PatrickHaller/the-stack-python-100k · Datasets at Hugging Face

0dfa05fb71babc8359addb393c00a0d8e644d775

1,934

py

Python

Protheus_WebApp/Modules/SIGAWMS/WMSA225TESTCASE.py

98llm/tir-script-samples

0bff8393b79356aa562e9e6512c11ee6e039b177

[ "MIT" ]

17

2018-09-24T17:27:08.000Z

2021-09-16T19:09:46.000Z

Protheus_WebApp/Modules/SIGAWMS/WMSA225TESTCASE.py

98llm/tir-script-samples

0bff8393b79356aa562e9e6512c11ee6e039b177

[ "MIT" ]

4

2018-09-24T17:30:32.000Z

2022-01-03T11:39:30.000Z

Protheus_WebApp/Modules/SIGAWMS/WMSA225TESTCASE.py

98llm/tir-script-samples

0bff8393b79356aa562e9e6512c11ee6e039b177

[ "MIT" ]

18

2019-06-07T17:41:34.000Z

2022-01-31T18:17:31.000Z

from tir import Webapp import unittest import time class WMSA225(unittest.TestCase): @classmethod def setUpClass(inst): inst.oHelper = Webapp() inst.oHelper.Setup("SIGAWMS", "10/12/2020", "T1", "M SP 01", "42") inst.oHelper.Program("WMSA225") def test_WMSA225_CT001(self): self.oHelper.SetButton('Incluir') self.oHelper.SetBranch('M SP 01') self.oHelper.SetValue('Armazém De ?','') self.oHelper.SetValue('Armazém Até ?','ZZ') self.oHelper.SetValue('Produto De ?','') self.oHelper.SetValue('Produto Até ?','ZZZZZZZZZZZZZZZ') self.oHelper.SetValue('Endereço De ?','') self.oHelper.SetValue('Endereço Até ?','ZZZZZZZZZZZZZZZ') self.oHelper.SetValue('Unitizador De ?','') self.oHelper.SetValue('Unitizador Até ?','ZZZZZZ') self.oHelper.SetValue('Tipo Transf. ?','Produto') self.oHelper.SetButton('Ok') self.oHelper.ClickBox('Produto','WMS00000G010') self.oHelper.SetValue('Quantidade', '1,00', grid=True, grid_number=2) self.oHelper.SetValue('Ender Des', 'A010001', grid=True, grid_number=2) self.oHelper.LoadGrid() self.oHelper.SetButton("Confirmar") self.oHelper.SetButton('Fechar') #self.oHelper.SearchBrowse('M SP 01 005WMS000000047 ') #self.oHelper.SetButton('Outras Ações','Visualizar') #self.oHelper.SetButton('Fechar') #self.oHelper.SearchBrowse(key=2, index=True) #self.oHelper.SearchBrowse('M SP 01 005WMS000025') #self.oHelper.SetButton('Excluir') #elf.oHelper.SetButton('Confirmar') #self.oHelper.SetButton('Fechar') self.oHelper.SetButton('X') self.oHelper.AssertTrue() @classmethod def tearDownClass(inst): inst.oHelper.TearDown() if __name__ == "__main__": unittest.main()

33.929825

79

0.618925

04f49a0fa56e9e3c427a54c5a5c46e451fd007e1

576

py

Python

scripts/colors.py

perlun/jetbrains-one-dark-theme

0daeaf372bec421640b8d6fdb4f26123df419ab0

[ "MIT" ]

1

2019-11-04T09:41:22.000Z

scripts/colors.py

Noman-ahk97/jetbrains-one-dark-theme

5b422df1de40e808fcff42df97252970b740e709

[ "MIT" ]

null

scripts/colors.py

Noman-ahk97/jetbrains-one-dark-theme

5b422df1de40e808fcff42df97252970b740e709

[ "MIT" ]

null

import os import yaml CONFIG_DIR = os.path.join(os.path.dirname(__file__), 'config') if __name__ == '__main__': with open(os.path.join(CONFIG_DIR, 'colors.yaml'), 'r') as input_file: colors = yaml.load(input_file, Loader=yaml.FullLoader) theme_file_path = os.path.join(CONFIG_DIR, 'theme.yaml') with open(theme_file_path, 'r') as theme_file: content = theme_file.read() for name, value in colors.items(): content = content.replace(value, name) with open(theme_file_path, 'w') as theme_file: theme_file.write(content)

27.428571

74

0.680556

ddf722296b045e4651d40786b03f7d0884ace687

6,340

py

Python

odps/df/expr/tests/test_datetimes.py

Emersonxuelinux/aliyun-odps-python-sdk

0b38c777711c95ed1775fa67822febf88fc3d642

[ "Apache-2.0" ]

412

2015-11-01T09:27:52.000Z

2022-03-26T05:04:03.000Z

odps/df/expr/tests/test_datetimes.py

Emersonxuelinux/aliyun-odps-python-sdk

0b38c777711c95ed1775fa67822febf88fc3d642

[ "Apache-2.0" ]

168

2015-11-16T09:46:39.000Z

2022-03-17T06:35:26.000Z

odps/df/expr/tests/test_datetimes.py

Emersonxuelinux/aliyun-odps-python-sdk

0b38c777711c95ed1775fa67822febf88fc3d642

[ "Apache-2.0" ]

103

2015-12-01T08:10:09.000Z

2022-02-21T12:46:35.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright 1999-2017 Alibaba Group Holding Ltd. # # 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 datetime import datetime from odps.tests.core import TestBase from odps.compat import unittest from odps.models import Schema from odps.df.types import validate_data_type from odps.df.expr.tests.core import MockTable from odps.df.expr.datetimes import * class Test(TestBase): def setup(self): datatypes = lambda *types: [validate_data_type(t) for t in types] schema = Schema.from_lists(['name', 'id', 'fid', 'isMale', 'scale', 'birth'], datatypes('string', 'int64', 'float64', 'boolean', 'decimal', 'datetime')) table = MockTable(name='pyodps_test_expr_table', schema=schema) self.expr = CollectionExpr(_source_data=table, _schema=schema) def testDatetimes(self): self.assertRaises(AttributeError, lambda: self.expr.id.hour) self.assertRaises(AttributeError, lambda: self.expr.fid.minute) self.assertRaises(AttributeError, lambda: self.expr.isMale.week) self.assertRaises(AttributeError, lambda: self.expr.scale.year) self.assertRaises(AttributeError, lambda: self.expr.name.strftime('%Y')) self.assertIsInstance(self.expr.birth.date, Date) self.assertIsInstance(self.expr.birth.date, DatetimeSequenceExpr) self.assertIsInstance(self.expr.birth.max().date, DatetimeScalar) self.assertIsInstance(self.expr.birth.time, Time) self.assertIsInstance(self.expr.birth.time, DatetimeSequenceExpr) self.assertIsInstance(self.expr.birth.max().time, DatetimeScalar) self.assertIsInstance(self.expr.birth.year, Year) self.assertIsInstance(self.expr.birth.year, Int64SequenceExpr) self.assertIsInstance(self.expr.birth.max().year, Int64Scalar) self.assertIsInstance(self.expr.birth.month, Month) self.assertIsInstance(self.expr.birth.month, Int64SequenceExpr) self.assertIsInstance(self.expr.birth.max().month, Int64Scalar) self.assertIsInstance(self.expr.birth.day, Day) self.assertIsInstance(self.expr.birth.day, Int64SequenceExpr) self.assertIsInstance(self.expr.birth.max().day, Int64Scalar) self.assertIsInstance(self.expr.birth.hour, Hour) self.assertIsInstance(self.expr.birth.hour, Int64SequenceExpr) self.assertIsInstance(self.expr.birth.max().hour, Int64Scalar) self.assertIsInstance(self.expr.birth.minute, Minute) self.assertIsInstance(self.expr.birth.minute, Int64SequenceExpr) self.assertIsInstance(self.expr.birth.max().minute, Int64Scalar) self.assertIsInstance(self.expr.birth.second, Second) self.assertIsInstance(self.expr.birth.second, Int64SequenceExpr) self.assertIsInstance(self.expr.birth.max().second, Int64Scalar) self.assertIsInstance(self.expr.birth.microsecond, MicroSecond) self.assertIsInstance(self.expr.birth.microsecond, Int64SequenceExpr) self.assertIsInstance(self.expr.birth.max().microsecond, Int64Scalar) self.assertIsInstance(self.expr.birth.week, Week) self.assertIsInstance(self.expr.birth.week, Int64SequenceExpr) self.assertIsInstance(self.expr.birth.max().week, Int64Scalar) self.assertIsInstance(self.expr.birth.weekofyear, WeekOfYear) self.assertIsInstance(self.expr.birth.weekofyear, Int64SequenceExpr) self.assertIsInstance(self.expr.birth.max().weekofyear, Int64Scalar) self.assertIsInstance(self.expr.birth.dayofweek, WeekDay) self.assertIsInstance(self.expr.birth.dayofweek, Int64SequenceExpr) self.assertIsInstance(self.expr.birth.max().dayofweek, Int64Scalar) self.assertIsInstance(self.expr.birth.weekday, WeekDay) self.assertIsInstance(self.expr.birth.weekday, Int64SequenceExpr) self.assertIsInstance(self.expr.birth.max().weekday, Int64Scalar) self.assertIsInstance(self.expr.birth.dayofyear, DayOfYear) self.assertIsInstance(self.expr.birth.dayofyear, Int64SequenceExpr) self.assertIsInstance(self.expr.birth.max().dayofyear, Int64Scalar) self.assertIsInstance(self.expr.birth.is_month_start, IsMonthStart) self.assertIsInstance(self.expr.birth.is_month_start, BooleanSequenceExpr) self.assertIsInstance(self.expr.birth.max().is_month_start, BooleanScalar) self.assertIsInstance(self.expr.birth.is_month_end, IsMonthEnd) self.assertIsInstance(self.expr.birth.is_month_end, BooleanSequenceExpr) self.assertIsInstance(self.expr.birth.max().is_month_end, BooleanScalar) self.assertIsInstance(self.expr.birth.is_year_start, IsYearStart) self.assertIsInstance(self.expr.birth.is_year_start, BooleanSequenceExpr) self.assertIsInstance(self.expr.birth.max().is_year_start, BooleanScalar) self.assertIsInstance(self.expr.birth.is_year_end, IsYearEnd) self.assertIsInstance(self.expr.birth.is_year_end, BooleanSequenceExpr) self.assertIsInstance(self.expr.birth.max().is_year_end, BooleanScalar) self.assertIsInstance(self.expr.birth.strftime('%Y'), Strftime) self.assertIsInstance(self.expr.birth.strftime('%Y'), StringSequenceExpr) self.assertIsInstance(self.expr.birth.max().strftime('%Y'), StringScalar) expr = self.expr.birth + hour(10) self.assertIsInstance(expr, DatetimeSequenceExpr) expr = self.expr.birth - microsecond(100) self.assertIsInstance(expr, DatetimeSequenceExpr) expr = self.expr.birth - datetime.now() self.assertIsInstance(expr, Int64SequenceExpr) self.assertRaises(ExpressionError, lambda: self.expr.birth + datetime.now()) if __name__ == '__main__': unittest.main()

47.669173

109

0.730915

2276bce9ffcd8566b4b0502e557c58343a7d098f

1,070

py

Python

predicators/src/args.py

williamshen-nz/predicators

6a6b3444108a6d2da3ec3c7d85bbe60ae3b113b9

[ "MIT" ]

null

predicators/src/args.py

williamshen-nz/predicators

6a6b3444108a6d2da3ec3c7d85bbe60ae3b113b9

[ "MIT" ]

null

predicators/src/args.py

williamshen-nz/predicators

6a6b3444108a6d2da3ec3c7d85bbe60ae3b113b9

[ "MIT" ]

null

"""Contains settings that vary per run. All global, immutable settings should be in settings.py. """ import argparse def create_arg_parser(env_required: bool = True, approach_required: bool = True, seed_required: bool = True) -> argparse.ArgumentParser: """Defines command line argument parser.""" parser = argparse.ArgumentParser() parser.add_argument("--env", required=env_required, type=str) parser.add_argument("--approach", required=approach_required, type=str) parser.add_argument("--excluded_predicates", default="", type=str) parser.add_argument("--seed", required=seed_required, type=int) parser.add_argument("--timeout", default=10, type=float) parser.add_argument("--make_videos", action="store_true") parser.add_argument("--make_failure_videos", action="store_true") parser.add_argument("--load_approach", action="store_true") parser.add_argument("--load_data", action="store_true") parser.add_argument("--experiment_id", default="", type=str) return parser

42.8

77

0.703738

f73092a62dc94f15d8005c38a7f97315b3879895

2,582

py

Python

src/openstack_cli/commands/conf/keys/export.py

hapylestat/openstack_cli

be627f0b3c7ab9bf1032c36faca2ad101e53fb0e

[ "Apache-2.0" ]

null

src/openstack_cli/commands/conf/keys/export.py

hapylestat/openstack_cli

be627f0b3c7ab9bf1032c36faca2ad101e53fb0e

[ "Apache-2.0" ]

1

2021-03-02T07:15:43.000Z

src/openstack_cli/commands/conf/keys/export.py

hapylestat/openstack_cli

be627f0b3c7ab9bf1032c36faca2ad101e53fb0e

[ "Apache-2.0" ]

1

2021-03-23T10:00:56.000Z

# Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to You under the Apache License, Version 2.0 # (the "License"); you may not use this file except in compliance with # the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os from openstack_cli.commands.conf.keys.list import _keys_list from openstack_cli.modules.apputils.terminal import Console from openstack_cli.core.config import Configuration from openstack_cli.modules.apputils.discovery import CommandMetaInfo from openstack_cli.modules.openstack import VMKeypairItemValue, OpenStack __module__ = CommandMetaInfo("export", "Export ssh keys to disk") __args__ = __module__.arg_builder\ .add_default_argument("name", str, "Name of the key to be exported", default="") def _keys_export(conf: Configuration, ostack: OpenStack, name: str): if not name: _keys = _keys_list(conf, ostack, True) item = Console.ask("Select key to export", _type=int) if item is None or item > len(_keys) - 1: Console.print_warning("Invalid selection, aborting") return name = _keys[item].name _key: VMKeypairItemValue try: _key = conf.get_key(name) except KeyError as e: Console.print_error(str(e)) return d = os.getcwd() _public_file_path = os.path.join(d, f"{_key.name}.public.key") _private_file_path = os.path.join(d, f"{_key.name}.private.key") if _key.public_key: try: with open(_public_file_path, "w+", encoding="UTF-8") as f: f.write(_key.public_key) Console.print(f"Public key: {_public_file_path}") except IOError as e: Console.print_error(f"{_key.name}(public): {str(e)}") if _key.private_key: try: with open(_private_file_path, "w+", encoding="UTF-8") as f: f.write(_key.private_key) Console.print(f"Private key: {_private_file_path}") except IOError as e: Console.print_error(f"{_key.name}(private): {str(e)}") def __init__(conf: Configuration, name: str): ostack = OpenStack(conf) _keys_export(conf, ostack, name)

36.366197

82

0.72773

0c9785c2d570ef00ef949f268c2c51f80508c758

1,118

py

Python

features/other.py

jacobjaeger/amadeus

5499528236845c2d7a22bcc88d10f02ddbdcfa51

[ "MIT" ]

null

features/other.py

jacobjaeger/amadeus

5499528236845c2d7a22bcc88d10f02ddbdcfa51

[ "MIT" ]

null

features/other.py

jacobjaeger/amadeus

5499528236845c2d7a22bcc88d10f02ddbdcfa51

[ "MIT" ]

null

from discord.ext.commands import Cog, command, Context, Converter, BadArgument from discord import Embed, Member import discord from aiohttp import ClientSession from .common import invalid_arg from random import choice, randint from typing import Optional, Union from re import compile class Color(Converter): REGEX = compile(r"#?([A-Fa-f0-9]{2}){3}") async def convert(self, ctx: Context, argument): if not self.REGEX.match(argument): raise BadArgument(f"{argument} is not a valid hex color") argument = argument if not argument.startswith("#") else argument[1:] return tuple((argument[:2], argument[2:4], argument[4:])) class Other(Cog): @command("color", help="show info about a color") async def color(self, ctx: Context, color: Color): # noqa em = Embed( title="#" + "".join(color), description=f"rgb({int(color[0], base=16)}, {int(color[1], base=16)}, {int(color[2], base=16)})", color=int("".join(color), base=16) ) # noqa await ctx.send(embed=em) def setup(bot): bot.add_cog(Other())

32.882353

109

0.644902

a3460454e5b162ba28f47ea8a372e786114cdb8a

422

py

Python

forum/migrations/0003_auto_20190928_2259.py

duplxey/NForum

990215e5a841ac054fd3c0a167dee37298a70fb8

[ "MIT" ]

7

2019-11-12T14:01:17.000Z

2022-01-29T19:17:09.000Z

forum/migrations/0003_auto_20190928_2259.py

duplxey/NForum

990215e5a841ac054fd3c0a167dee37298a70fb8

[ "MIT" ]

4

2019-12-08T10:03:21.000Z

2020-04-07T20:27:53.000Z

forum/migrations/0003_auto_20190928_2259.py

duplxey/NForum

990215e5a841ac054fd3c0a167dee37298a70fb8

[ "MIT" ]

1

2022-01-29T13:44:24.000Z

# Generated by Django 2.2.5 on 2019-09-28 20:59 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('forum', '0002_auto_20190928_2258'), ] operations = [ migrations.AlterField( model_name='category', name='subcategories', field=models.ManyToManyField(default=None, to='forum.Subcategory'), ), ]

22.210526

79

0.618483

09566356a21669017ea7782a85c4f30055cb40a7

1,125

py

Python

tests/unit/workflow/processors/test_target_postchecker.py

thoughteer/edera

c4ddb5d8a25906c3bd773c91afb3260fc0b704f2

[ "MIT" ]

3

2018-11-27T15:45:19.000Z

2018-12-21T20:32:10.000Z

tests/unit/workflow/processors/test_target_postchecker.py

thoughteer/edera

c4ddb5d8a25906c3bd773c91afb3260fc0b704f2

[ "MIT" ]

18

2018-12-02T18:38:59.000Z

2020-02-05T22:09:37.000Z

tests/unit/workflow/processors/test_target_postchecker.py

thoughteer/edera

c4ddb5d8a25906c3bd773c91afb3260fc0b704f2

[ "MIT" ]

null

import pytest from edera import Condition from edera import Task from edera.exceptions import TargetVerificationError from edera.requisites import shortcut from edera.workflow import WorkflowBuilder from edera.workflow.processors import TargetPostChecker def test_target_postchecker_always_executes_task(): class C(Condition): def check(self): return True class T(Task): target = C() def execute(self): pass class X(Task): @shortcut def requisite(self): return T() workflow = WorkflowBuilder().build(X()) TargetPostChecker().process(workflow) assert workflow[X()].item.phony workflow[T()].item.execute() def test_target_postchecker_verifies_target_after_task_execution(): class C(Condition): def check(self): return False class T(Task): target = C() def execute(self): pass workflow = WorkflowBuilder().build(T()) TargetPostChecker().process(workflow) with pytest.raises(TargetVerificationError): workflow[T()].item.execute()

20.454545

67

0.662222

3ba33eff0dd0499dcd0011a5603e946a533ab149

2,200

py

Python

linux-distro/package/nuxleus/Source/Vendor/Microsoft/IronPython-2.0.1/Lib/headstock/lib/logger.py

mdavid/nuxleus

653f1310d8bf08eaa5a7e3326c2349e56a6abdc2

[ "BSD-3-Clause" ]

1

2017-03-28T06:41:51.000Z

linux-distro/package/nuxleus/Source/Vendor/Microsoft/IronPython-2.0.1/Lib/headstock/lib/logger.py

mdavid/nuxleus

653f1310d8bf08eaa5a7e3326c2349e56a6abdc2

[ "BSD-3-Clause" ]

null

linux-distro/package/nuxleus/Source/Vendor/Microsoft/IronPython-2.0.1/Lib/headstock/lib/logger.py

mdavid/nuxleus

653f1310d8bf08eaa5a7e3326c2349e56a6abdc2

[ "BSD-3-Clause" ]

1

2016-12-13T21:08:58.000Z

# -*- coding: utf-8 -*- from Axon.Component import component from Axon.Ipc import shutdownMicroprocess, producerFinished from bridge import Element as E import logging from logging import handlers class Logger(component): Inboxes = {"inbox" : "String to be logged", "control" : "Closes the logger",} Outboxes = {"outbox" : "UNUSED", "signal" : "UNUSED",} def __init__(self, path=None, stdout=False, name=None): super(Logger, self).__init__() self.path = path self.with_stdout = stdout self.name = name def main(self): logger = logging.getLogger("kamaelia.logger.%s" % self.name or '') logger.setLevel(logging.DEBUG) logfmt = logging.Formatter("[%(asctime)s] %(message)s") if self.path: h = handlers.RotatingFileHandler(self.path, maxBytes=1048576, backupCount=5) h.setLevel(logging.DEBUG) h.setFormatter(logfmt) logger.addHandler(h) if self.with_stdout: import sys h = logging.StreamHandler(sys.stdout) h.setLevel(logging.DEBUG) h.setFormatter(logfmt) logger.addHandler(h) yield 1 while 1: if self.dataReady("control"): mes = self.recv("control") if isinstance(mes, shutdownMicroprocess) or isinstance(mes, producerFinished): logger.close() self.send(producerFinished(), "signal") break if self.dataReady("inbox"): msg = token = self.recv("inbox") if isinstance(token, tuple): if isinstance(token[1], E): msg = "%s : %s" % (msg[0], msg[1].xml(omit_declaration=True, indent=False)) else: msg = "%s : %s" % (msg[0], msg[1]) elif isinstance(msg, E): msg = msg.xml(omit_declaration=True, indent=False) logger.debug(msg) if not self.anyReady(): self.pause() yield 1

30.555556

99

0.522727

45bbe38b7b488aacbf20334b1b53e675b7c9d345

1,417

py

Python

keno/utility/stop_watch.py

matthewdeanmartin/keno

04724f5ebf53b5f11d150ec587750a18d0d3fee0

[ "MIT" ]

null

keno/utility/stop_watch.py

matthewdeanmartin/keno

04724f5ebf53b5f11d150ec587750a18d0d3fee0

[ "MIT" ]

17

2018-07-14T17:48:04.000Z

2020-09-16T10:25:59.000Z

keno/utility/stop_watch.py

matthewdeanmartin/keno

04724f5ebf53b5f11d150ec587750a18d0d3fee0

[ "MIT" ]

null

# coding=utf-8 """ How long is this taking """ import datetime class Timer(object): """A simple timer class""" def __init__(self) -> None: self.split_start: datetime.datetime self.start_time: datetime.datetime self.stop_time: datetime.datetime def start(self) -> datetime.datetime: """Starts the timer""" self.start_time = datetime.datetime.now() return self.start_time def stop(self, message: str = "Total: ") -> str: """Stops the timer. Returns the time elapsed""" self.stop_time = datetime.datetime.now() return message + str(self.stop_time - self.start_time) def now(self, message: str = "Now: ") -> str: """Returns the current time with a message""" return message + ": " + str(datetime.datetime.now()) def elapsed(self, message: str = "Elapsed: ") -> str: """Time elapsed since start was called""" return message + " " + str(datetime.datetime.now() - self.start_time) def split(self, message: str = "Split started at: ") -> str: """Start a split timer""" self.split_start = datetime.datetime.now() return message + str(self.split_start) def unsplit(self, message: str = "Unsplit: ") -> str: """Stops a split. Returns the time elapsed since split was called""" return message + str(datetime.datetime.now() - self.split_start)

33.738095

77

0.62103

a03d6120ff4b8c32e66b0cde7b7ebd965af76042

473

py

Python

Django/scraping/news_scraping.py

hanana1253/TIL

301ba13acdba677b19be1c03fcd4135ff06d862c

[ "MIT" ]

3

2021-06-01T08:31:54.000Z

2022-02-02T06:03:18.000Z

Django/scraping/news_scraping.py

hanana1253/TIL

301ba13acdba677b19be1c03fcd4135ff06d862c

[ "MIT" ]

null

Django/scraping/news_scraping.py

hanana1253/TIL

301ba13acdba677b19be1c03fcd4135ff06d862c

[ "MIT" ]

2

2021-03-27T06:45:12.000Z

2022-01-07T09:34:32.000Z

import requests from bs4 import BeautifulSoup for startnum in range(1, 100, 10): url = 'https://search.naver.com/search.naver?where=news&sm=tab_jum&query=%EC%BD%94%EB%A1%9C%EB%82%98&start={}'.format(startnum) response = requests.get(url) html_text = response.text soup = BeautifulSoup(html_text, 'html.parser') news_headings = soup.find_all("a", "news_tit") for heading in news_headings: print(heading['title'], heading['href'], sep='\n')

36.384615

131

0.691332

b749425984be001e996f17e1336c011eb82344d4

1,749

py

Python

Predictive_Tracker/BoxKalmanFilter.py

Royzon/3d-tracking-approaches

58cdf55b5fef5c6ceae67f0a44d589d08b24959c

[ "MIT" ]

1

2021-12-17T16:23:56.000Z

Predictive_Tracker/BoxKalmanFilter.py

Royzon/3d-tracking-approaches

58cdf55b5fef5c6ceae67f0a44d589d08b24959c

[ "MIT" ]

null

Predictive_Tracker/BoxKalmanFilter.py

Royzon/3d-tracking-approaches

58cdf55b5fef5c6ceae67f0a44d589d08b24959c

[ "MIT" ]

null

import numpy as np from filterpy.kalman import KalmanFilter # (x, y, z, w, l, h, angle, v_x, v_y, v_z, v_angle) class BoxKalmanFilter(): def __init__(self, box, id, type): self.id = id self.type = type self.age = 0 self.blind_time = 0 self.kf = KalmanFilter(dim_x=11, dim_z=7) self.kf.F = np.array([[1,0,0,0,0,0,0,1,0,0,0], [0,1,0,0,0,0,0,0,1,0,0], [0,0,1,0,0,0,0,0,0,1,0], [0,0,0,1,0,0,0,0,0,0,0], [0,0,0,0,1,0,0,0,0,0,0], [0,0,0,0,0,1,0,0,0,0,0], [0,0,0,0,0,0,1,0,0,0,1], [0,0,0,0,0,0,0,1,0,0,0], [0,0,0,0,0,0,0,0,1,0,0], [0,0,0,0,0,0,0,0,0,1,0], [0,0,0,0,0,0,0,0,0,0,1]]) self.kf.H = np.array([[1,0,0,0,0,0,0,0,0,0,0], [0,1,0,0,0,0,0,0,0,0,0], [0,0,1,0,0,0,0,0,0,0,0], [0,0,0,1,0,0,0,0,0,0,0], [0,0,0,0,1,0,0,0,0,0,0], [0,0,0,0,0,1,0,0,0,0,0], [0,0,0,0,0,0,1,0,0,0,0]]) self.kf.P[7:,7:] *= 1000. self.kf.P *= 10. self.kf.Q[7:,7:] *= 0.01 self.kf.x[:7] = box.reshape((7, 1)) def update(self, box): self.blind_time = 0 self.kf.update(box) def predict(self): self.blind_time += 1 self.age += 1 self.kf.predict() def get_state(self): return self.kf.x[:7].reshape((7,))

33.634615

57

0.348771

4f6ee2e5176bc62a36b9c645fe8eab63d82c438e

704

py

Python

components/google-cloud/google_cloud_pipeline_components/version.py

Iuiu1234/pipelines

1e032f550ce23cd40bfb6827b995248537b07d08

[ "Apache-2.0" ]

1

2021-11-22T12:22:14.000Z

components/google-cloud/google_cloud_pipeline_components/version.py

Iuiu1234/pipelines

1e032f550ce23cd40bfb6827b995248537b07d08

[ "Apache-2.0" ]

13

2021-07-06T06:47:43.000Z

2022-03-08T23:35:13.000Z

components/google-cloud/google_cloud_pipeline_components/version.py

Iuiu1234/pipelines

1e032f550ce23cd40bfb6827b995248537b07d08

[ "Apache-2.0" ]

1

2021-08-22T15:51:17.000Z

# Copyright 2021 The Kubeflow 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. """Contains the version string of Google Cloud Pipeline Components.""" __version__ = "0.2.1.dev"

41.411765

74

0.761364

73ac107c68e67c1f540c7dd532312b929cb8b662

1,341

py

Python

ffield.py

Nlioxa/ffield

0ffb150b6c0336a12b5f5d7f448054c385dd56b4

[ "MIT" ]

1

2020-12-16T11:50:45.000Z

ffield.py

Nlioxa/ffield

0ffb150b6c0336a12b5f5d7f448054c385dd56b4

[ "MIT" ]

null

ffield.py

Nlioxa/ffield

0ffb150b6c0336a12b5f5d7f448054c385dd56b4

[ "MIT" ]

null

class FNumber: def __init__(self, ffield, value): self.f = ffield self.v = int(value) % self.f.p def __neg__(self): return FNumber(self.f, -self.v) def __add__(self, other): return FNumber(self.f, int(self) + int(other)) def __sub__(self, other): return -other + self def __mul__(self, other): return FNumber(self.f, int(self) * int(other)) def __int__(self): return self.v def __truediv__(self, other): return self.f.mod(self.f.find(int(self)) - self.f.find(int(other))) def __pow__(self, other): return self.f.mod(self.f.find(int(self)) * int(other)) def __eq__(self, other): return int(self) == int(other) def __str__(self): return str(int(self)) def __repr__(self): return str(int(self)) class FField: def __init__(self, p, w): self.p = p self.w = w def mod(self, j): b = 1 for _ in range(j % self.p - 1 if j < 0 else j): b = (b * self.w) % self.p return self.num(b) def find(self, b): for j in range(self.p): if int(b) == self.mod(j): return j return -1 def num(self, value): return FNumber(self, value)

24.381818

76

0.517524

4483510427ca1667081aa51f724a87f274a66909

917

py

Python

src/apps/domains/location/migrations/0007_auto_20190721_0529.py

antiline/jun2

00928cea1f4b8cd6634cf9a1ae6dc19c95d0e54c

[ "MIT" ]

null

src/apps/domains/location/migrations/0007_auto_20190721_0529.py

antiline/jun2

00928cea1f4b8cd6634cf9a1ae6dc19c95d0e54c

[ "MIT" ]

17

2019-06-24T14:11:49.000Z

2021-06-04T22:19:59.000Z

src/apps/domains/location/migrations/0007_auto_20190721_0529.py

antiline/jun2

00928cea1f4b8cd6634cf9a1ae6dc19c95d0e54c

[ "MIT" ]

null

# Generated by Django 2.2.2 on 2019-07-20 20:29 from django.db import migrations, models import uuid class Migration(migrations.Migration): dependencies = [ ('location', '0006_auto_20190721_0341'), ] operations = [ migrations.AlterField( model_name='extractgpsfromexifstatus', name='image_path', field=models.FilePathField(allow_files=False, allow_folders=True, path='/Users/antiline/Documents/Jun2/mount', recursive=True, verbose_name='Image 경로'), ), migrations.AlterField( model_name='gpxshare', name='share_uuid', field=models.UUIDField(default=uuid.UUID('84574781-f532-4aaa-991e-5437c65c7247'), unique=True, verbose_name='공유 UUID'), ), migrations.AlterModelTable( name='extractgpsfromexifstatus', table='extract_gps_from_exif_status', ), ]

31.62069

164

0.642312

aee9c25fd03d501f2094e2fd83c83bb5971b3b8e

11,214

py

Python

tensorflow_ranking/python/feature_test.py

we1559/ranking

7de1bd2d366d942c0acaabc91876035cc1d9c3f0

[ "Apache-2.0" ]

2

2019-07-29T00:56:01.000Z

2021-05-18T01:01:33.000Z

tensorflow_ranking/python/feature_test.py

rishabhmehrotra/ranking

8536405845e8ca180dae01c4d0e34ecb1be89b65

[ "Apache-2.0" ]

null

tensorflow_ranking/python/feature_test.py

rishabhmehrotra/ranking

8536405845e8ca180dae01c4d0e34ecb1be89b65

[ "Apache-2.0" ]

null

# Copyright 2019 The TensorFlow Ranking Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tests for feature transformations.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from absl.testing import parameterized import numpy as np import tensorflow as tf from google.protobuf import text_format from tensorflow.python.feature_column import feature_column_lib as feature_column from tensorflow_ranking.python import feature as feature_lib EXAMPLE_1_PROTO = """features: { feature: { key: "example_0.age" value: { int64_list: { value: [ 41 ] } } } feature: { key: "example_0.weight" value: { int64_list: { value: [ 77 ] } } } feature: { key: "example_0.label" value: { float_list: { value: [ 1.0 ] } } } feature: { key: "example_1.age" value: { int64_list: { value: [ 43 ] } } } feature: { key: "example_1.weight" value: { int64_list: { value: [ 78 ] } } } feature: { key: "example_1.label" value: { float_list: { value: [ 0.0 ] } } } }""" EXAMPLE_2_PROTO = """features: { feature: { key: "example_0.age" value: { int64_list: { value: [ 25 ] } } } feature: { key: "example_0.weight" value: { int64_list: { value: [ 95 ] } } } feature: { key: "example_0.label" value: { float_list: { value: [ 1.0 ] } } } feature: { key: "example_1.age" value: { int64_list: { value: [ 53 ] } } } feature: { key: "example_1.weight" value: { int64_list: { value: [ 85 ] } } } feature: { key: "example_1.label" value: { float_list: { value: [ 0.0 ] } } } } """ def _create_input_fn(): def my_input_fn(): feature_to_type = { "example_0.age": tf.io.FixedLenFeature([1], tf.int64), "example_1.age": tf.io.FixedLenFeature([1], tf.int64), "example_0.weight": tf.io.FixedLenFeature([1], tf.int64), "example_1.weight": tf.io.FixedLenFeature([1], tf.int64), "example_0.label": tf.io.FixedLenFeature([1], tf.float32), "example_1.label": tf.io.FixedLenFeature([1], tf.float32) } feature_1_proto = tf.train.Example() feature_2_proto = tf.train.Example() text_format.Merge(EXAMPLE_1_PROTO, feature_1_proto) text_format.Merge(EXAMPLE_2_PROTO, feature_2_proto) features_tensor = tf.io.parse_example( serialized=[ feature_1_proto.SerializeToString(), feature_2_proto.SerializeToString() ], features=feature_to_type) # Create the dataset. dataset = tf.data.Dataset.from_tensor_slices(features_tensor).batch(2) return tf.compat.v1.data.make_one_shot_iterator(dataset).get_next() return my_input_fn class FeatureLibTest(tf.test.TestCase, parameterized.TestCase): def test_make_identity_transform_fn(self): features = { "context": # Input size: (batch_size=2, num_features=2). tf.convert_to_tensor(value=[[1.0, 1.0], [1.0, 1.0]]), "per_example": tf.convert_to_tensor(value=[[[10.0]], [[10.0]]]), } with tf.compat.v1.Session() as sess: transform_fn = feature_lib.make_identity_transform_fn(["context"]) context_features, per_example_features = sess.run( transform_fn(features, 1)) self.assertEqual(["context"], sorted(context_features)) self.assertAllEqual([[1.0, 1.0], [1.0, 1.0]], context_features["context"]) self.assertEqual(["per_example"], sorted(per_example_features)) self.assertAllEqual([[[10.0]], [[10.0]]], per_example_features["per_example"]) def test_encode_features(self): # Inputs. vocabulary_size = 4 # -1 values are ignored. input_a = np.array([ [3, -1, -1], # example 0, ids [3] [0, 1, -1], # example 1, ids [0, 1] ]) input_b = np.array([ [0, -1, -1], # example 0, ids [0] [-1, -1, -1], # example 1, ids [] ]) input_features = {"aaa": input_a, "bbb": input_b} # Embedding variable. embedding_dimension = 2 embedding_values = ( (1., 2.), # id 0 (3., 5.), # id 1 (7., 11.), # id 2 (9., 13.) # id 3 ) # Expected lookup result, using combiner='mean'. expected_lookups_a = ( # example 0: (9., 13.), # ids [3], embedding = [9, 13] # example 1: (2., 3.5), # ids [0, 1], embedding = mean([1, 2] + [3, 5]) = [2, 3.5] ) expected_lookups_b = ( # example 0: (1., 2.), # ids [0], embedding = [1, 2] # example 1: (0., 0.), # ids [], embedding = [0, 0] ) # Build columns. categorical_column_a = feature_column.categorical_column_with_identity( key="aaa", num_buckets=vocabulary_size) categorical_column_b = feature_column.categorical_column_with_identity( key="bbb", num_buckets=vocabulary_size) embed_column_a, embed_column_b = feature_column.shared_embedding_columns( [categorical_column_a, categorical_column_b], dimension=embedding_dimension, initializer=lambda shape, dtype, partition_info: embedding_values, shared_embedding_collection_name="custom_collection_name") feature_columns = {"aaa": embed_column_a, "bbb": embed_column_b} cols_to_tensors = feature_lib.encode_features( input_features, feature_columns.values(), mode=tf.estimator.ModeKeys.EVAL) embedding_lookup_a = cols_to_tensors[feature_columns["aaa"]] embedding_lookup_b = cols_to_tensors[feature_columns["bbb"]] # Assert expected embedding variable and lookups. global_vars = tf.compat.v1.get_collection( tf.compat.v1.GraphKeys.GLOBAL_VARIABLES) embedding_var = global_vars[0] with tf.compat.v1.Session() as sess: sess.run(tf.compat.v1.global_variables_initializer()) sess.run(tf.compat.v1.tables_initializer()) self.assertAllEqual(embedding_values, embedding_var.eval()) self.assertAllEqual(expected_lookups_a, embedding_lookup_a.eval()) self.assertAllEqual(expected_lookups_b, embedding_lookup_b.eval()) def test_encode_listwise_features(self): # Batch size = 2, list_size = 2. features = { "query_length": tf.convert_to_tensor(value=[[1], [2]]), "utility": tf.convert_to_tensor(value=[[[1.0], [0.0]], [[0.0], [1.0]]]), "unigrams": tf.SparseTensor( indices=[[0, 0, 0], [0, 1, 0], [1, 0, 0], [1, 1, 0]], values=["ranking", "regression", "classification", "ordinal"], dense_shape=[2, 2, 1]) } context_feature_columns = { "query_length": feature_column.numeric_column( "query_length", shape=(1,), default_value=0, dtype=tf.int64) } example_feature_columns = { "utility": feature_column.numeric_column( "utility", shape=(1,), default_value=0.0, dtype=tf.float32), "unigrams": feature_column.embedding_column( feature_column.categorical_column_with_vocabulary_list( "unigrams", vocabulary_list=[ "ranking", "regression", "classification", "ordinal" ]), dimension=10) } with self.assertRaisesRegexp( ValueError, r"2nd dimesion of tensor must be equal to input size: 3, but found .*"): feature_lib.encode_listwise_features( features, input_size=3, context_feature_columns=context_feature_columns, example_feature_columns=example_feature_columns) context_features, example_features = feature_lib.encode_listwise_features( features, input_size=2, context_feature_columns=context_feature_columns, example_feature_columns=example_feature_columns) self.assertAllEqual(["query_length"], sorted(context_features)) self.assertAllEqual(["unigrams", "utility"], sorted(example_features)) self.assertAllEqual([2, 2, 10], example_features["unigrams"].get_shape().as_list()) with tf.compat.v1.Session() as sess: sess.run(tf.compat.v1.global_variables_initializer()) sess.run(tf.compat.v1.tables_initializer()) context_features, example_features = sess.run( [context_features, example_features]) self.assertAllEqual([[1], [2]], context_features["query_length"]) self.assertAllEqual([[[1.0], [0.0]], [[0.0], [1.0]]], example_features["utility"]) def test_encode_pointwise_features(self): # Batch size = 2, tf.Example input format. features = { "query_length": tf.convert_to_tensor(value=[[1], [1]]), # Repeated context feature. "utility": tf.convert_to_tensor(value=[[1.0], [0.0]]), "unigrams": tf.SparseTensor( indices=[[0, 0], [1, 0]], values=["ranking", "regression"], dense_shape=[2, 1]) } context_feature_columns = { "query_length": tf.feature_column.numeric_column( "query_length", shape=(1,), default_value=0, dtype=tf.int64) } example_feature_columns = { "utility": tf.feature_column.numeric_column( "utility", shape=(1,), default_value=0.0, dtype=tf.float32), "unigrams": tf.feature_column.embedding_column( feature_column.categorical_column_with_vocabulary_list( "unigrams", vocabulary_list=[ "ranking", "regression", "classification", "ordinal" ]), dimension=10) } (context_features, example_features) = feature_lib.encode_pointwise_features( features, context_feature_columns=context_feature_columns, example_feature_columns=example_feature_columns) self.assertAllEqual(["query_length"], sorted(context_features)) self.assertAllEqual(["unigrams", "utility"], sorted(example_features)) # Unigrams dense tensor has shape: [batch_size=2, list_size=1, dim=10]. self.assertAllEqual([2, 1, 10], example_features["unigrams"].get_shape().as_list()) with tf.compat.v1.Session() as sess: sess.run(tf.compat.v1.global_variables_initializer()) sess.run(tf.compat.v1.tables_initializer()) context_features, example_features = sess.run( [context_features, example_features]) self.assertAllEqual([[1], [1]], context_features["query_length"]) # Utility tensor has shape: [batch_size=2, list_size=1, 1]. self.assertAllEqual([[[1.0]], [[0.0]]], example_features["utility"]) if __name__ == "__main__": tf.test.main()

39.625442

81

0.630462

41bc4868190dd457679f2c9eb5066100ad0c5a15

17,190

py

Python

CoreHandler.py

hiramegl/AaMakro5oul

fda92a4da1182965eea8bb4c508afdf43e2a8704

[ "Apache-2.0" ]

3

2019-05-01T20:33:12.000Z

2021-02-15T10:38:35.000Z

CoreHandler.py

hiramegl/AaMakro5oul

fda92a4da1182965eea8bb4c508afdf43e2a8704

[ "Apache-2.0" ]

1

2020-10-12T00:14:11.000Z

2020-10-13T11:50:36.000Z

CoreHandler.py

hiramegl/AaMakro5oul

fda92a4da1182965eea8bb4c508afdf43e2a8704

[ "Apache-2.0" ]

null

# ****************************************************************************** # This file is part of the AaMakro5oul project # (An OSC/MIDI controller for Ableton Live with DJ features) # # Full project source: https://github.com/hiramegl/AaMakro5oul # # License : Apache License 2.0 # Full license: https://github.com/hiramegl/AaMakro5oul/blob/master/LICENSE # # Copyright 2018, 2019 by Hiram Galicia (hiramegl@yahoo.com) # http://www.unasystems.com # # All rights reserved. # ****************************************************************************** from BaseHandler import BaseHandler # ****************************************************************************** # Core Handler: gives advanced control of Ableton Live features # and keeps track of static variables shared by several modules # ****************************************************************************** class CoreHandler(BaseHandler): # session state static variables m_nGuiTrackOffset = 0 # remote GUI track offset m_nGuiVisTracks = 8 # remote GUI visible tracks in the screen m_nGuiSceneOffset = 0 # remove GUI scene offset m_nGuiVisScenes = 8 # remove GUI visible scenes in the screen m_nGuiVisSends = 8 # max num sends in remote GUI that we manage in session (tools modal) and sequence tabs m_nGuiVisReturns = 8 # max num returns in remote GUI that we manage in session tab (tools modal) # beat sequence static variables m_nGuiVisBeats = 8 # number of visible beats in beat grid m_nGuiVisNotes = 12 # number of visible notes in beat grid m_nSeqOffset = -1 # sequence offset for beat grid (in beats, time axis) m_nNoteOffset = -1 # note offset for beat grid (idx abs, note axis) m_nSelNoteRelIdx = -1 # selected note index # track clip static variables (for build-ups and drop-downs) m_bNextSelectionOn = False m_aNextClips = [] m_aTrackIndeces = [str(i) for i in xrange(m_nGuiVisTracks)] + ['master', 'selected'] m_aSceneIndeces = [str(i) for i in xrange(m_nGuiVisScenes)] + ['selected'] m_aBeatsIndeces = [str(i) for i in xrange(m_nGuiVisBeats)] m_hBeats = { "6400": 64.0, "3200": 32.0, "1600": 16.0, "0800": 8.0, "0400": 4.0, "0200": 2.0, "0100": 1.0, "0050": 0.5, "0025": 0.25, "0012": 0.125, "0006": 0.0625, "0003": 0.03125, "0001": 0.015625 } # track selection for device control m_hDevTrackSel = { 'a': ['none', -1], 'b': ['none', -1], 'c': ['none', -1], 'd': ['none', -1] } m_nMaxTrackSels = 8 m_nMaxReturnSels = 8 m_aChannels = ['a', 'b', 'c', 'd'] m_aChannelsFx = ['a', 'b', 'c', 'd', 'x'] # track selection for effects channel m_aFxTrackSel = ['none', -1] """ Note-offset explanation diagram: Start Note C1 ---+ +--- Middle C | | V V | Button | Octave |-2 -1 0 1 2 3 4 5 6 7 8 | row | Off Rel Abs | Off Rel Abs | Off Rel Abs | Note +------------------------------------------------------+--------+---------------+---------------+----------------+ B | 11 23 35 47 59 71 83 95 107 [119] | 0 | 11 - 0 = 11 | 47 - 0 = 47 | 119 - 0 = 119 | A# | 10 22 34 46 58 70 82 94 106 118 | 1 | | | --- --- | A | 9 21 33 45 57 69 81 93 105 117 | 2 | | | Max Max | G# | 8 20 32 44 56 68 80 92 104 116 | 3 | | | offset val | G | 7 19 31 43 55 67 79 91 103 115 127 | 4 | | | | F# | 6 18 30 42 54 66 78 90 102 114 126 | 5 | | | | F | 5 17 29 41 53 65 77 89 101 113 125 | 6 | | | | E | 4 16 28 40 52 64 76 88 100 112 124 | 7 | | [Octave 1] | | D# | 3 15 27 39 51 63 75 87 99 111 123 | 8 | offset val | C1 | | D | 2 14 26 38 50 62 74 86 98 110 122 | 9 | Min Min | Default | | C# | 1 13 25 37 49 61 73 85 97 109 121 | 10 | -- --- | -- -- | | C |[0] 12 24 (36) 48 _60_ 72 84 96 108 120 | 11 | 11 - 11 = 0 | 47 - 11 = 36 | 119 - 11 = 108 | +------------------------------------------------------+--------+---------------+---------------+----------------+ """ # constructor def __init__(self, _oCtrlInstance, _oOscServer, _hConfig): BaseHandler.__init__(self, _oCtrlInstance, _oOscServer, _hConfig) # CONSTANTS self.m_nNumBeatsInBar = 4 # number of beats in a bar (4 for 4/4 notation) self.m_nNumBitsInBeat = 4 # number of bits in a beat (the divisions within a beat) self.m_nNumNotesInOct = 12 # number of notes per octave self.m_nDefNoteOffset = 47 # default note offset for Octave 1 (C1 note) self.m_nMinNoteOffset = 11 # minimum note offset self.m_nMaxNoteOffset = 119 # maximum note offset # beat note accessors ******************************************** def gui_vis_beats(self, _nGuiVisBeats = -1): if (_nGuiVisBeats == -1): return CoreHandler.m_nGuiVisBeats CoreHandler.m_nGuiVisBeats = _nGuiVisBeats def seq_offset(self, _nSeqOffset = -1): if (_nSeqOffset == -1): return CoreHandler.m_nSeqOffset CoreHandler.m_nSeqOffset = _nSeqOffset def note_offset(self, _nNoteOffset = -1): if (_nNoteOffset == -1): return CoreHandler.m_nNoteOffset CoreHandler.m_nNoteOffset = _nNoteOffset # input: value between 0 and 11 # output: value between 0 and 119 (should be 127 in the future) def note_idx_abs(self, _nNoteIdxRel): return CoreHandler.m_nNoteOffset - _nNoteIdxRel # input: value between 0 and 119 (should be 127 in the future) # output: value between 0 and 11 def note_idx_rel(self, _nNoteIdxAbs): return CoreHandler.m_nNoteOffset - _nNoteIdxAbs def note_time_abs(self, _nNoteTimeRel): return _nNoteTimeRel + CoreHandler.m_nSeqOffset def note_time_rel(self, _nNoteTimeAbs): return _nNoteTimeAbs - CoreHandler.m_nSeqOffset # input: value between 0 and 127 def is_note_visible(self, _nNoteIdxAbs, _nNoteTimeAbs): nNoteIdxRel = self.note_idx_rel(_nNoteIdxAbs) nNoteTimeRel = self.note_time_rel(_nNoteTimeAbs) return (nNoteIdxRel >= 0 and nNoteIdxRel < CoreHandler.m_nGuiVisNotes and nNoteTimeRel >= 0 and nNoteTimeRel < CoreHandler.m_nGuiVisBeats) def current_octave(self): return (CoreHandler.m_nNoteOffset / self.m_nNumNotesInOct) - 2 # MIDI Standard starts in octave -2 # selected note, relative index: from 0 to 11 def sel_note_rel_idx(self, _nSelNodeRelIdx = -1): if (_nSelNodeRelIdx == -1): return CoreHandler.m_nSelNoteRelIdx CoreHandler.m_nSelNoteRelIdx = _nSelNodeRelIdx # track and returns accessors ************************************ def tracks(self): return self.song().visible_tracks def returns(self): return self.song().return_tracks def master(self): return self.song().master_track def tracks_and_returns(self): return tuple(self.tracks()) + tuple(self.returns()) def get_track_or_return(self, _nTrackIdxAbs): aTracksAndReturns = self.tracks_and_returns() return aTracksAndReturns[_nTrackIdxAbs] def get_track(self, _nTrackIdxAbs): aTracks = self.tracks() return aTracks[_nTrackIdxAbs] def get_return(self, _nTrackIdxAbs): aReturns = self.returns() return aReturns[_nTrackIdxAbs] def sel_track(self, _oTrack = None): if (_oTrack != None): self.song().view.selected_track = _oTrack return self.song().view.selected_track def sel_track_idx_abs(self): aAllTracks = self.tracks_and_returns() oSelTrack = self.sel_track() return list(aAllTracks).index(oSelTrack) def is_track_available(self, _nTrackIdxAbs): return (_nTrackIdxAbs < len(self.tracks())) def is_return_available(self, _nReturnIdxAbs): return (_nReturnIdxAbs < len(self.returns())) def is_return_track(self, _oTrack): return (_oTrack in self.returns()) # only audio tracks (0 ... num audio tracks - 1) def tracks_range(self): return range(len(self.tracks())) # only return tracks (0 ... num return tracks - 1) def returns_range(self): return range(len(self.returns())) # both audio tracks and return tracks (0 ... num tracks + num returns - 1) def tracks_and_returns_range(self): return range(len(self.tracks_and_returns())) # scene accessors ************************************************ def scenes(self): return self.song().scenes def get_scene(self, _nSceneIdxAbs): aAllScenes = self.scenes() return aAllScenes[_nSceneIdxAbs] def sel_scene(self, _oScene = None): if (_oScene != None): self.song().view.selected_scene = _oScene return self.song().view.selected_scene def sel_scene_idx_abs(self): aAllScenes = self.scenes() oSelScene = self.sel_scene() return list(aAllScenes).index(oSelScene) def is_scene_available(self, _nSceneIdxAbs): return (_nSceneIdxAbs < len(self.scenes())) def available_scenes(self): return range(len(self.scenes())) # clip accessors ************************************************* def sel_clip_slot(self): return self.song().view.highlighted_clip_slot def is_clip_available(self, _nTrackIdxAbs, _nSceneIdxAbs): return self.is_track_available(_nTrackIdxAbs) and self.is_scene_available(_nSceneIdxAbs) # next clips accessors ********************************* def next_selection_on(self, _bNextSelectionOn = None): if (_bNextSelectionOn != None): CoreHandler.m_bNextSelectionOn = _bNextSelectionOn return CoreHandler.m_bNextSelectionOn def next_clips_add(self, _nTrackIdxAbs, _nSceneIdxAbs): CoreHandler.m_aNextClips.append([_nTrackIdxAbs, _nSceneIdxAbs]) def next_clips_clear(self): CoreHandler.m_aNextClips = [] def next_clips(self): return CoreHandler.m_aNextClips # gui track accessors ********************************** def gui_track_offset(self, _nGuiTrackOffset = -1): if (_nGuiTrackOffset == -1): return CoreHandler.m_nGuiTrackOffset CoreHandler.m_nGuiTrackOffset = _nGuiTrackOffset def gui_num_tracks(self, _nGuiNumTracks = -1): if (_nGuiNumTracks == -1): return CoreHandler.m_nGuiVisTracks CoreHandler.m_nGuiVisTracks = _nGuiNumTracks def gui_visible_tracks_abs_range(self): return range(CoreHandler.m_nGuiTrackOffset, CoreHandler.m_nGuiTrackOffset + CoreHandler.m_nGuiVisTracks) def gui_visible_tracks_rel_range(self, _aExtra = None): aTracks = list(range(CoreHandler.m_nGuiVisTracks)) # using a list to be able to add _aExtra elements return aTracks if (_aExtra == None) else aTracks + _aExtra def track_idx_rel(self, _nTrackIdxAbs): return (_nTrackIdxAbs - CoreHandler.m_nGuiTrackOffset) % CoreHandler.m_nGuiVisTracks def track_idx_abs(self, _nTrackIdxRel): return _nTrackIdxRel + CoreHandler.m_nGuiTrackOffset def is_track_visible(self, _nTrackIdxAbs): nTrackIdxRel = _nTrackIdxAbs - CoreHandler.m_nGuiTrackOffset return (nTrackIdxRel >= 0 and nTrackIdxRel < CoreHandler.m_nGuiVisTracks) # gui scene accessors ********************************** def gui_scene_offset(self, _nGuiSceneOffset = -1): if (_nGuiSceneOffset == -1): return CoreHandler.m_nGuiSceneOffset CoreHandler.m_nGuiSceneOffset = _nGuiSceneOffset def gui_num_scenes(self, _nGuiNumScenes = -1): if (_nGuiNumScenes == -1): return CoreHandler.m_nGuiVisScenes CoreHandler.m_nGuiVisScenes = _nGuiNumScenes def gui_visible_scenes_abs_range(self): return range(CoreHandler.m_nGuiSceneOffset, CoreHandler.m_nGuiSceneOffset + CoreHandler.m_nGuiVisScenes) def gui_visible_scenes_rel_range(self, _aExtra = None): aScenes = list(range(CoreHandler.m_nGuiVisScenes)) # using a list to be able to add _aExtra elements return aScenes if (_aExtra == None) else aScenes + _aExtra def scene_idx_rel(self, _nSceneIdxAbs): return (_nSceneIdxAbs - CoreHandler.m_nGuiSceneOffset) % CoreHandler.m_nGuiVisScenes def scene_idx_abs(self, _nSceneIdxRel): return _nSceneIdxRel + CoreHandler.m_nGuiSceneOffset def is_scene_visible(self, _nSceneIdxAbs): nSceneIdxRel = _nSceneIdxAbs - CoreHandler.m_nGuiSceneOffset return (nSceneIdxRel >= 0 and nSceneIdxRel < CoreHandler.m_nGuiVisScenes) # clip, sends and returns accessors ******************** def is_clip_visible(self, _nTrackIdxAbs, _nSceneIdxAbs): nTrackIdxRel = _nTrackIdxAbs - CoreHandler.m_nGuiTrackOffset nSceneIdxRel = _nSceneIdxAbs - CoreHandler.m_nGuiSceneOffset return (nTrackIdxRel >= 0 and nTrackIdxRel < CoreHandler.m_nGuiVisTracks and nSceneIdxRel >= 0 and nSceneIdxRel < CoreHandler.m_nGuiVisScenes) def gui_num_vis_sends(self): return CoreHandler.m_nGuiVisSends def gui_vis_sends_range(self): return range(CoreHandler.m_nGuiVisSends) def gui_num_vis_returns(self): return CoreHandler.m_nGuiVisReturns def gui_vis_returns_range(self): return range(CoreHandler.m_nGuiVisReturns) # ****************************************************** def track_indeces_list(self): return list(CoreHandler.m_aTrackIndeces) def scene_indeces_list(self): return list(CoreHandler.m_aSceneIndeces) def beats_indeces_list(self): return list(CoreHandler.m_aBeatsIndeces) def beats_list(self): return list(CoreHandler.m_hBeats.keys()) def beat_value(self, _sBeat): return CoreHandler.m_hBeats.get(_sBeat, 0.0) # ****************************************************** # selected track to handle device control events def sel_track_dev(self, _sChannel, _sTrackType = None, _nIdxAbs = -1): if (_nIdxAbs == -1): # check if is in remover mode if (_sTrackType == 'none'): CoreHandler.m_hDevTrackSel[_sChannel] = ['none', -1] # is in getter mode elif (_sTrackType == None): return CoreHandler.m_hDevTrackSel[_sChannel] # is in setter mode else: CoreHandler.m_hDevTrackSel[_sChannel] = [_sTrackType, _nIdxAbs] def max_track_sels(self): return CoreHandler.m_nMaxTrackSels def max_return_sels(self): return CoreHandler.m_nMaxReturnSels # ****************************************************** def sel_track_fx(self, _sTrackType = None, _nIdxAbs = -1): if (_nIdxAbs == -1): # check if is in remover mode if (_sTrackType == 'none'): CoreHandler.m_aFxTrackSel = ['none', -1] # is in getter mode elif (_sTrackType == None): return CoreHandler.m_aFxTrackSel # is in setter mode else: CoreHandler.m_aFxTrackSel = [_sTrackType, _nIdxAbs] # ****************************************************** def value_gui_to_param(self, _nGuiValue, _oParam): if (_oParam.is_quantized == True): # just copy the value for quantized params nParamValue = _nGuiValue else: # adjust the value in case is continuous (not discrete) nParamMin = _oParam.min nParamMax = _oParam.max nParamValue = (_nGuiValue * (nParamMax - nParamMin)) + nParamMin return nParamValue # value between min and max def value_param_to_gui(self, _nParamValue, _oParam): if (_oParam.is_quantized == True): # just copy the value for quantized params nGuiValue = _nParamValue else: # adjust the value in case is continuous (not discrete) nParamMin = _oParam.min nParamMax = _oParam.max nGuiValue = (_nParamValue - nParamMin) / (nParamMax - nParamMin) return nGuiValue # value between 0 and 1

33.905325

125

0.576847

86d365a64e9c422a8dba7681b586fd4f8b5b6277

6,926

py

Python

cloudera-framework-assembly/src/main/assembly/root/lib/manager/python/environment.py

ggear/cloudera-framework

a1240d85aeacfb2c467d01032d04bdb72b9e55a7

[ "Apache-2.0" ]

11

2015-07-01T02:34:37.000Z

2019-05-02T06:06:11.000Z

cloudera-framework-assembly/src/main/assembly/root/lib/manager/python/environment.py

ggear/cloudera-framework

a1240d85aeacfb2c467d01032d04bdb72b9e55a7

[ "Apache-2.0" ]

3

2015-12-16T22:29:21.000Z

2018-01-02T06:54:06.000Z

cloudera-framework-assembly/src/main/assembly/root/lib/manager/python/environment.py

ggear/cloudera-framework

a1240d85aeacfb2c467d01032d04bdb72b9e55a7

[ "Apache-2.0" ]

7

2016-06-19T10:30:38.000Z

2019-03-28T16:01:37.000Z

#!/usr/bin/env python """ Provide a flat list of cluster connectivity parameters Usage: %s [options] Options: -h --help Show help --host=<cm-server-host> Specify a Cloudera Manager Server host Defaults to 'localhost' --port=<cm-server-port> Override the default Cloudera Manager Server port Defaults to '7180' --user=<cm-server-user> The Cloudera Manager user Defaults to 'admin' --user=<cm-server-user-password> The Cloudera Manager user password Defaults to 'admin' --cluster_name=<cluster-name> Name the cluster to reflect on Defaults to the zeroth cluster defined --service_role_name=<service-role-type> Filter to apply to service and role name Defaults to all roles defined in the cluster --random_index=<true|false> For parameters that are multi-value, define singular value via a random index rather than the zeroth element Defaults to 'false' """ import getopt import inspect import logging import sys import textwrap from random import randint from cm_api import api_client from cm_api.api_client import ApiResource LOG = logging.getLogger(__name__) MAN_API_VERSION = 16 # Do not use api_client.API_CURRENT_VERSION, it is often +1 current production version def do_print_header(): print '###############################################################################' print '# ' print '# Cluster Environment' print '#' print '###############################################################################' print '' def do_print_footer(): print '###############################################################################' def do_print_line_item_manual(service_name, role_name, keys, values): service_role_name = service_name.upper() + '_' + role_name.upper(); print '# ' + service_role_name for key, value in zip(keys, values): print service_role_name + '_' + key + '=' + value print '' return True def do_print_line_item(api, service, service_role_name_filter, random_index, service_name, role_name, role_port_name, hosts, ports): service_role_name = service_name.upper() + '_' + role_name.upper(); if service_role_name_filter is None or service_role_name_filter.upper() == service_role_name and service.type == service_name: if len(hosts) == 0: for role in service.get_roles_by_type(role_name): host = api.get_host(role.hostRef.hostId).hostname port = role.get_config('full')[role_port_name].value if port is None: port = role.get_config('full')[role_port_name].default hosts.append(host) ports.append(port) if len(hosts) > 0: index = 0 if str(random_index).title() == 'True': index = randint(0, len(hosts) - 1) print '# ' + service_role_name print service_role_name + '_HOST=' + hosts[index] print service_role_name + '_PORT=' + ports[index] print service_role_name + '_HOSTS=' + ",".join(hosts) print service_role_name + '_PORTS=' + ",".join(ports) print service_role_name + '_HOSTS_AND_PORTS=' + ','.join([host + ':' + port for host, port in zip(hosts, ports)]) print '' return True return False def do_call(host, port, user, password, cluster_name, service_role_name, random_index): api = ApiResource(host, port, user, password, False, MAN_API_VERSION); for cluster in api.get_all_clusters(): if cluster_name is None: break elif cluster_name == cluster.name: break if cluster_name is not None and cluster_name != cluster.name: print >> sys.stderr, "Cloud not find cluster: " + cluster_name return -2; do_print_header() for service in cluster.get_all_services(): do_print_line_item(api, service, service_role_name, random_index, 'HDFS', 'NAMENODE', 'namenode_port', [], []) do_print_line_item(api, service, service_role_name, random_index, 'KUDU', 'KUDU_MASTER', 'webserver_port', [], []) do_print_line_item(api, service, service_role_name, random_index, 'HUE', 'HUE_SERVER', 'hue_http_port', [], []) do_print_line_item(api, service, service_role_name, random_index, 'HIVE', 'HIVESERVER2', 'hs2_thrift_address_port', [], []) do_print_line_item(api, service, service_role_name, random_index, 'IMPALA', 'IMPALAD', 'beeswax_port', [], []) do_print_line_item(api, service, service_role_name, random_index, 'FLUME', 'AGENT', 'agent_http_port', [], []) do_print_line_item(api, service, service_role_name, random_index, 'KAFKA', 'KAFKA_BROKER', 'port', [], []) do_print_line_item(api, service, service_role_name, random_index, 'ZOOKEEPER', 'SERVER', 'clientPort', [], []) do_print_footer() def usage(): doc = inspect.getmodule(usage).__doc__ print >> sys.stderr, textwrap.dedent(doc % (sys.argv[0],)) def setup_logging(level): logging.basicConfig() logging.getLogger().setLevel(level) def main(argv): setup_logging(logging.INFO) host = 'localhost' port = 7180 user = 'admin' password = 'admin' cluster_name = None service_role_name = None random_index = False try: opts, args = getopt.getopt(sys.argv[1:], "h", ["help", "host=", "port=", "user=", "password=", "random_index=", "cluster_name=", "service_role_name="]) except getopt.GetoptError, err: print >> sys.stderr, err usage() return -1 for option, value in opts: if option in ("-h", "--help"): usage() return -1 elif option in ("--host"): host = value; elif option in ("--port"): port = value; elif option in ("--user"): user = value; elif option in ("--password"): password = value; elif option in ("--random_index"): random_index = value; elif option in ("--cluster_name"): cluster_name = value; elif option in ("--service_role_name"): service_role_name = value; else: print >> sys.stderr, "Unknown option or flag: " + option usage() return -1 do_call(host, port, user, password, cluster_name, service_role_name, random_index) return 0 if __name__ == '__main__': sys.exit(main(sys.argv))

42.490798

140

0.574069

1b6cdeb92d2787f82075304727b31637982bbfeb

1,967

py

Python

snakemake_workflow_template/release_modules/replace_identifiers/lib/arguments.py

regulondbunam/RegulonDB-MCO-Felipe

6e5546907399d5712a8e69f2f8320af8d30b9d7b

[ "MIT" ]

null

snakemake_workflow_template/release_modules/replace_identifiers/lib/arguments.py

regulondbunam/RegulonDB-MCO-Felipe

6e5546907399d5712a8e69f2f8320af8d30b9d7b

[ "MIT" ]

null

snakemake_workflow_template/release_modules/replace_identifiers/lib/arguments.py

regulondbunam/RegulonDB-MCO-Felipe

6e5546907399d5712a8e69f2f8320af8d30b9d7b

[ "MIT" ]

null

import argparse def load_arguments(): parser = argparse.ArgumentParser(description="Replaces the data's source identifiers with RegulonDB's identifiers.", formatter_class=argparse.ArgumentDefaultsHelpFormatter, epilog="Either Multigenomic or Datamarts should be selected through arguments options" ) parser.add_argument( "-org", "--organism", help="organism name corresponding to the input data", metavar="ECOLI", required=True ) parser.add_argument( "-i", "--inputdir", help="Input JSON data path whose source identifiers will be replaced with RegulonDB's identifiers", metavar="/Users/user/Proyectos/RegulonDB/Results/source/ecocyc", required=True ) parser.add_argument( "-o", "--outputdir", help="Directory where the JSON files with the RegulonDB IDs replaced will be stored", metavar="/Users/user/Proyectos/RegulonDB/Results/replaced_identifiers/multigenomic/ecocyc", required=True ) parser.add_argument( "-u", "--url", help="URL to establish a connection between the process and MongoDB", metavar="mongodb://127.0.0.1:27017/regulondbidentifiers", required=True ) parser.add_argument( "-l", "--log", help="Path where the log is going to be saved.", metavar="/Users/user/Proyectos/RegulonDB/Results/log", ) parser.add_argument( "-db", "--database", choices=["regulondbmultigenomic", "regulondbht"], help="database where the input data belongs", metavar="regulondbmultigenomic", required=True ) parser.add_argument( "-v", "--version", help="RegulonDB's release version", metavar="10.8", required=True ) arguments = parser.parse_args() return arguments

32.245902

123

0.613625

2bc5b8e5380fac52bfaa8045af40040ac06e1b5d

1,982

py

Python

test/test_inline_response2007_message.py

RiskAmerica/api-client-python

468c554a0440bef5086828631e25d99d41e28571

[ "MIT" ]

null

test/test_inline_response2007_message.py

RiskAmerica/api-client-python

468c554a0440bef5086828631e25d99d41e28571

[ "MIT" ]

null

test/test_inline_response2007_message.py

RiskAmerica/api-client-python

468c554a0440bef5086828631e25d99d41e28571

[ "MIT" ]

1

2021-04-14T15:52:03.000Z

# coding: utf-8 """ APIs RISKAMERICA A continuación les presentamos la documentación las **APIs** **de** **RiskAmerica**, el cual es un servicio pagado ofrecido por RiskAmerica que se contrata por separado a nuestras otras ofertas de software. Algunas consideraciones que debe tener al momento de usar las APIs: - El APIKEY o Token lo puede conseguir solicitándolo al equipo comercial de RiskAmerica - El request necesita ser enviado con el header **Accept:** **application/json** para que responda en formato **JSON** (de no ser enviado con esto se responderá en formato **XML**) - Todos los Servicios son **REST** y sus parametros pueden ser enviados tanto en **POST** como **GET** - El uso de las APIs puede llevar un cobro asociado según se pacte en el acuerdo comercial, por lo que le recomendamos ser cuidadosos en el uso de éstas para evitar sobre-cargos innecesarios. - RiskAmerica funciona con un mecanismo de **WhiteList** **de** **IPs** para las consultas de las API. Para habilitar o modificar la lista de IPs permitidas debe contactarse al mail **contacto@riskamerica.com**. # noqa: E501 OpenAPI spec version: 1.0.0 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import unittest import riam_api_client from riam_api_client.models.inline_response2007_message import InlineResponse2007Message # noqa: E501 from riam_api_client.rest import ApiException class TestInlineResponse2007Message(unittest.TestCase): """InlineResponse2007Message unit test stubs""" def setUp(self): pass def tearDown(self): pass def testInlineResponse2007Message(self): """Test InlineResponse2007Message""" # FIXME: construct object with mandatory attributes with example values # model = riam_api_client.models.inline_response2007_message.InlineResponse2007Message() # noqa: E501 pass if __name__ == '__main__': unittest.main()

49.55

1,070

0.750757

d6c0273a2adf56645a606f545fd038f7db980b6c

2,479

py

Python

parlai/core/logs.py

albert-magyar/ParlAI

3133b4e8290f8a42679b93a65fc25e76ac7f6761

[ "MIT" ]

null

parlai/core/logs.py

albert-magyar/ParlAI

3133b4e8290f8a42679b93a65fc25e76ac7f6761

[ "MIT" ]

null

parlai/core/logs.py

albert-magyar/ParlAI

3133b4e8290f8a42679b93a65fc25e76ac7f6761

[ "MIT" ]

1

2020-11-04T06:13:59.000Z

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. """ Log metrics to tensorboard. This file provides interface to log any metrics in tensorboard, could be extended to any other tool like visdom. .. code-block: none tensorboard --logdir <PARLAI_DATA/tensorboard> --port 8888. """ import json import numbers from parlai.core.opt import Opt from parlai.core.metrics import Metric from parlai.utils.io import PathManager import parlai.utils.logging as logging class TensorboardLogger(object): """ Log objects to tensorboard. """ @staticmethod def add_cmdline_args(argparser): """ Add tensorboard CLI args. """ logger = argparser.add_argument_group('Tensorboard Arguments') logger.add_argument( '-tblog', '--tensorboard-log', type='bool', default=False, help="Tensorboard logging of metrics, default is %(default)s", hidden=False, ) def __init__(self, opt: Opt): try: # tensorboard is a very expensive thing to import. Wait until the # last second to import it. from tensorboardX import SummaryWriter except ImportError: raise ImportError('Please run `pip install tensorboard tensorboardX`.') tbpath = opt['model_file'] + '.tensorboard' logging.debug(f'Saving tensorboard logs to: {tbpath}') if not PathManager.exists(tbpath): PathManager.mkdirs(tbpath) self.writer = SummaryWriter(tbpath, comment=json.dumps(opt)) def log_metrics(self, setting, step, report): """ Add all metrics from tensorboard_metrics opt key. :param setting: One of train/valid/test. Will be used as the title for the graph. :param step: Number of parleys :param report: The report to log """ for k, v in report.items(): if isinstance(v, numbers.Number): self.writer.add_scalar(f'{k}/{setting}', v, global_step=step) elif isinstance(v, Metric): self.writer.add_scalar(f'{k}/{setting}', v.value(), global_step=step) else: logging.error(f'k {k} v {v} is not a number') def flush(self): self.writer.flush()

30.9875

85

0.621218

c9c53273a4434902758419f5bac37babc0d032fc

37,252

py

Python

official/recommendation/data_pipeline.py

denis-choi/models

b478430d89f0b40fe3caf281f4e24cefda825412

[ "Apache-2.0" ]

3

2019-10-12T07:28:40.000Z

2019-10-16T00:58:10.000Z

official/recommendation/data_pipeline.py

denis-choi/models

b478430d89f0b40fe3caf281f4e24cefda825412

[ "Apache-2.0" ]

6

2021-06-08T21:30:48.000Z

2022-03-12T00:29:00.000Z

official/recommendation/data_pipeline.py

denis-choi/models

b478430d89f0b40fe3caf281f4e24cefda825412

[ "Apache-2.0" ]

7

2017-07-01T22:47:51.000Z

2021-05-15T10:48:22.000Z

# Copyright 2018 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Asynchronous data producer for the NCF pipeline.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import atexit import functools import os import sys import tempfile import threading import time import timeit import traceback import typing import numpy as np import six from six.moves import queue import tensorflow as tf from absl import logging from official.recommendation import constants as rconst from official.recommendation import movielens from official.recommendation import popen_helper from official.recommendation import stat_utils SUMMARY_TEMPLATE = """General: {spacer}Num users: {num_users} {spacer}Num items: {num_items} Training: {spacer}Positive count: {train_pos_ct} {spacer}Batch size: {train_batch_size} {multiplier} {spacer}Batch count per epoch: {train_batch_ct} Eval: {spacer}Positive count: {eval_pos_ct} {spacer}Batch size: {eval_batch_size} {multiplier} {spacer}Batch count per epoch: {eval_batch_ct}""" class DatasetManager(object): """Helper class for handling TensorFlow specific data tasks. This class takes the (relatively) framework agnostic work done by the data constructor classes and handles the TensorFlow specific portions (TFRecord management, tf.Dataset creation, etc.). """ def __init__(self, is_training, stream_files, batches_per_epoch, shard_root=None, deterministic=False, num_train_epochs=None): # type: (bool, bool, int, typing.Optional[str], bool, int) -> None """Constructs a `DatasetManager` instance. Args: is_training: Boolean of whether the data provided is training or evaluation data. This determines whether to reuse the data (if is_training=False) and the exact structure to use when storing and yielding data. stream_files: Boolean indicating whether data should be serialized and written to file shards. batches_per_epoch: The number of batches in a single epoch. shard_root: The base directory to be used when stream_files=True. deterministic: Forgo non-deterministic speedups. (i.e. sloppy=True) num_train_epochs: Number of epochs to generate. If None, then each call to `get_dataset()` increments the number of epochs requested. """ self._is_training = is_training self._deterministic = deterministic self._stream_files = stream_files self._writers = [] self._write_locks = [threading.RLock() for _ in range(rconst.NUM_FILE_SHARDS)] if stream_files else [] self._batches_per_epoch = batches_per_epoch self._epochs_completed = 0 self._epochs_requested = num_train_epochs if num_train_epochs else 0 self._shard_root = shard_root self._result_queue = queue.Queue() self._result_reuse = [] @property def current_data_root(self): subdir = (rconst.TRAIN_FOLDER_TEMPLATE.format(self._epochs_completed) if self._is_training else rconst.EVAL_FOLDER) return os.path.join(self._shard_root, subdir) def buffer_reached(self): # Only applicable for training. return (self._epochs_completed - self._epochs_requested >= rconst.CYCLES_TO_BUFFER and self._is_training) @staticmethod def serialize(data): """Convert NumPy arrays into a TFRecords entry.""" def create_int_feature(values): return tf.train.Feature(int64_list=tf.train.Int64List(value=list(values))) feature_dict = { k: create_int_feature(v.astype(np.int64)) for k, v in data.items() } return tf.train.Example( features=tf.train.Features(feature=feature_dict)).SerializeToString() @staticmethod def deserialize(serialized_data, batch_size=None, is_training=True): """Convert serialized TFRecords into tensors. Args: serialized_data: A tensor containing serialized records. batch_size: The data arrives pre-batched, so batch size is needed to deserialize the data. is_training: Boolean, whether data to deserialize to training data or evaluation data. """ def _get_feature_map(batch_size, is_training=True): """Returns data format of the serialized tf record file.""" if is_training: return { movielens.USER_COLUMN: tf.io.FixedLenFeature([batch_size, 1], dtype=tf.int64), movielens.ITEM_COLUMN: tf.io.FixedLenFeature([batch_size, 1], dtype=tf.int64), rconst.VALID_POINT_MASK: tf.io.FixedLenFeature([batch_size, 1], dtype=tf.int64), "labels": tf.io.FixedLenFeature([batch_size, 1], dtype=tf.int64) } else: return { movielens.USER_COLUMN: tf.io.FixedLenFeature([batch_size, 1], dtype=tf.int64), movielens.ITEM_COLUMN: tf.io.FixedLenFeature([batch_size, 1], dtype=tf.int64), rconst.DUPLICATE_MASK: tf.io.FixedLenFeature([batch_size, 1], dtype=tf.int64) } features = tf.io.parse_single_example( serialized_data, _get_feature_map(batch_size, is_training=is_training)) users = tf.cast(features[movielens.USER_COLUMN], rconst.USER_DTYPE) items = tf.cast(features[movielens.ITEM_COLUMN], rconst.ITEM_DTYPE) if is_training: valid_point_mask = tf.cast(features[rconst.VALID_POINT_MASK], tf.bool) fake_dup_mask = tf.zeros_like(users) return { movielens.USER_COLUMN: users, movielens.ITEM_COLUMN: items, rconst.VALID_POINT_MASK: valid_point_mask, rconst.TRAIN_LABEL_KEY: tf.reshape(tf.cast(features["labels"], tf.bool), (batch_size, 1)), rconst.DUPLICATE_MASK: fake_dup_mask } else: labels = tf.cast(tf.zeros_like(users), tf.bool) fake_valid_pt_mask = tf.cast(tf.zeros_like(users), tf.bool) return { movielens.USER_COLUMN: users, movielens.ITEM_COLUMN: items, rconst.DUPLICATE_MASK: tf.cast(features[rconst.DUPLICATE_MASK], tf.bool), rconst.VALID_POINT_MASK: fake_valid_pt_mask, rconst.TRAIN_LABEL_KEY: labels } def put(self, index, data): # type: (int, dict) -> None """Store data for later consumption. Because there are several paths for storing and yielding data (queues, lists, files) the data producer simply provides the data in a standard format at which point the dataset manager handles storing it in the correct form. Args: index: Used to select shards when writing to files. data: A dict of the data to be stored. This method mutates data, and therefore expects to be the only consumer. """ if self._is_training: mask_start_index = data.pop(rconst.MASK_START_INDEX) batch_size = data[movielens.ITEM_COLUMN].shape[0] data[rconst.VALID_POINT_MASK] = np.expand_dims( np.less(np.arange(batch_size), mask_start_index), -1) if self._stream_files: example_bytes = self.serialize(data) with self._write_locks[index % rconst.NUM_FILE_SHARDS]: self._writers[index % rconst.NUM_FILE_SHARDS].write(example_bytes) else: self._result_queue.put(( data, data.pop("labels")) if self._is_training else data) def start_construction(self): if self._stream_files: tf.io.gfile.makedirs(self.current_data_root) template = os.path.join(self.current_data_root, rconst.SHARD_TEMPLATE) self._writers = [tf.io.TFRecordWriter(template.format(i)) for i in range(rconst.NUM_FILE_SHARDS)] def end_construction(self): if self._stream_files: [writer.close() for writer in self._writers] self._writers = [] self._result_queue.put(self.current_data_root) self._epochs_completed += 1 def data_generator(self, epochs_between_evals): """Yields examples during local training.""" assert not self._stream_files assert self._is_training or epochs_between_evals == 1 if self._is_training: for _ in range(self._batches_per_epoch * epochs_between_evals): yield self._result_queue.get(timeout=300) else: if self._result_reuse: assert len(self._result_reuse) == self._batches_per_epoch for i in self._result_reuse: yield i else: # First epoch. for _ in range(self._batches_per_epoch * epochs_between_evals): result = self._result_queue.get(timeout=300) self._result_reuse.append(result) yield result def increment_request_epoch(self): self._epochs_requested += 1 def get_dataset(self, batch_size, epochs_between_evals): """Construct the dataset to be used for training and eval. For local training, data is provided through Dataset.from_generator. For remote training (TPUs) the data is first serialized to files and then sent to the TPU through a StreamingFilesDataset. Args: batch_size: The per-replica batch size of the dataset. epochs_between_evals: How many epochs worth of data to yield. (Generator mode only.) """ self.increment_request_epoch() if self._stream_files: if epochs_between_evals > 1: raise ValueError("epochs_between_evals > 1 not supported for file " "based dataset.") epoch_data_dir = self._result_queue.get(timeout=300) if not self._is_training: self._result_queue.put(epoch_data_dir) # Eval data is reused. file_pattern = os.path.join( epoch_data_dir, rconst.SHARD_TEMPLATE.format("*")) # TODO(seemuch): remove this contrib import # pylint: disable=line-too-long from tensorflow.contrib.tpu.python.tpu.datasets import StreamingFilesDataset # pylint: enable=line-too-long dataset = StreamingFilesDataset( files=file_pattern, worker_job=popen_helper.worker_job(), num_parallel_reads=rconst.NUM_FILE_SHARDS, num_epochs=1, sloppy=not self._deterministic) map_fn = functools.partial( self.deserialize, batch_size=batch_size, is_training=self._is_training) dataset = dataset.map(map_fn, num_parallel_calls=16) else: types = {movielens.USER_COLUMN: rconst.USER_DTYPE, movielens.ITEM_COLUMN: rconst.ITEM_DTYPE} shapes = { movielens.USER_COLUMN: tf.TensorShape([batch_size, 1]), movielens.ITEM_COLUMN: tf.TensorShape([batch_size, 1]) } if self._is_training: types[rconst.VALID_POINT_MASK] = np.bool shapes[rconst.VALID_POINT_MASK] = tf.TensorShape([batch_size, 1]) types = (types, np.bool) shapes = (shapes, tf.TensorShape([batch_size, 1])) else: types[rconst.DUPLICATE_MASK] = np.bool shapes[rconst.DUPLICATE_MASK] = tf.TensorShape([batch_size, 1]) data_generator = functools.partial( self.data_generator, epochs_between_evals=epochs_between_evals) dataset = tf.data.Dataset.from_generator( generator=data_generator, output_types=types, output_shapes=shapes) return dataset.prefetch(16) def make_input_fn(self, batch_size): """Create an input_fn which checks for batch size consistency.""" def input_fn(params): """Returns batches for training.""" # Estimator passes batch_size during training and eval_batch_size during # eval. TPUEstimator only passes batch_size. param_batch_size = (params["batch_size"] if self._is_training else params.get("eval_batch_size") or params["batch_size"]) if batch_size != param_batch_size: raise ValueError("producer batch size ({}) differs from params batch " "size ({})".format(batch_size, param_batch_size)) epochs_between_evals = (params.get("epochs_between_evals", 1) if self._is_training else 1) return self.get_dataset(batch_size=batch_size, epochs_between_evals=epochs_between_evals) return input_fn class BaseDataConstructor(threading.Thread): """Data constructor base class. This class manages the control flow for constructing data. It is not meant to be used directly, but instead subclasses should implement the following two methods: self.construct_lookup_variables self.lookup_negative_items """ def __init__( self, maximum_number_epochs, # type: int num_users, # type: int num_items, # type: int user_map, # type: dict item_map, # type: dict train_pos_users, # type: np.ndarray train_pos_items, # type: np.ndarray train_batch_size, # type: int batches_per_train_step, # type: int num_train_negatives, # type: int eval_pos_users, # type: np.ndarray eval_pos_items, # type: np.ndarray eval_batch_size, # type: int batches_per_eval_step, # type: int stream_files, # type: bool deterministic=False, # type: bool epoch_dir=None, # type: str num_train_epochs=None, # type: int create_data_offline=False # type: bool ): # General constants self._maximum_number_epochs = maximum_number_epochs self._num_users = num_users self._num_items = num_items self.user_map = user_map self.item_map = item_map self._train_pos_users = train_pos_users self._train_pos_items = train_pos_items self.train_batch_size = train_batch_size self._num_train_negatives = num_train_negatives self._batches_per_train_step = batches_per_train_step self._eval_pos_users = eval_pos_users self._eval_pos_items = eval_pos_items self.eval_batch_size = eval_batch_size self.num_train_epochs = num_train_epochs self.create_data_offline = create_data_offline # Training if self._train_pos_users.shape != self._train_pos_items.shape: raise ValueError( "User positives ({}) is different from item positives ({})".format( self._train_pos_users.shape, self._train_pos_items.shape)) (self._train_pos_count,) = self._train_pos_users.shape self._elements_in_epoch = (1 + num_train_negatives) * self._train_pos_count self.train_batches_per_epoch = self._count_batches( self._elements_in_epoch, train_batch_size, batches_per_train_step) # Evaluation if eval_batch_size % (1 + rconst.NUM_EVAL_NEGATIVES): raise ValueError("Eval batch size {} is not divisible by {}".format( eval_batch_size, 1 + rconst.NUM_EVAL_NEGATIVES)) self._eval_users_per_batch = int( eval_batch_size // (1 + rconst.NUM_EVAL_NEGATIVES)) self._eval_elements_in_epoch = num_users * (1 + rconst.NUM_EVAL_NEGATIVES) self.eval_batches_per_epoch = self._count_batches( self._eval_elements_in_epoch, eval_batch_size, batches_per_eval_step) # Intermediate artifacts self._current_epoch_order = np.empty(shape=(0,)) self._shuffle_iterator = None self._shuffle_with_forkpool = not stream_files if stream_files: self._shard_root = epoch_dir or tempfile.mkdtemp(prefix="ncf_") atexit.register(tf.io.gfile.rmtree, dirname=self._shard_root) else: self._shard_root = None self._train_dataset = DatasetManager(True, stream_files, self.train_batches_per_epoch, self._shard_root, deterministic, num_train_epochs) self._eval_dataset = DatasetManager(False, stream_files, self.eval_batches_per_epoch, self._shard_root, deterministic, num_train_epochs) # Threading details super(BaseDataConstructor, self).__init__() self.daemon = True self._stop_loop = False self._fatal_exception = None self.deterministic = deterministic def __str__(self): multiplier = ("(x{} devices)".format(self._batches_per_train_step) if self._batches_per_train_step > 1 else "") summary = SUMMARY_TEMPLATE.format( spacer=" ", num_users=self._num_users, num_items=self._num_items, train_pos_ct=self._train_pos_count, train_batch_size=self.train_batch_size, train_batch_ct=self.train_batches_per_epoch, eval_pos_ct=self._num_users, eval_batch_size=self.eval_batch_size, eval_batch_ct=self.eval_batches_per_epoch, multiplier=multiplier) return super(BaseDataConstructor, self).__str__() + "\n" + summary @staticmethod def _count_batches(example_count, batch_size, batches_per_step): """Determine the number of batches, rounding up to fill all devices.""" x = (example_count + batch_size - 1) // batch_size return (x + batches_per_step - 1) // batches_per_step * batches_per_step def stop_loop(self): self._stop_loop = True def construct_lookup_variables(self): """Perform any one time pre-compute work.""" raise NotImplementedError def lookup_negative_items(self, **kwargs): """Randomly sample negative items for given users.""" raise NotImplementedError def _run(self): atexit.register(self.stop_loop) self._start_shuffle_iterator() self.construct_lookup_variables() self._construct_training_epoch() self._construct_eval_epoch() for _ in range(self._maximum_number_epochs - 1): self._construct_training_epoch() self.stop_loop() def run(self): try: self._run() except Exception as e: # The Thread base class swallows stack traces, so unfortunately it is # necessary to catch and re-raise to get debug output traceback.print_exc() self._fatal_exception = e sys.stderr.flush() raise def _start_shuffle_iterator(self): if self._shuffle_with_forkpool: pool = popen_helper.get_forkpool(3, closing=False) else: pool = popen_helper.get_threadpool(1, closing=False) atexit.register(pool.close) args = [(self._elements_in_epoch, stat_utils.random_int32()) for _ in range(self._maximum_number_epochs)] imap = pool.imap if self.deterministic else pool.imap_unordered self._shuffle_iterator = imap(stat_utils.permutation, args) def _get_training_batch(self, i): """Construct a single batch of training data. Args: i: The index of the batch. This is used when stream_files=True to assign data to file shards. """ batch_indices = self._current_epoch_order[i * self.train_batch_size: (i + 1) * self.train_batch_size] (mask_start_index,) = batch_indices.shape batch_ind_mod = np.mod(batch_indices, self._train_pos_count) users = self._train_pos_users[batch_ind_mod] negative_indices = np.greater_equal(batch_indices, self._train_pos_count) negative_users = users[negative_indices] negative_items = self.lookup_negative_items(negative_users=negative_users) items = self._train_pos_items[batch_ind_mod] items[negative_indices] = negative_items labels = np.logical_not(negative_indices) # Pad last partial batch pad_length = self.train_batch_size - mask_start_index if pad_length: # We pad with arange rather than zeros because the network will still # compute logits for padded examples, and padding with zeros would create # a very "hot" embedding key which can have performance implications. user_pad = np.arange(pad_length, dtype=users.dtype) % self._num_users item_pad = np.arange(pad_length, dtype=items.dtype) % self._num_items label_pad = np.zeros(shape=(pad_length,), dtype=labels.dtype) users = np.concatenate([users, user_pad]) items = np.concatenate([items, item_pad]) labels = np.concatenate([labels, label_pad]) self._train_dataset.put( i, { movielens.USER_COLUMN: np.reshape(users, (self.train_batch_size, 1)), movielens.ITEM_COLUMN: np.reshape(items, (self.train_batch_size, 1)), rconst.MASK_START_INDEX: np.array(mask_start_index, dtype=np.int32), "labels": np.reshape(labels, (self.train_batch_size, 1)), }) def _wait_to_construct_train_epoch(self): count = 0 while self._train_dataset.buffer_reached() and not self._stop_loop: time.sleep(0.01) count += 1 if count >= 100 and np.log10(count) == np.round(np.log10(count)): logging.info( "Waited {} times for training data to be consumed".format(count)) def _construct_training_epoch(self): """Loop to construct a batch of training data.""" if not self.create_data_offline: self._wait_to_construct_train_epoch() start_time = timeit.default_timer() if self._stop_loop: return self._train_dataset.start_construction() map_args = list(range(self.train_batches_per_epoch)) self._current_epoch_order = next(self._shuffle_iterator) get_pool = (popen_helper.get_fauxpool if self.deterministic else popen_helper.get_threadpool) with get_pool(6) as pool: pool.map(self._get_training_batch, map_args) self._train_dataset.end_construction() logging.info("Epoch construction complete. Time: {:.1f} seconds".format( timeit.default_timer() - start_time)) @staticmethod def _assemble_eval_batch(users, positive_items, negative_items, users_per_batch): """Construct duplicate_mask and structure data accordingly. The positive items should be last so that they lose ties. However, they should not be masked out if the true eval positive happens to be selected as a negative. So instead, the positive is placed in the first position, and then switched with the last element after the duplicate mask has been computed. Args: users: An array of users in a batch. (should be identical along axis 1) positive_items: An array (batch_size x 1) of positive item indices. negative_items: An array of negative item indices. users_per_batch: How many users should be in the batch. This is passed as an argument so that ncf_test.py can use this method. Returns: User, item, and duplicate_mask arrays. """ items = np.concatenate([positive_items, negative_items], axis=1) # We pad the users and items here so that the duplicate mask calculation # will include padding. The metric function relies on all padded elements # except the positive being marked as duplicate to mask out padded points. if users.shape[0] < users_per_batch: pad_rows = users_per_batch - users.shape[0] padding = np.zeros(shape=(pad_rows, users.shape[1]), dtype=np.int32) users = np.concatenate([users, padding.astype(users.dtype)], axis=0) items = np.concatenate([items, padding.astype(items.dtype)], axis=0) duplicate_mask = stat_utils.mask_duplicates(items, axis=1).astype(np.bool) items[:, (0, -1)] = items[:, (-1, 0)] duplicate_mask[:, (0, -1)] = duplicate_mask[:, (-1, 0)] assert users.shape == items.shape == duplicate_mask.shape return users, items, duplicate_mask def _get_eval_batch(self, i): """Construct a single batch of evaluation data. Args: i: The index of the batch. """ low_index = i * self._eval_users_per_batch high_index = (i + 1) * self._eval_users_per_batch users = np.repeat(self._eval_pos_users[low_index:high_index, np.newaxis], 1 + rconst.NUM_EVAL_NEGATIVES, axis=1) positive_items = self._eval_pos_items[low_index:high_index, np.newaxis] negative_items = (self.lookup_negative_items(negative_users=users[:, :-1]) .reshape(-1, rconst.NUM_EVAL_NEGATIVES)) users, items, duplicate_mask = self._assemble_eval_batch( users, positive_items, negative_items, self._eval_users_per_batch) self._eval_dataset.put( i, { movielens.USER_COLUMN: np.reshape(users.flatten(), (self.eval_batch_size, 1)), movielens.ITEM_COLUMN: np.reshape(items.flatten(), (self.eval_batch_size, 1)), rconst.DUPLICATE_MASK: np.reshape(duplicate_mask.flatten(), (self.eval_batch_size, 1)), }) def _construct_eval_epoch(self): """Loop to construct data for evaluation.""" if self._stop_loop: return start_time = timeit.default_timer() self._eval_dataset.start_construction() map_args = [i for i in range(self.eval_batches_per_epoch)] get_pool = (popen_helper.get_fauxpool if self.deterministic else popen_helper.get_threadpool) with get_pool(6) as pool: pool.map(self._get_eval_batch, map_args) self._eval_dataset.end_construction() logging.info("Eval construction complete. Time: {:.1f} seconds".format( timeit.default_timer() - start_time)) def make_input_fn(self, is_training): # It isn't feasible to provide a foolproof check, so this is designed to # catch most failures rather than provide an exhaustive guard. if self._fatal_exception is not None: raise ValueError("Fatal exception in the data production loop: {}" .format(self._fatal_exception)) return ( self._train_dataset.make_input_fn(self.train_batch_size) if is_training else self._eval_dataset.make_input_fn(self.eval_batch_size)) def increment_request_epoch(self): self._train_dataset.increment_request_epoch() class DummyConstructor(threading.Thread): """Class for running with synthetic data.""" def __init__(self, *args, **kwargs): super(DummyConstructor, self).__init__(*args, **kwargs) self.train_batches_per_epoch = rconst.SYNTHETIC_BATCHES_PER_EPOCH self.eval_batches_per_epoch = rconst.SYNTHETIC_BATCHES_PER_EPOCH def run(self): pass def stop_loop(self): pass def increment_request_epoch(self): pass @staticmethod def make_input_fn(is_training): """Construct training input_fn that uses synthetic data.""" def input_fn(params): """Returns dummy input batches for training.""" # Estimator passes batch_size during training and eval_batch_size during # eval. TPUEstimator only passes batch_size. batch_size = (params["batch_size"] if is_training else params.get("eval_batch_size") or params["batch_size"]) num_users = params["num_users"] num_items = params["num_items"] users = tf.random.uniform([batch_size, 1], dtype=tf.int32, minval=0, maxval=num_users) items = tf.random.uniform([batch_size, 1], dtype=tf.int32, minval=0, maxval=num_items) if is_training: valid_point_mask = tf.cast( tf.random.uniform([batch_size, 1], dtype=tf.int32, minval=0, maxval=2), tf.bool) labels = tf.cast( tf.random.uniform([batch_size, 1], dtype=tf.int32, minval=0, maxval=2), tf.bool) data = { movielens.USER_COLUMN: users, movielens.ITEM_COLUMN: items, rconst.VALID_POINT_MASK: valid_point_mask, }, labels else: dupe_mask = tf.cast( tf.random.uniform([batch_size, 1], dtype=tf.int32, minval=0, maxval=2), tf.bool) data = { movielens.USER_COLUMN: users, movielens.ITEM_COLUMN: items, rconst.DUPLICATE_MASK: dupe_mask, } dataset = tf.data.Dataset.from_tensors(data).repeat( rconst.SYNTHETIC_BATCHES_PER_EPOCH * params["batches_per_step"]) dataset = dataset.prefetch(32) return dataset return input_fn class MaterializedDataConstructor(BaseDataConstructor): """Materialize a table of negative examples for fast negative generation. This class creates a table (num_users x num_items) containing all of the negative examples for each user. This table is conceptually ragged; that is to say the items dimension will have a number of unused elements at the end equal to the number of positive elements for a given user. For instance: num_users = 3 num_items = 5 positives = [[1, 3], [0], [1, 2, 3, 4]] will generate a negative table: [ [0 2 4 int32max int32max], [1 2 3 4 int32max], [0 int32max int32max int32max int32max], ] and a vector of per-user negative counts, which in this case would be: [3, 4, 1] When sampling negatives, integers are (nearly) uniformly selected from the range [0, per_user_neg_count[user]) which gives a column_index, at which point the negative can be selected as: negative_table[user, column_index] This technique will not scale; however MovieLens is small enough that even a pre-compute which is quadratic in problem size will still fit in memory. A more scalable lookup method is in the works. """ def __init__(self, *args, **kwargs): super(MaterializedDataConstructor, self).__init__(*args, **kwargs) self._negative_table = None self._per_user_neg_count = None def construct_lookup_variables(self): # Materialize negatives for fast lookup sampling. start_time = timeit.default_timer() inner_bounds = np.argwhere(self._train_pos_users[1:] - self._train_pos_users[:-1])[:, 0] + 1 (upper_bound,) = self._train_pos_users.shape index_bounds = [0] + inner_bounds.tolist() + [upper_bound] self._negative_table = np.zeros(shape=(self._num_users, self._num_items), dtype=rconst.ITEM_DTYPE) # Set the table to the max value to make sure the embedding lookup will fail # if we go out of bounds, rather than just overloading item zero. self._negative_table += np.iinfo(rconst.ITEM_DTYPE).max assert self._num_items < np.iinfo(rconst.ITEM_DTYPE).max # Reuse arange during generation. np.delete will make a copy. full_set = np.arange(self._num_items, dtype=rconst.ITEM_DTYPE) self._per_user_neg_count = np.zeros( shape=(self._num_users,), dtype=np.int32) # Threading does not improve this loop. For some reason, the np.delete # call does not parallelize well. Multiprocessing incurs too much # serialization overhead to be worthwhile. for i in range(self._num_users): positives = self._train_pos_items[index_bounds[i]:index_bounds[i+1]] negatives = np.delete(full_set, positives) self._per_user_neg_count[i] = self._num_items - positives.shape[0] self._negative_table[i, :self._per_user_neg_count[i]] = negatives logging.info("Negative sample table built. Time: {:.1f} seconds".format( timeit.default_timer() - start_time)) def lookup_negative_items(self, negative_users, **kwargs): negative_item_choice = stat_utils.very_slightly_biased_randint( self._per_user_neg_count[negative_users]) return self._negative_table[negative_users, negative_item_choice] class BisectionDataConstructor(BaseDataConstructor): """Use bisection to index within positive examples. This class tallies the number of negative items which appear before each positive item for a user. This means that in order to select the ith negative item for a user, it only needs to determine which two positive items bound it at which point the item id for the ith negative is a simply algebraic expression. """ def __init__(self, *args, **kwargs): super(BisectionDataConstructor, self).__init__(*args, **kwargs) self.index_bounds = None self._sorted_train_pos_items = None self._total_negatives = None def _index_segment(self, user): lower, upper = self.index_bounds[user:user+2] items = self._sorted_train_pos_items[lower:upper] negatives_since_last_positive = np.concatenate( [items[0][np.newaxis], items[1:] - items[:-1] - 1]) return np.cumsum(negatives_since_last_positive) def construct_lookup_variables(self): start_time = timeit.default_timer() inner_bounds = np.argwhere(self._train_pos_users[1:] - self._train_pos_users[:-1])[:, 0] + 1 (upper_bound,) = self._train_pos_users.shape self.index_bounds = np.array([0] + inner_bounds.tolist() + [upper_bound]) # Later logic will assume that the users are in sequential ascending order. assert np.array_equal(self._train_pos_users[self.index_bounds[:-1]], np.arange(self._num_users)) self._sorted_train_pos_items = self._train_pos_items.copy() for i in range(self._num_users): lower, upper = self.index_bounds[i:i+2] self._sorted_train_pos_items[lower:upper].sort() self._total_negatives = np.concatenate([ self._index_segment(i) for i in range(self._num_users)]) logging.info("Negative total vector built. Time: {:.1f} seconds".format( timeit.default_timer() - start_time)) def lookup_negative_items(self, negative_users, **kwargs): output = np.zeros(shape=negative_users.shape, dtype=rconst.ITEM_DTYPE) - 1 left_index = self.index_bounds[negative_users] right_index = self.index_bounds[negative_users + 1] - 1 num_positives = right_index - left_index + 1 num_negatives = self._num_items - num_positives neg_item_choice = stat_utils.very_slightly_biased_randint(num_negatives) # Shortcuts: # For points where the negative is greater than or equal to the tally before # the last positive point there is no need to bisect. Instead the item id # corresponding to the negative item choice is simply: # last_postive_index + 1 + (neg_choice - last_negative_tally) # Similarly, if the selection is less than the tally at the first positive # then the item_id is simply the selection. # # Because MovieLens organizes popular movies into low integers (which is # preserved through the preprocessing), the first shortcut is very # efficient, allowing ~60% of samples to bypass the bisection. For the same # reason, the second shortcut is rarely triggered (<0.02%) and is therefore # not worth implementing. use_shortcut = neg_item_choice >= self._total_negatives[right_index] output[use_shortcut] = ( self._sorted_train_pos_items[right_index] + 1 + (neg_item_choice - self._total_negatives[right_index]) )[use_shortcut] if np.all(use_shortcut): # The bisection code is ill-posed when there are no elements. return output not_use_shortcut = np.logical_not(use_shortcut) left_index = left_index[not_use_shortcut] right_index = right_index[not_use_shortcut] neg_item_choice = neg_item_choice[not_use_shortcut] num_loops = np.max( np.ceil(np.log2(num_positives[not_use_shortcut])).astype(np.int32)) for i in range(num_loops): mid_index = (left_index + right_index) // 2 right_criteria = self._total_negatives[mid_index] > neg_item_choice left_criteria = np.logical_not(right_criteria) right_index[right_criteria] = mid_index[right_criteria] left_index[left_criteria] = mid_index[left_criteria] # Expected state after bisection pass: # The right index is the smallest index whose tally is greater than the # negative item choice index. assert np.all((right_index - left_index) <= 1) output[not_use_shortcut] = ( self._sorted_train_pos_items[right_index] - (self._total_negatives[right_index] - neg_item_choice) ) assert np.all(output >= 0) return output def get_constructor(name): if name == "bisection": return BisectionDataConstructor if name == "materialized": return MaterializedDataConstructor raise ValueError("Unrecognized constructor: {}".format(name))

38.723493

82

0.679963

139f1a357b258158eb71195d5eb8a97e707a810f

20,322

py

Python

shot_chart/core.py

theccalderon/shot_chart

97e4bd5da920bb894059c648d93e73743887d6e7

[ "Apache-2.0" ]

16

2020-04-14T11:48:14.000Z

2020-12-16T03:32:48.000Z

shot_chart/core.py

theccalderon/shot_chart

97e4bd5da920bb894059c648d93e73743887d6e7

[ "Apache-2.0" ]

4

2020-03-31T11:13:26.000Z

2021-05-20T12:45:17.000Z

shot_chart/core.py

theccalderon/shot_chart

97e4bd5da920bb894059c648d93e73743887d6e7

[ "Apache-2.0" ]

8

2020-10-14T15:17:04.000Z

2021-12-21T12:03:42.000Z

# AUTOGENERATED! DO NOT EDIT! File to edit: 00_core.ipynb (unless otherwise specified). __all__ = ['Config', 'URLs', 'download_url', 'download_data', 'file_extract', 'untar_data', 'make_df', 'delegates', 'Shots', 'list_teams', 'list_game_ids', 'TeamShots', 'list_team_players', 'PlayerShots'] # Cell from pathlib import Path import os import yaml import pandas as pd import requests import tarfile from fastprogress.fastprogress import master_bar, progress_bar import matplotlib.pyplot as plt from typing import Sequence, Tuple, TypeVar, Union from typing import Any, AnyStr, Callable, Collection, Dict, Hashable, Iterator, List, Mapping, NewType, Optional import boto3 import hashlib import shutil import numpy as np import inspect # Cell class Config: config_path = Path(os.getenv('SHOTCHART_HOME', '~/.shot_chart')).expanduser() config_file = config_path/'config.yml' def __init__(self): self.config_path.mkdir(parents=True, exist_ok=True) if not self.config_file.exists(): self.create_config() self.d = self.load_config() def __getitem__(self,k): k = k.lower() if k not in self.d: k = k+'_path' if k in ["my_dpi","fig_height","fig_width"]: return self.d[k] return Path(self.d[k]) def __getattr__(self,k): if k=='d': raise AttributeError return self[k] def __setitem__(self,k,v): self.d[k] = str(v) def __contains__(self,k): return k in self.d def load_config(self): with open(self.config_file, 'r') as f: config = yaml.safe_load(f) if 'version' in config and config['version'] == 1: return config self.create_config() return self.load_config() def create_config(self): F = plt.gcf() # Now check everything with the defaults: DPI = F.get_dpi() config = {'data_path': str(self.config_path/'data'), 'archive_path': str(self.config_path/'archive'), 'version': 1, 'my_dpi': DPI, 'fig_height': 472, 'fig_width': 500 } self.save_file(config) def save(self): self.save_file(self.d) def save_file(self, config): with self.config_file.open('w') as f: yaml.dump(config, f, default_flow_style=False) # Cell class URLs(): "Global constants for dataset and model URLs." LOCAL_PATH = Path.cwd() S3 = 'https://nba-shot-charts.s3.amazonaws.com/' SHOTS_2000 = f'{S3}shots-2000.tgz' SHOTS_2001 = f'{S3}shots-2001.tgz' SHOTS_2002 = f'{S3}shots-2002.tgz' SHOTS_2003 = f'{S3}shots-2003.tgz' SHOTS_2004 = f'{S3}shots-2004.tgz' SHOTS_2005 = f'{S3}shots-2005.tgz' SHOTS_2006 = f'{S3}shots-2006.tgz' SHOTS_2007 = f'{S3}shots-2007.tgz' SHOTS_2008 = f'{S3}shots-2008.tgz' SHOTS_2009 = f'{S3}shots-2009.tgz' SHOTS_2010 = f'{S3}shots-2010.tgz' SHOTS_2011 = f'{S3}shots-2011.tgz' SHOTS_2012 = f'{S3}shots-2012.tgz' SHOTS_2013 = f'{S3}shots-2013.tgz' SHOTS_2014 = f'{S3}shots-2014.tgz' SHOTS_2015 = f'{S3}shots-2015.tgz' SHOTS_2016 = f'{S3}shots-2016.tgz' SHOTS_2017 = f'{S3}shots-2017.tgz' SHOTS_2018 = f'{S3}shots-2018.tgz' SHOTS_2019 = f'{S3}shots-2019.tgz' SHOTS_2020 = f'{S3}shots-2020.tgz' def path(url, c_key='archive'): fname = url.split('/')[-1] local_path = URLs.LOCAL_PATH/('data' if c_key=='data' else 'archive')/fname if local_path.exists(): return local_path return Config()[c_key]/fname # Cell def download_url(url, dest, overwrite=False, pbar=None, show_progress=True, chunk_size=1024*1024, timeout=4, retries=5): "Download `url` to `dest` unless it exists and not `overwrite`" if os.path.exists(dest) and not overwrite: return s = requests.Session() s.mount('http://',requests.adapters.HTTPAdapter(max_retries=retries)) # additional line to identify as a firefox browser, see fastai/#2438 s.headers.update({'User-Agent': 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:71.0) Gecko/20100101 Firefox/71.0'}) u = s.get(url, stream=True, timeout=timeout) try: file_size = int(u.headers["Content-Length"]) except: show_progress = False with open(dest, 'wb') as f: nbytes = 0 if show_progress: pbar = progress_bar(range(file_size), leave=False, parent=pbar) try: if show_progress: pbar.update(0) for chunk in u.iter_content(chunk_size=chunk_size): nbytes += len(chunk) if show_progress: pbar.update(nbytes) f.write(chunk) except requests.exceptions.ConnectionError as e: fname = url.split('/')[-1] data_dir = dest.parent print(f'\n Download of {url} has failed after {retries} retries\n' f' Fix the download manually:\n' f'$ mkdir -p {data_dir}\n' f'$ cd {data_dir}\n' f'$ wget -c {url}\n' f'$ tar xf {fname}\n' f' And re-run your code once the download is successful\n') # Cell def download_data(url, fname=None, c_key='archive', force_download=False): "Download `url` to `fname`." fname = Path(fname or URLs.path(url, c_key=c_key)) fname.parent.mkdir(parents=True, exist_ok=True) if not fname.exists() or force_download: download_url(url, fname, overwrite=force_download) return fname # Cell def file_extract(fname, dest='.'): "Extract `fname` to `dest` using `tarfile` or `zipfile" fname = str(fname) if fname.endswith('gz'): tarfile.open(fname, 'r:gz').extractall(dest) elif fname.endswith('zip'): zipfile.ZipFile(fname ).extractall(dest) else: raise Exception(f'Unrecognized archive: {fname}') # Cell def _get_check(url): s3 = boto3.client('s3') s3_resp = s3.head_object(Bucket=URLs.S3.split(".")[0].split("//")[1],Key=url.split("/")[-1]) ETag = s3_resp['ETag'].strip('"') # Open,close, read file and calculate MD5 on its contents with open(Path(URLs.path(url)),"rb") as file_to_check: # read contents of the file data = file_to_check.read() # pipe contents of the file through md5_returned = hashlib.md5(data).hexdigest() return ETag != md5_returned # Cell def untar_data(url, fname=None, dest=None, c_key='data', force_download=False, extract_func=file_extract): "Download `url` to `fname` if `dest` doesn't exist, and un-tgz to folder `dest`." default_dest = URLs.path(url, c_key=c_key).with_suffix('.csv') dest = default_dest if dest is None else Path(dest)/default_dest.name fname = Path(fname or URLs.path(url)) if fname.exists() and _get_check(url): print("A new version of this dataset is available, downloading...") force_download = True if force_download: if fname.exists(): os.remove(fname) if dest.exists(): os.remove(dest) if not dest.exists(): fname = download_data(url, fname=fname, c_key=c_key) extract_func(fname, dest.parent) return dest # Cell def make_df(path): "Creates a pandas dataframe from `path`" return pd.read_csv(path) # Cell def delegates(to=None, keep=False): "Decorator: replace `**kwargs` in signature with params from `to`" def _f(f): if to is None: to_f,from_f = f.__base__.__init__,f.__init__ else: to_f,from_f = to,f sig = inspect.signature(from_f) sigd = dict(sig.parameters) k = sigd.pop('kwargs') s2 = {k:v for k,v in inspect.signature(to_f).parameters.items() if v.default != inspect.Parameter.empty and k not in sigd} sigd.update(s2) if keep: sigd['kwargs'] = k from_f.__signature__ = sig.replace(parameters=sigd.values()) return f return _f # Cell class Shots: "Plots shot chart and most/least effective shots using `plot_shots` and `plot_effective`" def __init__(self, dataframe): self.dataframe = dataframe self.__X_MODIFIER = 10 self.__Y_MODIFIER = 454 @property def teams(self): return self.dataframe['team'].drop_duplicates() @property def players(self): return self.dataframe['shots_by'].drop_duplicates() @property def fg_pct(self): return self.__calculate_metric(self.dataframe,"fg") @property def efg_pct(self): return self.__calculate_metric(self.dataframe,"efg") def __calculate_metric(self, dataframe, metric="efg"): if len(dataframe)==0: return 0 if metric == "fg": return round(len(dataframe.loc[dataframe['outcome']=='made'])/len(dataframe),2) else: return round( (len(dataframe.loc[dataframe['outcome']=='made']) + 0.5 *len(dataframe.loc[(dataframe['outcome']=='made') & (dataframe['attempt']=='3-pointer')]))/len(dataframe),2) def __plot_shot_chart(self, dataframe, metric:str="efg",attempt:str="all", distance_limit:Union[int,tuple]=29): if type(distance_limit) == int: distances = [str(x)+"ft" for x in range(distance_limit+1)] else: distances = [str(x)+"ft" for x in range(distance_limit[0],distance_limit[1]+1)] plt.figure(figsize=(2 * Config().fig_height/Config().my_dpi, Config().fig_width/Config().my_dpi), dpi=Config().my_dpi) ax = plt.subplot(1, 2, 1) plt.title("Shot chart") img = plt.imread("http://d2p3bygnnzw9w3.cloudfront.net/req/1/images/bbr/nbahalfcourt.png") implot = plt.imshow(img, extent=[0,500,0,472]) if attempt == "2-pointer": shots_df = dataframe.loc[(dataframe["attempt"]=="2-pointer") & (dataframe["distance"].isin(distances))] elif attempt == "3-pointer": shots_df = dataframe.loc[(dataframe["attempt"]=="3-pointer") & (dataframe["distance"].isin(distances))] else: shots_df = dataframe if len(shots_df) > 200: # print(len(shots_df)) ys = shots_df['x'].apply(lambda x: x.split("px")[0]).to_list() ys = [self.__Y_MODIFIER - int(x) for x in ys] xs = shots_df['y'].apply(lambda x: int(x.split("px")[0])).to_list() xs = [x + self.__X_MODIFIER for x in xs] mycmap = plt.cm.Reds mycmap._init() mycmap._lut[:,-1] = np.linspace(0, 0.8, 256+3) plt.hexbin(xs, ys, gridsize=(50,47), bins='log',cmap=mycmap) # cb = plt.colorbar(label='count in bin') else: made_df = shots_df.loc[shots_df['outcome']=="made"] missed_df = shots_df.loc[shots_df['outcome']=="missed"] ys = made_df['x'].apply(lambda x: x.split("px")[0]).to_list() ys = [self.__Y_MODIFIER - int(x) for x in ys] xs = made_df['y'].apply(lambda x: int(x.split("px")[0])).to_list() xs = [x + self.__X_MODIFIER for x in xs] # if most_or_least == 'most': plt.scatter(xs, ys,c='g',marker='o',s=10, alpha=1) # else: # plt.scatter(xs, ys,c='g',marker='o',s=10, alpha=0.2) ys = missed_df['x'].apply(lambda x: x.split("px")[0]).to_list() ys = [self.__Y_MODIFIER - int(x) for x in ys] xs = missed_df['y'].apply(lambda x: int(x.split("px")[0])).to_list() xs = [x + self.__X_MODIFIER for x in xs] # if most_or_least == 'least': # plt.scatter(xs, ys,c='g',marker='o',s=10, alpha=1) # else: plt.scatter(xs, ys,c='orange',marker='x',s=10, alpha=0.5) # plt.scatter(xs, ys,c='orange',marker='x',s=10) return def __plot_hist_volume(self, dataframe, fg_pct:float, efg_pct:float, most_or_least:str=None, final_distance:str=None, final_attempt:str=None,made:bool=True,missed:bool=True): if made: ax = plt.subplot(1, 2, 2) plt.title("Shot distribution - all distances") distances_all_shots = dataframe['distance'].apply(lambda x: int(x.split('ft')[0])).to_list() make_shots = dataframe.loc[dataframe['outcome']=='made']['distance'].apply(lambda x: int(x.split('ft')[0])).to_list() shots_to_plot = [make_shots,distances_all_shots] plt.hist(shots_to_plot, bins = range( 0, max(distances_all_shots)+1, 1), align="left",stacked=True, label=['made','all'], color=['green', '#ff7f0e']) plt.legend(loc="upper center") if most_or_least and final_distance and final_attempt: ax.text(30 + 12, 1, most_or_least+" effective shot: "+str(final_distance)+"\n Attempt: "+final_attempt+"\n\nMetrics:\n FG%: "+str(fg_pct)+"\n eFG%: "+str(efg_pct), bbox=dict(facecolor='red', alpha=0.5)) else: ax.text(30 + 12, 1, "Metrics:\n FG%: "+str(fg_pct)+"\n eFG%: "+str(efg_pct), bbox=dict(facecolor='red', alpha=0.5)) return def list_game_ids(self,year,month,day): "Lists unique game ids in `dataframe` for a given date" return self.dataframe.loc[(self.dataframe['year']==year) & (self.dataframe['month']==month) & (self.dataframe['day']==day)][['game_id','winner','loser']].drop_duplicates() @delegates(__plot_shot_chart) def plot_shots(self,date_range:Union[str,tuple,int]="all",**kwargs): "Plots the shot chart for a given `date_range` including `made`, `missed` and `attempt` shots within `distances`" #use kwargs if date_range == "all": shots_df = self.dataframe elif type(date_range) == str: shots_df = self.dataframe.loc[self.dataframe['game_id']==date_range] elif type(date_range) == int: #means month shots_df = self.dataframe.loc[self.dataframe["month"]==date_range] else: copy_df = self.dataframe.copy() copy_df['Timestamp'] = pd.to_datetime(copy_df.loc[:][['year','month','day']]) # Create a DatetimeIndex and assign it to the dataframe. copy_df.index = pd.DatetimeIndex(copy_df.Timestamp) shots_df = copy_df.loc[str(str(date_range[0][0])+"-"+str(date_range[0][1])+"-"+str(date_range[0][2])):str(str(date_range[1][0])+"-"+str(date_range[1][1])+"-"+str(date_range[1][2]))] self.__plot_shot_chart(shots_df, **kwargs) self.__plot_hist_volume(shots_df, self.__calculate_metric(self.dataframe, "fg"), self.__calculate_metric(self.dataframe, "efg")) plt.show() @delegates(__plot_shot_chart) def plot_effective(self, most_or_least="most",metric:str="efg", min_shots:Union[str,int]="none", exclude:Union[str,List["str"]]="none", **kwargs): "Plots the shot chart based on `most_or_least` considering a given `metric` for `date_range` including `made`, `missed` and `attempt` shots within `distances`. You can optionally `exclude` some shots. The `min_shots` option lets you filter based on a minimum ammount of shots taken per distance, auto == uniform distribution [0ft,29ft] as tracked by https://stats.nba.com/players/shooting/?sort=25-29%20ft.%20FGA&dir=1&Season=2019-20&SeasonType=Regular%20Season&CF=PLAYER_NAME*E*" distances = ["0ft","1ft","2ft","3ft","4ft","5ft","6ft","7ft","8ft","9ft","10ft","11ft","12ft","13ft","14ft","15ft","16ft","17ft","18ft","19ft","20ft","21ft","22ft","23ft","24ft","25ft","26ft","27ft","28ft"] distances = set(distances).intersection(set(self.dataframe['distance'].drop_duplicates().to_list())) if type(exclude) == list: distances = list(set(distances) - set(exclude)) #if auto, use uniform distro if min_shots == "none": new_df = self.dataframe elif min_shots == "auto": min_value = round(len(self.dataframe)/30,0) # [0ft, 29ft] new_df = pd.DataFrame(columns = self.dataframe.columns) for distance in distances: if len(self.dataframe.loc[self.dataframe['distance']==distance]) >= min_value: new_df = new_df.append(self.dataframe.loc[self.dataframe['distance']==distance]) else: min_value = min_shots new_df = pd.DataFrame(columns = self.dataframe.columns) for distance in distances: if len(self.dataframe.loc[self.dataframe['distance']==distance]) >= min_value: new_df = new_df.append(self.dataframe.loc[self.dataframe['distance']==distance]) if most_or_least == "most": max_fg = -1 max_efg = -1 final_distance = "" for attempt in ["2-pointer", "3-pointer"]: for distance in distances: player_df = new_df.loc[(new_df["distance"]==distance) & (new_df["attempt"]==attempt)] if len(player_df) == 0: continue fg_pct = self.__calculate_metric(player_df, "fg") efg_pct = self.__calculate_metric(player_df, "efg") if metric == "fg": if fg_pct > max_fg: max_fg = fg_pct max_efg = efg_pct final_distance = distance final_attempt = attempt else: if efg_pct > max_efg: max_fg = fg_pct max_efg = efg_pct final_distance = distance final_attempt = attempt else: max_fg = 101 max_efg = 101 final_distance = "" for attempt in ["2-pointer", "3-pointer"]: for distance in distances: player_df = new_df.loc[(new_df["distance"]==distance) & (new_df["attempt"]==attempt)] if len(player_df) == 0: continue fg_pct = self.__calculate_metric(player_df, "fg") efg_pct = self.__calculate_metric(player_df, "efg") if metric == "fg": if fg_pct < max_fg: max_fg = fg_pct max_efg = efg_pct final_distance = distance final_attempt = attempt else: if efg_pct < max_efg: max_fg = fg_pct max_efg = efg_pct final_distance = distance final_attempt = attempt player_df = new_df.loc[(new_df["distance"]==final_distance) & (new_df["attempt"] == final_attempt)] self.__plot_shot_chart(player_df, **kwargs) all_shots = self.dataframe self.__plot_hist_volume(all_shots, fg_pct=max_fg, efg_pct=max_efg, most_or_least=most_or_least, final_distance=final_distance, final_attempt=final_attempt) plt.show() # Cell def list_teams(dataframe): "Lists all the teams in `dataframe`" return dataframe['team'].drop_duplicates() # Cell def list_game_ids(dataframe,year,month,day): "Lists unique game ids in `dataframe` for a given date" return dataframe.loc[(dataframe['year']==year) & (dataframe['month']==month) & (dataframe['day']==day)][['game_id','winner','loser']].drop_duplicates() # Cell class TeamShots(Shots): "Team shots" def __init__(self, dataframe, team): dataframe = dataframe.loc[dataframe['team']==team].copy() self.team = team super().__init__(dataframe) # Cell def list_team_players(dataframe, team): "Lists the players in `dataframe` who took shots for a `team`" return dataframe.loc[dataframe['team']==team].groupby('shots_by').shots_by.count().reset_index(name='count').sort_values(['count'], ascending=False) # Cell class PlayerShots(Shots): "Player shots" def __init__(self, dataframe, player): self.team_total_shots = len(dataframe.loc[dataframe['team'] == dataframe.loc[dataframe['shots_by']==player]['team'].to_list()[0]]) dataframe = dataframe.loc[dataframe['shots_by']==player].copy() self.player = player super().__init__(dataframe)

46.717241

488

0.600581

ff8e52c2a7ebf6a9b097e8dd37f7073af382f713

308

py

Python

python/graphscope/experimental/nx/tests/algorithms/forward/centrality/test_reaching.py

wenyuanyu/GraphScope

a40ccaf70557e608d8b091eb25ab04477f99ce21

[ "Apache-2.0" ]

2

2020-12-15T08:42:10.000Z

2022-01-14T09:13:16.000Z

python/graphscope/experimental/nx/tests/algorithms/forward/centrality/test_reaching.py

wenyuanyu/GraphScope

a40ccaf70557e608d8b091eb25ab04477f99ce21

[ "Apache-2.0" ]

1

2020-12-22T13:15:40.000Z

python/graphscope/experimental/nx/tests/algorithms/forward/centrality/test_reaching.py

wenyuanyu/GraphScope

a40ccaf70557e608d8b091eb25ab04477f99ce21

[ "Apache-2.0" ]

1

2021-11-23T03:40:43.000Z

import networkx.algorithms.centrality.tests.test_reaching import pytest from graphscope.experimental.nx.utils.compat import import_as_graphscope_nx import_as_graphscope_nx(networkx.algorithms.centrality.tests.test_reaching, decorators=pytest.mark.usefixtures("graphscope_session"))

38.5

81

0.811688

0942113b2c99f43b18d9c3e9632075ae208fc1fe

22,525

py

Python

sdk/compute/azure-mgmt-compute/azure/mgmt/compute/v2019_03_01/aio/operations_async/_gallery_image_versions_operations_async.py

LianwMS/azure-sdk-for-python

612d7bca9de86ee1bd1fa59291d7bf897ba9213f

[ "MIT" ]

2

2019-05-17T21:24:53.000Z

2020-02-12T11:13:42.000Z

sdk/compute/azure-mgmt-compute/azure/mgmt/compute/v2019_03_01/aio/operations_async/_gallery_image_versions_operations_async.py

LianwMS/azure-sdk-for-python

612d7bca9de86ee1bd1fa59291d7bf897ba9213f

[ "MIT" ]

15

2019-07-12T18:18:04.000Z

2019-07-25T20:55:51.000Z

sdk/compute/azure-mgmt-compute/azure/mgmt/compute/v2019_03_01/aio/operations_async/_gallery_image_versions_operations_async.py

LianwMS/azure-sdk-for-python

612d7bca9de86ee1bd1fa59291d7bf897ba9213f

[ "MIT" ]

2

2020-05-21T22:51:22.000Z

2020-05-26T20:53:01.000Z

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- from typing import Any, AsyncIterable, Callable, Dict, Generic, Optional, TypeVar, Union import warnings from azure.core.async_paging import AsyncItemPaged, AsyncList from azure.core.exceptions import HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error from azure.core.pipeline import PipelineResponse from azure.core.pipeline.transport import AsyncHttpResponse, HttpRequest from azure.core.polling import AsyncLROPoller, AsyncNoPolling, AsyncPollingMethod from azure.mgmt.core.exceptions import ARMErrorFormat from azure.mgmt.core.polling.async_arm_polling import AsyncARMPolling from ... import models T = TypeVar('T') ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]] class GalleryImageVersionsOperations: """GalleryImageVersionsOperations async operations. You should not instantiate this class directly. Instead, you should create a Client instance that instantiates it for you and attaches it as an attribute. :ivar models: Alias to model classes used in this operation group. :type models: ~azure.mgmt.compute.v2019_03_01.models :param client: Client for service requests. :param config: Configuration of service client. :param serializer: An object model serializer. :param deserializer: An object model deserializer. """ models = models def __init__(self, client, config, serializer, deserializer) -> None: self._client = client self._serialize = serializer self._deserialize = deserializer self._config = config async def _create_or_update_initial( self, resource_group_name: str, gallery_name: str, gallery_image_name: str, gallery_image_version_name: str, gallery_image_version: "models.GalleryImageVersion", **kwargs ) -> "models.GalleryImageVersion": cls = kwargs.pop('cls', None) # type: ClsType["models.GalleryImageVersion"] error_map = {404: ResourceNotFoundError, 409: ResourceExistsError} error_map.update(kwargs.pop('error_map', {})) api_version = "2019-03-01" content_type = kwargs.pop("content_type", "application/json") # Construct URL url = self._create_or_update_initial.metadata['url'] # type: ignore path_format_arguments = { 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'galleryName': self._serialize.url("gallery_name", gallery_name, 'str'), 'galleryImageName': self._serialize.url("gallery_image_name", gallery_image_name, 'str'), 'galleryImageVersionName': self._serialize.url("gallery_image_version_name", gallery_image_version_name, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Content-Type'] = self._serialize.header("content_type", content_type, 'str') header_parameters['Accept'] = 'application/json' # Construct and send request body_content_kwargs = {} # type: Dict[str, Any] body_content = self._serialize.body(gallery_image_version, 'GalleryImageVersion') body_content_kwargs['content'] = body_content request = self._client.put(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 201, 202]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) deserialized = None if response.status_code == 200: deserialized = self._deserialize('GalleryImageVersion', pipeline_response) if response.status_code == 201: deserialized = self._deserialize('GalleryImageVersion', pipeline_response) if response.status_code == 202: deserialized = self._deserialize('GalleryImageVersion', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized _create_or_update_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/galleries/{galleryName}/images/{galleryImageName}/versions/{galleryImageVersionName}'} # type: ignore async def begin_create_or_update( self, resource_group_name: str, gallery_name: str, gallery_image_name: str, gallery_image_version_name: str, gallery_image_version: "models.GalleryImageVersion", **kwargs ) -> "models.GalleryImageVersion": """Create or update a gallery Image Version. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param gallery_name: The name of the Shared Image Gallery in which the Image Definition resides. :type gallery_name: str :param gallery_image_name: The name of the gallery Image Definition in which the Image Version is to be created. :type gallery_image_name: str :param gallery_image_version_name: The name of the gallery Image Version to be created. Needs to follow semantic version name pattern: The allowed characters are digit and period. Digits must be within the range of a 32-bit integer. Format: :code:`<MajorVersion>`.:code:`<MinorVersion>`.:code:`<Patch>`. :type gallery_image_version_name: str :param gallery_image_version: Parameters supplied to the create or update gallery Image Version operation. :type gallery_image_version: ~azure.mgmt.compute.v2019_03_01.models.GalleryImageVersion :keyword callable cls: A custom type or function that will be passed the direct response :keyword str continuation_token: A continuation token to restart a poller from a saved state. :keyword polling: True for ARMPolling, False for no polling, or a polling object for personal polling strategy :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. :return: GalleryImageVersion, or the result of cls(response) :rtype: ~azure.mgmt.compute.v2019_03_01.models.GalleryImageVersion :raises ~azure.core.exceptions.HttpResponseError: """ polling = kwargs.pop('polling', True) # type: Union[bool, AsyncPollingMethod] cls = kwargs.pop('cls', None) # type: ClsType["models.GalleryImageVersion"] lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) # type: Optional[str] if cont_token is None: raw_result = await self._create_or_update_initial( resource_group_name=resource_group_name, gallery_name=gallery_name, gallery_image_name=gallery_image_name, gallery_image_version_name=gallery_image_version_name, gallery_image_version=gallery_image_version, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) kwargs.pop('content_type', None) def get_long_running_output(pipeline_response): deserialized = self._deserialize('GalleryImageVersion', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized if polling is True: polling_method = AsyncARMPolling(lro_delay, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_create_or_update.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/galleries/{galleryName}/images/{galleryImageName}/versions/{galleryImageVersionName}'} # type: ignore async def get( self, resource_group_name: str, gallery_name: str, gallery_image_name: str, gallery_image_version_name: str, expand: Optional[str] = "ReplicationStatus", **kwargs ) -> "models.GalleryImageVersion": """Retrieves information about a gallery Image Version. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param gallery_name: The name of the Shared Image Gallery in which the Image Definition resides. :type gallery_name: str :param gallery_image_name: The name of the gallery Image Definition in which the Image Version resides. :type gallery_image_name: str :param gallery_image_version_name: The name of the gallery Image Version to be retrieved. :type gallery_image_version_name: str :param expand: The expand expression to apply on the operation. :type expand: str :keyword callable cls: A custom type or function that will be passed the direct response :return: GalleryImageVersion, or the result of cls(response) :rtype: ~azure.mgmt.compute.v2019_03_01.models.GalleryImageVersion :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["models.GalleryImageVersion"] error_map = {404: ResourceNotFoundError, 409: ResourceExistsError} error_map.update(kwargs.pop('error_map', {})) api_version = "2019-03-01" # Construct URL url = self.get.metadata['url'] # type: ignore path_format_arguments = { 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'galleryName': self._serialize.url("gallery_name", gallery_name, 'str'), 'galleryImageName': self._serialize.url("gallery_image_name", gallery_image_name, 'str'), 'galleryImageVersionName': self._serialize.url("gallery_image_version_name", gallery_image_version_name, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] if expand is not None: query_parameters['$expand'] = self._serialize.query("expand", expand, 'str') query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Accept'] = 'application/json' # Construct and send request request = self._client.get(url, query_parameters, header_parameters) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) deserialized = self._deserialize('GalleryImageVersion', pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/galleries/{galleryName}/images/{galleryImageName}/versions/{galleryImageVersionName}'} # type: ignore async def _delete_initial( self, resource_group_name: str, gallery_name: str, gallery_image_name: str, gallery_image_version_name: str, **kwargs ) -> None: cls = kwargs.pop('cls', None) # type: ClsType[None] error_map = {404: ResourceNotFoundError, 409: ResourceExistsError} error_map.update(kwargs.pop('error_map', {})) api_version = "2019-03-01" # Construct URL url = self._delete_initial.metadata['url'] # type: ignore path_format_arguments = { 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'galleryName': self._serialize.url("gallery_name", gallery_name, 'str'), 'galleryImageName': self._serialize.url("gallery_image_name", gallery_image_name, 'str'), 'galleryImageVersionName': self._serialize.url("gallery_image_version_name", gallery_image_version_name, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') # Construct headers header_parameters = {} # type: Dict[str, Any] # Construct and send request request = self._client.delete(url, query_parameters, header_parameters) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200, 202, 204]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) if cls: return cls(pipeline_response, None, {}) _delete_initial.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/galleries/{galleryName}/images/{galleryImageName}/versions/{galleryImageVersionName}'} # type: ignore async def begin_delete( self, resource_group_name: str, gallery_name: str, gallery_image_name: str, gallery_image_version_name: str, **kwargs ) -> None: """Delete a gallery Image Version. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param gallery_name: The name of the Shared Image Gallery in which the Image Definition resides. :type gallery_name: str :param gallery_image_name: The name of the gallery Image Definition in which the Image Version resides. :type gallery_image_name: str :param gallery_image_version_name: The name of the gallery Image Version to be deleted. :type gallery_image_version_name: str :keyword callable cls: A custom type or function that will be passed the direct response :keyword str continuation_token: A continuation token to restart a poller from a saved state. :keyword polling: True for ARMPolling, False for no polling, or a polling object for personal polling strategy :paramtype polling: bool or ~azure.core.polling.AsyncPollingMethod :keyword int polling_interval: Default waiting time between two polls for LRO operations if no Retry-After header is present. :return: None, or the result of cls(response) :rtype: None :raises ~azure.core.exceptions.HttpResponseError: """ polling = kwargs.pop('polling', True) # type: Union[bool, AsyncPollingMethod] cls = kwargs.pop('cls', None) # type: ClsType[None] lro_delay = kwargs.pop( 'polling_interval', self._config.polling_interval ) cont_token = kwargs.pop('continuation_token', None) # type: Optional[str] if cont_token is None: raw_result = await self._delete_initial( resource_group_name=resource_group_name, gallery_name=gallery_name, gallery_image_name=gallery_image_name, gallery_image_version_name=gallery_image_version_name, cls=lambda x,y,z: x, **kwargs ) kwargs.pop('error_map', None) kwargs.pop('content_type', None) def get_long_running_output(pipeline_response): if cls: return cls(pipeline_response, None, {}) if polling is True: polling_method = AsyncARMPolling(lro_delay, **kwargs) elif polling is False: polling_method = AsyncNoPolling() else: polling_method = polling if cont_token: return AsyncLROPoller.from_continuation_token( polling_method=polling_method, continuation_token=cont_token, client=self._client, deserialization_callback=get_long_running_output ) else: return AsyncLROPoller(self._client, raw_result, get_long_running_output, polling_method) begin_delete.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/galleries/{galleryName}/images/{galleryImageName}/versions/{galleryImageVersionName}'} # type: ignore def list_by_gallery_image( self, resource_group_name: str, gallery_name: str, gallery_image_name: str, **kwargs ) -> AsyncIterable["models.GalleryImageVersionList"]: """List gallery Image Versions in a gallery Image Definition. :param resource_group_name: The name of the resource group. :type resource_group_name: str :param gallery_name: The name of the Shared Image Gallery in which the Image Definition resides. :type gallery_name: str :param gallery_image_name: The name of the Shared Image Gallery Image Definition from which the Image Versions are to be listed. :type gallery_image_name: str :keyword callable cls: A custom type or function that will be passed the direct response :return: An iterator like instance of either GalleryImageVersionList or the result of cls(response) :rtype: ~azure.core.async_paging.AsyncItemPaged[~azure.mgmt.compute.v2019_03_01.models.GalleryImageVersionList] :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop('cls', None) # type: ClsType["models.GalleryImageVersionList"] error_map = {404: ResourceNotFoundError, 409: ResourceExistsError} error_map.update(kwargs.pop('error_map', {})) api_version = "2019-03-01" def prepare_request(next_link=None): if not next_link: # Construct URL url = self.list_by_gallery_image.metadata['url'] # type: ignore path_format_arguments = { 'subscriptionId': self._serialize.url("self._config.subscription_id", self._config.subscription_id, 'str'), 'resourceGroupName': self._serialize.url("resource_group_name", resource_group_name, 'str'), 'galleryName': self._serialize.url("gallery_name", gallery_name, 'str'), 'galleryImageName': self._serialize.url("gallery_image_name", gallery_image_name, 'str'), } url = self._client.format_url(url, **path_format_arguments) # Construct parameters query_parameters = {} # type: Dict[str, Any] query_parameters['api-version'] = self._serialize.query("api_version", api_version, 'str') else: url = next_link query_parameters = {} # type: Dict[str, Any] # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters['Accept'] = 'application/json' # Construct and send request request = self._client.get(url, query_parameters, header_parameters) return request async def extract_data(pipeline_response): deserialized = self._deserialize('GalleryImageVersionList', pipeline_response) list_of_elem = deserialized.value if cls: list_of_elem = cls(list_of_elem) return deserialized.next_link or None, AsyncList(list_of_elem) async def get_next(next_link=None): request = prepare_request(next_link) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) raise HttpResponseError(response=response, error_format=ARMErrorFormat) return pipeline_response return AsyncItemPaged( get_next, extract_data ) list_by_gallery_image.metadata = {'url': '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.Compute/galleries/{galleryName}/images/{galleryImageName}/versions'} # type: ignore

50.167038

247

0.677647

56fcb70df1f09764611db868fdd757d4f7135e38

78,531

py

Python

FIDL/decompiler_utils.py

mrj0n3s/FIDL

e6ceb000cda43b450717eb171309c02dee06dd4f

[ "MIT" ]

null

FIDL/decompiler_utils.py

mrj0n3s/FIDL

e6ceb000cda43b450717eb171309c02dee06dd4f

[ "MIT" ]

null

FIDL/decompiler_utils.py

mrj0n3s/FIDL

e6ceb000cda43b450717eb171309c02dee06dd4f

[ "MIT" ]

null

# -*- coding: utf-8 -*- # =========================================================================== # Decompiler utils # # This is a set of utilities wrapping the decompiler API into something # sane. This code focus on vulnerability research and bug hunting, # however most of the functionality is generic enough to be used for # broader reverse engineering purposes. # # Carlos Garcia Prado # <carlos.garcia@fireeye.com> # =========================================================================== __version__ = '1.2' from idc import * from idaapi import * from idautils import * import ida_hexrays from FIDL.compiler_consts import expr_condition from FIDL.compiler_consts import expr_ctype # To pretty print debug messages from FIDL.compiler_consts import expr_final, expr_assignments, insn_conditions import os import random import traceback import networkx as nx from collections import namedtuple, defaultdict, OrderedDict from six.moves import xrange DEBUG = False # =========================================================== # Auxiliary functions # =========================================================== def dprint(s=""): """This will print a debug message only if debugging is active :param s: The debug message :type s: str, optional """ if DEBUG: print(s) # networkx expects nodes to be hashable. We monkey patch some of IDA's type to # implement the __hash__ method so they can be used as nodes. _hash_from_obj_id = lambda self: hash(self.obj_id) ida_hexrays.cexpr_t.__hash__ = _hash_from_obj_id ida_hexrays.cinsn_t.__hash__ = _hash_from_obj_id ida_hexrays.carg_t.__hash__ = _hash_from_obj_id def debug_get_break_statements(c): for n in c.g.nodes(): cex = get_expr(n) if cex.op == cit_break: return [c.ea] operands = blowup_expression(cex) for operand in operands: if operand.op == cit_break: return [c.ea] return [0] def debug_stahp(): """Toggles ``DEBUG`` value, useful for testing """ global DEBUG DEBUG = not DEBUG def NonLibFunctions(start_ea=None, min_size=0): """Generator yielding only non-lib functions :param start_ea: Address to start looking for non-library functions. :type start_ea: int, optional :param min_size: Minimum function size. Useful to filter small, \ uninteresting functions. :type min_size: int, optional """ for f_ea in Functions(start=start_ea): flags = get_func_attr(f_ea, FUNCATTR_FLAGS) if flags & FUNC_LIB or flags & FUNC_THUNK: continue # Min size check f = get_func(f_ea) if f.size() < min_size: continue yield f_ea def do_for_all_funcs(func, fast=True, start_ea=None, blacklist=None, min_size=100, **kwargs): """This is a generic wrapper for all kinds of logic that we want to apply \ to all the functions in the binary. :param func: function "pointer" performing the analysis. \ Its only mandatory argument is a :class:`controlFlowinator` object. :type func: function :param fast: parameter fast for the :class:`controlFlowinator` object. :type fast: boolean, optional :param start_ea: Address to start looking for non-library functions. :type start_ea: int, optional :param blacklist: a function determining whether to process a function.\ Implemented via dependency injection. :type blacklist: function, optional :return: A list of JSON-like messages (individual function results) :rtype: list """ results = [] for f_ea in NonLibFunctions(start_ea=start_ea, min_size=min_size): # Since this is pretty CPU intensive, the following # offers a mechanism for skipping some functions def nope(x): return False is_blacklisted = blacklist or nope if is_blacklisted(f_ea): continue try: c = controlFlowinator(ea=f_ea, fast=fast) # CFG except Exception as e: print(e) continue res = func(c, **kwargs) # Functions return a __list__ of JSON-like messages # Consolidate all lists into one by using the # overloaded addition operator if res: results += res return results def my_get_func_name(ea): """Wrapper for :class:`get_func_name` handling some corner cases. :param ea: Address of the function to resolve its name :type ea: int """ f_name = get_func_name(ea) if not f_name: # This could be a function pointer # stored in a global variable f_name = get_name(ea) if not f_name: # Maybe an import? # TODO: some kind of caching or memoization imp = cImporter() import_dict = imp.get_imports_info() f_name = import_dict.get(ea, "") return f_name class cImporter: """Collect import information This is mainly to work around the fact that :func:``get_func_name`` does \ not resolve imports... """ def __init__(self): self.import_dict = {} def _imp_cb(self, ea, name, ord): if name: self.import_dict[ea] = name else: print("Could not resolve import name!") return True def _find_imports_info(self): nimps = get_import_module_qty() for i in xrange(0, nimps): name = get_import_module_name(i) if not name: continue enum_import_names(i, self._imp_cb) def get_imports_info(self): self._find_imports_info() return self.import_dict class BBGraph(object): """Representation of the assembly CFG for a function """ def __init__(self, f_ea): self.f_ea = f_ea self.f = FlowChart(get_func(f_ea), None, FC_PREDS) self.bb_list = self._get_basic_blocks() def _get_basic_blocks(self): """List of tuples containing basic blocks limits""" return [(bb.startEA, bb.endEA) for bb in self.f] def _get_function_graph(self): """It creates a graph of basic blocks and their children. returns: dict { block_ea: [branch1_ea, branch2_ea], ... } """ bb_dict = defaultdict(set) # Dict of BasicBlock objects for bb in self.f: for child in bb.succs(): bb_dict[bb.startEA].add(child.startEA) return bb_dict def _graph_to_networkx(self): """Gets a bb_dict (see _get_function_graph) and converts \ this to a NetworkX format """ bb_dict = self._get_function_graph() if not bb_dict: return None try: dg = nx.DiGraph() except NameError as e: # NetworkX is not installed return None for node, children in bb_dict.items(): for child in children: dg.add_edge(node, child) return dg def _get_block_from_ea(self, ea): """It returns the _BasicBlock_ containing ``ea`` or None """ for bb in self.f: # Remember that bb.endEA is bb.startEA of the next one! if ea >= bb.startEA and ea < bb.endEA: return bb return None def get_node(self, addr): """Given a function's address, returns the basic block (address) that \ contains it (or None) :param addr: address within a function :type addr: int :return: Address of the node containing the input address :rtype: int """ node_addr = None for start, end in self.bb_list: if addr >= start and addr < end: # The address is within this basic block node_addr = start break return node_addr def find_connected_paths(self, bb_start, bb_end, co=10): """Leverages NetworkX to find all connected paths :param bb_start: Initial basic block :type bb_start: Basic block :param bb_end: Final basic block :type bb_end: Basic block :param co: Cutoff parameter :type co: int, optional NOTE: the cutoff parameter in :class:`nx.all_simple_paths` serves \ two purposes: - reduce the chances of CPU melting (algo is O(n!)) - nobody will inspect (manually) monstruous paths :returns: generator of lists or None """ g = self._graph_to_networkx() if not g: return None # Sanity check. # Basic blocks within current function? bbl = [bb.startEA for bb in self.f] # bbl contains startEA's. However, we may have clicked # *somewhere* within the basic block _bb_start = self._get_block_from_ea(bb_start).startEA _bb_end = self._get_block_from_ea(bb_end).startEA if _bb_start in bbl and _bb_end in bbl: paths = nx.all_simple_paths( g, source=_bb_start, target=_bb_end, cutoff=co) return paths else: print('[!] find_connected_paths: check bb_start, bb_end parameters') return None # =========================================================== # Lighthouse utilities # (from: https://github.com/gaasedelen/lighthouse) # These help processing the displayed decompiled code # For example: painting specific lines of code # =========================================================== def map_line2citem(decompilation_text): """Part of `Lighthouse plugin <https://github.com/gaasedelen/lighthouse>`_ Map decompilation line numbers to citems. This function allows us to build a relationship between citems in the ctree and specific lines in the hexrays decompilation text. """ line2citem = {} # # it turns out that citem indexes are actually stored inline with the # decompilation text output, hidden behind COLOR_ADDR tokens. # # here we pass each line of raw decompilation text to our lexer, # extracting any COLOR_ADDR tokens as citem indexes # for line_number in xrange(decompilation_text.size()): line_text = decompilation_text[line_number].line line2citem[line_number] = lex_citem_indexes(line_text) return line2citem def map_line2node(cfunc, line2citem): """Part of `Lighthouse plugin <https://github.com/gaasedelen/lighthouse>`_ Map decompilation line numbers to node (basic blocks) addresses. This function allows us to build a relationship between graph nodes (basic blocks) and specific lines in the hexrays decompilation text. """ line2node = {} treeitems = cfunc.treeitems function_address = cfunc.entry_ea bb_graph = BBGraph(function_address) # # prior to this function, a line2citem map was built to tell us which # citems reside on any given line of text in the decompilation output. # # now, we walk through this line2citem map one 'line_number' at a time in # an effort to resolve the set of graph nodes associated with its citems. # for line_number, citem_indexes in line2citem.items(): nodes = set() # # we are at the level of a single line (line_number). we now consume # its set of citems (citem_indexes) and attempt to identify the explict # graph nodes they claim to be sourced from (by their reported EA) # for index in citem_indexes: # get the code address of the given citem try: item = treeitems[index] address = item.ea # apparently this is a thing on IDA 6.95 except IndexError as e: continue # find the graph node (eg, basic block) that generated this citem node_addr = bb_graph.get_node(address) # address not mapped to a node... weird. continue to the next citem if not node_addr: continue # # we made it this far, so we must have found a node that contains # this citem. save the computed node_id to the list of of known # nodes we have associated with this line of text # nodes.add(node_addr) # # finally, save the completed list of node ids as identified for this # line of decompilation text to the line2node map that we are building # line2node[line_number] = nodes # all done, return the computed map return line2node def lex_citem_indexes(line): """Part of `Lighthouse plugin <https://github.com/gaasedelen/lighthouse>`_ Lex all ctree item indexes from a given line of text. The HexRays decompiler output contains invisible text tokens that can be used to attribute spans of text to the ctree items that produced them. """ i = 0 indexes = set([]) line_length = len(line) # lex COLOR_ADDR tokens from the line of text while i < line_length: # does this character mark the start of a new COLOR_* token? if line[i] == COLOR_ON: # yes, so move past the COLOR_ON byte i += 1 # is this sequence for a COLOR_ADDR? if ord(line[i]) == COLOR_ADDR: # yes, so move past the COLOR_ADDR byte i += 1 # # A COLOR_ADDR token is followed by either 8, or 16 characters # (a hex encoded number) that represents an address/pointer. # in this context, it is actually the index number of a citem # citem_index = int(line[i:i + COLOR_ADDR_SIZE], 16) i += COLOR_ADDR_SIZE # SANITY CHECK # NOTE: this value is arbitrary (although reasonable) # FIX: get this from cfunc.treeitems.size() if citem_index < 0x1000: # save the extracted citem index indexes.add(citem_index) # skip to the next iteration as i has moved continue # nothing we care about happened, keep lexing forward i += 1 # return all the citem indexes extracted from this line of text return indexes def map_node2lines(line2node): """Part of `Lighthouse plugin <https://github.com/gaasedelen/lighthouse>`_ Creates a mapping of nodes to lines of code """ node2lines = defaultdict(set) for line, node_set in line2node.items(): for node_addr in node_set: node2lines[node_addr].add(line) return node2lines def map_citem2line(line2citem): """Part of `Lighthouse plugin <https://github.com/gaasedelen/lighthouse>`_ Creates a mapping of citem indexes to lines of code """ citem2line = {} for line, citem_l in line2citem.items(): for citem_idx in citem_l: citem2line[citem_idx] = line return citem2line def citem2higher(citem): """This gets the higher representation of a given :class:``citem``, \ that is, a :class:``cinsn_t`` or :class:``cexpr_t`` :param citem: a :class:``citem`` object :type citem: :class:``citem`` """ if citem.is_expr(): return citem.cexpr return citem.cinsn # =========================================================== # Hex-rays hacks :) # =========================================================== class my_var_t: """This wraps the :class:`lvar_t` nicely into a more usable data structure. It aggregates several interesting pieces of information in one place. \ eg. ``is_arg``, ``is_constrained``, ``is_initialized``, etc. The most commonly used attributes for this class are: - name - type_name - size - is_arg - is_pointer - is_array - is_signed :param var: an object representing a local variable or function argument :type var: :class:`lvar_t` """ def __init__(self, var): self.var = var self.name = self.var.name self.type_name = '' self.size = 0 self.ti = None self.is_signed = False self.is_array = False self.array_type = None self.element_size = 0 self.array_len = 0 self.complex_type = None self.is_pointer = False self.pointed_type = None # Convenience self.is_arg = self.var.is_arg_var # Shit gets real here self.is_tainted = False self.is_constrained = False self.is_initialized = False # Auxiliary for taint/constraint tracking # A specific variable might be a function # of others, ex: v1 = v2 * v3 + 1 # is_a_function_of -> [v2, v3] self.is_a_function_of = [] self._get_var_type() def _get_var_type(self): """Variable type information""" tif = self.var.type() # tinfo_t # This is useful to call its `is_int` # and similar functions # Ex. `my_var_t.ti.is_int()` self.ti = tif # Interesting for integer types self.is_signed = tif.is_signed() # Something like `__int64` self.type_name = str(tif) # For an array this is the number of elements # Ex: char arr[1234] -> 1234 self.size = tif.get_size() # This adds the type of the array # Ex: __int16 arr[123] -> __int16 if tif.is_array(): self.is_array = True self.array_type = tif.get_array_element() self.element_size = self.array_type.get_size() if self.element_size: self.array_len = int(self.size / self.element_size) # Ex: char *str -> char (None if the type is not a pointer) if tif.is_ptr(): self.is_pointer = True self.pointed_type = tif.get_pointed_object() or None # Ex: struct _SYSTEMTIME st -> _SYSTEMTIME if self.is_pointer: _tif = self.pointed_type else: _tif = tif self.complex_type = _tif.get_type_name() or "" def __repr__(self): print("Name: {}".format(self.name)) print(" Type name: {}".format(self.type_name)) print(" Size: {}".format(self.size)) # Optional stuff (not all vars have this) if self.array_type: print(" Array type: {}".format(self.array_type)) print(" Array element size: {}".format(self.element_size)) print(" Array length: {}".format(self.array_len)) if self.complex_type: print(" Complex type: {}".format(self.complex_type)) # At a first glance, this may seem odd. It is correct. pointed_type_s = "{}".format(self.pointed_type) if len(pointed_type_s) and pointed_type_s != 'None': print(" Pointed object: {}".format(pointed_type_s)) return "" def get_return_type(cf=None): """Hack to get the return value of a function. :param cf: the result of ``decompile()`` :type cf: :class:`ida_hexrays.cfuncptr_t` :return: Type information for the return value :rtype: :class:`tinfo_t` """ if not cf: raise ValueError ty = cf.type # tinfo_t (entire prototype) ti = ty.get_rettype() # tinfo_t (return value) return ti # =========================================================== # Convenience functions # =========================================================== class pseudoViewer: """This wraps the :class:`pseudoViewer` API neatly. We need it because some things don't work unless \ you previously visited (or are currently visiting) \ the function whose decompiled form you want to analyze. \ Thus, we are forced to "Hack like in the movies" TODO: probably deprecate this after IDA 7.5 changes NOTE: the performance penalty is negligible """ silent_flags = ida_hexrays.OPF_REUSE | ida_hexrays.OPF_NO_WAIT def __init__(self): self.vdui = None self.p_twidget = None def show(self, ea=0, flags=silent_flags): """Displays the pseudoviewer widget :param ea: adress of the function to display :type ea: int, optional :param flags: how to flags an existing pseudocode display, if any :type flags: int, optional """ try: self.vdui = open_pseudocode(ea, flags) if self.vdui: self.p_twidget = self.vdui.ct except Exception as e: print("ERROR (pseudoViewer.show) @ {:#08x}".format(ea)) print(e) def close(self): """Closes the pseudoviewer widget """ close_pseudocode(self.p_twidget) def get_function_vars(c=None, ea=0, only_args=False, only_locals=False): """Populates a dict of :class:`my_var_t` for the function containing the specified ``ea`` :param c: a :class:`controlFlowinator` object, optional :type c: :class:`controlFlowinator` :param ea: the function address :type ea: int :param only_args: extract only function arguments :type only_args: bool, optional :param only_locals: extract only local variables :type only_locals: bool, optional :return: A dictionary of :class:`my_var_t`, indexed by their index """ if not c: try: cf = my_decompile(ea=ea) except DecompilationFailure as e: print("ERROR (get_function_vars)") print(repr(str(e))) return {} else: cf = c.cf # Successful decompilation at `ea` point # `cf.lvars` is an array of `lvars_t` # `idx` is the index into this array to be used later # # I need to re-order the list of arguments. # No idea why, but IDA does not spit the arguments in order. # It keeps however a list of how the indexes are messed up in ``c.cf.argidx`` ordered_vars = [None] * len(cf.lvars) for i, v in enumerate(cf.lvars): if v.is_arg_var: # Need to fix order idx = cf.argidx[i] else: # Local vars seem to be fine idx = i ordered_vars[idx] = v if only_args: return OrderedDict({idx: my_var_t(v) for idx, v in enumerate(ordered_vars) if v.is_arg_var and v.name}) elif only_locals: return OrderedDict({idx: my_var_t(v) for idx, v in enumerate(ordered_vars) if not v.is_arg_var and v.name}) else: return OrderedDict({idx: my_var_t(v) for idx, v in enumerate(ordered_vars)}) def ref2var(ref, c=None, cf=None): """Convenient wrapper to streamline the conversions between ``var_ref_t`` and ``lvar_t`` :param c: a :class:`controlFlowinator` object, optional :type c: :class:`controlFlowinator` :param cf: a decompilation object (usually the result of ``decompile``), optional :type cf: a :class:`cfunc_t` object :param ref: a reference to a variable in the pseudocode :type ref: :class:`var_ref_t` :return: a :class:`lvar_t` object :rtype: :class:`lvar_t` """ if not c and not cf: raise RuntimeError('Need c or cf parameters. None was passed to ref2var') if not cf and c: cf = c.cf return cf.lvars[ref.v.idx] def is_arithmetic_expression(cex, only_these=[]): """Checks whether this is an arithmetic expression. :param cex: expression, usually this is a *node*. :type cex: :class:`cexpr_t` :param only_these: a list of arithmetic expressions to look for. \ These are defined in :mod:`ida_hexrays` :type only_these: a list of ``cot_*`` constants, eg. ``cot_add``. :return: True or False :rtype: bool """ arith_ops = [cot_add, cot_mul, cot_sub] interesting_ops = only_these or arith_ops if cex.op in interesting_ops: return True # Let's go down only a level since most common expressions # are something like "v4 + 1", "v3 * 2 + 1", etc. _x = cex.x _y = cex.y for e in (_x, _y): # e: left / right hand side of the comparison if e and e.op in interesting_ops: return True return False def is_binary_truncation(cex): """Looking for expressions truncating a number These expressions are of the form ``v1 & 0xFFFF`` or alike :param cex: an expression :type cex: :class:cexpr_t :return: True or False :rtype: bool """ res = find_elements_of_type(cex, element_type=cot_band) if not res: return False for band_e in res: rhs = band_e.y if is_number(rhs): return True return False # ============================================================ # The following wrappers are not rocket science # but the code resulting from using them # is __way__ more legible... # ============================================================ def is_array_indexing(ins): if ins.op == cot_idx: return True return False def is_cast(ins): if ins.op == cot_cast: return True return False def decast(ins): """Remove the ``cast``, returning the casted element """ if is_cast(ins): ins = ins.x return ins def is_asg(ins): if ins.op in expr_assignments.keys(): return True return False def is_call(ins): if ins.op == cot_call: return True return False def is_helper(ins): """Helpers are IDA macros, e.g. __ROR__ or LOBYTE """ if ins.op == cot_helper: return True return False def is_ref(ins): if ins.op == cot_ref: return True return False def ref_to(ins): if ins.op == cot_ref: ins = ins.x return ins def is_ptr(ins): if ins.op == cot_ptr: return True return False def points_to(ins): if ins.op == cot_ptr: ins = ins.x return ins def is_number(ins): """Convenience wrapper""" if ins.op == cot_num: return True return False def num_value(ins): """Returns the numerical value of ``ins`` :param ins: :class:`cexpr_t` or :class:`insn_t` """ if not is_number(ins): raise TypeError return ins.n.value(ins.type) def is_string(ins): """Convenience wrapper""" if ins.is_cstr(): return True return False def is_member_pointer(ins): """Convenience wrapper""" if ins.op == cot_memptr: return True return False def is_struct_member(ins): """Convenience wrapper""" if ins.op == cot_memref: return True return False def member_info(ins): """Returns info about a structure member or a pointer to it :param ins: :class:`cexpr_t` or :class:`insn_t` """ m_info = { 'type_name': str(ins.type), 'offset': ins.m, 'size': ins.type.get_size(), 'struct_var_idx': ins.x.v.idx } return m_info def string_value(ins): """Gets the string corresponding to ``ins`` Works with *C-str* and *Unicode* :param ins: :class:`cexpr_t` or :class:`insn_t` :return: string for this ``ins`` :rtype: string """ if not is_string(ins): raise TypeError str_ea = ins.obj_ea str_type = get_str_type(str_ea) & 0xF # Python 3: get_strlit_contents returns bytes now str_b = get_strlit_contents(str_ea, -1, str_type) return str_b.decode('utf-8') def is_var(ins): """Whether this ``ins`` corresponds to a variable Remember that if this evaluates to True, we are dealing with an object of type ``var_ref_t`` which are pretty much useless. We may want to convert this to a ``lvar_t`` and even better to a :class:`my_var_t` afterwards. :func:`ref2var` is a simple wrapper to perform the conversion between reference and variable """ if ins.op == cot_var: return True return False def is_global_var(ins): """Tells whether ``ins`` is a global variable TODO: enhance this heuristic :param ins: :class:`cexpr_t` or :class:`insn_t` :return: True or False :rtype: bool """ if ins.op == cot_obj: if not ins.is_cstr(): if ins.obj_ea > 0: return True return False def value_of_global(ins): """Returns the value of a global variable """ if not is_global_var(ins): raise TypeError return ins.obj_ea def is_if(ins): if ins.op == cit_if: return True return False # =========================================================== # Auxiliary # =========================================================== def my_decompile(ea=None): """This sets flags necessary to use this programmatically. :param ea: Address within the function to decompile :type ea: int :return: decompilation object :rtype: a :class:`cfunc_t` """ if not ea: print("Please specify an address (ea)") return None try: cf = decompile( ea=ea, flags=ida_hexrays.DECOMP_NO_WAIT | ida_hexrays.DECOMP_NO_CACHE ) cf.refresh_func_ctext() except ida_hexrays.DecompilationFailure as e: print("Failed to decompile @ {:X}".format(ea)) cf = None return cf def dump_pseudocode(ea=0): """Debugging helper. """ print("~~~~ Dumping pseudocode ~~~~") cf = my_decompile(ea=ea) if not cf: print("Failed to decompile @ {:X}".format(ea)) return None ps = cf.pseudocode for idx, sline in enumerate(ps): print("[{}] {}".format(idx, tag_remove(sline.line))) def dump_lvars(ea=0): """Debugging helper. """ print("~~~~ Dumping local variables ~~~~") try: c = controlFlowinator(ea=ea) except Exception as e: print("Failed to decompile {:X}".format(ea)) return None print() print(c.lvars) def lines_and_code(cf=None, ea=0): """Mapping of line numbers and code :param cf: a decompilation object :type cf: an :class:`cfunc_t` object, optional :param ea: Address within the function to decompile, if no `cf` is provided :type ea: int, optional :return: a dictionary of lines of code, indexed by line number :rtype: dict """ code = {} if not cf: cf = my_decompile(ea=ea) if cf: # Decompilation successful ps = cf.pseudocode for idx, sline in enumerate(ps): # Lines of code start at 1 code[idx + 1] = tag_remove(sline.line) return code def all_paths_between(c, start_node=None, end_node=None, co=40): """Calculates all paths between ``start_node`` and ``end_node`` Calculating paths is one of these things that \ is better done with the paralell index graph (``c.i_cfg``) \ It haywires when done with complex elements. FIXME: the co (cutoff) param is necessary to avoid complexity explosion. However, there is a problem if it's reached... :param c: a :class:`controlFlowinator` object :type c: :class:`controlFlowinator` :param start_node: a :class:`controlFlowinator` node :type start_node: :class:`cexpr_t` :param start_node: a :class:`controlFlowinator` node :type start_node: :class:`cexpr_t` :param co: the *cutoff* value controls the maximum path length. :type co: int, optional :return: it **yields** a list of nodes for each path :rtype: list """ if not start_node: start_index = min(c.i_cfg.nodes) else: start_index = c.node2index[start_node] # Use the node with the highest index # as default value if not end_node: node_indexes = [n.index for n in c.g.nodes()] # higher value is usually # the last return node if node_indexes: end_index = max(node_indexes) else: end_index = start_index else: end_index = c.node2index[end_node] # Find all *simple* paths # These paths are lists of citem indexes try: all_paths_i = nx.all_simple_paths( c.i_cfg, source=start_index, target=end_index, cutoff=co) except nx.NodeNotFound as e: # TODO: this problem is related to # the cutoff. Figure out what happens... traceback.print_exc() all_paths_i = [] # ------------------------------------------------ # Translate the indexes to cinsn_t, cexpr_t nodes # before yielding back to the caller # ------------------------------------------------ for p_i in all_paths_i: p_nodes = [c.index2node[x] for x in p_i] yield p_nodes def display_node(c=None, node=None, color=None): """Displays a given node in the ``pseudoviewer`` :param c: a :class:`controlFlowinator` object :type c: :class:`controlFlowinator` :param node: a :class:`controlFlowinator` node :type node: :class:`cexpr_t` :param color: color to mark the line of code corresponding to `node` :type color: int, optional """ cf = c.cf pseudo = cf.pseudocode line2citem = map_line2citem(pseudo) citem2line = map_citem2line(line2citem) code = lines_and_code(cf=cf) if not code: print("Mapping between lines and code not available!") return if not color: color = random.randint(0x202020, 0x808080) try: line = citem2line[node.index] except KeyError as e: print("display_node :: Error @ citem2line") print("{}: {}".format(line + 1, code[line + 1])) # Paint it! pseudo[line].bgcolor = color refresh_idaview_anyway() def display_path(cf=None, path=None, color=None): """Shows a path's code and colors its lines. :param cf: a decompilation object :type cf: an :class:`cfunc_t` object, optional :param path: a list of ::class:`controlFlowinator` nodes :type path: list :param color: color to mark the lines of code corresponding to `path` :type color: int, optional :return: a list of function lines (path nodes) :rtype: list """ path_lines = [] if not cf: cf = my_decompile(ea=ea) pseudo = cf.pseudocode line2citem = map_line2citem(pseudo) citem2line = map_citem2line(line2citem) code = lines_and_code(cf=cf) if not code: print("Mapping between lines and code not available!") return [] if not color: color = random.randint(0x202020, 0x808080) for node in path: try: line = citem2line[node.index] except KeyError as e: continue print("{}: {}".format(line + 1, code[line + 1])) path_lines.append(line + 1) # Paint it! pseudo[line].bgcolor = color refresh_idaview_anyway() return path_lines def display_line_at(ea, silent=False): """Displays the line of pseudocode corresponding to ``ea`` This is useful to quickly answer questions like: - "Is this function always called with its first parameter being a constant?" - "I want to see all the error messages displayed by this function" - etc. :param ea: address of an element contained within the line to display :type ea: int :param silent: flag controlling verbose output :type silent: bool """ err_fmt = "{:X} FAIL: {}" try: c = controlFlowinator(ea=ea) except Exception as e: err = "Failed to decompile" return err_fmt.format(ea, err) pseudo = c.cf.pseudocode line2citem = map_line2citem(pseudo) citem2line = map_citem2line(line2citem) code = lines_and_code(cf=c.cf) if not code: print("Mapping between lines and code not available!") found = None for item in c.cf.treeitems: # `node` is a `cexpr_t` that may contain the call within # a more complex expression. Need to peek inside if item.ea == ea: found = True break if not found: err = "Could find that address" return err_fmt.format(ea, err) try: line = citem2line[item.index] line_info = "{:X} {}: {}".format(ea, line + 1, code[line + 1]) if not silent: print(line_info) return line_info except KeyError as e: err = "KeyError:", e return err_fmt.format(ea, err) def display_all_calls_to(func_name): """Wrapping :func:`display_line_at` since this is the most common use of this API :param func_name: name of the function to search references :type func_name: string """ f_ea = get_name_ea_simple(func_name) if f_ea == BADADDR: print("Can not find {}".format(func_name)) return None # We'll save the results and display all at the end, # otherwise the output gets clobbered with IDA's logging lines = [] for ref in XrefsTo(f_ea, True): if not ref.iscode: continue lines.append(display_line_at(ref.frm, silent=True)) print("==================================================") print("= All calls to {}".format(func_name)) print("==================================================") for line in lines: print("- {}".format(line)) # =========================================================== # CFG reconstruction # =========================================================== class controlFlowinator: """This is the main object of FIDL's API. It finds all decompiled code "blocks" and recreates a CFG based on this information. This gives us the best of both worlds: the possibility to analyze a graph (like in disassembly mode) and the power of :class:``citem`` based analysis. Some analysis is performed *after* the CFG has been constructed. These are rather cost intensive, so they are turned off by default. Use ``fast=False`` to apply these and get a better CFG. :param ea: address of the function to analyze :type ea: int :param fast: Set to ``False`` for an object with richer information :type fast: bool """ def __init__(self, ea=None, fast=True): self.cf = my_decompile(ea=ea) if not self.cf: raise RuntimeError("controlFlowinator: failed to decompile") self.ea = self.cf.entry_ea self.fast = fast self.g = None # Information about local variables within this function # This is a dict of `my_var_t` objects self.lvars = None # Information about _this_ function arguments # This is a dict of `my_var_t` objects self.args = None # The return type of this function self.ret_type = None # A map of graph nodes and their indexes self.index2node = {} # Convenient to ve the reverse mapping self.node2index = {} # Superblock is the root superblock = self.cf.body.cblock self.i_cfg = self._get_root_cfg(superblock) blocks = self._get_blocks_to_expand(superblock) # Interim CFG of citem_t indexes # It is easier to create the initial graph # using indexes as nodes but convoluted to use self._generate_i_cfg(blocks_to_expand=blocks) # A `nx.Digraph` of `cinsn_t` or `cexpr_t` # It is easier to operate on this one, # it reduces API complexity self._generate_better_cfg() # ============================================================= # The hybrid-CFG is ready now. We add some (optional) features # to this object, since they're useful in most cases # ============================================================= # Information about calls within this function # This is a list of `callObj` objects self.calls = [] # Retrieves a list of `callObj's` self._get_all_function_calls() if not fast: self.lvars = get_function_vars(c=self, only_locals=True) self.args = get_function_vars(c=self, only_args=True) self.ret_type = get_return_type(cf=self.cf) def _e(self, index): """Syntactic sugar""" try: return self.cf.treeitems[index] except Exception as e: print(e) def _stitch_together(self, nodes): """Stitches the cinsn_t in a block together This is an auxiliary function since the operation appears so commonly (A, B, C, ..., X) => A->B->C->...->X Returns a list of edges: [(A, B), (B, C), ...] """ edges = [] list_indexes = [x.index for x in nodes] if len(list_indexes) < 2: return [] for idx in xrange(len(list_indexes) - 1): u = list_indexes[idx] v = list_indexes[idx + 1] edges.append((u, v)) return edges def _get_root_cfg(self, super_block): """Creates the initial graph from the super_block It contains all blocks at recursion level 0 Returns a `nx.Digraph` """ root_edges = self._stitch_together(super_block) root = nx.DiGraph() if root_edges: root.add_edges_from(root_edges) else: # The function contains a single call root_index = self.cf.body.index + 1 root.add_node(root_index) return root def _get_blocks_to_expand(self, node_list, succ=None): """Finds collapsed blocks to be expanded. Returns a list of cinsn_t """ to_expand = [] complex_blocks = (cit_if, cit_do, cit_while, cit_switch, cit_for, cit_goto) for n in node_list: if n.op in complex_blocks: n.succ = succ to_expand.append(n) return to_expand def _get_block_successor(self, block): """Gets the successor for a given block FIXME: This has a problem with "void" function prototypes. The lack of _return_ node results in an absence of an implicit _ielse_ for the switch statements. Maybe introduce a dummy "ret" node in the CF? """ succs = None try: succs = list(self.i_cfg.successors(block.index)) if len(succs) > 1: print("More than one successor!") print("Check this out") if succs: succ = succs[0] else: succ = None except nx.NetworkXError as e: print("_get_block_successor: {}".format(e)) succ = None if not succ: print("Block: {:#08x}".format(block.ea)) print(" succs: {}".format(succs)) print(" No successor! Take a look into this!") return succ def _expand_if_block(self, block): """Expands a given `if` block""" new_blocks = [] iblock = {'ithen': block.cif.ithen} ielse = block.cif.ielse if ielse: iblock['ielse'] = ielse # 1. Find and save the original successor # NOTE: There may not be a successor? # ex: leaf branch succ = self._get_block_successor(block) # 2. If we have an ielse block, remove # the implicit ielse (the existing edge) if 'ielse' in iblock and succ: try: self.i_cfg.remove_edge(block.index, succ) except nx.NetworkXError as e: print(e) # 3. Add the edges to this node # (ithen and maybe ielse) for what, bl in iblock.items(): branch = bl.cblock # Calculate new blocks to expand new_blocks += self._get_blocks_to_expand(branch, block.succ) # Stitch the instructions together if_edges = self._stitch_together(branch) if if_edges: self.i_cfg.add_edges_from(if_edges) # The cblock has one ins at least branch_ins = list(branch) if block.succ: for node in branch_ins: if node.op == cit_break: self.i_cfg.add_edge(node.index, block.succ) first = branch_ins[0] last = branch_ins[-1] # Add the edge between the if ins and # the first cblock element self.i_cfg.add_edge(block.index, first.index) # Connect to the orphaned successor # Unless it is a return, etc. if succ: if last.op not in (cit_return, cit_break): self.i_cfg.add_edge(last.index, succ) return new_blocks def _expand_switch_block(self, block): """Expands a given `switch` block""" # 1. Find and save the original successor # NOTE: There may not be a successor? # ex: leaf branch new_blocks = [] succ = self._get_block_successor(block) # Remove the original edge if succ: self.i_cfg.remove_edge(block.index, succ) switch = block.cswitch for case in switch.cases: case_block = case.cblock u = block case_ins = [i for i in case_block] new_blocks += self._get_blocks_to_expand(case_ins) for v in case_ins: self.i_cfg.add_edge(u.index, v.index) u = v # If the last element is a 'break' we can throw # this out and connect the last meaningful # citem to the end of the switch if v.op == cit_break: self.i_cfg.remove_node(v.index) if succ: # u -> break => u -> succ self.i_cfg.add_edge(case_ins[-2].index, succ) return new_blocks def _expand_do_block(self, block): """Expand a given `do` block""" succ = self._get_block_successor(block) # Remove the original edge if succ: self.i_cfg.remove_edge(block.index, succ) do_body = block.cdo.body do_block = do_body.cblock # Calculate new blocks to expand new_blocks = self._get_blocks_to_expand(do_block, succ) # Stitch the instructions together do_edges = self._stitch_together(do_block) if do_edges: self.i_cfg.add_edges_from(do_edges) # The cblock has one ins at least block_ins = list(do_block) if succ: for node in block_ins: if node.op == cit_break: self.i_cfg.add_edge(node.index, succ) first = block_ins[0] last = block_ins[-1] # Add the edge between the do ins and # the first cblock element self.i_cfg.add_edge(block.index, first.index) # Add the edge to the original successor if succ: self.i_cfg.add_edge(last.index, succ) # Optional: Add a reference to show the looping structure self.i_cfg.add_edge(last.index, block.index) return new_blocks def _expand_while_block(self, block): """Expand a given `while` block""" succ = self._get_block_successor(block) # Remove the original edge if succ: self.i_cfg.remove_edge(block.index, succ) while_body = block.cwhile.body while_block = while_body.cblock # Calculate new blocks to expand new_blocks = self._get_blocks_to_expand(while_block, succ) # Stitch the instructions together while_edges = self._stitch_together(while_block) if while_edges: self.i_cfg.add_edges_from(while_edges) # The cblock has one ins at least block_ins = list(while_block) if succ: for node in block_ins: if node.op == cit_break: self.i_cfg.add_edge(node.index, succ) first = block_ins[0] last = block_ins[-1] # Add the edge between the while ins and # the first cblock element self.i_cfg.add_edge(block.index, first.index) # Add the edge to the original successor if succ: self.i_cfg.add_edge(last.index, succ) # Optional: Add a reference to show the looping structure self.i_cfg.add_edge(last.index, block.index) return new_blocks def _expand_for_block(self, block): """Expand a given `for` block""" succ = self._get_block_successor(block) # Remove the original edge if succ: self.i_cfg.remove_edge(block.index, succ) for_body = block.cfor.body for_block = for_body.cblock # Calculate new blocks to expand new_blocks = self._get_blocks_to_expand(for_block, succ) # Stitch the instructions together for_edges = self._stitch_together(for_block) if for_edges: self.i_cfg.add_edges_from(for_edges) # The cblock has one ins at least block_ins = list(for_block) if succ: for node in block_ins: if node.op == cit_break: self.i_cfg.add_edge(node.index, succ) first = block_ins[0] last = block_ins[-1] # Add the edge between the for ins and # the first cblock element self.i_cfg.add_edge(block.index, first.index) # Add the edge to the original successor if succ: self.i_cfg.add_edge(last.index, succ) # Optional: Add a reference to show the looping structure self.i_cfg.add_edge(last.index, block.index) return new_blocks def _expand_goto_block(self, block): """Expands a given `goto` block""" # Remove the current's `goto` successor # pointing to the next instruction succ = self._get_block_successor(block) if succ: self.i_cfg.remove_edge(block.index, succ) # Target is identified by its label number # NOTE: All citem_t has a label_number target_label = block.cgoto.label_num # Find the citem corresponding to that label target_citem = self.cf.find_label(target_label) target_idx = target_citem.index if target_idx: # goto -> target self.i_cfg.add_edge(block.index, target_idx) # This operation does not add new blocks return [] def _generate_i_cfg(self, blocks_to_expand=[]): """This expands interesting blocks creating an increasingly complex graph. Each one of the methods `_expand_xxx_block` modify the `self.i_cfg` Digraph This works recursively until there are no blocks left to expand """ if not blocks_to_expand: return for block in blocks_to_expand: new_blocks = [] # Remove this block from the list blocks_to_expand.remove(block) # Modify CFG if block.op == cit_if: dprint() dprint(">> IF block @ {:#08x}...".format(block.ea)) new_blocks = self._expand_if_block(block) elif block.op == cit_do: dprint() dprint(">> DO block @ {:#08x}...".format(block.ea)) new_blocks = self._expand_do_block(block) elif block.op == cit_while: dprint() dprint(">> WHILE block @ {:#08x}...".format(block.ea)) new_blocks = self._expand_while_block(block) elif block.op == cit_switch: dprint() dprint(">> SWITCH block @ {:#08x}...".format(block.ea)) new_blocks = self._expand_switch_block(block) elif block.op == cit_for: dprint() dprint(">> FOR block @ {:#08x}...".format(block.ea)) new_blocks = self._expand_for_block(block) elif block.op == cit_goto: dprint() dprint(">> GOTO block @ {:#08x}...".format(block.ea)) new_blocks = self._expand_goto_block(block) # Add new found blocks to the list blocks_to_expand += new_blocks self._generate_i_cfg(blocks_to_expand=blocks_to_expand) def _generate_better_cfg(self): """Create a better representation of the interim CFG This one has {cinsn_t, cexpr_t} as nodes, instead of indexes """ _lifted = {} self.g = nx.DiGraph() # ================================================== # Mapping between nodes {cinsn_t, cexpr_t} # and their indexes (from the computed i_cfg) # ================================================== _nodez = {} j = 0 sind = 0 # single node index for i in self.i_cfg.nodes(): obj = self._e(i) hi = citem2higher(obj) # cinsn_t if hi.op == cit_expr: hi = hi.cexpr j = hi.index # i -> j _lifted[i] = j i = j else: _lifted[i] = i sind = i _nodez[i] = hi # cinsn_t or cexpr_t # Replicate the interim graph's edges # with {cinsn_t, cexpr_t} objects as nodes # Update the `i_cfg` with the lifted node indexes i_edges = list(self.i_cfg.edges()) if not i_edges and len(self.i_cfg.nodes()) == 1: # Corner case: # Only one node and no edges self.g.add_node(_nodez[sind]) else: # We have more than one node # and edges between them self.i_cfg = nx.DiGraph() for u, v in i_edges: # i -> j u = _lifted.get(u, None) or u v = _lifted.get(v, None) or v self.g.add_edge(_nodez[u], _nodez[v]) self.i_cfg.add_edge(u, v) # Save these mappings for later self.index2node = _nodez self.node2index = {v: k for k, v in _nodez.items()} def _get_all_function_calls(self, with_helpers=True): """It does exactly what the name says This is needed because calls don't always appear in their own nodes (that'd be a `sub_xxx();`) but may be 'embedded' in other expressions. Ex: `v1 = sub_xxx();` (asg) Returns: None (it sets self.calls) """ for n in self.g.nodes(): # Which nodes are prone to contain function calls? cex = get_expr(n) # This catches nodes that are pure calls # ex: sub_xxx(1, 2); if is_call(cex): name = my_get_func_name(cex.x.obj_ea) or 'sub_unknown' co = callObj(c=self, name=name, node=n, expr=cex) self.calls.append(co) # This recurses into more complex expressions # looking for calls. A very simple example # would be to check both sides of an assignment operands = blowup_expression(cex) for operand in operands: if is_call(operand): # The node in our CFG is the expression # containing the function call name = my_get_func_name(operand.x.obj_ea) or 'sub_unknown' co = callObj(c=self, name=name, node=n, expr=operand) self.calls.append(co) if not with_helpers: return # # Second pass to try to locate helper functions # for co in self.calls: if co.call_ea == BADADDR: # This may be a helper helperz = find_elements_of_type(co.node, cot_helper) if not helperz or len(helperz) > 1: continue h = helperz.pop() co.name = h.helper co.is_helper = True # ================================================================================ # Debugging utilities # ================================================================================ def dump_i_cfg(self): """Dump interim CFG for debugging purposes """ print("[DEBUG] Dumping CFG") for u, v in self.i_cfg.edges(): print("{} -> {}".format(u, v)) print("[DEBUG] Writing GraphML...") # Labeling the nodes for node in self.i_cfg.nodes(): self.i_cfg.node[node]['label'] = "{}".format(node) nx.write_graphml(self.i_cfg, r"D:\graphs\di.graphml") def dump_cfg(self, out_dir): """Dump the CFG for debugging purposes This dumps a representation of the CFG in DOT format. To generate an image: ``dot.exe -Tpng decompiled.dot -o decompiled.png`` """ dot = "digraph D {\n" dot += "node [shape=record style=rounded fontname=\"Sans serif\" fontsize=\"8\"];\n" # Labeling the nodes for node in self.g.nodes(): node_fmt = "node_{} [label=\"{} ({}) ({:X})\"];\n" # We can set different node attributes for specific opcodes if node.opname == 'call': node_fmt = "node_{} [fillcolor=lightblue style=\"rounded, filled\" label=\"{} ({}) ({:X})\"];\n" elif node.opname.startswith('asg'): node_fmt = "node_{} [fillcolor=green style=\"rounded, filled\" label=\"{} ({}) ({:X})\"];\n" elif node.opname == 'if': node_fmt = "node_{} [shape=diamond fillcolor=yellow style=filled label=\"{} ({}) ({:X})\"];\n" elif node.opname == 'return': node_fmt = "node_{} [shape=box fillcolor=red style=filled label=\"{} ({}) ({:X})\"];\n" dot += node_fmt.format( node.index, node.opname, node.index, node.ea) # Adding edges for u, v in self.g.edges(): dot += "node_{} -> node_{};\n".format( u.index, v.index) dot += "}\n" print("[DEBUG] Writing DOT file...") od = os.path.join(out_dir, "decompiled.dot") with open(od, 'wb') as f: f.write(bytes(bytearray(dot, "utf-8"))) print("[DEBUG] Done.") def get_cfg_for_ea(ea, dot_exe, out_dir): """Debugging helper. Uses ``DOT`` to create a ``.PNG`` graphic of the :class:`ControlFlowinator` CFG and displays it. :param ea: address of the function to analyze :type ea: int :param dot_exe: path to the ``DOT`` binary :type dot_exe: string :param out_dir: directory to write the ``.DOT`` file :type out_dir: string """ try: c = controlFlowinator(ea=ea) except Exception as e: print(e) return c.dump_cfg(out_dir) cmd = "{dot_exe} -Tpng -o '{png_file}' '{dot_file}'".format( dot_exe=dot_exe, dot_file=os.path.join(out_dir, "decompiled.dot"), png_file=os.path.join(out_dir, "decompiled.png")) cmd2 = os.path.join(out_dir, "decompiled.png") print("Trying to run: {}...".format(cmd)) os.system(cmd) print("Trying to run: {}...".format(cmd2)) os.system(cmd2) def debug_blownup_expressions(c=None, node=None): """ Debugging helper. Show all blown up expressions for this function. :param c: a :class:`controlFlowinator` object :type c: :class:`controlFlowinator` """ if not c and not node: print("I need a controlFlowinator object") return if node: elems = blowup_expression(node) s_elems = ",".join([expr_ctype[e.op] for e in elems]) print("{:X} {}".format(node.ea, s_elems)) else: for node in c.g.nodes: ea = node.ea if type(node) == cinsn_t: node = get_cond_from_statement(node) if not node: print("{:X} ???".format(ea)) continue elems = blowup_expression(node) s_elems = ",".join([expr_ctype[e.op] for e in elems]) print("{:X} {}".format(ea, s_elems)) def create_comment(c=None, ea=0, comment=""): """Displays a comment at the line corresponding to ``ea`` :param c: a :class:`controlFlowinator` object :type c: :class:`controlFlowinator` :param ea: address for the comment :type ea: int :param comment: the comment to add :type comment: string :return: returns True if comment successfully created :rtype: bool """ if not c: # No `controlFlowinator` object supplied # We have to decompile manually if ea: cf = my_decompile(ea=ea) else: raise ValueError else: cf = c.cf tl = treeloc_t() tl.ea = ea #for all cases see https://www.hex-rays.com/products/ida/support/idapython_docs/ida_hexrays-module.html for itp in [ITP_SEMI, ITP_CURLY1, ITP_CURLY2, ITP_COLON, ITP_BRACE1, ITP_BRACE2, ITP_ASM, ITP_ELSE, ITP_DO, ITP_CASE] + list(range(65)):#the range covers ITP_ARG1 to ITP_ARG64 and ITP_EMPTY(0) tl.itp = itp cf.set_user_cmt(tl, comment) cf.save_user_cmts() cf.__str__()#trigger string representation, otherwise orphan comments aren't detected if not cf.has_orphan_cmts(): return True cf.del_orphan_cmts() return False # =========================================================== # Processing the CFG # Convenience functions for cexpr_t elements # =========================================================== class callObj: """Auxiliary object for code clarity. It represents the occurrence of a ``call`` expression. :param name: name of the function called :type name: string, optional :param node: a :class:`controlFlowinator` node containing the call expression :type node: :class:`controlFlowinator` :param expr: the ``call`` expression element :type expr: :class:`cexpr_t` """ def __init__(self, c=None, name="", node=None, expr=None): self.c = c self.name = name self.ida_args = [] self.args = {} self.ea = None self.call_ea = None self.ret_type = None self.is_helper = False # Node in our CFG containing the call expr self.node = node # The call expr itself (cot_call) self.expr = expr if self.expr: # This is the Ea of the function # being called at location `self.ea` self.call_ea = self.expr.x.obj_ea # This is the Ea of the `call` instruction # and obviously of the decompiled function call self.ea = self.expr.ea self._populate_args() self._populate_return_type() def _populate_args(self): """Performs some arguments preprocessing""" self.ida_args = list(self.expr.a) self.args = {} Rep = namedtuple('Rep', 'type val') for i, raw_arg in enumerate(self.ida_args): # To be sure. Idempotent. arg = decast(raw_arg) if is_number(arg): rep = Rep(type='number', val=num_value(arg)) elif is_string(arg): rep = Rep(type='string', val=string_value(arg)) elif is_var(arg): # :class:`var_ref_t` -> :class:`lvar_t` -> :class:`my_var_t` lv = ref2var(arg, c=self.c) rep = Rep(type='var', val=my_var_t(lv)) elif is_global_var(arg): rep = Rep(type='global', val=value_of_global(arg)) elif is_ref(arg): # &v1 rep = Rep(type='ref', val=ref_to(arg)) elif is_ptr(arg): # *v1 rep = Rep(type='ptr', val=points_to(arg)) else: rep = Rep(type='unk', val=arg) self.args[i] = rep def _populate_return_type(self): """Finds the return type for the function being called """ tif = tinfo_t() get_tinfo(tif, self.call_ea) or guess_tinfo(tif, self.call_ea) self.ret_type = tif.get_rettype() def __repr__(self): """Display a pretty representation for print""" print("--------------------------------------") print("Ea: {:X}".format(self.ea)) print("Target's Name: {}".format(self.name)) print("Target's Ea: {:X}".format(self.call_ea)) print("Target's ret: {}".format(self.ret_type)) print("Is helper: {}".format(self.is_helper)) print("Args:") for i, arg in self.args.items(): print(" - {}: {}".format(i, arg)) return "" def get_expr(n): """Returns the corresponding :class:`cexpr_t` in case `n` is of type :class:`cinsn_t`. Idempotent otherwise. """ if n.op == cit_if: cex = n.cif.expr elif n.op == cit_return: cex = n.creturn.expr elif n.op == cit_for: cex = n.cfor.expr elif n.op == cit_while: cex = n.cwhile.expr elif n.op == cit_do: cex = n.cdo.expr else: cex = n return cex def blowup_expression(cex, final_operands=None): """Extracts all elements of an expression Ex: ``x + 1 < y`` -> ``{x, 1, y}`` :param cex: a :class:`cexpr_t` object :type cex: :class:`cexpr_t` :return: a *set* of elements (the *final_operands*) :rtype: set """ # Recursion and default values in Python... if final_operands is None: # First call, before recursion final_operands = set([]) operands = {} # Quick and dirty operand extraction # ---------------------------------- # NOTE: for now this does not look inside member offsets ('m') op_names = ('x', 'y', 'z') for name in op_names: if hasattr(cex, name): attr = getattr(cex, name) if attr: operands[name] = attr # Special code to handle call arguments if hasattr(cex, 'a') and cex.a: for arg in cex.a: arg_name = "a{}".format(arg.index) operands[arg_name] = arg # Corner case: this expression is a variable if hasattr(cex, 'v') and cex.v: v_name = "v{}".format(cex.v.idx) operands[v_name] = cex for op_name, operand in operands.items(): if is_final_expr(cex): # If the expression itself is a final one final_operands.add(cex) elif not is_final_expr(operand): # if visited_operands != None: # visited_operands.append(operand) if is_call(operand): final_operands.add(operand) blowup_expression(operand, final_operands) else: final_operands.add(operand) dprint("> final: {} {:#08x}".format( expr_ctype[operand.op], operand.ea)) return final_operands def get_all_vars_in_node(cex): """Extracts all variables involved in an expression. :param cex: typically a :class:`controlFlowinator` node :type cex: :class:`cexpr_t` :return: list of ``var_t`` indexes (to ``cf.lvars``) :rtype: list """ set_elem = blowup_expression(cex) var_indexes = [x.v.idx for x in set_elem if is_var(x)] return var_indexes def find_all_calls_to_within(f_name, ea=0, c=None): """Finds all calls to a function with the given name \ within the function containing the ``ea`` address. Note that the string comparison is relaxed to find variants of it, that is, searching for ``malloc`` will match as well ``_malloc``, ``malloc_0``, etc. :param f_name: the function name to search for :type f_name: string :param ea: any address within the function that may contain the calls :type ea: int :param c: if specified, work on this ``controlFlowinator`` object :type c: :class:`controlFlowinator`, optional :return: a list of :class:`callObj` :rtype: list """ call_objs = [] if c is None: try: c = controlFlowinator(ea=ea, fast=False) except Exception as e: print("Failed to find_all_calls_to_within {}".format(f_name)) print(e) return [] for co in c.calls: if f_name.lower() in co.name.lower(): call_objs.append(co) return call_objs def find_all_calls_to(f_name): """Finds all calls to a function with the given name Note that the string comparison is relaxed to find variants of it, that is, searching for ``malloc`` will match as well ``_malloc``, ``malloc_0``, etc. :param f_name: the function name to search for :type f_name: string :return: a list of :class:`callObj` :rtype: list """ f_ea = get_name_ea_simple(f_name) if f_ea == BADADDR: print("Failed to resolve address for {}".format(f_name)) return [] callz = [] callers = set() for ref in XrefsTo(f_ea, True): if not ref.iscode: continue # Get a set of unique *function* callers f = get_func(ref.frm) if f is None: continue f_ea = f.start_ea callers.add(f_ea) for caller_ea in callers: c = find_all_calls_to_within(f_name, caller_ea) callz += c return callz def find_elements_of_type(cex, element_type, elements=None): """Recursively extracts expression elements until \ a :class:`cexpr_t` from a specific group is found :param cex: a :class:`cexpr_t` object :type cex: :class:`cexpr_t` :param element_type: the type of element we are looking for \ (as a ``cot_xxx`` value, see ``compiler_consts.py``) :type element_type: a ``cot_xxx`` value (eg. ``cot_add``) :return: a set of :class:`cexpr_t` of the specified type :rtype: set """ if elements is None: elements = set([]) operands = {} # =========================================== # This covers the case cex is itself of # the type currently searched for :) # =========================================== if cex.op == element_type: elements.add(cex) # Quick and dirty operand extraction # ---------------------------------- # TODO: for now this does not look inside member offsets ('m') op_names = ('x', 'y', 'z') for name in op_names: if hasattr(cex, name): attr = getattr(cex, name) if attr: operands[name] = attr # Special code to handle call arguments if hasattr(cex, 'a') and cex.a: for arg in cex.a: arg_name = "a{}".format(arg.index) operands[arg_name] = arg # Corner case: this expression is a variable if hasattr(cex, 'v') and cex.v: v_name = "v{}".format(cex.v.idx) operands[v_name] = cex for op_name, operand in operands.items(): if operand.op == element_type: # If the expression itself is a sought one elements.add(operand) elif not is_final_expr(operand): find_elements_of_type(operand, element_type, elements) return elements def is_final_expr(cex): """Helper for internal functions. A final expression will be defined as one that \ can not be further decomposed, eg. number, var, string, etc. Normally, you should not need to use this. :param cex: a :class:`cexpr_t` object :type cex: :class:`cexpr_t` :return: True or False :rtype: bool """ if cex.op in expr_final: return True else: return False def get_cond_from_statement(ins): """Given a ``cinsn_t`` representing a control flow structure \ (do, while, for, etc.), it returns the corresponding ``cexpr_t`` \ representing the condition/argument for that code construct. This is useful since we usually want to peek into \ conditional statements... :param ins: the :class:`cinsn_t` associated with a control flow structure :type ins: :class:`cinsn_t` :return: the condition or argument within that control flow structure :rtype: :class:`cexpr_t` """ if ins.op == cit_while: res = ins.cwhile.expr elif ins.op == cit_do: res = ins.cdo.expr elif ins.op == cit_for: res = ins.cfor.expr elif ins.op == cit_if: res = ins.cif.expr elif ins.op == cit_return: res = ins.creturn.expr elif ins.op == cit_switch: res = ins.cswitch.expr else: res = None return res def assigns_to_var(cex): """Does this :class:``cexpr_t`` assign a value to any variable? TODO: this is limited for now to expressions of the type: ``v1 = something something`` :param cex: a :class:`cexpr_t` object :type cex: :class:`cexpr_t` :return: the assigned var index (to ``cf.lvars`` array) or -1 if the \ :class:`cexpr_t` does not assign to any variable :rtype: int """ v = None lvar_idx = -1 if not is_asg(cex): # Currently assignments only return -1 else: # left hand side _x = cex.x # Get the lhs variable if is_var(_x): # v1 = ... v = _x elif is_ptr(_x): # *v1 = ... # *v1 -> v1 __x = _x.x if is_var(__x): v = __x if v: lvar_idx = v.v.idx return lvar_idx def does_constrain(node): """This tries to answer the question: "Does this ``node`` constrains variables in any way?" Essentially it is looking for the occurrence of variables within known \ *constrainer constructs*, eg. inside an ``if`` condition. TODO: many more heuristics can be included here :param node: typically a :class:`controlFlowinator` node :type node: :class:`cinsn_t` or :class:`cexpr_t` :return: a set of variable indexes (to ``cf.lvars`` array) :rtype: set """ constrained_var_idxs = set([]) if is_asg(node): lhs = node.x rhs = node.y # ================================= # Binary truncation # ex: v1 = v4 & 0xFFFF # ================================= if is_binary_truncation(rhs): var_indexes = get_all_vars_in_node(lhs) # TODO: Refine this algorithm v_idx = var_indexes[0] return set([v_idx]) # =========================== # Something simple as v1 = 0 # =========================== if is_var(lhs) and is_number(rhs): v_idx = lhs.v.idx constrained_var_idxs.add(v_idx) return constrained_var_idxs # =================================== # Statements containing a condition # ex: if(v < MAX), # for(i=0; i<10; i++), etc. # =================================== insn_cond = insn_conditions.keys() if node.op in insn_cond: # Unwrap real condition (ex. x < y) cond = get_cond_from_statement(node) if not cond: return constrained_var_idxs constrainers = expr_condition.keys() if cond.op in constrainers: # We are mostly interested in the left # hand side (x) of the expression lhs = cond.x lhs_vars = find_elements_of_type(lhs, cot_var) for e in lhs_vars: v_idx = e.v.idx constrained_var_idxs.add(v_idx) dprint("Constraint found @ {:#08x}: {}".format( node.ea, expr_ctype[cond.op])) return constrained_var_idxs # TODO: More constraining cases here return set([]) def get_interesting_calls(c, user_defined=[]): """Not all functions are created equal. We are interested in functions with certain names or substrings in it. :param c: a :class:`controlFlowinator` object :type c: :class:`controlFlowinator` :param user_defined: a list of names (or substrings), if not supplied a \ hard-coded default list will be used. :type user_defined: list, optional :return: a list of :class:`callObj` :rtype: list """ default_list = ['check', 'log', 'assert', 'cpy', 'copy', 'alloc', 'move', 'memset'] interesting_calls = user_defined or default_list result = [] for co in c.calls: # Check against a number of interesting function names for f_name in interesting_calls: if f_name in co.name.lower(): result.append(co) return result def is_write(node): """Try to find write primitives. Looking for things like:: *(_DWORD *)(something) = v38 arr[i] = v21 TODO: Rather rough, it is a first version... :param node: a :class:`controlFlowinator` node :type node: :class:`cinsn_t` or :class:`cexpr_t` :return: True or False :rtype: bool """ if not is_asg(node): return False lhs = node.x if is_ptr(lhs): # *something return True if is_array_indexing(lhs): # arr[i] return True return False def is_read(ins): """Try to find read primitives. Looking for things like:: v3 = *(_DWORD *)(v5 + 784) NOTE: this will find expressions that are read && write, since they are not mutually exclusive TODO: Rather rough, it is a first version... :param node: a :class:`controlFlowinator` node :type node: :class:`cinsn_t` or :class:`cexpr_t` :return: True or False :rtype: bool """ if not is_asg(ins): return False rhs = ins.y if is_ptr(rhs): # *something return True return False # =========================================================== def main(): if not init_hexrays_plugin(): print("No decompiler found! :(") return # Ascii banner :) print(r""" ███████╗██╗██████╗ ██╗ ██╔════╝██║██╔══██╗██║ █████╗ ██║██║ ██║██║ ██╔══╝ ██║██║ ██║██║ ██║ ██║██████╔╝███████╗ ╚═╝ ╚═╝╚═════╝ ╚══════╝ """) print("Hex-rays version {} detected".format(get_hexrays_version())) print("FIDL v.{} (\"{}\") loaded...".format(__version__, __codename__)) print("") if __name__ == '__main__': main()

28.640044

196

0.575225

63a01777f9add1543c801b6fb8a7f8397133783a

2,060

py

Python

tests/test_security/test_security_api_key_query_description.py

dantownsend/xpresso

a4c4dbe96972a6f0339f30d7d794932f70510eea

[ "MIT" ]

null

tests/test_security/test_security_api_key_query_description.py

dantownsend/xpresso

a4c4dbe96972a6f0339f30d7d794932f70510eea

[ "MIT" ]

null

tests/test_security/test_security_api_key_query_description.py

dantownsend/xpresso

a4c4dbe96972a6f0339f30d7d794932f70510eea

[ "MIT" ]

null

from typing import Any, Dict, Generator import pytest from pydantic import BaseModel from xpresso import App, Dependant, Path, Security from xpresso.security import APIKeyQuery from xpresso.testclient import TestClient from xpresso.typing import Annotated api_key = APIKeyQuery(name="key", description="API Key Query") class User(BaseModel): username: str def get_current_user(oauth_header: Annotated[str, Security(api_key)]): user = User(username=oauth_header) return user def read_current_user(current_user: Annotated[User, Dependant(get_current_user)]): return current_user app = App([Path("/users/me", get=read_current_user)]) @pytest.fixture def client() -> Generator[TestClient, None, None]: with TestClient(app) as client: yield client openapi_schema: Dict[str, Any] = { "openapi": "3.0.3", "info": {"title": "API", "version": "0.1.0"}, "paths": { "/users/me": { "get": { "responses": { "200": { "description": "Successful Response", } }, "security": [{"APIKeyQuery": []}], } } }, "components": { "securitySchemes": { "APIKeyQuery": { "type": "apiKey", "name": "key", "in": "query", "description": "API Key Query", } } }, } def test_openapi_schema(client: TestClient): response = client.get("/openapi.json") assert response.status_code == 200, response.text assert response.json() == openapi_schema def test_security_api_key(client: TestClient): response = client.get("/users/me?key=secret") assert response.status_code == 200, response.text assert response.json() == {"username": "secret"} def test_security_api_key_no_key(client: TestClient): response = client.get("/users/me") assert response.status_code == 401, response.text assert response.json() == {"detail": "Not authenticated"}

25.75

82

0.604854

56e550512d7e699360010d4b0986b534798a3b8a

3,085

py

Python

resources/lib/MSLHttpRequestHandler.py

hcordobes/plugin.video.netflix

0bf52c68b0202d61c799480f2c9d047f55c433e0

[ "MIT" ]

17

2018-08-09T20:54:53.000Z

2019-11-23T03:17:55.000Z

resources/lib/MSLHttpRequestHandler.py

hcordobes/plugin.video.netflix

0bf52c68b0202d61c799480f2c9d047f55c433e0

[ "MIT" ]

1

2019-01-07T19:44:07.000Z

2019-02-09T14:12:16.000Z

resources/lib/MSLHttpRequestHandler.py

hcordobes/plugin.video.netflix

0bf52c68b0202d61c799480f2c9d047f55c433e0

[ "MIT" ]

14

2018-11-17T05:14:22.000Z

2019-06-03T12:22:11.000Z

# -*- coding: utf-8 -*- # Author: trummerjo # Module: MSLHttpRequestHandler # Created on: 26.01.2017 # License: MIT https://goo.gl/5bMj3H """Handles & translates requests from Inputstream to Netflix""" import base64 import BaseHTTPServer from urlparse import urlparse, parse_qs from SocketServer import TCPServer from resources.lib.MSL import MSL class MSLHttpRequestHandler(BaseHTTPServer.BaseHTTPRequestHandler): """Handles & translates requests from Inputstream to Netflix""" # pylint: disable=invalid-name def do_HEAD(self): """Answers head requests with a success code""" self.send_response(200) # pylint: disable=invalid-name def do_POST(self): """Loads the licence for the requested resource""" length = int(self.headers.get('content-length')) post = self.rfile.read(length) data = post.split('!') if len(data) is 2: challenge = data[0] sid = base64.standard_b64decode(data[1]) b64license = self.server.msl_handler.get_license(challenge, sid) if b64license is not '': self.send_response(200) self.end_headers() self.wfile.write(base64.standard_b64decode(b64license)) self.finish() else: self.server.nx_common.log(msg='Error getting License') self.send_response(400) else: self.server.nx_common.log(msg='Error in License Request') self.send_response(400) # pylint: disable=invalid-name def do_GET(self): """Loads the XML manifest for the requested resource""" url = urlparse(self.path) params = parse_qs(url.query) if 'id' not in params: self.send_response(400, 'No id') else: # Get the manifest with the given id dolby = (True if 'dolby' in params and params['dolby'][0].lower() == 'true' else 'false') hevc = (True if 'hevc' in params and params['hevc'][0].lower() == 'true' else 'false') data = self.server.msl_handler.load_manifest( int(params['id'][0]), dolby, hevc) self.send_response(200) self.send_header('Content-type', 'application/xml') self.end_headers() self.wfile.write(data) def log_message(self, *args): """Disable the BaseHTTPServer Log""" pass ################################## class MSLTCPServer(TCPServer): """Override TCPServer to allow usage of shared members""" def __init__(self, server_address, nx_common): """Initialization of MSLTCPServer""" nx_common.log(msg='Constructing MSLTCPServer') self.nx_common = nx_common self.msl_handler = MSL(nx_common) TCPServer.__init__(self, server_address, MSLHttpRequestHandler) def reset_msl_data(self): """Initialization of MSLTCPServerResets MSL data (perform handshake)""" self.msl_handler.perform_key_handshake()

33.901099

79

0.610049

7daafc6e210a6ac5e88736d87fd75a925f311747

3,686

py

Python

libraryBackend/Account/migrations/0001_initial.py

junaidgirkar/departmental-library-management-app

f14320214ff5903c296ee785a329335183b35402

[ "MIT" ]

1

2021-11-17T20:13:01.000Z

libraryBackend/Account/migrations/0001_initial.py

junaidgirkar/departmental-library-management-app

f14320214ff5903c296ee785a329335183b35402

[ "MIT" ]

null

libraryBackend/Account/migrations/0001_initial.py

junaidgirkar/departmental-library-management-app

f14320214ff5903c296ee785a329335183b35402

[ "MIT" ]

null

# Generated by Django 3.1.1 on 2020-09-22 13:26 import Account.managers from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ("auth", "0012_alter_user_first_name_max_length"), ] operations = [ migrations.CreateModel( name="User", fields=[ ( "id", models.AutoField( auto_created=True, primary_key=True, serialize=False, verbose_name="ID", ), ), ("password", models.CharField(max_length=128, verbose_name="password")), ( "last_login", models.DateTimeField( blank=True, null=True, verbose_name="last login" ), ), ( "email", models.EmailField( max_length=254, unique=True, verbose_name="email address" ), ), ( "first_name", models.CharField(max_length=40, verbose_name="first_name"), ), ( "last_name", models.CharField(max_length=40, verbose_name="last name"), ), ( "date_joined", models.DateTimeField(auto_now_add=True, verbose_name="date joined"), ), ("is_active", models.BooleanField(default=True, verbose_name="active")), ( "is_staff", models.BooleanField(default=False, verbose_name="staff status"), ), ( "is_superuser", models.BooleanField(default=False, verbose_name="is superuser"), ), ( "is_admin", models.BooleanField(default=False, verbose_name="is admin"), ), ( "is_student", models.BooleanField(default=False, verbose_name="is student"), ), ( "is_teacher", models.BooleanField(default=False, verbose_name="is teacher"), ), ( "groups", models.ManyToManyField( blank=True, help_text="The groups this user belongs to. A user will get all permissions granted to each of their groups.", related_name="user_set", related_query_name="user", to="auth.Group", verbose_name="groups", ), ), ( "user_permissions", models.ManyToManyField( blank=True, help_text="Specific permissions for this user.", related_name="user_set", related_query_name="user", to="auth.Permission", verbose_name="user permissions", ), ), ], options={ "verbose_name": "user", "verbose_name_plural": "users", }, managers=[ ("objects", Account.managers.UserManager()), ], ), ]

34.773585

134

0.405317

c675a9c0ff5ae0fef3967630b1f90ee58bbc7ac1

1,771

py

Python

invoice/views.py

AhmedElmawary/erp

c998787c62194e26e10e3cbc61e35935e901e56d

[ "MIT" ]

null

invoice/views.py

AhmedElmawary/erp

c998787c62194e26e10e3cbc61e35935e901e56d

[ "MIT" ]

null

invoice/views.py

AhmedElmawary/erp

c998787c62194e26e10e3cbc61e35935e901e56d

[ "MIT" ]

null

# import rest_framework # from django.shortcuts import get_list_or_404, get_object_or_404 # from rest_framework.views import APIView # from rest_framework.authentication import TokenAuthentication # from rest_framework.permissions import (IsAuthenticated, IsAdminUser) # from rest_framework.status import (HTTP_200_OK, HTTP_201_CREATED) # from rest_framework.response import Response # from .serializers import ( # InvoiceCreateSerializer, # InvoiceGetSerializer, # InvoiceListSerializer, # ) # from typing import (Dict, List, Union) # # from .models import SupplierInvoice # class MainAuth: # pass # # authentication_classes = (TokenAuthentication, ) # # permission_classes = (IsAuthenticated,) # class InitInvoice(MainAuth, APIView): # queryset = SupplierInvoice.objects.filter() # # Trying to start code as strict code # class InvoiceCreate(InitInvoice): # serializer_class: rest_framework.serializers.Serializer = InvoiceCreateSerializer # def post(self, request, format=None): # serialized = self.serializer_class(data=request.data) # serialized.is_valid(raise_exception=True) # serialized.save() # return Response(serialized.data, HTTP_201_CREATED) # class InvoiceGet(InitInvoice): # serializer_class = InvoiceGetSerializer # def get(self, request, pk): # serilaized = self.serializer_class(get_object_or_404(self.queryset, pk= pk)) # return Response(serilaized.data, HTTP_200_OK) # class InvoiceList(InitInvoice): # serializer_class = InvoiceListSerializer # def get(self, request): # serialized = self.serializer_class(get_list_or_404(self.queryset), many=True) # return Response(serialized.data, HTTP_200_OK)

33.415094

87

0.733484

edb1312d87ddd307efc63101c913e0890979fa06

673

py

Python

tests/conftest.py

SurajDonthi/Aspect-Based-Sentiment-Analysis

6f20357218b495f7ffa2d6fe9dfcb43b42a5c4f1

[ "Apache-2.0" ]

351

2020-04-20T13:08:25.000Z

2022-03-28T11:02:13.000Z

tests/conftest.py

SurajDonthi/Aspect-Based-Sentiment-Analysis

6f20357218b495f7ffa2d6fe9dfcb43b42a5c4f1

[ "Apache-2.0" ]

58

2020-04-23T05:58:14.000Z

2022-03-31T14:09:07.000Z

tests/conftest.py

SurajDonthi/Aspect-Based-Sentiment-Analysis

6f20357218b495f7ffa2d6fe9dfcb43b42a5c4f1

[ "Apache-2.0" ]

72

2020-04-22T05:39:19.000Z

2022-03-23T20:49:56.000Z

import pytest def pytest_addoption(parser): parser.addoption("--run-slow", action="store_true", default=False, help="Run slow tests") def pytest_configure(config): dsc = "slow: mark test, which can be slow to run" config.addinivalue_line("markers", dsc) def pytest_collection_modifyitems(config, items): if config.getoption("--run-slow"): # --run-slow given in cli: do not skip slow checks return reason_desc = "need --run-slow option to run" skip_sanity_check = pytest.mark.skip(reason=reason_desc) for item in items: if "slow" in item.keywords: item.add_marker(skip_sanity_check)

29.26087

60

0.667162

353b1dd4c84736cde9f4cdd80cd06b2cdd7f50e1

17

py

Python

dcssearch/__init__.py

CharlieNPie/dcsdata

c1935e2cd969494364bd4949d08c558cbec7befd

[ "MIT" ]

null

dcssearch/__init__.py

CharlieNPie/dcsdata

c1935e2cd969494364bd4949d08c558cbec7befd

[ "MIT" ]

null

dcssearch/__init__.py

CharlieNPie/dcsdata

c1935e2cd969494364bd4949d08c558cbec7befd

[ "MIT" ]

null

name = "dcsdata"

8.5

16

0.647059

4342546beac7efc0ef2c665f76c35b72b5de18e3

355

py

Python

src/pyoffice/outlook/windows/dasl/operator/LEOperator.py

qq809326636/pyoffice

a3c036ef82f6b0438c1e38a7675eb1f06c61144d

[ "MIT" ]

7

2020-06-19T03:11:48.000Z

2020-11-18T06:14:21.000Z

src/pyoffice/outlook/windows/dasl/operator/LEOperator.py

qq809326636/pyoffice

a3c036ef82f6b0438c1e38a7675eb1f06c61144d

[ "MIT" ]

null

src/pyoffice/outlook/windows/dasl/operator/LEOperator.py

qq809326636/pyoffice

a3c036ef82f6b0438c1e38a7675eb1f06c61144d

[ "MIT" ]

null

from .BaseOperator import BaseOperator __all__ = ['LEOperator'] class LEOperator(BaseOperator): def __init__(self, *args, **kwargs): BaseOperator.__init__(self, *args, **kwargs) self.code = 21 self.op = '<='

18.684211

40

0.430986

c389d7ffb3ea217cf4b456fbcc2b13418cf6a676

34,228

py

Python

piicrgmms/sklearn_mixture_piicr/_strict_bayesian_mixture.py

colinweber27/piicrgmms

d74fb00c584b16f341c47a42a930e27efe4e360d

[ "MIT" ]

null

piicrgmms/sklearn_mixture_piicr/_strict_bayesian_mixture.py

colinweber27/piicrgmms

d74fb00c584b16f341c47a42a930e27efe4e360d

[ "MIT" ]

null

piicrgmms/sklearn_mixture_piicr/_strict_bayesian_mixture.py

colinweber27/piicrgmms

d74fb00c584b16f341c47a42a930e27efe4e360d

[ "MIT" ]

null

"""Bayesian Gaussian Mixture Model.""" # Author: Wei Xue <xuewei4d@gmail.com> # Thierry Guillemot <thierry.guillemot.work@gmail.com> # Contributor: Colin Weber <colin.weber.27@gmail.com> # License: BSD 3 clause import math import numpy as np from scipy.special import betaln, digamma, gammaln from ._base import StrictBayesianBaseModel, _check_shape from ._gaussian_mixture import _check_precision_matrix from ._gaussian_mixture import _check_precision_positivity from ._gaussian_mixture import _compute_log_det_cholesky from ._gaussian_mixture import _compute_precision_cholesky from ._gaussian_mixture import _estimate_gaussian_parameters from ._gaussian_mixture import _estimate_log_gaussian_prob from sklearn.utils import check_array from sklearn.utils.validation import _deprecate_positional_args def _log_dirichlet_norm(dirichlet_concentration): """Compute the log of the Dirichlet distribution normalization term. Parameters ---------- dirichlet_concentration : array-like, shape (n_samples,) The parameters values of the Dirichlet distribution. Returns ------- log_dirichlet_norm : float The log normalization of the Dirichlet distribution. """ return (gammaln(np.sum(dirichlet_concentration)) - np.sum(gammaln(dirichlet_concentration))) def _log_wishart_norm(degrees_of_freedom, log_det_precisions_chol, n_features): """Compute the log of the Wishart distribution normalization term. Parameters ---------- degrees_of_freedom : array-like, shape (n_components,) The number of degrees of freedom on the covariance Wishart distributions. log_det_precisions_chol : array-like, shape (n_components,) The determinant of the precision matrix for each component. n_features : int The number of features. Return ------ log_wishart_norm : array-like, shape (n_components,) The log normalization of the Wishart distribution. """ # To simplify the computation we have removed the np.log(np.pi) term return -(degrees_of_freedom * log_det_precisions_chol + degrees_of_freedom * n_features * .5 * math.log(2.) + np.sum(gammaln(.5 * (degrees_of_freedom - np.arange(n_features)[:, np.newaxis])), 0)) class StrictBayesianGaussianMixture(StrictBayesianBaseModel): """Strict Variational Bayesian estimation of a Gaussian mixture. This class allows to infer an approximate posterior distribution over the parameters of a Gaussian mixture distribution. The number of components is given by the value of n_components, and remains fixed throughout the fit. In order to accomplish this, the fit stops when the smallest cluster contains 0.1% of the samples in the data set. Therefore, it rarely converges. This class implements two types of prior for the weights distribution: a finite mixture model with Dirichlet distribution and an infinite mixture model with the Dirichlet Process. In practice Dirichlet Process inference algorithm is approximated and uses a truncated distribution with a fixed maximum number of components (called the Stick-breaking representation). The number of components actually used almost always depends on the data. .. version:: 0.1 Read more in the :ref:`User Guide <bgmm>`. Parameters ---------- n_components : int, defaults to 1. The number of mixture components. covariance_type : {'full', 'tied', 'diag', 'spherical'}, defaults to 'full' String describing the type of covariance parameters to use. Must be one of:: 'full' (each component has its own general covariance matrix), 'tied' (all components share the same general covariance matrix), 'diag' (each component has its own diagonal covariance matrix), 'spherical' (each component has its own single variance). tol : float, defaults to 1e-3. The convergence threshold. EM iterations will stop when the lower bound average gain on the likelihood (of the training data with respect to the model) is below this threshold. reg_covar : float, defaults to 1e-6. Non-negative regularization added to the diagonal of covariance. Allows to assure that the covariance matrices are all positive. max_iter : int, defaults to 100. The number of EM iterations to perform. n_init : int, defaults to 1. The number of initializations to perform. The result with the highest lower bound value on the likelihood is kept. init_params : {'kmeans', 'random'}, defaults to 'kmeans'. The method used to initialize the weights, the means and the covariances. Must be one of:: 'kmeans' : responsibilities are initialized using kmeans. 'random' : responsibilities are initialized randomly. weight_concentration_prior_type : str, defaults to 'dirichlet_process'. String describing the type of the weight concentration prior. Must be one of:: 'dirichlet_process' (using the Stick-breaking representation), 'dirichlet_distribution' (can favor more uniform weights). weight_concentration_prior : float | None, optional. The dirichlet concentration of each component on the weight distribution (Dirichlet). This is commonly called gamma in the literature. The higher concentration puts more mass in the center and will lead to more components being active, while a lower concentration parameter will lead to more mass at the edge of the mixture weights simplex. The value of the parameter must be greater than 0. If it is None, it's set to ``1. / n_components``. mean_precision_prior : float | None, optional. The precision prior on the mean distribution (Gaussian). Controls the extent of where means can be placed. Larger values concentrate the cluster means around `mean_prior`. The value of the parameter must be greater than 0. If it is None, it is set to 1. mean_prior : array-like, shape (n_features,), optional The prior on the mean distribution (Gaussian). If it is None, it is set to the mean of X. degrees_of_freedom_prior : float | None, optional. The prior of the number of degrees of freedom on the covariance distributions (Wishart). If it is None, it's set to `n_features`. covariance_prior : float or array-like, optional The prior on the covariance distribution (Wishart). If it is None, the emiprical covariance prior is initialized using the covariance of X. The shape depends on `covariance_type`:: (n_features, n_features) if 'full', (n_features, n_features) if 'tied', (n_features) if 'diag', float if 'spherical' random_state : int, RandomState instance or None, optional (default=None) Controls the random seed given to the method chosen to initialize the parameters (see `init_params`). In addition, it controls the generation of random samples from the fitted distribution (see the method `sample`). Pass an int for reproducible output across multiple function calls. See :term:`Glossary <random_state>`. warm_start : bool, default to False. If 'warm_start' is True, the solution of the last fitting is used as initialization for the next call of fit(). This can speed up convergence when fit is called several times on similar problems. See :term:`the Glossary <warm_start>`. verbose : int, default to 0. Enable verbose output. If 1 then it prints the current initialization and each iteration step. If greater than 1 then it prints also the log probability and the time needed for each step. verbose_interval : int, default to 10. Number of iteration done before the next print. Attributes ---------- weights_ : array-like, shape (n_components,) The weights of each mixture components. means_ : array-like, shape (n_components, n_features) The mean of each mixture component. covariances_ : array-like The covariance of each mixture component. The shape depends on `covariance_type`:: (n_components,) if 'spherical', (n_features, n_features) if 'tied', (n_components, n_features) if 'diag', (n_components, n_features, n_features) if 'full' precisions_ : array-like The precision matrices for each component in the mixture. A precision matrix is the inverse of a covariance matrix. A covariance matrix is symmetric positive definite so the mixture of Gaussian can be equivalently parameterized by the precision matrices. Storing the precision matrices instead of the covariance matrices makes it more efficient to compute the log-likelihood of new samples at test time. The shape depends on ``covariance_type``:: (n_components,) if 'spherical', (n_features, n_features) if 'tied', (n_components, n_features) if 'diag', (n_components, n_features, n_features) if 'full' precisions_cholesky_ : array-like The cholesky decomposition of the precision matrices of each mixture component. A precision matrix is the inverse of a covariance matrix. A covariance matrix is symmetric positive definite so the mixture of Gaussian can be equivalently parameterized by the precision matrices. Storing the precision matrices instead of the covariance matrices makes it more efficient to compute the log-likelihood of new samples at test time. The shape depends on ``covariance_type``:: (n_components,) if 'spherical', (n_features, n_features) if 'tied', (n_components, n_features) if 'diag', (n_components, n_features, n_features) if 'full' converged_ : bool True when convergence was reached in fit(), False otherwise. n_iter_ : int Number of step used by the best fit of inference to reach the convergence. lower_bound_ : float Lower bound value on the likelihood (of the training data with respect to the model) of the best fit of inference. weight_concentration_prior_ : tuple or float The dirichlet concentration of each component on the weight distribution (Dirichlet). The type depends on ``weight_concentration_prior_type``:: (float, float) if 'dirichlet_process' (Beta parameters), float if 'dirichlet_distribution' (Dirichlet parameters). The higher concentration puts more mass in the center and will lead to more components being active, while a lower concentration parameter will lead to more mass at the edge of the simplex. weight_concentration_ : array-like, shape (n_components,) The dirichlet concentration of each component on the weight distribution (Dirichlet). mean_precision_prior_ : float The precision prior on the mean distribution (Gaussian). Controls the extent of where means can be placed. Larger values concentrate the cluster means around `mean_prior`. If mean_precision_prior is set to None, `mean_precision_prior_` is set to 1. mean_precision_ : array-like, shape (n_components,) The precision of each components on the mean distribution (Gaussian). mean_prior_ : array-like, shape (n_features,) The prior on the mean distribution (Gaussian). degrees_of_freedom_prior_ : float The prior of the number of degrees of freedom on the covariance distributions (Wishart). degrees_of_freedom_ : array-like, shape (n_components,) The number of degrees of freedom of each components in the model. covariance_prior_ : float or array-like The prior on the covariance distribution (Wishart). The shape depends on `covariance_type`:: (n_features, n_features) if 'full', (n_features, n_features) if 'tied', (n_features) if 'diag', float if 'spherical' See Also -------- GaussianMixture : Finite Gaussian mixture fit with EM. References ---------- .. [1] `Bishop, Christopher M. (2006). "Pattern recognition and machine learning". Vol. 4 No. 4. New York: Springer. <https://www.springer.com/kr/book/9780387310732>`_ .. [2] `Hagai Attias. (2000). "A Variational Bayesian Framework for Graphical Models". In Advances in Neural Information Processing Systems 12. <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.36.2841&rep=rep1&type=pdf>`_ .. [3] `Blei, David M. and Michael I. Jordan. (2006). "Variational inference for Dirichlet process mixtures". Bayesian analysis 1.1 <https://www.cs.princeton.edu/courses/archive/fall11/cos597C/reading/BleiJordan2005.pdf>`_ """ @_deprecate_positional_args def __init__(self, *, n_components=1, covariance_type='full', tol=1e-3, reg_covar=1e-6, max_iter=100, n_init=1, init_params='kmeans', weight_concentration_prior_type='dirichlet_process', weight_concentration_prior=None, mean_precision_prior=None, mean_prior=None, degrees_of_freedom_prior=None, covariance_prior=None, random_state=None, warm_start=False, verbose=0, verbose_interval=10, min_cluster_size=0.001): super().__init__( n_components=n_components, tol=tol, reg_covar=reg_covar, max_iter=max_iter, n_init=n_init, init_params=init_params, random_state=random_state, warm_start=warm_start, verbose=verbose, verbose_interval=verbose_interval, min_cluster_size=min_cluster_size) self.covariance_type = covariance_type self.weight_concentration_prior_type = weight_concentration_prior_type self.weight_concentration_prior = weight_concentration_prior self.mean_precision_prior = mean_precision_prior self.mean_prior = mean_prior self.degrees_of_freedom_prior = degrees_of_freedom_prior self.covariance_prior = covariance_prior self.min_cluster_size = min_cluster_size def _check_parameters(self, X): """Check that the parameters are well defined. Parameters ---------- X : array-like, shape (n_samples, n_features) """ if self.covariance_type not in ['spherical', 'tied', 'diag', 'full']: raise ValueError("Invalid value for 'covariance_type': %s " "'covariance_type' should be in " "['spherical', 'tied', 'diag', 'full']" % self.covariance_type) if (self.weight_concentration_prior_type not in ['dirichlet_process', 'dirichlet_distribution']): raise ValueError( "Invalid value for 'weight_concentration_prior_type': %s " "'weight_concentration_prior_type' should be in " "['dirichlet_process', 'dirichlet_distribution']" % self.weight_concentration_prior_type) self._check_weights_parameters() self._check_means_parameters(X) self._check_precision_parameters(X) self._checkcovariance_prior_parameter(X) self._check_min_cluster_size_parameter() def _check_weights_parameters(self): """Check the parameter of the Dirichlet distribution.""" if self.weight_concentration_prior is None: self.weight_concentration_prior_ = 1. / self.n_components elif self.weight_concentration_prior > 0.: self.weight_concentration_prior_ = ( self.weight_concentration_prior) else: raise ValueError("The parameter 'weight_concentration_prior' " "should be greater than 0., but got %.3f." % self.weight_concentration_prior) def _check_means_parameters(self, X): """Check the parameters of the Gaussian distribution. Parameters ---------- X : array-like, shape (n_samples, n_features) """ _, n_features = X.shape if self.mean_precision_prior is None: self.mean_precision_prior_ = 1. elif self.mean_precision_prior > 0.: self.mean_precision_prior_ = self.mean_precision_prior else: raise ValueError("The parameter 'mean_precision_prior' should be " "greater than 0., but got %.3f." % self.mean_precision_prior) if self.mean_prior is None: self.mean_prior_ = X.mean(axis=0) else: self.mean_prior_ = check_array(self.mean_prior, dtype=[np.float64, np.float32], ensure_2d=False) _check_shape(self.mean_prior_, (n_features, ), 'means') def _check_precision_parameters(self, X): """Check the prior parameters of the precision distribution. Parameters ---------- X : array-like, shape (n_samples, n_features) """ _, n_features = X.shape if self.degrees_of_freedom_prior is None: self.degrees_of_freedom_prior_ = n_features elif self.degrees_of_freedom_prior > n_features - 1.: self.degrees_of_freedom_prior_ = self.degrees_of_freedom_prior else: raise ValueError("The parameter 'degrees_of_freedom_prior' " "should be greater than %d, but got %.3f." % (n_features - 1, self.degrees_of_freedom_prior)) def _checkcovariance_prior_parameter(self, X): """Check the `covariance_prior_`. Parameters ---------- X : array-like, shape (n_samples, n_features) """ _, n_features = X.shape if self.covariance_prior is None: self.covariance_prior_ = { 'full': np.atleast_2d(np.cov(X.T)), 'tied': np.atleast_2d(np.cov(X.T)), 'diag': np.var(X, axis=0, ddof=1), 'spherical': np.var(X, axis=0, ddof=1).mean() }[self.covariance_type] elif self.covariance_type in ['full', 'tied']: self.covariance_prior_ = check_array( self.covariance_prior, dtype=[np.float64, np.float32], ensure_2d=False) _check_shape(self.covariance_prior_, (n_features, n_features), '%s covariance_prior' % self.covariance_type) _check_precision_matrix(self.covariance_prior_, self.covariance_type) elif self.covariance_type == 'diag': self.covariance_prior_ = check_array( self.covariance_prior, dtype=[np.float64, np.float32], ensure_2d=False) _check_shape(self.covariance_prior_, (n_features,), '%s covariance_prior' % self.covariance_type) _check_precision_positivity(self.covariance_prior_, self.covariance_type) # spherical case elif self.covariance_prior > 0.: self.covariance_prior_ = self.covariance_prior else: raise ValueError("The parameter 'spherical covariance_prior' " "should be greater than 0., but got %.3f." % self.covariance_prior) def _check_min_cluster_size_parameter(self): if self.min_cluster_size < 0 or self.min_cluster_size >= 1: raise ValueError("The parameter 'min_cluster_size' should" "be a percent in the range [0,1), but got %.3f." % self.min_cluster_size) def _initialize(self, X, resp): """Initialization of the mixture parameters. Parameters ---------- X : array-like, shape (n_samples, n_features) resp : array-like, shape (n_samples, n_components) """ nk, xk, sk = _estimate_gaussian_parameters(X, resp, self.reg_covar, self.covariance_type) self._estimate_weights(nk) self._estimate_means(nk, xk) self._estimate_precisions(nk, xk, sk) def _estimate_weights(self, nk): """Estimate the parameters of the Dirichlet distribution. Parameters ---------- nk : array-like, shape (n_components,) """ if self.weight_concentration_prior_type == 'dirichlet_process': # For dirichlet process weight_concentration will be a tuple # containing the two parameters of the beta distribution self.weight_concentration_ = ( 1. + nk, (self.weight_concentration_prior_ + np.hstack((np.cumsum(nk[::-1])[-2::-1], 0)))) else: # case Variationnal Gaussian mixture with dirichlet distribution self.weight_concentration_ = self.weight_concentration_prior_ + nk def _estimate_means(self, nk, xk): """Estimate the parameters of the Gaussian distribution. Parameters ---------- nk : array-like, shape (n_components,) xk : array-like, shape (n_components, n_features) """ self.mean_precision_ = self.mean_precision_prior_ + nk self.means_ = ((self.mean_precision_prior_ * self.mean_prior_ + nk[:, np.newaxis] * xk) / self.mean_precision_[:, np.newaxis]) def _estimate_precisions(self, nk, xk, sk): """Estimate the precisions parameters of the precision distribution. Parameters ---------- nk : array-like, shape (n_components,) xk : array-like, shape (n_components, n_features) sk : array-like The shape depends of `covariance_type`: 'full' : (n_components, n_features, n_features) 'tied' : (n_features, n_features) 'diag' : (n_components, n_features) 'spherical' : (n_components,) """ {"full": self._estimate_wishart_full, "tied": self._estimate_wishart_tied, "diag": self._estimate_wishart_diag, "spherical": self._estimate_wishart_spherical }[self.covariance_type](nk, xk, sk) self.precisions_cholesky_ = _compute_precision_cholesky( self.covariances_, self.covariance_type) def _estimate_wishart_full(self, nk, xk, sk): """Estimate the full Wishart distribution parameters. Parameters ---------- X : array-like, shape (n_samples, n_features) nk : array-like, shape (n_components,) xk : array-like, shape (n_components, n_features) sk : array-like, shape (n_components, n_features, n_features) """ _, n_features = xk.shape # Warning : in some Bishop book, there is a typo on the formula 10.63 # `degrees_of_freedom_k = degrees_of_freedom_0 + Nk` is # the correct formula self.degrees_of_freedom_ = self.degrees_of_freedom_prior_ + nk self.covariances_ = np.empty((self.n_components, n_features, n_features)) for k in range(self.n_components): diff = xk[k] - self.mean_prior_ self.covariances_[k] = (self.covariance_prior_ + nk[k] * sk[k] + nk[k] * self.mean_precision_prior_ / self.mean_precision_[k] * np.outer(diff, diff)) # Contrary to the original bishop book, we normalize the covariances self.covariances_ /= ( self.degrees_of_freedom_[:, np.newaxis, np.newaxis]) def _estimate_wishart_tied(self, nk, xk, sk): """Estimate the tied Wishart distribution parameters. Parameters ---------- X : array-like, shape (n_samples, n_features) nk : array-like, shape (n_components,) xk : array-like, shape (n_components, n_features) sk : array-like, shape (n_features, n_features) """ _, n_features = xk.shape # Warning : in some Bishop book, there is a typo on the formula 10.63 # `degrees_of_freedom_k = degrees_of_freedom_0 + Nk` # is the correct formula self.degrees_of_freedom_ = ( self.degrees_of_freedom_prior_ + nk.sum() / self.n_components) diff = xk - self.mean_prior_ self.covariances_ = ( self.covariance_prior_ + sk * nk.sum() / self.n_components + self.mean_precision_prior_ / self.n_components * np.dot( (nk / self.mean_precision_) * diff.T, diff)) # Contrary to the original bishop book, we normalize the covariances self.covariances_ /= self.degrees_of_freedom_ def _estimate_wishart_diag(self, nk, xk, sk): """Estimate the diag Wishart distribution parameters. Parameters ---------- X : array-like, shape (n_samples, n_features) nk : array-like, shape (n_components,) xk : array-like, shape (n_components, n_features) sk : array-like, shape (n_components, n_features) """ _, n_features = xk.shape # Warning : in some Bishop book, there is a typo on the formula 10.63 # `degrees_of_freedom_k = degrees_of_freedom_0 + Nk` # is the correct formula self.degrees_of_freedom_ = self.degrees_of_freedom_prior_ + nk diff = xk - self.mean_prior_ self.covariances_ = ( self.covariance_prior_ + nk[:, np.newaxis] * ( sk + (self.mean_precision_prior_ / self.mean_precision_)[:, np.newaxis] * np.square(diff))) # Contrary to the original bishop book, we normalize the covariances self.covariances_ /= self.degrees_of_freedom_[:, np.newaxis] def _estimate_wishart_spherical(self, nk, xk, sk): """Estimate the spherical Wishart distribution parameters. Parameters ---------- X : array-like, shape (n_samples, n_features) nk : array-like, shape (n_components,) xk : array-like, shape (n_components, n_features) sk : array-like, shape (n_components,) """ _, n_features = xk.shape # Warning : in some Bishop book, there is a typo on the formula 10.63 # `degrees_of_freedom_k = degrees_of_freedom_0 + Nk` # is the correct formula self.degrees_of_freedom_ = self.degrees_of_freedom_prior_ + nk diff = xk - self.mean_prior_ self.covariances_ = ( self.covariance_prior_ + nk * ( sk + self.mean_precision_prior_ / self.mean_precision_ * np.mean(np.square(diff), 1))) # Contrary to the original bishop book, we normalize the covariances self.covariances_ /= self.degrees_of_freedom_ def _m_step(self, X, log_resp): """M step. Parameters ---------- X : array-like, shape (n_samples, n_features) log_resp : array-like, shape (n_samples, n_components) Logarithm of the posterior probabilities (or responsibilities) of the point of each sample in X. """ n_samples, _ = X.shape nk, xk, sk = _estimate_gaussian_parameters( X, np.exp(log_resp), self.reg_covar, self.covariance_type) self._estimate_weights(nk) self._estimate_means(nk, xk) self._estimate_precisions(nk, xk, sk) def _estimate_log_weights(self): if self.weight_concentration_prior_type == 'dirichlet_process': digamma_sum = digamma(self.weight_concentration_[0] + self.weight_concentration_[1]) digamma_a = digamma(self.weight_concentration_[0]) digamma_b = digamma(self.weight_concentration_[1]) return (digamma_a - digamma_sum + np.hstack((0, np.cumsum(digamma_b - digamma_sum)[:-1]))) else: # case Variationnal Gaussian mixture with dirichlet distribution return (digamma(self.weight_concentration_) - digamma(np.sum(self.weight_concentration_))) def _estimate_log_prob(self, X): _, n_features = X.shape # We remove `n_features * np.log(self.degrees_of_freedom_)` because # the precision matrix is normalized log_gauss = (_estimate_log_gaussian_prob( X, self.means_, self.precisions_cholesky_, self.covariance_type) - .5 * n_features * np.log(self.degrees_of_freedom_)) log_lambda = n_features * np.log(2.) + np.sum(digamma( .5 * (self.degrees_of_freedom_ - np.arange(0, n_features)[:, np.newaxis])), 0) return log_gauss + .5 * (log_lambda - n_features / self.mean_precision_) def _compute_lower_bound(self, log_resp, log_prob_norm): """Estimate the lower bound of the model. The lower bound on the likelihood (of the training data with respect to the model) is used to detect the convergence and has to decrease at each iteration. Parameters ---------- X : array-like, shape (n_samples, n_features) log_resp : array, shape (n_samples, n_components) Logarithm of the posterior probabilities (or responsibilities) of the point of each sample in X. log_prob_norm : float Logarithm of the probability of each sample in X. Returns ------- lower_bound : float """ # Contrary to the original formula, we have done some simplification # and removed all the constant terms. n_features, = self.mean_prior_.shape # We removed `.5 * n_features * np.log(self.degrees_of_freedom_)` # because the precision matrix is normalized. log_det_precisions_chol = (_compute_log_det_cholesky( self.precisions_cholesky_, self.covariance_type, n_features) - .5 * n_features * np.log(self.degrees_of_freedom_)) if self.covariance_type == 'tied': log_wishart = self.n_components * np.float64(_log_wishart_norm( self.degrees_of_freedom_, log_det_precisions_chol, n_features)) else: log_wishart = np.sum(_log_wishart_norm( self.degrees_of_freedom_, log_det_precisions_chol, n_features)) if self.weight_concentration_prior_type == 'dirichlet_process': log_norm_weight = -np.sum(betaln(self.weight_concentration_[0], self.weight_concentration_[1])) else: log_norm_weight = _log_dirichlet_norm(self.weight_concentration_) return (-np.sum(np.exp(log_resp) * log_resp) - log_wishart - log_norm_weight - 0.5 * n_features * np.sum(np.log(self.mean_precision_))) def _get_parameters(self): return (self.weight_concentration_, self.mean_precision_, self.means_, self.degrees_of_freedom_, self.covariances_, self.precisions_cholesky_) def _set_parameters(self, params): (self.weight_concentration_, self.mean_precision_, self.means_, self.degrees_of_freedom_, self.covariances_, self.precisions_cholesky_) = params # Weights computation if self.weight_concentration_prior_type == "dirichlet_process": weight_dirichlet_sum = (self.weight_concentration_[0] + self.weight_concentration_[1]) tmp = self.weight_concentration_[1] / weight_dirichlet_sum self.weights_ = ( self.weight_concentration_[0] / weight_dirichlet_sum * np.hstack((1, np.cumprod(tmp[:-1])))) self.weights_ /= np.sum(self.weights_) else: self. weights_ = (self.weight_concentration_ / np.sum(self.weight_concentration_)) # Precisions matrices computation if self.covariance_type == 'full': self.precisions_ = np.array([ np.dot(prec_chol, prec_chol.T) for prec_chol in self.precisions_cholesky_]) elif self.covariance_type == 'tied': self.precisions_ = np.dot(self.precisions_cholesky_, self.precisions_cholesky_.T) else: self.precisions_ = self.precisions_cholesky_ ** 2

43.162673

98

0.616951

e93d65770f0f554b0eead56af918ca135e612609

5,495

py

Python

tensorflow/python/keras/utils/version_utils_test.py

EricRemmerswaal/tensorflow

141ff27877579c81a213fa113bd1b474c1749aca

[ "Apache-2.0" ]

190,993

2015-11-09T13:17:30.000Z

2022-03-31T23:05:27.000Z

tensorflow/python/keras/utils/version_utils_test.py

EricRemmerswaal/tensorflow

141ff27877579c81a213fa113bd1b474c1749aca

[ "Apache-2.0" ]

48,461

2015-11-09T14:21:11.000Z

2022-03-31T23:17:33.000Z

tensorflow/python/keras/utils/version_utils_test.py

EricRemmerswaal/tensorflow

141ff27877579c81a213fa113bd1b474c1749aca

[ "Apache-2.0" ]

104,981

2015-11-09T13:40:17.000Z

2022-03-31T19:51:54.000Z

# Copyright 2019 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the 'License'); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an 'AS IS' BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Tests for Keras utilities to split v1 and v2 classes.""" import abc import numpy as np from tensorflow.python import keras from tensorflow.python.framework import ops from tensorflow.python.keras import keras_parameterized from tensorflow.python.keras.engine import base_layer from tensorflow.python.keras.engine import base_layer_v1 from tensorflow.python.keras.engine import training from tensorflow.python.keras.engine import training_v1 from tensorflow.python.platform import test @keras_parameterized.run_all_keras_modes class SplitUtilsTest(keras_parameterized.TestCase): def _check_model_class(self, model_class): if ops.executing_eagerly_outside_functions(): self.assertEqual(model_class, training.Model) else: self.assertEqual(model_class, training_v1.Model) def _check_layer_class(self, layer): if ops.executing_eagerly_outside_functions(): self.assertIsInstance(layer, base_layer.Layer) self.assertNotIsInstance(layer, base_layer_v1.Layer) else: self.assertIsInstance(layer, base_layer_v1.Layer) def test_functional_model(self): inputs = keras.Input(10) outputs = keras.layers.Dense(1)(inputs) model = keras.Model(inputs, outputs) self._check_model_class(model.__class__.__bases__[0]) self._check_layer_class(model) def test_subclass_model_with_functional_init(self): inputs = keras.Input(10) outputs = keras.layers.Dense(1)(inputs) class MyModel(keras.Model): pass model = MyModel(inputs, outputs) model_class = model.__class__.__bases__[0].__bases__[0] self._check_model_class(model_class) self._check_layer_class(model) def test_subclass_model_with_functional_init_interleaved_v1_functional(self): with ops.Graph().as_default(): inputs = keras.Input(10) outputs = keras.layers.Dense(1)(inputs) _ = keras.Model(inputs, outputs) inputs = keras.Input(10) outputs = keras.layers.Dense(1)(inputs) class MyModel(keras.Model): pass model = MyModel(inputs, outputs) model_class = model.__class__.__bases__[0].__bases__[0] self._check_model_class(model_class) self._check_layer_class(model) def test_sequential_model(self): model = keras.Sequential([keras.layers.Dense(1)]) model_class = model.__class__.__bases__[0].__bases__[0] self._check_model_class(model_class) self._check_layer_class(model) def test_subclass_model(self): class MyModel(keras.Model): def call(self, x): return 2 * x model = MyModel() model_class = model.__class__.__bases__[0] self._check_model_class(model_class) self._check_layer_class(model) def test_layer(self): class IdentityLayer(base_layer.Layer): """A layer that returns it's input. Useful for testing a layer without a variable. """ def call(self, inputs): return inputs layer = IdentityLayer() self._check_layer_class(layer) def test_multiple_subclass_model(self): class Model1(keras.Model): pass class Model2(Model1): def call(self, x): return 2 * x model = Model2() model_class = model.__class__.__bases__[0].__bases__[0] self._check_model_class(model_class) self._check_layer_class(model) def test_user_provided_metaclass(self): class AbstractModel(keras.Model, metaclass=abc.ABCMeta): @abc.abstractmethod def call(self, inputs): """Calls the model.""" class MyModel(AbstractModel): def call(self, inputs): return 2 * inputs with self.assertRaisesRegex(TypeError, 'instantiate abstract class'): AbstractModel() # pylint: disable=abstract-class-instantiated model = MyModel() model_class = model.__class__.__bases__[0].__bases__[0] self._check_model_class(model_class) self._check_layer_class(model) def test_multiple_inheritance(self): class Return2(object): def return_2(self): return 2 class MyModel(keras.Model, Return2): def call(self, x): return self.return_2() * x model = MyModel() bases = model.__class__.__bases__ self._check_model_class(bases[0]) self.assertEqual(bases[1], Return2) self.assertEqual(model.return_2(), 2) self._check_layer_class(model) def test_fit_error(self): if not ops.executing_eagerly_outside_functions(): # Error only appears on the v2 class. return model = keras.Sequential([keras.layers.Dense(1)]) model.compile('sgd', 'mse') x, y = np.ones((10, 10)), np.ones((10, 1)) with ops.get_default_graph().as_default(): with self.assertRaisesRegex( ValueError, 'instance was constructed with eager mode enabled'): model.fit(x, y, batch_size=2) if __name__ == '__main__': test.main()

29.702703

80

0.709372

1b436334bc8e240a39d933c5919a621bb0ee0f80

913

py

Python

mediawiki/__init__.py

arnavk/mediawiki

2f74f80e18489bb3d22de3977fc2a8e5ba2c0737

[ "MIT" ]

null

mediawiki/__init__.py

arnavk/mediawiki

2f74f80e18489bb3d22de3977fc2a8e5ba2c0737

[ "MIT" ]

1

2018-09-19T18:56:06.000Z

mediawiki/__init__.py

arnavk/mediawiki

2f74f80e18489bb3d22de3977fc2a8e5ba2c0737

[ "MIT" ]

null

''' mediawiki module initialization ''' from __future__ import (unicode_literals, absolute_import) from .mediawiki import (MediaWiki, URL, VERSION) from .mediawikipage import (MediaWikiPage) from .exceptions import (MediaWikiException, PageError, MediaWikiGeoCoordError, RedirectError, DisambiguationError, MediaWikiAPIURLError, HTTPTimeoutError, MediaWikiCategoryTreeError) __author__ = 'Tyler Barrus' __maintainer__ = 'Tyler Barrus' __email__ = 'barrust@gmail.com' __license__ = 'MIT' __version__ = VERSION __credits__ = ['Jonathan Goldsmith'] __url__ = URL __bugtrack_url__ = '{0}/issues'.format(URL) __all__ = ['MediaWiki', 'PageError', 'RedirectError', 'MediaWikiException', 'DisambiguationError', 'MediaWikiAPIURLError', 'HTTPTimeoutError', 'MediaWikiGeoCoordError', 'MediaWikiCategoryTreeError']

36.52

79

0.709748

91de5af1966701dd6344a6f0a300ea28d1f41974

6,834

py

Python

backend/sources/tests/unit/test_routers/test_form_router.py

TeamUnibuc/SmartForms

db558b057468dafd3adbb85d6405697f8c7b7342

[ "MIT" ]

null

backend/sources/tests/unit/test_routers/test_form_router.py

TeamUnibuc/SmartForms

db558b057468dafd3adbb85d6405697f8c7b7342

[ "MIT" ]

2

2022-02-18T09:43:17.000Z

2022-03-02T23:18:03.000Z

backend/sources/tests/unit/test_routers/test_form_router.py

TeamUnibuc/SmartForms

db558b057468dafd3adbb85d6405697f8c7b7342

[ "MIT" ]

null

import base64 import json import os, sys sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.realpath(__file__)))))) import main import time import random import unittest from main import init_state import database import smart_forms_types from PyPDF2 import PdfFileReader from fastapi.testclient import TestClient import io def get_generic_form_description(): """ returns a generic description used for testing contains 2 questions. First is a text question, second is a multiple choice question """ return smart_forms_types.FormDescription( title="form_title", formId="formId", description="Description", questions=[ smart_forms_types.FormTextQuestion( title="question_title", description="question description", maxAnswerLength=12 ), smart_forms_types.FormMultipleChoiceQuestion( title="question2_title", description="question 2 description", choices = ["Yes", "No", "Maybe"] ) ], canBeFilledOnline=True, needsToBeSignedInToSubmit=False, authorEmail=True ) class TestFormEndpointNoAuthChecks(unittest.TestCase): def setUpClass() -> None: init_state() # disable authentication checks main.routers.AUTHENTICATION_CHECKS = False def setUp(self) -> None: self.client = TestClient(main.app) def test_preview_endpoint(self): """ Send a correct message and expect to receive a valid answer. """ form = get_generic_form_description() response = self.client.post("/api/form/preview", json=form.dict()) self.assertEqual(response.status_code, 200) self.assertTrue("formPdfBase64" in json.loads(response.content)) def test_create_and_description_endpoint(self): """ Send a correct message and expect to receive a valid answer. """ form = get_generic_form_description() response = self.client.post("/api/form/create", json=form.dict()) self.assertEqual(response.status_code, 200) content = response.json() self.assertTrue("formPdfBase64" in content) self.assertTrue("formId" in content) form_id = response.json()["formId"] # try to get form from /description response_description = self.client.get( f"/api/form/description/{form_id}" ) self.assertEqual(response_description.status_code, 200) extracted_form = smart_forms_types.FormDescription( **response_description.json() ) self.assertEqual(extracted_form.formId, form_id) self.assertIsNotNone(extracted_form.creationDate) def test_list_endpoint(self): """ Creates a form, and expects list to return at least one form. """ form = get_generic_form_description() response = self.client.post("/api/form/create", json=form.dict()) self.assertEqual(response.status_code, 200) content = json.loads(response.content) self.assertTrue("formId" in content) # try to get form from /list response = self.client.post( f"/api/form/list", json={ "offset": 0, "count": 10, "isOwner": False, } ) self.assertEqual(response.status_code, 200) self.assertTrue("forms" in response.json()) self.assertGreater(len(response.json()["forms"]), 0) def test_pdf_endpoint(self): """ Creates a form, and tries to retrieve it. """ form = get_generic_form_description() response = self.client.post("/api/form/create", json=form.dict()) self.assertEqual(response.status_code, 200) content = json.loads(response.content) self.assertTrue("formId" in content) form_id = content["formId"] # try to get form from /list response = self.client.get( f"/api/form/pdf/{form_id}" ) content = base64.b64decode( response.json()["formPdfBase64"] ) # try to read content as pdf pdf = PdfFileReader(io.BytesIO(content)) # shouldn't be none self.assertIsNotNone(pdf) # should have one page self.assertEqual(pdf.numPages, 1) # should contain the title (form_title) self.assertNotEqual( pdf.getPage(0).extractText().find("form_title"), -1 ) def test_delete_endpoint(self): """ Creates a form, and tries to delete it. """ form = get_generic_form_description() response = self.client.post("/api/form/create", json=form.dict()) self.assertEqual(response.status_code, 200) content = json.loads(response.content) self.assertTrue("formId" in content) form_id = content["formId"] # try to get form from /list response = self.client.delete( f"/api/form/delete/{form_id}" ) # no errors deleting the form self.assertEqual(response.status_code, 200) # try to read the form again response = self.client.get( f"/api/form/description/{form_id}" ) # should get NOT FOUND self.assertEqual(response.status_code, 201) def test_visibility_endpoint(self): """ Creates a form, and tries to delete it. """ form = get_generic_form_description() form.canBeFilledOnline = False form.needsToBeSignedInToSubmit = False response = self.client.post("/api/form/create", json=form.dict()) self.assertEqual(response.status_code, 200) form_id = response.json()["formId"] # try to get form from /list response = self.client.put( f"/api/form/online-access/{form_id}", json={ "canBeFilledOnline": True, "needsToBeSignedInToSubmit": True } ) # no errors deleting the form self.assertEqual(response.status_code, 200) # try to read the form again response = self.client.get( f"/api/form/description/{form_id}" ) # should get 200 self.assertEqual(response.status_code, 200) extracted_form = smart_forms_types.FormDescription( **response.json() ) # should be the same, exept the two fields self.assertEqual(extracted_form.formId, form_id) self.assertTrue(extracted_form.needsToBeSignedInToSubmit) self.assertTrue(extracted_form.canBeFilledOnline) if __name__ == '__main__': unittest.main()

30.783784

111

0.613257

546397f4cae0674d2db8e17766f65e23645c886d

6,452

py

Python

test/test_samples.py

ricky40403/Fcos_seg

e2803108ca0114b906c739aa348e8b28f3c6342c

[ "MIT" ]

null

test/test_samples.py

ricky40403/Fcos_seg

e2803108ca0114b906c739aa348e8b28f3c6342c

[ "MIT" ]

1

2021-09-29T17:10:01.000Z

test/test_samples.py

ricky40403/Fcos_seg

e2803108ca0114b906c739aa348e8b28f3c6342c

[ "MIT" ]

null

import os import sys import argparse import torch sys.path.append('..') from Fcos_seg.data.coco_dataset import COCODataset from Fcos_seg.data.transform import get_transform from Fcos_seg.data.data_meta import COCO_CLASS_NAME from Fcos_seg.core.config import get_cfg_defaults from Fcos_seg.utils.sampler_helper import get_sampler, make_batch_data_sampler from Fcos_seg.utils.dataset_helper import collate_fn from Fcos_seg.utils.norm_helper import UnNormalize from Fcos_seg.loss.fcos_loss import FcosLoss from Fcos_seg.detector.fcos import FCOS from PIL import Image, ImageDraw, ImageFont import torchvision.transforms as transforms def parse_args(): parser = argparse.ArgumentParser( description="Test COCO dataset" ) parser.add_argument('data', metavar='DIR', help='path to coco folder') return parser, parser.parse_args() def main(): _, args = parse_args() cfg = get_cfg_defaults() COCO_train_img_PATH = os.path.join(args.data, "images", "train2017") COCO_train_xml_PATH = os.path.join(args.data, "annotations", "instances_train2017.json") COCO_val_img_PATH = os.path.join(args.data, "images", "val2017") COCO_val_xml_PATH = os.path.join(args.data, "annotations", "instances_val2017.json") train_dataset = COCODataset(cfg, COCO_train_xml_PATH, COCO_train_img_PATH, True, transform= get_transform(cfg, train = True)) # val_dataset = COCODataset(cfg, COCO_val_xml_PATH, COCO_val_img_PATH, True, transform= get_transform(cfg, train = False)) train_sampler = get_sampler(train_dataset, shuffle = False, distributed = False) # val_sampler = get_sampler(val_dataset, shuffle = False, distributed = False) batch_per_gpu = cfg.TRAIN.BATCH train_sampler = make_batch_data_sampler(train_dataset, train_sampler, aspect_grouping = [1], images_per_batch = batch_per_gpu) # val_sampler = make_batch_data_sampler(val_dataset, val_sampler, aspect_grouping = [1], images_per_batch = batch_per_gpu) train_output_path = "test_coco_dataset/train_samples" # val_output_path = "test_coco_dataset/val" print("==> testing output folder for train: {}".format(train_output_path)) # print("==> testing output folder for val: {}".format(val_output_path)) if not os.path.exists(train_output_path): os.makedirs(train_output_path) # if not os.path.exists(val_output_path): # os.makedirs(val_output_path) train_loader = torch.utils.data.DataLoader(train_dataset, batch_sampler = train_sampler, num_workers = cfg.TRAIN.WORKER, collate_fn = collate_fn(cfg)) # val_loader = torch.utils.data.DataLoader(val_dataset, # batch_sampler = val_sampler, # num_workers = cfg.TRAIN.WORKER, # collate_fn = collate_fn(cfg)) # only test 100 image iter_num = 10 cur_iter_num = 0 un_norm = UnNormalize(mean = (103.53, 116.28, 123.675), std = (57.375, 57.12, 58.395)) model = FCOS(cfg) test_loss_sample = FcosLoss(cfg) # testing train dataset for imgs, targets, idx in train_loader: if cur_iter_num >= iter_num: break locations = model.get_locations(imgs, targets) cls_sample, reg_sample, locations = test_loss_sample.test_target_samples(locations, targets, imgs) for l in range(len(locations)): cls_sample_per_level = cls_sample[l] reg_sample_per_level = reg_sample[l] img_per_batch = imgs.tensors[0].clone() img_per_batch = img_per_batch * 0 PIL_image = transforms.ToPILImage()(img_per_batch) drawObj = ImageDraw.Draw(PIL_image) for loc in locations[l]: drawObj.rectangle((loc[0]-2, loc[1]-2,loc[0]+2, loc[1]+2), outline="red") PIL_image.save(os.path.join(train_output_path, "samplelocation_level{}.jpg".format(l))) for b in range(len(cls_sample)): img_per_batch = imgs.tensors[b].clone() img_per_batch = un_norm(img_per_batch) im_w, im_h = imgs.image_sizes[b] img_per_batch = img_per_batch[:, :im_w, :im_h] # batch_cls_per_level = cls_sample_per_level[b] # batch_reg_per_level = reg_sample_per_level[b] f_size = locations[l].shape[0] batch_cls_per_level = cls_sample_per_level[b*f_size:(b+1)*f_size] batch_reg_per_level = reg_sample_per_level[b*f_size:(b+1)*f_size,:] print(batch_cls_per_level.size()) print(batch_reg_per_level.size()) pos_inds = torch.nonzero(batch_cls_per_level > 0).squeeze(1) PIL_image = transforms.ToPILImage()(img_per_batch) drawObj = ImageDraw.Draw(PIL_image) for p in pos_inds: x1 = (locations[l][p][0] - batch_reg_per_level[p, 0]).cpu() y1 = (locations[l][p][1] - batch_reg_per_level[p, 2]).cpu() x2 = (locations[l][p][0] + batch_reg_per_level[p, 1]).cpu() y2 = (locations[l][p][1] + batch_reg_per_level[p, 3]).cpu() drawObj.rectangle((x1, y1, x2, y2), outline="red") drawObj.text((x1, y1), "Class: {}".format(COCO_CLASS_NAME[int(batch_cls_per_level[p])]), fill="red") PIL_image.save(os.path.join(train_output_path, "sample_{}_level{}.jpg".format(idx[b], l))) exit() if __name__ == "__main__": main()

36.451977

134

0.565871

cfd5c81386506bd99f8876433d93823b61628a8b

920

py

Python

preprocess/geocode.py

code-for-magdeburg/history-of-your-house

07f0dc04eb0259ad8d9c86034c5c2231a6922f8d

[ "MIT" ]

1

2018-04-28T20:24:48.000Z

preprocess/geocode.py

code-for-magdeburg/history-of-your-house

07f0dc04eb0259ad8d9c86034c5c2231a6922f8d

[ "MIT" ]

null

preprocess/geocode.py

code-for-magdeburg/history-of-your-house

07f0dc04eb0259ad8d9c86034c5c2231a6922f8d

[ "MIT" ]

null

import dataset import requests import time db = dataset.connect('sqlite:///databse.db') table = db['history'] key = "XAI2eQAhITGGep9PYpBj7ZQM90NtKSpd" baseUrl = "http://www.mapquestapi.com/geocoding/v1/address?street=%s&city=Magdeburg&country=Germany&maxResults=1&key=" + key def geocode(row): address = row['anschrift'] if row['lat'] == None: print(address) url = baseUrl % address response = requests.get(url).json() lat = response['results'][0]['locations'][0]['latLng']['lat'] lng = response['results'][0]['locations'][0]['latLng']['lng'] row['lng'] = lng row['lat'] = lat table.update(row, ['id']) # time.sleep(0.5) else: print(row['lat']) for row in list(table.all()): geocode(row) # dataset.freeze(table, format='csv', filename='history.csv') dataset.freeze(table, format='json', filename='history.json')

24.210526

124

0.620652

9e96da72b21911bfe4eb357c52c55d5591ffe000

2,017

py

Python

nahash/messages.py

jwiggins/nahash

51a41ed5e02c4414783ec4e301fb9c12a4a403ae

[ "MIT" ]

3

2018-06-13T00:42:14.000Z

2021-04-22T00:50:08.000Z

nahash/messages.py

jwiggins/nahash

51a41ed5e02c4414783ec4e301fb9c12a4a403ae

[ "MIT" ]

1

2019-11-27T10:04:16.000Z

2020-03-03T16:38:09.000Z

nahash/messages.py

jwiggins/nahash

51a41ed5e02c4414783ec4e301fb9c12a4a403ae

[ "MIT" ]

null

import subprocess import textwrap import time from .tables import MESSAGE_HANDLE_ID, MESSAGE_ROWID, MESSAGE_TEXT from .util import get_db_conn WAIT_TIMEOUT = 5.0 def send_message(recipient, text): """ Send a single message to a recipient Original: https://gist.github.com/tk512/9b578739730eb5b83c7cbf502c38cf3a """ script = textwrap.dedent(""" on run {targetBuddyPhone, targetMessage} tell application "Messages" set targetService to 1st service whose service type = iMessage set targetBuddy to buddy targetBuddyPhone of targetService send targetMessage to targetBuddy end tell end run """) command = ['osascript', '-', recipient.phone_or_email, text] proc = subprocess.Popen(command, stdin=subprocess.PIPE) proc.communicate(script.encode('utf8')) return proc.wait() def wait_for_next_message(recipients, last_rowid=0): """ Poll forever until a message arrives from any of `recipients`. """ if not isinstance(recipients, (list, tuple)): recipients = (recipients,) # Turn recipients into a dictionary recipients = {r.index: r for r in recipients} condition = 'is_from_me=0 AND handle_id IN ( {} )' condition = condition.format(', '.join(map(str, recipients.keys()))) sql = ('SELECT * FROM `message` ' 'WHERE ' + condition + 'ORDER BY ROWID DESC ' 'LIMIT 1') connection = get_db_conn() with connection: c = connection.cursor() while True: time.sleep(WAIT_TIMEOUT) c.execute(sql) row = c.fetchone() # Nothing there yet if row is None: continue if last_rowid == 0: last_rowid = row[MESSAGE_ROWID] elif row[MESSAGE_ROWID] > last_rowid: r_index = row[MESSAGE_HANDLE_ID] row_id = row[MESSAGE_ROWID] return row[MESSAGE_TEXT], recipients[r_index], row_id

30.104478

76

0.627169

2f143e59638a462a78589d830766ed148d50e92e

1,334

py

Python

clock/contrib/sites/migrations/0001_initial.py

mimischi/clock

3914da6a48b89cb80ab5205c6ce1c279012472fe

[ "MIT" ]

6

2016-03-02T14:20:14.000Z

2021-01-04T04:30:27.000Z

clock/contrib/sites/migrations/0001_initial.py

mimischi/clock

3914da6a48b89cb80ab5205c6ce1c279012472fe

[ "MIT" ]

340

2015-09-24T15:30:12.000Z

2020-03-14T09:05:40.000Z

clock/contrib/sites/migrations/0001_initial.py

mimischi/clock

3914da6a48b89cb80ab5205c6ce1c279012472fe

[ "MIT" ]

4

2016-04-04T13:18:18.000Z

2018-08-02T02:16:19.000Z

# -*- coding: utf-8 -*- import django.contrib.sites.models from django.db import migrations, models class Migration(migrations.Migration): dependencies = [] operations = [ migrations.CreateModel( name="Site", fields=[ ( "id", models.AutoField( verbose_name="ID", primary_key=True, serialize=False, auto_created=True, ), ), ( "domain", models.CharField( verbose_name="domain name", max_length=100, validators=[ django.contrib.sites.models._simple_domain_name_validator ], ), ), ("name", models.CharField(verbose_name="display name", max_length=50)), ], options={ "verbose_name_plural": "sites", "verbose_name": "site", "db_table": "django_site", "ordering": ("domain",), }, managers=[("objects", django.contrib.sites.models.SiteManager())], ) ]

29

87

0.407046

91fab78c4d322247bbc36cb8d278166efcb2710f

3,442

py

Python

examples/http/ghcn/get_ghcn.py

jjaraalm/hsds

936c9033fbbffcfe5093dd55c4f2992458ac2df5

[ "Apache-2.0" ]

1

2020-03-12T12:26:26.000Z

examples/http/ghcn/get_ghcn.py

jjaraalm/hsds

936c9033fbbffcfe5093dd55c4f2992458ac2df5

[ "Apache-2.0" ]

null

examples/http/ghcn/get_ghcn.py

jjaraalm/hsds

936c9033fbbffcfe5093dd55c4f2992458ac2df5

[ "Apache-2.0" ]

null

import sys import json import requests import helper import config import hsds_logger as log def printUsage(): print("Usage: get_ghcn.py [station] [obstype]") print(" Get ghcn results for given station and obstype") print(" If command line arguments are not given, default to:") print(" station=ITE00100554") print(" obstype=TMAX") def main(): domain = "/home/" + config.get("user_name") + "/" + config.get("domain_name") print("domain: {}".format(domain) ) station = "ITE00100554" obstype = "TMAX" if len(sys.argv) > 1 and (sys.argv[1] == "-h" or sys.argv[1] == "--help"): printUsage() sys.exit(1) if len(sys.argv) > 1: station = sys.argv[1].upper() if len(sys.argv) > 2: obstype = sys.argv[2].upper() print("Station: ", station) print("Obstype: ", obstype) # get root uuid req = helper.getEndpoint() + '/' headers = helper.getRequestHeaders(domain=domain) log.info("Req: " + req) rsp = requests.get(req, headers=headers) root_uuid = None if rsp.status_code == 200: rspJson = json.loads(rsp.text) root_uuid = rspJson["root"] log.info("root uuid: " + root_uuid) # get data uuid data_uuid = None req = helper.getEndpoint() + '/groups/' + root_uuid + "/links/data" log.info("Req: " + req) rsp = requests.get(req, headers=headers) if rsp.status_code == 200: rspJson = json.loads(rsp.text) link_json = rspJson["link"] data_uuid = link_json["id"] log.info("data uuid: " + data_uuid) #get station uuid station_uuid = None req = helper.getEndpoint() + '/groups/' + data_uuid + "/links/" + station log.info("Req: " + req) rsp = requests.get(req, headers=headers) if rsp.status_code == 200: rspJson = json.loads(rsp.text) link_json = rspJson["link"] station_uuid = link_json["id"] elif rsp.status_code == 404: print("Station not found") sys.exit() else: print("unexpected error: ", rsp.status_code) sys.exit() log.info("station uuid: " + station_uuid) #get obs uuid obs_uuid = None req = helper.getEndpoint() + '/groups/' + station_uuid + "/links/" + obstype log.info("Req: " + req) rsp = requests.get(req, headers=headers) if rsp.status_code == 200: rspJson = json.loads(rsp.text) link_json = rspJson["link"] obs_uuid = link_json["id"] elif rsp.status_code == 404: print("No observation of requested type") sys.exit() else: print("unexpected error: ", rsp.status_code) sys.exit() log.info("obs uuid: " + obs_uuid) # finally get the actual observations attrs = None req = helper.getEndpoint() + '/groups/' + obs_uuid + "/attributes?IncludeData=T" log.info("Req: " + req) rsp = requests.get(req, headers=headers) if rsp.status_code == 200: rspJson = json.loads(rsp.text) attrs = rspJson["attributes"] else: print("unexpected error: ", rsp.status_code) sys.exit() print("{} observations".format(len(attrs))) # convert list of attributes to a dict of data/value pairs data = {} for attr in attrs: data[attr["name"]] = attr["value"] keys = list(data.keys()) keys.sort() for k in keys: print("{}: {}".format(k, data[k])) main()

28.92437

84

0.590354

001d5fa7914ac83f78b65f038d6f04de7dcd2f92

1,701

py

Python

poem/Poem/poem/migrations/0006_groupofthresholdsprofiles_thresholdsprofiles.py

kevangel79/poem-2

75cda3cdd302df9c85b963bd91b7ce7182dfa220

[ "Apache-2.0" ]

null

poem/Poem/poem/migrations/0006_groupofthresholdsprofiles_thresholdsprofiles.py

kevangel79/poem-2

75cda3cdd302df9c85b963bd91b7ce7182dfa220

[ "Apache-2.0" ]

139

2020-04-06T09:22:16.000Z

2021-08-02T06:39:22.000Z

poem/Poem/poem/migrations/0006_groupofthresholdsprofiles_thresholdsprofiles.py

kevangel79/poem-2

75cda3cdd302df9c85b963bd91b7ce7182dfa220

[ "Apache-2.0" ]

3

2019-07-10T09:37:38.000Z

2020-04-02T10:48:38.000Z

# Generated by Django 2.2.5 on 2019-11-06 11:16 import django.contrib.auth.models from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('auth', '0011_update_proxy_permissions'), ('poem', '0005_tenanthistory'), ] operations = [ migrations.CreateModel( name='ThresholdsProfiles', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=128)), ('apiid', models.CharField(max_length=128)), ('groupname', models.CharField(default='', max_length=128)), ], options={ 'permissions': (('thresholdsprofilesown', 'Read/Write/Modify'),), }, ), migrations.CreateModel( name='GroupOfThresholdsProfiles', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=80, unique=True, verbose_name='name')), ('permissions', models.ManyToManyField(blank=True, to='auth.Permission', verbose_name='permissions')), ('thresholdsprofiles', models.ManyToManyField(blank=True, to='poem.ThresholdsProfiles')), ], options={ 'verbose_name': 'Group of thresholds profiles', 'verbose_name_plural': 'Groups of thresholds profiles', }, managers=[ ('objects', django.contrib.auth.models.GroupManager()), ], ), ]

38.659091

118

0.573192

4f39989fff61d1fa4b37977cfa1bf5bc5a354a41

442

py

Python

examples/fetch_tabix/fetch_tabix.py

k8iechen/vcfpy

89f7097cb5bc7bfec198d8c02fe031b44786b14b

[ "MIT" ]

48

2016-09-25T16:07:53.000Z

2022-01-19T17:44:46.000Z

examples/fetch_tabix/fetch_tabix.py

k8iechen/vcfpy

89f7097cb5bc7bfec198d8c02fe031b44786b14b

[ "MIT" ]

83

2016-09-16T12:20:02.000Z

2021-05-12T09:12:10.000Z

examples/fetch_tabix/fetch_tabix.py

k8iechen/vcfpy

89f7097cb5bc7bfec198d8c02fe031b44786b14b

[ "MIT" ]

9

2016-10-04T15:10:06.000Z

2022-02-08T21:10:51.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- import vcfpy # Open input, add FILTER header, and open output file reader = vcfpy.Reader.from_path("input.vcf.gz") writer = vcfpy.Writer.from_path("/dev/stdout", reader.header) # Fetch region 20:1,110,694-1,230,237. Note that the coordinates # in the API call are zero-based and describe half-open intervals. for record in reader.fetch("20", 1110695, 1230237): writer.write_record(record)

34

66

0.730769

76e57e77adb623fa451995104b08d00acd7bc02e

1,319

py

Python

work/PaddleVideo/paddlevideo/modeling/__init__.py

zmgaoscut/2021-CCF-BDCI---7-

bdf8139d840a31120f62c9f87542076a3db813ac

[ "Apache-2.0" ]

883

2020-11-12T11:46:46.000Z

2022-03-31T18:27:10.000Z

paddlevideo/modeling/__init__.py

arkofgalaxy/PaddleVideo

64251233c83b7eb681061b454da198a9082309a6

[ "Apache-2.0" ]

233

2020-12-09T06:04:59.000Z

2022-03-28T08:16:51.000Z

paddlevideo/modeling/__init__.py

arkofgalaxy/PaddleVideo

64251233c83b7eb681061b454da198a9082309a6

[ "Apache-2.0" ]

225

2020-11-13T06:21:55.000Z

2022-03-31T05:36:11.000Z

# Copyright (c) 2020 PaddlePaddle 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 .backbones import ResNet from .builder import (build_backbone, build_head, build_recognizer, build_localizer, build_loss) from .heads import BaseHead, TSNHead from .losses import CrossEntropyLoss from .framework.recognizers import BaseRecognizer, recognizer2d from .registry import BACKBONES, HEADS, LOSSES, RECOGNIZERS, LOCALIZERS from .weight_init import weight_init_ __all__ = [ 'BACKBONES', 'HEADS', 'RECOGNIZERS', 'LOCALIZERS', 'LOSSES', 'build_recognizer', 'build_localizer', 'build_head', 'build_backbone', 'build_loss', 'ResNet', 'TSNHead', 'BaseHead', 'BaseRecognizer', 'Recognizer2d', 'CrossEntropyLoss', ]

31.404762

74

0.727066

4a5e6dd36ca47bb2b91a79417c7c470af14d5528

6,020

py

Python

data/p3BR/R2/benchmark/startQiskit_QC154.py

UCLA-SEAL/QDiff

d968cbc47fe926b7f88b4adf10490f1edd6f8819

[ "BSD-3-Clause" ]

null

data/p3BR/R2/benchmark/startQiskit_QC154.py

UCLA-SEAL/QDiff

d968cbc47fe926b7f88b4adf10490f1edd6f8819

[ "BSD-3-Clause" ]

null

data/p3BR/R2/benchmark/startQiskit_QC154.py

UCLA-SEAL/QDiff

d968cbc47fe926b7f88b4adf10490f1edd6f8819

[ "BSD-3-Clause" ]

null

# qubit number=3 # total number=28 import numpy as np from qiskit import QuantumCircuit, execute, Aer, QuantumRegister, ClassicalRegister, transpile, BasicAer, IBMQ from qiskit.visualization import plot_histogram from typing import * from pprint import pprint from math import log2 from collections import Counter from qiskit.test.mock import FakeVigo, FakeYorktown kernel = 'circuit/bernstein' def bitwise_xor(s: str, t: str) -> str: length = len(s) res = [] for i in range(length): res.append(str(int(s[i]) ^ int(t[i]))) return ''.join(res[::-1]) def bitwise_dot(s: str, t: str) -> str: length = len(s) res = 0 for i in range(length): res += int(s[i]) * int(t[i]) return str(res % 2) def build_oracle(n: int, f: Callable[[str], str]) -> QuantumCircuit: # implement the oracle O_f # NOTE: use multi_control_toffoli_gate ('noancilla' mode) # https://qiskit.org/documentation/_modules/qiskit/aqua/circuits/gates/multi_control_toffoli_gate.html # https://quantumcomputing.stackexchange.com/questions/3943/how-do-you-implement-the-toffoli-gate-using-only-single-qubit-and-cnot-gates # https://quantumcomputing.stackexchange.com/questions/2177/how-can-i-implement-an-n-bit-toffoli-gate controls = QuantumRegister(n, "ofc") target = QuantumRegister(1, "oft") oracle = QuantumCircuit(controls, target, name="Of") for i in range(2 ** n): rep = np.binary_repr(i, n) if f(rep) == "1": for j in range(n): if rep[j] == "0": oracle.x(controls[j]) oracle.mct(controls, target[0], None, mode='noancilla') for j in range(n): if rep[j] == "0": oracle.x(controls[j]) # oracle.barrier() # oracle.draw('mpl', filename=(kernel + '-oracle.png')) return oracle def build_circuit(n: int, f: Callable[[str], str]) -> QuantumCircuit: # implement the Bernstein-Vazirani circuit zero = np.binary_repr(0, n) b = f(zero) # initial n + 1 bits input_qubit = QuantumRegister(n+1, "qc") classicals = ClassicalRegister(n, "qm") prog = QuantumCircuit(input_qubit, classicals) # inverse last one (can be omitted if using O_f^\pm) prog.x(input_qubit[n]) # circuit begin prog.h(input_qubit[1]) # number=1 prog.cx(input_qubit[0],input_qubit[2]) # number=11 prog.x(input_qubit[2]) # number=12 prog.h(input_qubit[2]) # number=25 prog.cz(input_qubit[0],input_qubit[2]) # number=26 prog.h(input_qubit[2]) # number=27 prog.h(input_qubit[1]) # number=7 prog.cz(input_qubit[2],input_qubit[1]) # number=8 prog.h(input_qubit[1]) # number=9 prog.h(input_qubit[1]) # number=18 prog.cz(input_qubit[2],input_qubit[1]) # number=19 prog.h(input_qubit[1]) # number=20 prog.y(input_qubit[1]) # number=14 prog.h(input_qubit[1]) # number=22 prog.cz(input_qubit[2],input_qubit[1]) # number=23 prog.h(input_qubit[1]) # number=24 prog.z(input_qubit[2]) # number=3 prog.x(input_qubit[1]) # number=17 prog.y(input_qubit[2]) # number=5 prog.x(input_qubit[2]) # number=21 # apply H to get superposition for i in range(n): prog.h(input_qubit[i]) prog.h(input_qubit[n]) prog.barrier() # apply oracle O_f oracle = build_oracle(n, f) prog.append( oracle.to_gate(), [input_qubit[i] for i in range(n)] + [input_qubit[n]]) # apply H back (QFT on Z_2^n) for i in range(n): prog.h(input_qubit[i]) prog.barrier() # measure return prog def get_statevector(prog: QuantumCircuit) -> Any: state_backend = Aer.get_backend('statevector_simulator') statevec = execute(prog, state_backend).result() quantum_state = statevec.get_statevector() qubits = round(log2(len(quantum_state))) quantum_state = { "|" + np.binary_repr(i, qubits) + ">": quantum_state[i] for i in range(2 ** qubits) } return quantum_state def evaluate(backend_str: str, prog: QuantumCircuit, shots: int, b: str) -> Any: # Q: which backend should we use? # get state vector quantum_state = get_statevector(prog) # get simulate results # provider = IBMQ.load_account() # backend = provider.get_backend(backend_str) # qobj = compile(prog, backend, shots) # job = backend.run(qobj) # job.result() backend = Aer.get_backend(backend_str) # transpile/schedule -> assemble -> backend.run results = execute(prog, backend, shots=shots).result() counts = results.get_counts() a = Counter(counts).most_common(1)[0][0][::-1] return { "measurements": counts, # "state": statevec, "quantum_state": quantum_state, "a": a, "b": b } def bernstein_test_1(rep: str): """011 . x + 1""" a = "011" b = "1" return bitwise_xor(bitwise_dot(a, rep), b) def bernstein_test_2(rep: str): """000 . x + 0""" a = "000" b = "0" return bitwise_xor(bitwise_dot(a, rep), b) def bernstein_test_3(rep: str): """111 . x + 1""" a = "111" b = "1" return bitwise_xor(bitwise_dot(a, rep), b) if __name__ == "__main__": n = 2 a = "11" b = "1" f = lambda rep: \ bitwise_xor(bitwise_dot(a, rep), b) prog = build_circuit(n, f) sample_shot =4000 writefile = open("../data/startQiskit_QC154.csv", "w") # prog.draw('mpl', filename=(kernel + '.png')) IBMQ.load_account() provider = IBMQ.get_provider(hub='ibm-q') provider.backends() backend = provider.get_backend("ibmq_5_yorktown") circuit1 = transpile(prog, FakeYorktown()) circuit1.h(qubit=2) circuit1.x(qubit=3) circuit1.measure_all() info = execute(circuit1,backend=backend, shots=sample_shot).result().get_counts() print(info, file=writefile) print("results end", file=writefile) print(circuit1.depth(), file=writefile) print(circuit1, file=writefile) writefile.close()

29.950249

140

0.631561

7618c197413d6dbde4c9b102fff4171ca1b06f72

2,307

py

Python

tests/ut/python/parallel/test_auto_parallel_matmul_drop.py

PowerOlive/mindspore

bda20724a94113cedd12c3ed9083141012da1f15

[ "Apache-2.0" ]

3,200

2020-02-17T12:45:41.000Z

2022-03-31T20:21:16.000Z

tests/ut/python/parallel/test_auto_parallel_matmul_drop.py

zimo-geek/mindspore

665ec683d4af85c71b2a1f0d6829356f2bc0e1ff

[ "Apache-2.0" ]

176

2020-02-12T02:52:11.000Z

2022-03-28T22:15:55.000Z

tests/ut/python/parallel/test_auto_parallel_matmul_drop.py

zimo-geek/mindspore

665ec683d4af85c71b2a1f0d6829356f2bc0e1ff

[ "Apache-2.0" ]

621

2020-03-09T01:31:41.000Z

2022-03-30T03:43:19.000Z

# Copyright 2019 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numpy as np import mindspore as ms import mindspore.nn as nn from mindspore import Tensor from mindspore import context from mindspore.common.api import _cell_graph_executor from mindspore.ops import composite as C from mindspore.ops import operations as P from tests.ut.python.ops.test_math_ops import VirtualLoss grad_all = C.GradOperation(get_all=True) class NetWithLoss(nn.Cell): def __init__(self, network): super(NetWithLoss, self).__init__() self.loss = VirtualLoss() self.network = network def construct(self, x, y, b): predict = self.network(x, y, b) return self.loss(predict) class GradWrap(nn.Cell): def __init__(self, network): super(GradWrap, self).__init__() self.network = network def construct(self, x, y, b): return grad_all(self.network)(x, y, b) # model_parallel test def test_two_matmul_dropout(): class Net(nn.Cell): def __init__(self): super().__init__() self.matmul1 = P.MatMul() self.dropout = nn.Dropout() self.matmul2 = P.MatMul() def construct(self, x, y, b): out = self.matmul1(x, y) out = self.dropout(out) out = self.matmul2(out, b) return out context.set_auto_parallel_context(device_num=8, global_rank=0) net = GradWrap(NetWithLoss(Net())) context.set_auto_parallel_context(parallel_mode="auto_parallel") net.set_auto_parallel() x = Tensor(np.ones([128, 32]), dtype=ms.float32) y = Tensor(np.ones([32, 64]), dtype=ms.float32) b = Tensor(np.ones([64, 64]), dtype=ms.float32) net.set_train() _cell_graph_executor.compile(net, x, y, b)

30.76

74

0.683572

2c24708a3bffb2bd86287214dbc7b1011691595e

60,660

py

Python

django/test/testcases.py

hashlash/django

3baf92cf8230ad3a932986170fd07c8feae7ff2f

[ "CNRI-Python-GPL-Compatible", "BSD-3-Clause" ]

5

2020-07-04T09:43:05.000Z

2022-02-23T12:22:24.000Z

django/test/testcases.py

Warlockk/django

e12fea24f06f8911ddc2af1d6cbfb1adb529c1f2

[ "CNRI-Python-GPL-Compatible", "BSD-3-Clause" ]

null

django/test/testcases.py

Warlockk/django

e12fea24f06f8911ddc2af1d6cbfb1adb529c1f2

[ "CNRI-Python-GPL-Compatible", "BSD-3-Clause" ]

1

2020-06-24T10:24:20.000Z

import asyncio import difflib import json import posixpath import sys import threading import unittest from collections import Counter from contextlib import contextmanager from copy import copy from difflib import get_close_matches from functools import wraps from unittest.suite import _DebugResult from unittest.util import safe_repr from urllib.parse import ( parse_qsl, unquote, urlencode, urljoin, urlparse, urlsplit, urlunparse, ) from urllib.request import url2pathname from asgiref.sync import async_to_sync from django.apps import apps from django.conf import settings from django.core import mail from django.core.exceptions import ImproperlyConfigured, ValidationError from django.core.files import locks from django.core.handlers.wsgi import WSGIHandler, get_path_info from django.core.management import call_command from django.core.management.color import no_style from django.core.management.sql import emit_post_migrate_signal from django.core.servers.basehttp import ThreadedWSGIServer, WSGIRequestHandler from django.db import DEFAULT_DB_ALIAS, connection, connections, transaction from django.forms.fields import CharField from django.http import QueryDict from django.http.request import split_domain_port, validate_host from django.test.client import Client from django.test.html import HTMLParseError, parse_html from django.test.signals import setting_changed, template_rendered from django.test.utils import ( CaptureQueriesContext, ContextList, compare_xml, modify_settings, override_settings, ) from django.utils.functional import classproperty from django.views.static import serve __all__ = ('TestCase', 'TransactionTestCase', 'SimpleTestCase', 'skipIfDBFeature', 'skipUnlessDBFeature') def to_list(value): """ Put value into a list if it's not already one. Return an empty list if value is None. """ if value is None: value = [] elif not isinstance(value, list): value = [value] return value def assert_and_parse_html(self, html, user_msg, msg): try: dom = parse_html(html) except HTMLParseError as e: standardMsg = '%s\n%s' % (msg, e) self.fail(self._formatMessage(user_msg, standardMsg)) return dom class _AssertNumQueriesContext(CaptureQueriesContext): def __init__(self, test_case, num, connection): self.test_case = test_case self.num = num super().__init__(connection) def __exit__(self, exc_type, exc_value, traceback): super().__exit__(exc_type, exc_value, traceback) if exc_type is not None: return executed = len(self) self.test_case.assertEqual( executed, self.num, "%d queries executed, %d expected\nCaptured queries were:\n%s" % ( executed, self.num, '\n'.join( '%d. %s' % (i, query['sql']) for i, query in enumerate(self.captured_queries, start=1) ) ) ) class _AssertTemplateUsedContext: def __init__(self, test_case, template_name): self.test_case = test_case self.template_name = template_name self.rendered_templates = [] self.rendered_template_names = [] self.context = ContextList() def on_template_render(self, sender, signal, template, context, **kwargs): self.rendered_templates.append(template) self.rendered_template_names.append(template.name) self.context.append(copy(context)) def test(self): return self.template_name in self.rendered_template_names def message(self): return '%s was not rendered.' % self.template_name def __enter__(self): template_rendered.connect(self.on_template_render) return self def __exit__(self, exc_type, exc_value, traceback): template_rendered.disconnect(self.on_template_render) if exc_type is not None: return if not self.test(): message = self.message() if self.rendered_templates: message += ' Following templates were rendered: %s' % ( ', '.join(self.rendered_template_names) ) else: message += ' No template was rendered.' self.test_case.fail(message) class _AssertTemplateNotUsedContext(_AssertTemplateUsedContext): def test(self): return self.template_name not in self.rendered_template_names def message(self): return '%s was rendered.' % self.template_name class _DatabaseFailure: def __init__(self, wrapped, message): self.wrapped = wrapped self.message = message def __call__(self): raise AssertionError(self.message) class SimpleTestCase(unittest.TestCase): # The class we'll use for the test client self.client. # Can be overridden in derived classes. client_class = Client _overridden_settings = None _modified_settings = None databases = set() _disallowed_database_msg = ( 'Database %(operation)s to %(alias)r are not allowed in SimpleTestCase ' 'subclasses. Either subclass TestCase or TransactionTestCase to ensure ' 'proper test isolation or add %(alias)r to %(test)s.databases to silence ' 'this failure.' ) _disallowed_connection_methods = [ ('connect', 'connections'), ('temporary_connection', 'connections'), ('cursor', 'queries'), ('chunked_cursor', 'queries'), ] @classmethod def setUpClass(cls): super().setUpClass() if cls._overridden_settings: cls._cls_overridden_context = override_settings(**cls._overridden_settings) cls._cls_overridden_context.enable() if cls._modified_settings: cls._cls_modified_context = modify_settings(cls._modified_settings) cls._cls_modified_context.enable() cls._add_databases_failures() @classmethod def _validate_databases(cls): if cls.databases == '__all__': return frozenset(connections) for alias in cls.databases: if alias not in connections: message = '%s.%s.databases refers to %r which is not defined in settings.DATABASES.' % ( cls.__module__, cls.__qualname__, alias, ) close_matches = get_close_matches(alias, list(connections)) if close_matches: message += ' Did you mean %r?' % close_matches[0] raise ImproperlyConfigured(message) return frozenset(cls.databases) @classmethod def _add_databases_failures(cls): cls.databases = cls._validate_databases() for alias in connections: if alias in cls.databases: continue connection = connections[alias] for name, operation in cls._disallowed_connection_methods: message = cls._disallowed_database_msg % { 'test': '%s.%s' % (cls.__module__, cls.__qualname__), 'alias': alias, 'operation': operation, } method = getattr(connection, name) setattr(connection, name, _DatabaseFailure(method, message)) @classmethod def _remove_databases_failures(cls): for alias in connections: if alias in cls.databases: continue connection = connections[alias] for name, _ in cls._disallowed_connection_methods: method = getattr(connection, name) setattr(connection, name, method.wrapped) @classmethod def tearDownClass(cls): cls._remove_databases_failures() if hasattr(cls, '_cls_modified_context'): cls._cls_modified_context.disable() delattr(cls, '_cls_modified_context') if hasattr(cls, '_cls_overridden_context'): cls._cls_overridden_context.disable() delattr(cls, '_cls_overridden_context') super().tearDownClass() def __call__(self, result=None): """ Wrapper around default __call__ method to perform common Django test set up. This means that user-defined Test Cases aren't required to include a call to super().setUp(). """ self._setup_and_call(result) def debug(self): """Perform the same as __call__(), without catching the exception.""" debug_result = _DebugResult() self._setup_and_call(debug_result, debug=True) def _setup_and_call(self, result, debug=False): """ Perform the following in order: pre-setup, run test, post-teardown, skipping pre/post hooks if test is set to be skipped. If debug=True, reraise any errors in setup and use super().debug() instead of __call__() to run the test. """ testMethod = getattr(self, self._testMethodName) skipped = ( getattr(self.__class__, "__unittest_skip__", False) or getattr(testMethod, "__unittest_skip__", False) ) # Convert async test methods. if asyncio.iscoroutinefunction(testMethod): setattr(self, self._testMethodName, async_to_sync(testMethod)) if not skipped: try: self._pre_setup() except Exception: if debug: raise result.addError(self, sys.exc_info()) return if debug: super().debug() else: super().__call__(result) if not skipped: try: self._post_teardown() except Exception: if debug: raise result.addError(self, sys.exc_info()) return def _pre_setup(self): """ Perform pre-test setup: * Create a test client. * Clear the mail test outbox. """ self.client = self.client_class() mail.outbox = [] def _post_teardown(self): """Perform post-test things.""" pass def settings(self, **kwargs): """ A context manager that temporarily sets a setting and reverts to the original value when exiting the context. """ return override_settings(**kwargs) def modify_settings(self, **kwargs): """ A context manager that temporarily applies changes a list setting and reverts back to the original value when exiting the context. """ return modify_settings(**kwargs) def assertRedirects(self, response, expected_url, status_code=302, target_status_code=200, msg_prefix='', fetch_redirect_response=True): """ Assert that a response redirected to a specific URL and that the redirect URL can be loaded. Won't work for external links since it uses the test client to do a request (use fetch_redirect_response=False to check such links without fetching them). """ if msg_prefix: msg_prefix += ": " if hasattr(response, 'redirect_chain'): # The request was a followed redirect self.assertTrue( response.redirect_chain, msg_prefix + "Response didn't redirect as expected: Response code was %d (expected %d)" % (response.status_code, status_code) ) self.assertEqual( response.redirect_chain[0][1], status_code, msg_prefix + "Initial response didn't redirect as expected: Response code was %d (expected %d)" % (response.redirect_chain[0][1], status_code) ) url, status_code = response.redirect_chain[-1] scheme, netloc, path, query, fragment = urlsplit(url) self.assertEqual( response.status_code, target_status_code, msg_prefix + "Response didn't redirect as expected: Final Response code was %d (expected %d)" % (response.status_code, target_status_code) ) else: # Not a followed redirect self.assertEqual( response.status_code, status_code, msg_prefix + "Response didn't redirect as expected: Response code was %d (expected %d)" % (response.status_code, status_code) ) url = response.url scheme, netloc, path, query, fragment = urlsplit(url) # Prepend the request path to handle relative path redirects. if not path.startswith('/'): url = urljoin(response.request['PATH_INFO'], url) path = urljoin(response.request['PATH_INFO'], path) if fetch_redirect_response: # netloc might be empty, or in cases where Django tests the # HTTP scheme, the convention is for netloc to be 'testserver'. # Trust both as "internal" URLs here. domain, port = split_domain_port(netloc) if domain and not validate_host(domain, settings.ALLOWED_HOSTS): raise ValueError( "The test client is unable to fetch remote URLs (got %s). " "If the host is served by Django, add '%s' to ALLOWED_HOSTS. " "Otherwise, use assertRedirects(..., fetch_redirect_response=False)." % (url, domain) ) # Get the redirection page, using the same client that was used # to obtain the original response. extra = response.client.extra or {} redirect_response = response.client.get( path, QueryDict(query), secure=(scheme == 'https'), **extra, ) self.assertEqual( redirect_response.status_code, target_status_code, msg_prefix + "Couldn't retrieve redirection page '%s': response code was %d (expected %d)" % (path, redirect_response.status_code, target_status_code) ) self.assertURLEqual( url, expected_url, msg_prefix + "Response redirected to '%s', expected '%s'" % (url, expected_url) ) def assertURLEqual(self, url1, url2, msg_prefix=''): """ Assert that two URLs are the same, ignoring the order of query string parameters except for parameters with the same name. For example, /path/?x=1&y=2 is equal to /path/?y=2&x=1, but /path/?a=1&a=2 isn't equal to /path/?a=2&a=1. """ def normalize(url): """Sort the URL's query string parameters.""" url = str(url) # Coerce reverse_lazy() URLs. scheme, netloc, path, params, query, fragment = urlparse(url) query_parts = sorted(parse_qsl(query)) return urlunparse((scheme, netloc, path, params, urlencode(query_parts), fragment)) self.assertEqual( normalize(url1), normalize(url2), msg_prefix + "Expected '%s' to equal '%s'." % (url1, url2) ) def _assert_contains(self, response, text, status_code, msg_prefix, html): # If the response supports deferred rendering and hasn't been rendered # yet, then ensure that it does get rendered before proceeding further. if hasattr(response, 'render') and callable(response.render) and not response.is_rendered: response.render() if msg_prefix: msg_prefix += ": " self.assertEqual( response.status_code, status_code, msg_prefix + "Couldn't retrieve content: Response code was %d" " (expected %d)" % (response.status_code, status_code) ) if response.streaming: content = b''.join(response.streaming_content) else: content = response.content if not isinstance(text, bytes) or html: text = str(text) content = content.decode(response.charset) text_repr = "'%s'" % text else: text_repr = repr(text) if html: content = assert_and_parse_html(self, content, None, "Response's content is not valid HTML:") text = assert_and_parse_html(self, text, None, "Second argument is not valid HTML:") real_count = content.count(text) return (text_repr, real_count, msg_prefix) def assertContains(self, response, text, count=None, status_code=200, msg_prefix='', html=False): """ Assert that a response indicates that some content was retrieved successfully, (i.e., the HTTP status code was as expected) and that ``text`` occurs ``count`` times in the content of the response. If ``count`` is None, the count doesn't matter - the assertion is true if the text occurs at least once in the response. """ text_repr, real_count, msg_prefix = self._assert_contains( response, text, status_code, msg_prefix, html) if count is not None: self.assertEqual( real_count, count, msg_prefix + "Found %d instances of %s in response (expected %d)" % (real_count, text_repr, count) ) else: self.assertTrue(real_count != 0, msg_prefix + "Couldn't find %s in response" % text_repr) def assertNotContains(self, response, text, status_code=200, msg_prefix='', html=False): """ Assert that a response indicates that some content was retrieved successfully, (i.e., the HTTP status code was as expected) and that ``text`` doesn't occurs in the content of the response. """ text_repr, real_count, msg_prefix = self._assert_contains( response, text, status_code, msg_prefix, html) self.assertEqual(real_count, 0, msg_prefix + "Response should not contain %s" % text_repr) def assertFormError(self, response, form, field, errors, msg_prefix=''): """ Assert that a form used to render the response has a specific field error. """ if msg_prefix: msg_prefix += ": " # Put context(s) into a list to simplify processing. contexts = to_list(response.context) if not contexts: self.fail(msg_prefix + "Response did not use any contexts to render the response") # Put error(s) into a list to simplify processing. errors = to_list(errors) # Search all contexts for the error. found_form = False for i, context in enumerate(contexts): if form not in context: continue found_form = True for err in errors: if field: if field in context[form].errors: field_errors = context[form].errors[field] self.assertTrue( err in field_errors, msg_prefix + "The field '%s' on form '%s' in" " context %d does not contain the error '%s'" " (actual errors: %s)" % (field, form, i, err, repr(field_errors)) ) elif field in context[form].fields: self.fail( msg_prefix + "The field '%s' on form '%s' in context %d contains no errors" % (field, form, i) ) else: self.fail( msg_prefix + "The form '%s' in context %d does not contain the field '%s'" % (form, i, field) ) else: non_field_errors = context[form].non_field_errors() self.assertTrue( err in non_field_errors, msg_prefix + "The form '%s' in context %d does not" " contain the non-field error '%s'" " (actual errors: %s)" % (form, i, err, non_field_errors or 'none') ) if not found_form: self.fail(msg_prefix + "The form '%s' was not used to render the response" % form) def assertFormsetError(self, response, formset, form_index, field, errors, msg_prefix=''): """ Assert that a formset used to render the response has a specific error. For field errors, specify the ``form_index`` and the ``field``. For non-field errors, specify the ``form_index`` and the ``field`` as None. For non-form errors, specify ``form_index`` as None and the ``field`` as None. """ # Add punctuation to msg_prefix if msg_prefix: msg_prefix += ": " # Put context(s) into a list to simplify processing. contexts = to_list(response.context) if not contexts: self.fail(msg_prefix + 'Response did not use any contexts to ' 'render the response') # Put error(s) into a list to simplify processing. errors = to_list(errors) # Search all contexts for the error. found_formset = False for i, context in enumerate(contexts): if formset not in context: continue found_formset = True for err in errors: if field is not None: if field in context[formset].forms[form_index].errors: field_errors = context[formset].forms[form_index].errors[field] self.assertTrue( err in field_errors, msg_prefix + "The field '%s' on formset '%s', " "form %d in context %d does not contain the " "error '%s' (actual errors: %s)" % (field, formset, form_index, i, err, repr(field_errors)) ) elif field in context[formset].forms[form_index].fields: self.fail( msg_prefix + "The field '%s' on formset '%s', form %d in context %d contains no errors" % (field, formset, form_index, i) ) else: self.fail( msg_prefix + "The formset '%s', form %d in context %d does not contain the field '%s'" % (formset, form_index, i, field) ) elif form_index is not None: non_field_errors = context[formset].forms[form_index].non_field_errors() self.assertFalse( not non_field_errors, msg_prefix + "The formset '%s', form %d in context %d " "does not contain any non-field errors." % (formset, form_index, i) ) self.assertTrue( err in non_field_errors, msg_prefix + "The formset '%s', form %d in context %d " "does not contain the non-field error '%s' (actual errors: %s)" % (formset, form_index, i, err, repr(non_field_errors)) ) else: non_form_errors = context[formset].non_form_errors() self.assertFalse( not non_form_errors, msg_prefix + "The formset '%s' in context %d does not " "contain any non-form errors." % (formset, i) ) self.assertTrue( err in non_form_errors, msg_prefix + "The formset '%s' in context %d does not " "contain the non-form error '%s' (actual errors: %s)" % (formset, i, err, repr(non_form_errors)) ) if not found_formset: self.fail(msg_prefix + "The formset '%s' was not used to render the response" % formset) def _assert_template_used(self, response, template_name, msg_prefix): if response is None and template_name is None: raise TypeError('response and/or template_name argument must be provided') if msg_prefix: msg_prefix += ": " if template_name is not None and response is not None and not hasattr(response, 'templates'): raise ValueError( "assertTemplateUsed() and assertTemplateNotUsed() are only " "usable on responses fetched using the Django test Client." ) if not hasattr(response, 'templates') or (response is None and template_name): if response: template_name = response response = None # use this template with context manager return template_name, None, msg_prefix template_names = [t.name for t in response.templates if t.name is not None] return None, template_names, msg_prefix def assertTemplateUsed(self, response=None, template_name=None, msg_prefix='', count=None): """ Assert that the template with the provided name was used in rendering the response. Also usable as context manager. """ context_mgr_template, template_names, msg_prefix = self._assert_template_used( response, template_name, msg_prefix) if context_mgr_template: # Use assertTemplateUsed as context manager. return _AssertTemplateUsedContext(self, context_mgr_template) if not template_names: self.fail(msg_prefix + "No templates used to render the response") self.assertTrue( template_name in template_names, msg_prefix + "Template '%s' was not a template used to render" " the response. Actual template(s) used: %s" % (template_name, ', '.join(template_names)) ) if count is not None: self.assertEqual( template_names.count(template_name), count, msg_prefix + "Template '%s' was expected to be rendered %d " "time(s) but was actually rendered %d time(s)." % (template_name, count, template_names.count(template_name)) ) def assertTemplateNotUsed(self, response=None, template_name=None, msg_prefix=''): """ Assert that the template with the provided name was NOT used in rendering the response. Also usable as context manager. """ context_mgr_template, template_names, msg_prefix = self._assert_template_used( response, template_name, msg_prefix ) if context_mgr_template: # Use assertTemplateNotUsed as context manager. return _AssertTemplateNotUsedContext(self, context_mgr_template) self.assertFalse( template_name in template_names, msg_prefix + "Template '%s' was used unexpectedly in rendering the response" % template_name ) @contextmanager def _assert_raises_or_warns_cm(self, func, cm_attr, expected_exception, expected_message): with func(expected_exception) as cm: yield cm self.assertIn(expected_message, str(getattr(cm, cm_attr))) def _assertFooMessage(self, func, cm_attr, expected_exception, expected_message, *args, **kwargs): callable_obj = None if args: callable_obj, *args = args cm = self._assert_raises_or_warns_cm(func, cm_attr, expected_exception, expected_message) # Assertion used in context manager fashion. if callable_obj is None: return cm # Assertion was passed a callable. with cm: callable_obj(*args, **kwargs) def assertRaisesMessage(self, expected_exception, expected_message, *args, **kwargs): """ Assert that expected_message is found in the message of a raised exception. Args: expected_exception: Exception class expected to be raised. expected_message: expected error message string value. args: Function to be called and extra positional args. kwargs: Extra kwargs. """ return self._assertFooMessage( self.assertRaises, 'exception', expected_exception, expected_message, *args, **kwargs ) def assertWarnsMessage(self, expected_warning, expected_message, *args, **kwargs): """ Same as assertRaisesMessage but for assertWarns() instead of assertRaises(). """ return self._assertFooMessage( self.assertWarns, 'warning', expected_warning, expected_message, *args, **kwargs ) def assertFieldOutput(self, fieldclass, valid, invalid, field_args=None, field_kwargs=None, empty_value=''): """ Assert that a form field behaves correctly with various inputs. Args: fieldclass: the class of the field to be tested. valid: a dictionary mapping valid inputs to their expected cleaned values. invalid: a dictionary mapping invalid inputs to one or more raised error messages. field_args: the args passed to instantiate the field field_kwargs: the kwargs passed to instantiate the field empty_value: the expected clean output for inputs in empty_values """ if field_args is None: field_args = [] if field_kwargs is None: field_kwargs = {} required = fieldclass(*field_args, **field_kwargs) optional = fieldclass(*field_args, **{**field_kwargs, 'required': False}) # test valid inputs for input, output in valid.items(): self.assertEqual(required.clean(input), output) self.assertEqual(optional.clean(input), output) # test invalid inputs for input, errors in invalid.items(): with self.assertRaises(ValidationError) as context_manager: required.clean(input) self.assertEqual(context_manager.exception.messages, errors) with self.assertRaises(ValidationError) as context_manager: optional.clean(input) self.assertEqual(context_manager.exception.messages, errors) # test required inputs error_required = [required.error_messages['required']] for e in required.empty_values: with self.assertRaises(ValidationError) as context_manager: required.clean(e) self.assertEqual(context_manager.exception.messages, error_required) self.assertEqual(optional.clean(e), empty_value) # test that max_length and min_length are always accepted if issubclass(fieldclass, CharField): field_kwargs.update({'min_length': 2, 'max_length': 20}) self.assertIsInstance(fieldclass(*field_args, **field_kwargs), fieldclass) def assertHTMLEqual(self, html1, html2, msg=None): """ Assert that two HTML snippets are semantically the same. Whitespace in most cases is ignored, and attribute ordering is not significant. The arguments must be valid HTML. """ dom1 = assert_and_parse_html(self, html1, msg, 'First argument is not valid HTML:') dom2 = assert_and_parse_html(self, html2, msg, 'Second argument is not valid HTML:') if dom1 != dom2: standardMsg = '%s != %s' % ( safe_repr(dom1, True), safe_repr(dom2, True)) diff = ('\n' + '\n'.join(difflib.ndiff( str(dom1).splitlines(), str(dom2).splitlines(), ))) standardMsg = self._truncateMessage(standardMsg, diff) self.fail(self._formatMessage(msg, standardMsg)) def assertHTMLNotEqual(self, html1, html2, msg=None): """Assert that two HTML snippets are not semantically equivalent.""" dom1 = assert_and_parse_html(self, html1, msg, 'First argument is not valid HTML:') dom2 = assert_and_parse_html(self, html2, msg, 'Second argument is not valid HTML:') if dom1 == dom2: standardMsg = '%s == %s' % ( safe_repr(dom1, True), safe_repr(dom2, True)) self.fail(self._formatMessage(msg, standardMsg)) def assertInHTML(self, needle, haystack, count=None, msg_prefix=''): needle = assert_and_parse_html(self, needle, None, 'First argument is not valid HTML:') haystack = assert_and_parse_html(self, haystack, None, 'Second argument is not valid HTML:') real_count = haystack.count(needle) if count is not None: self.assertEqual( real_count, count, msg_prefix + "Found %d instances of '%s' in response (expected %d)" % (real_count, needle, count) ) else: self.assertTrue(real_count != 0, msg_prefix + "Couldn't find '%s' in response" % needle) def assertJSONEqual(self, raw, expected_data, msg=None): """ Assert that the JSON fragments raw and expected_data are equal. Usual JSON non-significant whitespace rules apply as the heavyweight is delegated to the json library. """ try: data = json.loads(raw) except json.JSONDecodeError: self.fail("First argument is not valid JSON: %r" % raw) if isinstance(expected_data, str): try: expected_data = json.loads(expected_data) except ValueError: self.fail("Second argument is not valid JSON: %r" % expected_data) self.assertEqual(data, expected_data, msg=msg) def assertJSONNotEqual(self, raw, expected_data, msg=None): """ Assert that the JSON fragments raw and expected_data are not equal. Usual JSON non-significant whitespace rules apply as the heavyweight is delegated to the json library. """ try: data = json.loads(raw) except json.JSONDecodeError: self.fail("First argument is not valid JSON: %r" % raw) if isinstance(expected_data, str): try: expected_data = json.loads(expected_data) except json.JSONDecodeError: self.fail("Second argument is not valid JSON: %r" % expected_data) self.assertNotEqual(data, expected_data, msg=msg) def assertXMLEqual(self, xml1, xml2, msg=None): """ Assert that two XML snippets are semantically the same. Whitespace in most cases is ignored and attribute ordering is not significant. The arguments must be valid XML. """ try: result = compare_xml(xml1, xml2) except Exception as e: standardMsg = 'First or second argument is not valid XML\n%s' % e self.fail(self._formatMessage(msg, standardMsg)) else: if not result: standardMsg = '%s != %s' % (safe_repr(xml1, True), safe_repr(xml2, True)) diff = ('\n' + '\n'.join( difflib.ndiff(xml1.splitlines(), xml2.splitlines()) )) standardMsg = self._truncateMessage(standardMsg, diff) self.fail(self._formatMessage(msg, standardMsg)) def assertXMLNotEqual(self, xml1, xml2, msg=None): """ Assert that two XML snippets are not semantically equivalent. Whitespace in most cases is ignored and attribute ordering is not significant. The arguments must be valid XML. """ try: result = compare_xml(xml1, xml2) except Exception as e: standardMsg = 'First or second argument is not valid XML\n%s' % e self.fail(self._formatMessage(msg, standardMsg)) else: if result: standardMsg = '%s == %s' % (safe_repr(xml1, True), safe_repr(xml2, True)) self.fail(self._formatMessage(msg, standardMsg)) class TransactionTestCase(SimpleTestCase): # Subclasses can ask for resetting of auto increment sequence before each # test case reset_sequences = False # Subclasses can enable only a subset of apps for faster tests available_apps = None # Subclasses can define fixtures which will be automatically installed. fixtures = None databases = {DEFAULT_DB_ALIAS} _disallowed_database_msg = ( 'Database %(operation)s to %(alias)r are not allowed in this test. ' 'Add %(alias)r to %(test)s.databases to ensure proper test isolation ' 'and silence this failure.' ) # If transactions aren't available, Django will serialize the database # contents into a fixture during setup and flush and reload them # during teardown (as flush does not restore data from migrations). # This can be slow; this flag allows enabling on a per-case basis. serialized_rollback = False def _pre_setup(self): """ Perform pre-test setup: * If the class has an 'available_apps' attribute, restrict the app registry to these applications, then fire the post_migrate signal -- it must run with the correct set of applications for the test case. * If the class has a 'fixtures' attribute, install those fixtures. """ super()._pre_setup() if self.available_apps is not None: apps.set_available_apps(self.available_apps) setting_changed.send( sender=settings._wrapped.__class__, setting='INSTALLED_APPS', value=self.available_apps, enter=True, ) for db_name in self._databases_names(include_mirrors=False): emit_post_migrate_signal(verbosity=0, interactive=False, db=db_name) try: self._fixture_setup() except Exception: if self.available_apps is not None: apps.unset_available_apps() setting_changed.send( sender=settings._wrapped.__class__, setting='INSTALLED_APPS', value=settings.INSTALLED_APPS, enter=False, ) raise # Clear the queries_log so that it's less likely to overflow (a single # test probably won't execute 9K queries). If queries_log overflows, # then assertNumQueries() doesn't work. for db_name in self._databases_names(include_mirrors=False): connections[db_name].queries_log.clear() @classmethod def _databases_names(cls, include_mirrors=True): # Only consider allowed database aliases, including mirrors or not. return [ alias for alias in connections if alias in cls.databases and ( include_mirrors or not connections[alias].settings_dict['TEST']['MIRROR'] ) ] def _reset_sequences(self, db_name): conn = connections[db_name] if conn.features.supports_sequence_reset: sql_list = conn.ops.sequence_reset_by_name_sql( no_style(), conn.introspection.sequence_list()) if sql_list: with transaction.atomic(using=db_name): with conn.cursor() as cursor: for sql in sql_list: cursor.execute(sql) def _fixture_setup(self): for db_name in self._databases_names(include_mirrors=False): # Reset sequences if self.reset_sequences: self._reset_sequences(db_name) # Provide replica initial data from migrated apps, if needed. if self.serialized_rollback and hasattr(connections[db_name], "_test_serialized_contents"): if self.available_apps is not None: apps.unset_available_apps() connections[db_name].creation.deserialize_db_from_string( connections[db_name]._test_serialized_contents ) if self.available_apps is not None: apps.set_available_apps(self.available_apps) if self.fixtures: # We have to use this slightly awkward syntax due to the fact # that we're using *args and **kwargs together. call_command('loaddata', *self.fixtures, **{'verbosity': 0, 'database': db_name}) def _should_reload_connections(self): return True def _post_teardown(self): """ Perform post-test things: * Flush the contents of the database to leave a clean slate. If the class has an 'available_apps' attribute, don't fire post_migrate. * Force-close the connection so the next test gets a clean cursor. """ try: self._fixture_teardown() super()._post_teardown() if self._should_reload_connections(): # Some DB cursors include SQL statements as part of cursor # creation. If you have a test that does a rollback, the effect # of these statements is lost, which can affect the operation of # tests (e.g., losing a timezone setting causing objects to be # created with the wrong time). To make sure this doesn't # happen, get a clean connection at the start of every test. for conn in connections.all(): conn.close() finally: if self.available_apps is not None: apps.unset_available_apps() setting_changed.send(sender=settings._wrapped.__class__, setting='INSTALLED_APPS', value=settings.INSTALLED_APPS, enter=False) def _fixture_teardown(self): # Allow TRUNCATE ... CASCADE and don't emit the post_migrate signal # when flushing only a subset of the apps for db_name in self._databases_names(include_mirrors=False): # Flush the database inhibit_post_migrate = ( self.available_apps is not None or ( # Inhibit the post_migrate signal when using serialized # rollback to avoid trying to recreate the serialized data. self.serialized_rollback and hasattr(connections[db_name], '_test_serialized_contents') ) ) call_command('flush', verbosity=0, interactive=False, database=db_name, reset_sequences=False, allow_cascade=self.available_apps is not None, inhibit_post_migrate=inhibit_post_migrate) def assertQuerysetEqual(self, qs, values, transform=repr, ordered=True, msg=None): items = map(transform, qs) if not ordered: return self.assertEqual(Counter(items), Counter(values), msg=msg) values = list(values) # For example qs.iterator() could be passed as qs, but it does not # have 'ordered' attribute. if len(values) > 1 and hasattr(qs, 'ordered') and not qs.ordered: raise ValueError("Trying to compare non-ordered queryset " "against more than one ordered values") return self.assertEqual(list(items), values, msg=msg) def assertNumQueries(self, num, func=None, *args, using=DEFAULT_DB_ALIAS, **kwargs): conn = connections[using] context = _AssertNumQueriesContext(self, num, conn) if func is None: return context with context: func(*args, **kwargs) def connections_support_transactions(aliases=None): """ Return whether or not all (or specified) connections support transactions. """ conns = connections.all() if aliases is None else (connections[alias] for alias in aliases) return all(conn.features.supports_transactions for conn in conns) class TestCase(TransactionTestCase): """ Similar to TransactionTestCase, but use `transaction.atomic()` to achieve test isolation. In most situations, TestCase should be preferred to TransactionTestCase as it allows faster execution. However, there are some situations where using TransactionTestCase might be necessary (e.g. testing some transactional behavior). On database backends with no transaction support, TestCase behaves as TransactionTestCase. """ @classmethod def _enter_atomics(cls): """Open atomic blocks for multiple databases.""" atomics = {} for db_name in cls._databases_names(): atomics[db_name] = transaction.atomic(using=db_name) atomics[db_name].__enter__() return atomics @classmethod def _rollback_atomics(cls, atomics): """Rollback atomic blocks opened by the previous method.""" for db_name in reversed(cls._databases_names()): transaction.set_rollback(True, using=db_name) atomics[db_name].__exit__(None, None, None) @classmethod def _databases_support_transactions(cls): return connections_support_transactions(cls.databases) @classmethod def setUpClass(cls): super().setUpClass() if not cls._databases_support_transactions(): return cls.cls_atomics = cls._enter_atomics() if cls.fixtures: for db_name in cls._databases_names(include_mirrors=False): try: call_command('loaddata', *cls.fixtures, **{'verbosity': 0, 'database': db_name}) except Exception: cls._rollback_atomics(cls.cls_atomics) cls._remove_databases_failures() raise try: cls.setUpTestData() except Exception: cls._rollback_atomics(cls.cls_atomics) cls._remove_databases_failures() raise @classmethod def tearDownClass(cls): if cls._databases_support_transactions(): cls._rollback_atomics(cls.cls_atomics) for conn in connections.all(): conn.close() super().tearDownClass() @classmethod def setUpTestData(cls): """Load initial data for the TestCase.""" pass def _should_reload_connections(self): if self._databases_support_transactions(): return False return super()._should_reload_connections() def _fixture_setup(self): if not self._databases_support_transactions(): # If the backend does not support transactions, we should reload # class data before each test self.setUpTestData() return super()._fixture_setup() assert not self.reset_sequences, 'reset_sequences cannot be used on TestCase instances' self.atomics = self._enter_atomics() def _fixture_teardown(self): if not self._databases_support_transactions(): return super()._fixture_teardown() try: for db_name in reversed(self._databases_names()): if self._should_check_constraints(connections[db_name]): connections[db_name].check_constraints() finally: self._rollback_atomics(self.atomics) def _should_check_constraints(self, connection): return ( connection.features.can_defer_constraint_checks and not connection.needs_rollback and connection.is_usable() ) class CheckCondition: """Descriptor class for deferred condition checking.""" def __init__(self, *conditions): self.conditions = conditions def add_condition(self, condition, reason): return self.__class__(*self.conditions, (condition, reason)) def __get__(self, instance, cls=None): # Trigger access for all bases. if any(getattr(base, '__unittest_skip__', False) for base in cls.__bases__): return True for condition, reason in self.conditions: if condition(): # Override this descriptor's value and set the skip reason. cls.__unittest_skip__ = True cls.__unittest_skip_why__ = reason return True return False def _deferredSkip(condition, reason, name): def decorator(test_func): nonlocal condition if not (isinstance(test_func, type) and issubclass(test_func, unittest.TestCase)): @wraps(test_func) def skip_wrapper(*args, **kwargs): if (args and isinstance(args[0], unittest.TestCase) and connection.alias not in getattr(args[0], 'databases', {})): raise ValueError( "%s cannot be used on %s as %s doesn't allow queries " "against the %r database." % ( name, args[0], args[0].__class__.__qualname__, connection.alias, ) ) if condition(): raise unittest.SkipTest(reason) return test_func(*args, **kwargs) test_item = skip_wrapper else: # Assume a class is decorated test_item = test_func databases = getattr(test_item, 'databases', None) if not databases or connection.alias not in databases: # Defer raising to allow importing test class's module. def condition(): raise ValueError( "%s cannot be used on %s as it doesn't allow queries " "against the '%s' database." % ( name, test_item, connection.alias, ) ) # Retrieve the possibly existing value from the class's dict to # avoid triggering the descriptor. skip = test_func.__dict__.get('__unittest_skip__') if isinstance(skip, CheckCondition): test_item.__unittest_skip__ = skip.add_condition(condition, reason) elif skip is not True: test_item.__unittest_skip__ = CheckCondition((condition, reason)) return test_item return decorator def skipIfDBFeature(*features): """Skip a test if a database has at least one of the named features.""" return _deferredSkip( lambda: any(getattr(connection.features, feature, False) for feature in features), "Database has feature(s) %s" % ", ".join(features), 'skipIfDBFeature', ) def skipUnlessDBFeature(*features): """Skip a test unless a database has all the named features.""" return _deferredSkip( lambda: not all(getattr(connection.features, feature, False) for feature in features), "Database doesn't support feature(s): %s" % ", ".join(features), 'skipUnlessDBFeature', ) def skipUnlessAnyDBFeature(*features): """Skip a test unless a database has any of the named features.""" return _deferredSkip( lambda: not any(getattr(connection.features, feature, False) for feature in features), "Database doesn't support any of the feature(s): %s" % ", ".join(features), 'skipUnlessAnyDBFeature', ) class QuietWSGIRequestHandler(WSGIRequestHandler): """ A WSGIRequestHandler that doesn't log to standard output any of the requests received, so as to not clutter the test result output. """ def log_message(*args): pass class FSFilesHandler(WSGIHandler): """ WSGI middleware that intercepts calls to a directory, as defined by one of the *_ROOT settings, and serves those files, publishing them under *_URL. """ def __init__(self, application): self.application = application self.base_url = urlparse(self.get_base_url()) super().__init__() def _should_handle(self, path): """ Check if the path should be handled. Ignore the path if: * the host is provided as part of the base_url * the request's path isn't under the media path (or equal) """ return path.startswith(self.base_url[2]) and not self.base_url[1] def file_path(self, url): """Return the relative path to the file on disk for the given URL.""" relative_url = url[len(self.base_url[2]):] return url2pathname(relative_url) def get_response(self, request): from django.http import Http404 if self._should_handle(request.path): try: return self.serve(request) except Http404: pass return super().get_response(request) def serve(self, request): os_rel_path = self.file_path(request.path) os_rel_path = posixpath.normpath(unquote(os_rel_path)) # Emulate behavior of django.contrib.staticfiles.views.serve() when it # invokes staticfiles' finders functionality. # TODO: Modify if/when that internal API is refactored final_rel_path = os_rel_path.replace('\\', '/').lstrip('/') return serve(request, final_rel_path, document_root=self.get_base_dir()) def __call__(self, environ, start_response): if not self._should_handle(get_path_info(environ)): return self.application(environ, start_response) return super().__call__(environ, start_response) class _StaticFilesHandler(FSFilesHandler): """ Handler for serving static files. A private class that is meant to be used solely as a convenience by LiveServerThread. """ def get_base_dir(self): return settings.STATIC_ROOT def get_base_url(self): return settings.STATIC_URL class _MediaFilesHandler(FSFilesHandler): """ Handler for serving the media files. A private class that is meant to be used solely as a convenience by LiveServerThread. """ def get_base_dir(self): return settings.MEDIA_ROOT def get_base_url(self): return settings.MEDIA_URL class LiveServerThread(threading.Thread): """Thread for running a live http server while the tests are running.""" def __init__(self, host, static_handler, connections_override=None, port=0): self.host = host self.port = port self.is_ready = threading.Event() self.error = None self.static_handler = static_handler self.connections_override = connections_override super().__init__() def run(self): """ Set up the live server and databases, and then loop over handling HTTP requests. """ if self.connections_override: # Override this thread's database connections with the ones # provided by the main thread. for alias, conn in self.connections_override.items(): connections[alias] = conn try: # Create the handler for serving static and media files handler = self.static_handler(_MediaFilesHandler(WSGIHandler())) self.httpd = self._create_server() # If binding to port zero, assign the port allocated by the OS. if self.port == 0: self.port = self.httpd.server_address[1] self.httpd.set_app(handler) self.is_ready.set() self.httpd.serve_forever() except Exception as e: self.error = e self.is_ready.set() finally: connections.close_all() def _create_server(self): return ThreadedWSGIServer((self.host, self.port), QuietWSGIRequestHandler, allow_reuse_address=False) def terminate(self): if hasattr(self, 'httpd'): # Stop the WSGI server self.httpd.shutdown() self.httpd.server_close() self.join() class LiveServerTestCase(TransactionTestCase): """ Do basically the same as TransactionTestCase but also launch a live HTTP server in a separate thread so that the tests may use another testing framework, such as Selenium for example, instead of the built-in dummy client. It inherits from TransactionTestCase instead of TestCase because the threads don't share the same transactions (unless if using in-memory sqlite) and each thread needs to commit all their transactions so that the other thread can see the changes. """ host = 'localhost' port = 0 server_thread_class = LiveServerThread static_handler = _StaticFilesHandler @classproperty def live_server_url(cls): return 'http://%s:%s' % (cls.host, cls.server_thread.port) @classproperty def allowed_host(cls): return cls.host @classmethod def setUpClass(cls): super().setUpClass() connections_override = {} for conn in connections.all(): # If using in-memory sqlite databases, pass the connections to # the server thread. if conn.vendor == 'sqlite' and conn.is_in_memory_db(): # Explicitly enable thread-shareability for this connection conn.inc_thread_sharing() connections_override[conn.alias] = conn cls._live_server_modified_settings = modify_settings( ALLOWED_HOSTS={'append': cls.allowed_host}, ) cls._live_server_modified_settings.enable() cls.server_thread = cls._create_server_thread(connections_override) cls.server_thread.daemon = True cls.server_thread.start() # Wait for the live server to be ready cls.server_thread.is_ready.wait() if cls.server_thread.error: # Clean up behind ourselves, since tearDownClass won't get called in # case of errors. cls._tearDownClassInternal() raise cls.server_thread.error @classmethod def _create_server_thread(cls, connections_override): return cls.server_thread_class( cls.host, cls.static_handler, connections_override=connections_override, port=cls.port, ) @classmethod def _tearDownClassInternal(cls): # There may not be a 'server_thread' attribute if setUpClass() for some # reasons has raised an exception. if hasattr(cls, 'server_thread'): # Terminate the live server's thread cls.server_thread.terminate() # Restore sqlite in-memory database connections' non-shareability. for conn in cls.server_thread.connections_override.values(): conn.dec_thread_sharing() @classmethod def tearDownClass(cls): cls._tearDownClassInternal() cls._live_server_modified_settings.disable() super().tearDownClass() class SerializeMixin: """ Enforce serialization of TestCases that share a common resource. Define a common 'lockfile' for each set of TestCases to serialize. This file must exist on the filesystem. Place it early in the MRO in order to isolate setUpClass()/tearDownClass(). """ lockfile = None @classmethod def setUpClass(cls): if cls.lockfile is None: raise ValueError( "{}.lockfile isn't set. Set it to a unique value " "in the base class.".format(cls.__name__)) cls._lockfile = open(cls.lockfile) locks.lock(cls._lockfile, locks.LOCK_EX) super().setUpClass() @classmethod def tearDownClass(cls): super().tearDownClass() cls._lockfile.close()

40.413058

115

0.604105

85690fa8450025a8f7489b0ea456ab35da84c04c

2,738

py

Python

Exporters/Blender/src/babylon-js/exporter_settings_panel.py

harunpehlivan/Babylon.js

57275f4acf02e3d5a908672dcbda0922c7307f18

[ "Apache-2.0" ]

1

2019-04-08T07:44:09.000Z

Exporters/Blender/src/babylon-js/exporter_settings_panel.py

harunpehlivan/Babylon.js

57275f4acf02e3d5a908672dcbda0922c7307f18

[ "Apache-2.0" ]

null

Exporters/Blender/src/babylon-js/exporter_settings_panel.py

harunpehlivan/Babylon.js

57275f4acf02e3d5a908672dcbda0922c7307f18

[ "Apache-2.0" ]

1

2019-04-08T07:49:27.000Z

from .package_level import * import bpy # Panel displayed in Scene Tab of properties, so settings can be saved in a .blend file class ExporterSettingsPanel(bpy.types.Panel): bl_label = get_title() bl_space_type = 'PROPERTIES' bl_region_type = 'WINDOW' bl_context = 'scene' bpy.types.Scene.export_onlySelectedLayer = bpy.props.BoolProperty( name='Export only selected layers', description='Export only selected layers', default = False, ) bpy.types.Scene.export_flatshadeScene = bpy.props.BoolProperty( name='Flat shade entire scene', description='Use face normals on all meshes. Increases vertices.', default = False, ) bpy.types.Scene.force64Kmeshes = bpy.props.BoolProperty( name='Force 64k per Mesh Vertex Limit', description='When true, break up meshes with > 64k vertices for older\nhardware. No effect when no qualifying meshes.', default = True, ) bpy.types.Scene.attachedSound = bpy.props.StringProperty( name='Sound', description='', default = '' ) bpy.types.Scene.loopSound = bpy.props.BoolProperty( name='Loop sound', description='', default = True ) bpy.types.Scene.autoPlaySound = bpy.props.BoolProperty( name='Auto play sound', description='', default = True ) bpy.types.Scene.inlineTextures = bpy.props.BoolProperty( name='inline', description='turn textures into encoded strings, for direct inclusion into source code', default = False ) bpy.types.Scene.textureDir = bpy.props.StringProperty( name='Sub-directory', description='The path below the output directory to write texture files (any separators OS dependent)', default = '' ) bpy.types.Scene.ignoreIKBones = bpy.props.BoolProperty( name='Ignore IK Bones', description="Do not export bones with either '.ik' or 'ik.'(not case sensitive) in the name", default = False, ) def draw(self, context): layout = self.layout scene = context.scene layout.prop(scene, 'export_onlySelectedLayer') layout.prop(scene, 'export_flatshadeScene') layout.prop(scene, 'force64Kmeshes') layout.prop(scene, 'ignoreIKBones') box = layout.box() box.label(text='Texture Location:') box.prop(scene, 'inlineTextures') row = box.row() row.enabled = not scene.inlineTextures row.prop(scene, 'textureDir') box = layout.box() box.prop(scene, 'attachedSound') box.prop(scene, 'autoPlaySound') box.prop(scene, 'loopSound')

36.026316

128

0.635135

d273ecebddb6d0970334840a297d5346b4190ec1

186

py

Python

LEVEL1/제일작은수제거하기/solution.py

seunghwanly/CODING-TEST

a820da950c163d399594770199aa2e782d1fbbde

[ "MIT" ]

null

LEVEL1/제일작은수제거하기/solution.py

seunghwanly/CODING-TEST

a820da950c163d399594770199aa2e782d1fbbde

[ "MIT" ]

null

LEVEL1/제일작은수제거하기/solution.py

seunghwanly/CODING-TEST

a820da950c163d399594770199aa2e782d1fbbde

[ "MIT" ]

null

def solution(arr): if len(arr) > 1: item = min(arr) del arr[arr.index(item)] return arr else: return [-1] print(solution([4,3,2,1])) print(solution([4]))

20.666667

32

0.548387

f1fd337a65bb09ced55edf055a2ba205b2a75705

3,905

py

Python

snoop/content_types.py

hoover/snoop

bd49b081418a8a01a1e469ab17759a4c5b20d850

[ "MIT" ]

5

2017-01-03T00:52:03.000Z

2019-10-27T03:32:35.000Z

snoop/content_types.py

hoover/snoop

bd49b081418a8a01a1e469ab17759a4c5b20d850

[ "MIT" ]

25

2016-08-21T11:26:44.000Z

2018-03-13T12:19:20.000Z

snoop/content_types.py

hoover/snoop

bd49b081418a8a01a1e469ab17759a4c5b20d850

[ "MIT" ]

6

2016-09-27T13:03:45.000Z

2019-10-27T03:32:30.000Z

import mimetypes import magic mimetypes.add_type('message/x-emlx', '.emlx') mimetypes.add_type('message/x-emlxpart', '.emlxpart') mimetypes.add_type('application/vnd.ms-outlook', '.msg') mimetypes.add_type('application/x-hoover-pst', '.pst') mimetypes.add_type('application/x-hoover-pst', '.ost') mimetypes.add_type('application/x-pgp-encrypted-ascii', '.asc') mimetypes.add_type('application/x-pgp-encrypted-binary', '.pgp') def guess_content_type(filename): return mimetypes.guess_type(filename, strict=False)[0] or '' FILE_TYPES = { 'application/x-directory': 'folder', 'application/pdf': 'pdf', 'text/plain': 'text', 'text/html': 'html', 'application/x-hush-pgp-encrypted-html-body': 'html', 'message/x-emlx': 'email', 'message/rfc822': 'email', 'application/vnd.ms-outlook': 'email', 'application/x-hoover-pst': 'email-archive', 'application/msword': 'doc', 'application/vnd.openxmlformats-officedocument.wordprocessingml.document': 'doc', 'application/vnd.openxmlformats-officedocument.wordprocessingml.template': 'doc', 'application/vnd.ms-word.document.macroEnabled.12': 'doc', 'application/vnd.ms-word.template.macroEnabled.12': 'doc', 'application/vnd.oasis.opendocument.text': 'doc', 'application/vnd.oasis.opendocument.text-template': 'doc', 'application/rtf': 'doc', 'application/vnd.ms-excel': 'xls', 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet': 'xls', 'application/vnd.openxmlformats-officedocument.spreadsheetml.template': 'xls', 'application/vnd.ms-excel.sheet.macroEnabled.12': 'xls', 'application/vnd.ms-excel.template.macroEnabled.12': 'xls', 'application/vnd.ms-excel.addin.macroEnabled.12': 'xls', 'application/vnd.ms-excel.sheet.binary.macroEnabled.12': 'xls', 'application/vnd.oasis.opendocument.spreadsheet-template': 'xls', 'application/vnd.oasis.opendocument.spreadsheet': 'xls', 'application/vnd.openxmlformats-officedocument.presentationml.presentation': 'ppt', 'application/vnd.openxmlformats-officedocument.presentationml.template': 'ppt', 'application/vnd.openxmlformats-officedocument.presentationml.slideshow': 'ppt', 'application/vnd.ms-powerpoint': 'ppt', 'application/vnd.ms-powerpoint.addin.macroEnabled.12': 'ppt', 'application/vnd.ms-powerpoint.presentation.macroEnabled.12': 'ppt', 'application/vnd.ms-powerpoint.template.macroEnabled.12': 'ppt', 'application/vnd.ms-powerpoint.slideshow.macroEnabled.12': 'ppt', 'application/vnd.oasis.opendocument.presentation': 'ppt', 'application/vnd.oasis.opendocument.presentation-template': 'ppt', 'application/zip': 'archive', 'application/rar': 'archive', 'application/x-7z-compressed': 'archive', 'application/x-tar': 'archive', 'application/x-bzip2': 'archive', 'application/x-zip': 'archive', 'application/x-gzip': 'archive', 'application/x-zip-compressed': 'archive', 'application/x-rar-compressed': 'archive', } MAGIC_DESCRIPTION_TYPES = { "Microsoft Outlook email folder (>=2003)": "application/x-hoover-pst", "Composite Document File V2 Document": "application/vnd.ms-outlook", } MAGIC_READ_LIMIT = 24 * 1024 * 1024 def libmagic_guess_content_type(file, filesize): buffer = file.read(min(MAGIC_READ_LIMIT, filesize)) content_type = magic.from_buffer(buffer, mime=True) if content_type in FILE_TYPES: return content_type magic_description = magic.from_buffer(buffer, mime=False) return MAGIC_DESCRIPTION_TYPES.get(magic_description, content_type or '') def guess_filetype(doc): content_type = doc.content_type.split(';')[0] if content_type in FILE_TYPES: return FILE_TYPES[content_type] else: supertype = content_type.split('/')[0] if supertype in ['audio', 'video', 'image']: return supertype return None

41.542553

87

0.714725

2f840aca672bc20d880ebd6129769eebdc3f89f5

903

py

Python

ctrl/cmorph/old/cmorph_regrid.py

markmuetz/cosmic

f215c499bfc8f1d717dea6aa78a58632a4e89113

[ "Apache-2.0" ]

null

ctrl/cmorph/old/cmorph_regrid.py

markmuetz/cosmic

f215c499bfc8f1d717dea6aa78a58632a4e89113

[ "Apache-2.0" ]

null

ctrl/cmorph/old/cmorph_regrid.py

markmuetz/cosmic

f215c499bfc8f1d717dea6aa78a58632a4e89113

[ "Apache-2.0" ]

1

2021-01-26T02:25:48.000Z

import sys from pathlib import Path import iris from cosmic.util import load_module, filepath_regrid def main(year, month): target_filepath = Path(f'/gws/nopw/j04/cosmic/mmuetz/data/u-al508/ap9.pp/precip_200501/al508a.p9200501.asia_precip.nc') cmorph_datadir = Path(f'/gws/nopw/j04/cosmic/mmuetz/data/cmorph_data') cmorph_filepath = cmorph_datadir / f'8km-30min/precip_{year}{month:02}/cmorph_ppt_{year}{month:02}.asia.nc' output_filepath = cmorph_filepath.parent / (cmorph_filepath.stem + '.N1280.nc') print(f'Regrid {cmorph_filepath} -> {output_filepath}') print(f' using {target_filepath} resolution') coarse_cube = filepath_regrid(cmorph_filepath, target_filepath) iris.save(coarse_cube, str(output_filepath), zlib=True) if __name__ == '__main__': config = load_module(sys.argv[1]) config_key = sys.argv[2] main(*config.SCRIPT_ARGS[config_key])

33.444444

123

0.744186

74aa3d3bb96a81c9140e5a38d934c9e9cfee3e46

226

py

Python

Dict comp/aula67.py

pinheirogus/Curso-Python-Udemy

d6d52320426172e924081b9df619490baa8c6016

[ "MIT" ]

1

2021-09-01T01:58:13.000Z

Dict comp/aula67.py

pinheirogus/Curso-Python-Udemy

d6d52320426172e924081b9df619490baa8c6016

[ "MIT" ]

null

Dict comp/aula67.py

pinheirogus/Curso-Python-Udemy

d6d52320426172e924081b9df619490baa8c6016

[ "MIT" ]

null

lista = [ ('chave', 'valor'), ('chave2', 'valor2'), ('chave3', 3), ] # d1 = { x: y*2 for x, y in lista} # d1 = { x.upper(): y.upper()*2 for x, y in lista} d1 = { f'chave_{x}': x**2 for x in range(5)} print(d1)

16.142857

50

0.482301

ae7a4dfbe07b2f2204d8e33df76264e85d3847fe

419

py

Python

sudoku/src/main.py

klesnkri/sudoku-solver-aco

10170bf2a66877811fe78a65d45cb4d752c0e6f2

[ "MIT" ]

2

2021-05-14T14:51:37.000Z

2022-02-19T16:59:41.000Z

sudoku/src/main.py

christee8/sudoku-solver-aco

10170bf2a66877811fe78a65d45cb4d752c0e6f2

[ "MIT" ]

null

sudoku/src/main.py

christee8/sudoku-solver-aco

10170bf2a66877811fe78a65d45cb4d752c0e6f2

[ "MIT" ]

1

2021-06-06T20:32:15.000Z

import sys from App import App def main(): # Command line arguments if len(sys.argv) != 3 or (sys.argv[2] != "gui" and sys.argv[2] != "console"): print("usage: " + sys.argv[0] + " <input sudoku filename> <gui/console>") return sudoku_file = sys.argv[1] gui_active = (sys.argv[2] == "gui") app = App(gui_active, sudoku_file) app.run() if __name__ == "__main__": main()

19.952381

81

0.582339

0ec5dfb1b134a65644022f2513b464c8072c835b

253

py

Python

manage.py

klebercode/protocolle

31b464e41e20b77d3bce2a4688b0f4f7dff2c3af

[ "MIT" ]

null

manage.py

klebercode/protocolle

31b464e41e20b77d3bce2a4688b0f4f7dff2c3af

[ "MIT" ]

null

manage.py

klebercode/protocolle

31b464e41e20b77d3bce2a4688b0f4f7dff2c3af

[ "MIT" ]

null

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

23

74

0.774704

2071dd49fd312292976a8179dd06fcde8a122b3d

10,932

py

Python

ZabbixAlertYTWorkflow.py

devopshq/zabbix-youtrack-action

e4d8354e059687cc0ae13b1c5c25bf70de03c31f

[ "MIT" ]

5

2017-05-20T21:12:47.000Z

2021-03-26T14:53:09.000Z

ZabbixAlertYTWorkflow.py

devopshq/zabbix-youtrack-action

e4d8354e059687cc0ae13b1c5c25bf70de03c31f

[ "MIT" ]

null

ZabbixAlertYTWorkflow.py

devopshq/zabbix-youtrack-action

e4d8354e059687cc0ae13b1c5c25bf70de03c31f

[ "MIT" ]

3

2016-07-30T11:38:00.000Z

2019-02-28T10:39:28.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # (c) DevOpsHQ, 2016 # Integration YouTrack and Zabbix alerts. import yaml from pyzabbix import ZabbixAPI import sys import urllib import logging import time from youtrack.connection import Connection import re from requests.packages.urllib3 import disable_warnings disable_warnings() # disable ssl certificate errors # ------------ START Constants ------------ YT_PROJECT_NAME = 'CM' # ID project in Youtrack YT_ASSIGNEE = 'Zabbix' # Assignee to after create issue YT_TYPE = 'Error' # Youtrack Issue type YT_SERVICE = 'Zabbix' # Youtrack Issue service YT_SUBSYSTEM = 'DevOps' # Youtrack Issue subsystem YT_USER = 'Zabbix' # Youtrack Issue create user YT_PASSWORD = sys.argv[4] # Youtrack user password YT_TIME = 'About 1 hour' # Estimated time # YT_TIME = 'Undefined' # Estimated time YT_COMMENT = "Now is {status}. \n{text}\n\n" # Add this comment in issue LOG_FILE_NAME = '/var/log/zabbix/PtZabbixAlertYTWorkflow.log' # Path to Log-file for debug # LOG_FILE_NAME = 'PtZabbixAlertYTWorkflow.log' # Uncomment for debug in Windows-OS ZABBIX_SERVER = "https://zabbix.example.com/zabbix" ZBX_USER = "zabbix_api" ZBX_PASSWORD = sys.argv[5] # ------------ END Constants ------------ # ------------ START Setup logging ------------ # Use logger to log information logger = logging.getLogger() logger.setLevel(logging.DEBUG) formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s') # Log to file fh = logging.FileHandler(LOG_FILE_NAME) fh.setLevel(logging.DEBUG) fh.setFormatter(formatter) logger.addHandler(fh) # Log to stdout ch = logging.StreamHandler() ch.setLevel(logging.DEBUG) ch.setFormatter(formatter) logger.addHandler(ch) # Use logger to log information # Log from pyzabbix log = logging.getLogger('pyzabbix') log.addHandler(ch) log.addHandler(fh) log.setLevel(logging.DEBUG) # ------------ END Setup logging ------------ # ------------ START ZabbixAPI block ------------ Zbx = ZabbixAPI(ZABBIX_SERVER) Zbx.session.verify = False Zbx.login(ZBX_USER, ZBX_PASSWORD) # ------------ END ZabbixAPI block ------------ # ------------ START Function declaration ------------ def ExecAndLog(connection, issueId, command="", comment=""): logger.debug("Run command in {issueId}: {command}. {comment}".format(issueId=issueId, command=command, comment=comment )) connection.executeCommand(issueId=issueId, command=command, comment=comment, ) # ------------ END Function declaration ------------ def Main(sendTo, subject, yamlMessage): """ Workflow Zabbix-YouTrack :param sendTo: URL to Youtrack (ex. https://youtrack.example.com) :param subject: subject from Zabbix Action :param yamlMessage: message from Zabbix Action :return: """ # ----- Use below example yamlMessage to debug ----- # yamlMessage = """Name: 'Test Zabbix-YT workflow, ignore it' # Text: 'Agent ping (server:agent.ping()): DOWN (1) ' # Hostname: 'server.exmpale.ru' # Status: "OK" # Severity: "High" # EventID: "96976" # TriggerID: "123456789012" """ messages = yaml.load(yamlMessage) # ----- START Issue parameters ----- # Correspondence between the YouTrackPriority and ZabbixSeverity # Critical >= High # Normal < High ytPriority = 'Normal' if messages['Severity'] == 'Disaster' or messages['Severity'] == 'High': ytPriority = 'Critical' ytName = "{} ZabbixTriggerID::{}".format(messages['Name'], messages['TriggerID']) # ----- END Issue parameters ----- # ----- START Youtrack Issue description ----- # Search link to other issue searchString = "Hostname: '{}'".format(messages['Hostname']) linkToHostIssue = "{youtrack}/issues/{projectname}?q={query}".format( youtrack=sendTo, projectname=YT_PROJECT_NAME, query=urllib.parse.quote(searchString, safe='') ) issueDescription = """ {ytName} ----- {yamlMessage} ----- - [https://zabbix.example.com/zabbix.php?action=dashboard.view Zabbix Dashboard] - Show [{linkToHostIssue} all issue for *this host*] """.format( ytName=ytName, yamlMessage=yamlMessage, linkToHostIssue=linkToHostIssue, ) # ----- END Youtrack Issue description ----- # ----- START Youtrack current week ----- # Create connect to Youtrack API connection = Connection(sendTo, YT_USER, YT_PASSWORD) # Get current week in YT format (Sprint planned) version = connection.getAllBundles('version') for fixVersion in version[0].values: if fixVersion['archived'] == False and fixVersion['released'] == False: fixVersionWeek = fixVersion['name'] break # ----- END Youtrack current week ----- # ----- START Youtrack get or create issue ----- # Get issue if exist # Search for TriggerID createNewIssue = False logger.debug("Get issue with text '{}'".format(messages['TriggerID'])) issue = connection.getIssues(YT_PROJECT_NAME, "ZabbixTriggerID::{}".format(messages['TriggerID']), 0, 1) if len(issue) == 0: createNewIssue = True else: # if issue contains TriggerID in summary, then it's good issue # else create new issue, this is bad issue, not from Zabbix if "ZabbixTriggerID::{}".format(messages['TriggerID']) in issue[0]['summary']: issueId = issue[0]['id'] issue = connection.getIssue(issueId) else: createNewIssue = True # Create new issue if createNewIssue: logger.debug("Create new issue because it is not exist") issue = connection.createIssue(YT_PROJECT_NAME, 'Unassigned', ytName, issueDescription, priority=ytPriority, subsystem=YT_SUBSYSTEM, type=YT_TYPE, ) time.sleep(3) # Parse ID for new issue result = re.search(r'(CM-\d*)', issue[0]['location']) issueId = result.group(0) issue = connection.getIssue(issueId) logger.debug("Issue have id={}".format(issueId)) # Set issue service ExecAndLog(connection, issueId, "Service {}".format(YT_SERVICE)) # Update priority ExecAndLog(connection, issueId, "Priority {}".format(ytPriority)) # ----- END Youtrack get or create issue ----- # ----- START PROBLEM block ------ if messages['Status'] == "PROBLEM": # Issue exist and NOT Hold on, Unnassigned and Estimated time set if issue['State'] != 'Hold on': # Estimated time ExecAndLog(connection, issueId, "Estimated time {}".format(YT_TIME)) # Update fix version ExecAndLog(connection=connection, issueId=issueId, command="Sprint planned {}".format(fixVersionWeek)) # Reopen if Fixed or Verified or Canceled if issue['State'] == 'Fixed' or issue['State'] == 'Verified' or issue['State'] == 'Canceled': # Reopen Issue ExecAndLog(connection, issueId, "State reopen") # Assignee issue ExecAndLog(connection, issueId, command="Assignee Unassigned") # Update summary and description for issue logger.debug("Run command in {issueId}: {command}".format(issueId=issueId, command="""Update summary and description with connection.updateIssue method""" )) connection.updateIssue(issueId=issueId, summary=ytName, description=issueDescription) # Add comment logger.debug("Run command in {issueId}: {command}".format(issueId=issueId, command="""Now is PROBLEM {}""".format( messages['Text']) )) connection.executeCommand(issueId=issueId, command="", comment=YT_COMMENT.format( status=messages['Status'], text=messages['Text']) ) # Send ID to Zabbix: logger.debug("ZABBIX-API: Send Youtrack ID to {}".format(messages['EventID'])) Zbx.event.acknowledge(eventids=messages['EventID'], message="Create Youtrack task") Zbx.event.acknowledge(eventids=messages['EventID'], message="https://youtrack.example.com/issue/{}".format(issueId)) # ----- End PROBLEM block ------ # ----- Start OK block ----- if messages['Status'] == "OK": if issue['State'] == 'Hold on' or issue['State'] == 'Registered': # Cancel if not in work ExecAndLog(connection, issueId, command="State Cancel") # Assignee issue ExecAndLog(connection, issueId, command="Assignee {}".format(YT_ASSIGNEE)) if issue['State'] == 'Fixed': # Verify if Fixed ExecAndLog(connection, issueId, command="State verify") logger.debug("Run command in {issueId}: {command}".format(issueId=issueId, command="""Now is OK {}""".format(messages['Text']) )) connection.executeCommand(issueId=issueId, command="", comment=YT_COMMENT.format( status=messages['Status'], text=messages['Text']) ) # ----- End OK block ----- if __name__ == "__main__": logger.debug("Start script with arguments: {}".format(sys.argv[1:])) try: Main( # Arguments WIKI: https://www.zabbix.com/documentation/3.0/ru/manual/config/notifications/media/script sys.argv[1], # to sys.argv[2], # subject sys.argv[3], # body # FYI: Next argument used in code: # sys.argv[4], # YT_PASSWORD # sys.argv[5], # ZBX_PASSWORD ) except Exception: logger.exception("Exit with error") # Output exception exit(1)

36.198675

145

0.554885

f614c47b3a151d4e70d4c3cda4b3c4265d64f7cd

5,186

py

Python

ironic_python_agent/openstack/common/ironic_lib/utils.py

faizan-barmawer/ironic-python-agent

589b198e960a69fc9d71825fdf204cbbd59f39dd

[ "Apache-2.0" ]

null

ironic_python_agent/openstack/common/ironic_lib/utils.py

faizan-barmawer/ironic-python-agent

589b198e960a69fc9d71825fdf204cbbd59f39dd

[ "Apache-2.0" ]

null

ironic_python_agent/openstack/common/ironic_lib/utils.py

faizan-barmawer/ironic-python-agent

589b198e960a69fc9d71825fdf204cbbd59f39dd

[ "Apache-2.0" ]

null

# Copyright 2010 United States Government as represented by the # Administrator of the National Aeronautics and Space Administration. # Copyright 2011 Justin Santa Barbara # Copyright (c) 2012 NTT DOCOMO, INC. # All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. """Utilities and helper functions.""" import errno import logging import os from oslo_concurrency import processutils from oslo_config import cfg from oslo_utils import excutils from ironic_python_agent.openstack.common.ironic_lib import exception from ironic_python_agent.openstack.common._i18n import _LE from ironic_python_agent.openstack.common._i18n import _LW utils_opts = [ cfg.StrOpt('rootwrap_config', default="", help='Path to the rootwrap configuration file to use for ' 'running commands as root.'), cfg.StrOpt('rootwrap_helper_cmd', default="", help='Path to the rootwrap configuration file to use for ' 'running commands as root.'), cfg.StrOpt('tempdir', help='Explicitly specify the temporary working directory.'), ] CONF = cfg.CONF CONF.register_opts(utils_opts) LOG = logging.getLogger(__name__) def _get_root_helper(): return '%s %s' % (CONF.rootwrap_helper_cmd, CONF.rootwrap_config) def execute(*cmd, **kwargs): """Convenience wrapper around oslo's execute() method. :param cmd: Passed to processutils.execute. :param use_standard_locale: True | False. Defaults to False. If set to True, execute command with standard locale added to environment variables. :returns: (stdout, stderr) from process execution :raises: UnknownArgumentError :raises: ProcessExecutionError """ use_standard_locale = kwargs.pop('use_standard_locale', False) if use_standard_locale: env = kwargs.pop('env_variables', os.environ.copy()) env['LC_ALL'] = 'C' kwargs['env_variables'] = env if kwargs.get('run_as_root') and 'root_helper' not in kwargs: kwargs['root_helper'] = _get_root_helper() result = processutils.execute(*cmd, **kwargs) LOG.debug('Execution completed, command line is "%s"', ' '.join(map(str, cmd))) LOG.debug('Command stdout is: "%s"' % result[0]) LOG.debug('Command stderr is: "%s"' % result[1]) return result def mkfs(fs, path, label=None): """Format a file or block device :param fs: Filesystem type (examples include 'swap', 'ext3', 'ext4' 'btrfs', etc.) :param path: Path to file or block device to format :param label: Volume label to use """ if fs == 'swap': args = ['mkswap'] else: args = ['mkfs', '-t', fs] # add -F to force no interactive execute on non-block device. if fs in ('ext3', 'ext4'): args.extend(['-F']) if label: if fs in ('msdos', 'vfat'): label_opt = '-n' else: label_opt = '-L' args.extend([label_opt, label]) args.append(path) try: execute(*args, run_as_root=True, use_standard_locale=True) except processutils.ProcessExecutionError as e: with excutils.save_and_reraise_exception() as ctx: if os.strerror(errno.ENOENT) in e.stderr: ctx.reraise = False LOG.exception(_LE('Failed to make file system. ' 'File system %s is not supported.'), fs) raise exception.FileSystemNotSupported(fs=fs) else: LOG.exception(_LE('Failed to create a file system ' 'in %(path)s. Error: %(error)s'), {'path': path, 'error': e}) def unlink_without_raise(path): try: os.unlink(path) except OSError as e: if e.errno == errno.ENOENT: return else: LOG.warn(_LW("Failed to unlink %(path)s, error: %(e)s"), {'path': path, 'e': e}) def dd(src, dst, *args): """Execute dd from src to dst. :param src: the input file for dd command. :param dst: the output file for dd command. :param args: a tuple containing the arguments to be passed to dd command. :raises: processutils.ProcessExecutionError if it failed to run the process. """ LOG.debug("Starting dd process.") execute('dd', 'if=%s' % src, 'of=%s' % dst, *args, run_as_root=True, check_exit_code=[0]) def is_http_url(url): url = url.lower() return url.startswith('http://') or url.startswith('https://')

34.805369

78

0.625916

d10e8863134faadc53cd2057a122c4e6b512fc9b

5,698

py

Python

sdk/python/pulumi_azure_native/network/v20200801/get_route_filter_rule.py

pulumi-bot/pulumi-azure-native

f7b9490b5211544318e455e5cceafe47b628e12c

[ "Apache-2.0" ]

null

sdk/python/pulumi_azure_native/network/v20200801/get_route_filter_rule.py

pulumi-bot/pulumi-azure-native

f7b9490b5211544318e455e5cceafe47b628e12c

[ "Apache-2.0" ]

null

sdk/python/pulumi_azure_native/network/v20200801/get_route_filter_rule.py

pulumi-bot/pulumi-azure-native

f7b9490b5211544318e455e5cceafe47b628e12c

[ "Apache-2.0" ]

null

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union from ... import _utilities, _tables __all__ = [ 'GetRouteFilterRuleResult', 'AwaitableGetRouteFilterRuleResult', 'get_route_filter_rule', ] @pulumi.output_type class GetRouteFilterRuleResult: """ Route Filter Rule Resource. """ def __init__(__self__, access=None, communities=None, etag=None, id=None, location=None, name=None, provisioning_state=None, route_filter_rule_type=None): if access and not isinstance(access, str): raise TypeError("Expected argument 'access' to be a str") pulumi.set(__self__, "access", access) if communities and not isinstance(communities, list): raise TypeError("Expected argument 'communities' to be a list") pulumi.set(__self__, "communities", communities) if etag and not isinstance(etag, str): raise TypeError("Expected argument 'etag' to be a str") pulumi.set(__self__, "etag", etag) if id and not isinstance(id, str): raise TypeError("Expected argument 'id' to be a str") pulumi.set(__self__, "id", id) if location and not isinstance(location, str): raise TypeError("Expected argument 'location' to be a str") pulumi.set(__self__, "location", location) if name and not isinstance(name, str): raise TypeError("Expected argument 'name' to be a str") pulumi.set(__self__, "name", name) if provisioning_state and not isinstance(provisioning_state, str): raise TypeError("Expected argument 'provisioning_state' to be a str") pulumi.set(__self__, "provisioning_state", provisioning_state) if route_filter_rule_type and not isinstance(route_filter_rule_type, str): raise TypeError("Expected argument 'route_filter_rule_type' to be a str") pulumi.set(__self__, "route_filter_rule_type", route_filter_rule_type) @property @pulumi.getter def access(self) -> str: """ The access type of the rule. """ return pulumi.get(self, "access") @property @pulumi.getter def communities(self) -> Sequence[str]: """ The collection for bgp community values to filter on. e.g. ['12076:5010','12076:5020']. """ return pulumi.get(self, "communities") @property @pulumi.getter def etag(self) -> str: """ A unique read-only string that changes whenever the resource is updated. """ return pulumi.get(self, "etag") @property @pulumi.getter def id(self) -> Optional[str]: """ Resource ID. """ return pulumi.get(self, "id") @property @pulumi.getter def location(self) -> Optional[str]: """ Resource location. """ return pulumi.get(self, "location") @property @pulumi.getter def name(self) -> Optional[str]: """ The name of the resource that is unique within a resource group. This name can be used to access the resource. """ return pulumi.get(self, "name") @property @pulumi.getter(name="provisioningState") def provisioning_state(self) -> str: """ The provisioning state of the route filter rule resource. """ return pulumi.get(self, "provisioning_state") @property @pulumi.getter(name="routeFilterRuleType") def route_filter_rule_type(self) -> str: """ The rule type of the rule. """ return pulumi.get(self, "route_filter_rule_type") class AwaitableGetRouteFilterRuleResult(GetRouteFilterRuleResult): # pylint: disable=using-constant-test def __await__(self): if False: yield self return GetRouteFilterRuleResult( access=self.access, communities=self.communities, etag=self.etag, id=self.id, location=self.location, name=self.name, provisioning_state=self.provisioning_state, route_filter_rule_type=self.route_filter_rule_type) def get_route_filter_rule(resource_group_name: Optional[str] = None, route_filter_name: Optional[str] = None, rule_name: Optional[str] = None, opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetRouteFilterRuleResult: """ Route Filter Rule Resource. :param str resource_group_name: The name of the resource group. :param str route_filter_name: The name of the route filter. :param str rule_name: The name of the rule. """ __args__ = dict() __args__['resourceGroupName'] = resource_group_name __args__['routeFilterName'] = route_filter_name __args__['ruleName'] = rule_name if opts is None: opts = pulumi.InvokeOptions() if opts.version is None: opts.version = _utilities.get_version() __ret__ = pulumi.runtime.invoke('azure-native:network/v20200801:getRouteFilterRule', __args__, opts=opts, typ=GetRouteFilterRuleResult).value return AwaitableGetRouteFilterRuleResult( access=__ret__.access, communities=__ret__.communities, etag=__ret__.etag, id=__ret__.id, location=__ret__.location, name=__ret__.name, provisioning_state=__ret__.provisioning_state, route_filter_rule_type=__ret__.route_filter_rule_type)

35.6125

158

0.647596

58f20085decd164e0fc59b01031be872e82942d8

1,216

py

Python

test/test_videos_service_api.py

stanionascu/python-embyapi

a3f7aa49aea4052277cc43605c0d89bc6ff21913

[ "BSD-3-Clause" ]

null

test/test_videos_service_api.py

stanionascu/python-embyapi

a3f7aa49aea4052277cc43605c0d89bc6ff21913

[ "BSD-3-Clause" ]

null

test/test_videos_service_api.py

stanionascu/python-embyapi

a3f7aa49aea4052277cc43605c0d89bc6ff21913

[ "BSD-3-Clause" ]

null

# coding: utf-8 """ Emby Server API Explore the Emby Server API # noqa: E501 OpenAPI spec version: 4.1.1.0 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import unittest import embyapi from embyapi.api.videos_service_api import VideosServiceApi # noqa: E501 from embyapi.rest import ApiException class TestVideosServiceApi(unittest.TestCase): """VideosServiceApi unit test stubs""" def setUp(self): self.api = VideosServiceApi() # noqa: E501 def tearDown(self): pass def test_delete_videos_by_id_alternatesources(self): """Test case for delete_videos_by_id_alternatesources Removes alternate video sources. # noqa: E501 """ pass def test_get_videos_by_id_additionalparts(self): """Test case for get_videos_by_id_additionalparts Gets additional parts for a video. # noqa: E501 """ pass def test_post_videos_mergeversions(self): """Test case for post_videos_mergeversions Merges videos into a single record # noqa: E501 """ pass if __name__ == '__main__': unittest.main()

22.109091

73

0.676809

7a8ee979453b2a33c07f33d4054b781bb33c56d0

1,073

py

Python

peas/server/reconnect_task.py

13thProgression/peas-blockchain

8e058cbfe0c1ab73f7c1ec41bedb39071c63141c

[ "Apache-2.0" ]

2

2021-08-16T17:45:07.000Z

2021-09-18T19:00:58.000Z

peas/server/reconnect_task.py

13thProgression/peas-blockchain

8e058cbfe0c1ab73f7c1ec41bedb39071c63141c

[ "Apache-2.0" ]

4

2021-09-26T15:50:20.000Z

2021-10-06T06:18:51.000Z

peas/server/reconnect_task.py

13thProgression/peas-blockchain

8e058cbfe0c1ab73f7c1ec41bedb39071c63141c

[ "Apache-2.0" ]

3

2021-09-29T19:08:41.000Z

2022-03-15T08:47:28.000Z

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

37

106

0.643989

51148bca23f588e0b7392640d1c53e0333ab77fe

1,134

py

Python

speakeasy/winenv/defs/windows/netapi32.py

mwilliams31/speakeasy

7b75f767017f665d66e6736f9f42cd32c1f9476d

[ "MIT" ]

null

speakeasy/winenv/defs/windows/netapi32.py

mwilliams31/speakeasy

7b75f767017f665d66e6736f9f42cd32c1f9476d

[ "MIT" ]

null

speakeasy/winenv/defs/windows/netapi32.py

mwilliams31/speakeasy

7b75f767017f665d66e6736f9f42cd32c1f9476d

[ "MIT" ]

null

# Copyright (C) 2021 FireEye, Inc. All Rights Reserved. from speakeasy.struct import EmuStruct, Ptr import ctypes as ct NERR_Success = 0 ERROR_INVALID_LEVEL = 0x7C class WKSTA_INFO_100(EmuStruct): def __init__(self, ptr_size): super().__init__(ptr_size) self.wki_platform_id = Ptr self.wki_computername = Ptr self.wki_langroup = Ptr self.wki_ver_major = ct.c_uint32 self.wki_ver_minor = ct.c_uint32 class WKSTA_INFO_101(EmuStruct): def __init__(self, ptr_size): super().__init__(ptr_size) self.wki_platform_id = Ptr self.wki_computername = Ptr self.wki_langroup = Ptr self.wki_ver_major = ct.c_uint32 self.wki_ver_minor = ct.c_uint32 self.wki_lanroot = Ptr class WKSTA_INFO_102(EmuStruct): def __init__(self, ptr_size): super().__init__(ptr_size) self.wki_platform_id = Ptr self.wki_computername = Ptr self.wki_langroup = Ptr self.wki_ver_major = ct.c_uint32 self.wki_ver_minor = ct.c_uint32 self.wki_lanroot = Ptr self.wki_logged_on_users = Ptr

27.658537

55

0.671958

eb943ea0b3ef035285705a00961cc5940adf0958

25,453

py

Python

src/tests/conftest.py

wjwoodson/pretalx

935d0295e408a3518bf472147cc16722c44fe3fb

[ "Apache-2.0" ]

1

2021-06-19T02:33:29.000Z

src/tests/conftest.py

AKJK-Internet-32002/pretalx

062177a8a361dd57a8889e2dd422beec505881a9

[ "Apache-2.0" ]

null

src/tests/conftest.py

AKJK-Internet-32002/pretalx

062177a8a361dd57a8889e2dd422beec505881a9

[ "Apache-2.0" ]

null

import datetime as dt import pytest import pytz from django.core import management from django.core.files.uploadedfile import SimpleUploadedFile from django.utils.timezone import now from django_scopes import scope, scopes_disabled from pretalx.event.models import Event, Organiser, Team, TeamInvite from pretalx.mail.models import MailTemplate from pretalx.person.models import SpeakerInformation, SpeakerProfile, User from pretalx.schedule.models import Availability, Room, TalkSlot from pretalx.submission.models import ( Answer, AnswerOption, Feedback, Question, QuestionVariant, Resource, Review, Submission, SubmissionType, SubmitterAccessCode, Track, ) @pytest.fixture(scope="session", autouse=True) def collect_static(request): management.call_command("collectstatic", "--noinput", "--clear") @pytest.fixture def template_patch(monkeypatch): # Patch out template rendering for performance improvements monkeypatch.setattr( "django.template.backends.django.Template.render", lambda *args, **kwargs: "mocked template", ) @pytest.fixture def organiser(): with scopes_disabled(): o = Organiser.objects.create(name="Super Organiser", slug="superorganiser") Team.objects.create( name="Organisers", organiser=o, can_create_events=True, can_change_teams=True, can_change_organiser_settings=True, can_change_event_settings=True, can_change_submissions=True, ) Team.objects.create( name="Organisers and reviewers", organiser=o, can_create_events=True, can_change_teams=True, can_change_organiser_settings=True, can_change_event_settings=True, can_change_submissions=True, is_reviewer=True, ) Team.objects.create(name="Reviewers", organiser=o, is_reviewer=True) return o @pytest.fixture def other_organiser(): with scopes_disabled(): o = Organiser.objects.create(name="Different Organiser", slug="diffo") Team.objects.create( name="Organisers", organiser=o, can_create_events=True, can_change_teams=True, can_change_organiser_settings=True, can_change_event_settings=True, can_change_submissions=True, ) Team.objects.create( name="Organisers and reviewers", organiser=o, can_create_events=True, can_change_teams=True, can_change_organiser_settings=True, can_change_event_settings=True, can_change_submissions=True, is_reviewer=True, ) Team.objects.create(name="Reviewers", organiser=o, is_reviewer=True) return o @pytest.fixture def event(organiser): today = dt.date.today() with scopes_disabled(): event = Event.objects.create( name="Fancy testevent", is_public=True, slug="test", email="orga@orga.org", date_from=today, date_to=today + dt.timedelta(days=3), organiser=organiser, ) # exporting takes quite some time, so this speeds up our tests event.settings.export_html_on_schedule_release = False for team in organiser.teams.all(): team.limit_events.add(event) return event @pytest.fixture def other_event(other_organiser): with scopes_disabled(): event = Event.objects.create( name="Boring testevent", is_public=True, slug="other", email="orga2@orga.org", date_from=dt.date.today() + dt.timedelta(days=1), date_to=dt.date.today() + dt.timedelta(days=1), organiser=other_organiser, ) event.settings.export_html_on_schedule_release = False for team in other_organiser.teams.all(): team.limit_events.add(event) return event @pytest.fixture def multilingual_event(organiser): with scopes_disabled(): today = dt.date.today() event = Event.objects.create( name="Fancy testevent", is_public=True, slug="test2", email="orga@orga.org", date_from=today, date_to=today + dt.timedelta(days=3), locale_array="en,de", organiser=organiser, ) event.settings.export_html_on_schedule_release = False for team in organiser.teams.all(): team.limit_events.add(event) return event @pytest.fixture def resource(submission): f = SimpleUploadedFile("testresource.txt", b"a resource") with scope(event=submission.event): return Resource.objects.create( submission=submission, resource=f, description="Test resource" ) @pytest.fixture def confirmed_resource(confirmed_submission): f = SimpleUploadedFile("confirmed_testresource.txt", b"a confirmed resource") return Resource.objects.create( submission=confirmed_submission, resource=f, description="Confirmed test resource", ) @pytest.fixture def other_resource(submission): f = SimpleUploadedFile("testresource2.txt", b"another resource") with scope(event=submission.event): return Resource.objects.create( submission=submission, resource=f, description="Test resource 2" ) @pytest.fixture def question(event): with scope(event=event): return Question.objects.create( event=event, question="How much do you like green, on a scale from 1-10?", variant=QuestionVariant.NUMBER, target="submission", required=False, contains_personal_data=False, position=1, ) @pytest.fixture def inactive_question(event): with scope(event=event): return Question.objects.create( event=event, question="So, on a scale from 1–100, how much do you like red?", variant=QuestionVariant.NUMBER, target="submission", required=False, active=False, position=2, ) @pytest.fixture def answer(event, submission, question): with scope(event=event): return Answer.objects.create( answer="11", submission=submission, question=question ) @pytest.fixture def speaker_question(event): with scope(event=event): return Question.objects.create( event=event, question="What is your favourite color?", variant=QuestionVariant.STRING, target="speaker", required=False, position=3, ) @pytest.fixture def review_question(event): with scope(event=event): return Question.objects.create( event=event, question="What is your favourite color?", variant=QuestionVariant.STRING, target="reviewer", required=True, position=4, ) @pytest.fixture def speaker_boolean_question(event): with scope(event=event): return Question.objects.create( event=event, question="Do you like green?", variant=QuestionVariant.BOOLEAN, target="speaker", required=False, position=5, ) @pytest.fixture def boolean_question(event): with scope(event=event): return Question.objects.create( event=event, question="Do you like green?", variant=QuestionVariant.BOOLEAN, target="submission", required=False, position=6, ) @pytest.fixture def file_question(event): with scope(event=event): return Question.objects.create( event=event, question="Please submit your paper.", variant=QuestionVariant.FILE, target="submission", required=False, position=7, ) @pytest.fixture def speaker_file_question(event): with scope(event=event): return Question.objects.create( event=event, question="Please submit your CV.", variant=QuestionVariant.FILE, target="speaker", required=False, position=8, ) @pytest.fixture def choice_question(event): with scope(event=event): question = Question.objects.create( event=event, question="How much do you like green?", variant=QuestionVariant.CHOICES, target="speaker", required=False, position=9, ) for answer in ["very", "incredibly", "omggreen"]: AnswerOption.objects.create(question=question, answer=answer) return question @pytest.fixture def answered_choice_question(choice_question, submission, speaker): with scope(event=submission.event): a = Answer.objects.create( submission=submission, question=choice_question, person=speaker ) a.options.set([choice_question.options.first()]) a.save() return choice_question @pytest.fixture def multiple_choice_question(event): with scope(event=event): question = Question.objects.create( event=event, question="Which colors other than green do you like?", variant=QuestionVariant.MULTIPLE, target="speaker", required=False, position=10, ) for answer in ["yellow", "blue", "black"]: AnswerOption.objects.create(question=question, answer=answer) return question @pytest.fixture def speaker_text_question(event): with scope(event=event): return Question.objects.create( event=event, question="Please elaborat on your like/dislike of green.", variant=QuestionVariant.TEXT, target="speaker", required=False, position=11, ) @pytest.fixture def personal_question(submission): with scope(event=submission.event): return Question.objects.create( event=submission.event, target="submission", variant="boolean", question="Do you identify as a hacker?", contains_personal_data=True, position=12, ) @pytest.fixture def impersonal_answer(question, speaker, submission): with scope(event=question.event): return Answer.objects.create( answer="True", submission=submission, person=speaker, question=question ) @pytest.fixture def personal_answer(personal_question, speaker, submission): with scope(event=personal_question.event): return Answer.objects.create( answer="True", person=speaker, question=personal_question, submission=submission, ) @pytest.fixture def user(): with scopes_disabled(): return User.objects.create_user( email="testuser@example.com", password="testpassw0rd!" ) @pytest.fixture def administrator(): with scopes_disabled(): u = User.objects.create_superuser( email="testuser@examplecom", password="testpassw0rd!" ) u.is_administrator = True u.save() return u @pytest.fixture def orga_user(event): with scopes_disabled(): user = User.objects.create_user( password="orgapassw0rd", email="orgauser@orga.org" ) team = event.organiser.teams.filter( can_change_organiser_settings=True, is_reviewer=False ).first() team.members.add(user) team.save() return user @pytest.fixture def other_orga_user(event): with scopes_disabled(): user = User.objects.create_user( password="orgapassw0rd", email="evilorgauser@orga.org" ) team = event.organiser.teams.filter( can_change_organiser_settings=True, is_reviewer=False ).first() team.members.add(user) team.save() return user @pytest.fixture def review_user(organiser, event): with scopes_disabled(): user = User.objects.create_user( password="reviewpassw0rd", email="reviewuser@orga.org" ) if not event.organiser: event.organiser = organiser event.save() team, _ = event.organiser.teams.get_or_create( can_change_organiser_settings=False, is_reviewer=True ) team.members.add(user) team.save() return user @pytest.fixture def other_review_user(event): with scopes_disabled(): user = User.objects.create_user( password="reviewpassw0rd", email="evilreviewuser@orga.org" ) team = event.organiser.teams.filter( can_change_organiser_settings=False, is_reviewer=True ).first() team.members.add(user) team.save() return user @pytest.fixture def orga_reviewer_user(event): with scopes_disabled(): user = User.objects.create_user( password="orgapassw0rd", email="multiuser@orga.org" ) team = event.organiser.teams.filter( can_change_organiser_settings=True, is_reviewer=True ).first() team.members.add(user) team.save() return user @pytest.fixture def orga_client(orga_user, client): client.force_login(orga_user) return client @pytest.fixture def review_client(review_user, client): client.force_login(review_user) return client @pytest.fixture def other_review_client(other_review_user, client): client.force_login(other_review_user) return client @pytest.fixture def administrator_client(administrator, client): client.force_login(administrator) return client @pytest.fixture def submission_type(event): with scope(event=event): return SubmissionType.objects.create( name="Workshop", event=event, default_duration=60 ) @pytest.fixture def default_submission_type(event): return event.cfp.default_type @pytest.fixture def speaker(event): with scopes_disabled(): user = User.objects.create_user( password="speakerpwd1!", name="Jane Speaker", email="jane@speaker.org" ) with scope(event=event): SpeakerProfile.objects.create( user=user, event=event, biography="Best speaker in the world." ) return user @pytest.fixture def speaker_client(client, speaker): client.force_login(speaker) return client @pytest.fixture def other_speaker(event): with scopes_disabled(): user = User.objects.create_user( email="speaker2@example.org", password="speakerpwd1!", name="Krümelmonster" ) with scope(event=event): SpeakerProfile.objects.create(user=user, event=event, biography="COOKIIIIES!!") return user @pytest.fixture def other_speaker_client(client, other_speaker): client.force_login(other_speaker) return client @pytest.fixture def submission_data(event, submission_type): return { "title": "Lametta im Wandel der Zeiten", "submission_type": submission_type, "description": "Früher war es nämlich mehr. Und wir mussten es bügeln.", "abstract": "Ich habe Quellen!", "notes": "Und mein Enkel braucht auch noch ein Geschenk.", "internal_notes": "Ich habe gestern mit dem Redner telefoniert. -- slubby", "content_locale": "en", "event": event, } @pytest.fixture def submission(submission_data, speaker, event): with scope(event=event): sub = Submission.objects.create(**submission_data) sub.save() sub.speakers.add(speaker) return sub @pytest.fixture def other_submission(event, other_speaker): with scope(event=event): sub = Submission.objects.create( title="Albrecht Dürer. Sein Leben, seine Zeit", event=event, submission_type=event.cfp.default_type, description="1 guter Talk", abstract="Verstehste?", notes="I like cookies A LOT", content_locale="en", ) sub.save() sub.speakers.add(other_speaker) return sub @pytest.fixture def accepted_submission(speaker, submission_data, event): with scope(event=event): sub = Submission.objects.create(**submission_data) sub.save() sub.speakers.add(speaker) sub.accept() return sub @pytest.fixture def other_accepted_submission(other_submission): with scope(event=other_submission.event): other_submission.accept() return other_submission @pytest.fixture def rejected_submission(submission_data, other_speaker, event): with scope(event=event): sub = Submission.objects.create(**submission_data) sub.save() sub.speakers.add(other_speaker) sub.reject() return sub @pytest.fixture def confirmed_submission(submission_data, speaker, event): with scope(event=event): sub = Submission.objects.create(**submission_data) sub.save() sub.speakers.add(speaker) sub.accept() sub.confirm() return sub @pytest.fixture def other_confirmed_submission(other_accepted_submission, event): with scope(event=event): other_accepted_submission.confirm() return other_accepted_submission @pytest.fixture def canceled_submission(submission_data, speaker, event): with scope(event=event): sub = Submission.objects.create(**submission_data) sub.save() sub.speakers.add(speaker) sub.cancel(force=True) return sub @pytest.fixture def withdrawn_submission(submission_data, speaker, event): with scope(event=event): sub = Submission.objects.create(**submission_data) sub.save() sub.speakers.add(speaker) sub.withdraw(force=True) return sub @pytest.fixture def deleted_submission(event, submission_data, other_speaker): with scope(event=event): sub = Submission.objects.create(**submission_data) sub.speakers.add(other_speaker) sub.remove(force=True) return sub @pytest.fixture def invitation(event): with scope(event=event): team = event.organiser.teams.filter( can_change_organiser_settings=True, is_reviewer=False ).first() return TeamInvite.objects.create( team=team, token="testtoken", email="some@test.mail" ) @pytest.fixture def mail_template(event): with scope(event=event): return MailTemplate.objects.create( event=event, subject="Some Mail", text="Whee mail content!", reply_to="orga@orga.org", ) @pytest.fixture(scope="function") def mail(mail_template, speaker, event): with scope(event=event): return mail_template.to_mail(speaker, event) @pytest.fixture(scope="function") def other_mail(mail_template, event, speaker): with scope(event=event): return mail_template.to_mail(speaker, event) @pytest.fixture def sent_mail(mail_template, speaker, event): with scope(event=event): mail = mail_template.to_mail(speaker, event) mail.send() return mail @pytest.fixture def room(event): with scope(event=event): return Room.objects.create( event=event, name="Testroom", description="A fancy room", position=2, capacity=50, ) @pytest.fixture def room_availability(event, room, availability): with scope(event=event): availability.room = room availability.save() return availability @pytest.fixture def other_room(event): with scope(event=event): return Room.objects.create( event=event, name="Second Testroom", description="A less fancy room", position=1, capacity=10, ) @pytest.fixture def availability(event): with scope(event=event): return Availability( event=event, start=dt.datetime.combine(event.date_from, dt.time.min, tzinfo=pytz.utc), end=dt.datetime.combine(event.date_to, dt.time.max, tzinfo=pytz.utc), ) @pytest.fixture def schedule(event): with scope(event=event): event.release_schedule("🍪 Version") return event.current_schedule @pytest.fixture def slot(confirmed_submission, room, schedule): with scope(event=room.event): TalkSlot.objects.update_or_create( submission=confirmed_submission, schedule=room.event.wip_schedule, defaults={"is_visible": True}, ) TalkSlot.objects.update_or_create( submission=confirmed_submission, schedule=schedule, defaults={"is_visible": True}, ) slots = TalkSlot.objects.filter(submission=confirmed_submission) slots.update( start=room.event.datetime_from, end=room.event.datetime_from + dt.timedelta(minutes=60), room=room, ) return slots.get(schedule=schedule) @pytest.fixture def unreleased_slot(confirmed_submission, room): with scope(event=room.event): schedule = confirmed_submission.event.wip_schedule slot = schedule.talks.filter(submission=confirmed_submission) slot.update( start=room.event.datetime_from, end=room.event.datetime_from + dt.timedelta(minutes=30), room=room, schedule=schedule, is_visible=True, ) slot = slot.first() return slot @pytest.fixture def past_slot(other_confirmed_submission, room, schedule, speaker): with scope(event=room.event): slot = ( other_confirmed_submission.slots.filter(schedule=schedule).first() or other_confirmed_submission.slots.first() ) slot.start = now() - dt.timedelta(minutes=60) slot.end = now() - dt.timedelta(minutes=30) slot.room = room slot.schedule = schedule slot.is_visible = True slot.save() return slot @pytest.fixture def break_slot(room, schedule): with scope(event=schedule.event): TalkSlot.objects.create( description="Break", schedule=schedule.event.wip_schedule, ) slot = TalkSlot.objects.create( description="Break", schedule=schedule, is_visible=True, ) slots = TalkSlot.objects.filter(submission__isnull=True) slots.update( start=room.event.datetime_from + dt.timedelta(minutes=90), end=room.event.datetime_from + dt.timedelta(minutes=120), room=room, ) return slot @pytest.fixture def canceled_talk(past_slot): with scope(event=past_slot.submission.event): past_slot.submission.cancel(force=True) past_slot.submission.event.wip_schedule.freeze("vcanceled") return past_slot @pytest.fixture def feedback(past_slot): with scope(event=past_slot.submission.event): return Feedback.objects.create(talk=past_slot.submission, review="I liked it!") @pytest.fixture def other_slot(other_confirmed_submission, room, schedule): with scope(event=room.event): return TalkSlot.objects.create( start=room.event.datetime_from + dt.timedelta(minutes=60), end=room.event.datetime_from + dt.timedelta(minutes=90), submission=other_confirmed_submission, room=room, schedule=schedule, is_visible=True, ) @pytest.fixture def schedule_schema(): from lxml import etree with open("tests/fixtures/schedule.xsd", "r") as xsd: source = xsd.read() schema = etree.XML(source) return etree.XMLSchema(schema) @pytest.fixture def review(submission, review_user): with scope(event=submission.event): return Review.objects.create( score=1, submission=submission, user=review_user, text="Looks great!" ) @pytest.fixture def other_review(other_submission, other_review_user): with scope(event=other_submission.event): return Review.objects.create( score=0, submission=other_submission, user=other_review_user, text="Looks horrible!", ) @pytest.fixture def information(event): with scope(event=event): return SpeakerInformation.objects.create( event=event, title="Information title", text="Important information" ) @pytest.fixture def track(event): with scope(event=event): event.settings.use_tracks = True return Track.objects.create(name="Test Track", color="00ff00", event=event) @pytest.fixture def other_track(event): with scope(event=event): event.settings.use_tracks = True return Track.objects.create( name="Second Test Track", color="ff0000", event=event ) @pytest.fixture def access_code(event): with scope(event=event): return SubmitterAccessCode.objects.create(event=event)

27.847921

87

0.636664

4f16eb57653ebd4735974abc23e2cfc478c28867

18,288

py

Python

app/ctaStrategy/ctaHistoryData.py

Michael-JC/inverstment_analysis

724bcda7606d3049e6299f7f3390872954f6628f

[ "Apache-2.0" ]

1

2021-05-25T06:34:13.000Z

app/ctaStrategy/ctaHistoryData.py

Michael-JC/inverstment_analysis

724bcda7606d3049e6299f7f3390872954f6628f

[ "Apache-2.0" ]

null

app/ctaStrategy/ctaHistoryData.py

Michael-JC/inverstment_analysis

724bcda7606d3049e6299f7f3390872954f6628f

[ "Apache-2.0" ]

null

# encoding: UTF-8 """ 本模块中主要包含： 1. 从通联数据下载历史行情的引擎 2. 用来把MultiCharts导出的历史数据载入到MongoDB中用的函数 3. 增加从通达信导出的历史数据载入到MongoDB中的函数 """ from datetime import datetime, timedelta from time import time from multiprocessing.pool import ThreadPool import pymongo from vnpy.data.datayes import DatayesApi from trader.vtFunction import globalSetting from trader.vtObject import VtBarData from language.chinese.vt_constant import * from .ctaBase import SETTING_DB_NAME, TICK_DB_NAME, MINUTE_DB_NAME, DAILY_DB_NAME # 以下为vn.trader和通联数据规定的交易所代码映射 VT_TO_DATAYES_EXCHANGE = {} VT_TO_DATAYES_EXCHANGE[EXCHANGE_CFFEX] = 'CCFX' # 中金所 VT_TO_DATAYES_EXCHANGE[EXCHANGE_SHFE] = 'XSGE' # 上期所 VT_TO_DATAYES_EXCHANGE[EXCHANGE_CZCE] = 'XZCE' # 郑商所 VT_TO_DATAYES_EXCHANGE[EXCHANGE_DCE] = 'XDCE' # 大商所 DATAYES_TO_VT_EXCHANGE = {v:k for k,v in VT_TO_DATAYES_EXCHANGE.items()} ######################################################################## class HistoryDataEngine(object): """CTA模块用的历史数据引擎""" #---------------------------------------------------------------------- def __init__(self, token): """Constructor""" self.dbClient = pymongo.MongoClient(globalSetting['mongoHost'], globalSetting['mongoPort']) self.datayesClient = DatayesApi(token) #---------------------------------------------------------------------- def lastTradeDate(self): """获取最近交易日（只考虑工作日，无法检查国内假期）""" today = datetime.now() oneday = timedelta(1) if today.weekday() == 5: today = today - oneday elif today.weekday() == 6: today = today - oneday*2 return today.strftime('%Y%m%d') #---------------------------------------------------------------------- def readFuturesProductSymbol(self): """查询所有期货产品代码""" cx = self.dbClient[SETTING_DB_NAME]['FuturesSymbol'].find() return set([d['productSymbol'] for d in cx]) # 这里返回的是集合（因为会重复） #---------------------------------------------------------------------- def readFuturesSymbol(self): """查询所有期货合约代码""" cx = self.dbClient[SETTING_DB_NAME]['FuturesSymbol'].find() return [d['symbol'] for d in cx] # 这里返回的是列表 #---------------------------------------------------------------------- def downloadFuturesSymbol(self, tradeDate=''): """下载所有期货的代码""" if not tradeDate: tradeDate = self.lastTradeDate() self.dbClient[SETTING_DB_NAME]['FuturesSymbol'].ensure_index([('symbol', pymongo.ASCENDING)], unique=True) path = 'api/market/getMktMFutd.json' params = {} params['tradeDate'] = tradeDate data = self.datayesClient.downloadData(path, params) if data: for d in data: symbolDict = {} symbolDict['symbol'] = d['ticker'] symbolDict['productSymbol'] = d['contractObject'] flt = {'symbol': d['ticker']} self.dbClient[SETTING_DB_NAME]['FuturesSymbol'].update_one(flt, {'$set':symbolDict}, upsert=True) print u'期货合约代码下载完成' else: print u'期货合约代码下载失败' #---------------------------------------------------------------------- def downloadFuturesDailyBar(self, symbol): """ 下载期货合约的日行情，symbol是合约代码，若最后四位为0000（如IF0000），代表下载连续合约。 """ print u'开始下载%s日行情' %symbol # 查询数据库中已有数据的最后日期 cl = self.dbClient[DAILY_DB_NAME][symbol] cx = cl.find(sort=[('datetime', pymongo.DESCENDING)]) if cx.count(): last = cx[0] else: last = '' # 主力合约 if '0000' in symbol: path = 'api/market/getMktMFutd.json' params = {} params['contractObject'] = symbol.replace('0000', '') params['mainCon'] = 1 if last: params['startDate'] = last['date'] # 交易合约 else: path = 'api/market/getMktFutd.json' params = {} params['ticker'] = symbol if last: params['startDate'] = last['date'] # 开始下载数据 data = self.datayesClient.downloadData(path, params) if data: # 创建datetime索引 self.dbClient[DAILY_DB_NAME][symbol].ensure_index([('datetime', pymongo.ASCENDING)], unique=True) for d in data: bar = VtBarData() bar.vtSymbol = symbol bar.symbol = symbol try: bar.exchange = DATAYES_TO_VT_EXCHANGE.get(d.get('exchangeCD', ''), '') bar.open = d.get('openPrice', 0) bar.high = d.get('highestPrice', 0) bar.low = d.get('lowestPrice', 0) bar.close = d.get('closePrice', 0) bar.date = d.get('tradeDate', '').replace('-', '') bar.time = '' bar.datetime = datetime.strptime(bar.date, '%Y%m%d') bar.volume = d.get('turnoverVol', 0) bar.openInterest = d.get('openInt', 0) except KeyError: print d flt = {'datetime': bar.datetime} self.dbClient[DAILY_DB_NAME][symbol].update_one(flt, {'$set':bar.__dict__}, upsert=True) print u'%s下载完成' %symbol else: print u'找不到合约%s' %symbol #---------------------------------------------------------------------- def downloadAllFuturesDailyBar(self): """下载所有期货的主力合约日行情""" start = time() print u'开始下载所有期货的主力合约日行情' productSymbolSet = self.readFuturesProductSymbol() print u'代码列表读取成功，产品代码：%s' %productSymbolSet # 这里也测试了线程池，但可能由于下载函数中涉及较多的数据格 # 式转换，CPU开销较大，多线程效率并无显著改变。 #p = ThreadPool(10) #p.map(self.downloadFuturesDailyBar, productSymbolSet) #p.close() #p.join() for productSymbol in productSymbolSet: self.downloadFuturesDailyBar(productSymbol+'0000') print u'所有期货的主力合约日行情已经全部下载完成, 耗时%s秒' %(time()-start) #---------------------------------------------------------------------- def downloadFuturesIntradayBar(self, symbol): """下载期货的日内分钟行情""" print u'开始下载%s日内分钟行情' %symbol # 日内分钟行情只有具体合约 path = 'api/market/getFutureBarRTIntraDay.json' params = {} params['instrumentID'] = symbol params['unit'] = 1 data = self.datayesClient.downloadData(path, params) if data: today = datetime.now().strftime('%Y%m%d') # 创建datetime索引 self.dbClient[MINUTE_DB_NAME][symbol].ensure_index([('datetime', pymongo.ASCENDING)], unique=True) for d in data: bar = VtBarData() bar.vtSymbol = symbol bar.symbol = symbol try: bar.exchange = DATAYES_TO_VT_EXCHANGE.get(d.get('exchangeCD', ''), '') bar.open = d.get('openPrice', 0) bar.high = d.get('highestPrice', 0) bar.low = d.get('lowestPrice', 0) bar.close = d.get('closePrice', 0) bar.date = today bar.time = d.get('barTime', '') bar.datetime = datetime.strptime(bar.date + ' ' + bar.time, '%Y%m%d %H:%M') bar.volume = d.get('totalVolume', 0) bar.openInterest = 0 except KeyError: print d flt = {'datetime': bar.datetime} self.dbClient[MINUTE_DB_NAME][symbol].update_one(flt, {'$set':bar.__dict__}, upsert=True) print u'%s下载完成' %symbol else: print u'找不到合约%s' %symbol #---------------------------------------------------------------------- def downloadEquitySymbol(self, tradeDate=''): """下载所有股票的代码""" if not tradeDate: tradeDate = self.lastTradeDate() self.dbClient[SETTING_DB_NAME]['EquitySymbol'].ensure_index([('symbol', pymongo.ASCENDING)], unique=True) path = 'api/market/getMktEqud.json' params = {} params['tradeDate'] = tradeDate data = self.datayesClient.downloadData(path, params) if data: for d in data: symbolDict = {} symbolDict['symbol'] = d['ticker'] flt = {'symbol': d['ticker']} self.dbClient[SETTING_DB_NAME]['EquitySymbol'].update_one(flt, {'$set':symbolDict}, upsert=True) print u'股票代码下载完成' else: print u'股票代码下载失败' #---------------------------------------------------------------------- def downloadEquityDailyBar(self, symbol): """ 下载股票的日行情，symbol是股票代码 """ print u'开始下载%s日行情' %symbol # 查询数据库中已有数据的最后日期 cl = self.dbClient[DAILY_DB_NAME][symbol] cx = cl.find(sort=[('datetime', pymongo.DESCENDING)]) if cx.count(): last = cx[0] else: last = '' # 开始下载数据 path = 'api/market/getMktEqud.json' params = {} params['ticker'] = symbol if last: params['beginDate'] = last['date'] data = self.datayesClient.downloadData(path, params) if data: # 创建datetime索引 self.dbClient[DAILY_DB_NAME][symbol].ensure_index([('datetime', pymongo.ASCENDING)], unique=True) for d in data: bar = VtBarData() bar.vtSymbol = symbol bar.symbol = symbol try: bar.exchange = DATAYES_TO_VT_EXCHANGE.get(d.get('exchangeCD', ''), '') bar.open = d.get('openPrice', 0) bar.high = d.get('highestPrice', 0) bar.low = d.get('lowestPrice', 0) bar.close = d.get('closePrice', 0) bar.date = d.get('tradeDate', '').replace('-', '') bar.time = '' bar.datetime = datetime.strptime(bar.date, '%Y%m%d') bar.volume = d.get('turnoverVol', 0) except KeyError: print d flt = {'datetime': bar.datetime} self.dbClient[DAILY_DB_NAME][symbol].update_one(flt, {'$set':bar.__dict__}, upsert=True) print u'%s下载完成' %symbol else: print u'找不到合约%s' %symbol #---------------------------------------------------------------------- def downloadEquityDailyBarts(self, symbol): """ 下载股票的日行情，symbol是股票代码 """ print u'开始下载%s日行情' %symbol # 查询数据库中已有数据的最后日期 cl = self.dbClient[DAILY_DB_NAME][symbol] cx = cl.find(sort=[('datetime', pymongo.DESCENDING)]) if cx.count(): last = cx[0] else: last = '' # 开始下载数据 import tushare as ts if last: start = last['date'][:4]+'-'+last['date'][4:6]+'-'+last['date'][6:] data = ts.get_k_data(symbol,start) if not data.empty: # 创建datetime索引 self.dbClient[DAILY_DB_NAME][symbol].ensure_index([('datetime', pymongo.ASCENDING)], unique=True) for index, d in data.iterrows(): bar = VtBarData() bar.vtSymbol = symbol bar.symbol = symbol try: bar.open = d.get('open') bar.high = d.get('high') bar.low = d.get('low') bar.close = d.get('close') bar.date = d.get('date').replace('-', '') bar.time = '' bar.datetime = datetime.strptime(bar.date, '%Y%m%d') bar.volume = d.get('volume') except KeyError: print d flt = {'datetime': bar.datetime} self.dbClient[DAILY_DB_NAME][symbol].update_one(flt, {'$set':bar.__dict__}, upsert=True) print u'%s下载完成' %symbol else: print u'找不到合约%s' %symbol #---------------------------------------------------------------------- def loadMcCsv(fileName, dbName, symbol): """将Multicharts导出的csv格式的历史数据插入到Mongo数据库中""" import csv start = time() print u'开始读取CSV文件%s中的数据插入到%s的%s中' %(fileName, dbName, symbol) # 锁定集合，并创建索引 client = pymongo.MongoClient(globalSetting['mongoHost'], globalSetting['mongoPort']) collection = client[dbName][symbol] collection.ensure_index([('datetime', pymongo.ASCENDING)], unique=True) # 读取数据和插入到数据库 reader = csv.DictReader(file(fileName, 'r')) for d in reader: bar = VtBarData() bar.vtSymbol = symbol bar.symbol = symbol bar.open = float(d['Open']) bar.high = float(d['High']) bar.low = float(d['Low']) bar.close = float(d['Close']) bar.date = datetime.strptime(d['Date'], '%Y-%m-%d').strftime('%Y%m%d') bar.time = d['Time'] bar.datetime = datetime.strptime(bar.date + ' ' + bar.time, '%Y%m%d %H:%M:%S') bar.volume = d['TotalVolume'] flt = {'datetime': bar.datetime} collection.update_one(flt, {'$set':bar.__dict__}, upsert=True) print bar.date, bar.time print u'插入完毕，耗时：%s' % (time()-start) #---------------------------------------------------------------------- def loadTbCsv(fileName, dbName, symbol): """将TradeBlazer导出的csv格式的历史分钟数据插入到Mongo数据库中""" import csv start = time() print u'开始读取CSV文件%s中的数据插入到%s的%s中' %(fileName, dbName, symbol) # 锁定集合，并创建索引 client = pymongo.MongoClient(globalSetting['mongoHost'], globalSetting['mongoPort']) collection = client[dbName][symbol] collection.ensure_index([('datetime', pymongo.ASCENDING)], unique=True) # 读取数据和插入到数据库 reader = csv.reader(file(fileName, 'r')) for d in reader: bar = VtBarData() bar.vtSymbol = symbol bar.symbol = symbol bar.open = float(d[1]) bar.high = float(d[2]) bar.low = float(d[3]) bar.close = float(d[4]) bar.date = datetime.strptime(d[0].split(' ')[0], '%Y/%m/%d').strftime('%Y%m%d') bar.time = d[0].split(' ')[1]+":00" bar.datetime = datetime.strptime(bar.date + ' ' + bar.time, '%Y%m%d %H:%M:%S') bar.volume = d[5] bar.openInterest = d[6] flt = {'datetime': bar.datetime} collection.update_one(flt, {'$set':bar.__dict__}, upsert=True) print bar.date, bar.time print u'插入完毕，耗时：%s' % (time()-start) #---------------------------------------------------------------------- def loadTbPlusCsv(fileName, dbName, symbol): """将TB极速版导出的csv格式的历史分钟数据插入到Mongo数据库中""" import csv start = time() print u'开始读取CSV文件%s中的数据插入到%s的%s中' %(fileName, dbName, symbol) # 锁定集合，并创建索引 client = pymongo.MongoClient(globalSetting['mongoHost'], globalSetting['mongoPort']) collection = client[dbName][symbol] collection.ensure_index([('datetime', pymongo.ASCENDING)], unique=True) # 读取数据和插入到数据库 reader = csv.reader(file(fileName, 'r')) for d in reader: bar = VtBarData() bar.vtSymbol = symbol bar.symbol = symbol bar.open = float(d[2]) bar.high = float(d[3]) bar.low = float(d[4]) bar.close = float(d[5]) bar.date = str(d[0]) tempstr=str(round(float(d[1])*10000)).split(".")[0].zfill(4) bar.time = tempstr[:2]+":"+tempstr[2:4]+":00" bar.datetime = datetime.strptime(bar.date + ' ' + bar.time, '%Y%m%d %H:%M:%S') bar.volume = d[6] bar.openInterest = d[7] flt = {'datetime': bar.datetime} collection.update_one(flt, {'$set':bar.__dict__}, upsert=True) print bar.date, bar.time print u'插入完毕，耗时：%s' % (time()-start) #---------------------------------------------------------------------- def loadTdxCsv(fileName, dbName, symbol): """将通达信导出的csv格式的历史分钟数据插入到Mongo数据库中""" import csv start = time() print u'开始读取CSV文件%s中的数据插入到%s的%s中' %(fileName, dbName, symbol) # 锁定集合，并创建索引 client = pymongo.MongoClient(globalSetting['mongoHost'], globalSetting['mongoPort']) collection = client[dbName][symbol] collection.ensure_index([('datetime', pymongo.ASCENDING)], unique=True) # 读取数据和插入到数据库 reader = csv.reader(file(fileName, 'r')) for d in reader: bar = VtBarData() bar.vtSymbol = symbol bar.symbol = symbol bar.open = float(d[2]) bar.high = float(d[3]) bar.low = float(d[4]) bar.close = float(d[5]) bar.date = datetime.strptime(d[0], '%Y/%m/%d').strftime('%Y%m%d') bar.time = d[1][:2]+':'+d[1][2:4]+':00' bar.datetime = datetime.strptime(bar.date + ' ' + bar.time, '%Y%m%d %H:%M:%S') bar.volume = d[6] bar.openInterest = d[7] flt = {'datetime': bar.datetime} collection.update_one(flt, {'$set':bar.__dict__}, upsert=True) print bar.date, bar.time print u'插入完毕，耗时：%s' % (time()-start)

36

117

0.481026

09a5ae08d304994c4bd6f597cd6882012a6c7cdd

5,892

py

Python

app/cogs/owner/owner_commands.py

tayron1/Starboard-2

ff83e33e026107d94c306250827373ff2c32aa7d

[ "MIT" ]

null

app/cogs/owner/owner_commands.py

tayron1/Starboard-2

ff83e33e026107d94c306250827373ff2c32aa7d

[ "MIT" ]

null

app/cogs/owner/owner_commands.py

tayron1/Starboard-2

ff83e33e026107d94c306250827373ff2c32aa7d

[ "MIT" ]

null

import io import textwrap import time import traceback from contextlib import redirect_stdout import discord from asyncpg.exceptions import InterfaceError from discord.ext import commands from ... import checks, menus, utils from ...classes.bot import Bot class Owner(commands.Cog): "Owner-only commands" def __init__(self, bot: Bot) -> None: self.bot = bot @commands.command() @checks.is_owner() async def evall(self, ctx, *, body: str): """Evaluates code on all clusters and returns their response""" _msgs = await self.bot.websocket.send_command( "eval", {"content": body}, expect_resp=True ) msgs = [f"```py\n{m['author']}: {m['data']}\n```" for m in _msgs] await ctx.send(" ".join(msgs)) @commands.command(name="eval") @checks.is_owner() async def _eval(self, ctx, *, body: str): """Evaluates python code on the current cluster""" env = { "bot": self.bot, "ctx": ctx, "channel": ctx.channel, "author": ctx.author, "guild": ctx.guild, "message": ctx.message, "_": self.bot._last_result, } env.update(globals()) body = self.bot.cleanup_code(body) stdout = io.StringIO() to_compile = f'async def func():\n{textwrap.indent(body, " ")}' try: exec(to_compile, env) except Exception as e: return await ctx.send(f"```py\n{e.__class__.__name__}: {e}\n```") func = env["func"] try: with redirect_stdout(stdout): ret = await func() except Exception: value = stdout.getvalue() await ctx.send(f"```py\n{value}{traceback.format_exc()}\n```") else: value = stdout.getvalue() try: await ctx.message.add_reaction("\u2705") except (discord.Forbidden, discord.NotFound): pass if ret is None: if value: await ctx.send(f"```py\n{value}\n```") else: self.bot._last_result = ret await ctx.send(f"```py\n{value}{ret}\n```") @commands.command(name="sqltimes") @checks.is_owner() async def get_sql_times( self, ctx: commands.Context, sort_by: str = "total" ) -> None: """Shows stats on SQL queries""" if sort_by not in ["avg", "total", "exec"]: await ctx.send("Valid optons are `avg`, `total`, and `exec`.") return times = [] for sql in self.bot.db.sql_times: total_time = 0 executions = 0 for exec_time in self.bot.db.sql_times[sql]: executions += 1 total_time += exec_time times.append( (sql, (total_time / executions, total_time, executions)) ) pag = commands.Paginator(prefix="", suffix="", max_size=1000) if len(times) == 0: await ctx.send("Nothing to show") return def sorter(t): if sort_by == "avg": return t[1][0] elif sort_by == "total": return t[1][1] elif sort_by == "exec": return t[1][2] times.sort(key=sorter, reverse=True) for sql, exec_time in times: pag.add_line( f"```{sql}```" f"{utils.ms(exec_time[0])} MS AVG | " f"{round(exec_time[1], 2)} SECONDS TOTAL | " f"{exec_time[2]} EXECUTIONS\n" ) await menus.Paginator( embeds=[ discord.Embed( title="SQL Times", description=p, color=self.bot.theme_color, ) for p in pag.pages ], delete_after=True, ).start(ctx) @commands.command(name="restart") @checks.is_owner() async def restart_bot(self, ctx: commands.Context) -> None: """Restars all clusters""" if not await menus.Confirm("Restart all clusters?").start(ctx): await ctx.send("Cancelled") return await ctx.send("Restarting...") cmd: commands.Command = self.bot.get_command("evall") await ctx.invoke(cmd, body="await bot.logout()") @commands.command( name="runpg", aliases=["timepg", "timeit", "runtime"], brief="Time postgres queries", description="Time postgres queries", ) @checks.is_owner() async def time_postgres(self, ctx: commands.Context, *args: list) -> None: result = "None" times = 1 runtimes = [] try: async with self.bot.db.pool.acquire() as con: async with con.transaction(): for a in args: a = "".join(a) try: times = int(a) except Exception: start = time.time() for i in range(0, times): try: result = await con.fetch(a) except Exception as e: await ctx.send(e) raise Exception("rollback") runtimes.append((time.time() - start) / times) times = 1 raise Exception("Rollback") except (Exception, InterfaceError): pass for x, r in enumerate(runtimes): await ctx.send(f"Query {x} took {round(r*1000, 2)} ms") await ctx.send(result[0:500]) def setup(bot: Bot) -> None: bot.add_cog(Owner(bot))

31.508021

78

0.492702

b259bdd63388720b6b17c5711e3bfab2cb736754

1,981

py

Python

VPython/backup/7/HeliumAtom.py

programizer/HeliumAtom

01ba9cfe930f40c137b2232447fc4f67b61e9275

[ "MIT" ]

null

VPython/backup/7/HeliumAtom.py

programizer/HeliumAtom

01ba9cfe930f40c137b2232447fc4f67b61e9275

[ "MIT" ]

null

VPython/backup/7/HeliumAtom.py

programizer/HeliumAtom

01ba9cfe930f40c137b2232447fc4f67b61e9275

[ "MIT" ]

null

# from math import * # from visual import * # from visual.graph import * from vpython import * # import vpython scene.fullscreen = True G = 0.5 spheres = [ sphere(pos=vector(0,0,0),radius =0.6,color=color.red,charge=sqrt(200),mass=7200,velocity=vector(0,0,0),a = vector(0,0,0),trail=curve(color=color.red)), sphere(pos=vector(10,0,0),radius=0.2,color=color.blue,charge=-sqrt(10),mass=1,velocity=vector(0,0,0.75),a=vector(0,0,0),trail=curve(color=color.blue)), #sphere(pos=vector(0,12,0),radius=.08,color=color.green,mass=sqrt(4),velocity=vector(1.2,0,0.6),a=vector(0,0,0),trail=curve(color=color.green)), sphere(pos=vector(0,5,0),radius=0.2,color=color.white,charge=-sqrt(10),mass=1,velocity=vector(1.5,0,0),a=vector(0,0,0),trail=curve(color=color.white)), #sphere(pos=vector(0,28,0),radius=.4,color=color.orange,mass=sqrt(80),velocity=vector(0.7,0,0.4),a=vector(0,0,0),trail=curve(color=color.orange)), #sphere(pos=vector(0,32,0),radius=0.2,color=color.white,mass=-sqrt(10),velocity=vector(1.5,0,0.4),a=vector(0,0,0),trail=curve(color=color.white)) ] #print(spheres[0].a) #print(len(spheres)) def acceleration1on2(sphere2,sphere1): r = sphere2.pos - sphere1.pos r_mag = mag(r) normal_r = norm(r) g = ((G*sphere1.charge*sphere2.charge)/pow(r_mag,2))/sphere2.mass*normal_r #print(g) return g t = 0 dt = .01 while 1: rate(100) for i in spheres: i.a = vector(0,0,0) soi = vector(0,0,0) for j in spheres: if i!=j: i.a = i.a + acceleration1on2(i,j) for i in spheres: #print(i.velocity) i.velocity = i.velocity + i.a *dt i.pos = i.pos+i.velocity*dt #print(i.a) i.trail.append(pos=i.pos) scene.center=vector(spheres[0].pos.x,spheres[0].pos.y,spheres[0].pos.z) # print(i.a)

31.951613

152

0.596668

7ade01ea7ac83b8e61dc4944ad49c197b8b4a198

4,568

py

Python

experiments/steven-images/ddpg_spatial.py

Asap7772/railrl_evalsawyer

baba8ce634d32a48c7dfe4dc03b123e18e96e0a3

[ "MIT" ]

null

experiments/steven-images/ddpg_spatial.py

Asap7772/railrl_evalsawyer

baba8ce634d32a48c7dfe4dc03b123e18e96e0a3

[ "MIT" ]

null

experiments/steven-images/ddpg_spatial.py

Asap7772/railrl_evalsawyer

baba8ce634d32a48c7dfe4dc03b123e18e96e0a3

[ "MIT" ]

null

import rlkit.misc.hyperparameter as hyp import rlkit.torch.pytorch_util as ptu from rlkit.launchers.launcher_util import run_experiment from torch import nn as nn from rlkit.torch.networks.experimental import HuberLoss from rlkit.torch.ddpg.feat_point_ddpg import FeatPointDDPG from rlkit.torch.networks import FeatPointMlp from rlkit.torch.networks import ConcatMlp, AETanhPolicy from rlkit.exploration_strategies.gaussian_strategy import GaussianStrategy from rlkit.exploration_strategies.base import \ PolicyWrappedWithExplorationStrategy from rlkit.envs.wrappers import ImageMujocoWithObsEnv #from rlkit.images.camera import pusher_2d_init_camera #from rlkit.envs.mujoco.simple_sawyer import SawyerXYZEnv from rlkit.envs.mujoco.sawyer_gripper_env import SawyerXYZEnv import rlkit.images.camera as camera from rlkit.data_management.env_replay_buffer import AEEnvReplayBuffer def experiment(variant): feat_points = 16 history = 1 latent_obs_dim = feat_points * 2 * history imsize = 64 downsampled_size = 32 env = SawyerXYZEnv() extra_fc_size = env.obs_dim env = ImageMujocoWithObsEnv(env, imsize=imsize, normalize=True, grayscale=True, keep_prev=history-1, init_camera=camera.sawyer_init_camera) """env = ImageMujocoEnv(env, imsize=imsize, keep_prev=history-1, init_camera=camera.sawyer_init_camera)""" es = GaussianStrategy( action_space=env.action_space, ) obs_dim = env.observation_space.low.size action_dim = env.action_space.low.size ae = FeatPointMlp( input_size=imsize, downsample_size=downsampled_size, input_channels=1, num_feat_points=feat_points ) replay_buffer = AEEnvReplayBuffer( int(1e4), env, imsize=imsize, history_length=history, downsampled_size=downsampled_size ) qf = ConcatMlp( input_size= latent_obs_dim + extra_fc_size + action_dim, output_size=1, hidden_sizes=[400, 300] ) policy = AETanhPolicy( input_size=latent_obs_dim + extra_fc_size, ae=ae, env=env, history_length=history, output_size=action_dim, hidden_sizes=[400, 300], ) exploration_policy = PolicyWrappedWithExplorationStrategy( exploration_strategy=es, policy=policy, ) algorithm = FeatPointDDPG( ae, history, env=env, qf=qf, policy=policy, exploration_policy=exploration_policy, replay_buffer=replay_buffer, extra_fc_size=extra_fc_size, imsize=imsize, downsampled_size=downsampled_size, **variant['algo_params'] ) algorithm.to(ptu.device) algorithm.train() if __name__ == "__main__": # noinspection PyTypeChecker variant = dict( algo_params=dict( num_epochs=400, num_steps_per_epoch=1000, num_steps_per_eval=500, batch_size=64, max_path_length=200, discount=.99, use_soft_update=True, tau=1e-2, qf_learning_rate=1e-3, policy_learning_rate=1e-4, save_replay_buffer=False, ), qf_criterion_class=nn.MSELoss, env_id='Reacher-v2' ) search_space = { 'env_id': [ # 'Acrobot-v1', #'CartPole-v0', 'Reacher-v2', #'InvertedPendulum-v1', # 'CartPole-v1', # 'MountainCar-v0', ], # 'algo_params.use_hard_updates': [True, False], 'qf_criterion_class': [ #nn.MSELoss, HuberLoss, ], } # setup_logger('dqn-images-experiment', variant=variant) # experiment(variant) sweeper = hyp.DeterministicHyperparameterSweeper( search_space, default_parameters=variant, ) for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()): # for i in range(2): run_experiment( experiment, variant=variant, exp_id=exp_id, exp_prefix="sawyer-spatial-FREEZE", mode='local', # use_gpu=False, # exp_prefix="double-vs-dqn-huber-sweep-cartpole", # mode='local', use_gpu=True, )

29.470968

75

0.611646

b1fb1146e425b6cd89f062575e2792fad50a1050

3,417

py

Python

src/cmds/netspace_funcs.py

13767849/chia-blockchain

ad7d7e0cced7f2f6deddc9e006dbaeee6dab8f66

[ "Apache-2.0" ]

null

src/cmds/netspace_funcs.py

13767849/chia-blockchain

ad7d7e0cced7f2f6deddc9e006dbaeee6dab8f66

[ "Apache-2.0" ]

null

src/cmds/netspace_funcs.py

13767849/chia-blockchain

ad7d7e0cced7f2f6deddc9e006dbaeee6dab8f66

[ "Apache-2.0" ]

null

import aiohttp from src.rpc.full_node_rpc_client import FullNodeRpcClient from src.util.byte_types import hexstr_to_bytes from src.util.config import load_config from src.util.default_root import DEFAULT_ROOT_PATH from src.util.ints import uint16 async def netstorge_async(rpc_port: int, delta_block_height: str, start: str) -> None: """ Calculates the estimated space on the network given two block header hashes. """ try: config = load_config(DEFAULT_ROOT_PATH, "config.yaml") self_hostname = config["self_hostname"] if rpc_port is None: rpc_port = config["full_node"]["rpc_port"] client = await FullNodeRpcClient.create(self_hostname, uint16(rpc_port), DEFAULT_ROOT_PATH, config) if delta_block_height: if start == "": blockchain_state = await client.get_blockchain_state() if blockchain_state["peak"] is None: print("No blocks in blockchain") client.close() await client.await_closed() return None newer_block_height = blockchain_state["peak"].height else: newer_block = await client.get_block_record(hexstr_to_bytes(start)) if newer_block is None: print("Block header hash", start, "not found.") client.close() await client.await_closed() return None else: print("newer_height", newer_block.height) newer_block_height = newer_block.height newer_block_header = await client.get_block_record_by_height(newer_block_height) older_block_height = max(0, newer_block_height - int(delta_block_height)) older_block_header = await client.get_block_record_by_height(older_block_height) network_space_bytes_estimate = await client.get_network_space( newer_block_header.header_hash, older_block_header.header_hash ) print( "Older Block\n" f"Block Height: {older_block_header.height}\n" f"Weight: {older_block_header.weight}\n" f"VDF Iterations: {older_block_header.total_iters}\n" f"Header Hash: 0x{older_block_header.header_hash}\n" ) print( "Newer Block\n" f"Block Height: {newer_block_header.height}\n" f"Weight: {newer_block_header.weight}\n" f"VDF Iterations: {newer_block_header.total_iters}\n" f"Header Hash: 0x{newer_block_header.header_hash}\n" ) network_space_terabytes_estimate = network_space_bytes_estimate / 1024 ** 4 if network_space_terabytes_estimate > 1024: print(f"The network has an estimated {network_space_terabytes_estimate / 1024:.3f} PiB") else: print(f"The network has an estimated {network_space_terabytes_estimate:.3f} TiB") except Exception as e: if isinstance(e, aiohttp.client_exceptions.ClientConnectorError): print(f"Connection error. Check if full node rpc is running at {rpc_port}") else: print(f"Exception {e}") client.close() await client.await_closed()

44.960526

107

0.614282

990b0f51f2e563f1e3f053091a8c20837b22851d

21,234

py

Python

T5_shared.py

Verylovenlp/MinTL-SKKU

15b5cb870c7d6dcd0f5d895aac2806539cc5101f

[ "MIT" ]

60

2020-09-24T06:17:49.000Z

2022-02-24T08:44:52.000Z

T5_shared.py

Verylovenlp/MinTL-SKKU

15b5cb870c7d6dcd0f5d895aac2806539cc5101f

[ "MIT" ]

6

2020-11-11T02:04:23.000Z

2022-03-02T23:58:01.000Z

T5_shared.py

salesforce/CASPI

3e4cd23f4f3d1fa7132ba89805366472c9fe5983

[ "BSD-3-Clause" ]

13

2020-09-28T07:29:05.000Z

2022-02-06T15:04:27.000Z

from transformers import (AdamW, T5Tokenizer, T5ForConditionalGeneration, WEIGHTS_NAME,CONFIG_NAME) from copy import deepcopy import torch from torch.nn import CrossEntropyLoss import time class MiniT5(T5ForConditionalGeneration): def __init__(self, config): super().__init__(config) #make a copy of decoder for dst # decoder_config = deepcopy(config) # decoder_config.is_decoder = True # self.dst_decoder = type(self.decoder)(decoder_config, self.shared) # self.dst_decoder.load_state_dict(self.decoder.state_dict()) # self.dst_lm_head = type(self.lm_head)(config.d_model, config.vocab_size, bias=False) # self.dst_lm_head.load_state_dict(self.lm_head.state_dict()) def tie_decoder(self): pass # def forward( # self, # input_ids=None, # attention_mask=None, # encoder_outputs=None, # decoder_input_ids=None, # decoder_attention_mask=None, # lm_labels=None, # inputs_embeds=None, # decoder_inputs_embeds=None, # head_mask=None, # ): # # DST forward or Response generation forward? # if decoder_input_ids[0,0] == self.config.decoder_start_token_id: # decoder = self.dst_decoder # lm_head = self.dst_lm_head # else: # decoder = self.decoder # lm_head = self.lm_head # # Encode if needed (training, first prediction pass) # if encoder_outputs is None: # # Convert encoder inputs in embeddings if needed # encoder_outputs = self.encoder( # input_ids=input_ids, attention_mask=attention_mask, inputs_embeds=inputs_embeds, head_mask=head_mask # ) # hidden_states = encoder_outputs[0] # if lm_labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None: # # get decoder inputs from shifting lm labels to the right # decoder_input_ids = self._shift_right(lm_labels) # # Decode # decoder_outputs = decoder( # input_ids=decoder_input_ids, # attention_mask=decoder_attention_mask, # inputs_embeds=decoder_inputs_embeds, # encoder_hidden_states=hidden_states, # encoder_attention_mask=attention_mask, # head_mask=head_mask, # ) # sequence_output = decoder_outputs[0] # # Rescale output before projecting on vocab # # See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586 # sequence_output = sequence_output * (self.model_dim ** -0.5) # lm_logits = lm_head(sequence_output) # decoder_outputs = (lm_logits,) #+ decoder_outputs[1:] # Add hidden states and attention if they are here # if lm_labels is not None: # loss_fct = CrossEntropyLoss(ignore_index=-100) # loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), lm_labels.view(-1)) # decoder_outputs = ( # loss, # ) + decoder_outputs # TODO(thom): Add z_loss https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L666 # return decoder_outputs + encoder_outputs @torch.no_grad() def generate( self, input_ids=None, max_length=None, min_length=None, do_sample=None, early_stopping=None, num_beams=None, temperature=None, top_k=None, top_p=None, repetition_penalty=None, bad_words_ids=None, bos_token_id=None, pad_token_id=None, eos_token_id=None, length_penalty=None, no_repeat_ngram_size=None, num_return_sequences=None, attention_mask=None, decoder_start_token_id=None, ): # We cannot generate if the model does not have a LM head if self.get_output_embeddings() is None: raise AttributeError( "You tried to generate sequences with a model that does not have a LM Head." "Please use another model class (e.g. `OpenAIGPTLMHeadModel`, `XLNetLMHeadModel`, `GPT2LMHeadModel`, `CTRLLMHeadModel`, `T5WithLMHeadModel`, `TransfoXLLMHeadModel`, `XLMWithLMHeadModel`, `BartForConditionalGeneration` )" ) max_length = max_length if max_length is not None else self.config.max_length min_length = min_length if min_length is not None else self.config.min_length do_sample = do_sample if do_sample is not None else self.config.do_sample early_stopping = early_stopping if early_stopping is not None else self.config.early_stopping num_beams = num_beams if num_beams is not None else self.config.num_beams temperature = temperature if temperature is not None else self.config.temperature top_k = top_k if top_k is not None else self.config.top_k top_p = top_p if top_p is not None else self.config.top_p repetition_penalty = repetition_penalty if repetition_penalty is not None else self.config.repetition_penalty bos_token_id = bos_token_id if bos_token_id is not None else self.config.bos_token_id pad_token_id = pad_token_id if pad_token_id is not None else self.config.pad_token_id eos_token_id = eos_token_id if eos_token_id is not None else self.config.eos_token_id length_penalty = length_penalty if length_penalty is not None else self.config.length_penalty no_repeat_ngram_size = ( no_repeat_ngram_size if no_repeat_ngram_size is not None else self.config.no_repeat_ngram_size ) bad_words_ids = bad_words_ids if bad_words_ids is not None else self.config.bad_words_ids num_return_sequences = ( num_return_sequences if num_return_sequences is not None else self.config.num_return_sequences ) decoder_start_token_id = ( decoder_start_token_id if decoder_start_token_id is not None else self.config.decoder_start_token_id ) if input_ids is not None: batch_size = input_ids.shape[0] # overriden by the input batch_size else: batch_size = 1 assert isinstance(max_length, int) and max_length > 0, "`max_length` should be a strictly positive integer." assert isinstance(min_length, int) and min_length >= 0, "`min_length` should be a positive integer." assert isinstance(do_sample, bool), "`do_sample` should be a boolean." assert isinstance(early_stopping, bool), "`early_stopping` should be a boolean." assert isinstance(num_beams, int) and num_beams > 0, "`num_beams` should be a strictly positive integer." assert temperature > 0, "`temperature` should be strictly positive." assert isinstance(top_k, int) and top_k >= 0, "`top_k` should be a positive integer." assert 0 <= top_p <= 1, "`top_p` should be between 0 and 1." assert repetition_penalty >= 1.0, "`repetition_penalty` should be >= 1." assert input_ids is not None or ( isinstance(bos_token_id, int) and bos_token_id >= 0 ), "If input_ids is not defined, `bos_token_id` should be a positive integer." assert pad_token_id is None or ( isinstance(pad_token_id, int) and (pad_token_id >= 0) ), "`pad_token_id` should be a positive integer." assert (eos_token_id is None) or ( isinstance(eos_token_id, int) and (eos_token_id >= 0) ), "`eos_token_id` should be a positive integer." assert length_penalty > 0, "`length_penalty` should be strictly positive." assert ( isinstance(no_repeat_ngram_size, int) and no_repeat_ngram_size >= 0 ), "`no_repeat_ngram_size` should be a positive integer." assert ( isinstance(num_return_sequences, int) and num_return_sequences > 0 ), "`num_return_sequences` should be a strictly positive integer." assert ( bad_words_ids is None or isinstance(bad_words_ids, list) and isinstance(bad_words_ids[0], list) ), "`bad_words_ids` is either `None` or a list of lists of tokens that should not be generated" if input_ids is None: assert isinstance(bos_token_id, int) and bos_token_id >= 0, ( "you should either supply a context to complete as `input_ids` input " "or a `bos_token_id` (integer >= 0) as a first token to start the generation." ) input_ids = torch.full( (batch_size, 1), bos_token_id, dtype=torch.long, device=next(self.parameters()).device, ) else: assert input_ids.dim() == 2, "Input prompt should be of shape (batch_size, sequence length)." # not allow to duplicate outputs when greedy decoding if do_sample is False: if num_beams == 1: # no_beam_search greedy generation conditions assert ( num_return_sequences == 1 ), "Greedy decoding will always produce the same output for num_beams == 1 and num_return_sequences > 1. Please set num_return_sequences = 1" else: # beam_search greedy generation conditions assert ( num_beams >= num_return_sequences ), "Greedy beam search decoding cannot return more sequences than it has beams. Please set num_beams >= num_return_sequences" # create attention mask if necessary # TODO (PVP): this should later be handled by the forward fn() in each model in the future see PR 3140 if (attention_mask is None) and (pad_token_id is not None) and (pad_token_id in input_ids): attention_mask = input_ids.ne(pad_token_id).long() elif attention_mask is None: attention_mask = input_ids.new_ones(input_ids.shape) # set pad_token_id to eos_token_id if not set. Important that this is done after # attention_mask is created if pad_token_id is None and eos_token_id is not None: logger.warning( "Setting `pad_token_id` to {} (first `eos_token_id`) to generate sequence".format(eos_token_id) ) pad_token_id = eos_token_id # current position and vocab size vocab_size = self.config.vocab_size # set effective batch size and effective batch multiplier according to do_sample if do_sample: effective_batch_size = batch_size * num_return_sequences effective_batch_mult = num_return_sequences else: effective_batch_size = batch_size effective_batch_mult = 1 if self.config.is_encoder_decoder: if decoder_start_token_id is None: decoder_start_token_id = bos_token_id assert ( decoder_start_token_id is not None ), "decoder_start_token_id or bos_token_id has to be defined for encoder-decoder generation" assert hasattr(self, "get_encoder"), "{} should have a 'get_encoder' function defined".format(self) assert callable(self.get_encoder), "{} should be a method".format(self.get_encoder) # get encoder and store encoder outputs encoder = self.get_encoder() encoder_outputs = encoder(input_ids, attention_mask=attention_mask) # Expand input ids if num_beams > 1 or num_return_sequences > 1 if num_return_sequences > 1 or num_beams > 1: input_ids_len = input_ids.shape[-1] input_ids = input_ids.unsqueeze(1).expand(batch_size, effective_batch_mult * num_beams, input_ids_len) attention_mask = attention_mask.unsqueeze(1).expand( batch_size, effective_batch_mult * num_beams, input_ids_len ) input_ids = input_ids.contiguous().view( effective_batch_size * num_beams, input_ids_len ) # shape: (batch_size * num_return_sequences * num_beams, cur_len) attention_mask = attention_mask.contiguous().view( effective_batch_size * num_beams, input_ids_len ) # shape: (batch_size * num_return_sequences * num_beams, cur_len) if self.config.is_encoder_decoder: # create empty decoder_input_ids if isinstance(decoder_start_token_id, int): input_ids = torch.full( (effective_batch_size * num_beams, 1), decoder_start_token_id, dtype=torch.long, device=next(self.parameters()).device, ) else: # pass a batch of start tokens, but doesn't support beam search and sampling input_ids=decoder_start_token_id cur_len = 1 assert ( batch_size == encoder_outputs[0].shape[0] ), f"expected encoder_outputs[0] to have 1st dimension bs={batch_size}, got {encoder_outputs[0].shape[0]} " # expand batch_idx to assign correct encoder output for expanded input_ids (due to num_beams > 1 and num_return_sequences > 1) expanded_batch_idxs = ( torch.arange(batch_size) .view(-1, 1) .repeat(1, num_beams * effective_batch_mult) .view(-1) .to(input_ids.device) ) # expand encoder_outputs encoder_outputs = (encoder_outputs[0].index_select(0, expanded_batch_idxs), *encoder_outputs[1:]) else: encoder_outputs = None cur_len = input_ids.shape[-1] if num_beams > 1: output = self._generate_beam_search( input_ids, cur_len=cur_len, max_length=max_length, min_length=min_length, do_sample=do_sample, early_stopping=early_stopping, temperature=temperature, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, no_repeat_ngram_size=no_repeat_ngram_size, bad_words_ids=bad_words_ids, bos_token_id=bos_token_id, pad_token_id=pad_token_id, decoder_start_token_id=decoder_start_token_id, eos_token_id=eos_token_id, batch_size=effective_batch_size, num_return_sequences=num_return_sequences, length_penalty=length_penalty, num_beams=num_beams, vocab_size=vocab_size, encoder_outputs=encoder_outputs, attention_mask=attention_mask, ) else: output = self._generate_no_beam_search( input_ids, cur_len=cur_len, max_length=max_length, min_length=min_length, do_sample=do_sample, temperature=temperature, top_k=top_k, top_p=top_p, repetition_penalty=repetition_penalty, no_repeat_ngram_size=no_repeat_ngram_size, bad_words_ids=bad_words_ids, bos_token_id=bos_token_id, pad_token_id=pad_token_id, decoder_start_token_id=decoder_start_token_id, eos_token_id=eos_token_id, batch_size=effective_batch_size, encoder_outputs=encoder_outputs, attention_mask=attention_mask, ) return output def inference( self, tokenizer, reader, prev, input_ids=None, attention_mask=None, turn_domain=None, db=None ): #start = time.time() dst_outputs = self.generate(input_ids=input_ids, attention_mask=attention_mask, eos_token_id=tokenizer.encode("<eos_b>")[0], decoder_start_token_id=self.config.decoder_start_token_id, max_length=200, ) #dst_time = time.time()-start #print(dst_time) dst_outputs = dst_outputs.tolist() #length = len(dst_outputs[0]) #print(dst_outputs) # DST_UPDATE -> DST #check whether need to add eos #dst_outputs = [dst+tokenizer.encode("<eos_b>") for dst in dst_outputs] batch_size = input_ids.shape[0] constraint_dict_updates = [reader.bspan_to_constraint_dict(tokenizer.decode(dst_outputs[i])) for i in range(batch_size)] if prev['bspn']: # update the belief state dst_outputs = [reader.update_bspn(prev_bspn=prev['bspn'][i], bspn_update=dst_outputs[i]) for i in range(batch_size)] # compute the DB state using the updated domain db_state = [] for bi, bspn_list in enumerate(dst_outputs): # if not constraint_dict_updates[bi]: # # if nothing to update # db_state.append(tokenizer.encode("[db_state0]")) # else: # turn_domain = 'general' # for domain in constraint_dict_updates[bi].keys(): # #the last updated domain # turn_domain=domain # follow damd for fair comparison db_vector = reader.bspan_to_DBpointer(tokenizer.decode(bspn_list), turn_domain[bi]) if sum(db_vector)==0: db_state.append(tokenizer.encode("[db_state0]")) else: db_state.append([tokenizer.encode("[db_state0]")[0] + db_vector.index(1)+1]) # use gold booking pointer, because we cannot issue BOOKING API if db[bi][0]>=tokenizer.encode("[db_state0+bookfail]")[0]: if db[bi][0]>=tokenizer.encode("[db_state0+booksuccess]")[0]: db_state[-1][0]+=10 else: db_state[-1][0]+=5 db_state = torch.tensor( db_state, dtype=torch.long, device=next(self.parameters()).device, ) resp_outputs = self.generate(input_ids=input_ids, attention_mask=attention_mask, eos_token_id=tokenizer.encode("<eos_r>")[0], decoder_start_token_id=db_state, max_length=200, ) resp_outputs = resp_outputs[:,1:].tolist() #skip DB state # print("DST:", tokenizer.decode(dst_outputs[0])) # print("RESP:", tokenizer.decode(resp_outputs[0])) return dst_outputs, resp_outputs#, dst_time, length def inference_sequicity( self, tokenizer, reader, prev, input_ids=None, attention_mask=None, turn_domain=None, db=None ): #start = time.time() dst_outputs = self.generate(input_ids=input_ids, attention_mask=attention_mask, eos_token_id=tokenizer.encode("<eos_b>")[0], decoder_start_token_id=self.config.decoder_start_token_id, max_length=200, ) #dst_time = time.time() - start #print(dst_time) dst_outputs = dst_outputs.tolist() #length = len(dst_outputs[0]) # compute the DB state using the updated domain db_state = [] for bi, bspn_list in enumerate(dst_outputs): db_vector = reader.bspan_to_DBpointer(tokenizer.decode(bspn_list), turn_domain[bi]) if sum(db_vector)==0: db_state.append(tokenizer.encode("[db_state0]")) else: db_state.append([tokenizer.encode("[db_state0]")[0] + db_vector.index(1)+1]) # use gold booking pointer, because we cannot issue BOOKING API if db[bi][0]>=tokenizer.encode("[db_state0+bookfail]")[0]: if db[bi][0]>=tokenizer.encode("[db_state0+booksuccess]")[0]: db_state[-1][0]+=10 else: db_state[-1][0]+=5 db_state = torch.tensor( db_state, dtype=torch.long, device=next(self.parameters()).device, ) resp_outputs = self.generate(input_ids=input_ids, attention_mask=attention_mask, eos_token_id=tokenizer.encode("<eos_r>")[0], decoder_start_token_id=db_state, max_length=200, ) resp_outputs = resp_outputs[:,1:].tolist() #skip DB state # print("DST:", tokenizer.decode(dst_outputs[0])) # print("RESP:", tokenizer.decode(resp_outputs[0])) return dst_outputs, resp_outputs#, dst_time, length

45.468951

236

0.607658

1015ae15731d32ea6468eb098985d6db3acfabd6

10

py

Python

test_data/parse_retree/expected/character_set/complementing/suffix_dash/source.py

aas-core-works/aas-core-codegen

afec2cf363b6cb69816e7724a2b58626e2165869

[ "MIT" ]

5

2021-12-29T12:55:34.000Z

2022-03-01T17:57:21.000Z

test_data/parse_retree/expected/character_set/complementing/suffix_dash/source.py

aas-core-works/aas-core-codegen

afec2cf363b6cb69816e7724a2b58626e2165869

[ "MIT" ]

10

2021-12-29T02:15:55.000Z

2022-03-09T11:04:22.000Z

test_data/parse_retree/expected/character_set/complementing/suffix_dash/source.py

aas-core-works/aas-core-codegen

afec2cf363b6cb69816e7724a2b58626e2165869

[ "MIT" ]

2

2021-12-29T01:42:12.000Z

2022-02-15T13:46:33.000Z

"[^a-z-]"

5

9

0.2

58dafc08268a1a80cb3439805d5aaaa73e99cf05

132

py

Python

config.py

delldu/SCNN

a6fadfbd9e593a28684dcaf29ca8ad425c1c700f

[ "MIT" ]

1

2019-09-04T10:34:53.000Z

config.py

delldu/SCNN

a6fadfbd9e593a28684dcaf29ca8ad425c1c700f

[ "MIT" ]

null

config.py

delldu/SCNN

a6fadfbd9e593a28684dcaf29ca8ad425c1c700f

[ "MIT" ]

null

Dataset_Path = dict( CULane_path = "/home/lion/Dataset/CULane/data/CULane", Tusimple_path = "/home/lion/Dataset/tusimple" )

26.4

58

0.712121

1fab4aa952e3c0cfd4c7853024159ef86fa37499

114,523

py

Python

use_case_WebScrapingTextData.py

lilulamili/STATY_dev_test

7353572ec626647a69694f82d483039b6e1f5c40

[ "Apache-2.0" ]

null

use_case_WebScrapingTextData.py

lilulamili/STATY_dev_test

7353572ec626647a69694f82d483039b6e1f5c40

[ "Apache-2.0" ]

null

use_case_WebScrapingTextData.py

lilulamili/STATY_dev_test

7353572ec626647a69694f82d483039b6e1f5c40

[ "Apache-2.0" ]

2

2021-12-15T18:24:56.000Z

2021-12-15T19:07:41.000Z

#################### # IMPORT LIBRARIES # #################### from selectors import BaseSelector import streamlit as st import pandas as pd import numpy as np import plotly as dd import plotly.express as px import seaborn as sns import matplotlib.pyplot as plt import matplotlib.font_manager import plotly.graph_objects as go import functions as fc import modelling as ml import os import altair as alt import bs4 import requests from collections import Counter import streamlit.components.v1 as components import yfinance as yf import datetime from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator import sys import platform import re import base64 import time from io import BytesIO from pysummarization.nlpbase.auto_abstractor import AutoAbstractor from pysummarization.tokenizabledoc.simple_tokenizer import SimpleTokenizer from pysummarization.web_scraping import WebScraping from pysummarization.abstractabledoc.std_abstractor import StdAbstractor from pysummarization.abstractabledoc.top_n_rank_abstractor import TopNRankAbstractor from sklearn.feature_extraction.text import CountVectorizer from difflib import SequenceMatcher #---------------------------------------------------------------------------------------------- def app(): # Clear cache #st.legacy_caching.clear_cache() # Hide traceback in error messages (comment out for de-bugging) #sys.tracebacklimit = 0 # workaround for Firefox bug- hide the scrollbar while keeping the scrolling functionality st.markdown(""" <style> .ReactVirtualized__Grid::-webkit-scrollbar { display: none; } .ReactVirtualized__Grid { -ms-overflow-style: none; /* IE and Edge */ scrollbar-width: none; /* Firefox */ } </style> """, unsafe_allow_html=True) #------------------------------------------------------------------------- # RESET INPUT #Session state if 'key' not in st.session_state: st.session_state['key'] = 0 reset_clicked = st.sidebar.button("Reset all your input") if reset_clicked: st.session_state['key'] = st.session_state['key'] + 1 st.sidebar.markdown("") #------------------------------------------------------------------------------------------ # SETTINGS settings_expander=st.sidebar.expander('Settings') with settings_expander: st.caption("**Precision**") user_precision=int(st.number_input('Number of digits after the decimal point',min_value=0,max_value=10,step=1,value=4, key = st.session_state['key'])) #st.caption("**Help**") #sett_hints = st.checkbox('Show learning hints', value=False) st.caption("**Appearance**") sett_wide_mode = st.checkbox('Wide mode', value=False, key = st.session_state['key']) sett_theme = st.selectbox('Theme', ["Light", "Dark"], key = st.session_state['key']) #sett_info = st.checkbox('Show methods info', value=False) #sett_prec = st.number_input('Set the number of diggits for the output', min_value=0, max_value=8, value=2) st.sidebar.markdown("") st.sidebar.markdown("") # Check if wide mode if sett_wide_mode: fc.wide_mode_func() # Check theme if sett_theme == "Dark": fc.theme_func_dark() if sett_theme == "Light": fc.theme_func_light() fc.theme_func_dl_button() def cc(): st.legacy_caching.clear_cache() st.session_state['load_data_button'] = None #++++++++++++++++++++++++++++++++++++++++++++ # Text Mining and web-scraping #++++++++++++++++++++++++++++++++++++++++++++ basic_text="Let STATY do text/web processing for you and start exploring your data stories right below... " st.header('**Web scraping and text data**') tw_meth = ['Text analysis','Web-Page summary', 'Financial analysis'] tw_classifier = st.selectbox('What analysis would you like to perform?', list('-')+tw_meth, key = st.session_state['key'], on_change=cc) if tw_classifier in tw_meth: st.markdown("") st.markdown("") st.header('**'+tw_classifier+'**') st.markdown(basic_text) #------------------------------------------------------------ # Text summarization # ----------------------------------------------------------- if tw_classifier=='Web-Page summary': # Clear cache st.legacy_caching.clear_cache() user_path = st.text_input("What what web page should I summarize in five sentences for you?","https://en.wikipedia.org/wiki/Data_mining") run_models = st.button("Press to start the data processing...") if run_models: # Pysummarization of a web page: def pysumMain(url): web_scrape = WebScraping() # Web-scraping: document = web_scrape.scrape(url) auto_abstractor = AutoAbstractor() auto_abstractor.tokenizable_doc = SimpleTokenizer() # Set delimiter for a sentence: auto_abstractor.delimiter_list = [".", "\n"] abstractable_doc = TopNRankAbstractor() # Summarize a document: result_dict = auto_abstractor.summarize(document, abstractable_doc) # Set the limit for the number of output sentences: limit = 5 i = 1 for sentence in result_dict["summarize_result"]: st.write(sentence) if i >= limit: break i += 1 #user_path = st.text_input("What what web page should I summarize in five sentences for you?","https://en.wikipedia.org/wiki/Data_mining") if user_path !='': a1, a2 = st.columns(2) with a1: st.subheader('Web page preview:') st.text("") components.iframe(user_path,width=None,height=500,scrolling=True) with a2: st.subheader('Web page summary:') st.text("") pysumMain(user_path) if tw_classifier =='Stock data analysis': # Clear cache st.legacy_caching.clear_cache() # dwonload first the list of comanies in the S&P500 and DAX indices payload=pd.read_html('https://en.wikipedia.org/wiki/List_of_S%26P_500_companies') first_table = payload[0] df = first_table symbols = df['Symbol'].values.tolist() company = df['Security'].values.tolist() sector = df['GICS Sector'].values.tolist() #sectors = set(sectors) payload1=pd.read_html('https://en.wikipedia.org/wiki/DAX') DAXtable = payload1[3] df=DAXtable DAXsymbols = df['Ticker symbol'].values.tolist() DAXSector = df['Prime Standard Sector'].values.tolist() DAXcompany= df['Company'].values.tolist() #Merge indices data symbols_all=symbols+DAXsymbols sector_all=sector+DAXSector company_all=company+DAXcompany #ticker specification st.subheader('Stock data analysis') a3, a4 = st.columns(2) with a3: selected_stock = st.text_input("Enter a stock ticker symbol", "TSLA") symbols_all=list('-')+symbols_all selected_symbol = st.selectbox('You can add an additional stock for comparision...',symbols_all) with a4: today = datetime.date.today() last_year = today - datetime.timedelta(days=365) start_date = st.date_input('Select start date', last_year) end_date = st.date_input('Select end date', today) if start_date > end_date: st.error('ERROR: End date must fall after start date.') st.markdown("") add_data_show=st.checkbox("Get additional data (cashflow, balance sheet etc.)", value = False) st.markdown("") dev_expander_perf = st.expander("Stock performance") with dev_expander_perf: #get data for a selected ticker symbol: stock_data = yf.Ticker(selected_stock) stock_df = stock_data.history(period='1d', start=start_date, end=end_date) add_stock_data = yf.Ticker(selected_symbol) add_stock_df = add_stock_data.history(period='1d', start=start_date, end=end_date) #print stock values if st.checkbox("Show stock data for " + selected_stock, value = True): st.write(stock_df) if selected_symbol !="-": if st.checkbox("Show stock data for " + selected_symbol, value = False): st.write(add_stock_df) comparision_check=st.checkbox('Compare '+ selected_stock + " & " + selected_symbol, value = True) #draw line chart with stock prices a5, a6 = st.columns(2) with a5: stock_para= st.selectbox('Select ' + selected_stock + " info to draw", stock_df.columns) if selected_symbol !="-": if comparision_check: st.subheader('Daily data comparision '+ selected_stock + " & " + selected_symbol) c1=selected_stock + " " + stock_para c2=selected_symbol + " " + stock_para c1_data=stock_df[[stock_para]] c1_data.rename(columns={c1_data.columns[0]: c1 }, inplace = True) c2_data=add_stock_df[[stock_para]] c2_data.rename(columns={c2_data.columns[0]: c2 }, inplace = True) stock_dataToplot=pd.concat([c1_data, c2_data], axis=1) #st.write(stock_dataToplot) st.line_chart(stock_dataToplot) else: st.subheader(stock_para + " price for " + selected_stock + " (daily)") stock_dataToplot=stock_df[stock_para] st.line_chart(stock_dataToplot) else: st.subheader(stock_para + " price for " + selected_stock + " (daily)") stock_dataToplot=stock_df[stock_para] st.line_chart(stock_dataToplot) with a6: stock_para2= st.selectbox('Select ' + selected_stock + " info to draw", stock_df.columns, index=3) if selected_symbol !="-": if comparision_check: st.subheader('Daily data comparision '+ selected_stock + " & " + selected_symbol) c3=selected_stock + " " + stock_para2 c4=selected_symbol + " " + stock_para2 c3_data=stock_df[[stock_para2]] c3_data.rename(columns={c3_data.columns[0]: c3 }, inplace = True) c4_data=add_stock_df[[stock_para2]] c4_data.rename(columns={c4_data.columns[0]: c4 }, inplace = True) stock_dataToplot2=pd.concat([c3_data, c4_data], axis=1) #st.write(stock_dataToplot) st.line_chart(stock_dataToplot2) else: st.subheader(stock_para2 + " price for " + selected_stock + " (daily)") stock_dataToplot2=stock_df[stock_para2] st.line_chart(stock_dataToplot2) else: st.subheader(stock_para2 + " price for " + selected_stock + " (daily)") stock_dataToplot2=stock_df[stock_para2] st.line_chart(stock_dataToplot2) if add_data_show: dev_expander_cf = st.expander("Cashflow") with dev_expander_cf: st.subheader(selected_stock) stock_data_cf = yf.Ticker(selected_stock).cashflow st.write(stock_data_cf) if selected_symbol !='-': st.subheader(selected_symbol) st.write(yf.Ticker(selected_symbol).cashflow) dev_expander_bs = st.expander("Balance sheet") with dev_expander_bs: st.subheader(selected_stock) stock_data = yf.Ticker(selected_stock) stock_data_fi = stock_data.balance_sheet st.write(stock_data_fi) if selected_symbol !='-': st.subheader(selected_symbol) st.write(yf.Ticker(selected_symbol).balance_sheet) dev_expander_fi = st.expander("Other financials") with dev_expander_fi: st.subheader(selected_stock) stock_data = yf.Ticker(selected_stock) stock_data_fi = stock_data.financials st.write(stock_data_fi) if selected_symbol !='-': st.subheader(selected_symbol) st.write(yf.Ticker(selected_symbol).financials) dev_expander_info = st.expander("Stock basic info") with dev_expander_info: st.subheader(selected_stock) stock_data = yf.Ticker(selected_stock) st.write(stock_data.info ['longBusinessSummary']) if selected_symbol !='-': st.subheader(selected_symbol) st.write(yf.Ticker(selected_symbol).info ['longBusinessSummary']) # ---------------------------------------------------------------- # Text Mining #----------------------------------------------------------------- if tw_classifier=='Text analysis': # Clear cache st.legacy_caching.clear_cache() run_text_OK=False text_cv = CountVectorizer() user_color=21 def random_color_func(user_col,word=None, font_size=None, position=None, orientation=None, font_path=None, random_state=None): h = int(user_color) s = int(100.0 * 255.0 / 255.0) l = int(100.0 * float(random_state.randint(60, 120)) / 255.0) return "hsl({}, {}%, {}%)".format(h, s, l) #specify the data source word_sl=st.radio('Select the data source for text analysis',['text input','web page']) if word_sl=='text input': user_text=st.text_area('Please enter or copy your text here', value='STATY \n\n STATY is growing out of the effort to bring more data insights to university education across all disciplines of the natural and social sciences. It is motivated by the belief that fostering data literacy, creativity and critical thinking are more effective towards innovation, than bringing endless units of introduction to programming to students who find learning programming an overwhelming task. By providing easy access to the methods of classical statistics and machine learning, STATY’s approach is to inspire students to explore issues they are studying in the curriculum directly on real data, practice interpreting the results and check the source code to see how it is done or to improve the code. STATY can be used in the process of teaching and learning data science, demonstrations of theoretical concepts across various disciplines, active learning, promotion of teamwork, research and beyond.', height=600, key = st.session_state['key'] ) st.write("") if len(user_text)>0: run_text_OK = True elif word_sl=='web page': user_path_wp = st.text_input("What web page should I analyse?","https://en.wikipedia.org/wiki/Data_mining", key = st.session_state['key']) st.write("") if user_path_wp !='': web_scrape = WebScraping() user_text = web_scrape.scrape(user_path_wp) run_text_OK = True if run_text_OK == True: # Basic text processing: text_cv_fit=text_cv.fit_transform([user_text]) wordcount= pd.DataFrame(text_cv_fit.toarray().sum(axis=0), index=text_cv.get_feature_names(),columns=["Word count"]) word_sorted=wordcount.sort_values(by=["Word count"], ascending=False) #Stop words handling: stopword_selection=st.selectbox("Select stop word option",["No stop words (use all words)","Manually select stop words", "Use a built-in list of stop words in German", "Use a built-in list of stop words in English", "Specify stop words"], index=3, key=st.session_state['key']) if stopword_selection=="No stop words (use all words)": word_stopwords=[] elif stopword_selection=="Manually select stop words": word_stopwords=st.multiselect("Select stop words (words to be removed from the text)", word_sorted.index.tolist(),word_sorted.index[1:min(10,len(word_sorted.index))].tolist(), key = st.session_state['key']) elif stopword_selection=="Use a built-in list of stop words in German": 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elif stopword_selection=="Use a built-in list of stop words in English": word_stopwords=['a','about','above','after','again','against','ain','all','am','an','and','any','are','aren',"aren't",'as','at','be','because','been','before','being','below','between','both','but','by','can','couldn',"couldn't",'d','did','didn',"didn't",'do','does','doesn',"doesn't",'doing','don',"don't",'down','during','each','few','for','from','further','had','hadn',"hadn't",'has','hasn',"hasn't",'have','haven',"haven't",'having','he','her','here','hers','herself','him','himself','his','how','i','if','in','into','is','isn',"isn't",'it',"it's",'its','itself','just','ll','m','ma','me','mightn',"mightn't",'more','most','mustn',"mustn't",'my','myself','needn',"needn't",'no','nor','not','now','o','of','off','on','once','only','or','other','our','ours','ourselves','out','over','own','re','s','same','shan',"shan't",'she',"she's",'should',"should've",'shouldn',"shouldn't",'so','some','such','t','than','that',"that'll",'the','their','theirs','them','themselves','then','there','these','they','this','those','through','to','too','under','until','up','ve','very','was','wasn',"wasn't",'we','were','weren',"weren't",'what','when','where','which','while','who','whom','why','will','with','won',"won't",'wouldn',"wouldn't",'y','you',"you'd","you'll","you're","you've",'your','yours','yourself','yourselves'] elif stopword_selection=="Specify stop words": word_stopwords=[] user_stopwords=st.text_area('Please enter or copy stop words here', value='', height=200, key = st.session_state['key'] ) if len(user_stopwords)>0: stopwords_cv = CountVectorizer() stopwords_cv_fit=stopwords_cv.fit_transform([user_stopwords]) word_stopwords=stopwords_cv.get_feature_names() st.write("") a4,a5=st.columns(2) with a4: # user specification of words to search word_list=pd.DataFrame(columns=word_sorted.index) #words_cleaned=word_list.drop(word_stopwords,axis=1) words_cleaned=sorted(list(set(word_list)-set(word_stopwords))) find_words=st.multiselect("Search sentences with following words", words_cleaned, key = st.session_state['key']) with a5: #user-specification of n-grams user_ngram=st.number_input("Specify the number of words to be extracted (n-grams)", min_value=1, value=2, key = st.session_state['key']) if st.checkbox('Show a word count', value = False, key = st.session_state['key']): st.write(word_sorted) st.write("") number_remove=st.checkbox("Remove numbers from text", value=True, key = st.session_state['key']) a4,a5=st.columns(2) with a4: #WordCloud color specification st.write("") draw_WordCloud=st.checkbox("Create a Word Cloud", value=True, key = st.session_state['key']) with a5: if draw_WordCloud==True: #color options for the WordCloud (user selection) color_options= pd.DataFrame(np.array([[21, 120, 12, 240, 30]]), columns=['orange', 'green', 'red','blue','brown']) user_color_name=st.selectbox('Select the main color of your WordCloud',color_options.columns, key = st.session_state['key']) user_color=color_options[user_color_name] st.write("") st.write("") run_text = st.button("Press to start text processing...") if run_text: st.write("") st.write("") st.info("Text processing progress") text_bar = st.progress(0.0) progress = 0 #--------------------------------------------------------------------------------- # Basic NLP metrics and visualisations #--------------------------------------------------------------------------------- wfreq_output = st.expander("Basic NLP metrics and visualisations ", expanded = False) with wfreq_output: # Word frequency st.subheader('Word count') #calculate word frequency - stop words exluded: word_sorted=fc.cv_text(user_text, word_stopwords, 1,user_precision,number_remove) st.write("") st.write("Number of words: ", word_sorted["Word count"].sum()) st.write("Number of sentences", len(re.findall(r"([^.]*\.)" ,user_text))) if len(word_stopwords)>0: st.warning("All analyses are based on text with stop words removed!") else: st.warning("No stop words are removed from the text!") st.write(word_sorted.style.format({"Rel. freq.": "{:.2f}"})) a4,a5=st.columns(2) with a4: # relative frequency for the top 10 words txt_bar=word_sorted.head(min(len(word_sorted),10)) fig = go.Figure() fig.add_trace(go.Bar(x=txt_bar["Rel. freq."], y=txt_bar.index, name='',marker_color = 'indianred',opacity=0.5,orientation='h')) fig.update_layout({'plot_bgcolor': 'rgba(0, 0, 0, 0)',}) fig.update_layout(xaxis=dict(title='relative fraction %', titlefont_size=14, tickfont_size=14,),) fig.update_layout(hoverlabel=dict(bgcolor="white",align="left")) fig.update_layout(height=400,width=400) st.plotly_chart(fig, use_container_width=True) st.info("Top " + str(min(len(word_sorted),10)) + " words relative frequency") with a5: fig = go.Figure(data=[go.Histogram(x=word_sorted["Word length"], histnorm='probability',marker_color ='steelblue',opacity=0.5)]) fig.update_layout({'plot_bgcolor': 'rgba(0, 0, 0, 0)',}) fig.update_layout(xaxis=dict(title='word length', titlefont_size=14, tickfont_size=14,),) fig.update_layout(hoverlabel=dict(bgcolor="white",align="left")) fig.update_layout(height=400,width=400) st.plotly_chart(fig, use_container_width=True) st.info("Word length distribution") #word similarity vs. word length & word frequency word_similarity=[] for word in txt_bar.index: d=0 for sword in txt_bar.index: seq = SequenceMatcher(None,word,sword) d = d+(seq.ratio()*100) word_similarity.append([d]) txt_bar["Similarity"]=(np.float_(word_similarity)/len(txt_bar.index)).round(user_precision) a4,a5=st.columns(2) with a4: # bubble chart fig = go.Figure(data=[go.Scatter( y=txt_bar.index, x=txt_bar["Rel. freq."], mode='markers',text=txt_bar["Similarity"], marker_size=txt_bar["Similarity"],marker_color='indianred') ]) fig.update_layout({'plot_bgcolor': 'rgba(0, 0, 0, 0)',}) fig.update_layout(xaxis=dict(title='relative frequency', titlefont_size=14, tickfont_size=14,),) fig.update_layout(hoverlabel=dict(bgcolor="white",align="left")) fig.update_layout(height=400,width=400) st.plotly_chart(fig, use_container_width=True) st.info("Bubble size eq. average word similarity across the top " + str(min(len(word_sorted),10)) +" words") with a5: df_to_plot=word_sorted df_to_plot['word']=word_sorted.index fig = px.scatter(data_frame=df_to_plot, x='Word length', y='Rel. freq.',hover_data=['word','Word length', 'Rel. freq.'], color_discrete_sequence=['steelblue']) fig.update_layout({'plot_bgcolor': 'rgba(0, 0, 0, 0)',}) fig.update_layout(xaxis=dict(title="word length", titlefont_size=14, tickfont_size=14,),) fig.update_layout(yaxis=dict(title="word frequency", titlefont_size=14, tickfont_size=14,),) fig.update_layout(hoverlabel=dict(bgcolor="white", )) fig.update_layout(height=400,width=400) st.plotly_chart(fig) st.info("A comparision of frequencies of short and long words") # bigram distribution cv2_output=fc.cv_text(user_text, word_stopwords, 2,user_precision,number_remove) # trigram distribution cv3_output=fc.cv_text(user_text, word_stopwords, 3,user_precision,number_remove) a4,a5=st.columns(2) with a4: txt_bar=cv2_output.head(min(len(cv2_output),10)) fig = go.Figure() fig.add_trace(go.Bar(x=txt_bar["Rel. freq."], y=txt_bar.index, name='',marker_color = 'indianred',opacity=0.5,orientation='h')) fig.update_layout({'plot_bgcolor': 'rgba(0, 0, 0, 0)',}) fig.update_layout(xaxis=dict(title='relative fraction %', titlefont_size=14, tickfont_size=14,),) fig.update_layout(hoverlabel=dict(bgcolor="white",align="left")) fig.update_layout(height=400,width=400) st.plotly_chart(fig, use_container_width=True) st.info("Top " + str(min(len(cv2_output),10)) + " bigrams relative frequency") with a5: txt_bar=cv3_output.head(10) fig = go.Figure() fig.add_trace(go.Bar(x=txt_bar["Rel. freq."], y=txt_bar.index, name='',marker_color = 'indianred',opacity=0.5,orientation='h')) fig.update_layout({'plot_bgcolor': 'rgba(0, 0, 0, 0)',}) fig.update_layout(xaxis=dict(title='relative fraction %', titlefont_size=14, tickfont_size=14,),) fig.update_layout(hoverlabel=dict(bgcolor="white",align="left")) fig.update_layout(height=400,width=400) st.plotly_chart(fig, use_container_width=True) st.info("Top " + str(min(len(cv2_output),10)) + " trigrams relative frequency") if draw_WordCloud==True: #Draw WordCloud wordcloud = WordCloud(background_color="white", contour_color="white",max_words=100,stopwords=word_stopwords, width=600,height=400,color_func=random_color_func).generate(user_text) fig_text, ax = plt.subplots() ax=plt.imshow(wordcloud, interpolation='bilinear') plt.axis("off") st.subheader('WordCloud') st.pyplot(fig_text) progress += 1 text_bar.progress(progress/3) # Download link st.write("") output = BytesIO() excel_file = pd.ExcelWriter(output, engine="xlsxwriter") word_sorted.to_excel(excel_file, sheet_name="words",index=False) if len(word_stopwords)>0: pd.DataFrame(word_stopwords,columns=['stop words']).to_excel(excel_file, sheet_name="stop words",index=False) if len(cv2_output)>0: cv2_output.to_excel(excel_file, sheet_name="bigrams",index=True) if len(cv3_output)>0: cv3_output.to_excel(excel_file, sheet_name="trigrams",index=True) excel_file.save() excel_file = output.getvalue() b64 = base64.b64encode(excel_file) dl_file_name = "BasicTextAnalysis.xlsx" st.markdown( f""" <a href="data:file/excel_file;base64,{b64.decode()}" id="button_dl" download="{dl_file_name}">Download basic NLP metrics</a> """, unsafe_allow_html=True) st.write("") #--------------------------------------------------------------------------------- # Sentences with specific words #--------------------------------------------------------------------------------- if len(find_words)>0: # extract all sentences with specific words: sentences_list=[] sentences = re.findall(r"([^.]*\.)" ,user_text) for sentence in sentences: if all(word in sentence for word in find_words): if len(sentence)<1000: # threshold for to long sentences is 1000 characters sentences_list.append(sentence) if len(sentences_list)>0: sentences_output = st.expander("Sentences with specific words", expanded = False) with sentences_output: for sentence in sentences_list: st.write(sentence) #st.table(pd.DataFrame({'Sentences':sentences_list})) # Download link st.write("") output = BytesIO() excel_file = pd.ExcelWriter(output, engine="xlsxwriter") pd.DataFrame({'Sentences':sentences_list}).to_excel(excel_file, sheet_name="Sentences",index=False) excel_file.save() excel_file = output.getvalue() b64 = base64.b64encode(excel_file) dl_file_name = "Sentences with specific words.xlsx" st.markdown( f""" <a href="data:file/excel_file;base64,{b64.decode()}" id="button_dl" download="{dl_file_name}">Download sentences</a> """, unsafe_allow_html=True) st.write("") progress += 1 text_bar.progress(progress/3) #--------------------------------------------------------------------------------- # User specific n-grams #--------------------------------------------------------------------------------- #extract n-grams: ngram_list=[] text_cv = fc.cv_text(user_text, word_stopwords,user_ngram,user_precision,number_remove) #CountVectorizer(analyzer='word', stop_words=set(word_stopwords), ngram_range=(user_ngram, user_ngram)) #text_cv_fit=text_cv.fit_transform([user_text]) #listToString='. '.join(text_cv.get_feature_names()) listToString='. '.join(text_cv.index) sentences = re.findall(r"([^.]*\.)" ,listToString) for sentence in sentences: if all(word in sentence for word in find_words): sentence=re.sub('[.]', '', sentence) ngram_list.append(sentence) if len(ngram_list)>0: ngram_output = st.expander("n-grams", expanded = False) with ngram_output: st.write("") st.subheader("n-grams") st.write("") for sentence in ngram_list: st.write(sentence) # Download link st.write("") output = BytesIO() excel_file = pd.ExcelWriter(output, engine="xlsxwriter") pd.DataFrame({'n-gram':ngram_list}).to_excel(excel_file, sheet_name=str(user_ngram) +"-gram",index=False) excel_file.save() excel_file = output.getvalue() b64 = base64.b64encode(excel_file) dl_file_name = str(user_ngram)+"gram.xlsx" st.markdown( f""" <a href="data:file/excel_file;base64,{b64.decode()}" id="button_dl" download="{dl_file_name}">Download n-grams</a> """, unsafe_allow_html=True) st.write("") progress += 1 text_bar.progress(progress/3) # Success message #st.success('Text processing completed') #---------------------------------------------------- # Stock data analysis - Yahoo Finance # --------------------------------------------------- if tw_classifier=='Financial analysis': st.write('Check stock prices and key performance indicators for companies included in [S&P 500] (https://en.wikipedia.org/wiki/List_of_S%26P_500_companies), [DAX] (https://en.wikipedia.org/wiki/DAX), [FTSE 100] (https://en.wikipedia.org/wiki/FTSE_100_Index), [CSI 300](https://en.wikipedia.org/wiki/CSI_300_Index), [Nikkei 225] (https://de.wikipedia.org/wiki/Nikkei_225), [CAC 40] (https://en.wikipedia.org/wiki/CAC_40), [BSE SENSEX] (https://en.wikipedia.org/wiki/BSE_SENSEX) and [KOSPI](https://en.wikipedia.org/wiki/KOSPI) indexes, or for any company available via [Yahoo Finance](https://finance.yahoo.com/). Note, the bulk download and data processing may take some time!') a1,a2,a3=st.columns([1,3,1]) with a1: stock_search_option=st.radio('',['Indexes', 'Symbol']) with a2: st.markdown("") st.markdown("") st.write("Start your analysis by either selecting companies from specific key indexes, or by entering a ticker symbol") # delete session state if input widget change def in_wid_change(): st.session_state['load_data_button'] = None if stock_search_option !='Symbol': # select ticker for KPI-Dashboard co1 = st.container() st.write('Consider stocks from the following indices:') c1, c2, c3,c4 = st.columns(4) ticker_options = [] SP500 = c1.checkbox('S&P 500', True, on_change=in_wid_change) CSI300 = c1.checkbox('CSI 300', False, on_change=in_wid_change) DAX = c2.checkbox('DAX', True, on_change=in_wid_change) NIKKEI225 = c2.checkbox('NIKKEI 225', False, on_change=in_wid_change) CAC40=c3.checkbox('CAC40', False, on_change=in_wid_change) BSE_SENSEX=c3.checkbox('BSE SENSEX', False, on_change=in_wid_change) FTSE = c4.checkbox('FTSE', False, on_change=in_wid_change) KOSPI = c4.checkbox('KOSPI', False, on_change=in_wid_change) else: #selected option is 'Symbol' selected_stock = st.text_input("Enter at least one stock ticker symbol! Please use space for ticker symbol separation!", "TSLA") selected_stock=(list(selected_stock.split(" "))) selected_stock = list(filter(None, selected_stock)) list_companies=[] list_symbols = [] list_sectors = [] list_stockindex = [] for i in range(len(selected_stock)): sel_stock_string=selected_stock[i] try: yf.Ticker(sel_stock_string).info['longName'] except: st.error('Cannot get any data on ' + sel_stock_string + ', so the ticker probably does not exist! \n Please check spelling, or try another ticker symbol!') return list_companies.append(yf.Ticker(selected_stock[i]).info['longName']) list_sectors.append('XX') list_stockindex.append('XX') list_symbols = selected_stock if len(selected_stock)==0: selected_stock=[] #-------------------------------- # Get lists of company's/tickers # FUNCTIONS # download list of companies @st.cache() def load_ticker(): #--------------------------------------------------------------------------------------------- symbols_SP, symbols_DAX, symbols_FTSE, symbols_BSE, symbols_CAC, symbols_CSI, symbols_KO, symbols_NIK=[],[],[],[],[],[],[],[] company_SP , company_DAX , company_FTSE, company_BSE,company_CAC,company_CSI,company_KO,company_NIK=[],[],[],[],[],[],[],[] sector_SP , sector_DAX , sector_FTSE, sector_BSE,sector_CAC,sector_CSI,sector_KO,sector_NIK=[],[],[],[],[],[],[],[] index_SP , index_DAX , index_FTSE, index_BSE,index_CAC,index_CSI,index_KO,index_NIK=[],[],[],[],[],[],[],[] #S&P500: if SP500: (symbols_SP,company_SP,sector_SP,index_SP)= fc.get_stock_list('S&P500','https://en.wikipedia.org/wiki/List_of_S%26P_500_companies',0, 0, 1, 3) #DAX if DAX: (symbols_DAX,company_DAX,sector_DAX,index_DAX)=fc.get_stock_list('DAX','https://en.wikipedia.org/wiki/DAX',3, 3, 1, 2) #FTSE if FTSE: (symbols_FTSE,company_FTSE,sector_FTSE,index_FTSE)=fc.get_stock_list('FTSE100','https://en.wikipedia.org/wiki/FTSE_100_Index',3, 1, 0, 2) #CSI300 if CSI300: (symbols_CSI,company_CSI,sector_CSI,index_CSI)=fc.get_stock_list('CSI300','https://en.wikipedia.org/wiki/CSI_300_Index',3, 0, 1, 4) #NIKKEI225 if NIKKEI225: (symbols_NIK,company_NIK,sector_NIK,index_NIK)=fc.get_stock_list('NIKKEI225','https://topforeignstocks.com/indices/the-components-of-the-nikkei-225-index/',0, 2, 1, 3) #CAC40 if CAC40: (symbols_CAC,company_CAC,sector_CAC,index_CAC)=fc.get_stock_list('CAC40','https://en.wikipedia.org/wiki/CAC_40',3, 3, 0, 1) #BSE_SENSEX if BSE_SENSEX: (symbols_BSE,company_BSE,sector_BSE,index_BSE)=fc.get_stock_list('BSE_SENSEX','https://en.wikipedia.org/wiki/BSE_SENSEX',1, 2, 3, 4) #S&P_TSX60 #(symbols_TS,company_TS,sector_TS,index_TS)=fc.get_stock_list('S&P_TSX60','https://topforeignstocks.com/indices/the-components-of-the-sptsx-composite-index/') #KOSPI if KOSPI: (symbols_KO,company_KO,sector_KO,index_KO)=fc.get_stock_list('KOSPI','https://topforeignstocks.com/indices/the-components-of-the-korea-stock-exchange-kospi-index/',0, 2, 1, 3) #--------------------------------------------------------------------------------------------- # merge into one dataframe list_symbols = symbols_SP + symbols_DAX + symbols_FTSE+ symbols_BSE+symbols_CAC+symbols_CSI+symbols_KO+symbols_NIK list_companies = company_SP + company_DAX + company_FTSE+ company_BSE+company_CAC+company_CSI+company_KO+company_NIK list_sectors = sector_SP + sector_DAX + sector_FTSE+ sector_BSE+sector_CAC+sector_CSI+sector_KO+sector_NIK list_stockindex = index_SP + index_DAX + index_FTSE+ index_BSE+index_CAC+index_CSI+index_KO+index_NIK df_indicesdata = pd.DataFrame({'Ticker': list_symbols, 'Company': list_companies, 'Sector': list_sectors, 'Stock index': list_stockindex}) return df_indicesdata, symbols_SP, symbols_DAX, symbols_FTSE, symbols_BSE, symbols_CAC, symbols_CSI, symbols_KO, symbols_NIK # define ticker object @st.cache(allow_output_mutation=True) def function_ticker(): ticker = [] for i in range(len(list_symbols)): ticker.append(Class_ticker()) return ticker # function for multiselect ticker vs. company def ticker_dict_func(option): return ticker_dict[option] # function for multiselect index vs. company def index_dict_func(option): return ticker[option].company # create df_selected @st.cache(allow_output_mutation=True) def function_df_selected(): df_selected = pd.DataFrame(columns=['Ticker', 'Company', 'Sector']) index_list_rename = [] for i in range(len(selected_stock)): df_selected = df_selected.append(df_indicesdata[df_indicesdata['Ticker']==selected_stock[i]]) index_list_rename += [i+1] index_list = df_selected.index.tolist() df_selected.index = index_list_rename return df_selected, index_list def data_available(label, source, selected_year): if label in source.index and source.at[label, selected_year] is not None and source.at[label, selected_year] != 0 : return True else: return False # calculate average def average_func(index_list, df_selected, df): if len(index_list) > 1: if len(df_selected['Stock index'])*2 == (len(df_selected[df_selected['Stock index']==df_selected.at[1, 'Stock index']])+len(df_selected[df_selected['Sector']==df_selected.at[1, 'Sector']])): means = df.mean(axis=1) df.insert(0, 'average' , means) return df # create df def fill_df_func(df, ticker, i, label_index, label_column): if df.empty: df = pd.DataFrame(ticker[i].kpis, index=label_index, columns=[label_column]) else: df.insert(len(df.columns) , label_column, ticker[i].kpis) return df #---------------------------------------------------------------------------------------------- # define class of tickers class Class_ticker: # master data def __init__(self): self.symbol = None self.company = None self.sector = None self.dict = None def master_data(self, i): self.symbol = list_symbols[i] self.company = list_companys[i] self.sector = list_sectors[i] self.stockindex = list_stockindex[i] # data loading def load_data(self): self.data = yf.Ticker(self.symbol) #---------------------------------------------------------------------------------------------- # calculate kpis #profitability def kpi_profitability(self, selected_year): if data_available('Ebit', self.fi, selected_year) and data_available('Total Assets', self.bs, selected_year): self.roi = self.fi.at['Ebit', selected_year] / self.bs.at['Total Assets', selected_year] * 100 else: self.roi = np.nan if data_available('Ebit', self.fi, selected_year) and data_available('Total Stockholder Equity', self.bs, selected_year): self.roe = self.fi.at['Ebit', selected_year] / self.bs.at['Total Stockholder Equity', selected_year] * 100 else: self.roe = np.nan if data_available('Total Revenue', self.fi, selected_year): self.revenues = self.fi.at['Total Revenue', selected_year] / 1000000000 else: self.revenues = np.nan if data_available('Ebit', self.fi, selected_year) and data_available('Depreciation', self.cf, selected_year) and data_available('Total Revenue', self.fi, selected_year): self.ebitda_margin = (self.fi.at['Ebit', selected_year] + self.cf.at['Depreciation', selected_year]) / self.fi.at['Total Revenue', selected_year] *100 else: self.ebitda_margin = np.nan if data_available('Ebit', self.fi, selected_year) and data_available('Total Revenue', self.fi, selected_year): self.ebit_margin = self.fi.at['Ebit', selected_year] / self.fi.at['Total Revenue', selected_year] * 100 else: self.ebit_margin = np.nan self.kpis = [self.roi, self.roe, self.revenues, self.ebitda_margin, self.ebit_margin] #debt capital def kpi_debt_capital(self, selected_year): if data_available('Total Liab', self.bs, selected_year) and data_available('Cash', self.bs, selected_year) and data_available('Ebit', self.fi, selected_year) and data_available('Depreciation', self.cf, selected_year): self.netdebt_ebitda = (self.bs.at['Total Liab', selected_year] - self.bs.at['Cash', selected_year]) / (self.fi.at['Ebit', selected_year] + self.cf.at['Depreciation', selected_year]) else: self.netdebt_ebitda = np.nan if data_available('Ebit', self.fi, selected_year) and data_available('Depreciation', self.cf, selected_year) and data_available('Interest Expense', self.fi, selected_year): self.ebita_interest = (self.fi.at['Ebit', selected_year] + self.cf.at['Depreciation', selected_year]) / self.fi.at['Interest Expense', selected_year] * -1 else: self.ebita_interest = np.nan if data_available('Total Current Liabilities', self.bs, selected_year) and data_available('Total Current Assets', self.bs, selected_year): self.current_ratio = self.bs.at['Total Current Liabilities', selected_year] / self.bs.at['Total Current Assets', selected_year] else: self.current_ratio = np.nan if data_available('Accounts Payable', self.bs, selected_year) and data_available('Cost Of Revenue', self.fi, selected_year) : self.dpo = self.bs.at['Accounts Payable', selected_year] * 365 / self.fi.at['Cost Of Revenue', selected_year] else: self.dpo = np.nan self.kpis = [self.netdebt_ebitda, self.ebita_interest, self.current_ratio, self.dpo] #equity capital def kpi_equity_capital(self): if 'revenuePerShare' in self.info: self.revenuepershare = self.info['revenuePerShare'] else: self.revenuepershare = np.nan if 'forwardEps' in self.info: self.eps = self.info['forwardEps'] else: self.eps = np.nan if 'dividendRate' in self.info: self.dividendrate = self.info['dividendRate'] else: self.dividendrate = np.nan self.kpis = [self.revenuepershare, self.eps, self.dividendrate] #valuation def kpi_valuation(self): if 'forwardPE' in self.info: self.forwardPE = self.info['forwardPE'] else: self.forwardPE = np.nan if 'pegRatio' in self.info: self.pegratio = self.info['pegRatio'] else: self.pegratio = np.nan if 'priceToBook' in self.info: self.pricetobook = self.info['priceToBook'] else: self.pricetobook = np.nan if 'enterpriseValue' in self.info: self.ev = self.info['enterpriseValue'] / 1000000000 else: self.ev = np.nan if 'enterpriseToRevenue' in self.info: self.evtorevenue = self.info['enterpriseToRevenue'] else: self.evtorevenue = np.nan if 'enterpriseToEbitda' in self.info: self.evtoebitda = self.info['enterpriseToEbitda'] else: self.evtoebitda = np.nan self.kpis = [self.forwardPE, self.pegratio, self.pricetobook, self.ev, self.evtorevenue, self.evtoebitda] def stock_history(self): self.history = self.data.history(period=str(stock_period)) #capital procurement def kpi_capital_procurement(self, selected_year): if data_available('Total Cash From Operating Activities', self.cf, selected_year) and data_available('Total Cashflows From Investing Activities', self.cf, selected_year): self.selffinancingratio = self.cf.at['Total Cash From Operating Activities', selected_year]/ self.cf.at['Total Cashflows From Investing Activities', selected_year] * -1 else: self.selffinancingratio = np.nan if data_available('Total Stockholder Equity', self.bs, selected_year) and data_available('Total Assets', self.bs, selected_year): self.equityratio = self.bs.at['Total Stockholder Equity', selected_year] / self.bs.at['Total Assets', selected_year] * 100 else: self.equityratio = np.nan self.kpis = [self.selffinancingratio, self.equityratio] #capital allocation def kpi_capital_allocation(self, selected_year): if data_available('Capital Expenditures', self.cf, selected_year): self.capexrevenueratio = self.cf.at['Capital Expenditures', selected_year] * -1 / self.fi.at['Total Revenue', selected_year] else: self.capexrevenueratio = np.nan if data_available('Research Development', self.fi, selected_year): self.RDrevenueratio = self.fi.at['Research Development', selected_year] / self.fi.at['Total Revenue', selected_year] else: self.RDrevenueratio = np.nan if data_available('Inventory', self.bs, selected_year) and data_available('Net Receivables', self.bs, selected_year): self.ccc = (self.bs.at['Inventory', selected_year] * 365 / self.fi.at['Total Revenue', selected_year]) + (self.bs.at['Net Receivables', selected_year] * 365 / self.fi.at['Total Revenue', selected_year]) - self.dpo else: self.ccc = np.nan self.kpis = [self.capexrevenueratio, self.RDrevenueratio, self.ccc] #procurement market def kpi_procurement_market(self, selected_year): if selected_year == self.fi.columns[0] and 'fullTimeEmployees' in self.info and data_available('Total Revenue', self.fi, selected_year): self.labour_productivity = self.fi.at['Total Revenue', self.fi.columns[0]] / 1000 / self.info['fullTimeEmployees'] else: self.labour_productivity = np.nan if data_available('Total Revenue', self.fi, selected_year) and data_available('Net Receivables', self.bs, selected_year): self.asset_turnover = self.fi.at['Total Revenue', selected_year] / self.bs.at['Net Receivables', selected_year] * 100 else: self.asset_turnover = np.nan self.kpis = [self.labour_productivity, self.asset_turnover] #---------------------------------------------------------------------------------------------- # load ticker if stock_search_option=='Symbol': df_indicesdata = pd.DataFrame({'Ticker': list_symbols, 'Company': list_companies, 'Sector': list_sectors, 'Stock index': list_stockindex}) symbols_SP, symbols_DAX, symbols_FTSE, symbols_BSE, symbols_CAC, symbols_CSI, symbols_KO, symbols_NIK=[],[],[],[],[],[] ,[] ,[] else: df_indicesdata, symbols_SP, symbols_DAX, symbols_FTSE, symbols_BSE, symbols_CAC, symbols_CSI, symbols_KO, symbols_NIK = load_ticker() list_symbols = df_indicesdata['Ticker'].values.tolist() list_companys = df_indicesdata['Company'].values.tolist() list_sectors = df_indicesdata['Sector'].values.tolist() list_stockindex = df_indicesdata['Stock index'].values.tolist() # ini_msg=st.info("Initializing stock data loading...") #my_bar = st.progress(0.0) # progress_sum=len(list_symbols) #progress = 0 # define ticker object and load data ticker = function_ticker() # create dictonary, master data, load data ticker_dict = {} for i in range(len(list_symbols)): ticker[i].master_data(i) ticker_dict[ticker[i].symbol] = ticker[i].company ticker[i].load_data() #progress += 1 #my_bar.progress(progress/progress_sum) #suc_msg=st.success('Stock data loading... done!') # time.sleep(1) # my_bar.empty() # suc_msg.empty() #ini_msg.empty() # check session state if 'selected_stock' not in st.session_state: st.session_state['selected_stock'] = None if 'load_data_button' not in st.session_state: st.session_state['load_data_button'] = None if 'list_years' not in st.session_state: st.session_state['list_years'] = None #---------------------------------------------------------------------------------------------- if stock_search_option !='Symbol': co1 = st.container() if SP500: ticker_options += symbols_SP if DAX: ticker_options +=symbols_DAX if FTSE: ticker_options += symbols_FTSE if CSI300: ticker_options += symbols_CSI if NIKKEI225: ticker_options += symbols_NIK if BSE_SENSEX: ticker_options += symbols_BSE if CAC40: ticker_options += symbols_CAC if KOSPI: ticker_options += symbols_KO if SP500: default = 'MSFT' elif DAX: default = 'VOW3.DE' elif FTSE: default = 'VOD.L' elif CSI300: default = '601318.SS' elif NIKKEI225: default = '9983.T' elif BSE_SENSEX: default = 'HDFCBANK.BO' elif CAC40: default = 'BNP.PA' elif KOSPI: default = '005930.KS' else: st.error('Please select at least one stock index.') st.stop() st.markdown("") selected_stock = st.multiselect('Select at least one company', ticker_options, default , format_func=ticker_dict_func, on_change=in_wid_change) else: list_symbols = selected_stock #list_companies = ticker[0].data.info['longName'] #list_sectors = 'XX' #list_stockindex = 'XX' df_indicesdata = pd.DataFrame({'Ticker': list_symbols, 'Company': list_companies, 'Sector': list_sectors, 'Stock index': list_stockindex}) #st.write(df_indicesdata) # output specification tb_options=['Basic info','Institutional Holders','Stock Price', 'Balance Sheet', 'Cashflow','Other Financials'] tkpi_options= ['Profitability', 'Debt Capital', 'Equity Capital','Valuation', 'Capital Procurement','Capital Allocation','Procurement Market'] tb_output=st.multiselect('Select ticker basic information',tb_options,['Stock Price', 'Balance Sheet','Cashflow','Other Financials']) tkpi_output=st.multiselect('Select performance indicators',tkpi_options,['Profitability', 'Debt Capital', 'Valuation']) if len(selected_stock) == 0: st.error('Please select at least one ticker.') st.session_state['selected_stock'] = None st.stop() # ticker from the same sector for 1 selection only if len(selected_stock) == 1: sector_list = [] sector_ticker = [] sector_list = df_indicesdata[df_indicesdata['Ticker']==selected_stock[0]].index.tolist() for i in range(len(ticker)): if ticker[i].sector == ticker[sector_list[0]].sector and ticker[i].stockindex == ticker[sector_list[0]].stockindex and ticker[i].company != ticker[sector_list[0]].company: sector_list += [i] sector_ticker += [ticker[i].symbol] # c1 = st.container() # c2 = st.container() # select_all = c2.checkbox('Select all companies of '+ ticker[sector_list[0]].sector + ' in ' + ticker[sector_list[0]].stockindex + ' (can take some time)', on_change=in_wid_change) # if select_all: # sector_comparison = c1.multiselect('Select a Ticker from the same sector and stock index for comparison', sector_ticker, sector_ticker, format_func=ticker_dict_func, on_change=in_wid_change) # else: # sector_comparison = c1.multiselect('Select a Ticker from the same sector and stock index for comparison', sector_ticker, format_func=ticker_dict_func, on_change=in_wid_change) # selected_stock += sector_comparison #else: select_all = None # create df_selected df_selected, index_list = function_df_selected() #st.write('**Your selection:**') #st.write(df_selected) # company information # if len(index_list) < 2: # for i in index_list: # if st.checkbox('Show company information for ' + ticker[i].company): # st.write(ticker[i].data.info['longBusinessSummary']) # else: # company_info = st.multiselect('Show company information for ', index_list, format_func=index_dict_func) # for i in company_info: # st.markdown('Company information for ' + ticker[i].company) # st.write(ticker[i].data.info['longBusinessSummary']) #---------------------------------------------------------------------------------------------- ######## DATA LOADING ######### st.markdown("") b1, b5 = st.columns([5,1]) load_data_button = b1.button('Get Financial Figures') if b5.button('Clear Cache', on_click=in_wid_change): st.legacy_caching.clear_cache() if load_data_button: st.session_state['load_data_button'] = load_data_button if st.session_state['load_data_button']: c3 = st.container() my_bar = st.progress(0.0) progress_sum = len(index_list) + len(tb_options)+len(tkpi_options) progress = 0 if selected_stock != st.session_state['selected_stock'] or load_data_button: st.session_state['selected_stock'] = selected_stock list_years = [] for i in index_list: ticker[i].bs = ticker[i].data.balance_sheet if ticker[i].bs.empty: index_list.remove(i) st.warning('No data found for ' + ticker[i].company) else: ticker[i].bs.columns = pd.DatetimeIndex(ticker[i].bs.columns).year for element in ticker[i].bs.columns: if element not in list_years: list_years.append(element) ticker[i].cf = ticker[i].data.cashflow ticker[i].cf.columns = pd.DatetimeIndex(ticker[i].cf.columns).year ticker[i].fi = ticker[i].data.financials ticker[i].fi.columns = pd.DatetimeIndex(ticker[i].fi.columns).year ticker[i].info = ticker[i].data.info # check and adjust duplicate years list_columns = ticker[i].bs.columns.values.tolist() for n in range(len(ticker[i].bs.columns)): if any(ticker[i].bs.columns.duplicated()): list_duplicate_bool = ticker[i].bs.columns.duplicated() index_duplicate = [i for i, x in enumerate(list_duplicate_bool) if x] list_columns[index_duplicate[0]] = list_columns[index_duplicate[0]]-1 ticker[i].bs.columns = list_columns ticker[i].cf.columns = list_columns ticker[i].fi.columns = list_columns progress += 1 my_bar.progress(progress/progress_sum) # sort list of years list_years.sort(reverse=True) st.session_state['list_years'] = list_years else: list_years = st.session_state['list_years'] progress += len(index_list) my_bar.progress(progress/progress_sum) #---------------------------------------------------------------------------------------------- st.subheader('Ticker basics') #---------------------------------------------------------------------------------------------- # basic info if 'Basic info' in tb_output: with st.expander('Company Info'): if len(index_list) == 1: st.markdown('Company information for ' + ticker[0].company) st.write(ticker[0].data.info['longBusinessSummary']) else: for i in index_list: st.markdown("") st.markdown('Company information for ' + ticker[i].company) st.write(ticker[0].data.info['longBusinessSummary']) #progress progress += 1 my_bar.progress(progress/progress_sum) # basic info if 'Institutional Holders' in tb_output: with st.expander('Institutional Holders'): if len(index_list) == 1: st.markdown('Institutional holders for ' + ticker[0].company +":") st.write(ticker[0].data.institutional_holders) else: for i in index_list: st.markdown("") st.markdown('Institutional holders for ' + ticker[i].company +":") st.write(ticker[i].data.institutional_holders) #progress progress += 1 my_bar.progress(progress/progress_sum) #Stock price if 'Stock Price' in tb_output: with st.expander('Stock Price'): st.write('**Stock Price Development**') # Selection of parameter #if len(index_list) == 1: # stock_para = st.multiselect('Select parameter for stock price visualization', ['Open', 'High', 'Low', 'Close', 'Volume'], 'Open') #elif len(index_list) > 1: stock_para = st.selectbox('Select stock price info', ['Open', 'High', 'Low', 'Close', 'Volume']) stock_period = st.selectbox('Select time period', ['start/end day','max','10y','5y','2y','1y','ytd','6mo','3mo','1mo','5d', '1d'], 2) if stock_period=='start/end day': today = datetime.date.today() last_year = today - datetime.timedelta(days=365) a1,a2=st.columns(2) with a1: start_date = st.date_input('Select start date', last_year, key=2) with a2: end_date = st.date_input('Select end date', today,key=3) if start_date > end_date: st.error('ERROR: End date must fall after start date.') if len(index_list) < 6: df_history = pd.DataFrame() selected_company = [] for i in index_list: if stock_period=='start/end day': ticker[i].history = ticker[i].data.history(period='1d', start=start_date, end=end_date) else: ticker[i].history = ticker[i].data.history(period=str(stock_period)) if df_history.empty: df_history = pd.DataFrame(ticker[i].history[stock_para]) selected_company += [ticker[i].company] else: df_history.insert(len(df_history.columns) , ticker[i].company, ticker[i].history[stock_para]) selected_company += [ticker[i].company] df_history.columns = selected_company else: df_history = pd.DataFrame() selected_company = [] for i in st.multiselect('Select company for visualization', index_list, index_list[0], format_func=index_dict_func): if stock_period=='start & end day': ticker[i].history = ticker[i].data.history(period='1d',start=start_date,end=end_date) else: ticker[i].history = ticker[i].data.history(period=str(stock_period)) if df_history.empty: df_history = pd.DataFrame(ticker[i].history[stock_para]) selected_company += [ticker[i].company] else: df_history.insert(len(df_history.columns) , ticker[i].company, ticker[i].history[stock_para]) selected_company += [ticker[i].company] df_history.columns = selected_company st.line_chart(df_history) #progress progress += 1 my_bar.progress(progress/progress_sum) # Balance Sheet if 'Balance Sheet' in tb_output: with st.expander('Balance Sheet'): df_bs = pd.DataFrame() no_rows=len(ticker[index_list[0]].data.balance_sheet.index) label_index=ticker[index_list[0]].data.balance_sheet.index if len(index_list) == 1: for y in ticker[index_list[0]].data.balance_sheet.columns: df_bs=ticker[index_list[0]].data.balance_sheet if len(index_list) > 1: selected_year = st.selectbox('select year', list_years, key=4) year_id=max(list_years)-selected_year for i in index_list: if selected_year in pd.DatetimeIndex(ticker[i].data.balance_sheet.columns).year: year_id=max(list_years)-selected_year df_col=ticker[i].data.balance_sheet.iloc[:,[year_id]] df_col.columns=[ticker[i].company] else: df_col =np.empty(no_rows) df_col[:] = np.NaN df_col=pd.DataFrame(df_col,index=label_index,columns=[ticker[i].company]) if df_bs.empty: df_bs = df_col else: df_bs[ticker[i].company]=df_col st.dataframe(df_bs.style.format("{:.2f}")) #progress progress += 1 my_bar.progress(progress/progress_sum) # Cashflow if 'Cashflow' in tb_output: with st.expander('Cashflow'): df_cf = pd.DataFrame() no_rows=len(ticker[index_list[0]].data.cashflow.index) label_index=ticker[index_list[0]].data.cashflow.index if len(index_list) == 1: for y in ticker[index_list[0]].cf.columns: df_cf=ticker[index_list[0]].data.cashflow if len(index_list) > 1: selected_year = st.selectbox('select year', list_years, key=5) year_id=max(list_years)-selected_year for i in index_list: if selected_year in ticker[i].cf.columns: year_id=max(list_years)-selected_year df_col=ticker[i].data.cashflow.iloc[:,[year_id]] df_col.columns=[ticker[i].company] else: df_col =np.empty(no_rows) df_col[:] = np.NaN df_col=pd.DataFrame(df_col,index=label_index,columns=[ticker[i].company]) if df_cf.empty: df_cf = df_col else: df_cf[ticker[i].company]=df_col st.dataframe(df_cf.style.format("{:.2f}")) #progress progress += 1 my_bar.progress(progress/progress_sum) # Other financials if 'Other Financials' in tb_output: with st.expander('Other Financials'): df_of = pd.DataFrame() no_rows=len(ticker[index_list[0]].data.financials.index) label_index=ticker[index_list[0]].data.financials.index if len(index_list) == 1: for y in pd.DatetimeIndex(ticker[0].data.financials.columns).year: df_of=ticker[index_list[0]].data.financials if len(index_list) > 1: selected_year = st.selectbox('select year', list_years, key=6) year_id=max(list_years)-selected_year for i in index_list: if selected_year in pd.DatetimeIndex(ticker[i].data.financials.columns).year: year_id=max(list_years)-selected_year df_col=ticker[i].data.financials.iloc[:,[year_id]] df_col.columns=[ticker[i].company] else: df_col =np.empty(no_rows) df_col[:] = np.NaN df_col=pd.DataFrame(df_col,index=label_index,columns=[ticker[i].company]) if df_of.empty: df_of = df_col else: df_of[ticker[i].company]=df_col st.dataframe(df_of) #progress progress += 1 my_bar.progress(progress/progress_sum) if len(tb_output)>0: #download excel file st.markdown("") output = BytesIO() excel_file = pd.ExcelWriter(output, engine="xlsxwriter") if 'Balance Sheet' in tb_output: df_bs.to_excel(excel_file, sheet_name='Balance sheet') if 'Stock Price' in tb_output: df_history.to_excel(excel_file, sheet_name='Stock Price') if 'Cashflow' in tb_output: df_cf.to_excel(excel_file, sheet_name='Cashflow') if 'Other Financials' in tb_output: df_of.to_excel(excel_file, sheet_name='Other Financials') excel_file.save() excel_file = output.getvalue() b64 = base64.b64encode(excel_file) dl_file_name = "Stock Basic Info.xlsx" st.markdown( f""" <a href="data:file/excel_file;base64,{b64.decode()}" id="button_dl" download="{dl_file_name}">Download Stock Basic Info</a> """, unsafe_allow_html=True) #------------------------------------------------------------------------ st.subheader('KPI-Dashboard') # profitability if 'Profitability' in tkpi_output: with st.expander('Profitability'): label_index = 'Return on Investment (ROI) [in %]', 'Return on Equity (ROE) [in %]', 'Total Revenue [in billion]', 'EBITDA-Margin [in %]', 'EBIT-Margin [in %]' df_profitability = pd.DataFrame() if len(index_list) == 1: for y in ticker[index_list[0]].bs.columns: ticker[index_list[0]].kpi_profitability(y) df_profitability = fill_df_func(df_profitability, ticker, index_list[0], label_index, y) if len(index_list) > 1: selected_year = st.selectbox('select year', list_years, key=7) for i in index_list: if selected_year in ticker[i].bs.columns: ticker[i].kpi_profitability(selected_year) else: ticker[i].kpis = np.empty(len(label_index)) ticker[i].kpis.fill(np.nan) df_profitability = fill_df_func(df_profitability, ticker, i, label_index, ticker[i].company) df_profitability = average_func(index_list, df_selected, df_profitability) st.dataframe(df_profitability.style.format("{:.2f}")) #progress progress += 1 my_bar.progress(progress/progress_sum) #---------------------------------------------------------------------------------------------- # debt capital if 'Debt Capital' in tkpi_output: with st.expander('Debt capital'): label_index = 'Net Debt/EBITDA', 'EBITDA/Interest', 'Current Ratio', 'Days Payable Outstanding [in days]' df_debt_capital = pd.DataFrame() if len(index_list) == 1: for y in ticker[index_list[0]].bs.columns: ticker[index_list[0]].kpi_debt_capital(y) df_debt_capital = fill_df_func(df_debt_capital, ticker, index_list[0], label_index, y) if len(index_list) > 1: selected_year = st.selectbox('select year', list_years, key=8) for i in index_list: if selected_year in ticker[i].bs.columns: ticker[i].kpi_debt_capital(selected_year) else: ticker[i].kpis = np.empty(len(label_index)) ticker[i].kpis.fill(np.nan) df_debt_capital = fill_df_func(df_debt_capital, ticker, i, label_index, ticker[i].company) df_debt_capital = average_func(index_list, df_selected, df_debt_capital) st.dataframe(df_debt_capital.style.format("{:.2f}")) #progress progress += 1 my_bar.progress(progress/progress_sum) #---------------------------------------------------------------------------------------------- # equity capital if 'Equity Capital' in tkpi_output: with st.expander('Equity capital'): label_index = 'Revenues per Share', ' Forward EPS', 'Forward Annual Dividend Rate' df_equity_capital = pd.DataFrame() for i in index_list: ticker[i].kpi_equity_capital() df_equity_capital = fill_df_func(df_equity_capital, ticker, i, label_index, ticker[i].company) df_equity_capital = average_func(index_list, df_selected, df_equity_capital) st.dataframe(df_equity_capital.style.format("{:.2f}")) #progress progress += 1 my_bar.progress(progress/progress_sum) #---------------------------------------------------------------------------------------------- # valuation if 'Valuation' in tkpi_output: with st.expander('Valuation'): label_index = 'Forward P/E', 'PEG Ratio (5yr expected)', 'P/B Ratio', 'Enterprise Value (EV) [in billion]', 'EV/Revenue', 'EV/EBITDA' df_valuation = pd.DataFrame() for i in index_list: ticker[i].kpi_valuation() df_valuation = fill_df_func(df_valuation, ticker, i, label_index, ticker[i].company) df_valuation = average_func(index_list, df_selected, df_valuation) st.dataframe(df_valuation.style.format(formatter="{:.2f}")) st.write('') #progress progress += 1 my_bar.progress(progress/progress_sum) #---------------------------------------------------------------------------------------------- # capital procurement if 'Capital Procurement' in tkpi_output: with st.expander('Capital Procurement'): label_index = 'Self-Financing Ratio', 'Equity Ratio [in %]' df_capital_procurement = pd.DataFrame() if len(index_list) == 1: for y in ticker[index_list[0]].bs.columns: ticker[index_list[0]].kpi_capital_procurement(y) df_capital_procurement = fill_df_func(df_capital_procurement, ticker, index_list[0], label_index, y) if len(index_list) > 1: selected_year = st.selectbox('select year', list_years, key=9) for i in index_list: if selected_year in ticker[i].bs.columns: ticker[i].kpi_capital_procurement(selected_year) else: ticker[i].kpis = np.empty(len(label_index)) ticker[i].kpis.fill(np.nan) df_capital_procurement = fill_df_func(df_capital_procurement, ticker, i, label_index, ticker[i].company) df_capital_procurement = average_func(index_list, df_selected, df_capital_procurement) st.dataframe(df_capital_procurement.style.format("{:.2f}")) #progress progress += 1 my_bar.progress(progress/progress_sum) #---------------------------------------------------------------------------------------------- # capital allocation if 'Capital Allocation' in tkpi_output: with st.expander('Capital Allocation'): label_index = 'CapEx/Revenue', 'Research & Development/Revenue', 'Cash Conversion Cycle [in days]' df_capital_allocation = pd.DataFrame() if len(index_list) == 1: for y in ticker[index_list[0]].bs.columns: ticker[index_list[0]].kpi_capital_allocation(y) df_capital_allocation = fill_df_func(df_capital_allocation, ticker, index_list[0], label_index, y) if len(index_list) > 1: selected_year = st.selectbox('select year', list_years, key=10) for i in index_list: if selected_year in ticker[i].bs.columns: ticker[i].kpi_capital_allocation(selected_year) else: ticker[i].kpis = np.empty(len(label_index)) ticker[i].kpis.fill(np.nan) df_capital_allocation = fill_df_func(df_capital_allocation, ticker, i, label_index, ticker[i].company) df_capital_allocation = average_func(index_list, df_selected, df_capital_allocation) st.dataframe(df_capital_allocation.style.format("{:.2f}")) #progress progress += 1 my_bar.progress(progress/progress_sum) #---------------------------------------------------------------------------------------------- # procurement market if 'Procurement Market' in tkpi_output: with st.expander('Procurement Market'): label_index = 'Labour Productivity [in T per employee]', 'Asset turnover [in %]' df_procurement_market = pd.DataFrame() if len(index_list) == 1: for y in ticker[index_list[0]].bs.columns: ticker[index_list[0]].kpi_procurement_market(y) df_procurement_market = fill_df_func(df_procurement_market, ticker, index_list[0], label_index, y) if len(index_list) > 1: selected_year = st.selectbox('select year', list_years, key=11) for i in index_list: if selected_year in ticker[i].bs.columns: ticker[i].kpi_procurement_market(selected_year) else: ticker[i].kpis = np.empty(len(label_index)) ticker[i].kpis.fill(np.nan) df_procurement_market = fill_df_func(df_procurement_market, ticker, i, label_index, ticker[i].company) df_procurement_market = average_func(index_list, df_selected, df_procurement_market) st.dataframe(df_procurement_market.style.format("{:.2f}")) #progress progress += 1 my_bar.progress(progress/progress_sum) if progress == progress_sum: c3suc_msg=c3.success('Data loading is completed!') time.sleep(2) my_bar.empty() c3suc_msg.empty() if len(tkpi_output)>0: #download excel file st.markdown("") output = BytesIO() excel_file = pd.ExcelWriter(output, engine="xlsxwriter") if 'Profitability' in tkpi_output: df_profitability.to_excel(excel_file, sheet_name='KPI-Dashboard', startrow=3) excel_file.sheets['KPI-Dashboard'].write(1, 0, 'Profitability') if 'Debt Capital' in tkpi_output: df_debt_capital.to_excel(excel_file, sheet_name='KPI-Dashboard', startrow=12) excel_file.sheets['KPI-Dashboard'].write(10, 0, 'Dept Capital') if 'Equity Capital' in tkpi_output: df_equity_capital.to_excel(excel_file, sheet_name='KPI-Dashboard', startrow=20) excel_file.sheets['KPI-Dashboard'].write(18, 0, 'Equity Capital') if 'Valuation' in tkpi_output: df_valuation.to_excel(excel_file, sheet_name='KPI-Dashboard', startrow=27) excel_file.sheets['KPI-Dashboard'].write(25, 0, 'Valuation') if 'Capital Procurement' in tkpi_output: df_capital_procurement.to_excel(excel_file, sheet_name='KPI-Dashboard', startrow=37) excel_file.sheets['KPI-Dashboard'].write(35, 0, 'Capital Procurement') if 'Capital Allocation' in tkpi_output: df_capital_allocation.to_excel(excel_file, sheet_name='KPI-Dashboard', startrow=43) excel_file.sheets['KPI-Dashboard'].write(41, 0, 'Capital Allocation') if 'Procurement Market' in tkpi_output: df_procurement_market.to_excel(excel_file, sheet_name='KPI-Dashboard', startrow=50) excel_file.sheets['KPI-Dashboard'].write(48, 0, 'Procurement Market') excel_file.save() excel_file = output.getvalue() b64 = base64.b64encode(excel_file) dl_file_name = "Financial Analysis.xlsx" st.markdown( f""" <a href="data:file/excel_file;base64,{b64.decode()}" id="button_dl" download="{dl_file_name}">Download KPI-Dashboard</a> """, unsafe_allow_html=True)

67.525354

9,951

0.541961

912295f351682079d811a627513285af70f638e6

3,072

py

Python

bin/gftools-update-families.py

hyvyys/gftools

85ef924f9307f290be08b15115805cc5e3287d33

[ "Apache-2.0" ]

150

2018-07-04T12:53:34.000Z

2022-03-31T21:16:10.000Z

bin/gftools-update-families.py

hyvyys/gftools

85ef924f9307f290be08b15115805cc5e3287d33

[ "Apache-2.0" ]

365

2018-06-28T19:35:22.000Z

2022-03-30T17:07:56.000Z

bin/gftools-update-families.py

hyvyys/gftools

85ef924f9307f290be08b15115805cc5e3287d33

[ "Apache-2.0" ]

53

2018-07-20T01:47:44.000Z

2022-01-31T16:43:25.000Z

#!/usr/bin/env python3 # Copyright 2016 The Font Bakery Authors. # Copyright 2017 The Google Font Tools Authors # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # __author__="The Google Fonts Tools Authors" import argparse import glob import logging import os # set up some command line argument processing parser = argparse.ArgumentParser(description="Compare TTF files when upgrading families.") parser.add_argument('arg_filepaths', nargs='+', help='font file path(s) to check.' ' Wildcards like *.ttf are allowed.') parser.add_argument('-v', '--verbose', action='count', default=0, help="increase output verbosity") #===================================== # Main sequence of checkers & fixers def main(): # set up a basic logging config # to include timestamps # log_format = '%(asctime)s %(levelname)-8s %(message)s' log_format = '%(levelname)-8s %(message)s ' logger = logging.getLogger() handler = logging.StreamHandler() formatter = logging.Formatter(log_format) handler.setFormatter(formatter) logger.addHandler(handler) args = parser.parse_args() if args.verbose == 1: logger.setLevel(logging.INFO) elif args.verbose >= 2: logger.setLevel(logging.DEBUG) else: logger.setLevel(logging.ERROR) #------------------------------------------------------ logging.debug("Checking each file is a ttf") fonts_to_check = [] for arg_filepath in sorted(args.arg_filepaths): # use glob.glob to accept *.ttf for fullpath in glob.glob(arg_filepath): file_path, file_name = os.path.split(fullpath) if file_name.endswith(".ttf"): logging.debug("Adding '{}'".format(file_name)) fonts_to_check.append(fullpath) else: logging.warning("Skipping '{}' as file is not a ttf".format(file_name)) fonts_to_check.sort() #------------------------------------------------------ for new_file in fonts_to_check: logging.debug("Comparison of filesizes") old_file = new_file + "-old" new_filesize = os.path.getsize(new_file) old_filesize = os.path.getsize(old_file) delta = new_filesize - old_filesize percentage = float(delta) / old_filesize if delta>0: logging.warning("New font file '{}' is {} bytes larger".format( new_file, delta)) elif delta<0: logging.warning("New font file '{}' is {} bytes smaller".format( new_file, -delta)) else: logging.info("New font file '{}' preserves filesize.".format(new_file)) if __name__=='__main__': main()

35.72093

99

0.657552

91358f34676ca4623ec13cce4ba102738cc821db

742

py

Python

lldb/packages/Python/lldbsuite/test/functionalities/thread/concurrent_events/TestConcurrentDelaySignalWatch.py

medismailben/llvm-project

e334a839032fe500c3bba22bf976ab7af13ce1c1

[ "Apache-2.0" ]

305

2019-09-14T17:16:05.000Z

2022-03-31T15:05:20.000Z

lldb/packages/Python/lldbsuite/test/functionalities/thread/concurrent_events/TestConcurrentDelaySignalWatch.py

medismailben/llvm-project

e334a839032fe500c3bba22bf976ab7af13ce1c1

[ "Apache-2.0" ]

14

2020-02-03T23:39:51.000Z

2021-07-20T16:24:25.000Z

lldb/packages/Python/lldbsuite/test/functionalities/thread/concurrent_events/TestConcurrentDelaySignalWatch.py

medismailben/llvm-project

e334a839032fe500c3bba22bf976ab7af13ce1c1

[ "Apache-2.0" ]

24

2019-10-03T11:22:11.000Z

2022-01-25T09:59:30.000Z

import unittest2 from lldbsuite.test.decorators import * from lldbsuite.test.concurrent_base import ConcurrentEventsBase from lldbsuite.test.lldbtest import TestBase @skipIfWindows class ConcurrentDelaySignalWatch(ConcurrentEventsBase): mydir = ConcurrentEventsBase.compute_mydir(__file__) @skipIfFreeBSD # timing out on buildbot # Atomic sequences are not supported yet for MIPS in LLDB. @skipIf(triple='^mips') @add_test_categories(["watchpoint"]) def test(self): """Test a watchpoint and a (1 second delay) signal in multiple threads.""" self.build(dictionary=self.getBuildFlags()) self.do_thread_actions( num_delay_signal_threads=1, num_watchpoint_threads=1)

30.916667

82

0.738544

dcb9477eacc34042874ebb3d11b8f0cd6b3f4ee8

5,943

py

Python

simple_qt/gui/goto_frame.py

zleffke/nexstar

8676fe6184b89e1ee0399da207b11e9d9c8d0bb4

[ "MIT" ]

null

simple_qt/gui/goto_frame.py

zleffke/nexstar

8676fe6184b89e1ee0399da207b11e9d9c8d0bb4

[ "MIT" ]

null

simple_qt/gui/goto_frame.py

zleffke/nexstar

8676fe6184b89e1ee0399da207b11e9d9c8d0bb4

[ "MIT" ]

null

#!/usr/bin/env python3 #-- coding: utf-8 -- from PyQt5 import QtCore from PyQt5 import Qt from nexstar import * class goto_frame(Qt.QFrame): def __init__(self, parent=None): super(goto_frame, self).__init__() self.parent = parent self.initUI() def initUI(self): self.setFrameShape(Qt.QFrame.StyledPanel) self.init_widgets() self.connect_signals() def init_widgets(self): lbl_width = 50 val_width = 125 lbl_height = 12 btn_height = 20 frame_lbl = Qt.QLabel("GoTo Controls:") frame_lbl.setAlignment(Qt.Qt.AlignLeft|Qt.Qt.AlignVCenter) frame_lbl.setStyleSheet("QLabel {font:12pt; font-weight:bold; text-decoration: underline; color:rgb(255,0,0);}") frame_lbl.setFixedWidth(200) frame_lbl.setFixedHeight(20) self.azLabel = Qt.QLabel("Target Azimuth:") self.azLabel.setAlignment(QtCore.Qt.AlignRight|QtCore.Qt.AlignVCenter) self.azLabel.setStyleSheet("QLabel {font:10pt; color:rgb(255,0,0);}") self.azTextBox = Qt.QLineEdit() self.azTextBox.setText("000.000") self.azTextBox.setInputMask("#00.000;") self.azTextBox.setEchoMode(Qt.QLineEdit.Normal) self.azTextBox.setStyleSheet("QLineEdit {font:10pt; background-color:rgb(200,75,75); color:rgb(0,0,0);}") self.azTextBox.setMaxLength(3) self.azTextBox.setFixedWidth(80) self.azTextBox.setFixedHeight(20) az_hbox = Qt.QHBoxLayout() az_hbox.addWidget(self.azLabel) az_hbox.addWidget(self.azTextBox) self.elLabel = Qt.QLabel("Target Elevation:") self.elLabel.setAlignment(QtCore.Qt.AlignRight|QtCore.Qt.AlignVCenter) self.elLabel.setStyleSheet("QLabel {font:10pt; color:rgb(255,0,0);}") self.elTextBox = Qt.QLineEdit() self.elTextBox.setText("00.000") self.elTextBox.setInputMask("#00.000;") self.elTextBox.setEchoMode(Qt.QLineEdit.Normal) self.elTextBox.setStyleSheet("QLineEdit {font:10pt; background-color:rgb(200,75,75); color:rgb(0,0,0);}") self.elTextBox.setMaxLength(3) self.elTextBox.setFixedWidth(80) self.elTextBox.setFixedHeight(20) el_hbox = Qt.QHBoxLayout() el_hbox.addWidget(self.elLabel) el_hbox.addWidget(self.elTextBox) self.gotoButton = Qt.QPushButton("GoTo") self.gotoButton.setFixedHeight(btn_height) self.gotoButton.setStyleSheet("QPushButton { font:10pt; background-color:rgb(200,0,0); }") self.cancelButton = Qt.QPushButton("Cancel") self.cancelButton.setFixedHeight(btn_height) self.cancelButton.setStyleSheet("QPushButton { font:10pt; background-color:rgb(200,0,0); }") btn_hbox = Qt.QHBoxLayout() btn_hbox.addWidget(self.gotoButton) btn_hbox.addWidget(self.cancelButton) lbl = Qt.QLabel("Tracking Mode:") lbl.setAlignment(Qt.Qt.AlignRight|Qt.Qt.AlignVCenter) lbl.setStyleSheet("QLabel {font:10pt; color:rgb(255,0,0);}") lbl.setFixedWidth(100) lbl.setFixedHeight(lbl_height) self.tmRb_off = Qt.QRadioButton("OFF") self.tmRb_off.setChecked(True) self.tmRb_off.mode = 0 self.tmRb_off.setStyleSheet("QRadioButton { font:10pt; color:rgb(200,0,0); }") self.tmRb_altaz = Qt.QRadioButton("ALT AZ") self.tmRb_altaz.setChecked(False) self.tmRb_altaz.mode = 1 self.tmRb_altaz.setStyleSheet("QRadioButton { font:10pt; color:rgb(200,0,0); }") self.getTmButton = Qt.QPushButton("Get Tracking Mode") self.getTmButton.setFixedHeight(btn_height) self.getTmButton.setStyleSheet("QPushButton { font:10pt; background-color:rgb(200,0,0); }") tm_hbox = Qt.QHBoxLayout() tm_hbox.addStretch(1) tm_hbox.addWidget(lbl) tm_hbox.addWidget(self.tmRb_off) tm_hbox.addWidget(self.tmRb_altaz) vbox = Qt.QVBoxLayout() vbox.addWidget(frame_lbl) vbox.addLayout(az_hbox) vbox.addLayout(el_hbox) vbox.addLayout(btn_hbox) vbox.addWidget(self.getTmButton) vbox.addLayout(tm_hbox) self.setLayout(vbox) def connect_signals(self): self.cancelButton.clicked.connect(self.cancelButton_event) self.gotoButton.clicked.connect(self.gotoButton_event) self.tmRb_off.toggled.connect(self.set_track_mode) self.tmRb_altaz.toggled.connect(self.set_track_mode) self.getTmButton.clicked.connect(self.getTmButton_event) def set_track_mode(self): rb = self.sender() if rb.isChecked(): self.parent.set_tracking_mode(rb.mode) def getTmButton_event(self): self.parent.getTrackingMode() def cancelButton_event(self): self.parent.cancel_goto() def gotoButton_event(self): self.tar_az = float(self.azTextBox.text()) self.tar_el = float(self.elTextBox.text()) self.parent.goto_position(self.tar_az, self.tar_el) def increment_target_angle(self, az_el, val): #Called by button control frame if az_el == 'az': self.tar_az += val self.update_target_azimuth() elif az_el == 'el': self.tar_el += val self.update_target_elevation() #self.callback.set_position(self.tar_az, self.tar_el, True) def update_target_azimuth(self): if self.tar_az < -180.0: self.tar_az = -180.0 if self.tar_az > 540.0: self.tar_az = 540.0 #self.az_compass.set_tar_az(self.tar_az) #self.az_lcd_fr.set_tar(self.tar_az) #self.azTextBox.setText(str(self.tar_az)) def update_target_elevation(self): if self.tar_el < 0: self.tar_el = 0 if self.tar_el > 180: self.tar_el = 180 #self.el_compass.set_tar_el(self.tar_el) #self.el_lcd_fr.set_tar(self.tar_el) #self.elTextBox.setText(str(self.tar_el))

39.62

120

0.659936

3fc4ed575afd56a4dcc20ea5bd196c4097a08511

1,550

py

Python

3_estrutura_de_repeticao/43_cardapio_lanchonete.py

cecilmalone/lista_de_exercicios_pybr

6d7c4aeddf8d1b1d839ad05ef5b5813a8fe611b5

[ "MIT" ]

null

3_estrutura_de_repeticao/43_cardapio_lanchonete.py

cecilmalone/lista_de_exercicios_pybr

6d7c4aeddf8d1b1d839ad05ef5b5813a8fe611b5

[ "MIT" ]

null

3_estrutura_de_repeticao/43_cardapio_lanchonete.py

cecilmalone/lista_de_exercicios_pybr

6d7c4aeddf8d1b1d839ad05ef5b5813a8fe611b5

[ "MIT" ]

null

""" 43. O cardápio de uma lanchonete é o seguinte: Especificação Código Preço Cachorro Quente 100 R$ 1,20 Bauru Simples 101 R$ 1,30 Bauru com ovo 102 R$ 1,50 Hambúrguer 103 R$ 1,20 Cheeseburguer 104 R$ 1,30 Refrigerante 105 R$ 1,00 Faça um programa que leia o código dos itens pedidos e as quantidades desejadas. Calcule e mostre o valor a ser pago por item (preço * quantidade) e o total geral do pedido. Considere que o cliente deve informar quando o pedido deve ser encerrado. """ cardapio = [{'especificacao': 'Cachorro Quente', 'codigo': 100, 'valor': 1.20}, {'especificacao': 'Bauru Simples', 'codigo': 101, 'valor': 1.30}, {'especificacao': 'Bauru com ovo', 'codigo': 102, 'valor': 1.50}, {'especificacao': 'Hambúrguer', 'codigo': 103, 'valor': 1.20}, {'especificacao': 'Cheeseburguer', 'codigo': 104, 'valor': 1.30}, {'especificacao': 'Refrigerante', 'codigo': 105, 'valor': 1.00}] itens = [] codigo = 1 while codigo != 0: codigo = int(input("Informe o código do produto: ")) quantidade = int(input("Informe a quantidade do produto: ")) itens.append({'codigo': codigo, 'quantidade': quantidade}) print("Especificação Quantidade Preço") for item in itens: print("{} {} {}".format(cardapio['espeficacao'], item['quantidade'], item['quantidade'] * cardapio['valor']))

37.804878

80

0.579355

ae44aa1313c1bd612e44f5582947192de0c55da7

4,056

py

Python

scripts/demo.py

jetsunwhitton/pico-ner-relations

a11d48f2bd7a8ede73865f13123862262f5227d8

[ "Apache-2.0" ]

null

scripts/demo.py

jetsunwhitton/pico-ner-relations

a11d48f2bd7a8ede73865f13123862262f5227d8

[ "Apache-2.0" ]

null

scripts/demo.py

jetsunwhitton/pico-ner-relations

a11d48f2bd7a8ede73865f13123862262f5227d8

[ "Apache-2.0" ]

null

import spacy_streamlit, spacy, operator import streamlit as st from spacy import displacy from spacy.pipeline import merge_entities # make the factory work from rel_pipe import make_relation_extractor, score_relations # make the config work from rel_model import create_relation_model, create_classification_layer, create_instances, create_tensors from tabulate import relation_extraction, tabulate_pico_entities import base64 # set page config st.set_page_config( page_title="RCT-ART", page_icon="logo.jpg" ) st.sidebar.image("logo.jpg") st.sidebar.markdown("RCT-ART is an NLP pipeline built with spaCy for converting clinical trial result sentences into tables through jointly extracting intervention, outcome and outcome measure entities and their relations. ") st.sidebar.subheader("Current constraints:") st.sidebar.markdown(""" - Only abstracts from studies with 2 trial arms - Must be a sentence with study results - Sentence must contain at least least one intervention (e.g. drug name), outcome description (e.g. blood pressure) and non-comparative outcome measure) """) st.title("Demo") st.header("Randomised Controlled Trial Abstract Result Tabulator") ner_model = "trained_models/biobert/ner/all_domains/model-best" rel_model = "trained_models/biobert/rel/all_domains/model-best" default_text = "Somnolence , the most frequently reported adverse event , was noted in 72.5 % versus 7.7 % of subjects ( risperidone vs placebo ) and seemed manageable with dose/dose-schedule modification ." st.subheader("Enter result sentence for analysis") text = st.text_area("Input should follow constraints outlined in sidebar", default_text, height=200) nlp = spacy.load("trained_models/biobert/ner/all_domains/model-best") ent_doc = nlp(text) st.subheader("NER analysis") spacy_streamlit.visualize_ner( ent_doc, labels=["INTV", "OC", "MEAS"], show_table=False, title=False ) rel_doc = relation_extraction(rel_model,[ent_doc])[0] deps = {"words": [],"arcs": []} for tok in rel_doc: deps["words"].append({"text": tok.text, "tag": tok.ent_type_}) for key in rel_doc._.rel: rel = rel_doc._.rel[key] # get relation pred_rel = max(rel.items(), key=operator.itemgetter(1)) # selects relation type with highest probability if pred_rel[1] > 0.5: # includes relation if above set threshold for probability if key[0] > key[1] and rel_doc[key[1]].ent_type_ != "MEAS": deps["arcs"].append({"start": key[1], "end": key[0], "label": pred_rel[0], "dir": "right"}) elif key[0] > key[1]: deps["arcs"].append({"start": key[1], "end": key[0], "label": pred_rel[0], "dir": "left"}) elif rel_doc[key[1]].ent_type_ != "MEAS": deps["arcs"].append({"start": key[0], "end": key[1], "label": pred_rel[0], "dir": "left"}) else: deps["arcs"].append({"start": key[0], "end": key[1], "label": pred_rel[0], "dir": "right"}) html = displacy.render(deps, style="dep", manual=True, options={'distance':80}) st.subheader("RE analysis") st.write(spacy_streamlit.util.get_svg(html), unsafe_allow_html=True) heading_properties = [('font-size', '16px')] cell_properties = [('font-size', '16px')] dfstyle = [dict(selector="th", props=heading_properties),dict(selector="td", props=cell_properties)] df = tabulate_pico_entities(rel_doc) print(rel_doc._.rel) #df.style.set_table_styles([cell_hover, index_names, headers]) st.subheader("Tabulation") st.table(df.style.set_table_styles(dfstyle)) def get_table_download_link(df): """Generates a link allowing the data in a given panda dataframe to be downloaded in: dataframe out: href string """ csv = df.to_csv(index=False) b64 = base64.b64encode(csv.encode()).decode() # some strings <-> bytes conversions necessary here href = f'<a href="data:file/csv;base64,{b64}" download="result_sentence.csv">Download csv file</a>' return href st.markdown(get_table_download_link(df), unsafe_allow_html=True)

41.387755

225

0.708826

2564c9ff1fd1030d40199b21786eb111492f0536

3,008

py

Python

superlists/functional_tests/base.py

alvarocneto/TDD_python

d9646802f08a18f3aa4743bbe48f8a22eebf07f7

[ "MIT" ]

null

superlists/functional_tests/base.py

alvarocneto/TDD_python

d9646802f08a18f3aa4743bbe48f8a22eebf07f7

[ "MIT" ]

null

superlists/functional_tests/base.py

alvarocneto/TDD_python

d9646802f08a18f3aa4743bbe48f8a22eebf07f7

[ "MIT" ]

null

import os import time from django.conf import settings from django.contrib.auth import (BACKEND_SESSION_KEY, SESSION_KEY, get_user_model) from django.contrib.sessions.backends.db import SessionStore from django.contrib.staticfiles.testing import StaticLiveServerTestCase from selenium import webdriver from selenium.common.exceptions import WebDriverException from selenium.webdriver.common.keys import Keys User = get_user_model() MAX_WAIT = 10 def wait(fn): def modified_fn(*args, **kwargs): start_time = time.time() while True: try: return fn(*args, **kwargs) except (AssertionError, WebDriverException) as e: if time.time() - start_time > MAX_WAIT: raise e time.sleep(0.5) return modified_fn class FunctionalTest(StaticLiveServerTestCase): def setUp(self): self.browser = webdriver.Firefox() staging_server = os.environ.get('STAGING_SERVER') if staging_server: self.live_server_url = f'http://{staging_server}' def tearDown(self): self.browser.quit() @wait def wait_for_row_in_list_table(self, row_text): table = self.browser.find_element_by_id('id_list_table') rows = table.find_elements_by_tag_name('tr') self.assertIn(row_text, [row.text for row in rows]) @wait def wait_for(self, lambda_function): return lambda_function() def get_item_input_box(self): return self.browser.find_element_by_id('id_text') @wait def wait_to_be_logged_in(self, email): self.browser.find_element_by_link_text('Log Out') navbar = self.browser.find_element_by_css_selector('.navbar') self.assertIn(email, navbar.text) @wait def wait_to_be_logged_out(self, email): self.browser.find_element_by_name('email') navbar = self.browser.find_element_by_css_selector('.navbar') self.assertNotIn(email, navbar.text) def add_list_item(self, item_text): num_rows = len(self.browser.find_elements_by_css_selector('#id_list_table tr')) self.get_item_input_box().send_keys(item_text) self.get_item_input_box().send_keys(Keys.ENTER) item_number = num_rows + 1 self.wait_for_row_in_list_table(f'{item_number}: {item_text}') def create_pre_authenticated_session(self, email): user = User.objects.create(email=email) session = SessionStore() session[SESSION_KEY] = user.pk session[BACKEND_SESSION_KEY] = settings.AUTHENTICATION_BACKENDS[0] session.save() ## para definir um cookie, precisamos antes acessar o domínio. ## as páginas 404 são as que carregam mais rapidamente! self.browser.get(self.live_server_url + "/404_no_such_url/") self.browser.add_cookie(dict( name=settings.SESSION_COOKIE_NAME, value=session.session_key, path="/", ))

34.976744

87

0.674202

cd32c2f4b4816f2614ab859a372240546577a239

5,640

py

Python

pyprof/prof/misc.py

ethem-kinginthenorth/PyProf

5430eb3a95f9afc4443eb329f2e831df62cecfe6

[ "Apache-2.0" ]

null

pyprof/prof/misc.py

ethem-kinginthenorth/PyProf

5430eb3a95f9afc4443eb329f2e831df62cecfe6

[ "Apache-2.0" ]

null

pyprof/prof/misc.py

ethem-kinginthenorth/PyProf

5430eb3a95f9afc4443eb329f2e831df62cecfe6

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # Copyright (c) 2020, NVIDIA CORPORATION. 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 collections import OrderedDict from utility import Utility from base import OperatorLayerBase class Foo(OperatorLayerBase): """ An object of Foo is instantiated when we detect an unsupported operator. """ def __init__(self, d): marker = eval(d.argMarker[0]) mod = marker['mod'] op = marker['op'] args = marker['args'] self.marker = marker self.mod_ = mod self.op_ = op self.args = args shapes = [] types = [] for arg in args: if arg['type'] == "tensor": shapes.append(arg['shape']) types.append(arg['dtype']) self.shape = shapes self.type = types def params(self): p = OrderedDict([('T', self.shape), ('type', self.type)]) return p def tc(self): return "-" def op(self): return self.op_ def mod(self): return self.mod_ def flops(self): return 0 def bytes(self): return 0 class Copy(OperatorLayerBase): def __init__(self, d): marker = eval(d.argMarker[0]) mod = marker['mod'] op = marker['op'] args = marker['args'] self.marker = marker self.mod_ = mod self.op_ = op self.args = args assert (mod == "Tensor") assert (op == "copy_") assert (len(args) == 2) dst, src = args assert (src['type'] == dst['type']) assert (src['shape'] == dst['shape']) self.shape = src['shape'] self.stype = src['dtype'] self.dtype = dst['dtype'] def params(self): #The data type might be different p = OrderedDict([('T', self.shape), ('stype', self.stype), ('dtype', self.dtype)]) return p def tc(self): return "-" def op(self): return self.op_ def mod(self): return self.mod_ def flops(self): return 0 def elems(self): return Utility.numElems(self.shape) def bytes(self): return self.elems() * (Utility.typeToBytes(self.stype) + Utility.typeToBytes(self.dtype)) class Clone(OperatorLayerBase): def __init__(self, d): marker = eval(d.argMarker[0]) mod = marker['mod'] op = marker['op'] args = marker['args'] self.marker = marker self.mod_ = mod self.op_ = op self.args = args assert (mod == "Tensor") assert (op == "clone") assert (len(args) == 1) t = args[0] self.shape = t['shape'] self.type = t['dtype'] def params(self): p = OrderedDict([('T', self.shape), ('type', self.type)]) return p def flops(self): return 0 def tc(self): return "-" def op(self): return self.op_ def mod(self): return self.mod_ def elems(self): return Utility.numElems(self.shape) def bytes(self): return 2 * self.elems() * Utility.typeToBytes(self.type) class Contiguous(OperatorLayerBase): def __init__(self, d): marker = eval(d.argMarker[0]) mod = marker['mod'] op = marker['op'] args = marker['args'] self.marker = marker self.mod_ = mod self.op_ = op self.args = args assert (mod == "Tensor") assert (op == "contiguous") assert (len(args) == 1) t = args[0] self.shape = t['shape'] self.type = t['dtype'] def params(self): p = OrderedDict([('T', self.shape), ('type', self.type)]) return p def flops(self): return 0 def bytes(self): return 2 * Utility.numElems(self.shape) * Utility.typeToBytes(self.type) def tc(self): return "-" def op(self): return self.op_ def mod(self): return self.mod_ class Any(OperatorLayerBase): def __init__(self, d): marker = eval(d.argMarker[0]) mod = marker['mod'] op = marker['op'] args = marker['args'] self.marker = marker self.mod_ = mod self.op_ = op self.args = args assert (mod == "Tensor") assert (op == "any") assert (len(args) in [1,2]) t = args[0] # The input can be a tensor or scalar assert (t['type'] in ["tensor", "bool"]) if t['type'] == "tensor": self.shape = t['shape'] self.type = t['dtype'] else: self.shape = (1,) self.type = t['type'] self.sub = d.sub return def params(self): p = OrderedDict([('T', self.shape), ('type', self.type)]) return p def op(self): return self.op_ def mod(self): return self.mod_ def tc(self): return "-" def flops(self): return 0 def bytes(self): return Utility.numElems(self.shape) * Utility.typeToBytes(self.type)

22.56

97

0.542553

ecbf80fdbd8c15bfdbf3d6ddf2b1edebe1d5480d

4,246

py

Python

docs/examples/use_cases/paddle/tsm/infer.py

cyyever/DALI

e2b2d5a061da605e3e9e681017a7b2d53fe41a62

[ "ECL-2.0", "Apache-2.0" ]

3,967

2018-06-19T04:39:09.000Z

2022-03-31T10:57:53.000Z

docs/examples/use_cases/paddle/tsm/infer.py

cyyever/DALI

e2b2d5a061da605e3e9e681017a7b2d53fe41a62

[ "ECL-2.0", "Apache-2.0" ]

3,494

2018-06-21T07:09:58.000Z

2022-03-31T19:44:51.000Z

docs/examples/use_cases/paddle/tsm/infer.py

cyyever/DALI

e2b2d5a061da605e3e9e681017a7b2d53fe41a62

[ "ECL-2.0", "Apache-2.0" ]

531

2018-06-19T23:53:10.000Z

2022-03-30T08:35:59.000Z

# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. # Copyright (c) 2017-2019, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import json import os from paddle import fluid import paddle from nvidia.dali.pipeline import Pipeline import nvidia.dali.types as types import nvidia.dali.fn as fn from nvidia.dali.plugin.paddle import DALIGenericIterator from tsm import TSM from utils import load_weights PRETRAIN_WEIGHTS = 'https://paddlemodels.bj.bcebos.com/video_classification/TSM_final.pdparams' def create_video_pipe(video_files, sequence_length=8, target_size=224,stride=30): pipeline = Pipeline(1, 4, 0, seed=42) with pipeline: images = fn.readers.video(device="gpu", filenames=video_files, sequence_length=sequence_length, stride=stride, shard_id=0, num_shards=1, random_shuffle=False, pad_last_batch=True, name="Reader") images = fn.crop_mirror_normalize(images, dtype=types.FLOAT, output_layout="FCHW", crop=(target_size, target_size), mean=[0.485 * 255, 0.456 * 255, 0.406 * 255], std=[0.229 * 255, 0.224 * 255, 0.225 * 255]) pipeline.set_outputs(images) return pipeline def build(seg_num=8, target_size=224): image_shape = [seg_num, 3, target_size, target_size] image = fluid.layers.data( name='image', shape=image_shape, dtype='float32') model = TSM() return model(image) def main(): seg_num = 8 target_size = 224 video_files = [FLAGS.data + '/' + f for f in os.listdir(FLAGS.data)] pipeline = create_video_pipe(video_files, seg_num, target_size, FLAGS.stride) video_loader = DALIGenericIterator( pipeline, ['image'], reader_name="Reader", dynamic_shape=True) exe = fluid.Executor(fluid.CUDAPlace(0)) startup_prog = fluid.Program() eval_prog = fluid.Program() with fluid.program_guard(eval_prog, startup_prog): with fluid.unique_name.guard(): fetch_list = build(seg_num, target_size) exe.run(startup_prog) compiled_eval_prog = fluid.CompiledProgram(eval_prog) load_weights(exe, eval_prog, PRETRAIN_WEIGHTS) labels = json.load(open("kinetics_labels.json")) for idx, batch in enumerate(video_loader): fetches = exe.run( compiled_eval_prog, feed=batch, fetch_list=fetch_list) pred = fetches[0][0] topk_indices = pred.argsort()[0 - FLAGS.topk:] topk_labels = [labels[i] for i in topk_indices] filename = video_files[idx] print("prediction for {} is: {}".format(filename, topk_labels)) if __name__ == '__main__': parser = argparse.ArgumentParser( description='Paddle Temporal Shift Module Inference') parser.add_argument('data', metavar='DIR', help='Path to video files') parser.add_argument('--topk', '-k', default=1, type=int, metavar='K', help='Top k results (default: 1)') parser.add_argument('--stride', '-s', default=30, type=int, metavar='S', help='Distance between frames (default: 30)') FLAGS = parser.parse_args() assert FLAGS.data, "error: must provide data path" # In PaddlePaddle 2.x, we turn on dynamic graph mode by default, and 'data()' is only supported in static graph mode. # So if you want to use this api, please call 'paddle.enable_static()' before this api to enter static graph mode. paddle.enable_static() main()

38.252252

121

0.653085

40b82c2bfbf3cb2f4cd840fa06e5c1ad4f1746ff

10,550

py

Python

trabalho01.py

paulazomig/INE5609-DataStructure

a1d979784063f7280516604d80793abb7ba69c33

[ "MIT" ]

null

trabalho01.py

paulazomig/INE5609-DataStructure

a1d979784063f7280516604d80793abb7ba69c33

[ "MIT" ]

null

trabalho01.py

paulazomig/INE5609-DataStructure

a1d979784063f7280516604d80793abb7ba69c33

[ "MIT" ]

1

2021-08-24T11:09:05.000Z

class Nodo: def __init__(self, id:int, dado=None, anterior=None, posterior=None): self.__id = id self.__dado = dado self.__anterior = anterior self.__posterior = posterior @property def id(self): return self.__id @id.setter def id(self, id): self.__id = id @property def dado(self): return self.__dado @dado.setter def dado(self, dado): self.__dado = dado @property def anterior(self) -> 'Nodo': return self.__anterior @anterior.setter def anterior(self, anterior: 'Nodo'): self.__anterior = anterior @property def posterior(self) -> 'Nodo': return self.__posterior @posterior.setter def posterior(self, posterior: 'Nodo'): self.__posterior = posterior class Cursor: def __init__(self, lista:'ListaEncadeada', nodo_atual:Nodo): self.__lista = lista self.__nodo_atual = nodo_atual self.__posicao = 0 @property def nodo_atual(self) -> Nodo: return self.__nodo_atual @nodo_atual.setter def nodo_atual(self, nodo: Nodo): self.__nodo_atual = nodo @property def posicao(self) -> int: return self.__posicao @posicao.setter def posicao(self, posicao: int): self.__posicao = posicao def ir_para_o_primeiro(self): # vai para o primeiro nodo da lista if self.__lista.esta_vazia(): self.__posicao = 0 self.__nodo_atual = self.__lista.rabo else: self.__posicao = 1 self.__nodo_atual = self.__lista.cabeca.posterior def ir_para_o_ultimo(self): # vai para o ultimo nodo da lista if not self.__lista.esta_vazia(): self.__posicao = self.__lista.tamanho self.__nodo_atual = self.__lista.rabo.anterior else: self.__posicao = 0 self.__nodo_atual = self.__lista.cabeca def avancar_k_posicoes(self, k:int): # avanca k posicoes a partir da posicao atual if self.__lista.esta_vazia(): return if self.__posicao + k > self.__lista.tamanho: self.ir_para_o_ultimo() return contador = 0 while contador < k: self.__posicao += 1 contador += 1 self.__nodo_atual = self.__nodo_atual.posterior def retroceder_k_posicoes(self, k:int): # retrocede k posicoes a partir da posicao atual if self.__lista.esta_vazia(): return if self.__posicao - k < 1: self.ir_para_o_primeiro() return contador = 0 while contador < k: self.__posicao -= 1 contador += 1 self.__nodo_atual = self.__nodo_atual.anterior class ListaEncadeada: def __init__(self) -> None: self.__conta_id = 0 self.__tamanho = 0 self.__cabeca = Nodo(id=0) self.__rabo = Nodo(id=-1) self.__cabeca.posterior = self.__rabo self.__rabo.anterior = self.__cabeca self.__cursor = Cursor(lista=self, nodo_atual=self.__cabeca) @property def cabeca(self) -> Nodo: return self.__cabeca @property def rabo(self) -> Nodo: return self.__rabo @property def cursor(self) -> Cursor: return self.__cursor @property def tamanho(self) -> int: return self.__tamanho def acessar_atual(self) -> Nodo: # retorna o elemento atual sob o cursor return self.__cursor.nodo_atual def esta_vazia(self) -> bool: # verifica se a lista esta vazia return self.__tamanho == 0 def __len__(self): # retorna o numero de elementos da lista return self.__tamanho def posicao_de(self, dado): # retorna a posicao e o nodo de um valor procurado na lista if not self.contem_dado(dado): return posicao = 1 proximo_nodo = self.__cabeca.posterior while proximo_nodo.dado != None: if proximo_nodo.dado == dado: return posicao, proximo_nodo else: posicao += 1 proximo_nodo = proximo_nodo.posterior def inserir_depois_de(self, dado, atual:Nodo): # inseri um novo elemento depois da posicao de um outro elemento self.__conta_id += 1 posterior = atual.posterior novo_nodo = Nodo(id=self.__conta_id, dado=dado, anterior=atual, posterior=posterior) atual.posterior = novo_nodo posterior.anterior = novo_nodo self.__tamanho += 1 self.__cursor.avancar_k_posicoes(1) def inserir_antes_de(self, dado, atual:Nodo): # inseri um novo elemento antes da posicao de um outro elemento self.__conta_id += 1 anterior = atual.anterior novo_nodo = Nodo(id=self.__conta_id, dado=dado, anterior=anterior, posterior=atual) anterior.posterior = novo_nodo atual.anterior = novo_nodo self.__tamanho += 1 self.__cursor.retroceder_k_posicoes(1) def inserir_depois_do_atual(self, dado): # inseri um novo elemento depois da posicao do elemento sob o cursor atual = self.acessar_atual() self.inserir_depois_de(dado, atual) def inserir_antes_do_atual(self, dado): # inseri um novo elemento antes da posicao do elemento sob o cursor atual = self.acessar_atual() self.inserir_antes_de(dado, atual) def inserir_por_primero(self, dado): # inseri um novo elemento no incio da lista self.__cursor.ir_para_o_primeiro() atual = self.acessar_atual() self.inserir_antes_de(dado, atual) def inserir_por_ultimo(self, dado): # inseri um novo elemento no fim da lista self.__cursor.ir_para_o_ultimo() atual = self.acessar_atual() self.inserir_depois_de(dado, atual) def inserir_na_posicao(self, posicao:int, dado): # inseri um novo elemento na posicao desejada self.__cursor.ir_para_o_primeiro() self.__cursor.avancar_k_posicoes(k=posicao-1) self.inserir_antes_do_atual(dado) self.__cursor.ir_para_o_primeiro() self.__cursor.avancar_k_posicoes(k=posicao-1) def deletar_nodo(self, nodo:Nodo): # deleta um nodo if self.esta_vazia(): return anterior = nodo.anterior posterior = nodo.posterior anterior.posterior = posterior posterior.anterior = anterior self.__tamanho -= 1 nodo.anterior = nodo.posterior = nodo.dado = None def deletar_atual(self): # deleta um nodo sob o cursor if self.esta_vazia(): return atual = self.acessar_atual() self.deletar_nodo(atual) self.__cursor.ir_para_o_primeiro() def deletar_primeiro(self): # deleta o primeiro nodo da lista if self.esta_vazia(): return self.__cursor.ir_para_o_primeiro() self.deletar_atual() def deletar_ultimo(self): # deleta o ultimo nodo da lista if self.esta_vazia(): return self.__cursor.ir_para_o_ultimo() self.deletar_atual() def deletar_dado(self, dado): # deleta o nodo que contem o dado como valor if self.esta_vazia(): return _, nodo = self.posicao_de(dado) if nodo: self.deletar_nodo(nodo) self.__cursor.ir_para_o_primeiro() def deletar_da_posicao(self, posicao): # deleta o nodo da posicao self.__cursor.ir_para_o_primeiro() self.__cursor.avancar_k_posicoes(k=posicao-1) self.deletar_atual() self.__cursor.ir_para_o_primeiro() def contem_dado(self, dado) -> bool: # verifica se a lista contem o dado proximo_nodo = self.__cabeca.posterior while proximo_nodo.dado != None: if proximo_nodo.dado == dado: return True else: proximo_nodo = proximo_nodo.posterior return False def imprimir_lista(self): # imprime a lista proximo_nodo = self.__cabeca.posterior contador = 0 while proximo_nodo.dado != None: contador += 1 dado = proximo_nodo.dado print(f'[{contador}º: {dado}] ', end='') proximo_nodo = proximo_nodo.posterior if contador > 10: break print() if __name__ == '__main__': lista = ListaEncadeada() print(f'tamanho da lista: {len(lista)}') print(f'Cursor atual: {lista.cursor.nodo_atual.dado}, posicao {lista.cursor.posicao}') print() print('Insere 7 em primero') lista.inserir_por_primero(7) lista.imprimir_lista() print(f'Cursor atual: {lista.cursor.nodo_atual.dado}, posicao {lista.cursor.posicao}') print() print('Insere 6 em primero') lista.inserir_por_primero(6) lista.imprimir_lista() print(f'Cursor atual: {lista.cursor.nodo_atual.dado}, posicao {lista.cursor.posicao}') print() print('Insere 12 em ultimo') lista.inserir_por_ultimo(12) lista.imprimir_lista() print(f'Cursor atual: {lista.cursor.nodo_atual.dado}, posicao {lista.cursor.posicao}') print() print('Insere 4 em primero') lista.inserir_por_primero(4) lista.imprimir_lista() print(f'Cursor atual: {lista.cursor.nodo_atual.dado}, posicao {lista.cursor.posicao}') print() print('Insere -1 em ultimo') lista.inserir_por_ultimo(-1) lista.imprimir_lista() print(f'Cursor atual: {lista.cursor.nodo_atual.dado}, posicao {lista.cursor.posicao}') print() print('Movimentar o cursor para primeira posicao') lista.cursor.ir_para_o_primeiro() print(f'Cursor atual: {lista.cursor.nodo_atual.dado}, posicao {lista.cursor.posicao}') print() print('Verifica se contem dados na lista') print(f'Contem 5: {lista.contem_dado(5)}') print(f'Contem 7: {lista.contem_dado(7)}') lista.imprimir_lista() print() print('Insere dado 11 na posicao 3') lista.inserir_na_posicao(3, 11) print(f'Cursor atual: {lista.cursor.nodo_atual.dado}, posicao {lista.cursor.posicao}') lista.imprimir_lista() print() print('Deletar Nodo da posicao 4') lista.deletar_da_posicao(4) print(f'Cursor atual: {lista.cursor.nodo_atual.dado}, posicao {lista.cursor.posicao}') lista.imprimir_lista() print()

30.668605

92

0.622275

38d32d3e18274862d02b63e43a2ae970215ae5ac

28,677

py

Python

gropylib.py

rpkarandev/propylib

e2813b045761e43515cdb93859342566bc3edbbb

[ "CC0-1.0" ]

null

gropylib.py

rpkarandev/propylib

e2813b045761e43515cdb93859342566bc3edbbb

[ "CC0-1.0" ]

null

gropylib.py

rpkarandev/propylib

e2813b045761e43515cdb93859342566bc3edbbb

[ "CC0-1.0" ]

null

r#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Wed Apr 3 10:10:05 2019 functions to handle gromacs output files @author: karan """ import re from glob import glob import numpy as np import pandas as pd def assign_mass(s): if (s=="C"): return 12.0107 elif (s=="H"): return 1.00794 elif (s=="N"): return 14.0067 elif (s=="O"): return 15.9994 elif (s=="P"): return 30.973762 elif (s=="S"): return 32.065 else: print("unidentified atom: error mass not assigned", s) return 0 class coord(): 'Special class for coordinates and related function' def __init__(self, d=3): self.n = 0 self.dim = d self.data = np.empty((self.n, self.dim), dtype=np.float) #### peformance tuning need to be done def append(self, newcoord): newcoord = np.reshape(newcoord, (1, self.dim)) self.data = np.append(self.data, newcoord, axis=0) self.n +=1 def extend(self, newcoords): newcoords = np.array(newcoords) self.data = np.append(self.data, newcoords, axis=0) self.n += newcoords.shape[0] def __getitem__(self, index): index1, index2 = index if isinstance(index1[0], bool) : index1 = [x for x in range(len(index1)) if index1[x] == True ] if isinstance(index2[0], bool) : index2 = [x for x in range(len(index2)) if index2[x] == True ] return self.data[np.ix_(index1, index2)] def transform(self, tcoord): self.data = self.data + tcoord def __len__(self): return self.n # include rotation class atomgrp_o: 'Structure for storing pdb data of atoms/hetatoms' def __init__(self): self.atomno = [] self.atomname =[] self.atomtype = [] self.occupancy = [] self.bfac = [] self.resno = [] self.resname = [] self.chain = [] self.coords = coord() def add(self, record): self.atomno.append(record[0]) self.atomname.append(record[1]) self.resname.append(record[2]) self.chain.append(record[3]) self.resno.append(record[4]) self.coords.append( record[5] ) self.occupancy.append(record[6]) self.bfac.append(record[7]) self.atomtype.append(record[8]) class atomgrp: 'Structure for storing pdb data of atoms/hetatoms' def __init__(self): self.atomno = np.empty((0), dtype=int) self.atomname =np.empty((0), dtype='<U3') self.atomtype = np.empty((0), dtype='<U2') self.occupancy = np.empty((0), dtype=float) self.bfac = np.empty((0), dtype=float) self.resno = np.empty((0), dtype=int) self.resname = np.empty((0), dtype='<U3') self.chain = np.empty((0), dtype='<U2') self.coords = coord() def add(self, record): self.atomno = np.append(self.atomno, record[0]) self.atomname = np.append(self.atomname, record[1]) self.resname = np.append(self.resname, record[2]) self.chain = np.append(self.chain, record[3]) self.resno = np.append(self.resno, record[4]) self.coords.append( record[5] ) self.occupancy = np.append(self.occupancy, record[6]) self.bfac = np.append(self.bfac, record[7]) self.atomtype = np.append(self.atomtype, record[8]) def read_xvg(filename): data = [] cols = [] comment = [] ylabel = [] with open(filename, 'r') as infile: line = infile.readline() while line: if line.startswith('@'): line_st = line.split() if line_st[1] == 'xaxis': m = re.search('"(.*)"', line) if m is not None: cols.append( m.groups()[0] ) elif line_st[1] == 'yaxis': m = re.search('"(.*)"', line) if m is not None: ylabel.append( m.groups()[0] ) elif line_st[1][0] == 's' and line_st[2] == 'legend' : m = re.search('legend\s+"(.*)"', line) if m is not None: cols.append( m.groups()[0] ) elif line.startswith('#'): comment.append( line.strip() ) else: #elif line.startswith(' '): #print(line) data.append( line.split() ) line = infile.readline() for i in range(len(data[0])-len(cols)): cols = cols + [str(i)] data = pd.DataFrame( data, columns=cols ).astype('float') return data, comment, ylabel def read_xvg_notnum(filename): data = [] cols = [] comment = [] ylabel = [] with open(filename, 'r') as infile: line = infile.readline() while line: if line.startswith('@'): line_st = line.split() if line_st[1] == 'xaxis': m = re.search('"(.*)"', line) if m is not None: cols.append( m.groups()[0] ) elif line_st[1] == 'yaxis': m = re.search('"(.*)"', line) if m is not None: ylabel.append( m.groups()[0] ) elif line_st[1][0] == 's' and line_st[2] == 'legend' : m = re.search('legend\s+"(.*)"', line) if m is not None: cols.append( m.groups()[0] ) elif line.startswith('#'): comment.append( line.strip() ) else: #elif line.startswith(' '): #print(line) data.append( line.split() ) line = infile.readline() for i in range(len(data[0])-len(cols)): cols = cols + [str(i)] data = pd.DataFrame( data, columns=cols )#.astype('float') return data, comment, ylabel def read_rawxpm(filename): data = [] comment = [] xlabel = '' ylabel = '' xaxis = [] yaxis=[] strings = [] legenddict = {} valdict = {} with open(filename, 'r') as infile: line = infile.readline() while line: #print(line) if xlabel == '': m = re.search('/\* x-label: "(.*)" \*/', line) if m != None : xlabel = m.groups()[0] elif ylabel == '': m = re.search('/\* y-label: "(.*)" \*/', line) if m != None : ylabel = m.groups()[0] elif line[0] == '"' and len(xaxis) == 0 : m = re.search('"(.*?)\s+[c]*\s+(.*)\s+" /\*\s"(.*)"\s\*/', line) if m != None: string = m.groups()[0] legenddict[ m.groups()[0] ] = m.groups()[2] valdict[ m.groups()[0] ] = m.groups()[1] elif line[:10] == "/* x-axis:": m = re.search('/\* x-axis:\s+(.*) \*/', line) xaxis.extend( m.groups()[0].strip().split() ) elif line[:10] == "/* y-axis:": m = re.search('/\* y-axis:\s+(.*) \*/', line) yaxis.extend( m.groups()[0].strip().split() ) elif line[0] == '"' and len(xaxis) > 0 : m = re.search('"(.*)"', line) string = m.groups()[0] strings.append( list(string) ) line = infile.readline() data = pd.DataFrame(strings, ).T data.columns = [ylabel + '_' + y for y in yaxis] data[xlabel] = pd.Series(xaxis).astype('float') data = data.loc[:, [ data.columns[-1] ] + data.columns[:-1].values.tolist() ] return data, comment, legenddict, valdict, ylabel def writeNDXfile(grpIndexDic, indexFileName): with open(indexFileName, 'w') as ofile: s = '' for key in grpIndexDic.keys(): s += f'[ {key} ]\n' ld = 10 for ai, a in enumerate( grpIndexDic[key] ): if ai%ld == 0: s += '\n' s += f'{a:5d} ' s += '\n\n' ofile.write(s) return None def read_numxpm(filename): data, comment, legenddict, valdict, ylabel = read_rawxpm(filename) if len( data ) > 0: data.iloc[:,1:] = data.iloc[:,1:].apply(lambda x: x.apply( lambda y: legenddict[y] ) ) return data, comment, legenddict, valdict, ylabel else: print("error reading xpm") return None def read_ssxpm(filename): data, comment, legenddict, valdict, ylabel = read_rawxpm(filename) if len( data ) > 0: cols = [] chsep = [] try: chsep = [x for x in legenddict.keys() if legenddict[x] == "Chain_Separator"] except: print("No chainseparators are found") chno = 1 resno = 1 for x in data.iloc[0, 1:]: # first row, assign chain based on that if x in chsep: chno += 1 resno = 1 cols.append( "chsep" ) else: cols.append( "ch"+str(chno)+"_res"+str(resno) ) resno += 1 data.columns = [data.columns[0]] + cols data = data.loc[:, data.columns != 'chsep'] return data, comment, legenddict, valdict, ylabel else: print("error reading xpm") return None def read_gro(filename): try : with open(filename, 'r') as infile: data = [] line = infile.readline() title = line m = re.search('t=([\d.]*)', line) if m != None: frtime = float(m.groups()[0]) else: frtime = 0.0 line = infile.readline() m = re.search('t=([\d.]*)', line) tnmol = int(line.strip()) line = infile.readline() line1 = infile.readline() while line1 : data.append( [ line[:5].strip(), line[5:10].strip(), line[10:15].strip(), line[15:20].strip(), line[20:28].strip(), line[28:36].strip(), line[36:44].strip(), line[44:52].strip(), line[52:60].strip(), line[60:68].strip() ] ) line = line1 line1 = infile.readline() box = line.strip().split() box = [float(x) for x in box] cols =['resno', 'resname', 'aname', 'ano', 'x', 'y', 'z', 'vx', 'vy', 'vz'] dtypes = ['int64', 'object', 'object', 'int64', 'float64', 'float64', 'float64', 'float64', 'float64', 'float64'] dtypes = ['int', 'object', 'object', 'int', 'float', 'float', 'float', 'float', 'float', 'float'] dtypesdict = dict( zip( cols, dtypes) ) data = pd.DataFrame( data, columns=cols) data = data.astype( dtypesdict ) return data, title, frtime, box except: print("read_gro: error -> unable to open the file") class read_pdbtraj: 'Class for storing PDB data' def __init__(self, filename, framestart=0, frameend=0, chainids=[], nresperch=0, verbose=1): self.fname = ((filename.split('/'))[-1].split('.'))[0] if type(chainids) != list : print("read_pdbtraj: Error chainids must be a list") return -100 elif type(nresperch) != int or nresperch < 0 : print("read_pdbtraj: Enter valid nresperch ") return -100 elif type(framestart) != int or framestart < 0 : print("read_pdbtraj: Enter valid framestart ") return -100 elif type(frameend) != int or frameend < 0 : print("read_pdbtraj: Enter valid framestart ") return -100 frameend = 10000000 if frameend < 1 else frameend nchs_to_get = len(chainids) self.chains={} self.resnos = [] self.resnames = [] self.resnames1 = [] self.atom = atomgrp() self.hetatom = atomgrp() self.indCalpha = [] self.indBB = [] self.indnH = [] self.res_many_occurence = [] modelflag = False modelcounter = -1 atom_recs_l = [] atom_coords_l = [] atom_coords_list = [] hetatom_recs_l = [] hetatom_coords_l = [] hetatom_coords_list = [] readmodelcounter = 0 framelist = [] timelist = [] boxlist = [] print( " framend ", frameend) if verbose > 0 else None with open(filename, 'r') as pdbfile : line = pdbfile.readline() while line and modelcounter <= frameend: #print( line ) if line[:5] == 'TITLE': #print(line) #m = re.search('t=\s+([0-9]*\.[0-9]*)\s+', line) m = re.search('t=\s+([0-9\.]*)\s+', line) if m != None: #print(m.groups()[0].strip()) frtime = float(m.groups()[0].strip()) else: frtime = 0.5 if line[:6] == 'CRYST1': #print(line) m = re.search('CRYST1\s+([0-9\.]*)\s+([0-9\.]*)\s+([0-9\.]*)\s+', line) if m != None: #print(m.groups()[0].strip()) box = [ float(m.groups()[0].strip()), float(m.groups()[0].strip()), float(m.groups()[0].strip())] else: box = [0.0, 0.0, 0.0] #print(box) if line[:5] == 'MODEL': modelcounter += 1 if modelcounter >= framestart and modelcounter<= frameend: modelflag = True print("read_pdbtraj: reading model ", modelcounter) if verbose > 0 else None readmodelcounter += 1 framelist.append(modelcounter) timelist.append( frtime ) boxlist.append( box ) if len( atom_coords_l ) > 0 or len( hetatom_coords_l ) > 0 : atom_coords_list.append( atom_coords_l ) atom_coords_l = [] hetatom_coords_list.append( hetatom_coords_l ) hetatom_coords_l = [] else: modelflag = False if readmodelcounter == 1: if modelflag and nchs_to_get == 0: ## Read atom coordinates if line[:6] == 'ATOM ' : atom_recs_l.append( [int(line[6:11].strip()), line[12:16].strip(), line[17:20].strip(), line[21:22].strip(), int(line[22:26].strip()), float(line[54:60].strip()), float(line[60:66].strip()), line[76:78].strip() ] ) #atom_coords_l.append([modelcounter, frtime, float(line[30:38].strip()), float(line[38:46].strip()), float(line[46:54].strip()) ] ) atom_coords_l.append([float(line[30:38].strip()), float(line[38:46].strip()), float(line[46:54].strip()) ] ) if line[:6] == 'HEATOM': hetatom_recs_l.append( [int(line[6:11].strip()), line[12:16].strip(), line[17:20].strip(), line[21:22].strip(), int(line[22:26].strip()), float(line[54:60].strip()), float(line[60:66].strip()), line[76:78].strip() ] ) #hetatom_coords_l.append([modelcounter, frtime, float(line[30:38].strip()), float(line[38:46].strip()), float(line[46:54].strip()) ] ) hetatom_coords_l.append([ float(line[30:38].strip()), float(line[38:46].strip()), float(line[46:54].strip()) ] ) #line = pdbfile.readline() if modelflag and nchs_to_get > 0: ## Read atom coordinates from chain if line[:6] == 'ATOM ' and line[20:22].strip() in chainids : atom_recs_l.append( [int(line[6:11].strip()), line[12:16].strip(), line[17:20].strip(), line[21:22].strip(), int(line[22:26].strip()), float(line[54:60].strip()), float(line[60:66].strip()), line[76:78].strip() ] ) #atom_coords_l.append([modelcounter, frtime, float(line[30:38].strip()), float(line[38:46].strip()), float(line[46:54].strip()) ] ) atom_coords_l.append([float(line[30:38].strip()), float(line[38:46].strip()), float(line[46:54].strip()) ] ) if line[:6] == 'HEATOM': hetatom_recs_l.append( [int(line[6:11].strip()), line[12:16].strip(), line[17:20].strip(), line[21:22].strip(), int(line[22:26].strip()), float(line[54:60].strip()), float(line[60:66].strip()), line[76:78].strip() ] ) #hetatom_coords_l.append([modelcounter, frtime, float(line[30:38].strip()), float(line[38:46].strip()), float(line[46:54].strip()) ] ) hetatom_coords_l.append([ float(line[30:38].strip()), float(line[38:46].strip()), float(line[46:54].strip()) ]) #line = pdbfile.readline() else: if modelflag and nchs_to_get == 0: ## Read atom coordinates if line[:6] == 'ATOM ' : #atom_coords_l.append([modelcounter, frtime, float(line[30:38].strip()), float(line[38:46].strip()), float(line[46:54].strip()) ] ) atom_coords_l.append([float(line[30:38].strip()), float(line[38:46].strip()), float(line[46:54].strip()) ] ) if line[:6] == 'HEATOM': #hetatom_coords_l.append([modelcounter, frtime, float(line[30:38].strip()), float(line[38:46].strip()), float(line[46:54].strip()) ] ) hetatom_coords_l.append([ float(line[30:38].strip()), float(line[38:46].strip()), float(line[46:54].strip()) ] ) #line = pdbfile.readline() if modelflag and nchs_to_get > 0: ## Read atom coordinates from chain if line[:6] == 'ATOM ' and line[20:22].strip() in chainids : #atom_coords_l.append([modelcounter, frtime, float(line[30:38].strip()), float(line[38:46].strip()), float(line[46:54].strip()) ] ) atom_coords_l.append([float(line[30:38].strip()), float(line[38:46].strip()), float(line[46:54].strip()) ] ) if line[:6] == 'HEATOM': #hetatom_coords_l.append([modelcounter, frtime, float(line[30:38].strip()), float(line[38:46].strip()), float(line[46:54].strip()) ] ) hetatom_coords_l.append([ float(line[30:38].strip()), float(line[38:46].strip()), float(line[46:54].strip()) ]) #line = pdbfile.readline() line = pdbfile.readline() #print(line) cols1 = ['atomno', 'atomname', 'resname', 'chain', 'resno', 'occupancy', 'bfac', 'atomtype'] #cols2 = ['frame', 'time', 'X', 'Y', 'Z'] self.atom_recs = pd.DataFrame( atom_recs_l, columns=cols1 ) self.hetatom_recs = pd.DataFrame( hetatom_recs_l, columns=cols1 ) #self.atom_coords = pd.DataFrame( atom_coords_l, columns=cols2 ) #self.hetatom_coords = pd.DataFrame( hetatom_coords_l, columns=cols2 ) self.atom_coords = np.array( atom_coords_list ) self.hetatom_coords = pd.DataFrame( hetatom_coords_list ) self.framelist = pd.DataFrame( ) self.framelist['frameno'] = framelist self.framelist['time'] = timelist self.framelist['boxxyz'] = boxlist if nresperch > 0: resnoc_list = [] # continuous resno starts from 0 resnoc = -1 resno_old = -100 for resno in self.atom_recs.resno: if resno_old != resno: resnoc += 1 resnoc_list.append( resnoc ) resno_old = resno else: resnoc_list.append( resnoc ) self.atom_recs['resnoc'] = resnoc_list self.atom_recs['chainidc'] = self.atom_recs.resnoc.apply(lambda x: int( np.floor(x/nresperch) ) ) self.atom_recs['resnoch'] = self.atom_recs.resnoc.apply(lambda x: int( x%nresperch ) ) self.atom_recs['m'] = self.atom_recs.atomtype.apply( assign_mass ) # self.resnos = [ [ self.atom.resno[y] for y in x ] for x in self.indCalpha] # self.resnames = [ [ self.atom.resname[y] for y in x ] for x in self.indCalpha] # self.resnames1 = [ [ aminoAAA2A(y) for y in x ] for x in self.resnames] # self.indBB = [[x for x in range(len(self.atom.atomname)) if (self.atom.atomname[x] == 'N') or (self.atom.atomname[x] == 'CA') or (self.atom.atomname[x] == 'C')] for y in self.chains] # self.indnH = [[x for x in range(len(self.atom.atomname)) if (self.atom.atomname[x] != 'H') or (self.atom.atomname[x] != 'D') ] for y in self.chains] def cal_principalaxes(chcoordsfr, chm): com = ( chcoordsfr * chm ).sum(axis=0)/chm.sum() chcoordsfr = chcoordsfr - com chcoordsfr2 = chcoordsfr * chcoordsfr I11 = (chm * (chcoordsfr2[:, 1] + chcoordsfr2[:, 2])).sum() I22 = (chm * (chcoordsfr2[:, 0] + chcoordsfr2[:, 2])).sum() I33 = (chm * (chcoordsfr2[:, 0] + chcoordsfr2[:, 1])).sum() I12 = -(chm * chcoordsfr[:, 0] * chcoordsfr[:, 1] ).sum() I13 = -(chm * chcoordsfr[:, 0] * chcoordsfr[:, 2] ).sum() I23 = -(chm * chcoordsfr[:, 1] * chcoordsfr[:, 2] ).sum() I = np.array( [I11, I12, I13, I12, I22, I23, I13, I23, I33]).reshape( (3,3) ) eigval, eigvec = np.linalg.eig( I ) return eigvec, com, eigval def cal_largestaxis(chcoordsfr, chm): #n = 1000 # chcoordsfr = np.random.normal(size=3*n).reshape( (n, 3) ) + 100 #chm = np.ones( (n,1) ) eigvec, com, eigval = cal_principalaxes(chcoordsfr, chm) eigvalmaxind = np.argmax(np.abs(eigval)) eigvecmax = eigvec[:, eigvalmaxind ] return eigvecmax, com def get_neighbours(clusters_temp, slist): temp = [] for s in slist: temp += clusters_temp[s] return list( set(temp) ) def get_clusters( distmat0, distcutoff ): distmat = distmat0 <= distcutoff cluster_rows = [x for x in range(len(distmat))] clusters_temp = [np.where( distmat[ci, :] )[0].tolist() for ci in cluster_rows] clusters_temp1 = [] while len(cluster_rows) > 0: ci = cluster_rows.pop( 0 ) clust = clusters_temp[ ci ] nclust = get_neighbours(clusters_temp, clust) while len(clust) != len(nclust): clust = nclust.copy() nclust = get_neighbours(clusters_temp, clust) cluster_rows = [x for x in cluster_rows if x not in nclust] clusters_temp1.append( nclust ) clus_centers = [] for clus in clusters_temp1: ind_min = np.argmin(np.mean( distmat0[ clus, : ][:, clus], axis=1)) clus_centers.append( clus[ ind_min ] ) return clusters_temp1, clus_centers def get_all_clusters(pairdist, distcutoff ): nch1 = int( np.sqrt( pairdist.shape[1]-1 ) ) distmats = pairdist.iloc[:,1:].values clus_stat = [] sizecounts = {} ncounts = {} for x in range( distmats.shape[0] ) : sizecount = {'1':0, '2':0, '3-4':0, '5-6':0, '7-8':0, '9+':0, '11+':0,'13+':0} ncount = {'1':0, '2':0, '3-4':0, '5-6':0, '7-8':0, '9+':0, '11+':0,'13+':0} distmat0 = distmats[x,:].reshape( (nch1, nch1) ) clusters, clus_centers = get_clusters( distmat0, distcutoff ) clus_lens = [ len(x) for x in clusters ] clus_stat.append( [len(clusters), np.max(clus_lens), np.mean(clus_lens) ]) for cl in clus_lens: if cl == 1: sizecount['1'] += cl ncount['1'] += 1 elif cl == 2: sizecount['2'] += cl ncount['2'] += 1 elif cl in [3,4]: sizecount['3-4'] += cl ncount['3-4'] += 1 elif cl in [5,6] : sizecount['5-6'] += cl ncount['5-6'] += 1 elif cl in [7,8] : sizecount['7-8'] += cl ncount['7-8'] += 1 elif cl > 8 : sizecount['9+'] += cl ncount['9+'] += 1 if cl > 10 : sizecount['11+'] += cl ncount['11+'] += 1 if cl > 12 : sizecount['13+'] += cl ncount['13+'] += 1 sizecounts[x] = sizecount ncounts[x] = ncount return np.array( clus_stat ), pd.DataFrame( sizecounts ).T, pd.DataFrame( ncounts ).T def get_all_clusters_update1(pairdist, distcutoff ): nch1 = int( np.sqrt( pairdist.shape[1]-1 ) ) distmats = pairdist.iloc[:,1:].values clus_stat = [] sizecounts = {} ncounts = {} for x in range( distmats.shape[0] ) : sizecount = {'1':0, '2':0, '3-8':0, '9-16':0, '17-24':0, '24+':0} ncount = {'1':0, '2':0, '3-8':0, '9-16':0, '17-24':0, '24+':0} distmat0 = distmats[x,:].reshape( (nch1, nch1) ) clusters, clus_centers = get_clusters( distmat0, distcutoff ) clus_lens = [ len(x) for x in clusters ] clus_stat.append( [len(clusters), np.max(clus_lens), np.mean(clus_lens) ]) for cl in clus_lens: if cl == 1: sizecount['1'] += cl ncount['1'] += 1 elif cl == 2: sizecount['2'] += cl ncount['2'] += 1 elif cl < 9: sizecount['3-8'] += cl ncount['3-8'] += 1 elif cl < 17 : sizecount['9-16'] += cl ncount['9-16'] += 1 elif cl < 25 : sizecount['17-24'] += cl ncount['17-24'] += 1 elif cl >= 25 : sizecount['24+'] += cl ncount['24+'] += 1 sizecounts[x] = sizecount ncounts[x] = ncount return np.array( clus_stat ), pd.DataFrame( sizecounts ).T, pd.DataFrame( ncounts ).T def get_all_clusters_detail(pairdist, distcutoff, frameno=-1 ): nch1 = int( np.sqrt( pairdist.shape[1]-1 ) ) distmats = pairdist.iloc[:,1:].values clus_stat = [] sizecount = {'1':0, '2':0, '3-4':0, '5-6':0, '7-8':0, '9+':0, '11+':0,'13+':0} ncount = {'1':0, '2':0, '3-4':0, '5-6':0, '7-8':0, '9+':0, '11+':0,'13+':0} ## Take only the last frame distmat0 = distmats[frameno,:].reshape( (nch1, nch1) ) clusters, clus_centers = get_clusters( distmat0, distcutoff ) clus_lens = [ len(x) for x in clusters ] clus_stat.append( [len(clusters), np.max(clus_lens), np.mean(clus_lens) ]) for cl in clus_lens: if cl == 1: sizecount['1'] += cl ncount['1'] += 1 elif cl == 2: sizecount['2'] += cl ncount['2'] += 1 elif cl in [3,4]: sizecount['3-4'] += cl ncount['3-4'] += 1 elif cl in [5,6] : sizecount['5-6'] += cl ncount['5-6'] += 1 elif cl in [7,8] : sizecount['7-8'] += cl ncount['7-8'] += 1 elif cl > 8 : sizecount['9+'] += cl ncount['9+'] += 1 if cl > 10 : sizecount['11+'] += cl ncount['11+'] += 1 if cl > 12 : sizecount['13+'] += cl ncount['13+'] += 1 return clusters, clus_centers, clus_lens, np.array( clus_stat ), sizecount, ncount

45.8832

249

0.481536

d32ea621d5d0dd51fdba343f3af6ccd0e65733d1

6,690

py

Python

ZipFolder/Fetch Shapefiles.py

mattCensus/PerlScripts

d2643d99abc3f0647ebfbd41f7e5faa704da3e91

[ "MIT" ]

null

ZipFolder/Fetch Shapefiles.py

mattCensus/PerlScripts

d2643d99abc3f0647ebfbd41f7e5faa704da3e91

[ "MIT" ]

null

ZipFolder/Fetch Shapefiles.py

mattCensus/PerlScripts

d2643d99abc3f0647ebfbd41f7e5faa704da3e91

[ "MIT" ]

null

''' This script will download TIGER data shapefiles from the Census FTP site. It can be used to download a set of geographies defined in GEO_TYPES_LIST, or can be used to fetch files for a single state and/or single geography type. Pass an -s argument to limit by state, pass a -g argument to limit to a single geography type, and/or pass a -y argument to change the year from 2012 to something else (e.g. 2015). >> python fetch_shapefiles.py >> python fetch_shapefiles.py -s WA >> python fetch_shapefiles.py -g place >> python fetch_shapefiles.py -y 2015 >> python fetch_shapefiles.py -s WA -g place -y 2015 If you use the -s argument to fetch files for a single state, the script will also download the national county, state and congressional district files that include data for your chosen state. The script will create DOWNLOAD_DIR and EXTRACT_DIR directories if necessary, fetch a zipfile or set of zipfiles from the Census website, then extract the shapefiles from each zipfile retrieved. DISABLE_AUTO_DOWNLOADS will prevent certain geography types from being automatically downloaded if no -g argument is passed to fetch_shapefiles.py. This may be useful because certain files, such as those for Zip Code Tabulation Areas, are extremely large. You can still target any geography in GEO_TYPES_LIST specifically, however. So to fetch the ZCTA data: >> python fetch_shapefiles.py -g zcta5 ''' import optparse import os import sys import zipfile from os.path import isdir, join, normpath try: from six.moves.urllib import request as urllib2 except ImportError: import urllib2 from __init__ import (DOWNLOAD_DIR, EXTRACT_DIR, STATE_ABBREV_LIST, GEO_TYPES_LIST, DISABLE_AUTO_DOWNLOADS, get_fips_code_for_state) FTP_HOME = 'ftp://ftp2.census.gov/geo/tiger/TIGER2021/' def get_filename_list_from_ftp(target, state): target_files = urllib2.urlopen(target).read().splitlines() filename_list = [] for line in target_files: filename = '%s%s' % (target, line.decode().split()[-1]) filename_list.append(filename) if state: state_check = '_%s_' % get_fips_code_for_state(state) filename_list = filter( lambda filename: state_check in filename or ('_us_' in filename and '_us_zcta5' not in filename), filename_list ) return filename_list def get_content_length(u): # u is returned by urllib2.urlopen if sys.version_info[0] == 2: return int(u.info().getheader("Content-Length")) else: return int(u.headers["Content-Length"]) def download_files_in_list(filename_list, force=False): downloaded_filename_list = [] for file_location in filename_list: filename = '%s/%s' % (DOWNLOAD_DIR, file_location.split('/')[-1]) if force or not os.path.exists(filename): # Only download if required. u = urllib2.urlopen(file_location) f = open(filename, 'wb') file_size = get_content_length(u) print("Downloading: %s Bytes: %s" % (filename, file_size)) file_size_dl = 0 block_sz = 8192 while True: buffer = u.read(block_sz) if not buffer: break file_size_dl += len(buffer) f.write(buffer) status = r"%10d [%3.2f%%]" % ( file_size_dl, file_size_dl * 100. / file_size) status = status + chr(8) * (len(status) + 1) sys.stdout.write(status) sys.stdout.flush() f.close() downloaded_filename_list.append(filename) return downloaded_filename_list def extract_downloaded_file(filename, remove_on_error=True): zip_dir = filename.replace('.zip', '').split('/')[-1] target_dir = normpath(join(EXTRACT_DIR, zip_dir)) print("Extracting: " + filename + " ...") try: zipped = zipfile.ZipFile(filename, 'r') except zipfile.BadZipFile as ze: if remove_on_error: os.remove(filename) raise Exception( "Removed corrupt zip file (%s). Retry download." % filename) raise ze zipped.extractall(target_dir) zipped.close() def get_one_geo_type(geo_type, state=None, year='2012'): target = '%s%s/' % (FTP_HOME.replace('2012', year), geo_type.upper()) print("Finding files in: " + target + " ...") filename_list = get_filename_list_from_ftp(target, state) downloaded_filename_list = download_files_in_list(filename_list) for filename in downloaded_filename_list: extract_downloaded_file(filename) def get_all_geo_types(state=None, year='2012'): AUTO_DOWNLOADS = filter( lambda geo_type: geo_type not in DISABLE_AUTO_DOWNLOADS, GEO_TYPES_LIST ) for geo_type in AUTO_DOWNLOADS: get_one_geo_type(geo_type, state, year) def process_options(arglist=None): global options, args parser = optparse.OptionParser() parser.add_option( '-s', '--state', dest='state', help='specific state to download', choices=STATE_ABBREV_LIST, default=None ) parser.add_option( '-g', '--geo', '--geo_type', dest='geo_type', help='specific geographic type to download', choices=GEO_TYPES_LIST, default=None ) parser.add_option( '-y', '--year', dest='year', help='specific year to download', default='2012' ) options, args = parser.parse_args(arglist) return options, args def main(args=None): """ >> python fetch_shapefiles.py >> python fetch_shapefiles.py -s WA >> python fetch_shapefiles.py -g place >> python fetch_shapefiles.py -s WA -g place """ if args is None: args = sys.argv[1:] options, args = process_options(args) # make sure we have the expected directories for path in [DOWNLOAD_DIR, EXTRACT_DIR]: if not isdir(path): os.makedirs(path) # get one geo_type or all geo_types if options.geo_type: get_one_geo_type( geo_type = options.geo_type, state = options.state, year=options.year ) else: get_all_geo_types( state = options.state, year=options.year ) if __name__ == '__main__': main()

32.955665

79

0.624664

0ac9ddaf8297571d4b89d9f57c35aa4196098fd7

13,956

py

Python

eoxserver/services/ows/wps/parameters/complexdata.py

ESA-VirES/eoxserver

d7b65adf9317538b267d5cbb1281acb72bc0de2c

[ "OML" ]

1

2017-11-21T22:23:30.000Z

eoxserver/services/ows/wps/parameters/complexdata.py

ESA-VirES/eoxserver

d7b65adf9317538b267d5cbb1281acb72bc0de2c

[ "OML" ]

null

eoxserver/services/ows/wps/parameters/complexdata.py

ESA-VirES/eoxserver

d7b65adf9317538b267d5cbb1281acb72bc0de2c

[ "OML" ]

null

#------------------------------------------------------------------------------- # # WPS Complex Data type # # Project: EOxServer <http://eoxserver.org> # Authors: Fabian Schindler <fabian.schindler@eox.at> # Martin Paces <martin.paces@eox.at> # #------------------------------------------------------------------------------- # Copyright (C) 2013 EOX IT Services GmbH # # 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 of this Software or works derived from this Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. #------------------------------------------------------------------------------- import os import os.path import json from lxml import etree from copy import deepcopy from StringIO import StringIO try: from cStringIO import StringIO as FastStringIO except ImportError: FastStringIO = StringIO try: # available in Python 2.7+ from collections import OrderedDict except ImportError: from django.utils.datastructures import SortedDict as OrderedDict from .base import Parameter from .formats import Format #------------------------------------------------------------------------------- # complex data - data containers class CDBase(object): """ Base class of the complex data container. """ def __init__(self, mime_type=None, encoding=None, schema=None, format=None): if isinstance(format, Format): self.mime_type = format.mime_type self.encoding = format.encoding self.schema = format.schema else: self.mime_type = mime_type self.encoding = encoding self.schema = schema class CDObject(CDBase): """ Complex data wraper arround an arbitraty python object. To be used to set custom format attributes for the XML and JSON payload. NOTE: CDObject is not used for the input JSON and XML. """ def __init__(self, data, mime_type=None, encoding=None, schema=None, format=None): CDBase.__init__(self, mime_type, encoding, schema, format) self.data = data class CDByteBuffer(StringIO, CDBase): """ Complex data binary in-memory buffer (StringIO). To be used to hold a generic binary (byte-stream) payload. """ def __init__(self, data='', mime_type=None, encoding=None, schema=None, format=None): StringIO.__init__(self, str(data)) CDBase.__init__(self, mime_type, encoding, schema, format) def write(self, data): StringIO.write(self, str(data)) @property def data(self): self.seek(0) return self.read() class CDTextBuffer(StringIO, CDBase): """ Complex data text (unicode) in-memory buffer (StringIO). To be used to hold generic text. The the text payload is stored as a unicode-stream. Set the 'text_encoding' parameter is unicode encoding/decoding shall be applied. """ def __init__(self, data=u'', mime_type=None, encoding=None, schema=None, format=None, text_encoding=None): StringIO.__init__(self, unicode(data)) CDBase.__init__(self, mime_type, encoding, schema, format) self.text_encoding = text_encoding @property def data(self): self.seek(0) return self.read() def write(self, data): if self.text_encoding is None: return StringIO.write(self, unicode(data)) else: return StringIO.write(self, unicode(data, self.text_encoding)) def read(self, size=None): if size is None: data = StringIO.read(self) else: data = StringIO.read(self, size) if self.text_encoding is None: return data else: return data.encode(self.text_encoding) class CDAsciiTextBuffer(CDByteBuffer): """ Complex data text (ascii) in-memory buffer (StringIO). To be used to hold generic ascii text. The the text payload is stored as a byte-stream and this class cannot hold characters outside of the 7-bit ascii characters' range. """ def __init__(self, data='', mime_type=None, encoding=None, schema=None, format=None, text_encoding=None): CDByteBuffer.__init__(self, data, mime_type, encoding, schema, format) self.text_encoding = text_encoding def write(self, data): if not isinstance(data, basestring): data = str(data) StringIO.write(self, data.encode('ascii')) def read(self, size=None): if size is None: data = StringIO.read(self) else: data = StringIO.read(self, size) if self.text_encoding is None: return data else: return data.encode(self.text_encoding) class CDFile(CDBase): """ Complex data binary file. To be used to hold a generic binary (byte-stream) payload. NOTE: The file allows you to specify whether the file is temporary (will be atomatically removed - by default) or permanent (preserverved after object destruction). """ def __init__(self, name, mode='r', buffering=-1, mime_type=None, encoding=None, schema=None, format=None, remove_file=True): CDBase.__init__(self, mime_type, encoding, schema, format) self._file = file(name, mode, buffering) self._remove_file = remove_file def __del__(self): name = self.name self.close() if self._remove_file: os.remove(name) @property def data(self): self.seek(0) return self.read() def __getattr__(self, attr): return getattr(self._file, attr) class CDPermanentFile(CDFile): """ Complex data permanent binary file. To be used to hold a generic binary (byte-stream) payload. NOTE: This class preserves the actual file. """ def __init__(self, remove_file, name, mode='r', buffering=-1, mime_type=None, encoding=None, schema=None, format=None): CDFile.__init__(name, mode, buffering, mime_type, encoding, schema, format, False) #------------------------------------------------------------------------------- class ComplexData(Parameter): """ complex-data parameter class """ def __init__(self, identifier, formats, *args, **kwargs): """ Object constructor. Parameters: identifier identifier of the parameter. title optional human-readable name (defaults to identifier). abstract optional human-readable verbose description. metadata optional metadata (title/URL dictionary). optional optional boolean flag indicating whether the input parameter is optional or not. formats List of supported formats. """ super(ComplexData, self).__init__(identifier, *args, **kwargs) self.formats = OrderedDict() if isinstance(formats, Format): formats = (formats,) for frm in formats: self.formats[(frm.mime_type, frm.encoding, frm.schema)] = frm @property def default_format(self): return self.formats.itervalues().next() def get_format(self, mime_type, encoding=None, schema=None): if mime_type is None: return self.default_format else: return self.formats.get((mime_type, encoding, schema)) # def verify_format(self, format): # """ Returns valid format or rise value error exception.""" # if format is None: # return self.default_format # tmp = (format.mime_type, format.encoding, format.schema) # if tmp in self.formats: # return format # raise ValueError("Invalid format %r"%format) def parse(self, data, mime_type, schema, encoding, **opt): """ parse input complex data """ format_ = self.get_format(mime_type, encoding, schema) if format_ is None: raise ValueError("Invalid format specification! mime_type=%r, " "encoding=%r, schema=%r"%(mime_type, encoding, schema)) text_encoding = getattr(format_, 'text_encoding', 'utf-8') fattr = { 'mime_type': format_.mime_type, 'encoding': format_.encoding, 'schema': format_.schema } if format_.is_xml: parsed_data = CDObject(etree.fromstring(data), **fattr) elif format_.is_json: parsed_data = CDObject(json.loads(_unicode(data, text_encoding)), **fattr) elif format_.is_text: parsed_data = CDTextBuffer(_unicode(data, text_encoding), **fattr) parsed_data.seek(0) else: # generic binary byte-stream parsed_data = CDByteBuffer(data, **fattr) if format_.encoding is not None: data_out = FastStringIO() for chunk in format_.decode(parsed_data, **opt): data_out.write(chunk) parsed_data = data_out parsed_data.seek(0) return parsed_data def encode_xml(self, data): """ encode complex data to be embedded to an XML document""" mime_type = getattr(data, 'mime_type', None) encoding = getattr(data, 'encoding', None) schema = getattr(data, 'schema', None) format_ = self.get_format(mime_type, encoding, schema) if format_ is None: raise ValueError("Invalid format specification! mime_type=%r, " "encoding=%r, schema=%r"%(mime_type, encoding, schema)) if not format_.allows_xml_embedding: raise ValueError("Selected format does not allows XML embedding! " "mime_type=%r, encoding=%r, schema=%r"%( mime_type, encoding, schema)) if isinstance(data, CDObject): data = data.data if format_.is_xml: if isinstance(data, etree._ElementTree): data = data.getroot() return deepcopy(data) elif format_.is_json: return json.dumps(data, ensure_ascii=False) elif format_.is_text: if not isinstance(data, basestring): data.seek(0) data = data.read() return data else: # generic binary byte-stream if format_.encoding is not None: data.seek(0) data_out = FastStringIO() for chunk in format_.encode(data): data_out.write(chunk) data = data_out data.seek(0) return data.read() def encode_raw(self, data): """ encode complex data for raw output """ def _rewind(fid): if hasattr(fid, 'seek'): fid.seek(0) return data mime_type = getattr(data, 'mime_type', None) encoding = getattr(data, 'encoding', None) schema = getattr(data, 'schema', None) format_ = self.get_format(mime_type, encoding, schema) text_encoding = getattr(format_, 'text_encoding', 'utf-8') if format_ is None: raise ValueError("Invalid format specification! mime_type=%r, " "encoding=%r, schema=%r"%(mime_type, encoding, schema)) if isinstance(data, CDObject): data = data.data if format_.is_xml: data = FastStringIO(etree.tostring(data, pretty_print=False, xml_declaration=True, encoding=text_encoding)) content_type = "%s; charset=%s"%(format_.mime_type, text_encoding) elif format_.is_json: data = FastStringIO(json.dumps(data, ensure_ascii=False).encode(text_encoding)) content_type = "%s; charset=%s"%(format_.mime_type, text_encoding) elif format_.is_text: if isinstance(data, (CDTextBuffer, CDAsciiTextBuffer)): data.text_encoding = text_encoding else: data = FastStringIO(_rewind(data).read().encode(text_encoding)) content_type = "%s; charset=%s"%(format_.mime_type, text_encoding) else: # generic binary byte-stream if format_.encoding is not None: data_out = FastStringIO() for chunk in format_.encode(_rewind(data)): data_out.write(chunk) data = data_out content_type = format_.mime_type return _rewind(data), content_type def _bytestring(data): if isinstance(data, str): return data raise TypeError("Byte string expected, %s received!"%type(data)) def _unicode(data, encoding): if isinstance(data, unicode): return data elif isinstance(data, str): return unicode(data, encoding) raise TypeError("Byte od unicode string expected, %s received!"%type(data))

37.923913

91

0.602178

4b567eca852dd7e2aa70a46416b97222852e8651

292

py

Python

onlinecourse/migrations/0003_delete_question.py

spacemc2/ProjectFinalDjango

0754120fd1ac742582acafdd77ed66026ab90104

[ "Apache-2.0" ]

null

onlinecourse/migrations/0003_delete_question.py

spacemc2/ProjectFinalDjango

0754120fd1ac742582acafdd77ed66026ab90104

[ "Apache-2.0" ]

null

onlinecourse/migrations/0003_delete_question.py

spacemc2/ProjectFinalDjango

0754120fd1ac742582acafdd77ed66026ab90104

[ "Apache-2.0" ]

null

# Generated by Django 3.1.3 on 2022-01-07 02:59 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('onlinecourse', '0002_question'), ] operations = [ migrations.DeleteModel( name='Question', ), ]

17.176471

47

0.599315

5cf1d1c6ed77517db3b301c5517161288f9c0892

3,621

py

Python

fyplot/dict_plot.py

intelligent-soft-robots/fyplot

642ba620105ee2a177cd82d147561d235bf30ad6

[ "BSD-3-Clause" ]

null

fyplot/dict_plot.py

intelligent-soft-robots/fyplot

642ba620105ee2a177cd82d147561d235bf30ad6

[ "BSD-3-Clause" ]

null

fyplot/dict_plot.py

intelligent-soft-robots/fyplot

642ba620105ee2a177cd82d147561d235bf30ad6

[ "BSD-3-Clause" ]

null

# Copyright(c) 2020 Max Planck Gesellschaft # Author: Vincent Berenz from pyqtgraph.Qt import QtGui, QtCore, QtWidgets import pyqtgraph as pg from collections import deque import threading,math,time pg.setConfigOptions(antialias=True) class Config: def __init__(self): self.channels = [] self.slots = {} self.slots_colors = {} self.data_size = 200 self.title = "untitled" self.windows_size = [800,800] self.start = False self.limits = {} _APPLICATION = QtGui.QApplication([]) _WIN = pg.GraphicsWindow(title="pasta") _WIN.resize(1,1) _WIN.setWindowTitle("") _FIRST_ITERATION = True _CHANNELS = {} _PLOTS = {} # key : channel , value : {slot:(plot,curve)} _CONFIG = None class _channel_data: def __init__(self,slots,data_size,y_range=[-math.pi,+math.pi]): self.data = {slot:deque([0 for _ in range(data_size)],data_size) for slot in slots } for slot in slots: self.data[slot][-2] = y_range[0] self.data[slot][-1] = y_range[1] self.lock = threading.Lock() def update(self,values): with self.lock: for slot,value in values.items(): self.data[slot].append(value) def get(self): with self.lock: r = {} for slot,queue in self.data.items(): r[slot] = [v for v in queue] return r def _init(): timer.timeout.connect(_update_plot) global _CONFIG,_CHANNELS,_PLOTS,_WIN _WIN.resize(_CONFIG.windows_size[0],_CONFIG.windows_size[1]) _WIN.setWindowTitle(_CONFIG.title) if isinstance(_CONFIG.channels[0],str): _CONFIG.channels = [_CONFIG.channels] all_channels = [] for channel_set in _CONFIG.channels: all_channels.extend(channel_set) def _get_y_range(channel): try : return _CONFIG.limits[channel] except : return None _CHANNELS = {channel:_channel_data(_CONFIG.slots[channel], _CONFIG.data_size, y_range=_get_y_range(channel)) for channel in all_channels} for channel_set in _CONFIG.channels: for channel in channel_set: p = _WIN.addPlot(title=channel) data = _CHANNELS[channel].get() curves = {} for slot in _CONFIG.slots[channel]: curve = p.plot(pen=_CONFIG.slots_colors[slot],name=slot) curves[slot]=(p,curve) _PLOTS[channel]=curves _WIN.nextRow() _CONFIG.start = True def set_data(data): global _CHANNELS for channel,d in data.items(): _CHANNELS[channel].update(d) def _update_plot(): global _CHANNELS,_FIRST_ITERATION,_PLOTS,win if not _CONFIG.start : return for channel in _CHANNELS.keys(): curves = _PLOTS[channel] data = _CHANNELS[channel].get() for slot in _CONFIG.slots[channel]: plot,curve = curves[slot] d = data[slot] curve.setData(d) if _FIRST_ITERATION : plot.enableAutoRange('xy', False) _FIRST_ITERATION = False timer = QtCore.QTimer() timer.start(50) def _start(target_function): _init() t = threading.Thread(target=target_function) t.start() def start_plotting(config,target_function): global _CONFIG _CONFIG = config _start(target_function) import sys if (sys.flags.interactive != 1) or not hasattr(QtCore, 'PYQT_VERSION'): QtGui.QApplication.instance().exec_()

25.864286

92

0.605634

a24937bab183e54df40e931f2f4621ec22ea83c4

18,011

py

Python

src/test/py/bazel/test_wrapper_test.py

sevki/bazel

b18915752a69fbbc6ed94e1710198167593565fc

[ "Apache-2.0" ]

null

src/test/py/bazel/test_wrapper_test.py

sevki/bazel

b18915752a69fbbc6ed94e1710198167593565fc

[ "Apache-2.0" ]

null

src/test/py/bazel/test_wrapper_test.py

sevki/bazel

b18915752a69fbbc6ed94e1710198167593565fc

[ "Apache-2.0" ]

null

# Copyright 2018 The Bazel Authors. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import unittest import zipfile from src.test.py.bazel import test_base class TestWrapperTest(test_base.TestBase): @staticmethod def _ReadFile(path): # Read the runfiles manifest. contents = [] with open(path, 'rt') as f: contents = [line.strip() for line in f.readlines()] return contents def _FailWithOutput(self, output): self.fail('FAIL:\n | %s\n---' % '\n | '.join(output)) def _CreateMockWorkspace(self): self.ScratchFile('WORKSPACE') # All test targets are called <something>.bat, for the benefit of Windows. # This makes test execution faster on Windows for the following reason: # # When building a sh_test rule, the main output's name is the same as the # rule. On Unixes, this output is a symlink to the main script (the first # entry in `srcs`), on Windows it's a copy of the file. In fact the main # "script" does not have to be a script, it may be any executable file. # # On Unixes anything with the +x permission can be executed; the file's # shebang line specifies the interpreter. On Windows, there's no such # mechanism; Bazel runs the main script (which is typically a ".sh" file) # through Bash. However, if the main file is a native executable, it's # faster to run it directly than through Bash (plus it removes the need for # Bash). # # Therefore on Windows, if the main script is a native executable (such as a # ".bat" file) and has the same extension as the main file, Bazel (in case # of sh_test) makes a copy of the file and runs it directly, rather than # through Bash. self.ScratchFile('foo/BUILD', [ 'sh_test(', ' name = "passing_test.bat",', ' srcs = ["passing.bat"],', ')', 'sh_test(', ' name = "failing_test.bat",', ' srcs = ["failing.bat"],', ')', 'sh_test(', ' name = "printing_test.bat",', ' srcs = ["printing.bat"],', ')', 'sh_test(', ' name = "runfiles_test.bat",', ' srcs = ["runfiles.bat"],', ' data = ["passing.bat"],', ')', 'sh_test(', ' name = "sharded_test.bat",', ' srcs = ["sharded.bat"],', ' shard_count = 2,', ')', 'sh_test(', ' name = "unexported_test.bat",', ' srcs = ["unexported.bat"],', ')', 'sh_test(', ' name = "testargs_test.bat",', ' srcs = ["testargs.bat"],', ' args = ["foo", "a b", "", "bar"],', ')', 'py_test(', ' name = "undecl_test",', ' srcs = ["undecl_test.py"],', ' data = ["dummy.ico", "dummy.dat"],', ' deps = ["@bazel_tools//tools/python/runfiles"],', ')', 'py_test(', ' name = "annot_test",', ' srcs = ["annot_test.py"],', ')', ]) self.ScratchFile('foo/passing.bat', ['@exit /B 0'], executable=True) self.ScratchFile('foo/failing.bat', ['@exit /B 1'], executable=True) self.ScratchFile( 'foo/printing.bat', [ '@echo lorem ipsum', '@echo HOME=%HOME%', '@echo TEST_SRCDIR=%TEST_SRCDIR%', '@echo TEST_TMPDIR=%TEST_TMPDIR%', '@echo USER=%USER%', ], executable=True) self.ScratchFile( 'foo/runfiles.bat', [ '@echo MF=%RUNFILES_MANIFEST_FILE%', '@echo ONLY=%RUNFILES_MANIFEST_ONLY%', '@echo DIR=%RUNFILES_DIR%', ], executable=True) self.ScratchFile( 'foo/sharded.bat', [ '@echo STATUS=%TEST_SHARD_STATUS_FILE%', '@echo INDEX=%TEST_SHARD_INDEX% TOTAL=%TEST_TOTAL_SHARDS%', ], executable=True) self.ScratchFile( 'foo/unexported.bat', [ '@echo GOOD=%HOME%', '@echo BAD=%TEST_UNDECLARED_OUTPUTS_MANIFEST%', ], executable=True) self.ScratchFile( 'foo/testargs.bat', [ '@echo arg=(%~nx0)', # basename of $0 '@echo arg=(%1)', '@echo arg=(%2)', '@echo arg=(%3)', '@echo arg=(%4)', '@echo arg=(%5)', '@echo arg=(%6)', '@echo arg=(%7)', '@echo arg=(%8)', '@echo arg=(%9)', ], executable=True) # A single white pixel as an ".ico" file. /usr/bin/file should identify this # as "image/x-icon". # The MIME type lookup logic of the test wrapper only looks at file names, # but the test-setup.sh calls /usr/bin/file which inspects file contents, so # we need a valid ".ico" file. ico_file = bytearray([ 0x00, 0x00, 0x01, 0x00, 0x01, 0x00, 0x01, 0x01, 0x00, 0x00, 0x01, 0x00, 0x18, 0x00, 0x30, 0x00, 0x00, 0x00, 0x16, 0x00, 0x00, 0x00, 0x28, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x18, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xff, 0xff, 0xff, 0x00, 0x00, 0x00, 0x00, 0x00 ]) # 16 bytes of random data. /usr/bin/file should identify this as # "application/octet-stream". # The MIME type lookup logic of the test wrapper only looks at file names, # but the test-setup.sh calls /usr/bin/file which inspects file contents, so # we need a valid ".ico" file. dat_file = bytearray([ 0x40, 0x5a, 0x2e, 0x7e, 0x53, 0x86, 0x98, 0x0e, 0x12, 0xc4, 0x92, 0x38, 0x27, 0xcd, 0x09, 0xf9 ]) ico_file_path = self.ScratchFile('foo/dummy.ico').replace('/', '\\') dat_file_path = self.ScratchFile('foo/dummy.dat').replace('/', '\\') with open(ico_file_path, 'wb') as f: f.write(ico_file) with open(dat_file_path, 'wb') as f: f.write(dat_file) self.ScratchFile( 'foo/undecl_test.py', [ 'from bazel_tools.tools.python.runfiles import runfiles', 'import os', 'import shutil', '', 'root = os.environ.get("TEST_UNDECLARED_OUTPUTS_DIR")', 'os.mkdir(os.path.join(root, "out1"))', 'os.mkdir(os.path.join(root, "out2"))', 'os.makedirs(os.path.join(root, "empty/sub"))', 'r = runfiles.Create()', 'shutil.copyfile(r.Rlocation("__main__/foo/dummy.ico"),', ' os.path.join(root, "out1", "data1.ico"))', 'shutil.copyfile(r.Rlocation("__main__/foo/dummy.dat"),', ' os.path.join(root, "out2", "data2.dat"))', ], executable=True) self.ScratchFile( 'foo/annot_test.py', [ 'import os', 'root = os.environ.get("TEST_UNDECLARED_OUTPUTS_ANNOTATIONS_DIR")', 'dir1 = os.path.join(root, "out1")', 'dir2 = os.path.join(root, "out2.part")', 'os.mkdir(dir1)', 'os.mkdir(dir2)', 'with open(os.path.join(root, "a.part"), "wt") as f:', ' f.write("Hello a")', 'with open(os.path.join(root, "b.txt"), "wt") as f:', ' f.write("Hello b")', 'with open(os.path.join(root, "c.part"), "wt") as f:', ' f.write("Hello c")', 'with open(os.path.join(dir1, "d.part"), "wt") as f:', ' f.write("Hello d")', 'with open(os.path.join(dir2, "e.part"), "wt") as f:', ' f.write("Hello e")', ], executable=True) def _AssertPassingTest(self, flag): exit_code, _, stderr = self.RunBazel([ 'test', '//foo:passing_test.bat', '-t-', flag, ]) self.AssertExitCode(exit_code, 0, stderr) def _AssertFailingTest(self, flag): exit_code, _, stderr = self.RunBazel([ 'test', '//foo:failing_test.bat', '-t-', flag, ]) self.AssertExitCode(exit_code, 3, stderr) def _AssertPrintingTest(self, flag): exit_code, stdout, stderr = self.RunBazel([ 'test', '//foo:printing_test.bat', '-t-', '--test_output=all', flag, ]) self.AssertExitCode(exit_code, 0, stderr) lorem = False for line in stderr + stdout: if line.startswith('lorem ipsum'): lorem = True elif line.startswith('HOME='): home = line[len('HOME='):] elif line.startswith('TEST_SRCDIR='): srcdir = line[len('TEST_SRCDIR='):] elif line.startswith('TEST_TMPDIR='): tmpdir = line[len('TEST_TMPDIR='):] elif line.startswith('USER='): user = line[len('USER='):] if not lorem: self._FailWithOutput(stderr + stdout) if not home: self._FailWithOutput(stderr + stdout) if not os.path.isabs(home): self._FailWithOutput(stderr + stdout) if not os.path.isdir(srcdir): self._FailWithOutput(stderr + stdout) if not os.path.isfile(os.path.join(srcdir, 'MANIFEST')): self._FailWithOutput(stderr + stdout) if not os.path.isabs(srcdir): self._FailWithOutput(stderr + stdout) if not os.path.isdir(tmpdir): self._FailWithOutput(stderr + stdout) if not os.path.isabs(tmpdir): self._FailWithOutput(stderr + stdout) if not user: self._FailWithOutput(stderr + stdout) def _AssertRunfiles(self, flag): exit_code, stdout, stderr = self.RunBazel([ 'test', '//foo:runfiles_test.bat', '-t-', '--test_output=all', # Ensure Bazel does not create a runfiles tree. '--enable_runfiles=no', flag, ]) self.AssertExitCode(exit_code, 0, stderr) mf = mf_only = rf_dir = None for line in stderr + stdout: if line.startswith('MF='): mf = line[len('MF='):] elif line.startswith('ONLY='): mf_only = line[len('ONLY='):] elif line.startswith('DIR='): rf_dir = line[len('DIR='):] if mf_only != '1': self._FailWithOutput(stderr + stdout) if not os.path.isfile(mf): self._FailWithOutput(stderr + stdout) mf_contents = TestWrapperTest._ReadFile(mf) # Assert that the data dependency is listed in the runfiles manifest. if not any( line.split(' ', 1)[0].endswith('foo/passing.bat') for line in mf_contents): self._FailWithOutput(mf_contents) if not os.path.isdir(rf_dir): self._FailWithOutput(stderr + stdout) def _AssertShardedTest(self, flag): exit_code, stdout, stderr = self.RunBazel([ 'test', '//foo:sharded_test.bat', '-t-', '--test_output=all', flag, ]) self.AssertExitCode(exit_code, 0, stderr) status = None index_lines = [] for line in stderr + stdout: if line.startswith('STATUS='): status = line[len('STATUS='):] elif line.startswith('INDEX='): index_lines.append(line) if not status: self._FailWithOutput(stderr + stdout) # Test test-setup.sh / test wrapper only ensure that the directory of the # shard status file exist, not that the file itself does too. if not os.path.isdir(os.path.dirname(status)): self._FailWithOutput(stderr + stdout) if sorted(index_lines) != ['INDEX=0 TOTAL=2', 'INDEX=1 TOTAL=2']: self._FailWithOutput(stderr + stdout) def _AssertUnexportsEnvvars(self, flag): exit_code, stdout, stderr = self.RunBazel([ 'test', '//foo:unexported_test.bat', '-t-', '--test_output=all', flag, ]) self.AssertExitCode(exit_code, 0, stderr) good = bad = None for line in stderr + stdout: if line.startswith('GOOD='): good = line[len('GOOD='):] elif line.startswith('BAD='): bad = line[len('BAD='):] if not good or bad: self._FailWithOutput(stderr + stdout) def _AssertTestArgs(self, flag, expected): exit_code, bazel_bin, stderr = self.RunBazel(['info', 'bazel-bin']) self.AssertExitCode(exit_code, 0, stderr) bazel_bin = bazel_bin[0] exit_code, stdout, stderr = self.RunBazel([ 'test', '//foo:testargs_test.bat', '-t-', '--test_output=all', '--test_arg=baz', '--test_arg="x y"', '--test_arg=""', '--test_arg=qux', flag, ]) self.AssertExitCode(exit_code, 0, stderr) actual = [] for line in stderr + stdout: if line.startswith('arg='): actual.append(str(line[len('arg='):])) self.assertListEqual(expected, actual) def _AssertUndeclaredOutputs(self, flag): exit_code, bazel_testlogs, stderr = self.RunBazel( ['info', 'bazel-testlogs']) self.AssertExitCode(exit_code, 0, stderr) bazel_testlogs = bazel_testlogs[0] exit_code, _, stderr = self.RunBazel([ 'test', '//foo:undecl_test', '-t-', '--test_output=errors', flag, ]) self.AssertExitCode(exit_code, 0, stderr) undecl_zip = os.path.join(bazel_testlogs, 'foo', 'undecl_test', 'test.outputs', 'outputs.zip') self.assertTrue(os.path.exists(undecl_zip)) zip_content = {} with zipfile.ZipFile(undecl_zip, 'r') as z: zip_content = {f: z.getinfo(f).file_size for f in z.namelist()} self.assertDictEqual( zip_content, { 'out1/': 0, 'out2/': 0, 'empty/': 0, 'empty/sub/': 0, 'out1/data1.ico': 70, 'out2/data2.dat': 16 }) undecl_mf = os.path.join(bazel_testlogs, 'foo', 'undecl_test', 'test.outputs_manifest', 'MANIFEST') self.assertTrue(os.path.exists(undecl_mf)) mf_content = [] with open(undecl_mf, 'rt') as f: mf_content = [line.strip() for line in f.readlines()] # Using an ".ico" file as example, because as of 2018-11-09 Bazel's CI # machines run Windows Server 2016 core which recognizes fewer MIME types # than desktop Windows versions, and one of the recognized types is ".ico" # files. self.assertListEqual(mf_content, [ 'out1/data1.ico\t70\timage/x-icon', 'out2/data2.dat\t16\tapplication/octet-stream' ]) def _AssertUndeclaredOutputsAnnotations(self, flag): exit_code, bazel_testlogs, stderr = self.RunBazel( ['info', 'bazel-testlogs']) self.AssertExitCode(exit_code, 0, stderr) bazel_testlogs = bazel_testlogs[0] exit_code, _, stderr = self.RunBazel([ 'test', '//foo:annot_test', '-t-', '--test_output=errors', flag, ]) self.AssertExitCode(exit_code, 0, stderr) undecl_annot = os.path.join(bazel_testlogs, 'foo', 'annot_test', 'test.outputs_manifest', 'ANNOTATIONS') self.assertTrue(os.path.exists(undecl_annot)) annot_content = [] with open(undecl_annot, 'rt') as f: annot_content = [line.strip() for line in f.readlines()] self.assertListEqual(annot_content, ['Hello aHello c']) def testTestExecutionWithTestSetupSh(self): self._CreateMockWorkspace() flag = '--noincompatible_windows_native_test_wrapper' self._AssertPassingTest(flag) self._AssertFailingTest(flag) self._AssertPrintingTest(flag) self._AssertRunfiles(flag) self._AssertShardedTest(flag) self._AssertUnexportsEnvvars(flag) self._AssertTestArgs( flag, [ '(testargs_test.bat)', '(foo)', '(a)', '(b)', '(bar)', # Note: debugging shows that test-setup.sh receives more-or-less # good arguments (let's ignore issues #6276 and #6277 for now), but # mangles the last few. # I (laszlocsomor@) don't know the reason (as of 2018-10-01) but # since I'm planning to phase out test-setup.sh on Windows in favor # of the native test wrapper, I don't intend to debug this further. # The test is here merely to guard against unwanted future change of # behavior. '(baz)', '("\\"x)', '(y\\"")', '("\\\\\\")', '(qux")' ]) self._AssertUndeclaredOutputs(flag) self._AssertUndeclaredOutputsAnnotations(flag) def testTestExecutionWithTestWrapperExe(self): self._CreateMockWorkspace() flag = '--incompatible_windows_native_test_wrapper' self._AssertPassingTest(flag) self._AssertFailingTest(flag) self._AssertPrintingTest(flag) self._AssertRunfiles(flag) self._AssertShardedTest(flag) self._AssertUnexportsEnvvars(flag) self._AssertTestArgs( flag, [ '(testargs_test.bat)', '(foo)', # TODO(laszlocsomor): assert that "a b" is passed as one argument, # not two, after https://github.com/bazelbuild/bazel/issues/6277 # is fixed. '(a)', '(b)', # TODO(laszlocsomor): assert that the empty string argument is # passed, after https://github.com/bazelbuild/bazel/issues/6276 # is fixed. '(bar)', '(baz)', '("x y")', '("")', '(qux)', '()' ]) self._AssertUndeclaredOutputs(flag) self._AssertUndeclaredOutputsAnnotations(flag) if __name__ == '__main__': unittest.main()

34.972816

80

0.574149

ef1f2e30baf75627f1814f8e6ce17027a9bb0977

187,767

py

Python

Mini-Project-III/Script/Neural Networks Mini Project-II.py

cankocagil/Neural-Networks

5cb51ccb7dcc8afd30c5745111a87498ec38e006

[ "MIT" ]

null

Mini-Project-III/Script/Neural Networks Mini Project-II.py

cankocagil/Neural-Networks

5cb51ccb7dcc8afd30c5745111a87498ec38e006

[ "MIT" ]

null

Mini-Project-III/Script/Neural Networks Mini Project-II.py

cankocagil/Neural-Networks

5cb51ccb7dcc8afd30c5745111a87498ec38e006

[ "MIT" ]

null

# Necessary imports : import numpy as np import matplotlib.pyplot as plt import h5py # %% # Necessary imports : #import numpy as np #import matplotlib.pyplot as plt #import h5py import math import pandas as pd import seaborn as sns import sys question = input('Please enter question number 1/3 :') def can_kocagil_21602218_hw3(question): if question == '1' : # To add a new cell, type '# %%' # To add a new markdown cell, type '# %% [markdown]' # %% # %% def get_data(path) -> dict : """ Given the path of the dataset, return training and testing images with respective labels. """ with h5py.File(path,'r') as F: # Names variable contains the names of training and testing file names = list(F.keys()) data = np.array(F[names[0]][()]) invXForm = np.array(F[names[1]][()]) xForm = np.array(F[names[2]][()]) return {'data' : data, 'invXForm': invXForm, 'xForm' : xForm} path = 'assign3_data1.h5' data_h5 = get_data(path) # %% data = data_h5['data'] invXForm = data_h5['invXForm'] xForm = data_h5['xForm'] # %% print(f'The data has a shape: {data.shape}') # %% data = np.swapaxes(data,1,3) # %% print(f'The data has a shape: {data.shape}') # %% class ImagePreprocessing: """ _____Image preprocessor_____ Functions : --- ToGray(data) -Takes an input image then converts to gray scale by Luminosity Model --- MeanRemoval(data) -Extracking the mean of each image themselves --- ClipStd(data) - Clipping the input image within given condition --- Normalize(data,min_scale,max_scale) - Normalizing input image to [min_scale,max_scale] --- Flatten(data) - Flattening input image """ def __init__(self): pass def ToGray(self,data): """ Given the input image converting gray scale according to luminosity model """ R_linear = 0.2126 G_linear = 0.7152 B_linear = 0.0722 gray_data = (data[:,:,:,0] * R_linear) + (data[:,:,:,1] * G_linear) + (data[:,:,:,2] * B_linear) return gray_data def MeanRemoval(self,data): """ Given the input image, substracking the mean of pixel intensity of each image """ axis = (1,2) mean_pixel = np.mean(data,axis = axis) num_samples = data.shape[0] # Substracking means of each image seperately : for i in range(num_samples): data[i] -= mean_pixel[i] return data def ClipStd(self,data,std_scaler): """ Given the data and range of standart deviation scaler, return clipped data """ std_pixel = np.std(data) min_cond = - std_scaler * std_pixel max_cond = std_scaler * std_pixel clipped_data = np.clip(data,min_cond,max_cond) return clipped_data def Normalize(self,data,min_scale,max_scale): """ Given the data, normalize to given interval [min_val,max_val] """ min = data.max() max = data.min() # First normalize in [0,1] norm_data = (data - min) / (max-min) # Normalizing in [min_scale,max_scale] range = max_scale - min_scale interval_scaled_data = (norm_data * range) + min_scale return interval_scaled_data def Flatten(self,data): """ Given the input image data returning flattened version of the data """ num_samples = data.shape[0] flatten = data.reshape(num_samples,-1) return flatten # %% # Defining preprocessor : preprocessor = ImagePreprocessing() # %% # Converting gray scale : gray_data = preprocessor.ToGray(data = data) # %% # Mean removing : mean_removed_data = preprocessor.MeanRemoval(data = gray_data) # %% # Standart deviation clipping : clipped_data = preprocessor.ClipStd(data = mean_removed_data,std_scaler = 3) # %% # Normalized data data_processed = preprocessor.Normalize(data = clipped_data, min_scale = 0.1, max_scale = 0.9) # %% print(f' Maximum val of data : {data_processed.max()}') print(f' Minimum val of data : {data_processed.min()}') # %% def plot_patches(data,num_patches, cmap = 'viridis'): np.random.seed(15) num_samples = data.shape[0] random_indexes = np.random.randint(num_samples,size = num_patches) plt.figure(figsize = (18,16)) for i in range(num_patches): plt.subplot(20,20,i+1) plt.imshow(data[random_indexes[i]],cmap = cmap) plt.axis('off') plt.show() # %% plot_patches(preprocessor.Normalize(data = data, min_scale = 0, max_scale = 1),num_patches = 200) # %% #plot_patches(data,num_patches = 200) # %% plot_patches(data_processed,num_patches = 200, cmap = 'gray') # %% class Autoencoder: """ ____Autoencoder____ Functions : --- __init__(input_size,hidden_size) - Building overall architecture of the model --- InitParams(input_size,hidden_size) - Initializing configurable parameters --- aeCost(W,data,params) - Calculating cost and it's derivatives --- Forward(X) - Forward pass --- Backward(X) - Calculation of gradients w.r.t. loss function --- KL_divergence() - Calculate KL divergence and it's gradients --- TykhonowRegulator(X,grad) - Computing Tykhonow regularization term and it's gradient --- Predict(X) - To make predictions --- Sigmoid(X, grad) - Compute sigmoidal activation and it's gradients --- History() - To keep track history of the model """ def __init__(self,input_size,hidden_size,lambd): """ Construction of the architecture of the autoencoder """ np.random.seed(1500) self.lambd = lambd self.beta = 1e-1 self.rho = 5e-2 self.learning_rate = 9e-1 self.params = {'L_in' : input_size, 'L_hidden' : hidden_size, 'Lambda' : self.lambd, 'Beta' : self.beta, 'Rho' : self.rho} self.W_e = self.InitParams(input_size,hidden_size) self.loss = [] def InitParams(self,input_size,hidden_size): """ Given the size of the input node and hidden node, initialize the weights drawn from uniform distribution ~ Uniform[- sqrt(6/(L_pre + L_post)) , sqrt(6/(L_pre + L_post))] """ self.input_size = input_size self.hidden_size = hidden_size self.output_size = input_size W1_high = self.w_o(input_size,hidden_size) W1_low = - W1_high W1_size = (input_size,hidden_size) self.W1 = np.random.uniform(W1_low,W1_high,size = W1_size) B1_size = (1,hidden_size) self.B1 = np.random.uniform(W1_low,W1_high,size = B1_size) W2_high = self.w_o(hidden_size,self.output_size) W2_low = - W2_high W2_size = (hidden_size,self.output_size) self.W2 = np.random.uniform(W2_low,W2_high,size = W2_size) B2_size = (1,self.output_size) self.B2 = np.random.uniform(W1_low,W1_high,size = B2_size) return {'W1' : self.W1, 'W2' : self.W2, 'B1' : self.B1, 'B2' : self.B2} def w_o(self,L_pre,L_post): return np.sqrt(6/(L_pre + L_post)) def sigmoid(self,X, grad = True): """ Computing sigmoid and it's gradient w.r.t. it's input """ sig = 1/(1 + np.exp(-X)) return sig * (1-sig) if grad else sig def forward(self,X): """ Forward propagation """ W1 = self.W_e['W1'] W2 = self.W_e['W2'] B1 = self.W_e['B1'] B2 = self.W_e['B2'] Z1 = np.dot(X,W1) + B1 A1 = self.sigmoid(Z1,grad = False) Z2 = np.dot(A1,W2) + B2 A2 = self.sigmoid(Z2,grad = False) return {"Z1": Z1,"A1": A1, "Z2": Z2,"A2": A2} def total_loss(self,outs,label): W1 = self.W_e['W1'] W2 = self.W_e['W2'] Lambda = self.params['Lambda'] beta = self.params['Beta'] rho = self.params['Rho'] J_mse = self.MSE(outs['A2'],label, grad = False) J_tykhonow = self.TykhonowRegularization(W1 = W1, W2 = W2,lambd = Lambda, grad = False) J_KL = self.KL_divergence(rho = rho,expected = np.mean(outs['A1']), beta = beta, grad = False) return J_mse + J_tykhonow + J_KL def MSE(self,pred,label, grad = True): """ Calculating Mean Sqaured Error and it's gradient w.r.t. output """ return 1/2 * (pred - label) if grad else 1/2 * np.sum((pred - label)**2)/pred.shape[0] def aeCost(self,data): outs = self.forward(data) loss = self.total_loss(outs,data) grads = self.backward(outs,data) return {'J' : loss, 'J_grad' : grads} def KL_divergence(self,rho,beta,expected,grad = True): """ Computing KL-divergence and it's gradients, note that gradients is only for W1 """ return np.tile(beta * (-(rho/expected) + (1-rho)/(1-expected) ), reps = (10240,1)) if grad else beta * (np.sum((rho * np.log(rho/expected)) + ((1-rho)*np.log((1-rho)/(1-expected))))) def TykhonowRegularization(self,W1,W2,lambd,grad = True): """ L2 based regularization computing and it's gradients """ return {'dW1': lambd * W1, 'dW2': lambd * W2} if grad else (lambd/2) * (np.sum(W1**2) + np.sum(W2**2)) def backward(self,outs,data): """ Given the forward pass outputs, input and their labels, returning gradients w.r.t. loss functions """ m = data.shape[0] Lambda = self.params['Lambda'] beta = self.params['Beta'] rho = self.params['Rho'] W1 = self.W_e['W1'] W2 = self.W_e['W2'] B1 = self.W_e['B1'] B2 = self.W_e['B2'] Z1 = outs['Z1'] A1 = outs['A1'] Z2 = outs['Z2'] A2 = outs['A2'] L2_grad = self.TykhonowRegularization(W1,W2,lambd = Lambda , grad = True) KL_grad_W1 = self.KL_divergence(rho,beta,expected = np.mean(A1),grad = True) dZ2 = self.MSE(A2,data, grad = True) * self.sigmoid(Z2, grad = True) dW2 = (1/m) * (np.dot(A1.T,dZ2) + L2_grad['dW2']) dB2 = (1/m) * (np.sum(dZ2, axis=0, keepdims=True)) dZ1 = (np.dot(dZ2,W2.T) + KL_grad_W1) * self.sigmoid(Z1,grad = True) dW1 = (1/m) * (np.dot(data.T,dZ1) + L2_grad['dW1']) dB1 = (1/m) * (np.sum(dZ1, axis=0, keepdims=True)) assert (dW1.shape == W1.shape and dW2.shape == W2.shape) return {"dW1": dW1, "dW2": dW2, "dB1": dB1, "dB2": dB2} def fit(self,data,epochs = 5000,verbose = True): """ Given the traning dataset,their labels and number of epochs fitting the model, and measure the performance by validating training dataset. """ for epoch in range(epochs): loss_and_grads = self.aeCost(data) self.step(grads = loss_and_grads['J_grad']) if verbose: print(f"[{epoch}/{epochs}] ----------> Loss :{loss_and_grads['J']}") self.loss.append(loss_and_grads['J']) def step(self,grads): """ Updating configurable parameters according to full-batch stochastic gradient update rule """ self.W_e['W1'] += -self.learning_rate * grads['dW1'] self.W_e['W2'] += -self.learning_rate * grads['dW2'] self.W_e['B1'] += -self.learning_rate * grads['dB1'] self.W_e['B2'] += -self.learning_rate * grads['dB2'] self.learning_rate *= 0.9999 def evaluate(self): plt.plot(self.loss, color = 'orange') plt.xlabel(' # of Epochs') plt.ylabel('Loss') plt.title('Training Loss versus Epochs') plt.legend([f'Loss : {self.loss[-1]}']) def display_weights(self): """ Display weights as a image for feature representation """ W1 = self.W_e['W1'] num_disp = W1.shape[1] fig = plt.figure(figsize = (9,8)) for i in range(num_disp): plt.subplot(8,8,i+1) plt.imshow(W1.T[i].reshape(16,16),cmap = 'gray') plt.axis('off') fig.suptitle('Hidden Layer Feature Representation') plt.show() def display_outputs(self,output,data,num = 4): """ Displaying outputs, please give only sqaured values, i.e., 1,4,16,... """ random_indexes = np.random.randint(output.shape[0],size = num) plt.figure(figsize=(12, 4)) for i in range(len(random_indexes)): ax = plt.subplot(2,5,i+1) plt.imshow(output[random_indexes[i]].reshape(16,16),cmap = 'gray') ax.get_xaxis().set_visible(False) ax.get_yaxis().set_visible(False) plt.title("Reconstructed Image") #plt.axis('off') ax = plt.subplot(2, 5, i + 1 + 5) plt.imshow(data[random_indexes[i]].reshape(16,16),cmap = 'gray') plt.title("Original Image") plt.gray() ax.get_xaxis().set_visible(False) ax.get_yaxis().set_visible(False) plt.show() def parameters(self): """ Returns configurable parameters """ return self.W_e def history(self): return {'Loss' : self.loss} # %% class Solver: """ Given as input, A Solver encapsulates all the logic necessary for training then implement gradients solver to minimize the cost.The Solver performs stochastic gradient descent. """ def __init__(self, model,data): self.model = model self.data = data def train(self,epochs = 5000,verbose = False): """ Optimization of the model by minimizing cost by solving gradients """ self.model.fit(self.data,epochs,verbose) def parameters(self): """ Returning configurable parameters of the network """ return self.model.parameters() # %% data_feed = preprocessor.Flatten(data_processed) input_size = data_feed.shape[1] hidden_size = 64 autoencoder = Autoencoder(input_size = input_size, hidden_size = hidden_size,lambd = 5e-4) # %% solver = Solver(model = autoencoder, data = data_feed) solver.train(verbose = True) # %% net_params = solver.parameters() net_history = autoencoder.history() # %% autoencoder.evaluate() # %% autoencoder.display_weights() # %% preds = autoencoder.forward(data_feed) autoencoder.display_outputs(preds['A2'],data_feed) # %% hidden_size_1 = 10 lambd_1 = 0 autoencoder_1 = Autoencoder(input_size = input_size, hidden_size = hidden_size_1, lambd = lambd_1) solver_1 = Solver(model = autoencoder_1, data = data_feed) solver_1.train() autoencoder_1.evaluate() autoencoder_1.display_weights() preds_1 = autoencoder_1.forward(data_feed) autoencoder_1.display_outputs(preds_1['A2'],data_feed) # %% hidden_size_2 = 10 lambd_2 = 1e-3 autoencoder_2 = Autoencoder(input_size = input_size, hidden_size = hidden_size_2, lambd = lambd_2) solver_2 = Solver(model = autoencoder_2, data = data_feed) solver_2.train() autoencoder_2.evaluate() autoencoder_2.display_weights() preds_2 = autoencoder_2.forward(data_feed) autoencoder_2.display_outputs(preds_2['A2'],data_feed) # %% hidden_size_3 = 10 lambd_3 = 1e-5 autoencoder_3 = Autoencoder(input_size = input_size, hidden_size = hidden_size_3, lambd = lambd_3) solver_3 = Solver(model = autoencoder_3, data = data_feed) solver_3.train() autoencoder_3.evaluate() autoencoder_3.display_weights() preds_3 = autoencoder_3.forward(data_feed) autoencoder_3.display_outputs(preds_3['A2'],data_feed) # %% hidden_size_4 = 50 lambd_4 = 0 autoencoder_4 = Autoencoder(input_size = input_size, hidden_size = hidden_size_4, lambd = lambd_4) solver_4 = Solver(model = autoencoder_4, data = data_feed) solver_4.train() autoencoder_4.evaluate() autoencoder_4.display_weights() preds_4 = autoencoder_4.forward(data_feed) autoencoder_4.display_outputs(preds_4['A2'],data_feed) # %% hidden_size_5 = 50 lambd_5 = 1e-3 autoencoder_5 = Autoencoder(input_size = input_size, hidden_size = hidden_size_5, lambd = lambd_5) solver_5 = Solver(model = autoencoder_5, data = data_feed) solver_5.train() autoencoder_5.evaluate() autoencoder_5.display_weights() preds_5 = autoencoder_5.forward(data_feed) autoencoder_5.display_outputs(preds_5['A2'],data_feed) # %% autoencoder_5.display_outputs(preds_5['A2'],data_feed) # %% hidden_size_6 = 50 lambd_6 = 1e-5 autoencoder_6 = Autoencoder(input_size = input_size, hidden_size = hidden_size_6, lambd = lambd_6) solver_6 = Solver(model = autoencoder_6, data = data_feed) solver_6.train() autoencoder_6.evaluate() autoencoder_6.display_weights() preds_6 = autoencoder_6.forward(data_feed) # %% autoencoder_6.display_outputs(preds_6['A2'],data_feed) # %% hidden_size_7 = 100 lambd_7 = 0 autoencoder_7 = Autoencoder(input_size = input_size, hidden_size = hidden_size_7, lambd = lambd_7) solver_7 = Solver(model = autoencoder_7, data = data_feed) solver_7.train() autoencoder_7.evaluate() #autoencoder_7.display_weights() preds_7 = autoencoder_7.forward(data_feed) autoencoder_7.display_outputs(preds_7['A2'],data_feed) # %% #autoencoder_7.display_weights() W1 = autoencoder_7.W_e['W1'] num_disp = W1.shape[1] fig = plt.figure(figsize = (9,8)) for i in range(num_disp): plt.subplot(10,10,i+1) plt.imshow(W1.T[i].reshape(16,16),cmap = 'gray') plt.axis('off') fig.suptitle('Hidden Layer Feature Representation') plt.show() preds_7 = autoencoder_7.forward(data_feed) autoencoder_7.display_outputs(preds_7['A2'],data_feed) # %% autoencoder_7.display_outputs(preds_7['A2'],data_feed) # %% hidden_size_8 = 100 lambd_8 = 1e-3 autoencoder_8 = Autoencoder(input_size = input_size, hidden_size = hidden_size_8, lambd = lambd_8) solver_8 = Solver(model = autoencoder_8, data = data_feed) solver_8.train() autoencoder_8.evaluate() W1 = autoencoder_8.W_e['W1'] num_disp = W1.shape[1] fig = plt.figure(figsize = (9,8)) for i in range(num_disp): plt.subplot(10,10,i+1) plt.imshow(W1.T[i].reshape(16,16),cmap = 'gray') plt.axis('off') fig.suptitle('Hidden Layer Feature Representation') plt.show() preds_8 = autoencoder_8.forward(data_feed) autoencoder_8.display_outputs(preds_8['A2'],data_feed) # %% hidden_size_9 = 100 lambd_9 = 1e-5 autoencoder_9 = Autoencoder(input_size = input_size, hidden_size = hidden_size_9, lambd = lambd_9) solver_9 = Solver(model = autoencoder_9, data = data_feed) solver_9.train() autoencoder_9.evaluate() W1 = autoencoder_9.W_e['W1'] num_disp = W1.shape[1] fig = plt.figure(figsize = (9,8)) for i in range(num_disp): plt.subplot(10,10,i+1) plt.imshow(W1.T[i].reshape(16,16),cmap = 'gray') plt.axis('off') fig.suptitle('Hidden Layer Feature Representation') plt.show() preds_9 = autoencoder_9.forward(data_feed) # %% autoencoder_9.display_outputs(preds_9['A2'],data_feed) # %% import tensorflow as tf from tensorflow.keras import layers from tensorflow import keras import tensorflow.keras.backend as K # %% if tf.test.gpu_device_name(): print('Default GPU Device:{}'.format(tf.test.gpu_device_name())) else: print("NO GPU, that's okey") # %% def MeanSquaredError(): def customMeanSquaredError(pred,label): return 1/2 * K.sum((pred - label)**2)/pred.shape[0] return customMeanSquaredError def KL_divergence(rho, beta): def customKL(out): kl1 = rho*K.log(rho/K.mean(out, axis=0)) kl2 = (1-rho)*K.log((1-rho)/(1-K.mean(out, axis=0))) return beta*K.sum(kl1+kl2) return customKL # %% def create_model(hidden_size,lambd): tf_weights = tf_weight_initializer(inp_dim = inp_dim, hidden_dim = hidden_size) input_img = keras.Input(shape=(inp_dim,)) encoded = layers.Dense(encoding_dim,activation='sigmoid', kernel_regularizer=tf.keras.regularizers.l2(lambd), activity_regularizer=KL_divergence(rho,beta), kernel_initializer = tf_weights['W1'], bias_initializer = tf_weights['B1'])(input_img) decoded = layers.Dense(inp_dim,activation='sigmoid', activity_regularizer=tf.keras.regularizers.l2(lambd), kernel_initializer = tf_weights['W2'], bias_initializer = tf_weights['B2'])(encoded) tf_autoencoder = keras.Model(input_img,decoded) optimizer = tf.keras.optimizers.SGD(learning_rate=0.9,momentum=0,nesterov=False) tf_autoencoder.compile(optimizer=optimizer,loss=MeanSquaredError()) return tf_autoencoder def plot_tf_weights(W1): num_disp = W1.shape[1] fig = plt.figure(figsize = (9,8)) for i in range(num_disp): plt.subplot(10,10,i+1) plt.imshow(W1.T[i].reshape(16,16),cmap = 'gray') plt.axis('off') fig.suptitle('Hidden Layer Feature Representation') plt.show() # %% tf_model_1 = create_model(hidden_size = 10,lambd = 0) tf_model_1.fit(data_feed, data_feed, epochs=5000, batch_size=data_feed.shape[0]) tf_history_1 = tf_model_1.history.history plt.plot(tf_history_1['loss']) plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Loss versus Epoch') #plt.legend([f'Loss : {tf_history_1['loss'][-1]}']) tf_preds_1 = tf_model_1.predict(data_feed) autoencoder.display_outputs(tf_preds_1,data_feed) tf_weights_1 = tf_model_1.get_weights() plot_tf_weights(tf_weights_1[0]) # %% tf_model_2 = create_model(hidden_size = 10,lambd = 1e-3) tf_model_2.fit(data_feed, data_feed, epochs=5000, batch_size=data_feed.shape[0]) tf_history_2 = tf_model_2.history.history plt.plot(tf_history_2['loss']) plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Loss versus Epoch') tf_preds_2 = tf_model_2.predict(data_feed) autoencoder.display_outputs(tf_preds_2,data_feed) tf_weights_2 = tf_model_2.get_weights() plot_tf_weights(tf_weights_2[0]) # %% tf_model_3 = create_model(hidden_size = 10,lambd = 1e-5) tf_model_3.fit(data_feed, data_feed, epochs=5000, batch_size=data_feed.shape[0]) tf_history_3 = tf_model_3.history.history plt.plot(tf_history_3['loss']) plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Loss versus Epoch') tf_preds_3 = tf_model_3.predict(data_feed) autoencoder.display_outputs(tf_preds_3,data_feed) tf_weights_3 = tf_model_3.get_weights() plot_tf_weights(tf_weights_3[0]) # %% tf_model_4 = create_model(hidden_size = 50,lambd = 0) tf_model_4.fit(data_feed, data_feed, epochs=5000, batch_size=data_feed.shape[0]) tf_history_4 = tf_model_4.history.history plt.plot(tf_history_4['loss']) plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Loss versus Epoch') tf_preds_4 = tf_model_4.predict(data_feed) autoencoder.display_outputs(tf_preds_4,data_feed) tf_weights_4 = tf_model_4.get_weights() plot_tf_weights(tf_weights_4[0]) # %% tf_model_5 = create_model(hidden_size = 50,lambd = 1e-3) tf_model_5.fit(data_feed, data_feed, epochs=5000, batch_size=data_feed.shape[0]) tf_history_5 = tf_model_5.history.history plt.plot(tf_history_5['loss']) plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Loss versus Epoch') tf_preds_5 = tf_model_5.predict(data_feed) autoencoder.display_outputs(tf_preds_5,data_feed) tf_weights_5 = tf_model_5.get_weights() plot_tf_weights(tf_weights_5[0]) # %% tf_model_6 = create_model(hidden_size = 50,lambd = 1e-5) tf_model_6.fit(data_feed, data_feed, epochs=5000, batch_size=data_feed.shape[0]) tf_history_6 = tf_model_6.history.history plt.plot(tf_history_6['loss']) plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Loss versus Epoch') tf_preds_6 = tf_model_6.predict(data_feed) autoencoder.display_outputs(tf_preds_6,data_feed) tf_weights_6 = tf_model_1.get_weights() plot_tf_weights(tf_weights_6[0]) # %% tf_model_7 = create_model(hidden_size = 100,lambd = 0) tf_model_7.fit(data_feed, data_feed, epochs=5000, batch_size=data_feed.shape[0]) tf_history_7 = tf_model_7.history.history plt.plot(tf_history_7['loss']) plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Loss versus Epoch') tf_preds_7 = tf_model_7.predict(data_feed) autoencoder.display_outputs(tf_preds_7,data_feed) tf_weights_7 = tf_model_7.get_weights() plot_tf_weights(tf_weights_7[0]) # %% tf_model_8 = create_model(hidden_size = 100,lambd = 1e-3) tf_model_8.fit(data_feed, data_feed, epochs=5000, batch_size=data_feed.shape[0]) tf_history_8 = tf_model_8.history.history plt.plot(tf_history_8['loss']) plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Loss versus Epoch') tf_preds_8 = tf_model_8.predict(data_feed) autoencoder.display_outputs(tf_preds_8,data_feed) tf_weights_8 = tf_model_8.get_weights() plot_tf_weights(tf_weights_8[0]) # %% tf_model_9 = create_model(hidden_size = 100,lambd = 1e-5) tf_model_9.fit(data_feed, data_feed, epochs=5000, batch_size=data_feed.shape[0]) tf_history_9 = tf_model_9.history.history plt.plot(tf_history_9['loss']) plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Loss versus Epoch') tf_preds_9 = tf_model_9.predict(data_feed) autoencoder.display_outputs(tf_preds_9,data_feed) tf_weights_9 = tf_model_9.get_weights() plot_tf_weights(tf_weights_9[0]) # %% #encoding_dim = 10 rho,beta = 5e-1,1e-1 inp_dim = 256 #lamb = 0 W_scaler = lambda L_pre,L_post : np.sqrt(6/(L_pre + L_post)) def tf_weight_initializer(inp_dim,hidden_dim): initializer_1 = tf.keras.initializers.RandomUniform(minval=-W_scaler(inp_dim,hidden_dim), maxval=W_scaler(inp_dim,hidden_dim)) values_2 = initializer_1(shape=(inp_dim,hidden_dim)) initializer_2 = tf.keras.initializers.RandomUniform(minval=-W_scaler(hidden_dim,inp_dim), maxval=W_scaler(hidden_dim,inp_dim)) values_2 = initializer_2(shape=(inp_dim,hidden_dim)) initializer_3 = tf.keras.initializers.RandomUniform(minval=-W_scaler(inp_dim,hidden_dim), maxval=W_scaler(inp_dim,hidden_dim)) values_3 = initializer_3(shape=(1,hidden_dim)) initializer_4 = tf.keras.initializers.RandomUniform(minval=-W_scaler(hidden_dim,inp_dim), maxval=W_scaler(hidden_dim,inp_dim)) values_4 = initializer_4(shape=(1,inp_dim)) return {'W1':initializer_1, 'W2':initializer_2, 'B1':initializer_3, 'B2':initializer_4} tf_weights = tf_weight_initializer(inp_dim = inp_dim, hidden_dim = encoding_dim) # %% input_img = keras.Input(shape=(inp_dim,)) encoded = layers.Dense(encoding_dim,activation='sigmoid', kernel_regularizer=tf.keras.regularizers.l2(lamb), activity_regularizer=KL_divergence(rho,beta), kernel_initializer = tf_weights['W1'], bias_initializer = tf_weights['B1'])(input_img) decoded = layers.Dense(inp_dim,activation='sigmoid', activity_regularizer=tf.keras.regularizers.l2(lamb), kernel_initializer = tf_weights['W2'], bias_initializer = tf_weights['B2'])(encoded) tf_autoencoder = keras.Model(input_img,decoded) # %% tf_autoencoder.summary() # %% optimizer = tf.keras.optimizers.SGD(learning_rate=0.9,momentum=0,nesterov=False) tf_autoencoder.compile(optimizer=optimizer,loss=MeanSquaredError()) # %% tf_autoencoder.fit(data_feed, data_feed, epochs=5000, batch_size=data_feed.shape[0]) # %% tf_history = tf_autoencoder.history.history plt.plot(tf_history['loss']) plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Loss versus Epoch') # %% tf_preds = tf_autoencoder.predict(data_feed) # %% autoencoder.display_outputs(tf_preds,data_feed) # %% tf_weights = tf_autoencoder.get_weights() # %% tf_weights = tf_autoencoder.get_weights() W1 = tf_weights[0] num_disp = W1.shape[1] fig = plt.figure(figsize = (9,8)) for i in range(num_disp): plt.subplot(8,8,i+1) plt.imshow(W1.T[i].reshape(16,16),cmap = 'gray') plt.axis('off') fig.suptitle('Hidden Layer Feature Representation') plt.show() # %% # %% input_img_optim = keras.Input(shape=(inp_dim,)) encoded_optim = layers.Dense(encoding_dim,activation='sigmoid', kernel_regularizer=tf.keras.regularizers.l2(5e-4), activity_regularizer=KL_divergence(rho,beta))(input_img_optim) decoded_optim = layers.Dense(inp_dim,activation='sigmoid', activity_regularizer=tf.keras.regularizers.l2(5e-4))(encoded_optim) tf_autoencoder_optim = keras.Model(input_img_optim,decoded_optim) tf_autoencoder.compile(optimizer='adam',loss=MeanSquaredError()) tf_autoencoder.summary() # %% tf_autoencoder.fit(data_feed, data_feed, epochs=5000, batch_size=data_feed.shape[0]) # %% tf_history_optim = tf_autoencoder.history.history plt.plot(tf_history_optim['loss'],color = 'green') plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Loss versus Epoch') # %% tf_preds_optim = tf_autoencoder.predict(data_feed) autoencoder.display_outputs(tf_preds_optim,data_feed) # %% tf_weights_optim = tf_autoencoder_optim.get_weights() W1 = tf_weights[0] num_disp = W1.shape[1] fig = plt.figure(figsize = (9,8)) for i in range(num_disp): plt.subplot(8,8,i+1) plt.imshow(W1.T[i].reshape(16,16),cmap = 'gray') plt.axis('off') fig.suptitle('Hidden Layer Feature Representation') plt.show() # %% elif question == '3' : # To add a new cell, type '# %%' # To add a new markdown cell, type '# %% [markdown]' # %% def sigmoid(x): c = np.clip(x,-700,700) return 1 / (1 + np.exp(-c)) def dsigmoid(y): return y * (1 - y) def tanh(x): return np.tanh(x) def dtanh(y): return 1 - y * y # %% with h5py.File('assign3_data3.h5','r') as F: # Names variable contains the names of training and testing file names = list(F.keys()) X_train = np.array(F[names[0]][()]) y_train = np.array(F[names[1]][()]) X_test = np.array(F[names[2]][()]) y_test = np.array(F[names[3]][()]) # %% class Metrics: """ Necessary metrics to evaluate the model. Functions(labels,preds): --- confusion_matrix --- accuracy_score """ def confusion_matrix(self,labels,preds): """ Takes desireds/labels and softmax predictions, return a confusion matrix. """ label = pd.Series(labels,name='Actual') pred = pd.Series(preds,name='Predicted') return pd.crosstab(label,pred) def accuracy_score(self,labels,preds): """ Takes desireds/labels and softmax predictions, return a accuracy_score. """ count = 0 size = labels.shape[0] for i in range(size): if preds[i] == labels[i]: count +=1 return 100 * (count/size) def accuracy(self,labels,preds): """ Takes desireds/labels and softmax predictions, return a accuracy. """ return 100 * (labels == preds).mean() # %% class Activations: """ Necessary activation functions for recurrent neural network(RNN,LSTM,GRU). """ def relu_alternative(self,X): """ Rectified linear unit activation(ReLU). """ return np.maximum(X, 0) def ReLU(self,X): """ Rectified linear unit activation(ReLU). Most time efficient version. """ return (abs(X) + X) / 2 def relu_another(self,X): """ Rectified linear unit activation(ReLU). """ return X * (X > 0) def tanh(self,X): return np.tanh(X) def tanh_manuel(self,X): """ Hyperbolic tangent activation(tanh). """ return (np.exp(X) - np.exp(-X))/(np.exp(X) + np.exp(-X)) def sigmoid(self,X): """ Sigmoidal activation. """ c = np.clip(X,-700,700) return 1/(1 + np.exp(-c)) def softmax(self,X): """ Stable version of softmax classifier, note that column sum is equal to 1. """ e_x = np.exp(X - np.max(X, axis=-1, keepdims=True)) return e_x / np.sum(e_x, axis=-1, keepdims=True) def softmax_stable(self,X): """ Less stable version of softmax activation """ e_x = np.exp(X - np.max(X)) return e_x / np.sum(e_x) def ReLUDerivative(self,X): """ The derivative of the ReLU function w.r.t. given input. """ return 1 * (X > 0) def ReLU_grad(self,X): """ The derivative of the ReLU function w.r.t. given input. """ X[X<=0] = 0 X[X>1] = 1 return X def dReLU(self,X): """ The derivative of the ReLU function w.r.t. given input. """ return np.where(X <= 0, 0, 1) def dtanh(self,X): """ The derivative of the tanh function w.r.t. given input. """ return 1-(np.tanh(X)**2) def dsigmoid(self,X): """ The derivative of the sigmoid function w.r.t. given input. """ return self.sigmoid(X) * (1-self.sigmoid(X)) def softmax_stable_gradient(self,soft_out): return soft_out * (1 - soft_out) def softmax_grad(self,softmax): s = softmax.reshape(-1,1) return np.diagflat(s) - np.dot(s, s.T) def softmax_gradient(self,Sz): """Computes the gradient of the softmax function. z: (T, 1) array of input values where the gradient is computed. T is the number of output classes. Returns D (T, T) the Jacobian matrix of softmax(z) at the given z. D[i, j] is DjSi - the partial derivative of Si w.r.t. input j. """ # -SjSi can be computed using an outer product between Sz and itself. Then # we add back Si for the i=j cases by adding a diagonal matrix with the # values of Si on its diagonal. D = -np.outer(Sz, Sz) + np.diag(Sz.flatten()) return D # %% class RNN(object): """ Recurrent Neural Network for classifying human activity. RNN encapsulates all necessary logic for training the network. """ def __init__(self,input_dim = 3,hidden_dim = 128, seq_len = 150, learning_rate = 1e-1, mom_coeff = 0.85, batch_size = 32, output_class = 6): """ Initialization of weights/biases and other configurable parameters. """ np.random.seed(150) self.input_dim = input_dim self.hidden_dim = hidden_dim # Unfold case T = 150 : self.seq_len = seq_len self.output_class = output_class self.learning_rate = learning_rate self.batch_size = batch_size self.mom_coeff = mom_coeff # Xavier uniform scaler : Xavier = lambda fan_in,fan_out : math.sqrt(6/(fan_in + fan_out)) lim_inp2hid = Xavier(self.input_dim,self.hidden_dim) self.W1 = np.random.uniform(-lim_inp2hid,lim_inp2hid,(self.input_dim,self.hidden_dim)) self.B1 = np.random.uniform(-lim_inp2hid,lim_inp2hid,(1,self.hidden_dim)) lim_hid2hid = Xavier(self.hidden_dim,self.hidden_dim) self.W1_rec= np.random.uniform(-lim_hid2hid,lim_hid2hid,(self.hidden_dim,self.hidden_dim)) lim_hid2out = Xavier(self.hidden_dim,self.output_class) self.W2 = np.random.uniform(-lim_hid2out,lim_hid2out,(self.hidden_dim,self.output_class)) self.B2 = np.random.uniform(-lim_inp2hid,lim_inp2hid,(1,self.output_class)) # To keep track loss and accuracy score : self.train_loss,self.test_loss,self.train_acc,self.test_acc = [],[],[],[] # Storing previous momentum updates : self.prev_updates = {'W1' : 0, 'B1' : 0, 'W1_rec' : 0, 'W2' : 0, 'B2' : 0} def forward(self,X) -> tuple: """ Forward propagation of the RNN through time. Inputs: --- X is the bacth. --- h_prev_state is the previous state of the hidden layer. Returns: --- (X_state,hidden_state,probs) as a tuple. ------ 1) X_state is the input across all time steps ------ 2) hidden_state is the hidden stages across time ------ 3) probs is the probabilities of each outputs, i.e. outputs of softmax """ X_state = dict() hidden_state = dict() output_state = dict() probs = dict() self.h_prev_state = np.zeros((1,self.hidden_dim)) hidden_state[-1] = np.copy(self.h_prev_state) # Loop over time T = 150 : for t in range(self.seq_len): # Selecting first record with 3 inputs, dimension = (batch_size,input_size) X_state[t] = X[:,t] # Recurrent hidden layer : hidden_state[t] = np.tanh(np.dot(X_state[t],self.W1) + np.dot(hidden_state[t-1],self.W1_rec) + self.B1) output_state[t] = np.dot(hidden_state[t],self.W2) + self.B2 # Per class probabilites : probs[t] = activations.softmax(output_state[t]) return (X_state,hidden_state,probs) def BPTT(self,cache,Y): """ Back propagation through time algorihm. Inputs: -- Cache = (X_state,hidden_state,probs) -- Y = desired output Returns: -- Gradients w.r.t. all configurable elements """ X_state,hidden_state,probs = cache # backward pass: compute gradients going backwards dW1, dW1_rec, dW2 = np.zeros_like(self.W1), np.zeros_like(self.W1_rec), np.zeros_like(self.W2) dB1, dB2 = np.zeros_like(self.B1), np.zeros_like(self.B2) dhnext = np.zeros_like(hidden_state[0]) dy = np.copy(probs[149]) dy[np.arange(len(Y)),np.argmax(Y,1)] -= 1 dB2 += np.sum(dy,axis = 0, keepdims = True) dW2 += np.dot(hidden_state[149].T,dy) for t in reversed(range(1,self.seq_len)): dh = np.dot(dy,self.W2.T) + dhnext dhrec = (1 - (hidden_state[t] * hidden_state[t])) * dh dB1 += np.sum(dhrec,axis = 0, keepdims = True) dW1 += np.dot(X_state[t].T,dhrec) dW1_rec += np.dot(hidden_state[t-1].T,dhrec) dhnext = np.dot(dhrec,self.W1_rec.T) for grad in [dW1,dB1,dW1_rec,dW2,dB2]: np.clip(grad, -10, 10, out = grad) return [dW1,dB1,dW1_rec,dW2,dB2] def earlyStopping(self,ce_train,ce_val,ce_threshold,acc_train,acc_val,acc_threshold): if ce_train - ce_val < ce_threshold or acc_train - acc_val > acc_threshold: return True else: return False def CategoricalCrossEntropy(self,labels,preds): """ Computes cross entropy between labels and model's predictions """ predictions = np.clip(preds, 1e-12, 1. - 1e-12) N = predictions.shape[0] return -np.sum(labels * np.log(predictions + 1e-9)) / N def step(self,grads,momentum = True): """ SGD on mini batches """ #for config_param,grad in zip([self.W1,self.B1,self.W1_rec,self.W2,self.B2],grads): #config_param -= self.learning_rate * grad if momentum: delta_W1 = -self.learning_rate * grads[0] + self.mom_coeff * self.prev_updates['W1'] delta_B1 = -self.learning_rate * grads[1] + self.mom_coeff * self.prev_updates['B1'] delta_W1_rec = -self.learning_rate * grads[2] + self.mom_coeff * self.prev_updates['W1_rec'] delta_W2 = -self.learning_rate * grads[3] + self.mom_coeff * self.prev_updates['W2'] delta_B2 = -self.learning_rate * grads[4] + self.mom_coeff * self.prev_updates['B2'] self.W1 += delta_W1 self.W1_rec += delta_W1_rec self.W2 += delta_W2 self.B1 += delta_B1 self.B2 += delta_B2 self.prev_updates['W1'] = delta_W1 self.prev_updates['W1_rec'] = delta_W1_rec self.prev_updates['W2'] = delta_W2 self.prev_updates['B1'] = delta_B1 self.prev_updates['B2'] = delta_B2 self.learning_rate *= 0.9999 def fit(self,X,Y,X_val,y_val,epochs = 50 ,verbose = True, earlystopping = False): """ Given the traning dataset,their labels and number of epochs fitting the model, and measure the performance by validating training dataset. """ for epoch in range(epochs): print(f'Epoch : {epoch + 1}') perm = np.random.permutation(3000) for i in range(round(X.shape[0]/self.batch_size)): batch_start = i * self.batch_size batch_finish = (i+1) * self.batch_size index = perm[batch_start:batch_finish] X_feed = X[index] y_feed = Y[index] cache_train = self.forward(X_feed) grads = self.BPTT(cache_train,y_feed) self.step(grads) cross_loss_train = self.CategoricalCrossEntropy(y_feed,cache_train[2][149]) predictions_train = self.predict(X) acc_train = metrics.accuracy(np.argmax(Y,1),predictions_train) _,__,probs_test = self.forward(X_val) cross_loss_val = self.CategoricalCrossEntropy(y_val,probs_test[149]) predictions_val = np.argmax(probs_test[149],1) acc_val = metrics.accuracy(np.argmax(y_val,1),predictions_val) if earlystopping: if self.earlyStopping(ce_train = cross_loss_train,ce_val = cross_loss_val,ce_threshold = 3.0,acc_train = acc_train,acc_val = acc_val,acc_threshold = 15): break if verbose: print(f"[{epoch + 1}/{epochs}] ------> Training : Accuracy : {acc_train}") print(f"[{epoch + 1}/{epochs}] ------> Training : Loss : {cross_loss_train}") print('______________________________________________________________________________________\n') print(f"[{epoch + 1}/{epochs}] ------> Testing : Accuracy : {acc_val}") print(f"[{epoch + 1}/{epochs}] ------> Testing : Loss : {cross_loss_val}") print('______________________________________________________________________________________\n') self.train_loss.append(cross_loss_train) self.test_loss.append(cross_loss_val) self.train_acc.append(acc_train) self.test_acc.append(acc_val) def predict(self,X): _,__,probs = self.forward(X) return np.argmax(probs[149],axis=1) def history(self): return {'TrainLoss' : self.train_loss, 'TrainAcc' : self.train_acc, 'TestLoss' : self.test_loss, 'TestAcc' : self.test_acc} # %% input_dim = 3 activations = Activations() metrics = Metrics() model = RNN(input_dim = input_dim,learning_rate = 1e-4, mom_coeff = 0.0, hidden_dim = 128) # %% model.fit(X_train,y_train,X_test,y_test,epochs = 35) # %% history = model.history() # %% plt.figure() plt.plot(history['TestLoss'],'-o') plt.plot(history['TrainLoss'],'-o') plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Categorical Cross Entropy over epochs') plt.legend(['Test Loss','Train Loss']) plt.show() # %% plt.figure() plt.plot(history['TestAcc'],'-o') plt.plot(history['TrainAcc'],'-o') plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Accuracy over epochs') plt.legend(['Test Acc','Train Acc']) plt.show() # %% train_preds = model.predict(X_train) test_preds = model.predict(X_test) # %% confusion_mat_train = metrics.confusion_matrix(np.argmax(y_train,1),train_preds) confusion_mat_test = metrics.confusion_matrix(np.argmax(y_test,1),test_preds) # %% body_movements = ['downstairs','jogging','sitting','standing','upstairs','walking'] confusion_mat_train_,confusion_mat_test_ = confusion_mat_train,confusion_mat_test confusion_mat_train.columns = body_movements confusion_mat_train.index = body_movements print(confusion_mat_train) # %% confusion_mat_test.columns = body_movements confusion_mat_test.index = body_movements print(confusion_mat_train) # %% sns.heatmap(confusion_mat_train/np.sum(confusion_mat_train), annot=True, fmt='.2%',cmap = 'Blues') plt.show() # %% print(confusion_mat_test) # %% sns.heatmap(confusion_mat_test/np.sum(confusion_mat_test), annot=True, fmt='.2%',cmap = 'Blues') plt.show() # %% plt.matshow(confusion_mat_test, cmap=plt.cm.gray_r) plt.title('Testing Confusion Matrix') plt.colorbar() tick_marks = np.arange(len(confusion_mat_test.columns)) plt.xticks(tick_marks, confusion_mat_test.columns, rotation=45) plt.yticks(tick_marks, confusion_mat_test.index) plt.tight_layout() plt.ylabel(confusion_mat_test.index.name) plt.xlabel(confusion_mat_test.columns.name) plt.show() # %% plt.matshow(confusion_mat_train, cmap=plt.cm.gray_r) plt.title('Training Confusion Matrix') plt.colorbar() tick_marks = np.arange(len(confusion_mat_train.columns)) plt.xticks(tick_marks, confusion_mat_train.columns, rotation=45) plt.yticks(tick_marks, confusion_mat_train.index) plt.tight_layout() plt.ylabel(confusion_mat_train.index.name) plt.xlabel(confusion_mat_train.columns.name) plt.show() # %% sns.heatmap(confusion_mat_test/np.sum(confusion_mat_test), annot=True, fmt='.2%',cmap = 'Greens') plt.show() # %% sns.heatmap(confusion_mat_test/np.sum(confusion_mat_test), annot=True, fmt='.2%',cmap = 'Blues') plt.show() # %% class Multi_Layer_RNN(object): """ Recurrent Neural Network for classifying human activity. RNN encapsulates all necessary logic for training the network. """ def __init__(self,input_dim = 3,hidden_dim_1 = 128, hidden_dim_2 = 64, seq_len = 150, learning_rate = 1e-1, mom_coeff = 0.85, batch_size = 32, output_class = 6): """ Initialization of weights/biases and other configurable parameters. """ np.random.seed(150) self.input_dim = input_dim self.hidden_dim_1 = hidden_dim_1 self.hidden_dim_2 = hidden_dim_2 # Unfold case T = 150 : self.seq_len = seq_len self.output_class = output_class self.learning_rate = learning_rate self.batch_size = batch_size self.mom_coeff = mom_coeff # Xavier uniform scaler : Xavier = lambda fan_in,fan_out : math.sqrt(6/(fan_in + fan_out)) lim_inp2hid = Xavier(self.input_dim,self.hidden_dim_1) self.W1 = np.random.uniform(-lim_inp2hid,lim_inp2hid,(self.input_dim,self.hidden_dim_1)) self.B1 = np.random.uniform(-lim_inp2hid,lim_inp2hid,(1,self.hidden_dim_1)) lim_hid2hid = Xavier(self.hidden_dim_1,self.hidden_dim_1) self.W1_rec= np.random.uniform(-lim_hid2hid,lim_hid2hid,(self.hidden_dim_1,self.hidden_dim_1)) lim_hid2hid2 = Xavier(self.hidden_dim_1,self.hidden_dim_2) self.W2 = np.random.uniform(-lim_hid2hid2,lim_hid2hid2,(self.hidden_dim_1,self.hidden_dim_2)) self.B2 = np.random.uniform(-lim_hid2hid2,lim_hid2hid2,(1,self.hidden_dim_2)) lim_hid2out = Xavier(self.hidden_dim_2,self.output_class) self.W3 = np.random.uniform(-lim_hid2out,lim_hid2out,(self.hidden_dim_2,self.output_class)) self.B3 = np.random.uniform(-lim_inp2hid,lim_inp2hid,(1,self.output_class)) # To keep track loss and accuracy score : self.train_loss,self.test_loss,self.train_acc,self.test_acc = [],[],[],[] # Storing previous momentum updates : self.prev_updates = {'W1' : 0, 'B1' : 0, 'W1_rec' : 0, 'W2' : 0, 'B2' : 0, 'W3' : 0, 'B3' : 0} def forward(self,X) -> tuple: """ Forward propagation of the RNN through time. __________________________________________________________ Inputs: --- X is the bacth. --- h_prev_state is the previous state of the hidden layer. __________________________________________________________ Returns: --- (X_state,hidden_state,probs) as a tuple. ------ 1) X_state is the input across all time steps ------ 2) hidden_state is the hidden stages across time ------ 3) probs is the probabilities of each outputs, i.e. outputs of softmax __________________________________________________________ """ X_state = dict() hidden_state_1 = dict() hidden_state_mlp = dict() output_state = dict() probs = dict() mlp_linear = dict() self.h_prev_state = np.zeros((1,self.hidden_dim_1)) hidden_state_1[-1] = np.copy(self.h_prev_state) # Loop over time T = 150 : for t in range(self.seq_len): # Selecting first record with 3 inputs, dimension = (batch_size,input_size) X_state[t] = X[:,t] # Recurrent hidden layer : hidden_state_1[t] = np.tanh(np.dot(X_state[t],self.W1) + np.dot(hidden_state_1[t-1],self.W1_rec) + self.B1) mlp_linear[t] = np.dot(hidden_state_1[t],self.W2) + self.B2 hidden_state_mlp[t] = activations.ReLU(mlp_linear[t]) output_state[t] = np.dot(hidden_state_mlp[t],self.W3) + self.B3 # Per class probabilites : probs[t] = activations.softmax(output_state[t]) return (X_state,hidden_state_1,mlp_linear,hidden_state_mlp,probs) def BPTT(self,cache,Y): """ Back propagation through time algorihm. Inputs: -- Cache = (X_state,hidden_state,probs) -- Y = desired output Returns: -- Gradients w.r.t. all configurable elements """ X_state,hidden_state_1,mlp_linear,hidden_state_mlp,probs = cache # backward pass: compute gradients going backwards dW1, dW1_rec, dW2, dW3 = np.zeros_like(self.W1), np.zeros_like(self.W1_rec), np.zeros_like(self.W2),np.zeros_like(self.W3) dB1, dB2,dB3 = np.zeros_like(self.B1), np.zeros_like(self.B2),np.zeros_like(self.B3) dhnext = np.zeros_like(hidden_state_1[0]) dy = np.copy(probs[149]) dy[np.arange(len(Y)),np.argmax(Y,1)] -= 1 #dy = probs[0] - Y[0] dW3 += np.dot(hidden_state_mlp[149].T,dy) dB3 += np.sum(dy,axis = 0, keepdims = True) dy1 = np.dot(dy,self.W3.T) * activations.ReLU_grad(mlp_linear[149]) dB2 += np.sum(dy1,axis = 0, keepdims = True) dW2 += np.dot(hidden_state_1[149].T,dy1) for t in reversed(range(1,self.seq_len)): dh = np.dot(dy1,self.W2.T) + dhnext dhrec = (1 - (hidden_state_1[t] * hidden_state_1[t])) * dh dB1 += np.sum(dhrec,axis = 0, keepdims = True) dW1 += np.dot(X_state[t].T,dhrec) dW1_rec += np.dot(hidden_state_1[t-1].T,dhrec) dhnext = np.dot(dhrec,self.W1_rec.T) for grad in [dW1,dB1,dW1_rec,dW2,dB2,dW3,dB3]: np.clip(grad, -10, 10, out = grad) return [dW1,dB1,dW1_rec,dW2,dB2,dW3,dB3] def CategoricalCrossEntropy(self,labels,preds): """ Computes cross entropy between labels and model's predictions """ predictions = np.clip(preds, 1e-12, 1. - 1e-12) N = predictions.shape[0] return -np.sum(labels * np.log(predictions + 1e-9)) / N def step(self,grads,momentum = True): #for config_param,grad in zip([self.W1,self.B1,self.W1_rec,self.W2,self.B2,self.W3,self.B3],grads): #config_param -= self.learning_rate * grad if momentum: delta_W1 = -self.learning_rate * grads[0] - self.mom_coeff * self.prev_updates['W1'] delta_B1 = -self.learning_rate * grads[1] - self.mom_coeff * self.prev_updates['B1'] delta_W1_rec = -self.learning_rate * grads[2] - self.mom_coeff * self.prev_updates['W1_rec'] delta_W2 = -self.learning_rate * grads[3] - self.mom_coeff * self.prev_updates['W2'] delta_B2 = -self.learning_rate * grads[4] - self.mom_coeff * self.prev_updates['B2'] delta_W3 = -self.learning_rate * grads[5] - self.mom_coeff * self.prev_updates['W3'] delta_B3 = -self.learning_rate * grads[6] - self.mom_coeff * self.prev_updates['B3'] self.W1 += delta_W1 self.W1_rec += delta_W1_rec self.W2 += delta_W2 self.B1 += delta_B1 self.B2 += delta_B2 self.W3 += delta_W3 self.B3 += delta_B3 self.prev_updates['W1'] = delta_W1 self.prev_updates['W1_rec'] = delta_W1_rec self.prev_updates['W2'] = delta_W2 self.prev_updates['B1'] = delta_B1 self.prev_updates['B2'] = delta_B2 self.prev_updates['W3'] = delta_W3 self.prev_updates['B3'] = delta_B3 self.learning_rate *= 0.9999 def fit(self,X,Y,X_val,y_val,epochs = 50 ,verbose = True, crossVal = False): """ Given the traning dataset,their labels and number of epochs fitting the model, and measure the performance by validating training dataset. """ for epoch in range(epochs): print(f'Epoch : {epoch + 1}') perm = np.random.permutation(3000) for i in range(round(X.shape[0]/self.batch_size)): batch_start = i * self.batch_size batch_finish = (i+1) * self.batch_size index = perm[batch_start:batch_finish] X_feed = X[index] y_feed = Y[index] cache_train = self.forward(X_feed) grads = self.BPTT(cache_train,y_feed) self.step(grads) if crossVal: stop = self.cross_validation(X,val_X,Y,val_Y,threshold = 5) if stop: break cross_loss_train = self.CategoricalCrossEntropy(y_feed,cache_train[4][149]) predictions_train = self.predict(X) acc_train = metrics.accuracy(np.argmax(Y,1),predictions_train) _,__,___,____, probs_test = self.forward(X_val) cross_loss_val = self.CategoricalCrossEntropy(y_val,probs_test[149]) predictions_val = np.argmax(probs_test[149],1) acc_val = metrics.accuracy(np.argmax(y_val,1),predictions_val) if verbose: print(f"[{epoch + 1}/{epochs}] ------> Training : Accuracy : {acc_train}") print(f"[{epoch + 1}/{epochs}] ------> Training : Loss : {cross_loss_train}") print('______________________________________________________________________________________\n') print(f"[{epoch + 1}/{epochs}] ------> Testing : Accuracy : {acc_val}") print(f"[{epoch + 1}/{epochs}] ------> Testing : Loss : {cross_loss_val}") print('______________________________________________________________________________________\n') self.train_loss.append(cross_loss_train) self.test_loss.append(cross_loss_val) self.train_acc.append(acc_train) self.test_acc.append(acc_val) def predict(self,X): _,__,___,____,probs = self.forward(X) return np.argmax(probs[149],axis=1) def history(self): return {'TrainLoss' : self.train_loss, 'TrainAcc' : self.train_acc, 'TestLoss' : self.test_loss, 'TestAcc' : self.test_acc} # %% multilayer_rnn = Multi_Layer_RNN(learning_rate=1e-4,mom_coeff=0.0,hidden_dim_1 = 128, hidden_dim_2 = 64) # %% multilayer_rnn.fit(X_train,y_train,X_test,y_test,epochs = 35) # %% multilayer_rnn_history = multilayer_rnn.history() # %% plt.figure() plt.plot(multilayer_rnn_history['TestLoss'],'-o') plt.plot(multilayer_rnn_history['TrainLoss'],'-o') plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Categorical Cross Entropy over epochs') plt.legend(['Test Loss','Train Loss']) plt.show() # %% plt.figure() plt.plot(multilayer_rnn_history['TestAcc'],'-o') plt.plot(multilayer_rnn_history['TrainAcc'],'-o') plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Accuracy over epochs') plt.legend(['Test Acc','Train Acc']) plt.show() # %% plt.figure() plt.plot(multilayer_rnn_history['TrainAcc'],'-o') plt.plot(history['TrainAcc'],'-o') plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Training Accuracy over epochs') plt.legend(['Multi Layer RNN','Vanilla RNN']) plt.show() # %% plt.plot(multilayer_rnn_history['TestAcc'],'-o') plt.plot(history['TestAcc'],'-o') plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Testing Accuracy over epochs') plt.legend(['Multi Layer RNN','Vanilla RNN']) plt.show() # %% train_preds_multilayer_rnn = multilayer_rnn.predict(X_train) test_preds_multilayer_rnn = multilayer_rnn.predict(X_test) confusion_mat_train_multilayer_rnn = metrics.confusion_matrix(np.argmax(y_train,1),train_preds_multilayer_rnn) confusion_mat_test_multilayer_rnn = metrics.confusion_matrix(np.argmax(y_test,1),test_preds_multilayer_rnn) body_movements = ['downstairs','jogging','sitting','standing','upstairs','walking'] confusion_mat_train_multilayer_rnn.columns = body_movements confusion_mat_train_multilayer_rnn.index = body_movements confusion_mat_test_multilayer_rnn.columns = body_movements confusion_mat_test_multilayer_rnn.index = body_movements print(confusion_mat_train_multilayer_rnn) # %% print(confusion_mat_test_multilayer_rnn) # %% sns.heatmap(confusion_mat_test_multilayer_rnn/np.sum(confusion_mat_test_multilayer_rnn), annot=True, fmt='.2%',cmap = 'Blues') plt.show() # %% sns.heatmap(confusion_mat_train_multilayer_rnn/np.sum(confusion_mat_train_multilayer_rnn), annot=True, fmt='.2%',cmap = 'Blues') plt.show() # %% class Three_Hidden_Layer_RNN(object): """ Recurrent Neural Network for classifying human activity. RNN encapsulates all necessary logic for training the network. """ def __init__(self,input_dim = 3,hidden_dim_1 = 128, hidden_dim_2 = 64,hidden_dim_3 = 32, seq_len = 150, learning_rate = 1e-1, mom_coeff = 0.85, batch_size = 32, output_class = 6): """ Initialization of weights/biases and other configurable parameters. """ np.random.seed(150) self.input_dim = input_dim self.hidden_dim_1 = hidden_dim_1 self.hidden_dim_2 = hidden_dim_2 self.hidden_dim_3 = hidden_dim_3 # Unfold case T = 150 : self.seq_len = seq_len self.output_class = output_class self.learning_rate = learning_rate self.batch_size = batch_size self.mom_coeff = mom_coeff # Xavier uniform scaler : Xavier = lambda fan_in,fan_out : math.sqrt(6/(fan_in + fan_out)) lim_inp2hid = Xavier(self.input_dim,self.hidden_dim_1) self.W1 = np.random.uniform(-lim_inp2hid,lim_inp2hid,(self.input_dim,self.hidden_dim_1)) self.B1 = np.random.uniform(-lim_inp2hid,lim_inp2hid,(1,self.hidden_dim_1)) lim_hid2hid = Xavier(self.hidden_dim_1,self.hidden_dim_1) self.W1_rec= np.random.uniform(-lim_hid2hid,lim_hid2hid,(self.hidden_dim_1,self.hidden_dim_1)) lim_hid2hid2 = Xavier(self.hidden_dim_1,self.hidden_dim_2) self.W2 = np.random.uniform(-lim_hid2hid2,lim_hid2hid2,(self.hidden_dim_1,self.hidden_dim_2)) self.B2 = np.random.uniform(-lim_hid2hid2,lim_hid2hid2,(1,self.hidden_dim_2)) lim_hid2hid3 = Xavier(self.hidden_dim_2,self.hidden_dim_3) self.W3 = np.random.uniform(-lim_hid2hid3,lim_hid2hid3,(self.hidden_dim_2,self.hidden_dim_3)) self.B3 = np.random.uniform(-lim_hid2hid3,lim_hid2hid3,(1,self.hidden_dim_3)) lim_hid2out = Xavier(self.hidden_dim_3,self.output_class) self.W4 = np.random.uniform(-lim_hid2out,lim_hid2out,(self.hidden_dim_3,self.output_class)) self.B4 = np.random.uniform(-lim_hid2out,lim_hid2out,(1,self.output_class)) # To keep track loss and accuracy score : self.train_loss,self.test_loss,self.train_acc,self.test_acc = [],[],[],[] # Storing previous momentum updates : self.prev_updates = {'W1' : 0, 'B1' : 0, 'W1_rec' : 0, 'W2' : 0, 'B2' : 0, 'W3' : 0, 'W4' : 0, 'B3' : 0, 'B4' : 0} def forward(self,X) -> tuple: """ Forward propagation of the RNN through time. __________________________________________________________ Inputs: --- X is the bacth. --- h_prev_state is the previous state of the hidden layer. __________________________________________________________ Returns: --- (X_state,hidden_state,probs) as a tuple. ------ 1) X_state is the input across all time steps ------ 2) hidden_state is the hidden stages across time ------ 3) probs is the probabilities of each outputs, i.e. outputs of softmax __________________________________________________________ """ X_state = dict() hidden_state_1 = dict() hidden_state_mlp = dict() hidden_state_mlp_2 = dict() output_state = dict() probs = dict() mlp_linear = dict() mlp_linear_2 = dict() self.h_prev_state = np.zeros((1,self.hidden_dim_1)) hidden_state_1[-1] = np.copy(self.h_prev_state) # Loop over time T = 150 : for t in range(self.seq_len): # Selecting first record with 3 inputs, dimension = (batch_size,input_size) X_state[t] = X[:,t] # Recurrent hidden layer : hidden_state_1[t] = np.tanh(np.dot(X_state[t],self.W1) + np.dot(hidden_state_1[t-1],self.W1_rec) + self.B1) mlp_linear[t] = np.dot(hidden_state_1[t],self.W2) + self.B2 hidden_state_mlp[t] = activations.ReLU(mlp_linear[t]) mlp_linear_2[t] = np.dot(hidden_state_mlp[t],self.W3) + self.B3 hidden_state_mlp_2[t] = activations.ReLU(mlp_linear_2[t]) output_state[t] = np.dot(hidden_state_mlp_2[t],self.W4) + self.B4 # Per class probabilites : probs[t] = activations.softmax(output_state[t]) return (X_state,hidden_state_1,mlp_linear,hidden_state_mlp,mlp_linear_2,hidden_state_mlp_2,probs) def BPTT(self,cache,Y): """ Back propagation through time algorihm. Inputs: -- Cache = (X_state,hidden_state,probs) -- Y = desired output Returns: -- Gradients w.r.t. all configurable elements """ X_state,hidden_state_1,mlp_linear,hidden_state_mlp,mlp_linear_2,hidden_state_mlp_2,probs = cache # backward pass: compute gradients going backwards dW1, dW1_rec, dW2, dW3, dW4 = np.zeros_like(self.W1), np.zeros_like(self.W1_rec), np.zeros_like(self.W2),np.zeros_like(self.W3),np.zeros_like(self.W4) dB1, dB2,dB3,dB4 = np.zeros_like(self.B1), np.zeros_like(self.B2),np.zeros_like(self.B3),np.zeros_like(self.B4) dhnext = np.zeros_like(hidden_state_1[0]) for t in reversed(range(1,self.seq_len)): dy = np.copy(probs[t]) dy[np.arange(len(Y)),np.argmax(Y,1)] -= 1 #dy = probs[0] - Y[0] dW4 += np.dot(hidden_state_mlp_2[t].T,dy) dB4 += np.sum(dy,axis = 0, keepdims = True) dy1 = np.dot(dy,self.W4.T) * activations.ReLU_grad(mlp_linear_2[t]) dW3 += np.dot(hidden_state_mlp[t].T,dy1) dB3 += np.sum(dy1,axis = 0, keepdims = True) dy2 = np.dot(dy1,self.W3.T) * activations.ReLU_grad(mlp_linear[t]) dB2 += np.sum(dy2,axis = 0, keepdims = True) dW2 += np.dot(hidden_state_1[t].T,dy2) dh = np.dot(dy2,self.W2.T) + dhnext dhrec = (1 - (hidden_state_1[t] * hidden_state_1[t])) * dh dB1 += np.sum(dhrec,axis = 0, keepdims = True) dW1 += np.dot(X_state[t].T,dhrec) dW1_rec += np.dot(hidden_state_1[t-1].T,dhrec) dhnext = np.dot(dhrec,self.W1_rec.T) for grad in [dW1,dB1,dW1_rec,dW2,dB2,dW3,dB3,dW4,dB4]: np.clip(grad, -10, 10, out = grad) return [dW1,dB1,dW1_rec,dW2,dB2,dW3,dB3,dW4,dB4] def CategoricalCrossEntropy(self,labels,preds): """ Computes cross entropy between labels and model's predictions """ predictions = np.clip(preds, 1e-12, 1. - 1e-12) N = predictions.shape[0] return -np.sum(labels * np.log(predictions + 1e-9)) / N def step(self,grads,momentum = True): #for config_param,grad in zip([self.W1,self.B1,self.W1_rec,self.W2,self.B2,self.W3,self.B3],grads): #config_param -= self.learning_rate * grad if momentum: delta_W1 = -self.learning_rate * grads[0] + self.mom_coeff * self.prev_updates['W1'] delta_B1 = -self.learning_rate * grads[1] + self.mom_coeff * self.prev_updates['B1'] delta_W1_rec = -self.learning_rate * grads[2] + self.mom_coeff * self.prev_updates['W1_rec'] delta_W2 = -self.learning_rate * grads[3] + self.mom_coeff * self.prev_updates['W2'] delta_B2 = -self.learning_rate * grads[4] + self.mom_coeff * self.prev_updates['B2'] delta_W3 = -self.learning_rate * grads[5] + self.mom_coeff * self.prev_updates['W3'] delta_B3 = -self.learning_rate * grads[6] + self.mom_coeff * self.prev_updates['B3'] delta_W4 = -self.learning_rate * grads[7] + self.mom_coeff * self.prev_updates['W4'] delta_B4 = -self.learning_rate * grads[8] + self.mom_coeff * self.prev_updates['B4'] self.W1 += delta_W1 self.W1_rec += delta_W1_rec self.W2 += delta_W2 self.B1 += delta_B1 self.B2 += delta_B2 self.W3 += delta_W3 self.B3 += delta_B3 self.W4 += delta_W4 self.B4 += delta_B4 self.prev_updates['W1'] = delta_W1 self.prev_updates['W1_rec'] = delta_W1_rec self.prev_updates['W2'] = delta_W2 self.prev_updates['B1'] = delta_B1 self.prev_updates['B2'] = delta_B2 self.prev_updates['W3'] = delta_W3 self.prev_updates['B3'] = delta_B3 self.prev_updates['W4'] = delta_W4 self.prev_updates['B4'] = delta_B4 self.learning_rate *= 0.9999 def fit(self,X,Y,X_val,y_val,epochs = 50 ,verbose = True, crossVal = False): """ Given the traning dataset,their labels and number of epochs fitting the model, and measure the performance by validating training dataset. """ for epoch in range(epochs): print(f'Epoch : {epoch + 1}') perm = np.random.permutation(3000) for i in range(round(X.shape[0]/self.batch_size)): batch_start = i * self.batch_size batch_finish = (i+1) * self.batch_size index = perm[batch_start:batch_finish] X_feed = X[index] y_feed = Y[index] cache_train = self.forward(X_feed) grads = self.BPTT(cache_train,y_feed) self.step(grads) if crossVal: stop = self.cross_validation(X,val_X,Y,val_Y,threshold = 5) if stop: break cross_loss_train = self.CategoricalCrossEntropy(y_feed,cache_train[6][149]) predictions_train = self.predict(X) acc_train = metrics.accuracy(np.argmax(Y,1),predictions_train) _,__,___,____,_____,______, probs_test = self.forward(X_val) cross_loss_val = self.CategoricalCrossEntropy(y_val,probs_test[149]) predictions_val = np.argmax(probs_test[149],1) acc_val = metrics.accuracy(np.argmax(y_val,1),predictions_val) if verbose: print(f"[{epoch + 1}/{epochs}] ------> Training : Accuracy : {acc_train}") print(f"[{epoch + 1}/{epochs}] ------> Training : Loss : {cross_loss_train}") print('______________________________________________________________________________________\n') print(f"[{epoch + 1}/{epochs}] ------> Testing : Accuracy : {acc_val}") print(f"[{epoch + 1}/{epochs}] ------> Testing : Loss : {cross_loss_val}") print('______________________________________________________________________________________\n') self.train_loss.append(cross_loss_train) self.test_loss.append(cross_loss_val) self.train_acc.append(acc_train) self.test_acc.append(acc_val) def predict(self,X): _,__,___,____,_____,______,probs = self.forward(X) return np.argmax(probs[149],axis=1) def history(self): return {'TrainLoss' : self.train_loss, 'TrainAcc' : self.train_acc, 'TestLoss' : self.test_loss, 'TestAcc' : self.test_acc} # %% three_layer_rnn = Three_Hidden_Layer_RNN(hidden_dim_1 = 128, hidden_dim_2 = 64,hidden_dim_3 = 32, learning_rate = 1e-4, mom_coeff = 0.0, batch_size = 32, output_class = 6) # %% three_layer_rnn.fit(X_train,y_train,X_test,y_test,epochs=15) # %% three_layer_rnn_v1 = Three_Hidden_Layer_RNN(hidden_dim_1 = 128, hidden_dim_2 = 64,hidden_dim_3 = 32, learning_rate = 5e-5, mom_coeff = 0.0, batch_size = 32, output_class = 6) three_layer_rnn_v1.fit(X_train,y_train,X_test,y_test,epochs=15) # %% three_layer_rnn_v2 = Three_Hidden_Layer_RNN(hidden_dim_1 = 128, hidden_dim_2 = 64,hidden_dim_3 = 32, learning_rate = 1e-4, mom_coeff = 0.0, batch_size = 32, output_class = 6) three_layer_rnn_v2.fit(X_train,y_train,X_test,y_test,epochs=15) # %% three_layer_rnn_history = three_layer_rnn.history() plt.figure() plt.plot(three_layer_rnn_history['TestLoss'],'-o') plt.plot(three_layer_rnn_history['TrainLoss'],'-o') plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Categorical Cross Entropy over epochs') plt.legend(['Test Loss','Train Loss']) plt.show() # %% plt.figure() plt.plot(three_layer_rnn_history['TestAcc'],'-o') plt.plot(three_layer_rnn_history['TrainAcc'],'-o') plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Accuracy over epochs') plt.legend(['Test Acc','Train Acc']) plt.show() # %% plt.figure() plt.plot(three_layer_rnn_history['TrainAcc'],'-o') plt.plot(multilayer_rnn_history['TrainAcc'],'-o') plt.plot(history['TrainAcc'],'-o') plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Training Accuracy over epochs') plt.legend(['3 hidden layer Rnn','Multi Layer RNN','Vanilla RNN']) plt.show() # %% plt.figure() plt.plot(three_layer_rnn_history['TestAcc'],'-o') plt.plot(multilayer_rnn_history['TestAcc'],'-o') plt.plot(history['TestAcc'],'-o') plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Testing Accuracy over epochs') plt.legend(['3 hidden layer Rnn','Multi Layer RNN','Vanilla RNN']) plt.show() # %% train_preds_three_layer_rnn_history = three_layer_rnn.predict(X_train) test_preds_three_layer_rnn_history = three_layer_rnn.predict(X_test) confusion_mat_train_three_layer_rnn_history = metrics.confusion_matrix(np.argmax(y_train,1),train_preds_three_layer_rnn_history) confusion_mat_test_three_layer_rnn_history = metrics.confusion_matrix(np.argmax(y_test,1),test_preds_three_layer_rnn_history) body_movements = ['downstairs','jogging','sitting','standing','upstairs','walking'] confusion_mat_train_three_layer_rnn_history.columns = body_movements confusion_mat_train_three_layer_rnn_history.index = body_movements confusion_mat_test_three_layer_rnn_history.columns = body_movements confusion_mat_test_three_layer_rnn_history.index = body_movements print(confusion_mat_train_three_layer_rnn_history) # %% sns.heatmap(confusion_mat_test_three_layer_rnn_history/np.sum(confusion_mat_test_three_layer_rnn_history), annot=True, fmt='.2%',cmap = 'Blues') plt.show() # %% sns.heatmap(confusion_mat_train_three_layer_rnn_history/np.sum(confusion_mat_train_three_layer_rnn_history), annot=True, fmt='.2%',cmap = 'Blues') plt.show() # %% class Five_Hidden_Layer_RNN(object): """ Recurrent Neural Network for classifying human activity. RNN encapsulates all necessary logic for training the network. """ def __init__(self,input_dim = 3,hidden_dim_1 = 128, hidden_dim_2 = 64,hidden_dim_3 = 32,hidden_dim_4 = 16 ,hidden_dim_5 = 8, seq_len = 150, learning_rate = 1e-1, mom_coeff = 0.85, batch_size = 32, output_class = 6): """ Initialization of weights/biases and other configurable parameters. """ np.random.seed(150) self.input_dim = input_dim self.hidden_dim_1 = hidden_dim_1 self.hidden_dim_2 = hidden_dim_2 self.hidden_dim_3 = hidden_dim_3 self.hidden_dim_4 = hidden_dim_4 self.hidden_dim_5 = hidden_dim_5 # Unfold case T = 150 : self.seq_len = seq_len self.output_class = output_class self.learning_rate = learning_rate self.batch_size = batch_size self.mom_coeff = mom_coeff # Xavier uniform scaler : Xavier = lambda fan_in,fan_out : math.sqrt(6/(fan_in + fan_out)) lim_inp2hid = Xavier(self.input_dim,self.hidden_dim_1) self.W1 = np.random.uniform(-lim_inp2hid,lim_inp2hid,(self.input_dim,self.hidden_dim_1)) self.B1 = np.random.uniform(-lim_inp2hid,lim_inp2hid,(1,self.hidden_dim_1)) lim_hid2hid = Xavier(self.hidden_dim_1,self.hidden_dim_1) self.W1_rec= np.random.uniform(-lim_hid2hid,lim_hid2hid,(self.hidden_dim_1,self.hidden_dim_1)) lim_hid2hid2 = Xavier(self.hidden_dim_1,self.hidden_dim_2) self.W2 = np.random.uniform(-lim_hid2hid2,lim_hid2hid2,(self.hidden_dim_1,self.hidden_dim_2)) self.B2 = np.random.uniform(-lim_hid2hid2,lim_hid2hid2,(1,self.hidden_dim_2)) lim_hid2hid3 = Xavier(self.hidden_dim_2,self.hidden_dim_3) self.W3 = np.random.uniform(-lim_hid2hid3,lim_hid2hid3,(self.hidden_dim_2,self.hidden_dim_3)) self.B3 = np.random.uniform(-lim_hid2hid3,lim_hid2hid3,(1,self.hidden_dim_3)) lim_hid2hid4 = Xavier(self.hidden_dim_3,self.hidden_dim_4) self.W4 = np.random.uniform(-lim_hid2hid4,lim_hid2hid4,(self.hidden_dim_3,self.hidden_dim_4)) self.B4 = np.random.uniform(-lim_hid2hid4,lim_hid2hid4,(1,self.hidden_dim_4)) lim_hid2hid5 = Xavier(self.hidden_dim_4,self.hidden_dim_5) self.W5 = np.random.uniform(-lim_hid2hid5,lim_hid2hid5,(self.hidden_dim_4,self.hidden_dim_5)) self.B5 = np.random.uniform(-lim_hid2hid5,lim_hid2hid5,(1,self.hidden_dim_5)) lim_hid2out = Xavier(self.hidden_dim_5,self.output_class) self.W6 = np.random.uniform(-lim_hid2out,lim_hid2out,(self.hidden_dim_5,self.output_class)) self.B6 = np.random.uniform(-lim_hid2out,lim_hid2out,(1,self.output_class)) # To keep track loss and accuracy score : self.train_loss,self.test_loss,self.train_acc,self.test_acc = [],[],[],[] # Storing previous momentum updates : self.prev_updates = {'W1' : 0, 'B1' : 0, 'W1_rec' : 0, 'W2' : 0, 'B2' : 0, 'W3' : 0, 'W4' : 0, 'B3' : 0, 'B4' : 0, 'W5' : 0, 'W6' : 0, 'B5' : 0, 'B6' : 0} def forward(self,X) -> tuple: """ Forward propagation of the RNN through time. __________________________________________________________ Inputs: --- X is the bacth. --- h_prev_state is the previous state of the hidden layer. __________________________________________________________ Returns: --- (X_state,hidden_state,probs) as a tuple. ------ 1) X_state is the input across all time steps ------ 2) hidden_state is the hidden stages across time ------ 3) probs is the probabilities of each outputs, i.e. outputs of softmax __________________________________________________________ """ X_state = dict() hidden_state_1 = dict() hidden_state_mlp = dict() hidden_state_mlp_2 = dict() hidden_state_mlp_3 = dict() hidden_state_mlp_4 = dict() output_state = dict() probs = dict() mlp_linear = dict() mlp_linear_2 = dict() mlp_linear_3 = dict() mlp_linear_4 = dict() self.h_prev_state = np.zeros((1,self.hidden_dim_1)) hidden_state_1[-1] = np.copy(self.h_prev_state) # Loop over time T = 150 : for t in range(self.seq_len): # Selecting first record with 3 inputs, dimension = (batch_size,input_size) X_state[t] = X[:,t] # Recurrent hidden layer : hidden_state_1[t] = np.tanh(np.dot(X_state[t],self.W1) + np.dot(hidden_state_1[t-1],self.W1_rec) + self.B1) mlp_linear[t] = np.dot(hidden_state_1[t],self.W2) + self.B2 hidden_state_mlp[t] = activations.ReLU(mlp_linear[t]) mlp_linear_2[t] = np.dot(hidden_state_mlp[t],self.W3) + self.B3 hidden_state_mlp_2[t] = activations.ReLU(mlp_linear_2[t]) mlp_linear_3[t] = np.dot(hidden_state_mlp_2[t],self.W4) + self.B4 hidden_state_mlp_3[t] = activations.ReLU(mlp_linear_3[t]) mlp_linear_4[t] = np.dot(hidden_state_mlp_3[t],self.W5) + self.B5 hidden_state_mlp_4[t] = activations.ReLU(mlp_linear_4[t]) output_state[t] = np.dot(hidden_state_mlp_4[t],self.W6) + self.B6 # Per class probabilites : probs[t] = activations.softmax(output_state[t]) return (X_state,hidden_state_1,mlp_linear,hidden_state_mlp,mlp_linear_2,hidden_state_mlp_2,mlp_linear_3,hidden_state_mlp_3,mlp_linear_4,hidden_state_mlp_4,probs) def BPTT(self,cache,Y): """ Back propagation through time algorihm. Inputs: -- Cache = (X_state,hidden_state_1,mlp_linear,hidden_state_mlp,mlp_linear_2,hidden_state_mlp_2,mlp_linear_3,hidden_state_mlp_3,mlp_linear_4,hidden_state_mlp_4,probs) -- Y = desired output Returns: -- Gradients w.r.t. all configurable elements """ X_state,hidden_state_1,mlp_linear,hidden_state_mlp,mlp_linear_2,hidden_state_mlp_2,mlp_linear_3,hidden_state_mlp_3,mlp_linear_4,hidden_state_mlp_4,probs = cache # backward pass: compute gradients going backwards dW1, dW1_rec, dW2, dW3, dW4, dW5, dW6 = np.zeros_like(self.W1), np.zeros_like(self.W1_rec), np.zeros_like(self.W2),np.zeros_like(self.W3),np.zeros_like(self.W4),np.zeros_like(self.W5),np.zeros_like(self.W6) dB1, dB2,dB3,dB4,dB5,dB6 = np.zeros_like(self.B1), np.zeros_like(self.B2),np.zeros_like(self.B3),np.zeros_like(self.B4),np.zeros_like(self.B5),np.zeros_like(self.B6) dhnext = np.zeros_like(hidden_state_1[0]) for t in reversed(range(1,self.seq_len)): dy = np.copy(probs[149]) dy[np.arange(len(Y)),np.argmax(Y,1)] -= 1 #dy = probs[0] - Y[0] dW6 += np.dot(hidden_state_mlp_4[t].T,dy) dB6 += np.sum(dy,axis = 0, keepdims = True) dy1 = np.dot(dy,self.W6.T) * activations.ReLU_grad(mlp_linear_4[t]) dW5 += np.dot(hidden_state_mlp_3[t].T,dy1) dB5 += np.sum(dy1,axis = 0, keepdims = True) dy2 = np.dot(dy1,self.W5.T) * activations.ReLU_grad(mlp_linear_3[t]) dW4 += np.dot(hidden_state_mlp_2[t].T,dy2) dB4 += np.sum(dy2,axis = 0, keepdims = True) dy3 = np.dot(dy2,self.W4.T) * activations.ReLU_grad(mlp_linear_2[t]) dW3 += np.dot(hidden_state_mlp[t].T,dy3) dB3 += np.sum(dy3,axis = 0, keepdims = True) dy4 = np.dot(dy3,self.W3.T) * activations.ReLU_grad(mlp_linear[t]) dB2 += np.sum(dy4,axis = 0, keepdims = True) dW2 += np.dot(hidden_state_1[t].T,dy4) dh = np.dot(dy4,self.W2.T) + dhnext dhrec = (1 - (hidden_state_1[t] * hidden_state_1[t])) * dh dB1 += np.sum(dhrec,axis = 0, keepdims = True) dW1 += np.dot(X_state[t].T,dhrec) dW1_rec += np.dot(hidden_state_1[t-1].T,dhrec) dhnext = np.dot(dhrec,self.W1_rec.T) for grad in [dW1,dB1,dW1_rec,dW2,dB2,dW3,dB3,dW4,dB4,dW5,dB5,dW6,dB6]: np.clip(grad, -10, 10, out = grad) return [dW1,dB1,dW1_rec,dW2,dB2,dW3,dB3,dW4,dB4,dW5,dB5,dW6,dB6] def CategoricalCrossEntropy(self,labels,preds): """ Computes cross entropy between labels and model's predictions """ predictions = np.clip(preds, 1e-12, 1. - 1e-12) N = predictions.shape[0] return -np.sum(labels * np.log(predictions + 1e-9)) / N def step(self,grads,momentum = True): #for config_param,grad in zip([self.W1,self.B1,self.W1_rec,self.W2,self.B2,self.W3,self.B3],grads): #config_param -= self.learning_rate * grad if momentum: delta_W1 = -self.learning_rate * grads[0] + self.mom_coeff * self.prev_updates['W1'] delta_B1 = -self.learning_rate * grads[1] + self.mom_coeff * self.prev_updates['B1'] delta_W1_rec = -self.learning_rate * grads[2] + self.mom_coeff * self.prev_updates['W1_rec'] delta_W2 = -self.learning_rate * grads[3] + self.mom_coeff * self.prev_updates['W2'] delta_B2 = -self.learning_rate * grads[4] + self.mom_coeff * self.prev_updates['B2'] delta_W3 = -self.learning_rate * grads[5] + self.mom_coeff * self.prev_updates['W3'] delta_B3 = -self.learning_rate * grads[6] + self.mom_coeff * self.prev_updates['B3'] delta_W4 = -self.learning_rate * grads[7] + self.mom_coeff * self.prev_updates['W4'] delta_B4 = -self.learning_rate * grads[8] + self.mom_coeff * self.prev_updates['B4'] delta_W5 = -self.learning_rate * grads[9] + self.mom_coeff * self.prev_updates['W5'] delta_B5 = -self.learning_rate * grads[10] + self.mom_coeff * self.prev_updates['B5'] delta_W6 = -self.learning_rate * grads[11] + self.mom_coeff * self.prev_updates['W6'] delta_B6 = -self.learning_rate * grads[12] + self.mom_coeff * self.prev_updates['B6'] self.W1 += delta_W1 self.W1_rec += delta_W1_rec self.W2 += delta_W2 self.B1 += delta_B1 self.B2 += delta_B2 self.W3 += delta_W3 self.B3 += delta_B3 self.W4 += delta_W4 self.B4 += delta_B4 self.W5 += delta_W5 self.B5 += delta_B5 self.W6 += delta_W6 self.B6 += delta_B6 self.prev_updates['W1'] = delta_W1 self.prev_updates['W1_rec'] = delta_W1_rec self.prev_updates['W2'] = delta_W2 self.prev_updates['B1'] = delta_B1 self.prev_updates['B2'] = delta_B2 self.prev_updates['W3'] = delta_W3 self.prev_updates['B3'] = delta_B3 self.prev_updates['W4'] = delta_W4 self.prev_updates['B4'] = delta_B4 self.prev_updates['W5'] = delta_W5 self.prev_updates['B5'] = delta_B5 self.prev_updates['W6'] = delta_W6 self.prev_updates['B6'] = delta_B6 self.learning_rate *= 0.9999 def fit(self,X,Y,X_val,y_val,epochs = 50 ,verbose = True, crossVal = False): """ Given the traning dataset,their labels and number of epochs fitting the model, and measure the performance by validating training dataset. """ for epoch in range(epochs): print(f'Epoch : {epoch + 1}') perm = np.random.permutation(3000) for i in range(round(X.shape[0]/self.batch_size)): batch_start = i * self.batch_size batch_finish = (i+1) * self.batch_size index = perm[batch_start:batch_finish] X_feed = X[index] y_feed = Y[index] cache_train = self.forward(X_feed) grads = self.BPTT(cache_train,y_feed) self.step(grads) if crossVal: stop = self.cross_validation(X,val_X,Y,val_Y,threshold = 5) if stop: break cross_loss_train = self.CategoricalCrossEntropy(y_feed,cache_train[10][149]) predictions_train = self.predict(X) acc_train = metrics.accuracy(np.argmax(Y,1),predictions_train) _,__,___,____,_____,______,_______,________,__________,___________, probs_test = self.forward(X_val) cross_loss_val = self.CategoricalCrossEntropy(y_val,probs_test[149]) predictions_val = np.argmax(probs_test[149],1) acc_val = metrics.accuracy(np.argmax(y_val,1),predictions_val) if verbose: print(f"[{epoch + 1}/{epochs}] ------> Training : Accuracy : {acc_train}") print(f"[{epoch + 1}/{epochs}] ------> Training : Loss : {cross_loss_train}") print('______________________________________________________________________________________\n') print(f"[{epoch + 1}/{epochs}] ------> Testing : Accuracy : {acc_val}") print(f"[{epoch + 1}/{epochs}] ------> Testing : Loss : {cross_loss_val}") print('______________________________________________________________________________________\n') self.train_loss.append(cross_loss_train) self.test_loss.append(cross_loss_val) self.train_acc.append(acc_train) self.test_acc.append(acc_val) def predict(self,X): _,__,___,____,_____,______,_______,________,__________,___________, probs = self.forward(X) return np.argmax(probs[149],axis=1) def history(self): return {'TrainLoss' : self.train_loss, 'TrainAcc' : self.train_acc, 'TestLoss' : self.test_loss, 'TestAcc' : self.test_acc} # %% five_hidden_layer_rnn = Five_Hidden_Layer_RNN(hidden_dim_1 = 128, hidden_dim_2 = 64,hidden_dim_3 = 32,hidden_dim_4 = 16 ,hidden_dim_5 = 8, learning_rate = 1e-4, mom_coeff = 0.0) # %% five_hidden_layer_rnn.fit(X_train,y_train,X_test,y_test,epochs = 35) # %% five_hidden_layer_rnn_history = five_hidden_layer_rnn.history() plt.figure() plt.plot(five_hidden_layer_rnn_history['TestLoss'],'-o') plt.plot(five_hidden_layer_rnn_history['TrainLoss'],'-o') plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Categorical Cross Entropy over epochs') plt.legend(['Test Loss','Train Loss']) plt.show() # %% plt.figure() plt.plot(five_hidden_layer_rnn_history['TestAcc'],'-o') plt.plot(five_hidden_layer_rnn_history['TrainAcc'],'-o') plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Accuracy over epochs') plt.legend(['Test Acc','Train Acc']) plt.show() # %% plt.figure() plt.plot(five_hidden_layer_rnn_history['TrainAcc'],'-o') plt.plot(three_layer_rnn_history['TrainAcc'],'-o') plt.plot(multilayer_rnn_history['TrainAcc'],'-o') plt.plot(history['TrainAcc'],'-o') plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Training Accuracy over epochs') plt.legend(['Five hidden layer RNN','3 hidden layer RNN','Multi Layer RNN','Vanilla RNN']) plt.show() # %% plt.figure() plt.plot(five_hidden_layer_rnn_history['TestAcc'],'-o') plt.plot(three_layer_rnn_history['TestAcc'],'-o') plt.plot(multilayer_rnn_history['TestAcc'],'-o') plt.plot(history['TestAcc'],'-o') plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Testing Accuracy over epochs') plt.legend(['Five hidden layer RNN','3 hidden layer RNN','Multi Layer RNN','Vanilla RNN']) plt.show() # %% train_preds_five_hidden_layer_rnn = five_hidden_layer_rnn.predict(X_train) test_preds_five_hidden_layer_rnn = five_hidden_layer_rnn.predict(X_test) confusion_mat_train_five_hidden_layer_rnn = metrics.confusion_matrix(np.argmax(y_train,1),train_preds_five_hidden_layer_rnn) confusion_mat_test_five_hidden_layer_rnn = metrics.confusion_matrix(np.argmax(y_test,1),test_preds_five_hidden_layer_rnn) body_movements = ['downstairs','jogging','sitting','standing','upstairs','walking'] confusion_mat_train_five_hidden_layer_rnn.columns = body_movements confusion_mat_train_five_hidden_layer_rnn.index = body_movements confusion_mat_test_five_hidden_layer_rnn.columns = body_movements confusion_mat_test_five_hidden_layer_rnn.index = body_movements print(confusion_mat_test_five_hidden_layer_rnn) # %% sns.heatmap(confusion_mat_test_five_hidden_layer_rnn/np.sum(confusion_mat_test_five_hidden_layer_rnn), annot=True, fmt='.2%',cmap = 'Blues') plt.show() # %% sns.heatmap(confusion_mat_train_five_hidden_layer_rnn/np.sum(confusion_mat_train_five_hidden_layer_rnn), annot=True, fmt='.2%',cmap = 'Blues') plt.show() # %% [markdown] # LSTM # %% def sigmoid(x): return 1 / (1 + np.exp(-x)) def dsigmoid(y): return y * (1 - y) def tanh(x): return np.tanh(x) def dtanh(y): return 1 - y * y # %% class LSTM(object): """ Long-Short Term Memory Recurrent neural network, encapsulates all necessary logic for training, then built the hyperparameters and architecture of the network. """ def __init__(self,input_dim = 3,hidden_dim = 100,output_class = 6,seq_len = 150,batch_size = 30,learning_rate = 1e-1,mom_coeff = 0.85): """ Initialization of weights/biases and other configurable parameters. """ np.random.seed(150) self.input_dim = input_dim self.hidden_dim = hidden_dim # Unfold case T = 150 : self.seq_len = seq_len self.output_class = output_class self.learning_rate = learning_rate self.batch_size = batch_size self.mom_coeff = mom_coeff self.input_stack_dim = self.input_dim + self.hidden_dim # Xavier uniform scaler : Xavier = lambda fan_in,fan_out : math.sqrt(6/(fan_in + fan_out)) lim1 = Xavier(self.input_dim,self.hidden_dim) self.W_f = np.random.uniform(-lim1,lim1,(self.input_stack_dim,self.hidden_dim)) self.B_f = np.random.uniform(-lim1,lim1,(1,self.hidden_dim)) self.W_i = np.random.uniform(-lim1,lim1,(self.input_stack_dim,self.hidden_dim)) self.B_i = np.random.uniform(-lim1,lim1,(1,self.hidden_dim)) self.W_c = np.random.uniform(-lim1,lim1,(self.input_stack_dim,self.hidden_dim)) self.B_c = np.random.uniform(-lim1,lim1,(1,self.hidden_dim)) self.W_o = np.random.uniform(-lim1,lim1,(self.input_stack_dim,self.hidden_dim)) self.B_o = np.random.uniform(-lim1,lim1,(1,self.hidden_dim)) lim2 = Xavier(self.hidden_dim,self.output_class) self.W = np.random.uniform(-lim2,lim2,(self.hidden_dim,self.output_class)) self.B = np.random.uniform(-lim2,lim2,(1,self.output_class)) # To keep track loss and accuracy score : self.train_loss,self.test_loss,self.train_acc,self.test_acc = [],[],[],[] # To keep previous updates in momentum : self.previous_updates = [0] * 10 # For AdaGrad: self.cache = [0] * 10 self.cache_rmsprop = [0] * 10 self.m = [0] * 10 self.v = [0] * 10 self.t = 1 def cell_forward(self,X,h_prev,C_prev): """ Takes input, previous hidden state and previous cell state, compute: --- Forget gate + Input gate + New candidate input + New cell state + output gate + hidden state. Then, classify by softmax. """ #print(X.shape,h_prev.shape) # Stacking previous hidden state vector with inputs: stack = np.column_stack([X,h_prev]) # Forget gate: forget_gate = activations.sigmoid(np.dot(stack,self.W_f) + self.B_f) # İnput gate: input_gate = activations.sigmoid(np.dot(stack,self.W_i) + self.B_i) # New candidate: cell_bar = np.tanh(np.dot(stack,self.W_c) + self.B_c) # New Cell state: cell_state = forget_gate * C_prev + input_gate * cell_bar # Output fate: output_gate = activations.sigmoid(np.dot(stack,self.W_o) + self.B_o) # Hidden state: hidden_state = output_gate * np.tanh(cell_state) # Classifiers (Softmax) : dense = np.dot(hidden_state,self.W) + self.B probs = activations.softmax(dense) return (stack,forget_gate,input_gate,cell_bar,cell_state,output_gate,hidden_state,dense,probs) def forward(self,X,h_prev,C_prev): x_s,z_s,f_s,i_s = {},{},{},{} C_bar_s,C_s,o_s,h_s = {},{},{},{} v_s,y_s = {},{} h_s[-1] = np.copy(h_prev) C_s[-1] = np.copy(C_prev) for t in range(self.seq_len): x_s[t] = X[:,t,:] z_s[t], f_s[t], i_s[t], C_bar_s[t], C_s[t], o_s[t], h_s[t],v_s[t], y_s[t] = self.cell_forward(x_s[t],h_s[t-1],C_s[t-1]) return (z_s, f_s, i_s, C_bar_s, C_s, o_s, h_s,v_s, y_s) def BPTT(self,outs,Y): z_s, f_s, i_s, C_bar_s, C_s, o_s, h_s,v_s, y_s = outs dW_f, dW_i,dW_c, dW_o,dW = np.zeros_like(self.W_f), np.zeros_like(self.W_i), np.zeros_like(self.W_c),np.zeros_like(self.W_o),np.zeros_like(self.W) dB_f, dB_i,dB_c,dB_o,dB = np.zeros_like(self.B_f), np.zeros_like(self.B_i),np.zeros_like(self.B_c),np.zeros_like(self.B_o),np.zeros_like(self.B) dh_next = np.zeros_like(h_s[0]) dC_next = np.zeros_like(C_s[0]) # w.r.t. softmax input ddense = np.copy(y_s[149]) ddense[np.arange(len(Y)),np.argmax(Y,1)] -= 1 #ddense[np.argmax(Y,1)] -=1 #ddense = y_s[149] - Y # Softmax classifier's : dW = np.dot(h_s[149].T,ddense) dB = np.sum(ddense,axis = 0, keepdims = True) # Backprop through time: for t in reversed(range(1,self.seq_len)): # Just equating more meaningful names stack,forget_gate,input_gate,cell_bar,cell_state,output_gate,hidden_state,dense,probs = z_s[t], f_s[t], i_s[t], C_bar_s[t], C_s[t], o_s[t], h_s[t],v_s[t], y_s[t] C_prev = C_s[t-1] # w.r.t. softmax input #ddense = np.copy(probs) #ddense[np.arange(len(Y)),np.argmax(Y,1)] -= 1 #ddense[np.arange(len(Y)),np.argmax(Y,1)] -=1 # Softmax classifier's : #dW += np.dot(hidden_state.T,ddense) #dB += np.sum(ddense,axis = 0, keepdims = True) # Output gate : dh = np.dot(ddense,self.W.T) + dh_next do = dh * np.tanh(cell_state) do = do * dsigmoid(output_gate) dW_o += np.dot(stack.T,do) dB_o += np.sum(do,axis = 0, keepdims = True) # Cell state: dC = np.copy(dC_next) dC += dh * output_gate * activations.dtanh(cell_state) dC_bar = dC * input_gate dC_bar = dC_bar * dtanh(cell_bar) dW_c += np.dot(stack.T,dC_bar) dB_c += np.sum(dC_bar,axis = 0, keepdims = True) # Input gate: di = dC * cell_bar di = dsigmoid(input_gate) * di dW_i += np.dot(stack.T,di) dB_i += np.sum(di,axis = 0,keepdims = True) # Forget gate: df = dC * C_prev df = df * dsigmoid(forget_gate) dW_f += np.dot(stack.T,df) dB_f += np.sum(df,axis = 0, keepdims = True) dz = np.dot(df,self.W_f.T) + np.dot(di,self.W_i.T) + np.dot(dC_bar,self.W_c.T) + np.dot(do,self.W_o.T) dh_next = dz[:,-self.hidden_dim:] dC_next = forget_gate * dC # List of gradients : grads = [dW,dB,dW_o,dB_o,dW_c,dB_c,dW_i,dB_i,dW_f,dB_f] # Clipping gradients anyway for grad in grads: np.clip(grad, -15, 15, out = grad) return h_s[self.seq_len - 1],C_s[self.seq_len -1 ],grads def fit(self,X,Y,X_val,y_val,epochs = 50 ,optimizer = 'SGD',verbose = True, crossVal = False): """ Given the traning dataset,their labels and number of epochs fitting the model, and measure the performance by validating training dataset. """ for epoch in range(epochs): print(f'Epoch : {epoch + 1}') perm = np.random.permutation(3000) h_prev,C_prev = np.zeros((self.batch_size,self.hidden_dim)),np.zeros((self.batch_size,self.hidden_dim)) for i in range(round(X.shape[0]/self.batch_size) - 1): batch_start = i * self.batch_size batch_finish = (i+1) * self.batch_size index = perm[batch_start:batch_finish] # Feeding random indexes: X_feed = X[index] y_feed = Y[index] # Forward + BPTT + SGD: cache_train = self.forward(X_feed,h_prev,C_prev) h,c,grads = self.BPTT(cache_train,y_feed) if optimizer == 'SGD': self.SGD(grads) elif optimizer == 'AdaGrad' : self.AdaGrad(grads) elif optimizer == 'RMSprop': self.RMSprop(grads) elif optimizer == 'VanillaAdam': self.VanillaAdam(grads) else: self.Adam(grads) # Hidden state -------> Previous hidden state # Cell state ---------> Previous cell state h_prev,C_prev = h,c # Training metrics calculations: cross_loss_train = self.CategoricalCrossEntropy(y_feed,cache_train[8][149]) predictions_train = self.predict(X) acc_train = metrics.accuracy(np.argmax(Y,1),predictions_train) # Validation metrics calculations: test_prevs = np.zeros((X_val.shape[0],self.hidden_dim)) _,__,___,____,_____,______,_______,________,probs_test = self.forward(X_val,test_prevs,test_prevs) cross_loss_val = self.CategoricalCrossEntropy(y_val,probs_test[149]) predictions_val = np.argmax(probs_test[149],1) acc_val = metrics.accuracy(np.argmax(y_val,1),predictions_val) if verbose: print(f"[{epoch + 1}/{epochs}] ------> Training : Accuracy : {acc_train}") print(f"[{epoch + 1}/{epochs}] ------> Training : Loss : {cross_loss_train}") print('______________________________________________________________________________________\n') print(f"[{epoch + 1}/{epochs}] ------> Testing : Accuracy : {acc_val}") print(f"[{epoch + 1}/{epochs}] ------> Testing : Loss : {cross_loss_val}") print('______________________________________________________________________________________\n') self.train_loss.append(cross_loss_train) self.test_loss.append(cross_loss_val) self.train_acc.append(acc_train) self.test_acc.append(acc_val) def params(self): """ Return all weights/biases in sequential order starting from end in list form. """ return [self.W,self.B,self.W_o,self.B_o,self.W_c,self.B_c,self.W_i,self.B_i,self.W_f,self.B_f] def SGD(self,grads): """ Stochastic gradient descent with momentum on mini-batches. """ prevs = [] for param,grad,prev_update in zip(self.params(),grads,self.previous_updates): delta = self.learning_rate * grad - self.mom_coeff * prev_update param -= delta prevs.append(delta) self.previous_updates = prevs self.learning_rate *= 0.99999 def AdaGrad(self,grads): """ AdaGrad adaptive optimization algorithm. """ i = 0 for param,grad in zip(self.params(),grads): self.cache[i] += grad **2 param += -self.learning_rate * grad / (np.sqrt(self.cache[i]) + 1e-6) i += 1 def RMSprop(self,grads,decay_rate = 0.9): """ RMSprop adaptive optimization algorithm """ i = 0 for param,grad in zip(self.params(),grads): self.cache_rmsprop[i] = decay_rate * self.cache_rmsprop[i] + (1-decay_rate) * grad **2 param += - self.learning_rate * grad / (np.sqrt(self.cache_rmsprop[i])+ 1e-6) i += 1 def VanillaAdam(self,grads,beta1 = 0.9,beta2 = 0.999): """ Adam optimizer, but bias correction is not implemented """ i = 0 for param,grad in zip(self.params(),grads): self.m[i] = beta1 * self.m[i] + (1-beta1) * grad self.v[i] = beta2 * self.v[i] + (1-beta2) * grad **2 param += -self.learning_rate * self.m[i] / (np.sqrt(self.v[i]) + 1e-8) i += 1 def Adam(self,grads,beta1 = 0.9,beta2 = 0.999): """ Adam optimizer, bias correction is implemented. """ i = 0 for param,grad in zip(self.params(),grads): self.m[i] = beta1 * self.m[i] + (1-beta1) * grad self.v[i] = beta2 * self.v[i] + (1-beta2) * grad **2 m_corrected = self.m[i] / (1-beta1**self.t) v_corrected = self.v[i] / (1-beta2**self.t) param += -self.learning_rate * m_corrected / (np.sqrt(v_corrected) + 1e-8) i += 1 self.t +=1 def CategoricalCrossEntropy(self,labels,preds): """ Computes cross entropy between labels and model's predictions """ predictions = np.clip(preds, 1e-12, 1. - 1e-12) N = predictions.shape[0] return -np.sum(labels * np.log(predictions + 1e-9)) / N def predict(self,X): """ Return predictions, (not one hot encoded format) """ # Give zeros to hidden/cell states: pasts = np.zeros((X.shape[0],self.hidden_dim)) _,__,___,____,_____,______,_______,_______,probs = self.forward(X,pasts,pasts) return np.argmax(probs[149],axis=1) def history(self): return {'TrainLoss' : self.train_loss, 'TrainAcc' : self.train_acc, 'TestLoss' : self.test_loss, 'TestAcc' : self.test_acc} # %% lstm = LSTM(learning_rate = 5e-4,mom_coeff = 0.0,batch_size = 32,hidden_dim=128) # %% lstm.fit(X_train,y_train,X_test,y_test,epochs = 15,optimizer='SGD') # %% lstm_history = lstm.history() # %% train_preds_lstm = lstm.predict(X_train) test_preds_lstm = lstm.predict(X_test) confusion_mat_train_lstm = metrics.confusion_matrix(np.argmax(y_train,1),train_preds_lstm) confusion_mat_test_lstm = metrics.confusion_matrix(np.argmax(y_test,1),test_preds_lstm) body_movements = ['downstairs','jogging','sitting','standing','upstairs','walking'] confusion_mat_train_lstm.columns = body_movements confusion_mat_train_lstm.index = body_movements confusion_mat_test_lstm.columns = body_movements confusion_mat_test_lstm.index = body_movements sns.heatmap(confusion_mat_train_lstm/np.sum(confusion_mat_train_lstm), annot=True, fmt='.2%',cmap = 'Blues') plt.show() sns.heatmap(confusion_mat_test_lstm/np.sum(confusion_mat_test_lstm), annot=True, fmt='.2%',cmap = 'Blues') plt.show() # %% lstm2 = LSTM(learning_rate = 2e-3,mom_coeff = 0.0,batch_size = 32,hidden_dim=128) lstm2.fit(X_train,y_train,X_test,y_test,epochs = 15,optimizer='RMSprop') # %% lstm2_history = lstm2.history() # %% lstm3 = LSTM(learning_rate = 3e-3,mom_coeff = 0.0,batch_size = 32,hidden_dim=128) lstm3.fit(X_train,y_train,X_test,y_test,epochs = 15,optimizer='Adam') # %% lstm4 = LSTM(learning_rate = 1e-3,mom_coeff = 0.0,batch_size = 32,hidden_dim=128) lstm4.fit(X_train,y_train,X_test,y_test,epochs = 15,optimizer='AdaGrad') # %% lstm5 = LSTM(learning_rate = 1e-3,mom_coeff = 0.0,batch_size = 32,hidden_dim=128) lstm5.fit(X_train,y_train,X_test,y_test,epochs = 15,optimizer='VanillaAdam') # %% lstm3_history = lstm3.history() lstm4_history = lstm4.history() lstm5_history = lstm5.history() plt.figure() plt.plot(lstm_history['TrainAcc'],'-o') plt.plot(lstm2_history['TrainAcc'],'-o') plt.plot(lstm3_history['TrainAcc'],'-o') plt.plot(lstm4_history['TrainAcc'],'-o') plt.plot(lstm5_history['TrainAcc'],'-o') plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Training Accuracy over epochs') plt.legend(['SGD','RMSprop','Adam','AdaGrad','Vanilla Adam']) plt.show() plt.figure() plt.plot(lstm_history['TestAcc'],'-o') plt.plot(lstm2_history['TestAcc'],'-o') plt.plot(lstm3_history['TestAcc'],'-o') plt.plot(lstm4_history['TestAcc'],'-o') plt.plot(lstm5_history['TestAcc'],'-o') plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Testing Accuracy over epochs') plt.legend(['SGD','RMSprop','Adam','AdaGrad','Vanilla Adam']) plt.show() plt.figure() plt.plot(lstm_history['TrainLoss'],'-o') plt.plot(lstm2_history['TrainLoss'],'-o') plt.plot(lstm3_history['TrainLoss'],'-o') plt.plot(lstm4_history['TrainLoss'],'-o') plt.plot(lstm5_history['TrainLoss'],'-o') plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Training Loss over epochs') plt.legend(['SGD','RMSprop','Adam','AdaGrad','Vanilla Adam']) plt.show() plt.figure() plt.plot(lstm_history['TestLoss'],'-o') plt.plot(lstm2_history['TestLoss'],'-o') plt.plot(lstm3_history['TestLoss'],'-o') plt.plot(lstm4_history['TestLoss'],'-o') plt.plot(lstm5_history['TestLoss'],'-o') plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Testing Loss over epochs') plt.legend(['SGD','RMSprop','Adam','AdaGrad','Vanilla Adam']) plt.show() # %% three_layer_rnn_v2_history = three_layer_rnn_v2.history() plt.figure() plt.plot(three_layer_rnn_v2_history['TrainAcc'],'-o') plt.plot(lstm_history['TrainAcc'],'-o') plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Training Accuracy over epochs') plt.legend(['Best RNN','Best LSTM']) plt.show() plt.figure() plt.plot(three_layer_rnn_v2_history['TestAcc'],'-o') plt.plot(lstm_history['TestAcc'],'-o') plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Testing Accuracy over epochs') plt.legend(['Best RNN','Best LSTM']) plt.show() plt.figure() plt.plot(three_layer_rnn_v2_history['TrainLoss'],'-o') plt.plot(lstm_history['TrainLoss'],'-o') plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Training Loss over epochs') plt.legend(['Best RNN','Best LSTM']) plt.show() plt.figure() plt.plot(three_layer_rnn_v2_history['TestLoss'],'-o') plt.plot(lstm_history['TestLoss'],'-o') plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Testing Loss over epochs') plt.legend(['Best RNN','Best LSTM']) plt.show() # %% train_preds_lstm = lstm3.predict(X_train) test_preds_lstm = lstm3.predict(X_test) confusion_mat_train_lstm = metrics.confusion_matrix(np.argmax(y_train,1),train_preds_lstm) confusion_mat_test_lstm = metrics.confusion_matrix(np.argmax(y_test,1),test_preds_lstm) body_movements = ['downstairs','jogging','sitting','standing','upstairs','walking'] confusion_mat_train_lstm.columns = body_movements confusion_mat_train_lstm.index = body_movements confusion_mat_test_lstm.columns = body_movements confusion_mat_test_lstm.index = body_movements sns.heatmap(confusion_mat_train_lstm/np.sum(confusion_mat_train_lstm), annot=True, fmt='.2%',cmap = 'Blues') plt.show() sns.heatmap(confusion_mat_test_lstm/np.sum(confusion_mat_test_lstm), annot=True, fmt='.2%',cmap = 'Blues') plt.show() # %% # %% class Multi_Layer_LSTM(object): """ Long-Short Term Memory Recurrent neural network, encapsulates all necessary logic for training, then built the hyperparameters and architecture of the network. """ def __init__(self,input_dim = 3,hidden_dim_1 = 128,hidden_dim_2 =64,output_class = 6,seq_len = 150,batch_size = 30,learning_rate = 1e-1,mom_coeff = 0.85): """ Initialization of weights/biases and other configurable parameters. """ np.random.seed(150) self.input_dim = input_dim self.hidden_dim_1 = hidden_dim_1 self.hidden_dim_2 = hidden_dim_2 # Unfold case T = 150 : self.seq_len = seq_len self.output_class = output_class self.learning_rate = learning_rate self.batch_size = batch_size self.mom_coeff = mom_coeff self.input_stack_dim = self.input_dim + self.hidden_dim_1 # Xavier uniform scaler : Xavier = lambda fan_in,fan_out : math.sqrt(6/(fan_in + fan_out)) lim1 = Xavier(self.input_dim,self.hidden_dim_1) self.W_f = np.random.uniform(-lim1,lim1,(self.input_stack_dim,self.hidden_dim_1)) self.B_f = np.random.uniform(-lim1,lim1,(1,self.hidden_dim_1)) self.W_i = np.random.uniform(-lim1,lim1,(self.input_stack_dim,self.hidden_dim_1)) self.B_i = np.random.uniform(-lim1,lim1,(1,self.hidden_dim_1)) self.W_c = np.random.uniform(-lim1,lim1,(self.input_stack_dim,self.hidden_dim_1)) self.B_c = np.random.uniform(-lim1,lim1,(1,self.hidden_dim_1)) self.W_o = np.random.uniform(-lim1,lim1,(self.input_stack_dim,self.hidden_dim_1)) self.B_o = np.random.uniform(-lim1,lim1,(1,self.hidden_dim_1)) lim2 = Xavier(self.hidden_dim_1,self.hidden_dim_2) self.W_hid = np.random.uniform(-lim2,lim2,(self.hidden_dim_1,self.hidden_dim_2)) self.B_hid = np.random.uniform(-lim2,lim2,(1,self.hidden_dim_2)) lim3 = Xavier(self.hidden_dim_2,self.output_class) self.W = np.random.uniform(-lim3,lim3,(self.hidden_dim_2,self.output_class)) self.B = np.random.uniform(-lim3,lim3,(1,self.output_class)) # To keep track loss and accuracy score : self.train_loss,self.test_loss,self.train_acc,self.test_acc = [],[],[],[] # To keep previous updates in momentum : self.previous_updates = [0] * 13 # For AdaGrad: self.cache = [0] * 13 self.cache_rmsprop = [0] * 13 self.m = [0] * 13 self.v = [0] * 13 self.t = 1 def cell_forward(self,X,h_prev,C_prev): """ Takes input, previous hidden state and previous cell state, compute: --- Forget gate + Input gate + New candidate input + New cell state + output gate + hidden state. Then, classify by softmax. """ #print(X.shape,h_prev.shape) # Stacking previous hidden state vector with inputs: stack = np.column_stack([X,h_prev]) # Forget gate: forget_gate = activations.sigmoid(np.dot(stack,self.W_f) + self.B_f) # İnput gate: input_gate = activations.sigmoid(np.dot(stack,self.W_i) + self.B_i) # New candidate: cell_bar = np.tanh(np.dot(stack,self.W_c) + self.B_c) # New Cell state: cell_state = forget_gate * C_prev + input_gate * cell_bar # Output fate: output_gate = activations.sigmoid(np.dot(stack,self.W_o) + self.B_o) # Hidden state: hidden_state = output_gate * np.tanh(cell_state) # Classifiers (Softmax) : dense_hid = np.dot(hidden_state,self.W_hid) + self.B_hid act = activations.ReLU(dense_hid) dense = np.dot(act,self.W) + self.B probs = activations.softmax(dense) return (stack,forget_gate,input_gate,cell_bar,cell_state,output_gate,hidden_state,dense,probs,dense_hid,act) def forward(self,X,h_prev,C_prev): x_s,z_s,f_s,i_s = {},{},{},{} C_bar_s,C_s,o_s,h_s = {},{},{},{} v_s,y_s,v_1s,y_1s = {},{},{},{} h_s[-1] = np.copy(h_prev) C_s[-1] = np.copy(C_prev) for t in range(self.seq_len): x_s[t] = X[:,t,:] z_s[t], f_s[t], i_s[t], C_bar_s[t], C_s[t], o_s[t], h_s[t],v_s[t], y_s[t],v_1s[t],y_1s[t] = self.cell_forward(x_s[t],h_s[t-1],C_s[t-1]) return (z_s, f_s, i_s, C_bar_s, C_s, o_s, h_s,v_s, y_s,v_1s,y_1s) def BPTT(self,outs,Y): z_s, f_s, i_s, C_bar_s, C_s, o_s, h_s,v_s, y_s,v_1s,y_1s = outs dW_f, dW_i,dW_c, dW_o,dW,dW_hid = np.zeros_like(self.W_f), np.zeros_like(self.W_i), np.zeros_like(self.W_c),np.zeros_like(self.W_o),np.zeros_like(self.W),np.zeros_like(self.W_hid) dB_f, dB_i,dB_c,dB_o,dB,dB_hid = np.zeros_like(self.B_f), np.zeros_like(self.B_i),np.zeros_like(self.B_c),np.zeros_like(self.B_o),np.zeros_like(self.B),np.zeros_like(self.B_hid) dh_next = np.zeros_like(h_s[0]) dC_next = np.zeros_like(C_s[0]) # w.r.t. softmax input ddense = np.copy(y_s[149]) ddense[np.arange(len(Y)),np.argmax(Y,1)] -= 1 #ddense[np.argmax(Y,1)] -=1 #ddense = y_s[149] - Y # Softmax classifier's : dW = np.dot(v_1s[149].T,ddense) dB = np.sum(ddense,axis = 0, keepdims = True) ddense_hid = np.dot(ddense,self.W.T) * activations.dReLU(v_1s[149]) dW_hid = np.dot(h_s[149].T,ddense_hid) dB_hid = np.sum(ddense_hid,axis = 0, keepdims = True) # Backprop through time: for t in reversed(range(1,self.seq_len)): # Just equating more meaningful names stack,forget_gate,input_gate,cell_bar,cell_state,output_gate,hidden_state,dense,probs = z_s[t], f_s[t], i_s[t], C_bar_s[t], C_s[t], o_s[t], h_s[t],v_s[t], y_s[t] C_prev = C_s[t-1] # w.r.t. softmax input #ddense = np.copy(probs) #ddense[np.arange(len(Y)),np.argmax(Y,1)] -= 1 #ddense[np.arange(len(Y)),np.argmax(Y,1)] -=1 # Softmax classifier's : #dW += np.dot(hidden_state.T,ddense) #dB += np.sum(ddense,axis = 0, keepdims = True) # Output gate : dh = np.dot(ddense_hid,self.W_hid.T) + dh_next do = dh * np.tanh(cell_state) do = do * dsigmoid(output_gate) dW_o += np.dot(stack.T,do) dB_o += np.sum(do,axis = 0, keepdims = True) # Cell state: dC = np.copy(dC_next) dC += dh * output_gate * activations.dtanh(cell_state) dC_bar = dC * input_gate dC_bar = dC_bar * dtanh(cell_bar) dW_c += np.dot(stack.T,dC_bar) dB_c += np.sum(dC_bar,axis = 0, keepdims = True) # Input gate: di = dC * cell_bar di = dsigmoid(input_gate) * di dW_i += np.dot(stack.T,di) dB_i += np.sum(di,axis = 0,keepdims = True) # Forget gate: df = dC * C_prev df = df * dsigmoid(forget_gate) dW_f += np.dot(stack.T,df) dB_f += np.sum(df,axis = 0, keepdims = True) dz = np.dot(df,self.W_f.T) + np.dot(di,self.W_i.T) + np.dot(dC_bar,self.W_c.T) + np.dot(do,self.W_o.T) dh_next = dz[:,-self.hidden_dim_1:] dC_next = forget_gate * dC # List of gradients : grads = [dW,dB,dW_hid,dB_hid,dW_o,dB_o,dW_c,dB_c,dW_i,dB_i,dW_f,dB_f] # Clipping gradients anyway for grad in grads: np.clip(grad, -15, 15, out = grad) return h_s[self.seq_len - 1],C_s[self.seq_len -1 ],grads def fit(self,X,Y,X_val,y_val,epochs = 50 ,optimizer = 'SGD',verbose = True, crossVal = False): """ Given the traning dataset,their labels and number of epochs fitting the model, and measure the performance by validating training dataset. """ for epoch in range(epochs): print(f'Epoch : {epoch + 1}') perm = np.random.permutation(3000) h_prev,C_prev = np.zeros((self.batch_size,self.hidden_dim_1)),np.zeros((self.batch_size,self.hidden_dim_1)) for i in range(round(X.shape[0]/self.batch_size) - 1): batch_start = i * self.batch_size batch_finish = (i+1) * self.batch_size index = perm[batch_start:batch_finish] # Feeding random indexes: X_feed = X[index] y_feed = Y[index] # Forward + BPTT + SGD: cache_train = self.forward(X_feed,h_prev,C_prev) h,c,grads = self.BPTT(cache_train,y_feed) if optimizer == 'SGD': self.SGD(grads) elif optimizer == 'AdaGrad' : self.AdaGrad(grads) elif optimizer == 'RMSprop': self.RMSprop(grads) elif optimizer == 'VanillaAdam': self.VanillaAdam(grads) else: self.Adam(grads) # Hidden state -------> Previous hidden state # Cell state ---------> Previous cell state h_prev,C_prev = h,c # Training metrics calculations: cross_loss_train = self.CategoricalCrossEntropy(y_feed,cache_train[8][149]) predictions_train = self.predict(X) acc_train = metrics.accuracy(np.argmax(Y,1),predictions_train) # Validation metrics calculations: test_prevs = np.zeros((X_val.shape[0],self.hidden_dim_1)) _,__,___,____,_____,______,_______,________,probs_test,a,b = self.forward(X_val,test_prevs,test_prevs) cross_loss_val = self.CategoricalCrossEntropy(y_val,probs_test[149]) predictions_val = np.argmax(probs_test[149],1) acc_val = metrics.accuracy(np.argmax(y_val,1),predictions_val) if verbose: print(f"[{epoch + 1}/{epochs}] ------> Training : Accuracy : {acc_train}") print(f"[{epoch + 1}/{epochs}] ------> Training : Loss : {cross_loss_train}") print('______________________________________________________________________________________\n') print(f"[{epoch + 1}/{epochs}] ------> Testing : Accuracy : {acc_val}") print(f"[{epoch + 1}/{epochs}] ------> Testing : Loss : {cross_loss_val}") print('______________________________________________________________________________________\n') self.train_loss.append(cross_loss_train) self.test_loss.append(cross_loss_val) self.train_acc.append(acc_train) self.test_acc.append(acc_val) def params(self): """ Return all weights/biases in sequential order starting from end in list form. """ return [self.W,self.B,self.W_hid,self.B_hid,self.W_o,self.B_o,self.W_c,self.B_c,self.W_i,self.B_i,self.W_f,self.B_f] def SGD(self,grads): """ Stochastic gradient descent with momentum on mini-batches. """ prevs = [] for param,grad,prev_update in zip(self.params(),grads,self.previous_updates): delta = self.learning_rate * grad - self.mom_coeff * prev_update param -= delta prevs.append(delta) self.previous_updates = prevs self.learning_rate *= 0.99999 def AdaGrad(self,grads): """ AdaGrad adaptive optimization algorithm. """ i = 0 for param,grad in zip(self.params(),grads): self.cache[i] += grad **2 param += -self.learning_rate * grad / (np.sqrt(self.cache[i]) + 1e-6) i += 1 def RMSprop(self,grads,decay_rate = 0.9): """ RMSprop adaptive optimization algorithm """ i = 0 for param,grad in zip(self.params(),grads): self.cache_rmsprop[i] = decay_rate * self.cache_rmsprop[i] + (1-decay_rate) * grad **2 param += - self.learning_rate * grad / (np.sqrt(self.cache_rmsprop[i])+ 1e-6) i += 1 def VanillaAdam(self,grads,beta1 = 0.9,beta2 = 0.999): """ Adam optimizer, but bias correction is not implemented """ i = 0 for param,grad in zip(self.params(),grads): self.m[i] = beta1 * self.m[i] + (1-beta1) * grad self.v[i] = beta2 * self.v[i] + (1-beta2) * grad **2 param += -self.learning_rate * self.m[i] / (np.sqrt(self.v[i]) + 1e-8) i += 1 def Adam(self,grads,beta1 = 0.9,beta2 = 0.999): """ Adam optimizer, bias correction is implemented. """ i = 0 for param,grad in zip(self.params(),grads): self.m[i] = beta1 * self.m[i] + (1-beta1) * grad self.v[i] = beta2 * self.v[i] + (1-beta2) * grad **2 m_corrected = self.m[i] / (1-beta1**self.t) v_corrected = self.v[i] / (1-beta2**self.t) param += -self.learning_rate * m_corrected / (np.sqrt(v_corrected) + 1e-8) i += 1 self.t +=1 def CategoricalCrossEntropy(self,labels,preds): """ Computes cross entropy between labels and model's predictions """ predictions = np.clip(preds, 1e-12, 1. - 1e-12) N = predictions.shape[0] return -np.sum(labels * np.log(predictions + 1e-9)) / N def predict(self,X): """ Return predictions, (not one hot encoded format) """ # Give zeros to hidden/cell states: pasts = np.zeros((X.shape[0],self.hidden_dim_1)) _,__,___,____,_____,______,_______,_______,probs,a,b = self.forward(X,pasts,pasts) return np.argmax(probs[149],axis=1) def history(self): return {'TrainLoss' : self.train_loss, 'TrainAcc' : self.train_acc, 'TestLoss' : self.test_loss, 'TestAcc' : self.test_acc} # %% mutl_layer_lstm = Multi_Layer_LSTM(learning_rate=1e-3,batch_size=32,hidden_dim_1 = 128,hidden_dim_2=64,mom_coeff=0.0) mutl_layer_lstm.fit(X_train,y_train,X_test,y_test,epochs=15,optimizer='Adam') # %% mutl_layer_lstm_history = mutl_layer_lstm.history() plt.figure() plt.plot(mutl_layer_lstm_history['TrainAcc'],'-o') plt.plot(lstm_history['TrainAcc'],'-o') plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Training Accuracy over epochs') plt.legend(['Multi Layer LSTM','LSTM']) plt.show() plt.figure() plt.plot(mutl_layer_lstm_history['TestAcc'],'-o') plt.plot(lstm_history['TestAcc'],'-o') plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Testing Accuracy over epochs') plt.legend(['Multi Layer LSTM','LSTM']) plt.show() plt.figure() plt.plot(mutl_layer_lstm_history['TrainLoss'],'-o') plt.plot(lstm_history['TrainLoss'],'-o') plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Training Loss over epochs') plt.legend(['Multi Layer LSTM','LSTM']) plt.show() plt.figure() plt.plot(mutl_layer_lstm_history['TestLoss'],'-o') plt.plot(lstm_history['TestLoss'],'-o') plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Testing Loss over epochs') plt.legend(['Multi Layer LSTM','LSTM']) plt.show() # %% mutl_layer_lstm.fit(X_train,y_train,X_test,y_test,epochs=15,optimizer = 'Vanilla') # %% mutl_layer_lstm_history = mutl_layer_lstm.history() # %% plt.figure() plt.plot(mutl_layer_lstm_history['TestLoss'],'-o') plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Testing Loss over epochs') plt.show() plt.figure() plt.plot(mutl_layer_lstm_history['TrainLoss'],'-o') plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Testing Loss over epochs') plt.show() plt.figure() plt.plot(mutl_layer_lstm_history['TestAcc'],'-o') plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Testing Loss over epochs') plt.show() plt.figure() plt.plot(mutl_layer_lstm_history['TrainAcc'],'-o') plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Testing Loss over epochs') plt.show() # %% class GRU(object): """ Gater recurrent unit, encapsulates all necessary logic for training, then built the hyperparameters and architecture of the network. """ def __init__(self,input_dim = 3,hidden_dim = 128,output_class = 6,seq_len = 150,batch_size = 32,learning_rate = 1e-1,mom_coeff = 0.85): """ Initialization of weights/biases and other configurable parameters. """ np.random.seed(32) self.input_dim = input_dim self.hidden_dim = hidden_dim # Unfold case T = 150 : self.seq_len = seq_len self.output_class = output_class self.learning_rate = learning_rate self.batch_size = batch_size self.mom_coeff = mom_coeff # Xavier uniform scaler : Xavier = lambda fan_in,fan_out : math.sqrt(6/(fan_in + fan_out)) lim1 = Xavier(self.input_dim,self.hidden_dim) lim1_hid = Xavier(self.hidden_dim,self.hidden_dim) self.W_z = np.random.uniform(-lim1,lim1,(self.input_dim,self.hidden_dim)) self.U_z = np.random.uniform(-lim1_hid,lim1_hid,(self.hidden_dim,self.hidden_dim)) self.B_z = np.random.uniform(-lim1,lim1,(1,self.hidden_dim)) self.W_r = np.random.uniform(-lim1,lim1,(self.input_dim,self.hidden_dim)) self.U_r = np.random.uniform(-lim1_hid,lim1_hid,(self.hidden_dim,self.hidden_dim)) self.B_r = np.random.uniform(-lim1,lim1,(1,self.hidden_dim)) self.W_h = np.random.uniform(-lim1,lim1,(self.input_dim,self.hidden_dim)) self.U_h = np.random.uniform(-lim1_hid,lim1_hid,(self.hidden_dim,self.hidden_dim)) self.B_h = np.random.uniform(-lim1,lim1,(1,self.hidden_dim)) lim2 = Xavier(self.hidden_dim,self.output_class) self.W = np.random.uniform(-lim2,lim2,(self.hidden_dim,self.output_class)) self.B = np.random.uniform(-lim2,lim2,(1,self.output_class)) # To keep track loss and accuracy score : self.train_loss,self.test_loss,self.train_acc,self.test_acc = [],[],[],[] # To keep previous updates in momentum : self.previous_updates = [0] * 10 # For AdaGrad: self.cache = [0] * 11 self.cache_rmsprop = [0] * 11 self.m = [0] * 11 self.v = [0] * 11 self.t = 1 def cell_forward(self,X,h_prev): """ Takes input, previous hidden state and previous cell state, compute: --- Forget gate + Input gate + New candidate input + New cell state + output gate + hidden state. Then, classify by softmax. """ # Update gate: update_gate = activations.sigmoid(np.dot(X,self.W_z) + np.dot(h_prev,self.U_z) + self.B_z) # Reset gate: reset_gate = activations.sigmoid(np.dot(X,self.W_r) + np.dot(h_prev,self.U_r) + self.B_r) # Current memory content: h_hat = np.tanh(np.dot(X,self.W_h) + np.dot(np.multiply(reset_gate,h_prev),self.U_h) + self.B_h) # Hidden state: hidden_state = np.multiply(update_gate,h_prev) + np.multiply((1-update_gate),h_hat) # Classifiers (Softmax) : dense = np.dot(hidden_state,self.W) + self.B probs = activations.softmax(dense) return (update_gate,reset_gate,h_hat,hidden_state,dense,probs) def forward(self,X,h_prev): x_s,z_s,r_s,h_hat = {},{},{},{} h_s = {} y_s,p_s = {},{} h_s[-1] = np.copy(h_prev) for t in range(self.seq_len): x_s[t] = X[:,t,:] z_s[t], r_s[t], h_hat[t], h_s[t], y_s[t], p_s[t] = self.cell_forward(x_s[t],h_s[t-1]) return (x_s,z_s, r_s, h_hat, h_s, y_s, p_s) def BPTT(self,outs,Y): x_s,z_s, r_s, h_hat, h_s, y_s, p_s = outs dW_z, dW_r,dW_h, dW = np.zeros_like(self.W_z), np.zeros_like(self.W_r), np.zeros_like(self.W_h),np.zeros_like(self.W) dU_z, dU_r,dU_h, = np.zeros_like(self.U_z), np.zeros_like(self.U_r), np.zeros_like(self.U_h) dB_z, dB_r,dB_h,dB = np.zeros_like(self.B_z), np.zeros_like(self.B_r),np.zeros_like(self.B_h),np.zeros_like(self.B) dh_next = np.zeros_like(h_s[0]) # w.r.t. softmax input ddense = np.copy(p_s[149]) ddense[np.arange(len(Y)),np.argmax(Y,1)] -= 1 #ddense[np.argmax(Y,1)] -=1 #ddense = y_s[149] - Y # Softmax classifier's : dW = np.dot(h_s[149].T,ddense) dB = np.sum(ddense,axis = 0, keepdims = True) # Backprop through time: for t in reversed(range(1,self.seq_len)): # w.r.t. softmax input #ddense = np.copy(probs) #ddense[np.arange(len(Y)),np.argmax(Y,1)] -= 1 #ddense[np.arange(len(Y)),np.argmax(Y,1)] -=1 # Softmax classifier's : #dW += np.dot(hidden_state.T,ddense) #dB += np.sum(ddense,axis = 0, keepdims = True) # Curernt memort state : dh = np.dot(ddense,self.W.T) + dh_next dh_hat = dh * (1-z_s[t]) dh_hat = dh_hat * dtanh(h_hat[t]) dW_h += np.dot(x_s[t].T,dh_hat) dU_h += np.dot((r_s[t] * h_s[t-1]).T,dh_hat) dB_h += np.sum(dh_hat,axis = 0, keepdims = True) # Reset gate: dr_1 = np.dot(dh_hat,self.U_h.T) dr = dr_1 * h_s[t-1] dr = dr * dsigmoid(r_s[t]) dW_r += np.dot(x_s[t].T,dr) dU_r += np.dot(h_s[t-1].T,dr) dB_r += np.sum(dr,axis = 0, keepdims = True) # Forget gate: dz = dh * (h_s[t-1] - h_hat[t]) dz = dz * dsigmoid(z_s[t]) dW_z += np.dot(x_s[t].T,dz) dU_z += np.dot(h_s[t-1].T,dz) dB_z += np.sum(dz,axis = 0, keepdims = True) # Nexts: dh_next = np.dot(dz,self.U_z.T) + (dh * z_s[t]) + (dr_1 * r_s[t]) + np.dot(dr,self.U_r.T) # List of gradients : grads = [dW,dB,dW_z,dU_z,dB_z,dW_r,dU_r,dB_r,dW_h,dU_h,dB_h] # Clipping gradients anyway for grad in grads: np.clip(grad, -15, 15, out = grad) return h_s[self.seq_len - 1],grads def fit(self,X,Y,X_val,y_val,epochs = 50 ,optimizer = 'SGD',verbose = True, crossVal = False): """ Given the traning dataset,their labels and number of epochs fitting the model, and measure the performance by validating training dataset. """ for epoch in range(epochs): print(f'Epoch : {epoch + 1}') perm = np.random.permutation(3000) h_prev = np.zeros((self.batch_size,self.hidden_dim)) for i in range(round(X.shape[0]/self.batch_size) - 1): batch_start = i * self.batch_size batch_finish = (i+1) * self.batch_size index = perm[batch_start:batch_finish] # Feeding random indexes: X_feed = X[index] y_feed = Y[index] # Forward + BPTT + SGD: cache_train = self.forward(X_feed,h_prev) h,grads = self.BPTT(cache_train,y_feed) if optimizer == 'SGD': self.SGD(grads) elif optimizer == 'AdaGrad' : self.AdaGrad(grads) elif optimizer == 'RMSprop': self.RMSprop(grads) elif optimizer == 'VanillaAdam': self.VanillaAdam(grads) else: self.Adam(grads) # Hidden state -------> Previous hidden state h_prev= h # Training metrics calculations: cross_loss_train = self.CategoricalCrossEntropy(y_feed,cache_train[6][149]) predictions_train = self.predict(X) acc_train = metrics.accuracy(np.argmax(Y,1),predictions_train) # Validation metrics calculations: test_prevs = np.zeros((X_val.shape[0],self.hidden_dim)) _,__,___,____,_____,______,probs_test = self.forward(X_val,test_prevs) cross_loss_val = self.CategoricalCrossEntropy(y_val,probs_test[149]) predictions_val = np.argmax(probs_test[149],1) acc_val = metrics.accuracy(np.argmax(y_val,1),predictions_val) if verbose: print(f"[{epoch + 1}/{epochs}] ------> Training : Accuracy : {acc_train}") print(f"[{epoch + 1}/{epochs}] ------> Training : Loss : {cross_loss_train}") print('______________________________________________________________________________________\n') print(f"[{epoch + 1}/{epochs}] ------> Testing : Accuracy : {acc_val}") print(f"[{epoch + 1}/{epochs}] ------> Testing : Loss : {cross_loss_val}") print('______________________________________________________________________________________\n') self.train_loss.append(cross_loss_train) self.test_loss.append(cross_loss_val) self.train_acc.append(acc_train) self.test_acc.append(acc_val) def params(self): """ Return all weights/biases in sequential order starting from end in list form. """ return [self.W,self.B,self.W_z,self.U_z,self.B_z,self.W_r,self.U_r,self.B_r,self.W_h,self.U_h,self.B_h] def SGD(self,grads): """ Stochastic gradient descent with momentum on mini-batches. """ prevs = [] for param,grad,prev_update in zip(self.params(),grads,self.previous_updates): delta = self.learning_rate * grad - self.mom_coeff * prev_update param -= delta prevs.append(delta) self.previous_updates = prevs self.learning_rate *= 0.99999 def AdaGrad(self,grads): """ AdaGrad adaptive optimization algorithm. """ i = 0 for param,grad in zip(self.params(),grads): self.cache[i] += grad **2 param += -self.learning_rate * grad / (np.sqrt(self.cache[i]) + 1e-6) i += 1 def RMSprop(self,grads,decay_rate = 0.9): """ RMSprop adaptive optimization algorithm """ i = 0 for param,grad in zip(self.params(),grads): self.cache_rmsprop[i] = decay_rate * self.cache_rmsprop[i] + (1-decay_rate) * grad **2 param += - self.learning_rate * grad / (np.sqrt(self.cache_rmsprop[i])+ 1e-6) i += 1 def VanillaAdam(self,grads,beta1 = 0.9,beta2 = 0.999): """ Adam optimizer, but bias correction is not implemented """ i = 0 for param,grad in zip(self.params(),grads): self.m[i] = beta1 * self.m[i] + (1-beta1) * grad self.v[i] = beta2 * self.v[i] + (1-beta2) * grad **2 param += -self.learning_rate * self.m[i] / (np.sqrt(self.v[i]) + 1e-8) i += 1 def Adam(self,grads,beta1 = 0.9,beta2 = 0.999): """ Adam optimizer, bias correction is implemented. """ i = 0 for param,grad in zip(self.params(),grads): self.m[i] = beta1 * self.m[i] + (1-beta1) * grad self.v[i] = beta2 * self.v[i] + (1-beta2) * grad **2 m_corrected = self.m[i] / (1-beta1**self.t) v_corrected = self.v[i] / (1-beta2**self.t) param += -self.learning_rate * m_corrected / (np.sqrt(v_corrected) + 1e-8) i += 1 self.t +=1 def CategoricalCrossEntropy(self,labels,preds): """ Computes cross entropy between labels and model's predictions """ predictions = np.clip(preds, 1e-12, 1. - 1e-12) N = predictions.shape[0] return -np.sum(labels * np.log(predictions + 1e-9)) / N def predict(self,X): """ Return predictions, (not one hot encoded format) """ # Give zeros to hidden/cell states: pasts = np.zeros((X.shape[0],self.hidden_dim)) _,__,___,____,_____,______,probs = self.forward(X,pasts) return np.argmax(probs[149],axis=1) def history(self): return {'TrainLoss' : self.train_loss, 'TrainAcc' : self.train_acc, 'TestLoss' : self.test_loss, 'TestAcc' : self.test_acc} # %% gru = GRU(hidden_dim=128,learning_rate=1e-3,batch_size=32,mom_coeff=0.0) # %% gru.fit(X_train,y_train,X_test,y_test,epochs = 15,optimizer = 'RMSprop') # %% gru_history = gru.history() # %% # For figure 97: plt.figure() plt.plot(gru_history['TrainLoss'],'-o') plt.plot(gru_history['TestLoss'],'-o') plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Loss over epochs') plt.legend(['Train Loss','Test Loss']) plt.show() plt.figure() plt.plot(gru_history['TrainAcc'],'-o') plt.plot(gru_history['TestAcc'],'-o') plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Accuracy over epochs') plt.legend(['Train Acc','Test Acc']) plt.show() # %% # For figure 98: multi_layer_gru_history = multi_layer_gru.history() plt.figure() plt.plot(multi_layer_gru_history['TrainAcc'],'-o') plt.plot(gru_history['TrainAcc'],'-o') plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Training Accuracy over epochs') plt.legend(['Multi Layer GRU','GRU']) plt.show() plt.figure() plt.plot(multi_layer_gru_history['TestAcc'],'-o') plt.plot(gru_history['TestAcc'],'-o') plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Testing Accuracy over epochs') plt.legend(['Multi Layer GRU','GRU']) plt.show() plt.figure() plt.plot(multi_layer_gru_history['TrainLoss'],'-o') plt.plot(gru_history['TrainLoss'],'-o') plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Training Loss over epochs') plt.legend(['Multi Layer GRU','GRU']) plt.show() plt.figure() plt.plot(multi_layer_gru_history['TestLoss'],'-o') plt.plot(gru_history['TestLoss'],'-o') plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Testing Loss over epochs') plt.legend(['Multi Layer GRU','GRU']) plt.show() # %% # For figure 99: three_layer_rnn_history = three_layer_rnn.history() plt.figure() plt.plot(gru_history['TrainAcc'],'-o') plt.plot(lstm_history['TrainAcc'],'-o') plt.plot(three_layer_rnn_history['TrainAcc'],'-o') plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Training Accuracy over epochs') plt.legend(['GRU','LSTM','RNN']) plt.show() plt.figure() plt.plot(gru_history['TestAcc'],'-o') plt.plot(lstm_history['TestAcc'],'-o') plt.plot(three_layer_rnn_history['TestAcc'],'-o') plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Testing Accuracy over epochs') plt.legend(['GRU','LSTM','RNN']) plt.show() plt.figure() plt.plot(gru_history['TrainLoss'],'-o') plt.plot(lstm_history['TrainLoss'],'-o') plt.plot(three_layer_rnn_history['TrainLoss'],'-o') plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Training Loss over epochs') plt.legend(['GRU','LSTM','RNN']) plt.show() plt.figure() plt.plot(gru_history['TestLoss'],'-o') plt.plot(lstm_history['TestLoss'],'-o') plt.plot(three_layer_rnn_history['TestLoss'],'-o') plt.xlabel('# of epochs') plt.ylabel('Loss') plt.title('Testing Loss over epochs') plt.legend(['GRU','LSTM','RNN']) plt.show() # %% train_preds_gru = gru.predict(X_train) test_preds_gru = gru.predict(X_test) confusion_mat_train_gru = metrics.confusion_matrix(np.argmax(y_train,1),train_preds_gru) confusion_mat_test_gru = metrics.confusion_matrix(np.argmax(y_test,1),test_preds_gru) body_movements = ['downstairs','jogging','sitting','standing','upstairs','walking'] confusion_mat_train_gru.columns = body_movements confusion_mat_train_gru.index = body_movements confusion_mat_test_gru.columns = body_movements confusion_mat_test_gru.index = body_movements sns.heatmap(confusion_mat_train_gru/np.sum(confusion_mat_train_gru), annot=True, fmt='.2%',cmap = 'Blues') plt.show() sns.heatmap(confusion_mat_test_gru/np.sum(confusion_mat_test_gru), annot=True, fmt='.2%',cmap = 'Blues') plt.show() # %% class Multi_layer_GRU(object): """ Gater recurrent unit, encapsulates all necessary logic for training, then built the hyperparameters and architecture of the network. """ def __init__(self,input_dim = 3,hidden_dim_1 = 128,hidden_dim_2 = 64,output_class = 6,seq_len = 150,batch_size = 32,learning_rate = 1e-1,mom_coeff = 0.85): """ Initialization of weights/biases and other configurable parameters. """ np.random.seed(150) self.input_dim = input_dim self.hidden_dim_1 = hidden_dim_1 self.hidden_dim_2 = hidden_dim_2 # Unfold case T = 150 : self.seq_len = seq_len self.output_class = output_class self.learning_rate = learning_rate self.batch_size = batch_size self.mom_coeff = mom_coeff # Xavier uniform scaler : Xavier = lambda fan_in,fan_out : math.sqrt(6/(fan_in + fan_out)) lim1 = Xavier(self.input_dim,self.hidden_dim_1) lim1_hid = Xavier(self.hidden_dim_1,self.hidden_dim_1) self.W_z = np.random.uniform(-lim1,lim1,(self.input_dim,self.hidden_dim_1)) self.U_z = np.random.uniform(-lim1_hid,lim1_hid,(self.hidden_dim_1,self.hidden_dim_1)) self.B_z = np.random.uniform(-lim1,lim1,(1,self.hidden_dim_1)) self.W_r = np.random.uniform(-lim1,lim1,(self.input_dim,self.hidden_dim_1)) self.U_r = np.random.uniform(-lim1_hid,lim1_hid,(self.hidden_dim_1,self.hidden_dim_1)) self.B_r = np.random.uniform(-lim1,lim1,(1,self.hidden_dim_1)) self.W_h = np.random.uniform(-lim1,lim1,(self.input_dim,self.hidden_dim_1)) self.U_h = np.random.uniform(-lim1_hid,lim1_hid,(self.hidden_dim_1,self.hidden_dim_1)) self.B_h = np.random.uniform(-lim1,lim1,(1,self.hidden_dim_1)) lim2_hid = Xavier(self.hidden_dim_1,self.hidden_dim_2) self.W_hid = np.random.uniform(-lim2_hid,lim2_hid,(self.hidden_dim_1,self.hidden_dim_2)) self.B_hid = np.random.uniform(-lim2_hid,lim2_hid,(1,self.hidden_dim_2)) lim2 = Xavier(self.hidden_dim_2,self.output_class) self.W = np.random.uniform(-lim2,lim2,(self.hidden_dim_2,self.output_class)) self.B = np.random.uniform(-lim2,lim2,(1,self.output_class)) # To keep track loss and accuracy score : self.train_loss,self.test_loss,self.train_acc,self.test_acc = [],[],[],[] # To keep previous updates in momentum : self.previous_updates = [0] * 13 # For AdaGrad: self.cache = [0] * 13 self.cache_rmsprop = [0] * 13 self.m = [0] * 13 self.v = [0] * 13 self.t = 1 def cell_forward(self,X,h_prev): # Update gate: update_gate = activations.sigmoid(np.dot(X,self.W_z) + np.dot(h_prev,self.U_z) + self.B_z) # Reset gate: reset_gate = activations.sigmoid(np.dot(X,self.W_r) + np.dot(h_prev,self.U_r) + self.B_r) # Current memory content: h_hat = np.tanh(np.dot(X,self.W_h) + np.dot(np.multiply(reset_gate,h_prev),self.U_h) + self.B_h) # Hidden state: hidden_state = np.multiply(update_gate,h_prev) + np.multiply((1-update_gate),h_hat) # Hidden MLP: hid_dense = np.dot(hidden_state,self.W_hid) + self.B_hid relu = activations.ReLU(hid_dense) # Classifiers (Softmax) : dense = np.dot(relu,self.W) + self.B probs = activations.softmax(dense) return (update_gate,reset_gate,h_hat,hidden_state,hid_dense,relu,dense,probs) def forward(self,X,h_prev): x_s,z_s,r_s,h_hat = {},{},{},{} h_s = {} hd_s,relu_s = {},{} y_s,p_s = {},{} h_s[-1] = np.copy(h_prev) for t in range(self.seq_len): x_s[t] = X[:,t,:] z_s[t], r_s[t], h_hat[t], h_s[t],hd_s[t],relu_s[t], y_s[t], p_s[t] = self.cell_forward(x_s[t],h_s[t-1]) return (x_s,z_s, r_s, h_hat, h_s, hd_s,relu_s, y_s, p_s) def BPTT(self,outs,Y): x_s,z_s, r_s, h_hat, h_s, hd_s,relu_s, y_s, p_s = outs dW_z, dW_r,dW_h, dW = np.zeros_like(self.W_z), np.zeros_like(self.W_r), np.zeros_like(self.W_h),np.zeros_like(self.W) dW_hid = np.zeros_like(self.W_hid) dU_z, dU_r,dU_h = np.zeros_like(self.U_z), np.zeros_like(self.U_r), np.zeros_like(self.U_h) dB_z, dB_r,dB_h,dB = np.zeros_like(self.B_z), np.zeros_like(self.B_r),np.zeros_like(self.B_h),np.zeros_like(self.B) dB_hid = np.zeros_like(self.B_hid) dh_next = np.zeros_like(h_s[0]) # w.r.t. softmax input ddense = np.copy(p_s[149]) ddense[np.arange(len(Y)),np.argmax(Y,1)] -= 1 #ddense[np.argmax(Y,1)] -=1 #ddense = y_s[149] - Y # Softmax classifier's : dW = np.dot(relu_s[149].T,ddense) dB = np.sum(ddense,axis = 0, keepdims = True) ddense_hid = np.dot(ddense,self.W.T) * activations.dReLU(hd_s[149]) dW_hid = np.dot(h_s[149].T,ddense_hid) dB_hid = np.sum(ddense_hid,axis = 0, keepdims = True) # Backprop through time: for t in reversed(range(1,self.seq_len)): # Curernt memort state : dh = np.dot(ddense_hid,self.W_hid.T) + dh_next dh_hat = dh * (1-z_s[t]) dh_hat = dh_hat * dtanh(h_hat[t]) dW_h += np.dot(x_s[t].T,dh_hat) dU_h += np.dot((r_s[t] * h_s[t-1]).T,dh_hat) dB_h += np.sum(dh_hat,axis = 0, keepdims = True) # Reset gate: dr_1 = np.dot(dh_hat,self.U_h.T) dr = dr_1 * h_s[t-1] dr = dr * dsigmoid(r_s[t]) dW_r += np.dot(x_s[t].T,dr) dU_r += np.dot(h_s[t-1].T,dr) dB_r += np.sum(dr,axis = 0, keepdims = True) # Forget gate: dz = dh * (h_s[t-1] - h_hat[t]) dz = dz * dsigmoid(z_s[t]) dW_z += np.dot(x_s[t].T,dz) dU_z += np.dot(h_s[t-1].T,dz) dB_z += np.sum(dz,axis = 0, keepdims = True) # Nexts: dh_next = np.dot(dz,self.U_z.T) + (dh * z_s[t]) + (dr_1 * r_s[t]) + np.dot(dr,self.U_r.T) # List of gradients : grads = [dW,dB,dW_hid,dB_hid,dW_z,dU_z,dB_z,dW_r,dU_r,dB_r,dW_h,dU_h,dB_h] # Clipping gradients anyway for grad in grads: np.clip(grad, -15, 15, out = grad) return h_s[self.seq_len - 1],grads def fit(self,X,Y,X_val,y_val,epochs = 50 ,optimizer = 'SGD',verbose = True, crossVal = False): """ Given the traning dataset,their labels and number of epochs fitting the model, and measure the performance by validating training dataset. """ for epoch in range(epochs): print(f'Epoch : {epoch + 1}') perm = np.random.permutation(3000) # Equate 0 in every epoch: h_prev = np.zeros((self.batch_size,self.hidden_dim_1)) for i in range(round(X.shape[0]/self.batch_size) - 1): batch_start = i * self.batch_size batch_finish = (i+1) * self.batch_size index = perm[batch_start:batch_finish] # Feeding random indexes: X_feed = X[index] y_feed = Y[index] # Forward + BPTT + Optimization: cache_train = self.forward(X_feed,h_prev) h,grads = self.BPTT(cache_train,y_feed) if optimizer == 'SGD': self.SGD(grads) elif optimizer == 'AdaGrad' : self.AdaGrad(grads) elif optimizer == 'RMSprop': self.RMSprop(grads) elif optimizer == 'VanillaAdam': self.VanillaAdam(grads) else: self.Adam(grads) # Hidden state -------> Previous hidden state h_prev = h # Training metrics calculations: cross_loss_train = self.CategoricalCrossEntropy(y_feed,cache_train[8][149]) predictions_train = self.predict(X) acc_train = metrics.accuracy(np.argmax(Y,1),predictions_train) # Validation metrics calculations: test_prevs = np.zeros((X_val.shape[0],self.hidden_dim_1)) _,__,___,____,_____,______,_______,________,probs_test = self.forward(X_val,test_prevs) cross_loss_val = self.CategoricalCrossEntropy(y_val,probs_test[149]) predictions_val = np.argmax(probs_test[149],1) acc_val = metrics.accuracy(np.argmax(y_val,1),predictions_val) if verbose: print(f"[{epoch + 1}/{epochs}] ------> Training : Accuracy : {acc_train}") print(f"[{epoch + 1}/{epochs}] ------> Training : Loss : {cross_loss_train}") print('______________________________________________________________________________________\n') print(f"[{epoch + 1}/{epochs}] ------> Testing : Accuracy : {acc_val}") print(f"[{epoch + 1}/{epochs}] ------> Testing : Loss : {cross_loss_val}") print('______________________________________________________________________________________\n') self.train_loss.append(cross_loss_train) self.test_loss.append(cross_loss_val) self.train_acc.append(acc_train) self.test_acc.append(acc_val) def params(self): """ Return all weights/biases in sequential order starting from end in list form. """ return [self.W,self.B,self.W_hid,self.B_hid,self.W_z,self.U_z,self.B_z,self.W_r,self.U_r,self.B_r,self.W_h,self.U_h,self.B_h] def SGD(self,grads): """ Stochastic gradient descent with momentum on mini-batches. """ prevs = [] for param,grad,prev_update in zip(self.params(),grads,self.previous_updates): delta = self.learning_rate * grad + self.mom_coeff * prev_update param -= delta prevs.append(delta) self.previous_updates = prevs self.learning_rate *= 0.99999 def AdaGrad(self,grads): """ AdaGrad adaptive optimization algorithm. """ i = 0 for param,grad in zip(self.params(),grads): self.cache[i] += grad **2 param += -self.learning_rate * grad / (np.sqrt(self.cache[i]) + 1e-6) i += 1 def RMSprop(self,grads,decay_rate = 0.9): """ RMSprop adaptive optimization algorithm """ i = 0 for param,grad in zip(self.params(),grads): self.cache_rmsprop[i] = decay_rate * self.cache_rmsprop[i] + (1-decay_rate) * grad **2 param += - self.learning_rate * grad / (np.sqrt(self.cache_rmsprop[i])+ 1e-6) i += 1 def VanillaAdam(self,grads,beta1 = 0.9,beta2 = 0.999): """ Adam optimizer, but bias correction is not implemented """ i = 0 for param,grad in zip(self.params(),grads): self.m[i] = beta1 * self.m[i] + (1-beta1) * grad self.v[i] = beta2 * self.v[i] + (1-beta2) * grad **2 param += -self.learning_rate * self.m[i] / (np.sqrt(self.v[i]) + 1e-8) i += 1 def Adam(self,grads,beta1 = 0.9,beta2 = 0.999): """ Adam optimizer, bias correction is implemented. """ i = 0 for param,grad in zip(self.params(),grads): self.m[i] = beta1 * self.m[i] + (1-beta1) * grad self.v[i] = beta2 * self.v[i] + (1-beta2) * grad **2 m_corrected = self.m[i] / (1-beta1**self.t) v_corrected = self.v[i] / (1-beta2**self.t) param += -self.learning_rate * m_corrected / (np.sqrt(v_corrected) + 1e-8) i += 1 self.t +=1 def CategoricalCrossEntropy(self,labels,preds): """ Computes cross entropy between labels and model's predictions """ predictions = np.clip(preds, 1e-12, 1. - 1e-12) N = predictions.shape[0] return -np.sum(labels * np.log(predictions + 1e-9)) / N def predict(self,X): """ Return predictions, (not one hot encoded format) """ # Give zeros to hidden states: pasts = np.zeros((X.shape[0],self.hidden_dim_1)) _,__,___,____,_____,______,_______,________,probs = self.forward(X,pasts) return np.argmax(probs[149],axis=1) def history(self): return {'TrainLoss' : self.train_loss, 'TrainAcc' : self.train_acc, 'TestLoss' : self.test_loss, 'TestAcc' : self.test_acc} # %% multi_layer_gru = Multi_layer_GRU(hidden_dim_1=128,hidden_dim_2=64,learning_rate=1e-3,mom_coeff=0.0,batch_size=32) # %% multi_layer_gru.fit(X_train,y_train,X_test,y_test,epochs = 15,optimizer = 'RMSprop') can_kocagil_21602218_hw3(question)

39.916454

227

0.506292

07efd382cad29753912a8dca26eb897c239dfb3c

2,045

py

Python

wsnsims/flower/cell.py

king4arabs/wsnsims2

13d4c49ea2ae9de7ea42d4aaa8d117b3368cda20

[ "MIT" ]

2

2019-11-01T08:42:42.000Z

2020-01-02T00:17:39.000Z

wsnsims/flower/cell.py

king4arabs/wsnsims2

13d4c49ea2ae9de7ea42d4aaa8d117b3368cda20

[ "MIT" ]

null

wsnsims/flower/cell.py

king4arabs/wsnsims2

13d4c49ea2ae9de7ea42d4aaa8d117b3368cda20

[ "MIT" ]

1

2020-01-02T02:46:17.000Z

import numpy as np from wsnsims.core import point def side_length(environment): """ :param environment: :type environment: core.environment.Environment :return: pq.meter """ return environment.comms_range / np.sqrt(2) class Cell(object): """ Defines a cell in the grid """ count = 0 def __init__(self, row, column, environment): """ :param row: :param column: :param environment: :type environment: core.environment.Environment """ self.cell_id = Cell.count Cell.count += 1 # Maintain the grid position. self.grid_location = np.array([row, column]) # Calculate the physical location of the center of this cell. side_len = side_length(environment) x_pos = column * side_len + (side_len / 2.) y_pos = row * side_len + (side_len / 2.) self.location = point.Vec2(np.array([x_pos, y_pos])) # * pq.meter # The segments within radio range of this cell. self.segments = list() # The (maximum eight) cells immediately adjacent to this cell. self.neighbors = list() # The number of segments within radio range of any neighbor cell self.signal_hop_count = 0 # The cell distance between this cell and the centroid cell, G. self.proximity = 0 # The numeric identifier of the virtual cluster this cell belongs to. self.virtual_cluster_id = -1 # The numeric identifier of the cluster this cell belongs to. self._cluster_id = -1 @property def cluster_id(self): return self._cluster_id @cluster_id.setter def cluster_id(self, value): self._cluster_id = value @property def access(self): """ The number of segments within radio range of this cell. """ return len(self.segments) def __str__(self): return "Cell {}".format(self.cell_id) def __repr__(self): return "C {}".format(self.cell_id)

25.246914

77

0.61467

6c2b3109f5602dd1a5d9ada1b6953c78bb1fcb17

6,762

py

Python

pygama/io/orca_helper.py

rauscher1995/pygama

7357e3fb0be7c6712010e4925d863b0f0f843c27

[ "Apache-2.0" ]

null

pygama/io/orca_helper.py

rauscher1995/pygama

7357e3fb0be7c6712010e4925d863b0f0f843c27

[ "Apache-2.0" ]

null

pygama/io/orca_helper.py

rauscher1995/pygama

7357e3fb0be7c6712010e4925d863b0f0f843c27

[ "Apache-2.0" ]

1

2021-12-18T14:43:33.000Z

import plistlib import sys import pandas as pd import numpy as np def parse_header(xmlfile): """ Opens the given file for binary read ('rb'), then grabs the first 8 bytes The first 4 bytes (1 long) of an orca data file are the total length in longs of the record The next 4 bytes (1 long) is the length of the header in bytes The header is then read in ... """ with open(xmlfile, 'rb') as xmlfile_handle: #read the first word: ba = bytearray(xmlfile_handle.read(8)) #Replacing this to be python2 friendly # #first 4 bytes: header length in long words # i = int.from_bytes(ba[:4], byteorder=sys.byteorder) # #second 4 bytes: header length in bytes # j = int.from_bytes(ba[4:], byteorder=sys.byteorder) big_endian = False if sys.byteorder == "little" else True i = from_bytes(ba[:4], big_endian=big_endian) j = from_bytes(ba[4:], big_endian=big_endian) #read in the next that-many bytes that occupy the plist header ba = bytearray(xmlfile_handle.read(j)) #convert to string #the readPlistFromBytes method doesn't exist in 2.7 if sys.version_info[0] < 3: header_string = ba.decode("utf-8") header_dict = plistlib.readPlistFromString(header_string) else: header_dict = plistlib.readPlistFromBytes(ba) return i, j, header_dict def from_bytes(data, big_endian=False): """ python2 doesn't have this function, so rewrite it for backwards compatibility """ if isinstance(data, str): data = bytearray(data) if big_endian: data = reversed(data) num = 0 for offset, byte in enumerate(data): num += byte << (offset * 8) return num def get_run_number(header_dict): """ header_dict parse functions, ORCA specific """ for d in (header_dict["ObjectInfo"]["DataChain"]): if "Run Control" in d: return (d["Run Control"]["RunNumber"]) raise ValueError("No run number found in header!") def get_data_id(header_dict, class_name, super_name): """ stored like this: `header_dict["dataDescription"]["ORRunModel"]["Run"]["dataId"]` but integer needs to be bitshifted by 18 """ id_int = header_dict["dataDescription"][class_name][super_name]["dataId"] return id_int >> 18 def flip_data_ids(header_dict): """ Returns an inverted dictionary such that: Could be extended somehow to give you all the supers associated with a given class name (maybe like) flipped[dataId] = [class_key, [super1, super2, ...]] """ flipped = dict() # header_dict["dataDescription"][class_name][super_name]["dataId"] for class_key in header_dict["dataDescription"].keys(): super_keys_list = [] for super_key in header_dict["dataDescription"][class_key].keys(): super_keys_list.append(super_key) ID_val = (header_dict["dataDescription"][class_key][super_key] ["dataId"]) >> 18 flipped[ID_val] = [class_key, super_keys_list] # this one just gives a single super flipped[dataId] = [class_key, super_key] # for class_key in header_dict["dataDescription"].keys(): # super_keys_list = header_dict["dataDescription"][class_key].keys() # ID_val = (header_dict["dataDescription"][class_key][super_keys_list[0]]["dataId"])>>18 # flipped[ID_val] = [class_key,super_keys_list] return flipped def get_decoder_for_id(header_dict): """ Returns a dictionary that goes: `dict[dataIdNum] = "decoderName"` e.g: d[5] = 'ORSIS3302DecoderForEnergy' """ d = dict() for class_key in header_dict["dataDescription"].keys(): super_keys_list = [] for super_key in header_dict["dataDescription"][class_key].keys(): super_keys_list.append(super_key) ID_val = (header_dict["dataDescription"][class_key][super_key] ["dataId"]) >> 18 decoderName = header_dict["dataDescription"][class_key][super_key][ "decoder"] d[ID_val] = decoderName return d def get_object_info(header_dict, class_name): """ returns a dict keyed by data id with all info from the header TODO: doesn't include all parts of the header yet! """ object_info_list = [] crates = header_dict["ObjectInfo"]["Crates"] for crate in crates: cards = crate["Cards"] for card in cards: if card["Class Name"] == class_name: card["Crate"] = crate["CrateNumber"] object_info_list.append(card) # AuxHw = header_dict["ObjectInfo"]["AuxHw"] # print("AUX IS:") # for aux in AuxHw: # print(aux.keys()) # exit() if len(object_info_list) == 0: # print("Warning: no object info parsed for {}".format(class_name)) return None df = pd.DataFrame.from_dict(object_info_list) df.set_index(['Crate', 'Card'], inplace=True) return df def get_next_event(f_in): """ Gets the next event, and some basic information about it Takes the file pointer as input Outputs: -event_data: a byte array of the data produced by the card (could be header + data) -data_id: This is the identifier for the type of data-taker (i.e. Gretina4M, etc) # number of bytes to read in = 8 (2x 32-bit words, 4 bytes each) # The read is set up to do two 32-bit integers, rather than bytes or shorts # This matches the bitwise arithmetic used elsewhere best, and is easy to implement """ try: # event header is 8 bytes (2 longs) head = np.fromstring(f_in.read(4), dtype=np.uint32) except Exception as e: print(e) raise Exception("Failed to read in the event orca header.") # Assuming we're getting an array of bytes: # record_length = (head[0] + (head[1]<<8) + ((head[2]&0x3)<<16)) # data_id = (head[2] >> 2) + (head[3]<<8) # slot = (head[6] & 0x1f) # crate = (head[6]>>5) + head[7]&0x1 # reserved = (head[4] + (head[5]<<8)) # Using an array of uint32 record_length = int((head[0] & 0x3FFFF)) data_id = int((head[0] >> 18)) # slot =int( (head[1] >> 16) & 0x1f) # crate =int( (head[1] >> 21) & 0xf) # reserved =int( (head[1] &0xFFFF)) # /* ========== read in the rest of the event data ========== */ try: # record_length is in longs, read gives bytes event_data = f_in.read(record_length * 4 - 4) except Exception as e: print(" No more data...\n") print(e) raise EOFError return event_data, data_id

35.036269

104

0.617125

42b61c317c4e3af41140df8e8a3ecfc2558530b4

93,412

py

Python

Lib/test/test_math.py

olegasics/cpython

769d7d0c66c5b86e2dd29b9ce67ac2daaab1bb38

[ "0BSD" ]

7

2021-08-07T07:30:24.000Z

2022-03-10T22:48:04.000Z

Lib/test/test_math.py

olegasics/cpython

769d7d0c66c5b86e2dd29b9ce67ac2daaab1bb38

[ "0BSD" ]

16

2018-06-03T02:04:29.000Z

2022-03-01T00:00:50.000Z

Lib/test/test_math.py

olegasics/cpython

769d7d0c66c5b86e2dd29b9ce67ac2daaab1bb38

[ "0BSD" ]

4

2021-07-30T16:40:00.000Z

2022-01-29T13:08:28.000Z

# Python test set -- math module # XXXX Should not do tests around zero only from test.support import run_unittest, verbose, requires_IEEE_754 from test import support import unittest import itertools import decimal import math import os import platform import random import struct import sys eps = 1E-05 NAN = float('nan') INF = float('inf') NINF = float('-inf') FLOAT_MAX = sys.float_info.max FLOAT_MIN = sys.float_info.min # detect evidence of double-rounding: fsum is not always correctly # rounded on machines that suffer from double rounding. x, y = 1e16, 2.9999 # use temporary values to defeat peephole optimizer HAVE_DOUBLE_ROUNDING = (x + y == 1e16 + 4) # locate file with test values if __name__ == '__main__': file = sys.argv[0] else: file = __file__ test_dir = os.path.dirname(file) or os.curdir math_testcases = os.path.join(test_dir, 'math_testcases.txt') test_file = os.path.join(test_dir, 'cmath_testcases.txt') def to_ulps(x): """Convert a non-NaN float x to an integer, in such a way that adjacent floats are converted to adjacent integers. Then abs(ulps(x) - ulps(y)) gives the difference in ulps between two floats. The results from this function will only make sense on platforms where native doubles are represented in IEEE 754 binary64 format. Note: 0.0 and -0.0 are converted to 0 and -1, respectively. """ n = struct.unpack('<q', struct.pack('<d', x))[0] if n < 0: n = ~(n+2**63) return n # Here's a pure Python version of the math.factorial algorithm, for # documentation and comparison purposes. # # Formula: # # factorial(n) = factorial_odd_part(n) << (n - count_set_bits(n)) # # where # # factorial_odd_part(n) = product_{i >= 0} product_{0 < j <= n >> i; j odd} j # # The outer product above is an infinite product, but once i >= n.bit_length, # (n >> i) < 1 and the corresponding term of the product is empty. So only the # finitely many terms for 0 <= i < n.bit_length() contribute anything. # # We iterate downwards from i == n.bit_length() - 1 to i == 0. The inner # product in the formula above starts at 1 for i == n.bit_length(); for each i # < n.bit_length() we get the inner product for i from that for i + 1 by # multiplying by all j in {n >> i+1 < j <= n >> i; j odd}. In Python terms, # this set is range((n >> i+1) + 1 | 1, (n >> i) + 1 | 1, 2). def count_set_bits(n): """Number of '1' bits in binary expansion of a nonnnegative integer.""" return 1 + count_set_bits(n & n - 1) if n else 0 def partial_product(start, stop): """Product of integers in range(start, stop, 2), computed recursively. start and stop should both be odd, with start <= stop. """ numfactors = (stop - start) >> 1 if not numfactors: return 1 elif numfactors == 1: return start else: mid = (start + numfactors) | 1 return partial_product(start, mid) * partial_product(mid, stop) def py_factorial(n): """Factorial of nonnegative integer n, via "Binary Split Factorial Formula" described at http://www.luschny.de/math/factorial/binarysplitfact.html """ inner = outer = 1 for i in reversed(range(n.bit_length())): inner *= partial_product((n >> i + 1) + 1 | 1, (n >> i) + 1 | 1) outer *= inner return outer << (n - count_set_bits(n)) def ulp_abs_check(expected, got, ulp_tol, abs_tol): """Given finite floats `expected` and `got`, check that they're approximately equal to within the given number of ulps or the given absolute tolerance, whichever is bigger. Returns None on success and an error message on failure. """ ulp_error = abs(to_ulps(expected) - to_ulps(got)) abs_error = abs(expected - got) # Succeed if either abs_error <= abs_tol or ulp_error <= ulp_tol. if abs_error <= abs_tol or ulp_error <= ulp_tol: return None else: fmt = ("error = {:.3g} ({:d} ulps); " "permitted error = {:.3g} or {:d} ulps") return fmt.format(abs_error, ulp_error, abs_tol, ulp_tol) def parse_mtestfile(fname): """Parse a file with test values -- starts a comment blank lines, or lines containing only a comment, are ignored other lines are expected to have the form id fn arg -> expected [flag]* """ with open(fname, encoding="utf-8") as fp: for line in fp: # strip comments, and skip blank lines if '--' in line: line = line[:line.index('--')] if not line.strip(): continue lhs, rhs = line.split('->') id, fn, arg = lhs.split() rhs_pieces = rhs.split() exp = rhs_pieces[0] flags = rhs_pieces[1:] yield (id, fn, float(arg), float(exp), flags) def parse_testfile(fname): """Parse a file with test values Empty lines or lines starting with -- are ignored yields id, fn, arg_real, arg_imag, exp_real, exp_imag """ with open(fname, encoding="utf-8") as fp: for line in fp: # skip comment lines and blank lines if line.startswith('--') or not line.strip(): continue lhs, rhs = line.split('->') id, fn, arg_real, arg_imag = lhs.split() rhs_pieces = rhs.split() exp_real, exp_imag = rhs_pieces[0], rhs_pieces[1] flags = rhs_pieces[2:] yield (id, fn, float(arg_real), float(arg_imag), float(exp_real), float(exp_imag), flags) def result_check(expected, got, ulp_tol=5, abs_tol=0.0): # Common logic of MathTests.(ftest, test_testcases, test_mtestcases) """Compare arguments expected and got, as floats, if either is a float, using a tolerance expressed in multiples of ulp(expected) or absolutely (if given and greater). As a convenience, when neither argument is a float, and for non-finite floats, exact equality is demanded. Also, nan==nan as far as this function is concerned. Returns None on success and an error message on failure. """ # Check exactly equal (applies also to strings representing exceptions) if got == expected: return None failure = "not equal" # Turn mixed float and int comparison (e.g. floor()) to all-float if isinstance(expected, float) and isinstance(got, int): got = float(got) elif isinstance(got, float) and isinstance(expected, int): expected = float(expected) if isinstance(expected, float) and isinstance(got, float): if math.isnan(expected) and math.isnan(got): # Pass, since both nan failure = None elif math.isinf(expected) or math.isinf(got): # We already know they're not equal, drop through to failure pass else: # Both are finite floats (now). Are they close enough? failure = ulp_abs_check(expected, got, ulp_tol, abs_tol) # arguments are not equal, and if numeric, are too far apart if failure is not None: fail_fmt = "expected {!r}, got {!r}" fail_msg = fail_fmt.format(expected, got) fail_msg += ' ({})'.format(failure) return fail_msg else: return None class FloatLike: def __init__(self, value): self.value = value def __float__(self): return self.value class IntSubclass(int): pass # Class providing an __index__ method. class MyIndexable(object): def __init__(self, value): self.value = value def __index__(self): return self.value class MathTests(unittest.TestCase): def ftest(self, name, got, expected, ulp_tol=5, abs_tol=0.0): """Compare arguments expected and got, as floats, if either is a float, using a tolerance expressed in multiples of ulp(expected) or absolutely, whichever is greater. As a convenience, when neither argument is a float, and for non-finite floats, exact equality is demanded. Also, nan==nan in this function. """ failure = result_check(expected, got, ulp_tol, abs_tol) if failure is not None: self.fail("{}: {}".format(name, failure)) def testConstants(self): # Ref: Abramowitz & Stegun (Dover, 1965) self.ftest('pi', math.pi, 3.141592653589793238462643) self.ftest('e', math.e, 2.718281828459045235360287) self.assertEqual(math.tau, 2*math.pi) def testAcos(self): self.assertRaises(TypeError, math.acos) self.ftest('acos(-1)', math.acos(-1), math.pi) self.ftest('acos(0)', math.acos(0), math.pi/2) self.ftest('acos(1)', math.acos(1), 0) self.assertRaises(ValueError, math.acos, INF) self.assertRaises(ValueError, math.acos, NINF) self.assertRaises(ValueError, math.acos, 1 + eps) self.assertRaises(ValueError, math.acos, -1 - eps) self.assertTrue(math.isnan(math.acos(NAN))) def testAcosh(self): self.assertRaises(TypeError, math.acosh) self.ftest('acosh(1)', math.acosh(1), 0) self.ftest('acosh(2)', math.acosh(2), 1.3169578969248168) self.assertRaises(ValueError, math.acosh, 0) self.assertRaises(ValueError, math.acosh, -1) self.assertEqual(math.acosh(INF), INF) self.assertRaises(ValueError, math.acosh, NINF) self.assertTrue(math.isnan(math.acosh(NAN))) def testAsin(self): self.assertRaises(TypeError, math.asin) self.ftest('asin(-1)', math.asin(-1), -math.pi/2) self.ftest('asin(0)', math.asin(0), 0) self.ftest('asin(1)', math.asin(1), math.pi/2) self.assertRaises(ValueError, math.asin, INF) self.assertRaises(ValueError, math.asin, NINF) self.assertRaises(ValueError, math.asin, 1 + eps) self.assertRaises(ValueError, math.asin, -1 - eps) self.assertTrue(math.isnan(math.asin(NAN))) def testAsinh(self): self.assertRaises(TypeError, math.asinh) self.ftest('asinh(0)', math.asinh(0), 0) self.ftest('asinh(1)', math.asinh(1), 0.88137358701954305) self.ftest('asinh(-1)', math.asinh(-1), -0.88137358701954305) self.assertEqual(math.asinh(INF), INF) self.assertEqual(math.asinh(NINF), NINF) self.assertTrue(math.isnan(math.asinh(NAN))) def testAtan(self): self.assertRaises(TypeError, math.atan) self.ftest('atan(-1)', math.atan(-1), -math.pi/4) self.ftest('atan(0)', math.atan(0), 0) self.ftest('atan(1)', math.atan(1), math.pi/4) self.ftest('atan(inf)', math.atan(INF), math.pi/2) self.ftest('atan(-inf)', math.atan(NINF), -math.pi/2) self.assertTrue(math.isnan(math.atan(NAN))) def testAtanh(self): self.assertRaises(TypeError, math.atan) self.ftest('atanh(0)', math.atanh(0), 0) self.ftest('atanh(0.5)', math.atanh(0.5), 0.54930614433405489) self.ftest('atanh(-0.5)', math.atanh(-0.5), -0.54930614433405489) self.assertRaises(ValueError, math.atanh, 1) self.assertRaises(ValueError, math.atanh, -1) self.assertRaises(ValueError, math.atanh, INF) self.assertRaises(ValueError, math.atanh, NINF) self.assertTrue(math.isnan(math.atanh(NAN))) def testAtan2(self): self.assertRaises(TypeError, math.atan2) self.ftest('atan2(-1, 0)', math.atan2(-1, 0), -math.pi/2) self.ftest('atan2(-1, 1)', math.atan2(-1, 1), -math.pi/4) self.ftest('atan2(0, 1)', math.atan2(0, 1), 0) self.ftest('atan2(1, 1)', math.atan2(1, 1), math.pi/4) self.ftest('atan2(1, 0)', math.atan2(1, 0), math.pi/2) # math.atan2(0, x) self.ftest('atan2(0., -inf)', math.atan2(0., NINF), math.pi) self.ftest('atan2(0., -2.3)', math.atan2(0., -2.3), math.pi) self.ftest('atan2(0., -0.)', math.atan2(0., -0.), math.pi) self.assertEqual(math.atan2(0., 0.), 0.) self.assertEqual(math.atan2(0., 2.3), 0.) self.assertEqual(math.atan2(0., INF), 0.) self.assertTrue(math.isnan(math.atan2(0., NAN))) # math.atan2(-0, x) self.ftest('atan2(-0., -inf)', math.atan2(-0., NINF), -math.pi) self.ftest('atan2(-0., -2.3)', math.atan2(-0., -2.3), -math.pi) self.ftest('atan2(-0., -0.)', math.atan2(-0., -0.), -math.pi) self.assertEqual(math.atan2(-0., 0.), -0.) self.assertEqual(math.atan2(-0., 2.3), -0.) self.assertEqual(math.atan2(-0., INF), -0.) self.assertTrue(math.isnan(math.atan2(-0., NAN))) # math.atan2(INF, x) self.ftest('atan2(inf, -inf)', math.atan2(INF, NINF), math.pi*3/4) self.ftest('atan2(inf, -2.3)', math.atan2(INF, -2.3), math.pi/2) self.ftest('atan2(inf, -0.)', math.atan2(INF, -0.0), math.pi/2) self.ftest('atan2(inf, 0.)', math.atan2(INF, 0.0), math.pi/2) self.ftest('atan2(inf, 2.3)', math.atan2(INF, 2.3), math.pi/2) self.ftest('atan2(inf, inf)', math.atan2(INF, INF), math.pi/4) self.assertTrue(math.isnan(math.atan2(INF, NAN))) # math.atan2(NINF, x) self.ftest('atan2(-inf, -inf)', math.atan2(NINF, NINF), -math.pi*3/4) self.ftest('atan2(-inf, -2.3)', math.atan2(NINF, -2.3), -math.pi/2) self.ftest('atan2(-inf, -0.)', math.atan2(NINF, -0.0), -math.pi/2) self.ftest('atan2(-inf, 0.)', math.atan2(NINF, 0.0), -math.pi/2) self.ftest('atan2(-inf, 2.3)', math.atan2(NINF, 2.3), -math.pi/2) self.ftest('atan2(-inf, inf)', math.atan2(NINF, INF), -math.pi/4) self.assertTrue(math.isnan(math.atan2(NINF, NAN))) # math.atan2(+finite, x) self.ftest('atan2(2.3, -inf)', math.atan2(2.3, NINF), math.pi) self.ftest('atan2(2.3, -0.)', math.atan2(2.3, -0.), math.pi/2) self.ftest('atan2(2.3, 0.)', math.atan2(2.3, 0.), math.pi/2) self.assertEqual(math.atan2(2.3, INF), 0.) self.assertTrue(math.isnan(math.atan2(2.3, NAN))) # math.atan2(-finite, x) self.ftest('atan2(-2.3, -inf)', math.atan2(-2.3, NINF), -math.pi) self.ftest('atan2(-2.3, -0.)', math.atan2(-2.3, -0.), -math.pi/2) self.ftest('atan2(-2.3, 0.)', math.atan2(-2.3, 0.), -math.pi/2) self.assertEqual(math.atan2(-2.3, INF), -0.) self.assertTrue(math.isnan(math.atan2(-2.3, NAN))) # math.atan2(NAN, x) self.assertTrue(math.isnan(math.atan2(NAN, NINF))) self.assertTrue(math.isnan(math.atan2(NAN, -2.3))) self.assertTrue(math.isnan(math.atan2(NAN, -0.))) self.assertTrue(math.isnan(math.atan2(NAN, 0.))) self.assertTrue(math.isnan(math.atan2(NAN, 2.3))) self.assertTrue(math.isnan(math.atan2(NAN, INF))) self.assertTrue(math.isnan(math.atan2(NAN, NAN))) def testCbrt(self): self.assertRaises(TypeError, math.cbrt) self.ftest('cbrt(0)', math.cbrt(0), 0) self.ftest('cbrt(1)', math.cbrt(1), 1) self.ftest('cbrt(8)', math.cbrt(8), 2) self.ftest('cbrt(0.0)', math.cbrt(0.0), 0.0) self.ftest('cbrt(-0.0)', math.cbrt(-0.0), -0.0) self.ftest('cbrt(1.2)', math.cbrt(1.2), 1.062658569182611) self.ftest('cbrt(-2.6)', math.cbrt(-2.6), -1.375068867074141) self.ftest('cbrt(27)', math.cbrt(27), 3) self.ftest('cbrt(-1)', math.cbrt(-1), -1) self.ftest('cbrt(-27)', math.cbrt(-27), -3) self.assertEqual(math.cbrt(INF), INF) self.assertEqual(math.cbrt(NINF), NINF) self.assertTrue(math.isnan(math.cbrt(NAN))) def testCeil(self): self.assertRaises(TypeError, math.ceil) self.assertEqual(int, type(math.ceil(0.5))) self.assertEqual(math.ceil(0.5), 1) self.assertEqual(math.ceil(1.0), 1) self.assertEqual(math.ceil(1.5), 2) self.assertEqual(math.ceil(-0.5), 0) self.assertEqual(math.ceil(-1.0), -1) self.assertEqual(math.ceil(-1.5), -1) self.assertEqual(math.ceil(0.0), 0) self.assertEqual(math.ceil(-0.0), 0) #self.assertEqual(math.ceil(INF), INF) #self.assertEqual(math.ceil(NINF), NINF) #self.assertTrue(math.isnan(math.ceil(NAN))) class TestCeil: def __ceil__(self): return 42 class FloatCeil(float): def __ceil__(self): return 42 class TestNoCeil: pass self.assertEqual(math.ceil(TestCeil()), 42) self.assertEqual(math.ceil(FloatCeil()), 42) self.assertEqual(math.ceil(FloatLike(42.5)), 43) self.assertRaises(TypeError, math.ceil, TestNoCeil()) t = TestNoCeil() t.__ceil__ = lambda *args: args self.assertRaises(TypeError, math.ceil, t) self.assertRaises(TypeError, math.ceil, t, 0) @requires_IEEE_754 def testCopysign(self): self.assertEqual(math.copysign(1, 42), 1.0) self.assertEqual(math.copysign(0., 42), 0.0) self.assertEqual(math.copysign(1., -42), -1.0) self.assertEqual(math.copysign(3, 0.), 3.0) self.assertEqual(math.copysign(4., -0.), -4.0) self.assertRaises(TypeError, math.copysign) # copysign should let us distinguish signs of zeros self.assertEqual(math.copysign(1., 0.), 1.) self.assertEqual(math.copysign(1., -0.), -1.) self.assertEqual(math.copysign(INF, 0.), INF) self.assertEqual(math.copysign(INF, -0.), NINF) self.assertEqual(math.copysign(NINF, 0.), INF) self.assertEqual(math.copysign(NINF, -0.), NINF) # and of infinities self.assertEqual(math.copysign(1., INF), 1.) self.assertEqual(math.copysign(1., NINF), -1.) self.assertEqual(math.copysign(INF, INF), INF) self.assertEqual(math.copysign(INF, NINF), NINF) self.assertEqual(math.copysign(NINF, INF), INF) self.assertEqual(math.copysign(NINF, NINF), NINF) self.assertTrue(math.isnan(math.copysign(NAN, 1.))) self.assertTrue(math.isnan(math.copysign(NAN, INF))) self.assertTrue(math.isnan(math.copysign(NAN, NINF))) self.assertTrue(math.isnan(math.copysign(NAN, NAN))) # copysign(INF, NAN) may be INF or it may be NINF, since # we don't know whether the sign bit of NAN is set on any # given platform. self.assertTrue(math.isinf(math.copysign(INF, NAN))) # similarly, copysign(2., NAN) could be 2. or -2. self.assertEqual(abs(math.copysign(2., NAN)), 2.) def testCos(self): self.assertRaises(TypeError, math.cos) self.ftest('cos(-pi/2)', math.cos(-math.pi/2), 0, abs_tol=math.ulp(1)) self.ftest('cos(0)', math.cos(0), 1) self.ftest('cos(pi/2)', math.cos(math.pi/2), 0, abs_tol=math.ulp(1)) self.ftest('cos(pi)', math.cos(math.pi), -1) try: self.assertTrue(math.isnan(math.cos(INF))) self.assertTrue(math.isnan(math.cos(NINF))) except ValueError: self.assertRaises(ValueError, math.cos, INF) self.assertRaises(ValueError, math.cos, NINF) self.assertTrue(math.isnan(math.cos(NAN))) @unittest.skipIf(sys.platform == 'win32' and platform.machine() in ('ARM', 'ARM64'), "Windows UCRT is off by 2 ULP this test requires accuracy within 1 ULP") def testCosh(self): self.assertRaises(TypeError, math.cosh) self.ftest('cosh(0)', math.cosh(0), 1) self.ftest('cosh(2)-2*cosh(1)**2', math.cosh(2)-2*math.cosh(1)**2, -1) # Thanks to Lambert self.assertEqual(math.cosh(INF), INF) self.assertEqual(math.cosh(NINF), INF) self.assertTrue(math.isnan(math.cosh(NAN))) def testDegrees(self): self.assertRaises(TypeError, math.degrees) self.ftest('degrees(pi)', math.degrees(math.pi), 180.0) self.ftest('degrees(pi/2)', math.degrees(math.pi/2), 90.0) self.ftest('degrees(-pi/4)', math.degrees(-math.pi/4), -45.0) self.ftest('degrees(0)', math.degrees(0), 0) def testExp(self): self.assertRaises(TypeError, math.exp) self.ftest('exp(-1)', math.exp(-1), 1/math.e) self.ftest('exp(0)', math.exp(0), 1) self.ftest('exp(1)', math.exp(1), math.e) self.assertEqual(math.exp(INF), INF) self.assertEqual(math.exp(NINF), 0.) self.assertTrue(math.isnan(math.exp(NAN))) self.assertRaises(OverflowError, math.exp, 1000000) def testFabs(self): self.assertRaises(TypeError, math.fabs) self.ftest('fabs(-1)', math.fabs(-1), 1) self.ftest('fabs(0)', math.fabs(0), 0) self.ftest('fabs(1)', math.fabs(1), 1) def testFactorial(self): self.assertEqual(math.factorial(0), 1) total = 1 for i in range(1, 1000): total *= i self.assertEqual(math.factorial(i), total) self.assertEqual(math.factorial(i), py_factorial(i)) self.assertRaises(ValueError, math.factorial, -1) self.assertRaises(ValueError, math.factorial, -10**100) def testFactorialNonIntegers(self): self.assertRaises(TypeError, math.factorial, 5.0) self.assertRaises(TypeError, math.factorial, 5.2) self.assertRaises(TypeError, math.factorial, -1.0) self.assertRaises(TypeError, math.factorial, -1e100) self.assertRaises(TypeError, math.factorial, decimal.Decimal('5')) self.assertRaises(TypeError, math.factorial, decimal.Decimal('5.2')) self.assertRaises(TypeError, math.factorial, "5") # Other implementations may place different upper bounds. @support.cpython_only def testFactorialHugeInputs(self): # Currently raises OverflowError for inputs that are too large # to fit into a C long. self.assertRaises(OverflowError, math.factorial, 10**100) self.assertRaises(TypeError, math.factorial, 1e100) def testFloor(self): self.assertRaises(TypeError, math.floor) self.assertEqual(int, type(math.floor(0.5))) self.assertEqual(math.floor(0.5), 0) self.assertEqual(math.floor(1.0), 1) self.assertEqual(math.floor(1.5), 1) self.assertEqual(math.floor(-0.5), -1) self.assertEqual(math.floor(-1.0), -1) self.assertEqual(math.floor(-1.5), -2) #self.assertEqual(math.ceil(INF), INF) #self.assertEqual(math.ceil(NINF), NINF) #self.assertTrue(math.isnan(math.floor(NAN))) class TestFloor: def __floor__(self): return 42 class FloatFloor(float): def __floor__(self): return 42 class TestNoFloor: pass self.assertEqual(math.floor(TestFloor()), 42) self.assertEqual(math.floor(FloatFloor()), 42) self.assertEqual(math.floor(FloatLike(41.9)), 41) self.assertRaises(TypeError, math.floor, TestNoFloor()) t = TestNoFloor() t.__floor__ = lambda *args: args self.assertRaises(TypeError, math.floor, t) self.assertRaises(TypeError, math.floor, t, 0) def testFmod(self): self.assertRaises(TypeError, math.fmod) self.ftest('fmod(10, 1)', math.fmod(10, 1), 0.0) self.ftest('fmod(10, 0.5)', math.fmod(10, 0.5), 0.0) self.ftest('fmod(10, 1.5)', math.fmod(10, 1.5), 1.0) self.ftest('fmod(-10, 1)', math.fmod(-10, 1), -0.0) self.ftest('fmod(-10, 0.5)', math.fmod(-10, 0.5), -0.0) self.ftest('fmod(-10, 1.5)', math.fmod(-10, 1.5), -1.0) self.assertTrue(math.isnan(math.fmod(NAN, 1.))) self.assertTrue(math.isnan(math.fmod(1., NAN))) self.assertTrue(math.isnan(math.fmod(NAN, NAN))) self.assertRaises(ValueError, math.fmod, 1., 0.) self.assertRaises(ValueError, math.fmod, INF, 1.) self.assertRaises(ValueError, math.fmod, NINF, 1.) self.assertRaises(ValueError, math.fmod, INF, 0.) self.assertEqual(math.fmod(3.0, INF), 3.0) self.assertEqual(math.fmod(-3.0, INF), -3.0) self.assertEqual(math.fmod(3.0, NINF), 3.0) self.assertEqual(math.fmod(-3.0, NINF), -3.0) self.assertEqual(math.fmod(0.0, 3.0), 0.0) self.assertEqual(math.fmod(0.0, NINF), 0.0) def testFrexp(self): self.assertRaises(TypeError, math.frexp) def testfrexp(name, result, expected): (mant, exp), (emant, eexp) = result, expected if abs(mant-emant) > eps or exp != eexp: self.fail('%s returned %r, expected %r'%\ (name, result, expected)) testfrexp('frexp(-1)', math.frexp(-1), (-0.5, 1)) testfrexp('frexp(0)', math.frexp(0), (0, 0)) testfrexp('frexp(1)', math.frexp(1), (0.5, 1)) testfrexp('frexp(2)', math.frexp(2), (0.5, 2)) self.assertEqual(math.frexp(INF)[0], INF) self.assertEqual(math.frexp(NINF)[0], NINF) self.assertTrue(math.isnan(math.frexp(NAN)[0])) @requires_IEEE_754 @unittest.skipIf(HAVE_DOUBLE_ROUNDING, "fsum is not exact on machines with double rounding") def testFsum(self): # math.fsum relies on exact rounding for correct operation. # There's a known problem with IA32 floating-point that causes # inexact rounding in some situations, and will cause the # math.fsum tests below to fail; see issue #2937. On non IEEE # 754 platforms, and on IEEE 754 platforms that exhibit the # problem described in issue #2937, we simply skip the whole # test. # Python version of math.fsum, for comparison. Uses a # different algorithm based on frexp, ldexp and integer # arithmetic. from sys import float_info mant_dig = float_info.mant_dig etiny = float_info.min_exp - mant_dig def msum(iterable): """Full precision summation. Compute sum(iterable) without any intermediate accumulation of error. Based on the 'lsum' function at http://code.activestate.com/recipes/393090/ """ tmant, texp = 0, 0 for x in iterable: mant, exp = math.frexp(x) mant, exp = int(math.ldexp(mant, mant_dig)), exp - mant_dig if texp > exp: tmant <<= texp-exp texp = exp else: mant <<= exp-texp tmant += mant # Round tmant * 2**texp to a float. The original recipe # used float(str(tmant)) * 2.0**texp for this, but that's # a little unsafe because str -> float conversion can't be # relied upon to do correct rounding on all platforms. tail = max(len(bin(abs(tmant)))-2 - mant_dig, etiny - texp) if tail > 0: h = 1 << (tail-1) tmant = tmant // (2*h) + bool(tmant & h and tmant & 3*h-1) texp += tail return math.ldexp(tmant, texp) test_values = [ ([], 0.0), ([0.0], 0.0), ([1e100, 1.0, -1e100, 1e-100, 1e50, -1.0, -1e50], 1e-100), ([2.0**53, -0.5, -2.0**-54], 2.0**53-1.0), ([2.0**53, 1.0, 2.0**-100], 2.0**53+2.0), ([2.0**53+10.0, 1.0, 2.0**-100], 2.0**53+12.0), ([2.0**53-4.0, 0.5, 2.0**-54], 2.0**53-3.0), ([1./n for n in range(1, 1001)], float.fromhex('0x1.df11f45f4e61ap+2')), ([(-1.)**n/n for n in range(1, 1001)], float.fromhex('-0x1.62a2af1bd3624p-1')), ([1e16, 1., 1e-16], 10000000000000002.0), ([1e16-2., 1.-2.**-53, -(1e16-2.), -(1.-2.**-53)], 0.0), # exercise code for resizing partials array ([2.**n - 2.**(n+50) + 2.**(n+52) for n in range(-1074, 972, 2)] + [-2.**1022], float.fromhex('0x1.5555555555555p+970')), ] # Telescoping sum, with exact differences (due to Sterbenz) terms = [1.7**i for i in range(1001)] test_values.append(( [terms[i+1] - terms[i] for i in range(1000)] + [-terms[1000]], -terms[0] )) for i, (vals, expected) in enumerate(test_values): try: actual = math.fsum(vals) except OverflowError: self.fail("test %d failed: got OverflowError, expected %r " "for math.fsum(%.100r)" % (i, expected, vals)) except ValueError: self.fail("test %d failed: got ValueError, expected %r " "for math.fsum(%.100r)" % (i, expected, vals)) self.assertEqual(actual, expected) from random import random, gauss, shuffle for j in range(1000): vals = [7, 1e100, -7, -1e100, -9e-20, 8e-20] * 10 s = 0 for i in range(200): v = gauss(0, random()) ** 7 - s s += v vals.append(v) shuffle(vals) s = msum(vals) self.assertEqual(msum(vals), math.fsum(vals)) def testGcd(self): gcd = math.gcd self.assertEqual(gcd(0, 0), 0) self.assertEqual(gcd(1, 0), 1) self.assertEqual(gcd(-1, 0), 1) self.assertEqual(gcd(0, 1), 1) self.assertEqual(gcd(0, -1), 1) self.assertEqual(gcd(7, 1), 1) self.assertEqual(gcd(7, -1), 1) self.assertEqual(gcd(-23, 15), 1) self.assertEqual(gcd(120, 84), 12) self.assertEqual(gcd(84, -120), 12) self.assertEqual(gcd(1216342683557601535506311712, 436522681849110124616458784), 32) x = 434610456570399902378880679233098819019853229470286994367836600566 y = 1064502245825115327754847244914921553977 for c in (652560, 576559230871654959816130551884856912003141446781646602790216406874): a = x * c b = y * c self.assertEqual(gcd(a, b), c) self.assertEqual(gcd(b, a), c) self.assertEqual(gcd(-a, b), c) self.assertEqual(gcd(b, -a), c) self.assertEqual(gcd(a, -b), c) self.assertEqual(gcd(-b, a), c) self.assertEqual(gcd(-a, -b), c) self.assertEqual(gcd(-b, -a), c) self.assertEqual(gcd(), 0) self.assertEqual(gcd(120), 120) self.assertEqual(gcd(-120), 120) self.assertEqual(gcd(120, 84, 102), 6) self.assertEqual(gcd(120, 1, 84), 1) self.assertRaises(TypeError, gcd, 120.0) self.assertRaises(TypeError, gcd, 120.0, 84) self.assertRaises(TypeError, gcd, 120, 84.0) self.assertRaises(TypeError, gcd, 120, 1, 84.0) self.assertEqual(gcd(MyIndexable(120), MyIndexable(84)), 12) def testHypot(self): from decimal import Decimal from fractions import Fraction hypot = math.hypot # Test different numbers of arguments (from zero to five) # against a straightforward pure python implementation args = math.e, math.pi, math.sqrt(2.0), math.gamma(3.5), math.sin(2.1) for i in range(len(args)+1): self.assertAlmostEqual( hypot(*args[:i]), math.sqrt(sum(s**2 for s in args[:i])) ) # Test allowable types (those with __float__) self.assertEqual(hypot(12.0, 5.0), 13.0) self.assertEqual(hypot(12, 5), 13) self.assertEqual(hypot(Decimal(12), Decimal(5)), 13) self.assertEqual(hypot(Fraction(12, 32), Fraction(5, 32)), Fraction(13, 32)) self.assertEqual(hypot(bool(1), bool(0), bool(1), bool(1)), math.sqrt(3)) # Test corner cases self.assertEqual(hypot(0.0, 0.0), 0.0) # Max input is zero self.assertEqual(hypot(-10.5), 10.5) # Negative input self.assertEqual(hypot(), 0.0) # Negative input self.assertEqual(1.0, math.copysign(1.0, hypot(-0.0)) # Convert negative zero to positive zero ) self.assertEqual( # Handling of moving max to the end hypot(1.5, 1.5, 0.5), hypot(1.5, 0.5, 1.5), ) # Test handling of bad arguments with self.assertRaises(TypeError): # Reject keyword args hypot(x=1) with self.assertRaises(TypeError): # Reject values without __float__ hypot(1.1, 'string', 2.2) int_too_big_for_float = 10 ** (sys.float_info.max_10_exp + 5) with self.assertRaises((ValueError, OverflowError)): hypot(1, int_too_big_for_float) # Any infinity gives positive infinity. self.assertEqual(hypot(INF), INF) self.assertEqual(hypot(0, INF), INF) self.assertEqual(hypot(10, INF), INF) self.assertEqual(hypot(-10, INF), INF) self.assertEqual(hypot(NAN, INF), INF) self.assertEqual(hypot(INF, NAN), INF) self.assertEqual(hypot(NINF, NAN), INF) self.assertEqual(hypot(NAN, NINF), INF) self.assertEqual(hypot(-INF, INF), INF) self.assertEqual(hypot(-INF, -INF), INF) self.assertEqual(hypot(10, -INF), INF) # If no infinity, any NaN gives a NaN. self.assertTrue(math.isnan(hypot(NAN))) self.assertTrue(math.isnan(hypot(0, NAN))) self.assertTrue(math.isnan(hypot(NAN, 10))) self.assertTrue(math.isnan(hypot(10, NAN))) self.assertTrue(math.isnan(hypot(NAN, NAN))) self.assertTrue(math.isnan(hypot(NAN))) # Verify scaling for extremely large values fourthmax = FLOAT_MAX / 4.0 for n in range(32): self.assertTrue(math.isclose(hypot(*([fourthmax]*n)), fourthmax * math.sqrt(n))) # Verify scaling for extremely small values for exp in range(32): scale = FLOAT_MIN / 2.0 ** exp self.assertEqual(math.hypot(4*scale, 3*scale), 5*scale) @requires_IEEE_754 @unittest.skipIf(HAVE_DOUBLE_ROUNDING, "hypot() loses accuracy on machines with double rounding") def testHypotAccuracy(self): # Verify improved accuracy in cases that were known to be inaccurate. # # The new algorithm's accuracy depends on IEEE 754 arithmetic # guarantees, on having the usual ROUND HALF EVEN rounding mode, on # the system not having double rounding due to extended precision, # and on the compiler maintaining the specified order of operations. # # This test is known to succeed on most of our builds. If it fails # some build, we either need to add another skipIf if the cause is # identifiable; otherwise, we can remove this test entirely. hypot = math.hypot Decimal = decimal.Decimal high_precision = decimal.Context(prec=500) for hx, hy in [ # Cases with a 1 ulp error in Python 3.7 compiled with Clang ('0x1.10e89518dca48p+29', '0x1.1970f7565b7efp+30'), ('0x1.10106eb4b44a2p+29', '0x1.ef0596cdc97f8p+29'), ('0x1.459c058e20bb7p+30', '0x1.993ca009b9178p+29'), ('0x1.378371ae67c0cp+30', '0x1.fbe6619854b4cp+29'), ('0x1.f4cd0574fb97ap+29', '0x1.50fe31669340ep+30'), ('0x1.494b2cdd3d446p+29', '0x1.212a5367b4c7cp+29'), ('0x1.f84e649f1e46dp+29', '0x1.1fa56bef8eec4p+30'), ('0x1.2e817edd3d6fap+30', '0x1.eb0814f1e9602p+29'), ('0x1.0d3a6e3d04245p+29', '0x1.32a62fea52352p+30'), ('0x1.888e19611bfc5p+29', '0x1.52b8e70b24353p+29'), # Cases with 2 ulp error in Python 3.8 ('0x1.538816d48a13fp+29', '0x1.7967c5ca43e16p+29'), ('0x1.57b47b7234530p+29', '0x1.74e2c7040e772p+29'), ('0x1.821b685e9b168p+30', '0x1.677dc1c1e3dc6p+29'), ('0x1.9e8247f67097bp+29', '0x1.24bd2dc4f4baep+29'), ('0x1.b73b59e0cb5f9p+29', '0x1.da899ab784a97p+28'), ('0x1.94a8d2842a7cfp+30', '0x1.326a51d4d8d8ap+30'), ('0x1.e930b9cd99035p+29', '0x1.5a1030e18dff9p+30'), ('0x1.1592bbb0e4690p+29', '0x1.a9c337b33fb9ap+29'), ('0x1.1243a50751fd4p+29', '0x1.a5a10175622d9p+29'), ('0x1.57a8596e74722p+30', '0x1.42d1af9d04da9p+30'), # Cases with 1 ulp error in version fff3c28052e6b0 ('0x1.ee7dbd9565899p+29', '0x1.7ab4d6fc6e4b4p+29'), ('0x1.5c6bfbec5c4dcp+30', '0x1.02511184b4970p+30'), ('0x1.59dcebba995cap+30', '0x1.50ca7e7c38854p+29'), ('0x1.768cdd94cf5aap+29', '0x1.9cfdc5571d38ep+29'), ('0x1.dcf137d60262ep+29', '0x1.1101621990b3ep+30'), ('0x1.3a2d006e288b0p+30', '0x1.e9a240914326cp+29'), ('0x1.62a32f7f53c61p+29', '0x1.47eb6cd72684fp+29'), ('0x1.d3bcb60748ef2p+29', '0x1.3f13c4056312cp+30'), ('0x1.282bdb82f17f3p+30', '0x1.640ba4c4eed3ap+30'), ('0x1.89d8c423ea0c6p+29', '0x1.d35dcfe902bc3p+29'), ]: x = float.fromhex(hx) y = float.fromhex(hy) with self.subTest(hx=hx, hy=hy, x=x, y=y): with decimal.localcontext(high_precision): z = float((Decimal(x)**2 + Decimal(y)**2).sqrt()) self.assertEqual(hypot(x, y), z) def testDist(self): from decimal import Decimal as D from fractions import Fraction as F dist = math.dist sqrt = math.sqrt # Simple exact cases self.assertEqual(dist((1.0, 2.0, 3.0), (4.0, 2.0, -1.0)), 5.0) self.assertEqual(dist((1, 2, 3), (4, 2, -1)), 5.0) # Test different numbers of arguments (from zero to nine) # against a straightforward pure python implementation for i in range(9): for j in range(5): p = tuple(random.uniform(-5, 5) for k in range(i)) q = tuple(random.uniform(-5, 5) for k in range(i)) self.assertAlmostEqual( dist(p, q), sqrt(sum((px - qx) ** 2.0 for px, qx in zip(p, q))) ) # Test non-tuple inputs self.assertEqual(dist([1.0, 2.0, 3.0], [4.0, 2.0, -1.0]), 5.0) self.assertEqual(dist(iter([1.0, 2.0, 3.0]), iter([4.0, 2.0, -1.0])), 5.0) # Test allowable types (those with __float__) self.assertEqual(dist((14.0, 1.0), (2.0, -4.0)), 13.0) self.assertEqual(dist((14, 1), (2, -4)), 13) self.assertEqual(dist((D(14), D(1)), (D(2), D(-4))), D(13)) self.assertEqual(dist((F(14, 32), F(1, 32)), (F(2, 32), F(-4, 32))), F(13, 32)) self.assertEqual(dist((True, True, False, True, False), (True, False, True, True, False)), sqrt(2.0)) # Test corner cases self.assertEqual(dist((13.25, 12.5, -3.25), (13.25, 12.5, -3.25)), 0.0) # Distance with self is zero self.assertEqual(dist((), ()), 0.0) # Zero-dimensional case self.assertEqual(1.0, # Convert negative zero to positive zero math.copysign(1.0, dist((-0.0,), (0.0,))) ) self.assertEqual(1.0, # Convert negative zero to positive zero math.copysign(1.0, dist((0.0,), (-0.0,))) ) self.assertEqual( # Handling of moving max to the end dist((1.5, 1.5, 0.5), (0, 0, 0)), dist((1.5, 0.5, 1.5), (0, 0, 0)) ) # Verify tuple subclasses are allowed class T(tuple): pass self.assertEqual(dist(T((1, 2, 3)), ((4, 2, -1))), 5.0) # Test handling of bad arguments with self.assertRaises(TypeError): # Reject keyword args dist(p=(1, 2, 3), q=(4, 5, 6)) with self.assertRaises(TypeError): # Too few args dist((1, 2, 3)) with self.assertRaises(TypeError): # Too many args dist((1, 2, 3), (4, 5, 6), (7, 8, 9)) with self.assertRaises(TypeError): # Scalars not allowed dist(1, 2) with self.assertRaises(TypeError): # Reject values without __float__ dist((1.1, 'string', 2.2), (1, 2, 3)) with self.assertRaises(ValueError): # Check dimension agree dist((1, 2, 3, 4), (5, 6, 7)) with self.assertRaises(ValueError): # Check dimension agree dist((1, 2, 3), (4, 5, 6, 7)) with self.assertRaises(TypeError): # Rejects invalid types dist("abc", "xyz") int_too_big_for_float = 10 ** (sys.float_info.max_10_exp + 5) with self.assertRaises((ValueError, OverflowError)): dist((1, int_too_big_for_float), (2, 3)) with self.assertRaises((ValueError, OverflowError)): dist((2, 3), (1, int_too_big_for_float)) # Verify that the one dimensional case is equivalent to abs() for i in range(20): p, q = random.random(), random.random() self.assertEqual(dist((p,), (q,)), abs(p - q)) # Test special values values = [NINF, -10.5, -0.0, 0.0, 10.5, INF, NAN] for p in itertools.product(values, repeat=3): for q in itertools.product(values, repeat=3): diffs = [px - qx for px, qx in zip(p, q)] if any(map(math.isinf, diffs)): # Any infinite difference gives positive infinity. self.assertEqual(dist(p, q), INF) elif any(map(math.isnan, diffs)): # If no infinity, any NaN gives a NaN. self.assertTrue(math.isnan(dist(p, q))) # Verify scaling for extremely large values fourthmax = FLOAT_MAX / 4.0 for n in range(32): p = (fourthmax,) * n q = (0.0,) * n self.assertTrue(math.isclose(dist(p, q), fourthmax * math.sqrt(n))) self.assertTrue(math.isclose(dist(q, p), fourthmax * math.sqrt(n))) # Verify scaling for extremely small values for exp in range(32): scale = FLOAT_MIN / 2.0 ** exp p = (4*scale, 3*scale) q = (0.0, 0.0) self.assertEqual(math.dist(p, q), 5*scale) self.assertEqual(math.dist(q, p), 5*scale) def testIsqrt(self): # Test a variety of inputs, large and small. test_values = ( list(range(1000)) + list(range(10**6 - 1000, 10**6 + 1000)) + [2**e + i for e in range(60, 200) for i in range(-40, 40)] + [3**9999, 10**5001] ) for value in test_values: with self.subTest(value=value): s = math.isqrt(value) self.assertIs(type(s), int) self.assertLessEqual(s*s, value) self.assertLess(value, (s+1)*(s+1)) # Negative values with self.assertRaises(ValueError): math.isqrt(-1) # Integer-like things s = math.isqrt(True) self.assertIs(type(s), int) self.assertEqual(s, 1) s = math.isqrt(False) self.assertIs(type(s), int) self.assertEqual(s, 0) class IntegerLike(object): def __init__(self, value): self.value = value def __index__(self): return self.value s = math.isqrt(IntegerLike(1729)) self.assertIs(type(s), int) self.assertEqual(s, 41) with self.assertRaises(ValueError): math.isqrt(IntegerLike(-3)) # Non-integer-like things bad_values = [ 3.5, "a string", decimal.Decimal("3.5"), 3.5j, 100.0, -4.0, ] for value in bad_values: with self.subTest(value=value): with self.assertRaises(TypeError): math.isqrt(value) def test_lcm(self): lcm = math.lcm self.assertEqual(lcm(0, 0), 0) self.assertEqual(lcm(1, 0), 0) self.assertEqual(lcm(-1, 0), 0) self.assertEqual(lcm(0, 1), 0) self.assertEqual(lcm(0, -1), 0) self.assertEqual(lcm(7, 1), 7) self.assertEqual(lcm(7, -1), 7) self.assertEqual(lcm(-23, 15), 345) self.assertEqual(lcm(120, 84), 840) self.assertEqual(lcm(84, -120), 840) self.assertEqual(lcm(1216342683557601535506311712, 436522681849110124616458784), 16592536571065866494401400422922201534178938447014944) x = 43461045657039990237 y = 10645022458251153277 for c in (652560, 57655923087165495981): a = x * c b = y * c d = x * y * c self.assertEqual(lcm(a, b), d) self.assertEqual(lcm(b, a), d) self.assertEqual(lcm(-a, b), d) self.assertEqual(lcm(b, -a), d) self.assertEqual(lcm(a, -b), d) self.assertEqual(lcm(-b, a), d) self.assertEqual(lcm(-a, -b), d) self.assertEqual(lcm(-b, -a), d) self.assertEqual(lcm(), 1) self.assertEqual(lcm(120), 120) self.assertEqual(lcm(-120), 120) self.assertEqual(lcm(120, 84, 102), 14280) self.assertEqual(lcm(120, 0, 84), 0) self.assertRaises(TypeError, lcm, 120.0) self.assertRaises(TypeError, lcm, 120.0, 84) self.assertRaises(TypeError, lcm, 120, 84.0) self.assertRaises(TypeError, lcm, 120, 0, 84.0) self.assertEqual(lcm(MyIndexable(120), MyIndexable(84)), 840) def testLdexp(self): self.assertRaises(TypeError, math.ldexp) self.ftest('ldexp(0,1)', math.ldexp(0,1), 0) self.ftest('ldexp(1,1)', math.ldexp(1,1), 2) self.ftest('ldexp(1,-1)', math.ldexp(1,-1), 0.5) self.ftest('ldexp(-1,1)', math.ldexp(-1,1), -2) self.assertRaises(OverflowError, math.ldexp, 1., 1000000) self.assertRaises(OverflowError, math.ldexp, -1., 1000000) self.assertEqual(math.ldexp(1., -1000000), 0.) self.assertEqual(math.ldexp(-1., -1000000), -0.) self.assertEqual(math.ldexp(INF, 30), INF) self.assertEqual(math.ldexp(NINF, -213), NINF) self.assertTrue(math.isnan(math.ldexp(NAN, 0))) # large second argument for n in [10**5, 10**10, 10**20, 10**40]: self.assertEqual(math.ldexp(INF, -n), INF) self.assertEqual(math.ldexp(NINF, -n), NINF) self.assertEqual(math.ldexp(1., -n), 0.) self.assertEqual(math.ldexp(-1., -n), -0.) self.assertEqual(math.ldexp(0., -n), 0.) self.assertEqual(math.ldexp(-0., -n), -0.) self.assertTrue(math.isnan(math.ldexp(NAN, -n))) self.assertRaises(OverflowError, math.ldexp, 1., n) self.assertRaises(OverflowError, math.ldexp, -1., n) self.assertEqual(math.ldexp(0., n), 0.) self.assertEqual(math.ldexp(-0., n), -0.) self.assertEqual(math.ldexp(INF, n), INF) self.assertEqual(math.ldexp(NINF, n), NINF) self.assertTrue(math.isnan(math.ldexp(NAN, n))) def testLog(self): self.assertRaises(TypeError, math.log) self.ftest('log(1/e)', math.log(1/math.e), -1) self.ftest('log(1)', math.log(1), 0) self.ftest('log(e)', math.log(math.e), 1) self.ftest('log(32,2)', math.log(32,2), 5) self.ftest('log(10**40, 10)', math.log(10**40, 10), 40) self.ftest('log(10**40, 10**20)', math.log(10**40, 10**20), 2) self.ftest('log(10**1000)', math.log(10**1000), 2302.5850929940457) self.assertRaises(ValueError, math.log, -1.5) self.assertRaises(ValueError, math.log, -10**1000) self.assertRaises(ValueError, math.log, NINF) self.assertEqual(math.log(INF), INF) self.assertTrue(math.isnan(math.log(NAN))) def testLog1p(self): self.assertRaises(TypeError, math.log1p) for n in [2, 2**90, 2**300]: self.assertAlmostEqual(math.log1p(n), math.log1p(float(n))) self.assertRaises(ValueError, math.log1p, -1) self.assertEqual(math.log1p(INF), INF) @requires_IEEE_754 def testLog2(self): self.assertRaises(TypeError, math.log2) # Check some integer values self.assertEqual(math.log2(1), 0.0) self.assertEqual(math.log2(2), 1.0) self.assertEqual(math.log2(4), 2.0) # Large integer values self.assertEqual(math.log2(2**1023), 1023.0) self.assertEqual(math.log2(2**1024), 1024.0) self.assertEqual(math.log2(2**2000), 2000.0) self.assertRaises(ValueError, math.log2, -1.5) self.assertRaises(ValueError, math.log2, NINF) self.assertTrue(math.isnan(math.log2(NAN))) @requires_IEEE_754 # log2() is not accurate enough on Mac OS X Tiger (10.4) @support.requires_mac_ver(10, 5) def testLog2Exact(self): # Check that we get exact equality for log2 of powers of 2. actual = [math.log2(math.ldexp(1.0, n)) for n in range(-1074, 1024)] expected = [float(n) for n in range(-1074, 1024)] self.assertEqual(actual, expected) def testLog10(self): self.assertRaises(TypeError, math.log10) self.ftest('log10(0.1)', math.log10(0.1), -1) self.ftest('log10(1)', math.log10(1), 0) self.ftest('log10(10)', math.log10(10), 1) self.ftest('log10(10**1000)', math.log10(10**1000), 1000.0) self.assertRaises(ValueError, math.log10, -1.5) self.assertRaises(ValueError, math.log10, -10**1000) self.assertRaises(ValueError, math.log10, NINF) self.assertEqual(math.log(INF), INF) self.assertTrue(math.isnan(math.log10(NAN))) def testModf(self): self.assertRaises(TypeError, math.modf) def testmodf(name, result, expected): (v1, v2), (e1, e2) = result, expected if abs(v1-e1) > eps or abs(v2-e2): self.fail('%s returned %r, expected %r'%\ (name, result, expected)) testmodf('modf(1.5)', math.modf(1.5), (0.5, 1.0)) testmodf('modf(-1.5)', math.modf(-1.5), (-0.5, -1.0)) self.assertEqual(math.modf(INF), (0.0, INF)) self.assertEqual(math.modf(NINF), (-0.0, NINF)) modf_nan = math.modf(NAN) self.assertTrue(math.isnan(modf_nan[0])) self.assertTrue(math.isnan(modf_nan[1])) def testPow(self): self.assertRaises(TypeError, math.pow) self.ftest('pow(0,1)', math.pow(0,1), 0) self.ftest('pow(1,0)', math.pow(1,0), 1) self.ftest('pow(2,1)', math.pow(2,1), 2) self.ftest('pow(2,-1)', math.pow(2,-1), 0.5) self.assertEqual(math.pow(INF, 1), INF) self.assertEqual(math.pow(NINF, 1), NINF) self.assertEqual((math.pow(1, INF)), 1.) self.assertEqual((math.pow(1, NINF)), 1.) self.assertTrue(math.isnan(math.pow(NAN, 1))) self.assertTrue(math.isnan(math.pow(2, NAN))) self.assertTrue(math.isnan(math.pow(0, NAN))) self.assertEqual(math.pow(1, NAN), 1) # pow(0., x) self.assertEqual(math.pow(0., INF), 0.) self.assertEqual(math.pow(0., 3.), 0.) self.assertEqual(math.pow(0., 2.3), 0.) self.assertEqual(math.pow(0., 2.), 0.) self.assertEqual(math.pow(0., 0.), 1.) self.assertEqual(math.pow(0., -0.), 1.) self.assertRaises(ValueError, math.pow, 0., -2.) self.assertRaises(ValueError, math.pow, 0., -2.3) self.assertRaises(ValueError, math.pow, 0., -3.) self.assertEqual(math.pow(0., NINF), INF) self.assertTrue(math.isnan(math.pow(0., NAN))) # pow(INF, x) self.assertEqual(math.pow(INF, INF), INF) self.assertEqual(math.pow(INF, 3.), INF) self.assertEqual(math.pow(INF, 2.3), INF) self.assertEqual(math.pow(INF, 2.), INF) self.assertEqual(math.pow(INF, 0.), 1.) self.assertEqual(math.pow(INF, -0.), 1.) self.assertEqual(math.pow(INF, -2.), 0.) self.assertEqual(math.pow(INF, -2.3), 0.) self.assertEqual(math.pow(INF, -3.), 0.) self.assertEqual(math.pow(INF, NINF), 0.) self.assertTrue(math.isnan(math.pow(INF, NAN))) # pow(-0., x) self.assertEqual(math.pow(-0., INF), 0.) self.assertEqual(math.pow(-0., 3.), -0.) self.assertEqual(math.pow(-0., 2.3), 0.) self.assertEqual(math.pow(-0., 2.), 0.) self.assertEqual(math.pow(-0., 0.), 1.) self.assertEqual(math.pow(-0., -0.), 1.) self.assertRaises(ValueError, math.pow, -0., -2.) self.assertRaises(ValueError, math.pow, -0., -2.3) self.assertRaises(ValueError, math.pow, -0., -3.) self.assertEqual(math.pow(-0., NINF), INF) self.assertTrue(math.isnan(math.pow(-0., NAN))) # pow(NINF, x) self.assertEqual(math.pow(NINF, INF), INF) self.assertEqual(math.pow(NINF, 3.), NINF) self.assertEqual(math.pow(NINF, 2.3), INF) self.assertEqual(math.pow(NINF, 2.), INF) self.assertEqual(math.pow(NINF, 0.), 1.) self.assertEqual(math.pow(NINF, -0.), 1.) self.assertEqual(math.pow(NINF, -2.), 0.) self.assertEqual(math.pow(NINF, -2.3), 0.) self.assertEqual(math.pow(NINF, -3.), -0.) self.assertEqual(math.pow(NINF, NINF), 0.) self.assertTrue(math.isnan(math.pow(NINF, NAN))) # pow(-1, x) self.assertEqual(math.pow(-1., INF), 1.) self.assertEqual(math.pow(-1., 3.), -1.) self.assertRaises(ValueError, math.pow, -1., 2.3) self.assertEqual(math.pow(-1., 2.), 1.) self.assertEqual(math.pow(-1., 0.), 1.) self.assertEqual(math.pow(-1., -0.), 1.) self.assertEqual(math.pow(-1., -2.), 1.) self.assertRaises(ValueError, math.pow, -1., -2.3) self.assertEqual(math.pow(-1., -3.), -1.) self.assertEqual(math.pow(-1., NINF), 1.) self.assertTrue(math.isnan(math.pow(-1., NAN))) # pow(1, x) self.assertEqual(math.pow(1., INF), 1.) self.assertEqual(math.pow(1., 3.), 1.) self.assertEqual(math.pow(1., 2.3), 1.) self.assertEqual(math.pow(1., 2.), 1.) self.assertEqual(math.pow(1., 0.), 1.) self.assertEqual(math.pow(1., -0.), 1.) self.assertEqual(math.pow(1., -2.), 1.) self.assertEqual(math.pow(1., -2.3), 1.) self.assertEqual(math.pow(1., -3.), 1.) self.assertEqual(math.pow(1., NINF), 1.) self.assertEqual(math.pow(1., NAN), 1.) # pow(x, 0) should be 1 for any x self.assertEqual(math.pow(2.3, 0.), 1.) self.assertEqual(math.pow(-2.3, 0.), 1.) self.assertEqual(math.pow(NAN, 0.), 1.) self.assertEqual(math.pow(2.3, -0.), 1.) self.assertEqual(math.pow(-2.3, -0.), 1.) self.assertEqual(math.pow(NAN, -0.), 1.) # pow(x, y) is invalid if x is negative and y is not integral self.assertRaises(ValueError, math.pow, -1., 2.3) self.assertRaises(ValueError, math.pow, -15., -3.1) # pow(x, NINF) self.assertEqual(math.pow(1.9, NINF), 0.) self.assertEqual(math.pow(1.1, NINF), 0.) self.assertEqual(math.pow(0.9, NINF), INF) self.assertEqual(math.pow(0.1, NINF), INF) self.assertEqual(math.pow(-0.1, NINF), INF) self.assertEqual(math.pow(-0.9, NINF), INF) self.assertEqual(math.pow(-1.1, NINF), 0.) self.assertEqual(math.pow(-1.9, NINF), 0.) # pow(x, INF) self.assertEqual(math.pow(1.9, INF), INF) self.assertEqual(math.pow(1.1, INF), INF) self.assertEqual(math.pow(0.9, INF), 0.) self.assertEqual(math.pow(0.1, INF), 0.) self.assertEqual(math.pow(-0.1, INF), 0.) self.assertEqual(math.pow(-0.9, INF), 0.) self.assertEqual(math.pow(-1.1, INF), INF) self.assertEqual(math.pow(-1.9, INF), INF) # pow(x, y) should work for x negative, y an integer self.ftest('(-2.)**3.', math.pow(-2.0, 3.0), -8.0) self.ftest('(-2.)**2.', math.pow(-2.0, 2.0), 4.0) self.ftest('(-2.)**1.', math.pow(-2.0, 1.0), -2.0) self.ftest('(-2.)**0.', math.pow(-2.0, 0.0), 1.0) self.ftest('(-2.)**-0.', math.pow(-2.0, -0.0), 1.0) self.ftest('(-2.)**-1.', math.pow(-2.0, -1.0), -0.5) self.ftest('(-2.)**-2.', math.pow(-2.0, -2.0), 0.25) self.ftest('(-2.)**-3.', math.pow(-2.0, -3.0), -0.125) self.assertRaises(ValueError, math.pow, -2.0, -0.5) self.assertRaises(ValueError, math.pow, -2.0, 0.5) # the following tests have been commented out since they don't # really belong here: the implementation of ** for floats is # independent of the implementation of math.pow #self.assertEqual(1**NAN, 1) #self.assertEqual(1**INF, 1) #self.assertEqual(1**NINF, 1) #self.assertEqual(1**0, 1) #self.assertEqual(1.**NAN, 1) #self.assertEqual(1.**INF, 1) #self.assertEqual(1.**NINF, 1) #self.assertEqual(1.**0, 1) def testRadians(self): self.assertRaises(TypeError, math.radians) self.ftest('radians(180)', math.radians(180), math.pi) self.ftest('radians(90)', math.radians(90), math.pi/2) self.ftest('radians(-45)', math.radians(-45), -math.pi/4) self.ftest('radians(0)', math.radians(0), 0) @requires_IEEE_754 def testRemainder(self): from fractions import Fraction def validate_spec(x, y, r): """ Check that r matches remainder(x, y) according to the IEEE 754 specification. Assumes that x, y and r are finite and y is nonzero. """ fx, fy, fr = Fraction(x), Fraction(y), Fraction(r) # r should not exceed y/2 in absolute value self.assertLessEqual(abs(fr), abs(fy/2)) # x - r should be an exact integer multiple of y n = (fx - fr) / fy self.assertEqual(n, int(n)) if abs(fr) == abs(fy/2): # If |r| == |y/2|, n should be even. self.assertEqual(n/2, int(n/2)) # triples (x, y, remainder(x, y)) in hexadecimal form. testcases = [ # Remainders modulo 1, showing the ties-to-even behaviour. '-4.0 1 -0.0', '-3.8 1 0.8', '-3.0 1 -0.0', '-2.8 1 -0.8', '-2.0 1 -0.0', '-1.8 1 0.8', '-1.0 1 -0.0', '-0.8 1 -0.8', '-0.0 1 -0.0', ' 0.0 1 0.0', ' 0.8 1 0.8', ' 1.0 1 0.0', ' 1.8 1 -0.8', ' 2.0 1 0.0', ' 2.8 1 0.8', ' 3.0 1 0.0', ' 3.8 1 -0.8', ' 4.0 1 0.0', # Reductions modulo 2*pi '0x0.0p+0 0x1.921fb54442d18p+2 0x0.0p+0', '0x1.921fb54442d18p+0 0x1.921fb54442d18p+2 0x1.921fb54442d18p+0', '0x1.921fb54442d17p+1 0x1.921fb54442d18p+2 0x1.921fb54442d17p+1', '0x1.921fb54442d18p+1 0x1.921fb54442d18p+2 0x1.921fb54442d18p+1', '0x1.921fb54442d19p+1 0x1.921fb54442d18p+2 -0x1.921fb54442d17p+1', '0x1.921fb54442d17p+2 0x1.921fb54442d18p+2 -0x0.0000000000001p+2', '0x1.921fb54442d18p+2 0x1.921fb54442d18p+2 0x0p0', '0x1.921fb54442d19p+2 0x1.921fb54442d18p+2 0x0.0000000000001p+2', '0x1.2d97c7f3321d1p+3 0x1.921fb54442d18p+2 0x1.921fb54442d14p+1', '0x1.2d97c7f3321d2p+3 0x1.921fb54442d18p+2 -0x1.921fb54442d18p+1', '0x1.2d97c7f3321d3p+3 0x1.921fb54442d18p+2 -0x1.921fb54442d14p+1', '0x1.921fb54442d17p+3 0x1.921fb54442d18p+2 -0x0.0000000000001p+3', '0x1.921fb54442d18p+3 0x1.921fb54442d18p+2 0x0p0', '0x1.921fb54442d19p+3 0x1.921fb54442d18p+2 0x0.0000000000001p+3', '0x1.f6a7a2955385dp+3 0x1.921fb54442d18p+2 0x1.921fb54442d14p+1', '0x1.f6a7a2955385ep+3 0x1.921fb54442d18p+2 0x1.921fb54442d18p+1', '0x1.f6a7a2955385fp+3 0x1.921fb54442d18p+2 -0x1.921fb54442d14p+1', '0x1.1475cc9eedf00p+5 0x1.921fb54442d18p+2 0x1.921fb54442d10p+1', '0x1.1475cc9eedf01p+5 0x1.921fb54442d18p+2 -0x1.921fb54442d10p+1', # Symmetry with respect to signs. ' 1 0.c 0.4', '-1 0.c -0.4', ' 1 -0.c 0.4', '-1 -0.c -0.4', ' 1.4 0.c -0.4', '-1.4 0.c 0.4', ' 1.4 -0.c -0.4', '-1.4 -0.c 0.4', # Huge modulus, to check that the underlying algorithm doesn't # rely on 2.0 * modulus being representable. '0x1.dp+1023 0x1.4p+1023 0x0.9p+1023', '0x1.ep+1023 0x1.4p+1023 -0x0.ap+1023', '0x1.fp+1023 0x1.4p+1023 -0x0.9p+1023', ] for case in testcases: with self.subTest(case=case): x_hex, y_hex, expected_hex = case.split() x = float.fromhex(x_hex) y = float.fromhex(y_hex) expected = float.fromhex(expected_hex) validate_spec(x, y, expected) actual = math.remainder(x, y) # Cheap way of checking that the floats are # as identical as we need them to be. self.assertEqual(actual.hex(), expected.hex()) # Test tiny subnormal modulus: there's potential for # getting the implementation wrong here (for example, # by assuming that modulus/2 is exactly representable). tiny = float.fromhex('1p-1074') # min +ve subnormal for n in range(-25, 25): if n == 0: continue y = n * tiny for m in range(100): x = m * tiny actual = math.remainder(x, y) validate_spec(x, y, actual) actual = math.remainder(-x, y) validate_spec(-x, y, actual) # Special values. # NaNs should propagate as usual. for value in [NAN, 0.0, -0.0, 2.0, -2.3, NINF, INF]: self.assertIsNaN(math.remainder(NAN, value)) self.assertIsNaN(math.remainder(value, NAN)) # remainder(x, inf) is x, for non-nan non-infinite x. for value in [-2.3, -0.0, 0.0, 2.3]: self.assertEqual(math.remainder(value, INF), value) self.assertEqual(math.remainder(value, NINF), value) # remainder(x, 0) and remainder(infinity, x) for non-NaN x are invalid # operations according to IEEE 754-2008 7.2(f), and should raise. for value in [NINF, -2.3, -0.0, 0.0, 2.3, INF]: with self.assertRaises(ValueError): math.remainder(INF, value) with self.assertRaises(ValueError): math.remainder(NINF, value) with self.assertRaises(ValueError): math.remainder(value, 0.0) with self.assertRaises(ValueError): math.remainder(value, -0.0) def testSin(self): self.assertRaises(TypeError, math.sin) self.ftest('sin(0)', math.sin(0), 0) self.ftest('sin(pi/2)', math.sin(math.pi/2), 1) self.ftest('sin(-pi/2)', math.sin(-math.pi/2), -1) try: self.assertTrue(math.isnan(math.sin(INF))) self.assertTrue(math.isnan(math.sin(NINF))) except ValueError: self.assertRaises(ValueError, math.sin, INF) self.assertRaises(ValueError, math.sin, NINF) self.assertTrue(math.isnan(math.sin(NAN))) def testSinh(self): self.assertRaises(TypeError, math.sinh) self.ftest('sinh(0)', math.sinh(0), 0) self.ftest('sinh(1)**2-cosh(1)**2', math.sinh(1)**2-math.cosh(1)**2, -1) self.ftest('sinh(1)+sinh(-1)', math.sinh(1)+math.sinh(-1), 0) self.assertEqual(math.sinh(INF), INF) self.assertEqual(math.sinh(NINF), NINF) self.assertTrue(math.isnan(math.sinh(NAN))) def testSqrt(self): self.assertRaises(TypeError, math.sqrt) self.ftest('sqrt(0)', math.sqrt(0), 0) self.ftest('sqrt(0)', math.sqrt(0.0), 0.0) self.ftest('sqrt(2.5)', math.sqrt(2.5), 1.5811388300841898) self.ftest('sqrt(0.25)', math.sqrt(0.25), 0.5) self.ftest('sqrt(25.25)', math.sqrt(25.25), 5.024937810560445) self.ftest('sqrt(1)', math.sqrt(1), 1) self.ftest('sqrt(4)', math.sqrt(4), 2) self.assertEqual(math.sqrt(INF), INF) self.assertRaises(ValueError, math.sqrt, -1) self.assertRaises(ValueError, math.sqrt, NINF) self.assertTrue(math.isnan(math.sqrt(NAN))) def testTan(self): self.assertRaises(TypeError, math.tan) self.ftest('tan(0)', math.tan(0), 0) self.ftest('tan(pi/4)', math.tan(math.pi/4), 1) self.ftest('tan(-pi/4)', math.tan(-math.pi/4), -1) try: self.assertTrue(math.isnan(math.tan(INF))) self.assertTrue(math.isnan(math.tan(NINF))) except: self.assertRaises(ValueError, math.tan, INF) self.assertRaises(ValueError, math.tan, NINF) self.assertTrue(math.isnan(math.tan(NAN))) def testTanh(self): self.assertRaises(TypeError, math.tanh) self.ftest('tanh(0)', math.tanh(0), 0) self.ftest('tanh(1)+tanh(-1)', math.tanh(1)+math.tanh(-1), 0, abs_tol=math.ulp(1)) self.ftest('tanh(inf)', math.tanh(INF), 1) self.ftest('tanh(-inf)', math.tanh(NINF), -1) self.assertTrue(math.isnan(math.tanh(NAN))) @requires_IEEE_754 def testTanhSign(self): # check that tanh(-0.) == -0. on IEEE 754 systems self.assertEqual(math.tanh(-0.), -0.) self.assertEqual(math.copysign(1., math.tanh(-0.)), math.copysign(1., -0.)) def test_trunc(self): self.assertEqual(math.trunc(1), 1) self.assertEqual(math.trunc(-1), -1) self.assertEqual(type(math.trunc(1)), int) self.assertEqual(type(math.trunc(1.5)), int) self.assertEqual(math.trunc(1.5), 1) self.assertEqual(math.trunc(-1.5), -1) self.assertEqual(math.trunc(1.999999), 1) self.assertEqual(math.trunc(-1.999999), -1) self.assertEqual(math.trunc(-0.999999), -0) self.assertEqual(math.trunc(-100.999), -100) class TestTrunc: def __trunc__(self): return 23 class FloatTrunc(float): def __trunc__(self): return 23 class TestNoTrunc: pass self.assertEqual(math.trunc(TestTrunc()), 23) self.assertEqual(math.trunc(FloatTrunc()), 23) self.assertRaises(TypeError, math.trunc) self.assertRaises(TypeError, math.trunc, 1, 2) self.assertRaises(TypeError, math.trunc, FloatLike(23.5)) self.assertRaises(TypeError, math.trunc, TestNoTrunc()) def testIsfinite(self): self.assertTrue(math.isfinite(0.0)) self.assertTrue(math.isfinite(-0.0)) self.assertTrue(math.isfinite(1.0)) self.assertTrue(math.isfinite(-1.0)) self.assertFalse(math.isfinite(float("nan"))) self.assertFalse(math.isfinite(float("inf"))) self.assertFalse(math.isfinite(float("-inf"))) def testIsnan(self): self.assertTrue(math.isnan(float("nan"))) self.assertTrue(math.isnan(float("-nan"))) self.assertTrue(math.isnan(float("inf") * 0.)) self.assertFalse(math.isnan(float("inf"))) self.assertFalse(math.isnan(0.)) self.assertFalse(math.isnan(1.)) def testIsinf(self): self.assertTrue(math.isinf(float("inf"))) self.assertTrue(math.isinf(float("-inf"))) self.assertTrue(math.isinf(1E400)) self.assertTrue(math.isinf(-1E400)) self.assertFalse(math.isinf(float("nan"))) self.assertFalse(math.isinf(0.)) self.assertFalse(math.isinf(1.)) @requires_IEEE_754 def test_nan_constant(self): self.assertTrue(math.isnan(math.nan)) @requires_IEEE_754 def test_inf_constant(self): self.assertTrue(math.isinf(math.inf)) self.assertGreater(math.inf, 0.0) self.assertEqual(math.inf, float("inf")) self.assertEqual(-math.inf, float("-inf")) # RED_FLAG 16-Oct-2000 Tim # While 2.0 is more consistent about exceptions than previous releases, it # still fails this part of the test on some platforms. For now, we only # *run* test_exceptions() in verbose mode, so that this isn't normally # tested. @unittest.skipUnless(verbose, 'requires verbose mode') def test_exceptions(self): try: x = math.exp(-1000000000) except: # mathmodule.c is failing to weed out underflows from libm, or # we've got an fp format with huge dynamic range self.fail("underflowing exp() should not have raised " "an exception") if x != 0: self.fail("underflowing exp() should have returned 0") # If this fails, probably using a strict IEEE-754 conforming libm, and x # is +Inf afterwards. But Python wants overflows detected by default. try: x = math.exp(1000000000) except OverflowError: pass else: self.fail("overflowing exp() didn't trigger OverflowError") # If this fails, it could be a puzzle. One odd possibility is that # mathmodule.c's macros are getting confused while comparing # Inf (HUGE_VAL) to a NaN, and artificially setting errno to ERANGE # as a result (and so raising OverflowError instead). try: x = math.sqrt(-1.0) except ValueError: pass else: self.fail("sqrt(-1) didn't raise ValueError") @requires_IEEE_754 def test_testfile(self): # Some tests need to be skipped on ancient OS X versions. # See issue #27953. SKIP_ON_TIGER = {'tan0064'} osx_version = None if sys.platform == 'darwin': version_txt = platform.mac_ver()[0] try: osx_version = tuple(map(int, version_txt.split('.'))) except ValueError: pass fail_fmt = "{}: {}({!r}): {}" failures = [] for id, fn, ar, ai, er, ei, flags in parse_testfile(test_file): # Skip if either the input or result is complex if ai != 0.0 or ei != 0.0: continue if fn in ['rect', 'polar']: # no real versions of rect, polar continue # Skip certain tests on OS X 10.4. if osx_version is not None and osx_version < (10, 5): if id in SKIP_ON_TIGER: continue func = getattr(math, fn) if 'invalid' in flags or 'divide-by-zero' in flags: er = 'ValueError' elif 'overflow' in flags: er = 'OverflowError' try: result = func(ar) except ValueError: result = 'ValueError' except OverflowError: result = 'OverflowError' # Default tolerances ulp_tol, abs_tol = 5, 0.0 failure = result_check(er, result, ulp_tol, abs_tol) if failure is None: continue msg = fail_fmt.format(id, fn, ar, failure) failures.append(msg) if failures: self.fail('Failures in test_testfile:\n ' + '\n '.join(failures)) @requires_IEEE_754 def test_mtestfile(self): fail_fmt = "{}: {}({!r}): {}" failures = [] for id, fn, arg, expected, flags in parse_mtestfile(math_testcases): func = getattr(math, fn) if 'invalid' in flags or 'divide-by-zero' in flags: expected = 'ValueError' elif 'overflow' in flags: expected = 'OverflowError' try: got = func(arg) except ValueError: got = 'ValueError' except OverflowError: got = 'OverflowError' # Default tolerances ulp_tol, abs_tol = 5, 0.0 # Exceptions to the defaults if fn == 'gamma': # Experimental results on one platform gave # an accuracy of <= 10 ulps across the entire float # domain. We weaken that to require 20 ulp accuracy. ulp_tol = 20 elif fn == 'lgamma': # we use a weaker accuracy test for lgamma; # lgamma only achieves an absolute error of # a few multiples of the machine accuracy, in # general. abs_tol = 1e-15 elif fn == 'erfc' and arg >= 0.0: # erfc has less-than-ideal accuracy for large # arguments (x ~ 25 or so), mainly due to the # error involved in computing exp(-x*x). # # Observed between CPython and mpmath at 25 dp: # x < 0 : err <= 2 ulp # 0 <= x < 1 : err <= 10 ulp # 1 <= x < 10 : err <= 100 ulp # 10 <= x < 20 : err <= 300 ulp # 20 <= x : < 600 ulp # if arg < 1.0: ulp_tol = 10 elif arg < 10.0: ulp_tol = 100 else: ulp_tol = 1000 failure = result_check(expected, got, ulp_tol, abs_tol) if failure is None: continue msg = fail_fmt.format(id, fn, arg, failure) failures.append(msg) if failures: self.fail('Failures in test_mtestfile:\n ' + '\n '.join(failures)) def test_prod(self): prod = math.prod self.assertEqual(prod([]), 1) self.assertEqual(prod([], start=5), 5) self.assertEqual(prod(list(range(2,8))), 5040) self.assertEqual(prod(iter(list(range(2,8)))), 5040) self.assertEqual(prod(range(1, 10), start=10), 3628800) self.assertEqual(prod([1, 2, 3, 4, 5]), 120) self.assertEqual(prod([1.0, 2.0, 3.0, 4.0, 5.0]), 120.0) self.assertEqual(prod([1, 2, 3, 4.0, 5.0]), 120.0) self.assertEqual(prod([1.0, 2.0, 3.0, 4, 5]), 120.0) # Test overflow in fast-path for integers self.assertEqual(prod([1, 1, 2**32, 1, 1]), 2**32) # Test overflow in fast-path for floats self.assertEqual(prod([1.0, 1.0, 2**32, 1, 1]), float(2**32)) self.assertRaises(TypeError, prod) self.assertRaises(TypeError, prod, 42) self.assertRaises(TypeError, prod, ['a', 'b', 'c']) self.assertRaises(TypeError, prod, ['a', 'b', 'c'], '') self.assertRaises(TypeError, prod, [b'a', b'c'], b'') values = [bytearray(b'a'), bytearray(b'b')] self.assertRaises(TypeError, prod, values, bytearray(b'')) self.assertRaises(TypeError, prod, [[1], [2], [3]]) self.assertRaises(TypeError, prod, [{2:3}]) self.assertRaises(TypeError, prod, [{2:3}]*2, {2:3}) self.assertRaises(TypeError, prod, [[1], [2], [3]], []) with self.assertRaises(TypeError): prod([10, 20], [30, 40]) # start is a keyword-only argument self.assertEqual(prod([0, 1, 2, 3]), 0) self.assertEqual(prod([1, 0, 2, 3]), 0) self.assertEqual(prod([1, 2, 3, 0]), 0) def _naive_prod(iterable, start=1): for elem in iterable: start *= elem return start # Big integers iterable = range(1, 10000) self.assertEqual(prod(iterable), _naive_prod(iterable)) iterable = range(-10000, -1) self.assertEqual(prod(iterable), _naive_prod(iterable)) iterable = range(-1000, 1000) self.assertEqual(prod(iterable), 0) # Big floats iterable = [float(x) for x in range(1, 1000)] self.assertEqual(prod(iterable), _naive_prod(iterable)) iterable = [float(x) for x in range(-1000, -1)] self.assertEqual(prod(iterable), _naive_prod(iterable)) iterable = [float(x) for x in range(-1000, 1000)] self.assertIsNaN(prod(iterable)) # Float tests self.assertIsNaN(prod([1, 2, 3, float("nan"), 2, 3])) self.assertIsNaN(prod([1, 0, float("nan"), 2, 3])) self.assertIsNaN(prod([1, float("nan"), 0, 3])) self.assertIsNaN(prod([1, float("inf"), float("nan"),3])) self.assertIsNaN(prod([1, float("-inf"), float("nan"),3])) self.assertIsNaN(prod([1, float("nan"), float("inf"),3])) self.assertIsNaN(prod([1, float("nan"), float("-inf"),3])) self.assertEqual(prod([1, 2, 3, float('inf'),-3,4]), float('-inf')) self.assertEqual(prod([1, 2, 3, float('-inf'),-3,4]), float('inf')) self.assertIsNaN(prod([1,2,0,float('inf'), -3, 4])) self.assertIsNaN(prod([1,2,0,float('-inf'), -3, 4])) self.assertIsNaN(prod([1, 2, 3, float('inf'), -3, 0, 3])) self.assertIsNaN(prod([1, 2, 3, float('-inf'), -3, 0, 2])) # Type preservation self.assertEqual(type(prod([1, 2, 3, 4, 5, 6])), int) self.assertEqual(type(prod([1, 2.0, 3, 4, 5, 6])), float) self.assertEqual(type(prod(range(1, 10000))), int) self.assertEqual(type(prod(range(1, 10000), start=1.0)), float) self.assertEqual(type(prod([1, decimal.Decimal(2.0), 3, 4, 5, 6])), decimal.Decimal) def testPerm(self): perm = math.perm factorial = math.factorial # Test if factorial definition is satisfied for n in range(100): for k in range(n + 1): self.assertEqual(perm(n, k), factorial(n) // factorial(n - k)) # Test for Pascal's identity for n in range(1, 100): for k in range(1, n): self.assertEqual(perm(n, k), perm(n - 1, k - 1) * k + perm(n - 1, k)) # Test corner cases for n in range(1, 100): self.assertEqual(perm(n, 0), 1) self.assertEqual(perm(n, 1), n) self.assertEqual(perm(n, n), factorial(n)) # Test one argument form for n in range(20): self.assertEqual(perm(n), factorial(n)) self.assertEqual(perm(n, None), factorial(n)) # Raises TypeError if any argument is non-integer or argument count is # not 1 or 2 self.assertRaises(TypeError, perm, 10, 1.0) self.assertRaises(TypeError, perm, 10, decimal.Decimal(1.0)) self.assertRaises(TypeError, perm, 10, "1") self.assertRaises(TypeError, perm, 10.0, 1) self.assertRaises(TypeError, perm, decimal.Decimal(10.0), 1) self.assertRaises(TypeError, perm, "10", 1) self.assertRaises(TypeError, perm) self.assertRaises(TypeError, perm, 10, 1, 3) self.assertRaises(TypeError, perm) # Raises Value error if not k or n are negative numbers self.assertRaises(ValueError, perm, -1, 1) self.assertRaises(ValueError, perm, -2**1000, 1) self.assertRaises(ValueError, perm, 1, -1) self.assertRaises(ValueError, perm, 1, -2**1000) # Returns zero if k is greater than n self.assertEqual(perm(1, 2), 0) self.assertEqual(perm(1, 2**1000), 0) n = 2**1000 self.assertEqual(perm(n, 0), 1) self.assertEqual(perm(n, 1), n) self.assertEqual(perm(n, 2), n * (n-1)) if support.check_impl_detail(cpython=True): self.assertRaises(OverflowError, perm, n, n) for n, k in (True, True), (True, False), (False, False): self.assertEqual(perm(n, k), 1) self.assertIs(type(perm(n, k)), int) self.assertEqual(perm(IntSubclass(5), IntSubclass(2)), 20) self.assertEqual(perm(MyIndexable(5), MyIndexable(2)), 20) for k in range(3): self.assertIs(type(perm(IntSubclass(5), IntSubclass(k))), int) self.assertIs(type(perm(MyIndexable(5), MyIndexable(k))), int) def testComb(self): comb = math.comb factorial = math.factorial # Test if factorial definition is satisfied for n in range(100): for k in range(n + 1): self.assertEqual(comb(n, k), factorial(n) // (factorial(k) * factorial(n - k))) # Test for Pascal's identity for n in range(1, 100): for k in range(1, n): self.assertEqual(comb(n, k), comb(n - 1, k - 1) + comb(n - 1, k)) # Test corner cases for n in range(100): self.assertEqual(comb(n, 0), 1) self.assertEqual(comb(n, n), 1) for n in range(1, 100): self.assertEqual(comb(n, 1), n) self.assertEqual(comb(n, n - 1), n) # Test Symmetry for n in range(100): for k in range(n // 2): self.assertEqual(comb(n, k), comb(n, n - k)) # Raises TypeError if any argument is non-integer or argument count is # not 2 self.assertRaises(TypeError, comb, 10, 1.0) self.assertRaises(TypeError, comb, 10, decimal.Decimal(1.0)) self.assertRaises(TypeError, comb, 10, "1") self.assertRaises(TypeError, comb, 10.0, 1) self.assertRaises(TypeError, comb, decimal.Decimal(10.0), 1) self.assertRaises(TypeError, comb, "10", 1) self.assertRaises(TypeError, comb, 10) self.assertRaises(TypeError, comb, 10, 1, 3) self.assertRaises(TypeError, comb) # Raises Value error if not k or n are negative numbers self.assertRaises(ValueError, comb, -1, 1) self.assertRaises(ValueError, comb, -2**1000, 1) self.assertRaises(ValueError, comb, 1, -1) self.assertRaises(ValueError, comb, 1, -2**1000) # Returns zero if k is greater than n self.assertEqual(comb(1, 2), 0) self.assertEqual(comb(1, 2**1000), 0) n = 2**1000 self.assertEqual(comb(n, 0), 1) self.assertEqual(comb(n, 1), n) self.assertEqual(comb(n, 2), n * (n-1) // 2) self.assertEqual(comb(n, n), 1) self.assertEqual(comb(n, n-1), n) self.assertEqual(comb(n, n-2), n * (n-1) // 2) if support.check_impl_detail(cpython=True): self.assertRaises(OverflowError, comb, n, n//2) for n, k in (True, True), (True, False), (False, False): self.assertEqual(comb(n, k), 1) self.assertIs(type(comb(n, k)), int) self.assertEqual(comb(IntSubclass(5), IntSubclass(2)), 10) self.assertEqual(comb(MyIndexable(5), MyIndexable(2)), 10) for k in range(3): self.assertIs(type(comb(IntSubclass(5), IntSubclass(k))), int) self.assertIs(type(comb(MyIndexable(5), MyIndexable(k))), int) @requires_IEEE_754 def test_nextafter(self): # around 2^52 and 2^63 self.assertEqual(math.nextafter(4503599627370496.0, -INF), 4503599627370495.5) self.assertEqual(math.nextafter(4503599627370496.0, INF), 4503599627370497.0) self.assertEqual(math.nextafter(9223372036854775808.0, 0.0), 9223372036854774784.0) self.assertEqual(math.nextafter(-9223372036854775808.0, 0.0), -9223372036854774784.0) # around 1.0 self.assertEqual(math.nextafter(1.0, -INF), float.fromhex('0x1.fffffffffffffp-1')) self.assertEqual(math.nextafter(1.0, INF), float.fromhex('0x1.0000000000001p+0')) # x == y: y is returned self.assertEqual(math.nextafter(2.0, 2.0), 2.0) self.assertEqualSign(math.nextafter(-0.0, +0.0), +0.0) self.assertEqualSign(math.nextafter(+0.0, -0.0), -0.0) # around 0.0 smallest_subnormal = sys.float_info.min * sys.float_info.epsilon self.assertEqual(math.nextafter(+0.0, INF), smallest_subnormal) self.assertEqual(math.nextafter(-0.0, INF), smallest_subnormal) self.assertEqual(math.nextafter(+0.0, -INF), -smallest_subnormal) self.assertEqual(math.nextafter(-0.0, -INF), -smallest_subnormal) self.assertEqualSign(math.nextafter(smallest_subnormal, +0.0), +0.0) self.assertEqualSign(math.nextafter(-smallest_subnormal, +0.0), -0.0) self.assertEqualSign(math.nextafter(smallest_subnormal, -0.0), +0.0) self.assertEqualSign(math.nextafter(-smallest_subnormal, -0.0), -0.0) # around infinity largest_normal = sys.float_info.max self.assertEqual(math.nextafter(INF, 0.0), largest_normal) self.assertEqual(math.nextafter(-INF, 0.0), -largest_normal) self.assertEqual(math.nextafter(largest_normal, INF), INF) self.assertEqual(math.nextafter(-largest_normal, -INF), -INF) # NaN self.assertIsNaN(math.nextafter(NAN, 1.0)) self.assertIsNaN(math.nextafter(1.0, NAN)) self.assertIsNaN(math.nextafter(NAN, NAN)) @requires_IEEE_754 def test_ulp(self): self.assertEqual(math.ulp(1.0), sys.float_info.epsilon) # use int ** int rather than float ** int to not rely on pow() accuracy self.assertEqual(math.ulp(2 ** 52), 1.0) self.assertEqual(math.ulp(2 ** 53), 2.0) self.assertEqual(math.ulp(2 ** 64), 4096.0) # min and max self.assertEqual(math.ulp(0.0), sys.float_info.min * sys.float_info.epsilon) self.assertEqual(math.ulp(FLOAT_MAX), FLOAT_MAX - math.nextafter(FLOAT_MAX, -INF)) # special cases self.assertEqual(math.ulp(INF), INF) self.assertIsNaN(math.ulp(math.nan)) # negative number: ulp(-x) == ulp(x) for x in (0.0, 1.0, 2 ** 52, 2 ** 64, INF): with self.subTest(x=x): self.assertEqual(math.ulp(-x), math.ulp(x)) def test_issue39871(self): # A SystemError should not be raised if the first arg to atan2(), # copysign(), or remainder() cannot be converted to a float. class F: def __float__(self): self.converted = True 1/0 for func in math.atan2, math.copysign, math.remainder: y = F() with self.assertRaises(TypeError): func("not a number", y) # There should not have been any attempt to convert the second # argument to a float. self.assertFalse(getattr(y, "converted", False)) # Custom assertions. def assertIsNaN(self, value): if not math.isnan(value): self.fail("Expected a NaN, got {!r}.".format(value)) def assertEqualSign(self, x, y): """Similar to assertEqual(), but compare also the sign with copysign(). Function useful to compare signed zeros. """ self.assertEqual(x, y) self.assertEqual(math.copysign(1.0, x), math.copysign(1.0, y)) class IsCloseTests(unittest.TestCase): isclose = math.isclose # subclasses should override this def assertIsClose(self, a, b, *args, **kwargs): self.assertTrue(self.isclose(a, b, *args, **kwargs), msg="%s and %s should be close!" % (a, b)) def assertIsNotClose(self, a, b, *args, **kwargs): self.assertFalse(self.isclose(a, b, *args, **kwargs), msg="%s and %s should not be close!" % (a, b)) def assertAllClose(self, examples, *args, **kwargs): for a, b in examples: self.assertIsClose(a, b, *args, **kwargs) def assertAllNotClose(self, examples, *args, **kwargs): for a, b in examples: self.assertIsNotClose(a, b, *args, **kwargs) def test_negative_tolerances(self): # ValueError should be raised if either tolerance is less than zero with self.assertRaises(ValueError): self.assertIsClose(1, 1, rel_tol=-1e-100) with self.assertRaises(ValueError): self.assertIsClose(1, 1, rel_tol=1e-100, abs_tol=-1e10) def test_identical(self): # identical values must test as close identical_examples = [(2.0, 2.0), (0.1e200, 0.1e200), (1.123e-300, 1.123e-300), (12345, 12345.0), (0.0, -0.0), (345678, 345678)] self.assertAllClose(identical_examples, rel_tol=0.0, abs_tol=0.0) def test_eight_decimal_places(self): # examples that are close to 1e-8, but not 1e-9 eight_decimal_places_examples = [(1e8, 1e8 + 1), (-1e-8, -1.000000009e-8), (1.12345678, 1.12345679)] self.assertAllClose(eight_decimal_places_examples, rel_tol=1e-8) self.assertAllNotClose(eight_decimal_places_examples, rel_tol=1e-9) def test_near_zero(self): # values close to zero near_zero_examples = [(1e-9, 0.0), (-1e-9, 0.0), (-1e-150, 0.0)] # these should not be close to any rel_tol self.assertAllNotClose(near_zero_examples, rel_tol=0.9) # these should be close to abs_tol=1e-8 self.assertAllClose(near_zero_examples, abs_tol=1e-8) def test_identical_infinite(self): # these are close regardless of tolerance -- i.e. they are equal self.assertIsClose(INF, INF) self.assertIsClose(INF, INF, abs_tol=0.0) self.assertIsClose(NINF, NINF) self.assertIsClose(NINF, NINF, abs_tol=0.0) def test_inf_ninf_nan(self): # these should never be close (following IEEE 754 rules for equality) not_close_examples = [(NAN, NAN), (NAN, 1e-100), (1e-100, NAN), (INF, NAN), (NAN, INF), (INF, NINF), (INF, 1.0), (1.0, INF), (INF, 1e308), (1e308, INF)] # use largest reasonable tolerance self.assertAllNotClose(not_close_examples, abs_tol=0.999999999999999) def test_zero_tolerance(self): # test with zero tolerance zero_tolerance_close_examples = [(1.0, 1.0), (-3.4, -3.4), (-1e-300, -1e-300)] self.assertAllClose(zero_tolerance_close_examples, rel_tol=0.0) zero_tolerance_not_close_examples = [(1.0, 1.000000000000001), (0.99999999999999, 1.0), (1.0e200, .999999999999999e200)] self.assertAllNotClose(zero_tolerance_not_close_examples, rel_tol=0.0) def test_asymmetry(self): # test the asymmetry example from PEP 485 self.assertAllClose([(9, 10), (10, 9)], rel_tol=0.1) def test_integers(self): # test with integer values integer_examples = [(100000001, 100000000), (123456789, 123456788)] self.assertAllClose(integer_examples, rel_tol=1e-8) self.assertAllNotClose(integer_examples, rel_tol=1e-9) def test_decimals(self): # test with Decimal values from decimal import Decimal decimal_examples = [(Decimal('1.00000001'), Decimal('1.0')), (Decimal('1.00000001e-20'), Decimal('1.0e-20')), (Decimal('1.00000001e-100'), Decimal('1.0e-100')), (Decimal('1.00000001e20'), Decimal('1.0e20'))] self.assertAllClose(decimal_examples, rel_tol=1e-8) self.assertAllNotClose(decimal_examples, rel_tol=1e-9) def test_fractions(self): # test with Fraction values from fractions import Fraction fraction_examples = [ (Fraction(1, 100000000) + 1, Fraction(1)), (Fraction(100000001), Fraction(100000000)), (Fraction(10**8 + 1, 10**28), Fraction(1, 10**20))] self.assertAllClose(fraction_examples, rel_tol=1e-8) self.assertAllNotClose(fraction_examples, rel_tol=1e-9) def test_main(): from doctest import DocFileSuite suite = unittest.TestSuite() suite.addTest(unittest.makeSuite(MathTests)) suite.addTest(unittest.makeSuite(IsCloseTests)) suite.addTest(DocFileSuite("ieee754.txt")) run_unittest(suite) if __name__ == '__main__': test_main()

41.739053

98

0.568342

7a9a8e0f955e87c6b580379552976c8e04e01719

21,004

py

Python

sfcollector/solidfire_graphite_collector.py

scaleoutsean/hcicollector

e30652c0e72f46937f049c7e44e3f8d5516a7b40

[ "Apache-2.0" ]

3

2020-01-30T19:56:27.000Z

2020-06-09T10:50:53.000Z

sfcollector/solidfire_graphite_collector.py

scaleoutsean/hcicollector

e30652c0e72f46937f049c7e44e3f8d5516a7b40

[ "Apache-2.0" ]

15

2020-02-04T14:09:05.000Z

2021-12-26T08:52:53.000Z

sfcollector/solidfire_graphite_collector.py

scaleoutsean/hcicollector

e30652c0e72f46937f049c7e44e3f8d5516a7b40

[ "Apache-2.0" ]

1

2020-02-20T04:21:28.000Z

#!/usr/bin/env python # solidfire_graphite_collector.py # # Version 1.0.5 # # Original authors: Colin Bieberstein, Aaron Patten # Contributors: Pablo Luis Zorzoli, Davide Obbi, scaleoutSean # # Copyright (c) 2020 NetApp, Inc. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import argparse import time import graphyte from solidfire.factory import ElementFactory import solidfire.common import logging def send_cluster_faults(sfe, prefix): """ send active cluster fault counts by: warning, error, critical exclude best practices, and only include current faults """ fault_list = sfe.list_cluster_faults(False, "current").to_json()['faults'] group = {'critical': 0, 'error': 0, 'warning': 0} for d in fault_list: if d['severity'] not in group: group[d['severity']] = 1 else: group[d['severity']] += 1 if to_graphite: for key in group: graphyte.send(prefix + '.fault.' + key, to_num(group[key])) else: for key in group: LOG.warning(prefix + '.fault.' + key + ' ' + str(group[key])) def send_cluster_stats(sfe, prefix): """ send a subset of GetClusterStats API call results to graphite. """ metrics = ['clientQueueDepth', 'clusterUtilization', 'readOpsLastSample', 'readBytesLastSample', 'writeOpsLastSample', 'writeBytesLastSample', 'actualIOPS', 'latencyUSec', 'normalizedIOPS', 'readBytes', 'readLatencyUSec', 'readOps', 'unalignedReads', 'unalignedWrites', 'writeLatencyUSec', 'writeOps', 'writeBytes'] cluster_stats_dict = sfe.get_cluster_stats().to_json()['clusterStats'] clusterUtilizationDec = float(cluster_stats_dict['clusterUtilization']) clusterUtilizationScaled = clusterUtilizationDec if to_graphite: graphyte.send(prefix + '.clusterUtilizationScaled', to_num(clusterUtilizationScaled)) else: LOG.warning(prefix + '.clusterUtilizationScaled ' + str(clusterUtilizationScaled)) for key in metrics: if to_graphite: graphyte.send(prefix + '.' + key, to_num(cluster_stats_dict[key])) else: LOG.warning(prefix + '.' + key + ' ' + str(cluster_stats_dict[key])) def send_cluster_capacity(sfe, prefix): """ send a subset of GetClusterCapacity API call results and derived metrics to graphite. """ metrics = ['activeBlockSpace', 'activeSessions', 'averageIOPS', 'clusterRecentIOSize', 'currentIOPS', 'maxIOPS', 'maxOverProvisionableSpace', 'maxProvisionedSpace', 'maxUsedMetadataSpace', 'maxUsedSpace', 'nonZeroBlocks', 'peakActiveSessions', 'peakIOPS', 'provisionedSpace', 'snapshotNonZeroBlocks', 'totalOps', 'uniqueBlocks', 'uniqueBlocksUsedSpace', 'usedMetadataSpace', 'usedMetadataSpaceInSnapshots', 'usedSpace', 'zeroBlocks'] result = sfe.get_cluster_capacity().to_json()['clusterCapacity'] for key in metrics: if to_graphite: graphyte.send(prefix + '.' + key, to_num(result[key])) else: LOG.warning(prefix + '.' + key + ' ' + str(result[key])) # Calculate & send derived metrics non_zero_blocks = to_num(result['nonZeroBlocks']) zero_blocks = to_num(result['zeroBlocks']) unique_blocks = to_num(result['uniqueBlocks']) unique_blocks_used_space = to_num(result['uniqueBlocksUsedSpace']) snapshot_non_zero_blocks = to_num(result['snapshotNonZeroBlocks']) if non_zero_blocks != 0: thin_factor = float((non_zero_blocks + zero_blocks)) / float(non_zero_blocks) else: thin_factor = 1 if to_graphite: graphyte.send(prefix + '.thin_factor', to_num(thin_factor)) else: LOG.warning(prefix + '.thin_factor ' + str(thin_factor)) if unique_blocks != 0: # cluster with volumes with snapshots can have dedupe ratio < 1 dedupe_factor = float(non_zero_blocks + snapshot_non_zero_blocks) / float(unique_blocks) else: dedupe_factor = 1 if to_graphite: graphyte.send(prefix + '.dedupe_factor', to_num(dedupe_factor)) else: LOG.warning(prefix + '.dedupe_factor ' + str(dedupe_factor)) if unique_blocks_used_space != 0: compression_factor = (unique_blocks * 4096.0) / (unique_blocks_used_space * .93) else: compression_factor = 1 if to_graphite: graphyte.send(prefix + '.compression_factor', to_num(compression_factor)) else: LOG.warning(prefix + '.compression_factor ' + str(compression_factor)) efficiency_cxd_factor = dedupe_factor * compression_factor efficiency_factor = thin_factor * efficiency_cxd_factor if to_graphite: graphyte.send(prefix + '.efficiency_factor', to_num(efficiency_factor)) # w/o Thin Provisioning graphyte.send(prefix + '.efficiency_cxd_factor', to_num(efficiency_cxd_factor)) else: LOG.warning(prefix + '.efficiency_factor ' + str(efficiency_factor)) LOG.warning(prefix + '.efficiency_cxd_factor ' + str(efficiency_cxd_factor)) def send_volume_stats(sfe, prefix): """ Send a subset of ListVolumeStatsByVolume results to graphite. Note: Calls ListVolumes to get volume names for use in metric path. """ metrics_list = ['actualIOPS', 'averageIOPSize', 'burstIOPSCredit', 'clientQueueDepth', 'latencyUSec', 'nonZeroBlocks', 'readBytes', 'readBytesLastSample', 'readLatencyUSec', 'readOps', 'readOpsLastSample', 'throttle', 'unalignedReads', 'unalignedWrites', 'volumeSize', 'volumeUtilization', 'writeBytes', 'writeBytesLastSample', 'writeLatencyUSec', 'writeOps', 'writeOpsLastSample', 'zeroBlocks'] volume_list = sfe.list_volumes(include_virtual_volumes=False).to_json()['volumes'] volinfo_by_id = list_to_dict(volume_list, key="volumeID") volstats = sfe.list_volume_stats_by_volume(include_virtual_volumes=False).to_json()['volumeStats'] for vs_dict in volstats: vol_name = volinfo_by_id[vs_dict['volumeID']]['name'] vol_id = volinfo_by_id[vs_dict['volumeID']]['volumeID'] vol_accountID = volinfo_by_id[vs_dict['volumeID']]['accountID'] vol_accountID = volinfo_by_id[vs_dict['volumeID']]['accountID'] vol_accountName = sfe.get_account_by_id(vol_accountID).to_json()['account']['username'] for key in metrics_list: if to_graphite: graphyte.send(prefix + '.accountID.' + str(vol_accountName) + '.volume.' + str(vol_name) + '.' + key, to_num(vs_dict[key])) graphyte.send(prefix + '.volumeID.' + str(vol_id) + '.' + key, to_num(vs_dict[key])) else: LOG.warning(prefix + '.accountID.' + str(vol_accountName) + '.volume.' + str(vol_name) + '.' + key + ' ' + str(vs_dict[key])) LOG.warning(prefix + '.volumeID.' + str(vol_accountName) + '.volumeID.' + str(vol_id) + '.' + key + ' ' + str(vs_dict[key])) def send_volume_histogram_stats(sfe, prefix): """ Send volume QoS histogram stats. Requires API v11 or above Note: as of August 2020, this API method is not well documented so stuff may not mean what we think it means. """ hmetrics = ['belowMinIopsPercentages', 'minToMaxIopsPercentages', 'minToMaxIopsPercentages', 'readBlockSizes', 'throttlePercentages', 'writeBlockSizes'] qosh = sfe.invoke_sfapi("ListVolumeQoSHistograms", parameters=None) for i in range(len(qosh['qosHistograms'])): for metric in hmetrics: for key, value in (qosh['qosHistograms'][i]['histograms'][metric]).items(): if to_graphite: graphyte.send(prefix + '.volumeID.' + str(qosh['qosHistograms'][i]['volumeID']) + '.' + metric + '.' + key, int(value)) else: LOG.warning(prefix + '.volumeID.' + str(qosh['qosHistograms'][i]['volumeID']) + '.' + metric + '.' + key + ' ' + str(value)) def send_drive_stats(sfe, prefix): """ Calculates summary statistics about drives by status and type at both cluster and node levels and submits them to graphite. Calls ListDrives and ListAllNodes """ # Cluster level status drive_list = sfe.list_drives().to_json()['drives'] for status in ['active', 'available', 'erasing', 'failed', 'removing']: value = count_if(drive_list, 'status', status) if to_graphite: graphyte.send(prefix + '.drives.status.' + status, to_num(value)) else: LOG.warning(prefix + '.drives.status.' + status + ' ' + str(value)) for dtype in ['volume', 'block', 'unknown']: value = count_if(drive_list, 'type', dtype) if to_graphite: graphyte.send(prefix + '.drives.type.' + dtype, to_num(value)) else: LOG.warning(prefix + '.drives.type.' + dtype + ' ' + str(value)) # Node level status node_list = sfe.list_all_nodes().to_json()['nodes'] nodeinfo_by_id = list_to_dict(node_list, key="nodeID") for node in nodeinfo_by_id: node_name = nodeinfo_by_id[node]['name'] for status in ['active', 'available', 'erasing', 'failed', 'removing']: value = count_ifs(drive_list, 'status', status, 'nodeID', node) if to_graphite: graphyte.send(prefix + '.node.' + node_name + '.drives.status.' + status, to_num(value)) else: LOG.warning(prefix + '.node.' + node_name + '.drives.status.' + status + ' ' + str(value)) for drive_type in ['volume', 'block', 'unknown']: value = count_ifs(drive_list, 'type', drive_type, 'nodeID', node) if to_graphite: graphyte.send(prefix + '.node.' + node_name + '.drives.type.' + drive_type, to_num(value)) else: LOG.warning(prefix + '.node.' + node_name + '.drives.type.' + drive_type + ' ' + str(value)) def send_ssd_stats(sfe, prefix): """ Send drive wear level from ListDriveStats API method results to graphite. We could store them under node name, but that doesn't seem necessary. Note: Calls ListDriveStats to get driveID to use in metric path. """ result = sfe.list_drive_stats().to_json()['driveStats'] for i in range(len(result)): driveId = result[i]['driveID'] lifePct = result[i]['lifeRemainingPercent'] if 'activeSessions' in result[i].keys(): sessions = result[i]['activeSessions'] if to_graphite: graphyte.send(prefix + '.drives.' + str(driveId) + '.sessions', int(sessions)) graphyte.send(prefix + '.drives.' + str(driveId) + '.lifeRemainingPercent', int(lifePct)) else: LOG.warning(prefix + '.drives.' + str(driveId) + '.sessions ' + str(sessions)) LOG.warning(prefix + '.drives.' + str(driveId) + '.lifeRemainingPercent ' + str(lifePct)) else: if to_graphite: graphyte.send(prefix + '.drives.' + str(driveId) + '.lifeRemainingPercent', int(lifePct)) else: LOG.warning(prefix + '.drives.' + str(driveId) + '.lifeRemainingPercent ' + str(lifePct)) def send_elem_version(sfe, prefix): """ Send the highest API version supported by current Element API """ result = sfe.get_cluster_version_info().to_json() sf_version = result['clusterAPIVersion'] if to_graphite: graphyte.send(prefix + '.version', float(sf_version)) else: LOG.warning(prefix + '.version ' + str(sf_version)) def send_acc_eff(sfe, prefix): """ Sends the CxD (no Thin Provisioning) account efficiency for all accounts with one or more volumes """ accounts = sfe.list_accounts().to_json()['accounts'] avw = [] for x in range(len(accounts)): if len(accounts[x]['volumes']) > 0: avw.append(accounts[x]['accountID']) for acc in avw: acc_eff = 0 acc_eff_info = sfe.get_account_efficiency(acc).to_json() acc_eff = round(acc_eff_info['compression'] * acc_eff_info['deduplication'],2) if to_graphite: graphyte.send(prefix + '.accountID.' + str(acc) + '.accountEfficiency', float(acc_eff)) else: LOG.warning(prefix + '.accountID.' + str(acc) + '.accountEfficiency ' + str(acc_eff)) def send_vol_efficiency(sfe, prefix): """ Send per-volume efficiency info (dedupe & compression aka CxD only) Can be used to identify low-efficiency volumes (e.g. < 1.5x) """ results = sfe.list_accounts().to_json() for account in results['accounts']: av = sfe.list_volumes(accounts=[account['accountID']]).to_json() if len(av['volumes']) > 0: for volume in av['volumes']: vol_eff_d = sfe.get_volume_efficiency(volume['volumeID']).to_json() vol_eff = round((vol_eff_d['deduplication'] * vol_eff_d['compression']),2) if to_graphite: graphyte.send(prefix + '.volumeID.' + str(volume['volumeID']) + '.volumeEfficiency', float(vol_eff)) else: print(prefix + '.volumeID.' + str(volume['volumeID']) + '.volumeEfficiency ' + str(vol_eff)) LOG.warning(prefix + '.volumeID.' + str(volume['volumeID']) + '.volumeEfficiency ' + str(vol_eff)) else: LOG.warning(prefix + ': account ID ' + str(account['accountID']) + ' has no volumes') def send_conn_stats(sfe, prefix): """ calculates iSCSI connection stats at both cluster and node levels and submits them to Graphite. Calls ListConnections """ result = sfe.list_iscsisessions().to_json()['sessions'] tgts = [] accts = [] for i in range(len(result)): tgts.append(result[i]['targetIP'].split(':')[0]) accts.append(result[i]['initiatorIP'].split(':')[0]) if to_graphite: graphyte.send(prefix + '.iscsiActiveSessionCount', len(result)) graphyte.send(prefix + '.iscsiTargetCount', len(set(tgts))) else: LOG.warning(prefix + '.iscsiActiveSessionCount ' + str(len(result))) LOG.warning(prefix + '.iscsiTargetCount ' + str(len(set(tgts)))) def send_node_stats(sfe, prefix): """ send a subset of ListNodeStats API call results to graphite. Note: Calls ListAllNodes to get node name to use in metric path. """ metrics_list = ['cpu', 'usedMemory', 'networkUtilizationStorage', 'networkUtilizationCluster', 'cBytesOut', 'cBytesIn', 'sBytesOut', 'sBytesIn', 'mBytesOut', 'mBytesIn', 'readOps', 'writeOps'] node_list = sfe.list_all_nodes().to_json()['nodes'] nodeinfo_by_id = list_to_dict(node_list, key="nodeID") nodestats = sfe.list_node_stats().to_json()['nodeStats']['nodes'] for ns_dict in nodestats: node_name = nodeinfo_by_id[ns_dict['nodeID']]['name'] for key in metrics_list: if to_graphite: graphyte.send(prefix + '.node.' + node_name + '.' + key, to_num(ns_dict[key])) else: LOG.warning(prefix + '.node.' + node_name + '.' + key + ' ' + str(ns_dict[key])) def list_to_dict(list_of_dicts, key): """ pivots a list of dicts into a dict of dicts, using key. """ x = dict((child[key], dict(child, index=index)) for (index, child) in \ enumerate(list_of_dicts)) return x def count_if(my_list, key, value): """ return number of records in my_list where key==value pair matches """ counter = (1 for item in my_list if item.get(key) == value) return sum(counter) def count_ifs(my_list, key, value, key2, value2): """ return number of records in my_list where both key==value pairs matches ToDo: convert to grab any number of key=value pairs """ counter = (1 for item in my_list if ((item.get(key) == value) and \ (item.get(key2) == value2))) return sum(counter) def to_num(metric): """ convert string to number (int or float) """ x = 0 try: x = float(metric) except ValueError: try: x = float(metric) except ValueError: x = float('NaN') finally: return x # Parse commandline arguments parser = argparse.ArgumentParser() parser.add_argument('-s', '--solidfire', help='MVIP or FQDN of SolidFire cluster from which metrics should be collected') parser.add_argument('-u', '--username', default='admin', help='username for SolidFire array. Default: admin (NOTE: consider using a dedicated reporting admin account)') parser.add_argument('-p', '--password', default='password', help='password for admin account on SolidFire cluster. Default: password') parser.add_argument('-o', '--timeout', type=int, default=10, help='timeout for SolidFire Collector to connect to SolidFire API. Default: 10 (seconds)') parser.add_argument('-a', '--apitimeout', type=int, default=20, help='timeout for SolidFire Collector to get response from the SolidFire API endpoint. Default: 20 (seconds)') parser.add_argument('-c', '--validatecert', default=False, help='Validate SF TLS certificate. Default: False (allow self-signed). For "True", --solidfire must use FQDN') parser.add_argument('-g', '--graphite', default='localhost', help='hostname of Graphite server to send to. Default: localhost. (NOTE: "debug" sends metrics to logfile)') parser.add_argument('-t', '--port', type=int, default=2003, help='port to send message to. Default: 2003. if the --graphite is set to debug, can be omitted') parser.add_argument('-v', '--version', default="11.0", help='Element API version. Default: 11.0. Version must be supported by SolidFire Python SDK in sfcollector') parser.add_argument('-m', '--metricroot', default='netapp.solidfire.cluster', help='Graphite metric root for sfcollector. Default: netapp.solidfire.cluster') parser.add_argument('-l', '--logfile', help='logfile. Default: none. Required if Graphite hostname is "debug" and metrics sent to logfile') args = parser.parse_args() to_graphite = True # Logger module configuration LOG = logging.getLogger('solidfire_graphite_collector.py') if args.logfile: logging.basicConfig(filename=args.logfile, level=logging.DEBUG, format='%(asctime)s %(message)s') LOG.warning("Starting Collector script as a daemon. No console output possible.") else: logging.basicConfig(level=logging.DEBUG, format='%(asctime)s %(message)s') # Initialize graphyte sender if args.graphite == "debug": LOG.warning("Starting collector in debug mode. All the metrics will be shipped to logfile") to_graphite = False else: graphyte.init(args.graphite, port=args.port, prefix=args.metricroot) LOG.info("Metrics Collection for array: {0}".format(args.solidfire)) try: sfe = ElementFactory.create(args.solidfire, args.username, args.password, args.version, verify_ssl=args.validatecert, print_ascii_art=False) # There are two kinds of timeouts (one is for individual API requests) # https://github.com/solidfire/solidfire-sdk-python/pull/39/files sfe.timeout(args.apitimeout) sfe.connect_timeout(args.timeout) except solidfire.common.ApiServerError as e: LOG.warning("ApiServerError: {0}".format(str(e))) sfe = None except Exception as e: LOG.warning("General Exception: {0}".format(str(e))) sfe = None try: cluster_name = sfe.get_cluster_info().to_json()['clusterInfo']['name'] send_cluster_faults(sfe, cluster_name) send_cluster_stats(sfe, cluster_name) send_cluster_capacity(sfe, cluster_name) send_volume_stats(sfe, cluster_name) send_volume_histogram_stats(sfe, cluster_name) send_drive_stats(sfe, cluster_name) send_ssd_stats(sfe, cluster_name) send_acc_eff(sfe, cluster_name) send_elem_version(sfe, cluster_name) send_vol_efficiency(sfe, cluster_name) send_node_stats(sfe, cluster_name) send_conn_stats(sfe, cluster_name) except solidfire.common.ApiServerError as e: LOG.warning("ApiServerError: {0}".format(str(e))) except Exception as e: LOG.warning("General Exception: {0}".format(str(e))) sfe = None

43.849687

144

0.646305

a8559d0c21545bf4f541239a0f19c839fc2241fc

3,133

py

Python

MyFirstCodeProjects_GH/recursion_base/anagram.py

MingChuan09/MyFirstCodeProjects

b8cb844c1471d070c0ed7df627ac71e10e4e013a

[ "MIT" ]

null

MyFirstCodeProjects_GH/recursion_base/anagram.py

MingChuan09/MyFirstCodeProjects

b8cb844c1471d070c0ed7df627ac71e10e4e013a

[ "MIT" ]

null

MyFirstCodeProjects_GH/recursion_base/anagram.py

MingChuan09/MyFirstCodeProjects

b8cb844c1471d070c0ed7df627ac71e10e4e013a

[ "MIT" ]

null

""" File: anagram.py Name: 陳名娟 Jenny Chen ---------------------------------- This program recursively finds all the anagram(s) for the word input by user and terminates when the input string matches the EXIT constant defined at line 19 If you correctly implement this program, you should see the number of anagrams for each word listed below: * arm -> 3 anagrams * contains -> 5 anagrams * stop -> 6 anagrams * tesla -> 10 anagrams * spear -> 12 anagrams """ import time # This file allows you to calculate the speed of your algorithm # Constants FILE = 'dictionary.txt' # This is the filename of an English dictionary EXIT = '-1' # Controls when to stop the loop # global variable dictionary = [] def main(): """ To find the anagrams of the word inputted by user. """ start = time.time() #################### print(f'Welcome to stanCode \"Anagram Generator\" (or {EXIT} to quit)') read_dictionary() while True: word = input('Find anagram for: ') if word == EXIT: break else: find_anagrams(word) #################### end = time.time() print('----------------------------------') print(f'The speed of your anagram algorithm: {end-start} seconds.') def read_dictionary(): with open(FILE, 'r') as f: for line in f: dictionary.append(line.strip()) def find_anagrams(s): """ :param s: str, the main word inputted by user to find anagrams """ count = [0] ans_list = [] find_anagrams_helper(s, '', ans_list, count, []) print('Searching...') print(str(count[0]) + ' anagrams: ' + str(ans_list)) def find_anagrams_helper(s, current_str, ans_list, count, index_list): """ :param s: str, to find anagrams of the main word inputted by user :param current_str: str, :param ans_list: list, include the anagrams :param count: list, the index 0 is the number of the anagrams :param index_list: list, the index list """ if len(current_str) == len(s): if current_str in dictionary: if current_str not in ans_list: ans_list.append(current_str) count[0] += 1 print('Searching...') print(f'Find: {current_str}') else: for i in range(len(s)): alpha = s[i] if i not in index_list: # 使用index以確保每個字母都有列入考慮 # choose index_list.append(i) current_str += alpha # explore if has_prefix(current_str): # 當有該字開頭之字首, 才繼續explore find_anagrams_helper(s, current_str, ans_list, count, index_list) # un-choose index_list.pop() current_str = current_str[0:len(current_str)-1] def has_prefix(sub_s): """ :param sub_s: str, the part of the word :return: the boolean answer """ for word in dictionary: if word.startswith(sub_s): return True return False if __name__ == '__main__': main()

28.225225

93

0.569103

081a337b911ad9aea657fd0ce8b7af0664fd1d70

546

py

Python

convertmask/utils/backup/getArea.py

wwdok/mask2json

403c6b3df677185d5951239d13187b55bda6465a

[ "Apache-2.0" ]

27

2020-06-11T01:47:57.000Z

2022-03-18T01:47:59.000Z

convertmask/utils/backup/getArea.py

wwdok/mask2json

403c6b3df677185d5951239d13187b55bda6465a

[ "Apache-2.0" ]

14

2020-06-11T01:50:41.000Z

2022-03-18T13:11:04.000Z

convertmask/utils/backup/getArea.py

wwdok/mask2json

403c6b3df677185d5951239d13187b55bda6465a

[ "Apache-2.0" ]

5

2020-10-21T01:51:59.000Z

2022-02-28T08:27:32.000Z

''' lanhuage: python Descripttion: version: beta Author: xiaoshuyui Date: 2020-08-17 13:48:46 LastEditors: xiaoshuyui LastEditTime: 2020-10-10 15:41:28 ''' def getAreaOfPolyGonbyVector(points): # 基于向量叉乘计算多边形面积 area = 0 if (len(points) < 3): raise Exception("points is not enough to calculate area!") for i in range(0, len(points) - 1): p1 = points[i] # print(p1) p2 = points[i + 1] triArea = (p1[0][0] * p2[0][1] - p2[0][0] * p1[0][1]) / 2 area += triArea return abs(area)

21

66

0.591575

4d3a9d936d0aba674062add9184bb01e46ae9e86

838

py

Python

admin/products/migrations/0001_initial.py

Branbados/microservices-application

3166defbb3713464138cb936d318de94819bd8f1

[ "MIT" ]

null

admin/products/migrations/0001_initial.py

Branbados/microservices-application

3166defbb3713464138cb936d318de94819bd8f1

[ "MIT" ]

null

admin/products/migrations/0001_initial.py

Branbados/microservices-application

3166defbb3713464138cb936d318de94819bd8f1

[ "MIT" ]

null

# Generated by Django 3.1.3 on 2021-07-08 23:15 from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Product', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('title', models.CharField(max_length=200)), ('image', models.CharField(max_length=200)), ('likes', models.PositiveBigIntegerField(default=0)), ], ), migrations.CreateModel( name='User', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ], ), ]

27.933333

114

0.557279

e06b82f2eb0926082dc5b212d2b791ef845898d4

54,080

py

Python

fsspec/spec.py

d4l3k/filesystem_spec

bd0f7d2442e1645a696a8ebd6e56c5ebdc8cb960

[ "BSD-3-Clause" ]

1

2021-11-19T11:39:54.000Z

fsspec/spec.py

d4l3k/filesystem_spec

bd0f7d2442e1645a696a8ebd6e56c5ebdc8cb960

[ "BSD-3-Clause" ]

null

fsspec/spec.py

d4l3k/filesystem_spec

bd0f7d2442e1645a696a8ebd6e56c5ebdc8cb960

[ "BSD-3-Clause" ]

null

import io import logging import os import threading import warnings import weakref from distutils.version import LooseVersion from errno import ESPIPE from glob import has_magic from hashlib import sha256 from .callbacks import _DEFAULT_CALLBACK from .config import apply_config, conf from .dircache import DirCache from .transaction import Transaction from .utils import ( get_package_version_without_import, other_paths, read_block, stringify_path, tokenize, ) logger = logging.getLogger("fsspec") def make_instance(cls, args, kwargs): return cls(*args, **kwargs) class _Cached(type): """ Metaclass for caching file system instances. Notes ----- Instances are cached according to * The values of the class attributes listed in `_extra_tokenize_attributes` * The arguments passed to ``__init__``. This creates an additional reference to the filesystem, which prevents the filesystem from being garbage collected when all *user* references go away. A call to the :meth:`AbstractFileSystem.clear_instance_cache` must *also* be made for a filesystem instance to be garbage collected. """ def __init__(cls, *args, **kwargs): super().__init__(*args, **kwargs) # Note: we intentionally create a reference here, to avoid garbage # collecting instances when all other references are gone. To really # delete a FileSystem, the cache must be cleared. if conf.get("weakref_instance_cache"): # pragma: no cover # debug option for analysing fork/spawn conditions cls._cache = weakref.WeakValueDictionary() else: cls._cache = {} cls._pid = os.getpid() def __call__(cls, *args, **kwargs): kwargs = apply_config(cls, kwargs) extra_tokens = tuple( getattr(cls, attr, None) for attr in cls._extra_tokenize_attributes ) token = tokenize( cls, cls._pid, threading.get_ident(), *args, *extra_tokens, **kwargs ) skip = kwargs.pop("skip_instance_cache", False) if os.getpid() != cls._pid: cls._cache.clear() cls._pid = os.getpid() if not skip and cls.cachable and token in cls._cache: return cls._cache[token] else: obj = super().__call__(*args, **kwargs) # Setting _fs_token here causes some static linters to complain. obj._fs_token_ = token obj.storage_args = args obj.storage_options = kwargs if obj.async_impl: from .asyn import mirror_sync_methods mirror_sync_methods(obj) if cls.cachable and not skip: cls._cache[token] = obj return obj pa_version = get_package_version_without_import("pyarrow") if pa_version and LooseVersion(pa_version) < LooseVersion("2.0"): try: import pyarrow as pa up = pa.filesystem.DaskFileSystem except ImportError: # pragma: no cover # pyarrow exists but doesn't import for some reason up = object else: # pragma: no cover up = object class AbstractFileSystem(up, metaclass=_Cached): """ An abstract super-class for pythonic file-systems Implementations are expected to be compatible with or, better, subclass from here. """ cachable = True # this class can be cached, instances reused _cached = False blocksize = 2 ** 22 sep = "/" protocol = "abstract" async_impl = False root_marker = "" # For some FSs, may require leading '/' or other character #: Extra *class attributes* that should be considered when hashing. _extra_tokenize_attributes = () def __init__(self, *args, **storage_options): """Create and configure file-system instance Instances may be cachable, so if similar enough arguments are seen a new instance is not required. The token attribute exists to allow implementations to cache instances if they wish. A reasonable default should be provided if there are no arguments. Subclasses should call this method. Parameters ---------- use_listings_cache, listings_expiry_time, max_paths: passed to ``DirCache``, if the implementation supports directory listing caching. Pass use_listings_cache=False to disable such caching. skip_instance_cache: bool If this is a cachable implementation, pass True here to force creating a new instance even if a matching instance exists, and prevent storing this instance. asynchronous: bool loop: asyncio-compatible IOLoop or None """ if self._cached: # reusing instance, don't change return self._cached = True self._intrans = False self._transaction = None self._invalidated_caches_in_transaction = [] self.dircache = DirCache(**storage_options) if storage_options.pop("add_docs", None): warnings.warn("add_docs is no longer supported.", FutureWarning) if storage_options.pop("add_aliases", None): warnings.warn("add_aliases has been removed.", FutureWarning) # This is set in _Cached self._fs_token_ = None @property def _fs_token(self): return self._fs_token_ def __dask_tokenize__(self): return self._fs_token def __hash__(self): return int(self._fs_token, 16) def __eq__(self, other): return isinstance(other, type(self)) and self._fs_token == other._fs_token def __reduce__(self): return make_instance, (type(self), self.storage_args, self.storage_options) @classmethod def _strip_protocol(cls, path): """Turn path from fully-qualified to file-system-specific May require FS-specific handling, e.g., for relative paths or links. """ if isinstance(path, list): return [cls._strip_protocol(p) for p in path] path = stringify_path(path) protos = (cls.protocol,) if isinstance(cls.protocol, str) else cls.protocol for protocol in protos: if path.startswith(protocol + "://"): path = path[len(protocol) + 3 :] elif path.startswith(protocol + "::"): path = path[len(protocol) + 2 :] path = path.rstrip("/") # use of root_marker to make minimum required path, e.g., "/" return path or cls.root_marker @staticmethod def _get_kwargs_from_urls(path): """If kwargs can be encoded in the paths, extract them here This should happen before instantiation of the class; incoming paths then should be amended to strip the options in methods. Examples may look like an sftp path "sftp://user@host:/my/path", where the user and host should become kwargs and later get stripped. """ # by default, nothing happens return {} @classmethod def current(cls): """Return the most recently created FileSystem If no instance has been created, then create one with defaults """ if not len(cls._cache): return cls() else: return list(cls._cache.values())[-1] @property def transaction(self): """A context within which files are committed together upon exit Requires the file class to implement `.commit()` and `.discard()` for the normal and exception cases. """ if self._transaction is None: self._transaction = Transaction(self) return self._transaction def start_transaction(self): """Begin write transaction for deferring files, non-context version""" self._intrans = True self._transaction = Transaction(self) return self.transaction def end_transaction(self): """Finish write transaction, non-context version""" self.transaction.complete() self._transaction = None # The invalid cache must be cleared after the transcation is completed. for path in self._invalidated_caches_in_transaction: self.invalidate_cache(path) self._invalidated_caches_in_transaction.clear() def invalidate_cache(self, path=None): """ Discard any cached directory information Parameters ---------- path: string or None If None, clear all listings cached else listings at or under given path. """ # Not necessary to implement invalidation mechanism, may have no cache. # But if have, you should call this method of parent class from your # subclass to ensure expiring caches after transacations correctly. # See the implementaion of FTPFileSystem in ftp.py if self._intrans: self._invalidated_caches_in_transaction.append(path) def mkdir(self, path, create_parents=True, **kwargs): """ Create directory entry at path For systems that don't have true directories, may create an for this instance only and not touch the real filesystem Parameters ---------- path: str location create_parents: bool if True, this is equivalent to ``makedirs`` kwargs: may be permissions, etc. """ pass # not necessary to implement, may not have directories def makedirs(self, path, exist_ok=False): """Recursively make directories Creates directory at path and any intervening required directories. Raises exception if, for instance, the path already exists but is a file. Parameters ---------- path: str leaf directory name exist_ok: bool (False) If True, will error if the target already exists """ pass # not necessary to implement, may not have directories def rmdir(self, path): """Remove a directory, if empty""" pass # not necessary to implement, may not have directories def ls(self, path, detail=True, **kwargs): """List objects at path. This should include subdirectories and files at that location. The difference between a file and a directory must be clear when details are requested. The specific keys, or perhaps a FileInfo class, or similar, is TBD, but must be consistent across implementations. Must include: - full path to the entry (without protocol) - size of the entry, in bytes. If the value cannot be determined, will be ``None``. - type of entry, "file", "directory" or other Additional information may be present, aproriate to the file-system, e.g., generation, checksum, etc. May use refresh=True|False to allow use of self._ls_from_cache to check for a saved listing and avoid calling the backend. This would be common where listing may be expensive. Parameters ---------- path: str detail: bool if True, gives a list of dictionaries, where each is the same as the result of ``info(path)``. If False, gives a list of paths (str). kwargs: may have additional backend-specific options, such as version information Returns ------- List of strings if detail is False, or list of directory information dicts if detail is True. """ raise NotImplementedError def _ls_from_cache(self, path): """Check cache for listing Returns listing, if found (may me empty list for a directly that exists but contains nothing), None if not in cache. """ parent = self._parent(path) if path.rstrip("/") in self.dircache: return self.dircache[path.rstrip("/")] try: files = [ f for f in self.dircache[parent] if f["name"] == path or (f["name"] == path.rstrip("/") and f["type"] == "directory") ] if len(files) == 0: # parent dir was listed but did not contain this file raise FileNotFoundError(path) return files except KeyError: pass def walk(self, path, maxdepth=None, **kwargs): """Return all files belows path List all files, recursing into subdirectories; output is iterator-style, like ``os.walk()``. For a simple list of files, ``find()`` is available. Note that the "files" outputted will include anything that is not a directory, such as links. Parameters ---------- path: str Root to recurse into maxdepth: int Maximum recursion depth. None means limitless, but not recommended on link-based file-systems. kwargs: passed to ``ls`` """ path = self._strip_protocol(path) full_dirs = {} dirs = {} files = {} detail = kwargs.pop("detail", False) try: listing = self.ls(path, detail=True, **kwargs) except (FileNotFoundError, IOError): if detail: return path, {}, {} return path, [], [] for info in listing: # each info name must be at least [path]/part , but here # we check also for names like [path]/part/ pathname = info["name"].rstrip("/") name = pathname.rsplit("/", 1)[-1] if info["type"] == "directory" and pathname != path: # do not include "self" path full_dirs[pathname] = info dirs[name] = info elif pathname == path: # file-like with same name as give path files[""] = info else: files[name] = info if detail: yield path, dirs, files else: yield path, list(dirs), list(files) if maxdepth is not None: maxdepth -= 1 if maxdepth < 1: return for d in full_dirs: yield from self.walk(d, maxdepth=maxdepth, detail=detail, **kwargs) def find(self, path, maxdepth=None, withdirs=False, **kwargs): """List all files below path. Like posix ``find`` command without conditions Parameters ---------- path : str maxdepth: int or None If not None, the maximum number of levels to descend withdirs: bool Whether to include directory paths in the output. This is True when used by glob, but users usually only want files. kwargs are passed to ``ls``. """ # TODO: allow equivalent of -name parameter path = self._strip_protocol(path) out = dict() detail = kwargs.pop("detail", False) for _, dirs, files in self.walk(path, maxdepth, detail=True, **kwargs): if withdirs: files.update(dirs) out.update({info["name"]: info for name, info in files.items()}) if not out and self.isfile(path): # walk works on directories, but find should also return [path] # when path happens to be a file out[path] = {} names = sorted(out) if not detail: return names else: return {name: out[name] for name in names} def du(self, path, total=True, maxdepth=None, **kwargs): """Space used by files within a path Parameters ---------- path: str total: bool whether to sum all the file sizes maxdepth: int or None maximum number of directory levels to descend, None for unlimited. kwargs: passed to ``ls`` Returns ------- Dict of {fn: size} if total=False, or int otherwise, where numbers refer to bytes used. """ sizes = {} for f in self.find(path, maxdepth=maxdepth, **kwargs): info = self.info(f) sizes[info["name"]] = info["size"] if total: return sum(sizes.values()) else: return sizes def glob(self, path, **kwargs): """ Find files by glob-matching. If the path ends with '/' and does not contain "*", it is essentially the same as ``ls(path)``, returning only files. We support ``"**"``, ``"?"`` and ``"[..]"``. We do not support ^ for pattern negation. Search path names that contain embedded characters special to this implementation of glob may not produce expected results; e.g., 'foo/bar/*starredfilename*'. kwargs are passed to ``ls``. """ import re ends = path.endswith("/") path = self._strip_protocol(path) indstar = path.find("*") if path.find("*") >= 0 else len(path) indques = path.find("?") if path.find("?") >= 0 else len(path) indbrace = path.find("[") if path.find("[") >= 0 else len(path) ind = min(indstar, indques, indbrace) detail = kwargs.pop("detail", False) if not has_magic(path): root = path depth = 1 if ends: path += "/*" elif self.exists(path): if not detail: return [path] else: return {path: self.info(path)} else: if not detail: return [] # glob of non-existent returns empty else: return {} elif "/" in path[:ind]: ind2 = path[:ind].rindex("/") root = path[: ind2 + 1] depth = None if "**" in path else path[ind2 + 1 :].count("/") + 1 else: root = "" depth = None if "**" in path else path[ind + 1 :].count("/") + 1 allpaths = self.find(root, maxdepth=depth, withdirs=True, detail=True, **kwargs) # Escape characters special to python regex, leaving our supported # special characters in place. # See https://www.gnu.org/software/bash/manual/html_node/Pattern-Matching.html # for shell globbing details. pattern = ( "^" + ( path.replace("\\", r"\\") .replace(".", r"\.") .replace("+", r"\+") .replace("//", "/") .replace("(", r"$") .replace(")", r"$") .replace("|", r"\|") .replace("^", r"\^") .replace("$", r"\$") .replace("{", r"\{") .replace("}", r"\}") .rstrip("/") .replace("?", ".") ) + "$" ) pattern = re.sub("[*]{2}", "=PLACEHOLDER=", pattern) pattern = re.sub("[*]", "[^/]*", pattern) pattern = re.compile(pattern.replace("=PLACEHOLDER=", ".*")) out = { p: allpaths[p] for p in sorted(allpaths) if pattern.match(p.replace("//", "/").rstrip("/")) } if detail: return out else: return list(out) def exists(self, path, **kwargs): """Is there a file at the given path""" try: self.info(path, **kwargs) return True except: # noqa: E722 # any exception allowed bar FileNotFoundError? return False def info(self, path, **kwargs): """Give details of entry at path Returns a single dictionary, with exactly the same information as ``ls`` would with ``detail=True``. The default implementation should calls ls and could be overridden by a shortcut. kwargs are passed on to ```ls()``. Some file systems might not be able to measure the file's size, in which case, the returned dict will include ``'size': None``. Returns ------- dict with keys: name (full path in the FS), size (in bytes), type (file, directory, or something else) and other FS-specific keys. """ path = self._strip_protocol(path) out = self.ls(self._parent(path), detail=True, **kwargs) out = [o for o in out if o["name"].rstrip("/") == path] if out: return out[0] out = self.ls(path, detail=True, **kwargs) path = path.rstrip("/") out1 = [o for o in out if o["name"].rstrip("/") == path] if len(out1) == 1: if "size" not in out1[0]: out1[0]["size"] = None return out1[0] elif len(out1) > 1 or out: return {"name": path, "size": 0, "type": "directory"} else: raise FileNotFoundError(path) def checksum(self, path): """Unique value for current version of file If the checksum is the same from one moment to another, the contents are guaranteed to be the same. If the checksum changes, the contents *might* have changed. This should normally be overridden; default will probably capture creation/modification timestamp (which would be good) or maybe access timestamp (which would be bad) """ return int(tokenize(self.info(path)), 16) def size(self, path): """Size in bytes of file""" return self.info(path).get("size", None) def isdir(self, path): """Is this entry directory-like?""" try: return self.info(path)["type"] == "directory" except IOError: return False def isfile(self, path): """Is this entry file-like?""" try: return self.info(path)["type"] == "file" except: # noqa: E722 return False def cat_file(self, path, start=None, end=None, **kwargs): """Get the content of a file Parameters ---------- path: URL of file on this filesystems start, end: int Bytes limits of the read. If negative, backwards from end, like usual python slices. Either can be None for start or end of file, respectively kwargs: passed to ``open()``. """ # explicitly set buffering off? with self.open(path, "rb", **kwargs) as f: if start is not None: if start >= 0: f.seek(start) else: f.seek(max(0, f.size + start)) if end is not None: if end < 0: end = f.size + end return f.read(end - f.tell()) return f.read() def pipe_file(self, path, value, **kwargs): """Set the bytes of given file""" with self.open(path, "wb") as f: f.write(value) def pipe(self, path, value=None, **kwargs): """Put value into path (counterpart to ``cat``) Parameters ---------- path: string or dict(str, bytes) If a string, a single remote location to put ``value`` bytes; if a dict, a mapping of {path: bytesvalue}. value: bytes, optional If using a single path, these are the bytes to put there. Ignored if ``path`` is a dict """ if isinstance(path, str): self.pipe_file(self._strip_protocol(path), value, **kwargs) elif isinstance(path, dict): for k, v in path.items(): self.pipe_file(self._strip_protocol(k), v, **kwargs) else: raise ValueError("path must be str or dict") def cat(self, path, recursive=False, on_error="raise", **kwargs): """Fetch (potentially multiple) paths' contents Returns a dict of {path: contents} if there are multiple paths or the path has been otherwise expanded on_error : "raise", "omit", "return" If raise, an underlying exception will be raised (converted to KeyError if the type is in self.missing_exceptions); if omit, keys with exception will simply not be included in the output; if "return", all keys are included in the output, but the value will be bytes or an exception instance. """ paths = self.expand_path(path, recursive=recursive) if ( len(paths) > 1 or isinstance(path, list) or paths[0] != self._strip_protocol(path) ): out = {} for path in paths: try: out[path] = self.cat_file(path, **kwargs) except Exception as e: if on_error == "raise": raise if on_error == "return": out[path] = e return out else: return self.cat_file(paths[0], **kwargs) def get_file(self, rpath, lpath, callback=_DEFAULT_CALLBACK, **kwargs): """Copy single remote file to local""" if self.isdir(rpath): os.makedirs(lpath, exist_ok=True) return None with self.open(rpath, "rb", **kwargs) as f1: callback.set_size(getattr(f1, "size", None)) with open(lpath, "wb") as f2: data = True while data: data = f1.read(self.blocksize) segment_len = f2.write(data) callback.relative_update(segment_len) def get(self, rpath, lpath, recursive=False, callback=_DEFAULT_CALLBACK, **kwargs): """Copy file(s) to local. Copies a specific file or tree of files (if recursive=True). If lpath ends with a "/", it will be assumed to be a directory, and target files will go within. Can submit a list of paths, which may be glob-patterns and will be expanded. Calls get_file for each source. """ from .implementations.local import make_path_posix if isinstance(lpath, str): lpath = make_path_posix(lpath) rpaths = self.expand_path(rpath, recursive=recursive) lpaths = other_paths(rpaths, lpath) callback.set_size(len(lpaths)) for lpath, rpath in callback.wrap(zip(lpaths, rpaths)): callback.branch(rpath, lpath, kwargs) self.get_file(rpath, lpath, **kwargs) def put_file(self, lpath, rpath, callback=_DEFAULT_CALLBACK, **kwargs): """Copy single file to remote""" if os.path.isdir(lpath): self.makedirs(rpath, exist_ok=True) return None with open(lpath, "rb") as f1: callback.set_size(f1.seek(0, 2)) f1.seek(0) self.mkdirs(os.path.dirname(rpath), exist_ok=True) with self.open(rpath, "wb", **kwargs) as f2: data = True while data: data = f1.read(self.blocksize) segment_len = f2.write(data) callback.relative_update(segment_len) def put(self, lpath, rpath, recursive=False, callback=_DEFAULT_CALLBACK, **kwargs): """Copy file(s) from local. Copies a specific file or tree of files (if recursive=True). If rpath ends with a "/", it will be assumed to be a directory, and target files will go within. Calls put_file for each source. """ from .implementations.local import LocalFileSystem, make_path_posix rpath = ( self._strip_protocol(rpath) if isinstance(rpath, str) else [self._strip_protocol(p) for p in rpath] ) if isinstance(lpath, str): lpath = make_path_posix(lpath) fs = LocalFileSystem() lpaths = fs.expand_path(lpath, recursive=recursive) rpaths = other_paths( lpaths, rpath, exists=isinstance(rpath, str) and self.isdir(rpath) ) callback.set_size(len(rpaths)) for lpath, rpath in callback.wrap(zip(lpaths, rpaths)): callback.branch(lpath, rpath, kwargs) self.put_file(lpath, rpath, **kwargs) def head(self, path, size=1024): """ Get the first ``size`` bytes from file """ with self.open(path, "rb") as f: return f.read(size) def tail(self, path, size=1024): """ Get the last ``size`` bytes from file """ with self.open(path, "rb") as f: f.seek(max(-size, -f.size), 2) return f.read() def cp_file(self, path1, path2, **kwargs): raise NotImplementedError def copy(self, path1, path2, recursive=False, on_error=None, **kwargs): """Copy within two locations in the filesystem on_error : "raise", "ignore" If raise, any not-found exceptions will be raised; if ignore any not-found exceptions will cause the path to be skipped; defaults to raise unless recursive is true, where the default is ignore """ if on_error is None and recursive: on_error = "ignore" elif on_error is None: on_error = "raise" paths = self.expand_path(path1, recursive=recursive) path2 = other_paths(paths, path2) for p1, p2 in zip(paths, path2): try: self.cp_file(p1, p2, **kwargs) except FileNotFoundError: if on_error == "raise": raise def expand_path(self, path, recursive=False, maxdepth=None): """Turn one or more globs or directories into a list of all matching paths to files or directories.""" if isinstance(path, str): out = self.expand_path([path], recursive, maxdepth) else: # reduce depth on each recursion level unless None or 0 maxdepth = maxdepth if not maxdepth else maxdepth - 1 out = set() path = [self._strip_protocol(p) for p in path] for p in path: if has_magic(p): bit = set(self.glob(p)) out |= bit if recursive: out |= set( self.expand_path( list(bit), recursive=recursive, maxdepth=maxdepth ) ) continue elif recursive: rec = set(self.find(p, maxdepth=maxdepth, withdirs=True)) out |= rec if p not in out and (recursive is False or self.exists(p)): # should only check once, for the root out.add(p) if not out: raise FileNotFoundError(path) return list(sorted(out)) def mv(self, path1, path2, recursive=False, maxdepth=None, **kwargs): """ Move file(s) from one location to another """ self.copy(path1, path2, recursive=recursive, maxdepth=maxdepth) self.rm(path1, recursive=recursive) def rm_file(self, path): """Delete a file""" self._rm(path) def _rm(self, path): """Delete one file""" # this is the old name for the method, prefer rm_file raise NotImplementedError def rm(self, path, recursive=False, maxdepth=None): """Delete files. Parameters ---------- path: str or list of str File(s) to delete. recursive: bool If file(s) are directories, recursively delete contents and then also remove the directory maxdepth: int or None Depth to pass to walk for finding files to delete, if recursive. If None, there will be no limit and infinite recursion may be possible. """ path = self.expand_path(path, recursive=recursive, maxdepth=maxdepth) for p in reversed(path): self.rm_file(p) @classmethod def _parent(cls, path): path = cls._strip_protocol(path.rstrip("/")) if "/" in path: parent = path.rsplit("/", 1)[0].lstrip(cls.root_marker) return cls.root_marker + parent else: return cls.root_marker def _open( self, path, mode="rb", block_size=None, autocommit=True, cache_options=None, **kwargs, ): """Return raw bytes-mode file-like from the file-system""" return AbstractBufferedFile( self, path, mode, block_size, autocommit, cache_options=cache_options, **kwargs, ) def open(self, path, mode="rb", block_size=None, cache_options=None, **kwargs): """ Return a file-like object from the filesystem The resultant instance must function correctly in a context ``with`` block. Parameters ---------- path: str Target file mode: str like 'rb', 'w' See builtin ``open()`` block_size: int Some indication of buffering - this is a value in bytes cache_options : dict, optional Extra arguments to pass through to the cache. encoding, errors, newline: passed on to TextIOWrapper for text mode """ import io path = self._strip_protocol(path) if "b" not in mode: mode = mode.replace("t", "") + "b" text_kwargs = { k: kwargs.pop(k) for k in ["encoding", "errors", "newline"] if k in kwargs } return io.TextIOWrapper( self.open(path, mode, block_size, **kwargs), **text_kwargs ) else: ac = kwargs.pop("autocommit", not self._intrans) f = self._open( path, mode=mode, block_size=block_size, autocommit=ac, cache_options=cache_options, **kwargs, ) if not ac and "r" not in mode: self.transaction.files.append(f) return f def touch(self, path, truncate=True, **kwargs): """Create empty file, or update timestamp Parameters ---------- path: str file location truncate: bool If True, always set file size to 0; if False, update timestamp and leave file unchanged, if backend allows this """ if truncate or not self.exists(path): with self.open(path, "wb", **kwargs): pass else: raise NotImplementedError # update timestamp, if possible def ukey(self, path): """Hash of file properties, to tell if it has changed""" return sha256(str(self.info(path)).encode()).hexdigest() def read_block(self, fn, offset, length, delimiter=None): """Read a block of bytes from Starting at ``offset`` of the file, read ``length`` bytes. If ``delimiter`` is set then we ensure that the read starts and stops at delimiter boundaries that follow the locations ``offset`` and ``offset + length``. If ``offset`` is zero then we start at zero. The bytestring returned WILL include the end delimiter string. If offset+length is beyond the eof, reads to eof. Parameters ---------- fn: string Path to filename offset: int Byte offset to start read length: int Number of bytes to read delimiter: bytes (optional) Ensure reading starts and stops at delimiter bytestring Examples -------- >>> fs.read_block('data/file.csv', 0, 13) # doctest: +SKIP b'Alice, 100\\nBo' >>> fs.read_block('data/file.csv', 0, 13, delimiter=b'\\n') # doctest: +SKIP b'Alice, 100\\nBob, 200\\n' Use ``length=None`` to read to the end of the file. >>> fs.read_block('data/file.csv', 0, None, delimiter=b'\\n') # doctest: +SKIP b'Alice, 100\\nBob, 200\\nCharlie, 300' See Also -------- utils.read_block """ with self.open(fn, "rb") as f: size = f.size if length is None: length = size if size is not None and offset + length > size: length = size - offset return read_block(f, offset, length, delimiter) def to_json(self): """ JSON representation of this filesystem instance Returns ------- str: JSON structure with keys cls (the python location of this class), protocol (text name of this class's protocol, first one in case of multiple), args (positional args, usually empty), and all other kwargs as their own keys. """ import json cls = type(self) cls = ".".join((cls.__module__, cls.__name__)) proto = ( self.protocol[0] if isinstance(self.protocol, (tuple, list)) else self.protocol ) return json.dumps( dict( **{"cls": cls, "protocol": proto, "args": self.storage_args}, **self.storage_options, ) ) @staticmethod def from_json(blob): """ Recreate a filesystem instance from JSON representation See ``.to_json()`` for the expected structure of the input Parameters ---------- blob: str Returns ------- file system instance, not necessarily of this particular class. """ import json from .registry import _import_class, get_filesystem_class dic = json.loads(blob) protocol = dic.pop("protocol") try: cls = _import_class(dic.pop("cls")) except (ImportError, ValueError, RuntimeError, KeyError): cls = get_filesystem_class(protocol) return cls(*dic.pop("args", ()), **dic) def _get_pyarrow_filesystem(self): """ Make a version of the FS instance which will be acceptable to pyarrow """ # all instances already also derive from pyarrow return self def get_mapper(self, root, check=False, create=False): """Create key/value store based on this file-system Makes a MutibleMapping interface to the FS at the given root path. See ``fsspec.mapping.FSMap`` for further details. """ from .mapping import FSMap return FSMap(root, self, check, create) @classmethod def clear_instance_cache(cls): """ Clear the cache of filesystem instances. Notes ----- Unless overridden by setting the ``cachable`` class attribute to False, the filesystem class stores a reference to newly created instances. This prevents Python's normal rules around garbage collection from working, since the instances refcount will not drop to zero until ``clear_instance_cache`` is called. """ cls._cache.clear() def created(self, path): """Return the created timestamp of a file as a datetime.datetime""" raise NotImplementedError def modified(self, path): """Return the modified timestamp of a file as a datetime.datetime""" raise NotImplementedError # ------------------------------------------------------------------------ # Aliases def makedir(self, path, create_parents=True, **kwargs): """Alias of :ref:`FilesystemSpec.mkdir`.""" return self.mkdir(path, create_parents=create_parents, **kwargs) def mkdirs(self, path, exist_ok=False): """Alias of :ref:`FilesystemSpec.makedirs`.""" return self.makedirs(path, exist_ok=exist_ok) def listdir(self, path, detail=True, **kwargs): """Alias of :ref:`FilesystemSpec.ls`.""" return self.ls(path, detail=detail, **kwargs) def cp(self, path1, path2, **kwargs): """Alias of :ref:`FilesystemSpec.copy`.""" return self.copy(path1, path2, **kwargs) def move(self, path1, path2, **kwargs): """Alias of :ref:`FilesystemSpec.mv`.""" return self.mv(path1, path2, **kwargs) def stat(self, path, **kwargs): """Alias of :ref:`FilesystemSpec.info`.""" return self.info(path, **kwargs) def disk_usage(self, path, total=True, maxdepth=None, **kwargs): """Alias of :ref:`FilesystemSpec.du`.""" return self.du(path, total=total, maxdepth=maxdepth, **kwargs) def rename(self, path1, path2, **kwargs): """Alias of :ref:`FilesystemSpec.mv`.""" return self.mv(path1, path2, **kwargs) def delete(self, path, recursive=False, maxdepth=None): """Alias of :ref:`FilesystemSpec.rm`.""" return self.rm(path, recursive=recursive, maxdepth=maxdepth) def upload(self, lpath, rpath, recursive=False, **kwargs): """Alias of :ref:`FilesystemSpec.put`.""" return self.put(lpath, rpath, recursive=recursive, **kwargs) def download(self, rpath, lpath, recursive=False, **kwargs): """Alias of :ref:`FilesystemSpec.get`.""" return self.get(rpath, lpath, recursive=recursive, **kwargs) def sign(self, path, expiration=100, **kwargs): """Create a signed URL representing the given path Some implementations allow temporary URLs to be generated, as a way of delegating credentials. Parameters ---------- path : str The path on the filesystem expiration : int Number of seconds to enable the URL for (if supported) Returns ------- URL : str The signed URL Raises ------ NotImplementedError : if method is not implemented for a fileystem """ raise NotImplementedError("Sign is not implemented for this filesystem") def _isfilestore(self): # Originally inherited from pyarrow DaskFileSystem. Keeping this # here for backwards compatibility as long as pyarrow uses its # legacy fsspec-compatible filesystems and thus accepts fsspec # filesystems as well return False class AbstractBufferedFile(io.IOBase): """Convenient class to derive from to provide buffering In the case that the backend does not provide a pythonic file-like object already, this class contains much of the logic to build one. The only methods that need to be overridden are ``_upload_chunk``, ``_initiate_upload`` and ``_fetch_range``. """ DEFAULT_BLOCK_SIZE = 5 * 2 ** 20 def __init__( self, fs, path, mode="rb", block_size="default", autocommit=True, cache_type="readahead", cache_options=None, **kwargs, ): """ Template for files with buffered reading and writing Parameters ---------- fs: instance of FileSystem path: str location in file-system mode: str Normal file modes. Currently only 'wb', 'ab' or 'rb'. Some file systems may be read-only, and some may not support append. block_size: int Buffer size for reading or writing, 'default' for class default autocommit: bool Whether to write to final destination; may only impact what happens when file is being closed. cache_type: {"readahead", "none", "mmap", "bytes"}, default "readahead" Caching policy in read mode. See the definitions in ``core``. cache_options : dict Additional options passed to the constructor for the cache specified by `cache_type`. kwargs: Gets stored as self.kwargs """ from .core import caches self.path = path self.fs = fs self.mode = mode self.blocksize = ( self.DEFAULT_BLOCK_SIZE if block_size in ["default", None] else block_size ) self.loc = 0 self.autocommit = autocommit self.end = None self.start = None self.closed = False if cache_options is None: cache_options = {} if "trim" in kwargs: warnings.warn( "Passing 'trim' to control the cache behavior has been deprecated. " "Specify it within the 'cache_options' argument instead.", FutureWarning, ) cache_options["trim"] = kwargs.pop("trim") self.kwargs = kwargs if mode not in {"ab", "rb", "wb"}: raise NotImplementedError("File mode not supported") if mode == "rb": if not hasattr(self, "details"): self.details = fs.info(path) self.size = self.details["size"] self.cache = caches[cache_type]( self.blocksize, self._fetch_range, self.size, **cache_options ) else: self.buffer = io.BytesIO() self.offset = None self.forced = False self.location = None @property def closed(self): # get around this attr being read-only in IOBase # use getattr here, since this can be called during del return getattr(self, "_closed", True) @closed.setter def closed(self, c): self._closed = c def __hash__(self): if "w" in self.mode: return id(self) else: return int(tokenize(self.details), 16) def __eq__(self, other): """Files are equal if they have the same checksum, only in read mode""" return self.mode == "rb" and other.mode == "rb" and hash(self) == hash(other) def commit(self): """Move from temp to final destination""" def discard(self): """Throw away temporary file""" def info(self): """ File information about this path """ if "r" in self.mode: return self.details else: raise ValueError("Info not available while writing") def tell(self): """ Current file location """ return self.loc def seek(self, loc, whence=0): """Set current file location Parameters ---------- loc: int byte location whence: {0, 1, 2} from start of file, current location or end of file, resp. """ loc = int(loc) if not self.mode == "rb": raise OSError(ESPIPE, "Seek only available in read mode") if whence == 0: nloc = loc elif whence == 1: nloc = self.loc + loc elif whence == 2: nloc = self.size + loc else: raise ValueError("invalid whence (%s, should be 0, 1 or 2)" % whence) if nloc < 0: raise ValueError("Seek before start of file") self.loc = nloc return self.loc def write(self, data): """ Write data to buffer. Buffer only sent on flush() or if buffer is greater than or equal to blocksize. Parameters ---------- data: bytes Set of bytes to be written. """ if self.mode not in {"wb", "ab"}: raise ValueError("File not in write mode") if self.closed: raise ValueError("I/O operation on closed file.") if self.forced: raise ValueError("This file has been force-flushed, can only close") out = self.buffer.write(data) self.loc += out if self.buffer.tell() >= self.blocksize: self.flush() return out def flush(self, force=False): """ Write buffered data to backend store. Writes the current buffer, if it is larger than the block-size, or if the file is being closed. Parameters ---------- force: bool When closing, write the last block even if it is smaller than blocks are allowed to be. Disallows further writing to this file. """ if self.closed: raise ValueError("Flush on closed file") if force and self.forced: raise ValueError("Force flush cannot be called more than once") if force: self.forced = True if self.mode not in {"wb", "ab"}: # no-op to flush on read-mode return if not force and self.buffer.tell() < self.blocksize: # Defer write on small block return if self.offset is None: # Initialize a multipart upload self.offset = 0 try: self._initiate_upload() except: # noqa: E722 self.closed = True raise if self._upload_chunk(final=force) is not False: self.offset += self.buffer.seek(0, 2) self.buffer = io.BytesIO() def _upload_chunk(self, final=False): """Write one part of a multi-block file upload Parameters ========== final: bool This is the last block, so should complete file, if self.autocommit is True. """ # may not yet have been initialized, may need to call _initialize_upload def _initiate_upload(self): """ Create remote file/upload """ pass def _fetch_range(self, start, end): """Get the specified set of bytes from remote""" raise NotImplementedError def read(self, length=-1): """ Return data from cache, or fetch pieces as necessary Parameters ---------- length: int (-1) Number of bytes to read; if <0, all remaining bytes. """ length = -1 if length is None else int(length) if self.mode != "rb": raise ValueError("File not in read mode") if length < 0: length = self.size - self.loc if self.closed: raise ValueError("I/O operation on closed file.") logger.debug("%s read: %i - %i" % (self, self.loc, self.loc + length)) if length == 0: # don't even bother calling fetch return b"" out = self.cache._fetch(self.loc, self.loc + length) self.loc += len(out) return out def readinto(self, b): """mirrors builtin file's readinto method https://docs.python.org/3/library/io.html#io.RawIOBase.readinto """ out = memoryview(b).cast("B") data = self.read(out.nbytes) out[: len(data)] = data return len(data) def readuntil(self, char=b"\n", blocks=None): """Return data between current position and first occurrence of char char is included in the output, except if the end of the tile is encountered first. Parameters ---------- char: bytes Thing to find blocks: None or int How much to read in each go. Defaults to file blocksize - which may mean a new read on every call. """ out = [] while True: start = self.tell() part = self.read(blocks or self.blocksize) if len(part) == 0: break found = part.find(char) if found > -1: out.append(part[: found + len(char)]) self.seek(start + found + len(char)) break out.append(part) return b"".join(out) def readline(self): """Read until first occurrence of newline character Note that, because of character encoding, this is not necessarily a true line ending. """ return self.readuntil(b"\n") def __next__(self): out = self.readline() if out: return out raise StopIteration def __iter__(self): return self def readlines(self): """Return all data, split by the newline character""" data = self.read() lines = data.split(b"\n") out = [l + b"\n" for l in lines[:-1]] if data.endswith(b"\n"): return out else: return out + [lines[-1]] # return list(self) ??? def readinto1(self, b): return self.readinto(b) def close(self): """Close file Finalizes writes, discards cache """ if getattr(self, "_unclosable", False): return if self.closed: return if self.mode == "rb": self.cache = None else: if not self.forced: self.flush(force=True) if self.fs is not None: self.fs.invalidate_cache(self.path) self.fs.invalidate_cache(self.fs._parent(self.path)) self.closed = True def readable(self): """Whether opened for reading""" return self.mode == "rb" and not self.closed def seekable(self): """Whether is seekable (only in read mode)""" return self.readable() def writable(self): """Whether opened for writing""" return self.mode in {"wb", "ab"} and not self.closed def __del__(self): if not self.closed: self.close() def __str__(self): return "<File-like object %s, %s>" % (type(self.fs).__name__, self.path) __repr__ = __str__ def __enter__(self): return self def __exit__(self, *args): self.close()

33.694704

88

0.561686

529aef37e88a072e5fce72fd562c6cbf8531551b

732

py

Python

sql_app/models.py

IEPEREZ/FastAPItutorial

cac10f4ddb60bb95aa5aa9a2871f019720f53d3d

[ "MIT" ]

null

sql_app/models.py

IEPEREZ/FastAPItutorial

cac10f4ddb60bb95aa5aa9a2871f019720f53d3d

[ "MIT" ]

null

sql_app/models.py

IEPEREZ/FastAPItutorial

cac10f4ddb60bb95aa5aa9a2871f019720f53d3d

[ "MIT" ]

null

from sqlalchemy import Boolean, Column, ForeignKey, Integer, String from sqlalchemy.orm import relationship from database import Base class User(Base): __tablename__ = "users" id = Column(Integer, primary_key=True, index=True) email = Column(String, unique=True, index=True) hashed_password = Column(String) is_active = Column(Boolean, default=True) items = relationship("Item", back_populates="owner") class Item(Base): __tablename__ = "items" id = Column(Integer, primary_key=True, index=True) title = Column(String, index=True) description = Column(String, index=True) owner_id = Column(Integer, ForeignKey("users.id")) owner = relationship("User", back_populates="items")

28.153846

67

0.717213

d9b777de06489891ff67e5f8c180b5f8eea1d101

2,574

py

Python

lc/models/torch/lenet300.py

akamaster/anon_submission

105189fc1720a6b0c27df7e686519a6ffa75632b

[ "BSD-3-Clause" ]

40

2020-05-19T01:31:02.000Z

2022-01-28T00:25:33.000Z

lc/models/torch/lenet300.py

akamaster/anon_submission

105189fc1720a6b0c27df7e686519a6ffa75632b

[ "BSD-3-Clause" ]

3

2020-09-09T16:04:17.000Z

2022-03-18T00:59:39.000Z

lc/models/torch/lenet300.py

akamaster/anon_submission

105189fc1720a6b0c27df7e686519a6ffa75632b

[ "BSD-3-Clause" ]

8

2020-09-08T14:11:16.000Z

2022-01-28T00:25:41.000Z

import torch.nn as nn import torch.nn.init as init from collections import OrderedDict from .utils import LambdaLayer __all__ = ['lenet300_classic', 'lenet300_classic_drop', 'lenet300_modern', 'lenet300_modern_drop'] def _weights_init(m): classname = m.__class__.__name__ print(classname) if isinstance(m, nn.Linear): init.xavier_uniform_(m.weight) m.bias.data.fill_(0.0) class LeNet300(nn.Module): """ Base LeNet300 module that allows to reconfigure it. LeNet300 is a network having 3 layers with weights of dimensions 784x300, 300x100 and 100x10 and trained on MNIST dataset. References: Yann LeCun, Leon Bottou, Yoshua Bengio, and Patrick Haffner Gradient-based learning applied to document recognition https://ieeexplore.ieee.org/document/726791 """ def __init__(self, dropout, nonlinearity): """ Constructor of LeNet300. With given options it is possible to set various nonlinearities and add dropout layers after them :param dropout: boolean, if True, the dropout layer will be added after every inter hidden layer, no dropout after last layer :param nonlinearity: function, a constructor that returns nonlinearity function """ super(LeNet300, self).__init__() layers = [(784, 300), (300, 100), (100, 10)] cfg = [('reshape', LambdaLayer(lambda x: x.view(x.size(0),-1)))] for i, l in enumerate(layers): cfg.append(('compressible_' + str(i), nn.Linear(*l))) if i != len(layers)-1: # only non terminal layers have nonlinearity and (possible) dropouts cfg.append(('nonlinearity_' + str(i), nonlinearity())) if dropout: cfg.append(('drop_'+str(i), nn.Dropout())) self.output = nn.Sequential(OrderedDict(cfg)) self.loss = nn.CrossEntropyLoss() self.apply(_weights_init) def forward(self, input): return self.output(input) def lenet300_classic(): """ Creates classical version of LeNet300, the one having tanh activation functions and no dropouts """ return LeNet300(dropout=False, nonlinearity=nn.Tanh) def lenet300_classic_drop(): """ Returns classical LeNet300 with intermediate dropouts between layers. """ return LeNet300(dropout=True, nonlinearity=nn.Tanh) def lenet300_modern(): return LeNet300(dropout=False, nonlinearity=nn.ReLU) def lenet300_modern_drop(): return LeNet300(dropout=True, nonlinearity=nn.ReLU)

34.32

109

0.669386

1cd9069665f600d44f09aed8a540010012676c2e

858

py

Python

wbb/modules/telegraph.py

divyanshmusic/WilliamButcherBot

1d905de0ee7906c530df1b91b1fada7909d5f1e8

[ "MIT" ]

1

2021-12-06T13:59:52.000Z

wbb/modules/telegraph.py

divyanshmusic/WilliamButcherBot

1d905de0ee7906c530df1b91b1fada7909d5f1e8

[ "MIT" ]

null

wbb/modules/telegraph.py

divyanshmusic/WilliamButcherBot

1d905de0ee7906c530df1b91b1fada7909d5f1e8

[ "MIT" ]

2

2022-03-18T17:40:10.000Z

2022-03-24T07:09:44.000Z

from pyrogram import filters from pyrogram.types import Message from wbb import app, telegraph from wbb.core.decorators.errors import capture_err __MODULE__ = "Telegraph" __HELP__ = "/telegraph [Page name]: Paste styled text on telegraph." @app.on_message(filters.command("telegraph")) @capture_err async def paste(_, message: Message): reply = message.reply_to_message if not reply or not reply.text: return await message.reply("Reply to a text message") if len(message.command) < 2: return await message.reply("**Usage:**\n /telegraph [Page name]") page_name = message.text.split(None, 1)[1] page = telegraph.create_page( page_name, html_content=reply.text.html.replace("\n", "<br>") ) return await message.reply( f"**Posted:** {page['url']}", disable_web_page_preview=True, )

28.6

73

0.691142

518038d29c2ce0faf7f863301b91310792219e41

41,201

py

Python

venv/lib/python3.6/site-packages/sqlalchemy/engine/interfaces.py

tchengatcincoai/cryptocoin-prices-compare

f295fecc7213a877bf717af0eb98414e9137b554

[ "MIT" ]

78

2017-08-19T03:46:13.000Z

2020-02-19T04:29:45.000Z

desktop/core/ext-py/SQLAlchemy-1.2.0b3/lib/sqlalchemy/engine/interfaces.py

zks888/hue

93a8c370713e70b216c428caa2f75185ef809deb

[ "Apache-2.0" ]

5

2017-08-21T16:33:08.000Z

2018-06-21T18:37:18.000Z

desktop/core/ext-py/SQLAlchemy-1.2.0b3/lib/sqlalchemy/engine/interfaces.py

zks888/hue

93a8c370713e70b216c428caa2f75185ef809deb

[ "Apache-2.0" ]

43

2018-02-05T23:23:46.000Z

2021-07-28T22:51:42.000Z

# engine/interfaces.py # Copyright (C) 2005-2017 the SQLAlchemy authors and contributors # <see AUTHORS file> # # This module is part of SQLAlchemy and is released under # the MIT License: http://www.opensource.org/licenses/mit-license.php """Define core interfaces used by the engine system.""" from .. import util # backwards compat from ..sql.compiler import Compiled, TypeCompiler class Dialect(object): """Define the behavior of a specific database and DB-API combination. Any aspect of metadata definition, SQL query generation, execution, result-set handling, or anything else which varies between databases is defined under the general category of the Dialect. The Dialect acts as a factory for other database-specific object implementations including ExecutionContext, Compiled, DefaultGenerator, and TypeEngine. All Dialects implement the following attributes: name identifying name for the dialect from a DBAPI-neutral point of view (i.e. 'sqlite') driver identifying name for the dialect's DBAPI positional True if the paramstyle for this Dialect is positional. paramstyle the paramstyle to be used (some DB-APIs support multiple paramstyles). convert_unicode True if Unicode conversion should be applied to all ``str`` types. encoding type of encoding to use for unicode, usually defaults to 'utf-8'. statement_compiler a :class:`.Compiled` class used to compile SQL statements ddl_compiler a :class:`.Compiled` class used to compile DDL statements server_version_info a tuple containing a version number for the DB backend in use. This value is only available for supporting dialects, and is typically populated during the initial connection to the database. default_schema_name the name of the default schema. This value is only available for supporting dialects, and is typically populated during the initial connection to the database. execution_ctx_cls a :class:`.ExecutionContext` class used to handle statement execution execute_sequence_format either the 'tuple' or 'list' type, depending on what cursor.execute() accepts for the second argument (they vary). preparer a :class:`~sqlalchemy.sql.compiler.IdentifierPreparer` class used to quote identifiers. supports_alter ``True`` if the database supports ``ALTER TABLE``. max_identifier_length The maximum length of identifier names. supports_unicode_statements Indicate whether the DB-API can receive SQL statements as Python unicode strings supports_unicode_binds Indicate whether the DB-API can receive string bind parameters as Python unicode strings supports_sane_rowcount Indicate whether the dialect properly implements rowcount for ``UPDATE`` and ``DELETE`` statements. supports_sane_multi_rowcount Indicate whether the dialect properly implements rowcount for ``UPDATE`` and ``DELETE`` statements when executed via executemany. preexecute_autoincrement_sequences True if 'implicit' primary key functions must be executed separately in order to get their value. This is currently oriented towards PostgreSQL. implicit_returning use RETURNING or equivalent during INSERT execution in order to load newly generated primary keys and other column defaults in one execution, which are then available via inserted_primary_key. If an insert statement has returning() specified explicitly, the "implicit" functionality is not used and inserted_primary_key will not be available. colspecs A dictionary of TypeEngine classes from sqlalchemy.types mapped to subclasses that are specific to the dialect class. This dictionary is class-level only and is not accessed from the dialect instance itself. supports_default_values Indicates if the construct ``INSERT INTO tablename DEFAULT VALUES`` is supported supports_sequences Indicates if the dialect supports CREATE SEQUENCE or similar. sequences_optional If True, indicates if the "optional" flag on the Sequence() construct should signal to not generate a CREATE SEQUENCE. Applies only to dialects that support sequences. Currently used only to allow PostgreSQL SERIAL to be used on a column that specifies Sequence() for usage on other backends. supports_native_enum Indicates if the dialect supports a native ENUM construct. This will prevent types.Enum from generating a CHECK constraint when that type is used. supports_native_boolean Indicates if the dialect supports a native boolean construct. This will prevent types.Boolean from generating a CHECK constraint when that type is used. dbapi_exception_translation_map A dictionary of names that will contain as values the names of pep-249 exceptions ("IntegrityError", "OperationalError", etc) keyed to alternate class names, to support the case where a DBAPI has exception classes that aren't named as they are referred to (e.g. IntegrityError = MyException). In the vast majority of cases this dictionary is empty. .. versionadded:: 1.0.5 """ _has_events = False def create_connect_args(self, url): """Build DB-API compatible connection arguments. Given a :class:`~sqlalchemy.engine.url.URL` object, returns a tuple consisting of a `*args`/`**kwargs` suitable to send directly to the dbapi's connect function. """ raise NotImplementedError() @classmethod def type_descriptor(cls, typeobj): """Transform a generic type to a dialect-specific type. Dialect classes will usually use the :func:`.types.adapt_type` function in the types module to accomplish this. The returned result is cached *per dialect class* so can contain no dialect-instance state. """ raise NotImplementedError() def initialize(self, connection): """Called during strategized creation of the dialect with a connection. Allows dialects to configure options based on server version info or other properties. The connection passed here is a SQLAlchemy Connection object, with full capabilities. The initialize() method of the base dialect should be called via super(). """ pass def reflecttable( self, connection, table, include_columns, exclude_columns): """Load table description from the database. Given a :class:`.Connection` and a :class:`~sqlalchemy.schema.Table` object, reflect its columns and properties from the database. The implementation of this method is provided by :meth:`.DefaultDialect.reflecttable`, which makes use of :class:`.Inspector` to retrieve column information. Dialects should **not** seek to implement this method, and should instead implement individual schema inspection operations such as :meth:`.Dialect.get_columns`, :meth:`.Dialect.get_pk_constraint`, etc. """ raise NotImplementedError() def get_columns(self, connection, table_name, schema=None, **kw): """Return information about columns in `table_name`. Given a :class:`.Connection`, a string `table_name`, and an optional string `schema`, return column information as a list of dictionaries with these keys: name the column's name type [sqlalchemy.types#TypeEngine] nullable boolean default the column's default value autoincrement boolean sequence a dictionary of the form {'name' : str, 'start' :int, 'increment': int, 'minvalue': int, 'maxvalue': int, 'nominvalue': bool, 'nomaxvalue': bool, 'cycle': bool, 'cache': int, 'order': bool} Additional column attributes may be present. """ raise NotImplementedError() def get_primary_keys(self, connection, table_name, schema=None, **kw): """Return information about primary keys in `table_name`. Deprecated. This method is only called by the default implementation of :meth:`.Dialect.get_pk_constraint`. Dialects should instead implement the :meth:`.Dialect.get_pk_constraint` method directly. """ raise NotImplementedError() def get_pk_constraint(self, connection, table_name, schema=None, **kw): """Return information about the primary key constraint on table_name`. Given a :class:`.Connection`, a string `table_name`, and an optional string `schema`, return primary key information as a dictionary with these keys: constrained_columns a list of column names that make up the primary key name optional name of the primary key constraint. """ raise NotImplementedError() def get_foreign_keys(self, connection, table_name, schema=None, **kw): """Return information about foreign_keys in `table_name`. Given a :class:`.Connection`, a string `table_name`, and an optional string `schema`, return foreign key information as a list of dicts with these keys: name the constraint's name constrained_columns a list of column names that make up the foreign key referred_schema the name of the referred schema referred_table the name of the referred table referred_columns a list of column names in the referred table that correspond to constrained_columns """ raise NotImplementedError() def get_table_names(self, connection, schema=None, **kw): """Return a list of table names for `schema`.""" raise NotImplementedError() def get_temp_table_names(self, connection, schema=None, **kw): """Return a list of temporary table names on the given connection, if supported by the underlying backend. """ raise NotImplementedError() def get_view_names(self, connection, schema=None, **kw): """Return a list of all view names available in the database. schema: Optional, retrieve names from a non-default schema. """ raise NotImplementedError() def get_temp_view_names(self, connection, schema=None, **kw): """Return a list of temporary view names on the given connection, if supported by the underlying backend. """ raise NotImplementedError() def get_view_definition(self, connection, view_name, schema=None, **kw): """Return view definition. Given a :class:`.Connection`, a string `view_name`, and an optional string `schema`, return the view definition. """ raise NotImplementedError() def get_indexes(self, connection, table_name, schema=None, **kw): """Return information about indexes in `table_name`. Given a :class:`.Connection`, a string `table_name` and an optional string `schema`, return index information as a list of dictionaries with these keys: name the index's name column_names list of column names in order unique boolean """ raise NotImplementedError() def get_unique_constraints( self, connection, table_name, schema=None, **kw): r"""Return information about unique constraints in `table_name`. Given a string `table_name` and an optional string `schema`, return unique constraint information as a list of dicts with these keys: name the unique constraint's name column_names list of column names in order \**kw other options passed to the dialect's get_unique_constraints() method. .. versionadded:: 0.9.0 """ raise NotImplementedError() def get_check_constraints( self, connection, table_name, schema=None, **kw): r"""Return information about check constraints in `table_name`. Given a string `table_name` and an optional string `schema`, return check constraint information as a list of dicts with these keys: name the check constraint's name sqltext the check constraint's SQL expression \**kw other options passed to the dialect's get_check_constraints() method. .. versionadded:: 1.1.0 """ raise NotImplementedError() def get_table_comment( self, connection, table_name, schema=None, **kw): r"""Return the "comment" for the table identified by `table_name`. Given a string `table_name` and an optional string `schema`, return table comment information as a dictionary with this key: text text of the comment Raises ``NotImplementedError`` for dialects that don't support comments. .. versionadded:: 1.2 """ raise NotImplementedError() def normalize_name(self, name): """convert the given name to lowercase if it is detected as case insensitive. this method is only used if the dialect defines requires_name_normalize=True. """ raise NotImplementedError() def denormalize_name(self, name): """convert the given name to a case insensitive identifier for the backend if it is an all-lowercase name. this method is only used if the dialect defines requires_name_normalize=True. """ raise NotImplementedError() def has_table(self, connection, table_name, schema=None): """Check the existence of a particular table in the database. Given a :class:`.Connection` object and a string `table_name`, return True if the given table (possibly within the specified `schema`) exists in the database, False otherwise. """ raise NotImplementedError() def has_sequence(self, connection, sequence_name, schema=None): """Check the existence of a particular sequence in the database. Given a :class:`.Connection` object and a string `sequence_name`, return True if the given sequence exists in the database, False otherwise. """ raise NotImplementedError() def _get_server_version_info(self, connection): """Retrieve the server version info from the given connection. This is used by the default implementation to populate the "server_version_info" attribute and is called exactly once upon first connect. """ raise NotImplementedError() def _get_default_schema_name(self, connection): """Return the string name of the currently selected schema from the given connection. This is used by the default implementation to populate the "default_schema_name" attribute and is called exactly once upon first connect. """ raise NotImplementedError() def do_begin(self, dbapi_connection): """Provide an implementation of ``connection.begin()``, given a DB-API connection. The DBAPI has no dedicated "begin" method and it is expected that transactions are implicit. This hook is provided for those DBAPIs that might need additional help in this area. Note that :meth:`.Dialect.do_begin` is not called unless a :class:`.Transaction` object is in use. The :meth:`.Dialect.do_autocommit` hook is provided for DBAPIs that need some extra commands emitted after a commit in order to enter the next transaction, when the SQLAlchemy :class:`.Connection` is used in its default "autocommit" mode. :param dbapi_connection: a DBAPI connection, typically proxied within a :class:`.ConnectionFairy`. """ raise NotImplementedError() def do_rollback(self, dbapi_connection): """Provide an implementation of ``connection.rollback()``, given a DB-API connection. :param dbapi_connection: a DBAPI connection, typically proxied within a :class:`.ConnectionFairy`. """ raise NotImplementedError() def do_commit(self, dbapi_connection): """Provide an implementation of ``connection.commit()``, given a DB-API connection. :param dbapi_connection: a DBAPI connection, typically proxied within a :class:`.ConnectionFairy`. """ raise NotImplementedError() def do_close(self, dbapi_connection): """Provide an implementation of ``connection.close()``, given a DBAPI connection. This hook is called by the :class:`.Pool` when a connection has been detached from the pool, or is being returned beyond the normal capacity of the pool. .. versionadded:: 0.8 """ raise NotImplementedError() def create_xid(self): """Create a two-phase transaction ID. This id will be passed to do_begin_twophase(), do_rollback_twophase(), do_commit_twophase(). Its format is unspecified. """ raise NotImplementedError() def do_savepoint(self, connection, name): """Create a savepoint with the given name. :param connection: a :class:`.Connection`. :param name: savepoint name. """ raise NotImplementedError() def do_rollback_to_savepoint(self, connection, name): """Rollback a connection to the named savepoint. :param connection: a :class:`.Connection`. :param name: savepoint name. """ raise NotImplementedError() def do_release_savepoint(self, connection, name): """Release the named savepoint on a connection. :param connection: a :class:`.Connection`. :param name: savepoint name. """ raise NotImplementedError() def do_begin_twophase(self, connection, xid): """Begin a two phase transaction on the given connection. :param connection: a :class:`.Connection`. :param xid: xid """ raise NotImplementedError() def do_prepare_twophase(self, connection, xid): """Prepare a two phase transaction on the given connection. :param connection: a :class:`.Connection`. :param xid: xid """ raise NotImplementedError() def do_rollback_twophase(self, connection, xid, is_prepared=True, recover=False): """Rollback a two phase transaction on the given connection. :param connection: a :class:`.Connection`. :param xid: xid :param is_prepared: whether or not :meth:`.TwoPhaseTransaction.prepare` was called. :param recover: if the recover flag was passed. """ raise NotImplementedError() def do_commit_twophase(self, connection, xid, is_prepared=True, recover=False): """Commit a two phase transaction on the given connection. :param connection: a :class:`.Connection`. :param xid: xid :param is_prepared: whether or not :meth:`.TwoPhaseTransaction.prepare` was called. :param recover: if the recover flag was passed. """ raise NotImplementedError() def do_recover_twophase(self, connection): """Recover list of uncommitted prepared two phase transaction identifiers on the given connection. :param connection: a :class:`.Connection`. """ raise NotImplementedError() def do_executemany(self, cursor, statement, parameters, context=None): """Provide an implementation of ``cursor.executemany(statement, parameters)``.""" raise NotImplementedError() def do_execute(self, cursor, statement, parameters, context=None): """Provide an implementation of ``cursor.execute(statement, parameters)``.""" raise NotImplementedError() def do_execute_no_params(self, cursor, statement, parameters, context=None): """Provide an implementation of ``cursor.execute(statement)``. The parameter collection should not be sent. """ raise NotImplementedError() def is_disconnect(self, e, connection, cursor): """Return True if the given DB-API error indicates an invalid connection""" raise NotImplementedError() def connect(self): """return a callable which sets up a newly created DBAPI connection. The callable accepts a single argument "conn" which is the DBAPI connection itself. It has no return value. This is used to set dialect-wide per-connection options such as isolation modes, unicode modes, etc. If a callable is returned, it will be assembled into a pool listener that receives the direct DBAPI connection, with all wrappers removed. If None is returned, no listener will be generated. """ return None def reset_isolation_level(self, dbapi_conn): """Given a DBAPI connection, revert its isolation to the default. Note that this is a dialect-level method which is used as part of the implementation of the :class:`.Connection` and :class:`.Engine` isolation level facilities; these APIs should be preferred for most typical use cases. .. seealso:: :meth:`.Connection.get_isolation_level` - view current level :attr:`.Connection.default_isolation_level` - view default level :paramref:`.Connection.execution_options.isolation_level` - set per :class:`.Connection` isolation level :paramref:`.create_engine.isolation_level` - set per :class:`.Engine` isolation level """ raise NotImplementedError() def set_isolation_level(self, dbapi_conn, level): """Given a DBAPI connection, set its isolation level. Note that this is a dialect-level method which is used as part of the implementation of the :class:`.Connection` and :class:`.Engine` isolation level facilities; these APIs should be preferred for most typical use cases. .. seealso:: :meth:`.Connection.get_isolation_level` - view current level :attr:`.Connection.default_isolation_level` - view default level :paramref:`.Connection.execution_options.isolation_level` - set per :class:`.Connection` isolation level :paramref:`.create_engine.isolation_level` - set per :class:`.Engine` isolation level """ raise NotImplementedError() def get_isolation_level(self, dbapi_conn): """Given a DBAPI connection, return its isolation level. When working with a :class:`.Connection` object, the corresponding DBAPI connection may be procured using the :attr:`.Connection.connection` accessor. Note that this is a dialect-level method which is used as part of the implementation of the :class:`.Connection` and :class:`.Engine` isolation level facilities; these APIs should be preferred for most typical use cases. .. seealso:: :meth:`.Connection.get_isolation_level` - view current level :attr:`.Connection.default_isolation_level` - view default level :paramref:`.Connection.execution_options.isolation_level` - set per :class:`.Connection` isolation level :paramref:`.create_engine.isolation_level` - set per :class:`.Engine` isolation level """ raise NotImplementedError() @classmethod def get_dialect_cls(cls, url): """Given a URL, return the :class:`.Dialect` that will be used. This is a hook that allows an external plugin to provide functionality around an existing dialect, by allowing the plugin to be loaded from the url based on an entrypoint, and then the plugin returns the actual dialect to be used. By default this just returns the cls. .. versionadded:: 1.0.3 """ return cls @classmethod def engine_created(cls, engine): """A convenience hook called before returning the final :class:`.Engine`. If the dialect returned a different class from the :meth:`.get_dialect_cls` method, then the hook is called on both classes, first on the dialect class returned by the :meth:`.get_dialect_cls` method and then on the class on which the method was called. The hook should be used by dialects and/or wrappers to apply special events to the engine or its components. In particular, it allows a dialect-wrapping class to apply dialect-level events. .. versionadded:: 1.0.3 """ pass class CreateEnginePlugin(object): """A set of hooks intended to augment the construction of an :class:`.Engine` object based on entrypoint names in a URL. The purpose of :class:`.CreateEnginePlugin` is to allow third-party systems to apply engine, pool and dialect level event listeners without the need for the target application to be modified; instead, the plugin names can be added to the database URL. Target applications for :class:`.CreateEnginePlugin` include: * connection and SQL performance tools, e.g. which use events to track number of checkouts and/or time spent with statements * connectivity plugins such as proxies Plugins are registered using entry points in a similar way as that of dialects:: entry_points={ 'sqlalchemy.plugins': [ 'myplugin = myapp.plugins:MyPlugin' ] A plugin that uses the above names would be invoked from a database URL as in:: from sqlalchemy import create_engine engine = create_engine( "mysql+pymysql://scott:tiger@localhost/test?plugin=myplugin") The ``plugin`` argument supports multiple instances, so that a URL may specify multiple plugins; they are loaded in the order stated in the URL:: engine = create_engine( "mysql+pymysql://scott:tiger@localhost/" "test?plugin=plugin_one&plugin=plugin_twp&plugin=plugin_three") A plugin can receive additional arguments from the URL string as well as from the keyword arguments passed to :func:`.create_engine`. The :class:`.URL` object and the keyword dictionary are passed to the constructor so that these arguments can be extracted from the url's :attr:`.URL.query` collection as well as from the dictionary:: class MyPlugin(CreateEnginePlugin): def __init__(self, url, kwargs): self.my_argument_one = url.query.pop('my_argument_one') self.my_argument_two = url.query.pop('my_argument_two') self.my_argument_three = kwargs.pop('my_argument_three', None) Arguments like those illustrated above would be consumed from the following:: from sqlalchemy import create_engine engine = create_engine( "mysql+pymysql://scott:tiger@localhost/" "test?plugin=myplugin&my_argument_one=foo&my_argument_two=bar", my_argument_three='bat') The URL and dictionary are used for subsequent setup of the engine as they are, so the plugin can modify their arguments in-place. Arguments that are only understood by the plugin should be popped or otherwise removed so that they aren't interpreted as erroneous arguments afterwards. When the engine creation process completes and produces the :class:`.Engine` object, it is again passed to the plugin via the :meth:`.CreateEnginePlugin.engine_created` hook. In this hook, additional changes can be made to the engine, most typically involving setup of events (e.g. those defined in :ref:`core_event_toplevel`). .. versionadded:: 1.1 """ def __init__(self, url, kwargs): """Contruct a new :class:`.CreateEnginePlugin`. The plugin object is instantiated individually for each call to :func:`.create_engine`. A single :class:`.Engine` will be passed to the :meth:`.CreateEnginePlugin.engine_created` method corresponding to this URL. :param url: the :class:`.URL` object. The plugin should inspect what it needs here as well as remove its custom arguments from the :attr:`.URL.query` collection. The URL can be modified in-place in any other way as well. :param kwargs: The keyword arguments passed to :func`.create_engine`. The plugin can read and modify this dictionary in-place, to affect the ultimate arguments used to create the engine. It should remove its custom arguments from the dictionary as well. """ self.url = url def handle_dialect_kwargs(self, dialect_cls, dialect_args): """parse and modify dialect kwargs""" def handle_pool_kwargs(self, pool_cls, pool_args): """parse and modify pool kwargs""" def engine_created(self, engine): """Receive the :class:`.Engine` object when it is fully constructed. The plugin may make additional changes to the engine, such as registering engine or connection pool events. """ class ExecutionContext(object): """A messenger object for a Dialect that corresponds to a single execution. ExecutionContext should have these data members: connection Connection object which can be freely used by default value generators to execute SQL. This Connection should reference the same underlying connection/transactional resources of root_connection. root_connection Connection object which is the source of this ExecutionContext. This Connection may have close_with_result=True set, in which case it can only be used once. dialect dialect which created this ExecutionContext. cursor DB-API cursor procured from the connection, compiled if passed to constructor, sqlalchemy.engine.base.Compiled object being executed, statement string version of the statement to be executed. Is either passed to the constructor, or must be created from the sql.Compiled object by the time pre_exec() has completed. parameters bind parameters passed to the execute() method. For compiled statements, this is a dictionary or list of dictionaries. For textual statements, it should be in a format suitable for the dialect's paramstyle (i.e. dict or list of dicts for non positional, list or list of lists/tuples for positional). isinsert True if the statement is an INSERT. isupdate True if the statement is an UPDATE. should_autocommit True if the statement is a "committable" statement. prefetch_cols a list of Column objects for which a client-side default was fired off. Applies to inserts and updates. postfetch_cols a list of Column objects for which a server-side default or inline SQL expression value was fired off. Applies to inserts and updates. """ exception = None """A DBAPI-level exception that was caught when this ExecutionContext attempted to execute a statement. This attribute is meaningful only within the :meth:`.ConnectionEvents.dbapi_error` event. .. versionadded:: 0.9.7 .. seealso:: :attr:`.ExecutionContext.is_disconnect` :meth:`.ConnectionEvents.dbapi_error` """ is_disconnect = None """Boolean flag set to True or False when a DBAPI-level exception is caught when this ExecutionContext attempted to execute a statement. This attribute is meaningful only within the :meth:`.ConnectionEvents.dbapi_error` event. .. versionadded:: 0.9.7 .. seealso:: :attr:`.ExecutionContext.exception` :meth:`.ConnectionEvents.dbapi_error` """ def create_cursor(self): """Return a new cursor generated from this ExecutionContext's connection. Some dialects may wish to change the behavior of connection.cursor(), such as postgresql which may return a PG "server side" cursor. """ raise NotImplementedError() def pre_exec(self): """Called before an execution of a compiled statement. If a compiled statement was passed to this ExecutionContext, the `statement` and `parameters` datamembers must be initialized after this statement is complete. """ raise NotImplementedError() def post_exec(self): """Called after the execution of a compiled statement. If a compiled statement was passed to this ExecutionContext, the `last_insert_ids`, `last_inserted_params`, etc. datamembers should be available after this method completes. """ raise NotImplementedError() def result(self): """Return a result object corresponding to this ExecutionContext. Returns a ResultProxy. """ raise NotImplementedError() def handle_dbapi_exception(self, e): """Receive a DBAPI exception which occurred upon execute, result fetch, etc.""" raise NotImplementedError() def should_autocommit_text(self, statement): """Parse the given textual statement and return True if it refers to a "committable" statement""" raise NotImplementedError() def lastrow_has_defaults(self): """Return True if the last INSERT or UPDATE row contained inlined or database-side defaults. """ raise NotImplementedError() def get_rowcount(self): """Return the DBAPI ``cursor.rowcount`` value, or in some cases an interpreted value. See :attr:`.ResultProxy.rowcount` for details on this. """ raise NotImplementedError() class Connectable(object): """Interface for an object which supports execution of SQL constructs. The two implementations of :class:`.Connectable` are :class:`.Connection` and :class:`.Engine`. Connectable must also implement the 'dialect' member which references a :class:`.Dialect` instance. """ def connect(self, **kwargs): """Return a :class:`.Connection` object. Depending on context, this may be ``self`` if this object is already an instance of :class:`.Connection`, or a newly procured :class:`.Connection` if this object is an instance of :class:`.Engine`. """ def contextual_connect(self): """Return a :class:`.Connection` object which may be part of an ongoing context. Depending on context, this may be ``self`` if this object is already an instance of :class:`.Connection`, or a newly procured :class:`.Connection` if this object is an instance of :class:`.Engine`. """ raise NotImplementedError() @util.deprecated("0.7", "Use the create() method on the given schema " "object directly, i.e. :meth:`.Table.create`, " ":meth:`.Index.create`, :meth:`.MetaData.create_all`") def create(self, entity, **kwargs): """Emit CREATE statements for the given schema entity. """ raise NotImplementedError() @util.deprecated("0.7", "Use the drop() method on the given schema " "object directly, i.e. :meth:`.Table.drop`, " ":meth:`.Index.drop`, :meth:`.MetaData.drop_all`") def drop(self, entity, **kwargs): """Emit DROP statements for the given schema entity. """ raise NotImplementedError() def execute(self, object, *multiparams, **params): """Executes the given construct and returns a :class:`.ResultProxy`.""" raise NotImplementedError() def scalar(self, object, *multiparams, **params): """Executes and returns the first column of the first row. The underlying cursor is closed after execution. """ raise NotImplementedError() def _run_visitor(self, visitorcallable, element, **kwargs): raise NotImplementedError() def _execute_clauseelement(self, elem, multiparams=None, params=None): raise NotImplementedError() class ExceptionContext(object): """Encapsulate information about an error condition in progress. This object exists solely to be passed to the :meth:`.ConnectionEvents.handle_error` event, supporting an interface that can be extended without backwards-incompatibility. .. versionadded:: 0.9.7 """ connection = None """The :class:`.Connection` in use during the exception. This member is present, except in the case of a failure when first connecting. .. seealso:: :attr:`.ExceptionContext.engine` """ engine = None """The :class:`.Engine` in use during the exception. This member should always be present, even in the case of a failure when first connecting. .. versionadded:: 1.0.0 """ cursor = None """The DBAPI cursor object. May be None. """ statement = None """String SQL statement that was emitted directly to the DBAPI. May be None. """ parameters = None """Parameter collection that was emitted directly to the DBAPI. May be None. """ original_exception = None """The exception object which was caught. This member is always present. """ sqlalchemy_exception = None """The :class:`sqlalchemy.exc.StatementError` which wraps the original, and will be raised if exception handling is not circumvented by the event. May be None, as not all exception types are wrapped by SQLAlchemy. For DBAPI-level exceptions that subclass the dbapi's Error class, this field will always be present. """ chained_exception = None """The exception that was returned by the previous handler in the exception chain, if any. If present, this exception will be the one ultimately raised by SQLAlchemy unless a subsequent handler replaces it. May be None. """ execution_context = None """The :class:`.ExecutionContext` corresponding to the execution operation in progress. This is present for statement execution operations, but not for operations such as transaction begin/end. It also is not present when the exception was raised before the :class:`.ExecutionContext` could be constructed. Note that the :attr:`.ExceptionContext.statement` and :attr:`.ExceptionContext.parameters` members may represent a different value than that of the :class:`.ExecutionContext`, potentially in the case where a :meth:`.ConnectionEvents.before_cursor_execute` event or similar modified the statement/parameters to be sent. May be None. """ is_disconnect = None """Represent whether the exception as occurred represents a "disconnect" condition. This flag will always be True or False within the scope of the :meth:`.ConnectionEvents.handle_error` handler. SQLAlchemy will defer to this flag in order to determine whether or not the connection should be invalidated subsequently. That is, by assigning to this flag, a "disconnect" event which then results in a connection and pool invalidation can be invoked or prevented by changing this flag. """ invalidate_pool_on_disconnect = True """Represent whether all connections in the pool should be invalidated when a "disconnect" condition is in effect. Setting this flag to False within the scope of the :meth:`.ConnectionEvents.handle_error` event will have the effect such that the full collection of connections in the pool will not be invalidated during a disconnect; only the current connection that is the subject of the error will actually be invalidated. The purpose of this flag is for custom disconnect-handling schemes where the invalidation of other connections in the pool is to be performed based on other conditions, or even on a per-connection basis. .. versionadded:: 1.0.3 """

31.790895

81

0.665324

27db26e3f14b195f96ff70eca888b1ed2bf89363

3,042

py

Python

project/parsers/price.py

nikolay-py/product_optimizer

3d7da484984e63791849ce8a12b285a1ba2daacf

[ "MIT" ]

null

project/parsers/price.py

nikolay-py/product_optimizer

3d7da484984e63791849ce8a12b285a1ba2daacf

[ "MIT" ]

1

2021-07-05T13:42:19.000Z

2021-07-05T14:14:44.000Z

project/parsers/price.py

nikolay-py/product_optimizer

3d7da484984e63791849ce8a12b285a1ba2daacf

[ "MIT" ]

null

"""We fill the base with products.""" from typing import Union import bs4 from .crud import create_goods def get_catalog_name(soup: bs4) -> Union[str, bool]: """Get catalog name.""" # Получаем имя каталога link_page = soup.find( 'li', class_='breadcrumb-page__item breadcrumb-page__current') try: catalog_name = link_page.text.strip() return catalog_name.lower() except AttributeError: print('Каталог без имени') return False def get_price_per_kg(price: str, weight: str) -> Union[str, bool]: """Price per kg.""" # Расчет цены за кг и проверка на пустые значения веса try: price_per_kg = float(price) / float(weight) return "{0:.2f}".format(price_per_kg) except (ValueError, ZeroDivisionError) as e: print('-------------------------------------------------------') print(f"Ошибка пустой строки {e}. Цена {price}, Вес{weight}") return False def get_float_price(str_price: str) -> str: """Get float price.""" # Вытаскиваем из разрозненного текста цену item_price = ''.join([i for i in str_price if i.isnumeric()]) price = float(item_price) / 100 return "{0:.2f}".format(price) def get_price(soup: bs4) -> None: """We get the main dictionary with the price of goods.""" # Получаем основной словарь с ценой товаров items = {} catalog_name = get_catalog_name(soup) # Находим все классы catalog-item, даже лишние, и перебираем их for item in soup.find_all('div', class_='product ok-theme'): # Название item_title = item.find('div', class_='product-name').find('a')['title'].lower() # Цена if item.find('span', class_='price label'): search = item.find('span', class_='price label').text item_price = get_float_price(search) else: search = item.find('div', class_='product-price').find('span').text item_price = get_float_price(search) # Вес и единица измерения try: product_weight = item.find('div', class_='product-weight') units = product_weight.find('span').text.strip() product_weight.find('span').extract() weight = product_weight.text.strip().replace(',', '.') except AttributeError: units = 'шт' weight = 1 # Цена за кг price_per_kg = get_price_per_kg(item_price, weight) if price_per_kg is not False and catalog_name is not False: # Наполняем словарь items = { 'key': catalog_name, 'name': item_title, 'price': float(item_price), 'weight': weight, 'units': units, 'price_per_kg': price_per_kg } # Сохраняем данные в базу данных create_goods(items) else: print(f"{catalog_name} - {item_title} не учтена") print('-------------------------------------------------------')

34.179775

87

0.571006