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codeparrot-valid-near-deduplication

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CodeParrot 110

PieterMostert/Lipgloss

view/pretty_names.py

1

1617

# LIPGLOSS - Graphical user interface for constructing glaze recipes # Copyright (C) 2017 Pieter Mostert # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, version 3 of the License. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # version 3 along with this program (see LICENCE.txt). If not, see # <http://www.gnu.org/licenses/>. # Contact: [email protected] # Construct prettify function pretty_dict = {'SiO2':'SiO\u2082', 'Al2O3':'Al\u2082O\u2083', 'B2O3':'B\u2082O\u2083', 'Li2O':'Li\u2082O', 'Na2O':'Na\u2082O', 'K2O':'K\u2082O', 'P2O5':'P\u2082O\u2085', 'Fe2O3':'Fe\u2082O\u2083', 'TiO2':'TiO\u2082', 'MnO2':'MnO\u2082', 'SiO2_Al2O3':'SiO\u2082 : Al\u2082O\u2083', 'cost':'Cost', 'mass_perc_':'% weight', 'mole_perc_':'% mole'} def prettify(text): try: return pretty_dict[text] except: return text def pretty_entry_type(text): if text == 'um': return ' UMF' elif text == 'ma': return ' % weight' elif text == 'mo': return ' % mole' else: return ''

gpl-3.0

gsnbng/erpnext

erpnext/patches/v4_2/fix_gl_entries_for_stock_transactions.py

2

2129

# Copyright (c) 2015, Frappe Technologies Pvt. Ltd. and Contributors # License: GNU General Public License v3. See license.txt from __future__ import print_function, unicode_literals import frappe from frappe.utils import flt def execute(): from erpnext.stock.stock_balance import repost repost(allow_zero_rate=True, only_actual=True) frappe.reload_doctype("Account") warehouse_account = frappe.db.sql("""select name, master_name from tabAccount where ifnull(account_type, '') = 'Warehouse'""") if warehouse_account: warehouses = [d[1] for d in warehouse_account] accounts = [d[0] for d in warehouse_account] stock_vouchers = frappe.db.sql("""select distinct sle.voucher_type, sle.voucher_no from `tabStock Ledger Entry` sle where sle.warehouse in (%s) order by sle.posting_date""" % ', '.join(['%s']*len(warehouses)), tuple(warehouses)) rejected = [] for voucher_type, voucher_no in stock_vouchers: stock_bal = frappe.db.sql("""select sum(stock_value_difference) from `tabStock Ledger Entry` where voucher_type=%s and voucher_no =%s and warehouse in (%s)""" % ('%s', '%s', ', '.join(['%s']*len(warehouses))), tuple([voucher_type, voucher_no] + warehouses)) account_bal = frappe.db.sql("""select ifnull(sum(ifnull(debit, 0) - ifnull(credit, 0)), 0) from `tabGL Entry` where voucher_type=%s and voucher_no =%s and account in (%s) group by voucher_type, voucher_no""" % ('%s', '%s', ', '.join(['%s']*len(accounts))), tuple([voucher_type, voucher_no] + accounts)) if stock_bal and account_bal and abs(flt(stock_bal[0][0]) - flt(account_bal[0][0])) > 0.1: try: print(voucher_type, voucher_no, stock_bal[0][0], account_bal[0][0]) frappe.db.sql("""delete from `tabGL Entry` where voucher_type=%s and voucher_no=%s""", (voucher_type, voucher_no)) voucher = frappe.get_doc(voucher_type, voucher_no) voucher.make_gl_entries() frappe.db.commit() except Exception as e: print(frappe.get_traceback()) rejected.append([voucher_type, voucher_no]) frappe.db.rollback() print("Failed to repost: ") print(rejected)

agpl-3.0

alessio/prey

platform/linux/prey-config.py

1

21957

#!/usr/bin/env python ################################################ # Prey Configurator for Linux # By Tomas Pollak # (c) 2010 - Fork Ltd. (usefork.com) ################################################ # if having trouble with the GTK theme as root, do this: # sudo ln -s ~/.themes/ /root/.themes ################################################ # base includes ################################################ import pygtk pygtk.require("2.0") import gtk import os # from xml.dom.minidom import parseString import re import urllib app_name = 'prey-config' lang_path = 'lang' script_path = os.sys.path[0] ################################################ # gettext localization ################################################ import locale import gettext # locale.setlocale(locale.LC_ALL, '') # locale.bindtextdomain(app_name, lang_path) gettext.bindtextdomain(app_name, lang_path) gettext.textdomain(app_name) _ = gettext.gettext ################################################ # vars and such ################################################ PREY_PATH = '/usr/share/prey' CONFIG_FILE = PREY_PATH + '/config' CONTROL_PANEL_URL = 'http://control.preyproject.com' CONTROL_PANEL_URL_SSL = 'https://control.preyproject.com' GUEST_ACCOUNT_NAME = 'guest_account' VERSION = os.popen("cat " + PREY_PATH + "/version 2> /dev/null").read().strip().replace('version=', '').replace("'",'') PAGES = ['report_options', 'control_panel_options', 'new_user', 'existing_user', 'existing_device', 'standalone_options'] class PreyConfigurator(object): ################################################ # helper functions ################################################ def get(self, name): return self.root.get_object(name) def text(self, name): return self.get(name).get_text() def checkbox(self, name): if self.get(name).get_active() == True: return 'y' else: return 'n' ################################################ # validations ################################################ def validate_email(self, string): if len(string) > 7: if re.match("^.+\\@(\\[?)[a-zA-Z0-9\\-\\.]+\\.([a-zA-Z]{2,3}|[0-9]{1,3})(\\]?)$", string) != None: return True return False def validate_fields(self): if self.text('user_name') == '': self.show_alert(_("Empty name!"), _("Please type in your name.")) return False if self.validate_email(self.text('email')) == False: self.show_alert(_("Invalid email"), _("Please make sure the email address you typed is valid.")) return False if len(self.text('password')) < 6: self.show_alert(_("Bad password"), _("Password should contain at least 6 chars. Please try again.")) return False elif self.text('password') != self.text('password_confirm'): self.show_alert(_("Passwords don't match"), _("Please make sure both passwords match!")) return False return True ################################################ # dialogs ################################################ def show_alert(self, title, message, quit = False): dialog = gtk.MessageDialog( parent = None, flags = gtk.DIALOG_MODAL | gtk.DIALOG_DESTROY_WITH_PARENT, type = gtk.MESSAGE_INFO, buttons = gtk.BUTTONS_OK, message_format = message) dialog.set_title(title) if quit == True: dialog.connect('response', lambda dialog, response: gtk.main_quit()) else: dialog.connect('response', lambda dialog, response: dialog.destroy()) self.center_dialog(dialog) dialog.show() def show_question(self, title, message): dialog = gtk.MessageDialog( parent = None, flags = gtk.DIALOG_MODAL | gtk.DIALOG_DESTROY_WITH_PARENT, type = gtk.MESSAGE_QUESTION, buttons = gtk.BUTTONS_YES_NO, message_format = message) dialog.set_title(title) self.center_dialog(dialog) response = dialog.run() dialog.destroy() return response def show_about(self): dialog = self.get('about_prey_config') self.center_dialog(dialog) dialog.show() def close_about(self, dialog, response): dialog.hide() def center_dialog(self, dialog): if 'window' in self.__dict__: dialog.set_transient_for(self.window) dialog.set_position(gtk.WIN_POS_CENTER_ON_PARENT) ################################################ # window and widget management ################################################ def get_page_name(self): return PAGES[self.pages.get_current_page()] def toggle_pg3_next_apply(self, button): button_next = self.get('button_next') button_apply = self.get('button_apply') if self.get('use_existing_device').get_active() == False: button_next.hide() button_apply.show() button_apply.grab_default() else: button_apply.hide() button_next.show() button_next.grab_default() def next_page(self, button): page_name = self.get_page_name() increment = 1 if page_name == 'control_panel_options' and self.get('new_user_option').get_active() == False: increment = 2 if page_name == 'report_options': if self.get('reporting_mode_cp').get_active() == True: if self.current_api_key != '': response = self.show_question(_("Hold your horses!"), _("Your device seems to be already synchronized with the Control Panel! Do you want to re-setup your account? (Not recommended)")) if response == gtk.RESPONSE_NO: return else: increment = 5 if page_name == 'existing_user': # then we are going to select an exising device if not self.get_existing_user(True): # login didn't work, so don't go to next page return self.pages.set_current_page(self.pages.get_current_page() + increment) self.toggle_buttons(button, None, 1) def prev_page(self, button): page_name = self.get_page_name() decrement = 1 if page_name == 'existing_user': decrement = 2 elif page_name == 'standalone_options': decrement = 5 if self.pages.get_current_page() != 0: self.pages.set_current_page(self.pages.get_current_page() - decrement) self.toggle_buttons(button, None, 1) def toggle_buttons(self, button, tab, tab_number): button_prev = self.get('button_prev') button_next = self.get('button_next') button_apply = self.get('button_apply') if tab_number == 0: #main settings tab button_prev.hide() button_next.hide() button_apply.show() self.hide_ssl() else: page_name = self.get_page_name() if page_name == 'report_options': button_prev.hide() else: button_prev.show() if page_name == 'report_options' or page_name == 'control_panel_options' or (page_name == 'existing_user' and self.get('use_existing_device').get_active() == True): button_apply.hide() button_next.show() button_next.grab_default() else: button_next.hide() button_apply.show() button_apply.grab_default() if self.get_page_name() == 'new_user' or self.get_page_name() == 'existing_user': self.show_ssl() else: self.hide_ssl() def hide_ssl(self): self.get('icon_ssl').hide() self.get('lbl_ssl').hide() def show_ssl(self): self.get('icon_ssl').show() self.get('lbl_ssl').show() def set_default_action(self,button,ctrl): button_cancel = self.get('button_cancel') cancel_has_default = button_cancel.flags() & gtk.HAS_DEFAULT button_prev = self.get('button_prev') prev_has_default = button_prev.flags() & gtk.HAS_DEFAULT button_next = self.get('button_next') button_apply = self.get('button_apply') if not cancel_has_default and not prev_has_default: if button_next.flags() & gtk.VISIBLE: button_next.grab_default() else: button_apply.grab_default() def ensure_visible(self,widget,event): #ensure the widget focused is visible in the scroll window self.get('delay').set_name('delay') self.get('extended_headers').set_name('extended_headers') widget_name = widget.get_name() scrollwindow = self.get('main_settings_scrollwindow') internal_height = self.get('main_settings').get_size()[1] port_height = scrollwindow.allocation.height port_vadjust = scrollwindow.get_vadjustment() port_posn = port_vadjust.value widget_posn = widget.allocation.y widget_height = widget.allocation.height if (widget_posn - port_posn) >= 0 and (widget_posn + widget_height - port_posn) <= port_height: #widget is fully visible (even if its description or icon is not), so do nothing return False # for now we know there are only two possible hidden widgets so we scroll all the way up or all the way down # if we add options to this page we will have to scroll differently if widget_name == 'delay': #scroll to top port_vadjust.set_value(0) elif widget_name == 'extended_headers': #scroll to bottom port_vadjust.set_value(internal_height - port_height) return True def key_pressed(self, widget, event): # show about dialog on F1 keypress if (event.keyval == gtk.keysyms.F1) \ and (event.state & gtk.gdk.CONTROL_MASK) == 0 \ and (event.state & gtk.gdk.SHIFT_MASK) == 0: self.show_about() return True return False ################################################ # setting getting ################################################ def prey_exists(self): if not os.path.exists(PREY_PATH + '/core'): self.show_alert(_("Prey not installed"), _("Couldn't find a Prey installation on this system. Sorry."), True) else: return True def is_config_writable(self): command = 'if [ ! -w "'+PREY_PATH+'/config" ]; then echo 1; fi' no_access = os.popen(command).read().strip() if no_access == '1': self.show_alert(_("Unauthorized"), _("You don't have access to manage Prey's configuration. Sorry."), True) else: return True def get_setting(self, var): command = 'grep \''+var+'=\' '+CONFIG_FILE+' | sed "s/'+var+'=\'\(.*\)\'/\\1/"' return os.popen(command).read().strip() def get_current_settings(self): self.current_delay = os.popen("crontab -l | grep prey | cut -c 3-4").read() if not self.current_delay: self.current_delay = 20 self.current_auto_connect = self.get_setting('auto_connect') self.current_extended_headers = self.get_setting('extended_headers') self.current_guest_account = self.guest_account_exists() self.current_lang = self.get_setting('lang') self.current_check_url = self.get_setting('check_url') self.current_post_method = self.get_setting('post_method') self.current_api_key = self.get_setting('api_key') self.current_device_key = self.get_setting('device_key') self.current_mail_to = self.get_setting('mail_to') self.current_smtp_server = self.get_setting('smtp_server') self.current_smtp_username = self.get_setting('smtp_username') def guest_account_exists(self): result = os.popen('id ' + GUEST_ACCOUNT_NAME + ' 2> /dev/null').read() if result.find("uid"): return False else: return True def toggle_guest_account(self, enabled): if enabled: # create user and leave password blank os.system("useradd -m " + GUEST_ACCOUNT_NAME + "; passwd -d " + GUEST_ACCOUNT_NAME) # Authorize login with no passwords in gdm os.system("sed -i 's/PasswordRequired=false/#PasswordRequired=false/' /etc/gdm/gdm.conf") # Authorize login with no passwords in pam os.system("sed -i 's/nullok_secure/nullok/' /etc/pam.d/common-auth") else: os.system("userdel -r " + GUEST_ACCOUNT_NAME) os.system("sed -i 's/#PasswordRequired=false/PasswordRequired=false/' /etc/gdm/gdm.conf") os.system("sed -i 's/nullok/nullok_secure/' /etc/pam.d/common-auth") def display_real_settings(self): self.get('delay').set_value(int(self.current_delay)) self.get('guest_account').set_active(self.current_guest_account) if self.current_auto_connect == 'y': self.get('auto_connect').set_active(True) if self.current_extended_headers == 'y': self.get('extended_headers').set_active(True) self.get('check_url').set_text(self.current_check_url) self.get('mail_to').set_text(self.current_mail_to) self.get('smtp_server').set_text(self.current_smtp_server) self.get('smtp_username').set_text(self.current_smtp_username) if self.current_post_method == 'email': self.get('reporting_mode_standalone').set_active(True) def check_if_configured(self): if self.current_post_method == 'http' and self.current_api_key == '': self.show_alert(_('Welcome!'), _("It seems this is the first time you run this setup. Please set up your reporting method now, otherwise Prey won't work!")) ################################################ # setting settings ################################################ def save(self, param, value): if param == 'check_url': value = value.replace('/', '\/') command = 'sed -i -e "s/'+param+'=\'.*\'/'+param+'=\''+value+'\'/" '+ CONFIG_FILE os.system(command) def apply_settings(self, button): self.get('button_apply').set_label(_("Saving...")) if self.get("main_tabs").get_current_page() == 0: # main settings page self.apply_main_settings() else: page_name = self.get_page_name() if page_name == 'new_user': if self.validate_fields(): self.create_user() elif page_name == "existing_user": # this is an apply event, so we are creating a new device (no "advanced" device selection) self.get_existing_user(False) elif page_name == "existing_device": self.apply_device_settings() elif page_name == "standalone_options": self.apply_standalone_settings() self.get('button_apply').set_label('gtk-apply') def apply_main_settings(self): # save('lang', text('lang')) self.save('auto_connect', self.checkbox('auto_connect')) self.save('extended_headers', self.checkbox('extended_headers')) if((self.checkbox('guest_account') == 'y') != self.current_guest_account): self.toggle_guest_account(self.checkbox('guest_account') == 'y') # check and change the crontab interval new_delay = self.get('delay').get_value_as_int() if new_delay != int(self.current_delay): # print 'Updating delay in crontab...' os.system('(crontab -l | grep -v prey; echo "*/'+str(new_delay)+' * * * * /usr/share/prey/prey.sh > /var/log/prey.log") | crontab -') if self.check_if_configured == False: self.show_alert(_("All good."), _("Configuration saved. Remember you still need to set up your posting method, otherwise Prey won't work!")) else: self.show_alert(_("All good."), _("Configuration saved!"), True) def apply_control_panel_settings(self): if self.current_post_method != 'http': self.save('post_method', 'http') if self.current_check_url != CONTROL_PANEL_URL: self.save('check_url', CONTROL_PANEL_URL) # we could eventually use the email as a checking method to remove prey # i.e. "under which email was this account set up?" # self.save('mail_to', self.email) self.save('api_key', self.api_key) if self.device_key != "": self.save('device_key', self.device_key) def apply_standalone_settings(self): if self.current_post_method != 'email': self.save('post_method', 'email') self.save('check_url', self.text('check_url')) self.save('mail_to', self.text('mail_to')) self.save('smtp_server', self.text('smtp_server')) self.save('smtp_username', self.text('smtp_username')) smtp_password = self.text('smtp_password') if smtp_password != '': encoded_pass = os.popen('echo -n "'+ smtp_password +'" | openssl enc -base64').read().strip() self.save('smtp_password', encoded_pass) self.exit_configurator() def exit_configurator(self): self.run_prey() self.show_alert(_("Success"), _("Configuration saved! Your device is now setup and being tracked by Prey. Happy hunting!"), True) def run_prey(self): os.system(PREY_PATH + '/prey.sh > /var/log/prey.log &') ################################################ # control panel api ################################################ def report_connection_issue(self): self.show_alert(_("Problem connecting"), _("We seem to be having a problem connecting to your Control Panel. This is likely a temporary issue. Please try again in a few moments.")) def user_has_available_slots(self, string): matches = re.search(r"<available_slots>(\w*)</available_slots>", string) if matches and int(matches.groups()[0]) > 0: return True else: return False def get_api_key(self, string): matches = re.search(r"<key>(\w*)</key>", string) if matches: self.api_key = matches.groups()[0] def get_device_keys(self, string, has_available_slots): hostname = os.popen("hostname").read().strip() devices = self.get('device') index = -1 chosen = index liststore = gtk.ListStore(str,str) devices.clear() matches = re.findall(r"<device>\s*<key>(\w*)</key>.*?<title>([\s\w]*)</title>\s*</device>", string, re.DOTALL) for match in matches: index += 1 key = match[0] title = match[1] liststore.append([title,key]) if key == self.current_device_key: #set the choice because we have a matching device key chosen = index elif title.lower() == hostname.lower and chosen < 0: #set the choice because we likely have a matching title (but device key takes precedence) chosen = index if index < 0: #self.get('create_new_device').set_active(True) self.show_alert(_("No devices exist"), _("There are no devices currently defined in your Control Panel.\n\nPlease select the option to create a new device.")) return False devices.set_model(liststore) cell = gtk.CellRendererText() devices.pack_start(cell, True) devices.add_attribute(cell, 'text', 0) devices.set_active(chosen) return True def create_user(self): self.email = self.text('email') params = urllib.urlencode({'user[name]': self.text('user_name'), 'user[email]': self.email, 'user[password]': self.text('password'), 'user[password_confirmation]' : self.text('password_confirm')}) # params = 'user[name]='+self.text('user_name')+'&user[email]='+self.email+'&user[password]='+self.text('password')+'&user[password_confirmation]='+self.text('password_confirm') result = os.popen('curl -i -s -k --connect-timeout 5 '+ CONTROL_PANEL_URL_SSL + '/users.xml -d \"'+params+'\"').read().strip() if result.find("<key>") != -1: self.get_api_key(result) self.device_key = "" elif result.find("Email has already been taken") != -1: self.show_alert(_("Email has already been taken"), _("That email address already exists! If you signed up previously, please go back and select the Existing User option.")) return else: self.show_alert(_("Couldn't create user!"), _("There was a problem creating your account. Please make sure the email address you entered is valid, as well as your password.")) return self.apply_control_panel_settings() self.run_prey() self.show_alert(_("Account created!"), _("Your account has been succesfully created and configured in Prey's Control Panel.\n\nPlease check your inbox now, you should have received a verification email."), True) def get_existing_user(self, show_devices): self.email = self.text('existing_email') password = self.text('existing_password') result = os.popen('curl -i -s -k --connect-timeout 5 '+ CONTROL_PANEL_URL_SSL + '/profile.xml -u '+self.email+":'"+password+"'").read().strip() if result.find('401 Unauthorized') != -1: self.show_alert(_("User does not exist"), _("Couldn't log you in. Remember you need to activate your account opening the link we emailed you.\n\nIf you forgot your password please visit preyproject.com.")) return if result.find("<user>") != -1: self.get_api_key(result) else: self.report_connection_issue() return False has_available_slots = self.user_has_available_slots(result) if not has_available_slots and not show_devices: self.show_alert(_("Not allowed"), _("It seems you've reached your limit for devices!\n\nIf you had previously added this PC, you should select the \"Device already exists\" option to select the device from a list of devices you have already defined.\n\nIf this is a new device, you can also upgrade to a Pro Account to increase your slot count and get access to additional features. For more information, please check\nhttp://preyproject.com/plans.")) return False if show_devices: result = os.popen('curl -i -s -k --connect-timeout 5 '+ CONTROL_PANEL_URL_SSL + '/devices.xml -u '+self.email+":'"+password+"'").read().strip() if result.find("</devices>") != -1: return self.get_device_keys(result,has_available_slots) else: self.report_connection_issue() return False else: self.device_key = "" self.apply_control_panel_settings() self.exit_configurator() def apply_device_settings(self): devices = self.get('device') model = devices.get_model() self.device_key = model.get_value(devices.get_active_iter(),1) self.apply_control_panel_settings() self.exit_configurator() def __init__(self): if not self.prey_exists() or not self.is_config_writable(): gtk.main() exit(1) self.get_current_settings() builder = gtk.Builder() builder.set_translation_domain(app_name) builder.add_from_file(script_path + "/prey-config.glade") builder.connect_signals({ "on_window_destroy" : gtk.main_quit, "prev_page" : self.prev_page, "next_page" : self.next_page, "toggle_buttons" : self.toggle_buttons, "apply_settings" : self.apply_settings, "toggle_pg3_next_apply" : self.toggle_pg3_next_apply, "set_default_action" : self.set_default_action, "ensure_visible" : self.ensure_visible, "key_pressed" : self.key_pressed, "close_about" : self.close_about }) self.window = builder.get_object("window") self.window.set_title(self.window.get_title() + " (v" + VERSION + ")") # self.window.get_settings().set_string_property('gtk-font-name', 'sans normal 11',''); self.pages = builder.get_object("reporting_mode_tabs") self.root = builder self.get('delay').grab_focus() about = self.get('about_prey_config') about.set_version(VERSION) self.display_real_settings() self.check_if_configured() if __name__ == "__main__": app = PreyConfigurator() gtk.main()

gpl-3.0

xrmx/pylokit

setup.py

1

1072

from setuptools import setup, find_packages import os VERSION = "0.8.1" CLASSIFIERS = [ 'Environment :: Console', 'Intended Audience :: Developers', 'Intended Audience :: System Administrators', 'License :: OSI Approved :: Mozilla Public License 2.0 (MPL 2.0)', 'Operating System :: OS Independent', 'Programming Language :: Python', 'Topic :: Software Development :: Libraries :: Python Modules', 'Topic :: Office/Business :: Office Suites', ] setup( author="Riccardo Magliocchetti", author_email="[email protected]", name='pylokit', version=VERSION, description='Python CFFI wrapper for LibreOfficeKit', long_description=open(os.path.join(os.path.dirname(__file__), 'README.rst')).read(), url="https://github.com/xrmx/pylokit", license='MPL 2.0', platforms=['OS Independent'], classifiers=CLASSIFIERS, install_requires=[ 'cffi', 'six', ], test_suite='pylokit.tests', packages=find_packages(), include_package_data=True, zip_safe = False, )

mpl-2.0

xuru/pyvisdk

pyvisdk/do/or_alarm_expression.py

1

1024

import logging from pyvisdk.exceptions import InvalidArgumentError ######################################## # Automatically generated, do not edit. ######################################## log = logging.getLogger(__name__) def OrAlarmExpression(vim, *args, **kwargs): '''A data object type that links multiple alarm expressions with OR operators.''' obj = vim.client.factory.create('ns0:OrAlarmExpression') # do some validation checking... if (len(args) + len(kwargs)) < 1: raise IndexError('Expected at least 2 arguments got: %d' % len(args)) required = [ 'expression' ] optional = [ 'dynamicProperty', 'dynamicType' ] for name, arg in zip(required+optional, args): setattr(obj, name, arg) for name, value in kwargs.items(): if name in required + optional: setattr(obj, name, value) else: raise InvalidArgumentError("Invalid argument: %s. Expected one of %s" % (name, ", ".join(required + optional))) return obj

mit

hroark13/android_kernel_zte_draconis

scripts/gcc-wrapper.py

2

3383

#! /usr/bin/env python # -*- coding: utf-8 -*- # Copyright (c) 2011-2012, The Linux Foundation. All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # * Neither the name of The Linux Foundation nor # the names of its contributors may be used to endorse or promote # products derived from this software without specific prior written # permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND # NON-INFRINGEMENT ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR # CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, # EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, # PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; # OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, # WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR # OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF # ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # Invoke gcc, looking for warnings, and causing a failure if there are # non-whitelisted warnings. import errno import re import os import sys import subprocess # Note that gcc uses unicode, which may depend on the locale. TODO: # force LANG to be set to en_US.UTF-8 to get consistent warnings. allowed_warnings = set([ "return_address.c:62", ]) # Capture the name of the object file, can find it. ofile = None warning_re = re.compile(r'''(.*/|)([^/]+\.[a-z]+:\d+):(\d+:)? warning:''') def interpret_warning(line): """Decode the message from gcc. The messages we care about have a filename, and a warning""" line = line.rstrip('\n') m = warning_re.match(line) if m and m.group(2) not in allowed_warnings: print "error, forbidden warning:", m.group(2) # If there is a warning, remove any object if it exists. if ofile: try: os.remove(ofile) except OSError: pass sys.exit(1) def run_gcc(): args = sys.argv[1:] # Look for -o try: i = args.index('-o') global ofile ofile = args[i+1] except (ValueError, IndexError): pass compiler = sys.argv[0] try: proc = subprocess.Popen(args, stderr=subprocess.PIPE) for line in proc.stderr: print line, # interpret_warning(line) result = proc.wait() except OSError as e: result = e.errno if result == errno.ENOENT: print args[0] + ':',e.strerror print 'Is your PATH set correctly?' else: print ' '.join(args), str(e) return result if __name__ == '__main__': status = run_gcc() sys.exit(status)

gpl-2.0

rouxcode/django-cms-plugins

cmsplugins/baseplugin/utils.py

1

1125

from __future__ import unicode_literals from importlib import import_module from django.utils import six from django.utils.html import mark_safe from django.utils.translation import ugettext_lazy as _ def get_indicator_hidden(request, instance): html = '' is_visible = getattr(instance, 'is_visible', True) if request.toolbar.edit_mode_active and not is_visible: name = _('hidden') css_class = 'plugin-indicator-hidden' html = '<span class="{}">{}</span>'.format( css_class, name, ) return mark_safe(html) def get_str_from_tuple(key='', properties=()): return dict((k, v) for k, v in properties).get(key, '') def load_object(import_path): if not isinstance(import_path, six.string_types): return import_path if '.' not in import_path: raise TypeError( "'import_path' argument to 'django_load.core.load_object'" " must contain at least one dot." ) module_name, object_name = import_path.rsplit('.', 1) module = import_module(module_name) return getattr(module, object_name)

mit

griffinfoster/shapelets

setup.py

1

1460

from setuptools import setup, find_packages #from Cython.Build import cythonize import numpy as np import os, sys, glob __version__ = '0.2' #this needs to be kept up to date with shapelets/__init__.py setup(name = 'shapelets', version = __version__, description = 'Shapelet fitting and plotting', long_description = 'Shapelet fitting and plotting', author = 'Griffin Foster', author_email = '[email protected]', url = 'https://github.com/griffinfoster/shapelets', platforms = ['*nix'], license='GPL', requires = ['distutils', 'numpy', 'astropy', 'scipy', 'matplotlib', 'json'], provides = ['shapelets', 'shapelets.phs'], packages = ['shapelets', 'shapelets.phs'], #ext_modules = cythonize('shapelets/cshapelet.pyx', annotate=True), include_dirs = [np.get_include()], #scripts = glob.glob('scripts/*.py'), scripts = ['scripts/fitShapelet.py', 'scripts/insertShapelet.py', 'scripts/plotCoeffs.py', 'scripts/plotImg.py', 'scripts/plotShapelets.py', 'scripts/solveShapelet.py'], classifiers = [ 'Development Status :: 4 - Beta', 'Environment :: Console', 'Natural Language :: English', 'Operating System :: POSIX :: Linux', 'Programming Language :: Python :: 2.7', 'Intended Audience :: Science/Research', 'License :: OSI Approved :: GNU General Public License (GPL)', 'Topic :: Scientific/Engineering :: Astronomy', ], )

bsd-3-clause

bsandrow/hn-saved-stories

hn_saved_stories/__init__.py

1

8406

import os import json import re import sys import requests import lxml.html from datetime import datetime, timedelta from pprint import pprint as PP from time import sleep from urlparse import urljoin from .utils import hn_relatime_to_datetime, get_story_id from .logger import logger def parse_date_header(date): errors = [] formats = [ "%a, %d %B %Y %H:%M:%S %Z", "%a, %d %b %Y %H:%M:%S %Z", ] for format in formats: try: return datetime.strptime(date, format) except ValueError as e: errors.append(e) raise errors[0] class HNSession(object): user_agent = 'hn-saved-stories/0.2 (https://github.com/bsandrow/hn-saved-stories/)' max_retries = 2 retry_delay = 30 def __init__(self, headers=None): headers = headers or {} headers['User-Agent'] = headers.get('User-Agent', self.user_agent) self.session = requests.Session() self.session.headers = headers self.last_response = None def last_response_time(self): """ Return the time of the last response """ if 'last_response' in self.__dict__ and self.last_response.headers.get('date'): return parse_date_header(self.last_response.headers.get('date')) else: return None def last_response_url(self): """ Return the url of the last response """ if 'last_response' in self.__dict__: return self.last_response.url else: return None def get(self, *args, **kwargs): """ requests.get() within the session Wraps requests.get() within the session (so it has access to session cookies), and also retries on failures, because timeouts seem to happen randomly. """ if 'timeout' not in kwargs: kwargs['timeout'] = 10 retries = 0 while True: try: request = self.session.get(*args, **kwargs) request.raise_for_status() return request except requests.exceptions.RequestException as e: if retries < self.max_retries: retries += 1 sleep(self.retry_delay) logger.info("[Sleeping between requests (%ss)]" % self.retry_delay) else: raise def resolve_url(self, url): """ Resolve :url: using the most appropriate base url """ base_url = self.last_response_url() or 'https://news.ycombinator.com' return urljoin(base_url, url) def login(self, username, password, debug=False): """ Log into the session using provided credentials """ try: response = self.get('https://news.ycombinator.com/newslogin') except requests.exceptions.HTTPError: raise Exception("Error: Unable to retrieve login page") doc = lxml.html.fromstring(response.text) fields = doc.xpath('.//form[1]/input') form_data = { x.get('name'): x.get('value') for x in fields } form_data['u'] = username form_data['p'] = password if debug: print "Login Form Data: ", import pprint pprint.pprint(form_data) response = self.session.post('https://news.ycombinator.com/y', data=form_data, timeout=10) if response.status_code != requests.codes.ok: raise Exception("Error: Unable to successfully login.") self.username = username self.last_response = response def get_saved_stories(self, max_pages=None, break_func=None): """ Fetch the list of 'saved stories' from a profile Fetch the list of saved stories for a Hacker News user account. The session needs to be logged into an account for this to work. break_func - A function that takes the current page's story list, and returns True if we should break out of the loop. max_pages - The maximum number of pages that we should go through before aborting. A value of None goes through all pages. """ def parse_story(title, subtext): """ Parse a story from title + subtext """ url_keys = ['url', 'comments', 'submitter_link'] story = {} title_anchor = title.xpath('./a')[0] comments_anchor = subtext.xpath('.//a[contains(text(), "comments") or contains(text(), "discuss")]')[0] # See Footnote [1] story['url'] = title_anchor.get('href') story['title'] = title_anchor.text story['comments'] = comments_anchor.get('href') story['submitter'] = subtext.xpath('.//a[1]//text()')[0] # See Footnote [4] story['submitter_link'] = subtext.xpath('.//a[1]/@href')[0] story['submitted_at'] = str( hn_relatime_to_datetime(self.last_response_time(), subtext.xpath('./text()')[1]) ) # Resolve all relative URLs for key in story.keys(): if key in url_keys and story.get(key): story[key] = self.resolve_url(story[key]) return get_story_id(story), story page = 1 stories = {} url = 'https://news.ycombinator.com/saved?id=%s' % self.username while max_pages is None or page <= max_pages: html = None try: logger.info("Page %d:" % page) logger.debug(" url = %s" % url) logger.info(" Fetching...") try: response = self.get(url) except requests.exceptions.HTTPError as e: raise Exception("Error: Failed to retrieve page %d, error:'%s', rurl: %s" % (page, str(e), url)) if response.text == "Can't display that.": raise Exception("Error: Got \"Can't display that\" response.") logger.info(" Parsing...") html = lxml.html.fromstring(response.text) basetime = parse_date_header(response.headers['date']) title = html.cssselect('td.title') # See Footnote [3] subtext = html.cssselect('td.subtext') page_stories = dict([ parse_story(*s) for s in zip(title[1::2], subtext) ]) try: next_link = title[-1].xpath('.//a[text() = "More"]/@href') except IndexError: sys.exit("Caught IndexError. Dumping HTML:" + lxml.html.tostring(html)) next_link = next_link[0] if next_link else None stories.update(page_stories) should_break = (break_func and break_func(page_stories)) or next_link is None if should_break: break url = self.resolve_url(next_link) page += 1 logger.info(" Sleeping (1s)...") sleep(1) except Exception as e: if html: logger.debug("Caught exception. Dumping page...") logger.debug("______________") logger.debug(lxml.html.tostring(html, pretty_print=True)) logger.debug("______________") raise logger.info("Done.") return stories # Footnotes # ~~~~~~~~~ # [1] Anchor text needs 'comments,' because Polls submitted by yourself there # is a 'add choice.' Also, if the story has no comments, then the anchor # text is just 'discuss.' # # [2] '[Dead]' links remove the 'href' attribute from the anchor, so you end up # with None as a URL. # # [3] 'td.title' selects 3 different things: # 1) the number of the story (in reverse, story #1 is # the most recently saved) # 2) the title + link of the story # 3) the 'More' link at the bottom of the page, which # goes to the next page in the series. # The series should look something like [1,2,1,2,1,2,1,2,3], #1 and #2 # alternating with #3 being the last in the list. #3 will be missing on the # final page. # # [4] The '//text()' part is needed because sometimes the submitter has a # <font> element colouring it, so text() is not a direct child of the # anchor. E.g.: # # <a href="user?id=foofoobar"><font color="#3c963c">foofoobar</font></a>

mit

rosarior/mayan

apps/main/__init__.py

1

2420

from __future__ import absolute_import from django.utils.translation import ugettext_lazy as _ from django.conf import settings from navigation.api import register_top_menu from navigation.api import register_links from project_setup.api import register_setup from project_tools.api import register_tool from .conf.settings import SIDE_BAR_SEARCH, DISABLE_HOME_VIEW __author__ = 'Roberto Rosario' __copyright__ = 'Copyright 2012 Roberto Rosario' __credits__ = ['Roberto Rosario',] __license__ = 'GPL' __maintainer__ = 'Roberto Rosario' __email__ = '[email protected]' __status__ = 'Production' __version_info__ = { 'major': 1, 'minor': 0, 'micro': 0, 'releaselevel': 'alpha', 'serial': 0 } def is_superuser(context): return context['request'].user.is_staff or context['request'].user.is_superuser maintenance_menu = {'text': _(u'maintenance'), 'view': 'maintenance_menu', 'famfam': 'wrench', 'icon': 'wrench.png'} statistics = {'text': _(u'statistics'), 'view': 'statistics', 'famfam': 'table', 'icon': 'blackboard_sum.png', 'condition': is_superuser, 'children_view_regex': [r'statistics']} diagnostics = {'text': _(u'diagnostics'), 'view': 'diagnostics', 'famfam': 'pill', 'icon': 'pill.png'} sentry = {'text': _(u'sentry'), 'view': 'sentry', 'famfam': 'bug', 'icon': 'bug.png', 'condition': is_superuser} admin_site = {'text': _(u'admin site'), 'view': 'admin:index', 'famfam': 'keyboard', 'icon': 'keyboard.png', 'condition': is_superuser} if not DISABLE_HOME_VIEW: register_top_menu('home', link={'text': _(u'home'), 'view': 'home', 'famfam': 'house'}, position=0) if not SIDE_BAR_SEARCH: register_top_menu('search', link={'text': _(u'search'), 'view': 'search', 'famfam': 'zoom'}, children_path_regex=[r'^search/']) def get_version(): ''' Return the formatted version information ''' vers = ['%(major)i.%(minor)i' % __version_info__, ] if __version_info__['micro']: vers.append('.%(micro)i' % __version_info__) if __version_info__['releaselevel'] != 'final': vers.append('%(releaselevel)s%(serial)i' % __version_info__) return ''.join(vers) __version__ = get_version() if 'django.contrib.admin' in settings.INSTALLED_APPS: register_setup(admin_site) register_tool(maintenance_menu) register_tool(statistics) register_tool(diagnostics) if 'sentry' in settings.INSTALLED_APPS: register_tool(sentry)

gpl-3.0

ssvlab/esbmc-gpu

regression/esbmc-cpp/resultados.py

1

2672

#!/bin/python ############################# # Script to display test suite results ############################## import sys import os from sys import argv import xml.etree.ElementTree as ET def error(message): sys.stderr.write("error: %s\n" % message) #sys.exit(1) error_file = "resultados_error.log" f = open(error_file, 'w') suc = 0 fai = 0 fsuc = 0 fneg = 0 total = 0 crash = 0 def disp_resul(): """ Display the verification result """ print "Verification Success: ", suc print "Verification Fail: ", fai print "False positive:", fsuc print "False negative:", fneg print "Crashed:", crash print "Total: ", total def resultados(ite_ex, ite_ac): """ Check the result. Sucess, Fail, false sucess and false fail """ global suc, fai, fsuc, fneg, total, crash if ite_ac.text == "[ABORTED]": crash+=1 #print "CRASH" elif ite_ex.text == ite_ac.text: if ite_ac.text == "[SUCCESSFUL]": suc+=1 #print "SUCCESSFUL" elif ite_ac.text in ["[FAILED]", "[CONVERSION_ERROR]", "[PARSING_ERROR]"]: fai+=1 #print "FAILED" elif ite_ex.text == "[FAILED]" and ite_ac.text == "[SUCCESSFUL]": fsuc+=1 #print "FALSO POSITIVO" else: fneg+=1 #print "FALSO NEGATIVO" total+=1 #print def show_info(path): """ Parse tree """ global suc, fai, fsuc, fneg, total, crash suc = fai = fsuc = fneg = total = crash = 0 os.chdir(path) print "##### Directory: " + path try: tree = ET.parse("test_log.xml") except IOError as e: #sys.exit("Could not open test_log.xml") error("Could not open test_log.xml") return #XML file root = tree.getroot() for res in root.findall('run-test'): ite_ex = res.find('item_09_expected-result') ite_ac = res.find('item_10_actual-result') #print res.find('item_01_test-name').text #print ite_ex.text #print ite_ac.text if ite_ex != None and ite_ac != None: resultados(ite_ex, ite_ac) else: f.write('Error file: ') f.write(res.find('item_01_test-name').text + '\n') error("Parser error at " + res.find('item_01_test-name').text) disp_resul() #display result def main(): if len(sys.argv) < 2: print "usage: %s <PATH>" % argv[0] sys.exit(1) path = argv[1]; listing = os.listdir(path) listing.sort() #sort files os.chdir(path) for infile in listing: if os.path.isdir(infile): show_info(infile) print os.chdir("..") if __name__ == "__main__": main()

apache-2.0

475Cumulus/TBone

tests/data/test_models.py

1

3358

#!/usr/bin/env python # encoding: utf-8 import pytest import datetime from itertools import zip_longest from tbone.data.fields import * from tbone.data.models import * from tbone.testing.fixtures import event_loop def test_model_repr(): ''' Test Model repr function ''' class M(Model): pass m = M() assert repr(m) == '<M instance>' @pytest.mark.asyncio async def test_model_creation_and_serialization(): ''' Simple model creation test ''' class M(Model): name = StringField() age = IntegerField() decimal = FloatField() dt = DateTimeField() m = M({'name': 'Ron Burgundy', 'age': 45, 'decimal': '34.77', 'dt': '2017-07-25T12:34:14.414471'}) # convert model to primitive form data = await m.serialize() # check result is dict assert isinstance(data, dict) # check keys match assert all(a == b for a, b in zip_longest(m._fields.keys(), data.keys(), fillvalue=None)) @pytest.mark.asyncio async def test_model_import(): class M(Model): first_name = StringField() last_name = StringField() m = M() m.import_data({'first_name': 'Ron', 'last_name': 'Burgundy'}) data = await m.serialize() assert data['first_name'] == 'Ron' assert data['last_name'] == 'Burgundy' with pytest.raises(ValueError): m.import_data('Ron Burgundy') @pytest.mark.asyncio async def test_model_serialize_decorator(): class M(Model): first_name = StringField() last_name = StringField() @serialize async def full_name(self): return '{} {}'.format(self.first_name, self.last_name) m = M({'first_name': 'Ron', 'last_name': 'Burgundy'}) data = await m.serialize() assert data['first_name'] == 'Ron' assert data['last_name'] == 'Burgundy' assert 'full_name' in data assert data['full_name'] == 'Ron Burgundy' def test_model_items(): class M(Model): first_name = StringField() last_name = StringField() dob = DateTimeField() data = {'first_name': 'Ron', 'last_name': 'Burgundy', 'dob': datetime.datetime.now()} mo = M(data) for key, value in mo.items(): assert value == data[key] @pytest.mark.asyncio async def test_field_projection(): class M(Model): first_name = StringField() last_name = StringField() dob = DateTimeField() number_of_views = IntegerField(default=0, projection=None) data = {'first_name': 'Ron', 'last_name': 'Burgundy', 'dob': datetime.datetime.now()} mo = M(data) serialized = await mo.serialize() for key in data.keys(): assert key in serialized assert 'number_of_views' not in serialized def test_model_field_exclusion(): class User(Model): username = StringField() password = StringField() first_name = StringField() last_name = StringField() @serialize async def full_name(self): return '{} {}'.format(self.first_name, self.last_name) class PublicUser(User): class Meta: exclude_fields = ['password', 'none_existing_field'] exclude_serialize = ['full_name', 'none_existing_serialize_method'] assert 'password' not in PublicUser._fields assert 'full_name' not in PublicUser._serialize_methods

mit

rbarlow/pulp

nodes/test/nodes_tests/base.py

1

3158

from ConfigParser import SafeConfigParser from unittest import TestCase import logging import mock import os import shutil import unittest import okaara import pymongo from pulp.bindings.bindings import Bindings from pulp.bindings.server import PulpConnection from pulp.client.extensions.core import PulpCli, ClientContext, PulpPrompt from pulp.client.extensions.exceptions import ExceptionHandler from pulp.common.config import Config from pulp.server.async import celery_instance from pulp.server.config import config as pulp_conf from pulp.server.db import connection from pulp.server.logs import start_logging, stop_logging from pulp.server.managers import factory as managers from pulp.server.managers.auth.cert.cert_generator import SerialNumber SerialNumber.PATH = '/tmp/sn.dat' class ServerTests(unittest.TestCase): TMP_ROOT = '/tmp/pulp/nodes' @classmethod def setUpClass(cls): # This will make Celery tasks run synchronously celery_instance.celery.conf.CELERY_ALWAYS_EAGER = True if not os.path.exists(cls.TMP_ROOT): os.makedirs(cls.TMP_ROOT) stop_logging() path = os.path.join( os.path.abspath(os.path.dirname(__file__)), 'data', 'pulp.conf') pulp_conf.read(path) start_logging() storage_dir = pulp_conf.get('server', 'storage_dir') if not os.path.exists(storage_dir): os.makedirs(storage_dir) shutil.rmtree(storage_dir + '/*', ignore_errors=True) managers.initialize() @classmethod def tearDownClass(cls): name = pulp_conf.get('database', 'name') db = pymongo.database.Database(connection._CONNECTION, name) for name in db.collection_names(): if name[:7] == 'system.': continue db.drop_collection(name) class ClientTests(TestCase): def setUp(self): TestCase.setUp(self) self.config = SafeConfigParser() path = os.path.join( os.path.abspath(os.path.dirname(__file__)), 'data', 'client.conf') self.config = Config(path) self.server_mock = mock.Mock() self.pulp_connection = \ PulpConnection('', server_wrapper=self.server_mock) self.bindings = Bindings(self.pulp_connection) self.recorder = okaara.prompt.Recorder() self.prompt = PulpPrompt(enable_color=False, output=self.recorder, record_tags=True) self.logger = logging.getLogger('pulp') self.exception_handler = ExceptionHandler(self.prompt, self.config) self.context = ClientContext( self.bindings, self.config, self.logger, self.prompt, self.exception_handler) self.cli = PulpCli(self.context) self.context.cli = self.cli class Response: def __init__(self, code, body): self.response_code = code self.response_body = body class Task: def __init__(self, task_id=0): self.task_id = task_id class TaskResult: def __init__(self, task_id): self.spawned_tasks = [Task(task_id)]

gpl-2.0

annarev/tensorflow

tensorflow/python/distribute/input_lib.py

1

101510

# 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. # ============================================================================== """Various classes representing distributed inputs.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import functools import sys import six from tensorflow.python import tf2 from tensorflow.python.data.experimental.ops import batching from tensorflow.python.data.experimental.ops import cardinality from tensorflow.python.data.experimental.ops import distribute from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.ops import iterator_ops from tensorflow.python.data.ops import multi_device_iterator_ops from tensorflow.python.data.ops import optional_ops from tensorflow.python.distribute import device_util from tensorflow.python.distribute import distribute_utils from tensorflow.python.distribute import distribution_strategy_context from tensorflow.python.distribute import input_ops from tensorflow.python.distribute import reduce_util from tensorflow.python.distribute import values from tensorflow.python.distribute.distribute_lib import InputReplicationMode from tensorflow.python.eager import context from tensorflow.python.framework import composite_tensor from tensorflow.python.framework import constant_op from tensorflow.python.framework import device as tf_device from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import tensor_util from tensorflow.python.framework import type_spec from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops.ragged import ragged_tensor from tensorflow.python.types import distribute as distribute_types from tensorflow.python.util import nest from tensorflow.python.util.compat import collections_abc from tensorflow.python.util.deprecation import deprecated from tensorflow.python.util.tf_export import tf_export from tensorflow.tools.docs import doc_controls def get_distributed_dataset(dataset, input_workers, strategy, num_replicas_in_sync=None, input_context=None, options=None): """Returns a distributed dataset from the given tf.data.Dataset instance. This is a common function that is used by all strategies to return a distributed dataset. The distributed dataset instance returned is different depending on if we are in a TF 1 or TF 2 context. The distributed dataset instances returned differ from each other in the APIs supported by each of them. Args: dataset: a tf.data.Dataset instance. input_workers: an InputWorkers object which specifies devices on which iterators should be created. strategy: a `tf.distribute.Strategy` object, used to run all-reduce to handle last partial batch. num_replicas_in_sync: Optional integer. If this is not None, the value is used to decide how to rebatch datasets into smaller batches so that the total batch size for each step (across all workers and replicas) adds up to `dataset`'s batch size. input_context: `InputContext` for sharding. Only pass this in for between graph multi-worker cases where there is only one `input_worker`. In these cases, we will shard based on the `input_pipeline_id` and `num_input_pipelines` in the `InputContext`. options: Default is None. `tf.distribute.InputOptions` used to control options on how this dataset is distributed. Returns: A distributed dataset instance. """ if tf2.enabled(): return DistributedDataset( input_workers, strategy, dataset, num_replicas_in_sync=num_replicas_in_sync, input_context=input_context, options=options) else: return DistributedDatasetV1( dataset, input_workers, strategy, num_replicas_in_sync=num_replicas_in_sync, input_context=input_context, options=options) def get_distributed_datasets_from_function(dataset_fn, input_workers, input_contexts, strategy, options=None): """Returns a distributed dataset from the given input function. This is a common function that is used by all strategies to return a distributed dataset. The distributed dataset instance returned is different depending on if we are in a TF 1 or TF 2 context. The distributed dataset instances returned differ from each other in the APIs supported by each of them. Args: dataset_fn: a function that returns a tf.data.Dataset instance. input_workers: an InputWorkers object which specifies devices on which iterators should be created. input_contexts: A list of `InputContext` instances to be passed to call(s) to `dataset_fn`. Length and order should match worker order in `worker_device_pairs`. strategy: a `tf.distribute.Strategy` object, used to run all-reduce to handle last partial batch. options: Default is None. `tf.distribute.InputOptions` used to control options on how this dataset is distributed. Returns: A distributed dataset instance. Raises: ValueError: if `options.experimental_replication_mode` and `options.experimental_place_dataset_on_device` are not consistent """ if (options is not None and options.experimental_replication_mode != InputReplicationMode.PER_REPLICA and options.experimental_place_dataset_on_device): raise ValueError( "When `experimental_place_dataset_on_device` is set for dataset " "placement, you must also specify `PER_REPLICA` for the " "replication mode") if (options is not None and options.experimental_replication_mode == InputReplicationMode.PER_REPLICA and options.experimental_fetch_to_device and options.experimental_place_dataset_on_device): raise ValueError( "`experimental_place_dataset_on_device` can not be set to True " "when experimental_fetch_to_device is True and " "replication mode is set to `PER_REPLICA`") if tf2.enabled(): return DistributedDatasetsFromFunction(input_workers, strategy, input_contexts, dataset_fn, options) else: return DistributedDatasetsFromFunctionV1( input_workers, strategy, input_contexts, dataset_fn, options) @tf_export("distribute.DistributedIterator", v1=[]) class DistributedIteratorInterface(collections_abc.Iterator, distribute_types.Iterator): """An iterator over `tf.distribute.DistributedDataset`. `tf.distribute.DistributedIterator` is the primary mechanism for enumerating elements of a `tf.distribute.DistributedDataset`. It supports the Python Iterator protocol, which means it can be iterated over using a for-loop or by fetching individual elements explicitly via `get_next()`. You can create a `tf.distribute.DistributedIterator` by calling `iter` on a `tf.distribute.DistributedDataset` or creating a python loop over a `tf.distribute.DistributedDataset`. Visit the [tutorial](https://www.tensorflow.org/tutorials/distribute/input) on distributed input for more examples and caveats. """ def get_next(self): """Returns the next input from the iterator for all replicas. Example use: >>> strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"]) >>> dataset = tf.data.Dataset.range(100).batch(2) >>> dist_dataset = strategy.experimental_distribute_dataset(dataset) >>> dist_dataset_iterator = iter(dist_dataset) >>> @tf.function ... def one_step(input): ... return input >>> step_num = 5 >>> for _ in range(step_num): ... strategy.run(one_step, args=(dist_dataset_iterator.get_next(),)) >>> strategy.experimental_local_results(dist_dataset_iterator.get_next()) (<tf.Tensor: shape=(1,), dtype=int64, numpy=array([10])>, <tf.Tensor: shape=(1,), dtype=int64, numpy=array([11])>) Returns: A single `tf.Tensor` or a `tf.distribute.DistributedValues` which contains the next input for all replicas. Raises: `tf.errors.OutOfRangeError`: If the end of the iterator has been reached. """ raise NotImplementedError( "DistributedIterator.get_next() must be implemented in descendants.") @property def element_spec(self): # pylint: disable=line-too-long """The type specification of an element of `tf.distribute.DistributedIterator`. Example usage: >>> global_batch_size = 16 >>> strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"]) >>> dataset = tf.data.Dataset.from_tensors(([1.],[2])).repeat(100).batch(global_batch_size) >>> distributed_iterator = iter(strategy.experimental_distribute_dataset(dataset)) >>> distributed_iterator.element_spec (PerReplicaSpec(TensorSpec(shape=(None, 1), dtype=tf.float32, name=None), TensorSpec(shape=(None, 1), dtype=tf.float32, name=None)), PerReplicaSpec(TensorSpec(shape=(None, 1), dtype=tf.int32, name=None), TensorSpec(shape=(None, 1), dtype=tf.int32, name=None))) Returns: A nested structure of `tf.TypeSpec` objects matching the structure of an element of this `tf.distribute.DistributedIterator`. This returned value is typically a `tf.distribute.DistributedValues` object and specifies the `tf.TensorSpec` of individual components. """ raise NotImplementedError( "DistributedIterator.element_spec() must be implemented in descendants") def get_next_as_optional(self): # pylint: disable=line-too-long """Returns a `tf.experimental.Optional` that contains the next value for all replicas. If the `tf.distribute.DistributedIterator` has reached the end of the sequence, the returned `tf.experimental.Optional` will have no value. Example usage: >>> strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"]) >>> global_batch_size = 2 >>> steps_per_loop = 2 >>> dataset = tf.data.Dataset.range(10).batch(global_batch_size) >>> distributed_iterator = iter( ... strategy.experimental_distribute_dataset(dataset)) >>> def step_fn(x): ... # train the model with inputs ... return x >>> @tf.function ... def train_fn(distributed_iterator): ... for _ in tf.range(steps_per_loop): ... optional_data = distributed_iterator.get_next_as_optional() ... if not optional_data.has_value(): ... break ... per_replica_results = strategy.run(step_fn, args=(optional_data.get_value(),)) ... tf.print(strategy.experimental_local_results(per_replica_results)) >>> train_fn(distributed_iterator) ... # ([0 1], [2 3]) ... # ([4], []) Returns: An `tf.experimental.Optional` object representing the next value from the `tf.distribute.DistributedIterator` (if it has one) or no value. """ # pylint: enable=line-too-long raise NotImplementedError( "get_next_as_optional() not implemented in descendants") @tf_export("distribute.DistributedDataset", v1=[]) class DistributedDatasetInterface(collections_abc.Iterable, distribute_types.Iterable): # pylint: disable=line-too-long """Represents a dataset distributed among devices and machines. A `tf.distribute.DistributedDataset` could be thought of as a "distributed" dataset. When you use `tf.distribute` API to scale training to multiple devices or machines, you also need to distribute the input data, which leads to a `tf.distribute.DistributedDataset` instance, instead of a `tf.data.Dataset` instance in the non-distributed case. In TF 2.x, `tf.distribute.DistributedDataset` objects are Python iterables. Note: `tf.distribute.DistributedDataset` instances are *not* of type `tf.data.Dataset`. It only supports two usages we will mention below: iteration and `element_spec`. We don't support any other APIs to transform or inspect the dataset. There are two APIs to create a `tf.distribute.DistributedDataset` object: `tf.distribute.Strategy.experimental_distribute_dataset(dataset)`and `tf.distribute.Strategy.distribute_datasets_from_function(dataset_fn)`. *When to use which?* When you have a `tf.data.Dataset` instance, and the regular batch splitting (i.e. re-batch the input `tf.data.Dataset` instance with a new batch size that is equal to the global batch size divided by the number of replicas in sync) and autosharding (i.e. the `tf.data.experimental.AutoShardPolicy` options) work for you, use the former API. Otherwise, if you are *not* using a canonical `tf.data.Dataset` instance, or you would like to customize the batch splitting or sharding, you can wrap these logic in a `dataset_fn` and use the latter API. Both API handles prefetch to device for the user. For more details and examples, follow the links to the APIs. There are two main usages of a `DistributedDataset` object: 1. Iterate over it to generate the input for a single device or multiple devices, which is a `tf.distribute.DistributedValues` instance. To do this, you can: * use a pythonic for-loop construct: >>> global_batch_size = 4 >>> strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"]) >>> dataset = tf.data.Dataset.from_tensors(([1.],[1.])).repeat(4).batch(global_batch_size) >>> dist_dataset = strategy.experimental_distribute_dataset(dataset) >>> @tf.function ... def train_step(input): ... features, labels = input ... return labels - 0.3 * features >>> for x in dist_dataset: ... # train_step trains the model using the dataset elements ... loss = strategy.run(train_step, args=(x,)) ... print("Loss is", loss) Loss is PerReplica:{ 0: tf.Tensor( [[0.7] [0.7]], shape=(2, 1), dtype=float32), 1: tf.Tensor( [[0.7] [0.7]], shape=(2, 1), dtype=float32) } Placing the loop inside a `tf.function` will give a performance boost. However `break` and `return` are currently not supported if the loop is placed inside a `tf.function`. We also don't support placing the loop inside a `tf.function` when using `tf.distribute.experimental.MultiWorkerMirroredStrategy` or `tf.distribute.experimental.TPUStrategy` with multiple workers. * use `__iter__` to create an explicit iterator, which is of type `tf.distribute.DistributedIterator` >>> global_batch_size = 4 >>> strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"]) >>> train_dataset = tf.data.Dataset.from_tensors(([1.],[1.])).repeat(50).batch(global_batch_size) >>> train_dist_dataset = strategy.experimental_distribute_dataset(train_dataset) >>> @tf.function ... def distributed_train_step(dataset_inputs): ... def train_step(input): ... loss = tf.constant(0.1) ... return loss ... per_replica_losses = strategy.run(train_step, args=(dataset_inputs,)) ... return strategy.reduce(tf.distribute.ReduceOp.SUM, per_replica_losses,axis=None) >>> EPOCHS = 2 >>> STEPS = 3 >>> for epoch in range(EPOCHS): ... total_loss = 0.0 ... num_batches = 0 ... dist_dataset_iterator = iter(train_dist_dataset) ... for _ in range(STEPS): ... total_loss += distributed_train_step(next(dist_dataset_iterator)) ... num_batches += 1 ... average_train_loss = total_loss / num_batches ... template = ("Epoch {}, Loss: {:.4f}") ... print (template.format(epoch+1, average_train_loss)) Epoch 1, Loss: 0.2000 Epoch 2, Loss: 0.2000 To achieve a performance improvement, you can also wrap the `strategy.run` call with a `tf.range` inside a `tf.function`. This runs multiple steps in a `tf.function`. Autograph will convert it to a `tf.while_loop` on the worker. However, it is less flexible comparing with running a single step inside `tf.function`. For example, you cannot run things eagerly or arbitrary python code within the steps. 2. Inspect the `tf.TypeSpec` of the data generated by `DistributedDataset`. `tf.distribute.DistributedDataset` generates `tf.distribute.DistributedValues` as input to the devices. If you pass the input to a `tf.function` and would like to specify the shape and type of each Tensor argument to the function, you can pass a `tf.TypeSpec` object to the `input_signature` argument of the `tf.function`. To get the `tf.TypeSpec` of the input, you can use the `element_spec` property of the `tf.distribute.DistributedDataset` or `tf.distribute.DistributedIterator` object. For example: >>> global_batch_size = 4 >>> epochs = 1 >>> steps_per_epoch = 1 >>> mirrored_strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"]) >>> dataset = tf.data.Dataset.from_tensors(([2.])).repeat(100).batch(global_batch_size) >>> dist_dataset = mirrored_strategy.experimental_distribute_dataset(dataset) >>> @tf.function(input_signature=[dist_dataset.element_spec]) ... def train_step(per_replica_inputs): ... def step_fn(inputs): ... return tf.square(inputs) ... return mirrored_strategy.run(step_fn, args=(per_replica_inputs,)) >>> for _ in range(epochs): ... iterator = iter(dist_dataset) ... for _ in range(steps_per_epoch): ... output = train_step(next(iterator)) ... print(output) PerReplica:{ 0: tf.Tensor( [[4.] [4.]], shape=(2, 1), dtype=float32), 1: tf.Tensor( [[4.] [4.]], shape=(2, 1), dtype=float32) } Visit the [tutorial](https://www.tensorflow.org/tutorials/distribute/input) on distributed input for more examples and caveats. """ def __iter__(self): """Creates an iterator for the `tf.distribute.DistributedDataset`. The returned iterator implements the Python Iterator protocol. Example usage: >>> global_batch_size = 4 >>> strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"]) >>> dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3, 4]).repeat().batch(global_batch_size) >>> distributed_iterator = iter(strategy.experimental_distribute_dataset(dataset)) >>> print(next(distributed_iterator)) PerReplica:{ 0: tf.Tensor([1 2], shape=(2,), dtype=int32), 1: tf.Tensor([3 4], shape=(2,), dtype=int32) } Returns: An `tf.distribute.DistributedIterator` instance for the given `tf.distribute.DistributedDataset` object to enumerate over the distributed data. """ raise NotImplementedError("Must be implemented in descendants") @property def element_spec(self): """The type specification of an element of this `tf.distribute.DistributedDataset`. Example usage: >>> global_batch_size = 16 >>> strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"]) >>> dataset = tf.data.Dataset.from_tensors(([1.],[2])).repeat(100).batch(global_batch_size) >>> dist_dataset = strategy.experimental_distribute_dataset(dataset) >>> dist_dataset.element_spec (PerReplicaSpec(TensorSpec(shape=(None, 1), dtype=tf.float32, name=None), TensorSpec(shape=(None, 1), dtype=tf.float32, name=None)), PerReplicaSpec(TensorSpec(shape=(None, 1), dtype=tf.int32, name=None), TensorSpec(shape=(None, 1), dtype=tf.int32, name=None))) Returns: A nested structure of `tf.TypeSpec` objects matching the structure of an element of this `tf.distribute.DistributedDataset`. This returned value is typically a `tf.distribute.DistributedValues` object and specifies the `tf.TensorSpec` of individual components. """ raise NotImplementedError( "DistributedDataset.element_spec must be implemented in descendants.") @doc_controls.do_not_generate_docs def reduce(self, initial_state, reduce_func): raise NotImplementedError( "DistributedDataset.reduce must be implemented in descendants.") class InputWorkers(object): """A 1-to-many mapping from input worker devices to compute devices.""" def __init__(self, worker_device_pairs): """Initialize an `InputWorkers` object. Args: worker_device_pairs: A sequence of pairs: `(input device, a tuple of compute devices fed by that input device)`. """ self._worker_device_pairs = worker_device_pairs self._input_worker_devices = tuple(d for d, _ in self._worker_device_pairs) self._fed_devices = tuple(tuple(device_util.canonicalize(d) for d in f) for _, f in self._worker_device_pairs) @property def num_workers(self): return len(self._input_worker_devices) @property def worker_devices(self): return self._input_worker_devices def compute_devices_for_worker(self, worker_index): return self._fed_devices[worker_index] def __repr__(self): devices = self.worker_devices debug_repr = ",\n".join(" %d %s: %s" % (i, devices[i], self._fed_devices[i]) for i in range(len(devices))) return "%s:{\n%s}" % (self.__class__.__name__, debug_repr) def serialize(self): return self._worker_device_pairs def deserialize(self, worker_device_pairs): return InputWorkers(worker_device_pairs) def _get_next_as_optional(iterator, strategy, return_per_replica=False): """Returns an empty dataset indicator and the next input from the iterator. Args: iterator: a DistributedIterator object. strategy: the `tf.distribute.Strategy` instance. return_per_replica: a boolean. If True, the returned data will be wrapped with `PerReplica` structure. Otherwise it is a 2D num_input_workers*num_replicas_per_worker list. Returns: A tuple (a boolean tensor indicating whether the next batch has value globally, data from all replicas). """ replicas = [] worker_has_values = [] worker_devices = [] for i, worker in enumerate(iterator._input_workers.worker_devices): # pylint: disable=protected-access with ops.device(worker): worker_has_value, next_element = ( iterator._iterators[i].get_next_as_list()) # pylint: disable=protected-access # Collective all-reduce requires explicit devices for inputs. with ops.device("/cpu:0"): # Converting to integers for all-reduce. worker_has_value = math_ops.cast(worker_has_value, dtypes.int64) worker_devices.append(worker_has_value.device) worker_has_values.append(worker_has_value) # Make `replicas` a flat list of values across all replicas. replicas.append(next_element) if return_per_replica: flattened_data = [] for per_worker_data in replicas: flattened_data.extend(per_worker_data) replicas = _create_per_replica(flattened_data, strategy) # Run an all-reduce to see whether any worker has values. # TODO(b/131423105): we should be able to short-cut the all-reduce in some # cases. if getattr(strategy.extended, "_support_per_replica_values", True): # `reduce` expects a `PerReplica`, so we pass it one, even # though it doesn't actually have a value per replica worker_has_values = values.PerReplica(worker_has_values) global_has_value = strategy.reduce( reduce_util.ReduceOp.SUM, worker_has_values, axis=None) else: assert len(worker_has_values) == 1 global_has_value = worker_has_values[0] global_has_value = array_ops.reshape( math_ops.cast(global_has_value, dtypes.bool), []) return global_has_value, replicas def _is_statically_shaped(element_spec): """Test if an iterator output is statically shaped. For sparse and ragged tensors this only tests the batch dimension. Args: element_spec: a nest structure of `tf.TypeSpec`. The element spec of the dataset of the iterator. Returns: True if the shape is static, false otherwise. """ for spec in nest.flatten(element_spec): if isinstance( spec, (sparse_tensor.SparseTensorSpec, ragged_tensor.RaggedTensorSpec)): # For sparse or ragged tensor, we should only check the first # dimension in order to get_next_as_optional. This is because # when these tensors get batched by dataset only the batch dimension # is set. if spec.shape.rank > 0 and spec.shape.as_list()[0] is None: return False else: for component in nest.flatten(spec._component_specs): # pylint: disable=protected-access if not component.shape.is_fully_defined(): return False return True class DistributedIteratorBase(DistributedIteratorInterface): """Common implementation for all input iterators.""" # pylint: disable=super-init-not-called def __init__(self, input_workers, iterators, strategy, enable_get_next_as_optional): assert isinstance(input_workers, InputWorkers) if not input_workers.worker_devices: raise ValueError("Should have at least one worker for input iterator.") self._iterators = iterators self._input_workers = input_workers self._strategy = strategy self._enable_get_next_as_optional = enable_get_next_as_optional def next(self): return self.__next__() def __next__(self): try: return self.get_next() except errors.OutOfRangeError: raise StopIteration def __iter__(self): return self def get_next_as_optional(self): global_has_value, replicas = _get_next_as_optional( self, self._strategy, return_per_replica=True) def return_none(): return optional_ops.Optional.empty(self._element_spec) return control_flow_ops.cond( global_has_value, lambda: optional_ops.Optional.from_value(replicas), return_none) def get_next(self, name=None): """Returns the next input from the iterator for all replicas.""" if not self._enable_get_next_as_optional: replicas = [] for i, worker in enumerate(self._input_workers.worker_devices): if name is not None: d = tf_device.DeviceSpec.from_string(worker) new_name = "%s_%s_%d" % (name, d.job, d.task) else: new_name = None with ops.device(worker): # Make `replicas` a flat list of values across all replicas. replicas.extend( self._iterators[i].get_next_as_list_static_shapes(new_name)) return _create_per_replica(replicas, self._strategy) out_of_range_replicas = [] def out_of_range_fn(worker_index, device): """This function will throw an OutOfRange error.""" # As this will be only called when there is no data left, so calling # get_next() will trigger an OutOfRange error. data = self._iterators[worker_index].get_next(device) out_of_range_replicas.append(data) return data global_has_value, replicas = _get_next_as_optional( self, self._strategy, return_per_replica=False) results = [] for i, worker in enumerate(self._input_workers.worker_devices): with ops.device(worker): devices = self._input_workers.compute_devices_for_worker(i) for j, device in enumerate(devices): with ops.device(device): # pylint: disable=undefined-loop-variable # pylint: disable=cell-var-from-loop # It is fine for the lambda to capture variables from the loop as # the lambda is executed in the loop as well. result = control_flow_ops.cond( global_has_value, lambda: replicas[i][j], lambda: out_of_range_fn(i, device), strict=True, ) # pylint: enable=cell-var-from-loop # pylint: enable=undefined-loop-variable results.append(result) replicas = results return _create_per_replica(replicas, self._strategy) class DistributedIteratorV1(DistributedIteratorBase): """Input Iterator for a distributed dataset.""" # We need a private initializer method for re-initializing multidevice # iterators when used with Keras training loops. If we don't reinitialize the # iterator we run into memory leak issues (b/123315763). @property def _initializer(self): init_ops = [] for it in self._iterators: init_ops.extend(it.initialize()) return control_flow_ops.group(init_ops) @deprecated(None, "Use the iterator's `initializer` property instead.") def initialize(self): """Initialize underlying iterators. Returns: A list of any initializer ops that should be run. """ return self._initializer @property def initializer(self): """Returns a list of ops that initialize the iterator.""" return self.initialize() # TODO(priyag): Remove when we switch to using `MultiDeviceIterator` for TPUs. @property def output_classes(self): return self._iterators[0].output_classes # TODO(priyag): Remove when we switch to using `MultiDeviceIterator` for TPUs. @property def output_shapes(self): return self._iterators[0].output_shapes # TODO(priyag): Remove when we switch to using `MultiDeviceIterator` for TPUs. @property def output_types(self): return self._iterators[0].output_types # TODO(priyag): Remove when we switch to using `MultiDeviceIterator` for TPUs. def get_iterator(self, worker): for i, w in enumerate(self._input_workers.worker_devices): if worker == w: return self._iterators[i] return None @property def element_spec(self): """The type specification of an element of this iterator.""" return self._element_spec class DistributedDatasetAndIteratorSpec(type_spec.TypeSpec): """Common Type specification for `DistributedDataset and DistributedDatasetsFromFunction.""" __slots__ = [ "_input_workers", "_element_spec", "_strategy", "_enable_get_next_as_optional", "_options" ] def __init__(self, input_workers, element_spec, strategy, options, enable_get_next_as_optional=None): # We don't want to allow deserialization of this class because we don't # serialize the strategy object. Currently the only places where # _deserialize is called is when we save/restore using SavedModels. if isinstance(input_workers, tuple): raise NotImplementedError("DistributedIteratorSpec does not have support " "for deserialization.") else: self._input_workers = input_workers self._element_spec = element_spec self._strategy = strategy self._enable_get_next_as_optional = enable_get_next_as_optional self._options = options def _serialize(self): # We cannot serialize the strategy object so we convert it to an id that we # can use for comparison. return (self._input_workers.serialize(), self._element_spec, id(self._strategy), id(self._options)) def _deserialize(self): raise ValueError( f"Deserialization is currently unsupported for {type(self)}.") def sanity_check_type(self, other): """Returns the most specific TypeSpec compatible with `self` and `other`. Args: other: A `TypeSpec`. Raises: ValueError: If there is no TypeSpec that is compatible with both `self` and `other`. """ # pylint: disable=protected-access if type(self) is not type(other): raise ValueError("No TypeSpec is compatible with both %s and %s" % (self, other)) if self._input_workers.serialize() != other._input_workers.serialize(): raise ValueError("_input_workers is not compatible with both %s " "and %s" % (self, other)) if self._strategy is not other._strategy: raise ValueError("tf.distribute strategy is not compatible with both %s " "and %s" % (self, other)) class DistributedIteratorSpec(DistributedDatasetAndIteratorSpec): """Type specification for `DistributedIterator`.""" def __init__(self, input_workers, element_spec, strategy, enable_get_next_as_optional, options): super(DistributedIteratorSpec, self).__init__(input_workers, element_spec, strategy, options, enable_get_next_as_optional) @property def value_type(self): return DistributedIterator # Overriding this method so that we can merge and reconstruct the spec object def most_specific_compatible_type(self, other): """Returns the most specific TypeSpec compatible with `self` and `other`. Args: other: A `TypeSpec`. Raises: ValueError: If there is no TypeSpec that is compatible with both `self` and `other`. """ # pylint: disable=protected-access self.sanity_check_type(other) element_spec = nest.map_structure( lambda a, b: a.most_specific_compatible_type(b), self._element_spec, other._element_spec) return DistributedIteratorSpec(self._input_workers, element_spec, self._strategy, self._enable_get_next_as_optional, self._options) @property def _component_specs(self): specs = [] worker_device_pairs = self._input_workers._worker_device_pairs # pylint: disable=protected-access for i, (input_device, compute_devices) in enumerate(worker_device_pairs): element_spec = nest.map_structure( functools.partial(_replace_per_replica_spec, i=i), self._element_spec) specs.append( _SingleWorkerDatasetIteratorSpec(input_device, compute_devices, element_spec, self._options)) return specs def _to_components(self, value): return value._iterators # pylint: disable=protected-access def _from_components(self, components): return DistributedIterator( input_workers=self._input_workers, iterators=None, components=components, element_spec=self._element_spec, strategy=self._strategy, enable_get_next_as_optional=self._enable_get_next_as_optional, options=self._options) @staticmethod def from_value(value): # pylint: disable=protected-access return DistributedIteratorSpec(value._input_workers, value._element_spec, value._strategy, value._enable_get_next_as_optional, value._options) def _with_tensor_ranks_only(self): element_spec = nest.map_structure( lambda s: s._with_tensor_ranks_only(), # pylint: disable=protected-access self._element_spec) return DistributedIteratorSpec(self._input_workers, element_spec, self._strategy, self._enable_get_next_as_optional, self._options) class DistributedIterator(DistributedIteratorBase, composite_tensor.CompositeTensor): """Input Iterator for a distributed dataset.""" def __init__(self, input_workers=None, iterators=None, strategy=None, components=None, element_spec=None, enable_get_next_as_optional=False, options=None): if input_workers is None: raise ValueError("`input_workers` should be " "provided.") error_message = ("Either `input_workers` or " "both `components` and `element_spec` need to be " "provided.") self._options = options if iterators is None: if (components is None or element_spec is None): raise ValueError(error_message) self._element_spec = element_spec self._input_workers = input_workers self._iterators = components self._strategy = strategy self._enable_get_next_as_optional = enable_get_next_as_optional else: if (components is not None and element_spec is not None): raise ValueError(error_message) super(DistributedIterator, self).__init__(input_workers, iterators, strategy, enable_get_next_as_optional) @property def element_spec(self): # When partial batch handling is enabled, always set the batch dimension to # None, otherwise we just follow element_spec of the underlying dataset # (whose batch dimension may also be None). This is because with partial # batching handling we could always produce empty batches. if (self._enable_get_next_as_optional and self._strategy.extended._in_multi_worker_mode()): # pylint: disable=protected-access return nest.map_structure( _rebatch_as_dynamic, self._element_spec, expand_composites=False) return self._element_spec @property def _type_spec(self): # Note that we use actual element_spec instead of the rebatched-as-dynamic # one to create DistributedIteratorSpec, to be consistent with the # underlying iterators' specs. return DistributedIteratorSpec(self._input_workers, self._element_spec, self._strategy, self._enable_get_next_as_optional, self._options) class _IterableInput(DistributedDatasetInterface): """Base class for iterable inputs for distribution strategies.""" # pylint: disable=super-init-not-called def __init__(self, input_workers): assert isinstance(input_workers, InputWorkers) self._input_workers = input_workers def __iter__(self): raise NotImplementedError("must be implemented in descendants") def reduce(self, initial_state, reduce_fn): """Execute a `reduce_fn` over all the elements of the input.""" iterator = iter(self) has_data, data = _get_next_as_optional( iterator, self._strategy, return_per_replica=True) def cond(has_data, data, state): del data, state # Unused. return has_data def loop_body(has_data, data, state): """Executes `reduce_fn` in a loop till the dataset is empty.""" del has_data # Unused. state = reduce_fn(state, data) has_data, data = _get_next_as_optional( iterator, self._strategy, return_per_replica=True) return has_data, data, state has_data, data, final_state = control_flow_ops.while_loop( cond, loop_body, [has_data, data, initial_state], parallel_iterations=1) return final_state class DistributedDatasetSpec(DistributedDatasetAndIteratorSpec): """Type specification for `DistributedDataset.""" def __init__(self, input_workers, element_spec, strategy, enable_get_next_as_optional, options): super(DistributedDatasetSpec, self).__init__(input_workers, element_spec, strategy, options, enable_get_next_as_optional) @property def value_type(self): return DistributedDataset # Overriding this method so that we can merge and reconstruct the spec object def most_specific_compatible_type(self, other): """Returns the most specific TypeSpec compatible with `self` and `other`. Args: other: A `TypeSpec`. Raises: ValueError: If there is no TypeSpec that is compatible with both `self` and `other`. """ # pylint: disable=protected-access self.sanity_check_type(other) element_spec = nest.map_structure( lambda a, b: a.most_specific_compatible_type(b), self._element_spec, other._element_spec) return DistributedDatasetSpec(self._input_workers, element_spec, self._strategy, self._enable_get_next_as_optional, self._options) @property def _component_specs(self): specs = [] worker_device_pairs = self._input_workers._worker_device_pairs # pylint: disable=protected-access for i, _ in enumerate(worker_device_pairs): element_spec = nest.map_structure( functools.partial(_replace_per_replica_spec, i=i), self._element_spec) specs.append(dataset_ops.DatasetSpec(element_spec)) return specs def _to_components(self, value): return value._cloned_datasets # pylint: disable=protected-access def _from_components(self, components): return DistributedDataset( input_workers=self._input_workers, strategy=self._strategy, components=components, element_spec=self._element_spec, enable_get_next_as_optional=self._enable_get_next_as_optional, options=self._options) @staticmethod def from_value(value): # pylint: disable=protected-access return DistributedDatasetSpec(value._input_workers, value._element_spec, value._strategy, value._enable_get_next_as_optional, value._options) class DistributedDataset(_IterableInput, composite_tensor.CompositeTensor): """Distributed dataset that supports prefetching to multiple devices.""" def __init__(self, input_workers, strategy, dataset=None, num_replicas_in_sync=None, input_context=None, components=None, element_spec=None, enable_get_next_as_optional=None, options=None): """Distribute the dataset on all workers. If `num_replicas_in_sync` is not None, we split each batch of the dataset into `num_replicas_in_sync` smaller batches, to be distributed among that worker's replicas, so that the batch size for a global step (across all workers and replicas) is as expected. Args: input_workers: an `InputWorkers` object. strategy: a `tf.distribute.Strategy` object, used to run all-reduce to handle last partial batch. dataset: `tf.data.Dataset` that will be used as the input source. Either dataset or components field should be passed when constructing DistributedDataset. Use this when contructing DistributedDataset from a new `tf.data.Dataset`. Use components when constructing using DistributedDatasetSpec. num_replicas_in_sync: Optional integer. If this is not None, the value is used to decide how to rebatch datasets into smaller batches so that the total batch size for each step (across all workers and replicas) adds up to `dataset`'s batch size. input_context: `InputContext` for sharding. Only pass this in for between graph multi-worker cases where there is only one `input_worker`. In these cases, we will shard based on the `input_pipeline_id` and `num_input_pipelines` in the `InputContext`. components: datasets when DistributedDataset is constructed from DistributedDatasetSpec. Either field dataset or components should be passed. element_spec: element spec for DistributedDataset when constructing from DistributedDatasetSpec. This will be used to set the element_spec for DistributedDataset and verified against element_spec from components. enable_get_next_as_optional: this is required when components is passed instead of dataset. options: `tf.distribute.InputOptions` used to control options on how this dataset is distributed. """ super(DistributedDataset, self).__init__(input_workers=input_workers) if input_workers is None or strategy is None: raise ValueError("input_workers and strategy are required arguments") if dataset is not None and components is not None: raise ValueError("Only one of dataset or components should be present") if dataset is None and components is None: raise ValueError("At least one of dataset or components should be passed") if dataset is not None: self._create_cloned_datasets_from_dataset(dataset, input_context, input_workers, strategy, num_replicas_in_sync) else: if enable_get_next_as_optional is None: raise ValueError( "When constructing DistributedDataset with components, " + "enable_get_next_as_optional should also be passed") self._cloned_datasets = components self._enable_get_next_as_optional = enable_get_next_as_optional self._input_workers = input_workers self._strategy = strategy self._options = options if element_spec is not None: if element_spec != _create_distributed_tensor_spec( self._strategy, self._cloned_datasets[0].element_spec): raise ValueError("Mismatched element_spec from the passed components") self._element_spec = element_spec else: self._element_spec = _create_distributed_tensor_spec( self._strategy, self._cloned_datasets[0].element_spec) def _create_cloned_datasets_from_dataset(self, dataset, input_context, input_workers, strategy, num_replicas_in_sync): # We clone and shard the dataset on each worker. The current setup tries to # shard the dataset by files if possible so that each worker sees a # different subset of files. If that is not possible, will attempt to shard # the final input such that each worker will run the entire preprocessing # pipeline and only receive its own shard of the dataset. # Additionally, we rebatch the dataset on each worker into # `num_replicas_in_sync` smaller batches to be distributed among that # worker's replicas, so that the batch size for a global step (across all # workers and replicas) adds up to the original dataset's batch size. if num_replicas_in_sync is not None: num_workers = input_context.num_input_pipelines if input_context else len( input_workers.worker_devices) rebatch_fn = self._make_rebatch_fn(dataset, num_workers, num_replicas_in_sync) else: rebatch_fn = None self._cloned_datasets = [] if input_context: # Between-graph where we rely on the input_context for sharding assert input_workers.num_workers == 1 if rebatch_fn is not None: dataset = rebatch_fn(dataset, input_context.input_pipeline_id) dataset = input_ops.auto_shard_dataset(dataset, input_context.num_input_pipelines, input_context.input_pipeline_id, num_replicas_in_sync) self._cloned_datasets.append(dataset) else: replicated_ds = distribute.replicate(dataset, input_workers.worker_devices) for i, worker in enumerate(input_workers.worker_devices): with ops.device(worker): cloned_dataset = replicated_ds[worker] cloned_dataset = cloned_dataset.with_options(dataset.options()) if rebatch_fn is not None: cloned_dataset = rebatch_fn(cloned_dataset, i) cloned_dataset = input_ops.auto_shard_dataset( cloned_dataset, len(input_workers.worker_devices), i, num_replicas_in_sync) self._cloned_datasets.append(cloned_dataset) self._enable_get_next_as_optional = _enable_get_next_as_optional( strategy, dataset) def _make_rebatch_fn(self, dataset, num_workers, num_replicas_in_sync): """Returns a callable that rebatches the input dataset. Args: dataset: A `tf.data.Dataset` representing the dataset to be distributed. num_workers: An integer representing the number of workers to distribute `dataset` among. num_replicas_in_sync: An integer representing the number of replicas in sync across all workers. """ if num_replicas_in_sync % num_workers: raise ValueError( "tf.distribute expects every worker to have the same number of " "replicas. However, encountered `num_replicas_in_sync` ({}) that " "cannot be divided by `num_workers` ({})".format( num_replicas_in_sync, num_workers)) num_replicas_per_worker = num_replicas_in_sync // num_workers with ops.colocate_with(dataset._variant_tensor): # pylint: disable=protected-access batch_size = distribute.compute_batch_size(dataset) def rebatch_fn(dataset, worker_index): try: # pylint: disable=protected-access def apply_rebatch(): batch_sizes = distribute.batch_sizes_for_worker( batch_size, num_workers, num_replicas_per_worker, worker_index) return distribute._RebatchDataset( dataset, batch_sizes).prefetch(num_replicas_per_worker) def apply_legacy_rebatch(): return distribute._LegacyRebatchDataset( dataset, num_replicas_in_sync).prefetch(num_replicas_per_worker) with ops.colocate_with(dataset._variant_tensor): return control_flow_ops.cond( math_ops.not_equal(batch_size, -1), true_fn=apply_rebatch, false_fn=apply_legacy_rebatch) except errors.InvalidArgumentError as e: if "without encountering a batch" in str(e): six.reraise( ValueError, ValueError( "Call the `batch` method on the input Dataset in order to be " "able to split your input across {} replicas.\n Please see " "the tf.distribute.Strategy guide. {}".format( num_replicas_in_sync, e)), sys.exc_info()[2]) else: raise return rebatch_fn def __iter__(self): if not (context.executing_eagerly() or ops.get_default_graph().building_function): raise RuntimeError("__iter__() is only supported inside of tf.function " "or when eager execution is enabled.") # This is an optional flag that can be used to turn off using # OwnedMultiDeviceIterators and instead use the legacy MultiDeviceIterators # as a stop gap solution that will allow us to roll out this change. enable_legacy_iterators = getattr(self._strategy, "_enable_legacy_iterators", False) worker_iterators = _create_iterators_per_worker(self._cloned_datasets, self._input_workers, enable_legacy_iterators, self._options) if enable_legacy_iterators: iterator = DistributedIteratorV1( self._input_workers, worker_iterators, self._strategy, enable_get_next_as_optional=self._enable_get_next_as_optional) else: iterator = DistributedIterator( self._input_workers, worker_iterators, self._strategy, enable_get_next_as_optional=self._enable_get_next_as_optional, options=self._options) iterator._element_spec = self._element_spec # pylint: disable=protected-access # When async eager is enabled, sometimes the iterator may not finish # initialization before passing to a multi device function, add a sync point # here to make sure all underlying iterators are initialized. if context.executing_eagerly(): context.async_wait() return iterator @property def element_spec(self): """The type specification of an element of this dataset.""" # When partial batch handling is enabled, always set the batch dimension to # None, otherwise we just follow element_spec of the underlying dataset # (whose batch dimension may also be None). This is because with partial # batching handling we could always produce empty batches. if (self._enable_get_next_as_optional and self._strategy.extended._in_multi_worker_mode()): # pylint: disable=protected-access return nest.map_structure( _rebatch_as_dynamic, self._element_spec, expand_composites=False) return self._element_spec @property def _type_spec(self): return DistributedDatasetSpec(self._input_workers, self._element_spec, self._strategy, self._enable_get_next_as_optional, self._options) class DistributedDatasetV1(DistributedDataset): """Distributed dataset that supports prefetching to multiple devices.""" def __init__(self, dataset, input_workers, strategy, num_replicas_in_sync=None, input_context=None, options=None): self._input_workers = input_workers super(DistributedDatasetV1, self).__init__( input_workers, strategy, dataset, num_replicas_in_sync=num_replicas_in_sync, input_context=input_context, options=options) def make_one_shot_iterator(self): """Get a one time use iterator for DistributedDatasetV1. Note: This API is deprecated. Please use `for ... in dataset:` to iterate over the dataset or `iter` to create an iterator. Returns: A DistributedIteratorV1 instance. """ return self._make_one_shot_iterator() def _make_one_shot_iterator(self): """Get an iterator for DistributedDatasetV1.""" # Graph mode with one shot iterator is disabled because we have to call # `initialize` on the iterator which is only required if we are using a # tf.distribute strategy. if not context.executing_eagerly(): raise ValueError("Cannot create a one shot iterator. Please use " "`make_initializable_iterator()` instead.") return self._get_iterator() def make_initializable_iterator(self): """Get an initializable iterator for DistributedDatasetV1. Note: This API is deprecated. Please use `tf.compat.v1.data.make_initializable_iterator(dataset)` to create an initializable iterator. Returns: A DistributedIteratorV1 instance. """ return self._make_initializable_iterator() def _make_initializable_iterator(self, shared_name=None): # pylint: disable=unused-argument """Get an initializable iterator for DistributedDatasetV1.""" # Eager mode generates already initialized iterators. Hence we cannot create # an initializable iterator. if context.executing_eagerly(): raise ValueError("Cannot create initializable iterator in Eager mode. " "Please use `iter()` instead.") return self._get_iterator() def _get_iterator(self): worker_iterators = _create_iterators_per_worker(self._cloned_datasets, self._input_workers, True, self._options) iterator = DistributedIteratorV1(self._input_workers, worker_iterators, self._strategy, self._enable_get_next_as_optional) iterator._element_spec = self.element_spec # pylint: disable=protected-access # When async eager is enabled, sometimes the iterator may not finish # initialization before passing to a multi device function, add a sync point # here to make sure all underlying iterators are initialized. if context.executing_eagerly(): context.async_wait() return iterator def __iter__(self): if (ops.executing_eagerly_outside_functions() or ops.get_default_graph().building_function): return self._get_iterator() raise RuntimeError("__iter__() is only supported inside of tf.function " "or when eager execution is enabled.") class DistributedDatasetsFromFunctionSpec(DistributedDatasetAndIteratorSpec): """Type specification for `DistributedDatasetsFromFunction.""" def __init__(self, input_workers, element_spec, strategy, options): super(DistributedDatasetsFromFunctionSpec, self).__init__(input_workers, element_spec, strategy, options) @property def value_type(self): return DistributedDatasetsFromFunction @property def _component_specs(self): specs = [] worker_device_pairs = self._input_workers._worker_device_pairs # pylint: disable=protected-access for i, _ in enumerate(worker_device_pairs): element_spec = nest.map_structure( functools.partial(_replace_per_replica_spec, i=i), self._element_spec) specs.append(dataset_ops.DatasetSpec(element_spec)) return specs # Overriding this method so that we can merge and reconstruct the spec object def most_specific_compatible_type(self, other): """Returns the most specific TypeSpec compatible with `self` and `other`. Args: other: A `TypeSpec`. Raises: ValueError: If there is no TypeSpec that is compatible with both `self` and `other`. """ # pylint: disable=protected-access self.sanity_check_type(other) element_spec = nest.map_structure( lambda a, b: a.most_specific_compatible_type(b), self._element_spec, other._element_spec) # pylint: disable=protected-access return DistributedDatasetsFromFunctionSpec(self._input_workers, element_spec, self._strategy, self._options) def _to_components(self, value): return value._datasets # pylint: disable=protected-access def _from_components(self, components): return DistributedDatasetsFromFunction( input_workers=self._input_workers, strategy=self._strategy, components=components, element_spec=self._element_spec, options=self._options) @staticmethod def from_value(value): # pylint: disable=protected-access return DistributedDatasetsFromFunctionSpec( input_workers=value._input_workers, element_spec=value._element_spec, strategy=value._strategy, options=value._options) # TODO(priyag): Add other replication modes. class DistributedDatasetsFromFunction(_IterableInput, composite_tensor.CompositeTensor): """Inputs created from dataset function.""" def __init__(self, input_workers, strategy, input_contexts=None, dataset_fn=None, options=None, components=None, element_spec=None): """Makes an iterable from datasets created by the given function. Args: input_workers: an `InputWorkers` object. strategy: a `tf.distribute.Strategy` object, used to run all-reduce to handle last partial batch. input_contexts: A list of `InputContext` instances to be passed to call(s) to `dataset_fn`. Length and order should match worker order in `worker_device_pairs`. dataset_fn: A function that returns a `Dataset` given an `InputContext`. Either dataset_fn or components should be passed to construct DistributedDatasetsFromFunction. Use this when contructing DistributedDataset using a function. Use components when constructing using DistributedDatasetsFromFunctionSpec. options: `tf.distribute.InputOptions` used to control options on how this dataset is distributed. components: datasets when DistributedDatasetsFromFunction is constructed from DistributedDatasetsFromFunctionSpec. Only one of dataset or components should be passed. element_spec: element spec for DistributedDataset when constructing from DistributedDatasetSpec. This will be used to set the element_spec for DistributedDatasetsFromFunctionSpec and verified against element_spec from components. """ super(DistributedDatasetsFromFunction, self).__init__( input_workers=input_workers) self._input_workers = input_workers self._strategy = strategy self._options = options if dataset_fn is not None and components is not None: raise ValueError("Only one of dataset_fn or components should be set") if dataset_fn is None and components is None: raise ValueError("At least one of dataset_fn or components should be set") if dataset_fn is not None: if input_workers.num_workers != len(input_contexts): raise ValueError( "Number of input workers (%d) is not same as number of " "input_contexts (%d)" % (input_workers.num_workers, len(input_contexts))) self._datasets, element_spec = ( _create_datasets_from_function_with_input_context( input_contexts, self._input_workers, dataset_fn)) self._element_spec = _create_distributed_tensor_spec( self._strategy, element_spec) else: if element_spec is None: raise ValueError( "element_spec should also be passed when passing components") self._element_spec = element_spec self._datasets = components self._enable_get_next_as_optional = _enable_get_next_as_optional( self._strategy, self._datasets[0]) def __iter__(self): if (ops.executing_eagerly_outside_functions() or ops.get_default_graph().building_function): # This is an optional flag that can be used to turn off using # OwnedMultiDeviceIterators and instead use the legacy # MultiDeviceIterators as a stop gap solution that will allow us to roll # out this change. enable_legacy_iterators = getattr(self._strategy, "_enable_legacy_iterators", False) iterators = _create_iterators_per_worker(self._datasets, self._input_workers, enable_legacy_iterators, self._options) if enable_legacy_iterators: iterator = DistributedIteratorV1( self._input_workers, iterators, self._strategy, enable_get_next_as_optional=self._enable_get_next_as_optional) else: iterator = DistributedIterator( input_workers=self._input_workers, iterators=iterators, strategy=self._strategy, enable_get_next_as_optional=self._enable_get_next_as_optional, options=self._options) iterator._element_spec = self._element_spec # pylint: disable=protected-access # When async eager is enabled, sometimes the iterator may not finish # initialization before passing to a multi device function, add a sync # point here to make sure all underlying iterators are initialized. if context.executing_eagerly(): context.async_wait() return iterator raise RuntimeError("__iter__() is only supported inside of tf.function " "or when eager execution is enabled.") @property def element_spec(self): """The type specification of an element of this dataset.""" # When partial batch handling is enabled, always set the batch dimension to # None, otherwise we just follow element_spec of the underlying dataset # (whose batch dimension may also be None). This is because with partial # batching handling we could always produce empty batches. if (self._enable_get_next_as_optional and self._strategy.extended._in_multi_worker_mode()): # pylint: disable=protected-access return nest.map_structure( _rebatch_as_dynamic, self._element_spec, expand_composites=False) return self._element_spec @property def _type_spec(self): return DistributedDatasetsFromFunctionSpec(self._input_workers, self._element_spec, self._strategy, self._options) class DistributedDatasetsFromFunctionV1(DistributedDatasetsFromFunction): """Inputs created from dataset function.""" def _make_initializable_iterator(self, shared_name=None): """Get an initializable iterator for DistributedDatasetsFromFunctionV1.""" del shared_name # Unused # Eager mode generates already initialized iterators. Hence we cannot create # an initializable iterator. if context.executing_eagerly(): raise ValueError("Cannot create initializable iterator in Eager mode. " "Please use `iter()` instead.") return self._get_iterator() def _make_one_shot_iterator(self): """Get an iterator for iterating over DistributedDatasetsFromFunctionV1.""" # Graph mode with one shot iterator is disabled because we have to call # `initialize` on the iterator which is only required if we are using a # tf.distribute strategy. if not context.executing_eagerly(): raise ValueError("Cannot create a one shot iterator. Please use " "`make_initializable_iterator()` instead.") return self._get_iterator() def _get_iterator(self): iterators = _create_iterators_per_worker(self._datasets, self._input_workers, True, self._options) iterator = DistributedIteratorV1(self._input_workers, iterators, self._strategy, self._enable_get_next_as_optional) iterator._element_spec = self._element_spec # pylint: disable=protected-access # When async eager is enabled, sometimes the iterator may not finish # initialization before passing to a multi device function, add a sync point # here to make sure all underlying iterators are initialized. if context.executing_eagerly(): context.async_wait() return iterator def __iter__(self): if (ops.executing_eagerly_outside_functions() or ops.get_default_graph().building_function): return self._get_iterator() raise RuntimeError("__iter__() is only supported inside of tf.function " "or when eager execution is enabled.") # TODO(anjalisridhar): This class will be soon removed in favor of newer # APIs. class InputFunctionIterator(DistributedIteratorV1): """Iterator created from input function.""" def __init__(self, input_fn, input_workers, input_contexts, strategy): """Make an iterator for input provided via an input function. Currently implements PER_WORKER mode, in which the `input_fn` is called once on each worker. TODO(priyag): Add other replication modes. Args: input_fn: Input function that returns a `tf.data.Dataset` object. input_workers: an `InputWorkers` object. input_contexts: A list of `InputContext` instances to be passed to call(s) to `input_fn`. Length and order should match worker order in `worker_device_pairs`. strategy: a `tf.distribute.Strategy` object, used to run all-reduce to handle last partial batch. """ assert isinstance(input_workers, InputWorkers) if input_workers.num_workers != len(input_contexts): raise ValueError( "Number of input workers (%d) is not same as number of " "input_contexts (%d)" % (input_workers.num_workers, len(input_contexts))) iterators = [] for i, ctx in enumerate(input_contexts): worker = input_workers.worker_devices[i] with ops.device(worker): result = input_fn(ctx) devices = input_workers.compute_devices_for_worker(i) if isinstance(result, dataset_ops.DatasetV2): iterator = _SingleWorkerDatasetIterator(result, worker, devices) elif callable(result): iterator = _SingleWorkerCallableIterator(result, worker, devices) else: raise ValueError( "input_fn must return a tf.data.Dataset or a callable.") iterators.append(iterator) super(InputFunctionIterator, self).__init__( input_workers, iterators, strategy, enable_get_next_as_optional=False) self._enable_get_next_as_optional = False # TODO(anjalisridhar): This class will soon be removed and users should move # to using DistributedIterator. class DatasetIterator(DistributedIteratorV1): """Iterator created from input dataset.""" def __init__(self, dataset, input_workers, strategy, num_replicas_in_sync=None, input_context=None): """Make an iterator for the dataset on given devices. If `num_replicas_in_sync` is not None, we split each batch of the dataset into `num_replicas_in_sync` smaller batches, to be distributed among that worker's replicas, so that the batch size for a global step (across all workers and replicas) is as expected. Args: dataset: `tf.data.Dataset` that will be used as the input source. input_workers: an `InputWorkers` object. strategy: a `tf.distribute.Strategy` object, used to run all-reduce to handle last partial batch. num_replicas_in_sync: Optional integer. If this is not None, the value is used to decide how to rebatch datasets into smaller batches so that the total batch size for each step (across all workers and replicas) adds up to `dataset`'s batch size. input_context: `InputContext` for sharding. Only pass this in for between graph multi-worker cases where there is only one `input_worker`. In these cases, we will shard based on the `input_pipeline_id` and `num_input_pipelines` in the `InputContext`. """ dist_dataset = DistributedDatasetV1( dataset, input_workers, strategy, num_replicas_in_sync=num_replicas_in_sync, input_context=input_context) worker_iterators = _create_iterators_per_worker( dist_dataset._cloned_datasets, input_workers, True) # pylint: disable=protected-access super(DatasetIterator, self).__init__(input_workers, worker_iterators, strategy, dist_dataset._enable_get_next_as_optional) # pylint: disable=protected-access self._element_spec = dist_dataset.element_spec def _dummy_tensor_fn(value_structure): """A function to create dummy tensors from `value_structure`.""" def create_dummy_tensor(spec): """Create a dummy tensor with possible batch dimensions set to 0.""" if isinstance(spec, ragged_tensor.RaggedTensorSpec): # Splice out the ragged dimensions. # pylint: disable=protected-access feature_shape = spec._shape[:1].concatenate( spec._shape[(1 + spec._ragged_rank):]) feature_type = spec._dtype # pylint: enable=protected-access else: feature_shape = spec.shape feature_type = spec.dtype # Ideally we should set the batch dimension to 0, however as in # DistributionStrategy we don't know the batch dimension, we try to # guess it as much as possible. If the feature has unknown dimensions, we # will set them to 0. If the feature shape is already static, we guess the # first dimension as batch dimension and set it to 0. dims = ([dim if dim is not None else 0 for dim in feature_shape.as_list()] if feature_shape else []) if dims and (isinstance(spec, ragged_tensor.RaggedTensorSpec) or feature_shape.is_fully_defined()): dims[0] = tensor_shape.Dimension(0) if isinstance(spec, sparse_tensor.SparseTensorSpec): return sparse_tensor.SparseTensor( values=array_ops.zeros(0, feature_type), indices=array_ops.zeros((0, len(dims)), dtypes.int64), dense_shape=dims) # Create the dummy tensor. dummy_tensor = array_ops.zeros(tensor_shape.TensorShape(dims), feature_type) if isinstance(spec, ragged_tensor.RaggedTensorSpec): # Reinsert the ragged dimensions with size 0. # pylint: disable=protected-access row_splits = array_ops.zeros(1, spec._row_splits_dtype) dummy_tensor = ragged_tensor.RaggedTensor.from_nested_row_splits( dummy_tensor, (row_splits,) * spec._ragged_rank, validate=False) # pylint: enable=protected-access return dummy_tensor return nest.map_structure(create_dummy_tensor, value_structure) def _recover_shape_fn(data, value_structure): """Recover the shape of `data` the same as shape of `value_structure`.""" flattened_data = nest.flatten(data) for i, spec in enumerate(nest.flatten(value_structure)): for target, source in zip( nest.flatten(flattened_data[i], expand_composites=True), nest.flatten(spec, expand_composites=True)): target.set_shape(source.shape) # `SparseTensor` shape is not determined by the shape of its component # tensors. Rather, its shape depends on a tensor's values. if isinstance(spec, sparse_tensor.SparseTensorSpec) and spec.shape: dense_shape = spec.shape with ops.device(flattened_data[i].op.device): # For partially defined shapes, fill in missing values from tensor. if not dense_shape.is_fully_defined(): dense_shape = array_ops.stack([ flattened_data[i].dense_shape[j] if dim is None else dim for j, dim in enumerate(dense_shape.as_list()) ]) flattened_data[i] = sparse_tensor.SparseTensor( indices=flattened_data[i].indices, values=flattened_data[i].values, dense_shape=dense_shape) data = nest.pack_sequence_as(data, flattened_data) return data class _SingleWorkerDatasetIteratorBase(object): """Iterator for a single `tf.data.Dataset`.""" def __init__(self, dataset, worker, devices, options=None): """Create iterator for the `dataset` to fetch data to worker's `devices` . A `MultiDeviceIterator` or `OwnedMultiDeviceIterator` is used to prefetch input to the devices on the given worker. Args: dataset: A `tf.data.Dataset` instance. worker: Worker on which ops should be created. devices: Distribute data from `dataset` to these devices. options: options. """ self._dataset = dataset self._worker = worker self._devices = devices self._element_spec = dataset.element_spec self._options = options self._make_iterator() def _make_iterator(self): raise NotImplementedError("must be implemented in descendants") def _format_data_list_with_options(self, data_list): """Change the data in to a list type if required. The OwnedMultiDeviceIterator returns the list data type, while the PER_REPLICA iterator (when used with prefetch disabled) returns without the enclosed list. This is to fix the inconsistency. Args: data_list: data_list Returns: list """ if (self._options and self._options.experimental_replication_mode == InputReplicationMode.PER_REPLICA and not self._options.experimental_fetch_to_device): return [data_list] else: return data_list def get_next(self, device, name=None): """Get next element for the given device.""" del name with ops.device(self._worker): if _should_use_multi_device_iterator(self._options): return self._iterator.get_next(device) else: return self._iterator.get_next() def get_next_as_list_static_shapes(self, name=None): """Get next element from the underlying iterator. Runs the iterator get_next() within a device scope. Since this doesn't use get_next_as_optional(), it is considerably faster than get_next_as_list() (but can only be used when the shapes are static). Args: name: not used. Returns: A list consisting of the next data from each device. """ del name with ops.device(self._worker): return self._format_data_list_with_options(self._iterator.get_next()) def get_next_as_list(self, name=None): """Get next element from underlying iterator. If there is no data left, a list of dummy tensors with possible batch dimensions set to 0 will be returned. Use of get_next_as_optional() and extra logic adds overhead compared to get_next_as_list_static_shapes(), but allows us to handle non-static shapes. Args: name: not used. Returns: A boolean tensor indicates whether there is any data in next element and the real data as the next element or a list of dummy tensors if no data left. """ del name with ops.device(self._worker): data_list = self._format_data_list_with_options( self._iterator.get_next_as_optional()) result = [] for i, data in enumerate(data_list): # Place the condition op in the same device as the data so the data # doesn't need to be sent back to the worker. with ops.device(self._devices[i]): # Data will be fetched in order, so we only need to check if the first # replica has value to see whether there is data left for this single # worker. if i == 0: worker_has_value = data.has_value() # pylint: disable=unnecessary-lambda # pylint: disable=cell-var-from-loop real_data = control_flow_ops.cond( data.has_value(), lambda: data.get_value(), lambda: _dummy_tensor_fn(data.element_spec), strict=True, ) # Some dimensions in `replicas` will become unknown after we # conditionally return the real tensors or the dummy tensors. Recover # the shapes from `data.element_spec`. We only need to do this in # non eager mode because we always know the runtime shape of the # tensors in eager mode. if not context.executing_eagerly(): real_data = _recover_shape_fn(real_data, data.element_spec) result.append(real_data) # pylint: enable=cell-var-from-loop # pylint: enable=unnecessary-lambda return worker_has_value, result class _SingleWorkerDatasetIteratorSpec(type_spec.TypeSpec): """Type specification for `_SingleWorkerOwnedDatasetIterator`.""" __slots__ = ["_worker", "_devices", "_element_spec", "_options"] def __init__(self, worker, devices, element_spec, options): self._worker = worker self._devices = tuple(device_util.canonicalize(d) for d in devices) self._element_spec = element_spec self._options = options @property def value_type(self): return _SingleWorkerOwnedDatasetIterator def _serialize(self): return (self._worker, self._devices, self._element_spec, self._options) @property def _component_specs(self): specs = [] if _should_use_multi_device_iterator(self._options): specs.append( multi_device_iterator_ops.MultiDeviceIteratorSpec( self._devices, self._worker, element_spec=self._element_spec)) else: specs.append(iterator_ops.IteratorSpec(element_spec=self._element_spec)) return specs def _to_components(self, value): return [value._iterator] # pylint: disable=protected-access def _from_components(self, components): return _SingleWorkerOwnedDatasetIterator( dataset=None, worker=self._worker, devices=self._devices, components=components, element_spec=self._element_spec, options=self._options) @staticmethod def from_value(value): # pylint: disable=protected-access return _SingleWorkerDatasetIteratorSpec(value._worker, value._devices, value._element_spec, value._options) class _SingleWorkerOwnedDatasetIterator(_SingleWorkerDatasetIteratorBase, composite_tensor.CompositeTensor): """Iterator for a DistributedDataset instance.""" def __init__(self, dataset=None, worker=None, devices=None, components=None, element_spec=None, options=None): """Create iterator for the `dataset` to fetch data to worker's `devices` . `OwnedMultiDeviceIterator` is used to prefetch input to the devices on the given worker. The lifetime of this iterator is tied to the encompassing python object. Once we go out of scope of the python object or return from a tf.function the underlying iterator resource is deleted. Args: dataset: A `tf.data.Dataset` instance. worker: Worker on which ops should be created. devices: Distribute data from `dataset` to these devices. components: Tensor components to construct the _SingleWorkerOwnedDatasetIterator from. element_spec: A nested structure of `TypeSpec` objects that represents the type specification of elements of the iterator. options: `tf.distribute.InputOptions` used to control options on how this dataset is distributed. """ if worker is None or devices is None: raise ValueError("Both `worker` and `devices` should be provided") error_message = ("Either `dataset` or both `components` and `element_spec` " "need to be provided.") self._options = options if dataset is None: if (components is None or element_spec is None): raise ValueError(error_message) self._element_spec = element_spec self._worker = worker self._devices = devices self._iterator = components[0] else: if (components is not None or element_spec is not None): raise ValueError(error_message) super(_SingleWorkerOwnedDatasetIterator, self).__init__(dataset, worker, devices, self._options) def _make_iterator(self): """Make appropriate iterator on the dataset.""" if not self._worker: raise ValueError("Worked device must be specified when creating an " "owned iterator.") if _should_use_multi_device_iterator(self._options): host_device = device_util.get_host_for_device(self._worker) with ops.device(self._worker): if self._options is not None: self._iterator = multi_device_iterator_ops.OwnedMultiDeviceIterator( self._dataset, self._devices, source_device=host_device, max_buffer_size=self._options .experimental_per_replica_buffer_size, prefetch_buffer_size=self._options .experimental_per_replica_buffer_size) else: self._iterator = multi_device_iterator_ops.OwnedMultiDeviceIterator( self._dataset, self._devices, source_device=host_device) else: with ops.device(self._worker): self._iterator = iter(self._dataset) @property def element_spec(self): return self._element_spec @property def _type_spec(self): return _SingleWorkerDatasetIteratorSpec(self._worker, self._devices, self._element_spec, self._options) @property def output_classes(self): """Returns the class of each component of an element of this iterator. The expected values are `tf.Tensor` and `tf.SparseTensor`. Returns: A nested structure of Python `type` objects corresponding to each component of an element of this dataset. """ return nest.map_structure( lambda component_spec: component_spec._to_legacy_output_classes(), # pylint: disable=protected-access self._element_spec) @property def output_shapes(self): """Returns the shape of each component of an element of this iterator. Returns: A nested structure of `tf.TensorShape` objects corresponding to each component of an element of this dataset. """ return nest.map_structure( lambda component_spec: component_spec._to_legacy_output_shapes(), # pylint: disable=protected-access self._element_spec) @property def output_types(self): """Returns the type of each component of an element of this iterator. Returns: A nested structure of `tf.DType` objects corresponding to each component of an element of this dataset. """ return nest.map_structure( lambda component_spec: component_spec._to_legacy_output_types(), # pylint: disable=protected-access self._element_spec) class _SingleWorkerDatasetIterator(_SingleWorkerDatasetIteratorBase): """Iterator for a single DistributedDatasetV1 instance.""" def _make_iterator(self): """Make appropriate iterator on the dataset.""" with ops.device(self._worker): if self._options is not None: self._iterator = multi_device_iterator_ops.MultiDeviceIterator( self._dataset, self._devices, max_buffer_size=self._options.experimental_per_replica_buffer_size, prefetch_buffer_size=self._options .experimental_per_replica_buffer_size) else: self._iterator = multi_device_iterator_ops.MultiDeviceIterator( self._dataset, self._devices, ) def initialize(self): """Initialize underlying iterator. In eager execution, this simply recreates the underlying iterator. In graph execution, it returns the initializer ops for the underlying iterator. Returns: A list of any initializer ops that should be run. """ if ops.executing_eagerly_outside_functions(): self._iterator._eager_reset() # pylint: disable=protected-access return [] else: return [self._iterator.initializer] @property def output_classes(self): return dataset_ops.get_legacy_output_classes(self._iterator) @property def output_shapes(self): return dataset_ops.get_legacy_output_shapes(self._iterator) @property def output_types(self): return dataset_ops.get_legacy_output_types(self._iterator) class _SingleWorkerCallableIterator(object): """Iterator for a single tensor-returning callable.""" def __init__(self, fn, worker, devices): self._fn = fn self._worker = worker self._devices = devices def get_next(self, device, name=None): """Get next element for the given device from the callable.""" del device, name with ops.device(self._worker): return self._fn() def get_next_as_list_static_shapes(self, name=None): """Get next element from the callable.""" del name with ops.device(self._worker): data_list = [self._fn() for _ in self._devices] return data_list def get_next_as_list(self, name=None): """Get next element from the callable.""" del name with ops.device(self._worker): data_list = [self._fn() for _ in self._devices] return constant_op.constant(True), data_list def initialize(self): # TODO(petebu) Should this throw an exception instead? return [] def _create_iterators_per_worker(worker_datasets, input_workers, enable_legacy_iterators, options=None): """Create a multidevice iterator on each of the workers.""" assert isinstance(input_workers, InputWorkers) assert len(worker_datasets) == len(input_workers.worker_devices) iterators = [] for i, worker in enumerate(input_workers.worker_devices): with ops.device(worker): worker_devices = input_workers.compute_devices_for_worker(i) if tf2.enabled() and not enable_legacy_iterators: iterator = _SingleWorkerOwnedDatasetIterator( dataset=worker_datasets[i], worker=worker, devices=worker_devices, options=options) else: iterator = _SingleWorkerDatasetIterator(worker_datasets[i], worker, worker_devices, options) iterators.append(iterator) return iterators def _create_datasets_from_function_with_input_context(input_contexts, input_workers, dataset_fn): """Create device datasets per worker given a dataset function.""" datasets = [] for i, ctx in enumerate(input_contexts): worker = input_workers.worker_devices[i] with ops.device(worker): dataset = dataset_fn(ctx) datasets.append(dataset) return datasets, dataset.element_spec # TODO(sourabhbajaj): Remove this in lieu of distributed datasets def _get_batched_dataset(d): """Get the batched dataset from `d`.""" # pylint: disable=protected-access if isinstance(d, dataset_ops.DatasetV1Adapter): d = d._dataset if isinstance(d, (dataset_ops.BatchDataset, batching._MapAndBatchDataset)): return d elif isinstance(d, (dataset_ops.PrefetchDataset, dataset_ops._OptionsDataset)): return _get_batched_dataset(d._input_dataset) raise ValueError( "Unable to get batched dataset from the input dataset. `batch` " "`map_and_batch` need to be the last operations on the dataset. " "The batch operations can be followed by a prefetch.") def _get_batched_dataset_attributes(d): """Get `batch_size`, `drop_remainder` of dataset.""" # pylint: disable=protected-access assert isinstance(d, (dataset_ops.BatchDataset, batching._MapAndBatchDataset)) if isinstance(d, dataset_ops.BatchDataset): batch_size = d._batch_size drop_remainder = d._drop_remainder elif isinstance(d, batching._MapAndBatchDataset): batch_size = d._batch_size_t drop_remainder = d._drop_remainder_t # pylint: enable=protected-access if tensor_util.is_tf_type(batch_size): batch_size = tensor_util.constant_value(batch_size) if tensor_util.is_tf_type(drop_remainder): drop_remainder = tensor_util.constant_value(drop_remainder) return batch_size, drop_remainder # TODO(sourabhbajaj): Remove this in lieu of distributed datasets def _get_dataset_attributes(dataset): """Get the underlying attributes from the dataset object.""" # pylint: disable=protected-access # First, get batch_size and drop_remainder from the dataset. We need # to walk back the dataset creation process and find the batched version in # order to get the attributes. batched_dataset = _get_batched_dataset(dataset) batch_size, drop_remainder = _get_batched_dataset_attributes(batched_dataset) # Second, prefetch buffer should be get from the original dataset. prefetch_buffer = None if isinstance(dataset, dataset_ops.PrefetchDataset): prefetch_buffer = dataset._buffer_size elif (isinstance(dataset, dataset_ops.DatasetV1Adapter) and isinstance(dataset._dataset, dataset_ops.PrefetchDataset)): prefetch_buffer = dataset._dataset._buffer_size return batch_size, drop_remainder, prefetch_buffer def _should_use_multi_device_iterator(options): """Determine whether to use multi_device_iterator_ops.""" if (options is None or options.experimental_replication_mode == InputReplicationMode.PER_WORKER or (options.experimental_replication_mode == InputReplicationMode.PER_REPLICA and options.experimental_fetch_to_device)): return True return False class MultiStepContext(object): """A context object that can be used to capture things when running steps. This context object is useful when running multiple steps at a time using the `experimental_run_steps_on_iterator` API. For e.g. it allows the user's step function to specify which outputs to emit at what frequency. Currently it supports capturing output from the last step, as well as capturing non tensor outputs. In the future it will be augmented to support other use cases such as output each N steps. """ def __init__(self): """Initialize an output context. Returns: A context object. """ self._last_step_outputs = {} self._last_step_outputs_reduce_ops = {} self._non_tensor_outputs = {} @property def last_step_outputs(self): """A dictionary consisting of outputs to be captured on last step. Keys in the dictionary are names of tensors to be captured, as specified when `set_last_step_output` is called. Values in the dictionary are the tensors themselves. If `set_last_step_output` was called with a `reduce_op` for this output, then the value is the reduced value. Returns: A dictionary with last step outputs. """ return self._last_step_outputs def _set_last_step_outputs(self, outputs): """Replace the entire dictionary of last step outputs.""" if not isinstance(outputs, dict): raise ValueError("Need a dictionary to set last_step_outputs.") self._last_step_outputs = outputs def set_last_step_output(self, name, output, reduce_op=None): """Set `output` with `name` to be outputted from the last step. Args: name: String, name to identify the output. Doesn't need to match tensor name. output: The tensors that should be outputted with `name`. See below for actual types supported. reduce_op: Reduction method to use to reduce outputs from multiple replicas. Required if `set_last_step_output` is called in a replica context. Optional in cross_replica_context. When present, the outputs from all the replicas are reduced using the current distribution strategy's `reduce` method. Hence, the type of `output` must be what's supported by the corresponding `reduce` method. For e.g. if using MirroredStrategy and reduction is set, output must be a `PerReplica` value. The reduce method is also recorded in a dictionary `_last_step_outputs_reduce_ops` for later interpreting of the outputs as already reduced or not. """ if distribution_strategy_context.in_cross_replica_context(): self._last_step_outputs_reduce_ops[name] = reduce_op if reduce_op is None: self._last_step_outputs[name] = output else: distribution = distribution_strategy_context.get_strategy() self._last_step_outputs[name] = distribution.reduce(reduce_op, output, axis=None) else: assert reduce_op is not None def merge_fn(distribution, value): self._last_step_outputs[name] = distribution.reduce(reduce_op, value, axis=None) # Setting this inside the `merge_fn` because all replicas share the same # context object, so it's more robust to set it only once (even if all # the replicas are trying to set the same value). self._last_step_outputs_reduce_ops[name] = reduce_op distribution_strategy_context.get_replica_context().merge_call( merge_fn, args=(output,)) @property def non_tensor_outputs(self): """A dictionary consisting of any non tensor outputs to be captured.""" return self._non_tensor_outputs def set_non_tensor_output(self, name, output): """Set `output` with `name` to be captured as a non tensor output.""" if distribution_strategy_context.in_cross_replica_context(): self._non_tensor_outputs[name] = output else: def merge_fn(distribution, value): # NOTE(priyag): For non tensor outputs, we simply return all the values # in a list as reduction doesn't make sense on non tensors. self._non_tensor_outputs[name] = ( distribution.experimental_local_results(value)) distribution_strategy_context.get_replica_context().merge_call( merge_fn, args=(output,)) def _create_distributed_tensor_spec(strategy, tensor_spec): """Create a `tf.TypeSpec` for a given strategy and input `tensor_spec`. Args: strategy: The given `tf.distribute` strategy. tensor_spec: `tf.TensorSpec` of a given value. The batch dimension of the shape should be None if you have partial batches. Returns: A `tf.TypeSpec` that matches the values produced by a given strategy. This can be a `tf.TensorSpec` or a `PerRelicaSpec`. """ num_replicas = len(strategy.extended.worker_devices) # For one device strategy that is not MultiWorkerMirroredStrategy, return the # tensor_spec as is, since we don't wrap the output with PerReplica in this # case. # TODO(b/166464552): remove after we always wrap for all strategies. if not _always_wrap(strategy): return tensor_spec # For other cases we assume the input to tf.function is a per replica type. def _get_value_per_replica(tensor_spec_per_input): value_specs = [tensor_spec_per_input for _ in range(num_replicas)] return values.PerReplicaSpec(*value_specs) return nest.map_structure(_get_value_per_replica, tensor_spec) def _replace_per_replica_spec(spec, i): """If `spec` is a `PerReplicaSpec`, then return its `i`th value_spec.""" if isinstance(spec, values.PerReplicaSpec): return spec._value_specs[i] # pylint: disable=protected-access else: return spec def _enable_get_next_as_optional(strategy, dataset): """Returns whether to enable using partial batch handling.""" # TODO(b/133073708): we currently need a flag to control the usage because # there is a performance difference between get_next() and # get_next_as_optional(). And we only enable get_next_as_optional when the # output shapes are not static. # # TODO(rxsang): We want to always enable the get_next_as_optional behavior # when user passed input_fn instead of dataset. if not getattr(strategy.extended, "experimental_enable_get_next_as_optional", False): return False if context.executing_eagerly(): # If the dataset is infinite, we don't need to enable last partial batch # support. Currently the logic only applies to the case that distributed # dataset is created in eager mode, as we need to evaluate the dataset # cardinality. with ops.device(dataset._variant_tensor.device): # pylint: disable=protected-access if dataset.cardinality().numpy() == cardinality.INFINITE: return False return not _is_statically_shaped( dataset.element_spec) or strategy.extended._in_multi_worker_mode() # pylint: disable=protected-access def _create_per_replica(value_list, strategy): """Creates a PerReplica. For strategies other than OneDeviceStrategy, it creates a PerReplica whose type spec is set to the element spec of the dataset. This helps avoid retracing for partial batches. Retracing is problematic for multi client when different client retraces different time, since retracing changes the collective keys in the tf.function, and causes mismatches among clients. For single client strategies, this simply calls distribute_utils.regroup(). Args: value_list: a list of values, one for each replica. strategy: the `tf.distribute.Strategy`. Returns: a structure of PerReplica. """ # TODO(b/166464552): always wrap for all one device strategies as well. always_wrap = _always_wrap(strategy) per_replicas = distribute_utils.regroup(value_list, always_wrap=always_wrap) return per_replicas def _always_wrap(strategy): """Returns whether to always wrap the values in a DistributedValues.""" return strategy.extended._in_multi_worker_mode() or len( # pylint: disable=protected-access strategy.extended.worker_devices) > 1 def _rebatch_as_dynamic(per_replica_spec): """Rebatch the spec to have a dynamic batch dimension.""" assert isinstance(per_replica_spec, values.PerReplicaSpec), per_replica_spec # pylint: disable=protected-access def _rebatch(spec): # Rebatch if possible. try: return spec._unbatch()._batch(None) except ValueError: pass return spec return values.PerReplicaSpec( *nest.map_structure(_rebatch, per_replica_spec._value_specs)) # pylint: enable=protected-access

apache-2.0

dellytools/maze

readfq.py

1

1674

# source: https://github.com/lh3/readfq def readfq(fp): # this is a generator function last = None # this is a buffer keeping the last unprocessed line while True: # mimic closure; is it a bad idea? if not last: # the first record or a record following a fastq for l in fp: # search for the start of the next record if l[0] in '>@': # fasta/q header line last = l[:-1] # save this line break if not last: break name, seqs, last = last[1:].partition(" ")[0], [], None for l in fp: # read the sequence if l[0] in '@+>': last = l[:-1] break seqs.append(l[:-1]) if not last or last[0] != '+': # this is a fasta record yield name, ''.join(seqs), None # yield a fasta record if not last: break else: # this is a fastq record seq, leng, seqs = ''.join(seqs), 0, [] for l in fp: # read the quality seqs.append(l[:-1]) leng += len(l) - 1 if leng >= len(seq): # have read enough quality last = None yield name, seq, ''.join(seqs); # yield a fastq record break if last: # reach EOF before reading enough quality yield name, seq, None # yield a fasta record instead break if __name__ == "__main__": import sys n, slen, qlen = 0, 0, 0 for name, seq, qual in readfq(sys.stdin): n += 1 slen += len(seq) qlen += qual and len(qual) or 0 print n, '\t', slen, '\t', qlen

mit

hello-base/web

apps/merchandise/music/managers.py

1

1125

# -*- coding: utf-8 -*- from django.db import models from django.db.models.query import QuerySet class EditionManager(models.Manager): def find_edition(self, release, edition, **kwargs): if release: kwargs[release.identifier] = release if edition: kwargs[edition.parent.identifier] = edition.parent qs = super(EditionManager, self).get_queryset().order_by('released', 'romanized_name') try: return qs.filter(**kwargs)[0] except IndexError: return qs.none() def primary_edition(self, release=None, edition=None): editions = [self.model.EDITIONS.regular, self.model.EDITIONS.limited, self.model.EDITIONS.digital] for kind in editions: edition = self.find_edition(release, edition, kind=kind) if edition: return edition return None class TrackQuerySet(QuerySet): def originals(self): return self.filter(original_track__isnull=True) class TrackOrderQuerySet(QuerySet): def original_only(self): return self.filter(is_instrumental=False)

apache-2.0

ndronen/pylearnutils

pylearnutils/datasets/sparse_expander.py

1

7801

# From https://gist.github.com/ccsevers/10295174 import os.path import numpy as np from .utils import take_subset from pylearn2.datasets.dataset import Dataset from pylearn2.datasets.dense_design_matrix import DenseDesignMatrix from pylearn2.utils.iteration import (SequentialSubsetIterator, FiniteDatasetIterator, resolve_iterator_class) import functools import logging import numpy import warnings from pylearn2.space import CompositeSpace, Conv2DSpace, VectorSpace, IndexSpace from pylearn2.utils import safe_zip try: import scipy.sparse except ImportError: warnings.warn("Couldn't import scipy.sparse") import theano import gzip floatX = theano.config.floatX logger = logging.getLogger(__name__) class SparseExpanderDataset(Dataset): """ SparseExpanderDataset takes a numpy/scipy sparse matrix and calls .todense() as the batches are passed out of the iterator. """ def __init__(self, X_path=None, y_path=None, from_scipy_sparse_dataset=None, zipped_npy=False, means_path=None, stds_path=None, start_fraction=None, end_fraction=None, start=None, stop=None): self.X_path = X_path self.y_path = y_path if self.X_path != None: if zipped_npy == True: logger.info('... loading sparse data set from a zip npy file') self.X = scipy.sparse.csr_matrix( numpy.load(gzip.open(X_path)), dtype=floatX) else: logger.info('... loading sparse data set from a npy file') self.X = scipy.sparse.csr_matrix( numpy.load(X_path).item(), dtype=floatX) else: logger.info('... building from given sparse dataset') self.X = from_scipy_sparse_dataset.astype(floatX) if self.y_path != None: if zipped_npy == True: logger.info('... loading sparse data set from a zip npy file') #self.y = scipy.sparse.csr_matrix( # numpy.load(gzip.open(y_path)), dtype=floatX).todense() self.y = numpy.load(gzip.open(y_path)) if not isinstance(self.y, np.ndarray): print("calling y.item") self.y = y.item() else: logger.info('... loading sparse data set from a npy file') self.y = numpy.load(y_path) if not isinstance(self.y, np.ndarray): print("calling y.item") self.y = self.y.item() # We load y as a sparse matrix, but convert it to a dense array, # because otherwise MLP.mean_of_targets breaks. orig_shape = self.y.shape if scipy.sparse.issparse(self.y): self.y = np.asarray(self.y.todense()) # Only make this a column vector if it's not one-hot. if 1 in orig_shape or len(orig_shape) == 1: nrow = np.max(orig_shape) self.y = self.y.reshape((nrow, 1)) else: self.y = None self.y = self.y.astype(floatX) self.X, self.y = take_subset(self.X, self.y, start_fraction, end_fraction, start, stop) self.data_n_rows = self.X.shape[0] self.num_examples = self.data_n_rows self.fancy = False self.stochastic = False X_space = VectorSpace(dim=self.X.shape[1]) X_source = 'features' if y_path is None: space = X_space source = X_source else: if self.y.ndim == 1: dim = 1 else: dim = self.y.shape[-1] y_space = VectorSpace(dim=dim) y_source = 'targets' space = CompositeSpace((X_space, y_space)) source = (X_source, y_source) if means_path is not None: self.means = np.load(means_path) if stds_path is not None: self.stds = np.load(stds_path) self.data_specs = (space, source) self.X_space = X_space self._iter_data_specs = (self.X_space, 'features') def get_design_matrix(self): return self.X def get_batch_design(self, batch_size, include_labels=False): """ method inherited from Dataset """ self.iterator(mode='sequential', batch_size=batch_size) return self.next() def get_batch_topo(self, batch_size): """ method inherited from Dataset """ raise NotImplementedError('Not implemented for sparse dataset') def get_data_specs(self): """ Returns the data_specs specifying how the data is internally stored. This is the format the data returned by `self.get_data()` will be. """ return self.data_specs def get_data(self): """ Returns ------- data : numpy matrix or 2-tuple of matrices Returns all the data, as it is internally stored. The definition and format of these data are described in `self.get_data_specs()`. """ if self.y is None: return self.X else: return (self.X, self.y) def get_num_examples(self): return self.X.shape[0] @functools.wraps(Dataset.iterator) def iterator(self, mode=None, batch_size=None, num_batches=None, topo=None, targets=None, rng=None, data_specs=None, return_tuple=False): """ method inherited from Dataset """ self.mode = mode self.batch_size = batch_size self._targets = targets self._return_tuple = return_tuple if data_specs is None: data_specs = self._iter_data_specs # If there is a view_converter, we have to use it to convert # the stored data for "features" into one that the iterator # can return. # if space, source = data_specs if isinstance(space, CompositeSpace): sub_spaces = space.components sub_sources = source else: sub_spaces = (space,) sub_sources = (source,) convert = [] for sp, src in safe_zip(sub_spaces, sub_sources): if src == 'features': conv_fn = lambda x: x.todense() elif src == 'targets': conv_fn = lambda x: x else: conv_fn = None convert.append(conv_fn) if mode is None: if hasattr(self, '_iter_subset_class'): mode = self._iter_subset_class else: raise ValueError('iteration mode not provided and no default ' 'mode set for %s' % str(self)) else: mode = resolve_iterator_class(mode) return FiniteDatasetIterator(self, mode(self.X.shape[0], batch_size, num_batches, rng), data_specs=data_specs, return_tuple=return_tuple, convert=convert) def __iter__(self): return self def next(self): indx = self.subset_iterator.next() try: rval = self.X[indx].todense() if self.center: rval = rval - self.means if self.scale: rval = rval / self.stds except IndexError: # the ind of minibatch goes beyond the boundary import ipdb; ipdb.set_trace() rval = tuple(rval) if not self._return_tuple and len(rval) == 1: rval, = rval return rval

bsd-3-clause

prov-suite/service-tests

prov_service_tests/test_provvalidator.py

1

8645

"""Test class for ProvValidator service. """ # Copyright (c) 2015 University of Southampton # # 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. from __future__ import (absolute_import, division, print_function, unicode_literals) import itertools import json import os import requests import unittest from nose.tools import istest from nose_parameterized import parameterized from prov_service_tests import http from prov_service_tests import standards from prov_service_tests.test_service import ServiceTestCase @istest class ProvValidatorTestCase(ServiceTestCase): """Test class for ProvValidator service. These tests check that ProvValidator is available and responds to requests directed against the `ProvValidator REST API <https://provenance.ecs.soton.ac.uk/validator/view/api.html>`_. The class expects one environment variable to be set: - ``PROVVALIDATOR_URL`` - ProvValidator base URL e.g. ``https://provenance.ecs.soton.ac.uk/validator/provapi/documents/`` """ URL_ENV = "PROVVALIDATOR_URL" """str or unicode: environment variable holding ProvValidator URL """ CONTENT_TYPES = { standards.PROVN: "text/provenance-notation", standards.TTL: "text/turtle", standards.TRIG: "application/trig", standards.PROVX: "application/provenance+xml", standards.JSON: "application/json" } """dict: mapping from :mod:`prov_service_tests.standards` formats to content types understood by ProvStore """ def setUp(self): super(ProvValidatorTestCase, self).setUp() self.url = os.environ[ProvValidatorTestCase.URL_ENV] def tearDown(self): super(ProvValidatorTestCase, self).tearDown() def post_translate(self, document, format=standards.JSON): """Submit POST /provapi/documents to translate a document. The request is requested not to allow redirects and a test is done to check that the response code is 303 SEE OTHER. :param document: document in given format :type document: str or unicode :param format: a :mod:`prov_service_tests.standards` format :type format: str or unicode :return: URL of stored document :rtype: :class:`requests.Response` """ headers={http.CONTENT_TYPE: ProvValidatorTestCase.CONTENT_TYPES[format]} response = requests.post( \ self.url, headers=headers, allow_redirects=False, data=document) self.assertEqual(requests.codes.see_other, response.status_code) return response @parameterized.expand(list(itertools.product(standards.FORMATS, standards.FORMATS))) def test_post_translate(self, format1, format2): """Test POST /provapi/documents/ for translation. """ headers = {http.CONTENT_TYPE: ProvValidatorTestCase.CONTENT_TYPES[format1], http.ACCEPT: ProvValidatorTestCase.CONTENT_TYPES[format2]} response = requests.post(self.url, headers=headers, data=self.get_primer(format1)) self.assertEqual(requests.codes.ok, response.status_code) def test_translate_get_document(self): """Test GET /provapi/documents/{docId}. """ response = self.post_translate(self.get_primer(standards.JSON), standards.JSON) graph_url = response.headers["location"] response = requests.get(graph_url) self.assertEqual(requests.codes.ok, response.status_code) def test_translate_get_document_original(self): """Test GET /provapi/documents/{docId}/original. """ response = self.post_translate(self.get_primer(standards.JSON), standards.JSON) graph_url = response.headers["location"] response = requests.get(graph_url + "/original") self.assertEqual(requests.codes.ok, response.status_code) @parameterized.expand(standards.FORMATS) def test_translate_get_document_type(self, format): """Test GET /provapi/documents/{docId}.{type}. """ response = self.post_translate(self.get_primer(standards.JSON), standards.JSON) graph_url = response.headers["location"] response = requests.get(graph_url + "." + format) self.assertEqual(requests.codes.ok, response.status_code) @parameterized.expand(standards.FORMATS) def test_post_validate(self, format): """Test POST /provapi/documents for validation. """ response = requests.post( \ self.url, files={"statements": self.get_primer(format)}, data={"validate": "Validate", "type": format}, allow_redirects=True) self.assertEqual(requests.codes.ok, response.status_code) def validate(self): """Submit POST /provapi/documents then GET /provapi/documents/{docId}/validation/report to validate document. - Submit POST /provapi/documents request with a JSON document. - Get the graph URL from the response header ``location`` field. - Submit GET /provapi/documents/{docId}/validation/report, to validate the document. - Test that the response to GET is 200 OK. Accessing the validation report is a pre-requisite of validation-related requests including /validation, /metrics, /normalForm and /matrix. :return: graph URL :rtype: str or unicode """ response = self.post_translate(self.get_primer(standards.JSON), standards.JSON) graph_url = response.headers["location"] response = requests.get(graph_url + "/validation/report") self.assertEqual(requests.codes.ok, response.status_code) return graph_url def test_get_metrics(self): """Test GET /provapi/documents/{docId}/metrics. """ graph_url = self.validate() response = requests.get(graph_url + "/metrics") self.assertEqual(requests.codes.ok, response.status_code) @parameterized.expand(["txt", "png"]) def test_get_validation_matrix_format(self, format): """Test GET /provapi/documents/{docId}/validation/matrix.txt and png. """ graph_url = self.validate() response = requests.get(graph_url + "/validation/matrix." + format) self.assertEqual(requests.codes.ok, response.status_code) def test_get_validation_matrix_diagonal(self): """Test GET /provapi/documents/{docId}/validation/matrix/diagonal. """ graph_url = self.validate() response = requests.get(graph_url + "/validation/matrix/diagonal") self.assertEqual(requests.codes.ok, response.status_code) def test_get_validation_normal_form(self): """Test GET /provapi/documents/{docId}/validation/normalForm. """ graph_url = self.validate() response = requests.get(graph_url + "/validation/normalForm") self.assertEqual(requests.codes.ok, response.status_code) @parameterized.expand(standards.FORMATS) def test_get_validation_normal_form_format(self, format): """Test GET /provapi/documents/{docId}/validation/normalForm.{type}. """ graph_url = self.validate() response = requests.get(graph_url + "/validation/normalForm." + format) self.assertEqual(requests.codes.ok, response.status_code) def test_get_random_nodes_degree(self): """Test GET /provapi/documents/random/{nodes}/{degree}. """ response = requests.get(self.url + "random/1/1") self.assertEqual(requests.codes.ok, response.status_code) def test_get_random_nodes_degree_seed(self): """Test GET /provapi/documents/random/{nodes}/{degree}/{seed}. """ response = requests.get(self.url + "random/1/2/3") self.assertEqual(requests.codes.ok, response.status_code)

mit

FedoraScientific/salome-paravis

test/VisuPrs/IsoSurfaces/E8.py

1

1513

# Copyright (C) 2010-2014 CEA/DEN, EDF R&D # # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Lesser General Public # License as published by the Free Software Foundation; either # version 2.1 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with this library; if not, write to the Free Software # Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA # # See http://www.salome-platform.org/ or email : [email protected] # # This case corresponds to: /visu/IsoSurfaces/E8 case # Create Iso Surface for all data of the given MED file import sys from paravistest import datadir, pictureext, get_picture_dir from presentations import CreatePrsForFile, PrsTypeEnum import pvserver as paravis # Create presentations myParavis = paravis.myParavis # Directory for saving snapshots picturedir = get_picture_dir("IsoSurfaces/E8") file = datadir + "KCOUPLEX1.med" print " --------------------------------- " print "file ", file print " --------------------------------- " print "CreatePrsForFile..." CreatePrsForFile(myParavis, file, [PrsTypeEnum.ISOSURFACES], picturedir, pictureext)

lgpl-2.1

watson-developer-cloud/python-primer-companion-code

episode-2/flask/src/translation.py

1

2054

# -*- coding: utf-8 -*- # Copyright 2016 IBM Corp. 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 # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json from watson_developer_cloud import LanguageTranslationV2 as LanguageTranslationService def getTranslationService(): return LanguageTranslationService(username='<your username key for the Watson language translation service>', password='<your password key for the service>') def identifyLanguage(app, data): txt = data.encode("utf-8", "replace") language_translation = getTranslationService() langsdetected = language_translation.identify(txt) app.logger.info(json.dumps(langsdetected, indent=2)) primarylang = langsdetected["languages"][0] retData = {key: primarylang[key] for key in ('language', 'confidence')} app.logger.info(json.dumps(retData, indent=2)) return retData def checkForTranslation(app, fromlang, tolang): supportedModels = [] lt = getTranslationService() models = lt.list_models() modelList = models.get("models") supportedModels = [model['model_id'] for model in modelList if fromlang == model['source'] and tolang == model['target']] return supportedModels def performTranslation(app, txt, primarylang, targetlang): lt = getTranslationService() translation = lt.translate(txt, source=primarylang, target=targetlang) theTranslation = None if translation and ("translations" in translation): theTranslation = translation['translations'][0]['translation'] return theTranslation

apache-2.0

justanr/pyxl

pyxl.py

1

13205

''' This simple module consists of the Pyxl class and a few helper functions. ''' from os.path import basename, join from glob import glob from PIL import Image, ImageDraw, ImageFont #import flickrapi #Helper functions. def buildHex(hexStr): ''' Accepts a supposed hex color string and ensures it's 6 characters long. ''' hexStr = hexStr.lower().replace(' ','').replace('#','') #TODO: Make this prettier. if len(hexStr) == 1: return hexStr * 6 elif len(hexStr) == 2: return hexStr * 3 elif len(hexStr) == 3: return (hexStr[0] * 2) + (hexStr[1] * 2) + (hexStr[2] * 2) elif len(hexStr) > 3 and len(hexStr) < 6: return '{0:0<6}'.format(hexStr) elif len(hexStr) > 6: return hexStr[0:6] else: return hexStr def hexToRGB(hexStr): '''Converts a hexStr color to a RGB tuple''' # Pretty self explainatory, but as a note this converts # each hex pair (base16) to a base10 value # hexToRGB('ff0000') would return (255, 0, 0) or pure red hexStr = buildHex(hexStr) return tuple([int(hexStr[i:i+2], 16) for i in range(0, 6, 2)]) def RGBToHex(RGB): '''Converts a RGB tuple into a hex color''' #TODO: Convert to new style formatting return '%02x%02x%02x' % RGB def calcGradDiff(startFill, stopFill, distance): ''' Calculates the difference between the start and stop fills over the specified distance. ''' # account for the last pixel distance = distance - 1.0 return tuple((stopFill[x] - startFill[x])/distance for x in range(3)) def buildPyxlName(pyxl): ''' Builds an MD5 hash from Pyxl.getInfo, Pyxl.getSize and Pyxl.getOptions ''' from hashlib import md5 name = '{}-{}-{}'.format(pyxl.getInfo(), pyxl.getSize(), pyxl.getOptions()) return md5(name).hexdigest() + ".jpg" def savePyxlImage(pyxl, path='imgs'): ''' A simple save function for pyxl. Consider replacing with your own. ''' import ImageFile ImageFile.MAXBLOCK = pyxl.image.size[0] * pyxl.image.size[1] fullpath = join(path, buildPyxlName(pyxl)) pyxl.image.save(fullpath, 'JPEG', optimize=True, progressive=True ) def shiftRGB(old, new, shift): ''' Shifts an RGB towards a new value. Shift can be anything that returns an integer or float. ''' change = lambda x: (x[1]*shift)+(x[0]*(1-shift)) return tuple(map(change, zip(old, new))) class Pyxl(object): ''' This class builds an image based on a series of inputs. Either constructing it solely in PIL or pulling one from flickr. ''' #TODO: Better documentation. def __init__(self, info, size, options=None, fonts='fonts'): # Initializing some very key variables. self.info = {} self.size = () self.options = {} self.fonts = {} self.draw = None self.image = None self.defaults = { 'font':'liberationsans', 'colors':[hexToRGB('ffffff'), hexToRGB('ff0000')] } # Build the fonts dictionary. self.loadFonts(fonts) # Load all the arguments passed to Pyxl self.setInfo(info) self.setSize(size) self.setOptions(options) def setInfo(self, info): ''' This function sets the information Pyxl needs to start an image. It accepts one of three string patterns: tag or a series of tags delimited by a comma -- In this case, it is a flickr image OR color:hex -- A solid color image OR gradient:hex,hex -- A gradient image, there is an optional h argument at the end The info variable contains the following bits: type: This tells Pyxl what sort of image to produce tags: This key is only set for a flickr image, it determines what tags to pull an image from. color: A list of RGB tuples. ''' # Determine which kind of image we want # No colon found, we want to contact flickr if info.find(':') == -1: self.info['type'] = 'flickr' self.info['tags'] = info.split(',') self.draw = self.drawFlickr # We are building an image with PIL else: info = info.split(':') # We're drawing a gradient. if info[1].find(',') != -1: self.draw = self.drawGradient self.info['type'] = 'gradient' info[1] = info[1].split(',') self.info['colors'] = [ hexToRGB(info[1][0]), hexToRGB(info[1][1]) ] # Specifically, a horizontal gradient if len(info[1]) == 3: self.info['type'] = 'hgradient' # Just a solid image please else: self.draw = self.drawColor self.info['type'] = 'color' self.info['colors'] = [hexToRGB(info[1])] def getInfo(self): '''Returns a string representation of info dictionary.''' if self.info['type'] == 'flickr': return ','.join(self.info['tags']) elif self.info['type'] == 'color': return 'color:{}'.format(RGBToHex(self.info['colors'][0])) else: colors = ','.join([RGBToHex(x) for x in self.info['colors']]) if self.info['type'] == 'hgradient': colors = colors + ',h' return 'gradient:{}'.format(colors) def setSize(self, size): ''' Sets the total size of the image. This function accepts a string in the form of widthxheight. This function will also ensure that the dimensions are between 1 and the maximum (currently 2500) ''' default = 200 maximum = 2000 # seriously, who needs an image this big sizes = [] for x in size.split('x'): try: # Probably a better way to do this, but no point in letting this # ruin the script Even though I highly doubt someone will # pass something like axd as the size argument from the API, # better safe than sorry. x = int(x) except ValueError: x = default if x > maximum: x = maximum elif x < 1: x = default sizes.append(x) if len(sizes) != 2: sizes = [sizes[0], sizes[0]] self.size = tuple(sizes) def getSize(self): ''' Returns string representation of the iamge size in form of widthxheight ''' return 'x'.join([str(x) for x in self.size]) def setOptions(self, options): ''' This function accepts a string for the options of Pyxl. It should be formatted as: option:value,option2:value. There are just a few current valid options: seed: This option is to create a new image from the same options. text: A hex color that is converted to a RGB tuple. dimensions: This SHOULD be set to hide, but if it's there, the dimensions are not displayed on the image. font: This sets the font for the image text, it uses a defaults if the font isn't listed in Pyxl.fonts ''' if options is None: #defaults ahoy! self.options = { 'text':self.defaults['colors'][0], 'font':self.setFont(self.defaults['font']) } else: valid = ['seed', 'dimensions', 'text', 'font'] for option in options.lower().split(','): option = option.split(':') #prevent a bunch of spamming non-recognized options if option[0] not in valid: continue elif option[0] == 'font': option[1] = self.setFont(option[1]) elif option[0] == 'text': try: # again, probably a better way # but better safe than sorry option[1] = hexToRGB(option[1]) except ValueError: option[1] = self.defaults['colors'][0] elif option[0] == 'dimensions': option[1] = 'hide' elif option[0] == 'seed' and self.info['type'] != 'flickr': # There's no point in a seed for a none flickr image continue self.options[option[0]] = option[1] #double check to make sure at least font and text got set. if 'font' not in self.options: self.options['font'] = self.setFont(self.defaults['font']) if 'text' not in self.options: self.options['text'] = self.defaults['colors'][0] def getOptions(self): '''Returns a string representation of all the options set.''' options = '' for key in sorted(self.options.keys()): if key == 'text': option = RGBToHex(self.options['text']) elif key == 'font': option = basename(self.options['font']).lower().split('.')[0] else: option = self.options[key] options = options + '{}:{},'.format(key, option) return options.rstrip(',') def loadFonts(self, location='fonts'): ''' This function scans the location folder for fonts and stores them in a dictionary. The keys are the lowercased version of the file name, split at the first dot. LiberationSans.ttf becomes {'liberationsans':'fonts/LiberationSans.ttf'} Currently, it is only implemented to find TrueType fonts. ''' fonts = glob(join(location, '*.ttf')) self.fonts = { basename(font).lower().split('.')[0]:font for font in fonts } def setFont(self, font): ''' This function sets the font for the text on the image. If it receives a font that isn't in Pyxl's font library, it sets it to the default. ''' if font not in self.fonts.keys(): return self.fonts[self.defaults['font']] return self.fonts[font] def drawColor(self): '''Creates a solid colored image.''' self.image = Image.new('RGB', self.size, self.info['colors'][0]) if 'dimensions' not in self.options: self.drawDimensions() def drawGradient(self): '''Creates a gradient image.''' # this'll be much easier to work with height = self.size[1] width = self.size[0] # set the correct distance if self.info['type'] == 'hgradient': distance = width else: distance = height # again, easier to work with start = self.info['colors'][0] stop = self.info['colors'][1] # make a new blank image self.image = Image.new('RGB', self.size, hexToRGB('ffffff')) draw = ImageDraw.Draw(self.image) for i in range(distance): # set the correct draw positions if self.info['type'] == 'hgradient': pos = (i, 0, i, height) else: pos = (0, i, width, i) # move the start color closer to the end color rgb = shiftRGB(start, stop, float(i)/distance) fill = tuple(map(int, map(round, rgb))) draw.line(pos, fill=fill) if 'dimensions' not in self.options: self.drawDimensions() def drawFlickr(self): '''Creates an image based on a flickr image.''' pass def getFlickrImage(self): ''' Retrieves a single flickr image based on Pyxl.info['tags'] ''' pass def drawDimensions(self): '''Creates the dimensions image.''' text = self.getSize() size = 1 font = ImageFont.truetype(self.options['font'], size) img_fraction = 0.5 while (font.getsize(text)[0] < int(self.size[0] * img_fraction)) and \ (font.getsize(text)[1] < int(self.size[1]*img_fraction)): size += 1 font = ImageFont.truetype(self.options['font'], size) font = ImageFont.truetype(self.options['font'], size) pos = ( (self.size[0] - font.getsize(text)[0])/2, (self.size[1] - font.getsize(text)[1])/2 ) draw = ImageDraw.Draw(self.image) draw.text(pos, text, font=font, fill=self.options['text'])

mit

elemel/drillion

drillion/cannon_entity_creator.py

1

1825

from drillion.animation_component import AnimationComponent from drillion.collision import CollisionBody from drillion.collision_component import CollisionComponent from drillion.entity import Entity from drillion.maths import Polygon2, Transform2 from drillion.sprite import PolygonSprite from drillion.sprite_component import SpriteComponent from drillion.transform_component import TransformComponent import random class CannonEntityCreator(object): def __init__(self, animation_update_phase, draw_phase, batch): self._animation_update_phase = animation_update_phase self._draw_phase = draw_phase self._batch = batch def create(self, ship_entity, position=(0.0, 0.0), angle=0.0, length=1.0, width=0.1, color=(255, 255, 255, 255)): vertices = [(0.0, -0.5), (1.0, -0.5), (1.0, 0.5), (0.0, 0.5)] polygon = Polygon2(vertices) parent_transform_component = \ ship_entity.find_component(TransformComponent) transform = Transform2() transform.rotate(angle) transform.scale(length, width) transform.translate(*position) transform_component = \ TransformComponent(transform, parent=parent_transform_component) sprite = PolygonSprite(vertices, color=color, transform=transform) sprite_component = SpriteComponent(sprite, self._batch) animation_component = AnimationComponent(transform_component, sprite_component, self._animation_update_phase, self._draw_phase) components = [transform_component, sprite_component, animation_component] return Entity(components, parent=ship_entity)

mit

desmo999r/cmssysadmin

cmssysadmin/__init__.py

1

1437

import os import socket import fcntl import struct import subprocess import logging logger = logging.getLogger(__name__) class CmdLine(object): options = {} class __metaclass__(type): def __new__(cls, *kargs, **kwargs): t = type.__new__(cls, *kargs, **kwargs) with open("/proc/cmdline") as f: for option in f.readline().strip().split(): fields = option.split("=") if len(fields) == 1: t.options[fields[0]] = True else: t.options[fields[0]] = fields[1] logger.info("/proc/cmdline options: " + str(t.options)) return t def get_bootif(): try: mac = CmdLine.options['BOOTIF'][3:].replace('-', ':').strip().lower() except KeyError: return None for n in os.listdir("/sys/class/net"): with open("/sys/class/net/" + n + "/address") as f: if mac == f.read().strip().lower(): return n, mac raise Exception("There is a BOOTIF param but no matching interface") def get_ip_address(ifname): """Returns the NIC current IPv4 address""" s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) ip = socket.inet_ntoa(fcntl.ioctl( s.fileno(), 0x8915, # SIOCGIFADDR struct.pack('256s', ifname[:15]) )[20:24]) logger.info("Current IP is %s", ip) return ip # vim: set ts=4 sw=4 tw=0 et :

gpl-2.0

spadev/chatlogsync

chatlogsync.py

1

10218

#!/usr/bin/env python # Copyright 2013 Evan Vitero # This file is part of chatlogsync. # chatlogsync is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # chatlogsync is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # You should have received a copy of the GNU General Public License # along with chatlogsync. If not, see <http://www.gnu.org/licenses/>. from __future__ import unicode_literals from __future__ import absolute_import import os import sys import signal import traceback from os.path import join, dirname, exists, isfile, isdir, realpath from argparse import ArgumentParser, ArgumentTypeError from multiprocessing import Process, cpu_count, Value, Manager, Lock import chatlogsync from chatlogsync import const, formats, util, timezones WORKERS = [] class Progress(object): """Thread-safe progress updater""" def __init__(self): self._nread = Value('i', 0, lock=False) self._nwrote = Value('i', 0, lock=False) self._nexisting = Value('i', 0, lock=False) self._nerror = Value('i', 0, lock=False) self._lock = Lock() def print_status(self, msg=None): dryrun = ' (DRY RUN)' if const.DRYRUN else '' if msg: print_v(msg) print_('\r[read:%i wrote:%i existing:%i error:%i]%s ' % (self.nread, self.nwrote, self.nexisting, self.nerror, dryrun), end='', flush=True, file=sys.stderr) if msg: print_v('\n') def _incr(self, var, n=1): with self._lock: var.value += n def read(self, path): self._incr(self._nread) def wrote(self, path): self._incr(self._nwrote) self.print_status('wrote %s' % path) def error(self, path): tb = traceback.format_exc() self._incr(self._nerror) print_e('%s\n%s' % (path, tb)) def existing(self, path): self._incr(self._nexisting) print_v('existing %s' % path) @property def nerror(self): return self._nerror.value @property def nwrote(self): return self._nwrote.value @property def nread(self): return self._nread.value @property def nexisting(self): return self._nexisting.value class Parser(Process): def __init__(self, outformat, force, destination, queue, files, progress, fslock): super(Parser, self).__init__() self.queue = queue self.progress = progress self.tempfiles = [] self.destination = destination self.outformat = outformat self.force = force self._files = files self._fslock = fslock self._modules = [x() for x in formats.all_formats.values()] self._modules_map = {x.type: x for x in self._modules} self._stopped = Value('i', 0) self._curpath = '' def stop(self): self._stopped.value = 1 @property def stopped(self): return self._stopped.value == 1 def cleanup(self): for tempfile in self.tempfiles: if exists(tempfile): os.unlink(tempfile) def _process_path(self, path): self._curpath = path for i, rmodule in enumerate(self._modules): parsed = rmodule.parse_path(path) if parsed: # try this module first next time if i != 0: self._modules[i] = self._modules[0] self._modules[0] = rmodule break # file is not a chatlog if not parsed: return None self.progress.read(path) wmodule = self._modules_map[self.outformat] \ if self.outformat else rmodule for c in parsed: self._curpath = path dstpath = wmodule.get_path(c) real_dstpath = realpath(join(self.destination, dstpath)) with self._fslock: if real_dstpath in self._files: f = 1 elif exists(real_dstpath): f = 2 else: f = 0 self._files[real_dstpath] = f if f: self.progress.existing(dstpath) self.progress.print_status() if not self.force: continue if const.DRYRUN: conversation = c else: conversation = rmodule.parse_conversation(c) tmppath = real_dstpath+'.tmp' self.tempfiles.append(tmppath) self._curpath = real_dstpath self._write_outfile(wmodule, real_dstpath, tmppath, [conversation]) del self.tempfiles[-1] self.progress.wrote(dstpath) def _write_outfile(self, module, path, tmppath, conversations): # return len(conversations) dstdir = dirname(path) with self._fslock: if not exists(dstdir): os.makedirs(dstdir) module.write(tmppath, conversations) os.rename(tmppath, path) return len(conversations) def run(self): signal.signal(signal.SIGINT, signal.SIG_IGN) path = '' while True: try: path = self.queue.get() if path is None: break self._process_path(path) except IOError as e: break except Exception as e: self.progress.error(self._curpath) self.cleanup() def isfileordir(value): if not isfile(value) and not isdir(value): raise ArgumentTypeError("'%s' is not a file or directory" % value) return value def isnotfile(value): if isfile(value): raise ArgumentTypeError("'%s' is not a file" % value) return value def parse_args(): parser = \ ArgumentParser(description=const.PROGRAM_DESCRIPTION, prog=const.PROGRAM_NAME) parser.add_argument('source', nargs='+', type=isfileordir, help=_('source log file or directory')) parser.add_argument('destination', type=isnotfile, help=_('destination log directory')) parser.add_argument("-d", "--debug", help=_("enable debug output"), action='store_true', default=False, ) parser.add_argument("-f", "--format", choices=[str(x) for x in formats.output_formats], help=_("format to use for output files"), default=None, ) parser.add_argument("-F", "--force", help=_("force regeneration of existing logs at " "destination"), action='store_true', default=False, ) parser.add_argument("-n", "--dry-run", help=_("perform a trial run with no changes made"), action='store_true', default=False, ) parser.add_argument("--no-comments", help=_("do not write comments to converted logs"), action='store_true', default=False, ) parser.add_argument("-q", "--quiet", help=_("suppress warnings"), action='store_true', default=False, ) parser.add_argument("-t", "--threads", metavar="NUM_THREADS", help=_("use NUM_THREADS worker processes for parsing"), type=int, default=cpu_count(), ) parser.add_argument("-v", "--verbose", help=_("enable verbose output"), action='store_true', default=False, ) options = parser.parse_args() if options.debug: const.DEBUG = True if options.verbose: const.VERBOSE = True if options.quiet: const.QUIET = True if options.dry_run: const.DRYRUN = True if options.no_comments: const.NO_COMMENTS = True return options def convert(paths, options): global WORKERS manager = Manager() fslock = Lock() progress = Progress() queue = manager.Queue() files = manager.dict() WORKERS = [Parser(options.format, options.force, options.destination, queue, files, progress, fslock) for i in range(options.threads)] for w in WORKERS: w.start() for path in paths: queue.put(path) for w in WORKERS: queue.put(None) for w in WORKERS: w.join() return 0 def main(options): print_('gathering paths...', end='', flush=True, file=sys.stderr) src_paths = util.get_paths(options.source) print_('done', file=sys.stderr) convert(src_paths, options) return 0 def cleanup(exitcode): progress = None for w in WORKERS: progress = w.progress w.stop() for w in WORKERS: w.join() if progress: progress.print_status('done') exitcode += progress.nerror if not const.VERBOSE: print_('') return exitcode if __name__ == "__main__": options = parse_args() exitcode = 0 try: timezones.init() exitcode = main(options) except KeyboardInterrupt: exitcode = 1 print_e("***aborted***") except Exception as e: exitcode = 1 traceback.print_exc() finally: sys.exit(cleanup(exitcode))

gpl-3.0

uyaly/test

testcase/0_add1/Case05_HZ_add.py

1

2884

# coding:utf-8 import time import unittest import ddt from pageobject.account.Page_Account import Page_Account from selenium import webdriver from pageobject.Page_Login import Page_Login from pageobject.account.Page_Account_HZ_ADD import Page_Account_HZ_ADD from utils.config import Config from utils.log1 import Log import sys reload(sys) sys.setdefaultencoding('utf-8') log = Log() @ddt.ddt class addHZ(unittest.TestCase): '''总监登录，新增会长''' @classmethod def setUpClass(self): self.url = Config().get('URL') self.driver = webdriver.Firefox() self.l = Page_Login(self.driver) # login参数是LoginPage的实例 self.A = Page_Account(self.driver) self.A_HZ_ADD = Page_Account_HZ_ADD(self.driver) self.l.open(self.url) def test01_login(self): '''总监登录''' self.username = Config().get('CEO_LOGINNAME') self.psw = Config().get('PASSWORD') self.l.login(self.username, self.psw) # 判断是否登录成功 self.assertTrue(self.l.is_text_in_element(self.A.loginout_loc, "退出", "-------总监登录 失败-------")) log.info("-------总监登录 用例结束-------") def test02_add(self): '''新增会长''' self.username = Config().get('HZ_LOGINNAME') self.psw = Config().get('PASSWORD') self.loginid = Config().get('HZ_NAME') self.phone = Config().get('PHONE') # 进入模块 self.A.IntoModule("帐号2会长2") # 点击新增按钮 i = self.driver.find_element_by_id("mainIframe") self.driver.switch_to.frame(i) self.A.add() self.driver.switch_to.default_content() # 新增界面 time.sleep(2) self.A_HZ_ADD.input_club(self.username) time.sleep(3) # 滚动到底部 self.driver.execute_script("$('#form>div')[0].scrollTop=500") time.sleep(3) self.A_HZ_ADD.input_loginid(self.loginid) time.sleep(2) self.A_HZ_ADD.input_psw(self.psw) time.sleep(2) self.A_HZ_ADD.input_psw1(self.psw) time.sleep(2) self.A_HZ_ADD.input_name(self.username) time.sleep(2) self.A_HZ_ADD.input_phone(self.phone) time.sleep(2) self.A_HZ_ADD.click_save() time.sleep(2) # 判断是否新建成功 self.assertTrue(self.l.is_text_in_element(self.A.alert_text, "新增成功", str(self.l.get_text(self.A.alert_text)))) # 确定 self.A_HZ_ADD.click_ok() log.info('-------新增会长 用例结束-------') @classmethod def tearDownClass(self): # 关闭浏览器 self.driver.quit() # 执行测试主函数 if __name__ == '__main__': # 执行main全局方法，将会执行上述所有以test开头的测试方法 unittest.main(verbosity=2)

gpl-2.0

aldryn/aldryn-news

setup.py

1

1204

# -*- coding: utf-8 -*- from setuptools import setup, find_packages from aldryn_news import __version__ REQUIREMENTS = [ 'django-filer', 'django-hvad', 'django_select2', # last version that supports django 1.5 'django-taggit<=0.18.1', 'djangocms-text-ckeditor', 'translitcodec', 'Unidecode', ] CLASSIFIERS = [ 'Development Status :: 2 - Pre-Alpha', 'Environment :: Web Environment', 'Framework :: Django', 'Intended Audience :: Developers', 'License :: OSI Approved :: BSD License', 'Operating System :: OS Independent', 'Programming Language :: Python', 'Topic :: Internet :: WWW/HTTP :: Dynamic Content', 'Topic :: Software Development', 'Topic :: Software Development :: Libraries :: Application Frameworks', ] setup( name='aldryn-news', version=__version__, description='Publish news in django CMS', author='Divio AG', author_email='[email protected]', url='https://github.com/aldryn/aldryn-news', packages=find_packages(), license='LICENSE.txt', platforms=['OS Independent'], install_requires=REQUIREMENTS, classifiers=CLASSIFIERS, include_package_data=True, zip_safe=False )

bsd-3-clause

KittyHawkIrc/modules

reddit.py

1

3560

import json, random, urllib2 #Update schema __url__ = "https://raw.githubusercontent.com/KittyHawkIrc/modules/production/" + __name__ + ".py" __version__ = 1.0 def declare(): return {"reddit": "privmsg", "guess": "privmsg"} def callback(self): channel = self.channel command = self.command user = self.user msg = self.message type = self.type isop = self.isop if command == 'guess': u = 'FitOrFat' else: try: u = str(msg.split(' ', 1)[1]) except: return self.msg(channel, "Please specify a subreddit!") try: req = urllib2.Request("https://www.reddit.com/r/" + u + "/new.json", headers={ 'User-Agent': 'UNIX:the_kgb:reddit https://github.com/stqism/THE_KGB-apps' }) fd = urllib2.urlopen(req) reddit_api = json.loads(fd.read()) fd.close() cringe = [] for i in reddit_api['data']['children']: url = i['data']['url'] title = i['data']['title'] selfpost = bool(i['data']['is_self']) post = "https://reddit.com" + i['data']['permalink'] if 'imgur' in url: if 'http://i.imgur.com' in url: #force https url = 'https://i.imgur.com/%s' % (url.split('/')[3]) if 'http://' in url and '/a/' not in url: #direct URLs if 'gallery' in url: url = 'https://i.imgur.com/%s.jpg' % (url.split('/')[4]) else: url = 'https://i.imgur.com/%s.jpg' % (url.split('/')[3]) cringe.append([title, url, post]) item = random.choice(cringe) if command == 'guess': try: u = str(msg.split(' ', 1)[1]) return self.msg(channel, u + ": Am I fit or fat? " + item[1]) except: return self.msg(channel, "Am I fit or fat? " + item[1]) else: if not selfpost: via = " (via: " + item[2] + ")" return self.msg(channel, str(item[0] + " " + item[1] + via)) else: return self.msg(channel, str(item[0] + " " + item[1])) except Exception, e: return self.msg('#the_kgb', str(e)) class api: def msg(self, channel, text): return "[%s] %s" % (channel, text) if __name__ == "__main__": api = api() c = "#test" setattr(api, 'isop', True) setattr(api, 'type', 'privmsg') setattr(api, 'command', 'reddit') setattr(api, 'user', 'joe!username@hostmask') setattr(api, 'channel', c) setattr(api, 'message', '^reddit') if callback(api) != '[%s] Please specify a subreddit!' % (c): print '[TESTFAIL] no arguments' exit(1) setattr(api, 'message', '^reddit fatpeoplehate') if callback(api) != '[#the_kgb] HTTP Error 404: Not Found': print '[TESTFAIL] error catcher' exit(1) setattr(api, 'message', '^reddit fatlogic') if not callback(api).startswith('[%s] ' % (c)): print '[TESTFAIL] Subreddit loader' exit(1) setattr(api, 'message', '^guess') setattr(api, 'command', 'guess') if not callback(api).startswith('[%s] Am I fit or fat?' % (c)): print '[TESTFAIL] guess no user' print '[%s] Am I male or female?' % (c) exit(1) n = 'bob' setattr(api, 'message', '^guess %s' % (n)) if not callback(api).startswith('[%s] %s: Am I fit or fat?' % (c, n)): print '[TESTFAIL] guess with user' exit(1)

apache-2.0

evilsephiroth/plugin.video.vvvvid

vvvvid.py

1

1369

import urllib2 def f(m): l = list() o = 0 b = None while not b and o < len(m): n = m[o] <<2 o +=1 k = -1 j = -1 if o < len(m): n += m[o] >> 4 o += 1 if o < len(m): k = (m[o - 1] << 4) & 255; k += m[o] >> 2; o += 1 if o < len(m): j = (m[o - 1] << 6) & 255; j += m[o] o += 1 else: b = True else: b = True else: b = True l.append(n) if k != -1: l.append(k) if j != -1: l.append(j) return l def dec_ei(h): g = 'MNOPIJKL89+/4567UVWXQRSTEFGHABCDcdefYZabstuvopqr0123wxyzklmnghij' c = list() for e in range(0,len(h)): c.append(g.find(h[e])) for e in range(len(c)*2-1,-1,-1): #print 'e=' + str(e) a = c[e % len(c)] ^ c[(e+1)%len(c)] #print 'a='+str(a) c[e%len(c)] = a #print 'c['+str(e % len(c))+']='+ str(c[e % len(c)]) c = f(c) d = '' for e in range(0,len(c)): d += '%'+ (('0'+ (str(format(c[e],'x'))))[-2:]) return urllib2.unquote(d)

gpl-2.0

yola/yolapy

docs/conf.py

1

9271

# -*- coding: utf-8 -*- # # Yolapy documentation build configuration file, created by # sphinx-quickstart on Thu Aug 27 12:47:53 2015. # # This file is execfile()d with the current directory set to its # containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. import sys import os import shlex import sphinx_rtd_theme # 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. sys.path.insert(0, os.path.abspath('..')) from yolapy import __version__ # -- General configuration ------------------------------------------------ # If your documentation needs a minimal Sphinx version, state it here. #needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ 'sphinx.ext.autodoc', 'sphinx.ext.viewcode', ] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: source_suffix = ['.rst'] # The encoding of source files. #source_encoding = 'utf-8-sig' # The master toctree document. master_doc = 'index' # General information about the project. project = u'Yolapy' copyright = u'2015, Yola' author = u'Yola' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = __version__ # The full version, including alpha/beta/rc tags. release = __version__ # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: #today = '' # Else, today_fmt is used as the format for a strftime call. #today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. exclude_patterns = ['_build'] # The reST default role (used for this markup: `text`) to use for all # documents. #default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. #add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). #add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. #show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # A list of ignored prefixes for module index sorting. #modindex_common_prefix = [] # If true, keep warnings as "system message" paragraphs in the built documents. #keep_warnings = False # If true, `todo` and `todoList` produce output, else they produce nothing. todo_include_todos = False # -- 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' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. #html_theme_options = {} # Add any paths that contain custom themes here, relative to this directory. html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] # The name for this set of Sphinx documents. If None, it defaults to # "<project> v<release> documentation". #html_title = None # A shorter title for the navigation bar. Default is the same as html_title. #html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. #html_logo = None # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. #html_favicon = None # 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'] # Add any extra paths that contain custom files (such as robots.txt or # .htaccess) here, relative to this directory. These files are copied # directly to the root of the documentation. #html_extra_path = [] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. #html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. #html_use_smartypants = True # Custom sidebar templates, maps document names to template names. #html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. #html_additional_pages = {} # If false, no module index is generated. #html_domain_indices = True # If false, no index is generated. #html_use_index = True # If true, the index is split into individual pages for each letter. #html_split_index = False # If true, links to the reST sources are added to the pages. #html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. #html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. #html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a <link> tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. #html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). #html_file_suffix = None # Language to be used for generating the HTML full-text search index. # Sphinx supports the following languages: # 'da', 'de', 'en', 'es', 'fi', 'fr', 'hu', 'it', 'ja' # 'nl', 'no', 'pt', 'ro', 'ru', 'sv', 'tr' #html_search_language = 'en' # A dictionary with options for the search language support, empty by default. # Now only 'ja' uses this config value #html_search_options = {'type': 'default'} # The name of a javascript file (relative to the configuration directory) that # implements a search results scorer. If empty, the default will be used. #html_search_scorer = 'scorer.js' # Output file base name for HTML help builder. htmlhelp_basename = 'Yolapydoc' # -- Options for LaTeX output --------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). #'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). #'pointsize': '10pt', # Additional stuff for the LaTeX preamble. #'preamble': '', # Latex figure (float) alignment #'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ (master_doc, 'Yolapy.tex', u'Yolapy Documentation', u'Yola', 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. #latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. #latex_use_parts = False # If true, show page references after internal links. #latex_show_pagerefs = False # If true, show URL addresses after external links. #latex_show_urls = False # Documents to append as an appendix to all manuals. #latex_appendices = [] # If false, no module index is generated. #latex_domain_indices = True # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). #man_pages = [ # (master_doc, 'yolapy', u'Yolapy Documentation', # [author], 1) #] # If true, show URL addresses after external links. #man_show_urls = False # -- Options for Texinfo output ------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) #texinfo_documents = [ # (master_doc, 'Yolapy', u'Yolapy Documentation', # author, 'Yolapy', 'One line description of project.', # 'Miscellaneous'), #] # Documents to append as an appendix to all manuals. #texinfo_appendices = [] # If false, no module index is generated. #texinfo_domain_indices = True # How to display URL addresses: 'footnote', 'no', or 'inline'. #texinfo_show_urls = 'footnote' # If true, do not generate a @detailmenu in the "Top" node's menu. #texinfo_no_detailmenu = False

mit

shree-shubham/Unitype

Coupling Passions.py

1

1941

import math # Enter your code here. Read input from STDIN. Print output to STDOUT def distance_between(point1, point2): EARTH_RADIUS = 6371 point1_lat_in_radians = math.radians( point1['latitude'] ) point2_lat_in_radians = math.radians( point2['latitude'] ) point1_long_in_radians = math.radians( point1['longitude'] ) point2_long_in_radians = math.radians( point2['longitude'] ) return math.acos( math.sin( point1_lat_in_radians ) * math.sin( point2_lat_in_radians ) + math.cos( point1_lat_in_radians ) * math.cos( point2_lat_in_radians ) * math.cos( point2_long_in_radians - point1_long_in_radians) ) * EARTH_RADIUS m = int(raw_input()) people = {} locations = {} interests = {} for p in xrange(m): s = raw_input().split(' ') people[p] = s[1:] for i in s[1:]: interests[i] = 1 z = int(raw_input()) for l in xrange(z): s = raw_input().split(' ') locations[s[0]] = {'latitude': float(s[1]), 'longitude': float(s[2])} locations[s[0]]['passions'] = set() for ll in xrange(4, 4 + int(s[3])): locations[s[0]]['passions'].add(s[ll]) res = [] for l in locations: interest_set = set() for i in interests: if i in locations[l]['passions']: interest_set.add(i) res += [[l, interest_set]] commons = 0 commons_list = [] for i in xrange(len(res)): for j in xrange(i+1, len(res)): temp = len(res[i][1] | res[j][1]) if temp >= commons: commons = temp if res[i][0] < res[j][0]: commons_list += [[res[i][0], res[j][0], commons, distance_between(locations[res[i][0]], locations[res[j][0]])]] else: commons_list += [[res[j][0], res[i][0], commons, distance_between(locations[res[i][0]], locations[res[j][0]])]] commons_list = sorted(commons_list, key = lambda x : (-x[2], x[3])) print commons_list[0][0] + ' ' + commons_list[0][1]

gpl-3.0

lfairchild/PmagPy

dialogs/pmag_er_magic_dialogs.py

1

52576

""" dialogs for ErMagicBuilder """ # pylint: disable=W0612,C0111,C0103,W0201,C0301 import os import wx import wx.grid import numpy as np from . import drop_down_menus2 as drop_down_menus from . import pmag_widgets as pw from . import magic_grid2 as magic_grid from . import grid_frame2 from . import grid_frame3 from pmagpy import find_pmag_dir from pmagpy import contribution_builder as cb class ErMagicCheckFrame3(wx.Frame): def __init__(self, parent, title, WD, contribution): wx.Frame.__init__(self, parent, -1, title) self.WD = WD self.main_frame = self.Parent self.contribution = contribution self.temp_data = {} self.grid = None self.deleteRowButton = None self.selected_rows = set() self.min_size = (1160, 350) self.contribution.propagate_ages() # re-do the 'quit' binding so that it only closes the current window self.main_frame.Bind(wx.EVT_MENU, lambda event: self.main_frame.menubar.on_quit(event, self), self.main_frame.menubar.file_quit) self.InitSpecCheck() def InitSpecCheck(self): """ make an interactive grid in which users can edit specimen names as well as which sample a specimen belongs to """ #wait = wx.BusyInfo("Please wait, working...") #wx.SafeYield() self.contribution.propagate_lithology_cols() spec_df = self.contribution.tables['specimens'].df self.panel = wx.Panel(self, style=wx.SIMPLE_BORDER) self.grid_frame = grid_frame3.GridFrame(self.contribution, self.WD, 'specimens', 'specimens', self.panel, main_frame=self.main_frame) # redefine default 'save & exit grid' button to go to next dialog instead self.grid_frame.exitButton.SetLabel('Save and continue') grid = self.grid_frame.grid self.grid_frame.Bind(wx.EVT_BUTTON, lambda event: self.onContinue(event, grid, self.InitSampCheck), self.grid_frame.exitButton) # add back button self.backButton = wx.Button(self.grid_frame.panel, id=-1, label='Back', name='back_btn') self.backButton.Disable() self.grid_frame.main_btn_vbox.Add(self.backButton, flag=wx.ALL, border=5) # re-do fit self.grid_frame.do_fit(None, self.min_size) # center self.grid_frame.Centre() return def InitSampCheck(self): """ make an interactive grid in which users can edit sample names as well as which site a sample belongs to """ # propagate any type/lithology/class data from sites to samples table # will only overwrite if sample values are blank self.contribution.propagate_lithology_cols() samp_df = self.contribution.tables['samples'].df self.panel = wx.Panel(self, style=wx.SIMPLE_BORDER) self.grid_frame = grid_frame3.GridFrame(self.contribution, self.WD, 'samples', 'samples', self.panel, main_frame=self.main_frame) # redefine default 'save & exit grid' button to go to next dialog instead self.grid_frame.exitButton.SetLabel('Save and continue') next_dia = self.InitSiteCheck prev_dia = self.InitSpecCheck grid = self.grid_frame.grid self.grid_frame.Bind(wx.EVT_BUTTON, lambda event: self.onContinue(event, grid, next_dia), self.grid_frame.exitButton) # add back button self.backButton = wx.Button(self.grid_frame.panel, id=-1, label='Back', name='back_btn') self.Bind(wx.EVT_BUTTON, lambda event: self.onbackButton(event, prev_dia), self.backButton) self.grid_frame.main_btn_vbox.Add(self.backButton, flag=wx.ALL, border=5) # re-do fit self.grid_frame.do_fit(None, self.min_size) # center self.grid_frame.Centre() return def InitSiteCheck(self): """ make an interactive grid in which users can edit site names as well as which location a site belongs to """ # propagate average lat/lon info from samples table if # available in samples and missing in sites self.contribution.propagate_average_up(cols=['lat', 'lon', 'height'], target_df_name='sites', source_df_name='samples') # propagate lithology columns self.contribution.propagate_lithology_cols() site_df = self.contribution.tables['sites'].df self.panel = wx.Panel(self, style=wx.SIMPLE_BORDER) self.grid_frame = grid_frame3.GridFrame(self.contribution, self.WD, 'sites', 'sites', self.panel, main_frame=self.main_frame) # redefine default 'save & exit grid' button to go to next dialog instead self.grid_frame.exitButton.SetLabel('Save and continue') grid = self.grid_frame.grid self.grid_frame.Bind(wx.EVT_BUTTON, lambda event: self.onContinue(event, grid, self.InitLocCheck), self.grid_frame.exitButton) # add back button self.backButton = wx.Button(self.grid_frame.panel, id=-1, label='Back', name='back_btn') self.Bind(wx.EVT_BUTTON, lambda event: self.onbackButton(event, self.InitSampCheck), self.backButton) self.grid_frame.main_btn_vbox.Add(self.backButton, flag=wx.ALL, border=5) # re-do fit self.grid_frame.do_fit(None, self.min_size) # center self.grid_frame.Centre() return def InitLocCheck(self): """ make an interactive grid in which users can edit locations """ # if there is a location without a name, name it 'unknown' self.contribution.rename_item('locations', 'nan', 'unknown') # propagate lat/lon values from sites table self.contribution.get_min_max_lat_lon() # propagate lithologies & geologic classes from sites table self.contribution.propagate_cols_up(['lithologies', 'geologic_classes'], 'locations', 'sites') res = self.contribution.propagate_min_max_up() if cb.not_null(res): self.contribution.propagate_cols_up(['age_unit'], 'locations', 'sites') # set up frame self.panel = wx.Panel(self, style=wx.SIMPLE_BORDER) self.grid_frame = grid_frame3.GridFrame(self.contribution, self.WD, 'locations', 'locations', self.panel, main_frame=self.main_frame) # redefine default 'save & exit grid' button to go to next dialog instead self.grid_frame.exitButton.SetLabel('Save and continue') grid = self.grid_frame.grid self.grid_frame.Bind(wx.EVT_BUTTON, lambda event: self.onContinue(event, grid, self.InitAgeCheck), self.grid_frame.exitButton) # add back button self.backButton = wx.Button(self.grid_frame.panel, id=-1, label='Back', name='back_btn') self.Bind(wx.EVT_BUTTON, lambda event: self.onbackButton(event, self.InitSiteCheck), self.backButton) self.grid_frame.main_btn_vbox.Add(self.backButton, flag=wx.ALL, border=5) # re-do fit self.grid_frame.do_fit(None, min_size=self.min_size) # center self.grid_frame.Centre() return def InitAgeCheck(self): """make an interactive grid in which users can edit ages""" age_df = self.contribution.tables['ages'].df self.panel = wx.Panel(self, style=wx.SIMPLE_BORDER) self.grid_frame = grid_frame3.GridFrame(self.contribution, self.WD, 'ages', 'ages', self.panel, main_frame=self.main_frame) self.grid_frame.exitButton.SetLabel('Save and continue') grid = self.grid_frame.grid self.grid_frame.Bind(wx.EVT_BUTTON, lambda event: self.onContinue(event, grid, None), self.grid_frame.exitButton) # add back button self.backButton = wx.Button(self.grid_frame.panel, id=-1, label='Back', name='back_btn') self.Bind(wx.EVT_BUTTON, lambda event: self.onbackButton(event, self.InitLocCheck), self.backButton) self.grid_frame.main_btn_vbox.Add(self.backButton, flag=wx.ALL, border=5) # re-do fit self.grid_frame.do_fit(None, self.min_size) # center self.grid_frame.Centre() return def on_close_grid_frame(self, event=None): # required placeholder pass def onContinue(self, event, grid, next_dia=None):#, age_data_type='site'): """ Save grid data in the data object """ # deselect column, including remove 'EDIT ALL' label if self.grid_frame.drop_down_menu: self.grid_frame.drop_down_menu.clean_up() # remove '**' and '^^' from col names #self.remove_starred_labels(grid) grid.remove_starred_labels() grid.SaveEditControlValue() # locks in value in cell currently edited grid_name = str(grid.GetName()) # save all changes to data object and write to file self.grid_frame.grid_builder.save_grid_data() # check that all required data are present validation_errors = self.validate(grid) if validation_errors: warn_string = "" for error_name, error_cols in list(validation_errors.items()): if error_cols: warn_string += "You have {}: {}.\n\n".format(error_name, ", ".join(error_cols)) warn_string += "Are you sure you want to continue?" result = pw.warning_with_override(warn_string) if result == wx.ID_YES: pass else: return False else: wx.MessageBox('Saved!', 'Info', style=wx.OK | wx.ICON_INFORMATION) self.panel.Destroy() if next_dia: next_dia() else: # propagate any type/lithology/class data from sites to samples table # will only overwrite if sample values are blank or "Not Specified" self.contribution.propagate_lithology_cols() wx.MessageBox('Done!', 'Info', style=wx.OK | wx.ICON_INFORMATION) def onbackButton(self, event=None, prev_dia=None): if prev_dia: alert = True if self.grid_frame.grid.changes else False self.grid_frame.onSave(event=None, alert=alert, destroy=True) #if self.grid_frame.grid.name == 'samples': # self.sample_window -= 2 self.panel.Destroy() prev_dia() def validate(self, grid): """ Using the MagIC data model, generate validation errors on a MagicGrid. Parameters ---------- grid : dialogs.magic_grid3.MagicGrid The MagicGrid to be validated Returns --------- warnings: dict Empty dict if no warnings, otherwise a dict with format {name of problem: [problem_columns]} """ grid_name = str(grid.GetName()) dmodel = self.contribution.dmodel reqd_headers = dmodel.get_reqd_headers(grid_name) df = self.contribution.tables[grid_name].df df = df.replace('', np.nan) # python does not view empty strings as null if df.empty: return {} col_names = set(df.columns) missing_headers = set(reqd_headers) - col_names present_headers = set(reqd_headers) - set(missing_headers) non_null_headers = df.dropna(how='all', axis='columns').columns null_reqd_headers = present_headers - set(non_null_headers) if any(missing_headers) or any (null_reqd_headers): warnings = {'missing required column(s)': sorted(missing_headers), 'no data in required column(s)': sorted(null_reqd_headers)} else: warnings = {} return warnings def on_saveButton(self, event, grid): """saves any editing of the grid but does not continue to the next window""" wait = wx.BusyInfo("Please wait, working...") wx.SafeYield() if self.grid_frame.drop_down_menu: # unhighlight selected columns, etc. self.grid_frame.drop_down_menu.clean_up() # remove '**' and '^^' from col labels starred_cols, hatted_cols = grid.remove_starred_labels() grid.SaveEditControlValue() # locks in value in cell currently edited grid.HideCellEditControl() # removes focus from cell that was being edited if grid.changes: self.onSave(grid) for col in starred_cols: label = grid.GetColLabelValue(col) grid.SetColLabelValue(col, label + '**') for col in hatted_cols: label = grid.GetColLabelValue(col) grid.SetColLabelValue(col, label + '^^') del wait def on_backButton(self, event, previous_dia, current_dia=None): # save first? if self.grid.changes: result = pw.warning_with_override("You have unsaved data which will be lost. Are you sure you want to go back?") if result == wx.ID_NO: return # go back to previous grid wait = wx.BusyInfo("Please wait, working...") wx.SafeYield() if current_dia == self.InitLocCheck: pass #elif previous_dia == self.InitSpecCheck or previous_dia == self.InitSampCheck: # self.sample_window = 0 self.panel.Destroy() previous_dia() del wait ### Manage data methods ### def onSave(self, grid):#, age_data_type='site'): """ Save grid data in the data object """ # deselect column, including remove 'EDIT ALL' label if self.grid_frame.drop_down_menu: self.grid_frame.drop_down_menu.clean_up() # save all changes to data object and write to file self.grid_builder.save_grid_data() wx.MessageBox('Saved!', 'Info', style=wx.OK | wx.ICON_INFORMATION) class ErMagicCheckFrame(wx.Frame): def __init__(self, parent, title, WD, magic_data): # magic_data was ErMagic wx.Frame.__init__(self, parent, -1, title) self.WD = WD self.main_frame = self.Parent self.er_magic_data = magic_data self.er_magic_data.no_pmag_data = set(['specimen', 'sample', 'site', 'location']) self.temp_data = {} self.drop_down_menu = None # sample window must be displayed (differently) twice, so it is useful to keep track self.sample_window = 0 self.grid = None self.deleteRowButton = None self.selected_rows = set() self.InitSpecCheck() def InitSpecCheck(self): """make an interactive grid in which users can edit specimen names as well as which sample a specimen belongs to""" self.panel = wx.Panel(self, style=wx.SIMPLE_BORDER) #import wx.lib.scrolledpanel as libpanel # does not work well #self.panel = libpanel.ScrolledPanel(self, style=wx.SIMPLE_BORDER) text = """Step 1: Check that all specimens belong to the correct sample (if sample name is simply wrong, that will be fixed in step 2)""" label = wx.StaticText(self.panel, label=text) self.grid_builder = grid_frame2.GridBuilder(self.er_magic_data, 'specimen', self.er_magic_data.headers, self.panel, 'sample') self.spec_grid = self.grid_builder.make_grid(incl_pmag=False) self.grid = self.spec_grid self.spec_grid.InitUI() self.grid_builder.add_data_to_grid(self.spec_grid, 'specimen', incl_pmag=False) samples = self.er_magic_data.make_name_list(self.er_magic_data.samples) self.drop_down_menu = drop_down_menus.Menus("specimen", self, self.spec_grid, samples) #### Create Buttons #### hbox_one = wx.BoxSizer(wx.HORIZONTAL) self.addSampleButton = wx.Button(self.panel, label="Add a new sample") self.samples = [name for name in self.er_magic_data.samples] self.Bind(wx.EVT_BUTTON, self.on_addSampleButton, self.addSampleButton) self.helpButton = wx.Button(self.panel, label="Help") self.Bind(wx.EVT_BUTTON, lambda event: self.on_helpButton(event, "ErMagicSpecimenHelp.html"), self.helpButton) hbox_one.Add(self.addSampleButton, flag=wx.ALIGN_LEFT|wx.RIGHT, border=10) hbox_one.Add(self.helpButton) # hboxok = wx.BoxSizer(wx.HORIZONTAL) self.saveButton = wx.Button(self.panel, id=-1, label='Save') self.Bind(wx.EVT_BUTTON, lambda event: self.on_saveButton(event, self.spec_grid), self.saveButton) self.cancelButton = wx.Button(self.panel, wx.ID_CANCEL, '&Cancel') self.Bind(wx.EVT_BUTTON, self.on_cancelButton, self.cancelButton) self.continueButton = wx.Button(self.panel, id=-1, label='Save and continue') self.Bind(wx.EVT_BUTTON, lambda event: self.on_continueButton(event, self.spec_grid, next_dia=self.InitSampCheck), self.continueButton) hboxok.Add(self.saveButton, flag=wx.ALIGN_LEFT|wx.RIGHT, border=10) hboxok.Add(self.cancelButton, flag=wx.ALIGN_LEFT|wx.RIGHT, border=10) hboxok.Add(self.continueButton, flag=wx.ALIGN_LEFT) # hboxgrid = pw.hbox_grid(self.panel, self.onDeleteRow, 'specimen', self.grid) self.deleteRowButton = hboxgrid.deleteRowButton self.panel.Bind(wx.grid.EVT_GRID_LABEL_LEFT_CLICK, self.onLeftClickLabel, self.grid) ### Create Containers ### vbox = wx.BoxSizer(wx.VERTICAL) vbox.AddSpacer(10) vbox.Add(label, flag=wx.ALIGN_CENTER|wx.TOP|wx.BOTTOM, border=10) vbox.Add(hbox_one, flag=wx.TOP|wx.LEFT|wx.BOTTOM, border=10) vbox.Add(hboxok, flag=wx.BOTTOM|wx.LEFT, border=10) vbox.Add(hboxgrid, flag=wx.BOTTOM|wx.LEFT, border=10) vbox.Add(self.spec_grid, flag=wx.ALL, border=10)#|wx.EXPAND, border=30) vbox.AddSpacer(20) self.hbox_all = wx.BoxSizer(wx.HORIZONTAL) self.hbox_all.AddSpacer(20) self.hbox_all.Add(vbox) self.hbox_all.AddSpacer(20) self.panel.SetSizer(self.hbox_all) #self.panel.SetScrollbars(20, 20, 50, 50) self.hbox_all.Fit(self) self.Centre() self.Show() self.Hide() self.Show() def InitSampCheck(self): """make an interactive grid in which users can edit sample names as well as which site a sample belongs to""" self.sample_window += 1 self.panel = wx.Panel(self, style=wx.SIMPLE_BORDER) if self.sample_window == 1: text = """Step 2: Check that all samples are correctly named, and that they belong to the correct site (if site name is simply wrong, that will be fixed in step 3)""" step_label = wx.StaticText(self.panel, label=text)#, size=(900, 100)) else: text = """Step 4: Some of the data from the er_sites table has propogated into er_samples. Check that these data are correct, and fill in missing cells using controlled vocabularies. The columns for class, lithology, and type can take multiple values in the form of a colon-delimited list. You may use the drop-down menus to add as many values as needed in these columns. (see Help button for more details)\n\n** Denotes controlled vocabulary""" step_label = wx.StaticText(self.panel, label=text)#, size=(900, 100)) if self.sample_window == 1: # provide no extra headers headers = {'sample': {'er': [[], [], []], 'pmag': [[], [], []]}} self.grid_builder = grid_frame2.GridBuilder(self.er_magic_data, 'sample', headers, self.panel, 'site') if self.sample_window > 1: self.grid_builder = grid_frame2.GridBuilder(self.er_magic_data, 'sample', self.er_magic_data.headers, self.panel, 'site') self.samp_grid = self.grid_builder.make_grid(incl_pmag=False) self.samp_grid.InitUI() self.grid_builder.add_data_to_grid(self.samp_grid, 'sample', incl_pmag=False) self.grid = self.samp_grid sites = sorted(self.er_magic_data.make_name_list(self.er_magic_data.sites)) self.drop_down_menu = drop_down_menus.Menus("sample", self, self.samp_grid, sites) # initialize all needed drop-down menus ### Create Buttons ### hbox_one = wx.BoxSizer(wx.HORIZONTAL) self.addSiteButton = wx.Button(self.panel, label="Add a new site") self.Bind(wx.EVT_BUTTON, self.on_addSiteButton, self.addSiteButton) hbox_one.Add(self.addSiteButton, flag=wx.RIGHT, border=10) if self.sample_window == 1: html_help = "ErMagicSampleHelp1.html" if self.sample_window > 1: html_help = "ErMagicSampleHelp.html" self.helpButton = wx.Button(self.panel, label="Help") self.Bind(wx.EVT_BUTTON, lambda event: self.on_helpButton(event, html_help), self.helpButton) hbox_one.Add(self.helpButton) hboxok = wx.BoxSizer(wx.HORIZONTAL) self.saveButton = wx.Button(self.panel, id=-1, label='Save') self.Bind(wx.EVT_BUTTON, lambda event: self.on_saveButton(event, self.samp_grid), self.saveButton) self.cancelButton = wx.Button(self.panel, wx.ID_CANCEL, '&Cancel') self.Bind(wx.EVT_BUTTON, self.on_cancelButton, self.cancelButton) self.continueButton = wx.Button(self.panel, id=-1, label='Save and continue') next_dia = self.InitSiteCheck if self.sample_window < 2 else self.InitLocCheck self.Bind(wx.EVT_BUTTON, lambda event: self.on_continueButton(event, self.samp_grid, next_dia=next_dia), self.continueButton) self.backButton = wx.Button(self.panel, wx.ID_ANY, "&Back") previous_dia = self.InitSpecCheck if self.sample_window < 2 else self.InitSiteCheck self.Bind(wx.EVT_BUTTON, lambda event: self.on_backButton(event, previous_dia=previous_dia), self.backButton) hboxok.Add(self.saveButton, flag=wx.RIGHT, border=10) hboxok.Add(self.cancelButton, flag=wx.RIGHT, border=10) hboxok.Add(self.continueButton, flag=wx.RIGHT, border=10) hboxok.Add(self.backButton) hboxgrid = pw.hbox_grid(self.panel, self.onDeleteRow, 'sample', self.grid) self.deleteRowButton = hboxgrid.deleteRowButton self.Bind(wx.grid.EVT_GRID_LABEL_LEFT_CLICK, self.onLeftClickLabel, self.grid) ### Make Containers ### vbox = wx.BoxSizer(wx.VERTICAL) vbox.Add(step_label, flag=wx.ALIGN_LEFT|wx.TOP|wx.BOTTOM, border=20) vbox.Add(hbox_one, flag=wx.BOTTOM|wx.LEFT, border=10) vbox.Add(hboxok, flag=wx.BOTTOM|wx.LEFT, border=10) vbox.Add(hboxgrid, flag=wx.BOTTOM|wx.LEFT, border=10) vbox.Add(self.samp_grid, flag=wx.ALL, border=10) # using wx.EXPAND or not does not affect re-size problem vbox.AddSpacer(20) self.hbox_all = wx.BoxSizer(wx.HORIZONTAL) self.hbox_all.AddSpacer(20) self.hbox_all.Add(vbox) self.hbox_all.AddSpacer(20) self.panel.SetSizer(self.hbox_all) #if sys.platform in ['win32', 'win64']: # self.panel.SetScrollbars(20, 20, 50, 50) self.hbox_all.Fit(self) self.Centre() self.Show() ## this combination may prevent a display error that (without the fix) only resolves on manually resizing the window self.panel.Refresh() self.samp_grid.ForceRefresh() self.panel.Refresh() self.Refresh() # this prevents display errors self.Hide() self.Show() #self.Fit() # this make it worse! #self.Layout() # doesn't fix display resize error #self.panel.Layout() # doesn't fix display resize error #self.main_frame.Layout()# doesn't fix display resize error def InitSiteCheck(self): """make an interactive grid in which users can edit site names as well as which location a site belongs to""" self.panel = wx.Panel(self, style=wx.SIMPLE_BORDER) text = """Step 3: Check that all sites are correctly named, and that they belong to the correct location. Fill in the additional columns with controlled vocabularies. The columns for class, lithology, and type can take multiple values in the form of a colon-delimited list. You may use the drop-down menus to add as many values as needed in these columns. (see the help button for more details) note: Changes to site_class, site_lithology, or site_type will overwrite er_samples.txt However, you will be able to edit sample_class, sample_lithology, and sample_type in step 4 **Denotes controlled vocabulary""" label = wx.StaticText(self.panel, label=text) #self.Data_hierarchy = self.ErMagic.Data_hierarchy self.sites = sorted(self.er_magic_data.make_name_list(self.er_magic_data.sites)) #for val in ['er_citation_names', 'er_location_name', 'er_site_name', 'site_class', 'site_lithology', 'site_type', 'site_definition', 'site_lat', 'site_lon']: # # try: # self.er_magic_data.headers['site']['er'][0].remove(val) # except ValueError: # pass self.grid_builder = grid_frame2.GridBuilder(self.er_magic_data, 'site', self.er_magic_data.headers, self.panel, 'location') self.site_grid = self.grid_builder.make_grid(incl_pmag=False) self.site_grid.InitUI() self.grid_builder.add_data_to_grid(self.site_grid, 'site', incl_pmag=False) self.grid = self.site_grid # populate site_definition as 's' by default if no value is provided (indicates that site is single, not composite) rows = self.site_grid.GetNumberRows() col = 6 for row in range(rows): cell = self.site_grid.GetCellValue(row, col) if not cell: self.site_grid.SetCellValue(row, col, 's') # initialize all needed drop-down menus locations = sorted(self.er_magic_data.make_name_list(self.er_magic_data.locations)) self.drop_down_menu = drop_down_menus.Menus("site", self, self.site_grid, locations) ### Create Buttons ### hbox_one = wx.BoxSizer(wx.HORIZONTAL) self.addLocButton = wx.Button(self.panel, label="Add a new location") self.Bind(wx.EVT_BUTTON, self.on_addLocButton, self.addLocButton) hbox_one.Add(self.addLocButton, flag=wx.RIGHT, border=10) self.helpButton = wx.Button(self.panel, label="Help") self.Bind(wx.EVT_BUTTON, lambda event: self.on_helpButton(event, "ErMagicSiteHelp.html"), self.helpButton) hbox_one.Add(self.helpButton) hboxok = wx.BoxSizer(wx.HORIZONTAL) self.saveButton = wx.Button(self.panel, id=-1, label='Save') self.Bind(wx.EVT_BUTTON, lambda event: self.on_saveButton(event, self.site_grid), self.saveButton) self.cancelButton = wx.Button(self.panel, wx.ID_CANCEL, '&Cancel') self.Bind(wx.EVT_BUTTON, self.on_cancelButton, self.cancelButton) self.continueButton = wx.Button(self.panel, id=-1, label='Save and continue') self.Bind(wx.EVT_BUTTON, lambda event: self.on_continueButton(event, self.site_grid, next_dia=self.InitSampCheck), self.continueButton) self.backButton = wx.Button(self.panel, wx.ID_ANY, "&Back") previous_dia = self.InitSampCheck self.Bind(wx.EVT_BUTTON, lambda event: self.on_backButton(event, previous_dia=previous_dia), self.backButton) hboxok.Add(self.saveButton, flag=wx.RIGHT, border=10) hboxok.Add(self.cancelButton, flag=wx.RIGHT, border=10) hboxok.Add(self.continueButton, flag=wx.RIGHT, border=10) hboxok.Add(self.backButton) # hboxgrid = pw.hbox_grid(self.panel, self.onDeleteRow, 'site', self.grid) self.deleteRowButton = hboxgrid.deleteRowButton self.Bind(wx.grid.EVT_GRID_LABEL_LEFT_CLICK, self.onLeftClickLabel, self.grid) ### Make Containers ### vbox = wx.BoxSizer(wx.VERTICAL) vbox.Add(label, flag=wx.ALIGN_CENTER|wx.BOTTOM|wx.TOP, border=20) vbox.Add(hbox_one, flag=wx.BOTTOM|wx.LEFT, border=10) vbox.Add(hboxok, flag=wx.BOTTOM|wx.LEFT, border=10) vbox.Add(hboxgrid, flag=wx.BOTTOM|wx.LEFT, border=10) vbox.Add(self.site_grid, flag=wx.ALL|wx.EXPAND, border=10) # EXPAND ?? vbox.AddSpacer(20) self.hbox_all = wx.BoxSizer(wx.HORIZONTAL) self.hbox_all.AddSpacer(20) self.hbox_all.Add(vbox) self.hbox_all.AddSpacer(20) self.panel.SetSizer(self.hbox_all) #if sys.platform in ['win32', 'win64']: # self.panel.SetScrollbars(20, 20, 50, 50) self.hbox_all.Fit(self) self.Centre() self.Show() # this combination prevents a display error that (without the fix) only resolves on manually resizing the window self.site_grid.ForceRefresh() self.panel.Refresh() self.Hide() self.Show() def InitLocCheck(self): """make an interactive grid in which users can edit specimen names as well as which sample a specimen belongs to""" self.panel = wx.Panel(self, style=wx.SIMPLE_BORDER) text = """Step 5: Check that locations are correctly named. Fill in any blank cells using controlled vocabularies. (See Help button for details) ** Denotes controlled vocabulary""" label = wx.StaticText(self.panel, label=text) #self.Data_hierarchy = self.ErMagic.Data_hierarchy self.locations = self.er_magic_data.locations # if not self.er_magic_data.locations: msg = "You have no data in er_locations, so we are skipping step 5.\n Note that location names must be entered at the measurements level,so you may need to re-import your data, or you can add a location in step 3" dlg = wx.MessageDialog(None, caption="Message:", message=msg, style=wx.OK|wx.ICON_INFORMATION) dlg.ShowModal() dlg.Destroy() self.panel.Destroy() self.InitAgeCheck() return self.grid_builder = grid_frame2.GridBuilder(self.er_magic_data, 'location', self.er_magic_data.headers, self.panel) self.loc_grid = self.grid_builder.make_grid(incl_pmag=False) self.loc_grid.InitUI() self.grid_builder.add_data_to_grid(self.loc_grid, 'location', incl_pmag=False) self.grid = self.loc_grid # initialize all needed drop-down menus self.drop_down_menu = drop_down_menus.Menus("location", self, self.loc_grid, None) # need to find max/min lat/lon here IF they were added in the previous grid sites = self.er_magic_data.sites location_lat_lon = self.er_magic_data.get_min_max_lat_lon(self.er_magic_data.locations) col_names = ('location_begin_lat', 'location_end_lat', 'location_begin_lon', 'location_end_lon') col_inds = [self.grid.col_labels.index(name) for name in col_names] col_info = list(zip(col_names, col_inds)) for loc in self.er_magic_data.locations: row_ind = self.grid.row_labels.index(loc.name) for col_name, col_ind in col_info: info = location_lat_lon[loc.name][col_name] self.grid.SetCellValue(row_ind, col_ind, str(info)) ### Create Buttons ### hbox_one = wx.BoxSizer(wx.HORIZONTAL) self.helpButton = wx.Button(self.panel, label="Help") self.Bind(wx.EVT_BUTTON, lambda event: self.on_helpButton(event, "ErMagicLocationHelp.html"), self.helpButton) hbox_one.Add(self.helpButton) hboxok = wx.BoxSizer(wx.HORIZONTAL) self.saveButton = wx.Button(self.panel, id=-1, label='Save') self.Bind(wx.EVT_BUTTON, lambda event: self.on_saveButton(event, self.loc_grid), self.saveButton) self.cancelButton = wx.Button(self.panel, wx.ID_CANCEL, '&Cancel') self.Bind(wx.EVT_BUTTON, self.on_cancelButton, self.cancelButton) self.continueButton = wx.Button(self.panel, id=-1, label='Save and continue') self.Bind(wx.EVT_BUTTON, lambda event: self.on_continueButton(event, self.loc_grid, next_dia=self.InitAgeCheck), self.continueButton) self.backButton = wx.Button(self.panel, wx.ID_ANY, "&Back") previous_dia = self.InitSampCheck self.Bind(wx.EVT_BUTTON, lambda event: self.on_backButton(event, previous_dia, current_dia=self.InitLocCheck), self.backButton) hboxok.Add(self.saveButton, flag=wx.RIGHT, border=10) hboxok.Add(self.cancelButton, flag=wx.RIGHT, border=10) hboxok.Add(self.continueButton, flag=wx.RIGHT, border=10) hboxok.Add(self.backButton) # hboxgrid = pw.hbox_grid(self.panel, self.onDeleteRow, 'location', self.grid) self.deleteRowButton = hboxgrid.deleteRowButton self.Bind(wx.grid.EVT_GRID_LABEL_LEFT_CLICK, self.onLeftClickLabel, self.grid) ### Make Containers ### vbox = wx.BoxSizer(wx.VERTICAL) vbox.Add(label, flag=wx.ALIGN_CENTER|wx.TOP|wx.BOTTOM, border=20) vbox.Add(hbox_one, flag=wx.BOTTOM|wx.ALIGN_LEFT, border=10) vbox.Add(hboxok, flag=wx.BOTTOM|wx.ALIGN_LEFT, border=10) vbox.Add(hboxgrid, flag=wx.BOTTOM|wx.ALIGN_LEFT, border=10) vbox.Add(self.loc_grid, flag=wx.TOP|wx.BOTTOM, border=10) vbox.AddSpacer(20) self.hbox_all = wx.BoxSizer(wx.HORIZONTAL) self.hbox_all.AddSpacer(20) self.hbox_all.Add(vbox) self.hbox_all.AddSpacer(20) self.panel.SetSizer(self.hbox_all) #if sys.platform in ['win32', 'win64']: # self.panel.SetScrollbars(20, 20, 50, 50) self.hbox_all.Fit(self) self.Centre() self.Show() self.Hide() self.Show() def InitAgeCheck(self): """make an interactive grid in which users can edit ages""" self.panel = wx.Panel(self, style=wx.SIMPLE_BORDER) text = """Step 6: Fill in or correct any cells with information about ages. The column for magic_method_codes can take multiple values in the form of a colon-delimited list. You may use the drop-down menus to add as many values as needed in these columns. (See Help button for details) **Denotes controlled vocabulary """ label = wx.StaticText(self.panel, label=text) self.items = self.er_magic_data.data_lists[self.er_magic_data.age_type][0] self.grid_builder = grid_frame2.GridBuilder(self.er_magic_data, 'age', self.er_magic_data.headers, self.panel, 'location') self.age_grid = self.grid_builder.make_grid(incl_pmag=False) self.age_grid.InitUI() self.grid_builder.add_data_to_grid(self.age_grid, 'age', incl_pmag=False) self.grid_builder.add_age_data_to_grid() self.grid = self.age_grid # # make it impossible to edit the 1st and 3rd columns for row in range(self.age_grid.GetNumberRows()): for col in (0, 2): self.age_grid.SetReadOnly(row, col, True) # initialize all needed drop-down menus self.drop_down_menu = drop_down_menus.Menus("age", self, self.age_grid, None) # re-set first column name self.age_grid.SetColLabelValue(0, 'er_site_name') ### Create Buttons ### hbox_one = wx.BoxSizer(wx.HORIZONTAL) self.helpButton = wx.Button(self.panel, label="Help") self.Bind(wx.EVT_BUTTON, lambda event: self.on_helpButton(event, "ErMagicAgeHelp.html"), self.helpButton) hbox_one.Add(self.helpButton) hboxok = wx.BoxSizer(wx.HORIZONTAL) self.saveButton = wx.Button(self.panel, id=-1, label='Save') self.Bind(wx.EVT_BUTTON, lambda event: self.on_saveButton(event, self.age_grid), self.saveButton) self.cancelButton = wx.Button(self.panel, wx.ID_CANCEL, '&Cancel') self.Bind(wx.EVT_BUTTON, self.on_cancelButton, self.cancelButton) self.continueButton = wx.Button(self.panel, id=-1, label='Save and continue') self.Bind(wx.EVT_BUTTON, lambda event: self.on_continueButton(event, self.age_grid, next_dia=None), self.continueButton) self.backButton = wx.Button(self.panel, wx.ID_ANY, "&Back") previous_dia = self.InitLocCheck self.Bind(wx.EVT_BUTTON, lambda event: self.on_backButton(event, previous_dia), self.backButton) self.panel.Bind(wx.grid.EVT_GRID_LABEL_LEFT_CLICK, self.onLeftClickLabel, self.grid) hboxok.Add(self.saveButton, flag=wx.RIGHT, border=10) hboxok.Add(self.cancelButton, flag=wx.RIGHT, border=10) hboxok.Add(self.continueButton, flag=wx.RIGHT, border=10) hboxok.Add(self.backButton) ### Make Containers ### vbox = wx.BoxSizer(wx.VERTICAL) vbox.Add(label, flag=wx.ALIGN_CENTER|wx.TOP|wx.BOTTOM, border=20)#, flag=wx.ALIGN_LEFT|wx.BOTTOM, border=20) vbox.Add(hbox_one, flag=wx.BOTTOM, border=10) vbox.Add(hboxok, flag=wx.BOTTOM, border=10) vbox.Add(self.age_grid, flag=wx.TOP|wx.BOTTOM, border=10) # EXPAND ?? vbox.AddSpacer(20) self.hbox_all = wx.BoxSizer(wx.HORIZONTAL) self.hbox_all.AddSpacer(20) self.hbox_all.Add(vbox) self.hbox_all.AddSpacer(20) self.panel.SetSizer(self.hbox_all) #if sys.platform in ['win32', 'win64']: # self.panel.SetScrollbars(20, 20, 50, 50) self.hbox_all.Fit(self) self.Centre() self.Show() self.Hide() self.Show() ### Grid methods ### def make_simple_table(self, column_labels, data_dict, grid_name): row_labels = sorted(data_dict.keys()) if len(row_labels) in range(1, 4): num_rows = len(row_labels) height = {1: 70, 2: 90, 3: 110, 4: 130} grid = magic_grid.MagicGrid(self.panel, grid_name, row_labels, column_labels, (-1, height[num_rows])) # autosizes width, but enforces fixed pxl height to prevent display problems else: grid = magic_grid.MagicGrid(self.panel, grid_name, row_labels, column_labels) data = grid.InitUI() if grid_name == 'ages': temp_data_key = 'ages' else: temp_data_key = column_labels[0] self.temp_data[temp_data_key] = data grid.add_data(data_dict) grid.size_grid() grid.do_event_bindings() return grid def onMouseOver(self, event, grid): """ Displays a tooltip over any cell in a certain column """ x, y = grid.CalcUnscrolledPosition(event.GetX(), event.GetY()) coords = grid.XYToCell(x, y) col = coords[1] row = coords[0] # creates tooltip message for cells with long values # note: this works with EPD for windows, and modern wxPython, but not with Canopy Python msg = grid.GetCellValue(row, col) if len(msg) > 15: event.GetEventObject().SetToolTipString(msg) else: event.GetEventObject().SetToolTipString('') def validate(self, grid): validations = ['er_specimen_name', 'er_sample_name', 'er_site_name', 'er_location_name', 'site_class', 'site_lithology', 'site_type', 'site_definition', 'site_lon', 'site_lat', 'sample_class', 'sample_lithology', 'sample_type', 'sample_lat', 'sample_lon', 'location_type', 'age_unit', 'age']#, 'magic_method_codes'] cols = list(range(grid.GetNumberCols())) rows = list(range(grid.GetNumberRows())) data_missing = [] for col in cols: col_label = str(grid.GetColLabelValue(col)) if col_label in validations: for row in rows: value = grid.GetCellValue(row, col) if not value: data_missing.append(col_label) break return data_missing ### Button methods ### def on_addSampleButton(self, event): def add_sample(sample, site): add_sample_data(sample, site) sites = self.er_magic_data.make_name_list(self.er_magic_data.sites) pw.AddItem(self, 'Sample', add_sample, owner_items=sites, belongs_to='site') # makes window for adding new data def add_sample_data(sample, site): # add sample self.er_magic_data.add_sample(sample, site) # re-Bind so that the updated samples list shows up on a left click samples = sorted(self.er_magic_data.make_name_list(self.er_magic_data.samples)) choices = self.drop_down_menu.choices choices[1] = (samples, False) self.drop_down_menu.update_drop_down_menu(self.spec_grid, choices) def on_addSiteButton(self, event): def add_site(site, location): add_site_data(site, location) locations = self.er_magic_data.make_name_list(self.er_magic_data.locations) pw.AddItem(self, 'Site', add_site, locations, 'location') def add_site_data(site, location): # add site self.er_magic_data.add_site(site, location) # re-Bind so that the updated sites list shows up on a left click sites = sorted(self.er_magic_data.make_name_list(self.er_magic_data.sites)) self.drop_down_menu.update_drop_down_menu(self.samp_grid, {1: (sites, False)}) def on_addLocButton(self, event): def add_loc(loc, parent=None): add_loc_data(loc) #def __init__(self, parent, title, data_items, data_method): if not self.er_magic_data.locations: pass pw.AddItem(self, 'Location', add_loc, owner_items=None, belongs_to=None) # makes window for adding new data def add_loc_data(loc): # add location self.er_magic_data.add_location(loc) # re-Bind so that the updated locations list shows up on a left click locations = self.er_magic_data.make_name_list(self.er_magic_data.locations) choices = self.drop_down_menu.choices choices[1] = (locations, False) self.drop_down_menu.update_drop_down_menu(self.site_grid, choices) def on_helpButton(self, event, page=None): """shows html help page""" # for use on the command line: path = find_pmag_dir.get_pmag_dir() # for use with pyinstaller #path = self.main_frame.resource_dir help_page = os.path.join(path, 'dialogs', 'help_files', page) # if using with py2app, the directory structure is flat, # so check to see where the resource actually is if not os.path.exists(help_page): help_page = os.path.join(path, 'help_files', page) html_frame = pw.HtmlFrame(self, page=help_page) html_frame.Show() def on_continueButton(self, event, grid, next_dia=None): """ pulls up next dialog, if there is one. gets any updated information from the current grid and runs ErMagicBuilder """ #wait = wx.BusyInfo("Please wait, working...") # unhighlight selected columns, etc. if self.drop_down_menu: self.drop_down_menu.clean_up() # remove '**' from col names #self.remove_starred_labels(grid) grid.remove_starred_labels() grid.SaveEditControlValue() # locks in value in cell currently edited grid_name = str(grid.GetName()) # check that all required data are present validation_errors = self.validate(grid) if validation_errors: result = pw.warning_with_override("You are missing required data in these columns: {}\nAre you sure you want to continue without these data?".format(', '.join(validation_errors))) if result == wx.ID_YES: pass else: return False if grid.changes: self.onSave(grid) self.deleteRowButton = None #self.panel.Destroy() # calling Destroy here breaks with Anaconda Python (segfault) # make sure that specimens get propagated with # any default sample info if next_dia == self.InitLocCheck: if self.er_magic_data.specimens: for spec in self.er_magic_data.specimens: spec.propagate_data() if next_dia: wait = wx.BusyInfo("Please wait, working...") wx.SafeYield() wx.CallAfter(self.panel.Destroy) # no segfault here! next_dia() # need to wait to process the resize: event = wx.PyCommandEvent(wx.EVT_SIZE.typeId, self.GetId()) wx.CallAfter(self.GetEventHandler().ProcessEvent, event) del wait else: wait = wx.BusyInfo("Please wait, writing data to files...") wx.SafeYield() # actually write data: self.er_magic_data.write_files() self.Destroy() del wait def on_saveButton(self, event, grid): """saves any editing of the grid but does not continue to the next window""" wait = wx.BusyInfo("Please wait, working...") wx.SafeYield() if self.drop_down_menu: # unhighlight selected columns, etc. self.drop_down_menu.clean_up() # remove '**' from col labels starred_cols = grid.remove_starred_labels() grid.SaveEditControlValue() # locks in value in cell currently edited grid.HideCellEditControl() # removes focus from cell that was being edited if grid.changes: self.onSave(grid) for col in starred_cols: label = grid.GetColLabelValue(col) grid.SetColLabelValue(col, label + '**') del wait def on_cancelButton(self, event): dlg = pw.YesNoCancelDialog(self, "Your changes so far have not been written to file.\nSave changes?", "Not so fast") res = dlg.ShowModal() dlg.Destroy() if res == wx.ID_YES: self.onSave(self.grid) self.er_magic_data.write_files() self.Destroy() if res == wx.ID_NO: self.Destroy() if res == wx.ID_CANCEL: pass def on_backButton(self, event, previous_dia, current_dia=None): wait = wx.BusyInfo("Please wait, working...") wx.SafeYield() if current_dia == self.InitLocCheck: pass elif previous_dia == self.InitSpecCheck or previous_dia == self.InitSampCheck: self.sample_window = 0 self.panel.Destroy() previous_dia() del wait def onDeleteRow(self, event, data_type): """ On button click, remove relevant object from both the data model and the grid. """ ancestry = self.er_magic_data.ancestry child_type = ancestry[ancestry.index(data_type) - 1] names = [self.grid.GetCellValue(row, 0) for row in self.selected_rows] if data_type == 'site': how_to_fix = 'Make sure to select a new site for each orphaned sample in the next step' else: how_to_fix = 'Go back a step and select a new {} for each orphaned {}'.format(data_type, child_type) orphans = [] for name in names: row = self.grid.row_labels.index(name) orphan = self.er_magic_data.delete_methods[data_type](name) if orphan: orphans.extend(orphan) self.grid.remove_row(row) if orphans: orphan_names = self.er_magic_data.make_name_list(orphans) pw.simple_warning('You have deleted:\n\n {}\n\nthe parent(s) of {}(s):\n\n {}\n\n{}'.format(', '.join(names), child_type, ', '.join(orphan_names), how_to_fix)) self.selected_rows = set() # update grid and data model self.update_grid(self.grid)#, grids[grid_name]) self.grid.Refresh() def onLeftClickLabel(self, event): """ When user clicks on a grid label, determine if it is a row label or a col label. Pass along the event to the appropriate function. (It will either highlight a column for editing all values, or highlight a row for deletion). """ if event.Col == -1 and event.Row == -1: pass elif event.Col < 0: self.onSelectRow(event) elif event.Row < 0: self.drop_down_menu.on_label_click(event) def onSelectRow(self, event): """ Highlight or unhighlight a row for possible deletion. """ grid = self.grid row = event.Row default = (255, 255, 255, 255) highlight = (191, 216, 216, 255) cell_color = grid.GetCellBackgroundColour(row, 0) attr = wx.grid.GridCellAttr() if cell_color == default: attr.SetBackgroundColour(highlight) self.selected_rows.add(row) else: attr.SetBackgroundColour(default) try: self.selected_rows.remove(row) except KeyError: pass if self.selected_rows and self.deleteRowButton: self.deleteRowButton.Enable() else: self.deleteRowButton.Disable() grid.SetRowAttr(row, attr) grid.Refresh() ### Manage data methods ### def update_grid(self, grid): """ takes in wxPython grid and ErMagic data object to be updated """ data_methods = {'specimen': self.er_magic_data.change_specimen, 'sample': self.er_magic_data.change_sample, 'site': self.er_magic_data.change_site, 'location': self.er_magic_data.change_location, 'age': self.er_magic_data.change_age} grid_name = str(grid.GetName()) cols = list(range(grid.GetNumberCols())) col_labels = [] for col in cols: col_labels.append(grid.GetColLabelValue(col)) for row in grid.changes: # go through changes and update data structures if row == -1: continue else: data_dict = {} for num, label in enumerate(col_labels): if label: data_dict[str(label)] = str(grid.GetCellValue(row, num)) new_name = str(grid.GetCellValue(row, 0)) old_name = self.temp_data[grid_name][row] data_methods[grid_name](new_name, old_name, data_dict) grid.changes = False def onSave(self, grid):#, age_data_type='site'): """ Save grid data in the data object """ # deselect column, including remove 'EDIT ALL' label if self.drop_down_menu: self.drop_down_menu.clean_up() # save all changes to er_magic data object self.grid_builder.save_grid_data() # don't actually write data in this step (time-consuming) # instead, write to files when user is done editing #self.er_magic_data.write_files() wx.MessageBox('Saved!', 'Info', style=wx.OK | wx.ICON_INFORMATION)

bsd-3-clause

kevinarpe/kevinarpe-rambutan3

tests/check_args/other/test_RRangeSizeStr.py

1

1887

import pytest from rambutan3.check_args.RCheckArgsError import RCheckArgsError from rambutan3.check_args.other.RRangeSizeStr import RRangeSizeStr from tests.check_args.collection import test_RRangeSizeMatcher def test_ctor(): test_RRangeSizeMatcher.core_test_ctor(RRangeSizeStr) def test_check_arg(): with pytest.raises(RCheckArgsError): __check_arg([123], min_size=1) with pytest.raises(RCheckArgsError): __check_arg([123], max_size=1) with pytest.raises(RCheckArgsError): __check_arg([123], min_size=1, max_size=2) with pytest.raises(RCheckArgsError): __check_arg(None, min_size=1) with pytest.raises(RCheckArgsError): __check_arg(123, min_size=1) __check_arg('abc', min_size=1) with pytest.raises(RCheckArgsError): __check_arg('abc', min_size=4) __check_arg('abc', max_size=3) with pytest.raises(RCheckArgsError): __check_arg('abc', max_size=2) with pytest.raises(RCheckArgsError): __check_arg('', min_size=1, max_size=3) __check_arg('a', min_size=1, max_size=3) __check_arg('ab', min_size=1, max_size=3) __check_arg('abc', min_size=1, max_size=3) with pytest.raises(RCheckArgsError): __check_arg('abcd', min_size=1, max_size=3) def __check_arg(value, *, min_size: int=-1, max_size: int=-1): m = RRangeSizeStr(min_size=min_size, max_size=max_size) assert value is m.check_arg(value, 'dummy_arg_name') def test__eq__and__ne__(): test_RRangeSizeMatcher.core_test__eq__and__ne__(RRangeSizeStr) def test__hash__(): test_RRangeSizeMatcher.core_test__hash__(RRangeSizeStr) def test__str__(): assert str(RRangeSizeStr(min_size=1)) == 'str where size >= 1' assert str(RRangeSizeStr(max_size=1)) == 'str where size <= 1' assert str(RRangeSizeStr(min_size=1, max_size=2)) == 'str where size >= 1 and size <= 2'

gpl-3.0

nohona/cron-crm

usr/local/certbot/certbot/tests/errors_test.py

4

1328

"""Tests for certbot.errors.""" import unittest import mock from acme import messages from certbot import achallenges from certbot.tests import acme_util class FailedChallengesTest(unittest.TestCase): """Tests for certbot.errors.FailedChallenges.""" def setUp(self): from certbot.errors import FailedChallenges self.error = FailedChallenges(set([achallenges.DNS( domain="example.com", challb=messages.ChallengeBody( chall=acme_util.DNS01, uri=None, error=messages.Error(typ="tls", detail="detail")))])) def test_str(self): self.assertTrue(str(self.error).startswith( "Failed authorization procedure. example.com (dns-01): tls")) class StandaloneBindErrorTest(unittest.TestCase): """Tests for certbot.errors.StandaloneBindError.""" def setUp(self): from certbot.errors import StandaloneBindError self.error = StandaloneBindError(mock.sentinel.error, 1234) def test_instance_args(self): self.assertEqual(mock.sentinel.error, self.error.socket_error) self.assertEqual(1234, self.error.port) def test_str(self): self.assertTrue(str(self.error).startswith( "Problem binding to port 1234: ")) if __name__ == "__main__": unittest.main() # pragma: no cover

gpl-3.0

project-hypr/hypr2

tests/providers/crud/test_crud_crud.py

1

12472

# Copyright 2014-2016 Morgan Delahaye-Prat. All Rights Reserved. # # Licensed under the Simplified BSD License (the "License"); # you may not use this file except in compliance with the License. """Test basic CRUD operations of the CRUDProvider.""" import json import pytest from hypr.providers import CRUDProvider def deserialize(data, model): """Deserialize JSON data.""" data = json.loads(data) if 'content' in data and 'count' in data: return data['count'], [model.load(r) for r in data['content']] return model.load(data) @pytest.fixture def app(app, model): """All the tests are conducted with application/json as default mime.""" provider = type('IntestProvider', (CRUDProvider,), {'__model__': model}) app.add_provider(provider, '/test', '/test/<int:id>') return app class TestModelCreate: """Test create.""" models = 'SQLiteModel', def test_create(self, app, model): """Create one resource.""" payload = json.dumps({'value': 'foo'}) with app.test_client() as client: rv = client.post('/test', data=payload) assert rv.status == 201 data = deserialize(rv.text, model) assert data == model.one(data.id) def test_bulk_create(self, app, model): """Create multiple resources at once.""" payload = json.dumps([ {'value': 'foo'}, {'value': 'bar'} ]) with app.test_client() as client: rv = client.post('/test?_bulk=1', data=payload) assert rv.status == 201 count, resources = deserialize(rv.text, model) for resource in resources: assert resource == model.one(resource.id) @pytest.mark.populate(5) class TestProviderRead: """Test read.""" models = 'SQLiteModel', def test_get_collection(self, app, model): """Test.""" with app.test_client() as client: rv = client.get('/test') assert rv.status == 200 count, resources = deserialize(rv.text, model) assert count == model.count() == 5 assert sorted(resources) == sorted(model.get()) def test_get_one(self, app, model): """Test.""" with app.test_client() as client: rv = client.get('/test/1') assert rv.status == 200 resource = deserialize(rv.text, model) assert resource == model.one(1) @pytest.mark.populate(5) class TestModelUpdate: """Test update.""" models = 'SQLiteModel', def test_update(self, app, model): """Update an instance with PATCH.""" ref = model.one(1) payload = json.dumps({'value': 'test_ok'}) with app.test_client() as client: rv = client.patch('/test/1', data=payload) assert rv.status == 200 resource = deserialize(rv.text, model) assert resource != ref assert resource == model.one(1) def test_update_alt(self, app, model): """Update an instance with PUT.""" ref = model.one(2) payload = json.dumps({'value': 'test_ok'}) with app.test_client() as client: rv = client.put('/test/2', data=payload) assert rv.status == 200 resource = deserialize(rv.text, model) assert resource != ref assert resource == model.one(2) def test_bulk_update(self, app, model): """Update multiple resources at once.""" ref = [model.one(3), model.one(4)] payload = json.dumps([ {'id': 3, 'value': 'test_ok0'}, {'id': 4, 'value': 'test_ok1'} ]) with app.test_client() as client: rv = client.put('/test?_bulk=1', data=payload) assert rv.status == 200 count, data = deserialize(rv.text, model) for instance in ref: assert instance != model.one(instance.id) for resource in data: assert resource == model.one(resource.id) @pytest.mark.populate(5) class TestModelDelete: """Test delete.""" models = 'SQLiteModel', def test_delete(self, app, model): """Delete a resource.""" with app.test_client() as client: rv = client.delete('/test/1') assert rv.status == 204 assert model.one(1) is None def test_bulk_delete(self, app, model): """Delete multiple resources at once.""" ref = [model.one(3), model.one(4)] payload = json.dumps([ {'id': 3}, {'id': 4} ]) with app.test_client() as client: rv = client.delete('/test?_bulk=1', data=payload) assert rv.status == 204 for instance in ref: assert model.one(instance.id) is None @pytest.mark.populate(5) class TestMissingPayloadException: """Test requests with missing payload.""" models = 'SQLiteModel', def test_create(self, app, model): """Create one resource.""" with app.test_client() as client: rv = client.post('/test') assert rv.status == 400 def test_bulk_create(self, app, model): """Create multiple resources at once.""" with app.test_client() as client: rv = client.post('/test?_bulk=1') assert rv.status == 400 def test_update(self, app, model): """Update an instance.""" with app.test_client() as client: rv = client.patch('/test/1') assert rv.status == 400 def test_bulk_update(self, app, model): """Update multiple resources at once.""" with app.test_client() as client: rv = client.put('/test?_bulk=1') assert rv.status == 400 def test_bulk_delete(self, app, model): """Delete multiple resources at once.""" with app.test_client() as client: rv = client.delete('/test?_bulk=1') assert rv.status == 400 @pytest.mark.populate(5) class TestInvalidPayloadException: """Test requests with invalid payload.""" models = 'SQLiteModel', def test_create(self, app): """Create one resource.""" payload = json.dumps({'invalid': 'property'}) with app.test_client() as client: rv = client.post('/test', data=payload) assert rv.status == 400 def test_update(self, app, model): """Update one resource.""" ref = model.one(1) payload = json.dumps({'invalid': 'property'}) with app.test_client() as client: rv = client.patch('/test/1', data=payload) assert rv.status == 400 assert ref == model.one(1) @pytest.mark.populate(5) class TestInvalidBulkRequest: """Test invalid bulk requests.""" models = 'SQLiteModel', def test_bulk_create_missing_flag(self, app, model): """A missing bulk flag returns an error 400.""" payload = json.dumps([ {'value': 'foo'}, {'value': 'bar'} ]) with app.test_client() as client: rv = client.post('/test', data=payload) assert rv.status == 400 assert model.count() == 5 def test_bulk_update_missing_flag(self, app, model): """Update multiple resources at once.""" ref = model.get() payload = json.dumps([ {'id': 3, 'value': 'test_ok0'}, {'id': 4, 'value': 'test_ok1'} ]) with app.test_client() as client: rv = client.put('/test', data=payload) assert rv.status == 400 assert sorted(ref) == sorted(model.get()) def test_bulk_delete_missing_flag(self, app, model): """Delete multiple resources at once.""" ref = model.get() payload = json.dumps([ {'id': 3}, {'id': 4} ]) with app.test_client() as client: rv = client.delete('/test', data=payload) assert rv.status == 400 assert sorted(ref) == sorted(model.get()) def test_bulk_update_on_single_resource(self, app, model): """Update multiple resources at once.""" ref = model.get() payload = json.dumps([ {'id': 3, 'value': 'test_ok0'}, {'id': 4, 'value': 'test_ok1'} ]) with app.test_client() as client: rv = client.put('/test/1?_bulk=1', data=payload) assert rv.status == 400 assert sorted(ref) == sorted(model.get()) def test_bulk_delete_on_single_resource(self, app, model): """Delete multiple resources at once.""" ref = model.get() payload = json.dumps([ {'id': 3}, {'id': 4} ]) with app.test_client() as client: rv = client.delete('/test/1?_bulk=1', data=payload) assert rv.status == 400 assert sorted(ref) == sorted(model.get()) def test_bulk_update_unknown_resource(self, app, model): """Update multiple resources at once.""" ref = model.get() payload = json.dumps([ {'id': 3, 'value': 'test_ok0'}, {'id': 100, 'value': 'test_ok1'} # unkwnown resource ]) with app.test_client() as client: rv = client.put('/test?_bulk=1', data=payload) assert rv.status == 400 assert sorted(ref) == sorted(model.get()) def test_bulk_delete_unknown_resource(self, app, model): """Delete multiple resources at once.""" ref = model.get() payload = json.dumps([ {'id': 3}, {'id': 100} # unknwon resource ]) with app.test_client() as client: rv = client.delete('/test?_bulk=1', data=payload) assert rv.status == 400 assert sorted(ref) == sorted(model.get()) def test_bulk_create_invalid_property(self, app, model): """Create multiple resources at once.""" payload = json.dumps([ {'value': 'foo'}, {'invalid': 'property'} ]) with app.test_client() as client: rv = client.post('/test?_bulk=1', data=payload) assert rv.status == 400 assert model.count() == 5 def test_bulk_update_invalid_property(self, app, model): """Update multiple resources at once.""" ref = model.get() payload = json.dumps([ {'id': 3, 'value': 'test_ok0'}, {'id': 4, 'invalid': 'property'} ]) with app.test_client() as client: rv = client.put('/test?_bulk=1', data=payload) assert rv.status == 400 assert sorted(ref) == sorted(model.get()) def test_bulk_update_missing_id(self, app, model): """Update multiple resources at once.""" ref = model.get() payload = json.dumps([ {'id': 3, 'value': 'test_ok0'}, {'value': 'test_ok1'} # missing id ]) with app.test_client() as client: rv = client.put('/test?_bulk=1', data=payload) assert rv.status == 400 assert sorted(ref) == sorted(model.get()) def test_bulk_delete_missing_id(self, app, model): """Delete multiple resources at once.""" ref = model.get() payload = json.dumps([ {'id': 3}, {} # missing id ]) with app.test_client() as client: rv = client.delete('/test?_bulk=1', data=payload) assert rv.status == 400 assert sorted(ref) == sorted(model.get()) class TestEmptySet: """Crud operations (except create) on an empty database.""" models = 'SQLiteModel', def test_get_collection(self, app, model): """Get an empty set.""" with app.test_client() as client: rv = client.get('/test') assert rv.status == 200 count, resources = deserialize(rv.text, model) assert count == 0 assert resources == [] def test_get_one(self, app, model): """Get an unknown resource.""" with app.test_client() as client: rv = client.get('/test/1') assert rv.status == 404 def test_update(self, app, model): """Update an unknown resource.""" payload = json.dumps({'value': 'test_ok'}) with app.test_client() as client: rv = client.patch('/test/1', data=payload) assert rv.status == 404 def test_delete(self, app, model): """Delete an unknown resource.""" with app.test_client() as client: rv = client.delete('/test/1') assert rv.status == 404

bsd-2-clause

sergei-maertens/django-systemjs

docs/_ext/djangodocs.py

1

2159

""" Taken from djangoproject/django docs. Sphinx plugins for Django documentation. """ import re from sphinx import addnodes from sphinx.util.compat import Directive from sphinx.writers.html import SmartyPantsHTMLTranslator # RE for option descriptions without a '--' prefix simple_option_desc_re = re.compile( r'([-_a-zA-Z0-9]+)(\s*.*?)(?=,\s+(?:/|-|--)|$)') def setup(app): app.add_directive('versionadded', VersionDirective) app.add_directive('versionchanged', VersionDirective) app.set_translator('djangohtml', DjangoHTMLTranslator) return {'parallel_read_safe': True} class VersionDirective(Directive): has_content = True required_arguments = 1 optional_arguments = 1 final_argument_whitespace = True option_spec = {} def run(self): if len(self.arguments) > 1: msg = """Only one argument accepted for directive '{directive_name}::'. Comments should be provided as content, not as an extra argument.""".format(directive_name=self.name) raise self.error(msg) env = self.state.document.settings.env ret = [] node = addnodes.versionmodified() ret.append(node) node['version'] = self.arguments[0] node['type'] = self.name if self.content: self.state.nested_parse(self.content, self.content_offset, node) env.note_versionchange(node['type'], node['version'], node, self.lineno) return ret class DjangoHTMLTranslator(SmartyPantsHTMLTranslator): version_text = { 'versionchanged': 'Changed in %s', 'versionadded': 'Added in %s', } def visit_versionmodified(self, node): self.body.append( self.starttag(node, 'div', CLASS=node['type']) ) version_text = self.version_text.get(node['type']) if version_text: title = "%s%s" % ( version_text % node['version'], ":" if len(node) else "." ) self.body.append('<span class="title">%s</span> ' % title) def depart_versionmodified(self, node): self.body.append("</div>\n")

mit

dmach/dnf

dnf/cli/commands/shell.py

1

9173

# shell.py # Shell CLI command. # # Copyright (C) 2016 Red Hat, Inc. # # This copyrighted material is made available to anyone wishing to use, # modify, copy, or redistribute it subject to the terms and conditions of # the GNU General Public License v.2, or (at your option) any later version. # This program is distributed in the hope that it will be useful, but WITHOUT # ANY WARRANTY expressed or implied, including the implied warranties of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General # Public License for more details. You should have received a copy of the # GNU General Public License along with this program; if not, write to the # Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA # 02110-1301, USA. Any Red Hat trademarks that are incorporated in the # source code or documentation are not subject to the GNU General Public # License and may only be used or replicated with the express permission of # Red Hat, Inc. # from dnf.cli import commands from dnf.i18n import _ import cmd import copy import dnf import logging import shlex import sys logger = logging.getLogger('dnf') # only demands we'd like to override class ShellDemandSheet(object): available_repos = True resolving = True root_user = True sack_activation = True class ShellCommand(commands.Command, cmd.Cmd): aliases = ('shell', 'sh') summary = _('run an interactive DNF shell') MAPPING = {'repo': 'repo', 'repository': 'repo', 'exit': 'quit', 'quit': 'quit', 'run': 'ts_run', 'ts': 'transaction', 'transaction': 'transaction', 'config': 'config', 'resolvedep': 'resolve', 'help': 'help' } def __init__(self, cli): commands.Command.__init__(self, cli) cmd.Cmd.__init__(self) self.prompt = '> ' @staticmethod def set_argparser(parser): parser.add_argument('script', nargs='?', metavar=_('SCRIPT'), help=_('Script to run in DNF shell')) def configure(self): # append to ShellDemandSheet missing demands from # dnf.cli.demand.DemandSheet with their default values. default_demands = self.cli.demands self.cli.demands = ShellDemandSheet() for attr in dir(default_demands): if attr.startswith('__'): continue try: getattr(self.cli.demands, attr) except AttributeError: setattr(self.cli.demands, attr, getattr(default_demands, attr)) def run(self): if self.opts.script: self._run_script(self.opts.script) else: self.cmdloop() def _clean(self): self.base._finalize_base() self.base._transaction = None self.base.fill_sack() def onecmd(self, line): if not line or line == '\n': return if line == 'EOF': line = 'quit' try: s_line = shlex.split(line) except: self._help() return opts = self.cli.optparser.parse_main_args(s_line) # Disable shell recursion. if opts.command == 'shell': return if opts.command in self.MAPPING: getattr(self, '_' + self.MAPPING[opts.command])(s_line[1::]) else: cmd_cls = self.cli.cli_commands.get(opts.command) if cmd_cls is not None: cmd = cmd_cls(self.cli) try: opts = self.cli.optparser.parse_command_args(cmd, s_line) cmd.cli.demands = copy.deepcopy(self.cli.demands) cmd.configure() cmd.run() except dnf.exceptions.Error as e: logger.error(_("Error:") + " " + e.value) except: return else: self._help() def _config(self, args=None): def print_or_set(key, val, conf): if val: setattr(conf, key, val) else: try: print('{}: {}'.format(key, getattr(conf, str(key)))) except: logger.warning(_('Unsupported key value.')) if not args or len(args) > 2: self._help('config') return key = args[0] val = args[1] if len(args) == 2 else None period = key.find('.') if period != -1: repo_name = key[:period] key = key[period+1:] repos = self.base.repos.get_matching(repo_name) for repo in repos: print_or_set(key, val, repo) if not repos: logger.warning(_('Could not find repository: %s'), repo_name) else: print_or_set(key, val, self.base.conf) def _help(self, args=None): """Output help information. :param args: the command to output help information about. If *args* is an empty, general help will be output. """ arg = args[0] if isinstance(args, list) and len(args) > 0 else args msg = None if arg: if arg == 'config': msg = _("""{} arg [value] arg: debuglevel, errorlevel, obsoletes, gpgcheck, assumeyes, exclude, repo_id.gpgcheck, repo_id.exclude If no value is given it prints the current value. If value is given it sets that value.""").format(arg) elif arg == 'help': msg = _("""{} [command] print help""").format(arg) elif arg in ['repo', 'repository']: msg = _("""{} arg [option] list: lists repositories and their status. option = [all | id | glob] enable: enable repositories. option = repository id disable: disable repositories. option = repository id""").format(arg) elif arg == 'resolvedep': msg = _("""{} resolve the transaction set""").format(arg) elif arg in ['transaction', 'ts']: msg = _("""{} arg list: lists the contents of the transaction reset: reset (zero-out) the transaction run: run the transaction""").format(arg) elif arg == 'run': msg = _("""{} run the transaction""").format(arg) elif arg in ['exit', 'quit']: msg = _("""{} exit the shell""").format(arg) if not msg: self.cli.optparser.print_help() msg = _("""Shell specific arguments: config set config options help print help repository (or repo) enable, disable or list repositories resolvedep resolve the transaction set transaction (or ts) list, reset or run the transaction set run resolve and run the transaction set exit (or quit) exit the shell""") print('\n' + msg) def _repo(self, args=None): cmd = args[0] if args else None if cmd in ['list', None]: self.onecmd('repolist ' + ' '.join(args[1:])) elif cmd in ['enable', 'disable']: repos = self.cli.base.repos fill_sack = False for repo in args[1::]: r = repos.get_matching(repo) if r: getattr(r, cmd)() fill_sack = True else: logger.critical(_("Error:") + " " + _("Unknown repo: '%s'"), self.base.output.term.bold(repo)) if fill_sack: self.base.fill_sack() else: self._help('repo') def _resolve(self, args=None): if self.cli.base.transaction is None: try: self.cli.base.resolve(self.cli.demands.allow_erasing) except dnf.exceptions.DepsolveError as e: print(e) def _run_script(self, file): try: with open(file, 'r') as fd: lines = fd.readlines() for line in lines: if not line.startswith('#'): self.onecmd(line) except IOError: logger.info(_('Error: Cannot open %s for reading'), self.base.output.term.bold(file)) sys.exit(1) def _transaction(self, args=None): cmd = args[0] if args else None if cmd == 'reset': self._clean() return self._resolve() if cmd in ['list', None]: if self.base._transaction: out = self.base.output.list_transaction(self.base._transaction) logger.info(out) elif cmd == 'run': try: self.base.do_transaction() except: pass self._clean() else: self._help('transaction') def _ts_run(self, args=None): self._transaction(['run']) def _quit(self, args=None): logger.info(_('Leaving Shell')) sys.exit(0)

gpl-2.0

matthew-brett/dmg-wheel-installer

make_installer.py

1

6064

#!/usr/bin/env python """ Make dmg installer for Python.org Python from Python wheels """ from __future__ import division, print_function DESCRIP = "Make dmg installer for Python.org Python from Python wheels" EPILOG = \ """Make DMG installer from wheels * Collect source packages for pip, setuptools * Collect needed wheels using "pip wheel" command * Write directory to DMG containing source and wheel packages * Write "postinstall" script to install setuptools, pip, then install wheels * Write "postinstall" script in ".pkg" double click installer * Package result into DMG file. """ import os from os.path import exists, join as pjoin import shutil from subprocess import check_call from argparse import ArgumentParser, RawDescriptionHelpFormatter try: from urllib2 import urlopen, URLError # Python 2 except ImportError: from urllib.request import urlopen, URLError # Python 3 # Defaults PYTHON_VERSION='2.7' # Constants # Installed location of Python.org Python PY_ORG_BASE='/Library/Frameworks/Python.framework/Versions/' # Path for directory that will become the dmg contents DMG_DIR='dmg_root' # Subdirectory containing wheels and source packages PKG_DIR = 'packages' # Package directory within dmg_directory DMG_PKG_DIR = DMG_DIR + '/' + PKG_DIR # get-pip.py URL GET_PIP_URL = 'https://bootstrap.pypa.io/get-pip.py' def rm_mk_dir(dirname): if exists(dirname): shutil.rmtree(dirname) os.makedirs(dirname) def mkdirs(): [rm_mk_dir(pth) for pth in ( DMG_PKG_DIR, 'scripts', 'pkg_template')] def get_pip_params(args): params = '--no-index' if args.no_index else [] for link in args.find_links: params.append('--find-links=' + link) return params def get_pippers(pip_params, get_pip_path=None): pip_cmd = ['pip', 'install', '--download', DMG_PKG_DIR, 'pip', 'setuptools'] + pip_params check_call(pip_cmd) if not get_pip_path is None: shutil.copy2(get_pip_path, DMG_PKG_DIR) return url_obj = urlopen(GET_PIP_URL) with open(DMG_PKG_DIR + '/get-pip.py', 'wt') as fobj: fobj.write(url_obj.read()) def get_wheels(version, requirements, pip_params): pip_exe = '{0}/{1}/bin/pip{1}'.format(PY_ORG_BASE, version, version) if not exists(pip_exe): raise RuntimeError('Need to install pip for python at ' + '{0}/bin/python{1}'.format(PY_ORG_BASE, version)) # Install wheel locally just in case check_call([pip_exe, 'install'] + pip_params + ['wheel']) check_call([pip_exe, 'wheel', '-w', DMG_PKG_DIR] + pip_params + list(requirements)) def write_post(py_version, requirements): to_install = ', '.join(['"{0}"'.format(r) for r in requirements]) with open('scripts/postinstall', 'wt') as fobj: fobj.write( r"""#!/usr/bin/env python # Install into Python.org python import sys import os from os.path import exists, dirname from subprocess import check_call # Find disk image files package_path = os.environ.get('PACKAGE_PATH') if package_path is None: sys.exit(10) package_dir = dirname(package_path) wheelhouse = package_dir + '/{pkg_dir}' # Find Python.org Python python_bin = '{py_org_base}/{py_version}/bin' python_path = python_bin + '/python{py_version}' if not exists(python_path): sys.exit(20) # Install pip check_call([python_path, wheelhouse + '/get-pip.py', '-f', wheelhouse, '--no-setuptools']) # Find pip expected_pip = python_bin + '/pip{py_version}' if not exists(expected_pip): sys.exit(30) pip_cmd = [expected_pip, 'install', '--no-index', '--upgrade', '--find-links', wheelhouse] check_call(pip_cmd + ['setuptools']) check_call(pip_cmd + [{to_install}]) """.format(py_org_base = PY_ORG_BASE, py_version = py_version, to_install = to_install, pkg_dir = PKG_DIR, )) check_call(['chmod', 'a+x', 'scripts/postinstall']) def write_pkg(identifier, version): pkg_fname = pjoin(DMG_DIR, '{0}-{1}.pkg'.format(identifier, version)) check_call(['pkgbuild', '--root', 'pkg_template', '--nopayload', '--scripts', 'scripts', '--identifier', identifier, '--version', version, pkg_fname]) def write_dmg(identifier, py_version, pkg_version): dmg_name = '{0}-py{1}-{2}'.format( identifier, py_version.replace('.', ''), pkg_version) check_call(['hdiutil', 'create', '-srcfolder', DMG_DIR, '-volname', dmg_name, dmg_name + '.dmg']) def main(): parser = ArgumentParser(description=DESCRIP, epilog=EPILOG, formatter_class=RawDescriptionHelpFormatter) parser.add_argument('pkg_name', type=str, help='root name of installer') parser.add_argument('pkg_version', type=str, help='version of installer') parser.add_argument('requirements', type=str, nargs='+', help='pip requirement strings') parser.add_argument('--python-version', type=str, default=PYTHON_VERSION, help='Python version in major.minor format, e.g "3.4"') parser.add_argument('--no-index', action='store_true', help='disable search of pip indices when fetching ' 'packages to make installer') parser.add_argument('--find-links', '-f', type=str, nargs='*', default=[], help='locations to find packages to make installer') parser.add_argument('--get-pip-path', type=str, help='local path to "get-pip.py"') # parse the command line args = parser.parse_args() pip_params = get_pip_params(args) mkdirs() get_pippers(pip_params, args.get_pip_path) get_wheels(args.python_version, args.requirements, pip_params) write_post(args.python_version, args.requirements) write_pkg(args.pkg_name, args.pkg_version) write_dmg(args.pkg_name, args.python_version, args.pkg_version) if __name__ == '__main__': main()

bsd-2-clause

dmccloskey/SBaaS_COBRA

SBaaS_COBRA/stage02_physiology_pairWiseTest_query.py

1

9833

#SBaaS from .stage02_physiology_pairWiseTest_postgresql_models import * from SBaaS_base.sbaas_base import sbaas_base from SBaaS_base.sbaas_base_query_update import sbaas_base_query_update from SBaaS_base.sbaas_base_query_drop import sbaas_base_query_drop from SBaaS_base.sbaas_base_query_initialize import sbaas_base_query_initialize from SBaaS_base.sbaas_base_query_insert import sbaas_base_query_insert from SBaaS_base.sbaas_base_query_select import sbaas_base_query_select from SBaaS_base.sbaas_base_query_delete import sbaas_base_query_delete from SBaaS_base.sbaas_template_query import sbaas_template_query #system from math import log class stage02_physiology_pairWiseTest_query(sbaas_template_query): def initialize_supportedTables(self): '''Set the supported tables dict for stage02_physiology_pairWiseTest ''' tables_supported = {'data_stage02_physiology_pairWiseTest':data_stage02_physiology_pairWiseTest, 'data_stage02_physiology_pairWiseTestMetabolites':data_stage02_physiology_pairWiseTestMetabolites, 'data_stage02_physiology_pairWiseTestSubsystems':data_stage02_physiology_pairWiseTestSubsystems, }; self.set_supportedTables(tables_supported); ## Query from data_stage02_physiology_pairWiseTest# Query data from data_stage02_physiology_pairWiseTest def get_RDataList_simulationIDs_dataStage02PhysiologyPairWiseTest(self,simulation_id_1_I,simulation_id_2_I): """get data from simulation_ids 1 and 2""" #Tested try: data = self.session.query( data_stage02_physiology_pairWiseTest.simulation_id_1, data_stage02_physiology_pairWiseTest.simulation_id_2, data_stage02_physiology_pairWiseTest.rxn_id, data_stage02_physiology_pairWiseTest.test_stat, data_stage02_physiology_pairWiseTest.test_description, data_stage02_physiology_pairWiseTest.pvalue, data_stage02_physiology_pairWiseTest.pvalue_corrected, data_stage02_physiology_pairWiseTest.pvalue_corrected_description, data_stage02_physiology_pairWiseTest.mean, data_stage02_physiology_pairWiseTest.ci_lb, data_stage02_physiology_pairWiseTest.ci_ub, data_stage02_physiology_pairWiseTest.ci_level, data_stage02_physiology_pairWiseTest.fold_change).filter( data_stage02_physiology_pairWiseTest.simulation_id_1.like(simulation_id_1_I), data_stage02_physiology_pairWiseTest.simulation_id_2.like(simulation_id_2_I), data_stage02_physiology_pairWiseTest.used_.is_(True)).group_by( data_stage02_physiology_pairWiseTest.simulation_id_1, data_stage02_physiology_pairWiseTest.simulation_id_2, data_stage02_physiology_pairWiseTest.rxn_id, data_stage02_physiology_pairWiseTest.test_stat, data_stage02_physiology_pairWiseTest.test_description, data_stage02_physiology_pairWiseTest.pvalue, data_stage02_physiology_pairWiseTest.pvalue_corrected, data_stage02_physiology_pairWiseTest.pvalue_corrected_description, data_stage02_physiology_pairWiseTest.mean, data_stage02_physiology_pairWiseTest.ci_lb, data_stage02_physiology_pairWiseTest.ci_ub, data_stage02_physiology_pairWiseTest.ci_level, data_stage02_physiology_pairWiseTest.fold_change).order_by( data_stage02_physiology_pairWiseTest.simulation_id_2.asc(), data_stage02_physiology_pairWiseTest.rxn_id.asc()).all(); data_O = []; for d in data: data_1 = {}; data_1['simulation_id_1'] = d.simulation_id_1; data_1['simulation_id_2'] = d.simulation_id_2; data_1['rxn_id'] = d.rxn_id; data_1['test_stat'] = d.test_stat; data_1['test_description'] = d.test_description; data_1['pvalue_negLog10'] = None; data_1['pvalue_corrected_description'] = None if d.pvalue_corrected: data_1['pvalue_corrected_negLog10'] = -log(d.pvalue_corrected,10); if d.pvalue: data_1['pvalue_negLog10'] = -log(d.pvalue,10); data_1['pvalue_corrected_description'] = d.pvalue_corrected_description; data_1['mean'] = d.mean; data_1['ci_lb'] = d.ci_lb; data_1['ci_ub'] = d.ci_ub; data_1['ci_level'] = d.ci_level; data_1['fold_change'] = d.fold_change; data_O.append(data_1); return data_O; except SQLAlchemyError as e: print(e); def get_rows_analysisID_dataStage02PhysiologyPairWiseTest(self,analysis_id_I): """get data from simulation_ids 1 and 2""" #Tested try: data = self.session.query(data_stage02_physiology_pairWiseTest).filter( data_stage02_physiology_pairWiseTest.analysis_id.like(analysis_id_I), data_stage02_physiology_pairWiseTest.used_.is_(True)).order_by( data_stage02_physiology_pairWiseTest.simulation_id_1.asc(), data_stage02_physiology_pairWiseTest.simulation_id_2.asc(), data_stage02_physiology_pairWiseTest.rxn_id.asc()).all(); data_O = [d.__repr__dict__() for d in data]; for d in data_O: d['pvalue_corrected_negLog10'] = None; d['pvalue_corrected_description'] = None if d['pvalue_corrected']: d['pvalue_corrected_negLog10'] = -log(d['pvalue_corrected'],10); if d['pvalue']: d['pvalue_negLog10'] = -log(d['pvalue'],10); return data_O; except SQLAlchemyError as e: print(e); def get_rows_analysisID_dataStage02PhysiologyPairWiseTestMetabolites(self,analysis_id_I): """get data from simulation_ids 1 and 2""" #Tested try: data = self.session.query(data_stage02_physiology_pairWiseTestMetabolites).filter( data_stage02_physiology_pairWiseTestMetabolites.analysis_id.like(analysis_id_I), data_stage02_physiology_pairWiseTestMetabolites.used_.is_(True)).order_by( data_stage02_physiology_pairWiseTestMetabolites.simulation_id_1.asc(), data_stage02_physiology_pairWiseTestMetabolites.simulation_id_2.asc(), data_stage02_physiology_pairWiseTestMetabolites.met_id.asc()).all(); data_O = [d.__repr__dict__() for d in data]; for d in data_O: d['pvalue_corrected_negLog10'] = None; d['pvalue_corrected_description'] = None if d['pvalue_corrected']: d['pvalue_corrected_negLog10'] = -log(d['pvalue_corrected'],10); if d['pvalue']: d['pvalue_negLog10'] = -log(d['pvalue'],10); return data_O; except SQLAlchemyError as e: print(e); def get_rows_analysisID_dataStage02PhysiologyPairWiseTestSubsystems(self,analysis_id_I): """get data from simulation_ids 1 and 2""" #Tested try: data = self.session.query(data_stage02_physiology_pairWiseTestSubsystems).filter( data_stage02_physiology_pairWiseTestSubsystems.analysis_id.like(analysis_id_I), data_stage02_physiology_pairWiseTestSubsystems.used_.is_(True)).order_by( data_stage02_physiology_pairWiseTestSubsystems.simulation_id_1.asc(), data_stage02_physiology_pairWiseTestSubsystems.simulation_id_2.asc(), data_stage02_physiology_pairWiseTestSubsystems.subsystem_id.asc()).all(); data_O = [d.__repr__dict__() for d in data]; for d in data_O: d['pvalue_corrected_negLog10'] = None; d['pvalue_corrected_description'] = None if d['pvalue_corrected']: d['pvalue_corrected_negLog10'] = -log(d['pvalue_corrected'],10); if d['pvalue']: d['pvalue_negLog10'] = -log(d['pvalue'],10); return data_O; except SQLAlchemyError as e: print(e); def reset_dataStage02_physiology_pairWiseTest(self, tables_I = [], analysis_id_I = None, warn_I=True): try: if not tables_I: tables_I = list(self.get_supportedTables().keys()); querydelete = sbaas_base_query_delete(session_I=self.session,engine_I=self.engine,settings_I=self.settings,data_I=self.data); for table in tables_I: query = {}; query['delete_from'] = [{'table_name':table}]; query['where'] = [{ 'table_name':table, 'column_name':'analysis_id', 'value':analysis_id_I, 'operator':'LIKE', 'connector':'AND' } ]; table_model = self.convert_tableStringList2SqlalchemyModelDict([table]); query = querydelete.make_queryFromString(table_model,query); querydelete.reset_table_sqlalchemyModel(query_I=query,warn_I=warn_I); except Exception as e: print(e);

mit

Arcbot-Org/Arcbot

bolt/discord/models/channel.py

1

1176

from bolt.discord.models.base import Snowflake, Model, Field, ListField, Enum, Timestamp from bolt.discord.models.user import User from bolt.discord.permissions import Permission class ChannelType(Enum): GUILD_TEXT = 0 DM = 1 GUILD_VOICE = 2 GROUP_DM = 3 GUILD_CATEGORY = 4 class PermissionOverwrite(Model): __repr_keys__ = ['id', 'type'] id = Field(Snowflake) type = Field(str) deny = Field(Permission) allow = Field(Permission) class Channel(Model): __repr_keys__ = ['id', 'name', 'type'] id = Field(Snowflake, required=True) type = Field(ChannelType, required=True) guild_id = Field(Snowflake) position = Field(int) permission_overwrites = ListField(PermissionOverwrite) name = Field(str, max_length=100) topic = Field(str, max_length=1024) nsfw = Field(bool) last_message_id = Field(Snowflake) bitrate = Field(int) user_limit = Field(int) rate_limit_per_user = Field(int) recipients = ListField(User) icon = Field(str) owner_id = Field(Snowflake) application_id = Field(Snowflake) parent_id = Field(Snowflake) last_pin_timestamp = Field(Timestamp)

gpl-3.0

gnowledge/OTM2

opentreemap/treemap/util.py

1

7219

# -*- coding: utf-8 -*- from __future__ import print_function from __future__ import unicode_literals from __future__ import division import datetime from collections import OrderedDict from urlparse import urlparse from django.shortcuts import get_object_or_404, resolve_url from django.http import HttpResponse from django.utils.encoding import force_str, force_text from django.utils.functional import Promise from django.core.serializers.json import DjangoJSONEncoder from django.contrib.auth import REDIRECT_FIELD_NAME from django.conf import settings from django.core.exceptions import ValidationError, MultipleObjectsReturned from django.utils.translation import ugettext_lazy as trans from django.db.models.fields.files import ImageFieldFile from django.contrib.gis.geos import Point from opentreemap.util import dict_pop from treemap.instance import Instance def safe_get_model_class(model_string): """ In a couple of cases we want to be able to convert a string into a valid django model class. For instance, if we have 'Plot' we want to get the actual class for 'treemap.models.Plot' in a safe way. This function returns the class represented by the given model if it exists in 'treemap.models' """ from treemap.models import MapFeature # All of our models live in 'treemap.models', so # we can start with that namespace models_module = __import__('treemap.models') if hasattr(models_module.models, model_string): return getattr(models_module.models, model_string) elif MapFeature.has_subclass(model_string): return MapFeature.get_subclass(model_string) else: raise ValidationError( trans('invalid model type: "%s"') % model_string) def add_visited_instance(request, instance): if not (hasattr(request, 'session') and request.session): return # get the visited instances as a list of tuples, read into # OrderedDict. OrderedDict has nice convenience methods for this # purpose, but doesn't serialize well, so we pass it through. visited_instances = request.session.get('visited_instances', []) visited_instances = OrderedDict(visited_instances) # delete the existing entry for this instance so it can be # reinserted as the most recent entry. if instance.pk in visited_instances: del visited_instances[instance.pk] stamp = datetime.datetime.now().isoformat() visited_instances[instance.pk] = stamp # turn back into a list of tuples request.session['visited_instances'] = visited_instances.items() request.session.modified = True def get_last_visited_instance(request): if not hasattr(request, 'session'): instance = None else: visited_instances = request.session.get('visited_instances', []) if not visited_instances: instance = None else: # get the first tuple member of the last entry # visited_instances have entries '(<pk>, <timestamp>)' instance_id = visited_instances[-1][0] try: instance = Instance.objects.get(pk=instance_id) except (Instance.DoesNotExist, MultipleObjectsReturned): instance = None return instance def login_redirect(request): # Reference: django/contrib/auth/decorators.py path = request.build_absolute_uri() # urlparse chokes on lazy objects in Python 3, force to str resolved_login_url = force_str( resolve_url(settings.LOGIN_URL)) # If the login url is the same scheme and net location then just # use the path as the "next" url. login_scheme, login_netloc = urlparse(resolved_login_url)[:2] current_scheme, current_netloc = urlparse(path)[:2] if (not login_scheme or login_scheme == current_scheme)\ and (not login_netloc or login_netloc == current_netloc): # NOQA path = request.get_full_path() from django.contrib.auth.views import redirect_to_login return redirect_to_login( path, resolved_login_url, REDIRECT_FIELD_NAME) def get_instance_or_404(**kwargs): url_name, found = dict_pop(kwargs, 'url_name') if found: kwargs['url_name__iexact'] = url_name return get_object_or_404(Instance, **kwargs) def package_field_errors(model_name, validation_error): """ validation_error contains a dictionary of error messages of the form {fieldname1: [messages], fieldname2: [messages]}. Return a version keyed by "objectname.fieldname" instead of "fieldname". """ dict = {'%s.%s' % (to_object_name(model_name), field): msgs for (field, msgs) in validation_error.message_dict.iteritems()} return dict # https://docs.djangoproject.com/en/dev/topics/serialization/#id2 class LazyEncoder(DjangoJSONEncoder): def default(self, obj): if isinstance(obj, Promise): return force_text(obj) elif hasattr(obj, 'dict'): return obj.dict() elif isinstance(obj, set): return list(obj) elif hasattr(obj, 'as_dict'): return obj.as_dict() elif isinstance(obj, Point): srid = 4326 obj.transform(srid) return {'x': obj.x, 'y': obj.y, 'srid': srid} # TODO: Handle S3 elif isinstance(obj, ImageFieldFile): if obj: return obj.url else: return None else: return super(LazyEncoder, self).default(obj) def all_subclasses(cls): """Return all subclasses of given class""" subclasses = set(cls.__subclasses__()) return subclasses | {clz for s in subclasses for clz in all_subclasses(s)} def leaf_subclasses(cls): """Return all leaf subclasses of given class""" all = all_subclasses(cls) leaves = {s for s in all if not s.__subclasses__()} return leaves def to_object_name(model_name): """BenefitCurrencyConversion -> benefitCurrencyConversion""" return model_name[0].lower() + model_name[1:] def to_model_name(object_name): """benefitCurrencyConversion -> BenefitCurrencyConversion""" return object_name[0].upper() + object_name[1:] def get_filterable_audit_models(): from treemap.models import MapFeature map_features = [c.__name__ for c in leaf_subclasses(MapFeature)] models = map_features + ['Tree'] return {model.lower(): model for model in models} def get_csv_response(filename): response = HttpResponse(content_type='text/csv') response['Content-Disposition'] = 'attachment; filename=%s;' % filename response['Cache-Control'] = 'no-cache' # add BOM to support CSVs in MS Excel # http://en.wikipedia.org/wiki/Byte_order_mark response.write(u'\ufeff'.encode('utf8')) return response def get_json_response(filename): response = HttpResponse(content_type='application/json') response['Content-Disposition'] = 'attachment; filename=%s;' % filename response['Cache-Control'] = 'no-cache' return response def can_read_as_super_admin(request): if not hasattr(request.user, 'is_super_admin'): return False else: return request.user.is_super_admin() and request.method == 'GET'

gpl-3.0

Ra93POL/VKAPI

__init__.py

1

4277

# -* coding: utf-8 -*- import VKAPI, dataMngt, time vk = None one_account = {'vk.com': True, 'ok.ru': True, 'disk.yandex.ru': True} number_account = dataMngt.get_number_account() def check_app_data(one_account, res_auth, site): if res_auth == 'frozen': print 'Account of "'+vk.user_data[site][1]+'" is frozen' if one_account[site] == False: reauthorize(site, account='next') elif not vk.app_data[site].has_key('access_token'): print 'Access token for "'+vk.user_data[site][1]+'" wasn\'t given!' if one_account[site] == False: reauthorize(site, account='next') def reauthorize(site, account='next'): global vk, number_account time.sleep(10) if account == 'same': number_account[site] -= 1 dataMngt.reload_user_data(vk.user_data, number_account, site) res_auth = vk.do_authorize(site) check_app_data(one_account, res_auth, site) def authorize(*sites): global vk, one_account, number_account user_data = dataMngt.load_user_data(one_account, number_account) vk = VKAPI.VK(user_data) for site in sites: res_auth = vk.do_authorize(site) check_app_data(one_account, res_auth, site) return vk ################# ------ OK.RU ----- ################ def ok_usersSetStatus(status): return vk.api('ok.ru', 'users.setStatus', {'status': status})[1] def ok_usersGetInfo(uid, fields, emptyPictures='false'): params = { 'uid': uid, 'fields': fields, 'emptyPictures': emptyPictures} return vk.api('ok.ru', 'users.getInfo', params)[1] def ok_photosEditPhoto(photo_id, description): params = { 'photo_id': photo_id, 'description': description} return vk.api('ok.ru', 'photos.editPhoto', params)[1] def ok_photosGetPhotos(uid, fid='', aid=''): params = { 'uid': uid, 'fid': fid, 'aid': aid} return vk.api('ok.ru', 'photos.getPhotos', params)[1] ################# ------ VK.COM ----- ################ def proccessing_error(cond, res): global one_account if cond == 'success': return res elif cond == 'error': code = res['code'] msg = res['msg'] oa = one_account['vk.com'] print code, msg if code == 5: reauthorize('vk.com', 'next') print '\n Connected to', vk.user_data['vk.com'][1], '\n' return 'reauthed' elif code == 15: pass elif code == 220: # защита от спама if oa == False: reauthorize('vk.com', 'next') print '\n Connected to', vk.user_data['vk.com'][1], '\n' return 'reauthed' def vk_usersGet(user_ids, fields, name_case='nom'): params = { 'user_ids': user_ids, 'fields': fields, 'name_case': name_case} cond, res = vk.api('vk.com', 'users.get', params) return proccessing_error(cond, res) def vk_wallPost(owner_id, message, attachments='', from_group=0): params = { 'owner_id': owner_id, 'message': message, 'attachments': attachments, 'from_group': from_group} cond, res = vk.api('vk.com', 'wall.post', params) return proccessing_error(cond, res) def vk_newsfeedSearch(q, count, start_from='', end_time='', extended=0): params = { 'q': q, 'count': count, 'start_from': start_from, 'end_time': end_time, 'extended': extended} cond, res = vk.api('vk.com', 'newsfeed.search', params) return proccessing_error(cond, res) def vk_groupsSearch(q, count, offset=0, city_id=''): parametrs = { 'q': q, 'offset': offset, 'count': count, 'sort': 2, 'city_id': city_id} cond, res = vk.api('vk.com', 'groups.search', parametrs) return proccessing_error(cond, res) def vk_groupsGetById(group_id, fields=''): parametrs = {'group_id': group_id, 'fields': fields} cond, res = vk.api('vk.com', 'groups.getById', parametrs) return proccessing_error(cond, res) def vk_groupsGetMembers(group_id, count, offset=0, fields=''): parametrs = { 'group_id': group_id, 'fields': fields, 'offset': offset, 'count': count} cond, res = vk.api('vk.com', 'groups.getMembers', parametrs) return proccessing_error(cond, res)

gpl-3.0

chrislit/abydos

abydos/distance/_lcprefix.py

1

4129

# Copyright 2018-2020 by Christopher C. Little. # This file is part of Abydos. # # Abydos is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # Abydos is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with Abydos. If not, see <http://www.gnu.org/licenses/>. """abydos.distance._lcprefix. Longest common prefix """ from os.path import commonprefix from typing import List, cast from ._distance import _Distance __all__ = ['LCPrefix'] class LCPrefix(_Distance): """Longest common prefix. .. versionadded:: 0.4.0 """ def lcprefix(self, strings: List[str]) -> str: """Return the longest common prefix of a list of strings. Longest common prefix (LCPrefix). Parameters ---------- strings : list of strings Strings for comparison Returns ------- str The longest common prefix Examples -------- >>> pfx = LCPrefix() >>> pfx.lcprefix(['cat', 'hat']) '' >>> pfx.lcprefix(['Niall', 'Neil']) 'N' >>> pfx.lcprefix(['aluminum', 'Catalan']) '' >>> pfx.lcprefix(['ATCG', 'TAGC']) '' .. versionadded:: 0.4.0 """ return cast(str, commonprefix(strings)) def dist_abs(self, src: str, tar: str, *args: str) -> int: """Return the length of the longest common prefix of the strings. Parameters ---------- src : str Source string for comparison tar : str Target string for comparison *args : strs Additional strings for comparison Raises ------ ValueError All arguments must be of type str Returns ------- int The length of the longest common prefix Examples -------- >>> pfx = LCPrefix() >>> pfx.dist_abs('cat', 'hat') 0 >>> pfx.dist_abs('Niall', 'Neil') 1 >>> pfx.dist_abs('aluminum', 'Catalan') 0 >>> pfx.dist_abs('ATCG', 'TAGC') 0 .. versionadded:: 0.4.0 """ strings = [src, tar] for arg in args: if isinstance(arg, str): strings.append(arg) else: raise TypeError('All arguments must be of type str') return len(self.lcprefix(strings)) def sim(self, src: str, tar: str, *args: str) -> float: r"""Return the longest common prefix similarity of two or more strings. Longest common prefix similarity (:math:`sim_{LCPrefix}`). This employs the LCPrefix function to derive a similarity metric: :math:`sim_{LCPrefix}(s,t) = \frac{|LCPrefix(s,t)|}{max(|s|, |t|)}` Parameters ---------- src : str Source string for comparison tar : str Target string for comparison *args : strs Additional strings for comparison Returns ------- float LCPrefix similarity Examples -------- >>> pfx = LCPrefix() >>> pfx.sim('cat', 'hat') 0.0 >>> pfx.sim('Niall', 'Neil') 0.2 >>> pfx.sim('aluminum', 'Catalan') 0.0 >>> pfx.sim('ATCG', 'TAGC') 0.0 .. versionadded:: 0.4.0 """ if src == tar: return 1.0 elif not src or not tar: return 0.0 dist = self.dist_abs(src, tar, *args) maxlen = max(len(src), len(tar), *[len(arg) for arg in args]) return dist / maxlen if __name__ == '__main__': import doctest doctest.testmod()

gpl-3.0

renalreg/radar

tests/api/serializers/test_salt_wasting_clinical_features_serializer.py

1

7742

from datetime import date from cornflake.exceptions import ValidationError import pytest from radar.api.serializers.salt_wasting import SaltWastingClinicalFeaturesSerializer from radar.models.patient_demographics import PatientDemographics from radar.models.patients import Patient from radar.models.users import User @pytest.fixture def patient(): patient = Patient() patient_demographics = PatientDemographics() patient_demographics.date_of_birth = date(2000, 1, 1) patient.patient_demographics.append(patient_demographics) return patient @pytest.fixture def clinical_features(patient): return { 'patient': patient, 'normal_pregnancy': False, 'abnormal_pregnancy_text': 'Foo', 'neurological_problems': True, 'seizures': True, 'abnormal_gait': True, 'deafness': True, 'other_neurological_problem': True, 'other_neurological_problem_text': 'Bar', 'joint_problems': True, 'joint_problems_age': 21, 'x_ray_abnormalities': True, 'chondrocalcinosis': True, 'other_x_ray_abnormality': True, 'other_x_ray_abnormality_text': 'Baz' } def test_valid(clinical_features): obj = valid(clinical_features) assert obj.normal_pregnancy is False assert obj.abnormal_pregnancy_text == 'Foo' assert obj.neurological_problems is True assert obj.seizures is True assert obj.abnormal_gait is True assert obj.deafness is True assert obj.other_neurological_problem is True assert obj.other_neurological_problem_text == 'Bar' assert obj.joint_problems is True assert obj.joint_problems_age == 21 assert obj.x_ray_abnormalities is True assert obj.chondrocalcinosis is True assert obj.other_x_ray_abnormality is True assert obj.other_x_ray_abnormality_text == 'Baz' def test_normal_pregnancy_true(clinical_features): clinical_features['normal_pregnancy'] = True obj = valid(clinical_features) assert obj.abnormal_pregnancy_text is None def test_normal_pregnancy_true_none(clinical_features): clinical_features['normal_pregnancy'] = None valid(clinical_features) def test_normal_pregnancy_true_text_none(clinical_features): clinical_features['normal_pregnancy'] = True clinical_features['abnormal_pregnancy_text'] = None obj = valid(clinical_features) assert obj.abnormal_pregnancy_text is None def test_normal_pregnancy_true_text_blank(clinical_features): clinical_features['normal_pregnancy'] = True clinical_features['abnormal_pregnancy_text'] = '' obj = valid(clinical_features) assert obj.abnormal_pregnancy_text is None def test_normal_pregnancy_false_text_none(clinical_features): clinical_features['abnormal_pregnancy_text'] = None invalid(clinical_features) def test_normal_pregnancy_false_text_blank(clinical_features): clinical_features['abnormal_pregnancy_text'] = '' invalid(clinical_features) def test_neurological_problems_false(clinical_features): obj = valid(clinical_features) obj.seizures = None obj.abnormal_gait = None obj.deafness = None obj.other_neurological_problem = None obj.other_neurological_problem_text = None def test_neurological_problems_none(clinical_features): clinical_features['neurological_problems'] = None valid(clinical_features) def test_neurological_problems_true_seizures_none(clinical_features): clinical_features['seizures'] = None invalid(clinical_features) def test_neurological_problems_false_seizures_none(clinical_features): clinical_features['neurological_problems'] = False clinical_features['seizures'] = None valid(clinical_features) def test_neurological_problems_true_abnormal_gait_none(clinical_features): clinical_features['abnormal_gait'] = None invalid(clinical_features) def test_neurological_problems_false_abnormal_gait_none(clinical_features): clinical_features['neurological_problems'] = False clinical_features['abnormal_gait'] = None valid(clinical_features) def test_neurological_problems_true_deafness_none(clinical_features): clinical_features['deafness'] = None invalid(clinical_features) def test_neurological_problems_false_deafness_none(clinical_features): clinical_features['neurological_problems'] = False clinical_features['deafness'] = None valid(clinical_features) def test_neurological_problems_true_other_neurological_problem_none(clinical_features): clinical_features['other_neurological_problem'] = None invalid(clinical_features) def test_other_neurological_problem_false_text_none(clinical_features): clinical_features['other_neurological_problem'] = False clinical_features['other_neurological_problem_text'] = None valid(clinical_features) def test_other_neurological_problem_true_text_blank(clinical_features): clinical_features['other_neurological_problem_text'] = '' invalid(clinical_features) def test_other_neurological_problem_true_text_none(clinical_features): clinical_features['other_neurological_problem_text'] = None invalid(clinical_features) def test_joint_problems_false(clinical_features): clinical_features['joint_problems'] = False obj = valid(clinical_features) assert obj.joint_problems_age is None assert obj.x_ray_abnormalities is None assert obj.chondrocalcinosis is None assert obj.other_x_ray_abnormality is None assert obj.other_x_ray_abnormality_text is None def test_joint_problems_none(clinical_features): clinical_features['neurological_problems'] = None valid(clinical_features) def test_joint_problems_true_joint_problems_age_none(clinical_features): clinical_features['joint_problems_age'] = None invalid(clinical_features) def test_joint_problems_false_joint_problems_age_none(clinical_features): clinical_features['joint_problems'] = False clinical_features['joint_problems_age'] = None valid(clinical_features) def test_joint_problems_true_joint_problems_age_too_young(clinical_features): clinical_features['joint_problems_age'] = -1 invalid(clinical_features) def test_joint_problems_true_joint_problems_age_too_old(clinical_features): clinical_features['x_ray_abnormalities'] = 121 invalid(clinical_features) def test_joint_problems_true_x_ray_abnormalities_none(clinical_features): clinical_features['x_ray_abnormalities'] = None invalid(clinical_features) def test_joint_problems_false_x_ray_abnormalities_none(clinical_features): clinical_features['joint_problems'] = False clinical_features['x_ray_abnormalities'] = None valid(clinical_features) def test_joint_problems_true_chondrocalcinosis_none(clinical_features): clinical_features['chondrocalcinosis'] = None invalid(clinical_features) def test_joint_problems_false_chondrocalcinosis_none(clinical_features): clinical_features['joint_problems'] = False clinical_features['chondrocalcinosis'] = None valid(clinical_features) def test_joint_problems_true_other_x_ray_abnormality_none(clinical_features): clinical_features['other_x_ray_abnormality'] = None invalid(clinical_features) def test_joint_problems_false_other_x_ray_abnormality_none(clinical_features): clinical_features['joint_problems'] = False clinical_features['other_x_ray_abnormality'] = None valid(clinical_features) def invalid(data): with pytest.raises(ValidationError) as e: valid(data) return e def valid(data): serializer = SaltWastingClinicalFeaturesSerializer(data=data, context={'user': User(is_admin=True)}) serializer.is_valid(raise_exception=True) return serializer.save()

agpl-3.0

AlexeyKruglov/Skeinforge-fabmetheus

skeinforge_application/skeinforge_plugins/craft_plugins/inset.py

1

21880

#! /usr/bin/env python """ This page is in the table of contents. Inset will inset the outside outlines by half the edge width, and outset the inside outlines by the same amount. The inset manual page is at: http://fabmetheus.crsndoo.com/wiki/index.php/Skeinforge_Inset ==Settings== ===Add Custom Code for Temperature Reading=== Default is on. When selected, the M105 custom code for temperature reading will be added at the beginning of the file. ===Infill in Direction of Bridge=== Default is on. When selected, the infill will be in the direction of any bridge across a gap, so that the fill will be able to span a bridge easier. ===Loop Order Choice=== Default loop order choice is 'Ascending Area'. When overlap is to be removed, for each loop, the overlap is checked against the list of loops already extruded. If the latest loop overlaps an already extruded loop, the overlap is removed from the latest loop. The loops are ordered according to their areas. ====Ascending Area==== When selected, the loops will be ordered in ascending area. With thin walled parts, if overlap is being removed the outside of the container will not be extruded. Holes will be the correct size. ====Descending Area==== When selected, the loops will be ordered in descending area. With thin walled parts, if overlap is being removed the inside of the container will not be extruded. Holes will be missing the interior wall so they will be slightly wider than model size. ===Overlap Removal Width over Perimeter Width=== Default is 0.6. Defines the ratio of the overlap removal width over the edge width. Any part of the extrusion that comes within the overlap removal width of another is removed. This is to prevent the extruder from depositing two extrusions right beside each other. If the 'Overlap Removal Width over Perimeter Width' is less than 0.2, the overlap will not be removed. ===Turn Extruder Heater Off at Shut Down=== Default is on. When selected, the M104 S0 gcode line will be added to the end of the file to turn the extruder heater off by setting the extruder heater temperature to 0. ===Volume Fraction=== Default: 0.93 The 'Volume Fraction' is the estimated volume of the thread compared to the box defined by the layer height and infill width. This is used in dwindle, splodge, and statistic. It is in inset because inset is a required extrusion tool, earlier in the chain than dwindle and splodge. In dwindle and splodge it is used to determine the filament volume, in statistic it is used to determine the extrusion diameter. ==Examples== The following examples inset the file Screw Holder Bottom.stl. The examples are run in a terminal in the folder which contains Screw Holder Bottom.stl and inset.py. > python inset.py This brings up the inset dialog. > python inset.py Screw Holder Bottom.stl The inset tool is parsing the file: Screw Holder Bottom.stl .. The inset tool has created the file: .. Screw Holder Bottom_inset.gcode """ from __future__ import absolute_import try: import psyco psyco.full() except: pass #Init has to be imported first because it has code to workaround the python bug where relative imports don't work if the module is imported as a main module. import __init__ from fabmetheus_utilities.fabmetheus_tools import fabmetheus_interpret from fabmetheus_utilities.geometry.solids import triangle_mesh from fabmetheus_utilities.vector3 import Vector3 from fabmetheus_utilities import archive from fabmetheus_utilities import euclidean from fabmetheus_utilities import gcodec from fabmetheus_utilities import intercircle from fabmetheus_utilities import settings from skeinforge_application.skeinforge_utilities import skeinforge_craft from skeinforge_application.skeinforge_utilities import skeinforge_polyfile from skeinforge_application.skeinforge_utilities import skeinforge_profile import cmath import math import os import sys __author__ = 'Enrique Perez ([email protected])' __date__ = '$Date: 2008/02/05 $' __license__ = 'GNU Affero General Public License http://www.gnu.org/licenses/agpl.html' def addAlreadyFilledArounds( alreadyFilledArounds, loop, radius ): "Add already filled loops around loop to alreadyFilledArounds." radius = abs(radius) alreadyFilledLoop = [] slightlyGreaterThanRadius = intercircle.globalIntercircleMultiplier * radius muchGreaterThanRadius = 2.5 * radius centers = intercircle.getCentersFromLoop( loop, slightlyGreaterThanRadius ) for center in centers: alreadyFilledInset = intercircle.getSimplifiedInsetFromClockwiseLoop( center, radius ) if intercircle.isLargeSameDirection( alreadyFilledInset, center, radius ): alreadyFilledLoop.append( alreadyFilledInset ) if len( alreadyFilledLoop ) > 0: alreadyFilledArounds.append( alreadyFilledLoop ) def addSegmentOutline( isThick, outlines, pointBegin, pointEnd, width ): "Add a diamond or hexagonal outline for a line segment." width = abs( width ) exclusionWidth = 0.6 * width slope = 0.2 if isThick: slope = 3.0 exclusionWidth = 0.8 * width segment = pointEnd - pointBegin segmentLength = abs(segment) if segmentLength == 0.0: return normalizedSegment = segment / segmentLength outline = [] segmentYMirror = complex(normalizedSegment.real, -normalizedSegment.imag) pointBeginRotated = segmentYMirror * pointBegin pointEndRotated = segmentYMirror * pointEnd along = 0.05 alongLength = along * segmentLength if alongLength > 0.1 * exclusionWidth: along *= 0.1 * exclusionWidth / alongLength alongEnd = 1.0 - along remainingToHalf = 0.5 - along alongToWidth = exclusionWidth / slope / segmentLength pointBeginIntermediate = euclidean.getIntermediateLocation( along, pointBeginRotated, pointEndRotated ) pointEndIntermediate = euclidean.getIntermediateLocation( alongEnd, pointBeginRotated, pointEndRotated ) outline.append( pointBeginIntermediate ) verticalWidth = complex( 0.0, exclusionWidth ) if alongToWidth > 0.9 * remainingToHalf: verticalWidth = complex( 0.0, slope * remainingToHalf * segmentLength ) middle = ( pointBeginIntermediate + pointEndIntermediate ) * 0.5 middleDown = middle - verticalWidth middleUp = middle + verticalWidth outline.append( middleUp ) outline.append( pointEndIntermediate ) outline.append( middleDown ) else: alongOutsideBegin = along + alongToWidth alongOutsideEnd = alongEnd - alongToWidth outsideBeginCenter = euclidean.getIntermediateLocation( alongOutsideBegin, pointBeginRotated, pointEndRotated ) outsideBeginCenterDown = outsideBeginCenter - verticalWidth outsideBeginCenterUp = outsideBeginCenter + verticalWidth outsideEndCenter = euclidean.getIntermediateLocation( alongOutsideEnd, pointBeginRotated, pointEndRotated ) outsideEndCenterDown = outsideEndCenter - verticalWidth outsideEndCenterUp = outsideEndCenter + verticalWidth outline.append( outsideBeginCenterUp ) outline.append( outsideEndCenterUp ) outline.append( pointEndIntermediate ) outline.append( outsideEndCenterDown ) outline.append( outsideBeginCenterDown ) outlines.append( euclidean.getRotatedComplexes( normalizedSegment, outline ) ) def getBridgeDirection(belowLoops, layerLoops, radius): 'Get span direction for the majority of the overhanging extrusion edge, if any.' if len(belowLoops) < 1: return None belowOutsetLoops = intercircle.getInsetLoopsFromLoops(belowLoops, -radius) bridgeRotation = complex() for loop in layerLoops: for pointIndex, point in enumerate(loop): previousIndex = (pointIndex + len(loop) - 1) % len(loop) bridgeRotation += getOverhangDirection(belowOutsetLoops, loop[previousIndex], point) if abs(bridgeRotation) < 0.75 * radius: return None else: return cmath.sqrt(bridgeRotation / abs(bridgeRotation)) def getCraftedText( fileName, text='', repository=None): "Inset the preface file or text." return getCraftedTextFromText(archive.getTextIfEmpty(fileName, text), repository) def getCraftedTextFromText(gcodeText, repository=None): "Inset the preface gcode text." if gcodec.isProcedureDoneOrFileIsEmpty( gcodeText, 'inset'): return gcodeText if repository == None: repository = settings.getReadRepository( InsetRepository() ) return InsetSkein().getCraftedGcode(gcodeText, repository) def getDoubledRoundZ( overhangingSegment, segmentRoundZ ): 'Get doubled plane angle around z of the overhanging segment.' endpoint = overhangingSegment[0] roundZ = endpoint.point - endpoint.otherEndpoint.point roundZ *= segmentRoundZ if abs( roundZ ) == 0.0: return complex() if roundZ.real < 0.0: roundZ *= - 1.0 roundZLength = abs( roundZ ) return roundZ * roundZ / roundZLength def getInteriorSegments(loops, segments): 'Get segments inside the loops.' interiorSegments = [] for segment in segments: center = 0.5 * (segment[0].point + segment[1].point) if euclidean.getIsInFilledRegion(loops, center): interiorSegments.append(segment) return interiorSegments def getIsIntersectingWithinList(loop, loopList): "Determine if the loop is intersecting or is within the loop list." leftPoint = euclidean.getLeftPoint(loop) for otherLoop in loopList: if euclidean.getNumberOfIntersectionsToLeft(otherLoop, leftPoint) % 2 == 1: return True return euclidean.isLoopIntersectingLoops(loop, loopList) def getNewRepository(): 'Get new repository.' return InsetRepository() def getOverhangDirection( belowOutsetLoops, segmentBegin, segmentEnd ): 'Add to span direction from the endpoint segments which overhang the layer below.' segment = segmentEnd - segmentBegin normalizedSegment = euclidean.getNormalized( complex( segment.real, segment.imag ) ) segmentYMirror = complex(normalizedSegment.real, -normalizedSegment.imag) segmentBegin = segmentYMirror * segmentBegin segmentEnd = segmentYMirror * segmentEnd solidXIntersectionList = [] y = segmentBegin.imag solidXIntersectionList.append( euclidean.XIntersectionIndex( - 1.0, segmentBegin.real ) ) solidXIntersectionList.append( euclidean.XIntersectionIndex( - 1.0, segmentEnd.real ) ) for belowLoopIndex in xrange( len( belowOutsetLoops ) ): belowLoop = belowOutsetLoops[ belowLoopIndex ] rotatedOutset = euclidean.getRotatedComplexes( segmentYMirror, belowLoop ) euclidean.addXIntersectionIndexesFromLoopY( rotatedOutset, belowLoopIndex, solidXIntersectionList, y ) overhangingSegments = euclidean.getSegmentsFromXIntersectionIndexes( solidXIntersectionList, y ) overhangDirection = complex() for overhangingSegment in overhangingSegments: overhangDirection += getDoubledRoundZ( overhangingSegment, normalizedSegment ) return overhangDirection def getSegmentsFromLoopListsPoints( loopLists, pointBegin, pointEnd ): "Get endpoint segments from the beginning and end of a line segment." normalizedSegment = pointEnd - pointBegin normalizedSegmentLength = abs( normalizedSegment ) if normalizedSegmentLength == 0.0: return [] normalizedSegment /= normalizedSegmentLength segmentYMirror = complex(normalizedSegment.real, -normalizedSegment.imag) pointBeginRotated = segmentYMirror * pointBegin pointEndRotated = segmentYMirror * pointEnd rotatedLoopLists = [] for loopList in loopLists: rotatedLoopLists.append(euclidean.getRotatedComplexLists(segmentYMirror, loopList)) xIntersectionIndexList = [] xIntersectionIndexList.append( euclidean.XIntersectionIndex( - 1, pointBeginRotated.real ) ) xIntersectionIndexList.append( euclidean.XIntersectionIndex( - 1, pointEndRotated.real ) ) euclidean.addXIntersectionIndexesFromLoopListsY( rotatedLoopLists, xIntersectionIndexList, pointBeginRotated.imag ) segments = euclidean.getSegmentsFromXIntersectionIndexes( xIntersectionIndexList, pointBeginRotated.imag ) for segment in segments: for endpoint in segment: endpoint.point *= normalizedSegment return segments def isCloseToLast( paths, point, radius ): "Determine if the point is close to the last point of the last path." if len(paths) < 1: return False lastPath = paths[-1] return abs( lastPath[-1] - point ) < radius def isIntersectingItself( loop, width ): "Determine if the loop is intersecting itself." outlines = [] for pointIndex in xrange(len(loop)): pointBegin = loop[pointIndex] pointEnd = loop[(pointIndex + 1) % len(loop)] if euclidean.isLineIntersectingLoops( outlines, pointBegin, pointEnd ): return True addSegmentOutline( False, outlines, pointBegin, pointEnd, width ) return False def isIntersectingWithinLists( loop, loopLists ): "Determine if the loop is intersecting or is within the loop lists." for loopList in loopLists: if getIsIntersectingWithinList( loop, loopList ): return True return False def writeOutput(fileName, shouldAnalyze=True): "Inset the carving of a gcode file." skeinforge_craft.writeChainTextWithNounMessage(fileName, 'inset', shouldAnalyze) class InsetRepository: "A class to handle the inset settings." def __init__(self): "Set the default settings, execute title & settings fileName." skeinforge_profile.addListsToCraftTypeRepository('skeinforge_application.skeinforge_plugins.craft_plugins.inset.html', self) self.baseNameSynonymDictionary = { 'Infill in Direction of Bridge' : 'carve.csv', 'Infill Width over Thickness (ratio):' : 'fill.csv'} self.fileNameInput = settings.FileNameInput().getFromFileName(fabmetheus_interpret.getGNUTranslatorGcodeFileTypeTuples(), 'Open File for Inset', self, '') self.openWikiManualHelpPage = settings.HelpPage().getOpenFromAbsolute('http://fabmetheus.crsndoo.com/wiki/index.php/Skeinforge_Inset') self.addCustomCodeForTemperatureReading = settings.BooleanSetting().getFromValue('Add Custom Code for Temperature Reading', self, True) self.infillInDirectionOfBridge = settings.BooleanSetting().getFromValue('Infill in Direction of Bridge', self, True) self.infillWidthOverThickness = settings.FloatSpin().getFromValue(1.3, 'Infill Width over Thickness (ratio):', self, 1.7, 1.5) self.loopOrderChoice = settings.MenuButtonDisplay().getFromName('Loop Order Choice:', self ) self.loopOrderAscendingArea = settings.MenuRadio().getFromMenuButtonDisplay(self.loopOrderChoice, 'Ascending Area', self, True) self.loopOrderDescendingArea = settings.MenuRadio().getFromMenuButtonDisplay(self.loopOrderChoice, 'Descending Area', self, False) self.overlapRemovalWidthOverEdgeWidth = settings.FloatSpin().getFromValue(0.3, 'Overlap Removal Width over Perimeter Width (ratio):', self, 0.9, 0.6) self.turnExtruderHeaterOffAtShutDown = settings.BooleanSetting().getFromValue('Turn Extruder Heater Off at Shut Down', self, True) self.volumeFraction = settings.FloatSpin().getFromValue(0.7, 'Volume Fraction (ratio):', self, 1.0, 0.93) self.executeTitle = 'Inset' def execute(self): "Inset button has been clicked." fileNames = skeinforge_polyfile.getFileOrDirectoryTypesUnmodifiedGcode(self.fileNameInput.value, fabmetheus_interpret.getImportPluginFileNames(), self.fileNameInput.wasCancelled) for fileName in fileNames: writeOutput(fileName) class InsetSkein: "A class to inset a skein of extrusions." def __init__(self): 'Initialize.' self.belowLoops = [] self.boundary = None self.distanceFeedRate = gcodec.DistanceFeedRate() self.layerCount = settings.LayerCount() self.lineIndex = 0 self.loopLayer = None def addGcodeFromPerimeterPaths(self, isIntersectingSelf, loop, loopLayer, loopLists, radius): "Add the edge paths to the output." segments = [] outlines = [] thickOutlines = [] allLoopLists = loopLists[:] + [thickOutlines] aroundLists = loopLists for pointIndex in xrange(len(loop)): pointBegin = loop[pointIndex] pointEnd = loop[(pointIndex + 1) % len(loop)] if isIntersectingSelf: if euclidean.isLineIntersectingLoops(outlines, pointBegin, pointEnd): segments += getSegmentsFromLoopListsPoints(allLoopLists, pointBegin, pointEnd) else: segments += getSegmentsFromLoopListsPoints(loopLists, pointBegin, pointEnd) addSegmentOutline(False, outlines, pointBegin, pointEnd, self.overlapRemovalWidth) addSegmentOutline(True, thickOutlines, pointBegin, pointEnd, self.overlapRemovalWidth) else: segments += getSegmentsFromLoopListsPoints(loopLists, pointBegin, pointEnd) edgePaths = [] path = [] muchSmallerThanRadius = 0.1 * radius segments = getInteriorSegments(loopLayer.loops, segments) for segment in segments: pointBegin = segment[0].point if not isCloseToLast(edgePaths, pointBegin, muchSmallerThanRadius): path = [pointBegin] edgePaths.append(path) path.append(segment[1].point) if len(edgePaths) > 1: firstPath = edgePaths[0] lastPath = edgePaths[-1] if abs(lastPath[-1] - firstPath[0]) < 0.1 * muchSmallerThanRadius: connectedBeginning = lastPath[: -1] + firstPath edgePaths[0] = connectedBeginning edgePaths.remove(lastPath) muchGreaterThanRadius = 6.0 * radius for edgePath in edgePaths: if euclidean.getPathLength(edgePath) > muchGreaterThanRadius: self.distanceFeedRate.addGcodeFromThreadZ(edgePath, loopLayer.z) def addGcodeFromRemainingLoop(self, loop, loopLayer, loopLists, radius): "Add the remainder of the loop which does not overlap the alreadyFilledArounds loops." centerOutset = intercircle.getLargestCenterOutsetLoopFromLoopRegardless(loop, radius) euclidean.addNestedRingBeginning(self.distanceFeedRate, centerOutset.outset, loopLayer.z) self.addGcodePerimeterBlockFromRemainingLoop(centerOutset.center, loopLayer, loopLists, radius) self.distanceFeedRate.addLine('(</boundaryPerimeter>)') self.distanceFeedRate.addLine('(</nestedRing>)') def addGcodePerimeterBlockFromRemainingLoop(self, loop, loopLayer, loopLists, radius): "Add the perimter block remainder of the loop which does not overlap the alreadyFilledArounds loops." if self.repository.overlapRemovalWidthOverEdgeWidth.value < 0.2: self.distanceFeedRate.addPerimeterBlock(loop, loopLayer.z) return isIntersectingSelf = isIntersectingItself(loop, self.overlapRemovalWidth) if isIntersectingWithinLists(loop, loopLists) or isIntersectingSelf: self.addGcodeFromPerimeterPaths(isIntersectingSelf, loop, loopLayer, loopLists, radius) else: self.distanceFeedRate.addPerimeterBlock(loop, loopLayer.z) addAlreadyFilledArounds(loopLists, loop, self.overlapRemovalWidth) def addInitializationToOutput(self): "Add initialization gcode to the output." if self.repository.addCustomCodeForTemperatureReading.value: self.distanceFeedRate.addLine('M105') # Custom code for temperature reading. def addInset(self, loopLayer): "Add inset to the layer." alreadyFilledArounds = [] extrudateLoops = intercircle.getInsetLoopsFromLoops(loopLayer.loops, self.halfEdgeWidth) if self.repository.infillInDirectionOfBridge.value: bridgeRotation = getBridgeDirection(self.belowLoops, extrudateLoops, self.halfEdgeWidth) if bridgeRotation != None: self.distanceFeedRate.addTagBracketedLine('bridgeRotation', bridgeRotation) self.belowLoops = loopLayer.loops triangle_mesh.sortLoopsInOrderOfArea(not self.repository.loopOrderAscendingArea.value, extrudateLoops) for extrudateLoop in extrudateLoops: self.addGcodeFromRemainingLoop(extrudateLoop, loopLayer, alreadyFilledArounds, self.halfEdgeWidth) def getCraftedGcode(self, gcodeText, repository): "Parse gcode text and store the bevel gcode." self.repository = repository self.lines = archive.getTextLines(gcodeText) self.parseInitialization() for line in self.lines[self.lineIndex :]: self.parseLine(line) return self.distanceFeedRate.output.getvalue() def parseInitialization(self): 'Parse gcode initialization and store the parameters.' for self.lineIndex in xrange(len(self.lines)): line = self.lines[self.lineIndex] splitLine = gcodec.getSplitLineBeforeBracketSemicolon(line) firstWord = gcodec.getFirstWord(splitLine) self.distanceFeedRate.parseSplitLine(firstWord, splitLine) if firstWord == '(<decimalPlacesCarried>': self.addInitializationToOutput() elif firstWord == '(</extruderInitialization>)': self.distanceFeedRate.addTagBracketedProcedure('inset') return elif firstWord == '(<layerHeight>': layerHeight = float(splitLine[1]) self.infillWidth = self.repository.infillWidthOverThickness.value * layerHeight self.distanceFeedRate.addTagRoundedLine('infillWidth', self.infillWidth) self.distanceFeedRate.addTagRoundedLine('volumeFraction', self.repository.volumeFraction.value) elif firstWord == '(<edgeWidth>': self.edgeWidth = float(splitLine[1]) self.halfEdgeWidth = 0.5 * self.edgeWidth self.overlapRemovalWidth = self.edgeWidth * self.repository.overlapRemovalWidthOverEdgeWidth.value self.distanceFeedRate.addLine(line) def parseLine(self, line): "Parse a gcode line and add it to the inset skein." splitLine = gcodec.getSplitLineBeforeBracketSemicolon(line) if len(splitLine) < 1: return firstWord = splitLine[0] if firstWord == '(<boundaryPoint>': location = gcodec.getLocationFromSplitLine(None, splitLine) self.boundary.append(location.dropAxis()) elif firstWord == '(</crafting>)': self.distanceFeedRate.addLine(line) if self.repository.turnExtruderHeaterOffAtShutDown.value: self.distanceFeedRate.addLine('M104 S0') # Turn extruder heater off. return elif firstWord == '(<layer>': self.layerCount.printProgressIncrement('inset') self.loopLayer = euclidean.LoopLayer(float(splitLine[1])) self.distanceFeedRate.addLine(line) elif firstWord == '(</layer>)': self.addInset(self.loopLayer) self.loopLayer = None elif firstWord == '(<nestedRing>)': self.boundary = [] self.loopLayer.loops.append(self.boundary) if self.loopLayer == None: self.distanceFeedRate.addLine(line) def main(): "Display the inset dialog." if len(sys.argv) > 1: writeOutput(' '.join(sys.argv[1 :])) else: settings.startMainLoopFromConstructor(getNewRepository()) if __name__ == "__main__": main()

agpl-3.0

wking/swc-amy

workshops/migrations/0054_self_organized_host.py

1

1799

# -*- coding: utf-8 -*- from __future__ import unicode_literals import re import django from django.db import models, migrations from django.db.models import Q def add_self_organized_host(apps, schema_editor): """Make new host: self-organized.""" Host = apps.get_model('workshops', 'Host') Host.objects.create(domain='self-organized', fullname='self-organized', country='W3') def update_administrator_to_self_organized(apps, schema_editor): """Find all events that were self-organized and set administrator for them to be "self-organized".""" Host = apps.get_model('workshops', 'Host') self_org = Host.objects.get(fullname='self-organized') Event = apps.get_model('workshops', 'Event') Event.objects.filter(administrator__isnull=True) \ .filter( Q(invoice_status='na-self-org') | Q(notes__contains='self-organized') | Q(notes__contains='self organized') ) \ .update(administrator=self_org) class Migration(migrations.Migration): dependencies = [ ('workshops', '0053_merge'), ] operations = [ # some missing migration, totally healthy (changes only validators for the field) migrations.AlterField( model_name='event', name='url', field=models.CharField(validators=[django.core.validators.RegexValidator(re.compile('https?://github\\.com/(?P<name>[^/]+)/(?P<repo>[^/]+)/?', 32), inverse_match=True)], unique=True, max_length=100, help_text='Setting this and startdate "publishes" the event.<br />Use link to the event\'s website.', blank=True, null=True), ), migrations.RunPython(add_self_organized_host), migrations.RunPython(update_administrator_to_self_organized), ]

mit

jackuess/listmodel

listmodel/models.py

1

6658

import re try: import ujson as json except ImportError: import json try: import jsonpath_rw except ImportError: jsonpath_rw = None try: import lxml.etree except ImportError: lxml = None try: import yaml except ImportError: yaml = None class QueryAttr(object): def __init__(self, query, factory=None): self.query = query self.factory = factory def __get__(self, obj, cls): if obj: return self.create(obj, obj.__document__.execute_query(self.query)) else: return self def __call__(self, func): self.create = func return self def create(self, obj, value): if self.factory: return self.factory(value) else: return value class CsvRow(object): class DocumentProxy(object): def __init__(self, row, header_map): self.row = row self.header_map = header_map def execute_query(self, column): if isinstance(column, int): return self.row[column] else: assert self.header_map return self.row[self.header_map[column]] def __init__(self, docproxy): self.__document__ = docproxy @classmethod def fromfile(cls, file, separator=",", read_header=False): if read_header: row = next(file) cols = row.strip().split(separator) header_map = {col: pos for pos, col in enumerate(cols)} else: header_map = None for row in file: yield cls(cls.DocumentProxy(row.rstrip().split(separator), header_map)) class XMLDoc(object): class DocumentProxy(object): @classmethod def create_parser(cls): return lxml.etree.XMLParser() def __init__(self, doc): self.doc = doc @classmethod def fromfile(cls, file): cls.assert_lxml() return cls(lxml.etree.parse(file, cls.create_parser())) @classmethod def fromstring(cls, str): cls.assert_lxml() return cls(lxml.etree.fromstring(str, cls.create_parser())) @classmethod def assert_lxml(cls): assert lxml, "'lxml' module required" def execute_query(self, xpath): # if xpath.startswith("//"): # xpath = ".{}".format(xpath) nodes = self.doc.xpath(xpath) if nodes: if len(nodes) == 1: return nodes[0] else: return nodes def set_iterables(self, query): self.iterables = iter(self.doc.xpath(query)) def get_next_iterable(self): return next(self.iterables) def __init__(self, docproxy): self.__document__ = docproxy @classmethod def fromfile(cls, file): return cls(docproxy=cls.DocumentProxy.fromfile(file)) @classmethod def fromstring(cls, str): return cls(docproxy=cls.DocumentProxy.fromstring(str)) def __iter__(self): self.__document__.set_iterables(self.Iterable.__query__) return self def __next__(self): iterable = self.__document__.get_next_iterable() return self.Iterable(self.DocumentProxy(iterable)) next = __next__ # Python 2 compatibility def __repr__(self): cls = self.__class__ query_attributes = ["{}={!r}".format(attr, getattr(self, attr)) for attr in dir(cls) if isinstance(getattr(cls, attr), QueryAttr)] return "<{class_name} ({query_attributes})>".format( class_name=cls.__name__, query_attributes=", ".join(query_attributes) ) class HTMLDoc(XMLDoc): class DocumentProxy(XMLDoc.DocumentProxy): @classmethod def create_parser(cls): return lxml.etree.HTMLParser() class JSONDoc(XMLDoc): class DocumentProxy(object): def __init__(self, doc): self.doc = doc @classmethod def fromfile(cls, file): return cls(json.load(file)) @classmethod def fromstring(cls, str): return cls(json.loads(str)) def execute_query(self, json_path): assert jsonpath_rw, "'jsonpath_rw' module required" path_expr = jsonpath_rw.parse(json_path) values = [match.value for match in path_expr.find(self.doc)] if values: if len(values) > 1: return values else: return values[0] def set_iterables(self, query): self.iterables = iter(self.execute_query(query)) def get_next_iterable(self): return next(self.iterables) class YAMLDoc(JSONDoc): class DocumentProxy(JSONDoc.DocumentProxy): @classmethod def fromfile(cls, file): assert yaml, "'yaml' module required" return cls(yaml.load(file)) @classmethod def fromstring(cls, string): return cls.fromfile(string) class TextDoc(XMLDoc): class DocumentProxy(object): def __init__(self, doc): self.doc = doc @classmethod def fromfile(cls, doc): return cls(doc.read()) @classmethod def fromstring(cls, doc): return cls(doc) def execute_query(self, regexp): def groupdict_or_groups(match): groupdict = match.groupdict() if groupdict: return match.groupdict() return match.groups() matches = list(re.finditer(regexp, self.doc, re.DOTALL)) if matches: if len(matches) == 1: return first_or_all(groupdict_or_groups(matches[0])) else: return map(first_or_all, [groupdict_or_groups(match) for match in matches]) def set_iterables(self, regexp): self.iterables = re.finditer(regexp, self.doc, re.DOTALL) def get_next_iterable(self): next_match = next(self.iterables) try: return next_match.group(1) except IndexError: return next_match.group(0) def first_or_all(subject): if len(subject) == 1: return subject[0] return subject def set_name(name): def decorator(decorated): decorated.__name__ = name return decorated return decorator

lgpl-3.0

johnwilmes/py-data-structures

py_data_structures/trie.py

1

8045

"""A simple trie, or prefix tree, data structure.""" import itertools import collections.abc class Trie(collections.abc.MutableSet): """A simple prefix tree data structure. A Trie is data structure for storing sequences of "names," which can be aribtrary hashable objects. In the prototypical trie, names are characters from an alphabet, and the trie is used to store words (see the subclass StringTrie). The Trie is implemented internally as a tree, each node of which is a Trie.Node object. Args: contents (optional): a collection of sequences of names to initially populate the Trie """ class Node(object): """A node of a Trie object. An instance represents a single node of a trie, corresponding a specific prefix sequence of names, which may or may not be a complete sequence. All attributes must be maintained by the user (Trie). Attributes: children (dict): mapping from names to child Nodes terminal (bool): True if a complete sequence ends here, False otherwise size (int): the number of complete sequences for which this is a prefix """ def __init__(self): self.children = dict() self.terminal = False self.size = 0 def __len__(self): return self.size def __iter__(self): """Iterate over complete suffixes from `self`.""" if self.terminal: yield iter(()) for name, child in self.children.items(): for suffix in child: yield itertools.chain((name,), suffix) def __contains__(self, seq): """Check if `seq` is a complete suffix from `self` Returns: True if `seq` is a valid suffix of `self, False otherwise. """ node = self for name in seq: if name not in node.children: return False node = node.children[name] return node.terminal class View(collections.abc.Set): """A view of a sub-trie of a Trie object. This class allows accessing (but not modifying) the sequences in the Trie completing a given prefix. Args: trie_root: the root node of the original Trie object of which this is a sub-trie prefix: the sequence of names prefixing everything in this sub-trie, corresponding to the path from the root of the original Trie to this sub-trie """ def __init__(self, trie_root, prefix): self.prefix = prefix self._trie_root = trie_root # The root node of this sub-trie, corresponding to prefix. It will # be found when needed self._prefix_root = None def _validate_root(self): """Ensure that `self._prefix_root` is valid for `self._trie_root` and `self.prefix`. If the entire sub-Trie at `self._prefix_root` is removed, then `self._prefix_root` will no longer be a descendant of `self._trie_root`. If a sequence with prefix `self.prefix` is added back into the Trie, it will use a new Trie.Node in place of self._prefix_root. We need to find that node and use it in place of self._prefix_root. """ root = self._prefix_root # check if root is still okay if root is not None and (root.children or root.terminal): return # everything is still okay # self._root is invalid; check for a replacement node self._prefix_root = None node = self._trie_root for name in self.prefix: if name not in node.children: return node = node.children[name] self._prefix_root = node def __iter__(self): self._validate_root() if self._prefix_root is None: return for suffix in self._prefix_root: yield itertools.chain(self.prefix, suffix) def __len__(self): self._validate_root() if self._prefix_root is not None: return self._prefix_root.size return 0 def __contains__(self, seq): self._validate_root() if self._prefix_root is None: return False seq = iter(seq) for name in self.prefix: if name != next(seq): return False return seq in self._prefix_root def __init__(self, contents=None): self._root = self.Node() # root node corresponding to empty prefix if contents is not None: for seq in contents: self.add(seq) def __len__(self): return self._root.size def __iter__(self): """Iterate over complete suffixes from `self`.""" return iter(self._root) def __contains__(self, seq): """Check if `seq` is a complete sequence in the Trie. Returns: True if `seq` is a valid suffix of `self, False otherwise. """ return seq in self._root def add(self, seq): """Insert a sequence into the Trie. After insertion, `seq` will be a valid suffix of `self`. Args: seq: an iterable of names to be inserted""" parent_stack = list() node = self._root for name in seq: parent_stack.append(node) if name not in node.children: node.children[name] = self.Node() node = node.children[name] if node.terminal: return node.terminal = True node.size += 1 while parent_stack: parent_stack.pop().size += 1 def discard(self, seq): """Remove `seq` from the Trie. Prunes the trie to remove all prefixes for which `seq` is the only valid completion Args: seq: an iterable of names to be removed """ parent_stack = list() node = self._root # Traverse to node representing `seq` for name in seq: parent_stack.append((node, name)) if name not in node.children: return node = node.children[name] if not node.terminal: return node.terminal = False descendents = node.children while parent_stack and not descendents: node, child_name = parent_stack.pop() del node.children[child_name] descendents = node.children def __getitem__(self, prefix): """Get a view of the Trie corresponding to `prefix`. `prefix` does not necessarily need to currently be in Trie. This view will be dynamically updated as sequences are added or removed from `self`. Args: prefix: a container (not a single-use iterator) with the sequence of names identifying the sub-Trie to be viewed. """ if prefix is iter(prefix): raise ValueError('prefix must be a container, not an iterator') return self.View(self._root, prefix) class StringTrie(Trie): """A Trie class specialized for storing strings, rather than arbitrary sequences of objects.""" class View(Trie.View): """A view of a sub-trie of a StringTrie object. This class specializes the Trie.View class to yield strings as appropriate, rather than generic iterators. """ def __iter__(self): for word in super().__iter__(): yield ''.join(word) def __iter__(self): """Override the default iterator to yield strings instead of iterators""" for word in super().__iter__(): yield ''.join(word)

mit

alex/changes

changes/jobs/create_job.py

1

1548

from flask import current_app from changes.backends.base import UnrecoverableException from changes.config import db from changes.constants import Status, Result from changes.jobs.sync_job import sync_job from changes.models import Job, JobPlan from changes.queue.task import tracked_task def abort_create(task): job = Job.query.get(task.kwargs['job_id']) job.status = Status.finished job.result = Result.aborted db.session.add(job) db.session.commit() current_app.logger.exception('Unrecoverable exception creating job %s', job.id) @tracked_task(on_abort=abort_create, max_retries=10) def create_job(job_id): job = Job.query.get(job_id) if not job: return # we might already be marked as finished for various reasons # (such as aborting the task) if job.status == Status.finished: return jobplan, implementation = JobPlan.get_build_step_for_job(job_id=job.id) if implementation is None: # TODO(dcramer): record a FailureReason? job.status = Status.finished job.result = Result.failed current_app.logger.exception('No build plan set %s', job_id) return try: implementation.execute(job=job) except UnrecoverableException: job.status = Status.finished job.result = Result.aborted current_app.logger.exception('Unrecoverable exception creating %s', job_id) return sync_job.delay( job_id=job.id.hex, task_id=job.id.hex, parent_task_id=job.build_id.hex, )

apache-2.0

adamatan/polycircles

docs/conf.py

1

8304

# -*- coding: utf-8 -*- # # Polycircles documentation build configuration file, created by # sphinx-quickstart on Mon Apr 21 13:22:59 2014. # # This file is execfile()d with the current directory set to its # containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. import sys import os # 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. #sys.path.insert(0, os.path.abspath('.')) # -- General configuration ------------------------------------------------ # If your documentation needs a minimal Sphinx version, state it here. #needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ 'sphinx.ext.autodoc', 'sphinx.ext.doctest', 'sphinx.ext.coverage', 'sphinx.ext.pngmath', 'sphinx.ext.viewcode', ] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix of source filenames. source_suffix = '.rst' # The encoding of source files. #source_encoding = 'utf-8-sig' # The master toctree document. master_doc = 'index' # General information about the project. project = u'Polycircles' copyright = u'2014, Adam Matan' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = '0.1' # The full version, including alpha/beta/rc tags. release = '0.1' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. #language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: #today = '' # Else, today_fmt is used as the format for a strftime call. #today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. exclude_patterns = ['_build'] # The reST default role (used for this markup: `text`) to use for all # documents. #default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. #add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). #add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. #show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # A list of ignored prefixes for module index sorting. #modindex_common_prefix = [] # If true, keep warnings as "system message" paragraphs in the built documents. #keep_warnings = False # -- 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 = 'default' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. #html_theme_options = {} # Add any paths that contain custom themes here, relative to this directory. #html_theme_path = [] # The name for this set of Sphinx documents. If None, it defaults to # "<project> v<release> documentation". #html_title = None # A shorter title for the navigation bar. Default is the same as html_title. #html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. #html_logo = None # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. #html_favicon = None # 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'] # Add any extra paths that contain custom files (such as robots.txt or # .htaccess) here, relative to this directory. These files are copied # directly to the root of the documentation. #html_extra_path = [] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. #html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. #html_use_smartypants = True # Custom sidebar templates, maps document names to template names. #html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. #html_additional_pages = {} # If false, no module index is generated. #html_domain_indices = True # If false, no index is generated. #html_use_index = True # If true, the index is split into individual pages for each letter. #html_split_index = False # If true, links to the reST sources are added to the pages. #html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. #html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. #html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a <link> tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. #html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). #html_file_suffix = None # Output file base name for HTML help builder. htmlhelp_basename = 'Polycirclesdoc' # -- Options for LaTeX output --------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). #'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). #'pointsize': '10pt', # Additional stuff for the LaTeX preamble. #'preamble': '', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ ('index', 'Polycircles.tex', u'Polycircles Documentation', u'Adam Matan', 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. #latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. #latex_use_parts = False # If true, show page references after internal links. #latex_show_pagerefs = False # If true, show URL addresses after external links. #latex_show_urls = False # Documents to append as an appendix to all manuals. #latex_appendices = [] # If false, no module index is generated. #latex_domain_indices = True # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ ('index', 'polycircles', u'Polycircles Documentation', [u'Adam Matan'], 1) ] # If true, show URL addresses after external links. #man_show_urls = False # -- Options for Texinfo output ------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ ('index', 'Polycircles', u'Polycircles Documentation', u'Adam Matan', 'Polycircles', 'One line description of project.', 'Miscellaneous'), ] # Documents to append as an appendix to all manuals. #texinfo_appendices = [] # If false, no module index is generated. #texinfo_domain_indices = True # How to display URL addresses: 'footnote', 'no', or 'inline'. #texinfo_show_urls = 'footnote' # If true, do not generate a @detailmenu in the "Top" node's menu. #texinfo_no_detailmenu = False

mit

willprice/python-omxplayer-wrapper

omxplayer/player.py

1

27179

import subprocess import time import os import signal import logging import threading import atexit import sys try: # python 3 from pathlib import Path except ImportError: # python2 from pathlib2 import Path from decorator import decorator from dbus import DBusException, Int64, String, ObjectPath import dbus.types from omxplayer.bus_finder import BusFinder from omxplayer.dbus_connection import DBusConnection, \ DBusConnectionError from evento import Event # CONSTANTS RETRY_DELAY = 0.05 # FILE GLOBAL OBJECTS logger = logging.getLogger(__name__) logger.addHandler(logging.NullHandler()) def _check_player_is_active(fn): # wraps is a decorator that improves debugging wrapped methods def wrapped(fn, self, *args, **kwargs): logger.debug('Checking if process is still alive') # poll determines whether the process has terminated, # if it hasn't it returns None. if self._process.poll() is None: logger.debug('OMXPlayer is running, so execute %s' % fn.__name__) return fn(self, *args, **kwargs) else: raise OMXPlayerDeadError('Process is no longer alive, can\'t run command') return decorator(wrapped, fn) def _from_dbus_type(fn): def from_dbus_type(dbusVal): def from_dbus_dict(dbusDict): d = dict() for dbusKey, dbusVal in dbusDict.items(): d[from_dbus_type(dbusKey)] = from_dbus_type(dbusVal) return d typeUnwrapper = { dbus.types.Dictionary: from_dbus_dict, dbus.types.Array: lambda x: list(map(from_dbus_type, x)), dbus.types.Double: float, dbus.types.Boolean: bool, dbus.types.Byte: int, dbus.types.Int16: int, dbus.types.Int32: int, dbus.types.Int64: int, dbus.types.UInt32: int, dbus.types.UInt64: int, dbus.types.ByteArray: str, dbus.types.ObjectPath: str, dbus.types.Signature: str, dbus.types.String: str } try: return typeUnwrapper[type(dbusVal)](dbusVal) except KeyError: return dbusVal def wrapped(fn, self, *args, **kwargs): return from_dbus_type(fn(self, *args, **kwargs)) return decorator(wrapped, fn) # CLASSES class FileNotFoundError(Exception): pass class OMXPlayerDeadError(Exception): pass class OMXPlayer(object): """ OMXPlayer controller This works by speaking to OMXPlayer over DBus sending messages. Args: source (str): Path to the file (as ~/Videos/my-video.mp4) or URL you wish to play args (list/str): used to pass option parameters to omxplayer. see: https://github.com/popcornmix/omxplayer#synopsis Multiple argument example: >>> OMXPlayer('path.mp4', args=['--no-osd', '--no-keys', '-b']) >>> OMXPlayer('path.mp4', args='--no-osd --no-keys -b') >>> OMXPlayer('path.mp4', dbus_name='org.mpris.MediaPlayer2.omxplayer2') """ def __init__(self, source, args=None, bus_address_finder=None, Connection=None, dbus_name=None, pause=False): logger.debug('Instantiating OMXPlayer') if args is None: self.args = [] elif isinstance(args, str): import shlex self.args = shlex.split(args) else: self.args = list(map(str, args)) self._is_playing = True self._source = Path(source) self._dbus_name = dbus_name self._Connection = Connection if Connection else DBusConnection self._bus_address_finder = bus_address_finder if bus_address_finder else BusFinder() #: Event called on pause ``callback(player)`` self.pauseEvent = Event() #: Event called on play ``callback(player)`` self.playEvent = Event() #: Event called on stop ``callback(player)`` self.stopEvent = Event() #: Event called on exit ``callback(player, exit_status)`` self.exitEvent = Event() #: Event called on seek ``callback(player, relative_position)`` self.seekEvent = Event() #: Event called on setting position ``callback(player, absolute_position)`` self.positionEvent = Event() self._process = None self._connection = None self.load(source, pause=pause) def _load_source(self, source): if self._process: self.quit() self._process = self._setup_omxplayer_process(source) self._rate = 1.0 self._is_muted = False self._connection = self._setup_dbus_connection(self._Connection, self._bus_address_finder) def _run_omxplayer(self, source, devnull): def on_exit(self, exit_status): logger.info("OMXPlayer process is dead, all DBus calls from here " "will fail") self.exitEvent(self, exit_status) def monitor(self, process, on_exit): process.wait() on_exit(self, process.returncode) try: source = str(source.resolve()) except AttributeError: pass command = ['omxplayer'] + self.args + [source] if self._dbus_name: command += ['--dbus_name', self._dbus_name] logger.debug("Opening omxplayer with the command: %s" % command) # By running os.setsid in the fork-ed process we create a process group # which is used to kill the subprocesses the `omxplayer` script # (it is a bash script itself that calls omxplayer.bin) creates. Without # doing this we end up in a scenario where we kill the shell script, but not # the forked children of the shell script. # See https://pymotw.com/2/subprocess/#process-groups-sessions for examples on this process = subprocess.Popen(command, stdin=devnull, stdout=devnull, preexec_fn=os.setsid) try: self._process_monitor = threading.Thread(target=monitor, args=(self, process, on_exit)) self._process_monitor.start() return process except: # Make sure to not leave any dangling process on failure self._terminate_process(process) raise def _setup_omxplayer_process(self, source): logger.debug('Setting up OMXPlayer process') with open(os.devnull, 'w') as devnull: process = self._run_omxplayer(source, devnull) logger.debug('Process opened with PID %s' % process.pid) atexit.register(self.quit) return process def _terminate_process(self, process): try: process_group_id = os.getpgid(process.pid) os.killpg(process_group_id, signal.SIGTERM) logger.debug('SIGTERM Sent to pid: %s' % process_group_id) except OSError: logger.error('Could not find the process to kill') def _setup_dbus_connection(self, Connection, bus_address_finder): logger.debug('Trying to connect to OMXPlayer via DBus') tries = 0 while tries < 50: logger.debug('DBus connect attempt: {}'.format(tries)) try: connection = Connection(bus_address_finder.get_address(), self._dbus_name) logger.debug( 'Connected to OMXPlayer at DBus address: %s' % connection) return connection except (DBusConnectionError, IOError): logger.debug('Failed to connect to OMXPlayer DBus address') tries += 1 time.sleep(RETRY_DELAY) raise SystemError('DBus cannot connect to the OMXPlayer process') """ Utilities """ def load(self, source, pause=False): """ Loads a new source (as a file) from ``source`` (a file path or URL) by killing the current ``omxplayer`` process and forking a new one. Args: source (string): Path to the file to play or URL """ self._source = source try: self._load_source(source) if pause: time.sleep(0.5) # Wait for the DBus interface to be initialised self.pause() except: # Make sure we do not leave any dangling process if self._process: self._terminate_process(self._process) self._process = None raise """ ROOT INTERFACE PROPERTIES """ @_check_player_is_active @_from_dbus_type def can_quit(self): """ Returns: bool: whether the player can quit or not """ return self._root_interface_property('CanQuit') @_check_player_is_active @_from_dbus_type def fullscreen(self): """ Returns: bool: whether the player is fullscreen or not """ return self._root_interface_property('Fullscreen') @_check_player_is_active @_from_dbus_type def can_set_fullscreen(self): """ Returns: bool: whether the player can go fullscreen """ return self._root_interface_property('CanSetFullscreen') @_check_player_is_active @_from_dbus_type def can_raise(self): """ Returns: bool: whether the player can raise the display window atop of all other windows""" return self._root_interface_property('CanRaise') @_check_player_is_active @_from_dbus_type def has_track_list(self): """ Returns: bool: whether the player has a track list or not""" return self._root_interface_property('HasTrackList') @_check_player_is_active @_from_dbus_type def identity(self): """ Returns: str: Returns `omxplayer`, the name of the player """ return self._root_interface_property('Identity') @_check_player_is_active @_from_dbus_type def supported_uri_schemes(self): """ Returns: str: list of supported URI schemes Examples: >>> player.supported_uri_schemes() ["file", "http", "rtsp", "rtmp"] """ return self._root_interface_property('SupportedUriSchemes') """ ROOT INTERFACE METHODS """ """ PLAYER INTERFACE PROPERTIES """ @_check_player_is_active @_from_dbus_type def can_go_next(self): """ Returns: bool: whether the player can move to the next item in the playlist """ return self._player_interface_property('CanGoNext') @_check_player_is_active @_from_dbus_type def can_go_previous(self): """ Returns: bool: whether the player can move to the previous item in the playlist """ return self._player_interface_property('CanGoPrevious') @_check_player_is_active @_from_dbus_type def can_seek(self): """ Returns: bool: whether the player can seek """ return self._player_interface_property('CanSeek') @_check_player_is_active @_from_dbus_type def can_control(self): """ Returns: bool: whether the player can be controlled""" return self._player_interface_property('CanControl') @_check_player_is_active @_from_dbus_type def can_play(self): """ Returns: bool: whether the player can play""" return self._player_interface_property('CanPlay') @_check_player_is_active @_from_dbus_type def can_pause(self): """ Returns: bool: whether the player can pause""" return self._player_interface_property('CanPause') @_check_player_is_active @_from_dbus_type def playback_status(self): """ Returns: str: one of ("Playing" | "Paused" | "Stopped") """ return self._player_interface_property('PlaybackStatus') @_check_player_is_active @_from_dbus_type def volume(self): """ Returns: float: current player volume """ if self._is_muted: return 0 return self._player_interface_property('Volume') @_check_player_is_active @_from_dbus_type def set_volume(self, volume): """ Args: float: volume in the interval [0, 10] """ # 0 isn't handled correctly so we have to set it to a very small value to achieve the same purpose if volume == 0: volume = 1e-10 return self._player_interface_property('Volume', dbus.Double(volume)) @_check_player_is_active @_from_dbus_type def _position_us(self): """ Returns: int: position in microseconds """ return self._player_interface_property('Position') def position(self): """ Returns: int: position in seconds """ return self._position_us() / (1000.0 * 1000.0) @_check_player_is_active @_from_dbus_type def minimum_rate(self): """ Returns: float: minimum playback rate (as proportion of normal rate) """ return self._player_interface_property('MinimumRate') @_check_player_is_active @_from_dbus_type def maximum_rate(self): """ Returns: float: maximum playback rate (as proportion of normal rate) """ return self._player_interface_property('MaximumRate') @_check_player_is_active @_from_dbus_type def rate(self): """ Returns: float: playback rate, 1 is the normal rate, 2 would be double speed. """ return self._rate @_check_player_is_active @_from_dbus_type def set_rate(self, rate): """ Set the playback rate of the video as a multiple of the default playback speed Examples: >>> player.set_rate(2) # Will play twice as fast as normal speed >>> player.set_rate(0.5) # Will play half speed """ self._rate = self._player_interface_property('Rate', dbus.Double(rate)) return self._rate @_check_player_is_active @_from_dbus_type def metadata(self): """ Returns: dict: containing track information ('URI', 'length') Examples: >>> player.metadata() { 'mpris:length': 19691000, 'xesam:url': 'file:///home/name/path/to/media/file.mp4' } """ return self._player_interface_property('Metadata') """ PLAYER INTERFACE NON-STANDARD PROPERTIES """ @_check_player_is_active @_from_dbus_type def aspect_ratio(self): """ Returns: float: aspect ratio """ return self._player_interface_property('Aspect') @_check_player_is_active @_from_dbus_type def video_stream_count(self): """ Returns: int: number of video streams """ return self._player_interface_property('VideoStreamCount') @_check_player_is_active @_from_dbus_type def width(self): """ Returns: int: video width in px """ return self._player_interface_property('ResWidth') @_check_player_is_active @_from_dbus_type def height(self): """ Returns: int: video height in px """ return self._player_interface_property('ResHeight') @_check_player_is_active @_from_dbus_type def _duration_us(self): """ Returns: int: total length in microseconds """ return self._player_interface_property('Duration') @_check_player_is_active def duration(self): """ Returns: float: duration in seconds """ return self._duration_us() / (1000.0 * 1000.0) """ PLAYER INTERFACE METHODS """ @_check_player_is_active def pause(self): """ Pause playback """ self._player_interface.Pause() self._is_playing = False self.pauseEvent(self) @_check_player_is_active def play_pause(self): """ Pause playback if currently playing, otherwise start playing if currently paused. """ self._player_interface.PlayPause() self._is_playing = not self._is_playing if self._is_playing: self.playEvent(self) else: self.pauseEvent(self) @_check_player_is_active @_from_dbus_type def stop(self): """ Stop the player, causing it to quit """ self._player_interface.Stop() self.stopEvent(self) @_check_player_is_active @_from_dbus_type def seek(self, relative_position): """ Seek the video by `relative_position` seconds Args: relative_position (float): The position in seconds to seek to. """ self._player_interface.Seek(Int64(1000.0 * 1000 * relative_position)) self.seekEvent(self, relative_position) @_check_player_is_active @_from_dbus_type def set_position(self, position): """ Set the video to playback position to `position` seconds from the start of the video Args: position (float): The position in seconds. """ self._player_interface.SetPosition(ObjectPath("/not/used"), Int64(position * 1000.0 * 1000)) self.positionEvent(self, position) @_check_player_is_active @_from_dbus_type def set_layer(self, layer): """ Set the layer of the Video (default 0). Higher layers are above lower layers Args: layer (int): The Layer to switch to. """ self._player_interface.SetLayer(Int64(layer)) @_check_player_is_active @_from_dbus_type def set_alpha(self, alpha): """ Set the transparency of the video overlay Args: alpha (float): The transparency (0..255) """ self._player_interface.SetAlpha(ObjectPath('/not/used'), Int64(alpha)) @_check_player_is_active def mute(self): """ Mute audio. If already muted, then this does not do anything """ self._is_muted = True self._player_interface.Mute() @_check_player_is_active def unmute(self): """ Unmutes the video. If already unmuted, then this does not do anything """ self._is_muted = False self._player_interface.Unmute() @_check_player_is_active @_from_dbus_type def set_aspect_mode(self, mode): """ Set the aspect mode of the video Args: mode (str): One of ("letterbox" | "fill" | "stretch") """ self._player_interface.SetAspectMode(ObjectPath('/not/used'), String(mode)) @_check_player_is_active @_from_dbus_type def set_video_pos(self, x1, y1, x2, y2): """ Set the video position on the screen Args: x1 (int): Top left x coordinate (px) y1 (int): Top left y coordinate (px) x2 (int): Bottom right x coordinate (px) y2 (int): Bottom right y coordinate (px) """ position = "%s %s %s %s" % (str(x1),str(y1),str(x2),str(y2)) self._player_interface.VideoPos(ObjectPath('/not/used'), String(position)) @_check_player_is_active def video_pos(self): """ Returns: (int, int, int, int): Video spatial position (x1, y1, x2, y2) where (x1, y1) is top left, and (x2, y2) is bottom right. All values in px. """ position_string = self._player_interface.VideoPos(ObjectPath('/not/used')) return list(map(int, position_string.split(" "))) @_check_player_is_active @_from_dbus_type def set_video_crop(self, x1, y1, x2, y2): """ Args: x1 (int): Top left x coordinate (px) y1 (int): Top left y coordinate (px) x2 (int): Bottom right x coordinate (px) y2 (int): Bottom right y coordinate (px) """ crop = "%s %s %s %s" % (str(x1),str(y1),str(x2),str(y2)) self._player_interface.SetVideoCropPos(ObjectPath('/not/used'), String(crop)) @_check_player_is_active def hide_video(self): """ Hides the video overlays """ self._player_interface.HideVideo() @_check_player_is_active def show_video(self): """ Shows the video (to undo a `hide_video`) """ self._player_interface.UnHideVideo() @_check_player_is_active @_from_dbus_type def list_audio(self): """ Returns: [str]: A list of all known audio streams, each item is in the format: ``<index>:<language>:<name>:<codec>:<active>`` """ return self._player_interface.ListAudio() @_check_player_is_active @_from_dbus_type def list_video(self): """ Returns: [str]: A list of all known video streams, each item is in the format: ``<index>:<language>:<name>:<codec>:<active>`` """ return self._player_interface.ListVideo() @_check_player_is_active @_from_dbus_type def list_subtitles(self): """ Returns: [str]: A list of all known subtitles, each item is in the format: ``<index>:<language>:<name>:<codec>:<active>`` """ return self._player_interface.ListSubtitles() @_check_player_is_active def select_subtitle(self, index): """ Enable a subtitle specified by the index it is listed in :class:`list_subtitles` Args: index (int): index of subtitle listing returned by :class:`list_subtitles` """ return self._player_interface.SelectSubtitle(dbus.Int32(index)) @_check_player_is_active def select_audio(self, index): """ Select audio stream specified by the index of the stream in :class:`list_audio` Args: index (int): index of audio stream returned by :class:`list_audio` """ return self._player_interface.SelectAudio(dbus.Int32(index)) @_check_player_is_active def show_subtitles(self): """ Shows subtitles after :class:`hide_subtitles` """ return self._player_interface.ShowSubtitles() @_check_player_is_active def hide_subtitles(self): """ Hide subtitles """ return self._player_interface.HideSubtitles() @_check_player_is_active @_from_dbus_type def action(self, code): """ Executes a keyboard command via a code Args: code (int): The key code you wish to emulate refer to ``keys.py`` for the possible keys """ self._player_interface.Action(code) @_check_player_is_active @_from_dbus_type def is_playing(self): """ Returns: bool: Whether the player is playing """ self._is_playing = (self.playback_status() == "Playing") logger.info("Playing?: %s" % self._is_playing) return self._is_playing @_check_player_is_active @_from_dbus_type def play_sync(self): """ Play the video and block whilst the video is playing """ self.play() logger.info("Playing synchronously") try: time.sleep(0.05) logger.debug("Wait for playing to start") while self.is_playing(): time.sleep(0.05) except DBusException: logger.error( "Cannot play synchronously any longer as DBus calls timed out." ) @_check_player_is_active @_from_dbus_type def play(self): """ Play the video asynchronously returning control immediately to the calling code """ if not self.is_playing(): self.play_pause() self._is_playing = True self.playEvent(self) @_check_player_is_active @_from_dbus_type def next(self): """ Skip to the next chapter Returns: bool: Whether the player skipped to the next chapter """ return self._player_interface.Next() @_check_player_is_active @_from_dbus_type def previous(self): """ Skip to the previous chapter Returns: bool: Whether the player skipped to the previous chapter """ return self._player_interface.Previous() @property def _root_interface(self): return self._connection.root_interface @property def _player_interface(self): return self._connection.player_interface @property def _properties_interface(self): return self._connection.properties_interface def _interface_property(self, interface, prop, val): if val: return self._properties_interface.Set(interface, prop, val) else: return self._properties_interface.Get(interface, prop) def _root_interface_property(self, prop, val=None): return self._interface_property(self._root_interface.dbus_interface, prop, val) def _player_interface_property(self, prop, val=None): return self._interface_property(self._player_interface.dbus_interface, prop, val) def quit(self): """ Quit the player, blocking until the process has died """ if self._process is None: logger.debug('Quit was called after self._process had already been released') return logger.debug('Quitting OMXPlayer') self._terminate_process(self._process) self._process_monitor.join() self._process = None @_check_player_is_active @_from_dbus_type def get_source(self): """ Get the source URI of the currently playing media Returns: str: source currently playing """ return self._source # For backward compatibility @_check_player_is_active @_from_dbus_type def get_filename(self): """ Returns: str: source currently playing .. deprecated:: 0.2.0 Use: :func:`get_source` instead. """ return self.get_source() # MediaPlayer2.Player types: # Track_Id: DBus ID of track # Plaback_Rate: Multiplier for playback speed (1 = normal speed) # Volume: 0--1, 0 is muted and 1 is full volume # Time_In_Us: Time in microseconds # Playback_Status: Playing|Paused|Stopped # Loop_Status: None|Track|Playlist

lgpl-3.0

kyubifire/softlayer-python

SoftLayer/CLI/image/export.py

1

1270

"""Export an image.""" # :license: MIT, see LICENSE for more details. import click import SoftLayer from SoftLayer.CLI import environment from SoftLayer.CLI import exceptions from SoftLayer.CLI import helpers @click.command() @click.argument('identifier') @click.argument('uri') @click.option('--ibm-api-key', default=None, help="The IBM Cloud API Key with access to IBM Cloud Object " "Storage instance. For help creating this key see " "https://console.bluemix.net/docs/services/cloud-object-" "storage/iam/users-serviceids.html#serviceidapikeys") @environment.pass_env def cli(env, identifier, uri, ibm_api_key): """Export an image to object storage. The URI for an object storage object (.vhd/.iso file) of the format: swift://<objectStorageAccount>@<cluster>/<container>/<objectPath> or cos://<regionName>/<bucketName>/<objectPath> if using IBM Cloud Object Storage """ image_mgr = SoftLayer.ImageManager(env.client) image_id = helpers.resolve_id(image_mgr.resolve_ids, identifier, 'image') result = image_mgr.export_image_to_uri(image_id, uri, ibm_api_key) if not result: raise exceptions.CLIAbort("Failed to export Image")

mit

skeletalbassman/pytix

wrappers/trello.py

1

10975

'''wrapper class for Trello REST API''' import requests import yaml import datetime BASE = "https://api.trello.com/1/" class Trello(): def __init__(self, project=None, username=None, password=None): self._key = None self._token = None self._authorize() if project: self._board = self.setProject(project) else: try: with open("projects.yaml", "r") as f: data = f.read() boards = yaml.load(data) self._board = boards["trello"] except IOError: print "If you have not previously set a Trello board as your current project, you must\nspecify a board name." board_name = raw_input("Board name: ") self._board = self.setProject(board_name) def _authorize(self): try: with open("credentials.yaml", "r") as f: data = f.read() creds = yaml.load(data) except IOError: creds = {} if not "trello" in creds: print "Your API key was not found on file." print "Navigate to the following link to obtain your API key\nand paste it into the terminal below. Make sure you are logged into Trello before following the link." print "Link: https://trello.com/app-key" key = raw_input("API key: ") print "\nNow please follow the link below and click 'Allow'." print "Copy and paste the resulting token back into the terminal. Pytix will\ncache this key and token for future use. This is a one-time procedure." print "https://trello.com/1/authorize?expiration=never&scope=read%2Cwrite&name=pytix&key={}&response_type=token".format(key) token = raw_input("API token: ") self._key = key self._token = token new_creds = {} new_creds["key"] = key new_creds["token"] = token creds["trello"] = new_creds with open("credentials.yaml", "w") as f: f.write(yaml.dump(creds)) def _getCreds(self): with open("credentials.yaml", "r") as f: data = f.read() creds = yaml.load(data) key = creds["trello"]["key"] token = creds["trello"]["token"] return key, token def setProject(self, proj_name): key, token = self._getCreds() url = BASE + "members/me?&boards=all&key={0}&token={1}".format(key, token) response = requests.get(url) boards = response.json()["boards"] for board in boards: print board if board["name"] == proj_name: self._board = board["id"] try: with open("projects.yaml", "r") as f: data = f.read() projs = yaml.load(data) except IOError: projs = {} projs["trello"] = board["id"] with open("projects.yaml", "w") as f: f.write(yaml.dump(projs)) return board["id"] def getProject(self): key, token = self._getCreds() board = self._board url = BASE + "boards/{0}?lists=open&cards=open&key={1}&token={2}".format(board, key, token) response = requests.get(url) #TODO deal with the response here #what do we want to show the user about the board? json = response.json() lists = json["lists"] cards = json["cards"] list_stats = {} max_length = 0 for item in lists: cur_length = len(item["name"]) if cur_length > max_length: max_length = cur_length list_stats[item["id"]] = { "name": item["name"], "no. of cards": 0 } for card in cards: list_stats[card["idList"]]["no. of cards"] += 1 left_side = " List Name " right_side = " No. of Cards ".format("no. of cards") if len(left_side)-2 > max_length: max_length = len(left_side)-2 print "\n"+json["name"] print "\nStatistics:" print "-"*(19+max_length) print "|{0:{1}}|{2}|".format(left_side, max_length+2, right_side) print "-"*(19+max_length) for key in list_stats: name = " {} ".format(list_stats[key]["name"]) num = " {} ".format(str(list_stats[key]["no. of cards"])) print "|{0:{1}}|{2:14}|".format( name, max_length+2, num) print "-"*(19+max_length) def getList(self, name): key, token = self._getCreds() board = self._board url = BASE + "boards/{0}?lists=open&key={1}&token={2}".format(board, key, token) response = requests.get(url) json = response.json() for item in json["lists"]: if item["name"] == name: list_id = item["id"] if list_id: url = BASE + "lists/{0}?cards=open&key={1}&token={2}".format(list_id, key, token) response = requests.get(url) json = response.json() cards = {} max_name_len = 0 max_id_len = 0 for card in json["cards"]: if len(card["name"]) > max_name_len: max_name_len = len(card["name"]) if len(card["id"]) > max_id_len: max_id_len = len(card["id"]) cards[card["id"]] = { "name": card["name"], "id": card["id"] } left_side = " Card Name " right_side = " Card ID " if len(left_side)-2 > max_name_len: max_name_len = len(left_side)-2 if len(right_side)-2 > max_id_len: max_id_len = len(right_side)-2 print "\n"+json["name"] print "-"*(7+max_id_len+max_name_len) print "|{0:{1}}|{2:{3}}|".format(left_side, max_name_len+2, right_side, max_id_len+2) print "-"*(7+max_id_len+max_name_len) for key in cards: name = " {} ".format(cards[key]["name"]) ID = " {} ".format(cards[key]["id"]) print "|{0:{1}}|{2:{3}}|".format( name, max_name_len+2, ID, max_id_len+2) print "-"*(7+max_id_len+max_name_len) else: print "List not found. Check your spelling." def getTask(self, name=None, ID=None): if not name and not ID: print "You must specify either a card name or a card ID." return None key, token = self._getCreds() board = self._board url = BASE + "boards/{0}?cards=open&key={1}&token={2}".format(board, key, token) response = requests.get(url) json = response.json() card_id = None if ID: card_id = ID else: for card in json["cards"]: if card["name"] == name: card_id = card["id"] if card_id: url = BASE + "cards/{0}?actions=commentCard&key={1}&token={2}".format(card_id, key, token) response = requests.get(url) json = response.json() comments = {} max_name_len = 0 max_text_len = 0 max_date_len = 0 for comment in json["actions"]: if len(comment["memberCreator"]["username"])-2 > max_name_len: max_name_len = len(comment["memberCreator"]["username"]) if len(comment["data"]["text"])-2 > max_text_len: max_text_len = len(comment["data"]["text"]) date = comment["date"].split("T")[0] if len(date)-2 > max_date_len: max_date_len = len(date) comments[comment["id"]] = { "username": comment["memberCreator"]["username"], "text": comment["data"]["text"], "date": date } name = json["name"] name_label = " Username " text_label = " Comment Text " date_label = " Date " if len(name_label)-2 > max_name_len: max_name_len = len(name_label)-2 if len(text_label)-2 > max_text_len: max_text_len = len(text_label)-2 print "\n"+name print "-"*(10+max_text_len+max_name_len+max_date_len) print "|{0:{1}}|{2:{3}}|{4:{5}}|".format(name_label, max_name_len+2, text_label, max_text_len+2, date_label, max_date_len+2) print "-"*(10+max_text_len+max_name_len+max_date_len) #TODO need to handle comments where overall table width > 80 chars for key in comments: name = " {} ".format(comments[key]["username"]) text = " {} ".format(comments[key]["text"]) date = " {} ".format(comments[key]["date"]) print "|{0:{1}}|{2:{3}}|{4:{5}}|".format( name, max_name_len+2, text, max_text_len+2, date, max_date_len+2) print "-"*(10+max_text_len+max_name_len+max_date_len) else: print "Card not found. Check your spelling." def moveTask(self, name, from_list, to_list): key, token = self._getCreds() board = self._board board_url = BASE + "boards/{0}?lists=open&key={1}&token={2}".format(board, key, token) response = requests.get(board_url) json = response.json() from_id = to_id = None for item in json["lists"]: if item["name"] == from_list: from_id = item["id"] elif item["name"] == to_list: to_id = item["id"] if not from_id: print "Source board not found." return None if not to_id: print "Destination board not found." return None url1 = BASE + "lists/{0}?cards=open&key={1}&token={2}".format(from_id, key, token) response = requests.get(url1) json = response.json() card_id = None for card in json["cards"]: if card["name"] == name: card_id = card["id"] if not card_id: print "Card not found." return None url = BASE + "cards/{0}?idList={1}&pos=bottom&key={2}&token={3}".format(card_id, to_id, key, token) response = requests.put(url) json = response.json() print "'{0}' moved to list '{1}'".format(json["name"], to_list) def addTask(self, name, to_list): key, token = self._getCreds() board = self._board board_url = BASE + "boards/{0}?lists=open&key={1}&token={2}".format(board, key, token) response = requests.get(board_url) json = response.json() to_id = None for item in json["lists"]: if item["name"] == to_list: to_id = item["id"] if not to_id: print "Destination list not found." return None url = BASE + "cards?name={0}&idList={1}&due=null&key={2}&token={3}".format(name, to_id, key, token) response = requests.post(url, data={}) json = response.json() print "'{0}' added to list '{1}'".format(json["name"], to_list) def commentTask(self, name, text): if not name and not ID: print "You must specify either a card name or a card ID." return None key, token = self._getCreds() board = self._board url = BASE + "boards/{0}?cards=open&key={1}&token={2}".format(board, key, token) response = requests.get(url) json = response.json() card_id = None for card in json["cards"]: if card["name"] == name: card_id = card["id"] if not card_id: print "Card not found." return None url = BASE + "cards/{0}/actions/comments?key={1}&token={2}".format(card_id, key, token) data = { "text": text } response = requests.post(url, data=data) json = response.json() if text == json["display"]["entities"]["comment"]["text"]: print "Comment added successfully." else: print "There was an error in processing your comment." def deleteTask(self, name): if not name and not ID: print "You must specify either a card name or a card ID." return None key, token = self._getCreds() board = self._board url = BASE + "boards/{0}?cards=open&key={1}&token={2}".format(board, key, token) response = requests.get(url) json = response.json() card_id = None for card in json["cards"]: if card["name"] == name: card_id = card["id"] if not card_id: print "Card not found." return None url = BASE + "cards/{0}?key={1}&token={2}".format(card_id, key, token) response = requests.delete(url, data={}) json = response.json() if "_value" in json: if json["_value"] == None: print "Card deleted successfully." else: print "Card could not be deleted." if __name__ == "__main__": trello = Trello() #trello.getList("Current Sprint") trello.deleteTask("Test Card")

mit

enki/muXTCP

scapyLink.py

1

1540

#!/usr/bin/python from muxlib.scapy import * import sys from twisted.internet import base, fdesc, reactor, protocol import socket import iptables class ScapyLink(base.BasePort): def __init__(self, interface=None, plusIPs=[]): base.BasePort.__init__(self, reactor) self.protocols = [] self.interface = interface if interface: self.listenIPs = [get_if_addr(interface)] self.listenIPs += plusIPs self.listenOnWire() def getHandle(self): return self.socket def listenOnWire(self): # self.socket = scapy.L3RawSocket(iface=self.interface, promisc=True, filter='') self.socket = L2Socket(iface=self.interface) reactor.addReader(self) def fileno(self): return self.socket.ins.fileno() def doRead(self): packet = self.socket.recv(MTU) for protocol in self.protocols: protocol.packetReceived(packet) def registerProtocol(self, protocol): if protocol not in self.protocols: self.protocols.append(protocol) # protocol.startProtocol() else: raise "Registered Protocol", protocol, "twice" protocol.setTransport(self) def unRegisterProtocol(self, protocol): if protocol in self.protocols: protocol.setTransport(None) self.protocols.remove(protocol) else: raise "Removed Protocol", protocol, "that isn't registered" def send(self, packet): self.socket.send(packet)

mit

eduNEXT/edunext-platform

common/djangoapps/util/course.py

1

2804

""" Utility methods related to course """ import logging import six from django.conf import settings from django.utils.timezone import now from openedx.core.djangoapps.site_configuration import helpers as configuration_helpers log = logging.getLogger(__name__) COURSE_SHARING_UTM_PARAMETERS = { 'facebook': { 'utm_medium': 'social', 'utm_campaign': 'social-sharing-db', 'utm_source': 'facebook', }, 'twitter': { 'utm_medium': 'social', 'utm_campaign': 'social-sharing-db', 'utm_source': 'twitter', }, } def get_encoded_course_sharing_utm_params(): """ Returns encoded Course Sharing UTM Parameters. """ return { utm_source: six.moves.urllib.parse.urlencode(utm_params) for utm_source, utm_params in six.iteritems(COURSE_SHARING_UTM_PARAMETERS) } def get_link_for_about_page(course): """ Arguments: course: This can be either a course overview object or a course descriptor. Returns the course sharing url, this can be one of course's social sharing url, marketing url, or lms course about url. """ is_social_sharing_enabled = configuration_helpers.get_value( 'SOCIAL_SHARING_SETTINGS', getattr(settings, 'SOCIAL_SHARING_SETTINGS', {}) ).get('CUSTOM_COURSE_URLS') if is_social_sharing_enabled and course.social_sharing_url: course_about_url = course.social_sharing_url elif settings.FEATURES.get('ENABLE_MKTG_SITE') and getattr(course, 'marketing_url', None): course_about_url = course.marketing_url else: about_base = configuration_helpers.get_value_for_org( course.id.org, 'LMS_ROOT_URL', settings.LMS_ROOT_URL ) course_about_url = u'{about_base_url}/courses/{course_key}/about'.format( about_base_url=about_base, course_key=six.text_type(course.id), ) return course_about_url def has_certificates_enabled(course): """ Arguments: course: This can be either a course overview object or a course descriptor. Returns a boolean if the course has enabled certificates """ if not settings.FEATURES.get('CERTIFICATES_HTML_VIEW', False): return False return course.cert_html_view_enabled def should_display_grade(course_overview): """ Returns True or False depending upon either certificate available date or course-end-date """ course_end_date = course_overview.end_date cert_available_date = course_overview.certificate_available_date current_date = now().replace(hour=0, minute=0, second=0, microsecond=0) if cert_available_date: return cert_available_date < current_date return course_end_date and course_end_date < current_date

agpl-3.0

sugartom/tensorflow-alien

tensorflow/contrib/layers/python/layers/layers.py

1

95215

# Copyright 2016 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. # ============================================================================== # pylint: disable=g-short-docstring-punctuation """Higher level ops for building layers.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import functools import six from tensorflow.contrib.framework.python.ops import add_arg_scope from tensorflow.contrib.framework.python.ops import variables from tensorflow.contrib.layers.python.layers import initializers from tensorflow.contrib.layers.python.layers import utils from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor from tensorflow.python.layers import convolutional as convolutional_layers from tensorflow.python.layers import core as core_layers from tensorflow.python.layers import normalization as normalization_layers from tensorflow.python.layers import pooling as pooling_layers from tensorflow.python.ops import array_ops from tensorflow.python.ops import check_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn from tensorflow.python.ops import sparse_ops from tensorflow.python.ops import standard_ops from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables as tf_variables from tensorflow.python.training import moving_averages # TODO(b/28426988): Replace legacy_* fns migrated from slim. # TODO(b/28426988): Remove legacy_* when all uses have migrated to new API. __all__ = ['avg_pool2d', 'batch_norm', 'bias_add', 'conv2d', 'conv2d_in_plane', 'conv2d_transpose', 'convolution', 'convolution2d', 'convolution2d_in_plane', 'convolution2d_transpose', 'dropout', 'flatten', 'fully_connected', 'layer_norm', 'linear', 'pool', 'max_pool2d', 'one_hot_encoding', 'relu', 'relu6', 'repeat', 'separable_conv2d', 'separable_convolution2d', 'softmax', 'stack', 'unit_norm', 'legacy_fully_connected', 'legacy_linear', 'legacy_relu'] DATA_FORMAT_NCHW = 'NCHW' DATA_FORMAT_NHWC = 'NHWC' @add_arg_scope def avg_pool2d(inputs, kernel_size, stride=2, padding='VALID', data_format=DATA_FORMAT_NHWC, outputs_collections=None, scope=None): """Adds a 2D average pooling op. It is assumed that the pooling is done per image but not in batch or channels. Args: inputs: A 4-D tensor of shape `[batch_size, height, width, channels]` if `data_format` is `NHWC`, and `[batch_size, channels, height, width]` if `data_format` is `NCHW`. kernel_size: A list of length 2: [kernel_height, kernel_width] of the pooling kernel over which the op is computed. Can be an int if both values are the same. stride: A list of length 2: [stride_height, stride_width]. Can be an int if both strides are the same. Note that presently both strides must have the same value. padding: The padding method, either 'VALID' or 'SAME'. data_format: A string. `NHWC` (default) and `NCHW` are supported. outputs_collections: The collections to which the outputs are added. scope: Optional scope for name_scope. Returns: A `Tensor` representing the results of the pooling operation. Raises: ValueError: If `data_format` is neither `NHWC` nor `NCHW`. """ if data_format not in (DATA_FORMAT_NCHW, DATA_FORMAT_NHWC): raise ValueError('data_format has to be either NCHW or NHWC.') with ops.name_scope(scope, 'AvgPool2D', [inputs]) as sc: inputs = ops.convert_to_tensor(inputs) df = ('channels_first' if data_format and data_format.startswith('NC') else 'channels_last') layer = pooling_layers.AveragePooling2D(pool_size=kernel_size, strides=stride, padding=padding, data_format=df, _scope=sc) outputs = layer.apply(inputs) return utils.collect_named_outputs(outputs_collections, sc, outputs) def _fused_batch_norm( inputs, decay=0.999, center=True, scale=False, epsilon=0.001, activation_fn=None, param_initializers=None, updates_collections=ops.GraphKeys.UPDATE_OPS, is_training=True, reuse=None, variables_collections=None, outputs_collections=None, trainable=True, data_format=DATA_FORMAT_NHWC, zero_debias_moving_mean=False, scope=None): """Adds a Batch Normalization layer from http://arxiv.org/abs/1502.03167. "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift" Sergey Ioffe, Christian Szegedy Can be used as a normalizer function for conv2d and fully_connected. Note: When is_training is True the moving_mean and moving_variance need to be updated, by default the update_ops are placed in `tf.GraphKeys.UPDATE_OPS` so they need to be added as a dependency to the `train_op`, example: update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) with tf.control_dependencies(update_ops): train_op = optimizer.minimize(loss) One can set updates_collections=None to force the updates in place, but that can have speed penalty, especially in distributed settings. Args: inputs: A tensor with 2 or more dimensions, where the first dimension has `batch_size`. The normalization is over all but the last dimension if `data_format` is `NHWC` and the second dimension if `data_format` is `NCHW`. decay: Decay for the moving average. Reasonable values for `decay` are close to 1.0, typically in the multiple-nines range: 0.999, 0.99, 0.9, etc. Lower `decay` value (recommend trying `decay`=0.9) if model experiences reasonably good training performance but poor validation and/or test performance. center: If True, add offset of `beta` to normalized tensor. If False, `beta` is ignored. scale: If True, multiply by `gamma`. If False, `gamma` is not used. When the next layer is linear (also e.g. `nn.relu`), this can be disabled since the scaling can be done by the next layer. epsilon: Small float added to variance to avoid dividing by zero. activation_fn: Activation function, default set to None to skip it and maintain a linear activation. param_initializers: Optional initializers for beta, gamma, moving mean and moving variance. updates_collections: Collections to collect the update ops for computation. The updates_ops need to be executed with the train_op. If None, a control dependency would be added to make sure the updates are computed in place. is_training: Whether or not the layer is in training mode. In training mode it would accumulate the statistics of the moments into `moving_mean` and `moving_variance` using an exponential moving average with the given `decay`. When it is not in training mode then it would use the values of the `moving_mean` and the `moving_variance`. reuse: Whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given. variables_collections: Optional collections for the variables. outputs_collections: Collections to add the outputs. trainable: If `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). data_format: A string. `NHWC` (default) and `NCHW` are supported. zero_debias_moving_mean: Use zero_debias for moving_mean. scope: Optional scope for `variable_scope`. Returns: A `Tensor` representing the output of the operation. Raises: ValueError: If `data_format` is neither `NHWC` nor `NCHW`. ValueError: If the rank of `inputs` is undefined. ValueError: If the rank of `inputs` is neither 2 or 4. ValueError: If rank or `C` dimension of `inputs` is undefined. """ if data_format not in (DATA_FORMAT_NCHW, DATA_FORMAT_NHWC): raise ValueError('data_format has to be either NCHW or NHWC.') with variable_scope.variable_scope( scope, 'BatchNorm', [inputs], reuse=reuse) as sc: inputs = ops.convert_to_tensor(inputs) original_shape = inputs.get_shape() original_rank = original_shape.ndims if original_rank is None: raise ValueError('Inputs %s has undefined rank' % inputs.name) elif original_rank not in [2, 4]: raise ValueError('Inputs %s has unsupported rank.' ' Expected 2 or 4 but got %d' % ( inputs.name, original_rank)) if original_rank == 2: channels = inputs.get_shape()[-1].value if channels is None: raise ValueError('`C` dimension must be known but is None') new_shape = [-1, 1, 1, channels] if data_format == DATA_FORMAT_NCHW: new_shape = [-1, channels, 1, 1] inputs = array_ops.reshape(inputs, new_shape) inputs_shape = inputs.get_shape() dtype = inputs.dtype.base_dtype if data_format == DATA_FORMAT_NHWC: params_shape = inputs_shape[-1:] else: params_shape = inputs_shape[1:2] if not params_shape.is_fully_defined(): raise ValueError('Inputs %s has undefined `C` dimension %s.' % (inputs.name, params_shape)) # Allocate parameters for the beta and gamma of the normalization. trainable_beta = trainable and center beta_collections = utils.get_variable_collections(variables_collections, 'beta') if not param_initializers: param_initializers = {} beta_initializer = param_initializers.get('beta', init_ops.zeros_initializer()) beta = variables.model_variable( 'beta', shape=params_shape, dtype=dtype, initializer=beta_initializer, collections=beta_collections, trainable=trainable_beta) trainable_gamma = trainable and scale gamma_collections = utils.get_variable_collections(variables_collections, 'gamma') gamma_initializer = param_initializers.get('gamma', init_ops.ones_initializer()) gamma = variables.model_variable( 'gamma', shape=params_shape, dtype=dtype, initializer=gamma_initializer, collections=gamma_collections, trainable=trainable_gamma) # Create moving_mean and moving_variance variables and add them to the # appropiate collections. moving_mean_collections = utils.get_variable_collections( variables_collections, 'moving_mean') moving_mean_initializer = param_initializers.get( 'moving_mean', init_ops.zeros_initializer()) moving_mean = variables.model_variable( 'moving_mean', shape=params_shape, dtype=dtype, initializer=moving_mean_initializer, trainable=False, collections=moving_mean_collections) moving_variance_collections = utils.get_variable_collections( variables_collections, 'moving_variance') moving_variance_initializer = param_initializers.get( 'moving_variance', init_ops.ones_initializer()) moving_variance = variables.model_variable( 'moving_variance', shape=params_shape, dtype=dtype, initializer=moving_variance_initializer, trainable=False, collections=moving_variance_collections) def _fused_batch_norm_training(): return nn.fused_batch_norm( inputs, gamma, beta, epsilon=epsilon, data_format=data_format) def _fused_batch_norm_inference(): return nn.fused_batch_norm( inputs, gamma, beta, mean=moving_mean, variance=moving_variance, epsilon=epsilon, is_training=False, data_format=data_format) outputs, mean, variance = utils.smart_cond(is_training, _fused_batch_norm_training, _fused_batch_norm_inference) # If `is_training` doesn't have a constant value, because it is a `Tensor`, # a `Variable` or `Placeholder` then is_training_value will be None and # `need_updates` will be true. is_training_value = utils.constant_value(is_training) need_updates = is_training_value is None or is_training_value if need_updates: if updates_collections is None: no_updates = lambda: outputs def _force_updates(): """Internal function forces updates moving_vars if is_training.""" update_moving_mean = moving_averages.assign_moving_average( moving_mean, mean, decay, zero_debias=zero_debias_moving_mean) update_moving_variance = moving_averages.assign_moving_average( moving_variance, variance, decay, zero_debias=False) with ops.control_dependencies( [update_moving_mean, update_moving_variance]): return array_ops.identity(outputs) outputs = utils.smart_cond(is_training, _force_updates, no_updates) else: moving_vars_fn = lambda: (moving_mean, moving_variance) def _delay_updates(): """Internal function that delay updates moving_vars if is_training.""" update_moving_mean = moving_averages.assign_moving_average( moving_mean, mean, decay, zero_debias=zero_debias_moving_mean) update_moving_variance = moving_averages.assign_moving_average( moving_variance, variance, decay, zero_debias=False) return update_moving_mean, update_moving_variance update_mean, update_variance = utils.smart_cond(is_training, _delay_updates, moving_vars_fn) ops.add_to_collections(updates_collections, update_mean) ops.add_to_collections(updates_collections, update_variance) outputs.set_shape(inputs_shape) if original_shape.ndims == 2: outputs = array_ops.reshape(outputs, original_shape) if activation_fn is not None: outputs = activation_fn(outputs) return utils.collect_named_outputs(outputs_collections, sc.original_name_scope, outputs) @add_arg_scope def batch_norm(inputs, decay=0.999, center=True, scale=False, epsilon=0.001, activation_fn=None, param_initializers=None, param_regularizers=None, updates_collections=ops.GraphKeys.UPDATE_OPS, is_training=True, reuse=None, variables_collections=None, outputs_collections=None, trainable=True, batch_weights=None, fused=False, data_format=DATA_FORMAT_NHWC, zero_debias_moving_mean=False, scope=None): """Adds a Batch Normalization layer from http://arxiv.org/abs/1502.03167. "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift" Sergey Ioffe, Christian Szegedy Can be used as a normalizer function for conv2d and fully_connected. Note: When is_training is True the moving_mean and moving_variance need to be updated, by default the update_ops are placed in `tf.GraphKeys.UPDATE_OPS` so they need to be added as a dependency to the `train_op`, example: update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) with tf.control_dependencies(update_ops): train_op = optimizer.minimize(loss) One can set updates_collections=None to force the updates in place, but that can have speed penalty, especially in distributed settings. Args: inputs: A tensor with 2 or more dimensions, where the first dimension has `batch_size`. The normalization is over all but the last dimension if `data_format` is `NHWC` and the second dimension if `data_format` is `NCHW`. decay: Decay for the moving average. Reasonable values for `decay` are close to 1.0, typically in the multiple-nines range: 0.999, 0.99, 0.9, etc. Lower `decay` value (recommend trying `decay`=0.9) if model experiences reasonably good training performance but poor validation and/or test performance. Try zero_debias_moving_mean=True for improved stability. center: If True, add offset of `beta` to normalized tensor. If False, `beta` is ignored. scale: If True, multiply by `gamma`. If False, `gamma` is not used. When the next layer is linear (also e.g. `nn.relu`), this can be disabled since the scaling can be done by the next layer. epsilon: Small float added to variance to avoid dividing by zero. activation_fn: Activation function, default set to None to skip it and maintain a linear activation. param_initializers: Optional initializers for beta, gamma, moving mean and moving variance. param_regularizers: Optional regularizer for beta and gamma. updates_collections: Collections to collect the update ops for computation. The updates_ops need to be executed with the train_op. If None, a control dependency would be added to make sure the updates are computed in place. is_training: Whether or not the layer is in training mode. In training mode it would accumulate the statistics of the moments into `moving_mean` and `moving_variance` using an exponential moving average with the given `decay`. When it is not in training mode then it would use the values of the `moving_mean` and the `moving_variance`. reuse: Whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given. variables_collections: Optional collections for the variables. outputs_collections: Collections to add the outputs. trainable: If `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see `tf.Variable`). batch_weights: An optional tensor of shape `[batch_size]`, containing a frequency weight for each batch item. If present, then the batch normalization uses weighted mean and variance. (This can be used to correct for bias in training example selection.) fused: Use nn.fused_batch_norm if True, nn.batch_normalization otherwise. data_format: A string. `NHWC` (default) and `NCHW` are supported. zero_debias_moving_mean: Use zero_debias for moving_mean. It creates a new pair of variables 'moving_mean/biased' and 'moving_mean/local_step'. scope: Optional scope for `variable_scope`. Returns: A `Tensor` representing the output of the operation. Raises: ValueError: If `batch_weights` is not None and `fused` is True. ValueError: If `param_regularizers` is not None and `fused` is True. ValueError: If `data_format` is neither `NHWC` nor `NCHW`. ValueError: If the rank of `inputs` is undefined. ValueError: If rank or channels dimension of `inputs` is undefined. """ if fused: if batch_weights is not None: raise ValueError('Weighted mean and variance is not currently ' 'supported for fused batch norm.') if param_regularizers is not None: raise ValueError('Regularizers are not currently ' 'supported for fused batch norm.') return _fused_batch_norm( inputs, decay=decay, center=center, scale=scale, epsilon=epsilon, activation_fn=activation_fn, param_initializers=param_initializers, updates_collections=updates_collections, is_training=is_training, reuse=reuse, variables_collections=variables_collections, outputs_collections=outputs_collections, trainable=trainable, data_format=data_format, zero_debias_moving_mean=zero_debias_moving_mean, scope=scope) if data_format not in (DATA_FORMAT_NCHW, DATA_FORMAT_NHWC): raise ValueError('data_format has to be either NCHW or NHWC.') layer_variable_getter = _build_variable_getter() with variable_scope.variable_scope( scope, 'BatchNorm', [inputs], reuse=reuse, custom_getter=layer_variable_getter) as sc: inputs = ops.convert_to_tensor(inputs) # Determine whether we can use the core layer class. if (batch_weights is None and updates_collections is ops.GraphKeys.UPDATE_OPS and not zero_debias_moving_mean): # Use the core layer class. axis = 1 if data_format == DATA_FORMAT_NCHW else -1 if not param_initializers: param_initializers = {} beta_initializer = param_initializers.get('beta', init_ops.zeros_initializer()) gamma_initializer = param_initializers.get('gamma', init_ops.ones_initializer()) moving_mean_initializer = param_initializers.get( 'moving_mean', init_ops.zeros_initializer()) moving_variance_initializer = param_initializers.get( 'moving_variance', init_ops.ones_initializer()) if not param_regularizers: param_regularizers = {} beta_regularizer = param_regularizers.get('beta') gamma_regularizer = param_regularizers.get('gamma') layer = normalization_layers.BatchNormalization( axis=axis, momentum=decay, epsilon=epsilon, center=center, scale=scale, beta_initializer=beta_initializer, gamma_initializer=gamma_initializer, moving_mean_initializer=moving_mean_initializer, moving_variance_initializer=moving_variance_initializer, beta_regularizer=beta_regularizer, gamma_regularizer=gamma_regularizer, trainable=trainable, name=sc.name, _scope=sc, _reuse=reuse) outputs = layer.apply(inputs, training=is_training) # Add variables to collections. _add_variable_to_collections( layer.moving_mean, variables_collections, 'moving_mean') _add_variable_to_collections( layer.moving_variance, variables_collections, 'moving_variance') if layer.beta: _add_variable_to_collections(layer.beta, variables_collections, 'beta') if layer.gamma: _add_variable_to_collections( layer.gamma, variables_collections, 'gamma') if activation_fn is not None: outputs = activation_fn(outputs) return utils.collect_named_outputs(outputs_collections, sc.original_name_scope, outputs) # Not supported by layer class: batch_weights argument, # and custom updates_collections. In that case, use the legacy BN # implementation. # Custom updates collections are not supported because the update logic # is different in this case, in particular w.r.t. "forced updates" and # update op reuse. inputs_shape = inputs.get_shape() inputs_rank = inputs_shape.ndims if inputs_rank is None: raise ValueError('Inputs %s has undefined rank.' % inputs.name) dtype = inputs.dtype.base_dtype if batch_weights is not None: batch_weights = ops.convert_to_tensor(batch_weights) inputs_shape[0:1].assert_is_compatible_with(batch_weights.get_shape()) # Reshape batch weight values so they broadcast across inputs. nshape = [-1] + [1 for _ in range(inputs_rank - 1)] batch_weights = array_ops.reshape(batch_weights, nshape) if data_format == DATA_FORMAT_NCHW: moments_axes = [0] + list(range(2, inputs_rank)) params_shape = inputs_shape[1:2] # For NCHW format, rather than relying on implicit broadcasting, we # explicitly reshape the params to params_shape_broadcast when computing # the moments and the batch normalization. params_shape_broadcast = list( [1, inputs_shape[1].value] + [1 for _ in range(2, inputs_rank)]) else: moments_axes = list(range(inputs_rank - 1)) params_shape = inputs_shape[-1:] params_shape_broadcast = None if not params_shape.is_fully_defined(): raise ValueError('Inputs %s has undefined channels dimension %s.' % ( inputs.name, params_shape)) # Allocate parameters for the beta and gamma of the normalization. beta, gamma = None, None if not param_initializers: param_initializers = {} if center: beta_collections = utils.get_variable_collections(variables_collections, 'beta') beta_initializer = param_initializers.get('beta', init_ops.zeros_initializer()) beta = variables.model_variable('beta', shape=params_shape, dtype=dtype, initializer=beta_initializer, collections=beta_collections, trainable=trainable) if scale: gamma_collections = utils.get_variable_collections(variables_collections, 'gamma') gamma_initializer = param_initializers.get('gamma', init_ops.ones_initializer()) gamma = variables.model_variable('gamma', shape=params_shape, dtype=dtype, initializer=gamma_initializer, collections=gamma_collections, trainable=trainable) # Create moving_mean and moving_variance variables and add them to the # appropiate collections. We disable variable partitioning while creating # them, because assign_moving_average is not yet supported for partitioned # variables. partitioner = variable_scope.get_variable_scope().partitioner try: variable_scope.get_variable_scope().set_partitioner(None) moving_mean_collections = utils.get_variable_collections( variables_collections, 'moving_mean') moving_mean_initializer = param_initializers.get( 'moving_mean', init_ops.zeros_initializer()) moving_mean = variables.model_variable( 'moving_mean', shape=params_shape, dtype=dtype, initializer=moving_mean_initializer, trainable=False, collections=moving_mean_collections) moving_variance_collections = utils.get_variable_collections( variables_collections, 'moving_variance') moving_variance_initializer = param_initializers.get( 'moving_variance', init_ops.ones_initializer()) moving_variance = variables.model_variable( 'moving_variance', shape=params_shape, dtype=dtype, initializer=moving_variance_initializer, trainable=False, collections=moving_variance_collections) finally: variable_scope.get_variable_scope().set_partitioner(partitioner) # If `is_training` doesn't have a constant value, because it is a `Tensor`, # a `Variable` or `Placeholder` then is_training_value will be None and # `needs_moments` will be true. is_training_value = utils.constant_value(is_training) need_moments = is_training_value is None or is_training_value if need_moments: # Calculate the moments based on the individual batch. if batch_weights is None: if data_format == DATA_FORMAT_NCHW: mean, variance = nn.moments(inputs, moments_axes, keep_dims=True) mean = array_ops.reshape(mean, [-1]) variance = array_ops.reshape(variance, [-1]) else: mean, variance = nn.moments(inputs, moments_axes) else: if data_format == DATA_FORMAT_NCHW: mean, variance = nn.weighted_moments(inputs, moments_axes, batch_weights, keep_dims=True) mean = array_ops.reshape(mean, [-1]) variance = array_ops.reshape(variance, [-1]) else: mean, variance = nn.weighted_moments(inputs, moments_axes, batch_weights) moving_vars_fn = lambda: (moving_mean, moving_variance) if updates_collections is None: def _force_updates(): """Internal function forces updates moving_vars if is_training.""" update_moving_mean = moving_averages.assign_moving_average( moving_mean, mean, decay, zero_debias=zero_debias_moving_mean) update_moving_variance = moving_averages.assign_moving_average( moving_variance, variance, decay, zero_debias=False) with ops.control_dependencies([update_moving_mean, update_moving_variance]): return array_ops.identity(mean), array_ops.identity(variance) mean, variance = utils.smart_cond(is_training, _force_updates, moving_vars_fn) else: def _delay_updates(): """Internal function that delay updates moving_vars if is_training.""" update_moving_mean = moving_averages.assign_moving_average( moving_mean, mean, decay, zero_debias=zero_debias_moving_mean) update_moving_variance = moving_averages.assign_moving_average( moving_variance, variance, decay, zero_debias=False) return update_moving_mean, update_moving_variance update_mean, update_variance = utils.smart_cond(is_training, _delay_updates, moving_vars_fn) ops.add_to_collections(updates_collections, update_mean) ops.add_to_collections(updates_collections, update_variance) # Use computed moments during training and moving_vars otherwise. vars_fn = lambda: (mean, variance) mean, variance = utils.smart_cond(is_training, vars_fn, moving_vars_fn) else: mean, variance = moving_mean, moving_variance if data_format == DATA_FORMAT_NCHW: mean = array_ops.reshape(mean, params_shape_broadcast) variance = array_ops.reshape(variance, params_shape_broadcast) beta = array_ops.reshape(beta, params_shape_broadcast) if gamma is not None: gamma = array_ops.reshape(gamma, params_shape_broadcast) # Compute batch_normalization. outputs = nn.batch_normalization(inputs, mean, variance, beta, gamma, epsilon) outputs.set_shape(inputs_shape) if activation_fn is not None: outputs = activation_fn(outputs) return utils.collect_named_outputs(outputs_collections, sc.original_name_scope, outputs) @add_arg_scope def bias_add(inputs, activation_fn=None, initializer=init_ops.zeros_initializer(), regularizer=None, reuse=None, variables_collections=None, outputs_collections=None, trainable=True, data_format=DATA_FORMAT_NHWC, scope=None): """Adds a bias to the inputs. Can be used as a normalizer function for conv2d and fully_connected. Args: inputs: A tensor of with at least rank 2 and value for the last dimension, e.g. `[batch_size, depth]`, `[None, None, None, depth]`. activation_fn: Activation function, default set to None to skip it and maintain a linear activation. initializer: An initializer for the bias, defaults to 0. regularizer: A regularizer like the result of `l1_regularizer` or `l2_regularizer`. reuse: Whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given. variables_collections: Optional collections for the variables. outputs_collections: Collections to add the outputs. trainable: If `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable). data_format: A string. 'NHWC' and 'NCHW' are supported. scope: Optional scope for variable_scope. Returns: A tensor representing the result of adding biases to the inputs. Raises: ValueError: If `data_format` is neither `NHWC` nor `NCHW`. ValueError: If `data_format` is `NCHW` and rank of `inputs` is not 4. ValueError: If the rank of `inputs` is undefined. ValueError: If rank or `C` dimension of `inputs` is undefined. """ if data_format not in (DATA_FORMAT_NCHW, DATA_FORMAT_NHWC): raise ValueError('data_format has to be either NCHW or NHWC.') with variable_scope.variable_scope(scope, 'BiasAdd', [inputs], reuse=reuse) as sc: inputs = ops.convert_to_tensor(inputs) dtype = inputs.dtype.base_dtype inputs_shape = inputs.get_shape() inputs_rank = inputs_shape.ndims if inputs_rank is None: raise ValueError('Dims of shape must be known but is None') elif inputs_rank != 4 and data_format == DATA_FORMAT_NCHW: raise ValueError('Data format NCHW only supports 4D Tensor') axis = 1 if data_format == DATA_FORMAT_NCHW else -1 num_features = inputs_shape[axis].value if num_features is None: raise ValueError('`C` dimension must be known but is None') biases_collections = utils.get_variable_collections(variables_collections, 'biases') biases = variables.model_variable('biases', shape=[num_features,], dtype=dtype, initializer=initializer, regularizer=regularizer, collections=biases_collections, trainable=trainable) outputs = nn.bias_add(inputs, biases, data_format=data_format) if activation_fn is not None: outputs = activation_fn(outputs) return utils.collect_named_outputs(outputs_collections, sc.original_name_scope, outputs) # TODO(jbms): change `rate` parameter to `dilation_rate` for consistency with # underlying op. @add_arg_scope def convolution(inputs, num_outputs, kernel_size, stride=1, padding='SAME', data_format=None, rate=1, activation_fn=nn.relu, normalizer_fn=None, normalizer_params=None, weights_initializer=initializers.xavier_initializer(), weights_regularizer=None, biases_initializer=init_ops.zeros_initializer(), biases_regularizer=None, reuse=None, variables_collections=None, outputs_collections=None, trainable=True, scope=None): """Adds an N-D convolution followed by an optional batch_norm layer. It is required that 1 <= N <= 3. `convolution` creates a variable called `weights`, representing the convolutional kernel, that is convolved (actually cross-correlated) with the `inputs` to produce a `Tensor` of activations. If a `normalizer_fn` is provided (such as `batch_norm`), it is then applied. Otherwise, if `normalizer_fn` is None and a `biases_initializer` is provided then a `biases` variable would be created and added the activations. Finally, if `activation_fn` is not `None`, it is applied to the activations as well. Performs a'trous convolution with input stride/dilation rate equal to `rate` if a value > 1 for any dimension of `rate` is specified. In this case `stride` values != 1 are not supported. Args: inputs: A Tensor of rank N+2 of shape `[batch_size] + input_spatial_shape + [in_channels]` if data_format does not start with "NC" (default), or `[batch_size, in_channels] + input_spatial_shape` if data_format starts with "NC". num_outputs: Integer, the number of output filters. kernel_size: A sequence of N positive integers specifying the spatial dimensions of of the filters. Can be a single integer to specify the same value for all spatial dimensions. stride: A sequence of N positive integers specifying the stride at which to compute output. Can be a single integer to specify the same value for all spatial dimensions. Specifying any `stride` value != 1 is incompatible with specifying any `rate` value != 1. padding: One of `"VALID"` or `"SAME"`. data_format: A string or None. Specifies whether the channel dimension of the `input` and output is the last dimension (default, or if `data_format` does not start with "NC"), or the second dimension (if `data_format` starts with "NC"). For N=1, the valid values are "NWC" (default) and "NCW". For N=2, the valid values are "NHWC" (default) and "NCHW". For N=3, currently the only valid value is "NDHWC". rate: A sequence of N positive integers specifying the dilation rate to use for a'trous convolution. Can be a single integer to specify the same value for all spatial dimensions. Specifying any `rate` value != 1 is incompatible with specifying any `stride` value != 1. activation_fn: Activation function. The default value is a ReLU function. Explicitly set it to None to skip it and maintain a linear activation. normalizer_fn: Normalization function to use instead of `biases`. If `normalizer_fn` is provided then `biases_initializer` and `biases_regularizer` are ignored and `biases` are not created nor added. default set to None for no normalizer function normalizer_params: Normalization function parameters. weights_initializer: An initializer for the weights. weights_regularizer: Optional regularizer for the weights. biases_initializer: An initializer for the biases. If None skip biases. biases_regularizer: Optional regularizer for the biases. reuse: Whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given. variables_collections: Optional list of collections for all the variables or a dictionary containing a different list of collection per variable. outputs_collections: Collection to add the outputs. trainable: If `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable). scope: Optional scope for `variable_scope`. Returns: A tensor representing the output of the operation. Raises: ValueError: If `data_format` is invalid. ValueError: Both 'rate' and `stride` are not uniformly 1. """ if data_format not in [None, 'NWC', 'NCW', 'NHWC', 'NCHW', 'NDHWC']: raise ValueError('Invalid data_format: %r' % (data_format,)) layer_variable_getter = _build_variable_getter( {'bias': 'biases', 'kernel': 'weights'}) with variable_scope.variable_scope( scope, 'Conv', [inputs], reuse=reuse, custom_getter=layer_variable_getter) as sc: inputs = ops.convert_to_tensor(inputs) input_rank = inputs.get_shape().ndims if input_rank == 3: layer_class = convolutional_layers.Convolution1D elif input_rank == 4: layer_class = convolutional_layers.Convolution2D elif input_rank == 5: layer_class = convolutional_layers.Convolution3D else: raise ValueError('Convolution not supported for input with rank', input_rank) df = ('channels_first' if data_format and data_format.startswith('NC') else 'channels_last') layer = layer_class(filters=num_outputs, kernel_size=kernel_size, strides=stride, padding=padding, data_format=df, dilation_rate=rate, activation=None, use_bias=not normalizer_fn and biases_initializer, kernel_initializer=weights_initializer, bias_initializer=biases_initializer, kernel_regularizer=weights_regularizer, bias_regularizer=biases_regularizer, activity_regularizer=None, trainable=trainable, name=sc.name, dtype=inputs.dtype.base_dtype, _scope=sc, _reuse=reuse) outputs = layer.apply(inputs) # Add variables to collections. _add_variable_to_collections(layer.kernel, variables_collections, 'weights') if layer.use_bias: _add_variable_to_collections(layer.bias, variables_collections, 'biases') if normalizer_fn is not None: normalizer_params = normalizer_params or {} outputs = normalizer_fn(outputs, **normalizer_params) if activation_fn is not None: outputs = activation_fn(outputs) return utils.collect_named_outputs(outputs_collections, sc.original_name_scope, outputs) convolution2d = convolution @add_arg_scope def convolution2d_in_plane( inputs, kernel_size, stride=1, padding='SAME', activation_fn=nn.relu, normalizer_fn=None, normalizer_params=None, weights_initializer=initializers.xavier_initializer(), weights_regularizer=None, biases_initializer=init_ops.zeros_initializer(), biases_regularizer=None, reuse=None, variables_collections=None, outputs_collections=None, trainable=True, scope=None): """Performs the same in-plane convolution to each channel independently. This is useful for performing various simple channel-independent convolution operations such as image gradients: image = tf.constant(..., shape=(16, 240, 320, 3)) vert_gradients = layers.conv2d_in_plane(image, kernel=[1, -1], kernel_size=[2, 1]) horz_gradients = layers.conv2d_in_plane(image, kernel=[1, -1], kernel_size=[1, 2]) Args: inputs: A 4-D tensor with dimensions [batch_size, height, width, channels]. kernel_size: A list of length 2 holding the [kernel_height, kernel_width] of of the pooling. Can be an int if both values are the same. stride: A list of length 2 `[stride_height, stride_width]`. Can be an int if both strides are the same. Note that presently both strides must have the same value. padding: The padding type to use, either 'SAME' or 'VALID'. activation_fn: Activation function. The default value is a ReLU function. Explicitly set it to None to skip it and maintain a linear activation. normalizer_fn: Normalization function to use instead of `biases`. If `normalizer_fn` is provided then `biases_initializer` and `biases_regularizer` are ignored and `biases` are not created nor added. default set to None for no normalizer function normalizer_params: Normalization function parameters. weights_initializer: An initializer for the weights. weights_regularizer: Optional regularizer for the weights. biases_initializer: An initializer for the biases. If None skip biases. biases_regularizer: Optional regularizer for the biases. reuse: Whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given. variables_collections: Optional list of collections for all the variables or a dictionary containing a different list of collection per variable. outputs_collections: Collection to add the outputs. trainable: If `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable). scope: Optional scope for `variable_scope`. Returns: A `Tensor` representing the output of the operation. """ with variable_scope.variable_scope( scope, 'ConvInPlane', [inputs], reuse=reuse) as sc: dtype = inputs.dtype.base_dtype kernel_h, kernel_w = utils.two_element_tuple(kernel_size) stride_h, stride_w = utils.two_element_tuple(stride) num_filters_in = utils.last_dimension(inputs.get_shape(), min_rank=4) weights_shape = [kernel_h, kernel_w, 1, 1] weights_collections = utils.get_variable_collections( variables_collections, 'weights') weights = variables.model_variable('weights', shape=weights_shape, dtype=dtype, initializer=weights_initializer, regularizer=weights_regularizer, collections=weights_collections, trainable=trainable) depthwise_weights = array_ops.tile(weights, [1, 1, num_filters_in, 1]) outputs = nn.depthwise_conv2d(inputs, depthwise_weights, [1, stride_h, stride_w, 1], padding) if normalizer_fn is not None: normalizer_params = normalizer_params or {} outputs = normalizer_fn(outputs, **normalizer_params) else: if biases_initializer is not None: biases_collections = utils.get_variable_collections( variables_collections, 'biases') biases = variables.model_variable('biases', shape=[num_filters_in,], dtype=dtype, initializer=biases_initializer, regularizer=biases_regularizer, collections=biases_collections, trainable=trainable) outputs = nn.bias_add(outputs, biases) if activation_fn is not None: outputs = activation_fn(outputs) return utils.collect_named_outputs(outputs_collections, sc.original_name_scope, outputs) @add_arg_scope def convolution2d_transpose( inputs, num_outputs, kernel_size, stride=1, padding='SAME', data_format=DATA_FORMAT_NHWC, activation_fn=nn.relu, normalizer_fn=None, normalizer_params=None, weights_initializer=initializers.xavier_initializer(), weights_regularizer=None, biases_initializer=init_ops.zeros_initializer(), biases_regularizer=None, reuse=None, variables_collections=None, outputs_collections=None, trainable=True, scope=None): """Adds a convolution2d_transpose with an optional batch normalization layer. The function creates a variable called `weights`, representing the kernel, that is convolved with the input. If `batch_norm_params` is `None`, a second variable called 'biases' is added to the result of the operation. Args: inputs: A 4-D `Tensor` of type `float` and shape `[batch, height, width, in_channels]` for `NHWC` data format or `[batch, in_channels, height, width]` for `NCHW` data format. num_outputs: Integer, the number of output filters. kernel_size: A list of length 2 holding the [kernel_height, kernel_width] of of the filters. Can be an int if both values are the same. stride: A list of length 2: [stride_height, stride_width]. Can be an int if both strides are the same. Note that presently both strides must have the same value. padding: One of 'VALID' or 'SAME'. data_format: A string. `NHWC` (default) and `NCHW` are supported. activation_fn: Activation function. The default value is a ReLU function. Explicitly set it to None to skip it and maintain a linear activation. normalizer_fn: Normalization function to use instead of `biases`. If `normalizer_fn` is provided then `biases_initializer` and `biases_regularizer` are ignored and `biases` are not created nor added. default set to None for no normalizer function normalizer_params: Normalization function parameters. weights_initializer: An initializer for the weights. weights_regularizer: Optional regularizer for the weights. biases_initializer: An initializer for the biases. If None skip biases. biases_regularizer: Optional regularizer for the biases. reuse: Whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given. variables_collections: Optional list of collections for all the variables or a dictionary containing a different list of collection per variable. outputs_collections: Collection to add the outputs. trainable: Whether or not the variables should be trainable or not. scope: Optional scope for variable_scope. Returns: A tensor representing the output of the operation. Raises: ValueError: If 'kernel_size' is not a list of length 2. ValueError: If `data_format` is neither `NHWC` nor `NCHW`. ValueError: If `C` dimension of `inputs` is None. """ layer_variable_getter = _build_variable_getter( {'bias': 'biases', 'kernel': 'weights'}) with variable_scope.variable_scope( scope, 'Conv2d_transpose', [inputs], reuse=reuse, custom_getter=layer_variable_getter) as sc: if data_format not in (DATA_FORMAT_NCHW, DATA_FORMAT_NHWC): raise ValueError('data_format has to be either NCHW or NHWC.') inputs = ops.convert_to_tensor(inputs) df = ('channels_first' if data_format and data_format.startswith('NC') else 'channels_last') layer = convolutional_layers.Convolution2DTranspose( filters=num_outputs, kernel_size=kernel_size, strides=stride, padding=padding, data_format=df, activation=None, use_bias=not normalizer_fn and biases_initializer, kernel_initializer=weights_initializer, bias_initializer=biases_initializer, kernel_regularizer=weights_regularizer, bias_regularizer=biases_regularizer, activity_regularizer=None, trainable=trainable, name=sc.name, dtype=inputs.dtype.base_dtype, _scope=sc, _reuse=reuse) outputs = layer.apply(inputs) # Add variables to collections. _add_variable_to_collections(layer.kernel, variables_collections, 'weights') if layer.bias: _add_variable_to_collections(layer.bias, variables_collections, 'biases') if normalizer_fn is not None: normalizer_params = normalizer_params or {} outputs = normalizer_fn(outputs, **normalizer_params) if activation_fn is not None: outputs = activation_fn(outputs) return utils.collect_named_outputs(outputs_collections, sc.original_name_scope, outputs) @add_arg_scope def dropout(inputs, keep_prob=0.5, noise_shape=None, is_training=True, outputs_collections=None, scope=None): """Returns a dropout op applied to the input. With probability `keep_prob`, outputs the input element scaled up by `1 / keep_prob`, otherwise outputs `0`. The scaling is so that the expected sum is unchanged. Args: inputs: The tensor to pass to the nn.dropout op. keep_prob: A scalar `Tensor` with the same type as x. The probability that each element is kept. noise_shape: A 1-D `Tensor` of type `int32`, representing the shape for randomly generated keep/drop flags. is_training: A bool `Tensor` indicating whether or not the model is in training mode. If so, dropout is applied and values scaled. Otherwise, inputs is returned. outputs_collections: Collection to add the outputs. scope: Optional scope for name_scope. Returns: A tensor representing the output of the operation. """ with variable_scope.variable_scope( scope, 'Dropout', [inputs], custom_getter=_model_variable_getter) as sc: inputs = ops.convert_to_tensor(inputs) layer = core_layers.Dropout(rate=1 - keep_prob, noise_shape=noise_shape, name=sc.name, _scope=sc) outputs = layer.apply(inputs, training=is_training) return utils.collect_named_outputs( outputs_collections, sc.original_name_scope, outputs) @add_arg_scope def flatten(inputs, outputs_collections=None, scope=None): """Flattens the input while maintaining the batch_size. Assumes that the first dimension represents the batch. Args: inputs: A tensor of size [batch_size, ...]. outputs_collections: Collection to add the outputs. scope: Optional scope for name_scope. Returns: A flattened tensor with shape [batch_size, k]. Raises: ValueError: If inputs rank is unknown or less than 2. """ with ops.name_scope(scope, 'Flatten', [inputs]) as sc: inputs = ops.convert_to_tensor(inputs) inputs_rank = inputs.get_shape().ndims if (inputs_rank is None) or (inputs_rank < 2): raise ValueError('Inputs must have a least 2 dimensions.') inputs_shape = array_ops.shape(inputs) batch_dim = array_ops.slice(inputs_shape, [0], [1]) spatial_dims = array_ops.slice(inputs_shape, [1], [inputs_rank - 1]) flat_spatial_dim = math_ops.reduce_prod(spatial_dims) flat_spatial_dim = array_ops.expand_dims(flat_spatial_dim, 0) flat_shape = array_ops.concat([batch_dim, flat_spatial_dim], 0) outputs = array_ops.reshape(inputs, flat_shape) # Attempt to propagate shape information, if it is defined. input_shape = inputs.get_shape().as_list() batch_dim, spatial_dims = input_shape[0], input_shape[1:] if all(spatial_dims): outputs.set_shape([batch_dim, functools.reduce(lambda x, y: x * y, spatial_dims)]) else: outputs.set_shape([batch_dim, None]) return utils.collect_named_outputs(outputs_collections, sc, outputs) def _sparse_inner_flatten(inputs, new_rank): """Helper function for `inner_flatten`.""" outer_dimensions = inputs.dense_shape[:new_rank - 1] inner_dimensions = inputs.dense_shape[new_rank - 1:] new_shape = array_ops.concat((outer_dimensions, [math_ops.reduce_prod(inner_dimensions)]), 0) flattened = sparse_ops.sparse_reshape(inputs, new_shape) return flattened def _dense_inner_flatten(inputs, new_rank): """Helper function for `inner_flatten`.""" rank_assertion = check_ops.assert_rank_at_least( inputs, new_rank, message='inputs has rank less than new_rank') with ops.control_dependencies([rank_assertion]): outer_dimensions = array_ops.strided_slice( array_ops.shape(inputs), [0], [new_rank - 1]) new_shape = array_ops.concat((outer_dimensions, [-1]), 0) reshaped = array_ops.reshape(inputs, new_shape) # if `new_rank` is an integer, try to calculate new shape. if isinstance(new_rank, six.integer_types): static_shape = inputs.get_shape() if static_shape is not None and static_shape.dims is not None: static_shape = static_shape.as_list() static_outer_dims = static_shape[:new_rank - 1] static_inner_dims = static_shape[new_rank - 1:] flattened_dimension = 1 for inner_dim in static_inner_dims: if inner_dim is None: flattened_dimension = None break flattened_dimension *= inner_dim reshaped.set_shape(static_outer_dims + [flattened_dimension]) return reshaped @add_arg_scope def _inner_flatten(inputs, new_rank, output_collections=None, scope=None): """Flattens inner dimensions of `inputs`, returns a Tensor with `new_rank`. For example: ''' x = tf.random_uniform(shape=[1, 2, 3, 4, 5, 6]) y = _inner_flatten(x, 4) assert y.get_shape().as_list() == [1, 2, 3, (4 * 5 * 6)] ''' This layer will fail at run time if `new_rank` is greater than the current rank of `inputs`. Args: inputs: A `Tensor` or `SparseTensor`. new_rank: The desired rank of the returned `Tensor` or `SparseTensor`. output_collections: Collection to which the outputs will be added. scope: Optional scope for `name_scope`. Returns: A `Tensor` or `SparseTensor` conataining the same values as `inputs`, but with innermost dimensions flattened to obtain rank `new_rank`. Raises: TypeError: `inputs` is not a `Tensor` or `SparseTensor`. """ with ops.name_scope(scope, 'InnerFlatten', [inputs, new_rank]) as sc: if isinstance(inputs, sparse_tensor.SparseTensor): flattened = _sparse_inner_flatten(inputs, new_rank) else: inputs = ops.convert_to_tensor(inputs) flattened = _dense_inner_flatten(inputs, new_rank) return utils.collect_named_outputs(output_collections, sc, flattened) def _model_variable_getter(getter, name, shape=None, dtype=None, initializer=None, regularizer=None, trainable=True, collections=None, caching_device=None, partitioner=None, rename=None, use_resource=None, **_): """Getter that uses model_variable for compatibility with core layers.""" short_name = name.split('/')[-1] if rename and short_name in rename: name_components = name.split('/') name_components[-1] = rename[short_name] name = '/'.join(name_components) return variables.model_variable( name, shape=shape, dtype=dtype, initializer=initializer, regularizer=regularizer, collections=collections, trainable=trainable, caching_device=caching_device, partitioner=partitioner, custom_getter=getter, use_resource=use_resource) def _build_variable_getter(rename=None): """Build a model variable getter that respects scope getter and renames.""" # VariableScope will nest the getters def layer_variable_getter(getter, *args, **kwargs): kwargs['rename'] = rename return _model_variable_getter(getter, *args, **kwargs) return layer_variable_getter def _add_variable_to_collections(variable, collections_set, collections_name): """Adds variable (or all its parts) to all collections with that name.""" collections = utils.get_variable_collections( collections_set, collections_name) or [] variables_list = [variable] if isinstance(variable, tf_variables.PartitionedVariable): variables_list = [v for v in variable] for collection in collections: for var in variables_list: if var not in ops.get_collection(collection): ops.add_to_collection(collection, var) @add_arg_scope def fully_connected(inputs, num_outputs, activation_fn=nn.relu, normalizer_fn=None, normalizer_params=None, weights_initializer=initializers.xavier_initializer(), weights_regularizer=None, biases_initializer=init_ops.zeros_initializer(), biases_regularizer=None, reuse=None, variables_collections=None, outputs_collections=None, trainable=True, scope=None): """Adds a fully connected layer. `fully_connected` creates a variable called `weights`, representing a fully connected weight matrix, which is multiplied by the `inputs` to produce a `Tensor` of hidden units. If a `normalizer_fn` is provided (such as `batch_norm`), it is then applied. Otherwise, if `normalizer_fn` is None and a `biases_initializer` is provided then a `biases` variable would be created and added the hidden units. Finally, if `activation_fn` is not `None`, it is applied to the hidden units as well. Note: that if `inputs` have a rank greater than 2, then `inputs` is flattened prior to the initial matrix multiply by `weights`. Args: inputs: A tensor of at least rank 2 and static value for the last dimension; i.e. `[batch_size, depth]`, `[None, None, None, channels]`. num_outputs: Integer or long, the number of output units in the layer. activation_fn: Activation function. The default value is a ReLU function. Explicitly set it to None to skip it and maintain a linear activation. normalizer_fn: Normalization function to use instead of `biases`. If `normalizer_fn` is provided then `biases_initializer` and `biases_regularizer` are ignored and `biases` are not created nor added. default set to None for no normalizer function normalizer_params: Normalization function parameters. weights_initializer: An initializer for the weights. weights_regularizer: Optional regularizer for the weights. biases_initializer: An initializer for the biases. If None skip biases. biases_regularizer: Optional regularizer for the biases. reuse: Whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given. variables_collections: Optional list of collections for all the variables or a dictionary containing a different list of collections per variable. outputs_collections: Collection to add the outputs. trainable: If `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable). scope: Optional scope for variable_scope. Returns: The tensor variable representing the result of the series of operations. Raises: ValueError: If x has rank less than 2 or if its last dimension is not set. """ if not isinstance(num_outputs, six.integer_types): raise ValueError('num_outputs should be int or long, got %s.', num_outputs) layer_variable_getter = _build_variable_getter({'bias': 'biases', 'kernel': 'weights'}) with variable_scope.variable_scope( scope, 'fully_connected', [inputs], reuse=reuse, custom_getter=layer_variable_getter) as sc: inputs = ops.convert_to_tensor(inputs) layer = core_layers.Dense( units=num_outputs, activation=None, use_bias=not normalizer_fn and biases_initializer, kernel_initializer=weights_initializer, bias_initializer=biases_initializer, kernel_regularizer=weights_regularizer, bias_regularizer=biases_regularizer, activity_regularizer=None, trainable=trainable, name=sc.name, dtype=inputs.dtype.base_dtype, _scope=sc, _reuse=reuse) outputs = layer.apply(inputs) # Add variables to collections. _add_variable_to_collections(layer.kernel, variables_collections, 'weights') if layer.bias is not None: _add_variable_to_collections(layer.bias, variables_collections, 'biases') # Apply normalizer function / layer. if normalizer_fn is not None: if not normalizer_params: normalizer_params = {} outputs = normalizer_fn(outputs, **normalizer_params) if activation_fn is not None: outputs = activation_fn(outputs) return utils.collect_named_outputs( outputs_collections, sc.original_name_scope, outputs) @add_arg_scope def layer_norm(inputs, center=True, scale=True, activation_fn=None, reuse=None, variables_collections=None, outputs_collections=None, trainable=True, scope=None): """Adds a Layer Normalization layer from https://arxiv.org/abs/1607.06450. "Layer Normalization" Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey E. Hinton Can be used as a normalizer function for conv2d and fully_connected. Args: inputs: A tensor with 2 or more dimensions. The normalization occurs over all but the first dimension. center: If True, add offset of `beta` to normalized tensor. If False, `beta` is ignored. scale: If True, multiply by `gamma`. If False, `gamma` is not used. When the next layer is linear (also e.g. `nn.relu`), this can be disabled since the scaling can be done by the next layer. activation_fn: Activation function, default set to None to skip it and maintain a linear activation. reuse: Whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given. variables_collections: Optional collections for the variables. outputs_collections: Collections to add the outputs. trainable: If `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable). scope: Optional scope for `variable_scope`. Returns: A `Tensor` representing the output of the operation. Raises: ValueError: If rank or last dimension of `inputs` is undefined. """ with variable_scope.variable_scope(scope, 'LayerNorm', [inputs], reuse=reuse) as sc: inputs = ops.convert_to_tensor(inputs) inputs_shape = inputs.get_shape() inputs_rank = inputs_shape.ndims if inputs_rank is None: raise ValueError('Inputs %s has undefined rank.' % inputs.name) dtype = inputs.dtype.base_dtype axis = list(range(1, inputs_rank)) params_shape = inputs_shape[-1:] if not params_shape.is_fully_defined(): raise ValueError('Inputs %s has undefined last dimension %s.' % ( inputs.name, params_shape)) # Allocate parameters for the beta and gamma of the normalization. beta, gamma = None, None if center: beta_collections = utils.get_variable_collections(variables_collections, 'beta') beta = variables.model_variable( 'beta', shape=params_shape, dtype=dtype, initializer=init_ops.zeros_initializer(), collections=beta_collections, trainable=trainable) if scale: gamma_collections = utils.get_variable_collections(variables_collections, 'gamma') gamma = variables.model_variable( 'gamma', shape=params_shape, dtype=dtype, initializer=init_ops.ones_initializer(), collections=gamma_collections, trainable=trainable) # Calculate the moments on the last axis (layer activations). mean, variance = nn.moments(inputs, axis, keep_dims=True) # Compute layer normalization using the batch_normalization function. variance_epsilon = 1E-12 outputs = nn.batch_normalization( inputs, mean, variance, beta, gamma, variance_epsilon) outputs.set_shape(inputs_shape) if activation_fn is not None: outputs = activation_fn(outputs) return utils.collect_named_outputs(outputs_collections, sc.original_name_scope, outputs) @add_arg_scope def max_pool2d(inputs, kernel_size, stride=2, padding='VALID', data_format=DATA_FORMAT_NHWC, outputs_collections=None, scope=None): """Adds a 2D Max Pooling op. It is assumed that the pooling is done per image but not in batch or channels. Args: inputs: A 4-D tensor of shape `[batch_size, height, width, channels]` if `data_format` is `NHWC`, and `[batch_size, channels, height, width]` if `data_format` is `NCHW`. kernel_size: A list of length 2: [kernel_height, kernel_width] of the pooling kernel over which the op is computed. Can be an int if both values are the same. stride: A list of length 2: [stride_height, stride_width]. Can be an int if both strides are the same. Note that presently both strides must have the same value. padding: The padding method, either 'VALID' or 'SAME'. data_format: A string. `NHWC` (default) and `NCHW` are supported. outputs_collections: The collections to which the outputs are added. scope: Optional scope for name_scope. Returns: A `Tensor` representing the results of the pooling operation. Raises: ValueError: If `data_format` is neither `NHWC` nor `NCHW`. ValueError: If 'kernel_size' is not a 2-D list """ if data_format not in (DATA_FORMAT_NCHW, DATA_FORMAT_NHWC): raise ValueError('data_format has to be either NCHW or NHWC.') with ops.name_scope(scope, 'MaxPool2D', [inputs]) as sc: inputs = ops.convert_to_tensor(inputs) df = ('channels_first' if data_format and data_format.startswith('NC') else 'channels_last') layer = pooling_layers.MaxPooling2D(pool_size=kernel_size, strides=stride, padding=padding, data_format=df, _scope=sc) outputs = layer.apply(inputs) return utils.collect_named_outputs(outputs_collections, sc, outputs) @add_arg_scope def pool(inputs, kernel_size, pooling_type, padding='VALID', data_format=None, dilation_rate=1, stride=1, outputs_collections=None, scope=None): # pylint: disable=line-too-long """Adds a pooling op. Args: inputs: Tensor of rank N+2, of shape `[batch_size] + input_spatial_shape + [num_channels]` if data_format does not start with "NC" (default), or `[batch_size, num_channels] + input_spatial_shape` if data_format starts with "NC". Pooling happens over the spatial dimensions only. kernel_size: Sequence of N ints >= 1. Can also be a single integer to specify the same value for all spatial dimensions. pooling_type: Specifies pooling operation, must be "AVG" or "MAX". padding: The padding algorithm, must be "SAME" or "VALID". data_format: A string or None. Specifies whether the channel dimension of the `input` and output is the last dimension (default, or if `data_format` does not start with "NC"), or the second dimension (if `data_format` starts with "NC"). For N=1, the valid values are "NWC" (default) and "NCW". For N=2, the valid values are "NHWC" (default) and "NCHW". For N=3, currently the only valid value is "NDHWC". dilation_rate: Optional. Dilation rate. Sequence of N ints >= 1. Defaults to [1]*N. Can also be a single integer to specify the same value for all spatial dimensions. If any value of dilation_rate is > 1, then all values of stride must be 1. stride: Optional. Sequence of N ints >= 1. Defaults to [1]*N. Can also be a single integer to specify the same value for all spatial dimensions. If any value of stride is > 1, then all values of dilation_rate must be 1. outputs_collections: The collections to which the outputs are added. scope: Optional scope for name_scope. Returns: A `Tensor` representing the results of the pooling operation. Raises: ValueError: If arguments are invalid. """ # pylint: enable=line-too-long with ops.name_scope(scope, '%s_pool' % (pooling_type.lower()), [inputs]) as sc: inputs = ops.convert_to_tensor(inputs) input_rank = inputs.get_shape().ndims if input_rank is None: raise ValueError('Rank of inputs must be known') if input_rank < 3: raise ValueError('Rank of inputs must be >= 3') num_spatial_dims = input_rank - 2 output = nn.pool( input=inputs, window_shape=utils.n_positive_integers(num_spatial_dims, kernel_size), pooling_type=pooling_type, padding=padding, data_format=data_format, dilation_rate=utils.n_positive_integers(num_spatial_dims, dilation_rate), strides=utils.n_positive_integers(num_spatial_dims, stride), name=sc) return utils.collect_named_outputs(outputs_collections, sc, output) @add_arg_scope def one_hot_encoding(labels, num_classes, on_value=1.0, off_value=0.0, outputs_collections=None, scope=None): """Transform numeric labels into onehot_labels using `tf.one_hot`. Args: labels: [batch_size] target labels. num_classes: Total number of classes. on_value: A scalar defining the on-value. off_value: A scalar defining the off-value. outputs_collections: Collection to add the outputs. scope: Optional scope for name_scope. Returns: One-hot encoding of the labels. """ with ops.name_scope(scope, 'OneHotEncoding', [labels, num_classes]) as sc: labels = ops.convert_to_tensor(labels) if labels.dtype == dtypes.int32: labels = standard_ops.to_int64(labels) outputs = standard_ops.one_hot(labels, num_classes, on_value=on_value, off_value=off_value) return utils.collect_named_outputs(outputs_collections, sc, outputs) def _apply_activation(y, activation_fn, output_collections): if activation_fn is not None: y = activation_fn(y) ops.add_to_collections(list(output_collections or []) + [ops.GraphKeys.ACTIVATIONS], y) return y def repeat(inputs, repetitions, layer, *args, **kwargs): """Applies the same layer with the same arguments repeatedly. ```python y = repeat(x, 3, conv2d, 64, [3, 3], scope='conv1') # It is equivalent to: x = conv2d(x, 64, [3, 3], scope='conv1/conv1_1') x = conv2d(x, 64, [3, 3], scope='conv1/conv1_2') y = conv2d(x, 64, [3, 3], scope='conv1/conv1_3') ``` If the `scope` argument is not given in `kwargs`, it is set to `layer.__name__`, or `layer.func.__name__` (for `functools.partial` objects). If neither `__name__` nor `func.__name__` is available, the layers are called with `scope='stack'`. Args: inputs: A `Tensor` suitable for layer. repetitions: Int, number of repetitions. layer: A layer with arguments `(inputs, *args, **kwargs)` *args: Extra args for the layer. **kwargs: Extra kwargs for the layer. Returns: A tensor result of applying the layer, repetitions times. Raises: ValueError: If the op is unknown or wrong. """ scope = kwargs.pop('scope', None) with variable_scope.variable_scope(scope, 'Repeat', [inputs]): inputs = ops.convert_to_tensor(inputs) if scope is None: if hasattr(layer, '__name__'): scope = layer.__name__ elif hasattr(layer, 'func') and hasattr(layer.func, '__name__'): scope = layer.func.__name__ # In case layer is a functools.partial. else: scope = 'repeat' outputs = inputs for i in range(repetitions): kwargs['scope'] = scope + '_' + str(i+1) outputs = layer(outputs, *args, **kwargs) return outputs @add_arg_scope def separable_convolution2d( inputs, num_outputs, kernel_size, depth_multiplier, stride=1, padding='SAME', rate=1, activation_fn=nn.relu, normalizer_fn=None, normalizer_params=None, weights_initializer=initializers.xavier_initializer(), weights_regularizer=None, biases_initializer=init_ops.zeros_initializer(), biases_regularizer=None, reuse=None, variables_collections=None, outputs_collections=None, trainable=True, scope=None): """Adds a depth-separable 2D convolution with optional batch_norm layer. This op first performs a depthwise convolution that acts separately on channels, creating a variable called `depthwise_weights`. If `num_outputs` is not None, it adds a pointwise convolution that mixes channels, creating a variable called `pointwise_weights`. Then, if `batch_norm_params` is None, it adds bias to the result, creating a variable called 'biases', otherwise it adds a batch normalization layer. It finally applies an activation function to produce the end result. Args: inputs: A tensor of size [batch_size, height, width, channels]. num_outputs: The number of pointwise convolution output filters. If is None, then we skip the pointwise convolution stage. kernel_size: A list of length 2: [kernel_height, kernel_width] of of the filters. Can be an int if both values are the same. depth_multiplier: The number of depthwise convolution output channels for each input channel. The total number of depthwise convolution output channels will be equal to `num_filters_in * depth_multiplier`. stride: A list of length 2: [stride_height, stride_width], specifying the depthwise convolution stride. Can be an int if both strides are the same. padding: One of 'VALID' or 'SAME'. rate: A list of length 2: [rate_height, rate_width], specifying the dilation rates for a'trous convolution. Can be an int if both rates are the same. If any value is larger than one, then both stride values need to be one. activation_fn: Activation function. The default value is a ReLU function. Explicitly set it to None to skip it and maintain a linear activation. normalizer_fn: Normalization function to use instead of `biases`. If `normalizer_fn` is provided then `biases_initializer` and `biases_regularizer` are ignored and `biases` are not created nor added. default set to None for no normalizer function normalizer_params: Normalization function parameters. weights_initializer: An initializer for the weights. weights_regularizer: Optional regularizer for the weights. biases_initializer: An initializer for the biases. If None skip biases. biases_regularizer: Optional regularizer for the biases. reuse: Whether or not the layer and its variables should be reused. To be able to reuse the layer scope must be given. variables_collections: Optional list of collections for all the variables or a dictionary containing a different list of collection per variable. outputs_collections: Collection to add the outputs. trainable: Whether or not the variables should be trainable or not. scope: Optional scope for variable_scope. Returns: A `Tensor` representing the output of the operation. """ layer_variable_getter = _build_variable_getter( {'bias': 'biases', 'depthwise_kernel': 'depthwise_weights', 'pointwise_kernel': 'pointwise_weights'}) with variable_scope.variable_scope( scope, 'SeparableConv2d', [inputs], reuse=reuse, custom_getter=layer_variable_getter) as sc: inputs = ops.convert_to_tensor(inputs) if num_outputs is not None: # Apply separable conv using the SeparableConvolution2D layer. layer = convolutional_layers.SeparableConvolution2D( filters=num_outputs, kernel_size=kernel_size, strides=stride, padding=padding, data_format='channels_last', dilation_rate=utils.two_element_tuple(rate), activation=None, depth_multiplier=depth_multiplier, use_bias=not normalizer_fn and biases_initializer, depthwise_initializer=weights_initializer, pointwise_initializer=weights_initializer, bias_initializer=biases_initializer, depthwise_regularizer=weights_regularizer, pointwise_regularizer=weights_regularizer, bias_regularizer=biases_regularizer, activity_regularizer=None, trainable=trainable, name=sc.name, dtype=inputs.dtype.base_dtype, _scope=sc, _reuse=reuse) outputs = layer.apply(inputs) # Add variables to collections. _add_variable_to_collections(layer.depthwise_kernel, variables_collections, 'weights') _add_variable_to_collections(layer.pointwise_kernel, variables_collections, 'weights') if layer.bias: _add_variable_to_collections(layer.bias, variables_collections, 'biases') if normalizer_fn is not None: normalizer_params = normalizer_params or {} outputs = normalizer_fn(outputs, **normalizer_params) else: # Actually apply depthwise conv instead of separable conv. dtype = inputs.dtype.base_dtype kernel_h, kernel_w = utils.two_element_tuple(kernel_size) stride_h, stride_w = utils.two_element_tuple(stride) num_filters_in = utils.last_dimension(inputs.get_shape(), min_rank=4) weights_collections = utils.get_variable_collections( variables_collections, 'weights') depthwise_shape = [kernel_h, kernel_w, num_filters_in, depth_multiplier] depthwise_weights = variables.model_variable( 'depthwise_weights', shape=depthwise_shape, dtype=dtype, initializer=weights_initializer, regularizer=weights_regularizer, trainable=trainable, collections=weights_collections) strides = [1, stride_h, stride_w, 1] outputs = nn.depthwise_conv2d(inputs, depthwise_weights, strides, padding, rate=utils.two_element_tuple(rate)) num_outputs = depth_multiplier * num_filters_in if normalizer_fn is not None: normalizer_params = normalizer_params or {} outputs = normalizer_fn(outputs, **normalizer_params) else: if biases_initializer is not None: biases_collections = utils.get_variable_collections( variables_collections, 'biases') biases = variables.model_variable('biases', shape=[num_outputs,], dtype=dtype, initializer=biases_initializer, regularizer=biases_regularizer, collections=biases_collections) outputs = nn.bias_add(outputs, biases) if activation_fn is not None: outputs = activation_fn(outputs) return utils.collect_named_outputs(outputs_collections, sc.original_name_scope, outputs) @add_arg_scope def softmax(logits, scope=None): """Performs softmax on Nth dimension of N-dimensional logit tensor. For two-dimensional logits this reduces to tf.nn.softmax. The N-th dimension needs to have a specified number of elements (number of classes). Args: logits: N-dimensional `Tensor` with logits, where N > 1. scope: Optional scope for variable_scope. Returns: A `Tensor` with same shape and type as logits. """ # TODO(jrru): Add axis argument which defaults to last dimension. with variable_scope.variable_scope(scope, 'softmax', [logits]): num_logits = utils.last_dimension(logits.get_shape(), min_rank=2) logits_2d = array_ops.reshape(logits, [-1, num_logits]) predictions = nn.softmax(logits_2d) predictions = array_ops.reshape(predictions, array_ops.shape(logits)) predictions.set_shape(logits.get_shape()) return predictions def stack(inputs, layer, stack_args, **kwargs): """Builds a stack of layers by applying layer repeatedly using stack_args. `stack` allows you to repeatedly apply the same operation with different arguments `stack_args[i]`. For each application of the layer, `stack` creates a new scope appended with an increasing number. For example: ```python y = stack(x, fully_connected, [32, 64, 128], scope='fc') # It is equivalent to: x = fully_connected(x, 32, scope='fc/fc_1') x = fully_connected(x, 64, scope='fc/fc_2') y = fully_connected(x, 128, scope='fc/fc_3') ``` If the `scope` argument is not given in `kwargs`, it is set to `layer.__name__`, or `layer.func.__name__` (for `functools.partial` objects). If neither `__name__` nor `func.__name__` is available, the layers are called with `scope='stack'`. Args: inputs: A `Tensor` suitable for layer. layer: A layer with arguments `(inputs, *args, **kwargs)` stack_args: A list/tuple of parameters for each call of layer. **kwargs: Extra kwargs for the layer. Returns: A `Tensor` result of applying the stacked layers. Raises: ValueError: If the op is unknown or wrong. """ scope = kwargs.pop('scope', None) if not isinstance(stack_args, (list, tuple)): raise ValueError('stack_args need to be a list or tuple') with variable_scope.variable_scope(scope, 'Stack', [inputs]): inputs = ops.convert_to_tensor(inputs) if scope is None: if hasattr(layer, '__name__'): scope = layer.__name__ elif hasattr(layer, 'func') and hasattr(layer.func, '__name__'): scope = layer.func.__name__ # In case layer is a functools.partial. else: scope = 'stack' outputs = inputs for i in range(len(stack_args)): kwargs['scope'] = scope + '_' + str(i+1) layer_args = stack_args[i] if not isinstance(layer_args, (list, tuple)): layer_args = [layer_args] outputs = layer(outputs, *layer_args, **kwargs) return outputs @add_arg_scope def unit_norm(inputs, dim, epsilon=1e-7, scope=None): """Normalizes the given input across the specified dimension to unit length. Note that the rank of `input` must be known. Args: inputs: A `Tensor` of arbitrary size. dim: The dimension along which the input is normalized. epsilon: A small value to add to the inputs to avoid dividing by zero. scope: Optional scope for variable_scope. Returns: The normalized `Tensor`. Raises: ValueError: If dim is smaller than the number of dimensions in 'inputs'. """ with variable_scope.variable_scope(scope, 'UnitNorm', [inputs]): if not inputs.get_shape(): raise ValueError('The input rank must be known.') input_rank = len(inputs.get_shape().as_list()) if dim < 0 or dim >= input_rank: raise ValueError( 'dim must be positive but smaller than the input rank.') lengths = math_ops.sqrt(epsilon + math_ops.reduce_sum( math_ops.square(inputs), dim, True)) multiples = [] if dim > 0: multiples.append(array_ops.ones([dim], dtypes.int32)) multiples.append( array_ops.strided_slice(array_ops.shape(inputs), [dim], [dim + 1])) if dim < (input_rank - 1): multiples.append(array_ops.ones([input_rank - 1 - dim], dtypes.int32)) multiples = array_ops.concat(multiples, 0) return math_ops.div(inputs, array_ops.tile(lengths, multiples)) def legacy_fully_connected(x, num_output_units, activation_fn=None, weight_init=initializers.xavier_initializer(), bias_init=init_ops.zeros_initializer(), name=None, weight_collections=(ops.GraphKeys.WEIGHTS,), bias_collections=(ops.GraphKeys.BIASES,), output_collections=(ops.GraphKeys.ACTIVATIONS,), trainable=True, weight_regularizer=None, bias_regularizer=None): # pylint: disable=anomalous-backslash-in-string r"""Adds the parameters for a fully connected layer and returns the output. A fully connected layer is generally defined as a matrix multiply: `y = f(w * x + b)` where `f` is given by `activation_fn`. If `activation_fn` is `None`, the result of `y = w * x + b` is returned. If `x` has shape [\\\(\\text{dim}_0, \\text{dim}_1, ..., \\text{dim}_n\\\)] with more than 2 dimensions (\\\(n > 1\\\)), then we repeat the matrix multiply along the first dimensions. The result r is a tensor of shape [\\\(\\text{dim}_0, ..., \\text{dim}_{n-1},\\\) `num_output_units`], where \\\( r_{i_0, ..., i_{n-1}, k} = \\sum_{0 \\leq j < \\text{dim}_n} x_{i_0, ... i_{n-1}, j} \cdot w_{j, k}\\\). This is accomplished by reshaping `x` to 2-D [\\\(\\text{dim}_0 \\cdot ... \\cdot \\text{dim}_{n-1}, \\text{dim}_n\\\)] before the matrix multiply and afterwards reshaping it to [\\\(\\text{dim}_0, ..., \\text{dim}_{n-1},\\\) `num_output_units`]. This op creates `w` and optionally `b`. Bias (`b`) can be disabled by setting `bias_init` to `None`. The variable creation is compatible with `tf.variable_scope` and so can be reused with `tf.variable_scope` or `tf.make_template`. Most of the details of variable creation can be controlled by specifying the initializers (`weight_init` and `bias_init`) and in which collections to place the created variables (`weight_collections` and `bias_collections`; note that the variables are always added to the `VARIABLES` collection). The output of the layer can be placed in custom collections using `output_collections`. The collections arguments default to `WEIGHTS`, `BIASES` and `ACTIVATIONS`, respectively. A per layer regularization can be specified by setting `weight_regularizer` and `bias_regularizer`, which are applied to the weights and biases respectively, and whose output is added to the `REGULARIZATION_LOSSES` collection. Args: x: The input `Tensor`. num_output_units: The size of the output. activation_fn: Activation function, default set to None to skip it and maintain a linear activation. weight_init: An optional weight initialization, defaults to `xavier_initializer`. bias_init: An initializer for the bias, defaults to 0. Set to `None` in order to disable bias. name: The name for this operation is used to name operations and to find variables. If specified it must be unique for this scope, otherwise a unique name starting with "fully_connected" will be created. See `tf.variable_scope` for details. weight_collections: List of graph collections to which weights are added. bias_collections: List of graph collections to which biases are added. output_collections: List of graph collections to which outputs are added. trainable: If `True` also add variables to the graph collection `GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable). weight_regularizer: A regularizer like the result of `l1_regularizer` or `l2_regularizer`. Used for weights. bias_regularizer: A regularizer like the result of `l1_regularizer` or `l2_regularizer`. Used for biases. Returns: The output of the fully connected layer. Raises: ValueError: If x has rank less than 2 or if its last dimension is not set. """ with variable_scope.variable_scope(name, 'fully_connected', [x]): x = ops.convert_to_tensor(x) dims = x.get_shape().dims if dims is None: raise ValueError('dims of x must be known but is None') if len(dims) < 2: raise ValueError('rank of x must be at least 2 not: %d' % len(dims)) num_input_units = dims[-1].value if num_input_units is None: raise ValueError('last dimension of x must be known but is None') dtype = x.dtype.base_dtype weight_collections = set(list(weight_collections or []) + [ops.GraphKeys.GLOBAL_VARIABLES]) w = variable_scope.get_variable('weights', shape=[num_input_units, num_output_units], dtype=dtype, initializer=weight_init, collections=weight_collections, regularizer=weight_regularizer, trainable=trainable) x_2_dim = x if len(dims) <= 2 else array_ops.reshape(x, [-1, num_input_units]) y = standard_ops.matmul(x_2_dim, w) if bias_init is not None: bias_collections = set(list(bias_collections or []) + [ops.GraphKeys.GLOBAL_VARIABLES]) b = variable_scope.get_variable('bias', shape=[num_output_units], dtype=dtype, initializer=bias_init, collections=bias_collections, regularizer=bias_regularizer, trainable=trainable) y = nn.bias_add(y, b) if len(dims) > 2: out_shape = array_ops.unstack(array_ops.shape(x)) out_shape[-1] = num_output_units y = array_ops.reshape(y, array_ops.stack(out_shape)) static_shape = x.get_shape().as_list() static_shape[-1] = num_output_units y.set_shape(static_shape) return _apply_activation(y, activation_fn, output_collections) # TODO(eiderm): Verify and fix autocomplete in colab (also relu6). # Simple aliases which remove the activation_fn parameter. legacy_relu = functools.partial(legacy_fully_connected, activation_fn=nn.relu) legacy_linear = functools.partial(legacy_fully_connected, activation_fn=None) relu = functools.partial(fully_connected, activation_fn=nn.relu) relu6 = functools.partial(fully_connected, activation_fn=nn.relu6) linear = functools.partial(fully_connected, activation_fn=None) # Simple alias. conv2d = convolution2d conv2d_transpose = convolution2d_transpose conv2d_in_plane = convolution2d_in_plane separable_conv2d = separable_convolution2d

apache-2.0

dataflow/DataStage

datastage/dataset/longliving/sword_statement_check.py

1

4734

import logging import time import thread import urllib2 import sys import datetime from django_longliving.base import LonglivingThread from datastage.dataset import SUBMISSION_QUEUE from datastage.web.dataset.models import DatasetSubmission from datastage.web.dataset import openers from sword2 import Connection, UrlLib2Layer logger = logging.getLogger(__name__) # list of all the error states that we can see in the statement that we want # to be able to react to ERROR_STATES = [ "http://databank.ox.ac.uk/errors/UnzippingIssue" ] # NOTE: this thread is resistant to being stopped. A KeyboardInterrupt will # NOT suffice, it will need to be killed with a "kill <pid>" on the command # line class SwordStatementCheckThread(LonglivingThread): # FIXME: not quite sure how the __init__ function on LonglivingThread, # so setting this as a class variable for the time being # this is how long the thread will sleep between requests (in seconds) throttle = 5 # this is how long the thread will sleep between retrys (in seconds) retry_delay = 3 # This is how long the thread will sleep between entire batches of updates. # This is particularly useful if the total number of submissions is quite # small - it will stop the while True loop just spinning aimlessly most of # the time. (in seconds) batch_throttle = 120 # this is how many times the thread will re-try contacting the server if # it suffers a major exception (i.e. not a sword exception, but something # network related) retry_count = 10 # this is the gap between attempts to check a specific item. If the item # has been checked more recently than this amount of time ago, it will not # be checked again on the current run. Specified in seconds (here it is # set to once per day). check_gap = 86400 def run(self): # just keep going until the thread is killed while True: self._check_all_datasets() time.sleep(SwordStatementCheckThread.batch_throttle) def _check_all_datasets(self): dss = DatasetSubmission.objects.all() for dataset_submission in dss: if not self._checkable(dataset_submission): continue self._check_dataset(dataset_submission) def _checkable(self, dataset_submission): last_checked = dataset_submission.last_accessed if last_checked is None: return True now = datetime.datetime.now() minimum = datetime.timedelta(0, SwordStatementCheckThread.check_gap) gap = now - last_checked return gap > minimum def _check_dataset(self, dataset_submission): retry_counter = 0 exception = None while retry_counter < SwordStatementCheckThread.retry_count: try: # logger.info("Checking state of dataset at " + dataset_submission.remote_url) opener = openers.get_opener(dataset_submission.repository, dataset_submission.submitting_user) conn = Connection(error_response_raises_exceptions=False, http_impl=UrlLib2Layer(opener)) receipt = conn.get_deposit_receipt(dataset_submission.remote_url) statement = conn.get_ore_sword_statement(receipt.ore_statement_iri) for state_uri, state_desc in statement.states: logger.info("Dataset has state URI: " + state_uri) if state_uri in ERROR_STATES: dataset_submission.status = 'error' logger.info("URI: " + state_uri + " is an error state ... setting 'error' state on submission record") break dataset_submission.last_accessed = datetime.datetime.now() dataset_submission.save() time.sleep(SwordStatementCheckThread.throttle) except urllib2.URLError as e: # if we get an exception, try again up to the limit logger.info("Got error connecting to the server ... retrying " + str(retry_counter + 1) + " of " + str(SwordStatementCheckThread.retry_count)) retry_counter += 1 exception = e time.sleep(SwordStatementCheckThread.retry_delay) continue else: # if we don't get an exception, we're done return # if we don't return from the else statement above, it means the retries # all failed, and we have a problem. Raise the last thrown exception. raise exception

mit

laurent-george/weboob

modules/cmso/web/pages.py

1

3661

# -*- coding: utf-8 -*- # Copyright(C) 2014 smurail # # This file is part of weboob. # # weboob is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # weboob is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Affero General Public License for more details. # # You should have received a copy of the GNU Affero General Public License # along with weboob. If not, see <http://www.gnu.org/licenses/>. import datetime from weboob.browser.pages import HTMLPage, LoggedPage, pagination from weboob.browser.elements import ListElement, ItemElement, method from weboob.browser.filters.standard import CleanText, CleanDecimal, Regexp, DateGuesser, Env from weboob.browser.filters.html import Link from weboob.capabilities.bank import Account from ..transaction import Transaction __all__ = ['LoginPage'] class LoginPage(HTMLPage): def login(self, username, password): form = self.get_form('//form[@id="formAuth"]') form['noPersonne'] = username form['motDePasse'] = password form.submit() class CmsoListElement(ListElement): item_xpath = '//table[@class="Tb" and tr[1][@class="LnTit"]]/tr[@class="LnA" or @class="LnB"]' class AccountsPage(LoggedPage, HTMLPage): @method class iter_accounts(CmsoListElement): class item(ItemElement): klass = Account obj__history_url = Link('./td[1]/a') obj_label = CleanText('./td[1]') obj_id = obj__history_url & Regexp(pattern="indCptSelectionne=(\d+)") | None obj_balance = CleanDecimal('./td[2]', replace_dots=True) def validate(self, obj): if obj.id is None: obj.id = obj.label.replace(' ', '') return True class CmsoTransactionElement(ItemElement): klass = Transaction def condition(self): return len(self.el) >= 5 and not self.el.get('id', '').startswith('libelleLong') class HistoryPage(LoggedPage, HTMLPage): def iter_history(self, *args, **kwargs): if self.doc.xpath('//a[@href="1-situationGlobaleProfessionnel.act"]'): return self.iter_history_rest_page(*args, **kwargs) return self.iter_history_first_page(*args, **kwargs) @method class iter_history_first_page(CmsoListElement): class item(CmsoTransactionElement): def validate(self, obj): return obj.date >= datetime.date.today().replace(day=1) def date(selector): return DateGuesser(CleanText(selector), Env('date_guesser')) | Transaction.Date(selector) obj_date = date('./td[1]') obj_vdate = date('./td[2]') # Each row is followed by a "long labelled" version obj_raw = Transaction.Raw('./following-sibling::tr[1][starts-with(@id, "libelleLong")]/td[3]') obj_amount = Transaction.Amount('./td[5]', './td[4]') @pagination @method class iter_history_rest_page(CmsoListElement): next_page = Link('//span[has-class("Rappel")]/following-sibling::*[1][@href]') class item(CmsoTransactionElement): obj_date = Transaction.Date('./td[2]') obj_vdate = Transaction.Date('./td[1]') obj_raw = Transaction.Raw('./td[3]') obj_amount = Transaction.Amount('./td[5]', './td[4]', replace_dots=False)

agpl-3.0

r0balo/pelisalacarta

python/main-classic/channels/yaske.py

1

68313

# -*- coding: utf-8 -*- #------------------------------------------------------------ # pelisalacarta - XBMC Plugin # Canal para yaske # http://blog.tvalacarta.info/plugin-xbmc/pelisalacarta/ #------------------------------------------------------------ import re, sys, urllib, urlparse from core import config from core import logger from core import httptools from core import scrapertools from core import servertools from core import channeltools from core import tmdb from core.item import Item HOST = 'http://www.yaske.ro' parameters= channeltools.get_channel_parameters('yaske') fanart_host= parameters['fanart'] thumbnail_host= parameters['thumbnail'] color1, color2, color3 = ['0xFFA5F6AF','0xFF5FDA6D','0xFF11811E'] def mainlist(item): logger.info() itemlist = [] item.url = HOST item.text_color = color2 item.fanart = fanart_host thumbnail = "https://raw.githubusercontent.com/master-1970/resources/master/images/genres/4/verdes/%s.png" itemlist.append(item.clone(title="Novedades", action="peliculas", text_blod= True, viewcontent='movies', thumbnail= thumbnail % 'novedades', viewmode = "movie_with_plot")) itemlist.append(item.clone(title="Estrenos", action="peliculas", text_blod=True, url= HOST + "/genero/premieres", thumbnail=thumbnail % 'estrenos')) itemlist.append(item.clone(title="", folder=False)) itemlist.append(Item(channel=item.channel, title="Filtrar por:", fanart=fanart_host, folder=False, text_color=color3, text_blod= True, thumbnail=thumbnail_host)) itemlist.append(item.clone(title=" Género", action="menu_buscar_contenido", text_color=color1, text_italic=True, extra="gender", thumbnail=thumbnail % 'generos', viewmode = "thumbnails" )) itemlist.append(item.clone(title=" Idioma", action="menu_buscar_contenido", text_color=color1, text_italic=True, extra="language", thumbnail=thumbnail % 'idiomas')) itemlist.append(item.clone(title=" Calidad", action="menu_buscar_contenido", text_color=color1, text_italic=True, extra="quality", thumbnail=thumbnail % 'calidad')) itemlist.append(item.clone(title=" Año", action="menu_buscar_contenido", text_color=color1, text_italic=True, extra="year", thumbnail=thumbnail % 'year')) itemlist.append(item.clone(title="", folder=False)) itemlist.append(item.clone(title="Buscar por título", action="search", thumbnail=thumbnail % 'buscar') ) return itemlist def search(item,texto): logger.info() itemlist = [] try: item.url = HOST + "/search/%s" % texto.replace(' ', '+') item.extra = "" itemlist.extend(peliculas(item)) if itemlist[-1].title == ">> Página siguiente": item_pag = itemlist[-1] itemlist = sorted(itemlist[:-1], key=lambda Item: Item.contentTitle) itemlist.append(item_pag) else: itemlist = sorted(itemlist, key=lambda Item: Item.contentTitle) return itemlist except: import sys for line in sys.exc_info(): logger.error( "%s" % line ) return [] def newest(categoria): logger.info() itemlist = [] item = Item() try: if categoria == 'peliculas': item.url = HOST+"/" elif categoria == 'infantiles': item.url = HOST+"/custom/?gender=animation" else: return [] itemlist = peliculas(item) if itemlist[-1].title == ">> Página siguiente": itemlist.pop() # Se captura la excepción, para no interrumpir al canal novedades si un canal falla except: import sys for line in sys.exc_info(): logger.error("{0}".format(line)) return [] return itemlist def peliculas(item): logger.info() itemlist = [] url_next_page = "" data = httptools.downloadpage(item.url).data data = re.sub(r"\n|\r|\t|\s{2}|&nbsp;","",data) patron = '<li class="item-movies.*?' patron += '<a class="image-block" href="([^"]+)" title="([^"]+)">' patron += '<img src="([^"]+).*?' patron += '<div class="moSinopsis">.*?</b>([^<]+).*?' patron += '<div class="moYear">.*?</b>([^<]+).*?' patron += '<ul class="bottombox">.*?<li>(<img.*?)</li>.*?</ul>' patron += '<div class="quality">([^<]+)</div>' matches = re.compile(patron,re.DOTALL).findall(data) # Paginacion if item.next_page != 'b': if len(matches) > 20: url_next_page = item.url matches = matches [:20] next_page = 'b' else: matches = matches[20:] next_page = 'a' patron_next_page = "<a href='([^']+)'>\&raquo\;</a>" matches_next_page = re.compile(patron_next_page, re.DOTALL).findall(data) if len(matches_next_page) > 0: url_next_page = urlparse.urljoin(item.url, matches_next_page[0]) for scrapedurl, scrapedtitle, scrapedthumbnail, scrapedplot, year, idiomas, calidad in matches: patronidiomas = "<img src='[^']+' title='([^']+)'" matchesidiomas = re.compile(patronidiomas,re.DOTALL).findall(idiomas) idiomas_disponibles = "" if matchesidiomas: idiomas_disponibles = "[" + "/".join(matchesidiomas).strip() + "]" contentTitle = decodeHtmlentities(scrapedtitle.strip()) title = "%s %s [%s]" %(contentTitle, idiomas_disponibles, calidad) plot = decodeHtmlentities(scrapedplot) itemlist.append(Item(channel=item.channel, action="findvideos", title=title, url=scrapedurl, contentQuality=calidad, thumbnail=scrapedthumbnail, plot=plot, contentTitle=contentTitle, infoLabels={"year":year}, text_color = color1)) # Obtenemos los datos basicos de todas las peliculas mediante multihilos tmdb.set_infoLabels(itemlist) # Si es necesario añadir paginacion if url_next_page: itemlist.append(Item(channel=item.channel, action="peliculas", title=">> Página siguiente", thumbnail=thumbnail_host, url=url_next_page, next_page=next_page, folder=True, text_color = color3, text_blod=True)) return itemlist def menu_buscar_contenido(item): logger.info() data = httptools.downloadpage(item.url).data data = scrapertools.get_match(data,'<select name="'+item.extra+'"(.*?)</select>') # Extrae las entradas patron = "<option value='([^']+)'>([^<]+)</option>" matches = re.compile(patron,re.DOTALL).findall(data) itemlist = [] adult_mode = config.get_setting("adult_mode") for scrapedurl,scrapedtitle in matches: thumbnail = "" if item.extra == 'gender': if scrapedtitle in ['Proximos', 'Series', 'Noticia'] or (scrapedtitle == 'Adultos' and adult_mode == "false"): continue url = HOST + "/genero/" + scrapedurl thumbnail = "https://raw.githubusercontent.com/master-1970/resources/master/images/genres/4/verdes/%s.png" \ % scrapedtitle.lower().replace(' ','%20') else: url = HOST+"/custom/?"+item.extra+"="+scrapedurl thumbnail = item.thumbnail itemlist.append( Item(channel=item.channel, action="peliculas", title=scrapedtitle, url=url, text_color = color1, thumbnail=thumbnail, contentType='movie', folder=True, viewmode="movie_with_plot") ) if item.extra in ['gender', 'language']: return sorted(itemlist, key=lambda i: i.title.lower()) else: return itemlist def findvideos(item): logger.info() langdict = {} itemlist = [] # Descarga la página data = httptools.downloadpage(item.url).data if not item.plot: item.plot = scrapertools.find_single_match(data,'<meta name="sinopsis" content="([^"]+)"') item.plot = decodeHtmlentities(item.plot) patron = '<tr bgcolor=(.*?)</tr>' matches = re.compile(patron,re.DOTALL).findall(data) for tr in matches: try: url = scrapertools.find_single_match(tr, '<a.*?href="([^"]+)"') if not url.startswith("http") or "olimpo.link" in url: continue title = scrapertools.find_single_match(tr,'<i class="icon-([^"]+)') server = scrapertools.find_single_match(tr,'"http\://www.google.com[^>]+>([^<]+)') idioma = scrapertools.find_single_match(tr, '<img src="http://www.yaske.[a-z]+/theme/01/data/images/flags/([a-z_]+).png"[^>]+>[^<]*<') subtitulos = scrapertools.find_single_match(tr, '<img src="http://www.yaske.[a-z]+/theme/01/data/images/flags/[^"]+"[^>]+>([^<]*)<') thumbnail = servertools.guess_server_thumbnail(server) # TODO: esto tarda un mundo, a ver si lo cambiamos if not thumbnail: thumbnail = thumbnail_host if title == 'play': title = " Ver en %s" % server elif title == 'download': title = " Descargar de %s" % server else: title = " %s en %s" % (title, server) sublist = langdict.get(idioma, list()) sublist.append(item.clone(action="play", title=title, url=url, server=server, thumbnail=thumbnail, folder=False, text_color=color1)) langdict[idioma] = sublist except: import traceback logger.info("Excepcion: "+traceback.format_exc()) # Añadir servidores encontrados, agrupandolos por idioma lang_trans = {"es_es": "Español:", "la_la": "Latino:", "en_es": "Subtitulado:", "en_en": "Ingles:"} for k in ["es_es", "la_la", "en_es", "en_en"]: if k in langdict: itemlist.append(Item(channel=item.channel, title=lang_trans[k], fanart=item.fanart, folder=False, text_color=color2, text_blod=True, thumbnail=thumbnail_host)) itemlist.extend(langdict.pop(k)) # Otros idiomas for k, v in langdict.items(): if subtitulos: title = "%s/s%:" % (k, subtitulos) else: title = "%s:" % k itemlist.append(Item(channel=item.channel, title=title, fanart=fanart_host, folder=False, text_color=color2, text_blod=True, thumbnail=thumbnail_host)) itemlist.extend(langdict.pop(k)) # Insertar items "Buscar trailer" y "Añadir a la biblioteca" if itemlist and item.extra != "library": title = "%s [%s]" % (item.contentTitle, item.contentQuality) itemlist.insert(0, item.clone(channel = "trailertools", action="buscartrailer", text_color=color3, title=title, viewmode="list")) if config.get_library_support(): itemlist.append(Item(channel=item.channel, title="Añadir película a la biblioteca", action="add_pelicula_to_library", url=item.url, text_color="green", contentTitle=item.contentTitle, extra="library", thumbnail=thumbnail_host)) return itemlist def play(item): logger.info("item.url="+item.url) itemlist=[] data = urllib.unquote(item.url) newdata = scrapertools.find_single_match(data,'olo.gg/s/[a-zA-Z0-9]+.s.(.*?)$') if newdata: data = urllib.unquote(newdata) logger.info("item.url=" + data) # Buscamos video por servidor ... devuelve = servertools.findvideosbyserver(data, item.server) if not devuelve: # ...sino lo encontramos buscamos en todos los servidores disponibles devuelve = servertools.findvideos(data) if devuelve: #logger.debug(devuelve) itemlist.append(Item(channel=item.channel, title=item.contentTitle, action="play", server=devuelve[0][2], url=devuelve[0][1], thumbnail=item.thumbnail, folder=False)) return itemlist # TODO: Esto es temporal hasta q se modifique scrapertools def decodeHtmlentities(data): entity_re = re.compile("&(#?)(\d{1,5}|\w{1,8})(;?)") # maps the HTML5 named character references to the equivalent Unicode character(s) html5 = {'CupCap;': '\u224d', 'minusdu;': '\u2a2a', 'aring': '\xe5', 'Ubreve;': '\u016c', 'lcedil;': '\u013c', 'Zacute;': '\u0179', 'NotVerticalBar;': '\u2224', 'bbrk;': '\u23b5', 'ThinSpace;': '\u2009', 'nwarhk;': '\u2923', 'rlm;': '\u200f', 'DoubleDownArrow;': '\u21d3', 'RightDownVectorBar;': '\u2955', 'jukcy;': '\u0454', 'frac12;': '\xbd', 'subrarr;': '\u2979', 'rsquo;': '\u2019', 'aacute;': '\xe1', 'Integral;': '\u222b', 'oS;': '\u24c8', 'eqslantgtr;': '\u2a96', 'Uuml': '\xdc', 'piv;': '\u03d6', 'iinfin;': '\u29dc', 'Ubrcy;': '\u040e', 'lhblk;': '\u2584', 'uml': '\xa8', 'backcong;': '\u224c', 'capdot;': '\u2a40', 'harr;': '\u2194', 'lsquor;': '\u201a', 'iscr;': '\U0001d4be', 'Lsh;': '\u21b0', 'Implies;': '\u21d2', 'Oacute': '\xd3', 'reg': '\xae', 'vsupnE;': '\u2acc\ufe00', 'Pcy;': '\u041f', 'nang;': '\u2220\u20d2', 'Kcy;': '\u041a', 'GT': '>', 'eacute;': '\xe9', 'breve;': '\u02d8', 'mfr;': '\U0001d52a', 'bnot;': '\u2310', 'racute;': '\u0155', 'dtrif;': '\u25be', 'cedil': '\xb8', 'gesdotol;': '\u2a84', 'sc;': '\u227b', 'npreceq;': '\u2aaf\u0338', 'NotTildeTilde;': '\u2249', 'nlE;': '\u2266\u0338', 'trianglerighteq;': '\u22b5', 'gfr;': '\U0001d524', 'odblac;': '\u0151', 'wedge;': '\u2227', 'solb;': '\u29c4', 'isinE;': '\u22f9', 'middot;': '\xb7', 'nshortparallel;': '\u2226', 'cudarrr;': '\u2935', 'loarr;': '\u21fd', 'UnderBar;': '_', 'mstpos;': '\u223e', 'Oacute;': '\xd3', 'ltdot;': '\u22d6', 'gacute;': '\u01f5', 'Tcy;': '\u0422', 'Jcy;': '\u0419', 'wr;': '\u2240', 'Amacr;': '\u0100', 'gtrdot;': '\u22d7', 'rarrap;': '\u2975', 'boxtimes;': '\u22a0', 'nearr;': '\u2197', 'ecaron;': '\u011b', 'angmsdad;': '\u29ab', 'ropf;': '\U0001d563', 'uacute;': '\xfa', 'nsucc;': '\u2281', 'nvap;': '\u224d\u20d2', 'udblac;': '\u0171', 'range;': '\u29a5', 'udhar;': '\u296e', 'nwarr;': '\u2196', 'lneq;': '\u2a87', 'Uuml;': '\xdc', 'Tab;': '\t', 'Lmidot;': '\u013f', 'Tfr;': '\U0001d517', 'TScy;': '\u0426', 'nvge;': '\u2265\u20d2', 'mp;': '\u2213', 'gl;': '\u2277', 'YAcy;': '\u042f', 'CenterDot;': '\xb7', 'iopf;': '\U0001d55a', 'varsigma;': '\u03c2', 'lbrack;': '[', 'icy;': '\u0438', 'boxDR;': '\u2554', 'nsubseteq;': '\u2288', 'Ocy;': '\u041e', 'integers;': '\u2124', 'THORN': '\xde', 'cwint;': '\u2231', 'downharpoonright;': '\u21c2', 'capbrcup;': '\u2a49', 'nGtv;': '\u226b\u0338', 'nge;': '\u2271', 'angmsdac;': '\u29aa', 'ropar;': '\u2986', 'boxdl;': '\u2510', 'bigcup;': '\u22c3', 'lsim;': '\u2272', 'gtquest;': '\u2a7c', 'lrhar;': '\u21cb', 'Aring': '\xc5', 'Cap;': '\u22d2', 'twoheadrightarrow;': '\u21a0', 'ngsim;': '\u2275', 'plus;': '+', 'LeftArrowBar;': '\u21e4', 'lesseqqgtr;': '\u2a8b', 'softcy;': '\u044c', 'ne;': '\u2260', 'Agrave': '\xc0', 'SmallCircle;': '\u2218', 'andd;': '\u2a5c', 'LeftArrow;': '\u2190', 'napE;': '\u2a70\u0338', 'iuml': '\xef', 'Lscr;': '\u2112', 'gla;': '\u2aa5', 'yicy;': '\u0457', 'bsime;': '\u22cd', 'gtreqqless;': '\u2a8c', 'female;': '\u2640', 'cupdot;': '\u228d', 'pound': '\xa3', 'yacy;': '\u044f', 'varkappa;': '\u03f0', 'lambda;': '\u03bb', 'circledcirc;': '\u229a', 'circlearrowleft;': '\u21ba', 'Beta;': '\u0392', 'REG': '\xae', 'drbkarow;': '\u2910', 'boxhu;': '\u2534', 'xvee;': '\u22c1', 'boxv;': '\u2502', 'igrave;': '\xec', 'SquareSupersetEqual;': '\u2292', 'Afr;': '\U0001d504', 'lacute;': '\u013a', 'Yacute;': '\xdd', 'xrArr;': '\u27f9', 'mnplus;': '\u2213', 'shchcy;': '\u0449', 'Hopf;': '\u210d', 'ucirc': '\xfb', 'tau;': '\u03c4', 'TSHcy;': '\u040b', 'Icirc': '\xce', 'imath;': '\u0131', 'qprime;': '\u2057', 'uhblk;': '\u2580', 'lbarr;': '\u290c', 'Hstrok;': '\u0126', 'NotLessGreater;': '\u2278', 'vsubne;': '\u228a\ufe00', 'DoubleLeftRightArrow;': '\u21d4', 'larrtl;': '\u21a2', 'LessEqualGreater;': '\u22da', 'boxVl;': '\u2562', 'csupe;': '\u2ad2', 'gesdoto;': '\u2a82', 'lEg;': '\u2a8b', 'zhcy;': '\u0436', 'icirc': '\xee', 'rmoust;': '\u23b1', 'RoundImplies;': '\u2970', 'subE;': '\u2ac5', 'zwj;': '\u200d', 'VerticalLine;': '|', 'ell;': '\u2113', 'larrbfs;': '\u291f', 'OpenCurlyDoubleQuote;': '\u201c', 'Hfr;': '\u210c', 'ddotseq;': '\u2a77', 'orderof;': '\u2134', 'Element;': '\u2208', 'circledast;': '\u229b', 'larrpl;': '\u2939', 'longmapsto;': '\u27fc', 'lessapprox;': '\u2a85', 'nLtv;': '\u226a\u0338', 'ast;': '*', 'DiacriticalTilde;': '\u02dc', 'lrm;': '\u200e', 'imagpart;': '\u2111', 'Ropf;': '\u211d', 'scE;': '\u2ab4', 'deg': '\xb0', 'll;': '\u226a', 'mopf;': '\U0001d55e', 'ograve;': '\xf2', 'notnivc;': '\u22fd', 'prnap;': '\u2ab9', 'CircleDot;': '\u2299', 'blank;': '\u2423', 'NotLeftTriangleEqual;': '\u22ec', 'num;': '#', 'langle;': '\u27e8', 'scaron;': '\u0161', 'subne;': '\u228a', 'prE;': '\u2ab3', 'Tau;': '\u03a4', 'trie;': '\u225c', 'times': '\xd7', 'eg;': '\u2a9a', 'rightharpoonup;': '\u21c0', 'nearhk;': '\u2924', 'pointint;': '\u2a15', 'Pscr;': '\U0001d4ab', 'quot': '"', 'Iacute;': '\xcd', 'dcy;': '\u0434', 'upsi;': '\u03c5', 'MediumSpace;': '\u205f', 'DownLeftVectorBar;': '\u2956', 'supdsub;': '\u2ad8', 'Ccirc;': '\u0108', 'luruhar;': '\u2966', 'LT': '<', 'chcy;': '\u0447', 'lsimg;': '\u2a8f', 'ljcy;': '\u0459', 'complexes;': '\u2102', 'dagger;': '\u2020', 'isinv;': '\u2208', 'PartialD;': '\u2202', 'prod;': '\u220f', 'subplus;': '\u2abf', 'digamma;': '\u03dd', 'Ccedil': '\xc7', 'blacktriangle;': '\u25b4', 'veeeq;': '\u225a', 'lesdotor;': '\u2a83', 'gcy;': '\u0433', 'ntgl;': '\u2279', 'Ouml': '\xd6', 'eparsl;': '\u29e3', 'xsqcup;': '\u2a06', 'glE;': '\u2a92', 'bowtie;': '\u22c8', 'SquareIntersection;': '\u2293', 'RightFloor;': '\u230b', 'Efr;': '\U0001d508', 'DownLeftRightVector;': '\u2950', 'hercon;': '\u22b9', 'ecy;': '\u044d', 'DoubleDot;': '\xa8', 'rcub;': '}', 'asympeq;': '\u224d', 'NotTildeFullEqual;': '\u2247', 'Gg;': '\u22d9', 'gtreqless;': '\u22db', 'Sscr;': '\U0001d4ae', 'cularrp;': '\u293d', 'DoubleUpArrow;': '\u21d1', 'sect': '\xa7', 'map;': '\u21a6', 'Del;': '\u2207', 'ctdot;': '\u22ef', 'Umacr;': '\u016a', 'copf;': '\U0001d554', 'minus;': '\u2212', 'smte;': '\u2aac', 'zfr;': '\U0001d537', 'measuredangle;': '\u2221', 'male;': '\u2642', 'angrtvbd;': '\u299d', 'NestedGreaterGreater;': '\u226b', 'uuml;': '\xfc', 'ograve': '\xf2', 'Alpha;': '\u0391', 'QUOT;': '"', 'timesd;': '\u2a30', 'hyphen;': '\u2010', 'dopf;': '\U0001d555', 'Backslash;': '\u2216', 'utrif;': '\u25b4', 'ntrianglerighteq;': '\u22ed', 'Hat;': '^', 'between;': '\u226c', 'zacute;': '\u017a', 'geqslant;': '\u2a7e', 'elinters;': '\u23e7', 'lvertneqq;': '\u2268\ufe00', 'Yscr;': '\U0001d4b4', 'NotPrecedesEqual;': '\u2aaf\u0338', 'otilde': '\xf5', 'rtriltri;': '\u29ce', 'SucceedsSlantEqual;': '\u227d', 'bsim;': '\u223d', 'dscy;': '\u0455', 'cirmid;': '\u2aef', 'gnapprox;': '\u2a8a', 'uharl;': '\u21bf', 'sqsube;': '\u2291', 'YIcy;': '\u0407', 'forall;': '\u2200', 'ogt;': '\u29c1', 'Vopf;': '\U0001d54d', 'ffllig;': '\ufb04', 'loz;': '\u25ca', 'Atilde;': '\xc3', 'ntlg;': '\u2278', 'vangrt;': '\u299c', 'it;': '\u2062', 'GreaterTilde;': '\u2273', 'rarrhk;': '\u21aa', 'smid;': '\u2223', 'kappa;': '\u03ba', 'Diamond;': '\u22c4', 'ngeq;': '\u2271', 'DownArrowBar;': '\u2913', 'expectation;': '\u2130', 'sup3': '\xb3', 'frasl;': '\u2044', 'Bscr;': '\u212c', 'geqq;': '\u2267', 'lat;': '\u2aab', 'macr;': '\xaf', 'longrightarrow;': '\u27f6', 'Gcirc;': '\u011c', 'Wcirc;': '\u0174', 'horbar;': '\u2015', 'dharr;': '\u21c2', 'DownRightTeeVector;': '\u295f', 'GreaterEqual;': '\u2265', 'rBarr;': '\u290f', 'precsim;': '\u227e', 'iuml;': '\xef', 'ZHcy;': '\u0416', 'vnsub;': '\u2282\u20d2', 'UnderParenthesis;': '\u23dd', 'RuleDelayed;': '\u29f4', 'bull;': '\u2022', 'swArr;': '\u21d9', 'nrtri;': '\u22eb', 'apE;': '\u2a70', 'nLt;': '\u226a\u20d2', 'LeftDownVectorBar;': '\u2959', 'succnapprox;': '\u2aba', 'szlig': '\xdf', 'vcy;': '\u0432', 'wcirc;': '\u0175', 'utri;': '\u25b5', 'Zeta;': '\u0396', 'Hcirc;': '\u0124', 'NotRightTriangle;': '\u22eb', 'NotGreaterEqual;': '\u2271', 'larrb;': '\u21e4', 'ecolon;': '\u2255', 'ascr;': '\U0001d4b6', 'RightUpVectorBar;': '\u2954', 'divide': '\xf7', 'npolint;': '\u2a14', 'nexist;': '\u2204', 'plusb;': '\u229e', 'boxvl;': '\u2524', 'searhk;': '\u2925', 'oror;': '\u2a56', 'tdot;': '\u20db', 'bigotimes;': '\u2a02', 'phone;': '\u260e', 'Gscr;': '\U0001d4a2', 'bumpe;': '\u224f', 'ang;': '\u2220', 'ltquest;': '\u2a7b', 'rightharpoondown;': '\u21c1', 'rdca;': '\u2937', 'cross;': '\u2717', 'Kopf;': '\U0001d542', 'IEcy;': '\u0415', 'leq;': '\u2264', 'rarrw;': '\u219d', 'rcy;': '\u0440', 'Mu;': '\u039c', 'nopf;': '\U0001d55f', 'Aopf;': '\U0001d538', 'CloseCurlyDoubleQuote;': '\u201d', 'lbrace;': '{', 'triangleq;': '\u225c', 'curlyeqprec;': '\u22de', 'LeftDownTeeVector;': '\u2961', 'subset;': '\u2282', 'xscr;': '\U0001d4cd', 'brvbar;': '\xa6', 'nles;': '\u2a7d\u0338', 'circeq;': '\u2257', 'boxVH;': '\u256c', 'lE;': '\u2266', 'zeta;': '\u03b6', 'congdot;': '\u2a6d', 'emsp13;': '\u2004', 'uogon;': '\u0173', 'xcap;': '\u22c2', 'eta;': '\u03b7', 'lAarr;': '\u21da', 'thicksim;': '\u223c', 'boxDl;': '\u2556', 'rmoustache;': '\u23b1', 'Sopf;': '\U0001d54a', 'uarr;': '\u2191', 'Otimes;': '\u2a37', 'boxvH;': '\u256a', 'lparlt;': '\u2993', 'nsime;': '\u2244', 'sqcaps;': '\u2293\ufe00', 'SquareUnion;': '\u2294', 'Rsh;': '\u21b1', 'Zcy;': '\u0417', 'ycirc;': '\u0177', 'rbrkslu;': '\u2990', 'Proportional;': '\u221d', 'Sup;': '\u22d1', 'curlyvee;': '\u22ce', 'rceil;': '\u2309', 'Xfr;': '\U0001d51b', 'minusd;': '\u2238', 'angmsdab;': '\u29a9', 'DiacriticalDoubleAcute;': '\u02dd', 'par;': '\u2225', 'lpar;': '(', 'lcy;': '\u043b', 'Nu;': '\u039d', 'euml;': '\xeb', 'CircleMinus;': '\u2296', 'lfloor;': '\u230a', 'Rightarrow;': '\u21d2', 'rect;': '\u25ad', 'dzigrarr;': '\u27ff', 'tcy;': '\u0442', 'vartheta;': '\u03d1', 'Idot;': '\u0130', 'Lleftarrow;': '\u21da', 'GT;': '>', 'emsp14;': '\u2005', 'vert;': '|', 'boxHu;': '\u2567', 'Rarrtl;': '\u2916', 'nprcue;': '\u22e0', 'para': '\xb6', 'nsucceq;': '\u2ab0\u0338', 'nhArr;': '\u21ce', 'ClockwiseContourIntegral;': '\u2232', 'Downarrow;': '\u21d3', 'Otilde': '\xd5', 'umacr;': '\u016b', 'varsubsetneq;': '\u228a\ufe00', 'cup;': '\u222a', 'longleftrightarrow;': '\u27f7', 'gg;': '\u226b', 'Barv;': '\u2ae7', 'Map;': '\u2905', 'Im;': '\u2111', 'ltcir;': '\u2a79', 'gdot;': '\u0121', 'Cayleys;': '\u212d', 'timesbar;': '\u2a31', 'Gdot;': '\u0120', 'Ucirc': '\xdb', 'bigvee;': '\u22c1', 'QUOT': '"', 'lang;': '\u27e8', 'Yfr;': '\U0001d51c', 'Larr;': '\u219e', 'leg;': '\u22da', 'cuesc;': '\u22df', 'rArr;': '\u21d2', 'mumap;': '\u22b8', 'RightVector;': '\u21c0', 'nisd;': '\u22fa', 'crarr;': '\u21b5', 'leftthreetimes;': '\u22cb', 'Fcy;': '\u0424', 'xotime;': '\u2a02', 'odash;': '\u229d', 'agrave;': '\xe0', 'LeftFloor;': '\u230a', 'scpolint;': '\u2a13', 'Pfr;': '\U0001d513', 'nvHarr;': '\u2904', 'quot;': '"', 'comp;': '\u2201', 'imagline;': '\u2110', 'telrec;': '\u2315', 'Sqrt;': '\u221a', 'supsub;': '\u2ad4', 'rarr;': '\u2192', 'gvertneqq;': '\u2269\ufe00', 'nbumpe;': '\u224f\u0338', 'Uacute': '\xda', 'gsim;': '\u2273', 'coprod;': '\u2210', 'ncongdot;': '\u2a6d\u0338', 'sscr;': '\U0001d4c8', 'lstrok;': '\u0142', 'TripleDot;': '\u20db', 'topfork;': '\u2ada', 'yacute': '\xfd', 'nrightarrow;': '\u219b', 'VerticalBar;': '\u2223', 'LeftDownVector;': '\u21c3', 'angzarr;': '\u237c', 'nsupset;': '\u2283\u20d2', 'rdldhar;': '\u2969', 'deg;': '\xb0', 'DoubleRightArrow;': '\u21d2', 'macr': '\xaf', 'ldca;': '\u2936', 'jcirc;': '\u0135', 'uml;': '\xa8', 'cupor;': '\u2a45', 'egrave': '\xe8', 'boxur;': '\u2514', 'Esim;': '\u2a73', 'hybull;': '\u2043', 'DownBreve;': '\u0311', 'order;': '\u2134', 'Vscr;': '\U0001d4b1', 'ApplyFunction;': '\u2061', 'Mellintrf;': '\u2133', 'ufisht;': '\u297e', 'Ycirc;': '\u0176', 'nedot;': '\u2250\u0338', 'Ugrave;': '\xd9', 'npar;': '\u2226', 'RightArrowLeftArrow;': '\u21c4', 'xnis;': '\u22fb', 'sharp;': '\u266f', 'twixt;': '\u226c', 'midcir;': '\u2af0', 'real;': '\u211c', 'npr;': '\u2280', 'oopf;': '\U0001d560', 'Ouml;': '\xd6', 'urtri;': '\u25f9', 'SucceedsTilde;': '\u227f', 'ngeqslant;': '\u2a7e\u0338', 'Eopf;': '\U0001d53c', 'LowerLeftArrow;': '\u2199', 'sqsubseteq;': '\u2291', 'preccurlyeq;': '\u227c', 'RightTriangle;': '\u22b3', 'ReverseUpEquilibrium;': '\u296f', 'simplus;': '\u2a24', 'Aogon;': '\u0104', 'NotGreater;': '\u226f', 'rpargt;': '\u2994', 'curarrm;': '\u293c', 'THORN;': '\xde', 'smtes;': '\u2aac\ufe00', 'Ntilde': '\xd1', 'Zscr;': '\U0001d4b5', 'Nscr;': '\U0001d4a9', 'sigma;': '\u03c3', 'Atilde': '\xc3', 'checkmark;': '\u2713', 'spades;': '\u2660', 'ycy;': '\u044b', 'shortmid;': '\u2223', 'NotLeftTriangleBar;': '\u29cf\u0338', 'SuchThat;': '\u220b', 'amacr;': '\u0101', 'bigcirc;': '\u25ef', 'Gt;': '\u226b', 'xopf;': '\U0001d569', 'puncsp;': '\u2008', 'Fscr;': '\u2131', 'gel;': '\u22db', 'sect;': '\xa7', 'cudarrl;': '\u2938', 'Iuml': '\xcf', 'squarf;': '\u25aa', 'seswar;': '\u2929', 'Eacute': '\xc9', 'scy;': '\u0441', 'subnE;': '\u2acb', 'Sacute;': '\u015a', 'doublebarwedge;': '\u2306', 'rnmid;': '\u2aee', 'djcy;': '\u0452', 'Odblac;': '\u0150', 'duhar;': '\u296f', 'nVDash;': '\u22af', 'NotPrecedes;': '\u2280', 'frac45;': '\u2158', 'ubrcy;': '\u045e', 'empty;': '\u2205', 'nbsp;': '\xa0', 'comma;': ',', 'RightArrow;': '\u2192', 'notnivb;': '\u22fe', 'nrarrw;': '\u219d\u0338', 'downdownarrows;': '\u21ca', 'ngE;': '\u2267\u0338', 'lcub;': '{', 'Kscr;': '\U0001d4a6', 'Utilde;': '\u0168', 'pertenk;': '\u2031', 'sstarf;': '\u22c6', 'bdquo;': '\u201e', 'psi;': '\u03c8', 'NotLeftTriangle;': '\u22ea', 'Jscr;': '\U0001d4a5', 'UpEquilibrium;': '\u296e', 'succneqq;': '\u2ab6', 'drcrop;': '\u230c', 'csube;': '\u2ad1', 'plusdu;': '\u2a25', 'nvlArr;': '\u2902', 'RightTeeArrow;': '\u21a6', 'apos;': "'", 'squf;': '\u25aa', 'blacktriangledown;': '\u25be', 'ShortDownArrow;': '\u2193', 'boxuL;': '\u255b', 'Lambda;': '\u039b', 'Darr;': '\u21a1', 'sup3;': '\xb3', 'xcirc;': '\u25ef', 'nscr;': '\U0001d4c3', 'UpArrowDownArrow;': '\u21c5', 'Auml': '\xc4', 'nrArr;': '\u21cf', 'nges;': '\u2a7e\u0338', 'parallel;': '\u2225', 'LeftUpTeeVector;': '\u2960', 'uwangle;': '\u29a7', 'napprox;': '\u2249', 'sol;': '/', 'nRightarrow;': '\u21cf', 'squ;': '\u25a1', 'natur;': '\u266e', 'Escr;': '\u2130', 'nLl;': '\u22d8\u0338', 'DD;': '\u2145', 'Chi;': '\u03a7', 'lBarr;': '\u290e', 'emptyset;': '\u2205', 'iexcl': '\xa1', 'rarrtl;': '\u21a3', 'gE;': '\u2267', 'LeftTeeVector;': '\u295a', 'DoubleUpDownArrow;': '\u21d5', 'Icirc;': '\xce', 'Racute;': '\u0154', 'vee;': '\u2228', 'bot;': '\u22a5', 'nleftrightarrow;': '\u21ae', 'atilde': '\xe3', 'frac35;': '\u2157', 'mDDot;': '\u223a', 'eqcolon;': '\u2255', 'bsolb;': '\u29c5', 'lltri;': '\u25fa', 'bsemi;': '\u204f', 'because;': '\u2235', 'Oslash': '\xd8', 'nu;': '\u03bd', 'rightarrow;': '\u2192', 'rangle;': '\u27e9', 'TRADE;': '\u2122', 'llhard;': '\u296b', 'LeftAngleBracket;': '\u27e8', 'scnsim;': '\u22e9', 'ccirc;': '\u0109', 'Jsercy;': '\u0408', 'nvsim;': '\u223c\u20d2', 'nleftarrow;': '\u219a', 'hopf;': '\U0001d559', 'Ccedil;': '\xc7', 'rrarr;': '\u21c9', 'twoheadleftarrow;': '\u219e', 'erDot;': '\u2253', 'epsiv;': '\u03f5', 'xi;': '\u03be', 'ring;': '\u02da', 'tscy;': '\u0446', 'mu;': '\u03bc', 'Uacute;': '\xda', 'Lang;': '\u27ea', 'ovbar;': '\u233d', 'nleq;': '\u2270', 'gbreve;': '\u011f', 'cedil;': '\xb8', 'gneq;': '\u2a88', 'wopf;': '\U0001d568', 'frac18;': '\u215b', 'Oscr;': '\U0001d4aa', 'Egrave': '\xc8', 'Igrave;': '\xcc', 'varnothing;': '\u2205', 'divideontimes;': '\u22c7', 'dot;': '\u02d9', 'EqualTilde;': '\u2242', 'NotTilde;': '\u2241', 'els;': '\u2a95', 'easter;': '\u2a6e', 'swarhk;': '\u2926', 'vnsup;': '\u2283\u20d2', 'ETH': '\xd0', 'blacksquare;': '\u25aa', 'bcong;': '\u224c', 'ocy;': '\u043e', 'rbrksld;': '\u298e', 'lhard;': '\u21bd', 'gtrarr;': '\u2978', 'nharr;': '\u21ae', 'rharu;': '\u21c0', 'Mfr;': '\U0001d510', 'npre;': '\u2aaf\u0338', 'oslash;': '\xf8', 'GreaterSlantEqual;': '\u2a7e', 'Ifr;': '\u2111', 'Pi;': '\u03a0', 'lrarr;': '\u21c6', 'sce;': '\u2ab0', 'NotSquareSubsetEqual;': '\u22e2', 'beta;': '\u03b2', 'tcedil;': '\u0163', 'Int;': '\u222c', 'Conint;': '\u222f', 'kappav;': '\u03f0', 'varphi;': '\u03d5', 'subsim;': '\u2ac7', 'nGt;': '\u226b\u20d2', 'blk14;': '\u2591', 'IJlig;': '\u0132', 'LeftUpVector;': '\u21bf', 'epsilon;': '\u03b5', 'ReverseElement;': '\u220b', 'angmsdaa;': '\u29a8', 'starf;': '\u2605', 'sung;': '\u266a', 'udarr;': '\u21c5', 'RightUpTeeVector;': '\u295c', 'gne;': '\u2a88', 'nlArr;': '\u21cd', 'Lcedil;': '\u013b', 'ccedil': '\xe7', 'dtri;': '\u25bf', 'nap;': '\u2249', 'neArr;': '\u21d7', 'boxVR;': '\u2560', 'verbar;': '|', 'omicron;': '\u03bf', 'precapprox;': '\u2ab7', 'Lcaron;': '\u013d', 'ugrave;': '\xf9', 'eDDot;': '\u2a77', 'NotTildeEqual;': '\u2244', 'pitchfork;': '\u22d4', 'top;': '\u22a4', 'quaternions;': '\u210d', 'imped;': '\u01b5', 'SquareSubset;': '\u228f', 'rarrbfs;': '\u2920', 'NotSquareSuperset;': '\u2290\u0338', 'boxvR;': '\u255e', 'ni;': '\u220b', 'gcirc;': '\u011d', 'ffr;': '\U0001d523', 'numsp;': '\u2007', 'notinvb;': '\u22f7', 'MinusPlus;': '\u2213', 'preceq;': '\u2aaf', 'boxH;': '\u2550', 'lsqb;': '[', 'lagran;': '\u2112', 'lnsim;': '\u22e6', 'triplus;': '\u2a39', 'angmsdah;': '\u29af', 'iff;': '\u21d4', 'LT;': '<', 'amp;': '&', 'rightrightarrows;': '\u21c9', 'operp;': '\u29b9', 'imacr;': '\u012b', 'frac38;': '\u215c', 'cent;': '\xa2', 'NotHumpEqual;': '\u224f\u0338', 'zeetrf;': '\u2128', 'DownTee;': '\u22a4', 'Scedil;': '\u015e', 'ShortLeftArrow;': '\u2190', 'Bernoullis;': '\u212c', 'prurel;': '\u22b0', 'gEl;': '\u2a8c', 'late;': '\u2aad', 'notniva;': '\u220c', 'robrk;': '\u27e7', 'alefsym;': '\u2135', 'eng;': '\u014b', 'sext;': '\u2736', 'roang;': '\u27ed', 'Tcedil;': '\u0162', 'NotLessLess;': '\u226a\u0338', 'efDot;': '\u2252', 'cscr;': '\U0001d4b8', 'dashv;': '\u22a3', 'cularr;': '\u21b6', 'numero;': '\u2116', 'caron;': '\u02c7', 'suphsub;': '\u2ad7', 'boxUr;': '\u2559', 'ncup;': '\u2a42', 'lozenge;': '\u25ca', 'lowast;': '\u2217', 'Ufr;': '\U0001d518', 'Gcedil;': '\u0122', 'curren;': '\xa4', 'Ycy;': '\u042b', 'NegativeThickSpace;': '\u200b', 'ulcorner;': '\u231c', 'sdotb;': '\u22a1', 'rdquor;': '\u201d', 'nvltrie;': '\u22b4\u20d2', 'LeftUpDownVector;': '\u2951', 'cap;': '\u2229', 'PrecedesEqual;': '\u2aaf', 'Ecirc;': '\xca', 'bscr;': '\U0001d4b7', 'UpArrow;': '\u2191', 'simg;': '\u2a9e', 'notin;': '\u2209', 'RightDownTeeVector;': '\u295d', 'disin;': '\u22f2', 'oacute;': '\xf3', 'nsube;': '\u2288', 'iquest': '\xbf', 'ltrif;': '\u25c2', 'ccups;': '\u2a4c', 'Because;': '\u2235', 'otimes;': '\u2297', 'Zopf;': '\u2124', 'supedot;': '\u2ac4', 'ee;': '\u2147', 'NotSucceedsSlantEqual;': '\u22e1', 'scap;': '\u2ab8', 'TildeEqual;': '\u2243', 'Colon;': '\u2237', 'rcaron;': '\u0159', 'GJcy;': '\u0403', 'curvearrowright;': '\u21b7', 'Barwed;': '\u2306', 'scirc;': '\u015d', 'Lopf;': '\U0001d543', 'hoarr;': '\u21ff', 'NotLess;': '\u226e', 'afr;': '\U0001d51e', 'homtht;': '\u223b', 'subsup;': '\u2ad3', 'NotRightTriangleEqual;': '\u22ed', 'raemptyv;': '\u29b3', 'ltrPar;': '\u2996', 'upsih;': '\u03d2', 'ccupssm;': '\u2a50', 'Longrightarrow;': '\u27f9', 'NotGreaterFullEqual;': '\u2267\u0338', 'bnequiv;': '\u2261\u20e5', 'lrtri;': '\u22bf', 'setminus;': '\u2216', 'supplus;': '\u2ac0', 'Rscr;': '\u211b', 'Popf;': '\u2119', 'NotSuperset;': '\u2283\u20d2', 'looparrowright;': '\u21ac', 'odot;': '\u2299', 'laquo': '\xab', 'sqcup;': '\u2294', 'hairsp;': '\u200a', 'Gamma;': '\u0393', 'lbrksld;': '\u298f', 'uplus;': '\u228e', 'equivDD;': '\u2a78', 'el;': '\u2a99', 'CHcy;': '\u0427', 'rarrsim;': '\u2974', 'ffilig;': '\ufb03', 'thorn;': '\xfe', 'ngtr;': '\u226f', 'qopf;': '\U0001d562', 'nvle;': '\u2264\u20d2', 'omid;': '\u29b6', 'vrtri;': '\u22b3', 'curvearrowleft;': '\u21b6', 'DownRightVector;': '\u21c1', 'frac58;': '\u215d', 'Uopf;': '\U0001d54c', 'AMP;': '&', 'equest;': '\u225f', 'succapprox;': '\u2ab8', 'intercal;': '\u22ba', 'rthree;': '\u22cc', 'gimel;': '\u2137', 'Uparrow;': '\u21d1', 'Ll;': '\u22d8', 'dzcy;': '\u045f', 'dfisht;': '\u297f', 'frac12': '\xbd', 'submult;': '\u2ac1', 'rang;': '\u27e9', 'Wscr;': '\U0001d4b2', 'Kcedil;': '\u0136', 'leqslant;': '\u2a7d', 'urcrop;': '\u230e', 'SOFTcy;': '\u042c', 'hamilt;': '\u210b', 'AMP': '&', 'pscr;': '\U0001d4c5', 'egs;': '\u2a96', 'supE;': '\u2ac6', 'searr;': '\u2198', 'varpi;': '\u03d6', 'nlarr;': '\u219a', 'nearrow;': '\u2197', 'ldsh;': '\u21b2', 'gesl;': '\u22db\ufe00', 'rarrfs;': '\u291e', 'LessTilde;': '\u2272', 'boxUL;': '\u255d', 'And;': '\u2a53', 'LeftDoubleBracket;': '\u27e6', 'rAtail;': '\u291c', 'eogon;': '\u0119', 'bepsi;': '\u03f6', 'vDash;': '\u22a8', 'Coproduct;': '\u2210', 'ngeqq;': '\u2267\u0338', 'supne;': '\u228b', 'REG;': '\xae', 'kopf;': '\U0001d55c', 'cire;': '\u2257', 'boxhD;': '\u2565', 'cir;': '\u25cb', 'awconint;': '\u2233', 'LowerRightArrow;': '\u2198', 'Wfr;': '\U0001d51a', 'ssmile;': '\u2323', 'ic;': '\u2063', 'boxHd;': '\u2564', 'Oopf;': '\U0001d546', 'trisb;': '\u29cd', 'longleftarrow;': '\u27f5', 'vprop;': '\u221d', 'qfr;': '\U0001d52e', 'frac34;': '\xbe', 'vsubnE;': '\u2acb\ufe00', 'odiv;': '\u2a38', 'nvinfin;': '\u29de', 'boxminus;': '\u229f', 'efr;': '\U0001d522', 'ForAll;': '\u2200', 'lsaquo;': '\u2039', 'yen': '\xa5', 'lAtail;': '\u291b', 'tint;': '\u222d', 'ltri;': '\u25c3', 'DownTeeArrow;': '\u21a7', 'Tilde;': '\u223c', 'nsce;': '\u2ab0\u0338', 'larr;': '\u2190', 'supsup;': '\u2ad6', 'frac16;': '\u2159', 'eth;': '\xf0', 'acirc;': '\xe2', 'ddarr;': '\u21ca', 'Iscr;': '\u2110', 'triangleright;': '\u25b9', 'capand;': '\u2a44', 'HARDcy;': '\u042a', 'sup;': '\u2283', 'NotSubset;': '\u2282\u20d2', 'searrow;': '\u2198', 'nsc;': '\u2281', 'sup1': '\xb9', 'sup2': '\xb2', 'Breve;': '\u02d8', 'epar;': '\u22d5', 'clubsuit;': '\u2663', 'approx;': '\u2248', 'NotGreaterLess;': '\u2279', 'mapsto;': '\u21a6', 'scsim;': '\u227f', 'notinE;': '\u22f9\u0338', 'hcirc;': '\u0125', 'rightthreetimes;': '\u22cc', 'geq;': '\u2265', 'Kappa;': '\u039a', 'vdash;': '\u22a2', 'Congruent;': '\u2261', 'boxdr;': '\u250c', 'DoubleContourIntegral;': '\u222f', 'upuparrows;': '\u21c8', 'csub;': '\u2acf', 'PrecedesSlantEqual;': '\u227c', 'boxbox;': '\u29c9', 'zdot;': '\u017c', 'sub;': '\u2282', 'andand;': '\u2a55', 'laemptyv;': '\u29b4', 'dstrok;': '\u0111', 'perp;': '\u22a5', 'HumpDownHump;': '\u224e', 'int;': '\u222b', 'RightUpDownVector;': '\u294f', 'LongRightArrow;': '\u27f6', 'hstrok;': '\u0127', 'ngt;': '\u226f', 'lbrke;': '\u298b', 'Ograve': '\xd2', 'nvrtrie;': '\u22b5\u20d2', 'leqq;': '\u2266', 'intprod;': '\u2a3c', 'centerdot;': '\xb7', 'emptyv;': '\u2205', 'infintie;': '\u29dd', 'lbbrk;': '\u2772', 'Cacute;': '\u0106', 'rscr;': '\U0001d4c7', 'otilde;': '\xf5', 'DiacriticalGrave;': '`', 'supe;': '\u2287', 'rotimes;': '\u2a35', 'die;': '\xa8', 'mapstodown;': '\u21a7', 'fjlig;': 'fj', 'SquareSuperset;': '\u2290', 'curren': '\xa4', 'GreaterLess;': '\u2277', 'smile;': '\u2323', 'NotHumpDownHump;': '\u224e\u0338', 'ucirc;': '\xfb', 'vArr;': '\u21d5', 'boxV;': '\u2551', 'Tcaron;': '\u0164', 'not;': '\xac', 'mho;': '\u2127', 'sfrown;': '\u2322', 'ZeroWidthSpace;': '\u200b', 'Acirc': '\xc2', 'gneqq;': '\u2269', 'Euml': '\xcb', 'Ccaron;': '\u010c', 'Iacute': '\xcd', 'Yopf;': '\U0001d550', 'aogon;': '\u0105', 'rationals;': '\u211a', 'Bopf;': '\U0001d539', 'uopf;': '\U0001d566', 'acE;': '\u223e\u0333', 'ETH;': '\xd0', 'intcal;': '\u22ba', 'clubs;': '\u2663', 'plussim;': '\u2a26', 'olt;': '\u29c0', 'tprime;': '\u2034', 'iogon;': '\u012f', 'diamondsuit;': '\u2666', 'ltlarr;': '\u2976', 'frac14': '\xbc', 'fscr;': '\U0001d4bb', 'aacute': '\xe1', 'dollar;': '$', 'xmap;': '\u27fc', 'vscr;': '\U0001d4cb', 'ShortRightArrow;': '\u2192', 'Square;': '\u25a1', 'blk12;': '\u2592', 'triangle;': '\u25b5', 'eacute': '\xe9', 'angrt;': '\u221f', 'circlearrowright;': '\u21bb', 'UpTee;': '\u22a5', 'copy;': '\xa9', 'scnE;': '\u2ab6', 'aelig;': '\xe6', 'doteq;': '\u2250', 'parsl;': '\u2afd', 'Ugrave': '\xd9', 'lfr;': '\U0001d529', 'gvnE;': '\u2269\ufe00', 'rarrc;': '\u2933', 'Acy;': '\u0410', 'rbrace;': '}', 'ccedil;': '\xe7', 'nwarrow;': '\u2196', 'njcy;': '\u045a', 'UpperRightArrow;': '\u2197', 'dHar;': '\u2965', 'gt': '>', 'jscr;': '\U0001d4bf', 'rarrpl;': '\u2945', 'varrho;': '\u03f1', 'Ocirc;': '\xd4', 'lowbar;': '_', 'Yacute': '\xdd', 'nsub;': '\u2284', 'lessdot;': '\u22d6', 'NotGreaterGreater;': '\u226b\u0338', 'darr;': '\u2193', 'mcomma;': '\u2a29', 'Cedilla;': '\xb8', 'vartriangleright;': '\u22b3', 'vfr;': '\U0001d533', 'rfisht;': '\u297d', 'PlusMinus;': '\xb1', 'planck;': '\u210f', 'NotPrecedesSlantEqual;': '\u22e0', 'Egrave;': '\xc8', 'rightarrowtail;': '\u21a3', 'Prime;': '\u2033', 'gtrless;': '\u2277', 'thetasym;': '\u03d1', 'bbrktbrk;': '\u23b6', 'nle;': '\u2270', 'mlcp;': '\u2adb', 'larrsim;': '\u2973', 'jcy;': '\u0439', 'drcorn;': '\u231f', 'harrw;': '\u21ad', 'updownarrow;': '\u2195', 'ubreve;': '\u016d', 'pluse;': '\u2a72', 'UpTeeArrow;': '\u21a5', 'prime;': '\u2032', 'COPY;': '\xa9', 'CirclePlus;': '\u2295', 'Longleftarrow;': '\u27f8', 'dArr;': '\u21d3', 'xcup;': '\u22c3', 'AElig': '\xc6', 'leftharpoonup;': '\u21bc', 'Uarr;': '\u219f', 'lsquo;': '\u2018', 'nVdash;': '\u22ae', 'nwnear;': '\u2927', 'gescc;': '\u2aa9', 'rdsh;': '\u21b3', 'grave;': '`', 'blk34;': '\u2593', 'LeftVector;': '\u21bc', 'uharr;': '\u21be', 'isins;': '\u22f4', 'lescc;': '\u2aa8', 'eplus;': '\u2a71', 'jmath;': '\u0237', 'kscr;': '\U0001d4c0', 'nsim;': '\u2241', 'Aacute;': '\xc1', 'NotLessEqual;': '\u2270', 'tshcy;': '\u045b', 'plusmn': '\xb1', 'ecir;': '\u2256', 'nsmid;': '\u2224', 'lesdoto;': '\u2a81', 'nvdash;': '\u22ac', 'Lt;': '\u226a', 'DownRightVectorBar;': '\u2957', 'asymp;': '\u2248', 'ggg;': '\u22d9', 'szlig;': '\xdf', 'lneqq;': '\u2268', 'loplus;': '\u2a2d', 'ExponentialE;': '\u2147', 'profline;': '\u2312', 'DDotrahd;': '\u2911', 'rarrlp;': '\u21ac', 'Scy;': '\u0421', 'le;': '\u2264', 'auml;': '\xe4', 'roarr;': '\u21fe', 'fltns;': '\u25b1', 'vellip;': '\u22ee', 'apacir;': '\u2a6f', 'circledS;': '\u24c8', 'rfloor;': '\u230b', 'Cross;': '\u2a2f', 'DoubleLeftTee;': '\u2ae4', 'subsetneqq;': '\u2acb', 'ordf': '\xaa', 'rightleftharpoons;': '\u21cc', 'fllig;': '\ufb02', 'ntilde': '\xf1', 'emsp;': '\u2003', 'iacute;': '\xed', 'xfr;': '\U0001d535', 'fflig;': '\ufb00', 'xlarr;': '\u27f5', 'leftarrow;': '\u2190', 'urcorner;': '\u231d', 'dharl;': '\u21c3', 'reals;': '\u211d', 'Re;': '\u211c', 'bemptyv;': '\u29b0', 'angrtvb;': '\u22be', 'mdash;': '\u2014', 'dotsquare;': '\u22a1', 'omacr;': '\u014d', 'Vvdash;': '\u22aa', 'pm;': '\xb1', 'OverBar;': '\u203e', 'nldr;': '\u2025', 'target;': '\u2316', 'hksearow;': '\u2925', 'ecirc': '\xea', 'swnwar;': '\u292a', 'nfr;': '\U0001d52b', 'Copf;': '\u2102', 'Rarr;': '\u21a0', 'raquo;': '\xbb', 'oline;': '\u203e', 'utilde;': '\u0169', 'hookrightarrow;': '\u21aa', 'Or;': '\u2a54', 'origof;': '\u22b6', 'Theta;': '\u0398', 'kfr;': '\U0001d528', 'Sfr;': '\U0001d516', 'aopf;': '\U0001d552', 'lArr;': '\u21d0', 'equiv;': '\u2261', 'ord;': '\u2a5d', 'Sigma;': '\u03a3', 'DScy;': '\u0405', 'PrecedesTilde;': '\u227e', 'gnsim;': '\u22e7', 'colone;': '\u2254', 'boxhU;': '\u2568', 'Ntilde;': '\xd1', 'NotNestedGreaterGreater;': '\u2aa2\u0338', 'NotSucceeds;': '\u2281', 'larrfs;': '\u291d', 'models;': '\u22a7', 'DifferentialD;': '\u2146', 'toea;': '\u2928', 'Zdot;': '\u017b', 'zscr;': '\U0001d4cf', 'gtlPar;': '\u2995', 'ii;': '\u2148', 'Zcaron;': '\u017d', 'Leftarrow;': '\u21d0', 'ohbar;': '\u29b5', 'orv;': '\u2a5b', 'OverParenthesis;': '\u23dc', 'Upsilon;': '\u03a5', 'plusdo;': '\u2214', 'nis;': '\u22fc', 'Poincareplane;': '\u210c', 'tfr;': '\U0001d531', 'DownArrow;': '\u2193', 'Sub;': '\u22d0', 'Ncedil;': '\u0145', 'Iota;': '\u0399', 'InvisibleComma;': '\u2063', 'Ucy;': '\u0423', 'lnap;': '\u2a89', 'angst;': '\xc5', 'sube;': '\u2286', 'Gopf;': '\U0001d53e', 'Succeeds;': '\u227b', 'ap;': '\u2248', 'andv;': '\u2a5a', 'eDot;': '\u2251', 'angsph;': '\u2222', 'Dscr;': '\U0001d49f', 'boxHD;': '\u2566', 'gamma;': '\u03b3', 'RightTeeVector;': '\u295b', 'straightphi;': '\u03d5', 'ohm;': '\u03a9', 'frac15;': '\u2155', 'itilde;': '\u0129', 'jfr;': '\U0001d527', 'NJcy;': '\u040a', 'notinva;': '\u2209', 'frac25;': '\u2156', 'Epsilon;': '\u0395', 'xoplus;': '\u2a01', 'zcy;': '\u0437', 'Union;': '\u22c3', 'lesssim;': '\u2272', 'trpezium;': '\u23e2', 'bcy;': '\u0431', 'succsim;': '\u227f', 'boxDr;': '\u2553', 'beth;': '\u2136', 'prap;': '\u2ab7', 'bumpeq;': '\u224f', 'NotSquareSubset;': '\u228f\u0338', 'nhpar;': '\u2af2', 'vBar;': '\u2ae8', 'rbrke;': '\u298c', 'Dot;': '\xa8', 'ENG;': '\u014a', 'and;': '\u2227', 'nsupseteqq;': '\u2ac6\u0338', 'blacklozenge;': '\u29eb', 'boxdL;': '\u2555', 'odsold;': '\u29bc', 'bigsqcup;': '\u2a06', 'trade;': '\u2122', 'half;': '\xbd', 'elsdot;': '\u2a97', 'iota;': '\u03b9', 'diam;': '\u22c4', 'block;': '\u2588', 'parsim;': '\u2af3', 'KHcy;': '\u0425', 'Lstrok;': '\u0141', 'lesseqgtr;': '\u22da', 'div;': '\xf7', 'planckh;': '\u210e', 'rfr;': '\U0001d52f', 'loang;': '\u27ec', 'lnapprox;': '\u2a89', 'triangleleft;': '\u25c3', 'nvDash;': '\u22ad', 'oint;': '\u222e', 'ecirc;': '\xea', 'Lfr;': '\U0001d50f', 'eqsim;': '\u2242', 'emacr;': '\u0113', 'DownLeftVector;': '\u21bd', 'succeq;': '\u2ab0', 'yucy;': '\u044e', 'biguplus;': '\u2a04', 'plusmn;': '\xb1', 'smashp;': '\u2a33', 'cuvee;': '\u22ce', 'prec;': '\u227a', 'chi;': '\u03c7', 'angmsdag;': '\u29ae', 'backprime;': '\u2035', 'nbump;': '\u224e\u0338', 'Mcy;': '\u041c', 'subseteq;': '\u2286', 'gtrapprox;': '\u2a86', 'lmoustache;': '\u23b0', 'circledR;': '\xae', 'gsiml;': '\u2a90', 'subseteqq;': '\u2ac5', 'rbbrk;': '\u2773', 'inodot;': '\u0131', 'fpartint;': '\u2a0d', 'barvee;': '\u22bd', 'egsdot;': '\u2a98', 'fcy;': '\u0444', 'qint;': '\u2a0c', 'Gammad;': '\u03dc', 'upharpoonright;': '\u21be', 'NotEqual;': '\u2260', 'boxVL;': '\u2563', 'dotminus;': '\u2238', 'esim;': '\u2242', 'lotimes;': '\u2a34', 'Xopf;': '\U0001d54f', 'divide;': '\xf7', 'RightTriangleEqual;': '\u22b5', 'af;': '\u2061', 'tridot;': '\u25ec', 'lvnE;': '\u2268\ufe00', 'multimap;': '\u22b8', 'rsh;': '\u21b1', 'Ascr;': '\U0001d49c', 'hkswarow;': '\u2926', 'suplarr;': '\u297b', 'VDash;': '\u22ab', 'uscr;': '\U0001d4ca', 'sccue;': '\u227d', 'SHcy;': '\u0428', 'ndash;': '\u2013', 'YUcy;': '\u042e', 'rppolint;': '\u2a12', 'Equilibrium;': '\u21cc', 'boxvL;': '\u2561', 'nlt;': '\u226e', 'Euml;': '\xcb', 'IOcy;': '\u0401', 'times;': '\xd7', 'mapstoup;': '\u21a5', 'epsi;': '\u03b5', 'xlArr;': '\u27f8', 'cacute;': '\u0107', 'capcap;': '\u2a4b', 'ntriangleleft;': '\u22ea', 'sqsupseteq;': '\u2292', 'NotCupCap;': '\u226d', 'RightUpVector;': '\u21be', 'rpar;': ')', 'Xi;': '\u039e', 'tilde;': '\u02dc', 'auml': '\xe4', 'esdot;': '\u2250', 'nleqslant;': '\u2a7d\u0338', 'rhard;': '\u21c1', 'Delta;': '\u0394', 'gsime;': '\u2a8e', 'lt': '<', 'SHCHcy;': '\u0429', 'varsupsetneq;': '\u228b\ufe00', 'LeftUpVectorBar;': '\u2958', 'simne;': '\u2246', 'lozf;': '\u29eb', 'LeftTeeArrow;': '\u21a4', 'spadesuit;': '\u2660', 'Pr;': '\u2abb', 'Eacute;': '\xc9', 'boxVh;': '\u256b', 'Dashv;': '\u2ae4', 'ccaron;': '\u010d', 'setmn;': '\u2216', 'Aring;': '\xc5', 'plustwo;': '\u2a27', 'Rcaron;': '\u0158', 'sdote;': '\u2a66', 'ifr;': '\U0001d526', 'roplus;': '\u2a2e', 'qscr;': '\U0001d4c6', 'bernou;': '\u212c', 'Dstrok;': '\u0110', 'not': '\xac', 'backepsilon;': '\u03f6', 'Otilde;': '\xd5', 'langd;': '\u2991', 'lopf;': '\U0001d55d', 'KJcy;': '\u040c', 'infin;': '\u221e', 'uacute': '\xfa', 'Fopf;': '\U0001d53d', 'backsim;': '\u223d', 'ape;': '\u224a', 'LeftArrowRightArrow;': '\u21c6', 'Wedge;': '\u22c0', 'DownLeftTeeVector;': '\u295e', 'Ffr;': '\U0001d509', 'rtrif;': '\u25b8', 'gjcy;': '\u0453', 'supmult;': '\u2ac2', 'gt;': '>', 'swarr;': '\u2199', 'amalg;': '\u2a3f', 'rho;': '\u03c1', 'triminus;': '\u2a3a', 'or;': '\u2228', 'nesim;': '\u2242\u0338', 'sime;': '\u2243', 'larrlp;': '\u21ab', 'Sum;': '\u2211', 'khcy;': '\u0445', 'wscr;': '\U0001d4cc', 'caret;': '\u2041', 'agrave': '\xe0', 'Ocirc': '\xd4', 'Iopf;': '\U0001d540', 'bump;': '\u224e', 'ratail;': '\u291a', 'simgE;': '\u2aa0', 'precneqq;': '\u2ab5', 'varpropto;': '\u221d', 'yuml;': '\xff', 'ntrianglelefteq;': '\u22ec', 'ouml': '\xf6', 'lt;': '<', 'alpha;': '\u03b1', 'gopf;': '\U0001d558', 'smt;': '\u2aaa', 'doteqdot;': '\u2251', 'LessSlantEqual;': '\u2a7d', 'mid;': '\u2223', 'simeq;': '\u2243', 'tstrok;': '\u0167', 'GreaterEqualLess;': '\u22db', 'escr;': '\u212f', 'Nfr;': '\U0001d511', 'nGg;': '\u22d9\u0338', 'simlE;': '\u2a9f', 'apid;': '\u224b', 'nvrArr;': '\u2903', 'dotplus;': '\u2214', 'cirscir;': '\u29c2', 'LeftTee;': '\u22a3', 'lnE;': '\u2268', 'topcir;': '\u2af1', 'egrave;': '\xe8', 'demptyv;': '\u29b1', 'copysr;': '\u2117', 'Vdashl;': '\u2ae6', 'yen;': '\xa5', 'gap;': '\u2a86', 'thetav;': '\u03d1', 'bumpE;': '\u2aae', 'Ncaron;': '\u0147', 'blacktriangleright;': '\u25b8', 'olcir;': '\u29be', 'UnderBracket;': '\u23b5', 'nsimeq;': '\u2244', 'downarrow;': '\u2193', 'Assign;': '\u2254', 'opar;': '\u29b7', 'diams;': '\u2666', 'jsercy;': '\u0458', 'SubsetEqual;': '\u2286', 'bkarow;': '\u290d', 'square;': '\u25a1', 'ntriangleright;': '\u22eb', 'nrarr;': '\u219b', 'Udblac;': '\u0170', 'sqsubset;': '\u228f', 'sup1;': '\xb9', 'ldrdhar;': '\u2967', 'erarr;': '\u2971', 'frown;': '\u2322', 'cemptyv;': '\u29b2', 'rtri;': '\u25b9', 'Hscr;': '\u210b', 'Cconint;': '\u2230', 'Edot;': '\u0116', 'hardcy;': '\u044a', 'there4;': '\u2234', 'frac56;': '\u215a', 'Gbreve;': '\u011e', 'ldquo;': '\u201c', 'wedgeq;': '\u2259', 'ncong;': '\u2247', 'prop;': '\u221d', 'isinsv;': '\u22f3', 'hbar;': '\u210f', 'supseteq;': '\u2287', 'Abreve;': '\u0102', 'swarrow;': '\u2199', 'lfisht;': '\u297c', 'siml;': '\u2a9d', 'equals;': '=', 'lesges;': '\u2a93', 'phiv;': '\u03d5', 'Proportion;': '\u2237', 'Dcy;': '\u0414', 'edot;': '\u0117', 'CounterClockwiseContourIntegral;': '\u2233', 'shortparallel;': '\u2225', 'frac34': '\xbe', 'solbar;': '\u233f', 'sbquo;': '\u201a', 'LessLess;': '\u2aa1', 'harrcir;': '\u2948', 'Jfr;': '\U0001d50d', 'Xscr;': '\U0001d4b3', 'NotNestedLessLess;': '\u2aa1\u0338', 'zcaron;': '\u017e', 'abreve;': '\u0103', 'nacute;': '\u0144', 'ultri;': '\u25f8', 'Bcy;': '\u0411', 'ThickSpace;': '\u205f\u200a', 'questeq;': '\u225f', 'DoubleLongLeftArrow;': '\u27f8', 'ccaps;': '\u2a4d', 'rHar;': '\u2964', 'upharpoonleft;': '\u21bf', 'iacute': '\xed', 'cong;': '\u2245', 'yopf;': '\U0001d56a', 'nvlt;': '<\u20d2', 'bopf;': '\U0001d553', 'Supset;': '\u22d1', 'Subset;': '\u22d0', 'varsubsetneqq;': '\u2acb\ufe00', 'Omega;': '\u03a9', 'lsh;': '\u21b0', 'iiiint;': '\u2a0c', 'copy': '\xa9', 'gscr;': '\u210a', 'Star;': '\u22c6', 'boxHU;': '\u2569', 'circ;': '\u02c6', 'lap;': '\u2a85', 'rlhar;': '\u21cc', 'percnt;': '%', 'NotLessSlantEqual;': '\u2a7d\u0338', 'maltese;': '\u2720', 'looparrowleft;': '\u21ab', 'LeftVectorBar;': '\u2952', 'nLeftrightarrow;': '\u21ce', 'bsolhsub;': '\u27c8', 'nsubseteqq;': '\u2ac5\u0338', 'Rfr;': '\u211c', 'lgE;': '\u2a91', 'RightTriangleBar;': '\u29d0', 'Superset;': '\u2283', 'reg;': '\xae', 'frac14;': '\xbc', 'RBarr;': '\u2910', 'realpart;': '\u211c', 'zwnj;': '\u200c', 'nrarrc;': '\u2933\u0338', 'pluscir;': '\u2a22', 'lharul;': '\u296a', 'thickapprox;': '\u2248', 'lscr;': '\U0001d4c1', 'caps;': '\u2229\ufe00', 'supsim;': '\u2ac8', 'cirfnint;': '\u2a10', 'boxvh;': '\u253c', 'therefore;': '\u2234', 'Verbar;': '\u2016', 'nsqsube;': '\u22e2', 'latail;': '\u2919', 'propto;': '\u221d', 'boxuR;': '\u2558', 'Omacr;': '\u014c', 'ges;': '\u2a7e', 'Scaron;': '\u0160', 'oslash': '\xf8', 'oast;': '\u229b', 'phi;': '\u03c6', 'cuwed;': '\u22cf', 'oplus;': '\u2295', 'ncedil;': '\u0146', 'scnap;': '\u2aba', 'Iogon;': '\u012e', 'bne;': '=\u20e5', 'Oslash;': '\xd8', 'xuplus;': '\u2a04', 'precnsim;': '\u22e8', 'bigtriangledown;': '\u25bd', 'iprod;': '\u2a3c', 'ange;': '\u29a4', 'RightTee;': '\u22a2', 'tosa;': '\u2929', 'Iukcy;': '\u0406', 'leftrightarrows;': '\u21c6', 'DoubleLeftArrow;': '\u21d0', 'COPY': '\xa9', 'frac13;': '\u2153', 'middot': '\xb7', 'pr;': '\u227a', 'rhov;': '\u03f1', 'Qopf;': '\u211a', 'weierp;': '\u2118', 'ofr;': '\U0001d52c', 'lrhard;': '\u296d', 'commat;': '@', 'nesear;': '\u2928', 'sopf;': '\U0001d564', 'raquo': '\xbb', 'malt;': '\u2720', 'OElig;': '\u0152', 'Uscr;': '\U0001d4b0', 'eqslantless;': '\u2a95', 'LeftTriangleEqual;': '\u22b4', 'oacute': '\xf3', 'andslope;': '\u2a58', 'yfr;': '\U0001d536', 'nsup;': '\u2285', 'NotElement;': '\u2209', 'angmsdaf;': '\u29ad', 'nsccue;': '\u22e1', 'ge;': '\u2265', 'fallingdotseq;': '\u2252', 'rbarr;': '\u290d', 'DoubleLongLeftRightArrow;': '\u27fa', 'uparrow;': '\u2191', 'orarr;': '\u21bb', 'Rcy;': '\u0420', 'acute;': '\xb4', 'NewLine;': '\n', 'lmoust;': '\u23b0', 'NegativeMediumSpace;': '\u200b', 'Nacute;': '\u0143', 'aelig': '\xe6', 'prcue;': '\u227c', 'ensp;': '\u2002', 'utdot;': '\u22f0', 'napos;': '\u0149', 'DoubleLongRightArrow;': '\u27f9', 'Vfr;': '\U0001d519', 'xutri;': '\u25b3', 'awint;': '\u2a11', 'leftrightsquigarrow;': '\u21ad', 'plusacir;': '\u2a23', 'FilledVerySmallSquare;': '\u25aa', 'Mscr;': '\u2133', 'leftrightharpoons;': '\u21cb', 'sqcups;': '\u2294\ufe00', 'LJcy;': '\u0409', 'circleddash;': '\u229d', 'NoBreak;': '\u2060', 'nlsim;': '\u2274', 'Uogon;': '\u0172', 'NotRightTriangleBar;': '\u29d0\u0338', 'Ecy;': '\u042d', 'sdot;': '\u22c5', 'smeparsl;': '\u29e4', 'niv;': '\u220b', 'kcedil;': '\u0137', 'xrarr;': '\u27f6', 'isindot;': '\u22f5', 'xodot;': '\u2a00', 'gtdot;': '\u22d7', 'natural;': '\u266e', 'eqvparsl;': '\u29e5', 'gnap;': '\u2a8a', 'Psi;': '\u03a8', 'Rho;': '\u03a1', 'micro;': '\xb5', 'cylcty;': '\u232d', 'gesles;': '\u2a94', 'uHar;': '\u2963', 'CircleTimes;': '\u2297', 'sqsub;': '\u228f', 'ldrushar;': '\u294b', 'bsol;': '\\', 'rcedil;': '\u0157', 'nprec;': '\u2280', 'vltri;': '\u22b2', 'atilde;': '\xe3', 'prsim;': '\u227e', 'primes;': '\u2119', 'Omicron;': '\u039f', 'ocirc;': '\xf4', 'iiint;': '\u222d', 'quest;': '?', 'daleth;': '\u2138', 'nbsp': '\xa0', 'nwArr;': '\u21d6', 'gammad;': '\u03dd', 'heartsuit;': '\u2665', 'wedbar;': '\u2a5f', 'OverBrace;': '\u23de', 'spar;': '\u2225', 'brvbar': '\xa6', 'blacktriangleleft;': '\u25c2', 'lopar;': '\u2985', 'xwedge;': '\u22c0', 'iexcl;': '\xa1', 'boxul;': '\u2518', 'Imacr;': '\u012a', 'ominus;': '\u2296', 'eopf;': '\U0001d556', 'DotDot;': '\u20dc', 'Scirc;': '\u015c', 'succnsim;': '\u22e9', 'sigmaf;': '\u03c2', 'ReverseEquilibrium;': '\u21cb', 'DiacriticalDot;': '\u02d9', 'AElig;': '\xc6', 'zigrarr;': '\u21dd', 'NegativeThinSpace;': '\u200b', 'approxeq;': '\u224a', 'Gcy;': '\u0413', 'Vert;': '\u2016', 'NotSquareSupersetEqual;': '\u22e3', 'srarr;': '\u2192', 'rtrie;': '\u22b5', 'VeryThinSpace;': '\u200a', 'RightDoubleBracket;': '\u27e7', 'dfr;': '\U0001d521', 'Eogon;': '\u0118', 'Cscr;': '\U0001d49e', 'gnE;': '\u2269', 'nparallel;': '\u2226', 'lsime;': '\u2a8d', 'lceil;': '\u2308', 'ijlig;': '\u0133', 'RightCeiling;': '\u2309', 'Icy;': '\u0418', 'yuml': '\xff', 'exist;': '\u2203', 'DiacriticalAcute;': '\xb4', 'boxVr;': '\u255f', 'mscr;': '\U0001d4c2', 'NotGreaterSlantEqual;': '\u2a7e\u0338', 'leftrightarrow;': '\u2194', 'Wopf;': '\U0001d54e', 'supset;': '\u2283', 'DownArrowUpArrow;': '\u21f5', 'glj;': '\u2aa4', 'Colone;': '\u2a74', 'prnsim;': '\u22e8', 'Zfr;': '\u2128', 'lbrkslu;': '\u298d', 'scedil;': '\u015f', 'Dcaron;': '\u010e', 'coloneq;': '\u2254', 'CapitalDifferentialD;': '\u2145', 'nshortmid;': '\u2224', 'trianglelefteq;': '\u22b4', 'rarrb;': '\u21e5', 'ssetmn;': '\u2216', 'ufr;': '\U0001d532', 'Acirc;': '\xc2', 'LeftRightArrow;': '\u2194', 'varr;': '\u2195', 'eth': '\xf0', 'varsupsetneqq;': '\u2acc\ufe00', 'HilbertSpace;': '\u210b', 'diamond;': '\u22c4', 'npart;': '\u2202\u0338', 'Cfr;': '\u212d', 'slarr;': '\u2190', 'cwconint;': '\u2232', 'ncaron;': '\u0148', 'theta;': '\u03b8', 'NotSupersetEqual;': '\u2289', 'nsubset;': '\u2282\u20d2', 'EmptySmallSquare;': '\u25fb', 'Tstrok;': '\u0166', 'lg;': '\u2276', 'urcorn;': '\u231d', 'acy;': '\u0430', 'DoubleVerticalBar;': '\u2225', 'Phi;': '\u03a6', 'imof;': '\u22b7', 'angle;': '\u2220', 'supdot;': '\u2abe', 'timesb;': '\u22a0', 'bfr;': '\U0001d51f', 'dcaron;': '\u010f', 'Aacute': '\xc1', 'cent': '\xa2', 'rdquo;': '\u201d', 'jopf;': '\U0001d55b', 'sup2;': '\xb2', 'triangledown;': '\u25bf', 'lHar;': '\u2962', 'leftarrowtail;': '\u21a2', 'HorizontalLine;': '\u2500', 'duarr;': '\u21f5', 'cupcap;': '\u2a46', 'euml': '\xeb', 'shy': '\xad', 'curarr;': '\u21b7', 'larrhk;': '\u21a9', 'Kfr;': '\U0001d50e', 'olarr;': '\u21ba', 'nsupE;': '\u2ac6\u0338', 'colon;': ':', 'Eta;': '\u0397', 'dsol;': '\u29f6', 'LessGreater;': '\u2276', 'dblac;': '\u02dd', 'vopf;': '\U0001d567', 'incare;': '\u2105', 'wreath;': '\u2240', 'NotSucceedsEqual;': '\u2ab0\u0338', 'lcaron;': '\u013e', 'conint;': '\u222e', 'napid;': '\u224b\u0338', 'Equal;': '\u2a75', 'dscr;': '\U0001d4b9', 'Itilde;': '\u0128', 'iiota;': '\u2129', 'UpDownArrow;': '\u2195', 'Vcy;': '\u0412', 'lobrk;': '\u27e6', 'thksim;': '\u223c', 'Ucirc;': '\xdb', 'Rcedil;': '\u0156', 'tritime;': '\u2a3b', 'boxh;': '\u2500', 'Fouriertrf;': '\u2131', 'realine;': '\u211b', 'rightleftarrows;': '\u21c4', 'wp;': '\u2118', 'thkap;': '\u2248', 'sqsupset;': '\u2290', 'CloseCurlyQuote;': '\u2019', 'SquareSubsetEqual;': '\u2291', 'Iuml;': '\xcf', 'sqsup;': '\u2290', 'NotDoubleVerticalBar;': '\u2226', 'ugrave': '\xf9', 'acd;': '\u223f', 'oscr;': '\u2134', 'Qfr;': '\U0001d514', 'ncap;': '\u2a43', 'Vdash;': '\u22a9', 'nrtrie;': '\u22ed', 'lesdot;': '\u2a7f', 'nltri;': '\u22ea', 'ncy;': '\u043d', 'Hacek;': '\u02c7', 'radic;': '\u221a', 'frac78;': '\u215e', 'NotReverseElement;': '\u220c', 'Therefore;': '\u2234', 'lates;': '\u2aad\ufe00', 'varepsilon;': '\u03f5', 'ruluhar;': '\u2968', 'rsaquo;': '\u203a', 'Tscr;': '\U0001d4af', 'subsetneq;': '\u228a', 'UnderBrace;': '\u23df', 'Uring;': '\u016e', 'acirc': '\xe2', 'check;': '\u2713', 'rsquor;': '\u2019', 'tbrk;': '\u23b4', 'NotLessTilde;': '\u2274', 'vsupne;': '\u228b\ufe00', 'wfr;': '\U0001d534', 'hellip;': '\u2026', 'nless;': '\u226e', 'Yuml;': '\u0178', 'FilledSmallSquare;': '\u25fc', 'SucceedsEqual;': '\u2ab0', 'frac23;': '\u2154', 'OverBracket;': '\u23b4', 'SupersetEqual;': '\u2287', 'gesdot;': '\u2a80', 'excl;': '!', 'UpArrowBar;': '\u2912', 'barwed;': '\u2305', 'barwedge;': '\u2305', 'notinvc;': '\u22f6', 'uArr;': '\u21d1', 'lthree;': '\u22cb', 'risingdotseq;': '\u2253', 'Mopf;': '\U0001d544', 'yacute;': '\xfd', 'otimesas;': '\u2a36', 'capcup;': '\u2a47', 'ofcir;': '\u29bf', 'Upsi;': '\u03d2', 'Ecaron;': '\u011a', 'Qscr;': '\U0001d4ac', 'hookleftarrow;': '\u21a9', 'Ograve;': '\xd2', 'precnapprox;': '\u2ab9', 'Uarrocir;': '\u2949', 'part;': '\u2202', 'subsub;': '\u2ad5', 'lmidot;': '\u0140', 'DJcy;': '\u0402', 'nexists;': '\u2204', 'NotEqualTilde;': '\u2242\u0338', 'profalar;': '\u232e', 'sum;': '\u2211', 'Precedes;': '\u227a', 'Ofr;': '\U0001d512', 'fopf;': '\U0001d557', 'iecy;': '\u0435', 'ShortUpArrow;': '\u2191', 'nparsl;': '\u2afd\u20e5', 'boxUR;': '\u255a', 'exponentiale;': '\u2147', 'upsilon;': '\u03c5', 'Jopf;': '\U0001d541', 'VerticalSeparator;': '\u2758', 'Dfr;': '\U0001d507', 'NonBreakingSpace;': '\xa0', 'bottom;': '\u22a5', 'orslope;': '\u2a57', 'boxDL;': '\u2557', 'bigcap;': '\u22c2', 'Vbar;': '\u2aeb', 'pound;': '\xa3', 'boxvr;': '\u251c', 'Cup;': '\u22d3', 'bigtriangleup;': '\u25b3', 'RightAngleBracket;': '\u27e9', 'lesg;': '\u22da\ufe00', 'RightDownVector;': '\u21c2', 'Gfr;': '\U0001d50a', 'shy;': '\xad', 'supnE;': '\u2acc', 'cirE;': '\u29c3', 'angmsdae;': '\u29ac', 'Bumpeq;': '\u224e', 'delta;': '\u03b4', 'thinsp;': '\u2009', 'EmptyVerySmallSquare;': '\u25ab', 'leftleftarrows;': '\u21c7', 'les;': '\u2a7d', 'ltcc;': '\u2aa6', 'TildeFullEqual;': '\u2245', 'iocy;': '\u0451', 'supsetneqq;': '\u2acc', 'rharul;': '\u296c', 'hArr;': '\u21d4', 'amp': '&', 'Cdot;': '\u010a', 'rbrack;': ']', 'nspar;': '\u2226', 'pcy;': '\u043f', 'NotSucceedsTilde;': '\u227f\u0338', 'acute': '\xb4', 'dlcrop;': '\u230d', 'subdot;': '\u2abd', 'UnionPlus;': '\u228e', 'mapstoleft;': '\u21a4', 'DoubleRightTee;': '\u22a8', 'sigmav;': '\u03c2', 'sfr;': '\U0001d530', 'Igrave': '\xcc', 'euro;': '\u20ac', 'complement;': '\u2201', 'profsurf;': '\u2313', 'nabla;': '\u2207', 'para;': '\xb6', 'Dopf;': '\U0001d53b', 'cdot;': '\u010b', 'sim;': '\u223c', 'popf;': '\U0001d561', 'ImaginaryI;': '\u2148', 'notni;': '\u220c', 'RightArrowBar;': '\u21e5', 'intlarhk;': '\u2a17', 'gtcir;': '\u2a7a', 'llcorner;': '\u231e', 'Bfr;': '\U0001d505', 'Rang;': '\u27eb', 'ddagger;': '\u2021', 'vBarv;': '\u2ae9', 'forkv;': '\u2ad9', 'angmsd;': '\u2221', 'ouml;': '\xf6', 'nvgt;': '>\u20d2', 'Dagger;': '\u2021', 'lharu;': '\u21bc', 'Exists;': '\u2203', 'LeftTriangleBar;': '\u29cf', 'ratio;': '\u2236', 'TildeTilde;': '\u2248', 'minusb;': '\u229f', 'race;': '\u223d\u0331', 'rAarr;': '\u21db', 'bigoplus;': '\u2a01', 'rangd;': '\u2992', 'micro': '\xb5', 'osol;': '\u2298', 'strns;': '\xaf', 'Longleftrightarrow;': '\u27fa', 'boxUl;': '\u255c', 'Sc;': '\u2abc', 'ocirc': '\xf4', 'ac;': '\u223e', 'nsubE;': '\u2ac5\u0338', 'DotEqual;': '\u2250', 'zopf;': '\U0001d56b', 'llarr;': '\u21c7', 'permil;': '\u2030', 'Topf;': '\U0001d54b', 'UpperLeftArrow;': '\u2196', 'ulcorn;': '\u231c', 'curlyeqsucc;': '\u22df', 'aleph;': '\u2135', 'image;': '\u2111', 'igrave': '\xec', 'NestedLessLess;': '\u226a', 'LongLeftRightArrow;': '\u27f7', 'sqsupe;': '\u2292', 'midast;': '*', 'dwangle;': '\u29a6', 'uring;': '\u016f', 'becaus;': '\u2235', 'GreaterFullEqual;': '\u2267', 'dd;': '\u2146', 'kcy;': '\u043a', 'Laplacetrf;': '\u2112', 'marker;': '\u25ae', 'simrarr;': '\u2972', 'Agrave;': '\xc0', 'bNot;': '\u2aed', 'ocir;': '\u229a', 'supsetneq;': '\u228b', 'fork;': '\u22d4', 'pi;': '\u03c0', 'topbot;': '\u2336', 'xharr;': '\u27f7', 'Jukcy;': '\u0404', 'naturals;': '\u2115', 'csup;': '\u2ad0', 'ltimes;': '\u22c9', 'mcy;': '\u043c', 'lessgtr;': '\u2276', 'uuml': '\xfc', 'iquest;': '\xbf', 'boxhd;': '\u252c', 'nsupe;': '\u2289', 'leftharpoondown;': '\u21bd', 'Lacute;': '\u0139', 'Emacr;': '\u0112', 'Vee;': '\u22c1', 'cupcup;': '\u2a4a', 'backsimeq;': '\u22cd', 'dlcorn;': '\u231e', 'bprime;': '\u2035', 'HumpEqual;': '\u224f', 'simdot;': '\u2a6a', 'oelig;': '\u0153', 'ntilde;': '\xf1', 'xdtri;': '\u25bd', 'hscr;': '\U0001d4bd', 'cups;': '\u222a\ufe00', 'pre;': '\u2aaf', 'yscr;': '\U0001d4ce', 'boxplus;': '\u229e', 'Jcirc;': '\u0134', 'suphsol;': '\u27c9', 'Nopf;': '\u2115', 'DZcy;': '\u040f', 'flat;': '\u266d', 'ldquor;': '\u201e', 'Leftrightarrow;': '\u21d4', 'veebar;': '\u22bb', 'Rrightarrow;': '\u21db', 'compfn;': '\u2218', 'succ;': '\u227b', 'NegativeVeryThinSpace;': '\u200b', 'cupbrcap;': '\u2a48', 'notindot;': '\u22f5\u0338', 'supseteqq;': '\u2ac6', 'plankv;': '\u210f', 'ordm': '\xba', 'nsupseteq;': '\u2289', 'sacute;': '\u015b', 'ordm;': '\xba', 'dtdot;': '\u22f1', 'NotSubsetEqual;': '\u2288', 'subedot;': '\u2ac3', 'curlywedge;': '\u22cf', 'GreaterGreater;': '\u2aa2', 'dbkarow;': '\u290f', 'quatint;': '\u2a16', 'ContourIntegral;': '\u222e', 'LeftTriangle;': '\u22b2', 'lrcorner;': '\u231f', 'RightVectorBar;': '\u2953', 'nequiv;': '\u2262', 'ltrie;': '\u22b4', 'divonx;': '\u22c7', 'topf;': '\U0001d565', 'cuepr;': '\u22de', 'LeftRightVector;': '\u294e', 'rtimes;': '\u22ca', 'LeftCeiling;': '\u2308', 'iukcy;': '\u0456', 'ordf;': '\xaa', 'OpenCurlyQuote;': '\u2018', 'fnof;': '\u0192', 'thorn': '\xfe', 'star;': '\u2606', 'lne;': '\u2a87', 'hearts;': '\u2665', 'dash;': '\u2010', 'vartriangleleft;': '\u22b2', 'shcy;': '\u0448', 'hfr;': '\U0001d525', 'uuarr;': '\u21c8', 'isin;': '\u2208', 'tcaron;': '\u0165', 'bigodot;': '\u2a00', 'lurdshar;': '\u294a', 'ucy;': '\u0443', 'nmid;': '\u2224', 'semi;': ';', 'laquo;': '\xab', 'bullet;': '\u2022', 'hslash;': '\u210f', 'gtrsim;': '\u2273', 'InvisibleTimes;': '\u2062', 'cfr;': '\U0001d520', 'tscr;': '\U0001d4c9', 'nltrie;': '\u22ec', 'succcurlyeq;': '\u227d', 'ogon;': '\u02db', 'NotExists;': '\u2204', 'kgreen;': '\u0138', 'seArr;': '\u21d8', 'Product;': '\u220f', 'sqcap;': '\u2293', 'rx;': '\u211e', 'nLeftarrow;': '\u21cd', 'Updownarrow;': '\u21d5', 'Ecirc': '\xca', 'Lcy;': '\u041b', 'icirc;': '\xee', 'bigstar;': '\u2605', 'gtcc;': '\u2aa7', 'olcross;': '\u29bb', 'in;': '\u2208', 'VerticalTilde;': '\u2240', 'filig;': '\ufb01', 'rightsquigarrow;': '\u219d', 'pfr;': '\U0001d52d', 'Intersection;': '\u22c2', 'Not;': '\u2aec', 'rsqb;': ']', 'Ncy;': '\u041d', 'period;': '.', 'xhArr;': '\u27fa', 'phmmat;': '\u2133', 'NotCongruent;': '\u2262', 'boxdR;': '\u2552', 'kjcy;': '\u045c', 'bigwedge;': '\u22c0', 'NotGreaterTilde;': '\u2275', 'nsqsupe;': '\u22e3', 'aring;': '\xe5', 'prnE;': '\u2ab5', 'LessFullEqual;': '\u2266', 'eqcirc;': '\u2256', 'downharpoonleft;': '\u21c3', 'rlarr;': '\u21c4', 'smallsetminus;': '\u2216', 'omega;': '\u03c9', 'mldr;': '\u2026', 'vzigzag;': '\u299a', 'nleqq;': '\u2266\u0338', 'ulcrop;': '\u230f', 'straightepsilon;': '\u03f5', 'Auml;': '\xc4', 'LongLeftArrow;': '\u27f5'} def substitute_entity(match): ent = match.group(2) + match.group(3) res = "" while not ent in html5 and not ent.endswith(";") and match.group(1) != "#": # Excepción para cuando '&' se usa como argumento en la urls contenidas en los datos try: res = ent[-1] + res ent = ent[:-1] except: break if match.group(1) == "#": ent = unichr(int(ent.replace(";",""))) return ent.encode('utf-8') else: cp = html5.get(ent) if cp: return cp.decode("unicode-escape").encode('utf-8') + res else: return match.group() return entity_re.subn(substitute_entity, data)[0]

gpl-3.0

CyrilWaechter/pyRevitMEP

pyRevitMEP.tab/Create.panel/BatchCreation.pulldown/BatchDependentViewCreation.pushbutton/script.py

1

2538

# coding: utf8 import rpw # noinspection PyUnresolvedReferences from rpw import revit, DB from pyrevit.forms import WPFWindow from pyrevit import script from pyrevitmep.workset import Workset # noinspection PyUnresolvedReferences from System.Collections.ObjectModel import ObservableCollection __doc__ = "Batch create dependent views corresponding to existing Scope Boxes for selected views" __title__ = "DependentViews" __author__ = "Cyril Waechter" __context__ = "selection" doc = rpw.revit.doc logger = script.get_logger() class Gui(WPFWindow): def __init__(self, xaml_file_name): WPFWindow.__init__(self, xaml_file_name) volume_of_interest = DB.FilteredElementCollector(doc).OfCategory(DB.BuiltInCategory.OST_VolumeOfInterest) self.data_grid_content = ObservableCollection[object](volume_of_interest) self.datagrid.ItemsSource = self.data_grid_content image_dict = { "plus_img": "icons8-plus-32.png", "minus_img": "icons8-minus-32.png", "import_img": "icons8-import-32.png", "ok_img": "icons8-checkmark-32.png" } for k, v in image_dict.items(): self.set_image_source(getattr(self, k), v) # noinspection PyUnusedLocal def ok_click(self, sender, e): for view_id in rpw.uidoc.Selection.GetElementIds(): view = doc.GetElement(view_id) try: with rpw.db.Transaction("BatchCreateDependentViews"): for volume_of_interest in self.data_grid_content: new_view_id = view.Duplicate(DB.ViewDuplicateOption.AsDependent) new_view = doc.GetElement(new_view_id) parameter = new_view.get_Parameter(DB.BuiltInParameter.VIEWER_VOLUME_OF_INTEREST_CROP) parameter.Set(volume_of_interest.Id) except AttributeError as e: print("{} doesn't seem to be a view".format(view)) logger.debug("{}".format(e.message)) # noinspection PyUnusedLocal def load_from_file_click(self, sender, e): for workset in Workset.read_from_txt(): self.data_grid_content.Add(workset) # noinspection PyUnusedLocal def add(self, sender, e): self.data_grid_content.Add(Workset("")) # noinspection PyUnusedLocal def remove(self, sender, e): for item in list(self.datagrid.SelectedItems): self.data_grid_content.Remove(item) gui = Gui("WPFWindow.xaml") gui.ShowDialog()

gpl-3.0

lmazuel/azure-sdk-for-python

azure-mgmt-network/azure/mgmt/network/v2017_06_01/models/topology_resource.py

1

1570

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is # regenerated. # -------------------------------------------------------------------------- from msrest.serialization import Model class TopologyResource(Model): """The network resource topology information for the given resource group. :param name: Name of the resource. :type name: str :param id: ID of the resource. :type id: str :param location: Resource location. :type location: str :param associations: Holds the associations the resource has with other resources in the resource group. :type associations: list[~azure.mgmt.network.v2017_06_01.models.TopologyAssociation] """ _attribute_map = { 'name': {'key': 'name', 'type': 'str'}, 'id': {'key': 'id', 'type': 'str'}, 'location': {'key': 'location', 'type': 'str'}, 'associations': {'key': 'associations', 'type': '[TopologyAssociation]'}, } def __init__(self, **kwargs): super(TopologyResource, self).__init__(**kwargs) self.name = kwargs.get('name', None) self.id = kwargs.get('id', None) self.location = kwargs.get('location', None) self.associations = kwargs.get('associations', None)

mit

eliben/code-for-blog

2018/type-inference/parser.py

1

7046

# EBNF specification for micro-ML. { x } means zero or more repetitions of x. # # The top-level is decl. # # decl: ID { ID } '=' expr # # expr: INT # | bool # | ID # | ID '(' { expr ',' } ')' # | '(' expr ')' # | expr op expr # | 'if' expr 'then' expr 'else' expr # | 'lambda' { ID } '->' expr # # op: + | * | - | == | > | >= | <= | < | != # bool: 'true' | 'false' # # ID: identifier # INT: an integer # # Eli Bendersky [http://eli.thegreenplace.net] # This code is in the public domain. import ast import lexer class ParseError(Exception): pass class Parser: """Parser for micro-ML. The only public method here is parse_decl that parses a 'decl' from a string. Usage: p = Parser() decl = p.parse_decl(<some micro-ML code>) # decl is now an ast.Decl node parse_decl() can be called multiple times with the same parser to parse multiple decls (state is wiped out between calls). """ def __init__(self): lex_rules = ( ('if', 'IF'), ('then', 'THEN'), ('else', 'ELSE'), ('true', 'TRUE'), ('false', 'FALSE'), ('lambda', 'LAMBDA'), ('\d+', 'INT'), ('->', 'ARROW'), ('!=', '!='), ('==', '=='), ('>=', '>='), ('<=', '<='), ('<', '<'), ('>', '>'), ('\+', '+'), ('\-', '-'), ('\*', '*'), ('\(', '('), ('\)', ')'), ('=', '='), (',', ','), ('[a-zA-Z_]\w*', 'ID'), ) self.lexer = lexer.Lexer(lex_rules, skip_whitespace=True) self.cur_token = None self.operators = {'!=', '==', '>=', '<=', '<', '>', '+', '-', '*'} def parse_decl(self, text): """Parse declaration given in text and return an AST node for it.""" self.lexer.input(text) self._get_next_token() decl = self._decl() if self.cur_token.type != None: self._error('Unexpected token "{}" (at #{})'.format( self.cur_token.val, self.cur_token.pos)) return decl def _error(self, msg): raise ParseError(msg) def _get_next_token(self): """Advances the parser's internal lexer to the next token. This method doesn't return anything; it assigns self.cur_token to the next token in the input stream. """ try: self.cur_token = self.lexer.token() if self.cur_token is None: self.cur_token = lexer.Token(None, None, None) except lexer.LexerError as e: self._error('Lexer error at position {}: {}'.format(e.pos, e)) def _match(self, type): """ The 'match' primitive of RD parsers. * Verifies that the current token is of the given type * Returns the value of the current token * Reads in the next token """ if self.cur_token.type == type: val = self.cur_token.val self._get_next_token() return val else: self._error('Unmatched {} (found {})'.format(type, self.cur_token.type)) def _decl(self): name = self._match('ID') argnames = [] # If we have arguments, collect them. Only IDs allowed here. while self.cur_token.type == 'ID': argnames.append(self.cur_token.val) self._get_next_token() self._match('=') expr = self._expr() if len(argnames) > 0: return ast.Decl(name, ast.LambdaExpr(argnames, expr)) else: return ast.Decl(name, expr) def _expr(self): """Parse an expr of the form: expr op expr We only allow a single operator between expressions. Additional operators should be nested using parens, e.g. x + (y * z) """ node = self._expr_component() if self.cur_token.type in self.operators: op = self.cur_token.type self._get_next_token() rhs = self._expr_component() return ast.OpExpr(op, node, rhs) else: return node def _expr_component(self): """Parse an expr component (components can be separated by an operator). """ curtok = self.cur_token if self.cur_token.type == 'INT': self._get_next_token() return ast.IntConstant(curtok.val) elif self.cur_token.type in ('FALSE', 'TRUE'): self._get_next_token() return ast.BoolConstant(curtok.val) elif self.cur_token.type == 'ID': self._get_next_token() if self.cur_token.type == '(': # ID followed by '(' is function application return self._app(curtok.val) else: return ast.Identifier(curtok.val) elif self.cur_token.type == '(': self._get_next_token() expr = self._expr() self._match(')') return expr elif self.cur_token.type == 'IF': return self._ifexpr() elif self.cur_token.type == 'LAMBDA': return self._lambda() else: self._error("Don't support {} yet".format(curtok.type)) def _ifexpr(self): self._match('IF') ifexpr = self._expr() self._match('THEN') thenexpr = self._expr() self._match('ELSE') elseexpr = self._expr() return ast.IfExpr(ifexpr, thenexpr, elseexpr) def _lambda(self): self._match('LAMBDA') argnames = [] while self.cur_token.type == 'ID': argnames.append(self.cur_token.val) self._get_next_token() if len(argnames) < 1: self._error('Expected non-empty argument list for lambda') self._match('ARROW') expr = self._expr() return ast.LambdaExpr(argnames, expr) def _app(self, name): self._match('(') args = [] while self.cur_token.type != ')': args.append(self._expr()) if self.cur_token.type == ',': self._get_next_token() elif self.cur_token.type == ')': pass # the loop will break else: self._error("Unexpected {} in application".format( self.cur_token.val)) self._match(')') return ast.AppExpr(ast.Identifier(name), args)

unlicense

nsi-iff/nsi_site

apps/news/migrations/0002_auto.py

1

6169

# encoding: utf-8 import datetime from south.db import db from south.v2 import SchemaMigration from django.db import models class Migration(SchemaMigration): def forwards(self, orm): # Removing M2M table for field project on 'New' db.delete_table('news_new_project') # Adding M2M table for field projects_relateds on 'New' db.create_table('news_new_projects_relateds', ( ('id', models.AutoField(verbose_name='ID', primary_key=True, auto_created=True)), ('new', models.ForeignKey(orm['news.new'], null=False)), ('project', models.ForeignKey(orm['projects.project'], null=False)) )) db.create_unique('news_new_projects_relateds', ['new_id', 'project_id']) def backwards(self, orm): # Adding M2M table for field project on 'New' db.create_table('news_new_project', ( ('id', models.AutoField(verbose_name='ID', primary_key=True, auto_created=True)), ('new', models.ForeignKey(orm['news.new'], null=False)), ('project', models.ForeignKey(orm['projects.project'], null=False)) )) db.create_unique('news_new_project', ['new_id', 'project_id']) # Removing M2M table for field projects_relateds on 'New' db.delete_table('news_new_projects_relateds') models = { 'auth.group': { 'Meta': {'object_name': 'Group'}, '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': "orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'}) }, 'auth.permission': { 'Meta': {'ordering': "('content_type__app_label', 'content_type__model', 'codename')", 'unique_together': "(('content_type', 'codename'),)", 'object_name': 'Permission'}, 'codename': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'content_type': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['contenttypes.ContentType']"}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '50'}) }, 'auth.user': { 'Meta': {'object_name': 'User'}, 'date_joined': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}), 'email': ('django.db.models.fields.EmailField', [], {'max_length': '75', 'blank': 'True'}), 'first_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}), 'groups': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['auth.Group']", 'symmetrical': 'False', 'blank': 'True'}), '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'}), 'password': ('django.db.models.fields.CharField', [], {'max_length': '128'}), 'user_permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': "orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'}), 'username': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '30'}) }, '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'}), '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'}) }, 'news.new': { 'Meta': {'object_name': 'New'}, 'author': ('django.db.models.fields.related.ForeignKey', [], {'to': "orm['auth.User']"}), 'body': ('django.db.models.fields.TextField', [], {}), 'datetime': ('django.db.models.fields.DateTimeField', [], {}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'image': ('django.db.models.fields.files.ImageField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}), 'projects_relateds': ('django.db.models.fields.related.ManyToManyField', [], {'symmetrical': 'False', 'to': "orm['projects.Project']", 'null': 'True', 'blank': 'True'}), 'summary': ('django.db.models.fields.CharField', [], {'max_length': '200', 'null': 'True', 'blank': 'True'}), 'title': ('django.db.models.fields.CharField', [], {'max_length': '200'}) }, 'projects.project': { 'Meta': {'object_name': 'Project'}, 'description': ('django.db.models.fields.TextField', [], {}), 'end_date': ('django.db.models.fields.DateField', [], {'null': 'True'}), 'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'logo': ('django.db.models.fields.files.ImageField', [], {'max_length': '100'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'sponsor': ('django.db.models.fields.CharField', [], {'max_length': '100'}), 'start_date': ('django.db.models.fields.DateField', [], {}), 'status': ('django.db.models.fields.CharField', [], {'max_length': '100'}) } } complete_apps = ['news']

mit

fifengine/fifengine

tests/fife_test/scripts/test.py

1

3991

#!/usr/bin/env python # -*- coding: utf-8 -*- # #################################################################### # Copyright (C) 2005-2019 by the FIFE team # http://www.fifengine.net # This file is part of FIFE. # # FIFE is free software; you can redistribute it and/or # modify it under the terms of the GNU Lesser General Public # License as published by the Free Software Foundation; either # version 2.1 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with this library; if not, write to the # Free Software Foundation, Inc., # 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA # #################################################################### from __future__ import print_function from builtins import object import os class TestManager(object): def __init__(self, engine, application, settings): self._engine = engine self._application = application self._settings = settings self._running = None self._testdir = "tests" self._tests = [] files = [] for f in os.listdir(self._testdir): path = os.path.join(self._testdir, f) if os.path.isfile(path) and os.path.splitext(f)[1] == ".py" and f != "__init__.py": files.append(os.path.splitext(f)[0]) for f in files: importtest = self._settings.get("Tests", f, False) if importtest: try: print("Importing test plugin: ", f) exec("import " + self._testdir + "." + f) test = eval(self._testdir + "." + f + "." + f + "()") if isinstance(test, Test) is False: print(f + " is not an instance of Test!") else: self._tests.append(test) except BaseException as error: print("Error: ", error) print("Invalid test: ", f) else: print("Not importing test: ", f) self._settings.set("Tests", f, importtest) def _getRunningTest(self): return self._running def runTest(self, test): if test in self._tests and not self._running: self._running = test self._running.create(self._engine, self._application) self._running.run() def stopTest(self): if self._running: if self._running.isRunning(): self._running.stop() self._running.destroy() self._running = None def resetTest(self): if self._running: if self._running.isRunning(): self._running.stop() self._running.destroy() self._running.create(self._engine, self._application) self._running.run() def _getTests(self): return self._tests def _getTestNameList(self): namelist = [] for t in self._tests: namelist.append(t.getName()) return namelist tests = property(_getTests) testnames = property(_getTestNameList) runningtest = property(_getRunningTest) class Test(object): """ The base calss for all tests. All tests must override these functions! """ def create(self, engine, application): raise NotImplementedError("Test has not implemented the init() function!") def destroy(self): raise NotImplementedError("Test has not implemented the destroy() function!") def run(self): raise NotImplementedError("Test has not implemented the run() function!") def stop(self): raise NotImplementedError("Test has not implemented the stop() function!") def isRunning(self): raise NotImplementedError("Test has not implemented the isRunning() function!") def getName(self): raise NotImplementedError("Test has not implemented the getName() function!") def getAuthor(self): return "unknown" def getDescription(self): return "none" def getHelp(self): return "You're on your own for this one!" def onConsoleCommand(self, cmd): return cmd[0] + ": not found." def pump(self): pass

lgpl-2.1

Patrick-Cole/pygmi

pygmi/clust/graphtool.py

1

26477

# ----------------------------------------------------------------------------- # Name: graph_tool.py (part of PyGMI) # # Author: Patrick Cole # E-Mail: [email protected] # # Copyright: (c) 2013 Council for Geoscience # Licence: GPL-3.0 # # This file is part of PyGMI # # PyGMI is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # PyGMI is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program. If not, see <http://www.gnu.org/licenses/>. # ----------------------------------------------------------------------------- """Multi-function graphing tool for use with cluster analysis.""" import numpy as np from PyQt5 import QtWidgets, QtCore from matplotlib.figure import Figure from matplotlib import cm from matplotlib.artist import Artist from matplotlib.patches import Polygon from matplotlib.lines import Line2D from matplotlib.path import Path from matplotlib.ticker import NullFormatter from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg class GraphHist(FigureCanvasQTAgg): """Graph Hist.""" def __init__(self, parent=None): self.figure = Figure() super().__init__(self.figure) self.setParent(parent) self.nullfmt = NullFormatter() self.pntxy = None self.polyi = None self.axhistx = None self.axhisty = None self.axscatter = None self.histx = None self.histy = None self.xcoord = None self.ycoord = None self.data = [] self.cindx = [0, 1, 0] self.cdata = [] self.csp = None def get_hist(self, bins): """ Routine to get the scattergram with histogram overlay. Parameters ---------- bins : int Number of bins. Returns ------- xymahist : numpy array Output data. """ xyhist = np.zeros((bins + 1, bins + 1)) xxx = self.xcoord.compressed() yyy = self.ycoord.compressed() xyhist = np.histogram2d(xxx, yyy, bins + 1) xymahist = np.ma.masked_equal(xyhist[0], 0) return xymahist def get_clust_scat(self, bins, dattmp, ctmp): """ Routine to get the scattergram with cluster overlay. Parameters ---------- bins : int Number of bins. dattmp : list Data. ctmp : list Cluster indices. Returns ------- xymahist : numpy array Output data. """ clust = np.ma.array(dattmp[ctmp[2] - 1].data.flatten()) clust.mask = np.ma.getmaskarray(self.xcoord) clust = clust.compressed() xxx = self.xcoord.compressed() yyy = self.ycoord.compressed() xyhist = np.zeros((bins + 1, bins + 1)) xyhist[xxx, yyy] = (clust + 1) xymahist = np.ma.masked_equal(xyhist, 0) return xymahist def init_graph(self): """ Initialize the Graph. Returns ------- None. """ self.figure.clf() left, width = 0.1, 0.65 bottom, height = 0.1, 0.65 bottom_h = bottom + height + 0.02 left_h = left + width + 0.02 rect_scatter = [left, bottom, width, height] rect_histx = [left, bottom_h, width, 0.2] rect_histy = [left_h, bottom, 0.2, height] self.axscatter = self.figure.add_axes(rect_scatter, label='s') self.axhistx = self.figure.add_axes(rect_histx, label='x') self.axhisty = self.figure.add_axes(rect_histy, label='y') # Setup the coordinates self.setup_coords() # setup 1d histograms self.setup_hist() # Compressed eliminates the masked values so that hist xymahist = self.get_hist(50) self.axscatter.get_xaxis().set_visible(False) self.axscatter.get_yaxis().set_visible(False) self.csp = self.axscatter.imshow(xymahist.T, interpolation='nearest', cmap=cm.get_cmap('jet'), aspect='auto') self.csp.set_clim(xymahist.min(), xymahist.max()) self.csp.changed() self.figure.canvas.draw() def polyint(self): """ Polygon Interactor routine. Returns ------- None. """ pntxy = np.transpose([self.xcoord, self.ycoord]) self.polyi = PolygonInteractor(self.axscatter, pntxy) self.polyi.ishist = True def setup_coords(self): """ Routine to setup the coordinates for the scattergram. Returns ------- None. """ self.xcoord = self.data[self.cindx[0]].data.flatten() self.ycoord = self.data[self.cindx[1]].data.flatten() self.xcoord -= self.xcoord.min() self.ycoord -= self.ycoord.min() xptp = self.xcoord.ptp() yptp = self.ycoord.ptp() xstep = xptp / 50 ystep = yptp / 50 self.xcoord /= xstep self.ycoord /= ystep self.xcoord = self.xcoord.astype(int) self.ycoord = self.ycoord.astype(int) def setup_hist(self): """ Routine to setup the 1D histograms. Returns ------- None. """ self.axhistx.xaxis.set_major_formatter(self.nullfmt) self.axhisty.yaxis.set_major_formatter(self.nullfmt) self.axhistx.yaxis.set_major_formatter(self.nullfmt) self.axhisty.xaxis.set_major_formatter(self.nullfmt) xrng = [self.xcoord.min(), self.xcoord.max()] yrng = [self.ycoord.min(), self.ycoord.max()] self.histx = self.axhistx.hist(self.xcoord.compressed(), 50) self.histy = self.axhisty.hist(self.ycoord.compressed(), 50, orientation='horizontal') self.axhistx.set_xlim(xrng) self.axhisty.set_ylim(yrng[::-1]) def update_graph(self, clearaxis=False): """ Draw Routine. Parameters ---------- clearaxis : bool, optional True to clear the axis. The default is False. Returns ------- None. """ if clearaxis is True: self.axhistx.cla() self.axhisty.cla() self.setup_coords() self.polyi.pntxy = np.array([self.xcoord, self.ycoord]).T self.setup_hist() if self.cindx[2] > 0: xymahist = self.get_clust_scat(50, self.cdata, self.cindx) else: xymahist = self.get_hist(50) if self.csp is None: return self.csp.set_data(xymahist.T) self.csp.set_clim(xymahist.min(), xymahist.max()) self.csp.changed() self.figure.canvas.draw() self.polyi.draw_callback() class GraphMap(FigureCanvasQTAgg): """ Graph Map. Attributes ---------- parent : parent reference to the parent routine """ def __init__(self, parent=None): self.figure = Figure() super().__init__(self.figure) self.setParent(parent) self.parent = parent self.polyi = None self.data = [] self.cdata = [] self.mindx = [0, 0] self.csp = None self.subplot = None def init_graph(self): """ Initialize the Graph. Returns ------- None. """ mtmp = self.mindx dat = self.data[mtmp[0]] self.figure.clf() self.subplot = self.figure.add_subplot(111) self.subplot.get_xaxis().set_visible(False) self.subplot.get_yaxis().set_visible(False) self.csp = self.subplot.imshow(dat.data, cmap=cm.get_cmap('jet')) self.subplot.figure.colorbar(self.csp) self.figure.canvas.draw() def polyint(self): """ Polygon Integrator. Returns ------- None. """ mtmp = self.mindx dat = self.data[mtmp[0]].data xtmp = np.arange(dat.shape[1]) ytmp = np.arange(dat.shape[0]) xmesh, ymesh = np.meshgrid(xtmp, ytmp) xmesh = np.ma.array(xmesh, dtype=float, mask=dat.mask) ymesh = np.ma.array(ymesh, dtype=float, mask=dat.mask) xmesh = xmesh.flatten() ymesh = ymesh.flatten() xmesh = xmesh.filled(np.nan) ymesh = ymesh.filled(np.nan) pntxy = np.transpose([xmesh, ymesh]) self.polyi = PolygonInteractor(self.subplot, pntxy) self.polyi.ishist = False def update_graph(self): """ Draw routine. Returns ------- None. """ mtmp = self.mindx dat = self.data[mtmp[0]] if mtmp[1] > 0: cdat = self.cdata[mtmp[1] - 1].data self.csp.set_data(cdat) self.csp.set_clim(cdat.min(), cdat.max()) else: self.csp.set_data(dat.data) self.csp.set_clim(dat.data.min(), dat.data.max()) self.csp.changed() self.figure.canvas.draw() self.polyi.draw_callback() class PolygonInteractor(QtCore.QObject): """Polygon Interactor.""" showverts = True epsilon = 5 # max pixel distance to count as a vertex hit polyi_changed = QtCore.pyqtSignal(list) def __init__(self, axtmp, pntxy): super().__init__() self.ax = axtmp self.poly = Polygon([(1, 1)], animated=True) self.ax.add_patch(self.poly) self.canvas = self.poly.figure.canvas self.poly.set_alpha(0.5) self.pntxy = pntxy self.ishist = True self.background = self.canvas.copy_from_bbox(self.ax.bbox) xtmp, ytmp = list(zip(*self.poly.xy)) self.line = Line2D(xtmp, ytmp, marker='o', markerfacecolor='r', color='y', animated=True) self.ax.add_line(self.line) self.poly.add_callback(self.poly_changed) self._ind = None # the active vert self.canvas.mpl_connect('button_press_event', self.button_press_callback) self.canvas.mpl_connect('button_release_event', self.button_release_callback) self.canvas.mpl_connect('motion_notify_event', self.motion_notify_callback) def draw_callback(self): """ Draw callback. Returns ------- None. """ self.background = self.canvas.copy_from_bbox(self.ax.bbox) QtWidgets.QApplication.processEvents() self.canvas.restore_region(self.background) self.ax.draw_artist(self.poly) self.ax.draw_artist(self.line) self.canvas.update() def new_poly(self, npoly): """ Create new Polygon. Parameters ---------- npoly : list New polygon coordinates. Returns ------- None. """ self.poly.set_xy(npoly) self.line.set_data(list(zip(*self.poly.xy))) self.canvas.draw() self.update_plots() def poly_changed(self, poly): """ Polygon changed. Parameters ---------- poly : TYPE DESCRIPTION. Returns ------- None. """ # this method is called whenever the polygon object is called # only copy the artist props to the line (except visibility) vis = self.line.get_visible() Artist.update_from(self.line, poly) self.line.set_visible(vis) # don't use the poly visibility state def get_ind_under_point(self, event): """ Get the index of vertex under point if within epsilon tolerance. Parameters ---------- event : TYPE DESCRIPTION. Returns ------- ind : int or None Index of vertex under point. """ # display coords xytmp = np.asarray(self.poly.xy) xyt = self.poly.get_transform().transform(xytmp) xtt, ytt = xyt[:, 0], xyt[:, 1] dtt = np.sqrt((xtt - event.x) ** 2 + (ytt - event.y) ** 2) indseq = np.nonzero(np.equal(dtt, np.amin(dtt)))[0] ind = indseq[0] if dtt[ind] >= self.epsilon: ind = None return ind def button_press_callback(self, event): """ Button press callback. Parameters ---------- event : TYPE DESCRIPTION. Returns ------- None. """ if event.inaxes is None: return if event.button != 1: return if self.ax.get_navigate_mode() is not None: return self._ind = self.get_ind_under_point(event) if self._ind is None: xys = self.poly.get_transform().transform(self.poly.xy) ptmp = self.poly.get_transform().transform([event.xdata, event.ydata]) # ptmp = event.x, event.y # display coords if len(xys) == 1: self.poly.xy = np.array( [(event.xdata, event.ydata)] + [(event.xdata, event.ydata)]) self.line.set_data(list(zip(*self.poly.xy))) self.canvas.restore_region(self.background) self.ax.draw_artist(self.poly) self.ax.draw_artist(self.line) self.canvas.update() return dmin = -1 imin = -1 for i in range(len(xys) - 1): s0tmp = xys[i] s1tmp = xys[i + 1] dtmp = dist_point_to_segment(ptmp, s0tmp, s1tmp) if dmin == -1: dmin = dtmp imin = i elif dtmp < dmin: dmin = dtmp imin = i i = imin if np.array_equal(self.poly.xy, np.ones((2, 2))): self.poly.set_xy([[event.xdata, event.ydata]]) else: self.poly.xy = np.array(list(self.poly.xy[:i + 1]) + [(event.xdata, event.ydata)] + list(self.poly.xy[i + 1:])) # self.poly.xy = np.array(list(self.poly.xy[:i + 1]) + # [(event.xdata, event.ydata)] + # list(self.poly.xy[i + 1:])) self.line.set_data(list(zip(*self.poly.xy))) self.canvas.restore_region(self.background) self.ax.draw_artist(self.poly) self.ax.draw_artist(self.line) self.canvas.update() def button_release_callback(self, event): """ Button release callback. Parameters ---------- event : TYPE DESCRIPTION. Returns ------- None. """ if event.button != 1: return self._ind = None self.update_plots() def update_plots(self): """ Update plots. Returns ------- None. """ polymask = Path(self.poly.xy).contains_points(self.pntxy) self.polyi_changed.emit(polymask.tolist()) def motion_notify_callback(self, event): """ Mouse notify callback. Parameters ---------- event : TYPE DESCRIPTION. Returns ------- None. """ if self._ind is None: return if event.inaxes is None: return if event.button != 1: return xtmp, ytmp = event.xdata, event.ydata self.poly.xy[self._ind] = xtmp, ytmp if self._ind == 0: self.poly.xy[-1] = xtmp, ytmp self.line.set_data(list(zip(*self.poly.xy))) self.canvas.restore_region(self.background) self.ax.draw_artist(self.poly) self.ax.draw_artist(self.line) self.canvas.update() class ScatterPlot(QtWidgets.QDialog): """ Main Graph Tool Routine. Attributes ---------- parent : parent reference to the parent routine indata : dictionary dictionary of input datasets outdata : dictionary dictionary of output datasets """ def __init__(self, parent=None): super().__init__(parent) self.indata = {} self.outdata = {} self.parent = parent self.m1 = 0 self.c = [0, 1, 0] self.m = [0, 0] self.dat_tmp = None if parent is None: self.showprocesslog = print else: self.showprocesslog = parent.showprocesslog self.map = GraphMap(self) self.hist = GraphHist(self) self.cp_dpoly = QtWidgets.QPushButton('Delete Polygon') self.cp_combo = QtWidgets.QComboBox() self.cp_combo2 = QtWidgets.QComboBox() self.cp_combo3 = QtWidgets.QComboBox() self.map_dpoly = QtWidgets.QPushButton('Delete Polygon') self.map_combo = QtWidgets.QComboBox() self.map_combo2 = QtWidgets.QComboBox() self.setupui() self.hist.cindx = self.c self.map.mindx = self.m def setupui(self): """ Set up UI. Returns ------- None. """ grid_main = QtWidgets.QGridLayout(self) group_cp = QtWidgets.QGroupBox('Cross Plot Settings') grid_left = QtWidgets.QGridLayout(group_cp) group_map = QtWidgets.QGroupBox('Map Settings') grid_right = QtWidgets.QGridLayout(group_map) self.setWindowTitle('Graph Window') lbl_combo_left = QtWidgets.QLabel('X Data Band:') lbl_combo2_left = QtWidgets.QLabel('Y Data Band:') lbl_combo3_left = QtWidgets.QLabel('Cluster Overlay:') lbl_combo_right = QtWidgets.QLabel('Data Band:') lbl_combo2_right = QtWidgets.QLabel('Cluster Overlay:') grid_left.addWidget(lbl_combo_left, 0, 0, 1, 1) grid_left.addWidget(lbl_combo2_left, 1, 0, 1, 1) grid_left.addWidget(lbl_combo3_left, 2, 0, 1, 1) grid_left.addWidget(self.cp_dpoly, 0, 2, 1, 1) grid_left.addWidget(self.cp_combo, 0, 1, 1, 1) grid_left.addWidget(self.cp_combo2, 1, 1, 1, 1) grid_left.addWidget(self.cp_combo3, 2, 1, 1, 1) grid_right.addWidget(lbl_combo_right, 0, 0, 1, 1) grid_right.addWidget(lbl_combo2_right, 1, 0, 1, 1) grid_right.addWidget(self.map_dpoly, 0, 2, 1, 1) grid_right.addWidget(self.map_combo, 0, 1, 1, 1) grid_right.addWidget(self.map_combo2, 1, 1, 1, 1) grid_main.addWidget(self.hist, 0, 0, 1, 1) grid_main.addWidget(self.map, 0, 1, 1, 1) grid_main.addWidget(group_cp, 1, 0, 1, 1) grid_main.addWidget(group_map, 1, 1, 1, 1) self.cp_dpoly.clicked.connect(self.on_cp_dpoly) self.map_dpoly.clicked.connect(self.on_map_dpoly) def on_cp_dpoly(self): """ On cp dpoly. Returns ------- None. """ self.hist.polyi.new_poly([[10, 10]]) mtmp = self.map_combo.currentIndex() mask = self.indata['Raster'][mtmp].data.mask dattmp = self.map.csp.get_array() dattmp.mask = mask self.map.csp.changed() self.map.figure.canvas.draw() def on_map_dpoly(self): """ On map dpoly. Returns ------- None. """ self.map.polyi.new_poly([[10, 10]]) dattmp = self.hist.csp.get_array() dattmp.mask = np.ma.getmaskarray(np.ma.masked_equal(dattmp.data, 0.)) self.hist.csp.changed() self.hist.figure.canvas.draw() def on_cp_combo(self): """ On cp combo. Returns ------- None. """ gstmp = self.cp_combo.currentIndex() if gstmp != self.c[0]: self.c[0] = gstmp self.hist.update_graph(clearaxis=True) self.map.polyi.update_plots() def on_cp_combo2(self): """ On cp combo 2. Returns ------- None. """ gstmp = self.cp_combo2.currentIndex() if gstmp != self.c[1]: self.c[1] = gstmp self.hist.update_graph(clearaxis=True) self.map.polyi.update_plots() def on_cp_combo3(self): """ On cp combo 3. Returns ------- None. """ self.c[2] = self.cp_combo3.currentIndex() self.hist.update_graph() self.map.polyi.update_plots() def on_map_combo(self): """ On map combo. Returns ------- None. """ self.m[0] = self.map_combo.currentIndex() self.map.update_graph() self.hist.polyi.update_plots() def on_map_combo2(self): """ On map combo 2. Returns ------- None. """ self.m[1] = self.map_combo2.currentIndex() self.map.update_graph() self.hist.polyi.update_plots() def settings(self, nodialog=False): """ Run. Returns ------- bool True if successful, False otherwise. """ if 'Raster' not in self.indata: self.showprocesslog('Error: You must have a multi-band raster ' 'dataset in addition to your cluster analysis' ' results') return False self.dat_tmp = self.indata['Raster'] self.map.data = self.indata['Raster'] self.hist.data = self.indata['Raster'] bands = [i.dataid for i in self.indata['Raster']] self.cp_combo.clear() self.cp_combo2.clear() self.map_combo.clear() self.cp_combo.addItems(bands) self.cp_combo2.addItems(bands) self.map_combo.addItems(bands) self.cp_combo2.setCurrentIndex(1) self.cp_combo.currentIndexChanged.connect(self.on_cp_combo) self.cp_combo2.currentIndexChanged.connect(self.on_cp_combo2) self.map_combo.currentIndexChanged.connect(self.on_map_combo) cbands = ['Scatter Amplitudes'] mbands = ['None'] if 'Cluster' in self.indata: self.hist.cdata = self.indata['Cluster'] self.map.cdata = self.indata['Cluster'] cbands += [i.dataid for i in self.indata['Cluster']] mbands += [i.dataid for i in self.indata['Cluster']] self.cp_combo3.clear() self.map_combo2.clear() self.cp_combo3.addItems(cbands) self.map_combo2.addItems(mbands) self.cp_combo3.currentIndexChanged.connect(self.on_cp_combo3) self.map_combo2.currentIndexChanged.connect(self.on_map_combo2) self.hist.init_graph() self.map.init_graph() self.show() self.hist.polyint() self.map.polyint() self.hist.polyi.polyi_changed.connect(self.update_map) self.map.polyi.polyi_changed.connect(self.update_hist) self.hist.update_graph(clearaxis=True) self.map.update_graph() return True def loadproj(self, projdata): """ Load project data into class. Parameters ---------- projdata : dictionary Project data loaded from JSON project file. Returns ------- chk : bool A check to see if settings was successfully run. """ return False def saveproj(self): """ Save project data from class. Returns ------- projdata : dictionary Project data to be saved to JSON project file. """ projdata = {} # projdata['ftype'] = '2D Mean' return projdata def update_map(self, polymask): """ Update map. Parameters ---------- polymask : numpy array Polygon mask. Returns ------- None. """ if max(polymask) is False: return mtmp = self.map_combo.currentIndex() mask = self.indata['Raster'][mtmp].data.mask polymask = np.array(polymask) polymask.shape = mask.shape polymask = np.logical_or(~polymask, mask) dattmp = self.map.csp.get_array() dattmp.mask = polymask self.map.csp.changed() self.map.figure.canvas.draw() def update_hist(self, polymask): """ Update histogram. Parameters ---------- polymask : numpy array Polygon mask. Returns ------- None. """ if max(polymask) is False: return polymask = np.array(polymask) dattmp = self.hist.csp.get_array() atmp = np.array([self.hist.xcoord[polymask], self.hist.ycoord[polymask]]).T dattmp.mask = np.ones_like(np.ma.getmaskarray(dattmp)) for i in atmp: dattmp.mask[i[1], i[0]] = False self.hist.csp.changed() self.hist.figure.canvas.draw() def dist_point_to_segment(p, s0, s1): """ Dist point to segment. Reimplementation of Matplotlib's dist_point_to_segment, after it was depreciated. Follows http://geomalgorithms.com/a02-_lines.html Parameters ---------- p : numpy array Point. s0 : numpy array Start of segment. s1 : numpy array End of segment. Returns ------- numpy array Distance of point to segment. """ p = np.array(p) s0 = np.array(s0) s1 = np.array(s1) v = s1 - s0 w = p - s0 c1 = np.dot(w, v) if c1 <= 0: return np.linalg.norm(p - s0) c2 = np.dot(v, v) if c2 <= c1: return np.linalg.norm(p - s1) b = c1/c2 pb = s0 + b*v return np.linalg.norm(p - pb)

gpl-3.0

aspose-pdf/Aspose.Pdf-for-Java

Plugins/Aspose_Pdf_Java_for_Python/WorkingWithDocumentObject/__init__.py

1

9774

__author__ = 'fahadadeel' import jpype import re import datetime class AddJavascript: def __init__(self, dataDir): self.dataDir = dataDir self.Document = jpype.JClass("com.aspose.pdf.Document") self.JavascriptAction=jpype.JClass("com.aspose.pdf.JavascriptAction") def main(self): # Open a pdf document. doc= self.Document() pdf = self.Document() pdf=self.dataDir + 'Template.pdf' # Adding JavaScript at Document Level # Instantiate JavascriptAction with desried JavaScript statement javaScript = self.JavascriptAction("this.print({bUI:true,bSilent:false,bShrinkToFit:true});"); # Assign JavascriptAction object to desired action of Document doc.setOpenAction(javaScript) js=self.JavascriptAction("app.alert('page 2 is opened')") # Adding JavaScript at Page Level doc.getPages.get_Item(2) doc.getActions().setOnOpen(js()) doc.getPages().get_Item(2).getActions().setOnClose(self.JavascriptAction("app.alert('page 2 is closed')")) # Save PDF Document doc.save(self.dataDir + "JavaScript-Added.pdf") print "Added JavaScript Successfully, please check the output file." class AddToc: def __init__(self, dataDir): self.dataDir = dataDir self.Document = jpype.JClass("com.aspose.pdf.Document") self.TocInfo=jpype.JClass("com.aspose.pdf.TocInfo") self.TextFragment=jpype.JClass("com.aspose.pdf.TextFragment") self.TextSegment=jpype.JClass("com.aspose.pdf.TextSegment") self.Heading=jpype.JClass("com.aspose.pdf.Heading") def main(self): # Open a pdf document. doc= self.Document() pdf = self.Document() pdf=self.dataDir + 'input1.pdf' # Get access to first page of PDF file toc_page = doc.getPages().insert(1) # Create object to represent TOC information toc_info = self.TocInfo() title = self.TextFragment("Table Of Contents") title.getTextState().setFontSize(20) # Set the title for TOC toc_info.setTitle(title) toc_page.setTocInfo(toc_info) # Create string objects which will be used as TOC elements titles = ["First page", "Second page"] i = 0; while (i < 2): # Create Heading object heading2 = self.Heading(1); segment2 = self.TextSegment heading2.setTocPage(toc_page) heading2.getSegments().add(segment2) # Specify the destination page for heading object heading2.setDestinationPage(doc.getPages().get_Item(i + 2)) # Destination page heading2.setTop(doc.getPages().get_Item(i + 2).getRect().getHeight()) # Destination coordinate segment2.setText(titles[i]) # Add heading to page containing TOC toc_page.getParagraphs().add(heading2) i +=1; # Save PDF Document doc.save(self.dataDir + "TOC.pdf") print "Added TOC Successfully, please check the output file." class GetDocumentWindow: def __init__(self, dataDir): self.dataDir = dataDir self.Document = jpype.JClass("com.aspose.pdf.Document") def main(self): doc= self.Document() pdf = self.Document() pdf=self.dataDir + 'input1.pdf' # Get different document properties # Position of document's window - Default: false print "CenterWindow :- " + str(doc.getCenterWindow()) # Predominant reading order; determine the position of page # when displayed side by side - Default: L2R print "Direction :- " + str(doc.getDirection()) # Whether window's title bar should display document title. # If false, title bar displays PDF file name - Default: false print "DisplayDocTitle :- " + str(doc.getDisplayDocTitle()) #Whether to resize the document's window to fit the size of #first displayed page - Default: false print "FitWindow :- " + str(doc.getFitWindow()) # Whether to hide menu bar of the viewer application - Default: false print "HideMenuBar :-" + str(doc.getHideMenubar()) # Whether to hide tool bar of the viewer application - Default: false print "HideToolBar :-" + str(doc.getHideToolBar()) # Whether to hide UI elements like scroll bars # and leaving only the page contents displayed - Default: false print "HideWindowUI :-" + str(doc.getHideWindowUI()) # The document's page mode. How to display document on exiting full-screen mode. print "NonFullScreenPageMode :-" + str(doc.getNonFullScreenPageMode()) # The page layout i.e. single page, one column print "PageLayout :-" + str(doc.getPageLayout()) #How the document should display when opened. print "pageMode :-" + str(doc.getPageMode()) class GetPdfFileInfo: def __init__(self, dataDir): self.dataDir = dataDir self.Document = jpype.JClass("com.aspose.pdf.Document") def main(self): doc= self.Document() pdf = self.Document() pdf=self.dataDir + 'input1.pdf' # Get document information doc_info = doc.getInfo(); # Show document information print "Author:-" + str(doc_info.getAuthor()) print "Creation Date:-" + str(doc_info.getCreationDate()) print "Keywords:-" + str(doc_info.getKeywords()) print "Modify Date:-" + str(doc_info.getModDate()) print "Subject:-" + str(doc_info.getSubject()) print "Title:-" + str(doc_info.getTitle()) class GetXMPMetadata: def __init__(self, dataDir): self.dataDir = dataDir self.Document = jpype.JClass("com.aspose.pdf.Document") def main(self): doc= self.Document() pdf = self.Document() pdf=self.dataDir + 'input1.pdf' # Get properties print "xmp:CreateDate: " + str(doc.getMetadata().get_Item("xmp:CreateDate")) print "xmp:Nickname: " + str(doc.getMetadata().get_Item("xmp:Nickname")) print "xmp:CustomProperty: " + str(doc.getMetadata().get_Item("xmp:CustomProperty")) class Optimize: def __init__(self, dataDir): self.dataDir = dataDir self.Document = jpype.JClass("com.aspose.pdf.Document") # self.OptimizationOptions=jpype.JClass("com.aspose.pdf.Document.OptimizationOptions") def main(self): doc= self.Document() pdf = self.Document() pdf=self.dataDir + 'input1.pdf' # Optimize for web doc.optimize(); #Save output document doc.save(self.dataDir + "Optimized_Web.pdf") print "Optimized PDF for the Web, please check output file." class RemoveMetadata: def __init__(self, dataDir): self.dataDir = dataDir self.Document = jpype.JClass("com.aspose.pdf.Document") def main(self): doc= self.Document() pdf = self.Document() pdf=self.dataDir + 'input1.pdf' if (re.findall('/pdfaid:part/',doc.getMetadata())): doc.getMetadata().removeItem("pdfaid:part") if (re.findall('/dc:format/',doc.getMetadata())): doc.getMetadata().removeItem("dc:format") # save update document with new information doc.save(self.dataDir + "Remove_Metadata.pdf") print "Removed metadata successfully, please check output file." class SetExpiration: def __init__(self, dataDir): self.dataDir = dataDir self.Document = jpype.JClass("com.aspose.pdf.Document") self.JavascriptAction=jpype.JClass("com.aspose.pdf.JavascriptAction") def main(self): doc= self.Document() pdf = self.Document() pdf=self.dataDir + 'input1.pdf' javascript = self.JavascriptAction( "var year=2014; var month=4;today = new Date();today = new Date(today.getFullYear(), today.getMonth());expiry = new Date(year, month);if (today.getTime() > expiry.getTime())app.alert('The file is expired. You need a new one.');"); doc.setOpenAction(javascript); # save update document with new information doc.save(self.dataDir + "set_expiration.pdf"); print "Update document information, please check output file." class SetPdfFileInfo: def __init__(self, dataDir): self.dataDir = dataDir self.Document = jpype.JClass("com.aspose.pdf.Document") def main(self): doc= self.Document() pdf = self.Document() pdf=self.dataDir + 'input1.pdf' # Get document information doc_info = doc.getInfo(); doc_info.setAuthor("Aspose.Pdf for java"); doc_info.setCreationDate(datetime.today.strftime("%m/%d/%Y")); doc_info.setKeywords("Aspose.Pdf, DOM, API"); doc_info.setModDate(datetime.today.strftime("%m/%d/%Y")); doc_info.setSubject("PDF Information"); doc_info.setTitle("Setting PDF Document Information"); # save update document with new information doc.save(self.dataDir + "Updated_Information.pdf") print "Update document information, please check output file."

mit

andr3wmac/metaTower

mt/EventManager.py

1

1710

""" * metaTower v0.4.5 * http://www.metatower.com * * Copyright 2012, Andrew Mac * http://www.andrewmac.ca * Licensed under GPL v3. * See license.txt * or http://www.metatower.com/license.txt """ import mt, inspect class EventManager: class EventItem: def __init__(self, event, function, source): self.event = event self.function = function self.source = source def __init__(self): self.events = [] def register(self, event, function): source = mt.utils.getSource() newEvent = self.EventItem(event, function, source) self.events.append(newEvent) def clear(self, function = None): if ( function == None ): self.events = [] else: new_list = [] for e in self.events: if e.function != function: new_list.append(e) self.events = new_list def clearSource(self, source): new_list = [] for e in self.events: if e.source != source: new_list.append(e) self.events = new_list def trigger(self, event, *args): #print "Triggering event: " + event + " with " + str(len(args)) + " arg(s)" result = None for e in self.events: if e.event == event: arg_count = len(inspect.getargspec(e.function).args) if ( arg_count == 0 ) : result = e.function() if ( arg_count > 0 ): if ( arg_count == len(args) ): result = e.function(*args) if ( arg_count < len(args) ): result = e.function(*args[:(arg_count-len(args))]) return result

gpl-3.0

andreagrandi/workshopvenues

workshopvenues/venues/migrations/0007_auto__add_country__chg_field_address_country__add_index_address_countr.py

1

3797

# -*- coding: utf-8 -*- import datetime from south.db import db from south.v2 import SchemaMigration from django.db import models class Migration(SchemaMigration): def forwards(self, orm): # Adding model 'Country' db.create_table(u'venues_country', ( (u'id', self.gf('django.db.models.fields.AutoField')(primary_key=True)), ('name', self.gf('django.db.models.fields.CharField')(max_length=30)), )) db.send_create_signal(u'venues', ['Country']) # Deleting field 'Address.country' db.delete_column(u'venues_address', 'country') def backwards(self, orm): # Deleting model 'Country' db.delete_table(u'venues_country') # Adding field 'Address.country' db.add_column(u'venues_address', 'country', self.gf('django.db.models.fields.CharField')(default='', max_length=30, blank=True), keep_default=False) models = { u'venues.address': { 'Meta': {'object_name': 'Address'}, u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'postcode': ('django.db.models.fields.CharField', [], {'max_length': '10'}), 'street': ('django.db.models.fields.CharField', [], {'max_length': '200'}), 'town': ('django.db.models.fields.CharField', [], {'max_length': '30'}) }, u'venues.country': { 'Meta': {'object_name': 'Country'}, u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '30'}) }, u'venues.facility': { 'Meta': {'object_name': 'Facility'}, u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '30'}) }, u'venues.image': { 'Meta': {'object_name': 'Image'}, 'filename': ('django.db.models.fields.CharField', [], {'max_length': '255'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'venue': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['venues.Venue']"}) }, u'venues.venue': { 'Meta': {'object_name': 'Venue'}, 'address': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['venues.Address']"}), 'capacity': ('django.db.models.fields.CharField', [], {'max_length': '200', 'blank': 'True'}), 'contact': ('django.db.models.fields.CharField', [], {'max_length': '50', 'blank': 'True'}), 'contact_email': ('django.db.models.fields.CharField', [], {'max_length': '50', 'blank': 'True'}), 'contact_twitter': ('django.db.models.fields.CharField', [], {'max_length': '200', 'blank': 'True'}), 'cost': ('django.db.models.fields.CharField', [], {'max_length': '200', 'blank': 'True'}), 'facilities': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['venues.Facility']", 'symmetrical': 'False'}), u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}), 'name': ('django.db.models.fields.CharField', [], {'max_length': '30'}), 'phone': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}), 'style': ('django.db.models.fields.CharField', [], {'max_length': '200', 'blank': 'True'}), 'twitter': ('django.db.models.fields.CharField', [], {'max_length': '200', 'blank': 'True'}), 'website': ('django.db.models.fields.CharField', [], {'max_length': '50', 'blank': 'True'}) } } complete_apps = ['venues']

bsd-3-clause

bswartz/cinder

cinder/volume/drivers/netapp/dataontap/fc_cmode.py

1

5282

# Copyright (c) - 2014, Clinton Knight. All rights reserved. # Copyright (c) - 2016 Mike Rooney. 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. """ Volume driver for NetApp Data ONTAP (C-mode) FibreChannel storage systems. """ from cinder import interface from cinder.volume import driver from cinder.volume.drivers.netapp.dataontap import block_cmode from cinder.zonemanager import utils as fczm_utils @interface.volumedriver class NetAppCmodeFibreChannelDriver(driver.BaseVD, driver.ConsistencyGroupVD, driver.ManageableVD, driver.ExtendVD, driver.TransferVD, driver.SnapshotVD): """NetApp C-mode FibreChannel volume driver.""" DRIVER_NAME = 'NetApp_FibreChannel_Cluster_direct' def __init__(self, *args, **kwargs): super(NetAppCmodeFibreChannelDriver, self).__init__(*args, **kwargs) self.library = block_cmode.NetAppBlockStorageCmodeLibrary( self.DRIVER_NAME, 'FC', **kwargs) def do_setup(self, context): self.library.do_setup(context) def check_for_setup_error(self): self.library.check_for_setup_error() def create_volume(self, volume): self.library.create_volume(volume) def create_volume_from_snapshot(self, volume, snapshot): self.library.create_volume_from_snapshot(volume, snapshot) def create_cloned_volume(self, volume, src_vref): self.library.create_cloned_volume(volume, src_vref) def delete_volume(self, volume): self.library.delete_volume(volume) def create_snapshot(self, snapshot): self.library.create_snapshot(snapshot) def delete_snapshot(self, snapshot): self.library.delete_snapshot(snapshot) def get_volume_stats(self, refresh=False): return self.library.get_volume_stats(refresh, self.get_filter_function(), self.get_goodness_function()) def get_default_filter_function(self): return self.library.get_default_filter_function() def get_default_goodness_function(self): return self.library.get_default_goodness_function() def extend_volume(self, volume, new_size): self.library.extend_volume(volume, new_size) def ensure_export(self, context, volume): return self.library.ensure_export(context, volume) def create_export(self, context, volume, connector): return self.library.create_export(context, volume) def remove_export(self, context, volume): self.library.remove_export(context, volume) def manage_existing(self, volume, existing_ref): return self.library.manage_existing(volume, existing_ref) def manage_existing_get_size(self, volume, existing_ref): return self.library.manage_existing_get_size(volume, existing_ref) def unmanage(self, volume): return self.library.unmanage(volume) @fczm_utils.AddFCZone def initialize_connection(self, volume, connector): return self.library.initialize_connection_fc(volume, connector) @fczm_utils.RemoveFCZone def terminate_connection(self, volume, connector, **kwargs): return self.library.terminate_connection_fc(volume, connector, **kwargs) def get_pool(self, volume): return self.library.get_pool(volume) def create_consistencygroup(self, context, group): return self.library.create_consistencygroup(group) def delete_consistencygroup(self, context, group, volumes): return self.library.delete_consistencygroup(group, volumes) def update_consistencygroup(self, context, group, add_volumes=None, remove_volumes=None): return self.library.update_consistencygroup(group, add_volumes=None, remove_volumes=None) def create_cgsnapshot(self, context, cgsnapshot, snapshots): return self.library.create_cgsnapshot(cgsnapshot, snapshots) def delete_cgsnapshot(self, context, cgsnapshot, snapshots): return self.library.delete_cgsnapshot(cgsnapshot, snapshots) def create_consistencygroup_from_src(self, context, group, volumes, cgsnapshot=None, snapshots=None, source_cg=None, source_vols=None): return self.library.create_consistencygroup_from_src( group, volumes, cgsnapshot=cgsnapshot, snapshots=snapshots, source_cg=source_cg, source_vols=source_vols)

apache-2.0

cloudbrain/cloudbrain_examples

sandbox/print_data.py

1

1223

import time from cloudbrain.subscribers.rabbitmq import PikaSubscriber from cloudbrain_examples.settings import (base_routing_key, metric_name, num_channels, buffer_size, rabbitmq_address, rabbitmq_user, rabbitmq_pwd) def _print_callback(unsed_ch, unsed_method, unsed_properties, body): print "==> %s" % body def main(): # Setup the subscriber subscriber = PikaSubscriber(base_routing_key=base_routing_key, rabbitmq_address=rabbitmq_address, rabbitmq_user=rabbitmq_user, rabbitmq_pwd=rabbitmq_pwd) subscriber.connect() subscriber.register(metric_name, num_channels) time.sleep(1) # Leave it some time to register # Get one message at a time one_message = subscriber.get_one_message(metric_name) print "\n==> Got one message: %s\n" % one_message time.sleep(2) # Give people time to read the message # Get message continuously print "==> Subscribing ..." try: subscriber.subscribe(metric_name, _print_callback) except KeyboardInterrupt: subscriber.disconnect() if __name__ == '__main__': main()

agpl-3.0

dazzzl/transwhat

transwhat.py

1

3061

#!/usr/bin/python __author__ = "Steffen Vogel" __copyright__ = "Copyright 2015, Steffen Vogel" __license__ = "GPLv3" __maintainer__ = "Steffen Vogel" __email__ = "[email protected]" """ This file is part of transWhat transWhat is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or any later version. transwhat is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with transWhat. If not, see <http://www.gnu.org/licenses/>. """ import argparse import traceback import logging import asyncore import sys, os import e4u import Queue import threadutils sys.path.insert(0, os.getcwd()) from Spectrum2.iochannel import IOChannel from config import SpectrumConfig from whatsappbackend import WhatsAppBackend from yowsup.common import YowConstants from yowsup.stacks import YowStack # Arguments parser = argparse.ArgumentParser() parser.add_argument('--debug', action='store_true') parser.add_argument('--log', type=str) parser.add_argument('--host', type=str, required=True) parser.add_argument('--port', type=int, required=True) parser.add_argument('--service.backend_id', metavar="ID", type=int, required=True) parser.add_argument('config', type=str) parser.add_argument('-j', type=str, required=True) args, unknown = parser.parse_known_args() YowConstants.PATH_STORAGE='/var/lib/spectrum2/' + args.j if args.log is None: args.log = '/var/log/spectrum2/' + args.j + '/backends/backend.log' # Logging logging.basicConfig( filename=args.log, format = "%(asctime)-15s %(levelname)s %(name)s: %(message)s", level = logging.DEBUG if args.debug else logging.INFO ) if args.config is not None: specConf = SpectrumConfig(args.config) else: specConf = None # Handler def handleTransportData(data): try: plugin.handleDataRead(data) except SystemExit as e: raise e except: logger = logging.getLogger('transwhat') logger.error(traceback.format_exc()) e4u.load() closed = False def connectionClosed(): global closed closed = True # Main io = IOChannel(args.host, args.port, handleTransportData, connectionClosed) plugin = WhatsAppBackend(io, args.j, specConf) plugin.handleBackendConfig({ 'features': [ ('send_buddies_on_login', 1), ('muc', 'true'), ], }) while True: try: asyncore.loop(timeout=1.0, count=10, use_poll = True) try: callback = YowStack._YowStack__detachedQueue.get(False) #doesn't block callback() except Queue.Empty: pass else: break if closed: break while True: try: callback = threadutils.eventQueue.get_nowait() except Queue.Empty: break else: callback() except SystemExit: break except: logger = logging.getLogger('transwhat') logger.error(traceback.format_exc())

gpl-3.0

GoogleCloudPlatform/Solutions-Using-ETL-tool-on-Google-Compute-Engine

gce_api_test.py

1

12371

#!/usr/bin/python # Copyright 2013 Google Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Unit tests of gce_api.py.""" import unittest import apiclient.discovery import apiclient.errors import mock from mock import MagicMock from mock import PropertyMock import oauth2client.client import oauth2client.file import oauth2client.tools from gce_api import GceApi class GceApiTest(unittest.TestCase): """Unit test class of GceApi.""" def setUp(self): self.gce_api = GceApi('gce_api_test', 'CLIENT_ID', 'CLIENT_SECRET', 'project-name', 'zone-name') def tearDown(self): mock.patch.stopall() def _MockGoogleClientApi(self, credentials_validity=True): """Sets up mocks for Google Client API library. Args: credentials_validity: Type/validity of locally cached credentials. None for no local credentials, False for invalid local credentials, True for valid local credentials. Returns: Dictionary that holds mocks created. """ mock_local_credentials = MagicMock( spec=oauth2client.client.Credentials, name='Mock Credentials') mock_http_local = MagicMock(name='HTTP authorized by local credentials') mock_local_credentials.authorize.return_value = mock_http_local mock_new_credentials = MagicMock( spec=oauth2client.client.Credentials, name='Mock Credentials') mock_http_new = MagicMock(name='HTTP authorized by new credentials') mock_new_credentials.authorize.return_value = mock_http_new mock_api = MagicMock(name='Google Client API') mock_storage_class = mock.patch('oauth2client.file.Storage').start() mock_flow_class = mock.patch('gce_api.OAuth2WebServerFlow').start() mock.patch('oauth2client.tools.run', return_value=mock_new_credentials).start() mock.patch('apiclient.discovery.build', return_value=mock_api).start() mock.patch('httplib2.Http').start() mock_storage = mock_storage_class.return_value if credentials_validity is None: mock_storage.get.return_value = None else: mock_storage.get.return_value = mock_local_credentials mock_local_credentials.invalid = not credentials_validity mock_flow = mock_flow_class.return_value apiclient.discovery.build = MagicMock(return_value=mock_api) return {'api': mock_api, 'storage_class': mock_storage_class, 'storage': mock_storage, 'flow_class': mock_flow_class, 'flow': mock_flow, 'local_credentials': mock_local_credentials, 'http_authorized_by_local_credentials': mock_http_local, 'new_credentials': mock_new_credentials, 'http_authorized_by_new_credentials': mock_http_new} def testGetApi_CachedCredentials(self): """Unit test of GetApi(). Local credentials are valid.""" my_mocks = self._MockGoogleClientApi() api = self.gce_api.GetApi() self.assertEqual(my_mocks['api'], api) self.assertEqual(1, my_mocks['storage_class'].call_count) # When cached credentials are valid, OAuth2 dance won't happen. self.assertFalse(my_mocks['flow_class'].called) self.assertFalse(oauth2client.tools.run.called) self.assertEqual(1, my_mocks['local_credentials'].authorize.call_count) apiclient.discovery.build.assert_called_once_with( 'compute', mock.ANY, http=my_mocks['http_authorized_by_local_credentials']) self.assertRegexpMatches( apiclient.discovery.build.call_args[0][1], '^v\\d') def testGetApi_InvalidCachedCredentials(self): """Unit test of GetApi(). Local credentials are invalid.""" my_mocks = self._MockGoogleClientApi(False) api = self.gce_api.GetApi() self.assertEqual(my_mocks['api'], api) self.assertEqual(1, my_mocks['storage_class'].call_count) self.assertTrue(my_mocks['flow_class'].called) oauth2client.tools.run.assert_called_once_with( my_mocks['flow'], my_mocks['storage']) # New credentials are used. self.assertEqual(1, my_mocks['new_credentials'].authorize.call_count) apiclient.discovery.build.assert_called_once_with( 'compute', mock.ANY, http=my_mocks['http_authorized_by_new_credentials']) self.assertRegexpMatches( apiclient.discovery.build.call_args[0][1], '^v\\d') def testGetApi_NoCachedCredentials(self): """Unit test of GetApi(). Local credentials are invalid.""" my_mocks = self._MockGoogleClientApi(None) api = self.gce_api.GetApi() self.assertEqual(my_mocks['api'], api) self.assertEqual(1, my_mocks['storage_class'].call_count) self.assertTrue(my_mocks['flow_class'].called) oauth2client.tools.run.assert_called_once_with( my_mocks['flow'], my_mocks['storage']) # New credentials are used. self.assertEqual(1, my_mocks['new_credentials'].authorize.call_count) apiclient.discovery.build.assert_called_once_with( 'compute', mock.ANY, http=my_mocks['http_authorized_by_new_credentials']) self.assertRegexpMatches( apiclient.discovery.build.call_args[0][1], '^v\\d') def testGetInstance(self): """Unit test of GetInstance().""" mock_api = MagicMock(name='Mock Google Client API') self.gce_api.GetApi = MagicMock(return_value=mock_api) instance_info = self.gce_api.GetInstance('instance-name') self.assertEqual(1, self.gce_api.GetApi.call_count) mock_api.instances.return_value.get.assert_called_once_with( project='project-name', zone='zone-name', instance='instance-name') (mock_api.instances.return_value.get.return_value.execute. assert_called_once_with()) self.assertEqual(mock_api.instances.return_value.get.return_value. execute.return_value, instance_info) def testListInstance_NoFilter(self): """Unit test of ListInstance() without filter string.""" mock_api = MagicMock(name='Mock Google Client API') self.gce_api.GetApi = MagicMock(return_value=mock_api) mock_api.instances.return_value.list.return_value.execute.return_value = { 'items': ['dummy', 'list'] } instance_list = self.gce_api.ListInstances() self.assertEqual(1, self.gce_api.GetApi.call_count) mock_api.instances.return_value.list.assert_called_once_with( project='project-name', zone='zone-name', filter=None) (mock_api.instances.return_value.list.return_value.execute. assert_called_once_with()) self.assertEqual(['dummy', 'list'], instance_list) def testListInstance_Filter(self): """Unit test of ListInstance() with filter string.""" mock_api = MagicMock(name='Mock Google Client API') self.gce_api.GetApi = MagicMock(return_value=mock_api) mock_api.instances.return_value.list.return_value.execute.return_value = { 'items': ['dummy', 'list'] } instance_list = self.gce_api.ListInstances('filter condition') self.assertEqual(1, self.gce_api.GetApi.call_count) mock_api.instances.return_value.list.assert_called_once_with( project='project-name', zone='zone-name', filter='filter condition') (mock_api.instances.return_value.list.return_value.execute. assert_called_once_with()) self.assertEqual(['dummy', 'list'], instance_list) def testCreateInstance_Success(self): """Unit test of CreateInstance() with success result.""" mock_api = MagicMock(name='Mock Google Client API') self.gce_api.GetApi = MagicMock(return_value=mock_api) mock_api.instances.return_value.insert.return_value.execute.return_value = { 'name': 'instance-name' } self.assertTrue(self.gce_api.CreateInstance( 'instance-name', 'machine-type', 'image-name')) self.assertEqual(1, self.gce_api.GetApi.call_count) mock_api.instances.return_value.insert.assert_called_once_with( project='project-name', zone='zone-name', body=mock.ANY) (mock_api.instances.return_value.insert.return_value.execute. assert_called_once_with()) def testCreateInstance_SuccessWithWarning(self): """Unit test of CreateInstance() with warning.""" mock_api = MagicMock(name='Mock Google Client API') self.gce_api.GetApi = MagicMock(return_value=mock_api) mock_api.instances.return_value.insert.return_value.execute.return_value = { 'name': 'instance-name', 'warnings': [ { 'code': 'some warning code', 'message': 'some warning message' } ] } self.assertTrue(self.gce_api.CreateInstance( 'instance-name', 'machine-type', 'image-name')) self.assertEqual(1, self.gce_api.GetApi.call_count) mock_api.instances.return_value.insert.assert_called_once_with( project='project-name', zone='zone-name', body=mock.ANY) (mock_api.instances.return_value.insert.return_value.execute. assert_called_once_with()) def testCreateInstance_Error(self): """Unit test of CreateInstance() with error.""" mock_api = MagicMock(name='Mock Google Client API') self.gce_api.GetApi = MagicMock(return_value=mock_api) mock_api.instances.return_value.insert.return_value.execute.return_value = { 'name': 'instance-name', 'error': { 'errors': [ { 'code': 'some error code', 'message': 'some error message' } ] } } # CreateInstance() returns False. self.assertFalse(self.gce_api.CreateInstance( 'instance-name', 'machine-type', 'image-name')) self.assertEqual(1, self.gce_api.GetApi.call_count) mock_api.instances.return_value.insert.assert_called_once_with( project='project-name', zone='zone-name', body=mock.ANY) (mock_api.instances.return_value.insert.return_value.execute. assert_called_once_with()) def testDeleteInstance(self): """Unit test of DeleteInstance().""" mock_api = MagicMock(name='Mock Google Client API') self.gce_api.GetApi = MagicMock(return_value=mock_api) mock_api.instances.return_value.insert.return_value.execute.return_value = { 'name': 'instance-name' } self.assertTrue(self.gce_api.DeleteInstance('instance-name')) self.assertEqual(1, self.gce_api.GetApi.call_count) mock_api.instances.return_value.delete.assert_called_once_with( project='project-name', zone='zone-name', instance='instance-name') (mock_api.instances.return_value.delete.return_value.execute. assert_called_once_with()) def testCreateFirewall_created(self): """Unit test of CreateFirewall() where a firewall is created.""" mock_api = MagicMock(name='Mock Google Client API') self.gce_api.GetApi = MagicMock(return_value=mock_api) property_mock = PropertyMock(side_effect=apiclient.errors.HttpError ('resp', 'content')) mock_api.firewalls.return_value.get = property_mock self.assertTrue(self.gce_api.CreateFirewall( 'firewall-name', [{'IPProtocol': 'tcp'}])) mock_api.firewalls.return_value.insert.assert_called_once_with( project='project-name', body=mock.ANY) (mock_api.firewalls.return_value.insert.return_value.execute. assert_called_once_with()) def testCreateFirewall_notCreated(self): """Unit test of CreateFirewall() with no firewall created.""" mock_api = MagicMock(name='Mock Google Client API') self.gce_api.GetApi = MagicMock(return_value=mock_api) mock_api.firewalls.return_value.get.return_value.execute.return_value = { 'name': 'firewall-name' } self.assertTrue(self.gce_api.CreateFirewall( 'firewall-name', [{'IPProtocol': 'tcp'}])) # Make sure firewall insert is not called self.assertFalse(mock_api.firewalls.return_value.insert.called) if __name__ == '__main__': unittest.main()

apache-2.0

Programie/Capture2Net

webinterface/generate.py

1

4933

#!/usr/bin/env python # -*- coding: utf-8 -*- ################################################################################ # # qooxdoo - the new era of web development # # http://qooxdoo.org # # Copyright: # 2008 - 2012 1&1 Internet AG, Germany, http://www.1und1.de # # License: # LGPL: http://www.gnu.org/licenses/lgpl.html # EPL: http://www.eclipse.org/org/documents/epl-v10.php # See the LICENSE file in the project's top-level directory for details. # # Authors: # * Thomas Herchenroeder (thron7) # ################################################################################ ## # This is a stub proxy for the real generator.py ## import sys, os, re, subprocess, codecs, optparse CMD_PYTHON = sys.executable QOOXDOO_PATH = '../../..' QX_PYLIB = "tool/pylib" ## # A derived OptionParser class that ignores unknown options (The parent # class raises in those cases, and stops further processing). # We need this, as we are only interested in -c/--config on this level, and # want to ignore pot. other options. # class MyOptionParser(optparse.OptionParser): ## # <rargs> is the raw argument list. The original _process_args mutates # rargs, processing options into <values> and copying interspersed args # into <largs>. This overridden version ignores unknown or ambiguous # options. def _process_args(self, largs, rargs, values): while rargs: try: optparse.OptionParser._process_args(self, largs, rargs, values) except (optparse.BadOptionError, optparse.AmbiguousOptionError): pass def parseArgs(): parser = MyOptionParser() parser.add_option( "-c", "--config", dest="config", metavar="CFGFILE", default="config.json", help="path to configuration file" ) parser.add_option( "-v", "--verbose", dest="verbose", action="store_true", default=False, help="run in verbose mode" ) (options, args) = parser.parse_args(sys.argv[1:]) return options, args ShellOptions, ShellArgs = parseArgs() # this is from misc.json, duplicated for decoupling _eolComment = re.compile(r'(?<![a-zA-Z]:)//.*$', re.M) # double $ for string.Template _mulComment = re.compile(r'/\*.*?\*/', re.S) def stripComments(s): b = _eolComment.sub('',s) b = _mulComment.sub('',b) return b def getQxPath(): path = QOOXDOO_PATH # OS env takes precedence if os.environ.has_key("QOOXDOO_PATH"): path = os.environ["QOOXDOO_PATH"] # else use QOOXDOO_PATH from config.json else: config_file = ShellOptions.config if os.path.exists(config_file): # try json parsing with qx json if not path.startswith('${'): # template macro has been resolved sys.path.insert(0, os.path.join(path, QX_PYLIB)) try: from misc import json got_json = True except: got_json = False got_path = False if got_json: config_str = codecs.open(config_file, "r", "utf-8").read() #config_str = stripComments(config_str) # not necessary under demjson config = json.loads(config_str) p = config.get("let") if p: p = p.get("QOOXDOO_PATH") if p: path = p got_path = True # regex parsing - error prone if not got_path: qpathr=re.compile(r'"QOOXDOO_PATH"\s*:\s*"([^"]*)"\s*,?') conffile = codecs.open(config_file, "r", "utf-8") aconffile = conffile.readlines() for line in aconffile: mo = qpathr.search(line) if mo: path = mo.group(1) break # assume first occurrence is ok path = os.path.normpath(os.path.join(os.path.dirname(os.path.abspath(sys.argv[0])), path)) return path os.chdir(os.path.dirname(os.path.abspath(sys.argv[0]))) # switch to skeleton dir qxpath = getQxPath() REAL_GENERATOR = os.path.join(qxpath, 'tool', 'bin', 'generator.py') if not os.path.exists(REAL_GENERATOR): print "Cannot find real generator script under: \"%s\"; aborting" % REAL_GENERATOR sys.exit(1) elif ShellOptions.verbose: print "\nInvoking real generator under %s ..." % REAL_GENERATOR argList = [] argList.append(CMD_PYTHON) argList.append(REAL_GENERATOR) argList.extend(sys.argv[1:]) if sys.platform == "win32": argList1=[] for arg in argList: if arg.find(' ')>-1: argList1.append('"%s"' % arg) else: argList1.append(arg) argList = argList1 else: argList = ['"%s"' % x for x in argList] # quote argv elements cmd = " ".join(argList) retval = subprocess.call(cmd, shell=True) sys.exit(retval)

mit

rokuz/pygeom

vec2.py

1

4197

import math import copy import geom_exceptions import functions import vec2_gen class Vec2(vec2_gen.GenVec2): """2D Vector.""" def __init__(self, x=0.0, y=0.0): vec2_gen.GenVec2.__init__(self, x, y) def __getitem__(self, key): if key == 0: return self.x elif key == 1: return self.y else: raise ValueError("Integer key in the range [0;1] required") def __setitem__(self, key, value): if key == 0: self.x = value elif key == 1: self.y = value else: raise ValueError("Integer key in the range [0;1] required") def __len__(self): return 2 def __str__(self): return 'Vec2({}; {})'.format(self.x, self.y) def __copy__(self): return Vec2(self.x, self.y) def __deepcopy__(self, memodict={}): return Vec2(self.x, self.y) def __add__(self, other): return Vec2(self.x + other[0], self.y + other[1]) def __iadd__(self, other): self.x += other[0] self.y += other[1] return self def __sub__(self, other): return Vec2(self.x - other[0], self.y - other[1]) def __isub__(self, other): self.x -= other[0] self.y -= other[1] return self def __mul__(self, scalar): return Vec2(self.x * scalar, self.y * scalar) def __imul__(self, scalar): self.x *= scalar self.y *= scalar return self def __div__(self, scalar): return Vec2(self.x / scalar, self.y / scalar) def __truediv__(self, scalar): return Vec2(self.x / scalar, self.y / scalar) def __idiv__(self, scalar): self.x /= scalar self.y /= scalar return self def __itruediv__(self, scalar): self.x /= scalar self.y /= scalar return self def __neg__(self): return Vec2(-self.x, -self.y) def __eq__(self, other): return functions.almost_equal(self.x, other[0]) and functions.almost_equal(self.y, other[1]) def __ne__(self, other): return not self.__eq__(other) def __lt__(self, other): if functions.almost_equal(self.x, other[0]): return self.y < other[1] return self.x < other[0] def __gt__(self, other): if functions.almost_equal(self.x, other[0]): return self.y > other[1] return self.x > other[0] def __le__(self, other): return self.__eq__(other) or self.__lt__(other) def __ge__(self, other): return self.__eq__(other) or self.__gt__(other) def length_squared(self): """Calculates squared length of a vector.""" return self.x * self.x + self.y * self.y def length(self): """Calculates length of a vector.""" return math.sqrt(self.length_squared()) def normalize(self): """Performs vector normalization. Raises VectorException in case of zero length.""" ls = self.length_squared() if ls == 0.0: raise geom_exceptions.VectorException("Zero-length normalization") l = math.sqrt(ls) self.x /= l self.y /= l def get_normalized(self): """Returns normalized copy of a vector. Raises VectorException in case of zero length.""" c = copy.copy(self) c.normalize() return c def dot(self, v2): """Calculated dot product of current vector and vector v2.""" return self.x * v2[0] + self.y * v2[1] def cross(self, v2): """Calculates cross product. It's a scalar which absolute value equals to square of a parallelogram constructed on the current vector and vector v2. The sign tells either v2 is on the left side (positive value) of the current vector or on the right side (negative value).""" return self.x * v2[1] - self.y * v2[0] @property def left_normal(self): """Calculates left normal vector to the current vector.""" return Vec2(-self.y, self.x) @property def right_normal(self): """Calculates right normal vector to the current vector.""" return Vec2(self.y, -self.x)

mit

HaebinShin/tensorflow

tensorflow/python/kernel_tests/seq2seq_test.py

1

31311

# Copyright 2015 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. # ============================================================================== """Tests for functional style sequence-to-sequence models.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import math import random import numpy as np import tensorflow as tf class Seq2SeqTest(tf.test.TestCase): def testRNNDecoder(self): with self.test_session() as sess: with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)): inp = [tf.constant(0.5, shape=[2, 2])] * 2 _, enc_state = tf.nn.rnn( tf.nn.rnn_cell.GRUCell(2), inp, dtype=tf.float32) dec_inp = [tf.constant(0.4, shape=[2, 2])] * 3 cell = tf.nn.rnn_cell.OutputProjectionWrapper( tf.nn.rnn_cell.GRUCell(2), 4) dec, mem = tf.nn.seq2seq.rnn_decoder(dec_inp, enc_state, cell) sess.run([tf.initialize_all_variables()]) res = sess.run(dec) self.assertEqual(3, len(res)) self.assertEqual((2, 4), res[0].shape) res = sess.run([mem]) self.assertEqual((2, 2), res[0].shape) def testBasicRNNSeq2Seq(self): with self.test_session() as sess: with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)): inp = [tf.constant(0.5, shape=[2, 2])] * 2 dec_inp = [tf.constant(0.4, shape=[2, 2])] * 3 cell = tf.nn.rnn_cell.OutputProjectionWrapper( tf.nn.rnn_cell.GRUCell(2), 4) dec, mem = tf.nn.seq2seq.basic_rnn_seq2seq(inp, dec_inp, cell) sess.run([tf.initialize_all_variables()]) res = sess.run(dec) self.assertEqual(3, len(res)) self.assertEqual((2, 4), res[0].shape) res = sess.run([mem]) self.assertEqual((2, 2), res[0].shape) def testTiedRNNSeq2Seq(self): with self.test_session() as sess: with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)): inp = [tf.constant(0.5, shape=[2, 2])] * 2 dec_inp = [tf.constant(0.4, shape=[2, 2])] * 3 cell = tf.nn.rnn_cell.OutputProjectionWrapper( tf.nn.rnn_cell.GRUCell(2), 4) dec, mem = tf.nn.seq2seq.tied_rnn_seq2seq(inp, dec_inp, cell) sess.run([tf.initialize_all_variables()]) res = sess.run(dec) self.assertEqual(3, len(res)) self.assertEqual((2, 4), res[0].shape) res = sess.run([mem]) self.assertEqual(1, len(res)) self.assertEqual((2, 2), res[0].shape) def testEmbeddingRNNDecoder(self): with self.test_session() as sess: with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)): inp = [tf.constant(0.5, shape=[2, 2])] * 2 cell = tf.nn.rnn_cell.BasicLSTMCell(2, state_is_tuple=True) _, enc_state = tf.nn.rnn(cell, inp, dtype=tf.float32) dec_inp = [tf.constant(i, tf.int32, shape=[2]) for i in range(3)] dec, mem = tf.nn.seq2seq.embedding_rnn_decoder( dec_inp, enc_state, cell, num_symbols=4, embedding_size=2) sess.run([tf.initialize_all_variables()]) res = sess.run(dec) self.assertEqual(3, len(res)) self.assertEqual((2, 2), res[0].shape) res = sess.run([mem]) self.assertEqual(1, len(res)) self.assertEqual((2, 2), res[0].c.shape) self.assertEqual((2, 2), res[0].h.shape) def testEmbeddingRNNSeq2Seq(self): with self.test_session() as sess: with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)): enc_inp = [tf.constant(1, tf.int32, shape=[2]) for i in range(2)] dec_inp = [tf.constant(i, tf.int32, shape=[2]) for i in range(3)] cell = tf.nn.rnn_cell.BasicLSTMCell(2, state_is_tuple=True) dec, mem = tf.nn.seq2seq.embedding_rnn_seq2seq( enc_inp, dec_inp, cell, num_encoder_symbols=2, num_decoder_symbols=5, embedding_size=2) sess.run([tf.initialize_all_variables()]) res = sess.run(dec) self.assertEqual(3, len(res)) self.assertEqual((2, 5), res[0].shape) res = sess.run([mem]) self.assertEqual((2, 2), res[0].c.shape) self.assertEqual((2, 2), res[0].h.shape) # Test with state_is_tuple=False. with tf.variable_scope("no_tuple"): cell1 = tf.nn.rnn_cell.BasicLSTMCell(2, state_is_tuple=False) dec, mem = tf.nn.seq2seq.embedding_rnn_seq2seq( enc_inp, dec_inp, cell1, num_encoder_symbols=2, num_decoder_symbols=5, embedding_size=2) sess.run([tf.initialize_all_variables()]) res = sess.run(dec) self.assertEqual(3, len(res)) self.assertEqual((2, 5), res[0].shape) res = sess.run([mem]) self.assertEqual((2, 4), res[0].shape) # Test externally provided output projection. w = tf.get_variable("proj_w", [2, 5]) b = tf.get_variable("proj_b", [5]) with tf.variable_scope("proj_seq2seq"): dec, _ = tf.nn.seq2seq.embedding_rnn_seq2seq( enc_inp, dec_inp, cell, num_encoder_symbols=2, num_decoder_symbols=5, embedding_size=2, output_projection=(w, b)) sess.run([tf.initialize_all_variables()]) res = sess.run(dec) self.assertEqual(3, len(res)) self.assertEqual((2, 2), res[0].shape) # Test that previous-feeding model ignores inputs after the first. dec_inp2 = [tf.constant(0, tf.int32, shape=[2]) for _ in range(3)] with tf.variable_scope("other"): d3, _ = tf.nn.seq2seq.embedding_rnn_seq2seq( enc_inp, dec_inp2, cell, num_encoder_symbols=2, num_decoder_symbols=5, embedding_size=2, feed_previous=tf.constant(True)) sess.run([tf.initialize_all_variables()]) tf.get_variable_scope().reuse_variables() d1, _ = tf.nn.seq2seq.embedding_rnn_seq2seq( enc_inp, dec_inp, cell, num_encoder_symbols=2, num_decoder_symbols=5, embedding_size=2, feed_previous=True) d2, _ = tf.nn.seq2seq.embedding_rnn_seq2seq( enc_inp, dec_inp2, cell, num_encoder_symbols=2, num_decoder_symbols=5, embedding_size=2, feed_previous=True) res1 = sess.run(d1) res2 = sess.run(d2) res3 = sess.run(d3) self.assertAllClose(res1, res2) self.assertAllClose(res1, res3) def testEmbeddingTiedRNNSeq2Seq(self): with self.test_session() as sess: with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)): enc_inp = [tf.constant(1, tf.int32, shape=[2]) for i in range(2)] dec_inp = [tf.constant(i, tf.int32, shape=[2]) for i in range(3)] cell = tf.nn.rnn_cell.BasicLSTMCell(2, state_is_tuple=True) dec, mem = tf.nn.seq2seq.embedding_tied_rnn_seq2seq( enc_inp, dec_inp, cell, num_symbols=5, embedding_size=2) sess.run([tf.initialize_all_variables()]) res = sess.run(dec) self.assertEqual(3, len(res)) self.assertEqual((2, 5), res[0].shape) res = sess.run([mem]) self.assertEqual((2, 2), res[0].c.shape) self.assertEqual((2, 2), res[0].h.shape) # Test when num_decoder_symbols is provided, the size of decoder output # is num_decoder_symbols. with tf.variable_scope("decoder_symbols_seq2seq"): dec, mem = tf.nn.seq2seq.embedding_tied_rnn_seq2seq( enc_inp, dec_inp, cell, num_symbols=5, num_decoder_symbols=3, embedding_size=2) sess.run([tf.initialize_all_variables()]) res = sess.run(dec) self.assertEqual(3, len(res)) self.assertEqual((2, 3), res[0].shape) # Test externally provided output projection. w = tf.get_variable("proj_w", [2, 5]) b = tf.get_variable("proj_b", [5]) with tf.variable_scope("proj_seq2seq"): dec, _ = tf.nn.seq2seq.embedding_tied_rnn_seq2seq( enc_inp, dec_inp, cell, num_symbols=5, embedding_size=2, output_projection=(w, b)) sess.run([tf.initialize_all_variables()]) res = sess.run(dec) self.assertEqual(3, len(res)) self.assertEqual((2, 2), res[0].shape) # Test that previous-feeding model ignores inputs after the first. dec_inp2 = [tf.constant(0, tf.int32, shape=[2])] * 3 with tf.variable_scope("other"): d3, _ = tf.nn.seq2seq.embedding_tied_rnn_seq2seq( enc_inp, dec_inp2, cell, num_symbols=5, embedding_size=2, feed_previous=tf.constant(True)) sess.run([tf.initialize_all_variables()]) tf.get_variable_scope().reuse_variables() d1, _ = tf.nn.seq2seq.embedding_tied_rnn_seq2seq( enc_inp, dec_inp, cell, num_symbols=5, embedding_size=2, feed_previous=True) d2, _ = tf.nn.seq2seq.embedding_tied_rnn_seq2seq( enc_inp, dec_inp2, cell, num_symbols=5, embedding_size=2, feed_previous=True) res1 = sess.run(d1) res2 = sess.run(d2) res3 = sess.run(d3) self.assertAllClose(res1, res2) self.assertAllClose(res1, res3) def testAttentionDecoder1(self): with self.test_session() as sess: with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)): cell = tf.nn.rnn_cell.GRUCell(2) inp = [tf.constant(0.5, shape=[2, 2])] * 2 enc_outputs, enc_state = tf.nn.rnn(cell, inp, dtype=tf.float32) attn_states = tf.concat(1, [tf.reshape(e, [-1, 1, cell.output_size]) for e in enc_outputs]) dec_inp = [tf.constant(0.4, shape=[2, 2])] * 3 dec, mem = tf.nn.seq2seq.attention_decoder( dec_inp, enc_state, attn_states, cell, output_size=4) sess.run([tf.initialize_all_variables()]) res = sess.run(dec) self.assertEqual(3, len(res)) self.assertEqual((2, 4), res[0].shape) res = sess.run([mem]) self.assertEqual((2, 2), res[0].shape) def testAttentionDecoder2(self): with self.test_session() as sess: with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)): cell = tf.nn.rnn_cell.GRUCell(2) inp = [tf.constant(0.5, shape=[2, 2])] * 2 enc_outputs, enc_state = tf.nn.rnn(cell, inp, dtype=tf.float32) attn_states = tf.concat(1, [tf.reshape(e, [-1, 1, cell.output_size]) for e in enc_outputs]) dec_inp = [tf.constant(0.4, shape=[2, 2])] * 3 dec, mem = tf.nn.seq2seq.attention_decoder( dec_inp, enc_state, attn_states, cell, output_size=4, num_heads=2) sess.run([tf.initialize_all_variables()]) res = sess.run(dec) self.assertEqual(3, len(res)) self.assertEqual((2, 4), res[0].shape) res = sess.run([mem]) self.assertEqual((2, 2), res[0].shape) def testEmbeddingAttentionDecoder(self): with self.test_session() as sess: with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)): inp = [tf.constant(0.5, shape=[2, 2])] * 2 cell = tf.nn.rnn_cell.GRUCell(2) enc_outputs, enc_state = tf.nn.rnn(cell, inp, dtype=tf.float32) attn_states = tf.concat(1, [tf.reshape(e, [-1, 1, cell.output_size]) for e in enc_outputs]) dec_inp = [tf.constant(i, tf.int32, shape=[2]) for i in range(3)] dec, mem = tf.nn.seq2seq.embedding_attention_decoder( dec_inp, enc_state, attn_states, cell, num_symbols=4, embedding_size=2, output_size=3) sess.run([tf.initialize_all_variables()]) res = sess.run(dec) self.assertEqual(3, len(res)) self.assertEqual((2, 3), res[0].shape) res = sess.run([mem]) self.assertEqual((2, 2), res[0].shape) def testEmbeddingAttentionSeq2Seq(self): with self.test_session() as sess: with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)): enc_inp = [tf.constant(1, tf.int32, shape=[2]) for i in range(2)] dec_inp = [tf.constant(i, tf.int32, shape=[2]) for i in range(3)] cell = tf.nn.rnn_cell.BasicLSTMCell(2, state_is_tuple=True) dec, mem = tf.nn.seq2seq.embedding_attention_seq2seq( enc_inp, dec_inp, cell, num_encoder_symbols=2, num_decoder_symbols=5, embedding_size=2) sess.run([tf.initialize_all_variables()]) res = sess.run(dec) self.assertEqual(3, len(res)) self.assertEqual((2, 5), res[0].shape) res = sess.run([mem]) self.assertEqual((2, 2), res[0].c.shape) self.assertEqual((2, 2), res[0].h.shape) # Test with state_is_tuple=False. with tf.variable_scope("no_tuple"): cell = tf.nn.rnn_cell.BasicLSTMCell(2, state_is_tuple=False) dec, mem = tf.nn.seq2seq.embedding_attention_seq2seq( enc_inp, dec_inp, cell, num_encoder_symbols=2, num_decoder_symbols=5, embedding_size=2) sess.run([tf.initialize_all_variables()]) res = sess.run(dec) self.assertEqual(3, len(res)) self.assertEqual((2, 5), res[0].shape) res = sess.run([mem]) self.assertEqual((2, 4), res[0].shape) # Test externally provided output projection. w = tf.get_variable("proj_w", [2, 5]) b = tf.get_variable("proj_b", [5]) with tf.variable_scope("proj_seq2seq"): dec, _ = tf.nn.seq2seq.embedding_attention_seq2seq( enc_inp, dec_inp, cell, num_encoder_symbols=2, num_decoder_symbols=5, embedding_size=2, output_projection=(w, b)) sess.run([tf.initialize_all_variables()]) res = sess.run(dec) self.assertEqual(3, len(res)) self.assertEqual((2, 2), res[0].shape) # Test that previous-feeding model ignores inputs after the first. dec_inp2 = [tf.constant(0, tf.int32, shape=[2]) for _ in range(3)] with tf.variable_scope("other"): d3, _ = tf.nn.seq2seq.embedding_attention_seq2seq( enc_inp, dec_inp2, cell, num_encoder_symbols=2, num_decoder_symbols=5, embedding_size=2, feed_previous=tf.constant(True)) sess.run([tf.initialize_all_variables()]) tf.get_variable_scope().reuse_variables() d1, _ = tf.nn.seq2seq.embedding_attention_seq2seq( enc_inp, dec_inp, cell, num_encoder_symbols=2, num_decoder_symbols=5, embedding_size=2, feed_previous=True) d2, _ = tf.nn.seq2seq.embedding_attention_seq2seq( enc_inp, dec_inp2, cell, num_encoder_symbols=2, num_decoder_symbols=5, embedding_size=2, feed_previous=True) res1 = sess.run(d1) res2 = sess.run(d2) res3 = sess.run(d3) self.assertAllClose(res1, res2) self.assertAllClose(res1, res3) def testOne2ManyRNNSeq2Seq(self): with self.test_session() as sess: with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)): enc_inp = [tf.constant(1, tf.int32, shape=[2]) for i in range(2)] dec_inp_dict = {} dec_inp_dict["0"] = [ tf.constant(i, tf.int32, shape=[2]) for i in range(3)] dec_inp_dict["1"] = [ tf.constant(i, tf.int32, shape=[2]) for i in range(4)] dec_symbols_dict = {"0": 5, "1": 6} cell = tf.nn.rnn_cell.BasicLSTMCell(2, state_is_tuple=True) outputs_dict, state_dict = tf.nn.seq2seq.one2many_rnn_seq2seq( enc_inp, dec_inp_dict, cell, 2, dec_symbols_dict, embedding_size=2) sess.run([tf.initialize_all_variables()]) res = sess.run(outputs_dict["0"]) self.assertEqual(3, len(res)) self.assertEqual((2, 5), res[0].shape) res = sess.run(outputs_dict["1"]) self.assertEqual(4, len(res)) self.assertEqual((2, 6), res[0].shape) res = sess.run([state_dict["0"]]) self.assertEqual((2, 2), res[0].c.shape) self.assertEqual((2, 2), res[0].h.shape) res = sess.run([state_dict["1"]]) self.assertEqual((2, 2), res[0].c.shape) self.assertEqual((2, 2), res[0].h.shape) # Test that previous-feeding model ignores inputs after the first, i.e. # dec_inp_dict2 has different inputs from dec_inp_dict after the first # time-step. dec_inp_dict2 = {} dec_inp_dict2["0"] = [ tf.constant(0, tf.int32, shape=[2]) for _ in range(3)] dec_inp_dict2["1"] = [ tf.constant(0, tf.int32, shape=[2]) for _ in range(4)] with tf.variable_scope("other"): outputs_dict3, _ = tf.nn.seq2seq.one2many_rnn_seq2seq( enc_inp, dec_inp_dict2, cell, 2, dec_symbols_dict, embedding_size=2, feed_previous=tf.constant(True)) sess.run([tf.initialize_all_variables()]) tf.get_variable_scope().reuse_variables() outputs_dict1, _ = tf.nn.seq2seq.one2many_rnn_seq2seq( enc_inp, dec_inp_dict, cell, 2, dec_symbols_dict, embedding_size=2, feed_previous=True) outputs_dict2, _ = tf.nn.seq2seq.one2many_rnn_seq2seq( enc_inp, dec_inp_dict2, cell, 2, dec_symbols_dict, embedding_size=2, feed_previous=True) res1 = sess.run(outputs_dict1["0"]) res2 = sess.run(outputs_dict2["0"]) res3 = sess.run(outputs_dict3["0"]) self.assertAllClose(res1, res2) self.assertAllClose(res1, res3) def testSequenceLoss(self): with self.test_session() as sess: logits = [tf.constant(i + 0.5, shape=[2, 5]) for i in range(3)] targets = [tf.constant(i, tf.int32, shape=[2]) for i in range(3)] weights = [tf.constant(1.0, shape=[2]) for i in range(3)] average_loss_per_example = tf.nn.seq2seq.sequence_loss( logits, targets, weights, average_across_timesteps=True, average_across_batch=True) res = sess.run(average_loss_per_example) self.assertAllClose(1.60944, res) average_loss_per_sequence = tf.nn.seq2seq.sequence_loss( logits, targets, weights, average_across_timesteps=False, average_across_batch=True) res = sess.run(average_loss_per_sequence) self.assertAllClose(4.828314, res) total_loss = tf.nn.seq2seq.sequence_loss( logits, targets, weights, average_across_timesteps=False, average_across_batch=False) res = sess.run(total_loss) self.assertAllClose(9.656628, res) def testSequenceLossByExample(self): with self.test_session() as sess: output_classes = 5 logits = [tf.constant(i + 0.5, shape=[2, output_classes]) for i in range(3)] targets = [tf.constant(i, tf.int32, shape=[2]) for i in range(3)] weights = [tf.constant(1.0, shape=[2]) for i in range(3)] average_loss_per_example = tf.nn.seq2seq.sequence_loss_by_example( logits, targets, weights, average_across_timesteps=True) res = sess.run(average_loss_per_example) self.assertAllClose(np.asarray([1.609438, 1.609438]), res) loss_per_sequence = tf.nn.seq2seq.sequence_loss_by_example( logits, targets, weights, average_across_timesteps=False) res = sess.run(loss_per_sequence) self.assertAllClose(np.asarray([4.828314, 4.828314]), res) def testModelWithBucketsScopeAndLoss(self): """Test that variable scope reuse is not reset after model_with_buckets.""" classes = 10 buckets = [(4, 4), (8, 8)] with self.test_session(): # Here comes a sample Seq2Seq model using GRU cells. def SampleGRUSeq2Seq(enc_inp, dec_inp, weights, per_example_loss): """Example sequence-to-sequence model that uses GRU cells.""" def GRUSeq2Seq(enc_inp, dec_inp): cell = tf.nn.rnn_cell.MultiRNNCell([tf.nn.rnn_cell.GRUCell(24)] * 2, state_is_tuple=True) return tf.nn.seq2seq.embedding_attention_seq2seq( enc_inp, dec_inp, cell, num_encoder_symbols=classes, num_decoder_symbols=classes, embedding_size=24) targets = [dec_inp[i+1] for i in range(len(dec_inp) - 1)] + [0] return tf.nn.seq2seq.model_with_buckets( enc_inp, dec_inp, targets, weights, buckets, GRUSeq2Seq, per_example_loss=per_example_loss) # Now we construct the copy model. inp = [tf.placeholder(tf.int32, shape=[None]) for _ in range(8)] out = [tf.placeholder(tf.int32, shape=[None]) for _ in range(8)] weights = [tf.ones_like(inp[0], dtype=tf.float32) for _ in range(8)] with tf.variable_scope("root"): _, losses1 = SampleGRUSeq2Seq(inp, out, weights, per_example_loss=False) # Now check that we did not accidentally set reuse. self.assertEqual(False, tf.get_variable_scope().reuse) # Construct one more model with per-example loss. tf.get_variable_scope().reuse_variables() _, losses2 = SampleGRUSeq2Seq(inp, out, weights, per_example_loss=True) # First loss is scalar, the second one is a 1-dimensinal tensor. self.assertEqual([], losses1[0].get_shape().as_list()) self.assertEqual([None], losses2[0].get_shape().as_list()) def testModelWithBuckets(self): """Larger tests that does full sequence-to-sequence model training.""" # We learn to copy 10 symbols in 2 buckets: length 4 and length 8. classes = 10 buckets = [(4, 4), (8, 8)] perplexities = [[], []] # Results for each bucket. tf.set_random_seed(111) random.seed(111) np.random.seed(111) with self.test_session() as sess: # We use sampled softmax so we keep output projection separate. w = tf.get_variable("proj_w", [24, classes]) w_t = tf.transpose(w) b = tf.get_variable("proj_b", [classes]) # Here comes a sample Seq2Seq model using GRU cells. def SampleGRUSeq2Seq(enc_inp, dec_inp, weights): """Example sequence-to-sequence model that uses GRU cells.""" def GRUSeq2Seq(enc_inp, dec_inp): cell = tf.nn.rnn_cell.MultiRNNCell([tf.nn.rnn_cell.GRUCell(24)] * 2, state_is_tuple=True) return tf.nn.seq2seq.embedding_attention_seq2seq( enc_inp, dec_inp, cell, num_encoder_symbols=classes, num_decoder_symbols=classes, embedding_size=24, output_projection=(w, b)) targets = [dec_inp[i+1] for i in range(len(dec_inp) - 1)] + [0] def SampledLoss(inputs, labels): labels = tf.reshape(labels, [-1, 1]) return tf.nn.sampled_softmax_loss(w_t, b, inputs, labels, 8, classes) return tf.nn.seq2seq.model_with_buckets( enc_inp, dec_inp, targets, weights, buckets, GRUSeq2Seq, softmax_loss_function=SampledLoss) # Now we construct the copy model. batch_size = 8 inp = [tf.placeholder(tf.int32, shape=[None]) for _ in range(8)] out = [tf.placeholder(tf.int32, shape=[None]) for _ in range(8)] weights = [tf.ones_like(inp[0], dtype=tf.float32) for _ in range(8)] with tf.variable_scope("root"): _, losses = SampleGRUSeq2Seq(inp, out, weights) updates = [] params = tf.all_variables() optimizer = tf.train.AdamOptimizer(0.03, epsilon=1e-5) for i in range(len(buckets)): full_grads = tf.gradients(losses[i], params) grads, _ = tf.clip_by_global_norm(full_grads, 30.0) update = optimizer.apply_gradients(zip(grads, params)) updates.append(update) sess.run([tf.initialize_all_variables()]) steps = 6 for _ in range(steps): bucket = random.choice(np.arange(len(buckets))) length = buckets[bucket][0] i = [np.array([np.random.randint(9) + 1 for _ in range(batch_size)], dtype=np.int32) for _ in range(length)] # 0 is our "GO" symbol here. o = [np.array([0] * batch_size, dtype=np.int32)] + i feed = {} for i1, i2, o1, o2 in zip(inp[:length], i[:length], out[:length], o[:length]): feed[i1.name] = i2 feed[o1.name] = o2 if length < 8: # For the 4-bucket, we need the 5th as target. feed[out[length].name] = o[length] res = sess.run([updates[bucket], losses[bucket]], feed) perplexities[bucket].append(math.exp(float(res[1]))) for bucket in range(len(buckets)): if len(perplexities[bucket]) > 1: # Assert that perplexity went down. self.assertLess(perplexities[bucket][-1], perplexities[bucket][0]) def testModelWithBooleanFeedPrevious(self): """Test the model behavior when feed_previous is True. For example, the following two cases have the same effect: - Train `embedding_rnn_seq2seq` with `feed_previous=True`, which contains a `embedding_rnn_decoder` with `feed_previous=True` and `update_embedding_for_previous=True`. The decoder is fed with "<Go>" and outputs "A, B, C". - Train `embedding_rnn_seq2seq` with `feed_previous=False`. The decoder is fed with "<Go>, A, B". """ num_encoder_symbols = 3 num_decoder_symbols = 5 batch_size = 2 num_enc_timesteps = 2 num_dec_timesteps = 3 def TestModel(seq2seq): with self.test_session(graph=tf.Graph()) as sess: tf.set_random_seed(111) random.seed(111) np.random.seed(111) enc_inp = [tf.constant(i + 1, tf.int32, shape=[batch_size]) for i in range(num_enc_timesteps)] dec_inp_fp_true = [tf.constant(i, tf.int32, shape=[batch_size]) for i in range(num_dec_timesteps)] dec_inp_holder_fp_false = [tf.placeholder(tf.int32, shape=[batch_size]) for _ in range(num_dec_timesteps)] targets = [tf.constant(i + 1, tf.int32, shape=[batch_size]) for i in range(num_dec_timesteps)] weights = [tf.constant(1.0, shape=[batch_size]) for i in range(num_dec_timesteps)] def ForwardBackward(enc_inp, dec_inp, feed_previous): scope_name = "fp_{}".format(feed_previous) with tf.variable_scope(scope_name): dec_op, _ = seq2seq(enc_inp, dec_inp, feed_previous=feed_previous) net_variables = tf.get_collection(tf.GraphKeys.VARIABLES, scope_name) optimizer = tf.train.AdamOptimizer(0.03, epsilon=1e-5) update_op = optimizer.minimize( tf.nn.seq2seq.sequence_loss(dec_op, targets, weights), var_list=net_variables) return dec_op, update_op, net_variables dec_op_fp_true, update_fp_true, variables_fp_true = ForwardBackward( enc_inp, dec_inp_fp_true, feed_previous=True) dec_op_fp_false, update_fp_false, variables_fp_false = ForwardBackward( enc_inp, dec_inp_holder_fp_false, feed_previous=False) sess.run(tf.initialize_all_variables()) # We only check consistencies between the variables existing in both # the models with True and False feed_previous. Variables created by # the loop_function in the model with True feed_previous are ignored. v_false_name_dict = {v.name.split('/', 1)[-1]: v for v in variables_fp_false} matched_variables = [(v, v_false_name_dict[v.name.split('/', 1)[-1]]) for v in variables_fp_true] for v_true, v_false in matched_variables: sess.run(tf.assign(v_false, v_true)) # Take the symbols generated by the decoder with feed_previous=True as # the true input symbols for the decoder with feed_previous=False. dec_fp_true = sess.run(dec_op_fp_true) output_symbols_fp_true = np.argmax(dec_fp_true, axis=2) dec_inp_fp_false = np.vstack((dec_inp_fp_true[0].eval(), output_symbols_fp_true[:-1])) sess.run(update_fp_true) sess.run(update_fp_false, {holder: inp for holder, inp in zip(dec_inp_holder_fp_false, dec_inp_fp_false)}) for v_true, v_false in matched_variables: self.assertAllClose(v_true.eval(), v_false.eval()) def EmbeddingRNNSeq2SeqF(enc_inp, dec_inp, feed_previous): cell = tf.nn.rnn_cell.BasicLSTMCell(2, state_is_tuple=True) return tf.nn.seq2seq.embedding_rnn_seq2seq( enc_inp, dec_inp, cell, num_encoder_symbols, num_decoder_symbols, embedding_size=2, feed_previous=feed_previous) def EmbeddingRNNSeq2SeqNoTupleF(enc_inp, dec_inp, feed_previous): cell = tf.nn.rnn_cell.BasicLSTMCell(2, state_is_tuple=False) return tf.nn.seq2seq.embedding_rnn_seq2seq( enc_inp, dec_inp, cell, num_encoder_symbols, num_decoder_symbols, embedding_size=2, feed_previous=feed_previous) def EmbeddingTiedRNNSeq2Seq(enc_inp, dec_inp, feed_previous): cell = tf.nn.rnn_cell.BasicLSTMCell(2, state_is_tuple=True) return tf.nn.seq2seq.embedding_tied_rnn_seq2seq( enc_inp, dec_inp, cell, num_decoder_symbols, embedding_size=2, feed_previous=feed_previous) def EmbeddingTiedRNNSeq2SeqNoTuple(enc_inp, dec_inp, feed_previous): cell = tf.nn.rnn_cell.BasicLSTMCell(2, state_is_tuple=False) return tf.nn.seq2seq.embedding_tied_rnn_seq2seq( enc_inp, dec_inp, cell, num_decoder_symbols, embedding_size=2, feed_previous=feed_previous) def EmbeddingAttentionSeq2Seq(enc_inp, dec_inp, feed_previous): cell = tf.nn.rnn_cell.BasicLSTMCell(2, state_is_tuple=True) return tf.nn.seq2seq.embedding_attention_seq2seq( enc_inp, dec_inp, cell, num_encoder_symbols, num_decoder_symbols, embedding_size=2, feed_previous=feed_previous) def EmbeddingAttentionSeq2SeqNoTuple(enc_inp, dec_inp, feed_previous): cell = tf.nn.rnn_cell.BasicLSTMCell(2, state_is_tuple=False) return tf.nn.seq2seq.embedding_attention_seq2seq( enc_inp, dec_inp, cell, num_encoder_symbols, num_decoder_symbols, embedding_size=2, feed_previous=feed_previous) for model in (EmbeddingRNNSeq2SeqF, EmbeddingRNNSeq2SeqNoTupleF, EmbeddingTiedRNNSeq2Seq, EmbeddingTiedRNNSeq2SeqNoTuple, EmbeddingAttentionSeq2Seq, EmbeddingAttentionSeq2SeqNoTuple): TestModel(model) if __name__ == "__main__": tf.test.main()

apache-2.0

j00zek/PolishTranslations

TranslationsUpdater/myComponents.py

1

15911

# -*- coding: utf-8 -*- # @j00zek 2015 from __init__ import * from Components.ActionMap import ActionMap from Components.config import * from Components.MenuList import MenuList from Components.ScrollLabel import ScrollLabel from Components.Sources.StaticText import StaticText from enigma import eConsoleAppContainer, eTimer from Screens.ChoiceBox import ChoiceBox from Screens.MessageBox import MessageBox from Screens.Screen import Screen # from os import system as os_system, popen as os_popen, path as os_path config.plugins.TranslationsUpdater = ConfigSubsection() config.plugins.TranslationsUpdater.SortowaniePoDacie = ConfigYesNo(default = False) config.plugins.TranslationsUpdater.UkrywanieNiezainstalowanych = ConfigYesNo(default = False) config.plugins.TranslationsUpdater.AutoUpdate = ConfigYesNo(default = False) config.plugins.TranslationsUpdater.UsunPlikiTMP = ConfigYesNo(default = True) def substring_2_translate(text): to_translate = text.split('_(', 2) text = to_translate[1] to_translate = text.split(')', 2) text = to_translate[0] return text def lastChance(text): NonStandardTranslations=[('Jan','Styczeń '),('Feb','Luty'),('Mar','Marzec'),('Apr','Kwiecień'),('May','Maj'), \ ('Jun','Czerwiec'),('Jul','Lipiec'),('Aug','Sierpień'),('Sep','Wrzesień'),('Oct','Październik'),('Nov','Listopad'),('Dec','Grudzień')] for tr in NonStandardTranslations: text=text.replace(tr[0],tr[1]) return text def __(txt): if txt.find('_(') == -1: txt = _(txt) else: index = 0 while txt.find('_(') != -1: tmptxt = substring_2_translate(txt) translated_tmptxt = _(tmptxt) if translated_tmptxt == tmptxt: translated_tmptxt = lastChance(tmptxt) txt = txt.replace('_(' + tmptxt + ')', translated_tmptxt) index += 1 if index == 10: break return txt class translatedConsole(Screen): #TODO move this to skin.xml skin = """ <screen position="center,center" size="550,450" title="Instalacja..." > <widget name="text" position="0,0" size="550,450" font="Console;14" /> </screen>""" def __init__(self, session, title = "translatedConsole", cmdlist = None, finishedCallback = None, closeOnSuccess = False): Screen.__init__(self, session) self.finishedCallback = finishedCallback self.closeOnSuccess = closeOnSuccess self.errorOcurred = False self["text"] = ScrollLabel("") self["actions"] = ActionMap(["WizardActions", "DirectionActions"], { "ok": self.cancel, "back": self.cancel, "up": self["text"].pageUp, "down": self["text"].pageDown }, -1) self.cmdlist = cmdlist self.newtitle = title.replace('\t',' ').replace(' ',' ').strip() self.onShown.append(self.updateTitle) self.container = eConsoleAppContainer() self.run = 0 self.container.appClosed.append(self.runFinished) self.container.dataAvail.append(self.dataAvail) self.onLayoutFinish.append(self.startRun) # dont start before gui is finished def updateTitle(self): self.setTitle(self.newtitle) def startRun(self): self["text"].setText("" + "\n\n") print "TranslatedConsole: executing in run", self.run, " the command:", self.cmdlist[self.run] if self.container.execute(self.cmdlist[self.run]): #start of container application failed... self.runFinished(-1) # so we must call runFinished manual def runFinished(self, retval): if retval: self.errorOcurred = True self.run += 1 if self.run != len(self.cmdlist): if self.container.execute(self.cmdlist[self.run]): #start of container application failed... self.runFinished(-1) # so we must call runFinished manual else: #lastpage = self["text"].isAtLastPage() #str = self["text"].getText() #str += _("\nUse up/down arrows to scroll text. OK closes window"); #self["text"].setText(str) #if lastpage: self["text"].lastPage() if self.finishedCallback is not None: self.finishedCallback() if not self.errorOcurred and self.closeOnSuccess: self.cancel() def cancel(self): from Screens.MessageBox import MessageBox def rebootQuestionAnswered(ret): if ret: from enigma import quitMainloop quitMainloop(3) try: self.close() except: pass return def doReboot(ret): self.session.openWithCallback(rebootQuestionAnswered, MessageBox,"Restart GUI now?", type = MessageBox.TYPE_YESNO, timeout = 10, default = False) if self.run == len(self.cmdlist): self.container.appClosed.remove(self.runFinished) self.container.dataAvail.remove(self.dataAvail) if os_path.exists("/tmp/.rebootGUI"): self.session.openWithCallback(doReboot,MessageBox, 'LICENCJA: Wszystkie tłumaczenia są autorstwem kolegów Mariusz1970P, Century, Kos i innych.\n\nMożesz z nich korzystać jedynie za pośrednictwem wtyczki "Aktualizator tłumaczeń".\nUszanuj pracę autorów i poświęcony czas i nie wykorzystuj ich bezpośrednio w swoich wtyczkach, czy paczkach.', MessageBox.TYPE_INFO, timeout=15) else: self.close() def dataAvail(self, str): #lastpage = self["text"].isAtLastPage() self["text"].setText(self["text"].getText() + __(str)) #if lastpage: self["text"].lastPage() ############################################ class j00zekTUMenu(Screen,): def __init__(self, session, MenuFolder = "" , MenuFile = '_MenuItems', MenuTitle = 'j00zekTUMenu'): self.myList = [] self.list = [] self.myPath = MenuFolder self.MenuFile = "/tmp/%s" % (MenuFile) self.SkryptOpcji = "" self.PIC = "" picHeight = 0 self.MenuTitle = MenuTitle skin = """ <screen name="j00zekTUMenu" position="center,center" size="520,520" title="j00zekTUMenu" > <widget name="list" position="5,30" font="Regular;20" size="510,350" scrollbarMode="showOnDemand" /> <eLabel text="Tłumaczenia: Mariusz1970, Century" position="0,390" size="520,30" font="Regular;22" foregroundColor="yellow" valign="center" halign="center" /> <eLabel text="Wtyczka: (c)2015,2016 j00zek" position="0,420" size="520,30" font="Regular;22" foregroundColor="yellow" valign="center" halign="center" /> <eLabel position=" 5,455" size="253, 30" zPosition="-10" backgroundColor="#20b81c46" /> <eLabel position="262,455" size="253, 30" zPosition="-10" backgroundColor="#20009f3c" /> <eLabel position=" 5,490" size="253, 30" zPosition="-10" backgroundColor="#209ca81b" /> <widget source="key_red" render="Label" position=" 5,455" size="253,30" zPosition="1" font="Regular;20" valign="center" halign="center" transparent="1" /> <widget source="key_green" render="Label" position="262,455" size="253,30" zPosition="1" font="Regular;20" valign="center" halign="center" transparent="1" /> <widget source="key_yellow" render="Label" position=" 5,490" size="253,30" zPosition="1" font="Regular;20" valign="center" halign="center" transparent="1" /> <widget source="Header1" render="Label" position=" 10,0" size="150,30" font="Regular;18" foregroundColor="#6DABBF" valign="center" halign="center" /> <widget source="Header2" render="Label" position="145,0" size="460,30" font="Regular;18" foregroundColor="#6DABBF" valign="center" halign="center" /> </screen>""" self.skin = skin self.session = session Screen.__init__(self, session) self["list"] = MenuList(self.list) self["actions"] = ActionMap(["OkCancelActions", "ColorActions"], {"ok": self.run, "cancel": self.close, "red": self.ZmienSortowanie, "green": self.ZmienUkrywanieNiezainstalowanych, "yellow": self.ZmienAutoUpdate, }, -1) self.onLayoutFinish.append(self.onStart) self.visible = True self.setTitle("Pobieranie danych...") self["key_red"] = StaticText("") self["key_green"] = StaticText("") self["key_yellow"] = StaticText("") self["Header1"] = StaticText("") self["Header2"] = StaticText("") def onStart(self): self.system( "rm -f /tmp/PolishTranslations.list" ) self.updateDataTimer = eTimer() self.updateDataTimer.callback.append(self.updateData) self.updateDataTimer.start(500, True) # singleshot def updateData(self): self.setButtons(czysc=True) self.setTitle("Pobieranie danych...") self.system( "%s/_MenuGenerator.sh %s" % (self.myPath, self.myPath) ) self.setTitle(self.MenuTitle) self.clearLIST() self.reloadLIST() self.setButtons() def ZmienAutoUpdate(self): config.plugins.TranslationsUpdater.AutoUpdate.value = not config.plugins.TranslationsUpdater.AutoUpdate.value config.plugins.TranslationsUpdater.AutoUpdate.save() configfile.save() self.setButtons() def ZmienSortowanie(self): config.plugins.TranslationsUpdater.SortowaniePoDacie.value = not config.plugins.TranslationsUpdater.SortowaniePoDacie.value config.plugins.TranslationsUpdater.SortowaniePoDacie.save() configfile.save() self.setButtons(czysc=True) self.clearLIST() self.updateDataTimer.start(100, True) # singleshot def ZmienUkrywanieNiezainstalowanych(self): config.plugins.TranslationsUpdater.UkrywanieNiezainstalowanych.value = not config.plugins.TranslationsUpdater.UkrywanieNiezainstalowanych.value config.plugins.TranslationsUpdater.UkrywanieNiezainstalowanych.save() configfile.save() self.setButtons(czysc=True) self.clearLIST() self.updateDataTimer.start(100, True) # singleshot def setButtons(self, czysc=False): if czysc == True: self["key_red"].setText("") self["key_green"].setText("") self["Header1"].setText("") self["Header2"].setText("") return if config.plugins.TranslationsUpdater.SortowaniePoDacie.value == True: self["key_red"].setText("Posortuj po nazwie") self["Header1"].setText("Z dnia") self["Header2"].setText("Plik") else: self["key_red"].setText("Posortuj po dacie") self["Header1"].setText("Plik") self["Header2"].setText("z dnia") if config.plugins.TranslationsUpdater.UkrywanieNiezainstalowanych.value == True: self["key_green"].setText("Pokaż wszystkie") else: self["key_green"].setText("Ukryj niezainstalowane") if config.plugins.TranslationsUpdater.AutoUpdate.value == True: self["key_yellow"].setText("Wył. AutoAktualizację") else: self["key_yellow"].setText("Wł. AutoAktualizację") def YESNO(self, decyzja): if decyzja is False: return self.system("%s" % self.SkryptOpcji) def system(self,komenda): with open("/proc/sys/vm/drop_caches", "w") as f: f.write("1\n") os_system(komenda) def run(self): selecteditem = self["list"].getCurrent() if selecteditem is not None: for opcja in self.myList: if opcja[0] == selecteditem: self.SkryptOpcji = opcja[2] if opcja[1] == "CONSOLE": self.session.openWithCallback(self.endrun ,translatedConsole, title = "%s" % selecteditem, cmdlist = [ ('chmod 775 %s 2>/dev/null' % self.SkryptOpcji),('%s' % self.SkryptOpcji) ]) if opcja[1] == "YESNO": self.session.openWithCallback(self.YESNO ,MessageBox,_("Execute %s?") % selecteditem, MessageBox.TYPE_YESNO) if opcja[1] == "SILENT": self.system("%s" % self.SkryptOpcji) self.endrun() elif opcja[1] == "RUN": self.system("%s" % self.SkryptOpcji) self.session.openWithCallback(self.endrun,MessageBox,_("%s executed!") %( selecteditem ), MessageBox.TYPE_INFO, timeout=5) elif opcja[1] == "MSG": msgline = "" popenret = os_popen( self.SkryptOpcji) for readline in popenret.readlines(): msgline += readline self.session.openWithCallback(self.endrun,MessageBox, "%s" %( msgline ), MessageBox.TYPE_INFO, timeout=15) def endConsole(self, ret =0, wymusUpdate=False): self.session.openWithCallback(self.endrun,MessageBox, 'LICENCJA: Wszystkie tłumaczenia są autorstwem kolegi Mariusz1970P.\nMożesz z nich korzystać jedynie za pośrednictwem wtyczki "Aktualizator tłumaczeń".\nUszanuj jego pracę i poświęcony czas i nie wykorzystuj ich bezpośrednio w swoich wtyczkach, czy paczkach.', MessageBox.TYPE_INFO, timeout=15) def endrun(self, ret =0, wymusUpdate=False): #odświerzamy menu if not os_path.exists(self.MenuFile) or wymusUpdate == True: self.system( "%s/_MenuGenerator.sh %s" % (self.myPath, self.myPath) ) self.clearLIST() self.reloadLIST() def SkryptOpcjiWithFullPAth(self, txt): if txt.startswith('/'): return txt elif txt.split(' ')[0] in ('opkg'): return txt else: return ('%s/%s') %(self.myPath,txt) def clearLIST(self): #czyścimy listę w ten dziwny sposób, aby GUI działało, bo nie zmienimy obiektów ;) while len(self.list) > 0: del self.myList[-1] del self.list[-1] self["list"].hide() self["list"].show() def reloadLIST(self): if os_path.exists(self.MenuFile) is True: self["list"].hide() with open (self.MenuFile, "r") as myMenufile: for MenuItem in myMenufile: MenuItem = MenuItem.rstrip('\n') if not MenuItem or MenuItem[0] == '#': #omijamy komentarze continue #interesują nas tylko pozycje menu if MenuItem[0:5] == "ITEM|": #teraz bierzemy pod uwage tylko te linie co mają odpowiednią ilość | #print MenuItem skladniki = MenuItem.replace("ITEM|","").split('|') if len(skladniki) == 3: (NazwaOpcji, TypOpcji, SkryptOpcji) = skladniki if NazwaOpcji != "": NazwaOpcji = __(NazwaOpcji) #NazwaOpcji = NazwaOpcji.replace(NazwaOpcji[:3],_(NazwaOpcji[:3])) self.myList.append( (NazwaOpcji, TypOpcji, self.SkryptOpcjiWithFullPAth(SkryptOpcji)) ) self.list.append( NazwaOpcji ) myMenufile.close() myIdx = self["list"].getSelectionIndex() if myIdx > len(self.list) -1: self["list"].moveToIndex(len(self.list) -1) self["list"].show()

gpl-2.0

a25kk/osm

src/osm.sitetheme/osm/sitetheme/tests/test_setup.py

1

1144

# -*- coding: utf-8 -*- """Setup/installation tests for this package.""" from osm.buildout.testing import IntegrationTestCase from plone import api class TestInstall(IntegrationTestCase): """Test installation of osm.buildout into Plone.""" def setUp(self): """Custom shared utility setup for tests.""" self.portal = self.layer['portal'] self.installer = api.portal.get_tool('portal_quickinstaller') def test_product_installed(self): """Test if osm.buildout is installed with portal_quickinstaller.""" self.assertTrue(self.installer.isProductInstalled('osm.buildout')) def test_uninstall(self): """Test if osm.buildout is cleanly uninstalled.""" self.installer.uninstallProducts(['osm.buildout']) self.assertFalse(self.installer.isProductInstalled('osm.buildout')) # browserlayer.xmlw def test_browserlayer(self): """Test that IosmBuildoutLayer is registered.""" from osm.buildout.interfaces import IosmBuildoutLayer from plone.browserlayer import utils self.failUnless(IosmBuildoutLayer in utils.registered_layers())

mit

borysiasty/inasafe

safe/gis/test/test_reclassify.py

1

1890

# coding=utf-8 """Tests for reclassify implementation.""" import unittest from collections import OrderedDict from qgis.core import QgsVectorLayer, QgsFeatureRequest from safe.gis.reclassify_gdal import reclassify_polygonize from safe.test.utilities import test_data_path, get_qgis_app QGIS_APP, CANVAS, IFACE, PARENT = get_qgis_app() class ReclassifyTest(unittest.TestCase): """Tests for reclassify a raster.""" def setUp(self): pass def tearDown(self): pass def test_reclassify_polygonize(self): """Test if we can reclassify a raster according to some thresholds.""" raster_path = test_data_path('hazard', 'continuous_flood_20_20.asc') ranges = OrderedDict() # value <= 0.2 ranges[1] = [None, 0.2] # 0.2 < value <= 1 ranges[2] = [0.2, 1] # 1 < value <= 1.3 and gap in output classes ranges[10] = [1, 1.3] # value > 1.3 ranges[11] = [1.3, None] output = reclassify_polygonize(raster_path, ranges) layer = QgsVectorLayer(output, 'test layer', 'ogr') self.assertEqual(layer.featureCount(), 61) expression = '"DN" = \'%s\'' % 1 request = QgsFeatureRequest().setFilterExpression(expression) self.assertEqual(sum(1 for _ in layer.getFeatures(request)), 20) expression = '"DN" = \'%s\'' % 2 request = QgsFeatureRequest().setFilterExpression(expression) self.assertEqual(sum(1 for _ in layer.getFeatures(request)), 1) expression = '"DN" = \'%s\'' % 10 request = QgsFeatureRequest().setFilterExpression(expression) self.assertEqual(sum(1 for _ in layer.getFeatures(request)), 20) expression = '"DN" = \'%s\'' % 11 request = QgsFeatureRequest().setFilterExpression(expression) self.assertEqual(sum(1 for _ in layer.getFeatures(request)), 20)

gpl-3.0

eddienigma/rpi-rht

GraphIndexTH.py

1

4632

# # pull data from sql, plot using matplotlib # see http://stackoverflow.com/questions/18663746/matplotlib-multiple-lines-with-common-date-on-x-axis-solved # # rev 1.0 12/02/2013 WPNS built from GraphAirmuxSD.py V1.1 # rev 1.1 12/02/2013 WPNS remove large delta values # rev 1.2 12/02/2013 WPNS remove -0.1 values (failed to read) # rev 1.3 12/02/2013 WPNS show count of anomalies # rev 1.4 12/03/2013 WPNS cleanup, release # rev 1.5 12/03/2013 WPNS better label # rev 1.6 12/03/2013 WPNS bugfix, release # rev 1.69 12/04/2013 WPNS release to Instructables # rev 2.0-JAS 1/11/2014 JAS adjusted graph ranges for current conditions and to use SQLite3 instead of MySQL import sys import os import time import math import datetime import numpy import sqlite3 as lite # so matplotlib has to have some of the setup parameters _before_ pyplot import matplotlib matplotlib.use('agg') #matplotlib.rcParams['figure.dpi'] = 100 #matplotlib.rcParams['figure.figsize'] = [10.24, 7.68] matplotlib.rcParams['lines.linewidth'] = 1 matplotlib.rcParams['axes.color_cycle'] = ['r','g','b','k'] matplotlib.rcParams['axes.labelsize'] = 'large' matplotlib.rcParams['font.size'] = 8 matplotlib.rcParams['grid.linestyle']='-' import matplotlib.pyplot as plt anomalies = 0 print "GraphTH.py V1.69 12/04/2013 WPNS",time.asctime(), print "GraphTH.py V1.0-JAS 12/22/2013 JAS" # open the database connection, read the last <many> seconds of data, put them in a Numpy array called Raw DBconn = lite.connect('/var/rht/db/rht.db') cursor = DBconn.cursor() sql = "select ComputerTime,TempF,Humidity from rht where ComputerTime >= (strftime('%s','now')-(60*60*24))" cursor.execute(sql) cursor2 = DBconn.cursor() sql2 = "SELECT datetime(ComputerTime,'unixepoch','localtime'),TempF,Humidity FROM rht WHERE ComputerTime = (select max(ComputerTime) from rht)" cursor2.execute(sql2) lastRow = cursor2.fetchone() Raw = numpy.fromiter(cursor.fetchall(), count=-1, dtype=[('', numpy.float)]*3) Raw = Raw.view(numpy.float).reshape(-1, 3) (samples,ports)=Raw.shape print 'Samples: {}, DataPoints: {}'.format(samples,ports), plotme=numpy.zeros((samples,ports-1)) # make an array the same shape minus the epoch numbers for y in range(ports-1): # print y for x in range(samples-1): # can't do last one, there's no (time) delta from previous sample seconds = Raw[x+1,0]-Raw[x,0] # if the number didn't overflow the counter plotme[x,y] = Raw[x,y+1] plotme[samples-1,y] = None # set last sample to "do not plot" for x in range(samples-1): # go thru the dataset again if (Raw[x+1,1] == -0.1): # if values are "reading failed" flag plotme[x+1,0] = plotme[x,0] # copy current sample over it plotme[x+1,1] = plotme[x,1] # for temperature and humidity both anomalies += 1 if (abs(Raw[x+1,1]-Raw[x,1]) > 10): # if temperature jumps more than 10 degrees in a minute plotme[x+1,0] = plotme[x,0] # copy current sample over it plotme[x+1,1] = plotme[x,1] # for temperature and humidity both anomalies += 1 print "Anomalies: ",anomalies, #print plotme # get an array of adatetime objects (askewchan from stackoverflow, above) dts = map(datetime.datetime.fromtimestamp, Raw[:,0]) # set up the plot details we want plt.grid(True) plt.ylabel('Temp F, RH %%') plt.axis(ymax=100,ymin=10) plt.xlabel(time.asctime()) plt.title("Outdoor: Temperature (Red), Humidity (Green)") plt.hold(True) # and some fiddly bits around formatting the X (date) axis plt.gca().xaxis.set_major_formatter(matplotlib.dates.DateFormatter('%m/%d %H:%M')) plt.gca().xaxis.set_major_locator(matplotlib.dates.HourLocator()) lines = plt.plot(dts,plotme) plt.gcf().autofmt_xdate() FileName = '/var/rht/images/TH.png' plt.savefig(FileName) web = open('/var/www/index.html', 'w') web.write('<HTML>\n') web.write('<HEAD>\n') web.write('<meta http-equiv=\"refresh\" content=\"60\">\n') web.write('<TITLE>Raspberry Pi Temperature and Humidity Readings</TITLE>\n') web.write('</HEAD>\n') web.write('\n') web.write('<BODY BGCOLOR="#FFFFFF">\n') web.write('<CENTER>\n') web.write('<IMG SRC="/images/TH.png">\n') web.write('<BR><BR>\n') web.write('<FONT COLOR=\"#FF0000\" SIZE=+2>Temp: ' + str(lastRow[1]) + 'F </FONT> &nbsp; &nbsp; &nbsp; <FONT COLOR=\"#00FF00\" SIZE=+2>Humidity: ' + str(lastRow[2]) + '% </FONT><BR>\n') web.write('<FONT SIZE=+2>Time: ' + str(lastRow[0]) + '</FONT><BR>\n') web.write('</CENTER>\n') web.write('</BODY>\n') web.write('\n') web.write('</HTML\n') print 'Done at',time.asctime()

gpl-3.0

chagaz/SamSpecCoEN

code/setupCV_computeNetworks.py

1

4177

# @Author # Chloe-Agathe Azencott # [email protected] # April 2016 import argparse import h5py import numpy as np import os import sys import CoExpressionNetwork def main(): """ Create sample-specific co-expression networks for one fold and one repeat of a cross-validation for which fold indices have already been computed. The data will be stored under <data_dir>/repeat<repeat idx> with the following structure: edges.gz: Gzipped file containing the list of edges of the co-expression networks. Each line is an undirected edge, formatted as: <index of gene 1> <index of gene 2> By convention, the index of gene 1 is smaller than that of gene 2. For k=0..(numFolds-1): <k>/lioness/edge_weights.gz: gzipped file containing the (self.numSamples, numEdges) array describing the edge weights of the LIONESS co-expression networks for the training samples. <k>/lioness/edge_weights_te.gz: gzipped file containing the (self.numSamples, numEdges) array describing the edge weights of the LIONESS co-expression networks for the test samples. <k>/regline/edge_weights.gz: gzipped file containing the (self.numSamples, numEdges) array describing the edge weights of the Regline co-expression networks for the training samples. <k>/regline/edge_weights_te.gz: gzipped file containing the (self.numSamples, numEdges) array describing the edge weights of the Regline co-expression networks for the test samples. Parameters ---------- aces_dir: path Path to the ACES folder. data_dir: path Path to the folder containing fold indices (under <data_dir>/repeat<repeat_idx>/fold<fold_idx>). fold: int Fold index. repeat: int Repeat index. Example ------- $ python setUpSubTypeStratifiedCV_computeNetworks.py ACES outputs/U133A_combat_RFS/subtype_stratified 0 0 Reference --------- Allahyar, A., and Ridder, J. de (2015). FERAL: network-based classifier with application to breast cancer outcome prediction. Bioinformatics 31, i311--i319. """ parser = argparse.ArgumentParser(description="Build sample-specific co-expression networks" + \ "for a 10-fold sub-type stratified CV on the RFS data", add_help=True) parser.add_argument("aces_dir", help="Path to ACES data") parser.add_argument("data_dir", help="Path to the fold indices") parser.add_argument("fold", help="Index of the fold", type=int) parser.add_argument("repeat", help="Index of the repeat", type=int) args = parser.parse_args() outDir = '%s/repeat%d' % (args.data_dir, args.repeat) # Get expression data, sample labels. # Do not normalize the data while loading it (so as not to use test data for normalization). f = h5py.File("%s/experiments/data/U133A_combat.h5" % args.aces_dir) expressionData = np.array(f['U133A_combat_RFS']['ExpressionData']) sampleLabels = np.array(f['U133A_combat_RFS']['PatientClassLabels']) f.close() foldNr = args.fold # Output directory foldDir = "%s/fold%d" % (outDir, foldNr) # Read train indices from file trIndicesF = '%s/train.indices' % foldDir trIndices = np.loadtxt(trIndicesF, dtype=int) sys.stdout.write("Read training indices for fold %d from %s\n" % (foldNr, trIndicesF)) # Read test indices from file teIndicesF = '%s/test.indices' % foldDir teIndices = np.loadtxt(teIndicesF, dtype=int) sys.stdout.write("Read training indices for fold %d from %s\n" % (foldNr, teIndicesF)) print teIndices print teIndices.shape # Create networks CoExpressionNetwork.run_whole_data(expressionData, sampleLabels, foldDir, trIndices=trIndices, teIndices=teIndices) if __name__ == "__main__": main()

mit

anaran/olympia

services/update.py

1

14315

import smtplib import sys import traceback from email.Utils import formatdate from email.mime.text import MIMEText from time import time from urlparse import parse_qsl from django.utils.http import urlencode import settings_local as settings # This has to be imported after the settings so statsd knows where to log to. from django_statsd.clients import statsd import commonware.log import MySQLdb as mysql import sqlalchemy.pool as pool try: from compare import version_int except ImportError: from apps.versions.compare import version_int from constants import applications, base from utils import (APP_GUIDS, get_mirror, log_configure, PLATFORMS, STATUSES_PUBLIC) # Go configure the log. log_configure() good_rdf = """<?xml version="1.0"?> <RDF:RDF xmlns:RDF="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:em="http://www.mozilla.org/2004/em-rdf#"> <RDF:Description about="urn:mozilla:%(type)s:%(guid)s"> <em:updates> <RDF:Seq> <RDF:li resource="urn:mozilla:%(type)s:%(guid)s:%(version)s"/> </RDF:Seq> </em:updates> </RDF:Description> <RDF:Description about="urn:mozilla:%(type)s:%(guid)s:%(version)s"> <em:version>%(version)s</em:version> <em:targetApplication> <RDF:Description> <em:id>%(appguid)s</em:id> <em:minVersion>%(min)s</em:minVersion> <em:maxVersion>%(max)s</em:maxVersion> <em:updateLink>%(url)s</em:updateLink> %(if_update)s %(if_hash)s </RDF:Description> </em:targetApplication> </RDF:Description> </RDF:RDF>""" bad_rdf = """<?xml version="1.0"?> <RDF:RDF xmlns:RDF="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:em="http://www.mozilla.org/2004/em-rdf#"> </RDF:RDF>""" no_updates_rdf = """<?xml version="1.0"?> <RDF:RDF xmlns:RDF="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:em="http://www.mozilla.org/2004/em-rdf#"> <RDF:Description about="urn:mozilla:%(type)s:%(guid)s"> <em:updates> <RDF:Seq> </RDF:Seq> </em:updates> </RDF:Description> </RDF:RDF>""" timing_log = commonware.log.getLogger('z.timer') error_log = commonware.log.getLogger('z.services') def getconn(): db = settings.SERVICES_DATABASE return mysql.connect(host=db['HOST'], user=db['USER'], passwd=db['PASSWORD'], db=db['NAME']) mypool = pool.QueuePool(getconn, max_overflow=10, pool_size=5, recycle=300) class Update(object): def __init__(self, data, compat_mode='strict'): self.conn, self.cursor = None, None self.data = data.copy() self.data['row'] = {} self.version_int = 0 self.compat_mode = compat_mode def is_valid(self): # If you accessing this from unit tests, then before calling # is valid, you can assign your own cursor. if not self.cursor: self.conn = mypool.connect() self.cursor = self.conn.cursor() data = self.data # Version can be blank. data['version'] = data.get('version', '') for field in ['reqVersion', 'id', 'appID', 'appVersion']: if field not in data: return False data['app_id'] = APP_GUIDS.get(data['appID']) if not data['app_id']: return False sql = """SELECT id, status, addontype_id, guid FROM addons WHERE guid = %(guid)s AND inactive = 0 AND status != %(STATUS_DELETED)s LIMIT 1;""" self.cursor.execute(sql, {'guid': self.data['id'], 'STATUS_DELETED': base.STATUS_DELETED}) result = self.cursor.fetchone() if result is None: return False data['id'], data['addon_status'], data['type'], data['guid'] = result data['version_int'] = version_int(data['appVersion']) if 'appOS' in data: for k, v in PLATFORMS.items(): if k in data['appOS']: data['appOS'] = v break else: data['appOS'] = None return True def get_update(self): data = self.data data.update(STATUSES_PUBLIC) data['STATUS_BETA'] = base.STATUS_BETA sql = [""" SELECT addons.guid as guid, addons.addontype_id as type, addons.inactive as disabled_by_user, applications.guid as appguid, appmin.version as min, appmax.version as max, files.id as file_id, files.status as file_status, files.hash, files.filename, versions.id as version_id, files.datestatuschanged as datestatuschanged, files.strict_compatibility as strict_compat, versions.releasenotes, versions.version as version, addons.premium_type FROM versions INNER JOIN addons ON addons.id = versions.addon_id AND addons.id = %(id)s INNER JOIN applications_versions ON applications_versions.version_id = versions.id INNER JOIN applications ON applications_versions.application_id = applications.id AND applications.id = %(app_id)s INNER JOIN appversions appmin ON appmin.id = applications_versions.min INNER JOIN appversions appmax ON appmax.id = applications_versions.max INNER JOIN files ON files.version_id = versions.id AND (files.platform_id = 1 """] if data.get('appOS'): sql.append(' OR files.platform_id = %(appOS)s') sql.append(""" ) -- Find a reference to the user's current version, if it exists. -- These should never be inner joins. We need results even if we -- can't find the current version. LEFT JOIN versions curver ON curver.addon_id = addons.id AND curver.version = %(version)s LEFT JOIN files curfile ON curfile.version_id = curver.id WHERE -- Note that the WHEN clauses here will evaluate to the same -- thing for each row we examine. The JOINs above narrow the -- rows matched by the WHERE clause to versions of a specific -- add-on, and the ORDER BY and LIMIT 1 clauses below make it -- unlikely that we'll be examining a large number of rows, -- so this is fairly cheap. CASE WHEN curfile.status = %(STATUS_BETA)s THEN -- User's current version is a known beta version. -- -- Serve only beta updates. Serving a full version here -- will forever kick users out of the beta update channel. -- -- If the add-on does not have full review, serve no -- updates. addons.status = %(STATUS_PUBLIC)s AND files.status = %(STATUS_BETA)s WHEN addons.status IN (%(STATUS_LITE)s, %(STATUS_LITE_AND_NOMINATED)s) AND (curfile.id IS NULL OR curfile.status = %(STATUS_LITE)s) THEN -- Add-on is prelim, and user's current version is either a -- known prelim, or an unknown version. -- -- Serve only prelim versions. Serving a full version here -- will prevent users from receiving further updates until -- the add-on achieves full review. files.status = %(STATUS_LITE)s ELSE -- Anything else, including: -- -- * Add-on has full review -- * User's current version has full review, regardless -- of add-on status -- -- Serve only full-reviewed updates. files.status = %(STATUS_PUBLIC)s END """) sql.append('AND appmin.version_int <= %(version_int)s ') if self.compat_mode == 'ignore': pass # no further SQL modification required. elif self.compat_mode == 'normal': # When file has strict_compatibility enabled, or file has binary # components, default to compatible is disabled. sql.append("""AND CASE WHEN files.strict_compatibility = 1 OR files.binary_components = 1 THEN appmax.version_int >= %(version_int)s ELSE 1 END """) # Filter out versions that don't have the minimum maxVersion # requirement to qualify for default-to-compatible. d2c_max = applications.D2C_MAX_VERSIONS.get(data['app_id']) if d2c_max: data['d2c_max_version'] = version_int(d2c_max) sql.append("AND appmax.version_int >= %(d2c_max_version)s ") # Filter out versions found in compat overrides sql.append("""AND NOT versions.id IN ( SELECT version_id FROM incompatible_versions WHERE app_id=%(app_id)s AND (min_app_version='0' AND max_app_version_int >= %(version_int)s) OR (min_app_version_int <= %(version_int)s AND max_app_version='*') OR (min_app_version_int <= %(version_int)s AND max_app_version_int >= %(version_int)s)) """) else: # Not defined or 'strict'. sql.append('AND appmax.version_int >= %(version_int)s ') # Special case for bug 1031516. if data['guid'] == '[email protected]': app_version = data['version_int'] hotfix_version = data['version'] if version_int('10') <= app_version <= version_int('16.0.1'): if hotfix_version < '20121019.01': sql.append("AND versions.version = '20121019.01' ") elif hotfix_version < '20130826.01': sql.append("AND versions.version = '20130826.01' ") elif version_int('16.0.2') <= app_version <= version_int('24.*'): if hotfix_version < '20130826.01': sql.append("AND versions.version = '20130826.01' ") sql.append('ORDER BY versions.id DESC LIMIT 1;') self.cursor.execute(''.join(sql), data) result = self.cursor.fetchone() if result: row = dict(zip([ 'guid', 'type', 'disabled_by_user', 'appguid', 'min', 'max', 'file_id', 'file_status', 'hash', 'filename', 'version_id', 'datestatuschanged', 'strict_compat', 'releasenotes', 'version', 'premium_type'], list(result))) row['type'] = base.ADDON_SLUGS_UPDATE[row['type']] row['url'] = get_mirror(data['addon_status'], data['id'], row) data['row'] = row return True return False def get_bad_rdf(self): return bad_rdf def get_rdf(self): if self.is_valid(): if self.get_update(): rdf = self.get_good_rdf() else: rdf = self.get_no_updates_rdf() else: rdf = self.get_bad_rdf() self.cursor.close() if self.conn: self.conn.close() return rdf def get_no_updates_rdf(self): name = base.ADDON_SLUGS_UPDATE[self.data['type']] return no_updates_rdf % ({'guid': self.data['guid'], 'type': name}) def get_good_rdf(self): data = self.data['row'] data['if_hash'] = '' if data['hash']: data['if_hash'] = ('<em:updateHash>%s</em:updateHash>' % data['hash']) data['if_update'] = '' if data['releasenotes']: data['if_update'] = ('<em:updateInfoURL>%s%s%s/%%APP_LOCALE%%/' '</em:updateInfoURL>' % (settings.SITE_URL, '/versions/updateInfo/', data['version_id'])) return good_rdf % data def format_date(self, secs): return '%s GMT' % formatdate(time() + secs)[:25] def get_headers(self, length): return [('Content-Type', 'text/xml'), ('Cache-Control', 'public, max-age=3600'), ('Last-Modified', self.format_date(0)), ('Expires', self.format_date(3600)), ('Content-Length', str(length))] def mail_exception(data): if settings.EMAIL_BACKEND != 'django.core.mail.backends.smtp.EmailBackend': return msg = MIMEText('%s\n\n%s' % ( '\n'.join(traceback.format_exception(*sys.exc_info())), data)) msg['Subject'] = '[Update] ERROR at /services/update' msg['To'] = ','.join([a[1] for a in settings.ADMINS]) msg['From'] = settings.DEFAULT_FROM_EMAIL conn = smtplib.SMTP(getattr(settings, 'EMAIL_HOST', 'localhost'), getattr(settings, 'EMAIL_PORT', '25')) conn.sendmail(settings.DEFAULT_FROM_EMAIL, msg['To'], msg.as_string()) conn.close() def log_exception(data): (typ, value, traceback) = sys.exc_info() error_log.error(u'Type: %s, %s. Query: %s' % (typ, value, data)) def application(environ, start_response): status = '200 OK' with statsd.timer('services.update'): data = dict(parse_qsl(environ['QUERY_STRING'])) compat_mode = data.pop('compatMode', 'strict') try: update = Update(data, compat_mode) output = update.get_rdf() start_response(status, update.get_headers(len(output))) except: #mail_exception(data) log_exception(data) raise return [output]

bsd-3-clause

pLeBlanc93/ArcREST

src/arcrest/manageorg/_portals.py

1

82353

from __future__ import absolute_import from __future__ import print_function from __future__ import division from ..security import PortalServerSecurityHandler from ..manageags import AGSAdministration from ..hostedservice import Services from ..common.general import local_time_to_online from .._abstract.abstract import BaseAGOLClass import os from ..packages.six.moves import urllib_parse as urlparse from . import _parameters as parameters import json ######################################################################## class Portals(BaseAGOLClass): """ A multitenant portal contains multiple portals, each one of which is owned by and represents an organization. Each user in the multitenant portal belongs to one of these organizational portals or to a default portal that includes all users who do not belong to an organization. The Portals Root resource is a root placeholder resource that covers all the portals contained in the multitenant portal. """ _url = None _securityHandler = None _proxy_url = None _proxy_port = None _culture = None _region = None #---------------------------------------------------------------------- def __init__(self, url, securityHandler=None, proxy_url=None, proxy_port=None): """Constructor""" if url.lower().endswith("/portals"): self._url = url else: self._url = "%s/portals" % url self._securityHandler = securityHandler self._proxy_port = proxy_port self._proxy_url = proxy_url #---------------------------------------------------------------------- @property def root(self): """gets the classes url""" return self._url #---------------------------------------------------------------------- @property def regions(self): """gets the regions value""" url = "%s/regions" % self.root params = {"f": "json"} return self._get(url=url, param_dict=params, proxy_url=self._proxy_url, proxy_port=self._proxy_port) #---------------------------------------------------------------------- @property def languages(self): """returns the site's languages""" url = "%s/languages" % self.root params = {'f': "json"} return self._get(url=url, param_dict=params, proxy_url=self._proxy_url, proxy_port=self._proxy_port) #---------------------------------------------------------------------- @property def info(self): """gets the sharing api information""" url = "%s/info" % self.root params = {"f": "json"} return self._get(url=url, param_dict=params, proxy_url=self._proxy_url, proxy_port=self._proxy_port) #---------------------------------------------------------------------- @property def portalSelf(self): """The portal to which the current user belongs. This is an organizational portal if the user belongs to an organization or the default portal if the user does not belong to one""" url = "%s/self" % self.root return Portal(url=url, securityHandler=self._securityHandler, proxy_url=self._proxy_url, proxy_port=self._proxy_port, ) #---------------------------------------------------------------------- def portal(self, portalID=None): """returns a specific reference to a portal""" if portalID is None: portalID = self.portalSelf.id url = "%s/%s" % (self.root, portalID) return Portal(url=url, securityHandler=self._securityHandler, proxy_url=self._proxy_url, proxy_port=self._proxy_port, initalize=True) #---------------------------------------------------------------------- @property def portalId(self): """gets the portal Id""" return self.portalSelf.id ######################################################################## class Portal(BaseAGOLClass): """ Portal returns information on your organization and is accessible to administrators. Publishers and information workers can view users and resources of the organization. """ _bingKey = None _authorizedCrossOriginDomains = None _url = None _securityHandler = None _proxy_url = None _proxy_port = None _json = None _json_dict = None _canSharePublic = None _defaultExtent = None _supportsHostedServices = None _homePageFeaturedContentCount = None _supportsOAuth = None _portalName = None _databaseUsage = None _culture = None _helpBase = None _galleryTemplatesGroupQuery = None _commentsEnabled = None _databaseQuota = None _id = None _canSearchPublic = None _customBaseUrl = None _allSSL = None _httpPort = None _featuredGroupsId = None _defaultBasemap = None _created = None _access = None _platform = None _isPortal = None _canSignInArcGIS = None _disableSignup = None _httpsPort = None _units = None _backgroundImage = None _mfaEnabled = None _featuredGroups = None _thumbnail = None _featuredItemsGroupQuery = None _canSignInIDP = None _useStandardizedQuery = None _rotatorPanels = None _description = None _homePageFeaturedContent = None _helperServices = None _canProvisionDirectPurchase = None _canListData = None _user = None _helpMap = None _canListPreProvisionedItems = None _colorSetsGroupQuery = None _canListApps = None _portalProperties = None _isWindows = None _name = None _supportsSceneServices = None _stylesGroupQuery = None _samlEnabled = None _symbolSetsGroupQuery = None _portalLocalHttpPort = None _storageQuota = None _canShareBingPublic = None _maxTokenExpirationMinutes = None _layerTemplatesGroupQuery = None _staticImagesUrl = None _modified = None _portalHostname = None _showHomePageDescription = None _availableCredits = None _portalMode = None _portalLocalHttpsPort = None _hostedServerHostedFolder = None _storageUsage = None _templatesGroupQuery = None _portalLocalHostname = None _basemapGalleryGroupQuery = None _mfaAdmins = None _portalId = None _subscriptionInfo = None _urlKey = None _metadataEditable = None _portalThumbnail = None _metadataFormats = None _ipCntryCode = None _livingAtlasGroupQuery = None _region = None _contacts = None _appInfo = None _creditAssignments = None _updateUserProfileDisabled = None _analysisLayersGroupQuery = None _defaultUserCreditAssignment = None _analysisLayersGroupQuery = None #---------------------------------------------------------------------- def __init__(self, url, securityHandler, proxy_url=None, proxy_port=None, initalize=False): """Constructor""" self._url = url self._securityHandler = securityHandler if not securityHandler is None: self._referer_url = securityHandler.referer_url self._proxy_port = proxy_port self._proxy_url = proxy_url if initalize: self.__init() #---------------------------------------------------------------------- def __init(self): """loads the property data into the class""" params = { "f" : "json" } json_dict = self._get(url=self.root, param_dict=params, securityHandler=self._securityHandler, proxy_port=self._proxy_port, proxy_url=self._proxy_url) self._json_dict = json_dict self._json = json.dumps(json_dict) attributes = [attr for attr in dir(self) if not attr.startswith('__') and \ not attr.startswith('_')] for k,v in json_dict.items(): if k in attributes: setattr(self, "_"+ k, json_dict[k]) else: setattr(self, k, v) print( k, " - attribute not implemented in Portal class.") #---------------------------------------------------------------------- def _findPortalId(self): """gets the portal id for a site if not known.""" if not self.root.lower().endswith("/self"): url = self.root + "/self" else: url = self.root params = { "f" : "json" } res = self._get(url=url, param_dict=params, securityHandler=self._securityHandler, proxy_port=self._proxy_port, proxy_url=self._proxy_url) if 'id' in res: return res['id'] return None @property def analysisLayersGroupQuery(self): if self._analysisLayersGroupQuery is None: self.__init() return self._analysisLayersGroupQuery #---------------------------------------------------------------------- @property def defaultUserCreditAssignment(self): """gets the property value for defaultUserCreditAssignment""" if self._defaultUserCreditAssignment is None: self.__init() return self._defaultUserCreditAssignment #---------------------------------------------------------------------- @property def analysisLayersGroupQueryt(self): """gets the property value for analysisLayersGroupQuery""" if self._analysisLayersGroupQuery is None: self.__init() return self._analysisLayersGroupQuery #---------------------------------------------------------------------- @property def updateUserProfileDisabled(self): '''gets the property value for updateUserProfileDisabled''' if self._updateUserProfileDisabled is None: self.__init() return self._updateUserProfileDisabled #---------------------------------------------------------------------- @property def bingKey(self): '''gets the property value for bingKey''' if self._bingKey is None: self.__init() return self._bingKey #---------------------------------------------------------------------- @property def subscriptionInfo(self): '''gets the property value for subscriptionInfo''' if self._subscriptionInfo is None: self.__init() return self._subscriptionInfo #---------------------------------------------------------------------- @property def authorizedCrossOriginDomains(self): """ gets the authorizedCrossOriginDomains property """ if self._authorizedCrossOriginDomains is None: self.__init() return self._authorizedCrossOriginDomains #---------------------------------------------------------------------- @property def appInfo(self): '''gets the property value for appInfo''' if self._appInfo is None: self.__init() return self._appInfo #---------------------------------------------------------------------- @property def contacts(self): '''gets the property value for contacts''' if self._contacts is None: self.__init() return self._contacts #---------------------------------------------------------------------- @property def urlKey(self): '''gets the property value for urlKey''' if self._urlKey is None: self.__init() return self._urlKey #---------------------------------------------------------------------- @property def metadataEditable(self): '''gets the property value for metadataEditable''' if self._metadataEditable is None: self.__init() return self._metadataEditable #---------------------------------------------------------------------- @property def portalThumbnail(self): '''gets the property value for portalThumbnail''' if self._portalThumbnail is None: self.__init() return self._portalThumbnail #---------------------------------------------------------------------- @property def metadataFormats(self): '''gets the property value for metadataFormats''' if self._metadataFormats is None: self.__init() return self._metadataFormats #---------------------------------------------------------------------- @property def ipCntryCode(self): '''gets the property value for ipCntryCode''' if self._ipCntryCode is None: self.__init() return self._ipCntryCode #---------------------------------------------------------------------- @property def livingAtlasGroupQuery(self): '''gets the property value for livingAtlasGroupQuery''' if self._livingAtlasGroupQuery is None: self.__init() return self._livingAtlasGroupQuery #---------------------------------------------------------------------- @property def region(self): '''gets the property value for region''' if self._region is None: self.__init() return self._region #---------------------------------------------------------------------- @property def portalId(self): """gets the portal Id""" if self._portalId is None: self._portalId = self._findPortalId() return self._portalId #---------------------------------------------------------------------- def __str__(self): """returns class as string""" if self._json is None: self.__init() return self._json #---------------------------------------------------------------------- def __iter__(self): """iterates through raw JSON""" if self._json_dict is None: self.__init() for k,v in self._json_dict.items(): yield [k,v] #---------------------------------------------------------------------- @property def root(self): """returns classes URL""" return self._url #---------------------------------------------------------------------- @property def canSharePublic(self): '''gets the property value for canSharePublic''' if self._canSharePublic is None: self.__init() return self._canSharePublic #---------------------------------------------------------------------- @property def defaultExtent(self): '''gets the property value for defaultExtent''' if self._defaultExtent is None: self.__init() return self._defaultExtent #---------------------------------------------------------------------- @property def supportsHostedServices(self): '''gets the property value for supportsHostedServices''' if self._supportsHostedServices is None: self.__init() return self._supportsHostedServices #---------------------------------------------------------------------- @property def homePageFeaturedContentCount(self): '''gets the property value for homePageFeaturedContentCount''' if self._homePageFeaturedContentCount is None: self.__init() return self._homePageFeaturedContentCount #---------------------------------------------------------------------- @property def supportsOAuth(self): '''gets the property value for supportsOAuth''' if self._supportsOAuth is None: self.__init() return self._supportsOAuth #---------------------------------------------------------------------- @property def portalName(self): '''gets the property value for portalName''' if self._portalName is None: self.__init() return self._portalName #---------------------------------------------------------------------- @property def databaseUsage(self): '''gets the property value for databaseUsage''' if self._databaseUsage is None: self.__init() return self._databaseUsage #---------------------------------------------------------------------- @property def culture(self): '''gets the property value for culture''' if self._culture is None: self.__init() return self._culture #---------------------------------------------------------------------- @property def helpBase(self): '''gets the property value for helpBase''' if self._helpBase is None: self.__init() return self._helpBase #---------------------------------------------------------------------- @property def galleryTemplatesGroupQuery(self): '''gets the property value for galleryTemplatesGroupQuery''' if self._galleryTemplatesGroupQuery is None: self.__init() return self._galleryTemplatesGroupQuery #---------------------------------------------------------------------- @property def commentsEnabled(self): '''gets the property value for commentsEnabled''' if self._commentsEnabled is None: self.__init() return self._commentsEnabled #---------------------------------------------------------------------- @property def databaseQuota(self): '''gets the property value for databaseQuota''' if self._databaseQuota is None: self.__init() return self._databaseQuota #---------------------------------------------------------------------- @property def id(self): '''gets the property value for id''' if self._id is None: self.__init() return self._id #---------------------------------------------------------------------- @property def canSearchPublic(self): '''gets the property value for canSearchPublic''' if self._canSearchPublic is None: self.__init() return self._canSearchPublic #---------------------------------------------------------------------- @property def customBaseUrl(self): '''gets the property value for customBaseUrl''' if self._customBaseUrl is None: self.__init() return self._customBaseUrl #---------------------------------------------------------------------- @property def allSSL(self): '''gets the property value for allSSL''' if self._allSSL is None: self.__init() return self._allSSL #---------------------------------------------------------------------- @property def httpPort(self): '''gets the property value for httpPort''' if self._httpPort is None: self.__init() return self._httpPort #---------------------------------------------------------------------- @property def featuredGroupsId(self): '''gets the property value for featuredGroupsId''' if self._featuredGroupsId is None: self.__init() return self._featuredGroupsId #---------------------------------------------------------------------- @property def defaultBasemap(self): '''gets the property value for defaultBasemap''' if self._defaultBasemap is None: self.__init() return self._defaultBasemap #---------------------------------------------------------------------- @property def created(self): '''gets the property value for created''' if self._created is None: self.__init() return self._created #---------------------------------------------------------------------- @property def access(self): '''gets the property value for access''' if self._access is None: self.__init() return self._access #---------------------------------------------------------------------- @property def platform(self): '''gets the property value for platform''' if self._platform is None: self.__init() return self._platform #---------------------------------------------------------------------- @property def isPortal(self): '''gets the property value for isPortal''' if self._isPortal is None: self.__init() return self._isPortal #---------------------------------------------------------------------- @property def canSignInArcGIS(self): '''gets the property value for canSignInArcGIS''' if self._canSignInArcGIS is None: self.__init() return self._canSignInArcGIS #---------------------------------------------------------------------- @property def disableSignup(self): '''gets the property value for disableSignup''' if self._disableSignup is None: self.__init() return self._disableSignup #---------------------------------------------------------------------- @property def httpsPort(self): '''gets the property value for httpsPort''' if self._httpsPort is None: self.__init() return self._httpsPort #---------------------------------------------------------------------- @property def units(self): '''gets the property value for units''' if self._units is None: self.__init() return self._units #---------------------------------------------------------------------- @property def backgroundImage(self): '''gets the property value for backgroundImage''' if self._backgroundImage is None: self.__init() return self._backgroundImage #---------------------------------------------------------------------- @property def mfaEnabled(self): '''gets the property value for mfaEnabled''' if self._mfaEnabled is None: self.__init() return self._mfaEnabled #---------------------------------------------------------------------- @property def featuredGroups(self): '''gets the property value for featuredGroups''' if self._featuredGroups is None: self.__init() return self._featuredGroups #---------------------------------------------------------------------- @property def thumbnail(self): '''gets the property value for thumbnail''' if self._thumbnail is None: self.__init() return self._thumbnail #---------------------------------------------------------------------- @property def featuredItemsGroupQuery(self): '''gets the property value for featuredItemsGroupQuery''' if self._featuredItemsGroupQuery is None: self.__init() return self._featuredItemsGroupQuery #---------------------------------------------------------------------- @property def canSignInIDP(self): '''gets the property value for canSignInIDP''' if self._canSignInIDP is None: self.__init() return self._canSignInIDP #---------------------------------------------------------------------- @property def useStandardizedQuery(self): '''gets the property value for useStandardizedQuery''' if self._useStandardizedQuery is None: self.__init() return self._useStandardizedQuery #---------------------------------------------------------------------- @property def rotatorPanels(self): '''gets the property value for rotatorPanels''' if self._rotatorPanels is None: self.__init() return self._rotatorPanels #---------------------------------------------------------------------- @property def description(self): '''gets the property value for description''' if self._description is None: self.__init() return self._description #---------------------------------------------------------------------- @property def homePageFeaturedContent(self): '''gets the property value for homePageFeaturedContent''' if self._homePageFeaturedContent is None: self.__init() return self._homePageFeaturedContent #---------------------------------------------------------------------- @property def helperServices(self): '''gets the property value for helperServices''' if self._helperServices is None: self.__init() return self._helperServices #---------------------------------------------------------------------- @property def canProvisionDirectPurchase(self): '''gets the property value for canProvisionDirectPurchase''' if self._canProvisionDirectPurchase is None: self.__init() return self._canProvisionDirectPurchase #---------------------------------------------------------------------- @property def canListData(self): '''gets the property value for canListData''' if self._canListData is None: self.__init() return self._canListData #---------------------------------------------------------------------- @property def user(self): '''gets the property value for user''' if self._user is None: self.__init() return self._user #---------------------------------------------------------------------- @property def helpMap(self): '''gets the property value for helpMap''' if self._helpMap is None: self.__init() return self._helpMap #---------------------------------------------------------------------- @property def canListPreProvisionedItems(self): '''gets the property value for canListPreProvisionedItems''' if self._canListPreProvisionedItems is None: self.__init() return self._canListPreProvisionedItems #---------------------------------------------------------------------- @property def colorSetsGroupQuery(self): '''gets the property value for colorSetsGroupQuery''' if self._colorSetsGroupQuery is None: self.__init() return self._colorSetsGroupQuery #---------------------------------------------------------------------- @property def canListApps(self): '''gets the property value for canListApps''' if self._canListApps is None: self.__init() return self._canListApps #---------------------------------------------------------------------- @property def portalProperties(self): '''gets the property value for portalProperties''' if self._portalProperties is None: self.__init() return self._portalProperties #---------------------------------------------------------------------- @property def isWindows(self): '''gets the property value for isWindows''' if self._isWindows is None: self.__init() return self._isWindows #---------------------------------------------------------------------- @property def name(self): '''gets the property value for name''' if self._name is None: self.__init() return self._name #---------------------------------------------------------------------- @property def supportsSceneServices(self): '''gets the property value for supportsSceneServices''' if self._supportsSceneServices is None: self.__init() return self._supportsSceneServices #---------------------------------------------------------------------- @property def stylesGroupQuery(self): '''gets the property value for stylesGroupQuery''' if self._stylesGroupQuery is None: self.__init() return self._stylesGroupQuery #---------------------------------------------------------------------- @property def samlEnabled(self): '''gets the property value for samlEnabled''' if self._samlEnabled is None: self.__init() return self._samlEnabled #---------------------------------------------------------------------- @property def symbolSetsGroupQuery(self): '''gets the property value for symbolSetsGroupQuery''' if self._symbolSetsGroupQuery is None: self.__init() return self._symbolSetsGroupQuery #---------------------------------------------------------------------- @property def portalLocalHttpPort(self): '''gets the property value for portalLocalHttpPort''' if self._portalLocalHttpPort is None: self.__init() return self._portalLocalHttpPort #---------------------------------------------------------------------- @property def storageQuota(self): '''gets the property value for storageQuota''' if self._storageQuota is None: self.__init() return self._storageQuota #---------------------------------------------------------------------- @property def canShareBingPublic(self): '''gets the property value for canShareBingPublic''' if self._canShareBingPublic is None: self.__init() return self._canShareBingPublic #---------------------------------------------------------------------- @property def maxTokenExpirationMinutes(self): '''gets the property value for maxTokenExpirationMinutes''' if self._maxTokenExpirationMinutes is None: self.__init() return self._maxTokenExpirationMinutes #---------------------------------------------------------------------- @property def layerTemplatesGroupQuery(self): '''gets the property value for layerTemplatesGroupQuery''' if self._layerTemplatesGroupQuery is None: self.__init() return self._layerTemplatesGroupQuery #---------------------------------------------------------------------- @property def staticImagesUrl(self): '''gets the property value for staticImagesUrl''' if self._staticImagesUrl is None: self.__init() return self._staticImagesUrl #---------------------------------------------------------------------- @property def modified(self): '''gets the property value for modified''' if self._modified is None: self.__init() return self._modified #---------------------------------------------------------------------- @property def portalHostname(self): '''gets the property value for portalHostname''' if self._portalHostname is None: self.__init() return self._portalHostname #---------------------------------------------------------------------- @property def showHomePageDescription(self): '''gets the property value for showHomePageDescription''' if self._showHomePageDescription is None: self.__init() return self._showHomePageDescription #---------------------------------------------------------------------- @property def availableCredits(self): '''gets the property value for availableCredits''' if self._availableCredits is None: self.__init() return self._availableCredits #---------------------------------------------------------------------- @property def portalMode(self): '''gets the property value for portalMode''' if self._portalMode is None: self.__init() return self._portalMode #---------------------------------------------------------------------- @property def portalLocalHttpsPort(self): '''gets the property value for portalLocalHttpsPort''' if self._portalLocalHttpsPort is None: self.__init() return self._portalLocalHttpsPort #---------------------------------------------------------------------- @property def hostedServerHostedFolder(self): '''gets the property value for hostedServerHostedFolder''' if self._hostedServerHostedFolder is None: self.__init() return self._hostedServerHostedFolder #---------------------------------------------------------------------- @property def storageUsage(self): '''gets the property value for storageUsage''' if self._storageUsage is None: self.__init() return self._storageUsage #---------------------------------------------------------------------- @property def templatesGroupQuery(self): '''gets the property value for templatesGroupQuery''' if self._templatesGroupQuery is None: self.__init() return self._templatesGroupQuery #---------------------------------------------------------------------- @property def portalLocalHostname(self): '''gets the property value for portalLocalHostname''' if self._portalLocalHostname is None: self.__init() return self._portalLocalHostname #---------------------------------------------------------------------- @property def basemapGalleryGroupQuery(self): '''gets the property value for basemapGalleryGroupQuery''' if self._basemapGalleryGroupQuery is None: self.__init() return self._basemapGalleryGroupQuery #---------------------------------------------------------------------- @property def mfaAdmins(self): '''gets the property value for mfaAdmins''' if self._mfaAdmins is None: self.__init() return self._mfaAdmins #---------------------------------------------------------------------- @property def creditAssignments(self): '''gets the property value for creditAssignments''' if self._creditAssignments is None: self.__init() return self._creditAssignments #---------------------------------------------------------------------- @property def urls(self): """gets the urls for a portal""" url = "%s/urls" % self.root params = {"f":"json"} return self._get(url=url, param_dict=params, securityHandler=self._securityHandler, proxy_url=self._proxy_url, proxy_port=self._proxy_port) #---------------------------------------------------------------------- @property def featureServers(self): """gets the hosting feature AGS Server""" services = [] if self.urls == {}: return {} urls = self.urls if 'https' in urls['urls']['features']: res = urls['urls']['features']['https'] else: res = urls['urls']['features']['http'] for https in res: if self.isPortal: url = "%s/admin" % https services.append(AGSAdministration(url=url, securityHandler=self._securityHandler, proxy_url=self._proxy_url, proxy_port=self._proxy_port) ) else: url = "https://%s/%s/ArcGIS/admin" % (https, self.portalId) services.append(Services(url=url, securityHandler=self._securityHandler, proxy_url=self._proxy_url, proxy_port=self._proxy_port)) return services #---------------------------------------------------------------------- @property def tileServers(self): """ Returns the objects to manage site's tile hosted services/servers. It returns AGSAdministration object if the site is Portal and it returns a hostedservice.Services object if it is AGOL. """ services = [] ishttps = False if self.urls == {}: return {} urls = self.urls["urls"]['tiles'] if 'https' in urls: res = urls['https'] ishttps = True else: res = urls['http'] for https in res: if ishttps: scheme = "https" else: scheme = "http" if self.isPortal == False: url = "%s://%s/tiles/%s/arcgis/admin/services" % (scheme, https, self.portalId) services.append(Services(url=url, securityHandler=self._securityHandler, proxy_url=self._proxy_url, proxy_port=self._proxy_port)) else: url = "%s/admin" % https servers = self.servers for server in servers.servers: url = server.adminUrl sh = PortalServerSecurityHandler(tokenHandler=self._securityHandler, serverUrl=url, referer=server.name.split(":")[0] ) services.append( AGSAdministration(url=url, securityHandler=sh, proxy_url=self._proxy_url, proxy_port=self._proxy_port, initialize=True) ) return services #---------------------------------------------------------------------- @property def purchases(self): """gets the portal's purchases""" url = "%s/purchases" % self.root params = {"f":"json"} return self._get(url=url, param_dict=params, securityHandler=self._securityHandler, proxy_url=self._proxy_url, proxy_port=self._proxy_port) #---------------------------------------------------------------------- @property def customers(self): """gets the site's customers""" url = "%s/customers" % self.root params = {"f":"json"} return self._get(url=url, param_dict=params, securityHandler=self._securityHandler, proxy_url=self._proxy_url, proxy_port=self._proxy_port) #---------------------------------------------------------------------- def exportCustomers(self, outPath): """exports customer list to a csv file Input: outPath - save location of the customer list """ url = "%s/customers/export" % self.root params = {"f":"csv"} dirPath = None fileName = None if outPath is not None: dirPath = os.path.dirname(outPath) fileName = os.path.basename(outPath) return self._get(url=url, param_dict=params, securityHandler=self._securityHandler, proxy_url=self._proxy_url, proxy_port=self._proxy_port, out_folder=dirPath, file_name=fileName) #---------------------------------------------------------------------- def update(self, updatePortalParameters, clearEmptyFields=False): """ The Update operation allows administrators only to update the organization information such as name, description, thumbnail, and featured groups. Inputs: updatePortalParamters - parameter.PortalParameters object that holds information to update clearEmptyFields - boolean that clears all whitespace from fields """ url = self.root + "/update" params = { "f" : "json", "clearEmptyFields" : clearEmptyFields } if isinstance(updatePortalParameters, parameters.PortalParameters): params.update(updatePortalParameters.value) elif isinstance(updatePortalParameters, dict): for k,v in updatePortalParameters.items(): params[k] = v else: raise AttributeError("updatePortalParameters must be of type parameter.PortalParameters") return self._post(url=url, param_dict=params, securityHandler=self._securityHandler, proxy_url=self._proxy_url, proxy_port=self._proxy_port) #---------------------------------------------------------------------- def updateUserRole(self, user, role): """ The Update User Role operation allows the administrator of an org anization to update the role of a user within a portal. Inputs: role - Sets the user's role. Roles are the following: org_user - Ability to add items, create groups, and share in the organization. org_publisher - Same privileges as org_user plus the ability to publish hosted services from ArcGIS for Desktop and ArcGIS Online. org_admin - In addition to add, create, share, and publish capabilities, an org_admin administers and customizes the organization. Example: role=org_publisher user - The username whose role you want to change. """ url = self._url + "/updateuserrole" params = { "f" : "json", "user" : user, "role" : role } return self._post(url=url, param_dict=params, securityHandler=self._securityHandler, proxy_url=self._proxy_url, proxy_port=self._proxy_port) #---------------------------------------------------------------------- def removeUser(self, users): """ The Remove Users operation allows the administrator to remove users from a portal. Before the administrator can remove the user, all of the user's content and groups must be reassigned or deleted. Inputs: users - Comma-separated list of usernames to remove. """ url = self._url + "/removeusers" params = { "f" : "json", "users" : users } return self._post(url=url, param_dict=params, securityHandler=self._securityHandler, proxy_url=self._proxy_url, proxy_port=self._proxy_port) #---------------------------------------------------------------------- def isServiceNameAvailable(self, name, serviceType): """ Checks to see if a given service name and type are available for publishing a new service. true indicates that the name and type is not found in the organization's services and is available for publishing. false means the requested name and type are not available. Inputs: name - requested name of service serviceType - type of service allowed values: Feature Service or Map Service """ _allowedTypes = ['Feature Service', "Map Service"] url = self._url + "/isServiceNameAvailable" params = { "f" : "json", "name" : name, "type" : serviceType } return self._get(url=url, param_dict=params, securityHandler=self._securityHandler, proxy_url=self._proxy_url, proxy_port=self._proxy_port) #---------------------------------------------------------------------- @property def servers(self): """gets the federated or registered servers for Portal""" url = "%s/servers" % self.root return Servers(url=url, securityHandler=self._securityHandler, proxy_url=self._proxy_url, proxy_port=self._proxy_port) #---------------------------------------------------------------------- def assignUserCredits(self, usernames, credits): """ assigns credit to a user. Inputs: usernames - list of users credits - number of credits to assign to the users Ouput: dictionary """ userAssignments = [] for name in usernames: userAssignments.append( { "username" : name, "credits" : credits } ) params = { "userAssignments" : userAssignments, "f" : "json" } url = self.root + "/assignUserCredits" return self._post(url=url, param_dict=params, securityHandler=self._securityHandler, proxy_url=self._proxy_url, proxy_port=self._proxy_port) #---------------------------------------------------------------------- def users(self, start=1, num=10, sortField="fullName", sortOrder="asc", role=None): """ Lists all the members of the organization. The start and num paging parameters are supported. Inputs: start - The number of the first entry in the result set response. The index number is 1-based. The default value of start is 1 (that is, the first search result). The start parameter, along with the num parameter, can be used to paginate the search results. num - The maximum number of results to be included in the result set response. The default value is 10, and the maximum allowed value is 100.The start parameter, along with the num parameter, can be used to paginate the search results. sortField - field to sort on sortOrder - asc or desc on the sortField role - name of the role or role id to search Output: list of User classes """ users = [] url = self._url + "/users" params = { "f" : "json", "start" : start, "num" : num } if not role is None: params['role'] = role if not sortField is None: params['sortField'] = sortField if not sortOrder is None: params['sortOrder'] = sortOrder from ._community import Community res = self._post(url=url, param_dict=params, securityHandler=self._securityHandler, proxy_url=self._proxy_url, proxy_port=self._proxy_port) if "users" in res: if len(res['users']) > 0: parsed = urlparse.urlparse(self._url) if parsed.netloc.lower().find('arcgis.com') == -1: cURL = "%s://%s/%s/sharing/rest/community" % (parsed.scheme, parsed.netloc, parsed.path[1:].split('/')[0]) else: cURL = "%s://%s/sharing/rest/community" % (parsed.scheme, parsed.netloc) com = Community(url=cURL, securityHandler=self._securityHandler, proxy_url=self._proxy_url, proxy_port=self._proxy_port) for r in res['users']: users.append( com.users.user(r["username"]) ) res['users'] = users return res #---------------------------------------------------------------------- def createRole(self, name, description): """ creates a role for a portal/agol site. Inputs: names - name of the role description - brief text string stating the nature of this role. Ouput: dictionary """ params = { "name" : name, "description" : description, "f" : "json" } url = self.root + "/createRole" return self._post(url=url, param_dict=params, securityHandler=self._securityHandler, proxy_url=self._proxy_url, proxy_port=self._proxy_port) #---------------------------------------------------------------------- @property def roles(self): """gets the roles class that allows admins to manage custom roles on portal""" return Roles(url="%s/roles" % self.root, securityHandler=self._securityHandler, proxy_url=self._proxy_url, proxy_port=self._proxy_port) #---------------------------------------------------------------------- def cost(self, tileStorage=0, fileStorage=0, featureStorage=0, generatedTileCount=0, loadedTileCount=0, enrichVariableCount=0, enrichReportCount=0, serviceAreaCount=0, geocodeCount=0): """ returns the cost values for a given portal Inputs: tileStorage - int - numbe of tiles to store in MBs fileStorage - int - size of file to store in MBs featureStorage - int - size in MBs generateTileCount - int - number of tiles to genearte on site loadedTileCount -int- cost to host a certian number of tiles enrichVariableCount - int - cost to enrich data enrichReportCount - int - cost to generate an enrichment report serviceAreaCount - int - cost to generate x number of service areas geocodeCount - int - cost to generate x number of addresses """ params = { "f" : "json", "tileStorage": tileStorage, "fileStorage": fileStorage, "featureStorage": featureStorage, "generatedTileCount": generatedTileCount, "loadedTileCount":loadedTileCount, "enrichVariableCount": enrichVariableCount, "enrichReportCount" : enrichReportCount, "serviceAreaCount" : serviceAreaCount, "geocodeCount" : geocodeCount } url = self._url + "/cost" return self._post(url=url, param_dict=params, securityHandler=self._securityHandler, proxy_url=self._proxy_url, proxy_port=self._proxy_port) #---------------------------------------------------------------------- def resources(self, start=1, num=10): """ Resources lists all file resources for the organization. The start and num paging parameters are supported. Inputs: start - the number of the first entry in the result set response The index number is 1-based and the default is 1 num - the maximum number of results to be returned as a whole # """ url = self._url + "/resources" params = { "f" : "json", "start" : start, "num" : num } return self._get(url=url, param_dict=params, securityHandler=self._securityHandler, proxy_url=self._proxy_url, proxy_port=self._proxy_port) #---------------------------------------------------------------------- def addResource(self, key, filePath, text): """ The add resource operation allows the administrator to add a file resource, for example, the organization's logo or custom banner. The resource can be used by any member of the organization. File resources use storage space from your quota and are scanned for viruses. Inputs: key - The name the resource should be stored under. filePath - path of file to upload text - Some text to be written (for example, JSON or JavaScript) directly to the resource from a web client. """ url = self.root + "/addresource" params = { "f": "json", "token" : self._securityHandler.token, "key" : key, "text" : text } files = {} files['file'] = filePath res = self._post(url=url, param_dict=params, files=files, securityHandler=self._securityHandler, proxy_url=self._proxy_url, proxy_port=self._proxy_port) return res #---------------------------------------------------------------------- def removeResource(self, key): """ The Remove Resource operation allows the administrator to remove a file resource. Input: key - name of resource to delete """ url = self._url + "/removeresource" params = { "key" : key, "f" : "json" } return self._post(url=url, param_dict=params, securityHandler=self._securityHandler, proxy_url=self._proxy_url, proxy_port=self._proxy_port) #---------------------------------------------------------------------- @property def securityPolicy(self): """gets the object to manage the portal's security policy""" url = "%s/securityPolicy" % self.root params = {'f': 'json'} return self._post(url=url, param_dict=params, securityHandler=self._securityHandler, proxy_url=self._proxy_url, proxy_port=self._proxy_port) #---------------------------------------------------------------------- def resetSecurityPolicy(self): """resets the security policy to default install""" params = {"f" : "json"} url = "%s/securityPolicy/reset" % self.root return self._post(url=url, param_dict=params, securityHandler=self._securityHandler, proxy_url=self._proxy_url, proxy_port=self._proxy_port) #---------------------------------------------------------------------- def updateSecurityPolicy(self, minLength=8, minUpper=None, minLower=None, minLetter=None, minDigit=None, minOther=None, expirationInDays=None, historySize=None): """updates the Portals security policy""" params = { "f" : "json", "minLength" : minLength, "minUpper": minUpper, "minLower": minLower, "minLetter": minLetter, "minDigit": minDigit, "minOther": minOther, "expirationInDays" : expirationInDays, "historySize": historySize } url = "%s/securityPolicy/update" % self.root return self._post(url=url, param_dict=params, securityHandler=self._securityHandler, proxy_url=self._proxy_url, proxy_port=self._proxy_port) #---------------------------------------------------------------------- @property def portalAdmin(self): """gets a reference to a portal administration class""" from ..manageportal import PortalAdministration return PortalAdministration(admin_url="https://%s/portaladmin" % self.portalHostname, securityHandler=self._securityHandler, proxy_url=self._proxy_url, proxy_port=self._proxy_port, initalize=False) #---------------------------------------------------------------------- def addUser(self, invitationList, subject, html): """ adds a user without sending an invitation email Inputs: invitationList - InvitationList class used to add users without sending an email subject - email subject html - email message sent to users in invitation list object """ url = self._url + "/invite" params = {"f" : "json"} if isinstance(invitationList, parameters.InvitationList): params['invitationList'] = invitationList.value() params['html'] = html params['subject'] = subject return self._post(url=url, param_dict=params, securityHandler=self._securityHandler, proxy_url=self._proxy_url, proxy_port=self._proxy_port) #---------------------------------------------------------------------- def inviteByEmail(self, emails, subject, text, html, role="org_user", mustApprove=True, expiration=1440): """Invites a user or users to a site. Inputs: emails - comma seperated list of emails subject - title of email text - email text html - email text in html role - site role (can't be administrator) mustApprove - verifies if user that is join must be approved by an administrator expiration - time in seconds. Default is 1 day 1440 """ url = self.root + "/inviteByEmail" params = { "f" : "json", "emails": emails, "subject": subject, "text": text, "html" : html, "role" : role, "mustApprove": mustApprove, "expiration" : expiration } return self._post(url=url, param_dict=params, securityHandler=self._securityHandler, proxy_url=self._proxy_url, proxy_port=self._proxy_port) #---------------------------------------------------------------------- @property def invitations(self): """gets all the invitations to the current portal""" params = {"f": "json"} url = "%s/invitations" % self.root return self._get(url=url, param_dict=params, securityHandler=self._securityHandler, proxy_url=self._proxy_url, proxy_port=self._proxy_port) #---------------------------------------------------------------------- def usage(self, startTime, endTime, vars=None, period=None, groupby=None, name=None, stype=None, etype=None, appId=None, deviceId=None, username=None, appOrgId=None, userOrgId=None, hostOrgId=None): """ returns the usage statistics value """ url = self.root + "/usage" startTime = str(int(local_time_to_online(dt=startTime))) endTime = str(int(local_time_to_online(dt=endTime))) params = { 'f' : 'json', 'startTime' : startTime, 'endTime' : endTime, 'vars' : vars, 'period' : period, 'groupby' : groupby, 'name' : name, 'stype' : stype, 'etype' : etype, 'appId' : appId, 'deviceId' : deviceId, 'username' : username, 'appOrgId' : appOrgId, 'userOrgId' : userOrgId, 'hostOrgId' : hostOrgId, } params = {key:item for key,item in params.items() if item is not None} return self._post(url=url, param_dict=params, securityHandler=self._securityHandler, proxy_url=self._proxy_url, proxy_port=self._proxy_port) #---------------------------------------------------------------------- @property def IDP(self): """gets the IDP information for the portal/agol""" url = "%s/idp" % self.root params = {"f": "json"} return self._get(url=url, param_dict=params, securityHandler=self._securityHandler, proxy_url=self._proxy_url, proxy_port=self._proxy_port) ######################################################################## class Servers(BaseAGOLClass): """This resource lists the ArcGIS Server sites that have been federated with the portal.This resource is not applicable to ArcGIS Online; it is only applicable to Portal for ArcGIS. """ _servers = None _surl = None _url = None _securityHandler = None _proxy_url = None _proxy_port = None _json = None _json_dict = None ######################################################################## class Server(BaseAGOLClass): _surl = None _url = None _id = None _name = None _adminUrl = None _url = None _isHosted = None _serverKey = None _serverType = None _surl = None _url = None _securityHandler = None _proxy_url = None _proxy_port = None _json = None _json_dict = None """represents a single server instance registers with portal""" #---------------------------------------------------------------------- def __init__(self, url, securityHandler, proxy_url=None, proxy_port=None, initalize=False): """Constructor""" self._surl = url self._securityHandler = securityHandler if not securityHandler is None: self._referer_url = securityHandler.referer_url self._proxy_port = proxy_port self._proxy_url = proxy_url if initalize: self.__init() #---------------------------------------------------------------------- def __init(self): """loads the property data into the class""" params = { "f" : "pjson" } json_dict = self._get(url=self._surl, param_dict=params, securityHandler=self._securityHandler, proxy_port=self._proxy_port, proxy_url=self._proxy_url) self._json_dict = json_dict self._json = json.dumps(json_dict) attributes = [attr for attr in dir(self) if not attr.startswith('__') and \ not attr.startswith('_')] for k,v in json_dict.items(): if k in attributes: setattr(self, "_"+ k, json_dict[k]) else: print( k, " - attribute not implemented in Servers.Server class.") #---------------------------------------------------------------------- def __str__(self): """returns class as string""" if self._json is None: self.__init() return self._json #---------------------------------------------------------------------- def __iter__(self): """iterates through raw JSON""" if self._json_dict is None: self.__init() for k,v in self._json_dict.items(): yield [k,v] #---------------------------------------------------------------------- @property def root(self): """returns classes URL""" return self._url #---------------------------------------------------------------------- @property def id(self): """gets the server id""" if self._id is None: self.__init() return self._id #---------------------------------------------------------------------- @property def name(self): """gets the server name""" if self._name is None: self.__init() return self._name #---------------------------------------------------------------------- @property def adminUrl(self): """gets the adminURL for the server""" if self._adminUrl is None: self.__init() return self._adminUrl #---------------------------------------------------------------------- @property def url(self): """gets the url for the server""" if self._url is None: self.__init() return self._url #---------------------------------------------------------------------- @property def isHosted(self): """gets the isHosted value""" if self._isHosted is None: self.__init() return self._isHosted #---------------------------------------------------------------------- @property def serverKey(self): """gets the server key""" if self._serverKey is None: self.__init() return self._serverKey #---------------------------------------------------------------------- @property def serverType(self): """gets the server type""" if self._serverType is None: self.__init() return self._serverType #---------------------------------------------------------------------- def unregister(self): """ This operation unregisters an ArcGIS Server site from the portal. The server is no longer federated with the portal after this operation completes. After this operation completes, you must invoke the Update Security Configuration operation on your ArcGIS Server site to specify how you want the server to work with users and roles. Inputs: serverId - unique identifier of the server """ url = self._url + "/unregister" params = { "f" : "json" } return self._post(url=url, param_dict=params, securityHandler=self._securityHandler, proxy_url=self._proxy_url, proxy_port=self._proxy_port) #---------------------------------------------------------------------- def update(self, name, url, adminUrl, isHosted, serverType): """ This operation updates the properties of an ArcGIS Server site that has been registered, or federated, with the portal. For example, you can use this operation to change the federated site that acts as the portal's hosting server. Inputs: name - The fully qualified name of the machine hosting the ArcGIS Server site, followed by the port. url - The externally visible URL of the ArcGIS Server site, using the fully qualified name of the machine. adminUrl - The administrative URL of the ArcGIS Server site, using the fully qualified name of the machine. isHosted - A Boolean property denoting whether the ArcGIS Server site will be allowed to host services for the portal (true) or will not be allowed to host services (false). serverType - The type of server being registered with the portal For example: ArcGIS. """ url = self._url + "/update" params = { "name" : name, "url" : url, "adminUrl" : adminUrl, "isHosted" : isHosted, "serverType" : serverType } return self._post(url=url, param_dict=params, securityHandler=self._securityHandler, proxy_url=self._proxy_url, proxy_port=self._proxy_port) #---------------------------------------------------------------------- def __init__(self, url, securityHandler, proxy_url=None, proxy_port=None, initalize=False): """Constructor""" if url.lower().endswith('/servers') == False: url = url + "/servers" self._surl = url self._securityHandler = securityHandler if not securityHandler is None: self._referer_url = securityHandler.referer_url self._proxy_port = proxy_port self._proxy_url = proxy_url if initalize: self.__init() #---------------------------------------------------------------------- def __init(self): """loads the property data into the class""" params = { "f" : "json" } json_dict = self._get(url=self._surl, param_dict=params, securityHandler=self._securityHandler, proxy_port=self._proxy_port, proxy_url=self._proxy_url) self._json_dict = json_dict self._json = json.dumps(json_dict) attributes = [attr for attr in dir(self) if not attr.startswith('__') and \ not attr.startswith('_')] for k,v in json_dict.items(): if k in attributes: setattr(self, "_"+ k, json_dict[k]) else: print( k, " - attribute not implemented in Servers class.") #---------------------------------------------------------------------- def __str__(self): """returns class as string""" if self._json is None: self.__init() return self._json #---------------------------------------------------------------------- def __iter__(self): """iterates through raw JSON""" if self._json_dict is None: self.__init() for k,v in self._json_dict.items(): yield [k,v] #---------------------------------------------------------------------- @property def root(self): """returns classes URL""" return self._surl #---------------------------------------------------------------------- def register(self, name, url, adminUrl, isHosted, serverType): """ You can optionally register (or "federate") an ArcGIS Server site with your Portal for ArcGIS deployment. This provides the following benefits: The server and the portal share the same user store (that of the portal). This results in a convenient single sign-on experience. Any items you publish to the server are automatically shared on the portal. You can optionally allow the server to host tiled map services and feature services published by portal users. After you register a server with your portal, you must invoke the Update Security Configuration operation on the ArcGIS Server site and configure the site's security store to take advantage of users and roles from the portal. This operation is only applicable to Portal for ArcGIS; it is not supported with ArcGIS Online. Inputs: name - The fully qualified name of the machine hosting the ArcGIS Server site, followed by the port. url - The externally visible URL of the ArcGIS Server site, using the fully qualified name of the machine. adminUrl - The administrative URL of your ArcGIS Server site, using the fully qualified name of the machine. isHosted - A Boolean property denoting whether the ArcGIS Server site will be allowed to host services for the portal (true) or not be allowed to host services (false). serverType - The type of server being registered with the portal For example: ArcGIS. """ url = self.root + "/register" params = { "f" : "json", "url" : url, "adminUrl" : adminUrl, "isHosted" : isHosted, "name" : name, "serverType" : serverType } return self._get(url=url, param_dict=params, securityHandler=self._securityHandler, proxy_url=self._proxy_url, proxy_port=self._proxy_port) #---------------------------------------------------------------------- @property def servers(self): """gets all the server resources""" self.__init() items = [] for k,v in self._json_dict.items(): if k == "servers": for s in v: if 'id' in s: url = "%s/%s" % (self.root, s['id']) items.append( self.Server(url=url, securityHandler=self._securityHandler, proxy_url=self._proxy_url, proxy_port=self._proxy_port)) del k,v return items ######################################################################## class Roles(BaseAGOLClass): """Handles the searching, creation, deletion and updating of roles on AGOL or Portal. """ _url = None _securityHandler = None _proxy_url = None _proxy_port = None #---------------------------------------------------------------------- def __init__(self, url, securityHandler, proxy_url=None, proxy_port=None): """Constructor""" if url.find('/roles') < 0: url = url + "/roles" self._url = url self._securityHandler = securityHandler self._proxy_url = proxy_url self._proxy_port = proxy_port #---------------------------------------------------------------------- def __str__(self): """returns the roles as a string""" nextCount = 0 start = 0 num = 100 results = [] while nextCount != -1: res = self.roles(start=start + nextCount, num=num) results = results + res['roles'] nextCount = int(res['nextStart']) return json.dumps(results) #---------------------------------------------------------------------- def __iter__(self): """iterator to loop through role entries""" nextCount = 0 start = 0 num = 100 results = [] while nextCount != -1: res = self.roles(start=start + nextCount, num=num) for r in res['roles']: yield r nextCount = int(res['nextStart']) #---------------------------------------------------------------------- def roles(self, start, num): """ lists the custom roles on the AGOL/Portal site Input: start - default 1 num - 100 - number of roles to return """ url = self._url params = { "f" : "json", "start" : start, "num" : num } return self._post(url=url, param_dict=params, securityHandler=self._securityHandler, proxy_url=self._proxy_url, proxy_port=self._proxy_port) #---------------------------------------------------------------------- def deleteRole(self, roleID): """ deletes a role by ID """ url = self._url + "/%s/delete" % roleID params = { "f" : "json" } return self._post(url=url, param_dict=params, proxy_url=self._proxy_url, proxy_port=self._proxy_port) #---------------------------------------------------------------------- def updateRole(self, roleID, name, description): """allows for the role name or description to be modified""" params = { "name" : name, "description" : description, "f" : "json" } url = self._url + "/%s/update" return self._post(url=url, param_dict=params, proxy_url=self._proxy_url, proxy_port=self._proxy_port) #---------------------------------------------------------------------- def info(self, roleID): """""" url = self._url + "/%s" % roleID params = {"f" : "json"} return self._post(url=url, param_dict=params, securityHandler=self._securityHandler, proxy_url=self._proxy_url, proxy_port=self._proxy_port) #---------------------------------------------------------------------- def findRoleID(self, name): """searches the roles by name and returns the role's ID""" for r in self: if r['name'].lower() == name.lower(): return r['id'] del r return None #---------------------------------------------------------------------- def privileges(self, roleID): """returns the assigned priveleges for a given custom role""" url = self._url + "/%s/privileges" % roleID params = {"f" : "json"} return self._post(url=url, param_dict=params, securityHandler=self._securityHandler, proxy_url=self._proxy_url, proxy_port=self._proxy_port) #---------------------------------------------------------------------- def setPrivileges(self, roleID, privileges): """ assigns a role a set of actions that the role can perform on the AGOL or Portal site. Input: roleID - unique id of the role privileges - list of privileges to assign to role. """ params = { "f" : "json", "privileges" : {"privileges": privileges}, "id": roleID } url = self._url + "/%s/setPrivileges" % roleID return self._post(url=url, param_dict=params, securityHandler=self._securityHandler, proxy_url=self._proxy_url, proxy_port=self._proxy_port)

apache-2.0

avanc/mopidy-usbplaylist

mopidy_usbplaylist/playlists.py

1

1414

from __future__ import unicode_literals import logging logger = logging.getLogger(__name__) from mopidy import backend from mopidy.models import Playlist from mopidy.models import Track import os import fnmatch import glob def find_files(path): matches = glob.glob(os.path.join(path,'*.mp3')) return matches def find_files2(path): matches = [] for root, dirnames, filenames in os.walk(path): for filename in fnmatch.filter(filenames, '*.mp3'): matches.append(os.path.join(root, filename)) return matches class USBPlaylistProvider(backend.PlaylistsProvider): def create(self, name): pass def delete(self, uri): pass def lookup(self, uri): path=self.backend.config['usbplaylist']['path'] for playlist in self.playlists: if playlist.uri == uri: files=find_files2(path) tracks =[] for file in files: tracks.append(Track(uri='file:'+file, name="USB-File")) return playlist.copy(tracks=tracks) def refresh(self): playlists=[] uri="usb://playall" playlist = Playlist(uri=uri, name="USB") playlists.append(playlist) self.playlists = playlists backend.BackendListener.send('playlists_loaded') def save(self, playlist): pass

apache-2.0

TetraAsh/baruwa2

baruwa/forms/accounts.py

1

6737

# -*- coding: utf-8 -*- # vim: ai ts=4 sts=4 et sw=4 # Baruwa - Web 2.0 MailScanner front-end. # Copyright (C) 2010-2012 Andrew Colin Kissa <[email protected]> # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program. If not, see <http://www.gnu.org/licenses/>. # """accounts forms""" from wtforms import PasswordField, validators, DecimalField, RadioField from wtforms import BooleanField, TextField, SelectField from wtforms.ext.sqlalchemy.fields import QuerySelectMultipleField from pylons.i18n.translation import lazy_ugettext as _ from sqlalchemy.orm.exc import NoResultFound from baruwa.forms import Form from baruwa.model.accounts import User from baruwa.model.domains import Domain from baruwa.model.meta import Session from baruwa.forms.organizations import check_pw_strength from baruwa.forms import TIMEZONE_TUPLES, REQ_MSG, EMAIL_MSG from baruwa.forms.messages import MultiCheckboxField ACCOUNT_TYPES = ( ('3', _('User')), ('2', _('Domain admin')), ('1', _('Administrator')), ) def check_password(form, field): "check password strength" check_pw_strength(field.data) def check_domain(form, field): "check domain" domain = field.data.split('@')[1] try: Session.query(Domain).filter(Domain.name == domain).one() except NoResultFound: raise validators.ValidationError( _(u'The domain: %(dom)s is not local') % dict(dom=domain) ) def check_account(form, field): "check account" if field.data == 3 and not form.domains.data: raise validators.ValidationError( _(u'Please select atleast one domain') ) def can_reset(form, field): "check account is legible to reset" try: user = Session.query(User)\ .filter(User.email == field.data)\ .one() if user.account_type != 3: raise validators.ValidationError( _("Admin accounts cannot be reset via the web")) except NoResultFound: raise validators.ValidationError(_("Account not found")) class AddUserForm(Form): """Add user""" username = TextField(_('Username'), [validators.Required(message=REQ_MSG), validators.Length(min=4, max=254)]) firstname = TextField(_('First name'), [validators.Length(max=254)]) lastname = TextField(_('Last name'), [validators.Length(max=254)]) password1 = PasswordField(_('New Password'), [check_password, validators.Required(message=REQ_MSG), validators.EqualTo('password2', message=_('Passwords must match'))]) password2 = PasswordField(_('Retype Password'), [validators.Required(message=REQ_MSG)]) email = TextField(_('Email address'), [validators.Required(message=REQ_MSG), validators.Email(message=EMAIL_MSG)]) timezone = SelectField(_('Timezone'), choices=TIMEZONE_TUPLES) account_type = SelectField(_('Account type'), choices=list(ACCOUNT_TYPES)) domains = QuerySelectMultipleField(_('Domains'), get_label='name', allow_blank=True) active = BooleanField(_('Enabled')) send_report = BooleanField(_('Send reports')) spam_checks = BooleanField(_('Enable spam checks'), default=True) low_score = DecimalField(_('Probable spam score'), places=1, default=0) high_score = DecimalField(_('Definite spam score'), places=1, default=0) def validate_domains(form, field): if int(form.account_type.data) == 3 and not field.data: raise validators.ValidationError( _(u'Please select atleast one domain')) class EditUserForm(Form): """Edit user""" username = TextField(_('Username'), [validators.Required(message=REQ_MSG), validators.Length(min=4, max=254)]) firstname = TextField(_('First name'), [validators.Length(max=254)]) lastname = TextField(_('Last name'), [validators.Length(max=254)]) email = TextField(_('Email address'), [validators.Required(message=REQ_MSG)]) timezone = SelectField(_('Timezone'), choices=TIMEZONE_TUPLES) domains = QuerySelectMultipleField(_('Domains'), get_label='name', allow_blank=False) active = BooleanField(_('Enabled')) send_report = BooleanField(_('Send reports')) spam_checks = BooleanField(_('Enable spam checks')) low_score = DecimalField(_('Spam low score'), places=1) high_score = DecimalField(_('Spam high score'), places=1) class BulkDelUsers(Form): """Bulk account delete form""" accountid = MultiCheckboxField('') whatdo = RadioField('', choices=[('delete', _('delete'),), ('disable', _('disable'),), ('enable', _('enable'),),]) class AddressForm(Form): """Add alias address""" address = TextField(_('Email Address'), [validators.Required(message=REQ_MSG), validators.Email(message=EMAIL_MSG), check_domain]) enabled = BooleanField(_('Enabled')) class ChangePasswordForm(Form): """Admin change user password""" password1 = PasswordField(_('New Password'), [check_password, validators.Required(message=REQ_MSG), validators.EqualTo('password2', message=_('Passwords must match'))]) password2 = PasswordField(_('Retype Password'), [validators.Required(message=REQ_MSG)]) class UserPasswordForm(ChangePasswordForm): """User password change""" password3 = PasswordField(_('Old Password'), [validators.Required(message=REQ_MSG)]) class ResetPwForm(Form): """User reset password form""" email = TextField(_('Email Address'), [validators.Required(message=REQ_MSG), validators.Email(message=EMAIL_MSG), can_reset])

gpl-3.0

Schille/weimar-graphstore

weimar.py

1

1485

''' Created on Mar 17, 2014 @author: mschilonka ''' import argparse, sys from remote import server as Server from remote import worker as Worker if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument("-w", "--worker", help="Starts a weimar worker instance.", action="store_true") parser.add_argument("-t", "--threads",type=int, dest='threads', help="The number of threads running in one a worker (Default=3).") parser.add_argument("-s", "--server", help="Starts a weimar graph server.", action="store_true") parser.add_argument("-i", "--hyperdex-ip",type=str ,dest='hyperdex_ip', help='The HyperDex coordinator IP address. Must be specified if a server is started.') parser.add_argument("-p", "--hyperdex-port",type=int ,dest='hyperdex_port', help="The HyperDex coordinator port number. Must be specified if a server is started.") args = parser.parse_args() if args.worker: if(args.threads is None): args.threads = 3 Worker.start_worker(args.threads) elif args.server: if(args.hyperdex_ip is None or args.hyperdex_port is None): print('When starting a Weimar server, please specify the HyperDex\'s coordinators ip and port.') parser.print_help() sys.exit(1) if(args.threads is not None): print('--threads only refers to a worker process and will be omitted.') Server.start_server(args.hyperdex_ip, args.hyperdex_port)

mit

yunify/qingcloud-cli

qingcloud/cli/iaas_client/actions/notification/describe_notification_items.py

1

2183

# ========================================================================= # Copyright 2012-present Yunify, Inc. # ------------------------------------------------------------------------- # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this work except in compliance with the License. # You may obtain a copy of the License in the LICENSE file, or 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 qingcloud.cli.iaas_client.actions.base import BaseAction class DescribeNotificationItemsAction(BaseAction): action = 'DescribeNotificationItems' command = 'describe-notification-items' usage = '%(prog)s [-i --notification_items...] [-f <conf_file>]' @classmethod def add_ext_arguments(cls, parser): parser.add_argument('-i', '--notification-items', dest='notification_items', action='store', type=str, default=None, help='An array including IDs of notification items.') parser.add_argument('-l', '--notification-list', dest='notification_list', action='store', type=str, default=None, help='The ID of notification list.') parser.add_argument('-t', '--notification-item-type', dest='notification_item_type', action='store', type=str, default=None, help='The type of notification item, including email, phone and webhook.') @classmethod def build_directive(cls, options): directive = { "notification_items": options.notification_items, "notification_list": options.notification_list, "notification_item_type": options.notification_item_type } return directive

apache-2.0

room77/py77

pylib/util/git_util.py

1

7051

#!/usr/bin/env python """ utility file for various git functions """ __author__ = '[email protected] (Nicholas Edelman)' __copyright__ = 'Copyright 2013 Room77, Inc.' import os import subprocess from pylib.base.exec_utils import ExecUtils from pylib.base.term_color import TermColor class Error(Exception): def __init__(self, value): self.value = value def __str__(self): return "'%s'" % self.value class EmptyHotfixError(Exception): """this exception is raised when a hotfix is applied twice""" def __init__(self, value): self.value = value def __str__(self): return "'%s'" % self.value class GitUtil(object): @classmethod def apply_hotfix(cls, branch, commit_hash=""): """applies a hotfix to a specific branch Args: branch (string) - the branch to apply the hotfix hash (string) - the commit hash to use Raises: EmptyHotfixError - raised when the hotfix is empty Error - critical error such as conflict stopped with hotfix from being applied """ print("moving to branch %s" % TermColor.ColorStr(branch, 'GREEN')) # get onto the appropriate branch cls.checkout_branch(branch) # try to cherry-pick print(TermColor.ColorStr("Applying hotfix to branch: %s" % branch, 'GREEN')) ret = ExecUtils.RunCmd('git cherry-pick %s' % commit_hash)[0] if not ret == 0: r = ExecUtils.RunCmd('git diff --name-only') if r[0]: raise Error(TermColor.ColorStr('error doing a git diff', 'RED')) files = r[1] if not files: raise EmptyHotfixError('hotfix is empty. likely already applied') # not an error if empty raise Error(TermColor.ColorStr( ('Hotfix apply failed at step cherry pick on branch %s.\n' 'You NEED to fix this NOW! Go to %s and fix the issue! ' 'Impacted files: %s') % ( cls.get_current_branch(), os.getcwd(), files), 'RED')) # push cherry-pick to remote ret = ExecUtils.RunCmd('git push origin %s' % branch)[0] if not ret == 0: raise Error(TermColor.ColorStr( 'Please manually resolve your merge conflicts,' + \ 'then commit, and finally run hotfix selecting the ' + \ 'branches that have not yet received the commit', 'RED')) print(TermColor.ColorStr('Applied hotfix to %s' % branch, 'GREEN')) print(TermColor.ColorStr('On branch %s' % branch, 'GREEN')) @classmethod def checkout_branch(cls, branch): """Checks out the specified branch with the latest code Args: branch (string) - the branch name """ # fetches the latest code ret = ExecUtils.RunCmd('git fetch origin')[0] if not ret == 0: raise Error(TermColor.ColorStr('error during git fetch origin!', 'RED')) #subprocess.check_call( # 'git checkout -b %s --track origin/%s 2>/dev/null' % \ # (branch, branch), # shell=True) ret = ExecUtils.RunCmd('git checkout -B %s --track origin/%s' % ( branch, branch))[0] if not ret == 0: raise Error(TermColor.ColorStr( 'error checking out branch %s' % branch, 'RED')) @classmethod def commit_push_hotfix(cls, files, msg, branch=''): """Commits/pushes the set of files to the CURRENT branch AND if a branch param is specified, hotfixes to the specified branch with this same commit Args: files (list) - the files to commit msg (string) - the commit message branch (string) - the name of the additional branch to hotfix if desired """ # commit/push the specified files cls.commit_push(files, msg) # find the SHA1 of the latest commit commit_hash = cls.get_latest_commit() # save the current branch current_branch = cls.get_current_branch() # hotfix to branch if not already on the branch if branch and not current_branch == branch: cls.apply_hotfix(branch, commit_hash) # get back on current branch cls.checkout_branch(current_branch) @classmethod def commit_push(cls, files, msg): """Commits to the current branch AND pushes to remote Args: files (list) - list of files to commit msg (string) - the commit message """ ret = ExecUtils.RunCmd('git commit %s -m "%s"' % (' '.join(files), msg))[0] if not ret == 0: raise Error(TermColor.ColorStr( 'error committing these files: %s' % ' '.join(files), 'RED')) ret = ExecUtils.RunCmd('git pull && git push')[0] if not ret == 0: raise Error(TermColor.ColorStr( 'Please manually resolve any conflicts preventing git push of ' + \ 'the commit to remote', 'RED')) @classmethod def create_branch(cls, name): """Create and checkout branch and push to origin for tracking. Simply checks out if the branch already exists Args: name (string) - the name of the branch to create """ # only create a new branch if did not exist before params = '' # check if the branch already exists ret = subprocess.call( 'git show-ref --verify refs/heads/%s' % name, shell=True) if ret: params = '-b' # checkout and/or create the branch subprocess.check_call('git checkout %s %s' % (params, name), shell=True) # push to remote for tracking subprocess.check_call('git push -u origin %s' % name, shell=True) @classmethod def get_current_branch(cls): """Returns the name of the current branch""" cmd = 'git rev-parse --abbrev-ref HEAD' r = ExecUtils.RunCmd(cmd) if r[0]: raise Error(TermColor.ColorStr('error executing cmd %s' % cmd, 'RED')) return r[1].strip() @classmethod def get_latest_commit(cls): """Returns the latest commit hash""" commit_hash = subprocess.check_output('git log -1 --pretty=format:%H', shell=True) if not commit_hash: raise Error(TermColor.ColorStr( 'unable to find the latest commit hash', 'RED')) return commit_hash @classmethod def get_latest_release_branch(cls): """Returns the name of the latest release branch""" return subprocess.check_output("git branch -r | grep release- | sed -e 's/^[ \t]*//' | sed 's/\* //' | sed 's/origin\///' | sort -r | head -n1", shell=True).strip() @classmethod def repo_root(cls): """Returns the root of the repository""" return subprocess.check_output('git rev-parse --show-toplevel', shell=True).strip() @classmethod def update_submodules(cls): """Does a git pull and then update the submodules to the latest version AND finally ensure the submodule is on master @warning if you run this from a module run that does a os.chdir, this os.chdir will NOT persist here """ if ExecUtils.RunCmd('git pull')[0]: raise Error(TermColor.ColorStr( 'unable to git pull as part of submodule update', 'RED')) if ExecUtils.RunCmd('git submodule init && git submodule update')[0]: raise Error(TermColor.ColorStr( 'git submodule update failed!', 'RED'))

mit

llvm/llvm-lnt

lnt/server/db/migrations/upgrade_10_to_11.py

1

1936

# Version 8 of the database updates FieldChanges as well as adds tables # for Regression Tracking features. import sqlalchemy from sqlalchemy import String, Integer, Column, ForeignKey # Import the original schema from upgrade_0_to_1 since upgrade_1_to_2 does not # change the actual schema, but rather adds functionality vis-a-vis orders. import lnt.server.db.migrations.upgrade_0_to_1 as upgrade_0_to_1 import lnt.server.db.migrations.upgrade_7_to_8 as upgrade_7_to_8 def add_baselines(test_suite): """Give test-suites a baseline order. """ # Grab the Base for the previous schema so that we have all # the definitions we need. base = upgrade_7_to_8.add_regressions(test_suite) # Grab our db_key_name for our test suite so we can properly # prefix our fields/table names. db_key_name = test_suite.db_key_name class Baseline(base): """Baselines to compare runs to.""" __tablename__ = db_key_name + '_Baseline' id = Column("ID", Integer, primary_key=True) name = Column("Name", String(32), unique=True) comment = Column("Comment", String(256)) order_id = Column("OrderID", Integer, ForeignKey("%s_Order.ID" % db_key_name), index=True) return base def upgrade_testsuite(engine, name): # Grab Test Suite. session = sqlalchemy.orm.sessionmaker(engine)() test_suite = session.query(upgrade_0_to_1.TestSuite). \ filter_by(name=name).first() assert (test_suite is not None) # Add FieldChange to the test suite. base = add_baselines(test_suite) base.metadata.create_all(engine) # Commit changes (also closing all relevant transactions with # respect to Postgres like databases). session.commit() session.close() def upgrade(engine): # Create our FieldChangeField table and commit. upgrade_testsuite(engine, 'nts') upgrade_testsuite(engine, 'compile')

apache-2.0

hack4impact/Givology

mainSite/source/proj/giv/captcha.py

1

4110

import urllib2, urllib from proj.settings import * API_SSL_SERVER="https://www.google.com/recaptcha/api" API_SERVER="http://www.google.com/recaptcha/api" VERIFY_SERVER="www.google.com" class RecaptchaResponse(object): def __init__(self, is_valid, error_code=None): self.is_valid = is_valid self.error_code = error_code def displayhtml (public_key, use_ssl = False, error = None): """Gets the HTML to display for reCAPTCHA public_key -- The public api key use_ssl -- Should the request be sent over ssl? error -- An error message to display (from RecaptchaResponse.error_code)""" error_param = '' if error: error_param = '&error=%s' % error if use_ssl: server = API_SSL_SERVER else: server = API_SERVER return """<script type="text/javascript" src="%(ApiServer)s/challenge?k=%(PublicKey)s%(ErrorParam)s"></script> <noscript> <iframe src="%(ApiServer)s/noscript?k=%(PublicKey)s%(ErrorParam)s" height="300" width="500" frameborder="0"></iframe><br /> <textarea name="recaptcha_challenge_field" rows="3" cols="40"></textarea> <input type='hidden' name='recaptcha_response_field' value='manual_challenge' /> </noscript> """ % { 'ApiServer' : server, 'PublicKey' : public_key, 'ErrorParam' : error_param, } def submit (recaptcha_challenge_field, recaptcha_response_field, private_key, remoteip): """ Submits a reCAPTCHA request for verification. Returns RecaptchaResponse for the request recaptcha_challenge_field -- The value of recaptcha_challenge_field from the form recaptcha_response_field -- The value of recaptcha_response_field from the form private_key -- your reCAPTCHA private key remoteip -- the user's ip address """ if not (recaptcha_response_field and recaptcha_challenge_field and len (recaptcha_response_field) and len (recaptcha_challenge_field)): return RecaptchaResponse (is_valid = False, error_code = 'incorrect-captcha-sol') def encode_if_necessary(s): if isinstance(s, unicode): return s.encode('utf-8') return s params = urllib.urlencode ({ 'privatekey': encode_if_necessary(private_key), 'remoteip' : encode_if_necessary(remoteip), 'challenge': encode_if_necessary(recaptcha_challenge_field), 'response' : encode_if_necessary(recaptcha_response_field), }) request = urllib2.Request ( url = "http://%s/recaptcha/api/verify" % VERIFY_SERVER, data = params, headers = { "Content-type": "application/x-www-form-urlencoded", "User-agent": "reCAPTCHA Python" } ) httpresp = urllib2.urlopen (request) return_values = httpresp.read ().splitlines (); httpresp.close(); return_code = return_values [0] if (return_code == "true"): return RecaptchaResponse (is_valid=True) else: return RecaptchaResponse (is_valid=False, error_code = return_values [1]) def check_captcha(request): captcha_challenge = request.POST.get('recaptcha_challenge_field') captcha_response = request.POST.get('recaptcha_response_field') captcha_result = None ip = None if 'HTTP_X_FORWARDED_FOR' in request.META: ip = request.META['HTTP_X_FORWARDED_FOR'] elif 'REMOTE_ADDR' in request.META: ip = request.META['REMOTE_ADDR'] if captcha_response is not None and captcha_challenge is not None: captcha_result = submit(captcha_challenge, captcha_response, recaptcha_private_key, ip) return captcha_result def new_captcha_html(captcha_result): if captcha_result is None: captcha_html = displayhtml(recaptcha_public_key, use_ssl=True) else: captcha_html = displayhtml(recaptcha_public_key, use_ssl=True, error = captcha_result.error_code) return captcha_html

mit

catalyst/l3overlay

src/l3overlay/l3overlayd/process/ipsec.py

1

6516

# # IPsec overlay network manager (l3overlay) # l3overlay/l3overlayd/process/ipsec.py - IPsec process manager # # Copyright (c) 2017 Catalyst.net Ltd # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program. If not, see <http://www.gnu.org/licenses/>. # ''' IPsec process manager. ''' import subprocess from l3overlay import util from l3overlay.util.exception import L3overlayError from l3overlay.util.worker import Worker class UnexpectedReturnCodeError(L3overlayError): ''' Exception to raise when the process returns an unexpected code. ''' def __init__(self, command, code): super().__init__("unexpected '%s' return code: %i" % (command, code)) # pylint: disable=too-many-instance-attributes class Process(Worker): ''' IPsec process manager. ''' description = "ipsec process" def __init__(self, daemon): ''' Set internal fields for the IPsec process. ''' super().__init__() self.dry_run = daemon.dry_run self.logger = daemon.logger self.use_ipsec = daemon.use_ipsec if not self.use_ipsec: return self.ipsec_manage = daemon.ipsec_manage self.template_dir = daemon.template_dir self.ipsec_conf = daemon.ipsec_conf self.ipsec_secrets = daemon.ipsec_secrets self.ipsec_conf_template = util.template_read(self.template_dir, "ipsec.conf") self.ipsec_secrets_template = util.template_read(self.template_dir, "ipsec.secrets") self.conns = dict() self.secrets = dict() for link in daemon.mesh_links.keys(): self.tunnel_add(link, daemon.ipsec_psk) for link, data in daemon.ipsec_tunnels.items(): psk = data["ipsec-psk"] if data["ipsec-psk"] else daemon.ipsec_psk self.tunnel_add(link, psk) self.ipsec = util.command_path("ipsec") if not self.dry_run else util.command_path("true") def start(self): ''' Start the IPsec process. ''' if not self.use_ipsec: return self.set_starting() self.logger.info("starting IPsec process") self.logger.debug("creating IPsec configuration file '%s'" % self.ipsec_conf) if not self.dry_run: with open(self.ipsec_conf, "w") as fil: fil.write(self.ipsec_conf_template.render( file=self.ipsec_conf, ipsec_manage=self.ipsec_manage, conns=self.conns, )) self.logger.debug("creating IPsec secrets file '%s'" % self.ipsec_secrets) if not self.dry_run: with open(self.ipsec_secrets, "w") as fil: fil.write(self.ipsec_secrets_template.render( file=self.ipsec_secrets, secrets=self.secrets, )) self.logger.debug("checking IPsec status") status = subprocess.call( [self.ipsec, "status"], stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, ) if status == 0: self.logger.debug("reloading IPsec secrets") subprocess.check_output([self.ipsec, "rereadsecrets"], stderr=subprocess.STDOUT) self.logger.debug("reloading IPsec configuration") subprocess.check_output([self.ipsec, "reload"], stderr=subprocess.STDOUT) elif status == 3: self.logger.debug("starting IPsec") subprocess.check_output([self.ipsec, "start"], stderr=subprocess.STDOUT) else: raise UnexpectedReturnCodeError("%s status" % self.ipsec, status) self.logger.info("finished starting IPsec process") self.set_started() def stop(self): ''' Stop the IPsec process. ''' if not self.use_ipsec: return self.set_stopping() self.logger.info("stopping IPsec process") self.logger.debug("removing IPsec configuration file '%s'" % self.ipsec_conf) if not self.dry_run: util.file_remove(self.ipsec_conf) self.logger.debug("removing IPsec secrets file '%s'" % self.ipsec_secrets) if not self.dry_run: util.file_remove(self.ipsec_secrets) if self.ipsec_manage: # When we manage IPsec, it is safe to stop it completely. self.logger.debug("stopping IPsec") if not self.dry_run: subprocess.check_output([self.ipsec, "stop"], stderr=subprocess.STDOUT) else: # When we don't, reload the configuration without the tunnels # configured, and shut down all of the tunnels. self.logger.debug("reloading IPsec secrets") if not self.dry_run: subprocess.check_output([self.ipsec, "rereadsecrets"], stderr=subprocess.STDOUT) self.logger.debug("reloading IPsec configuration") if not self.dry_run: subprocess.check_output([self.ipsec, "reload"], stderr=subprocess.STDOUT) for conn in self.conns: self.logger.debug("shutting down IPsec tunnel '%s'" % conn) if not self.dry_run: subprocess.check_output( [self.ipsec, "down", conn], stderr=subprocess.STDOUT, ) self.logger.info("finished stopping IPsec process") self.set_stopped() def tunnel_add(self, link, psk): ''' Add an IPsec tunnel and its corresponding PSK to the database which gets used to configure the IPsec process. ''' self.conns["%s-%s" % link] = link if not psk in self.secrets: self.secrets[psk] = set() self.secrets[psk].update(link) # pylint: disable=no-member Worker.register(Process) def create(daemon): ''' Create a IPsec process object. ''' return Process(daemon)

gpl-3.0

mbareta/edx-platform-ft

lms/djangoapps/instructor_task/models.py

1

12291

""" WE'RE USING MIGRATIONS! If you make changes to this model, be sure to create an appropriate migration file and check it in at the same time as your model changes. To do that, 1. Go to the edx-platform dir 2. ./manage.py schemamigration instructor_task --auto description_of_your_change 3. Add the migration file created in edx-platform/lms/djangoapps/instructor_task/migrations/ ASSUMPTIONS: modules have unique IDs, even across different module_types """ from uuid import uuid4 import csv import json import hashlib import os.path from django.conf import settings from django.contrib.auth.models import User from django.core.files.base import ContentFile from django.db import models, transaction from openedx.core.storage import get_storage from xmodule_django.models import CourseKeyField # define custom states used by InstructorTask QUEUING = 'QUEUING' PROGRESS = 'PROGRESS' class InstructorTask(models.Model): """ Stores information about background tasks that have been submitted to perform work by an instructor (or course staff). Examples include grading and rescoring. `task_type` identifies the kind of task being performed, e.g. rescoring. `course_id` uses the course run's unique id to identify the course. `task_key` stores relevant input arguments encoded into key value for testing to see if the task is already running (together with task_type and course_id). `task_input` stores input arguments as JSON-serialized dict, for reporting purposes. Examples include url of problem being rescored, id of student if only one student being rescored. `task_id` stores the id used by celery for the background task. `task_state` stores the last known state of the celery task `task_output` stores the output of the celery task. Format is a JSON-serialized dict. Content varies by task_type and task_state. `requester` stores id of user who submitted the task `created` stores date that entry was first created `updated` stores date that entry was last modified """ class Meta(object): app_label = "instructor_task" task_type = models.CharField(max_length=50, db_index=True) course_id = CourseKeyField(max_length=255, db_index=True) task_key = models.CharField(max_length=255, db_index=True) task_input = models.CharField(max_length=255) task_id = models.CharField(max_length=255, db_index=True) # max_length from celery_taskmeta task_state = models.CharField(max_length=50, null=True, db_index=True) # max_length from celery_taskmeta task_output = models.CharField(max_length=1024, null=True) requester = models.ForeignKey(User, db_index=True) created = models.DateTimeField(auto_now_add=True, null=True) updated = models.DateTimeField(auto_now=True) subtasks = models.TextField(blank=True) # JSON dictionary def __repr__(self): return 'InstructorTask<%r>' % ({ 'task_type': self.task_type, 'course_id': self.course_id, 'task_input': self.task_input, 'task_id': self.task_id, 'task_state': self.task_state, 'task_output': self.task_output, },) def __unicode__(self): return unicode(repr(self)) @classmethod def create(cls, course_id, task_type, task_key, task_input, requester): """ Create an instance of InstructorTask. """ # create the task_id here, and pass it into celery: task_id = str(uuid4()) json_task_input = json.dumps(task_input) # check length of task_input, and return an exception if it's too long: if len(json_task_input) > 255: fmt = 'Task input longer than 255: "{input}" for "{task}" of "{course}"' msg = fmt.format(input=json_task_input, task=task_type, course=course_id) raise ValueError(msg) # create the task, then save it: instructor_task = cls( course_id=course_id, task_type=task_type, task_id=task_id, task_key=task_key, task_input=json_task_input, task_state=QUEUING, requester=requester ) instructor_task.save_now() return instructor_task @transaction.atomic def save_now(self): """ Writes InstructorTask immediately, ensuring the transaction is committed. """ self.save() @staticmethod def create_output_for_success(returned_result): """ Converts successful result to output format. Raises a ValueError exception if the output is too long. """ # In future, there should be a check here that the resulting JSON # will fit in the column. In the meantime, just return an exception. json_output = json.dumps(returned_result) if len(json_output) > 1023: raise ValueError("Length of task output is too long: {0}".format(json_output)) return json_output @staticmethod def create_output_for_failure(exception, traceback_string): """ Converts failed result information to output format. Traceback information is truncated or not included if it would result in an output string that would not fit in the database. If the output is still too long, then the exception message is also truncated. Truncation is indicated by adding "..." to the end of the value. """ tag = '...' task_progress = {'exception': type(exception).__name__, 'message': unicode(exception.message)} if traceback_string is not None: # truncate any traceback that goes into the InstructorTask model: task_progress['traceback'] = traceback_string json_output = json.dumps(task_progress) # if the resulting output is too long, then first shorten the # traceback, and then the message, until it fits. too_long = len(json_output) - 1023 if too_long > 0: if traceback_string is not None: if too_long >= len(traceback_string) - len(tag): # remove the traceback entry entirely (so no key or value) del task_progress['traceback'] too_long -= (len(traceback_string) + len('traceback')) else: # truncate the traceback: task_progress['traceback'] = traceback_string[:-(too_long + len(tag))] + tag too_long = 0 if too_long > 0: # we need to shorten the message: task_progress['message'] = task_progress['message'][:-(too_long + len(tag))] + tag json_output = json.dumps(task_progress) return json_output @staticmethod def create_output_for_revoked(): """Creates standard message to store in output format for revoked tasks.""" return json.dumps({'message': 'Task revoked before running'}) class ReportStore(object): """ Simple abstraction layer that can fetch and store CSV files for reports download. Should probably refactor later to create a ReportFile object that can simply be appended to for the sake of memory efficiency, rather than passing in the whole dataset. Doing that for now just because it's simpler. """ @classmethod def from_config(cls, config_name): """ Return one of the ReportStore subclasses depending on django configuration. Look at subclasses for expected configuration. """ # Convert old configuration parameters to those expected by # DjangoStorageReportStore for backward compatibility config = getattr(settings, config_name, {}) storage_type = config.get('STORAGE_TYPE', '').lower() if storage_type == 's3': return DjangoStorageReportStore( storage_class='storages.backends.s3boto.S3BotoStorage', storage_kwargs={ 'bucket': config['BUCKET'], 'location': config['ROOT_PATH'], 'querystring_expire': 300, 'gzip': True, }, ) elif storage_type == 'localfs': return DjangoStorageReportStore( storage_class='django.core.files.storage.FileSystemStorage', storage_kwargs={ 'location': config['ROOT_PATH'], }, ) return DjangoStorageReportStore.from_config(config_name) def _get_utf8_encoded_rows(self, rows): """ Given a list of `rows` containing unicode strings, return a new list of rows with those strings encoded as utf-8 for CSV compatibility. """ for row in rows: yield [unicode(item).encode('utf-8') for item in row] class DjangoStorageReportStore(ReportStore): """ ReportStore implementation that delegates to django's storage api. """ def __init__(self, storage_class=None, storage_kwargs=None): if storage_kwargs is None: storage_kwargs = {} self.storage = get_storage(storage_class, **storage_kwargs) @classmethod def from_config(cls, config_name): """ By default, the default file storage specified by the `DEFAULT_FILE_STORAGE` setting will be used. To configure the storage used, add a dict in settings with the following fields:: STORAGE_CLASS : The import path of the storage class to use. If not set, the DEFAULT_FILE_STORAGE setting will be used. STORAGE_KWARGS : An optional dict of kwargs to pass to the storage constructor. This can be used to specify a different S3 bucket or root path, for example. Reference the setting name when calling `.from_config`. """ return cls( getattr(settings, config_name).get('STORAGE_CLASS'), getattr(settings, config_name).get('STORAGE_KWARGS'), ) def store(self, course_id, filename, buff): """ Store the contents of `buff` in a directory determined by hashing `course_id`, and name the file `filename`. `buff` can be any file-like object, ready to be read from the beginning. """ path = self.path_to(course_id, filename) self.storage.save(path, buff) def store_rows(self, course_id, filename, rows): """ Given a course_id, filename, and rows (each row is an iterable of strings), write the rows to the storage backend in csv format. """ output_buffer = ContentFile('') csvwriter = csv.writer(output_buffer) csvwriter.writerows(self._get_utf8_encoded_rows(rows)) output_buffer.seek(0) self.store(course_id, filename, output_buffer) def links_for(self, course_id): """ For a given `course_id`, return a list of `(filename, url)` tuples. Calls the `url` method of the underlying storage backend. Returned urls can be plugged straight into an href """ course_dir = self.path_to(course_id) try: _, filenames = self.storage.listdir(course_dir) except OSError: # Django's FileSystemStorage fails with an OSError if the course # dir does not exist; other storage types return an empty list. return [] files = [(filename, os.path.join(course_dir, filename)) for filename in filenames] files.sort(key=lambda f: self.storage.modified_time(f[1]), reverse=True) return [ (filename, self.storage.url(full_path)) for filename, full_path in files ] def path_to(self, course_id, filename=''): """ Return the full path to a given file for a given course. """ # FastTrac affiliate reports if isinstance(course_id, basestring): hashed_course_id = hashlib.sha1(course_id).hexdigest() else: hashed_course_id = hashlib.sha1(course_id.to_deprecated_string()).hexdigest() return os.path.join(hashed_course_id, filename)

agpl-3.0

meta-it/misc-addons

web_debranding/models/web_planner.py

1

1046

# -*- coding: utf-8 -*- import re from openerp import models, api class Planner(models.Model): _inherit = 'web.planner' @api.model def render(self, template_id, planner_app): res = super(Planner, self).render(template_id, planner_app) params = self.env['ir.config_parameter'].get_debranding_parameters() planner_footer = params.get('web_debranding.planner_footer') planner_footer = '<p>' + str(planner_footer) + '</p>' res = re.sub(r'<p>[^<]*to contact our accounting experts by using the[\s\S]*?</div>', planner_footer, res) res = re.sub(r'<p>[^<]*If you need help, do not hesitate to contact our experts[\s\S]*?</div>', planner_footer, res) res = re.sub(r'<h4>Don\'t hesitate to[\s\S]*logo.png"/>', '', res) res = re.sub(r'<p>Once it\'s fully working[\s\S]*odoo_logo.png"/>', planner_footer, res) res = re.sub(r'<div class="mt32">[\s\S]*Fabien Pinckaers, Founder[\s\S]*?</div>', planner_footer, res) return self.env['ir.translation']._debrand(res)

lgpl-3.0

radjkarl/dataArtist

dataArtist/items/GridROI.py

1

14750

# coding=utf-8 from __future__ import division from __future__ import absolute_import import pyqtgraph_karl as pg import numpy as np from math import cos, sin, pi import cv2 from qtpy import QtCore from .PseudoSquareROI import PseudoSquareROI from dataArtist.items.QPainterPath import QPainterPath class GridROI(pg.ROI): ''' An ROI displaying mini ROIs of different shapes as a grid ''' # TODO: default argument is mutable: Default argument values are evaluated only once at function definition time, # which means that modifying the default value of the argument will affect all subsequent calls of the function. def __init__(self, pos=[20, 20], size=[20, 20], grid=[4, 5], shape='Rect', gap=[0, 0], subgrid=([], []), subgrid_width=0.05, pen='w', **kwargs): ''' shape = ['Rect', 'Square', 'Circular', 'Pseudosquare'] ''' self.opts = {'shape': shape, 'grid': np.asarray(grid), 'gap': np.asfarray(gap), 'subgrid': subgrid, 'subgrid_width': subgrid_width } # TODO: limit max cell size while rescale self.maxCellSize = size / self.opts['grid'] self.cells = [] self._createCells() self._createSubgrid() # cannot set brush at the moment, so: if 'brush' in kwargs: kwargs.pop('brush') pg.ROI.__init__(self, pos, size, pen=pen, **kwargs) self.translatable = False self.mouseHovering = False self._setCellSize(self.state['size']) self._setCellPos(pos) self.layout_rescaling = False self.addScaleHandle([1, 1], [0, 0]) self.addScaleHandle([0, 0], [1, 1]) self.addScaleHandle([1, 0], [0, 1]) self.addScaleHandle([0, 1], [1, 0]) self.addRotateHandle([0.5, 1], [0.5, 0.5]) def getCellParameters(self, array, fn=np.mean): out = np.arange(len(self.cells), dtype=float).reshape(self.opts['grid']) s = array.shape for (i, j), n in np.ndenumerate(out): m = self.cells[int(n)].getMask(s) out[i, j] = fn(array[m]) return out def saveState(self): s = pg.ROI.saveState(self) o = self.opts s['gap'] = tuple(o['gap']) s['grid'] = tuple(o['grid']) s['shape'] = o['shape'] return s def painterPath(self): ''' Return a qpainterpath including all cells ''' p = self.cells[0].painterPath() for c in self.cells[1:]: p.addPath(c.painterPath()) return p def _createCells(self): grid = self.opts['grid'] cellClass = {'Rect': RectROI, 'Circle': CircleROI, 'Pseudosquare': CellPseudoSquareROI}[self.opts['shape']] self.layout_rescaling = True for c in self.cells: self.vb.removeItem(c) self.cells = [cellClass(pos=[1, 1]) for _ in range(grid[0] * grid[1])] i_scaleCell = -(grid[0] * grid[1] - grid[1] + 1) self._scaleCell = c = self.cells[i_scaleCell] c.setScaleCell() c.sigRegionChanged.connect(self._cellResized) def _createSubgrid(self): for c in self.cells: for line in c.subgrid: self.vb.removeItem(line) s = self.opts['subgrid'] w = self.opts['subgrid_width'] for c in self.cells: for pos in s[0]: c.subgrid.append(SubLine(c, orientation=0, pos=pos, thickness=w)) for pos in s[1]: c.subgrid.append(SubLine(c, orientation=1, pos=pos, thickness=w)) for n, line in enumerate(self._scaleCell.subgrid): line.setScaleLine() line.sigRegionChanged.connect(lambda line, n=n: self._lineResized(line, n)) def setPen(self, pen): pg.ROI.setPen(self, pen) for c in self.cells: c.setPen(pen) for line in c.subgrid: line.setPen(pen) def setBrush(self, pen): pass # TODO # pg.ROI.setB(pen) # for c in self.cells: # c.setBrush(pen) # #raises: AttributeError: 'RectROI' object has no attribute 'setBrush' def getMask(self, shape): m = self.cells[0].getMask(shape) for c in self.cells[1:]: m += c.getMask(shape) return m def __iter__(self): return iter(self.cells) def __len__(self): return len(self.cells) def _lineResized(self, line, n): if not self.layout_rescaling: #size = line.state['size'] pos = line.state['pos'] thick, pos = line.fromState() for c in self.cells: ln = c.subgrid[n] if ln != line: ln.thickness = thick ln.pos = pos ln.updatePos() ln.updateSize() def _cellResized(self, cell): if not self.layout_rescaling: size = cell.state['size'] self.opts['gap'] = (self.state['size'] - ( size * self.opts['grid'])) / (self.opts['grid'] - 1) for c in self.cells: if c != cell: c.setSize(size) self._setCellPos(self.state['pos'], True) def setAngle(self, angle, **kwargs): for c in self.cells: c.setAngle(angle, **kwargs) for line in c.subgrid: line.setAngle(angle, **kwargs) self._setCellPos(self.state['pos']) pg.ROI.setAngle(self, angle, **kwargs) def setPos(self, pos, **kwargs): pg.ROI.setPos(self, pos, **kwargs) self._setCellPos(pos) def setSubGrid(self, s): self.opts['subgrid'] = s self.refresh() def setGrid(self, x=None, y=None): g = self.opts['grid'] if x is not None: g[0] = x if y is not None: g[1] = y self.refresh() def setCellShape(self, shape): self.opts['shape'] = shape self.refresh() def refresh(self): self._createCells() self._setCellSize(self.state['size']) self._setCellPos(self.state['pos']) [self.vb.addItem(c) for c in self.cells] self._createSubgrid() [[self.vb.addItem(line) for line in c.subgrid] for c in self.cells] def setSize(self, size, update=True, finish=True): pg.ROI.setSize(self, size, update, finish) self.layout_rescaling = True self._setCellSize(size) self._setCellPos(self.state['pos']) self.layout_rescaling = False self.maxCellSize = size / self.opts['grid'] def _setCellSize(self, size): size_cell = (size - (self.opts['grid'] - 1) * self.opts['gap']) / self.opts['grid'] for c in self.cells: c.setSize(size_cell) for line in c.subgrid: line.updateSize() @staticmethod def _rotatePoint(point, angle, center): if angle == 0: return point x = point[0] y = point[1] cx = center[0] cy = center[1] point[0] = cos(angle) * (x - cx) - sin(angle) * (y - cy) + cx point[1] = sin(angle) * (x - cx) + cos(angle) * (y - cy) + cy def _setCellPos(self, pos, ignoreScaleCell=False): size_cell = self._scaleCell.state['size'] rad = self.state['angle'] * pi / 180 # center of rotation: c = self.state['pos'] if self.handles: # centre defined by both edges: c += 0.5 * self.handles[1]['item'].pos() n = 0 for x in range(self.opts['grid'][0]): for y in range(self.opts['grid'][1]): cell = self.cells[n] n += 1 if ignoreScaleCell and cell == self._scaleCell: for line in cell.subgrid: line.updatePos() continue p = pos + [x, y] * (size_cell + self.opts['gap']) self._rotatePoint(p, rad, c) cell.setPos(p) for line in cell.subgrid: line.updatePos() def setViewBox(self, v): ''' add grid and its cells to the ViewBox ''' self.vb = v v.addItem(self) [v.addItem(c) for c in self.cells] [[self.vb.addItem(line) for line in c.subgrid] for c in self.cells] def show(self): [c.show() for c in self.cells] [[line.show() for line in c.subgrid] for c in self.cells] pg.ROI.show(self) def hide(self): [c.hide() for c in self.cells] [[line.hide() for line in c.subgrid] for c in self.cells] pg.ROI.hide(self) def close(self): [self.vb.removeItem(c) for c in self.cells] self.vb.removeItem(self) class _CellBase(object): ''' Base class for all cells in a grid ''' def __init__(self, *args, **kwargs): self.subgrid = [] self.translatable = False self.mouseHovering = False class SubLine(pg.ROI): ''' one line for the subgrid ''' def __init__(self, cell, orientation, pos, thickness): pg.ROI.__init__(self, pos=(1, 1), size=(1, 1)) self.translatable = False self.mouseHovering = False self.pos = pos self.thickness = thickness if orientation == 0: self.i = 0 self.j = 1 else: self.i = 1 self.j = 0 self.cell = cell def fromState(self): ''' update thickness and position from current state ''' j = self.j s = self.state cs = self.cell.state p = self.pos = (s['pos'][j] - cs['pos'][j]) / cs['size'][j] t = self.thickness = s['size'][j] / cs['size'][j] return t, p def setScaleLine(self): self.addScaleHandle([0.5, 1], [0.5, 0]) self.addScaleHandle([0.5, 0], [0.5, 1]) def updateSize(self): s = self.cell.state['size'] pg.ROI.setSize(self, (s[self.i], self.thickness * s[self.j])) def updatePos(self): p = self.cell.state['pos'].copy() s = self.cell.state['size'] j = self.j p[j] += s[j] * self.pos pg.ROI.setPos(self, p) class RectROI(pg.ROI, _CellBase): def __init__(self, *args, **kwargs): pg.ROI.__init__(self, *args, **kwargs) _CellBase.__init__(self, *args, **kwargs) def setScaleCell(self): self.addScaleHandle([1, 0], [0, 1]) self.setPen('y') def painterPath(self): p = QPainterPath() a = self.boundingRect() a.moveTo(self.state['pos']) p.addRect(a) return p def getMask(self, shape): p = self.state['pos'] s = self.state['size'] center = p + s / 2 a = self.state['angle'] # opencv convention: shape = (shape[1], shape[0]) arr = np.zeros(shape, dtype=np.uint8) # draw rotated rectangle: vertices = np.int0(cv2.boxPoints((center, s, a))) cv2.drawContours(arr, [vertices], 0, color=1, thickness=-1) return arr.astype(bool).T class CircleROI(_CellBase, pg.EllipseROI): def __init__(self, *args, **kwargs): pg.ROI.__init__(self, *args, **kwargs) _CellBase.__init__(self, *args, **kwargs) self._ratioEllispeRectangle = 1 # only changed in CellPseudoSquareROI def setScaleCell(self): self.addScaleHandle([cos(1), sin(0)], [0, 1]) self.setPen('y') def painterPath(self): p = QPainterPath() a = self.boundingRect() a.moveTo(self.state['pos']) p.addEllipse(a) return p def getMask(self, shape): ''' returns bool array ''' p = self.state['pos'] s = self.state['size'] center = p + s / 2 a = self.state['angle'] # opencv convention: shape = (shape[1], shape[0]) arr = np.zeros(shape, dtype=np.uint8) # draw ellipse: cv2.ellipse(arr, (int(center[0]), int(center[1])), (int(s[0] / 2 * self._ratioEllispeRectangle), int(s[1] / 2 * self._ratioEllispeRectangle)), int(a), startAngle=0, endAngle=360, color=1, thickness=-1) return arr.astype(bool).T class CellPseudoSquareROI(_CellBase, PseudoSquareROI): def __init__(self, *args, **kwargs): PseudoSquareROI.__init__(self, *args, **kwargs) _CellBase.__init__(self, *args, **kwargs) def setScaleCell(self): self.addScaleHandle([1, 0], [0, 1]) self.setPen('y') def painterPath(self): p = QPainterPath() roundness = int(99 * float(self._alen) / 16 / 90) r = QtCore.QRectF(self._rect) r.moveTo(self.state['pos']) p.addRoundRect(r, roundness) return p if __name__ == '__main__': from pyqtgraph.Qt import QtGui app = QtWidgets.QApplication([]) w = pg.GraphicsWindow(size=(1000, 800), border=True) w.setWindowTitle('pyqtgraph example: ROI Examples') w1 = w.addLayout(row=0, col=0) #label1 = w1.addLabel('test', row=1, col=0) v = w1.addViewBox(row=1, col=0, lockAspect=True) v2 = w1.addViewBox(row=2, col=0, lockAspect=True) img1b = pg.ImageItem() v2.addItem(img1b) v3 = w1.addViewBox(row=3, col=0, lockAspect=True) img1c = pg.ImageItem() v3.addItem(img1c) # Create image to display arr = np.ones((100, 100), dtype=float) arr[45:55, 45:55] = 0 arr[25, :] = 5 arr[:, 25] = 5 arr[75, :] = 5 arr[:, 75] = 5 arr[50, :] = 10 arr[:, 50] = 10 arr += np.sin(np.linspace(0, 20, 100)).reshape(1, 100) arr += np.random.normal(size=(100, 100)) img1a = pg.ImageItem(arr) v.addItem(img1a) r = GridROI([20, 20], [20, 20], pen=(0, 9), subgrid=([0.3, 0.5, 1], []), shape='Pseudosquare') r.setViewBox(v) cell = r.cells[0] v.autoRange(False) def update(roi): img1b.setImage(roi.getArrayRegion(arr, img1a), levels=(0, arr.max())) img1c.setImage(np.int0(r.getMask(arr.shape))) # cell.sigRegionChanged.connect(update) # update(cell) app.exec_()

gpl-3.0

jekahy/EIASR

src/canny.py

1

3842

# coding: utf8 from math import pi import numpy as np from scipy.signal import convolve2d SOBEL_X = np.array([ [ 1, 0, -1], [ 2, 0, -2], [ 1, 0, -1], ]) SOBEL_Y = np.array([ [ 1, 2, 1], [ 0, 0, 0], [-1, -2, -1], ]) class GradientImage(object): def __init__(self, magnitudes, angles): self.magnitudes = magnitudes self.angles = angles @property def w(self): return self.magnitudes.shape[0] @property def h(self): return self.magnitudes.shape[1] @classmethod def from_partials(cls, dxs, dys): magnitudes = np.sqrt(dxs ** 2 + dys ** 2) angles = np.arctan2(dys, dxs) return cls(magnitudes, angles) def gradient(in_): dxs = convolve2d(in_, SOBEL_X, 'same', 'symm') dys = convolve2d(in_, SOBEL_Y, 'same', 'symm') return GradientImage.from_partials(dxs, dys) def thin_nonmaximum(gradient_image): thinned = np.copy(gradient_image.magnitudes) for idx, s in np.ndenumerate(gradient_image.magnitudes): s_nl = _neighbour_in_direction( gradient_image.magnitudes, idx, gradient_image.angles[idx]) s_nr = _neighbour_in_direction( gradient_image.magnitudes, idx, gradient_image.angles[idx] + pi) # TODO: consider angle at nl, nr if s < s_nl or s < s_nr: thinned[idx] = 0 return GradientImage(thinned, gradient_image.angles) def thin_hysteresis(gradient_image, t_high=0.2, t_low=0.1): # 8 pixel neighborhood x = [-1, 0, 1, -1, 1, -1, 0, 1] y = [-1, -1, -1, 0, 0, 1, 1, 1] magnitudes = gradient_image.magnitudes # Dimensions xdim, ydim = magnitudes.shape # Max magnitude max_magn = magnitudes.max() # Pixels > t_high are kept automatically thinned = np.where(magnitudes > (t_high * max_magn), magnitudes, 0) # Pixels > t_low will be ad ded later if they prove to be # adjacent to another pixel which has been included in the thinned list cands = np.where(magnitudes > (t_low * max_magn), magnitudes, 0) # Create an initial list of strong edge pixels prevx, prevy = thinned.nonzero() # If the previous loop of testing found no new pixels to move from # the cands list to the edge list, then stop while len(prevx) != 0: newx, newy = [], [] # Loop over new edge pixels discovered on previous iteration for ii in range(len(prevx)): # Loop through 8 pixel neighborhood for ij in range(len(x)): xidx = prevx[ii] + x[ij] yidx = prevy[ii] + y[ij] # Check if pixel index falls within image boundary if xidx >= 0 and xidx < xdim and yidx >= 0 and yidx < ydim: # Check if pixel is on the cands list but has not yet been added to the thinned list if cands[xidx][yidx] and not thinned[xidx][yidx]: # Transfer to thinned list thinned[xidx][yidx] = cands[xidx][yidx] # Keep track of indices for next loop iteration newx.append(xidx) newy.append(yidx) # Update for next iteration prevx = newx prevy = newy return GradientImage(thinned, gradient_image.angles) NEIGHBOURS = [ ( 0, 1), ( 1, 1), ( 1, 0), ( 1, -1), ( 0, -1), (-1, -1), (-1, 0), (-1, 1), ] def _neighbour_in_direction(a, (x, y), direction): w, h = a.shape ndir = len(NEIGHBOURS) discrete_direction = int((direction / (2*pi) * ndir + 0.5 * ndir) % ndir) dx, dy = NEIGHBOURS[discrete_direction] nx, ny = x + dx, y + dy if not (0 <= nx < w and 0 <= ny < h): return 0 return a[nx, ny]

mit

volpino/Yeps-EURAC

lib/galaxy/jobs/runners/sge.py

1

13219

import os, logging, threading, time from Queue import Queue, Empty from galaxy import model from paste.deploy.converters import asbool import pkg_resources try: pkg_resources.require( "DRMAA_python" ) DRMAA = __import__( "DRMAA" ) except: DRMAA = None log = logging.getLogger( __name__ ) if DRMAA is not None: DRMAA_state = { DRMAA.Session.UNDETERMINED: 'process status cannot be determined', DRMAA.Session.QUEUED_ACTIVE: 'job is queued and waiting to be scheduled', DRMAA.Session.SYSTEM_ON_HOLD: 'job is queued and in system hold', DRMAA.Session.USER_ON_HOLD: 'job is queued and in user hold', DRMAA.Session.USER_SYSTEM_ON_HOLD: 'job is queued and in user and system hold', DRMAA.Session.RUNNING: 'job is running', DRMAA.Session.SYSTEM_SUSPENDED: 'job is system suspended', DRMAA.Session.USER_SUSPENDED: 'job is user suspended', DRMAA.Session.DONE: 'job finished normally', DRMAA.Session.FAILED: 'job finished, but failed', } sge_template = """#!/bin/sh #$ -S /bin/sh GALAXY_LIB="%s" if [ "$GALAXY_LIB" != "None" ]; then if [ -n "$PYTHONPATH" ]; then PYTHONPATH="$GALAXY_LIB:$PYTHONPATH" else PYTHONPATH="$GALAXY_LIB" fi export PYTHONPATH fi cd %s %s """ class SGEJobState( object ): def __init__( self ): """ Encapsulates state related to a job that is being run via SGE and that we need to monitor. """ self.job_wrapper = None self.job_id = None self.old_state = None self.running = False self.job_file = None self.ofile = None self.efile = None self.runner_url = None class SGEJobRunner( object ): """ Job runner backed by a finite pool of worker threads. FIFO scheduling """ STOP_SIGNAL = object() def __init__( self, app ): """Initialize this job runner and start the monitor thread""" # Check if SGE was importable, fail if not if DRMAA is None: raise Exception( "SGEJobRunner requires DRMAA_python which was not found" ) self.app = app # 'watched' and 'queue' are both used to keep track of jobs to watch. # 'queue' is used to add new watched jobs, and can be called from # any thread (usually by the 'queue_job' method). 'watched' must only # be modified by the monitor thread, which will move items from 'queue' # to 'watched' and then manage the watched jobs. self.watched = [] self.queue = Queue() self.default_cell = self.determine_sge_cell( self.app.config.default_cluster_job_runner ) self.ds = DRMAA.Session() self.ds.init( self.default_cell ) self.monitor_thread = threading.Thread( target=self.monitor ) self.monitor_thread.start() log.debug( "ready" ) def determine_sge_cell( self, url ): """Determine what SGE cell we are using""" url_split = url.split("/") if url_split[0] == 'sge:': return url_split[2] # this could happen if sge is started, but is not the default runner else: return '' def determine_sge_queue( self, url ): """Determine what SGE queue we are submitting to""" url_split = url.split("/") queue = url_split[3] if queue == "": # None == server's default queue queue = None return queue def queue_job( self, job_wrapper ): """Create SGE script for a job and submit it to the SGE queue""" try: job_wrapper.prepare() command_line = job_wrapper.get_command_line() except: job_wrapper.fail( "failure preparing job", exception=True ) log.exception("failure running job %d" % job_wrapper.job_id) return runner_url = job_wrapper.tool.job_runner # This is silly, why would we queue a job with no command line? if not command_line: job_wrapper.finish( '', '' ) return # Check for deletion before we change state if job_wrapper.get_state() == model.Job.states.DELETED: log.debug( "Job %s deleted by user before it entered the SGE queue" % job_wrapper.job_id ) job_wrapper.cleanup() return # Change to queued state immediately job_wrapper.change_state( model.Job.states.QUEUED ) if self.determine_sge_cell( runner_url ) != self.default_cell: # TODO: support multiple cells log.warning( "(%s) Using multiple SGE cells is not supported. This job will be submitted to the default cell." % job_wrapper.job_id ) sge_queue_name = self.determine_sge_queue( runner_url ) # define job attributes ofile = "%s/database/pbs/%s.o" % (os.getcwd(), job_wrapper.job_id) efile = "%s/database/pbs/%s.e" % (os.getcwd(), job_wrapper.job_id) jt = self.ds.createJobTemplate() jt.remoteCommand = "%s/database/pbs/galaxy_%s.sh" % (os.getcwd(), job_wrapper.job_id) jt.outputPath = ":%s" % ofile jt.errorPath = ":%s" % efile if sge_queue_name is not None: jt.setNativeSpecification( "-q %s" % sge_queue_name ) script = sge_template % (job_wrapper.galaxy_lib_dir, os.path.abspath( job_wrapper.working_directory ), command_line) fh = file( jt.remoteCommand, "w" ) fh.write( script ) fh.close() os.chmod( jt.remoteCommand, 0750 ) # job was deleted while we were preparing it if job_wrapper.get_state() == model.Job.states.DELETED: log.debug( "Job %s deleted by user before it entered the SGE queue" % job_wrapper.job_id ) self.cleanup( ( ofile, efile, jt.remoteCommand ) ) job_wrapper.cleanup() return galaxy_job_id = job_wrapper.job_id log.debug("(%s) submitting file %s" % ( galaxy_job_id, jt.remoteCommand ) ) log.debug("(%s) command is: %s" % ( galaxy_job_id, command_line ) ) # runJob will raise if there's a submit problem job_id = self.ds.runJob(jt) if sge_queue_name is None: log.debug("(%s) queued in default queue as %s" % (galaxy_job_id, job_id) ) else: log.debug("(%s) queued in %s queue as %s" % (galaxy_job_id, sge_queue_name, job_id) ) # store runner information for tracking if Galaxy restarts job_wrapper.set_runner( runner_url, job_id ) # Store SGE related state information for job sge_job_state = SGEJobState() sge_job_state.job_wrapper = job_wrapper sge_job_state.job_id = job_id sge_job_state.ofile = ofile sge_job_state.efile = efile sge_job_state.job_file = jt.remoteCommand sge_job_state.old_state = 'new' sge_job_state.running = False sge_job_state.runner_url = runner_url # delete the job template self.ds.deleteJobTemplate( jt ) # Add to our 'queue' of jobs to monitor self.queue.put( sge_job_state ) def monitor( self ): """ Watches jobs currently in the PBS queue and deals with state changes (queued to running) and job completion """ while 1: # Take any new watched jobs and put them on the monitor list try: while 1: sge_job_state = self.queue.get_nowait() if sge_job_state is self.STOP_SIGNAL: # TODO: This is where any cleanup would occur self.ds.exit() return self.watched.append( sge_job_state ) except Empty: pass # Iterate over the list of watched jobs and check state self.check_watched_items() # Sleep a bit before the next state check time.sleep( 1 ) def check_watched_items( self ): """ Called by the monitor thread to look at each watched job and deal with state changes. """ new_watched = [] for sge_job_state in self.watched: job_id = sge_job_state.job_id galaxy_job_id = sge_job_state.job_wrapper.job_id old_state = sge_job_state.old_state try: state = self.ds.getJobProgramStatus( job_id ) except DRMAA.InvalidJobError: # we should only get here if an orphaned job was put into the queue at app startup log.debug("(%s/%s) job left SGE queue" % ( galaxy_job_id, job_id ) ) self.finish_job( sge_job_state ) continue except Exception, e: # so we don't kill the monitor thread log.exception("(%s/%s) Unable to check job status" % ( galaxy_job_id, job_id ) ) log.warning("(%s/%s) job will now be errored" % ( galaxy_job_id, job_id ) ) sge_job_state.job_wrapper.fail( "Cluster could not complete job" ) continue if state != old_state: log.debug("(%s/%s) state change: %s" % ( galaxy_job_id, job_id, DRMAA_state[state] ) ) if state == DRMAA.Session.RUNNING and not sge_job_state.running: sge_job_state.running = True sge_job_state.job_wrapper.change_state( model.Job.states.RUNNING ) if state == DRMAA.Session.DONE: self.finish_job( sge_job_state ) continue if state == DRMAA.Session.FAILED: sge_job_state.job_wrapper.fail( "Cluster could not complete job" ) sge_job_state.job_wrapper.cleanup() continue sge_job_state.old_state = state new_watched.append( sge_job_state ) # Replace the watch list with the updated version self.watched = new_watched def finish_job( self, sge_job_state ): """ Get the output/error for a finished job, pass to `job_wrapper.finish` and cleanup all the SGE temporary files. """ ofile = sge_job_state.ofile efile = sge_job_state.efile job_file = sge_job_state.job_file # collect the output try: ofh = file(ofile, "r") efh = file(efile, "r") stdout = ofh.read() stderr = efh.read() except: stdout = '' stderr = 'Job output not returned from cluster' log.debug(stderr) try: sge_job_state.job_wrapper.finish( stdout, stderr ) except: log.exception("Job wrapper finish method failed") # clean up the sge files self.cleanup( ( ofile, efile, job_file ) ) def cleanup( self, files ): if not asbool( self.app.config.get( 'debug', False ) ): for file in files: if os.access( file, os.R_OK ): os.unlink( file ) def put( self, job_wrapper ): """Add a job to the queue (by job identifier)""" self.queue_job( job_wrapper ) def shutdown( self ): """Attempts to gracefully shut down the monitor thread""" log.info( "sending stop signal to worker threads" ) self.queue.put( self.STOP_SIGNAL ) log.info( "sge job runner stopped" ) def stop_job( self, job ): """Attempts to delete a job from the SGE queue""" try: self.ds.control( job.job_runner_external_id, DRMAA.Session.TERMINATE ) log.debug( "(%s/%s) Removed from SGE queue at user's request" % ( job.id, job.job_runner_external_id ) ) except DRMAA.InvalidJobError: log.debug( "(%s/%s) User killed running job, but it was already dead" % ( job.id, job.job_runner_external_id ) ) def recover( self, job, job_wrapper ): """Recovers jobs stuck in the queued/running state when Galaxy started""" sge_job_state = SGEJobState() sge_job_state.ofile = "%s/database/pbs/%s.o" % (os.getcwd(), job.id) sge_job_state.efile = "%s/database/pbs/%s.e" % (os.getcwd(), job.id) sge_job_state.job_file = "%s/database/pbs/galaxy_%s.sh" % (os.getcwd(), job.id) sge_job_state.job_id = str( job.job_runner_external_id ) sge_job_state.runner_url = job_wrapper.tool.job_runner job_wrapper.command_line = job.command_line sge_job_state.job_wrapper = job_wrapper if job.state == model.Job.states.RUNNING: log.debug( "(%s/%s) is still in running state, adding to the SGE queue" % ( job.id, job.job_runner_external_id ) ) sge_job_state.old_state = DRMAA.Session.RUNNING sge_job_state.running = True self.queue.put( sge_job_state ) elif job.state == model.Job.states.QUEUED: log.debug( "(%s/%s) is still in SGE queued state, adding to the SGE queue" % ( job.id, job.job_runner_external_id ) ) sge_job_state.old_state = DRMAA.Session.QUEUED sge_job_state.running = False self.queue.put( sge_job_state )

mit

sharad/calibre

src/calibre/gui2/dbus_export/menu.py

1

14852

#!/usr/bin/env python # vim:fileencoding=utf-8 from __future__ import (unicode_literals, division, absolute_import, print_function) __license__ = 'GPL v3' __copyright__ = '2014, Kovid Goyal <kovid at kovidgoyal.net>' # Support for excporting Qt's MenuBars/Menus over DBUS. The API is defined in # dbus-menu.xml from the libdbusmenu project https://launchpad.net/libdbusmenu import dbus from PyQt5.Qt import ( QApplication, QMenu, QIcon, QKeySequence, QObject, QEvent, QTimer, pyqtSignal, Qt) from calibre.utils.dbus_service import Object, BusName, method as dbus_method, dbus_property, signal as dbus_signal from calibre.gui2.dbus_export.utils import ( setup_for_cli_run, swap_mnemonic_char, key_sequence_to_dbus_shortcut, icon_to_dbus_menu_icon) null = object() def PropDict(mapping=()): return dbus.Dictionary(mapping, signature='sv') def create_properties_for_action(ac, previous=None): ans = PropDict() if ac.isSeparator(): ans['type'] = 'separator' if not ac.isVisible(): ans['visible'] = False return ans text = ac.text() or ac.iconText() if text: ans['label'] = swap_mnemonic_char(text) if not ac.isEnabled(): ans['enabled'] = False if not ac.isVisible() or ac.property('blocked') is True: ans['visible'] = False if ac.menu() is not None: ans['children-display'] = 'submenu' if ac.isCheckable(): exclusive = ac.actionGroup() is not None and ac.actionGroup().isExclusive() ans['toggle-type'] = 'radio' if exclusive else 'checkmark' ans['toggle-state'] = int(ac.isChecked()) shortcuts = ac.shortcuts() if shortcuts: sc = dbus.Array(signature='as') for s in shortcuts: if not s.isEmpty(): for x in key_sequence_to_dbus_shortcut(s): sc.append(dbus.Array(x, signature='s')) if sc: ans['shortcut'] = sc[:1] # Unity fails to display the shortcuts at all if more than one is specified if ac.isIconVisibleInMenu(): icon = ac.icon() if previous and previous.get('x-qt-icon-cache-key') == icon.cacheKey(): for x in 'icon-data x-qt-icon-cache-key'.split(): ans[x] = previous[x] else: data = icon_to_dbus_menu_icon(ac.icon()) if data is not None: ans['icon-data'] = data ans['x-qt-icon-cache-key'] = icon.cacheKey() return ans def menu_actions(menu): try: return menu.actions() except TypeError: if isinstance(menu, QMenu): return QMenu.actions(menu) raise class DBusMenu(QObject): handle_event_signal = pyqtSignal(object, object, object, object) def __init__(self, object_path, parent=None, bus=None): QObject.__init__(self, parent) # Unity barfs is the Event DBUS method does not return immediately, so # handle it asynchronously self.handle_event_signal.connect(self.handle_event, type=Qt.QueuedConnection) self.dbus_api = DBusMenuAPI(self, object_path, bus=bus) self.set_status = self.dbus_api.set_status self._next_id = 0 self.action_changed_timer = t = QTimer(self) t.setInterval(0), t.setSingleShot(True), t.timeout.connect(self.actions_changed) self.layout_changed_timer = t = QTimer(self) t.setInterval(0), t.setSingleShot(True), t.timeout.connect(self.layouts_changed) self.init_maps() @property def object_path(self): return self.dbus_api._object_path def init_maps(self, qmenu=None): self.action_changes = set() self.layout_changes = set() self.qmenu = qmenu self._id_to_action, self._action_to_id = {}, {} self._action_properties = {} @property def next_id(self): self._next_id += 1 return self._next_id def id_to_action(self, action_id): if self.qmenu is None: return None return self._id_to_action.get(action_id) def action_to_id(self, action): if self.qmenu is None: return None return self._action_to_id.get(action) def action_properties(self, action_id, restrict_to=None): if self.qmenu is None: return {} ans = self._action_properties.get(action_id, PropDict()) if restrict_to: ans = PropDict({k:v for k, v in ans.iteritems() if k in restrict_to}) return ans def publish_new_menu(self, qmenu=None): self.init_maps(qmenu) if qmenu is not None: qmenu.destroyed.connect(lambda obj=None:self.publish_new_menu()) ac = qmenu.menuAction() self.add_action(ac) self.dbus_api.LayoutUpdated(self.dbus_api.revision, 0) def set_visible(self, visible): ac = self.id_to_action(0) if ac is not None and self.qmenu is not None: changed = False blocked = not visible for ac in menu_actions(ac.menu()): ac_id = self.action_to_id(ac) if ac_id is not None: old = ac.property('blocked') if old is not blocked: ac.setProperty('blocked', blocked) self.action_changes.add(ac_id) changed = True if changed: self.action_changed_timer.start() def add_action(self, ac): ac_id = 0 if ac.menu() is self.qmenu else self.next_id self._id_to_action[ac_id] = ac self._action_to_id[ac] = ac_id self._action_properties[ac_id] = create_properties_for_action(ac) if ac.menu() is not None: self.add_menu(ac.menu()) def add_menu(self, menu): menu.installEventFilter(self) for ac in menu_actions(menu): self.add_action(ac) def eventFilter(self, obj, ev): ac = getattr(obj, 'menuAction', lambda : None)() ac_id = self.action_to_id(ac) if ac_id is not None: etype = ev.type() if etype == QEvent.ActionChanged: ac_id = self.action_to_id(ev.action()) self.action_changes.add(ac_id) self.action_changed_timer.start() elif etype == QEvent.ActionAdded: self.layout_changes.add(ac_id) self.layout_changed_timer.start() self.add_action(ev.action()) elif etype == QEvent.ActionRemoved: self.layout_changes.add(ac_id) self.layout_changed_timer.start() self.action_removed(ev.action()) return False def actions_changed(self): updated_props = dbus.Array(signature='(ia{sv})') removed_props = dbus.Array(signature='(ias)') for ac_id in self.action_changes: ac = self.id_to_action(ac_id) if ac is None: continue old_props = self.action_properties(ac_id) new_props = self._action_properties[ac_id] = create_properties_for_action(ac, old_props) removed = set(old_props) - set(new_props) if removed: removed_props.append((ac_id, dbus.Array(removed, signature='as'))) updated = PropDict({k:v for k, v in new_props.iteritems() if v != old_props.get(k, null)}) if updated: updated_props.append((ac_id, updated)) self.action_changes = set() if updated_props or removed_props: self.dbus_api.ItemsPropertiesUpdated(updated_props, removed_props) return updated_props, removed_props def layouts_changed(self): changes = set() for ac_id in self.layout_changes: if ac_id in self._id_to_action: changes.add(ac_id) self.layout_changes = set() if changes: self.dbus_api.revision += 1 for change in changes: self.dbus_api.LayoutUpdated(self.dbus_api.revision, change) return changes def action_is_in_a_menu(self, ac): all_menus = {ac.menu() for ac in self._action_to_id} all_menus.discard(None) return bool(set(ac.associatedWidgets()).intersection(all_menus)) def action_removed(self, ac): if not self.action_is_in_a_menu(ac): ac_id = self._action_to_id.pop(ac, None) self._id_to_action.pop(ac_id, None) self._action_properties.pop(ac_id, None) def get_layout(self, parent_id, depth, property_names): # Ensure any pending updates are done, as they are needed now self.actions_changed() self.layouts_changed() property_names = property_names or None props = self.action_properties(parent_id, property_names) return parent_id, props, self.get_layout_children(parent_id, depth, property_names) def get_layout_children(self, parent_id, depth, property_names): ans = dbus.Array(signature='(ia{sv}av)') ac = self.id_to_action(parent_id) if ac is not None and depth != 0 and ac.menu() is not None: for child in menu_actions(ac.menu()): child_id = self.action_to_id(child) if child_id is not None: props = self.action_properties(child_id, property_names) ans.append((child_id, props, self.get_layout_children(child_id, depth - 1, property_names))) return ans def get_properties(self, ids=None, property_names=None): property_names = property_names or None ans = dbus.Array(signature='(ia{sv})') for action_id in (ids or self._id_to_action): ans.append((action_id, self.action_properties(action_id, property_names))) return ans def handle_event(self, action_id, event, data, timestamp): ac = self.id_to_action(action_id) if event == 'clicked': if ac.isCheckable(): ac.toggle() ac.triggered.emit(ac.isCheckable() and ac.isChecked()) def handle_about_to_show(self, ac): child_ids = {self.action_to_id(x) for x in menu_actions(ac.menu())} child_ids.discard(None) ac_id = self.action_to_id(ac) ac.menu().aboutToShow.emit() if ac_id in self.layout_changes or child_ids.intersection(self.action_changes): return True return False class DBusMenuAPI(Object): IFACE = 'com.canonical.dbusmenu' def __init__(self, menu, object_path, bus=None): if bus is None: bus = dbus.SessionBus() Object.__init__(self, bus, object_path) self.status = 'normal' self.menu = menu self.revision = 0 @dbus_property(IFACE, signature='u') def Version(self): return 3 # GTK 3 uses 3, KDE 4 uses 2 @dbus_property(IFACE, signature='s', emits_changed_signal=True) def Status(self): return self.status def set_status(self, normal=True): self.status = 'normal' if normal else 'notice' self.PropertiesChanged(self.IFACE, {'Status': self.status}, []) @dbus_property(IFACE, signature='s') def TextDirection(self): return 'ltr' if QApplication.instance().isLeftToRight() else 'rtl' @dbus_property(IFACE, signature='as') def IconThemePath(self): return dbus.Array(signature='s') @dbus_method(IFACE, in_signature='iias', out_signature='u(ia{sv}av)') def GetLayout(self, parentId, recursionDepth, propertyNames): layout = self.menu.get_layout(parentId, recursionDepth, propertyNames) return self.revision, layout @dbus_method(IFACE, in_signature='aias', out_signature='a(ia{sv})') def GetGroupProperties(self, ids, propertyNames): return self.menu.get_properties(ids, propertyNames) @dbus_method(IFACE, in_signature='is', out_signature='v') def GetProperty(self, id, name): return self.menu.action_properties(id).get(name, '') @dbus_method(IFACE, in_signature='isvu', out_signature='') def Event(self, id, eventId, data, timestamp): ''' This is called by the applet to notify the application an event happened on a menu item. eventId can be one of the following:: * "clicked" * "hovered" * "opened" * "closed" Vendor specific events can be added by prefixing them with "x-<vendor>-"''' if self.menu.id_to_action(id) is not None: self.menu.handle_event_signal.emit(id, eventId, data, timestamp) @dbus_method(IFACE, in_signature='a(isvu)', out_signature='ai') def EventGroup(self, events): ''' Used to pass a set of events as a single message for possibily several different menuitems. This is done to optimize DBus traffic. Should return a list of ids that are not found. events is a list of events in the same format as used for the Event method.''' missing = dbus.Array(signature='u') for id, eventId, data, timestamp in events: if self.menu.id_to_action(id) is not None: self.menu.handle_event_signal.emit(id, eventId, data, timestamp) else: missing.append(id) return missing @dbus_method(IFACE, in_signature='i', out_signature='b') def AboutToShow(self, id): ac = self.menu.id_to_action(id) if ac is not None and ac.menu() is not None: return self.menu.handle_about_to_show(ac) return False @dbus_method(IFACE, in_signature='ai', out_signature='aiai') def AboutToShowGroup(self, ids): updates_needed = dbus.Array(signature='i') id_errors = dbus.Array(signature='i') for ac_id in ids: ac = self.menu.id_to_action(id) if ac is not None and ac.menu() is not None: if self.menu.handle_about_to_show(ac): updates_needed.append(ac_id) else: id_errors.append(ac_id) return updates_needed, id_errors @dbus_signal(IFACE, 'a(ia{sv})a(ias)') def ItemsPropertiesUpdated(self, updatedProps, removedProps): pass @dbus_signal(IFACE, 'ui') def LayoutUpdated(self, revision, parent): pass @dbus_signal(IFACE, 'iu') def ItemActivationRequested(self, id, timestamp): pass def test(): setup_for_cli_run() app = QApplication([]) bus = dbus.SessionBus() dbus_name = BusName('com.calibre-ebook.TestDBusMenu', bus=bus, do_not_queue=True) m = QMenu() ac = m.addAction(QIcon(I('window-close.png')), 'Quit', app.quit) ac.setShortcut(QKeySequence('Ctrl+Q')) menu = DBusMenu('/Menu', bus=bus) menu.publish_new_menu(m) app.exec_() del dbus_name if __name__ == '__main__': test()

gpl-3.0

tensorflow/agents

tf_agents/networks/nest_map.py

1

8386

# coding=utf-8 # Copyright 2020 The TF-Agents 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 # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Network layer that allows mapping multiple inputs.""" from __future__ import absolute_import from __future__ import division # Using Type Annotations. from __future__ import print_function import copy import typing import tensorflow.compat.v2 as tf from tf_agents.networks import network from tf_agents.networks import sequential from tf_agents.typing import types from tf_agents.utils import nest_utils def NestFlatten() -> tf.keras.layers.Layer: # pylint: disable=invalid-name """Returns a Keras layer that takes a nest of inputs, and returns a list. Useful in combination with `NestMap` to combine processed inputs: ```python # Process inputs in dictionary {"inp1": ..., "inp2": ...}, then # flatten the resulting tensors into a list, and finally pass this # list to tf.keras.layers.Add() to sum the values element-wise. net = tf_agents.networks.Sequence([ NestMap({"inp1": layer1, "inp2": layer2}), NestFlatten(), tf.keras.layers.Add(), ]) combined_outputs, next_state = net({"inp1": inp1, "inp2": inp2}, state) ``` """ return tf.keras.layers.Lambda(tf.nest.flatten) class NestMap(network.Network): """The `NestMap` network processes nested inputs via nested layers. It is a TF-Agents network that can be used to process nested inputs. Stateful Keras layers (e.g. LSTMCell, RNN, LSTM, TF-Agents DynamicUnroll) are all supported. The `state_spec` of `NestMap` has a structure matching that of `nested_layers`. `NestMap` can be used in conjunction with `NestFlatten` and a combiner (e.g. `tf.keras.layers.Add` or `tf.keras.layers.Concatenate`) to process and aggregate in a preprocessing step. Usage: ```python net = NestMap({"inp1": layer1, "inp2": layer2}) outputs, next_state = net({"inp1": inp1, "inp2": inp2}, state) ``` """ def __init__(self, nested_layers: types.NestedLayer, input_spec: typing.Optional[types.NestedTensorSpec] = None, name: typing.Optional[typing.Text] = None): """Create a Sequential Network. Args: nested_layers: A nest of layers and/or networks. These will be used to process the inputs (input nest structure will have to match this structure). Any layers that are subclasses of `tf.keras.layers.{RNN,LSTM,GRU,...}` are wrapped in `tf_agents.keras_layers.RNNWrapper`. input_spec: (Optional.) A nest of `tf.TypeSpec` representing the input observations. The structure of `input_spec` must match that of `nested_layers`. name: (Optional.) Network name. Raises: TypeError: If any of the layers are not instances of keras `Layer`. ValueError: If `input_spec` is provided but its nest structure does not match that of `nested_layers`. RuntimeError: If not `tf.executing_eagerly()`; as this is required to be able to create deep copies of layers in `layers`. """ if not tf.executing_eagerly(): raise RuntimeError( 'Not executing eagerly - cannot make deep copies of `nested_layers`.') flat_nested_layers = tf.nest.flatten(nested_layers) for layer in flat_nested_layers: if not isinstance(layer, tf.keras.layers.Layer): raise TypeError( 'Expected all layers to be instances of keras Layer, but saw' ': \'{}\''.format(layer)) if input_spec is not None: nest_utils.assert_same_structure( nested_layers, input_spec, message=( '`nested_layers` and `input_spec` do not have matching structures' )) flat_input_spec = tf.nest.flatten(input_spec) else: flat_input_spec = [None] * len(flat_nested_layers) # Wrap in Sequential if necessary. flat_nested_layers = [ sequential.Sequential([m], s) if not isinstance(m, network.Network) else m for (s, m) in zip(flat_input_spec, flat_nested_layers) ] flat_nested_layers_state_specs = [m.state_spec for m in flat_nested_layers] nested_layers = tf.nest.pack_sequence_as(nested_layers, flat_nested_layers) # We use flattened layers and states here instead of tf.nest.map_structure # for several reason. One is that we perform several operations against # the layers and we want to avoid calling into tf.nest.map* multiple times. # But the main reason is that network states have a different *structure* # than the layers; e.g., `nested_layers` may just be tf.keras.layers.LSTM, # but the states would then have structure `[.,.]`. Passing these in # as args to tf.nest.map_structure causes it to fail. Instead we would # have to use nest.map_structure_up_to -- but that function is not part # of the public TF API. However, if we do everything in flatland and then # use pack_sequence_as, we bypass the more rigid structure tests. state_spec = tf.nest.pack_sequence_as( nested_layers, flat_nested_layers_state_specs) super(NestMap, self).__init__(input_tensor_spec=input_spec, state_spec=state_spec, name=name) self._nested_layers = nested_layers @property def nested_layers(self) -> types.NestedNetwork: # Return a shallow copy so users don't modify the layers list. return tf.nest.map_structure(lambda m: m, self._nested_layers) def copy(self, **kwargs) -> 'NestMap': """Make a copy of a `NestMap` instance. **NOTE** A copy of a `NestMap` instance always performs a deep copy of the underlying layers, so the new instance will not share weights with the original - but it will start with the same weights. Args: **kwargs: Args to override when recreating this network. Commonly overridden args include 'name'. Returns: A deep copy of this network. """ new_kwargs = dict(self._saved_kwargs, **kwargs) if 'nested_layers' not in new_kwargs: new_nested_layers = [copy.deepcopy(m) for m in self._nested_layers] new_kwargs['nested_layers'] = new_nested_layers return type(self)(**new_kwargs) def call(self, inputs, network_state=(), **kwargs): nest_utils.assert_same_structure( self._nested_layers, inputs, allow_shallow_nest1=True, message=( '`self.nested_layers` and `inputs` do not have matching structures') ) if network_state: nest_utils.assert_same_structure( self.state_spec, network_state, allow_shallow_nest1=True, message=( 'network_state and state_spec do not have matching structure')) nested_layers_state = network_state else: nested_layers_state = tf.nest.map_structure( lambda _: (), self._nested_layers) # Here we must use map_structure_up_to because nested_layers_state has a # "deeper" structure than self._nested_layers. For example, an LSTM # layer's state is composed of a list with two tensors. The # tf.nest.map_structure function would raise an error if two # "incompatible" structures are passed in this way. def _mapper(inp, layer, state): # pylint: disable=invalid-name return layer(inp, network_state=state, **kwargs) outputs_and_next_state = nest_utils.map_structure_up_to( self._nested_layers, _mapper, inputs, self._nested_layers, nested_layers_state) flat_outputs_and_next_state = nest_utils.flatten_up_to( self._nested_layers, outputs_and_next_state) flat_outputs, flat_next_state = zip(*flat_outputs_and_next_state) outputs = tf.nest.pack_sequence_as( self._nested_layers, flat_outputs) next_network_state = tf.nest.pack_sequence_as( self._nested_layers, flat_next_state) return outputs, next_network_state

apache-2.0

AmI-2014/Python-Lab1

fibonacci.py

1

1295

''' Created on Mar 18, 2014 @author: Dario Bonino <[email protected]> Copyright (c) 2014 Dario Bonino 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 ''' def fib(order): # initialization, we use a tuple a = (0, 1) # the resulting array fibonacci = [] # init while variable i = 0 # fill the array while i < order: a = (a[1], a[0] + a[1]) fibonacci.append(a[0]) i+=1 return fibonacci if __name__ == '__main__': # get the series order as a string order_as_string = raw_input("Insert the Fibonacci's series order:\n>") # convert the string to an integer number order = int(order_as_string) # get the Fibonacci's series value values = fib(order) # print the values print values

apache-2.0

mapzen/vector-datasource

integration-test/480-rest_area-services.py

2

3053

# -*- encoding: utf-8 -*- from shapely.wkt import loads as wkt_loads import dsl from . import FixtureTest class RestAreaServices(FixtureTest): def test_rest_area_node(self): self.generate_fixtures(dsl.way(159773030, wkt_loads('POINT (-76.73912905210828 40.99079246918038)'), {u'source': u'openstreetmap.org', u'highway': u'rest_area', u'name': u'Foo Rest Area'})) # noqa self.assert_has_feature( 16, 18798, 24573, 'pois', {'kind': 'rest_area', 'id': 159773030, 'min_zoom': 13}) def test_rest_area_way(self): # Way: Crystal Springs Rest Area (97057565) self.generate_fixtures(dsl.way(97057565, wkt_loads('POLYGON ((-122.365488944754 37.54048597695269, -122.363673359734 37.53894968362569, -122.363521634282 37.53881541316188, -122.363421831454 37.53868392047339, -122.363355445955 37.53852749658311, -122.363020823511 37.53696630264469, -122.36330406232 37.5367516065384, -122.365488944754 37.54048597695269))'), {u'drinking_water': u'yes', u'toilets': u'yes', u'handicapped_accessible': u'yes', u'vending': u'yes', u'name': u'Crystal Springs Rest Area', u'sanitation': u'no', u'area': u'yes', u'route': u'280', u'way_area': u'35229.6', u'pet_area': u'yes', u'phone': u'yes', u'picnic_tables': u'yes', u'source': u'openstreetmap.org', u'addr:county': u'San Mateo', u'attribution': u'Caltrans', u'caltrans:district': u'4', u'highway': u'rest_area', u'description': u'Near San Francisco Reservoir'})) # noqa self.assert_has_feature( 16, 10492, 25385, 'landuse', {'kind': 'rest_area', 'id': 97057565, 'sort_rank': 44}) def test_service_area_node(self): # NOTE: this has been remapped as an area now. the test data here # is superseded by the 1698-too-many-service-areas test. # node: Tiffin River self.generate_fixtures(dsl.way(200412620, wkt_loads('POINT (-84.41292493378698 41.6045519557572)'), {u'source': u'openstreetmap.org', u'name': u'Tiffin River', u'highway': u'services'})) # noqa self.assert_has_feature( 16, 17401, 24424, 'pois', {'kind': 'service_area', 'id': 200412620, 'min_zoom': 17}) def test_service_area_way(self): # Way: Nicole Driveway (274732386) self.generate_fixtures(dsl.way(274732386, wkt_loads('POLYGON ((-120.123766060274 38.09757738412661, -120.123761209371 38.0977196908478, -120.123658621766 38.0979925683359, -120.123633379106 38.0982482663423, -120.123585319239 38.098378271151, -120.123533216952 38.09837445372108, -120.123577234401 38.09825915310519, -120.123617928083 38.09797468287368, -120.123713957987 38.09768759586379, -120.123702639215 38.09747749355018, -120.123762826339 38.09746978790201, -120.123766060274 38.09757738412661))'), {u'source': u'openstreetmap.org', u'way_area': u'744.019', u'name': u'Nicole Driveway', u'highway': u'services', u'area': u'yes'})) # noqa self.assert_has_feature( 16, 10900, 25256, 'landuse', {'kind': 'service_area', 'id': 274732386, 'sort_rank': 45})

mit

janusnic/dj-21v

unit_07/mysite/blog/models.py

1

1898

from django.db import models import datetime from django.utils import timezone from django.utils.encoding import python_2_unicode_compatible class Category(models.Model): name = models.CharField(max_length=100) slug = models.SlugField(max_length=100, unique=True, verbose_name='slug') description = models.TextField(max_length=4096) def __str__(self): return '%s' % (self.name) class Tag(models.Model): name = models.CharField(max_length=100, unique=True) slug = models.SlugField(max_length=100, unique=True, verbose_name='slug') def __str__(self): return '%s' % (self.name) @python_2_unicode_compatible class Article(models.Model): ARTICLE_STATUS = ( ('D', 'Not Reviewed'), ('P', 'Published'), ('E', 'Expired'), ) title = models.CharField(max_length=100, unique=True) slug = models.SlugField(max_length=100, unique=True, verbose_name='slug') status = models.IntegerField(default=0) content = models.TextField() status = models.CharField(max_length=1, choices=ARTICLE_STATUS, default='D') category = models.ForeignKey(Category, verbose_name="the related category") tags = models.ManyToManyField(Tag, verbose_name="the related tags", related_name="keyword_set", blank=True) views = models.IntegerField(default=0) publish_date = models.DateTimeField(auto_now=True, editable=False, help_text="Please use the following format: <em>YYYY-MM-DD</em>.") created_date = models.DateTimeField(auto_now_add=True, editable=False) def was_published_recently(self): return self.publish_date >= timezone.now() - datetime.timedelta(days=1) was_published_recently.admin_order_field = 'publish_date' was_published_recently.boolean = True was_published_recently.short_description = 'Published recently?' def __str__(self): return '%s' % (self.title)

mit

dgjnpr/py-junos-eznc

lib/jnpr/junos/factory/viewfields.py

1

1923

class ViewFields(object): """ Used to dynamically create a field dictionary used with the RunstatView class """ def __init__(self): self._fields = dict() def _prockvargs(self, field, name, **kvargs): if not len(kvargs): return field[name].update(kvargs) @property def end(self): return self._fields def str(self, name, xpath=None, **kvargs): """ field is a string """ if xpath is None: xpath = name field = {name: {'xpath': xpath}} self._prockvargs(field, name, **kvargs) self._fields.update(field) return self def astype(self, name, xpath=None, astype=int, **kvargs): """ field string value will be passed to function :astype: This is typically used to do simple type conversions, but also works really well if you set :astype: to a function that does a basic converstion like look at the value and change it to a True/False. For example: astype=lambda x: True if x == 'enabled' else False """ if xpath is None: xpath = name field = { name: {'xpath': xpath, 'astype': astype} } self._prockvargs(field, name, **kvargs) self._fields.update(field) return self def int(self, name, xpath=None, **kvargs): """ field is an integer """ return self.astype(name, xpath, int, **kvargs) def flag(self, name, xpath=None, **kvargs): """ field is a flag, results in True/False if the xpath element exists or not. Model this as a boolean type <bool> """ return self.astype(name, xpath, bool, **kvargs) def table(self, name, table): """ field is a RunstatTable """ self._fields.update({ name: {'table': table} }) return self

apache-2.0