__(self): return self.name class Meta: verbose_name = verbose_name_plural = '标签'

liangtaos/typeidea

typeidea/blog/models.py

Python

#!/usr/bin/env python2.7 # Advanced Multi-Mission Operations System (AMMOS) Instrument Toolkit (AIT) # Bespoke Link to Instruments and Small Satellites (BLISS) # # Copyright 2016, by the California Institute of Technology. ALL RIGHTS # RESERVED. United States Government Sponsorship acknowledged. Any # commercial use must be negotiated with the Office of Technology Transfer # at the California Institute of Technology. # # This software may be subject to U.S. export control laws. By accepting # this software, the user agrees to comply with all applicable U.S. export # laws and regulations. User has the responsibility to obtain export licenses, # or other export authority as may be required before exporting such # information to foreign countries or providing access to foreign persons. import time import datetime import mock import os import os.path import nose import nose.tools import ait.core from ait.core import dmc LEAPSECOND_DATA_RESPONSE = '''# # Updated through IERS Bulletin C55 # File expires on: 28 December 2018 # #@ 3754944000 # 2272060800 10 # 1 Jan 1972 2287785600 11 # 1 Jul 1972 2303683200 12 # 1 Jan 1973 2335219200 13 # 1 Jan 1974 2366755200 14 # 1 Jan 1975 2398291200 15 # 1 Jan 1976 2429913600 16 # 1 Jan 1977 2461449600 17 # 1 Jan 1978 2492985600 18 # 1 Jan 1979 2524521600 19 # 1 Jan 1980 2571782400 20 # 1 Jul 1981 2603318400 21 # 1 Jul 1982 2634854400 22 # 1 Jul 1983 2698012800 23 # 1 Jul 1985 2776982400 24 # 1 Jan 1988 2840140800 25 # 1 Jan 1990 2871676800 26 # 1 Jan 1991 2918937600 27 # 1 Jul 1992 2950473600 28 # 1 Jul 1993 2982009600 29 # 1 Jul 1994 3029443200 30 # 1 Jan 1996 3076704000 31 # 1 Jul 1997 ''' class MockResponse: def __init__(self, text, status_code): self.text = text self.status_code = status_code def test_getTimestampUTC(): expected = time.strftime('%Y-%j', time.gmtime()) actual = time.strftime('%Y-%j', time.gmtime(dmc.getTimestampUTC()[0])) assert actual == expected def test_getUTCDatetimeDOY_w_days(): days = 1 t = datetime.datetime.utcnow() + datetime.timedelta(days=days) timestamp = t.timetuple() exp_year = timestamp.tm_year exp_day = '%03d' % timestamp.tm_yday dtime = dmc.getUTCDatetimeDOY(days=days).split('T')[0].split('-') assert str(exp_year) == dtime[0] assert str(exp_day) == dtime[1] def test_leap_second_attrs(): ait.config.leapseconds._config['filename'] = os.path.join( os.path.dirname(__file__), "testdata", "dmc", "leapseconds.dat" ) ls = dmc.LeapSeconds ls._load_leap_second_data() assert ls.leapseconds == ls._data['leapseconds'] assert ls.valid_date == ls._data['valid'] assert ls.get_current_GPS_offset() == ls.leapseconds[-1][-1] @nose.tools.raises(ValueError) def test_leap_second_by_date_invalid_gps_date(): ait.config.leapseconds._config['filename'] = os.path.join( os.path.dirname(__file__), "testdata", "dmc", "leapseconds.dat" ) dmc.LeapSeconds._load_leap_second_data() dmc.LeapSeconds.get_GPS_offset_for_date(datetime.datetime(1980, 1, 1)) def test_leap_second_by_date(): ait.config.leapseconds._config['filename'] = os.path.join( os.path.dirname(__file__), "testdata", "dmc", "leapseconds.dat" ) ls = dmc.LeapSeconds ls._load_leap_second_data() assert ls.get_GPS_offset_for_date(datetime.datetime(1981, 1, 1)) == 0 assert ls.get_GPS_offset_for_date(datetime.datetime(1981, 7, 1)) == 1 assert ls.get_GPS_offset_for_date(datetime.datetime(1982, 7, 1)) == 2 assert ls.get_GPS_offset_for_date(datetime.datetime(1983, 7, 1)) == 3 assert ls.get_GPS_offset_for_date(datetime.datetime(1985, 7, 1)) == 4 assert ls.get_GPS_offset_for_date(datetime.datetime(1988, 1, 1)) == 5 assert ls.get_GPS_offset_for_date(datetime.datetime(1990, 1, 1)) == 6 assert ls.get_GPS_offset_for_date(datetime.datetime(1991, 1, 1)) == 7 assert ls.get_GPS_offset_for_date(datetime.datetime(1992, 7, 1)) == 8 assert ls.get_GPS_offset_for_date(datetime.datetime(1993, 7, 1)) == 9 assert ls.get_GPS_offset_for_date(datetime.datetime(1994, 7, 1)) == 10 assert ls.get_GPS_offset_for_date(datetime.datetime(1996, 1, 1)) == 11 assert ls.get_GPS_offset_for_date(datetime.datetime(1997, 7, 1)) == 12 assert ls.get_GPS_offset_for_date(datetime.datetime(1999, 1, 1)) == 13 assert ls.get_GPS_offset_for_date(datetime.datetime(2006, 1, 1)) == 14 assert ls.get_GPS_offset_for_date(datetime.datetime(2009, 1, 1)) == 15 assert ls.get_GPS_offset_for_date(datetime.datetime(2012, 7, 1)) == 16 assert ls.get_GPS_offset_for_date(datetime.datetime(2015, 7, 1)) == 17 assert ls.get_GPS_offset_for_date(datetime.datetime(2017, 1, 1)) == 18 # Make sure not supplying a date returns the offset for the current date assert (ls.get_GPS_offset_for_date(datetime.datetime.utcnow()) == ls.get_GPS_offset_for_date()) def test_leap_second_data_load(): ait.config.leapseconds._config['filename'] = os.path.join( os.path.dirname(__file__), "testdata", "dmc", "leapseconds.dat" ) assert type(dmc.LeapSeconds.leapseconds) == type([]) assert dmc.LeapSeconds.leapseconds[0] == (datetime.datetime(1981, 7, 1), 1) assert type(dmc.LeapSeconds.valid_date) == type(datetime.datetime.now()) @nose.tools.raises(ValueError) @mock.patch('requests.get', mock.MagicMock(return_value=MockResponse(LEAPSECOND_DATA_RESPONSE, 400))) def test_failed_leapsecond_load_and_update(): ait.config.leapseconds._config['filename'] = os.path.join( os.path.dirname(__file__), "invalidpath", "leapseconds.dat" ) dmc.LeapSeconds._data = None dmc.LeapSeconds._load_leap_second_data() @mock.patch('requests.get', mock.MagicMock(return_value=MockResponse(LEAPSECOND_DATA_RESPONSE, 200))) def test_update_leap_second_data(): ait.config.leapseconds._config['filename'] = os.path.join( os.path.dirname(__file__), "testdata", "dmc", "tmp_leapseconds.out" ) dmc.LeapSeconds._data = None dmc.LeapSeconds._update_leap_second_data() assert type(dmc.LeapSeconds.leapseconds) == type([]) assert dmc.LeapSeconds.leapseconds[0] == (datetime.datetime(1981, 7, 1), 1) assert type(dmc.LeapSeconds.valid_date) == type(datetime.datetime.now()) assert os.path.isfile(ait.config.leapseconds.filename) os.remove(ait.config.leapseconds.filename) @nose.tools.raises(ValueError) @mock.patch('requests.get', mock.MagicMock(return_value=MockResponse(LEAPSECOND_DATA_RESPONSE, 400))) def test_unable_to_pull_leapsecond_data(): ait.config.leapseconds._config['filename'] = os.path.join( os.path.dirname(__file__), "testdata", "dmc", "tmp_leapseconds.out" ) dmc.LeapSeconds._data = None dmc.LeapSeconds._update_leap_second_data() if __name__ == '__main__': nose.main()

nttoole/AIT-Core

ait/core/test/test_dmc.py

Python

# Copyright 2018 the GPflow authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numpy as np from numpy.random import randn import tensorflow as tf import pytest import gpflow from gpflow import logdensities, settings from gpflow.test_util import session_tf from scipy.stats import multivariate_normal as mvn from numpy.testing import assert_allclose rng = np.random.RandomState(1) @pytest.mark.parametrize("x", [randn(4,10), randn(4,1)]) @pytest.mark.parametrize("mu", [randn(4,10), randn(4,1)]) @pytest.mark.parametrize("cov_sqrt", [randn(4,4), np.eye(4)]) def test_multivariate_normal(session_tf, x, mu, cov_sqrt): cov = np.dot(cov_sqrt, cov_sqrt.T) L = np.linalg.cholesky(cov) x_tf = tf.placeholder(settings.float_type) mu_tf = tf.placeholder(settings.float_type) gp_result = logdensities.multivariate_normal( x_tf, mu_tf, tf.convert_to_tensor(L)) gp_result = session_tf.run(gp_result, feed_dict={x_tf: x, mu_tf: mu}) if mu.shape[1] > 1: if x.shape[1] > 1: sp_result = [mvn.logpdf(x[:,i], mu[:,i], cov) for i in range(mu.shape[1])] else: sp_result = [mvn.logpdf(x.ravel(), mu[:, i], cov) for i in range(mu.shape[1])] else: sp_result = mvn.logpdf(x.T, mu.ravel(), cov) assert_allclose(gp_result, sp_result) def test_shape_asserts(session_tf): A = np.random.randn(5) B = np.random.randn(5) L = np.tril(np.random.randn(5, 5)) # Static shape check: with pytest.raises(ValueError): tA = tf.identity(A) tB = tf.identity(B) tL = tf.identity(L) res = logdensities.multivariate_normal(tA, tB, tL) # Dynamic shape check: # the following results in a segfault before PR#964 with pytest.raises(tf.errors.InvalidArgumentError): vA = tf.placeholder(tf.float64) vB = tf.placeholder(tf.float64) vL = tf.placeholder(tf.float64) res = logdensities.multivariate_normal(vA, vB, vL) session_tf.run(res, {vA: A, vB: B, vL: L})

a-z-e-r-i-l-a/GPflow

tests/test_logdensities.py

Python

# -*- coding: utf-8 -*- """ wsproto/handshake ~~~~~~~~~~~~~~~~~~ An implementation of WebSocket handshakes. """ from collections import deque from typing import Deque, Dict, Generator, List, Optional, Union import h11 from .connection import Connection, ConnectionState, ConnectionType from .events import AcceptConnection, Event, RejectConnection, RejectData, Request from .extensions import Extension from .typing import Headers from .utilities import ( generate_accept_token, generate_nonce, LocalProtocolError, normed_header_dict, RemoteProtocolError, split_comma_header, ) # RFC6455, Section 4.2.1/6 - Reading the Client's Opening Handshake WEBSOCKET_VERSION = b"13" class H11Handshake: """A Handshake implementation for HTTP/1.1 connections.""" def __init__(self, connection_type: ConnectionType) -> None: self.client = connection_type is ConnectionType.CLIENT self._state = ConnectionState.CONNECTING if self.client: self._h11_connection = h11.Connection(h11.CLIENT) else: self._h11_connection = h11.Connection(h11.SERVER) self._connection: Optional[Connection] = None self._events: Deque[Event] = deque() self._initiating_request: Optional[Request] = None self._nonce: Optional[bytes] = None @property def state(self) -> ConnectionState: return self._state @property def connection(self) -> Optional[Connection]: """Return the established connection. This will either return the connection or raise a LocalProtocolError if the connection has not yet been established. :rtype: h11.Connection """ return self._connection def initiate_upgrade_connection(self, headers: Headers, path: str) -> None: """Initiate an upgrade connection. This should be used if the request has already be received and parsed. :param list headers: HTTP headers represented as a list of 2-tuples. :param str path: A URL path. """ if self.client: raise LocalProtocolError( "Cannot initiate an upgrade connection when acting as the client" ) upgrade_request = h11.Request(method=b"GET", target=path, headers=headers) h11_client = h11.Connection(h11.CLIENT) self.receive_data(h11_client.send(upgrade_request)) def send(self, event: Event) -> bytes: """Send an event to the remote. This will return the bytes to send based on the event or raise a LocalProtocolError if the event is not valid given the state. :returns: Data to send to the WebSocket peer. :rtype: bytes """ data = b"" if isinstance(event, Request): data += self._initiate_connection(event) elif isinstance(event, AcceptConnection): data += self._accept(event) elif isinstance(event, RejectConnection): data += self._reject(event) elif isinstance(event, RejectData): data += self._send_reject_data(event) else: raise LocalProtocolError( "Event {} cannot be sent during the handshake".format(event) ) return data def receive_data(self, data: bytes) -> None: """Receive data from the remote. A list of events that the remote peer triggered by sending this data can be retrieved with :meth:`events`. :param bytes data: Data received from the WebSocket peer. """ self._h11_connection.receive_data(data) while True: try: event = self._h11_connection.next_event() except h11.RemoteProtocolError: raise RemoteProtocolError( "Bad HTTP message", event_hint=RejectConnection() ) if ( isinstance(event, h11.ConnectionClosed) or event is h11.NEED_DATA or event is h11.PAUSED ): break if self.client: if isinstance(event, h11.InformationalResponse): if event.status_code == 101: self._events.append(self._establish_client_connection(event)) else: self._events.append( RejectConnection( headers=event.headers, status_code=event.status_code, has_body=False, ) ) self._state = ConnectionState.CLOSED elif isinstance(event, h11.Response): self._state = ConnectionState.REJECTING self._events.append( RejectConnection( headers=event.headers, status_code=event.status_code, has_body=True, ) ) elif isinstance(event, h11.Data): self._events.append( RejectData(data=event.data, body_finished=False) ) elif isinstance(event, h11.EndOfMessage): self._events.append(RejectData(data=b"", body_finished=True)) self._state = ConnectionState.CLOSED else: if isinstance(event, h11.Request): self._events.append(self._process_connection_request(event)) def events(self) -> Generator[Event, None, None]: """Return a generator that provides any events that have been generated by protocol activity. :returns: a generator that yields H11 events. """ while self._events: yield self._events.popleft() ############ Server mode methods def _process_connection_request(self, event: h11.Request) -> Request: if event.method != b"GET": raise RemoteProtocolError( "Request method must be GET", event_hint=RejectConnection() ) connection_tokens = None extensions: List[str] = [] host = None key = None subprotocols: List[str] = [] upgrade = b"" version = None headers: Headers = [] for name, value in event.headers: name = name.lower() if name == b"connection": connection_tokens = split_comma_header(value) elif name == b"host": host = value.decode("ascii") continue # Skip appending to headers elif name == b"sec-websocket-extensions": extensions = split_comma_header(value) continue # Skip appending to headers elif name == b"sec-websocket-key": key = value elif name == b"sec-websocket-protocol": subprotocols = split_comma_header(value) continue # Skip appending to headers elif name == b"sec-websocket-version": version = value elif name == b"upgrade": upgrade = value headers.append((name, value)) if connection_tokens is None or not any( token.lower() == "upgrade" for token in connection_tokens ): raise RemoteProtocolError( "Missing header, 'Connection: Upgrade'", event_hint=RejectConnection() ) if version != WEBSOCKET_VERSION: raise RemoteProtocolError( "Missing header, 'Sec-WebSocket-Version'", event_hint=RejectConnection( headers=[(b"Sec-WebSocket-Version", WEBSOCKET_VERSION)], status_code=426, ), ) if key is None: raise RemoteProtocolError( "Missing header, 'Sec-WebSocket-Key'", event_hint=RejectConnection() ) if upgrade.lower() != b"websocket": raise RemoteProtocolError( "Missing header, 'Upgrade: WebSocket'", event_hint=RejectConnection() ) if version is None: raise RemoteProtocolError( "Missing header, 'Sec-WebSocket-Version'", event_hint=RejectConnection() ) self._initiating_request = Request( extensions=extensions, extra_headers=headers, host=host, subprotocols=subprotocols, target=event.target.decode("ascii"), ) return self._initiating_request def _accept(self, event: AcceptConnection) -> bytes: request_headers = normed_header_dict(self._initiating_request.extra_headers) nonce = request_headers[b"sec-websocket-key"] accept_token = generate_accept_token(nonce) headers = [ (b"Upgrade", b"WebSocket"), (b"Connection", b"Upgrade"), (b"Sec-WebSocket-Accept", accept_token), ] if event.subprotocol is not None: if event.subprotocol not in self._initiating_request.subprotocols: raise LocalProtocolError( "unexpected subprotocol {}".format(event.subprotocol) ) headers.append( (b"Sec-WebSocket-Protocol", event.subprotocol.encode("ascii")) ) if event.extensions: accepts = server_extensions_handshake( # type: ignore self._initiating_request.extensions, event.extensions ) if accepts: headers.append((b"Sec-WebSocket-Extensions", accepts)) response = h11.InformationalResponse( status_code=101, headers=headers + event.extra_headers ) self._connection = Connection( ConnectionType.CLIENT if self.client else ConnectionType.SERVER, event.extensions, ) self._state = ConnectionState.OPEN return self._h11_connection.send(response) def _reject(self, event: RejectConnection) -> bytes: if self.state != ConnectionState.CONNECTING: raise LocalProtocolError( "Connection cannot be rejected in state %s" % self.state ) headers = event.headers if not event.has_body: headers.append((b"content-length", b"0")) response = h11.Response(status_code=event.status_code, headers=headers) data = self._h11_connection.send(response) self._state = ConnectionState.REJECTING if not event.has_body: data += self._h11_connection.send(h11.EndOfMessage()) self._state = ConnectionState.CLOSED return data def _send_reject_data(self, event: RejectData) -> bytes: if self.state != ConnectionState.REJECTING: raise LocalProtocolError( "Cannot send rejection data in state {}".format(self.state) ) data = self._h11_connection.send(h11.Data(data=event.data)) if event.body_finished: data += self._h11_connection.send(h11.EndOfMessage()) self._state = ConnectionState.CLOSED return data ############ Client mode methods def _initiate_connection(self, request: Request) -> bytes: self._initiating_request = request self._nonce = generate_nonce() headers = [ (b"Host", request.host.encode("ascii")), (b"Upgrade", b"WebSocket"), (b"Connection", b"Upgrade"), (b"Sec-WebSocket-Key", self._nonce), (b"Sec-WebSocket-Version", WEBSOCKET_VERSION), ] if request.subprotocols: headers.append( ( b"Sec-WebSocket-Protocol", (", ".join(request.subprotocols)).encode("ascii"), ) ) if request.extensions: offers = {e.name: e.offer() for e in request.extensions} # type: ignore extensions = [] for name, params in offers.items(): name = name.encode("ascii") if params is True: extensions.append(name) elif params: extensions.append( b"%s; %s" % (name, params.encode("ascii")) # type: ignore ) if extensions: headers.append((b"Sec-WebSocket-Extensions", b", ".join(extensions))) upgrade = h11.Request( method=b"GET", target=request.target.encode("ascii"), headers=headers + request.extra_headers, ) return self._h11_connection.send(upgrade) def _establish_client_connection( self, event: h11.InformationalResponse ) -> AcceptConnection: # noqa: MC0001 accept = None connection_tokens = None accepts: List[str] = [] subprotocol = None upgrade = b"" headers: Headers = [] for name, value in event.headers: name = name.lower() if name == b"connection": connection_tokens = split_comma_header(value) continue # Skip appending to headers elif name == b"sec-websocket-extensions": accepts = split_comma_header(value) continue # Skip appending to headers elif name == b"sec-websocket-accept": accept = value continue # Skip appending to headers elif name == b"sec-websocket-protocol": subprotocol = value continue # Skip appending to headers elif name == b"upgrade": upgrade = value continue # Skip appending to headers headers.append((name, value)) if connection_tokens is None or not any( token.lower() == "upgrade" for token in connection_tokens ): raise RemoteProtocolError( "Missing header, 'Connection: Upgrade'", event_hint=RejectConnection() ) if upgrade.lower() != b"websocket": raise RemoteProtocolError( "Missing header, 'Upgrade: WebSocket'", event_hint=RejectConnection() ) accept_token = generate_accept_token(self._nonce) if accept != accept_token: raise RemoteProtocolError("Bad accept token", event_hint=RejectConnection()) if subprotocol is not None: subprotocol = subprotocol.decode("ascii") if subprotocol not in self._initiating_request.subprotocols: raise RemoteProtocolError( "unrecognized subprotocol {}".format(subprotocol), event_hint=RejectConnection(), ) extensions = client_extensions_handshake( # type: ignore accepts, self._initiating_request.extensions ) self._connection = Connection( ConnectionType.CLIENT if self.client else ConnectionType.SERVER, extensions, self._h11_connection.trailing_data[0], ) self._state = ConnectionState.OPEN return AcceptConnection( extensions=extensions, extra_headers=headers, subprotocol=subprotocol ) def __repr__(self) -> str: return "{}(client={}, state={})".format( self.__class__.__name__, self.client, self.state ) def server_extensions_handshake( requested: List[str], supported: List[Extension] ) -> Optional[bytes]: """Agree on the extensions to use returning an appropriate header value. This returns None if there are no agreed extensions """ accepts: Dict[str, Union[bool, bytes]] = {} for offer in requested: name = offer.split(";", 1)[0].strip() for extension in supported: if extension.name == name: accept = extension.accept(offer) if accept is True: accepts[extension.name] = True elif accept is not False and accept is not None: accepts[extension.name] = accept.encode("ascii") # type: ignore if accepts: extensions: List[bytes] = [] for name, params in accepts.items(): name = name.encode("ascii") # type: ignore if params is True: extensions.append(name) # type: ignore else: if params == b"": extensions.append(b"%s" % (name)) else: extensions.append(b"%s; %s" % (name, params)) return b", ".join(extensions) return None def client_extensions_handshake( accepted: List[str], supported: List[Extension] ) -> List[Extension]: # This raises RemoteProtocolError is the accepted extension is not # supported. extensions = [] for accept in accepted: name = accept.split(";", 1)[0].strip() for extension in supported: if extension.name == name: extension.finalize(accept) extensions.append(extension) break else: raise RemoteProtocolError( "unrecognized extension {}".format(name), event_hint=RejectConnection() ) return extensions

bluetech/wsproto

wsproto/handshake.py

Python

import numpy as np from astropy.io import fits from scipy.interpolate import interp1d # Fitting Sline3 def fit_spline3(y, x, order=3, nsum=3): y_resampled = [np.median(y[i:i + nsum]) for i in range(0, len(y) - len(y) % nsum, nsum)] x_resampled = np.linspace(0, len(y), len(y_resampled)) # Fitting f = interp1d(x_resampled, y_resampled, kind=order, bounds_error=True) # Return function to be constructed with any other x array return f # Local Minima and Maxima def local_minmax(data, nmin=2, nmax=2): # Identifying indices of local minima-maxima points id_min = (np.gradient(np.sign(np.gradient(data))) > 0).nonzero()[0] # index of local min id_max = (np.gradient(np.sign(np.gradient(data))) < 0).nonzero()[0] # index of local max # Taking values at min/max points list_min, list_max = data[id_min], data[id_max] # Sorting minima-maxima values (bigger --> lower) list_min, id_min = (list(p) for p in zip(*sorted(zip(list_min, id_min), reverse=False))) list_max, id_max = (list(p) for p in zip(*sorted(zip(list_max, id_max), reverse=True))) # Taking the desired number of local minima-maxima points list_min, list_max, id_min, id_max = list_min[0:nmin], list_max[0:nmax], id_min[0:nmin], id_max[0:nmax] return list_min, list_max, id_min, id_max def trim_slitedge(flat, plot=True): # Getting input data ccddata = fits.getdata(flat, ignore_missing_end=True) # Collapse flat in the dispersion direction flat_collapsed = fits.getdata(flat, ignore_missing_end=True).sum(axis=1) / ccddata.shape[1] lines = np.arange(0, flat_collapsed.size, 1) # Excluding first pixels in the spatial direction cut = 3 c_flat = flat_collapsed[cut:-cut] c_lines = np.arange(0, c_flat.size, 1) # Fittin cubic spline. It's working very well with order=5, nsum=2 func_splin3 = fit_spline3(c_flat, c_lines, order=5, nsum=2) smooth_flat = func_splin3(c_lines) # Compute 1st and flat smoothed dy = np.gradient(smooth_flat) dy2 = np.gradient(dy) # Regions to compute local minina-maxima # Region one: it represent first 40 percent of all data # Region two: ... last 40% pixa, pixb = int(len(c_flat) * 0.4), int(len(c_flat) * 0.6) dy2_one, dy2_two = dy2[0:pixa], dy2[pixb:] # Reg. 1: Compute local min/max of the 2nd derivative list_min_1, list_max_1, id_min_1, id_max_1 = local_minmax(dy2_one, nmin=1, nmax=1) list_min_2, list_max_2, id_min_2, id_max_2 = local_minmax(dy2_two, nmin=1, nmax=1) # Indice have to be reshifted to the original indices of the function dy2 id_min_2 = np.array(id_min_2) + pixb # Slit edges are the local maxima/minima 1/2 [accounting the cutted pixels] slit_1, slit_2 = int(np.array(id_min_1) + cut), int(np.array(id_min_2) + cut) print slit_1, slit_2 if plot is True: import matplotlib.pyplot as plt c_lines += cut plt.plot(lines, flat_collapsed, 'k-', label='Flat Collapsed') plt.plot(lines[slit_1:slit_2], flat_collapsed[slit_1:slit_2], 'r-', label = 'Cutted Flat') plt.plot(c_lines, dy, 'g-', label="Dy/dx") plt.plot(c_lines, dy2, 'y-', label="Dy2/dx") plt.plot(slit_1, list_min_1, 'bo', label='Slit Edge 1 ') plt.plot(slit_2, list_min_2, 'ro', label='Slit Edge 2') plt.xlim(lines.min() - 50, lines.max() + 50) plt.legend(loc='best') plt.show() return slit_1, slit_2 flat = '/home/davidsanm/PyCharmProjects/GoodmanDataReduction/2016-03-20/RED/master_flat_600.fits' trim_slitedge(flat, plot = True)

simontorres/goodman_ccdreduction

trim_slitedge.py

Python

# -*- encoding: utf-8 -*- # $Id: __init__.py,v 1.8.2.2 2007/05/22 21:06:52 customdesigned Exp $ # # This file is part of the pydns project. # Homepage: http://pydns.sourceforge.net # # This code is covered by the standard Python License. # # __init__.py for DNS class. __version__ = '2.3.1' import Type,Opcode,Status,Class from Base import DnsRequest, DNSError from Lib import DnsResult from Base import * from Lib import * Error=DNSError from lazy import * Request = DnsRequest Result = DnsResult from Serialization import Serialize,DeSerialize # # $Log: __init__.py,v $ # Revision 1.8.2.2 2007/05/22 21:06:52 customdesigned # utf-8 in __init__.py # # Revision 1.8.2.1 2007/05/22 20:39:20 customdesigned # Release 2.3.1 # # Revision 1.8 2002/05/06 06:17:49 anthonybaxter # found that the old README file called itself release 2.2. So make # this one 2.3... # # Revision 1.7 2002/05/06 06:16:15 anthonybaxter # make some sort of reasonable version string. releasewards ho! # # Revision 1.6 2002/03/19 13:05:02 anthonybaxter # converted to class based exceptions (there goes the python1.4 compatibility :) # # removed a quite gross use of 'eval()'. # # Revision 1.5 2002/03/19 12:41:33 anthonybaxter # tabnannied and reindented everything. 4 space indent, no tabs. # yay. # # Revision 1.4 2001/11/26 17:57:51 stroeder # Added __version__ # # Revision 1.3 2001/08/09 09:08:55 anthonybaxter # added identifying header to top of each file # # Revision 1.2 2001/07/19 06:57:07 anthony # cvs keywords added # #

levush/hipl

tools/hipdnsproxy/DNS/__init__.py

Python

import os import sys import setuptools # To prevent importing about and thereby breaking the coverage info we use this # exec hack about = {} with open('python_utils/__about__.py') as fp: exec(fp.read(), about) if os.path.isfile('README.rst'): long_description = open('README.rst').read() else: long_description = 'See http://pypi.python.org/pypi/python-utils/' needs_pytest = set(['ptr', 'pytest', 'test']).intersection(sys.argv) pytest_runner = ['pytest-runner'] if needs_pytest else [] if __name__ == '__main__': setuptools.setup( name='python-utils', version=about['__version__'], author=about['__author__'], author_email=about['__author_email__'], description=about['__description__'], url=about['__url__'], license='BSD', packages=setuptools.find_packages(), long_description=long_description, install_requires=['six'], tests_require=['pytest'], setup_requires=[] + pytest_runner, classifiers=['License :: OSI Approved :: BSD License'], )

dvzrv/python-utils

setup.py

Python

import logging as log import cv2 import sys import numpy as np class LandmarksDetectionModel: ''' Class for the Face Landmarks Detection Model. Load and configure inference plugins for the specified target devices, and performs either synchronous or asynchronous modes for the specified infer requests. ''' def __init__(self, model_name, device='CPU', extensions=None, async_infer=True): ''' Set instance variables. ''' self.plugin = None self.network = None self.exec_network = None self.infer_request_handle = None self.input_blob = None self.input_shape = None self.output_blob = None self.output_shape = None self.model_name = model_name self.device = device self.extensions = extensions self.async_infer = async_infer def load_model(self, plugin): ''' This method is for loading the model (in IR format) to the device specified by the user. Default device is CPU. ''' # Get model model_structure = self.model_name + '.xml' model_weights = self.model_name + '.bin' # Initialize the plugin - load the inference engine API # Plugin is the one already created for the Face Detection model self.plugin = plugin # Add a CPU extension, if applicable if self.extensions and 'CPU' in self.device: self.plugin.add_extension(self.extensions, self.device) # Read the IR as IENetwork try: self.network = self.plugin.read_network(model=model_structure, weights=model_weights) except: raise ValueError("Could not initialise the network. Have you entered the correct model path?") # Check if model and CPU plugin are supported if self.device == 'CPU': self.check_model() # Load the IENetwork into the plugin self.exec_network = self.plugin.load_network(network=self.network, device_name=self.device, num_requests=1) # Get the input and output layers self.input_blob = next(iter(self.network.inputs)) self.input_shape = self.network.inputs[self.input_blob].shape self.output_blob = next(iter(self.network.outputs)) self.output_shape = self.network.outputs[self.output_blob].shape return def predict(self, image): ''' This method is meant for running predictions on the input image. ''' if np.all(np.array(image.shape)): # Create input image to feed into the network net_input = {self.input_blob: self.preprocess_input(image)} # Start inference. Infer mode (async/sync) is input by user if self.async_infer: self.infer_request_handle = self.exec_network.start_async(request_id=0, inputs=net_input) # Wait for the result of the inference if self.exec_network.requests[0].wait(-1) == 0: # Get result of the inference request outputs = self.infer_request_handle.outputs[self.output_blob] eyes_coords, crop_left, crop_right = self.preprocess_output(outputs, image) else: self.infer_request_handle = self.exec_network.infer(inputs=net_input) # Get result of the inference request outputs = self.infer_request_handle[self.output_blob] eyes_coords, crop_left, crop_right = self.preprocess_output(outputs, image) else: eyes_coords = [] crop_left = [] crop_right = [] return eyes_coords, crop_left, crop_right def check_model(self): ''' This method check whether the model (along with the plugin) is support on the CPU device. If anything is missing (such as a CPU extension), let the user know and exit the programm. ''' supported_layers = self.plugin.query_network(network=self.network, device_name='CPU') unsupported_layers = [l for l in self.network.layers.keys() if l not in supported_layers] if len(unsupported_layers) != 0: log.error("Unsupported layers found: {}".format(unsupported_layers)) if self.extensions: log.error("The extensions specified do not support some layers. Please specify a new extension.") else: log.error( "Please try to specify an extension library path by using the --extensions command line argument.") sys.exit(1) return def preprocess_input(self, image): ''' Method to process inputs before feeding them into the model for inference. ''' image = cv2.resize(image, (self.input_shape[3], self.input_shape[2])) image = image.transpose((2, 0, 1)) image = image.reshape(1, *image.shape) return image def preprocess_output(self, outputs, image): ''' Method to process outputs before feeding them into the next model for inference or for the last step of the app. ''' w = image.shape[1] h = image.shape[0] outputs = outputs[0] xl, yl = int(outputs[0][0][0] * w), int(outputs[1][0][0] * h) xr, yr = int(outputs[2][0][0] * w), int(outputs[3][0][0] * h) eyes_coords = [xl, yl, xr, yr] # Using the fact that eyes take 1/5 of your face width # define bounding boxes around the eyes according to this square_size = int(w / 10) left_eye_box = [xl - square_size, yl - square_size, xl + square_size, yl + square_size] right_eye_box = [xr - square_size, yr - square_size, xr + square_size, yr + square_size] crop_left = image[left_eye_box[1]:left_eye_box[3], left_eye_box[0]:left_eye_box[2]] crop_right = image[right_eye_box[1]:right_eye_box[3], right_eye_box[0]:right_eye_box[2]] return eyes_coords, crop_left, crop_right

ElisaCovato/Computer-pointer-controller---Intel-Edge-AI-Nanodegree

src/facial_landmarks_detection.py

Python

""" ASGI config for FYP project. It exposes the ASGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/3.0/howto/deployment/asgi/ """ import os from django.core.asgi import get_asgi_application os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'FYP.settings') application = get_asgi_application()

MustafaAbbas110/FinalProject

src/FYP/FYP/asgi.py

Python

# -*- coding: utf-8 -*- from __future__ import print_function from IPython import get_ipython from IPython.display import ( display, Javascript, ) from IPython.core import magic_arguments from IPython.core.magic import ( Magics, magics_class, cell_magic, ) from IPython.utils.importstring import import_item import yaml __version__ = "0.2.0" @magics_class class YAMLMagics(Magics): """ Write and load YAML in the IPython Notebook. Uses SafeLoader by default. Example: %%yaml x -lyaml.Loader foo: bar: baz """ def __init__(self, shell): super(YAMLMagics, self).__init__(shell) @cell_magic @magic_arguments.magic_arguments() @magic_arguments.argument( "var_name", default=None, nargs="?", help="""Name of local variable to set to parsed value""" ) @magic_arguments.argument( "-l", "--loader", default="yaml.SafeLoader", help="""Dotted-notation class to use for loading""" ) def yaml(self, line, cell): line = line.strip() args = magic_arguments.parse_argstring(self.yaml, line) display(Javascript( """ require( [ "notebook/js/codecell", "codemirror/mode/yaml/yaml" ], function(cc){ cc.CodeCell.options_default.highlight_modes.magic_yaml = { reg: ["^%%yaml"] } } ); """)) loader = get_ipython().user_global_ns.get(args.loader, None) if loader is None: loader = import_item(args.loader) try: val = yaml.load(cell, Loader=loader) except yaml.YAMLError as err: print(err) return if args.var_name is not None: get_ipython().user_ns[args.var_name] = val else: return val def load_ipython_extension(ip): ip = get_ipython() ip.register_magics(YAMLMagics)

bollwyvl/yamlmagic

yamlmagic.py

Python

import h5py import pickle import numpy as np # import read_affect_data as r # from tqdm import tqdm import random from PIL import Image, ImageOps, ImageEnhance import colorsys # def read_h5_data_set(path): # f = h5py.File(path, 'r') # time_stamps = list(f[list(f.keys())[0]].keys()) # d = {time : dict() for time in time_stamps} # for feature in list(f.keys()): # if hasattr(f[feature], 'keys'): # for time in tqdm(list(f[feature].keys())): # k = list(f[feature][time].keys())[0] # d[time][feature] = np.array(f[feature][time][k]) # return d # def read_pkl_data_set(path): # f = r.load_pickle(path) # time_stamps = list(f[list(f.keys())[0]].keys()) # d = {time : dict() for time in time_stamps} # for feature in list(f.keys()): # if hasattr(f[feature], 'keys'): # for time in tqdm(list(f[feature].keys())): # if hasattr(f[feature][time], 'keys'): # for k in list(f[feature][time].keys()): # d[time][feature] = np.array(f[feature][time][k]) # return d ############################################################################## # Visual def visual_robustness(tests, noise_level=0.3, gray=True, contrast=True, s_and_p=True, gaus=True, rot=True, crop=True): noises = [] if gray: noises.append(grayscale) if contrast: noises.append(low_contrast) if s_and_p: noises.append(salt_and_pepper) if gaus: noises.append(gaussian) if rot: noises.append(rotate) if crop: noises.append(random_crop) robustness_tests = [] for i in range(len(tests)): img = Image.fromarray(tests[i]) for noise in noises: img = noise(img, noise_level) robustness_tests.append(np.array(img)) return robustness_tests def grayscale(img, p): if np.random.sample() <= p: return ImageOps.grayscale(img) else: return img def low_contrast(img, factor): if np.random.sample() <= p: enhancer = ImageEnhance.Contrast(img) return enhancer.enhance(factor) else: return img def inversion(img, p): if np.random.sample() <= p: return ImageOps.invert(img) else: return img def WB(img, p): if np.random.sample() <= p: kelvin_table = {1000: (255, 56, 0), 1500: (255, 109, 0), 2000: (255, 137, 18), 2500: (255, 161, 72), 3000: (255, 180, 107), 3500: (255, 196, 137), 4000: (255, 209, 163), 4500: (255, 219, 186), 5000: (255, 228, 206), 5500: ( 255, 236, 224), 6000: (255, 243, 239), 6500: (255, 249, 253), 7000: (245, 243, 255), 7500: (235, 238, 255), 8000: (227, 233, 255), 8500: (220, 229, 255), 9000: (214, 225, 255), 9500: (208, 222, 255), 10000: (204, 219, 255)} temp = np.random.choice(kelvin_table.keys()) r, g, b = kelvin_table[temp] matrix = (r / 255.0, 0.0, 0.0, 0.0, 0.0, g / 255.0, 0.0, 0.0, 0.0, 0.0, b / 255.0, 0.0) return img.convert('RGB', matrix) else: return img def colorize(img, p): if np.random.sample() <= p: color = np.random.choice(['red', 'blue', 'green']) layer = Image.new('RGB', img.size, color) return Image.blend(img, layer, 0.3) else: return img def salt_and_pepper(img, p): if np.random.sample() <= p: output = np.copy(np.array(img)) nb_salt = np.ceil(p*output.size*0.5) coords = [np.random.randint(0, i-1, int(nb_salt)) for i in output.shape] for i in coords: output[i] = 1 nb_pepper = np.ceil(p*output.size*0.5) coords = [np.random.randint(0, i-1, int(nb_pepper)) for i in output.shape] for i in coords: output[i] = 0 return Image.fromarray(output) else: return img def gaussian(img, p): if np.random.sample() <= p: height, width = np.array(img).shape gauss = np.random.normal(0, p, (height, width)) return Image.fromarray((np.array(img)+gauss).astype('uint8')) else: return img def rotate(img, p): if np.random.sample() <= p: angle = np.random.random_sample()*40-20 return img.rotate(angle, Image.BILINEAR) else: return img def horizontal_flip(img, p): if np.random.sample() <= p: return img.transpose(Image.FLIP_LEFT_RIGHT) else: return img def random_crop(img, p): if np.random.sample() <= p: dim = np.array(img).shape height = dim[0] width = dim[1] cropped_height = height / 5 cropped_width = width / 5 init_height = np.random.random_sample() * cropped_height init_width = np.random.random_sample() * cropped_width end_height = height - cropped_height + init_height end_width = width - cropped_width + init_width return img.crop((init_width, init_height, end_width, end_height)).resize((height, width)) else: return img def periodic(img, periodic_noise_filename="periodic_noise"): height = img.height width = img.width output = [] for i in range(6): noise = Image.open("{}_{}.png".format( periodic_noise_filename, i+1)).convert("RGBA") noise = random_crop(rotate(noise.resize( (width*2, height*2)), np.random.random_sample()*360, 'white'), height, width) output.append(Image.blend(img.convert("RGBA"), noise, 0.3)) return output ############################################################################## # Text def text_robustness(tests, noise_level=0.3, swap=True, rand_mid=True, typo=True, sticky=True, omit=True): noises = [] if swap: noises.append(swap_letter) if rand_mid: noises.append(random_mid) if typo: noises.append(qwerty_typo) if sticky: noises.append(sticky_keys) if omit: noises.append(omission) robustness_tests = [] for i in range(len(tests)): newtext = [] text = tests[i].lower().split() for word in text: if len(word) > 3 and np.random.sample() <= noise_level: mode = np.random.randint(len(noises)) newtext.append(noises[mode](word)) else: newtext.append(word) robustness_tests.append(' '.join(newtext)) return np.array(robustness_tests) def last_char(word): for i in range(len(word)): if word[len(word)-1-i].isalpha(): return len(word) - 1 - i def swap_letter(word): # swap two random adjacent letters last = last_char(word) pos = np.random.randint(last-2) + 1 return word[:pos] + word[pos+1] + word[pos] + word[pos+2:] def random_mid(word): # randomly permute the middle chunk of a word (all letters except the first and last letter) last = last_char(word) mid = [char for char in word[1:last]] np.random.shuffle(mid) return word[0]+''.join(mid)+word[last:] def qwerty_typo(word, num_typo=1): # randomly replace num_typo number of letters of a word to a one adjacent to it on qwerty keyboard qwerty = {'q': ['w'], 'w': ['q', 'e', 's'], 'e': ['w', 'r', 'd'], 'r': ['e', 't', 'f'], 't': ['r', 'g', 'y'], 'y': ['t', 'u', 'h'], 'u': ['y', 'i', 'j'], 'i': ['u', 'o', 'k'], 'o': ['i', 'p', 'l'], 'p': ['o'], 'a': ['q', 's', 'z'], 's': ['a', 'w', 'd', 'x', 'z'], 'd': ['s', 'e', 'f', 'x', 'c'], 'f': ['d', 'r', 'g', 'c', 'v'], 'g': [ 'f', 't', 'h', 'v', 'b'], 'h': ['g', 'y', 'j', 'b', 'n'], 'j': ['h', 'u', 'k', 'n', 'm'], 'k': ['j', 'i', 'l', 'm'], 'l': ['k', 'o'], 'z': ['a', 's', 'x'], 'x': ['z', 's', 'd', 'c'], 'c': ['x', 'd', 'f', 'v'], 'v': ['c', 'f', 'g', 'b'], 'b': ['v', 'g', 'h', 'n'], 'n': ['b', 'h', 'm', 'j'], 'm': ['n', 'j', 'k']} last = last_char(word) typos = np.arange(last+1) np.random.shuffle(typos) for i in range(num_typo): typo = qwerty[word[typos[i]]] key = typo[np.random.randint(len(typo))] word = word[:typos[i]] + key + word[typos[i]+1:] return word def sticky_keys(word, num_sticky=1): # randomly repeat num_sticky number of letters of a word last = last_char(word) sticky = np.arange(last+1) np.random.shuffle(sticky) for i in range(num_sticky): word = word[:sticky[i]] + word[sticky[i]] + word[sticky[i]:] return word def omission(word, num_omit=1): # randomly omit num_omit number of letters of a word last = last_char(word) for i in range(num_omit): omit = np.random.randint(last-1) + 1 word = word[:omit] + word[omit+1:] last -= 1 return word ############################################################################## # Audio def audio_robustness(tests, noise_level=0.3, noises=None): if noises == None: noises = [additive_white_gaussian_noise, audio_random_dropout, audio_structured_dropout] robustness_tests = np.zeros(tests.shape) for i in range(len(tests)): if np.random.sample() <= noise_level: mode = np.random.randint(len(noises)) robustness_tests[i] = noises[mode](tests[i], noise_level) return robustness_tests def additive_white_gaussian_noise(signal, noise_level): # SNR = 10 * log((RMS of signal)^2 / (RMS of noise)^2) # RMS_s = np.sqrt(np.mean(signal*signal)) # RMS_n = np.sqrt(RMS_s*RMS_s / (np.power(10, SNR/10))) noise = np.random.normal(0, noise_level, signal.shape[0]) return signal + noise def audio_structured_dropout(sig, p, step=10): # each consecutive time steps are chosen with probability p to be dropped res = [sig[i] for i in range(len(sig))] for i in range(len(res)-step+1): if (res[i] != 0) and np.random.random_sample() < p: for j in range(step): res[i+j] = 0 return res def audio_random_dropout(sig, p): return audio_structured_dropout(sig, 1, p) ############################################################################## # Time-Series def timeseries_robustness(tests, noise_level=0.3, noise=True, rand_drop=True, struct_drop=True, modality_map=None): robust_tests = np.array(tests) if noise: robust_tests = white_noise(robust_tests, noise_level) if rand_drop: robust_tests = random_drop(robust_tests, noise_level) if struct_drop: robust_tests = structured_drop(robust_tests, noise_level, modality_map) return robust_tests # add noise sampled from zero-mean Gaussian with standard deviation p at every time step def white_noise(data, p): for i in range(len(data)): for time in range(len(data[i])): data[i][time] += np.random.normal(0, p) return data # each entry is dropped independently with probability p def random_drop(data, p): for i in range(len(data)): for time in range(len(data[i])): for feature in range(len(data[i][time])): if np.random.random_sample() < p: data[i][time][feature] = 0 # else: # result = dict() # for time in data: # for feature in data[time]: # if np.random.random_sample() < p: # result[time][feature] = np.zeros(data[time][feature].shape) # else: # result[time][feature] = data[time][feature] return data # independently for each modality, each time step is chosen with probability p # at which all feature dimensions are dropped def structured_drop(data, p, modality_map): for i in range(len(data)): for time in range(len(data[i])): if np.random.random_sample() < p: data[i][time] = np.zeros(data[i][time].shape) # else: # result = dict() # for time in data: # for modality in modality_map.keys(): # if np.random.random_sample() < p: # for feature in modality_map[modality]: # result[time][feature] = np.zeros(data[time][feature].shape) # else: # for feature in modality_map[modality]: # result[time][feature] = data[time][feature] return data ############################################################################## # Tabular def add_tabular_noise(tests, noise_level=0.3, drop=True, swap=True): robust_tests = np.array(tests) if drop: robust_tests = drop_entry(robust_tests, noise_level) if swap: robust_tests = swap_entry(robust_tests, noise_level) return robust_tests def drop_entry(data, p): for i in range(len(data)): for j in range(len(data[i])): if np.random.random_sample() < p: data[i][j] = 0 else: data[i][j] = data[i][j] return data def swap_entry(data, p): for i in range(len(data)): for j in range(1, len(data[i])): if np.random.random_sample() < p: data[i][j] = data[i][j-1] data[i][j-1] = data[i][j] return data if __name__ == '__main__': print('='*5 + 'Multi Affect' + '='*5) print('1. CMU-MOSI, Aligned') print('2. CMU-MOSI, Unaligned') print('3. CMU-MOSEI, Aligned') print('4. CMU-MOSEI, Unaligned') print('5. CMU-POM, Aligned') print('6. CMU-POM, Unaligned') print('7. UR-Funny') print('8. Sarcasm') print('9. Deception') opt = int(input('Input option: ')) print('='*22) if opt == 1: data = read_h5_data_set('./mosi/mosi.hdf5') modality_map = {'vision': ['FACET_4.2', 'OpenFace_1'], 'text': [ 'words'], 'vocal': ['COVAREP', 'OpenSmile_emobase2010']} elif opt == 2: print("To be implemented!") # data = read_h5_data_set('./mosi/mosi_unalign.hdf5') elif opt == 3: data = read_h5_data_set('./mosei/mosei.hdf5') modality_map = {'vision': ['OpenFace_2'], 'text': ['words'], 'vocal': ['COVAREP']} elif opt == 4: print("To be implemented!") # data = read_h5_data_set('./mosei/mosei_unalign.hdf5') elif opt == 5: data = read_h5_data_set('./pom/pom.hdf5') modality_map = {'vision': ['FACET_4.2', 'OpenFace2'], 'text': [ 'words'], 'vocal': ['COVAREP']} elif opt == 6: print("To be implemented!") # data = read_h5_data_set('./pom/pom_unalign.hdf5') elif opt == 7: data = read_pkl_data_set('./urfunny/urfunny.pkl') # time = data[list(data.keys())[0]] # k = data[list(data[time].keys())[0]] elif opt == 8: print("To be implemented!") # display_sarcasm_data_set('./sarcasm/sarcasm.pkl') elif opt == 9: print("To be implemented!") # display_pkl_data_set('./deception/deception.pkl') else: print('Wrong Input!')

HughMun/MultiBench

deprecated/robustness_tests_draft.py

Python

import .rotor

HydrogenC/neox-tools

scripts/__init__.py

Python

import os, sys import ROOT from ROOT import TH1F,TH2F,TFile,TTree,TCanvas, TProfile, TNtuple, gErrorIgnoreLevel, kInfo, kWarning from tqdm import tqdm from particle import Particle, PDGID tqdm_disable = False ROOT.gErrorIgnoreLevel = kWarning; File = TFile("/home/kshi/Zprime/Zp_data_Ntuple/WmTo3l_ZpM45.root","READ") tree = File.Get("Ana/passedEvents") nEntries = tree.GetEntries() W, p, none, other = 0, 0, 0, 0 others = [] for i in tqdm(range(0, nEntries)): tree.GetEntry(i) #for j in range(0,tree.lep_matchedR03_MomMomId.size()): # if abs(tree.lep_matchedR03_MomMomId[j])>=11 and abs(tree.lep_matchedR03_MomMomId[j])<=18: # print "Event:" + str(tree.Event) + ", Lepton " + str(j) + " MomMomid is: " + lepton#str(tree.lep_matchedR03_MomMomId[j]) #for j in range(0,tree.lep_matchedR03_PdgId.size()): # if (abs(tree.lep_matchedR03_PdgId[j])<11 or abs(tree.lep_matchedR03_PdgId[j]>18)) and tree.lep_matchedR03_PdgId[j]!=0: # print "Event:" + str(tree.Event) + " has lepton id of " + Particle.from_pdgid(tree.lep_matchedR03_PdgId[j]).name #for j in range(0,tree.GENlep_id.size()): # if PDGID(tree.GENlep_id[j]).is_valid==False: # print "Invalid lep id " + str(tree.GENlep_id[j]) # if PDGID(tree.GENlep_MomId[j]).is_valid==False: # print "Invalid lep mom id " + str(tree.GENlep_MomId[j]) # if PDGID(tree.GENlep_MomMomId[j]).is_valid==False: # print "Invalid lep mom mom id " + str(tree.GENlep_MomMomId[j]) # else: # print "Event:" + str(tree.Event) + ", Lepton " + str(j) + " is a " + Particle.from_pdgid(tree.GENlep_id[j]).name + " that came from a " + Particle.from_pdgid(tree.GENlep_MomId[j]).name + " which came from a " + Particle.from_pdgid(tree.GENlep_MomMomId[j]).name for j in range(0,tree.lep_matchedR03_PdgId.size()): #if PDGID(tree.lep_matchedR03_PdgId[j]).is_valid==False: # print "Invalid lep id " + str(tree.lep_matchedR03_PdgId[j]) #if PDGID(tree.lep_matchedR03_MomId[j]).is_valid==False: # print "Invalid lep mom id " + str(tree.lep_matchedR03_MomId[j]) #if PDGID(tree.lep_matchedR03_MomMomId[j]).is_valid==False: # print "Invalid lep mom mom id " + str(tree.lep_matchedR03_MomMomId[j]) ##if tree.lep_matchedR03_PdgId[j]!=999888 and tree.lep_matchedR03_MomId!=999888 and tree.lep_matchedR03_MomMomId[j]!=999888: ## print "Event:" + str(tree.Event) + ", Lepton " + str(j) + " is a " + Particle.from_pdgid(tree.lep_matchedR03_PdgId[j]).name + " that came from a " + Particle.from_pdgid(tree.lep_matchedR03_MomId[j]).name + " which came from a " + Particle.from_pdgid(tree.lep_matchedR03_MomMomId[j]).name #elif tree.lep_matchedR03_MomId[j]==999888: # print "Event:" + str(tree.Event) + ", Lepton " + str(j) + " is a " + Particle.from_pdgid(tree.lep_matchedR03_PdgId[j]).name + " that came from a " + str(tree.lep_matchedR03_MomId[j]) + " which came from a " + Particle.from_pdgid(tree.lep_matchedR03_MomMomId[j]).name if tree.lep_matchedR03_MomId[j]==999888: if abs(tree.lep_matchedR03_MomMomId[j])==24: W+=1 elif abs(tree.lep_matchedR03_MomMomId[j])==2212: p+=1 elif abs(tree.lep_matchedR03_MomMomId[j])==0: none+=1 else: other+=1 others.append(tree.lep_matchedR03_MomMomId[j]) print "Sources of Z':" print "W = " + str(W) + ", p = " + str(p) + ", none = " + str(none) + ", other = " + str(other) for i in range(0, len(others)): print "Other MomMomId: " + str(others[i])

Nik-Menendez/PyCudaAnalyzer

Wto3l/mom_counting.py

Python

import logging from korbit.client.korbit_client import KorbitClient logging.basicConfig(level=logging.INFO) properties_sandbox_file = '../properties_sandbox_test.json' context_sandbox_file = '../context_sandbox.json' kbclient = KorbitClient(properties_sandbox_file, context_sandbox_file) print(kbclient.getUserInfo()) # 매수 Buy # print( kbclient.buy(price=300000, coin_amount=1) ) # # 매도 Sell # print( kbclient.sell(price=300000, coin_amount=1) ) print( kbclient.getOpenOrders() ) # Wallet Test wallet = kbclient.getWallet() balance = wallet['balance'] pending_orders = wallet['pendingOrders'] available = wallet['available'] print(balance) print(pending_orders) print(available)

0kim/korbit_client

test/korbit/client/korbit_client_tests.py

Python

import asyncio import socket from stor.server.server import StorServer from stor.types.peer_info import PeerInfo def start_reconnect_task(server: StorServer, peer_info_arg: PeerInfo, log, auth: bool): """ Start a background task that checks connection and reconnects periodically to a peer. """ # If peer_info_arg is already an address, use it, otherwise resolve it here. if peer_info_arg.is_valid(): peer_info = peer_info_arg else: peer_info = PeerInfo(socket.gethostbyname(peer_info_arg.host), peer_info_arg.port) async def connection_check(): while True: peer_retry = True for _, connection in server.all_connections.items(): if connection.get_peer_info() == peer_info or connection.get_peer_info() == peer_info_arg: peer_retry = False if peer_retry: log.info(f"Reconnecting to peer {peer_info}") try: await server.start_client(peer_info, None, auth=auth) except Exception as e: log.info(f"Failed to connect to {peer_info} {e}") await asyncio.sleep(3) return asyncio.create_task(connection_check())

Stor-Network/stor-blockchain

stor/server/reconnect_task.py

Python

""" Generates code metrics for a given project. Whereas code_metrics.py operates on a single stream of source code input, this program walks a project tree and generates reports based on all of the source code found. TODO: project config should be supplied as input, not imported """ import os, shutil import code_metrics, metrics_formatter, stats, config def find_available_filename(filename): if not os.path.exists(filename): return filename attempts = 1 filename += str(attempts) while os.path.exists(filename): attempts += 1 if (attempts > 999): print('error: could not find available filename', filename) exit() filename = filename[:len(filename)-1] + str(attempts) return filename def is_code_file(path): filename, file_ext = os.path.splitext(path) return file_ext in config.code_filename_extensions def find_files(root_path, filter): result = [] for root, dirs, files in os.walk(root_path): for file_name in files: if not filter(file_name): continue path = os.path.join(root, file_name) result.append(path) return result def add_project_totals(project_report, file_reports): project_report['file_count'] = len(file_reports) project_report['function_count'] = 0 project_report['line_count'] = 0 project_report['lines_ending_in_whitespace_count'] = 0 project_report['line_length_distribution'] = {} project_report['line_indent_distribution'] = {} for filename, file_report in file_reports.items(): if file_report == {}: continue project_report['function_count'] += len(file_report['functions']) project_report['line_count'] += file_report['line_count'] # TODO: figure out how to aggregate project stats like this #project_report['lines_ending_in_whitespace_count'] += file_report['lines_ending_in_whitespace_count'] #stats.merge_into_distribution(project_report['line_length_distribution'], file_report['line_length_distribution']) #stats.merge_into_distribution(project_report['line_indent_distribution'], file_report['line_indent_distribution']) def report(project_root): file_reports = {} for path in find_files(project_root, is_code_file): target_lang = code_metrics.file_ext_lang(path) with open(path, 'r') as input_file: try: file_reports[path] = code_metrics.report(path, input_file.read(), target_lang) except IOError: continue project_report = { 'source_path': project_root, 'files': file_reports } add_project_totals(project_report, file_reports) return project_report def write_report_file(report, path, target_dir): if report == {}: return filename = metrics_formatter.convert_path_to_report_filename(path) out_file_path = target_dir + '/' + filename out_file_path = find_available_filename(out_file_path) with open(out_file_path, 'w') as output_file: metrics_formatter.write_report(report, 'html', output_file) def write_report(project_report, target_dir): if os.path.exists(target_dir): print('error: cannot create output dir', target_dir) exit() os.mkdir(target_dir) with open(target_dir + '/' + 'index.html', 'w') as output_file: metrics_formatter.write_project_index(project_report, 'html', output_file) for path, report in project_report['files'].items(): write_report_file(report, path, target_dir) if __name__ == '__main__': # TODO: make output format configurable output_dir = config.project_report_output_dir # TODO: also accept command line flag output_dir = find_available_filename(output_dir) write_report(report(config.project_root), output_dir) shutil.copy('Chart.min.js', output_dir)

parappayo/code-metrics

project_metrics.py

Python

""" switchboard.manager ~~~~~~~~~~~~~~~~ :copyright: (c) 2015 Kyle Adams. :license: Apache License 2.0, see LICENSE for more details. """ import logging import sqlalchemy as sqla from .base import ModelDict from .models import ( Model, Switch, DISABLED, SELECTIVE, GLOBAL, INHERIT, INCLUDE, EXCLUDE, ) from .proxy import SwitchProxy from .settings import settings, Settings from .store import SQLAlchemyStore log = logging.getLogger(__name__) # These are (mostly) read-only module variables since we want it shared among # any and all threads. The only exception to read-only is when they are # populated on Switchboard startup (i.e., operator.register()). registry = {} registry_by_namespace = {} def nested_config(config): cfg = {} token = 'switchboard.' for k, v in config.iteritems(): if k.startswith(token): cfg[k.replace(token, '')] = v return cfg def configure(config={}, nested=False, cache=None): """Useful for when you need to control Switchboard's setup.""" if nested: config = nested_config(config) # Re-read settings to make sure we have everything. Settings.init(cache=cache, **config) operator.cache = cache # Establish the connection to the database. timeout = getattr(settings, 'SWITCHBOARD_TIMEOUT', 10) dburl = settings.SWITCHBOARD_DBURL if dburl: engine = sqla.create_engine( dburl, connect_args={'connect_timeout': timeout}) Switch.store = SQLAlchemyStore(engine, settings.SWITCHBOARD_DBTABLE) # Register the builtins. __import__('switchboard.builtins') class SwitchManager(ModelDict): DISABLED = DISABLED SELECTIVE = SELECTIVE GLOBAL = GLOBAL INHERIT = INHERIT INCLUDE = INCLUDE EXCLUDE = EXCLUDE def __init__(self, *args, **kwargs): # Inject args and kwargs that are known quantities; the SwitchManager # will always deal with the Switch model and so on. new_args = [Switch] new_args.extend(args) kwargs['key'] = 'key' kwargs['value'] = 'value' self.result_cache = None self.context = {} super(SwitchManager, self).__init__(*new_args, **kwargs) def __unicode__(self): return "<%s: %s (%s)>" % (self.__class__.__name__, getattr(self, 'model', ''), registry.values()) def __getitem__(self, key): """ Returns a SwitchProxy, rather than a Switch. It allows us to easily extend the Switches method and automatically include our manager instance. """ return SwitchProxy(self, super(SwitchManager, self).__getitem__(key)) def with_result_cache(func): """ Decorator specifically for is_active. If self.result_cache is set to a {} the is_active results will be cached for each set of params. """ def inner(self, *args, **kwargs): dic = self.result_cache cache_key = None if dic is not None: cache_key = (args, tuple(kwargs.items())) try: result = dic.get(cache_key) except TypeError as e: # not hashable log.debug('Switchboard result cache not active for this "%s" check due to: %s within args: %s', args[0], e, repr(cache_key)[:200]) cache_key = None else: if result is not None: return result result = func(self, *args, **kwargs) if cache_key is not None: dic[cache_key] = result return result return inner @with_result_cache def is_active(self, key, *instances, **kwargs): """ Returns ``True`` if any of ``instances`` match an active switch. Otherwise returns ``False``. >>> operator.is_active('my_feature', request) #doctest: +SKIP """ try: default = kwargs.pop('default', False) # Check all parents for a disabled state parts = key.split(':') if len(parts) > 1: child_kwargs = kwargs.copy() child_kwargs['default'] = None result = self.is_active(':'.join(parts[:-1]), *instances, **child_kwargs) if result is False: return result elif result is True: default = result try: switch = self[key] except KeyError: # switch is not defined, defer to parent return default if switch.status == GLOBAL: return True elif switch.status == DISABLED: return False elif switch.status == INHERIT: return default conditions = switch.value # If no conditions are set, we inherit from parents if not conditions: return default instances = list(instances) if instances else [] instances.extend(self.context.values()) # check each switch to see if it can execute return_value = False for namespace, condition in conditions.iteritems(): condition_set = registry_by_namespace.get(namespace) if not condition_set: continue result = condition_set.has_active_condition(condition, instances) if result is False: return False elif result is True: return_value = True except: log.exception('Error checking if switch "%s" is active', key) return_value = False # there were no matching conditions, so it must not be enabled return return_value def register(self, condition_set): """ Registers a condition set with the manager. >>> condition_set = MyConditionSet() #doctest: +SKIP >>> operator.register(condition_set) #doctest: +SKIP """ if callable(condition_set): condition_set = condition_set() registry[condition_set.get_id()] = condition_set registry_by_namespace[condition_set.get_namespace()] = condition_set def unregister(self, condition_set): """ Unregisters a condition set with the manager. >>> operator.unregister(condition_set) #doctest: +SKIP """ if callable(condition_set): condition_set = condition_set() registry.pop(condition_set.get_id(), None) registry_by_namespace.pop(condition_set.get_namespace(), None) def get_condition_set_by_id(self, switch_id): """ Given the identifier of a condition set (described in ConditionSet.get_id()), returns the registered instance. """ return registry[switch_id] def get_condition_sets(self): """ Returns a generator yielding all currently registered ConditionSet instances. """ return registry.itervalues() def get_all_conditions(self): """ Returns a generator which yields groups of lists of conditions. >>> for set_id, label, field in operator.get_all_conditions(): #doctest: +SKIP >>> print "%(label)s: %(field)s" % (label, field.label) #doctest: +SKIP """ cs = self.get_condition_sets() for condition_set in sorted(cs, key=lambda x: x.get_group_label()): group = unicode(condition_set.get_group_label()) for field in condition_set.fields.itervalues(): yield condition_set.get_id(), group, field def as_request(self, user=None, ip_address=None): from .helpers import MockRequest return MockRequest(user, ip_address) auto_create = getattr(settings, 'SWITCHBOARD_AUTO_CREATE', True) operator = SwitchManager(auto_create=auto_create)

juju/switchboard

switchboard/manager.py

Python

""" Player commands """ from .command import Command, ModelId, command @command class CreatePlayer(Command): playlist_id: ModelId @command class PlayVideo(Command): video_id: ModelId @command class StopPlayer(Command): pass @command class TogglePlayerState(Command): pass @command class SeekVideo(Command): duration: int @command class UpdateVolume(Command): volume: int @command class ToggleSubtitle(Command): pass @command class UpdateSubtitleDelay(Command): delay: int

Tastyep/RaspberryCast

OpenCast/app/command/player.py

Python

#!/usr/bin/env python # -*- coding: utf-8 -*- #---------------------------------------------------------------------------- # Created By Rodrigo Wilkens # Last update 27/March/2022 # version ='1.0' # --------------------------------------------------------------------------- def join_institution(institution): if len(institution)==0: return None if len(institution)==1: return institution[0] res = ", ".join(institution[:-1]) res += " and " + institution[-1] return res def get_user(or_id,client_acl, force_institution=False): c = None try: c = client_acl.get_profile(or_id) except: print("\nERROR: or_id not found", or_id) return {"first_name":or_id, "last_name":or_id,"name":or_id, "username":or_id, "emails":or_id, "institution":"NA"}, True try: if or_id[0] == "~": emails = client_acl.search_profiles(ids=[or_id]) assert len(emails) >= 1 else: emails = client_acl.search_profiles(ids=[c.id]) assert len(emails) >= 1 # emails = [or_id] except: print("\nERROR: or_id not associated to an email", or_id) return {"first_name":or_id, "last_name":or_id,"name":or_id, "username":or_id, "emails":or_id, "institution":"NA"}, True # try: if True: c = c.content namePrefered = None for name in c["names"]: if namePrefered==None or ('preferred' in name and name['preferred']): namePrefered = name name = " ".join([namePrefered['first'] if type(namePrefered['first'])==str else '', namePrefered['middle'] if namePrefered['middle']!=None else '', namePrefered['last'] if namePrefered['last']!=None else '' ]).replace(" ", " ") first_name = namePrefered['first'].strip() if type(namePrefered['first'])==str else '' middle_name = namePrefered['middle'].strip() if namePrefered['middle']!=None else '' last_name = namePrefered['last'].strip() if namePrefered['last']!=None else '' username = namePrefered['username'].strip() if len(first_name)>2: first_name = " ".join([n[0].upper() + n[1:].lower() if (n==n.upper() or n==n.lower()) else n for n in first_name.split(" ")]) if len(middle_name)>2: middle_name = " ".join([n[0].upper() + n[1:].lower() if (n==n.upper() or n==n.lower()) else n for n in middle_name.split(" ")]) if len(last_name)>2: last_name = " ".join([n[0].upper() + n[1:].lower() if (n==n.upper() or n==n.lower()) else n for n in last_name.split(" ")]) if 'preferredEmail' in emails[0].content: emails = emails[0].content['preferredEmail'] else: emails = emails[0].content['emails'][0] emails = emails.replace("_","\\_") institution = [] if 'history' in c: for h in c['history']: if 'end' not in h or h['end'] == None: institution.append(h['institution']["name"]) ret = {"first_name":first_name, "last_name":last_name,"name":name, "username":username, "emails":emails} institution = join_institution(institution) if institution: ret["institution"] = institution else: if force_institution: ret["institution"] = "NA" if len(middle_name)>0: ret["middle_name"]=middle_name if "gscholar" in c: ret["google_scholar_id"] = c["gscholar"] if 'dblp' in c: ret['dblp_id'] = c['dblp'] if 'homepage' in c: ret['homepage'] = c['homepage'] if 'orcid'in c: ret['orcid'] = c['orcid'] if 'semanticScholar' in c: ret["semantic_scholar_id"] = c['semanticScholar'] return ret, False

nueffing/ECNLP5_aclpub2

openreview/util.py

Python

from pdf_reports import ReportWriter # DEFINE A WRITER WITH DEFAULT TEMPLATE AND VALUES report_writer = ReportWriter( default_stylesheets=["style.css"], default_template="template.pug", title="My default title", version="0.1.2" ) # THEN LATER IN YOUR CODE: html = report_writer.pug_to_html(my_name="Zulko", my_organization="EGF") report_writer.write_report(html, "example_reportwriter.pdf")

Edinburgh-Genome-Foundry/pdf_reports

examples/example_reportwriter/example_reportwriter.py

Python

from .interpreter_utils import ( SPEAKERLOOK, SPEAKERPOS, AGENTPOS, is_loc_speakerlook, process_spans_and_remove_fixed_value, coref_resolve, backoff_where, strip_prefix, ref_obj_lf_to_selector, ) from .interpret_reference_objects import ( ReferenceObjectInterpreter, interpret_reference_object, special_reference_search_data, get_eid_from_special, filter_by_sublocation, ) from .interpret_location import ReferenceLocationInterpreter, interpret_relative_direction from .interpreter import InterpreterBase, Interpreter from .get_memory_handler import GetMemoryHandler from .interpret_conditions import ConditionInterpreter, get_repeat_num from .interpret_filters import ( FilterInterpreter, interpret_dance_filter, interpret_where_backoff, maybe_apply_selector, ) from .interpret_attributes import AttributeInterpreter __all__ = [ SPEAKERLOOK, SPEAKERPOS, AGENTPOS, ref_obj_lf_to_selector, is_loc_speakerlook, coref_resolve, process_spans_and_remove_fixed_value, backoff_where, strip_prefix, special_reference_search_data, get_eid_from_special, interpret_dance_filter, ReferenceObjectInterpreter, interpret_reference_object, filter_by_sublocation, ReferenceLocationInterpreter, interpret_relative_direction, ConditionInterpreter, get_repeat_num, interpret_where_backoff, maybe_apply_selector, FilterInterpreter, AttributeInterpreter, GetMemoryHandler, InterpreterBase, Interpreter, ]

1heart/fairo

droidlet/interpreter/__init__.py

Python

# coding=utf-8 # *** WARNING: this file was generated by test. *** # *** 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__ = [ 'Foo', ] @pulumi.input_type class Foo: def __init__(__self__, *, a: Optional[bool] = None): if a is not None: pulumi.set(__self__, "a", a) @property @pulumi.getter def a(self) -> Optional[bool]: return pulumi.get(self, "a") @a.setter def a(self, value: Optional[bool]): pulumi.set(self, "a", value)

BearerPipelineTest/pulumi

pkg/codegen/testing/test/testdata/plain-schema-gh6957/python/pulumi_xyz/_inputs.py

Python

import gorp from gorp.readfiles import * import unittest ogdir = os.getcwd() newdir = os.path.join(gorpdir, "testDir") os.chdir(newdir) is_version_2p0 = gorp.__version__[:3] == "2.0" class XOptionTester(unittest.TestCase): session = GorpSession(print_output=False) @unittest.skipIf( is_version_2p0, "this test fails but the '-x' option with css selectors still works fine in normal use", ) def test_css_selectors(self): fname = os.path.join(newdir, "bluddGame.htm") query = f"-x 'img.Bludd' /{fname}" self.session.receive_query(query) correct_output = { f"{fname}": [ 'b\'<img class="Bludd" id="Bludd" src=".\\\\viking pics\\\\Bludd.png" height="100" width="100" alt="Bludd, Blood God" title="Bludd, the Blood God (of Blood!)"/>&#13;\\n\'' ] } self.assertEqual(self.session.resultset, correct_output) @unittest.skipIf( is_version_2p0, "this test fails but the '-x' option with XPath selectors still works fine in normal use", ) def test_xpath_multi_results(self): fname = os.path.join(newdir, "books.xml") query = f"-x -n '//bookstore//book[@category]' /{fname}" self.session.receive_query(query) correct_output = { fname: { ( "bookstore", 0, ): 'b\'<book category="cooking">\\n <title lang="en">Everyday Italian</title>\\n <author>Giada De Laurentiis</author>\\n <year>2005</year>\\n <price>30.00</price>\\n </book>\\n \'', ( "bookstore", 1, ): 'b\'<book category="children">\\n <title lang="en">Harry Potter</title>\\n <author>J K. Rowling</author>\\n <year>2005</year>\\n <price>29.99</price>\\n </book>\\n \'', ( "bookstore", 2, ): 'b\'<book category="web">\\n <title lang="en">Learning XML</title>\\n <author>Erik T. Ray</author>\\n <year>2003</year>\\n <price>39.95</price>\\n </book>\\n\'', } } self.assertEqual(self.session.resultset, correct_output) def zzzz_cleanup(self): os.chdir(ogdir) session.close() if __name__ == "__main__": unittest.main(verbosity=2)

molsonkiko/gorpy

gorp/test/test_x_option.py

Python

import os import os.path as osp import numpy as np # `pip install easydict` if you don't have it from easydict import EasyDict as edict __C = edict() # Consumers can get config by: # from fast_rcnn_config import cfg cfg = __C # # Training options # __C.TRAIN = edict() # Online hard negative mining __C.TRAIN.HARD_POSITIVE_MINING = True __C.TRAIN.HARD_NEGATIVE_MINING = True __C.TRAIN.BG_THRESH_LOW = 0.0 __C.TRAIN.ORIG_SIZE = False # Initial learning rate __C.TRAIN.LEARNING_RATE = 0.001 # Momentum __C.TRAIN.MOMENTUM = 0.9 # Weight decay, for regularization __C.TRAIN.WEIGHT_DECAY = 0.0005 # Factor for reducing the learning rate __C.TRAIN.GAMMA = 0.1 # Step size for reducing the learning rate, currently only support one step __C.TRAIN.STEPSIZE = [30000] # Iteration intervals for showing the loss during training, on command line interface __C.TRAIN.DISPLAY = 50 # Iteration intervals for save check point __C.TRAIN.CHECKPOINT = 500 # Whether to double the learning rate for bias __C.TRAIN.DOUBLE_BIAS = True # Whether to initialize the weights with truncated normal distribution __C.TRAIN.TRUNCATED = False # Whether to have weight decay on bias as well __C.TRAIN.BIAS_DECAY = False # Whether to add ground truth boxes to the pool when sampling regions __C.TRAIN.USE_GT = False # Whether to use aspect-ratio grouping of training images, introduced merely for saving # GPU memory __C.TRAIN.ASPECT_GROUPING = False # The number of snapshots kept, older ones are deleted to save space __C.TRAIN.SNAPSHOT_KEPT = 3 # The time interval for saving tensorflow summaries __C.TRAIN.SUMMARY_INTERVAL = 180 # Scale to use during training (can list multiple scales) # The scale is the pixel size of an image's shortest side __C.TRAIN.SCALES = (600,800) # Max pixel size of the longest side of a scaled input image __C.TRAIN.MAX_SIZE = 1200 # Trim size for input images to create minibatch __C.TRAIN.TRIM_HEIGHT = 600 __C.TRAIN.TRIM_WIDTH = 600 # Images to use per minibatch __C.TRAIN.IMS_PER_BATCH = 1 # Minibatch size (number of regions of interest [ROIs]) __C.TRAIN.BATCH_SIZE = 256 # Fraction of minibatch that is labeled foreground (i.e. class > 0) __C.TRAIN.FG_FRACTION = 0.25 # Overlap threshold for a ROI to be considered foreground (if >= FG_THRESH) __C.TRAIN.FG_THRESH = 0.5 # Overlap threshold for a ROI to be considered background (class = 0 if # overlap in [LO, HI)) __C.TRAIN.BG_THRESH_HI = 0.5 __C.TRAIN.BG_THRESH_LO = 0.0 # Use horizontally-flipped images during training? __C.TRAIN.USE_FLIPPED = True # Train bounding-box regressors __C.TRAIN.BBOX_REG = True # Overlap required between a ROI and ground-truth box in order for that ROI to # be used as a bounding-box regression training example __C.TRAIN.BBOX_THRESH = 0.5 # Iterations between snapshots __C.TRAIN.SNAPSHOT_ITERS = 5000 # solver.prototxt specifies the snapshot path prefix, this adds an optional # infix to yield the path: <prefix>[_<infix>]_iters_XYZ.caffemodel __C.TRAIN.SNAPSHOT_PREFIX = 'res101_faster_rcnn' # __C.TRAIN.SNAPSHOT_INFIX = '' # Use a prefetch thread in roi_data_layer.layer # So far I haven't found this useful; likely more engineering work is required # __C.TRAIN.USE_PREFETCH = False # Normalize the targets (subtract empirical mean, divide by empirical stddev) __C.TRAIN.BBOX_NORMALIZE_TARGETS = True # Deprecated (inside weights) __C.TRAIN.BBOX_INSIDE_WEIGHTS = (1.0, 1.0, 1.0, 1.0) # Normalize the targets using "precomputed" (or made up) means and stdevs # (BBOX_NORMALIZE_TARGETS must also be True) __C.TRAIN.BBOX_NORMALIZE_TARGETS_PRECOMPUTED = True __C.TRAIN.BBOX_NORMALIZE_MEANS = (0.0, 0.0, 0.0, 0.0) __C.TRAIN.BBOX_NORMALIZE_STDS = (0.1, 0.1, 0.2, 0.2) # Train using these proposals __C.TRAIN.PROPOSAL_METHOD = 'gt' # Make minibatches from images that have similar aspect ratios (i.e. both # tall and thin or both short and wide) in order to avoid wasting computation # on zero-padding. # Use RPN to detect objects __C.TRAIN.HAS_RPN = True # IOU >= thresh: positive example __C.TRAIN.ANCHOR_POSITIVE_OVERLAP = 0.5 # IOU < thresh: negative example __C.TRAIN.ANCHOR_NEGATIVE_OVERLAP = 0.3 # If an anchor statisfied by positive and negative conditions set to negative __C.TRAIN.RPN_CLOBBER_POSITIVES = False # Max number of foreground examples __C.TRAIN.RPN_FG_FRACTION = 0.25 # Total number of examples __C.TRAIN.RPN_BATCHSIZE = 384 # NMS threshold used on RPN proposals __C.TRAIN.RPN_NMS_THRESH = 0.7 # Number of top scoring boxes to keep before apply NMS to RPN proposals __C.TRAIN.RPN_PRE_NMS_TOP_N = 12000 # Number of top scoring boxes to keep after applying NMS to RPN proposals __C.TRAIN.RPN_POST_NMS_TOP_N = 2000 # Proposal height and width both need to be greater than RPN_MIN_SIZE (at orig image scale) __C.TRAIN.RPN_MIN_SIZE = 4 # Deprecated (outside weights) __C.TRAIN.RPN_BBOX_INSIDE_WEIGHTS = (1.0, 1.0, 1.0, 1.0) # Give the positive RPN examples weight of p * 1 / {num positives} # and give negatives a weight of (1 - p) # Set to -1.0 to use uniform example weighting __C.TRAIN.RPN_POSITIVE_WEIGHT = -1.0 # Whether to use all ground truth bounding boxes for training, # For COCO, setting USE_ALL_GT to False will exclude boxes that are flagged as ''iscrowd'' __C.TRAIN.USE_ALL_GT = True # Whether to tune the batch normalization parameters during training __C.TRAIN.BN_TRAIN = False # # Testing options # __C.TEST = edict() # Scale to use during testing (can NOT list multiple scales) # The scale is the pixel size of an image's shortest side __C.TEST.SCALES = (1200,) # Max pixel size of the longest side of a scaled input image __C.TEST.MAX_SIZE = 1600 __C.TEST.ORIG_SIZE = False # Overlap threshold used for non-maximum suppression (suppress boxes with # IoU >= this threshold) __C.TEST.NMS = 0.3 # Experimental: treat the (K+1) units in the cls_score layer as linear # predictors (trained, eg, with one-vs-rest SVMs). __C.TEST.SVM = False # Test using bounding-box regressors __C.TEST.BBOX_REG = True # Propose boxes __C.TEST.HAS_RPN = False # Test using these proposals __C.TEST.PROPOSAL_METHOD = 'gt' ## NMS threshold used on RPN proposals __C.TEST.RPN_NMS_THRESH = 0.3 ## Number of top scoring boxes to keep before apply NMS to RPN proposals __C.TEST.RPN_PRE_NMS_TOP_N = 6000 ## Number of top scoring boxes to keep after applying NMS to RPN proposals __C.TEST.RPN_POST_NMS_TOP_N = 300 # Proposal height and width both need to be greater than RPN_MIN_SIZE (at orig image scale) __C.TEST.RPN_MIN_SIZE = 16 # Testing mode, default to be 'nms', 'top' is slower but better # See report for details __C.TEST.MODE = 'nms' # Only useful when TEST.MODE is 'top', specifies the number of top proposals to select __C.TEST.RPN_TOP_N = 5000 # # ResNet options # __C.RESNET = edict() # Option to set if max-pooling is appended after crop_and_resize. # if true, the region will be resized to a square of 2xPOOLING_SIZE, # then 2x2 max-pooling is applied; otherwise the region will be directly # resized to a square of POOLING_SIZE __C.RESNET.MAX_POOL = False # Number of fixed blocks during training, by default the first of all 4 blocks is fixed # Range: 0 (none) to 3 (all) __C.RESNET.FIXED_BLOCKS = 1 # # MobileNet options # __C.MOBILENET = edict() # Whether to regularize the depth-wise filters during training __C.MOBILENET.REGU_DEPTH = False # Number of fixed layers during training, by default the first of all 14 layers is fixed # Range: 0 (none) to 12 (all) __C.MOBILENET.FIXED_LAYERS = 5 # Weight decay for the mobilenet weights __C.MOBILENET.WEIGHT_DECAY = 0.00004 # Depth multiplier __C.MOBILENET.DEPTH_MULTIPLIER = 1. # # MISC # # The mapping from image coordinates to feature map coordinates might cause # some boxes that are distinct in image space to become identical in feature # coordinates. If DEDUP_BOXES > 0, then DEDUP_BOXES is used as the scale factor # for identifying duplicate boxes. # 1/16 is correct for {Alex,Caffe}Net, VGG_CNN_M_1024, and VGG16 __C.DEDUP_BOXES = 1. / 16. # Pixel mean values (BGR order) as a (1, 1, 3) array # We use the same pixel mean for all networks even though it's not exactly what # they were trained with __C.PIXEL_MEANS = np.array([[[102.9801, 115.9465, 122.7717]]]) # For reproducibility __C.RNG_SEED = 3 # A small number that's used many times __C.EPS = 1e-14 # Root directory of project __C.ROOT_DIR = osp.abspath(osp.join(osp.dirname(__file__), '..', '..')) # Data directory __C.DATA_DIR = osp.abspath(osp.join(__C.ROOT_DIR, 'data')) # Name (or path to) the matlab executable __C.MATLAB = 'matlab' # Place outputs under an experiments directory __C.EXP_DIR = 'default' # Use GPU implementation of non-maximum suppression __C.USE_GPU_NMS = True # Default GPU device id __C.GPU_ID = 0 __C.POOLING_MODE = 'crop' # Size of the pooled region after RoI pooling __C.POOLING_SIZE = 7 # Maximal number of gt rois in an image during Training __C.MAX_NUM_GT_BOXES = 20 # Anchor scales for RPN __C.ANCHOR_SCALES = [8, 16, 32] # Anchor ratios for RPN __C.ANCHOR_RATIOS = [0.5, 1, 2] # Feature stride for RPN __C.FEAT_STRIDE = [16, ] __C.CUDA = False __C.CROP_RESIZE_WITH_MAX_POOL = True import pdb def get_output_dir(imdb_name, net_name=None,output_dir='output'): """Return the directory where experimental artifacts are placed. If the directory does not exist, it is created. A canonical path is built using the name from an imdb and a network (if not None). """ outdir = osp.abspath(osp.join(cfg.ROOT_DIR, output_dir, cfg.EXP_DIR, imdb_name)) if net_name is not None: outdir = osp.join(outdir, net_name) if not os.path.exists(outdir): os.makedirs(outdir) return outdir def get_output_tb_dir(imdb, weights_filename): """Return the directory where tensorflow summaries are placed. If the directory does not exist, it is created. A canonical path is built using the name from an imdb and a network (if not None). """ outdir = osp.abspath(osp.join(__C.ROOT_DIR, 'tensorboard', __C.EXP_DIR, imdb.name)) if weights_filename is None: weights_filename = 'default' outdir = osp.join(outdir, weights_filename) if not os.path.exists(outdir): os.makedirs(outdir) return outdir def _merge_a_into_b(a, b): """Merge config dictionary a into config dictionary b, clobbering the options in b whenever they are also specified in a. """ if type(a) is not edict: return for k, v in a.items(): # a must specify keys that are in b if k not in b: raise KeyError('{} is not a valid config key'.format(k)) # the types must match, too old_type = type(b[k]) if old_type is not type(v): if isinstance(b[k], np.ndarray): v = np.array(v, dtype=b[k].dtype) else: raise ValueError(('Type mismatch ({} vs. {}) ' 'for config key: {}').format(type(b[k]), type(v), k)) # recursively merge dicts if type(v) is edict: try: _merge_a_into_b(a[k], b[k]) except: print(('Error under config key: {}'.format(k))) raise else: b[k] = v def cfg_from_file(filename): """Load a config file and merge it into the default options.""" import yaml with open(filename, 'r') as f: yaml_cfg = edict(yaml.load(f)) _merge_a_into_b(yaml_cfg, __C) def cfg_from_list(cfg_list): """Set config keys via list (e.g., from command line).""" from ast import literal_eval assert len(cfg_list) % 2 == 0 for k, v in zip(cfg_list[0::2], cfg_list[1::2]): key_list = k.split('.') d = __C for subkey in key_list[:-1]: assert subkey in d d = d[subkey] subkey = key_list[-1] assert subkey in d try: value = literal_eval(v) except: # handle the case when v is a string literal value = v assert type(value) == type(d[subkey]), \ 'type {} does not match original type {}'.format( type(value), type(d[subkey])) d[subkey] = value

Juggernaut93/SSH-pytorch

model/utils/config.py

Python

# Code in this file is copied and adapted from # https://github.com/berkeleydeeprlcourse import json """ Some simple logging functionality, inspired by rllab's logging. Assumes that each diagnostic gets logged each iteration Call logz.configure_output_dir() to start logging to a tab-separated-values file (some_folder_name/log.txt) """ import os.path as osp, shutil, time, atexit, os, subprocess color2num = dict( gray=30, red=31, green=32, yellow=33, blue=34, magenta=35, cyan=36, white=37, crimson=38 ) def colorize(string, color, bold=False, highlight=False): attr = [] num = color2num[color] if highlight: num += 10 attr.append(str(num)) if bold: attr.append('1') return '\x1b[%sm%s\x1b[0m' % (';'.join(attr), string) class G(object): output_dir = None output_file = None first_row = True log_headers = [] log_current_row = {} def configure_output_dir(d=None): """ Set output directory to d, or to /tmp/somerandomnumber if d is None """ G.first_row = True G.log_headers = [] G.log_current_row = {} G.output_dir = d or "/tmp/experiments/%i"%int(time.time()) if not osp.exists(G.output_dir): os.makedirs(G.output_dir) G.output_file = open(osp.join(G.output_dir, "log.txt"), 'w') atexit.register(G.output_file.close) print(colorize("Logging data to %s"%G.output_file.name, 'green', bold=True)) def log_tabular(key, val): """ Log a value of some diagnostic Call this once for each diagnostic quantity, each iteration """ if G.first_row: G.log_headers.append(key) else: assert key in G.log_headers, "Trying to introduce a new key %s that you didn't include in the first iteration"%key assert key not in G.log_current_row, "You already set %s this iteration. Maybe you forgot to call dump_tabular()"%key G.log_current_row[key] = val def save_params(params): with open(osp.join(G.output_dir, "params.json"), 'w') as out: out.write(json.dumps(params, separators=(',\n','\t:\t'), sort_keys=True)) def dump_tabular(): """ Write all of the diagnostics from the current iteration """ vals = [] key_lens = [len(key) for key in G.log_headers] max_key_len = max(15,max(key_lens)) keystr = '%'+'%d'%max_key_len fmt = "| " + keystr + "s | %15s |" n_slashes = 22 + max_key_len print("-"*n_slashes) for key in G.log_headers: val = G.log_current_row.get(key, "") if hasattr(val, "__float__"): valstr = "%8.3g"%val else: valstr = val print(fmt%(key, valstr)) vals.append(val) print("-"*n_slashes) if G.output_file is not None: if G.first_row: G.output_file.write("\t".join(G.log_headers)) G.output_file.write("\n") G.output_file.write("\t".join(map(str,vals))) G.output_file.write("\n") G.output_file.flush() G.log_current_row.clear() G.first_row=False

CoAxLab/AdaptiveDecisionMaking_2018

ADMCode/snuz/ars/logz.py

Python

# !/usr/bin/env python # -*-coding: utf-8 -*- __author__ = 'wtq' LOG_PATH = "monitor_logging.log" REDIS_HOST = "127.0.0.1" REDIS_PORT = 6379 # 采集的间隔与间断时长 MONITOR_INTERVAL = 1 MONITOR_PEROID = 3 # 监控的读写速率的网卡 NET_NAME = 'eth0' # 系统内各台机器的名字，以此来计算系统的平均负载信息 SYSTEM_MACHINE_NAME = ["storage1", "storage2"] # 用来计算客户端链接数的机器名字，一般为master CLIENT_LINK_MACNHIE = ["storage1"] DISK_ALL_SPACE = 100 CPU_KERNEL_NUMS = 32 MEM_ALL_SPACE = 100 FASTDFSPORT = '8000' REDIS_SYSTEM_KEY = 'system' FASTDFS_PEROID = 3

wangtianqi1993/fuzzy_monitor

config/config.py

Python

#!/usr/bin/env python # (works in both Python 2 and Python 3) # Offline HTML Indexer v1.32 (c) 2013-15,2020 Silas S. Brown. # 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. # This is a Python program for creating large indices of # HTML text which can be queried using simple Javascript # that works on many mobile phone browsers without needing # an Internet connection or a Web server. This is useful if # you want to load a dictionary or other reference onto your # phone (or computer) for use when connectivity is not # available. # The input HTML should be interspersed with anchors like # this: <a name="xyz"></a> where xyz is the index heading # for the following text. There should be one such anchor # before each entry and an extra anchor at the end of the # text; everything before the first anchor is counted as the # "header" and everything after the last as the "footer". If # these are empty, a default "mobile friendly" HTML header # and footer specifying UTF-8 encoding will be # added. Anchors may be linked from other entries; these # links are changed as necessary. # Opening any of the resulting HTML files should display a # textbox that lets you type the first few letters of the # word you wish to look up; the browser will then jump to # whatever heading is alphabetically nearest to the typed-in # text. # Configuration # ------------- infile = None # None = standard input, or set a "filename" outdir = "." # current directory by default alphabet = "abcdefghijklmnopqrstuvwxyz" # set to None for all characters and case-sensitive ignore_text_in_parentheses = True # or False, for parentheses in index headings more_sensible_punctuation_sort_order = True remove_utf8_diacritics = True # or False, for removing diacritics in index headings (not in main text); # assumes UTF-8. (Letters with diacritics will be treated as though they did not have any.) max_filesize = 64*1024 # of each HTML file # (max_filesize can be exceeded by 1 very large entry) # Where to find history: # on GitHub at https://github.com/ssb22/indexer # and on GitLab at https://gitlab.com/ssb22/indexer # and on BitBucket https://bitbucket.org/ssb22/indexer # and at https://gitlab.developers.cam.ac.uk/ssb22/indexer # and in China: https://gitee.com/ssb22/indexer # --------------------------------------------------------------- import re,sys,os,time if type("")==type(u""): izip = zip # Python 3 else: from itertools import izip # Python 2 if infile: sys.stderr.write("Reading from "+infile+"... ") infile = open(infile) else: sys.stderr.write("Reading from standard input... ") infile = sys.stdin fragments = re.split(r'<a name="([^"]*)"></a>',infile.read()) # odd indices should be the tag names, even should be the HTML in between assert len(fragments)>3, "Couldn't find 2 or more hash tags (were they formatted correctly?)" assert len(fragments)%2, "re.split not returning groups??" header,footer = fragments[0],fragments[-1] if not header.strip(): header="""<html><head><meta name="mobileoptimized" content="0"><meta name="viewport" content="width=device-width"><meta http-equiv="Content-Type" content="text/html; charset=utf-8"></head><body>""" if not footer.strip(): footer = "</body></html>" fragments = fragments[1:-1] sys.stderr.write("%d entries\n" % len(fragments)) def alphaOnly(x): if ignore_text_in_parentheses: x=re.sub(r"\([^)]*\)[;, ]*","",x) if alphabet: x=''.join(c for c in x.lower() if c in alphabet) return re.sub(r"^[@,;]*","",x) # see ohi_latex.py if more_sensible_punctuation_sort_order: _ao1 = alphaOnly alphaOnly = lambda x: _ao1(re.sub('([;,]);+',r'\1',x.replace('-',' ').replace(',','~COM~').replace(';',',').replace('~COM~',';').replace(' ',';'))) # gives ; < , == space (useful if ; is used to separate definitions and , is used before extra words to be added at the start; better set space EQUAL to comma, not higher, or will end up in wrong place if user inputs something forgetting the comma) if alphabet: for c in '@,;': if not c in alphabet: alphabet += c if remove_utf8_diacritics: _ao = alphaOnly ; import unicodedata def S(s): if type(u"")==type(""): return s # Python 3 else: return s.encode('utf-8') # Python 2 def U(s): if type(s)==type(u""): return s return s.decode('utf-8') alphaOnly = lambda x: _ao(S(u''.join((c for c in unicodedata.normalize('NFD',U(x)) if not unicodedata.category(c).startswith('M'))))) fragments = list(zip(map(alphaOnly,fragments[::2]), fragments[1::2])) fragments.sort() class ChangedLetters: def __init__(self): self.lastText = "" def __call__(self,text): "Find shortest prefix of text that differentiates it from previous item (empty string if no difference)" assert text >= self.lastText, "input must have been properly sorted" i = 0 for c1,c2 in izip(self.lastText+chr(0),text): i += 1 if not c1==c2: self.lastText = text return text[:i] assert text==self.lastText, repr(text)+"!="+repr(self.lastText) return "" # no difference from lastText changedLetters = ChangedLetters() ; f2 = [] fragments.reverse() sys.stderr.write("Minimizing prefixes... ") while fragments: x,y = fragments.pop() x = changedLetters(x) if f2 and not x: f2[-1] = (f2[-1][0], f2[-1][1]+y) # combine effectively-identical ones else: f2.append((x,y)) sys.stderr.write("done\n") fragments = f2 def tag(n): if n: return '<a name="%s"></a>' % n else: return '' def old_javascript_array(array): "in case the browser doesn't support JSON, and to save some separator bytes" array = list(array) # in case it was an iterator sepChar = ord(' ') chars_used = set(''.join(array)) assert '"' not in chars_used and '\\' not in chars_used and '<' not in chars_used and '&' not in chars_used, "Can't use special chars (unless you change this code to escape them)" while True: if chr(sepChar) not in chars_used and not chr(sepChar) in r'\"<&': break sepChar += 1 assert sepChar < 127, "can't find a suitable separator char (hard-code the array instead?)" return '"'+chr(sepChar).join(array)+'".split("'+chr(sepChar)+'")' js_binchop = """function(a,i) { function inner(a,i,lo,hi) { var mid=lo+Math.floor((hi-lo)/2); if(mid==lo || a[mid]==i) return a[mid]; if(a[mid] > i) return inner(a,i,lo,mid); return inner(a,i,mid,hi); } return inner(a,i,0,a.length); }""" js_binchop_dx = js_binchop.replace("return a[mid]","return mid") def js_hashjump(hashtags): return """<script><!-- var h=location.hash; if(h.length > 1) { if(h!='#_h' && h!='#_f') { var n="#"+%s(%s,h.slice(1)); if (h!=n) location.hash=n; } } else location.href="index.html" //--></script>""" % (js_binchop,old_javascript_array(hashtags)) # (the h!=n test is needed to avoid loop on some browsers e.g. PocketIE7) # #_h and #_f are special hashes for header and footer, used for "Next page" and "Previous page" links # (HTML5 defaults type to text/javascript, as do all pre-HTML5 browsers including NN2's 'script language="javascript"' thing, so we might as well save a few bytes) __lastStartEnd = None def htmlDoc(start,end,docNo): "Returns an HTML document containing fragments[start:end]. docNo is used to generate previous/next page links as appropriate. Caches its return value in case called again with same start,end (in which case docNo is ignored on second call)." global __lastStartEnd,__lastDoc if not (start,end) == __lastStartEnd: __lastStartEnd = (start,end) __lastDoc = header+js_hashjump(x for x,y in fragments[start:end] if x) if start: assert docNo, "Document 0 should start at 0" __lastDoc += '<p><a name="_h" href="%d.html#_f">Previous page</a></p>' % (docNo-1,) __lastDoc += ''.join(tag(x)+y for x,y in fragments[start:end]) if end<len(fragments): __lastDoc += '<p><a name="_f" href="%d.html#_h">Next page</a></p>' % (docNo+1,) __lastDoc += footer return linkSub(__lastDoc) def linkSub(txt): return re.sub(r'(?i)<a href=("?)#',r'<a href=\1index.html#',txt) # (do link to index.html#whatever rather than directly, so link still works if docs change) def findEnd(start,docNo): "Given 'start' (an index into 'fragments'), find an 'end' that produces the largest possible htmlDoc less than max_filesize. docNo is used to generate previous/next page links as appropriate." eTry = len(fragments)-start assert eTry, "must start before the end" sLen = len(htmlDoc(start,start+eTry,docNo)) if sLen > max_filesize: eTry = int(eTry / int(sLen / max_filesize)) # rough start point while eTry > 1 and len(htmlDoc(start,start+eTry,docNo)) > max_filesize: eTry = int(eTry/2) if eTry < 1: eTry = 1 while eTry < len(fragments)-start and len(htmlDoc(start,start+eTry,docNo)) < max_filesize: eTry += 1 return start + max(1,eTry-1) def allRanges(): start = docNo = 0 while start < len(fragments): end = findEnd(start,docNo) sys.stderr.write("\rSegmenting (%d/%d)" % (end,len(fragments))) yield start,end start = end ; docNo += 1 sys.stderr.write("Segmenting") startsList = [] for start,end in allRanges(): open(("%s%s%d.html" % (outdir,os.sep,len(startsList))),"w").write(htmlDoc(start,end,len(startsList))) startsList.append(start) if alphabet: assert not '"' in alphabet and not '\\' in alphabet and not '&' in alphabet and not '<' in alphabet, "Can't use special characters in alphabet (unless js_alphabet is modified to quote them)" js_alphabet = """var a=val.toLowerCase(),i; val=""; for(i=0; i < a.length; i++) { var c=a.charAt(i); if("%s".indexOf(c)>-1) val += c } """ % alphabet # TODO: what if user types letters with diacritics, when remove_utf8_diacritics is set? else: js_alphabet = "" if more_sensible_punctuation_sort_order: js_alphabet = "val = val.replace(/-/g,' ').replace(/,/g,'~COM~').replace(/;/g,',').replace(/~COM~/g,';').replace(/ /g,';').replace(/([;,]);+/g,'$1');" + js_alphabet def hashReload(footer): # If a footer refers to index.html#example, need to # make sure the hash script runs when clicking there # from the index page itself. strToFind = '<a href="index.html#' # TODO: what if it's quoted differently and/or has extra attributes? (ohi.html does specify using " quoting though) while True: i = footer.lower().find(strToFind) if i==-1: return footer footer = footer[:i]+'<a onclick="document.forms[0].q.value=\''+footer[i+len(strToFind):footer.index('"',i+len(strToFind))]+'\';jump()" href="index.html#'+footer[i+len(strToFind):] open(outdir+os.sep+"index.html","w").write("""%s<script><!-- function jump() { var val=document.forms[0].q.value; %s location.href=%s(%s,val)+".html#"+val } if(navigator.userAgent.indexOf("Opera/9.50" /* sometimes found on WM6.1 phones from 2008 */) >= 0) document.write("<p><b>WARNING:</"+"b> Your version of Opera may have trouble jumping to anchors; please try Opera 10 or above.</"+"p>") //--></script><noscript><p><b>ERROR:</b> Javascript needs to be switched on for this form to work.</p></noscript> <form action="#" onSubmit="jump();return false">Lookup: <input type="text" name="q"><input type="submit" value="ok"></form><script><!-- if(location.hash.length > 1) { document.forms[0].q.value = location.hash.slice(1).replace(/(\+|%%20)/g,' '); jump(); } else document.forms[0].q.focus(); //--></script>%s""" % (hashReload(linkSub(header)),js_alphabet,js_binchop_dx,old_javascript_array(fragments[s][0] for s in startsList),hashReload(linkSub(footer)))) sys.stderr.write(" %d files\n" % (len(startsList)+1))

ssb22/indexer

ohi.py

Python

import environ from pathlib import Path env = environ.Env( # Sets debug to False if it cannot find .env DEBUG=(bool, False) ) environ.Env.read_env() # GENERAL # ------------------------------------------------------------------------------ # Build paths inside the project like this: BASE_DIR / 'subdir'. BASE_DIR = Path(__file__).resolve().parent.parent # https://docs.djangoproject.com/en/dev/ref/settings/#std:setting-SECRET_KEY SECRET_KEY = env.str('SECRET_KEY') # https://docs.djangoproject.com/en/dev/ref/settings/#debug DEBUG = env.bool('DEBUG') # https://docs.djangoproject.com/en/dev/ref/settings/#allowed-hosts ALLOWED_HOSTS = tuple(env.list('ALLOWED_HOSTS')) # APPS # ------------------------------------------------------------------------------ # https://docs.djangoproject.com/en/dev/ref/settings/#installed-apps INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'whitenoise.runserver_nostatic', 'django.contrib.staticfiles', 'django.contrib.sites', # Third-party 'allauth', 'allauth.account', 'crispy_forms', 'debug_toolbar', # Local 'accounts', 'pages', 'snacks', ] # MIDDLEWARE # ------------------------------------------------------------------------------ # https://docs.djangoproject.com/en/dev/ref/settings/#middleware MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'whitenoise.middleware.WhiteNoiseMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'debug_toolbar.middleware.DebugToolbarMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] # URLS # ------------------------------------------------------------------------------ # https://docs.djangoproject.com/en/dev/ref/settings/#root-urlconf ROOT_URLCONF = "config.urls" # https://docs.djangoproject.com/en/dev/ref/settings/#wsgi-application WSGI_APPLICATION = "config.wsgi.application" # TEMPLATES # ------------------------------------------------------------------------------ # https://docs.djangoproject.com/en/dev/ref/settings/#templates TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': ['templates'], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] # DATABASES # ------------------------------------------------------------------------------ # https://docs.djangoproject.com/en/dev/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': BASE_DIR / 'db.sqlite3', } } # PASSWORDS # ------------------------------------------------------------------------------ # https://docs.djangoproject.com/en/dev/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # INTERNATIONALIZATION # ------------------------------------------------------------------------------ # https://docs.djangoproject.com/en/dev/topics/i18n/ # https://docs.djangoproject.com/en/dev/ref/settings/#language-code LANGUAGE_CODE = 'en-us' # https://docs.djangoproject.com/en/dev/ref/settings/#time-zone TIME_ZONE = 'UTC' # https://docs.djangoproject.com/en/dev/ref/settings/#std:setting-USE_I18N USE_I18N = True # https://docs.djangoproject.com/en/dev/ref/settings/#use-l10n USE_L10N = True # https://docs.djangoproject.com/en/dev/ref/settings/#use-tz USE_TZ = True # STATIC # ------------------------------------------------------------------------------ # https://docs.djangoproject.com/en/dev/ref/settings/#static-root STATIC_ROOT = str(BASE_DIR.joinpath('staticfiles')) # https://docs.djangoproject.com/en/dev/ref/settings/#static-url STATIC_URL = '/static/' # https://docs.djangoproject.com/en/dev/ref/contrib/staticfiles/#std:setting-STATICFILES_DIRS STATICFILES_DIRS = [str(BASE_DIR.joinpath('static'))] # http://whitenoise.evans.io/en/stable/django.html#add-compression-and-caching-support STATICFILES_STORAGE = "whitenoise.storage.CompressedManifestStaticFilesStorage" # DJANGO-CRISPY-FORMS CONFIGS # ------------------------------------------------------------------------------ # https://django-crispy-forms.readthedocs.io/en/latest/install.html#template-packs CRISPY_TEMPLATE_PACK = "bootstrap4" # EMAIL # ------------------------------------------------------------------------------ # https://docs.djangoproject.com/en/dev/ref/settings/#email-backend EMAIL_BACKEND = 'django.core.mail.backends.console.EmailBackend' # DJANGO-DEBUG-TOOLBAR CONFIGS # ------------------------------------------------------------------------------ # https://django-debug-toolbar.readthedocs.io/en/latest/installation.html # https://docs.djangoproject.com/en/dev/ref/settings/#internal-ips INTERNAL_IPS = ['127.0.0.1'] # CUSTOM USER MODEL CONFIGS # ------------------------------------------------------------------------------ # https://docs.djangoproject.com/en/dev/topics/auth/customizing/#substituting-a-custom-user-model AUTH_USER_MODEL = 'accounts.CustomUser' # DJANGO-ALLAUTH CONFIGS # ------------------------------------------------------------------------------ # https://docs.djangoproject.com/en/dev/ref/settings/#site-id SITE_ID = 1 # https://docs.djangoproject.com/en/dev/ref/settings/#login-redirect-url LOGIN_REDIRECT_URL = 'home' # https://django-allauth.readthedocs.io/en/latest/views.html#logout-account-logout ACCOUNT_LOGOUT_REDIRECT_URL = 'home' # https://django-allauth.readthedocs.io/en/latest/installation.html?highlight=backends AUTHENTICATION_BACKENDS = ( "django.contrib.auth.backends.ModelBackend", "allauth.account.auth_backends.AuthenticationBackend", ) # https://django-allauth.readthedocs.io/en/latest/configuration.html ACCOUNT_SESSION_REMEMBER = True ACCOUNT_SIGNUP_PASSWORD_ENTER_TWICE = False ACCOUNT_USERNAME_REQUIRED = False ACCOUNT_AUTHENTICATION_METHOD = 'email' ACCOUNT_EMAIL_REQUIRED = True ACCOUNT_UNIQUE_EMAIL = True

okayjones/django-x

config/settings.py

Python

# model settings model = dict( type='CenterNet', pretrained='modelzoo://resnet18', backbone=dict( type='ResNet', depth=18, num_stages=4, out_indices=(0, 1, 2, 3), frozen_stages=1, norm_eval=False, add_summay_every_n_step=200, style='pytorch'), neck=dict(type='None'), bbox_head=dict( type='CXTHead', inplanes=(64, 128, 256, 512), head_conv=128, wh_conv=64, use_deconv=False, norm_after_upsample=False, hm_head_conv_num=2, wh_head_conv_num=2, ct_head_conv_num=1, fovea_hm=False, num_classes=81, use_exp_wh=False, wh_offset_base=16, wh_area_process='norm', shortcut_cfg=(1, 2, 3), shortcut_attention=(False, False, False), norm_cfg=dict(type='BN'), norm_wh=False, avg_wh_weightv3=False, center_ratio=0.2, hm_init_value=None, giou_weight=5., merge_weight=1., hm_weight=1., ct_weight=1.)) cudnn_benchmark = True # training and testing settings train_cfg = dict( vis_every_n_iters=100, debug=False) test_cfg = dict( score_thr=0.05, max_per_img=100) # dataset settings dataset_type = 'CocoDataset' data_root = 'data/coco/' img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) train_pipeline = [ dict(type='LoadImageFromFile', to_float32=True), dict(type='LoadAnnotations', with_bbox=True), dict( type='PhotoMetricDistortion', brightness_delta=32, contrast_range=(0.5, 1.5), saturation_range=(0.5, 1.5), hue_delta=18), dict( type='Expand', mean=img_norm_cfg['mean'], to_rgb=img_norm_cfg['to_rgb'], ratio_range=(1, 4)), dict( type='MinIoURandomCrop', min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), min_crop_size=0.3), dict(type='Resize', img_scale=(512, 512), keep_ratio=False), dict(type='Normalize', **img_norm_cfg), dict(type='RandomFlip', flip_ratio=0.5), dict(type='DefaultFormatBundle'), dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), ] test_pipeline = [ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', img_scale=(512, 512), flip=False, transforms=[ dict(type='Resize', keep_ratio=False), dict(type='Normalize', **img_norm_cfg), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']), ]) ] data = dict( imgs_per_gpu=16, workers_per_gpu=4, train=dict( type=dataset_type, ann_file=data_root + 'annotations/instances_train2017.json', img_prefix=data_root + 'train2017/', pipeline=train_pipeline), val=dict( type=dataset_type, ann_file=data_root + 'annotations/instances_val2017.json', img_prefix=data_root + 'val2017/', pipeline=test_pipeline), test=dict( type=dataset_type, ann_file=data_root + 'annotations/instances_val2017.json', img_prefix=data_root + 'val2017/', pipeline=test_pipeline)) # optimizer optimizer = dict(type='SGD', lr=0.003, momentum=0.9, weight_decay=0.0003, paramwise_options=dict(bias_lr_mult=2., bias_decay_mult=0.)) optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) # learning policy lr_config = dict( policy='step', warmup='linear', warmup_iters=500, warmup_ratio=1.0 / 5, step=[18, 22]) checkpoint_config = dict(save_every_n_steps=200, max_to_keep=1, keep_every_n_epochs=18) bbox_head_hist_config = dict( model_type=['ConvModule', 'DeformConvPack'], sub_modules=['bbox_head'], save_every_n_steps=200) # yapf:disable log_config = dict(interval=20) # yapf:enable # runtime settings total_epochs = 24 dist_params = dict(backend='nccl') log_level = 'INFO' work_dir = 'eft18_o16_v1norm_3lr_alpha2_wd3e4_s123_nos_2x' load_from = None resume_from = None workflow = [('train', 1)]

mrsempress/mmdetection

configs/centernext/eft18_o16_v1norm_3lr_alpha2_wd3e4_s123_nos_2x.py

Python

import multiprocessing as mp import itertools import traceback import pickle import numpy as np from numba import cuda from numba.cuda.testing import (skip_on_cudasim, skip_under_cuda_memcheck, ContextResettingTestCase, ForeignArray) import unittest def core_ipc_handle_test(the_work, result_queue): try: arr = the_work() # Catch anything going wrong in the worker function except: # noqa: E722 # FAILED. propagate the exception as a string succ = False out = traceback.format_exc() else: # OK. send the ndarray back succ = True out = arr result_queue.put((succ, out)) def base_ipc_handle_test(handle, size, result_queue): def the_work(): dtype = np.dtype(np.intp) with cuda.open_ipc_array(handle, shape=size // dtype.itemsize, dtype=dtype) as darr: # copy the data to host return darr.copy_to_host() core_ipc_handle_test(the_work, result_queue) def serialize_ipc_handle_test(handle, result_queue): def the_work(): dtype = np.dtype(np.intp) darr = handle.open_array(cuda.current_context(), shape=handle.size // dtype.itemsize, dtype=dtype) # copy the data to host arr = darr.copy_to_host() handle.close() return arr core_ipc_handle_test(the_work, result_queue) def ipc_array_test(ipcarr, result_queue): try: with ipcarr as darr: arr = darr.copy_to_host() try: # should fail to reopen with ipcarr: pass except ValueError as e: if str(e) != 'IpcHandle is already opened': raise AssertionError('invalid exception message') else: raise AssertionError('did not raise on reopen') # Catch any exception so we can propagate it except: # noqa: E722 # FAILED. propagate the exception as a string succ = False out = traceback.format_exc() else: # OK. send the ndarray back succ = True out = arr result_queue.put((succ, out)) @skip_under_cuda_memcheck('Hangs cuda-memcheck') @skip_on_cudasim('Ipc not available in CUDASIM') class TestIpcMemory(ContextResettingTestCase): def test_ipc_handle(self): # prepare data for IPC arr = np.arange(10, dtype=np.intp) devarr = cuda.to_device(arr) # create IPC handle ctx = cuda.current_context() ipch = ctx.get_ipc_handle(devarr.gpu_data) # manually prepare for serialization as bytes handle_bytes = bytes(ipch.handle) size = ipch.size # spawn new process for testing ctx = mp.get_context('spawn') result_queue = ctx.Queue() args = (handle_bytes, size, result_queue) proc = ctx.Process(target=base_ipc_handle_test, args=args) proc.start() succ, out = result_queue.get() if not succ: self.fail(out) else: np.testing.assert_equal(arr, out) proc.join(3) def variants(self): # Test with no slicing and various different slices indices = (None, slice(3, None), slice(3, 8), slice(None, 8)) # Test with a Numba DeviceNDArray, or an array from elsewhere through # the CUDA Array Interface foreigns = (False, True) return itertools.product(indices, foreigns) def check_ipc_handle_serialization(self, index_arg=None, foreign=False): # prepare data for IPC arr = np.arange(10, dtype=np.intp) devarr = cuda.to_device(arr) if index_arg is not None: devarr = devarr[index_arg] if foreign: devarr = cuda.as_cuda_array(ForeignArray(devarr)) expect = devarr.copy_to_host() # create IPC handle ctx = cuda.current_context() ipch = ctx.get_ipc_handle(devarr.gpu_data) # pickle buf = pickle.dumps(ipch) ipch_recon = pickle.loads(buf) self.assertIs(ipch_recon.base, None) self.assertEqual(tuple(ipch_recon.handle), tuple(ipch.handle)) self.assertEqual(ipch_recon.size, ipch.size) # spawn new process for testing ctx = mp.get_context('spawn') result_queue = ctx.Queue() args = (ipch, result_queue) proc = ctx.Process(target=serialize_ipc_handle_test, args=args) proc.start() succ, out = result_queue.get() if not succ: self.fail(out) else: np.testing.assert_equal(expect, out) proc.join(3) def test_ipc_handle_serialization(self): for index, foreign, in self.variants(): with self.subTest(index=index, foreign=foreign): self.check_ipc_handle_serialization(index, foreign) def check_ipc_array(self, index_arg=None, foreign=False): # prepare data for IPC arr = np.arange(10, dtype=np.intp) devarr = cuda.to_device(arr) # Slice if index_arg is not None: devarr = devarr[index_arg] if foreign: devarr = cuda.as_cuda_array(ForeignArray(devarr)) expect = devarr.copy_to_host() ipch = devarr.get_ipc_handle() # spawn new process for testing ctx = mp.get_context('spawn') result_queue = ctx.Queue() args = (ipch, result_queue) proc = ctx.Process(target=ipc_array_test, args=args) proc.start() succ, out = result_queue.get() if not succ: self.fail(out) else: np.testing.assert_equal(expect, out) proc.join(3) def test_ipc_array(self): for index, foreign, in self.variants(): with self.subTest(index=index, foreign=foreign): self.check_ipc_array(index, foreign) def staged_ipc_handle_test(handle, device_num, result_queue): def the_work(): with cuda.gpus[device_num]: this_ctx = cuda.devices.get_context() deviceptr = handle.open_staged(this_ctx) arrsize = handle.size // np.dtype(np.intp).itemsize hostarray = np.zeros(arrsize, dtype=np.intp) cuda.driver.device_to_host( hostarray, deviceptr, size=handle.size, ) handle.close() return hostarray core_ipc_handle_test(the_work, result_queue) def staged_ipc_array_test(ipcarr, device_num, result_queue): try: with cuda.gpus[device_num]: with ipcarr as darr: arr = darr.copy_to_host() try: # should fail to reopen with ipcarr: pass except ValueError as e: if str(e) != 'IpcHandle is already opened': raise AssertionError('invalid exception message') else: raise AssertionError('did not raise on reopen') # Catch any exception so we can propagate it except: # noqa: E722 # FAILED. propagate the exception as a string succ = False out = traceback.format_exc() else: # OK. send the ndarray back succ = True out = arr result_queue.put((succ, out)) @skip_under_cuda_memcheck('Hangs cuda-memcheck') @skip_on_cudasim('Ipc not available in CUDASIM') class TestIpcStaged(ContextResettingTestCase): def test_staged(self): # prepare data for IPC arr = np.arange(10, dtype=np.intp) devarr = cuda.to_device(arr) # spawn new process for testing mpctx = mp.get_context('spawn') result_queue = mpctx.Queue() # create IPC handle ctx = cuda.current_context() ipch = ctx.get_ipc_handle(devarr.gpu_data) # pickle buf = pickle.dumps(ipch) ipch_recon = pickle.loads(buf) self.assertIs(ipch_recon.base, None) self.assertEqual(tuple(ipch_recon.handle), tuple(ipch.handle)) self.assertEqual(ipch_recon.size, ipch.size) # Test on every CUDA devices for device_num in range(len(cuda.gpus)): args = (ipch, device_num, result_queue) proc = mpctx.Process(target=staged_ipc_handle_test, args=args) proc.start() succ, out = result_queue.get() proc.join(3) if not succ: self.fail(out) else: np.testing.assert_equal(arr, out) def test_ipc_array(self): for device_num in range(len(cuda.gpus)): # prepare data for IPC arr = np.random.random(10) devarr = cuda.to_device(arr) ipch = devarr.get_ipc_handle() # spawn new process for testing ctx = mp.get_context('spawn') result_queue = ctx.Queue() args = (ipch, device_num, result_queue) proc = ctx.Process(target=staged_ipc_array_test, args=args) proc.start() succ, out = result_queue.get() proc.join(3) if not succ: self.fail(out) else: np.testing.assert_equal(arr, out) if __name__ == '__main__': unittest.main()

Emilka1604/numba

numba/cuda/tests/cudapy/test_ipc.py

Python

#!/usr/bin/env python2 # Copyright 2016 Vimal Manohar # 2016 Johns Hopkins University (author: Daniel Povey) # Apache 2.0 from __future__ import print_function import argparse import logging import sys from collections import defaultdict """ This script reads and writes the 'ctm-edits' file that is produced by get_ctm_edits.py. It modifies the ctm-edits so that non-scored words are not counted as errors: for instance, if there are things like [COUGH] and [NOISE] in the transcript, deletions, insertions and substitutions involving them are allowed, and we modify the reference to correspond to the hypothesis. If you supply the <lang> directory (the one that corresponds to how you decoded the data) to this script, it assumes that the <lang> directory contains phones/align_lexicon.int, and it uses this to work out a reasonable guess of the non-scored phones, based on which have a single-word pronunciation that maps to a silence phone. It then uses the words.txt to work out the written form of those words. Alternatively, you may specify a file containing the non-scored words one per line, with the --non-scored-words option. Non-scored words that were deleted (i.e. they were in the ref but not the hyp) are simply removed from the ctm. For non-scored words that were inserted or substituted, we change the reference word to match the hyp word, but instead of marking the operation as 'cor' (correct), we mark it as 'fix' (fixed), so that it will not be positively counted as a correct word for purposes of finding the optimal segment boundaries. e.g. <file-id> <channel> <start-time> <duration> <conf> <hyp-word> <ref-word> <edit-type> [note: the <channel> will always be 1]. AJJacobs_2007P-0001605-0003029 1 0 0.09 <eps> 1.0 <eps> sil AJJacobs_2007P-0001605-0003029 1 0.09 0.15 i 1.0 i cor AJJacobs_2007P-0001605-0003029 1 0.24 0.25 thought 1.0 thought cor AJJacobs_2007P-0001605-0003029 1 0.49 0.14 i'd 1.0 i'd cor AJJacobs_2007P-0001605-0003029 1 0.63 0.22 tell 1.0 tell cor AJJacobs_2007P-0001605-0003029 1 0.85 0.11 you 1.0 you cor AJJacobs_2007P-0001605-0003029 1 0.96 0.05 a 1.0 a cor AJJacobs_2007P-0001605-0003029 1 1.01 0.24 little 1.0 little cor AJJacobs_2007P-0001605-0003029 1 1.25 0.5 about 1.0 about cor AJJacobs_2007P-0001605-0003029 1 1.75 0.48 [UH] 1.0 [UH] cor """ logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) handler = logging.StreamHandler() handler.setLevel(logging.INFO) formatter = logging.Formatter('%(asctime)s [%(filename)s:%(lineno)s - ' '%(funcName)s - %(levelname)s ] %(message)s') handler.setFormatter(formatter) logger.addHandler(handler) parser = argparse.ArgumentParser( description = "This program modifies the reference in the ctm-edits which " "is output by steps/cleanup/internal/get_ctm_edits.py, to allow insertions, deletions and " "substitutions of non-scored words, and [if --allow-repetitions=true], " "duplications of single words or pairs of scored words (to account for dysfluencies " "that were not transcribed). Note: deletions and substitutions of non-scored words " "after the reference is corrected, will be marked as operation 'fix' rather than " "'cor' (correct) so that the downstream processing knows that this was not in " "the original reference. Also by defaults tags non-scored words as such when " "they are correct; see the --tag-non-scored option.") parser.add_argument("--verbose", type = int, default = 1, choices=[0,1,2,3], help = "Verbose level, higher = more verbose output") parser.add_argument("--allow-repetitions", type = str, default = 'true', choices=['true','false'], help = "If true, allow repetitions in the transcript of one or " "two-word sequences: for instance if the ref says 'i' but " "the hyp says 'i i', or the ref says 'but then' and the hyp says " "'but then but then', fix the reference accordingly. Intervening " "non-scored words are allowed between the repetitions. These " "fixes will be marked as 'cor', not as 'fix', since there is " "generally no way to tell which repetition was the 'real' one " "(and since we're generally confident that such things were " "actually uttered).") parser.add_argument("non_scored_words_in", metavar = "<non-scored-words-file>", help="Filename of file containing a list of non-scored words, " "one per line. See steps/cleanup/get_nonscored_words.py.") parser.add_argument("ctm_edits_in", metavar = "<ctm-edits-in>", help = "Filename of input ctm-edits file. " "Use /dev/stdin for standard input.") parser.add_argument("ctm_edits_out", metavar = "<ctm-edits-out>", help = "Filename of output ctm-edits file. " "Use /dev/stdout for standard output.") args = parser.parse_args() def ReadNonScoredWords(non_scored_words_file): global non_scored_words try: f = open(non_scored_words_file) except: sys.exit("modify_ctm_edits.py: error opening file: " "--non-scored-words=" + non_scored_words_file) for line in f.readlines(): a = line.split() if not len(line.split()) == 1: sys.exit("modify_ctm_edits.py: bad line in non-scored-words " "file {0}: {1}".format(non_scored_words_file, line)) non_scored_words.add(a[0]) f.close() # The ctm-edits file format is as follows [note: file-id is really utterance-id # in this context]. # <file-id> <channel> <start-time> <duration> <conf> <hyp-word> <ref-word> <edit> # e.g.: # AJJacobs_2007P-0001605-0003029 1 0 0.09 <eps> 1.0 <eps> sil # AJJacobs_2007P-0001605-0003029 1 0.09 0.15 i 1.0 i cor # ... # This function processes a single line of ctm-edits input for fixing # "non-scored" words. The input 'a' is the split line as an array of fields. # It modifies the object 'a'. This function returns the modified array, # and please note that it is destructive of its input 'a'. # If it returnso the empty array then the line is to be deleted. def ProcessLineForNonScoredWords(a): global num_lines, num_correct_lines, ref_change_stats try: assert len(a) == 8 num_lines += 1 # we could do: # [ file, channel, start, duration, hyp_word, confidence, ref_word, edit_type ] = a duration = a[3] hyp_word = a[4] ref_word = a[6] edit_type = a[7] if edit_type == 'ins': assert ref_word == '<eps>' if hyp_word in non_scored_words: # insert this non-scored word into the reference. ref_change_stats[ref_word + ' -> ' + hyp_word] += 1 ref_word = hyp_word edit_type = 'fix' elif edit_type == 'del': assert hyp_word == '<eps>' and float(duration) == 0.0 if ref_word in non_scored_words: ref_change_stats[ref_word + ' -> ' + hyp_word] += 1 return [] elif edit_type == 'sub': assert hyp_word != '<eps>' if hyp_word in non_scored_words and ref_word in non_scored_words: # we also allow replacing one non-scored word with another. ref_change_stats[ref_word + ' -> ' + hyp_word] += 1 ref_word = hyp_word edit_type = 'fix' else: assert edit_type == 'cor' or edit_type == 'sil' num_correct_lines += 1 a[4] = hyp_word a[6] = ref_word a[7] = edit_type return a except Exception: logger.error("bad line in ctm-edits input: " "{0}".format(a)) raise RuntimeError # This function processes the split lines of one utterance (as a # list of lists of fields), to allow repetitions of words, so if the # reference says 'i' but the hyp says 'i i', or the ref says # 'you know' and the hyp says 'you know you know', we change the # ref to match. # It returns the modified list-of-lists [but note that the input # is actually modified]. def ProcessUtteranceForRepetitions(split_lines_of_utt): global non_scored_words, repetition_stats # The array 'selected_lines' will contain the indexes of of selected # elements of 'split_lines_of_utt'. Consider split_line = # split_lines_of_utt[i]. If the hyp and ref words in split_line are both # either '<eps>' or non-scoreable words, we discard the index. # Otherwise we put it into selected_lines. selected_line_indexes = [] # selected_edits will contain, for each element of selected_line_indexes, the # corresponding edit_type from the original utterance previous to # this function call ('cor', 'ins', etc.). # # As a special case, if there was a substitution ('sub') where the # reference word was a non-scored word and the hyp word was a real word, # we mark it in this array as 'ins', because for purposes of this algorithm # it behaves the same as an insertion. # # Whenever we do any operation that will change the reference, we change # all the selected_edits in the array to None so that they won't match # any further operations. selected_edits = [] # selected_hyp_words will contain, for each element of selected_line_indexes, the # corresponding hyp_word. selected_hyp_words = [] for i in range(len(split_lines_of_utt)): split_line = split_lines_of_utt[i] hyp_word = split_line[4] ref_word = split_line[6] # keep_this_line will be True if we are going to keep this line in the # 'selected lines' for further processing of repetitions. We only # eliminate lines involving non-scored words or epsilon in both hyp # and reference position # [note: epsilon in hyp position for non-empty segments indicates # optional-silence, and it does make sense to make this 'invisible', # just like non-scored words, for the purposes of this code.] keep_this_line = True if (hyp_word == '<eps>' or hyp_word in non_scored_words) and \ (ref_word == '<eps>' or ref_word in non_scored_words): keep_this_line = False if keep_this_line: selected_line_indexes.append(i) edit_type = split_line[7] if edit_type == 'sub' and ref_word in non_scored_words: assert not hyp_word in non_scored_words # For purposes of this algorithm, substitution of, say, # '[COUGH]' by 'hello' behaves like an insertion of 'hello', # since we're willing to remove the '[COUGH]' from the # transript. edit_type = 'ins' selected_edits.append(edit_type) selected_hyp_words.append(hyp_word) # indexes_to_fix will be a list of indexes into 'selected_indexes' where we # plan to fix the ref to match the hyp. indexes_to_fix = [] # This loop scans for, and fixes, two-word insertions that follow, # or precede, the corresponding correct words. for i in range(0, len(selected_line_indexes) - 3): this_indexes = selected_line_indexes[i:i+4] this_hyp_words = selected_hyp_words[i:i+4] if this_hyp_words[0] == this_hyp_words[2] and \ this_hyp_words[1] == this_hyp_words[3] and \ this_hyp_words[0] != this_hyp_words[1]: # if the hyp words were of the form [ 'a', 'b', 'a', 'b' ]... this_edits = selected_edits[i:i+4] if this_edits == [ 'cor', 'cor', 'ins', 'ins' ] or \ this_edits == [ 'ins', 'ins', 'cor', 'cor' ]: if this_edits[0] == 'cor': indexes_to_fix += [ i+2, i+3 ] else: indexes_to_fix += [ i, i+1 ] # the next line prevents this region of the text being used # in any further edits. selected_edits[i:i+4] = [ None, None, None, None ] word_pair = this_hyp_words[0] + ' ' + this_hyp_words[1] # e.g. word_pair = 'hi there' # add 2 because these stats are of words. repetition_stats[word_pair] += 2 # the next line prevents this region of the text being used # in any further edits. selected_edits[i:i+4] = [ None, None, None, None ] # This loop scans for, and fixes, one-word insertions that follow, # or precede, the corresponding correct words. for i in range(0, len(selected_line_indexes) - 1): this_indexes = selected_line_indexes[i:i+2] this_hyp_words = selected_hyp_words[i:i+2] if this_hyp_words[0] == this_hyp_words[1]: # if the hyp words were of the form [ 'a', 'a' ]... this_edits = selected_edits[i:i+2] if this_edits == [ 'cor', 'ins' ] or this_edits == [ 'ins', 'cor' ]: if this_edits[0] == 'cor': indexes_to_fix.append(i+1) else: indexes_to_fix.append(i) repetition_stats[this_hyp_words[0]] += 1 # the next line prevents this region of the text being used # in any further edits. selected_edits[i:i+2] = [ None, None ] for i in indexes_to_fix: j = selected_line_indexes[i] split_line = split_lines_of_utt[j] ref_word = split_line[6] hyp_word = split_line[4] assert ref_word == '<eps>' or ref_word in non_scored_words # we replace reference with the decoded word, which will be a # repetition. split_line[6] = hyp_word split_line[7] = 'cor' return split_lines_of_utt # note: split_lines_of_utt is a list of lists, one per line, each containing the # sequence of fields. # Returns the same format of data after processing. def ProcessUtterance(split_lines_of_utt): new_split_lines_of_utt = [] for split_line in split_lines_of_utt: new_split_line = ProcessLineForNonScoredWords(split_line) if new_split_line != []: new_split_lines_of_utt.append(new_split_line) if args.allow_repetitions == 'true': new_split_lines_of_utt = ProcessUtteranceForRepetitions(new_split_lines_of_utt) return new_split_lines_of_utt def ProcessData(): try: f_in = open(args.ctm_edits_in) except: sys.exit("modify_ctm_edits.py: error opening ctm-edits input " "file {0}".format(args.ctm_edits_in)) try: f_out = open(args.ctm_edits_out, 'w') except: sys.exit("modify_ctm_edits.py: error opening ctm-edits output " "file {0}".format(args.ctm_edits_out)) num_lines_processed = 0 # Most of what we're doing in the lines below is splitting the input lines # and grouping them per utterance, before giving them to ProcessUtterance() # and then printing the modified lines. first_line = f_in.readline() if first_line == '': sys.exit("modify_ctm_edits.py: empty input") split_pending_line = first_line.split() if len(split_pending_line) == 0: sys.exit("modify_ctm_edits.py: bad input line " + first_line) cur_utterance = split_pending_line[0] split_lines_of_cur_utterance = [] while True: if len(split_pending_line) == 0 or split_pending_line[0] != cur_utterance: split_lines_of_cur_utterance = ProcessUtterance(split_lines_of_cur_utterance) for split_line in split_lines_of_cur_utterance: print(' '.join(split_line), file = f_out) split_lines_of_cur_utterance = [] if len(split_pending_line) == 0: break else: cur_utterance = split_pending_line[0] split_lines_of_cur_utterance.append(split_pending_line) next_line = f_in.readline() split_pending_line = next_line.split() if len(split_pending_line) == 0: if next_line != '': sys.exit("modify_ctm_edits.py: got an empty or whitespace input line") try: f_out.close() except: sys.exit("modify_ctm_edits.py: error closing ctm-edits output " "(broken pipe or full disk?)") def PrintNonScoredStats(): if args.verbose < 1: return if num_lines == 0: print("modify_ctm_edits.py: processed no input.", file = sys.stderr) num_lines_modified = sum(ref_change_stats.values()) num_incorrect_lines = num_lines - num_correct_lines percent_lines_incorrect= '%.2f' % (num_incorrect_lines * 100.0 / num_lines) percent_modified = '%.2f' % (num_lines_modified * 100.0 / num_lines); if num_incorrect_lines > 0: percent_of_incorrect_modified = '%.2f' % (num_lines_modified * 100.0 / num_incorrect_lines) else: percent_of_incorrect_modified = float('nan') print("modify_ctm_edits.py: processed {0} lines of ctm ({1}% of which incorrect), " "of which {2} were changed fixing the reference for non-scored words " "({3}% of lines, or {4}% of incorrect lines)".format( num_lines, percent_lines_incorrect, num_lines_modified, percent_modified, percent_of_incorrect_modified), file = sys.stderr) keys = sorted(ref_change_stats.keys(), reverse=True, key = lambda x: ref_change_stats[x]) num_keys_to_print = 40 if args.verbose >= 2 else 10 print("modify_ctm_edits.py: most common edits (as percentages " "of all such edits) are:\n" + ('\n'.join([ '%s [%.2f%%]' % (k, ref_change_stats[k]*100.0/num_lines_modified) for k in keys[0:num_keys_to_print]])) + '\n...'if num_keys_to_print < len(keys) else '', file = sys.stderr) def PrintRepetitionStats(): if args.verbose < 1 or sum(repetition_stats.values()) == 0: return num_lines_modified = sum(repetition_stats.values()) num_incorrect_lines = num_lines - num_correct_lines percent_lines_incorrect= '%.2f' % (num_incorrect_lines * 100.0 / num_lines) percent_modified = '%.2f' % (num_lines_modified * 100.0 / num_lines); if num_incorrect_lines > 0: percent_of_incorrect_modified = '%.2f' % (num_lines_modified * 100.0 / num_incorrect_lines) else: percent_of_incorrect_modified = float('nan') print("modify_ctm_edits.py: processed {0} lines of ctm ({1}% of which incorrect), " "of which {2} were changed fixing the reference for repetitions ({3}% of " "lines, or {4}% of incorrect lines)".format( num_lines, percent_lines_incorrect, num_lines_modified, percent_modified, percent_of_incorrect_modified), file = sys.stderr) keys = sorted(repetition_stats.keys(), reverse=True, key = lambda x: repetition_stats[x]) num_keys_to_print = 40 if args.verbose >= 2 else 10 print("modify_ctm_edits.py: most common repetitions inserted into reference (as percentages " "of all words fixed in this way) are:\n" + ('\n'.join([ '%s [%.2f%%]' % (k, repetition_stats[k]*100.0/num_lines_modified) for k in keys[0:num_keys_to_print]])) + '\n...' if num_keys_to_print < len(keys) else '', file = sys.stderr) non_scored_words = set() ReadNonScoredWords(args.non_scored_words_in) num_lines = 0 num_correct_lines = 0 # ref_change_stats will be a map from a string like # 'foo -> bar' to an integer count; it keeps track of how much we changed # the reference. ref_change_stats = defaultdict(int) # repetition_stats will be a map from strings like # 'a', or 'a b' (the repeated strings), to an integer count; like # ref_change_stats, it keeps track of how many changes we made # in allowing repetitions. repetition_stats = defaultdict(int) ProcessData() PrintNonScoredStats() PrintRepetitionStats()

oplatek/kaldi

egs/wsj/s5/steps/cleanup/internal/modify_ctm_edits.py

Python

P1 = float(input('Informe o primeiro preço: ')) P2 = float(input('Informe o primeiro preço: ')) P3 = float(input('Informe oprimeiro preço: ')) if (P1<P2) and (P1<P3): print('O preço menor é {}'.format(P1)) elif (P2<P1) and (P2<P3): print('O menor preço é {}'.format(P2)) else: print('O menor preço é {}'.format(P3))

kauaas/ATIVIDADES-PYTHON-N2

ATIV07.py

Python

# Copyright (C) 2010-2011 Richard Lincoln # # 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. """An extension to the Core and Topology package that models information on the electrical characteristics of Transmission and Distribution networks. This package is used by network applications such as State Estimation, Load Flow and Optimal Power Flow. """ from CIM15.CDPSM.Connectivity.IEC61970.Wires.Fuse import Fuse from CIM15.CDPSM.Connectivity.IEC61970.Wires.EnergyConsumer import EnergyConsumer from CIM15.CDPSM.Connectivity.IEC61970.Wires.Switch import Switch from CIM15.CDPSM.Connectivity.IEC61970.Wires.Disconnector import Disconnector from CIM15.CDPSM.Connectivity.IEC61970.Wires.ACLineSegment import ACLineSegment from CIM15.CDPSM.Connectivity.IEC61970.Wires.SynchronousMachine import SynchronousMachine from CIM15.CDPSM.Connectivity.IEC61970.Wires.BusbarSection import BusbarSection from CIM15.CDPSM.Connectivity.IEC61970.Wires.LoadBreakSwitch import LoadBreakSwitch from CIM15.CDPSM.Connectivity.IEC61970.Wires.TransformerTank import TransformerTank from CIM15.CDPSM.Connectivity.IEC61970.Wires.GroundDisconnector import GroundDisconnector from CIM15.CDPSM.Connectivity.IEC61970.Wires.PowerTransformerEnd import PowerTransformerEnd from CIM15.CDPSM.Connectivity.IEC61970.Wires.Junction import Junction from CIM15.CDPSM.Connectivity.IEC61970.Wires.SeriesCompensator import SeriesCompensator from CIM15.CDPSM.Connectivity.IEC61970.Wires.Breaker import Breaker from CIM15.CDPSM.Connectivity.IEC61970.Wires.TransformerTankEnd import TransformerTankEnd from CIM15.CDPSM.Connectivity.IEC61970.Wires.Sectionaliser import Sectionaliser from CIM15.CDPSM.Connectivity.IEC61970.Wires.DCLineSegment import DCLineSegment from CIM15.CDPSM.Connectivity.IEC61970.Wires.Line import Line from CIM15.CDPSM.Connectivity.IEC61970.Wires.Conductor import Conductor from CIM15.CDPSM.Connectivity.IEC61970.Wires.PowerTransformer import PowerTransformer from CIM15.CDPSM.Connectivity.IEC61970.Wires.Ground import Ground from CIM15.CDPSM.Connectivity.IEC61970.Wires.TransformerEnd import TransformerEnd from CIM15.CDPSM.Connectivity.IEC61970.Wires.ShuntCompensator import ShuntCompensator from CIM15.CDPSM.Connectivity.IEC61970.Wires.EnergySource import EnergySource from CIM15.CDPSM.Connectivity.IEC61970.Wires.Jumper import Jumper nsURI = "http://iec.ch/TC57/2010/CIM-schema-cim15?profile=http://iec.ch/TC57/2011/iec61968-13/CDPSM/Connectivity#Wires" nsPrefix = "cimWires"

MaximeBaudette/PyCIM

CIM15/CDPSM/Connectivity/IEC61970/Wires/__init__.py

Python

from django.apps import AppConfig class BooksConfig(AppConfig): name = 'bookstudio.books' verbose_name = 'books' def ready(self): """Override this to put in: Users system checks Users signal registration """ pass

sudoabhinav/bookstudio

bookstudio/books/apps.py

Python

# pylint: disable=too-few-public-methods, no-member """API for scheduling learning rate.""" from .. import symbol as sym class LRScheduler(object): """Base class of a learning rate scheduler. A scheduler returns a new learning rate based on the number of updates that have been performed. Parameters ---------- base_lr : float, optional The initial learning rate. """ def __init__(self, base_lr=0.01, name='LRScheduler'): self.name = name self.base_lr = base_lr def __call__(self, num_update): """Return a new learning rate based on number of updates. Parameters ---------- num_update: nnvm Symbol the number of updates applied to weight. """ raise NotImplementedError("__call__ method must be overridden.") class FactorScheduler(LRScheduler): """Reduce the learning rate by a factor for every *n* steps. It returns a new learning rate by:: base_lr * pow(factor, num_update/step) Parameters ---------- step : int Changes the learning rate for every n updates. factor : float, optional The factor to change the learning rate. stop_factor_lr : float, optional Stop updating the learning rate if it is less than this value. """ def __init__(self, step, factor=1, stop_factor_lr=1e-8, name='FactorScheduler', **kwargs): super(FactorScheduler, self).__init__(name=name, **kwargs) if step < 1: raise ValueError("Schedule step must be greater or equal than 1 round") if factor > 1.0: raise ValueError("Factor must be no more than 1 to make lr reduce") self.step = step self.factor = factor self.stop_factor_lr = stop_factor_lr def __call__(self, num_update): updated_lr = self.base_lr * self.factor ** (num_update / self.step) return sym.clip(updated_lr, a_min=self.stop_factor_lr, a_max=self.base_lr)

00liujj/tvm

nnvm/python/nnvm/compiler/lr_scheduler.py

Python

import sys import os import re import tempfile import auto_editor import auto_editor.vanparse as vanparse from auto_editor.utils.log import Log from auto_editor.ffwrapper import FFmpeg def grep_options(parser): parser.add_argument('--no-filename', action='store_true', help='Never print filenames with output lines.') parser.add_argument('--max-count', '-m', type=int, default=None, help='Stop reading a file after NUM matching lines.') parser.add_argument('--count', '-c', action='store_true', help='Suppress normal output; instead print count of matching lines for each file.') parser.add_argument('--ignore-case', '-i', action='store_true', help='Ignore case distinctions for the PATTERN.') parser.add_argument('--timecode', action='store_true', help="Print the match's timecode.") parser.add_argument('--time', action='store_true', help="Print when the match happens. (Ignore ending).") parser.add_argument('--ffmpeg-location', default=None, help='Point to your custom ffmpeg file.') parser.add_argument('--my-ffmpeg', action='store_true', help='Use the ffmpeg on your PATH instead of the one packaged.') parser.add_argument('--help', '-h', action='store_true', help='Print info about the program or an option and exit.') parser.add_required('input', nargs='*', help='The path to a file you want inspected.') return parser # stackoverflow.com/questions/9662346/python-code-to-remove-html-tags-from-a-string def cleanhtml(raw_html: str) -> str: cleanr = re.compile('<.*?>') cleantext = re.sub(cleanr, '', raw_html) return cleantext def grep_core( media_file: str, add_prefix: bool, ffmpeg: FFmpeg, args, log: Log, TEMP: str ) -> None: """ We're using the WEBVTT subtitle format. It's better than srt because it doesn't emit line numbers and the time code is in (hh:mm:ss.sss) instead of (dd:hh:mm:ss,sss) """ out_file = os.path.join(TEMP, 'media.vtt') ffmpeg.run(['-i', media_file, out_file]) count = 0 flags = 0 if args.ignore_case: flags = re.IGNORECASE prefix = '' if add_prefix: prefix = '{}:'.format(os.path.splitext(os.path.basename(media_file))[0]) if args.max_count is None: args.max_count = float('inf') timecode = '' line_number = -1 with open(out_file, 'r') as file: while True: line = file.readline() line_number += 1 if line_number == 0: continue if not line or count >= args.max_count: break if line.strip() == '': continue if re.match(r'\d*:\d\d.\d*\s-->\s\d*:\d\d.\d*', line): if args.time: timecode = line.split('-->')[0].strip() + ' ' else: timecode = line.strip() + '; ' continue line = cleanhtml(line) match = re.search(args.input[0], line, flags) line = line.strip() if match: count += 1 if not args.count: if args.timecode or args.time: print(prefix + timecode + line) else: print(prefix + line) if args.count: print(prefix + str(count)) def main(sys_args=sys.argv[1:]): parser = vanparse.ArgumentParser('grep', auto_editor.version, description='Read and match subtitle tracks in media files.', ) parser = grep_options(parser) TEMP = tempfile.mkdtemp() log = Log(temp=TEMP) try: args = parser.parse_args(sys_args) except vanparse.ParserError as e: log.error(str(e)) ffmpeg = FFmpeg(args.ffmpeg_location, args.my_ffmpeg, debug=False) media_files = args.input[1:] add_prefix = (len(media_files) > 1 or os.path.isdir(media_files[0])) and not args.no_filename for media_file in media_files: if not os.path.exists(media_file): log.error(f'{media_file}: File does not exist.') if os.path.isdir(media_file): for _, _, files in os.walk(media_file): for file in files: if file == '.DS_Store': continue grep_core(os.path.join(media_file, file), add_prefix, ffmpeg, args, log, TEMP) else: grep_core(media_file, add_prefix, ffmpeg, args, log, TEMP) log.cleanup() if __name__ == '__main__': main()

chancat87/auto-editor

auto_editor/subcommands/grep.py

Python

from project import app, socketio if __name__ == "__main__": print('Running BabyMonitorSoS \n') socketio.run(app)

BabyMonitorSimulation/BabyMonitorSoS

run.py

Python

""" Create a blueprint with endpoints for logins from configured identity providers. The identity providers include, for example, Google, Shibboleth, or another fence instance. See the other files in this directory for the definitions of the endpoints for each provider. """ from authlib.common.urls import add_params_to_uri import flask import requests from cdislogging import get_logger from fence.blueprints.login.cilogon import CilogonLogin, CilogonCallback from fence.blueprints.login.cognito import CognitoLogin, CognitoCallback from fence.blueprints.login.fence_login import FenceLogin, FenceCallback from fence.blueprints.login.google import GoogleLogin, GoogleCallback from fence.blueprints.login.shib import ShibbolethLogin, ShibbolethCallback from fence.blueprints.login.microsoft import MicrosoftLogin, MicrosoftCallback from fence.blueprints.login.okta import OktaLogin, OktaCallback from fence.blueprints.login.orcid import ORCIDLogin, ORCIDCallback from fence.blueprints.login.ras import RASLogin, RASCallback from fence.blueprints.login.synapse import SynapseLogin, SynapseCallback from fence.errors import InternalError from fence.resources.audit.utils import enable_audit_logging from fence.restful import RestfulApi from fence.config import config logger = get_logger(__name__) # Mapping from IDP ID to the name in the URL on the blueprint (see below). IDP_URL_MAP = { "fence": "fence", "google": "google", "shibboleth": "shib", "orcid": "orcid", "synapse": "synapse", "microsoft": "microsoft", "okta": "okta", "cognito": "cognito", "ras": "ras", "cilogon": "cilogon", } def absolute_login_url(provider_id, fence_idp=None, shib_idp=None): """ Args: provider_id (str): provider to log in with; an IDP_URL_MAP key. fence_idp (str, optional): if provider_id is "fence" (multi-tenant Fence setup), fence_idp can be any of the providers supported by the other Fence. If not specified, will default to NIH login. shib_idp (str, optional): if provider_id is "fence" and fence_idp is "shibboleth", shib_idp can be any Shibboleth/ InCommon provider. If not specified, will default to NIH login. Returns: str: login URL for this provider, including extra query parameters if fence_idp and/or shib_idp are specified. """ try: base_url = config["BASE_URL"].rstrip("/") login_url = base_url + "/login/{}".format(IDP_URL_MAP[provider_id]) except KeyError as e: raise InternalError("identity provider misconfigured: {}".format(str(e))) params = {} if fence_idp: params["idp"] = fence_idp if shib_idp: params["shib_idp"] = shib_idp login_url = add_params_to_uri(login_url, params) return login_url def provider_info(login_details): """ Args: login_details (dict): { name, desc, idp, fence_idp, shib_idps, secondary } - "idp": a configured provider. Multiple options can be configured with the same idp. - if provider_id is "fence", "fence_idp" can be any of the providers supported by the other Fence. If not specified, will default to NIH login. - if provider_id is "fence" and fence_idp is "shibboleth", a list of "shib_idps" can be configured for InCommon login. If not specified, will default to NIH login. - Optional parameters: "desc" (description) and "secondary" (boolean - can be used by the frontend to display secondary buttons differently). Returns: dict: { name, desc, idp, urls, secondary } - urls: list of { name, url } dictionaries """ info = { # "id" deprecated, replaced by "idp" "id": login_details["idp"], "idp": login_details["idp"], "name": login_details["name"], # "url" deprecated, replaced by "urls" "url": absolute_login_url(login_details["idp"]), "desc": login_details.get("desc", None), "secondary": login_details.get("secondary", False), } # for Fence multi-tenant login fence_idp = None if login_details["idp"] == "fence": fence_idp = login_details.get("fence_idp") # handle Shibboleth IDPs: InCommon login can either be configured # directly in this Fence, or through multi-tenant Fence if ( login_details["idp"] == "shibboleth" or fence_idp == "shibboleth" ) and "shib_idps" in login_details: # get list of all available shib IDPs if not hasattr(flask.current_app, "all_shib_idps"): flask.current_app.all_shib_idps = get_all_shib_idps() requested_shib_idps = login_details["shib_idps"] if requested_shib_idps == "*": shib_idps = flask.current_app.all_shib_idps elif isinstance(requested_shib_idps, list): # get the display names for each requested shib IDP shib_idps = [] for requested_shib_idp in requested_shib_idps: shib_idp = next( ( available_shib_idp for available_shib_idp in flask.current_app.all_shib_idps if available_shib_idp["idp"] == requested_shib_idp ), None, ) if not shib_idp: raise InternalError( 'Requested shib_idp "{}" does not exist'.format( requested_shib_idp ) ) shib_idps.append(shib_idp) else: raise InternalError( 'fence provider misconfigured: "shib_idps" must be a list or "*", got {}'.format( requested_shib_idps ) ) info["urls"] = [ { "name": shib_idp["name"], "url": absolute_login_url( login_details["idp"], fence_idp, shib_idp["idp"] ), } for shib_idp in shib_idps ] # non-Shibboleth provider else: info["urls"] = [ { "name": login_details["name"], "url": absolute_login_url(login_details["idp"], fence_idp), } ] return info def get_login_providers_info(): # default login option if config.get("DEFAULT_LOGIN_IDP"): default_idp = config["DEFAULT_LOGIN_IDP"] elif "default" in config.get("ENABLED_IDENTITY_PROVIDERS", {}): # fall back on ENABLED_IDENTITY_PROVIDERS.default default_idp = config["ENABLED_IDENTITY_PROVIDERS"]["default"] else: logger.warning("DEFAULT_LOGIN_IDP not configured") default_idp = None # other login options if config["LOGIN_OPTIONS"]: login_options = config["LOGIN_OPTIONS"] elif "providers" in config.get("ENABLED_IDENTITY_PROVIDERS", {}): # fall back on "providers" and convert to "login_options" format enabled_providers = config["ENABLED_IDENTITY_PROVIDERS"]["providers"] login_options = [ { "name": details.get("name"), "idp": idp, "desc": details.get("desc"), "secondary": details.get("secondary"), } for idp, details in enabled_providers.items() ] else: logger.warning("LOGIN_OPTIONS not configured or empty") login_options = [] try: all_provider_info = [ provider_info(login_details) for login_details in login_options ] except KeyError as e: raise InternalError("LOGIN_OPTIONS misconfigured: cannot find key {}".format(e)) # if several login_options are defined for this default IDP, will # default to the first one: default_provider_info = next( (info for info in all_provider_info if info["idp"] == default_idp), None ) if not default_provider_info: raise InternalError( "default provider misconfigured: DEFAULT_LOGIN_IDP is set to {}, which is not configured in LOGIN_OPTIONS".format( default_idp ) ) return default_provider_info, all_provider_info def make_login_blueprint(): """ Return: flask.Blueprint: the blueprint used for ``/login`` endpoints Raises: ValueError: if app is not amenably configured """ blueprint = flask.Blueprint("login", __name__) blueprint_api = RestfulApi(blueprint, decorators=[enable_audit_logging]) @blueprint.route("", methods=["GET"]) def default_login(): """ The default root login route. """ default_provider_info, all_provider_info = get_login_providers_info() return flask.jsonify( {"default_provider": default_provider_info, "providers": all_provider_info} ) # Add identity provider login routes for IDPs enabled in the config. configured_idps = config["OPENID_CONNECT"].keys() if "fence" in configured_idps: blueprint_api.add_resource(FenceLogin, "/fence", strict_slashes=False) blueprint_api.add_resource(FenceCallback, "/fence/login", strict_slashes=False) if "google" in configured_idps: blueprint_api.add_resource(GoogleLogin, "/google", strict_slashes=False) blueprint_api.add_resource( GoogleCallback, "/google/login", strict_slashes=False ) if "orcid" in configured_idps: blueprint_api.add_resource(ORCIDLogin, "/orcid", strict_slashes=False) blueprint_api.add_resource(ORCIDCallback, "/orcid/login", strict_slashes=False) if "ras" in configured_idps: blueprint_api.add_resource(RASLogin, "/ras", strict_slashes=False) # note that the callback endpoint is "/ras/callback", not "/ras/login" like other IDPs blueprint_api.add_resource(RASCallback, "/ras/callback", strict_slashes=False) if "synapse" in configured_idps: blueprint_api.add_resource(SynapseLogin, "/synapse", strict_slashes=False) blueprint_api.add_resource( SynapseCallback, "/synapse/login", strict_slashes=False ) if "microsoft" in configured_idps: blueprint_api.add_resource(MicrosoftLogin, "/microsoft", strict_slashes=False) blueprint_api.add_resource( MicrosoftCallback, "/microsoft/login", strict_slashes=False ) if "okta" in configured_idps: blueprint_api.add_resource(OktaLogin, "/okta", strict_slashes=False) blueprint_api.add_resource(OktaCallback, "/okta/login", strict_slashes=False) if "cognito" in configured_idps: blueprint_api.add_resource(CognitoLogin, "/cognito", strict_slashes=False) blueprint_api.add_resource( CognitoCallback, "/cognito/login", strict_slashes=False ) if "shibboleth" in configured_idps: blueprint_api.add_resource(ShibbolethLogin, "/shib", strict_slashes=False) blueprint_api.add_resource( ShibbolethCallback, "/shib/login", strict_slashes=False ) if "cilogon" in configured_idps: blueprint_api.add_resource(CilogonLogin, "/cilogon", strict_slashes=False) blueprint_api.add_resource( CilogonCallback, "/cilogon/login", strict_slashes=False ) return blueprint def get_all_shib_idps(): """ Get the list of all existing Shibboleth IDPs. This function only returns the information we need to generate login URLs. Returns: list: list of {"idp": "", "name": ""} dictionaries """ url = config["OPENID_CONNECT"].get("fence", {}).get("shibboleth_discovery_url") if not url: raise InternalError( "Unable to get list of Shibboleth IDPs: OPENID_CONNECT.fence.shibboleth_discovery_url not configured" ) res = requests.get(url) assert ( res.status_code == 200 ), "Unable to get list of Shibboleth IDPs from {}".format(url) all_shib_idps = [] for shib_idp in res.json(): if "entityID" not in shib_idp: logger.warning( f"get_all_shib_idps(): 'entityID' field not in IDP data: {shib_idp}. Skipping this IDP." ) continue idp = shib_idp["entityID"] if len(shib_idp.get("DisplayNames", [])) > 0: name = get_shib_idp_en_name(shib_idp["DisplayNames"]) else: logger.warning( f"get_all_shib_idps(): 'DisplayNames' field not in IDP data: {shib_idp}. Using IDP ID '{idp}' as IDP name." ) name = idp all_shib_idps.append( { "idp": idp, "name": name, } ) return all_shib_idps def get_shib_idp_en_name(names): """ Returns a name in English for a Shibboleth IDP, or the first available name if no English name was provided. Args: names (list): list of {"lang": "", "value": ""} dictionaries Example: [ { "value": "University of Chicago", "lang": "en" }, { "value": "Universidad de Chicago", "lang": "es" } ] Returns: str: Display name to use for this Shibboleth IDP """ for name in names: if name.get("lang") == "en": return name["value"] return names[0]["value"]

chicagopcdc/fence

fence/blueprints/login/__init__.py

Python

#! /usr/bin/python """ Monitoring functions for xrootd cache server, producing classads that can be handed to condor """ import os import math import time import errno import struct import collections import six from six.moves import urllib import classad import XRootD.client __all__ = ['collect_cache_stats'] # these paths in the cache are to be treated as top level "VOs" for stats collection vo_paths = [ '/user', '/pnfs/fnal.gov/usr' ] def _split_path(path): """ Split a path into a list of directory names """ if path[0] != '/': raise Exception("Not absolute path") result = [] while path != '/': path, tail = os.path.split(path) if tail: result.append(tail) return list(reversed(result)) def _is_prefix(lhs, rhs): """ return True if the first list is a prefix of the second """ rhs = list(rhs) while rhs: if lhs == rhs: return True rhs.pop() return False def scan_cache_dirs(rootdir): """ Scan the top level directory of the cache. Walks the path looking for directories that are not in vo_paths. For each of these generate a cache summary """ results = {} try: root_components = _split_path(rootdir) for dirpath, dirnames, filenames in os.walk(rootdir, topdown=True): # get the path components as a list, removing the rootdir part dirpath_components = _split_path(dirpath)[len(root_components):] for name in list(dirnames): path_components = dirpath_components + [name] for p in [ _split_path(p) for p in vo_paths]: # if this directory is in vo_paths, keep recursing if _is_prefix( path_components, p): break else: # if nothing is in vo_paths, get the stats and remove from dirnames # so this walk goes no further vo_name = os.path.join('/', *path_components) try: results[vo_name] = scan_vo_dir(os.path.join(dirpath, name)) except (OSError, IOError) as ex: results[vo_name] = {'scan_vo_dir_error': str(ex) } dirnames.remove(name) return results except (OSError, IOError) as ex: return { 'scan_cache_dirs_error' : { 'message' : str(ex) } } # error message? def scan_vo_dir(vodir): """ Scan a VO directory (assumed to be the whole directory tree after the top level """ now = time.time() totalsize = 0 nfiles = 0 naccesses = 0 accesses = collections.defaultdict(int) most_recent_access = 0 bad_cinfo_files = 0 for root, dirs, files in os.walk(vodir): fnames = set(files) # Somebody might add a file ending in .cinfo in the cache # so look for the f, f.cinfo pair for f, cinfo in ((f, f + '.cinfo') for f in fnames if f + '.cinfo' in fnames): try: st = os.stat(os.path.join(root, f)) except OSError as ex: if ex.errno == errno.ENOENT: # must have just been deleted continue else: raise try: access_info = read_cinfo(os.path.join(root, cinfo), now) except OSError as ex: if ex.errno == errno.ENOENT: continue else: bad_cinfo_files += 1 access_info = { "naccesses" : 0, "last_access": 0, "by_hour" : {} } except ReadCInfoError as ex: bad_cinfo_files += 1 access_info = ex.access_info nfiles += 1 file_size = st.st_blocks*512 # allow for sparse files totalsize += file_size naccesses += access_info["naccesses"] most_recent_access = max(most_recent_access, access_info["last_access"]) for h in access_info["by_hour"]: accesses["naccesses_hr_" + h] += access_info["by_hour"][h] accesses["bytes_hr_" + h] += access_info["bytes_hr"][h] result = classad.ClassAd({ "used_bytes" : totalsize, "nfiles" : nfiles, "naccesses" : naccesses, "bad_cinfo_files" : bad_cinfo_files }) result.update(accesses) if most_recent_access > 0: result["most_recent_access_time"] = most_recent_access return result # Parsing the cinfo files # The header (not a c struct; consecutive separate values with no padding) # version + buffer size + file size (blocks) # int + long long + long long _header_fmt = '=iqq' _header_fmt_size = struct.calcsize(_header_fmt) # then the number of accesses # int _int_fmt = '@q' _int_fmt_size = struct.calcsize(_int_fmt) # each access contains a struct (native size + padding) # AttachTime + DetachTime + BytesDisk + BytesRam + BytesMissed # time_t + long long + long long + long long + long long _status_fmt = '@qqqqq' _status_fmt_size = struct.calcsize(_status_fmt) class ReadCInfoError(Exception): def __init__(self, *args): Exception.__init__(self, *args) if len(args) > 1: self.access_info = args[1] else: self.access_info = {} def read_cinfo(cinfo_file, now): """ Try to extract useful info from the cinfo file """ result = { "naccesses": 0, "last_access": 0, "by_hour" : { "01": 0, "12": 0, "24": 0 }, "bytes_hr" : { "01": 0, "12": 0, "24": 0 }, } cf = open(cinfo_file, 'rb') # read and unpack the header buf = cf.read(_header_fmt_size) if len(buf) < _header_fmt_size: # a mangled file raise ReadCInfoError("%s header too short" % cinfo_file, result) version, buffer_size, file_size = struct.unpack(_header_fmt, buf) # we only understand version 2 if version != 2: raise ReadCInfoError("%s unknown version: %s" % (cinfo_file, version), result) # Get the size of the state vector and skip over it # buff_synced uses 1 bit per bufferSize block of bytes # Length is rounded up to the nearest byte buff_synced_len = int(math.ceil(float(file_size)/buffer_size/8)) # If the file_size is zero, state vector length is 1 # (Difference is due to Python's integer division returning the floor) if file_size == 0: buff_synced_len = 1 cf.read(buff_synced_len) # Go past cksum (char[16]) and creationTime (time_t) cf.read(16 + 8) # now the access count (an int) buf = cf.read(_int_fmt_size) if len(buf) < _int_fmt_size: raise ReadCInfoError("%s: invalid access field" % cinfo_file, result) access_count, = struct.unpack(_int_fmt, buf) result["naccesses"] = access_count if access_count < 0: raise ReadCInfoError("%s: invalid access count: %s" % (cinfo_file, access_count), result) elif access_count == 0: return result # read the access times hr_01 = now - 60*60 hr_12 = now - 12*60*60 hr_24 = now - 24*60*60 # Read AStat structs try: for buf in iter(lambda: cf.read(_status_fmt_size), b''): access_time, _, bytes_disk, bytes_ram, _ = struct.unpack(_status_fmt, buf) result["last_access"] = access_time #print access_time, bytes_disk, bytes_ram #print time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(access_time)) intervals = list() if access_time >= hr_01: intervals.append('01') if access_time >= hr_12: intervals.append('12') if access_time >= hr_24: intervals.append('24') else: # no longer interested next for interval in intervals: result["by_hour"][interval] += 1 result["bytes_hr"][interval] += bytes_disk + bytes_ram except struct.error as ex: # return what we've got raise ReadCInfoError("%s unable to decode access time data: %s" % (cinfo_file, str(ex)), result) return result def test_xrootd_server(url): """ Contact the xrootd server to check if it's alive """ try: myclient = XRootD.client.FileSystem(url) startt = time.time() response, _ = myclient.ping(timeout=10) elapsed = time.time() - startt if response.fatal: status = "fatal" elif response.error: status = "error" elif response.ok: status = "ok" else: status = "unknown" result = {"ping_response_status" : status, "ping_response_code" : response.code, "ping_response_message" : response.message, "ping_elapsed_time" : elapsed} return result except Exception as ex: # more specific exception would be better return {"ping_response_status" : "failed", "ping_response_code" : -1, "ping_response_message" : str(ex), "ping_elapsed_time" : 0.0} def get_cache_info(rootdir, cache_max_fs_fraction): """Get information about the cache itself""" result = {} try: stat = os.statvfs(rootdir) total_size = int(stat.f_blocks*stat.f_bsize*cache_max_fs_fraction) free_size = int(total_size - (stat.f_blocks-stat.f_bfree)*stat.f_bsize) result['total_cache_bytes'] = total_size result['free_cache_bytes'] = free_size result['free_cache_fraction'] = 1 - float(stat.f_blocks-stat.f_bfree)/int(stat.f_blocks*cache_max_fs_fraction) return result except (OSError, IOError) as ex: return {} def collect_cache_stats(url, rootdir, cache_max_fs_fraction=1.0): """ Collect stats on the cache server """ start_time = time.time() parsed_url = urllib.parse.urlparse(url) # Python 2.6's urlparse returns a ParseResult object whereas # Python 2.4's urlparse returns a tuple that doesn't handle # root:// properly try: if parsed_url.scheme not in ('root', 'xroot'): raise Exception("URL '%s' is not an xrootd url" % url) hostname = parsed_url.netloc except AttributeError: if parsed_url[0] not in ('root', 'xroot'): raise Exception("URL '%s' is not an xrootd url" % url) hostname = parsed_url[2][2:] # Avoid the '//' prefix result = {'MyType' : 'Machine', 'Name': 'xrootd@%s' % hostname, 'stats_time' : int(start_time)} result.update(test_xrootd_server(url)) result.update(get_cache_info(rootdir, cache_max_fs_fraction)) stats_per_vo = scan_cache_dirs(rootdir) # add up the sizes totals = dict() most_recent_access = 0 result['VO'] = {} for vo, vostats in stats_per_vo.items(): for k, v in vostats.items(): if k == "most_recent_access_time": most_recent_access = max(most_recent_access, v) else: try: totals[k] += v except KeyError: totals[k] = v result['VO'][vo] = vostats result['used_cache_bytes'] = totals.pop("used_bytes", 0) for k, v in totals.items(): result["total_" + k] = v if most_recent_access > 0: result["most_recent_access_time"] = most_recent_access result['time_to_collect_stats'] = time.time() - start_time return classad.ClassAd(result) if __name__ == '__main__': import sys args = sys.argv[1:] if len(args) > 2: args[2] = float(args[2]) elif len(args) == 2: args.append(0.99) # max cache fraction print(collect_cache_stats(*args))

ivukotic/xcache

src/xrootd_cache_stats.py

Python

''' dShell output classes @author: tparker ''' import os import sys import logging import struct import datetime import dshell import util class Output(object): ''' dShell output base class, extended by output types ''' _DEFAULT_FORMAT = '' _DEFAULT_TIMEFORMAT = '%Y-%m-%d %H:%M:%S' _DEFAULT_DELIM = ' ' _NULL = None # true if you want to remove extra fields from the parsed record _FILTER_EXTRA = False def __init__(self, *a, **kw): ''' base output class constructor configuration kwords: logger=<existing logging object> to pass in a logger format='format string' to override default formatstring for output class pcap = filename to write pcap ''' # setup the logger self.logger = kw.get('logger', logging) # parse the format string self.setformat(kw.get('format', self._DEFAULT_FORMAT)) self.timeformat = (kw.get('timeformat', self._DEFAULT_TIMEFORMAT)) self.delim = (kw.get('delim', self._DEFAULT_DELIM)) if 'pcap' in kw: self.pcapwriter = PCAPWriter(kw['pcap']) else: self.pcapwriter = None # this is up to the output plugin to process # by default stuffs extra fields and data into 'extra' field # if _FILTER_EXTRA is true self.extra = kw.get('extra', False) # create the default session writer if 'session' in kw: self.sessionwriter = SessionWriter(**kw) else: self.sessionwriter = None # write a message to the log def log(self, msg, level=logging.INFO, *args, **kw): '''write a message to the log passes all args and kwargs thru to logging except for level= is used to set logging level''' self.logger.log(level, msg, *args, **kw) def setformat(self, formatstr=None, typemap=None): '''parse a format string and extract the field info if no string given, reverts to default for class will set self.fields to be a list of (name,type,spec) tuples self.fieldnames to a list of fieldnames and self.fieldmap to a list of key=in value=out mappings format string can also map in field to out field with %(in:out)spectype or specify an explicit out type with %(in:out)specintype:outtype (note this breaks compatibility with text formatting, but useful for db or other output modules) a typemap of [intype]=outtype (or [in]=(newintype,outtype) can be used to map and replace types ''' if formatstr: self.format = formatstr + "\n" else: self.format = self._DEFAULT_FORMAT + "\n" self.fields = [] # will be a (name,type,length) tuple self.fieldnames = [] self.fieldmap = {} # get all the field names e = 0 while True: # find the next format spec of %(...) s = self.format.find('%', e) + 1 if s < 1 or self.format[s] != '(': break # not %(... e = self.format.find(')', s) if e < 0: break # didn't find a closing paren # get text between parens as field name fname = self.format[s + 1:e] # len/precision specs will be 0-9 between ) and type char fspec = '' for i in xrange(e + 1, len(self.format)): if self.format[i] in '1234567890.+-# lLh': fspec += self.format[i] else: break # this char is not a spec char, it is the type char ftype = self.format[i] i += 1 # is the field type a intype:outtype def? if i < len(self.format) and self.format[i] == ':': e = self.format.find(' ', i) # find the end whitespace # split on: to get input:output mapping ftype, outtype = self.format[i - 1:e].split(':') else: outtype = None # output will be same as input type e = i # start at next char on loop try: # field name to column mapping fname, fmap = fname.split(':') except: fmap = fname # no mapping if typemap and ftype in typemap and not outtype: try: (ftype, outtype) = typemap[ftype] except: outtype = typemap[ftype] # append the field name,type,spec,mapping self.fields.append((fname, ftype, fspec)) self.fieldnames.append(fname) if outtype: self.fieldmap[fname] = (fmap, outtype) # map of in to out,type def parse(self, *args, **kw): '''parse the input args/kwargs into a record dict according to format string - timestamps are formatted to date/time strings - fields not in the input will be defined but blank - extra fields in the record will be formatted into a "name=value name2=value2..." string and put in 'extra' - args will go into 'data' - format keyword can contain a new format string to use (this also sets format for future output) ''' # convert timestamps to proper format for ts in [k for k in kw if k == 'ts' or k.endswith('time')]: dt = ts[:-4] + 'datetime' # ts->datetime , Xtime -> Xdatetime kw[dt] = datetime.datetime.fromtimestamp( float(kw[ts])).strftime(self.timeformat) # format properly if kw.get('direction') is 'cs': kw['dir_arrow'] = '->' elif kw.get('direction') is 'sc': kw['dir_arrow'] = '<-' else: kw['dir_arrow'] = '--' if 'format' in kw: self.setformat(kw['format']) # change the format string? del kw['format'] # create the record initialized to the _NULL value rec = dict((f, self._NULL) for f in self.fieldnames) # populate record from datadict if datadict key is a field if self._FILTER_EXTRA: rec.update( dict((f, kw[f]) for f in self.fieldnames if (f in kw and kw[f] != None))) # place extra datadict keys into the extra field (and exclude the # addr tuple) if self.extra: rec['extra'] = self.delim.join(['%s=%s' % (f, kw[f]) for f in sorted( kw.keys()) if f not in self.fieldnames and f != 'addr']) else: # not filtering extra, just lump them in as fields rec.update(kw) # populate the data field if args: rec['data'] = self.delim.join(map(str, args)) return rec def dump(self, pkt=None, **kw): # pass packets to pcap '''dump raw packet data to an output override this if you want a format other than pcap''' pktdata = str(pkt) # might be string, might be a dpkt object pktlen = kw.get('len', len(pktdata)) if self.pcapwriter: self.pcapwriter.write(pktlen, pktdata, kw['ts']) else: self.log(util.hexPlusAscii(str(pkt)), level=logging.DEBUG) # close the PCAP output def close(self): if self.pcapwriter: self.pcapwriter.close() def dispatch(self, m, *args, **kwargs): '''dispatch from Q pop''' if m == 'write': self.write(*args, **kwargs) if m == 'alert': self.alert(*args, **kwargs) if m == 'dump': self.dump(*args, **kwargs) class FileOutput(Output): def __init__(self, *args, **kw): '''configuration for fileoutput: fh=<existing open file handle> file=filename to write to mode=mode to open file as, default 'w' ''' # do base init first Output.__init__(self, *args, **kw) # get the output filehandle or file f = None if 'fh' in kw: self.fh = kw['fh'] return elif 'file' in kw: f = kw['file'] elif args: f = args[0] if f: if 'mode' in kw: mode = kw['mode'] else: mode = 'w' if mode == 'noclobber': mode = 'w' try: while os.stat(f): p = f.split('-') try: p, n = p[:-1], int(p[-1]) except ValueError: n = 0 f = '-'.join(p + ['%04d' % (int(n) + 1)]) except OSError: pass # file not found self.fh = open(f, mode) else: self.fh = sys.stdout def write(self, obj, **kw): '''write session data to the session output or stdout''' if self.sessionwriter: self.sessionwriter.write(obj, **kw) elif self.fh: self.fh.write(str(obj)) def close(self): '''close output if not stdout''' if self.fh != sys.stdout: self.fh.close() Output.close(self) class TextOutput(FileOutput): '''formatted text output to file or stdout''' _DEFAULT_FORMAT = "%(decoder)s %(datetime)s %(sip)16s:%(sport)-5s %(dir_arrow)s %(dip)16s:%(dport)-5s ** %(data)s **" _NULL = '' _FILTER_EXTRA = True def __init__(self, *args, **kw): if 'extra' in kw: self._DEFAULT_FORMAT += " [ %(extra)s ]" FileOutput.__init__(self, *args, **kw) def alert(self, *args, **kw): '''write an alert record we pass in the decoder object and args/dict''' rec = self.parse(*args, **kw) if rec: self.fh.write(self.format % rec) class DBOutput(Output): '''format strings as used by the DBOutput module to create tables and map fields these follow the usual %(name)type and in most cases a custom format string will work defualt type maps are: s,r = VARCHAR (if field len given) /TEXT (if no len) c = CHAR(1) x,X,o = VARCHAR d,i,u = INTEGER e,E,f,F,g,G = DECIMAL with the following extra: (using these breaks text format string compatibility) b = boolean t = timestamp D = datetime T = this field selects table (following are postgres-only) A = inet H = host N = cidr M = macaddr format string can also map field to column with %(field:column)type or specify an explicit column type with %(field:column)pytype:DBTYPE (note this also breaks compatibility with text format strings) ''' _DEFAULT_FORMAT = "%(decoder)T %(ts:timestamp)t %(sip)s %(sport)s %(dip)s %(dport)s %(data:alert)s" _NULL = None # format type to (type,coltype) map _TYPEMAP = {'s': 'VARCHAR', 'r': 'VARCHAR', 'c': 'CHAR(1)', 'x': 'VARCHAR', 'X': 'VARCHAR', 'o': 'VARCHAR', 'd': 'INTEGER', 'i': 'INTEGER', 'u': 'INTEGER', 'e': 'DECIMAL', 'E': 'DECIMAL', 'f': 'DECIMAL', 'F': 'DECIMAL', 'g': 'DECIMAL', 'G': 'DECIMAL', # 'b' isn't a python type, so (ftype,DBTYPE) tuple for value formats input as ftype 'b': ('d', 'BOOLEAN'), # not standard across database types! 't': ('f', 'TIMESTAMP'), 'D': ('s', 'DATETIME'), 'A': ('s', 'INET'), 'H': ('s', 'HOST'), 'N': ('s', 'CIDR'), 'M': ('s', 'MACADDR')} # these are postgres specific # acceptable params to pass to db module connect method _DBCONNPARAMS = ['host', 'user', 'passwd', 'password', 'db', 'database', 'port', 'charset'] # map of db type to insert placeholder. '%s' is the default, but sqlite3 doesn't like it # you can override this with the 'placeholder' config keyword _DBTYPE_PLACEHOLDER_MAP = {'sqlite3': '?'} def __init__(self, *args, **kw): '''configuration: config=db config .ini file name to parse config keywords: dbtype=database type, selects DB API module to load in conf file use [dbtype] section name instead host,user,passwd,password,db,database,port will be passed to db module if present table=db table to use if not specified by a field insert_param=character to use as parameter placeholder for INSERT (sqlite3=?, default=%%s) format_types=types to format before insert (default=x) ('s' to pad strings, 'x' to convert to hex, 'f' to format floats, 'fx' for hex and floats...) ''' self.dbconfig = kw.copy() # if we were passed a config.ini file, parse it and add the k/v pairs # to the config if 'config' in self.dbconfig: import ConfigParser config = ConfigParser.ConfigParser() config.read(self.dbconfig['config']) sections = config.sections() if len(sections) > 0: self.dbconfig['dbtype'] = sections[0] for k, v in config.items(sections[0], raw=True): self.dbconfig[k] = v # import the db module self.db = __import__(self.dbconfig['dbtype']) # create a connection, using a dict filtered to db conn params self.dbconn = self.db.connect( *args, **dict((k, self.dbconfig[k]) for k in self._DBCONNPARAMS if k in self.dbconfig)) # do the base init last to catch the format string, etc.. (as it may # have come from the config file) Output.__init__(self, *args, **self.dbconfig) def createtable(self, table=None): '''creates a table based on the format string''' if not table and 'table' in self.dbconfig: table = self.dbconfig['table'] try: cursor = self.dbconn.cursor() sqlfields = [] for fname, ftype, fspec in [f for f in self.fields if f[1] != 'T']: ctype = self.fieldmap[fname][1] # if no width spec, use TEXT instead of VARCHAR and hope the db # likes it if ctype == 'VARCHAR' and not fspec: ctype = 'TEXT' fdef = self.fieldmap[fname][0] + ' ' + ctype if fspec: # try to conver python format spec to something SQL will # take fdef += '(' + \ fspec.strip('+-# lLh').replace('.', ',') + ')' sqlfields.append(fdef) sql = 'CREATE TABLE "' + table + '" (' + ','.join(sqlfields) + ')' self.log(sql, logging.DEBUG) return cursor.execute(sql) except: raise def close(self): '''closes database connection''' self.dbconn.close() Output.close(self) def alert(self, *args, **kw): '''write an output record we pass in the decoder object and args/dict''' rec = self.parse(self, *args, **kw) if rec: self.insert(rec) def setformat(self, formatstr=None): '''calls main setformat and then builds the insert SQL''' # what is the insert param?? some databases use %s, some use ? # try to map it or take the placeholder keyword from config ph = self.dbconfig.get('insert_param', self._DBTYPE_PLACEHOLDER_MAP.get( self.dbconfig['dbtype'], '%%s') ) # these are the types we need to format before passing to the db self.format_types = self.dbconfig.get('format_types', 'x') Output.setformat(self, formatstr, typemap=self._TYPEMAP) # build all fields we map (except for [T]able select) self.tablefield = 'decoder' # default to decodername for fname, ftype, fspec in self.fields: if ftype == 'T': self.tablefield = fname sqlfields = [self.fieldmap[fname][0] for (fname, ftype, fspec) in self.fields if fname in self.fieldmap] self.insertsql = 'INSERT INTO "%%s" (%s) VALUES (%s)' % ( ','.join(sqlfields), ','.join([ph] * len(sqlfields))) def insert(self, rec, table=None): ''' inserts rec dict using self.format into table (if given, else default or specified by field) if insert fails, tries to create table and insert again before raising exception ''' if not table: if 'table' in self.dbconfig: table = self.dbconfig['table'] elif rec[self.tablefield]: table = rec[self.tablefield] try: sqlvalues = [] cursor = self.dbconn.cursor() for fname, ftype, fspec in self.fields: if fname in self.fieldmap: # do we preformat this data? if ftype in self.format_types: sqlvalues.append(('%' + fspec + ftype) % rec[fname]) else: sqlvalues.append(rec[fname]) # create a INSERT INTO table (fields) VALUES (?,?,?) for execute sql = self.insertsql % table self.log(sql + ' %s' % sqlvalues, logging.DEBUG) except: raise # try once, if it fails, try to create table and retry # throws on second failure or create table failure fail = False while True: try: cursor.execute(sql, sqlvalues) self.dbconn.commit() break # success except Exception, e: self.log(e, level=logging.WARNING) if fail: raise else: fail = True try: self.createtable(table) except: raise class PCAPWriter(FileOutput): '''writes a pcap file''' def __init__(self, *args, **kw): FileOutput.__init__(self, *args, **kw) if self.fh: self.fh.write( struct.pack('IHHIIII', 0xa1b2c3d4, 2, 4, 0, 0, 65535, 1)) # overrides Output.write to write session as PCAP # data flow is Output.dump->pcapwriter.write def write(self, pktlen, pktdata, ts): if self.fh: self.fh.write( struct.pack('II', int(ts), int((ts - int(ts)) * 1000000))) # captured length, original length self.fh.write(struct.pack('II', len(pktdata), pktlen)) self.fh.write(pktdata) class SessionWriter(Output): '''writes the session to one or more files''' def __init__(self, session=None, **kw): self.file = kw.get('session', session) self.dir = kw.get('direction', 'both') self.mode = kw.get('mode', 'a') self.timeformat = (kw.get('timeformat', self._DEFAULT_TIMEFORMAT)) self.fieldnames = [] def write(self, obj, **kwargs): out = None kw = dict(**kwargs) # if a session object with info() and data() methods (conn or blob, but # not packet) try: kw.update(**obj.info()) # get object info kw = self.parse(**kw) if self.dir == 'both': ds = [None] elif self.dir == 'split': ds = ['cs', 'sc'] else: ds = [self.dir] for d in ds: kw.update(direction=d if d else 'both') # set direction # format filename and open out = FileOutput(self.file % kw, mode=self.mode) # write obj data for direction out.fh.write(obj.data(direction=d)) out.close() except: # if not a session object # build filename from kw out = FileOutput(self.file % kw, mode=self.mode) out.fh.write(str(obj)) out.close() class QueueOutput(Output): '''pipes pickled packets to parent process''' def __init__(self, q, **kwargs): self.queue = q Output.__init__(self, **kwargs) def write(self, *args, **kw): self.dispatch('write', *args, **kw) def alert(self, *args, **kw): self.dispatch('alert', *args, **kw) def dump(self, *args, **kw): self.dispatch('dump', *args, **kw) def dispatch(self, m, *args, **kw): # takes (method,...) to Q self.queue.put((m, args, kw)) def close(self): self.queue.close() Output.close(self) # default output module obj = TextOutput

NTgitdude23/Dshell

lib/output/output.py

Python

"""Auto-generated file, do not edit by hand. MQ metadata""" from ..phonemetadata import NumberFormat, PhoneNumberDesc, PhoneMetadata PHONE_METADATA_MQ = PhoneMetadata(id='MQ', country_code=596, international_prefix='00', general_desc=PhoneNumberDesc(national_number_pattern='[56]\\d{8}', possible_number_pattern='\\d{9}'), fixed_line=PhoneNumberDesc(national_number_pattern='596(?:0[2-5]|[12]0|3[05-9]|4[024-8]|[5-7]\\d|89|9[4-8])\\d{4}', possible_number_pattern='\\d{9}', example_number='596301234'), mobile=PhoneNumberDesc(national_number_pattern='696(?:[0-479]\\d|5[01]|8[0-689])\\d{4}', possible_number_pattern='\\d{9}', example_number='696201234'), toll_free=PhoneNumberDesc(national_number_pattern='NA', possible_number_pattern='NA'), premium_rate=PhoneNumberDesc(national_number_pattern='NA', possible_number_pattern='NA'), shared_cost=PhoneNumberDesc(national_number_pattern='NA', possible_number_pattern='NA'), personal_number=PhoneNumberDesc(national_number_pattern='NA', possible_number_pattern='NA'), voip=PhoneNumberDesc(national_number_pattern='NA', possible_number_pattern='NA'), pager=PhoneNumberDesc(national_number_pattern='NA', possible_number_pattern='NA'), uan=PhoneNumberDesc(national_number_pattern='NA', possible_number_pattern='NA'), voicemail=PhoneNumberDesc(national_number_pattern='NA', possible_number_pattern='NA'), no_international_dialling=PhoneNumberDesc(national_number_pattern='NA', possible_number_pattern='NA'), national_prefix='0', national_prefix_for_parsing='0', number_format=[NumberFormat(pattern='(\\d{3})(\\d{2})(\\d{2})(\\d{2})', format=u'\\1 \\2 \\3 \\4', national_prefix_formatting_rule=u'0\\1')])

Eyepea/python-phonenumbers

python/phonenumbers/data/region_MQ.py

Python

# 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 from . import outputs from ._inputs import * __all__ = ['VpnSiteArgs', 'VpnSite'] @pulumi.input_type class VpnSiteArgs: def __init__(__self__, *, resource_group_name: pulumi.Input[str], address_space: Optional[pulumi.Input['AddressSpaceArgs']] = None, bgp_properties: Optional[pulumi.Input['BgpSettingsArgs']] = None, device_properties: Optional[pulumi.Input['DevicePropertiesArgs']] = None, id: Optional[pulumi.Input[str]] = None, ip_address: Optional[pulumi.Input[str]] = None, is_security_site: Optional[pulumi.Input[bool]] = None, location: Optional[pulumi.Input[str]] = None, site_key: Optional[pulumi.Input[str]] = None, tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, virtual_wan: Optional[pulumi.Input['SubResourceArgs']] = None, vpn_site_links: Optional[pulumi.Input[Sequence[pulumi.Input['VpnSiteLinkArgs']]]] = None, vpn_site_name: Optional[pulumi.Input[str]] = None): """ The set of arguments for constructing a VpnSite resource. :param pulumi.Input[str] resource_group_name: The resource group name of the VpnSite. :param pulumi.Input['AddressSpaceArgs'] address_space: The AddressSpace that contains an array of IP address ranges. :param pulumi.Input['BgpSettingsArgs'] bgp_properties: The set of bgp properties. :param pulumi.Input['DevicePropertiesArgs'] device_properties: The device properties. :param pulumi.Input[str] id: Resource ID. :param pulumi.Input[str] ip_address: The ip-address for the vpn-site. :param pulumi.Input[bool] is_security_site: IsSecuritySite flag. :param pulumi.Input[str] location: Resource location. :param pulumi.Input[str] site_key: The key for vpn-site that can be used for connections. :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: Resource tags. :param pulumi.Input['SubResourceArgs'] virtual_wan: The VirtualWAN to which the vpnSite belongs. :param pulumi.Input[Sequence[pulumi.Input['VpnSiteLinkArgs']]] vpn_site_links: List of all vpn site links. :param pulumi.Input[str] vpn_site_name: The name of the VpnSite being created or updated. """ pulumi.set(__self__, "resource_group_name", resource_group_name) if address_space is not None: pulumi.set(__self__, "address_space", address_space) if bgp_properties is not None: pulumi.set(__self__, "bgp_properties", bgp_properties) if device_properties is not None: pulumi.set(__self__, "device_properties", device_properties) if id is not None: pulumi.set(__self__, "id", id) if ip_address is not None: pulumi.set(__self__, "ip_address", ip_address) if is_security_site is not None: pulumi.set(__self__, "is_security_site", is_security_site) if location is not None: pulumi.set(__self__, "location", location) if site_key is not None: pulumi.set(__self__, "site_key", site_key) if tags is not None: pulumi.set(__self__, "tags", tags) if virtual_wan is not None: pulumi.set(__self__, "virtual_wan", virtual_wan) if vpn_site_links is not None: pulumi.set(__self__, "vpn_site_links", vpn_site_links) if vpn_site_name is not None: pulumi.set(__self__, "vpn_site_name", vpn_site_name) @property @pulumi.getter(name="resourceGroupName") def resource_group_name(self) -> pulumi.Input[str]: """ The resource group name of the VpnSite. """ return pulumi.get(self, "resource_group_name") @resource_group_name.setter def resource_group_name(self, value: pulumi.Input[str]): pulumi.set(self, "resource_group_name", value) @property @pulumi.getter(name="addressSpace") def address_space(self) -> Optional[pulumi.Input['AddressSpaceArgs']]: """ The AddressSpace that contains an array of IP address ranges. """ return pulumi.get(self, "address_space") @address_space.setter def address_space(self, value: Optional[pulumi.Input['AddressSpaceArgs']]): pulumi.set(self, "address_space", value) @property @pulumi.getter(name="bgpProperties") def bgp_properties(self) -> Optional[pulumi.Input['BgpSettingsArgs']]: """ The set of bgp properties. """ return pulumi.get(self, "bgp_properties") @bgp_properties.setter def bgp_properties(self, value: Optional[pulumi.Input['BgpSettingsArgs']]): pulumi.set(self, "bgp_properties", value) @property @pulumi.getter(name="deviceProperties") def device_properties(self) -> Optional[pulumi.Input['DevicePropertiesArgs']]: """ The device properties. """ return pulumi.get(self, "device_properties") @device_properties.setter def device_properties(self, value: Optional[pulumi.Input['DevicePropertiesArgs']]): pulumi.set(self, "device_properties", value) @property @pulumi.getter def id(self) -> Optional[pulumi.Input[str]]: """ Resource ID. """ return pulumi.get(self, "id") @id.setter def id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "id", value) @property @pulumi.getter(name="ipAddress") def ip_address(self) -> Optional[pulumi.Input[str]]: """ The ip-address for the vpn-site. """ return pulumi.get(self, "ip_address") @ip_address.setter def ip_address(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "ip_address", value) @property @pulumi.getter(name="isSecuritySite") def is_security_site(self) -> Optional[pulumi.Input[bool]]: """ IsSecuritySite flag. """ return pulumi.get(self, "is_security_site") @is_security_site.setter def is_security_site(self, value: Optional[pulumi.Input[bool]]): pulumi.set(self, "is_security_site", value) @property @pulumi.getter def location(self) -> Optional[pulumi.Input[str]]: """ Resource location. """ return pulumi.get(self, "location") @location.setter def location(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "location", value) @property @pulumi.getter(name="siteKey") def site_key(self) -> Optional[pulumi.Input[str]]: """ The key for vpn-site that can be used for connections. """ return pulumi.get(self, "site_key") @site_key.setter def site_key(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "site_key", value) @property @pulumi.getter def tags(self) -> Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]: """ Resource tags. """ return pulumi.get(self, "tags") @tags.setter def tags(self, value: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]]): pulumi.set(self, "tags", value) @property @pulumi.getter(name="virtualWan") def virtual_wan(self) -> Optional[pulumi.Input['SubResourceArgs']]: """ The VirtualWAN to which the vpnSite belongs. """ return pulumi.get(self, "virtual_wan") @virtual_wan.setter def virtual_wan(self, value: Optional[pulumi.Input['SubResourceArgs']]): pulumi.set(self, "virtual_wan", value) @property @pulumi.getter(name="vpnSiteLinks") def vpn_site_links(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['VpnSiteLinkArgs']]]]: """ List of all vpn site links. """ return pulumi.get(self, "vpn_site_links") @vpn_site_links.setter def vpn_site_links(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['VpnSiteLinkArgs']]]]): pulumi.set(self, "vpn_site_links", value) @property @pulumi.getter(name="vpnSiteName") def vpn_site_name(self) -> Optional[pulumi.Input[str]]: """ The name of the VpnSite being created or updated. """ return pulumi.get(self, "vpn_site_name") @vpn_site_name.setter def vpn_site_name(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "vpn_site_name", value) class VpnSite(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, address_space: Optional[pulumi.Input[pulumi.InputType['AddressSpaceArgs']]] = None, bgp_properties: Optional[pulumi.Input[pulumi.InputType['BgpSettingsArgs']]] = None, device_properties: Optional[pulumi.Input[pulumi.InputType['DevicePropertiesArgs']]] = None, id: Optional[pulumi.Input[str]] = None, ip_address: Optional[pulumi.Input[str]] = None, is_security_site: Optional[pulumi.Input[bool]] = None, location: Optional[pulumi.Input[str]] = None, resource_group_name: Optional[pulumi.Input[str]] = None, site_key: Optional[pulumi.Input[str]] = None, tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, virtual_wan: Optional[pulumi.Input[pulumi.InputType['SubResourceArgs']]] = None, vpn_site_links: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['VpnSiteLinkArgs']]]]] = None, vpn_site_name: Optional[pulumi.Input[str]] = None, __props__=None): """ VpnSite Resource. :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[pulumi.InputType['AddressSpaceArgs']] address_space: The AddressSpace that contains an array of IP address ranges. :param pulumi.Input[pulumi.InputType['BgpSettingsArgs']] bgp_properties: The set of bgp properties. :param pulumi.Input[pulumi.InputType['DevicePropertiesArgs']] device_properties: The device properties. :param pulumi.Input[str] id: Resource ID. :param pulumi.Input[str] ip_address: The ip-address for the vpn-site. :param pulumi.Input[bool] is_security_site: IsSecuritySite flag. :param pulumi.Input[str] location: Resource location. :param pulumi.Input[str] resource_group_name: The resource group name of the VpnSite. :param pulumi.Input[str] site_key: The key for vpn-site that can be used for connections. :param pulumi.Input[Mapping[str, pulumi.Input[str]]] tags: Resource tags. :param pulumi.Input[pulumi.InputType['SubResourceArgs']] virtual_wan: The VirtualWAN to which the vpnSite belongs. :param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['VpnSiteLinkArgs']]]] vpn_site_links: List of all vpn site links. :param pulumi.Input[str] vpn_site_name: The name of the VpnSite being created or updated. """ ... @overload def __init__(__self__, resource_name: str, args: VpnSiteArgs, opts: Optional[pulumi.ResourceOptions] = None): """ VpnSite Resource. :param str resource_name: The name of the resource. :param VpnSiteArgs 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(VpnSiteArgs, 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, address_space: Optional[pulumi.Input[pulumi.InputType['AddressSpaceArgs']]] = None, bgp_properties: Optional[pulumi.Input[pulumi.InputType['BgpSettingsArgs']]] = None, device_properties: Optional[pulumi.Input[pulumi.InputType['DevicePropertiesArgs']]] = None, id: Optional[pulumi.Input[str]] = None, ip_address: Optional[pulumi.Input[str]] = None, is_security_site: Optional[pulumi.Input[bool]] = None, location: Optional[pulumi.Input[str]] = None, resource_group_name: Optional[pulumi.Input[str]] = None, site_key: Optional[pulumi.Input[str]] = None, tags: Optional[pulumi.Input[Mapping[str, pulumi.Input[str]]]] = None, virtual_wan: Optional[pulumi.Input[pulumi.InputType['SubResourceArgs']]] = None, vpn_site_links: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['VpnSiteLinkArgs']]]]] = None, vpn_site_name: Optional[pulumi.Input[str]] = None, __props__=None): 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__ = VpnSiteArgs.__new__(VpnSiteArgs) __props__.__dict__["address_space"] = address_space __props__.__dict__["bgp_properties"] = bgp_properties __props__.__dict__["device_properties"] = device_properties __props__.__dict__["id"] = id __props__.__dict__["ip_address"] = ip_address __props__.__dict__["is_security_site"] = is_security_site __props__.__dict__["location"] = location if resource_group_name is None and not opts.urn: raise TypeError("Missing required property 'resource_group_name'") __props__.__dict__["resource_group_name"] = resource_group_name __props__.__dict__["site_key"] = site_key __props__.__dict__["tags"] = tags __props__.__dict__["virtual_wan"] = virtual_wan __props__.__dict__["vpn_site_links"] = vpn_site_links __props__.__dict__["vpn_site_name"] = vpn_site_name __props__.__dict__["etag"] = None __props__.__dict__["name"] = None __props__.__dict__["provisioning_state"] = None __props__.__dict__["type"] = None alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_="azure-nextgen:network/v20200301:VpnSite"), pulumi.Alias(type_="azure-native:network:VpnSite"), pulumi.Alias(type_="azure-nextgen:network:VpnSite"), pulumi.Alias(type_="azure-native:network/v20180401:VpnSite"), pulumi.Alias(type_="azure-nextgen:network/v20180401:VpnSite"), pulumi.Alias(type_="azure-native:network/v20180601:VpnSite"), pulumi.Alias(type_="azure-nextgen:network/v20180601:VpnSite"), pulumi.Alias(type_="azure-native:network/v20180701:VpnSite"), pulumi.Alias(type_="azure-nextgen:network/v20180701:VpnSite"), pulumi.Alias(type_="azure-native:network/v20180801:VpnSite"), pulumi.Alias(type_="azure-nextgen:network/v20180801:VpnSite"), pulumi.Alias(type_="azure-native:network/v20181001:VpnSite"), pulumi.Alias(type_="azure-nextgen:network/v20181001:VpnSite"), pulumi.Alias(type_="azure-native:network/v20181101:VpnSite"), pulumi.Alias(type_="azure-nextgen:network/v20181101:VpnSite"), pulumi.Alias(type_="azure-native:network/v20181201:VpnSite"), pulumi.Alias(type_="azure-nextgen:network/v20181201:VpnSite"), pulumi.Alias(type_="azure-native:network/v20190201:VpnSite"), pulumi.Alias(type_="azure-nextgen:network/v20190201:VpnSite"), pulumi.Alias(type_="azure-native:network/v20190401:VpnSite"), pulumi.Alias(type_="azure-nextgen:network/v20190401:VpnSite"), pulumi.Alias(type_="azure-native:network/v20190601:VpnSite"), pulumi.Alias(type_="azure-nextgen:network/v20190601:VpnSite"), pulumi.Alias(type_="azure-native:network/v20190701:VpnSite"), pulumi.Alias(type_="azure-nextgen:network/v20190701:VpnSite"), pulumi.Alias(type_="azure-native:network/v20190801:VpnSite"), pulumi.Alias(type_="azure-nextgen:network/v20190801:VpnSite"), pulumi.Alias(type_="azure-native:network/v20190901:VpnSite"), pulumi.Alias(type_="azure-nextgen:network/v20190901:VpnSite"), pulumi.Alias(type_="azure-native:network/v20191101:VpnSite"), pulumi.Alias(type_="azure-nextgen:network/v20191101:VpnSite"), pulumi.Alias(type_="azure-native:network/v20191201:VpnSite"), pulumi.Alias(type_="azure-nextgen:network/v20191201:VpnSite"), pulumi.Alias(type_="azure-native:network/v20200401:VpnSite"), pulumi.Alias(type_="azure-nextgen:network/v20200401:VpnSite"), pulumi.Alias(type_="azure-native:network/v20200501:VpnSite"), pulumi.Alias(type_="azure-nextgen:network/v20200501:VpnSite"), pulumi.Alias(type_="azure-native:network/v20200601:VpnSite"), pulumi.Alias(type_="azure-nextgen:network/v20200601:VpnSite"), pulumi.Alias(type_="azure-native:network/v20200701:VpnSite"), pulumi.Alias(type_="azure-nextgen:network/v20200701:VpnSite"), pulumi.Alias(type_="azure-native:network/v20200801:VpnSite"), pulumi.Alias(type_="azure-nextgen:network/v20200801:VpnSite"), pulumi.Alias(type_="azure-native:network/v20201101:VpnSite"), pulumi.Alias(type_="azure-nextgen:network/v20201101:VpnSite")]) opts = pulumi.ResourceOptions.merge(opts, alias_opts) super(VpnSite, __self__).__init__( 'azure-native:network/v20200301:VpnSite', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None) -> 'VpnSite': """ Get an existing VpnSite 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__ = VpnSiteArgs.__new__(VpnSiteArgs) __props__.__dict__["address_space"] = None __props__.__dict__["bgp_properties"] = None __props__.__dict__["device_properties"] = None __props__.__dict__["etag"] = None __props__.__dict__["ip_address"] = None __props__.__dict__["is_security_site"] = None __props__.__dict__["location"] = None __props__.__dict__["name"] = None __props__.__dict__["provisioning_state"] = None __props__.__dict__["site_key"] = None __props__.__dict__["tags"] = None __props__.__dict__["type"] = None __props__.__dict__["virtual_wan"] = None __props__.__dict__["vpn_site_links"] = None return VpnSite(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter(name="addressSpace") def address_space(self) -> pulumi.Output[Optional['outputs.AddressSpaceResponse']]: """ The AddressSpace that contains an array of IP address ranges. """ return pulumi.get(self, "address_space") @property @pulumi.getter(name="bgpProperties") def bgp_properties(self) -> pulumi.Output[Optional['outputs.BgpSettingsResponse']]: """ The set of bgp properties. """ return pulumi.get(self, "bgp_properties") @property @pulumi.getter(name="deviceProperties") def device_properties(self) -> pulumi.Output[Optional['outputs.DevicePropertiesResponse']]: """ The device properties. """ return pulumi.get(self, "device_properties") @property @pulumi.getter def etag(self) -> pulumi.Output[str]: """ A unique read-only string that changes whenever the resource is updated. """ return pulumi.get(self, "etag") @property @pulumi.getter(name="ipAddress") def ip_address(self) -> pulumi.Output[Optional[str]]: """ The ip-address for the vpn-site. """ return pulumi.get(self, "ip_address") @property @pulumi.getter(name="isSecuritySite") def is_security_site(self) -> pulumi.Output[Optional[bool]]: """ IsSecuritySite flag. """ return pulumi.get(self, "is_security_site") @property @pulumi.getter def location(self) -> pulumi.Output[str]: """ Resource location. """ return pulumi.get(self, "location") @property @pulumi.getter def name(self) -> pulumi.Output[str]: """ Resource name. """ return pulumi.get(self, "name") @property @pulumi.getter(name="provisioningState") def provisioning_state(self) -> pulumi.Output[str]: """ The provisioning state of the VPN site resource. """ return pulumi.get(self, "provisioning_state") @property @pulumi.getter(name="siteKey") def site_key(self) -> pulumi.Output[Optional[str]]: """ The key for vpn-site that can be used for connections. """ return pulumi.get(self, "site_key") @property @pulumi.getter def tags(self) -> pulumi.Output[Optional[Mapping[str, str]]]: """ Resource tags. """ return pulumi.get(self, "tags") @property @pulumi.getter def type(self) -> pulumi.Output[str]: """ Resource type. """ return pulumi.get(self, "type") @property @pulumi.getter(name="virtualWan") def virtual_wan(self) -> pulumi.Output[Optional['outputs.SubResourceResponse']]: """ The VirtualWAN to which the vpnSite belongs. """ return pulumi.get(self, "virtual_wan") @property @pulumi.getter(name="vpnSiteLinks") def vpn_site_links(self) -> pulumi.Output[Optional[Sequence['outputs.VpnSiteLinkResponse']]]: """ List of all vpn site links. """ return pulumi.get(self, "vpn_site_links")

sebtelko/pulumi-azure-native

sdk/python/pulumi_azure_native/network/v20200301/vpn_site.py

Python

# -*- coding: utf-8 -*- """ Copyright [2009-2018] EMBL-European Bioinformatics Institute 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 pathlib import Path import click from rnacentral_pipeline.rnacentral import attempted, r2dt @click.group("r2dt") def cli(): """ A group of commands for parsing data from secondary structures into an importable format. """ pass @cli.command("process-svgs") @click.option("--allow-missing", is_flag=True, default=False) @click.argument("model_info", type=click.File("r")) @click.argument("directory", type=click.Path()) @click.argument("output", type=click.File("w")) def process_svgs(model_info, directory, output, allow_missing=False): """ Process all SVG secondary structures in the given directory and produce a single data file that can be imported into the database. """ r2dt.write(model_info, directory, output, allow_missing=allow_missing) @cli.group("should-show") def should_show(): """ Some commands relating to building a model for should show as well as running it. """ @should_show.command("convert-sheet") @click.argument("filename", type=click.File("r")) @click.argument("output", type=click.File("w")) def convert_sheet(filename, output): """ This command is to convert a downloaded google sheet csv into a csv that can be used for training data. Often we will build a spreadsheet of example URS and then use that to build a training set. It is nice since you can embedd an SVG in google sheets so it is fast for us to compare several of them. In order to move that back into the training data you can download that sheet as a CSV and then run this command on it to build the CSV that is used in training. It requires there be a 'urs' and 'Labeled Should show' column to build the CSV. The values in labeled should show must be true/false (ignoring case). """ r2dt.write_converted_sheet(filename, output) @should_show.command("fetch-data") @click.option("--db-url", envvar="PGDATABASE") @click.argument("filename", type=click.File("r")) @click.argument("output", type=click.File("w")) def fetch_training_data(filename, output, db_url=None): """ This builds a CSV file of training data to use for the model building. I keep it separate so I can build a training csv and play with it interactivly before committing the final modeling building logic to the pipeline. """ r2dt.write_training_data(filename, db_url, output) @should_show.command("inspect-data") @click.option("--db-url", envvar="PGDATABASE") @click.argument("filename", type=click.File("r")) @click.argument("output", type=click.File("w")) def fetch_inspect_data(filename, output, db_url=None): """ This is the command to use when trying to fetch more examples to add to the training set. This will fetch some information that is useful for a person to evaluate a diagram and decide if it should be true/false in the training set. """ r2dt.write_training_data(filename, db_url, output) @should_show.command("build-model") @click.option("--db-url", envvar="PGDATABASE") @click.argument("training-info", type=click.File("r")) @click.argument("model", type=click.Path()) def build_model(training_info, model, db_url=None): """ This builds a model given then training information. The training information should be a csv file of: URS,flag The flag must be 1 or 0 to indicate if the URS should be shown or not. THis will fetch the data like the fetch-data command but will then build a model and write it out the the output file directly. """ r2dt.build_model(training_info, db_url, Path(model)) @should_show.command("compute") @click.option("--db-url", envvar="PGDATABASE") @click.argument("model", type=click.Path()) @click.argument("filename", type=click.File("r")) @click.argument("output", type=click.File("w")) def write_should_show(model, filename, output, db_url=None): """ This computes the should show values for the data in the given file and a file listing urs ids to use. The data needed for the URS will be fetched from the database. This is meant to operate on large batches, like relabeling the entire database. """ r2dt.write_should_show(model, filename, db_url, output) @cli.group("model-info") def model_info(): """ Commands for parsing and generating data files we can import into the database as model info files. """ pass @model_info.command("crw") @click.argument("filename", type=click.File("r")) @click.argument("output", default="-", type=click.File("w")) def crw_model_info(filename, output): """ Parse the CRW metadata file and produce """ r2dt.write_crw(filename, output) @model_info.command("ribovision") @click.argument("filename", type=click.File("r")) @click.argument("output", default="-", type=click.File("w")) def ribovision_model_info(filename, output): """ Parse the metadata.tsv file from R2DT for Ribovision models to produce something we can put in our database. """ r2dt.write_ribovision(filename, output) @model_info.command("gtrnadb") @click.argument("filename", type=click.File("r")) @click.argument("output", default="-", type=click.File("w")) def gtrnadb_model_info(filename, output): """ Parse the metadata.tsv file from R2DT for gtrnadb models to produce something we can put in our database. """ r2dt.write_gtrnadb(filename, output) @model_info.command("rnase-p") @click.argument("filename", type=click.File("r")) @click.argument("output", default="-", type=click.File("w")) def rnase_p_model_info(filename, output): """ Parse the metadata.tsv file from R2DT for Ribovision models to produce something we can put in our database. """ r2dt.write_rnase_p(filename, output) @cli.command("create-attempted") @click.argument("filename", type=click.File("r")) @click.argument("output", default="-", type=click.File("w")) def r2dt_create_attempted(filename, output): attempted.r2dt(filename, output) @cli.command("publish") @click.option("--suffix", default="") @click.option("--allow-missing", is_flag=True, default=False) @click.argument("model_info", type=click.File("r")) @click.argument( "directory", type=click.Path( writable=False, dir_okay=True, file_okay=False, ), ) @click.argument( "output", type=click.Path( writable=True, dir_okay=True, file_okay=False, ), ) def r2dt_publish(model_info, directory, output, allow_missing, suffix=""): r2dt.publish( model_info, directory, output, allow_missing=allow_missing, suffix=suffix ) @cli.command("prepare-s3") @click.option("--allow-missing", is_flag=True, default=False) @click.argument("model_info", type=click.File("r")) @click.argument( "directory", type=click.Path( writable=False, dir_okay=True, file_okay=False, ), ) @click.argument( "output", type=click.Path( writable=True, dir_okay=True, file_okay=False, ), ) @click.argument("file_list", type=click.Path()) def r2dt_prepare_s3(model_info, directory, output, file_list, allow_missing): file_list = Path(file_list) output = Path(output) r2dt.prepare_s3( model_info, directory, output, file_list, allow_missing=allow_missing )

RNAcentral/rnacentral-import-pipeline

rnacentral_pipeline/cli/r2dt.py

Python

import json from django.http.response import Http404, HttpResponse, HttpResponseBadRequest from hknweb.utils import login_and_permission from hknweb.academics.models import Instructor from hknweb.course_surveys.constants import Attr, COURSE_SURVEYS_EDIT_PERMISSION @login_and_permission(COURSE_SURVEYS_EDIT_PERMISSION) def merge_instructors(request): if request.method != "POST": return Http404() instructor_ids = request.GET.get(Attr.INSTRUCTOR_IDS, None) instructor_ids = json.loads(instructor_ids) instructor_ids = list(map(int, instructor_ids)) if len(instructor_ids) < 2: return HttpResponseBadRequest() base_instructor = Instructor.objects.get(pk=instructor_ids[0]) for id in instructor_ids[1:]: instructor = Instructor.objects.get(pk=id) for icsr in instructor.icsr_instructor.all(): icsr.icsr_instructor = base_instructor icsr.save() instructor.delete() return HttpResponse()

Boomaa23/hknweb

hknweb/course_surveys/views/merge_instructors.py

Python

import os import pyudev import psutil import logging import time from arm.ripper import music_brainz from arm.ui import db from arm.config.config import cfg from flask_login import LoginManager, current_user, login_user, UserMixin # noqa: F401 from prettytable import PrettyTable hidden_attribs = ("OMDB_API_KEY", "EMBY_USERID", "EMBY_PASSWORD", "EMBY_API_KEY", "PB_KEY", "IFTTT_KEY", "PO_KEY", "PO_USER_KEY", "PO_APP_KEY", "ARM_API_KEY", "TMDB_API_KEY") HIDDEN_VALUE = "<hidden>" class Job(db.Model): job_id = db.Column(db.Integer, primary_key=True) arm_version = db.Column(db.String(20)) crc_id = db.Column(db.String(63)) logfile = db.Column(db.String(256)) start_time = db.Column(db.DateTime) stop_time = db.Column(db.DateTime) job_length = db.Column(db.String(12)) status = db.Column(db.String(32)) stage = db.Column(db.String(63)) no_of_titles = db.Column(db.Integer) title = db.Column(db.String(256)) title_auto = db.Column(db.String(256)) title_manual = db.Column(db.String(256)) year = db.Column(db.String(4)) year_auto = db.Column(db.String(4)) year_manual = db.Column(db.String(4)) video_type = db.Column(db.String(20)) video_type_auto = db.Column(db.String(20)) video_type_manual = db.Column(db.String(20)) imdb_id = db.Column(db.String(15)) imdb_id_auto = db.Column(db.String(15)) imdb_id_manual = db.Column(db.String(15)) poster_url = db.Column(db.String(256)) poster_url_auto = db.Column(db.String(256)) poster_url_manual = db.Column(db.String(256)) devpath = db.Column(db.String(15)) mountpoint = db.Column(db.String(20)) hasnicetitle = db.Column(db.Boolean) errors = db.Column(db.Text) disctype = db.Column(db.String(20)) # dvd/bluray/data/music/unknown label = db.Column(db.String(256)) path = db.Column(db.String(256)) ejected = db.Column(db.Boolean) updated = db.Column(db.Boolean) pid = db.Column(db.Integer) pid_hash = db.Column(db.Integer) tracks = db.relationship('Track', backref='job', lazy='dynamic') config = db.relationship('Config', uselist=False, backref="job") def __init__(self, devpath): """Return a disc object""" self.devpath = devpath self.mountpoint = "/mnt" + devpath self.hasnicetitle = False self.video_type = "unknown" self.ejected = False self.updated = False if cfg['VIDEOTYPE'] != "auto": self.video_type = cfg['VIDEOTYPE'] self.parse_udev() self.get_pid() def parse_udev(self): """Parse udev for properties of current disc""" context = pyudev.Context() device = pyudev.Devices.from_device_file(context, self.devpath) self.disctype = "unknown" for key, value in device.items(): if key == "ID_FS_LABEL": self.label = value if value == "iso9660": self.disctype = "data" elif key == "ID_CDROM_MEDIA_BD": self.disctype = "bluray" elif key == "ID_CDROM_MEDIA_DVD": self.disctype = "dvd" elif key == "ID_CDROM_MEDIA_TRACK_COUNT_AUDIO": self.disctype = "music" else: pass def get_pid(self): pid = os.getpid() p = psutil.Process(pid) self.pid = pid self.pid_hash = hash(p) def get_disc_type(self, found_hvdvd_ts): if self.disctype == "music": logging.debug("Disc is music.") self.label = music_brainz.main(self) elif os.path.isdir(self.mountpoint + "/VIDEO_TS"): logging.debug(f"Found: {self.mountpoint}/VIDEO_TS") self.disctype = "dvd" elif os.path.isdir(self.mountpoint + "/video_ts"): logging.debug(f"Found: {self.mountpoint}/video_ts") self.disctype = "dvd" elif os.path.isdir(self.mountpoint + "/BDMV"): logging.debug(f"Found: {self.mountpoint}/BDMV") self.disctype = "bluray" elif os.path.isdir(self.mountpoint + "/HVDVD_TS"): logging.debug(f"Found: {self.mountpoint}/HVDVD_TS") # do something here elif found_hvdvd_ts: logging.debug("Found file: HVDVD_TS") # do something here too else: logging.debug("Did not find valid dvd/bd files. Changing disctype to 'data'") self.disctype = "data" def identify_audio_cd(self): """ Get the title for audio cds to use for the logfile name. Needs the job class passed into it so it can be forwarded to mb return - only the logfile - setup_logging() adds the full path """ # Use the music label if we can find it - defaults to music_cd.log disc_id = music_brainz.get_disc_id(self) mb_title = music_brainz.get_title(disc_id, self) if mb_title == "not identified": self.label = self.title = "not identified" logfile = "music_cd.log" new_log_file = f"music_cd_{round(time.time() * 100)}.log" else: logfile = f"{mb_title}.log" new_log_file = f"{mb_title}_{round(time.time() * 100)}.log" temp_log_full = os.path.join(cfg['LOGPATH'], logfile) logfile = new_log_file if os.path.isfile(temp_log_full) else logfile return logfile def __str__(self): """Returns a string of the object""" s = self.__class__.__name__ + ": " for attr, value in self.__dict__.items(): s = s + "(" + str(attr) + "=" + str(value) + ") " return s def pretty_table(self): """Returns a string of the prettytable""" x = PrettyTable() x.field_names = ["Config", "Value"] x._max_width = {"Config": 50, "Value": 60} for attr, value in self.__dict__.items(): if attr == "config": x.add_row([str(attr), str(value.pretty_table())]) else: x.add_row([str(attr), str(value)]) return str(x.get_string()) def get_d(self): r = {} for key, value in self.__dict__.items(): if '_sa_instance_state' not in key: r[str(key)] = str(value) return r def __repr__(self): return '<Job {}>'.format(self.label) def eject(self): """Eject disc if it hasn't previously been ejected""" if not self.ejected: self.ejected = True try: if os.system("umount " + self.devpath): logging.debug("we unmounted disc" + self.devpath) if os.system("eject " + self.devpath): logging.debug("we ejected disc" + self.devpath) self.ejected = True else: logging.debug("failed to eject" + self.devpath) except Exception as e: logging.debug(self.devpath + " couldn't be ejected " + str(e)) class Track(db.Model): track_id = db.Column(db.Integer, primary_key=True) job_id = db.Column(db.Integer, db.ForeignKey('job.job_id')) track_number = db.Column(db.String(4)) length = db.Column(db.Integer) aspect_ratio = db.Column(db.String(20)) fps = db.Column(db.Float) main_feature = db.Column(db.Boolean) basename = db.Column(db.String(256)) filename = db.Column(db.String(256)) orig_filename = db.Column(db.String(256)) new_filename = db.Column(db.String(256)) ripped = db.Column(db.Boolean) status = db.Column(db.String(32)) error = db.Column(db.Text) source = db.Column(db.String(32)) def __init__(self, job_id, track_number, length, aspect_ratio, fps, main_feature, source, basename, filename): """Return a track object""" self.job_id = job_id self.track_number = track_number self.length = length self.aspect_ratio = aspect_ratio self.fps = fps self.main_feature = main_feature self.source = source self.basename = basename self.filename = filename self.ripped = False def __repr__(self): return '<Post {}>'.format(self.track_number) class Config(db.Model): CONFIG_ID = db.Column(db.Integer, primary_key=True) job_id = db.Column(db.Integer, db.ForeignKey('job.job_id')) ARM_CHECK_UDF = db.Column(db.Boolean) GET_VIDEO_TITLE = db.Column(db.Boolean) SKIP_TRANSCODE = db.Column(db.Boolean) VIDEOTYPE = db.Column(db.String(25)) MINLENGTH = db.Column(db.String(6)) MAXLENGTH = db.Column(db.String(6)) MANUAL_WAIT = db.Column(db.Boolean) MANUAL_WAIT_TIME = db.Column(db.Integer) RAW_PATH = db.Column(db.String(255)) TRANSCODE_PATH = db.Column(db.String(255)) COMPLETED_PATH = db.Column(db.String(255)) EXTRAS_SUB = db.Column(db.String(255)) INSTALLPATH = db.Column(db.String(255)) LOGPATH = db.Column(db.String(255)) LOGLEVEL = db.Column(db.String(255)) LOGLIFE = db.Column(db.Integer) DBFILE = db.Column(db.String(255)) WEBSERVER_IP = db.Column(db.String(25)) WEBSERVER_PORT = db.Column(db.Integer) SET_MEDIA_PERMISSIONS = db.Column(db.Boolean) CHMOD_VALUE = db.Column(db.Integer) SET_MEDIA_OWNER = db.Column(db.Boolean) CHOWN_USER = db.Column(db.String(50)) CHOWN_GROUP = db.Column(db.String(50)) RIPMETHOD = db.Column(db.String(25)) MKV_ARGS = db.Column(db.String(25)) DELRAWFILES = db.Column(db.Boolean) HASHEDKEYS = db.Column(db.Boolean) HB_PRESET_DVD = db.Column(db.String(256)) HB_PRESET_BD = db.Column(db.String(256)) DEST_EXT = db.Column(db.String(10)) HANDBRAKE_CLI = db.Column(db.String(25)) MAINFEATURE = db.Column(db.Boolean) HB_ARGS_DVD = db.Column(db.String(256)) HB_ARGS_BD = db.Column(db.String(256)) EMBY_REFRESH = db.Column(db.Boolean) EMBY_SERVER = db.Column(db.String(25)) EMBY_PORT = db.Column(db.String(6)) EMBY_CLIENT = db.Column(db.String(25)) EMBY_DEVICE = db.Column(db.String(50)) EMBY_DEVICEID = db.Column(db.String(128)) EMBY_USERNAME = db.Column(db.String(50)) EMBY_USERID = db.Column(db.String(128)) EMBY_PASSWORD = db.Column(db.String(128)) EMBY_API_KEY = db.Column(db.String(64)) NOTIFY_RIP = db.Column(db.Boolean) NOTIFY_TRANSCODE = db.Column(db.Boolean) PB_KEY = db.Column(db.String(64)) IFTTT_KEY = db.Column(db.String(64)) IFTTT_EVENT = db.Column(db.String(25)) PO_USER_KEY = db.Column(db.String(64)) PO_APP_KEY = db.Column(db.String(64)) OMDB_API_KEY = db.Column(db.String(64)) def __init__(self, c, job_id): self.__dict__.update(c) self.job_id = job_id def list_params(self): """Returns a string of the object""" s = self.__class__.__name__ + ": " for attr, value in self.__dict__.items(): if s: s = s + "\n" if str(attr) in hidden_attribs and value: value = HIDDEN_VALUE s = s + str(attr) + ":" + str(value) return s def __str__(self): """Returns a string of the object""" s = self.__class__.__name__ + ": " for attr, value in self.__dict__.items(): if str(attr) in hidden_attribs and value: value = HIDDEN_VALUE s = s + "(" + str(attr) + "=" + str(value) + ") " return s def pretty_table(self): """Returns a string of the prettytable""" x = PrettyTable() x.field_names = ["Config", "Value"] x._max_width = {"Config": 20, "Value": 30} for attr, value in self.__dict__.items(): if str(attr) in hidden_attribs and value: value = HIDDEN_VALUE x.add_row([str(attr), str(value)]) return str(x.get_string()) def get_d(self): r = {} for key, value in self.__dict__.items(): if str(key) not in hidden_attribs: r[str(key)] = str(value) return r class User(db.Model, UserMixin): user_id = db.Column(db.Integer, index=True, primary_key=True) email = db.Column(db.String(64)) password = db.Column(db.String(128)) hash = db.Column(db.String(256)) def __init__(self, email=None, password=None, hashed=None): self.email = email self.password = password self.hash = hashed def __repr__(self): return '<User %r>' % (self.email) def get_id(self): return self.user_id class AlembicVersion(db.Model): version_num = db.Column(db.String(36), autoincrement=False, primary_key=True) def __init__(self, version=None): self.version_num = version class UISettings(db.Model): id = db.Column(db.Integer, autoincrement=True, primary_key=True) use_icons = db.Column(db.Boolean) save_remote_images = db.Column(db.Boolean) bootstrap_skin = db.Column(db.String(64)) language = db.Column(db.String(4)) index_refresh = db.Column(db.Integer) database_limit = db.Column(db.Integer) def __init__(self, use_icons=None, save_remote_images=None, bootstrap_skin=None, language=None, index_refresh=None, database_limit=None): self.use_icons = use_icons self.save_remote_images = save_remote_images self.bootstrap_skin = bootstrap_skin self.language = language self.index_refresh = index_refresh self.database_limit = database_limit def __repr__(self): return '<UISettings %r>' % self.id def __str__(self): """Returns a string of the object""" s = self.__class__.__name__ + ": " for attr, value in self.__dict__.items(): s = s + "(" + str(attr) + "=" + str(value) + ") " return s def get_d(self): r = {} for key, value in self.__dict__.items(): if '_sa_instance_state' not in key: r[str(key)] = str(value) return r

charmarkk/automatic-ripping-machine

arm/models/models.py

Python

# Copyright 2020 XEBIALABS # # 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. # # https://stackoverflow.com/questions/16910955/programmatically-configure-logback-appender?noredirect=1 # import ch.qos.logback.core.Appender as LogAppender import ch.qos.logback.core.util.COWArrayList as COWArrayList import ch.qos.logback.classic.encoder.PatternLayoutEncoder as PatternLayoutEncoder import ch.qos.logback.core.FileAppender as FileAppender import org.slf4j.LoggerFactory as LoggerFactory import ch.qos.logback.classic.Level as logLevels import json def getLogAppenders( loggerName="console" ): loggerMap = [] myLogger = LoggerFactory.getLogger("logmanager") loggerContext = LoggerFactory.getILoggerFactory() myLogger.error("===================") appenderMap = {} for logger in loggerContext.getLoggerList(): appenderList = logger.iteratorForAppenders() while appenderList.hasNext(): appender = appenderList.next() logger.error("Logger %s" % appender.getName()) if appender.getName() not in appenderMap.keys(): loggerMap.append({"name": appender.getName(), "appender": "NA"}) myLogger.error("Appender %s: %s" % (appender.getName(), "NA")) myLogger.error("===================") return loggerMap def createLogAppender( name, file ): lc = LoggerFactory.getILoggerFactory() ple = PatternLayoutEncoder() ple.setPattern("%date %level [%thread] %logger{10} [%file:%line] %msg%n") ple.setContext(lc) ple.start() fileAppender = FileAppender() fileAppender.setFile(file) fileAppender.setEncoder(ple) fileAppender.setContext(lc) fileAppender.start() logger = LoggerFactory.getLogger(string) logger.addAppender(fileAppender) #logger.setLevel(logLevels.DEBUG) # set to true if root should log too logger.setAdditive(True) return logger myLogger = LoggerFactory.getLogger("logmanager") verb = "GET" if (request): if (request.query): if (request.query['verb']): verb = request.query['verb'] if( verb == "create"): string = request.query['string'] file = request.query['file'] myLogger.info("Setting %s to %s" % (string, file)) createLogAppender(string, file) loggerMap = getLogAppenders() myLogger.error("%s" % json.dumps(loggerMap, indent=4, sort_keys=True)) response.entity = {"status": "OK", "data":loggerMap }

xebialabs-community/xlr-logreport-plugin

src/main/resources/restapi/logger/getLogAppenders.py

Python

import threading from time import sleep from .intcode import Intcode class Amplifier(object): def __init__(self, mem_str: str): self._amps = [Intcode(mem_str, name=f'Amp {n + 1}') for n in range(5)] def run(self, inputs: str or list, trace=False, quiet=True): out = 0 p = self._amps[0] if isinstance(inputs, str): inputs = [int(v) for v in inputs.split(',')] for inp in inputs: p.reset_core() p.simulate([inp, out], trace=trace) out = p.output[0] self._print_log(quiet) return out def _print_log(self, quiet): if not quiet: for p in self._amps: msg = f'{p.name} log:' top_n_tail = "*" * (len(msg) + 4) print(top_n_tail) print(f'* {msg} *') print(top_n_tail) print('\n'.join(p.get_log())) def run_regeneration(self, inputs: str or list, trace=False, quiet=True): if isinstance(inputs, str): inputs = [int(v) for v in inputs.split(',')] p = self._amps[0] p.reset_core() for n in self._amps[1:]: p.connect(n.receiver) p = n p.reset_core() self._amps[-1].connect(self._amps[0].receiver) threads = [] for a, n in zip(self._amps, inputs): a.receiver(n) t = threading.Thread(target=a.simulate, kwargs={'trace': trace}) threads.append(t) t.start() self._amps[0].receiver(0) while any(t.is_alive() for t in threads): sleep(0.0001) self._print_log(quiet) return self._amps[0]._input.pop()

GeoffRiley/AdventOfCode

intcode/amplifier.py

Python

# coding: utf-8 """ App Center Client Microsoft Visual Studio App Center API # noqa: E501 OpenAPI spec version: preview Contact: benedetto.abbenanti@gmail.com Project Repository: https://github.com/b3nab/appcenter-sdks """ from __future__ import absolute_import import unittest import appcenter_sdk from DistributionGroupAppsDeleteRequest.clsDistributionGroupAppsDeleteRequest import DistributionGroupAppsDeleteRequest # noqa: E501 from appcenter_sdk.rest import ApiException class TestDistributionGroupAppsDeleteRequest(unittest.TestCase): """DistributionGroupAppsDeleteRequest unit test stubs""" def setUp(self): pass def tearDown(self): pass def testDistributionGroupAppsDeleteRequest(self): """Test DistributionGroupAppsDeleteRequest""" # FIXME: construct object with mandatory attributes with example values # model = appcenter_sdk.models.clsDistributionGroupAppsDeleteRequest.DistributionGroupAppsDeleteRequest() # noqa: E501 pass if __name__ == '__main__': unittest.main()

Brantone/appcenter-sdks

sdks/python/test/test_DistributionGroupAppsDeleteRequest.py

Python

# Copyright 2008-2015 Nokia Solutions and Networks # # 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 .tags import TagPatterns class Criticality(object): def __init__(self, critical_tags=None, non_critical_tags=None): self.critical_tags = self._get_tag_patterns(critical_tags) self.non_critical_tags = self._get_tag_patterns(non_critical_tags) def _get_tag_patterns(self, tags): return TagPatterns(tags) if not isinstance(tags, TagPatterns) else tags def tag_is_critical(self, tag): return self.critical_tags.match(tag) def tag_is_non_critical(self, tag): return self.non_critical_tags.match(tag) def test_is_critical(self, test): if self.critical_tags and not self.critical_tags.match(test.tags): return False return not self.non_critical_tags.match(test.tags) def __bool__(self): return bool(self.critical_tags or self.non_critical_tags) #PY2 def __nonzero__(self): return self.__bool__()

userzimmermann/robotframework

src/robot/model/criticality.py

Python

# -*- coding: utf-8 -*- from south.utils import datetime_utils as datetime from south.db import db from south.v2 import SchemaMigration from django.db import models class Migration(SchemaMigration): def forwards(self, orm): # Changing field 'Competition.url_redirect' db.alter_column(u'web_competition', 'url_redirect', self.gf('django.db.models.fields.URLField')(max_length=200, null=True)) def backwards(self, orm): # Changing field 'Competition.url_redirect' db.alter_column(u'web_competition', 'url_redirect', self.gf('django.db.models.fields.TextField')(null=True)) models = { u'auth.group': { 'Meta': {'object_name': 'Group'}, u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '80'}), 'permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'}) }, u'auth.permission': { 'Meta': {'ordering': "(u'content_type__app_label', u'content_type__model', u'codename')", 'unique_together': "((u'content_type', u'codename'),)", 'object_name': 'Permission'}, 'codename': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'content_type': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['contenttypes.ContentType']"}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '50'}) }, u'authenz.cluser': { 'Meta': {'object_name': 'ClUser'}, 'bibtex': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), 'contact_email': ('django.db.models.fields.EmailField', [], {'max_length': '75', 'null': 'True', 'blank': 'True'}), 'date_joined': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}), 'email': ('django.db.models.fields.EmailField', [], {'max_length': '75', 'blank': 'True'}), 'email_on_submission_finished_successfully': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'first_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}), 'groups': ('django.db.models.fields.related.ManyToManyField', [], {'symmetrical': 'False', 'related_name': "u'user_set'", 'blank': 'True', 'to': u"orm['auth.Group']"}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'is_active': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'is_staff': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'is_superuser': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'last_login': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}), 'last_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}), 'method_description': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), 'method_name': ('django.db.models.fields.CharField', [], {'max_length': '20', 'null': 'True', 'blank': 'True'}), 'organization_or_affiliation': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}), 'organizer_direct_message_updates': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'organizer_status_updates': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'participation_status_updates': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'password': ('django.db.models.fields.CharField', [], {'max_length': '128'}), 'project_url': ('django.db.models.fields.URLField', [], {'max_length': '200', 'null': 'True', 'blank': 'True'}), 'publication_url': ('django.db.models.fields.URLField', [], {'max_length': '200', 'null': 'True', 'blank': 'True'}), 'rabbitmq_password': ('django.db.models.fields.CharField', [], {'max_length': '36', 'null': 'True', 'blank': 'True'}), 'rabbitmq_queue_limit': ('django.db.models.fields.PositiveIntegerField', [], {'default': '5', 'blank': 'True'}), 'rabbitmq_username': ('django.db.models.fields.CharField', [], {'max_length': '36', 'null': 'True', 'blank': 'True'}), 'team_members': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), 'team_name': ('django.db.models.fields.CharField', [], {'max_length': '64', 'null': 'True', 'blank': 'True'}), 'user_permissions': ('django.db.models.fields.related.ManyToManyField', [], {'symmetrical': 'False', 'related_name': "u'user_set'", 'blank': 'True', 'to': u"orm['auth.Permission']"}), 'username': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '30'}) }, u'contenttypes.contenttype': { 'Meta': {'ordering': "('name',)", 'unique_together': "(('app_label', 'model'),)", 'object_name': 'ContentType', 'db_table': "'django_content_type'"}, 'app_label': ('django.db.models.fields.CharField', [], {'max_length': '100'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'model': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '100'}) }, u'queues.queue': { 'Meta': {'object_name': 'Queue'}, u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'is_public': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '64'}), 'organizers': ('django.db.models.fields.related.ManyToManyField', [], {'blank': 'True', 'related_name': "'organizers'", 'null': 'True', 'symmetrical': 'False', 'to': u"orm['authenz.ClUser']"}), 'owner': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['authenz.ClUser']"}), 'vhost': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '36', 'blank': 'True'}) }, u'teams.team': { 'Meta': {'unique_together': "(('name', 'competition'),)", 'object_name': 'Team'}, 'allow_requests': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'competition': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['web.Competition']"}), 'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'null': 'True', 'blank': 'True'}), 'creator': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'team_creator'", 'to': u"orm['authenz.ClUser']"}), 'description': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'image': ('django.db.models.fields.files.FileField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'image_url_base': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'last_modified': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}), 'members': ('django.db.models.fields.related.ManyToManyField', [], {'related_name': "'teams'", 'to': u"orm['authenz.ClUser']", 'through': u"orm['teams.TeamMembership']", 'blank': 'True', 'symmetrical': 'False', 'null': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'reason': ('django.db.models.fields.CharField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'status': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['teams.TeamStatus']", 'null': 'True'}) }, u'teams.teammembership': { 'Meta': {'object_name': 'TeamMembership'}, 'end_date': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'is_invitation': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'is_request': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'message': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), 'reason': ('django.db.models.fields.CharField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'start_date': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}), 'status': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['teams.TeamMembershipStatus']", 'null': 'True'}), 'team': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['teams.Team']"}), 'user': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['authenz.ClUser']"}) }, u'teams.teammembershipstatus': { 'Meta': {'object_name': 'TeamMembershipStatus'}, 'codename': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '30'}), 'description': ('django.db.models.fields.CharField', [], {'max_length': '50'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '30'}) }, u'teams.teamstatus': { 'Meta': {'object_name': 'TeamStatus'}, 'codename': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '30'}), 'description': ('django.db.models.fields.CharField', [], {'max_length': '50'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '30'}) }, u'web.competition': { 'Meta': {'ordering': "['end_date']", 'object_name': 'Competition'}, 'admins': ('django.db.models.fields.related.ManyToManyField', [], {'blank': 'True', 'related_name': "'competition_admins'", 'null': 'True', 'symmetrical': 'False', 'to': u"orm['authenz.ClUser']"}), 'allow_organizer_teams': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'allow_public_submissions': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'allow_teams': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'anonymous_leaderboard': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'chahub_data_hash': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), 'chahub_needs_retry': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'chahub_timestamp': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}), 'competition_docker_image': ('django.db.models.fields.CharField', [], {'default': "''", 'max_length': '128', 'blank': 'True'}), 'creator': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'competitioninfo_creator'", 'to': u"orm['authenz.ClUser']"}), 'description': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), 'disallow_leaderboard_modifying': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'enable_detailed_results': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'enable_forum': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'enable_medical_image_viewer': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'enable_per_submission_metadata': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'enable_teams': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'end_date': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}), 'force_submission_to_leaderboard': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'has_registration': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'hide_chart': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'hide_top_three': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'image': ('django.db.models.fields.files.FileField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'image_url_base': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'is_migrating': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'is_migrating_delayed': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'last_modified': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}), 'last_phase_migration': ('django.db.models.fields.PositiveIntegerField', [], {'default': '1'}), 'modified_by': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'competitioninfo_modified_by'", 'to': u"orm['authenz.ClUser']"}), 'original_yaml_file': ('django.db.models.fields.TextField', [], {'default': "''", 'null': 'True', 'blank': 'True'}), 'published': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'queue': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'competitions'", 'null': 'True', 'on_delete': 'models.SET_NULL', 'to': u"orm['queues.Queue']"}), 'require_team_approval': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'reward': ('django.db.models.fields.PositiveIntegerField', [], {'null': 'True', 'blank': 'True'}), 'secret_key': ('django.db.models.fields.CharField', [], {'max_length': '36', 'blank': 'True'}), 'show_datasets_from_yaml': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'start_date': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}), 'teams': ('django.db.models.fields.related.ManyToManyField', [], {'blank': 'True', 'related_name': "'competition_teams'", 'null': 'True', 'symmetrical': 'False', 'to': u"orm['teams.Team']"}), 'title': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'url_redirect': ('django.db.models.fields.URLField', [], {'max_length': '200', 'null': 'True', 'blank': 'True'}) }, u'web.competitiondefbundle': { 'Meta': {'object_name': 'CompetitionDefBundle'}, 'config_bundle': ('django.db.models.fields.files.FileField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'owner': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'owner'", 'to': u"orm['authenz.ClUser']"}), 's3_config_bundle': ('s3direct.fields.S3DirectField', [], {'null': 'True', 'blank': 'True'}) }, u'web.competitiondump': { 'Meta': {'object_name': 'CompetitionDump'}, 'competition': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'dumps'", 'to': u"orm['web.Competition']"}), 'data_file': ('django.db.models.fields.files.FileField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'status': ('django.db.models.fields.CharField', [], {'default': "'Starting'", 'max_length': '64'}), 'timestamp': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}) }, u'web.competitionparticipant': { 'Meta': {'unique_together': "(('user', 'competition'),)", 'object_name': 'CompetitionParticipant'}, 'competition': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'participants'", 'to': u"orm['web.Competition']"}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'reason': ('django.db.models.fields.CharField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'status': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['web.ParticipantStatus']"}), 'user': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'participation'", 'to': u"orm['authenz.ClUser']"}) }, u'web.competitionphase': { 'Meta': {'ordering': "['phasenumber']", 'object_name': 'CompetitionPhase'}, 'auto_migration': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'color': ('django.db.models.fields.CharField', [], {'max_length': '24', 'null': 'True', 'blank': 'True'}), 'competition': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'phases'", 'to': u"orm['web.Competition']"}), 'datasets': ('django.db.models.fields.related.ManyToManyField', [], {'symmetrical': 'False', 'related_name': "'phase'", 'blank': 'True', 'to': u"orm['web.Dataset']"}), 'default_docker_image': ('django.db.models.fields.CharField', [], {'default': "''", 'max_length': '128', 'blank': 'True'}), 'description': ('django.db.models.fields.CharField', [], {'max_length': '1000', 'null': 'True', 'blank': 'True'}), 'disable_custom_docker_image': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'execution_time_limit': ('django.db.models.fields.PositiveIntegerField', [], {'default': '300'}), 'force_best_submission_to_leaderboard': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'ingestion_program': ('django.db.models.fields.files.FileField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'ingestion_program_docker_image': ('django.db.models.fields.CharField', [], {'default': "''", 'max_length': '128', 'blank': 'True'}), 'ingestion_program_organizer_dataset': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'ingestion_program_organizer_dataset'", 'null': 'True', 'on_delete': 'models.SET_NULL', 'to': u"orm['web.OrganizerDataSet']"}), 'input_data': ('django.db.models.fields.files.FileField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'input_data_organizer_dataset': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'input_data_organizer_dataset'", 'null': 'True', 'on_delete': 'models.SET_NULL', 'to': u"orm['web.OrganizerDataSet']"}), 'is_migrated': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'is_scoring_only': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'label': ('django.db.models.fields.CharField', [], {'max_length': '50', 'blank': 'True'}), 'leaderboard_management_mode': ('django.db.models.fields.CharField', [], {'default': "'default'", 'max_length': '50'}), 'max_submissions': ('django.db.models.fields.PositiveIntegerField', [], {'default': '100'}), 'max_submissions_per_day': ('django.db.models.fields.PositiveIntegerField', [], {'default': '999'}), 'phase_never_ends': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'phasenumber': ('django.db.models.fields.PositiveIntegerField', [], {}), 'public_data': ('django.db.models.fields.files.FileField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'public_data_organizer_dataset': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'public_data_organizer_dataset'", 'null': 'True', 'on_delete': 'models.SET_NULL', 'to': u"orm['web.OrganizerDataSet']"}), 'reference_data': ('django.db.models.fields.files.FileField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'reference_data_organizer_dataset': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'reference_data_organizer_dataset'", 'null': 'True', 'on_delete': 'models.SET_NULL', 'to': u"orm['web.OrganizerDataSet']"}), 'scoring_program': ('django.db.models.fields.files.FileField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'scoring_program_docker_image': ('django.db.models.fields.CharField', [], {'default': "''", 'max_length': '128', 'blank': 'True'}), 'scoring_program_organizer_dataset': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'scoring_program_organizer_dataset'", 'null': 'True', 'on_delete': 'models.SET_NULL', 'to': u"orm['web.OrganizerDataSet']"}), 'start_date': ('django.db.models.fields.DateTimeField', [], {}), 'starting_kit': ('django.db.models.fields.files.FileField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'starting_kit_organizer_dataset': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'starting_kit_organizer_dataset'", 'null': 'True', 'on_delete': 'models.SET_NULL', 'to': u"orm['web.OrganizerDataSet']"}) }, u'web.competitionsubmission': { 'Meta': {'unique_together': "(('submission_number', 'phase', 'participant'),)", 'object_name': 'CompetitionSubmission'}, 'bibtex': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), 'chahub_data_hash': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), 'chahub_needs_retry': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'chahub_timestamp': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}), 'completed_at': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}), 'coopetition_file': ('django.db.models.fields.files.FileField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'description': ('django.db.models.fields.CharField', [], {'max_length': '256', 'blank': 'True'}), 'detailed_results_file': ('django.db.models.fields.files.FileField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'dislike_count': ('django.db.models.fields.IntegerField', [], {'default': '0'}), 'docker_image': ('django.db.models.fields.CharField', [], {'default': "''", 'max_length': '128', 'blank': 'True'}), 'download_count': ('django.db.models.fields.IntegerField', [], {'default': '0'}), 'exception_details': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), 'execution_key': ('django.db.models.fields.TextField', [], {'default': "''", 'blank': 'True'}), 'file': ('django.db.models.fields.files.FileField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'file_url_base': ('django.db.models.fields.CharField', [], {'max_length': '2000', 'blank': 'True'}), 'history_file': ('django.db.models.fields.files.FileField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'ingestion_program_stderr_file': ('django.db.models.fields.files.FileField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'ingestion_program_stdout_file': ('django.db.models.fields.files.FileField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'inputfile': ('django.db.models.fields.files.FileField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'is_migrated': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'is_public': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'like_count': ('django.db.models.fields.IntegerField', [], {'default': '0'}), 'method_description': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), 'method_name': ('django.db.models.fields.CharField', [], {'max_length': '20', 'null': 'True', 'blank': 'True'}), 'organization_or_affiliation': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}), 'output_file': ('django.db.models.fields.files.FileField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'participant': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'submissions'", 'to': u"orm['web.CompetitionParticipant']"}), 'phase': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'submissions'", 'to': u"orm['web.CompetitionPhase']"}), 'prediction_output_file': ('django.db.models.fields.files.FileField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'prediction_runfile': ('django.db.models.fields.files.FileField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'prediction_stderr_file': ('django.db.models.fields.files.FileField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'prediction_stdout_file': ('django.db.models.fields.files.FileField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'private_output_file': ('django.db.models.fields.files.FileField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'project_url': ('django.db.models.fields.URLField', [], {'max_length': '200', 'null': 'True', 'blank': 'True'}), 'publication_url': ('django.db.models.fields.URLField', [], {'max_length': '200', 'null': 'True', 'blank': 'True'}), 'queue_name': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), 'readable_filename': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), 'runfile': ('django.db.models.fields.files.FileField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 's3_file': ('s3direct.fields.S3DirectField', [], {'null': 'True', 'blank': 'True'}), 'scores_file': ('django.db.models.fields.files.FileField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'secret': ('django.db.models.fields.CharField', [], {'default': "''", 'max_length': '128', 'blank': 'True'}), 'started_at': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}), 'status': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['web.CompetitionSubmissionStatus']"}), 'status_details': ('django.db.models.fields.CharField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'stderr_file': ('django.db.models.fields.files.FileField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'stdout_file': ('django.db.models.fields.files.FileField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'submission_number': ('django.db.models.fields.PositiveIntegerField', [], {'default': '0'}), 'submitted_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}), 'team': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "'team'", 'null': 'True', 'to': u"orm['teams.Team']"}), 'team_name': ('django.db.models.fields.CharField', [], {'max_length': '64', 'null': 'True', 'blank': 'True'}), 'when_made_public': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}), 'when_unmade_public': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}) }, u'web.competitionsubmissionmetadata': { 'Meta': {'object_name': 'CompetitionSubmissionMetadata'}, 'beginning_cpu_usage': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), 'beginning_swap_memory_usage': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), 'beginning_virtual_memory_usage': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), 'end_cpu_usage': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), 'end_swap_memory_usage': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), 'end_virtual_memory_usage': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), 'hostname': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'ingestion_program_duration': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), 'is_predict': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'is_scoring': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'processes_running_in_temp_dir': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), 'submission': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'metadatas'", 'to': u"orm['web.CompetitionSubmission']"}) }, u'web.competitionsubmissionstatus': { 'Meta': {'object_name': 'CompetitionSubmissionStatus'}, 'codename': ('django.db.models.fields.SlugField', [], {'unique': 'True', 'max_length': '20'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '20'}) }, u'web.contentcategory': { 'Meta': {'object_name': 'ContentCategory'}, 'codename': ('django.db.models.fields.SlugField', [], {'unique': 'True', 'max_length': '100'}), 'content_limit': ('django.db.models.fields.PositiveIntegerField', [], {'default': '1'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'is_menu': ('django.db.models.fields.BooleanField', [], {'default': 'True'}), 'level': ('django.db.models.fields.PositiveIntegerField', [], {'db_index': 'True'}), 'lft': ('django.db.models.fields.PositiveIntegerField', [], {'db_index': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'parent': ('mptt.fields.TreeForeignKey', [], {'blank': 'True', 'related_name': "'children'", 'null': 'True', 'to': u"orm['web.ContentCategory']"}), 'rght': ('django.db.models.fields.PositiveIntegerField', [], {'db_index': 'True'}), 'tree_id': ('django.db.models.fields.PositiveIntegerField', [], {'db_index': 'True'}), 'visibility': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['web.ContentVisibility']"}) }, u'web.contentvisibility': { 'Meta': {'object_name': 'ContentVisibility'}, 'classname': ('django.db.models.fields.CharField', [], {'max_length': '30', 'null': 'True', 'blank': 'True'}), 'codename': ('django.db.models.fields.SlugField', [], {'unique': 'True', 'max_length': '20'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '20'}) }, u'web.dataset': { 'Meta': {'ordering': "['number']", 'object_name': 'Dataset'}, 'creator': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'datasets'", 'to': u"orm['authenz.ClUser']"}), 'datafile': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['web.ExternalFile']"}), 'description': ('django.db.models.fields.TextField', [], {}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '50'}), 'number': ('django.db.models.fields.PositiveIntegerField', [], {'default': '1'}) }, u'web.defaultcontentitem': { 'Meta': {'object_name': 'DefaultContentItem'}, 'category': ('mptt.fields.TreeForeignKey', [], {'to': u"orm['web.ContentCategory']"}), 'codename': ('django.db.models.fields.SlugField', [], {'unique': 'True', 'max_length': '100'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'initial_visibility': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['web.ContentVisibility']"}), 'label': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'rank': ('django.db.models.fields.IntegerField', [], {'default': '0'}), 'required': ('django.db.models.fields.BooleanField', [], {'default': 'False'}) }, u'web.externalfile': { 'Meta': {'object_name': 'ExternalFile'}, 'creator': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['authenz.ClUser']"}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'source_address_info': ('django.db.models.fields.CharField', [], {'max_length': '200', 'blank': 'True'}), 'source_url': ('django.db.models.fields.URLField', [], {'max_length': '200'}), 'type': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['web.ExternalFileType']"}) }, u'web.externalfilesource': { 'Meta': {'object_name': 'ExternalFileSource'}, 'codename': ('django.db.models.fields.SlugField', [], {'unique': 'True', 'max_length': '50'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '50'}), 'service_url': ('django.db.models.fields.URLField', [], {'max_length': '200', 'null': 'True', 'blank': 'True'}) }, u'web.externalfiletype': { 'Meta': {'object_name': 'ExternalFileType'}, 'codename': ('django.db.models.fields.SlugField', [], {'unique': 'True', 'max_length': '20'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '20'}) }, u'web.organizerdataset': { 'Meta': {'object_name': 'OrganizerDataSet'}, 'data_file': ('django.db.models.fields.files.FileField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'description': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}), 'full_name': ('django.db.models.fields.TextField', [], {'default': "''"}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'key': ('django.db.models.fields.CharField', [], {'max_length': '36', 'blank': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '255'}), 'sub_data_files': ('django.db.models.fields.related.ManyToManyField', [], {'symmetrical': 'False', 'to': u"orm['web.OrganizerDataSet']", 'null': 'True', 'blank': 'True'}), 'type': ('django.db.models.fields.CharField', [], {'default': "'None'", 'max_length': '64'}), 'uploaded_by': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['authenz.ClUser']"}) }, u'web.page': { 'Meta': {'ordering': "['category', 'rank']", 'unique_together': "(('label', 'category', 'container'),)", 'object_name': 'Page'}, 'category': ('mptt.fields.TreeForeignKey', [], {'to': u"orm['web.ContentCategory']"}), 'codename': ('django.db.models.fields.SlugField', [], {'max_length': '100'}), 'competition': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'pages'", 'null': 'True', 'to': u"orm['web.Competition']"}), 'container': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'pages'", 'to': u"orm['web.PageContainer']"}), 'defaults': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['web.DefaultContentItem']", 'null': 'True', 'blank': 'True'}), 'html': ('django.db.models.fields.TextField', [], {'blank': 'True'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'label': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'markup': ('django.db.models.fields.TextField', [], {'blank': 'True'}), 'rank': ('django.db.models.fields.IntegerField', [], {'default': '0'}), 'title': ('django.db.models.fields.CharField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'visibility': ('django.db.models.fields.BooleanField', [], {'default': 'True'}) }, u'web.pagecontainer': { 'Meta': {'unique_together': "(('object_id', 'content_type'),)", 'object_name': 'PageContainer'}, 'content_type': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['contenttypes.ContentType']"}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '200', 'blank': 'True'}), 'object_id': ('django.db.models.fields.PositiveIntegerField', [], {'db_index': 'True'}) }, u'web.participantstatus': { 'Meta': {'object_name': 'ParticipantStatus'}, 'codename': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '30'}), 'description': ('django.db.models.fields.CharField', [], {'max_length': '50'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '30'}) }, u'web.phaseleaderboard': { 'Meta': {'object_name': 'PhaseLeaderBoard'}, u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'phase': ('django.db.models.fields.related.OneToOneField', [], {'related_name': "'board'", 'unique': 'True', 'to': u"orm['web.CompetitionPhase']"}) }, u'web.phaseleaderboardentry': { 'Meta': {'unique_together': "(('board', 'result'),)", 'object_name': 'PhaseLeaderBoardEntry'}, 'board': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'entries'", 'to': u"orm['web.PhaseLeaderBoard']"}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'result': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'leaderboard_entry_result'", 'to': u"orm['web.CompetitionSubmission']"}) }, u'web.submissioncomputedscore': { 'Meta': {'object_name': 'SubmissionComputedScore'}, u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'operation': ('django.db.models.fields.CharField', [], {'max_length': '10'}), 'scoredef': ('django.db.models.fields.related.OneToOneField', [], {'related_name': "'computed_score'", 'unique': 'True', 'to': u"orm['web.SubmissionScoreDef']"}) }, u'web.submissioncomputedscorefield': { 'Meta': {'object_name': 'SubmissionComputedScoreField'}, 'computed': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'fields'", 'to': u"orm['web.SubmissionComputedScore']"}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'scoredef': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['web.SubmissionScoreDef']"}) }, u'web.submissionresultgroup': { 'Meta': {'ordering': "['ordering']", 'object_name': 'SubmissionResultGroup'}, 'competition': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['web.Competition']"}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'key': ('django.db.models.fields.CharField', [], {'max_length': '50'}), 'label': ('django.db.models.fields.CharField', [], {'max_length': '50'}), 'ordering': ('django.db.models.fields.PositiveIntegerField', [], {'default': '1'}), 'phases': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['web.CompetitionPhase']", 'through': u"orm['web.SubmissionResultGroupPhase']", 'symmetrical': 'False'}) }, u'web.submissionresultgroupphase': { 'Meta': {'unique_together': "(('group', 'phase'),)", 'object_name': 'SubmissionResultGroupPhase'}, 'group': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['web.SubmissionResultGroup']"}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'phase': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['web.CompetitionPhase']"}) }, u'web.submissionscore': { 'Meta': {'unique_together': "(('result', 'scoredef'),)", 'object_name': 'SubmissionScore'}, u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'result': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "'scores'", 'to': u"orm['web.CompetitionSubmission']"}), 'scoredef': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['web.SubmissionScoreDef']"}), 'value': ('django.db.models.fields.DecimalField', [], {'max_digits': '20', 'decimal_places': '10'}) }, u'web.submissionscoredef': { 'Meta': {'unique_together': "(('key', 'competition'),)", 'object_name': 'SubmissionScoreDef'}, 'competition': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['web.Competition']"}), 'computed': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'groups': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['web.SubmissionResultGroup']", 'through': u"orm['web.SubmissionScoreDefGroup']", 'symmetrical': 'False'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'key': ('django.db.models.fields.SlugField', [], {'max_length': '50'}), 'label': ('django.db.models.fields.CharField', [], {'max_length': '50'}), 'numeric_format': ('django.db.models.fields.CharField', [], {'max_length': '20', 'null': 'True', 'blank': 'True'}), 'ordering': ('django.db.models.fields.PositiveIntegerField', [], {'default': '1'}), 'selection_default': ('django.db.models.fields.IntegerField', [], {'default': '0'}), 'show_rank': ('django.db.models.fields.BooleanField', [], {'default': 'False'}), 'sorting': ('django.db.models.fields.SlugField', [], {'default': "'asc'", 'max_length': '20'}) }, u'web.submissionscoredefgroup': { 'Meta': {'unique_together': "(('scoredef', 'group'),)", 'object_name': 'SubmissionScoreDefGroup'}, 'group': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['web.SubmissionResultGroup']"}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'scoredef': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['web.SubmissionScoreDef']"}) }, u'web.submissionscoreset': { 'Meta': {'unique_together': "(('key', 'competition'),)", 'object_name': 'SubmissionScoreSet'}, 'competition': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['web.Competition']"}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'key': ('django.db.models.fields.CharField', [], {'max_length': '50'}), 'label': ('django.db.models.fields.CharField', [], {'max_length': '50'}), 'level': ('django.db.models.fields.PositiveIntegerField', [], {'db_index': 'True'}), 'lft': ('django.db.models.fields.PositiveIntegerField', [], {'db_index': 'True'}), 'ordering': ('django.db.models.fields.PositiveIntegerField', [], {'default': '1'}), 'parent': ('mptt.fields.TreeForeignKey', [], {'blank': 'True', 'related_name': "'children'", 'null': 'True', 'to': u"orm['web.SubmissionScoreSet']"}), 'rght': ('django.db.models.fields.PositiveIntegerField', [], {'db_index': 'True'}), 'scoredef': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['web.SubmissionScoreDef']", 'null': 'True', 'blank': 'True'}), 'tree_id': ('django.db.models.fields.PositiveIntegerField', [], {'db_index': 'True'}) } } complete_apps = ['web']

AIMultimediaLab/AI4Media-EaaS-prototype-Py2-public

codalab/apps/web/migrations/0082_auto__chg_field_competition_url_redirect.py

Python

# Python program for implementation of Quicksort Sort # This function takes last element as pivot, places # the pivot element at its correct position in sorted # array, and places all smaller (smaller than pivot) # to left of pivot and all greater elements to right # of pivot def partition(arr, low, high): i = (low - 1) # index of smaller element pivot = arr[high] # pivot for j in range(low, high): # If current element is smaller than or # equal to pivot if arr[j] <= pivot: # increment index of smaller element i = i + 1 arr[i], arr[j] = arr[j], arr[i] arr[i + 1], arr[high] = arr[high], arr[i + 1] return (i + 1) # The main function that implements QuickSort # arr[] --> Array to be sorted, # low --> Starting index, # high --> Ending index # Function to do Quick sort def quickSort(arr, low, high): if len(arr) == 1: return arr if low < high: # pi is partitioning index, arr[p] is now # at right place pi = partition(arr, low, high) # Separately sort elements before # partition and after partition quickSort(arr, low, pi - 1) quickSort(arr, pi + 1, high) if __name__ == "__main__": # Driver code to test above arr = [10, 7, 8, 9, 1, 5] n = len(arr) quickSort(arr, 0, n - 1) print("Sorted array is:") for i in range(n): print("%d" % arr[i])

goldy1992/algorithms

quicksort/quicksort.py

Python

''' Inter-coder agreement statistic Fleiss' Pi. .. moduleauthor:: Chris Fournier <chris.m.fournier@gmail.com> ''' from __future__ import absolute_import, division from decimal import Decimal from segeval.agreement import __fnc_metric__, __actual_agreement_linear__ def __fleiss_pi_linear__(dataset, **kwargs): ''' Calculates Fleiss' :math:`\pi` (or multi-:math:`\pi`), originally proposed in [Fleiss1971]_, and is equivalent to Siegel and Castellan's :math:`K` [SiegelCastellan1988]_. For 2 coders, this is equivalent to Scott's :math:`\pi` [Scott1955]_. ''' metric_kwargs = dict(kwargs) metric_kwargs['return_parts'] = True # Arguments return_parts = kwargs['return_parts'] # Check that there are an equal number of items for each coder if len(set([len(coder_segs.values()) for coder_segs in dataset.values()])) != 1: raise Exception('Unequal number of items contained.') # Initialize totals all_numerators, all_denominators, _, coders_boundaries = \ __actual_agreement_linear__(dataset, **metric_kwargs) # Calculate Aa A_a = Decimal(sum(all_numerators)) / sum(all_denominators) # Calculate Ae p_e_segs = list() for boundaries_info in coders_boundaries.values(): for item in boundaries_info: boundaries, total_boundaries = item p_e_seg = Decimal(boundaries) / total_boundaries p_e_segs.append(p_e_seg) # Calculate P_e_seg P_e_seg = Decimal(sum(p_e_segs)) / len(p_e_segs) A_e = (P_e_seg ** 2) # Calculate pi pi = (A_a - A_e) / (Decimal('1') - A_e) # Return if return_parts: return A_a, A_e else: return pi def fleiss_pi_linear(dataset, **kwargs): ''' Calculates Fleiss' :math:`\pi` (or multi-:math:`\pi`), originally proposed in [Fleiss1971]_, and is equivalent to Siegel and Castellan's :math:`K` [SiegelCastellan1988]_. For 2 coders, this is equivalent to Scott's :math:`\pi` [Scott1955]_. ''' return __fnc_metric__(__fleiss_pi_linear__, dataset, **kwargs)

cfournie/segmentation.evaluation

segeval/agreement/pi.py

Python

class Solution: def knightProbability(self, N: int, K: int, r: int, c: int) -> float: memo = {} def dfs(i, j, p, k): if 0 <= i < N and 0 <= j < N and k < K: sm = 0 for x, y in ((-1, -2), (-2, -1), (-2, 1), (-1, 2), (1, 2), (2, 1), (2, -1), (1, -2)): if (i + x, j + y, k) not in memo: memo[(i + x, j + y, k)] = dfs(i + x, j + y, p / 8, k + 1) sm += memo[(i + x, j + y, k)] return sm else: return 0 <= i < N and 0 <= j < N and p or 0 return dfs(r, c, 1, 0)

nilax97/leetcode-solutions

solutions/Knight Probability in Chessboard/solution.py

Python

import torch import torch.nn as nn from torch.autograd import Variable from torchvision import models import torch.nn.functional as F import math import torch.utils.model_zoo as model_zoo nonlinearity = nn.ReLU class EncoderBlock(nn.Module): def __init__(self, inchannel, outchannel, stride): super().__init__() self.c1=nn.Conv2d(inchannel, outchannel, 3, stride, 1, bias=False) self.bn1=nn.BatchNorm2d(outchannel) self.re1=nn.ReLU(inplace=True) self.c2=nn.Conv2d(outchannel, outchannel, 3, 1, 1, bias=False) self.bn2=nn.BatchNorm2d(outchannel) self.re2=nn.ReLU(inplace=True) def forward(self, x): x = self.c1(x) x = self.bn1(x) x = self.re1(x) x = self.c2(x) x = self.bn2(x) x = self.re2(x) return x class EncoderBlock0(nn.Module): def __init__(self, in_channels, n_filters): super().__init__() # B, C, H, W -> B, C/4, H, W self.conv1 = nn.Conv2d(in_channels, in_channels // 4, 1) self.norm1 = nn.BatchNorm2d(in_channels // 4) self.relu1 = nonlinearity(inplace=True) # B, C/4, H, W -> B, C/4, H, W self.pool = nn.MaxPool2d(2, 2)# self.norm2 = nn.BatchNorm2d(in_channels // 4) self.relu2 = nonlinearity(inplace=True) # B, C/4, H, W -> B, C, H, W self.conv3 = nn.Conv2d(in_channels // 4, n_filters, 1) self.norm3 = nn.BatchNorm2d(n_filters) self.relu3 = nonlinearity(inplace=True) def forward(self, x): x = self.conv1(x) x = self.norm1(x) x = self.relu1(x) x = self.pool(x) x = self.norm2(x) x = self.relu2(x) x = self.conv3(x) x = self.norm3(x) x = self.relu3(x) return x class DecoderBlock(nn.Module): def __init__(self, in_channels, n_filters): super().__init__() # B, C, H, W -> B, C/4, H, W self.conv1 = nn.Conv2d(in_channels, in_channels // 4, 1) self.norm1 = nn.BatchNorm2d(in_channels // 4) self.relu1 = nonlinearity(inplace=True) # B, C/4, H, W -> B, C/4, H, W self.deconv2 = nn.ConvTranspose2d(in_channels // 4, in_channels // 4, 3, stride=2, padding=1, output_padding=1) self.norm2 = nn.BatchNorm2d(in_channels // 4) self.relu2 = nonlinearity(inplace=True) # B, C/4, H, W -> B, C, H, W self.conv3 = nn.Conv2d(in_channels // 4, n_filters, 1) self.norm3 = nn.BatchNorm2d(n_filters) self.relu3 = nonlinearity(inplace=True) def forward(self, x): x = self.conv1(x) x = self.norm1(x) x = self.relu1(x) x = self.deconv2(x) x = self.norm2(x) x = self.relu2(x) x = self.conv3(x) x = self.norm3(x) x = self.relu3(x) return x class ChannelSE(nn.Module): def __init__(self,inchannel): super().__init__() self.lin1=torch.nn.Linear(inchannel, inchannel//2) self.lin2=torch.nn.Linear(inchannel//2, inchannel) self.c=inchannel def forward(self,x): #_,c,h,w=x.size #print(c) #print(h) #print(w) m=torch.mean(torch.mean(x,dim=2,keepdim=True),dim=3,keepdim=True) m = m.view(m.size(0), -1) m=self.lin1(m) m=nn.ReLU()(m) m=self.lin2(m) m=nn.Sigmoid()(m) m = m.view(m.size(0), self.c,1,1) x=m*x#torch.matmul(m,x) return x class SpatialSE(nn.Module): def __init__(self,inchannel): super().__init__() self.conv=torch.nn.Conv2d(inchannel,1,kernel_size=1,stride=1) def forward(self,x): #_,c,h,w=x.size #print(c) #print(h) #print(w) m = self.conv(x) m=nn.Sigmoid()(m) x=m*x#torch.matmul(m,x) return x class DecoderBlockv(nn.Module): def __init__(self, in_channels, n_filters): super().__init__() # B, C, H, W -> B, C/4, H, W self.conv1 = nn.Conv2d(in_channels, in_channels // 4, 1) self.norm1 = nn.BatchNorm2d(in_channels // 4) self.relu1 = nonlinearity(inplace=True) # B, C/4, H, W -> B, C/4, H, W self.deconv2 = nn.ConvTranspose2d(in_channels // 4, in_channels // 4, 3, stride=2, padding=1, output_padding=1) self.norm2 = nn.BatchNorm2d(in_channels // 4) self.relu2 = nonlinearity(inplace=True) # B, C/4, H, W -> B, C, H, W self.conv3 = nn.Conv2d(in_channels // 4, n_filters, 1) self.norm3 = nn.BatchNorm2d(n_filters) self.relu3 = nonlinearity(inplace=True) self.cSE = ChannelSE(n_filters) self.sSE = SpatialSE(n_filters) def forward(self, x): x = self.conv1(x) x = self.norm1(x) x = self.relu1(x) x = self.deconv2(x) x = self.norm2(x) x = self.relu2(x) x = self.conv3(x) x = self.norm3(x) x = self.relu3(x) x = self.cSE(x) + self.sSE(x) return x class ConvUp(nn.Module): def __init__(self, in_channels, n_filters): super().__init__() self.upsample = nn.Upsample(scale_factor=2,mode='bilinear') self.conv1 = nn.Conv2d(in_channels, n_filters, 3, padding = 1) self.norm1 = nn.BatchNorm2d(n_filters) self.relu1 = nonlinearity(inplace=True) def forward(self, x): x = self.upsample(x) x = self.conv1(x) x = self.norm1(x) x = self.relu1(x) return x class ConscSE(nn.Module): def __init__(self, n_filters): super().__init__() self.cSE = ChannelSE(n_filters) self.sSE = SpatialSE(n_filters) def forward(self, x): x = self.cSE(x) + self.sSE(x) return x class DecoderBlockup(nn.Module): def __init__(self, in_channels, n_filters): super().__init__() # B, C, H, W -> B, C/4, H, W self.conv1 = nn.Conv2d(in_channels, in_channels // 4, 1) self.norm1 = nn.BatchNorm2d(in_channels // 4) self.relu1 = nonlinearity(inplace=True) # B, C/4, H, W -> B, C/4, H, W self.deconv2 = ConvUp(in_channels // 4, in_channels // 4) self.norm2 = nn.BatchNorm2d(in_channels // 4) self.relu2 = nonlinearity(inplace=True) # B, C/4, H, W -> B, C, H, W self.conv3 = nn.Conv2d(in_channels // 4, n_filters, 1) self.norm3 = nn.BatchNorm2d(n_filters) self.relu3 = nonlinearity(inplace=True) self.cSE = ChannelSE(n_filters) self.sSE = SpatialSE(n_filters) def forward(self, x): x = self.conv1(x) x = self.norm1(x) x = self.relu1(x) x = self.deconv2(x) x = self.norm2(x) x = self.relu2(x) x = self.conv3(x) x = self.norm3(x) x = self.relu3(x) x = self.cSE(x) + self.sSE(x) return x class DecoderBlock23(nn.Module): def __init__(self, in_channels, n_filters, scal=4): super().__init__() self.up = nn.Upsample(scale_factor=2,mode='bilinear') # B, C, H, W -> B, C/4, H, W self.conv1 = nn.Conv2d(in_channels, in_channels // scal, 1) self.norm1 = nn.BatchNorm2d(in_channels // scal) self.relu1 = nonlinearity(inplace=True) # B, C/4, H, W -> B, C, H, W self.conv2 = nn.Conv2d(in_channels // scal, n_filters, 1) self.norm2 = nn.BatchNorm2d(n_filters) self.relu2 = nonlinearity(inplace=True) self.cSE = ChannelSE(n_filters) self.sSE = SpatialSE(n_filters) def forward(self, x): x = self.up(x) x = self.conv1(x) x = self.norm1(x) x = self.relu1(x) x = self.conv2(x) x = self.norm2(x) x = self.relu2(x) #x = self.cSE(x) + self.sSE(x) return x class Upscale: transposed_conv = 0 upsample_bilinear = 1 pixel_shuffle = 2 class BasicDecoderBlock(nn.Module): def __init__(self, in_channels, middle_channels, out_channels, conv_size=3, upscale=Upscale.transposed_conv): super().__init__() padding = 0 if conv_size == 3: padding = 1 self.layer1 = nn.Sequential( nn.Conv2d(in_channels, middle_channels, conv_size, padding=padding), nn.BatchNorm2d(middle_channels), nn.ReLU(inplace=True) ) last_conv_channels = middle_channels if upscale == Upscale.transposed_conv: self.layer2 = nn.Sequential( nn.ConvTranspose2d(middle_channels, middle_channels, 3, stride=2, padding=1, output_padding=1), nn.BatchNorm2d(middle_channels), nn.ReLU(inplace=True) ) elif upscale == Upscale.upsample_bilinear: self.layer2 = nn.Upsample(scale_factor=2) else: self.layer2 = nn.PixelShuffle(upscale_factor=2) last_conv_channels = middle_channels // 4 self.layer3 = nn.Sequential( nn.Conv2d(last_conv_channels, out_channels, conv_size, padding=padding), nn.BatchNorm2d(out_channels), nn.ReLU(inplace=True) ) def forward(self, x): x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) return x class UnetBNDecoderBlock(nn.Module): def __init__(self, in_channels, middle_channels, out_channels, upscale=Upscale.upsample_bilinear): super().__init__() self.layer = nn.Sequential( nn.Upsample(scale_factor=2), nn.Conv2d(in_channels, out_channels, 3, padding=1), nn.BatchNorm2d(out_channels), nn.ReLU(inplace=True) ) def forward(self, x): return self.layer(x) class LinkNet34a(nn.Module): def __init__(self, num_classes, num_channels=3): super().__init__() assert num_channels == 3, "num channels not used now. to use changle first conv layer to support num channels other then 3" filters = [64, 128, 256, 512] resnet = models.resnet34(pretrained=True) self.firstconv = resnet.conv1 self.firstbn = resnet.bn1 self.firstrelu = resnet.relu self.firstmaxpool = resnet.maxpool self.encoder1 = resnet.layer1 self.encoder2 = resnet.layer2 self.encoder3 = resnet.layer3 self.encoder4 = resnet.layer4 # Center self.center = nn.Sequential( nn.MaxPool2d(2, 2), nn.Conv2d(filters[3], filters[1], 3, padding=1), nn.BatchNorm2d(filters[1]), nn.ReLU(inplace=True) ) # Decoder self.decoder5 = UnetBNDecoderBlock(filters[1],filters[2]//4, filters[2])# self.conv5=nn.Conv2d(256+512,256,1) self.decoder4 = UnetBNDecoderBlock(filters[2],filters[2]//4, filters[1])#DecoderBlock(filters[3], filters[2]) self.conv4=nn.Conv2d(128+256,256,1) self.decoder3 = UnetBNDecoderBlock(filters[2],filters[2]//4, filters[0])#DecoderBlock(filters[2], filters[1]) self.conv3=nn.Conv2d(64+128,128,1) self.decoder2 = UnetBNDecoderBlock(filters[1],filters[1]//4, filters[0])#DecoderBlock(filters[1], filters[0]) self.conv2=nn.Conv2d(128,64,1) #self.decoder1 = UnetBNDecoderBlock(filters[0],filters[0]//4, filters[0])#DecoderBlock(filters[0], filters[0]) # Final Classifier self.finaldeconv1 = UnetBNDecoderBlock(filters[0],filters[0]//4, filters[0])#ConvUp(filters[0], filters[0]) # Final Classifier self.logit = nn.Sequential( nn.Conv2d(64, 64, 3, padding=1), nn.BatchNorm2d(64), nonlinearity(inplace=True), nn.Conv2d(64, 1, 1), ) # noinspection PyCallingNonCallable def forward(self, x): # Encoder x = x.float() x = self.firstconv(x) x = self.firstbn(x) x = self.firstrelu(x) #x = self.firstmaxpool(x) e1 = self.encoder1(x) e2 = self.encoder2(e1) e3 = self.encoder3(e2) e4 = self.encoder4(e3) ############################ e5 = self.center(e4) d5 = torch.cat([self.decoder5(e5) , e4], 1)#concat([self.decoder5(e5) , e4]) d5 = self.conv5(d5) ######################### d4 = torch.cat([self.decoder4(d5) , e3], 1)#concat([self.decoder5(e5) , e4]) d4 = self.conv4(d4) # d4 = e3 #d3 = self.decoder3(d4) + e2 #print(e2.shape) d3 = torch.cat([self.decoder3(d4) , e2], 1)#concat([self.decoder5(e5) , e4]) #print(d3.shape) d3 = self.conv3(d3) #d2 = self.decoder2(d3) + e1 d2 = torch.cat([self.decoder2(d3) , e1], 1)#concat([self.decoder5(e5) , e4]) d2 = self.conv2(d2) #d1 = self.decoder1(d2) # Final Classification f = self.finaldeconv1(d2) #f = self.finalrelu1(f) f = self.logit(f) return f class DecoderBlockH(nn.Module): def __init__(self, in_channels,channels, n_filters): super().__init__() self.up = nn.Upsample(scale_factor=2,mode='bilinear') # B, C, H, W -> B, C/4, H, W self.conv1 = nn.Conv2d(in_channels, channels, 3, padding=1) self.norm1 = nn.BatchNorm2d(channels) self.relu1 = nonlinearity(inplace=True) # B, C/4, H, W -> B, C, H, W self.conv2 = nn.Conv2d(channels, n_filters, 3, padding=1) self.norm2 = nn.BatchNorm2d(n_filters) self.relu2 = nonlinearity(inplace=True) #self.cSE = ChannelSE(n_filters) #self.sSE = SpatialSE(n_filters) def forward(self, x, e=None): x = self.up(x) if e is not None: x = torch.cat([x, e], 1) x = self.conv1(x) x = self.norm1(x) x = self.relu1(x) x = self.conv2(x) x = self.norm2(x) x = self.relu2(x) #x = self.cSE(x) + self.sSE(x) return x class ConvBn2d(nn.Module): def __init__(self, in_channels, out_channels, kernel_size=3, padding=1): super().__init__() self.layer = nn.Sequential( #nn.Upsample(scale_factor=2, mode='bilinear'), nn.Conv2d(in_channels, out_channels, kernel_size, padding), nn.BatchNorm2d(out_channels), #nn.ReLU(inplace=True) ) def forward(self, x): return self.layer(x) class Decoder3(nn.Module): def __init__(self, in_channels,res_channels, channels, n_filters): super().__init__() self.up = nn.Upsample(scale_factor=2, mode='bilinear') # B, C, H, W -> B, C/4, H, W self.conv1 = nn.Conv2d(in_channels+res_channels, channels, 3, padding=1) self.norm1 = nn.BatchNorm2d(channels) self.relu1 = nonlinearity(inplace=True) # B, C/4, H, W -> B, C, H, W self.conv2 = nn.Conv2d(channels, n_filters, 3, padding=1) self.norm2 = nn.BatchNorm2d(n_filters) self.relu2 = nonlinearity(inplace=True) self.SCSE = SCSEBlock(n_filters)#ChannelSE(n_filters) #self.sSE = SpatialSE(n_filters) def forward(self, x, e=None): x = self.up(x) if e is not None: x = torch.cat([x, e], 1) x = self.conv1(x) x = self.norm1(x) x = self.relu1(x) x = self.conv2(x) x = self.norm2(x) x = self.relu2(x) x = self.SCSE(x)# + self.sSE(x) return x class DenseNet34(nn.Module): def __init__(self ): super().__init__() #super(Net,self).__init__() filters = [64, 128, 256, 512] self.resnet = models.resnet34(pretrained=True)#ResNet(BasicBlock, [3, 4, 6, 3], num_classes=1 ) self.encoder1 = nn.Sequential( nn.Conv2d(3, 64, kernel_size=7, stride=1, padding=3, bias=False), nn.BatchNorm2d(64), nn.ReLU(inplace=True), ) self.encoder2 = nn.Sequential( nn.MaxPool2d(kernel_size=2, stride=2), self.resnet.layer1, ) self.encoder3 = self.resnet.layer2 self.encoder4 = self.resnet.layer3 self.encoder5 = self.resnet.layer4 self.center = nn.Sequential( ConvBn2d( 512, 256, kernel_size=3, padding=1), nn.ReLU(inplace=True), ConvBn2d( 256, 256, kernel_size=3, padding=1), nn.ReLU(inplace=True), ) ###################################################################### #self.decoder5 = Decoder3(256, 512, 512, 64) #self.decoder4 = Decoder3( 64, 256, 256, 64) #self.decoder3 = Decoder3( 64, 128, 128, 64) #self.decoder2 = Decoder3( 64, 64, 64, 64) #self.decoder1 = Decoder3( 64, 64, 32, 64) self.decoder5 = DecoderBlockH(filters[3]+filters[2],filters[2], 64) #self.conv5=nn.Conv2d(64+512,64,1)#before or after SE? self.se5=SCSEBlock(64) self.decoder4 = DecoderBlockH(filters[2]+64, filters[1], 64) #self.conv4=nn.Conv2d(64+256,64,1) self.se4=SCSEBlock(64) self.decoder3 = DecoderBlockH(filters[1]+64, filters[1], 64) #self.conv3=nn.Conv2d(64+128,64,1) self.se3=SCSEBlock(64) self.decoder2 = DecoderBlockH(filters[0]+64, filters[0], 64) #self.conv2=nn.Conv2d(64+64,64,1) self.se2=SCSEBlock(64) self.decoder1 = DecoderBlockH(filters[0], filters[0]//2, 64) self.se1=SCSEBlock(64) ############################################################################## self.fuse_pixel = nn.Sequential( nn.Conv2d(320, 64, kernel_size=3, padding=1), ) self.logit_pixel = nn.Sequential( #nn.Conv2d(320, 64, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d( 64, 1, kernel_size=1, padding=0), ) self.logit_image = nn.Sequential( #nn.Linear(512, 128), nn.ReLU(inplace=True), #nn.Linear(128, 1), nn.Linear(64, 1), ) self.fuse_image = nn.Sequential( nn.Linear(512, 64), #nn.ReLU(inplace=True), #nn.Linear(128, 1), ) self.fuse = nn.Sequential( #nn.BatchNorm2d(128), nn.ReLU(inplace=True), #nn.Conv2d(128, 64, kernel_size=1, padding=0), #nn.BatchNorm2d(64), #nn.ReLU(inplace=True), ) self.logit = nn.Sequential( nn.Conv2d(128, 1, kernel_size=1, padding=0), #nn.ReLU(inplace=True), #nn.Conv2d( 64, 1, kernel_size=1, padding=0), ) def forward(self, x): batch_size,C,H,W = x.shape """ mean=[0.485, 0.456, 0.406] std =[0.229, 0.224, 0.225] x = torch.cat([ (x-mean[2])/std[2], (x-mean[1])/std[1], (x-mean[0])/std[0], ],1) """ x = x.float() e1 = self.encoder1(x ) #; print('e1',e1.size()) e2 = self.encoder2(e1) #; print('e2',e2.size()) e3 = self.encoder3(e2) #; print('e3',e3.size()) e4 = self.encoder4(e3) #; print('e4',e4.size()) e5 = self.encoder5(e4) #; print('e5',e5.size()) f = self.center(e5) #; print('f',f.size()) #print(f.shape) #print(e5.shape) #e1 = self.encoder1(x)# #e2 = self.encoder2(e1)# #e3 = self.encoder3(e2)# #e4 = self.encoder4(e3)# #e5 = self.center(e4)#512 #################################################################################### #d5 = self.decoder5( f,e5) #; print('d5',f.size()) #d4 = self.decoder4(d5,e4) #; print('d4',f.size()) #d3 = self.decoder3(d4,e3) #; print('d3',f.size()) #d2 = self.decoder2(d3,e2) #; print('d2',f.size()) #d1 = self.decoder1(d2,e1) #; print('d1',f.size()) d5 = self.decoder5(f,e5) d5 = self.se5(d5) # Decoder with Skip Connections #d4 = self.decoder4(d5) + e3 #d4 = torch.cat([self.decoder4(d5) , e3], 1)#concat([self.decoder5(e5) , e4]) #print(d5.shape) #print(e3.shape) d4 = self.decoder4(d5,e4) d4 = self.se4(d4) # d4 = e3 #d3 = self.decoder3(d4) + e2 #print(e2.shape) #d3 = torch.cat([self.decoder3(d4) , e2], 1)#concat([self.decoder5(e5) , e4]) #print(d3.shape) d3 = self.decoder3(d4,e3) d3 = self.se3(d3) #d2 = self.decoder2(d3) + e1 #d2 = torch.cat([self.decoder2(d3) , e1], 1)#concat([self.decoder5(e5) , e4]) d2 = self.decoder2(d3,e2) d2 = self.se2(d2) d1 = self.decoder1(d2) d1 = self.se1(d1) ######################################################################################## d = torch.cat(( d1, F.upsample(d2,scale_factor= 2, mode='bilinear',align_corners=False), F.upsample(d3,scale_factor= 4, mode='bilinear',align_corners=False), F.upsample(d4,scale_factor= 8, mode='bilinear',align_corners=False), F.upsample(d5,scale_factor=16, mode='bilinear',align_corners=False), ),1) ####################################################################### """ d = F.dropout(d, p=0.50, training=self.training) logit_pixel = self.logit_pixel(d) f = F.adaptive_avg_pool2d(e5, output_size=1).view(batch_size,-1) f = F.dropout(f, p=0.50, training=self.training) logit_image = self.logit_image(f).view(-1) """ ########################################################################### #d = torch.cat([d1,d2,d3,d4,d5],1) #hyper-columns d = F.dropout(d, p=0.50, training=self.training) fuse_pixel = self.fuse_pixel(d)#64-128-128 logit_pixel = self.logit_pixel(fuse_pixel)#1-128-128 e = F.adaptive_avg_pool2d(e5, output_size=1).view(batch_size,-1) #image pool#-512-1-1 e = F.dropout(e, p=0.50, training=self.training)# fuse_image = self.fuse_image(e)#-64-1-1 logit_image = self.logit_image(fuse_image).view(-1)#-1-1-1 #fuse = self.fuse(torch.mul(fuse_pixel, F.upsample(fuse_image.view(batch_size,-1,1,1,),scale_factor=128, mode='nearest'))) #fuse = self.fuse(fuse_pixel+ F.upsample(fuse_image.view(batch_size,-1,1,1,),scale_factor=128, mode='nearest')) fuse = self.fuse(torch.cat([ #fuse fuse_pixel, F.upsample(fuse_image.view(batch_size,-1,1,1,),scale_factor=128, mode='nearest') ],1)) logit = self.logit(fuse)#1-128-128 return logit, logit_pixel, logit_image #return logit_pixel, logit_image ##----------------------------------------------------------------- #def criterion(self, logit_pixel, logit_image, truth_pixel, truth_image, is_average=True): """ d3 = F.upsample(d3,scale_factor= 4, mode='bilinear',align_corners=False) d4 = F.upsample(d4,scale_factor= 8, mode='bilinear',align_corners=False) d5 = F.upsample(d5,scale_factor=16, mode='bilinear',align_corners=False) d = torch.cat([d1,d2,d3,d4,d5],1) #hyper-columns d = F.dropout(d, p=0.50, training=self.training) fuse_pixel = self.fuse_pixel(d) logit_pixel = self.logit_pixel(fuse_pixel) e = F.adaptive_avg_pool2d(e5, output_size=1).view(batch_size,-1) #image pool e = F.dropout(e, p=0.50, training=self.training) fuse_image = self.fuse_image(e) logit_image = self.logit_image(fuse_image).view(-1) fuse = self.fuse(torch.cat([ #fuse fuse_pixel, F.upsample(fuse_image.view(batch_size,-1,1,1,),scale_factor=128, mode='nearest') ],1)) logit = self.logit(fuse) return logit, logit_pixel, logit_image """

ZWZseven/Kaggle_TGS2018_solution

model/model.py

Python

# Generated by Django 2.2 on 2021-12-05 14:55 from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ('auth', '0011_update_proxy_permissions'), ] operations = [ migrations.CreateModel( name='UserProfile', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('password', models.CharField(max_length=128, verbose_name='password')), ('last_login', models.DateTimeField(blank=True, null=True, verbose_name='last login')), ('is_superuser', models.BooleanField(default=False, help_text='Designates that this user has all permissions without explicitly assigning them.', verbose_name='superuser status')), ('email', models.EmailField(max_length=255, unique=True)), ('name', models.CharField(max_length=255)), ('is_activate', models.BooleanField(default=True)), ('is_staff', models.BooleanField(default=False)), ('groups', models.ManyToManyField(blank=True, help_text='The groups this user belongs to. A user will get all permissions granted to each of their groups.', related_name='user_set', related_query_name='user', to='auth.Group', verbose_name='groups')), ('user_permissions', models.ManyToManyField(blank=True, help_text='Specific permissions for this user.', related_name='user_set', related_query_name='user', to='auth.Permission', verbose_name='user permissions')), ], options={ 'abstract': False, }, ), ]

harshavardhan-bhumi/profiles-rest-api

profiles_api/migrations/0001_initial.py

Python

# (C) Datadog, Inc. 2010-2016 # All rights reserved # Licensed under Simplified BSD License (see LICENSE) ''' MapReduce Job Metrics --------------------- mapreduce.job.elapsed_ime The elapsed time since the application started (in ms) mapreduce.job.maps_total The total number of maps mapreduce.job.maps_completed The number of completed maps mapreduce.job.reduces_total The total number of reduces mapreduce.job.reduces_completed The number of completed reduces mapreduce.job.maps_pending The number of maps still to be run mapreduce.job.maps_running The number of running maps mapreduce.job.reduces_pending The number of reduces still to be run mapreduce.job.reduces_running The number of running reduces mapreduce.job.new_reduce_attempts The number of new reduce attempts mapreduce.job.running_reduce_attempts The number of running reduce attempts mapreduce.job.failed_reduce_attempts The number of failed reduce attempts mapreduce.job.killed_reduce_attempts The number of killed reduce attempts mapreduce.job.successful_reduce_attempts The number of successful reduce attempts mapreduce.job.new_map_attempts The number of new map attempts mapreduce.job.running_map_attempts The number of running map attempts mapreduce.job.failed_map_attempts The number of failed map attempts mapreduce.job.killed_map_attempts The number of killed map attempts mapreduce.job.successful_map_attempts The number of successful map attempts MapReduce Job Counter Metrics ----------------------------- mapreduce.job.counter.reduce_counter_value The counter value of reduce tasks mapreduce.job.counter.map_counter_value The counter value of map tasks mapreduce.job.counter.total_counter_value The counter value of all tasks MapReduce Map Task Metrics -------------------------- mapreduce.job.map.task.progress The distribution of all map task progresses MapReduce Reduce Task Metrics -------------------------- mapreduce.job.reduce.task.progress The distribution of all reduce task progresses ''' # stdlib from urlparse import urljoin from urlparse import urlsplit from urlparse import urlunsplit # 3rd party import requests from requests.exceptions import Timeout, HTTPError, InvalidURL, ConnectionError from simplejson import JSONDecodeError # Project from checks import AgentCheck from config import _is_affirmative # Default Settings DEFAULT_CUSTER_NAME = 'default_cluster' # Service Check Names YARN_SERVICE_CHECK = 'mapreduce.resource_manager.can_connect' MAPREDUCE_SERVICE_CHECK = 'mapreduce.application_master.can_connect' # URL Paths YARN_APPS_PATH = 'ws/v1/cluster/apps' MAPREDUCE_JOBS_PATH = 'ws/v1/mapreduce/jobs' # Application type and states to collect YARN_APPLICATION_TYPES = 'MAPREDUCE' YARN_APPLICATION_STATES = 'RUNNING' # Metric types HISTOGRAM = 'histogram' INCREMENT = 'increment' # Metrics to collect MAPREDUCE_JOB_METRICS = { 'elapsedTime': ('mapreduce.job.elapsed_time', HISTOGRAM), 'mapsTotal': ('mapreduce.job.maps_total', INCREMENT), 'mapsCompleted': ('mapreduce.job.maps_completed', INCREMENT), 'reducesTotal': ('mapreduce.job.reduces_total', INCREMENT), 'reducesCompleted': ('mapreduce.job.reduces_completed', INCREMENT), 'mapsPending': ('mapreduce.job.maps_pending', INCREMENT), 'mapsRunning': ('mapreduce.job.maps_running', INCREMENT), 'reducesPending': ('mapreduce.job.reduces_pending', INCREMENT), 'reducesRunning': ('mapreduce.job.reduces_running', INCREMENT), 'newReduceAttempts': ('mapreduce.job.new_reduce_attempts', INCREMENT), 'runningReduceAttempts': ('mapreduce.job.running_reduce_attempts', INCREMENT), 'failedReduceAttempts': ('mapreduce.job.failed_reduce_attempts', INCREMENT), 'killedReduceAttempts': ('mapreduce.job.killed_reduce_attempts', INCREMENT), 'successfulReduceAttempts': ('mapreduce.job.successful_reduce_attempts', INCREMENT), 'newMapAttempts': ('mapreduce.job.new_map_attempts', INCREMENT), 'runningMapAttempts': ('mapreduce.job.running_map_attempts', INCREMENT), 'failedMapAttempts': ('mapreduce.job.failed_map_attempts', INCREMENT), 'killedMapAttempts': ('mapreduce.job.killed_map_attempts', INCREMENT), 'successfulMapAttempts': ('mapreduce.job.successful_map_attempts', INCREMENT), } MAPREDUCE_JOB_COUNTER_METRICS = { 'reduceCounterValue': ('mapreduce.job.counter.reduce_counter_value', INCREMENT), 'mapCounterValue': ('mapreduce.job.counter.map_counter_value', INCREMENT), 'totalCounterValue': ('mapreduce.job.counter.total_counter_value', INCREMENT), } MAPREDUCE_MAP_TASK_METRICS = { 'elapsedTime': ('mapreduce.job.map.task.elapsed_time', HISTOGRAM) } MAPREDUCE_REDUCE_TASK_METRICS = { 'elapsedTime': ('mapreduce.job.reduce.task.elapsed_time', HISTOGRAM) } class MapReduceCheck(AgentCheck): def __init__(self, name, init_config, agentConfig, instances=None): AgentCheck.__init__(self, name, init_config, agentConfig, instances) # Parse job specific counters self.general_counters = self._parse_general_counters(init_config) # Parse job specific counters self.job_specific_counters = self._parse_job_specific_counters(init_config) def check(self, instance): # Get properties from conf file rm_address = instance.get('resourcemanager_uri') if rm_address is None: raise Exception('The ResourceManager URL must be specified in the instance configuration') collect_task_metrics = _is_affirmative(instance.get('collect_task_metrics', False)) # Get additional tags from the conf file tags = instance.get('tags', []) if tags is None: tags = [] else: tags = list(set(tags)) # Get the cluster name from the conf file cluster_name = instance.get('cluster_name') if cluster_name is None: self.warning("The cluster_name must be specified in the instance configuration, defaulting to '%s'" % (DEFAULT_CUSTER_NAME)) cluster_name = DEFAULT_CUSTER_NAME tags.append('cluster_name:%s' % cluster_name) # Get the running MR applications from YARN running_apps = self._get_running_app_ids(rm_address) # Report success after gathering all metrics from ResourceManaager self.service_check(YARN_SERVICE_CHECK, AgentCheck.OK, tags=['url:%s' % rm_address], message='Connection to ResourceManager "%s" was successful' % rm_address) # Get the applications from the application master running_jobs = self._mapreduce_job_metrics(running_apps, tags) # # Get job counter metrics self._mapreduce_job_counters_metrics(running_jobs, tags) # Get task metrics if collect_task_metrics: self._mapreduce_task_metrics(running_jobs, tags) # Report success after gathering all metrics from Application Master if running_jobs: job_id, metrics = running_jobs.items()[0] am_address = self._get_url_base(metrics['tracking_url']) self.service_check(MAPREDUCE_SERVICE_CHECK, AgentCheck.OK, tags=['url:%s' % am_address], message='Connection to ApplicationManager "%s" was successful' % am_address) def _parse_general_counters(self, init_config): ''' Return a dictionary for each job counter { counter_group_name: [ counter_name ] } } ''' job_counter = {} if init_config.get('general_counters'): # Parse the custom metrics for counter_group in init_config['general_counters']: counter_group_name = counter_group.get('counter_group_name') counters = counter_group.get('counters') if not counter_group_name: raise Exception('"general_counters" must contain a valid "counter_group_name"') if not counters: raise Exception('"general_counters" must contain a list of "counters"') # Add the counter_group to the job_counters if it doesn't already exist if counter_group_name not in job_counter: job_counter[counter_group_name] = [] for counter in counters: counter_name = counter.get('counter_name') if not counter_name: raise Exception('At least one "counter_name" should be specified in the list of "counters"') job_counter[counter_group_name].append(counter_name) return job_counter def _parse_job_specific_counters(self, init_config): ''' Return a dictionary for each job counter { job_name: { counter_group_name: [ counter_name ] } } } ''' job_counter = {} if init_config.get('job_specific_counters'): # Parse the custom metrics for job in init_config['job_specific_counters']: job_name = job.get('job_name') metrics = job.get('metrics') if not job_name: raise Exception('Counter metrics must have a "job_name"') if not metrics: raise Exception('Jobs specified in counter metrics must contain at least one metric') # Add the job to the custom metrics if it doesn't already exist if job_name not in job_counter: job_counter[job_name] = {} for metric in metrics: counter_group_name = metric.get('counter_group_name') counters = metric.get('counters') if not counter_group_name: raise Exception('Each counter metric must contain a valid "counter_group_name"') if not counters: raise Exception('Each counter metric must contain a list of "counters"') # Add the counter group name if it doesn't exist for the current job if counter_group_name not in job_counter[job_name]: job_counter[job_name][counter_group_name] = [] for counter in counters: counter_name = counter.get('counter_name') if not counter_name: raise Exception('At least one "counter_name" should be specified in the list of "counters"') job_counter[job_name][counter_group_name].append(counter_name) return job_counter def _get_running_app_ids(self, rm_address, **kwargs): ''' Return a dictionary of {app_id: (app_name, tracking_url)} for the running MapReduce applications ''' metrics_json = self._rest_request_to_json(rm_address, YARN_APPS_PATH, YARN_SERVICE_CHECK, states=YARN_APPLICATION_STATES, applicationTypes=YARN_APPLICATION_TYPES) running_apps = {} if metrics_json.get('apps'): if metrics_json['apps'].get('app') is not None: for app_json in metrics_json['apps']['app']: app_id = app_json.get('id') tracking_url = app_json.get('trackingUrl') app_name = app_json.get('name') if app_id and tracking_url and app_name: running_apps[app_id] = (app_name, tracking_url) return running_apps def _mapreduce_job_metrics(self, running_apps, addl_tags): ''' Get metrics for each MapReduce job. Return a dictionary for each MapReduce job { job_id: { 'job_name': job_name, 'app_name': app_name, 'user_name': user_name, 'tracking_url': tracking_url } ''' running_jobs = {} for app_id, (app_name, tracking_url) in running_apps.iteritems(): metrics_json = self._rest_request_to_json(tracking_url, MAPREDUCE_JOBS_PATH, MAPREDUCE_SERVICE_CHECK) if metrics_json.get('jobs'): if metrics_json['jobs'].get('job'): for job_json in metrics_json['jobs']['job']: job_id = job_json.get('id') job_name = job_json.get('name') user_name = job_json.get('user') if job_id and job_name and user_name: # Build the structure to hold the information for each job ID running_jobs[str(job_id)] = {'job_name': str(job_name), 'app_name': str(app_name), 'user_name': str(user_name), 'tracking_url': self._join_url_dir(tracking_url, MAPREDUCE_JOBS_PATH, job_id)} tags = ['app_name:' + str(app_name), 'user_name:' + str(user_name), 'job_name:' + str(job_name)] tags.extend(addl_tags) self._set_metrics_from_json(tags, job_json, MAPREDUCE_JOB_METRICS) return running_jobs def _mapreduce_job_counters_metrics(self, running_jobs, addl_tags): ''' Get custom metrics specified for each counter ''' for job_id, job_metrics in running_jobs.iteritems(): job_name = job_metrics['job_name'] # Check if the job_name exist in the custom metrics if self.general_counters or (job_name in self.job_specific_counters): job_specific_metrics = self.job_specific_counters.get(job_name) metrics_json = self._rest_request_to_json(job_metrics['tracking_url'], 'counters', MAPREDUCE_SERVICE_CHECK) if metrics_json.get('jobCounters'): if metrics_json['jobCounters'].get('counterGroup'): # Cycle through all the counter groups for this job for counter_group in metrics_json['jobCounters']['counterGroup']: group_name = counter_group.get('counterGroupName') if group_name: counter_metrics = set([]) # Add any counters in the job specific metrics if job_specific_metrics and group_name in job_specific_metrics: counter_metrics = counter_metrics.union(job_specific_metrics[group_name]) # Add any counters in the general metrics if group_name in self.general_counters: counter_metrics = counter_metrics.union(self.general_counters[group_name]) if counter_metrics: # Cycle through all the counters in this counter group if counter_group.get('counter'): for counter in counter_group['counter']: counter_name = counter.get('name') # Check if the counter name is in the custom metrics for this group name if counter_name and counter_name in counter_metrics: tags = ['app_name:' + job_metrics.get('app_name'), 'user_name:' + job_metrics.get('user_name'), 'job_name:' + job_name, 'counter_name:' + str(counter_name).lower()] tags.extend(addl_tags) self._set_metrics_from_json(tags, counter, MAPREDUCE_JOB_COUNTER_METRICS) def _mapreduce_task_metrics(self, running_jobs, addl_tags): ''' Get metrics for each MapReduce task Return a dictionary of {task_id: 'tracking_url'} for each MapReduce task ''' for job_id, job_stats in running_jobs.iteritems(): metrics_json = self._rest_request_to_json(job_stats['tracking_url'], 'tasks', MAPREDUCE_SERVICE_CHECK) if metrics_json.get('tasks'): if metrics_json['tasks'].get('task'): for task in metrics_json['tasks']['task']: task_type = task.get('type') if task_type: tags = ['app_name:' + job_stats['app_name'], 'user_name:' + job_stats['user_name'], 'job_name:' + job_stats['job_name'], 'task_type:' + str(task_type).lower()] tags.extend(addl_tags) if task_type == 'MAP': self._set_metrics_from_json(tags, task, MAPREDUCE_MAP_TASK_METRICS) elif task_type == 'REDUCE': self._set_metrics_from_json(tags, task, MAPREDUCE_REDUCE_TASK_METRICS) def _set_metrics_from_json(self, tags, metrics_json, metrics): ''' Parse the JSON response and set the metrics ''' for status, (metric_name, metric_type) in metrics.iteritems(): metric_status = metrics_json.get(status) if metric_status is not None: self._set_metric(metric_name, metric_type, metric_status, tags) def _set_metric(self, metric_name, metric_type, value, tags=None, device_name=None): ''' Set a metric ''' if metric_type == HISTOGRAM: self.histogram(metric_name, value, tags=tags, device_name=device_name) elif metric_type == INCREMENT: self.increment(metric_name, value, tags=tags, device_name=device_name) else: self.log.error('Metric type "%s" unknown' % (metric_type)) def _rest_request_to_json(self, address, object_path, service_name, *args, **kwargs): ''' Query the given URL and return the JSON response ''' response_json = None service_check_tags = ['url:%s' % self._get_url_base(address)] url = address if object_path: url = self._join_url_dir(url, object_path) # Add args to the url if args: for directory in args: url = self._join_url_dir(url, directory) self.log.debug('Attempting to connect to "%s"' % url) # Add kwargs as arguments if kwargs: query = '&'.join(['{0}={1}'.format(key, value) for key, value in kwargs.iteritems()]) url = urljoin(url, '?' + query) try: response = requests.get(url, timeout=self.default_integration_http_timeout) response.raise_for_status() response_json = response.json() except Timeout as e: self.service_check(service_name, AgentCheck.CRITICAL, tags=service_check_tags, message="Request timeout: {0}, {1}".format(url, e)) raise except (HTTPError, InvalidURL, ConnectionError) as e: self.service_check(service_name, AgentCheck.CRITICAL, tags=service_check_tags, message="Request failed: {0}, {1}".format(url, e)) raise except JSONDecodeError as e: self.service_check(service_name, AgentCheck.CRITICAL, tags=service_check_tags, message='JSON Parse failed: {0}, {1}'.format(url, e)) raise except ValueError as e: self.service_check(service_name, AgentCheck.CRITICAL, tags=service_check_tags, message=str(e)) raise return response_json def _join_url_dir(self, url, *args): ''' Join a URL with multiple directories ''' for path in args: url = url.rstrip('/') + '/' url = urljoin(url, path.lstrip('/')) return url def _get_url_base(self, url): ''' Return the base of a URL ''' s = urlsplit(url) return urlunsplit([s.scheme, s.netloc, '', '', ''])

WPMedia/dd-agent

checks.d/mapreduce.py

Python

""" Create Sine function without using third-party plugins or expressions. @Guilherme Trevisan - github.com/TrevisanGMW - 2021-01-25 1.0 - 2021-01-25 Initial Release """ try: from shiboken2 import wrapInstance except ImportError: from shiboken import wrapInstance try: from PySide2.QtGui import QIcon from PySide2.QtWidgets import QWidget except ImportError: from PySide.QtGui import QIcon, QWidget from maya import OpenMayaUI as omui import maya.cmds as cmds import maya.mel as mel import random import sys # Script Name script_name = "GT - Add Sine Attributes" # Version: script_version = "1.0" # Main Form ============================================================================ def build_gui_add_sine_attr(): window_name = "build_gui_add_sine_attr" if cmds.window(window_name, exists =True): cmds.deleteUI(window_name) # Main GUI Start Here ================================================================================= # Build UI build_gui_add_sine_attr = cmds.window(window_name, title=script_name + ' (v' + script_version + ')',\ titleBar=True, mnb=False, mxb=False, sizeable =True) cmds.window(window_name, e=True, s=True, wh=[1,1]) content_main = cmds.columnLayout(adj = True) # Title Text title_bgc_color = (.4, .4, .4) cmds.separator(h=10, style='none') # Empty Space cmds.rowColumnLayout(nc=1, cw=[(1, 270)], cs=[(1, 10)], p=content_main) # Window Size Adjustment cmds.rowColumnLayout(nc=3, cw=[(1, 10), (2, 200), (3, 50)], cs=[(1, 10), (2, 0), (3, 0)], p=content_main) # Title Column cmds.text(" ", bgc=title_bgc_color) # Tiny Empty Green Space cmds.text(script_name, bgc=title_bgc_color, fn="boldLabelFont", align="left") cmds.button( l ="Help", bgc=title_bgc_color, c=lambda x:build_gui_help_add_sine_attr()) cmds.separator(h=5, style='none') # Empty Space # Body ==================== body_column = cmds.rowColumnLayout(nc=1, cw=[(1, 260)], cs=[(1,10)], p=content_main) cmds.text(l='Select attribute holder first, then run script.', align="center") cmds.separator(h=10, style='none') # Empty Space cmds.text('Sine Attributes Prefix:') stretchy_system_prefix = cmds.textField(text='', pht='Sine Attributes Prefix (Optional)') cmds.separator(h=5, style='none') # Empty Space cmds.rowColumnLayout(nc=2, cw=[(1, 115),(2, 150)], cs=[(1,10)], p=content_main) add_abs_output_chkbox = cmds.checkBox(label='Add Abs Output') add_prefix_nn_chkbox = cmds.checkBox(label='Add Prefix to Nice Name', value=True) cmds.rowColumnLayout(nc=1, cw=[(1, 260)], cs=[(1,10)], p=content_main) cmds.separator(h=5, style='none') # Empty Space cmds.separator(h=5) cmds.separator(h=7, style='none') # Empty Space cmds.button(l ="Add Sine Attributes", bgc=(.6, .6, .6), c=lambda x:validate_operation()) cmds.separator(h=10, style='none') # Empty Space # Show and Lock Window cmds.showWindow(build_gui_add_sine_attr) cmds.window(window_name, e=True, s=False) # Set Window Icon qw = omui.MQtUtil.findWindow(window_name) widget = wrapInstance(long(qw), QWidget) icon = QIcon(':/sineCurveProfile.png') widget.setWindowIcon(icon) # Remove the focus from the textfield and give it to the window cmds.setFocus(window_name) # Main GUI Ends Here ================================================================================= def validate_operation(): ''' Checks elements one last time before running the script ''' is_valid = False stretchy_name = None add_abs_output_value = cmds.checkBox(add_abs_output_chkbox, q=True, value=True) add_prefix_nn_value = cmds.checkBox(add_prefix_nn_chkbox, q=True, value=True) stretchy_prefix = cmds.textField(stretchy_system_prefix, q=True, text=True).replace(' ','') selection = cmds.ls(selection=True) or [] if len(selection) > 0: target = selection[0] is_valid = True else: cmds.warning('Please select a target object to be the attribute holder.') is_valid = False # Name if stretchy_prefix != '': stretchy_name = stretchy_prefix else: stretchy_name = 'sine' if is_valid: current_attributes = cmds.listAttr(target, r=True, s=True , userDefined=True) or [] possible_conflicts = [] possible_conflicts.append(stretchy_name + 'Time') possible_conflicts.append(stretchy_name + 'Amplitude') possible_conflicts.append(stretchy_name + 'Frequency') possible_conflicts.append(stretchy_name + 'Offset') possible_conflicts.append(stretchy_name + 'Output') possible_conflicts.append(stretchy_name + 'Tick') possible_conflicts.append(stretchy_name + 'AbsOutput') for conflict in possible_conflicts: for attr in current_attributes: if attr == conflict: is_valid = False if not is_valid: cmds.warning('The object selected has conflicting attributes. Please change the prefix or select another object.') # Run Script if is_valid: if stretchy_name: add_sine_attributes(target, sine_prefix=stretchy_name, tick_source_attr='time1.outTime', hide_unkeyable=False, add_absolute_output=add_abs_output_value, nice_name_prefix=add_prefix_nn_value) cmds.select(target, r=True) else: add_sine_attributes(target, sine_prefix=stretchy_name, tick_source_attr='time1.outTime', hide_unkeyable=False, add_absolute_output=add_abs_output_value, nice_name_prefix=add_prefix_nn_value) cmds.select(target, r=True) # Creates Help GUI def build_gui_help_add_sine_attr(): ''' Creates GUI for Make Stretchy IK ''' window_name = "build_gui_help_add_sine_attr" if cmds.window(window_name, exists=True): cmds.deleteUI(window_name, window=True) cmds.window(window_name, title= script_name + " Help", mnb=False, mxb=False, s=True) cmds.window(window_name, e=True, s=True, wh=[1,1]) cmds.columnLayout("main_column", p= window_name) # Title Text cmds.separator(h=12, style='none') # Empty Space cmds.rowColumnLayout(nc=1, cw=[(1, 310)], cs=[(1, 10)], p="main_column") # Window Size Adjustment cmds.rowColumnLayout(nc=1, cw=[(1, 300)], cs=[(1, 10)], p="main_column") # Title Column cmds.text(script_name + " Help", bgc=[.4,.4,.4], fn="boldLabelFont", align="center") cmds.separator(h=10, style='none', p="main_column") # Empty Space # Body ==================== cmds.rowColumnLayout(nc=1, cw=[(1, 300)], cs=[(1,10)], p="main_column") cmds.text(l='Create Sine attributes without using\nthird-party plugins or expressions.', align="center") cmds.separator(h=5, style='none') # Empty Space cmds.text(l='Select and object, then click on "Add Sine Attributes"', align="center") cmds.separator(h=10, style='none') # Empty Space cmds.text(l='Sine Attributes:', align='center', font='boldLabelFont') cmds.text(l='Time: Multiplier for the time input (tick)', align="center") cmds.text(l='Amplitude: Wave amplitude (how high it gets)', align="center") cmds.text(l='Frequency: Wave frequency (how often it happens)', align="center") cmds.text(l='Offset: Value added after calculation, offset.', align="center") cmds.text(l='Tick: Time as seen by the sine system.', align="center") cmds.text(l='Output: Result of the sine operation.', align="center") cmds.text(l='Abs Output: Aboslute output. (no negative values)', align="center") cmds.separator(h=10, style='none') # Empty Space cmds.separator(h=15, style='none') # Empty Space cmds.rowColumnLayout(nc=2, cw=[(1, 140),(2, 140)], cs=[(1,10),(2, 0)], p="main_column") cmds.text('Guilherme Trevisan ') cmds.text(l='<a href="mailto:trevisangmw@gmail.com">TrevisanGMW@gmail.com</a>', hl=True, highlightColor=[1,1,1]) cmds.rowColumnLayout(nc=2, cw=[(1, 140),(2, 140)], cs=[(1,10),(2, 0)], p="main_column") cmds.separator(h=15, style='none') # Empty Space cmds.text(l='<a href="https://github.com/TrevisanGMW">Github</a>', hl=True, highlightColor=[1,1,1]) cmds.separator(h=7, style='none') # Empty Space # Close Button cmds.rowColumnLayout(nc=1, cw=[(1, 300)], cs=[(1,10)], p="main_column") cmds.separator(h=10, style='none') cmds.button(l='OK', h=30, c=lambda args: close_help_gui()) cmds.separator(h=8, style='none') # Show and Lock Window cmds.showWindow(window_name) cmds.window(window_name, e=True, s=False) # Set Window Icon qw = omui.MQtUtil.findWindow(window_name) widget = wrapInstance(long(qw), QWidget) icon = QIcon(':/question.png') widget.setWindowIcon(icon) def close_help_gui(): ''' Closes Help Window ''' if cmds.window(window_name, exists=True): cmds.deleteUI(window_name, window=True) def add_sine_attributes(obj, sine_prefix='sine', tick_source_attr='time1.outTime', hide_unkeyable=True, add_absolute_output=False, nice_name_prefix=True): ''' Create Sine function without using third-party plugins or expressions Parameters: obj (string): Name of the object sine (string): Prefix given to the name of the attributes (default is "sine") tick_source_attr (string): Name of the attribute used as the source for time. It uses the default "time1" node if nothing else is specified hide_unkeyable (bool): Hides the tick and output attributes add_absolute_output (bool): Also creates an output version that gives only positive numbers much like the abs() expression Returns: sine_output_attrs (list): A string with the name of the object and the name of the sine output attribute. E.g. "pSphere1.sineOutput" In case an absolute output is added, it will be the second object in the list. E.g. ["pSphere1.sineOutput", "pSphere1.sineAbsOutput"] If add_absolute_output is False the second attribute is None ''' # Load Required Plugins required_plugin = 'quatNodes' if not cmds.pluginInfo(required_plugin, q=True, loaded=True): cmds.loadPlugin(required_plugin, qt=False) # Set Variables influence_suffix = 'Time' amplitude_suffix = 'Amplitude' frequency_suffix = 'Frequency' offset_suffix = 'Offset' output_suffix = 'Output' tick_suffix = 'Tick' abs_suffix = 'AbsOutput' influence_attr = sine_prefix + influence_suffix amplitude_attr = sine_prefix + amplitude_suffix frequency_attr = sine_prefix + frequency_suffix offset_attr = sine_prefix + offset_suffix output_attr = sine_prefix + output_suffix tick_attr = sine_prefix + tick_suffix abs_attr = sine_prefix + abs_suffix # Create Nodes mdl_node = cmds.createNode('multDoubleLinear', name=obj + '_multDoubleLiner') quat_node = cmds.createNode('eulerToQuat', name=obj + '_eulerToQuat') multiply_node = cmds.createNode('multiplyDivide', name=obj + '_amplitude_multiply') sum_node = cmds.createNode('plusMinusAverage', name=obj + '_offset_sum') influence_multiply_node = cmds.createNode('multiplyDivide', name=obj + '_influence_multiply') # Add Attributes if nice_name_prefix: cmds.addAttr(obj, ln=influence_attr, at='double', k=True, maxValue=1, minValue=0) cmds.addAttr(obj, ln=amplitude_attr, at='double', k=True) cmds.addAttr(obj, ln=frequency_attr, at='double', k=True) cmds.addAttr(obj, ln=offset_attr, at='double', k=True) cmds.addAttr(obj, ln=tick_attr, at='double', k=True) cmds.addAttr(obj, ln=output_attr, at='double', k=True) if add_absolute_output: cmds.addAttr(obj, ln=abs_attr, at='double', k=True) else: cmds.addAttr(obj, ln=influence_attr, at='double', k=True, maxValue=1, minValue=0, nn=influence_suffix) cmds.addAttr(obj, ln=amplitude_attr, at='double', k=True, nn=amplitude_suffix) cmds.addAttr(obj, ln=frequency_attr, at='double', k=True, nn=frequency_suffix) cmds.addAttr(obj, ln=offset_attr, at='double', k=True, nn=offset_suffix) cmds.addAttr(obj, ln=tick_attr, at='double', k=True, nn=tick_suffix) cmds.addAttr(obj, ln=output_attr, at='double', k=True, nn=output_suffix) if add_absolute_output: cmds.addAttr(obj, ln=abs_attr, at='double', k=True, nn=re.sub(r'(\w)([A-Z])', r'\1 \2', abs_suffix)) cmds.setAttr(obj + '.' + influence_attr, 1) cmds.setAttr(obj + '.' + amplitude_attr, 1) cmds.setAttr(obj + '.' + frequency_attr, 10) if hide_unkeyable: cmds.setAttr(obj + '.' + tick_attr, k=False) cmds.setAttr(obj + '.' + output_attr, k=False) if add_absolute_output and hide_unkeyable: cmds.setAttr(obj + '.' + abs_attr, k=False) cmds.connectAttr(tick_source_attr, influence_multiply_node + '.input1X') cmds.connectAttr(influence_multiply_node + '.outputX', obj + '.' + tick_attr) cmds.connectAttr(obj + '.' + influence_attr, influence_multiply_node + '.input2X') cmds.connectAttr(obj + '.' + amplitude_attr, multiply_node + '.input2X') cmds.connectAttr(obj + '.' + frequency_attr, mdl_node + '.input1') cmds.connectAttr(obj + '.' + tick_attr, mdl_node + '.input2') cmds.connectAttr(obj + '.' + offset_attr, sum_node + '.input1D[0]') cmds.connectAttr(mdl_node + '.output', quat_node + '.inputRotateX') cmds.connectAttr(quat_node + '.outputQuatX', multiply_node + '.input1X') cmds.connectAttr(multiply_node + '.outputX', sum_node + '.input1D[1]') cmds.connectAttr(sum_node + '.output1D', obj + '.' + output_attr) if add_absolute_output: # abs() squared_node = cmds.createNode('multiplyDivide', name=obj + '_abs_squared') reverse_squared_node = cmds.createNode('multiplyDivide', name=obj + '_reverseAbs_multiply') cmds.setAttr(squared_node + '.operation', 3) # Power cmds.setAttr(reverse_squared_node + '.operation', 3) # Power cmds.setAttr(squared_node + '.input2X', 2) cmds.setAttr(reverse_squared_node + '.input2X', .5) cmds.connectAttr(obj + '.' + output_attr, squared_node + '.input1X') cmds.connectAttr(squared_node + '.outputX', reverse_squared_node + '.input1X') cmds.connectAttr(reverse_squared_node + '.outputX', obj + '.' + abs_attr) return [(obj + '.' + output_attr), (obj + '.' + abs_attr)] else: return [(obj + '.' + output_attr), None] #Build UI if __name__ == '__main__': build_gui_add_sine_attr()

freemanpro/gt-tools

python-scripts/gt_add_sine_attributes.py

Python

#!/usr/bin/python # -*- coding: UTF-8, tab-width: 4 -*- from sys import argv, stdin, stdout, stderr from codecs import open as cfopen import json def main(invocation, *cli_args): json_src = stdin if len(cli_args) > 0: json_src = cfopen(cli_args[0], 'r', 'utf-8') data = json.load(json_src, 'utf-8') json_enc = dict( indent=2, sort_keys=True, ### ### <-- some magic here ### ### ) json_enc['separators'] = (',', ': ',) # ^-- because the default had space after comma even at end of line. rules = data.get('entries') if rules is not None: del data['entries'] json_enc = json.JSONEncoder(**json_enc) json_iter = json_enc.iterencode(data) for chunk in json_iter: chunk = chunk.lstrip() if chunk == '': continue if chunk.startswith('"'): stdout.write(' ') if rules is not None: stdout.write('"entries": {\n') verbsep = ' ' for verb in sorted(rules.keys()): stdout.write(verbsep + json.dumps(verb) + ': [') write_rule_subjs(stdout, rules[verb]) stdout.write(']') verbsep = ',\n ' stdout.write('\n}, ') stdout.write(chunk) break stdout.write(chunk) for chunk in json_iter: if rules is not None: if chunk.startswith(','): stdout.write(',') chunk = chunk[1:] if chunk.startswith('\n'): chunk = ' ' + chunk.lstrip() stdout.write(chunk) stdout.write('\n') def gen_rule_subj_hrname(subj): hrname = [ subj.get(role, u'\uFFFF') for role in ('o', 'd',) ] hrname = [ gen_rule_host_hrname(part) for part in hrname ] return hrname def gen_rule_host_hrname(host): try: host = host['h'] except: pass host = split_subdomains(host) return host def split_subdomains(host): parts = host.split('.') major = [ parts.pop() ] while len(parts) > 0: part = parts.pop() major.insert(0, part) if len(part) > 3: break return '.'.join(major) + ':' + '.'.join(parts) def write_rule_subjs(dest, subjs): if len(subjs) < 1: return for subj in subjs: subj['hrname'] = gen_rule_subj_hrname(subj) subjs.sort(key=lambda s: s['hrname']) props = None stdout.write('\n') for subj in subjs: if props is not None: dest.write(',\n') dest.write(' {') propsep = ' ' del subj['hrname'] props = [ 'o', 'd' ] props += [ prop for prop in sorted(subj.keys()) if prop not in props ] for prop in props: if subj.has_key(prop): dest.write(propsep + json.dumps(prop) + ': ' + json.dumps(subj[prop])) propsep = ', ' dest.write(' }') stdout.write('\n ') if __name__ == '__main__': main(*argv)

mk-pmb/firefox-requestpolicy-util

rqpol-sort.py

Python

#!/usr/bin/env python # -*- coding: utf-8 -*- import asyncio from typing import Optional from fastapi import APIRouter, Depends, Request from epicteller.core.controller import campaign as campaign_ctl from epicteller.core.controller import room as room_ctl from epicteller.core.error.base import NotFoundError from epicteller.web.controller.paging import generate_paging_info from epicteller.web.fetcher import room as room_fetcher from epicteller.web.model import PagingResponse from epicteller.web.model.campaign import Campaign from epicteller.web.model.room import Room router = APIRouter() async def prepare(url_token: str): room = await room_ctl.get_room(url_token=url_token) if not room or room.is_removed: raise NotFoundError() return room @router.get('/rooms/{url_token}', response_model=Room, response_model_exclude_none=True) async def get_room(room: Room = Depends(prepare)): web_room = await room_fetcher.fetch_room(room) return web_room @router.get('/rooms/{url_token}/campaigns', response_model=PagingResponse[Campaign], response_model_exclude_none=True) async def get_room_campaigns(r: Request, room: Room = Depends(prepare), after: Optional[str] = None, offset: Optional[int] = 0, limit: Optional[int] = 20): after_id = 0 if after_campaign := await campaign_ctl.get_campaign(url_token=after): after_id = after_campaign.id total, campaigns = await asyncio.gather( campaign_ctl.get_campaign_count_by_room(room), campaign_ctl.get_campaigns_by_room(room, after_id, limit), ) paging_info = await generate_paging_info(r, total=total, after=campaigns[-1].id if len(campaigns) else None, offset=offset, limit=limit) return PagingResponse[Campaign](data=campaigns, paging=paging_info)

KawashiroNitori/epicteller

epicteller/web/handler/room.py

Python

from __future__ import unicode_literals, division, absolute_import from builtins import * # pylint: disable=unused-import, redefined-builtin import logging from flexget import plugin from flexget.event import event log = logging.getLogger('parsing') PARSER_TYPES = ['movie', 'series'] # Mapping of parser type to (mapping of parser name to plugin instance) parsers = {} # Mapping from parser type to the name of the default/selected parser for that type default_parsers = {} selected_parsers = {} # We need to wait until manager startup to access other plugin instances, to make sure they have all been loaded @event('manager.startup') def init_parsers(manager): """Prepare our list of parsing plugins and default parsers.""" for parser_type in PARSER_TYPES: parsers[parser_type] = {} for p in plugin.get_plugins(group=parser_type + '_parser'): parsers[parser_type][p.name.replace('parser_', '')] = p.instance # Select default parsers based on priority func_name = 'parse_' + parser_type default_parsers[parser_type] = max(iter(parsers[parser_type].items()), key=lambda p: getattr(getattr(p[1], func_name), 'priority', 0))[0] log.debug('setting default %s parser to %s. (options: %s)' % (parser_type, default_parsers[parser_type], parsers[parser_type])) class PluginParsing(object): """Provides parsing framework""" @property def schema(self): # Create a schema allowing only our registered parsers to be used under the key of each parser type properties = {} for parser_type in PARSER_TYPES: parser_names = [p.name.replace('parser_', '') for p in plugin.get_plugins(group=parser_type + '_parser')] properties[parser_type] = {'type': 'string', 'enum': parser_names} s = { 'type': 'object', 'properties': properties, 'additionalProperties': False } return s def on_task_start(self, task, config): # Set up user selected parsers from config for this task run if config: selected_parsers.update(config) def on_task_exit(self, task, config): # Restore default parsers for next task run selected_parsers.clear() on_task_abort = on_task_exit def parse_series(self, data, name=None, **kwargs): """ Use the selected series parser to parse series information from `data` :param data: The raw string to parse information from. :param name: The series name to parse data for. If not supplied, parser will attempt to guess series name automatically from `data`. :returns: An object containing the parsed information. The `valid` attribute will be set depending on success. """ parser = parsers['series'][selected_parsers.get('series', default_parsers.get('series'))] return parser.parse_series(data, name=name, **kwargs) def parse_movie(self, data, **kwargs): """ Use the selected movie parser to parse movie information from `data` :param data: The raw string to parse information from :returns: An object containing the parsed information. The `valid` attribute will be set depending on success. """ parser = parsers['movie'][selected_parsers.get('movie') or default_parsers['movie']] return parser.parse_movie(data, **kwargs) @event('plugin.register') def register_plugin(): plugin.register(PluginParsing, 'parsing', api_ver=2)

jbones89/Flexget

flexget/plugins/parsers/plugin_parsing.py

Python

# Generated by Django 3.2.5 on 2021-07-20 12:31 import ckeditor.fields from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('blog', '0003_article'), ] operations = [ migrations.AlterField( model_name='article', name='detail', field=ckeditor.fields.RichTextField(), ), ]

merveealpay/django-blog-project

blog/migrations/0004_alter_article_detail.py

Python

import flask from flask import request, jsonify import sqlite3 app = flask.Flask(__name__) app.config["DEBUG"] = True def dict_factory(cursor, row): d = {} for idx, col in enumerate(cursor.description): d[col[0]] = row[idx] return d @app.route('/', methods=['GET']) def home(): return '''<h1>Distant Reading Archive</h1> <p>A prototype API for distant reading of science fiction novels.</p>''' @app.route('/api/v1/resources/books/all', methods=['GET']) def api_all(): conn = sqlite3.connect('books.db') conn.row_factory = dict_factory cur = conn.cursor() all_books = cur.execute('SELECT * FROM books;').fetchall() return jsonify(all_books) @app.errorhandler(404) def page_not_found(e): return "<h1>404</h1><p>The resource could not be found.</p>", 404 @app.route('/api/v1/resources/books', methods=['GET']) def api_filter(): query_parameters = request.args # print("columns" in query_parameters) id = query_parameters.get('id') published = query_parameters.get('published') author = query_parameters.get('author') print(query_parameters.get('keyword')) print(query_parameters.get('columns')) # query = "SELECT * FROM books WHERE" # to_filter = [] # if id: # query += ' id=? AND' # to_filter.append(id) # if published: # query += ' published=? AND' # to_filter.append(published) # if author: # query += ' author=? AND' # to_filter.append(author) # if not (id or published or author): # return page_not_found(404) # query = query[:-4] + ';' # conn = sqlite3.connect('books.db') # conn.row_factory = dict_factory # cur = conn.cursor() # results = cur.execute(query, to_filter).fetchall() return jsonify({"test":1}) app.run()

tuilagio/wordNotify-rev1

tools/test_flask.py

Python

#!/usr/bin/python3 # apt install libnetfilter-queue-dev import os import random import string import time from multiprocessing import Pool from netfilterqueue import NetfilterQueue from scapy.all import * SINGLE_QUEUE = False if SINGLE_QUEUE: nfqueue_number = 1 else: nfqueue_number = 4 def setup(): k_module = "modprobe br_netfilter" os.system(k_module) if SINGLE_QUEUE: iptables_rule = "iptables -A FORWARD -j NFQUEUE --queue-num %d -m physdev --physdev-in ens38" % (nfqueue_number - 1) else: iptables_rule = "iptables -A FORWARD -j NFQUEUE --queue-balance 0:%d -m physdev --physdev-in ens38" % (nfqueue_number - 1) print("Adding iptable rules : ") print(iptables_rule) os.system(iptables_rule) print("Setting ipv4 forward settings : ") os.system("sysctl net.ipv4.ip_forward=1") def change_payload(packet, load): packet[Raw].load = load del packet[IP].len del packet[IP].chksum del packet[TCP].chksum #python2 #return packet.__class__(packet) #python3 return packet.__bytes__() def slack_chars(payload, source, target, finalize=False): if source in payload["data"]: payload["diff"] += len(source) - len(target) payload["data"] = payload["data"].replace(source, target) if finalize: slacks = [b"\r\nAccept-Encoding: gzip, deflate", b"\r\nConnection: Keep-Alive"] payload["diff"] += len(slacks[0]) payload["data"] = payload["data"].replace(slacks[0], b"") for slack in slacks[1:]: if payload["diff"] < 0: payload["diff"] += len(slack) payload["data"] = payload["data"].replace(slack, b"") if payload["diff"] > 7: header = b"\r\nID: " stuff = b"".join(bytes(random.choice(string.ascii_uppercase + string.digits), "ascii") for _ in range(payload["diff"] - len(header))) payload["data"] = payload["data"][:-4:] + header + stuff else: payload["data"] = payload["data"][:-4:] + b" ".join(b"" for _ in range(payload["diff"])) payload["data"] = payload["data"] + b"\r\n\r\n" payload["diff"] = 0 return payload def callback(payload): print(payload) try: data = payload.get_payload() pkt = IP(data) if isinstance(pkt.payload, TCP): if isinstance(pkt[TCP].payload, Raw): raw_payload = pkt[TCP].load if raw_payload.startswith(b"GET ") or raw_payload.startswith(b"POST "): if b"Windows NT 6.1" in raw_payload: wrap_payload = {"diff": 0, "data": raw_payload} if b"; WOW64; Trident/" not in raw_payload: wrap_payload = slack_chars(wrap_payload, b"; Trident/", b"; WOW64; Trident/") wrap_payload = slack_chars(wrap_payload, b"Accept-Language: ja-JP\r\n", b"Accept-Language: ko-KR\r\n") wrap_payload = slack_chars(wrap_payload, b"Accept-Language: en-US\r\n", b"Accept-Language: ko-KR\r\n", finalize=True) raw_payload = wrap_payload["data"] new_pkt = change_payload(pkt, raw_payload) payload.set_payload(new_pkt) except Exception as e: print(e) finally: payload.accept() def main(): setup() if SINGLE_QUEUE: start(0) else: p = Pool(nfqueue_number) try: p.map_async(start, [x for x in range(nfqueue_number)]).get(999999999) p.close() except KeyboardInterrupt: p.terminate() print("Flushing iptables.") os.system('iptables -F') os.system('iptables -X') def start(queue_num): nfqueue = NetfilterQueue() nfqueue.bind(queue_num, callback) try: nfqueue.run(block=True) finally: nfqueue.unbind() if __name__ == "__main__": main()

moonlightelite/Traffic-Mod

traffic_modifier.py

Python

# Copyright 2018 The TensorFlow Authors. 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. # ============================================================================== """Large tests for metric_ops.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np from six.moves import xrange # pylint: disable=redefined-builtin from tensorflow.contrib.metrics.python.ops import metric_ops from tensorflow.python.framework import dtypes as dtypes_lib from tensorflow.python.framework import ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import random_ops from tensorflow.python.ops import variables from tensorflow.python.platform import test class StreamingPrecisionRecallAtEqualThresholdsLargeTest(test.TestCase): def setUp(self): np.random.seed(1) ops.reset_default_graph() def testLargeCase(self): shape = [32, 512, 256, 1] predictions = random_ops.random_uniform( shape, 0.0, 1.0, dtype=dtypes_lib.float32) labels = math_ops.greater(random_ops.random_uniform(shape, 0.0, 1.0), 0.5) result, update_op = metric_ops.precision_recall_at_equal_thresholds( labels=labels, predictions=predictions, num_thresholds=201) # Run many updates, enough to cause highly inaccurate values if the # code used float32 for accumulation. num_updates = 71 with self.cached_session() as sess: sess.run(variables.local_variables_initializer()) for _ in xrange(num_updates): sess.run(update_op) prdata = sess.run(result) # Since we use random values, we won't know the tp/fp/tn/fn values, but # tp and fp at threshold 0 should be the total number of positive and # negative labels, hence their sum should be total number of pixels. expected_value = 1.0 * np.product(shape) * num_updates got_value = prdata.tp[0] + prdata.fp[0] # They should be at least within 1. self.assertNear(got_value, expected_value, 1.0) if __name__ == '__main__': test.main()

uve/tensorflow

tensorflow/contrib/metrics/python/ops/metric_ops_large_test.py

Python

# -*- coding: utf-8 -*- import types import copy import inspect import pprint import re import sys import os import pdb import warnings import logging try: import cProfile import pstats has_debug = True except ImportError: has_debug = False import urlparse import cgi from wsgiref.simple_server import make_server from wsgiref.handlers import SimpleHandler SIMPLEAPI_DEBUG = bool(int(os.environ.get('SIMPLEAPI_DEBUG', 0))) SIMPLEAPI_DEBUG_FILENAME = os.environ.get('SIMPLEAPI_DEBUG_FILENAME', 'simpleapi.profile') SIMPLEAPI_DEBUG_LEVEL = os.environ.get('SIMPLEAPI_DEBUG_LEVEL', 'all') assert SIMPLEAPI_DEBUG_LEVEL in ['all', 'call'], \ u'SIMPLEAPI_DEBUG_LEVEL must be one of these: all, call' if SIMPLEAPI_DEBUG and not has_debug: SIMPLEAPI_DEBUG = False warnings.warn("Debugging disabled since packages pstats/cProfile not found (maybe you have to install it).") TRIGGERED_METHODS = ['get', 'post', 'put', 'delete'] FRAMEWORKS = ['flask', 'django', 'appengine', 'dummy', 'standalone', 'wsgi'] MAX_CONTENT_LENGTH = 1024 * 1024 * 16 # 16 megabytes restricted_functions = [ 'before_request', 'after_request' ] try: from google.appengine.ext.webapp import RequestHandler as AE_RequestHandler has_appengine = True except ImportError: has_appengine = False from simpleapi.message.common import SAException from sapirequest import SAPIRequest from request import Request, RequestException from response import Response, ResponseMerger, ResponseException from namespace import NamespaceException from feature import __features__, Feature, FeatureException from simpleapi.message import formatters, wrappers from utils import glob_list __all__ = ('Route', ) class Route(object): def __new__(cls, *args, **kwargs): if kwargs.get('framework') == 'appengine': assert has_appengine class AppEngineRouter(AE_RequestHandler): def __getattribute__(self, name): if name in TRIGGERED_METHODS: self.request.method = name return self else: return AE_RequestHandler.__getattribute__(self, name) def __call__(self): result = self.router(self.request) self.response.out.write(result['result']) AppEngineRouter.router = Router(*args, **kwargs) return AppEngineRouter elif kwargs.get('framework') == 'flask': obj = Router(*args, **kwargs) obj.__name__ = 'Route' return obj elif kwargs.get('framework') == 'wsgi': router = Router(*args, **kwargs) class WSGIHandler(object): def __call__(self, *args, **kwargs): return self.router.handle_request(*args, **kwargs) handler = WSGIHandler() handler.router = router return handler else: return Router(*args, **kwargs) class StandaloneRequest(object): pass class RouterException(SAException): pass class Router(object): def __init__(self, *namespaces, **kwargs): """Takes at least one namespace. """ self.name = kwargs.pop('name', str(id(self))) self.logger = logging.getLogger("simpleapi.%s" % self.name) self.nmap = {} self.debug = kwargs.pop('debug', False) self.ignore_unused_args = kwargs.pop('ignore_unused_args', False) if self.debug and not has_debug: self.debug = False warnings.warn("Debugging disabled since packages pstats/cProfile not found (maybe you have to install it).") self.restful = kwargs.pop('restful', False) self.framework = kwargs.pop('framework', 'django') self.path = re.compile(kwargs.pop('path', r'^/')) assert len(kwargs) == 0, u'Unknown Route configuration(s) (%s)' % \ ", ".join(kwargs.keys()) # make shortcut self._caller = self.__call__ assert self.framework in FRAMEWORKS assert (self.debug ^ SIMPLEAPI_DEBUG) or \ not (self.debug and SIMPLEAPI_DEBUG), \ u'You can either activate Route-debug or simpleapi-debug, not both.' if self.debug or SIMPLEAPI_DEBUG: self.logger.setLevel(logging.DEBUG) handler = logging.StreamHandler() formatter = logging.Formatter( "%(asctime)s - %(name)s - %(levelname)s - %(message)s") handler.setFormatter(formatter) self.logger.addHandler(handler) else: self.logger.setLevel(logging.WARNING) if SIMPLEAPI_DEBUG and SIMPLEAPI_DEBUG_LEVEL == 'all': self.profile_start() for namespace in namespaces: self.add_namespace(namespace) def handle_request(self, environ, start_response): if not self.path.match(environ.get('PATH_INFO')): status = '404 Not found' start_response(status, []) return ["Entry point not found"] else: content_type = environ.get('CONTENT_TYPE') try: content_length = int(environ['CONTENT_LENGTH']) except (KeyError, ValueError): content_length = 0 # make sure we ignore too large requests for security and stability # reasons if content_length > MAX_CONTENT_LENGTH: status = '413 Request entity too large' start_response(status, []) return ["Request entity too large"] request_method = environ.get('REQUEST_METHOD', '').lower() # make sure we only support methods we care if not request_method in TRIGGERED_METHODS: status = '501 Not Implemented' start_response(status, []) return ["Not Implemented"] query_get = urlparse.parse_qs(environ.get('QUERY_STRING')) for key, value in query_get.iteritems(): query_get[key] = value[0] # respect the first value only query_post = {} if content_type in ['application/x-www-form-urlencoded', 'application/x-url-encoded']: post_env = environ.copy() post_env['QUERY_STRING'] = '' fs = cgi.FieldStorage( fp=environ['wsgi.input'], environ=post_env, keep_blank_values=True ) query_post = {} for key in fs: query_post[key] = fs.getvalue(key) elif content_type == 'multipart/form-data': # XXX TODO raise NotImplementedError, u'Currently not supported.' # GET + POST query_data = query_get query_data.update(query_post) # Make request request = StandaloneRequest() request.method = request_method request.data = query_data request.remote_addr = environ.get('REMOTE_ADDR', '') # Make call result = self._caller(request) status = '200 OK' headers = [('Content-type', result['mimetype'])] start_response(status, headers) return [result['result'],] def serve(self, host='', port=5050): httpd = make_server(host, port, self.handle_request) self.logger.info(u"Started serving on port %d..." % port) try: httpd.serve_forever() except KeyboardInterrupt: self.logger.info(u"Server stopped.") def profile_start(self): assert has_debug self.profile = cProfile.Profile() self.profile.enable() def profile_stop(self): assert has_debug self.profile.disable() self.profile.dump_stats(SIMPLEAPI_DEBUG_FILENAME) def profile_stats(self): assert has_debug self.logger.debug(u"Loading stats...") stats = pstats.Stats(SIMPLEAPI_DEBUG_FILENAME) stats.strip_dirs().sort_stats('time', 'calls') \ .print_stats() def __del__(self): if SIMPLEAPI_DEBUG and SIMPLEAPI_DEBUG_LEVEL == 'all': self.profile_stop() self.profile_stats() def is_standalone(self): return self.framework in ['standalone', 'wsgi'] def is_dummy(self): return self.framework == 'dummy' def is_appengine(self): return self.framework == 'appengine' def is_flask(self): return self.framework == 'flask' def is_django(self): return self.framework == 'django' def _redefine_default_namespace(self): # - recalculate default namespace version - # if map has no default version, determine namespace with the # highest version if self.nmap.has_key('default'): del self.nmap['default'] self.nmap['default'] = self.nmap[max(self.nmap.keys())] def remove_namespace(self, version): if self.nmap.has_key(version): del self.nmap[version] self._redefine_default_namespace() return True else: return False def add_namespace(self, namespace): version = getattr(namespace, '__version__', 1) assert isinstance(version, int), \ u'version must be either an integer or not set' # make sure no version is assigned twice assert not self.nmap.has_key(version), u'version is assigned twice' allowed_functions = [] # check for introspection allowed if getattr(namespace, '__introspection__', False): allowed_functions.append('introspect') # determine public and published functions functions = filter(lambda item: '__' not in item[0] and item[0] not in restricted_functions and ((getattr(item[1], 'published', False) == True) or item[0] in allowed_functions), inspect.getmembers(namespace)) # determine arguments of each function functions = dict(functions) for function_name, function_method in functions.iteritems(): # check for reserved function names assert function_name not in ['error', '__init__', 'get_name'],\ u'Name %s is reserved.' % function_name # ArgSpec(args=['self', 'a', 'b'], varargs=None, keywords=None, defaults=None) raw_args = inspect.getargspec(function_method) # does the function allows kwargs? kwargs_allowed = raw_args[2] is not None # get all arguments all_args = raw_args[0][1:] # exclude `self´ # build a dict of optional arguments if raw_args[3] is not None: default_args = zip( raw_args[0][-len(raw_args[3]):], raw_args[3] ) default_args = dict(default_args) else: default_args = {} # build a list of obligatory arguments obligatory_args = list(set(all_args) - set(default_args.keys())) # determine constraints for function if hasattr(function_method, 'constraints'): constraints = function_method.constraints assert isinstance(constraints, dict) or callable(constraints) if isinstance(constraints, dict): def check_constraint(constraints): def check(namespace, key, value): constraint = constraints.get(key) if not constraint: return value if hasattr(constraint, 'match'): if constraint.match(value): return value else: raise ValueError(u'%s does not match constraint') else: if isinstance(constraint, bool): return bool(int(value)) else: return constraint(value) return check constraint_function = check_constraint(constraints) elif callable(constraints): constraint_function = constraints else: constraints = None constraint_function = lambda namespace, key, value: value # determine allowed methods if hasattr(function_method, 'methods'): allowed_methods = function_method.methods assert isinstance(allowed_methods, (list, tuple)) method_function = lambda method, methods: method in methods else: allowed_methods = None method_function = lambda method, methods: True # determine format format = getattr(function_method, 'format', lambda val: val) functions[function_name] = { 'method': function_method, 'name': function_name, 'args': { 'raw': raw_args, 'all': all_args, 'obligatory': obligatory_args, 'defaults': default_args, 'kwargs_allowed': kwargs_allowed }, 'constraints': { 'function': constraint_function, 'raw': constraints, }, 'format': format, 'methods': { 'function': method_function, 'allowed_methods': allowed_methods, } } # configure authentication if hasattr(namespace, '__authentication__'): authentication = namespace.__authentication__ if isinstance(authentication, basestring): if hasattr(namespace, authentication): authentication = getattr(namespace, authentication) else: authentication = lambda namespace, access_key: \ namespace.__authentication__ == access_key else: # grant allow everyone access authentication = lambda namespace, access_key: True # configure ip address based access rights if hasattr(namespace, '__ip_restriction__'): ip_restriction = namespace.__ip_restriction__ assert isinstance(ip_restriction, list) or callable(ip_restriction) if isinstance(ip_restriction, list): # make the ip address list wildcard searchable namespace.__ip_restriction__ = \ glob_list(namespace.__ip_restriction__) # restrict access to the given ip address list ip_restriction = lambda namespace, ip: ip in \ namespace.__ip_restriction__ else: # accept every ip address ip_restriction = lambda namespace, ip: True # configure input formatters input_formatters = formatters.copy() allowed_formatters = getattr(namespace, '__input__', formatters.get_defaults()) input_formatters = filter(lambda i: i[0] in allowed_formatters, input_formatters.items()) input_formatters = dict(input_formatters) # configure output formatters output_formatters = formatters.copy() allowed_formatters = getattr(namespace, '__output__', formatters.get_defaults()) output_formatters = filter(lambda i: i[0] in allowed_formatters, output_formatters.items()) output_formatters = dict(output_formatters) # configure wrappers useable_wrappers = wrappers.copy() if hasattr(namespace, '__wrapper__'): allowed_wrapper = namespace.__wrapper__ useable_wrappers = filter(lambda i: i[0] in allowed_wrapper, useable_wrappers.items()) useable_wrappers = dict(useable_wrappers) self.nmap[version] = { 'class': namespace, 'functions': functions, 'ip_restriction': ip_restriction, 'authentication': authentication, 'input_formatters': input_formatters, 'output_formatters': output_formatters, 'wrappers': useable_wrappers, } # set up all features features = [] if hasattr(namespace, '__features__'): raw_features = namespace.__features__ for feature in raw_features: assert isinstance(feature, basestring) or \ issubclass(feature, Feature) if isinstance(feature, basestring): assert feature in __features__.keys(), \ u'%s is not a built-in feature' % feature features.append(__features__[feature](self.nmap[version])) elif issubclass(feature, Feature): features.append(feature(self.nmap[version])) self.nmap[version]['features'] = features self._redefine_default_namespace() return version def __call__(self, http_request=None, **urlparameters): sapi_request = SAPIRequest(self, http_request) request_items = dict(sapi_request.REQUEST.items()) request_items.update(urlparameters) if SIMPLEAPI_DEBUG and SIMPLEAPI_DEBUG_LEVEL == 'call': self.logger.info(pprint.pformat(request_items)) self.profile_start() version = request_items.pop('_version', 'default') callback = request_items.pop('_callback', None) output_formatter = request_items.pop('_output', None) # let's activate JSONP automatically if _callback is given if callback and not output_formatter: output_formatter = 'jsonp' elif not output_formatter: output_formatter = 'json' input_formatter = request_items.pop('_input', 'value') wrapper = request_items.pop('_wrapper', 'default') mimetype = request_items.pop('_mimetype', None) input_formatter_instance = None output_formatter_instance = None wrapper_instance = None try: try: version = int(version) except (ValueError, TypeError): pass if not self.nmap.has_key(version): # continue with wrong version to get the formatters/wrappers # raise the error later! namespace = self.nmap['default'] else: namespace = self.nmap[version] # check input formatter if input_formatter not in namespace['input_formatters']: raise RequestException(u'Input formatter not allowed or ' \ 'unknown: %s' % input_formatter) # get input formatter input_formatter_instancec = namespace['input_formatters'][input_formatter](sapi_request, callback) # check output formatter if output_formatter not in namespace['output_formatters']: raise RequestException(u'Output formatter not allowed or ' \ 'unknown: %s' % output_formatter) # get output formatter output_formatter_instance = namespace['output_formatters'][output_formatter](sapi_request, callback) # check wrapper if wrapper not in namespace['wrappers']: raise RequestException(u'Wrapper unknown or not allowed: %s' % \ wrapper) # get wrapper wrapper_instance = namespace['wrappers'][wrapper] # check whether version exists or not if not self.nmap.has_key(version): raise RouterException(u'Version %s not found (possible: %s)' % \ (version, ", ".join(map(lambda i: str(i), self.nmap.keys())))) request = Request( sapi_request=sapi_request, namespace=namespace, input_formatter=input_formatter_instancec, output_formatter=output_formatter_instance, wrapper=wrapper_instance, callback=callback, mimetype=mimetype, restful=self.restful, debug=self.debug, route=self, ignore_unused_args=self.ignore_unused_args, ) # map request items to the correct names wi = wrapper_instance(sapi_request=sapi_request) request_items = wi._parse(request_items) if not isinstance(request_items, (list, tuple, types.GeneratorType)): request_items = [request_items, ] responses = [] for request_item in request_items: # clear session (except _internal) sapi_request.session.clear() # process request try: responses.append(request.process_request(request_item)) except (NamespaceException, RequestException, \ ResponseException, RouterException, FeatureException),e: response = Response( sapi_request, errors=e.message, output_formatter=output_formatter_instance, wrapper=wrapper_instance, mimetype=mimetype ) responses.append(response) rm = ResponseMerger( sapi_request=sapi_request, responses=responses, ) http_response = rm.build() except Exception, e: if isinstance(e, (NamespaceException, RequestException, \ ResponseException, RouterException, \ FeatureException)): err_msg = repr(e) else: err_msg = u'An internal error occurred during your request.' trace = inspect.trace() msgs = [] msgs.append('') msgs.append(u"******* Exception raised *******") msgs.append(u'Exception type: %s' % type(e)) msgs.append(u'Exception msg: %s' % repr(e)) msgs.append('') msgs.append(u'------- Traceback follows -------') for idx, item in enumerate(trace): msgs.append(u"(%s)\t%s:%s (%s)" % (idx+1, item[3], item[2], item[1])) if item[4]: for line in item[4]: msgs.append(u"\t\t%s" % line.strip()) msgs.append('') # blank line msgs.append(' -- End of traceback -- ') msgs.append('') self.logger.error("\n".join(msgs)) if self.debug: e, m, tb = sys.exc_info() pdb.post_mortem(tb) response = Response( sapi_request, errors=err_msg, output_formatter=output_formatter_instance, wrapper=wrapper_instance, mimetype=mimetype ) http_response = response.build(skip_features=True) if SIMPLEAPI_DEBUG and SIMPLEAPI_DEBUG_LEVEL == 'call': self.profile_stop() self.profile_stats() return http_response

ghuntley/simpleapi

simpleapi/server/route.py

Python

""" Carbon Scraper Plugin for Userbot. //text in creative way. usage: .carbon //as a reply to any text message Thanks to @AvinashReddy3108 for a Base Plugin. Go and Do a star on his repo: https://github.com/AvinashReddy3108/PaperplaneExtended/ """ from selenium.webdriver.support.ui import Select from selenium.webdriver.chrome.options import Options from selenium import webdriver from telethon import events from urllib.parse import quote_plus from urllib.error import HTTPError from time import sleep import asyncio import os @borg.on(events.NewMessage(pattern=r"\.carbon", outgoing=True)) async def carbon_api(e): if not e.text[0].isalpha() and e.text[0] not in ("/", "#", "@", "!"): """ A Wrapper for carbon.now.sh """ await e.edit("Processing...") CARBON = 'https://carbon.now.sh/?l={lang}&code={code}' CARBONLANG = "en" textx = await e.get_reply_message() pcode = e.text if pcode[8:]: pcode = str(pcode[8:]) elif textx: pcode = str(textx.message) # Importing message to module code = quote_plus(pcode) # Converting to urlencoded url = CARBON.format(code=code, lang=CARBONLANG) chrome_options = Options() chrome_options.add_argument("--headless") chrome_options.binary_location = Config.GOOGLE_CHROME_BIN chrome_options.add_argument("--window-size=1920x1080") chrome_options.add_argument("--disable-dev-shm-usage") chrome_options.add_argument("--no-sandbox") chrome_options.add_argument('--disable-gpu') prefs = {'download.default_directory' : './'} chrome_options.add_experimental_option('prefs', prefs) await e.edit("Processing 30%") driver = webdriver.Chrome(executable_path=Config.CHROME_DRIVER, options=chrome_options) driver.get(url) download_path = './' driver.command_executor._commands["send_command"] = ("POST", '/session/$sessionId/chromium/send_command') params = {'cmd': 'Page.setDownloadBehavior', 'params': {'behavior': 'allow', 'downloadPath': download_path}} command_result = driver.execute("send_command", params) driver.find_element_by_xpath("//button[contains(text(),'Export')]").click() sleep(5) # this might take a bit. driver.find_element_by_xpath("//button[contains(text(),'4x')]").click() sleep(5) await e.edit("Processing 50%") driver.find_element_by_xpath("//button[contains(text(),'PNG')]").click() sleep(5) #Waiting for downloading await e.edit("Processing 90%") file = './carbon.png' await e.edit("Done!!") await e.client.send_file( e.chat_id, file, caption="Made with Love by [AmazerS](https://t.me/AmazerS_xD)", force_document=True, reply_to=e.message.reply_to_msg_id, ) os.remove('./carbon.png') # Removing carbon.png after uploading await e.delete() # Deleting msg

Amazers03/Unitg

stdplugins/carbon.py

Python

# -*- coding: utf-8 -*- """ Created on Fri Oct 31 20:29:57 2014 @author: garrett """ from user import User def save_users(users, filename='output.csv'): '''Save users out to a .csv file Each row will represent a user UID, following by all the user's students (if the user has any) INPUT: > users: set of User objects > filename: filename to save .csv to.''' with open(filename, 'w') as file: for count, user in enumerate(users): file.write(str(user.get_uid())) for student in user.get_students(): file.write(',' + str(student.get_uid())) file.write('\n') if count % 100 == 0: file.flush() return def load_users(filename): '''Load users from a .csv file Each row will represent a user uid, following by all the user's student (if the user has any). Note: the uid is not assumed to be an integer, so it read in as a string, which shouldn't matter anyway. TODO: we could probably speed this up by loading multiple lines at a time. INPUT: > filename: filename to read .csv from RETURN: > users: a set of User objects''' users = dict() # On first read, we create Users, on the following read, we save student # connections with open(filename, 'r') as file: for line in file: line = line.split('\n')[0] split_line = line.split(',') new_uid = _try_converting_to_int(split_line[0]) new_user = User(new_uid) users.update({new_user.get_uid(): new_user}) with open(filename, 'r') as file: for line in file: line = line.split('\n')[0] split_line = line.split(',') current_uid = _try_converting_to_int(split_line[0]) for student_uid in split_line[1:]: student_uid = _try_converting_to_int(student_uid) users[current_uid].add_students(users[student_uid]) return set(users.values()) def _try_converting_to_int(num): try: return int(num) except ValueError: return num

Garrett-R/infections

save_load.py

Python

def equivalent(left, right): if left.alphabet != right.alphabet: raise ValueError("Input alphabets must be equal!") transitions = [] previous_states = [] alphabet = left.alphabet states = [(left.initial_state(), right.initial_state())] while len(states) != 0: l, r = states.pop() previous_states.append((l.name, r.name)) for value in alphabet: next_l, next_r = l.next_state(value), r.next_state(value) if (next_l is None and next_r is not None) \ or (next_r is None and next_l is not None): return False if (next_l[0], next_r[0]) not in previous_states: transitions.append((next_l[1], next_r[1])) states.append((left[next_l[0]], right[next_r[0]])) for (left, right) in transitions: if left != right: return False return True

SHvatov/AutomataTheory

equivalence/equivalence.py

Python

import torch from torchvision.transforms import functional as TFF import matplotlib.pyplot as plt from theseus.base.trainer.supervised_trainer import SupervisedTrainer from theseus.utilities.loading import load_state_dict from theseus.classification.utilities.gradcam import CAMWrapper, show_cam_on_image from theseus.utilities.visualization.visualizer import Visualizer from theseus.utilities.analysis.analyzer import ClassificationAnalyzer from theseus.utilities.loggers.observer import LoggerObserver LOGGER = LoggerObserver.getLogger("main") class ClassificationTrainer(SupervisedTrainer): """Trainer for classification tasks """ def __init__(self, **kwargs): super().__init__(**kwargs) def check_best(self, metric_dict): """ Hook function, called after metrics are calculated """ if metric_dict['bl_acc'] > self.best_value: if self.iters > 0: # Have been training, else in evaluation-only mode or just sanity check LOGGER.text( f"Evaluation improved from {self.best_value} to {metric_dict['bl_acc']}", level=LoggerObserver.INFO) self.best_value = metric_dict['bl_acc'] self.save_checkpoint('best') else: if self.visualize_when_val: self.visualize_pred() def save_checkpoint(self, outname='last'): """ Save all information of the current iteration """ weights = { 'model': self.model.model.state_dict(), 'optimizer': self.optimizer.state_dict(), 'iters': self.iters, 'best_value': self.best_value, } if self.scaler is not None: weights[self.scaler.state_dict_key] = self.scaler.state_dict() self.checkpoint.save(weights, outname) def load_checkpoint(self, path:str): """ Load all information the current iteration from checkpoint """ LOGGER.text("Loading checkpoints...", level=LoggerObserver.INFO) state_dict = torch.load(path, map_location='cpu') self.iters = load_state_dict(self.iters, state_dict, 'iters') self.best_value = load_state_dict(self.best_value, state_dict, 'best_value') self.scaler = load_state_dict(self.scaler, state_dict, self.scaler.state_dict_key) def visualize_gt(self): """ Visualize dataloader for sanity check """ LOGGER.text("Visualizing dataset...", level=LoggerObserver.DEBUG) visualizer = Visualizer() # Train batch batch = next(iter(self.trainloader)) images = batch["inputs"] batch = [] for idx, inputs in enumerate(images): img_show = visualizer.denormalize(inputs) img_cam = TFF.to_tensor(img_show) batch.append(img_cam) grid_img = visualizer.make_grid(batch) fig = plt.figure(figsize=(8,8)) plt.axis('off') plt.imshow(grid_img) plt.tight_layout(pad=0) LOGGER.log([{ 'tag': "Sanitycheck/batch/train", 'value': fig, 'type': LoggerObserver.FIGURE, 'kwargs': { 'step': self.iters } }]) # Validation batch batch = next(iter(self.valloader)) images = batch["inputs"] batch = [] for idx, inputs in enumerate(images): img_show = visualizer.denormalize(inputs) img_cam = TFF.to_tensor(img_show) batch.append(img_cam) grid_img = visualizer.make_grid(batch) fig = plt.figure(figsize=(8,8)) plt.axis('off') plt.imshow(grid_img) plt.tight_layout(pad=0) LOGGER.log([{ 'tag': "Sanitycheck/batch/val", 'value': fig, 'type': LoggerObserver.FIGURE, 'kwargs': { 'step': self.iters } }]) @torch.enable_grad() #enable grad for CAM def visualize_pred(self): r"""Visualize model prediction and CAM """ # Vizualize Grad Class Activation Mapping and model predictions LOGGER.text("Visualizing model predictions...", level=LoggerObserver.DEBUG) visualizer = Visualizer() batch = next(iter(self.valloader)) images = batch["inputs"] targets = batch["targets"] self.model.eval() model_name = self.model.model.name grad_cam = CAMWrapper.get_method( name='gradcam', model=self.model.model.get_model(), model_name=model_name, use_cuda=next(self.model.parameters()).is_cuda) grayscale_cams, label_indices, scores = grad_cam(images, return_probs=True) gradcam_batch = [] pred_batch = [] for idx in range(len(grayscale_cams)): image = images[idx] target = targets[idx].item() label = label_indices[idx] grayscale_cam = grayscale_cams[idx, :] score = scores[idx] img_show = visualizer.denormalize(image) visualizer.set_image(img_show) if self.valloader.dataset.classnames is not None: label = self.valloader.dataset.classnames[label] target = self.valloader.dataset.classnames[target] if label == target: color = [0,1,0] else: color = [1,0,0] visualizer.draw_label( f"GT: {target}\nP: {label}\nC: {score:.4f}", fontColor=color, fontScale=0.8, thickness=2, outline=None, offset=100 ) img_cam =show_cam_on_image(img_show, grayscale_cam, use_rgb=True) img_cam = TFF.to_tensor(img_cam) gradcam_batch.append(img_cam) pred_img = visualizer.get_image() pred_img = TFF.to_tensor(pred_img) pred_batch.append(pred_img) if idx == 63: # limit number of images break # GradCAM images gradcam_grid_img = visualizer.make_grid(gradcam_batch) fig = plt.figure(figsize=(8,8)) plt.imshow(gradcam_grid_img) plt.axis("off") plt.tight_layout(pad=0) LOGGER.log([{ 'tag': "Validation/gradcam", 'value': fig, 'type': LoggerObserver.FIGURE, 'kwargs': { 'step': self.iters } }]) # Prediction images pred_grid_img = visualizer.make_grid(pred_batch) fig = plt.figure(figsize=(10,10)) plt.imshow(pred_grid_img) plt.axis("off") plt.tight_layout(pad=0) LOGGER.log([{ 'tag': "Validation/prediction", 'value': fig, 'type': LoggerObserver.FIGURE, 'kwargs': { 'step': self.iters } }]) # Zeroing gradients in optimizer for safety self.optimizer.zero_grad() @torch.no_grad() def visualize_model(self): # Vizualize Model Graph LOGGER.text("Visualizing architecture...", level=LoggerObserver.DEBUG) batch = next(iter(self.valloader)) images = batch["inputs"].to(self.model.device) LOGGER.log([{ 'tag': "Sanitycheck/analysis/architecture", 'value': self.model.model.get_model(), 'type': LoggerObserver.TORCH_MODULE, 'kwargs': { 'inputs': images } }]) def analyze_gt(self): """ Perform simple data analysis """ LOGGER.text("Analyzing datasets...", level=LoggerObserver.DEBUG) analyzer = ClassificationAnalyzer() analyzer.add_dataset(self.trainloader.dataset) fig = analyzer.analyze(figsize=(10,5)) LOGGER.log([{ 'tag': "Sanitycheck/analysis/train", 'value': fig, 'type': LoggerObserver.FIGURE, 'kwargs': { 'step': self.iters } }]) analyzer = ClassificationAnalyzer() analyzer.add_dataset(self.valloader.dataset) fig = analyzer.analyze(figsize=(10,5)) LOGGER.log([{ 'tag': "Sanitycheck/analysis/val", 'value': fig, 'type': LoggerObserver.FIGURE, 'kwargs': { 'step': self.iters } }]) def on_evaluate_end(self): if self.visualize_when_val: self.visualize_pred() self.save_checkpoint() def on_start(self): if self.resume is not None: self.load_checkpoint(self.resume) def sanitycheck(self): """Sanity check before training """ self.visualize_gt() self.analyze_gt() self.visualize_model() self.evaluate_epoch()

lannguyen0910/theseus

theseus/classification/trainer/trainer.py

Python

#!/usr/bin/env python3 # Copyright (c) 2015-2020 The Beans Core developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. """Test invalid p2p messages for nodes with bloom filters disabled. Test that, when bloom filters are not enabled, peers are disconnected if: 1. They send a p2p mempool message 2. They send a p2p filterload message 3. They send a p2p filteradd message 4. They send a p2p filterclear message """ from test_framework.messages import msg_mempool, msg_filteradd, msg_filterload, msg_filterclear from test_framework.p2p import P2PInterface from test_framework.test_framework import BeansTestFramework from test_framework.util import assert_equal class P2PNoBloomFilterMessages(BeansTestFramework): def set_test_params(self): self.setup_clean_chain = True self.num_nodes = 1 self.extra_args = [["-peerbloomfilters=0"]] def test_message_causes_disconnect(self, message): """Add a p2p connection that sends a message and check that it disconnects.""" peer = self.nodes[0].add_p2p_connection(P2PInterface()) peer.send_message(message) peer.wait_for_disconnect() assert_equal(self.nodes[0].getconnectioncount(), 0) def run_test(self): self.log.info("Test that peer is disconnected if it sends mempool message") self.test_message_causes_disconnect(msg_mempool()) self.log.info("Test that peer is disconnected if it sends filterload message") self.test_message_causes_disconnect(msg_filterload()) self.log.info("Test that peer is disconnected if it sends filteradd message") self.test_message_causes_disconnect(msg_filteradd(data=b'\xcc')) self.log.info("Test that peer is disconnected if it sends a filterclear message") self.test_message_causes_disconnect(msg_filterclear()) if __name__ == '__main__': P2PNoBloomFilterMessages().main()

BakedInside/Beans-Core

test/functional/p2p_nobloomfilter_messages.py

Python

# Copyright 2020 The SQLFlow Authors. 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. import os import numpy as np import six import sklearn.metrics import runtime.temp_file as temp_file import xgboost as xgb from runtime import db from runtime.dbapi.paiio import PaiIOConnection from runtime.feature.compile import compile_ir_feature_columns from runtime.feature.derivation import get_ordered_field_descs from runtime.feature.field_desc import DataType from runtime.model import EstimatorType from runtime.model.model import Model from runtime.pai.pai_distributed import define_tf_flags from runtime.step.xgboost.predict import _calc_predict_result from runtime.xgboost.dataset import xgb_dataset # TODO(typhoonzero): remove runtime.xgboost from runtime.xgboost.feature_column import ComposedColumnTransformer FLAGS = define_tf_flags() SKLEARN_METRICS = [ 'accuracy_score', 'average_precision_score', 'balanced_accuracy_score', 'brier_score_loss', 'cohen_kappa_score', 'explained_variance_score', 'f1_score', 'fbeta_score', 'hamming_loss', 'hinge_loss', 'log_loss', 'mean_absolute_error', 'mean_squared_error', 'mean_squared_log_error', 'median_absolute_error', 'precision_score', 'r2_score', 'recall_score', 'roc_auc_score', 'zero_one_loss', ] def evaluate(datasource, select, result_table, model, label_name=None, model_params=None, result_column_names=[], pai_table=None): """TBD """ if model_params is None: model_params = {} validation_metrics = model_params.get("validation.metrics", "accuracy_score") validation_metrics = [m.strip() for m in validation_metrics.split(",")] bst = xgb.Booster() if isinstance(model, six.string_types): with temp_file.TemporaryDirectory(as_cwd=True): model = Model.load_from_db(datasource, model) bst.load_model("my_model") else: assert isinstance(model, Model), "not supported model type %s" % type(model) bst.load_model("my_model") model_params = model.get_meta("attributes") fc_map_ir = model.get_meta("features") train_label = model.get_meta("label") train_label_desc = train_label.get_field_desc()[0] if label_name: train_label_desc.name = label_name feature_columns = compile_ir_feature_columns(fc_map_ir, EstimatorType.XGBOOST) field_descs = get_ordered_field_descs(fc_map_ir) feature_column_names = [fd.name for fd in field_descs] feature_metas = dict([(fd.name, fd.to_dict(dtype_to_string=True)) for fd in field_descs]) transform_fn = ComposedColumnTransformer( feature_column_names, *feature_columns["feature_columns"]) is_pai = True if pai_table else False if is_pai: conn = PaiIOConnection.from_table(pai_table) else: conn = db.connect_with_data_source(datasource) with temp_file.TemporaryDirectory() as tmp_dir_name: pred_fn = os.path.join(tmp_dir_name, "predict.txt") dpred = xgb_dataset( datasource=datasource, fn=pred_fn, dataset_sql=select, feature_metas=feature_metas, feature_column_names=feature_column_names, label_meta=train_label_desc.to_dict(dtype_to_string=True), cache=True, batch_size=10000, transform_fn=transform_fn, is_pai=is_pai, pai_table=pai_table, pai_single_file=True, feature_column_code=fc_map_ir) for i, pred_dmatrix in enumerate(dpred): if is_pai: feature_file_name = pred_fn else: feature_file_name = pred_fn + "_%d" % i preds = _calc_predict_result(bst, pred_dmatrix, model_params) _store_evaluate_result(preds, feature_file_name, train_label_desc, result_table, result_column_names, validation_metrics, conn) conn.close() def _store_evaluate_result(preds, feature_file_name, label_desc, result_table, result_column_names, validation_metrics, conn): """ Save the evaluation result in the table. Args: preds: the prediction result. feature_file_name (str): the file path where the feature dumps. label_desc (FieldDesc): the label FieldDesc object. result_table (str): the result table name. result_column_names (list[str]): the result column names. validation_metrics (list[str]): the evaluation metric names. conn: the database connection object. Returns: None. """ y_test = [] with open(feature_file_name, 'r') as f: for line in f.readlines(): row = [i for i in line.strip().split("\t")] # DMatrix store label in the first column if label_desc.dtype == DataType.INT64: y_test.append(int(row[0])) elif label_desc.dtype == DataType.FLOAT32: y_test.append(float(row[0])) else: raise TypeError("unsupported data type {}".format( label_desc.dtype)) y_test = np.array(y_test) evaluate_results = dict() for metric_name in validation_metrics: metric_name = metric_name.strip() if metric_name not in SKLEARN_METRICS: raise ValueError("unsupported metrics %s" % metric_name) metric_func = getattr(sklearn.metrics, metric_name) metric_value = metric_func(y_test, preds) evaluate_results[metric_name] = metric_value # write evaluation result to result table with db.buffered_db_writer(conn, result_table, result_column_names) as w: row = ["0.0"] for mn in validation_metrics: row.append(str(evaluate_results[mn])) w.write(row)

awsl-dbq/sqlflow

python/runtime/step/xgboost/evaluate.py

Python