Datasets:

code-rag-bench

/

github-repos-python

like 2

Follow

CodeRAG-Bench 12

import psycopg2 from psycopg2 import extras from flask import Flask, request, session, g, redirect, url_for, \ abort, render_template, flash import config from contextlib import closing from database import connect_db # create our little application :) app = Flask(__name__) app.config.from_object(config) app.config.from_envvar('FLASKR_SETTINGS', silent=True) def init_db(): with closing(connect_db()) as db: with app.open_resource('schema.sql', mode='r') as file: db.cursor().execute(file.read()) db.commit() def add_paprika(): with closing(connect_db()) as db: db.execute('UPDATE entries set title = ?', ['PAPRIKA']) db.commit() def query_db(query, args=(), one=False): dict_cur = g.db.cursor(cursor_factory=psycopg2.extras.DictCursor) dict_cur.execute(query, args) rv = dict_cur.fetchall() dict_cur.close() return (rv[0] if rv else None) if one else rv @app.before_request def before_request(): g.db = connect_db() @app.teardown_request def teardown_request(exception): db = getattr(g, 'db', None) if db is not None: db.close() @app.route('/') def show_entries(): entries = query_db('select title, text from entries order by id desc') return render_template('show_entries.html', entries=entries) @app.route('/add', methods=['POST']) def add_entry(): if not session.get('logged_in'): abort(401) dict_cur = g.db.cursor(cursor_factory=psycopg2.extras.DictCursor) dict_cur.execute('insert into entries (title, text) values (%s,%s)', [request.form['title'], request.form['text']]) dict_cur.execute('COMMIT') flash('New entry was successfully posted') return redirect(url_for('show_entries')) @app.route('/login', methods=['GET', 'POST']) def login(): error = None if request.method == 'POST': if request.form['username'] != app.config['USERNAME']: error = 'Invalid username' elif request.form['password'] != app.config['PASSWORD']: error = 'Invalid password' else: session['logged_in'] = True flash('You were logged in') return redirect(url_for('show_entries')) return render_template('login.html', error=error) @app.route('/logout') def logout(): session.pop('logged_in', None) flash('You were logged out') return redirect(url_for('show_entries')) if __name__ == '__main__': app.run()

from sqlalchemy import create_engine from sqlalchemy.orm import sessionmaker from setupDB import Base, Project, Category, Entry engine = create_engine('sqlite:///projects.db') # Bind the engine to the metadata of the Base class so that the # declaratives can be accessed through a DBSession instance Base.metadata.bind = engine DBSession = sessionmaker(bind=engine) # A DBSession() instance establishes all conversations with the database # and represents a "staging zone" for all the objects loaded into the # database session object. Any change made against the objects in the # session won't be persisted into the database until you call # session.commit(). If you're not happy about the changes, you can # revert all of them back to the last commit by calling # session.rollback() session = DBSession() project1 = Project(name = "TEST PROJECT") session.add(project1) session.commit() category1 = Category(name = "TEST CATEGORY", project_id = 1) session.add(category1) session.commit() entry1 = Entry(description = "THIS IS A TEST DESCRIPTION.", category_id = 1, project_id = 1) session.add(entry1) session.commit()

def build_from_name(city_name): return { 'match':{ 'name':{ 'query':city_name, 'operator':'and', 'fuzziness':'AUTO' } } } def build_from_guide(guide, regions=None, weathers=None, months=None, ignore_cids=None, ignore_name=None): must = [{ 'match':{'guide':{ 'query':guide, 'cutoff_frequency':0.005 }} }] should = [] must_not = [] if regions: if isinstance(regions, (unicode,str)): regions = [regions] must.append({ 'match':{'regions':{ 'query':regions, 'operator':'or', 'fuzziness':'AUTO' }} }) if weathers: if isinstance(weathers, dict): weathers = [weathers] should += list(yield_weather_conditions(*weathers)) if months: should.append({'terms':{'months_ideal':months}}) if ignore_cids: must_not.append({'terms':{'cid':ignore_cids}}) if ignore_name: must_not.append(build_from_name(ignore_name)) query = {'bool':{'must':must}} if should: query['bool']['should'] = should if must_not: query['bool']['must_not'] = must_not return query def yield_weather_conditions(*weathers): temps = [w['temperature']['celsius'] for w in weathers if 'celsius' in w.get('temperature',{})] if temps: yield {'range':{'weather.temperature.celsius':{ 'gte':min(temps) - 3, 'lte':max(temps) + 3 }}} humidities = [w['humidity'] for w in weathers if 'humidity' in w] if humidities: yield {'range':{'weather.humidity':{ 'gte':min(humidities) - 0.1, 'lte':max(humidities) + 0.1 }}} patterns = [w['pattern'] for w in weathers if 'pattern' in w] if patterns: yield {'terms':{'weather.pattern':patterns}}

from copy import deepcopy from enum import Enum, unique import json from kii import exceptions as exc class Clause: def __str__(self): return '<Clause Object:{0}> {1}'.format( id(self), json.dumps(self.query(), ensure_ascii=False)) @classmethod def all(cls): return AllClause() @classmethod def and_(cls, *clauses): return AndClause(*clauses) @classmethod def eq(cls, field, value): return EqualClause(field, value) @classmethod def in_(cls, field, values): return InClause(field, values) @classmethod def not_(cls, clause): return NotClause(clause) @classmethod def or_(cls, *clauses): return OrClause(*clauses) @classmethod def prefix(cls, field, prefix): return PrefixClause(field, prefix) def clone(self): return deepcopy(self) class AllClause(Clause): def query(self): return {'type': 'all'} class AndClause(Clause): def __init__(self, *clauses): for c in clauses: if not isinstance(c, Clause): raise exc.KiiInvalidClauseError self.children = list(clauses) def __len__(self): return len(self.children) def add(self, clause): if not isinstance(clause, Clause): raise exc.KiiInvalidClauseError self.children.append(clause) return self def query(self): return { 'type': 'and', 'clauses': [c.query() for c in self.children], } class EqualClause(Clause): def __init__(self, field, value): self.field = field self.value = value def query(self): return { 'type': 'eq', 'field': self.field, 'value': self.value, } class GeoBoxClause(Clause): def __init__(self, field, ne_lat, ne_lon, sw_lat, sw_lon): self.field = field self.ne_lat = ne_lat self.ne_lon = ne_lon self.sw_lat = sw_lat self.sw_lon = sw_lon def query(self): return { 'type': 'geobox', 'field': self.field, 'box': { 'ne': { '_type': 'point', 'lat': self.ne_lat, 'lon': self.ne_lon, }, 'sw': { '_type': 'point', 'lat': self.sw_lat, 'lon': self.sw_lon, } } } class GeoDistanceClause(Clause): def __init__(self, field, center_lat, center_lon, radius, put_distance_into=None): self.field = field self.center_lat = center_lat self.center_lon = center_lon self.radius = radius self.put_distance_into = put_distance_into def query(self): params = { 'type': 'geodistance', 'field': self.field, 'center': { '_type': 'point', 'lat': self.center_lat, 'lon': self.center_lon, }, 'radius': self.radius, } if self.put_distance_into is not None: params['putDistanceInto'] = self.put_distance_into return params class HasFieldClause(Clause): @unique class Types(Enum): string = 'STRING' integer = 'INTEGER' decimal = 'DECIMAL' boolean = 'BOOLEAN' def __init__(self, field, field_type): self.field = field if not isinstance(field_type, HasFieldClause.Types): field_type = HasFieldClause.Types(field_type) self.field_type = field_type def query(self): return { 'type': 'hasField', 'field': self.field, 'fieldType': self.field_type.value, } class InClause(Clause): def __init__(self, field, values): self.field = field if not isinstance(values, (tuple, list)): values = tuple(values) self.values = values def query(self): return { 'type': 'in', 'field': self.field, 'values': self.values, } class NotClause(Clause): def __init__(self, clause): if not isinstance(clause, Clause): raise exc.KiiInvalidClauseError self.clause = clause def query(self): return { 'type': 'not', 'clause': self.clause.query(), } class OrClause(AndClause): def query(self): return { 'type': 'or', 'clauses': [c.query() for c in self.children], } class PrefixClause(Clause): def __init__(self, field, prefix): self.field = field self.prefix = prefix def query(self): return { 'type': 'prefix', 'field': self.field, 'prefix': self.prefix, } class RangeClause(Clause): def __init__(self, field): self.field = field self.lower_limit = None self.lower_included = True self.upper_limit = None self.upper_included = True def query(self): query = { 'type': 'range', 'field': self.field, } if self.lower_limit is not None: query['lowerLimit'] = self.lower_limit if not self.lower_included: query['lowerIncluded'] = self.lower_included if self.upper_limit is not None: query['upperLimit'] = self.upper_limit if not self.upper_included: query['upperIncluded'] = self.upper_included return query def ge(self, lower_limit): self.lower_limit = lower_limit self.lower_included = True return self def gt(self, lower_limit): self.lower_limit = lower_limit self.lower_included = False return self def le(self, upper_limit): self.upper_limit = upper_limit self.upper_included = True return self def lt(self, upper_limit): self.upper_limit = upper_limit self.upper_included = False return self

"""Test Python APIs for working with formatters""" from __future__ import print_function import lldb from lldbsuite.test.decorators import * from lldbsuite.test.lldbtest import * from lldbsuite.test import lldbutil class SBFormattersAPITestCase(TestBase): mydir = TestBase.compute_mydir(__file__) NO_DEBUG_INFO_TESTCASE = True def setUp(self): # Call super's setUp(). TestBase.setUp(self) self.line = line_number('main.cpp', '// Set break point at this line.') @add_test_categories(['pyapi']) def test_formatters_api(self): """Test Python APIs for working with formatters""" self.build() self.setTearDownCleanup() """Test Python APIs for working with formatters""" self.runCmd("file " + self.getBuildArtifact("a.out"), CURRENT_EXECUTABLE_SET) lldbutil.run_break_set_by_file_and_line( self, "main.cpp", self.line, num_expected_locations=1, loc_exact=True) self.runCmd("run", RUN_SUCCEEDED) # The stop reason of the thread should be breakpoint. self.expect("thread list", STOPPED_DUE_TO_BREAKPOINT, substrs=['stopped', 'stop reason = breakpoint']) # This is the function to remove the custom formats in order to have a # clean slate for the next test case. def cleanup(): self.runCmd('type format clear', check=False) self.runCmd('type summary clear', check=False) self.runCmd('type filter clear', check=False) self.runCmd('type synthetic clear', check=False) self.runCmd('type category delete foobar', check=False) self.runCmd('type category delete JASSynth', check=False) self.runCmd('type category delete newbar', check=False) # Execute the cleanup function during test case tear down. self.addTearDownHook(cleanup) format = lldb.SBTypeFormat(lldb.eFormatHex) category = self.dbg.GetDefaultCategory() category.AddTypeFormat(lldb.SBTypeNameSpecifier("int"), format) self.expect("frame variable foo.A", substrs=['0x00000001']) self.expect("frame variable foo.E", matching=False, substrs=['b8cca70a']) category.AddTypeFormat(lldb.SBTypeNameSpecifier("long"), format) self.expect("frame variable foo.A", substrs=['0x00000001']) self.expect("frame variable foo.E", substrs=['b8cca70a']) format.SetFormat(lldb.eFormatOctal) category.AddTypeFormat(lldb.SBTypeNameSpecifier("int"), format) self.expect("frame variable foo.A", substrs=[' 01']) self.expect("frame variable foo.E", substrs=['b8cca70a']) category.DeleteTypeFormat(lldb.SBTypeNameSpecifier("int")) category.DeleteTypeFormat(lldb.SBTypeNameSpecifier("long")) self.expect("frame variable foo.A", matching=False, substrs=[' 01']) self.expect("frame variable foo.E", matching=False, substrs=['b8cca70a']) summary = lldb.SBTypeSummary.CreateWithSummaryString( "the hello world you'll never see") summary.SetSummaryString('hello world') new_category = self.dbg.GetCategory("foobar") self.assertFalse( new_category.IsValid(), "getting a non-existing category worked") new_category = self.dbg.CreateCategory("foobar") new_category.SetEnabled(True) new_category.AddTypeSummary( lldb.SBTypeNameSpecifier( "^.*t$", True, # is_regexp ), summary) self.expect("frame variable foo.A", substrs=['hello world']) self.expect("frame variable foo.E", matching=False, substrs=['hello world']) self.expect("frame variable foo.B", substrs=['hello world']) self.expect("frame variable foo.F", substrs=['hello world']) new_category.SetEnabled(False) self.expect("frame variable foo.A", matching=False, substrs=['hello world']) self.expect("frame variable foo.E", matching=False, substrs=['hello world']) self.expect("frame variable foo.B", matching=False, substrs=['hello world']) self.expect("frame variable foo.F", matching=False, substrs=['hello world']) self.dbg.DeleteCategory(new_category.GetName()) self.expect("frame variable foo.A", matching=False, substrs=['hello world']) self.expect("frame variable foo.E", matching=False, substrs=['hello world']) self.expect("frame variable foo.B", matching=False, substrs=['hello world']) self.expect("frame variable foo.F", matching=False, substrs=['hello world']) filter = lldb.SBTypeFilter(0) filter.AppendExpressionPath("A") filter.AppendExpressionPath("D") self.assertTrue( filter.GetNumberOfExpressionPaths() == 2, "filter with two items does not have two items") category.AddTypeFilter(lldb.SBTypeNameSpecifier("JustAStruct"), filter) self.expect("frame variable foo", substrs=['A = 1', 'D = 6.28']) self.expect("frame variable foo", matching=False, substrs=['B = ', 'C = ', 'E = ', 'F = ']) category.DeleteTypeFilter( lldb.SBTypeNameSpecifier( "JustAStruct", True)) self.expect("frame variable foo", substrs=['A = 1', 'D = 6.28']) self.expect("frame variable foo", matching=False, substrs=['B = ', 'C = ', 'E = ', 'F = ']) category.DeleteTypeFilter( lldb.SBTypeNameSpecifier( "JustAStruct", False)) self.expect("frame variable foo", substrs=['A = 1', 'D = 6.28']) self.expect("frame variable foo", matching=True, substrs=['B = ', 'C = ', 'E = ', 'F = ']) self.runCmd("command script import --allow-reload ./synth.py") self.expect("frame variable foo", matching=False, substrs=['X = 1']) self.dbg.GetCategory("JASSynth").SetEnabled(True) self.expect("frame variable foo", matching=True, substrs=['X = 1']) self.dbg.GetCategory("CCCSynth").SetEnabled(True) self.expect( "frame variable ccc", matching=True, substrs=[ 'CCC object with leading value (int) a = 111', 'a = 111', 'b = 222', 'c = 333']) foo_var = self.dbg.GetSelectedTarget().GetProcess( ).GetSelectedThread().GetSelectedFrame().FindVariable('foo') self.assertTrue(foo_var.IsValid(), 'could not find foo') self.assertTrue( foo_var.GetDeclaration().IsValid(), 'foo declaration is invalid') self.assertTrue( foo_var.GetNumChildren() == 2, 'synthetic value has wrong number of child items (synth)') self.assertTrue( foo_var.GetChildMemberWithName('X').GetValueAsUnsigned() == 1, 'foo_synth.X has wrong value (synth)') self.assertFalse( foo_var.GetChildMemberWithName('B').IsValid(), 'foo_synth.B is valid but should not (synth)') self.dbg.GetCategory("JASSynth").SetEnabled(False) foo_var = self.dbg.GetSelectedTarget().GetProcess( ).GetSelectedThread().GetSelectedFrame().FindVariable('foo') self.assertTrue(foo_var.IsValid(), 'could not find foo') self.assertFalse( foo_var.GetNumChildren() == 2, 'still seeing synthetic value') filter = lldb.SBTypeFilter(0) filter.AppendExpressionPath("A") filter.AppendExpressionPath("D") category.AddTypeFilter(lldb.SBTypeNameSpecifier("JustAStruct"), filter) self.expect("frame variable foo", substrs=['A = 1', 'D = 6.28']) foo_var = self.dbg.GetSelectedTarget().GetProcess( ).GetSelectedThread().GetSelectedFrame().FindVariable('foo') self.assertTrue(foo_var.IsValid(), 'could not find foo') self.assertTrue( foo_var.GetNumChildren() == 2, 'synthetic value has wrong number of child items (filter)') self.assertTrue( foo_var.GetChildMemberWithName('X').GetValueAsUnsigned() == 0, 'foo_synth.X has wrong value (filter)') self.assertTrue( foo_var.GetChildMemberWithName('A').GetValueAsUnsigned() == 1, 'foo_synth.A has wrong value (filter)') self.assertTrue(filter.ReplaceExpressionPathAtIndex( 0, "C"), "failed to replace an expression path in filter") self.expect("frame variable foo", substrs=['A = 1', 'D = 6.28']) category.AddTypeFilter(lldb.SBTypeNameSpecifier("JustAStruct"), filter) self.expect("frame variable foo", substrs=["C = 'e'", 'D = 6.28']) category.AddTypeFilter(lldb.SBTypeNameSpecifier("FooType"), filter) filter.ReplaceExpressionPathAtIndex(1, "F") self.expect("frame variable foo", substrs=["C = 'e'", 'D = 6.28']) category.AddTypeFilter(lldb.SBTypeNameSpecifier("JustAStruct"), filter) self.expect("frame variable foo", substrs=["C = 'e'", 'F = 0']) self.expect("frame variable bar", substrs=["C = 'e'", 'D = 6.28']) foo_var = self.dbg.GetSelectedTarget().GetProcess( ).GetSelectedThread().GetSelectedFrame().FindVariable('foo') self.assertTrue(foo_var.IsValid(), 'could not find foo') self.assertTrue( foo_var.GetChildMemberWithName('C').GetValueAsUnsigned() == ord('e'), 'foo_synth.C has wrong value (filter)') chosen = self.dbg.GetFilterForType( lldb.SBTypeNameSpecifier("JustAStruct")) self.assertTrue( chosen.count == 2, "wrong filter found for JustAStruct") self.assertTrue( chosen.GetExpressionPathAtIndex(0) == 'C', "wrong item at index 0 for JustAStruct") self.assertTrue( chosen.GetExpressionPathAtIndex(1) == 'F', "wrong item at index 1 for JustAStruct") self.assertFalse( category.DeleteTypeFilter( lldb.SBTypeNameSpecifier("NoSuchType")), "deleting a non-existing filter worked") self.assertFalse( category.DeleteTypeSummary( lldb.SBTypeNameSpecifier("NoSuchType")), "deleting a non-existing summary worked") self.assertFalse( category.DeleteTypeFormat( lldb.SBTypeNameSpecifier("NoSuchType")), "deleting a non-existing format worked") self.assertFalse( category.DeleteTypeSynthetic( lldb.SBTypeNameSpecifier("NoSuchType")), "deleting a non-existing synthetic worked") self.assertFalse( category.DeleteTypeFilter( lldb.SBTypeNameSpecifier("")), "deleting a filter for '' worked") self.assertFalse( category.DeleteTypeSummary( lldb.SBTypeNameSpecifier("")), "deleting a summary for '' worked") self.assertFalse( category.DeleteTypeFormat( lldb.SBTypeNameSpecifier("")), "deleting a format for '' worked") self.assertFalse( category.DeleteTypeSynthetic( lldb.SBTypeNameSpecifier("")), "deleting a synthetic for '' worked") try: self.assertFalse( category.AddTypeSummary( lldb.SBTypeNameSpecifier("NoneSuchType"), None), "adding a summary valued None worked") except: pass else: self.assertFalse(True, "adding a summary valued None worked") try: self.assertFalse( category.AddTypeFilter( lldb.SBTypeNameSpecifier("NoneSuchType"), None), "adding a filter valued None worked") except: pass else: self.assertFalse(True, "adding a filter valued None worked") try: self.assertFalse( category.AddTypeSynthetic( lldb.SBTypeNameSpecifier("NoneSuchType"), None), "adding a synthetic valued None worked") except: pass else: self.assertFalse(True, "adding a synthetic valued None worked") try: self.assertFalse( category.AddTypeFormat( lldb.SBTypeNameSpecifier("NoneSuchType"), None), "adding a format valued None worked") except: pass else: self.assertFalse(True, "adding a format valued None worked") self.assertFalse( category.AddTypeSummary( lldb.SBTypeNameSpecifier("EmptySuchType"), lldb.SBTypeSummary()), "adding a summary without value worked") self.assertFalse( category.AddTypeFilter( lldb.SBTypeNameSpecifier("EmptySuchType"), lldb.SBTypeFilter()), "adding a filter without value worked") self.assertFalse( category.AddTypeSynthetic( lldb.SBTypeNameSpecifier("EmptySuchType"), lldb.SBTypeSynthetic()), "adding a synthetic without value worked") self.assertFalse( category.AddTypeFormat( lldb.SBTypeNameSpecifier("EmptySuchType"), lldb.SBTypeFormat()), "adding a format without value worked") self.assertFalse( category.AddTypeSummary( lldb.SBTypeNameSpecifier(""), lldb.SBTypeSummary.CreateWithSummaryString("")), "adding a summary for an invalid type worked") self.assertFalse( category.AddTypeFilter( lldb.SBTypeNameSpecifier(""), lldb.SBTypeFilter(0)), "adding a filter for an invalid type worked") self.assertFalse( category.AddTypeSynthetic( lldb.SBTypeNameSpecifier(""), lldb.SBTypeSynthetic.CreateWithClassName("")), "adding a synthetic for an invalid type worked") self.assertFalse( category.AddTypeFormat( lldb.SBTypeNameSpecifier(""), lldb.SBTypeFormat( lldb.eFormatHex)), "adding a format for an invalid type worked") new_category = self.dbg.CreateCategory("newbar") new_category.AddTypeSummary( lldb.SBTypeNameSpecifier("JustAStruct"), lldb.SBTypeSummary.CreateWithScriptCode("return 'hello scripted world';")) self.expect("frame variable foo", matching=False, substrs=['hello scripted world']) new_category.SetEnabled(True) self.expect("frame variable foo", matching=True, substrs=['hello scripted world']) self.expect("frame variable foo_ptr", matching=True, substrs=['hello scripted world']) new_category.AddTypeSummary( lldb.SBTypeNameSpecifier("JustAStruct"), lldb.SBTypeSummary.CreateWithScriptCode( "return 'hello scripted world';", lldb.eTypeOptionSkipPointers)) self.expect("frame variable foo", matching=True, substrs=['hello scripted world']) frame = self.dbg.GetSelectedTarget().GetProcess( ).GetSelectedThread().GetSelectedFrame() foo_ptr = frame.FindVariable("foo_ptr") summary = foo_ptr.GetTypeSummary() self.assertFalse( summary.IsValid(), "summary found for foo* when none was planned") self.expect("frame variable foo_ptr", matching=False, substrs=['hello scripted world']) new_category.AddTypeSummary( lldb.SBTypeNameSpecifier("JustAStruct"), lldb.SBTypeSummary.CreateWithSummaryString( "hello static world", lldb.eTypeOptionNone)) summary = foo_ptr.GetTypeSummary() self.assertTrue( summary.IsValid(), "no summary found for foo* when one was in place") self.assertTrue( summary.GetData() == "hello static world", "wrong summary found for foo*") self.expect("frame variable e1", substrs=["I am an empty Empty1 {}"]) self.expect("frame variable e2", substrs=["I am an empty Empty2"]) self.expect( "frame variable e2", substrs=["I am an empty Empty2 {}"], matching=False) self.assertTrue( self.dbg.GetCategory( lldb.eLanguageTypeObjC) is not None, "ObjC category is None") @add_test_categories(['pyapi']) def test_force_synth_off(self): """Test that one can have the public API return non-synthetic SBValues if desired""" self.build(dictionary={'EXE': 'no_synth'}) self.setTearDownCleanup() self.runCmd("file " + self.getBuildArtifact("no_synth"), CURRENT_EXECUTABLE_SET) lldbutil.run_break_set_by_file_and_line( self, "main.cpp", self.line, num_expected_locations=1, loc_exact=True) self.runCmd("run", RUN_SUCCEEDED) # The stop reason of the thread should be breakpoint. self.expect("thread list", STOPPED_DUE_TO_BREAKPOINT, substrs=['stopped', 'stop reason = breakpoint']) # This is the function to remove the custom formats in order to have a # clean slate for the next test case. def cleanup(): self.runCmd('type format clear', check=False) self.runCmd('type summary clear', check=False) self.runCmd('type filter clear', check=False) self.runCmd('type synthetic clear', check=False) self.runCmd('type category delete foobar', check=False) self.runCmd('type category delete JASSynth', check=False) self.runCmd('type category delete newbar', check=False) self.runCmd('settings set target.enable-synthetic-value true') # Execute the cleanup function during test case tear down. self.addTearDownHook(cleanup) frame = self.dbg.GetSelectedTarget().GetProcess( ).GetSelectedThread().GetSelectedFrame() int_vector = frame.FindVariable("int_vector") if self.TraceOn(): print(int_vector) self.assertTrue( int_vector.GetNumChildren() == 0, 'synthetic vector is empty') self.runCmd('settings set target.enable-synthetic-value false') frame = self.dbg.GetSelectedTarget().GetProcess( ).GetSelectedThread().GetSelectedFrame() int_vector = frame.FindVariable("int_vector") if self.TraceOn(): print(int_vector) self.assertFalse( int_vector.GetNumChildren() == 0, '"physical" vector is not empty') self.runCmd('settings set target.enable-synthetic-value true') frame = self.dbg.GetSelectedTarget().GetProcess( ).GetSelectedThread().GetSelectedFrame() int_vector = frame.FindVariable("int_vector") if self.TraceOn(): print(int_vector) self.assertTrue( int_vector.GetNumChildren() == 0, 'synthetic vector is still empty')

"""Add a new file to the ``fdep.yml`` file. .. code:: bash fdep add [--version=if_any] <local path> <remote path> .. note:: This doesn't download the file. Use ``fdep install`` to download the files. """ import sys from fdep.commands import ConfigRequiredMixin, SubcommandRunner class AddCommandRunner(SubcommandRunner, ConfigRequiredMixin): """Handle add commands.""" COMMAND_NAME = 'add' def run(self, *args, **kwargs): if len(args) != 2: sys.stderr.write(self.messages.ERROR_INVALID_ARGUMENT) self.root_runner.commands['help'].run() return False entry = self.path_helper.resolve_path_to_entry(args[0]) source = args[1] self.entries[entry] = {"source": source} version = kwargs.get('version') if version: self.entries[entry]['version'] = version self.config.save() print(self.messages.ADDED.format(entry)) return True

import logging import json import os import io import unittest import hashlib import xml.etree.ElementTree as ET import requests from base64 import b64encode from environs import Env from dvvset import DVVSet import boto3 from botocore.config import Config from botocore.utils import fix_s3_host env = Env() env.read_env('.env') #logger = logging.getLogger(__name__) # pylint: disable=invalid-name for name in ['botocore', 's3transfer', 'boto3']: logging.getLogger(name).setLevel(logging.CRITICAL) BASE_URL = env.str("BASE_URL", "https://lightupon.cloud") USERNAME_1 = env.str("USERNAME_1") PASSWORD_1 = env.str("PASSWORD_1") USERNAME_2 = env.str("USERNAME_2") PASSWORD_2 = env.str("PASSWORD_2") TEST_BUCKET_1 = env.str("TEST_BUCKET_1") TEST_BUCKET_2 = env.str("TEST_BUCKET_2") UPLOADS_BUCKET_NAME = env.str("UPLOADS_BUCKET_NAME") ACCESS_KEY = env.str("ACCESS_KEY") SECRET_KEY = env.str("SECRET_KEY") HTTP_PROXY = env.str("HTTP_PROXY") RIAK_ACTION_LOG_FILENAME = ".riak_action_log.xml" FILE_UPLOAD_CHUNK_SIZE = 2000000 def configure_boto3(): session = boto3.Session( aws_access_key_id=ACCESS_KEY, aws_secret_access_key=SECRET_KEY, ) resource = session.resource('s3', config=Config(proxies={'http': HTTP_PROXY}), use_ssl=False) resource.meta.client.meta.events.unregister('before-sign.s3', fix_s3_host) boto3.set_stream_logger('botocore') return resource class TestClient(unittest.TestCase): def setUp(self): creds = {"login": USERNAME_1, "password": PASSWORD_1} response = requests.post("{}/riak/login".format(BASE_URL), data=json.dumps(creds), headers={'content-type': 'application/json'}) data = response.json() self.token = data['token'] self.user_id = data['id'] self.resource = configure_boto3() self.purge_test_buckets() def get_json(self, url, status=200, **kwargs): response = requests.get(url, headers={'content-type': 'application/json', 'authorization': 'Token {}'.format(self.token)}, **kwargs) assert response.status_code == status assert response.headers['content-type'] == 'application/json' return response.json() def post_json(self, url, data, status=200, **kwargs): response = requests.post(url, data=json.dumps(data), headers={'content-type': 'application/json', 'authorization': 'Token {}'.format(self.token)}) assert response.status_code == status return json.loads(response.data.decode('utf8')) def delete_json(self, url, data, status=200, **kwargs): response = requests.delete(url, data=json.dumps(data), headers={'content-type': 'application/json', 'authorization': 'Token {}'.format(self.token)}) assert response.status_code == status assert response.headers['content-type'] == 'application/json' return response.json() def upload_file(self, url, fn, prefix='', guid='', last_seen_version=None, form_data=None, **kwargs): """ Uploads file to server by splitting it to chunks and testing if server has chunk already, before actual upload. ``url`` -- The base upload API endpoint ``fn`` -- filename ``prefix`` -- an object's prefix on server ``guid`` -- unique identifier ( UUID4 ) for tracking history of changes ``last_seen_version`` -- casual history value, generated by DVVSet() """ data = {} stat = os.stat(fn) modified_utc = str(int(stat.st_mtime)) size = stat.st_size if not last_seen_version: dvvset = DVVSet() dot = dvvset.create(dvvset.new(modified_utc), self.user_id) version = b64encode(json.dumps(dot).encode()) else: # increment version context = dvvset.join(last_seen_version) new_dot = dvvset.update(dvvset.new_with_history(context, modified_utc), dot, self.user_id) version = dvvset.sync([last_seen_version, new_dot]) version = b64encode(json.dumps(version)).encode() result = None with open(fn, 'rb') as fd: _read_chunk = lambda: fd.read(FILE_UPLOAD_CHUNK_SIZE) part_num = 1 md5_list = [] upload_id = None offset = 0 for chunk in iter(_read_chunk, ''): md5 = hashlib.md5(chunk) md5_digest = md5.hexdigest() md5_list.append(md5_digest) multipart_form_data = { 'files[]': (fn, ''), 'md5': md5_digest, 'prefix': prefix, 'guid': guid, 'version': version } chunk_size = len(chunk) if form_data: multipart_form_data.update(form_data) if size > FILE_UPLOAD_CHUNK_SIZE: offset = (part_num-1) * FILE_UPLOAD_CHUNK_SIZE limit = offset+chunk_size-1 if limit < 0: limit = 0 ct_range = "bytes {}-{}/{}".format(offset, limit, size) else: ct_range = "bytes 0-{}/{}".format(size-1, size) headers = { 'accept': 'application/json', 'authorization': 'Token {}'.format(self.token), 'content-range': ct_range } if part_num == 1: r_url = url else: r_url = "{}{}/{}/".format(url, upload_id, part_num) if offset+chunk_size == size: # last chunk etags = ",".join(["{},{}".format(i+1, md5_list[i]) for i in range(len(md5_list))]) multipart_form_data.update({ 'etags[]': etags }) # send request without binary data first response = requests.post(r_url, files=multipart_form_data, headers=headers) if response.status_code == 206: # skip chunk upload, as server has it aleady response_json = response.json() upload_id = response_json['upload_id'] guid = response_json['guid'] part_num += 1 if offset+chunk_size == size: result = response_json break else: continue self.assertEqual(response.status_code, 200) response_json = response.json() upload_id = response_json['upload_id'] guid = response_json['guid'] # server could change GUID server_md5 = response_json['md5'] self.assertEqual(md5_digest, server_md5) # upload an actual data now multipart_form_data.update({ 'files[]': (fn, chunk), 'guid': guid # GUID could change }) response = requests.post(r_url, files=multipart_form_data, headers=headers) self.assertEqual(response.status_code, 200) response_json = response.json() if offset+chunk_size == size: # the last chunk has been processed, expect complete_upload response expected = set(['lock_user_tel', 'lock_user_name', 'guid', 'upload_id', 'lock_modified_utc', 'lock_user_id', 'is_locked', 'author_tel', 'is_deleted', 'upload_time', 'md5', 'version', 'height', 'author_id', 'author_name', 'object_key', 'bytes', 'width', 'orig_name', 'end_byte']) self.assertEqual(expected, set(response_json.keys())) result = response_json break else: self.assertEqual(set(['end_byte', 'upload_id', 'guid', 'upload_id', 'md5']), set(response_json.keys())) #self.assertEqual(response_json['guid'], guid) #self.assertEqual(response_json['upload_id'], upload_id) server_md5 = response_json['md5'] self.assertEqual(md5_digest, server_md5) upload_id = response_json['upload_id'] part_num += 1 return result def download_object(self, bucketId, objectKey): """ This method downloads aby object from the object storage. Unlike download_file, it queries Riak CS directly. """ bucket = self.resource.Bucket(bucketId) content = io.BytesIO() bucket.download_fileobj(Fileobj=content, Key=objectKey) return content.getvalue() def download_file(self, bucketId, objectKey): """ This method uses /riak/download/ API endpoint to download file """ url = '{}/riak/download/{}/{}'.format(BASE_URL, bucketId, objectKey) response = requests.get(url, headers={"authorization": "Token {}".format(self.token)}) return response.content def head(self, bucketId, objectKey): obj = self.resource.Object(bucketId, objectKey) obj.load() return obj.metadata def remove_object(self, bucketId, objectKey): bucket = self.resource.Bucket(bucketId) bucket.Object(objectKey).delete() def purge_test_buckets(self): """ Deletes all objects from bucket """ bucket = self.resource.Bucket(TEST_BUCKET_1) try: objects = [i for i in bucket.objects.all()] except self.resource.meta.client.exceptions.NoSuchBucket: objects = [] for obj in objects: obj.delete() bucket = self.resource.Bucket(UPLOADS_BUCKET_NAME) try: objects = [i for i in bucket.objects.all()] except self.resource.meta.client.exceptions.NoSuchBucket: objects = [] for obj in objects: obj.delete() def create_bucket(self, name): pass def parse_action_log(self, xmlstring): tree = ET.ElementTree(ET.fromstring(xmlstring)) root = tree.getroot() record = root.find("record") action = record.find("action").text details = record.find("details").text user_name = record.find("user_name").text tenant_name = record.find("tenant_name").text return { 'action': action, 'details': details, 'user_name': user_name, 'tenant_name': tenant_name } def create_pseudo_directory(self, name): req_headers = { 'content-type': 'application/json', 'authorization': 'Token {}'.format(self.token), } data = { 'prefix': '', 'directory_name': name } url = "{}/riak/list/{}/".format(BASE_URL, TEST_BUCKET_1) return requests.post(url, json=data, headers=req_headers)

r"""Find the full path to commands. which(command, path=None, verbose=0, exts=None) Return the full path to the first match of the given command on the path. whichall(command, path=None, verbose=0, exts=None) Return a list of full paths to all matches of the given command on the path. whichgen(command, path=None, verbose=0, exts=None) Return a generator which will yield full paths to all matches of the given command on the path. By default the PATH environment variable is searched (as well as, on Windows, the AppPaths key in the registry), but a specific 'path' list to search may be specified as well. On Windows, the PATHEXT environment variable is applied as appropriate. If "verbose" is true then a tuple of the form (<fullpath>, <matched-where-description>) is returned for each match. The latter element is a textual description of where the match was found. For example: from PATH element 0 from HKLM\SOFTWARE\...\perl.exe """ _cmdlnUsage = """ Show the full path of commands. Usage: which [<options>...] [<command-name>...] Options: -h, --help Print this help and exit. -V, --version Print the version info and exit. -a, --all Print *all* matching paths. -v, --verbose Print out how matches were located and show near misses on stderr. -q, --quiet Just print out matches. I.e., do not print out near misses. -p <altpath>, --path=<altpath> An alternative path (list of directories) may be specified for searching. -e <exts>, --exts=<exts> Specify a list of extensions to consider instead of the usual list (';'-separate list, Windows only). Show the full path to the program that would be run for each given command name, if any. Which, like GNU's which, returns the number of failed arguments, or -1 when no <command-name> was given. Near misses include duplicates, non-regular files and (on Un*x) files without executable access. """ __revision__ = "$Id: which.py 1448 2007-02-28 19:13:06Z trentm $" __version_info__ = (1, 1, 3) __version__ = '.'.join(map(str, __version_info__)) __all__ = ["which", "whichall", "whichgen", "WhichError"] import os import sys import getopt import stat #---- exceptions class WhichError(Exception): pass #---- internal support stuff def _getRegisteredExecutable(exeName): """Windows allow application paths to be registered in the registry.""" registered = None if sys.platform.startswith('win'): if os.path.splitext(exeName)[1].lower() != '.exe': exeName += '.exe' import _winreg try: key = "SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\App Paths\\" +\ exeName value = _winreg.QueryValue(_winreg.HKEY_LOCAL_MACHINE, key) registered = (value, "from HKLM\\"+key) except _winreg.error: pass if registered and not os.path.exists(registered[0]): registered = None return registered def _samefile(fname1, fname2): if sys.platform.startswith('win'): return ( os.path.normpath(os.path.normcase(fname1)) ==\ os.path.normpath(os.path.normcase(fname2)) ) else: return os.path.samefile(fname1, fname2) def _cull(potential, matches, verbose=0): """Cull inappropriate matches. Possible reasons: - a duplicate of a previous match - not a disk file - not executable (non-Windows) If 'potential' is approved it is returned and added to 'matches'. Otherwise, None is returned. """ for match in matches: # don't yield duplicates if _samefile(potential[0], match[0]): if verbose: sys.stderr.write("duplicate: %s (%s)\n" % potential) return None else: if not stat.S_ISREG(os.stat(potential[0]).st_mode): if verbose: sys.stderr.write("not a regular file: %s (%s)\n" % potential) elif sys.platform != "win32" \ and not os.access(potential[0], os.X_OK): if verbose: sys.stderr.write("no executable access: %s (%s)\n"\ % potential) else: matches.append(potential) return potential #---- module API def whichgen(command, path=None, verbose=0, exts=None): """Return a generator of full paths to the given command. "command" is a the name of the executable to search for. "path" is an optional alternate path list to search. The default it to use the PATH environment variable. "verbose", if true, will cause a 2-tuple to be returned for each match. The second element is a textual description of where the match was found. "exts" optionally allows one to specify a list of extensions to use instead of the standard list for this system. This can effectively be used as an optimization to, for example, avoid stat's of "foo.vbs" when searching for "foo" and you know it is not a VisualBasic script but ".vbs" is on PATHEXT. This option is only supported on Windows. This method returns a generator which yields either full paths to the given command or, if verbose, tuples of the form (<path to command>, <where path found>). """ matches = [] if path is None: usingGivenPath = 0 path = os.environ.get("PATH", "").split(os.pathsep) if sys.platform.startswith("win"): path.insert(0, os.curdir) # implied by Windows shell else: usingGivenPath = 1 # Windows has the concept of a list of extensions (PATHEXT env var). if sys.platform.startswith("win"): if exts is None: exts = os.environ.get("PATHEXT", "").split(os.pathsep) # If '.exe' is not in exts then obviously this is Win9x and # or a bogus PATHEXT, then use a reasonable default. for ext in exts: if ext.lower() == ".exe": break else: exts = ['.COM', '.EXE', '.BAT'] elif not isinstance(exts, list): raise TypeError("'exts' argument must be a list or None") else: if exts is not None: raise WhichError("'exts' argument is not supported on "\ "platform '%s'" % sys.platform) exts = [] # File name cannot have path separators because PATH lookup does not # work that way. if os.sep in command or os.altsep and os.altsep in command: if os.path.exists(command): match = _cull((command, "explicit path given"), matches, verbose) if verbose: yield match else: yield match[0] else: for i in range(len(path)): dirName = path[i] # On windows the dirName *could* be quoted, drop the quotes if sys.platform.startswith("win") and len(dirName) >= 2\ and dirName[0] == '"' and dirName[-1] == '"': dirName = dirName[1:-1] for ext in ['']+exts: absName = os.path.abspath( os.path.normpath(os.path.join(dirName, command+ext))) if os.path.isfile(absName): if usingGivenPath: fromWhere = "from given path element %d" % i elif not sys.platform.startswith("win"): fromWhere = "from PATH element %d" % i elif i == 0: fromWhere = "from current directory" else: fromWhere = "from PATH element %d" % (i-1) match = _cull((absName, fromWhere), matches, verbose) if match: if verbose: yield match else: yield match[0] match = _getRegisteredExecutable(command) if match is not None: match = _cull(match, matches, verbose) if match: if verbose: yield match else: yield match[0] def which(command, path=None, verbose=0, exts=None): """Return the full path to the first match of the given command on the path. "command" is a the name of the executable to search for. "path" is an optional alternate path list to search. The default it to use the PATH environment variable. "verbose", if true, will cause a 2-tuple to be returned. The second element is a textual description of where the match was found. "exts" optionally allows one to specify a list of extensions to use instead of the standard list for this system. This can effectively be used as an optimization to, for example, avoid stat's of "foo.vbs" when searching for "foo" and you know it is not a VisualBasic script but ".vbs" is on PATHEXT. This option is only supported on Windows. If no match is found for the command, a WhichError is raised. """ try: match = whichgen(command, path, verbose, exts).next() except StopIteration: raise WhichError("Could not find '%s' on the path." % command) return match def whichall(command, path=None, verbose=0, exts=None): """Return a list of full paths to all matches of the given command on the path. "command" is a the name of the executable to search for. "path" is an optional alternate path list to search. The default it to use the PATH environment variable. "verbose", if true, will cause a 2-tuple to be returned for each match. The second element is a textual description of where the match was found. "exts" optionally allows one to specify a list of extensions to use instead of the standard list for this system. This can effectively be used as an optimization to, for example, avoid stat's of "foo.vbs" when searching for "foo" and you know it is not a VisualBasic script but ".vbs" is on PATHEXT. This option is only supported on Windows. """ return list( whichgen(command, path, verbose, exts) ) #---- mainline def main(argv): all = 0 verbose = 0 altpath = None exts = None try: optlist, args = getopt.getopt(argv[1:], 'haVvqp:e:', ['help', 'all', 'version', 'verbose', 'quiet', 'path=', 'exts=']) except getopt.GetoptError, msg: sys.stderr.write("which: error: %s. Your invocation was: %s\n"\ % (msg, argv)) sys.stderr.write("Try 'which --help'.\n") return 1 for opt, optarg in optlist: if opt in ('-h', '--help'): print _cmdlnUsage return 0 elif opt in ('-V', '--version'): print "which %s" % __version__ return 0 elif opt in ('-a', '--all'): all = 1 elif opt in ('-v', '--verbose'): verbose = 1 elif opt in ('-q', '--quiet'): verbose = 0 elif opt in ('-p', '--path'): if optarg: altpath = optarg.split(os.pathsep) else: altpath = [] elif opt in ('-e', '--exts'): if optarg: exts = optarg.split(os.pathsep) else: exts = [] if len(args) == 0: return -1 failures = 0 for arg in args: #print "debug: search for %r" % arg nmatches = 0 for match in whichgen(arg, path=altpath, verbose=verbose, exts=exts): if verbose: print "%s (%s)" % match else: print match nmatches += 1 if not all: break if not nmatches: failures += 1 return failures if __name__ == "__main__": sys.exit( main(sys.argv) )

import copy import functools import os import re import stat import json import shlex import shutil import textwrap import platform from enum import Enum from pathlib import PurePath from .. import mlog from .. import mesonlib from ..compilers import clib_langs from ..mesonlib import MesonException, OrderedSet from ..mesonlib import Popen_safe, version_compare_many, version_compare, listify # These must be defined in this file to avoid cyclical references. packages = {} _packages_accept_language = set() class DependencyException(MesonException): '''Exceptions raised while trying to find dependencies''' class DependencyMethods(Enum): # Auto means to use whatever dependency checking mechanisms in whatever order meson thinks is best. AUTO = 'auto' PKGCONFIG = 'pkg-config' QMAKE = 'qmake' # Just specify the standard link arguments, assuming the operating system provides the library. SYSTEM = 'system' # This is only supported on OSX - search the frameworks directory by name. EXTRAFRAMEWORK = 'extraframework' # Detect using the sysconfig module. SYSCONFIG = 'sysconfig' # Specify using a "program"-config style tool CONFIG_TOOL = 'config-tool' # For backwards compatibility SDLCONFIG = 'sdlconfig' CUPSCONFIG = 'cups-config' PCAPCONFIG = 'pcap-config' LIBWMFCONFIG = 'libwmf-config' # Misc DUB = 'dub' class Dependency: @classmethod def _process_method_kw(cls, kwargs): method = kwargs.get('method', 'auto') if method not in [e.value for e in DependencyMethods]: raise DependencyException('method {!r} is invalid'.format(method)) method = DependencyMethods(method) # This sets per-tool config methods which are deprecated to to the new # generic CONFIG_TOOL value. if method in [DependencyMethods.SDLCONFIG, DependencyMethods.CUPSCONFIG, DependencyMethods.PCAPCONFIG, DependencyMethods.LIBWMFCONFIG]: mlog.warning(textwrap.dedent("""\ Configuration method {} has been deprecated in favor of 'config-tool'. This will be removed in a future version of meson.""".format(method))) method = DependencyMethods.CONFIG_TOOL # Set the detection method. If the method is set to auto, use any available method. # If method is set to a specific string, allow only that detection method. if method == DependencyMethods.AUTO: methods = cls.get_methods() elif method in cls.get_methods(): methods = [method] else: raise DependencyException( 'Unsupported detection method: {}, allowed methods are {}'.format( method.value, mlog.format_list([x.value for x in [DependencyMethods.AUTO] + cls.get_methods()]))) return methods def __init__(self, type_name, kwargs): self.name = "null" self.version = None self.language = None # None means C-like self.is_found = False self.type_name = type_name self.compile_args = [] self.link_args = [] # Raw -L and -l arguments without manual library searching # If None, self.link_args will be used self.raw_link_args = None self.sources = [] self.methods = self._process_method_kw(kwargs) def __repr__(self): s = '<{0} {1}: {2}>' return s.format(self.__class__.__name__, self.name, self.is_found) def get_compile_args(self): return self.compile_args def get_link_args(self, raw=False): if raw and self.raw_link_args is not None: return self.raw_link_args return self.link_args def found(self): return self.is_found def get_sources(self): """Source files that need to be added to the target. As an example, gtest-all.cc when using GTest.""" return self.sources @staticmethod def get_methods(): return [DependencyMethods.AUTO] def get_name(self): return self.name def get_version(self): if self.version: return self.version else: return 'unknown' def get_exe_args(self, compiler): return [] def need_openmp(self): return False def need_threads(self): return False def get_pkgconfig_variable(self, variable_name, kwargs): raise DependencyException('{!r} is not a pkgconfig dependency'.format(self.name)) def get_configtool_variable(self, variable_name): raise DependencyException('{!r} is not a config-tool dependency'.format(self.name)) def get_partial_dependency(self, *, compile_args=False, link_args=False, links=False, includes=False, sources=False): """Create a new dependency that contains part of the parent dependency. The following options can be inherited: links -- all link_with arguemnts includes -- all include_directory and -I/-isystem calls sources -- any source, header, or generated sources compile_args -- any compile args link_args -- any link args Additionally the new dependency will have the version parameter of it's parent (if any) and the requested values of any dependencies will be added as well. """ RuntimeError('Unreachable code in partial_dependency called') class InternalDependency(Dependency): def __init__(self, version, incdirs, compile_args, link_args, libraries, whole_libraries, sources, ext_deps): super().__init__('internal', {}) self.version = version self.is_found = True self.include_directories = incdirs self.compile_args = compile_args self.link_args = link_args self.libraries = libraries self.whole_libraries = whole_libraries self.sources = sources self.ext_deps = ext_deps def get_pkgconfig_variable(self, variable_name, kwargs): raise DependencyException('Method "get_pkgconfig_variable()" is ' 'invalid for an internal dependency') def get_configtool_variable(self, variable_name): raise DependencyException('Method "get_configtool_variable()" is ' 'invalid for an internal dependency') def get_partial_dependency(self, *, compile_args=False, link_args=False, links=False, includes=False, sources=False): compile_args = self.compile_args.copy() if compile_args else [] link_args = self.link_args.copy() if link_args else [] libraries = self.libraries.copy() if links else [] whole_libraries = self.whole_libraries.copy() if links else [] sources = self.sources.copy() if sources else [] includes = self.include_directories.copy() if includes else [] deps = [d.get_partial_dependency( compile_args=compile_args, link_args=link_args, links=links, includes=includes, sources=sources) for d in self.ext_deps] return InternalDependency( self.version, includes, compile_args, link_args, libraries, whole_libraries, sources, deps) class ExternalDependency(Dependency): def __init__(self, type_name, environment, language, kwargs): super().__init__(type_name, kwargs) self.env = environment self.name = type_name # default self.is_found = False self.language = language self.version_reqs = kwargs.get('version', None) if isinstance(self.version_reqs, str): self.version_reqs = [self.version_reqs] self.required = kwargs.get('required', True) self.silent = kwargs.get('silent', False) self.static = kwargs.get('static', False) if not isinstance(self.static, bool): raise DependencyException('Static keyword must be boolean') # Is this dependency for cross-compilation? if 'native' in kwargs and self.env.is_cross_build(): self.want_cross = not kwargs['native'] else: self.want_cross = self.env.is_cross_build() self.clib_compiler = None # Set the compiler that will be used by this dependency # This is only used for configuration checks if self.want_cross: compilers = self.env.coredata.cross_compilers else: compilers = self.env.coredata.compilers # Set the compiler for this dependency if a language is specified, # else try to pick something that looks usable. if self.language: if self.language not in compilers: m = self.name.capitalize() + ' requires a {0} compiler, but ' \ '{0} is not in the list of project languages' raise DependencyException(m.format(self.language.capitalize())) self.clib_compiler = compilers[self.language] else: # Try to find a compiler that can find C libraries for # running compiler.find_library() for lang in clib_langs: self.clib_compiler = compilers.get(lang, None) if self.clib_compiler: break def get_compiler(self): return self.clib_compiler def get_partial_dependency(self, *, compile_args=False, link_args=False, links=False, includes=False, sources=False): new = copy.copy(self) if not compile_args: new.compile_args = [] if not link_args: new.link_args = [] if not sources: new.sources = [] return new def log_details(self): return '' def log_info(self): return '' def log_tried(self): return '' # Check if dependency version meets the requirements def _check_version(self): if not self.is_found: return if self.version_reqs: # an unknown version can never satisfy any requirement if not self.version: found_msg = ['Dependency', mlog.bold(self.name), 'found:'] found_msg += [mlog.red('NO'), 'unknown version, but need:', self.version_reqs] mlog.log(*found_msg) if self.required: m = 'Unknown version of dependency {!r}, but need {!r}.' raise DependencyException(m.format(self.name, self.version_reqs)) else: (self.is_found, not_found, found) = \ version_compare_many(self.version, self.version_reqs) if not self.is_found: found_msg = ['Dependency', mlog.bold(self.name), 'found:'] found_msg += [mlog.red('NO'), 'found {!r} but need:'.format(self.version), ', '.join(["'{}'".format(e) for e in not_found])] if found: found_msg += ['; matched:', ', '.join(["'{}'".format(e) for e in found])] mlog.log(*found_msg) if self.required: m = 'Invalid version of dependency, need {!r} {!r} found {!r}.' raise DependencyException(m.format(self.name, not_found, self.version)) return class NotFoundDependency(Dependency): def __init__(self, environment): super().__init__('not-found', {}) self.env = environment self.name = 'not-found' self.is_found = False class ConfigToolDependency(ExternalDependency): """Class representing dependencies found using a config tool.""" tools = None tool_name = None __strip_version = re.compile(r'^[0-9.]*') def __init__(self, name, environment, language, kwargs): super().__init__('config-tool', environment, language, kwargs) self.name = name self.native = kwargs.get('native', False) self.tools = listify(kwargs.get('tools', self.tools)) req_version = kwargs.get('version', None) tool, version = self.find_config(req_version) self.config = tool self.is_found = self.report_config(version, req_version) if not self.is_found: self.config = None return self.version = version if getattr(self, 'finish_init', None): self.finish_init(self) def _sanitize_version(self, version): """Remove any non-numeric, non-point version suffixes.""" m = self.__strip_version.match(version) if m: # Ensure that there isn't a trailing '.', such as an input like # `1.2.3.git-1234` return m.group(0).rstrip('.') return version @classmethod def factory(cls, name, environment, language, kwargs, tools, tool_name, finish_init=None): """Constructor for use in dependencies that can be found multiple ways. In addition to the standard constructor values, this constructor sets the tool_name and tools values of the instance. """ # This deserves some explanation, because metaprogramming is hard. # This uses type() to create a dynamic subclass of ConfigToolDependency # with the tools and tool_name class attributes set, this class is then # instantiated and returned. The reduce function (method) is also # attached, since python's pickle module won't be able to do anything # with this dynamically generated class otherwise. def reduce(self): return (cls._unpickle, (), self.__dict__) sub = type('{}Dependency'.format(name.capitalize()), (cls, ), {'tools': tools, 'tool_name': tool_name, '__reduce__': reduce, 'finish_init': staticmethod(finish_init)}) return sub(name, environment, language, kwargs) @classmethod def _unpickle(cls): return cls.__new__(cls) def find_config(self, versions=None): """Helper method that searchs for config tool binaries in PATH and returns the one that best matches the given version requirements. """ if not isinstance(versions, list) and versions is not None: versions = listify(versions) if self.env.is_cross_build() and not self.native: cross_file = self.env.cross_info.config['binaries'] try: tools = [cross_file[self.tool_name]] except KeyError: mlog.warning('No entry for {0} specified in your cross file. ' 'Falling back to searching PATH. This may find a ' 'native version of {0}!'.format(self.tool_name)) tools = self.tools else: tools = self.tools best_match = (None, None) for tool in tools: try: p, out = Popen_safe([tool, '--version'])[:2] except (FileNotFoundError, PermissionError): continue if p.returncode != 0: continue out = self._sanitize_version(out.strip()) # Some tools, like pcap-config don't supply a version, but also # don't fail with --version, in that case just assume that there is # only one version and return it. if not out: return (tool, None) if versions: is_found = version_compare_many(out, versions)[0] # This allows returning a found version without a config tool, # which is useful to inform the user that you found version x, # but y was required. if not is_found: tool = None if best_match[1]: if version_compare(out, '> {}'.format(best_match[1])): best_match = (tool, out) else: best_match = (tool, out) return best_match def report_config(self, version, req_version): """Helper method to print messages about the tool.""" if self.config is None: if version is not None: mlog.log('Found', mlog.bold(self.tool_name), repr(version), mlog.red('NO'), '(needed', req_version, ')') else: mlog.log('Found', mlog.bold(self.tool_name), repr(req_version), mlog.red('NO')) return False mlog.log('Found {}:'.format(self.tool_name), mlog.bold(shutil.which(self.config)), '({})'.format(version)) return True def get_config_value(self, args, stage): p, out, err = Popen_safe([self.config] + args) # This is required to keep shlex from stripping path separators on # Windows. Also, don't put escape sequences in config values, okay? out = out.replace('\\', '\\\\') if p.returncode != 0: if self.required: raise DependencyException( 'Could not generate {} for {}.\n{}'.format( stage, self.name, err)) return [] return shlex.split(out) @staticmethod def get_methods(): return [DependencyMethods.AUTO, DependencyMethods.CONFIG_TOOL] def get_configtool_variable(self, variable_name): p, out, _ = Popen_safe([self.config, '--{}'.format(variable_name)]) if p.returncode != 0: if self.required: raise DependencyException( 'Could not get variable "{}" for dependency {}'.format( variable_name, self.name)) variable = out.strip() mlog.debug('Got config-tool variable {} : {}'.format(variable_name, variable)) return variable def log_tried(self): return self.type_name class PkgConfigDependency(ExternalDependency): # The class's copy of the pkg-config path. Avoids having to search for it # multiple times in the same Meson invocation. class_pkgbin = None # We cache all pkg-config subprocess invocations to avoid redundant calls pkgbin_cache = {} def __init__(self, name, environment, kwargs, language=None): super().__init__('pkgconfig', environment, language, kwargs) self.name = name self.is_libtool = False # Store a copy of the pkg-config path on the object itself so it is # stored in the pickled coredata and recovered. self.pkgbin = None # When finding dependencies for cross-compiling, we don't care about # the 'native' pkg-config if self.want_cross: if 'pkgconfig' not in environment.cross_info.config['binaries']: if self.required: raise DependencyException('Pkg-config binary missing from cross file') else: potential_pkgbin = ExternalProgram.from_cross_info(environment.cross_info, 'pkgconfig') if potential_pkgbin.found(): self.pkgbin = potential_pkgbin else: mlog.debug('Cross pkg-config %s not found.' % potential_pkgbin.name) # Only search for the native pkg-config the first time and # store the result in the class definition elif PkgConfigDependency.class_pkgbin is None: self.pkgbin = self.check_pkgconfig() PkgConfigDependency.class_pkgbin = self.pkgbin else: self.pkgbin = PkgConfigDependency.class_pkgbin if not self.pkgbin: if self.required: raise DependencyException('Pkg-config not found.') return mlog.debug('Determining dependency {!r} with pkg-config executable ' '{!r}'.format(name, self.pkgbin.get_path())) ret, self.version = self._call_pkgbin(['--modversion', name]) if ret != 0: return try: # Fetch cargs to be used while using this dependency self._set_cargs() # Fetch the libraries and library paths needed for using this self._set_libs() except DependencyException as e: if self.required: raise else: self.compile_args = [] self.link_args = [] self.is_found = False self.reason = e self.is_found = True def __repr__(self): s = '<{0} {1}: {2} {3}>' return s.format(self.__class__.__name__, self.name, self.is_found, self.version_reqs) def _call_pkgbin_real(self, args, env): cmd = self.pkgbin.get_command() + args p, out = Popen_safe(cmd, env=env)[0:2] rc, out = p.returncode, out.strip() call = ' '.join(cmd) mlog.debug("Called `{}` -> {}\n{}".format(call, rc, out)) return rc, out def _call_pkgbin(self, args, env=None): if env is None: fenv = env env = os.environ else: fenv = frozenset(env.items()) targs = tuple(args) cache = PkgConfigDependency.pkgbin_cache if (self.pkgbin, targs, fenv) not in cache: cache[(self.pkgbin, targs, fenv)] = self._call_pkgbin_real(args, env) return cache[(self.pkgbin, targs, fenv)] def _convert_mingw_paths(self, args): ''' Both MSVC and native Python on Windows cannot handle MinGW-esque /c/foo paths so convert them to C:/foo. We cannot resolve other paths starting with / like /home/foo so leave them as-is so that the user gets an error/warning from the compiler/linker. ''' if not mesonlib.is_windows(): return args converted = [] for arg in args: pargs = [] # Library search path if arg.startswith('-L/'): pargs = PurePath(arg[2:]).parts tmpl = '-L{}:/{}' elif arg.startswith('-I/'): pargs = PurePath(arg[2:]).parts tmpl = '-I{}:/{}' # Full path to library or .la file elif arg.startswith('/'): pargs = PurePath(arg).parts tmpl = '{}:/{}' if len(pargs) > 1 and len(pargs[1]) == 1: arg = tmpl.format(pargs[1], '/'.join(pargs[2:])) converted.append(arg) return converted def _set_cargs(self): env = None if self.language == 'fortran': # gfortran doesn't appear to look in system paths for INCLUDE files, # so don't allow pkg-config to suppress -I flags for system paths env = os.environ.copy() env['PKG_CONFIG_ALLOW_SYSTEM_CFLAGS'] = '1' ret, out = self._call_pkgbin(['--cflags', self.name], env=env) if ret != 0: raise DependencyException('Could not generate cargs for %s:\n\n%s' % (self.name, out)) self.compile_args = self._convert_mingw_paths(shlex.split(out)) def _search_libs(self, out, out_raw): ''' @out: PKG_CONFIG_ALLOW_SYSTEM_LIBS=1 pkg-config --libs @out_raw: pkg-config --libs We always look for the file ourselves instead of depending on the compiler to find it with -lfoo or foo.lib (if possible) because: 1. We want to be able to select static or shared 2. We need the full path of the library to calculate RPATH values 3. De-dup of libraries is easier when we have absolute paths Libraries that are provided by the toolchain or are not found by find_library() will be added with -L -l pairs. ''' # Library paths should be safe to de-dup # # First, figure out what library paths to use. Originally, we were # doing this as part of the loop, but due to differences in the order # of -L values between pkg-config and pkgconf, we need to do that as # a separate step. See: # https://github.com/mesonbuild/meson/issues/3951 # https://github.com/mesonbuild/meson/issues/4023 # # Separate system and prefix paths, and ensure that prefix paths are # always searched first. prefix_libpaths = OrderedSet() # We also store this raw_link_args on the object later raw_link_args = self._convert_mingw_paths(shlex.split(out_raw)) for arg in raw_link_args: if arg.startswith('-L') and not arg.startswith(('-L-l', '-L-L')): prefix_libpaths.add(arg[2:]) system_libpaths = OrderedSet() full_args = self._convert_mingw_paths(shlex.split(out)) for arg in full_args: if arg.startswith(('-L-l', '-L-L')): # These are D language arguments, not library paths continue if arg.startswith('-L') and arg[2:] not in prefix_libpaths: system_libpaths.add(arg[2:]) # Use this re-ordered path list for library resolution libpaths = list(prefix_libpaths) + list(system_libpaths) # Track -lfoo libraries to avoid duplicate work libs_found = OrderedSet() # Track not-found libraries to know whether to add library paths libs_notfound = [] libtype = 'static' if self.static else 'default' # Generate link arguments for this library link_args = [] for lib in full_args: if lib.startswith(('-L-l', '-L-L')): # These are D language arguments, add them as-is pass elif lib.startswith('-L'): # We already handled library paths above continue elif lib.startswith('-l'): # Don't resolve the same -lfoo argument again if lib in libs_found: continue if self.clib_compiler: args = self.clib_compiler.find_library(lib[2:], self.env, libpaths, libtype) # If the project only uses a non-clib language such as D, Rust, # C#, Python, etc, all we can do is limp along by adding the # arguments as-is and then adding the libpaths at the end. else: args = None if args is not None: libs_found.add(lib) # Replace -l arg with full path to library if available # else, library is either to be ignored, or is provided by # the compiler, can't be resolved, and should be used as-is if args: if not args[0].startswith('-l'): lib = args[0] else: continue else: # Library wasn't found, maybe we're looking in the wrong # places or the library will be provided with LDFLAGS or # LIBRARY_PATH from the environment (on macOS), and many # other edge cases that we can't account for. # # Add all -L paths and use it as -lfoo if lib in libs_notfound: continue if self.static: mlog.warning('Static library {!r} not found for dependency {!r}, may ' 'not be statically linked'.format(lib[2:], self.name)) libs_notfound.append(lib) elif lib.endswith(".la"): shared_libname = self.extract_libtool_shlib(lib) shared_lib = os.path.join(os.path.dirname(lib), shared_libname) if not os.path.exists(shared_lib): shared_lib = os.path.join(os.path.dirname(lib), ".libs", shared_libname) if not os.path.exists(shared_lib): raise DependencyException('Got a libtools specific "%s" dependencies' 'but we could not compute the actual shared' 'library path' % lib) self.is_libtool = True lib = shared_lib if lib in link_args: continue link_args.append(lib) # Add all -Lbar args if we have -lfoo args in link_args if libs_notfound: # Order of -L flags doesn't matter with ld, but it might with other # linkers such as MSVC, so prepend them. link_args = ['-L' + lp for lp in prefix_libpaths] + link_args return link_args, raw_link_args def _set_libs(self): env = None libcmd = [self.name, '--libs'] if self.static: libcmd.append('--static') # Force pkg-config to output -L fields even if they are system # paths so we can do manual searching with cc.find_library() later. env = os.environ.copy() env['PKG_CONFIG_ALLOW_SYSTEM_LIBS'] = '1' ret, out = self._call_pkgbin(libcmd, env=env) if ret != 0: raise DependencyException('Could not generate libs for %s:\n\n%s' % (self.name, out)) # Also get the 'raw' output without -Lfoo system paths for adding -L # args with -lfoo when a library can't be found, and also in # gnome.generate_gir + gnome.gtkdoc which need -L -l arguments. ret, out_raw = self._call_pkgbin(libcmd) if ret != 0: raise DependencyException('Could not generate libs for %s:\n\n%s' % (self.name, out_raw)) self.link_args, self.raw_link_args = self._search_libs(out, out_raw) def get_pkgconfig_variable(self, variable_name, kwargs): options = ['--variable=' + variable_name, self.name] if 'define_variable' in kwargs: definition = kwargs.get('define_variable', []) if not isinstance(definition, list): raise MesonException('define_variable takes a list') if len(definition) != 2 or not all(isinstance(i, str) for i in definition): raise MesonException('define_variable must be made up of 2 strings for VARIABLENAME and VARIABLEVALUE') options = ['--define-variable=' + '='.join(definition)] + options ret, out = self._call_pkgbin(options) variable = '' if ret != 0: if self.required: raise DependencyException('dependency %s not found.' % (self.name)) else: variable = out.strip() # pkg-config doesn't distinguish between empty and non-existent variables # use the variable list to check for variable existence if not variable: ret, out = self._call_pkgbin(['--print-variables', self.name]) if not re.search(r'^' + variable_name + r'$', out, re.MULTILINE): if 'default' in kwargs: variable = kwargs['default'] else: mlog.warning("pkgconfig variable '%s' not defined for dependency %s." % (variable_name, self.name)) mlog.debug('Got pkgconfig variable %s : %s' % (variable_name, variable)) return variable @staticmethod def get_methods(): return [DependencyMethods.PKGCONFIG] def check_pkgconfig(self): evar = 'PKG_CONFIG' if evar in os.environ: pkgbin = os.environ[evar].strip() else: pkgbin = 'pkg-config' pkgbin = ExternalProgram(pkgbin, silent=True) if pkgbin.found(): try: p, out = Popen_safe(pkgbin.get_command() + ['--version'])[0:2] if p.returncode != 0: mlog.warning('Found pkg-config {!r} but couldn\'t run it' ''.format(' '.join(pkgbin.get_command()))) # Set to False instead of None to signify that we've already # searched for it and not found it pkgbin = False except (FileNotFoundError, PermissionError): pkgbin = False else: pkgbin = False if not self.silent: if pkgbin: mlog.log('Found pkg-config:', mlog.bold(pkgbin.get_path()), '(%s)' % out.strip()) else: mlog.log('Found Pkg-config:', mlog.red('NO')) return pkgbin def extract_field(self, la_file, fieldname): with open(la_file) as f: for line in f: arr = line.strip().split('=') if arr[0] == fieldname: return arr[1][1:-1] return None def extract_dlname_field(self, la_file): return self.extract_field(la_file, 'dlname') def extract_libdir_field(self, la_file): return self.extract_field(la_file, 'libdir') def extract_libtool_shlib(self, la_file): ''' Returns the path to the shared library corresponding to this .la file ''' dlname = self.extract_dlname_field(la_file) if dlname is None: return None # Darwin uses absolute paths where possible; since the libtool files never # contain absolute paths, use the libdir field if mesonlib.is_osx(): dlbasename = os.path.basename(dlname) libdir = self.extract_libdir_field(la_file) if libdir is None: return dlbasename return os.path.join(libdir, dlbasename) # From the comments in extract_libtool(), older libtools had # a path rather than the raw dlname return os.path.basename(dlname) def log_tried(self): return self.type_name class DubDependency(ExternalDependency): class_dubbin = None def __init__(self, name, environment, kwargs): super().__init__('dub', environment, 'd', kwargs) self.name = name self.compiler = super().get_compiler() self.module_path = None if 'required' in kwargs: self.required = kwargs.get('required') if DubDependency.class_dubbin is None: self.dubbin = self._check_dub() DubDependency.class_dubbin = self.dubbin else: self.dubbin = DubDependency.class_dubbin if not self.dubbin: if self.required: raise DependencyException('DUB not found.') self.is_found = False return mlog.debug('Determining dependency {!r} with DUB executable ' '{!r}'.format(name, self.dubbin.get_path())) # we need to know the target architecture arch = self.compiler.arch # Ask dub for the package ret, res = self._call_dubbin(['describe', name, '--arch=' + arch]) if ret != 0: self.is_found = False return comp = self.compiler.get_id().replace('llvm', 'ldc').replace('gcc', 'gdc') packages = [] description = json.loads(res) for package in description['packages']: packages.append(package['name']) if package['name'] == name: self.is_found = True not_lib = True if 'targetType' in package: if package['targetType'] == 'library': not_lib = False if not_lib: mlog.error(mlog.bold(name), "found but it isn't a library") self.is_found = False return self.module_path = self._find_right_lib_path(package['path'], comp, description, True, package['targetFileName']) if not os.path.exists(self.module_path): # check if the dependency was built for other archs archs = [['x86_64'], ['x86'], ['x86', 'x86_mscoff']] for a in archs: description_a = copy.deepcopy(description) description_a['architecture'] = a arch_module_path = self._find_right_lib_path(package['path'], comp, description_a, True, package['targetFileName']) if arch_module_path: mlog.error(mlog.bold(name), "found but it wasn't compiled for", mlog.bold(arch)) self.is_found = False return mlog.error(mlog.bold(name), "found but it wasn't compiled with", mlog.bold(comp)) self.is_found = False return self.version = package['version'] self.pkg = package if self.pkg['targetFileName'].endswith('.a'): self.static = True self.compile_args = [] for flag in self.pkg['dflags']: self.link_args.append(flag) for path in self.pkg['importPaths']: self.compile_args.append('-I' + os.path.join(self.pkg['path'], path)) self.link_args = self.raw_link_args = [] for flag in self.pkg['lflags']: self.link_args.append(flag) self.link_args.append(os.path.join(self.module_path, self.pkg['targetFileName'])) # Handle dependencies libs = [] def add_lib_args(field_name, target): if field_name in target['buildSettings']: for lib in target['buildSettings'][field_name]: if lib not in libs: libs.append(lib) if os.name is not 'nt': pkgdep = PkgConfigDependency(lib, environment, {'required': 'true', 'silent': 'true'}) for arg in pkgdep.get_compile_args(): self.compile_args.append(arg) for arg in pkgdep.get_link_args(): self.link_args.append(arg) for arg in pkgdep.get_link_args(raw=True): self.raw_link_args.append(arg) for target in description['targets']: if target['rootPackage'] in packages: add_lib_args('libs', target) add_lib_args('libs-{}'.format(platform.machine()), target) for file in target['buildSettings']['linkerFiles']: lib_path = self._find_right_lib_path(file, comp, description) if lib_path: self.link_args.append(lib_path) else: self.is_found = False def get_compiler(self): return self.compiler def _find_right_lib_path(self, default_path, comp, description, folder_only=False, file_name=''): module_path = lib_file_name = '' if folder_only: module_path = default_path lib_file_name = file_name else: module_path = os.path.dirname(default_path) lib_file_name = os.path.basename(default_path) module_build_path = os.path.join(module_path, '.dub', 'build') # Get D version implemented in the compiler # gdc doesn't support this ret, res = self._call_dubbin(['--version']) if ret != 0: mlog.error('Failed to run {!r}', mlog.bold(comp)) return d_ver = re.search('v[0-9].[0-9][0-9][0-9].[0-9]', res) # Ex.: v2.081.2 if d_ver is not None: d_ver = d_ver.group().rsplit('.', 1)[0].replace('v', '').replace('.', '') # Fix structure. Ex.: 2081 else: d_ver = '' # gdc if not os.path.isdir(module_build_path): return '' # Ex.: library-debug-linux.posix-x86_64-ldc_2081-EF934983A3319F8F8FF2F0E107A363BA build_name = 'library-{}-{}-{}-{}_{}'.format(description['buildType'], '.'.join(description['platform']), '.'.join(description['architecture']), comp, d_ver) for entry in os.listdir(module_build_path): if entry.startswith(build_name): for file in os.listdir(os.path.join(module_build_path, entry)): if file == lib_file_name: if folder_only: return os.path.join(module_build_path, entry) else: return os.path.join(module_build_path, entry, lib_file_name) return '' def _call_dubbin(self, args, env=None): p, out = Popen_safe(self.dubbin.get_command() + args, env=env)[0:2] return p.returncode, out.strip() def _call_copmbin(self, args, env=None): p, out = Popen_safe(self.compiler.get_exelist() + args, env=env)[0:2] return p.returncode, out.strip() def _check_dub(self): dubbin = ExternalProgram('dub', silent=True) if dubbin.found(): try: p, out = Popen_safe(dubbin.get_command() + ['--version'])[0:2] if p.returncode != 0: mlog.warning('Found dub {!r} but couldn\'t run it' ''.format(' '.join(dubbin.get_command()))) # Set to False instead of None to signify that we've already # searched for it and not found it dubbin = False except (FileNotFoundError, PermissionError): dubbin = False else: dubbin = False if dubbin: mlog.log('Found DUB:', mlog.bold(dubbin.get_path()), '(%s)' % out.strip()) else: mlog.log('Found DUB:', mlog.red('NO')) return dubbin @staticmethod def get_methods(): return [DependencyMethods.DUB] class ExternalProgram: windows_exts = ('exe', 'msc', 'com', 'bat', 'cmd') def __init__(self, name, command=None, silent=False, search_dir=None): self.name = name if command is not None: self.command = listify(command) else: self.command = self._search(name, search_dir) # Set path to be the last item that is actually a file (in order to # skip options in something like ['python', '-u', 'file.py']. If we # can't find any components, default to the last component of the path. self.path = self.command[-1] for i in range(len(self.command) - 1, -1, -1): arg = self.command[i] if arg is not None and os.path.isfile(arg): self.path = arg break if not silent: if self.found(): mlog.log('Program', mlog.bold(name), 'found:', mlog.green('YES'), '(%s)' % ' '.join(self.command)) else: mlog.log('Program', mlog.bold(name), 'found:', mlog.red('NO')) def __repr__(self): r = '<{} {!r} -> {!r}>' return r.format(self.__class__.__name__, self.name, self.command) def description(self): '''Human friendly description of the command''' return ' '.join(self.command) @staticmethod def from_cross_info(cross_info, name): if name not in cross_info.config['binaries']: return NonExistingExternalProgram() command = cross_info.config['binaries'][name] if not isinstance(command, (list, str)): raise MesonException('Invalid type {!r} for binary {!r} in cross file' ''.format(command, name)) if isinstance(command, list): if len(command) == 1: command = command[0] # We cannot do any searching if the command is a list, and we don't # need to search if the path is an absolute path. if isinstance(command, list) or os.path.isabs(command): return ExternalProgram(name, command=command, silent=True) # Search for the command using the specified string! return ExternalProgram(command, silent=True) @staticmethod def _shebang_to_cmd(script): """ Check if the file has a shebang and manually parse it to figure out the interpreter to use. This is useful if the script is not executable or if we're on Windows (which does not understand shebangs). """ try: with open(script) as f: first_line = f.readline().strip() if first_line.startswith('#!'): # In a shebang, everything before the first space is assumed to # be the command to run and everything after the first space is # the single argument to pass to that command. So we must split # exactly once. commands = first_line[2:].split('#')[0].strip().split(maxsplit=1) if mesonlib.is_windows(): # Windows does not have UNIX paths so remove them, # but don't remove Windows paths if commands[0].startswith('/'): commands[0] = commands[0].split('/')[-1] if len(commands) > 0 and commands[0] == 'env': commands = commands[1:] # Windows does not ship python3.exe, but we know the path to it if len(commands) > 0 and commands[0] == 'python3': commands = mesonlib.python_command + commands[1:] elif mesonlib.is_haiku(): # Haiku does not have /usr, but a lot of scripts assume that # /usr/bin/env always exists. Detect that case and run the # script with the interpreter after it. if commands[0] == '/usr/bin/env': commands = commands[1:] # We know what python3 is, we're running on it if len(commands) > 0 and commands[0] == 'python3': commands = mesonlib.python_command + commands[1:] return commands + [script] except Exception as e: mlog.debug(e) pass mlog.debug('Unusable script {!r}'.format(script)) return False def _is_executable(self, path): suffix = os.path.splitext(path)[-1].lower()[1:] if mesonlib.is_windows(): if suffix in self.windows_exts: return True elif os.access(path, os.X_OK): return not os.path.isdir(path) return False def _search_dir(self, name, search_dir): if search_dir is None: return False trial = os.path.join(search_dir, name) if os.path.exists(trial): if self._is_executable(trial): return [trial] # Now getting desperate. Maybe it is a script file that is # a) not chmodded executable, or # b) we are on windows so they can't be directly executed. return self._shebang_to_cmd(trial) else: if mesonlib.is_windows(): for ext in self.windows_exts: trial_ext = '{}.{}'.format(trial, ext) if os.path.exists(trial_ext): return [trial_ext] return False def _search_windows_special_cases(self, name, command): ''' Lots of weird Windows quirks: 1. PATH search for @name returns files with extensions from PATHEXT, but only self.windows_exts are executable without an interpreter. 2. @name might be an absolute path to an executable, but without the extension. This works inside MinGW so people use it a lot. 3. The script is specified without an extension, in which case we have to manually search in PATH. 4. More special-casing for the shebang inside the script. ''' if command: # On Windows, even if the PATH search returned a full path, we can't be # sure that it can be run directly if it's not a native executable. # For instance, interpreted scripts sometimes need to be run explicitly # with an interpreter if the file association is not done properly. name_ext = os.path.splitext(command)[1] if name_ext[1:].lower() in self.windows_exts: # Good, it can be directly executed return [command] # Try to extract the interpreter from the shebang commands = self._shebang_to_cmd(command) if commands: return commands return [None] # Maybe the name is an absolute path to a native Windows # executable, but without the extension. This is technically wrong, # but many people do it because it works in the MinGW shell. if os.path.isabs(name): for ext in self.windows_exts: command = '{}.{}'.format(name, ext) if os.path.exists(command): return [command] # On Windows, interpreted scripts must have an extension otherwise they # cannot be found by a standard PATH search. So we do a custom search # where we manually search for a script with a shebang in PATH. search_dirs = os.environ.get('PATH', '').split(';') for search_dir in search_dirs: commands = self._search_dir(name, search_dir) if commands: return commands return [None] def _search(self, name, search_dir): ''' Search in the specified dir for the specified executable by name and if not found search in PATH ''' commands = self._search_dir(name, search_dir) if commands: return commands # Do a standard search in PATH command = shutil.which(name) if mesonlib.is_windows(): return self._search_windows_special_cases(name, command) # On UNIX-like platforms, shutil.which() is enough to find # all executables whether in PATH or with an absolute path return [command] def found(self): return self.command[0] is not None def get_command(self): return self.command[:] def get_path(self): return self.path def get_name(self): return self.name class NonExistingExternalProgram(ExternalProgram): "A program that will never exist" def __init__(self): self.name = 'nonexistingprogram' self.command = [None] self.path = None def __repr__(self): r = '<{} {!r} -> {!r}>' return r.format(self.__class__.__name__, self.name, self.command) def found(self): return False class EmptyExternalProgram(ExternalProgram): ''' A program object that returns an empty list of commands. Used for cases such as a cross file exe_wrapper to represent that it's not required. ''' def __init__(self): self.name = None self.command = [] self.path = None def __repr__(self): r = '<{} {!r} -> {!r}>' return r.format(self.__class__.__name__, self.name, self.command) def found(self): return True class ExternalLibrary(ExternalDependency): def __init__(self, name, link_args, environment, language, silent=False): super().__init__('library', environment, language, {}) self.name = name self.language = language self.is_found = False if link_args: self.is_found = True self.link_args = link_args if not silent: if self.is_found: mlog.log('Library', mlog.bold(name), 'found:', mlog.green('YES')) else: mlog.log('Library', mlog.bold(name), 'found:', mlog.red('NO')) def get_link_args(self, language=None, **kwargs): ''' External libraries detected using a compiler must only be used with compatible code. For instance, Vala libraries (.vapi files) cannot be used with C code, and not all Rust library types can be linked with C-like code. Note that C++ libraries *can* be linked with C code with a C++ linker (and vice-versa). ''' # Using a vala library in a non-vala target, or a non-vala library in a vala target # XXX: This should be extended to other non-C linkers such as Rust if (self.language == 'vala' and language != 'vala') or \ (language == 'vala' and self.language != 'vala'): return [] return super().get_link_args(**kwargs) def get_partial_dependency(self, *, compile_args=False, link_args=False, links=False, includes=False, sources=False): # External library only has link_args, so ignore the rest of the # interface. new = copy.copy(self) if not link_args: new.link_args = [] return new class ExtraFrameworkDependency(ExternalDependency): def __init__(self, name, required, path, env, lang, kwargs): super().__init__('extraframeworks', env, lang, kwargs) self.name = name self.required = required self.detect(name, path) if self.found(): self.compile_args = ['-I' + os.path.join(self.path, self.name, 'Headers')] self.link_args = ['-F' + self.path, '-framework', self.name.split('.')[0]] def detect(self, name, path): # should use the compiler to look for frameworks, rather than peering at # the filesystem, so we can also find them when cross-compiling if self.want_cross: return lname = name.lower() if path is None: paths = ['/System/Library/Frameworks', '/Library/Frameworks'] else: paths = [path] for p in paths: for d in os.listdir(p): fullpath = os.path.join(p, d) if lname != d.rsplit('.', 1)[0].lower(): continue if not stat.S_ISDIR(os.stat(fullpath).st_mode): continue self.path = p self.name = d self.is_found = True return def log_info(self): return os.path.join(self.path, self.name) def log_tried(self): return 'framework' def get_dep_identifier(name, kwargs, want_cross): # Need immutable objects since the identifier will be used as a dict key version_reqs = listify(kwargs.get('version', [])) if isinstance(version_reqs, list): version_reqs = frozenset(version_reqs) identifier = (name, version_reqs, want_cross) for key, value in kwargs.items(): # 'version' is embedded above as the second element for easy access # 'native' is handled above with `want_cross` # 'required' is irrelevant for caching; the caller handles it separately # 'fallback' subprojects cannot be cached -- they must be initialized if key in ('version', 'native', 'required', 'fallback',): continue # All keyword arguments are strings, ints, or lists (or lists of lists) if isinstance(value, list): value = frozenset(listify(value)) identifier += (key, value) return identifier display_name_map = { 'boost': 'Boost', 'dub': 'DUB', 'gmock': 'GMock', 'gtest': 'GTest', 'llvm': 'LLVM', 'mpi': 'MPI', 'openmp': 'OpenMP', 'wxwidgets': 'WxWidgets', } def find_external_dependency(name, env, kwargs): assert(name) required = kwargs.get('required', True) if not isinstance(required, bool): raise DependencyException('Keyword "required" must be a boolean.') if not isinstance(kwargs.get('method', ''), str): raise DependencyException('Keyword "method" must be a string.') lname = name.lower() if lname not in _packages_accept_language and 'language' in kwargs: raise DependencyException('%s dependency does not accept "language" keyword argument' % (name, )) if not isinstance(kwargs.get('version', ''), (str, list)): raise DependencyException('Keyword "Version" must be string or list.') # display the dependency name with correct casing display_name = display_name_map.get(lname, lname) # if this isn't a cross-build, it's uninteresting if native: is used or not if not env.is_cross_build(): type_text = 'Dependency' else: type_text = 'Native' if kwargs.get('native', False) else 'Cross' type_text += ' dependency' # build a list of dependency methods to try candidates = _build_external_dependency_list(name, env, kwargs) pkg_exc = None pkgdep = [] details = '' for c in candidates: # try this dependency method try: d = c() d._check_version() pkgdep.append(d) except Exception as e: mlog.debug(str(e)) # store the first exception we see if not pkg_exc: pkg_exc = e else: details = d.log_details() if details: details = '(' + details + ') ' if 'language' in kwargs: details += 'for ' + d.language + ' ' # if the dependency was found if d.found(): info = [] if d.version: info.append(d.version) log_info = d.log_info() if log_info: info.append('(' + log_info + ')') info = ' '.join(info) mlog.log(type_text, mlog.bold(display_name), details + 'found:', mlog.green('YES'), info) return d # otherwise, the dependency could not be found tried_methods = [d.log_tried() for d in pkgdep if d.log_tried()] if tried_methods: tried = '{}'.format(mlog.format_list(tried_methods)) else: tried = '' mlog.log(type_text, mlog.bold(display_name), details + 'found:', mlog.red('NO'), '(tried {})'.format(tried) if tried else '') if required: # if exception(s) occurred, re-raise the first one (on the grounds that # it came from a preferred dependency detection method) if pkg_exc: raise pkg_exc # we have a list of failed ExternalDependency objects, so we can report # the methods we tried to find the dependency raise DependencyException('Dependency "%s" not found' % (name) + (', tried %s' % (tried) if tried else '')) # return the last failed dependency object if pkgdep: return pkgdep[-1] # this should never happen raise DependencyException('Dependency "%s" not found, but no dependency object to return' % (name)) def _build_external_dependency_list(name, env, kwargs): # Is there a specific dependency detector for this dependency? lname = name.lower() if lname in packages: # Create the list of dependency object constructors using a factory # class method, if one exists, otherwise the list just consists of the # constructor if getattr(packages[lname], '_factory', None): dep = packages[lname]._factory(env, kwargs) else: dep = [functools.partial(packages[lname], env, kwargs)] return dep candidates = [] # If it's explicitly requested, use the dub detection method (only) if 'dub' == kwargs.get('method', ''): candidates.append(functools.partial(DubDependency, name, env, kwargs)) return candidates # TBD: other values of method should control what method(s) are used # Otherwise, just use the pkgconfig dependency detector candidates.append(functools.partial(PkgConfigDependency, name, env, kwargs)) # On OSX, also try framework dependency detector if mesonlib.is_osx(): candidates.append(functools.partial(ExtraFrameworkDependency, name, False, None, env, None, kwargs)) return candidates def strip_system_libdirs(environment, link_args): """Remove -L<system path> arguments. leaving these in will break builds where a user has a version of a library in the system path, and a different version not in the system path if they want to link against the non-system path version. """ exclude = {'-L{}'.format(p) for p in environment.get_compiler_system_dirs()} return [l for l in link_args if l not in exclude]

from peewee import * from playhouse.sqlite_ext import * import datetime from pushbullet import PushBullet import ConfigParser import re config = ConfigParser.RawConfigParser() config.read('app.cfg') db = SqliteDatabase("eventbullet.db") api_key = config.get("pushbullet", "api_key") title_base = config.get("pushbullet", "title") message_base = config.get("pushbullet", "message") pb = PushBullet(api_key) class Event(Model): title = CharField() url = CharField(unique=True) event_from = DateTimeField(null=True) event_to = DateTimeField(null=True) description = TextField(null=True) # tags = TextField(null=True) notified = BooleanField(default=False) # Is notified this event updated_at = DateTimeField(default=datetime.datetime.now) created_at = DateTimeField(default=datetime.datetime.now) class Meta: database = db def notify(self, force=False): if force == True or self.notified == False: success, push = pb.push_note(self.event_title(), self.event_message()) self.notified = True self.save() def event_title(self): return self.replace_message_tags(title_base) def event_message(self): return self.replace_message_tags(message_base) @staticmethod def add_event(title, url, tags, description=None, event_from=None, event_to=None): ev = Event() ev.title = title ev.url = url ev.description = description ev.save() for tag in tags: Tag.add_tag(ev, tag) def update_date(self, event_from=None, event_to=None): if event_from is not None: self.event_from = event_from if event_to is not None: self.event_to = event_to self.notified = False self.save() def replace_message_tags(self, base): r = base r = re.sub("#title#", self.title, r) r = re.sub("#description#", self.description, r) r = re.sub("#from#", (self.event_from.strftime("%Y/%m/%d %H:%M") if self.event_from is not None else "") , r) r = re.sub("#from_time#", (self.event_from.strftime("%H:%M") if self.event_from is not None else "") , r) r = re.sub("#to#", (self.event_to.strftime("%Y/%m/%d %H:%M") if self.event_to is not None else ""), r) r = re.sub("#to_time#", (self.event_to.strftime("%H:%M") if self.event_to is not None else ""), r) r = re.sub("#url#", self.url, r) return r @staticmethod def get_not_end_events(): events = Event.select().where(Event.event_to > datetime.datetime.now()) return events class Tag(Model): event = ForeignKeyField(Event, related_name="event") name = CharField() created_at = DateTimeField(default=datetime.datetime.now) class Meta: database = db @staticmethod def add_tag(event, name): tag = Tag() tag.event = event tag.name = name tag.save() db.create_tables([Event, Tag], True)

from __future__ import absolute_import import errno import os import sys import signal from mock import Mock, patch, call from celery import _find_option_with_arg from celery import platforms from celery.five import open_fqdn from celery.platforms import ( get_fdmax, ignore_errno, set_process_title, signals, maybe_drop_privileges, setuid, setgid, initgroups, parse_uid, parse_gid, detached, DaemonContext, create_pidlock, Pidfile, LockFailed, setgroups, _setgroups_hack, close_open_fds, ) try: import resource except ImportError: # pragma: no cover resource = None # noqa from celery.tests.case import ( Case, WhateverIO, override_stdouts, mock_open, SkipTest, ) class test_find_option_with_arg(Case): def test_long_opt(self): self.assertEqual( _find_option_with_arg(['--foo=bar'], long_opts=['--foo']), 'bar' ) def test_short_opt(self): self.assertEqual( _find_option_with_arg(['-f', 'bar'], short_opts=['-f']), 'bar' ) class test_close_open_fds(Case): def test_closes(self): with patch('os.close') as _close: with patch('celery.platforms.get_fdmax') as fdmax: fdmax.return_value = 3 close_open_fds() _close.assert_has_calls([call(2), call(1), call(0)]) _close.side_effect = OSError() _close.side_effect.errno = errno.EBADF close_open_fds() class test_ignore_errno(Case): def test_raises_EBADF(self): with ignore_errno('EBADF'): exc = OSError() exc.errno = errno.EBADF raise exc def test_otherwise(self): with self.assertRaises(OSError): with ignore_errno('EBADF'): exc = OSError() exc.errno = errno.ENOENT raise exc class test_set_process_title(Case): def when_no_setps(self): prev = platforms._setproctitle = platforms._setproctitle, None try: set_process_title('foo') finally: platforms._setproctitle = prev class test_Signals(Case): @patch('signal.getsignal') def test_getitem(self, getsignal): signals['SIGINT'] getsignal.assert_called_with(signal.SIGINT) def test_supported(self): self.assertTrue(signals.supported('INT')) self.assertFalse(signals.supported('SIGIMAGINARY')) def test_reset_alarm(self): if sys.platform == 'win32': raise SkipTest('signal.alarm not available on Windows') with patch('signal.alarm') as _alarm: signals.reset_alarm() _alarm.assert_called_with(0) def test_arm_alarm(self): if hasattr(signal, 'setitimer'): with patch('signal.setitimer', create=True) as seti: signals.arm_alarm(30) self.assertTrue(seti.called) def test_signum(self): self.assertEqual(signals.signum(13), 13) self.assertEqual(signals.signum('INT'), signal.SIGINT) self.assertEqual(signals.signum('SIGINT'), signal.SIGINT) with self.assertRaises(TypeError): signals.signum('int') signals.signum(object()) @patch('signal.signal') def test_ignore(self, set): signals.ignore('SIGINT') set.assert_called_with(signals.signum('INT'), signals.ignored) signals.ignore('SIGTERM') set.assert_called_with(signals.signum('TERM'), signals.ignored) @patch('signal.signal') def test_setitem(self, set): handle = lambda *a: a signals['INT'] = handle set.assert_called_with(signal.SIGINT, handle) @patch('signal.signal') def test_setitem_raises(self, set): set.side_effect = ValueError() signals['INT'] = lambda *a: a if not platforms.IS_WINDOWS: class test_get_fdmax(Case): @patch('resource.getrlimit') def test_when_infinity(self, getrlimit): getrlimit.return_value = [None, resource.RLIM_INFINITY] default = object() self.assertIs(get_fdmax(default), default) @patch('resource.getrlimit') def test_when_actual(self, getrlimit): getrlimit.return_value = [None, 13] self.assertEqual(get_fdmax(None), 13) class test_maybe_drop_privileges(Case): @patch('celery.platforms.parse_uid') @patch('pwd.getpwuid') @patch('celery.platforms.setgid') @patch('celery.platforms.setuid') @patch('celery.platforms.initgroups') def test_with_uid(self, initgroups, setuid, setgid, getpwuid, parse_uid): class pw_struct(object): pw_gid = 50001 def raise_on_second_call(*args, **kwargs): setuid.side_effect = OSError() setuid.side_effect.errno = errno.EPERM setuid.side_effect = raise_on_second_call getpwuid.return_value = pw_struct() parse_uid.return_value = 5001 maybe_drop_privileges(uid='user') parse_uid.assert_called_with('user') getpwuid.assert_called_with(5001) setgid.assert_called_with(50001) initgroups.assert_called_with(5001, 50001) setuid.assert_has_calls([call(5001), call(0)]) @patch('celery.platforms.parse_uid') @patch('celery.platforms.parse_gid') @patch('celery.platforms.setgid') @patch('celery.platforms.setuid') @patch('celery.platforms.initgroups') def test_with_guid(self, initgroups, setuid, setgid, parse_gid, parse_uid): def raise_on_second_call(*args, **kwargs): setuid.side_effect = OSError() setuid.side_effect.errno = errno.EPERM setuid.side_effect = raise_on_second_call parse_uid.return_value = 5001 parse_gid.return_value = 50001 maybe_drop_privileges(uid='user', gid='group') parse_uid.assert_called_with('user') parse_gid.assert_called_with('group') setgid.assert_called_with(50001) initgroups.assert_called_with(5001, 50001) setuid.assert_has_calls([call(5001), call(0)]) setuid.side_effect = None with self.assertRaises(RuntimeError): maybe_drop_privileges(uid='user', gid='group') setuid.side_effect = OSError() setuid.side_effect.errno = errno.EINVAL with self.assertRaises(OSError): maybe_drop_privileges(uid='user', gid='group') @patch('celery.platforms.setuid') @patch('celery.platforms.setgid') @patch('celery.platforms.parse_gid') def test_only_gid(self, parse_gid, setgid, setuid): parse_gid.return_value = 50001 maybe_drop_privileges(gid='group') parse_gid.assert_called_with('group') setgid.assert_called_with(50001) self.assertFalse(setuid.called) class test_setget_uid_gid(Case): @patch('celery.platforms.parse_uid') @patch('os.setuid') def test_setuid(self, _setuid, parse_uid): parse_uid.return_value = 5001 setuid('user') parse_uid.assert_called_with('user') _setuid.assert_called_with(5001) @patch('celery.platforms.parse_gid') @patch('os.setgid') def test_setgid(self, _setgid, parse_gid): parse_gid.return_value = 50001 setgid('group') parse_gid.assert_called_with('group') _setgid.assert_called_with(50001) def test_parse_uid_when_int(self): self.assertEqual(parse_uid(5001), 5001) @patch('pwd.getpwnam') def test_parse_uid_when_existing_name(self, getpwnam): class pwent(object): pw_uid = 5001 getpwnam.return_value = pwent() self.assertEqual(parse_uid('user'), 5001) @patch('pwd.getpwnam') def test_parse_uid_when_nonexisting_name(self, getpwnam): getpwnam.side_effect = KeyError('user') with self.assertRaises(KeyError): parse_uid('user') def test_parse_gid_when_int(self): self.assertEqual(parse_gid(50001), 50001) @patch('grp.getgrnam') def test_parse_gid_when_existing_name(self, getgrnam): class grent(object): gr_gid = 50001 getgrnam.return_value = grent() self.assertEqual(parse_gid('group'), 50001) @patch('grp.getgrnam') def test_parse_gid_when_nonexisting_name(self, getgrnam): getgrnam.side_effect = KeyError('group') with self.assertRaises(KeyError): parse_gid('group') class test_initgroups(Case): @patch('pwd.getpwuid') @patch('os.initgroups', create=True) def test_with_initgroups(self, initgroups_, getpwuid): getpwuid.return_value = ['user'] initgroups(5001, 50001) initgroups_.assert_called_with('user', 50001) @patch('celery.platforms.setgroups') @patch('grp.getgrall') @patch('pwd.getpwuid') def test_without_initgroups(self, getpwuid, getgrall, setgroups): prev = getattr(os, 'initgroups', None) try: delattr(os, 'initgroups') except AttributeError: pass try: getpwuid.return_value = ['user'] class grent(object): gr_mem = ['user'] def __init__(self, gid): self.gr_gid = gid getgrall.return_value = [grent(1), grent(2), grent(3)] initgroups(5001, 50001) setgroups.assert_called_with([1, 2, 3]) finally: if prev: os.initgroups = prev class test_detached(Case): def test_without_resource(self): prev, platforms.resource = platforms.resource, None try: with self.assertRaises(RuntimeError): detached() finally: platforms.resource = prev @patch('celery.platforms._create_pidlock') @patch('celery.platforms.signals') @patch('celery.platforms.maybe_drop_privileges') @patch('os.geteuid') @patch(open_fqdn) def test_default(self, open, geteuid, maybe_drop, signals, pidlock): geteuid.return_value = 0 context = detached(uid='user', gid='group') self.assertIsInstance(context, DaemonContext) signals.reset.assert_called_with('SIGCLD') maybe_drop.assert_called_with(uid='user', gid='group') open.return_value = Mock() geteuid.return_value = 5001 context = detached(uid='user', gid='group', logfile='/foo/bar') self.assertIsInstance(context, DaemonContext) self.assertTrue(context.after_chdir) context.after_chdir() open.assert_called_with('/foo/bar', 'a') open.return_value.close.assert_called_with() context = detached(pidfile='/foo/bar/pid') self.assertIsInstance(context, DaemonContext) self.assertTrue(context.after_chdir) context.after_chdir() pidlock.assert_called_with('/foo/bar/pid') class test_DaemonContext(Case): @patch('os.fork') @patch('os.setsid') @patch('os._exit') @patch('os.chdir') @patch('os.umask') @patch('os.close') @patch('os.closerange') @patch('os.open') @patch('os.dup2') def test_open(self, dup2, open, close, closer, umask, chdir, _exit, setsid, fork): x = DaemonContext(workdir='/opt/workdir') fork.return_value = 0 with x: self.assertTrue(x._is_open) with x: pass self.assertEqual(fork.call_count, 2) setsid.assert_called_with() self.assertFalse(_exit.called) chdir.assert_called_with(x.workdir) umask.assert_called_with(x.umask) self.assertTrue(dup2.called) fork.reset_mock() fork.return_value = 1 x = DaemonContext(workdir='/opt/workdir') with x: pass self.assertEqual(fork.call_count, 1) _exit.assert_called_with(0) x = DaemonContext(workdir='/opt/workdir', fake=True) x._detach = Mock() with x: pass self.assertFalse(x._detach.called) x.after_chdir = Mock() with x: pass x.after_chdir.assert_called_with() class test_Pidfile(Case): @patch('celery.platforms.Pidfile') def test_create_pidlock(self, Pidfile): p = Pidfile.return_value = Mock() p.is_locked.return_value = True p.remove_if_stale.return_value = False with self.assertRaises(SystemExit): create_pidlock('/var/pid') p.remove_if_stale.return_value = True ret = create_pidlock('/var/pid') self.assertIs(ret, p) def test_context(self): p = Pidfile('/var/pid') p.write_pid = Mock() p.remove = Mock() with p as _p: self.assertIs(_p, p) p.write_pid.assert_called_with() p.remove.assert_called_with() def test_acquire_raises_LockFailed(self): p = Pidfile('/var/pid') p.write_pid = Mock() p.write_pid.side_effect = OSError() with self.assertRaises(LockFailed): with p: pass @patch('os.path.exists') def test_is_locked(self, exists): p = Pidfile('/var/pid') exists.return_value = True self.assertTrue(p.is_locked()) exists.return_value = False self.assertFalse(p.is_locked()) def test_read_pid(self): with mock_open() as s: s.write('1816\n') s.seek(0) p = Pidfile('/var/pid') self.assertEqual(p.read_pid(), 1816) def test_read_pid_partially_written(self): with mock_open() as s: s.write('1816') s.seek(0) p = Pidfile('/var/pid') with self.assertRaises(ValueError): p.read_pid() def test_read_pid_raises_ENOENT(self): exc = IOError() exc.errno = errno.ENOENT with mock_open(side_effect=exc): p = Pidfile('/var/pid') self.assertIsNone(p.read_pid()) def test_read_pid_raises_IOError(self): exc = IOError() exc.errno = errno.EAGAIN with mock_open(side_effect=exc): p = Pidfile('/var/pid') with self.assertRaises(IOError): p.read_pid() def test_read_pid_bogus_pidfile(self): with mock_open() as s: s.write('eighteensixteen\n') s.seek(0) p = Pidfile('/var/pid') with self.assertRaises(ValueError): p.read_pid() @patch('os.unlink') def test_remove(self, unlink): unlink.return_value = True p = Pidfile('/var/pid') p.remove() unlink.assert_called_with(p.path) @patch('os.unlink') def test_remove_ENOENT(self, unlink): exc = OSError() exc.errno = errno.ENOENT unlink.side_effect = exc p = Pidfile('/var/pid') p.remove() unlink.assert_called_with(p.path) @patch('os.unlink') def test_remove_EACCES(self, unlink): exc = OSError() exc.errno = errno.EACCES unlink.side_effect = exc p = Pidfile('/var/pid') p.remove() unlink.assert_called_with(p.path) @patch('os.unlink') def test_remove_OSError(self, unlink): exc = OSError() exc.errno = errno.EAGAIN unlink.side_effect = exc p = Pidfile('/var/pid') with self.assertRaises(OSError): p.remove() unlink.assert_called_with(p.path) @patch('os.kill') def test_remove_if_stale_process_alive(self, kill): p = Pidfile('/var/pid') p.read_pid = Mock() p.read_pid.return_value = 1816 kill.return_value = 0 self.assertFalse(p.remove_if_stale()) kill.assert_called_with(1816, 0) p.read_pid.assert_called_with() kill.side_effect = OSError() kill.side_effect.errno = errno.ENOENT self.assertFalse(p.remove_if_stale()) @patch('os.kill') def test_remove_if_stale_process_dead(self, kill): with override_stdouts(): p = Pidfile('/var/pid') p.read_pid = Mock() p.read_pid.return_value = 1816 p.remove = Mock() exc = OSError() exc.errno = errno.ESRCH kill.side_effect = exc self.assertTrue(p.remove_if_stale()) kill.assert_called_with(1816, 0) p.remove.assert_called_with() def test_remove_if_stale_broken_pid(self): with override_stdouts(): p = Pidfile('/var/pid') p.read_pid = Mock() p.read_pid.side_effect = ValueError() p.remove = Mock() self.assertTrue(p.remove_if_stale()) p.remove.assert_called_with() def test_remove_if_stale_no_pidfile(self): p = Pidfile('/var/pid') p.read_pid = Mock() p.read_pid.return_value = None p.remove = Mock() self.assertTrue(p.remove_if_stale()) p.remove.assert_called_with() @patch('os.fsync') @patch('os.getpid') @patch('os.open') @patch('os.fdopen') @patch(open_fqdn) def test_write_pid(self, open_, fdopen, osopen, getpid, fsync): getpid.return_value = 1816 osopen.return_value = 13 w = fdopen.return_value = WhateverIO() w.close = Mock() r = open_.return_value = WhateverIO() r.write('1816\n') r.seek(0) p = Pidfile('/var/pid') p.write_pid() w.seek(0) self.assertEqual(w.readline(), '1816\n') self.assertTrue(w.close.called) getpid.assert_called_with() osopen.assert_called_with(p.path, platforms.PIDFILE_FLAGS, platforms.PIDFILE_MODE) fdopen.assert_called_with(13, 'w') fsync.assert_called_with(13) open_.assert_called_with(p.path) @patch('os.fsync') @patch('os.getpid') @patch('os.open') @patch('os.fdopen') @patch(open_fqdn) def test_write_reread_fails(self, open_, fdopen, osopen, getpid, fsync): getpid.return_value = 1816 osopen.return_value = 13 w = fdopen.return_value = WhateverIO() w.close = Mock() r = open_.return_value = WhateverIO() r.write('11816\n') r.seek(0) p = Pidfile('/var/pid') with self.assertRaises(LockFailed): p.write_pid() class test_setgroups(Case): @patch('os.setgroups', create=True) def test_setgroups_hack_ValueError(self, setgroups): def on_setgroups(groups): if len(groups) <= 200: setgroups.return_value = True return raise ValueError() setgroups.side_effect = on_setgroups _setgroups_hack(list(range(400))) setgroups.side_effect = ValueError() with self.assertRaises(ValueError): _setgroups_hack(list(range(400))) @patch('os.setgroups', create=True) def test_setgroups_hack_OSError(self, setgroups): exc = OSError() exc.errno = errno.EINVAL def on_setgroups(groups): if len(groups) <= 200: setgroups.return_value = True return raise exc setgroups.side_effect = on_setgroups _setgroups_hack(list(range(400))) setgroups.side_effect = exc with self.assertRaises(OSError): _setgroups_hack(list(range(400))) exc2 = OSError() exc.errno = errno.ESRCH setgroups.side_effect = exc2 with self.assertRaises(OSError): _setgroups_hack(list(range(400))) @patch('os.sysconf') @patch('celery.platforms._setgroups_hack') def test_setgroups(self, hack, sysconf): sysconf.return_value = 100 setgroups(list(range(400))) hack.assert_called_with(list(range(100))) @patch('os.sysconf') @patch('celery.platforms._setgroups_hack') def test_setgroups_sysconf_raises(self, hack, sysconf): sysconf.side_effect = ValueError() setgroups(list(range(400))) hack.assert_called_with(list(range(400))) @patch('os.getgroups') @patch('os.sysconf') @patch('celery.platforms._setgroups_hack') def test_setgroups_raises_ESRCH(self, hack, sysconf, getgroups): sysconf.side_effect = ValueError() esrch = OSError() esrch.errno = errno.ESRCH hack.side_effect = esrch with self.assertRaises(OSError): setgroups(list(range(400))) @patch('os.getgroups') @patch('os.sysconf') @patch('celery.platforms._setgroups_hack') def test_setgroups_raises_EPERM(self, hack, sysconf, getgroups): sysconf.side_effect = ValueError() eperm = OSError() eperm.errno = errno.EPERM hack.side_effect = eperm getgroups.return_value = list(range(400)) setgroups(list(range(400))) getgroups.assert_called_with() getgroups.return_value = [1000] with self.assertRaises(OSError): setgroups(list(range(400))) getgroups.assert_called_with()

from ..excel_comparsion_test import ExcelComparisonTest from ...workbook import Workbook class TestCompareXLSXFiles(ExcelComparisonTest): """ Test file created by XlsxWriter against a file created by Excel. """ def setUp(self): self.maxDiff = None filename = 'chart_gradient03.xlsx' test_dir = 'xlsxwriter/test/comparison/' self.got_filename = test_dir + '_test_' + filename self.exp_filename = test_dir + 'xlsx_files/' + filename self.ignore_files = [] self.ignore_elements = {} def test_create_file(self): """Test the creation of a simple XlsxWriter file.""" workbook = Workbook(self.got_filename) worksheet = workbook.add_worksheet() chart = workbook.add_chart({'type': 'column'}) chart.axis_ids = [61363328, 61364864] chart.axis_ids = [61363712, 61365248] data = [ [1, 2, 3, 4, 5], [2, 4, 6, 8, 10], [3, 6, 9, 12, 15], ] worksheet.write_column('A1', data[0]) worksheet.write_column('B1', data[1]) worksheet.write_column('C1', data[2]) chart.add_series({ 'values': '=Sheet1!$A$1:$A$5', 'gradient': { 'colors': ['#DDEBCF', '#9CB86E', '#156B13'], 'positions': [0, 40, 100], } }) chart.add_series({'values': '=Sheet1!$B$1:$B$5'}) chart.add_series({'values': '=Sheet1!$C$1:$C$5'}) worksheet.insert_chart('E9', chart) workbook.close() self.assertExcelEqual()

from chunsabot.database import Database def make_initial_config(): Database.mkdir() if Database.config_exists(): print("There exists account info. \r\nReally overwrite config file? (Y / N)") if input().lower() == "y": result = True else: result = False else: print("There isn't default config file (data/config.yaml). Do you want to generate default config file? (Y / N)") if input().lower() == "y": result = True else: result = False if result: Database.save_config('leave', u"안녕히 계세요!") Database.save_config('debug_users', []) Database.save_config('curse_map', []) Database.save_config('sensitive_map', []) Database.save_config('debug_allowed_room', []) Database.save_config('debug_users', []) Database.save_config('debug_mode', False) Database.save_config('google_api_key', '') Database.save_config('weather_url_all', 'http://www.kma.go.kr/weather/forecast/mid-term-rss3.jsp') print("Config sucessfully saved.") else: print("Configmaker work cancelled.")

""" Can be run to install all the subpieces. """ from __future__ import print_function try: from setuptools import setup from setuptools.command.install import install from setuptools.command.sdist import sdist except ImportError: from distutils.core import setup from distutils.command.install import install from distutils.command.sdist import sdist from distutils.cmd import Command import os import sys from contextlib import contextmanager VERSION = "1.16.0" @contextmanager def chdir(dir): prev = os.getcwd() try: os.chdir(dir) yield finally: os.chdir(prev) def run_cmd(command_string): import subprocess try: print(f"+ {command_string}") rc = subprocess.call(command_string, shell=True) if rc < 0: print(f"Command terminated by signal {-rc}", file=sys.stderr) except OSError as e: print(f"Command failed: {e}", file=sys.stderr) PACKAGES = [ "bdc", "course", "db_edu_util", "gendbc", "master_parse", ] class TestCommand(Command): description = "run all tests" user_options = [] def __init__(self, dist): Command.__init__(self, dist) def initialize_options(self): pass def finalize_options(self): pass def run(self): run_cmd("./run-tests.sh") setup( name="db-build-tooling", packages=PACKAGES, cmdclass={"test": TestCommand}, install_requires=[ "databricks-cli >= 0.8.7", "docopt >= 0.6.2", "GitPython >= 3.1.2", "grizzled-python >= 2.2.0", "markdown2 >= 2.3.7", "parsimonious >= 0.8.1", "pystache >= 0.5.4", "PyYAML >= 5.1", "nbformat >= 4.4.0", "requests >= 2.22.0", "termcolor >= 1.1.0", "WeasyPrint >= 45", ], entry_points={ "console_scripts": [ "bdc=bdc:main", "course=course:main", "gendbc=gendbc:main", "master_parse=master_parse:main", ] }, version=VERSION, description="Wrapper package for Databricks Training build tools", author="Databricks Education Team", author_email="training-logins@databricks.com", license="Creative Commons Attribution-NonCommercial 4.0 International", )

"""Test for kubernetes_e2e.py""" import os import shutil import string import tempfile import urllib2 import unittest import time import kubernetes_e2e FAKE_WORKSPACE_STATUS = 'STABLE_BUILD_GIT_COMMIT 599539dc0b99976fda0f326f4ce47e93ec07217c\n' \ 'STABLE_BUILD_SCM_STATUS clean\n' \ 'STABLE_BUILD_SCM_REVISION v1.7.0-alpha.0.1320+599539dc0b9997\n' \ 'STABLE_BUILD_MAJOR_VERSION 1\n' \ 'STABLE_BUILD_MINOR_VERSION 7+\n' \ 'STABLE_gitCommit 599539dc0b99976fda0f326f4ce47e93ec07217c\n' \ 'STABLE_gitTreeState clean\n' \ 'STABLE_gitVersion v1.7.0-alpha.0.1320+599539dc0b9997\n' \ 'STABLE_gitMajor 1\n' \ 'STABLE_gitMinor 7+\n' FAKE_WORKSPACE_STATUS_V1_6 = 'STABLE_BUILD_GIT_COMMIT 84febd4537dd190518657405b7bdb921dfbe0387\n' \ 'STABLE_BUILD_SCM_STATUS clean\n' \ 'STABLE_BUILD_SCM_REVISION v1.6.4-beta.0.18+84febd4537dd19\n' \ 'STABLE_BUILD_MAJOR_VERSION 1\n' \ 'STABLE_BUILD_MINOR_VERSION 6+\n' \ 'STABLE_gitCommit 84febd4537dd190518657405b7bdb921dfbe0387\n' \ 'STABLE_gitTreeState clean\n' \ 'STABLE_gitVersion v1.6.4-beta.0.18+84febd4537dd19\n' \ 'STABLE_gitMajor 1\n' \ 'STABLE_gitMinor 6+\n' FAKE_DESCRIBE_FROM_FAMILY_RESPONSE = """ archiveSizeBytes: '1581831882' creationTimestamp: '2017-06-16T10:37:57.681-07:00' description: 'Google, Container-Optimized OS, 59-9460.64.0 stable, Kernel: ChromiumOS-4.4.52 Kubernetes: 1.6.4 Docker: 1.11.2' diskSizeGb: '10' family: cos-stable id: '2388425242502080922' kind: compute#image labelFingerprint: 42WmSpB8rSM= licenses: - https://www.googleapis.com/compute/v1/projects/cos-cloud/global/licenses/cos name: cos-stable-59-9460-64-0 rawDisk: containerType: TAR source: '' selfLink: https://www.googleapis.com/compute/v1/projects/cos-cloud/global/images/cos-stable-59-9460-64-0 sourceType: RAW status: READY """ def fake_pass(*_unused, **_unused2): """Do nothing.""" pass def fake_bomb(*a, **kw): """Always raise.""" raise AssertionError('Should not happen', a, kw) def raise_urllib2_error(*_unused, **_unused2): """Always raise a urllib2.URLError""" raise urllib2.URLError("test failure") def always_kubernetes(*_unused, **_unused2): """Always return 'kubernetes'""" return 'kubernetes' class Stub(object): """Replace thing.param with replacement until exiting with.""" def __init__(self, thing, param, replacement): self.thing = thing self.param = param self.replacement = replacement self.old = getattr(thing, param) setattr(thing, param, self.replacement) def __enter__(self, *a, **kw): return self.replacement def __exit__(self, *a, **kw): setattr(self.thing, self.param, self.old) class ClusterNameTest(unittest.TestCase): def test_name_filled(self): """Return the cluster name if set.""" name = 'foo' build = '1984' os.environ['BUILD_ID'] = build actual = kubernetes_e2e.cluster_name(name) self.assertTrue(actual) self.assertIn(name, actual) self.assertNotIn(build, actual) def test_name_empty_short_build(self): """Return the build number if name is empty.""" name = '' build = '1984' os.environ['BUILD_ID'] = build actual = kubernetes_e2e.cluster_name(name) self.assertTrue(actual) self.assertIn(build, actual) def test_name_empty_long_build(self): """Return a short hash of a long build number if name is empty.""" name = '' build = '0' * 63 os.environ['BUILD_ID'] = build actual = kubernetes_e2e.cluster_name(name) self.assertTrue(actual) self.assertNotIn(build, actual) if len(actual) > 32: # Some firewall names consume half the quota self.fail('Name should be short: %s' % actual) class ScenarioTest(unittest.TestCase): # pylint: disable=too-many-public-methods """Test for e2e scenario.""" callstack = [] envs = {} def setUp(self): self.boiler = [ Stub(kubernetes_e2e, 'check', self.fake_check), Stub(shutil, 'copy', fake_pass), ] def tearDown(self): for stub in self.boiler: with stub: # Leaving with restores things pass self.callstack[:] = [] self.envs.clear() def fake_check(self, *cmd): """Log the command.""" self.callstack.append(string.join(cmd)) def fake_check_env(self, env, *cmd): """Log the command with a specific env.""" self.envs.update(env) self.callstack.append(string.join(cmd)) def fake_output_work_status(self, *cmd): """fake a workstatus blob.""" self.callstack.append(string.join(cmd)) return FAKE_WORKSPACE_STATUS def fake_output_work_status_v1_6(self, *cmd): """fake a workstatus blob for v1.6.""" self.callstack.append(string.join(cmd)) return FAKE_WORKSPACE_STATUS_V1_6 def fake_output_get_latest_image(self, *cmd): """fake a `gcloud compute images describe-from-family` response.""" self.callstack.append(string.join(cmd)) return FAKE_DESCRIBE_FROM_FAMILY_RESPONSE def test_local(self): """Make sure local mode is fine overall.""" args = kubernetes_e2e.parse_args() with Stub(kubernetes_e2e, 'check_env', self.fake_check_env): kubernetes_e2e.main(args) self.assertNotEqual(self.envs, {}) for call in self.callstack: self.assertFalse(call.startswith('docker')) def test_check_leaks(self): """Ensure --check-leaked-resources=true sends flag to kubetest.""" args = kubernetes_e2e.parse_args(['--check-leaked-resources=true']) with Stub(kubernetes_e2e, 'check_env', self.fake_check_env): kubernetes_e2e.main(args) self.assertIn('--check-leaked-resources=true', self.callstack[-1]) def test_check_leaks_false(self): """Ensure --check-leaked-resources=true sends flag to kubetest.""" args = kubernetes_e2e.parse_args(['--check-leaked-resources=false']) with Stub(kubernetes_e2e, 'check_env', self.fake_check_env): kubernetes_e2e.main(args) self.assertIn('--check-leaked-resources=false', self.callstack[-1]) def test_check_leaks_default(self): """Ensure --check-leaked-resources=true sends flag to kubetest.""" args = kubernetes_e2e.parse_args(['--check-leaked-resources']) with Stub(kubernetes_e2e, 'check_env', self.fake_check_env): kubernetes_e2e.main(args) self.assertIn('--check-leaked-resources', self.callstack[-1]) def test_check_leaks_unset(self): """Ensure --check-leaked-resources=true sends flag to kubetest.""" args = kubernetes_e2e.parse_args(['--mode=local']) with Stub(kubernetes_e2e, 'check_env', self.fake_check_env): kubernetes_e2e.main(args) self.assertNotIn('--check-leaked-resources', self.callstack[-1]) def test_migrated_kubetest_args(self): migrated = [ '--stage-suffix=panda', '--random-flag', 'random-value', '--multiple-federations', 'arg1', 'arg2', '--federation', '--kubemark', '--extract=this', '--extract=that', '--perf-tests', '--save=somewhere', '--skew', '--publish=location', '--timeout=42m', '--upgrade_args=ginkgo', '--check-leaked-resources=true', '--charts', ] explicit_passthrough_args = [ '--deployment=yay', '--provider=gce', ] args = kubernetes_e2e.parse_args(migrated + explicit_passthrough_args + ['--test=false']) self.assertEquals(migrated, args.kubetest_args) with Stub(kubernetes_e2e, 'check_env', self.fake_check_env): kubernetes_e2e.main(args) lastcall = self.callstack[-1] for arg in migrated: self.assertIn(arg, lastcall) for arg in explicit_passthrough_args: self.assertIn(arg, lastcall) def test_updown_default(self): args = kubernetes_e2e.parse_args([]) with Stub(kubernetes_e2e, 'check_env', self.fake_check_env): kubernetes_e2e.main(args) lastcall = self.callstack[-1] self.assertIn('--up', lastcall) self.assertIn('--down', lastcall) def test_updown_set(self): args = kubernetes_e2e.parse_args(['--up=false', '--down=true']) with Stub(kubernetes_e2e, 'check_env', self.fake_check_env): kubernetes_e2e.main(args) lastcall = self.callstack[-1] self.assertNotIn('--up', lastcall) self.assertIn('--down', lastcall) def test_kubeadm_ci(self): """Make sure kubeadm ci mode is fine overall.""" args = kubernetes_e2e.parse_args(['--kubeadm=ci']) self.assertEqual(args.kubeadm, 'ci') with Stub(kubernetes_e2e, 'check_env', self.fake_check_env): with Stub(kubernetes_e2e, 'check_output', self.fake_output_work_status): kubernetes_e2e.main(args) self.assertNotIn('E2E_OPT', self.envs) version = 'gs://kubernetes-release-dev/ci/v1.7.0-alpha.0.1320+599539dc0b9997-bazel/bin/linux/amd64/' # pylint: disable=line-too-long self.assertIn('--kubernetes-anywhere-kubeadm-version=%s' % version, self.callstack[-1]) called = False for call in self.callstack: self.assertFalse(call.startswith('docker')) if call == 'hack/print-workspace-status.sh': called = True self.assertTrue(called) def test_local_env(self): """ Ensure that host variables (such as GOPATH) are included, and added envs/env files overwrite os environment. """ mode = kubernetes_e2e.LocalMode('/orig-workspace', '/random-artifacts') mode.add_environment(*( 'FOO=BAR', 'GOPATH=/go/path', 'WORKSPACE=/new/workspace')) mode.add_os_environment(*('USER=jenkins', 'FOO=BAZ', 'GOOS=linux')) with tempfile.NamedTemporaryFile() as temp: temp.write('USER=prow') temp.flush() mode.add_file(temp.name) with Stub(kubernetes_e2e, 'check_env', self.fake_check_env): mode.start([]) self.assertIn(('FOO', 'BAR'), self.envs.viewitems()) self.assertIn(('WORKSPACE', '/new/workspace'), self.envs.viewitems()) self.assertIn(('GOPATH', '/go/path'), self.envs.viewitems()) self.assertIn(('USER', 'prow'), self.envs.viewitems()) self.assertIn(('GOOS', 'linux'), self.envs.viewitems()) self.assertNotIn(('USER', 'jenkins'), self.envs.viewitems()) self.assertNotIn(('FOO', 'BAZ'), self.envs.viewitems()) def test_kubeadm_periodic(self): """Make sure kubeadm periodic mode is fine overall.""" args = kubernetes_e2e.parse_args(['--kubeadm=periodic']) self.assertEqual(args.kubeadm, 'periodic') with Stub(kubernetes_e2e, 'check_env', self.fake_check_env): with Stub(kubernetes_e2e, 'check_output', self.fake_output_work_status): kubernetes_e2e.main(args) self.assertNotIn('E2E_OPT', self.envs) version = 'gs://kubernetes-release-dev/ci/v1.7.0-alpha.0.1320+599539dc0b9997-bazel/bin/linux/amd64/' # pylint: disable=line-too-long self.assertIn('--kubernetes-anywhere-kubeadm-version=%s' % version, self.callstack[-1]) called = False for call in self.callstack: self.assertFalse(call.startswith('docker')) if call == 'hack/print-workspace-status.sh': called = True self.assertTrue(called) def test_kubeadm_pull(self): """Make sure kubeadm pull mode is fine overall.""" args = kubernetes_e2e.parse_args([ '--kubeadm=pull', '--use-shared-build=bazel' ]) self.assertEqual(args.kubeadm, 'pull') self.assertEqual(args.use_shared_build, 'bazel') gcs_bucket = "gs://kubernetes-release-dev/bazel/v1.8.0-beta.1.132+599539dc0b9997" def fake_gcs_path(path): bazel_default = os.path.join( 'gs://kubernetes-jenkins/shared-results', 'bazel-build-location.txt') self.assertEqual(path, bazel_default) return gcs_bucket with Stub(kubernetes_e2e, 'check_env', self.fake_check_env): with Stub(kubernetes_e2e, 'read_gcs_path', fake_gcs_path): kubernetes_e2e.main(args) self.assertNotIn('E2E_OPT', self.envs) version = '%s/bin/linux/amd64/' % gcs_bucket self.assertIn('--kubernetes-anywhere-kubeadm-version=%s' % version, self.callstack[-1]) def test_kubeadm_invalid(self): """Make sure kubeadm invalid mode exits unsuccessfully.""" with self.assertRaises(SystemExit) as sysexit: kubernetes_e2e.parse_args(['--mode=local', '--kubeadm=deploy']) self.assertEqual(sysexit.exception.code, 2) def test_parse_args_order_agnostic(self): args = kubernetes_e2e.parse_args([ '--some-kubetest-arg=foo', '--cluster=test']) self.assertEqual(args.kubetest_args, ['--some-kubetest-arg=foo']) self.assertEqual(args.cluster, 'test') def test_gcp_network(self): args = kubernetes_e2e.parse_args(['--mode=local', '--cluster=test']) with Stub(kubernetes_e2e, 'check_env', self.fake_check_env): kubernetes_e2e.main(args) lastcall = self.callstack[-1] self.assertIn('--gcp-network=test', lastcall) def test_env_local(self): env = 'FOO' value = 'BLAT' args = kubernetes_e2e.parse_args([ '--mode=local', '--env={env}={value}'.format(env=env, value=value), ]) with Stub(kubernetes_e2e, 'check_env', self.fake_check_env): kubernetes_e2e.main(args) self.assertIn(env, self.envs) self.assertEqual(self.envs[env], value) def test_aws(self): temp = tempfile.NamedTemporaryFile() args = kubernetes_e2e.parse_args([ '--aws', '--cluster=foo', '--aws-cluster-domain=test-aws.k8s.io', '--aws-ssh=%s' % temp.name, '--aws-pub=%s' % temp.name, '--aws-cred=%s' % temp.name, ]) with Stub(kubernetes_e2e, 'check_env', self.fake_check_env): kubernetes_e2e.main(args) lastcall = self.callstack[-1] self.assertIn('kops-e2e-runner.sh', lastcall) self.assertIn('--kops-cluster=foo.test-aws.k8s.io', lastcall) self.assertIn('--kops-zones', lastcall) self.assertIn('--kops-state=s3://k8s-kops-prow/', lastcall) self.assertIn('--kops-nodes=4', lastcall) self.assertIn('--kops-ssh-key', lastcall) self.assertNotIn('kubetest', lastcall) self.assertIn('kops-e2e-runner.sh', lastcall) self.assertEqual( self.envs['JENKINS_AWS_SSH_PRIVATE_KEY_FILE'], temp.name) self.assertEqual( self.envs['JENKINS_AWS_SSH_PUBLIC_KEY_FILE'], temp.name) self.assertEqual( self.envs['JENKINS_AWS_CREDENTIALS_FILE'], temp.name) def test_kops_aws(self): temp = tempfile.NamedTemporaryFile() args = kubernetes_e2e.parse_args([ '--provider=aws', '--deployment=kops', '--cluster=foo.example.com', '--aws-ssh=%s' % temp.name, '--aws-pub=%s' % temp.name, '--aws-cred=%s' % temp.name, ]) with Stub(kubernetes_e2e, 'check_env', self.fake_check_env): kubernetes_e2e.main(args) lastcall = self.callstack[-1] self.assertIn('kubetest', lastcall) self.assertIn('--provider=aws', lastcall) self.assertIn('--deployment=kops', lastcall) self.assertIn('--kops-cluster=foo.example.com', lastcall) self.assertIn('--kops-zones', lastcall) self.assertIn('--kops-state=s3://k8s-kops-prow/', lastcall) self.assertIn('--kops-nodes=4', lastcall) self.assertIn('--kops-ssh-key', lastcall) self.assertIn('kubetest', lastcall) self.assertNotIn('kops-e2e-runner.sh', lastcall) def test_kops_gce(self): temp = tempfile.NamedTemporaryFile() args = kubernetes_e2e.parse_args([ '--provider=gce', '--deployment=kops', '--cluster=foo.example.com', '--gce-ssh=%s' % temp.name, '--gce-pub=%s' % temp.name, ]) with Stub(kubernetes_e2e, 'check_env', self.fake_check_env): kubernetes_e2e.main(args) lastcall = self.callstack[-1] self.assertIn('kubetest', lastcall) self.assertIn('--provider=gce', lastcall) self.assertIn('--deployment=kops', lastcall) self.assertIn('--kops-cluster=foo.example.com', lastcall) self.assertIn('--kops-zones', lastcall) self.assertIn('--kops-state=gs://k8s-kops-gce/', lastcall) self.assertIn('--kops-nodes=4', lastcall) self.assertIn('--kops-ssh-key', lastcall) def test_use_shared_build(self): # normal path args = kubernetes_e2e.parse_args([ '--use-shared-build=bazel' ]) def expect_bazel_gcs(path): bazel_default = os.path.join( 'gs://kubernetes-jenkins/shared-results', 'bazel-build-location.txt') self.assertEqual(path, bazel_default) return always_kubernetes() with Stub(kubernetes_e2e, 'check_env', self.fake_check_env): with Stub(kubernetes_e2e, 'read_gcs_path', expect_bazel_gcs): with Stub(time, 'sleep', fake_pass): kubernetes_e2e.main(args) lastcall = self.callstack[-1] self.assertIn('--extract=kubernetes', lastcall) # normal path, not bazel args = kubernetes_e2e.parse_args([ '--use-shared-build' ]) def expect_normal_gcs(path): bazel_default = os.path.join( 'gs://kubernetes-jenkins/shared-results', 'build-location.txt') self.assertEqual(path, bazel_default) return always_kubernetes() with Stub(kubernetes_e2e, 'check_env', self.fake_check_env): with Stub(kubernetes_e2e, 'read_gcs_path', expect_normal_gcs): kubernetes_e2e.main(args) lastcall = self.callstack[-1] self.assertIn('--extract=kubernetes', lastcall) # test failure to read shared path from GCS with Stub(kubernetes_e2e, 'check_env', self.fake_check_env): with Stub(kubernetes_e2e, 'read_gcs_path', raise_urllib2_error): with Stub(os, 'getcwd', always_kubernetes): with Stub(time, 'sleep', fake_pass): try: kubernetes_e2e.main(args) except RuntimeError as err: if not err.message.startswith('Failed to get shared build location'): raise err if __name__ == '__main__': unittest.main()

import datetime import unittest from unittest.mock import Mock, patch import pendulum from airflow import settings from airflow.models.dag import DAG from airflow.models.skipmixin import SkipMixin from airflow.models.taskinstance import TaskInstance as TI from airflow.operators.dummy_operator import DummyOperator from airflow.utils import timezone from airflow.utils.state import State from airflow.utils.types import DagRunType DEFAULT_DATE = timezone.datetime(2016, 1, 1) class TestSkipMixin(unittest.TestCase): @patch('airflow.utils.timezone.utcnow') def test_skip(self, mock_now): session = settings.Session() now = datetime.datetime.utcnow().replace(tzinfo=pendulum.timezone('UTC')) mock_now.return_value = now dag = DAG( 'dag', start_date=DEFAULT_DATE, ) with dag: tasks = [DummyOperator(task_id='task')] dag_run = dag.create_dagrun( run_type=DagRunType.MANUAL, execution_date=now, state=State.FAILED, ) SkipMixin().skip(dag_run=dag_run, execution_date=now, tasks=tasks, session=session) session.query(TI).filter( TI.dag_id == 'dag', TI.task_id == 'task', TI.state == State.SKIPPED, TI.start_date == now, TI.end_date == now, ).one() @patch('airflow.utils.timezone.utcnow') def test_skip_none_dagrun(self, mock_now): session = settings.Session() now = datetime.datetime.utcnow().replace(tzinfo=pendulum.timezone('UTC')) mock_now.return_value = now dag = DAG( 'dag', start_date=DEFAULT_DATE, ) with dag: tasks = [DummyOperator(task_id='task')] SkipMixin().skip(dag_run=None, execution_date=now, tasks=tasks, session=session) session.query(TI).filter( TI.dag_id == 'dag', TI.task_id == 'task', TI.state == State.SKIPPED, TI.start_date == now, TI.end_date == now, ).one() def test_skip_none_tasks(self): session = Mock() SkipMixin().skip(dag_run=None, execution_date=None, tasks=[], session=session) self.assertFalse(session.query.called) self.assertFalse(session.commit.called) def test_skip_all_except(self): dag = DAG( 'dag_test_skip_all_except', start_date=DEFAULT_DATE, ) with dag: task1 = DummyOperator(task_id='task1') task2 = DummyOperator(task_id='task2') task3 = DummyOperator(task_id='task3') task1 >> [task2, task3] ti1 = TI(task1, execution_date=DEFAULT_DATE) ti2 = TI(task2, execution_date=DEFAULT_DATE) ti3 = TI(task3, execution_date=DEFAULT_DATE) SkipMixin().skip_all_except(ti=ti1, branch_task_ids=['task2']) def get_state(ti): ti.refresh_from_db() return ti.state assert get_state(ti2) == State.NONE assert get_state(ti3) == State.SKIPPED

import datetime from pyoperant import hwio, utils, ComponentError class BaseComponent(object): """Base class for physcal component""" def __init__(self, name=None, *args, **kwargs): self.name = name pass ## Hopper ## class HopperActiveError(ComponentError): """raised when the hopper is up when it shouldn't be""" pass class HopperInactiveError(ComponentError): """raised when the hopper is down when it shouldn't be""" pass class HopperAlreadyUpError(HopperActiveError): """raised when the hopper is already up before it goes up""" pass class HopperWontComeUpError(HopperInactiveError): """raised when the hopper won't come up""" pass class HopperWontDropError(HopperActiveError): """raised when the hopper won't drop""" pass class Hopper(BaseComponent): """ Class which holds information about a hopper Parameters ---------- solenoid : `hwio.BooleanOutput` output channel to activate the solenoid & raise the hopper IR : :class:`hwio.BooleanInput` input channel for the IR beam to check if the hopper is up max_lag : float, optional time in seconds to wait before checking to make sure the hopper is up (default=0.3) Attributes ---------- solenoid : hwio.BooleanOutput output channel to activate the solenoid & raise the hopper IR : hwio.BooleanInput input channel for the IR beam to check if the hopper is up max_lag : float time in seconds to wait before checking to make sure the hopper is up """ def __init__(self,IR,solenoid,max_lag=0.3,*args,**kwargs): super(Hopper, self).__init__(*args,**kwargs) self.max_lag = max_lag if isinstance(IR,hwio.BooleanInput): self.IR = IR else: raise ValueError('%s is not an input channel' % IR) if isinstance(solenoid,hwio.BooleanOutput): self.solenoid = solenoid else: raise ValueError('%s is not an output channel' % solenoid) def check(self): """reads the status of solenoid & IR beam, then throws an error if they don't match Returns ------- bool True if the hopper is up. Raises ------ HopperActiveError The Hopper is up and it shouldn't be. (The IR beam is tripped, but the solenoid is not active.) HopperInactiveError The Hopper is down and it shouldn't be. (The IR beam is not tripped, but the solenoid is active.) """ IR_status = self.IR.read() solenoid_status = self.solenoid.read() if IR_status != solenoid_status: if IR_status: raise HopperActiveError elif solenoid_status: raise HopperInactiveError else: raise ComponentError("the IR & solenoid don't match: IR:%s,solenoid:%s" % (IR_status,solenoid_status)) else: return IR_status def up(self): """Raises the hopper up. Returns ------- bool True if the hopper comes up. Raises ------ HopperWontComeUpError The Hopper did not raise. """ self.solenoid.write(True) time_up = self.IR.poll(timeout=self.max_lag) if time_up is None: # poll timed out self.solenoid.write(False) raise HopperWontComeUpError else: return time_up def down(self): """Lowers the hopper. Returns ------- bool True if the hopper drops. Raises ------ HopperWontDropError The Hopper did not drop. """ self.solenoid.write(False) time_down = datetime.datetime.now() utils.wait(self.max_lag) try: self.check() except HopperActiveError as e: raise HopperWontDropError(e) return time_down def feed(self,dur=2.0,error_check=True): """Performs a feed Parameters --------- dur : float, optional duration of feed in seconds Returns ------- (datetime, float) Timestamp of the feed and the feed duration Raises ------ HopperAlreadyUpError The Hopper was already up at the beginning of the feed. HopperWontComeUpError The Hopper did not raise for the feed. HopperWontDropError The Hopper did not drop fater the feed. """ assert self.max_lag < dur, "max_lag (%ss) must be shorter than duration (%ss)" % (self.max_lag,dur) try: self.check() except HopperActiveError as e: self.solenoid.write(False) raise HopperAlreadyUpError(e) feed_time = self.up() utils.wait(dur) feed_over = self.down() feed_duration = feed_over - feed_time return (feed_time,feed_duration) def reward(self,value=2.0): """wrapper for `feed`, passes *value* into *dur* """ return self.feed(dur=value) ## Peck Port ## class PeckPort(BaseComponent): """ Class which holds information about peck ports Parameters ---------- LED : hwio.BooleanOutput output channel to activate the LED in the peck port IR : hwio.BooleanInput input channel for the IR beam to check for a peck Attributes ---------- LED : hwio.BooleanOutput output channel to activate the LED in the peck port IR : hwio.BooleanInput input channel for the IR beam to check for a peck """ def __init__(self,IR,LED,*args,**kwargs): super(PeckPort, self).__init__(*args,**kwargs) if isinstance(IR,hwio.BooleanInput): self.IR = IR else: raise ValueError('%s is not an input channel' % IR) if isinstance(LED,hwio.BooleanOutput): self.LED = LED else: raise ValueError('%s is not an output channel' % LED) def status(self): """reads the status of the IR beam Returns ------- bool True if beam is broken """ return self.IR.read() def off(self): """ Turns the LED off Returns ------- bool True if successful """ self.LED.write(False) return True def on(self): """Turns the LED on Returns ------- bool True if successful """ self.LED.write(True) return True def flash(self,dur=1.0,isi=0.1): """Flashes the LED on and off with *isi* seconds high and low for *dur* seconds, then revert LED to prior state. Parameters ---------- dur : float, optional Duration of the light flash in seconds. isi : float,optional Time interval between toggles. (0.5 * period) Returns ------- (datetime, float) Timestamp of the flash and the flash duration """ LED_state = self.LED.read() flash_time = datetime.datetime.now() flash_duration = datetime.datetime.now() - flash_time while flash_duration < datetime.timedelta(seconds=dur): self.LED.toggle() utils.wait(isi) flash_duration = datetime.datetime.now() - flash_time self.LED.write(LED_state) return (flash_time,flash_duration) def poll(self,timeout=None): """ Polls the peck port until there is a peck Returns ------- datetime Timestamp of the IR beam being broken. """ return self.IR.poll(timeout) ## House Light ## class HouseLight(BaseComponent): """ Class which holds information about the house light Keywords -------- light : hwio.BooleanOutput output channel to turn the light on and off Methods: on() -- off() -- timeout(dur) -- turns off the house light for 'dur' seconds (default=10.0) punish() -- calls timeout() for 'value' as 'dur' """ def __init__(self,light,*args,**kwargs): super(HouseLight, self).__init__(*args,**kwargs) if isinstance(light,hwio.BooleanOutput): self.light = light else: raise ValueError('%s is not an output channel' % light) def off(self): """Turns the house light off. Returns ------- bool True if successful. """ self.light.write(False) return True def on(self): """Turns the house light on. Returns ------- bool True if successful. """ self.light.write(True) return True def timeout(self,dur=10.0): """Turn off the light for *dur* seconds Keywords ------- dur : float, optional The amount of time (in seconds) to turn off the light. Returns ------- (datetime, float) Timestamp of the timeout and the timeout duration """ timeout_time = datetime.datetime.now() self.light.write(False) utils.wait(dur) timeout_duration = datetime.datetime.now() - timeout_time self.light.write(True) return (timeout_time,timeout_duration) def punish(self,value=10.0): """Calls `timeout(dur)` with *value* as *dur* """ return self.timeout(dur=value) ## Cue Light ## class RGBLight(BaseComponent): """ Class which holds information about an RGB cue light Keywords -------- red : hwio.BooleanOutput output channel for the red LED green : hwio.BooleanOutput output channel for the green LED blue : hwio.BooleanOutput output channel for the blue LED """ def __init__(self,red,green,blue,*args,**kwargs): super(RGBLight, self).__init__(*args,**kwargs) if isinstance(red,hwio.BooleanOutput): self._red = red else: raise ValueError('%s is not an output channel' % red) if isinstance(green,hwio.BooleanOutput): self._green = green else: raise ValueError('%s is not an output channel' % green) if isinstance(blue,hwio.BooleanOutput): self._blue = blue else: raise ValueError('%s is not an output channel' % blue) def red(self): """Turns the cue light to red Returns ------- bool `True` if successful. """ self._green.write(False) self._blue.write(False) return self._red.write(True) def green(self): """Turns the cue light to green Returns ------- bool `True` if successful. """ self._red.write(False) self._blue.write(False) return self._green.write(True) def blue(self): """Turns the cue light to blue Returns ------- bool `True` if successful. """ self._red.write(False) self._green.write(False) return self._blue.write(True) def off(self): """Turns the cue light off Returns ------- bool `True` if successful. """ self._red.write(False) self._green.write(False) self._blue.write(False) return True # ## Perch ## # class Perch(BaseComponent): # """Class which holds information about a perch # Has parts: # - IR Beam (input) # - speaker # """ # def __init__(self,*args,**kwargs): # super(Perch, self).__init__(*args,**kwargs)

""" ORCID Member No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) # noqa: E501 OpenAPI spec version: Latest Generated by: https://github.com/swagger-api/swagger-codegen.git """ import pprint import re # noqa: F401 import six from orcid_api_v3.models.created_date_v30_rc2 import CreatedDateV30Rc2 # noqa: F401,E501 from orcid_api_v3.models.external_i_ds_v30_rc2 import ExternalIDsV30Rc2 # noqa: F401,E501 from orcid_api_v3.models.fuzzy_date_v30_rc2 import FuzzyDateV30Rc2 # noqa: F401,E501 from orcid_api_v3.models.last_modified_date_v30_rc2 import LastModifiedDateV30Rc2 # noqa: F401,E501 from orcid_api_v3.models.organization_v30_rc2 import OrganizationV30Rc2 # noqa: F401,E501 from orcid_api_v3.models.source_v30_rc2 import SourceV30Rc2 # noqa: F401,E501 from orcid_api_v3.models.url_v30_rc2 import UrlV30Rc2 # noqa: F401,E501 class DistinctionV30Rc2(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ """ Attributes: swagger_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ swagger_types = { 'created_date': 'CreatedDateV30Rc2', 'last_modified_date': 'LastModifiedDateV30Rc2', 'source': 'SourceV30Rc2', 'put_code': 'int', 'path': 'str', 'department_name': 'str', 'role_title': 'str', 'start_date': 'FuzzyDateV30Rc2', 'end_date': 'FuzzyDateV30Rc2', 'organization': 'OrganizationV30Rc2', 'url': 'UrlV30Rc2', 'external_ids': 'ExternalIDsV30Rc2', 'display_index': 'str', 'visibility': 'str' } attribute_map = { 'created_date': 'created-date', 'last_modified_date': 'last-modified-date', 'source': 'source', 'put_code': 'put-code', 'path': 'path', 'department_name': 'department-name', 'role_title': 'role-title', 'start_date': 'start-date', 'end_date': 'end-date', 'organization': 'organization', 'url': 'url', 'external_ids': 'external-ids', 'display_index': 'display-index', 'visibility': 'visibility' } def __init__(self, created_date=None, last_modified_date=None, source=None, put_code=None, path=None, department_name=None, role_title=None, start_date=None, end_date=None, organization=None, url=None, external_ids=None, display_index=None, visibility=None): # noqa: E501 """DistinctionV30Rc2 - a model defined in Swagger""" # noqa: E501 self._created_date = None self._last_modified_date = None self._source = None self._put_code = None self._path = None self._department_name = None self._role_title = None self._start_date = None self._end_date = None self._organization = None self._url = None self._external_ids = None self._display_index = None self._visibility = None self.discriminator = None if created_date is not None: self.created_date = created_date if last_modified_date is not None: self.last_modified_date = last_modified_date if source is not None: self.source = source if put_code is not None: self.put_code = put_code if path is not None: self.path = path if department_name is not None: self.department_name = department_name if role_title is not None: self.role_title = role_title self.start_date = start_date if end_date is not None: self.end_date = end_date self.organization = organization if url is not None: self.url = url if external_ids is not None: self.external_ids = external_ids if display_index is not None: self.display_index = display_index if visibility is not None: self.visibility = visibility @property def created_date(self): """Gets the created_date of this DistinctionV30Rc2. # noqa: E501 :return: The created_date of this DistinctionV30Rc2. # noqa: E501 :rtype: CreatedDateV30Rc2 """ return self._created_date @created_date.setter def created_date(self, created_date): """Sets the created_date of this DistinctionV30Rc2. :param created_date: The created_date of this DistinctionV30Rc2. # noqa: E501 :type: CreatedDateV30Rc2 """ self._created_date = created_date @property def last_modified_date(self): """Gets the last_modified_date of this DistinctionV30Rc2. # noqa: E501 :return: The last_modified_date of this DistinctionV30Rc2. # noqa: E501 :rtype: LastModifiedDateV30Rc2 """ return self._last_modified_date @last_modified_date.setter def last_modified_date(self, last_modified_date): """Sets the last_modified_date of this DistinctionV30Rc2. :param last_modified_date: The last_modified_date of this DistinctionV30Rc2. # noqa: E501 :type: LastModifiedDateV30Rc2 """ self._last_modified_date = last_modified_date @property def source(self): """Gets the source of this DistinctionV30Rc2. # noqa: E501 :return: The source of this DistinctionV30Rc2. # noqa: E501 :rtype: SourceV30Rc2 """ return self._source @source.setter def source(self, source): """Sets the source of this DistinctionV30Rc2. :param source: The source of this DistinctionV30Rc2. # noqa: E501 :type: SourceV30Rc2 """ self._source = source @property def put_code(self): """Gets the put_code of this DistinctionV30Rc2. # noqa: E501 :return: The put_code of this DistinctionV30Rc2. # noqa: E501 :rtype: int """ return self._put_code @put_code.setter def put_code(self, put_code): """Sets the put_code of this DistinctionV30Rc2. :param put_code: The put_code of this DistinctionV30Rc2. # noqa: E501 :type: int """ self._put_code = put_code @property def path(self): """Gets the path of this DistinctionV30Rc2. # noqa: E501 :return: The path of this DistinctionV30Rc2. # noqa: E501 :rtype: str """ return self._path @path.setter def path(self, path): """Sets the path of this DistinctionV30Rc2. :param path: The path of this DistinctionV30Rc2. # noqa: E501 :type: str """ self._path = path @property def department_name(self): """Gets the department_name of this DistinctionV30Rc2. # noqa: E501 :return: The department_name of this DistinctionV30Rc2. # noqa: E501 :rtype: str """ return self._department_name @department_name.setter def department_name(self, department_name): """Sets the department_name of this DistinctionV30Rc2. :param department_name: The department_name of this DistinctionV30Rc2. # noqa: E501 :type: str """ self._department_name = department_name @property def role_title(self): """Gets the role_title of this DistinctionV30Rc2. # noqa: E501 :return: The role_title of this DistinctionV30Rc2. # noqa: E501 :rtype: str """ return self._role_title @role_title.setter def role_title(self, role_title): """Sets the role_title of this DistinctionV30Rc2. :param role_title: The role_title of this DistinctionV30Rc2. # noqa: E501 :type: str """ self._role_title = role_title @property def start_date(self): """Gets the start_date of this DistinctionV30Rc2. # noqa: E501 :return: The start_date of this DistinctionV30Rc2. # noqa: E501 :rtype: FuzzyDateV30Rc2 """ return self._start_date @start_date.setter def start_date(self, start_date): """Sets the start_date of this DistinctionV30Rc2. :param start_date: The start_date of this DistinctionV30Rc2. # noqa: E501 :type: FuzzyDateV30Rc2 """ if start_date is None: raise ValueError("Invalid value for `start_date`, must not be `None`") # noqa: E501 self._start_date = start_date @property def end_date(self): """Gets the end_date of this DistinctionV30Rc2. # noqa: E501 :return: The end_date of this DistinctionV30Rc2. # noqa: E501 :rtype: FuzzyDateV30Rc2 """ return self._end_date @end_date.setter def end_date(self, end_date): """Sets the end_date of this DistinctionV30Rc2. :param end_date: The end_date of this DistinctionV30Rc2. # noqa: E501 :type: FuzzyDateV30Rc2 """ self._end_date = end_date @property def organization(self): """Gets the organization of this DistinctionV30Rc2. # noqa: E501 :return: The organization of this DistinctionV30Rc2. # noqa: E501 :rtype: OrganizationV30Rc2 """ return self._organization @organization.setter def organization(self, organization): """Sets the organization of this DistinctionV30Rc2. :param organization: The organization of this DistinctionV30Rc2. # noqa: E501 :type: OrganizationV30Rc2 """ if organization is None: raise ValueError("Invalid value for `organization`, must not be `None`") # noqa: E501 self._organization = organization @property def url(self): """Gets the url of this DistinctionV30Rc2. # noqa: E501 :return: The url of this DistinctionV30Rc2. # noqa: E501 :rtype: UrlV30Rc2 """ return self._url @url.setter def url(self, url): """Sets the url of this DistinctionV30Rc2. :param url: The url of this DistinctionV30Rc2. # noqa: E501 :type: UrlV30Rc2 """ self._url = url @property def external_ids(self): """Gets the external_ids of this DistinctionV30Rc2. # noqa: E501 :return: The external_ids of this DistinctionV30Rc2. # noqa: E501 :rtype: ExternalIDsV30Rc2 """ return self._external_ids @external_ids.setter def external_ids(self, external_ids): """Sets the external_ids of this DistinctionV30Rc2. :param external_ids: The external_ids of this DistinctionV30Rc2. # noqa: E501 :type: ExternalIDsV30Rc2 """ self._external_ids = external_ids @property def display_index(self): """Gets the display_index of this DistinctionV30Rc2. # noqa: E501 :return: The display_index of this DistinctionV30Rc2. # noqa: E501 :rtype: str """ return self._display_index @display_index.setter def display_index(self, display_index): """Sets the display_index of this DistinctionV30Rc2. :param display_index: The display_index of this DistinctionV30Rc2. # noqa: E501 :type: str """ self._display_index = display_index @property def visibility(self): """Gets the visibility of this DistinctionV30Rc2. # noqa: E501 :return: The visibility of this DistinctionV30Rc2. # noqa: E501 :rtype: str """ return self._visibility @visibility.setter def visibility(self, visibility): """Sets the visibility of this DistinctionV30Rc2. :param visibility: The visibility of this DistinctionV30Rc2. # noqa: E501 :type: str """ allowed_values = ["LIMITED", "REGISTERED_ONLY", "PUBLIC", "PRIVATE"] # noqa: E501 if visibility not in allowed_values: raise ValueError( "Invalid value for `visibility` ({0}), must be one of {1}" # noqa: E501 .format(visibility, allowed_values) ) self._visibility = visibility def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value if issubclass(DistinctionV30Rc2, dict): for key, value in self.items(): result[key] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, DistinctionV30Rc2): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other

import json import logging import urllib2 from celery.schedules import crontab from celery.task import periodic_task from django.conf import settings from corehq.apps.accounting.models import Currency update_logger = logging.getLogger("currency_update") smsbillables_logger = logging.getLogger("smsbillables") @periodic_task(run_every=crontab(minute=0, hour=9), queue=getattr(settings, 'CELERY_PERIODIC_QUEUE','celery')) def update_exchange_rates(app_id=settings.OPEN_EXCHANGE_RATES_ID): try: update_logger.info("Updating exchange rates...") rates = json.load(urllib2.urlopen( 'https://openexchangerates.org/api/latest.json?app_id=%s' % app_id))['rates'] default_rate = float(rates[Currency.get_default().code]) for code, rate in rates.items(): currency, _ = Currency.objects.get_or_create(code=code) currency.rate_to_default = float(rate) / default_rate currency.save() update_logger.info("Exchange rates updated.") except Exception as e: smsbillables_logger.error(e.message)

def quicksort(myList, start, end): if start < end: # partition the list pivot = partition(myList, start, end) # sort both halves quicksort(myList, start, pivot-1) quicksort(myList, pivot+1, end) return myList def partition(myList, start, end): pivot = myList[start] left = start+1 right = end done = False while not done: while left <= right and myList[left] <= pivot: left = left + 1 while myList[right] >= pivot and right >=left: right = right -1 if right < left: done= True else: # swap places temp=myList[left] myList[left]=myList[right] myList[right]=temp # swap start with myList[right] temp=myList[start] myList[start]=myList[right] myList[right]=temp return right if __name__=='__main__': # Use case description array = [2,5,1,3,4,8,11,9,10,12] print quicksort(array, 0, 9)

from OpenGLCffi.GLES1 import params @params(api='gles1', prms=['mode']) def glBlendEquationOES(mode): pass

from abc import ABC, abstractmethod class Serializable(ABC): def __init__(self): super(Serializable, self).__init__() @abstractmethod def serialize(self): pass @staticmethod @abstractmethod def unserialize(marshall): pass

""" Yet Another Django Profiler middleware implementation """ from __future__ import unicode_literals import cProfile import logging import marshal import os import pstats import subprocess import tempfile try: from unittest import mock except ImportError: import mock from django.core.exceptions import MiddlewareNotUsed from django.utils.six.moves import cStringIO as StringIO from django.utils.translation import ugettext as _ from .conf import settings log = logging.getLogger(__name__) def func_strip_path(func_name): """Replacement for pstats.func_strip_path which yields qualified module names""" filename, line, name = func_name return settings.path_to_module_function(filename), line, name def text_response(response, content): """Return a plain text message as the response content.""" response.content = content response['Content-type'] = 'text/plain' return response def which(program): """Return the path of the named program in the PATH, or None if no such executable program can be found Used to make sure that required binaries are in place before attempting to call them.""" def is_exe(file_path): return os.path.isfile(file_path) and os.access(file_path, os.X_OK) program_path, _name = os.path.split(program) if program_path: if is_exe(program): return program else: for path in os.environ["PATH"].split(os.pathsep): path = path.strip('"') exe_file = os.path.join(path, program) if is_exe(exe_file): return exe_file return None class ProfilerMiddleware(object): """ Code profiling middleware which can display either a call graph PDF or a table of called functions ordered by the desired statistic. For the call graph, just append "?profile" to the URL. For the graph generation to work, install Graphviz from http://www.graphviz.org/Download.php For a statistics table, use the statistic you want to sort by as the parameter (such as "?profile=time"). Sorting options include: * calls (call count) * cumulative (cumulative time) * file (file name) * module (file name) * pcalls (primitive call count) * line (line number) * name (function name) * nfl (name/file/line) * stdname (standard name) * time (internal time) Additional parameters can be added when generating a statistics table: * fraction - The fraction of total function calls to display (the default of .2 is omitted if max_calls or pattern are specified) * max_calls - The maximum number of function calls to display * pattern - Regular expression filter for function display names To get these instructions in the app if you forget the usage options, use "?profile=help" in the URL. Inspiration: * https://gist.github.com/kesor/1229681 * https://bitbucket.org/brodie/geordi """ def __init__(self): if not settings.YADP_ENABLED: # Disable the middleware completely when YADP_ENABLED = False raise MiddlewareNotUsed() self.error = None self.profiler = None def process_view(self, request, callback, callback_args, callback_kwargs): if settings.YADP_ENABLED and (settings.YADP_PROFILE_PARAMETER in request.REQUEST): self.error = None if settings.YADP_PROFILER_BACKEND == 'yappi': try: from .yadp_yappi import YappiProfile wall = request.REQUEST.get(settings.YADP_CLOCK_PARAMETER, None) == 'wall' self.profiler = YappiProfile(wall=wall) except Exception as e: log.exception(e) self.error = _('Could not find Yappi; please install Yappi to be able to use it for profiling') return None else: self.profiler = cProfile.Profile() args = (request,) + callback_args return self.profiler.runcall(callback, *args, **callback_kwargs) def process_response(self, request, response): if settings.YADP_ENABLED and settings.YADP_PROFILE_PARAMETER in request.REQUEST: if self.error: return text_response(response, self.error) self.profiler.create_stats() mode = request.REQUEST[settings.YADP_PROFILE_PARAMETER] if mode == 'file': # Work around bug on Python versions >= 2.7.4 mode = 'fil' if not mode: if not which('dot'): return text_response(response, _('Could not find "dot" from Graphviz; please install Graphviz to enable call graph generation')) if not which('gprof2dot.py'): return text_response(response, _('Could not find gprof2dot.py, which should have been installed by yet-another-django-profiler')) with tempfile.NamedTemporaryFile() as stats: stats.write(marshal.dumps(self.profiler.stats)) stats.flush() cmd = ('gprof2dot.py -f pstats {} | dot -Tpdf'.format(stats.name)) process = subprocess.Popen(cmd, shell=True, stdin=subprocess.PIPE, stdout=subprocess.PIPE) output = process.communicate()[0] return_code = process.poll() if return_code: raise Exception(_('gprof2dot.py exited with {return_code}').format(return_code=return_code)) response.content = output response['Content-Type'] = 'application/pdf' return response elif mode == 'help': return text_response(response, ProfilerMiddleware.__doc__) else: out = StringIO() stats = pstats.Stats(self.profiler, stream=out) with mock.patch('pstats.func_strip_path') as mock_func_strip_path: mock_func_strip_path.side_effect = func_strip_path stats.strip_dirs() restrictions = [] if settings.YADP_PATTERN_PARAMETER in request.REQUEST: restrictions.append(request.REQUEST[settings.YADP_PATTERN_PARAMETER]) if settings.YADP_FRACTION_PARAMETER in request.REQUEST: restrictions.append(float(request.REQUEST[settings.YADP_FRACTION_PARAMETER])) elif settings.YADP_MAX_CALLS_PARAMETER in request.REQUEST: restrictions.append(int(request.REQUEST[settings.YADP_MAX_CALLS_PARAMETER])) elif settings.YADP_PATTERN_PARAMETER not in request.REQUEST: restrictions.append(.2) stats.sort_stats(mode).print_stats(*restrictions) return text_response(response, out.getvalue()) return response

import dragonfly import bee from bee import * from bee.spyderhive.hivemaphive import hivemapframe import Spyder from dragonfly.commandhive import commandhive, commandapp from panda3d.core import getModelPath import os getModelPath().prependPath(os.getcwd()) from bee import hivemodule class myhivemapframe(hivemapframe): hm = Spyder.Hivemap.fromfile("tut-worker-4b.web") class myapp(commandapp): def on_tick(self): taskMgr.step() taskMgr.step() class myhive(commandhive): _hivecontext = hivemodule.appcontext(myapp) h = myhivemapframe() raiser = bee.raiser() bee.connect("evexc", raiser) m = myhive().getinstance() m.build("m") m.place() m.close() m.init() m.run()

''' This script shows how to use a Motor Stage ''' from basil.dut import Dut dut = Dut('mercury_pyserial.yaml') dut.init() print(dut["MotorStage"].get_position()) # dut["MotorStage"].set_position(100000)

"""FixMatch with Distribution Alignment and Adaptative Confidence Ratio. """ import os import sys from typing import Callable import jax import jax.numpy as jn import objax from absl import app from absl import flags from absl.flags import FLAGS from objax.typing import JaxArray from semi_supervised_domain_adaptation.lib.data import MixData, CTAData from semi_supervised_domain_adaptation.lib.train import TrainableSSDAModule from shared.data.fsl import DATASETS as FSL_DATASETS from shared.data.ssl import DATASETS as SSL_DATASETS, DataSetSSL from shared.train import ScheduleCos from shared.util import setup_tf, MyParallel from shared.zoo.models import network, ARCHS class Baseline(TrainableSSDAModule): def __init__(self, nclass: int, model: Callable, **kwargs): super().__init__(nclass, kwargs) self.model: objax.Module = model(colors=3, nclass=nclass, **kwargs) self.model_ema = objax.optimizer.ExponentialMovingAverageModule(self.model, momentum=0.999) if FLAGS.arch.endswith('pretrain'): # Initialize weights of EMA with pretrained model's weights. self.model_ema.ema.momentum = 0 self.model_ema.update_ema() self.model_ema.ema.momentum = 0.999 self.stats = objax.Module() self.stats.keygen = objax.random.DEFAULT_GENERATOR self.stats.p_labeled = objax.nn.ExponentialMovingAverage((nclass,), init_value=1 / nclass) self.stats.p_unlabeled = objax.nn.MovingAverage((nclass,), buffer_size=128, init_value=1 / nclass) train_vars = self.model.vars() + self.stats.vars() self.opt = objax.optimizer.Momentum(train_vars) self.lr = ScheduleCos(self.params.lr, self.params.lr_decay) @objax.Function.with_vars(self.model_ema.vars()) def eval_op(x: JaxArray, domain: int) -> JaxArray: return objax.functional.softmax(self.model_ema(x, training=False, domain=domain)) def loss_function(sx, sy, tu): c, h, w = sx.shape[-3:] xu = jn.concatenate((sx, tu)).reshape((-1, c, h, w)) logit = self.model(xu, training=True) logit_sx = jn.split(logit, (2 * sx.shape[0],))[0] logit_sx_weak, logit_sx_strong = logit_sx[::2], logit_sx[1::2] xe = 0.5 * (objax.functional.loss.cross_entropy_logits(logit_sx_weak, sy).mean() + objax.functional.loss.cross_entropy_logits(logit_sx_strong, sy).mean()) wd = 0.5 * sum((v.value ** 2).sum() for k, v in train_vars.items() if k.endswith('.w')) loss = xe + self.params.wd * wd return loss, {'losses/xe': xe, 'losses/wd': wd} gv = objax.GradValues(loss_function, train_vars) @objax.Function.with_vars(self.vars()) def train_op(step, sx, sy, tx, ty, tu, probe=None): y_probe = eval_op(probe, 1) if probe is not None else None p = step / (FLAGS.train_mimg << 20) lr = self.lr(p) g, v = gv(jn.concatenate((sx, tx)), jn.concatenate((sy, ty)), tu) self.opt(lr, objax.functional.parallel.pmean(g)) self.model_ema.update_ema() return objax.functional.parallel.pmean({'monitors/lr': lr, **v[1]}), y_probe self.train_op = MyParallel(train_op, reduce=lambda x: x) self.eval_op = MyParallel(eval_op, static_argnums=(1,)) def main(argv): del argv print('JAX host: %d / %d' % (jax.host_id(), jax.host_count())) print('JAX devices:\n%s' % '\n'.join(str(d) for d in jax.devices()), flush=True) setup_tf() source = FSL_DATASETS()[f'{FLAGS.dataset}_{FLAGS.source}-0']() target_name, target_samples_per_class, target_seed = DataSetSSL.parse_name(f'{FLAGS.dataset}_{FLAGS.target}') target_labeled = SSL_DATASETS()[target_name](target_samples_per_class, target_seed) target_unlabeled = FSL_DATASETS()[f'{target_name}-0']() testsets = [target_unlabeled.test, source.test] # Ordered by domain (unlabeled always first) module = Baseline(source.nclass, network(FLAGS.arch), lr=FLAGS.lr, lr_decay=FLAGS.lr_decay, wd=FLAGS.wd, arch=FLAGS.arch, batch=FLAGS.batch, uratio=FLAGS.uratio) logdir = f'SSDA/{FLAGS.dataset}/{FLAGS.source}/{FLAGS.target}/{FLAGS.augment}/{module.__class__.__name__}/' logdir += '_'.join(sorted('%s%s' % k for k in module.params.items())) logdir = os.path.join(FLAGS.logdir, logdir) test = {} for domain, testset in enumerate(testsets): test.update((k, v.parse().batch(FLAGS.batch).nchw().map(lambda d: {**d, 'domain': domain}).prefetch(16)) for k, v in testset.items()) if FLAGS.augment.startswith('('): train = MixData(source.train, target_labeled.train, target_unlabeled.train, source.nclass, FLAGS.batch, FLAGS.uratio) elif FLAGS.augment.startswith('CTA('): train = CTAData(source.train, target_labeled.train, target_unlabeled.train, source.nclass, FLAGS.batch, FLAGS.uratio) else: raise ValueError(f'Augment flag value {FLAGS.augment} not supported.') module.train(FLAGS.train_mimg << 10, FLAGS.report_kimg, train, test, logdir, FLAGS.keep_ckpts) train.stop() if __name__ == '__main__': flags.DEFINE_enum('arch', 'wrn28-2', ARCHS, 'Model architecture.') flags.DEFINE_float('lr', 0.03, 'Learning rate.') flags.DEFINE_float('lr_decay', 0.25, 'Learning rate decay.') flags.DEFINE_float('wd', 0.001, 'Weight decay.') flags.DEFINE_integer('batch', 64, 'Batch size') flags.DEFINE_integer('uratio', 3, 'Unlabeled batch size ratio') flags.DEFINE_integer('report_kimg', 64, 'Reporting period in kibi-images.') flags.DEFINE_integer('train_mimg', 8, 'Training duration in mega-images.') flags.DEFINE_integer('keep_ckpts', 5, 'Number of checkpoints to keep (0 for all).') flags.DEFINE_string('logdir', 'experiments', 'Directory where to save checkpoints and tensorboard data.') flags.DEFINE_string('dataset', 'domainnet32', 'Source data to train on.') flags.DEFINE_string('source', 'clipart', 'Source data to train on.') flags.DEFINE_string('target', 'infograph(10,seed=1)', 'Target data to train on.') FLAGS.set_default('augment', 'CTA(sm,sm,probe=1)') FLAGS.set_default('para_augment', 8) app.run(main)

from os.path import abspath, dirname, join import astropy.config as _config import astropy.io.registry as io_registry from astropy import extern from .table import Table class Conf(_config.ConfigNamespace): """ Configuration parameters for `astropy.table.jsviewer`. """ jquery_url = _config.ConfigItem( 'https://code.jquery.com/jquery-3.6.0.min.js', 'The URL to the jquery library.') datatables_url = _config.ConfigItem( 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min.js', 'The URL to the jquery datatables library.') css_urls = _config.ConfigItem( ['https://cdn.datatables.net/1.10.12/css/jquery.dataTables.css'], 'The URLs to the css file(s) to include.', cfgtype='string_list') conf = Conf() EXTERN_JS_DIR = abspath(join(dirname(extern.__file__), 'jquery', 'data', 'js')) EXTERN_CSS_DIR = abspath(join(dirname(extern.__file__), 'jquery', 'data', 'css')) _SORTING_SCRIPT_PART_1 = """ var astropy_sort_num = function(a, b) {{ var a_num = parseFloat(a); var b_num = parseFloat(b); if (isNaN(a_num) && isNaN(b_num)) return ((a < b) ? -1 : ((a > b) ? 1 : 0)); else if (!isNaN(a_num) && !isNaN(b_num)) return ((a_num < b_num) ? -1 : ((a_num > b_num) ? 1 : 0)); else return isNaN(a_num) ? -1 : 1; }} """ _SORTING_SCRIPT_PART_2 = """ jQuery.extend( jQuery.fn.dataTableExt.oSort, {{ "optionalnum-asc": astropy_sort_num, "optionalnum-desc": function (a,b) {{ return -astropy_sort_num(a, b); }} }}); """ IPYNB_JS_SCRIPT = """ <script> %(sorting_script1)s require.config({{paths: {{ datatables: '{datatables_url}' }}}}); require(["datatables"], function(){{ console.log("$('#{tid}').dataTable()"); %(sorting_script2)s $('#{tid}').dataTable({{ order: [], pageLength: {display_length}, lengthMenu: {display_length_menu}, pagingType: "full_numbers", columnDefs: [{{targets: {sort_columns}, type: "optionalnum"}}] }}); }}); </script> """ % dict(sorting_script1=_SORTING_SCRIPT_PART_1, sorting_script2=_SORTING_SCRIPT_PART_2) HTML_JS_SCRIPT = _SORTING_SCRIPT_PART_1 + _SORTING_SCRIPT_PART_2 + """ $(document).ready(function() {{ $('#{tid}').dataTable({{ order: [], pageLength: {display_length}, lengthMenu: {display_length_menu}, pagingType: "full_numbers", columnDefs: [{{targets: {sort_columns}, type: "optionalnum"}}] }}); }} ); """ # Default CSS for the JSViewer writer DEFAULT_CSS = """\ body {font-family: sans-serif;} table.dataTable {width: auto !important; margin: 0 !important;} .dataTables_filter, .dataTables_paginate {float: left !important; margin-left:1em} """ # Default CSS used when rendering a table in the IPython notebook DEFAULT_CSS_NB = """\ table.dataTable {clear: both; width: auto !important; margin: 0 !important;} .dataTables_info, .dataTables_length, .dataTables_filter, .dataTables_paginate{ display: inline-block; margin-right: 1em; } .paginate_button { margin-right: 5px; } """ class JSViewer: """Provides an interactive HTML export of a Table. This class provides an interface to the `DataTables <https://datatables.net/>`_ library, which allow to visualize interactively an HTML table. It is used by the `~astropy.table.Table.show_in_browser` method. Parameters ---------- use_local_files : bool, optional Use local files or a CDN for JavaScript libraries. Default False. display_length : int, optional Number or rows to show. Default to 50. """ def __init__(self, use_local_files=False, display_length=50): self._use_local_files = use_local_files self.display_length_menu = [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, "All"]] self.display_length = display_length for L in self.display_length_menu: if display_length not in L: L.insert(0, display_length) @property def jquery_urls(self): if self._use_local_files: return ['file://' + join(EXTERN_JS_DIR, 'jquery-3.6.0.min.js'), 'file://' + join(EXTERN_JS_DIR, 'jquery.dataTables.min.js')] else: return [conf.jquery_url, conf.datatables_url] @property def css_urls(self): if self._use_local_files: return ['file://' + join(EXTERN_CSS_DIR, 'jquery.dataTables.css')] else: return conf.css_urls def _jstable_file(self): if self._use_local_files: return 'file://' + join(EXTERN_JS_DIR, 'jquery.dataTables.min') else: return conf.datatables_url[:-3] def ipynb(self, table_id, css=None, sort_columns='[]'): html = f'<style>{css if css is not None else DEFAULT_CSS_NB}</style>' html += IPYNB_JS_SCRIPT.format( display_length=self.display_length, display_length_menu=self.display_length_menu, datatables_url=self._jstable_file(), tid=table_id, sort_columns=sort_columns) return html def html_js(self, table_id='table0', sort_columns='[]'): return HTML_JS_SCRIPT.format( display_length=self.display_length, display_length_menu=self.display_length_menu, tid=table_id, sort_columns=sort_columns).strip() def write_table_jsviewer(table, filename, table_id=None, max_lines=5000, table_class="display compact", jskwargs=None, css=DEFAULT_CSS, htmldict=None, overwrite=False): if table_id is None: table_id = f'table{id(table)}' jskwargs = jskwargs or {} jsv = JSViewer(**jskwargs) sortable_columns = [i for i, col in enumerate(table.columns.values()) if col.info.dtype.kind in 'iufc'] html_options = { 'table_id': table_id, 'table_class': table_class, 'css': css, 'cssfiles': jsv.css_urls, 'jsfiles': jsv.jquery_urls, 'js': jsv.html_js(table_id=table_id, sort_columns=sortable_columns) } if htmldict: html_options.update(htmldict) if max_lines < len(table): table = table[:max_lines] table.write(filename, format='html', htmldict=html_options, overwrite=overwrite) io_registry.register_writer('jsviewer', Table, write_table_jsviewer)

from django.http import HttpResponse from django.shortcuts import render from django.contrib.auth import authenticate, login def persona_login(request): user = authenticate(assertion=request.POST['assertion']) if user: login(request, user) return HttpResponse('OK')

from django.db import models class Election(models.Model): name = models.CharField(max_length=128) description = models.TextField() country = models.ForeignKey('core.Country', null=True, blank=True) live_date = models.DateTimeField() dead_date = models.DateTimeField() active = models.BooleanField(default=True) def __unicode__(self): return self.name

import collections import time from logging import getLogger import django from django.db import models from django.db.models import query from django.db.models.fields.related_descriptors import ForwardManyToOneDescriptor __version__ = '1.2.3' logger = getLogger(__name__) class PrefetchManagerMixin(models.Manager): use_for_related_fields = True prefetch_definitions = {} @classmethod def get_queryset_class(cls): return PrefetchQuerySet def __init__(self): super(PrefetchManagerMixin, self).__init__() for name, prefetcher in self.prefetch_definitions.items(): if prefetcher.__class__ is not Prefetcher and not callable(prefetcher): raise InvalidPrefetch("Invalid prefetch definition %s. This prefetcher needs to be a class not an instance." % name) def get_queryset(self): qs = self.get_queryset_class()( self.model, prefetch_definitions=self.prefetch_definitions ) if getattr(self, '_db', None) is not None: qs = qs.using(self._db) return qs def prefetch(self, *args): return self.get_queryset().prefetch(*args) class PrefetchManager(PrefetchManagerMixin): def __init__(self, **kwargs): self.prefetch_definitions = kwargs super(PrefetchManager, self).__init__() class PrefetchIterable(query.ModelIterable): def __iter__(self): data = list(super(PrefetchIterable, self).__iter__()) for name, (forwarders, prefetcher) in self.queryset._prefetch.items(): prefetcher.fetch(data, name, self.queryset.model, forwarders, getattr(self.queryset, '_db', None)) return iter(data) class InvalidPrefetch(Exception): pass class PrefetchOption(object): def __init__(self, name, *args, **kwargs): self.name = name self.args = args self.kwargs = kwargs P = PrefetchOption class PrefetchQuerySet(query.QuerySet): def __init__(self, model=None, query=None, using=None, prefetch_definitions=None, **kwargs): super(PrefetchQuerySet, self).__init__(model, query, using, **kwargs) self._prefetch = {} self.prefetch_definitions = prefetch_definitions self._iterable_class = PrefetchIterable if django.VERSION < (2, 0): def _clone(self, **kwargs): return super(PrefetchQuerySet, self). \ _clone(_prefetch=self._prefetch, prefetch_definitions=self.prefetch_definitions, **kwargs) else: def _clone(self): c = super(PrefetchQuerySet, self)._clone() c._prefetch = self._prefetch c.prefetch_definitions = self.prefetch_definitions return c def prefetch(self, *names): obj = self._clone() for opt in names: if isinstance(opt, PrefetchOption): name = opt.name else: name = opt opt = None parts = name.split('__') forwarders = [] prefetcher = None model = self.model prefetch_definitions = self.prefetch_definitions for what in parts: if not prefetcher: if what in prefetch_definitions: prefetcher = prefetch_definitions[what] continue descriptor = getattr(model, what, None) if isinstance(descriptor, ForwardManyToOneDescriptor): field = descriptor.field forwarders.append(field.name) model = field.remote_field.model manager = model.objects if not isinstance(manager, PrefetchManagerMixin): raise InvalidPrefetch('Manager for %s is not a PrefetchManagerMixin instance.' % model) prefetch_definitions = manager.prefetch_definitions else: raise InvalidPrefetch("Invalid part %s in prefetch call for %s on model %s. " "The name is not a prefetcher nor a forward relation (fk)." % ( what, name, self.model)) else: raise InvalidPrefetch("Invalid part %s in prefetch call for %s on model %s. " "You cannot have any more relations after the prefetcher." % ( what, name, self.model)) if not prefetcher: raise InvalidPrefetch("Invalid prefetch call with %s for on model %s. " "The last part isn't a prefetch definition." % (name, self.model)) if opt: if prefetcher.__class__ is Prefetcher: raise InvalidPrefetch("Invalid prefetch call with %s for on model %s. " "This prefetcher (%s) needs to be a subclass of Prefetcher." % ( name, self.model, prefetcher)) obj._prefetch[name] = forwarders, prefetcher(*opt.args, **opt.kwargs) else: obj._prefetch[name] = forwarders, prefetcher if prefetcher.__class__ is Prefetcher else prefetcher() for forwarders, prefetcher in obj._prefetch.values(): if forwarders: obj = obj.select_related('__'.join(forwarders)) return obj def iterator(self): return self._iterable_class(self) class Prefetcher(object): """ Prefetch definitition. For convenience you can either subclass this and define the methods on the subclass or just pass the functions to the contructor. Eg, subclassing:: class GroupPrefetcher(Prefetcher): @staticmethod def filter(ids): return User.groups.through.objects.filter(user__in=ids).select_related('group') @staticmethod def reverse_mapper(user_group_association): return [user_group_association.user_id] @staticmethod def decorator(user, user_group_associations=()): setattr(user, 'prefetched_groups', [i.group for i in user_group_associations]) Or with contructor:: Prefetcher( filter = lambda ids: User.groups.through.objects.filter(user__in=ids).select_related('group'), reverse_mapper = lambda user_group_association: [user_group_association.user_id], decorator = lambda user, user_group_associations=(): setattr(user, 'prefetched_groups', [ i.group for i in user_group_associations ]) ) Glossary: * filter(list_of_ids): A function that returns a queryset containing all the related data for a given list of keys. Takes a list of ids as argument. * reverse_mapper(related_object): A function that takes the related object as argument and returns a list of keys that maps that related object to the objects in the queryset. * mapper(object): Optional (defaults to ``lambda obj: obj.pk``). A function that returns the key for a given object in your query set. * decorator(object, list_of_related_objects): A function that will save the related data on each of your objects in your queryset. Takes the object and a list of related objects as arguments. Note that you should not override existing attributes on the model instance here. """ collect = False def __init__(self, filter=None, reverse_mapper=None, decorator=None, mapper=None, collect=None): if filter: self.filter = filter elif not hasattr(self, 'filter'): raise RuntimeError("You must define a filter function") if reverse_mapper: self.reverse_mapper = reverse_mapper elif not hasattr(self, 'reverse_mapper'): raise RuntimeError("You must define a reverse_mapper function") if decorator: self.decorator = decorator elif not hasattr(self, 'decorator'): raise RuntimeError("You must define a decorator function") if mapper: self.mapper = mapper if collect is not None: self.collect = collect @staticmethod def mapper(obj): return obj.pk def fetch(self, dataset, name, model, forwarders, db): collect = self.collect or forwarders try: data_mapping = collections.defaultdict(list) t1 = time.time() for obj in dataset: for field in forwarders: obj = getattr(obj, field, None) if not obj: continue if collect: data_mapping[self.mapper(obj)].append(obj) else: data_mapping[self.mapper(obj)] = obj self.decorator(obj) t2 = time.time() logger.debug("Creating data_mapping for %s query took %.3f secs for the %s prefetcher.", model.__name__, t2-t1, name) t1 = time.time() related_data = self.filter(data_mapping.keys()) if db is not None: related_data = related_data.using(db) related_data_len = len(related_data) t2 = time.time() logger.debug("Filtering for %s related objects for %s query took %.3f secs for the %s prefetcher.", related_data_len, model.__name__, t2-t1, name) relation_mapping = collections.defaultdict(list) t1 = time.time() for obj in related_data: for id_ in self.reverse_mapper(obj): if id_: relation_mapping[id_].append(obj) for id_, related_items in relation_mapping.items(): if id_ in data_mapping: if collect: for item in data_mapping[id_]: self.decorator(item, related_items) else: self.decorator(data_mapping[id_], related_items) t2 = time.time() logger.debug("Adding the related objects on the %s query took %.3f secs for the %s prefetcher.", model.__name__, t2-t1, name) return dataset except Exception: logger.exception("Prefetch failed for %s prefetch on the %s model:", name, model.__name__) raise

"""SCons.Tool.sunf95 Tool-specific initialization for sunf95, the Sun Studio F95 compiler. There normally shouldn't be any need to import this module directly. It will usually be imported through the generic SCons.Tool.Tool() selection method. """ # # Copyright (c) 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014 The SCons Foundation # # 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. # __revision__ = "src/engine/SCons/Tool/sunf95.py 2014/03/02 14:18:15 garyo" import SCons.Util from FortranCommon import add_all_to_env compilers = ['sunf95', 'f95'] def generate(env): """Add Builders and construction variables for sunf95 to an Environment.""" add_all_to_env(env) fcomp = env.Detect(compilers) or 'f95' env['FORTRAN'] = fcomp env['F95'] = fcomp env['SHFORTRAN'] = '$FORTRAN' env['SHF95'] = '$F95' env['SHFORTRANFLAGS'] = SCons.Util.CLVar('$FORTRANFLAGS -KPIC') env['SHF95FLAGS'] = SCons.Util.CLVar('$F95FLAGS -KPIC') def exists(env): return env.Detect(compilers) # Local Variables: # tab-width:4 # indent-tabs-mode:nil # End: # vim: set expandtab tabstop=4 shiftwidth=4:

from datetime import datetime, timedelta import json from django import template from django.conf import settings from django.core.urlresolvers import reverse from django.template.loader import render_to_string from django.utils.safestring import mark_safe from django.utils.translation import ugettext as _ from corehq.apps.domain.models import Domain from dimagi.utils.couch.cache import cache_core from dimagi.utils.logging import notify_exception from dimagi.utils.web import json_handler register = template.Library() @register.filter def JSON(obj): return mark_safe(json.dumps(obj, default=json_handler)) @register.filter def to_javascript_string(obj): # seriously: http://stackoverflow.com/a/1068548/8207 return mark_safe(JSON(obj).replace('</script>', '<" + "/script>')) @register.filter def BOOL(obj): try: obj = obj.to_json() except AttributeError: pass return 'true' if obj else 'false' @register.filter def dict_lookup(dict, key): '''Get an item from a dictionary.''' return dict.get(key) @register.filter def array_lookup(array, index): '''Get an item from an array.''' if index < len(array): return array[index] @register.simple_tag def dict_as_query_string(dict, prefix=""): '''Convert a dictionary to a query string, minus the initial ?''' return "&".join(["%s%s=%s" % (prefix, key, value) for key, value in dict.items()]) @register.filter def add_days(date, days=1): '''Return a date with some days added''' span = timedelta(days=days) try: return date + span except: return datetime.strptime(date,'%m/%d/%Y').date() + span @register.filter def concat(str1, str2): """Concatenate two strings""" return "%s%s" % (str1, str2) try: from resource_versions import resource_versions except (ImportError, SyntaxError): resource_versions = {} @register.simple_tag def static(url): resource_url = url version = resource_versions.get(resource_url) url = settings.STATIC_URL + url if version: url += "?version=%s" % version return url @register.simple_tag def get_report_analytics_tag(request): # todo: change this to takes_context=True and check the active_tab context # variable to see exactly whether the reports tab is active if 'reports' in request.path_info: try: report_name = request.path_info.split('reports/')[1][:-1].replace('_', ' ') except IndexError: return '' return "_gaq.push(['_setCustomVar', 2, 'report', '%s', 3]);\n_gaq.push(['_trackEvent', 'Viewed Report', '%s']);" % (report_name, report_name) return '' @register.simple_tag def domains_for_user(request, selected_domain=None): """ Generate pulldown menu for domains. Cache the entire string alongside the couch_user's doc_id that can get invalidated when the user doc updates via save. """ lst = list() lst.append('<ul class="dropdown-menu nav-list dropdown-orange">') new_domain_url = reverse("registration_domain") if selected_domain == 'public': # viewing the public domain with a different db, so the user's domains can't readily be accessed. lst.append('<li><a href="%s">%s...</a></li>' % (reverse("domain_select"), _("Back to My Projects"))) lst.append('<li class="divider"></li>') else: cached_domains = cache_core.get_cached_prop(request.couch_user.get_id, 'domain_list') if cached_domains: domain_list = [Domain.wrap(x) for x in cached_domains] else: try: domain_list = Domain.active_for_user(request.couch_user) cache_core.cache_doc_prop(request.couch_user.get_id, 'domain_list', [x.to_json() for x in domain_list]) except Exception: if settings.DEBUG: raise else: domain_list = Domain.active_for_user(request.user) notify_exception(request) if len(domain_list) > 0: lst.append('<li class="nav-header">%s</li>' % _('My Projects')) for domain in domain_list: default_url = reverse("domain_homepage", args=[domain.name]) lst.append('<li><a href="%s">%s</a></li>' % (default_url, domain.long_display_name())) else: lst.append('<li class="nav-header">No Projects</li>') lst.append('<li class="divider"></li>') lst.append('<li><a href="%s">%s...</a></li>' % (new_domain_url, _('New Project'))) lst.append('<li><a href="%s">%s...</a></li>' % (reverse("appstore"), _('CommCare Exchange'))) lst.append("</ul>") domain_list_str = "".join(lst) return domain_list_str @register.simple_tag def list_my_domains(request): cached_val = cache_core.get_cached_prop(request.couch_user.get_id, 'list_my_domains') if cached_val: return cached_val.get('list_my_domains', "") domain_list = Domain.active_for_user(request.user) lst = list() lst.append('<ul class="nav nav-pills nav-stacked">') for domain in domain_list: default_url = reverse("domain_homepage", args=[domain.name]) lst.append('<li><a href="%s">%s</a></li>' % (default_url, domain.display_name())) lst.append('</ul>') my_domain_list_str = "".join(lst) ret = {"list_my_domains": my_domain_list_str} cache_core.cache_doc_prop(request.couch_user.get_id, 'list_my_domains', ret) return my_domain_list_str @register.simple_tag def list_my_orgs(request): org_list = request.couch_user.get_organizations() lst = list() lst.append('<ul class="nav nav-pills nav-stacked">') for org in org_list: default_url = reverse("orgs_landing", args=[org.name]) lst.append('<li><a href="%s">%s</a></li>' % (default_url, org.title)) lst.append('</ul>') return "".join(lst) @register.simple_tag def commcare_user(): return _(settings.COMMCARE_USER_TERM) @register.simple_tag def hq_web_user(): return _(settings.WEB_USER_TERM) @register.filter def mod(value, arg): return value % arg # This is taken verbatim from https://code.djangoproject.com/ticket/15583 @register.filter(name='sort') def listsort(value): if isinstance(value,dict): new_dict = SortedDict() key_list = value.keys() key_list.sort() for key in key_list: new_dict[key] = value[key] return new_dict elif isinstance(value, list): new_list = list(value) new_list.sort() return new_list else: return value listsort.is_safe = True @register.filter(name='getattr') def get_attribute(obj, arg): """ Get attribute from obj Usage: {{ couch_user|getattr:"full_name" }} """ return getattr(obj, arg, None) @register.filter def pretty_doc_info(doc_info): return render_to_string('hqwebapp/pretty_doc_info.html', { 'doc_info': doc_info, })

import time from struct import pack from electrum_arg.i18n import _ from electrum_arg.util import PrintError, UserCancelled from electrum_arg.keystore import bip39_normalize_passphrase from electrum_arg.bitcoin import serialize_xpub class GuiMixin(object): # Requires: self.proto, self.device messages = { 3: _("Confirm the transaction output on your %s device"), 4: _("Confirm internal entropy on your %s device to begin"), 5: _("Write down the seed word shown on your %s"), 6: _("Confirm on your %s that you want to wipe it clean"), 7: _("Confirm on your %s device the message to sign"), 8: _("Confirm the total amount spent and the transaction fee on your " "%s device"), 10: _("Confirm wallet address on your %s device"), 'default': _("Check your %s device to continue"), } def callback_Failure(self, msg): # BaseClient's unfortunate call() implementation forces us to # raise exceptions on failure in order to unwind the stack. # However, making the user acknowledge they cancelled # gets old very quickly, so we suppress those. The NotInitialized # one is misnamed and indicates a passphrase request was cancelled. if msg.code in (self.types.Failure_PinCancelled, self.types.Failure_ActionCancelled, self.types.Failure_NotInitialized): raise UserCancelled() raise RuntimeError(msg.message) def callback_ButtonRequest(self, msg): message = self.msg if not message: message = self.messages.get(msg.code, self.messages['default']) self.handler.show_message(message % self.device, self.cancel) return self.proto.ButtonAck() def callback_PinMatrixRequest(self, msg): if msg.type == 2: msg = _("Enter a new PIN for your %s:") elif msg.type == 3: msg = (_("Re-enter the new PIN for your %s.\n\n" "NOTE: the positions of the numbers have changed!")) else: msg = _("Enter your current %s PIN:") pin = self.handler.get_pin(msg % self.device) if not pin: return self.proto.Cancel() return self.proto.PinMatrixAck(pin=pin) def callback_PassphraseRequest(self, req): if self.creating_wallet: msg = _("Enter a passphrase to generate this wallet. Each time " "you use this wallet your %s will prompt you for the " "passphrase. If you forget the passphrase you cannot " "access the argentums in the wallet.") % self.device else: msg = _("Enter the passphrase to unlock this wallet:") passphrase = self.handler.get_passphrase(msg, self.creating_wallet) if passphrase is None: return self.proto.Cancel() passphrase = bip39_normalize_passphrase(passphrase) return self.proto.PassphraseAck(passphrase=passphrase) def callback_WordRequest(self, msg): self.step += 1 msg = _("Step %d/24. Enter seed word as explained on " "your %s:") % (self.step, self.device) word = self.handler.get_word(msg) # Unfortunately the device can't handle self.proto.Cancel() return self.proto.WordAck(word=word) def callback_CharacterRequest(self, msg): char_info = self.handler.get_char(msg) if not char_info: return self.proto.Cancel() return self.proto.CharacterAck(**char_info) class TrezorClientBase(GuiMixin, PrintError): def __init__(self, handler, plugin, proto): assert hasattr(self, 'tx_api') # ProtocolMixin already constructed? self.proto = proto self.device = plugin.device self.handler = handler self.tx_api = plugin self.types = plugin.types self.msg = None self.creating_wallet = False self.used() def __str__(self): return "%s/%s" % (self.label(), self.features.device_id) def label(self): '''The name given by the user to the device.''' return self.features.label def is_initialized(self): '''True if initialized, False if wiped.''' return self.features.initialized def is_pairable(self): return not self.features.bootloader_mode def used(self): self.last_operation = time.time() def prevent_timeouts(self): self.last_operation = float('inf') def timeout(self, cutoff): '''Time out the client if the last operation was before cutoff.''' if self.last_operation < cutoff: self.print_error("timed out") self.clear_session() @staticmethod def expand_path(n): '''Convert bip32 path to list of uint32 integers with prime flags 0/-1/1' -> [0, 0x80000001, 0x80000001]''' # This code is similar to code in trezorlib where it unforunately # is not declared as a staticmethod. Our n has an extra element. PRIME_DERIVATION_FLAG = 0x80000000 path = [] for x in n.split('/')[1:]: prime = 0 if x.endswith("'"): x = x.replace('\'', '') prime = PRIME_DERIVATION_FLAG if x.startswith('-'): prime = PRIME_DERIVATION_FLAG path.append(abs(int(x)) | prime) return path def cancel(self): '''Provided here as in keepkeylib but not trezorlib.''' self.transport.write(self.proto.Cancel()) def i4b(self, x): return pack('>I', x) def address_from_derivation(self, derivation): return self.get_address('Argentum', self.expand_path(derivation)) def toggle_passphrase(self): if self.features.passphrase_protection: self.msg = _("Confirm on your %s device to disable passphrases") else: self.msg = _("Confirm on your %s device to enable passphrases") enabled = not self.features.passphrase_protection self.apply_settings(use_passphrase=enabled) def change_label(self, label): self.msg = _("Confirm the new label on your %s device") self.apply_settings(label=label) def change_homescreen(self, homescreen): self.msg = _("Confirm on your %s device to change your home screen") self.apply_settings(homescreen=homescreen) def set_pin(self, remove): if remove: self.msg = _("Confirm on your %s device to disable PIN protection") elif self.features.pin_protection: self.msg = _("Confirm on your %s device to change your PIN") else: self.msg = _("Confirm on your %s device to set a PIN") self.change_pin(remove) def clear_session(self): '''Clear the session to force pin (and passphrase if enabled) re-entry. Does not leak exceptions.''' self.print_error("clear session:", self) self.prevent_timeouts() try: super(TrezorClientBase, self).clear_session() except BaseException as e: # If the device was removed it has the same effect... self.print_error("clear_session: ignoring error", str(e)) pass def get_public_node(self, address_n, creating): self.creating_wallet = creating return super(TrezorClientBase, self).get_public_node(address_n) def close(self): '''Called when Our wallet was closed or the device removed.''' self.print_error("closing client") self.clear_session() # Release the device self.transport.close() def firmware_version(self): f = self.features return (f.major_version, f.minor_version, f.patch_version) def atleast_version(self, major, minor=0, patch=0): return cmp(self.firmware_version(), (major, minor, patch)) >= 0 @staticmethod def wrapper(func): '''Wrap methods to clear any message box they opened.''' def wrapped(self, *args, **kwargs): try: self.prevent_timeouts() return func(self, *args, **kwargs) finally: self.used() self.handler.finished() self.creating_wallet = False self.msg = None return wrapped @staticmethod def wrap_methods(cls): for method in ['apply_settings', 'change_pin', 'decrypt_message', 'get_address', 'get_public_node', 'load_device_by_mnemonic', 'load_device_by_xprv', 'recovery_device', 'reset_device', 'sign_message', 'sign_tx', 'wipe_device']: setattr(cls, method, cls.wrapper(getattr(cls, method)))

from setuptools import setup, find_packages with open('README.md') as f: long_description = f.read() with open('requirements.txt') as f: requirements = f.read().splitlines() with open('extra_requirements.txt') as f: extra_requirements = f.read().splitlines() setup( name='nussl', version="1.1.9", classifiers=[ 'Development Status :: 3 - Alpha', 'Environment :: Console', 'Intended Audience :: Developers', 'Intended Audience :: Education', 'Intended Audience :: Science/Research', 'Natural Language :: English', 'Operating System :: OS Independent', 'Programming Language :: Python :: 3.7', 'Topic :: Artistic Software', 'Topic :: Multimedia', 'Topic :: Multimedia :: Sound/Audio', 'Topic :: Multimedia :: Sound/Audio :: Editors', 'Topic :: Software Development :: Libraries', ], description='A flexible sound source separation library.', long_description=long_description, long_description_content_type='text/markdown', author='E. Manilow, P. Seetharaman, F. Pishdadian, N. Shelly, A. Bugler, B. Pardo', author_email='ethanmanilow@u.northwestern.edu', maintainer='E. Manilow, P. Seetharaman, F. Pishdadian, N. Shelly, A. Bugler, B. Pardo', maintainer_email='ethanmanilow@u.northwestern.edu', url='https://github.com/interactiveaudiolab/nussl', license='MIT', packages=find_packages(), package_data={'': ['core/templates/multitrack.html']}, keywords=['audio', 'source', 'separation', 'music', 'sound', 'source separation'], install_requires=requirements, extras_require={ 'melodia': [ 'vamp' ], 'extras': extra_requirements, } )

"""Wrapper for pulse Generated with: tools/genwrappers.py pulseaudio Do not modify this file. """ __docformat__ = 'restructuredtext' __version__ = '$Id: wrap.py 1694 2008-01-30 23:12:00Z Alex.Holkner $' import ctypes from ctypes import * import pyglet.lib _lib = pyglet.lib.load_library('pulse') _int_types = (c_int16, c_int32) if hasattr(ctypes, 'c_int64'): # Some builds of ctypes apparently do not have c_int64 # defined; it's a pretty good bet that these builds do not # have 64-bit pointers. _int_types += (ctypes.c_int64,) for t in _int_types: if sizeof(t) == sizeof(c_size_t): c_ptrdiff_t = t class c_void(Structure): # c_void_p is a buggy return type, converting to int, so # POINTER(None) == c_void_p is actually written as # POINTER(c_void), so it can be treated as a real pointer. _fields_ = [('dummy', c_int)] class struct_pa_mainloop_api(Structure): __slots__ = [ ] struct_pa_mainloop_api._fields_ = [ ('_opaque_struct', c_int) ] class struct_pa_mainloop_api(Structure): __slots__ = [ ] struct_pa_mainloop_api._fields_ = [ ('_opaque_struct', c_int) ] # /usr/include/pulse/mainloop-api.h:51 pa_mainloop_api = struct_pa_mainloop_api enum_pa_io_event_flags = c_int PA_IO_EVENT_NULL = 0 PA_IO_EVENT_INPUT = 1 PA_IO_EVENT_OUTPUT = 2 PA_IO_EVENT_HANGUP = 4 PA_IO_EVENT_ERROR = 8 # /usr/include/pulse/mainloop-api.h:60 pa_io_event_flags_t = enum_pa_io_event_flags class struct_pa_io_event(Structure): __slots__ = [ ] struct_pa_io_event._fields_ = [ ('_opaque_struct', c_int) ] class struct_pa_io_event(Structure): __slots__ = [ ] struct_pa_io_event._fields_ = [ ('_opaque_struct', c_int) ] pa_io_event = struct_pa_io_event # /usr/include/pulse/mainloop-api.h:63 pa_io_event_cb_t = CFUNCTYPE(None, POINTER(pa_mainloop_api), POINTER(pa_io_event), c_int, pa_io_event_flags_t, POINTER( None)) # /usr/include/pulse/mainloop-api.h:65 pa_io_event_destroy_cb_t = CFUNCTYPE(None, POINTER(pa_mainloop_api), POINTER(pa_io_event), POINTER( None)) # /usr/include/pulse/mainloop-api.h:67 class struct_pa_time_event(Structure): __slots__ = [ ] struct_pa_time_event._fields_ = [ ('_opaque_struct', c_int) ] class struct_pa_time_event(Structure): __slots__ = [ ] struct_pa_time_event._fields_ = [ ('_opaque_struct', c_int) ] pa_time_event = struct_pa_time_event # /usr/include/pulse/mainloop-api.h:70 class struct_timeval(Structure): __slots__ = [ ] struct_timeval._fields_ = [ ('_opaque_struct', c_int) ] class struct_timeval(Structure): __slots__ = [ ] struct_timeval._fields_ = [ ('_opaque_struct', c_int) ] pa_time_event_cb_t = CFUNCTYPE(None, POINTER(pa_mainloop_api), POINTER(pa_time_event), POINTER(struct_timeval), POINTER( None)) # /usr/include/pulse/mainloop-api.h:72 pa_time_event_destroy_cb_t = CFUNCTYPE(None, POINTER(pa_mainloop_api), POINTER(pa_time_event), POINTER( None)) # /usr/include/pulse/mainloop-api.h:74 class struct_pa_defer_event(Structure): __slots__ = [ ] struct_pa_defer_event._fields_ = [ ('_opaque_struct', c_int) ] class struct_pa_defer_event(Structure): __slots__ = [ ] struct_pa_defer_event._fields_ = [ ('_opaque_struct', c_int) ] pa_defer_event = struct_pa_defer_event # /usr/include/pulse/mainloop-api.h:77 pa_defer_event_cb_t = CFUNCTYPE(None, POINTER(pa_mainloop_api), POINTER(pa_defer_event), POINTER( None)) # /usr/include/pulse/mainloop-api.h:79 pa_defer_event_destroy_cb_t = CFUNCTYPE(None, POINTER(pa_mainloop_api), POINTER(pa_defer_event), POINTER( None)) # /usr/include/pulse/mainloop-api.h:81 # /usr/include/pulse/mainloop-api.h:120 pa_mainloop_api_once = _lib.pa_mainloop_api_once pa_mainloop_api_once.restype = None pa_mainloop_api_once.argtypes = [POINTER(pa_mainloop_api), CFUNCTYPE(None, POINTER(pa_mainloop_api), POINTER(None)), POINTER(None)] PA_CHANNELS_MAX = 32 # /usr/include/pulse/sample.h:117 PA_RATE_MAX = 192000 # /usr/include/pulse/sample.h:120 enum_pa_sample_format = c_int PA_SAMPLE_U8 = 0 PA_SAMPLE_ALAW = 1 PA_SAMPLE_ULAW = 2 PA_SAMPLE_S16LE = 3 PA_SAMPLE_S16BE = 4 PA_SAMPLE_FLOAT32LE = 5 PA_SAMPLE_FLOAT32BE = 6 PA_SAMPLE_S32LE = 7 PA_SAMPLE_S32BE = 8 PA_SAMPLE_MAX = 9 PA_SAMPLE_INVALID = 10 pa_sample_format_t = enum_pa_sample_format # /usr/include/pulse/sample.h:135 class struct_pa_sample_spec(Structure): __slots__ = [ 'format', 'rate', 'channels', ] struct_pa_sample_spec._fields_ = [ ('format', pa_sample_format_t), ('rate', c_uint32), ('channels', c_uint8), ] pa_sample_spec = struct_pa_sample_spec # /usr/include/pulse/sample.h:173 pa_usec_t = c_uint64 # /usr/include/pulse/sample.h:176 # /usr/include/pulse/sample.h:179 pa_bytes_per_second = _lib.pa_bytes_per_second pa_bytes_per_second.restype = c_size_t pa_bytes_per_second.argtypes = [POINTER(pa_sample_spec)] # /usr/include/pulse/sample.h:182 pa_frame_size = _lib.pa_frame_size pa_frame_size.restype = c_size_t pa_frame_size.argtypes = [POINTER(pa_sample_spec)] # /usr/include/pulse/sample.h:185 pa_sample_size = _lib.pa_sample_size pa_sample_size.restype = c_size_t pa_sample_size.argtypes = [POINTER(pa_sample_spec)] # /usr/include/pulse/sample.h:188 pa_bytes_to_usec = _lib.pa_bytes_to_usec pa_bytes_to_usec.restype = pa_usec_t pa_bytes_to_usec.argtypes = [c_uint64, POINTER(pa_sample_spec)] # /usr/include/pulse/sample.h:191 pa_usec_to_bytes = _lib.pa_usec_to_bytes pa_usec_to_bytes.restype = c_size_t pa_usec_to_bytes.argtypes = [pa_usec_t, POINTER(pa_sample_spec)] # /usr/include/pulse/sample.h:194 pa_sample_spec_valid = _lib.pa_sample_spec_valid pa_sample_spec_valid.restype = c_int pa_sample_spec_valid.argtypes = [POINTER(pa_sample_spec)] # /usr/include/pulse/sample.h:197 pa_sample_spec_equal = _lib.pa_sample_spec_equal pa_sample_spec_equal.restype = c_int pa_sample_spec_equal.argtypes = [POINTER(pa_sample_spec), POINTER(pa_sample_spec)] # /usr/include/pulse/sample.h:200 pa_sample_format_to_string = _lib.pa_sample_format_to_string pa_sample_format_to_string.restype = c_char_p pa_sample_format_to_string.argtypes = [pa_sample_format_t] # /usr/include/pulse/sample.h:203 pa_parse_sample_format = _lib.pa_parse_sample_format pa_parse_sample_format.restype = pa_sample_format_t pa_parse_sample_format.argtypes = [c_char_p] PA_SAMPLE_SPEC_SNPRINT_MAX = 32 # /usr/include/pulse/sample.h:206 # /usr/include/pulse/sample.h:209 pa_sample_spec_snprint = _lib.pa_sample_spec_snprint pa_sample_spec_snprint.restype = c_char_p pa_sample_spec_snprint.argtypes = [c_char_p, c_size_t, POINTER(pa_sample_spec)] # /usr/include/pulse/sample.h:212 pa_bytes_snprint = _lib.pa_bytes_snprint pa_bytes_snprint.restype = c_char_p pa_bytes_snprint.argtypes = [c_char_p, c_size_t, c_uint] enum_pa_context_state = c_int PA_CONTEXT_UNCONNECTED = 0 PA_CONTEXT_CONNECTING = 1 PA_CONTEXT_AUTHORIZING = 2 PA_CONTEXT_SETTING_NAME = 3 PA_CONTEXT_READY = 4 PA_CONTEXT_FAILED = 5 PA_CONTEXT_TERMINATED = 6 pa_context_state_t = enum_pa_context_state # /usr/include/pulse/def.h:49 enum_pa_stream_state = c_int PA_STREAM_UNCONNECTED = 0 PA_STREAM_CREATING = 1 PA_STREAM_READY = 2 PA_STREAM_FAILED = 3 PA_STREAM_TERMINATED = 4 pa_stream_state_t = enum_pa_stream_state # /usr/include/pulse/def.h:58 enum_pa_operation_state = c_int PA_OPERATION_RUNNING = 0 PA_OPERATION_DONE = 1 PA_OPERATION_CANCELED = 2 pa_operation_state_t = enum_pa_operation_state # /usr/include/pulse/def.h:65 enum_pa_context_flags = c_int PA_CONTEXT_NOAUTOSPAWN = 1 pa_context_flags_t = enum_pa_context_flags # /usr/include/pulse/def.h:73 enum_pa_stream_direction = c_int PA_STREAM_NODIRECTION = 0 PA_STREAM_PLAYBACK = 1 PA_STREAM_RECORD = 2 PA_STREAM_UPLOAD = 3 pa_stream_direction_t = enum_pa_stream_direction # /usr/include/pulse/def.h:81 enum_pa_stream_flags = c_int PA_STREAM_START_CORKED = 1 PA_STREAM_INTERPOLATE_TIMING = 2 PA_STREAM_NOT_MONOTONOUS = 4 PA_STREAM_AUTO_TIMING_UPDATE = 8 PA_STREAM_NO_REMAP_CHANNELS = 16 PA_STREAM_NO_REMIX_CHANNELS = 32 PA_STREAM_FIX_FORMAT = 64 PA_STREAM_FIX_RATE = 128 PA_STREAM_FIX_CHANNELS = 256 PA_STREAM_DONT_MOVE = 512 PA_STREAM_VARIABLE_RATE = 1024 pa_stream_flags_t = enum_pa_stream_flags # /usr/include/pulse/def.h:212 class struct_pa_buffer_attr(Structure): __slots__ = [ 'maxlength', 'tlength', 'prebuf', 'minreq', 'fragsize', ] struct_pa_buffer_attr._fields_ = [ ('maxlength', c_uint32), ('tlength', c_uint32), ('prebuf', c_uint32), ('minreq', c_uint32), ('fragsize', c_uint32), ] pa_buffer_attr = struct_pa_buffer_attr # /usr/include/pulse/def.h:221 enum_pa_subscription_mask = c_int PA_SUBSCRIPTION_MASK_NULL = 0 PA_SUBSCRIPTION_MASK_SINK = 1 PA_SUBSCRIPTION_MASK_SOURCE = 2 PA_SUBSCRIPTION_MASK_SINK_INPUT = 4 PA_SUBSCRIPTION_MASK_SOURCE_OUTPUT = 8 PA_SUBSCRIPTION_MASK_MODULE = 16 PA_SUBSCRIPTION_MASK_CLIENT = 32 PA_SUBSCRIPTION_MASK_SAMPLE_CACHE = 64 PA_SUBSCRIPTION_MASK_SERVER = 128 PA_SUBSCRIPTION_MASK_AUTOLOAD = 256 PA_SUBSCRIPTION_MASK_ALL = 511 # /usr/include/pulse/def.h:261 pa_subscription_mask_t = enum_pa_subscription_mask enum_pa_subscription_event_type = c_int PA_SUBSCRIPTION_EVENT_SINK = 0 PA_SUBSCRIPTION_EVENT_SOURCE = 1 PA_SUBSCRIPTION_EVENT_SINK_INPUT = 2 PA_SUBSCRIPTION_EVENT_SOURCE_OUTPUT = 3 PA_SUBSCRIPTION_EVENT_MODULE = 4 PA_SUBSCRIPTION_EVENT_CLIENT = 5 PA_SUBSCRIPTION_EVENT_SAMPLE_CACHE = 6 PA_SUBSCRIPTION_EVENT_SERVER = 7 PA_SUBSCRIPTION_EVENT_AUTOLOAD = 8 PA_SUBSCRIPTION_EVENT_FACILITY_MASK = 15 PA_SUBSCRIPTION_EVENT_NEW = 0 PA_SUBSCRIPTION_EVENT_CHANGE = 16 PA_SUBSCRIPTION_EVENT_REMOVE = 32 PA_SUBSCRIPTION_EVENT_TYPE_MASK = 1632 # /usr/include/pulse/def.h:280 pa_subscription_event_type_t = enum_pa_subscription_event_type class struct_pa_timing_info(Structure): __slots__ = [ 'timestamp', 'synchronized_clocks', 'sink_usec', 'source_usec', 'transport_usec', 'playing', 'write_index_corrupt', 'write_index', 'read_index_corrupt', 'read_index', ] class struct_timeval(Structure): __slots__ = [ ] struct_timeval._fields_ = [ # TODO: HACK struct timeval wasn't picked up by wraptypes # ('_opaque_struct', c_int) ('tv_sec', c_long), ('tv_usec', c_long), ] struct_pa_timing_info._fields_ = [ ('timestamp', struct_timeval), ('synchronized_clocks', c_int), ('sink_usec', pa_usec_t), ('source_usec', pa_usec_t), ('transport_usec', pa_usec_t), ('playing', c_int), ('write_index_corrupt', c_int), ('write_index', c_int64), ('read_index_corrupt', c_int), ('read_index', c_int64), ] pa_timing_info = struct_pa_timing_info # /usr/include/pulse/def.h:347 class struct_pa_spawn_api(Structure): __slots__ = [ 'prefork', 'postfork', 'atfork', ] struct_pa_spawn_api._fields_ = [ ('prefork', POINTER(CFUNCTYPE(None))), ('postfork', POINTER(CFUNCTYPE(None))), ('atfork', POINTER(CFUNCTYPE(None))), ] pa_spawn_api = struct_pa_spawn_api # /usr/include/pulse/def.h:366 enum_pa_seek_mode = c_int PA_SEEK_RELATIVE = 0 PA_SEEK_ABSOLUTE = 1 PA_SEEK_RELATIVE_ON_READ = 2 PA_SEEK_RELATIVE_END = 3 pa_seek_mode_t = enum_pa_seek_mode # /usr/include/pulse/def.h:374 enum_pa_sink_flags = c_int PA_SINK_HW_VOLUME_CTRL = 1 PA_SINK_LATENCY = 2 PA_SINK_HARDWARE = 4 PA_SINK_NETWORK = 8 pa_sink_flags_t = enum_pa_sink_flags # /usr/include/pulse/def.h:382 enum_pa_source_flags = c_int PA_SOURCE_HW_VOLUME_CTRL = 1 PA_SOURCE_LATENCY = 2 PA_SOURCE_HARDWARE = 4 PA_SOURCE_NETWORK = 8 pa_source_flags_t = enum_pa_source_flags # /usr/include/pulse/def.h:390 pa_free_cb_t = CFUNCTYPE(None, POINTER(None)) # /usr/include/pulse/def.h:393 class struct_pa_operation(Structure): __slots__ = [ ] struct_pa_operation._fields_ = [ ('_opaque_struct', c_int) ] class struct_pa_operation(Structure): __slots__ = [ ] struct_pa_operation._fields_ = [ ('_opaque_struct', c_int) ] pa_operation = struct_pa_operation # /usr/include/pulse/operation.h:36 # /usr/include/pulse/operation.h:39 pa_operation_ref = _lib.pa_operation_ref pa_operation_ref.restype = POINTER(pa_operation) pa_operation_ref.argtypes = [POINTER(pa_operation)] # /usr/include/pulse/operation.h:42 pa_operation_unref = _lib.pa_operation_unref pa_operation_unref.restype = None pa_operation_unref.argtypes = [POINTER(pa_operation)] # /usr/include/pulse/operation.h:45 pa_operation_cancel = _lib.pa_operation_cancel pa_operation_cancel.restype = None pa_operation_cancel.argtypes = [POINTER(pa_operation)] # /usr/include/pulse/operation.h:48 pa_operation_get_state = _lib.pa_operation_get_state pa_operation_get_state.restype = pa_operation_state_t pa_operation_get_state.argtypes = [POINTER(pa_operation)] class struct_pa_context(Structure): __slots__ = [ ] struct_pa_context._fields_ = [ ('_opaque_struct', c_int) ] class struct_pa_context(Structure): __slots__ = [ ] struct_pa_context._fields_ = [ ('_opaque_struct', c_int) ] pa_context = struct_pa_context # /usr/include/pulse/context.h:160 pa_context_notify_cb_t = CFUNCTYPE(None, POINTER(pa_context), POINTER( None)) # /usr/include/pulse/context.h:163 pa_context_success_cb_t = CFUNCTYPE(None, POINTER(pa_context), c_int, POINTER( None)) # /usr/include/pulse/context.h:166 # /usr/include/pulse/context.h:170 pa_context_new = _lib.pa_context_new pa_context_new.restype = POINTER(pa_context) pa_context_new.argtypes = [POINTER(pa_mainloop_api), c_char_p] # /usr/include/pulse/context.h:173 pa_context_unref = _lib.pa_context_unref pa_context_unref.restype = None pa_context_unref.argtypes = [POINTER(pa_context)] # /usr/include/pulse/context.h:176 pa_context_ref = _lib.pa_context_ref pa_context_ref.restype = POINTER(pa_context) pa_context_ref.argtypes = [POINTER(pa_context)] # /usr/include/pulse/context.h:179 pa_context_set_state_callback = _lib.pa_context_set_state_callback pa_context_set_state_callback.restype = None pa_context_set_state_callback.argtypes = [POINTER(pa_context), pa_context_notify_cb_t, POINTER(None)] # /usr/include/pulse/context.h:182 pa_context_errno = _lib.pa_context_errno pa_context_errno.restype = c_int pa_context_errno.argtypes = [POINTER(pa_context)] # /usr/include/pulse/context.h:185 pa_context_is_pending = _lib.pa_context_is_pending pa_context_is_pending.restype = c_int pa_context_is_pending.argtypes = [POINTER(pa_context)] # /usr/include/pulse/context.h:188 pa_context_get_state = _lib.pa_context_get_state pa_context_get_state.restype = pa_context_state_t pa_context_get_state.argtypes = [POINTER(pa_context)] # /usr/include/pulse/context.h:197 pa_context_connect = _lib.pa_context_connect pa_context_connect.restype = c_int pa_context_connect.argtypes = [POINTER(pa_context), c_char_p, pa_context_flags_t, POINTER(pa_spawn_api)] # /usr/include/pulse/context.h:200 pa_context_disconnect = _lib.pa_context_disconnect pa_context_disconnect.restype = None pa_context_disconnect.argtypes = [POINTER(pa_context)] # /usr/include/pulse/context.h:203 pa_context_drain = _lib.pa_context_drain pa_context_drain.restype = POINTER(pa_operation) pa_context_drain.argtypes = [POINTER(pa_context), pa_context_notify_cb_t, POINTER(None)] # /usr/include/pulse/context.h:208 pa_context_exit_daemon = _lib.pa_context_exit_daemon pa_context_exit_daemon.restype = POINTER(pa_operation) pa_context_exit_daemon.argtypes = [POINTER(pa_context), pa_context_success_cb_t, POINTER(None)] # /usr/include/pulse/context.h:211 pa_context_set_default_sink = _lib.pa_context_set_default_sink pa_context_set_default_sink.restype = POINTER(pa_operation) pa_context_set_default_sink.argtypes = [POINTER(pa_context), c_char_p, pa_context_success_cb_t, POINTER(None)] # /usr/include/pulse/context.h:214 pa_context_set_default_source = _lib.pa_context_set_default_source pa_context_set_default_source.restype = POINTER(pa_operation) pa_context_set_default_source.argtypes = [POINTER(pa_context), c_char_p, pa_context_success_cb_t, POINTER(None)] # /usr/include/pulse/context.h:217 pa_context_is_local = _lib.pa_context_is_local pa_context_is_local.restype = c_int pa_context_is_local.argtypes = [POINTER(pa_context)] # /usr/include/pulse/context.h:220 pa_context_set_name = _lib.pa_context_set_name pa_context_set_name.restype = POINTER(pa_operation) pa_context_set_name.argtypes = [POINTER(pa_context), c_char_p, pa_context_success_cb_t, POINTER(None)] # /usr/include/pulse/context.h:223 pa_context_get_server = _lib.pa_context_get_server pa_context_get_server.restype = c_char_p pa_context_get_server.argtypes = [POINTER(pa_context)] # /usr/include/pulse/context.h:226 pa_context_get_protocol_version = _lib.pa_context_get_protocol_version pa_context_get_protocol_version.restype = c_uint32 pa_context_get_protocol_version.argtypes = [POINTER(pa_context)] # /usr/include/pulse/context.h:229 pa_context_get_server_protocol_version = _lib.pa_context_get_server_protocol_version pa_context_get_server_protocol_version.restype = c_uint32 pa_context_get_server_protocol_version.argtypes = [POINTER(pa_context)] enum_pa_channel_position = c_int PA_CHANNEL_POSITION_INVALID = 0 PA_CHANNEL_POSITION_MONO = 0 PA_CHANNEL_POSITION_LEFT = 1 PA_CHANNEL_POSITION_RIGHT = 2 PA_CHANNEL_POSITION_CENTER = 3 PA_CHANNEL_POSITION_FRONT_LEFT = 0 PA_CHANNEL_POSITION_FRONT_RIGHT = 0 PA_CHANNEL_POSITION_FRONT_CENTER = 0 PA_CHANNEL_POSITION_REAR_CENTER = 1 PA_CHANNEL_POSITION_REAR_LEFT = 2 PA_CHANNEL_POSITION_REAR_RIGHT = 3 PA_CHANNEL_POSITION_LFE = 4 PA_CHANNEL_POSITION_SUBWOOFER = 0 PA_CHANNEL_POSITION_FRONT_LEFT_OF_CENTER = 1 PA_CHANNEL_POSITION_FRONT_RIGHT_OF_CENTER = 2 PA_CHANNEL_POSITION_SIDE_LEFT = 3 PA_CHANNEL_POSITION_SIDE_RIGHT = 4 PA_CHANNEL_POSITION_AUX0 = 5 PA_CHANNEL_POSITION_AUX1 = 6 PA_CHANNEL_POSITION_AUX2 = 7 PA_CHANNEL_POSITION_AUX3 = 8 PA_CHANNEL_POSITION_AUX4 = 9 PA_CHANNEL_POSITION_AUX5 = 10 PA_CHANNEL_POSITION_AUX6 = 11 PA_CHANNEL_POSITION_AUX7 = 12 PA_CHANNEL_POSITION_AUX8 = 13 PA_CHANNEL_POSITION_AUX9 = 14 PA_CHANNEL_POSITION_AUX10 = 15 PA_CHANNEL_POSITION_AUX11 = 16 PA_CHANNEL_POSITION_AUX12 = 17 PA_CHANNEL_POSITION_AUX13 = 18 PA_CHANNEL_POSITION_AUX14 = 19 PA_CHANNEL_POSITION_AUX15 = 20 PA_CHANNEL_POSITION_AUX16 = 21 PA_CHANNEL_POSITION_AUX17 = 22 PA_CHANNEL_POSITION_AUX18 = 23 PA_CHANNEL_POSITION_AUX19 = 24 PA_CHANNEL_POSITION_AUX20 = 25 PA_CHANNEL_POSITION_AUX21 = 26 PA_CHANNEL_POSITION_AUX22 = 27 PA_CHANNEL_POSITION_AUX23 = 28 PA_CHANNEL_POSITION_AUX24 = 29 PA_CHANNEL_POSITION_AUX25 = 30 PA_CHANNEL_POSITION_AUX26 = 31 PA_CHANNEL_POSITION_AUX27 = 32 PA_CHANNEL_POSITION_AUX28 = 33 PA_CHANNEL_POSITION_AUX29 = 34 PA_CHANNEL_POSITION_AUX30 = 35 PA_CHANNEL_POSITION_AUX31 = 36 PA_CHANNEL_POSITION_TOP_CENTER = 37 PA_CHANNEL_POSITION_TOP_FRONT_LEFT = 38 PA_CHANNEL_POSITION_TOP_FRONT_RIGHT = 39 PA_CHANNEL_POSITION_TOP_FRONT_CENTER = 40 PA_CHANNEL_POSITION_TOP_REAR_LEFT = 41 PA_CHANNEL_POSITION_TOP_REAR_RIGHT = 42 PA_CHANNEL_POSITION_TOP_REAR_CENTER = 43 PA_CHANNEL_POSITION_MAX = 44 # /usr/include/pulse/channelmap.h:140 pa_channel_position_t = enum_pa_channel_position enum_pa_channel_map_def = c_int PA_CHANNEL_MAP_AIFF = 0 PA_CHANNEL_MAP_ALSA = 1 PA_CHANNEL_MAP_AUX = 2 PA_CHANNEL_MAP_WAVEEX = 3 PA_CHANNEL_MAP_OSS = 4 PA_CHANNEL_MAP_DEFAULT = 0 # /usr/include/pulse/channelmap.h:151 pa_channel_map_def_t = enum_pa_channel_map_def class struct_pa_channel_map(Structure): __slots__ = [ 'channels', 'map', ] struct_pa_channel_map._fields_ = [ ('channels', c_uint8), ('map', pa_channel_position_t * 32), ] pa_channel_map = struct_pa_channel_map # /usr/include/pulse/channelmap.h:159 # /usr/include/pulse/channelmap.h:162 pa_channel_map_init = _lib.pa_channel_map_init pa_channel_map_init.restype = POINTER(pa_channel_map) pa_channel_map_init.argtypes = [POINTER(pa_channel_map)] # /usr/include/pulse/channelmap.h:165 pa_channel_map_init_mono = _lib.pa_channel_map_init_mono pa_channel_map_init_mono.restype = POINTER(pa_channel_map) pa_channel_map_init_mono.argtypes = [POINTER(pa_channel_map)] # /usr/include/pulse/channelmap.h:168 pa_channel_map_init_stereo = _lib.pa_channel_map_init_stereo pa_channel_map_init_stereo.restype = POINTER(pa_channel_map) pa_channel_map_init_stereo.argtypes = [POINTER(pa_channel_map)] # /usr/include/pulse/channelmap.h:172 pa_channel_map_init_auto = _lib.pa_channel_map_init_auto pa_channel_map_init_auto.restype = POINTER(pa_channel_map) pa_channel_map_init_auto.argtypes = [POINTER(pa_channel_map), c_uint, pa_channel_map_def_t] # /usr/include/pulse/channelmap.h:175 pa_channel_position_to_string = _lib.pa_channel_position_to_string pa_channel_position_to_string.restype = c_char_p pa_channel_position_to_string.argtypes = [pa_channel_position_t] # /usr/include/pulse/channelmap.h:178 pa_channel_position_to_pretty_string = _lib.pa_channel_position_to_pretty_string pa_channel_position_to_pretty_string.restype = c_char_p pa_channel_position_to_pretty_string.argtypes = [pa_channel_position_t] PA_CHANNEL_MAP_SNPRINT_MAX = 336 # /usr/include/pulse/channelmap.h:181 # /usr/include/pulse/channelmap.h:184 pa_channel_map_snprint = _lib.pa_channel_map_snprint pa_channel_map_snprint.restype = c_char_p pa_channel_map_snprint.argtypes = [c_char_p, c_size_t, POINTER(pa_channel_map)] # /usr/include/pulse/channelmap.h:187 pa_channel_map_parse = _lib.pa_channel_map_parse pa_channel_map_parse.restype = POINTER(pa_channel_map) pa_channel_map_parse.argtypes = [POINTER(pa_channel_map), c_char_p] # /usr/include/pulse/channelmap.h:190 pa_channel_map_equal = _lib.pa_channel_map_equal pa_channel_map_equal.restype = c_int pa_channel_map_equal.argtypes = [POINTER(pa_channel_map), POINTER(pa_channel_map)] # /usr/include/pulse/channelmap.h:193 pa_channel_map_valid = _lib.pa_channel_map_valid pa_channel_map_valid.restype = c_int pa_channel_map_valid.argtypes = [POINTER(pa_channel_map)] pa_volume_t = c_uint32 # /usr/include/pulse/volume.h:101 PA_VOLUME_NORM = 65536 # /usr/include/pulse/volume.h:104 PA_VOLUME_MUTED = 0 # /usr/include/pulse/volume.h:107 class struct_pa_cvolume(Structure): __slots__ = [ 'channels', 'values', ] struct_pa_cvolume._fields_ = [ ('channels', c_uint8), ('values', pa_volume_t * 32), ] pa_cvolume = struct_pa_cvolume # /usr/include/pulse/volume.h:113 # /usr/include/pulse/volume.h:116 pa_cvolume_equal = _lib.pa_cvolume_equal pa_cvolume_equal.restype = c_int pa_cvolume_equal.argtypes = [POINTER(pa_cvolume), POINTER(pa_cvolume)] # /usr/include/pulse/volume.h:125 pa_cvolume_set = _lib.pa_cvolume_set pa_cvolume_set.restype = POINTER(pa_cvolume) pa_cvolume_set.argtypes = [POINTER(pa_cvolume), c_uint, pa_volume_t] PA_CVOLUME_SNPRINT_MAX = 64 # /usr/include/pulse/volume.h:128 # /usr/include/pulse/volume.h:131 pa_cvolume_snprint = _lib.pa_cvolume_snprint pa_cvolume_snprint.restype = c_char_p pa_cvolume_snprint.argtypes = [c_char_p, c_size_t, POINTER(pa_cvolume)] # /usr/include/pulse/volume.h:134 pa_cvolume_avg = _lib.pa_cvolume_avg pa_cvolume_avg.restype = pa_volume_t pa_cvolume_avg.argtypes = [POINTER(pa_cvolume)] # /usr/include/pulse/volume.h:137 pa_cvolume_valid = _lib.pa_cvolume_valid pa_cvolume_valid.restype = c_int pa_cvolume_valid.argtypes = [POINTER(pa_cvolume)] # /usr/include/pulse/volume.h:140 pa_cvolume_channels_equal_to = _lib.pa_cvolume_channels_equal_to pa_cvolume_channels_equal_to.restype = c_int pa_cvolume_channels_equal_to.argtypes = [POINTER(pa_cvolume), pa_volume_t] # /usr/include/pulse/volume.h:149 pa_sw_volume_multiply = _lib.pa_sw_volume_multiply pa_sw_volume_multiply.restype = pa_volume_t pa_sw_volume_multiply.argtypes = [pa_volume_t, pa_volume_t] # /usr/include/pulse/volume.h:152 pa_sw_cvolume_multiply = _lib.pa_sw_cvolume_multiply pa_sw_cvolume_multiply.restype = POINTER(pa_cvolume) pa_sw_cvolume_multiply.argtypes = [POINTER(pa_cvolume), POINTER(pa_cvolume), POINTER(pa_cvolume)] # /usr/include/pulse/volume.h:155 pa_sw_volume_from_dB = _lib.pa_sw_volume_from_dB pa_sw_volume_from_dB.restype = pa_volume_t pa_sw_volume_from_dB.argtypes = [c_double] # /usr/include/pulse/volume.h:158 pa_sw_volume_to_dB = _lib.pa_sw_volume_to_dB pa_sw_volume_to_dB.restype = c_double pa_sw_volume_to_dB.argtypes = [pa_volume_t] # /usr/include/pulse/volume.h:161 pa_sw_volume_from_linear = _lib.pa_sw_volume_from_linear pa_sw_volume_from_linear.restype = pa_volume_t pa_sw_volume_from_linear.argtypes = [c_double] # /usr/include/pulse/volume.h:164 pa_sw_volume_to_linear = _lib.pa_sw_volume_to_linear pa_sw_volume_to_linear.restype = c_double pa_sw_volume_to_linear.argtypes = [pa_volume_t] PA_DECIBEL_MININFTY = -200 # /usr/include/pulse/volume.h:170 class struct_pa_stream(Structure): __slots__ = [ ] struct_pa_stream._fields_ = [ ('_opaque_struct', c_int) ] class struct_pa_stream(Structure): __slots__ = [ ] struct_pa_stream._fields_ = [ ('_opaque_struct', c_int) ] pa_stream = struct_pa_stream # /usr/include/pulse/stream.h:268 pa_stream_success_cb_t = CFUNCTYPE(None, POINTER(pa_stream), c_int, POINTER( None)) # /usr/include/pulse/stream.h:271 pa_stream_request_cb_t = CFUNCTYPE(None, POINTER(pa_stream), c_size_t, POINTER( None)) # /usr/include/pulse/stream.h:274 pa_stream_notify_cb_t = CFUNCTYPE(None, POINTER(pa_stream), POINTER( None)) # /usr/include/pulse/stream.h:277 # /usr/include/pulse/stream.h:280 pa_stream_new = _lib.pa_stream_new pa_stream_new.restype = POINTER(pa_stream) pa_stream_new.argtypes = [POINTER(pa_context), c_char_p, POINTER(pa_sample_spec), POINTER(pa_channel_map)] # /usr/include/pulse/stream.h:287 pa_stream_unref = _lib.pa_stream_unref pa_stream_unref.restype = None pa_stream_unref.argtypes = [POINTER(pa_stream)] # /usr/include/pulse/stream.h:290 pa_stream_ref = _lib.pa_stream_ref pa_stream_ref.restype = POINTER(pa_stream) pa_stream_ref.argtypes = [POINTER(pa_stream)] # /usr/include/pulse/stream.h:293 pa_stream_get_state = _lib.pa_stream_get_state pa_stream_get_state.restype = pa_stream_state_t pa_stream_get_state.argtypes = [POINTER(pa_stream)] # /usr/include/pulse/stream.h:296 pa_stream_get_context = _lib.pa_stream_get_context pa_stream_get_context.restype = POINTER(pa_context) pa_stream_get_context.argtypes = [POINTER(pa_stream)] # /usr/include/pulse/stream.h:302 pa_stream_get_index = _lib.pa_stream_get_index pa_stream_get_index.restype = c_uint32 pa_stream_get_index.argtypes = [POINTER(pa_stream)] # /usr/include/pulse/stream.h:312 pa_stream_get_device_index = _lib.pa_stream_get_device_index pa_stream_get_device_index.restype = c_uint32 pa_stream_get_device_index.argtypes = [POINTER(pa_stream)] # /usr/include/pulse/stream.h:322 pa_stream_get_device_name = _lib.pa_stream_get_device_name pa_stream_get_device_name.restype = c_char_p pa_stream_get_device_name.argtypes = [POINTER(pa_stream)] # /usr/include/pulse/stream.h:328 pa_stream_is_suspended = _lib.pa_stream_is_suspended pa_stream_is_suspended.restype = c_int pa_stream_is_suspended.argtypes = [POINTER(pa_stream)] # /usr/include/pulse/stream.h:331 pa_stream_connect_playback = _lib.pa_stream_connect_playback pa_stream_connect_playback.restype = c_int pa_stream_connect_playback.argtypes = [POINTER(pa_stream), c_char_p, POINTER(pa_buffer_attr), pa_stream_flags_t, POINTER(pa_cvolume), POINTER(pa_stream)] # /usr/include/pulse/stream.h:340 pa_stream_connect_record = _lib.pa_stream_connect_record pa_stream_connect_record.restype = c_int pa_stream_connect_record.argtypes = [POINTER(pa_stream), c_char_p, POINTER(pa_buffer_attr), pa_stream_flags_t] # /usr/include/pulse/stream.h:347 pa_stream_disconnect = _lib.pa_stream_disconnect pa_stream_disconnect.restype = c_int pa_stream_disconnect.argtypes = [POINTER(pa_stream)] # /usr/include/pulse/stream.h:356 pa_stream_write = _lib.pa_stream_write pa_stream_write.restype = c_int pa_stream_write.argtypes = [POINTER(pa_stream), POINTER(None), c_size_t, pa_free_cb_t, c_int64, pa_seek_mode_t] # /usr/include/pulse/stream.h:369 pa_stream_peek = _lib.pa_stream_peek pa_stream_peek.restype = c_int pa_stream_peek.argtypes = [POINTER(pa_stream), POINTER(POINTER(None)), POINTER(c_size_t)] # /usr/include/pulse/stream.h:376 pa_stream_drop = _lib.pa_stream_drop pa_stream_drop.restype = c_int pa_stream_drop.argtypes = [POINTER(pa_stream)] # /usr/include/pulse/stream.h:379 pa_stream_writable_size = _lib.pa_stream_writable_size pa_stream_writable_size.restype = c_size_t pa_stream_writable_size.argtypes = [POINTER(pa_stream)] # /usr/include/pulse/stream.h:382 pa_stream_readable_size = _lib.pa_stream_readable_size pa_stream_readable_size.restype = c_size_t pa_stream_readable_size.argtypes = [POINTER(pa_stream)] # /usr/include/pulse/stream.h:385 pa_stream_drain = _lib.pa_stream_drain pa_stream_drain.restype = POINTER(pa_operation) pa_stream_drain.argtypes = [POINTER(pa_stream), pa_stream_success_cb_t, POINTER(None)] # /usr/include/pulse/stream.h:391 pa_stream_update_timing_info = _lib.pa_stream_update_timing_info pa_stream_update_timing_info.restype = POINTER(pa_operation) pa_stream_update_timing_info.argtypes = [POINTER(pa_stream), pa_stream_success_cb_t, POINTER(None)] # /usr/include/pulse/stream.h:394 pa_stream_set_state_callback = _lib.pa_stream_set_state_callback pa_stream_set_state_callback.restype = None pa_stream_set_state_callback.argtypes = [POINTER(pa_stream), pa_stream_notify_cb_t, POINTER(None)] # /usr/include/pulse/stream.h:398 pa_stream_set_write_callback = _lib.pa_stream_set_write_callback pa_stream_set_write_callback.restype = None pa_stream_set_write_callback.argtypes = [POINTER(pa_stream), pa_stream_request_cb_t, POINTER(None)] # /usr/include/pulse/stream.h:402 pa_stream_set_read_callback = _lib.pa_stream_set_read_callback pa_stream_set_read_callback.restype = None pa_stream_set_read_callback.argtypes = [POINTER(pa_stream), pa_stream_request_cb_t, POINTER(None)] # /usr/include/pulse/stream.h:405 pa_stream_set_overflow_callback = _lib.pa_stream_set_overflow_callback pa_stream_set_overflow_callback.restype = None pa_stream_set_overflow_callback.argtypes = [POINTER(pa_stream), pa_stream_notify_cb_t, POINTER(None)] # /usr/include/pulse/stream.h:408 pa_stream_set_underflow_callback = _lib.pa_stream_set_underflow_callback pa_stream_set_underflow_callback.restype = None pa_stream_set_underflow_callback.argtypes = [POINTER(pa_stream), pa_stream_notify_cb_t, POINTER(None)] # /usr/include/pulse/stream.h:413 pa_stream_set_latency_update_callback = _lib.pa_stream_set_latency_update_callback pa_stream_set_latency_update_callback.restype = None pa_stream_set_latency_update_callback.argtypes = [POINTER(pa_stream), pa_stream_notify_cb_t, POINTER(None)] # /usr/include/pulse/stream.h:420 pa_stream_set_moved_callback = _lib.pa_stream_set_moved_callback pa_stream_set_moved_callback.restype = None pa_stream_set_moved_callback.argtypes = [POINTER(pa_stream), pa_stream_notify_cb_t, POINTER(None)] # /usr/include/pulse/stream.h:430 pa_stream_set_suspended_callback = _lib.pa_stream_set_suspended_callback pa_stream_set_suspended_callback.restype = None pa_stream_set_suspended_callback.argtypes = [POINTER(pa_stream), pa_stream_notify_cb_t, POINTER(None)] # /usr/include/pulse/stream.h:433 pa_stream_cork = _lib.pa_stream_cork pa_stream_cork.restype = POINTER(pa_operation) pa_stream_cork.argtypes = [POINTER(pa_stream), c_int, pa_stream_success_cb_t, POINTER(None)] # /usr/include/pulse/stream.h:438 pa_stream_flush = _lib.pa_stream_flush pa_stream_flush.restype = POINTER(pa_operation) pa_stream_flush.argtypes = [POINTER(pa_stream), pa_stream_success_cb_t, POINTER(None)] # /usr/include/pulse/stream.h:442 pa_stream_prebuf = _lib.pa_stream_prebuf pa_stream_prebuf.restype = POINTER(pa_operation) pa_stream_prebuf.argtypes = [POINTER(pa_stream), pa_stream_success_cb_t, POINTER(None)] # /usr/include/pulse/stream.h:447 pa_stream_trigger = _lib.pa_stream_trigger pa_stream_trigger.restype = POINTER(pa_operation) pa_stream_trigger.argtypes = [POINTER(pa_stream), pa_stream_success_cb_t, POINTER(None)] # /usr/include/pulse/stream.h:450 pa_stream_set_name = _lib.pa_stream_set_name pa_stream_set_name.restype = POINTER(pa_operation) pa_stream_set_name.argtypes = [POINTER(pa_stream), c_char_p, pa_stream_success_cb_t, POINTER(None)] # /usr/include/pulse/stream.h:467 pa_stream_get_time = _lib.pa_stream_get_time pa_stream_get_time.restype = c_int pa_stream_get_time.argtypes = [POINTER(pa_stream), POINTER(pa_usec_t)] # /usr/include/pulse/stream.h:473 pa_stream_get_latency = _lib.pa_stream_get_latency pa_stream_get_latency.restype = c_int pa_stream_get_latency.argtypes = [POINTER(pa_stream), POINTER(pa_usec_t), POINTER(c_int)] # /usr/include/pulse/stream.h:485 pa_stream_get_timing_info = _lib.pa_stream_get_timing_info pa_stream_get_timing_info.restype = POINTER(pa_timing_info) pa_stream_get_timing_info.argtypes = [POINTER(pa_stream)] # /usr/include/pulse/stream.h:488 pa_stream_get_sample_spec = _lib.pa_stream_get_sample_spec pa_stream_get_sample_spec.restype = POINTER(pa_sample_spec) pa_stream_get_sample_spec.argtypes = [POINTER(pa_stream)] # /usr/include/pulse/stream.h:491 pa_stream_get_channel_map = _lib.pa_stream_get_channel_map pa_stream_get_channel_map.restype = POINTER(pa_channel_map) pa_stream_get_channel_map.argtypes = [POINTER(pa_stream)] # /usr/include/pulse/stream.h:496 pa_stream_get_buffer_attr = _lib.pa_stream_get_buffer_attr pa_stream_get_buffer_attr.restype = POINTER(pa_buffer_attr) pa_stream_get_buffer_attr.argtypes = [POINTER(pa_stream)] # /usr/include/pulse/stream.h:504 pa_stream_set_buffer_attr = _lib.pa_stream_set_buffer_attr pa_stream_set_buffer_attr.restype = POINTER(pa_operation) pa_stream_set_buffer_attr.argtypes = [POINTER(pa_stream), POINTER(pa_buffer_attr), pa_stream_success_cb_t, POINTER(None)] # /usr/include/pulse/stream.h:511 pa_stream_update_sample_rate = _lib.pa_stream_update_sample_rate pa_stream_update_sample_rate.restype = POINTER(pa_operation) pa_stream_update_sample_rate.argtypes = [POINTER(pa_stream), c_uint32, pa_stream_success_cb_t, POINTER(None)] class struct_pa_sink_info(Structure): __slots__ = [ 'name', 'index', 'description', 'sample_spec', 'channel_map', 'owner_module', 'volume', 'mute', 'monitor_source', 'monitor_source_name', 'latency', 'driver', 'flags', ] struct_pa_sink_info._fields_ = [ ('name', c_char_p), ('index', c_uint32), ('description', c_char_p), ('sample_spec', pa_sample_spec), ('channel_map', pa_channel_map), ('owner_module', c_uint32), ('volume', pa_cvolume), ('mute', c_int), ('monitor_source', c_uint32), ('monitor_source_name', c_char_p), ('latency', pa_usec_t), ('driver', c_char_p), ('flags', pa_sink_flags_t), ] pa_sink_info = struct_pa_sink_info # /usr/include/pulse/introspect.h:224 pa_sink_info_cb_t = CFUNCTYPE(None, POINTER(pa_context), POINTER(pa_sink_info), c_int, POINTER( None)) # /usr/include/pulse/introspect.h:227 # /usr/include/pulse/introspect.h:230 pa_context_get_sink_info_by_name = _lib.pa_context_get_sink_info_by_name pa_context_get_sink_info_by_name.restype = POINTER(pa_operation) pa_context_get_sink_info_by_name.argtypes = [POINTER(pa_context), c_char_p, pa_sink_info_cb_t, POINTER(None)] # /usr/include/pulse/introspect.h:233 pa_context_get_sink_info_by_index = _lib.pa_context_get_sink_info_by_index pa_context_get_sink_info_by_index.restype = POINTER(pa_operation) pa_context_get_sink_info_by_index.argtypes = [POINTER(pa_context), c_uint32, pa_sink_info_cb_t, POINTER(None)] # /usr/include/pulse/introspect.h:236 pa_context_get_sink_info_list = _lib.pa_context_get_sink_info_list pa_context_get_sink_info_list.restype = POINTER(pa_operation) pa_context_get_sink_info_list.argtypes = [POINTER(pa_context), pa_sink_info_cb_t, POINTER(None)] class struct_pa_source_info(Structure): __slots__ = [ 'name', 'index', 'description', 'sample_spec', 'channel_map', 'owner_module', 'volume', 'mute', 'monitor_of_sink', 'monitor_of_sink_name', 'latency', 'driver', 'flags', ] struct_pa_source_info._fields_ = [ ('name', c_char_p), ('index', c_uint32), ('description', c_char_p), ('sample_spec', pa_sample_spec), ('channel_map', pa_channel_map), ('owner_module', c_uint32), ('volume', pa_cvolume), ('mute', c_int), ('monitor_of_sink', c_uint32), ('monitor_of_sink_name', c_char_p), ('latency', pa_usec_t), ('driver', c_char_p), ('flags', pa_source_flags_t), ] pa_source_info = struct_pa_source_info # /usr/include/pulse/introspect.h:253 pa_source_info_cb_t = CFUNCTYPE(None, POINTER(pa_context), POINTER(pa_source_info), c_int, POINTER( None)) # /usr/include/pulse/introspect.h:256 # /usr/include/pulse/introspect.h:259 pa_context_get_source_info_by_name = _lib.pa_context_get_source_info_by_name pa_context_get_source_info_by_name.restype = POINTER(pa_operation) pa_context_get_source_info_by_name.argtypes = [POINTER(pa_context), c_char_p, pa_source_info_cb_t, POINTER(None)] # /usr/include/pulse/introspect.h:262 pa_context_get_source_info_by_index = _lib.pa_context_get_source_info_by_index pa_context_get_source_info_by_index.restype = POINTER(pa_operation) pa_context_get_source_info_by_index.argtypes = [POINTER(pa_context), c_uint32, pa_source_info_cb_t, POINTER(None)] # /usr/include/pulse/introspect.h:265 pa_context_get_source_info_list = _lib.pa_context_get_source_info_list pa_context_get_source_info_list.restype = POINTER(pa_operation) pa_context_get_source_info_list.argtypes = [POINTER(pa_context), pa_source_info_cb_t, POINTER(None)] class struct_pa_server_info(Structure): __slots__ = [ 'user_name', 'host_name', 'server_version', 'server_name', 'sample_spec', 'default_sink_name', 'default_source_name', 'cookie', ] struct_pa_server_info._fields_ = [ ('user_name', c_char_p), ('host_name', c_char_p), ('server_version', c_char_p), ('server_name', c_char_p), ('sample_spec', pa_sample_spec), ('default_sink_name', c_char_p), ('default_source_name', c_char_p), ('cookie', c_uint32), ] pa_server_info = struct_pa_server_info # /usr/include/pulse/introspect.h:277 pa_server_info_cb_t = CFUNCTYPE(None, POINTER(pa_context), POINTER(pa_server_info), POINTER( None)) # /usr/include/pulse/introspect.h:280 # /usr/include/pulse/introspect.h:283 pa_context_get_server_info = _lib.pa_context_get_server_info pa_context_get_server_info.restype = POINTER(pa_operation) pa_context_get_server_info.argtypes = [POINTER(pa_context), pa_server_info_cb_t, POINTER(None)] class struct_pa_module_info(Structure): __slots__ = [ 'index', 'name', 'argument', 'n_used', 'auto_unload', ] struct_pa_module_info._fields_ = [ ('index', c_uint32), ('name', c_char_p), ('argument', c_char_p), ('n_used', c_uint32), ('auto_unload', c_int), ] pa_module_info = struct_pa_module_info # /usr/include/pulse/introspect.h:292 pa_module_info_cb_t = CFUNCTYPE(None, POINTER(pa_context), POINTER(pa_module_info), c_int, POINTER( None)) # /usr/include/pulse/introspect.h:295 # /usr/include/pulse/introspect.h:298 pa_context_get_module_info = _lib.pa_context_get_module_info pa_context_get_module_info.restype = POINTER(pa_operation) pa_context_get_module_info.argtypes = [POINTER(pa_context), c_uint32, pa_module_info_cb_t, POINTER(None)] # /usr/include/pulse/introspect.h:301 pa_context_get_module_info_list = _lib.pa_context_get_module_info_list pa_context_get_module_info_list.restype = POINTER(pa_operation) pa_context_get_module_info_list.argtypes = [POINTER(pa_context), pa_module_info_cb_t, POINTER(None)] class struct_pa_client_info(Structure): __slots__ = [ 'index', 'name', 'owner_module', 'driver', ] struct_pa_client_info._fields_ = [ ('index', c_uint32), ('name', c_char_p), ('owner_module', c_uint32), ('driver', c_char_p), ] pa_client_info = struct_pa_client_info # /usr/include/pulse/introspect.h:309 pa_client_info_cb_t = CFUNCTYPE(None, POINTER(pa_context), POINTER(pa_client_info), c_int, POINTER( None)) # /usr/include/pulse/introspect.h:312 # /usr/include/pulse/introspect.h:315 pa_context_get_client_info = _lib.pa_context_get_client_info pa_context_get_client_info.restype = POINTER(pa_operation) pa_context_get_client_info.argtypes = [POINTER(pa_context), c_uint32, pa_client_info_cb_t, POINTER(None)] # /usr/include/pulse/introspect.h:318 pa_context_get_client_info_list = _lib.pa_context_get_client_info_list pa_context_get_client_info_list.restype = POINTER(pa_operation) pa_context_get_client_info_list.argtypes = [POINTER(pa_context), pa_client_info_cb_t, POINTER(None)] class struct_pa_sink_input_info(Structure): __slots__ = [ 'index', 'name', 'owner_module', 'client', 'sink', 'sample_spec', 'channel_map', 'volume', 'buffer_usec', 'sink_usec', 'resample_method', 'driver', 'mute', ] struct_pa_sink_input_info._fields_ = [ ('index', c_uint32), ('name', c_char_p), ('owner_module', c_uint32), ('client', c_uint32), ('sink', c_uint32), ('sample_spec', pa_sample_spec), ('channel_map', pa_channel_map), ('volume', pa_cvolume), ('buffer_usec', pa_usec_t), ('sink_usec', pa_usec_t), ('resample_method', c_char_p), ('driver', c_char_p), ('mute', c_int), ] # /usr/include/pulse/introspect.h:335 pa_sink_input_info = struct_pa_sink_input_info pa_sink_input_info_cb_t = CFUNCTYPE(None, POINTER(pa_context), POINTER(pa_sink_input_info), c_int, POINTER( None)) # /usr/include/pulse/introspect.h:338 # /usr/include/pulse/introspect.h:341 pa_context_get_sink_input_info = _lib.pa_context_get_sink_input_info pa_context_get_sink_input_info.restype = POINTER(pa_operation) pa_context_get_sink_input_info.argtypes = [POINTER(pa_context), c_uint32, pa_sink_input_info_cb_t, POINTER(None)] # /usr/include/pulse/introspect.h:344 pa_context_get_sink_input_info_list = _lib.pa_context_get_sink_input_info_list pa_context_get_sink_input_info_list.restype = POINTER(pa_operation) pa_context_get_sink_input_info_list.argtypes = [POINTER(pa_context), pa_sink_input_info_cb_t, POINTER(None)] class struct_pa_source_output_info(Structure): __slots__ = [ 'index', 'name', 'owner_module', 'client', 'source', 'sample_spec', 'channel_map', 'buffer_usec', 'source_usec', 'resample_method', 'driver', ] struct_pa_source_output_info._fields_ = [ ('index', c_uint32), ('name', c_char_p), ('owner_module', c_uint32), ('client', c_uint32), ('source', c_uint32), ('sample_spec', pa_sample_spec), ('channel_map', pa_channel_map), ('buffer_usec', pa_usec_t), ('source_usec', pa_usec_t), ('resample_method', c_char_p), ('driver', c_char_p), ] # /usr/include/pulse/introspect.h:359 pa_source_output_info = struct_pa_source_output_info pa_source_output_info_cb_t = CFUNCTYPE(None, POINTER(pa_context), POINTER(pa_source_output_info), c_int, POINTER( None)) # /usr/include/pulse/introspect.h:362 # /usr/include/pulse/introspect.h:365 pa_context_get_source_output_info = _lib.pa_context_get_source_output_info pa_context_get_source_output_info.restype = POINTER(pa_operation) pa_context_get_source_output_info.argtypes = [POINTER(pa_context), c_uint32, pa_source_output_info_cb_t, POINTER(None)] # /usr/include/pulse/introspect.h:368 pa_context_get_source_output_info_list = _lib.pa_context_get_source_output_info_list pa_context_get_source_output_info_list.restype = POINTER(pa_operation) pa_context_get_source_output_info_list.argtypes = [POINTER(pa_context), pa_source_output_info_cb_t, POINTER(None)] # /usr/include/pulse/introspect.h:371 pa_context_set_sink_volume_by_index = _lib.pa_context_set_sink_volume_by_index pa_context_set_sink_volume_by_index.restype = POINTER(pa_operation) pa_context_set_sink_volume_by_index.argtypes = [POINTER(pa_context), c_uint32, POINTER(pa_cvolume), pa_context_success_cb_t, POINTER(None)] # /usr/include/pulse/introspect.h:374 pa_context_set_sink_volume_by_name = _lib.pa_context_set_sink_volume_by_name pa_context_set_sink_volume_by_name.restype = POINTER(pa_operation) pa_context_set_sink_volume_by_name.argtypes = [POINTER(pa_context), c_char_p, POINTER(pa_cvolume), pa_context_success_cb_t, POINTER(None)] # /usr/include/pulse/introspect.h:377 pa_context_set_sink_mute_by_index = _lib.pa_context_set_sink_mute_by_index pa_context_set_sink_mute_by_index.restype = POINTER(pa_operation) pa_context_set_sink_mute_by_index.argtypes = [POINTER(pa_context), c_uint32, c_int, pa_context_success_cb_t, POINTER(None)] # /usr/include/pulse/introspect.h:380 pa_context_set_sink_mute_by_name = _lib.pa_context_set_sink_mute_by_name pa_context_set_sink_mute_by_name.restype = POINTER(pa_operation) pa_context_set_sink_mute_by_name.argtypes = [POINTER(pa_context), c_char_p, c_int, pa_context_success_cb_t, POINTER(None)] # /usr/include/pulse/introspect.h:383 pa_context_set_sink_input_volume = _lib.pa_context_set_sink_input_volume pa_context_set_sink_input_volume.restype = POINTER(pa_operation) pa_context_set_sink_input_volume.argtypes = [POINTER(pa_context), c_uint32, POINTER(pa_cvolume), pa_context_success_cb_t, POINTER(None)] # /usr/include/pulse/introspect.h:386 pa_context_set_sink_input_mute = _lib.pa_context_set_sink_input_mute pa_context_set_sink_input_mute.restype = POINTER(pa_operation) pa_context_set_sink_input_mute.argtypes = [POINTER(pa_context), c_uint32, c_int, pa_context_success_cb_t, POINTER(None)] # /usr/include/pulse/introspect.h:389 pa_context_set_source_volume_by_index = _lib.pa_context_set_source_volume_by_index pa_context_set_source_volume_by_index.restype = POINTER(pa_operation) pa_context_set_source_volume_by_index.argtypes = [POINTER(pa_context), c_uint32, POINTER(pa_cvolume), pa_context_success_cb_t, POINTER(None)] # /usr/include/pulse/introspect.h:392 pa_context_set_source_volume_by_name = _lib.pa_context_set_source_volume_by_name pa_context_set_source_volume_by_name.restype = POINTER(pa_operation) pa_context_set_source_volume_by_name.argtypes = [POINTER(pa_context), c_char_p, POINTER(pa_cvolume), pa_context_success_cb_t, POINTER(None)] # /usr/include/pulse/introspect.h:395 pa_context_set_source_mute_by_index = _lib.pa_context_set_source_mute_by_index pa_context_set_source_mute_by_index.restype = POINTER(pa_operation) pa_context_set_source_mute_by_index.argtypes = [POINTER(pa_context), c_uint32, c_int, pa_context_success_cb_t, POINTER(None)] # /usr/include/pulse/introspect.h:398 pa_context_set_source_mute_by_name = _lib.pa_context_set_source_mute_by_name pa_context_set_source_mute_by_name.restype = POINTER(pa_operation) pa_context_set_source_mute_by_name.argtypes = [POINTER(pa_context), c_char_p, c_int, pa_context_success_cb_t, POINTER(None)] class struct_pa_stat_info(Structure): __slots__ = [ 'memblock_total', 'memblock_total_size', 'memblock_allocated', 'memblock_allocated_size', 'scache_size', ] struct_pa_stat_info._fields_ = [ ('memblock_total', c_uint32), ('memblock_total_size', c_uint32), ('memblock_allocated', c_uint32), ('memblock_allocated_size', c_uint32), ('scache_size', c_uint32), ] pa_stat_info = struct_pa_stat_info # /usr/include/pulse/introspect.h:407 pa_stat_info_cb_t = CFUNCTYPE(None, POINTER(pa_context), POINTER(pa_stat_info), POINTER( None)) # /usr/include/pulse/introspect.h:410 # /usr/include/pulse/introspect.h:413 pa_context_stat = _lib.pa_context_stat pa_context_stat.restype = POINTER(pa_operation) pa_context_stat.argtypes = [POINTER(pa_context), pa_stat_info_cb_t, POINTER(None)] class struct_pa_sample_info(Structure): __slots__ = [ 'index', 'name', 'volume', 'sample_spec', 'channel_map', 'duration', 'bytes', 'lazy', 'filename', ] struct_pa_sample_info._fields_ = [ ('index', c_uint32), ('name', c_char_p), ('volume', pa_cvolume), ('sample_spec', pa_sample_spec), ('channel_map', pa_channel_map), ('duration', pa_usec_t), ('bytes', c_uint32), ('lazy', c_int), ('filename', c_char_p), ] pa_sample_info = struct_pa_sample_info # /usr/include/pulse/introspect.h:426 pa_sample_info_cb_t = CFUNCTYPE(None, POINTER(pa_context), POINTER(pa_sample_info), c_int, POINTER( None)) # /usr/include/pulse/introspect.h:429 # /usr/include/pulse/introspect.h:432 pa_context_get_sample_info_by_name = _lib.pa_context_get_sample_info_by_name pa_context_get_sample_info_by_name.restype = POINTER(pa_operation) pa_context_get_sample_info_by_name.argtypes = [POINTER(pa_context), c_char_p, pa_sample_info_cb_t, POINTER(None)] # /usr/include/pulse/introspect.h:435 pa_context_get_sample_info_by_index = _lib.pa_context_get_sample_info_by_index pa_context_get_sample_info_by_index.restype = POINTER(pa_operation) pa_context_get_sample_info_by_index.argtypes = [POINTER(pa_context), c_uint32, pa_sample_info_cb_t, POINTER(None)] # /usr/include/pulse/introspect.h:438 pa_context_get_sample_info_list = _lib.pa_context_get_sample_info_list pa_context_get_sample_info_list.restype = POINTER(pa_operation) pa_context_get_sample_info_list.argtypes = [POINTER(pa_context), pa_sample_info_cb_t, POINTER(None)] # /usr/include/pulse/introspect.h:441 pa_context_kill_client = _lib.pa_context_kill_client pa_context_kill_client.restype = POINTER(pa_operation) pa_context_kill_client.argtypes = [POINTER(pa_context), c_uint32, pa_context_success_cb_t, POINTER(None)] # /usr/include/pulse/introspect.h:444 pa_context_kill_sink_input = _lib.pa_context_kill_sink_input pa_context_kill_sink_input.restype = POINTER(pa_operation) pa_context_kill_sink_input.argtypes = [POINTER(pa_context), c_uint32, pa_context_success_cb_t, POINTER(None)] # /usr/include/pulse/introspect.h:447 pa_context_kill_source_output = _lib.pa_context_kill_source_output pa_context_kill_source_output.restype = POINTER(pa_operation) pa_context_kill_source_output.argtypes = [POINTER(pa_context), c_uint32, pa_context_success_cb_t, POINTER(None)] pa_context_index_cb_t = CFUNCTYPE(None, POINTER(pa_context), c_uint32, POINTER( None)) # /usr/include/pulse/introspect.h:450 # /usr/include/pulse/introspect.h:453 pa_context_load_module = _lib.pa_context_load_module pa_context_load_module.restype = POINTER(pa_operation) pa_context_load_module.argtypes = [POINTER(pa_context), c_char_p, c_char_p, pa_context_index_cb_t, POINTER(None)] # /usr/include/pulse/introspect.h:456 pa_context_unload_module = _lib.pa_context_unload_module pa_context_unload_module.restype = POINTER(pa_operation) pa_context_unload_module.argtypes = [POINTER(pa_context), c_uint32, pa_context_success_cb_t, POINTER(None)] enum_pa_autoload_type = c_int PA_AUTOLOAD_SINK = 0 PA_AUTOLOAD_SOURCE = 1 # /usr/include/pulse/introspect.h:462 pa_autoload_type_t = enum_pa_autoload_type class struct_pa_autoload_info(Structure): __slots__ = [ 'index', 'name', 'type', 'module', 'argument', ] struct_pa_autoload_info._fields_ = [ ('index', c_uint32), ('name', c_char_p), ('type', pa_autoload_type_t), ('module', c_char_p), ('argument', c_char_p), ] # /usr/include/pulse/introspect.h:471 pa_autoload_info = struct_pa_autoload_info pa_autoload_info_cb_t = CFUNCTYPE(None, POINTER(pa_context), POINTER(pa_autoload_info), c_int, POINTER( None)) # /usr/include/pulse/introspect.h:474 # /usr/include/pulse/introspect.h:477 pa_context_get_autoload_info_by_name = _lib.pa_context_get_autoload_info_by_name pa_context_get_autoload_info_by_name.restype = POINTER(pa_operation) pa_context_get_autoload_info_by_name.argtypes = [POINTER(pa_context), c_char_p, pa_autoload_type_t, pa_autoload_info_cb_t, POINTER(None)] # /usr/include/pulse/introspect.h:480 pa_context_get_autoload_info_by_index = _lib.pa_context_get_autoload_info_by_index pa_context_get_autoload_info_by_index.restype = POINTER(pa_operation) pa_context_get_autoload_info_by_index.argtypes = [POINTER(pa_context), c_uint32, pa_autoload_info_cb_t, POINTER(None)] # /usr/include/pulse/introspect.h:483 pa_context_get_autoload_info_list = _lib.pa_context_get_autoload_info_list pa_context_get_autoload_info_list.restype = POINTER(pa_operation) pa_context_get_autoload_info_list.argtypes = [POINTER(pa_context), pa_autoload_info_cb_t, POINTER(None)] # /usr/include/pulse/introspect.h:486 pa_context_add_autoload = _lib.pa_context_add_autoload pa_context_add_autoload.restype = POINTER(pa_operation) pa_context_add_autoload.argtypes = [POINTER(pa_context), c_char_p, pa_autoload_type_t, c_char_p, c_char_p, pa_context_index_cb_t, POINTER(None)] # /usr/include/pulse/introspect.h:489 pa_context_remove_autoload_by_name = _lib.pa_context_remove_autoload_by_name pa_context_remove_autoload_by_name.restype = POINTER(pa_operation) pa_context_remove_autoload_by_name.argtypes = [POINTER(pa_context), c_char_p, pa_autoload_type_t, pa_context_success_cb_t, POINTER(None)] # /usr/include/pulse/introspect.h:492 pa_context_remove_autoload_by_index = _lib.pa_context_remove_autoload_by_index pa_context_remove_autoload_by_index.restype = POINTER(pa_operation) pa_context_remove_autoload_by_index.argtypes = [POINTER(pa_context), c_uint32, pa_context_success_cb_t, POINTER(None)] # /usr/include/pulse/introspect.h:495 pa_context_move_sink_input_by_name = _lib.pa_context_move_sink_input_by_name pa_context_move_sink_input_by_name.restype = POINTER(pa_operation) pa_context_move_sink_input_by_name.argtypes = [POINTER(pa_context), c_uint32, c_char_p, pa_context_success_cb_t, POINTER(None)] # /usr/include/pulse/introspect.h:498 pa_context_move_sink_input_by_index = _lib.pa_context_move_sink_input_by_index pa_context_move_sink_input_by_index.restype = POINTER(pa_operation) pa_context_move_sink_input_by_index.argtypes = [POINTER(pa_context), c_uint32, c_uint32, pa_context_success_cb_t, POINTER(None)] # /usr/include/pulse/introspect.h:501 pa_context_move_source_output_by_name = _lib.pa_context_move_source_output_by_name pa_context_move_source_output_by_name.restype = POINTER(pa_operation) pa_context_move_source_output_by_name.argtypes = [POINTER(pa_context), c_uint32, c_char_p, pa_context_success_cb_t, POINTER(None)] # /usr/include/pulse/introspect.h:504 pa_context_move_source_output_by_index = _lib.pa_context_move_source_output_by_index pa_context_move_source_output_by_index.restype = POINTER(pa_operation) pa_context_move_source_output_by_index.argtypes = [POINTER(pa_context), c_uint32, c_uint32, pa_context_success_cb_t, POINTER(None)] # /usr/include/pulse/introspect.h:507 pa_context_suspend_sink_by_name = _lib.pa_context_suspend_sink_by_name pa_context_suspend_sink_by_name.restype = POINTER(pa_operation) pa_context_suspend_sink_by_name.argtypes = [POINTER(pa_context), c_char_p, c_int, pa_context_success_cb_t, POINTER(None)] # /usr/include/pulse/introspect.h:510 pa_context_suspend_sink_by_index = _lib.pa_context_suspend_sink_by_index pa_context_suspend_sink_by_index.restype = POINTER(pa_operation) pa_context_suspend_sink_by_index.argtypes = [POINTER(pa_context), c_uint32, c_int, pa_context_success_cb_t, POINTER(None)] # /usr/include/pulse/introspect.h:513 pa_context_suspend_source_by_name = _lib.pa_context_suspend_source_by_name pa_context_suspend_source_by_name.restype = POINTER(pa_operation) pa_context_suspend_source_by_name.argtypes = [POINTER(pa_context), c_char_p, c_int, pa_context_success_cb_t, POINTER(None)] # /usr/include/pulse/introspect.h:516 pa_context_suspend_source_by_index = _lib.pa_context_suspend_source_by_index pa_context_suspend_source_by_index.restype = POINTER(pa_operation) pa_context_suspend_source_by_index.argtypes = [POINTER(pa_context), c_uint32, c_int, pa_context_success_cb_t, POINTER(None)] pa_context_subscribe_cb_t = CFUNCTYPE(None, POINTER(pa_context), pa_subscription_event_type_t, c_uint32, POINTER( None)) # /usr/include/pulse/subscribe.h:54 # /usr/include/pulse/subscribe.h:57 pa_context_subscribe = _lib.pa_context_subscribe pa_context_subscribe.restype = POINTER(pa_operation) pa_context_subscribe.argtypes = [POINTER(pa_context), pa_subscription_mask_t, pa_context_success_cb_t, POINTER(None)] # /usr/include/pulse/subscribe.h:60 pa_context_set_subscribe_callback = _lib.pa_context_set_subscribe_callback pa_context_set_subscribe_callback.restype = None pa_context_set_subscribe_callback.argtypes = [POINTER(pa_context), pa_context_subscribe_cb_t, POINTER(None)] # /usr/include/pulse/scache.h:83 pa_stream_connect_upload = _lib.pa_stream_connect_upload pa_stream_connect_upload.restype = c_int pa_stream_connect_upload.argtypes = [POINTER(pa_stream), c_size_t] # /usr/include/pulse/scache.h:87 pa_stream_finish_upload = _lib.pa_stream_finish_upload pa_stream_finish_upload.restype = c_int pa_stream_finish_upload.argtypes = [POINTER(pa_stream)] # /usr/include/pulse/scache.h:90 pa_context_play_sample = _lib.pa_context_play_sample pa_context_play_sample.restype = POINTER(pa_operation) pa_context_play_sample.argtypes = [POINTER(pa_context), c_char_p, c_char_p, pa_volume_t, pa_context_success_cb_t, POINTER(None)] # /usr/include/pulse/scache.h:99 pa_context_remove_sample = _lib.pa_context_remove_sample pa_context_remove_sample.restype = POINTER(pa_operation) pa_context_remove_sample.argtypes = [POINTER(pa_context), c_char_p, pa_context_success_cb_t, POINTER(None)] # /usr/include/pulse/version.h:43 pa_get_library_version = _lib.pa_get_library_version pa_get_library_version.restype = c_char_p pa_get_library_version.argtypes = list() PA_API_VERSION = 11 # /usr/include/pulse/version.h:48 PA_PROTOCOL_VERSION = 12 # /usr/include/pulse/version.h:52 # /usr/include/pulse/error.h:37 pa_strerror = _lib.pa_strerror pa_strerror.restype = c_char_p pa_strerror.argtypes = [c_int] # /usr/include/pulse/xmalloc.h:40 pa_xmalloc = _lib.pa_xmalloc pa_xmalloc.restype = POINTER(c_void) pa_xmalloc.argtypes = [c_size_t] # /usr/include/pulse/xmalloc.h:43 pa_xmalloc0 = _lib.pa_xmalloc0 pa_xmalloc0.restype = POINTER(c_void) pa_xmalloc0.argtypes = [c_size_t] # /usr/include/pulse/xmalloc.h:46 pa_xrealloc = _lib.pa_xrealloc pa_xrealloc.restype = POINTER(c_void) pa_xrealloc.argtypes = [POINTER(None), c_size_t] # /usr/include/pulse/xmalloc.h:49 pa_xfree = _lib.pa_xfree pa_xfree.restype = None pa_xfree.argtypes = [POINTER(None)] # /usr/include/pulse/xmalloc.h:52 pa_xstrdup = _lib.pa_xstrdup pa_xstrdup.restype = c_char_p pa_xstrdup.argtypes = [c_char_p] # /usr/include/pulse/xmalloc.h:55 pa_xstrndup = _lib.pa_xstrndup pa_xstrndup.restype = c_char_p pa_xstrndup.argtypes = [c_char_p, c_size_t] # /usr/include/pulse/xmalloc.h:58 pa_xmemdup = _lib.pa_xmemdup pa_xmemdup.restype = POINTER(c_void) pa_xmemdup.argtypes = [POINTER(None), c_size_t] # /usr/include/pulse/utf8.h:37 pa_utf8_valid = _lib.pa_utf8_valid pa_utf8_valid.restype = c_char_p pa_utf8_valid.argtypes = [c_char_p] # /usr/include/pulse/utf8.h:40 pa_utf8_filter = _lib.pa_utf8_filter pa_utf8_filter.restype = c_char_p pa_utf8_filter.argtypes = [c_char_p] # /usr/include/pulse/utf8.h:43 pa_utf8_to_locale = _lib.pa_utf8_to_locale pa_utf8_to_locale.restype = c_char_p pa_utf8_to_locale.argtypes = [c_char_p] # /usr/include/pulse/utf8.h:46 pa_locale_to_utf8 = _lib.pa_locale_to_utf8 pa_locale_to_utf8.restype = c_char_p pa_locale_to_utf8.argtypes = [c_char_p] class struct_pa_threaded_mainloop(Structure): __slots__ = [ ] struct_pa_threaded_mainloop._fields_ = [ ('_opaque_struct', c_int) ] class struct_pa_threaded_mainloop(Structure): __slots__ = [ ] struct_pa_threaded_mainloop._fields_ = [ ('_opaque_struct', c_int) ] # /usr/include/pulse/thread-mainloop.h:242 pa_threaded_mainloop = struct_pa_threaded_mainloop # /usr/include/pulse/thread-mainloop.h:247 pa_threaded_mainloop_new = _lib.pa_threaded_mainloop_new pa_threaded_mainloop_new.restype = POINTER(pa_threaded_mainloop) pa_threaded_mainloop_new.argtypes = list() # /usr/include/pulse/thread-mainloop.h:252 pa_threaded_mainloop_free = _lib.pa_threaded_mainloop_free pa_threaded_mainloop_free.restype = None pa_threaded_mainloop_free.argtypes = [POINTER(pa_threaded_mainloop)] # /usr/include/pulse/thread-mainloop.h:255 pa_threaded_mainloop_start = _lib.pa_threaded_mainloop_start pa_threaded_mainloop_start.restype = c_int pa_threaded_mainloop_start.argtypes = [POINTER(pa_threaded_mainloop)] # /usr/include/pulse/thread-mainloop.h:259 pa_threaded_mainloop_stop = _lib.pa_threaded_mainloop_stop pa_threaded_mainloop_stop.restype = None pa_threaded_mainloop_stop.argtypes = [POINTER(pa_threaded_mainloop)] # /usr/include/pulse/thread-mainloop.h:267 pa_threaded_mainloop_lock = _lib.pa_threaded_mainloop_lock pa_threaded_mainloop_lock.restype = None pa_threaded_mainloop_lock.argtypes = [POINTER(pa_threaded_mainloop)] # /usr/include/pulse/thread-mainloop.h:270 pa_threaded_mainloop_unlock = _lib.pa_threaded_mainloop_unlock pa_threaded_mainloop_unlock.restype = None pa_threaded_mainloop_unlock.argtypes = [POINTER(pa_threaded_mainloop)] # /usr/include/pulse/thread-mainloop.h:279 pa_threaded_mainloop_wait = _lib.pa_threaded_mainloop_wait pa_threaded_mainloop_wait.restype = None pa_threaded_mainloop_wait.argtypes = [POINTER(pa_threaded_mainloop)] # /usr/include/pulse/thread-mainloop.h:286 pa_threaded_mainloop_signal = _lib.pa_threaded_mainloop_signal pa_threaded_mainloop_signal.restype = None pa_threaded_mainloop_signal.argtypes = [POINTER(pa_threaded_mainloop), c_int] # /usr/include/pulse/thread-mainloop.h:292 pa_threaded_mainloop_accept = _lib.pa_threaded_mainloop_accept pa_threaded_mainloop_accept.restype = None pa_threaded_mainloop_accept.argtypes = [POINTER(pa_threaded_mainloop)] # /usr/include/pulse/thread-mainloop.h:295 pa_threaded_mainloop_get_retval = _lib.pa_threaded_mainloop_get_retval pa_threaded_mainloop_get_retval.restype = c_int pa_threaded_mainloop_get_retval.argtypes = [POINTER(pa_threaded_mainloop)] # /usr/include/pulse/thread-mainloop.h:298 pa_threaded_mainloop_get_api = _lib.pa_threaded_mainloop_get_api pa_threaded_mainloop_get_api.restype = POINTER(pa_mainloop_api) pa_threaded_mainloop_get_api.argtypes = [POINTER(pa_threaded_mainloop)] # /usr/include/pulse/thread-mainloop.h:301 pa_threaded_mainloop_in_thread = _lib.pa_threaded_mainloop_in_thread pa_threaded_mainloop_in_thread.restype = c_int pa_threaded_mainloop_in_thread.argtypes = [POINTER(pa_threaded_mainloop)] class struct_pa_mainloop(Structure): __slots__ = [ ] struct_pa_mainloop._fields_ = [ ('_opaque_struct', c_int) ] class struct_pa_mainloop(Structure): __slots__ = [ ] struct_pa_mainloop._fields_ = [ ('_opaque_struct', c_int) ] pa_mainloop = struct_pa_mainloop # /usr/include/pulse/mainloop.h:79 # /usr/include/pulse/mainloop.h:82 pa_mainloop_new = _lib.pa_mainloop_new pa_mainloop_new.restype = POINTER(pa_mainloop) pa_mainloop_new.argtypes = list() # /usr/include/pulse/mainloop.h:85 pa_mainloop_free = _lib.pa_mainloop_free pa_mainloop_free.restype = None pa_mainloop_free.argtypes = [POINTER(pa_mainloop)] # /usr/include/pulse/mainloop.h:90 pa_mainloop_prepare = _lib.pa_mainloop_prepare pa_mainloop_prepare.restype = c_int pa_mainloop_prepare.argtypes = [POINTER(pa_mainloop), c_int] # /usr/include/pulse/mainloop.h:93 pa_mainloop_poll = _lib.pa_mainloop_poll pa_mainloop_poll.restype = c_int pa_mainloop_poll.argtypes = [POINTER(pa_mainloop)] # /usr/include/pulse/mainloop.h:97 pa_mainloop_dispatch = _lib.pa_mainloop_dispatch pa_mainloop_dispatch.restype = c_int pa_mainloop_dispatch.argtypes = [POINTER(pa_mainloop)] # /usr/include/pulse/mainloop.h:100 pa_mainloop_get_retval = _lib.pa_mainloop_get_retval pa_mainloop_get_retval.restype = c_int pa_mainloop_get_retval.argtypes = [POINTER(pa_mainloop)] # /usr/include/pulse/mainloop.h:108 pa_mainloop_iterate = _lib.pa_mainloop_iterate pa_mainloop_iterate.restype = c_int pa_mainloop_iterate.argtypes = [POINTER(pa_mainloop), c_int, POINTER(c_int)] # /usr/include/pulse/mainloop.h:111 pa_mainloop_run = _lib.pa_mainloop_run pa_mainloop_run.restype = c_int pa_mainloop_run.argtypes = [POINTER(pa_mainloop), POINTER(c_int)] # /usr/include/pulse/mainloop.h:114 pa_mainloop_get_api = _lib.pa_mainloop_get_api pa_mainloop_get_api.restype = POINTER(pa_mainloop_api) pa_mainloop_get_api.argtypes = [POINTER(pa_mainloop)] # /usr/include/pulse/mainloop.h:117 pa_mainloop_quit = _lib.pa_mainloop_quit pa_mainloop_quit.restype = None pa_mainloop_quit.argtypes = [POINTER(pa_mainloop), c_int] # /usr/include/pulse/mainloop.h:120 pa_mainloop_wakeup = _lib.pa_mainloop_wakeup pa_mainloop_wakeup.restype = None pa_mainloop_wakeup.argtypes = [POINTER(pa_mainloop)] class struct_pollfd(Structure): __slots__ = [ ] struct_pollfd._fields_ = [ ('_opaque_struct', c_int) ] class struct_pollfd(Structure): __slots__ = [ ] struct_pollfd._fields_ = [ ('_opaque_struct', c_int) ] pa_poll_func = CFUNCTYPE(c_int, POINTER(struct_pollfd), c_ulong, c_int, POINTER(None)) # /usr/include/pulse/mainloop.h:123 # /usr/include/pulse/mainloop.h:126 pa_mainloop_set_poll_func = _lib.pa_mainloop_set_poll_func pa_mainloop_set_poll_func.restype = None pa_mainloop_set_poll_func.argtypes = [POINTER(pa_mainloop), pa_poll_func, POINTER(None)] # /usr/include/pulse/mainloop-signal.h:43 pa_signal_init = _lib.pa_signal_init pa_signal_init.restype = c_int pa_signal_init.argtypes = [POINTER(pa_mainloop_api)] # /usr/include/pulse/mainloop-signal.h:46 pa_signal_done = _lib.pa_signal_done pa_signal_done.restype = None pa_signal_done.argtypes = list() class struct_pa_signal_event(Structure): __slots__ = [ ] struct_pa_signal_event._fields_ = [ ('_opaque_struct', c_int) ] class struct_pa_signal_event(Structure): __slots__ = [ ] struct_pa_signal_event._fields_ = [ ('_opaque_struct', c_int) ] # /usr/include/pulse/mainloop-signal.h:49 pa_signal_event = struct_pa_signal_event # /usr/include/pulse/mainloop-signal.h:52 pa_signal_new = _lib.pa_signal_new pa_signal_new.restype = POINTER(pa_signal_event) pa_signal_new.argtypes = [c_int, CFUNCTYPE(None, POINTER(pa_mainloop_api), POINTER(pa_signal_event), c_int, POINTER(None)), POINTER(None)] # /usr/include/pulse/mainloop-signal.h:55 pa_signal_free = _lib.pa_signal_free pa_signal_free.restype = None pa_signal_free.argtypes = [POINTER(pa_signal_event)] # /usr/include/pulse/mainloop-signal.h:58 pa_signal_set_destroy = _lib.pa_signal_set_destroy pa_signal_set_destroy.restype = None pa_signal_set_destroy.argtypes = [POINTER(pa_signal_event), CFUNCTYPE(None, POINTER(pa_mainloop_api), POINTER(pa_signal_event), POINTER(None))] # /usr/include/pulse/util.h:38 pa_get_user_name = _lib.pa_get_user_name pa_get_user_name.restype = c_char_p pa_get_user_name.argtypes = [c_char_p, c_size_t] # /usr/include/pulse/util.h:41 pa_get_host_name = _lib.pa_get_host_name pa_get_host_name.restype = c_char_p pa_get_host_name.argtypes = [c_char_p, c_size_t] # /usr/include/pulse/util.h:44 pa_get_fqdn = _lib.pa_get_fqdn pa_get_fqdn.restype = c_char_p pa_get_fqdn.argtypes = [c_char_p, c_size_t] # /usr/include/pulse/util.h:47 pa_get_home_dir = _lib.pa_get_home_dir pa_get_home_dir.restype = c_char_p pa_get_home_dir.argtypes = [c_char_p, c_size_t] # /usr/include/pulse/util.h:51 pa_get_binary_name = _lib.pa_get_binary_name pa_get_binary_name.restype = c_char_p pa_get_binary_name.argtypes = [c_char_p, c_size_t] # /usr/include/pulse/util.h:55 pa_path_get_filename = _lib.pa_path_get_filename pa_path_get_filename.restype = c_char_p pa_path_get_filename.argtypes = [c_char_p] # /usr/include/pulse/util.h:58 pa_msleep = _lib.pa_msleep pa_msleep.restype = c_int pa_msleep.argtypes = [c_ulong] PA_MSEC_PER_SEC = 1000 # /usr/include/pulse/timeval.h:36 PA_USEC_PER_SEC = 1000000 # /usr/include/pulse/timeval.h:37 PA_NSEC_PER_SEC = 1000000000 # /usr/include/pulse/timeval.h:38 PA_USEC_PER_MSEC = 1000 # /usr/include/pulse/timeval.h:39 class struct_timeval(Structure): __slots__ = [ ] struct_timeval._fields_ = [ ('_opaque_struct', c_int) ] class struct_timeval(Structure): __slots__ = [ ] struct_timeval._fields_ = [ ('_opaque_struct', c_int) ] # /usr/include/pulse/timeval.h:44 pa_gettimeofday = _lib.pa_gettimeofday pa_gettimeofday.restype = POINTER(struct_timeval) pa_gettimeofday.argtypes = [POINTER(struct_timeval)] class struct_timeval(Structure): __slots__ = [ ] struct_timeval._fields_ = [ ('_opaque_struct', c_int) ] class struct_timeval(Structure): __slots__ = [ ] struct_timeval._fields_ = [ ('_opaque_struct', c_int) ] # /usr/include/pulse/timeval.h:48 pa_timeval_diff = _lib.pa_timeval_diff pa_timeval_diff.restype = pa_usec_t pa_timeval_diff.argtypes = [POINTER(struct_timeval), POINTER(struct_timeval)] class struct_timeval(Structure): __slots__ = [ ] struct_timeval._fields_ = [ ('_opaque_struct', c_int) ] class struct_timeval(Structure): __slots__ = [ ] struct_timeval._fields_ = [ ('_opaque_struct', c_int) ] # /usr/include/pulse/timeval.h:51 pa_timeval_cmp = _lib.pa_timeval_cmp pa_timeval_cmp.restype = c_int pa_timeval_cmp.argtypes = [POINTER(struct_timeval), POINTER(struct_timeval)] class struct_timeval(Structure): __slots__ = [ ] struct_timeval._fields_ = [ ('_opaque_struct', c_int) ] # /usr/include/pulse/timeval.h:54 pa_timeval_age = _lib.pa_timeval_age pa_timeval_age.restype = pa_usec_t pa_timeval_age.argtypes = [POINTER(struct_timeval)] class struct_timeval(Structure): __slots__ = [ ] struct_timeval._fields_ = [ ('_opaque_struct', c_int) ] class struct_timeval(Structure): __slots__ = [ ] struct_timeval._fields_ = [ ('_opaque_struct', c_int) ] # /usr/include/pulse/timeval.h:57 pa_timeval_add = _lib.pa_timeval_add pa_timeval_add.restype = POINTER(struct_timeval) pa_timeval_add.argtypes = [POINTER(struct_timeval), pa_usec_t] class struct_timeval(Structure): __slots__ = [ ] struct_timeval._fields_ = [ ('_opaque_struct', c_int) ] class struct_timeval(Structure): __slots__ = [ ] struct_timeval._fields_ = [ ('_opaque_struct', c_int) ] # /usr/include/pulse/timeval.h:60 pa_timeval_store = _lib.pa_timeval_store pa_timeval_store.restype = POINTER(struct_timeval) pa_timeval_store.argtypes = [POINTER(struct_timeval), pa_usec_t] class struct_timeval(Structure): __slots__ = [ ] struct_timeval._fields_ = [ ('_opaque_struct', c_int) ] # /usr/include/pulse/timeval.h:63 pa_timeval_load = _lib.pa_timeval_load pa_timeval_load.restype = pa_usec_t pa_timeval_load.argtypes = [POINTER(struct_timeval)] __all__ = ['pa_mainloop_api', 'pa_io_event_flags_t', 'PA_IO_EVENT_NULL', 'PA_IO_EVENT_INPUT', 'PA_IO_EVENT_OUTPUT', 'PA_IO_EVENT_HANGUP', 'PA_IO_EVENT_ERROR', 'pa_io_event', 'pa_io_event_cb_t', 'pa_io_event_destroy_cb_t', 'pa_time_event', 'pa_time_event_cb_t', 'pa_time_event_destroy_cb_t', 'pa_defer_event', 'pa_defer_event_cb_t', 'pa_defer_event_destroy_cb_t', 'pa_mainloop_api_once', 'PA_CHANNELS_MAX', 'PA_RATE_MAX', 'pa_sample_format_t', 'PA_SAMPLE_U8', 'PA_SAMPLE_ALAW', 'PA_SAMPLE_ULAW', 'PA_SAMPLE_S16LE', 'PA_SAMPLE_S16BE', 'PA_SAMPLE_FLOAT32LE', 'PA_SAMPLE_FLOAT32BE', 'PA_SAMPLE_S32LE', 'PA_SAMPLE_S32BE', 'PA_SAMPLE_MAX', 'PA_SAMPLE_INVALID', 'pa_sample_spec', 'pa_usec_t', 'pa_bytes_per_second', 'pa_frame_size', 'pa_sample_size', 'pa_bytes_to_usec', 'pa_usec_to_bytes', 'pa_sample_spec_valid', 'pa_sample_spec_equal', 'pa_sample_format_to_string', 'pa_parse_sample_format', 'PA_SAMPLE_SPEC_SNPRINT_MAX', 'pa_sample_spec_snprint', 'pa_bytes_snprint', 'pa_context_state_t', 'PA_CONTEXT_UNCONNECTED', 'PA_CONTEXT_CONNECTING', 'PA_CONTEXT_AUTHORIZING', 'PA_CONTEXT_SETTING_NAME', 'PA_CONTEXT_READY', 'PA_CONTEXT_FAILED', 'PA_CONTEXT_TERMINATED', 'pa_stream_state_t', 'PA_STREAM_UNCONNECTED', 'PA_STREAM_CREATING', 'PA_STREAM_READY', 'PA_STREAM_FAILED', 'PA_STREAM_TERMINATED', 'pa_operation_state_t', 'PA_OPERATION_RUNNING', 'PA_OPERATION_DONE', 'PA_OPERATION_CANCELED', 'pa_context_flags_t', 'PA_CONTEXT_NOAUTOSPAWN', 'pa_stream_direction_t', 'PA_STREAM_NODIRECTION', 'PA_STREAM_PLAYBACK', 'PA_STREAM_RECORD', 'PA_STREAM_UPLOAD', 'pa_stream_flags_t', 'PA_STREAM_START_CORKED', 'PA_STREAM_INTERPOLATE_TIMING', 'PA_STREAM_NOT_MONOTONOUS', 'PA_STREAM_AUTO_TIMING_UPDATE', 'PA_STREAM_NO_REMAP_CHANNELS', 'PA_STREAM_NO_REMIX_CHANNELS', 'PA_STREAM_FIX_FORMAT', 'PA_STREAM_FIX_RATE', 'PA_STREAM_FIX_CHANNELS', 'PA_STREAM_DONT_MOVE', 'PA_STREAM_VARIABLE_RATE', 'pa_buffer_attr', 'pa_subscription_mask_t', 'PA_SUBSCRIPTION_MASK_NULL', 'PA_SUBSCRIPTION_MASK_SINK', 'PA_SUBSCRIPTION_MASK_SOURCE', 'PA_SUBSCRIPTION_MASK_SINK_INPUT', 'PA_SUBSCRIPTION_MASK_SOURCE_OUTPUT', 'PA_SUBSCRIPTION_MASK_MODULE', 'PA_SUBSCRIPTION_MASK_CLIENT', 'PA_SUBSCRIPTION_MASK_SAMPLE_CACHE', 'PA_SUBSCRIPTION_MASK_SERVER', 'PA_SUBSCRIPTION_MASK_AUTOLOAD', 'PA_SUBSCRIPTION_MASK_ALL', 'pa_subscription_event_type_t', 'PA_SUBSCRIPTION_EVENT_SINK', 'PA_SUBSCRIPTION_EVENT_SOURCE', 'PA_SUBSCRIPTION_EVENT_SINK_INPUT', 'PA_SUBSCRIPTION_EVENT_SOURCE_OUTPUT', 'PA_SUBSCRIPTION_EVENT_MODULE', 'PA_SUBSCRIPTION_EVENT_CLIENT', 'PA_SUBSCRIPTION_EVENT_SAMPLE_CACHE', 'PA_SUBSCRIPTION_EVENT_SERVER', 'PA_SUBSCRIPTION_EVENT_AUTOLOAD', 'PA_SUBSCRIPTION_EVENT_FACILITY_MASK', 'PA_SUBSCRIPTION_EVENT_NEW', 'PA_SUBSCRIPTION_EVENT_CHANGE', 'PA_SUBSCRIPTION_EVENT_REMOVE', 'PA_SUBSCRIPTION_EVENT_TYPE_MASK', 'pa_timing_info', 'pa_spawn_api', 'pa_seek_mode_t', 'PA_SEEK_RELATIVE', 'PA_SEEK_ABSOLUTE', 'PA_SEEK_RELATIVE_ON_READ', 'PA_SEEK_RELATIVE_END', 'pa_sink_flags_t', 'PA_SINK_HW_VOLUME_CTRL', 'PA_SINK_LATENCY', 'PA_SINK_HARDWARE', 'PA_SINK_NETWORK', 'pa_source_flags_t', 'PA_SOURCE_HW_VOLUME_CTRL', 'PA_SOURCE_LATENCY', 'PA_SOURCE_HARDWARE', 'PA_SOURCE_NETWORK', 'pa_free_cb_t', 'pa_operation', 'pa_operation_ref', 'pa_operation_unref', 'pa_operation_cancel', 'pa_operation_get_state', 'pa_context', 'pa_context_notify_cb_t', 'pa_context_success_cb_t', 'pa_context_new', 'pa_context_unref', 'pa_context_ref', 'pa_context_set_state_callback', 'pa_context_errno', 'pa_context_is_pending', 'pa_context_get_state', 'pa_context_connect', 'pa_context_disconnect', 'pa_context_drain', 'pa_context_exit_daemon', 'pa_context_set_default_sink', 'pa_context_set_default_source', 'pa_context_is_local', 'pa_context_set_name', 'pa_context_get_server', 'pa_context_get_protocol_version', 'pa_context_get_server_protocol_version', 'pa_channel_position_t', 'PA_CHANNEL_POSITION_INVALID', 'PA_CHANNEL_POSITION_MONO', 'PA_CHANNEL_POSITION_LEFT', 'PA_CHANNEL_POSITION_RIGHT', 'PA_CHANNEL_POSITION_CENTER', 'PA_CHANNEL_POSITION_FRONT_LEFT', 'PA_CHANNEL_POSITION_FRONT_RIGHT', 'PA_CHANNEL_POSITION_FRONT_CENTER', 'PA_CHANNEL_POSITION_REAR_CENTER', 'PA_CHANNEL_POSITION_REAR_LEFT', 'PA_CHANNEL_POSITION_REAR_RIGHT', 'PA_CHANNEL_POSITION_LFE', 'PA_CHANNEL_POSITION_SUBWOOFER', 'PA_CHANNEL_POSITION_FRONT_LEFT_OF_CENTER', 'PA_CHANNEL_POSITION_FRONT_RIGHT_OF_CENTER', 'PA_CHANNEL_POSITION_SIDE_LEFT', 'PA_CHANNEL_POSITION_SIDE_RIGHT', 'PA_CHANNEL_POSITION_AUX0', 'PA_CHANNEL_POSITION_AUX1', 'PA_CHANNEL_POSITION_AUX2', 'PA_CHANNEL_POSITION_AUX3', 'PA_CHANNEL_POSITION_AUX4', 'PA_CHANNEL_POSITION_AUX5', 'PA_CHANNEL_POSITION_AUX6', 'PA_CHANNEL_POSITION_AUX7', 'PA_CHANNEL_POSITION_AUX8', 'PA_CHANNEL_POSITION_AUX9', 'PA_CHANNEL_POSITION_AUX10', 'PA_CHANNEL_POSITION_AUX11', 'PA_CHANNEL_POSITION_AUX12', 'PA_CHANNEL_POSITION_AUX13', 'PA_CHANNEL_POSITION_AUX14', 'PA_CHANNEL_POSITION_AUX15', 'PA_CHANNEL_POSITION_AUX16', 'PA_CHANNEL_POSITION_AUX17', 'PA_CHANNEL_POSITION_AUX18', 'PA_CHANNEL_POSITION_AUX19', 'PA_CHANNEL_POSITION_AUX20', 'PA_CHANNEL_POSITION_AUX21', 'PA_CHANNEL_POSITION_AUX22', 'PA_CHANNEL_POSITION_AUX23', 'PA_CHANNEL_POSITION_AUX24', 'PA_CHANNEL_POSITION_AUX25', 'PA_CHANNEL_POSITION_AUX26', 'PA_CHANNEL_POSITION_AUX27', 'PA_CHANNEL_POSITION_AUX28', 'PA_CHANNEL_POSITION_AUX29', 'PA_CHANNEL_POSITION_AUX30', 'PA_CHANNEL_POSITION_AUX31', 'PA_CHANNEL_POSITION_TOP_CENTER', 'PA_CHANNEL_POSITION_TOP_FRONT_LEFT', 'PA_CHANNEL_POSITION_TOP_FRONT_RIGHT', 'PA_CHANNEL_POSITION_TOP_FRONT_CENTER', 'PA_CHANNEL_POSITION_TOP_REAR_LEFT', 'PA_CHANNEL_POSITION_TOP_REAR_RIGHT', 'PA_CHANNEL_POSITION_TOP_REAR_CENTER', 'PA_CHANNEL_POSITION_MAX', 'pa_channel_map_def_t', 'PA_CHANNEL_MAP_AIFF', 'PA_CHANNEL_MAP_ALSA', 'PA_CHANNEL_MAP_AUX', 'PA_CHANNEL_MAP_WAVEEX', 'PA_CHANNEL_MAP_OSS', 'PA_CHANNEL_MAP_DEFAULT', 'pa_channel_map', 'pa_channel_map_init', 'pa_channel_map_init_mono', 'pa_channel_map_init_stereo', 'pa_channel_map_init_auto', 'pa_channel_position_to_string', 'pa_channel_position_to_pretty_string', 'PA_CHANNEL_MAP_SNPRINT_MAX', 'pa_channel_map_snprint', 'pa_channel_map_parse', 'pa_channel_map_equal', 'pa_channel_map_valid', 'pa_volume_t', 'PA_VOLUME_NORM', 'PA_VOLUME_MUTED', 'pa_cvolume', 'pa_cvolume_equal', 'pa_cvolume_set', 'PA_CVOLUME_SNPRINT_MAX', 'pa_cvolume_snprint', 'pa_cvolume_avg', 'pa_cvolume_valid', 'pa_cvolume_channels_equal_to', 'pa_sw_volume_multiply', 'pa_sw_cvolume_multiply', 'pa_sw_volume_from_dB', 'pa_sw_volume_to_dB', 'pa_sw_volume_from_linear', 'pa_sw_volume_to_linear', 'PA_DECIBEL_MININFTY', 'pa_stream', 'pa_stream_success_cb_t', 'pa_stream_request_cb_t', 'pa_stream_notify_cb_t', 'pa_stream_new', 'pa_stream_unref', 'pa_stream_ref', 'pa_stream_get_state', 'pa_stream_get_context', 'pa_stream_get_index', 'pa_stream_get_device_index', 'pa_stream_get_device_name', 'pa_stream_is_suspended', 'pa_stream_connect_playback', 'pa_stream_connect_record', 'pa_stream_disconnect', 'pa_stream_write', 'pa_stream_peek', 'pa_stream_drop', 'pa_stream_writable_size', 'pa_stream_readable_size', 'pa_stream_drain', 'pa_stream_update_timing_info', 'pa_stream_set_state_callback', 'pa_stream_set_write_callback', 'pa_stream_set_read_callback', 'pa_stream_set_overflow_callback', 'pa_stream_set_underflow_callback', 'pa_stream_set_latency_update_callback', 'pa_stream_set_moved_callback', 'pa_stream_set_suspended_callback', 'pa_stream_cork', 'pa_stream_flush', 'pa_stream_prebuf', 'pa_stream_trigger', 'pa_stream_set_name', 'pa_stream_get_time', 'pa_stream_get_latency', 'pa_stream_get_timing_info', 'pa_stream_get_sample_spec', 'pa_stream_get_channel_map', 'pa_stream_get_buffer_attr', 'pa_stream_set_buffer_attr', 'pa_stream_update_sample_rate', 'pa_sink_info', 'pa_sink_info_cb_t', 'pa_context_get_sink_info_by_name', 'pa_context_get_sink_info_by_index', 'pa_context_get_sink_info_list', 'pa_source_info', 'pa_source_info_cb_t', 'pa_context_get_source_info_by_name', 'pa_context_get_source_info_by_index', 'pa_context_get_source_info_list', 'pa_server_info', 'pa_server_info_cb_t', 'pa_context_get_server_info', 'pa_module_info', 'pa_module_info_cb_t', 'pa_context_get_module_info', 'pa_context_get_module_info_list', 'pa_client_info', 'pa_client_info_cb_t', 'pa_context_get_client_info', 'pa_context_get_client_info_list', 'pa_sink_input_info', 'pa_sink_input_info_cb_t', 'pa_context_get_sink_input_info', 'pa_context_get_sink_input_info_list', 'pa_source_output_info', 'pa_source_output_info_cb_t', 'pa_context_get_source_output_info', 'pa_context_get_source_output_info_list', 'pa_context_set_sink_volume_by_index', 'pa_context_set_sink_volume_by_name', 'pa_context_set_sink_mute_by_index', 'pa_context_set_sink_mute_by_name', 'pa_context_set_sink_input_volume', 'pa_context_set_sink_input_mute', 'pa_context_set_source_volume_by_index', 'pa_context_set_source_volume_by_name', 'pa_context_set_source_mute_by_index', 'pa_context_set_source_mute_by_name', 'pa_stat_info', 'pa_stat_info_cb_t', 'pa_context_stat', 'pa_sample_info', 'pa_sample_info_cb_t', 'pa_context_get_sample_info_by_name', 'pa_context_get_sample_info_by_index', 'pa_context_get_sample_info_list', 'pa_context_kill_client', 'pa_context_kill_sink_input', 'pa_context_kill_source_output', 'pa_context_index_cb_t', 'pa_context_load_module', 'pa_context_unload_module', 'pa_autoload_type_t', 'PA_AUTOLOAD_SINK', 'PA_AUTOLOAD_SOURCE', 'pa_autoload_info', 'pa_autoload_info_cb_t', 'pa_context_get_autoload_info_by_name', 'pa_context_get_autoload_info_by_index', 'pa_context_get_autoload_info_list', 'pa_context_add_autoload', 'pa_context_remove_autoload_by_name', 'pa_context_remove_autoload_by_index', 'pa_context_move_sink_input_by_name', 'pa_context_move_sink_input_by_index', 'pa_context_move_source_output_by_name', 'pa_context_move_source_output_by_index', 'pa_context_suspend_sink_by_name', 'pa_context_suspend_sink_by_index', 'pa_context_suspend_source_by_name', 'pa_context_suspend_source_by_index', 'pa_context_subscribe_cb_t', 'pa_context_subscribe', 'pa_context_set_subscribe_callback', 'pa_stream_connect_upload', 'pa_stream_finish_upload', 'pa_context_play_sample', 'pa_context_remove_sample', 'pa_get_library_version', 'PA_API_VERSION', 'PA_PROTOCOL_VERSION', 'pa_strerror', 'pa_xmalloc', 'pa_xmalloc0', 'pa_xrealloc', 'pa_xfree', 'pa_xstrdup', 'pa_xstrndup', 'pa_xmemdup', 'pa_utf8_valid', 'pa_utf8_filter', 'pa_utf8_to_locale', 'pa_locale_to_utf8', 'pa_threaded_mainloop', 'pa_threaded_mainloop_new', 'pa_threaded_mainloop_free', 'pa_threaded_mainloop_start', 'pa_threaded_mainloop_stop', 'pa_threaded_mainloop_lock', 'pa_threaded_mainloop_unlock', 'pa_threaded_mainloop_wait', 'pa_threaded_mainloop_signal', 'pa_threaded_mainloop_accept', 'pa_threaded_mainloop_get_retval', 'pa_threaded_mainloop_get_api', 'pa_threaded_mainloop_in_thread', 'pa_mainloop', 'pa_mainloop_new', 'pa_mainloop_free', 'pa_mainloop_prepare', 'pa_mainloop_poll', 'pa_mainloop_dispatch', 'pa_mainloop_get_retval', 'pa_mainloop_iterate', 'pa_mainloop_run', 'pa_mainloop_get_api', 'pa_mainloop_quit', 'pa_mainloop_wakeup', 'pa_poll_func', 'pa_mainloop_set_poll_func', 'pa_signal_init', 'pa_signal_done', 'pa_signal_event', 'pa_signal_new', 'pa_signal_free', 'pa_signal_set_destroy', 'pa_get_user_name', 'pa_get_host_name', 'pa_get_fqdn', 'pa_get_home_dir', 'pa_get_binary_name', 'pa_path_get_filename', 'pa_msleep', 'PA_MSEC_PER_SEC', 'PA_USEC_PER_SEC', 'PA_NSEC_PER_SEC', 'PA_USEC_PER_MSEC', 'pa_gettimeofday', 'pa_timeval_diff', 'pa_timeval_cmp', 'pa_timeval_age', 'pa_timeval_add', 'pa_timeval_store', 'pa_timeval_load']

from multiprocessing.connection import Listener, Client from os import path, environ import datetime address = path.join(environ['XDG_RUNTIME_DIR'], 'i3timer') authkey = bytes(environ['XDG_SESSION_COOKIE'], 'ascii') class Timer(object): def __init__(self): self.stop() @property def current(self): if self.accumulator is None: return self.running r = self.running if r is None: return self.accumulator else: return self.accumulator + r @property def running(self): return datetime.datetime.now() - self.last_start if self.last_start is not None else None @property def is_running(self): return self.last_start is not None def get_state(self): return {'current': self.current, 'running': self.running} def start(self): self.last_start = datetime.datetime.now() def pause(self): self.accumulator = self.current self.last_start = None def toggle(self): if self.is_running: self.pause() else: self.start() def stop(self): self.last_start = None self.accumulator = None def listen_forever(): timer = Timer() with Listener(address=address, authkey=authkey) as listener: while True: conn = listener.accept() try: message = conn.recv() conn.send(getattr(timer, message)()) conn.close() except EOFError as e: pass except Exception as e: print('exception occurred: %s' % e) class TimerClient(object): def __init__(self): pass def execute(self, command): client = Client(address=address, authkey=authkey) client.send(command) return client.recv() @property def state(self): return self.execute('get_state')

import os from flask.ext.script import Manager from flask.ext.migrate import Migrate, MigrateCommand from app import app, db #app.config.from_object(os.environ['APP_SETTINGS']) migrate = Migrate(app, db) manager = Manager(app) manager.add_command('db', MigrateCommand) if __name__ == '__main__': manager.run()

from scipy import linspace from scipy.stats import norm import numpy as np import math def densitiesPlot( densities, unit, legend = None ): import matplotlib.pyplot as plt L = len( densities[0] )/2 pts = symmetric_lattice( L=L, unit=unit ) for density in densities: plt.plot( pts, density ) if legend is not None: plt.legend( legend ) plt.grid() plt.show() def integer_shift( cdf, k ): """ Shift cdf to the *right* so it represents the cdf for Y ~ X + k*unit :param cdf: :param k: int Number of lattice points :return: """ if k < 0: return np.append( cdf[ abs(k):], cdf[-1]*np.ones( abs(k) ) ) elif k==0: return cdf else: return np.append( np.zeros(k), cdf[:-k] ) def fractional_shift( cdf, x ): """ Shift cdf to the *right* so it represents the cdf for Y ~ X + x*unit :param cdf: :param x: float Number of lattice points to shift (need not be integer) :return: """ ( l, lc), ( u, uc) = _low_high( x ) return lc * integer_shift( cdf, l ) + uc * integer_shift( cdf, u ) def _low_high( offset ): l = math.floor( offset ) u = math.ceil( offset ) r = offset - l return (l, 1-r ), (u, r) def fractional_shift_density( density, x ): """ Shift pdf to the *right* so it represents the pdf for Y ~ X + x*unit """ cdf = pdf_to_cdf(density) shifted_cdf = fractional_shift( cdf, x ) return cdf_to_pdf(shifted_cdf) return shifted_pdf def center_density( density ): """ Shift density to near its mean """ m = mean_of_density(density, unit=1.0) return fractional_shift_density( density, -m ) def mean_of_density( density, unit ): L = len( density) /2 pts = symmetric_lattice( L=L, unit=unit ) return np.inner( density, pts ) def symmetric_lattice(L, unit): return unit*linspace( -L, L, 2*L+1 ) ############################################# # Simple family of skewed distributions # ############################################# def skew_normal_density( L, unit, loc=0, scale=1.0, a=2.0): """ Skew normal as a lattice density """ lattice = symmetric_lattice(L=L, unit=unit) density = np.array( [ _unnormalized_skew_cdf(x, loc=loc, scale=scale, a=a ) for x in lattice]) density = density / np.sum( density ) return density def _unnormalized_skew_cdf(x, loc=0, scale=1, a=2.0): """ Proportional to skew-normal density :param x: :param loc: location :param scale: scale :param a: controls skew (a>0 means fat tail on right) :return: np.array length 2*L+1 """ t = (x-loc) / scale return 2 / scale * norm.pdf(t) * norm.cdf(a*t) # Probabilities on lattices # Nothing below here depends on the unit chosen ############################################# # Order statistics on lattices # ############################################# def pdf_to_cdf( density ): """ Prob( X <= k*unit ) """ return np.cumsum( density ) def cdf_to_pdf(cumulative): """ Given cumulative distribution on lattice, return the pdf """ prepended = np.insert( cumulative, 0, 0.) return np.diff( prepended ) def winner_of_many( densities, multiplicities = None): """ The PDF of the minimum of the random variables represented by densities :param densities: [ np.array ] :return: np.array """ d = densities[0] multiplicities = multiplicities or [ None for _ in densities ] m = multiplicities[0] for d2, m2 in zip( densities[1:], multiplicities[1:] ): d, m = _winner_of_two_pdf( d, d2, multiplicityA=m, multiplicityB = m2 ) return d, m def sample_from_cdf( cdf, nSamples ): """ Monte Carlo sample """ rvs = np.random.rand( nSamples ) return [ sum( [ rv>c for c in cdf ] ) for rv in rvs ] def sample_winner_of_many( densities, nSamples = 5000 ): """ The PDF of the minimum of the integer random variables represented by densities, by Monte Carlo """ cdfs = [pdf_to_cdf(density) for density in densities] cols = [sample_from_cdf(cdf, nSamples) for cdf in cdfs] rows = map( list, zip( *cols )) D = [ min( row ) for row in rows ] density = np.bincount( D, minlength=len( densities[0] ) ) / (1.0*nSamples) return density def expected_payoff( density, densityAll, multiplicityAll, cdf = None, cdfAll = None): """ Returns expected _conditional_payoff_against_rest broken down by score, where _conditional_payoff_against_rest is 1 if we are better than rest (lower) and 1/(1+multiplicity) if we are equal """ # Use np.sum( expected_payoff ) for the expectation if cdf is None: cdf = pdf_to_cdf(density) if cdfAll is None: cdfAll = pdf_to_cdf(densityAll) if density is None: density = cdf_to_pdf(cdf) if densityAll is None: densityAll = cdf_to_pdf(cdfAll) S = 1 - cdfAll S1 = 1 - cdf Srest = ( S + 1e-18 ) / ( S1 + 1e-6 ) cdfRest = 1 - Srest # Multiplicity inversion (uses notation from blog post) # This is written up in my blog post m = multiplicityAll f1 = density m1 = 1.0 fRest = cdf_to_pdf(cdfRest) # numer = m*f1*Srest + m*(f1+S1)*fRest - m1*f1*( Srest + fRest ) # denom = fRest*(f1+S1) numer = m*f1*Srest + m*(f1+S1)*fRest - m1*f1*( Srest + fRest ) denom = fRest*(f1+S1) multiplicityLeftTail = (1e-18 + numer ) / ( 1e-18 + denom ) multiplicityRest = multiplicityLeftTail T1 = (S1 +1.0e-18) / (f1 + 1e-6 ) # This calculation is more stable on the right tail. It should tend to zero eventually Trest = (Srest + 1e-18) / ( fRest + 1e-6 ) multiplicityRightTail = m*Trest / (1 + T1) + m - m1 * (1 + Trest ) / (1 + T1 ) k = list( f1 == max(f1) ).index( True ) multiplicityRest[k:] = multiplicityRightTail[k:] return _conditional_payoff_against_rest(density = density, densityRest = None, multiplicityRest = multiplicityRest, cdf = cdf, cdfRest = cdfRest) def _winner_of_two_pdf( densityA, densityB, multiplicityA = None, multiplicityB = None, cdfA = None, cdfB = None): """ The PDF of the minimum of two random variables represented by densities :param densityA: np.array :param densityB: np.array :return: density, multiplicity """ cdfA = pdf_to_cdf(densityA) cdfB = pdf_to_cdf(densityB) cdfMin = 1 - np.multiply( 1 - cdfA, 1- cdfB ) density = cdf_to_pdf(cdfMin) L = len( density ) / 2 if multiplicityA is None: multiplicityA = np.ones( 2*L+1 ) if multiplicityB is None: multiplicityB = np.ones( 2*L+1 ) winA, draw, winB = _conditional_win_draw_loss( densityA, densityB, cdfA, cdfB ) multiplicity = ( winA*multiplicityA + draw*(multiplicityA+multiplicityB) + winB*multiplicityB +1e-18) / ( winA+draw+winB+1e-18) return density, multiplicity def _conditional_win_draw_loss(densityA, densityB, cdfA, cdfB): """ Conditional win, draw and loss probability lattices for a two horse race """ win = densityA * ( 1 - cdfB ) draw = densityA * densityB lose = densityB * ( 1 - cdfA ) return win, draw, lose def _conditional_payoff_against_rest(density, densityRest, multiplicityRest, cdf = None, cdfRest = None): """ Returns expected _conditional_payoff_against_rest broken down by score, where _conditional_payoff_against_rest is 1 if we are better than rest (lower) and 1/(1+multiplicity) if we are equal """ # use np.sum( _conditional_payoff_against_rest) for the expectation if cdf is None: cdf = pdf_to_cdf(density) if cdfRest is None: cdfRest = pdf_to_cdf(densityRest) if density is None: density = cdf_to_pdf(cdf) if densityRest is None: densityRest = cdf_to_pdf(cdfRest) win, draw, loss = _conditional_win_draw_loss(density, densityRest, cdf, cdfRest) return win + draw / (1+multiplicityRest ) def densities_and_coefs_from_offsets( density, offsets ): """ Given a density and a list of offsets (which might be non-integer) :param density: np.ndarray :param offsets: [ float ] :return: [ np.ndarray ] """ cdf = pdf_to_cdf(density) coefs = [ _low_high( offset ) for offset in offsets ] cdfs = [lc * integer_shift(cdf, l) + uc * integer_shift(cdf, u) for (l, lc), (u, uc) in coefs] return [cdf_to_pdf(cdf) for cdf in cdfs], coefs def densities_from_offsets( density, offsets ): return densities_and_coefs_from_offsets( density, offsets)[0] def state_prices_from_offsets( density, offsets ): densities = densities_from_offsets( density, offsets ) densityAll, multiplicityAll = winner_of_many( densities ) return implicit_state_prices( density, densityAll = densityAll, multiplicityAll=multiplicityAll, offsets=offsets ) def implicit_state_prices( density, densityAll, multiplicityAll = None, cdf = None, cdfAll = None, offsets = None ): """ Returns the expected _conditional_payoff_against_rest as a function of location changes in cdf """ L = len( density )/2 if cdf is None: cdf = pdf_to_cdf( density ) if cdfAll is None: cdfAll = pdf_to_cdf(densityAll) if multiplicityAll is None: multiplicityAll = np.ones( 2*L+1 ) if offsets is None: offsets = xrange( -L/2, L/2 ) implicit = list() for k in offsets: if k==int( k ): offset_cdf = integer_shift( cdf, k ) ip = expected_payoff( density = None, densityAll = densityAll, multiplicityAll=multiplicityAll, cdf = offset_cdf, cdfAll = cdfAll) implicit.append( np.sum( ip ) ) else: (l, l_coef ), ( r, r_coef) = _low_high( k ) offset_cdf_left = integer_shift(cdf, l) offset_cdf_right = integer_shift(cdf, r) ip_left = expected_payoff(density = None, densityAll = densityAll, multiplicityAll=multiplicityAll, cdf = offset_cdf_left, cdfAll = cdfAll) ip_right = expected_payoff(density = None, densityAll = densityAll, multiplicityAll=multiplicityAll, cdf = offset_cdf_right, cdfAll = cdfAll) implicit.append( l_coef*np.sum( ip_left ) + r_coef*np.sum( ip_right ) ) return implicit

from django.conf import settings from django.conf.urls import patterns, include, url urlpatterns = patterns('members.views', url(r'^$', 'members_home', name="home"), url(r'^login/$', 'members_login', name="login"), url(r'^cancel/$', 'members_cancel', name="cancel"), url(r'^purchase/(\S+)/$', 'purchase', name="purchase"), url(r'^charge/(\S+)/$', 'charge', name="charge"), url(r'^edit-name/$', 'edit_name', name="edit-name"), url(r'^paypal-success/$', 'paypal_success', name="paypal-success"), url(r'^support/$', 'support', name="support"), )

""" Admin support code for DurationFields. """ import ttcal from django.forms.fields import Field from django.forms import ValidationError from django.forms.utils import flatatt from django.forms.widgets import TextInput from django.utils.safestring import mark_safe from django.utils.encoding import force_text class DurationInput(TextInput): """Duration input widget. """ def render(self, name, value, attrs=None): """output.append(u'<li>%(cb)s<label%(for)s>%(label)s</label></li>' % {"for": label_for, "label": option_label, "cb": rendered_cb}) """ if value is None: value = '' final_attrs = self.build_attrs(attrs, type=self.input_type, name=name) if value != '': # Only add the 'value' attribute if a value is non-empty. if isinstance(value, int): # Database backends serving different types value = ttcal.Duration(seconds=value) # Otherwise, we've got a timedelta already final_attrs['value'] = force_text(value) return mark_safe(u'<input%s />' % flatatt(final_attrs)) class DurationField(Field): """Form field for DurationField custom database field. """ widget = DurationInput def __init__(self, *args, **kwargs): super(DurationField, self).__init__(*args, **kwargs) def clean(self, value): """Returns a datetime.timedelta object. """ super(DurationField, self).clean(value) try: return ttcal.Duration.parse(value) except (ValueError, TypeError): raise ValidationError('Enter a valid duration.') def to_python(self, value): # pylint:disable=R0201 """Convert form input to python value. """ try: return ttcal.Duration.parse(value) except (ValueError, TypeError): raise ValidationError('Enter a valid duration.')

import scrapy from scrapy.loader.processors import Join, MapCompose, TakeFirst from w3lib.html import remove_tags from classics_spider.utils import Sanitizer class ClassicsSpiderItem(scrapy.Item): post_id = scrapy.Field( input_processor=MapCompose(remove_tags), output_processor=Join(), ) post_author = scrapy.Field( input_processor=MapCompose(remove_tags, Sanitizer.trim), output_processor=Join(), ) post_datetime = scrapy.Field( input_processor=MapCompose(remove_tags, Sanitizer.extract_date), output_processor=Join(), ) post_content = scrapy.Field( input_processor=MapCompose(remove_tags, Sanitizer.extract_content), output_processor=Join(), )

from decimal import Decimal from typing import List, Union from ...asset import Asset from ...call_builder.base import BaseStrictReceivePathsCallBuilder from ...call_builder.call_builder_sync.base_call_builder import BaseCallBuilder from ...client.base_sync_client import BaseSyncClient from ...type_checked import type_checked __all__ = ["StrictReceivePathsCallBuilder"] @type_checked class StrictReceivePathsCallBuilder(BaseCallBuilder, BaseStrictReceivePathsCallBuilder): """Creates a new :class:`StrictReceivePathsCallBuilder` pointed to server defined by horizon_url. Do not create this object directly, use :func:`stellar_sdk.Server.strict_receive_paths`. The Stellar Network allows payments to be made across assets through path payments. A path payment specifies a series of assets to route a payment through, from source asset (the asset debited from the payer) to destination asset (the asset credited to the payee). A path search is specified using: - The source address or source assets. - The asset and amount that the destination account should receive. As part of the search, horizon will load a list of assets available to the source address and will find any payment paths from those source assets to the desired destination asset. The search's amount parameter will be used to determine if there a given path can satisfy a payment of the desired amount. If a list of assets is passed as the source, horizon will find any payment paths from those source assets to the desired destination asset. See `List Strict Receive Payment Paths <https://developers.stellar.org/api/aggregations/paths/strict-receive/>`__ for more information. :param horizon_url: Horizon server URL. :param client: The client instance used to send request. :param source: The sender's account ID or a list of Assets. Any returned path must use a source that the sender can hold. :param destination_asset: The destination asset. :param destination_amount: The amount, denominated in the destination asset, that any returned path should be able to satisfy. """ def __init__( self, horizon_url: str, client: BaseSyncClient, source: Union[str, List[Asset]], destination_asset: Asset, destination_amount: Union[str, Decimal], ) -> None: super().__init__( # type: ignore[call-arg] horizon_url=horizon_url, client=client, source=source, destination_asset=destination_asset, destination_amount=destination_amount, )

"""Isomap for manifold learning""" # Author: Jake Vanderplas -- <vanderplas@astro.washington.edu> # License: BSD, (C) 2011 import numpy as np from ..base import BaseEstimator from ..neighbors import NearestNeighbors, kneighbors_graph from ..utils.graph import graph_shortest_path from ..decomposition import KernelPCA from ..preprocessing import KernelCenterer class Isomap(BaseEstimator): """Isomap Embedding Non-linear dimensionality reduction through Isometric Mapping Parameters ---------- n_neighbors : integer number of neighbors to consider for each point. out_dim : integer number of coordinates for the manifold eigen_solver : ['auto'|'arpack'|'dense'] 'auto' : attempt to choose the most efficient solver for the given problem. 'arpack' : use Arnoldi decomposition to find the eigenvalues and eigenvectors. Note that arpack can handle both dense and sparse data efficiently 'dense' : use a direct solver (i.e. LAPACK) for the eigenvalue decomposition. tol : float convergence tolerance passed to arpack or lobpcg. not used if eigen_solver == 'dense' max_iter : integer maximum number of iterations for the arpack solver. not used if eigen_solver == 'dense' path_method : string ['auto'|'FW'|'D'] method to use in finding shortest path. 'auto' : attempt to choose the best algorithm automatically 'FW' : Floyd-Warshall algorithm 'D' : Dijkstra algorithm with Fibonacci Heaps neighbors_algorithm : string ['auto'|'brute'|'kd_tree'|'ball_tree'] algorithm to use for nearest neighbors search, passed to neighbors.NearestNeighbors instance Attributes ---------- `embedding_` : array-like, shape (n_samples, out_dim) Stores the embedding vectors `kernel_pca_` : `KernelPCA` object used to implement the embedding `training_data_` : array-like, shape (n_samples, n_features) Stores the training data `nbrs_` : sklearn.neighbors.NearestNeighbors instance Stores nearest neighbors instance, including BallTree or KDtree if applicable. `dist_matrix_` : array-like, shape (n_samples, n_samples) Stores the geodesic distance matrix of training data Notes ----- **References**: [1] Tenenbaum, J.B.; De Silva, V.; & Langford, J.C. A global geometric framework for nonlinear dimensionality reduction. Science 290 (5500) """ def __init__(self, n_neighbors=5, out_dim=2, eigen_solver='auto', tol=0, max_iter=None, path_method='auto', neighbors_algorithm='auto'): self.n_neighbors = n_neighbors self.out_dim = out_dim self.eigen_solver = eigen_solver self.tol = tol self.max_iter = max_iter self.path_method = path_method self.neighbors_algorithm = neighbors_algorithm self.nbrs_ = NearestNeighbors(n_neighbors=n_neighbors, algorithm=neighbors_algorithm) def _fit_transform(self, X): self.nbrs_.fit(X) self.training_data_ = self.nbrs_._fit_X self.kernel_pca_ = KernelPCA(n_components=self.out_dim, kernel="precomputed", eigen_solver=self.eigen_solver, tol=self.tol, max_iter=self.max_iter) kng = kneighbors_graph(self.nbrs_, self.n_neighbors, mode='distance') self.dist_matrix_ = graph_shortest_path(kng, method=self.path_method, directed=False) G = self.dist_matrix_ ** 2 G *= -0.5 self.embedding_ = self.kernel_pca_.fit_transform(G) def reconstruction_error(self): """Compute the reconstruction error for the embedding. Returns ------- reconstruction_error : float Notes ------- The cost function of an isomap embedding is ``E = frobenius_norm[K(D) - K(D_fit)] / n_samples`` Where D is the matrix of distances for the input data X, D_fit is the matrix of distances for the output embedding X_fit, and K is the isomap kernel: ``K(D) = -0.5 * (I - 1/n_samples) * D^2 * (I - 1/n_samples)`` """ G = -0.5 * self.dist_matrix_ ** 2 G_center = KernelCenterer().fit_transform(G) evals = self.kernel_pca_.lambdas_ return np.sqrt(np.sum(G_center ** 2) - np.sum(evals ** 2)) / G.shape[0] def fit(self, X, y=None): """Compute the embedding vectors for data X Parameters ---------- X : {array-like, sparse matrix, BallTree, cKDTree, NearestNeighbors} Sample data, shape = (n_samples, n_features), in the form of a numpy array, sparse array, precomputed tree, or NearestNeighbors object. Returns ------- self : returns an instance of self. """ self._fit_transform(X) return self def fit_transform(self, X, y=None): """Fit the model from data in X and transform X. Parameters ---------- X: {array-like, sparse matrix, BallTree, cKDTree} Training vector, where n_samples in the number of samples and n_features is the number of features. Returns ------- X_new: array-like, shape (n_samples, out_dim) """ self._fit_transform(X) return self.embedding_ def transform(self, X): """Transform X. This is implemented by linking the points X into the graph of geodesic distances of the training data. First the `n_neighbors` nearest neighbors of X are found in the training data, and from these the shortest geodesic distances from each point in X to each point in the training data are computed in order to construct the kernel. The embedding of X is the projection of this kernel onto the embedding vectors of the training set. Parameters ---------- X: array-like, shape (n_samples, n_features) Returns ------- X_new: array-like, shape (n_samples, out_dim) """ distances, indices = self.nbrs_.kneighbors(X, return_distance=True) #Create the graph of shortest distances from X to self.training_data_ # via the nearest neighbors of X. #This can be done as a single array operation, but it potentially # takes a lot of memory. To avoid that, use a loop: G_X = np.zeros((X.shape[0], self.training_data_.shape[0])) for i in range(X.shape[0]): G_X[i] = np.min((self.dist_matrix_[indices[i]] + distances[i][:, None]), 0) G_X **= 2 G_X *= -0.5 return self.kernel_pca_.transform(G_X)

def test_import(): import pelican_jupyter assert pelican_jupyter.__version__ is not None assert pelican_jupyter.__version__ != "0.0.0" assert len(pelican_jupyter.__version__) > 0 def test_import_markup(): from pelican_jupyter import markup as nb_markup assert nb_markup def test_import_liquid(): from pelican_jupyter import liquid as nb_liquid assert nb_liquid

import web import hashlib import cgi #import Image from PIL import Image import os import os.path import random import string import time import hashlib import socket from sign import sign from config import upload_path,app_root from conn import client cgi.maxlen = 5 * 1024 * 1024 #文件大小限制，需要 try except db = client.pyblog def transformPosts(posts,artists): for i in posts: if str(i['artist']) not in artists: artists[str(i['artist'])] = db['users'].find_one({'_id': i['artist']}) i['artist'] = artists[str(i['artist'])] return posts def listToHashByArtists(list): hash = {} for i in list: hash[str(i['_id'])] = i return hash def getArtistByKey(cursor,key): collections = list(cursor) ids = [] for i in collections: ids.append(i[key]) return list(db['users'].find({'_id': {'$in': ids}})) #检测登录 def checkLogin(): if web.ctx.has_key('session'): return web.ctx.session.hasLogin else: return False # user = web.cookies().get('pyname') # connect = web.cookies().get('pyconnect') # if user and connect: # return connect == sign(user) # else: # return False # 上传 def upload(file,path='/',mediaType='pic'): if not os.path.exists(upload_path+path+'/thumbs'): os.mkdir(upload_path+path+'/thumbs') THUMBS_WIDTH = 500 pic_width = 1280 # filename = file.filename.replace('\\','/').split('/')[-1] # 随机名 extname = os.path.splitext(file.filename)[1] filename = createRandomName() + extname img = Image.open(file.file) img_w,img_h = img.size ratio = 1.0 * img_w / img_h new_size_t = (THUMBS_WIDTH, int(THUMBS_WIDTH / ratio)) if mediaType is 'avatar': pic_width = 150 new_size = (pic_width, int(pic_width / ratio)) img.thumbnail(new_size,Image.ANTIALIAS) img.save(upload_path+path+filename) if mediaType is not 'avatar': img.thumbnail(new_size_t,Image.ANTIALIAS) img.save(upload_path+path+'/thumbs/'+filename) return '/static/upload'+path+filename def writeSession(arg): for i in arg: web.ctx.session[i] = arg[i] def createRandomName(): # http://tocode.sinaapp.com/4 # hashlib.md5(str(time.time())).digest() salt = ''.join(random.sample(string.ascii_letters + string.digits, 13)) return salt def randomString(num=16): return ''.join(map(lambda xx:(hex(ord(xx))[2:]),os.urandom(num))) def get_my_ip(): try: csock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) csock.connect(('8.8.8.8', 80)) (addr, port) = csock.getsockname() csock.close() return addr except socket.error: return "127.0.0.1" def isTrue(str): return str.lower() == 'true' def addQuery(url, query): qs = [] for i in query: qs.append(i + '=' + str(query[i])) qs = '&'.join(qs) if url.find('?') == -1: url += '?' + qs else: url += '&' + qs return url def handlerSpecPostType(posts,userId): for i in posts: if i.get('private'): if userId == i['artist']: i['showPost'] = i['private'] = True else: i['showPost'] = False continue if i.get('assigns'): if userId == i['artist'] or str(userId) in i.get('assigns'): i['showPost'] = i['assign'] = True else: i['showPost'] = False else: i['showPost'] = True

import argparse, sys, copy, gzip, time, math, re import numpy as np import pandas as pd from scipy import stats from collections import Counter, defaultdict, namedtuple import statsmodels.formula.api as smf from operator import itemgetter import warnings from svtools.vcf.file import Vcf from svtools.vcf.genotype import Genotype from svtools.vcf.variant import Variant import svtools.utils as su CN_rec = namedtuple ('CN_rec', 'var_id sample svtype svlen AF GT CN AB log_len log2r') # http://stackoverflow.com/questions/8930370/where-can-i-find-mad-mean-absolute-deviation-in-scipy def mad(arr): """ Median Absolute Deviation: a "Robust" version of standard deviation. Indices variabililty of the sample. https://en.wikipedia.org/wiki/Median_absolute_deviation """ arr = np.ma.array(arr).compressed() # should be faster to not use masked arrays. med = np.median(arr) return np.median(np.abs(arr - med)) def to_bnd_strings(var, fixed_gts): old_type = var.info['SVTYPE'] old_id = var.var_id old_pos = var.pos old_end = var.info['END'] old_ciend = var.info['CIEND'] old_cipos = var.info['CIPOS'] old_cipos95 = var.info['CIPOS95'] old_ciend95 = var.info['CIEND95'] #for both ends var.info['SVTYPE'] = 'BND' var.info['EVENT'] = old_id del var.info['SVLEN'] del var.info['END'] #var1 var.var_id = old_id + "_1" var.info['MATEID'] = old_id + "_2" if old_type == 'DEL': var.alt = 'N[%s:%s[' % (var.chrom, old_end) else: var.alt = ']%s:%s]N' % (var.chrom, old_end) var1=var.get_var_string(fixed_gts) #var2 var.var_id = old_id + "_2" var.info['MATEID'] = old_id + "_1" var.info['CIPOS'] = old_ciend var.info['CIEND'] = old_cipos var.info['CIPOS95'] = old_ciend95 var.info['CIEND95'] = old_cipos95 var.pos = old_end var.info['SECONDARY'] = True if old_type == 'DEL': var.alt = ']%s:%s]N' % (var.chrom, old_pos) else: var.alt = 'N[%s:%s[' % (var.chrom, old_pos) var2=var.get_var_string(fixed_gts) return var1, var2 def reciprocal_overlap(a, b_list): overlap = 0 b_aggregate = 0 # catch divide by zero error if a[1] == a[0]: return 0 # update the overlap and b_aggregate for b in b_list: b_aggregate += (b[1] - b[0]) overlap += float(min(a[1], b[1]) - max(a[0], b[0])) # catch divide by zero error if b_aggregate == 0: return 0 return min(overlap / (a[1] - a[0]), overlap / b_aggregate) def collapse_bed_records(bed_list): bed_list_sorted = sorted(bed_list, key=itemgetter(1)) collapsed_bed_list = [] i = 0 curr_rec = bed_list_sorted[i] while i < len(bed_list_sorted): # end at last element in list if i == len(bed_list_sorted) - 1: collapsed_bed_list.append(copy.copy(curr_rec)) break # load next entry next_rec = bed_list_sorted[i + 1] # merge is overlap if curr_rec[1] >= next_rec[0]: curr_rec[1] = next_rec[1] i += 1 # write out if no overlap else: collapsed_bed_list.append(copy.copy(curr_rec)) i += 1 curr_rec = bed_list_sorted[i] # print 'collapsed:', collapsed_bed_list return collapsed_bed_list def annotation_intersect(var, ae_dict, threshold): best_frac_overlap = 0 best_feature = '' slop = 0 # dictionary with number of bases of overlap for each class class_overlap = {} # first check for reciprocal overlap if var.chrom in ae_dict: var_start = var.pos var_end = int(var.info['END']) i = 0 while 1: # bail if end of dict if i >= len(ae_dict[var.chrom]): break feature = ae_dict[var.chrom][i] if feature[0] - slop < var_end: if feature[1] + slop > var_start: try: class_overlap[feature[2]].append(feature) except KeyError: class_overlap[feature[2]] = [feature] else: break i += 1 # print class_overlap for me_class in class_overlap: class_overlap[me_class] = collapse_bed_records(class_overlap[me_class]) frac_overlap = reciprocal_overlap([var_start, var_end], class_overlap[me_class]) if frac_overlap > best_frac_overlap: best_frac_overlap = frac_overlap best_feature = me_class if best_frac_overlap >= threshold: return best_feature return None def lowQuantile(xx): return np.percentile(xx,2.5) def highQuantile(xx): return np.percentile(xx,97.5) def lld(xx, mean, sd): ll = 1 / sd * math.exp(-(xx-mean) * (xx-mean) / (2*sd*sd)) return ll def calc_params(vcf_path): tSet = list() epsilon=0.1 header=[] in_header = True vcf = Vcf() if vcf_path.endswith('.gz'): vcf_file = gzip.open(vcf_path, 'rb') else: vcf_file = open(vcf_path, 'r') for line in vcf_file: if in_header: if line[0] == '#': header.append(line) if line[1] != '#': vcf_samples = line.rstrip().split('\t')[9:] in_header = False vcf.add_header(header) continue else: v = line.rstrip().split('\t') info = v[7].split(';') svtype = None for x in info: if x.startswith('SVTYPE='): svtype = x.split('=')[1] break if svtype not in ['DEL', 'DUP'] or v[0]=="X" or v[0]=="Y": continue var = Variant(v, vcf) for sample in vcf_samples: sample_genotype = var.genotype(sample) if sample_genotype.get_format('GT') != './.': log2r = math.log((float(sample_genotype.get_format('CN'))+ epsilon)/2,2) #to avoid log(0) tSet.append(CN_rec(var.var_id, sample, var.info['SVTYPE'], abs(float(var.info['SVLEN'])), var.info['AF'], sample_genotype.get_format('GT'), sample_genotype.get_format('CN'), sample_genotype.get_format('AB'), math.log(abs(float(var.info['SVLEN']))), log2r)) df=pd.DataFrame(tSet, columns=CN_rec._fields) #exclude from training data, DELs and DUPs with CN in the tails of the distribution df.loc[:,'q_low']=df.groupby(['sample', 'svtype', 'GT'])['log2r'].transform(lowQuantile) df.loc[:,'q_high']=df.groupby(['sample', 'svtype', 'GT'])['log2r'].transform(highQuantile) df=df[(df.log2r>=df.q_low) & (df.log2r<=df.q_high)] #df.to_csv('./train.csv') #adjust copy number for small deletions (<1kb), no strong relationship b/w cn and size for dups evident so far small_het_dels = df[(df.svtype=="DEL") & (df.GT=="0/1") & (df.svlen<1000) & (df.svlen>=50)].copy() small_hom_dels = df[(df.svtype=="DEL") & (df.GT=="1/1") & (df.svlen<1000) & (df.svlen>=50)].copy() het_del_mean=np.mean(df[(df.svlen>1000) & (df.GT=="0/1") & (df.svtype=="DEL")]['log2r']) hom_del_mean=np.mean(df[(df.svlen>1000) & (df.GT=="1/1") & (df.svtype=="DEL")]['log2r']) small_het_dels.loc[:,'offset']=small_het_dels.loc[:,'log2r']-het_del_mean small_hom_dels.loc[:,'offset']=small_hom_dels.loc[:,'log2r']-hom_del_mean with warnings.catch_warnings(): warnings.filterwarnings("ignore") hom_del_fit=smf.ols('offset~log_len',small_hom_dels).fit() het_del_fit=smf.ols('offset~log_len',small_het_dels).fit() #print hom_del_fit.summary() #print het_del_fit.summary() small_hom_dels.loc[:,'log2r_adj'] = small_hom_dels.loc[:,'log2r'] - hom_del_fit.predict(small_hom_dels) small_het_dels.loc[:,'log2r_adj'] = small_het_dels.loc[:,'log2r'] - het_del_fit.predict(small_het_dels) small_dels=small_hom_dels.append(small_het_dels) small_dels=small_dels[['var_id', 'sample', 'svtype', 'svlen', 'AF', 'GT', 'CN', 'log_len', 'log2r', 'q_low', 'q_high', 'log2r_adj']] # dels of length<100 bp are excluded here df1=df.loc[(df.svtype!="DEL") | (df.GT=="0/0") | (df.svlen>=1000), :].copy() df1.loc[:,'log2r_adj']=df1.loc[:,'log2r'] df1=df1.append(small_dels) params=df1.groupby(['sample', 'svtype', 'GT'])['log2r_adj'].aggregate([np.mean,np.var, len]).reset_index() params=pd.pivot_table(params, index=['sample', 'svtype'], columns='GT', values=['mean', 'var', 'len']).reset_index() params.columns=['sample', 'svtype', 'mean0', 'mean1', 'mean2', 'var0', 'var1', 'var2', 'len0', 'len1', 'len2'] params['std_pooled']=np.sqrt((params['var0']*params['len0']+params['var1']*params['len1']+params['var2']*params['len2'])/(params['len0']+params['len1']+params['len2'])) #params.to_csv('./params.csv') return (params, het_del_fit, hom_del_fit) def rd_support_nb(temp, p_cnv): tr = pd.DataFrame({'p0' : [1.0, 0.1, 0.0], 'p1' : [0.0, 0.7, 0.25], 'p2' : [0.0, 0.2, 0.75], 'GT' : ["0/0", "0/1", "1/1"]}) temp = pd.merge(temp, tr, on='GT', how='left') temp['p_mix'] = temp['lld0'] * temp['p0'] + temp['lld1'] * temp['p1'] + temp['lld2'] * temp['p2'] return np.log(p_cnv)+np.sum(np.log(temp['p_mix'])) > np.log(1-p_cnv)+np.sum(np.log(temp['lld0'])) def has_rd_support_by_nb(test_set, het_del_fit, hom_del_fit, params, p_cnv = 0.5): svtype=test_set['svtype'][0] svlen=test_set['svlen'][0] log_len=test_set['log_len'][0] if svtype == 'DEL' and svlen<1000: params1=params[params.svtype=='DEL'].copy() if svlen<50: params1['log_len']=math.log(50) else: params1['log_len']=log_len params1.loc[:,'mean1_adj'] = params1.loc[:,'mean1'] + het_del_fit.predict(params1) params1.loc[:,'mean2_adj'] = params1.loc[:,'mean2'] + hom_del_fit.predict(params1) else: params1=params.copy() params1.loc[:,'mean1_adj'] = params1.loc[:,'mean1'] params1.loc[:,'mean2_adj'] = params1.loc[:,'mean2'] v0=test_set.loc[test_set.GT=="0/0", 'log2r'].values v1=test_set.loc[test_set.GT=="0/1", 'log2r'].values v2=test_set.loc[test_set.GT=="1/1", 'log2r'].values if len(v0)>0: med0=np.median(v0) else: if len(v1)>0: med0=med1=np.median(v1) elif len(v2)>0: med0=med1=med2=np.median(v2) else: return False if len(v1)>0: med1=np.median(v1) else: med1=med0 if len(v2)>0: med2=np.median(v2) else: med2=med1 if svtype=='DEL' and ( med1>med0 or med2>med0 ): return False elif svtype=='DUP' and (med1<med0 or med2<med0): return False mm=pd.merge(test_set, params1, how='left') mm.loc[:,'lld0'] = mm.apply(lambda row:lld(row["log2r"], row["mean0"],row["std_pooled"]), axis=1) mm.loc[:,'lld1'] = mm.apply(lambda row:lld(row["log2r"], row["mean1_adj"],row["std_pooled"]), axis=1) mm.loc[:,'lld2'] = mm.apply(lambda row:lld(row["log2r"], row["mean2_adj"],row["std_pooled"]), axis=1) return rd_support_nb(mm, p_cnv) def load_df(var, exclude, sex): epsilon=0.1 test_set = list() for s in var.sample_list: if s in exclude: continue cn = var.genotype(s).get_format('CN') if (var.chrom == 'X' or var.chrom == 'Y') and sex[s] == 1: cn=str(float(cn)*2) log2r = math.log((float(cn)+epsilon)/2, 2) # to avoid log(0) test_set.append(CN_rec(var.var_id, s, var.info['SVTYPE'], abs(float(var.info['SVLEN'])), var.info['AF'], var.genotype(s).get_format('GT'), cn , var.genotype(s).get_format('AB'), math.log(abs(float(var.info['SVLEN']))), log2r)) test_set = pd.DataFrame(data = test_set, columns=CN_rec._fields) return test_set # test for read depth support of low frequency variants def has_low_freq_depth_support(test_set, mad_threshold=2, absolute_cn_diff=0.5): mad_quorum = 0.5 # this fraction of the pos. genotyped results must meet the mad_threshold hom_ref_cn=test_set[test_set.GT=="0/0"]['CN'].values.astype(float) hom_het_alt_cn=test_set[(test_set.GT=="0/1") | (test_set.GT=="1/1")]['CN'].values.astype(float) if len(hom_ref_cn) > 0: cn_median = np.median(hom_ref_cn) cn_mad = mad(hom_ref_cn) else: cn_median = None cn_mad = None # bail after writing out diagnostic info, if no ref samples or all ref samples if (len(hom_ref_cn) == 0 or len(hom_het_alt_cn) == 0): return False # tally up the pos. genotyped samples meeting the mad_threshold resid=hom_het_alt_cn-cn_median #if test_set['svtype'][0]=='DEL': if test_set.loc[0, 'svtype']=='DEL': resid=-resid resid=resid[(resid > (cn_mad * mad_threshold) ) & (resid>absolute_cn_diff)] if float(len(resid))/len(hom_het_alt_cn)>mad_quorum: return True else: return False # test whether variant has read depth support by regression def has_high_freq_depth_support(df, slope_threshold, rsquared_threshold): rd = df[[ 'AB', 'CN']][df['AB']!='.'].values.astype(float) if len(np.unique(rd[0,:])) > 1 and len(np.unique(rd[1,:])) > 1: (slope, intercept, r_value, p_value, std_err) = stats.linregress(rd) if df['svtype'][0] == 'DEL': slope=-slope #sys.stderr.write(df['var_id'][0]+"\t"+str(slope)+"\t"+str(r_value)+"\n") if (slope < slope_threshold or r_value*r_value < rsquared_threshold): return False return True return False def has_rd_support_by_ls(df, slope_threshold, rsquared_threshold, num_pos_samps, mad_threshold=2, absolute_cn_diff=0.5): min_pos_samps_for_regression=10 if num_pos_samps>min_pos_samps_for_regression: return has_high_freq_depth_support(df, slope_threshold, rsquared_threshold) else: return has_low_freq_depth_support(df, mad_threshold, absolute_cn_diff) return False def has_rd_support_hybrid(df, het_del_fit, hom_del_fit, params, p_cnv, slope_threshold, rsquared_threshold, num_pos_samps): hybrid_support=False nb_support=has_rd_support_by_nb(df, het_del_fit, hom_del_fit, params, p_cnv) ls_support=has_rd_support_by_ls(df, slope_threshold, rsquared_threshold, num_pos_samps) if nb_support and ls_support: hybrid_support=True elif nb_support and has_rd_support_by_ls(df, 2*slope_threshold, 2*rsquared_threshold, num_pos_samps, 2, 0.75): hybrid_support=True elif ls_support and has_rd_support_by_nb(df, het_del_fit, hom_del_fit, params, 0.2*p_cnv): hybrid_support=True return [ls_support, nb_support, hybrid_support] # primary function def sv_classify(vcf_in, vcf_out, gender_file, exclude_file, ae_dict, f_overlap, slope_threshold, rsquared_threshold, p_cnv, het_del_fit, hom_del_fit, params, diag_outfile, method): vcf = Vcf() header = [] in_header = True sex = {} # read sample genders for line in gender_file: v = line.rstrip().split('\t') sex[v[0]] = int(v[1]) exclude = [] if exclude_file is not None: for line in exclude_file: exclude.append(line.rstrip()) if diag_outfile is not None: outf=open(diag_outfile, 'w', 4096) outf.write("varid\torig_svtype\tsvlen\tnum_pos_samps\tnb_support\tls_support\thybrid_support\thas_rd_support\n") for line in vcf_in: if in_header: if line[0] == '#': header.append(line) continue else: in_header = False vcf.add_header(header) vcf_out.write(vcf.get_header() + '\n') v = line.rstrip().split('\t') info = v[7].split(';') svtype = None for x in info: if x.startswith('SVTYPE='): svtype = x.split('=')[1] break # bail if not DEL or DUP prior to reclassification if svtype not in ['DEL', 'DUP']: vcf_out.write(line) continue var = Variant(v, vcf) # check intersection with mobile elements if ae_dict is not None and var.info['SVTYPE'] in ['DEL']: ae = annotation_intersect(var, ae_dict, f_overlap) if ae is not None: if ae.startswith('SINE') or ae.startswith('LINE') or ae.split('|')[2].startswith('SVA'): ae = 'ME:' + ae var.alt = '<DEL:%s>' % ae var.info['SVTYPE'] = 'MEI' vcf_out.write(var.get_var_string(True) + '\n') continue #count positively genotyped samples num_pos_samps = 0 num_total_samps=len(var.sample_list) for s in var.sample_list: if var.genotype(s).get_format('GT') not in ["./.", "0/0"]: num_pos_samps += 1 nb_support = False ls_support = False hybrid_support = False has_rd_support = False if num_pos_samps == 0: vcf_out.write(line) else: df=load_df(var, exclude, sex) if method=='large_sample': ls_support = has_rd_support_by_ls(df, slope_threshold, rsquared_threshold, num_pos_samps) has_rd_support=ls_support elif method=='naive_bayes': nb_support = has_rd_support_by_nb(df, het_del_fit, hom_del_fit, params, p_cnv) has_rd_support=nb_support elif method=='hybrid': ls_support, nb_support, hybrid_support = has_rd_support_hybrid(df, het_del_fit, hom_del_fit, params, p_cnv, slope_threshold, rsquared_threshold, num_pos_samps) has_rd_support=hybrid_support if has_rd_support: vcf_out.write(line) else: for m_var in to_bnd_strings(var, True): vcf_out.write(m_var + '\n') if diag_outfile is not None: svlen=df['svlen'][0] outf.write(var.var_id+"\t"+svtype+"\t"+str(svlen)+"\t"+str(num_pos_samps)+"\t"+str(nb_support)+"\t"+str(ls_support)+"\t"+str(hybrid_support)+"\t"+str(has_rd_support)+"\n") vcf_out.close() if diag_outfile is not None: outf.close() vcf_in.close() vcf_out.close() gender_file.close() if exclude_file is not None: exclude_file.close() return def get_ae_dict(ae_path): if ae_path.endswith('.gz'): ae_bedfile = gzip.open(ae_path, 'rb') else: ae_bedfile = open(ae_path, 'r') ae_dict = {} for line in ae_bedfile: v = line.rstrip().split('\t') if len(v) < 4: continue v[1] = int(v[1]) v[2] = int(v[2]) if v[0] in ae_dict: ae_dict[v[0]].append(v[1:]) else: ae_dict[v[0]] = [v[1:]] ae_bedfile.close() return ae_dict def run_reclassifier(vcf_file, vcf_out, sex_file, ae_path, f_overlap, exclude_list, slope_threshold, rsquared_threshold, training_data, method, diag_outfile): ae_dict = None params = None het_del_fit = None hom_del_fit = None p_cnv=0.5 # prior probability that CNV is real if ae_path is not None: sys.stderr.write("loading annotations\n") ae_dict=get_ae_dict(ae_path) if(method!="large_sample"): sys.stderr.write("calculating parameters\n") #calculate per-sample CN profiles on training set [params, het_del_fit, hom_del_fit]=calc_params(training_data) sys.stderr.write("reclassifying\n") sv_classify(vcf_file, vcf_out, sex_file, exclude_list, ae_dict, f_overlap, slope_threshold, rsquared_threshold, p_cnv, het_del_fit, hom_del_fit, params, diag_outfile, method) def add_arguments_to_parser(parser): parser.add_argument('-i', '--input', metavar='<VCF>', default=None, help='VCF input') #parser.add_argument('-i', '--input', metavar='<STRING>', dest='vcf_in', type=argparse.FileType('r'), default=None, help='VCF input [stdin]') parser.add_argument('-o', '--output', metavar='<VCF>', dest='vcf_out', type=argparse.FileType('w'), default=sys.stdout, help='VCF output [stdout]') parser.add_argument('-g', '--gender', metavar='<FILE>', dest='gender', type=argparse.FileType('r'), required=True, default=None, help='tab delimited file of sample genders (male=1, female=2)\nex: SAMPLE_A\t2') parser.add_argument('-a', '--annotation', metavar='<BED>', dest='ae_path', type=str, default=None, help='BED file of annotated elements') parser.add_argument('-f', '--fraction', metavar='<FLOAT>', dest='f_overlap', type=float, default=0.9, help='fraction of reciprocal overlap to apply annotation to variant [0.9]') parser.add_argument('-e', '--exclude', metavar='<FILE>', dest='exclude', type=argparse.FileType('r'), required=False, default=None, help='list of samples to exclude from classification algorithms') parser.add_argument('-s', '--slope_threshold', metavar='<FLOAT>', dest='slope_threshold', type=float, default=1.0, help='minimum slope absolute value of regression line to classify as DEL or DUP[1.0]') parser.add_argument('-r', '--rsquared_threshold', metavar='<FLOAT>', dest='rsquared_threshold', type=float, default=0.2, help='minimum R^2 correlation value of regression line to classify as DEL or DUP [0.2], for large sample reclassification') parser.add_argument('-t', '--tSet', metavar='<STRING>', dest='tSet', type=str, default=None, required=False, help='high quality deletions & duplications training dataset[vcf], required by naive Bayes reclassification') parser.add_argument('-m', '--method', metavar='<STRING>', dest='method', type=str, default="large_sample", required=False, help='reclassification method, one of (large_sample, naive_bayes, hybrid)', choices=['large_sample', 'naive_bayes', 'hybrid']) parser.add_argument('-d', '--diag_file', metavar='<STRING>', dest='diag_outfile', type=str, default=None, required=False, help='text file to output method comparisons') parser.set_defaults(entry_point=run_from_args) def description(): return 'reclassify DEL and DUP based on read depth information' def command_parser(): parser = argparse.ArgumentParser(description=description()) add_arguments_to_parser(parser) return parser def run_from_args(args): # sys.stderr.write(args.vcf_in) if args.tSet is None: if args.method!="large_sample": sys.stderr.write("Training data required for naive Bayes or hybrid classifiers\n") parser.print_help() sys.exit(1) with su.InputStream(args.input) as stream: run_reclassifier(stream, args.vcf_out, args.gender, args.ae_path, args.f_overlap, args.exclude, args.slope_threshold, args.rsquared_threshold, args.tSet, args.method, args.diag_outfile) if __name__ == '__main__': parser = command_parser() args=parser.parse_args() sys.exit(args.entry_point(args))

import unittest from django.test import TestCase from django.utils import six from django.db.models import (CharField, TextField, BooleanField, ForeignKey, SmallIntegerField) from django.contrib.auth.models import User from django.utils.translation import ugettext as _ from django.utils.timezone import now, timedelta from deck.models import Event, Proposal, Vote, Jury from test_utils import get_all_field_names EVENT_DATA = { 'title': 'RuPy', 'slug': 'rupy', 'description': 'A really good event.', 'author_id': 1, 'is_published': False, 'slots': 30, 'closing_date': now() + timedelta(days=7), } PROPOSAL_DATA = { 'title': 'Python For Zombies', 'slug': 'python-for-zombies', 'description': 'Brain...', 'author_id': 1, 'slides_url': 'jane_doe/talk' } ANOTHER_PROPOSAL_DATA = { 'title': 'A Python 3 Metaprogramming Tutorial', 'slug': 'python-3-metaprogramming', 'description': 'An advanced tutorial on Python 3 and Metaprogramming', 'author_id': 1 } class EventModelIntegrityTest(TestCase): def setUp(self): self.fields = { field.name: field for field in Event._meta.fields } def test_assert_event_should_have_a_verbose_name(self): self.assertEquals(_('Event'), Event._meta.verbose_name) def test_assert_event_should_have_a_verbose_name_plural(self): self.assertEquals(_('Events'), Event._meta.verbose_name_plural) def test_assert_event_should_have_a_title(self): self.assertIn('title', get_all_field_names(Event)) def test_assert_event_title_should_be_a_CharField(self): self.assertIsInstance(self.fields['title'], CharField) def test_assert_event_title_should_be_required(self): self.assertEquals(False, self.fields['title'].null) self.assertEquals(False, self.fields['title'].blank) def test_assert_event_title_should_have_at_most_200_characters(self): self.assertEquals(200, self.fields['title'].max_length) def test_assert_event_should_have_a_description(self): self.assertIn('description', get_all_field_names(Event)) def test_assert_event_description_should_be_a_TextField(self): self.assertIsInstance(self.fields['description'], TextField) def test_assert_event_description_should_be_nullable_but_needs_to_be_blank(self): self.assertEquals(False, self.fields['description'].null) self.assertEquals(True, self.fields['description'].blank) def test_assert_event_description_should_have_at_most_10000_characters(self): self.assertEquals(10000, self.fields['description'].max_length) def test_assert_event_should_allow_public_voting(self): self.assertIn('allow_public_voting', get_all_field_names(Event)) def test_assert_event_allow_public_voting_should_be_a_BooleanField(self): self.assertIsInstance(self.fields['allow_public_voting'], BooleanField) def test_assert_event_allow_public_voting_should_be_True_as_default(self): self.assertEquals(True, self.fields['allow_public_voting'].default) def test_assert_event_should_have_a_author(self): self.assertIn('author', get_all_field_names(Event)) def test_assert_event_author_should_be_an_User(self): self.assertEquals(User, self.fields['author'].rel.to) def test_assert_event_author_should_be_a_ForeignKey(self): self.assertIsInstance(self.fields['author'], ForeignKey) def test_assert_event_author_should_be_required(self): self.assertEquals(False, self.fields['author'].null) self.assertEquals(False, self.fields['author'].blank) def test_assert_event_author_should_have_a_related_name(self): self.assertEquals('events', self.fields['author'].rel.related_name) def test_assert_event_should_have_a_publish_flag(self): self.assertIn('is_published', get_all_field_names(Event)) def test_assert_event_is_published_should_be_a_BooleanField(self): self.assertIsInstance(self.fields['is_published'], BooleanField) def test_assert_event_is_published_should_be_True_as_default(self): self.assertEquals(True, self.fields['is_published'].default) def test_assert_event_should_have_a_jury(self): self.assertIn('jury', get_all_field_names(Event)) def test_assert_event_jury_should_be_an_Jury(self): self.assertEquals(Jury, self.fields['jury'].rel.to) def test_assert_event_jury_should_be_a_ForeignKey(self): self.assertIsInstance(self.fields['jury'], ForeignKey) def test_assert_event_jury_should_not_be_required(self): self.assertEquals(True, self.fields['jury'].null) self.assertEquals(True, self.fields['jury'].blank) def test_assert_event_jury_should_have_a_related_name(self): self.assertEquals('event', self.fields['jury'].rel.related_name) class EventObjectTest(TestCase): def setUp(self): self.event = Event(**EVENT_DATA) @unittest.skipIf(six.PY3, 'not test unicode on python3') def test_assert_event_unicode_representation(self): self.assertEquals(u'RuPy', six.text_type(self.event)) def test_assert_event_title(self): self.assertEquals(u'RuPy', self.event.title) def test_assert_event_description(self): self.assertEquals(u'A really good event.', self.event.description) def test_assert_event_author(self): self.assertEquals(1, self.event.author_id) def test_assert_event_allow_public_voting(self): self.assertEquals(True, self.event.allow_public_voting) def test_assert_event_is_published(self): self.assertEquals(False, self.event.is_published) class ProposalModelIntegrityTest(TestCase): def setUp(self): self.fields = { field.name: field for field in Proposal._meta.fields } def test_assert_proposal_should_have_a_verbose_name(self): self.assertEquals(_('Proposal'), Proposal._meta.verbose_name) def test_assert_proposal_should_have_a_verbose_name_plural(self): self.assertEquals(_('Proposals'), Proposal._meta.verbose_name_plural) def test_assert_proposal_should_have_a_title(self): self.assertIn('title', get_all_field_names(Proposal)) def test_assert_proposal_title_should_be_a_CharField(self): self.assertIsInstance(self.fields['title'], CharField) def test_assert_proposal_title_should_be_required(self): self.assertEquals(False, self.fields['title'].null) self.assertEquals(False, self.fields['title'].blank) def test_assert_proposal_title_should_have_at_most_200_characters(self): self.assertEquals(200, self.fields['title'].max_length) def test_assert_proposal_should_have_a_description(self): self.assertIn('description', get_all_field_names(Proposal)) def test_assert_proposal_description_should_be_a_TextField(self): self.assertIsInstance(self.fields['description'], TextField) def test_assert_proposal_description_should_be_nullable_but_needs_to_be_blank(self): self.assertEquals(False, self.fields['description'].null) self.assertEquals(True, self.fields['description'].blank) def test_assert_proposal_description_should_have_at_most_10000_characters(self): self.assertEquals(10000, self.fields['description'].max_length) def test_assert_proposal_should_have_a_author(self): self.assertIn('author', get_all_field_names(Proposal)) def test_assert_proposal_author_should_be_an_User(self): self.assertEquals(User, self.fields['author'].rel.to) def test_assert_proposal_author_should_be_a_ForeignKey(self): self.assertIsInstance(self.fields['author'], ForeignKey) def test_assert_proposal_author_should_be_required(self): self.assertEquals(False, self.fields['author'].null) self.assertEquals(False, self.fields['author'].blank) def test_assert_proposal_event_should_have_a_related_name(self): self.assertEquals('proposals', self.fields['event'].rel.related_name) def test_assert_proposal_should_have_a_event(self): self.assertIn('event', get_all_field_names(Proposal)) def test_assert_proposal_event_should_be_an_Event(self): self.assertEquals(Event, self.fields['event'].rel.to) def test_assert_proposal_event_should_be_a_ForeignKey(self): self.assertIsInstance(self.fields['event'], ForeignKey) def test_assert_proposal_event_should_be_required(self): self.assertEquals(False, self.fields['event'].null) self.assertEquals(False, self.fields['event'].blank) def test_assert_proposal_should_have_a_publish_flag(self): self.assertIn('is_published', get_all_field_names(Proposal)) def test_assert_proposal_is_published_should_be_a_BooleanField(self): self.assertIsInstance(self.fields['is_published'], BooleanField) def test_assert_proposal_is_published_should_be_True_as_default(self): self.assertEquals(True, self.fields['is_published'].default) def test_assert_proposal_is_approved_should_be_a_BooleanField(self): self.assertIsInstance(self.fields['is_approved'], BooleanField) def test_assert_proposal_is_approved_should_be_False_as_default(self): self.assertEquals(False, self.fields['is_approved'].default) def test_assert_proposal_slides_url_should_not_be_required(self): self.assertEquals(True, self.fields['slides_url'].null) self.assertEquals(True, self.fields['slides_url'].blank) def test_assert_proposal_slides_url_should_be_a_CharField(self): self.assertIsInstance(self.fields['slides_url'], CharField) def test_assert_proposal_slides_url_should_have_at_most_250_characters(self): self.assertEquals(250, self.fields['slides_url'].max_length) class ProposalObjectTest(TestCase): fixtures = ['user.json'] def setUp(self): self.user = User.objects.first() self.event = Event(**EVENT_DATA) self.proposal = Proposal(**PROPOSAL_DATA) self.vote = Vote(user_id=self.event.author_id, proposal=self.proposal, rate=3) @unittest.skipIf(six.PY3, 'not test unicode on python3') def test_assert_proposal_unicode_representation(self): self.assertEquals(u'Python For Zombies', six.text_type(self.proposal)) def test_assert_proposal_title(self): self.assertEquals(u'Python For Zombies', self.proposal.title) def test_assert_proposal_description(self): self.assertEquals(u'Brain...', self.proposal.description) def test_assert_proposal_slides_url(self): self.assertEquals(u'jane_doe/talk', self.proposal.slides_url) def test_assert_proposal_get_full_slides_url(self): self.assertEquals(u'http://www.speakerdeck.com/jane_doe/talk', self.proposal.get_full_slides_url()) def test_assert_proposal_author(self): self.assertEquals(1, self.proposal.author_id) def test_assert_proposal_rate(self): self.assertEquals(0, self.proposal.get_rate) def test_get_absolute_url(self): self.proposal.event = self.event self.assertEquals('/events/rupy/#python-for-zombies', self.proposal.get_absolute_url()) def test_assert_user_cannot_vote_multiple_times(self): self.event.save() self.proposal.event = self.event self.proposal.author = User.objects.get(id=2) self.proposal.save() self.vote.proposal = self.proposal self.vote.save() self.assertTrue(self.proposal.user_already_voted(self.user)) def test_assert_proposal_is_published(self): self.assertEquals(True, self.proposal.is_published) def test_assert_proposal_approve(self): self.event.save() self.proposal.event = self.event self.proposal.save() self.assertEquals(False, self.proposal.is_approved) self.proposal.approve() self.assertEquals(True, self.proposal.is_approved) def test_assert_proposal_disapprove(self): self.event.save() self.proposal.event = self.event self.proposal.is_approved = True self.proposal.save() self.assertEquals(True, self.proposal.is_approved) self.proposal.disapprove() self.assertEquals(False, self.proposal.is_approved) class VoteModelIntegrityTest(TestCase): def setUp(self): self.fields = { field.name: field for field in Vote._meta.fields } def test_assert_vote_should_have_a_verbose_name(self): self.assertEquals(_('Vote'), Vote._meta.verbose_name) def test_assert_vote_should_have_a_verbose_name_plural(self): self.assertEquals(_('Votes'), Vote._meta.verbose_name_plural) def test_assert_vote_should_have_a_unique_together_constraint(self): self.assertEquals((('proposal', 'user'),), Vote._meta.unique_together) def test_assert_vote_should_have_a_rate(self): self.assertIn('rate', get_all_field_names(Vote)) def test_assert_vote_rate_should_be_a_SmallIntegerField(self): self.assertIsInstance(self.fields['rate'], SmallIntegerField) def test_assert_vote_rate_should_be_required(self): self.assertEquals(True, self.fields['rate'].null) self.assertEquals(True, self.fields['rate'].blank) def test_assert_vote_should_have_a_proposal(self): self.assertIn('proposal', get_all_field_names(Vote)) def test_assert_vote_proposal_should_be_an_Proposal(self): self.assertEquals(Proposal, self.fields['proposal'].rel.to) def test_assert_vote_proposal_should_be_a_ForeignKey(self): self.assertIsInstance(self.fields['proposal'], ForeignKey) def test_assert_vote_proposal_should_be_required(self): self.assertEquals(False, self.fields['proposal'].null) self.assertEquals(False, self.fields['proposal'].blank) def test_assert_vote_proposal_should_have_a_related_name(self): self.assertEquals('votes', self.fields['proposal'].rel.related_name) def test_assert_vote_should_have_a_author(self): self.assertIn('user', get_all_field_names(Vote)) def test_assert_vote_user_should_be_an_User(self): self.assertEquals(User, self.fields['user'].rel.to) def test_assert_vote_user_should_be_a_ForeignKey(self): self.assertIsInstance(self.fields['user'], ForeignKey) def test_assert_vote_user_should_be_required(self): self.assertEquals(False, self.fields['user'].null) self.assertEquals(False, self.fields['user'].blank) def test_assert_vote_event_should_have_a_related_name(self): self.assertEquals('votes', self.fields['user'].rel.related_name) class VoteObjectTest(TestCase): def setUp(self): self.event = Event(**EVENT_DATA) self.proposal = Proposal(event=self.event, **PROPOSAL_DATA) self.vote = Vote(user_id=self.event.author_id, proposal=self.proposal, rate=3) @unittest.skipIf(six.PY3, 'not test unicode on python3') def test_assert_vote_unicode_representation(self): self.vote.user = User(username='User') self.assertEquals(u'User: 3 in Python For Zombies', six.text_type(self.vote)) def test_assert_vote_rate(self): self.assertEquals(3, self.vote.rate) def test_assert_vote_proposal(self): self.assertEquals(self.proposal, self.vote.proposal) def test_assert_vote_author(self): self.assertEquals(1, self.vote.user_id)

""" Utilities for handling command line-related tasks - parsing arguments for program options, expanding args into paths etc. @author James Skinner @version 0.1 """ __all__ = [ "WinShlex", "Args", "parseargs" ] import getopt import shlex from collections import namedtuple from spiralx.props import Props class WindowsShlex(shlex.shlex): """ Sub-class of the shlex class in the shlex module which is initialised for splitting program arguments in Windows, i.e. using ^ as the quote character and treating backslashes as normal characters. """ def __init__(self, s): super(WindowsShlex, self).__init__(s, posix=True) self.escape = "^" @classmethod def split(cls, s): """ >>> WindowsShlex.split("first second third") ['first', 'second', 'third'] """ ws = cls(s) ws.whitespace_split = True return list(ws) class ArgParser(object): def __init__(self, opts=None, long_opts=None, defaults=None): if opts is None: opts = "" if long_opts is None: long_opts = [] if defaults is None: defaults = {} self.opts = opts self.long_opts = long_opts self.allopts = list(opts.replace(":", "")) + \ map(lambda o: o.replace("=", ""), long_opts) self.defaults = defaults def parse(self, windows=True): if windows: args = WindowsShlex.split(" ".join(sys.argv[1:])) else: args = sys.argv[1:] o, a = getopt.getopt(args, self.opts, self.long_opts) options = {} options.update(self.defaults) options.update((k.lstrip("-"), v) for k, v in o) return options, a class Args(namedtuple("Args", "opts args")): def __new__(cls, options): o, a = getopt(sys.argv[1:], _aslist(opts)) o = Props((k[1:], v) for k, v in opts) return tuple.__new__(cls, (o, a)) def __str__(self): ostr = " ".join("{0}='{1}'".format(k, v) for k, v in self[0]) astr = ", ".join("'{0}'".format(a) for a in self[1]) return "Args(opts: {0}, args: {1})".format(ostr, astr) def _aslist(v): """ If v is a string then split it on whitespace using the shlex module to handle quoted strings correctly, otherwise return it as a list. >>> _aslist("first second third") ... ['first', 'second', 'third'] """ return shlex.split(v) if isinstance(v, str) else list(v) if __name__ == "__main__": import doctest doctest.testmod() print("\nDoctest finished.\n")

from pyswagger import App from ..utils import get_test_data_folder import unittest _json = 'application/json' _xml = 'application/xml' class PatchObjTestCase(unittest.TestCase): """ test patch_obj.py """ @classmethod def setUpClass(kls): kls.app = App._create_(get_test_data_folder( version='2.0', which='patch' )) def test_operation_produces_consumes(self): """ test patch Operation with produces and consumes """ p = self.app.s('/pc') self.assertEqual(p.get.produces, [_json]) self.assertEqual(p.get.consumes, [_json]) self.assertEqual(p.post.produces, [_xml]) self.assertEqual(p.post.consumes, [_json]) self.assertEqual(p.put.produces, [_json]) self.assertEqual(p.put.consumes, [_xml]) self.assertEqual(p.delete.produces, [_xml]) self.assertEqual(p.delete.consumes, [_xml]) def test_operation_parameters(self): """ test patch Operation with parameters """ p = self.app.s('/param') pp = p.get.parameters self.assertEqual(len(pp), 2) self.assertEqual(pp[0].name, 'p1') self.assertEqual(getattr(pp[0], 'in'), 'query') self.assertEqual(getattr(pp[0], 'type'), 'string') self.assertEqual(pp[1].name, 'p2') self.assertEqual(getattr(pp[1], 'in'), 'query') self.assertEqual(getattr(pp[1], 'type'), 'string') pp = p.post.parameters self.assertEqual(len(pp), 2) self.assertEqual(pp[0].name, 'p1') self.assertEqual(getattr(pp[0], 'in'), 'path') self.assertEqual(getattr(pp[0], 'type'), 'string') self.assertEqual(pp[1].name, 'p2') self.assertEqual(getattr(pp[1], 'in'), 'query') self.assertEqual(getattr(pp[1], 'type'), 'string') def test_operation_scheme(self): """ test patch Operation with scheme """ p = self.app.s('/s') self.assertEqual(p.get.cached_schemes, self.app.root.schemes) self.assertEqual(p.get.cached_schemes, ['http', 'https']) def test_operation_security(self): """ test patch Operation with Swagger.security """ p = self.app.s('/op_security') # when security is something, do not overwrite self.assertTrue(len(p.put.security) == 1) self.assertTrue("internalApiKey" in p.put.security[0]) # when security is [], do not overwrite self.assertEqual(p.get.security, []) # when security is not provided, overwrite with global self.assertTrue(len(p.post.security) == 2) self.assertTrue("githubAccessCode" in p.post.security[0]) self.assertTrue("internalApiKey" in p.post.security[1]) def test_path_item(self): """ test patch PathItem """ p = self.app.s('/pc') self.assertEqual(p.get.method, 'get') self.assertEqual(p.get.url, '//test.com/v1/pc') self.assertEqual(p.get.path, '/pc') self.assertEqual(p.get.base_path, '/v1') def test_schema(self): """ test patch Schema """ s = self.app.resolve('#/definitions/schema1') self.assertEqual(s.name, 'schema1')

import numpy as np import pandas as pd import sklearn.preprocessing def load_glove(vectors_file, normalize=False): """ Load a GloVe formatted file, which is simply of the format <word_0><space><vec_0,0><space><vec_0,1><space>...<newline> <word_1><space><vec_1,0><space><vec_1,1><space>...<newline> ... See https://github.com/stanfordnlp/GloVe for more information. That link also has information on how to download the pre-trained word vectorizer models. If the file you download is compressed, you will need to uncompress it before using this function. Note that the loading speed and memory usage is highly depdendent on what model you use. The downloadable model "glove.840B.300d.txt" will take a few minutes to load and use 2.8 GB of memory, whereas the model "glove.6B.50d.txt" will take a few seconds and use < 200 MB of memory. Sample usage: >>> vectors = load_glove('tagnews/geoloc/glove.6B.50d.txt') >>> text = 'This is a sentence and stuff.' >>> # you should use an actual tokenizer for this step. >>> vectorized_text = vectors.loc[[word.lower() ... for word in text.split()]] >>> print(vectorized_text.shape) (6, 300) >>> k = 5 >>> import numpy as np >>> def euc(word): ... return np.sum((vectors.values-vectors.loc[word].values)**2.0, 1) ... >>> vectors.index[np.argpartition(euc('murder'), range(k))[:k]] Inputs: vectors_file: path to file that contains GloVe formatted word vectors. normalize: Should the word vectors be normalized? See https://stats.stackexchange.com/questions/177905/ for a good discussion on the topic. Retuns: vectors: NxM pandas dataframe whose rows are indexed by the word. """ with open(vectors_file, 'r', encoding='utf-8') as f: for vocab_size, line in enumerate(f): pass vocab_size += 1 vec_size = len(line.split(' ')) - 1 vectors = np.zeros((vocab_size, vec_size), dtype=np.float32) words = np.empty(shape=(vocab_size), dtype=np.dtype('object')) with open(vectors_file, 'r', encoding='utf-8') as f: for i, line in enumerate(f): line = line.split(' ') words[i] = line[0] vectors[i] = [float(x) for x in line[1:]] vectors = pd.DataFrame(vectors, index=words, copy=False) vectors = vectors.loc[~vectors.index.duplicated()] if normalize: sklearn.preprocessing.normalize(vectors, copy=False) return vectors

import json import pathlib import proto import re import shutil import tempfile import requests from typing import ( Any, Dict, List, NamedTuple, Optional, Sequence, Tuple, TYPE_CHECKING, Union, ) from google.api_core import operation from google.api_core import exceptions as api_exceptions from google.auth import credentials as auth_credentials from google.auth.transport import requests as google_auth_requests from google.cloud import aiplatform from google.cloud.aiplatform import base from google.cloud.aiplatform import constants from google.cloud.aiplatform import explain from google.cloud.aiplatform import initializer from google.cloud.aiplatform import jobs from google.cloud.aiplatform import models from google.cloud.aiplatform import utils from google.cloud.aiplatform.utils import gcs_utils from google.cloud.aiplatform import model_evaluation from google.cloud.aiplatform.compat.services import endpoint_service_client from google.cloud.aiplatform.compat.types import ( encryption_spec as gca_encryption_spec, endpoint as gca_endpoint_compat, explanation as gca_explanation_compat, io as gca_io_compat, machine_resources as gca_machine_resources_compat, model as gca_model_compat, model_service as gca_model_service_compat, env_var as gca_env_var_compat, ) from google.cloud.aiplatform.constants import ( prediction as prediction_constants, ) from google.protobuf import field_mask_pb2, timestamp_pb2 from google.protobuf import json_format if TYPE_CHECKING: from google.cloud.aiplatform.prediction import LocalModel _DEFAULT_MACHINE_TYPE = "n1-standard-2" _DEPLOYING_MODEL_TRAFFIC_SPLIT_KEY = "0" _SUCCESSFUL_HTTP_RESPONSE = 300 _RAW_PREDICT_DEPLOYED_MODEL_ID_KEY = "X-Vertex-AI-Deployed-Model-Id" _RAW_PREDICT_MODEL_RESOURCE_KEY = "X-Vertex-AI-Model" _RAW_PREDICT_MODEL_VERSION_ID_KEY = "X-Vertex-AI-Model-Version-Id" _LOGGER = base.Logger(__name__) _SUPPORTED_MODEL_FILE_NAMES = [ "model.pkl", "model.joblib", "model.bst", "saved_model.pb", "saved_model.pbtxt", ] class VersionInfo(NamedTuple): """VersionInfo class envelopes returned Model version information. Attributes: version_id: The version ID of the model. create_time: Timestamp when this Model version was uploaded into Vertex AI. update_time: Timestamp when this Model version was most recently updated. model_display_name: The user-defined name of the model this version belongs to. model_resource_name: The fully-qualified model resource name. e.g. projects/{project}/locations/{location}/models/{model_display_name} version_aliases: User provided version aliases so that a model version can be referenced via alias (i.e. projects/{project}/locations/{location}/models/{model_display_name}@{version_alias}). Default is None. version_description: The description of this version. Default is None. """ version_id: str version_create_time: timestamp_pb2.Timestamp version_update_time: timestamp_pb2.Timestamp model_display_name: str model_resource_name: str version_aliases: Optional[Sequence[str]] = None version_description: Optional[str] = None class Prediction(NamedTuple): """Prediction class envelopes returned Model predictions and the Model id. Attributes: predictions: The predictions that are the output of the predictions call. The schema of any single prediction may be specified via Endpoint's DeployedModels' [Model's][google.cloud.aiplatform.v1beta1.DeployedModel.model] [PredictSchemata's][google.cloud.aiplatform.v1beta1.Model.predict_schemata] deployed_model_id: ID of the Endpoint's DeployedModel that served this prediction. model_version_id: ID of the DeployedModel's version that served this prediction. model_resource_name: The fully-qualified resource name of the model that served this prediction. explanations: The explanations of the Model's predictions. It has the same number of elements as instances to be explained. Default is None. """ predictions: List[Dict[str, Any]] deployed_model_id: str model_version_id: Optional[str] = None model_resource_name: Optional[str] = None explanations: Optional[Sequence[gca_explanation_compat.Explanation]] = None class Endpoint(base.VertexAiResourceNounWithFutureManager): client_class = utils.EndpointClientWithOverride _resource_noun = "endpoints" _getter_method = "get_endpoint" _list_method = "list_endpoints" _delete_method = "delete_endpoint" _parse_resource_name_method = "parse_endpoint_path" _format_resource_name_method = "endpoint_path" def __init__( self, endpoint_name: str, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[auth_credentials.Credentials] = None, ): """Retrieves an endpoint resource. Args: endpoint_name (str): Required. A fully-qualified endpoint resource name or endpoint ID. Example: "projects/123/locations/us-central1/endpoints/456" or "456" when project and location are initialized or passed. project (str): Optional. Project to retrieve endpoint from. If not set, project set in aiplatform.init will be used. location (str): Optional. Location to retrieve endpoint from. If not set, location set in aiplatform.init will be used. credentials (auth_credentials.Credentials): Optional. Custom credentials to use to upload this model. Overrides credentials set in aiplatform.init. """ super().__init__( project=project, location=location, credentials=credentials, resource_name=endpoint_name, ) endpoint_name = utils.full_resource_name( resource_name=endpoint_name, resource_noun="endpoints", parse_resource_name_method=self._parse_resource_name, format_resource_name_method=self._format_resource_name, project=project, location=location, ) # Lazy load the Endpoint gca_resource until needed self._gca_resource = gca_endpoint_compat.Endpoint(name=endpoint_name) self._prediction_client = self._instantiate_prediction_client( location=self.location, credentials=credentials, ) self.authorized_session = None self.raw_predict_request_url = None def _skipped_getter_call(self) -> bool: """Check if GAPIC resource was populated by call to get/list API methods Returns False if `_gca_resource` is None or fully populated. Returns True if `_gca_resource` is partially populated """ return self._gca_resource and not self._gca_resource.create_time def _sync_gca_resource_if_skipped(self) -> None: """Sync GAPIC service representation of Endpoint class resource only if get_endpoint() was never called.""" if self._skipped_getter_call(): self._gca_resource = self._get_gca_resource( resource_name=self._gca_resource.name ) def _assert_gca_resource_is_available(self) -> None: """Ensures Endpoint getter was called at least once before asserting on gca_resource's availability.""" super()._assert_gca_resource_is_available() self._sync_gca_resource_if_skipped() @property def traffic_split(self) -> Dict[str, int]: """A map from a DeployedModel's ID to the percentage of this Endpoint's traffic that should be forwarded to that DeployedModel. If a DeployedModel's ID is not listed in this map, then it receives no traffic. The traffic percentage values must add up to 100, or map must be empty if the Endpoint is to not accept any traffic at a moment. """ self._sync_gca_resource() return dict(self._gca_resource.traffic_split) @property def network(self) -> Optional[str]: """The full name of the Google Compute Engine [network](https://cloud.google.com/vpc/docs/vpc#networks) to which this Endpoint should be peered. Takes the format `projects/{project}/global/networks/{network}`. Where {project} is a project number, as in `12345`, and {network} is a network name. Private services access must already be configured for the network. If left unspecified, the Endpoint is not peered with any network. """ self._assert_gca_resource_is_available() return getattr(self._gca_resource, "network", None) @classmethod def create( cls, display_name: Optional[str] = None, description: Optional[str] = None, labels: Optional[Dict[str, str]] = None, metadata: Optional[Sequence[Tuple[str, str]]] = (), project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[auth_credentials.Credentials] = None, encryption_spec_key_name: Optional[str] = None, sync=True, create_request_timeout: Optional[float] = None, endpoint_id: Optional[str] = None, ) -> "Endpoint": """Creates a new endpoint. Args: display_name (str): Optional. The user-defined name of the Endpoint. The name can be up to 128 characters long and can be consist of any UTF-8 characters. description (str): Optional. The description of the Endpoint. labels (Dict[str, str]): Optional. The labels with user-defined metadata to organize your Endpoints. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. metadata (Sequence[Tuple[str, str]]): Optional. Strings which should be sent along with the request as metadata. project (str): Required. Project to retrieve endpoint from. If not set, project set in aiplatform.init will be used. location (str): Required. Location to retrieve endpoint from. If not set, location set in aiplatform.init will be used. credentials (auth_credentials.Credentials): Optional. Custom credentials to use to upload this model. Overrides credentials set in aiplatform.init. encryption_spec_key_name (str): Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the model. Has the form: ``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``. The key needs to be in the same region as where the compute resource is created. If set, this Endpoint and all sub-resources of this Endpoint will be secured by this key. Overrides encryption_spec_key_name set in aiplatform.init. sync (bool): Whether to execute this method synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed. create_request_timeout (float): Optional. The timeout for the create request in seconds. endpoint_id (str): Optional. The ID to use for endpoint, which will become the final component of the endpoint resource name. If not provided, Vertex AI will generate a value for this ID. This value should be 1-10 characters, and valid characters are /[0-9]/. When using HTTP/JSON, this field is populated based on a query string argument, such as ``?endpoint_id=12345``. This is the fallback for fields that are not included in either the URI or the body. Returns: endpoint (aiplatform.Endpoint): Created endpoint. """ api_client = cls._instantiate_client(location=location, credentials=credentials) if not display_name: display_name = cls._generate_display_name() utils.validate_display_name(display_name) if labels: utils.validate_labels(labels) project = project or initializer.global_config.project location = location or initializer.global_config.location return cls._create( api_client=api_client, display_name=display_name, project=project, location=location, description=description, labels=labels, metadata=metadata, credentials=credentials, encryption_spec=initializer.global_config.get_encryption_spec( encryption_spec_key_name=encryption_spec_key_name ), sync=sync, create_request_timeout=create_request_timeout, endpoint_id=endpoint_id, ) @classmethod @base.optional_sync() def _create( cls, api_client: endpoint_service_client.EndpointServiceClient, display_name: str, project: str, location: str, description: Optional[str] = None, labels: Optional[Dict[str, str]] = None, metadata: Optional[Sequence[Tuple[str, str]]] = (), credentials: Optional[auth_credentials.Credentials] = None, encryption_spec: Optional[gca_encryption_spec.EncryptionSpec] = None, network: Optional[str] = None, sync=True, create_request_timeout: Optional[float] = None, endpoint_id: Optional[str] = None, ) -> "Endpoint": """Creates a new endpoint by calling the API client. Args: api_client (EndpointServiceClient): Required. An instance of EndpointServiceClient with the correct api_endpoint already set based on user's preferences. display_name (str): Required. The user-defined name of the Endpoint. The name can be up to 128 characters long and can be consist of any UTF-8 characters. project (str): Required. Project to retrieve endpoint from. If not set, project set in aiplatform.init will be used. location (str): Required. Location to retrieve endpoint from. If not set, location set in aiplatform.init will be used. description (str): Optional. The description of the Endpoint. labels (Dict[str, str]): Optional. The labels with user-defined metadata to organize your Endpoints. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. metadata (Sequence[Tuple[str, str]]): Optional. Strings which should be sent along with the request as metadata. credentials (auth_credentials.Credentials): Optional. Custom credentials to use to upload this model. Overrides credentials set in aiplatform.init. encryption_spec (gca_encryption_spec.EncryptionSpec): Optional. The Cloud KMS customer managed encryption key used to protect the dataset. The key needs to be in the same region as where the compute resource is created. If set, this Dataset and all sub-resources of this Dataset will be secured by this key. network (str): Optional. The full name of the Compute Engine network to which this Endpoint will be peered. E.g. "projects/12345/global/networks/myVPC". Private services access must already be configured for the network. Read more about PrivateEndpoints [in the documentation](https://cloud.google.com/vertex-ai/docs/predictions/using-private-endpoints). sync (bool): Whether to create this endpoint synchronously. create_request_timeout (float): Optional. The timeout for the create request in seconds. endpoint_id (str): Optional. The ID to use for endpoint, which will become the final component of the endpoint resource name. If not provided, Vertex AI will generate a value for this ID. This value should be 1-10 characters, and valid characters are /[0-9]/. When using HTTP/JSON, this field is populated based on a query string argument, such as ``?endpoint_id=12345``. This is the fallback for fields that are not included in either the URI or the body. Returns: endpoint (aiplatform.Endpoint): Created endpoint. """ parent = initializer.global_config.common_location_path( project=project, location=location ) gapic_endpoint = gca_endpoint_compat.Endpoint( display_name=display_name, description=description, labels=labels, encryption_spec=encryption_spec, network=network, ) operation_future = api_client.create_endpoint( parent=parent, endpoint=gapic_endpoint, endpoint_id=endpoint_id, metadata=metadata, timeout=create_request_timeout, ) _LOGGER.log_create_with_lro(cls, operation_future) created_endpoint = operation_future.result() _LOGGER.log_create_complete(cls, created_endpoint, "endpoint") return cls._construct_sdk_resource_from_gapic( gapic_resource=created_endpoint, project=project, location=location, credentials=credentials, ) @classmethod def _construct_sdk_resource_from_gapic( cls, gapic_resource: proto.Message, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[auth_credentials.Credentials] = None, ) -> "Endpoint": """Given a GAPIC Endpoint object, return the SDK representation. Args: gapic_resource (proto.Message): A GAPIC representation of a Endpoint resource, usually retrieved by a get_* or in a list_* API call. project (str): Optional. Project to construct Endpoint object from. If not set, project set in aiplatform.init will be used. location (str): Optional. Location to construct Endpoint object from. If not set, location set in aiplatform.init will be used. credentials (auth_credentials.Credentials): Optional. Custom credentials to use to construct Endpoint. Overrides credentials set in aiplatform.init. Returns: Endpoint (aiplatform.Endpoint): An initialized Endpoint resource. """ endpoint = cls._empty_constructor( project=project, location=location, credentials=credentials ) endpoint._gca_resource = gapic_resource endpoint._prediction_client = cls._instantiate_prediction_client( location=endpoint.location, credentials=credentials, ) return endpoint @staticmethod def _allocate_traffic( traffic_split: Dict[str, int], traffic_percentage: int, ) -> Dict[str, int]: """Allocates desired traffic to new deployed model and scales traffic of older deployed models. Args: traffic_split (Dict[str, int]): Required. Current traffic split of deployed models in endpoint. traffic_percentage (int): Required. Desired traffic to new deployed model. Returns: new_traffic_split (Dict[str, int]): Traffic split to use. """ new_traffic_split = {} old_models_traffic = 100 - traffic_percentage if old_models_traffic: unallocated_traffic = old_models_traffic for deployed_model in traffic_split: current_traffic = traffic_split[deployed_model] new_traffic = int(current_traffic / 100 * old_models_traffic) new_traffic_split[deployed_model] = new_traffic unallocated_traffic -= new_traffic # will likely under-allocate. make total 100. for deployed_model in new_traffic_split: if unallocated_traffic == 0: break new_traffic_split[deployed_model] += 1 unallocated_traffic -= 1 new_traffic_split[_DEPLOYING_MODEL_TRAFFIC_SPLIT_KEY] = traffic_percentage return new_traffic_split @staticmethod def _unallocate_traffic( traffic_split: Dict[str, int], deployed_model_id: str, ) -> Dict[str, int]: """Sets deployed model id's traffic to 0 and scales the traffic of other deployed models. Args: traffic_split (Dict[str, int]): Required. Current traffic split of deployed models in endpoint. deployed_model_id (str): Required. Desired traffic to new deployed model. Returns: new_traffic_split (Dict[str, int]): Traffic split to use. """ new_traffic_split = traffic_split.copy() del new_traffic_split[deployed_model_id] deployed_model_id_traffic = traffic_split[deployed_model_id] traffic_percent_left = 100 - deployed_model_id_traffic if traffic_percent_left: unallocated_traffic = 100 for deployed_model in new_traffic_split: current_traffic = traffic_split[deployed_model] new_traffic = int(current_traffic / traffic_percent_left * 100) new_traffic_split[deployed_model] = new_traffic unallocated_traffic -= new_traffic # will likely under-allocate. make total 100. for deployed_model in new_traffic_split: if unallocated_traffic == 0: break new_traffic_split[deployed_model] += 1 unallocated_traffic -= 1 new_traffic_split[deployed_model_id] = 0 return new_traffic_split @staticmethod def _validate_deploy_args( min_replica_count: int, max_replica_count: int, accelerator_type: Optional[str], deployed_model_display_name: Optional[str], traffic_split: Optional[Dict[str, int]], traffic_percentage: Optional[int], explanation_metadata: Optional[aiplatform.explain.ExplanationMetadata] = None, explanation_parameters: Optional[ aiplatform.explain.ExplanationParameters ] = None, ): """Helper method to validate deploy arguments. Args: min_replica_count (int): Required. The minimum number of machine replicas this deployed model will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed. max_replica_count (int): Required. The maximum number of replicas this deployed model may be deployed on when the traffic against it increases. If requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the deployed model increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, the larger value of min_replica_count or 1 will be used. If value provided is smaller than min_replica_count, it will automatically be increased to be min_replica_count. accelerator_type (str): Required. Hardware accelerator type. One of ACCELERATOR_TYPE_UNSPECIFIED, NVIDIA_TESLA_K80, NVIDIA_TESLA_P100, NVIDIA_TESLA_V100, NVIDIA_TESLA_P4, NVIDIA_TESLA_T4 deployed_model_display_name (str): Required. The display name of the DeployedModel. If not provided upon creation, the Model's display_name is used. traffic_split (Dict[str, int]): Optional. A map from a DeployedModel's ID to the percentage of this Endpoint's traffic that should be forwarded to that DeployedModel. If a DeployedModel's ID is not listed in this map, then it receives no traffic. The traffic percentage values must add up to 100, or map must be empty if the Endpoint is to not accept any traffic at the moment. Key for model being deployed is "0". Should not be provided if traffic_percentage is provided. traffic_percentage (int): Optional. Desired traffic to newly deployed model. Defaults to 0 if there are pre-existing deployed models. Defaults to 100 if there are no pre-existing deployed models. Negative values should not be provided. Traffic of previously deployed models at the endpoint will be scaled down to accommodate new deployed model's traffic. Should not be provided if traffic_split is provided. explanation_metadata (aiplatform.explain.ExplanationMetadata): Optional. Metadata describing the Model's input and output for explanation. `explanation_metadata` is optional while `explanation_parameters` must be specified when used. For more details, see `Ref docs <http://tinyurl.com/1igh60kt>` explanation_parameters (aiplatform.explain.ExplanationParameters): Optional. Parameters to configure explaining for Model's predictions. For more details, see `Ref docs <http://tinyurl.com/1an4zake>` Raises: ValueError: if Min or Max replica is negative. Traffic percentage > 100 or < 0. Or if traffic_split does not sum to 100. ValueError: if explanation_metadata is specified while explanation_parameters is not. """ if min_replica_count < 0: raise ValueError("Min replica cannot be negative.") if max_replica_count < 0: raise ValueError("Max replica cannot be negative.") if deployed_model_display_name is not None: utils.validate_display_name(deployed_model_display_name) if traffic_split is None: if traffic_percentage > 100: raise ValueError("Traffic percentage cannot be greater than 100.") if traffic_percentage < 0: raise ValueError("Traffic percentage cannot be negative.") elif traffic_split: if sum(traffic_split.values()) != 100: raise ValueError( "Sum of all traffic within traffic split needs to be 100." ) if bool(explanation_metadata) and not bool(explanation_parameters): raise ValueError( "To get model explanation, `explanation_parameters` must be specified." ) # Raises ValueError if invalid accelerator if accelerator_type: utils.validate_accelerator_type(accelerator_type) def deploy( self, model: "Model", deployed_model_display_name: Optional[str] = None, traffic_percentage: int = 0, traffic_split: Optional[Dict[str, int]] = None, machine_type: Optional[str] = None, min_replica_count: int = 1, max_replica_count: int = 1, accelerator_type: Optional[str] = None, accelerator_count: Optional[int] = None, service_account: Optional[str] = None, explanation_metadata: Optional[aiplatform.explain.ExplanationMetadata] = None, explanation_parameters: Optional[ aiplatform.explain.ExplanationParameters ] = None, metadata: Optional[Sequence[Tuple[str, str]]] = (), sync=True, deploy_request_timeout: Optional[float] = None, autoscaling_target_cpu_utilization: Optional[int] = None, autoscaling_target_accelerator_duty_cycle: Optional[int] = None, ) -> None: """Deploys a Model to the Endpoint. Args: model (aiplatform.Model): Required. Model to be deployed. deployed_model_display_name (str): Optional. The display name of the DeployedModel. If not provided upon creation, the Model's display_name is used. traffic_percentage (int): Optional. Desired traffic to newly deployed model. Defaults to 0 if there are pre-existing deployed models. Defaults to 100 if there are no pre-existing deployed models. Negative values should not be provided. Traffic of previously deployed models at the endpoint will be scaled down to accommodate new deployed model's traffic. Should not be provided if traffic_split is provided. traffic_split (Dict[str, int]): Optional. A map from a DeployedModel's ID to the percentage of this Endpoint's traffic that should be forwarded to that DeployedModel. If a DeployedModel's ID is not listed in this map, then it receives no traffic. The traffic percentage values must add up to 100, or map must be empty if the Endpoint is to not accept any traffic at the moment. Key for model being deployed is "0". Should not be provided if traffic_percentage is provided. machine_type (str): Optional. The type of machine. Not specifying machine type will result in model to be deployed with automatic resources. min_replica_count (int): Optional. The minimum number of machine replicas this deployed model will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed. max_replica_count (int): Optional. The maximum number of replicas this deployed model may be deployed on when the traffic against it increases. If requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the deployed model increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, the larger value of min_replica_count or 1 will be used. If value provided is smaller than min_replica_count, it will automatically be increased to be min_replica_count. accelerator_type (str): Optional. Hardware accelerator type. Must also set accelerator_count if used. One of ACCELERATOR_TYPE_UNSPECIFIED, NVIDIA_TESLA_K80, NVIDIA_TESLA_P100, NVIDIA_TESLA_V100, NVIDIA_TESLA_P4, NVIDIA_TESLA_T4 accelerator_count (int): Optional. The number of accelerators to attach to a worker replica. service_account (str): The service account that the DeployedModel's container runs as. Specify the email address of the service account. If this service account is not specified, the container runs as a service account that doesn't have access to the resource project. Users deploying the Model must have the `iam.serviceAccounts.actAs` permission on this service account. explanation_metadata (aiplatform.explain.ExplanationMetadata): Optional. Metadata describing the Model's input and output for explanation. `explanation_metadata` is optional while `explanation_parameters` must be specified when used. For more details, see `Ref docs <http://tinyurl.com/1igh60kt>` explanation_parameters (aiplatform.explain.ExplanationParameters): Optional. Parameters to configure explaining for Model's predictions. For more details, see `Ref docs <http://tinyurl.com/1an4zake>` metadata (Sequence[Tuple[str, str]]): Optional. Strings which should be sent along with the request as metadata. sync (bool): Whether to execute this method synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed. deploy_request_timeout (float): Optional. The timeout for the deploy request in seconds. autoscaling_target_cpu_utilization (int): Target CPU Utilization to use for Autoscaling Replicas. A default value of 60 will be used if not specified. autoscaling_target_accelerator_duty_cycle (int): Target Accelerator Duty Cycle. Must also set accelerator_type and accelerator_count if specified. A default value of 60 will be used if not specified. """ self._sync_gca_resource_if_skipped() self._validate_deploy_args( min_replica_count=min_replica_count, max_replica_count=max_replica_count, accelerator_type=accelerator_type, deployed_model_display_name=deployed_model_display_name, traffic_split=traffic_split, traffic_percentage=traffic_percentage, explanation_metadata=explanation_metadata, explanation_parameters=explanation_parameters, ) self._deploy( model=model, deployed_model_display_name=deployed_model_display_name, traffic_percentage=traffic_percentage, traffic_split=traffic_split, machine_type=machine_type, min_replica_count=min_replica_count, max_replica_count=max_replica_count, accelerator_type=accelerator_type, accelerator_count=accelerator_count, service_account=service_account, explanation_metadata=explanation_metadata, explanation_parameters=explanation_parameters, metadata=metadata, sync=sync, deploy_request_timeout=deploy_request_timeout, autoscaling_target_cpu_utilization=autoscaling_target_cpu_utilization, autoscaling_target_accelerator_duty_cycle=autoscaling_target_accelerator_duty_cycle, ) @base.optional_sync() def _deploy( self, model: "Model", deployed_model_display_name: Optional[str] = None, traffic_percentage: Optional[int] = 0, traffic_split: Optional[Dict[str, int]] = None, machine_type: Optional[str] = None, min_replica_count: int = 1, max_replica_count: int = 1, accelerator_type: Optional[str] = None, accelerator_count: Optional[int] = None, service_account: Optional[str] = None, explanation_metadata: Optional[aiplatform.explain.ExplanationMetadata] = None, explanation_parameters: Optional[ aiplatform.explain.ExplanationParameters ] = None, metadata: Optional[Sequence[Tuple[str, str]]] = (), sync=True, deploy_request_timeout: Optional[float] = None, autoscaling_target_cpu_utilization: Optional[int] = None, autoscaling_target_accelerator_duty_cycle: Optional[int] = None, ) -> None: """Deploys a Model to the Endpoint. Args: model (aiplatform.Model): Required. Model to be deployed. deployed_model_display_name (str): Optional. The display name of the DeployedModel. If not provided upon creation, the Model's display_name is used. traffic_percentage (int): Optional. Desired traffic to newly deployed model. Defaults to 0 if there are pre-existing deployed models. Defaults to 100 if there are no pre-existing deployed models. Negative values should not be provided. Traffic of previously deployed models at the endpoint will be scaled down to accommodate new deployed model's traffic. Should not be provided if traffic_split is provided. traffic_split (Dict[str, int]): Optional. A map from a DeployedModel's ID to the percentage of this Endpoint's traffic that should be forwarded to that DeployedModel. If a DeployedModel's ID is not listed in this map, then it receives no traffic. The traffic percentage values must add up to 100, or map must be empty if the Endpoint is to not accept any traffic at the moment. Key for model being deployed is "0". Should not be provided if traffic_percentage is provided. machine_type (str): Optional. The type of machine. Not specifying machine type will result in model to be deployed with automatic resources. min_replica_count (int): Optional. The minimum number of machine replicas this deployed model will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed. max_replica_count (int): Optional. The maximum number of replicas this deployed model may be deployed on when the traffic against it increases. If requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the deployed model increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, the larger value of min_replica_count or 1 will be used. If value provided is smaller than min_replica_count, it will automatically be increased to be min_replica_count. accelerator_type (str): Optional. Hardware accelerator type. Must also set accelerator_count if used. One of ACCELERATOR_TYPE_UNSPECIFIED, NVIDIA_TESLA_K80, NVIDIA_TESLA_P100, NVIDIA_TESLA_V100, NVIDIA_TESLA_P4, NVIDIA_TESLA_T4 accelerator_count (int): Optional. The number of accelerators to attach to a worker replica. service_account (str): The service account that the DeployedModel's container runs as. Specify the email address of the service account. If this service account is not specified, the container runs as a service account that doesn't have access to the resource project. Users deploying the Model must have the `iam.serviceAccounts.actAs` permission on this service account. explanation_metadata (aiplatform.explain.ExplanationMetadata): Optional. Metadata describing the Model's input and output for explanation. `explanation_metadata` is optional while `explanation_parameters` must be specified when used. For more details, see `Ref docs <http://tinyurl.com/1igh60kt>` explanation_parameters (aiplatform.explain.ExplanationParameters): Optional. Parameters to configure explaining for Model's predictions. For more details, see `Ref docs <http://tinyurl.com/1an4zake>` metadata (Sequence[Tuple[str, str]]): Optional. Strings which should be sent along with the request as metadata. sync (bool): Whether to execute this method synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed. deploy_request_timeout (float): Optional. The timeout for the deploy request in seconds. autoscaling_target_cpu_utilization (int): Target CPU Utilization to use for Autoscaling Replicas. A default value of 60 will be used if not specified. autoscaling_target_accelerator_duty_cycle (int): Target Accelerator Duty Cycle. Must also set accelerator_type and accelerator_count if specified. A default value of 60 will be used if not specified. """ _LOGGER.log_action_start_against_resource( f"Deploying Model {model.resource_name} to", "", self ) self._deploy_call( api_client=self.api_client, endpoint_resource_name=self.resource_name, model=model, endpoint_resource_traffic_split=self._gca_resource.traffic_split, network=self.network, deployed_model_display_name=deployed_model_display_name, traffic_percentage=traffic_percentage, traffic_split=traffic_split, machine_type=machine_type, min_replica_count=min_replica_count, max_replica_count=max_replica_count, accelerator_type=accelerator_type, accelerator_count=accelerator_count, service_account=service_account, explanation_metadata=explanation_metadata, explanation_parameters=explanation_parameters, metadata=metadata, deploy_request_timeout=deploy_request_timeout, autoscaling_target_cpu_utilization=autoscaling_target_cpu_utilization, autoscaling_target_accelerator_duty_cycle=autoscaling_target_accelerator_duty_cycle, ) _LOGGER.log_action_completed_against_resource("model", "deployed", self) self._sync_gca_resource() @classmethod def _deploy_call( cls, api_client: endpoint_service_client.EndpointServiceClient, endpoint_resource_name: str, model: "Model", endpoint_resource_traffic_split: Optional[proto.MapField] = None, network: Optional[str] = None, deployed_model_display_name: Optional[str] = None, traffic_percentage: Optional[int] = 0, traffic_split: Optional[Dict[str, int]] = None, machine_type: Optional[str] = None, min_replica_count: int = 1, max_replica_count: int = 1, accelerator_type: Optional[str] = None, accelerator_count: Optional[int] = None, service_account: Optional[str] = None, explanation_metadata: Optional[aiplatform.explain.ExplanationMetadata] = None, explanation_parameters: Optional[ aiplatform.explain.ExplanationParameters ] = None, metadata: Optional[Sequence[Tuple[str, str]]] = (), deploy_request_timeout: Optional[float] = None, autoscaling_target_cpu_utilization: Optional[int] = None, autoscaling_target_accelerator_duty_cycle: Optional[int] = None, ): """Helper method to deploy model to endpoint. Args: api_client (endpoint_service_client.EndpointServiceClient): Required. endpoint_service_client.EndpointServiceClient to make call. endpoint_resource_name (str): Required. Endpoint resource name to deploy model to. model (aiplatform.Model): Required. Model to be deployed. endpoint_resource_traffic_split (proto.MapField): Optional. Endpoint current resource traffic split. network (str): Optional. The full name of the Compute Engine network to which this Endpoint will be peered. E.g. "projects/123/global/networks/my_vpc". Private services access must already be configured for the network. deployed_model_display_name (str): Optional. The display name of the DeployedModel. If not provided upon creation, the Model's display_name is used. traffic_percentage (int): Optional. Desired traffic to newly deployed model. Defaults to 0 if there are pre-existing deployed models. Defaults to 100 if there are no pre-existing deployed models. Negative values should not be provided. Traffic of previously deployed models at the endpoint will be scaled down to accommodate new deployed model's traffic. Should not be provided if traffic_split is provided. traffic_split (Dict[str, int]): Optional. A map from a DeployedModel's ID to the percentage of this Endpoint's traffic that should be forwarded to that DeployedModel. If a DeployedModel's ID is not listed in this map, then it receives no traffic. The traffic percentage values must add up to 100, or map must be empty if the Endpoint is to not accept any traffic at the moment. Key for model being deployed is "0". Should not be provided if traffic_percentage is provided. machine_type (str): Optional. The type of machine. Not specifying machine type will result in model to be deployed with automatic resources. min_replica_count (int): Optional. The minimum number of machine replicas this deployed model will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed. max_replica_count (int): Optional. The maximum number of replicas this deployed model may be deployed on when the traffic against it increases. If requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the deployed model increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, the larger value of min_replica_count or 1 will be used. If value provided is smaller than min_replica_count, it will automatically be increased to be min_replica_count. accelerator_type (str): Optional. Hardware accelerator type. Must also set accelerator_count if used. One of ACCELERATOR_TYPE_UNSPECIFIED, NVIDIA_TESLA_K80, NVIDIA_TESLA_P100, NVIDIA_TESLA_V100, NVIDIA_TESLA_P4, NVIDIA_TESLA_T4 accelerator_count (int): Optional. The number of accelerators to attach to a worker replica. service_account (str): The service account that the DeployedModel's container runs as. Specify the email address of the service account. If this service account is not specified, the container runs as a service account that doesn't have access to the resource project. Users deploying the Model must have the `iam.serviceAccounts.actAs` permission on this service account. explanation_metadata (aiplatform.explain.ExplanationMetadata): Optional. Metadata describing the Model's input and output for explanation. `explanation_metadata` is optional while `explanation_parameters` must be specified when used. For more details, see `Ref docs <http://tinyurl.com/1igh60kt>` explanation_parameters (aiplatform.explain.ExplanationParameters): Optional. Parameters to configure explaining for Model's predictions. For more details, see `Ref docs <http://tinyurl.com/1an4zake>` metadata (Sequence[Tuple[str, str]]): Optional. Strings which should be sent along with the request as metadata. deploy_request_timeout (float): Optional. The timeout for the deploy request in seconds. autoscaling_target_cpu_utilization (int): Optional. Target CPU Utilization to use for Autoscaling Replicas. A default value of 60 will be used if not specified. autoscaling_target_accelerator_duty_cycle (int): Optional. Target Accelerator Duty Cycle. Must also set accelerator_type and accelerator_count if specified. A default value of 60 will be used if not specified. Raises: ValueError: If only `accelerator_type` or `accelerator_count` is specified. ValueError: If model does not support deployment. ValueError: If there is not current traffic split and traffic percentage is not 0 or 100. """ max_replica_count = max(min_replica_count, max_replica_count) if bool(accelerator_type) != bool(accelerator_count): raise ValueError( "Both `accelerator_type` and `accelerator_count` should be specified or None." ) if autoscaling_target_accelerator_duty_cycle is not None and ( not accelerator_type or not accelerator_count ): raise ValueError( "Both `accelerator_type` and `accelerator_count` should be set " "when specifying autoscaling_target_accelerator_duty_cycle`" ) deployed_model = gca_endpoint_compat.DeployedModel( model=model.versioned_resource_name, display_name=deployed_model_display_name, service_account=service_account, ) supports_automatic_resources = ( gca_model_compat.Model.DeploymentResourcesType.AUTOMATIC_RESOURCES in model.supported_deployment_resources_types ) supports_dedicated_resources = ( gca_model_compat.Model.DeploymentResourcesType.DEDICATED_RESOURCES in model.supported_deployment_resources_types ) provided_custom_machine_spec = ( machine_type or accelerator_type or accelerator_count or autoscaling_target_accelerator_duty_cycle or autoscaling_target_cpu_utilization ) # If the model supports both automatic and dedicated deployment resources, # decide based on the presence of machine spec customizations use_dedicated_resources = supports_dedicated_resources and ( not supports_automatic_resources or provided_custom_machine_spec ) if provided_custom_machine_spec and not use_dedicated_resources: _LOGGER.info( "Model does not support dedicated deployment resources. " "The machine_type, accelerator_type and accelerator_count," "autoscaling_target_accelerator_duty_cycle," "autoscaling_target_cpu_utilization parameters are ignored." ) if use_dedicated_resources and not machine_type: machine_type = _DEFAULT_MACHINE_TYPE _LOGGER.info(f"Using default machine_type: {machine_type}") if use_dedicated_resources: dedicated_resources = gca_machine_resources_compat.DedicatedResources( min_replica_count=min_replica_count, max_replica_count=max_replica_count, ) machine_spec = gca_machine_resources_compat.MachineSpec( machine_type=machine_type ) if autoscaling_target_cpu_utilization: autoscaling_metric_spec = gca_machine_resources_compat.AutoscalingMetricSpec( metric_name="aiplatform.googleapis.com/prediction/online/cpu/utilization", target=autoscaling_target_cpu_utilization, ) dedicated_resources.autoscaling_metric_specs.extend( [autoscaling_metric_spec] ) if accelerator_type and accelerator_count: utils.validate_accelerator_type(accelerator_type) machine_spec.accelerator_type = accelerator_type machine_spec.accelerator_count = accelerator_count if autoscaling_target_accelerator_duty_cycle: autoscaling_metric_spec = gca_machine_resources_compat.AutoscalingMetricSpec( metric_name="aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle", target=autoscaling_target_accelerator_duty_cycle, ) dedicated_resources.autoscaling_metric_specs.extend( [autoscaling_metric_spec] ) dedicated_resources.machine_spec = machine_spec deployed_model.dedicated_resources = dedicated_resources elif supports_automatic_resources: deployed_model.automatic_resources = ( gca_machine_resources_compat.AutomaticResources( min_replica_count=min_replica_count, max_replica_count=max_replica_count, ) ) else: raise ValueError( "Model does not support deployment. " "See https://cloud.google.com/vertex-ai/docs/reference/rpc/google.cloud.aiplatform.v1#google.cloud.aiplatform.v1.Model.FIELDS.repeated.google.cloud.aiplatform.v1.Model.DeploymentResourcesType.google.cloud.aiplatform.v1.Model.supported_deployment_resources_types" ) # Service will throw error if explanation_parameters is not provided if explanation_parameters: explanation_spec = gca_endpoint_compat.explanation.ExplanationSpec() explanation_spec.parameters = explanation_parameters if explanation_metadata: explanation_spec.metadata = explanation_metadata deployed_model.explanation_spec = explanation_spec # Checking if traffic percentage is valid # TODO(b/221059294) PrivateEndpoint should support traffic split if traffic_split is None and not network: # new model traffic needs to be 100 if no pre-existing models if not endpoint_resource_traffic_split: # default scenario if traffic_percentage == 0: traffic_percentage = 100 # verify user specified 100 elif traffic_percentage < 100: raise ValueError( """There are currently no deployed models so the traffic percentage for this deployed model needs to be 100.""" ) traffic_split = cls._allocate_traffic( traffic_split=dict(endpoint_resource_traffic_split), traffic_percentage=traffic_percentage, ) operation_future = api_client.deploy_model( endpoint=endpoint_resource_name, deployed_model=deployed_model, traffic_split=traffic_split, metadata=metadata, timeout=deploy_request_timeout, ) _LOGGER.log_action_started_against_resource_with_lro( "Deploy", "model", cls, operation_future ) operation_future.result() def undeploy( self, deployed_model_id: str, traffic_split: Optional[Dict[str, int]] = None, metadata: Optional[Sequence[Tuple[str, str]]] = (), sync=True, ) -> None: """Undeploys a deployed model. The model to be undeployed should have no traffic or user must provide a new traffic_split with the remaining deployed models. Refer to `Endpoint.traffic_split` for the current traffic split mapping. Args: deployed_model_id (str): Required. The ID of the DeployedModel to be undeployed from the Endpoint. traffic_split (Dict[str, int]): Optional. A map of DeployedModel IDs to the percentage of this Endpoint's traffic that should be forwarded to that DeployedModel. Required if undeploying a model with non-zero traffic from an Endpoint with multiple deployed models. The traffic percentage values must add up to 100, or map must be empty if the Endpoint is to not accept any traffic at the moment. If a DeployedModel's ID is not listed in this map, then it receives no traffic. metadata (Sequence[Tuple[str, str]]): Optional. Strings which should be sent along with the request as metadata. """ self._sync_gca_resource_if_skipped() if traffic_split is not None: if deployed_model_id in traffic_split and traffic_split[deployed_model_id]: raise ValueError("Model being undeployed should have 0 traffic.") if sum(traffic_split.values()) != 100: raise ValueError( "Sum of all traffic within traffic split needs to be 100." ) # Two or more models deployed to Endpoint and remaining traffic will be zero elif ( len(self.traffic_split) > 1 and deployed_model_id in self._gca_resource.traffic_split and self._gca_resource.traffic_split[deployed_model_id] == 100 ): raise ValueError( f"Undeploying deployed model '{deployed_model_id}' would leave the remaining " "traffic split at 0%. Traffic split must add up to 100% when models are " "deployed. Please undeploy the other models first or provide an updated " "traffic_split." ) self._undeploy( deployed_model_id=deployed_model_id, traffic_split=traffic_split, metadata=metadata, sync=sync, ) @base.optional_sync() def _undeploy( self, deployed_model_id: str, traffic_split: Optional[Dict[str, int]] = None, metadata: Optional[Sequence[Tuple[str, str]]] = (), sync=True, ) -> None: """Undeploys a deployed model. Proportionally adjusts the traffic_split among the remaining deployed models of the endpoint. Args: deployed_model_id (str): Required. The ID of the DeployedModel to be undeployed from the Endpoint. traffic_split (Dict[str, int]): Optional. A map from a DeployedModel's ID to the percentage of this Endpoint's traffic that should be forwarded to that DeployedModel. If a DeployedModel's ID is not listed in this map, then it receives no traffic. The traffic percentage values must add up to 100, or map must be empty if the Endpoint is to not accept any traffic at the moment. Key for model being deployed is "0". Should not be provided if traffic_percentage is provided. metadata (Sequence[Tuple[str, str]]): Optional. Strings which should be sent along with the request as metadata. """ self._sync_gca_resource_if_skipped() current_traffic_split = traffic_split or dict(self._gca_resource.traffic_split) if deployed_model_id in current_traffic_split: current_traffic_split = self._unallocate_traffic( traffic_split=current_traffic_split, deployed_model_id=deployed_model_id, ) current_traffic_split.pop(deployed_model_id) _LOGGER.log_action_start_against_resource("Undeploying", "model", self) operation_future = self.api_client.undeploy_model( endpoint=self.resource_name, deployed_model_id=deployed_model_id, traffic_split=current_traffic_split, metadata=metadata, ) _LOGGER.log_action_started_against_resource_with_lro( "Undeploy", "model", self.__class__, operation_future ) # block before returning operation_future.result() _LOGGER.log_action_completed_against_resource("model", "undeployed", self) # update local resource self._sync_gca_resource() @staticmethod def _instantiate_prediction_client( location: Optional[str] = None, credentials: Optional[auth_credentials.Credentials] = None, ) -> utils.PredictionClientWithOverride: """Helper method to instantiates prediction client with optional overrides for this endpoint. Args: location (str): The location of this endpoint. credentials (google.auth.credentials.Credentials): Optional custom credentials to use when accessing interacting with the prediction client. Returns: prediction_client (prediction_service_client.PredictionServiceClient): Initialized prediction client with optional overrides. """ return initializer.global_config.create_client( client_class=utils.PredictionClientWithOverride, credentials=credentials, location_override=location, prediction_client=True, ) def update( self, display_name: Optional[str] = None, description: Optional[str] = None, labels: Optional[Dict[str, str]] = None, traffic_split: Optional[Dict[str, int]] = None, request_metadata: Optional[Sequence[Tuple[str, str]]] = (), update_request_timeout: Optional[float] = None, ) -> "Endpoint": """Updates an endpoint. Example usage: my_endpoint = my_endpoint.update( display_name='my-updated-endpoint', description='my updated description', labels={'key': 'value'}, traffic_split={ '123456': 20, '234567': 80, }, ) Args: display_name (str): Optional. The display name of the Endpoint. The name can be up to 128 characters long and can be consist of any UTF-8 characters. description (str): Optional. The description of the Endpoint. labels (Dict[str, str]): Optional. The labels with user-defined metadata to organize your Endpoints. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. traffic_split (Dict[str, int]): Optional. A map from a DeployedModel's ID to the percentage of this Endpoint's traffic that should be forwarded to that DeployedModel. If a DeployedModel's ID is not listed in this map, then it receives no traffic. The traffic percentage values must add up to 100, or map must be empty if the Endpoint is to not accept any traffic at a moment. request_metadata (Sequence[Tuple[str, str]]): Optional. Strings which should be sent along with the request as metadata. update_request_timeout (float): Optional. The timeout for the update request in seconds. Returns: Endpoint (aiplatform.Prediction): Updated endpoint resource. Raises: ValueError: If `labels` is not the correct format. """ self.wait() current_endpoint_proto = self.gca_resource copied_endpoint_proto = current_endpoint_proto.__class__(current_endpoint_proto) update_mask: List[str] = [] if display_name: utils.validate_display_name(display_name) copied_endpoint_proto.display_name = display_name update_mask.append("display_name") if description: copied_endpoint_proto.description = description update_mask.append("description") if labels: utils.validate_labels(labels) copied_endpoint_proto.labels = labels update_mask.append("labels") if traffic_split: update_mask.append("traffic_split") copied_endpoint_proto.traffic_split = traffic_split update_mask = field_mask_pb2.FieldMask(paths=update_mask) _LOGGER.log_action_start_against_resource( "Updating", "endpoint", self, ) self._gca_resource = self.api_client.update_endpoint( endpoint=copied_endpoint_proto, update_mask=update_mask, metadata=request_metadata, timeout=update_request_timeout, ) _LOGGER.log_action_completed_against_resource("endpoint", "updated", self) return self def predict( self, instances: List, parameters: Optional[Dict] = None, timeout: Optional[float] = None, use_raw_predict: Optional[bool] = False, ) -> Prediction: """Make a prediction against this Endpoint. Args: instances (List): Required. The instances that are the input to the prediction call. A DeployedModel may have an upper limit on the number of instances it supports per request, and when it is exceeded the prediction call errors in case of AutoML Models, or, in case of customer created Models, the behaviour is as documented by that Model. The schema of any single instance may be specified via Endpoint's DeployedModels' [Model's][google.cloud.aiplatform.v1beta1.DeployedModel.model] [PredictSchemata's][google.cloud.aiplatform.v1beta1.Model.predict_schemata] ``instance_schema_uri``. parameters (Dict): The parameters that govern the prediction. The schema of the parameters may be specified via Endpoint's DeployedModels' [Model's ][google.cloud.aiplatform.v1beta1.DeployedModel.model] [PredictSchemata's][google.cloud.aiplatform.v1beta1.Model.predict_schemata] ``parameters_schema_uri``. timeout (float): Optional. The timeout for this request in seconds. use_raw_predict (bool): Optional. Default value is False. If set to True, the underlying prediction call will be made against Endpoint.raw_predict(). Returns: prediction (aiplatform.Prediction): Prediction with returned predictions and Model ID. """ self.wait() if use_raw_predict: raw_predict_response = self.raw_predict( body=json.dumps({"instances": instances, "parameters": parameters}), headers={"Content-Type": "application/json"}, ) json_response = raw_predict_response.json() return Prediction( predictions=json_response["predictions"], deployed_model_id=raw_predict_response.headers[ _RAW_PREDICT_DEPLOYED_MODEL_ID_KEY ], model_resource_name=raw_predict_response.headers[ _RAW_PREDICT_MODEL_RESOURCE_KEY ], model_version_id=raw_predict_response.headers.get( _RAW_PREDICT_MODEL_VERSION_ID_KEY, None ), ) else: prediction_response = self._prediction_client.predict( endpoint=self._gca_resource.name, instances=instances, parameters=parameters, timeout=timeout, ) return Prediction( predictions=[ json_format.MessageToDict(item) for item in prediction_response.predictions.pb ], deployed_model_id=prediction_response.deployed_model_id, model_version_id=prediction_response.model_version_id, model_resource_name=prediction_response.model, ) def raw_predict( self, body: bytes, headers: Dict[str, str] ) -> requests.models.Response: """Makes a prediction request using arbitrary headers. Example usage: my_endpoint = aiplatform.Endpoint(ENDPOINT_ID) response = my_endpoint.raw_predict( body = b'{"instances":[{"feat_1":val_1, "feat_2":val_2}]}' headers = {'Content-Type':'application/json'} ) status_code = response.status_code results = json.dumps(response.text) Args: body (bytes): The body of the prediction request in bytes. This must not exceed 1.5 mb per request. headers (Dict[str, str]): The header of the request as a dictionary. There are no restrictions on the header. Returns: A requests.models.Response object containing the status code and prediction results. """ if not self.authorized_session: self.credentials._scopes = constants.base.DEFAULT_AUTHED_SCOPES self.authorized_session = google_auth_requests.AuthorizedSession( self.credentials ) self.raw_predict_request_url = f"https://{self.location}-{constants.base.API_BASE_PATH}/v1/projects/{self.project}/locations/{self.location}/endpoints/{self.name}:rawPredict" return self.authorized_session.post(self.raw_predict_request_url, body, headers) def explain( self, instances: List[Dict], parameters: Optional[Dict] = None, deployed_model_id: Optional[str] = None, timeout: Optional[float] = None, ) -> Prediction: """Make a prediction with explanations against this Endpoint. Example usage: response = my_endpoint.explain(instances=[...]) my_explanations = response.explanations Args: instances (List): Required. The instances that are the input to the prediction call. A DeployedModel may have an upper limit on the number of instances it supports per request, and when it is exceeded the prediction call errors in case of AutoML Models, or, in case of customer created Models, the behaviour is as documented by that Model. The schema of any single instance may be specified via Endpoint's DeployedModels' [Model's][google.cloud.aiplatform.v1beta1.DeployedModel.model] [PredictSchemata's][google.cloud.aiplatform.v1beta1.Model.predict_schemata] ``instance_schema_uri``. parameters (Dict): The parameters that govern the prediction. The schema of the parameters may be specified via Endpoint's DeployedModels' [Model's ][google.cloud.aiplatform.v1beta1.DeployedModel.model] [PredictSchemata's][google.cloud.aiplatform.v1beta1.Model.predict_schemata] ``parameters_schema_uri``. deployed_model_id (str): Optional. If specified, this ExplainRequest will be served by the chosen DeployedModel, overriding this Endpoint's traffic split. timeout (float): Optional. The timeout for this request in seconds. Returns: prediction (aiplatform.Prediction): Prediction with returned predictions, explanations, and Model ID. """ self.wait() explain_response = self._prediction_client.explain( endpoint=self.resource_name, instances=instances, parameters=parameters, deployed_model_id=deployed_model_id, timeout=timeout, ) return Prediction( predictions=[ json_format.MessageToDict(item) for item in explain_response.predictions.pb ], deployed_model_id=explain_response.deployed_model_id, explanations=explain_response.explanations, ) @classmethod def list( cls, filter: Optional[str] = None, order_by: Optional[str] = None, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[auth_credentials.Credentials] = None, ) -> List["models.Endpoint"]: """List all Endpoint resource instances. Example Usage: aiplatform.Endpoint.list( filter='labels.my_label="my_label_value" OR display_name=!"old_endpoint"', ) Args: filter (str): Optional. An expression for filtering the results of the request. For field names both snake_case and camelCase are supported. order_by (str): Optional. A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: `display_name`, `create_time`, `update_time` project (str): Optional. Project to retrieve list from. If not set, project set in aiplatform.init will be used. location (str): Optional. Location to retrieve list from. If not set, location set in aiplatform.init will be used. credentials (auth_credentials.Credentials): Optional. Custom credentials to use to retrieve list. Overrides credentials set in aiplatform.init. Returns: List[models.Endpoint]: A list of Endpoint resource objects """ return cls._list_with_local_order( cls_filter=lambda ep: not bool( ep.network ), # `network` is empty for public Endpoints filter=filter, order_by=order_by, project=project, location=location, credentials=credentials, ) def list_models(self) -> List[gca_endpoint_compat.DeployedModel]: """Returns a list of the models deployed to this Endpoint. Returns: deployed_models (List[aiplatform.gapic.DeployedModel]): A list of the models deployed in this Endpoint. """ self._sync_gca_resource() return list(self._gca_resource.deployed_models) def undeploy_all(self, sync: bool = True) -> "Endpoint": """Undeploys every model deployed to this Endpoint. Args: sync (bool): Whether to execute this method synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed. """ self._sync_gca_resource() models_to_undeploy = sorted( # Undeploy zero traffic models first self._gca_resource.traffic_split.keys(), key=lambda id: self._gca_resource.traffic_split[id], ) for deployed_model in models_to_undeploy: self._undeploy(deployed_model_id=deployed_model, sync=sync) return self def delete(self, force: bool = False, sync: bool = True) -> None: """Deletes this Vertex AI Endpoint resource. If force is set to True, all models on this Endpoint will be undeployed prior to deletion. Args: force (bool): Required. If force is set to True, all deployed models on this Endpoint will be undeployed first. Default is False. sync (bool): Whether to execute this method synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed. Raises: FailedPrecondition: If models are deployed on this Endpoint and force = False. """ if force: self.undeploy_all(sync=sync) super().delete(sync=sync) class PrivateEndpoint(Endpoint): """ Represents a Vertex AI PrivateEndpoint resource. Read more [about private endpoints in the documentation.](https://cloud.google.com/vertex-ai/docs/predictions/using-private-endpoints) """ def __init__( self, endpoint_name: str, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[auth_credentials.Credentials] = None, ): """Retrieves a PrivateEndpoint resource. Example usage: my_private_endpoint = aiplatform.PrivateEndpoint( endpoint_name="projects/123/locations/us-central1/endpoints/1234567891234567890" ) or (when project and location are initialized) my_private_endpoint = aiplatform.PrivateEndpoint( endpoint_name="1234567891234567890" ) Args: endpoint_name (str): Required. A fully-qualified endpoint resource name or endpoint ID. Example: "projects/123/locations/us-central1/endpoints/my_endpoint_id" or "my_endpoint_id" when project and location are initialized or passed. project (str): Optional. Project to retrieve endpoint from. If not set, project set in aiplatform.init will be used. location (str): Optional. Location to retrieve endpoint from. If not set, location set in aiplatform.init will be used. credentials (auth_credentials.Credentials): Optional. Custom credentials to use to upload this model. Overrides credentials set in aiplatform.init. Raises: ValueError: If the Endpoint being retrieved is not a PrivateEndpoint. ImportError: If there is an issue importing the `urllib3` package. """ try: import urllib3 except ImportError: raise ImportError( "Cannot import the urllib3 HTTP client. Please install google-cloud-aiplatform[private_endpoints]." ) super().__init__( endpoint_name=endpoint_name, project=project, location=location, credentials=credentials, ) if not self.network: raise ValueError( "Please ensure the Endpoint being retrieved is a PrivateEndpoint." ) self._http_client = urllib3.PoolManager() @property def predict_http_uri(self) -> Optional[str]: """HTTP path to send prediction requests to, used when calling `PrivateEndpoint.predict()`""" if not self._gca_resource.deployed_models: return None return self._gca_resource.deployed_models[0].private_endpoints.predict_http_uri @property def explain_http_uri(self) -> Optional[str]: """HTTP path to send explain requests to, used when calling `PrivateEndpoint.explain()`""" if not self._gca_resource.deployed_models: return None return self._gca_resource.deployed_models[0].private_endpoints.explain_http_uri @property def health_http_uri(self) -> Optional[str]: """HTTP path to send health check requests to, used when calling `PrivateEndpoint.health_check()`""" if not self._gca_resource.deployed_models: return None return self._gca_resource.deployed_models[0].private_endpoints.health_http_uri @classmethod def create( cls, display_name: str, project: Optional[str] = None, location: Optional[str] = None, network: Optional[str] = None, description: Optional[str] = None, labels: Optional[Dict[str, str]] = None, credentials: Optional[auth_credentials.Credentials] = None, encryption_spec_key_name: Optional[str] = None, sync=True, ) -> "PrivateEndpoint": """Creates a new PrivateEndpoint. Example usage: my_private_endpoint = aiplatform.PrivateEndpoint.create( display_name="my_endpoint_name", project="my_project_id", location="us-central1", network="projects/123456789123/global/networks/my_vpc" ) or (when project and location are initialized) my_private_endpoint = aiplatform.PrivateEndpoint.create( display_name="my_endpoint_name", network="projects/123456789123/global/networks/my_vpc" ) Args: display_name (str): Required. The user-defined name of the Endpoint. The name can be up to 128 characters long and can be consist of any UTF-8 characters. project (str): Optional. Project to retrieve endpoint from. If not set, project set in aiplatform.init will be used. location (str): Optional. Location to retrieve endpoint from. If not set, location set in aiplatform.init will be used. network (str): Optional. The full name of the Compute Engine network to which this Endpoint will be peered. E.g. "projects/123456789123/global/networks/my_vpc". Private services access must already be configured for the network. If left unspecified, the network set with aiplatform.init will be used. description (str): Optional. The description of the Endpoint. labels (Dict[str, str]): Optional. The labels with user-defined metadata to organize your Endpoints. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. credentials (auth_credentials.Credentials): Optional. Custom credentials to use to upload this model. Overrides credentials set in aiplatform.init. encryption_spec_key_name (str): Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the model. Has the form: ``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``. The key needs to be in the same region as where the compute resource is created. If set, this Model and all sub-resources of this Model will be secured by this key. Overrides encryption_spec_key_name set in aiplatform.init. sync (bool): Whether to execute this method synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed. Returns: endpoint (aiplatform.PrivateEndpoint): Created endpoint. Raises: ValueError: A network must be instantiated when creating a PrivateEndpoint. """ api_client = cls._instantiate_client(location=location, credentials=credentials) utils.validate_display_name(display_name) if labels: utils.validate_labels(labels) project = project or initializer.global_config.project location = location or initializer.global_config.location network = network or initializer.global_config.network if not network: raise ValueError( "Please provide required argument `network` or set network" "using aiplatform.init(network=...)" ) return cls._create( api_client=api_client, display_name=display_name, project=project, location=location, description=description, labels=labels, credentials=credentials, encryption_spec=initializer.global_config.get_encryption_spec( encryption_spec_key_name=encryption_spec_key_name ), network=network, sync=sync, ) @classmethod def _construct_sdk_resource_from_gapic( cls, gapic_resource: proto.Message, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[auth_credentials.Credentials] = None, ) -> "PrivateEndpoint": """Given a GAPIC PrivateEndpoint object, return the SDK representation. Args: gapic_resource (proto.Message): A GAPIC representation of a PrivateEndpoint resource, usually retrieved by a get_* or in a list_* API call. project (str): Optional. Project to construct Endpoint object from. If not set, project set in aiplatform.init will be used. location (str): Optional. Location to construct Endpoint object from. If not set, location set in aiplatform.init will be used. credentials (auth_credentials.Credentials): Optional. Custom credentials to use to construct Endpoint. Overrides credentials set in aiplatform.init. Returns: endpoint (aiplatform.PrivateEndpoint): An initialized PrivateEndpoint resource. Raises: ImportError: If there is an issue importing the `urllib3` package. """ try: import urllib3 except ImportError: raise ImportError( "Cannot import the urllib3 HTTP client. Please install google-cloud-aiplatform[private_endpoints]." ) endpoint = cls._empty_constructor( project=project, location=location, credentials=credentials ) endpoint._gca_resource = gapic_resource endpoint._http_client = urllib3.PoolManager() return endpoint def _http_request( self, method: str, url: str, body: Optional[Dict[Any, Any]] = None, headers: Optional[Dict[str, str]] = None, ) -> "urllib3.response.HTTPResponse": # type: ignore # noqa: F821 """Helper function used to perform HTTP requests for PrivateEndpoint. Args: method (str): Required. The HTTP request method to use. Example: "POST" or "GET" url (str): Required. The url used to send requests and get responses from. body (Dict[Any, Any]): Optional. Data sent to the url in the HTTP request. For a PrivateEndpoint, an instance is sent and a prediction response is expected. headers (Dict[str, str]): Optional. Header in the HTTP request. Returns: urllib3.response.HTTPResponse: A HTTP Response container. Raises: ImportError: If there is an issue importing the `urllib3` package. RuntimeError: If a HTTP request could not be made. RuntimeError: A connection could not be established with the PrivateEndpoint and a HTTP request could not be made. """ try: import urllib3 except ImportError: raise ImportError( "Cannot import the urllib3 HTTP client. Please install google-cloud-aiplatform[private_endpoints]." ) try: response = self._http_client.request( method=method, url=url, body=body, headers=headers ) if response.status < _SUCCESSFUL_HTTP_RESPONSE: return response else: raise RuntimeError( f"{response.status} - Failed to make request, see response: " + response.data.decode("utf-8") ) except urllib3.exceptions.MaxRetryError as exc: raise RuntimeError( f"Failed to make a {method} request to this URI, make sure: " " this call is being made inside the network this PrivateEndpoint is peered to " f"({self._gca_resource.network}), calling health_check() returns True, " f"and that {url} is a valid URL." ) from exc def predict(self, instances: List, parameters: Optional[Dict] = None) -> Prediction: """Make a prediction against this PrivateEndpoint using a HTTP request. This method must be called within the network the PrivateEndpoint is peered to. Otherwise, the predict() call will fail with error code 404. To check, use `PrivateEndpoint.network`. Example usage: response = my_private_endpoint.predict(instances=[...]) my_predictions = response.predictions Args: instances (List): Required. The instances that are the input to the prediction call. Instance types mut be JSON serializable. A DeployedModel may have an upper limit on the number of instances it supports per request, and when it is exceeded the prediction call errors in case of AutoML Models, or, in case of customer created Models, the behaviour is as documented by that Model. The schema of any single instance may be specified via Endpoint's DeployedModels' [Model's][google.cloud.aiplatform.v1beta1.DeployedModel.model] [PredictSchemata's][google.cloud.aiplatform.v1beta1.Model.predict_schemata] ``instance_schema_uri``. parameters (Dict): The parameters that govern the prediction. The schema of the parameters may be specified via Endpoint's DeployedModels' [Model's ][google.cloud.aiplatform.v1beta1.DeployedModel.model] [PredictSchemata's][google.cloud.aiplatform.v1beta1.Model.predict_schemata] ``parameters_schema_uri``. Returns: prediction (aiplatform.Prediction): Prediction object with returned predictions and Model ID. Raises: RuntimeError: If a model has not been deployed a request cannot be made. """ self.wait() self._sync_gca_resource_if_skipped() if not self._gca_resource.deployed_models: raise RuntimeError( "Cannot make a predict request because a model has not been deployed on this Private" "Endpoint. Please ensure a model has been deployed." ) response = self._http_request( method="POST", url=self.predict_http_uri, body=json.dumps({"instances": instances}), headers={"Content-Type": "application/json"}, ) prediction_response = json.loads(response.data) return Prediction( predictions=prediction_response.get("predictions"), deployed_model_id=self._gca_resource.deployed_models[0].id, ) def raw_predict( self, body: bytes, headers: Dict[str, str] ) -> requests.models.Response: """Make a prediction request using arbitrary headers. This method must be called within the network the PrivateEndpoint is peered to. Otherwise, the predict() call will fail with error code 404. To check, use `PrivateEndpoint.network`. Example usage: my_endpoint = aiplatform.PrivateEndpoint(ENDPOINT_ID) response = my_endpoint.raw_predict( body = b'{"instances":[{"feat_1":val_1, "feat_2":val_2}]}' headers = {'Content-Type':'application/json'} ) status_code = response.status_code results = json.dumps(response.text) Args: body (bytes): The body of the prediction request in bytes. This must not exceed 1.5 mb per request. headers (Dict[str, str]): The header of the request as a dictionary. There are no restrictions on the header. Returns: A requests.models.Response object containing the status code and prediction results. """ self.wait() return self._http_request( method="POST", url=self.predict_http_uri, body=body, headers=headers, ) def explain(self): raise NotImplementedError( f"{self.__class__.__name__} class does not support 'explain' as of now." ) def health_check(self) -> bool: """ Makes a request to this PrivateEndpoint's health check URI. Must be within network that this PrivateEndpoint is in. Example Usage: if my_private_endpoint.health_check(): print("PrivateEndpoint is healthy!") Returns: bool: Checks if calls can be made to this PrivateEndpoint. Raises: RuntimeError: If a model has not been deployed a request cannot be made. """ self.wait() self._sync_gca_resource_if_skipped() if not self._gca_resource.deployed_models: raise RuntimeError( "Cannot make a health check request because a model has not been deployed on this Private" "Endpoint. Please ensure a model has been deployed." ) response = self._http_request( method="GET", url=self.health_http_uri, ) return response.status < _SUCCESSFUL_HTTP_RESPONSE @classmethod def list( cls, filter: Optional[str] = None, order_by: Optional[str] = None, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[auth_credentials.Credentials] = None, ) -> List["models.PrivateEndpoint"]: """List all PrivateEndpoint resource instances. Example Usage: my_private_endpoints = aiplatform.PrivateEndpoint.list() or my_private_endpoints = aiplatform.PrivateEndpoint.list( filter='labels.my_label="my_label_value" OR display_name=!"old_endpoint"', ) Args: filter (str): Optional. An expression for filtering the results of the request. For field names both snake_case and camelCase are supported. order_by (str): Optional. A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: `display_name`, `create_time`, `update_time` project (str): Optional. Project to retrieve list from. If not set, project set in aiplatform.init will be used. location (str): Optional. Location to retrieve list from. If not set, location set in aiplatform.init will be used. credentials (auth_credentials.Credentials): Optional. Custom credentials to use to retrieve list. Overrides credentials set in aiplatform.init. Returns: List[models.PrivateEndpoint]: A list of PrivateEndpoint resource objects. """ return cls._list_with_local_order( cls_filter=lambda ep: bool( ep.network ), # Only PrivateEndpoints have a network set filter=filter, order_by=order_by, project=project, location=location, credentials=credentials, ) def deploy( self, model: "Model", deployed_model_display_name: Optional[str] = None, machine_type: Optional[str] = None, min_replica_count: int = 1, max_replica_count: int = 1, accelerator_type: Optional[str] = None, accelerator_count: Optional[int] = None, service_account: Optional[str] = None, explanation_metadata: Optional[aiplatform.explain.ExplanationMetadata] = None, explanation_parameters: Optional[ aiplatform.explain.ExplanationParameters ] = None, metadata: Optional[Sequence[Tuple[str, str]]] = (), sync=True, ) -> None: """Deploys a Model to the PrivateEndpoint. Example Usage: my_private_endpoint.deploy( model=my_model ) Args: model (aiplatform.Model): Required. Model to be deployed. deployed_model_display_name (str): Optional. The display name of the DeployedModel. If not provided upon creation, the Model's display_name is used. machine_type (str): Optional. The type of machine. Not specifying machine type will result in model to be deployed with automatic resources. min_replica_count (int): Optional. The minimum number of machine replicas this deployed model will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed. max_replica_count (int): Optional. The maximum number of replicas this deployed model may be deployed on when the traffic against it increases. If requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the deployed model increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, the larger value of min_replica_count or 1 will be used. If value provided is smaller than min_replica_count, it will automatically be increased to be min_replica_count. accelerator_type (str): Optional. Hardware accelerator type. Must also set accelerator_count if used. One of ACCELERATOR_TYPE_UNSPECIFIED, NVIDIA_TESLA_K80, NVIDIA_TESLA_P100, NVIDIA_TESLA_V100, NVIDIA_TESLA_P4, NVIDIA_TESLA_T4 accelerator_count (int): Optional. The number of accelerators to attach to a worker replica. service_account (str): The service account that the DeployedModel's container runs as. Specify the email address of the service account. If this service account is not specified, the container runs as a service account that doesn't have access to the resource project. Users deploying the Model must have the `iam.serviceAccounts.actAs` permission on this service account. explanation_metadata (aiplatform.explain.ExplanationMetadata): Optional. Metadata describing the Model's input and output for explanation. `explanation_metadata` is optional while `explanation_parameters` must be specified when used. For more details, see `Ref docs <http://tinyurl.com/1igh60kt>` explanation_parameters (aiplatform.explain.ExplanationParameters): Optional. Parameters to configure explaining for Model's predictions. For more details, see `Ref docs <http://tinyurl.com/1an4zake>` metadata (Sequence[Tuple[str, str]]): Optional. Strings which should be sent along with the request as metadata. sync (bool): Whether to execute this method synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed. """ self._validate_deploy_args( min_replica_count=min_replica_count, max_replica_count=max_replica_count, accelerator_type=accelerator_type, deployed_model_display_name=deployed_model_display_name, traffic_split=None, traffic_percentage=100, explanation_metadata=explanation_metadata, explanation_parameters=explanation_parameters, ) self._deploy( model=model, deployed_model_display_name=deployed_model_display_name, traffic_percentage=100, traffic_split=None, machine_type=machine_type, min_replica_count=min_replica_count, max_replica_count=max_replica_count, accelerator_type=accelerator_type, accelerator_count=accelerator_count, service_account=service_account, explanation_metadata=explanation_metadata, explanation_parameters=explanation_parameters, metadata=metadata, sync=sync, ) def undeploy( self, deployed_model_id: str, sync=True, ) -> None: """Undeploys a deployed model from the PrivateEndpoint. Example Usage: my_private_endpoint.undeploy( deployed_model_id="1234567891232567891" ) or my_deployed_model_id = my_private_endpoint.list_models()[0].id my_private_endpoint.undeploy( deployed_model_id=my_deployed_model_id ) Args: deployed_model_id (str): Required. The ID of the DeployedModel to be undeployed from the PrivateEndpoint. Use PrivateEndpoint.list_models() to get the deployed model ID. sync (bool): Whether to execute this method synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed. """ self._sync_gca_resource_if_skipped() # TODO(b/211351292): Add traffic splitting for PrivateEndpoint self._undeploy( deployed_model_id=deployed_model_id, traffic_split=None, sync=sync, ) def delete(self, force: bool = False, sync: bool = True) -> None: """Deletes this Vertex AI PrivateEndpoint resource. If force is set to True, all models on this PrivateEndpoint will be undeployed prior to deletion. Args: force (bool): Required. If force is set to True, all deployed models on this Endpoint will be undeployed first. Default is False. sync (bool): Whether to execute this method synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed. Raises: FailedPrecondition: If models are deployed on this Endpoint and force = False. """ if force and self._gca_resource.deployed_models: self.undeploy( deployed_model_id=self._gca_resource.deployed_models[0].id, sync=sync, ) super().delete(force=False, sync=sync) class Model(base.VertexAiResourceNounWithFutureManager): client_class = utils.ModelClientWithOverride _resource_noun = "models" _getter_method = "get_model" _list_method = "list_models" _delete_method = "delete_model" _parse_resource_name_method = "parse_model_path" _format_resource_name_method = "model_path" @property def uri(self) -> Optional[str]: """Path to the directory containing the Model artifact and any of its supporting files. Not present for AutoML Models.""" self._assert_gca_resource_is_available() return self._gca_resource.artifact_uri or None @property def description(self) -> str: """Description of the model.""" self._assert_gca_resource_is_available() return self._gca_resource.description @property def supported_export_formats( self, ) -> Dict[str, List[gca_model_compat.Model.ExportFormat.ExportableContent]]: """The formats and content types in which this Model may be exported. If empty, this Model is not available for export. For example, if this model can be exported as a Tensorflow SavedModel and have the artifacts written to Cloud Storage, the expected value would be: {'tf-saved-model': [<ExportableContent.ARTIFACT: 1>]} """ self._assert_gca_resource_is_available() return { export_format.id: [ gca_model_compat.Model.ExportFormat.ExportableContent(content) for content in export_format.exportable_contents ] for export_format in self._gca_resource.supported_export_formats } @property def supported_deployment_resources_types( self, ) -> List[aiplatform.gapic.Model.DeploymentResourcesType]: """List of deployment resource types accepted for this Model. When this Model is deployed, its prediction resources are described by the `prediction_resources` field of the objects returned by `Endpoint.list_models()`. Because not all Models support all resource configuration types, the configuration types this Model supports are listed here. If no configuration types are listed, the Model cannot be deployed to an `Endpoint` and does not support online predictions (`Endpoint.predict()` or `Endpoint.explain()`). Such a Model can serve predictions by using a `BatchPredictionJob`, if it has at least one entry each in `Model.supported_input_storage_formats` and `Model.supported_output_storage_formats`.""" self._assert_gca_resource_is_available() return list(self._gca_resource.supported_deployment_resources_types) @property def supported_input_storage_formats(self) -> List[str]: """The formats this Model supports in the `input_config` field of a `BatchPredictionJob`. If `Model.predict_schemata.instance_schema_uri` exists, the instances should be given as per that schema. [Read the docs for more on batch prediction formats](https://cloud.google.com/vertex-ai/docs/predictions/batch-predictions#batch_request_input) If this Model doesn't support any of these formats it means it cannot be used with a `BatchPredictionJob`. However, if it has `supported_deployment_resources_types`, it could serve online predictions by using `Endpoint.predict()` or `Endpoint.explain()`. """ self._assert_gca_resource_is_available() return list(self._gca_resource.supported_input_storage_formats) @property def supported_output_storage_formats(self) -> List[str]: """The formats this Model supports in the `output_config` field of a `BatchPredictionJob`. If both `Model.predict_schemata.instance_schema_uri` and `Model.predict_schemata.prediction_schema_uri` exist, the predictions are returned together with their instances. In other words, the prediction has the original instance data first, followed by the actual prediction content (as per the schema). [Read the docs for more on batch prediction formats](https://cloud.google.com/vertex-ai/docs/predictions/batch-predictions) If this Model doesn't support any of these formats it means it cannot be used with a `BatchPredictionJob`. However, if it has `supported_deployment_resources_types`, it could serve online predictions by using `Endpoint.predict()` or `Endpoint.explain()`. """ self._assert_gca_resource_is_available() return list(self._gca_resource.supported_output_storage_formats) @property def predict_schemata(self) -> Optional[aiplatform.gapic.PredictSchemata]: """The schemata that describe formats of the Model's predictions and explanations, if available.""" self._assert_gca_resource_is_available() return getattr(self._gca_resource, "predict_schemata") @property def training_job(self) -> Optional["aiplatform.training_jobs._TrainingJob"]: """The TrainingJob that uploaded this Model, if any. Raises: api_core.exceptions.NotFound: If the Model's training job resource cannot be found on the Vertex service. """ self._assert_gca_resource_is_available() job_name = getattr(self._gca_resource, "training_pipeline") if not job_name: return None try: return aiplatform.training_jobs._TrainingJob._get_and_return_subclass( resource_name=job_name, project=self.project, location=self.location, credentials=self.credentials, ) except api_exceptions.NotFound: raise api_exceptions.NotFound( f"The training job used to create this model could not be found: {job_name}" ) @property def container_spec(self) -> Optional[aiplatform.gapic.ModelContainerSpec]: """The specification of the container that is to be used when deploying this Model. Not present for AutoML Models.""" self._assert_gca_resource_is_available() return getattr(self._gca_resource, "container_spec") @property def version_id(self) -> str: """The version ID of the model. A new version is committed when a new model version is uploaded or trained under an existing model id. It is an auto-incrementing decimal number in string representation.""" self._assert_gca_resource_is_available() return getattr(self._gca_resource, "version_id") @property def version_aliases(self) -> Sequence[str]: """User provided version aliases so that a model version can be referenced via alias (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_alias} instead of auto-generated version id (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_id}). The format is [a-z][a-zA-Z0-9-]{0,126}[a-z0-9] to distinguish from version_id. A default version alias will be created for the first version of the model, and there must be exactly one default version alias for a model.""" self._assert_gca_resource_is_available() return getattr(self._gca_resource, "version_aliases") @property def version_create_time(self) -> timestamp_pb2.Timestamp: """Timestamp when this version was created.""" self._assert_gca_resource_is_available() return getattr(self._gca_resource, "version_create_time") @property def version_update_time(self) -> timestamp_pb2.Timestamp: """Timestamp when this version was updated.""" self._assert_gca_resource_is_available() return getattr(self._gca_resource, "version_update_time") @property def version_description(self) -> str: """The description of this version.""" self._assert_gca_resource_is_available() return getattr(self._gca_resource, "version_description") @property def resource_name(self) -> str: """Full qualified resource name, without any version ID.""" self._assert_gca_resource_is_available() return ModelRegistry._parse_versioned_name(self._gca_resource.name)[0] @property def name(self) -> str: """Name of this resource.""" self._assert_gca_resource_is_available() return ModelRegistry._parse_versioned_name(super().name)[0] @property def versioned_resource_name(self) -> str: """The fully-qualified resource name, including the version ID. For example, projects/{project}/locations/{location}/models/{model_id}@{version_id} """ self._assert_gca_resource_is_available() return ModelRegistry._get_versioned_name( self.resource_name, self.version_id, ) @property def versioning_registry(self) -> "ModelRegistry": """The registry of model versions associated with this Model instance.""" return self._registry def __init__( self, model_name: str, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[auth_credentials.Credentials] = None, version: Optional[str] = None, ): """Retrieves the model resource and instantiates its representation. Args: model_name (str): Required. A fully-qualified model resource name or model ID. Example: "projects/123/locations/us-central1/models/456" or "456" when project and location are initialized or passed. May optionally contain a version ID or version alias in {model_name}@{version} form. See version arg. project (str): Optional project to retrieve model from. If not set, project set in aiplatform.init will be used. location (str): Optional location to retrieve model from. If not set, location set in aiplatform.init will be used. credentials: Optional[auth_credentials.Credentials]=None, Custom credentials to use to upload this model. If not set, credentials set in aiplatform.init will be used. version (str): Optional. Version ID or version alias. When set, the specified model version will be targeted unless overridden in method calls. When not set, the model with the "default" alias will be targeted unless overridden in method calls. No behavior change if only one version of a model exists. Raises: ValueError: If `version` is passed alongside a model_name referencing a different version. """ # If the version was passed in model_name, parse it model_name, parsed_version = ModelRegistry._parse_versioned_name(model_name) if parsed_version: if version and version != parsed_version: raise ValueError( f"A version of {version} was passed that conflicts with the version of {parsed_version} in the model_name." ) version = parsed_version super().__init__( project=project, location=location, credentials=credentials, resource_name=model_name, ) # Model versions can include @{version} in the resource name. self._resource_id_validator = super()._revisioned_resource_id_validator # Create a versioned model_name, if it exists, for getting the GCA model versioned_model_name = ModelRegistry._get_versioned_name(model_name, version) self._gca_resource = self._get_gca_resource(resource_name=versioned_model_name) # Create ModelRegistry with the unversioned resource name self._registry = ModelRegistry( self.resource_name, location=location, project=project, credentials=credentials, ) def update( self, display_name: Optional[str] = None, description: Optional[str] = None, labels: Optional[Dict[str, str]] = None, ) -> "Model": """Updates a model. Example usage: my_model = my_model.update( display_name="my-model", description="my description", labels={'key': 'value'}, ) Args: display_name (str): The display name of the Model. The name can be up to 128 characters long and can be consist of any UTF-8 characters. description (str): The description of the model. labels (Dict[str, str]): Optional. The labels with user-defined metadata to organize your Models. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. Returns: model (aiplatform.Model): Updated model resource. Raises: ValueError: If `labels` is not the correct format. """ self.wait() current_model_proto = self.gca_resource copied_model_proto = current_model_proto.__class__(current_model_proto) update_mask: List[str] = [] # Updates to base model properties cannot occur if a versioned model is passed. # Use the unversioned model resource name. copied_model_proto.name = self.resource_name if display_name: utils.validate_display_name(display_name) copied_model_proto.display_name = display_name update_mask.append("display_name") if description: copied_model_proto.description = description update_mask.append("description") if labels: utils.validate_labels(labels) copied_model_proto.labels = labels update_mask.append("labels") update_mask = field_mask_pb2.FieldMask(paths=update_mask) self.api_client.update_model(model=copied_model_proto, update_mask=update_mask) self._sync_gca_resource() return self # TODO(b/170979552) Add support for predict schemata # TODO(b/170979926) Add support for metadata and metadata schema @classmethod @base.optional_sync() def upload( cls, serving_container_image_uri: Optional[str] = None, *, artifact_uri: Optional[str] = None, model_id: Optional[str] = None, parent_model: Optional[str] = None, is_default_version: bool = True, version_aliases: Optional[Sequence[str]] = None, version_description: Optional[str] = None, serving_container_predict_route: Optional[str] = None, serving_container_health_route: Optional[str] = None, description: Optional[str] = None, serving_container_command: Optional[Sequence[str]] = None, serving_container_args: Optional[Sequence[str]] = None, serving_container_environment_variables: Optional[Dict[str, str]] = None, serving_container_ports: Optional[Sequence[int]] = None, local_model: Optional["LocalModel"] = None, instance_schema_uri: Optional[str] = None, parameters_schema_uri: Optional[str] = None, prediction_schema_uri: Optional[str] = None, explanation_metadata: Optional[explain.ExplanationMetadata] = None, explanation_parameters: Optional[explain.ExplanationParameters] = None, display_name: Optional[str] = None, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[auth_credentials.Credentials] = None, labels: Optional[Dict[str, str]] = None, encryption_spec_key_name: Optional[str] = None, staging_bucket: Optional[str] = None, sync=True, upload_request_timeout: Optional[float] = None, ) -> "Model": """Uploads a model and returns a Model representing the uploaded Model resource. Example usage: my_model = Model.upload( display_name="my-model", artifact_uri="gs://my-model/saved-model", serving_container_image_uri="tensorflow/serving" ) Args: serving_container_image_uri (str): Optional. The URI of the Model serving container. This parameter is required if the parameter `local_model` is not specified. artifact_uri (str): Optional. The path to the directory containing the Model artifact and any of its supporting files. Leave blank for custom container prediction. Not present for AutoML Models. model_id (str): Optional. The ID to use for the uploaded Model, which will become the final component of the model resource name. This value may be up to 63 characters, and valid characters are `[a-z0-9_-]`. The first character cannot be a number or hyphen. parent_model (str): Optional. The resource name or model ID of an existing model that the newly-uploaded model will be a version of. Only set this field when uploading a new version of an existing model. is_default_version (bool): Optional. When set to True, the newly uploaded model version will automatically have alias "default" included. Subsequent uses of this model without a version specified will use this "default" version. When set to False, the "default" alias will not be moved. Actions targeting the newly-uploaded model version will need to specifically reference this version by ID or alias. New model uploads, i.e. version 1, will always be "default" aliased. version_aliases (Sequence[str]): Optional. User provided version aliases so that a model version can be referenced via alias instead of auto-generated version ID. A default version alias will be created for the first version of the model. The format is [a-z][a-zA-Z0-9-]{0,126}[a-z0-9] version_description (str): Optional. The description of the model version being uploaded. serving_container_predict_route (str): Optional. An HTTP path to send prediction requests to the container, and which must be supported by it. If not specified a default HTTP path will be used by Vertex AI. serving_container_health_route (str): Optional. An HTTP path to send health check requests to the container, and which must be supported by it. If not specified a standard HTTP path will be used by Vertex AI. description (str): The description of the model. serving_container_command: Optional[Sequence[str]]=None, The command with which the container is run. Not executed within a shell. The Docker image's ENTRYPOINT is used if this is not provided. Variable references $(VAR_NAME) are expanded using the container's environment. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not. serving_container_args: Optional[Sequence[str]]=None, The arguments to the command. The Docker image's CMD is used if this is not provided. Variable references $(VAR_NAME) are expanded using the container's environment. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not. serving_container_environment_variables: Optional[Dict[str, str]]=None, The environment variables that are to be present in the container. Should be a dictionary where keys are environment variable names and values are environment variable values for those names. serving_container_ports: Optional[Sequence[int]]=None, Declaration of ports that are exposed by the container. This field is primarily informational, it gives Vertex AI information about the network connections the container uses. Listing or not a port here has no impact on whether the port is actually exposed, any port listening on the default "0.0.0.0" address inside a container will be accessible from the network. local_model (Optional[LocalModel]): Optional. A LocalModel instance that includes a `serving_container_spec`. If provided, the `serving_container_spec` of the LocalModel instance will overwrite the values of all other serving container parameters. instance_schema_uri (str): Optional. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used in ``PredictRequest.instances``, ``ExplainRequest.instances`` and ``BatchPredictionJob.input_config``. The schema is defined as an OpenAPI 3.0.2 `Schema Object <https://tinyurl.com/y538mdwt#schema-object>`__. AutoML Models always have this field populated by AI Platform. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access. parameters_schema_uri (str): Optional. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via ``PredictRequest.parameters``, ``ExplainRequest.parameters`` and ``BatchPredictionJob.model_parameters``. The schema is defined as an OpenAPI 3.0.2 `Schema Object <https://tinyurl.com/y538mdwt#schema-object>`__. AutoML Models always have this field populated by AI Platform, if no parameters are supported it is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access. prediction_schema_uri (str): Optional. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via ``PredictResponse.predictions``, ``ExplainResponse.explanations``, and ``BatchPredictionJob.output_config``. The schema is defined as an OpenAPI 3.0.2 `Schema Object <https://tinyurl.com/y538mdwt#schema-object>`__. AutoML Models always have this field populated by AI Platform. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access. explanation_metadata (aiplatform.explain.ExplanationMetadata): Optional. Metadata describing the Model's input and output for explanation. `explanation_metadata` is optional while `explanation_parameters` must be specified when used. For more details, see `Ref docs <http://tinyurl.com/1igh60kt>` explanation_parameters (aiplatform.explain.ExplanationParameters): Optional. Parameters to configure explaining for Model's predictions. For more details, see `Ref docs <http://tinyurl.com/1an4zake>` display_name (str): Optional. The display name of the Model. The name can be up to 128 characters long and can be consist of any UTF-8 characters. project: Optional[str]=None, Project to upload this model to. Overrides project set in aiplatform.init. location: Optional[str]=None, Location to upload this model to. Overrides location set in aiplatform.init. credentials: Optional[auth_credentials.Credentials]=None, Custom credentials to use to upload this model. Overrides credentials set in aiplatform.init. labels (Dict[str, str]): Optional. The labels with user-defined metadata to organize your Models. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. encryption_spec_key_name (Optional[str]): Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the model. Has the form: ``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``. The key needs to be in the same region as where the compute resource is created. If set, this Model and all sub-resources of this Model will be secured by this key. Overrides encryption_spec_key_name set in aiplatform.init. staging_bucket (str): Optional. Bucket to stage local model artifacts. Overrides staging_bucket set in aiplatform.init. upload_request_timeout (float): Optional. The timeout for the upload request in seconds. Returns: model (aiplatform.Model): Instantiated representation of the uploaded model resource. Raises: ValueError: If explanation_metadata is specified while explanation_parameters is not. Also if model directory does not contain a supported model file. If `local_model` is specified but `serving_container_spec.image_uri` in the `local_model` is None. If `local_model` is not specified and `serving_container_image_uri` is None. """ if not display_name: display_name = cls._generate_display_name() utils.validate_display_name(display_name) if labels: utils.validate_labels(labels) if bool(explanation_metadata) and not bool(explanation_parameters): raise ValueError( "To get model explanation, `explanation_parameters` must be specified." ) appended_user_agent = None if local_model: container_spec = local_model.get_serving_container_spec() appended_user_agent = [prediction_constants.CUSTOM_PREDICTION_ROUTINES] else: if not serving_container_image_uri: raise ValueError( "The parameter `serving_container_image_uri` is required " "if no `local_model` is provided." ) env = None ports = None if serving_container_environment_variables: env = [ gca_env_var_compat.EnvVar(name=str(key), value=str(value)) for key, value in serving_container_environment_variables.items() ] if serving_container_ports: ports = [ gca_model_compat.Port(container_port=port) for port in serving_container_ports ] container_spec = gca_model_compat.ModelContainerSpec( image_uri=serving_container_image_uri, command=serving_container_command, args=serving_container_args, env=env, ports=ports, predict_route=serving_container_predict_route, health_route=serving_container_health_route, ) model_predict_schemata = None if any([instance_schema_uri, parameters_schema_uri, prediction_schema_uri]): model_predict_schemata = gca_model_compat.PredictSchemata( instance_schema_uri=instance_schema_uri, parameters_schema_uri=parameters_schema_uri, prediction_schema_uri=prediction_schema_uri, ) # TODO(b/182388545) initializer.global_config.get_encryption_spec from a sync function encryption_spec = initializer.global_config.get_encryption_spec( encryption_spec_key_name=encryption_spec_key_name, ) parent_model = ModelRegistry._get_true_version_parent( location=location, project=project, parent_model=parent_model ) version_aliases = ModelRegistry._get_true_alias_list( version_aliases=version_aliases, is_default_version=is_default_version ) managed_model = gca_model_compat.Model( display_name=display_name, description=description, version_aliases=version_aliases, version_description=version_description, container_spec=container_spec, predict_schemata=model_predict_schemata, labels=labels, encryption_spec=encryption_spec, ) if artifact_uri and not artifact_uri.startswith("gs://"): model_dir = pathlib.Path(artifact_uri) # Validating the model directory if not model_dir.exists(): raise ValueError(f"artifact_uri path does not exist: '{artifact_uri}'") PREBUILT_IMAGE_RE = "(us|europe|asia)-docker.pkg.dev/vertex-ai/prediction/" if re.match(PREBUILT_IMAGE_RE, serving_container_image_uri): if not model_dir.is_dir(): raise ValueError( f"artifact_uri path must be a directory: '{artifact_uri}' when using prebuilt image '{serving_container_image_uri}'" ) if not any( (model_dir / file_name).exists() for file_name in _SUPPORTED_MODEL_FILE_NAMES ): raise ValueError( "artifact_uri directory does not contain any supported model files. " f"When using a prebuilt serving image, the upload method only supports the following model files: '{_SUPPORTED_MODEL_FILE_NAMES}'" ) # Uploading the model staged_data_uri = gcs_utils.stage_local_data_in_gcs( data_path=str(model_dir), staging_gcs_dir=staging_bucket, project=project, location=location, credentials=credentials, ) artifact_uri = staged_data_uri if artifact_uri: managed_model.artifact_uri = artifact_uri # Override explanation_spec if required field is provided if explanation_parameters: explanation_spec = gca_endpoint_compat.explanation.ExplanationSpec() explanation_spec.parameters = explanation_parameters if explanation_metadata: explanation_spec.metadata = explanation_metadata managed_model.explanation_spec = explanation_spec request = gca_model_service_compat.UploadModelRequest( parent=initializer.global_config.common_location_path(project, location), model=managed_model, parent_model=parent_model, model_id=model_id, ) api_client = cls._instantiate_client( location, credentials, appended_user_agent=appended_user_agent ) lro = api_client.upload_model( request=request, timeout=upload_request_timeout, ) _LOGGER.log_create_with_lro(cls, lro) model_upload_response = lro.result() this_model = cls( model_upload_response.model, version=model_upload_response.model_version_id ) _LOGGER.log_create_complete(cls, this_model._gca_resource, "model") return this_model def deploy( self, endpoint: Optional[Union["Endpoint", "PrivateEndpoint"]] = None, deployed_model_display_name: Optional[str] = None, traffic_percentage: Optional[int] = 0, traffic_split: Optional[Dict[str, int]] = None, machine_type: Optional[str] = None, min_replica_count: int = 1, max_replica_count: int = 1, accelerator_type: Optional[str] = None, accelerator_count: Optional[int] = None, service_account: Optional[str] = None, explanation_metadata: Optional[aiplatform.explain.ExplanationMetadata] = None, explanation_parameters: Optional[ aiplatform.explain.ExplanationParameters ] = None, metadata: Optional[Sequence[Tuple[str, str]]] = (), encryption_spec_key_name: Optional[str] = None, network: Optional[str] = None, sync=True, deploy_request_timeout: Optional[float] = None, autoscaling_target_cpu_utilization: Optional[int] = None, autoscaling_target_accelerator_duty_cycle: Optional[int] = None, ) -> Union[Endpoint, PrivateEndpoint]: """Deploys model to endpoint. Endpoint will be created if unspecified. Args: endpoint (Union[Endpoint, PrivateEndpoint]): Optional. Public or private Endpoint to deploy model to. If not specified, endpoint display name will be model display name+'_endpoint'. deployed_model_display_name (str): Optional. The display name of the DeployedModel. If not provided upon creation, the Model's display_name is used. traffic_percentage (int): Optional. Desired traffic to newly deployed model. Defaults to 0 if there are pre-existing deployed models. Defaults to 100 if there are no pre-existing deployed models. Negative values should not be provided. Traffic of previously deployed models at the endpoint will be scaled down to accommodate new deployed model's traffic. Should not be provided if traffic_split is provided. traffic_split (Dict[str, int]): Optional. A map from a DeployedModel's ID to the percentage of this Endpoint's traffic that should be forwarded to that DeployedModel. If a DeployedModel's ID is not listed in this map, then it receives no traffic. The traffic percentage values must add up to 100, or map must be empty if the Endpoint is to not accept any traffic at the moment. Key for model being deployed is "0". Should not be provided if traffic_percentage is provided. machine_type (str): Optional. The type of machine. Not specifying machine type will result in model to be deployed with automatic resources. min_replica_count (int): Optional. The minimum number of machine replicas this deployed model will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed. max_replica_count (int): Optional. The maximum number of replicas this deployed model may be deployed on when the traffic against it increases. If requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the deployed model increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, the smaller value of min_replica_count or 1 will be used. accelerator_type (str): Optional. Hardware accelerator type. Must also set accelerator_count if used. One of ACCELERATOR_TYPE_UNSPECIFIED, NVIDIA_TESLA_K80, NVIDIA_TESLA_P100, NVIDIA_TESLA_V100, NVIDIA_TESLA_P4, NVIDIA_TESLA_T4 accelerator_count (int): Optional. The number of accelerators to attach to a worker replica. service_account (str): The service account that the DeployedModel's container runs as. Specify the email address of the service account. If this service account is not specified, the container runs as a service account that doesn't have access to the resource project. Users deploying the Model must have the `iam.serviceAccounts.actAs` permission on this service account. explanation_metadata (aiplatform.explain.ExplanationMetadata): Optional. Metadata describing the Model's input and output for explanation. `explanation_metadata` is optional while `explanation_parameters` must be specified when used. For more details, see `Ref docs <http://tinyurl.com/1igh60kt>` explanation_parameters (aiplatform.explain.ExplanationParameters): Optional. Parameters to configure explaining for Model's predictions. For more details, see `Ref docs <http://tinyurl.com/1an4zake>` metadata (Sequence[Tuple[str, str]]): Optional. Strings which should be sent along with the request as metadata. encryption_spec_key_name (Optional[str]): Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the model. Has the form: ``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``. The key needs to be in the same region as where the compute resource is created. If set, this Endpoint and all sub-resources of this Endpoint will be secured by this key. Overrides encryption_spec_key_name set in aiplatform.init. network (str): Optional. The full name of the Compute Engine network to which the Endpoint, if created, will be peered to. E.g. "projects/12345/global/networks/myVPC". Private services access must already be configured for the network. If set or aiplatform.init(network=...) has been set, a PrivateEndpoint will be created. If left unspecified, an Endpoint will be created. Read more about PrivateEndpoints [in the documentation](https://cloud.google.com/vertex-ai/docs/predictions/using-private-endpoints). sync (bool): Whether to execute this method synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed. deploy_request_timeout (float): Optional. The timeout for the deploy request in seconds. autoscaling_target_cpu_utilization (int): Optional. Target CPU Utilization to use for Autoscaling Replicas. A default value of 60 will be used if not specified. autoscaling_target_accelerator_duty_cycle (int): Optional. Target Accelerator Duty Cycle. Must also set accelerator_type and accelerator_count if specified. A default value of 60 will be used if not specified. Returns: endpoint (Union[Endpoint, PrivateEndpoint]): Endpoint with the deployed model. Raises: ValueError: If `traffic_split` is set for PrivateEndpoint. """ network = network or initializer.global_config.network Endpoint._validate_deploy_args( min_replica_count=min_replica_count, max_replica_count=max_replica_count, accelerator_type=accelerator_type, deployed_model_display_name=deployed_model_display_name, traffic_split=traffic_split, traffic_percentage=traffic_percentage, explanation_metadata=explanation_metadata, explanation_parameters=explanation_parameters, ) if isinstance(endpoint, PrivateEndpoint): if traffic_split: raise ValueError( "Traffic splitting is not yet supported for PrivateEndpoint. " "Try calling deploy() without providing `traffic_split`. " "A maximum of one model can be deployed to each private Endpoint." ) return self._deploy( endpoint=endpoint, deployed_model_display_name=deployed_model_display_name, traffic_percentage=traffic_percentage, traffic_split=traffic_split, machine_type=machine_type, min_replica_count=min_replica_count, max_replica_count=max_replica_count, accelerator_type=accelerator_type, accelerator_count=accelerator_count, service_account=service_account, explanation_metadata=explanation_metadata, explanation_parameters=explanation_parameters, metadata=metadata, encryption_spec_key_name=encryption_spec_key_name or initializer.global_config.encryption_spec_key_name, network=network, sync=sync, deploy_request_timeout=deploy_request_timeout, autoscaling_target_cpu_utilization=autoscaling_target_cpu_utilization, autoscaling_target_accelerator_duty_cycle=autoscaling_target_accelerator_duty_cycle, ) @base.optional_sync(return_input_arg="endpoint", bind_future_to_self=False) def _deploy( self, endpoint: Optional[Union["Endpoint", "PrivateEndpoint"]] = None, deployed_model_display_name: Optional[str] = None, traffic_percentage: Optional[int] = 0, traffic_split: Optional[Dict[str, int]] = None, machine_type: Optional[str] = None, min_replica_count: int = 1, max_replica_count: int = 1, accelerator_type: Optional[str] = None, accelerator_count: Optional[int] = None, service_account: Optional[str] = None, explanation_metadata: Optional[aiplatform.explain.ExplanationMetadata] = None, explanation_parameters: Optional[ aiplatform.explain.ExplanationParameters ] = None, metadata: Optional[Sequence[Tuple[str, str]]] = (), encryption_spec_key_name: Optional[str] = None, network: Optional[str] = None, sync: bool = True, deploy_request_timeout: Optional[float] = None, autoscaling_target_cpu_utilization: Optional[int] = None, autoscaling_target_accelerator_duty_cycle: Optional[int] = None, ) -> Union[Endpoint, PrivateEndpoint]: """Deploys model to endpoint. Endpoint will be created if unspecified. Args: endpoint (Union[Endpoint, PrivateEndpoint]): Optional. Public or private Endpoint to deploy model to. If not specified, endpoint display name will be model display name+'_endpoint'. deployed_model_display_name (str): Optional. The display name of the DeployedModel. If not provided upon creation, the Model's display_name is used. traffic_percentage (int): Optional. Desired traffic to newly deployed model. Defaults to 0 if there are pre-existing deployed models. Defaults to 100 if there are no pre-existing deployed models. Negative values should not be provided. Traffic of previously deployed models at the endpoint will be scaled down to accommodate new deployed model's traffic. Should not be provided if traffic_split is provided. traffic_split (Dict[str, int]): Optional. A map from a DeployedModel's ID to the percentage of this Endpoint's traffic that should be forwarded to that DeployedModel. If a DeployedModel's ID is not listed in this map, then it receives no traffic. The traffic percentage values must add up to 100, or map must be empty if the Endpoint is to not accept any traffic at the moment. Key for model being deployed is "0". Should not be provided if traffic_percentage is provided. machine_type (str): Optional. The type of machine. Not specifying machine type will result in model to be deployed with automatic resources. min_replica_count (int): Optional. The minimum number of machine replicas this deployed model will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed. max_replica_count (int): Optional. The maximum number of replicas this deployed model may be deployed on when the traffic against it increases. If requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale the model to that many replicas is guaranteed (barring service outages). If traffic against the deployed model increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, the smaller value of min_replica_count or 1 will be used. accelerator_type (str): Optional. Hardware accelerator type. Must also set accelerator_count if used. One of ACCELERATOR_TYPE_UNSPECIFIED, NVIDIA_TESLA_K80, NVIDIA_TESLA_P100, NVIDIA_TESLA_V100, NVIDIA_TESLA_P4, NVIDIA_TESLA_T4 accelerator_count (int): Optional. The number of accelerators to attach to a worker replica. service_account (str): The service account that the DeployedModel's container runs as. Specify the email address of the service account. If this service account is not specified, the container runs as a service account that doesn't have access to the resource project. Users deploying the Model must have the `iam.serviceAccounts.actAs` permission on this service account. explanation_metadata (aiplatform.explain.ExplanationMetadata): Optional. Metadata describing the Model's input and output for explanation. `explanation_metadata` is optional while `explanation_parameters` must be specified when used. For more details, see `Ref docs <http://tinyurl.com/1igh60kt>` explanation_parameters (aiplatform.explain.ExplanationParameters): Optional. Parameters to configure explaining for Model's predictions. For more details, see `Ref docs <http://tinyurl.com/1an4zake>` metadata (Sequence[Tuple[str, str]]): Optional. Strings which should be sent along with the request as metadata. encryption_spec_key_name (Optional[str]): Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the model. Has the form: ``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``. The key needs to be in the same region as where the compute resource is created. If set, this Model and all sub-resources of this Model will be secured by this key. Overrides encryption_spec_key_name set in aiplatform.init network (str): Optional. The full name of the Compute Engine network to which the Endpoint, if created, will be peered to. E.g. "projects/12345/global/networks/myVPC". Private services access must already be configured for the network. Read more about PrivateEndpoints [in the documentation](https://cloud.google.com/vertex-ai/docs/predictions/using-private-endpoints). sync (bool): Whether to execute this method synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed. deploy_request_timeout (float): Optional. The timeout for the deploy request in seconds. autoscaling_target_cpu_utilization (int): Optional. Target CPU Utilization to use for Autoscaling Replicas. A default value of 60 will be used if not specified. autoscaling_target_accelerator_duty_cycle (int): Optional. Target Accelerator Duty Cycle. Must also set accelerator_type and accelerator_count if specified. A default value of 60 will be used if not specified. Returns: endpoint (Union[Endpoint, PrivateEndpoint]): Endpoint with the deployed model. """ if endpoint is None: display_name = self.display_name[:118] + "_endpoint" if not network: endpoint = Endpoint.create( display_name=display_name, project=self.project, location=self.location, credentials=self.credentials, encryption_spec_key_name=encryption_spec_key_name, ) else: endpoint = PrivateEndpoint.create( display_name=display_name, network=network, project=self.project, location=self.location, credentials=self.credentials, encryption_spec_key_name=encryption_spec_key_name, ) _LOGGER.log_action_start_against_resource("Deploying model to", "", endpoint) endpoint._deploy_call( endpoint.api_client, endpoint.resource_name, self, endpoint._gca_resource.traffic_split, network=network, deployed_model_display_name=deployed_model_display_name, traffic_percentage=traffic_percentage, traffic_split=traffic_split, machine_type=machine_type, min_replica_count=min_replica_count, max_replica_count=max_replica_count, accelerator_type=accelerator_type, accelerator_count=accelerator_count, service_account=service_account, explanation_metadata=explanation_metadata, explanation_parameters=explanation_parameters, metadata=metadata, deploy_request_timeout=deploy_request_timeout, autoscaling_target_cpu_utilization=autoscaling_target_cpu_utilization, autoscaling_target_accelerator_duty_cycle=autoscaling_target_accelerator_duty_cycle, ) _LOGGER.log_action_completed_against_resource("model", "deployed", endpoint) endpoint._sync_gca_resource() return endpoint def batch_predict( self, job_display_name: Optional[str] = None, gcs_source: Optional[Union[str, Sequence[str]]] = None, bigquery_source: Optional[str] = None, instances_format: str = "jsonl", gcs_destination_prefix: Optional[str] = None, bigquery_destination_prefix: Optional[str] = None, predictions_format: str = "jsonl", model_parameters: Optional[Dict] = None, machine_type: Optional[str] = None, accelerator_type: Optional[str] = None, accelerator_count: Optional[int] = None, starting_replica_count: Optional[int] = None, max_replica_count: Optional[int] = None, generate_explanation: Optional[bool] = False, explanation_metadata: Optional[aiplatform.explain.ExplanationMetadata] = None, explanation_parameters: Optional[ aiplatform.explain.ExplanationParameters ] = None, labels: Optional[Dict[str, str]] = None, credentials: Optional[auth_credentials.Credentials] = None, encryption_spec_key_name: Optional[str] = None, sync: bool = True, create_request_timeout: Optional[float] = None, batch_size: Optional[int] = None, ) -> jobs.BatchPredictionJob: """Creates a batch prediction job using this Model and outputs prediction results to the provided destination prefix in the specified `predictions_format`. One source and one destination prefix are required. Example usage: my_model.batch_predict( job_display_name="prediction-123", gcs_source="gs://example-bucket/instances.csv", instances_format="csv", bigquery_destination_prefix="projectId.bqDatasetId.bqTableId" ) Args: job_display_name (str): Optional. The user-defined name of the BatchPredictionJob. The name can be up to 128 characters long and can be consist of any UTF-8 characters. gcs_source: Optional[Sequence[str]] = None Google Cloud Storage URI(-s) to your instances to run batch prediction on. They must match `instances_format`. bigquery_source: Optional[str] = None BigQuery URI to a table, up to 2000 characters long. For example: `bq://projectId.bqDatasetId.bqTableId` instances_format: str = "jsonl" The format in which instances are provided. Must be one of the formats listed in `Model.supported_input_storage_formats`. Default is "jsonl" when using `gcs_source`. If a `bigquery_source` is provided, this is overridden to "bigquery". gcs_destination_prefix: Optional[str] = None The Google Cloud Storage location of the directory where the output is to be written to. In the given directory a new directory is created. Its name is ``prediction-<model-display-name>-<job-create-time>``, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files ``predictions_0001.<extension>``, ``predictions_0002.<extension>``, ..., ``predictions_N.<extension>`` are created where ``<extension>`` depends on chosen ``predictions_format``, and N may equal 0001 and depends on the total number of successfully predicted instances. If the Model has both ``instance`` and ``prediction`` schemata defined then each such file contains predictions as per the ``predictions_format``. If prediction for any instance failed (partially or completely), then an additional ``errors_0001.<extension>``, ``errors_0002.<extension>``,..., ``errors_N.<extension>`` files are created (N depends on total number of failed predictions). These files contain the failed instances, as per their schema, followed by an additional ``error`` field which as value has ```google.rpc.Status`` <Status>`__ containing only ``code`` and ``message`` fields. bigquery_destination_prefix: Optional[str] = None The BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: ``bq://projectId`` or ``bq://projectId.bqDatasetId`` or ``bq://projectId.bqDatasetId.bqTableId``. If no Dataset is specified, a new one is created with the name ``prediction_<model-display-name>_<job-create-time>`` where the table name is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created, ``predictions``, and ``errors``. If the Model has both ``instance`` and ``prediction`` schemata defined then the tables have columns as follows: The ``predictions`` table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model's instance and prediction schemata. The ``errors`` table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single "errors" column, which as values has ```google.rpc.Status`` <Status>`__ represented as a STRUCT, and containing only ``code`` and ``message``. predictions_format: str = "jsonl" Required. The format in which Vertex AI outputs the predictions, must be one of the formats specified in `Model.supported_output_storage_formats`. Default is "jsonl" when using `gcs_destination_prefix`. If a `bigquery_destination_prefix` is provided, this is overridden to "bigquery". model_parameters: Optional[Dict] = None Optional. The parameters that govern the predictions. The schema of the parameters may be specified via the Model's `parameters_schema_uri`. machine_type: Optional[str] = None Optional. The type of machine for running batch prediction on dedicated resources. Not specifying machine type will result in batch prediction job being run with automatic resources. accelerator_type: Optional[str] = None Optional. The type of accelerator(s) that may be attached to the machine as per `accelerator_count`. Only used if `machine_type` is set. accelerator_count: Optional[int] = None Optional. The number of accelerators to attach to the `machine_type`. Only used if `machine_type` is set. starting_replica_count: Optional[int] = None The number of machine replicas used at the start of the batch operation. If not set, Vertex AI decides starting number, not greater than `max_replica_count`. Only used if `machine_type` is set. max_replica_count: Optional[int] = None The maximum number of machine replicas the batch operation may be scaled to. Only used if `machine_type` is set. Default is 10. generate_explanation (bool): Optional. Generate explanation along with the batch prediction results. This will cause the batch prediction output to include explanations based on the `prediction_format`: - `bigquery`: output includes a column named `explanation`. The value is a struct that conforms to the [aiplatform.gapic.Explanation] object. - `jsonl`: The JSON objects on each line include an additional entry keyed `explanation`. The value of the entry is a JSON object that conforms to the [aiplatform.gapic.Explanation] object. - `csv`: Generating explanations for CSV format is not supported. explanation_metadata (aiplatform.explain.ExplanationMetadata): Optional. Explanation metadata configuration for this BatchPredictionJob. Can be specified only if `generate_explanation` is set to `True`. This value overrides the value of `Model.explanation_metadata`. All fields of `explanation_metadata` are optional in the request. If a field of the `explanation_metadata` object is not populated, the corresponding field of the `Model.explanation_metadata` object is inherited. For more details, see `Ref docs <http://tinyurl.com/1igh60kt>` explanation_parameters (aiplatform.explain.ExplanationParameters): Optional. Parameters to configure explaining for Model's predictions. Can be specified only if `generate_explanation` is set to `True`. This value overrides the value of `Model.explanation_parameters`. All fields of `explanation_parameters` are optional in the request. If a field of the `explanation_parameters` object is not populated, the corresponding field of the `Model.explanation_parameters` object is inherited. For more details, see `Ref docs <http://tinyurl.com/1an4zake>` labels: Optional[Dict[str, str]] = None Optional. The labels with user-defined metadata to organize your BatchPredictionJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. credentials: Optional[auth_credentials.Credentials] = None Optional. Custom credentials to use to create this batch prediction job. Overrides credentials set in aiplatform.init. encryption_spec_key_name (Optional[str]): Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the model. Has the form: ``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``. The key needs to be in the same region as where the compute resource is created. If set, this Model and all sub-resources of this Model will be secured by this key. Overrides encryption_spec_key_name set in aiplatform.init. create_request_timeout (float): Optional. The timeout for the create request in seconds. batch_size (int): Optional. The number of the records (e.g. instances) of the operation given in each batch to a machine replica. Machine type, and size of a single record should be considered when setting this parameter, higher value speeds up the batch operation's execution, but too high value will result in a whole batch not fitting in a machine's memory, and the whole operation will fail. The default value is 64. Returns: job (jobs.BatchPredictionJob): Instantiated representation of the created batch prediction job. """ return jobs.BatchPredictionJob.create( job_display_name=job_display_name, model_name=self, instances_format=instances_format, predictions_format=predictions_format, gcs_source=gcs_source, bigquery_source=bigquery_source, gcs_destination_prefix=gcs_destination_prefix, bigquery_destination_prefix=bigquery_destination_prefix, model_parameters=model_parameters, machine_type=machine_type, accelerator_type=accelerator_type, accelerator_count=accelerator_count, starting_replica_count=starting_replica_count, max_replica_count=max_replica_count, batch_size=batch_size, generate_explanation=generate_explanation, explanation_metadata=explanation_metadata, explanation_parameters=explanation_parameters, labels=labels, project=self.project, location=self.location, credentials=credentials or self.credentials, encryption_spec_key_name=encryption_spec_key_name, sync=sync, create_request_timeout=create_request_timeout, ) @classmethod def list( cls, filter: Optional[str] = None, order_by: Optional[str] = None, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[auth_credentials.Credentials] = None, ) -> List["models.Model"]: """List all Model resource instances. Example Usage: aiplatform.Model.list( filter='labels.my_label="my_label_value" AND display_name="my_model"', ) Args: filter (str): Optional. An expression for filtering the results of the request. For field names both snake_case and camelCase are supported. order_by (str): Optional. A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: `display_name`, `create_time`, `update_time` project (str): Optional. Project to retrieve list from. If not set, project set in aiplatform.init will be used. location (str): Optional. Location to retrieve list from. If not set, location set in aiplatform.init will be used. credentials (auth_credentials.Credentials): Optional. Custom credentials to use to retrieve list. Overrides credentials set in aiplatform.init. Returns: List[models.Model]: A list of Model resource objects """ return cls._list( filter=filter, order_by=order_by, project=project, location=location, credentials=credentials, ) @classmethod def _construct_sdk_resource_from_gapic( cls, gapic_resource: gca_model_compat.Model, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[auth_credentials.Credentials] = None, ) -> "Model": """Override base._construct_sdk_resource_from_gapic to allow for setting a ModelRegistry and resource_id_validator. Args: gapic_resource (gca_model_compat.Model): A GAPIC representation of a Model resource. project (str): Optional. Project to construct SDK object from. If not set, project set in aiplatform.init will be used. location (str): Optional. Location to construct SDK object from. If not set, location set in aiplatform.init will be used. credentials (auth_credentials.Credentials): Optional. Custom credentials to use to construct SDK object. Overrides credentials set in aiplatform.init. Returns: Model: An initialized SDK Model object that represents the Model GAPIC type. """ sdk_resource = super()._construct_sdk_resource_from_gapic( gapic_resource=gapic_resource, project=project, location=location, credentials=credentials, ) sdk_resource._resource_id_validator = super()._revisioned_resource_id_validator sdk_resource._registry = ModelRegistry( sdk_resource.resource_name, location=location, project=project, credentials=credentials, ) return sdk_resource @base.optional_sync() def _wait_on_export(self, operation_future: operation.Operation, sync=True) -> None: operation_future.result() def export_model( self, export_format_id: str, artifact_destination: Optional[str] = None, image_destination: Optional[str] = None, sync: bool = True, ) -> Dict[str, str]: """Exports a trained, exportable Model to a location specified by the user. A Model is considered to be exportable if it has at least one `supported_export_formats`. Either `artifact_destination` or `image_destination` must be provided. Example Usage: my_model.export( export_format_id="tf-saved-model", artifact_destination="gs://my-bucket/models/" ) or my_model.export( export_format_id="custom-model", image_destination="us-central1-docker.pkg.dev/projectId/repo/image" ) Args: export_format_id (str): Required. The ID of the format in which the Model must be exported. The list of export formats that this Model supports can be found by calling `Model.supported_export_formats`. artifact_destination (str): The Cloud Storage location where the Model artifact is to be written to. Under the directory given as the destination a new one with name "``model-export-<model-display-name>-<timestamp-of-export-call>``", where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format, will be created. Inside, the Model and any of its supporting files will be written. This field should only be set when, in [Model.supported_export_formats], the value for the key given in `export_format_id` contains ``ARTIFACT``. image_destination (str): The Google Container Registry or Artifact Registry URI where the Model container image will be copied to. Accepted forms: - Google Container Registry path. For example: ``gcr.io/projectId/imageName:tag``. - Artifact Registry path. For example: ``us-central1-docker.pkg.dev/projectId/repoName/imageName:tag``. This field should only be set when, in [Model.supported_export_formats], the value for the key given in `export_format_id` contains ``IMAGE``. sync (bool): Whether to execute this export synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed. Returns: output_info (Dict[str, str]): Details of the completed export with output destination paths to the artifacts or container image. Raises: ValueError: If model does not support exporting. ValueError: If invalid arguments or export formats are provided. """ self.wait() # Model does not support exporting if not self.supported_export_formats: raise ValueError(f"The model `{self.resource_name}` is not exportable.") # No destination provided if not any((artifact_destination, image_destination)): raise ValueError( "Please provide an `artifact_destination` or `image_destination`." ) export_format_id = export_format_id.lower() # Unsupported export type if export_format_id not in self.supported_export_formats: raise ValueError( f"'{export_format_id}' is not a supported export format for this model. " f"Choose one of the following: {self.supported_export_formats}" ) content_types = gca_model_compat.Model.ExportFormat.ExportableContent supported_content_types = self.supported_export_formats[export_format_id] if ( artifact_destination and content_types.ARTIFACT not in supported_content_types ): raise ValueError( "This model can not be exported as an artifact in '{export_format_id}' format. " "Try exporting as a container image by passing the `image_destination` argument." ) if image_destination and content_types.IMAGE not in supported_content_types: raise ValueError( "This model can not be exported as a container image in '{export_format_id}' format. " "Try exporting the model artifacts by passing a `artifact_destination` argument." ) # Construct request payload output_config = gca_model_service_compat.ExportModelRequest.OutputConfig( export_format_id=export_format_id ) if artifact_destination: output_config.artifact_destination = gca_io_compat.GcsDestination( output_uri_prefix=artifact_destination ) if image_destination: output_config.image_destination = ( gca_io_compat.ContainerRegistryDestination(output_uri=image_destination) ) _LOGGER.log_action_start_against_resource("Exporting", "model", self) model_name = self.versioned_resource_name operation_future = self.api_client.export_model( name=model_name, output_config=output_config ) _LOGGER.log_action_started_against_resource_with_lro( "Export", "model", self.__class__, operation_future ) # Block before returning self._wait_on_export(operation_future=operation_future, sync=sync) _LOGGER.log_action_completed_against_resource("model", "exported", self) return json_format.MessageToDict(operation_future.metadata.output_info._pb) @classmethod @base.optional_sync() def upload_xgboost_model_file( cls, model_file_path: str, xgboost_version: Optional[str] = None, display_name: Optional[str] = None, description: Optional[str] = None, model_id: Optional[str] = None, parent_model: Optional[str] = None, is_default_version: Optional[bool] = True, version_aliases: Optional[Sequence[str]] = None, version_description: Optional[str] = None, instance_schema_uri: Optional[str] = None, parameters_schema_uri: Optional[str] = None, prediction_schema_uri: Optional[str] = None, explanation_metadata: Optional[aiplatform.explain.ExplanationMetadata] = None, explanation_parameters: Optional[ aiplatform.explain.ExplanationParameters ] = None, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[auth_credentials.Credentials] = None, labels: Optional[Dict[str, str]] = None, encryption_spec_key_name: Optional[str] = None, staging_bucket: Optional[str] = None, sync=True, upload_request_timeout: Optional[float] = None, ) -> "Model": """Uploads a model and returns a Model representing the uploaded Model resource. Note: This function is *experimental* and can be changed in the future. Example usage: my_model = Model.upload_xgboost_model_file( model_file_path="iris.xgboost_model.bst" ) Args: model_file_path (str): Required. Local file path of the model. xgboost_version (str): Optional. The version of the XGBoost serving container. Supported versions: ["0.82", "0.90", "1.1", "1.2", "1.3", "1.4"]. If the version is not specified, the latest version is used. display_name (str): Optional. The display name of the Model. The name can be up to 128 characters long and can be consist of any UTF-8 characters. description (str): The description of the model. model_id (str): Optional. The ID to use for the uploaded Model, which will become the final component of the model resource name. This value may be up to 63 characters, and valid characters are `[a-z0-9_-]`. The first character cannot be a number or hyphen. parent_model (str): Optional. The resource name or model ID of an existing model that the newly-uploaded model will be a version of. Only set this field when uploading a new version of an existing model. is_default_version (bool): Optional. When set to True, the newly uploaded model version will automatically have alias "default" included. Subsequent uses of this model without a version specified will use this "default" version. When set to False, the "default" alias will not be moved. Actions targeting the newly-uploaded model version will need to specifically reference this version by ID or alias. New model uploads, i.e. version 1, will always be "default" aliased. version_aliases (Sequence[str]): Optional. User provided version aliases so that a model version can be referenced via alias instead of auto-generated version ID. A default version alias will be created for the first version of the model. The format is [a-z][a-zA-Z0-9-]{0,126}[a-z0-9] version_description (str): Optional. The description of the model version being uploaded. instance_schema_uri (str): Optional. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used in ``PredictRequest.instances``, ``ExplainRequest.instances`` and ``BatchPredictionJob.input_config``. The schema is defined as an OpenAPI 3.0.2 `Schema Object <https://tinyurl.com/y538mdwt#schema-object>`__. AutoML Models always have this field populated by AI Platform. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access. parameters_schema_uri (str): Optional. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via ``PredictRequest.parameters``, ``ExplainRequest.parameters`` and ``BatchPredictionJob.model_parameters``. The schema is defined as an OpenAPI 3.0.2 `Schema Object <https://tinyurl.com/y538mdwt#schema-object>`__. AutoML Models always have this field populated by AI Platform, if no parameters are supported it is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access. prediction_schema_uri (str): Optional. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via ``PredictResponse.predictions``, ``ExplainResponse.explanations``, and ``BatchPredictionJob.output_config``. The schema is defined as an OpenAPI 3.0.2 `Schema Object <https://tinyurl.com/y538mdwt#schema-object>`__. AutoML Models always have this field populated by AI Platform. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access. explanation_metadata (aiplatform.explain.ExplanationMetadata): Optional. Metadata describing the Model's input and output for explanation. `explanation_metadata` is optional while `explanation_parameters` must be specified when used. For more details, see `Ref docs <http://tinyurl.com/1igh60kt>` explanation_parameters (aiplatform.explain.ExplanationParameters): Optional. Parameters to configure explaining for Model's predictions. For more details, see `Ref docs <http://tinyurl.com/1an4zake>` project: Optional[str]=None, Project to upload this model to. Overrides project set in aiplatform.init. location: Optional[str]=None, Location to upload this model to. Overrides location set in aiplatform.init. credentials: Optional[auth_credentials.Credentials]=None, Custom credentials to use to upload this model. Overrides credentials set in aiplatform.init. labels (Dict[str, str]): Optional. The labels with user-defined metadata to organize your Models. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. encryption_spec_key_name (Optional[str]): Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the model. Has the form: ``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``. The key needs to be in the same region as where the compute resource is created. If set, this Model and all sub-resources of this Model will be secured by this key. Overrides encryption_spec_key_name set in aiplatform.init. staging_bucket (str): Optional. Bucket to stage local model artifacts. Overrides staging_bucket set in aiplatform.init. upload_request_timeout (float): Optional. The timeout for the upload request in seconds. Returns: model (aiplatform.Model): Instantiated representation of the uploaded model resource. Raises: ValueError: If model directory does not contain a supported model file. """ if not display_name: display_name = cls._generate_display_name("XGBoost model") XGBOOST_SUPPORTED_MODEL_FILE_EXTENSIONS = [ ".pkl", ".joblib", ".bst", ] container_image_uri = aiplatform.helpers.get_prebuilt_prediction_container_uri( region=location, framework="xgboost", framework_version=xgboost_version or "1.4", accelerator="cpu", ) model_file_path_obj = pathlib.Path(model_file_path) if not model_file_path_obj.is_file(): raise ValueError( f"model_file_path path must point to a file: '{model_file_path}'" ) model_file_extension = model_file_path_obj.suffix if model_file_extension not in XGBOOST_SUPPORTED_MODEL_FILE_EXTENSIONS: _LOGGER.warning( f"Only the following XGBoost model file extensions are currently supported: '{XGBOOST_SUPPORTED_MODEL_FILE_EXTENSIONS}'" ) _LOGGER.warning( "Treating the model file as a binary serialized XGBoost Booster." ) model_file_extension = ".bst" # Preparing model directory # We cannot clean up the directory immediately after calling Model.upload since # that call may be asynchronous and return before the model file has been read. # To work around this, we make this method asynchronous (decorate with @base.optional_sync) # but call Model.upload with sync=True. with tempfile.TemporaryDirectory() as prepared_model_dir: prepared_model_file_path = pathlib.Path(prepared_model_dir) / ( "model" + model_file_extension ) shutil.copy(model_file_path_obj, prepared_model_file_path) return cls.upload( serving_container_image_uri=container_image_uri, artifact_uri=prepared_model_dir, display_name=display_name, description=description, model_id=model_id, parent_model=parent_model, is_default_version=is_default_version, version_aliases=version_aliases, version_description=version_description, instance_schema_uri=instance_schema_uri, parameters_schema_uri=parameters_schema_uri, prediction_schema_uri=prediction_schema_uri, explanation_metadata=explanation_metadata, explanation_parameters=explanation_parameters, project=project, location=location, credentials=credentials, labels=labels, encryption_spec_key_name=encryption_spec_key_name, staging_bucket=staging_bucket, sync=True, upload_request_timeout=upload_request_timeout, ) @classmethod @base.optional_sync() def upload_scikit_learn_model_file( cls, model_file_path: str, sklearn_version: Optional[str] = None, display_name: Optional[str] = None, description: Optional[str] = None, model_id: Optional[str] = None, parent_model: Optional[str] = None, is_default_version: Optional[bool] = True, version_aliases: Optional[Sequence[str]] = None, version_description: Optional[str] = None, instance_schema_uri: Optional[str] = None, parameters_schema_uri: Optional[str] = None, prediction_schema_uri: Optional[str] = None, explanation_metadata: Optional[aiplatform.explain.ExplanationMetadata] = None, explanation_parameters: Optional[ aiplatform.explain.ExplanationParameters ] = None, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[auth_credentials.Credentials] = None, labels: Optional[Dict[str, str]] = None, encryption_spec_key_name: Optional[str] = None, staging_bucket: Optional[str] = None, sync=True, upload_request_timeout: Optional[float] = None, ) -> "Model": """Uploads a model and returns a Model representing the uploaded Model resource. Note: This function is *experimental* and can be changed in the future. Example usage: my_model = Model.upload_scikit_learn_model_file( model_file_path="iris.sklearn_model.joblib" ) Args: model_file_path (str): Required. Local file path of the model. sklearn_version (str): Optional. The version of the Scikit-learn serving container. Supported versions: ["0.20", "0.22", "0.23", "0.24", "1.0"]. If the version is not specified, the latest version is used. display_name (str): Optional. The display name of the Model. The name can be up to 128 characters long and can be consist of any UTF-8 characters. description (str): The description of the model. model_id (str): Optional. The ID to use for the uploaded Model, which will become the final component of the model resource name. This value may be up to 63 characters, and valid characters are `[a-z0-9_-]`. The first character cannot be a number or hyphen. parent_model (str): Optional. The resource name or model ID of an existing model that the newly-uploaded model will be a version of. Only set this field when uploading a new version of an existing model. is_default_version (bool): Optional. When set to True, the newly uploaded model version will automatically have alias "default" included. Subsequent uses of this model without a version specified will use this "default" version. When set to False, the "default" alias will not be moved. Actions targeting the newly-uploaded model version will need to specifically reference this version by ID or alias. New model uploads, i.e. version 1, will always be "default" aliased. version_aliases (Sequence[str]): Optional. User provided version aliases so that a model version can be referenced via alias instead of auto-generated version ID. A default version alias will be created for the first version of the model. The format is [a-z][a-zA-Z0-9-]{0,126}[a-z0-9] version_description (str): Optional. The description of the model version being uploaded. instance_schema_uri (str): Optional. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used in ``PredictRequest.instances``, ``ExplainRequest.instances`` and ``BatchPredictionJob.input_config``. The schema is defined as an OpenAPI 3.0.2 `Schema Object <https://tinyurl.com/y538mdwt#schema-object>`__. AutoML Models always have this field populated by AI Platform. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access. parameters_schema_uri (str): Optional. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via ``PredictRequest.parameters``, ``ExplainRequest.parameters`` and ``BatchPredictionJob.model_parameters``. The schema is defined as an OpenAPI 3.0.2 `Schema Object <https://tinyurl.com/y538mdwt#schema-object>`__. AutoML Models always have this field populated by AI Platform, if no parameters are supported it is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access. prediction_schema_uri (str): Optional. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via ``PredictResponse.predictions``, ``ExplainResponse.explanations``, and ``BatchPredictionJob.output_config``. The schema is defined as an OpenAPI 3.0.2 `Schema Object <https://tinyurl.com/y538mdwt#schema-object>`__. AutoML Models always have this field populated by AI Platform. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access. explanation_metadata (aiplatform.explain.ExplanationMetadata): Optional. Metadata describing the Model's input and output for explanation. `explanation_metadata` is optional while `explanation_parameters` must be specified when used. For more details, see `Ref docs <http://tinyurl.com/1igh60kt>` explanation_parameters (aiplatform.explain.ExplanationParameters): Optional. Parameters to configure explaining for Model's predictions. For more details, see `Ref docs <http://tinyurl.com/1an4zake>` project: Optional[str]=None, Project to upload this model to. Overrides project set in aiplatform.init. location: Optional[str]=None, Location to upload this model to. Overrides location set in aiplatform.init. credentials: Optional[auth_credentials.Credentials]=None, Custom credentials to use to upload this model. Overrides credentials set in aiplatform.init. labels (Dict[str, str]): Optional. The labels with user-defined metadata to organize your Models. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. encryption_spec_key_name (Optional[str]): Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the model. Has the form: ``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``. The key needs to be in the same region as where the compute resource is created. If set, this Model and all sub-resources of this Model will be secured by this key. Overrides encryption_spec_key_name set in aiplatform.init. staging_bucket (str): Optional. Bucket to stage local model artifacts. Overrides staging_bucket set in aiplatform.init. sync (bool): Whether to execute this method synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed. upload_request_timeout (float): Optional. The timeout for the upload request in seconds. Returns: model (aiplatform.Model): Instantiated representation of the uploaded model resource. Raises: ValueError: If explanation_metadata is specified while explanation_parameters is not. Also if model directory does not contain a supported model file. """ if not display_name: display_name = cls._generate_display_name("Scikit-Learn model") SKLEARN_SUPPORTED_MODEL_FILE_EXTENSIONS = [ ".pkl", ".joblib", ] container_image_uri = aiplatform.helpers.get_prebuilt_prediction_container_uri( region=location, framework="sklearn", framework_version=sklearn_version or "1.0", accelerator="cpu", ) model_file_path_obj = pathlib.Path(model_file_path) if not model_file_path_obj.is_file(): raise ValueError( f"model_file_path path must point to a file: '{model_file_path}'" ) model_file_extension = model_file_path_obj.suffix if model_file_extension not in SKLEARN_SUPPORTED_MODEL_FILE_EXTENSIONS: _LOGGER.warning( f"Only the following Scikit-learn model file extensions are currently supported: '{SKLEARN_SUPPORTED_MODEL_FILE_EXTENSIONS}'" ) _LOGGER.warning( "Treating the model file as a pickle serialized Scikit-learn model." ) model_file_extension = ".pkl" # Preparing model directory # We cannot clean up the directory immediately after calling Model.upload since # that call may be asynchronous and return before the model file has been read. # To work around this, we make this method asynchronous (decorate with @base.optional_sync) # but call Model.upload with sync=True. with tempfile.TemporaryDirectory() as prepared_model_dir: prepared_model_file_path = pathlib.Path(prepared_model_dir) / ( "model" + model_file_extension ) shutil.copy(model_file_path_obj, prepared_model_file_path) return cls.upload( serving_container_image_uri=container_image_uri, artifact_uri=prepared_model_dir, display_name=display_name, description=description, model_id=model_id, parent_model=parent_model, is_default_version=is_default_version, version_aliases=version_aliases, version_description=version_description, instance_schema_uri=instance_schema_uri, parameters_schema_uri=parameters_schema_uri, prediction_schema_uri=prediction_schema_uri, explanation_metadata=explanation_metadata, explanation_parameters=explanation_parameters, project=project, location=location, credentials=credentials, labels=labels, encryption_spec_key_name=encryption_spec_key_name, staging_bucket=staging_bucket, sync=True, upload_request_timeout=upload_request_timeout, ) @classmethod def upload_tensorflow_saved_model( cls, saved_model_dir: str, tensorflow_version: Optional[str] = None, use_gpu: bool = False, display_name: Optional[str] = None, description: Optional[str] = None, model_id: Optional[str] = None, parent_model: Optional[str] = None, is_default_version: Optional[bool] = True, version_aliases: Optional[Sequence[str]] = None, version_description: Optional[str] = None, instance_schema_uri: Optional[str] = None, parameters_schema_uri: Optional[str] = None, prediction_schema_uri: Optional[str] = None, explanation_metadata: Optional[aiplatform.explain.ExplanationMetadata] = None, explanation_parameters: Optional[ aiplatform.explain.ExplanationParameters ] = None, project: Optional[str] = None, location: Optional[str] = None, credentials: Optional[auth_credentials.Credentials] = None, labels: Optional[Dict[str, str]] = None, encryption_spec_key_name: Optional[str] = None, staging_bucket: Optional[str] = None, sync=True, upload_request_timeout: Optional[str] = None, ) -> "Model": """Uploads a model and returns a Model representing the uploaded Model resource. Note: This function is *experimental* and can be changed in the future. Example usage: my_model = Model.upload_scikit_learn_model_file( model_file_path="iris.tensorflow_model.SavedModel" ) Args: saved_model_dir (str): Required. Local directory of the Tensorflow SavedModel. tensorflow_version (str): Optional. The version of the Tensorflow serving container. Supported versions: ["0.15", "2.1", "2.2", "2.3", "2.4", "2.5", "2.6", "2.7"]. If the version is not specified, the latest version is used. use_gpu (bool): Whether to use GPU for model serving. display_name (str): Optional. The display name of the Model. The name can be up to 128 characters long and can be consist of any UTF-8 characters. description (str): The description of the model. model_id (str): Optional. The ID to use for the uploaded Model, which will become the final component of the model resource name. This value may be up to 63 characters, and valid characters are `[a-z0-9_-]`. The first character cannot be a number or hyphen. parent_model (str): Optional. The resource name or model ID of an existing model that the newly-uploaded model will be a version of. Only set this field when uploading a new version of an existing model. is_default_version (bool): Optional. When set to True, the newly uploaded model version will automatically have alias "default" included. Subsequent uses of this model without a version specified will use this "default" version. When set to False, the "default" alias will not be moved. Actions targeting the newly-uploaded model version will need to specifically reference this version by ID or alias. New model uploads, i.e. version 1, will always be "default" aliased. version_aliases (Sequence[str]): Optional. User provided version aliases so that a model version can be referenced via alias instead of auto-generated version ID. A default version alias will be created for the first version of the model. The format is [a-z][a-zA-Z0-9-]{0,126}[a-z0-9] version_description (str): Optional. The description of the model version being uploaded. instance_schema_uri (str): Optional. Points to a YAML file stored on Google Cloud Storage describing the format of a single instance, which are used in ``PredictRequest.instances``, ``ExplainRequest.instances`` and ``BatchPredictionJob.input_config``. The schema is defined as an OpenAPI 3.0.2 `Schema Object <https://tinyurl.com/y538mdwt#schema-object>`__. AutoML Models always have this field populated by AI Platform. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access. parameters_schema_uri (str): Optional. Points to a YAML file stored on Google Cloud Storage describing the parameters of prediction and explanation via ``PredictRequest.parameters``, ``ExplainRequest.parameters`` and ``BatchPredictionJob.model_parameters``. The schema is defined as an OpenAPI 3.0.2 `Schema Object <https://tinyurl.com/y538mdwt#schema-object>`__. AutoML Models always have this field populated by AI Platform, if no parameters are supported it is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access. prediction_schema_uri (str): Optional. Points to a YAML file stored on Google Cloud Storage describing the format of a single prediction produced by this Model, which are returned via ``PredictResponse.predictions``, ``ExplainResponse.explanations``, and ``BatchPredictionJob.output_config``. The schema is defined as an OpenAPI 3.0.2 `Schema Object <https://tinyurl.com/y538mdwt#schema-object>`__. AutoML Models always have this field populated by AI Platform. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access. explanation_metadata (aiplatform.explain.ExplanationMetadata): Optional. Metadata describing the Model's input and output for explanation. `explanation_metadata` is optional while `explanation_parameters` must be specified when used. For more details, see `Ref docs <http://tinyurl.com/1igh60kt>` explanation_parameters (aiplatform.explain.ExplanationParameters): Optional. Parameters to configure explaining for Model's predictions. For more details, see `Ref docs <http://tinyurl.com/1an4zake>` project: Optional[str]=None, Project to upload this model to. Overrides project set in aiplatform.init. location: Optional[str]=None, Location to upload this model to. Overrides location set in aiplatform.init. credentials: Optional[auth_credentials.Credentials]=None, Custom credentials to use to upload this model. Overrides credentials set in aiplatform.init. labels (Dict[str, str]): Optional. The labels with user-defined metadata to organize your Models. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels. encryption_spec_key_name (Optional[str]): Optional. The Cloud KMS resource identifier of the customer managed encryption key used to protect the model. Has the form: ``projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key``. The key needs to be in the same region as where the compute resource is created. If set, this Model and all sub-resources of this Model will be secured by this key. Overrides encryption_spec_key_name set in aiplatform.init. staging_bucket (str): Optional. Bucket to stage local model artifacts. Overrides staging_bucket set in aiplatform.init. sync (bool): Whether to execute this method synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed. upload_request_timeout (float): Optional. The timeout for the upload request in seconds. Returns: model (aiplatform.Model): Instantiated representation of the uploaded model resource. Raises: ValueError: If explanation_metadata is specified while explanation_parameters is not. Also if model directory does not contain a supported model file. """ if not display_name: display_name = cls._generate_display_name("Tensorflow model") container_image_uri = aiplatform.helpers.get_prebuilt_prediction_container_uri( region=location, framework="tensorflow", framework_version=tensorflow_version or "2.7", accelerator="gpu" if use_gpu else "cpu", ) return cls.upload( serving_container_image_uri=container_image_uri, artifact_uri=saved_model_dir, display_name=display_name, description=description, model_id=model_id, parent_model=parent_model, is_default_version=is_default_version, version_aliases=version_aliases, version_description=version_description, instance_schema_uri=instance_schema_uri, parameters_schema_uri=parameters_schema_uri, prediction_schema_uri=prediction_schema_uri, explanation_metadata=explanation_metadata, explanation_parameters=explanation_parameters, project=project, location=location, credentials=credentials, labels=labels, encryption_spec_key_name=encryption_spec_key_name, staging_bucket=staging_bucket, sync=sync, upload_request_timeout=upload_request_timeout, ) def list_model_evaluations( self, ) -> List["model_evaluation.ModelEvaluation"]: """List all Model Evaluation resources associated with this model. If this Model resource was instantiated with a version, the Model Evaluation resources for that version will be returned. If no version was provided when the Model resource was instantiated, Model Evaluation resources will be returned for the default version. Example Usage: my_model = Model( model_name="projects/123/locations/us-central1/models/456@1" ) my_evaluations = my_model.list_model_evaluations() Returns: List[model_evaluation.ModelEvaluation]: List of ModelEvaluation resources for the model. """ return model_evaluation.ModelEvaluation._list( parent=self.versioned_resource_name, credentials=self.credentials, ) def get_model_evaluation( self, evaluation_id: Optional[str] = None, ) -> Optional[model_evaluation.ModelEvaluation]: """Returns a ModelEvaluation resource and instantiates its representation. If no evaluation_id is passed, it will return the first evaluation associated with this model. If the aiplatform.Model resource was instantiated with a version, this will return a Model Evaluation from that version. If no version was specified when instantiating the Model resource, this will return an Evaluation from the default version. Example usage: my_model = Model( model_name="projects/123/locations/us-central1/models/456" ) my_evaluation = my_model.get_model_evaluation( evaluation_id="789" ) # If no arguments are passed, this method returns the first evaluation for the model my_evaluation = my_model.get_model_evaluation() Args: evaluation_id (str): Optional. The ID of the model evaluation to retrieve. Returns: model_evaluation.ModelEvaluation: Instantiated representation of the ModelEvaluation resource. """ evaluations = self.list_model_evaluations() if not evaluation_id: if len(evaluations) > 1: _LOGGER.warning( f"Your model has more than one model evaluation, this is returning only one evaluation resource: {evaluations[0].resource_name}" ) return evaluations[0] if evaluations else evaluations else: resource_uri_parts = self._parse_resource_name(self.resource_name) evaluation_resource_name = ( model_evaluation.ModelEvaluation._format_resource_name( **resource_uri_parts, evaluation=evaluation_id, ) ) return model_evaluation.ModelEvaluation( evaluation_name=evaluation_resource_name, credentials=self.credentials, ) # TODO (b/232546878): Async support class ModelRegistry: def __init__( self, model: Union[Model, str], location: Optional[str] = None, project: Optional[str] = None, credentials: Optional[auth_credentials.Credentials] = None, ): """Creates a ModelRegistry instance for version management of a registered model. Args: model (Union[Model, str]): Required. One of the following: 1. A Model instance 2. A fully-qualified model resource name 3. A model ID. A location and project must be provided. location (str): Optional. The model location. Used when passing a model name as model. If not set, project set in aiplatform.init will be used. project (str): Optional. The model project. Used when passing a model name as model. If not set, project set in aiplatform.init will be used. credentials (auth_credentials.Credentials): Optional. Custom credentials to use with model access. If not set, credentials set in aiplatform.init will be used. """ if isinstance(model, Model): self.model_resource_name = model.resource_name else: self.model_resource_name = utils.full_resource_name( resource_name=model, resource_noun="models", parse_resource_name_method=Model._parse_resource_name, format_resource_name_method=Model._format_resource_name, project=project, location=location, resource_id_validator=base.VertexAiResourceNoun._revisioned_resource_id_validator, ) self.credentials = credentials or ( model.credentials if isinstance(model, Model) else initializer.global_config.credentials ) self.client = Model._instantiate_client(location, self.credentials) def get_model( self, version: Optional[str] = None, ) -> Model: """Gets a registered model with optional version. Args: version (str): Optional. A model version ID or alias to target. Defaults to the model with the "default" alias. Returns: Model: An instance of a Model from this ModelRegistry. """ return Model( self.model_resource_name, version=version, credentials=self.credentials ) def list_versions( self, ) -> List[VersionInfo]: """Lists the versions and version info of a model. Returns: List[VersionInfo]: A list of VersionInfo, each containing info about specific model versions. """ _LOGGER.info(f"Getting versions for {self.model_resource_name}") page_result = self.client.list_model_versions( name=self.model_resource_name, ) versions = [ VersionInfo( version_id=model.version_id, version_create_time=model.version_create_time, version_update_time=model.version_update_time, model_display_name=model.display_name, model_resource_name=self._parse_versioned_name(model.name)[0], version_aliases=model.version_aliases, version_description=model.version_description, ) for model in page_result ] return versions def get_version_info( self, version: str, ) -> VersionInfo: """Gets information about a specific model version. Args: version (str): Required. The model version to obtain info for. Returns: VersionInfo: Contains info about the model version. """ _LOGGER.info(f"Getting version {version} info for {self.model_resource_name}") model = self.client.get_model( name=self._get_versioned_name(self.model_resource_name, version), ) return VersionInfo( version_id=model.version_id, version_create_time=model.version_create_time, version_update_time=model.version_update_time, model_display_name=model.display_name, model_resource_name=self._parse_versioned_name(model.name)[0], version_aliases=model.version_aliases, version_description=model.version_description, ) def delete_version( self, version: str, ) -> None: """Deletes a model version from the registry. Cannot delete a version if it is the last remaining version. Use Model.delete() in that case. Args: version (str): Required. The model version ID or alias to delete. """ lro = self.client.delete_model_version( name=self._get_versioned_name(self.model_resource_name, version), ) _LOGGER.info(f"Deleting version {version} for {self.model_resource_name}") lro.result() _LOGGER.info(f"Deleted version {version} for {self.model_resource_name}") def add_version_aliases( self, new_aliases: List[str], version: str, ) -> None: """Adds version alias(es) to a model version. Args: new_aliases (List[str]): Required. The alias(es) to add to a model version. version (str): Required. The version ID to receive the new alias(es). """ self._merge_version_aliases( version_aliases=new_aliases, version=version, ) def remove_version_aliases( self, target_aliases: List[str], version: str, ) -> None: """Removes version alias(es) from a model version. Args: target_aliases (List[str]): Required. The alias(es) to remove from a model version. version (str): Required. The version ID to be stripped of the target alias(es). """ self._merge_version_aliases( version_aliases=[f"-{alias}" for alias in target_aliases], version=version, ) def _merge_version_aliases( self, version_aliases: List[str], version: str, ) -> None: """Merges a list of version aliases with a model's existing alias list. Args: version_aliases (List[str]): Required. The version alias change list. version (str): Required. The version ID to have its alias list changed. """ _LOGGER.info(f"Merging version aliases for {self.model_resource_name}") self.client.merge_version_aliases( name=self._get_versioned_name(self.model_resource_name, version), version_aliases=version_aliases, ) _LOGGER.info( f"Completed merging version aliases for {self.model_resource_name}" ) @staticmethod def _get_versioned_name( resource_name: str, version: Optional[str] = None, ) -> str: """Creates a versioned form of a model resource name. Args: resource_name (str): Required. A fully-qualified resource name or resource ID. version (str): Optional. The version or alias of the resource. Returns: versioned_name (str): The versioned resource name in revisioned format. """ if version: return f"{resource_name}@{version}" return resource_name @staticmethod def _parse_versioned_name( model_name: str, ) -> Tuple[str, Optional[str]]: """Return a model name and, if included in the model name, a model version. Args: model_name (str): Required. A fully-qualified model name or model ID, optionally with an included version. Returns: parsed_version_name (Tuple[str, Optional[str]]): A tuple containing the model name or ID as the first element, and the model version as the second element, if present in `model_name`. Raises: ValueError: If the `model_name` is invalid and contains too many '@' symbols. """ if "@" not in model_name: return model_name, None elif model_name.count("@") > 1: raise ValueError( f"Received an invalid model_name with too many `@`s: {model_name}" ) else: return model_name.split("@") @staticmethod def _get_true_version_parent( parent_model: Optional[str] = None, project: Optional[str] = None, location: Optional[str] = None, ) -> Optional[str]: """Gets the true `parent_model` with full resource name. Args: parent_model (str): Optional. A fully-qualified resource name or resource ID of the model that would be the parent of another model. project (str): Optional. The project of `parent_model`, if not included in `parent_model`. location (str): Optional. The location of `parent_model`, if not included in `parent_model`. Returns: true_parent_model (str): Optional. The true resource name of the parent model, if one should exist. """ if parent_model: existing_resource = utils.full_resource_name( resource_name=parent_model, resource_noun="models", parse_resource_name_method=Model._parse_resource_name, format_resource_name_method=Model._format_resource_name, project=project, location=location, ) parent_model = existing_resource return parent_model @staticmethod def _get_true_alias_list( version_aliases: Optional[Sequence[str]] = None, is_default_version: bool = True, ) -> Optional[Sequence[str]]: """Gets the true `version_aliases` list based on `is_default_version`. Args: version_aliases (Sequence[str]): Optional. The user-provided list of model aliases. is_default_version (bool): Optional. When set, includes the "default" alias in `version_aliases`. Defaults to True. Returns: true_alias_list (Sequence[str]): Optional: The true alias list, should one exist, containing "default" if specified. """ if is_default_version: if version_aliases and "default" not in version_aliases: version_aliases.append("default") elif not version_aliases: version_aliases = ["default"] return version_aliases

from __future__ import absolute_import, division, print_function import time import WatchmanEdenTestCase class TestEdenJournal(WatchmanEdenTestCase.WatchmanEdenTestCase): def test_eden_journal(self): def populate(repo): repo.write_file("hello", "hola\n") repo.commit("initial commit.") root = self.makeEdenMount(populate) repo = self.repoForPath(root) initial_commit = repo.get_head_hash() res = self.watchmanCommand("watch", root) self.assertEqual("eden", res["watcher"]) clock = self.watchmanCommand("clock", root) self.touchRelative(root, "newfile") res = self.watchmanCommand("query", root, {"fields": ["name"], "since": clock}) clock = res["clock"] self.assertFileListsEqual(res["files"], ["newfile"]) repo.add_file("newfile") repo.commit(message="add newfile") res = self.watchmanCommand( "query", root, { "expression": [ "not", [ "anyof", ["dirname", ".hg"], ["match", "checklink*"], ["match", "hg-check*"], ], ], "fields": ["name"], "since": clock, }, ) clock = res["clock"] self.assertFileListsEqual( res["files"], ["newfile"], message="We expect to report the files changed in the commit", ) # Test the the journal has the correct contents across a "reset" like # operation where the parents are poked directly. This is using # debugsetparents rather than reset because the latter isn't enabled # by default for hg in the watchman test machinery. self.touchRelative(root, "unclean") repo.hg("debugsetparents", initial_commit) res = self.watchmanCommand( "query", root, { "expression": ["not", ["dirname", ".hg"]], "fields": ["name"], "since": clock, }, ) self.assertFileListsEqual( res["files"], ["newfile", "unclean"], message=( "We expect to report the file changed in the commit " "as well as the unclean file" ), ) # make sure that we detect eden getting unmounted. This sleep is unfortunate # and ugly. Without it, the unmount will fail because something is accessing # the filesystem. I haven't been able to find out what it is because fuser # takes too long to run and by the time it has run, whatever that blocker # was is not longer there. Ordinarily I'd prefer to poll on some condition # in a loop rather than just sleeping an arbitrary amount, but I just don't # know what the offending thing is and running the unmount in a loop is prone # to false negatives. time.sleep(1) self.eden.remove(root) watches = self.watchmanCommand("watch-list") self.assertNotIn(root, watches["roots"])

from django.core.paginator import Paginator, PageNotAnInteger, EmptyPage from django.shortcuts import render from django.views.generic import DetailView, TemplateView from django.http import HttpResponse from ojoalplato.cards.forms import RestaurantSearchForm from ojoalplato.cards.models import Restaurant, Wine from haystack.query import SearchQuerySet import json # class MapView(SearchView): # model = Restaurant # template_name = "blog/wpfamily/map_list.html" # form = HighlightedModelSearchForm class MapView(TemplateView): template_name = "blog/wpfamily/map_list.html" class RestaurantDetailView(DetailView): model = Restaurant template_name = "cards/restaurant_detail.html" context_object_name = "card" class WineDetailView(DetailView): model = Wine template_name = "cards/wine_detail.html" context_object_name = "card" def restaurant_search(request): query = request.GET.get('q') page = request.GET.get('page') form = RestaurantSearchForm(request.GET) restaurants = form.search() paginator = Paginator(restaurants, 10) # Show 10 results per page try: restaurants = paginator.page(page) except PageNotAnInteger: # If page is not an integer, deliver first page. restaurants = paginator.page(1) except EmptyPage: # If page is out of range (e.g. 9999), deliver last page of results. restaurants = paginator.page(paginator.num_pages) return render(request, 'blog/wpfamily/map_list.html', { 'query': query, 'page': restaurants }) def autocomplete(request): sqs = SearchQuerySet().autocomplete(content_auto=request.GET.get('q', '')) suggestions = [{"name": r.object.name, "chef": r.object.chef, "address": r.object.address, "url": r.object.get_absolute_url(), "img": r.object.img_src, "stars": r.object.stars, "suns": r.object.suns} for r in sqs] # Make sure you return a JSON object, not a bare list. # Otherwise, you could be vulnerable to an XSS attack. the_data = json.dumps(suggestions) return HttpResponse(the_data, content_type='application/json')

import time from azure.cli.testsdk import (ScenarioTest, ResourceGroupPreparer, live_only) from knack.util import CLIError # pylint: disable=line-too-long # pylint: disable=too-many-lines class SBNSMigrationCRUDScenarioTest(ScenarioTest): from azure.cli.testsdk.scenario_tests import AllowLargeResponse # Test playback fails and the live-only flag will be removed once it is addressed @live_only() @AllowLargeResponse() @ResourceGroupPreparer(name_prefix='cli_test_sb_migration') def test_sb_migration(self, resource_group): from azure.mgmt.servicebus.models import ProvisioningStateDR self.kwargs.update({ 'loc_south': 'SouthCentralUS', 'loc_north': 'NorthCentralUS', 'namespacenamestandard': self.create_random_name(prefix='sb-std-nscli', length=20), 'namespacenamepremium': self.create_random_name(prefix='sb-pre-nscli', length=20), 'tags': {'tag1: value1', 'tag2: value2'}, 'sku': 'Premium', 'sku_std': 'Standard', 'authoname': self.create_random_name(prefix='cliAutho', length=20), 'defaultauthorizationrule': 'RootManageSharedAccessKey', 'accessrights': 'Send', 'primary': 'PrimaryKey', 'secondary': 'SecondaryKey', 'postmigrationname': self.create_random_name(prefix='clipostmigration', length=20), 'alternatename': self.create_random_name(prefix='cliAlter', length=20), 'id': '', 'test': '', 'queuename': '', 'topicname': '', 'partnernamespaceid': '' }) self.cmd('servicebus namespace exists --name {namespacenamestandard}', checks=[self.check('nameAvailable', True)]) # Create Namespace - Standard self.cmd( 'servicebus namespace create --resource-group {rg} --name {namespacenamestandard} --location {loc_south} --tags {tags} --sku {sku_std}', checks=[self.check('sku.name', '{sku_std}')]) # Get Created Namespace - Standard self.cmd('servicebus namespace show --resource-group {rg} --name {namespacenamestandard}', checks=[self.check('sku.name', '{sku_std}')]) # Create Namespace - Primary self.cmd( 'servicebus namespace create --resource-group {rg} --name {namespacenamepremium} --location {loc_north} --tags {tags} --sku {sku}', checks=[self.check('sku.name', '{sku}')]) # Get Created Namespace - Primary getnamespace2result = self.cmd( 'servicebus namespace show --resource-group {rg} --name {namespacenamepremium}', checks=[self.check('sku.name', '{sku}')]).get_output_in_json() # Create Authoriazation Rule self.cmd( 'servicebus namespace authorization-rule create --resource-group {rg} --namespace-name {namespacenamestandard} --name {authoname} --rights {accessrights}', checks=[self.check('name', '{authoname}')]) partnernamespaceid = getnamespace2result['id'] self.kwargs.update({'id': partnernamespaceid}) # Get Create Authorization Rule self.cmd( 'servicebus namespace authorization-rule show --resource-group {rg} --namespace-name {namespacenamestandard} --name {authoname}', checks=[self.check('name', '{authoname}')]) # Create Queues under Standrad namespace for x in range(0, 10): queuenamestr = 'queue' + repr(x) self.kwargs.update({'queuename': queuenamestr}) self.cmd( 'servicebus queue create --resource-group {rg} --namespace-name {namespacenamestandard} --name {queuename}', checks=[self.check('name', '{queuename}')]) # Create Topics under Standrad namespace for x in range(0, 10): topicnamestr = 'topic' + repr(x) self.kwargs.update({'topicname': topicnamestr}) self.cmd( 'servicebus topic create --resource-group {rg} --namespace-name {namespacenamestandard} --name {topicname}', checks=[self.check('name', '{topicname}')]) time.sleep(10) # Create Migration self.cmd( 'servicebus migration start --resource-group {rg} --name {namespacenamestandard} --target-namespace {id} --post-migration-name {postmigrationname}') # get Migration getmigration = self.cmd( 'servicebus migration show --resource-group {rg} --name {namespacenamestandard}').get_output_in_json() # Complete Migration self.cmd( 'servicebus migration complete --resource-group {rg} --name {namespacenamestandard}') # get Migration getmigration = self.cmd( 'servicebus migration show --resource-group {rg} --name {namespacenamestandard}').get_output_in_json() # check for the migration provisioning succeeded while getmigration['provisioningState'] != ProvisioningStateDR.succeeded.value: time.sleep(30) getmigration = self.cmd( 'servicebus migration show --resource-group {rg} --name {namespacenamestandard}').get_output_in_json() # check for the migration PendingReplicationOperationsCount is 0 or null while getmigration['migrationState'] != 'Active': time.sleep(30) getmigration = self.cmd( 'servicebus migration show --resource-group {rg} --name {namespacenamestandard}').get_output_in_json() # Get Authorization Rule - Premium self.cmd( 'servicebus namespace authorization-rule show --resource-group {rg} --namespace-name {namespacenamepremium} --name {authoname}', checks=[self.check('name', '{authoname}')]) # Get all queues from Premium namespace listqueues1 = self.cmd( 'servicebus queue list --resource-group {rg} --namespace-name {namespacenamepremium}').get_output_in_json() self.assertIsNotNone(listqueues1) self.assertGreaterEqual(len(listqueues1), 10, 'Premium - get all queues count not 10') # Get all queues from Premium namespace listtopics = self.cmd( 'servicebus topic list --resource-group {rg} --namespace-name {namespacenamepremium}').get_output_in_json() self.assertIsNotNone(listtopics) self.assertGreaterEqual(len(listtopics), 10, 'Premium - get all topics count not 10') time.sleep(30) # get namespace getnamespace = self.cmd( 'servicebus namespace show --resource-group {rg} --name {namespacenamestandard}').get_output_in_json() # check for the namespace provisioning succeeded while getnamespace['provisioningState'] != ProvisioningStateDR.succeeded.value: time.sleep(30) getnamespace = self.cmd( 'servicebus namespace show --resource-group {rg} --name {namespacenamestandard}').get_output_in_json() # Delete Namespace - Standard self.cmd('servicebus namespace delete --resource-group {rg} --name {namespacenamestandard}') # get namespace getnamespace = self.cmd( 'servicebus namespace show --resource-group {rg} --name {namespacenamepremium}').get_output_in_json() # check for the namespace provisioning succeeded while getnamespace['provisioningState'] != ProvisioningStateDR.succeeded.value: time.sleep(30) getnamespace = self.cmd( 'servicebus namespace show --resource-group {rg} --name {namespacenamepremium}').get_output_in_json()

""" kindlepush ~~~~~~~~~~ Kindlepush is trying to rescue you from manually clicking the deliver button to send the doc from your kindle library to your kindle. It is for 3G devices, such as kindle dx. It was created by @blahgeek, now maintained by @lord63. :copyright: (c) 2014 BlahGeek. :copyright: (c) 2014 lord63. :license: MIT, see LICENSE for more details. """ __version__ = "0.3.3" __title__ = "kindlepush" __author__ = "BlahGeek" __maintainer__ = "lord63" __license__ = "MIT" __copyright__ = "Copyright 2014 BlahGeek 2014 lord63"

"""The base classes for RDFValue tests.""" import time from grr.client.components.rekall_support import rekall_types as rdf_rekall_types from grr.lib import aff4 from grr.lib import rdfvalue from grr.lib import test_lib from grr.lib import type_info from grr.lib.rdfvalues import client as rdf_client from grr.lib.rdfvalues import flows as rdf_flows from grr.lib.rdfvalues import paths as rdf_paths from grr.lib.rdfvalues import structs as rdf_structs # pylint:mode=test class RDFValueBaseTest(test_lib.GRRBaseTest): pass class GenericRDFProtoTest(RDFValueBaseTest): def testNestedProtobufAssignment(self): """Check that we can assign a nested protobuf.""" container = rdf_rekall_types.RekallRequest() pathspec = rdf_paths.PathSpec(path=r"\\.\pmem", pathtype=1) # Should raise - incompatible RDFType. self.assertRaises(ValueError, setattr, container, "device", rdfvalue.RDFString("hello")) # Should raise - incompatible RDFProto type. self.assertRaises( ValueError, setattr, container, "device", rdf_client.StatEntry(st_size=5)) # Assign directly. container.device = pathspec self.assertEqual(container.device.path, r"\\.\pmem") # Clear the field. container.device = None # Check the protobuf does not have the field set at all. self.assertFalse(container.HasField("device")) def testSimpleTypeAssignment(self): sample = rdf_client.StatEntry() sample.AddDescriptor( rdf_structs.ProtoRDFValue( name="test", field_number=45, default=rdfvalue.RDFInteger(0), rdf_type=rdfvalue.RDFInteger)) self.assertIsInstance(sample.test, rdfvalue.RDFInteger) # Can we assign an RDFValue instance? sample.test = rdfvalue.RDFInteger(5) self.assertEqual(sample.test, 5) # Check that bare values can be coerced. sample.test = 6 self.assertIsInstance(sample.test, rdfvalue.RDFInteger) self.assertEqual(sample.test, 6) # Assign an enum. sample.registry_type = sample.RegistryType.REG_DWORD self.assertEqual(sample.registry_type, sample.RegistryType.REG_DWORD) sample.registry_type = rdf_client.StatEntry.RegistryType.REG_SZ self.assertEqual(sample.registry_type, sample.RegistryType.REG_SZ) # We can also assign the string value. sample.registry_type = "REG_QWORD" self.assertEqual(sample.registry_type, sample.RegistryType.REG_QWORD) # Check that coercing works. sample.test = "10" self.assertEqual(sample.test, 10) # Assign an RDFValue which can not be coerced. self.assertRaises(type_info.TypeValueError, setattr, sample, "test", rdfvalue.RDFString("hello")) def testComplexConstruction(self): """Test that we can construct RDFProtos with nested fields.""" pathspec = rdf_paths.PathSpec( path="/foobar", pathtype=rdf_paths.PathSpec.PathType.TSK) sample = rdf_client.StatEntry(pathspec=pathspec, st_size=5) self.assertEqual(sample.pathspec.path, "/foobar") self.assertEqual(sample.st_size, 5) self.assertRaises(AttributeError, rdf_client.StatEntry, foobar=1) def testUnicodeSupport(self): pathspec = rdf_paths.PathSpec( path="/foobar", pathtype=rdf_paths.PathSpec.PathType.TSK) pathspec.path = u"Grüezi" self.assertEqual(pathspec.path, u"Grüezi") def testRDFTypes(self): """Test that types are properly serialized.""" # Create an object to carry attributes obj = aff4.FACTORY.Create("foobar", aff4.AFF4Object, token=self.token) # Make a url object str_url = "aff4:/users" url = rdfvalue.RDFURN(str_url, age=1) # Store it # We must use a proper Attribute() instance self.assertRaises(AttributeError, obj.Set, "aff4:stored", url) self.assertRaises(ValueError, obj.Set, obj.Schema.STORED, str_url) old_time = time.time try: time.time = lambda: 100 obj.Set(obj.Schema.STORED, url) obj.Close() # Check that its ok obj = aff4.FACTORY.Open("foobar", token=self.token) url = obj.Get(obj.Schema.STORED) # It must be a real RDFURN and be the same as the original string self.assertEqual(url.__class__, rdfvalue.RDFURN) self.assertEqual(str(url), str_url) # The time of the stored property reflects the time of the Set() call. self.assertEqual(url.age, 100 * 1e6) finally: time.time = old_time def testRepeatedFields(self): """Test handling of protobuf repeated fields.""" sample = rdf_client.Interface() # Add a simple string. sample.ip4_addresses.Append("127.0.0.1") self.assertEqual(sample.ip4_addresses[0], "127.0.0.1") # Add an invalid type. self.assertRaises(type_info.TypeValueError, sample.addresses.Append, 2) # Add a protobuf sample.addresses.Append(human_readable="127.0.0.1") self.assertEqual(sample.addresses[0].human_readable, "127.0.0.1") self.assertEqual(len(sample.addresses), 1) def testEnums(self): """Check that enums are wrapped in a descriptor class.""" sample = rdf_flows.GrrStatus() self.assertEqual(str(sample.status), "OK") class RDFValueTestCase(RDFValueBaseTest): """The base class for testing RDFValue implementations.""" # This should be overridden by the RDFValue class we want to test. rdfvalue_class = lambda *args, **kw: None __abstract = True # Do not register this class so pylint: disable=g-bad-name def GenerateSample(self, number=0): """Create a pre-populated instance of the RDFValue. Args: number: A sample number. Derived classes should return a different sample for each number. """ _ = number return self.rdfvalue_class() def CheckRDFValue(self, value, sample): """Check that the rdfproto is the same as the sample.""" self.assertIsInstance(sample, self.rdfvalue_class) self.assertIsInstance(value, self.rdfvalue_class) self.assertRDFValuesEqual(value, sample) def testComparisons(self): """Checks that object comparisons work.""" sample1 = self.GenerateSample(1) self.assertTrue(sample1 == self.GenerateSample(1)) self.assertFalse(sample1 == self.GenerateSample(2)) self.assertTrue(sample1 != self.GenerateSample(2)) self.assertFalse(sample1 != self.GenerateSample(1)) def testHashability(self): """RDFValue instances need to act as keys in a dict.""" sample1 = self.GenerateSample(1) # Different instances with the same value need to hash to the same. self.assertTrue(hash(sample1) == hash(self.GenerateSample(1))) self.assertTrue(hash(sample1) != hash(self.GenerateSample(2))) def testInitialization(self): """Check that we can use an empty initializer. RDFValues are created in many different ways, sometimes in stages by gradually populating fields. The only time you can be sure the user has finished creating a proto is when it is serialized. This means strong validation that requires all fields populated can't be done in init, but should be done in SerializeToString. """ self.rdfvalue_class() # Initialize from another instance. sample = self.GenerateSample() self.CheckRDFValue(self.rdfvalue_class(sample), sample) def testSerialization(self, sample=None): """Make sure the RDFValue instance can be serialized.""" if sample is None: sample = self.GenerateSample() # Serializing to a string must produce a string. serialized = sample.SerializeToString() self.assertIsInstance(serialized, str) # Ensure we can parse it again. rdfvalue_object = self.rdfvalue_class.FromSerializedString(serialized) self.CheckRDFValue(rdfvalue_object, sample) # Serializing to data store must produce something the data store can # handle. serialized = sample.SerializeToDataStore() if self.rdfvalue_class.data_store_type == "bytes": self.assertIsInstance(serialized, str) elif self.rdfvalue_class.data_store_type == "string": self.assertIsInstance(serialized, unicode) elif self.rdfvalue_class.data_store_type in ["unsigned_integer", "integer"]: self.assertIsInstance(serialized, (int, long)) else: self.fail("%s has no valid data_store_type" % self.rdfvalue_class) # Ensure we can parse it again. rdfvalue_object = self.rdfvalue_class.FromDatastoreValue(serialized) self.CheckRDFValue(rdfvalue_object, sample) class RDFProtoTestCase(RDFValueTestCase): """A harness for testing RDFProto implementations.""" __abstract = True # Do not register this class so pylint: disable=g-bad-name def testInitializationEx(self): """Check we can initialize from additional parts.""" sample = self.GenerateSample() # RDFProto can be initialized from a serialized protobuf. serialized = sample.SerializeToString() rdfvalue_sample = self.rdfvalue_class.FromSerializedString(serialized) self.CheckRDFValue(rdfvalue_sample, sample) # RDFProto can be initialized from another RDFProto. new_rdfvalue_sample = self.rdfvalue_class(rdfvalue_sample) self.CheckRDFValue(new_rdfvalue_sample, rdfvalue_sample) # In this case the ages should be identical self.assertEqual(int(new_rdfvalue_sample.age), int(rdfvalue_sample.age))

from asynctest import TestCase from parameterized import parameterized from async_hvac import AsyncClient from async_hvac.tests.util import RequestsMocker class TestApproleRoutes(TestCase): """Unit tests providing coverage for approle auth backend-related methods/routes.""" @parameterized.expand([ ("default mount point", None, "application1"), ("custom mount point", "my-approle-path", "application2"), ]) @RequestsMocker() async def test_create_role(self, test_label, mount_point, role_name, requests_mocker): expected_status_code = 204 mock_url = 'http://127.0.0.1:8200/v1/auth/{0}/role/{1}'.format( 'approle' if mount_point is None else mount_point, role_name, ) requests_mocker.register_uri( method='POST', url=mock_url, status_code=expected_status_code, ) client = AsyncClient() if mount_point is None: actual_response = await client.create_role( role_name=role_name, ) else: actual_response = await client.create_role( role_name=role_name, mount_point=mount_point, ) self.assertEqual( first=expected_status_code, second=actual_response.status, ) await client.close() @parameterized.expand([ ("default mount point", None, "application1"), ("custom mount point", "my-approle-path", "application2"), ]) @RequestsMocker() async def test_list_roles(self, test_label, mount_point, role_name, requests_mocker): expected_status_code = 200 mock_response = { "auth": None, "data": { "keys": [ role_name, ] }, "lease_duration": 0, "lease_id": "", "renewable": False, "request_id": "e4c219fb-0a78-2be2-8d3c-b3715dccb920", "warnings": None, "wrap_info": None } mock_url = 'http://127.0.0.1:8200/v1/auth/{0}/role?list=true'.format( 'approle' if mount_point is None else mount_point, ) requests_mocker.register_uri( method='GET', url=mock_url, status_code=expected_status_code, json=mock_response, ) client = AsyncClient() if mount_point is None: actual_response = await client.list_roles() else: actual_response = await client.list_roles( mount_point=mount_point, ) # ensure we received our mock response data back successfully self.assertEqual(mock_response, actual_response) await client.close() @parameterized.expand([ ("default mount point", None, "application1", "40b3c82d-12a6-838c-9e74-1f1133867e06"), ("custom mount point", "my-approle-path", "application2", "5fs3c82d-12a6-838c-9e74-1f1133867esf"), ]) @RequestsMocker() async def test_get_role_id(self, test_label, mount_point, role_name, role_id, requests_mocker): expected_status_code = 200 mock_response = { "auth": None, "data": { "role_id": role_id }, "lease_duration": 0, "lease_id": "", "renewable": False, "request_id": "85590a1a-6dd7-de79-01b0-1c285d505bf2", "warnings": None, "wrap_info": None } mock_url = 'http://127.0.0.1:8200/v1/auth/{0}/role/{1}/role-id'.format( 'approle' if mount_point is None else mount_point, role_name, ) requests_mocker.register_uri( method='GET', url=mock_url, status_code=expected_status_code, json=mock_response, ) client = AsyncClient() if mount_point is None: actual_response = await client.get_role_id( role_name=role_name ) else: actual_response = await client.get_role_id( role_name=role_name, mount_point=mount_point ) # ensure we received our mock response data back successfully self.assertEqual( first=role_id, second=actual_response ) await client.close() @parameterized.expand([ ("default mount point", None, "application1", "custom-role-id-1"), ("custom mount point", "my-approle-path", "application2", "custom-role-id-2"), ]) @RequestsMocker() async def test_set_role_id(self, test_label, mount_point, role_name, role_id, requests_mocker): expected_status_code = 204 mock_url = 'http://127.0.0.1:8200/v1/auth/{0}/role/{1}/role-id'.format( 'approle' if mount_point is None else mount_point, role_name, ) requests_mocker.register_uri( method='POST', url=mock_url, status_code=expected_status_code, ) client = AsyncClient() if mount_point is None: actual_response = await client.set_role_id( role_name=role_name, role_id=role_id ) else: actual_response = await client.set_role_id( role_name=role_name, role_id=role_id, mount_point=mount_point, ) self.assertEqual( first=expected_status_code, second=actual_response.status, ) await client.close() @parameterized.expand([ ("default mount point", None, "application1"), ("custom mount point", "my-approle-path", "application2"), ]) @RequestsMocker() async def test_get_role(self, test_label, mount_point, role_name, requests_mocker): expected_status_code = 200 mock_response = { "auth": None, "data": { "bind_secret_id": True, "bound_cidr_list": "", "period": 0, "policies": [ "default" ], "secret_id_num_uses": 0, "secret_id_ttl": 0, "token_max_ttl": 900, "token_num_uses": 0, "token_ttl": 600 }, "lease_duration": 0, "lease_id": "", "renewable": False, "request_id": "0aab655f-ecd2-b3d4-3817-35b5bdfd3f28", "warnings": None, "wrap_info": None } mock_url = 'http://127.0.0.1:8200/v1/auth/{0}/role/{1}'.format( 'approle' if mount_point is None else mount_point, role_name, ) requests_mocker.register_uri( method='GET', url=mock_url, status_code=expected_status_code, json=mock_response, ) client = AsyncClient() if mount_point is None: actual_response = await client.get_role( role_name=role_name, ) else: actual_response = await client.get_role( role_name=role_name, mount_point=mount_point, ) self.assertEqual( first=mock_response, second=actual_response, ) await client.close() @parameterized.expand([ ("default mount point", None, "application1"), ("custom mount point", "my-approle-path", "application2"), ]) @RequestsMocker() async def test_create_role_secret_id(self, test_label, mount_point, role_name, requests_mocker): expected_status_code = 200 mock_response = { "auth": None, "data": { "secret_id": "be78e3ca-f644-b099-3291-e8a6f5985cfe", "secret_id_accessor": "b58fd0ee-130c-33bb-5f69-6d4fd1731e5f" }, "lease_duration": 0, "lease_id": "", "renewable": False, "request_id": "2310dc21-0fea-a2de-2d94-bb4edd59f1e9", "warnings": None, "wrap_info": None } mock_url = 'http://127.0.0.1:8200/v1/auth/{0}/role/{1}/secret-id'.format( 'approle' if mount_point is None else mount_point, role_name, ) requests_mocker.register_uri( method='POST', url=mock_url, status_code=expected_status_code, json=mock_response, ) client = AsyncClient() if mount_point is None: actual_response = await client.create_role_secret_id( role_name=role_name, ) else: actual_response = await client.create_role_secret_id( role_name=role_name, mount_point=mount_point, ) self.assertEqual( first=mock_response, second=actual_response, ) await client.close() @parameterized.expand([ ("default mount point", None, "application1", "be78e3ca-f644-b099-3291-e8a6f5985cfe"), ("custom mount point", "my-approle-path", "application2", "ce78e3ca-f644-b099-3291-e8a6f5985cfe"), ]) @RequestsMocker() async def test_get_role_secret_id(self, test_label, mount_point, role_name, secret_id, requests_mocker): expected_status_code = 200 mock_response = { "auth": None, "data": { "SecretIDNumUses": 0, "cidr_list": [], "creation_time": "2018-06-11T07:33:57.771908-05:00", "expiration_time": "0001-01-01T00:00:00Z", "last_updated_time": "2018-06-11T07:33:57.771908-05:00", "metadata": {}, "secret_id_accessor": "b58fd0ee-130c-33bb-5f69-6d4fd1731e5f", "secret_id_num_uses": 0, "secret_id_ttl": 0 }, "lease_duration": 0, "lease_id": "", "renewable": False, "request_id": "718a00fa-e76f-f1fc-9b9e-f9c4baa766b3", "wrap_info": None } mock_url = 'http://127.0.0.1:8200/v1/auth/{0}/role/{1}/secret-id/lookup'.format( 'approle' if mount_point is None else mount_point, role_name, ) requests_mocker.register_uri( method='POST', url=mock_url, status_code=expected_status_code, json=mock_response, ) client = AsyncClient() if mount_point is None: actual_response = await client.get_role_secret_id( role_name=role_name, secret_id=secret_id, ) else: actual_response = await client.get_role_secret_id( role_name=role_name, secret_id=secret_id, mount_point=mount_point, ) self.assertEqual( first=mock_response, second=actual_response, ) await client.close() @parameterized.expand([ ("default mount point", None, "application1", "be78e3ca-f644-b099-3291-e8a6f5985cfe"), ("custom mount point", "my-approle-path", "application2", "ce78e3ca-f644-b099-3291-e8a6f5985cfe"), ]) @RequestsMocker() async def test_list_role_secrets(self, test_label, mount_point, role_name, secret_id, requests_mocker): expected_status_code = 200 mock_response = { "auth": None, "data": { "keys": [ secret_id ] }, "lease_duration": 0, "lease_id": "", "renewable": False, "request_id": "eb805845-f6ce-a514-9238-6914664dd601", "warnings": None, "wrap_info": None } mock_url = 'http://127.0.0.1:8200/v1/auth/{0}/role/{1}/secret-id?list=true'.format( 'approle' if mount_point is None else mount_point, role_name, ) requests_mocker.register_uri( method='GET', url=mock_url, status_code=expected_status_code, json=mock_response, ) client = AsyncClient() if mount_point is None: actual_response = await client.list_role_secrets( role_name=role_name, ) else: actual_response = await client.list_role_secrets( role_name=role_name, mount_point=mount_point, ) self.assertEqual( first=mock_response, second=actual_response, ) await client.close() @parameterized.expand([ ("default mount point", None, "application1", "be78e3ca-f644-b099-3291-e8a6f5985cfe"), ("custom mount point", "my-approle-path", "application2", "ce78e3ca-f644-b099-3291-e8a6f5985cfe"), ]) @RequestsMocker() async def test_get_role_secret_id_accessor(self, test_label, mount_point, role_name, secret_id_accessor, requests_mocker): expected_status_code = 200 mock_response = { "auth": None, "data": { "SecretIDNumUses": 0, "cidr_list": [], "creation_time": "2018-06-11T07:33:57.771908-05:00", "expiration_time": "0001-01-01T00:00:00Z", "last_updated_time": "2018-06-11T07:33:57.771908-05:00", "metadata": {}, "secret_id_accessor": secret_id_accessor, "secret_id_num_uses": 0, "secret_id_ttl": 0 }, "lease_duration": 0, "lease_id": "", "renewable": False, "request_id": "2c9fcba6-425d-e4c0-45fa-ee90450a3c00", "wrap_info": None } mock_url = 'http://127.0.0.1:8200/v1/auth/{0}/role/{1}/secret-id-accessor/lookup'.format( 'approle' if mount_point is None else mount_point, role_name, ) requests_mocker.register_uri( method='POST', url=mock_url, status_code=expected_status_code, json=mock_response, ) client = AsyncClient() if mount_point is None: actual_response = await client.get_role_secret_id_accessor( role_name=role_name, secret_id_accessor=secret_id_accessor, ) else: actual_response = await client.get_role_secret_id_accessor( role_name=role_name, secret_id_accessor=secret_id_accessor, mount_point=mount_point, ) self.assertEqual( first=mock_response, second=actual_response, ) await client.close() @parameterized.expand([ ("default mount point", None, "application1", "be78e3ca-f644-b099-3291-e8a6f5985cfe"), ("custom mount point", "my-approle-path", "application2", "ce78e3ca-f644-b099-3291-e8a6f5985cfe"), ]) @RequestsMocker() async def test_delete_role_secret_id(self, test_label, mount_point, role_name, secret_id, requests_mocker): expected_status_code = 204 mock_url = 'http://127.0.0.1:8200/v1/auth/{0}/role/{1}/secret-id/destroy'.format( 'approle' if mount_point is None else mount_point, role_name, ) requests_mocker.register_uri( method='POST', url=mock_url, status_code=expected_status_code, ) client = AsyncClient() if mount_point is None: actual_response = await client.delete_role_secret_id( role_name=role_name, secret_id=secret_id, ) else: actual_response = await client.delete_role_secret_id( role_name=role_name, secret_id=secret_id, mount_point=mount_point, ) self.assertEqual( first=expected_status_code, second=actual_response.status, ) await client.close() @parameterized.expand([ ("default mount point", None, "application1", "be78e3ca-f644-b099-3291-e8a6f5985cfe"), ("custom mount point", "my-approle-path", "application2", "ce78e3ca-f644-b099-3291-e8a6f5985cfe"), ]) @RequestsMocker() async def test_delete_role_secret_id_accessor(self, test_label, mount_point, role_name, secret_id_accessor, requests_mocker): expected_status_code = 204 mock_url = 'http://127.0.0.1:8200/v1/auth/{0}/role/{1}/secret-id-accessor/{2}'.format( 'approle' if mount_point is None else mount_point, role_name, secret_id_accessor, ) requests_mocker.register_uri( method='DELETE', url=mock_url, status_code=expected_status_code, ) client = AsyncClient() if mount_point is None: actual_response = await client.delete_role_secret_id_accessor( role_name=role_name, secret_id_accessor=secret_id_accessor, ) else: actual_response = await client.delete_role_secret_id_accessor( role_name=role_name, secret_id_accessor=secret_id_accessor, mount_point=mount_point, ) self.assertEqual( first=expected_status_code, second=actual_response.status, ) await client.close() @parameterized.expand([ ("default mount point", None, "application1", "be78e3ca-f644-b099-3291-e8a6f5985cfe"), ("custom mount point", "my-approle-path", "application2", "ce78e3ca-f644-b099-3291-e8a6f5985cfe"), ]) @RequestsMocker() async def test_create_role_custom_secret_id(self, test_label, mount_point, role_name, secret_id, requests_mocker): expected_status_code = 200 mock_response = { "auth": None, "data": { "secret_id": secret_id, "secret_id_accessor": "f5cb4b7d-9111-320e-6f24-73bf45d3845d" }, "lease_duration": 0, "lease_id": "", "renewable": False, "request_id": "e7c8b2e1-95e8-cb17-e98a-6c428201f1d5", "warnings": None, "wrap_info": None } mock_url = 'http://127.0.0.1:8200/v1/auth/{0}/role/{1}/custom-secret-id'.format( 'approle' if mount_point is None else mount_point, role_name, ) requests_mocker.register_uri( method='POST', url=mock_url, status_code=expected_status_code, json=mock_response, ) client = AsyncClient() if mount_point is None: actual_response = await client.create_role_custom_secret_id( role_name=role_name, secret_id=secret_id, ) else: actual_response = await client.create_role_custom_secret_id( role_name=role_name, secret_id=secret_id, mount_point=mount_point, ) self.assertEqual( first=mock_response, second=actual_response, ) await client.close() @parameterized.expand([ ("default mount point", None, "c7f93182-c6b1-4b6a-9dfb-03bdb6df0026", "26089502-b7d3-412a-b3e6-3d44300f9bd1"), ("custom mount point", "my-approle-path", "cf6b7c2e-3866-48f8-a764-3bcb5782a85a", "7156c666-0491-4c49-af40-7a97300fbaff"), ]) @RequestsMocker() async def test_auth_approle(self, test_label, mount_point, role_id, secret_id, requests_mocker): expected_status_code = 200 mock_response = { "auth": { "accessor": "f8b576f9-9146-4173-e174-40257d58015a", "client_token": "3db3d089-7d3c-f531-cd3e-bfe44696a92c", "lease_duration": 600, "metadata": { "role_name": "application1" }, "policies": [ "default" ], "renewable": True }, "data": None, "lease_duration": 0, "lease_id": "", "renewable": False, "request_id": "2eb635ad-a763-926a-9815-4cb4d14a40f9", "warnings": None, "wrap_info": None } mock_url = 'http://127.0.0.1:8200/v1/auth/{0}/login'.format( 'approle' if mount_point is None else mount_point, ) requests_mocker.register_uri( method='POST', url=mock_url, status_code=expected_status_code, json=mock_response, ) client = AsyncClient() if mount_point is None: actual_response = await client.auth_approle( role_id=role_id, secret_id=secret_id, ) else: actual_response = await client.auth_approle( role_id=role_id, secret_id=secret_id, mount_point=mount_point, ) self.assertEqual( first=mock_response, second=actual_response, ) await client.close()

""" This module contains XMLSchema classes creator for xmlschema package. Two schema classes are created at the end of this module, XMLSchema10 for XSD 1.0 and XMLSchema11 for XSD 1.1. The latter class parses also XSD 1.0 schemas, as prescribed by the standard. Those are the differences between XSD 1.0 and XSD 1.1 and their current development status: * All model extended for content groups * Assertions for simple types * Default attributes for complex types * Alternative type for elements * Inheritable attributes * targetNamespace for restricted element and attributes * Assert for complex types * TODO: OpenContent and XSD 1.1 wildcards for complex types * schema overrides """ import os from collections import namedtuple, Counter from abc import ABCMeta import warnings from ..compat import add_metaclass from ..exceptions import XMLSchemaTypeError, XMLSchemaURLError, XMLSchemaValueError, XMLSchemaOSError from ..qnames import XSD_SCHEMA, XSD_ANNOTATION, XSD_NOTATION, XSD_ATTRIBUTE, XSD_ATTRIBUTE_GROUP, \ XSD_GROUP, XSD_SIMPLE_TYPE, XSD_COMPLEX_TYPE, XSD_ELEMENT, XSD_SEQUENCE, XSD_ANY, \ XSD_ANY_ATTRIBUTE, XSD_REDEFINE, XSD_OVERRIDE from ..helpers import has_xsd_components, get_xsd_derivation_attribute, get_xsd_form_attribute from ..namespaces import XSD_NAMESPACE, XML_NAMESPACE, XSI_NAMESPACE, XHTML_NAMESPACE, \ XLINK_NAMESPACE, NamespaceResourcesMap, NamespaceView from ..etree import etree_element, etree_tostring, ParseError from ..resources import is_remote_url, url_path_is_file, fetch_resource, XMLResource from ..converters import XMLSchemaConverter from ..xpath import ElementPathMixin from .exceptions import XMLSchemaParseError, XMLSchemaValidationError, XMLSchemaEncodeError, \ XMLSchemaNotBuiltError, XMLSchemaIncludeWarning, XMLSchemaImportWarning from .xsdbase import XSD_VALIDATION_MODES, XsdValidator, ValidationMixin, XsdComponent from .notations import XsdNotation from .simple_types import xsd_simple_type_factory, XsdUnion, XsdAtomicRestriction, \ Xsd11AtomicRestriction, Xsd11Union from .attributes import XsdAttribute, XsdAttributeGroup, Xsd11Attribute from .complex_types import XsdComplexType, Xsd11ComplexType from .groups import XsdGroup, Xsd11Group from .elements import XsdElement, Xsd11Element from .wildcards import XsdAnyElement, XsdAnyAttribute, Xsd11AnyElement, Xsd11AnyAttribute from .globals_ import iterchildren_xsd_import, iterchildren_xsd_include, \ iterchildren_xsd_redefine, iterchildren_xsd_override, XsdGlobals # Elements for building dummy groups ATTRIBUTE_GROUP_ELEMENT = etree_element(XSD_ATTRIBUTE_GROUP) ANY_ATTRIBUTE_ELEMENT = etree_element( XSD_ANY_ATTRIBUTE, attrib={'namespace': '##any', 'processContents': 'lax'} ) SEQUENCE_ELEMENT = etree_element(XSD_SEQUENCE) ANY_ELEMENT = etree_element( XSD_ANY, attrib={ 'namespace': '##any', 'processContents': 'lax', 'minOccurs': '0', 'maxOccurs': 'unbounded' }) SCHEMAS_DIR = os.path.join(os.path.dirname(__file__), 'schemas/') XML_SCHEMA_FILE = os.path.join(SCHEMAS_DIR, 'xml_minimal.xsd') HFP_SCHEMA_FILE = os.path.join(SCHEMAS_DIR, 'XMLSchema-hasFacetAndProperty_minimal.xsd') XSI_SCHEMA_FILE = os.path.join(SCHEMAS_DIR, 'XMLSchema-instance_minimal.xsd') XLINK_SCHEMA_FILE = os.path.join(SCHEMAS_DIR, 'xlink.xsd') class XMLSchemaMeta(ABCMeta): def __new__(mcs, name, bases, dict_): def get_attribute(attr, *args): for obj in args: if hasattr(obj, attr): return getattr(obj, attr) meta_schema = dict_.get('meta_schema') or get_attribute('meta_schema', *bases) if meta_schema is None: # Defining a subclass without a meta-schema (eg. XMLSchemaBase) return super(XMLSchemaMeta, mcs).__new__(mcs, name, bases, dict_) dict_['meta_schema'] = None xsd_version = dict_.get('XSD_VERSION') or get_attribute('XSD_VERSION', *bases) if xsd_version not in ('1.0', '1.1'): raise XMLSchemaValueError("Validator class XSD version must be '1.0' or '1.1', not %r." % xsd_version) builders = dict_.get('BUILDERS') or get_attribute('BUILDERS', *bases) if isinstance(builders, dict): dict_['BUILDERS'] = namedtuple('Builders', builders)(**builders) dict_['BUILDERS_MAP'] = { XSD_NOTATION: builders['notation_class'], XSD_SIMPLE_TYPE: builders['simple_type_factory'], XSD_COMPLEX_TYPE: builders['complex_type_class'], XSD_ATTRIBUTE: builders['attribute_class'], XSD_ATTRIBUTE_GROUP: builders['attribute_group_class'], XSD_GROUP: builders['group_class'], XSD_ELEMENT: builders['element_class'], } elif builders is None: raise XMLSchemaValueError("Validator class doesn't have defined XSD builders.") elif get_attribute('BUILDERS_MAP', *bases) is None: raise XMLSchemaValueError("Validator class doesn't have a builder map for XSD globals.") # Build the new meta-schema class meta_schema_class_name = 'Meta' + name meta_schema_class = super(XMLSchemaMeta, mcs).__new__(mcs, meta_schema_class_name, bases, dict_) meta_schema_class.__qualname__ = meta_schema_class_name globals()[meta_schema_class_name] = meta_schema_class # Build the new meta-schema instance schema_location = meta_schema.url if isinstance(meta_schema, XMLSchemaBase) else meta_schema meta_schema = meta_schema_class.create_meta_schema(schema_location) meta_schema.maps.build() dict_['meta_schema'] = meta_schema return super(XMLSchemaMeta, mcs).__new__(mcs, name, bases, dict_) def __init__(cls, name, bases, dict_): super(XMLSchemaMeta, cls).__init__(name, bases, dict_) @add_metaclass(XMLSchemaMeta) class XMLSchemaBase(XsdValidator, ValidationMixin, ElementPathMixin): """ Base class for an XML Schema instance. :param source: an URI that reference to a resource or a file path or a file-like \ object or a string containing the schema or an Element or an ElementTree document. :type source: Element or ElementTree or str or file-like object :param namespace: is an optional argument that contains the URI of the namespace. \ When specified it must be equal to the *targetNamespace* declared in the schema. :type namespace: str or None :param validation: defines the XSD validation mode to use for build the schema, \ it's value can be 'strict', 'lax' or 'skip'. :type validation: str :param global_maps: is an optional argument containing an :class:`XsdGlobals` \ instance, a mediator object for sharing declaration data between dependents \ schema instances. :type global_maps: XsdGlobals or None :param converter: is an optional argument that can be an :class:`XMLSchemaConverter` \ subclass or instance, used for defining the default XML data converter for XML Schema instance. :type converter: XMLSchemaConverter or None :param locations: schema location hints for namespace imports. Can be a dictionary or \ a sequence of couples (namespace URI, resource URL). :type locations: dict or list or None :param base_url: is an optional base URL, used for the normalization of relative paths \ when the URL of the schema resource can't be obtained from the source argument. :type base_url: str or None :param defuse: defines when to defuse XML data. Can be 'always', 'remote' or 'never'. \ For default defuse only remote XML data. :type defuse: str or None :param timeout: the timeout in seconds for fetching resources. Default is `300`. :type timeout: int :param build: defines whether build the schema maps. Default is `True`. :type build: bool :param use_meta: if `True` the schema processor uses the package meta-schema, otherwise the \ meta-schema is added at the end. In the latter case the meta-schema is rebuilt if any base \ namespace has been overridden by an import. Ignored if the argument *global_maps* is provided. :type use_meta: bool :cvar XSD_VERSION: store the XSD version (1.0 or 1.1). :vartype XSD_VERSION: str :cvar BUILDERS: a namedtuple with attributes related to schema components classes. \ Used for build local components within parsing methods. :vartype BUILDERS: namedtuple :cvar BUILDERS_MAP: a dictionary that maps from tag to class for XSD global components. \ Used for build global components within lookup functions. :vartype BUILDERS_MAP: dict :cvar BASE_SCHEMAS: a dictionary from namespace to schema resource for meta-schema bases. :vartype BASE_SCHEMAS: dict :cvar meta_schema: the XSD meta-schema instance. :vartype meta_schema: XMLSchema :cvar attribute_form_default: the schema's *attributeFormDefault* attribute, defaults to 'unqualified'. :vartype attribute_form_default: str :cvar element_form_default: the schema's *elementFormDefault* attribute, defaults to 'unqualified' :vartype element_form_default: str :cvar block_default: the schema's *blockDefault* attribute, defaults to ''. :vartype block_default: str :cvar final_default: the schema's *finalDefault* attribute, defaults to ''. :vartype final_default: str :cvar default_attributes: the XSD 1.1 schema's *defaultAttributes* attribute, defaults to ``None``. :vartype default_attributes: XsdAttributeGroup :ivar target_namespace: is the *targetNamespace* of the schema, the namespace to which \ belong the declarations/definitions of the schema. If it's empty no namespace is associated \ with the schema. In this case the schema declarations can be reused from other namespaces as \ *chameleon* definitions. :vartype target_namespace: str :ivar validation: validation mode, can be 'strict', 'lax' or 'skip'. :vartype validation: str :ivar maps: XSD global declarations/definitions maps. This is an instance of :class:`XsdGlobal`, \ that store the global_maps argument or a new object when this argument is not provided. :vartype maps: XsdGlobals :ivar converter: the default converter used for XML data decoding/encoding. :vartype converter: XMLSchemaConverter :ivar locations: schema location hints. :vartype locations: NamespaceResourcesMap :ivar namespaces: a dictionary that maps from the prefixes used by the schema into namespace URI. :vartype namespaces: dict :ivar imports: a dictionary of namespace imports of the schema, that maps namespace URI to imported schema \ object, or `None` in case of unsuccessful import. :vartype imports: dict :ivar includes: a dictionary of included schemas, that maps a schema location to an included schema. \ It also comprehend schemas included by "xs:redefine" or "xs:override" statements. :vartype warnings: dict :ivar warnings: warning messages about failure of import and include elements. :vartype warnings: list :ivar notations: `xsd:notation` declarations. :vartype notations: NamespaceView :ivar types: `xsd:simpleType` and `xsd:complexType` global declarations. :vartype types: NamespaceView :ivar attributes: `xsd:attribute` global declarations. :vartype attributes: NamespaceView :ivar attribute_groups: `xsd:attributeGroup` definitions. :vartype attribute_groups: NamespaceView :ivar groups: `xsd:group` global definitions. :vartype groups: NamespaceView :ivar elements: `xsd:element` global declarations. :vartype elements: NamespaceView """ XSD_VERSION = None BUILDERS = None BUILDERS_MAP = None BASE_SCHEMAS = None meta_schema = None # Schema defaults target_namespace = '' attribute_form_default = 'unqualified' element_form_default = 'unqualified' block_default = '' final_default = '' default_attributes = None # for XSD 1.1 def __init__(self, source, namespace=None, validation='strict', global_maps=None, converter=None, locations=None, base_url=None, defuse='remote', timeout=300, build=True, use_meta=True): super(XMLSchemaBase, self).__init__(validation) self.source = XMLResource(source, base_url, defuse, timeout, lazy=False) self.imports = {} self.includes = {} self.warnings = [] self._root_elements = None root = self.source.root # Parse namespaces and targetNamespace self.namespaces = {'xml': XML_NAMESPACE} # the XML namespace is implicit self.namespaces.update(self.source.get_namespaces()) try: self.target_namespace = root.attrib['targetNamespace'] except KeyError: pass else: if self.target_namespace == '': # Ref: https://www.w3.org/TR/2004/REC-xmlschema-1-20041028/structures.html#element-schema self.parse_error("The attribute 'targetNamespace' cannot be an empty string.", root) if namespace is not None and self.target_namespace != namespace: if self.target_namespace: msg = u"wrong namespace (%r instead of %r) for XSD resource %r." self.parse_error(msg % (self.target_namespace, namespace, self.url), root) # Chameleon schema case: set the target namespace and the default namespace self.target_namespace = namespace if '' not in self.namespaces: self.namespaces[''] = namespace # Parses the schema defaults if 'attributeFormDefault' in root.attrib: try: self.attribute_form_default = get_xsd_form_attribute(root, 'attributeFormDefault') except ValueError as err: self.parse_error(err, root) if 'elementFormDefault' in root.attrib: try: self.element_form_default = get_xsd_form_attribute(root, 'elementFormDefault') except ValueError as err: self.parse_error(err, root) if 'blockDefault' in root.attrib: try: self.block_default = get_xsd_derivation_attribute( root, 'blockDefault', {'extension', 'restriction', 'substitution'} ) except ValueError as err: self.parse_error(err, root) if 'finalDefault' in root.attrib: try: self.final_default = get_xsd_derivation_attribute(root, 'finalDefault') except ValueError as err: self.parse_error(err, root) if self.XSD_VERSION > '1.0': # XSD 1.1: "defaultAttributes" and "xpathDefaultNamespace" self.xpath_default_namespace = self._parse_xpath_default_namespace(root) if 'defaultAttributes' in root.attrib: try: self.default_attributes = self.resolve_qname(root.attrib['defaultAttributes']) except XMLSchemaValueError as error: self.parse_error(str(error), root) # Set locations hints map and converter self.locations = NamespaceResourcesMap(self.source.get_locations(locations)) if self.meta_schema is not None: # Add fallback schema location hint for XHTML self.locations[XHTML_NAMESPACE] = os.path.join(SCHEMAS_DIR, 'xhtml1-strict.xsd') self.converter = self.get_converter(converter) # Create or set the XSD global maps instance if self.meta_schema is None: self.maps = global_maps or XsdGlobals(self) return # Meta-schemas don't need to be checked or built and don't process include/imports elif global_maps is None: if use_meta is False: self.maps = XsdGlobals(self, validation) self.locations.update(self.BASE_SCHEMAS) elif self.target_namespace not in self.BASE_SCHEMAS: self.maps = self.meta_schema.maps.copy(self, validation=validation) else: base_schemas = {k: v for k, v in self.BASE_SCHEMAS.items() if k != self.target_namespace} meta_schema = self.create_meta_schema(base_schemas=base_schemas) self.maps = meta_schema.maps self.meta_schema = meta_schema elif isinstance(global_maps, XsdGlobals): self.maps = global_maps else: raise XMLSchemaTypeError("'global_maps' argument must be a %r instance." % XsdGlobals) # Validate the schema document if validation == 'strict': self.check_schema(root, self.namespaces) elif validation == 'lax': self.errors.extend([e for e in self.meta_schema.iter_errors(root, namespaces=self.namespaces)]) # Includes and imports schemas (errors are treated as warnings) self._include_schemas() self._import_namespaces() if '' not in self.namespaces: self.namespaces[''] = '' # For default local names are mapped to no namespace if build: self.maps.build() def __repr__(self): if self.url: basename = os.path.basename(self.url) return u'%s(basename=%r, namespace=%r)' % (self.__class__.__name__, basename, self.target_namespace) else: return u'%s(namespace=%r)' % (self.__class__.__name__, self.target_namespace) def __setattr__(self, name, value): if name == 'root' and value.tag not in (XSD_SCHEMA, 'schema'): raise XMLSchemaValueError("schema root element must has %r tag." % XSD_SCHEMA) elif name == 'maps': if self.meta_schema is None and hasattr(self, 'maps'): raise XMLSchemaValueError("cannot change the global maps instance of a meta-schema") super(XMLSchemaBase, self).__setattr__(name, value) self.notations = NamespaceView(value.notations, self.target_namespace) self.types = NamespaceView(value.types, self.target_namespace) self.attributes = NamespaceView(value.attributes, self.target_namespace) self.attribute_groups = NamespaceView(value.attribute_groups, self.target_namespace) self.groups = NamespaceView(value.groups, self.target_namespace) self.elements = NamespaceView(value.elements, self.target_namespace) self.substitution_groups = NamespaceView(value.substitution_groups, self.target_namespace) self.constraints = NamespaceView(value.constraints, self.target_namespace) self.global_maps = (self.notations, self.types, self.attributes, self.attribute_groups, self.groups, self.elements) value.register(self) elif name == 'validation' and value not in ('strict', 'lax', 'skip'): raise XMLSchemaValueError("Wrong value %r for attribute 'validation'." % value) else: super(XMLSchemaBase, self).__setattr__(name, value) def __iter__(self): for xsd_element in sorted(self.elements.values(), key=lambda x: x.name): yield xsd_element def __reversed__(self): for xsd_element in sorted(self.elements.values(), key=lambda x: x.name, reverse=True): yield xsd_element def __len__(self): return len(self.elements) # XML resource attributes access @property def root(self): """Root element of the schema.""" return self.source.root def get_text(self): """ Gets the XSD text of the schema. If the source text is not available creates an encoded string representation of the XSD tree. """ if self.source.text is None: if self.source.url is None: return etree_tostring(self.source.root, self.namespaces, xml_declaration=True) else: try: self.source.load() except XMLSchemaOSError: return etree_tostring(self.source.root, self.namespaces, xml_declaration=True) return self.source.text @property def url(self): """Schema resource URL, is `None` if the schema is built from a string.""" return self.source.url @property def base_url(self): """The base URL of the source of the schema.""" return self.source.base_url @property def defuse(self): """Defines when to defuse XML data, can be 'always', 'remote' or 'never'.""" return self.source.defuse @property def timeout(self): """Timeout in seconds for fetching resources.""" return self.source.timeout @property def use_meta(self): """Returns `True` if the meta-schema is imported.""" return self.meta_schema is not None and XSD_NAMESPACE in self.maps.namespaces # Schema root attributes @property def tag(self): """Schema root tag. For compatibility with the ElementTree API.""" return self.root.tag @property def id(self): """The schema's *id* attribute, defaults to ``None``.""" return self.root.get('id') @property def version(self): """The schema's *version* attribute, defaults to ``None``.""" return self.root.get('version') @property def schema_location(self): """A list of location hints extracted from the *xsi:schemaLocation* attribute of the schema.""" return [(k, v) for k, v in self.source.iter_location_hints() if k] @property def no_namespace_schema_location(self): """A location hint extracted from the *xsi:noNamespaceSchemaLocation* attribute of the schema.""" for k, v in self.source.iter_location_hints(): if not k: return v @property def default_namespace(self): """The namespace associated to the empty prefix ''.""" return self.namespaces.get('') @property def target_prefix(self): """The prefix associated to the *targetNamespace*.""" for prefix, namespace in self.namespaces.items(): if namespace == self.target_namespace: return prefix return '' @classmethod def builtin_types(cls): """An accessor for XSD built-in types.""" try: return cls.meta_schema.maps.namespaces[XSD_NAMESPACE][0].types except KeyError: raise XMLSchemaNotBuiltError(cls.meta_schema, "missing XSD namespace in meta-schema") except AttributeError: raise XMLSchemaNotBuiltError(cls.meta_schema, "meta-schema unavailable for %r" % cls) @property def root_elements(self): """ The list of global elements that are not used by reference in any model of the schema. This is implemented as lazy property because it's computationally expensive to build when the schema model is complex. """ if not self.elements: return [] elif len(self.elements) == 1: return list(self.elements.values()) elif self._root_elements is None: names = set(e.name for e in self.elements.values()) for xsd_element in self.elements.values(): for e in xsd_element.iter(): if e is xsd_element or isinstance(e, XsdAnyElement): continue elif e.ref or e.is_global: if e.name in names: names.discard(e.name) if not names: break self._root_elements = list(names) return [e for e in self.elements.values() if e.name in self._root_elements] @classmethod def create_meta_schema(cls, source=None, base_schemas=None, global_maps=None): """ Creates a new meta-schema instance. :param source: an optional argument referencing to or containing the XSD meta-schema \ resource. Required if the schema class doesn't already have a meta-schema. :param base_schemas: an optional dictionary that contains namespace URIs and schema locations. \ If provided it's used as substitute for class 's BASE_SCHEMAS. Also a sequence of (namespace, \ location) items can be provided if there are more schema documents for one or more namespaces. :param global_maps: is an optional argument containing an :class:`XsdGlobals` \ instance for the new meta schema. If not provided a new map is created. """ if source is None: try: source = cls.meta_schema.url except AttributeError: raise XMLSchemaValueError( "The argument 'source' is required when the class doesn't already have a meta-schema" ) if base_schemas is None: base_schemas = cls.BASE_SCHEMAS.items() elif isinstance(base_schemas, dict): base_schemas = base_schemas.items() else: try: base_schemas = [(n, l) for n, l in base_schemas] except ValueError: raise ValueError("The argument 'base_schemas' is not a dictionary nor a sequence of items") meta_schema_class = cls if cls.meta_schema is None else cls.meta_schema.__class__ meta_schema = meta_schema_class(source, XSD_NAMESPACE, global_maps=global_maps, defuse='never', build=False) for ns, location in base_schemas: if ns == XSD_NAMESPACE: meta_schema.include_schema(location=location) else: meta_schema.import_schema(namespace=ns, location=location) return meta_schema @classmethod def create_schema(cls, *args, **kwargs): """Creates a new schema instance of the same class of the caller.""" return cls(*args, **kwargs) def create_any_content_group(self, parent, name=None): """Creates a model group related to schema instance that accepts any content.""" group = self.BUILDERS.group_class(SEQUENCE_ELEMENT, self, parent, name) group.append(XsdAnyElement(ANY_ELEMENT, self, group)) return group def create_any_attribute_group(self, parent, name=None): """Creates an attribute group related to schema instance that accepts any attribute.""" attribute_group = self.BUILDERS.attribute_group_class(ATTRIBUTE_GROUP_ELEMENT, self, parent, name) attribute_group[None] = XsdAnyAttribute(ANY_ATTRIBUTE_ELEMENT, self, attribute_group) return attribute_group def copy(self): """Makes a copy of the schema instance. The new instance has independent maps of shared XSD components.""" schema = object.__new__(self.__class__) schema.__dict__.update(self.__dict__) schema.source = self.source.copy() schema.errors = self.errors[:] schema.warnings = self.warnings[:] schema.namespaces = self.namespaces.copy() schema.locations = NamespaceResourcesMap(self.locations) schema.imports = dict(self.imports) schema.includes = dict(self.includes) schema.maps = self.maps.copy(validator=schema) return schema __copy__ = copy @classmethod def check_schema(cls, schema, namespaces=None): """ Validates the given schema against the XSD meta-schema (:attr:`meta_schema`). :param schema: the schema instance that has to be validated. :param namespaces: is an optional mapping from namespace prefix to URI. :raises: :exc:`XMLSchemaValidationError` if the schema is invalid. """ for error in cls.meta_schema.iter_errors(schema, namespaces=namespaces): raise error def build(self): """Builds the schema XSD global maps.""" self.maps.build() @property def built(self): xsd_global = None for xsd_global in self.iter_globals(self): if not isinstance(xsd_global, XsdComponent): return False if not xsd_global.built: return False if xsd_global is not None: return True prefix = '{%s}' % self.target_namespace if self.target_namespace else '' for child in filter(lambda x: x.tag != XSD_ANNOTATION, self.root): if child.tag in (XSD_REDEFINE, XSD_OVERRIDE): for e in filter(lambda x: x.tag in self.BUILDERS_MAP, child): name = e.get('name') if name is not None: try: if not self.maps.lookup(e.tag, prefix + name if prefix else name).built: return False except KeyError: return False elif child.tag in self.BUILDERS_MAP: name = child.get('name') if name is not None: try: if not self.maps.lookup(child.tag, prefix + name if prefix else name).built: return False except KeyError: return False return True @property def validation_attempted(self): if self.built: return 'full' elif any([comp.validation_attempted == 'partial' for comp in self.iter_globals()]): return 'partial' else: return 'none' def iter_globals(self, schema=None): """ Creates an iterator for XSD global definitions/declarations related to schema namespace. :param schema: Optional argument for filtering only globals related to a schema instance. """ if schema is None: for global_map in self.global_maps: for obj in global_map.values(): yield obj else: for global_map in self.global_maps: for obj in global_map.values(): if isinstance(obj, tuple): if obj[1] == schema: yield obj elif obj.schema == schema: yield obj def iter_components(self, xsd_classes=None): if xsd_classes is None or isinstance(self, xsd_classes): yield self for xsd_global in self.iter_globals(self): for obj in xsd_global.iter_components(xsd_classes): yield obj def get_locations(self, namespace): """ Get a list of location hints for a namespace. """ try: return list(self.locations[namespace]) except KeyError: return [] def get_converter(self, converter=None, namespaces=None, **kwargs): """ Returns a new converter instance. :param converter: can be a converter class or instance. If it's an instance \ the new instance is copied from it and configured with the provided arguments. :param namespaces: is an optional mapping from namespace prefix to URI. :param kwargs: optional arguments for initialize the converter instance. :return: a converter instance. """ if converter is None: converter = getattr(self, 'converter', XMLSchemaConverter) if isinstance(converter, XMLSchemaConverter): return converter.copy(namespaces=namespaces, **kwargs) elif issubclass(converter, XMLSchemaConverter): return converter(namespaces, **kwargs) else: msg = "'converter' argument must be a %r subclass or instance: %r" raise XMLSchemaTypeError(msg % (XMLSchemaConverter, converter)) def get_element(self, tag, path=None, namespaces=None): if not path: return self.find(tag) elif path[-1] == '*': return self.find(path[:-1] + tag, namespaces) else: return self.find(path, namespaces) def _include_schemas(self): """Processes schema document inclusions and redefinitions.""" for child in iterchildren_xsd_include(self.root): try: self.include_schema(child.attrib['schemaLocation'], self.base_url) except KeyError: pass except (OSError, IOError) as err: # Attribute missing error already found by validation against meta-schema. # It is not an error if the location fail to resolve: # https://www.w3.org/TR/2012/REC-xmlschema11-1-20120405/#compound-schema # https://www.w3.org/TR/2012/REC-xmlschema11-1-20120405/#src-include self.warnings.append("Include schema failed: %s." % str(err)) warnings.warn(self.warnings[-1], XMLSchemaIncludeWarning, stacklevel=3) except (XMLSchemaURLError, XMLSchemaParseError, XMLSchemaTypeError, ParseError) as err: msg = 'cannot include schema %r: %s' % (child.attrib['schemaLocation'], err) if isinstance(err, (XMLSchemaParseError, ParseError)): self.parse_error(msg) elif self.validation == 'strict': raise type(err)(msg) else: self.errors.append(type(err)(msg)) for child in iterchildren_xsd_redefine(self.root): try: self.include_schema(child.attrib['schemaLocation'], self.base_url) except KeyError: pass # Attribute missing error already found by validation against meta-schema except (OSError, IOError) as err: # If the redefine doesn't contain components (annotation excluded) the statement # is equivalent to an include, so no error is generated. Otherwise fails. self.warnings.append("Redefine schema failed: %s." % str(err)) warnings.warn(self.warnings[-1], XMLSchemaIncludeWarning, stacklevel=3) if has_xsd_components(child): self.parse_error(str(err), child) except (XMLSchemaURLError, XMLSchemaParseError, XMLSchemaTypeError, ParseError) as err: msg = 'cannot redefine schema %r: %s' % (child.attrib['schemaLocation'], err) if isinstance(err, (XMLSchemaParseError, ParseError)): self.parse_error(msg) elif self.validation == 'strict': raise type(err)(msg) else: self.errors.append(type(err)(msg)) def include_schema(self, location, base_url=None): """ Includes a schema for the same namespace, from a specific URL. :param location: is the URL of the schema. :param base_url: is an optional base URL for fetching the schema resource. :return: the included :class:`XMLSchema` instance. """ schema_url = fetch_resource(location, base_url) for schema in self.maps.namespaces[self.target_namespace]: if schema_url == schema.url: break else: schema = self.create_schema( schema_url, self.target_namespace, self.validation, self.maps, self.converter, self.locations, self.base_url, self.defuse, self.timeout, False ) if location not in self.includes: self.includes[location] = schema elif self.includes[location] != schema: self.includes[schema_url] = schema return schema def _import_namespaces(self): """ Processes namespace imports. Imports are done on namespace basis not on resource: this is the standard and also avoids import loops that sometimes are hard to detect. """ namespace_imports = NamespaceResourcesMap(map( lambda x: (x.get('namespace'), x.get('schemaLocation')), iterchildren_xsd_import(self.root) )) for namespace, locations in namespace_imports.items(): # Checks the namespace against the targetNamespace of the schema if namespace is None: namespace = '' if namespace == self.target_namespace: self.parse_error("if the 'namespace' attribute is not present on the import statement " "then the importing schema must has a 'targetNamespace'") continue elif namespace == self.target_namespace: self.parse_error("the attribute 'namespace' must be different from schema's 'targetNamespace'") continue # Skip import of already imported namespaces if self.imports.get(namespace) is not None: continue elif namespace in self.maps.namespaces: self.imports[namespace] = self.maps.namespaces[namespace][0] continue locations = [url for url in locations if url] if not namespace: pass elif not locations: locations = self.get_locations(namespace) elif all(is_remote_url(url) for url in locations): # If all import schema locations are remote URLs and there are local hints # that match a local file path, try the local hints before schema locations. # This is not the standard processing for XSD imports, but resolve the problem # of local processing of schemas tested to work from a http server, providing # explicit local hints. local_hints = [url for url in self.get_locations(namespace) if url and url_path_is_file(url)] if local_hints: locations = local_hints + locations import_error = None for url in locations: try: self.import_schema(namespace, url, self.base_url) except (OSError, IOError) as err: # It's not an error if the location access fails (ref. section 4.2.6.2): # https://www.w3.org/TR/2012/REC-xmlschema11-1-20120405/#composition-schemaImport if import_error is None: import_error = err except (XMLSchemaURLError, XMLSchemaParseError, XMLSchemaTypeError, ParseError) as err: if namespace: msg = "cannot import namespace %r: %s." % (namespace, err) else: msg = "cannot import chameleon schema: %s." % err if isinstance(err, (XMLSchemaParseError, ParseError)): self.parse_error(msg) elif self.validation == 'strict': raise type(err)(msg) else: self.errors.append(type(err)(msg)) except XMLSchemaValueError as err: self.parse_error(err) else: break else: if import_error is not None: self.warnings.append("Namespace import failed: %s." % str(import_error)) warnings.warn(self.warnings[-1], XMLSchemaImportWarning, stacklevel=3) self.imports[namespace] = None def import_schema(self, namespace, location, base_url=None, force=False): """ Imports a schema for an external namespace, from a specific URL. :param namespace: is the URI of the external namespace. :param location: is the URL of the schema. :param base_url: is an optional base URL for fetching the schema resource. :param force: is set to `True` imports the schema also if the namespace is already imported. :return: the imported :class:`XMLSchema` instance. """ if not force: if self.imports.get(namespace) is not None: return self.imports[namespace] elif namespace in self.maps.namespaces: self.imports[namespace] = self.maps.namespaces[namespace][0] return self.imports[namespace] schema_url = fetch_resource(location, base_url) if self.imports.get(namespace) is not None and self.imports[namespace].url == schema_url: return self.imports[namespace] elif namespace in self.maps.namespaces: for schema in self.maps.namespaces[namespace]: if schema_url == schema.url: self.imports[namespace] = schema return schema schema = self.create_schema( schema_url, None, self.validation, self.maps, self.converter, self.locations, self.base_url, self.defuse, self.timeout, False ) if schema.target_namespace != namespace: raise XMLSchemaValueError('imported schema %r has an unmatched namespace %r' % (location, namespace)) self.imports[namespace] = schema return schema def resolve_qname(self, qname): """ QName resolution for a schema instance. :param qname: a string in xs:QName format. :returns: an expanded QName in the format "{*namespace-URI*}*local-name*". :raises: `XMLSchemaValueError` for an invalid xs:QName or if the namespace prefix is not \ declared in the schema instance or if the namespace is not the *targetNamespace* and \ the namespace is not imported by the schema. """ qname = qname.strip() if not qname or ' ' in qname or '\t' in qname or '\n' in qname: raise XMLSchemaValueError("{!r} is not a valid value for xs:QName".format(qname)) if qname[0] == '{': try: namespace, local_name = qname[1:].split('}') except ValueError: raise XMLSchemaValueError("{!r} is not a valid value for xs:QName".format(qname)) elif ':' in qname: try: prefix, local_name = qname.split(':') except ValueError: raise XMLSchemaValueError("{!r} is not a valid value for xs:QName".format(qname)) else: try: namespace = self.namespaces[prefix] except KeyError: raise XMLSchemaValueError("prefix %r not found in namespace map" % prefix) else: namespace, local_name = self.namespaces.get('', ''), qname if not namespace: return local_name elif self.meta_schema is not None and namespace != self.target_namespace and \ namespace not in {XSD_NAMESPACE, XSI_NAMESPACE} and namespace not in self.imports: raise XMLSchemaValueError( "the QName {!r} is mapped to the namespace {!r}, but this namespace has " "not an xs:import statement in the schema.".format(qname, namespace) ) return '{%s}%s' % (namespace, local_name) def validate(self, source, path=None, schema_path=None, use_defaults=True, namespaces=None): """ Validates an XML data against the XSD schema/component instance. :raises: :exc:`XMLSchemaValidationError` if XML *data* instance is not a valid. """ for error in self.iter_errors(source, path, schema_path, use_defaults, namespaces): raise error def is_valid(self, source, path=None, schema_path=None, use_defaults=True, namespaces=None): """ Like :meth:`validate` except that do not raises an exception but returns ``True`` if the XML document is valid, ``False`` if it's invalid. """ error = next(self.iter_errors(source, path, schema_path, use_defaults, namespaces), None) return error is None def iter_errors(self, source, path=None, schema_path=None, use_defaults=True, namespaces=None): """ Creates an iterator for the errors generated by the validation of an XML data against the XSD schema/component instance. :param source: the source of XML data. Can be an :class:`XMLResource` instance, a \ path to a file or an URI of a resource or an opened file-like object or an Element \ instance or an ElementTree instance or a string containing the XML data. :param path: is an optional XPath expression that matches the elements of the XML \ data that have to be decoded. If not provided the XML root element is selected. :param schema_path: an alternative XPath expression to select the XSD element to use for \ decoding. Useful if the root of the XML data doesn't match an XSD global element of the schema. :param use_defaults: Use schema's default values for filling missing data. :param namespaces: is an optional mapping from namespace prefix to URI. """ if not self.built: raise XMLSchemaNotBuiltError(self, "schema %r is not built." % self) elif not isinstance(source, XMLResource): source = XMLResource(source=source, defuse=self.defuse, timeout=self.timeout, lazy=False) if not schema_path and path: schema_path = path if path.startswith('/') else '/%s/%s' % (source.root.tag, path) namespaces = {} if namespaces is None else namespaces.copy() namespaces.update(source.get_namespaces()) id_map = Counter() if source.is_lazy() and path is None: # TODO: Document validation in lazy mode. # Validation is done pushing a _no_deep argument for root node and with # a path='*' for validating children. This is a feature under test. xsd_element = self.get_element(source.root.tag, schema_path) if xsd_element is None: yield self.validation_error('lax', "%r is not an element of the schema" % source.root, source.root) for result in xsd_element.iter_decode(source.root, source=source, namespaces=namespaces, use_defaults=use_defaults, id_map=id_map, _no_deep=None): if isinstance(result, XMLSchemaValidationError): yield result else: del result path = '*' if not schema_path: schema_path = '/%s/*' % source.root.tag for elem in source.iterfind(path, namespaces): xsd_element = self.get_element(elem.tag, schema_path, namespaces) if xsd_element is None: yield self.validation_error('lax', "%r is not an element of the schema" % elem, elem) for result in xsd_element.iter_decode(elem, source=source, namespaces=namespaces, use_defaults=use_defaults, id_map=id_map): if isinstance(result, XMLSchemaValidationError): yield result else: del result def iter_decode(self, source, path=None, schema_path=None, validation='lax', process_namespaces=True, namespaces=None, use_defaults=True, decimal_type=None, datetime_types=False, converter=None, filler=None, fill_missing=False, **kwargs): """ Creates an iterator for decoding an XML source to a data structure. :param source: the source of XML data. Can be an :class:`XMLResource` instance, a \ path to a file or an URI of a resource or an opened file-like object or an Element \ instance or an ElementTree instance or a string containing the XML data. :param path: is an optional XPath expression that matches the elements of the XML \ data that have to be decoded. If not provided the XML root element is selected. :param schema_path: an alternative XPath expression to select the XSD element to use for \ decoding. Useful if the root of the XML data doesn't match an XSD global element of the schema. :param validation: defines the XSD validation mode to use for decode, can be 'strict', \ 'lax' or 'skip'. :param process_namespaces: indicates whether to use namespace information in the decoding \ process, using the map provided with the argument *namespaces* and the map extracted from \ the XML document. :param namespaces: is an optional mapping from namespace prefix to URI. :param use_defaults: indicates whether to use default values for filling missing data. :param decimal_type: conversion type for `Decimal` objects (generated by XSD `decimal` \ built-in and derived types), useful if you want to generate a JSON-compatible data structure. :param datetime_types: if set to `True` the datetime and duration XSD types are decoded, \ otherwise their origin XML string is returned. :param converter: an :class:`XMLSchemaConverter` subclass or instance to use for the decoding. :param filler: an optional callback function to fill undecodable data with a typed value. \ The callback function must accepts one positional argument, that can be an XSD Element or \ an attribute declaration. If not provided undecodable data is replaced by `None`. :param fill_missing: if set to `True` the decoder fills also missing attributes. \ The filling value is `None` or a typed value if the *filler* callback is provided. :param kwargs: keyword arguments with other options for converter and decoder. :return: yields a decoded data object, eventually preceded by a sequence of validation \ or decoding errors. """ if not self.built: raise XMLSchemaNotBuiltError(self, "schema %r is not built." % self) elif validation not in XSD_VALIDATION_MODES: raise XMLSchemaValueError("validation argument can be 'strict', 'lax' or 'skip': %r" % validation) elif not isinstance(source, XMLResource): source = XMLResource(source=source, defuse=self.defuse, timeout=self.timeout, lazy=False) if not schema_path and path: schema_path = path if path.startswith('/') else '/%s/%s' % (source.root.tag, path) if process_namespaces: namespaces = {} if namespaces is None else namespaces.copy() namespaces.update(source.get_namespaces()) else: namespaces = {} converter = self.get_converter(converter, namespaces, **kwargs) id_map = Counter() if decimal_type is not None: kwargs['decimal_type'] = decimal_type for elem in source.iterfind(path, namespaces): xsd_element = self.get_element(elem.tag, schema_path, namespaces) if xsd_element is None: yield self.validation_error(validation, "%r is not an element of the schema" % elem, elem) for obj in xsd_element.iter_decode( elem, validation, converter=converter, source=source, namespaces=namespaces, use_defaults=use_defaults, datetime_types=datetime_types, filler=filler, fill_missing=fill_missing, id_map=id_map, **kwargs): yield obj def decode(self, source, path=None, schema_path=None, validation='strict', *args, **kwargs): """ Decodes XML data. Takes the same arguments of the method :func:`XMLSchema.iter_decode`. """ data, errors = [], [] for result in self.iter_decode(source, path, schema_path, validation, *args, **kwargs): if not isinstance(result, XMLSchemaValidationError): data.append(result) elif validation == 'lax': errors.append(result) else: raise result if not data: return (None, errors) if validation == 'lax' else None elif len(data) == 1: return (data[0], errors) if validation == 'lax' else data[0] else: return (data, errors) if validation == 'lax' else data to_dict = decode def iter_encode(self, obj, path=None, validation='lax', namespaces=None, converter=None, **kwargs): """ Creates an iterator for encoding a data structure to an ElementTree's Element. :param obj: the data that has to be encoded to XML data. :param path: is an optional XPath expression for selecting the element of the schema \ that matches the data that has to be encoded. For default the first global element of \ the schema is used. :param validation: the XSD validation mode. Can be 'strict', 'lax' or 'skip'. :param namespaces: is an optional mapping from namespace prefix to URI. :param converter: an :class:`XMLSchemaConverter` subclass or instance to use for the encoding. :param kwargs: Keyword arguments containing options for converter and encoding. :return: yields an Element instance/s or validation/encoding errors. """ if not self.built: raise XMLSchemaNotBuiltError(self, "schema %r is not built." % self) elif validation not in XSD_VALIDATION_MODES: raise XMLSchemaValueError("validation argument can be 'strict', 'lax' or 'skip': %r" % validation) elif not self.elements: yield XMLSchemaValueError("encoding needs at least one XSD element declaration!") namespaces = {} if namespaces is None else namespaces.copy() converter = self.get_converter(converter, namespaces, **kwargs) if path is not None: xsd_element = self.find(path, namespaces=namespaces) elif isinstance(obj, dict) and len(obj) == 1: xsd_element = self.elements.get(list(obj.keys())[0]) elif len(self.elements) == 1: xsd_element = list(self.elements.values())[0] else: root_elements = self.root_elements xsd_element = root_elements[0] if len(root_elements) == 1 else None if not isinstance(xsd_element, XsdElement): if path is not None: msg = "the path %r doesn't match any element of the schema!" % path else: msg = "unable to select an element for decoding data, provide a valid 'path' argument." yield XMLSchemaEncodeError(self, obj, self.elements, reason=msg) else: for result in xsd_element.iter_encode(obj, validation, converter=converter, **kwargs): yield result def encode(self, obj, path=None, validation='strict', *args, **kwargs): """ Encodes to XML data. Takes the same arguments of the method :func:`XMLSchema.iter_encode`. :return: An ElementTree's Element or a list containing a sequence of ElementTree's \ elements if the argument *path* matches multiple XML data chunks. If *validation* \ argument is 'lax' a 2-items tuple is returned, where the first item is the encoded \ object and the second item is a list containing the errors. """ data, errors = [], [] for result in self.iter_encode(obj, path, validation, *args, **kwargs): if not isinstance(result, XMLSchemaValidationError): data.append(result) elif validation == 'lax': errors.append(result) else: raise result if not data: return (None, errors) if validation == 'lax' else None elif len(data) == 1: return (data[0], errors) if validation == 'lax' else data[0] else: return (data, errors) if validation == 'lax' else data to_etree = encode class XMLSchema10(XMLSchemaBase): """ XSD 1.0 schema class. <schema attributeFormDefault = (qualified | unqualified) : unqualified blockDefault = (#all | List of (extension | restriction | substitution)) : '' elementFormDefault = (qualified | unqualified) : unqualified finalDefault = (#all | List of (extension | restriction | list | union)) : '' id = ID targetNamespace = anyURI version = token xml:lang = language {any attributes with non-schema namespace . . .}> Content: ((include | import | redefine | annotation)*, (((simpleType | complexType | group | attributeGroup) | element | attribute | notation), annotation*)*) </schema> """ XSD_VERSION = '1.0' BUILDERS = { 'notation_class': XsdNotation, 'complex_type_class': XsdComplexType, 'attribute_class': XsdAttribute, 'any_attribute_class': XsdAnyAttribute, 'attribute_group_class': XsdAttributeGroup, 'group_class': XsdGroup, 'element_class': XsdElement, 'any_element_class': XsdAnyElement, 'restriction_class': XsdAtomicRestriction, 'union_class': XsdUnion, 'simple_type_factory': xsd_simple_type_factory } meta_schema = os.path.join(SCHEMAS_DIR, 'XSD_1.0/XMLSchema.xsd') BASE_SCHEMAS = { XML_NAMESPACE: XML_SCHEMA_FILE, # HFP_NAMESPACE: HFP_SCHEMA_FILE, XSI_NAMESPACE: XSI_SCHEMA_FILE, XLINK_NAMESPACE: XLINK_SCHEMA_FILE, } # ++++ UNDER DEVELOPMENT, DO NOT USE!!! ++++ class XMLSchema11(XMLSchemaBase): """ XSD 1.1 schema class. <schema attributeFormDefault = (qualified | unqualified) : unqualified blockDefault = (#all | List of (extension | restriction | substitution)) : '' defaultAttributes = QName xpathDefaultNamespace = (anyURI | (##defaultNamespace | ##targetNamespace | ##local)) : ##local elementFormDefault = (qualified | unqualified) : unqualified finalDefault = (#all | List of (extension | restriction | list | union)) : '' id = ID targetNamespace = anyURI version = token xml:lang = language {any attributes with non-schema namespace . . .}> Content: ((include | import | redefine | override | annotation)*, (defaultOpenContent, annotation*)?, ((simpleType | complexType | group | attributeGroup | element | attribute | notation), annotation*)*) </schema> <schema attributeFormDefault = (qualified | unqualified) : unqualified blockDefault = (#all | List of (extension | restriction | substitution)) : '' elementFormDefault = (qualified | unqualified) : unqualified finalDefault = (#all | List of (extension | restriction | list | union)) : '' id = ID targetNamespace = anyURI version = token xml:lang = language {any attributes with non-schema namespace . . .}> Content: ((include | import | redefine | annotation)*, (((simpleType | complexType | group | attributeGroup) | element | attribute | notation), annotation*)*) </schema> """ XSD_VERSION = '1.1' BUILDERS = { 'notation_class': XsdNotation, 'complex_type_class': Xsd11ComplexType, 'attribute_class': Xsd11Attribute, 'any_attribute_class': Xsd11AnyAttribute, 'attribute_group_class': XsdAttributeGroup, 'group_class': Xsd11Group, 'element_class': Xsd11Element, 'any_element_class': Xsd11AnyElement, 'restriction_class': Xsd11AtomicRestriction, 'union_class': Xsd11Union, 'simple_type_factory': xsd_simple_type_factory } meta_schema = os.path.join(SCHEMAS_DIR, 'XSD_1.1/XMLSchema.xsd') BASE_SCHEMAS = { XSD_NAMESPACE: os.path.join(SCHEMAS_DIR, 'XSD_1.1/list_builtins.xsd'), XML_NAMESPACE: XML_SCHEMA_FILE, # HFP_NAMESPACE: HFP_SCHEMA_FILE, XSI_NAMESPACE: XSI_SCHEMA_FILE, XLINK_NAMESPACE: XLINK_SCHEMA_FILE, } def _include_schemas(self): super(XMLSchema11, self)._include_schemas() for child in iterchildren_xsd_override(self.root): try: self.include_schema(child.attrib['schemaLocation'], self.base_url) except KeyError: pass # Attribute missing error already found by validation against meta-schema except (OSError, IOError) as err: # If the override doesn't contain components (annotation excluded) the statement # is equivalent to an include, so no error is generated. Otherwise fails. self.warnings.append("Override schema failed: %s." % str(err)) warnings.warn(self.warnings[-1], XMLSchemaIncludeWarning, stacklevel=3) if has_xsd_components(child): self.parse_error(str(err), child) XMLSchema = XMLSchema10 """The default class for schema instances."""

"""The :mod:`~deap.creator` is a meta-factory allowing to create classes that will fulfill the needs of your evolutionary algorithms. In effect, new classes can be built from any imaginable type, from :class:`list` to :class:`set`, :class:`dict`, :class:`~deap.gp.PrimitiveTree` and more, providing the possibility to implement genetic algorithms, genetic programming, evolution strategies, particle swarm optimizers, and many more. """ import array import copy import warnings import copyreg as copy_reg class_replacers = {} """Some classes in Python's standard library as well as third party library may be in part incompatible with the logic used in DEAP. To palliate this problem, the method :func:`create` uses the dictionary `class_replacers` to identify if the base type provided is problematic, and if so the new class inherits from the replacement class instead of the original base class. `class_replacers` keys are classes to be replaced and the values are the replacing classes. """ try: import numpy (numpy.ndarray, numpy.array) except ImportError: # Numpy is not present, skip the definition of the replacement class. pass except AttributeError: # Numpy is present, but there is either no ndarray or array in numpy, # also skip the definition of the replacement class. pass else: class _numpy_array(numpy.ndarray): def __deepcopy__(self, memo): """Overrides the deepcopy from numpy.ndarray that does not copy the object's attributes. This one will deepcopy the array and its :attr:`__dict__` attribute. """ copy_ = numpy.ndarray.copy(self) copy_.__dict__.update(copy.deepcopy(self.__dict__, memo)) return copy_ @staticmethod def __new__(cls, iterable): """Creates a new instance of a numpy.ndarray from a function call. Adds the possibility to instanciate from an iterable.""" return numpy.array(list(iterable)).view(cls) def __setstate__(self, state): self.__dict__.update(state) def __reduce__(self): return (self.__class__, (list(self),), self.__dict__) class_replacers[numpy.ndarray] = _numpy_array class _array(array.array): @staticmethod def __new__(cls, seq=()): return super(_array, cls).__new__(cls, cls.typecode, seq) def __deepcopy__(self, memo): """Overrides the deepcopy from array.array that does not copy the object's attributes and class type. """ cls = self.__class__ copy_ = cls.__new__(cls, self) memo[id(self)] = copy_ copy_.__dict__.update(copy.deepcopy(self.__dict__, memo)) return copy_ def __reduce__(self): return (self.__class__, (list(self),), self.__dict__) class_replacers[array.array] = _array class CreatorMeta(type): def __new__(meta, name, base, dct): return super(CreatorMeta, meta).__new__(meta, name, (base,), dct) def __init__(cls, name, base, dct): # A DeprecationWarning is raised when the object inherits from the # class "object" which leave the option of passing arguments, but # raise a warning stating that it will eventually stop permitting # this option. Usually this happens when the base class does not # override the __init__ method from object. dict_inst = {} dict_cls = {} for obj_name, obj in dct.items(): if isinstance(obj, type): dict_inst[obj_name] = obj else: dict_cls[obj_name] = obj def initType(self, *args, **kargs): """Replace the __init__ function of the new type, in order to add attributes that were defined with **kargs to the instance. """ for obj_name, obj in dict_inst.items(): setattr(self, obj_name, obj()) if base.__init__ is not object.__init__: base.__init__(self, *args, **kargs) cls.__init__ = initType cls.reduce_args = (name, base, dct) super(CreatorMeta, cls).__init__(name, (base,), dict_cls) def __reduce__(cls): return (meta_creator, cls.reduce_args) copy_reg.pickle(CreatorMeta, CreatorMeta.__reduce__) def meta_creator(name, base, dct): class_ = CreatorMeta(name, base, dct) globals()[name] = class_ return class_ def create(name, base, **kargs): """Creates a new class named *name* inheriting from *base* in the :mod:`~deap.creator` module. The new class can have attributes defined by the subsequent keyword arguments passed to the function create. If the argument is a class (without the parenthesis), the __init__ function is called in the initialization of an instance of the new object and the returned instance is added as an attribute of the class' instance. Otherwise, if the argument is not a class, (for example an :class:`int`), it is added as a "static" attribute of the class. :param name: The name of the class to create. :param base: A base class from which to inherit. :param attribute: One or more attributes to add on instanciation of this class, optional. The following is used to create a class :class:`Foo` inheriting from the standard :class:`list` and having an attribute :attr:`bar` being an empty dictionary and a static attribute :attr:`spam` initialized to 1. :: create("Foo", list, bar=dict, spam=1) This above line is exactly the same as defining in the :mod:`creator` module something like the following. :: class Foo(list): spam = 1 def __init__(self): self.bar = dict() The :ref:`creating-types` tutorial gives more examples of the creator usage. """ if name in globals(): warnings.warn("A class named '{0}' has already been created and it " "will be overwritten. Consider deleting previous " "creation of that class or rename it.".format(name), RuntimeWarning) # Check if the base class has to be replaced if base in class_replacers: base = class_replacers[base] meta_creator(name, base, kargs)

import os from os.path import join import numpy as np import pandas as pd from hagelslag.evaluation import DistributedROC, DistributedReliability eval_path = "/glade/p/work/dgagne/ncar_coarse_neighbor_eval_2016_s_2/" eval_files = sorted(os.listdir(eval_path)) eval_test = pd.read_csv(join(eval_path, eval_files[0])) models = eval_test.columns[eval_test.columns.str.contains("mean")] run_dates = pd.DatetimeIndex([e.split("_")[-1][:8] for e in eval_files]) thresholds = [25, 50, 75] prob_thresholds = np.concatenate(([0, 0.01], np.arange(0.1, 1.1, 0.1), [1.05])) brier = {} roc = {} for thresh in thresholds: brier[thresh] = pd.DataFrame(index=run_dates, columns=models, dtype=object) roc[thresh] = pd.DataFrame(index=run_dates, columns=models, dtype=object) for ev, eval_file in enumerate(eval_files): print(eval_file) eval_data = pd.read_csv(join(eval_path, eval_file)) us_mask = eval_data["us_mask"] == 1 for thresh in thresholds: obs = eval_data.loc[us_mask, "MESH_Max_60min_00.50_{0:2d}".format(thresh)] for model in models: brier[thresh].loc[run_dates[ev], model] = DistributedReliability(thresholds=prob_thresholds) brier[thresh].loc[run_dates[ev], model].update(eval_data.loc[us_mask, model], obs) roc[thresh].loc[run_dates[ev], model] = DistributedROC(thresholds=prob_thresholds) roc[thresh].loc[run_dates[ev], model].update(eval_data.loc[us_mask, model], obs) out_path = "/glade/p/work/dgagne/ncar_coarse_neighbor_scores_2016/" for thresh in [25, 50, 75]: brier[thresh].to_csv(join(out_path, "ncar_2016_s_2_brier_objs_{0:02d}.csv".format(thresh)), index_label="Date") roc[thresh].to_csv(join(out_path, "ncar_2016_s_2_roc_objs_{0:02d}.csv".format(thresh)), index_label="Date")

from zeus.models import TestCaseMeta def test_repository_test_details( client, db_session, default_login, default_testcase, default_build, default_repo, default_repo_access, ): db_session.add( TestCaseMeta( repository_id=default_testcase.repository_id, name=default_testcase.name, hash=default_testcase.hash, first_build_id=default_build.id, ) ) resp = client.get( "/api/repos/{}/tests/{}".format( default_repo.get_full_name(), default_testcase.hash ) ) assert resp.status_code == 200 data = resp.json() assert data["hash"] == str(default_testcase.hash) assert data["name"] == default_testcase.name assert data["first_build"]["id"] == str(default_build.id) assert data["last_build"]["id"] == str(default_build.id)

from copy import deepcopy # Copied from django/utils/html.py # TODO(robryk): Remove once we start using a version of Django that includes these from django.utils.html import conditional_escape, mark_safe def format_html(format_string, *args, **kwargs): """ Similar to str.format, but passes all arguments through conditional_escape, and calls 'mark_safe' on the result. This function should be used instead of str.format or % interpolation to build up small HTML fragments. """ args_safe = map(conditional_escape, args) kwargs_safe = dict([(k, conditional_escape(v)) for (k, v) in kwargs.iteritems()]) return mark_safe(format_string.format(*args_safe, **kwargs_safe)) def format_html_join(sep, format_string, args_generator): """ A wrapper format_html, for the common case of a group of arguments that need to be formatted using the same format string, and then joined using 'sep'. 'sep' is also passed through conditional_escape. 'args_generator' should be an iterator that returns the sequence of 'args' that will be passed to format_html. Example: format_html_join('\n', "<li>{0} {1}</li>", ((u.first_name, u.last_name) for u in users)) """ return mark_safe(conditional_escape(sep).join( format_html(format_string, *tuple(args)) for args in args_generator)) class FilterFunction(object): def __init__(self, prefix, name='', choices=[], check=(lambda table, i, value : True),default=None,showall=True): self.check = check self.choices = choices if showall: self.choices = [['disable_filter','-------']]+self.choices self.name = name self.prefix = prefix self.default = default class TableField(object): def __init__(self,id,name,sortable=True,value=(lambda table,i: unicode(i)+'th value'),render=None,filter=None,choices=[],css=None): if render == None: render = value self.id = unicode(id) self.name = name self.sortable = sortable self.render = render self.value = value self.filter = filter self.choices = choices self.css = css def class_string(self): if isinstance(self.css,str) or isinstance(self.css,unicode): self.css=[self.css] if self.css: return format_html(u' class="{0}"', ' '.join(self.css)) else: return '' class ResultTable(object): def length(self): return len(self.results) @staticmethod def default_limit(): return 0 def __init__(self, req = {}, prefix='', autosort=True, default_sort=None, default_desc=False): self.results = [] self.params = {} self.filters = {} self.filter_functions = [] self.other = {} self.total = 0 self.prefix = prefix self.autosort = autosort self.fields = [] for k in req.keys(): if k.find(prefix)==0: self.params[k[len(prefix)+1:]] = req[k] else: self.other[k] = req[k] for k in self.params.keys(): if k.find('filter')==0: if self.params[k]!='disable_filter': self.filters[k[7:]] = self.params[k] del self.params[k] if 'page' not in self.params.keys(): self.params['page'] = 1 else: self.params['page'] = int(self.params['page']) if self.params['page'] <= 0: self.params['page'] = 1 if 'limit' not in self.params.keys(): self.params['limit'] = self.default_limit() else: self.params['limit'] = int(self.params['limit']) if self.params['limit'] < 0: self.params['limit'] = self.default_limit() if 'sort' not in self.params.keys() and default_sort: self.params['sort'] = unicode(default_sort) if default_desc: self.params['order'] = 'desc' def add_autofilter(self,field): def autocheck(table,i,v): return v!='disable_filter' and field.value(table,i)==v choices = [] for i in range(0,self.length()): choices.append(field.value(self,i)) choices.sort() choices = [ [choices[i],choices[i]] for i in range(0,len(choices)) if i==0 or choices[i]!=choices[i-1]] self.filter_functions.append(FilterFunction(name=field.name,prefix=unicode(field.id),choices=choices,check=autocheck)) def getparams(self,filters={},**kwargs): p = deepcopy(self.params) for key in kwargs: p[key] = kwargs[key] for key in filters: p['filter_'+key] = filters[key] for key in self.filters: if not key in filters.keys(): p['filter_'+key] = self.filters[key] return '?'+'&'.join([self.prefix+'_'+unicode(k)+'='+unicode(v) for (k,v) in p.iteritems()]) def render_header(self): def sort_link(f): if self.params.get('sort',None)!=unicode(f.id) or self.params.get('order',None)=='desc': return self.getparams(sort=f.id,order='asc') return self.getparams(sort=f.id,order='desc') def header(f): if self.autosort and f.sortable: return format_html(u'<a class="stdlink" href="{0}">{1}</a>', sort_link(f), f.name) else: return f.name s = format_html_join(u'', u'<th>{0}</th>', [(header(f),) for f in self.fields]) return format_html(u'<tr>{0}</tr>', s) def render_row(self,i): s = format_html_join(u'', u'<td{0}>{1}</td>', [(f.class_string(), f.render(self,i)) for f in self.fields]) return format_html(u'<tr>{0}</tr>', s) def render_table(self): f_key = None order = [] for i in range(0,self.length()): ok = True for ff in self.filter_functions: v = self.filters.get(ff.prefix,ff.default) if v and not ff.check(self,i,v): ok = False for f in self.fields: if f.filter=='auto' and unicode(f.id) in self.filters.keys() and self.filters[f.id]!='disable_filter' and f.value(self,i)!=self.filters[f.id]: ok = False if ok: order.append(i) if 'sort' in self.params.keys(): for f in self.fields: if unicode(f.id) == self.params['sort']: f_key = f if self.autosort and f_key: order.sort(key=lambda i: f_key.value(self,i),reverse=(self.params.get('order',None)=='desc')) limit = self.params['limit'] page = self.params['page'] if self.autosort and limit>0: order = order[(page-1)*limit:page*limit] s = format_html_join(u'', u'{0}', [(self.render_row(i),) for i in order]) return format_html(u'{0}{1}', self.render_header(), s) def render_scrollbar(self): limit = self.params['limit'] if limit==0: return '' page = self.params['page'] tpages = (self.total+limit-1)/limit + 1 def render_wheelitem(i): if i == page: return format_html(u'<span class="wheelsel">{0}</span>', i) else: return format_html(u'<a class="wheelitem" href="{0}">{1}</a>',self.getparams(page=i), i) s = format_html_join(u'', u'{0}', [(render_wheelitem(i),) for i in range(1, tpages)]) return format_html(u'<div class="wheel">{0}</div>', s) def render_filters(self): s = '<form action="" method="GET">' s += ''.join(['<input type="hidden" name="'+self.prefix+'_'+unicode(k)+'" value="'+unicode(v)+'"/>' for (k,v) in self.params.iteritems() if k!='page']) s += ''.join(['<input type="hidden" name="'+unicode(k)+'" value="'+unicode(v)+'"/>' for (k,v) in self.other.iteritems()]) for ff in self.filter_functions: current = self.filters.get(ff.prefix,None) s += ff.name+': <select name="'+self.prefix+'_filter_'+ff.prefix+'">' for v in ff.choices: s += '<option value="'+unicode(v[0])+'"' if current and unicode(v[0])==unicode(current): s+='selected' s += ' >'+ unicode(v[1])+'</option>' s += '</select>' for f in self.fields: if f.filter=='custom': current=self.filters.get(unicode(f.id),None) s += f.name+': <select name="'+self.prefix+'_filter_'+unicode(f.id)+'">' s += '<option value="disable_filter">Show all</option>' for v in f.choices: s += '<option value="'+unicode(v[1])+'"' if unicode(v[1])==current: s+='selected' s += ' >'+ unicode(v[0])+'</option>' s += '</select>' s += '<input type="submit" class="button" value="Filter"/></form>' # FIXME(robryk): Make this saner, although this doesn't seem to be a vulnerability now. return mark_safe(s)

"""The Euler sampling method for ito processes.""" from typing import Callable, List, Optional import tensorflow.compat.v2 as tf from tf_quant_finance import types from tf_quant_finance import utils as tff_utils from tf_quant_finance.math import custom_loops from tf_quant_finance.math import random from tf_quant_finance.models import utils def sample( dim: int, drift_fn: Callable[..., types.RealTensor], volatility_fn: Callable[..., types.RealTensor], times: types.RealTensor, time_step: Optional[types.RealTensor] = None, num_time_steps: Optional[types.IntTensor] = None, num_samples: types.IntTensor = 1, initial_state: Optional[types.RealTensor] = None, random_type: Optional[random.RandomType] = None, seed: Optional[types.IntTensor] = None, swap_memory: bool = True, skip: types.IntTensor = 0, precompute_normal_draws: bool = True, times_grid: Optional[types.RealTensor] = None, normal_draws: Optional[types.RealTensor] = None, watch_params: Optional[List[types.RealTensor]] = None, validate_args: bool = False, tolerance: Optional[types.RealTensor] = None, dtype: Optional[tf.DType] = None, name: Optional[str] = None) -> types.RealTensor: """Returns a sample paths from the process using Euler method. For an Ito process, ``` dX = a(t, X_t) dt + b(t, X_t) dW_t X(t=0) = x0 ``` with given drift `a` and volatility `b` functions Euler method generates a sequence {X_n} as ``` X_{n+1} = X_n + a(t_n, X_n) dt + b(t_n, X_n) (N(0, t_{n+1}) - N(0, t_n)), X_0 = x0 ``` where `dt = t_{n+1} - t_n` and `N` is a sample from the Normal distribution. See [1] for details. #### Example Sampling from 2-dimensional Ito process of the form: ```none dX_1 = mu_1 * sqrt(t) dt + s11 * dW_1 + s12 * dW_2 dX_2 = mu_2 * sqrt(t) dt + s21 * dW_1 + s22 * dW_2 ``` ```python import tensorflow as tf import tf_quant_finance as tff import numpy as np mu = np.array([0.2, 0.7]) s = np.array([[0.3, 0.1], [0.1, 0.3]]) num_samples = 10000 dim = 2 dtype = tf.float64 # Define drift and volatility functions def drift_fn(t, x): return mu * tf.sqrt(t) * tf.ones([num_samples, dim], dtype=dtype) def vol_fn(t, x): return s * tf.ones([num_samples, dim, dim], dtype=dtype) # Set starting location x0 = np.array([0.1, -1.1]) # Sample `num_samples` paths at specified `times` using Euler scheme. times = [0.1, 1.0, 2.0] paths = tff.models.euler_sampling.sample( dim=dim, drift_fn=drift_fn, volatility_fn=vol_fn, times=times, num_samples=num_samples, initial_state=x0, time_step=0.01, seed=42, dtype=dtype) # Expected: paths.shape = [10000, 3, 2] ``` #### References [1]: Wikipedia. Euler-Maruyama method: https://en.wikipedia.org/wiki/Euler-Maruyama_method Args: dim: Python int greater than or equal to 1. The dimension of the Ito Process. drift_fn: A Python callable to compute the drift of the process. The callable should accept two real `Tensor` arguments of the same dtype. The first argument is the scalar time t, the second argument is the value of Ito process X - tensor of shape `batch_shape + [num_samples, dim]`. `batch_shape` is the shape of the independent stochastic processes being modelled and is inferred from the initial state `x0`. The result is value of drift a(t, X). The return value of the callable is a real `Tensor` of the same dtype as the input arguments and of shape `batch_shape + [num_samples, dim]`. volatility_fn: A Python callable to compute the volatility of the process. The callable should accept two real `Tensor` arguments of the same dtype and shape `times_shape`. The first argument is the scalar time t, the second argument is the value of Ito process X - tensor of shape `batch_shape + [num_samples, dim]`. The result is value of drift b(t, X). The return value of the callable is a real `Tensor` of the same dtype as the input arguments and of shape `batch_shape + [num_samples, dim, dim]`. times: Rank 1 `Tensor` of increasing positive real values. The times at which the path points are to be evaluated. time_step: An optional scalar real `Tensor` - maximal distance between points in grid in Euler schema. Either this or `num_time_steps` should be supplied. Default value: `None`. num_time_steps: An optional Scalar integer `Tensor` - a total number of time steps performed by the algorithm. The maximal distance betwen points in grid is bounded by `times[-1] / (num_time_steps - times.shape[0])`. Either this or `time_step` should be supplied. Default value: `None`. num_samples: Positive scalar `int`. The number of paths to draw. Default value: 1. initial_state: `Tensor` of shape broadcastable with `batch_shape + [num_samples, dim]`. The initial state of the process. `batch_shape` represents the shape of the independent batches of the stochastic process. Note that `batch_shape` is inferred from the `initial_state` and hence when sampling is requested for a batch of stochastic processes, the shape of `initial_state` should be at least `batch_shape + [1, 1]`. Default value: None which maps to a zero initial state. random_type: Enum value of `RandomType`. The type of (quasi)-random number generator to use to generate the paths. Default value: None which maps to the standard pseudo-random numbers. seed: Seed for the random number generator. The seed is only relevant if `random_type` is one of `[STATELESS, PSEUDO, HALTON_RANDOMIZED, PSEUDO_ANTITHETIC, STATELESS_ANTITHETIC]`. For `PSEUDO`, `PSEUDO_ANTITHETIC` and `HALTON_RANDOMIZED` the seed should be a Python integer. For `STATELESS` and `STATELESS_ANTITHETIC `must be supplied as an integer `Tensor` of shape `[2]`. Default value: `None` which means no seed is set. swap_memory: A Python bool. Whether GPU-CPU memory swap is enabled for this op. See an equivalent flag in `tf.while_loop` documentation for more details. Useful when computing a gradient of the op since `tf.while_loop` is used to propagate stochastic process in time. Default value: True. skip: `int32` 0-d `Tensor`. The number of initial points of the Sobol or Halton sequence to skip. Used only when `random_type` is 'SOBOL', 'HALTON', or 'HALTON_RANDOMIZED', otherwise ignored. Default value: `0`. precompute_normal_draws: Python bool. Indicates whether the noise increments `N(0, t_{n+1}) - N(0, t_n)` are precomputed. For `HALTON` and `SOBOL` random types the increments are always precomputed. While the resulting graph consumes more memory, the performance gains might be significant. Default value: `True`. times_grid: An optional rank 1 `Tensor` representing time discretization grid. If `times` are not on the grid, then the nearest points from the grid are used. When supplied, `num_time_steps` and `time_step` are ignored. Default value: `None`, which means that times grid is computed using `time_step` and `num_time_steps`. normal_draws: A `Tensor` of shape broadcastable with `batch_shape + [num_samples, num_time_points, dim]` and the same `dtype` as `times`. Represents random normal draws to compute increments `N(0, t_{n+1}) - N(0, t_n)`. When supplied, `num_samples` argument is ignored and the first dimensions of `normal_draws` is used instead. Default value: `None` which means that the draws are generated by the algorithm. By default normal_draws for each model in the batch are independent. watch_params: An optional list of zero-dimensional `Tensor`s of the same `dtype` as `initial_state`. If provided, specifies `Tensor`s with respect to which the differentiation of the sampling function will happen. A more efficient algorithm is used when `watch_params` are specified. Note the the function becomes differentiable onlhy wrt to these `Tensor`s and the `initial_state`. The gradient wrt any other `Tensor` is set to be zero. validate_args: Python `bool`. When `True` performs multiple checks: * That `times` are increasing with the minimum increments of the specified tolerance. * If `normal_draws` are supplied, checks that `normal_draws.shape[1]` is equal to `num_time_steps` that is either supplied as an argument or computed from `time_step`. When `False` invalid dimension may silently render incorrect outputs. Default value: `False`. tolerance: A non-negative scalar `Tensor` specifying the minimum tolerance for discernible times on the time grid. Times that are closer than the tolerance are perceived to be the same. Default value: `None` which maps to `1-e6` if the for single precision `dtype` and `1e-10` for double precision `dtype`. dtype: `tf.Dtype`. If supplied the dtype for the input and output `Tensor`s. Default value: None which means that the dtype implied by `times` is used. name: Python string. The name to give this op. Default value: `None` which maps to `euler_sample`. Returns: A real `Tensor` of shape batch_shape_process + [num_samples, k, n] where `k` is the size of the `times`, `n` is the dimension of the process. Raises: ValueError: (a) When `times_grid` is not supplied, and neither `num_time_steps` nor `time_step` are supplied or if both are supplied. (b) If `normal_draws` is supplied and `dim` is mismatched. tf.errors.InvalidArgumentError: If `normal_draws` is supplied and `num_time_steps` is mismatched. """ name = name or 'euler_sample' with tf.name_scope(name): times = tf.convert_to_tensor(times, dtype=dtype) if dtype is None: dtype = times.dtype asserts = [] if tolerance is None: tolerance = 1e-10 if dtype == tf.float64 else 1e-6 tolerance = tf.convert_to_tensor(tolerance, dtype=dtype) if validate_args: asserts.append( tf.assert_greater( times[1:], times[:-1] + tolerance, message='`times` increments should be greater ' 'than tolerance {0}'.format(tolerance))) if initial_state is None: initial_state = tf.zeros(dim, dtype=dtype) initial_state = tf.convert_to_tensor(initial_state, dtype=dtype, name='initial_state') batch_shape = tff_utils.get_shape(initial_state)[:-2] num_requested_times = tff_utils.get_shape(times)[0] # Create a time grid for the Euler scheme. if num_time_steps is not None and time_step is not None: raise ValueError( 'When `times_grid` is not supplied only one of either ' '`num_time_steps` or `time_step` should be defined but not both.') if times_grid is None: if time_step is None: if num_time_steps is None: raise ValueError( 'When `times_grid` is not supplied, either `num_time_steps` ' 'or `time_step` should be defined.') num_time_steps = tf.convert_to_tensor( num_time_steps, dtype=tf.int32, name='num_time_steps') time_step = times[-1] / tf.cast(num_time_steps, dtype=dtype) else: time_step = tf.convert_to_tensor(time_step, dtype=dtype, name='time_step') else: times_grid = tf.convert_to_tensor(times_grid, dtype=dtype, name='times_grid') if validate_args: asserts.append( tf.assert_greater( times_grid[1:], times_grid[:-1] + tolerance, message='`times_grid` increments should be greater ' 'than tolerance {0}'.format(tolerance))) times, keep_mask, time_indices = utils.prepare_grid( times=times, time_step=time_step, num_time_steps=num_time_steps, times_grid=times_grid, tolerance=tolerance, dtype=dtype) if normal_draws is not None: normal_draws = tf.convert_to_tensor(normal_draws, dtype=dtype, name='normal_draws') # Shape [num_time_points] + batch_shape + [num_samples, dim] normal_draws_rank = normal_draws.shape.rank perm = tf.concat( [[normal_draws_rank-2], tf.range(normal_draws_rank-2), [normal_draws_rank-1]], axis=0) normal_draws = tf.transpose(normal_draws, perm=perm) num_samples = tf.shape(normal_draws)[-2] draws_dim = normal_draws.shape[-1] if dim != draws_dim: raise ValueError( '`dim` should be equal to `normal_draws.shape[2]` but are ' '{0} and {1} respectively'.format(dim, draws_dim)) if validate_args: draws_times = tff_utils.get_shape(normal_draws)[0] asserts.append(tf.assert_equal( draws_times, tf.shape(keep_mask)[0] - 1, message='`num_time_steps` should be equal to ' '`tf.shape(normal_draws)[1]`')) if validate_args: with tf.control_dependencies(asserts): times = tf.identity(times) if watch_params is not None: watch_params = [tf.convert_to_tensor(param, dtype=dtype) for param in watch_params] return _sample( dim=dim, batch_shape=batch_shape, drift_fn=drift_fn, volatility_fn=volatility_fn, times=times, keep_mask=keep_mask, num_requested_times=num_requested_times, num_samples=num_samples, initial_state=initial_state, random_type=random_type, seed=seed, swap_memory=swap_memory, skip=skip, precompute_normal_draws=precompute_normal_draws, normal_draws=normal_draws, watch_params=watch_params, time_indices=time_indices, dtype=dtype) def _sample(*, dim, batch_shape, drift_fn, volatility_fn, times, keep_mask, num_requested_times, num_samples, initial_state, random_type, seed, swap_memory, skip, precompute_normal_draws, watch_params, time_indices, normal_draws, dtype): """Returns a sample of paths from the process using Euler method.""" dt = times[1:] - times[:-1] sqrt_dt = tf.sqrt(dt) # current_state.shape = batch_shape + [num_samples, dim] current_state = initial_state + tf.zeros([num_samples, dim], dtype=dtype) steps_num = tff_utils.get_shape(dt)[-1] wiener_mean = None if normal_draws is None: # In order to use low-discrepancy random_type we need to generate the # sequence of independent random normals upfront. We also precompute random # numbers for stateless random type in order to ensure independent samples # for multiple function calls with different seeds. if precompute_normal_draws or random_type in ( random.RandomType.SOBOL, random.RandomType.HALTON, random.RandomType.HALTON_RANDOMIZED, random.RandomType.STATELESS, random.RandomType.STATELESS_ANTITHETIC): normal_draws = utils.generate_mc_normal_draws( num_normal_draws=dim, num_time_steps=steps_num, num_sample_paths=num_samples, batch_shape=batch_shape, random_type=random_type, dtype=dtype, seed=seed, skip=skip) wiener_mean = None else: # If pseudo or anthithetic sampling is used, proceed with random sampling # at each step. wiener_mean = tf.zeros((dim,), dtype=dtype, name='wiener_mean') normal_draws = None if watch_params is None: # Use while_loop if `watch_params` is not passed return _while_loop( steps_num=steps_num, current_state=current_state, drift_fn=drift_fn, volatility_fn=volatility_fn, wiener_mean=wiener_mean, num_samples=num_samples, times=times, dt=dt, sqrt_dt=sqrt_dt, keep_mask=keep_mask, num_requested_times=num_requested_times, swap_memory=swap_memory, random_type=random_type, seed=seed, normal_draws=normal_draws, dtype=dtype) else: # Use custom for_loop if `watch_params` is specified return _for_loop( batch_shape=batch_shape, steps_num=steps_num, current_state=current_state, drift_fn=drift_fn, volatility_fn=volatility_fn, wiener_mean=wiener_mean, num_samples=num_samples, times=times, dt=dt, sqrt_dt=sqrt_dt, time_indices=time_indices, keep_mask=keep_mask, watch_params=watch_params, random_type=random_type, seed=seed, normal_draws=normal_draws) def _while_loop(*, steps_num, current_state, drift_fn, volatility_fn, wiener_mean, num_samples, times, dt, sqrt_dt, num_requested_times, keep_mask, swap_memory, random_type, seed, normal_draws, dtype): """Sample paths using tf.while_loop.""" written_count = 0 if isinstance(num_requested_times, int) and num_requested_times == 1: record_samples = False result = current_state else: # If more than one sample has to be recorded, create a TensorArray record_samples = True element_shape = current_state.shape result = tf.TensorArray(dtype=dtype, size=num_requested_times, element_shape=element_shape, clear_after_read=False) # Include initial state, if necessary result = result.write(written_count, current_state) written_count += tf.cast(keep_mask[0], dtype=tf.int32) # Define sampling while_loop body function def cond_fn(i, written_count, *args): # It can happen that `times_grid[-1] > times[-1]` in which case we have # to terminate when `written_count` reaches `num_requested_times` del args return tf.math.logical_and(i < steps_num, written_count < num_requested_times) def step_fn(i, written_count, current_state, result): return _euler_step( i=i, written_count=written_count, current_state=current_state, result=result, drift_fn=drift_fn, volatility_fn=volatility_fn, wiener_mean=wiener_mean, num_samples=num_samples, times=times, dt=dt, sqrt_dt=sqrt_dt, keep_mask=keep_mask, random_type=random_type, seed=seed, normal_draws=normal_draws, record_samples=record_samples) # Sample paths _, _, _, result = tf.while_loop( cond_fn, step_fn, (0, written_count, current_state, result), maximum_iterations=steps_num, swap_memory=swap_memory) if not record_samples: # shape batch_shape + [num_samples, 1, dim] return tf.expand_dims(result, axis=-2) # Shape [num_time_points] + batch_shape + [num_samples, dim] result = result.stack() # transpose to shape batch_shape + [num_samples, num_time_points, dim] n = result.shape.rank perm = list(range(1, n-1)) + [0, n - 1] return tf.transpose(result, perm) def _for_loop(*, batch_shape, steps_num, current_state, drift_fn, volatility_fn, wiener_mean, watch_params, num_samples, times, dt, sqrt_dt, time_indices, keep_mask, random_type, seed, normal_draws): """Sample paths using custom for_loop.""" del batch_shape num_time_points = time_indices.shape.as_list()[:-1] if isinstance(num_time_points, int) and num_time_points == 1: iter_nums = steps_num else: iter_nums = time_indices def step_fn(i, current_state): # Unpack current_state current_state = current_state[0] _, _, next_state, _ = _euler_step( i=i, written_count=0, current_state=current_state, result=current_state, drift_fn=drift_fn, volatility_fn=volatility_fn, wiener_mean=wiener_mean, num_samples=num_samples, times=times, dt=dt, sqrt_dt=sqrt_dt, keep_mask=keep_mask, random_type=random_type, seed=seed, normal_draws=normal_draws, record_samples=False) return [next_state] result = custom_loops.for_loop( body_fn=step_fn, initial_state=[current_state], params=watch_params, num_iterations=iter_nums)[0] if num_time_points == 1: return tf.expand_dims(result, axis=-2) # result.shape=[num_time_points] + batch_shape + [num_samples, dim] # transpose to shape=batch_shape + [num_time_points, num_samples, dim] n = result.shape.rank perm = list(range(1, n-1)) + [0, n - 1] return tf.transpose(result, perm) def _euler_step(*, i, written_count, current_state, drift_fn, volatility_fn, wiener_mean, num_samples, times, dt, sqrt_dt, keep_mask, random_type, seed, normal_draws, result, record_samples): """Performs one step of Euler scheme.""" current_time = times[i + 1] written_count = tf.cast(written_count, tf.int32) if normal_draws is not None: dw = normal_draws[i] else: dw = random.mv_normal_sample( (num_samples,), mean=wiener_mean, random_type=random_type, seed=seed) dw = dw * sqrt_dt[i] dt_inc = dt[i] * drift_fn(current_time, current_state) # pylint: disable=not-callable dw_inc = tf.linalg.matvec(volatility_fn(current_time, current_state), dw) # pylint: disable=not-callable next_state = current_state + dt_inc + dw_inc if record_samples: result = result.write(written_count, next_state) else: result = next_state written_count += tf.cast(keep_mask[i + 1], dtype=tf.int32) return i + 1, written_count, next_state, result __all__ = ['sample']

from django.db import models from django.utils.encoding import python_2_unicode_compatible @python_2_unicode_compatible class Series(models.Model): name = models.CharField(max_length=128) number = models.PositiveSmallIntegerField() year = models.PositiveSmallIntegerField() parent = models.ForeignKey('Series', default=None, blank=True, null=True) imdb_id = models.CharField(max_length=9, unique=True, default=None, blank=True, null=True) trakt_id = models.IntegerField(unique=True, default=None, blank=True, null=True) class Meta(object): ordering = ('year',) verbose_name_plural = 'series' def __str__(self): if self.parent: return '{series} ({season})'.format(series=self.parent.name, season=self.name) return '{name}'.format(name=self.name) @property def seasons(self): return Series.objects.filter(parent=self) @python_2_unicode_compatible class Episode(models.Model): series = models.ForeignKey(Series, related_name='episodes') name = models.CharField(max_length=128) number = models.PositiveSmallIntegerField() imdb_id = models.CharField(max_length=9, unique=True, default=None, blank=True, null=True) trakt_id = models.IntegerField(unique=True, default=None, blank=True, null=True) def __str__(self): return self.name

from __future__ import unicode_literals import appointment.models from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('appointment', '0002_vaccineappointment_20181031_1852'), ] operations = [ migrations.AddField( model_name='appointment', name='pt_showed', field=models.NullBooleanField(help_text=b'Did the patient come to this appointment?', verbose_name=b'Patient Showed'), ), migrations.AddField( model_name='historicalappointment', name='pt_showed', field=models.NullBooleanField(help_text=b'Did the patient come to this appointment?', verbose_name=b'Patient Showed'), ), migrations.AlterField( model_name='appointment', name='clintime', field=models.TimeField(default=appointment.models.generate_default_appointment_time, verbose_name=b'Time of Appointment'), ), migrations.AlterField( model_name='historicalappointment', name='clintime', field=models.TimeField(default=appointment.models.generate_default_appointment_time, verbose_name=b'Time of Appointment'), ), ]

''' Copyright 2017 Dell Inc. or its subsidiaries. All Rights Reserved. Author(s): George Paulos RackHD Functional Integration Test (FIT) library This is the main common function library for RackHD FIT tests. ''' # Standard imports import fit_path # NOQA: unused import import os import sys import json import subprocess import time import unittest # NOQA: imported but unused import re import requests import pexpect import inspect import argparse from mkcfg import mkcfg VERBOSITY = 1 TEST_PATH = None CONFIG_PATH = None API_PORT = "None" API_PROTOCOL = "None" AUTH_TOKEN = "None" REDFISH_TOKEN = "None" BMC_LIST = [] def fitcfg(): """ returns the configuration dictionary :return: dictionary """ return mkcfg().get() def fitrackhd(): """ returns the ['rackhd-config'] dictionary :return: dictionary or None """ return fitcfg().get('rackhd-config', None) def fitargs(): """ returns the ['cmd-args-list'] dictionary :return: dictionary or None """ return fitcfg().get('cmd-args-list', None) def fitcreds(): """ returns the ['credentials'] dictionary :return: dictionary or None """ return fitcfg().get('credentials', None) def fitinstall(): """ returns the ['install-config']['install'] dictionary :return: dictionary or None """ if 'install-config' not in fitcfg(): return None return fitcfg()['install-config'].get('install', None) def fitports(): """ returns the ['install-config']['ports'] dictionary :return: dictionary or None """ if 'install-config' not in fitcfg(): return None return fitcfg()['install-config'].get('ports', None) def fitcit(): """ returns the ['cit-config'] dictionary :return: dictionary or None """ return fitcfg().get('cit-config', None) def fitglobals(): """ returns the ['install-config']['global'] dictionary :return: dictionary or None """ return fitcfg().get('globals', None) def fitproxy(): """ returns the ['install-config']['proxy'] dictionary :return: dictionary or None """ if 'install-config' not in fitcfg(): return None return fitcfg()['install-config'].get('proxy', None) def fitskupack(): if 'install-config' not in fitcfg(): return None return fitcfg()['install-config'].get('skupack', None) def compose_config(use_sysargs=False): """ creates a configuration based on :param use_sysargs: set to true if sys.argv is to be processed. :return: None """ # create configuration object cfg_obj = mkcfg() if cfg_obj.config_is_loaded(): # a previously generated configuration has been loaded # restore previously setup globals update_globals() else: # create new configuration # * add cmd-args-list section # * add the default config json file composition. # * add stack overlay # * save off environment # * generate a few globals # * save (generate) the configuration to a file args_list = {} if use_sysargs: # Args from command line, pass -config option to create args_list['cmd-args-list'] = mkargs() config = args_list['cmd-args-list']['config'] cfg_obj.create(config) else: # Args from default set no_args = {} args_list['cmd-args-list'] = mkargs(no_args) cfg_obj.create() # add the 'cmd-args-list' section cfg_obj.add_from_dict(args_list) if fitargs()['config'] != 'config': print "*** Using config file path:", fitcfg()['cmd-args-list']['config'] if cfg_obj.get_path() is None: default_composition = ['rackhd_default.json', 'credentials_default.json', 'install_default.json', 'cit_default.json'] # config file composition cfg_obj.add_from_file_list(default_composition) # stack overlay configuration apply_stack_config() # apply any additional configurations specified on command line if fitcfg()['cmd-args-list']['extra']: cfg_obj.add_from_file_list(fitcfg()['cmd-args-list']['extra'].split(',')) # add significant environment variables cfg_obj.add_from_dict({ 'env': { 'HOME': os.environ['HOME'], 'PATH': os.environ['PATH'] } }) add_globals() # generate the configuration file cfg_obj.generate() print "*** Using config file: {0}".format(cfg_obj.get_path()) def apply_stack_config(): """ does the necessary stack configuration changes :return: None """ stack = fitargs()['stack'] if stack is not None: mkcfg().add_from_file('stack_config.json', stack) if fitargs()['rackhd_host'] == 'localhost' and 'rackhd_host' in fitcfg(): fitargs()['rackhd_host'] = fitcfg()['rackhd_host'] if 'bmc' in fitcfg(): fitargs()['bmc'] = fitcfg()['bmc'] if 'hyper' in fitcfg(): fitargs()['hyper'] = fitcfg()['hyper'] def add_globals(): """ create a handlful of global shortcuts :return: """ global TEST_PATH global CONFIG_PATH global API_PORT global API_PROTOCOL global VERBOSITY # set api port and protocol from command line if fitargs()['http'] is True: API_PROTOCOL = "http" API_PORT = str(fitports()['http']) elif fitargs()['https'] is True: API_PROTOCOL = "https" API_PORT = str(fitports()['https']) else: # default protocol is http API_PROTOCOL = "http" API_PORT = str(fitports()['http']) if fitargs()['port'] != "None": # port override via command line argument -port API_PORT = fitargs()['port'] # add globals section to base configuration TEST_PATH = fit_path.fit_path_root + '/' CONFIG_PATH = TEST_PATH + fitargs()['config'] + "/" mkcfg().add_from_dict({ 'globals': { 'API_PORT': API_PORT, 'API_PROTOCOL': API_PROTOCOL, 'TEST_PATH': TEST_PATH, 'CONFIG_PATH': CONFIG_PATH, 'VERBOSITY': fitargs()['v'] } }) # set OVA template from command line argument -template if fitargs()["template"] == "None": fitargs()["template"] = fitcfg()['install-config']['template'] def update_globals(): global API_PORT global API_PROTOCOL global TEST_PATH global CONFIG_PATH global VERBOSITY API_PORT = fitglobals()['API_PORT'] API_PROTOCOL = fitglobals()['API_PROTOCOL'] TEST_PATH = fitglobals()['TEST_PATH'] CONFIG_PATH = fitglobals()['CONFIG_PATH'] VERBOSITY = fitglobals()['VERBOSITY'] def _fix_check_unicode(value): """ function to help with unicode characters in command line arguments. * will subsitute a single '-' for a couple of different single-dash-like unicode chars * will subsitute a double '--' for an em_dash unicode character If there still unicode in the string once the substituion is complete that would prevent converting to pure-ascii, None is returned. Otherwise the fixed string is. """ # First turn from byte-string to utf-8 value = value.decode("utf-8") # These are the various hyphen/dashes that # look like single '-'s... h_minus = u'\u002d' hyphen = u'\u2010' en_dash = u'\u2013' single_dash_list = [h_minus, hyphen, en_dash] # walk through and substitute single-dash-like unicode to plain minus for convert_dash in single_dash_list: value = value.replace(convert_dash, '-') # now do the em_dash, which is the '--' em_dash = u'\u2014' value = value.replace(em_dash, '--') # Now convert to ascii and complain if we can't try: final_value = value.decode('ascii') except UnicodeEncodeError: final_value = None return final_value def mkargs(in_args=None): """ processes the command line options as passed in by in_args. :param in_args: input arguments :return: dictionary of processed arguments """ if in_args is None: in_args = sys.argv[1:] # command line argument parser returns cmd_args dict arg_parser = argparse.ArgumentParser( description="Command Help", add_help=False) arg_parser.add_argument('-h', '--help', action='store_true', default=False, help='show this help message and exit') arg_parser.add_argument("-test", default="tests/", help="test to execute, default: tests/") arg_parser.add_argument("-config", default="config", help="config file location, default: config") arg_parser.add_argument("-extra", default=None, help="comma separated list of extra config files (found in 'config' directory)") arg_parser.add_argument("-group", default="all", help="test group to execute: 'smoke', 'regression', 'extended', default: 'all'") arg_parser.add_argument("-stack", default="vagrant", help="stack label (test bed)") arg_parser.add_argument("-rackhd_host", default="localhost", help="RackHD appliance IP address or hostname, default: localhost") arg_parser.add_argument("-template", default="None", help="path or URL link to OVA template or RackHD OVA") arg_parser.add_argument("-xunit", default="False", action="store_true", help="generates xUnit XML report files") arg_parser.add_argument("-numvms", default=1, type=int, help="number of virtual machines for deployment on specified stack") arg_parser.add_argument("-list", default="False", action="store_true", help="generates test list only") arg_parser.add_argument("-sku", default="all", help="node SKU name, example: Quanta-T41, default=all") group = arg_parser.add_mutually_exclusive_group(required=False) group.add_argument("-obmmac", default="all", help="node OBM MAC address, example:00:1e:67:b1:d5:64") group.add_argument("-nodeid", default="None", help="node identifier string of a discovered node, example: 56ddcf9a8eff16614e79ec74") group2 = arg_parser.add_mutually_exclusive_group(required=False) group2.add_argument("-http", default="False", action="store_true", help="forces the tests to utilize the http API protocol") group2.add_argument("-https", default="False", action="store_true", help="forces the tests to utilize the https API protocol") arg_parser.add_argument("-port", default="None", help="API port number override, default from install_config.json") arg_parser.add_argument("-v", default=4, type=int, help="Verbosity level of console and log output (see -nose-help for more options), Built Ins: " + "0: Minimal logging, " + "1: Display ERROR and CRITICAL to console and to files, " + "3: Display INFO to console and to files, " + "4: (default) Display INFO to console, and DEBUG to files, " + "5: Display infra.run and test.run DEBUG to both, " + "6: Add display of test.data (rest calls and status) DEBUG to both, " + "7: Add display of infra.data (ipmi, ssh) DEBUG to both, " + "9: Display infra.* and test.* at DEBUG_9 (max output) ") arg_parser.add_argument("-nose-help", default=False, action="store_true", dest="nose_help", help="display help from underlying nosetests command, including additional log options") fixed_args = [] for arg in in_args: new_value = _fix_check_unicode(arg) if new_value is None: arg_parser.error( "Argument '{0}' of {1} had unknown unicode characters in it, likely from a cut-and-paste.".format( arg, in_args)) fixed_args.append(new_value) in_args = fixed_args # we want to grab the arguments we want, and pass the rest # into the nosetest invocation. parse_results, other_args = arg_parser.parse_known_args(in_args) # if 'help' was set, handle it as best we can. We use argparse to # display usage and arguments, and then give nose a shot at printing # things out (if they set that option) if parse_results.help: arg_parser.print_help() if parse_results.nose_help: print print "NOTE: below is the --help output from nosetests." print rcode = _run_nose_help() else: rcode = 0 sys.exit(rcode) # And if they only did --nose-help if parse_results.nose_help: rcode = _run_nose_help() sys.exit(rcode) # Now handle mapping -v to infra-logging. Check stream-monitor/flogging/README.md # for how loggers and handlers fit together. if parse_results.v >= 9: # Turn them all up to 11. vargs = ['--sm-set-combo-level', 'console*', 'DEBUG_9'] elif parse_results.v >= 7: # ends up turning everything up to DEBUG_5 (levels 5 + 6 + infra.data) vargs = ['--sm-set-combo-level', 'console*', 'DEBUG_5'] elif parse_results.v >= 6: # infra.run and test.* to DEBUG (level 5 + test.data) vargs = ['--sm-set-combo-level', 'console*:(test.data|*.run)', 'DEBUG_5'] elif parse_results.v >= 5: # infra and test.run to DEBUG vargs = ['--sm-set-combo-level', 'console*:*.run', 'DEBUG_5'] elif parse_results.v >= 4: # default vargs = [] elif parse_results.v >= 3: # dial BACK output to files to INFO_5 vargs = ['--sm-set-logger-level', '*', 'INFO_5'] elif parse_results.v >= 1: # dial BACK output to everything to just ERROR, CRITICAL to console and logs vargs = ['--sm-set-combo-level', '*', 'ERROR_5'] else: # 0 and 1 currently try to squish ALL logging output. vargs = ['--sm-set-combo-level', '*', 'CRITICAL_0'] other_args.extend(vargs) # Put all the args we did not use and put them # into the parse_results so they can be found # by run_nose() parse_results.unhandled_arguments = other_args # parse arguments to cmd_args dict cmd_args = vars(parse_results) return cmd_args def timestamp(): # return formatted current timestamp return time.strftime("%Y-%m-%dT%H:%M:%S", time.gmtime()) # This routine executes a sleep with countdown def countdown(sleep_time, sleep_interval=1): sys.stdout.write("Sleeping for " + str(sleep_time * sleep_interval) + " seconds.") sys.stdout.flush() for _ in range(0, sleep_time): time.sleep(sleep_interval) sys.stdout.write(".") sys.stdout.flush() print "Waking!" return def remote_shell(shell_cmd, expect_receive="", expect_send="", timeout=300, address=None, user=None, password=None, vmnum=1): ''' Run ssh based shell command on a remote machine at fitargs()['rackhd_host'] :param shell_cmd: string based command :param expect_receive: :param expect_send: :param timeout: in seconds :param address: IP or hostname of remote host :param user: username of remote host :param password: password of remote host :return: dict = {'stdout': str:ouput, 'exitcode': return code} ''' if not address: if (vmnum == 1): address = fitargs()['rackhd_host'] else: address = fitargs()['rackhd_host'].replace("ora", "ora-" + str(vmnum - 1)) if not user: user = fitcreds()['rackhd_host'][0]['username'] if not password: password = fitcreds()['rackhd_host'][0]['password'] logfile_redirect = None if VERBOSITY >= 4: print "VM number: ", vmnum print "remote_shell: Host =", address print "remote_shell: Command =", shell_cmd if VERBOSITY >= 9: print "remote_shell: STDOUT =\n" logfile_redirect = sys.stdout # if localhost just run the command local if fitargs()['rackhd_host'] == 'localhost': (command_output, exitstatus) = \ pexpect.run("sudo bash -c \"" + shell_cmd + "\"", withexitstatus=1, events={"assword": password + "\n"}, timeout=timeout, logfile=logfile_redirect) return {'stdout': command_output, 'exitcode': exitstatus} # this clears the ssh key from ~/.ssh/known_hosts subprocess.call(["touch ~/.ssh/known_hosts;ssh-keygen -R " + address + " -f ~/.ssh/known_hosts >/dev/null 2>&1"], shell=True) shell_cmd.replace("'", "\\\'") if expect_receive == "" or expect_send == "": (command_output, exitstatus) = \ pexpect.run("ssh -q -o StrictHostKeyChecking=no -t " + user + "@" + address + " sudo bash -c \\\"" + shell_cmd + "\\\"", withexitstatus=1, events={"assword": password + "\n"}, timeout=timeout, logfile=logfile_redirect) else: (command_output, exitstatus) = \ pexpect.run("ssh -q -o StrictHostKeyChecking=no -t " + user + "@" + address + " sudo bash -c \\\"" + shell_cmd + "\\\"", withexitstatus=1, events={"assword": password + "\n", expect_receive: expect_send + "\n"}, timeout=timeout, logfile=logfile_redirect) if VERBOSITY >= 4: print shell_cmd, "\nremote_shell: Exit Code =", exitstatus return {'stdout': command_output, 'exitcode': exitstatus} def scp_file_to_ora(src_file_name, vmnum=1): # legacy call scp_file_to_host(src_file_name, vmnum) def scp_file_to_host(src_file_name, vmnum=1): ''' scp the given file over to the RackHD host and place it in the home directory. :param src_file_name: name of file to copy over. May include path :type src_file_name: basestring :return: just name of file on target (no path) :rtype: basestring ''' logfile_redirect = file('/dev/null', 'w') just_fname = os.path.basename(src_file_name) # if localhost just copy to home dir if fitargs()['rackhd_host'] == 'localhost': remote_shell('cp ' + src_file_name + ' ~/' + src_file_name) return src_file_name if (vmnum == 1): rackhd_hostname = fitargs()['rackhd_host'] else: rackhd_hostname = fitargs()['rackhd_host'].replace("ora", "ora-" + str(vmnum - 1)) scp_target = fitcreds()['rackhd_host'][0]['username'] + '@{0}:'.format(rackhd_hostname) cmd = 'scp -o StrictHostKeyChecking=no {0} {1}'.format(src_file_name, scp_target) if VERBOSITY >= 4: print "scp_file_to_host: '{0}'".format(cmd) if VERBOSITY >= 9: logfile_redirect = sys.stdout (command_output, ecode) = pexpect.run( cmd, withexitstatus=1, events={'(?i)assword: ': fitcreds()['rackhd_host'][0]['password'] + '\n'}, logfile=logfile_redirect) if VERBOSITY >= 4: print "scp_file_to_host: Exit Code = {0}".format(ecode) assert ecode == 0, \ 'failed "{0}" because {1}. Output={2}'.format(cmd, ecode, command_output) return just_fname def get_auth_token(): # This is run once to get an auth token which is set to global AUTH_TOKEN and used for rest of session global AUTH_TOKEN global REDFISH_TOKEN api_login = {"username": fitcreds()["api"][0]["admin_user"], "password": fitcreds()["api"][0]["admin_pass"]} redfish_login = {"UserName": fitcreds()["api"][0]["admin_user"], "Password": fitcreds()["api"][0]["admin_pass"]} try: restful("https://" + fitargs()['rackhd_host'] + ":" + str(fitports()['https']) + "/login", rest_action="post", rest_payload=api_login, rest_timeout=2) except: AUTH_TOKEN = "Unavailable" return False else: api_data = restful("https://" + fitargs()['rackhd_host'] + ":" + str(fitports()['https']) + "/login", rest_action="post", rest_payload=api_login, rest_timeout=2) if api_data['status'] == 200: AUTH_TOKEN = str(api_data['json']['token']) redfish_data = restful("https://" + fitargs()['rackhd_host'] + ":" + str(fitports()['https']) + "/redfish/v1/SessionService/Sessions", rest_action="post", rest_payload=redfish_login, rest_timeout=2) if 'x-auth-token' in redfish_data['headers']: REDFISH_TOKEN = redfish_data['headers']['x-auth-token'] return True else: print "WARNING: Redfish API token not available." else: AUTH_TOKEN = "Unavailable" return False def rackhdapi(url_cmd, action='get', payload=[], timeout=None, headers={}): ''' This routine will build URL for RackHD API, enable port, execute, and return data Example: rackhdapi('/api/current/nodes') - simple 'get' command Example: rackhdapi("/api/current/nodes/ID/dhcp/whitelist", action="post") :param url_cmd: url command for monorail api :param action: rest action (get/put/post/delete) :param payload: rest payload :param timeout: rest timeout :param headers: rest_headers :return: {'json':result_data.json(), 'text':result_data.text, 'status':result_data.status_code, 'headers':result_data.headers.get('content-type'), 'timeout':False} ''' # Retrieve authentication token for the session if AUTH_TOKEN == "None": get_auth_token() return restful(API_PROTOCOL + "://" + fitargs()['rackhd_host'] + ":" + str(API_PORT) + url_cmd, rest_action=action, rest_payload=payload, rest_timeout=timeout, rest_headers=headers) def restful(url_command, rest_action='get', rest_payload=[], rest_timeout=None, sslverify=False, rest_headers={}): ''' This routine executes a rest API call to the host. :param url_command: the full URL for the command :param rest_action: what the restful do (get/post/put/delete) :param rest_payload: payload for rest request :param rest_headers: headers (JSON dict) :param rest_timeout: timeout for rest request :param sslverify: ssl Verify (True/False) :return: {'json':result_data.json(), 'text':result_data.text, 'status':result_data.status_code, 'headers':result_data.headers, 'timeout':False} ''' result_data = None # print URL and action if VERBOSITY >= 4: print "restful: Action = ", rest_action, ", URL = ", url_command # prepare payload for XML output payload_print = [] try: json.dumps(rest_payload) except: payload_print = [] else: payload_print = json.dumps(rest_payload, sort_keys=True, indent=4,) if len(payload_print) > 4096: payload_print = payload_print[0:4096] + '\n...truncated...\n' if VERBOSITY >= 7 and rest_payload != []: print "restful: Payload =\n", payload_print rest_headers.update({"Content-Type": "application/json"}) if VERBOSITY >= 5: print "restful: Request Headers =", rest_headers, "\n" # If AUTH_TOKEN is set, add to header if AUTH_TOKEN != "None" and AUTH_TOKEN != "Unavailable" and "authorization" not in rest_headers: rest_headers.update({"authorization": "JWT " + AUTH_TOKEN, "X-Auth-Token": REDFISH_TOKEN}) # Perform rest request try: if rest_action == "get": result_data = requests.get(url_command, timeout=rest_timeout, verify=sslverify, headers=rest_headers) if rest_action == "delete": result_data = requests.delete(url_command, data=json.dumps(rest_payload), timeout=rest_timeout, verify=sslverify, headers=rest_headers) if rest_action == "put": result_data = requests.put(url_command, data=json.dumps(rest_payload), headers=rest_headers, timeout=rest_timeout, verify=sslverify, ) if rest_action == "binary-put": rest_headers.update({"Content-Type": "application/x-www-form-urlencoded"}) result_data = requests.put(url_command, data=rest_payload, headers=rest_headers, timeout=rest_timeout, verify=sslverify, ) if rest_action == "text-put": rest_headers.update({"Content-Type": "text/plain"}) result_data = requests.put(url_command, data=rest_payload, headers=rest_headers, timeout=rest_timeout, verify=sslverify, ) if rest_action == "post": result_data = requests.post(url_command, data=json.dumps(rest_payload), headers=rest_headers, timeout=rest_timeout, verify=sslverify ) if rest_action == "binary-post": rest_headers.update({"Content-Type": "application/x-www-form-urlencoded"}) result_data = requests.post(url_command, data=rest_payload, headers=rest_headers, timeout=rest_timeout, verify=sslverify ) if rest_action == "text-post": rest_headers.update({"Content-Type": "text/plain"}) result_data = requests.post(url_command, data=rest_payload, headers=rest_headers, timeout=rest_timeout, verify=sslverify ) if rest_action == "patch": result_data = requests.patch(url_command, data=json.dumps(rest_payload), headers=rest_headers, timeout=rest_timeout, verify=sslverify ) except requests.exceptions.Timeout: return {'json': {}, 'text': '', 'status': 0, 'headers': '', 'timeout': True} try: result_data.json() except ValueError: if VERBOSITY >= 9: print "restful: TEXT =\n" print result_data.text if VERBOSITY >= 6: print "restful: Response Headers =", result_data.headers, "\n" if VERBOSITY >= 4: print "restful: Status code =", result_data.status_code, "\n" return {'json': {}, 'text': result_data.text, 'status': result_data.status_code, 'headers': result_data.headers, 'timeout': False} else: if VERBOSITY >= 9: print "restful: JSON = \n" print json.dumps(result_data.json(), sort_keys=True, indent=4) if VERBOSITY >= 6: print "restful: Response Headers =", result_data.headers, "\n" if VERBOSITY >= 4: print "restful: Status code =", result_data.status_code, "\n" return {'json': result_data.json(), 'text': result_data.text, 'status': result_data.status_code, 'headers': result_data.headers, 'timeout': False} # Get the list of BMC IP addresses that we can find def get_bmc_ips(): idlist = [] # list of unique dcmi node IDs # If we have already done this, use that list if len(BMC_LIST) == 0: ipscan = remote_shell('arp')['stdout'].split() for ipaddr in ipscan: if ipaddr[0:3] == "172" and remote_shell('ping -c 1 -w 5 ' + ipaddr)['exitcode'] == 0: # iterate through all known IPMI users for item in fitcreds()['bmc']: # check BMC credentials ipmicheck = remote_shell('ipmitool -I lanplus -H ' + ipaddr + ' -U ' + item['username'] + ' -P ' + item['password'] + ' -R 1 -N 3 chassis power status') if ipmicheck['exitcode'] == 0: # retrieve the ID string return_code = remote_shell('ipmitool -I lanplus -H ' + ipaddr + ' -U ' + item['username'] + ' -P ' + item['password'] + ' -R 1 -N 3 dcmi get_mc_id_string') bmc_info = {"ip": ipaddr, "user": item['username'], "pw": item['password']} if return_code['exitcode'] == 0 and return_code['stdout'] not in idlist: # add to list if unique idlist.append(return_code['stdout']) BMC_LIST.append(bmc_info) break else: # simulated nodes don't yet support dcmi, remove this else branch when supported BMC_LIST.append(bmc_info) break if VERBOSITY >= 6: print "get_bmc_ips: " print "**** BMC IP node count =", len(BMC_LIST), "****" return len(BMC_LIST) # power on/off all compute nodes in the stack via the BMC def power_control_all_nodes(state): if state != "on" and state != "off": print "power_control_all_nodes: invalid state " + state return # Get the list of BMCs that we know about node_count = get_bmc_ips() # Send power on/off to all of them for bmc in BMC_LIST: return_code = remote_shell('ipmitool -I lanplus -H ' + bmc['ip'] + ' -U ' + bmc['user'] + ' -P ' + bmc['pw'] + ' -R 4 -N 3 chassis power ' + state) if return_code['exitcode'] != 0: print "Error powering " + state + " node: " + bmc['ip'] return node_count def mongo_reset(): # clears the Mongo database on host to default, returns True if successful exitcode = 0 if int(remote_shell('pm2 stop rackhd-pm2-config.yml')['exitcode']) == 0: # for pm2-based source installations exitcode = exitcode + int(remote_shell("mongo pxe --eval 'db.dropDatabase\\\(\\\)'")['exitcode']) exitcode = exitcode + int(remote_shell('rm -f /var/lib/dhcp/dhcpd.leases')['exitcode']) exitcode = exitcode + int(remote_shell('pm2 start rackhd-pm2-config.yml')['exitcode']) else: # for package-based installations exitcode = exitcode + int(remote_shell('sudo service on-http stop')['exitcode']) exitcode = exitcode + int(remote_shell('sudo service on-dhcp-proxy stop')['exitcode']) exitcode = exitcode + int(remote_shell('sudo service on-syslog stop')['exitcode']) exitcode = exitcode + int(remote_shell('sudo service on-taskgraph stop')['exitcode']) exitcode = exitcode + int(remote_shell('sudo service on-tftp stop')['exitcode']) exitcode = exitcode + int(remote_shell("mongo pxe --eval 'db.dropDatabase\\\(\\\)'")['exitcode']) exitcode = exitcode + int(remote_shell('rm -f /var/lib/dhcp/dhcpd.leases')['exitcode']) exitcode = exitcode + int(remote_shell('sudo service on-http start')['exitcode']) exitcode = exitcode + int(remote_shell('sudo service on-dhcp-proxy start')['exitcode']) exitcode = exitcode + int(remote_shell('sudo service on-syslog start')['exitcode']) exitcode = exitcode + int(remote_shell('sudo service on-taskgraph start')['exitcode']) exitcode = exitcode + int(remote_shell('sudo service on-tftp start')['exitcode']) if exitcode == 0: return True else: return False def node_select(): # returns a list with valid compute node IDs that match fitargs()["sku"] in 'Name' or 'Model' field # and matches node BMC MAC address in fitargs()["obmmac"] if specified # Otherwise returns list of all IDs that are not 'Unknown' or 'Unmanaged' nodelist = [] skuid = "None" # check if user specified a single nodeid to run against # user must know the nodeid and any check for a valid nodeid is skipped if fitargs()["nodeid"] != 'None': nodelist.append(fitargs()["nodeid"]) return nodelist else: # Find SKU ID skumap = rackhdapi('/api/2.0/skus') if skumap['status'] != 200: print '**** Unable to retrieve SKU list via API.\n' sys.exit(255) for skuentry in skumap['json']: if str(fitargs()['sku']) in json.dumps(skuentry): skuid = skuentry['id'] # Collect node IDs catalog = rackhdapi('/api/2.0/nodes') if skumap['status'] != 200: print '**** Unable to retrieve node list via API.\n' sys.exit(255) # Select node by SKU for nodeentry in catalog['json']: if fitargs()["sku"] == 'all': # Select only managed compute nodes if nodeentry['type'] == 'compute': nodelist.append(nodeentry['id']) else: if 'sku' in nodeentry and skuid in json.dumps(nodeentry['sku']): nodelist.append(nodeentry['id']) # Select by node BMC MAC addr if fitargs()["obmmac"] != 'all': idlist = nodelist nodelist = [] for member in idlist: nodeentry = rackhdapi('/api/2.0/nodes/' + member) if fitargs()["obmmac"] in json.dumps(nodeentry['json']): nodelist = [member] break if VERBOSITY >= 6: print "Node List:" print nodelist, '\n' if len(nodelist) == 0: print '**** Empty node list.\n' return nodelist def list_skus(): # return list of installed SKU names skunames = [] api_data = rackhdapi('/api/2.0/skus')['json'] for item in api_data: skunames.append(item['name']) return skunames def get_node_sku(nodeid): # return name field of node SKU if available nodetype = "" sku = "" # get node info mondata = rackhdapi("/api/2.0/nodes/" + nodeid) if mondata['status'] == 200: # get the sku id contained in the node sku = mondata['json'].get("sku") if sku: skudata = rackhdapi(sku) if skudata['status'] == 200: nodetype = skudata['json'].get("name") else: if VERBOSITY >= 2: errmsg = "Error: SKU API failed {}, return code {} ".format(sku, skudata['status']) print errmsg return "unknown" else: return "unknown" return nodetype def check_active_workflows(nodeid): # Return True if active workflows are found on node workflows = rackhdapi('/api/2.0/nodes/' + nodeid + '/workflows')['json'] for item in workflows: if '_status' in item: if item['_status'] in ['running', 'pending']: return True if 'status' in item: if item['status'] in ['running', 'pending']: return True else: return False return False def cancel_active_workflows(nodeid): # cancel all active workflows on node exitstatus = True apistatus = rackhdapi('/api/2.0/nodes/' + nodeid + '/workflows/action', action='put', payload={"command": "cancel"})['status'] if apistatus != 202: exitstatus = False return exitstatus def apply_obm_settings(retry=30): # New routine to install OBM credentials via workflows in parallel count = 0 for creds in fitcreds()['bmc']: # greate graph for setting OBM credentials payload = { "friendlyName": "IPMI" + str(count), "injectableName": 'Graph.Obm.Ipmi.CreateSettings' + str(count), "options": { "obm-ipmi-task": { "user": creds["username"], "password": creds["password"] } }, "tasks": [ { "label": "obm-ipmi-task", "taskName": "Task.Obm.Ipmi.CreateSettings" } ] } api_data = rackhdapi("/api/2.0/workflows/graphs", action="put", payload=payload) if api_data['status'] != 201: print "**** OBM workflow failed to load!" return False count += 1 # Setup additional OBM settings for nodes that currently use RMM port (still same bmc username/password used) count = 0 for creds in fitcreds()['bmc']: # greate graph for setting OBM credentials for RMM payload = { "friendlyName": "RMM.IPMI" + str(count), "injectableName": 'Graph.Obm.Ipmi.CreateSettings.RMM' + str(count), "options": { "obm-ipmi-task": { "ipmichannel": "3", "user": creds["username"], "password": creds["password"] } }, "tasks": [ { "label": "obm-ipmi-task", "taskName": "Task.Obm.Ipmi.CreateSettings" } ] } api_data = rackhdapi("/api/2.0/workflows/graphs", action="put", payload=payload) if api_data['status'] != 201: print "**** OBM workflow failed to load!" return False count += 1 # run each OBM credential workflow on each node in parallel until success nodestatus = {} # dictionary with node IDs and status of each node for dummy in range(0, retry): nodelist = node_select() for node in nodelist: if node not in nodestatus: nodestatus[node] = {"status": "pending", "instanceId": "", "sku": get_node_sku(node), "retry": 0} for num in range(0, count): for node in nodelist: # try workflow if nodestatus[node]['status'] == "pending": skuid = rackhdapi('/api/2.0/nodes/' + node)['json'].get("sku") if skuid: if nodestatus[node]['sku'] == "unknown": nodestatus[node].update({"sku": get_node_sku(node)}) skudata = rackhdapi(skuid)['text'] if "rmm.data.MAC" in skudata: workflow = {"name": 'Graph.Obm.Ipmi.CreateSettings.RMM' + str(num)} else: workflow = {"name": 'Graph.Obm.Ipmi.CreateSettings' + str(num)} result = rackhdapi("/api/2.0/nodes/" + node + "/workflows", action="post", payload=workflow) if result['status'] == 201: nodestatus[node].update({"status": "running", "instanceId": result['json']["instanceId"]}) for node in nodelist: # check OBM workflow status if nodestatus[node]['status'] == "running": nodestatus[node]['retry'] += 1 state_data = rackhdapi("/api/2.0/workflows/" + nodestatus[node]['instanceId']) if state_data['status'] == 200: if "_status" in state_data['json']: state = state_data['json']['_status'] else: state = state_data['json']['status'] if state == "succeeded": nodestatus[node]['status'] = "succeeded" if state in ["failed", "cancelled", "timeout"]: nodestatus[node]['status'] = "pending" # if the workflow left an invalid OBM, delete it result = rackhdapi("/api/2.0/nodes/" + node) if result['status'] == 200: if result['json']['obms']: for ref in result['json']['obms']: obmref = ref.get('ref') if obmref: rackhdapi(obmref, action="delete") if VERBOSITY >= 4: print "**** Node(s) OBM status:\n", json.dumps(nodestatus, sort_keys=True, indent=4,) if "pending" not in str(nodestatus) and "running" not in str(nodestatus): # All OBM settings successful return True time.sleep(30) # Failures occurred print "**** Node(s) OBM settings failed." return False def run_nose(nosepath=None): if not nosepath: nosepath = fitcfg()['cmd-args-list']['test'] # this routine runs nosetests from wrapper using path spec 'nosepath' def _noserunner(pathspecs, noseopts): xmlfile = str(time.time()) + ".xml" # XML report file name env = { 'FIT_CONFIG': mkcfg().get_path(), 'HOME': os.environ['HOME'], 'PATH': os.environ['PATH'], 'PYTHONPATH': ':'.join(sys.path) } argv = ['nosetests'] argv.extend(noseopts) argv.append('--xunit-file') argv.append(xmlfile) argv.extend(pathspecs) argv.extend(fitcfg()['cmd-args-list']['unhandled_arguments']) return subprocess.call(argv, env=env) exitcode = 0 # set nose options noseopts = ['--exe', '--with-nosedep', '--with-stream-monitor'] if fitargs()['group'] != 'all' and fitargs()['group'] != '': noseopts.append('-a') noseopts.append(str(fitargs()['group'])) if fitargs()['list'] is True or fitargs()['list'] == "True": noseopts.append('--collect-only') fitargs()['v'] = 0 print "\nTest Listing for:", fitargs()['test'] print "----------------------------------------------------------------------" if fitargs()['xunit'] is True or fitargs()['xunit'] == "True": noseopts.append('--with-xunit') else: noseopts.append('-s') noseopts.append('-v') # if nosepath is a directory, recurse through subdirs else run single test file if os.path.isdir(nosepath): # Skip the CIT test directories that match these expressions regex = '(tests/*$)|(tests/api-cit/*)|(tests/api$)|(tests/api/.*)' pathspecs = [] for root, _, _ in os.walk(nosepath): if not re.search(regex, root): pathspecs.append(root) exitcode += _noserunner(pathspecs, noseopts) else: exitcode += _noserunner([nosepath], noseopts) return exitcode def _run_nose_help(): # This is used ONLY to fire off 'nosetests --help' for use from mkargs() when # it is handling --help itself. argv = ['nosetests', '--help'] return subprocess.call(argv) def run_from_module(file_name): # Use this method in 'name == "__main__"' style test invocations # within individual test files run_nose(file_name) # determine who imported us. importer = inspect.getframeinfo(inspect.getouterframes(inspect.currentframe())[1][0])[0] if 'run_tests.py' in importer: # we are being imported through run_tests.py (the fit wrapper) # process sys.args as received by run_tests.py compose_config(True) else: # we are being imported directly through a unittest module # args will be nose-base args compose_config(False)

""" Unit tests for the NetApp Data ONTAP cDOT multi-SVM storage driver library. """ import copy import ddt import mock from oslo_log import log from manila import context from manila import exception from manila.share.drivers.netapp.dataontap.client import api as netapp_api from manila.share.drivers.netapp.dataontap.cluster_mode import lib_base from manila.share.drivers.netapp.dataontap.cluster_mode import lib_multi_svm from manila.share.drivers.netapp import utils as na_utils from manila import test from manila.tests.share.drivers.netapp.dataontap.client import fakes as c_fake from manila.tests.share.drivers.netapp.dataontap import fakes as fake @ddt.ddt class NetAppFileStorageLibraryTestCase(test.TestCase): def setUp(self): super(NetAppFileStorageLibraryTestCase, self).setUp() self.mock_object(na_utils, 'validate_driver_instantiation') # Mock loggers as themselves to allow logger arg validation mock_logger = log.getLogger('mock_logger') self.mock_object(lib_multi_svm.LOG, 'warning', mock.Mock(side_effect=mock_logger.warning)) self.mock_object(lib_multi_svm.LOG, 'error', mock.Mock(side_effect=mock_logger.error)) kwargs = { 'configuration': fake.get_config_cmode(), 'private_storage': mock.Mock(), 'app_version': fake.APP_VERSION } self.library = lib_multi_svm.NetAppCmodeMultiSVMFileStorageLibrary( fake.DRIVER_NAME, **kwargs) self.library._client = mock.Mock() self.library._client.get_ontapi_version.return_value = (1, 21) self.client = self.library._client def test_check_for_setup_error_cluster_creds_no_vserver(self): self.library._have_cluster_creds = True self.mock_object(self.library, '_find_matching_aggregates', mock.Mock(return_value=fake.AGGREGATES)) mock_super = self.mock_object(lib_base.NetAppCmodeFileStorageLibrary, 'check_for_setup_error') self.library.check_for_setup_error() self.assertTrue(self.library._find_matching_aggregates.called) mock_super.assert_called_once_with() def test_check_for_setup_error_cluster_creds_with_vserver(self): self.library._have_cluster_creds = True self.library.configuration.netapp_vserver = fake.VSERVER1 self.mock_object(self.library, '_find_matching_aggregates', mock.Mock(return_value=fake.AGGREGATES)) mock_super = self.mock_object(lib_base.NetAppCmodeFileStorageLibrary, 'check_for_setup_error') self.library.check_for_setup_error() mock_super.assert_called_once_with() self.assertTrue(self.library._find_matching_aggregates.called) self.assertTrue(lib_multi_svm.LOG.warning.called) def test_check_for_setup_error_vserver_creds(self): self.library._have_cluster_creds = False self.assertRaises(exception.InvalidInput, self.library.check_for_setup_error) def test_check_for_setup_error_no_aggregates(self): self.library._have_cluster_creds = True self.mock_object(self.library, '_find_matching_aggregates', mock.Mock(return_value=[])) self.assertRaises(exception.NetAppException, self.library.check_for_setup_error) self.assertTrue(self.library._find_matching_aggregates.called) def test_get_vserver_no_share_server(self): self.assertRaises(exception.InvalidInput, self.library._get_vserver) def test_get_vserver_no_backend_details(self): fake_share_server = copy.deepcopy(fake.SHARE_SERVER) fake_share_server.pop('backend_details') kwargs = {'share_server': fake_share_server} self.assertRaises(exception.VserverNotSpecified, self.library._get_vserver, **kwargs) def test_get_vserver_none_backend_details(self): fake_share_server = copy.deepcopy(fake.SHARE_SERVER) fake_share_server['backend_details'] = None kwargs = {'share_server': fake_share_server} self.assertRaises(exception.VserverNotSpecified, self.library._get_vserver, **kwargs) def test_get_vserver_no_vserver(self): fake_share_server = copy.deepcopy(fake.SHARE_SERVER) fake_share_server['backend_details'].pop('vserver_name') kwargs = {'share_server': fake_share_server} self.assertRaises(exception.VserverNotSpecified, self.library._get_vserver, **kwargs) def test_get_vserver_none_vserver(self): fake_share_server = copy.deepcopy(fake.SHARE_SERVER) fake_share_server['backend_details']['vserver_name'] = None kwargs = {'share_server': fake_share_server} self.assertRaises(exception.VserverNotSpecified, self.library._get_vserver, **kwargs) def test_get_vserver_not_found(self): self.library._client.vserver_exists.return_value = False kwargs = {'share_server': fake.SHARE_SERVER} self.assertRaises(exception.VserverNotFound, self.library._get_vserver, **kwargs) def test_get_vserver(self): self.library._client.vserver_exists.return_value = True self.mock_object(self.library, '_get_api_client', mock.Mock(return_value='fake_client')) result = self.library._get_vserver(share_server=fake.SHARE_SERVER) self.assertTupleEqual((fake.VSERVER1, 'fake_client'), result) def test_get_ems_pool_info(self): self.mock_object(self.library, '_find_matching_aggregates', mock.Mock(return_value=['aggr1', 'aggr2'])) result = self.library._get_ems_pool_info() expected = { 'pools': { 'vserver': None, 'aggregates': ['aggr1', 'aggr2'], }, } self.assertEqual(expected, result) def test_handle_housekeeping_tasks(self): self.mock_object(self.client, 'prune_deleted_nfs_export_policies') self.mock_object(self.client, 'prune_deleted_snapshots') mock_super = self.mock_object(lib_base.NetAppCmodeFileStorageLibrary, '_handle_housekeeping_tasks') self.library._handle_housekeeping_tasks() self.assertTrue(self.client.prune_deleted_nfs_export_policies.called) self.assertTrue(self.client.prune_deleted_snapshots.called) self.assertTrue(mock_super.called) def test_find_matching_aggregates(self): mock_list_non_root_aggregates = self.mock_object( self.client, 'list_non_root_aggregates', mock.Mock(return_value=fake.AGGREGATES)) self.library.configuration.netapp_aggregate_name_search_pattern = ( '.*_aggr_1') result = self.library._find_matching_aggregates() self.assertListEqual([fake.AGGREGATES[0]], result) mock_list_non_root_aggregates.assert_called_once_with() def test_setup_server(self): mock_get_vserver_name = self.mock_object( self.library, '_get_vserver_name', mock.Mock(return_value=fake.VSERVER1)) mock_create_vserver = self.mock_object(self.library, '_create_vserver') mock_validate_network_type = self.mock_object( self.library, '_validate_network_type') result = self.library.setup_server(fake.NETWORK_INFO) self.assertTrue(mock_validate_network_type.called) self.assertTrue(mock_get_vserver_name.called) self.assertTrue(mock_create_vserver.called) self.assertDictEqual({'vserver_name': fake.VSERVER1}, result) def test_setup_server_with_error(self): mock_get_vserver_name = self.mock_object( self.library, '_get_vserver_name', mock.Mock(return_value=fake.VSERVER1)) fake_exception = exception.ManilaException("fake") mock_create_vserver = self.mock_object( self.library, '_create_vserver', mock.Mock(side_effect=fake_exception)) mock_validate_network_type = self.mock_object( self.library, '_validate_network_type') self.assertRaises( exception.ManilaException, self.library.setup_server, fake.NETWORK_INFO) self.assertTrue(mock_validate_network_type.called) self.assertTrue(mock_get_vserver_name.called) self.assertTrue(mock_create_vserver.called) self.assertDictEqual( {'server_details': {'vserver_name': fake.VSERVER1}}, fake_exception.detail_data) @ddt.data( {'network_info': {'network_type': 'vlan', 'segmentation_id': 1000}}, {'network_info': {'network_type': None, 'segmentation_id': None}}, {'network_info': {'network_type': 'flat', 'segmentation_id': None}}) @ddt.unpack def test_validate_network_type_with_valid_network_types(self, network_info): self.library._validate_network_type(network_info) @ddt.data( {'network_info': {'network_type': 'vxlan', 'segmentation_id': 1000}}, {'network_info': {'network_type': 'gre', 'segmentation_id': 100}}) @ddt.unpack def test_validate_network_type_with_invalid_network_types(self, network_info): self.assertRaises(exception.NetworkBadConfigurationException, self.library._validate_network_type, network_info) def test_get_vserver_name(self): vserver_id = fake.NETWORK_INFO['server_id'] vserver_name = fake.VSERVER_NAME_TEMPLATE % vserver_id actual_result = self.library._get_vserver_name(vserver_id) self.assertEqual(vserver_name, actual_result) def test_create_vserver(self): versions = ['fake_v1', 'fake_v2'] self.library.configuration.netapp_enabled_share_protocols = versions vserver_id = fake.NETWORK_INFO['server_id'] vserver_name = fake.VSERVER_NAME_TEMPLATE % vserver_id vserver_client = mock.Mock() self.mock_object(context, 'get_admin_context', mock.Mock(return_value='fake_admin_context')) self.mock_object(self.library, '_get_api_client', mock.Mock(return_value=vserver_client)) self.mock_object(self.library._client, 'vserver_exists', mock.Mock(return_value=False)) self.mock_object(self.library, '_find_matching_aggregates', mock.Mock(return_value=fake.AGGREGATES)) self.mock_object(self.library, '_create_ipspace', mock.Mock(return_value=fake.IPSPACE)) self.mock_object(self.library, '_create_vserver_lifs') self.mock_object(self.library, '_create_vserver_admin_lif') self.mock_object(self.library, '_create_vserver_routes') self.library._create_vserver(vserver_name, fake.NETWORK_INFO) self.library._create_ipspace.assert_called_once_with(fake.NETWORK_INFO) self.library._client.create_vserver.assert_called_once_with( vserver_name, fake.ROOT_VOLUME_AGGREGATE, fake.ROOT_VOLUME, fake.AGGREGATES, fake.IPSPACE) self.library._get_api_client.assert_called_once_with( vserver=vserver_name) self.library._create_vserver_lifs.assert_called_once_with( vserver_name, vserver_client, fake.NETWORK_INFO, fake.IPSPACE) self.library._create_vserver_admin_lif.assert_called_once_with( vserver_name, vserver_client, fake.NETWORK_INFO, fake.IPSPACE) self.library._create_vserver_routes.assert_called_once_with( vserver_client, fake.NETWORK_INFO) vserver_client.enable_nfs.assert_called_once_with(versions) self.library._client.setup_security_services.assert_called_once_with( fake.NETWORK_INFO['security_services'], vserver_client, vserver_name) def test_create_vserver_already_present(self): vserver_id = fake.NETWORK_INFO['server_id'] vserver_name = fake.VSERVER_NAME_TEMPLATE % vserver_id self.mock_object(context, 'get_admin_context', mock.Mock(return_value='fake_admin_context')) self.mock_object(self.library._client, 'vserver_exists', mock.Mock(return_value=True)) self.assertRaises(exception.NetAppException, self.library._create_vserver, vserver_name, fake.NETWORK_INFO) @ddt.data(netapp_api.NaApiError, exception.NetAppException) def test_create_vserver_lif_creation_failure(self, lif_exception): vserver_id = fake.NETWORK_INFO['server_id'] vserver_name = fake.VSERVER_NAME_TEMPLATE % vserver_id vserver_client = mock.Mock() self.mock_object(context, 'get_admin_context', mock.Mock(return_value='fake_admin_context')) self.mock_object(self.library, '_get_api_client', mock.Mock(return_value=vserver_client)) self.mock_object(self.library._client, 'vserver_exists', mock.Mock(return_value=False)) self.mock_object(self.library, '_find_matching_aggregates', mock.Mock(return_value=fake.AGGREGATES)) self.mock_object(self.library, '_create_ipspace', mock.Mock(return_value=fake.IPSPACE)) self.mock_object(self.library, '_create_vserver_lifs', mock.Mock(side_effect=lif_exception)) self.mock_object(self.library, '_delete_vserver') self.assertRaises(lif_exception, self.library._create_vserver, vserver_name, fake.NETWORK_INFO) self.library._get_api_client.assert_called_with(vserver=vserver_name) self.assertTrue(self.library._client.create_vserver.called) self.library._create_vserver_lifs.assert_called_with( vserver_name, vserver_client, fake.NETWORK_INFO, fake.IPSPACE) self.library._delete_vserver.assert_called_once_with( vserver_name, security_services=None) self.assertFalse(vserver_client.enable_nfs.called) self.assertEqual(1, lib_multi_svm.LOG.error.call_count) def test_get_valid_ipspace_name(self): result = self.library._get_valid_ipspace_name(fake.IPSPACE_ID) expected = 'ipspace_' + fake.IPSPACE_ID.replace('-', '_') self.assertEqual(expected, result) def test_create_ipspace_not_supported(self): self.library._client.features.IPSPACES = False result = self.library._create_ipspace(fake.NETWORK_INFO) self.assertIsNone(result) @ddt.data(None, 'flat') def test_create_ipspace_not_vlan(self, network_type): self.library._client.features.IPSPACES = True network_info = copy.deepcopy(fake.NETWORK_INFO) network_info['network_allocations'][0]['segmentation_id'] = None network_info['network_allocations'][0]['network_type'] = network_type result = self.library._create_ipspace(network_info) self.assertEqual('Default', result) def test_create_ipspace_already_present(self): self.library._client.features.IPSPACES = True self.mock_object(self.library._client, 'ipspace_exists', mock.Mock(return_value=True)) result = self.library._create_ipspace(fake.NETWORK_INFO) expected = self.library._get_valid_ipspace_name( fake.NETWORK_INFO['neutron_subnet_id']) self.assertEqual(expected, result) self.library._client.ipspace_exists.assert_has_calls([ mock.call(expected)]) self.assertFalse(self.library._client.create_ipspace.called) def test_create_ipspace(self): self.library._client.features.IPSPACES = True self.mock_object(self.library._client, 'ipspace_exists', mock.Mock(return_value=False)) result = self.library._create_ipspace(fake.NETWORK_INFO) expected = self.library._get_valid_ipspace_name( fake.NETWORK_INFO['neutron_subnet_id']) self.assertEqual(expected, result) self.library._client.ipspace_exists.assert_has_calls([ mock.call(expected)]) self.library._client.create_ipspace.assert_has_calls([ mock.call(expected)]) def test_create_vserver_lifs(self): self.mock_object(self.library._client, 'list_cluster_nodes', mock.Mock(return_value=fake.CLUSTER_NODES)) self.mock_object(self.library, '_get_lif_name', mock.Mock(side_effect=['fake_lif1', 'fake_lif2'])) self.mock_object(self.library, '_create_lif') self.library._create_vserver_lifs(fake.VSERVER1, 'fake_vserver_client', fake.NETWORK_INFO, fake.IPSPACE) self.library._create_lif.assert_has_calls([ mock.call('fake_vserver_client', fake.VSERVER1, fake.IPSPACE, fake.CLUSTER_NODES[0], 'fake_lif1', fake.NETWORK_INFO['network_allocations'][0]), mock.call('fake_vserver_client', fake.VSERVER1, fake.IPSPACE, fake.CLUSTER_NODES[1], 'fake_lif2', fake.NETWORK_INFO['network_allocations'][1])]) def test_create_vserver_admin_lif(self): self.mock_object(self.library._client, 'list_cluster_nodes', mock.Mock(return_value=fake.CLUSTER_NODES)) self.mock_object(self.library, '_get_lif_name', mock.Mock(return_value='fake_admin_lif')) self.mock_object(self.library, '_create_lif') self.library._create_vserver_admin_lif(fake.VSERVER1, 'fake_vserver_client', fake.NETWORK_INFO, fake.IPSPACE) self.library._create_lif.assert_has_calls([ mock.call('fake_vserver_client', fake.VSERVER1, fake.IPSPACE, fake.CLUSTER_NODES[0], 'fake_admin_lif', fake.NETWORK_INFO['admin_network_allocations'][0])]) def test_create_vserver_admin_lif_no_admin_network(self): fake_network_info = copy.deepcopy(fake.NETWORK_INFO) fake_network_info['admin_network_allocations'] = [] self.mock_object(self.library._client, 'list_cluster_nodes', mock.Mock(return_value=fake.CLUSTER_NODES)) self.mock_object(self.library, '_get_lif_name', mock.Mock(return_value='fake_admin_lif')) self.mock_object(self.library, '_create_lif') self.library._create_vserver_admin_lif(fake.VSERVER1, 'fake_vserver_client', fake_network_info, fake.IPSPACE) self.assertFalse(self.library._create_lif.called) @ddt.data( fake.get_network_info(fake.USER_NETWORK_ALLOCATIONS, fake.ADMIN_NETWORK_ALLOCATIONS), fake.get_network_info(fake.USER_NETWORK_ALLOCATIONS_IPV6, fake.ADMIN_NETWORK_ALLOCATIONS)) def test_create_vserver_routes(self, network_info): expected_gateway = network_info['network_allocations'][0]['gateway'] vserver_client = mock.Mock() self.mock_object(vserver_client, 'create_route') retval = self.library._create_vserver_routes( vserver_client, network_info) self.assertIsNone(retval) vserver_client.create_route.assert_called_once_with(expected_gateway) def test_get_node_data_port(self): self.mock_object(self.client, 'list_node_data_ports', mock.Mock(return_value=fake.NODE_DATA_PORTS)) self.library.configuration.netapp_port_name_search_pattern = 'e0c' result = self.library._get_node_data_port(fake.CLUSTER_NODE) self.assertEqual('e0c', result) self.library._client.list_node_data_ports.assert_has_calls([ mock.call(fake.CLUSTER_NODE)]) def test_get_node_data_port_no_match(self): self.mock_object(self.client, 'list_node_data_ports', mock.Mock(return_value=fake.NODE_DATA_PORTS)) self.library.configuration.netapp_port_name_search_pattern = 'ifgroup1' self.assertRaises(exception.NetAppException, self.library._get_node_data_port, fake.CLUSTER_NODE) def test_get_lif_name(self): result = self.library._get_lif_name( 'fake_node', fake.NETWORK_INFO['network_allocations'][0]) self.assertEqual('os_132dbb10-9a36-46f2-8d89-3d909830c356', result) @ddt.data(fake.MTU, None, 'not-present') def test_create_lif(self, mtu): """Tests cases where MTU is a valid value, None or not present.""" expected_mtu = (mtu if mtu not in (None, 'not-present') else fake.DEFAULT_MTU) network_allocations = copy.deepcopy( fake.NETWORK_INFO['network_allocations'][0]) network_allocations['mtu'] = mtu if mtu == 'not-present': network_allocations.pop('mtu') vserver_client = mock.Mock() vserver_client.network_interface_exists = mock.Mock( return_value=False) self.mock_object(self.library, '_get_node_data_port', mock.Mock(return_value='fake_port')) self.library._create_lif(vserver_client, 'fake_vserver', 'fake_ipspace', 'fake_node', 'fake_lif', network_allocations) self.library._client.create_network_interface.assert_has_calls([ mock.call('10.10.10.10', '255.255.255.0', '1000', 'fake_node', 'fake_port', 'fake_vserver', 'fake_lif', 'fake_ipspace', expected_mtu)]) def test_create_lif_if_nonexistent_already_present(self): vserver_client = mock.Mock() vserver_client.network_interface_exists = mock.Mock( return_value=True) self.mock_object(self.library, '_get_node_data_port', mock.Mock(return_value='fake_port')) self.library._create_lif(vserver_client, 'fake_vserver', fake.IPSPACE, 'fake_node', 'fake_lif', fake.NETWORK_INFO['network_allocations'][0]) self.assertFalse(self.library._client.create_network_interface.called) def test_get_network_allocations_number(self): self.library._client.list_cluster_nodes.return_value = ( fake.CLUSTER_NODES) result = self.library.get_network_allocations_number() self.assertEqual(len(fake.CLUSTER_NODES), result) def test_get_admin_network_allocations_number(self): result = self.library.get_admin_network_allocations_number( 'fake_admin_network_api') self.assertEqual(1, result) def test_get_admin_network_allocations_number_no_admin_network(self): result = self.library.get_admin_network_allocations_number(None) self.assertEqual(0, result) def test_teardown_server(self): self.library._client.vserver_exists.return_value = True mock_delete_vserver = self.mock_object(self.library, '_delete_vserver') self.library.teardown_server( fake.SHARE_SERVER['backend_details'], security_services=fake.NETWORK_INFO['security_services']) self.library._client.vserver_exists.assert_called_once_with( fake.VSERVER1) mock_delete_vserver.assert_called_once_with( fake.VSERVER1, security_services=fake.NETWORK_INFO['security_services']) @ddt.data(None, {}, {'vserver_name': None}) def test_teardown_server_no_share_server(self, server_details): mock_delete_vserver = self.mock_object(self.library, '_delete_vserver') self.library.teardown_server(server_details) self.assertFalse(mock_delete_vserver.called) self.assertTrue(lib_multi_svm.LOG.warning.called) def test_teardown_server_no_vserver(self): self.library._client.vserver_exists.return_value = False mock_delete_vserver = self.mock_object(self.library, '_delete_vserver') self.library.teardown_server( fake.SHARE_SERVER['backend_details'], security_services=fake.NETWORK_INFO['security_services']) self.library._client.vserver_exists.assert_called_once_with( fake.VSERVER1) self.assertFalse(mock_delete_vserver.called) self.assertTrue(lib_multi_svm.LOG.warning.called) def test_delete_vserver_no_ipspace(self): self.mock_object(self.library._client, 'get_vserver_ipspace', mock.Mock(return_value=None)) vserver_client = mock.Mock() self.mock_object(self.library, '_get_api_client', mock.Mock(return_value=vserver_client)) mock_delete_vserver_vlans = self.mock_object(self.library, '_delete_vserver_vlans') net_interfaces = copy.deepcopy(c_fake.NETWORK_INTERFACES_MULTIPLE) net_interfaces_with_vlans = [net_interfaces[0]] self.mock_object(vserver_client, 'get_network_interfaces', mock.Mock(return_value=net_interfaces)) security_services = fake.NETWORK_INFO['security_services'] self.library._delete_vserver(fake.VSERVER1, security_services=security_services) self.library._client.get_vserver_ipspace.assert_called_once_with( fake.VSERVER1) self.library._client.delete_vserver.assert_called_once_with( fake.VSERVER1, vserver_client, security_services=security_services) self.assertFalse(self.library._client.delete_ipspace.called) mock_delete_vserver_vlans.assert_called_once_with( net_interfaces_with_vlans) def test_delete_vserver_ipspace_has_data_vservers(self): self.mock_object(self.library._client, 'get_vserver_ipspace', mock.Mock(return_value=fake.IPSPACE)) vserver_client = mock.Mock() self.mock_object(self.library, '_get_api_client', mock.Mock(return_value=vserver_client)) self.mock_object(self.library._client, 'ipspace_has_data_vservers', mock.Mock(return_value=True)) mock_delete_vserver_vlans = self.mock_object(self.library, '_delete_vserver_vlans') self.mock_object( vserver_client, 'get_network_interfaces', mock.Mock(return_value=c_fake.NETWORK_INTERFACES_MULTIPLE)) security_services = fake.NETWORK_INFO['security_services'] self.library._delete_vserver(fake.VSERVER1, security_services=security_services) self.library._client.get_vserver_ipspace.assert_called_once_with( fake.VSERVER1) self.library._client.delete_vserver.assert_called_once_with( fake.VSERVER1, vserver_client, security_services=security_services) self.assertFalse(self.library._client.delete_ipspace.called) mock_delete_vserver_vlans.assert_called_once_with( [c_fake.NETWORK_INTERFACES_MULTIPLE[0]]) @ddt.data([], c_fake.NETWORK_INTERFACES) def test_delete_vserver_with_ipspace(self, interfaces): self.mock_object(self.library._client, 'get_vserver_ipspace', mock.Mock(return_value=fake.IPSPACE)) vserver_client = mock.Mock() self.mock_object(self.library, '_get_api_client', mock.Mock(return_value=vserver_client)) self.mock_object(self.library._client, 'ipspace_has_data_vservers', mock.Mock(return_value=False)) mock_delete_vserver_vlans = self.mock_object(self.library, '_delete_vserver_vlans') self.mock_object(vserver_client, 'get_network_interfaces', mock.Mock(return_value=interfaces)) security_services = fake.NETWORK_INFO['security_services'] self.library._delete_vserver(fake.VSERVER1, security_services=security_services) self.library._client.get_vserver_ipspace.assert_called_once_with( fake.VSERVER1) self.library._client.delete_vserver.assert_called_once_with( fake.VSERVER1, vserver_client, security_services=security_services) self.library._client.delete_ipspace.assert_called_once_with( fake.IPSPACE) mock_delete_vserver_vlans.assert_called_once_with(interfaces) def test_delete_vserver_vlans(self): self.library._delete_vserver_vlans(c_fake.NETWORK_INTERFACES) for interface in c_fake.NETWORK_INTERFACES: home_port = interface['home-port'] port, vlan = home_port.split('-') node = interface['home-node'] self.library._client.delete_vlan.assert_called_once_with( node, port, vlan) def test_delete_vserver_vlans_client_error(self): mock_exception_log = self.mock_object(lib_multi_svm.LOG, 'exception') self.mock_object( self.library._client, 'delete_vlan', mock.Mock(side_effect=exception.NetAppException("fake error"))) self.library._delete_vserver_vlans(c_fake.NETWORK_INTERFACES) for interface in c_fake.NETWORK_INTERFACES: home_port = interface['home-port'] port, vlan = home_port.split('-') node = interface['home-node'] self.library._client.delete_vlan.assert_called_once_with( node, port, vlan) self.assertEqual(1, mock_exception_log.call_count)

""" Django settings for qr_code_demo project. Generated by 'django-admin startproject' using Django 2.2. For more information on this file, see https://docs.djangoproject.com/en/2.2/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/2.2/ref/settings/ """ import os import django # Build paths inside the project like this: os.path.join(BASE_DIR, ...) from qr_code.qrcode import constants BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/3.1/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = "8l4)()f1&tg*dtxh6whlew#k-d5&79npe#j_dg9l0b)m8^g#8u" # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = ["django.contrib.auth", "django.contrib.contenttypes", "django.contrib.staticfiles", "qr_code", "qr_code_demo"] MIDDLEWARE = [ "django.middleware.security.SecurityMiddleware", "django.contrib.sessions.middleware.SessionMiddleware", "django.middleware.common.CommonMiddleware", "django.middleware.csrf.CsrfViewMiddleware", "django.contrib.auth.middleware.AuthenticationMiddleware", "django.contrib.messages.middleware.MessageMiddleware", "django.middleware.clickjacking.XFrameOptionsMiddleware", ] ROOT_URLCONF = "demo_site.urls" TEMPLATES = [ { "BACKEND": "django.template.backends.django.DjangoTemplates", "DIRS": [], "APP_DIRS": True, "OPTIONS": { "context_processors": [ "django.template.context_processors.debug", "django.template.context_processors.request", "django.contrib.auth.context_processors.auth", "django.contrib.messages.context_processors.messages", ], }, }, ] WSGI_APPLICATION = "demo_site.wsgi.application" # Database # https://docs.djangoproject.com/en/3.1/ref/settings/#databases DATABASES = {} # Password validation # https://docs.djangoproject.com/en/3.1/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { "NAME": "django.contrib.auth.password_validation.UserAttributeSimilarityValidator", }, { "NAME": "django.contrib.auth.password_validation.MinimumLengthValidator", }, { "NAME": "django.contrib.auth.password_validation.CommonPasswordValidator", }, { "NAME": "django.contrib.auth.password_validation.NumericPasswordValidator", }, ] # Internationalization # https://docs.djangoproject.com/en/3.1/topics/i18n/ LANGUAGE_CODE = "en-us" TIME_ZONE = "UTC" USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.1/howto/static-files/ STATIC_URL = "/static/" STATIC_ROOT = os.path.join(BASE_DIR, "staticfiles") # Caches. CACHES = { "default": { "BACKEND": "django.core.cache.backends.locmem.LocMemCache", }, "qr-code": {"BACKEND": "django.core.cache.backends.locmem.LocMemCache", "LOCATION": "qr-code-cache", "TIMEOUT": 3600}, } # Django QR Code specific options. QR_CODE_CACHE_ALIAS = "qr-code" QR_CODE_URL_PROTECTION = { constants.TOKEN_LENGTH: 30, # Optional random token length for URL protection. Defaults to 20. constants.SIGNING_KEY: "my-secret-signing-key", # Optional signing key for URL token. Uses SECRET_KEY if not defined. constants.SIGNING_SALT: "my-signing-salt", # Optional signing salt for URL token. constants.ALLOWS_EXTERNAL_REQUESTS_FOR_REGISTERED_USER: lambda u: True, # Tells whether a registered user can request the QR code URLs from outside a site that uses this app. It can be a boolean value used for any user, or a callable that takes a user as parameter. Defaults to False (nobody can access the URL without the security token). } SERVE_QR_CODE_IMAGE_PATH = "qr-code-image/"

import logging import time from universe import pyprofile from universe.vectorized import core logger = logging.getLogger(__name__) DEFAULT_MAX_EPISODE_SECONDS = 20 * 60. # Default to 20 minutes if there is no explicit limit class TimeLimit(core.Wrapper): def _configure(self, **kwargs): super(TimeLimit, self)._configure(**kwargs) self._max_episode_seconds = self.env.spec.tags.get('wrapper_config.TimeLimit.max_episode_seconds', None) self._max_episode_steps = self.env.spec.tags.get('wrapper_config.TimeLimit.max_episode_steps', None) if self._max_episode_seconds is None and self._max_episode_steps is None: self._max_episode_seconds = DEFAULT_MAX_EPISODE_SECONDS self._elapsed_steps = 0 self._episode_started_at = None @property def _elapsed_seconds(self): return time.time() - self._episode_started_at def _past_limit(self): """Return true if we are past our limit""" if self._max_episode_steps is not None and self._max_episode_steps <= self._elapsed_steps: logger.debug("Env has passed the step limit defined by TimeLimit.") return True if self._max_episode_seconds is not None and self._max_episode_seconds <= self._elapsed_seconds: logger.debug("Env has passed the seconds limit defined by TimeLimit.") return True return False def _step(self, action_n): assert self._episode_started_at is not None, "Cannot call env.step() before calling reset()" observation_n, reward_n, done_n, info = self.env.step(action_n) self._elapsed_steps += 1 if self._past_limit(): _ = self.reset() # Force a reset, discard the observation done_n = [True] * self.n # Force a done = True return observation_n, reward_n, done_n, info def _reset(self): self._episode_started_at = time.time() self._elapsed_steps = 0 return self.env.reset()

from .pulsectl import Module class Module(Module): def __init__(self, config, theme): super().__init__(config, theme, "sink") # vim: tabstop=8 expandtab shiftwidth=4 softtabstop=4

"""Tests the Tensorboard mesh plugin.""" import collections.abc import os import shutil import numpy as np import tensorflow as tf import time from unittest import mock from werkzeug import test as werkzeug_test from werkzeug import wrappers from tensorboard.backend import application from tensorboard.backend.event_processing import data_provider from tensorboard.backend.event_processing import ( plugin_event_multiplexer as event_multiplexer, ) from tensorboard.plugins import base_plugin from tensorboard.plugins.mesh import mesh_plugin from tensorboard.plugins.mesh import summary from tensorboard.plugins.mesh import plugin_data_pb2 from tensorboard.plugins.mesh import test_utils from tensorboard.util import test_util as tensorboard_test_util class MeshPluginTest(tf.test.TestCase): """Tests for mesh plugin server.""" def setUp(self): # We use numpy.random to generate meshes. We seed to avoid non-determinism # in this test. np.random.seed(17) # Log dir to save temp events into. self.log_dir = self.get_temp_dir() # Create mesh summary. with tf.compat.v1.Graph().as_default(): tf_placeholder = tf.compat.v1.placeholder sess = tf.compat.v1.Session() point_cloud = test_utils.get_random_mesh(1000) point_cloud_vertices = tf_placeholder( tf.float32, point_cloud.vertices.shape ) mesh_no_color = test_utils.get_random_mesh(2000, add_faces=True) mesh_no_color_extended = test_utils.get_random_mesh( 2500, add_faces=True ) mesh_no_color_vertices = tf_placeholder(tf.float32, [1, None, 3]) mesh_no_color_faces = tf_placeholder(tf.int32, [1, None, 3]) mesh_color = test_utils.get_random_mesh( 3000, add_faces=True, add_colors=True ) mesh_color_vertices = tf_placeholder( tf.float32, mesh_color.vertices.shape ) mesh_color_faces = tf_placeholder(tf.int32, mesh_color.faces.shape) mesh_color_colors = tf_placeholder( tf.uint8, mesh_color.colors.shape ) self.data = [ point_cloud, mesh_no_color, mesh_no_color_extended, mesh_color, ] # In case when name is present and display_name is not, we will reuse name # as display_name. Summaries below intended to test both cases. self.names = ["point_cloud", "mesh_no_color", "mesh_color"] summary.op( self.names[0], point_cloud_vertices, description="just point cloud", ) summary.op( self.names[1], mesh_no_color_vertices, faces=mesh_no_color_faces, display_name="name_to_display_in_ui", description="beautiful mesh in grayscale", ) summary.op( self.names[2], mesh_color_vertices, faces=mesh_color_faces, colors=mesh_color_colors, description="mesh with random colors", ) merged_summary_op = tf.compat.v1.summary.merge_all() self.runs = ["bar"] self.steps = 20 bar_directory = os.path.join(self.log_dir, self.runs[0]) with tensorboard_test_util.FileWriterCache.get( bar_directory ) as writer: writer.add_graph(sess.graph) for step in range(self.steps): # Alternate between two random meshes with different number of # vertices. no_color = ( mesh_no_color if step % 2 == 0 else mesh_no_color_extended ) with mock.patch.object(time, "time", return_value=step): writer.add_summary( sess.run( merged_summary_op, feed_dict={ point_cloud_vertices: point_cloud.vertices, mesh_no_color_vertices: no_color.vertices, mesh_no_color_faces: no_color.faces, mesh_color_vertices: mesh_color.vertices, mesh_color_faces: mesh_color.faces, mesh_color_colors: mesh_color.colors, }, ), global_step=step, ) # Start a server that will receive requests. multiplexer = event_multiplexer.EventMultiplexer( { "bar": bar_directory, } ) provider = data_provider.MultiplexerDataProvider( multiplexer, self.log_dir ) self.context = base_plugin.TBContext( logdir=self.log_dir, data_provider=provider ) self.plugin = mesh_plugin.MeshPlugin(self.context) # Wait until after plugin construction to reload the multiplexer because the # plugin caches data from the multiplexer upon construction and this affects # logic tested later down. # TODO(https://github.com/tensorflow/tensorboard/issues/2579): Eliminate the # caching of data at construction time and move this Reload() up to just # after the multiplexer is created. multiplexer.Reload() wsgi_app = application.TensorBoardWSGI([self.plugin]) self.server = werkzeug_test.Client(wsgi_app, wrappers.Response) self.routes = self.plugin.get_plugin_apps() def tearDown(self): shutil.rmtree(self.log_dir, ignore_errors=True) def testRoutes(self): """Tests that the /tags route offers the correct run to tag mapping.""" self.assertIsInstance(self.routes["/tags"], collections.abc.Callable) self.assertIsInstance(self.routes["/meshes"], collections.abc.Callable) self.assertIsInstance(self.routes["/data"], collections.abc.Callable) def testTagsRoute(self): """Tests that the /tags route offers the correct run to tag mapping.""" response = self.server.get("/data/plugin/mesh/tags") self.assertEqual(200, response.status_code) tags = test_utils.deserialize_json_response(response.get_data()) self.assertIn(self.runs[0], tags) for name in self.names: self.assertIn(name, tags[self.runs[0]]) def validate_data_response( self, run, tag, sample, content_type, dtype, ground_truth_data, step=0 ): """Makes request and checks that response has expected data.""" response = self.server.get( "/data/plugin/mesh/data?run=%s&tag=%s&sample=%d&content_type=" "%s&step=%d" % (run, tag, sample, content_type, step) ) self.assertEqual(200, response.status_code) data = test_utils.deserialize_array_buffer_response( next(response.response), dtype ) self.assertEqual(ground_truth_data.reshape(-1).tolist(), data.tolist()) def testDataRoute(self): """Tests that the /data route returns correct data for meshes.""" self.validate_data_response( self.runs[0], self.names[0], 0, "VERTEX", np.float32, self.data[0].vertices, ) self.validate_data_response( self.runs[0], self.names[1], 0, "FACE", np.int32, self.data[1].faces ) # Validate that the same summary has mesh with different number of faces at # different step=1. self.validate_data_response( self.runs[0], self.names[1], 0, "FACE", np.int32, self.data[2].faces, step=1, ) self.validate_data_response( self.runs[0], self.names[2], 0, "COLOR", np.uint8, self.data[3].colors, ) def testMetadataRoute(self): """Tests that the /meshes route returns correct metadata for meshes.""" response = self.server.get( "/data/plugin/mesh/meshes?run=%s&tag=%s&sample=%d" % (self.runs[0], self.names[0], 0) ) self.assertEqual(200, response.status_code) metadata = test_utils.deserialize_json_response(response.get_data()) self.assertEqual(len(metadata), self.steps) self.assertAllEqual( metadata[0]["content_type"], plugin_data_pb2.MeshPluginData.VERTEX ) self.assertAllEqual( metadata[0]["data_shape"], self.data[0].vertices.shape ) def testsEventsAlwaysSortedByStep(self): """Tests that events always sorted by step.""" response = self.server.get( "/data/plugin/mesh/meshes?run=%s&tag=%s&sample=%d" % (self.runs[0], self.names[1], 0) ) self.assertEqual(200, response.status_code) metadata = test_utils.deserialize_json_response(response.get_data()) for i in range(1, self.steps): # Step will be equal when two tensors of different content type # belong to the same mesh. self.assertLessEqual(metadata[i - 1]["step"], metadata[i]["step"]) def testIsActive(self): self.assertFalse(self.plugin.is_active()) if __name__ == "__main__": tf.test.main()

"""Sliver manager. The sliver manager has several functions. It is responsible for creating, resource limiting, starting, stopping, and destroying slivers. It provides an API for users to access these functions and also to make inter-sliver resource loans. The sliver manager is also responsible for handling delegation accounts. """ import string import re import time import logger import api, api_calls import database import account import controller try: import sliver_lxc implementation='lxc' sliver_default_type='sliver.LXC' sliver_class_to_register = sliver_lxc.Sliver_LXC sliver_password_shell = sliver_lxc.Sliver_LXC.SHELL except: try: import sliver_vs implementation='vs' sliver_default_type='sliver.VServer' sliver_class_to_register = sliver_vs.Sliver_VS sliver_password_shell = sliver_vs.Sliver_VS.SHELL except: logger.log("Could not import either sliver_lxc or sliver_vs - bailing out") exit(1) # just being safe try: from plnode.bwlimit import bwmin, bwmax except: bwmin, bwmax = 8, 1000*1000*1000 priority = 10 DEFAULT_ALLOCATION = { 'enabled': 1, # CPU parameters 'cpu_pct': 0, # percent CPU reserved 'cpu_share': 1, # proportional share 'cpu_cores': "0b", # reserved cpu cores <num_cores>[b] 'cpu_freezable': 0, # freeze processes if cpu_cores is 0 # bandwidth parameters 'net_min_rate': bwmin / 1000, # kbps 'net_max_rate': bwmax / 1000, # kbps 'net_share': 1, # proportional share # bandwidth parameters over routes exempt from node bandwidth limits 'net_i2_min_rate': bwmin / 1000, # kbps 'net_i2_max_rate': bwmax / 1000, # kbps 'net_i2_share': 1, # proportional share 'net_max_kbyte' : 10546875, #Kbyte 'net_thresh_kbyte': 9492187, #Kbyte 'net_i2_max_kbyte': 31640625, 'net_i2_thresh_kbyte': 28476562, # disk space limit 'disk_max': 10000000, # bytes # capabilities 'capabilities': '', # IP addresses 'ip_addresses': '0.0.0.0', # NOTE: this table is further populated with resource names and # default amounts via the start() function below. This probably # should be changeg and these values should be obtained via the # API to myplc. } start_requested = False # set to True in order to request that all slivers be started # check leases and adjust the 'reservation_alive' field in slivers # this is not expected to be saved as it will change for the next round def adjustReservedSlivers (data): """ On reservable nodes, tweak the 'reservation_alive' field to instruct cyclic loop about what to do with slivers. """ # only impacts reservable nodes if 'reservation_policy' not in data: return policy=data['reservation_policy'] if policy not in ['lease_or_idle', 'lease_or_shared']: if policy is not None: logger.log ("unexpected reservation_policy %(policy)s"%locals()) return logger.log("slivermanager.adjustReservedSlivers") now=int(time.time()) # scan leases that are expected to show in ascending order active_lease=None for lease in data['leases']: if lease['t_from'] <= now and now <= lease['t_until']: active_lease=lease break def is_system_sliver (sliver): for d in sliver['attributes']: if d['tagname']=='system' and d['value']: return True return False # mark slivers as appropriate for sliver in data['slivers']: # system slivers must be kept alive if is_system_sliver(sliver): sliver['reservation_alive']=True continue # regular slivers if not active_lease: # with 'idle_or_shared', just let the field out, behave like a shared node # otherwise, mark all slivers as being turned down if policy == 'lease_or_idle': sliver['reservation_alive']=False else: # there is an active lease, mark it alive and the other not sliver['reservation_alive'] = sliver['name']==active_lease['name'] @database.synchronized def GetSlivers(data, config = None, plc=None, fullupdate=True): """This function has two purposes. One, convert GetSlivers() data into a more convenient format. Two, even if no updates are coming in, use the GetSlivers() heartbeat as a cue to scan for expired slivers.""" logger.verbose("slivermanager: Entering GetSlivers with fullupdate=%r"%fullupdate) for key in list(data.keys()): logger.verbose('slivermanager: GetSlivers key : ' + key) node_id = None try: f = open('/etc/planetlab/node_id') try: node_id = int(f.read()) finally: f.close() except: logger.log_exc("slivermanager: GetSlivers failed to read /etc/planetlab/node_id") if 'node_id' in data and data['node_id'] != node_id: return if 'networks' in data: for network in data['networks']: if network['is_primary'] and network['bwlimit'] is not None: DEFAULT_ALLOCATION['net_max_rate'] = network['bwlimit'] / 1000 # Take initscripts (global) returned by API, build a hash scriptname->code iscripts_hash = {} if 'initscripts' not in data: logger.log_missing_data("slivermanager.GetSlivers", 'initscripts') return for initscript_rec in data['initscripts']: logger.verbose("slivermanager: initscript: %s" % initscript_rec['name']) iscripts_hash[str(initscript_rec['name'])] = initscript_rec['script'] adjustReservedSlivers (data) for sliver in data['slivers']: logger.verbose("slivermanager: %s: slivermanager.GetSlivers in slivers loop"%sliver['name']) rec = sliver.copy() rec.setdefault('timestamp', data['timestamp']) # convert attributes field to a proper dict attributes = {} for attr in rec.pop('attributes'): attributes[attr['tagname']] = attr['value'] rec.setdefault("attributes", attributes) # squash keys keys = rec.pop('keys') rec.setdefault('keys', '\n'.join([key_struct['key'] for key_struct in keys])) ## 'Type' isn't returned by GetSlivers() for whatever reason. We're overloading ## instantiation here, but i suppose its the same thing when you think about it. -FA # Handle nm-controller here if rec['instantiation'].lower() == 'nm-controller': rec.setdefault('type', attributes.get('type', 'controller.Controller')) else: rec.setdefault('type', attributes.get('type', sliver_default_type)) # set the vserver reference. If none, set to default. rec.setdefault('vref', attributes.get('vref', 'default')) ### set initscripts; set empty rec['initscript'] if not # if tag 'initscript_code' is set, that's what we use iscode = attributes.get('initscript_code', '') if iscode: rec['initscript'] = iscode else: isname = attributes.get('initscript') if isname is not None and isname in iscripts_hash: rec['initscript'] = iscripts_hash[isname] else: rec['initscript'] = '' # set delegations, if none, set empty rec.setdefault('delegations', attributes.get("delegations", [])) # extract the implied rspec rspec = {} rec['rspec'] = rspec for resname, default_amount in DEFAULT_ALLOCATION.items(): try: t = type(default_amount) amount = t.__new__(t, attributes[resname]) except (KeyError, ValueError): amount = default_amount rspec[resname] = amount # add in sysctl attributes into the rspec for key in list(attributes.keys()): if key.find("sysctl.") == 0: rspec[key] = attributes[key] # also export tags in rspec so they make it to the sliver_vs.start call rspec['tags'] = attributes database.db.deliver_record(rec) if fullupdate: database.db.set_min_timestamp(data['timestamp']) # slivers are created here. database.db.sync() def deliver_ticket(data): return GetSlivers(data, fullupdate=False) def start(): # No default allocation values for LXC yet, think if its necessary given # that they are also default allocation values in this module if implementation == 'vs': for resname, default_amount in sliver_vs.DEFAULT_ALLOCATION.items(): DEFAULT_ALLOCATION[resname]=default_amount account.register_class(sliver_class_to_register) account.register_class(controller.Controller) database.start() api_calls.deliver_ticket = deliver_ticket api.start() ### check if a sliver is running ### a first step to a unified code for codemux def is_running (name): if implementation=='vs': import vserver return vserver.VServer(name).is_running() else: import libvirt running = False try: conn = libvirt.open('lxc://') dom = conn.lookupByName(name) running = dom.info()[0] == libvirt.VIR_DOMAIN_RUNNING finally: conn.close() return running

from ..excel_comparison_test import ExcelComparisonTest from ...workbook import Workbook class TestCompareXLSXFiles(ExcelComparisonTest): """ Test file created by XlsxWriter against a file created by Excel. """ def setUp(self): self.set_filename('textbox11.xlsx') def test_create_file(self): """Test the creation of a simple XlsxWriter file with textbox(s).""" workbook = Workbook(self.got_filename) worksheet = workbook.add_worksheet() worksheet.insert_textbox('E9', 'This is some text', {'fill': {'color': 'red'}}) workbook.close() self.assertExcelEqual()

import RPi.GPIO as GPIO import time import os import subprocess as sub import sys import socket from clint.textui import colored BCN3DSigmaxLCDPath = "home/pi/BCN3DSigmaxLCD" BCN3DSigmaPath = "/home/pi/BCN3DSigma" BCN3DPlusPath = "/home/pi/BCN3DSigmax" BCN3DSigmaScreenPath = "home/pi/BCN3DSigmaLCD" repoPath = "/home/pi/sd-auto-loader" codePath = "/home/pi/sd-auto-loader/Code" #Tuple that holds all the input options of the software inputOptions = ['sync','help'] #Pin declarations LED1 = 21 LED2 = 20 LED3 = 16 LED4 = 12 LED5 = 7 LED6 = 8 LED7 = 25 LED8 = 24 LED9 = 23 def haveInternet(): os.system("sudo ifup eth0") REMOTE_SERVER = "www.google.com" try: host = socket.gethostbyname(REMOTE_SERVER) s = socket.create_connection((host, 443)) return True except: pass return False def syncGithub(): #Update the Repo if haveInternet(): print (colored.green("=========Internet is ON!===========")) try: print "Getting updates from Github" os.chdir(repoPath) currentDirectory = os.getcwd() print "The current directory is: %s" % currentDirectory os.system("git pull origin master") except: print "Something went wrong, check your internet connection" pass else: print (colored.red("=============No internet, no github sync=============")) def manageInputs(): print "Setting the switches to inputs" GPIO.setwarnings(False) GPIO.setmode(GPIO.BCM) #Set pull-ups to pins GPIO.setup(5, GPIO.IN, pull_up_down=GPIO.PUD_UP) GPIO.setup(26, GPIO.IN, pull_up_down=GPIO.PUD_UP) GPIO.setup(19, GPIO.IN, pull_up_down=GPIO.PUD_UP) GPIO.setup(13, GPIO.IN, pull_up_down=GPIO.PUD_UP) GPIO.setup(6, GPIO.IN, pull_up_down=GPIO.PUD_UP) #Read inputs just one time input_state_5 = GPIO.input(5) input_state_26 = GPIO.input(26) input_state_19 = GPIO.input(19) input_state_13 = GPIO.input(13) input_state_6 = GPIO.input(6) GPIO.setmode(GPIO.BCM) GPIO.setup(LED1, GPIO.OUT) GPIO.setup(LED2, GPIO.OUT) GPIO.setup(LED3, GPIO.OUT) GPIO.setup(LED4, GPIO.OUT) GPIO.setup(LED5, GPIO.OUT) GPIO.setup(LED6, GPIO.OUT) GPIO.setup(LED7, GPIO.OUT) GPIO.setup(LED8, GPIO.OUT) GPIO.setup(LED9, GPIO.OUT) def turnOnLED(pin): #print "turning on led: %d" % pin GPIO.output(pin, GPIO.HIGH) time.sleep(0.1) def turnOffLED(pin): #print "turning off led: %d" % pin GPIO.output(pin, GPIO.LOW) time.sleep(0.1) def turnOffAllLEDs(): GPIO.output(21, GPIO.LOW) GPIO.output(20, GPIO.LOW) GPIO.output(16, GPIO.LOW) GPIO.output(12, GPIO.LOW) GPIO.output(7, GPIO.LOW) GPIO.output(8, GPIO.LOW) GPIO.output(25, GPIO.LOW) GPIO.output(24, GPIO.LOW) GPIO.output(23, GPIO.LOW) def turnOnAllLEDs(): GPIO.output(21, GPIO.HIGH) GPIO.output(20, GPIO.HIGH) GPIO.output(16, GPIO.HIGH) GPIO.output(12, GPIO.HIGH) GPIO.output(7, GPIO.HIGH) GPIO.output(8, GPIO.HIGH) GPIO.output(25, GPIO.HIGH) GPIO.output(24, GPIO.HIGH) GPIO.output(23, GPIO.HIGH) def blinkLED(LED): turnOnLED(LED) time.sleep(0.25) turnOffLED(LED) def startUpLEDS(times): #Just a sequence of LEDs to know that the system is running the program print "Lightning some LEDs..." for x in range(0,times): print ". . . . ." turnOnLED(LED1) turnOnLED(LED2) turnOnLED(LED3) turnOnLED(LED4) turnOnLED(LED5) turnOnLED(LED6) turnOnLED(LED7) turnOnLED(LED8) turnOnLED(LED9) time.sleep(0.2) turnOffLED(LED9) turnOffLED(LED8) turnOffLED(LED7) turnOffLED(LED6) turnOffLED(LED5) turnOffLED(LED4) turnOffLED(LED3) turnOffLED(LED2) turnOffLED(LED1) #GPIO.cleanup() def loadBCN3DSigmaSD(): os.chdir(codePath) startUpLEDS(1) time.sleep(2) proc = sub.Popen(['./formatAndCopy.sh', 'Sigma']) while (proc.returncode == None): turnOnAllLEDs() time.sleep(0.5) turnOffAllLEDs() proc.poll() if (proc.returncode != 0): print (colored.red("*************An error ocurred loading SD's***********")) turnOffAllLEDs() else: print (colored.green("----------------SD's Loaded Successfully!-----------------")) turnOnAllLEDs() time.sleep(2) #Sleep for 2 seconds for x in range(0,5): turnOnAllLEDs() time.sleep(0.25) turnOffAllLEDs() def loadBCN3DSigmaScreenSD(): os.chdir(codePath) startUpLEDS(1) time.sleep(2) proc = sub.Popen(['./formatAndCopy.sh', 'LCD_Sigma']) while (proc.returncode == None): turnOnAllLEDs() time.sleep(0.5) turnOffAllLEDs() proc.poll() if (proc.returncode != 0): print (colored.red("*************An error ocurred loading SD's***********")) turnOffAllLEDs() else: print (colored.green("----------------SD's Loaded Successfully!-----------------")) turnOnAllLEDs() time.sleep(2) #Sleep for 2 seconds for x in range(0,5): turnOnAllLEDs() time.sleep(0.25) turnOffAllLEDs() def loadBCN3DSigmaxSD(): os.chdir(codePath) startUpLEDS(1) time.sleep(2) proc = sub.Popen(['./formatAndCopy.sh', 'Sigmax']) while (proc.returncode == None): turnOnAllLEDs() time.sleep(0.5) turnOffAllLEDs() proc.poll() if (proc.returncode != 0): print (colored.red("*************An error ocurred loading SD's***********")) turnOffAllLEDs() else: print (colored.green("----------------SD's Loaded Successfully!-----------------")) turnOnAllLEDs() time.sleep(2) #Sleep for 2 seconds for x in range(0,5): turnOnAllLEDs() time.sleep(0.25) turnOffAllLEDs() def loadBCN3DSigmaxLCDSD(): os.chdir(codePath) startUpLEDS(1) time.sleep(2) proc = sub.Popen(['./formatAndCopy.sh', 'LCD_Sigmax']) while (proc.returncode == None): turnOnAllLEDs() time.sleep(0.5) turnOffAllLEDs() proc.poll() if (proc.returncode != 0): print (colored.red("*************An error ocurred loading SD's***********")) turnOffAllLEDs() else: print (colored.green("----------------SD's Loaded Successfully!-----------------")) turnOnAllLEDs() time.sleep(2) #Sleep for 2 seconds for x in range(0,5): turnOnAllLEDs() time.sleep(0.25) turnOffAllLEDs() def printButtonStatus(): print "Switch 1 is set to: %d" % GPIO.input(6) print "Switch 2 is set to: %d" % GPIO.input(13) print "Switch 3 is set to: %d" % GPIO.input(19) print "Switch 4 is set to: %d" % GPIO.input(26) def checkButtons(channel): #Read the status of the switches and buttons try: print "Reading the switch buttons..." input_state_26 = GPIO.input(26) input_state_19 = GPIO.input(19) input_state_13 = GPIO.input(13) input_state_6 = GPIO.input(6) printButtonStatus() if input_state_26 == False and input_state_19 == True and input_state_13 == True and input_state_6 == True: print 'Loading BCN3D Sigma SD' loadBCN3DSigmaSD() time.sleep(2) if input_state_26 == True and input_state_19 == False and input_state_13 == True and input_state_6 == True: print 'Loading BCN3D Sigma Display uSD' loadBCN3DSigmaScreenSD() time.sleep(2) if input_state_26 == True and input_state_19 == True and input_state_13 == False and input_state_6 == True: print 'Loading BCN3DSigmax SD' loadBCN3DSigmaxSD() time.sleep(2) if input_state_26 == True and input_state_19 == True and input_state_13 == True and input_state_6 == False: print 'Loading BCN3D Sigmax Display uSD' loadBCN3DSigmaxLCDSD() time.sleep(2) if input_state_26 == False and input_state_19 == False and input_state_13 == False and input_state_6 == False: turnOffAllLEDs() for x in range(0,5): turnOnAllLEDs(); time.sleep(0.25); turnOffAllLEDs(); print "Powering OFF The system" GPIO.cleanup() os.system("sudo poweroff") #if input_state_26 == True and input_state_19 == True and input_state_13 == True and input_state_6 == False: except KeyboardInterrupt: #If we press ctrl + c print "Program closed by user" GPIO.cleanup() sys.exit() except: print "Other error or exception ocurred!" GPIO.cleanup() sys.exit() def printHelp(): #function that prints the options available as input commands try: print "This are the available options: " print '\n' i = 0 for option in inputOptions: print " %d. %s" % (i,option) i+=1 print '\n' print "Use: sudo python sdloader.py [OPTION]" except KeyboardInterrupt: #If we press ctrl + c print "Program closed by user" GPIO.cleanup() sys.exit() #------------------------------------MAIN FLOW----------------------------- def main(): if len(sys.argv) > 1: if sys.argv[1] == inputOptions[0]: syncGithub() #Only sync then quit sys.exit() elif sys.argv[1] == inputOptions[1]: printHelp() #When a keyboard is detected, exit program elif len(sys.argv) > 1 and sys.argv[1] not in inputOptions: #When input arguments are wrong print "command/s " + str(sys.argv[1:]) + " not recognised. Please type " + sys.argv[0] + " \"help\" to see commands" time.sleep(3) sys.exit() else: syncGithub() manageInputs() startUpLEDS(3) #Callback function in PIN 5. Whenever a Falling Edge is detected, run checkButtons function GPIO.add_event_detect(5, GPIO.FALLING, callback=checkButtons, bouncetime=150) while True: time.sleep(0.5) #print "waiting for the load button..." #Just the regular boilerplate to start the program if __name__ == '__main__': main()

import json import urllib2 import pymongo # connect to mongo connection = pymongo.MongoClient("mongodb://localhost") # get a handle to the reddit database db = connection.reddit stories = db.stories # get the reddit home page reddit_page = urllib2.urlopen("http://www.reddit.com/r/technology/.json") # parse the json parsed = json.loads(reddit_page.read()) print parsed # iterate through every news item on the page for item in parsed['data']['children']: # # put it in mongo stories.insert(item['data'])

import argparse import json from taxonomyschema.request import Requestor from taxonomyschema.vocabulary import VocabularyEncoder, VocabularyManifest from requests.exceptions import HTTPError def run(models_dir, url): r = Requestor(url) data = json.dumps(VocabularyManifest(models_dir), cls=VocabularyEncoder) try: resp = r.update_service(data) except HTTPError: resp_json = resp.json() if 'error' in resp_json: print('[ERROR]: API update failed') for e in resp_json['error']: print('code: {}'.format(getattr(e, 'code', ''))) print('message: {}'.format(getattr(e, 'message', ''))) print('') def main(): parser = argparse.ArgumentParser( description=( 'Updates Taxonomy Service datamodels via API call' ) ) parser.add_argument( 'models', type=str, default='datamodels', help='path to models directory' ) parser.add_argument( 'url', type=str, help='url of API to POST models to' ) args = parser.parse_args() run(models_dir=args.models, url=args.url) if __name__ == '__main__': main()

"""A very simple MNIST classifier. See extensive documentation at https://www.tensorflow.org/get_started/mnist/beginners """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import argparse import sys from tensorflow.examples.tutorials.mnist import input_data import tensorflow as tf FLAGS = None def main(_): # Import data mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True) # Create the model x = tf.placeholder(tf.float32, [None, 784]) W = tf.Variable(tf.zeros([784, 10])) b = tf.Variable(tf.zeros([10])) y = tf.matmul(x, W) + b # Define loss and optimizer y_ = tf.placeholder(tf.float32, [None, 10]) # The raw formulation of cross-entropy, # # tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.nn.softmax(y)), # reduction_indices=[1])) # # can be numerically unstable. # # So here we use tf.nn.softmax_cross_entropy_with_logits on the raw # outputs of 'y', and then average across the batch. cross_entropy = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y)) train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy) sess = tf.InteractiveSession() tf.global_variables_initializer().run() # Train for _ in range(1000): batch_xs, batch_ys = mnist.train.next_batch(100) sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}) # Test trained model correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels})) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--data_dir', type=str, default='/tmp/tensorflow/mnist/input_data', help='Directory for storing input data') FLAGS, unparsed = parser.parse_known_args() tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)

from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals from builtins import str from past.builtins import basestring from past.utils import old_div import copy from datetime import datetime, timedelta import dateutil.parser from functools import wraps import inspect from itertools import chain, product import json import logging import os import socket import sys import time import traceback from flask._compat import PY2 from flask import ( Flask, url_for, Markup, Blueprint, redirect, flash, Response, render_template) from flask.ext.admin import Admin, BaseView, expose, AdminIndexView from flask.ext.admin.form import DateTimePickerWidget from flask.ext.admin import base from flask.ext.admin.contrib.sqla import ModelView from flask.ext.cache import Cache from flask import request import sqlalchemy as sqla from wtforms import ( widgets, Form, DateTimeField, SelectField, TextAreaField, PasswordField, StringField) from pygments import highlight, lexers from pygments.formatters import HtmlFormatter import chartkick import jinja2 import markdown from sqlalchemy import or_ import airflow from airflow import jobs, login, models, settings, utils from airflow.configuration import conf from airflow.models import State from airflow.settings import Session from airflow.utils import AirflowException from airflow.www import utils as wwwutils login_required = login.login_required current_user = login.current_user logout_user = login.logout_user from airflow import default_login as login if conf.getboolean('webserver', 'AUTHENTICATE'): try: # Environment specific login import airflow_login as login except ImportError as e: logging.error( "authenticate is set to True in airflow.cfg, " "but airflow_login failed to import %s" % e) login_required = login.login_required current_user = login.current_user logout_user = login.logout_user AUTHENTICATE = conf.getboolean('webserver', 'AUTHENTICATE') if AUTHENTICATE is False: login_required = lambda x: x FILTER_BY_OWNER = False if conf.getboolean('webserver', 'FILTER_BY_OWNER'): # filter_by_owner if authentication is enabled and filter_by_owner is true FILTER_BY_OWNER = AUTHENTICATE class VisiblePasswordInput(widgets.PasswordInput): def __init__(self, hide_value=False): self.hide_value = hide_value class VisiblePasswordField(PasswordField): widget = VisiblePasswordInput() def superuser_required(f): ''' Decorator for views requiring superuser access ''' @wraps(f) def decorated_function(*args, **kwargs): if ( not AUTHENTICATE or (not current_user.is_anonymous() and current_user.is_superuser()) ): return f(*args, **kwargs) else: flash("This page requires superuser privileges", "error") return redirect(url_for('admin.index')) return decorated_function def data_profiling_required(f): ''' Decorator for views requiring data profiling access ''' @wraps(f) def decorated_function(*args, **kwargs): if ( not AUTHENTICATE or (not current_user.is_anonymous() and current_user.data_profiling()) ): return f(*args, **kwargs) else: flash("This page requires data profiling privileges", "error") return redirect(url_for('admin.index')) return decorated_function QUERY_LIMIT = 100000 CHART_LIMIT = 200000 def pygment_html_render(s, lexer=lexers.TextLexer): return highlight( s, lexer(), HtmlFormatter(linenos=True), ) def wrapped_markdown(s): return '<div class="rich_doc">' + markdown.markdown(s) + "</div>" def render(obj, lexer): out = "" if isinstance(obj, basestring): out += pygment_html_render(obj, lexer) elif isinstance(obj, (tuple, list)): for i, s in enumerate(obj): out += "<div>List item #{}</div>".format(i) out += "<div>" + pygment_html_render(s, lexer) + "</div>" elif isinstance(obj, dict): for k, v in obj.items(): out += '<div>Dict item "{}"</div>'.format(k) out += "<div>" + pygment_html_render(v, lexer) + "</div>" return out attr_renderer = { 'bash_command': lambda x: render(x, lexers.BashLexer), 'hql': lambda x: render(x, lexers.SqlLexer), 'sql': lambda x: render(x, lexers.SqlLexer), 'doc': lambda x: render(x, lexers.TextLexer), 'doc_json': lambda x: render(x, lexers.JsonLexer), 'doc_rst': lambda x: render(x, lexers.RstLexer), 'doc_yaml': lambda x: render(x, lexers.YamlLexer), 'doc_md': wrapped_markdown, 'python_callable': lambda x: render( inspect.getsource(x), lexers.PythonLexer), } dagbag = models.DagBag(os.path.expanduser(conf.get('core', 'DAGS_FOLDER'))) utils.pessimistic_connection_handling() app = Flask(__name__) app.config['SQLALCHEMY_POOL_RECYCLE'] = 3600 app.secret_key = conf.get('webserver', 'SECRET_KEY') login.login_manager.init_app(app) cache = Cache( app=app, config={'CACHE_TYPE': 'filesystem', 'CACHE_DIR': '/tmp'}) # Init for chartkick, the python wrapper for highcharts ck = Blueprint( 'ck_page', __name__, static_folder=chartkick.js(), static_url_path='/static') app.register_blueprint(ck, url_prefix='/ck') app.jinja_env.add_extension("chartkick.ext.charts") @app.context_processor def jinja_globals(): return { 'hostname': socket.gethostname(), } class DateTimeForm(Form): # Date filter form needed for gantt and graph view execution_date = DateTimeField( "Execution date", widget=DateTimePickerWidget()) class GraphForm(Form): execution_date = DateTimeField( "Execution date", widget=DateTimePickerWidget()) arrange = SelectField("Layout", choices=( ('LR', "Left->Right"), ('RL', "Right->Left"), ('TB', "Top->Bottom"), ('BT', "Bottom->Top"), )) class TreeForm(Form): base_date = DateTimeField( "Anchor date", widget=DateTimePickerWidget(), default=datetime.now()) num_runs = SelectField("Number of runs", default=25, choices=( (5, "5"), (25, "25"), (50, "50"), (100, "100"), (365, "365"), )) @app.route('/') def index(): return redirect(url_for('admin.index')) @app.route('/health') def health(): """ We can add an array of tests here to check the server's health """ content = Markup(markdown.markdown("The server is healthy!")) return content @app.teardown_appcontext def shutdown_session(exception=None): settings.Session.remove() def dag_link(v, c, m, p): url = url_for( 'airflow.graph', dag_id=m.dag_id) return Markup( '<a href="{url}">{m.dag_id}</a>'.format(**locals())) class DagModelView(wwwutils.SuperUserMixin, ModelView): column_list = ('dag_id', 'owners') column_editable_list = ('is_paused',) form_excluded_columns = ('is_subdag', 'is_active') column_searchable_list = ('dag_id',) column_filters = ( 'dag_id', 'owners', 'is_paused', 'is_active', 'is_subdag', 'last_scheduler_run', 'last_expired') form_widget_args = { 'last_scheduler_run': {'disabled': True}, 'fileloc': {'disabled': True}, 'is_paused': {'disabled': True}, 'last_pickled': {'disabled': True}, 'pickle_id': {'disabled': True}, 'last_loaded': {'disabled': True}, 'last_expired': {'disabled': True}, 'pickle_size': {'disabled': True}, 'scheduler_lock': {'disabled': True}, 'owners': {'disabled': True}, } column_formatters = dict( dag_id=dag_link, ) can_delete = False can_create = False page_size = 50 list_template = 'airflow/list_dags.html' named_filter_urls = True def get_query(self): """ Default filters for model """ return ( super(DagModelView, self) .get_query() .filter(or_(models.DagModel.is_active, models.DagModel.is_paused)) .filter(~models.DagModel.is_subdag) ) def get_count_query(self): """ Default filters for model """ return ( super(DagModelView, self) .get_count_query() .filter(models.DagModel.is_active) .filter(~models.DagModel.is_subdag) ) class HomeView(AdminIndexView): @expose("/") @login_required def index(self): session = Session() DM = models.DagModel qry = None # filter the dags if filter_by_owner and current user is not superuser do_filter = FILTER_BY_OWNER and (not current_user.is_superuser()) if do_filter: qry = ( session.query(DM) .filter( ~DM.is_subdag, DM.is_active, DM.owners == current_user.username) .all() ) else: qry = session.query(DM).filter(~DM.is_subdag, DM.is_active).all() orm_dags = {dag.dag_id: dag for dag in qry} import_errors = session.query(models.ImportError).all() for ie in import_errors: flash( "Broken DAG: [{ie.filename}] {ie.stacktrace}".format(ie=ie), "error") session.expunge_all() session.commit() session.close() dags = dagbag.dags.values() if do_filter: dags = { dag.dag_id: dag for dag in dags if ( dag.owner == current_user.username and (not dag.parent_dag) ) } else: dags = {dag.dag_id: dag for dag in dags if not dag.parent_dag} all_dag_ids = sorted(set(orm_dags.keys()) | set(dags.keys())) return self.render( 'airflow/dags.html', dags=dags, orm_dags=orm_dags, all_dag_ids=all_dag_ids) admin = Admin( app, name="Airflow", index_view=HomeView(name="DAGs"), template_mode='bootstrap3') class Airflow(BaseView): def is_visible(self): return False @expose('/') @login_required def index(self): return self.render('airflow/dags.html') @expose('/chart_data') @data_profiling_required @wwwutils.gzipped # @cache.cached(timeout=3600, key_prefix=wwwutils.make_cache_key) def chart_data(self): session = settings.Session() chart_id = request.args.get('chart_id') csv = request.args.get('csv') == "true" chart = session.query(models.Chart).filter_by(id=chart_id).first() db = session.query( models.Connection).filter_by(conn_id=chart.conn_id).first() session.expunge_all() session.commit() session.close() payload = {} payload['state'] = 'ERROR' payload['error'] = '' # Processing templated fields try: args = eval(chart.default_params) if type(args) is not type(dict()): raise AirflowException('Not a dict') except: args = {} payload['error'] += ( "Default params is not valid, string has to evaluate as " "a Python dictionary. ") request_dict = {k: request.args.get(k) for k in request.args} from airflow import macros args.update(request_dict) args['macros'] = macros sql = jinja2.Template(chart.sql).render(**args) label = jinja2.Template(chart.label).render(**args) payload['sql_html'] = Markup(highlight( sql, lexers.SqlLexer(), # Lexer call HtmlFormatter(noclasses=True)) ) payload['label'] = label import pandas as pd pd.set_option('display.max_colwidth', 100) hook = db.get_hook() try: df = hook.get_pandas_df(wwwutils.limit_sql(sql, CHART_LIMIT, conn_type=db.conn_type)) df = df.fillna(0) except Exception as e: payload['error'] += "SQL execution failed. Details: " + str(e) if csv: return Response( response=df.to_csv(index=False), status=200, mimetype="application/text") if not payload['error'] and len(df) == CHART_LIMIT: payload['warning'] = ( "Data has been truncated to {0}" " rows. Expect incomplete results.").format(CHART_LIMIT) if not payload['error'] and len(df) == 0: payload['error'] += "Empty result set. " elif ( not payload['error'] and chart.sql_layout == 'series' and chart.chart_type != "datatable" and len(df.columns) < 3): payload['error'] += "SQL needs to return at least 3 columns. " elif ( not payload['error'] and chart.sql_layout == 'columns'and len(df.columns) < 2): payload['error'] += "SQL needs to return at least 2 columns. " elif not payload['error']: import numpy as np chart_type = chart.chart_type data = None if chart_type == "datatable": chart.show_datatable = True if chart.show_datatable: data = df.to_dict(orient="split") data['columns'] = [{'title': c} for c in data['columns']] # Trying to convert time to something Highcharts likes x_col = 1 if chart.sql_layout == 'series' else 0 if chart.x_is_date: try: # From string to datetime df[df.columns[x_col]] = pd.to_datetime( df[df.columns[x_col]]) except Exception as e: raise AirflowException(str(e)) df[df.columns[x_col]] = df[df.columns[x_col]].apply( lambda x: int(x.strftime("%s")) * 1000) series = [] colorAxis = None if chart_type == 'datatable': payload['data'] = data payload['state'] = 'SUCCESS' return Response( response=json.dumps( payload, indent=4, cls=utils.AirflowJsonEncoder), status=200, mimetype="application/json") elif chart_type == 'para': df.rename(columns={ df.columns[0]: 'name', df.columns[1]: 'group', }, inplace=True) return Response( response=df.to_csv(index=False), status=200, mimetype="application/text") elif chart_type == 'heatmap': color_perc_lbound = float( request.args.get('color_perc_lbound', 0)) color_perc_rbound = float( request.args.get('color_perc_rbound', 1)) color_scheme = request.args.get('color_scheme', 'blue_red') if color_scheme == 'blue_red': stops = [ [color_perc_lbound, '#00D1C1'], [ color_perc_lbound + ((color_perc_rbound - color_perc_lbound)/2), '#FFFFCC' ], [color_perc_rbound, '#FF5A5F'] ] elif color_scheme == 'blue_scale': stops = [ [color_perc_lbound, '#FFFFFF'], [color_perc_rbound, '#2222FF'] ] elif color_scheme == 'fire': diff = float(color_perc_rbound - color_perc_lbound) stops = [ [color_perc_lbound, '#FFFFFF'], [color_perc_lbound + 0.33*diff, '#FFFF00'], [color_perc_lbound + 0.66*diff, '#FF0000'], [color_perc_rbound, '#000000'] ] else: stops = [ [color_perc_lbound, '#FFFFFF'], [ color_perc_lbound + ((color_perc_rbound - color_perc_lbound)/2), '#888888' ], [color_perc_rbound, '#000000'], ] xaxis_label = df.columns[1] yaxis_label = df.columns[2] data = [] for row in df.itertuples(): data.append({ 'x': row[2], 'y': row[3], 'value': row[4], }) x_format = '{point.x:%Y-%m-%d}' \ if chart.x_is_date else '{point.x}' series.append({ 'data': data, 'borderWidth': 0, 'colsize': 24 * 36e5, 'turboThreshold': sys.float_info.max, 'tooltip': { 'headerFormat': '', 'pointFormat': ( df.columns[1] + ': ' + x_format + '<br/>' + df.columns[2] + ': {point.y}<br/>' + df.columns[3] + ': <b>{point.value}</b>' ), }, }) colorAxis = { 'stops': stops, 'minColor': '#FFFFFF', 'maxColor': '#000000', 'min': 50, 'max': 2200, } else: if chart.sql_layout == 'series': # User provides columns (series, x, y) xaxis_label = df.columns[1] yaxis_label = df.columns[2] df[df.columns[2]] = df[df.columns[2]].astype(np.float) df = df.pivot_table( index=df.columns[1], columns=df.columns[0], values=df.columns[2], aggfunc=np.sum) else: # User provides columns (x, y, metric1, metric2, ...) xaxis_label = df.columns[0] yaxis_label = 'y' df.index = df[df.columns[0]] df = df.sort(df.columns[0]) del df[df.columns[0]] for col in df.columns: df[col] = df[col].astype(np.float) for col in df.columns: series.append({ 'name': col, 'data': [ (k, df[col][k]) for k in df[col].keys() if not np.isnan(df[col][k])] }) series = [serie for serie in sorted( series, key=lambda s: s['data'][0][1], reverse=True)] if chart_type == "stacked_area": stacking = "normal" chart_type = 'area' elif chart_type == "percent_area": stacking = "percent" chart_type = 'area' else: stacking = None hc = { 'chart': { 'type': chart_type }, 'plotOptions': { 'series': { 'marker': { 'enabled': False } }, 'area': {'stacking': stacking}, }, 'title': {'text': ''}, 'xAxis': { 'title': {'text': xaxis_label}, 'type': 'datetime' if chart.x_is_date else None, }, 'yAxis': { 'title': {'text': yaxis_label}, }, 'colorAxis': colorAxis, 'tooltip': { 'useHTML': True, 'backgroundColor': None, 'borderWidth': 0, }, 'series': series, } if chart.y_log_scale: hc['yAxis']['type'] = 'logarithmic' hc['yAxis']['minorTickInterval'] = 0.1 if 'min' in hc['yAxis']: del hc['yAxis']['min'] payload['state'] = 'SUCCESS' payload['hc'] = hc payload['data'] = data payload['request_dict'] = request_dict return Response( response=json.dumps( payload, indent=4, cls=utils.AirflowJsonEncoder), status=200, mimetype="application/json") @expose('/chart') @data_profiling_required def chart(self): session = settings.Session() chart_id = request.args.get('chart_id') embed = request.args.get('embed') chart = session.query(models.Chart).filter_by(id=chart_id).first() session.expunge_all() session.commit() session.close() if chart.chart_type == 'para': return self.render('airflow/para/para.html', chart=chart) sql = "" if chart.show_sql: sql = Markup(highlight( chart.sql, lexers.SqlLexer(), # Lexer call HtmlFormatter(noclasses=True)) ) return self.render( 'airflow/highchart.html', chart=chart, title="Airflow - Chart", sql=sql, label=chart.label, embed=embed) @expose('/dag_stats') @login_required def dag_stats(self): states = [ State.SUCCESS, State.RUNNING, State.FAILED, State.UPSTREAM_FAILED, State.UP_FOR_RETRY, State.QUEUED, ] task_ids = [] dag_ids = [] for dag in dagbag.dags.values(): task_ids += dag.task_ids if not dag.is_subdag: dag_ids.append(dag.dag_id) TI = models.TaskInstance session = Session() qry = ( session.query(TI.dag_id, TI.state, sqla.func.count(TI.task_id)) .filter(TI.task_id.in_(task_ids)) .filter(TI.dag_id.in_(dag_ids)) .group_by(TI.dag_id, TI.state) ) data = {} for dag_id, state, count in qry: if dag_id not in data: data[dag_id] = {} data[dag_id][state] = count session.commit() session.close() payload = {} for dag in dagbag.dags.values(): payload[dag.safe_dag_id] = [] for state in states: try: count = data[dag.dag_id][state] except: count = 0 d = { 'state': state, 'count': count, 'dag_id': dag.dag_id, 'color': State.color(state) } payload[dag.safe_dag_id].append(d) return Response( response=json.dumps(payload, indent=4), status=200, mimetype="application/json") @expose('/code') @login_required def code(self): dag_id = request.args.get('dag_id') dag = dagbag.get_dag(dag_id) code = "".join(open(dag.full_filepath, 'r').readlines()) title = dag.filepath html_code = highlight( code, lexers.PythonLexer(), HtmlFormatter(linenos=True)) return self.render( 'airflow/dag_code.html', html_code=html_code, dag=dag, title=title, root=request.args.get('root'), demo_mode=conf.getboolean('webserver', 'demo_mode')) @app.errorhandler(404) def circles(self): return render_template( 'airflow/circles.html', hostname=socket.gethostname()), 404 @app.errorhandler(500) def show_traceback(self): return render_template( 'airflow/traceback.html', info=traceback.format_exc()), 500 @expose('/sandbox') @login_required def sandbox(self): from airflow import configuration title = "Sandbox Suggested Configuration" cfg_loc = configuration.AIRFLOW_CONFIG + '.sandbox' f = open(cfg_loc, 'r') config = f.read() f.close() code_html = Markup(highlight( config, lexers.IniLexer(), # Lexer call HtmlFormatter(noclasses=True)) ) return self.render( 'airflow/code.html', code_html=code_html, title=title, subtitle=cfg_loc) @expose('/noaccess') def noaccess(self): return self.render('airflow/noaccess.html') @expose('/headers') def headers(self): d = {k: v for k, v in request.headers} if hasattr(current_user, 'is_superuser'): d['is_superuser'] = current_user.is_superuser() d['data_profiling'] = current_user.data_profiling() d['is_anonymous'] = current_user.is_anonymous() d['is_authenticated'] = current_user.is_authenticated() return Response( response=json.dumps(d, indent=4), status=200, mimetype="application/json") @expose('/login', methods=['GET', 'POST']) def login(self): return login.login(self, request) @expose('/logout') def logout(self): logout_user() return redirect(url_for('admin.index')) @expose('/rendered') @login_required def rendered(self): dag_id = request.args.get('dag_id') task_id = request.args.get('task_id') execution_date = request.args.get('execution_date') dttm = dateutil.parser.parse(execution_date) form = DateTimeForm(data={'execution_date': dttm}) dag = dagbag.get_dag(dag_id) task = copy.copy(dag.get_task(task_id)) ti = models.TaskInstance(task=task, execution_date=dttm) try: ti.render_templates() except Exception as e: flash("Error rendering template: " + str(e), "error") title = "Rendered Template" html_dict = {} for template_field in task.__class__.template_fields: content = getattr(task, template_field) if template_field in attr_renderer: html_dict[template_field] = attr_renderer[template_field](content) else: html_dict[template_field] = ( "<pre><code>" + str(content) + "</pre></code>") return self.render( 'airflow/ti_code.html', html_dict=html_dict, dag=dag, task_id=task_id, execution_date=execution_date, form=form, title=title,) @expose('/log') @login_required def log(self): BASE_LOG_FOLDER = os.path.expanduser( conf.get('core', 'BASE_LOG_FOLDER')) dag_id = request.args.get('dag_id') task_id = request.args.get('task_id') execution_date = request.args.get('execution_date') dag = dagbag.get_dag(dag_id) log_relative = "/{dag_id}/{task_id}/{execution_date}".format( **locals()) loc = BASE_LOG_FOLDER + log_relative loc = loc.format(**locals()) log = "" TI = models.TaskInstance session = Session() dttm = dateutil.parser.parse(execution_date) ti = session.query(TI).filter( TI.dag_id == dag_id, TI.task_id == task_id, TI.execution_date == dttm).first() dttm = dateutil.parser.parse(execution_date) form = DateTimeForm(data={'execution_date': dttm}) if ti: host = ti.hostname if socket.gethostname() == host: try: f = open(loc) log += "".join(f.readlines()) f.close() except: log = "Log file isn't where expected.\n".format(loc) else: WORKER_LOG_SERVER_PORT = \ conf.get('celery', 'WORKER_LOG_SERVER_PORT') url = ( "http://{host}:{WORKER_LOG_SERVER_PORT}/log" "{log_relative}").format(**locals()) log += "Log file isn't local.\n" log += "Fetching here: {url}\n".format(**locals()) try: import requests log += requests.get(url).text except: log += "Failed to fetch log file.".format(**locals()) session.commit() session.close() log = log.decode('utf-8') if PY2 else log title = "Log" return self.render( 'airflow/ti_code.html', code=log, dag=dag, title=title, task_id=task_id, execution_date=execution_date, form=form) @expose('/task') @login_required def task(self): dag_id = request.args.get('dag_id') task_id = request.args.get('task_id') # Carrying execution_date through, even though it's irrelevant for # this context execution_date = request.args.get('execution_date') dttm = dateutil.parser.parse(execution_date) form = DateTimeForm(data={'execution_date': dttm}) dag = dagbag.get_dag(dag_id) if not dag or task_id not in dag.task_ids: flash( "Task [{}.{}] doesn't seem to exist" " at the moment".format(dag_id, task_id), "error") return redirect('/admin/') task = dag.get_task(task_id) task = copy.copy(task) task.resolve_template_files() attributes = [] for attr_name in dir(task): if not attr_name.startswith('_'): attr = getattr(task, attr_name) if type(attr) != type(self.task) and \ attr_name not in attr_renderer: attributes.append((attr_name, str(attr))) title = "Task Details" # Color coding the special attributes that are code special_attrs_rendered = {} for attr_name in attr_renderer: if hasattr(task, attr_name): source = getattr(task, attr_name) special_attrs_rendered[attr_name] = attr_renderer[attr_name](source) return self.render( 'airflow/task.html', attributes=attributes, task_id=task_id, execution_date=execution_date, special_attrs_rendered=special_attrs_rendered, form=form, dag=dag, title=title) @expose('/action') @login_required def action(self): action = request.args.get('action') dag_id = request.args.get('dag_id') task_id = request.args.get('task_id') origin = request.args.get('origin') dag = dagbag.get_dag(dag_id) task = dag.get_task(task_id) execution_date = request.args.get('execution_date') execution_date = dateutil.parser.parse(execution_date) confirmed = request.args.get('confirmed') == "true" upstream = request.args.get('upstream') == "true" downstream = request.args.get('downstream') == "true" future = request.args.get('future') == "true" past = request.args.get('past') == "true" if action == "run": from airflow.executors import DEFAULT_EXECUTOR as executor from airflow.executors import CeleryExecutor if not isinstance(executor, CeleryExecutor): flash("Only works with the CeleryExecutor, sorry", "error") return redirect(origin) force = request.args.get('force') == "true" deps = request.args.get('deps') == "true" ti = models.TaskInstance(task=task, execution_date=execution_date) executor.start() executor.queue_task_instance( ti, force=force, ignore_dependencies=deps) executor.heartbeat() flash( "Sent {} to the message queue, " "it should start any moment now.".format(ti)) return redirect(origin) elif action == 'clear': dag = dag.sub_dag( task_regex=r"^{0}$".format(task_id), include_downstream=downstream, include_upstream=upstream) end_date = execution_date if not future else None start_date = execution_date if not past else None if confirmed: count = dag.clear( start_date=start_date, end_date=end_date) flash("{0} task instances have been cleared".format(count)) return redirect(origin) else: tis = dag.clear( start_date=start_date, end_date=end_date, dry_run=True) if not tis: flash("No task instances to clear", 'error') response = redirect(origin) else: details = "\n".join([str(t) for t in tis]) response = self.render( 'airflow/confirm.html', message=( "Here's the list of task instances you are about " "to clear:"), details=details,) return response elif action == 'success': MAX_PERIODS = 1000 # Flagging tasks as successful session = settings.Session() task_ids = [task_id] end_date = ((dag.latest_execution_date or datetime.now()) if future else execution_date) if 'start_date' in dag.default_args: start_date = dag.default_args['start_date'] elif dag.start_date: start_date = dag.start_date else: start_date = execution_date if execution_date < start_date or end_date < start_date: flash("Selected date before DAG start date", 'error') return redirect(origin) start_date = execution_date if not past else start_date if downstream: task_ids += [ t.task_id for t in task.get_flat_relatives(upstream=False)] if upstream: task_ids += [ t.task_id for t in task.get_flat_relatives(upstream=True)] TI = models.TaskInstance dates = utils.date_range(start_date, end_date) tis = session.query(TI).filter( TI.dag_id == dag_id, TI.execution_date.in_(dates), TI.task_id.in_(task_ids)).all() tis_to_change = session.query(TI).filter( TI.dag_id == dag_id, TI.execution_date.in_(dates), TI.task_id.in_(task_ids), TI.state != State.SUCCESS).all() tasks = list(product(task_ids, dates)) tis_to_create = list( set(tasks) - set([(ti.task_id, ti.execution_date) for ti in tis])) tis_all_altered = list(chain( [(ti.task_id, ti.execution_date) for ti in tis_to_change], tis_to_create)) if len(tis_all_altered) > MAX_PERIODS: flash("Too many tasks at once (>{0})".format( MAX_PERIODS), 'error') return redirect(origin) if confirmed: for ti in tis_to_change: ti.state = State.SUCCESS session.commit() for task_id, task_execution_date in tis_to_create: ti = TI( task=dag.get_task(task_id), execution_date=task_execution_date, state=State.SUCCESS) session.add(ti) session.commit() session.commit() session.close() flash("Marked success on {} task instances".format( len(tis_all_altered))) return redirect(origin) else: if not tis_all_altered: flash("No task instances to mark as successful", 'error') response = redirect(origin) else: tis = [] for task_id, task_execution_date in tis_all_altered: tis.append(TI( task=dag.get_task(task_id), execution_date=task_execution_date, state=State.SUCCESS)) details = "\n".join([str(t) for t in tis]) response = self.render( 'airflow/confirm.html', message=( "Here's the list of task instances you are about " "to mark as successful:"), details=details,) return response @expose('/tree') @login_required @wwwutils.gzipped def tree(self): dag_id = request.args.get('dag_id') blur = conf.getboolean('webserver', 'demo_mode') dag = dagbag.get_dag(dag_id) root = request.args.get('root') if root: dag = dag.sub_dag( task_regex=root, include_downstream=False, include_upstream=True) session = settings.Session() start_date = dag.start_date if not start_date and 'start_date' in dag.default_args: start_date = dag.default_args['start_date'] base_date = request.args.get('base_date') num_runs = request.args.get('num_runs') num_runs = int(num_runs) if num_runs else 25 if not base_date: # New DAGs will not have a latest execution date if dag.latest_execution_date: base_date = dag.latest_execution_date + 2 * dag.schedule_interval else: base_date = datetime.now() else: base_date = dateutil.parser.parse(base_date) start_date = dag.start_date if not start_date and 'start_date' in dag.default_args: start_date = dag.default_args['start_date'] # if a specific base_date is requested, don't round it if not request.args.get('base_date'): if start_date: base_date = utils.round_time( base_date, dag.schedule_interval, start_date) else: base_date = utils.round_time(base_date, dag.schedule_interval) form = TreeForm(data={'base_date': base_date, 'num_runs': num_runs}) from_date = (base_date - (num_runs * dag.schedule_interval)) dates = utils.date_range( from_date, base_date, dag.schedule_interval) task_instances = {} for ti in dag.get_task_instances(session, from_date): task_instances[(ti.task_id, ti.execution_date)] = ti expanded = [] # The default recursion traces every path so that tree view has full # expand/collapse functionality. After 5,000 nodes we stop and fall # back on a quick DFS search for performance. See PR #320. node_count = [0] node_limit = 5000 / max(1, len(dag.roots)) def recurse_nodes(task, visited): visited.add(task) node_count[0] += 1 children = [ recurse_nodes(t, visited) for t in task.upstream_list if node_count[0] < node_limit or t not in visited] # D3 tree uses children vs _children to define what is # expanded or not. The following block makes it such that # repeated nodes are collapsed by default. children_key = 'children' if task.task_id not in expanded: expanded.append(task.task_id) elif children: children_key = "_children" return { 'name': task.task_id, 'instances': [ utils.alchemy_to_dict( task_instances.get((task.task_id, d))) or { 'execution_date': d.isoformat(), 'task_id': task.task_id } for d in dates], children_key: children, 'num_dep': len(task.upstream_list), 'operator': task.task_type, 'retries': task.retries, 'owner': task.owner, 'start_date': task.start_date, 'end_date': task.end_date, 'depends_on_past': task.depends_on_past, 'ui_color': task.ui_color, } if len(dag.roots) > 1: # d3 likes a single root data = { 'name': 'root', 'instances': [], 'children': [recurse_nodes(t, set()) for t in dag.roots] } elif len(dag.roots) == 1: data = recurse_nodes(dag.roots[0], set()) else: flash("No tasks found.", "error") data = [] data = json.dumps(data, indent=4, default=utils.json_ser) session.commit() session.close() return self.render( 'airflow/tree.html', operators=sorted( list(set([op.__class__ for op in dag.tasks])), key=lambda x: x.__name__ ), root=root, form=form, dag=dag, data=data, blur=blur) @expose('/graph') @login_required @wwwutils.gzipped def graph(self): session = settings.Session() dag_id = request.args.get('dag_id') blur = conf.getboolean('webserver', 'demo_mode') arrange = request.args.get('arrange', "LR") dag = dagbag.get_dag(dag_id) if dag_id not in dagbag.dags: flash('DAG "{0}" seems to be missing.'.format(dag_id), "error") return redirect('/admin/') root = request.args.get('root') if root: dag = dag.sub_dag( task_regex=root, include_upstream=True, include_downstream=False) nodes = [] edges = [] for task in dag.tasks: nodes.append({ 'id': task.task_id, 'value': { 'label': task.task_id, 'labelStyle': "fill:{0};".format(task.ui_fgcolor), 'style': "fill:{0};".format(task.ui_color), } }) def get_upstream(task): for t in task.upstream_list: edge = { 'u': t.task_id, 'v': task.task_id, } if edge not in edges: edges.append(edge) get_upstream(t) for t in dag.roots: get_upstream(t) dttm = request.args.get('execution_date') if dttm: dttm = dateutil.parser.parse(dttm) else: dttm = dag.latest_execution_date or datetime.now().date() form = GraphForm(data={'execution_date': dttm, 'arrange': arrange}) task_instances = { ti.task_id: utils.alchemy_to_dict(ti) for ti in dag.get_task_instances(session, dttm, dttm) } tasks = { t.task_id: { 'dag_id': t.dag_id, 'task_type': t.task_type, } for t in dag.tasks } if not tasks: flash("No tasks found", "error") session.commit() session.close() doc_md = markdown.markdown(dag.doc_md) if hasattr(dag, 'doc_md') else '' return self.render( 'airflow/graph.html', dag=dag, form=form, width=request.args.get('width', "100%"), height=request.args.get('height', "800"), execution_date=dttm.isoformat(), doc_md=doc_md, arrange=arrange, operators=sorted( list(set([op.__class__ for op in dag.tasks])), key=lambda x: x.__name__ ), blur=blur, root=root or '', task_instances=json.dumps(task_instances, indent=2), tasks=json.dumps(tasks, indent=2), nodes=json.dumps(nodes, indent=2), edges=json.dumps(edges, indent=2),) @expose('/duration') @login_required def duration(self): session = settings.Session() dag_id = request.args.get('dag_id') days = int(request.args.get('days', 30)) dag = dagbag.get_dag(dag_id) from_date = (datetime.today()-timedelta(days)).date() from_date = datetime.combine(from_date, datetime.min.time()) root = request.args.get('root') if root: dag = dag.sub_dag( task_regex=root, include_upstream=True, include_downstream=False) all_data = [] for task in dag.tasks: data = [] for ti in task.get_task_instances(session, from_date): if ti.duration: data.append([ ti.execution_date.isoformat(), float(ti.duration) / (60*60) ]) if data: all_data.append({'data': data, 'name': task.task_id}) session.commit() session.close() return self.render( 'airflow/chart.html', dag=dag, data=all_data, chart_options={'yAxis': {'title': {'text': 'hours'}}}, height="700px", demo_mode=conf.getboolean('webserver', 'demo_mode'), root=root, ) @expose('/landing_times') @login_required def landing_times(self): session = settings.Session() dag_id = request.args.get('dag_id') days = int(request.args.get('days', 30)) dag = dagbag.get_dag(dag_id) from_date = (datetime.today()-timedelta(days)).date() from_date = datetime.combine(from_date, datetime.min.time()) root = request.args.get('root') if root: dag = dag.sub_dag( task_regex=root, include_upstream=True, include_downstream=False) all_data = [] for task in dag.tasks: data = [] for ti in task.get_task_instances(session, from_date): if ti.end_date: data.append([ ti.execution_date.isoformat(), old_div(( ti.end_date - ( ti.execution_date + task.schedule_interval) ).total_seconds(),(60*60)) ]) all_data.append({'data': data, 'name': task.task_id}) session.commit() session.close() return self.render( 'airflow/chart.html', dag=dag, data=all_data, height="700px", chart_options={'yAxis': {'title': {'text': 'hours after 00:00'}}}, demo_mode=conf.getboolean('webserver', 'demo_mode'), root=root, ) @expose('/paused') @login_required def paused(self): DagModel = models.DagModel dag_id = request.args.get('dag_id') session = settings.Session() orm_dag = session.query( DagModel).filter(DagModel.dag_id == dag_id).first() if request.args.get('is_paused') == 'false': orm_dag.is_paused = True else: orm_dag.is_paused = False session.merge(orm_dag) session.commit() session.close() dagbag.get_dag(dag_id) return "OK" @expose('/refresh') @login_required def refresh(self): DagModel = models.DagModel dag_id = request.args.get('dag_id') session = settings.Session() orm_dag = session.query( DagModel).filter(DagModel.dag_id == dag_id).first() if orm_dag: orm_dag.last_expired = datetime.now() session.merge(orm_dag) session.commit() session.close() dagbag.get_dag(dag_id) flash("DAG [{}] is now fresh as a daisy".format(dag_id)) return redirect('/') @expose('/refresh_all') @login_required def refresh_all(self): dagbag.collect_dags(only_if_updated=False) flash("All DAGs are now up to date") return redirect('/') @expose('/gantt') @login_required def gantt(self): session = settings.Session() dag_id = request.args.get('dag_id') dag = dagbag.get_dag(dag_id) demo_mode = conf.getboolean('webserver', 'demo_mode') root = request.args.get('root') if root: dag = dag.sub_dag( task_regex=root, include_upstream=True, include_downstream=False) dttm = request.args.get('execution_date') if dttm: dttm = dateutil.parser.parse(dttm) else: dttm = dag.latest_execution_date or datetime.now().date() form = DateTimeForm(data={'execution_date': dttm}) tis = [ ti for ti in dag.get_task_instances(session, dttm, dttm) if ti.start_date] tis = sorted(tis, key=lambda ti: ti.start_date) tasks = [] data = [] for i, ti in enumerate(tis): end_date = ti.end_date or datetime.now() tasks += [ti.task_id] color = State.color(ti.state) data.append({ 'x': i, 'low': int(ti.start_date.strftime('%s')) * 1000, 'high': int(end_date.strftime('%s')) * 1000, 'color': color, }) height = (len(tis) * 25) + 50 session.commit() session.close() hc = { 'chart': { 'type': 'columnrange', 'inverted': True, 'height': height, }, 'xAxis': {'categories': tasks}, 'yAxis': {'type': 'datetime'}, 'title': { 'text': None }, 'plotOptions': { 'series': { 'cursor': 'pointer', 'minPointLength': 4, }, }, 'legend': { 'enabled': False }, 'series': [{ 'data': data }] } return self.render( 'airflow/gantt.html', dag=dag, execution_date=dttm.isoformat(), form=form, hc=json.dumps(hc, indent=4), height=height, demo_mode=demo_mode, root=root, ) @expose('/variables/<form>', methods=["GET", "POST"]) @login_required def variables(self, form): try: if request.method == 'POST': data = request.json if data: session = settings.Session() var = models.Variable(key=form, val=json.dumps(data)) session.add(var) session.commit() return "" else: return self.render( 'airflow/variables/{}.html'.format(form) ) except: return ("Error: form airflow/variables/{}.html " "not found.").format(form), 404 admin.add_view(Airflow(name='DAGs')) class QueryView(wwwutils.DataProfilingMixin, BaseView): @expose('/') @wwwutils.gzipped def query(self): session = settings.Session() dbs = session.query(models.Connection).order_by( models.Connection.conn_id).all() session.expunge_all() db_choices = list( ((db.conn_id, db.conn_id) for db in dbs if db.get_hook())) conn_id_str = request.args.get('conn_id') csv = request.args.get('csv') == "true" sql = request.args.get('sql') class QueryForm(Form): conn_id = SelectField("Layout", choices=db_choices) sql = TextAreaField("SQL", widget=wwwutils.AceEditorWidget()) data = { 'conn_id': conn_id_str, 'sql': sql, } results = None has_data = False error = False if conn_id_str: db = [db for db in dbs if db.conn_id == conn_id_str][0] hook = db.get_hook() try: df = hook.get_pandas_df( wwwutils.limit_sql( sql, QUERY_LIMIT, conn_type=db.conn_type)) has_data = len(df) > 0 df = df.fillna('') results = df.to_html( classes=[ 'table', 'table-bordered', 'table-striped', 'no-wrap'], index=False, na_rep='', ) if has_data else '' except Exception as e: flash(str(e), 'error') error = True if has_data and len(df) == QUERY_LIMIT: flash( "Query output truncated at " + str(QUERY_LIMIT) + " rows", 'info') if not has_data and error: flash('No data', 'error') if csv: return Response( response=df.to_csv(index=False), status=200, mimetype="application/text") form = QueryForm(request.form, data=data) session.commit() session.close() return self.render( 'airflow/query.html', form=form, title="Ad Hoc Query", results=results or '', has_data=has_data) admin.add_view(QueryView(name='Ad Hoc Query', category="Data Profiling")) class AirflowModelView(ModelView): list_template = 'airflow/model_list.html' edit_template = 'airflow/model_edit.html' create_template = 'airflow/model_create.html' page_size = 500 class ModelViewOnly(wwwutils.LoginMixin, AirflowModelView): """ Modifying the base ModelView class for non edit, browse only operations """ named_filter_urls = True can_create = False can_edit = False can_delete = False column_display_pk = True def log_link(v, c, m, p): url = url_for( 'airflow.log', dag_id=m.dag_id, task_id=m.task_id, execution_date=m.execution_date.isoformat()) return Markup( '<a href="{url}">' ' <span class="glyphicon glyphicon-book" aria-hidden="true">' '</span></a>').format(**locals()) def task_instance_link(v, c, m, p): url = url_for( 'airflow.task', dag_id=m.dag_id, task_id=m.task_id, execution_date=m.execution_date.isoformat()) url_root = url_for( 'airflow.graph', dag_id=m.dag_id, root=m.task_id, execution_date=m.execution_date.isoformat()) return Markup( """ <span style="white-space: nowrap;"> <a href="{url}">{m.task_id}</a> <a href="{url_root}" title="Filter on this task and upstream"> <span class="glyphicon glyphicon-filter" style="margin-left: 0px;" aria-hidden="true"></span> </a> </span> """.format(**locals())) def state_f(v, c, m, p): color = State.color(m.state) return Markup( '<span class="label" style="background-color:{color};">' '{m.state}</span>'.format(**locals())) def duration_f(v, c, m, p): if m.end_date and m.duration: return timedelta(seconds=m.duration) def datetime_f(v, c, m, p): attr = getattr(m, p) dttm = attr.isoformat() if attr else '' if datetime.now().isoformat()[:4] == dttm[:4]: dttm = dttm[5:] return Markup("<nobr>{}</nobr>".format(dttm)) def nobr_f(v, c, m, p): return Markup("<nobr>{}</nobr>".format(getattr(m, p))) class JobModelView(ModelViewOnly): verbose_name_plural = "jobs" verbose_name = "job" column_default_sort = ('start_date', True) column_filters = ( 'job_type', 'dag_id', 'state', 'unixname', 'hostname', 'start_date', 'end_date', 'latest_heartbeat') column_formatters = dict( start_date=datetime_f, end_date=datetime_f, hostname=nobr_f, state=state_f, latest_heartbeat=datetime_f) mv = JobModelView(jobs.BaseJob, Session, name="Jobs", category="Browse") admin.add_view(mv) class LogModelView(ModelViewOnly): verbose_name_plural = "logs" verbose_name = "log" column_default_sort = ('dttm', True) column_filters = ('dag_id', 'task_id', 'execution_date') column_formatters = dict( dttm=datetime_f, execution_date=datetime_f, dag_id=dag_link) mv = LogModelView( models.Log, Session, name="Logs", category="Browse") admin.add_view(mv) class TaskInstanceModelView(ModelViewOnly): verbose_name_plural = "task instances" verbose_name = "task instance" column_filters = ( 'state', 'dag_id', 'task_id', 'execution_date', 'hostname', 'queue', 'pool', 'operator', 'start_date', 'end_date') named_filter_urls = True column_formatters = dict( log=log_link, task_id=task_instance_link, hostname=nobr_f, state=state_f, execution_date=datetime_f, start_date=datetime_f, end_date=datetime_f, queued_dttm=datetime_f, dag_id=dag_link, duration=duration_f) column_searchable_list = ('dag_id', 'task_id', 'state') column_default_sort = ('start_date', True) column_list = ( 'state', 'dag_id', 'task_id', 'execution_date', 'operator', 'start_date', 'end_date', 'duration', 'job_id', 'hostname', 'unixname', 'priority_weight', 'queue', 'queued_dttm', 'log') can_delete = True page_size = 500 mv = TaskInstanceModelView( models.TaskInstance, Session, name="Task Instances", category="Browse") admin.add_view(mv) mv = DagModelView( models.DagModel, Session, name=None) admin.add_view(mv) # Hack to not add this view to the menu admin._menu = admin._menu[:-1] class ConnectionModelView(wwwutils.SuperUserMixin, AirflowModelView): create_template = 'airflow/conn_create.html' edit_template = 'airflow/conn_edit.html' list_template = 'airflow/conn_list.html' form_columns = ( 'conn_id', 'conn_type', 'host', 'schema', 'login', 'password', 'port', 'extra', 'extra__jdbc__drv_path', 'extra__jdbc__drv_clsname', ) verbose_name = "Connection" verbose_name_plural = "Connections" column_default_sort = ('conn_id', False) column_list = ('conn_id', 'conn_type', 'host', 'port', 'is_encrypted',) form_overrides = dict(_password=VisiblePasswordField) form_widget_args = { 'is_encrypted': {'disabled': True}, } # Used to customized the form, the forms elements get rendered # and results are stored in the extra field as json. All of these # need to be prefixed with extra__ and then the conn_type ___ as in # extra__{conn_type}__name. You can also hide form elements and rename # others from the connection_form.js file form_extra_fields = { 'extra__jdbc__drv_path' : StringField('Driver Path'), 'extra__jdbc__drv_clsname': StringField('Driver Class'), } form_choices = { 'conn_type': [ ('ftp', 'FTP',), ('hdfs', 'HDFS',), ('http', 'HTTP',), ('hive_cli', 'Hive Client Wrapper',), ('hive_metastore', 'Hive Metastore Thrift',), ('hiveserver2', 'Hive Server 2 Thrift',), ('jdbc', 'Jdbc Connection',), ('mysql', 'MySQL',), ('postgres', 'Postgres',), ('oracle', 'Oracle',), ('vertica', 'Vertica',), ('presto', 'Presto',), ('s3', 'S3',), ('samba', 'Samba',), ('sqlite', 'Sqlite',), ('mssql', 'Microsoft SQL Server'), ('mesos_framework-id', 'Mesos Framework ID'), ] } def on_model_change(self, form, model, is_created): formdata = form.data if formdata['conn_type'] in ['jdbc']: extra = { key:formdata[key] for key in self.form_extra_fields.keys() if key in formdata} model.extra = json.dumps(extra) @classmethod def alert_fernet_key(cls): return not conf.has_option('core', 'fernet_key') @classmethod def is_secure(self): """ Used to display a message in the Connection list view making it clear that the passwords can't be encrypted. """ is_secure = False try: import cryptography conf.get('core', 'fernet_key') is_secure = True except: pass return is_secure def on_form_prefill(self, form, id): try: d = json.loads(form.data.get('extra', '{}')) except Exception as e: d = {} for field in list(self.form_extra_fields.keys()): value = d.get(field, '') if value: field = getattr(form, field) field.data = value mv = ConnectionModelView( models.Connection, Session, name="Connections", category="Admin") admin.add_view(mv) class UserModelView(wwwutils.SuperUserMixin, AirflowModelView): verbose_name = "User" verbose_name_plural = "Users" column_default_sort = 'username' mv = UserModelView(models.User, Session, name="Users", category="Admin") admin.add_view(mv) class ConfigurationView(wwwutils.SuperUserMixin, BaseView): @expose('/') def conf(self): from airflow import configuration raw = request.args.get('raw') == "true" title = "Airflow Configuration" subtitle = configuration.AIRFLOW_CONFIG if conf.getboolean("webserver", "expose_config"): with open(configuration.AIRFLOW_CONFIG, 'r') as f: config = f.read() else: config = ( "# You Airflow administrator chose not to expose the " "configuration, most likely for security reasons.") if raw: return Response( response=config, status=200, mimetype="application/text") else: code_html = Markup(highlight( config, lexers.IniLexer(), # Lexer call HtmlFormatter(noclasses=True)) ) return self.render( 'airflow/code.html', pre_subtitle=settings.HEADER + " v" + airflow.__version__, code_html=code_html, title=title, subtitle=subtitle) admin.add_view(ConfigurationView(name='Configuration', category="Admin")) def label_link(v, c, m, p): try: default_params = eval(m.default_params) except: default_params = {} url = url_for( 'airflow.chart', chart_id=m.id, iteration_no=m.iteration_no, **default_params) return Markup("<a href='{url}'>{m.label}</a>".format(**locals())) class ChartModelView(wwwutils.DataProfilingMixin, AirflowModelView): verbose_name = "chart" verbose_name_plural = "charts" form_columns = ( 'label', 'owner', 'conn_id', 'chart_type', 'show_datatable', 'x_is_date', 'y_log_scale', 'show_sql', 'height', 'sql_layout', 'sql', 'default_params',) column_list = ( 'label', 'conn_id', 'chart_type', 'owner', 'last_modified',) column_formatters = dict(label=label_link, last_modified=datetime_f) column_default_sort = ('last_modified', True) create_template = 'airflow/chart/create.html' edit_template = 'airflow/chart/edit.html' column_filters = ('label', 'owner.username', 'conn_id') column_searchable_list = ('owner.username', 'label', 'sql') column_descriptions = { 'label': "Can include {{ templated_fields }} and {{ macros }}", 'chart_type': "The type of chart to be displayed", 'sql': "Can include {{ templated_fields }} and {{ macros }}.", 'height': "Height of the chart, in pixels.", 'conn_id': "Source database to run the query against", 'x_is_date': ( "Whether the X axis should be casted as a date field. Expect most " "intelligible date formats to get casted properly." ), 'owner': ( "The chart's owner, mostly used for reference and filtering in " "the list view." ), 'show_datatable': "Whether to display an interactive data table under the chart.", 'default_params': ( 'A dictionary of {"key": "values",} that define what the ' 'templated fields (parameters) values should be by default. ' 'To be valid, it needs to "eval" as a Python dict. ' 'The key values will show up in the url\'s querystring ' 'and can be altered there.' ), 'show_sql': "Whether to display the SQL statement as a collapsible " "section in the chart page.", 'y_log_scale': "Whether to use a log scale for the Y axis.", 'sql_layout': ( "Defines the layout of the SQL that the application should " "expect. Depending on the tables you are sourcing from, it may " "make more sense to pivot / unpivot the metrics." ), } column_labels = { 'sql': "SQL", 'height': "Chart Height", 'sql_layout': "SQL Layout", 'show_sql': "Display the SQL Statement", 'default_params': "Default Parameters", } form_choices = { 'chart_type': [ ('line', 'Line Chart'), ('spline', 'Spline Chart'), ('bar', 'Bar Chart'), ('para', 'Parallel Coordinates'), ('column', 'Column Chart'), ('area', 'Overlapping Area Chart'), ('stacked_area', 'Stacked Area Chart'), ('percent_area', 'Percent Area Chart'), ('heatmap', 'Heatmap'), ('datatable', 'No chart, data table only'), ], 'sql_layout': [ ('series', 'SELECT series, x, y FROM ...'), ('columns', 'SELECT x, y (series 1), y (series 2), ... FROM ...'), ], 'conn_id': [ (c.conn_id, c.conn_id) for c in ( Session().query(models.Connection.conn_id) .group_by(models.Connection.conn_id) ) ] } def on_model_change(self, form, model, is_created=True): if model.iteration_no is None: model.iteration_no = 0 else: model.iteration_no += 1 if AUTHENTICATE and not model.user_id and current_user: model.user_id = current_user.id model.last_modified = datetime.now() mv = ChartModelView( models.Chart, Session, name="Charts", category="Data Profiling") admin.add_view(mv) admin.add_link( base.MenuLink( category='Docs', name='Documentation', url='http://pythonhosted.org/airflow/')) admin.add_link( base.MenuLink( category='Docs', name='Github', url='https://github.com/airbnb/airflow')) class KnowEventView(wwwutils.DataProfilingMixin, AirflowModelView): verbose_name = "known event" verbose_name_plural = "known events" form_columns = ( 'label', 'event_type', 'start_date', 'end_date', 'reported_by', 'description') column_list = ( 'label', 'event_type', 'start_date', 'end_date', 'reported_by') column_default_sort = ("start_date", True) mv = KnowEventView( models.KnownEvent, Session, name="Known Events", category="Data Profiling") admin.add_view(mv) class KnowEventTypeView(wwwutils.DataProfilingMixin, AirflowModelView): pass ''' # For debugging / troubleshooting mv = KnowEventTypeView( models.KnownEventType, Session, name="Known Event Types", category="Manage") admin.add_view(mv) class DagPickleView(SuperUserMixin, ModelView): pass mv = DagPickleView( models.DagPickle, Session, name="Pickles", category="Manage") admin.add_view(mv) ''' class VariableView(wwwutils.LoginMixin, AirflowModelView): verbose_name = "Variable" verbose_name_plural = "Variables" column_list = ('key',) column_filters = ('key', 'val') column_searchable_list = ('key', 'val') form_widget_args = { 'val': { 'rows': 20, } } mv = VariableView( models.Variable, Session, name="Variables", category="Admin") admin.add_view(mv) def pool_link(v, c, m, p): url = '/admin/taskinstance/?flt1_pool_equals=' + m.pool return Markup("<a href='{url}'>{m.pool}</a>".format(**locals())) def fused_slots(v, c, m, p): url = ( '/admin/taskinstance/' + '?flt1_pool_equals=' + m.pool + '&flt2_state_equals=running') return Markup("<a href='{0}'>{1}</a>".format(url, m.used_slots())) def fqueued_slots(v, c, m, p): url = ( '/admin/taskinstance/' + '?flt1_pool_equals=' + m.pool + '&flt2_state_equals=queued&sort=10&desc=1') return Markup("<a href='{0}'>{1}</a>".format(url, m.queued_slots())) class PoolModelView(wwwutils.SuperUserMixin, AirflowModelView): column_list = ('pool', 'slots', 'used_slots', 'queued_slots') column_formatters = dict( pool=pool_link, used_slots=fused_slots, queued_slots=fqueued_slots) named_filter_urls = True mv = PoolModelView(models.Pool, Session, name="Pools", category="Admin") admin.add_view(mv) class SlaMissModelView(wwwutils.SuperUserMixin, ModelViewOnly): verbose_name_plural = "SLA misses" verbose_name = "SLA miss" column_list = ( 'dag_id', 'task_id', 'execution_date', 'email_sent', 'timestamp') column_formatters = dict( task_id=task_instance_link, execution_date=datetime_f, timestamp=datetime_f, dag_id=dag_link) named_filter_urls = True column_searchable_list = ('dag_id', 'task_id',) column_filters = ( 'dag_id', 'task_id', 'email_sent', 'timestamp', 'execution_date') form_widget_args = { 'email_sent': {'disabled': True}, 'timestamp': {'disabled': True}, } mv = SlaMissModelView( models.SlaMiss, Session, name="SLA Misses", category="Browse") admin.add_view(mv) def integrate_plugins(): """Integrate plugins to the context""" from airflow.plugins_manager import ( admin_views, flask_blueprints, menu_links) for v in admin_views: admin.add_view(v) for bp in flask_blueprints: print(bp) app.register_blueprint(bp) for ml in menu_links: admin.add_link(ml) integrate_plugins()

from __future__ import absolute_import from rest_framework.response import Response from django.db.models import Count from sentry.api.bases import SentryAppsBaseEndpoint from sentry.models import SentryApp from sentry.api.permissions import SuperuserPermission class SentryAppsStatsEndpoint(SentryAppsBaseEndpoint): permission_classes = (SuperuserPermission,) def get(self, request): sentry_apps = ( SentryApp.objects.filter(installations__date_deleted=None) .annotate(Count("installations")) .order_by() ) if "per_page" in request.query_params: sentry_apps = sentry_apps[: int(request.query_params["per_page"])] apps = [ {"id": app.id, "slug": app.slug, "name": app.name, "installs": app.installations__count} for app in sentry_apps ] return Response(apps)

"""Tensor utility functions.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections import functools import inspect import re from tensorflow.python.platform import tf_logging as logging from tensorflow.python.util import decorator_utils from tensorflow.python.util import is_in_graph_mode from tensorflow.python.util import tf_contextlib from tensorflow.python.util import tf_decorator from tensorflow.python.util import tf_inspect from tensorflow.tools.docs import doc_controls # Allow deprecation warnings to be silenced temporarily with a context manager. _PRINT_DEPRECATION_WARNINGS = True # Remember which deprecation warnings have been printed already. _PRINTED_WARNING = {} class DeprecatedNamesAlreadySet(Exception): """Raised when setting deprecated names multiple times for the same symbol.""" pass def _add_deprecated_function_notice_to_docstring(doc, date, instructions): """Adds a deprecation notice to a docstring for deprecated functions.""" main_text = ['THIS FUNCTION IS DEPRECATED. It will be removed %s.' % ('in a future version' if date is None else ('after %s' % date))] if instructions: main_text.append('Instructions for updating:') return decorator_utils.add_notice_to_docstring( doc, instructions, 'DEPRECATED FUNCTION', '(deprecated)', main_text) def _add_deprecated_arg_notice_to_docstring(doc, date, instructions, deprecated_names): """Adds a deprecation notice to a docstring for deprecated arguments.""" deprecation_string = ', '.join(sorted(deprecated_names)) return decorator_utils.add_notice_to_docstring( doc, instructions, 'DEPRECATED FUNCTION ARGUMENTS', '(deprecated arguments)', [ 'SOME ARGUMENTS ARE DEPRECATED: `(%s)`. ' 'They will be removed %s.' % (deprecation_string, 'in a future version' if date is None else ('after %s' % date)), 'Instructions for updating:' ]) def _add_deprecated_arg_value_notice_to_docstring(doc, date, instructions, deprecated_name_value_dict): """Adds a deprecation notice to a docstring for deprecated arguments.""" deprecation_string = ', '.join( '%s=%r' % (key, value) for key, value in sorted(deprecated_name_value_dict.items())) when = 'in a future version' if date is None else ('after %s' % date) return decorator_utils.add_notice_to_docstring( doc, instructions, 'DEPRECATED FUNCTION ARGUMENT VALUES', '(deprecated argument values)', [ 'SOME ARGUMENT VALUES ARE DEPRECATED: `(%s)`. ' 'They will be removed %s.' % (deprecation_string, when), 'Instructions for updating:' ]) def _validate_deprecation_args(date, instructions): if date is not None and not re.match(r'20\d\d-[01]\d-[0123]\d', date): raise ValueError(f'Date must be in format YYYY-MM-DD. Received: {date}') if not instructions: raise ValueError( 'Don\'t deprecate things without conversion instructions! Specify ' 'the `instructions` argument.') def _call_location(outer=False): """Returns call location given level up from current call.""" # Two up: <_call_location>, <_call_location's caller> # tf_inspect is not required here. Please ignore the lint warning by adding # DISABLE_IMPORT_INSPECT_CHECK=TRUE to your cl description. Using it caused # test timeouts (b/189384061). f = inspect.currentframe().f_back.f_back parent = f.f_back if outer and parent is not None: f = parent return '{}:{}'.format(f.f_code.co_filename, f.f_lineno) def _safe_eq(a, b): if a is None or b is None: return a is None and b is None return a == b def _wrap_decorator(wrapped_function): """Indicate that one function wraps another. This decorator wraps a function using `tf_decorator.make_decorator` so that doc generation scripts can pick up original function signature. It would be better to use @functools.wrap decorator, but it would not update function signature to match wrapped function in Python 2. Args: wrapped_function: The function that decorated function wraps. Returns: Function that accepts wrapper function as an argument and returns `TFDecorator` instance. """ def wrapper(wrapper_func): return tf_decorator.make_decorator(wrapped_function, wrapper_func) return wrapper def deprecated_alias(deprecated_name, name, func_or_class, warn_once=True): """Deprecate a symbol in favor of a new name with identical semantics. This function is meant to be used when defining a backwards-compatibility alias for a symbol which has been moved. For example: module1.py: ```python class NewNameForClass: pass ``` module2.py: ```python import module1 DeprecatedNameForClass = deprecated_alias( deprecated_name='module2.DeprecatedNameForClass', name='module1.NewNameForClass', func_or_class=module1.NewNameForClass) ``` This function works for classes and functions. For classes, it creates a new class which is functionally identical (it inherits from the original, and overrides its constructor), but which prints a deprecation warning when an instance is created. It also adds a deprecation notice to the class' docstring. For functions, it returns a function wrapped by `tf_decorator.make_decorator`. That function prints a warning when used, and has a deprecation notice in its docstring. This is more or less equivalent (the deprecation warning has slightly different text) to writing: ```python @deprecated def deprecated_alias(original_args): real_function(original_args) ``` Args: deprecated_name: The name of the symbol that is being deprecated, to be used in the warning message. This should be its fully qualified name to avoid confusion. name: The name of the symbol that is to be used instead of the deprecated name. This should be a fully qualified name to avoid confusion. func_or_class: The (non-deprecated) class or function for which a deprecated alias should be created. warn_once: If True (the default), only print a deprecation warning the first time this function is used, or the class is instantiated. Returns: A wrapped version of `func_or_class` which prints a deprecation warning on use and has a modified docstring. """ if tf_inspect.isclass(func_or_class): # Make a new class with __init__ wrapped in a warning. class _NewClass(func_or_class): # pylint: disable=missing-docstring __doc__ = decorator_utils.add_notice_to_docstring( func_or_class.__doc__, 'Please use %s instead.' % name, 'DEPRECATED CLASS', '(deprecated)', ['THIS CLASS IS DEPRECATED. ' 'It will be removed in a future version. ']) __name__ = func_or_class.__name__ __module__ = _call_location(outer=True) @_wrap_decorator(func_or_class.__init__) def __init__(self, *args, **kwargs): if hasattr(_NewClass.__init__, '__func__'): # Python 2 _NewClass.__init__.__func__.__doc__ = func_or_class.__init__.__doc__ else: # Python 3 _NewClass.__init__.__doc__ = func_or_class.__init__.__doc__ if _PRINT_DEPRECATION_WARNINGS: # We're making the alias as we speak. The original may have other # aliases, so we cannot use it to check for whether it's already been # warned about. if _NewClass.__init__ not in _PRINTED_WARNING: if warn_once: _PRINTED_WARNING[_NewClass.__init__] = True logging.warning( 'From %s: The name %s is deprecated. Please use %s instead.\n', _call_location(), deprecated_name, name) super(_NewClass, self).__init__(*args, **kwargs) return _NewClass else: decorator_utils.validate_callable(func_or_class, 'deprecated') # Make a wrapper for the original @functools.wraps(func_or_class) def new_func(*args, **kwargs): # pylint: disable=missing-docstring if _PRINT_DEPRECATION_WARNINGS: # We're making the alias as we speak. The original may have other # aliases, so we cannot use it to check for whether it's already been # warned about. if new_func not in _PRINTED_WARNING: if warn_once: _PRINTED_WARNING[new_func] = True logging.warning( 'From %s: The name %s is deprecated. Please use %s instead.\n', _call_location(), deprecated_name, name) return func_or_class(*args, **kwargs) return tf_decorator.make_decorator( func_or_class, new_func, 'deprecated', _add_deprecated_function_notice_to_docstring( func_or_class.__doc__, None, 'Please use %s instead.' % name)) def deprecated_endpoints(*args): """Decorator for marking endpoints deprecated. This decorator does not print deprecation messages. TODO(annarev): eventually start printing deprecation warnings when @deprecation_endpoints decorator is added. Args: *args: Deprecated endpoint names. Returns: A function that takes symbol as an argument and adds _tf_deprecated_api_names to that symbol. _tf_deprecated_api_names would be set to a list of deprecated endpoint names for the symbol. """ def deprecated_wrapper(func): # pylint: disable=protected-access if '_tf_deprecated_api_names' in func.__dict__: raise DeprecatedNamesAlreadySet( f'Cannot set deprecated names for {func.__name__} to {args}. ' 'Deprecated names are already set to ' f'{func._tf_deprecated_api_names}.') func._tf_deprecated_api_names = args # pylint: disable=protected-access return func return deprecated_wrapper def deprecated(date, instructions, warn_once=True): """Decorator for marking functions or methods deprecated. This decorator logs a deprecation warning whenever the decorated function is called. It has the following format: <function> (from <module>) is deprecated and will be removed after <date>. Instructions for updating: <instructions> If `date` is None, 'after <date>' is replaced with 'in a future version'. <function> will include the class name if it is a method. It also edits the docstring of the function: ' (deprecated)' is appended to the first line of the docstring and a deprecation notice is prepended to the rest of the docstring. Args: date: String or None. The date the function is scheduled to be removed. Must be ISO 8601 (YYYY-MM-DD), or None. instructions: String. Instructions on how to update code using the deprecated function. warn_once: Boolean. Set to `True` to warn only the first time the decorated function is called. Otherwise, every call will log a warning. Returns: Decorated function or method. Raises: ValueError: If date is not None or in ISO 8601 format, or instructions are empty. """ _validate_deprecation_args(date, instructions) def deprecated_wrapper(func_or_class): """Deprecation wrapper.""" if isinstance(func_or_class, type): # If a class is deprecated, you actually want to wrap the constructor. cls = func_or_class if cls.__new__ is object.__new__: func = cls.__init__ constructor_name = '__init__' else: func = cls.__new__ constructor_name = '__new__' else: cls = None constructor_name = None func = func_or_class decorator_utils.validate_callable(func, 'deprecated') @functools.wraps(func) def new_func(*args, **kwargs): # pylint: disable=missing-docstring if _PRINT_DEPRECATION_WARNINGS: if func not in _PRINTED_WARNING: if warn_once: _PRINTED_WARNING[func] = True logging.warning( 'From %s: %s (from %s) is deprecated and will be removed %s.\n' 'Instructions for updating:\n%s', _call_location(), decorator_utils.get_qualified_name(func), func.__module__, 'in a future version' if date is None else ('after %s' % date), instructions) return func(*args, **kwargs) doc_controls.set_deprecated(new_func) new_func = tf_decorator.make_decorator( func, new_func, 'deprecated', _add_deprecated_function_notice_to_docstring(func.__doc__, date, instructions)) if cls is None: return new_func else: # Insert the wrapped function as the constructor setattr(cls, constructor_name, new_func) # And update the docstring of the class. cls.__doc__ = _add_deprecated_function_notice_to_docstring( cls.__doc__, date, instructions) return cls return deprecated_wrapper DeprecatedArgSpec = collections.namedtuple( 'DeprecatedArgSpec', ['position', 'has_ok_value', 'ok_value']) def deprecated_args(date, instructions, *deprecated_arg_names_or_tuples, **kwargs): """Decorator for marking specific function arguments as deprecated. This decorator logs a deprecation warning whenever the decorated function is called with the deprecated argument. It has the following format: Calling <function> (from <module>) with <arg> is deprecated and will be removed after <date>. Instructions for updating: <instructions> If `date` is None, 'after <date>' is replaced with 'in a future version'. <function> includes the class name if it is a method. It also edits the docstring of the function: ' (deprecated arguments)' is appended to the first line of the docstring and a deprecation notice is prepended to the rest of the docstring. Args: date: String or None. The date the function is scheduled to be removed. Must be ISO 8601 (YYYY-MM-DD), or None. instructions: String. Instructions on how to update code using the deprecated function. *deprecated_arg_names_or_tuples: String or 2-Tuple (String, ok_val). The string is the deprecated argument name. Optionally, an ok-value may be provided. If the user provided argument equals this value, the warning is suppressed. **kwargs: If `warn_once=False` is passed, every call with a deprecated argument will log a warning. The default behavior is to only warn the first time the function is called with any given deprecated argument. All other kwargs raise `ValueError`. Returns: Decorated function or method. Raises: ValueError: If date is not None or in ISO 8601 format, instructions are empty, the deprecated arguments are not present in the function signature, the second element of a deprecated_tuple is not a list, or if a kwarg other than `warn_once` is passed. """ _validate_deprecation_args(date, instructions) if not deprecated_arg_names_or_tuples: raise ValueError('Specify which argument is deprecated.') if kwargs and list(kwargs.keys()) != ['warn_once']: kwargs.pop('warn_once', None) raise ValueError(f'Illegal argument passed to deprecated_args: {kwargs}') warn_once = kwargs.get('warn_once', True) def _get_arg_names_to_ok_vals(): """Returns a dict mapping arg_name to DeprecatedArgSpec w/o position.""" d = {} for name_or_tuple in deprecated_arg_names_or_tuples: if isinstance(name_or_tuple, tuple): d[name_or_tuple[0]] = DeprecatedArgSpec(-1, True, name_or_tuple[1]) else: d[name_or_tuple] = DeprecatedArgSpec(-1, False, None) return d def _get_deprecated_positional_arguments(names_to_ok_vals, arg_spec): """Builds a dictionary from deprecated arguments to their spec. Returned dict is keyed by argument name. Each value is a DeprecatedArgSpec with the following fields: position: The zero-based argument position of the argument within the signature. None if the argument isn't found in the signature. ok_values: Values of this argument for which warning will be suppressed. Args: names_to_ok_vals: dict from string arg_name to a list of values, possibly empty, which should not elicit a warning. arg_spec: Output from tf_inspect.getfullargspec on the called function. Returns: Dictionary from arg_name to DeprecatedArgSpec. """ # Extract argument list arg_space = arg_spec.args + arg_spec.kwonlyargs arg_name_to_pos = { name: pos for pos, name in enumerate(arg_space)} deprecated_positional_args = {} for arg_name, spec in iter(names_to_ok_vals.items()): if arg_name in arg_name_to_pos: pos = arg_name_to_pos[arg_name] deprecated_positional_args[arg_name] = DeprecatedArgSpec( pos, spec.has_ok_value, spec.ok_value) return deprecated_positional_args deprecated_arg_names = _get_arg_names_to_ok_vals() def deprecated_wrapper(func): """Deprecation decorator.""" decorator_utils.validate_callable(func, 'deprecated_args') arg_spec = tf_inspect.getfullargspec(func) deprecated_positions = _get_deprecated_positional_arguments( deprecated_arg_names, arg_spec) is_varargs_deprecated = arg_spec.varargs in deprecated_arg_names is_kwargs_deprecated = arg_spec.varkw in deprecated_arg_names if (len(deprecated_positions) + is_varargs_deprecated + is_kwargs_deprecated != len(deprecated_arg_names_or_tuples)): known_args = (arg_spec.args + arg_spec.kwonlyargs + [arg_spec.varargs, arg_spec.varkw]) missing_args = [arg_name for arg_name in deprecated_arg_names if arg_name not in known_args] raise ValueError('The following deprecated arguments are not present ' f'in the function signature: {missing_args}. ' 'Expected arguments from the following list: ' f'{known_args}.') def _same_value(a, b): """A comparison operation that works for multiple object types. Returns True for two empty lists, two numeric values with the same value, etc. Returns False for (pd.DataFrame, None), and other pairs which should not be considered equivalent. Args: a: value one of the comparison. b: value two of the comparison. Returns: A boolean indicating whether the two inputs are the same value for the purposes of deprecation. """ if a is b: return True try: equality = a == b if isinstance(equality, bool): return equality except TypeError: return False return False @functools.wraps(func) def new_func(*args, **kwargs): """Deprecation wrapper.""" # TODO(apassos) figure out a way to have reasonable performance with # deprecation warnings and eager mode. if is_in_graph_mode.IS_IN_GRAPH_MODE() and _PRINT_DEPRECATION_WARNINGS: invalid_args = [] named_args = tf_inspect.getcallargs(func, *args, **kwargs) for arg_name, spec in iter(deprecated_positions.items()): if (spec.position < len(args) and not (spec.has_ok_value and _same_value(named_args[arg_name], spec.ok_value))): invalid_args.append(arg_name) if is_varargs_deprecated and len(args) > len(arg_spec.args): invalid_args.append(arg_spec.varargs) if is_kwargs_deprecated and kwargs: invalid_args.append(arg_spec.varkw) for arg_name in deprecated_arg_names: if (arg_name in kwargs and not (deprecated_positions[arg_name].has_ok_value and _same_value(named_args[arg_name], deprecated_positions[arg_name].ok_value))): invalid_args.append(arg_name) for arg_name in invalid_args: if (func, arg_name) not in _PRINTED_WARNING: if warn_once: _PRINTED_WARNING[(func, arg_name)] = True logging.warning( 'From %s: calling %s (from %s) with %s is deprecated and will ' 'be removed %s.\nInstructions for updating:\n%s', _call_location(), decorator_utils.get_qualified_name(func), func.__module__, arg_name, 'in a future version' if date is None else ('after %s' % date), instructions) return func(*args, **kwargs) doc = _add_deprecated_arg_notice_to_docstring( func.__doc__, date, instructions, sorted(deprecated_arg_names.keys())) return tf_decorator.make_decorator(func, new_func, 'deprecated', doc) return deprecated_wrapper def deprecated_arg_values(date, instructions, warn_once=True, **deprecated_kwargs): """Decorator for marking specific function argument values as deprecated. This decorator logs a deprecation warning whenever the decorated function is called with the deprecated argument values. It has the following format: Calling <function> (from <module>) with <arg>=<value> is deprecated and will be removed after <date>. Instructions for updating: <instructions> If `date` is None, 'after <date>' is replaced with 'in a future version'. <function> will include the class name if it is a method. It also edits the docstring of the function: ' (deprecated arguments)' is appended to the first line of the docstring and a deprecation notice is prepended to the rest of the docstring. Args: date: String or None. The date the function is scheduled to be removed. Must be ISO 8601 (YYYY-MM-DD), or None instructions: String. Instructions on how to update code using the deprecated function. warn_once: If `True`, warn only the first time this function is called with deprecated argument values. Otherwise, every call (with a deprecated argument value) will log a warning. **deprecated_kwargs: The deprecated argument values. Returns: Decorated function or method. Raises: ValueError: If date is not None or in ISO 8601 format, or instructions are empty. """ _validate_deprecation_args(date, instructions) if not deprecated_kwargs: raise ValueError('Specify which argument values are deprecated.') def deprecated_wrapper(func): """Deprecation decorator.""" decorator_utils.validate_callable(func, 'deprecated_arg_values') @functools.wraps(func) def new_func(*args, **kwargs): """Deprecation wrapper.""" if _PRINT_DEPRECATION_WARNINGS: named_args = tf_inspect.getcallargs(func, *args, **kwargs) for arg_name, arg_value in deprecated_kwargs.items(): if arg_name in named_args and _safe_eq(named_args[arg_name], arg_value): if (func, arg_name) not in _PRINTED_WARNING: if warn_once: _PRINTED_WARNING[(func, arg_name)] = True logging.warning( 'From %s: calling %s (from %s) with %s=%s is deprecated and ' 'will be removed %s.\nInstructions for updating:\n%s', _call_location(), decorator_utils.get_qualified_name(func), func.__module__, arg_name, arg_value, 'in a future version' if date is None else ('after %s' % date), instructions) return func(*args, **kwargs) doc = _add_deprecated_arg_value_notice_to_docstring( func.__doc__, date, instructions, deprecated_kwargs) return tf_decorator.make_decorator(func, new_func, 'deprecated', doc) return deprecated_wrapper def deprecated_argument_lookup(new_name, new_value, old_name, old_value): """Looks up deprecated argument name and ensures both are not used. Args: new_name: new name of argument new_value: value of new argument (or None if not used) old_name: old name of argument old_value: value of old argument (or None if not used) Returns: The effective argument that should be used. Raises: ValueError: if new_value and old_value are both non-null """ if old_value is not None: if new_value is not None: raise ValueError(f"Cannot specify both '{old_name}' and '{new_name}'.") return old_value return new_value def rewrite_argument_docstring(old_doc, old_argument, new_argument): return old_doc.replace('`%s`' % old_argument, '`%s`' % new_argument).replace( '%s:' % old_argument, '%s:' % new_argument) @tf_contextlib.contextmanager def silence(): """Temporarily silence deprecation warnings.""" global _PRINT_DEPRECATION_WARNINGS print_deprecation_warnings = _PRINT_DEPRECATION_WARNINGS _PRINT_DEPRECATION_WARNINGS = False yield _PRINT_DEPRECATION_WARNINGS = print_deprecation_warnings class HiddenTfApiAttribute(property): """Hides a class attribute from the public API. Attributes in public classes can be hidden from the API by having an '_' in front of the name (e.g. ClassName._variables). This doesn't work when attributes or methods are inherited from a parent class. To hide inherited attributes, set their values to be `deprecation.hide_attribute_from_api`. For example, this is used in V2 Estimator to hide the deprecated export_savedmodel method: class EstimatorV2(Estimator): export_savedmodel = deprecation.hide_attribute_from_api('...') """ def __init__(self, deprecation_message): def raise_error(unused_self): raise AttributeError(deprecation_message) super(HiddenTfApiAttribute, self).__init__(raise_error) hide_attribute_from_api = HiddenTfApiAttribute # pylint: disable=invalid-name # TODO(kathywu): Remove once cl/246395236 is submitted. HIDDEN_ATTRIBUTE = HiddenTfApiAttribute('This attribute has been deprecated.')

from openerp import models, fields, api class BarcodeEventsMixin(models.AbstractModel): """ Mixin class for objects reacting when a barcode is scanned in their form views which contains `<field name="_barcode_scanned" widget="barcode_handler"/>`. Models using this mixin must implement the method on_barcode_scanned. It works like an onchange and receives the scanned barcode in parameter. """ _name = 'barcodes.barcode_events_mixin' _barcode_scanned = fields.Char("Barcode Scanned", help="Value of the last barcode scanned.", store=False) @api.onchange('_barcode_scanned') def _on_barcode_scanned(self): barcode = self._barcode_scanned if barcode: self._barcode_scanned = "" return self.on_barcode_scanned(barcode) def on_barcode_scanned(self, barcode): raise NotImplementedError("In order to use barcodes.barcode_events_mixin, method on_barcode_scanned must be implemented")

"""Tests for the Find flow.""" from grr.client import vfs from grr.lib import action_mocks from grr.lib import aff4 from grr.lib import flags from grr.lib import test_lib from grr.lib import type_info from grr.lib import utils # pylint: disable=unused-import from grr.lib.flows.general import find # pylint: enable=unused-import from grr.lib.rdfvalues import client as rdf_client from grr.lib.rdfvalues import paths as rdf_paths class TestFindFlow(test_lib.FlowTestsBaseclass): """Test the interrogate flow.""" def setUp(self): super(TestFindFlow, self).setUp() # Install the mock vfs_type = rdf_paths.PathSpec.PathType.OS vfs.VFS_HANDLERS[vfs_type] = test_lib.ClientVFSHandlerFixture def testInvalidFindSpec(self): """Test that its impossible to produce an invalid findspec.""" # The regular expression is not valid. self.assertRaises(type_info.TypeValueError, rdf_client.FindSpec, path_regex="[") def testFindFiles(self): """Test that the Find flow works with files.""" client_mock = action_mocks.ActionMock("Find") output_path = "analysis/FindFlowTest1" # Prepare a findspec. findspec = rdf_client.FindSpec( path_regex="bash", pathspec=rdf_paths.PathSpec( path="/", pathtype=rdf_paths.PathSpec.PathType.OS)) for _ in test_lib.TestFlowHelper( "FindFiles", client_mock, client_id=self.client_id, token=self.token, output=output_path, findspec=findspec): pass # Check the output file is created fd = aff4.FACTORY.Open(self.client_id.Add(output_path), token=self.token) # Should match ["bash" and "rbash"]. matches = set([x.aff4path.Basename() for x in fd]) self.assertEqual(sorted(matches), ["bash", "rbash"]) self.assertEqual(len(fd), 4) for child in fd: path = utils.SmartStr(child.aff4path) self.assertTrue(path.endswith("bash")) self.assertEqual(child.__class__.__name__, "StatEntry") def testFindFilesWithGlob(self): """Test that the Find flow works with glob.""" client_mock = action_mocks.ActionMock("Find") output_path = "analysis/FindFlowTest1" # Prepare a findspec. findspec = rdf_client.FindSpec( path_glob="bash*", pathspec=rdf_paths.PathSpec( path="/", pathtype=rdf_paths.PathSpec.PathType.OS)) for _ in test_lib.TestFlowHelper( "FindFiles", client_mock, client_id=self.client_id, token=self.token, output=output_path, findspec=findspec): pass # Check the output file is created fd = aff4.FACTORY.Open(self.client_id.Add(output_path), token=self.token) # Make sure that bash is a file. matches = set([x.aff4path.Basename() for x in fd]) self.assertEqual(sorted(matches), ["bash"]) self.assertEqual(len(fd), 2) for child in fd: path = utils.SmartStr(child.aff4path) self.assertTrue(path.endswith("bash")) self.assertEqual(child.__class__.__name__, "StatEntry") def testFindDirectories(self): """Test that the Find flow works with directories.""" client_mock = action_mocks.ActionMock("Find") output_path = "analysis/FindFlowTest2" # Prepare a findspec. findspec = rdf_client.FindSpec( path_regex="bin", pathspec=rdf_paths.PathSpec(path="/", pathtype=rdf_paths.PathSpec.PathType.OS)) for _ in test_lib.TestFlowHelper( "FindFiles", client_mock, client_id=self.client_id, token=self.token, output=output_path, findspec=findspec): pass # Check the output file is created fd = aff4.FACTORY.Open(self.client_id.Add(output_path), token=self.token) # Make sure that bin is a directory self.assertEqual(len(fd), 2) for child in fd: path = utils.SmartStr(child.aff4path) self.assertTrue("bin" in path) self.assertEqual(child.__class__.__name__, "StatEntry") def testFindWithMaxFiles(self): """Test that the Find flow works when specifying proto directly.""" client_mock = action_mocks.ActionMock("Find") output_path = "analysis/FindFlowTest4" # Prepare a findspec. findspec = rdf_client.FindSpec( path_regex=".*", pathspec=rdf_paths.PathSpec(path="/", pathtype=rdf_paths.PathSpec.PathType.OS)) for _ in test_lib.TestFlowHelper( "FindFiles", client_mock, client_id=self.client_id, token=self.token, findspec=findspec, iteration_count=3, output=output_path, max_results=7): pass # Check the output file is created fd = aff4.FACTORY.Open(self.client_id.Add(output_path), token=self.token) # Make sure we got the right number of results. self.assertEqual(len(fd), 7) def testCollectionOverwriting(self): """Test we overwrite the collection every time the flow is executed.""" client_mock = action_mocks.ActionMock("Find") output_path = "analysis/FindFlowTest5" # Prepare a findspec. findspec = rdf_client.FindSpec() findspec.path_regex = "bin" findspec.pathspec.path = "/" findspec.pathspec.pathtype = rdf_paths.PathSpec.PathType.OS for _ in test_lib.TestFlowHelper( "FindFiles", client_mock, client_id=self.client_id, token=self.token, findspec=findspec, output=output_path): pass # Check the output file with the right number of results. fd = aff4.FACTORY.Open(self.client_id.Add(output_path), token=self.token) self.assertEqual(len(fd), 2) # Now find a new result, should overwrite the collection findspec.path_regex = "dd" for _ in test_lib.TestFlowHelper( "FindFiles", client_mock, client_id=self.client_id, token=self.token, findspec=findspec, output=output_path, max_results=1): pass fd = aff4.FACTORY.Open(self.client_id.Add(output_path), token=self.token) self.assertEqual(len(fd), 1) def main(argv): # Run the full test suite test_lib.GrrTestProgram(argv=argv) if __name__ == "__main__": flags.StartMain(main)

from approvaltests import verify from approval_utilities.utilities.markdown_table import MarkdownTable def test_markdown_table(): # begin-snippet: markdown_table_example inputs = ["verify json", "verify all", "verify parameters", "verify as json"] table = MarkdownTable.with_headers( "Input", "Camel Case", "Snake Case", "Kebab Case" ) table.add_rows_for_inputs(inputs, to_camel_case, to_snake_case, to_kebab_case) verify(table) # end-snippet def to_camel_case(text: str) -> str: words = text.split() output = "" for w in words: output += w[0].capitalize() + w[1:] return output[0].lower() + output[1:] def to_snake_case(text: str) -> str: return text.lower().replace(" ", "_") def to_kebab_case(text: str) -> str: return text.lower().replace(" ", "-")

''' Copyright 2015 University of Auckland Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ''' from PySide import QtCore, QtGui from numbers import Number from opencmiss.zinc.field import Field from opencmiss.zinc.element import Element from opencmiss.zinc.glyph import Glyph from opencmiss.zinc.graphics import Graphics, GraphicsStreamlines, Graphicslineattributes from opencmiss.zinc.scenecoordinatesystem import SCENECOORDINATESYSTEM_LOCAL from opencmiss.zinc.spectrum import Spectrum from opencmiss.zinc.status import OK as ZINC_OK from opencmiss.neon.core.neonlogger import NeonLogger from opencmiss.neon.ui.zincwidgets.fieldconditions import * from opencmiss.neon.ui.zincwidgets.ui_graphicseditorwidget import Ui_GraphicsEditorWidget STRING_FLOAT_FORMAT = '{:.5g}' class GraphicsEditorWidget(QtGui.QWidget): def __init__(self, parent=None): ''' Call the super class init functions ''' QtGui.QWidget.__init__(self, parent) self._graphics = None # Using composition to include the visual element of the GUI. self.ui = Ui_GraphicsEditorWidget() self.ui.setupUi(self) # base graphics attributes self.ui.coordinate_field_chooser.setNullObjectName('-') self.ui.coordinate_field_chooser.setConditional(FieldIsCoordinateCapable) self.ui.data_field_chooser.setNullObjectName('-') self.ui.data_field_chooser.setConditional(FieldIsRealValued) self.ui.spectrum_chooser.setNullObjectName('-') # contours self.ui.isoscalar_field_chooser.setNullObjectName('- choose -') self.ui.isoscalar_field_chooser.setConditional(FieldIsScalar) # streamlines self.ui.stream_vector_field_chooser.setNullObjectName('- choose -') self.ui.stream_vector_field_chooser.setConditional(FieldIsStreamVectorCapable) # line attributes self.ui.line_orientation_scale_field_chooser.setNullObjectName('-') self.ui.line_orientation_scale_field_chooser.setConditional(FieldIsScalar) # point attributes self.ui.glyph_chooser.setNullObjectName('-') self.ui.point_orientation_scale_field_chooser.setNullObjectName('-') self.ui.point_orientation_scale_field_chooser.setConditional(FieldIsOrientationScaleCapable) self.ui.label_field_chooser.setNullObjectName('-') def _updateWidgets(self): # base graphics attributes coordinateField = None material = None dataField = None spectrum = None tessellation = None isExterior = False isWireframe = False pointattributes = None lineattributes = None samplingattributes = None contours = None streamlines = None if self._graphics: coordinateField = self._graphics.getCoordinateField() material = self._graphics.getMaterial() dataField = self._graphics.getDataField() spectrum = self._graphics.getSpectrum() tessellation = self._graphics.getTessellation() isExterior = self._graphics.isExterior() isWireframe = self._graphics.getRenderPolygonMode() == Graphics.RENDER_POLYGON_MODE_WIREFRAME contours = self._graphics.castContours() streamlines = self._graphics.castStreamlines() pointattributes = self._graphics.getGraphicspointattributes() lineattributes = self._graphics.getGraphicslineattributes() samplingattributes = self._graphics.getGraphicssamplingattributes() self.ui.general_groupbox.show() else: self.ui.general_groupbox.hide() self.ui.coordinate_field_chooser.setField(coordinateField) self._scenecoordinatesystemDisplay() self.ui.material_chooser.setMaterial(material) self.ui.data_field_chooser.setField(dataField) self.ui.spectrum_chooser.setSpectrum(spectrum) self.ui.tessellation_chooser.setTessellation(tessellation) self.ui.exterior_checkbox.setCheckState(QtCore.Qt.Checked if isExterior else QtCore.Qt.Unchecked) self._faceDisplay() self.ui.wireframe_checkbox.setCheckState(QtCore.Qt.Checked if isWireframe else QtCore.Qt.Unchecked) # contours isoscalarField = None if contours and contours.isValid(): isoscalarField = contours.getIsoscalarField() self.ui.contours_groupbox.show() else: self.ui.contours_groupbox.hide() self.ui.isoscalar_field_chooser.setField(isoscalarField) self._isovaluesDisplay() # streamlines streamVectorField = None if streamlines and streamlines.isValid(): streamVectorField = streamlines.getStreamVectorField() self.ui.streamlines_groupbox.show() else: self.ui.streamlines_groupbox.hide() self.ui.stream_vector_field_chooser.setField(streamVectorField) self._streamlinesTrackLengthDisplay() self._streamlinesTrackDirectionDisplay() self._streamlinesColourDataTypeDisplay() # line attributes lineOrientationScaleField = None if lineattributes and lineattributes.isValid(): lineOrientationScaleField = lineattributes.getOrientationScaleField() self.ui.lines_groupbox.show() else: self.ui.lines_groupbox.hide() self._lineShapeDisplay() self._lineBaseSizeDisplay() self.ui.line_orientation_scale_field_chooser.setField(lineOrientationScaleField) self._lineScaleFactorsDisplay() isStreamline = (streamlines is not None) and streamlines.isValid() if not isStreamline: isStreamline = False model = self.ui.line_shape_combobox.model() model.item(1, 0).setEnabled(isStreamline) model.item(3, 0).setEnabled(isStreamline) self.ui.line_orientation_scale_field_label.setEnabled(not isStreamline) self.ui.line_orientation_scale_field_chooser.setEnabled(not isStreamline) self.ui.line_scale_factors_label.setEnabled(not isStreamline) self.ui.line_scale_factors_lineedit.setEnabled(not isStreamline) # point attributes glyph = None pointOrientationScaleField = None labelField = None if pointattributes and pointattributes.isValid(): glyph = pointattributes.getGlyph() pointOrientationScaleField = pointattributes.getOrientationScaleField() labelField = pointattributes.getLabelField() self.ui.points_groupbox.show() else: self.ui.points_groupbox.hide() self.ui.glyph_chooser.setGlyph(glyph) self._pointBaseSizeDisplay() self.ui.point_orientation_scale_field_chooser.setField(pointOrientationScaleField) self._pointScaleFactorsDisplay() self.ui.label_field_chooser.setField(labelField) # sampling attributes if samplingattributes and samplingattributes.isValid(): self.ui.sampling_groupbox.show() else: self.ui.sampling_groupbox.hide() self._samplingModeDisplay() def setScene(self, scene): ''' Set when scene changes to initialised widgets dependent on scene ''' self.ui.material_chooser.setMaterialmodule(scene.getMaterialmodule()) self.ui.glyph_chooser.setGlyphmodule(scene.getGlyphmodule()) self.ui.spectrum_chooser.setSpectrummodule(scene.getSpectrummodule()) self.ui.tessellation_chooser.setTessellationmodule(scene.getTessellationmodule()) region = scene.getRegion() self.ui.coordinate_field_chooser.setRegion(region) self.ui.data_field_chooser.setRegion(region) self.ui.isoscalar_field_chooser.setRegion(region) self.ui.stream_vector_field_chooser.setRegion(region) self.ui.point_orientation_scale_field_chooser.setRegion(region) self.ui.label_field_chooser.setRegion(region) self.ui.line_orientation_scale_field_chooser.setRegion(region) def getGraphics(self): ''' Get the graphics currently in the editor ''' return self._graphics def setGraphics(self, graphics): ''' Set the graphics to be edited ''' if graphics and graphics.isValid(): self._graphics = graphics else: self._graphics = None self._updateWidgets() def _displayReal(self, widget, value): ''' Display real value in a widget ''' newText = STRING_FLOAT_FORMAT.format(value) widget.setText(newText) def _displayScale(self, widget, values, numberFormat=STRING_FLOAT_FORMAT): ''' Display vector values in a widget, separated by '*' ''' newText = "*".join(numberFormat.format(value) for value in values) widget.setText(newText) def _parseScale(self, widget): ''' Return real vector from comma separated text in line edit widget ''' text = widget.text() values = [float(value) for value in text.split('*')] return values def _parseScaleInteger(self, widget): ''' Return integer vector from comma separated text in line edit widget ''' text = widget.text() values = [int(value) for value in text.split('*')] return values def _displayVector(self, widget, values, numberFormat=STRING_FLOAT_FORMAT): ''' Display real vector values in a widget. Also handle scalar ''' if isinstance(values, Number): newText = STRING_FLOAT_FORMAT.format(values) else: newText = ", ".join(numberFormat.format(value) for value in values) widget.setText(newText) def _parseVector(self, widget): ''' Return real vector from comma separated text in line edit widget ''' text = widget.text() values = [float(value) for value in text.split(',')] return values def coordinateFieldChanged(self, index): ''' An item was selected at index in coordinate field chooser widget ''' if self._graphics: coordinateField = self.ui.coordinate_field_chooser.getField() if coordinateField: self._graphics.setCoordinateField(coordinateField) else: self._graphics.setCoordinateField(Field()) def _scenecoordinatesystemDisplay(self): ''' Show the current state of the scenecoordinatesystem combo box ''' scenecoordinatesystem = SCENECOORDINATESYSTEM_LOCAL if self._graphics: scenecoordinatesystem = self._graphics.getScenecoordinatesystem() self.ui.scenecoordinatesystem_combobox.blockSignals(True) self.ui.scenecoordinatesystem_combobox.setCurrentIndex(scenecoordinatesystem - SCENECOORDINATESYSTEM_LOCAL) self.ui.scenecoordinatesystem_combobox.blockSignals(False) def scenecoordinatesystemChanged(self, index): if self._graphics: self._graphics.setScenecoordinatesystem(index + SCENECOORDINATESYSTEM_LOCAL) def dataFieldChanged(self, index): ''' An item was selected at index in data field chooser widget ''' if self._graphics: dataField = self.ui.data_field_chooser.getField() if dataField: scene = self._graphics.getScene() scene.beginChange() spectrum = self._graphics.getSpectrum() if not spectrum.isValid(): spectrummodule = scene.getSpectrummodule() spectrum = spectrummodule.getDefaultSpectrum() self._graphics.setSpectrum(spectrum) self.ui.spectrum_chooser.setSpectrum(spectrum) self._graphics.setDataField(dataField) scene.endChange() else: self._graphics.setDataField(Field()) def spectrumChanged(self, index): if self._graphics: spectrum = self.ui.spectrum_chooser.getSpectrum() if spectrum: self._graphics.setSpectrum(spectrum) else: self._graphics.setSpectrum(Spectrum()) def tessellationChanged(self, index): ''' An item was selected at index in tessellation chooser widget ''' if self._graphics: tessellation = self.ui.tessellation_chooser.getTessellation() self._graphics.setTessellation(tessellation) def exteriorClicked(self, isChecked): ''' The exterior radiobutton was clicked ''' if self._graphics: self._graphics.setExterior(isChecked) def _faceDisplay(self): ''' Show the current state of the face combo box ''' faceType = Element.FACE_TYPE_ALL if self._graphics: faceType = self._graphics.getElementFaceType() self.ui.face_combobox.blockSignals(True) self.ui.face_combobox.setCurrentIndex(faceType - Element.FACE_TYPE_ALL) self.ui.face_combobox.blockSignals(False) def faceChanged(self, index): ''' Element face combo box changed ''' if self._graphics: self._graphics.setElementFaceType(index + Element.FACE_TYPE_ALL) def wireframeClicked(self, isChecked): ''' The wireframe surface render radiobutton was clicked ''' if self._graphics: self._graphics.setRenderPolygonMode(Graphics.RENDER_POLYGON_MODE_WIREFRAME if isChecked else Graphics.RENDER_POLYGON_MODE_SHADED) def glyphChanged(self, index): ''' An item was selected at index in glyph chooser widget ''' if self._graphics: pointattributes = self._graphics.getGraphicspointattributes() if (pointattributes.isValid()): glyph = self.ui.glyph_chooser.getGlyph() if glyph: pointattributes.setGlyph(glyph) else: pointattributes.setGlyph(Glyph()) def materialChanged(self, index): ''' An item was selected at index in material chooser widget ''' if self._graphics: material = self.ui.material_chooser.getMaterial() self._graphics.setMaterial(material) def isoscalarFieldChanged(self, index): if self._graphics: contours = self._graphics.castContours() if contours.isValid(): isoscalarField = self.ui.isoscalar_field_chooser.getField() if not isoscalarField: isoscalarField = Field() contours.setIsoscalarField(isoscalarField) def _isovaluesDisplay(self): ''' Display the current iso values list ''' if self._graphics: contours = self._graphics.castContours() if contours.isValid(): count, isovalues = contours.getListIsovalues(1) if count > 1: count, isovalues = contours.getListIsovalues(count) if count > 0: self._displayVector(self.ui.isovalues_lineedit, isovalues) return self.ui.isovalues_lineedit.setText('') def isovaluesEntered(self): ''' Set iso values list from text in widget ''' try: isovalues = self._parseVector(self.ui.isovalues_lineedit) contours = self._graphics.castContours() if contours.isValid(): if contours.setListIsovalues(isovalues) != ZINC_OK: raise except: NeonLogger.getLogger().error("Invalid isovalues") self._isovaluesDisplay() def streamVectorFieldChanged(self, index): if self._graphics: streamlines = self._graphics.castStreamlines() if streamlines.isValid(): streamVectorField = self.ui.stream_vector_field_chooser.getField() if not streamVectorField: streamVectorField = Field() streamlines.setStreamVectorField(streamVectorField) def _streamlinesTrackLengthDisplay(self): ''' Display the current streamlines length ''' if self._graphics: streamlines = self._graphics.castStreamlines() if streamlines.isValid(): trackLength = streamlines.getTrackLength() self._displayReal(self.ui.streamlines_track_length_lineedit, trackLength) return self.ui.streamlines_track_length_lineedit.setText('') def streamlinesTrackLengthEntered(self): ''' Set iso values list from text in widget ''' streamlinesLengthText = self.ui.streamlines_track_length_lineedit.text() try: trackLength = float(streamlinesLengthText) streamlines = self._graphics.castStreamlines() if streamlines.isValid(): if streamlines.setTrackLength(trackLength) != ZINC_OK: raise except: print("Invalid streamlines track length", streamlinesLengthText) self._streamlinesTrackLengthDisplay() def _streamlinesTrackDirectionDisplay(self): ''' Show the current state of the streamlines track direction combo box ''' streamlinesTrackDirection = GraphicsStreamlines.TRACK_DIRECTION_FORWARD if self._graphics: streamlines = self._graphics.castStreamlines() if streamlines.isValid(): streamlinesTrackDirection = streamlines.getTrackDirection() self.ui.streamlines_track_direction_combobox.blockSignals(True) self.ui.streamlines_track_direction_combobox.setCurrentIndex(streamlinesTrackDirection - GraphicsStreamlines.TRACK_DIRECTION_FORWARD) self.ui.streamlines_track_direction_combobox.blockSignals(False) def streamlinesTrackDirectionChanged(self, index): ''' Element streamlines track direction combo box changed ''' if self._graphics: streamlines = self._graphics.castStreamlines() if streamlines.isValid(): streamlines.setTrackDirection(index + GraphicsStreamlines.TRACK_DIRECTION_FORWARD) def _streamlinesColourDataTypeDisplay(self): ''' Show the current state of the streamlines colour data type combo box ''' streamlinesColourDataType = GraphicsStreamlines.COLOUR_DATA_TYPE_FIELD if self._graphics: streamlines = self._graphics.castStreamlines() if streamlines.isValid(): streamlinesColourDataType = streamlines.getColourDataType() self.ui.streamlines_colour_data_type_combobox.blockSignals(True) self.ui.streamlines_colour_data_type_combobox.setCurrentIndex(streamlinesColourDataType - GraphicsStreamlines.COLOUR_DATA_TYPE_FIELD) self.ui.streamlines_colour_data_type_combobox.blockSignals(False) def streamlinesColourDataTypeChanged(self, index): ''' Element streamlines colour data type combo box changed ''' if self._graphics: streamlines = self._graphics.castStreamlines() if streamlines.isValid(): scene = self._graphics.getScene() scene.beginChange() spectrum = self._graphics.getSpectrum() if not spectrum.isValid(): spectrummodule = scene.getSpectrummodule() spectrum = spectrummodule.getDefaultSpectrum() self._graphics.setSpectrum(spectrum) streamlines.setColourDataType(index + GraphicsStreamlines.COLOUR_DATA_TYPE_FIELD) scene.endChange() def _lineShapeDisplay(self): ''' Show the current state of the lineShape combo box ''' lineShapeType = Graphicslineattributes.SHAPE_TYPE_LINE if self._graphics: lineattributes = self._graphics.getGraphicslineattributes() if lineattributes.isValid(): lineShapeType = lineattributes.getShapeType() self.ui.line_shape_combobox.blockSignals(True) self.ui.line_shape_combobox.setCurrentIndex(lineShapeType - Graphicslineattributes.SHAPE_TYPE_LINE) self.ui.line_shape_combobox.blockSignals(False) def lineShapeChanged(self, index): ''' Element lineShape combo box changed ''' if self._graphics: lineattributes = self._graphics.getGraphicslineattributes() if lineattributes.isValid(): lineattributes.setShapeType(index + Graphicslineattributes.SHAPE_TYPE_LINE) def _lineBaseSizeDisplay(self): ''' Display the current line base size ''' if self._graphics: lineattributes = self._graphics.getGraphicslineattributes() if lineattributes.isValid(): _, baseSize = lineattributes.getBaseSize(2) self._displayScale(self.ui.line_base_size_lineedit, baseSize) return self.ui.line_base_size_lineedit.setText('0') def lineBaseSizeEntered(self): ''' Set line base size from text in widget ''' try: baseSize = self._parseScale(self.ui.line_base_size_lineedit) lineattributes = self._graphics.getGraphicslineattributes() if lineattributes.setBaseSize(baseSize) != ZINC_OK: raise except: print("Invalid line base size") self._lineBaseSizeDisplay() def lineOrientationScaleFieldChanged(self, index): if self._graphics: lineattributes = self._graphics.getGraphicslineattributes() if lineattributes.isValid(): orientationScaleField = self.ui.line_orientation_scale_field_chooser.getField() if not orientationScaleField: orientationScaleField = Field() lineattributes.setOrientationScaleField(orientationScaleField) def _lineScaleFactorsDisplay(self): ''' Display the current line scale factors ''' if self._graphics: lineattributes = self._graphics.getGraphicslineattributes() if lineattributes.isValid(): _, scaleFactors = lineattributes.getScaleFactors(2) self._displayScale(self.ui.line_scale_factors_lineedit, scaleFactors) return self.ui.line_scale_factors_lineedit.setText('0') def lineScaleFactorsEntered(self): ''' Set line scale factors from text in widget ''' try: scaleFactors = self._parseScale(self.ui.line_scale_factors_lineedit) lineattributes = self._graphics.getGraphicslineattributes() if lineattributes.setScaleFactors(scaleFactors) != ZINC_OK: raise except: print("Invalid line scale factors") self._lineScaleFactorsDisplay() def _pointBaseSizeDisplay(self): ''' Display the current point base size ''' if self._graphics: pointattributes = self._graphics.getGraphicspointattributes() if pointattributes.isValid(): _, baseSize = pointattributes.getBaseSize(3) self._displayScale(self.ui.point_base_size_lineedit, baseSize) return self.ui.point_base_size_lineedit.setText('0') def pointBaseSizeEntered(self): ''' Set point base size from text in widget ''' try: baseSize = self._parseScale(self.ui.point_base_size_lineedit) pointattributes = self._graphics.getGraphicspointattributes() if pointattributes.setBaseSize(baseSize) != ZINC_OK: raise except: print("Invalid point base size") self._pointBaseSizeDisplay() def pointOrientationScaleFieldChanged(self, index): if self._graphics: pointattributes = self._graphics.getGraphicspointattributes() if pointattributes.isValid(): orientationScaleField = self.ui.point_orientation_scale_field_chooser.getField() if not orientationScaleField: orientationScaleField = Field() pointattributes.setOrientationScaleField(orientationScaleField) def _pointScaleFactorsDisplay(self): ''' Display the current point scale factors ''' if self._graphics: pointattributes = self._graphics.getGraphicspointattributes() if pointattributes.isValid(): _, scaleFactors = pointattributes.getScaleFactors(3) self._displayScale(self.ui.point_scale_factors_lineedit, scaleFactors) return self.ui.point_scale_factors_lineedit.setText('0') def pointScaleFactorsEntered(self): ''' Set point scale factors from text in widget ''' try: scaleFactors = self._parseScale(self.ui.point_scale_factors_lineedit) pointattributes = self._graphics.getGraphicspointattributes() if pointattributes.setScaleFactors(scaleFactors) != ZINC_OK: raise except: print("Invalid point scale factors") self._pointScaleFactorsDisplay() def labelFieldChanged(self, index): if self._graphics: pointattributes = self._graphics.getGraphicspointattributes() if pointattributes.isValid(): labelField = self.ui.label_field_chooser.getField() if not labelField: labelField = Field() pointattributes.setLabelField(labelField) def _samplingModeDisplay(self): ''' Show the current state of the sampling mode combo box ''' samplingMode = Element.POINT_SAMPLING_MODE_CELL_CENTRES if self._graphics: samplingattributes = self._graphics.getGraphicssamplingattributes() if samplingattributes.isValid(): samplingMode = samplingattributes.getElementPointSamplingMode() self.ui.sampling_mode_combobox.blockSignals(True) self.ui.sampling_mode_combobox.setCurrentIndex(samplingMode - Element.POINT_SAMPLING_MODE_CELL_CENTRES) self.ui.sampling_mode_combobox.blockSignals(False) def samplingModeChanged(self, index): ''' Sampling mode combo box changed ''' if self._graphics: samplingattributes = self._graphics.getGraphicssamplingattributes() if samplingattributes.isValid(): samplingattributes.setElementPointSamplingMode(index + Element.POINT_SAMPLING_MODE_CELL_CENTRES)

import pygame, sys, math class Meme(pygame.sprite.Sprite): def __init__(self, screensize, levelNumber, speed=[0,0], pos=[0,0], size=None): #print screensize, levelNumber pygame.sprite.Sprite.__init__(self, self.containers) self.image = pygame.image.load("rsc/levelmat" + str(levelNumber) + "/meme/meme.png") if size: self.image = pygame.transform.scale(self.image, [size,size]) self.rect = self.image.get_rect(center = pos) self.speedx = speed[0] self.speedy = speed[1] self.speed = [self.speedx, self.speedy] self.radius = self.rect.width/2 -1 self.didBounceX = False self.didBounceY = False self.screenHeight = screensize[1] self.damage = 50 def shiftX(self, amount): self.rect.x += amount def getPos(self, key="center"): if key == "center": return self.rect.center def update(self, walls): self.calc_grav() self.rect.x += self.speedx block_hit_list = pygame.sprite.spritecollide(self, walls, False) for block in block_hit_list: if self.speedx > 0: self.rect.right = block.rect.left self.jump(walls) elif self.speedx < 0: self.rect.left = block.rect.right self.jump(walls) self.rect.y += self.speedy block_hit_list = pygame.sprite.spritecollide(self, walls, False) for block in block_hit_list: if self.speedy > 0: self.rect.bottom = block.rect.top elif self.speedy < 0: self.rect.top = block.rect.bottom self.speedy = 0 def jump(self, walls): self.rect.y += 2 platform_hit_list = pygame.sprite.spritecollide(self, walls, False) self.rect.y -= 2 #print len(platform_hit_list) if len(platform_hit_list) > 0 or self.rect.bottom >= self.screenHeight: self.speedy = -10 def calc_grav(self): if self.speedy == 0: self.speedy = 1 else: self.speedy += .35 if (self.rect.y >= (self.screenHeight - self.rect.height)) and (self.speedy >= 0): self.speedy = 0 self.rect.y = self.screenHeight - self.rect.height def animate(self): if self.animationTimer < self.animationTimerMax: self.animationTimer += 1 else: self.animationTimer = 0 if self.frame < self.maxFrame: self.frame += 1 else: self.frame = 0 self.image = self.images[self.frame] def move(self): self.didBounceX = False self.didBounceY = False self.speed = [self.speedx, self.speedy] self.rect = self.rect.move(self.speed) def bounceScreen(self, size): width = size[0] height = size[1] if self.rect.left < 0 or self.rect.right > width: self.speedx = -self.speedx self.didBounceX = True if self.rect.top < 0 or self.rect.bottom > height: self.speedy = -self.speedy self.didBounceY = True def bounceMeme(self, other): if not self.didBounceX: self.speedx = -self.speedx if not self.didBounceY: self.speedy = -self.speedy def dist(self, pt): x1 = self.rect.center[0] y1 = self.rect.center[1] x2 = pt[0] y2 = pt[1] xDiff = x1 - x2 yDiff = y1 - y2 return math.sqrt(xDiff**2 + yDiff**2)

from kde import KDE from smoothers_lowess import lowess import bandwidths

import sys from lxml import etree dtd = etree.DTD(open("dotscene.dtd")) root = etree.parse(sys.argv[1]) if dtd.validate(root): print("validation successful") else: print(dtd.error_log.filter_from_errors())

from plotly.basedatatypes import BaseTraceType as _BaseTraceType import copy as _copy class Histogram2dContour(_BaseTraceType): # class properties # -------------------- _parent_path_str = "" _path_str = "histogram2dcontour" _valid_props = { "autobinx", "autobiny", "autocolorscale", "autocontour", "bingroup", "coloraxis", "colorbar", "colorscale", "contours", "customdata", "customdatasrc", "histfunc", "histnorm", "hoverinfo", "hoverinfosrc", "hoverlabel", "hovertemplate", "hovertemplatesrc", "ids", "idssrc", "legendgroup", "line", "marker", "meta", "metasrc", "name", "nbinsx", "nbinsy", "ncontours", "opacity", "reversescale", "showlegend", "showscale", "stream", "type", "uid", "uirevision", "visible", "x", "xaxis", "xbingroup", "xbins", "xcalendar", "xsrc", "y", "yaxis", "ybingroup", "ybins", "ycalendar", "ysrc", "z", "zauto", "zhoverformat", "zmax", "zmid", "zmin", "zsrc", } # autobinx # -------- @property def autobinx(self): """ Obsolete: since v1.42 each bin attribute is auto-determined separately and `autobinx` is not needed. However, we accept `autobinx: true` or `false` and will update `xbins` accordingly before deleting `autobinx` from the trace. The 'autobinx' property must be specified as a bool (either True, or False) Returns ------- bool """ return self["autobinx"] @autobinx.setter def autobinx(self, val): self["autobinx"] = val # autobiny # -------- @property def autobiny(self): """ Obsolete: since v1.42 each bin attribute is auto-determined separately and `autobiny` is not needed. However, we accept `autobiny: true` or `false` and will update `ybins` accordingly before deleting `autobiny` from the trace. The 'autobiny' property must be specified as a bool (either True, or False) Returns ------- bool """ return self["autobiny"] @autobiny.setter def autobiny(self, val): self["autobiny"] = val # autocolorscale # -------------- @property def autocolorscale(self): """ Determines whether the colorscale is a default palette (`autocolorscale: true`) or the palette determined by `colorscale`. In case `colorscale` is unspecified or `autocolorscale` is true, the default palette will be chosen according to whether numbers in the `color` array are all positive, all negative or mixed. The 'autocolorscale' property must be specified as a bool (either True, or False) Returns ------- bool """ return self["autocolorscale"] @autocolorscale.setter def autocolorscale(self, val): self["autocolorscale"] = val # autocontour # ----------- @property def autocontour(self): """ Determines whether or not the contour level attributes are picked by an algorithm. If True, the number of contour levels can be set in `ncontours`. If False, set the contour level attributes in `contours`. The 'autocontour' property must be specified as a bool (either True, or False) Returns ------- bool """ return self["autocontour"] @autocontour.setter def autocontour(self, val): self["autocontour"] = val # bingroup # -------- @property def bingroup(self): """ Set the `xbingroup` and `ybingroup` default prefix For example, setting a `bingroup` of 1 on two histogram2d traces will make them their x-bins and y-bins match separately. The 'bingroup' property is a string and must be specified as: - A string - A number that will be converted to a string Returns ------- str """ return self["bingroup"] @bingroup.setter def bingroup(self, val): self["bingroup"] = val # coloraxis # --------- @property def coloraxis(self): """ Sets a reference to a shared color axis. References to these shared color axes are "coloraxis", "coloraxis2", "coloraxis3", etc. Settings for these shared color axes are set in the layout, under `layout.coloraxis`, `layout.coloraxis2`, etc. Note that multiple color scales can be linked to the same color axis. The 'coloraxis' property is an identifier of a particular subplot, of type 'coloraxis', that may be specified as the string 'coloraxis' optionally followed by an integer >= 1 (e.g. 'coloraxis', 'coloraxis1', 'coloraxis2', 'coloraxis3', etc.) Returns ------- str """ return self["coloraxis"] @coloraxis.setter def coloraxis(self, val): self["coloraxis"] = val # colorbar # -------- @property def colorbar(self): """ The 'colorbar' property is an instance of ColorBar that may be specified as: - An instance of :class:`plotly.graph_objs.histogram2dcontour.ColorBar` - A dict of string/value properties that will be passed to the ColorBar constructor Supported dict properties: bgcolor Sets the color of padded area. bordercolor Sets the axis line color. borderwidth Sets the width (in px) or the border enclosing this color bar. dtick Sets the step in-between ticks on this axis. Use with `tick0`. Must be a positive number, or special strings available to "log" and "date" axes. If the axis `type` is "log", then ticks are set every 10^(n*dtick) where n is the tick number. For example, to set a tick mark at 1, 10, 100, 1000, ... set dtick to 1. To set tick marks at 1, 100, 10000, ... set dtick to 2. To set tick marks at 1, 5, 25, 125, 625, 3125, ... set dtick to log_10(5), or 0.69897000433. "log" has several special values; "L<f>", where `f` is a positive number, gives ticks linearly spaced in value (but not position). For example `tick0` = 0.1, `dtick` = "L0.5" will put ticks at 0.1, 0.6, 1.1, 1.6 etc. To show powers of 10 plus small digits between, use "D1" (all digits) or "D2" (only 2 and 5). `tick0` is ignored for "D1" and "D2". If the axis `type` is "date", then you must convert the time to milliseconds. For example, to set the interval between ticks to one day, set `dtick` to 86400000.0. "date" also has special values "M<n>" gives ticks spaced by a number of months. `n` must be a positive integer. To set ticks on the 15th of every third month, set `tick0` to "2000-01-15" and `dtick` to "M3". To set ticks every 4 years, set `dtick` to "M48" exponentformat Determines a formatting rule for the tick exponents. For example, consider the number 1,000,000,000. If "none", it appears as 1,000,000,000. If "e", 1e+9. If "E", 1E+9. If "power", 1x10^9 (with 9 in a super script). If "SI", 1G. If "B", 1B. len Sets the length of the color bar This measure excludes the padding of both ends. That is, the color bar length is this length minus the padding on both ends. lenmode Determines whether this color bar's length (i.e. the measure in the color variation direction) is set in units of plot "fraction" or in *pixels. Use `len` to set the value. nticks Specifies the maximum number of ticks for the particular axis. The actual number of ticks will be chosen automatically to be less than or equal to `nticks`. Has an effect only if `tickmode` is set to "auto". outlinecolor Sets the axis line color. outlinewidth Sets the width (in px) of the axis line. separatethousands If "true", even 4-digit integers are separated showexponent If "all", all exponents are shown besides their significands. If "first", only the exponent of the first tick is shown. If "last", only the exponent of the last tick is shown. If "none", no exponents appear. showticklabels Determines whether or not the tick labels are drawn. showtickprefix If "all", all tick labels are displayed with a prefix. If "first", only the first tick is displayed with a prefix. If "last", only the last tick is displayed with a suffix. If "none", tick prefixes are hidden. showticksuffix Same as `showtickprefix` but for tick suffixes. thickness Sets the thickness of the color bar This measure excludes the size of the padding, ticks and labels. thicknessmode Determines whether this color bar's thickness (i.e. the measure in the constant color direction) is set in units of plot "fraction" or in "pixels". Use `thickness` to set the value. tick0 Sets the placement of the first tick on this axis. Use with `dtick`. If the axis `type` is "log", then you must take the log of your starting tick (e.g. to set the starting tick to 100, set the `tick0` to 2) except when `dtick`=*L<f>* (see `dtick` for more info). If the axis `type` is "date", it should be a date string, like date data. If the axis `type` is "category", it should be a number, using the scale where each category is assigned a serial number from zero in the order it appears. tickangle Sets the angle of the tick labels with respect to the horizontal. For example, a `tickangle` of -90 draws the tick labels vertically. tickcolor Sets the tick color. tickfont Sets the color bar's tick label font tickformat Sets the tick label formatting rule using d3 formatting mini-languages which are very similar to those in Python. For numbers, see: https://github.com/d3/d3-3.x-api- reference/blob/master/Formatting.md#d3_format And for dates see: https://github.com/d3/d3-3.x-api- reference/blob/master/Time-Formatting.md#format We add one item to d3's date formatter: "%{n}f" for fractional seconds with n digits. For example, *2016-10-13 09:15:23.456* with tickformat "%H~%M~%S.%2f" would display "09~15~23.46" tickformatstops A tuple of :class:`plotly.graph_objects.histogr am2dcontour.colorbar.Tickformatstop` instances or dicts with compatible properties tickformatstopdefaults When used in a template (as layout.template.dat a.histogram2dcontour.colorbar.tickformatstopdef aults), sets the default property values to use for elements of histogram2dcontour.colorbar.tickformatstops ticklen Sets the tick length (in px). tickmode Sets the tick mode for this axis. If "auto", the number of ticks is set via `nticks`. If "linear", the placement of the ticks is determined by a starting position `tick0` and a tick step `dtick` ("linear" is the default value if `tick0` and `dtick` are provided). If "array", the placement of the ticks is set via `tickvals` and the tick text is `ticktext`. ("array" is the default value if `tickvals` is provided). tickprefix Sets a tick label prefix. ticks Determines whether ticks are drawn or not. If "", this axis' ticks are not drawn. If "outside" ("inside"), this axis' are drawn outside (inside) the axis lines. ticksuffix Sets a tick label suffix. ticktext Sets the text displayed at the ticks position via `tickvals`. Only has an effect if `tickmode` is set to "array". Used with `tickvals`. ticktextsrc Sets the source reference on Chart Studio Cloud for ticktext . tickvals Sets the values at which ticks on this axis appear. Only has an effect if `tickmode` is set to "array". Used with `ticktext`. tickvalssrc Sets the source reference on Chart Studio Cloud for tickvals . tickwidth Sets the tick width (in px). title :class:`plotly.graph_objects.histogram2dcontour .colorbar.Title` instance or dict with compatible properties titlefont Deprecated: Please use histogram2dcontour.colorbar.title.font instead. Sets this color bar's title font. Note that the title's font used to be set by the now deprecated `titlefont` attribute. titleside Deprecated: Please use histogram2dcontour.colorbar.title.side instead. Determines the location of color bar's title with respect to the color bar. Note that the title's location used to be set by the now deprecated `titleside` attribute. x Sets the x position of the color bar (in plot fraction). xanchor Sets this color bar's horizontal position anchor. This anchor binds the `x` position to the "left", "center" or "right" of the color bar. xpad Sets the amount of padding (in px) along the x direction. y Sets the y position of the color bar (in plot fraction). yanchor Sets this color bar's vertical position anchor This anchor binds the `y` position to the "top", "middle" or "bottom" of the color bar. ypad Sets the amount of padding (in px) along the y direction. Returns ------- plotly.graph_objs.histogram2dcontour.ColorBar """ return self["colorbar"] @colorbar.setter def colorbar(self, val): self["colorbar"] = val # colorscale # ---------- @property def colorscale(self): """ Sets the colorscale. The colorscale must be an array containing arrays mapping a normalized value to an rgb, rgba, hex, hsl, hsv, or named color string. At minimum, a mapping for the lowest (0) and highest (1) values are required. For example, `[[0, 'rgb(0,0,255)'], [1, 'rgb(255,0,0)']]`. To control the bounds of the colorscale in color space, use`zmin` and `zmax`. Alternatively, `colorscale` may be a palette name string of the following list: Greys,YlGnBu,Greens,YlOrRd,Bluered,RdBu,Reds,Bl ues,Picnic,Rainbow,Portland,Jet,Hot,Blackbody,Earth,Electric,Vi ridis,Cividis. The 'colorscale' property is a colorscale and may be specified as: - A list of colors that will be spaced evenly to create the colorscale. Many predefined colorscale lists are included in the sequential, diverging, and cyclical modules in the plotly.colors package. - A list of 2-element lists where the first element is the normalized color level value (starting at 0 and ending at 1), and the second item is a valid color string. (e.g. [[0, 'green'], [0.5, 'red'], [1.0, 'rgb(0, 0, 255)']]) - One of the following named colorscales: ['aggrnyl', 'agsunset', 'algae', 'amp', 'armyrose', 'balance', 'blackbody', 'bluered', 'blues', 'blugrn', 'bluyl', 'brbg', 'brwnyl', 'bugn', 'bupu', 'burg', 'burgyl', 'cividis', 'curl', 'darkmint', 'deep', 'delta', 'dense', 'earth', 'edge', 'electric', 'emrld', 'fall', 'geyser', 'gnbu', 'gray', 'greens', 'greys', 'haline', 'hot', 'hsv', 'ice', 'icefire', 'inferno', 'jet', 'magenta', 'magma', 'matter', 'mint', 'mrybm', 'mygbm', 'oranges', 'orrd', 'oryel', 'peach', 'phase', 'picnic', 'pinkyl', 'piyg', 'plasma', 'plotly3', 'portland', 'prgn', 'pubu', 'pubugn', 'puor', 'purd', 'purp', 'purples', 'purpor', 'rainbow', 'rdbu', 'rdgy', 'rdpu', 'rdylbu', 'rdylgn', 'redor', 'reds', 'solar', 'spectral', 'speed', 'sunset', 'sunsetdark', 'teal', 'tealgrn', 'tealrose', 'tempo', 'temps', 'thermal', 'tropic', 'turbid', 'twilight', 'viridis', 'ylgn', 'ylgnbu', 'ylorbr', 'ylorrd']. Appending '_r' to a named colorscale reverses it. Returns ------- str """ return self["colorscale"] @colorscale.setter def colorscale(self, val): self["colorscale"] = val # contours # -------- @property def contours(self): """ The 'contours' property is an instance of Contours that may be specified as: - An instance of :class:`plotly.graph_objs.histogram2dcontour.Contours` - A dict of string/value properties that will be passed to the Contours constructor Supported dict properties: coloring Determines the coloring method showing the contour values. If "fill", coloring is done evenly between each contour level If "heatmap", a heatmap gradient coloring is applied between each contour level. If "lines", coloring is done on the contour lines. If "none", no coloring is applied on this trace. end Sets the end contour level value. Must be more than `contours.start` labelfont Sets the font used for labeling the contour levels. The default color comes from the lines, if shown. The default family and size come from `layout.font`. labelformat Sets the contour label formatting rule using d3 formatting mini-language which is very similar to Python, see: https://github.com/d3/d3-3.x-api- reference/blob/master/Formatting.md#d3_format operation Sets the constraint operation. "=" keeps regions equal to `value` "<" and "<=" keep regions less than `value` ">" and ">=" keep regions greater than `value` "[]", "()", "[)", and "(]" keep regions inside `value[0]` to `value[1]` "][", ")(", "](", ")[" keep regions outside `value[0]` to value[1]` Open vs. closed intervals make no difference to constraint display, but all versions are allowed for consistency with filter transforms. showlabels Determines whether to label the contour lines with their values. showlines Determines whether or not the contour lines are drawn. Has an effect only if `contours.coloring` is set to "fill". size Sets the step between each contour level. Must be positive. start Sets the starting contour level value. Must be less than `contours.end` type If `levels`, the data is represented as a contour plot with multiple levels displayed. If `constraint`, the data is represented as constraints with the invalid region shaded as specified by the `operation` and `value` parameters. value Sets the value or values of the constraint boundary. When `operation` is set to one of the comparison values (=,<,>=,>,<=) "value" is expected to be a number. When `operation` is set to one of the interval values ([],(),[),(],][,)(,](,)[) "value" is expected to be an array of two numbers where the first is the lower bound and the second is the upper bound. Returns ------- plotly.graph_objs.histogram2dcontour.Contours """ return self["contours"] @contours.setter def contours(self, val): self["contours"] = val # customdata # ---------- @property def customdata(self): """ Assigns extra data each datum. This may be useful when listening to hover, click and selection events. Note that, "scatter" traces also appends customdata items in the markers DOM elements The 'customdata' property is an array that may be specified as a tuple, list, numpy array, or pandas Series Returns ------- numpy.ndarray """ return self["customdata"] @customdata.setter def customdata(self, val): self["customdata"] = val # customdatasrc # ------------- @property def customdatasrc(self): """ Sets the source reference on Chart Studio Cloud for customdata . The 'customdatasrc' property must be specified as a string or as a plotly.grid_objs.Column object Returns ------- str """ return self["customdatasrc"] @customdatasrc.setter def customdatasrc(self, val): self["customdatasrc"] = val # histfunc # -------- @property def histfunc(self): """ Specifies the binning function used for this histogram trace. If "count", the histogram values are computed by counting the number of values lying inside each bin. If "sum", "avg", "min", "max", the histogram values are computed using the sum, the average, the minimum or the maximum of the values lying inside each bin respectively. The 'histfunc' property is an enumeration that may be specified as: - One of the following enumeration values: ['count', 'sum', 'avg', 'min', 'max'] Returns ------- Any """ return self["histfunc"] @histfunc.setter def histfunc(self, val): self["histfunc"] = val # histnorm # -------- @property def histnorm(self): """ Specifies the type of normalization used for this histogram trace. If "", the span of each bar corresponds to the number of occurrences (i.e. the number of data points lying inside the bins). If "percent" / "probability", the span of each bar corresponds to the percentage / fraction of occurrences with respect to the total number of sample points (here, the sum of all bin HEIGHTS equals 100% / 1). If "density", the span of each bar corresponds to the number of occurrences in a bin divided by the size of the bin interval (here, the sum of all bin AREAS equals the total number of sample points). If *probability density*, the area of each bar corresponds to the probability that an event will fall into the corresponding bin (here, the sum of all bin AREAS equals 1). The 'histnorm' property is an enumeration that may be specified as: - One of the following enumeration values: ['', 'percent', 'probability', 'density', 'probability density'] Returns ------- Any """ return self["histnorm"] @histnorm.setter def histnorm(self, val): self["histnorm"] = val # hoverinfo # --------- @property def hoverinfo(self): """ Determines which trace information appear on hover. If `none` or `skip` are set, no information is displayed upon hovering. But, if `none` is set, click and hover events are still fired. The 'hoverinfo' property is a flaglist and may be specified as a string containing: - Any combination of ['x', 'y', 'z', 'text', 'name'] joined with '+' characters (e.g. 'x+y') OR exactly one of ['all', 'none', 'skip'] (e.g. 'skip') - A list or array of the above Returns ------- Any|numpy.ndarray """ return self["hoverinfo"] @hoverinfo.setter def hoverinfo(self, val): self["hoverinfo"] = val # hoverinfosrc # ------------ @property def hoverinfosrc(self): """ Sets the source reference on Chart Studio Cloud for hoverinfo . The 'hoverinfosrc' property must be specified as a string or as a plotly.grid_objs.Column object Returns ------- str """ return self["hoverinfosrc"] @hoverinfosrc.setter def hoverinfosrc(self, val): self["hoverinfosrc"] = val # hoverlabel # ---------- @property def hoverlabel(self): """ The 'hoverlabel' property is an instance of Hoverlabel that may be specified as: - An instance of :class:`plotly.graph_objs.histogram2dcontour.Hoverlabel` - A dict of string/value properties that will be passed to the Hoverlabel constructor Supported dict properties: align Sets the horizontal alignment of the text content within hover label box. Has an effect only if the hover label text spans more two or more lines alignsrc Sets the source reference on Chart Studio Cloud for align . bgcolor Sets the background color of the hover labels for this trace bgcolorsrc Sets the source reference on Chart Studio Cloud for bgcolor . bordercolor Sets the border color of the hover labels for this trace. bordercolorsrc Sets the source reference on Chart Studio Cloud for bordercolor . font Sets the font used in hover labels. namelength Sets the default length (in number of characters) of the trace name in the hover labels for all traces. -1 shows the whole name regardless of length. 0-3 shows the first 0-3 characters, and an integer >3 will show the whole name if it is less than that many characters, but if it is longer, will truncate to `namelength - 3` characters and add an ellipsis. namelengthsrc Sets the source reference on Chart Studio Cloud for namelength . Returns ------- plotly.graph_objs.histogram2dcontour.Hoverlabel """ return self["hoverlabel"] @hoverlabel.setter def hoverlabel(self, val): self["hoverlabel"] = val # hovertemplate # ------------- @property def hovertemplate(self): """ Template string used for rendering the information that appear on hover box. Note that this will override `hoverinfo`. Variables are inserted using %{variable}, for example "y: %{y}". Numbers are formatted using d3-format's syntax %{variable:d3-format}, for example "Price: %{y:$.2f}". https://github.com/d3/d3-3.x-api- reference/blob/master/Formatting.md#d3_format for details on the formatting syntax. Dates are formatted using d3-time- format's syntax %{variable|d3-time-format}, for example "Day: %{2019-01-01|%A}". https://github.com/d3/d3-3.x-api- reference/blob/master/Time-Formatting.md#format for details on the date formatting syntax. The variables available in `hovertemplate` are the ones emitted as event data described at this link https://plotly.com/javascript/plotlyjs-events/#event- data. Additionally, every attributes that can be specified per- point (the ones that are `arrayOk: true`) are available. variable `z` Anything contained in tag `<extra>` is displayed in the secondary box, for example "<extra>{fullData.name}</extra>". To hide the secondary box completely, use an empty tag `<extra></extra>`. The 'hovertemplate' property is a string and must be specified as: - A string - A number that will be converted to a string - A tuple, list, or one-dimensional numpy array of the above Returns ------- str|numpy.ndarray """ return self["hovertemplate"] @hovertemplate.setter def hovertemplate(self, val): self["hovertemplate"] = val # hovertemplatesrc # ---------------- @property def hovertemplatesrc(self): """ Sets the source reference on Chart Studio Cloud for hovertemplate . The 'hovertemplatesrc' property must be specified as a string or as a plotly.grid_objs.Column object Returns ------- str """ return self["hovertemplatesrc"] @hovertemplatesrc.setter def hovertemplatesrc(self, val): self["hovertemplatesrc"] = val # ids # --- @property def ids(self): """ Assigns id labels to each datum. These ids for object constancy of data points during animation. Should be an array of strings, not numbers or any other type. The 'ids' property is an array that may be specified as a tuple, list, numpy array, or pandas Series Returns ------- numpy.ndarray """ return self["ids"] @ids.setter def ids(self, val): self["ids"] = val # idssrc # ------ @property def idssrc(self): """ Sets the source reference on Chart Studio Cloud for ids . The 'idssrc' property must be specified as a string or as a plotly.grid_objs.Column object Returns ------- str """ return self["idssrc"] @idssrc.setter def idssrc(self, val): self["idssrc"] = val # legendgroup # ----------- @property def legendgroup(self): """ Sets the legend group for this trace. Traces part of the same legend group hide/show at the same time when toggling legend items. The 'legendgroup' property is a string and must be specified as: - A string - A number that will be converted to a string Returns ------- str """ return self["legendgroup"] @legendgroup.setter def legendgroup(self, val): self["legendgroup"] = val # line # ---- @property def line(self): """ The 'line' property is an instance of Line that may be specified as: - An instance of :class:`plotly.graph_objs.histogram2dcontour.Line` - A dict of string/value properties that will be passed to the Line constructor Supported dict properties: color Sets the color of the contour level. Has no effect if `contours.coloring` is set to "lines". dash Sets the dash style of lines. Set to a dash type string ("solid", "dot", "dash", "longdash", "dashdot", or "longdashdot") or a dash length list in px (eg "5px,10px,2px,2px"). smoothing Sets the amount of smoothing for the contour lines, where 0 corresponds to no smoothing. width Sets the contour line width in (in px) Returns ------- plotly.graph_objs.histogram2dcontour.Line """ return self["line"] @line.setter def line(self, val): self["line"] = val # marker # ------ @property def marker(self): """ The 'marker' property is an instance of Marker that may be specified as: - An instance of :class:`plotly.graph_objs.histogram2dcontour.Marker` - A dict of string/value properties that will be passed to the Marker constructor Supported dict properties: color Sets the aggregation data. colorsrc Sets the source reference on Chart Studio Cloud for color . Returns ------- plotly.graph_objs.histogram2dcontour.Marker """ return self["marker"] @marker.setter def marker(self, val): self["marker"] = val # meta # ---- @property def meta(self): """ Assigns extra meta information associated with this trace that can be used in various text attributes. Attributes such as trace `name`, graph, axis and colorbar `title.text`, annotation `text` `rangeselector`, `updatemenues` and `sliders` `label` text all support `meta`. To access the trace `meta` values in an attribute in the same trace, simply use `%{meta[i]}` where `i` is the index or key of the `meta` item in question. To access trace `meta` in layout attributes, use `%{data[n[.meta[i]}` where `i` is the index or key of the `meta` and `n` is the trace index. The 'meta' property accepts values of any type Returns ------- Any|numpy.ndarray """ return self["meta"] @meta.setter def meta(self, val): self["meta"] = val # metasrc # ------- @property def metasrc(self): """ Sets the source reference on Chart Studio Cloud for meta . The 'metasrc' property must be specified as a string or as a plotly.grid_objs.Column object Returns ------- str """ return self["metasrc"] @metasrc.setter def metasrc(self, val): self["metasrc"] = val # name # ---- @property def name(self): """ Sets the trace name. The trace name appear as the legend item and on hover. The 'name' property is a string and must be specified as: - A string - A number that will be converted to a string Returns ------- str """ return self["name"] @name.setter def name(self, val): self["name"] = val # nbinsx # ------ @property def nbinsx(self): """ Specifies the maximum number of desired bins. This value will be used in an algorithm that will decide the optimal bin size such that the histogram best visualizes the distribution of the data. Ignored if `xbins.size` is provided. The 'nbinsx' property is a integer and may be specified as: - An int (or float that will be cast to an int) in the interval [0, 9223372036854775807] Returns ------- int """ return self["nbinsx"] @nbinsx.setter def nbinsx(self, val): self["nbinsx"] = val # nbinsy # ------ @property def nbinsy(self): """ Specifies the maximum number of desired bins. This value will be used in an algorithm that will decide the optimal bin size such that the histogram best visualizes the distribution of the data. Ignored if `ybins.size` is provided. The 'nbinsy' property is a integer and may be specified as: - An int (or float that will be cast to an int) in the interval [0, 9223372036854775807] Returns ------- int """ return self["nbinsy"] @nbinsy.setter def nbinsy(self, val): self["nbinsy"] = val # ncontours # --------- @property def ncontours(self): """ Sets the maximum number of contour levels. The actual number of contours will be chosen automatically to be less than or equal to the value of `ncontours`. Has an effect only if `autocontour` is True or if `contours.size` is missing. The 'ncontours' property is a integer and may be specified as: - An int (or float that will be cast to an int) in the interval [1, 9223372036854775807] Returns ------- int """ return self["ncontours"] @ncontours.setter def ncontours(self, val): self["ncontours"] = val # opacity # ------- @property def opacity(self): """ Sets the opacity of the trace. The 'opacity' property is a number and may be specified as: - An int or float in the interval [0, 1] Returns ------- int|float """ return self["opacity"] @opacity.setter def opacity(self, val): self["opacity"] = val # reversescale # ------------ @property def reversescale(self): """ Reverses the color mapping if true. If true, `zmin` will correspond to the last color in the array and `zmax` will correspond to the first color. The 'reversescale' property must be specified as a bool (either True, or False) Returns ------- bool """ return self["reversescale"] @reversescale.setter def reversescale(self, val): self["reversescale"] = val # showlegend # ---------- @property def showlegend(self): """ Determines whether or not an item corresponding to this trace is shown in the legend. The 'showlegend' property must be specified as a bool (either True, or False) Returns ------- bool """ return self["showlegend"] @showlegend.setter def showlegend(self, val): self["showlegend"] = val # showscale # --------- @property def showscale(self): """ Determines whether or not a colorbar is displayed for this trace. The 'showscale' property must be specified as a bool (either True, or False) Returns ------- bool """ return self["showscale"] @showscale.setter def showscale(self, val): self["showscale"] = val # stream # ------ @property def stream(self): """ The 'stream' property is an instance of Stream that may be specified as: - An instance of :class:`plotly.graph_objs.histogram2dcontour.Stream` - A dict of string/value properties that will be passed to the Stream constructor Supported dict properties: maxpoints Sets the maximum number of points to keep on the plots from an incoming stream. If `maxpoints` is set to 50, only the newest 50 points will be displayed on the plot. token The stream id number links a data trace on a plot with a stream. See https://chart- studio.plotly.com/settings for more details. Returns ------- plotly.graph_objs.histogram2dcontour.Stream """ return self["stream"] @stream.setter def stream(self, val): self["stream"] = val # uid # --- @property def uid(self): """ Assign an id to this trace, Use this to provide object constancy between traces during animations and transitions. The 'uid' property is a string and must be specified as: - A string - A number that will be converted to a string Returns ------- str """ return self["uid"] @uid.setter def uid(self, val): self["uid"] = val # uirevision # ---------- @property def uirevision(self): """ Controls persistence of some user-driven changes to the trace: `constraintrange` in `parcoords` traces, as well as some `editable: true` modifications such as `name` and `colorbar.title`. Defaults to `layout.uirevision`. Note that other user-driven trace attribute changes are controlled by `layout` attributes: `trace.visible` is controlled by `layout.legend.uirevision`, `selectedpoints` is controlled by `layout.selectionrevision`, and `colorbar.(x|y)` (accessible with `config: {editable: true}`) is controlled by `layout.editrevision`. Trace changes are tracked by `uid`, which only falls back on trace index if no `uid` is provided. So if your app can add/remove traces before the end of the `data` array, such that the same trace has a different index, you can still preserve user-driven changes if you give each trace a `uid` that stays with it as it moves. The 'uirevision' property accepts values of any type Returns ------- Any """ return self["uirevision"] @uirevision.setter def uirevision(self, val): self["uirevision"] = val # visible # ------- @property def visible(self): """ Determines whether or not this trace is visible. If "legendonly", the trace is not drawn, but can appear as a legend item (provided that the legend itself is visible). The 'visible' property is an enumeration that may be specified as: - One of the following enumeration values: [True, False, 'legendonly'] Returns ------- Any """ return self["visible"] @visible.setter def visible(self, val): self["visible"] = val # x # - @property def x(self): """ Sets the sample data to be binned on the x axis. The 'x' property is an array that may be specified as a tuple, list, numpy array, or pandas Series Returns ------- numpy.ndarray """ return self["x"] @x.setter def x(self, val): self["x"] = val # xaxis # ----- @property def xaxis(self): """ Sets a reference between this trace's x coordinates and a 2D cartesian x axis. If "x" (the default value), the x coordinates refer to `layout.xaxis`. If "x2", the x coordinates refer to `layout.xaxis2`, and so on. The 'xaxis' property is an identifier of a particular subplot, of type 'x', that may be specified as the string 'x' optionally followed by an integer >= 1 (e.g. 'x', 'x1', 'x2', 'x3', etc.) Returns ------- str """ return self["xaxis"] @xaxis.setter def xaxis(self, val): self["xaxis"] = val # xbingroup # --------- @property def xbingroup(self): """ Set a group of histogram traces which will have compatible x-bin settings. Using `xbingroup`, histogram2d and histogram2dcontour traces (on axes of the same axis type) can have compatible x-bin settings. Note that the same `xbingroup` value can be used to set (1D) histogram `bingroup` The 'xbingroup' property is a string and must be specified as: - A string - A number that will be converted to a string Returns ------- str """ return self["xbingroup"] @xbingroup.setter def xbingroup(self, val): self["xbingroup"] = val # xbins # ----- @property def xbins(self): """ The 'xbins' property is an instance of XBins that may be specified as: - An instance of :class:`plotly.graph_objs.histogram2dcontour.XBins` - A dict of string/value properties that will be passed to the XBins constructor Supported dict properties: end Sets the end value for the x axis bins. The last bin may not end exactly at this value, we increment the bin edge by `size` from `start` until we reach or exceed `end`. Defaults to the maximum data value. Like `start`, for dates use a date string, and for category data `end` is based on the category serial numbers. size Sets the size of each x axis bin. Default behavior: If `nbinsx` is 0 or omitted, we choose a nice round bin size such that the number of bins is about the same as the typical number of samples in each bin. If `nbinsx` is provided, we choose a nice round bin size giving no more than that many bins. For date data, use milliseconds or "M<n>" for months, as in `axis.dtick`. For category data, the number of categories to bin together (always defaults to 1). start Sets the starting value for the x axis bins. Defaults to the minimum data value, shifted down if necessary to make nice round values and to remove ambiguous bin edges. For example, if most of the data is integers we shift the bin edges 0.5 down, so a `size` of 5 would have a default `start` of -0.5, so it is clear that 0-4 are in the first bin, 5-9 in the second, but continuous data gets a start of 0 and bins [0,5), [5,10) etc. Dates behave similarly, and `start` should be a date string. For category data, `start` is based on the category serial numbers, and defaults to -0.5. Returns ------- plotly.graph_objs.histogram2dcontour.XBins """ return self["xbins"] @xbins.setter def xbins(self, val): self["xbins"] = val # xcalendar # --------- @property def xcalendar(self): """ Sets the calendar system to use with `x` date data. The 'xcalendar' property is an enumeration that may be specified as: - One of the following enumeration values: ['gregorian', 'chinese', 'coptic', 'discworld', 'ethiopian', 'hebrew', 'islamic', 'julian', 'mayan', 'nanakshahi', 'nepali', 'persian', 'jalali', 'taiwan', 'thai', 'ummalqura'] Returns ------- Any """ return self["xcalendar"] @xcalendar.setter def xcalendar(self, val): self["xcalendar"] = val # xsrc # ---- @property def xsrc(self): """ Sets the source reference on Chart Studio Cloud for x . The 'xsrc' property must be specified as a string or as a plotly.grid_objs.Column object Returns ------- str """ return self["xsrc"] @xsrc.setter def xsrc(self, val): self["xsrc"] = val # y # - @property def y(self): """ Sets the sample data to be binned on the y axis. The 'y' property is an array that may be specified as a tuple, list, numpy array, or pandas Series Returns ------- numpy.ndarray """ return self["y"] @y.setter def y(self, val): self["y"] = val # yaxis # ----- @property def yaxis(self): """ Sets a reference between this trace's y coordinates and a 2D cartesian y axis. If "y" (the default value), the y coordinates refer to `layout.yaxis`. If "y2", the y coordinates refer to `layout.yaxis2`, and so on. The 'yaxis' property is an identifier of a particular subplot, of type 'y', that may be specified as the string 'y' optionally followed by an integer >= 1 (e.g. 'y', 'y1', 'y2', 'y3', etc.) Returns ------- str """ return self["yaxis"] @yaxis.setter def yaxis(self, val): self["yaxis"] = val # ybingroup # --------- @property def ybingroup(self): """ Set a group of histogram traces which will have compatible y-bin settings. Using `ybingroup`, histogram2d and histogram2dcontour traces (on axes of the same axis type) can have compatible y-bin settings. Note that the same `ybingroup` value can be used to set (1D) histogram `bingroup` The 'ybingroup' property is a string and must be specified as: - A string - A number that will be converted to a string Returns ------- str """ return self["ybingroup"] @ybingroup.setter def ybingroup(self, val): self["ybingroup"] = val # ybins # ----- @property def ybins(self): """ The 'ybins' property is an instance of YBins that may be specified as: - An instance of :class:`plotly.graph_objs.histogram2dcontour.YBins` - A dict of string/value properties that will be passed to the YBins constructor Supported dict properties: end Sets the end value for the y axis bins. The last bin may not end exactly at this value, we increment the bin edge by `size` from `start` until we reach or exceed `end`. Defaults to the maximum data value. Like `start`, for dates use a date string, and for category data `end` is based on the category serial numbers. size Sets the size of each y axis bin. Default behavior: If `nbinsy` is 0 or omitted, we choose a nice round bin size such that the number of bins is about the same as the typical number of samples in each bin. If `nbinsy` is provided, we choose a nice round bin size giving no more than that many bins. For date data, use milliseconds or "M<n>" for months, as in `axis.dtick`. For category data, the number of categories to bin together (always defaults to 1). start Sets the starting value for the y axis bins. Defaults to the minimum data value, shifted down if necessary to make nice round values and to remove ambiguous bin edges. For example, if most of the data is integers we shift the bin edges 0.5 down, so a `size` of 5 would have a default `start` of -0.5, so it is clear that 0-4 are in the first bin, 5-9 in the second, but continuous data gets a start of 0 and bins [0,5), [5,10) etc. Dates behave similarly, and `start` should be a date string. For category data, `start` is based on the category serial numbers, and defaults to -0.5. Returns ------- plotly.graph_objs.histogram2dcontour.YBins """ return self["ybins"] @ybins.setter def ybins(self, val): self["ybins"] = val # ycalendar # --------- @property def ycalendar(self): """ Sets the calendar system to use with `y` date data. The 'ycalendar' property is an enumeration that may be specified as: - One of the following enumeration values: ['gregorian', 'chinese', 'coptic', 'discworld', 'ethiopian', 'hebrew', 'islamic', 'julian', 'mayan', 'nanakshahi', 'nepali', 'persian', 'jalali', 'taiwan', 'thai', 'ummalqura'] Returns ------- Any """ return self["ycalendar"] @ycalendar.setter def ycalendar(self, val): self["ycalendar"] = val # ysrc # ---- @property def ysrc(self): """ Sets the source reference on Chart Studio Cloud for y . The 'ysrc' property must be specified as a string or as a plotly.grid_objs.Column object Returns ------- str """ return self["ysrc"] @ysrc.setter def ysrc(self, val): self["ysrc"] = val # z # - @property def z(self): """ Sets the aggregation data. The 'z' property is an array that may be specified as a tuple, list, numpy array, or pandas Series Returns ------- numpy.ndarray """ return self["z"] @z.setter def z(self, val): self["z"] = val # zauto # ----- @property def zauto(self): """ Determines whether or not the color domain is computed with respect to the input data (here in `z`) or the bounds set in `zmin` and `zmax` Defaults to `false` when `zmin` and `zmax` are set by the user. The 'zauto' property must be specified as a bool (either True, or False) Returns ------- bool """ return self["zauto"] @zauto.setter def zauto(self, val): self["zauto"] = val # zhoverformat # ------------ @property def zhoverformat(self): """ Sets the hover text formatting rule using d3 formatting mini- languages which are very similar to those in Python. See: https://github.com/d3/d3-3.x-api- reference/blob/master/Formatting.md#d3_format The 'zhoverformat' property is a string and must be specified as: - A string - A number that will be converted to a string Returns ------- str """ return self["zhoverformat"] @zhoverformat.setter def zhoverformat(self, val): self["zhoverformat"] = val # zmax # ---- @property def zmax(self): """ Sets the upper bound of the color domain. Value should have the same units as in `z` and if set, `zmin` must be set as well. The 'zmax' property is a number and may be specified as: - An int or float Returns ------- int|float """ return self["zmax"] @zmax.setter def zmax(self, val): self["zmax"] = val # zmid # ---- @property def zmid(self): """ Sets the mid-point of the color domain by scaling `zmin` and/or `zmax` to be equidistant to this point. Value should have the same units as in `z`. Has no effect when `zauto` is `false`. The 'zmid' property is a number and may be specified as: - An int or float Returns ------- int|float """ return self["zmid"] @zmid.setter def zmid(self, val): self["zmid"] = val # zmin # ---- @property def zmin(self): """ Sets the lower bound of the color domain. Value should have the same units as in `z` and if set, `zmax` must be set as well. The 'zmin' property is a number and may be specified as: - An int or float Returns ------- int|float """ return self["zmin"] @zmin.setter def zmin(self, val): self["zmin"] = val # zsrc # ---- @property def zsrc(self): """ Sets the source reference on Chart Studio Cloud for z . The 'zsrc' property must be specified as a string or as a plotly.grid_objs.Column object Returns ------- str """ return self["zsrc"] @zsrc.setter def zsrc(self, val): self["zsrc"] = val # type # ---- @property def type(self): return self._props["type"] # Self properties description # --------------------------- @property def _prop_descriptions(self): return """\ autobinx Obsolete: since v1.42 each bin attribute is auto- determined separately and `autobinx` is not needed. However, we accept `autobinx: true` or `false` and will update `xbins` accordingly before deleting `autobinx` from the trace. autobiny Obsolete: since v1.42 each bin attribute is auto- determined separately and `autobiny` is not needed. However, we accept `autobiny: true` or `false` and will update `ybins` accordingly before deleting `autobiny` from the trace. autocolorscale Determines whether the colorscale is a default palette (`autocolorscale: true`) or the palette determined by `colorscale`. In case `colorscale` is unspecified or `autocolorscale` is true, the default palette will be chosen according to whether numbers in the `color` array are all positive, all negative or mixed. autocontour Determines whether or not the contour level attributes are picked by an algorithm. If True, the number of contour levels can be set in `ncontours`. If False, set the contour level attributes in `contours`. bingroup Set the `xbingroup` and `ybingroup` default prefix For example, setting a `bingroup` of 1 on two histogram2d traces will make them their x-bins and y-bins match separately. coloraxis Sets a reference to a shared color axis. References to these shared color axes are "coloraxis", "coloraxis2", "coloraxis3", etc. Settings for these shared color axes are set in the layout, under `layout.coloraxis`, `layout.coloraxis2`, etc. Note that multiple color scales can be linked to the same color axis. colorbar :class:`plotly.graph_objects.histogram2dcontour.ColorBa r` instance or dict with compatible properties colorscale Sets the colorscale. The colorscale must be an array containing arrays mapping a normalized value to an rgb, rgba, hex, hsl, hsv, or named color string. At minimum, a mapping for the lowest (0) and highest (1) values are required. For example, `[[0, 'rgb(0,0,255)'], [1, 'rgb(255,0,0)']]`. To control the bounds of the colorscale in color space, use`zmin` and `zmax`. Alternatively, `colorscale` may be a palette name string of the following list: Greys,YlGnBu,Greens,YlOrR d,Bluered,RdBu,Reds,Blues,Picnic,Rainbow,Portland,Jet,H ot,Blackbody,Earth,Electric,Viridis,Cividis. contours :class:`plotly.graph_objects.histogram2dcontour.Contour s` instance or dict with compatible properties customdata Assigns extra data each datum. This may be useful when listening to hover, click and selection events. Note that, "scatter" traces also appends customdata items in the markers DOM elements customdatasrc Sets the source reference on Chart Studio Cloud for customdata . histfunc Specifies the binning function used for this histogram trace. If "count", the histogram values are computed by counting the number of values lying inside each bin. If "sum", "avg", "min", "max", the histogram values are computed using the sum, the average, the minimum or the maximum of the values lying inside each bin respectively. histnorm Specifies the type of normalization used for this histogram trace. If "", the span of each bar corresponds to the number of occurrences (i.e. the number of data points lying inside the bins). If "percent" / "probability", the span of each bar corresponds to the percentage / fraction of occurrences with respect to the total number of sample points (here, the sum of all bin HEIGHTS equals 100% / 1). If "density", the span of each bar corresponds to the number of occurrences in a bin divided by the size of the bin interval (here, the sum of all bin AREAS equals the total number of sample points). If *probability density*, the area of each bar corresponds to the probability that an event will fall into the corresponding bin (here, the sum of all bin AREAS equals 1). hoverinfo Determines which trace information appear on hover. If `none` or `skip` are set, no information is displayed upon hovering. But, if `none` is set, click and hover events are still fired. hoverinfosrc Sets the source reference on Chart Studio Cloud for hoverinfo . hoverlabel :class:`plotly.graph_objects.histogram2dcontour.Hoverla bel` instance or dict with compatible properties hovertemplate Template string used for rendering the information that appear on hover box. Note that this will override `hoverinfo`. Variables are inserted using %{variable}, for example "y: %{y}". Numbers are formatted using d3-format's syntax %{variable:d3-format}, for example "Price: %{y:$.2f}". https://github.com/d3/d3-3.x-api- reference/blob/master/Formatting.md#d3_format for details on the formatting syntax. Dates are formatted using d3-time-format's syntax %{variable|d3-time- format}, for example "Day: %{2019-01-01|%A}". https://github.com/d3/d3-3.x-api- reference/blob/master/Time-Formatting.md#format for details on the date formatting syntax. The variables available in `hovertemplate` are the ones emitted as event data described at this link https://plotly.com/javascript/plotlyjs-events/#event- data. Additionally, every attributes that can be specified per-point (the ones that are `arrayOk: true`) are available. variable `z` Anything contained in tag `<extra>` is displayed in the secondary box, for example "<extra>{fullData.name}</extra>". To hide the secondary box completely, use an empty tag `<extra></extra>`. hovertemplatesrc Sets the source reference on Chart Studio Cloud for hovertemplate . ids Assigns id labels to each datum. These ids for object constancy of data points during animation. Should be an array of strings, not numbers or any other type. idssrc Sets the source reference on Chart Studio Cloud for ids . legendgroup Sets the legend group for this trace. Traces part of the same legend group hide/show at the same time when toggling legend items. line :class:`plotly.graph_objects.histogram2dcontour.Line` instance or dict with compatible properties marker :class:`plotly.graph_objects.histogram2dcontour.Marker` instance or dict with compatible properties meta Assigns extra meta information associated with this trace that can be used in various text attributes. Attributes such as trace `name`, graph, axis and colorbar `title.text`, annotation `text` `rangeselector`, `updatemenues` and `sliders` `label` text all support `meta`. To access the trace `meta` values in an attribute in the same trace, simply use `%{meta[i]}` where `i` is the index or key of the `meta` item in question. To access trace `meta` in layout attributes, use `%{data[n[.meta[i]}` where `i` is the index or key of the `meta` and `n` is the trace index. metasrc Sets the source reference on Chart Studio Cloud for meta . name Sets the trace name. The trace name appear as the legend item and on hover. nbinsx Specifies the maximum number of desired bins. This value will be used in an algorithm that will decide the optimal bin size such that the histogram best visualizes the distribution of the data. Ignored if `xbins.size` is provided. nbinsy Specifies the maximum number of desired bins. This value will be used in an algorithm that will decide the optimal bin size such that the histogram best visualizes the distribution of the data. Ignored if `ybins.size` is provided. ncontours Sets the maximum number of contour levels. The actual number of contours will be chosen automatically to be less than or equal to the value of `ncontours`. Has an effect only if `autocontour` is True or if `contours.size` is missing. opacity Sets the opacity of the trace. reversescale Reverses the color mapping if true. If true, `zmin` will correspond to the last color in the array and `zmax` will correspond to the first color. showlegend Determines whether or not an item corresponding to this trace is shown in the legend. showscale Determines whether or not a colorbar is displayed for this trace. stream :class:`plotly.graph_objects.histogram2dcontour.Stream` instance or dict with compatible properties uid Assign an id to this trace, Use this to provide object constancy between traces during animations and transitions. uirevision Controls persistence of some user-driven changes to the trace: `constraintrange` in `parcoords` traces, as well as some `editable: true` modifications such as `name` and `colorbar.title`. Defaults to `layout.uirevision`. Note that other user-driven trace attribute changes are controlled by `layout` attributes: `trace.visible` is controlled by `layout.legend.uirevision`, `selectedpoints` is controlled by `layout.selectionrevision`, and `colorbar.(x|y)` (accessible with `config: {editable: true}`) is controlled by `layout.editrevision`. Trace changes are tracked by `uid`, which only falls back on trace index if no `uid` is provided. So if your app can add/remove traces before the end of the `data` array, such that the same trace has a different index, you can still preserve user-driven changes if you give each trace a `uid` that stays with it as it moves. visible Determines whether or not this trace is visible. If "legendonly", the trace is not drawn, but can appear as a legend item (provided that the legend itself is visible). x Sets the sample data to be binned on the x axis. xaxis Sets a reference between this trace's x coordinates and a 2D cartesian x axis. If "x" (the default value), the x coordinates refer to `layout.xaxis`. If "x2", the x coordinates refer to `layout.xaxis2`, and so on. xbingroup Set a group of histogram traces which will have compatible x-bin settings. Using `xbingroup`, histogram2d and histogram2dcontour traces (on axes of the same axis type) can have compatible x-bin settings. Note that the same `xbingroup` value can be used to set (1D) histogram `bingroup` xbins :class:`plotly.graph_objects.histogram2dcontour.XBins` instance or dict with compatible properties xcalendar Sets the calendar system to use with `x` date data. xsrc Sets the source reference on Chart Studio Cloud for x . y Sets the sample data to be binned on the y axis. yaxis Sets a reference between this trace's y coordinates and a 2D cartesian y axis. If "y" (the default value), the y coordinates refer to `layout.yaxis`. If "y2", the y coordinates refer to `layout.yaxis2`, and so on. ybingroup Set a group of histogram traces which will have compatible y-bin settings. Using `ybingroup`, histogram2d and histogram2dcontour traces (on axes of the same axis type) can have compatible y-bin settings. Note that the same `ybingroup` value can be used to set (1D) histogram `bingroup` ybins :class:`plotly.graph_objects.histogram2dcontour.YBins` instance or dict with compatible properties ycalendar Sets the calendar system to use with `y` date data. ysrc Sets the source reference on Chart Studio Cloud for y . z Sets the aggregation data. zauto Determines whether or not the color domain is computed with respect to the input data (here in `z`) or the bounds set in `zmin` and `zmax` Defaults to `false` when `zmin` and `zmax` are set by the user. zhoverformat Sets the hover text formatting rule using d3 formatting mini-languages which are very similar to those in Python. See: https://github.com/d3/d3-3.x-api- reference/blob/master/Formatting.md#d3_format zmax Sets the upper bound of the color domain. Value should have the same units as in `z` and if set, `zmin` must be set as well. zmid Sets the mid-point of the color domain by scaling `zmin` and/or `zmax` to be equidistant to this point. Value should have the same units as in `z`. Has no effect when `zauto` is `false`. zmin Sets the lower bound of the color domain. Value should have the same units as in `z` and if set, `zmax` must be set as well. zsrc Sets the source reference on Chart Studio Cloud for z . """ def __init__( self, arg=None, autobinx=None, autobiny=None, autocolorscale=None, autocontour=None, bingroup=None, coloraxis=None, colorbar=None, colorscale=None, contours=None, customdata=None, customdatasrc=None, histfunc=None, histnorm=None, hoverinfo=None, hoverinfosrc=None, hoverlabel=None, hovertemplate=None, hovertemplatesrc=None, ids=None, idssrc=None, legendgroup=None, line=None, marker=None, meta=None, metasrc=None, name=None, nbinsx=None, nbinsy=None, ncontours=None, opacity=None, reversescale=None, showlegend=None, showscale=None, stream=None, uid=None, uirevision=None, visible=None, x=None, xaxis=None, xbingroup=None, xbins=None, xcalendar=None, xsrc=None, y=None, yaxis=None, ybingroup=None, ybins=None, ycalendar=None, ysrc=None, z=None, zauto=None, zhoverformat=None, zmax=None, zmid=None, zmin=None, zsrc=None, **kwargs ): """ Construct a new Histogram2dContour object The sample data from which statistics are computed is set in `x` and `y` (where `x` and `y` represent marginal distributions, binning is set in `xbins` and `ybins` in this case) or `z` (where `z` represent the 2D distribution and binning set, binning is set by `x` and `y` in this case). The resulting distribution is visualized as a contour plot. Parameters ---------- arg dict of properties compatible with this constructor or an instance of :class:`plotly.graph_objs.Histogram2dContour` autobinx Obsolete: since v1.42 each bin attribute is auto- determined separately and `autobinx` is not needed. However, we accept `autobinx: true` or `false` and will update `xbins` accordingly before deleting `autobinx` from the trace. autobiny Obsolete: since v1.42 each bin attribute is auto- determined separately and `autobiny` is not needed. However, we accept `autobiny: true` or `false` and will update `ybins` accordingly before deleting `autobiny` from the trace. autocolorscale Determines whether the colorscale is a default palette (`autocolorscale: true`) or the palette determined by `colorscale`. In case `colorscale` is unspecified or `autocolorscale` is true, the default palette will be chosen according to whether numbers in the `color` array are all positive, all negative or mixed. autocontour Determines whether or not the contour level attributes are picked by an algorithm. If True, the number of contour levels can be set in `ncontours`. If False, set the contour level attributes in `contours`. bingroup Set the `xbingroup` and `ybingroup` default prefix For example, setting a `bingroup` of 1 on two histogram2d traces will make them their x-bins and y-bins match separately. coloraxis Sets a reference to a shared color axis. References to these shared color axes are "coloraxis", "coloraxis2", "coloraxis3", etc. Settings for these shared color axes are set in the layout, under `layout.coloraxis`, `layout.coloraxis2`, etc. Note that multiple color scales can be linked to the same color axis. colorbar :class:`plotly.graph_objects.histogram2dcontour.ColorBa r` instance or dict with compatible properties colorscale Sets the colorscale. The colorscale must be an array containing arrays mapping a normalized value to an rgb, rgba, hex, hsl, hsv, or named color string. At minimum, a mapping for the lowest (0) and highest (1) values are required. For example, `[[0, 'rgb(0,0,255)'], [1, 'rgb(255,0,0)']]`. To control the bounds of the colorscale in color space, use`zmin` and `zmax`. Alternatively, `colorscale` may be a palette name string of the following list: Greys,YlGnBu,Greens,YlOrR d,Bluered,RdBu,Reds,Blues,Picnic,Rainbow,Portland,Jet,H ot,Blackbody,Earth,Electric,Viridis,Cividis. contours :class:`plotly.graph_objects.histogram2dcontour.Contour s` instance or dict with compatible properties customdata Assigns extra data each datum. This may be useful when listening to hover, click and selection events. Note that, "scatter" traces also appends customdata items in the markers DOM elements customdatasrc Sets the source reference on Chart Studio Cloud for customdata . histfunc Specifies the binning function used for this histogram trace. If "count", the histogram values are computed by counting the number of values lying inside each bin. If "sum", "avg", "min", "max", the histogram values are computed using the sum, the average, the minimum or the maximum of the values lying inside each bin respectively. histnorm Specifies the type of normalization used for this histogram trace. If "", the span of each bar corresponds to the number of occurrences (i.e. the number of data points lying inside the bins). If "percent" / "probability", the span of each bar corresponds to the percentage / fraction of occurrences with respect to the total number of sample points (here, the sum of all bin HEIGHTS equals 100% / 1). If "density", the span of each bar corresponds to the number of occurrences in a bin divided by the size of the bin interval (here, the sum of all bin AREAS equals the total number of sample points). If *probability density*, the area of each bar corresponds to the probability that an event will fall into the corresponding bin (here, the sum of all bin AREAS equals 1). hoverinfo Determines which trace information appear on hover. If `none` or `skip` are set, no information is displayed upon hovering. But, if `none` is set, click and hover events are still fired. hoverinfosrc Sets the source reference on Chart Studio Cloud for hoverinfo . hoverlabel :class:`plotly.graph_objects.histogram2dcontour.Hoverla bel` instance or dict with compatible properties hovertemplate Template string used for rendering the information that appear on hover box. Note that this will override `hoverinfo`. Variables are inserted using %{variable}, for example "y: %{y}". Numbers are formatted using d3-format's syntax %{variable:d3-format}, for example "Price: %{y:$.2f}". https://github.com/d3/d3-3.x-api- reference/blob/master/Formatting.md#d3_format for details on the formatting syntax. Dates are formatted using d3-time-format's syntax %{variable|d3-time- format}, for example "Day: %{2019-01-01|%A}". https://github.com/d3/d3-3.x-api- reference/blob/master/Time-Formatting.md#format for details on the date formatting syntax. The variables available in `hovertemplate` are the ones emitted as event data described at this link https://plotly.com/javascript/plotlyjs-events/#event- data. Additionally, every attributes that can be specified per-point (the ones that are `arrayOk: true`) are available. variable `z` Anything contained in tag `<extra>` is displayed in the secondary box, for example "<extra>{fullData.name}</extra>". To hide the secondary box completely, use an empty tag `<extra></extra>`. hovertemplatesrc Sets the source reference on Chart Studio Cloud for hovertemplate . ids Assigns id labels to each datum. These ids for object constancy of data points during animation. Should be an array of strings, not numbers or any other type. idssrc Sets the source reference on Chart Studio Cloud for ids . legendgroup Sets the legend group for this trace. Traces part of the same legend group hide/show at the same time when toggling legend items. line :class:`plotly.graph_objects.histogram2dcontour.Line` instance or dict with compatible properties marker :class:`plotly.graph_objects.histogram2dcontour.Marker` instance or dict with compatible properties meta Assigns extra meta information associated with this trace that can be used in various text attributes. Attributes such as trace `name`, graph, axis and colorbar `title.text`, annotation `text` `rangeselector`, `updatemenues` and `sliders` `label` text all support `meta`. To access the trace `meta` values in an attribute in the same trace, simply use `%{meta[i]}` where `i` is the index or key of the `meta` item in question. To access trace `meta` in layout attributes, use `%{data[n[.meta[i]}` where `i` is the index or key of the `meta` and `n` is the trace index. metasrc Sets the source reference on Chart Studio Cloud for meta . name Sets the trace name. The trace name appear as the legend item and on hover. nbinsx Specifies the maximum number of desired bins. This value will be used in an algorithm that will decide the optimal bin size such that the histogram best visualizes the distribution of the data. Ignored if `xbins.size` is provided. nbinsy Specifies the maximum number of desired bins. This value will be used in an algorithm that will decide the optimal bin size such that the histogram best visualizes the distribution of the data. Ignored if `ybins.size` is provided. ncontours Sets the maximum number of contour levels. The actual number of contours will be chosen automatically to be less than or equal to the value of `ncontours`. Has an effect only if `autocontour` is True or if `contours.size` is missing. opacity Sets the opacity of the trace. reversescale Reverses the color mapping if true. If true, `zmin` will correspond to the last color in the array and `zmax` will correspond to the first color. showlegend Determines whether or not an item corresponding to this trace is shown in the legend. showscale Determines whether or not a colorbar is displayed for this trace. stream :class:`plotly.graph_objects.histogram2dcontour.Stream` instance or dict with compatible properties uid Assign an id to this trace, Use this to provide object constancy between traces during animations and transitions. uirevision Controls persistence of some user-driven changes to the trace: `constraintrange` in `parcoords` traces, as well as some `editable: true` modifications such as `name` and `colorbar.title`. Defaults to `layout.uirevision`. Note that other user-driven trace attribute changes are controlled by `layout` attributes: `trace.visible` is controlled by `layout.legend.uirevision`, `selectedpoints` is controlled by `layout.selectionrevision`, and `colorbar.(x|y)` (accessible with `config: {editable: true}`) is controlled by `layout.editrevision`. Trace changes are tracked by `uid`, which only falls back on trace index if no `uid` is provided. So if your app can add/remove traces before the end of the `data` array, such that the same trace has a different index, you can still preserve user-driven changes if you give each trace a `uid` that stays with it as it moves. visible Determines whether or not this trace is visible. If "legendonly", the trace is not drawn, but can appear as a legend item (provided that the legend itself is visible). x Sets the sample data to be binned on the x axis. xaxis Sets a reference between this trace's x coordinates and a 2D cartesian x axis. If "x" (the default value), the x coordinates refer to `layout.xaxis`. If "x2", the x coordinates refer to `layout.xaxis2`, and so on. xbingroup Set a group of histogram traces which will have compatible x-bin settings. Using `xbingroup`, histogram2d and histogram2dcontour traces (on axes of the same axis type) can have compatible x-bin settings. Note that the same `xbingroup` value can be used to set (1D) histogram `bingroup` xbins :class:`plotly.graph_objects.histogram2dcontour.XBins` instance or dict with compatible properties xcalendar Sets the calendar system to use with `x` date data. xsrc Sets the source reference on Chart Studio Cloud for x . y Sets the sample data to be binned on the y axis. yaxis Sets a reference between this trace's y coordinates and a 2D cartesian y axis. If "y" (the default value), the y coordinates refer to `layout.yaxis`. If "y2", the y coordinates refer to `layout.yaxis2`, and so on. ybingroup Set a group of histogram traces which will have compatible y-bin settings. Using `ybingroup`, histogram2d and histogram2dcontour traces (on axes of the same axis type) can have compatible y-bin settings. Note that the same `ybingroup` value can be used to set (1D) histogram `bingroup` ybins :class:`plotly.graph_objects.histogram2dcontour.YBins` instance or dict with compatible properties ycalendar Sets the calendar system to use with `y` date data. ysrc Sets the source reference on Chart Studio Cloud for y . z Sets the aggregation data. zauto Determines whether or not the color domain is computed with respect to the input data (here in `z`) or the bounds set in `zmin` and `zmax` Defaults to `false` when `zmin` and `zmax` are set by the user. zhoverformat Sets the hover text formatting rule using d3 formatting mini-languages which are very similar to those in Python. See: https://github.com/d3/d3-3.x-api- reference/blob/master/Formatting.md#d3_format zmax Sets the upper bound of the color domain. Value should have the same units as in `z` and if set, `zmin` must be set as well. zmid Sets the mid-point of the color domain by scaling `zmin` and/or `zmax` to be equidistant to this point. Value should have the same units as in `z`. Has no effect when `zauto` is `false`. zmin Sets the lower bound of the color domain. Value should have the same units as in `z` and if set, `zmax` must be set as well. zsrc Sets the source reference on Chart Studio Cloud for z . Returns ------- Histogram2dContour """ super(Histogram2dContour, self).__init__("histogram2dcontour") if "_parent" in kwargs: self._parent = kwargs["_parent"] return # Validate arg # ------------ if arg is None: arg = {} elif isinstance(arg, self.__class__): arg = arg.to_plotly_json() elif isinstance(arg, dict): arg = _copy.copy(arg) else: raise ValueError( """\ The first argument to the plotly.graph_objs.Histogram2dContour constructor must be a dict or an instance of :class:`plotly.graph_objs.Histogram2dContour`""" ) # Handle skip_invalid # ------------------- self._skip_invalid = kwargs.pop("skip_invalid", False) self._validate = kwargs.pop("_validate", True) # Populate data dict with properties # ---------------------------------- _v = arg.pop("autobinx", None) _v = autobinx if autobinx is not None else _v if _v is not None: self["autobinx"] = _v _v = arg.pop("autobiny", None) _v = autobiny if autobiny is not None else _v if _v is not None: self["autobiny"] = _v _v = arg.pop("autocolorscale", None) _v = autocolorscale if autocolorscale is not None else _v if _v is not None: self["autocolorscale"] = _v _v = arg.pop("autocontour", None) _v = autocontour if autocontour is not None else _v if _v is not None: self["autocontour"] = _v _v = arg.pop("bingroup", None) _v = bingroup if bingroup is not None else _v if _v is not None: self["bingroup"] = _v _v = arg.pop("coloraxis", None) _v = coloraxis if coloraxis is not None else _v if _v is not None: self["coloraxis"] = _v _v = arg.pop("colorbar", None) _v = colorbar if colorbar is not None else _v if _v is not None: self["colorbar"] = _v _v = arg.pop("colorscale", None) _v = colorscale if colorscale is not None else _v if _v is not None: self["colorscale"] = _v _v = arg.pop("contours", None) _v = contours if contours is not None else _v if _v is not None: self["contours"] = _v _v = arg.pop("customdata", None) _v = customdata if customdata is not None else _v if _v is not None: self["customdata"] = _v _v = arg.pop("customdatasrc", None) _v = customdatasrc if customdatasrc is not None else _v if _v is not None: self["customdatasrc"] = _v _v = arg.pop("histfunc", None) _v = histfunc if histfunc is not None else _v if _v is not None: self["histfunc"] = _v _v = arg.pop("histnorm", None) _v = histnorm if histnorm is not None else _v if _v is not None: self["histnorm"] = _v _v = arg.pop("hoverinfo", None) _v = hoverinfo if hoverinfo is not None else _v if _v is not None: self["hoverinfo"] = _v _v = arg.pop("hoverinfosrc", None) _v = hoverinfosrc if hoverinfosrc is not None else _v if _v is not None: self["hoverinfosrc"] = _v _v = arg.pop("hoverlabel", None) _v = hoverlabel if hoverlabel is not None else _v if _v is not None: self["hoverlabel"] = _v _v = arg.pop("hovertemplate", None) _v = hovertemplate if hovertemplate is not None else _v if _v is not None: self["hovertemplate"] = _v _v = arg.pop("hovertemplatesrc", None) _v = hovertemplatesrc if hovertemplatesrc is not None else _v if _v is not None: self["hovertemplatesrc"] = _v _v = arg.pop("ids", None) _v = ids if ids is not None else _v if _v is not None: self["ids"] = _v _v = arg.pop("idssrc", None) _v = idssrc if idssrc is not None else _v if _v is not None: self["idssrc"] = _v _v = arg.pop("legendgroup", None) _v = legendgroup if legendgroup is not None else _v if _v is not None: self["legendgroup"] = _v _v = arg.pop("line", None) _v = line if line is not None else _v if _v is not None: self["line"] = _v _v = arg.pop("marker", None) _v = marker if marker is not None else _v if _v is not None: self["marker"] = _v _v = arg.pop("meta", None) _v = meta if meta is not None else _v if _v is not None: self["meta"] = _v _v = arg.pop("metasrc", None) _v = metasrc if metasrc is not None else _v if _v is not None: self["metasrc"] = _v _v = arg.pop("name", None) _v = name if name is not None else _v if _v is not None: self["name"] = _v _v = arg.pop("nbinsx", None) _v = nbinsx if nbinsx is not None else _v if _v is not None: self["nbinsx"] = _v _v = arg.pop("nbinsy", None) _v = nbinsy if nbinsy is not None else _v if _v is not None: self["nbinsy"] = _v _v = arg.pop("ncontours", None) _v = ncontours if ncontours is not None else _v if _v is not None: self["ncontours"] = _v _v = arg.pop("opacity", None) _v = opacity if opacity is not None else _v if _v is not None: self["opacity"] = _v _v = arg.pop("reversescale", None) _v = reversescale if reversescale is not None else _v if _v is not None: self["reversescale"] = _v _v = arg.pop("showlegend", None) _v = showlegend if showlegend is not None else _v if _v is not None: self["showlegend"] = _v _v = arg.pop("showscale", None) _v = showscale if showscale is not None else _v if _v is not None: self["showscale"] = _v _v = arg.pop("stream", None) _v = stream if stream is not None else _v if _v is not None: self["stream"] = _v _v = arg.pop("uid", None) _v = uid if uid is not None else _v if _v is not None: self["uid"] = _v _v = arg.pop("uirevision", None) _v = uirevision if uirevision is not None else _v if _v is not None: self["uirevision"] = _v _v = arg.pop("visible", None) _v = visible if visible is not None else _v if _v is not None: self["visible"] = _v _v = arg.pop("x", None) _v = x if x is not None else _v if _v is not None: self["x"] = _v _v = arg.pop("xaxis", None) _v = xaxis if xaxis is not None else _v if _v is not None: self["xaxis"] = _v _v = arg.pop("xbingroup", None) _v = xbingroup if xbingroup is not None else _v if _v is not None: self["xbingroup"] = _v _v = arg.pop("xbins", None) _v = xbins if xbins is not None else _v if _v is not None: self["xbins"] = _v _v = arg.pop("xcalendar", None) _v = xcalendar if xcalendar is not None else _v if _v is not None: self["xcalendar"] = _v _v = arg.pop("xsrc", None) _v = xsrc if xsrc is not None else _v if _v is not None: self["xsrc"] = _v _v = arg.pop("y", None) _v = y if y is not None else _v if _v is not None: self["y"] = _v _v = arg.pop("yaxis", None) _v = yaxis if yaxis is not None else _v if _v is not None: self["yaxis"] = _v _v = arg.pop("ybingroup", None) _v = ybingroup if ybingroup is not None else _v if _v is not None: self["ybingroup"] = _v _v = arg.pop("ybins", None) _v = ybins if ybins is not None else _v if _v is not None: self["ybins"] = _v _v = arg.pop("ycalendar", None) _v = ycalendar if ycalendar is not None else _v if _v is not None: self["ycalendar"] = _v _v = arg.pop("ysrc", None) _v = ysrc if ysrc is not None else _v if _v is not None: self["ysrc"] = _v _v = arg.pop("z", None) _v = z if z is not None else _v if _v is not None: self["z"] = _v _v = arg.pop("zauto", None) _v = zauto if zauto is not None else _v if _v is not None: self["zauto"] = _v _v = arg.pop("zhoverformat", None) _v = zhoverformat if zhoverformat is not None else _v if _v is not None: self["zhoverformat"] = _v _v = arg.pop("zmax", None) _v = zmax if zmax is not None else _v if _v is not None: self["zmax"] = _v _v = arg.pop("zmid", None) _v = zmid if zmid is not None else _v if _v is not None: self["zmid"] = _v _v = arg.pop("zmin", None) _v = zmin if zmin is not None else _v if _v is not None: self["zmin"] = _v _v = arg.pop("zsrc", None) _v = zsrc if zsrc is not None else _v if _v is not None: self["zsrc"] = _v # Read-only literals # ------------------ self._props["type"] = "histogram2dcontour" arg.pop("type", None) # Process unknown kwargs # ---------------------- self._process_kwargs(**dict(arg, **kwargs)) # Reset skip_invalid # ------------------ self._skip_invalid = False

"""TensorFlow ops for array / tensor manipulation.""" # Copyright 2015-present The Scikit Flow 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. from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf def split_squeeze(dim, num_split, tensor_in): """Splits input on given dimension and then squeezes that dimension. Args: dim: Dimension to split and squeeze on. num_split: integer, the number of ways to split. tensor_in: Input tensor of shape [N1, N2, .. Ndim, .. Nx]. Returns: List of tensors [N1, N2, .. Ndim-1, Ndim+1, .. Nx]. """ return [tf.squeeze(t, squeeze_dims=[dim]) for t in tf.split(dim, num_split, tensor_in)] def expand_concat(dim, inputs): """Expands inputs on given dimension and then concatenates them. Args: dim: Dimension to expand and concatenate on. inputs: List of tensors of the same shape [N1, ... Nx]. Returns: A tensor of shape [N1, .. Ndim, ... Nx] """ return tf.concat(dim, [tf.expand_dims(t, dim) for t in inputs]) def one_hot_matrix(tensor_in, num_classes, on_value=1.0, off_value=0.0): """Encodes indices from given tensor as one-hot tensor. TODO(ilblackdragon): Ideally implementation should be part of TensorFlow with Eigen-native operation. Args: tensor_in: Input tensor of shape [N1, N2]. num_classes: Number of classes to expand index into. on_value: Tensor or float, value to fill-in given index. off_value: Tensor or float, value to fill-in everything else. Returns: Tensor of shape [N1, N2, num_classes] with 1.0 for each id in original tensor. """ tensor_in = tf.convert_to_tensor(tensor_in) sparse_values = tf.to_int64(tf.reshape(tensor_in, [-1, 1])) size = tf.shape(sparse_values)[0] dims = tf.shape(tensor_in) indices = tf.to_int64(tf.reshape(tf.range(0, size), [-1, 1])) indices_values = tf.concat(1, [indices, sparse_values]) outshape = tf.to_int64(expand_concat(0, [size, num_classes])) one_hot_vector = tf.sparse_to_dense(indices_values, outshape, on_value, off_value) ret = tf.reshape(one_hot_vector, tf.concat(0, [dims, [num_classes]])) ret.set_shape(tensor_in.get_shape().concatenate(num_classes)) return ret

def create_multipliers(): return [lambda x : i * x for i in range(5)] if __name__ == '__main__': for multiplier in create_multipliers(): print(multiplier(2)) # Devrait afficher 0, 2, 4, 6, 8

import os import re import hashlib from functools import partial class Disc: def __init__(self, id, size, intitial_position, delay=1, angular_velocity=1): self.id = int(id) self.size = int(size) self.initial_position = int(intitial_position) self.delay = self.id self.angular_velocity = angular_velocity def get_position(self, time): return (self.angular_velocity*time + self.initial_position) % self.size def get_position_with_delay(self, time): return (self.angular_velocity * (time + self.delay) + self.initial_position) % self.size def min_time_to_0(self): return self.size - (self.angular_velocity * self.delay + self.initial_position) % self.size class Sculpture: regex = re.compile(r'Disc #([0-9]+) has ([0-9]+) positions; at time=0, ' r'it is at position ([0-9]+).') def __init__(self, description): self.discs = [] for line in description: match = Sculpture.regex.match(line) if match: self.discs.append(Disc(*match.groups())) self.sort_discs() def sort_discs(self): self.discs = sorted(self.discs, key=lambda disc: disc.size) def solve(self): sol = None index = 0 while sol is None: time = self.discs[-1].min_time_to_0() + index * self.discs[-1].size for disc in self.discs: if disc.get_position_with_delay(time) != 0: break else: return time index += 1 if __name__ == '__main__': dir = os.path.dirname(__file__) file = os.path.join(dir, 'input.txt') instructions = [] with open(file) as fd: for line in fd: instructions.append(line.strip()) sculpture = Sculpture(instructions) print('Part1: ', sculpture.solve()) instructions.append('Disc #7 has 11 positions; at time=0, it is at position 0.') sculpture = Sculpture(instructions) print('Part 2:', sculpture.solve())

"""User-facing customization options to create and train a text classifier.""" import dataclasses from typing import Optional from mediapipe.model_maker.python.core import hyperparameters as hp from mediapipe.model_maker.python.text.text_classifier import model_options as mo from mediapipe.model_maker.python.text.text_classifier import model_spec as ms @dataclasses.dataclass class TextClassifierOptions: """User-facing options for creating the text classifier. Attributes: supported_model: A preconfigured model spec. hparams: Training hyperparameters the user can set to override the ones in `supported_model`. model_options: Model options the user can set to override the ones in `supported_model`. The model options type should be consistent with the architecture of the `supported_model`. """ supported_model: ms.SupportedModels hparams: Optional[hp.BaseHParams] = None model_options: Optional[mo.TextClassifierModelOptions] = None

import xmlrpc.client url = "http://www.pythonchallenge.com/pc/phonebook.php" proxy = xmlrpc.client.ServerProxy(url) print(proxy.system.listMethods()) print(proxy.system.methodHelp("phone")) #from http://www.pythonchallenge.com/pc/return/evil4.jpg print(proxy.phone("Bert")) #555-ITALY

from __future__ import unicode_literals from calaccess_raw import fields from .base import CalAccessBaseModel, DocumentCloud from django.template.defaultfilters import floatformat from django.utils.encoding import python_2_unicode_compatible from django.contrib.humanize.templatetags.humanize import intcomma @python_2_unicode_compatible class FilernameCd(CalAccessBaseModel): """ A combination of CAL-ACCESS tables to provide the analyst with filer information. Full name of all PACs, firms, and employers are in the last name field. Major donors can be split between first and last name fields, but usually are contained in the last name field only. Individual names of lobbyists, candidates/officeholders, treasurers/responsible officers, and major donors (when they are only an individual's name) use both the first and last name fields in conjunction. """ UNIQUE_KEY = ("FILER_ID", "NAMID") xref_filer_id = fields.CharField( verbose_name='crossreference filer ID', max_length=15, db_column='XREF_FILER_ID', db_index=True, help_text="Alternative filer ID found on many forms" ) DOCUMENTCLOUD_PAGES = [ DocumentCloud(id='2711615-FAQ', start_page=2) ] filer_id = fields.IntegerField( verbose_name='filer ID', db_column='FILER_ID', db_index=True, null=True, help_text="Filer's unique identification number" ) FILER_TYPE_CHOICES = ( (' NOT DEFINED', 'Undefined'), ('ALL FILERS', 'All filers'), ('CANDIDATE/OFFICEHOLDER', 'Candidate/officeholder'), ('CLIENT', 'Client'), ('EMPLOYER', 'Employer'), ('FIRM', 'Firm'), ('INDIVIDUAL', 'Individual'), ('INITIATIVE', 'Initiative'), ('LOBBYIST', 'Lobbyist'), ( 'MAJOR DONOR/INDEPENDENT EXPENDITURE COMMITTEE', 'Major donor or indenpendent expenditure committee' ), ('PAYMENT TO INFLUENCE', 'Payment to influence'), ('PREPAID ACCOUNT', 'Prepaid account'), ('PROPONENT', 'Proponent'), ('PROPOSITION', 'Proposition'), ('RECIPIENT COMMITTEE', 'Recipient committee'), ('SLATE MAILER ORGANIZATIONS', 'Slate mailer organization'), ( 'TREASURER/RESPONSIBLE OFFICER', 'Treasurer/responsible officer' ) ) filer_type = fields.CharField( max_length=45, db_column='FILER_TYPE', db_index=True, choices=FILER_TYPE_CHOICES, help_text='The type of filer' ) STATUS_CHOICES = ( ('', 'Undefined'), ('A', ''), ('ACTIVE', ''), ('INACTIVE', ''), ('P', ''), ('R', ''), ('S', ''), ('TERMINATED', ''), ('W', ''), ) status = fields.CharField( max_length=10, db_column='STATUS', db_index=True, choices=STATUS_CHOICES, blank=True, help_text='The status of the filer' ) effect_dt = fields.DateField( db_column='EFFECT_DT', help_text="Effective date for status", null=True, ) naml = fields.CharField( max_length=200, db_column='NAML', help_text="Last name, sometimes full name" ) namf = fields.CharField( max_length=55, db_column='NAMF', blank=True, help_text="First name" ) namt = fields.CharField( max_length=70, db_column='NAMT', blank=True, help_text="Name prefix or title" ) nams = fields.CharField( max_length=32, db_column='NAMS', blank=True, help_text="Name suffix" ) adr1 = fields.CharField( max_length=200, db_column='ADR1', blank=True, help_text="First line of street address" ) adr2 = fields.CharField( max_length=200, db_column='ADR2', blank=True, help_text="Second line of street address" ) city = fields.CharField( max_length=55, db_column='CITY', blank=True, help_text="City address" ) st = fields.CharField( max_length=4, db_column='ST', blank=True, verbose_name="State" ) zip4 = fields.CharField( max_length=10, db_column='ZIP4', blank=True, help_text="ZIP Code" ) phon = fields.CharField( max_length=60, db_column='PHON', blank=True, verbose_name="Phone", help_text="Phone number" ) fax = fields.CharField( max_length=60, db_column='FAX', blank=True, help_text="Fax number" ) email = fields.CharField( max_length=60, db_column='EMAIL', blank=True, help_text="Email address" ) class Meta: app_label = 'calaccess_raw' db_table = 'FILERNAME_CD' verbose_name = 'FILERNAME_CD' verbose_name_plural = 'FILERNAME_CD' ordering = ("naml", "namf",) def __str__(self): return str(self.filer_id) @python_2_unicode_compatible class FilerFilingsCd(CalAccessBaseModel): """ Key table that links filers to their paper, key data entry, legacy, and electronic filings. This table is used as an index to locate filing information. """ UNIQUE_KEY = ( "FILER_ID", "FILING_ID", "FORM_ID", "FILING_SEQUENCE" ) filer_id = fields.IntegerField( verbose_name='filer ID', db_column='FILER_ID', db_index=True, null=True, help_text="Filer's unique identification number" ) filing_id = fields.IntegerField( db_column='FILING_ID', db_index=True, verbose_name='filing ID', help_text="Unique filing identificiation number" ) period_id = fields.IntegerField( null=True, db_column='PERIOD_ID', blank=True, help_text="Identifies the period when the filing was recieved." ) FORM_ID_CHOICES = ( ('E530', ''), ('F111', ''), ('F400', ''), ('F401', ''), ('F402', ''), ('F405', ''), ('F410', ''), ('F410 AT', ''), ('F410ATR', ''), ('F415', ''), ('F416', ''), ('F419', ''), ('F420', ''), ('F421', ''), ('F425', ''), ('F430', ''), ('F440', ''), ('F450', ''), ('F460', ''), ('F461', ''), ('F465', ''), ('F470', ''), ('F470S', ''), ('F480', ''), ('F490', ''), ('F495', ''), ('F496', ''), ('F497', ''), ('F498', ''), ('F500', ''), ('F501', ''), ('F501502', ''), ('F502', ''), ('F555', ''), ('F601', ''), ('F602', ''), ('F603', ''), ('F604', ''), ('F605', ''), ('F606', ''), ('F607', ''), ('F615', ''), ('F625', ''), ('F635', ''), ('F645', ''), ('F666', ''), ('F690', ''), ('F700', ''), ('F777', ''), ('F888', ''), ('F900', ''), ('F999', ''), ) form_id = fields.CharField( max_length=7, db_column='FORM_ID', db_index=True, verbose_name='form type', choices=FORM_ID_CHOICES, help_text="Form identification code" ) filing_sequence = fields.IntegerField( db_column='FILING_SEQUENCE', db_index=True, help_text="Amendment number where 0 is an original filing and 1 to \ 999 are amendments" ) filing_date = fields.DateField( db_column='FILING_DATE', help_text="Date the filing entered into the system", null=True ) STATEMENT_TYPE_CHOICES = ( (0, ''), (10001, ''), (10002, ''), (10003, ''), (10004, ''), (10005, ''), (10006, ''), (10007, ''), ) stmnt_type = fields.IntegerField( db_column='STMNT_TYPE', verbose_name="statement type", db_index=True, choices=STATEMENT_TYPE_CHOICES, help_text="Type of statement" ) STATEMENT_STATUS_CHOICES = ( (0, ''), (11001, ''), (11002, ''), (11003, ''), ) stmnt_status = fields.IntegerField( db_column='STMNT_STATUS', db_index=True, null=True, help_text="The status of the statement. If the filing has been \ reviewed or not reviewed.", verbose_name='statement status', choices=STATEMENT_STATUS_CHOICES, ) session_id = fields.IntegerField( verbose_name='session ID', db_column='SESSION_ID', help_text='Legislative session identification number', null=True, ) user_id = fields.CharField( max_length=12, db_column='USER_ID', verbose_name="User ID", help_text="User identifier of the PRD user who logged the filing" ) special_audit = fields.IntegerField( null=True, db_column='SPECIAL_AUDIT', blank=True, help_text="Denotes whether the filing has been audited for money \ laundering or other special condition." ) fine_audit = fields.IntegerField( null=True, db_column='FINE_AUDIT', blank=True, help_text="Indicates whether a filing has been audited for a fine" ) rpt_start = fields.DateField( null=True, db_column='RPT_START', blank=True, help_text="Starting date for the period the filing represents", ) rpt_end = fields.DateField( null=True, db_column='RPT_END', blank=True, help_text="Ending date for the period the filing represents", ) rpt_date = fields.DateField( null=True, db_column='RPT_DATE', blank=True, help_text="Date filing received", ) FILING_TYPE_CHOICES = ( (0, '0 (Unknown)'), (22001, 'Electronic'), (22006, 'Cal Online'), ) filing_type = fields.IntegerField( db_column='FILING_TYPE', null=True, blank=True, choices=FILING_TYPE_CHOICES, help_text="The type of filing" ) class Meta: app_label = 'calaccess_raw' db_table = 'FILER_FILINGS_CD' verbose_name = 'FILER_FILINGS_CD' verbose_name_plural = 'FILER_FILINGS_CD' def __str__(self): return str("%s %s" % (self.filer_id, self.filing_id)) @python_2_unicode_compatible class FilingsCd(CalAccessBaseModel): """ This table is the parent table from which all links and association to a filing are derived. """ UNIQUE_KEY = "FILING_ID" filing_id = fields.IntegerField( db_column='FILING_ID', db_index=True, verbose_name='filing ID', help_text="Unique filing identificiation number" ) FILING_TYPE_CHOICES = ( (22001, 'Electronic'), (22002, 'Key data entry'), (22003, 'Historical lobby'), (22004, 'Historical campaign'), (22005, 'AMS'), (22006, 'Cal Online'), ) filing_type = fields.IntegerField( db_column='FILING_TYPE', db_index=True, choices=FILING_TYPE_CHOICES, help_text="The type of filing" ) class Meta: app_label = 'calaccess_raw' db_table = 'FILINGS_CD' verbose_name = 'FILINGS_CD' verbose_name_plural = 'FILINGS_CD' def __str__(self): return str("%s %s" % (self.filing_id, self.filing_type)) @python_2_unicode_compatible class HdrCd(CalAccessBaseModel): """ Electronic filing record header data """ UNIQUE_KEY = ("FILING_ID", "AMEND_ID") amend_id = fields.IntegerField( db_column='AMEND_ID', db_index=True, help_text="Amendment identification number. A number of 0 is the \ original filing and 1 to 999 amendments.", verbose_name="amendment ID" ) cal_ver = fields.CharField( max_length=4, db_column='CAL_VER', blank=True, help_text="CAL Version number the filing was made using" ) ef_type = fields.CharField( max_length=3, db_column='EF_TYPE', blank=True, help_text='Electronic filing type. This will always have the \ value of "CAL".' ) filing_id = fields.IntegerField( db_column='FILING_ID', db_index=True, verbose_name='filing ID', help_text="Unique filing identificiation number" ) hdr_comment = fields.CharField( max_length=200, db_column='HDRCOMMENT', blank=True, verbose_name="Header comment", help_text="Typically used for development and test filings" ) REC_TYPE_CHOICES = ( ("HDR", "HDR"), ) rec_type = fields.CharField( verbose_name='record type', db_column='REC_TYPE', max_length=4, db_index=True, choices=REC_TYPE_CHOICES, ) soft_name = fields.CharField( max_length=90, db_column='SOFT_NAME', blank=True, help_text="Filing software name used to electronically file" ) soft_ver = fields.CharField( max_length=16, db_column='SOFT_VER', blank=True, help_text="Filing software version number" ) state_cd = fields.CharField( max_length=2, db_column='STATE_CD', blank=True, verbose_name='State code', help_text="The state code value entered in the electronic filing" ) class Meta: app_label = 'calaccess_raw' db_table = 'HDR_CD' verbose_name = 'HDR_CD' verbose_name_plural = 'HDR_CD' def __str__(self): return str(self.filing_id) @python_2_unicode_compatible class HeaderCd(CalAccessBaseModel): """ Lookup table used to report form 460 information in the AMS. """ UNIQUE_KEY = ("LINE_NUMBER", "FORM_ID", "REC_TYPE") line_number = fields.IntegerField( db_column='LINE_NUMBER', help_text="This field is undocumented" ) form_id = fields.CharField( db_column='FORM_ID', max_length=5, help_text="Form identification code", verbose_name="Form ID" ) REC_TYPE_CHOICES = ( ("AP1", "AP1"), ("AP2", "AP2"), ("SMRY_HEADER", "SMRY_HEADER"), ) rec_type = fields.CharField( verbose_name='record type', db_column='REC_TYPE', max_length=11, db_index=True, choices=REC_TYPE_CHOICES, ) section_label = fields.CharField( db_column='SECTION_LABEL', max_length=58, blank=True, help_text="This field is undocumented" ) comments1 = fields.CharField( db_column='COMMENTS1', max_length=48, blank=True, help_text="This field is undocumented" ) comments2 = fields.CharField( db_column='COMMENTS2', max_length=48, blank=True, help_text="This field is undocumented" ) label = fields.CharField( db_column='LABEL', max_length=98, help_text="This field is undocumented" ) column_a = fields.IntegerField( db_column='COLUMN_A', blank=True, null=True, help_text="This field is undocumented" ) column_b = fields.IntegerField( db_column='COLUMN_B', blank=True, null=True, help_text="This field is undocumented" ) column_c = fields.IntegerField( db_column='COLUMN_C', blank=True, null=True, help_text="This field is undocumented" ) show_c = fields.IntegerField( db_column='SHOW_C', blank=True, null=True, help_text="This field is undocumented" ) show_b = fields.IntegerField( db_column='SHOW_B', blank=True, null=True, help_text="This field is undocumented" ) class Meta: app_label = 'calaccess_raw' db_table = 'HEADER_CD' verbose_name = 'HEADER_CD' verbose_name_plural = 'HEADER_CD' def __str__(self): return str(self.form_id) @python_2_unicode_compatible class SmryCd(CalAccessBaseModel): """ Summary totals from filings. """ UNIQUE_KEY = ( "FILING_ID", "AMEND_ID", "LINE_ITEM", "REC_TYPE", "FORM_TYPE", ) filing_id = fields.IntegerField( db_column='FILING_ID', db_index=True, verbose_name='filing ID', help_text="Unique filing identificiation number" ) amend_id = fields.IntegerField( db_column='AMEND_ID', db_index=True, help_text="Amendment identification number. A number of 0 is the \ original filing and 1 to 999 amendments.", verbose_name="amendment ID" ) line_item = fields.CharField( max_length=8, db_column='LINE_ITEM', db_index=True, help_text="Line item number of this record" ) REC_TYPE_CHOICES = ( ('SMRY', 'SMRY'), ) rec_type = fields.CharField( max_length=4, db_column='REC_TYPE', db_index=True, choices=REC_TYPE_CHOICES, verbose_name='record type', ) FORM_TYPE_CHOICES = ( ('401A', 'Form 401 (Slate mailer organization campaign statement): \ Schedule A, payments received'), ('401B', 'Form 401 (Slate mailer organization campaign statement): \ Schedule B, payments made'), ('401B-1', 'Form 401 (Slate mailer organization campaign statement): \ Schedule B1, payments made by agent or independent contractor'), ('A', 'Form 460 (Recipient committee campaign statement): \ Schedule A, '), ('B1', 'Form 460 (Recipient committee campaign statement): \ Schedule B1, '), ('B2', 'Form 460 (Recipient committee campaign statement): \ Schedule B2, '), ('B3', 'Form 460 (Recipient committee campaign statement): \ Schedule B3, '), ('C', 'Form 460 (Recipient committee campaign statement): \ Schedule C, '), ('D', 'Form 460 (Recipient committee campaign statement): \ Schedule D, '), ('E', 'Form 460 (Recipient committee campaign statement): \ Schedule E, '), ('F', 'Form 460 (Recipient committee campaign statement): \ Schedule F, '), ('G', 'Form 460 (Recipient committee campaign statement): \ Schedule G, '), ('H', 'Form 460 (Recipient committee campaign statement): \ Schedule H, '), ('H1', 'Form 460 (Recipient committee campaign statement): \ Schedule H1, '), ('H2', 'Form 460 (Recipient committee campaign statement): \ Schedule H2, '), ('H3', 'Form 460 (Recipient committee campaign statement): \ Schedule H3, '), ('I', 'Form 460 (Recipient committee campaign statement): \ Schedule I, '), ('F401', 'Form 401 (Slate mailer organization campaign statement)'), ('F450', 'Form 450 (Recipient committee campaign statement, \ short form)'), ('F460', 'Form 460 (Recipient committee campaign statement)'), ('F461', 'Form 461 (Independent expenditure and major donor \ committee campaign statement)'), ('F465', 'Form 465 ()'), ('F625', 'Form 625 (Report of lobbying firm)'), ('F625P2', 'Form 625 (Report of lobbying firm): \ Part 2, payments received in connection with lobbying activity'), ('F625P3A', 'Form 625 (Report of lobbying firm): \ Part 3A, payments for activity expenses made in connection with \ lobbying activities'), ('F625P3B', 'Form 625 (Report of lobbying firm): \ Part 3B, payments to other lobbying firms made in connection with \ lobbying activities'), ('F635', 'Form 635 (Report of lobbyist employer and lobbying \ coalition)'), ('F635P3A', 'Form 635 (Report of lobbyist employer and lobbying \ coalition): Part 3A, payments in in-house employee lobbyists'), ('F635P3B', 'Form 635 (Report of lobbyist employer and lobbying \ coalition): Part 3B, payments to lobbying firms'), ('F635P3C', 'Form 635 (Report of lobbyist employer and lobbying \ coalition): Part 3C, activity expenses'), ('F635P3D', 'Form 635 (Report of lobbyist employer and lobbying \ coalition): Part 3D, other payments to influence legislative or \ administrative action'), ('F635P3E', 'Form 635 (Report of lobbyist employer and lobbying \ coalition): Part 3E, payments in connection with administrative testimony \ in ratemaking proceedings before the California Public Utilities Commission'), ('F645', 'Form 645 (Report of person spending $5,000 or more to \ influence legislative or administrative action)'), ('F645P2A', 'Form 645 (Report of person spending $5,000 or more to \ influence legislative or administrative action): Part 2A, activity expenses'), ('F645P2B', 'Form 645 (Report of person spending $5,000 or more to \ influence legislative or administrative action): Part 2B, \ other payments to influence legislative or administrative action'), ('F645P2C', 'Form 645 (Report of person spending $5,000 or more to \ influence legislative or administrative action): Part 2C, \ payments in connection with administrative testimony in ratemaking \ proceedings before the California Public Utilities Commission'), ('F900', 'Form 900 (Form 900 (Public Employee\'s Retirement Board \ Candidate Campaign Statement)'), ('S640', 'Form 640 (Governmental agencies reporting ther payments to \ influence legislative or administrative action attachment)'), ) form_type = fields.CharField( max_length=8, db_column='FORM_TYPE', db_index=True, choices=FORM_TYPE_CHOICES, help_text='Name of the source filing form or schedule' ) amount_a = fields.DecimalField( decimal_places=2, null=True, max_digits=14, db_column='AMOUNT_A', blank=True, help_text='Summary amount from column A', verbose_name='amount A' ) amount_b = fields.DecimalField( decimal_places=2, null=True, max_digits=14, db_column='AMOUNT_B', blank=True, help_text='Summary amount from column B', verbose_name='amount B' ) amount_c = fields.DecimalField( decimal_places=2, null=True, max_digits=14, db_column='AMOUNT_C', blank=True, help_text='Summary amount from column C', verbose_name='amount C' ) elec_dt = fields.DateField( db_column='ELEC_DT', null=True, blank=True, verbose_name='election date' ) class Meta: app_label = 'calaccess_raw' db_table = 'SMRY_CD' verbose_name = 'SMRY_CD' verbose_name_plural = 'SMRY_CD' ordering = ("filing_id", "-amend_id", 'form_type', "line_item") def __str__(self): return str(self.filing_id) def pretty_amount_a(self): if self.amount_a is None: return None return "$%s" % intcomma(floatformat(self.amount_a, 0)) pretty_amount_a.short_description = 'amount A' def pretty_amount_b(self): if self.amount_b is None: return None return "$%s" % intcomma(floatformat(self.amount_b, 0)) pretty_amount_b.short_description = 'amount B' def pretty_amount_c(self): if self.amount_c is None: return None return "$%s" % intcomma(floatformat(self.amount_c, 0)) pretty_amount_c.short_description = 'amount C' @python_2_unicode_compatible class CvrE530Cd(CalAccessBaseModel): """ This table method is undocumented. """ UNIQUE_KEY = ("FILING_ID", "AMEND_ID") filing_id = fields.IntegerField( db_column='FILING_ID', db_index=True, verbose_name='filing ID', help_text="Unique filing identificiation number" ) amend_id = fields.IntegerField( db_column='AMEND_ID', db_index=True, help_text="Amendment identification number. A number of 0 is the \ original filing and 1 to 999 amendments.", verbose_name="amendment ID" ) REC_TYPE_CHOICES = ( ("CVR", "CVR"), ) rec_type = fields.CharField( verbose_name='record type', db_column='REC_TYPE', max_length=4, db_index=True, choices=REC_TYPE_CHOICES, ) FORM_TYPE_CHOICES = ( ('E530', 'Form 530 (Issue advocacy report)'), ) form_type = fields.CharField( db_column='FORM_TYPE', max_length=4, db_index=True, help_text='Name of the source filing form or schedule', choices=FORM_TYPE_CHOICES, ) ENTITY_CODE_CHOICES = ( # Defined here: # http://www.documentcloud.org/documents/1308003-cal-access-cal-format.html#document/p9 ('', 'Unknown'), ) entity_cd = fields.CharField( db_column='ENTITY_CD', max_length=32, blank=True, verbose_name='entity code', choices=ENTITY_CODE_CHOICES ) filer_naml = fields.CharField( db_column='FILER_NAML', max_length=200, help_text="Filer last name" ) filer_namf = fields.CharField( db_column='FILER_NAMF', max_length=4, blank=True, help_text="Filer first name" ) filer_namt = fields.CharField( db_column='FILER_NAMT', max_length=32, blank=True, help_text="Filer title or prefix" ) filer_nams = fields.CharField( db_column='FILER_NAMS', max_length=32, blank=True, help_text="Filer suffix" ) report_num = fields.CharField( db_column='REPORT_NUM', max_length=32, blank=True, help_text="This field is undocumented" ) rpt_date = fields.DateField( db_column='RPT_DATE', null=True, help_text="This field is undocumented" ) filer_city = fields.CharField( db_column='FILER_CITY', max_length=16, blank=True, help_text='Filer city' ) filer_st = fields.CharField( db_column='FILER_ST', max_length=4, blank=True, verbose_name='Filer state' ) filer_zip4 = fields.CharField( db_column='FILER_ZIP4', max_length=10, blank=True, help_text='Filer ZIP Code' ) occupation = fields.CharField( db_column='OCCUPATION', max_length=15, blank=True, help_text="This field is undocumented" ) employer = fields.CharField( db_column='EMPLOYER', max_length=13, blank=True, help_text="This field is undocumented" ) cand_naml = fields.CharField( db_column='CAND_NAML', max_length=46, help_text="Candidate last name" ) cand_namf = fields.CharField( db_column='CAND_NAMF', max_length=21, blank=True, help_text="Candidate first name" ) cand_namt = fields.CharField( db_column='CAND_NAMT', max_length=32, blank=True, help_text="Candidate title or prefix" ) cand_nams = fields.CharField( db_column='CAND_NAMS', max_length=32, blank=True, help_text="Candidate suffix" ) district_cd = fields.IntegerField( db_column='DISTRICT_CD', help_text="This field is undocumented" ) OFFICE_CODE_CHOICES = ( (30001, "PRESIDENT"), (30002, "GOVERNOR"), (30003, "LIEUTENANT GOVERNOR"), (30004, "SECRETARY OF STATE"), (30005, "CONTROLLER"), (30006, "TREASURER"), (30007, "ATTORNEY GENERAL"), (30008, "SUPERINTENDENT OF PUBLIC INSTRUCTION"), (30009, "MEMBER BOARD OF EQUALIZATION"), (30010, "OXNARD HARBOR COMMISSIONER"), (30011, "CITY CONTROLLER"), (30012, "STATE SENATE"), (30013, "ASSEMBLY"), (30014, "INSURANCE COMMISSIONER"), (30015, "JUDGE"), (30016, "BOARD MEMBER"), (30017, "TAX COLLECTOR"), (30018, "TRUSTEE"), (30019, "SUPERVISOR"), (30020, "SHERIFF"), (30021, "CORONER"), (30022, "MARSHALL"), (30023, "CITY CLERK"), (30024, "SCHOOL BOARD"), (30025, "HARBOR COMMISSIONER"), (30026, "DISTRICT ATTORNEY"), (30027, "COUNTY CLERK"), (30028, "AUDITOR"), (30029, "MAYOR"), (30030, "CITY ATTORNEY"), (30031, "DEMOCRATIC COUNTY CENTRAL COMMITTEE"), (30032, "TOWN COUNCIL"), (30033, "ASSESSOR"), (30034, "CITY TREASURER"), (30035, "CITY COUNCIL"), (30036, "COMMISSIONER"), (30037, "REPUBLICAN COUNTY CENTRAL COMMITTEE"), (30038, "DIRECTOR"), (30039, "DIRECTOR OF ZONE 7"), (30040, "COMMUNITY COLLEGE BOARD"), (30041, "POLICE CHIEF"), (30042, "CHIEF OF POLICE"), (30043, "CENTRAL COMMITTEE"), (30044, "BOARD OF EDUCATION"), (30045, "BOARD OF DIRECTORS"), (30046, "COLLEGE BOARD"), (30047, "BART BOARD DIRECTOR"), (30048, "BOARD OF TRUSTEES"), (30049, "IRRIGATION"), (30050, "WATER BOARD"), (30051, "COMMUNITY PLANNING GROUP"), (30052, "BOARD OF SUPERVISORS"), (30053, "SUPERIOR COURT JUDGE"), (30054, "DISTRICT ATTORNEY/PUBLIC DEFENDER"), (30055, "MEASURE"), (30056, "CITY PROSECUTOR"), (30057, "SUPREME COURT JUDGE"), (30058, "PUBLIC EMPLOYEES RETIREMENT BOARD"), (30059, "APPELLATE COURT JUDGE"), (50001, "Ag"), (50002, "Assembly"), (50003, "Assessor"), (50004, "Assessor/Clerk/Recorder"), (50005, "Assessor/County Clerk/Recorder"), (50006, "Assessor/Recorder"), (50007, "Associate Justice"), (50008, "Auditor"), (50009, "Auditor/Controller"), (50010, "Auditor/Controller/Clerk/Recorder"), (50011, "Auditor/Controller/Recorder"), (50012, "Auditor/Controller/Treasurer/Tax Collector"), (50013, "Auditor/Recorder"), (50014, "Board Member"), (50015, "Board Of Director"), (50016, "Board Of Supervisor"), (50017, "Boe"), (50018, "Chief Justice"), (50019, "City"), (50020, "City Attorney"), (50021, "City Auditor"), (50022, "City Clerk"), (50023, "City Council"), (50024, "City Of Los Angeles"), (50025, "City Of South El Monte"), (50026, "City Prosecutor"), (50027, "City Treasurer"), (50028, "Clerk/Auditor"), (50029, "Clerk/Record/Public Admin"), (50030, "Clerk/Recorder"), (50031, "Clerk/Recorder/Registar"), (50032, "Clerk/Recorder/Registrar"), (50033, "Commissioner"), (50034, "Controller"), (50035, "Costa Mesa"), (50036, "Council Member"), (50037, "County Clerk"), (50038, "County Clerk/Auditor"), (50039, "County Clerk/Auditor/Controller"), (50040, "County Clerk/Recorder"), (50041, "County Clerk/Recorder/Assessor"), (50042, "County Clerk/Recorder/Public Admin"), (50043, "Democratic County Central Committee"), (50044, "Director"), (50045, "District Attorney"), (50046, "District Attorney/Public Administrator"), (50047, "Gccc"), (50048, "Governor"), (50049, "Harbor Commissioner"), (50050, "Ic"), (50051, "Irrigation Dist"), (50052, "Judge"), (50053, "Justice"), (50054, "Legislature"), (50055, "Lieutenant Governor"), (50056, "Mayor"), (50057, "N/A"), (50058, "Placentia"), (50059, "Public Administrator"), (50060, "Public Administrator/Guardian"), (50061, "Rent Stabilization Board"), (50062, "Republican Central Committee"), (50063, "San Francisco Dccc"), (50064, "Sanger"), (50065, "School Board"), (50066, "Secretary Of State"), (50067, "Senator"), (50068, "Sheriff"), (50069, "Sheriff/Coroner"), (50070, "Sheriff/Coroner/Marshall"), (50071, "Sheriff/Coroner/Public Administrator"), (50072, "Solana Beach"), (50073, "Superintendent"), (50074, "Supervisor"), (50075, "Supt Of Schools"), (50076, "Tax Collector"), (50077, "Town Council"), (50078, "Treasurer"), (50079, "Treasurer/Tax Collector"), (50080, "Treasurer/Tax Collector/Clerk"), (50081, "Treasurer/Tax Collector/Public Administrator"), (50082, "Treasurer/Tax Collector/Public Administrator/County Clerk"), (50083, "Treasurer/Tax Collector/Recorder"), (50084, "Trustee"), (50085, "Weed Recreation Board Member"), ) office_cd = fields.IntegerField( db_column='OFFICE_CD', verbose_name="office code", help_text="Identifies the office being sought", choices=OFFICE_CODE_CHOICES ) pmnt_dt = fields.DateField( db_column='PMNT_DT', null=True, help_text="This field is undocumented" ) pmnt_amount = fields.FloatField( db_column='PMNT_AMOUNT', help_text="This field is undocumented" ) type_literature = fields.IntegerField( db_column='TYPE_LITERATURE', help_text="This field is undocumented" ) type_printads = fields.IntegerField( db_column='TYPE_PRINTADS', help_text="This field is undocumented" ) type_radio = fields.IntegerField( db_column='TYPE_RADIO', help_text="This field is undocumented" ) type_tv = fields.IntegerField( db_column='TYPE_TV', help_text="This field is undocumented" ) type_it = fields.IntegerField( db_column='TYPE_IT', help_text="This field is undocumented" ) type_billboards = fields.IntegerField( db_column='TYPE_BILLBOARDS', help_text="This field is undocumented" ) type_other = fields.IntegerField( db_column='TYPE_OTHER', help_text="This field is undocumented" ) other_desc = fields.CharField( db_column='OTHER_DESC', max_length=49, help_text="This field is undocumented" ) class Meta: app_label = 'calaccess_raw' db_table = 'CVR_E530_CD' verbose_name = 'CVR_E530_CD' verbose_name_plural = 'CVR_E530_CD' def __str__(self): return str(self.filing_id) @python_2_unicode_compatible class SpltCd(CalAccessBaseModel): """ Split records """ UNIQUE_KEY = ( "FILING_ID", "AMEND_ID", "LINE_ITEM", "PFORM_TYPE" ) amend_id = fields.IntegerField( db_column='AMEND_ID', db_index=True, help_text="Amendment identification number. A number of 0 is the \ original filing and 1 to 999 amendments.", verbose_name="amendment ID" ) elec_amount = fields.DecimalField( max_digits=16, decimal_places=2, db_column='ELEC_AMOUNT', help_text="This field is undocumented" ) elec_code = fields.CharField( max_length=2, db_column='ELEC_CODE', blank=True, help_text='This field is undocumented', ) elec_date = fields.DateField( db_column='ELEC_DATE', null=True, help_text="This field is undocumented" ) filing_id = fields.IntegerField( db_column='FILING_ID', db_index=True, verbose_name='filing ID', help_text="Unique filing identificiation number" ) line_item = fields.IntegerField( db_column='LINE_ITEM', help_text="Line item number of this record", db_index=True, ) PFORM_TYPE_CHOICES = ( ('A', ''), ('B1', ''), ('B2', ''), ('C', ''), ('D', ''), ('F450P5', ''), ('H', ''), ) pform_type = fields.CharField( max_length=7, db_column='PFORM_TYPE', db_index=True, choices=PFORM_TYPE_CHOICES, help_text='This field is undocumented', ) ptran_id = fields.CharField( verbose_name='transaction ID', max_length=32, db_column='PTRAN_ID', blank=True, help_text='Permanent value unique to this item', ) class Meta: app_label = 'calaccess_raw' db_table = 'SPLT_CD' verbose_name = 'SPLT_CD' verbose_name_plural = 'SPLT_CD' def __str__(self): return str(self.filing_id) @python_2_unicode_compatible class TextMemoCd(CalAccessBaseModel): """ Text memos attached to electronic filings """ UNIQUE_KEY = ( "FILING_ID", "AMEND_ID", "LINE_ITEM", "REC_TYPE", "FORM_TYPE" ) filing_id = fields.IntegerField( db_column='FILING_ID', db_index=True, verbose_name='filing ID', help_text="Unique filing identificiation number" ) amend_id = fields.IntegerField( db_column='AMEND_ID', db_index=True, help_text="Amendment identification number. A number of 0 is the \ original filing and 1 to 999 amendments.", verbose_name="amendment ID" ) line_item = fields.IntegerField( db_column='LINE_ITEM', help_text="Line item number of this record", db_index=True, ) REC_TYPE_CHOICES = ( ('i', 'i'), ('MEMO', 'MEMO'), ('TEXT', 'TEXT'), ('trun', 'trun'), ('Unde', 'Unde'), ) rec_type = fields.CharField( db_column='REC_TYPE', max_length=4, db_index=True, choices=REC_TYPE_CHOICES, verbose_name='record type' ) FORM_TYPE_CHOICES = ( (' E', ''), ('410', ''), ('460', ''), ('461', ''), ('465', ''), ('496', ''), ('497', ''), ('497P1', ''), ('497P2', ''), ('A', ''), ('A4', ''), ('A6', ''), ('B', ''), ('B1', ''), ('B2', ''), ('B3', ''), ('C', ''), ('COMMENTS', ''), ('CVR', ''), ('D', ''), ('DEBTF', ''), ('E', ''), ('EXPNT', ''), ('F', ''), ('F401', ''), ('F401A', ''), ('F401B', ''), ('F401B-1', ''), ('F405', ''), ('F410', ''), ('F425', ''), ('F450', ''), ('F450P5', ''), ('F460', ''), ('F461', ''), ('F461P1', ''), ('F461P2', ''), ('F461P5', ''), ('F465', ''), ('F465P3', ''), ('F496', ''), ('F496P3', ''), ('F497', ''), ('F497P1', ''), ('F497P2', ''), ('F498-A', ''), ('F498-R', ''), ('F601', ''), ('F601P2A', ''), ('F601P2B', ''), ('F602', ''), ('F603', ''), ('F604', ''), ('F605', ''), ('F606', ''), ('F607', ''), ('F615', ''), ('F615P1', ''), ('F615P2', ''), ('F625', ''), ('F625P2', ''), ('F625P3A', ''), ('F625P3B', ''), ('F625P4B', ''), ('F635', ''), ('F635P3B', ''), ('F635P3C', ''), ('F635P4B', ''), ('F645', ''), ('F645P2A', ''), ('F645P3B', ''), ('G', ''), ('H', ''), ('H1', ''), ('H2', ''), ('H3', ''), ('I', ''), ('PT5', ''), ('RCPTB1', ''), ('RCPTC', ''), ('RCPTI', ''), ('S497', ''), ('S630', ''), ('S635-C', ''), ('S635C', ''), ('S640', ''), ('SCH A', ''), ('SF', ''), ('SMRY', ''), ('SPLT', ''), ('SUM', ''), ('SUMMARY', ''), ) form_type = fields.CharField( db_column='FORM_TYPE', max_length=8, help_text='Name of the source filing form or schedule', db_index=True, choices=FORM_TYPE_CHOICES ) ref_no = fields.CharField( db_column='REF_NO', max_length=20, blank=True, help_text='Links text memo to a specific record', verbose_name='reference number' ) text4000 = fields.CharField( db_column='TEXT4000', max_length=4000, blank=True, help_text='Contents of the text memo', verbose_name='text' ) class Meta: app_label = 'calaccess_raw' db_table = 'TEXT_MEMO_CD' verbose_name = 'TEXT_MEMO_CD' verbose_name_plural = 'TEXT_MEMO_CD' def __str__(self): return str(self.filing_id)

import keyword as kw from .repr import ReprPrinter from .str import StrPrinter # A list of classes that should be printed using StrPrinter STRPRINT = ('Add', 'Infinity', 'Integer', 'Mul', 'NegativeInfinity', 'Pow', 'Zero') class PythonPrinter(ReprPrinter, StrPrinter): """A printer which converts an expression into its Python interpretation.""" def __init__(self, settings=None): ReprPrinter.__init__(self) StrPrinter.__init__(self, settings) self.symbols = [] self.functions = [] # Create print methods for classes that should use StrPrinter instead # of ReprPrinter. for name in STRPRINT: f_name = f'_print_{name}' f = getattr(StrPrinter, f_name) setattr(PythonPrinter, f_name, f) def _print_Function(self, expr): import diofant func = expr.func.__name__ if not hasattr(diofant, func) and func not in self.functions: self.functions.append(func) return StrPrinter._print_Function(self, expr) # procedure (!) for defining symbols which have be defined in python() def _print_Symbol(self, expr): symbol = self._str(expr) if symbol not in self.symbols: self.symbols.append(symbol) return StrPrinter._print_Symbol(self, expr) _print_BaseSymbol = StrPrinter._print_BaseSymbol def python(expr, **settings): """Return Python interpretation of passed expression (can be passed to the exec() function without any modifications) """ from ..core import Function, Symbol printer = PythonPrinter(settings) exprp = printer.doprint(expr) result = '' # Returning found symbols and functions renamings = {} for symbolname in printer.symbols: newsymbolname = symbolname # Escape symbol names that are reserved python keywords if kw.iskeyword(newsymbolname): while True: newsymbolname += '_' if (newsymbolname not in printer.symbols and newsymbolname not in printer.functions): renamings[Symbol(symbolname)] = Symbol(newsymbolname) break result += newsymbolname + " = Symbol('" + symbolname + "')\n" for functionname in printer.functions: newfunctionname = functionname # Escape function names that are reserved python keywords if kw.iskeyword(newfunctionname): while True: newfunctionname += '_' if (newfunctionname not in printer.symbols and newfunctionname not in printer.functions): renamings[Function(functionname)] = Function(newfunctionname) break result += newfunctionname + " = Function('" + functionname + "')\n" if len(renamings) != 0: exprp = expr.subs(renamings) result += 'e = ' + printer._str(exprp) return result

""" http://adventofcode.com/day/11 Part 1 ------ Santa's previous password expired, and he needs help choosing a new one. To help him remember his new password after the old one expires, Santa has devised a method of coming up with a password based on the previous one. Corporate policy dictates that passwords must be exactly eight lowercase letters (for security reasons), so he finds his new password by incrementing his old password string repeatedly until it is valid. Incrementing is just like counting with numbers: xx, xy, xz, ya, yb, and so on. Increase the rightmost letter one step; if it was z, it wraps around to a, and repeat with the next letter to the left until one doesn't wrap around. Unfortunately for Santa, a new Security-Elf recently started, and he has imposed some additional password requirements: - Passwords must include one increasing straight of at least three letters, like abc, bcd, cde, and so on, up to xyz. They cannot skip letters; abd doesn't count. - Passwords may not contain the letters i, o, or l, as these letters can be mistaken for other characters and are therefore confusing. - Passwords must contain at least two different, non-overlapping pairs of letters, like aa, bb, or zz. For example: - hijklmmn meets the first requirement (because it contains the straight hij) but fails the second requirement requirement (because it contains i and l). - abbceffg meets the third requirement (because it repeats bb and ff) but fails the first requirement. - abbcegjk fails the third requirement, because it only has one double letter (bb). - The next password after abcdefgh is abcdffaa. - The next password after ghijklmn is ghjaabcc, because you eventually skip all the passwords that start with ghi..., since i is not allowed. Given Santa's current password (your puzzle input), what should his next password be? Part 2 ------ Santa's password expired again. What's the next one? """ from __future__ import print_function, unicode_literals import os import re import sys INFILE = 'inputs/input11.txt' MINLENGTH = 8 MAXLENGTH = MINLENGTH VALIDCHARS = 'abcdefghijklmnopqrstuvwxyz' VALID = re.compile('^[a-z]+$') BAD = re.compile(r'[ilo]') double = [ch * 2 for ch in VALIDCHARS] DOUBLE = re.compile('|'.join(double)) straights = [VALIDCHARS[i:i + 3] for i in xrange(len(VALIDCHARS) - 2)] STRAIGHTS = re.compile('|'.join(straights)) def check(password): valid = True valid = valid and len(password) >= MINLENGTH valid = valid and len(password) <= MAXLENGTH valid = valid and VALID.match(password) valid = valid and len(STRAIGHTS.findall(password)) == 1 valid = valid and not BAD.match(password) valid = valid and len(DOUBLE.findall(password)) >= 2 return valid def next(password): chars = [c for c in password] # Temporarily reverse the string so we can go # left to right chars.reverse() # Increment characters for i, char in enumerate(chars): try: current = VALIDCHARS.index(char) chars[i] = VALIDCHARS[current + 1] except IndexError: chars[i] = VALIDCHARS[0] else: # If we were able to increment a letter, we # don't need to move on to the next column break # Reverse the string again to make it normal chars.reverse() return ''.join(chars) def make(current_password): new_password = next(current_password) while not check(new_password): new_password = next(new_password) return new_password def main(): current_password = None with open(INFILE) as f: for line in f: current_password = line.strip() if current_password is not None: # Part 1 new_password = make(current_password) msg = '[Python] Puzzle 11-1: {}' print(msg.format(new_password)) # Part 2 new_password = make(new_password) msg = '[Python] Puzzle 11-2: {}' print(msg.format(new_password)) if __name__ == '__main__': main()

from metadata.models import TanitJobsCategory from django.core.management.base import BaseCommand CHOICES = [ 'Tous secteurs', 'Informatique', "Centres d'appels", 'Industrie', 'Ingenierie', "Technologie de l'information", 'Commerce', 'Formation', 'Marketing', 'Télécommunications', 'Vente', 'Transport', 'Stratégie-Planification', 'Science', 'Commerce de détail', 'Restauration', 'Recherche', 'Immobilier', 'Controle Qualite', 'Achat - Approvisionnement', 'Pharmaceutiques', 'Services a la clientele', 'Media-Journalisme', 'Gestion', 'Juridique', 'Assurances', 'Installation-Entretien-Reparation', 'Ressources humaines', 'Sante', 'Fonction publique', 'Services veterinaires', 'Finance', 'Enseignement', 'Distribution', 'Design', 'Consulting', 'Construction', 'Developpement des affaires', 'Biotechnologie', 'Banque', 'Automobile', 'Administration', 'Comptabilite', 'Autres', ] class Command(BaseCommand): help = 'Fill the Database with the interests details' def handle(self, **options): objects = [] for name in CHOICES: print name objects.append(TanitJobsCategory(name=name)) TanitJobsCategory.objects.bulk_create(objects) print 'Tanit Jobs Category saved'

import pyat.sync import time import random from .util import a_task, a_failing_task, make_args, make_kwargs, \ FailedTaskException MIN_TIME = 1.0 MAX_TIME = 2.0 DELTA_TIME = MAX_TIME - MIN_TIME STEP_TIME = 0.1 TIMEOUT_DELAY = 0.5 def test_future(): ''' Test a task scheduled N seconds in the future is executed close to the requested time. ''' # Create scheduler scheduler = pyat.sync.SynchronousTaskScheduler() # Pick some random arguments args = make_args() kwargs = make_kwargs() # Pick a time between 1-5 seconds in the future delay = MIN_TIME + (random.random() * DELTA_TIME) at_time = time.time() + delay # Schedule task task = scheduler.schedule(at_time, a_task, *args, **kwargs) # Pick a timeout timeout = at_time + TIMEOUT_DELAY # Wait for timeout while time.time() < timeout: time.sleep(STEP_TIME) scheduler.poll() # Did our function get run? try: (run_at, run_args, run_kwargs) = task.result assert run_args == args, 'args does not match' assert run_kwargs == kwargs, 'kwargs does not match' assert run_at > at_time, 'Ran too early' assert run_at < (at_time + STEP_TIME), 'Ran too late' except pyat.sync.NotExecutedYet: assert False, 'Did not get executed' def test_future_exception(): ''' Test a task scheduled N seconds in the future that fails. ''' # Create scheduler scheduler = pyat.sync.SynchronousTaskScheduler() # Pick some random arguments args = make_args() kwargs = make_kwargs() # Pick a time between 1-5 seconds in the future delay = MIN_TIME + (random.random() * DELTA_TIME) at_time = time.time() + delay # Schedule task task = scheduler.schedule(at_time, a_failing_task, *args, **kwargs) # Pick a timeout timeout = at_time + TIMEOUT_DELAY # Wait for timeout while time.time() < timeout: time.sleep(STEP_TIME) scheduler.poll() # Did our function get run? try: (run_at, run_args, run_kwargs) = task.result assert False, 'Did not fail' except FailedTaskException as e: (msg, run_at, run_args, run_kwargs) = e.args assert run_args == args, 'args does not match' assert run_kwargs == kwargs, 'kwargs does not match' assert run_at > at_time, 'Ran too early' assert run_at < (at_time + STEP_TIME), 'Ran too late' except pyat.sync.NotExecutedYet: assert False, 'Did not get executed' def test_future_cancelled(): ''' Test a task scheduled N seconds in the future then cancelled doesn't execute. ''' # Create scheduler scheduler = pyat.sync.SynchronousTaskScheduler() # Pick some random arguments args = make_args() kwargs = make_kwargs() # Pick a time between 1-5 seconds in the future delay = MIN_TIME + (random.random() * DELTA_TIME) at_time = time.time() + delay # Schedule task task = scheduler.schedule(at_time, a_task, *args, **kwargs) # Poll once scheduler.poll() # Cancel the task task.cancel() assert task.cancelled, 'Not cancelled' # Pick a timeout timeout = at_time + TIMEOUT_DELAY # Wait for timeout while time.time() < timeout: time.sleep(STEP_TIME) scheduler.poll() # Did our function get run? try: (run_at, run_args, run_kwargs) = task.result assert False, 'Task executed' except pyat.sync.NotExecutedYet: pass def test_cancel_all(): ''' Test we can cancel all tasks. ''' # Create scheduler scheduler = pyat.sync.SynchronousTaskScheduler() # Pick some random arguments args = make_args() kwargs = make_kwargs() # Pick a time between 1-5 seconds in the future delay = MIN_TIME + (random.random() * DELTA_TIME) at_time = time.time() + delay # Schedule tasks task1 = scheduler.schedule(at_time, a_task, *args, **kwargs) task2 = scheduler.schedule(at_time + 1, a_task, *args, **kwargs) task3 = scheduler.schedule(at_time + 2, a_task, *args, **kwargs) # Poll once scheduler.poll() # Cancel all tasks scheduler.cancel_all() # Pick a timeout timeout = at_time + TIMEOUT_DELAY # Wait for timeout while time.time() < timeout: time.sleep(STEP_TIME) scheduler.poll() # Did our functions get run? for task in (task1, task2, task3): try: (run_at, run_args, run_kwargs) = task.result assert False, 'Task executed' except pyat.sync.NotExecutedYet: pass def test_not_yet_executed(): ''' Test NotYetExecuted gets raised if we ask the task early. ''' # Create scheduler scheduler = pyat.sync.SynchronousTaskScheduler() # Pick some random arguments args = make_args() kwargs = make_kwargs() # Pick a time between 1-5 seconds in the future delay = MIN_TIME + (random.random() * DELTA_TIME) at_time = time.time() + delay # Schedule task task = scheduler.schedule(at_time, a_task, *args, **kwargs) # Poll once scheduler.poll() # Did our function get run? try: (run_at, run_args, run_kwargs) = task.result assert False, 'Task executed early' except pyat.sync.NotExecutedYet: pass

exec(open('parse.py').read()) Node = dict Leaf = str def typeExpression(env, e): if type(e) == Leaf: pass # Complete base cases for booleans for Problem #3. if type(e) == Node: for label in e: children = e[label] if label == 'Number': return 'TyNumber' elif label == 'Variable': pass # Complete case for 'Variable' for Problem #3. elif label == 'Element': pass # Complete case for 'Variable' for Problem #3. elif label == 'Plus': pass # Complete case for 'Plus' for Problem #3. def typeProgram(env, s): if type(s) == Leaf: if s == 'End': return 'TyVoid' elif type(s) == Node: for label in s: if label == 'Print': [e, p] = s[label] pass # Complete case(s) for 'Print' for Problem #3. if label == 'Assign': [xTree, e0, e1, e2, p] = s[label] x = xTree['Variable'][0] pass # Complete case(s) for 'Assign' for Problem #3. if label == 'Loop': [xTree, nTree, p1, p2] = s[label] x = xTree['Variable'][0] n = nTree['Number'][0] pass # Complete case for 'Loop' for Problem #3. #eof

from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('passive_data_kit', '0050_datasourcereference'), ] operations = [ migrations.AddField( model_name='datapoint', name='source_reference', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='data_points', to='passive_data_kit.DataSourceReference'), ), ]