PatrickHaller/the-stack-python-1M · Datasets at Hugging Face

546ee763a66e875517fa6ca313b6ecb60b91f69b

1,617

py

Python

assets/python_scripts/xml2txt.py

weiSupreme/weiSupreme.github.io

59d419f2d8207c7da0a427330b21546f4f0d85d0

[ "CC-BY-4.0" ]

3

2018-09-15T05:47:35.000Z

2019-04-08T07:00:02.000Z

assets/python_scripts/xml2txt.py

weiSupreme/weiSupreme.github.io

59d419f2d8207c7da0a427330b21546f4f0d85d0

[ "CC-BY-4.0" ]

null

assets/python_scripts/xml2txt.py

weiSupreme/weiSupreme.github.io

59d419f2d8207c7da0a427330b21546f4f0d85d0

[ "CC-BY-4.0" ]

null

#!/usr/bin/evn python # coding:utf-8 import os try: import xml.etree.cElementTree as ET except ImportError: import xml.etree.ElementTree as ET import sys xml_dir = r"D:\Python\PythonProjects\333_xml" xml_paths = [os.path.join(xml_dir, f) for f in os.listdir(xml_dir) if os.path.isfile(os.path.join(xml_dir, f)) and f.endswith(".xml")] strTemp = '' # 将txt文件清空 with open(os.path.join(xml_dir, "mytxt.txt"), "w") as f: f.write(str(strTemp)) for xml_path in xml_paths: f = xml_path tree = ET.parse(f) # 打开xml文档 root = tree.getroot() # 获得root节点 print "*" * 10 filename = root.find('filename').text #filename = filename[:-4] print filename ######################################## for size in root.findall('size'): # 找到root节点下的size节点 width = size.find('width').text # 子节点下节点width的值 height = size.find('height').text # 子节点下节点height的值 print width, height ######################################## for object in root.findall('object'): # 找到root节点下的所有object节点 name = object.find('name').text # 子节点下节点name的值 print name bndbox = object.find('bndbox') # 子节点下属性bndbox的值 xmin = bndbox.find('xmin').text ymin = bndbox.find('ymin').text xmax = bndbox.find('xmax').text ymax = bndbox.find('ymax').text print xmin, ymin, xmax, ymax strTemp=filename+'.png '+'basketball'+' '+str(xmin)+' '+str(ymin)+' '+str(xmax)+' '+str(ymax)+'\n' with open(os.path.join(xml_dir, "mytxt.txt"), "a") as f: f.write(str(strTemp))

29.4

102

0.577613

310be3133b5a907e7e7cbd37d3c67bd8cd6b05b5

1,153

py

Python

tests/unit/test_utils.py

Informasjonsforvaltning/organization-page-bffe

473dc9606649f864618f4f8bfc4a6a2a035f06d7

[ "Apache-2.0" ]

null

tests/unit/test_utils.py

Informasjonsforvaltning/organization-page-bffe

473dc9606649f864618f4f8bfc4a6a2a035f06d7

[ "Apache-2.0" ]

47

2020-05-14T07:54:48.000Z

2022-03-29T22:17:08.000Z

tests/unit/test_utils.py

Informasjonsforvaltning/organization-page-bffe

473dc9606649f864618f4f8bfc4a6a2a035f06d7

[ "Apache-2.0" ]

null

"""Unit test cases for utils.""" import pytest from fdk_organization_bff.utils.utils import resource_is_new, url_with_params @pytest.mark.unit def test_url_with_params() -> None: """Params is added correctly.""" url_0 = url_with_params("http://localhost:8000/endpoint", None) url_1 = url_with_params("http://localhost:8000/endpoint", {}) url_2 = url_with_params("http://localhost:8000/endpoint", {"key0": "value0"}) url_3 = url_with_params( "http://localhost:8000/endpoint", {"key0": "value0", "key1": "value1"} ) assert url_0 == "http://localhost:8000/endpoint" assert url_1 == "http://localhost:8000/endpoint" assert url_2 == "http://localhost:8000/endpoint?key0=value0" assert url_3 == "http://localhost:8000/endpoint?key0=value0&key1=value1" @pytest.mark.unit def test_resource_is_new_handles_bad_date_format() -> None: """Check that resource_is_new handles bad data.""" res_0 = resource_is_new({}) res_1 = resource_is_new({"issued": {}}) res_2 = resource_is_new({"issued": {"value": "2020/10/10"}}) assert res_0 is False assert res_1 is False assert res_2 is False

34.939394

81

0.690373

a39ec4558c0977b5ee346411c8ee3f8e0b782534

1,221

py

Python

mycli/clistyle.py

steverobbins/mycli

ad19b143da483f97a657a69eeff88f9a74966520

[ "BSD-3-Clause" ]

2

2015-08-01T03:39:58.000Z

2015-11-08T07:54:59.000Z

mycli/clistyle.py

steverobbins/mycli

ad19b143da483f97a657a69eeff88f9a74966520

[ "BSD-3-Clause" ]

null

mycli/clistyle.py

steverobbins/mycli

ad19b143da483f97a657a69eeff88f9a74966520

[ "BSD-3-Clause" ]

null

from pygments.token import Token from pygments.style import Style from pygments.util import ClassNotFound from prompt_toolkit.styles import default_style_extensions import pygments.styles def style_factory(name): try: style = pygments.styles.get_style_by_name(name) except ClassNotFound: style = pygments.styles.get_style_by_name('native') class CLIStyle(Style): styles = {} styles.update(style.styles) styles.update(default_style_extensions) styles.update({ Token.Menu.Completions.Completion.Current: 'bg:#00aaaa #000000', Token.Menu.Completions.Completion: 'bg:#008888 #ffffff', Token.Menu.Completions.ProgressButton: 'bg:#003333', Token.Menu.Completions.ProgressBar: 'bg:#00aaaa', Token.SelectedText: '#ffffff bg:#6666aa', Token.IncrementalSearchMatch: '#ffffff bg:#4444aa', Token.IncrementalSearchMatch.Current: '#ffffff bg:#44aa44', Token.Toolbar: 'bg:#440044 #ffffff', Token.Toolbar: 'bg:#222222 #aaaaaa', Token.Toolbar.Off: 'bg:#222222 #888888', Token.Toolbar.On: 'bg:#222222 #ffffff', }) return CLIStyle

35.911765

76

0.657658

94a18da4560be081da6b3059d998fab14276d9a8

1,052

py

Python

api/test/api/responders_test/test_data/constants.py

korenlev/calipso-cvim

39278a5cf09c40b26a8a143ccc0c8d437961abc2

[ "Apache-2.0" ]

null

api/test/api/responders_test/test_data/constants.py

korenlev/calipso-cvim

39278a5cf09c40b26a8a143ccc0c8d437961abc2

[ "Apache-2.0" ]

null

api/test/api/responders_test/test_data/constants.py

korenlev/calipso-cvim

39278a5cf09c40b26a8a143ccc0c8d437961abc2

[ "Apache-2.0" ]

null

############################################################################### # Copyright (c) 2017-2020 Koren Lev (Cisco Systems), # # Yaron Yogev (Cisco Systems), Ilia Abashin (Cisco Systems) and others # # # # All rights reserved. This program and the accompanying materials # # are made available under the terms of the Apache License, Version 2.0 # # which accompanies this distribution, and is available at # # http://www.apache.org/licenses/LICENSE-2.0 # ############################################################################### URL = "/constants" UNKNOWN_NAME = "unknown constant" NAME = "distributions" CONSTANTS_WITH_SPECIFIC_NAME = [{ "id": "YmPDAQAchr39KjECQ", "name": NAME, "data": [{ "value": "Canonical-icehouse", "label": "Canonical-icehouse" }, { "value": "Canonical-juno", "label": "Canonical-juno" }], }]

43.833333

79

0.455323

14180ef85d9e9750aa4cb6f06a8af20b49de6651

1,754

py

Python

eggs/bx_python-0.7.2-py2.6-linux-x86_64-ucs4.egg/EGG-INFO/scripts/maf_to_concat_fasta.py

psnehal/MethylSig

5efad71e71ff2515feff2e49579c856ef9a1bbd8

[ "CC-BY-3.0" ]

null

eggs/bx_python-0.7.2-py2.6-linux-x86_64-ucs4.egg/EGG-INFO/scripts/maf_to_concat_fasta.py

psnehal/MethylSig

5efad71e71ff2515feff2e49579c856ef9a1bbd8

[ "CC-BY-3.0" ]

null

eggs/bx_python-0.7.2-py2.6-linux-x86_64-ucs4.egg/EGG-INFO/scripts/maf_to_concat_fasta.py

psnehal/MethylSig

5efad71e71ff2515feff2e49579c856ef9a1bbd8

[ "CC-BY-3.0" ]

null

#!/afs/bx.psu.edu/project/pythons/py2.6-linux-x86_64-ucs4/bin/python2.6 """ Read a maf and print the text as a fasta file, concatenating blocks. A specific subset of species can be chosen. usage %prog [options] species1,species2,... < maf_file > fasta_file --fill="expression": Insert this between blocks --wrap=columns: Wrap FASTA to this many columns """ from optparse import OptionParser import textwrap import sys from bx.align import maf def __main__(): # Parse command line arguments parser = OptionParser() parser.add_option( "--fill", action="store", default=None, type="string", help="" ) parser.add_option( "--wrap", action="store", default=None, type="int", help="" ) parser.add_option( "--nowrap", action="store_true", default=False, dest="nowrap", help="" ) ( options, args ) = parser.parse_args() species = [] for arg in args: species.extend(arg.split(',')) fill = "" if options.fill: fill = eval( options.fill ) wrap = 50 if (options.wrap != None): wrap = options.wrap elif (options.nowrap): wrap = 0 # create the concatenated sequences texts = {} for s in species: texts[s] = [] maf_reader = maf.Reader( sys.stdin ) for m in maf_reader: for s in species: c = m.get_component_by_src_start( s ) if c: texts[s].append( c.text ) else: texts[s].append( "-" * m.text_size ) for s in species: print ">" + s print_n( fill.join( texts[s] ), wrap ) def print_n( s, n, f = sys.stdout ): if (n <= 0): print >> f, s else: p = 0 while p < len( s ): print >> f, s[p:min(p+n,len(s))] p += n if __name__ == "__main__": __main__()

28.290323

95

0.600912

a74c597dc4960b95584b13a311f8948e26638c61

3,549

py

Python

sdk/servicebus/azure-servicebus/samples/async_samples/mgmt_subscription_async.py

rsdoherty/azure-sdk-for-python

6bba5326677468e6660845a703686327178bb7b1

[ "MIT" ]

2,728

2015-01-09T10:19:32.000Z

2022-03-31T14:50:33.000Z

sdk/servicebus/azure-servicebus/samples/async_samples/mgmt_subscription_async.py

rsdoherty/azure-sdk-for-python

6bba5326677468e6660845a703686327178bb7b1

[ "MIT" ]

17,773

2015-01-05T15:57:17.000Z

2022-03-31T23:50:25.000Z

sdk/servicebus/azure-servicebus/samples/async_samples/mgmt_subscription_async.py

rsdoherty/azure-sdk-for-python

6bba5326677468e6660845a703686327178bb7b1

[ "MIT" ]

1,916

2015-01-19T05:05:41.000Z

2022-03-31T19:36:44.000Z

#!/usr/bin/env python # -------------------------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # -------------------------------------------------------------------------------------------- """ Example to show managing subscription entities under a ServiceBus namespace, including - Create a subscription - Get subscription properties and runtime information - Update a subscription - Delete a subscription - List subscriptions under the given ServiceBus Namespace """ # pylint: disable=C0111 import os import asyncio import uuid from azure.servicebus.aio.management import ServiceBusAdministrationClient CONNECTION_STR = os.environ['SERVICE_BUS_CONNECTION_STR'] TOPIC_NAME = os.environ['SERVICE_BUS_TOPIC_NAME'] SUBSCRIPTION_NAME = "sb_mgmt_sub" + str(uuid.uuid4()) async def create_subscription(servicebus_mgmt_client): print("-- Create Subscription") await servicebus_mgmt_client.create_subscription(TOPIC_NAME, SUBSCRIPTION_NAME) print("Subscription {} is created.".format(SUBSCRIPTION_NAME)) print("") async def delete_subscription(servicebus_mgmt_client): print("-- Delete Subscription") await servicebus_mgmt_client.delete_subscription(TOPIC_NAME, SUBSCRIPTION_NAME) print("Subscription {} is deleted.".format(SUBSCRIPTION_NAME)) print("") async def list_subscriptions(servicebus_mgmt_client): print("-- List Subscriptions") async for subscription_properties in servicebus_mgmt_client.list_subscriptions(TOPIC_NAME): print("Subscription Name:", subscription_properties.name) print("") async def get_and_update_subscription(servicebus_mgmt_client): print("-- Get and Update Subscription") subscription_properties = await servicebus_mgmt_client.get_subscription(TOPIC_NAME, SUBSCRIPTION_NAME) print("Subscription Name:", subscription_properties.name) print("Please refer to SubscriptionDescription for complete available settings.") print("") # update by updating the properties in the model subscription_properties.max_delivery_count = 5 await servicebus_mgmt_client.update_subscription(TOPIC_NAME, subscription_properties) # update by passing keyword arguments subscription_properties = await servicebus_mgmt_client.get_subscription(TOPIC_NAME, SUBSCRIPTION_NAME) await servicebus_mgmt_client.update_subscription(TOPIC_NAME, subscription_properties, max_delivery_count=3) async def get_subscription_runtime_properties(servicebus_mgmt_client): print("-- Get Subscription Runtime Properties") get_subscription_runtime_properties = await servicebus_mgmt_client.get_subscription_runtime_properties(TOPIC_NAME, SUBSCRIPTION_NAME) print("Subscription Name:", get_subscription_runtime_properties.name) print("Please refer to SubscriptionRuntimeProperties from complete available runtime properties.") print("") async def main(): async with ServiceBusAdministrationClient.from_connection_string(CONNECTION_STR) as servicebus_mgmt_client: await create_subscription(servicebus_mgmt_client) await list_subscriptions(servicebus_mgmt_client) await get_and_update_subscription(servicebus_mgmt_client) await get_subscription_runtime_properties(servicebus_mgmt_client) await delete_subscription(servicebus_mgmt_client) loop = asyncio.get_event_loop() loop.run_until_complete(main())

43.280488

137

0.75796

15e89279227612cb8f967e13981f4d21eb54e8d4

2,423

py

Python

oscar/lib/python2.7/site-packages/prompt_toolkit/styles/from_pygments.py

sainjusajan/django-oscar

466e8edc807be689b0a28c9e525c8323cc48b8e1

[ "BSD-3-Clause" ]

null

oscar/lib/python2.7/site-packages/prompt_toolkit/styles/from_pygments.py

sainjusajan/django-oscar

466e8edc807be689b0a28c9e525c8323cc48b8e1

[ "BSD-3-Clause" ]

null

oscar/lib/python2.7/site-packages/prompt_toolkit/styles/from_pygments.py

sainjusajan/django-oscar

466e8edc807be689b0a28c9e525c8323cc48b8e1

[ "BSD-3-Clause" ]

null

""" Adaptor for building prompt_toolkit styles, starting from a Pygments style. Usage:: from pygments.styles.tango import TangoStyle style = style_from_pygments(pygments_style_cls=TangoStyle) """ from __future__ import unicode_literals from .base import Style from .from_dict import style_from_dict __all__ = ( 'PygmentsStyle', 'style_from_pygments', ) # Following imports are only needed when a ``PygmentsStyle`` class is used. try: from pygments.style import Style as pygments_Style from pygments.styles.default import DefaultStyle as pygments_DefaultStyle except ImportError: pygments_Style = None pygments_DefaultStyle = None def style_from_pygments(style_cls=pygments_DefaultStyle, style_dict=None, include_defaults=True): """ Shortcut to create a :class:`.Style` instance from a Pygments style class and a style dictionary. Example:: from prompt_toolkit.styles.from_pygments import style_from_pygments from pygments.styles import get_style_by_name style = style_from_pygments(get_style_by_name('monokai')) :param style_cls: Pygments style class to start from. :param style_dict: Dictionary for this style. `{Token: style}`. :param include_defaults: (`bool`) Include prompt_toolkit extensions. """ assert style_dict is None or isinstance(style_dict, dict) assert style_cls is None or issubclass(style_cls, pygments_Style) styles_dict = {} if style_cls is not None: styles_dict.update(style_cls.styles) if style_dict is not None: styles_dict.update(style_dict) return style_from_dict(styles_dict, include_defaults=include_defaults) class PygmentsStyle(Style): " Deprecated. " def __new__(cls, pygments_style_cls): assert issubclass(pygments_style_cls, pygments_Style) return style_from_dict(pygments_style_cls.styles) def invalidation_hash(self): pass @classmethod def from_defaults(cls, style_dict=None, pygments_style_cls=pygments_DefaultStyle, include_extensions=True): " Deprecated. " return style_from_pygments( style_cls=pygments_style_cls, style_dict=style_dict, include_defaults=include_extensions)

31.064103

78

0.689641

0152fec4750f3db6055010ef2270dd2c335f8656

1,354

py

Python

backend/corpora/common/utils/cloudfront.py

chanzuckerberg/dcp-prototype

24d2323ba5ae1482395da35ea11c42708e3a52ce

[ "MIT" ]

2

2020-02-07T18:12:12.000Z

2020-02-11T14:59:03.000Z

backend/corpora/common/utils/cloudfront.py

HumanCellAtlas/dcp-prototype

44ca66a266004124f39d7d3e3dd75e9076012ff0

[ "MIT" ]

173

2020-01-29T17:48:02.000Z

2020-03-20T02:52:58.000Z

backend/corpora/common/utils/cloudfront.py

HumanCellAtlas/dcp-prototype

44ca66a266004124f39d7d3e3dd75e9076012ff0

[ "MIT" ]

1

2020-03-20T17:06:54.000Z

import boto3 import uuid from typing import List from backend.corpora.common.corpora_config import CorporaCloudfrontConfig import logging client = boto3.client("cloudfront") # Since Cloudfront is only used in deployed environments (dev, staging, prod), # only trigger an invalidation if the distribution_id is defined in secrets manager. # Otherwise this will be a no-op def create_invalidation(paths: List[str]): try: distribution = CorporaCloudfrontConfig().distribution_id except RuntimeError: # Will be raised if the attribute is not found (i.e. in rdev) logging.debug("No Cloudfront distribution found in secrets, will not invalidate") return None return _create_invalidation(distribution, paths) def _create_invalidation(distribution: str, paths: List[str]): invalidation_id = str(uuid.uuid4()) logging.info(f"Requesting invalidation {invalidation_id} for distribution {distribution}") return client.create_invalidation( DistributionId=distribution, InvalidationBatch={ "Paths": { "Quantity": len(paths), "Items": paths, }, "CallerReference": invalidation_id, }, ) def create_invalidation_for_index_paths(): return create_invalidation(["/dp/v1/datasets/index", "/dp/v1/collections/index"])

33.02439

94

0.70901

3890e1baae75008f888c9888e11fda7dee555729

4,897

py

Python

axiol/functions.py

ramnreddy15/Axiol

39c6828c74709ac4dc27af666945c4a48d37258d

[ "MIT" ]

null

axiol/functions.py

ramnreddy15/Axiol

39c6828c74709ac4dc27af666945c4a48d37258d

[ "MIT" ]

null

axiol/functions.py

ramnreddy15/Axiol

39c6828c74709ac4dc27af666945c4a48d37258d

[ "MIT" ]

null

import asyncio import random from variables import DEFAULT_PREFIX from database import PREFIXES, LEVELDATABASE, PLUGINS, PERMISSIONS async def get_prefix(ctx): prefix = await PREFIXES.find_one({"_id": ctx.guild.id}) if prefix is not None: return prefix["prefix"] else: return DEFAULT_PREFIX async def get_xprange(guild_id): collection = LEVELDATABASE.get_collection(str(guild_id)) settings = await collection.find_one({"_id": 0}) return settings["xprange"] async def get_randomtext(typing_time): f = open("resources/words.txt").read() words = f.split("\n") if typing_time == 60: r = range(60) elif typing_time == 30: r = range(40) elif typing_time == 15: r = range(25) elif typing_time == 10: r = range(15) else: r = range(1) return " ".join([random.choice(words) for i in r]) def get_code(amount): return ''.join(random.choices("ABCDEFGHIJKLMNOPQRSTUVWXYZ" + "1234567890", k = amount)) """ Some functions to counter errors and warnings while working locally :p To get everything work properly database needs to be updates even if it's working locally on a single guild, this is because lots of places have major database dependencies. First function simply updates all plugin and permissions documents with a new plugin, only used when some new plugin is added, not required to use this function to fix any errors or warnings. Second function does the main job, it checks for all plugin, permission, leveling (if enabled) and prefix documents, then updates/adds them if they aren't there. I would have loved to say that I did this intentionally to avoid people from stealing code but it was just me writing bad code which ended up benefiting ¯\_(ツ)_/¯ """ #Adding new plugin and permissions async def update_plugins_and_permissions(plugin): await PLUGINS.update_many( { plugin: { "$exists": False } }, { "$set": { plugin : True } } ) await PERMISSIONS.update_many( { plugin: {"$exists": False} }, { "$set": { plugin: {}} } ) #updating leveling, plugin, prefix and permission data async def update_db(guild_ids): plugins_update = [] permissions_update = [] leveling_update = [] for guild_id in guild_ids: Guild_Plugins = await PLUGINS.find_one({"_id": guild_id}) if not await PLUGINS.count_documents({"_id": guild_id}, limit=1): PLUGINS.insert_one({ "_id": guild_id, "Leveling": False, "Moderation": True, "ReactionRoles": True, "Welcome": False, "Verification": False, "Chatbot": True, "AutoMod": False, "Karma": False, "Fun": True, "Giveaway": True }) plugins_update.append(guild_id) print(f"✅{guild_id} - Plugins 🔧") if not await PERMISSIONS.count_documents({"_id": guild_id}, limit=1): PERMISSIONS.insert_one({ "_id": guild_id, "Leveling": {}, "Moderation": {}, "ReactionRoles": {}, "Welcome": {}, "Verification": {}, "Chatbot": {}, "Commands": {}, "AutoMod": {}, "Karma": {}, "Fun": {}, "Giveaway": {} }) permissions_update.append(guild_id) print(f"✅{guild_id} - Permissions 🔨") if Guild_Plugins["Leveling"]: if str(guild_id) not in await LEVELDATABASE.list_collection_names(): GuildLevelDB = await LEVELDATABASE.create_collection(str(guild_id)) await GuildLevelDB.insert_one({ "_id": 0, "xprange": [15, 25], "alertchannel": None, "blacklistedchannels": [], "alerts": True }) leveling_update.append(guild_id) print(f"✅{guild_id} - Leveling 📊") #Only use this when working locally try: await PREFIXES.insert_one({ "_id": guild_id, "prefix": "ax" }) print(f"✅{guild_id} - Prefix ⚪") except: print(f"❌{guild_id} - Prefix ⚪") print(f"Update results\n{len(plugins_update)} plugins\n{len(permissions_update)} permissions\n{len(leveling_update)} leveling") # serveridlist = [843516084266729512, 751491708465840159] # loop = asyncio.get_event_loop() # loop.run_until_complete(updatedb(serveridlist)) #update_plugins_and_permissions("Giveaway")

30.416149

131

0.566061

920cbe75be00713016fbf37939080542d048245b

3,740

py

Python

git_commenter/cli.py

fossabot/git-commenter

5634e639b48368df3ffc47451fadb17adb20c300

[ "MIT" ]

null

git_commenter/cli.py

fossabot/git-commenter

5634e639b48368df3ffc47451fadb17adb20c300

[ "MIT" ]

null

git_commenter/cli.py

fossabot/git-commenter

5634e639b48368df3ffc47451fadb17adb20c300

[ "MIT" ]

null

import argparse import pyperclip from termcolor import cprint from .git import GitUtility from .question import Question class InvalidArgumentError(ValueError): def __str__(self): return "The multiple modes can not be specified." class CLI: def __init__(self, message): """ Args: message (str, None): Message input as an argument """ self.git = GitUtility() self.question = Question() self.message = message self.modes = [] def register_mode(self, args): self.modes = [mode for mode, value in vars(args).items() if value] if not self.modes or self.modes == ["clipboard"]: self.modes.append("normal") if "message" in self.modes and "template" in self.modes: raise InvalidArgumentError() def run(self): if "clean" in self.modes: if self.question.ask_clean_use_history(): self.question.data_loader.clean_use_history() cprint("Use history cleaned.", "yellow") else: emoji, verb, object_, template = (None, None, None, None) # ask if "normal" in self.modes: emoji = self.question.ask_emoji() verb = self.question.ask_verb() object_ = self.question.ask_object() modifier = self.question.ask_modifier() self.message = self.make_message(verb, object_, modifier) commit_message = f"{emoji} : {self.message}" elif "message" in self.modes: emoji = self.question.ask_emoji() commit_message = f"{emoji} : {self.message}" elif "template" in self.modes: template = self.question.ask_template() commit_message = template # commit if self.question.ask_commit(commit_message): self.question.data_loader.store_history( emoji, verb, object_, template ) if "clipboard" in self.modes: pyperclip.copy(commit_message) cprint("Commit message copied to clipboard.", "yellow") else: self.git.commit(commit_message) @staticmethod def make_message(verb, object_, modifier): if verb is not None: verb = verb.capitalize() words = [ word for word in [verb, object_, modifier] if word is not None ] message = " ".join(words) return message def main(): from . import __version__ cprint( r""" ____ ____ ____ _ U /"___|uU /"___|U /"___||"| ___ \| | _ /\| | u \| | uU | | u |_"_| | |_| | | |/__ | |/__\| |/__ | | \____| \____| \____||_____| U/| |\u _)(|_ _// \\ _// \\ // \\.-,_|___|_,-. (__)__) (__)(__)(__)(__|_")("_)\_)-' '-(_/ """, "magenta", ) parser = argparse.ArgumentParser(description="Git Commenter") parser.add_argument( "-c", "--clipboard", action="store_true", help="copy to clipboard" ) parser.add_argument( "-m", "--message", help="commit with input message", type=str ) parser.add_argument( "-t", "--template", action="store_true", help="commit with selected template", ) parser.add_argument( "--clean", action="store_true", help="clean up use history" ) parser.add_argument( "--version", action="version", version=__version__, help="show version and exit", ) args = parser.parse_args() cli = CLI(message=args.message) cli.register_mode(args) cli.run()

28.549618

75

0.543048

d400aa1345560450bf3ec0cfc9543ba0a7b90a5a

17,117

bzl

Python

go/private/rules/test.bzl

alxn/rules_go

678db7ff7269d3bb13af342617b8deddec6ad7ae

[ "Apache-2.0" ]

null

go/private/rules/test.bzl

alxn/rules_go

678db7ff7269d3bb13af342617b8deddec6ad7ae

[ "Apache-2.0" ]

null

go/private/rules/test.bzl

alxn/rules_go

678db7ff7269d3bb13af342617b8deddec6ad7ae

[ "Apache-2.0" ]

null

# Copyright 2014 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. load( "//go/private:context.bzl", "go_context", ) load( "//go/private:common.bzl", "asm_exts", "cgo_exts", "go_exts", "pkg_dir", "split_srcs", ) load( "//go/private/rules:binary.bzl", "gc_linkopts", ) load( "//go/private:providers.bzl", "GoArchive", "GoLibrary", "GoSource", "INFERRED_PATH", "get_archive", ) load( "//go/private/rules:transition.bzl", "go_transition_rule", ) load( "//go/private:mode.bzl", "LINKMODE_NORMAL", ) load( "@bazel_skylib//lib:structs.bzl", "structs", ) def _testmain_library_to_source(go, attr, source, merge): source["deps"] = source["deps"] + [attr.library] def _go_test_impl(ctx): """go_test_impl implements go testing. It emits an action to run the test generator, and then compiles the test into a binary.""" go = go_context(ctx) # Compile the library to test with internal white box tests internal_library = go.new_library(go, testfilter = "exclude") internal_source = go.library_to_source(go, ctx.attr, internal_library, ctx.coverage_instrumented()) internal_archive = go.archive(go, internal_source) go_srcs = split_srcs(internal_source.srcs).go # Compile the library with the external black box tests external_library = go.new_library( go, name = internal_library.name + "_test", importpath = internal_library.importpath + "_test", testfilter = "only", ) external_source = go.library_to_source(go, struct( srcs = [struct(files = go_srcs)], deps = internal_archive.direct + [internal_archive], x_defs = ctx.attr.x_defs, ), external_library, ctx.coverage_instrumented()) external_source, internal_archive = _recompile_external_deps(go, external_source, internal_archive, [t.label for t in ctx.attr.embed]) external_archive = go.archive(go, external_source) external_srcs = split_srcs(external_source.srcs).go # now generate the main function if ctx.attr.rundir: if ctx.attr.rundir.startswith("/"): run_dir = ctx.attr.rundir else: run_dir = pkg_dir(ctx.label.workspace_root, ctx.attr.rundir) else: run_dir = pkg_dir(ctx.label.workspace_root, ctx.label.package) main_go = go.declare_file(go, path = "testmain.go") arguments = go.builder_args(go, "gentestmain") arguments.add("-output", main_go) if ctx.configuration.coverage_enabled: arguments.add("-coverage") arguments.add( # the l is the alias for the package under test, the l_test must be the # same with the test suffix "-import", "l=" + internal_source.library.importpath, ) arguments.add( "-import", "l_test=" + external_source.library.importpath, ) arguments.add("-pkgname", internal_source.library.importpath) arguments.add_all(go_srcs, before_each = "-src", format_each = "l=%s") ctx.actions.run( inputs = go_srcs, outputs = [main_go], mnemonic = "GoTestGenTest", executable = go.toolchain._builder, arguments = [arguments], ) test_gc_linkopts = gc_linkopts(ctx) if not go.mode.debug: # Disable symbol table and DWARF generation for test binaries. test_gc_linkopts.extend(["-s", "-w"]) # Link in the run_dir global for bzltestutil test_gc_linkopts.extend(["-X", "github.com/bazelbuild/rules_go/go/tools/bzltestutil.RunDir=" + run_dir]) # Now compile the test binary itself test_library = GoLibrary( name = go._ctx.label.name + "~testmain", label = go._ctx.label, importpath = "testmain", importmap = "testmain", importpath_aliases = (), pathtype = INFERRED_PATH, is_main = True, resolve = None, ) test_deps = external_archive.direct + [external_archive] + ctx.attr._testmain_additional_deps if ctx.configuration.coverage_enabled: test_deps.append(go.coverdata) test_source = go.library_to_source(go, struct( srcs = [struct(files = [main_go])], deps = test_deps, ), test_library, False) test_archive, executable, runfiles = go.binary( go, name = ctx.label.name, source = test_source, test_archives = [internal_archive.data], gc_linkopts = test_gc_linkopts, version_file = ctx.version_file, info_file = ctx.info_file, ) # Bazel only looks for coverage data if the test target has an # InstrumentedFilesProvider. If the provider is found and at least one # source file is present, Bazel will set the COVERAGE_OUTPUT_FILE # environment variable during tests and will save that file to the build # events + test outputs. return [ test_archive, DefaultInfo( files = depset([executable]), runfiles = runfiles, executable = executable, ), OutputGroupInfo( compilation_outputs = [internal_archive.data.file], ), coverage_common.instrumented_files_info( ctx, source_attributes = ["srcs"], dependency_attributes = ["deps", "embed"], extensions = ["go"], ), ] _go_test_kwargs = { "implementation": _go_test_impl, "attrs": { "data": attr.label_list(allow_files = True), "srcs": attr.label_list(allow_files = go_exts + asm_exts + cgo_exts), "deps": attr.label_list(providers = [GoLibrary]), "embed": attr.label_list(providers = [GoLibrary]), "importpath": attr.string(), "gc_goopts": attr.string_list(), "gc_linkopts": attr.string_list(), "rundir": attr.string(), "x_defs": attr.string_dict(), "linkmode": attr.string(default = LINKMODE_NORMAL), "cgo": attr.bool(), "cdeps": attr.label_list(), "cppopts": attr.string_list(), "copts": attr.string_list(), "cxxopts": attr.string_list(), "clinkopts": attr.string_list(), "_go_context_data": attr.label(default = "//:go_context_data"), "_testmain_additional_deps": attr.label_list( providers = [GoLibrary], default = ["@io_bazel_rules_go//go/tools/bzltestutil"], ), # Workaround for bazelbuild/bazel#6293. See comment in lcov_merger.sh. "_lcov_merger": attr.label( executable = True, default = "@io_bazel_rules_go//go/tools/builders:lcov_merger", cfg = "target", ), }, "executable": True, "test": True, "toolchains": ["@io_bazel_rules_go//go:toolchain"], } go_test = rule(**_go_test_kwargs) go_transition_test = go_transition_rule(**_go_test_kwargs) def _recompile_external_deps(go, external_source, internal_archive, library_labels): """Recompiles some archives in order to split internal and external tests. go_test, like 'go test', splits tests into two separate archives: an internal archive ('package foo') and an external archive ('package foo_test'). The library under test is embedded into the internal archive. The external archive may import it and may depend on symbols defined in the internal test files. To avoid conflicts, the library under test must not be linked into the test binary, since the internal test archive embeds the same sources. Libraries imported by the external test that transitively import the library under test must be recompiled too, or the linker will complain that export data they were compiled with doesn't match the export data they are linked with. This function identifies which archives may need to be recompiled, then declares new output files and actions to recompile them. This is an unfortunately an expensive process requiring O(V+E) time and space in the size of the test's dependency graph for each test. Args: go: go object returned by go_context. external_source: GoSource for the external archive. internal_archive: GoArchive for the internal archive. library_labels: labels for embedded libraries under test. Returns: external_soruce: recompiled GoSource for the external archive. If no recompilation is needed, the original GoSource is returned. internal_archive: recompiled GoArchive for the internal archive. If no recompilation is needed, the original GoSource is returned. """ # If no libraries are embedded in the internal archive, then nothing needs # to be recompiled. if not library_labels: return external_source, internal_archive # Build a map from labels to GoArchiveData. # If none of the librares embedded in the internal archive are in the # dependency graph, then nothing needs to be recompiled. arc_data_list = depset(transitive = [get_archive(dep).transitive for dep in external_source.deps]).to_list() label_to_arc_data = {a.label: a for a in arc_data_list} if all([l not in label_to_arc_data for l in library_labels]): return external_source, internal_archive # Build a depth-first post-order list of dependencies starting with the # external archive. Each archive appears after its dependencies and before # its dependents. # # This is tricky because Starlark doesn't support recursion or while loops. # We simulate a while loop by iterating over a list of 2N elements where # N is the number of archives. Each archive is pushed onto the stack # twice: once before its dependencies are pushed, and once after. # dep_list is the post-order list of dependencies we're building. dep_list = [] # stack is a stack of targets to process. We're done when it's empty. stack = [get_archive(dep).data.label for dep in external_source.deps] # deps_pushed tracks the status of each target. # DEPS_UNPROCESSED means the target is on the stack, but its dependencies # are not. # ON_DEP_LIST means the target and its dependencies have been added to # dep_list. # Non-negative integers are the number of dependencies on the stack that # still need to be processed. # A target is on the stack if its status is DEPS_UNPROCESSED or 0. DEPS_UNPROCESSED = -1 ON_DEP_LIST = -2 deps_pushed = {l: DEPS_UNPROCESSED for l in stack} # dependents maps labels to lists of known dependents. When a target is # processed, its dependents' deps_pushed count is deprecated. dependents = {l: [] for l in stack} # step is a list to iterate over to simulate a while loop. i tracks # iterations. step = [None] * (2 * len(arc_data_list)) i = 0 for _ in step: if len(stack) == 0: break i += 1 label = stack.pop() if deps_pushed[label] == 0: # All deps have been added to dep_list. Append this target to the # list. If a dependent is not waiting for anything else, push # it back onto the stack. dep_list.append(label) for p in dependents.get(label, []): deps_pushed[p] -= 1 if deps_pushed[p] == 0: stack.append(p) continue # deps_pushed[label] == None, indicating we don't know whether this # targets dependencies have been processed. Other targets processed # earlier may depend on them. deps_pushed[label] = 0 arc_data = label_to_arc_data[label] for c in arc_data._dep_labels: if c not in deps_pushed: # Dependency not seen yet; push it. stack.append(c) deps_pushed[c] = None deps_pushed[label] += 1 dependents[c] = [label] elif deps_pushed[c] != 0: # Dependency pushed, not processed; wait for it. deps_pushed[label] += 1 dependents[c].append(label) if deps_pushed[label] == 0: # No dependencies to wait for; push self. stack.append(label) if i != len(step): fail("assertion failed: iterated %d times instead of %d" % (i, len(step))) # Determine which dependencies need to be recompiled because they depend # on embedded libraries. need_recompile = {} for label in dep_list: arc_data = label_to_arc_data[label] need_recompile[label] = any([ dep in library_labels or need_recompile[dep] for dep in arc_data._dep_labels ]) # Recompile the internal archive without dependencies that need # recompilation. This breaks a cycle which occurs because the deps list # is shared between the internal and external archive. The internal archive # can't import anything that imports itself. internal_source = internal_archive.source internal_deps = [dep for dep in internal_source.deps if not need_recompile[get_archive(dep).data.label]] attrs = structs.to_dict(internal_source) attrs["deps"] = internal_deps internal_source = GoSource(**attrs) internal_archive = go.archive(go, internal_source, _recompile_suffix = ".recompileinternal") # Build a map from labels to possibly recompiled GoArchives. label_to_archive = {} i = 0 for label in dep_list: i += 1 recompile_suffix = ".recompile%d" % i # If this library is the internal archive, use the recompiled version. if label == internal_archive.data.label: label_to_archive[label] = internal_archive continue # If this is a library embedded into the internal test archive, # use the internal test archive instead. if label in library_labels: label_to_archive[label] = internal_archive continue # Create a stub GoLibrary and GoSource from the archive data. arc_data = label_to_arc_data[label] library = GoLibrary( name = arc_data.name, label = arc_data.label, importpath = arc_data.importpath, importmap = arc_data.importmap, importpath_aliases = arc_data.importpath_aliases, pathtype = arc_data.pathtype, resolve = None, testfilter = None, is_main = False, ) deps = [label_to_archive[d] for d in arc_data._dep_labels] source = GoSource( library = library, mode = go.mode, srcs = arc_data.srcs, orig_srcs = arc_data.orig_srcs, orig_src_map = dict(zip(arc_data.srcs, arc_data._orig_src_map)), cover = arc_data._cover, x_defs = dict(arc_data._x_defs), deps = deps, gc_goopts = arc_data._gc_goopts, runfiles = go._ctx.runfiles(files = arc_data.data_files), cgo = arc_data._cgo, cdeps = arc_data._cdeps, cppopts = arc_data._cppopts, copts = arc_data._copts, cxxopts = arc_data._cxxopts, clinkopts = arc_data._clinkopts, cgo_exports = arc_data._cgo_exports, ) # If this archive needs to be recompiled, use go.archive. # Otherwise, create a stub GoArchive, using the original file. if need_recompile[label]: recompile_suffix = ".recompile%d" % i archive = go.archive(go, source, _recompile_suffix = recompile_suffix) else: archive = GoArchive( source = source, data = arc_data, direct = deps, libs = depset(direct = [arc_data.file], transitive = [a.libs for a in deps]), transitive = depset(direct = [arc_data], transitive = [a.transitive for a in deps]), x_defs = source.x_defs, cgo_deps = depset(direct = arc_data._cgo_deps, transitive = [a.cgo_deps for a in deps]), cgo_exports = depset(direct = list(source.cgo_exports), transitive = [a.cgo_exports for a in deps]), runfiles = source.runfiles, mode = go.mode, ) label_to_archive[label] = archive # Finally, we need to replace external_source.deps with the recompiled # archives. attrs = structs.to_dict(external_source) attrs["deps"] = [label_to_archive[get_archive(dep).data.label] for dep in external_source.deps] return GoSource(**attrs), internal_archive

39.259174

138

0.647543

c41f64a60e0266e67ca8d294c0123334e3e86f44

561

py

Python

test_login.py

olga121/Selenium_Webdriver_2107

13e43aa5e4a2e04c6453ba634778fcbb5c793f40

[ "Apache-2.0" ]

null

test_login.py

olga121/Selenium_Webdriver_2107

13e43aa5e4a2e04c6453ba634778fcbb5c793f40

[ "Apache-2.0" ]

null

test_login.py

olga121/Selenium_Webdriver_2107

13e43aa5e4a2e04c6453ba634778fcbb5c793f40

[ "Apache-2.0" ]

null

import pytest from selenium import webdriver from selenium.webdriver.support.wait import WebDriverWait from selenium.webdriver.support import expected_conditions as EC @pytest.fixture def driver(request): wd = webdriver.Chrome() request.addfinalizer(wd.quit) return wd def test_example(driver): driver.get("https://www.nasa.gov/") driver.find_element_by_name('query').send_keys('Leonov') driver.find_element_by_xpath("//input[@value='Search']").click() WebDriverWait(driver,10).until(EC.title_is("Leonov - NASA Search Results"))

29.526316

79

0.755793

0d05be740e09157b590f036be1d2972da65a7a29

270

py

Python

ros2_draw_squares/launch/call_move_robot_in_square_service_server.launch.py

samialperen/ros2_move_robot

b80693589632a37b20a3cac26004a4a546f1717f

[ "MIT" ]

null

ros2_draw_squares/launch/call_move_robot_in_square_service_server.launch.py

samialperen/ros2_move_robot

b80693589632a37b20a3cac26004a4a546f1717f

[ "MIT" ]

3

2022-03-30T15:22:36.000Z

2022-03-30T18:23:13.000Z

ros2_draw_squares/launch/call_move_robot_in_square_service_server.launch.py

samialperen/ros2_move_robot

b80693589632a37b20a3cac26004a4a546f1717f

[ "MIT" ]

null

from launch import LaunchDescription import launch_ros.actions def generate_launch_description(): return LaunchDescription([ launch_ros.actions.Node( package='ros2_draw_squares', executable='move_robot_service_client', output='screen'), ])

27

98

0.744444

74890957a4a2779f58b2ffb16f5cbbdb24b071e2

979

py

Python

scripts/slave/recipe_modules/syzygy/gclient_config.py

bopopescu/build

4e95fd33456e552bfaf7d94f7d04b19273d1c534

[ "BSD-3-Clause" ]

null

scripts/slave/recipe_modules/syzygy/gclient_config.py

bopopescu/build

4e95fd33456e552bfaf7d94f7d04b19273d1c534

[ "BSD-3-Clause" ]

null

scripts/slave/recipe_modules/syzygy/gclient_config.py

bopopescu/build

4e95fd33456e552bfaf7d94f7d04b19273d1c534

[ "BSD-3-Clause" ]

1

2020-07-23T10:57:32.000Z

# Copyright 2014 The Chromium Authors. All rights reserved. # Use of this source code is governed by a BSD-style license that can be # found in the LICENSE file. import DEPS CONFIG_CTX = DEPS['gclient'].CONFIG_CTX @CONFIG_CTX() def syzygy(c): # A syzygy configuration for the gclient module. c.got_revision_mapping['src'] = 'got_revision' c.delete_unversioned_trees = True # Configure the checkout of the Syzygy repository. s = c.solutions.add() s.name = 'src' s.url = ('https://chromium.googlesource.com/external/' + 'github.com/google/syzygy.git/') s.deps_file = 'DEPS' s.managed = False # Configure the src-internal checkout. s = c.solutions.add() s.name = 'src-internal' s.url = ('https://chrome-internal.googlesource.com/chrome/syzygy/' + 'internal.DEPS.git') s.managed = False @CONFIG_CTX(includes=['syzygy']) def syzygy_official(dummy_c): pass @CONFIG_CTX(includes=['syzygy']) def kasko_official(dummy_c): pass

26.459459

72

0.702758

79c4e773afe00fe8c4655e5ccde6337f7e73d45f

407

py

Python

indoorair_front/indoorair_front/wsgi.py

ydang5/indoorair-front

24264fbbd0da0ab007d9dc058311dd4914953d59

[ "BSD-3-Clause" ]

null

indoorair_front/indoorair_front/wsgi.py

ydang5/indoorair-front

24264fbbd0da0ab007d9dc058311dd4914953d59

[ "BSD-3-Clause" ]

null

indoorair_front/indoorair_front/wsgi.py

ydang5/indoorair-front

24264fbbd0da0ab007d9dc058311dd4914953d59

[ "BSD-3-Clause" ]

null

""" WSGI config for indoorair_front project. It exposes the WSGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/2.2/howto/deployment/wsgi/ """ import os from django.core.wsgi import get_wsgi_application os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'indoorair_front.settings') application = get_wsgi_application()

23.941176

78

0.793612

26d3a0a46bdd79f94d4fec343460258a1465dd73

1,764

py

Python

rabbitai/charts/commands/importers/dispatcher.py

psbsgic/rabbitai

769e120ba605d56ac076f810a549c38dac410c8e

[ "Apache-2.0" ]

null

rabbitai/charts/commands/importers/dispatcher.py

psbsgic/rabbitai

769e120ba605d56ac076f810a549c38dac410c8e

[ "Apache-2.0" ]

null

rabbitai/charts/commands/importers/dispatcher.py

psbsgic/rabbitai

769e120ba605d56ac076f810a549c38dac410c8e

[ "Apache-2.0" ]

1

2021-07-09T16:29:50.000Z

import logging from typing import Any, Dict from marshmallow.exceptions import ValidationError from rabbitai.charts.commands.importers import v1 from rabbitai.commands.base import BaseCommand from rabbitai.commands.exceptions import CommandInvalidError from rabbitai.commands.importers.exceptions import IncorrectVersionError logger = logging.getLogger(__name__) command_versions = [ v1.ImportChartsCommand, ] class ImportChartsCommand(BaseCommand): """ Import charts. This command dispatches the import to different versions of the command until it finds one that matches. """ # pylint: disable=unused-argument def __init__(self, contents: Dict[str, str], *args: Any, **kwargs: Any): self.contents = contents self.args = args self.kwargs = kwargs def run(self) -> None: # iterate over all commands until we find a version that can # handle the contents for version in command_versions: command = version(self.contents, *self.args, **self.kwargs) try: command.run() return except IncorrectVersionError: logger.debug("File not handled by command, skipping") except (CommandInvalidError, ValidationError) as exc: # found right version, but file is invalid logger.info("Command failed validation") raise exc except Exception as exc: # validation succeeded but something went wrong logger.exception("Error running import command") raise exc raise CommandInvalidError("Could not find a valid command to import file") def validate(self) -> None: pass

32.072727

82

0.65873

ed34d314b62d11e9c93e8f9bab37da59ed218373

14,808

py

Python

qiime2/core/type/signature.py

ebolyen/qiime2

f8d53101035ab4618587fe608f21f6e467acc74a

[ "BSD-3-Clause" ]

null

qiime2/core/type/signature.py

ebolyen/qiime2

f8d53101035ab4618587fe608f21f6e467acc74a

[ "BSD-3-Clause" ]

null

qiime2/core/type/signature.py

ebolyen/qiime2

f8d53101035ab4618587fe608f21f6e467acc74a

[ "BSD-3-Clause" ]

null

# ---------------------------------------------------------------------------- # Copyright (c) 2016-2017, QIIME 2 development team. # # Distributed under the terms of the Modified BSD License. # # The full license is in the file LICENSE, distributed with this software. # ---------------------------------------------------------------------------- import collections import inspect import copy import qiime2.sdk from .grammar import TypeExpression from .primitive import is_primitive_type from .semantic import is_semantic_type from .visualization import Visualization from ..util import ImmutableBase class _NoValue: def __repr__(self): return "NOVALUE" class ParameterSpec(ImmutableBase): NOVALUE = _NoValue() def __init__(self, qiime_type=NOVALUE, view_type=NOVALUE, default=NOVALUE, description=NOVALUE): self.qiime_type = qiime_type self.view_type = view_type self.default = default self.description = description self._freeze_() def has_qiime_type(self): return self.qiime_type is not self.NOVALUE def has_view_type(self): return self.view_type is not self.NOVALUE def has_default(self): return self.default is not self.NOVALUE def has_description(self): return self.description is not self.NOVALUE def __repr__(self): return ("ParameterSpec(qiime_type=%r, view_type=%r, default=%r, " "description=%r)" % (self.qiime_type, self.view_type, self.default, self.description)) def __eq__(self, other): return (self.qiime_type == other.qiime_type and self.view_type == other.view_type and self.default == other.default and self.description == other.description) def __ne__(self, other): return not (self == other) # Note: Pipeline doesn't exist yet but it is expected to accept zero or more # input semantic types, zero or more parameters, and produce one or more output # semantic types or Visualization types. class PipelineSignature: builtin_args = ('ctx',) def __init__(self, callable, inputs, parameters, outputs, input_descriptions=None, parameter_descriptions=None, output_descriptions=None): """ Parameters ---------- callable : callable Callable with view type annotations on parameters and return. inputs : dict Parameter name to semantic type. parameters : dict Parameter name to primitive type. outputs : list of tuple Each tuple contains the name of the output (str) and its QIIME type. input_descriptions : dict, optional Input name to description string. parameter_descriptions : dict, optional Parameter name to description string. output_descriptions : dict, optional Output name to description string. """ inputs, parameters, outputs, signature_order = \ self._parse_signature(callable, inputs, parameters, outputs, input_descriptions, parameter_descriptions, output_descriptions) self._assert_valid_inputs(inputs) self._assert_valid_parameters(parameters) self._assert_valid_outputs(outputs) self.inputs = inputs self.parameters = parameters self.outputs = outputs self.signature_order = signature_order def _parse_signature(self, callable, inputs, parameters, outputs, input_descriptions=None, parameter_descriptions=None, output_descriptions=None): # Initialize dictionaries if non-existant. if input_descriptions is None: input_descriptions = {} if parameter_descriptions is None: parameter_descriptions = {} if output_descriptions is None: output_descriptions = {} # Copy so we can "exhaust" the collections and check for missing params inputs = copy.copy(inputs) parameters = copy.copy(parameters) input_descriptions = copy.copy(input_descriptions) parameter_descriptions = copy.copy(parameter_descriptions) output_descriptions = copy.copy(output_descriptions) builtin_args = list(self.builtin_args) annotated_inputs = collections.OrderedDict() annotated_parameters = collections.OrderedDict() annotated_outputs = collections.OrderedDict() signature_order = collections.OrderedDict() for name, parameter in inspect.signature(callable).parameters.items(): if (parameter.kind == parameter.VAR_POSITIONAL or parameter.kind == parameter.VAR_KEYWORD): raise TypeError("Variadic definitions are unsupported: %r" % name) if builtin_args: if builtin_args[0] != name: raise TypeError("Missing builtin argument %r, got %r" % (builtin_args[0], name)) builtin_args = builtin_args[1:] continue view_type = ParameterSpec.NOVALUE if parameter.annotation is not parameter.empty: view_type = parameter.annotation default = ParameterSpec.NOVALUE if parameter.default is not parameter.empty: default = parameter.default if name in inputs: description = input_descriptions.pop(name, ParameterSpec.NOVALUE) param_spec = ParameterSpec( qiime_type=inputs.pop(name), view_type=view_type, default=default, description=description) annotated_inputs[name] = param_spec signature_order[name] = param_spec elif name in parameters: description = parameter_descriptions.pop(name, ParameterSpec.NOVALUE) param_spec = ParameterSpec( qiime_type=parameters.pop(name), view_type=view_type, default=default, description=description) annotated_parameters[name] = param_spec signature_order[name] = param_spec elif name not in self.builtin_args: raise TypeError("Parameter in callable without QIIME type:" " %r" % name) # we should have popped both of these empty by this point if inputs or parameters: raise TypeError("Callable does not have parameter(s): %r" % (list(inputs) + list(parameters))) if 'return' in callable.__annotations__: output_views = qiime2.core.util.tuplize( callable.__annotations__['return']) if len(output_views) != len(outputs): raise TypeError("Number of registered outputs (%r) does not" " match annotation (%r)" % (len(outputs), len(output_views))) for (name, qiime_type), view_type in zip(outputs, output_views): description = output_descriptions.pop(name, ParameterSpec.NOVALUE) annotated_outputs[name] = ParameterSpec( qiime_type=qiime_type, view_type=view_type, description=description) else: for name, qiime_type in outputs: description = output_descriptions.pop(name, ParameterSpec.NOVALUE) annotated_outputs[name] = ParameterSpec( qiime_type=qiime_type, description=description) # we should have popped the descriptions empty by this point if input_descriptions or parameter_descriptions or output_descriptions: raise TypeError( "Callable does not have parameter(s)/output(s) found in " "descriptions: %r" % [*input_descriptions, *parameter_descriptions, *output_descriptions]) return (annotated_inputs, annotated_parameters, annotated_outputs, signature_order) def _assert_valid_inputs(self, inputs): for input_name, spec in inputs.items(): if not is_semantic_type(spec.qiime_type): raise TypeError( "Input %r must be a semantic QIIME type, not %r" % (input_name, spec.qiime_type)) if not isinstance(spec.qiime_type, TypeExpression): raise TypeError( "Input %r must be a complete semantic type expression, " "not %r" % (input_name, spec.qiime_type)) if spec.has_default() and spec.default is not None: raise ValueError( "Input %r has a default value of %r. Only a default " "value of `None` is supported for inputs." % (input_name, spec.default)) def _assert_valid_parameters(self, parameters): for param_name, spec in parameters.items(): if not is_primitive_type(spec.qiime_type): raise TypeError( "Parameter %r must be a primitive QIIME type, not %r" % (param_name, spec.qiime_type)) if not isinstance(spec.qiime_type, TypeExpression): raise TypeError( "Parameter %r must be a complete primitive type " "expression, not %r" % (param_name, spec.qiime_type)) if (spec.has_default() and spec.default is not None and spec.default not in spec.qiime_type): raise TypeError("Default value for parameter %r is not of " "semantic QIIME type %r or `None`." % (param_name, spec.qiime_type)) def _assert_valid_outputs(self, outputs): if len(outputs) == 0: raise TypeError("%s requires at least one output" % self.__class__.__name__) for output_name, spec in outputs.items(): if not (is_semantic_type(spec.qiime_type) or spec.qiime_type == Visualization): raise TypeError( "Output %r must be a semantic QIIME type or " "Visualization, not %r" % (output_name, spec.qiime_type)) if not isinstance(spec.qiime_type, TypeExpression): raise TypeError( "Output %r must be a complete type expression, not %r" % (output_name, spec.qiime_type)) def decode_parameters(self, **kwargs): params = {} for key, spec in self.parameters.items(): if (spec.has_default() and spec.default is None and kwargs[key] is None): params[key] = None else: params[key] = spec.qiime_type.decode(kwargs[key]) return params def check_types(self, **kwargs): for name, spec in self.signature_order.items(): if kwargs[name] not in spec.qiime_type: # A type mismatch is unacceptable unless the value is None # and this parameter's default value is None. if not (spec.has_default() and spec.default is None and kwargs[name] is None): raise TypeError("Argument to parameter %r is not a " "subtype of %r." % (name, spec.qiime_type)) def solve_output(self, **input_types): # TODO implement solving here. The check for concrete output types may # be unnecessary here if the signature's constructor can detect # unsolvable signatures and ensure that solving will always produce # concrete output types. solved_outputs = self.outputs for output_name, spec in solved_outputs.items(): if not spec.qiime_type.is_concrete(): raise TypeError( "Solved output %r must be a concrete type, not %r" % (output_name, spec.qiime_type)) return solved_outputs def __repr__(self): lines = [] for group in 'inputs', 'parameters', 'outputs': lookup = getattr(self, group) lines.append('%s:' % group) for name, spec in lookup.items(): lines.append(' %s: %r' % (name, spec)) return '\n'.join(lines) def __eq__(self, other): return (type(self) is type(other) and self.inputs == other.inputs and self.parameters == other.parameters and self.outputs == other.outputs and self.signature_order == other.signature_order) def __ne__(self, other): return not (self == other) class MethodSignature(PipelineSignature): builtin_args = () def _assert_valid_outputs(self, outputs): super()._assert_valid_outputs(outputs) # Assert all output types are semantic types. The parent class is less # strict in its output type requirements. for output_name, spec in outputs.items(): if not is_semantic_type(spec.qiime_type): raise TypeError( "Output %r must be a semantic QIIME type, not %r" % (output_name, spec.qiime_type)) class VisualizerSignature(PipelineSignature): builtin_args = ('output_dir',) def __init__(self, callable, inputs, parameters, input_descriptions=None, parameter_descriptions=None): outputs = [('visualization', Visualization)] output_descriptions = None super().__init__(callable, inputs, parameters, outputs, input_descriptions, parameter_descriptions, output_descriptions) def _assert_valid_outputs(self, outputs): super()._assert_valid_outputs(outputs) output = outputs['visualization'] if output.has_view_type() and output.view_type is not None: raise TypeError( "Visualizer callable cannot return anything. Its return " "annotation must be `None`, not %r. Write output to " "`output_dir`." % output.view_type)

41.712676

79

0.577256

14acd9a44b7a5cc95962cb159185a8d4148f3999

13,519

py

Python

tests/terraform/graph/graph_builder/test_graph_builder.py

jamesholland-uk/checkov

d73fd4bd7096d48ab3434a92a177bcc55605460a

[ "Apache-2.0" ]

null

tests/terraform/graph/graph_builder/test_graph_builder.py

jamesholland-uk/checkov

d73fd4bd7096d48ab3434a92a177bcc55605460a

[ "Apache-2.0" ]

null

tests/terraform/graph/graph_builder/test_graph_builder.py

jamesholland-uk/checkov

d73fd4bd7096d48ab3434a92a177bcc55605460a

[ "Apache-2.0" ]

null

import os import shutil from unittest import TestCase from checkov.common.graph.db_connectors.networkx.networkx_db_connector import NetworkxConnector from checkov.terraform.graph_builder.graph_components.block_types import BlockType from checkov.terraform.graph_builder.graph_to_tf_definitions import convert_graph_vertices_to_tf_definitions from checkov.terraform.graph_manager import TerraformGraphManager from checkov.terraform.parser import external_modules_download_path TEST_DIRNAME = os.path.dirname(os.path.realpath(__file__)) class TestGraphBuilder(TestCase): def test_build_graph(self): resources_dir = os.path.join(TEST_DIRNAME, '../resources/general_example') graph_manager = TerraformGraphManager(db_connector=NetworkxConnector()) graph, tf_definitions = graph_manager.build_graph_from_source_directory(resources_dir) expected_num_of_var_nodes = 3 expected_num_of_locals_nodes = 1 expected_num_of_resources_nodes = 1 expected_num_of_provider_nodes = 1 vertices_by_block_type = graph.vertices_by_block_type self.assertEqual(expected_num_of_var_nodes, len(vertices_by_block_type[BlockType.VARIABLE])) self.assertEqual(expected_num_of_locals_nodes, len(vertices_by_block_type[BlockType.LOCALS])) self.assertEqual(expected_num_of_resources_nodes, len(vertices_by_block_type[BlockType.RESOURCE])) self.assertEqual(expected_num_of_provider_nodes, len(vertices_by_block_type[BlockType.PROVIDER])) provider_node = graph.vertices[vertices_by_block_type[BlockType.PROVIDER][0]] resource_node = graph.vertices[vertices_by_block_type[BlockType.RESOURCE][0]] local_node = graph.vertices[vertices_by_block_type[BlockType.LOCALS][0]] var_bucket_name_node = None var_region_node = None var_aws_profile_node = None for index in vertices_by_block_type[BlockType.VARIABLE]: var_node = graph.vertices[index] if var_node.name == 'aws_profile': var_aws_profile_node = var_node if var_node.name == 'bucket_name': var_bucket_name_node = var_node if var_node.name == 'region': var_region_node = var_node self.check_edge(graph, resource_node, local_node, 'bucket') self.check_edge(graph, resource_node, provider_node, 'provider') self.check_edge(graph, resource_node, var_region_node, 'region') self.check_edge(graph, provider_node, var_aws_profile_node, 'profile') self.check_edge(graph, local_node, var_bucket_name_node, 'bucket_name') def check_edge(self, graph, node_from, node_to, expected_label): hashed_from = node_from.get_hash() hashed_to = node_to.get_hash() matching_edges = [] for edge in graph.edges: if graph.vertices[edge.origin].get_hash() == hashed_from and graph.vertices[edge.dest].get_hash() == hashed_to: matching_edges.append(edge) self.assertGreater(len(matching_edges), 0, f'expected to find edge from [{node_from.block_type} {node_from.name}] to [{node_to.block_type} {node_to.name}] with label [{expected_label}]') if not any(e.label == expected_label for e in matching_edges): self.fail( f'expected to find edge from [{node_from.block_type} {node_from.name}] to [{node_to.block_type} {node_to.name}] with label [{expected_label}], found edges: {[str(e) for e in matching_edges]}') @staticmethod def get_vertex_by_name_and_type(local_graph, block_type, name, multiple=False): vertices = [local_graph.vertices[i] for i in local_graph.vertices_block_name_map[block_type][name]] if multiple: return vertices return vertices[0] def test_update_vertices_configs_deep_nesting(self): resources_dir = os.path.join(TEST_DIRNAME, '../resources/variable_rendering/render_deep_nesting') graph_manager = TerraformGraphManager(NetworkxConnector()) local_graph, _ = graph_manager.build_graph_from_source_directory(resources_dir, render_variables=True) expected_config = {'aws_s3_bucket': {'default': {'server_side_encryption_configuration': [ {'rule': [{'apply_server_side_encryption_by_default': [ {'sse_algorithm': ['AES256'], 'kms_master_key_id': ['']}]}]}]}}} actual_config = local_graph.vertices[local_graph.vertices_by_block_type.get(BlockType.RESOURCE)[0]].config self.assertDictEqual(expected_config, actual_config) print('') def test_build_graph_with_linked_modules(self): # see the image to view the expected graph in tests/resources/modules/linked_modules/expected_graph.png resources_dir = os.path.realpath(os.path.join(TEST_DIRNAME, '../resources/modules/linked_modules')) graph_manager = TerraformGraphManager(NetworkxConnector()) local_graph, tf_definitions = graph_manager.build_graph_from_source_directory(resources_dir, render_variables=False) vertices_by_block_type = local_graph.vertices_by_block_type expected_vertices_num_by_type = { BlockType.VARIABLE: 5, BlockType.RESOURCE: 5, BlockType.OUTPUT: 3, BlockType.MODULE: 2, BlockType.DATA: 1, } for block_type, count in expected_vertices_num_by_type.items(): self.assertEqual(count, len(vertices_by_block_type[block_type])) output_this_lambda_func_arn = self.get_vertex_by_name_and_type(local_graph, BlockType.OUTPUT, 'this_lambda_function_arn') output_this_lambda_func_name = self.get_vertex_by_name_and_type(local_graph, BlockType.OUTPUT, 'this_lambda_function_name') output_this_s3_bucket_id = self.get_vertex_by_name_and_type(local_graph, BlockType.OUTPUT, 'this_s3_bucket_id') resource_aws_lambda_function = self.get_vertex_by_name_and_type(local_graph, BlockType.RESOURCE, 'aws_lambda_function.this') resource_aws_s3_bucket_policy = self.get_vertex_by_name_and_type(local_graph, BlockType.RESOURCE, 'aws_s3_bucket_policy.this') resource_aws_s3_bucket = self.get_vertex_by_name_and_type(local_graph, BlockType.RESOURCE, 'aws_s3_bucket.this') self.check_edge(local_graph, node_from=output_this_lambda_func_arn, node_to=resource_aws_lambda_function, expected_label='value') self.check_edge(local_graph, node_from=output_this_lambda_func_name, node_to=resource_aws_lambda_function, expected_label='value') self.check_edge(local_graph, node_from=output_this_s3_bucket_id, node_to=resource_aws_s3_bucket_policy, expected_label='value') self.check_edge(local_graph, node_from=output_this_s3_bucket_id, node_to=resource_aws_s3_bucket, expected_label='value') def test_build_graph_with_linked_registry_modules(self): resources_dir = os.path.realpath( os.path.join(TEST_DIRNAME, '../resources/modules/registry_security_group_inner_module')) graph_manager = TerraformGraphManager(NetworkxConnector()) local_graph, tf_definitions = graph_manager.build_graph_from_source_directory(resources_dir, render_variables=True, download_external_modules=True) outputs_vpcs = self.get_vertex_by_name_and_type(local_graph, BlockType.OUTPUT, 'security_group_vpc_id', multiple=True) resource_flow_log = self.get_vertex_by_name_and_type(local_graph, BlockType.RESOURCE, 'aws_flow_log.related_flow_log') resource_security_group_this = self.get_vertex_by_name_and_type(local_graph, BlockType.RESOURCE, 'aws_security_group.this') resource_security_group_this_name_prefix = self.get_vertex_by_name_and_type(local_graph, BlockType.RESOURCE, 'aws_security_group.this_name_prefix') output_this_security_group_vpc_id_inner = [o for o in outputs_vpcs if 'http-80' in o.path][0] output_this_security_group_vpc_id_outer = [o for o in outputs_vpcs if 'http-80' not in o.path][0] self.check_edge(local_graph, node_from=resource_flow_log, node_to=output_this_security_group_vpc_id_inner, expected_label='vpc_id') self.check_edge(local_graph, node_from=output_this_security_group_vpc_id_outer, node_to=resource_security_group_this, expected_label='value') self.check_edge(local_graph, node_from=output_this_security_group_vpc_id_outer, node_to=resource_security_group_this_name_prefix, expected_label='value') # cleanup if os.path.exists(os.path.join(resources_dir, external_modules_download_path)): shutil.rmtree(os.path.join(resources_dir, external_modules_download_path)) def test_build_graph_with_deep_nested_edges(self): resources_dir = os.path.realpath(os.path.join(TEST_DIRNAME, '../resources/k8_service')) graph_manager = TerraformGraphManager(NetworkxConnector()) local_graph, tf = graph_manager.build_graph_from_source_directory(resources_dir, render_variables=True) resource_kubernetes_deployment = self.get_vertex_by_name_and_type(local_graph, BlockType.RESOURCE, 'kubernetes_deployment.bazel_remote_cache') locals_name = self.get_vertex_by_name_and_type(local_graph, BlockType.LOCALS, 'name') locals_labels = self.get_vertex_by_name_and_type(local_graph, BlockType.LOCALS, 'labels') self.check_edge(local_graph, node_from=locals_labels, node_to=locals_name, expected_label="labels.app.kubernetes.io/name") self.check_edge(local_graph, node_from=resource_kubernetes_deployment, node_to=locals_name, expected_label="metadata.name") self.check_edge(local_graph, node_from=resource_kubernetes_deployment, node_to=locals_name, expected_label="spec.template.metadata.name") self.check_edge(local_graph, node_from=resource_kubernetes_deployment, node_to=locals_name, expected_label="spec.template.spec.container.name") self.check_edge(local_graph, node_from=resource_kubernetes_deployment, node_to=locals_name, expected_label="spec.template.spec.volume.1.config_map.name") def test_blocks_from_local_graph_module(self): resources_dir = os.path.realpath(os.path.join(TEST_DIRNAME, '../resources/modules/stacks')) graph_manager = TerraformGraphManager(NetworkxConnector()) local_graph, tf = graph_manager.build_graph_from_source_directory(resources_dir, render_variables=True) tf, _ = convert_graph_vertices_to_tf_definitions(local_graph.vertices, resources_dir) found_results = 0 for key, value in tf.items(): if key.startswith(os.path.join(os.path.dirname(resources_dir), 's3_inner_modules', 'inner', 'main.tf')): conf = value['resource'][0]['aws_s3_bucket']['inner_s3'] if 'stage/main' in key or 'prod/main' in key: self.assertTrue(conf['versioning'][0]['enabled'][0]) found_results += 1 elif 'test/main' in key: self.assertFalse(conf['versioning'][0]['enabled'][0]) found_results += 1 self.assertEqual(found_results, 3) def test_build_graph_with_dynamic_blocks(self): resources_dir = os.path.realpath(os.path.join(TEST_DIRNAME, '../resources/dynamic_lambda_function')) graph_manager = TerraformGraphManager(NetworkxConnector()) local_graph, tf = graph_manager.build_graph_from_source_directory(resources_dir, render_variables=True) lambda_attributes = local_graph.vertices[0].attributes self.assertIn("dead_letter_config", lambda_attributes.keys()) def test_get_attribute_dict_with_list_value(self): # given resources_dir = os.path.join(TEST_DIRNAME, "../resources/s3_bucket_grant") graph_manager = TerraformGraphManager(NetworkxConnector()) local_graph, _ = graph_manager.build_graph_from_source_directory(resources_dir, render_variables=True) # when attributes = local_graph.vertices[ local_graph.vertices_by_block_type.get(BlockType.RESOURCE)[0] ].get_attribute_dict() # then expected_grant_attribute = [ {"permissions": ["READ_ACP"], "type": "Group", "uri": "http://acs.amazonaws.com/groups/global/AllUsers"}, {"id": "1234567890", "permissions": ["FULL_CONTROL"], "type": "CanonicalUser"}, ] self.assertCountEqual(expected_grant_attribute, attributes["grant"])

61.171946

208

0.684074

bb68d08f2f5d89eca636785c1c25060b28d7ac52

2,222

py

Python

filter.py

pitpig/rozowoo

79ec617978252deed688aaf33c67fa1b0cebe56a

[ "MIT" ]

1

2019-01-09T19:15:30.000Z

filter.py

pitpig/rozowoo

79ec617978252deed688aaf33c67fa1b0cebe56a

[ "MIT" ]

null

filter.py

pitpig/rozowoo

79ec617978252deed688aaf33c67fa1b0cebe56a

[ "MIT" ]

null

# -*- coding: utf-8 -*- import logging from django import template from model import * import django.template.defaultfilters as defaultfilters import urllib register = template.Library() from datetime import * @register.filter def datetz(date,format): #datetime with timedelta t=timedelta(seconds=3600*g_blog.timedelta) return defaultfilters.date(date+t,format) @register.filter def TimestampISO8601(t): """Seconds since epoch (1970-01-01) --> ISO 8601 time string.""" return time.strftime('%Y-%m-%dT%H:%M:%SZ', time.gmtime(t)) @register.filter def urlencode(value): return urllib.quote(value.encode('utf8')) @register.filter def check_current(v1,v2): if v1==v2: return "current" else: return "" @register.filter def excerpt_more(entry,value='..more'): return entry.get_content_excerpt(value.decode('utf8')) @register.filter def dict_value(v1,v2): return v1[v2] from app.html_filter import html_filter plog_filter = html_filter() plog_filter.allowed = { 'a': ('href', 'target', 'name'), 'b': (), 'blockquote': (), 'pre': (), 'em': (), 'i': (), 'img': ('src', 'width', 'height', 'alt', 'title'), 'strong': (), 'u': (), 'font': ('color', 'size'), 'p': (), 'h1': (), 'h2': (), 'h3': (), 'h4': (), 'h5': (), 'h6': (), 'table': (), 'tr': (), 'th': (), 'td': (), 'ul': (), 'ol': (), 'li': (), 'br': (), 'hr': (), } plog_filter.no_close += ('br',) plog_filter.allowed_entities += ('nbsp','ldquo', 'rdquo', 'hellip',) plog_filter.make_clickable_urls = False # enable this will get a bug about a and img @register.filter def do_filter(data): return plog_filter.go(data) ''' tag like {%mf header%}xxx xxx{%endmf%} ''' @register.tag("mf") def do_mf(parser, token): nodelist = parser.parse(('endmf',)) parser.delete_first_token() return MfNode(nodelist,token) class MfNode(template.Node): def __init__(self, nodelist,token): self.nodelist = nodelist self.token=token def render(self, context): tokens= self.token.split_contents() if len(tokens)<2: raise TemplateSyntaxError, "'mf' tag takes one argument: the filter name is needed" fname=tokens[1] output = self.nodelist.render(context) return g_blog.tigger_filter(fname,output)

22.22

86

0.654815

0daf571315d900b1297ea876d815cc733e1908fe

10,367

py

Python

genmakefiles.py

mtk-watch/android_external_v8

29eb30806a59123b1f9faf9083a12d26fa418fad

[ "BSD-3-Clause" ]

null

genmakefiles.py

mtk-watch/android_external_v8

29eb30806a59123b1f9faf9083a12d26fa418fad

[ "BSD-3-Clause" ]

null

genmakefiles.py

mtk-watch/android_external_v8

29eb30806a59123b1f9faf9083a12d26fa418fad

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python import os import subprocess import sys from antlr4 import * from gnparser.gnLexer import gnLexer from gnparser.gnParser import gnParser from gnparser.gnListener import gnListener from string import Template DBG = False # Reformat the specified Android.bp file def _bpFmt(filename): ## NOTE: bpfmt does not set error code even when the bp file is illegal. print subprocess.check_output(["bpfmt", "-w", filename]) def _bpList(entries): return '[' + ",".join(['"' + x + '"' for x in entries]) + ']' # Write an Android.bp in the simpler format used by v8_libplatform and # v8_libsampler def _writeBP(filename, module_name, sources): if not sources: raise ValueError('No sources for ' + filename) with open(filename, 'w') as out: out.write(Template(''' // GENERATED, do not edit // for changes, see genmakefiles.py cc_library_static { name: "$module_name", defaults: ["v8_defaults"], srcs: $srcs, local_include_dirs: ["src", "include"], } ''').substitute({'module_name': module_name, 'srcs' : _bpList(sorted(sources))})) _bpFmt(filename) def _writeV8SrcBP(getSourcesFunc): sources = getSourcesFunc(None) if not sources: raise ValueError('Must specify v8_base target properties') sources.add('src/setup-isolate-full.cc') # sources.add('src/builtins/setup-builtins-internal.cc') # sources.add('src/interpreter/setup-interpreter-internal.cc') arm_src = list(getSourcesFunc('arm') - sources) arm64_src = list(getSourcesFunc('arm64') - sources) x86_src = list(getSourcesFunc('x86') - sources) x86_64_src = list(getSourcesFunc('x64') - sources) mips_src = list(getSourcesFunc('mips') - sources) mips64_src = list(getSourcesFunc('mips64') - sources) filename = 'Android.v8.bp' with open(filename, 'w') as out: out.write(Template(''' // GENERATED, do not edit // for changes, see genmakefiles.py cc_library_static { name: "libv8src", defaults: ["v8_defaults"], srcs: $srcs, arch: { arm: { srcs: $arm_src, }, arm64: { srcs: $arm64_src, }, mips: { srcs: $mips_src, }, mips64: { srcs: $mips64_src, }, x86: { srcs: $x86_src, }, x86_64: { srcs: $x86_64_src, }, }, target: { android: { cflags: ["-DANDROID_LINK_SHARED_ICU4C"], }, }, local_include_dirs: ["src"], header_libs: ["libicuuc_headers", "libicui18n_headers"], generated_headers: ["v8_torque_file"], generated_sources: ["v8_torque_file_cc"], } ''').substitute({'srcs': _bpList(sorted(sources)), 'arm_src': _bpList(sorted(arm_src)), 'arm64_src': _bpList(sorted(arm64_src)), 'mips_src': _bpList(sorted(mips_src)), 'mips64_src': _bpList(sorted(mips64_src)), 'x86_src': _bpList(sorted(x86_src)), 'x86_64_src': _bpList(sorted(x86_64_src)), })) _bpFmt(filename) def _writeGeneratedFilesBP(sources): if not sources: raise ValueError('Must specify j2sc target properties') filename = 'Android.v8gen.bp' with open(filename, 'w') as out: out.write(Template(''' // GENERATED, do not edit // for changes, see genmakefiles.py filegroup { name: "v8_js_lib_files", srcs: $srcs, } ''').substitute({'srcs' : _bpList(sources)})) ## Not sorted intentionally _bpFmt(filename) def _writeLibBaseBP(sources): if not sources: raise ValueError('Must specify v8_libbase target properties') filename = 'Android.base.bp' with open(filename, 'w') as out: out.write(Template(''' // GENERATED, do not edit // for changes, see genmakefiles.py cc_library_static { name: "libv8base", defaults: ["v8_defaults"], host_supported: true, srcs: $srcs, local_include_dirs: ["src"], target: { android: { srcs: ["src/base/debug/stack_trace_android.cc"], }, linux: { srcs: ["src/base/platform/platform-linux.cc"], }, host: { srcs: ["src/base/debug/stack_trace_posix.cc"], cflags: ["-UANDROID"], }, darwin: { srcs: ["src/base/platform/platform-macos.cc"], }, }, } ''').substitute({'srcs' : _bpList(sorted(sources))})) _bpFmt(filename) def _expr_to_str(expr): val = expr.unaryexpr().primaryexpr() if val.String(): return val.String().getText()[1:-1] ## Strip quotation marks around string elif val.Identifier(): return val.Identifier().getText() else: if DBG: print 'WARN: unhandled primary expression' return None class V8GnListener(gnListener): def __init__(self, target, arch, only_cc_files): super(gnListener, self).__init__() self._match = False self._depth = 0 self._target = target self._arch = arch self._sources = [] self._fixed_conditions = { 'use_jumbo_build' : True, 'use_jumbo_build==true' : True, 'is_win' : False, 'is_linux' : False, 'v8_postmortem_support' : False, 'v8_enable_i18n_support': True, '!v8_enable_i18n_support': False, 'current_os!="aix"' : True, 'is_posix||is_fuchsia' : True, 'v8_current_cpu=="arm"' : arch == 'arm', 'v8_current_cpu=="arm64"' : arch == 'arm64', 'v8_current_cpu=="x86"' : arch == 'x86', 'v8_current_cpu=="x64"' : arch == 'x64', 'v8_current_cpu=="mips"||v8_current_cpu=="mipsel"' : arch == 'mips', 'v8_current_cpu=="mips64"||v8_current_cpu=="mips64el"' : arch == 'mips64', 'v8_current_cpu=="ppc"||v8_current_cpu=="ppc64"' : False, 'v8_current_cpu=="s390"||v8_current_cpu=="s390x"' : False, } self._only_cc_files = only_cc_files def _match_call_target(self, ctx): call_type = ctx.Identifier().getText() if not call_type in ['v8_source_set', 'v8_component', 'action']: return False call_name = _expr_to_str(ctx.exprlist().expr(0)) return call_name == self._target def enterCall(self, ctx): if self._depth == 1 and self._match_call_target(ctx): self._match = True self._conditions = [] ## [(value, condition), ...] if DBG: print 'Found call', str(ctx.Identifier()), ctx.exprlist().getText() def exitCall(self, ctx): if self._match and self._match_call_target(ctx): self._match = False self._conditions = [] if DBG: print 'Left call' def _extract_sources(self, ctx): op = ctx.AssignOp().getText() if not ctx.expr().unaryexpr().primaryexpr().exprlist(): ## sources += check_header_includes_sources return srcs = map(_expr_to_str, ctx.expr().unaryexpr().primaryexpr().exprlist().expr()) if self._only_cc_files: srcs = [x for x in srcs if x.endswith('.cc')] if DBG: print '_extract_sources: ', len(srcs), "condition:", self._conditions if op == '=': if self._sources: print "WARN: override sources" self._sources = srcs elif op == '+=': self._sources.extend(srcs) def _compute_condition(self, ctx): condition = ctx.expr().getText() if DBG: print '_extract_condition', condition if condition in self._fixed_conditions: result = self._fixed_conditions[condition] else: print 'WARN: unknown condition, assume False', condition self._fixed_conditions[condition] = False result = False if DBG: print 'Add condition:', condition self._conditions.append((result, condition)) def enterCondition(self, ctx): if not self._match: return self._compute_condition(ctx) def enterElsec(self, ctx): if not self._match: return c = self._conditions[-1] self._conditions[-1] = (not c[0], c[1]) if DBG: print 'Negate condition:', self._conditions[-1] def exitCondition(self, ctx): if not self._match: return if DBG: print 'Remove conditions: ', self._conditions[-1] del self._conditions[-1] def _flatten_conditions(self): if DBG: print '_flatten_conditions: ', self._conditions for condition, _ in self._conditions: if not condition: return False return True def enterAssignment(self, ctx): if not self._match: return if ctx.lvalue().Identifier().getText() == "sources": if self._flatten_conditions(): self._extract_sources(ctx) def enterStatement(self, ctx): self._depth += 1 def exitStatement(self, ctx): self._depth -= 1 def get_sources(self): seen = set() result = [] ## Deduplicate list while maintaining ordering. needed for js2c files for s in self._sources: if not s in seen: result.append(s) seen.add(s) return result def parseSources(tree, target, arch = None, only_cc_files = True): listener = V8GnListener(target, arch, only_cc_files) ParseTreeWalker().walk(listener, tree) return listener.get_sources() def GenerateMakefiles(): f = FileStream(os.path.join(os.getcwd(), './BUILD.gn')) lexer = gnLexer(f) stream = CommonTokenStream(lexer) parser = gnParser(stream) tree = parser.r() _writeBP('Android.platform.bp', 'libv8platform', parseSources(tree, "v8_libplatform")) _writeBP('Android.sampler.bp', 'libv8sampler', parseSources(tree, "v8_libsampler")) _writeV8SrcBP(lambda arch: set(parseSources(tree, "v8_base", arch) + parseSources(tree, "v8_initializers", arch))) _writeGeneratedFilesBP(parseSources(tree, "js2c", None, False)) _writeLibBaseBP(parseSources(tree, "v8_libbase")) if __name__ == '__main__': GenerateMakefiles()

33.334405

118

0.593904

f3e1beb5f7bd251084f124c79eb225d34be1c904

3,735

py

Python

abtools/utils/decorators.py

menis/abtools

bfc7c6c508b174bb3b74d8f152156242ddd2ee77

[ "MIT" ]

9

2016-06-13T20:00:04.000Z

2022-03-19T19:07:23.000Z

abtools/utils/decorators.py

menis/abtools

bfc7c6c508b174bb3b74d8f152156242ddd2ee77

[ "MIT" ]

null

abtools/utils/decorators.py

menis/abtools

bfc7c6c508b174bb3b74d8f152156242ddd2ee77

[ "MIT" ]

4

2018-04-10T09:05:21.000Z

2022-01-27T21:23:06.000Z

#!/usr/bin/env python # filename: decorators.py # # Copyright (c) 2015 Bryan Briney # License: The MIT license (http://opensource.org/licenses/MIT) # # Permission is hereby granted, free of charge, to any person obtaining a copy of this software # and associated documentation files (the "Software"), to deal in the Software without restriction, # including without limitation the rights to use, copy, modify, merge, publish, distribute, # sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all copies or # substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING # BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND # NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, # DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # def lazy_property(func): ''' Wraps a property to provide lazy evaluation. Eliminates boilerplate. Also provides for setting and deleting the property. Use as you would use the @property decorator:: # OLD: class MyClass(): def __init__(): self._compute = None @property def compute(self): if self._compute is None: # computationally intense stuff # ... # ... self._compute = result return self._compute @compute.setter def compute(self, value): self._compute = value # NEW: class MyClass(): def __init__(): pass @lazy_property def compute(self): # computationally intense stuff # ... # ... return result .. note: Properties wrapped with ``lazy_property`` are only evaluated once. If the instance state changes, lazy properties will not be automatically re-evaulated and the update must be explicitly called for:: c = MyClass(data) prop = c.lazy_property # If you update some data that affects c.lazy_property c.data = new_data # c.lazy_property won't change prop == c.lazy_property # TRUE # If you want to update c.lazy_property, you can delete it, which will # force it to be recomputed (with the new data) the next time you use it del c.lazy_property new_prop = c.lazy_property new_prop == prop # FALSE ''' attr_name = '_lazy_' + func.__name__ @property def _lazy_property(self): if not hasattr(self, attr_name): setattr(self, attr_name, func(self)) return getattr(self, attr_name) @_lazy_property.deleter def _lazy_property(self): if hasattr(self, attr_name): delattr(self, attr_name) @_lazy_property.setter def _lazy_property(self, value): setattr(self, attr_name, value) return _lazy_property def coroutine(func): ''' Initializes a coroutine -- essentially it just takes a generator function and calls generator.next() to get things going. ''' def start(*args, **kwargs): cr = func(*args, **kwargs) cr.next() return cr return start

31.923077

99

0.624096

51819166b8d9013f41886275a6f2467e807ce7e0

7,744

py

Python

qiskit/aqua/algorithms/adaptive/vq_algorithm.py

aaita92/qiskit-aqua

8681045790123eefc347dfd05dee547bddc3d2df

[ "Apache-2.0" ]

2

2020-01-22T23:45:59.000Z

2020-01-22T23:46:02.000Z

qiskit/aqua/algorithms/adaptive/vq_algorithm.py

aaita92/qiskit-aqua

8681045790123eefc347dfd05dee547bddc3d2df

[ "Apache-2.0" ]

null

qiskit/aqua/algorithms/adaptive/vq_algorithm.py

aaita92/qiskit-aqua

8681045790123eefc347dfd05dee547bddc3d2df

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # This code is part of Qiskit. # # (C) Copyright IBM 2019. # # This code is licensed under the Apache License, Version 2.0. You may # obtain a copy of this license in the LICENSE.txt file in the root directory # of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. # # Any modifications or derivative works of this code must retain this # copyright notice, and modified files need to carry a notice indicating # that they have been altered from the originals. """ The Variational Quantum Algorithm Base Class. This class can be used an interface for working with Variation Quantum Algorithms, such as VQE, QAOA, or VSVM, and also provides helper utilities for implementing new variational algorithms. Writing a new variational algorithm is a simple as extending this class, implementing a cost function for the new algorithm to pass to the optimizer, and running the find_minimum() function below to begin the optimization. Alternatively, all of the functions below can be overridden to opt-out of this infrastructure but still meet the interface requirements. """ from typing import Optional, Callable import time import logging from abc import abstractmethod import numpy as np from qiskit.aqua import AquaError from qiskit.aqua.algorithms import QuantumAlgorithm from qiskit.aqua.components.optimizers import Optimizer from qiskit.aqua.components.variational_forms import VariationalForm logger = logging.getLogger(__name__) # pylint: disable=invalid-name class VQAlgorithm(QuantumAlgorithm): """ The Variational Quantum Algorithm Base Class. """ def __init__(self, var_form: VariationalForm, optimizer: Optimizer, cost_fn: Optional[Callable] = None, initial_point: Optional[np.ndarray] = None) -> None: super().__init__() if var_form is None: raise AquaError('Missing variational form.') self._var_form = var_form if optimizer is None: raise AquaError('Missing optimizer.') self._optimizer = optimizer self._cost_fn = cost_fn self._initial_point = initial_point self._parameterized_circuits = None @abstractmethod def get_optimal_cost(self): """ get optimal cost """ raise NotImplementedError() @abstractmethod def get_optimal_circuit(self): """ get optimal circuit """ raise NotImplementedError() @abstractmethod def get_optimal_vector(self): """ get optimal vector """ raise NotImplementedError() def find_minimum(self, initial_point=None, var_form=None, cost_fn=None, optimizer=None, gradient_fn=None): """Optimize to find the minimum cost value. Returns: dict: Optimized variational parameters, and corresponding minimum cost value. Raises: ValueError: invalid input """ initial_point = initial_point if initial_point is not None else self._initial_point var_form = var_form if var_form is not None else self._var_form cost_fn = cost_fn if cost_fn is not None else self._cost_fn optimizer = optimizer if optimizer is not None else self._optimizer nparms = var_form.num_parameters bounds = var_form.parameter_bounds if initial_point is not None and len(initial_point) != nparms: raise ValueError( 'Initial point size {} and parameter size {} mismatch'.format( len(initial_point), nparms)) if len(bounds) != nparms: raise ValueError('Variational form bounds size does not match parameter size') # If *any* value is *equal* in bounds array to None then the problem does *not* have bounds problem_has_bounds = not np.any(np.equal(bounds, None)) # Check capabilities of the optimizer if problem_has_bounds: if not optimizer.is_bounds_supported: raise ValueError('Problem has bounds but optimizer does not support bounds') else: if optimizer.is_bounds_required: raise ValueError('Problem does not have bounds but optimizer requires bounds') if initial_point is not None: if not optimizer.is_initial_point_supported: raise ValueError('Optimizer does not support initial point') else: if optimizer.is_initial_point_required: low = [(l if l is not None else -2 * np.pi) for (l, u) in bounds] high = [(u if u is not None else 2 * np.pi) for (l, u) in bounds] initial_point = self.random.uniform(low, high) start = time.time() if not optimizer.is_gradient_supported: # ignore the passed gradient function gradient_fn = None logger.info('Starting optimizer.\nbounds=%s\ninitial point=%s', bounds, initial_point) opt_params, opt_val, num_optimizer_evals = optimizer.optimize(var_form.num_parameters, cost_fn, variable_bounds=bounds, initial_point=initial_point, gradient_function=gradient_fn) eval_time = time.time() - start ret = {} ret['num_optimizer_evals'] = num_optimizer_evals ret['min_val'] = opt_val ret['opt_params'] = opt_params ret['eval_time'] = eval_time return ret def get_prob_vector_for_params(self, construct_circuit_fn, params_s, quantum_instance, construct_circuit_args=None): """ Helper function to get probability vectors for a set of params """ circuits = [] for params in params_s: circuit = construct_circuit_fn(params, **construct_circuit_args) circuits.append(circuit) results = quantum_instance.execute(circuits) probs_s = [] for circuit in circuits: if quantum_instance.is_statevector: sv = results.get_statevector(circuit) probs = np.real(sv * np.conj(sv)) probs_s.append(probs) else: counts = results.get_counts(circuit) probs_s.append(self.get_probabilities_for_counts(counts)) return np.array(probs_s) def get_probabilities_for_counts(self, counts): """ get probabilities for counts """ shots = sum(counts.values()) states = int(2 ** len(list(counts.keys())[0])) probs = np.zeros(states) for k, v in counts.items(): probs[int(k, 2)] = v / shots return probs @property def initial_point(self): """ returns initial point """ return self._initial_point @initial_point.setter def initial_point(self, new_value): """ set initial point """ self._initial_point = new_value @property @abstractmethod def optimal_params(self): """ returns optimal parameters """ raise NotImplementedError() @property def var_form(self): """ returns var forms """ return self._var_form @var_form.setter def var_form(self, new_value): """ sets var forms """ self._var_form = new_value @property def optimizer(self): """ returns optimizer """ return self._optimizer def cleanup_parameterized_circuits(self): """ set parameterized circuits to None """ self._parameterized_circuits = None

37.230769

100

0.634168

7247f929364d2de8f8be14e48728efc4d36406ae

5,508

py

Python

service/paginator.py

getcircle/python-soa

590b53691ff959713e331c25353d8c9280e10100

[ "MIT" ]

null

service/paginator.py

getcircle/python-soa

590b53691ff959713e331c25353d8c9280e10100

[ "MIT" ]

null

service/paginator.py

getcircle/python-soa

590b53691ff959713e331c25353d8c9280e10100

[ "MIT" ]

null

# Copy of Django's paginator: https://github.com/django/django/blob/master/django/core/paginator.py import collections from math import ceil class InvalidPage(Exception): pass class PageNotAnInteger(InvalidPage): pass class EmptyPage(InvalidPage): pass class Paginator(object): def __init__(self, object_list, per_page, orphans=0, allow_empty_first_page=True, count=None, disabled=False): self.object_list = object_list self.per_page = int(per_page) self.orphans = int(orphans) self.allow_empty_first_page = allow_empty_first_page self._num_pages = self._count = None self._pre_calculated = count is not None if count is not None: self._count = int(count) if disabled: # if the list is empty, just use the default page size self.per_page = len(object_list) or self.per_page def validate_number(self, number): """ Validates the given 1-based page number. """ try: number = int(number) except (TypeError, ValueError): raise PageNotAnInteger('That page number is not an integer') if number < 1: raise EmptyPage('That page number is less than 1') if number > self.num_pages: if number == 1 and self.allow_empty_first_page: pass else: raise EmptyPage('That page contains no results') return number def get_page_bottom_top(self, number): number = self.validate_number(number) bottom = (number - 1) * self.per_page top = bottom + self.per_page if top + self.orphans >= self.count: top = self.count return bottom, top def page(self, number): """ Returns a Page object for the given 1-based page number. """ bottom, top = self.get_page_bottom_top(number) if not self._pre_calculated: page_object_list = self.object_list[bottom:top] else: page_object_list = self.object_list return self._get_page(page_object_list, number, self) def _get_page(self, *args, **kwargs): """ Returns an instance of a single page. This hook can be used by subclasses to use an alternative to the standard :cls:`Page` object. """ return Page(*args, **kwargs) def _get_count(self): """ Returns the total number of objects, across all pages. """ if self._count is None: try: self._count = self.object_list.count() except (AttributeError, TypeError): # AttributeError if object_list has no count() method. # TypeError if object_list.count() requires arguments # (i.e. is of type list). self._count = len(self.object_list) return self._count count = property(_get_count) def _get_num_pages(self): """ Returns the total number of pages. """ if self._num_pages is None: if self.count == 0 and not self.allow_empty_first_page: self._num_pages = 0 else: hits = max(1, self.count - self.orphans) self._num_pages = int(ceil(hits / float(self.per_page))) return self._num_pages num_pages = property(_get_num_pages) def _get_page_range(self): """ Returns a 1-based range of pages for iterating through within a template for loop. """ return list(range(1, self.num_pages + 1)) page_range = property(_get_page_range) class Page(collections.Sequence): def __init__(self, object_list, number, paginator): self.object_list = object_list self.number = number self.paginator = paginator def __repr__(self): return '<Page %s of %s>' % (self.number, self.paginator.num_pages) def __len__(self): return len(self.object_list) def __getitem__(self, index): # The object_list is converted to a list so that if it was a QuerySet # it won't be a database hit per __getitem__. if not isinstance(self.object_list, list): self.object_list = list(self.object_list) return self.object_list[index] def has_next(self): return self.number < self.paginator.num_pages def has_previous(self): return self.number > 1 def has_other_pages(self): return self.has_previous() or self.has_next() def next_page_number(self): return self.paginator.validate_number(self.number + 1) def previous_page_number(self): return self.paginator.validate_number(self.number - 1) def start_index(self): """ Returns the 1-based index of the first object on this page, relative to total objects in the paginator. """ # Special case, return zero if no items. if self.paginator.count == 0: return 0 return (self.paginator.per_page * (self.number - 1)) + 1 def end_index(self): """ Returns the 1-based index of the last object on this page, relative to total objects found (hits). """ # Special case for the last page because there can be orphans. if self.number == self.paginator.num_pages: return self.paginator.count return self.number * self.paginator.per_page

32.210526

99

0.611837

741cfd4513df7d3ca1fdbcaf14b96a5a5972210e

1,511

py

Python

Pypractice/test.py

JaydenYL/Projects

b51c0476f7be80f0b0d6aa84592966ecb4343d76

[ "MIT" ]

5

2021-09-06T04:27:56.000Z

2021-12-14T14:50:27.000Z

Pypractice/test.py

JaydenYL/Projects

b51c0476f7be80f0b0d6aa84592966ecb4343d76

[ "MIT" ]

null

Pypractice/test.py

JaydenYL/Projects

b51c0476f7be80f0b0d6aa84592966ecb4343d76

[ "MIT" ]

null

import unittest import random import string import subprocess def rand_string(length=15): printable = string.printable[:-5] random_string = ''.join([ printable[random.randint( 0, len(printable) - 1 )] for _ in range(length) ]) return random_string global_key = rand_string() class SampleTest(unittest.TestCase): def __init__(self, *args, **kwargs): self.class_key = rand_string() super().__init__(*args, **kwargs) def guess(self, process, key): guess = process.stdout.readline().decode('utf-8').strip('\n') response = "{guess} {{message}}".format(guess=guess) if guess == key: response = response.format(message="is correct!") else: response = response.format(message=self) process.stdin.write((response + '\n').encode()) process.stdin.flush() return guess def key_check(self, key): process = subprocess.Popen('python pybreak.py'.split(), stdin=subprocess.PIPE, stdout=subprocess.PIPE ) first_guess = self.guess(process, key) print(first_guess) second_guess = self.guess(process, key) print(second_guess) process.kill() if (key not in first_guess) and (key not in second_guess): self.assertIn(key, second_guess) return def test_global_key(self): global global_key key = global_key self.key_check(key) return def test_class_key(self): key = self.class_key self.key_check(key) return def __repr__(self): return "Is not the key!" if __name__ == '__main__': unittest.main()

21.898551

63

0.684977

a61d581f084c3f6bf19a46821e4d2b2362444ce1

1,239

py

Python

setup.py

afeinstein20/tess_infos

c546f2c775fea203f2dfce1610305cae184a9b69

[ "MIT" ]

null

setup.py

afeinstein20/tess_infos

c546f2c775fea203f2dfce1610305cae184a9b69

[ "MIT" ]

null

setup.py

afeinstein20/tess_infos

c546f2c775fea203f2dfce1610305cae184a9b69

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Wed Sep 16 13:37:42 2020 @author: Maximilian N. Günther MIT Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02109, USA Email: maxgue@mit.edu Web: www.mnguenther.com """ from setuptools import setup, find_packages setup( name = 'tess_infos', packages = find_packages(), version = '0.1.0', description = 'Extremely fast Pythonic access to the full TICv8, GAIA DR2 and Banyan Sigma parameters for all TESS short-cadence targets', author = 'Maximilian N. Günther', author_email = 'maxgue@mit.edu', url = 'https://github.com/MNGuenther/tess_infos', download_url = 'https://github.com/MNGuenther/tess_infos', license='MIT', classifiers=['Development Status :: 4 - Beta', #3 - Alpha / 4 - Beta / 5 - Production/Stable 'Intended Audience :: Science/Research', 'License :: OSI Approved :: MIT License', 'Programming Language :: Python'], install_requires=['feather-format>=0.4.1', 'pyarrow', 'pandas', 'numpy', 'tqdm', 'matplotlib'], include_package_data = False )

28.159091

142

0.653753

3ac4349d4f7370291c657a23307f34ddd86fd394

8,095

py

Python

sgk/sparse/sparse_matrix.py

egonrian/google-research

8177adbe9ca0d7e5a9463b54581fe6dd27be0974

[ "Apache-2.0" ]

3

2021-01-18T04:46:49.000Z

2021-03-05T09:21:40.000Z

sgk/sparse/sparse_matrix.py

Alfaxad/google-research

2c0043ecd507e75e2df9973a3015daf9253e1467

[ "Apache-2.0" ]

7

2021-11-10T19:44:38.000Z

2022-02-10T06:48:39.000Z

sgk/sparse/sparse_matrix.py

Alfaxad/google-research

2c0043ecd507e75e2df9973a3015daf9253e1467

[ "Apache-2.0" ]

4

2021-02-08T10:25:45.000Z

2021-04-17T14:46:26.000Z

# coding=utf-8 # Copyright 2020 The Google Research 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. """Defines primitive sparse matrix type for use with sparse ops.""" import numpy as np import tensorflow.compat.v1 as tf from sgk.sparse import connectors from sgk.sparse import initializers SPARSE_MATRIX_COLLECTION = "sparse_matrices" def get_trainable_sparse_matrices(): """Returns a list of all trainable sparse matrices.""" return tf.get_collection(SPARSE_MATRIX_COLLECTION) def track_trainable_sparse_matrix(sm): """Adds a sparse matrix to the collection.""" assert sm.trainable if sm not in tf.get_collection_ref(SPARSE_MATRIX_COLLECTION): tf.add_to_collection(SPARSE_MATRIX_COLLECTION, sm) def _dense_to_sparse(matrix): """Converts dense numpy matrix to a csr sparse matrix.""" assert len(matrix.shape) == 2 # Extract the nonzero values. values = matrix.compress((matrix != 0).flatten()) # Calculate the offset of each row. mask = (matrix != 0).astype(np.int32) row_offsets = np.concatenate(([0], np.cumsum(np.add.reduce(mask, axis=1))), axis=0) # Create the row indices and sort them. row_indices = np.argsort(-1 * np.diff(row_offsets)) # Extract the column indices for the nonzero values. x = mask * (np.arange(matrix.shape[1]) + 1) column_indices = x.compress((x != 0).flatten()) column_indices = column_indices - 1 # Cast the desired precision. values = values.astype(np.float32) row_indices, row_offsets, column_indices = [ x.astype(np.uint32) for x in [row_indices, row_offsets, column_indices] ] return values, row_indices, row_offsets, column_indices class SparseTopology(object): """Describes a sparse matrix, with no values.""" def __init__(self, name, shape=None, mask=None, connector=connectors.Uniform(0.8), dtype=tf.float32): if mask is None: assert shape is not None and len(shape) == 2 mask = connector(np.ones(shape)) self._shape = shape else: assert shape is None assert len(mask.shape) == 2 self._shape = mask.shape self._name = name self._dtype = dtype self._sparsity = 1.0 - np.count_nonzero(mask) / mask.size # Create a numpy version of the sparse mask. _, row_indices_, row_offsets_, column_indices_ = _dense_to_sparse(mask) # Create tensors for the mask shape on the host. These are for internal # use and should generally not be used by end-user. Use the normal python # 'shape' property instead. with tf.device("cpu"): self._rows = tf.get_variable( initializer=self._shape[0], trainable=False, name=self._name + "_rows", dtype=tf.int32) self._columns = tf.get_variable( initializer=self._shape[1], trainable=False, name=self._name + "_columns", dtype=tf.int32) # Convert the sparse mask to TensorFlow variables. self._row_indices = tf.get_variable( initializer=row_indices_, trainable=False, name=self._name + "_row_indices", dtype=tf.uint32) self._row_offsets = tf.get_variable( initializer=row_offsets_, trainable=False, name=self._name + "_row_offsets", dtype=tf.uint32) self._column_indices = tf.get_variable( initializer=column_indices_, trainable=False, name=self._name + "_column_indices", dtype=tf.uint32) @property def name(self): return self._name @property def shape(self): return self._shape @property def size(self): return np.prod(self._shape) @property def dtype(self): return self._dtype @property def sparsity(self): return self._sparsity @property def row_indices(self): return self._row_indices @property def row_offsets(self): return self._row_offsets @property def column_indices(self): return self._column_indices class SparseMatrix(object): """Compressed sparse row matrix type.""" def __init__(self, name, shape=None, matrix=None, initializer=initializers.Uniform(), connector=connectors.Uniform(0.8), trainable=True, dtype=tf.float32): if matrix is None: assert shape is not None and len(shape) == 2 matrix = connector(initializer(shape)) self._shape = shape else: assert shape is None assert len(matrix.shape) == 2 self._shape = matrix.shape self._name = name self._trainable = trainable self._dtype = dtype self._sparsity = 1.0 - np.count_nonzero(matrix) / matrix.size # Create a numpy version of the sparse matrix. values_, row_indices_, row_offsets_, column_indices_ = _dense_to_sparse( matrix) # Create tensors for the matrix shape on the host. These are for internal # use and should generally not be used by end-user. Use the normal python # 'shape' property instead. with tf.device("cpu"): self._rows = tf.get_variable( initializer=self._shape[0], trainable=False, name=self._name + "_rows", dtype=tf.int32) self._columns = tf.get_variable( initializer=self._shape[1], trainable=False, name=self._name + "_columns", dtype=tf.int32) # Convert the sparse matrix to TensorFlow variables. self._values = tf.get_variable( initializer=values_, trainable=self.trainable, name=self._name + "_values", dtype=self._dtype) self._row_indices = tf.get_variable( initializer=row_indices_, trainable=False, name=self._name + "_row_indices", dtype=tf.uint32) self._row_offsets = tf.get_variable( initializer=row_offsets_, trainable=False, name=self._name + "_row_offsets", dtype=tf.uint32) self._column_indices = tf.get_variable( initializer=column_indices_, trainable=False, name=self._name + "_column_indices", dtype=tf.uint32) # Add this matrix to the collection of trainable matrices. track_trainable_sparse_matrix(self) @classmethod def _wrap_existing(cls, shape, rows, columns, values, row_indices, row_offsets, column_indices): """Helper to wrap existing tensors in a SparseMatrix object.""" matrix = cls.__new__(cls) # Set the members appropriately. # # pylint: disable=protected-access matrix._shape = shape matrix._rows = rows matrix._columns = columns matrix._values = values matrix._row_indices = row_indices matrix._row_offsets = row_offsets matrix._column_indices = column_indices matrix._name = values.name matrix._trainable = False matrix._dtype = values.dtype # pylint: enable=protected-access return matrix @property def name(self): return self._name @property def shape(self): return self._shape @property def size(self): return np.prod(self._shape) @property def trainable(self): return self._trainable @property def dtype(self): return self._dtype @property def sparsity(self): return self._sparsity @property def values(self): return self._values @property def row_indices(self): return self._row_indices @property def row_offsets(self): return self._row_offsets @property def column_indices(self): return self._column_indices

28.403509

77

0.665596

0d037cc912676edd5833bb17061c211595ca2116

6,714

py

Python

jolly-jellyfish/src/page_maker/templatetags/render_thumbnail.py

Vthechamp22/summer-code-jam-2021

0a8bf1f22f6c73300891fd779da36efd8e1304c1

[ "MIT" ]

40

2020-08-02T07:38:22.000Z

2021-07-26T01:46:50.000Z

jolly-jellyfish/src/page_maker/templatetags/render_thumbnail.py

Vthechamp22/summer-code-jam-2021

0a8bf1f22f6c73300891fd779da36efd8e1304c1

[ "MIT" ]

134

2020-07-31T12:15:45.000Z

2020-12-13T04:42:19.000Z

jolly-jellyfish/src/page_maker/templatetags/render_thumbnail.py

Artemis21/summer-code-jam-2020

1323288cb1e75b3aa4141c2c6e378f9219cf73d0

[ "MIT" ]

101

2020-07-31T12:00:47.000Z

2021-11-01T09:06:58.000Z

import os from pathlib import Path from typing import Union from PIL import Image from django import template from django.conf import settings from django.http import HttpRequest from django.utils import timezone from selenium import webdriver from selenium.webdriver.chrome.options import Options as ChromeOptions from selenium.webdriver.firefox.options import Options as FireOptions from ..models import Webpage, Template register = template.Library() # registers function in this file as template tags for use within Django templates try: DRIVER_PATH = os.environ['SELENIUM_DRIVER'] except KeyError: raise KeyError('SELENIUM_DRIVER system environment variable not found.\n' 'Check the README for details on setting it.\n' 'If you have already done so and this error persists, try restarting your PC.') def add_options( options_object: Union[ChromeOptions, FireOptions], window_size: str) -> Union[ChromeOptions, FireOptions]: """ Used to quickly add options to options_object - since these are shared by both ChromeOptions & FireOptions :param options_object: Options object imported from selenium.webdriver.BROWSER.options :param window_size: string in format 'height, width' :return Options object with common arguments added """ options_object.add_argument('--headless') options_object.add_argument('--hide-scrollbars') options_object.add_argument(f'--window-size={window_size}') return options_object @register.simple_tag() def render_thumbnail(request: HttpRequest, url_to_render: str, page_obj: Union[Webpage, Template]) -> None: """ This function needs to be called from within django html templates using: {% load render_thumbnail %} {% render_thumbnail request url_to_render page_obj as none %} It saves a thumbnail/screenshot of the page from within which it is called to page_obj's thumbnail attribute. This is accomplished using a selenium browser driver running in a headless configuration. Since, when running a local server, this results in the server being pinged twice (once within itself), a ConnectionResetError may be output to the console. This is safe to ignore (but can't be escaped, with except, for some reason). :param request: A Django HttpRequest object, sourced from the page which is calling this function. This is needed in order to access additional GET info and to build a full url (including domain). :param url_to_render: The relative url of the webpage to render (e.g. '/pages/page_name'). Accessed within a template using something like: {% url 'webpage-view' pagename=webpage.name as url_to_render %}. Also needed to build full url. :param page_obj: Either a 'Webpage' or 'Template' Django object. Accessed in order to save thumbnail path. """ is_template = isinstance(page_obj, Template) # True if object is an instance of models.Template is_outdated = False if not is_template: # only Webpages have the thumbnail_edit_date attr (since they can be edited by the user) # True if the thumbnail is outdated or does not exist yet (thumbnail_edit_date datetime set to 1, 1, 1) is_outdated = page_obj.thumbnail_edit_date < page_obj.last_edit_date if is_template: # str(page_obj.thumbnail) returns rel path to thumbnail in str form (not comparable otherwise) if 'placeholder_img.png' not in str(page_obj.thumbnail): # checks if the template actually needs a thumbnail is_template = False # To avoid infinite loops as the page calls itself to render, # ensures function only runs if url doesn't end with '?rendering=true' (added below) is_rendering = bool(request.GET.get('rendering', '')) if (is_template or is_outdated) and not is_rendering: # if a thumbnail is needed base_dir = Path(settings.BASE_DIR) media_dir = Path(settings.MEDIA_ROOT) # 'template_' prefix for Template Objects; 'pg_' for Webpages. image_prefix = 'template_' if is_template else 'pg_' # Can't use only the obj's name as this may contain characters not permitted by file system image_path = Path('thumbnails') / f'{image_prefix}thumb-{page_obj.id}.png' full_os_path = str(base_dir / media_dir / image_path) if is_template: window_size = '500,500' # thumbnails for Templates, due to less content, can be much smaller else: window_size = '1980,1080' if 'chromedriver' in DRIVER_PATH: options = add_options(ChromeOptions(), window_size) driver = webdriver.Chrome(executable_path=DRIVER_PATH, options=options) elif 'geckodriver' in DRIVER_PATH: options = add_options(FireOptions(), window_size) driver = webdriver.Firefox(executable_path=DRIVER_PATH, options=options) else: raise Exception('The driver specified in SELENIUM_DRIVER is not supported.\n' "Currently, only Chrome/Chromium ('chromedriver') and " "Firefox ('geckodriver') are supported.\n" 'Please install one of these browsers and the associated driver.') url = request.build_absolute_uri(url_to_render) + '?rendering=true' # NB: This function raises: "ConnectionResetError: [WinError 10054] ... # An existing connection was forcibly closed by the remote host" in console. # This has no functional effect on the program and # is likely just a side-effect of the server being pinged from within itself essentially. # # Celery (for asynchronous tasks) no longer supports Windows - same with Redis. # This means that, in this current dev environment where there are multiple OSs at play, # having a functioning webpage, while this function executes in the background, # doesn't seem possible 😭 TODO: possible using Docker? driver.get(url) driver.save_screenshot(full_os_path) driver.close() img = Image.open(full_os_path) # image shrunk (aspect ration maintained) to reduce storage space and reduce footprint used on actual webpage img.thumbnail(size=(500, 500)) img.save(full_os_path) page_obj.thumbnail = f'{image_path}' # saves relative path to thumbnail to ImageField attribute # only Webpage objects can be edited by user so only they have the thumbnail_edit_date attribute if not is_template: page_obj.thumbnail_edit_date = timezone.now() page_obj.save() # actually commit changes to model database

50.481203

117

0.70703

defb4a5b23287240ed28ec1695d2fdfbec9ac122

3,189

py

Python

voicecontrol/pinyin_typing.py

thautwarm/voicecontrol

dc5565d114fe80f0f06a0e7c541ee447fb7712f3

[ "MIT" ]

2

2021-06-05T08:27:44.000Z

2021-06-05T13:46:27.000Z

voicecontrol/pinyin_typing.py

thautwarm/voicecontrol

dc5565d114fe80f0f06a0e7c541ee447fb7712f3

[ "MIT" ]

null

voicecontrol/pinyin_typing.py

thautwarm/voicecontrol

dc5565d114fe80f0f06a0e7c541ee447fb7712f3

[ "MIT" ]

null

import os from pynput.keyboard import Key, Controller import psutil from voicecontrol.st import * keyboard = Controller() voice_input_exe = "iFlyVoice.exe" def get_process(name: str): for p in psutil.process_iter(): if p.name() == name: return p def pressed(k): keyboard.press(k) keyboard.release(k) def get_en_US_model_path(): import speech_recognition as sr import os.path as p return p.join(p.dirname(sr.__file__), "pocketsphinx-data", "en-US") class 休息模式(DSModel, CommandsFromMethods): path = get_en_US_model_path() decider = staticmethod(just_hyp) def okay(self, st: StateMachine): st.model = 控制模式() class 输入模式(DSModel, CommandsFromMethods): path = get_en_US_model_path() decider = staticmethod(just_hyp) def okay(self, st: StateMachine): pressed(Key.f8) st.model = 控制模式() class 控制模式(DSModel, CommandsFromMethods): path = os.path.join(os.path.dirname(__file__), "models", "control") decider = staticmethod(vote_most_common_word) def 符(self, st: StateMachine): tb = ('，', " 。","“”", "！","？","【】","「」","（）") tb = ('，', " 。","“”", "！","？","【】","「」","（）") i = st.state if i <= len(tb): for k in tb[i-1]: pressed(k) st.state = 1 def 五(self, st: StateMachine): st.state += 5 def 四(self, st: StateMachine): st.state += 4 def 三(self, st: StateMachine): st.state += 3 def 二(self, st: StateMachine): st.state += 2 def 一(self, st: StateMachine): st.state += 1 def 上(self, st: StateMachine): for _ in range(st.state): pressed(Key.up) st.state = 1 def 下(self, st: StateMachine): for _ in range(st.state): pressed(Key.down) st.state = 1 def 左(self, st: StateMachine): for _ in range(st.state): pressed(Key.left) st.state = 1 def 右(self, st: StateMachine): for _ in range(st.state): pressed(Key.right) st.state = 1 def 撤(self, st: StateMachine): with keyboard.pressed(Key.ctrl): pressed("z") def 重(self, st: StateMachine): with keyboard.pressed(Key.ctrl): with keyboard.pressed(Key.shift): pressed("z") def 删除(self, st: StateMachine): for _ in range(st.state): pressed(Key.backspace) st.state = 1 def 首(self, st: StateMachine): with keyboard.pressed(Key.ctrl): pressed("a") def 末(self, st: StateMachine): with keyboard.pressed(Key.ctrl): pressed("e") def 行(self, st: StateMachine): pressed(Key.enter) def 休息(self, st: StateMachine): st.model = 休息模式() def 输入(self, st: StateMachine): st.model = 输入模式() pressed(Key.f8) def main(): from voicecontrol.indicator import create_root st = StateMachine(1, 控制模式()) st.add_event(create_root()) st.start_loop()

23.622222

72

0.541549

886d9901ecbf2c8cd2bbab9ed49548abcb8f9bdc

2,059

py

Python

tests/step16_tests.py

svaningelgem/advent_of_code_2021

80351508d6d6953392bc57af20e1fac05ab3ec2a

[ "MIT" ]

null

tests/step16_tests.py

svaningelgem/advent_of_code_2021

80351508d6d6953392bc57af20e1fac05ab3ec2a

[ "MIT" ]

null

tests/step16_tests.py

svaningelgem/advent_of_code_2021

80351508d6d6953392bc57af20e1fac05ab3ec2a

[ "MIT" ]

null

from pathlib import Path from step16 import decode_hex, Packet, get_version_sum TEST_INPUT = Path(__file__).parent / 'step16.txt' REAL_INPUT = Path(__file__).parent.parent / 'src/step16.txt' def test_step16(): assert decode_hex('D2FE28') == '110100101111111000101000' assert decode_hex('38006F45291200') == '00111000000000000110111101000101001010010001001000000000' assert decode_hex('EE00D40C823060') == '11101110000000001101010000001100100000100011000001100000' sut = Packet('D2FE28') assert sut.version == 6 assert sut.packet_id == 4 assert sut.is_literal assert sut._value == 2021 sut = Packet('38006F45291200') assert sut.version == 1 assert sut.packet_id == 6 assert not sut.is_literal assert sut._value is None assert len(sut.sub_packets) == 2 assert sut.sub_packets[0]._value == 10 assert sut.sub_packets[1]._value == 20 sut = Packet('EE00D40C823060') assert sut.version == 7 assert sut.packet_id == 3 assert not sut.is_literal assert sut._value is None assert len(sut.sub_packets) == 3 assert sut.sub_packets[0]._value == 1 assert sut.sub_packets[1]._value == 2 assert sut.sub_packets[2]._value == 3 assert get_version_sum('8A004A801A8002F478') == 16 assert get_version_sum('620080001611562C8802118E34') == 12 assert get_version_sum('C0015000016115A2E0802F182340') == 23 assert get_version_sum('A0016C880162017C3686B18A3D4780') == 31 def test_step16_real_data(): assert get_version_sum(REAL_INPUT.read_text()) == 879 def test_step16_part2(): assert Packet('C200B40A82').value == 3 assert Packet('04005AC33890').value == 54 assert Packet('880086C3E88112').value == 7 assert Packet('CE00C43D881120').value == 9 assert Packet('D8005AC2A8F0').value == 1 assert Packet('F600BC2D8F').value == 0 assert Packet('9C005AC2F8F0').value == 0 assert Packet('9C0141080250320F1802104A08').value == 1 def test_step16_part2_real_data(): assert Packet(REAL_INPUT.read_text()).value == 539051801941

33.209677

101

0.718796

932afd4ea007227b4e807dcdbb7690b3100d8a9f

1,817

py

Python

scapy/error.py

kosciolek/J-Tracert

89ed802f700e02600138ad7132e6a856463620dd

[ "MIT" ]

3

2019-04-09T22:59:33.000Z

2019-06-14T09:23:24.000Z

scapy/error.py

kosciolek/J-Tracert

89ed802f700e02600138ad7132e6a856463620dd

[ "MIT" ]

null

scapy/error.py

kosciolek/J-Tracert

89ed802f700e02600138ad7132e6a856463620dd

[ "MIT" ]

1

2018-11-15T12:37:04.000Z

## This file is part of Scapy ## See http://www.secdev.org/projects/scapy for more informations ## Copyright (C) Philippe Biondi <phil@secdev.org> ## This program is published under a GPLv2 license """ Logging subsystem and basic exception class. """ ############################# ##### Logging subsystem ##### ############################# class Scapy_Exception(Exception): pass import logging, traceback, time class ScapyFreqFilter(logging.Filter): def __init__(self): logging.Filter.__init__(self) self.warning_table = {} def filter(self, record): from scapy.config import conf wt = conf.warning_threshold if wt > 0: stk = traceback.extract_stack() caller=None for f,l,n,c in stk: if n == 'warning': break caller = l tm,nb = self.warning_table.get(caller, (0,0)) ltm = time.time() if ltm-tm > wt: tm = ltm nb = 0 else: if nb < 2: nb += 1 if nb == 2: record.msg = "more "+record.msg else: return 0 self.warning_table[caller] = (tm,nb) return 1 log_scapy = logging.getLogger("scapy") log_scapy.addHandler(logging.NullHandler()) log_runtime = logging.getLogger("scapy.runtime") # logs at runtime log_runtime.addFilter(ScapyFreqFilter()) log_interactive = logging.getLogger("scapy.interactive") # logs in interactive functions log_loading = logging.getLogger("scapy.loading") # logs when loading Scapy def warning(x, *args, **kargs): """ Prints a warning during runtime. """ log_runtime.warning(x, *args, **kargs)

29.786885

89

0.549257

c1e575cc633f6fc2fb98acfe03f36e3654118c5a

30,336

py

Python

pretorched/gans/biggan.py

schwettmann/pretorched-x

ce8c3712434b3cd5d85dcbe8582ff51ddfa7d4ed

[ "MIT" ]

null

pretorched/gans/biggan.py

schwettmann/pretorched-x

ce8c3712434b3cd5d85dcbe8582ff51ddfa7d4ed

[ "MIT" ]

null

pretorched/gans/biggan.py

schwettmann/pretorched-x

ce8c3712434b3cd5d85dcbe8582ff51ddfa7d4ed

[ "MIT" ]

null

"""biggan.py Implements BigGAN architecture from Large Scale GAN Training for High Fidelity Natural Image Synthesis by Andrew Brock, Jeff Donahue, and Karen Simonyan. Adapted from https://github.com/ajbrock/BigGAN-PyTorch. """ import functools import os import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torch.nn import init import pretorched.layers as layers class GBlock(nn.Module): """Residual block for generator. Note that this class assumes the kernel size and padding (and any other settings) have been selected in the main generator module and passed in through the conv_func arg. Similar rules apply with bn_func (the input size [which is actually the number of channels of the conditional info] must be preselected) """ def __init__(self, in_channels, out_channels, conv_func=nn.Conv2d, bn_func=layers.bn, activation=None, upsample=None): super().__init__() self.in_channels, self.out_channels = in_channels, out_channels self.conv_func, self.bn_func = conv_func, bn_func self.activation = activation self.upsample = upsample # Conv layers self.conv1 = self.conv_func(self.in_channels, self.out_channels) self.conv2 = self.conv_func(self.out_channels, self.out_channels) self.learnable_sc = in_channels != out_channels or upsample if self.learnable_sc: self.conv_sc = self.conv_func(in_channels, out_channels, kernel_size=1, padding=0) # Batchnorm layers self.bn1 = self.bn_func(in_channels) self.bn2 = self.bn_func(out_channels) def forward(self, x, y): h = self.activation(self.bn1(x, y)) if self.upsample: h = self.upsample(h) x = self.upsample(x) h = self.conv1(h) h = self.activation(self.bn2(h, y)) h = self.conv2(h) if self.learnable_sc: x = self.conv_sc(x) return h + x class DBlock(nn.Module): """ Residual block for discriminator.""" def __init__(self, in_channels, out_channels, conv_func=layers.SNConv2d, wide=True, preactivation=False, activation=None, downsample=None,): super().__init__() self.in_channels, self.out_channels = in_channels, out_channels # If using wide D (as in SA-GAN and BigGAN), change the channel pattern self.hidden_channels = self.out_channels if wide else self.in_channels self.conv_func = conv_func self.preactivation = preactivation self.activation = activation self.downsample = downsample # Conv layers self.conv1 = self.conv_func(self.in_channels, self.hidden_channels) self.conv2 = self.conv_func(self.hidden_channels, self.out_channels) self.learnable_sc = True if (in_channels != out_channels) or downsample else False if self.learnable_sc: self.conv_sc = self.conv_func(in_channels, out_channels, kernel_size=1, padding=0) def shortcut(self, x): if self.preactivation: if self.learnable_sc: x = self.conv_sc(x) if self.downsample: x = self.downsample(x) else: if self.downsample: x = self.downsample(x) if self.learnable_sc: x = self.conv_sc(x) return x def forward(self, x): if self.preactivation: h = F.relu(x) # This line *must* be an out-of-place ReLU or it will negatively affect the shortcut connection. else: h = x h = self.conv1(h) h = self.conv2(self.activation(h)) if self.downsample: h = self.downsample(h) return h + self.shortcut(x) def G_arch(ch=64, attention='64', ksize='333333', dilation='111111'): """Architectures for G. Attention is passed in in the format '32_64' to mean applying an attention block at both resolution 32x32 and 64x64. Just '64' will apply at 64x64 """ arch = {} arch[512] = {'in_channels': [ch * item for item in [16, 16, 8, 8, 4, 2, 1]], 'out_channels': [ch * item for item in [16, 8, 8, 4, 2, 1, 1]], 'upsample': [True] * 7, 'resolution': [8, 16, 32, 64, 128, 256, 512], 'attention': {2**i: (2**i in [int(item) for item in attention.split('_')]) for i in range(3, 10)}} arch[256] = {'in_channels': [ch * item for item in [16, 16, 8, 8, 4, 2]], 'out_channels': [ch * item for item in [16, 8, 8, 4, 2, 1]], 'upsample': [True] * 6, 'resolution': [8, 16, 32, 64, 128, 256], 'attention': {2**i: (2**i in [int(item) for item in attention.split('_')]) for i in range(3, 9)}} arch[128] = {'in_channels': [ch * item for item in [16, 16, 8, 4, 2]], 'out_channels': [ch * item for item in [16, 8, 4, 2, 1]], 'upsample': [True] * 5, 'resolution': [8, 16, 32, 64, 128], 'attention': {2**i: (2**i in [int(item) for item in attention.split('_')]) for i in range(3, 8)}} arch[64] = {'in_channels': [ch * item for item in [16, 16, 8, 4]], 'out_channels': [ch * item for item in [16, 8, 4, 2]], 'upsample': [True] * 4, 'resolution': [8, 16, 32, 64], 'attention': {2**i: (2**i in [int(item) for item in attention.split('_')]) for i in range(3, 7)}} arch[32] = {'in_channels': [ch * item for item in [4, 4, 4]], 'out_channels': [ch * item for item in [4, 4, 4]], 'upsample': [True] * 3, 'resolution': [8, 16, 32], 'attention': {2**i: (2**i in [int(item) for item in attention.split('_')]) for i in range(3, 6)}} return arch class Generator(nn.Module): def __init__(self, G_ch=64, dim_z=128, bottom_width=4, resolution=128, G_kernel_size=3, G_attn='64', n_classes=1000, num_G_SVs=1, num_G_SV_itrs=1, G_shared=True, shared_dim=0, hier=False, cross_replica=False, mybn=False, G_activation=nn.ReLU(inplace=False), G_lr=5e-5, G_B1=0.0, G_B2=0.999, adam_eps=1e-8, BN_eps=1e-5, SN_eps=1e-12, G_mixed_precision=False, G_fp16=False, G_init='ortho', skip_init=False, no_optim=False, G_param='SN', norm_style='bn', verbose=False, **kwargs): super(Generator, self).__init__() # Channel width mulitplier self.ch = G_ch # Dimensionality of the latent space self.dim_z = dim_z # The initial spatial dimensions self.bottom_width = bottom_width # Resolution of the output self.resolution = resolution # Kernel size? self.kernel_size = G_kernel_size # Attention? self.attention = G_attn # number of classes, for use in categorical conditional generation self.n_classes = n_classes # Use shared embeddings? self.G_shared = G_shared # Dimensionality of the shared embedding? Unused if not using G_shared self.shared_dim = shared_dim if shared_dim > 0 else dim_z # Hierarchical latent space? self.hier = hier # Cross replica batchnorm? self.cross_replica = cross_replica # Use my batchnorm? self.mybn = mybn # nonlinearity for residual blocks self.activation = G_activation # Initialization style self.init = G_init # Parameterization style self.G_param = G_param # Normalization style self.norm_style = norm_style # Epsilon for BatchNorm? self.BN_eps = BN_eps # Epsilon for Spectral Norm? self.SN_eps = SN_eps # fp16? self.fp16 = G_fp16 # Print out model information during init? self.verbose = verbose # Architecture dict self.arch = G_arch(self.ch, self.attention)[resolution] # If using hierarchical latents, adjust z if self.hier: # Number of places z slots into self.num_slots = len(self.arch['in_channels']) + 1 self.z_chunk_size = (self.dim_z // self.num_slots) # Recalculate latent dimensionality for even splitting into chunks self.dim_z = self.z_chunk_size * self.num_slots else: self.num_slots = 1 self.z_chunk_size = 0 # Which convs, batchnorms, and linear layers to use if self.G_param == 'SN': self.conv_func = functools.partial(layers.SNConv2d, kernel_size=3, padding=1, num_svs=num_G_SVs, num_itrs=num_G_SV_itrs, eps=self.SN_eps) self.linear_func = functools.partial(layers.SNLinear, num_svs=num_G_SVs, num_itrs=num_G_SV_itrs, eps=self.SN_eps) else: self.conv_func = functools.partial(nn.Conv2d, kernel_size=3, padding=1) self.linear_func = nn.Linear # We use a non-spectral-normed embedding here regardless; # For some reason applying SN to G's embedding seems to randomly cripple G self.embedding_func = nn.Embedding bn_linear = (functools.partial(self.linear_func, bias=False) if self.G_shared else self.embedding_func) self.bn_func = functools.partial(layers.ccbn, linear_func=bn_linear, cross_replica=self.cross_replica, mybn=self.mybn, input_size=(self.shared_dim + self.z_chunk_size if self.G_shared else self.n_classes), norm_style=self.norm_style, eps=self.BN_eps) # Prepare model # If not using shared embeddings, self.shared is just a passthrough self.shared = (self.embedding_func(n_classes, self.shared_dim) if G_shared else nn.Identity()) # First linear layer self.linear = self.linear_func(self.dim_z // self.num_slots, self.arch['in_channels'][0] * (self.bottom_width ** 2)) # self.blocks is a doubly-nested list of modules, the outer loop intended # to be over blocks at a given resolution (resblocks and/or self-attention) # while the inner loop is over a given block self.blocks = [] for index in range(len(self.arch['out_channels'])): self.blocks += [[GBlock(in_channels=self.arch['in_channels'][index], out_channels=self.arch['out_channels'][index], conv_func=self.conv_func, bn_func=self.bn_func, activation=self.activation, upsample=(functools.partial(F.interpolate, scale_factor=2) if self.arch['upsample'][index] else None))]] # If attention on this block, attach it to the end if self.arch['attention'][self.arch['resolution'][index]]: if self.verbose: print('Adding attention layer in G at resolution {}'.format(self.arch['resolution'][index])) self.blocks[-1] += [layers.Attention(self.arch['out_channels'][index], self.conv_func)] # Turn self.blocks into a ModuleList so that it's all properly registered. self.blocks = nn.ModuleList([nn.ModuleList(block) for block in self.blocks]) # output layer: batchnorm-relu-conv. # Consider using a non-spectral conv here self.output_layer = nn.Sequential(layers.bn(self.arch['out_channels'][-1], cross_replica=self.cross_replica, mybn=self.mybn), self.activation, self.conv_func(self.arch['out_channels'][-1], 3)) # Initialize weights. Optionally skip init for testing. if not skip_init: self.init_weights() # Set up optimizer # If this is an EMA copy, no need for an optim, so just return now if no_optim: return self.lr, self.B1, self.B2, self.adam_eps = G_lr, G_B1, G_B2, adam_eps if G_mixed_precision: if self.verbose: print('Using fp16 adam in G...') from . import utils self.optim = utils.Adam16(params=self.parameters(), lr=self.lr, betas=(self.B1, self.B2), weight_decay=0, eps=self.adam_eps) else: self.optim = optim.Adam(params=self.parameters(), lr=self.lr, betas=(self.B1, self.B2), weight_decay=0, eps=self.adam_eps) # LR scheduling, left here for forward compatibility # self.lr_sched = {'itr' : 0}# if self.progressive else {} # self.j = 0 # Initialize def init_weights(self): self.param_count = 0 for module in self.modules(): if (isinstance(module, nn.Conv2d) or isinstance(module, nn.Linear) or isinstance(module, nn.Embedding)): if self.init == 'ortho': init.orthogonal_(module.weight) elif self.init == 'N02': init.normal_(module.weight, 0, 0.02) elif self.init in ['glorot', 'xavier']: init.xavier_uniform_(module.weight) else: print('Init style not recognized...') self.param_count += sum([p.data.nelement() for p in module.parameters()]) if self.verbose: print('Param count for G''s initialized parameters: {}'.format(self.param_count)) # Note on this forward function: we pass in a y vector which has # already been passed through G.shared to enable easy class-wise # interpolation later. If we passed in the one-hot and then ran it through # G.shared in this forward function, it would be harder to handle. def forward(self, z, y, embed=False, layer=None): if embed: y = self.shared(y) # If hierarchical, concatenate zs and ys if self.hier: zs = torch.split(z, self.z_chunk_size, 1) z = zs[0] ys = [torch.cat([y, item], 1) for item in zs[1:]] else: ys = [y] * len(self.blocks) # First linear layer h = self.linear(z) # Reshape h = h.view(h.size(0), -1, self.bottom_width, self.bottom_width) # Loop over blocks for index, blocklist in enumerate(self.blocks): if index == layer: return h # Second inner loop in case block has multiple layers for block in blocklist: h = block(h, ys[index]) # Apply batchnorm-relu-conv-tanh at output return torch.tanh(self.output_layer(h)) @property def z_dim(self): return self.dim_z def D_arch(ch=64, attention='64', ksize='333333', dilation='111111'): """Discriminator architecture, same paradigm as G's above.""" arch = {} arch[256] = {'in_channels': [3] + [ch * item for item in [1, 2, 4, 8, 8, 16]], 'out_channels': [item * ch for item in [1, 2, 4, 8, 8, 16, 16]], 'downsample': [True] * 6 + [False], 'resolution': [128, 64, 32, 16, 8, 4, 4], 'attention': {2**i: 2**i in [int(item) for item in attention.split('_')] for i in range(2, 8)}} arch[128] = {'in_channels': [3] + [ch * item for item in [1, 2, 4, 8, 16]], 'out_channels': [item * ch for item in [1, 2, 4, 8, 16, 16]], 'downsample': [True] * 5 + [False], 'resolution': [64, 32, 16, 8, 4, 4], 'attention': {2**i: 2**i in [int(item) for item in attention.split('_')] for i in range(2, 8)}} arch[64] = {'in_channels': [3] + [ch * item for item in [1, 2, 4, 8]], 'out_channels': [item * ch for item in [1, 2, 4, 8, 16]], 'downsample': [True] * 4 + [False], 'resolution': [32, 16, 8, 4, 4], 'attention': {2**i: 2**i in [int(item) for item in attention.split('_')] for i in range(2, 7)}} arch[32] = {'in_channels': [3] + [item * ch for item in [4, 4, 4]], 'out_channels': [item * ch for item in [4, 4, 4, 4]], 'downsample': [True, True, False, False], 'resolution': [16, 16, 16, 16], 'attention': {2**i: 2**i in [int(item) for item in attention.split('_')] for i in range(2, 6)}} return arch class Discriminator(nn.Module): def __init__(self, D_ch=64, D_wide=True, resolution=128, D_kernel_size=3, D_attn='64', n_classes=1000, num_D_SVs=1, num_D_SV_itrs=1, D_activation=nn.ReLU(inplace=False), D_lr=2e-4, D_B1=0.0, D_B2=0.999, adam_eps=1e-8, SN_eps=1e-12, output_dim=1, D_mixed_precision=False, D_fp16=False, D_init='ortho', skip_init=False, D_param='SN', verbose=False, **kwargs): super().__init__() # Width multiplier self.ch = D_ch # Use Wide D as in BigGAN and SA-GAN or skinny D as in SN-GAN? self.D_wide = D_wide # Resolution self.resolution = resolution # Kernel size self.kernel_size = D_kernel_size # Attention? self.attention = D_attn # Number of classes self.n_classes = n_classes # Activation self.activation = D_activation # Initialization style self.init = D_init # Parameterization style self.D_param = D_param # Epsilon for Spectral Norm? self.SN_eps = SN_eps # Fp16? self.fp16 = D_fp16 # Print model info during init? self.verbose = verbose # Architecture self.arch = D_arch(self.ch, self.attention)[resolution] # Which convs, batchnorms, and linear layers to use # No option to turn off SN in D right now if self.D_param == 'SN': self.conv_func = functools.partial(layers.SNConv2d, kernel_size=3, padding=1, num_svs=num_D_SVs, num_itrs=num_D_SV_itrs, eps=self.SN_eps) self.linear_func = functools.partial(layers.SNLinear, num_svs=num_D_SVs, num_itrs=num_D_SV_itrs, eps=self.SN_eps) self.embedding_func = functools.partial(layers.SNEmbedding, num_svs=num_D_SVs, num_itrs=num_D_SV_itrs, eps=self.SN_eps) # Prepare model # self.blocks is a doubly-nested list of modules, the outer loop intended # to be over blocks at a given resolution (resblocks and/or self-attention) self.blocks = [] for index in range(len(self.arch['out_channels'])): self.blocks += [[DBlock(in_channels=self.arch['in_channels'][index], out_channels=self.arch['out_channels'][index], conv_func=self.conv_func, wide=self.D_wide, activation=self.activation, preactivation=(index > 0), downsample=(nn.AvgPool2d(2) if self.arch['downsample'][index] else None))]] # If attention on this block, attach it to the end if self.arch['attention'][self.arch['resolution'][index]]: if self.verbose: print('Adding attention layer in D at resolution %d' % self.arch['resolution'][index]) self.blocks[-1] += [layers.Attention(self.arch['out_channels'][index], self.conv_func)] # Turn self.blocks into a ModuleList so that it's all properly registered. self.blocks = nn.ModuleList([nn.ModuleList(block) for block in self.blocks]) # Linear output layer. The output dimension is typically 1, but may be # larger if we're e.g. turning this into a VAE with an inference output self.linear = self.linear_func(self.arch['out_channels'][-1], output_dim) # Embedding for projection discrimination self.embed = self.embedding_func(self.n_classes, self.arch['out_channels'][-1]) # Initialize weights if not skip_init: self.init_weights() # Set up optimizer self.lr, self.B1, self.B2, self.adam_eps = D_lr, D_B1, D_B2, adam_eps if D_mixed_precision: if self.verbose: print('Using fp16 adam in D...') from . import utils self.optim = utils.Adam16(params=self.parameters(), lr=self.lr, betas=(self.B1, self.B2), weight_decay=0, eps=self.adam_eps) else: self.optim = optim.Adam(params=self.parameters(), lr=self.lr, betas=(self.B1, self.B2), weight_decay=0, eps=self.adam_eps) # LR scheduling, left here for forward compatibility # self.lr_sched = {'itr' : 0}# if self.progressive else {} # self.j = 0 # Initialize def init_weights(self): self.param_count = 0 for module in self.modules(): if (isinstance(module, nn.Conv2d) or isinstance(module, nn.Linear) or isinstance(module, nn.Embedding)): if self.init == 'ortho': init.orthogonal_(module.weight) elif self.init == 'N02': init.normal_(module.weight, 0, 0.02) elif self.init in ['glorot', 'xavier']: init.xavier_uniform_(module.weight) else: print('Init style not recognized...') self.param_count += sum([p.data.nelement() for p in module.parameters()]) if self.verbose: print('Param count for D''s initialized parameters: %d' % self.param_count) def forward(self, x, y=None): # Stick x into h for cleaner for loops without flow control h = x # Loop over blocks for index, blocklist in enumerate(self.blocks): for block in blocklist: h = block(h) # Apply global sum pooling as in SN-GAN h = torch.sum(self.activation(h), [2, 3]) # Get initial class-unconditional output out = self.linear(h) # Get projection of final featureset onto class vectors and add to evidence out = out + torch.sum(self.embed(y) * h, 1, keepdim=True) return out class G_D(nn.Module): """Parallelized G_D to minimize cross-gpu communication. Without this, Generator outputs would get all-gathered and then rebroadcast. """ def __init__(self, G, D): super().__init__() self.G = G self.D = D def forward(self, z, gy, x=None, dy=None, train_G=False, return_G_z=False, split_D=False): # If training G, enable grad tape with torch.set_grad_enabled(train_G): # Get Generator output given noise G_z = self.G(z, self.G.shared(gy)) # Cast as necessary if self.G.fp16 and not self.D.fp16: G_z = G_z.float() if self.D.fp16 and not self.G.fp16: G_z = G_z.half() # Split_D means to run D once with real data and once with fake, # rather than concatenating along the batch dimension. if split_D: D_fake = self.D(G_z, gy) if x is not None: D_real = self.D(x, dy) return D_fake, D_real else: if return_G_z: return D_fake, G_z else: return D_fake # If real data is provided, concatenate it with the Generator's output # along the batch dimension for improved efficiency. else: D_input = torch.cat([G_z, x], 0) if x is not None else G_z D_class = torch.cat([gy, dy], 0) if dy is not None else gy # Get Discriminator output D_out = self.D(D_input, D_class) if x is not None: return torch.split(D_out, [G_z.shape[0], x.shape[0]]) # D_fake, D_real else: if return_G_z: return D_out, G_z else: return D_out root = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'weights') model_weights = { 'imagenet': { 128: { 'G': os.path.join(root, '100k', 'G.pth'), 'G_ema': os.path.join(root, '100k', 'G_ema.pth'), 'state_dict': os.path.join(root, '100k', 'state_dict.pth'), } } } root_url = 'http://pretorched-x.csail.mit.edu/gans/BigGAN' ganocracy_root_url = 'http://ganocracy.csail.mit.edu/models' tfhub_urls = { 'imagenet': { 128: os.path.join(ganocracy_root_url, 'tfbiggan_128-13f17ff2.pth'), 256: os.path.join(ganocracy_root_url, 'tfbiggan_256-a4cf3382.pth'), 512: os.path.join(ganocracy_root_url, 'tfbiggan_512-447bfb81.pth'), } } model_urls = { 'places365': { 128: { 96: { 'D': os.path.join(root_url, 'biggan128_D_places365-8afb2a4d.pth'), 'G': os.path.join(root_url, 'biggan128_G_places365-43cd58c0.pth'), 'G_ema': os.path.join(root_url, 'biggan128_G_ema_places365-78c78abe.pth'), 'state_dict': os.path.join(root_url, 'biggan128_state_dict_places365-3d39f6bb.pth') }}, 256: { 96: { 'D': os.path.join(root_url, 'biggan256_D_ch96_places365-44bf2902.pth'), 'G': os.path.join(root_url, 'biggan256_G_ch96_places365-5adac787.pth'), 'G_ema': os.path.join(root_url, 'biggan256_G_ema_ch96_places365-ac277771.pth'), 'state_dict': os.path.join(root_url, 'biggan256_state_dict_ch96_places365-b4f6daf6.pth'), }, 128: { 'D': os.path.join(root_url, 'biggan256_D_ch128_places365-a6f7d3b6.pth'), 'G': os.path.join(root_url, 'biggan256_G_ch128_places365-47f6e48c.pth'), 'G_ema': os.path.join(root_url, 'biggan256_G_ema_ch128_places365-6fb66feb.pth'), 'state_dict': os.path.join(root_url, 'biggan256_state_dict_ch128_places365-7594847d.pth'), }}, }, 'imagenet': { 128: { 96: { 'D': os.path.join(root_url, 'biggan128_D_imagenet-9fd72e50.pth'), 'G': os.path.join(root_url, 'biggan128_G_imagenet-94e0b761.pth'), 'G_ema': os.path.join(root_url, 'biggan128_G_ema_imagenet-c9706dfb.pth'), 'state_dict': os.path.join(root_url, 'biggan128_state_dict_imagenet-4aad5089.pth'), }}, } } def BigGAN(resolution=256, pretrained='imagenet', load_ema=True, tfhub=True, ch=128): if resolution == 128: # Set default for now. ch = 96 attn = {128: '64', 256: '128', 512: '64'} dim_z = {128: 120, 256: 140, 512: 128} config = { 'G_param': 'SN', 'D_param': 'SN', 'G_ch': 96, 'D_ch': 96, 'D_wide': True, 'G_shared': True, 'shared_dim': 128, 'dim_z': dim_z[resolution], 'hier': True, 'cross_replica': False, 'mybn': False, 'G_activation': nn.ReLU(inplace=True), 'G_attn': attn[resolution], 'norm_style': 'bn', 'G_init': 'ortho', 'skip_init': True, 'no_optim': True, 'G_fp16': False, 'G_mixed_precision': False, 'accumulate_stats': False, 'num_standing_accumulations': 16, 'G_eval_mode': True, 'BN_eps': 1e-04, 'SN_eps': 1e-04, 'num_G_SVs': 1, 'num_G_SV_itrs': 1, 'resolution': resolution, 'n_classes': 1000 } version = 'G_ema' if load_ema else 'G' if tfhub and pretrained == 'imagenet': url = tfhub_urls[pretrained][resolution] weights = torch.hub.load_state_dict_from_url(url) G = Generator(**config) G.load_state_dict(weights, strict=False) G.eval() return G elif pretrained is not None: url = model_urls[pretrained][resolution][ch][version] sd_url = model_urls[pretrained][resolution][ch]['state_dict'] weights = torch.hub.load_state_dict_from_url(url) state_dict = torch.hub.load_state_dict_from_url(sd_url) G = Generator(**state_dict['config']) G.load_state_dict(weights, strict=False) G.eval() return G G = Generator(**config) return G def fix_class(G, y): f = G.forward def forward(self, z): bs = z.size(0) c = y * torch.ones(bs, device=z.device).long() return f(z, c, embed=True) setattr(G.__class__, 'forward', forward) return G

43.901592

123

0.548688

06c38ca5a3d50d9e9dfbe617e18a755385e83173

3,267

py

Python

src/primaires/pnj/commandes/pspawn/__init__.py

vlegoff/tsunami

36b3b974f6eefbf15cd5d5f099fc14630e66570b

[ "BSD-3-Clause" ]

14

2015-08-21T19:15:21.000Z

2017-11-26T13:59:17.000Z

src/primaires/pnj/commandes/pspawn/__init__.py

vincent-lg/tsunami

36b3b974f6eefbf15cd5d5f099fc14630e66570b

[ "BSD-3-Clause" ]

20

2015-09-29T20:50:45.000Z

2018-06-21T12:58:30.000Z

src/primaires/pnj/commandes/pspawn/__init__.py

vlegoff/tsunami

36b3b974f6eefbf15cd5d5f099fc14630e66570b

[ "BSD-3-Clause" ]

3

2015-05-02T19:42:03.000Z

2018-09-06T10:55:00.000Z

# -*-coding:Utf-8 -* # Copyright (c) 2010-2017 LE GOFF Vincent # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright notice, this # list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # * Neither the name of the copyright holder nor the names of its contributors # may be used to endorse or promote products derived from this software # without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT # OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. """Package contenant la commande 'pspawn'. """ from primaires.interpreteur.commande.commande import Commande class CmdPspawn(Commande): """Commande 'pspawn'. """ def __init__(self): """Constructeur de la commande""" Commande.__init__(self, "pspawn", "pspawn") self.groupe = "administrateur" self.nom_categorie = "batisseur" self.schema = "(<nombre>) <ident_prototype_pnj>" self.aide_courte = "fait apparaître un PNJ dans la salle" self.aide_longue = \ "Cette commande permet de faire apparaître un PNJ dans " \ "la salle où vous vous trouvez. Elle prend en paramètre " \ "obligatoire le prototype depuis lequel créer le PNJ." def interpreter(self, personnage, dic_masques): """Interprétation de la commande""" prototype = dic_masques["ident_prototype_pnj"].prototype salle = personnage.salle nb_pnj = 1 if prototype.squelette is None: personnage << "|err|Ce prototype n'a aucun squelette défini.|ff|" return if dic_masques["nombre"] is not None: nb_pnj = dic_masques["nombre"].nombre i = 0 while i < nb_pnj: pnj = type(self).importeur.pnj.creer_PNJ(prototype) pnj.salle = salle i += 1 salle.envoyer("{} apparaissent du néant.".format( prototype.get_nom(nb_pnj))) else: pnj = type(self).importeur.pnj.creer_PNJ(prototype) pnj.salle = salle salle.envoyer("{} apparaît du néant.".format( pnj.nom_singulier))

41.884615

79

0.676155

1f15f325c10419b6635c94e464dad42538b22998

87

py

Python

ComRISB/pygtool/gs_ml.py

comscope/comsuite

d51c43cad0d15dc3b4d1f45e7df777cdddaa9d6c

[ "BSD-3-Clause" ]

18

2019-06-15T18:08:21.000Z

2022-01-30T05:01:29.000Z

ComRISB/pygtool/gs_ml.py

comscope/Comsuite

b80ca9f34c519757d337487c489fb655f7598cc2

[ "BSD-3-Clause" ]

null

ComRISB/pygtool/gs_ml.py

comscope/Comsuite

b80ca9f34c519757d337487c489fb655f7598cc2

[ "BSD-3-Clause" ]

11

2019-06-05T02:57:55.000Z

2021-12-29T02:54:25.000Z

#!/usr/bin/env python from pyglib.gsolver.gs_ml_v2 import driver_gs_ml driver_gs_ml()

17.4

48

0.804598

b9eb6dc2a8a3d52943c35a49f2065c09bd0cd92d

2,298

py

Python

networks/carn_arch.py

xyzhu1/EMASRN

a0f1dc9b78ba814a8b7e3291a0625598cf6c7609

[ "MIT" ]

19

2020-09-18T08:49:19.000Z

2022-03-02T01:56:48.000Z

networks/carn_arch.py

zhuxyme/EMASRN

d92c12d37797514aaa8024168d5b96fb21d26f15

[ "MIT" ]

1

2021-12-06T12:59:47.000Z

networks/carn_arch.py

zhuxyme/EMASRN

d92c12d37797514aaa8024168d5b96fb21d26f15

[ "MIT" ]

8

2020-09-23T02:32:40.000Z

2022-01-18T15:18:40.000Z

import torch import torch.nn as nn import networks.carn_ops as ops class Block(nn.Module): def __init__(self, in_channels, out_channels, group=1): super(Block, self).__init__() self.b1 = ops.ResidualBlock(64, 64) self.b2 = ops.ResidualBlock(64, 64) self.b3 = ops.ResidualBlock(64, 64) self.c1 = ops.BasicBlock(64 * 2, 64, 1, 1, 0) self.c2 = ops.BasicBlock(64 * 3, 64, 1, 1, 0) self.c3 = ops.BasicBlock(64 * 4, 64, 1, 1, 0) def forward(self, x): c0 = o0 = x b1 = self.b1(o0) c1 = torch.cat([c0, b1], dim=1) o1 = self.c1(c1) b2 = self.b2(o1) c2 = torch.cat([c1, b2], dim=1) o2 = self.c2(c2) b3 = self.b3(o2) c3 = torch.cat([c2, b3], dim=1) o3 = self.c3(c3) return o3 class CARN(nn.Module): def __init__(self): super(CARN, self).__init__() scale = 2 multi_scale = False group = 1 self.sub_mean = ops.MeanShift((0.4488, 0.4371, 0.4040), sub=True) self.add_mean = ops.MeanShift((0.4488, 0.4371, 0.4040), sub=False) self.entry = nn.Conv2d(3, 64, 3, 1, 1) self.b1 = Block(64, 64) self.b2 = Block(64, 64) self.b3 = Block(64, 64) self.c1 = ops.BasicBlock(64 * 2, 64, 1, 1, 0) self.c2 = ops.BasicBlock(64 * 3, 64, 1, 1, 0) self.c3 = ops.BasicBlock(64 * 4, 64, 1, 1, 0) self.upsample = ops.UpsampleBlock(64, scale=scale, multi_scale=multi_scale, group=group) self.exit = nn.Conv2d(64, 3, 3, 1, 1) def forward(self, x): scale=2 x = self.sub_mean(x) x = self.entry(x) c0 = o0 = x b1 = self.b1(o0) c1 = torch.cat([c0, b1], dim=1) o1 = self.c1(c1) b2 = self.b2(o1) c2 = torch.cat([c1, b2], dim=1) o2 = self.c2(c2) b3 = self.b3(o2) c3 = torch.cat([c2, b3], dim=1) o3 = self.c3(c3) out = self.upsample(o3, scale=scale) out = self.exit(out) out = self.add_mean(out) return out

27.035294

75

0.474326

0527809f637e7960647774d14659f7c40f425019

12,360

py

Python

skrf/media/circularWaveguide.py

dxxx9/scikit-rf

8bbb23ff5ef25ae054cfd45b4558cbdfce3378a7

[ "BSD-3-Clause" ]

379

2015-01-25T12:19:19.000Z

2022-03-29T14:01:07.000Z

skrf/media/circularWaveguide.py

jhillairet/scikit-rf

060257568f3c0bdcc817e89742ec29445df81a09

[ "BSD-3-Clause" ]

456

2015-01-06T19:15:55.000Z

2022-03-31T06:42:57.000Z

skrf/media/circularWaveguide.py

jhillairet/scikit-rf

060257568f3c0bdcc817e89742ec29445df81a09

[ "BSD-3-Clause" ]

211

2015-01-06T17:14:06.000Z

2022-03-31T01:36:00.000Z

""" circularWaveguide (:mod:`skrf.media.circularWaveguide`) ================================================================ .. autosummary:: :toctree: generated/ CircularWaveguide """ from scipy.constants import epsilon_0, mu_0, pi from scipy.special import jn_zeros, jnp_zeros from numpy import sqrt, where import numpy as npy from .media import Media from ..data import materials from .freespace import Freespace from ..constants import NumberLike from typing import Union, TYPE_CHECKING if TYPE_CHECKING: from .. frequency import Frequency class CircularWaveguide(Media): r""" A single mode of a homogeneously filled Circular Waveguide Represents a single mode of a homogeneously filled circular waveguide of cross-section `r^2 pi`. The mode is determined by `mode-type` (`'te'` or `'tm'`) and mode indices ( `m` and `n` ). Corrugated circular waveguides, which also support HE modes, are not supported. ==================================== ============= =============== Quantity Symbol Variable ==================================== ============= =============== Characteristic Wave Number :math:`k_0` :attr:`k0` Cut-off Wave Number :math:`k_c` :attr:`kc` Longitudinal Wave Number :math:`k_z` :attr:`gamma` Transverse Wave Number (a) :math:`k_x` :attr:`kx` Transverse Wave Number (b) :math:`k_y` :attr:`ky` Characteristic Impedance :math:`z_0` :attr:`z0` ==================================== ============= =============== Parameters ---------- frequency : :class:`~skrf.frequency.Frequency` object frequency band of this transmission line medium z0 : number, array-like, or None the port impedance for media. Only needed if it's different from the characteristic impedance of the transmission line. if z0 is None then will default to Z0 r : number radius of the waveguide, in meters. mode_type : ['te','tm'] mode type, transverse electric (te) or transverse magnetic (tm) to-z. where z is direction of propagation m : int mode index in 'phi'-direction, the azimuthal index n : int mode index in 'r'-direction, the radial index ep_r : number, array-like, filling material's relative permittivity mu_r : number, array-like filling material's relative permeability rho : number, array-like, string resistivity (ohm-m) of the conductor walls. If array-like must be same length as frequency. if str, it must be a key in `skrf.data.materials`. \*args, \*\*kwargs : arguments, keyword arguments passed to :class:`~skrf.media.media.Media`'s constructor (:func:`~skrf.media.media.Media.__init__` Examples -------- In the following example an ideal waveguide of 2.39 mm diameter is constructed for the high W band, operated in the fundamental TE11 mode. If no conductivity is provided the walls are treated as perfect electric conductors. >>> freq = rf.Frequency(88, 110, 101, 'ghz') >>> rf.CircularWaveguide(freq, r=0.5 * 2.39e-3) """ def __init__(self, frequency: Union['Frequency', None] = None, z0: Union[NumberLike, None] = None, r: NumberLike = 1, mode_type: str = 'te', m: int = 1, n: int = 1, ep_r: NumberLike = 1, mu_r: NumberLike = 1, rho: Union[NumberLike, str, None] = None, *args, **kwargs): Media.__init__(self, frequency=frequency,z0=z0) if mode_type.lower() not in ['te','tm']: raise ValueError('mode_type must be either \'te\' or \'tm\'') self.r = r self.mode_type = mode_type.lower() self.m = m self.n = n self.ep_r = ep_r self.mu_r = mu_r self.rho = rho def __str__(self) -> str: f=self.frequency output = \ 'Circular Waveguide Media. %i-%i %s. %i points'%\ (f.f_scaled[0], f.f_scaled[-1], f.unit, f.npoints) + \ '\n r= %.2em'% \ (self.r) return output def __repr__(self) -> str: return self.__str__() @classmethod def from_Z0(cls, frequency: 'Frequency', Z0: NumberLike, f: NumberLike, ep_r: NumberLike = 1, mu_r: NumberLike = 1, **kwargs): r""" Initialize from specified impedance at a given frequency, assuming the fundamental TE11 mode. Parameters ---------- frequency : Frequency Object Z0 : number /array characteristic impedance to create at `f` f : number frequency (in Hz) at which the resultant waveguide has the characteristic impedance Z0 ep_r : number, array-like, filling material's relative permittivity mu_r : number, array-like filling material's relative permeability \*\*kwargs : arguments, keyword arguments passed to :class:`~skrf.media.media.Media`'s constructor (:func:`~skrf.media.media.Media.__init__` """ mu = mu_0*mu_r ep = epsilon_0*ep_r w = 2*pi*f # if self.mode_type =="te": u = jnp_zeros(1, 1)[-1] r =u/(w*mu) * 1./sqrt((1/(Z0*1j)**2+ep/mu)) kwargs.update(dict(frequency=frequency, r=r, m=1, n=1, ep_r=ep_r, mu_r=mu_r)) return cls(**kwargs) @property def ep(self) -> NumberLike: """ The permativity of the filling material. Returns ------- ep : number filling material's relative permittivity """ return self.ep_r * epsilon_0 @property def mu(self) -> NumberLike: """ The permeability of the filling material. Returns ------- mu : number filling material's relative permeability """ return self.mu_r * mu_0 @property def k0(self) -> NumberLike: r""" Characteristic wave number. .. math:: k_0 = \omega \sqrt{\varepsilon \mu} Returns ------- k0 : number characteristic wave number """ return 2*pi*self.frequency.f*sqrt(self.ep * self.mu) @property def kc(self) -> NumberLike: r""" Cut-off wave number. Defined as .. math:: k_c = \frac{u_{mn}}{R} where R is the radius of the waveguide, and u_mn is: * the n-th root of the m-th Bessel function for 'tm' mode * the n-th root of the Derivative of the m-th Bessel function for 'te' mode. Returns ------- kc : number cut-off wavenumber """ if self.mode_type =="te": u = jnp_zeros(self.m, self.n)[-1] elif self.mode_type =="tm": u = jn_zeros(self.m,self.n)[-1] return u/self.r @property def f_cutoff(self) -> NumberLike: r""" cutoff frequency for this mode .. math:: f_c = \frac{v}{2 \pi} \frac{u_{mn}}{R} where R is the radius of the waveguide, and u_mn is: * the n-th root of the m-th Bessel function for 'tm' mode * the n-th root of the Derivative of the m-th Bessel function for 'te' mode. and v= 1/sqrt(ep*mu) is the bulk velocity inside the filling material. """ v = 1/sqrt(self.ep*self.mu) return v* self.kc/(2*npy.pi) @property def f_norm(self) -> NumberLike: """ frequency vector normalized to cutoff """ return self.frequency.f/self.f_cutoff @property def rho(self) -> NumberLike: """ conductivity of sidewalls in ohm*m Parameters -------------- val : float, array-like or str the conductivity in ohm*m. If array-like must be same length as self.frequency. if str, it must be a key in `skrf.data.materials`. Examples --------- >>> wg.rho = 2.8e-8 >>> wg.rho = 2.8e-8 * ones(len(wg.frequency)) >>> wg.rho = 'al' >>> wg.rho = 'aluminum' """ # if self.roughness != None: # delta = skin_depth(self.frequency.f, self._rho, self.mu_r) # k_w = 1. +exp(-(delta/(2*self.roughness))**1.6) # return self._rho*k_w**2 return self._rho @rho.setter def rho(self, val): if isinstance(val, str): self._rho = materials[val.lower()]['resistivity(ohm*m)'] else: self._rho=val @property def lambda_guide(self) -> NumberLike: r""" Guide wavelength. .. math:: \lambda_g = 2\pi/\beta the distance in which the phase of the field increases by 2 pi. """ return 2*pi/self.beta @property def lambda_cutoff(self) -> NumberLike: r""" Cutoff wavelength. .. math:: \lambda_c = v/f_c where v= 1/sqrt(ep*mu) """ v = 1/sqrt(self.ep*self.mu) return v/self.f_cutoff @property def gamma(self) -> NumberLike: r""" The propagation constant (aka Longitudinal wave number) Defined as .. math:: k_z = \pm j \sqrt {k_0^2 - k_c^2} This is: * IMAGINARY for propagating modes * REAL for non-propagating modes, Returns ------- gamma : number The propagation constant """ # This also holds for the circular waveguide ## haringtons form if False: #self.m==1 and self.n==0: fs = Freespace(frequency=self.frequency, ep_r=self.ep_r, mu_r=self.mu_r) g= where(self.f_norm>1., sqrt(1-self.f_norm**(-2))*fs.gamma, # cutton -1j*sqrt(1-self.f_norm**(2))*fs.gamma)# cutoff else: # TODO: fix this for lossy ep/mu (remove abs?) k0,kc = self.k0, self.kc g= 1j*sqrt(abs(k0**2 - kc**2)) * (k0>kc) +\ sqrt(abs(kc**2- k0**2))*(k0<kc) + \ 0*(kc==k0) g = g + self.alpha_c *(self.rho is not None) return g @property def alpha_c(self) -> NumberLike: """ Loss due to finite conductivity of the sidewalls for the fundamental mode TE11. Higher order modes are not implemented, as well as effects due to surface roughness. In units of Np/m See property `rho` for setting conductivity. Effects of finite conductivity are taken from [#]_, but expressed in the same terms as in [#]_. References ---------- .. [#] Eq. (3.133), Chapter 3.4, Microwave Engineering, Pozar David, 2011 .. [#] Eq. (9.8.1), Chapter 9, Electromagnetic Waves and Antennas by Sophocles J. Orfanidis http://eceweb1.rutgers.edu/~orfanidi/ewa/ See Also -------- rho """ # TODO: Generalize to higher order modes if (self.mode_type != "te") or (self.m != 1) or (self.n != 1): raise NotImplementedError if self.rho is None: return 0 r, w, ep, rho, f_n = self.r, self.frequency.w, self.ep, \ self.rho, self.f_norm u= self.kc*r return 1./r * sqrt( (w*ep)/(2./rho) ) * ( (1/f_n)**2 + 1/(u**2 - 1) ) \ /sqrt(1-(1/f_n)**2) @property def Z0(self) -> NumberLike: """ The characteristic impedance. """ omega = self.frequency.w impedance_dict = {'te': 1j*omega*self.mu/(self.gamma), 'tm': -1j*self.gamma/(omega*self.ep),\ } return impedance_dict[self.mode_type]

30.44335

103

0.521117

d51da784719b9971a212649528c060503f9b8225

1,145

py

Python

Easy/Que100.py

HuangZengPei/LeetCode

d2b8a1dfe986d71d02d2568b55bad6e5b1c81492

[ "MIT" ]

2

2019-11-20T14:05:27.000Z

2019-11-20T14:05:28.000Z

Easy/Que100.py

HuangZengPei/LeetCode

d2b8a1dfe986d71d02d2568b55bad6e5b1c81492

[ "MIT" ]

null

Easy/Que100.py

HuangZengPei/LeetCode

d2b8a1dfe986d71d02d2568b55bad6e5b1c81492

[ "MIT" ]

null

# Definition for a binary tree node. # class TreeNode(object): # def __init__(self, x): # self.val = x # self.left = None # self.right = None class Solution(object): # 不能单独用前序遍历，[1,2]以及[1,null,2]结果相同 def preOrder(self,p,list): if p == None: return else: list.append(p.val) self.preOrder(p.left,list) self.preOrder(p.right,list) def isSameTree(self, p, q): """ :type p: TreeNode :type q: TreeNode :rtype: bool """ list1 = [] list2 = [] self.preOrder(p,list1) self.preOrder(q,list2) return list1 == list2 def isSameTree(self, p, q): """ :type p: TreeNode :type q: TreeNode :rtype: bool """ if not p and not q: return True if not p or not q: return False if p.val != q.val: return False else: return self.isSameTree(p.left,q.left) and self.isSameTree(p.right,q.right)

26.022727

86

0.464629

ef72a06ada490805daac90b4aa87656bee7afee1

650

py

Python

pysbolgraph/S2ProvUsage.py

zhfanrui/pysbolgraph

c4914705bd9b22a2b69db0fc4d43049fcb07ad17

[ "BSD-2-Clause" ]

4

2018-06-29T10:43:08.000Z

2019-03-27T22:33:33.000Z

pysbolgraph/S2ProvUsage.py

zhfanrui/pysbolgraph

c4914705bd9b22a2b69db0fc4d43049fcb07ad17

[ "BSD-2-Clause" ]

14

2019-01-22T16:03:12.000Z

2019-11-11T19:05:32.000Z

pysbolgraph/S2ProvUsage.py

zhfanrui/pysbolgraph

c4914705bd9b22a2b69db0fc4d43049fcb07ad17

[ "BSD-2-Clause" ]

12

2018-07-01T10:59:37.000Z

2021-03-01T08:48:20.000Z

from .S2Identified import S2Identified from .terms import Prov from .terms import SBOL2 from rdflib import URIRef from rdflib.namespace import RDF class S2ProvUsage(S2Identified): def __init__(self, g, uri): super(S2ProvUsage, self).__init__(g, uri) @property def entity(self): return self.get_identified_property(Prov.entity) @entity.setter def entity(self, entity): self.set_identified_property(Prov.entity, entity) @property def role(self): return self.get_uri_property(Prov.hadRole) @role.setter def role(self, role): self.set_uri_property(Prov.hadRole, role)

20.967742

57

0.701538

031a3d34cb4e54b14cb038a2d0f88bb00a191756

305

py

Python

src/xdist/__init__.py

kianmeng/pytest-xdist

290b322a5d48290397ad698fc1dcb729cbe62e07

[ "MIT" ]

883

2015-09-01T22:41:20.000Z

2022-03-30T22:32:43.000Z

src/xdist/__init__.py

kianmeng/pytest-xdist

290b322a5d48290397ad698fc1dcb729cbe62e07

[ "MIT" ]

623

2015-09-02T00:06:07.000Z

2022-03-31T11:40:44.000Z

src/xdist/__init__.py

kianmeng/pytest-xdist

290b322a5d48290397ad698fc1dcb729cbe62e07

[ "MIT" ]

190

2015-09-01T18:56:08.000Z

2022-03-25T17:50:56.000Z

from xdist.plugin import ( is_xdist_worker, is_xdist_master, get_xdist_worker_id, is_xdist_controller, ) from xdist._version import version as __version__ __all__ = [ "__version__", "is_xdist_worker", "is_xdist_master", "is_xdist_controller", "get_xdist_worker_id", ]

19.0625

49

0.714754

4c45351b800bd72bfa76522864c7dd66725bab61

2,954

py

Python

keras_transformer/keras_transformer/core/encoder_decoder/DecoderBlock.py

erelcan/keras-transformer

ae88985dd4f1b5f91737e80c7e9c3157b60b4c4f

[ "Apache-2.0" ]

3

2021-02-14T17:10:59.000Z

2021-02-14T18:09:17.000Z

keras_transformer/keras_transformer/core/encoder_decoder/DecoderBlock.py

erelcan/keras-transformer

ae88985dd4f1b5f91737e80c7e9c3157b60b4c4f

[ "Apache-2.0" ]

null

keras_transformer/keras_transformer/core/encoder_decoder/DecoderBlock.py

erelcan/keras-transformer

ae88985dd4f1b5f91737e80c7e9c3157b60b4c4f

[ "Apache-2.0" ]

null

from keras.layers import Layer from keras_transformer.core.encoder_decoder.sub_layers.SelfAttentionSublayer import SelfAttentionSublayer from keras_transformer.core.encoder_decoder.sub_layers.PositionWiseFeedForwardSublayer import PositionWiseFeedForwardSublayer class DecoderBlock(Layer): def __init__(self, d_model, attention_info, pff_info, **kwargs): super().__init__(**kwargs) self.supports_masking = True self._d_model = d_model self._attention_info = attention_info self._pff_info = pff_info # masked_self_attention masks the future for each query~. Hence, using left_context_mask. self._masked_self_attention = SelfAttentionSublayer(self._attention_info["head_num"], "left_context_mask", self._attention_info["dropout_rate"]) self._self_attention = SelfAttentionSublayer(self._attention_info["head_num"], None, self._attention_info["dropout_rate"]) self._position_wise_feed_forward = PositionWiseFeedForwardSublayer(self._d_model, self._pff_info["inner_length"], self._pff_info["dropout_rate"]) def call(self, inputs, mask=None, **kwargs): # Assuming that inputs is a tensor which will be copied to Q, K and V in multi-head attention. # We may customize this for taking a list of tensors representing Q, K and V which will be directly mapped # in multi-head attention. # input: [encoder_output, decoder_input] # mask: [encoder_output_mask, decoder_mask] encoder_output, decoder_input = inputs encoder_output_mask, decoder_mask = mask # In sublayer2, encoder_output is used for keys and queries. sublayer1_output = self._masked_self_attention(decoder_input, mask=decoder_mask) sublayer2_output = self._self_attention([sublayer1_output, encoder_output, encoder_output], [decoder_mask, encoder_output_mask, encoder_output_mask]) output = self._position_wise_feed_forward(sublayer2_output, mask=decoder_mask) return output def compute_output_shape(self, input_shape): return input_shape[1][0], input_shape[1][1], self._d_model def compute_mask(self, inputs, mask=None): # The input mask is [encoder_output_mask, decoder_mask] # This part is tricky: # If we want to explicitly manage the encoder and decoder masks outside; this method should have no effect. # Depending on the usage, we may need to return both masks or just the one on the decoder output/sequence. # For now, we return the decoder_mask only; for next decoder in decoder layer, mask will be prepared by # the DecoderStack. return mask[1] def get_config(self): config = { "d_model": self._d_model, "attention_info": self._attention_info, "pff_info": self._pff_info } base_config = super().get_config() config.update(base_config) return config

48.42623

157

0.721733

2dd69fb5fb52e08a04fd1621a2a947048539e632

2,730

py

Python

tools/lint.py

mvaisakh/art-testing

89dab669dca6d768a79101b1e480794fa46012d5

[ "Apache-2.0" ]

null

tools/lint.py

mvaisakh/art-testing

89dab669dca6d768a79101b1e480794fa46012d5

[ "Apache-2.0" ]

null

tools/lint.py

mvaisakh/art-testing

89dab669dca6d768a79101b1e480794fa46012d5

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 # Copyright 2015 ARM Limited # # 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 multiprocessing import os import subprocess import sys import utils def BuildOptions(): parser = argparse.ArgumentParser( description = "Lint the java code in the repository.", # Print default values. formatter_class = argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('--jobs', '-j', metavar='N', type=int, nargs='?', default=multiprocessing.cpu_count(), help='Lint using N jobs') return parser.parse_args() def GetJavaFiles(): java_files = [] for dir_java_files in [utils.dir_framework, utils.dir_benchmarks]: for root, dirs, files in os.walk(dir_java_files): files = map(lambda x : os.path.join(root, x), files) java_files += [f for f in files if f.endswith('.java')] java_files.sort() return java_files def Lint(filename): command = \ [os.path.join(utils.dir_tools, 'checkstyle', 'checkstyle'), filename] print(' '.join(command)) process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE) out, err = process.communicate() rc = process.wait() if rc != 0: print(out) return rc def EnsureCheckstyleAvailable(): # Run the checkstyle script once to ensure the checkstyle jar file as # available. p = subprocess.check_output([os.path.join(utils.dir_tools, 'checkstyle', 'checkstyle')]) def LintFiles(files, jobs = 1): EnsureCheckstyleAvailable() pool = multiprocessing.Pool(jobs) # The '.get(9999999)' is workaround to allow killing the test script with # ctrl+C from the shell. This bug is documented at # http://bugs.python.org/issue8296. try: results = pool.map_async(Lint, files).get(9999999) pool.close() pool.join() except KeyboardInterrupt: pool.terminate() sys.exit(1) n_incorrectly_formatted_files = sum(results) return n_incorrectly_formatted_files if __name__ == "__main__": args = BuildOptions() rc = LintFiles(GetJavaFiles(), args.jobs) sys.exit(rc)

30.674157

90

0.675824

565c3fc7bbc6b0d7e26084a7a3226b3a2e8db8f9

1,534

py

Python

build_site.py

devinbrady/openanc

5e69dceb08120dd80a902fdfcaaf1a2a9947afaa

[ "CC0-1.0" ]

4

2020-08-04T20:55:18.000Z

2022-03-14T23:18:47.000Z

build_site.py

devinbrady/openanc

5e69dceb08120dd80a902fdfcaaf1a2a9947afaa

[ "CC0-1.0" ]

12

2020-07-16T17:46:12.000Z

2022-01-24T22:02:07.000Z

build_site.py

devinbrady/openanc

5e69dceb08120dd80a902fdfcaaf1a2a9947afaa

[ "CC0-1.0" ]

1

2020-08-04T02:59:24.000Z

""" All steps necessary to build OpenANC pages """ import argparse from datetime import datetime from scripts.refresh_data import RefreshData from scripts.index import BuildIndex from scripts.districts import BuildDistricts from scripts.ancs import BuildANCs from scripts.wards import BuildWards start_time = datetime.now() parser = argparse.ArgumentParser() parser.add_argument('-r', '--refresh-data', action='store_true', help='Refresh local CSVs from Google Sheets') parser.add_argument('-i', '--build-index', action='store_true', help='Build index and other top-level pages') parser.add_argument('-w', '--build-wards', action='store_true', help='Build page for each Ward') parser.add_argument('-a', '--build-ancs', action='store_true', help='Build page for each ANC') parser.add_argument('-d', '--build-districts', action='store_true', help='Build page for each SMD') args = parser.parse_args() if args.refresh_data: r = RefreshData() r.run() if args.build_index: bi = BuildIndex() bi.run() if args.build_wards: bw = BuildWards() bw.run() if args.build_ancs: ba = BuildANCs() ba.run() if args.build_districts: bd = BuildDistricts() bd.run() if not any([args.refresh_data, args.build_index, args.build_wards, args.build_ancs, args.build_districts]): print('No arguments provided to build_site script, exiting.') end_time = datetime.now() time_elapsed = end_time - start_time seconds_elapsed = time_elapsed.total_seconds() print(f'build_site: {seconds_elapsed:.1f} seconds')

28.943396

110

0.736636

89c567a4b46e20dafe52b34e4a3eb8118180fb68

1,646

py

Python

promoterz/representation/oldschool.py

emillj/gekkoJaponicus

d77c8c7a303b97a3643eb3f3c8b995b8b393f3f7

[ "MIT" ]

null

promoterz/representation/oldschool.py

emillj/gekkoJaponicus

d77c8c7a303b97a3643eb3f3c8b995b8b393f3f7

[ "MIT" ]

null

promoterz/representation/oldschool.py

emillj/gekkoJaponicus

d77c8c7a303b97a3643eb3f3c8b995b8b393f3f7

[ "MIT" ]

1

2021-11-29T20:18:25.000Z

#!/bin/python import random import json import os from copy import deepcopy from deap import base from deap import creator from deap import tools from deap import algorithms import numpy as np from .. import functions def constructPhenotype(stratSettings, individue): # THIS FUNCTION IS UGLYLY WRITTEN; USE WITH CAUTION; # (still works :}) Strategy = individue.Strategy R = lambda V, lim: ((lim[1]-lim[0])/100) * V + lim[0] AttributeNames = sorted(list(stratSettings.keys())) Phenotype = {} for K in range(len(AttributeNames)): Value = R(individue[K], stratSettings[AttributeNames[K]]) Phenotype[AttributeNames[K]] = Value Phenotype = functions.expandNestedParameters(Phenotype) return Phenotype def createRandomVarList(IndSize): VAR_LIST = [random.randrange(0,100) for x in range(IndSize)] return VAR_LIST def initInd(Criterion, Attributes): w = Criterion() IndSize =len(list(Attributes.keys())) w[:] = createRandomVarList(IndSize) return w def getToolbox(Strategy, genconf, Attributes): toolbox = base.Toolbox() creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Individual", list, fitness=creator.FitnessMax, Strategy=Strategy) toolbox.register("newind", initInd, creator.Individual, Attributes) toolbox.register("population", tools.initRepeat, list, toolbox.newind) toolbox.register("mate", tools.cxTwoPoint) toolbox.register("mutate", tools.mutUniformInt, low=10, up=10, indpb=0.2) toolbox.register("constructPhenotype", constructPhenotype, Attributes) return toolbox

27.433333

77

0.709599

924fba0647ef5146df4f41163e8bc74a3044652e

2,054

py

Python

mm2d/core/backend/pymunk/body.py

adamheins/mm2d

9a7b6ef63998757172c3100b916621a25d165d65

[ "MIT" ]

null

mm2d/core/backend/pymunk/body.py

adamheins/mm2d

9a7b6ef63998757172c3100b916621a25d165d65

[ "MIT" ]

null

mm2d/core/backend/pymunk/body.py

adamheins/mm2d

9a7b6ef63998757172c3100b916621a25d165d65

[ "MIT" ]

null

import pymunk from mm2d.shapes import Circle, Segment, Polygon from mm2d.body import Fixture, Body from mm2d.spatial import Transform, Twist, State from .util import transform_to_pymunk def shape_to_pymunk(shape) -> pymunk.Shape: if isinstance(shape, Circle): pymunk_shape = pymunk.Circle(radius=shape.radius, offset=shape.position) elif isinstance(shape, Segment): pymunk_shape = pymunk.Segment(a=shape.a, b=shape.b, radius=shape.radius) elif isinstance(shape, Polygon): pymunk_transform = None if shape.transform is not None: pymunk_transform = transform_to_pymunk(shape.transform) pymunk_shape = pymunk.Poly( vertices=shape.vertices, radius=shape.radius, transform=pymunk_transform ) else: raise TypeError(f"{shape} is not a valid shape.") return pymunk_shape def fixture_to_pymunk(fixture: Fixture) -> pymunk.Shape: pymunk_shape = shape_to_pymunk(fixture.shape) pymunk_shape.mass = fixture.mass pymunk_shape.friction = fixture.friction pymunk_shape.elasticity = fixture.elasticity return pymunk_shape def body_to_pymunk(body: Body) -> pymunk.Body: pymunk_body = pymunk.Body() pymunk_shapes = [] for fixture in body.fixtures: pymunk_shape = fixture_to_pymunk(fixture) pymunk_shape.body = pymunk_body pymunk_shapes.append(pymunk_shape) # TODO not sure if I'm missing something with setting position and angle - # should I only do this after adding shapes or something? pymunk_body.position = body.pose.position pymunk_body.angle = body.pose.angle return pymunk_body, pymunk_shapes class PymunkBodyBackend: """Rigid body backend using pymunk.""" def __init__(self, body): self.pymunk_body, self.pymunk_shapes = body_to_pymunk(body) def state(self): pose = Transform(self.pymunk_body.position, self.pymunk_body.angle) twist = Twist(self.pymunk_body.velocity, self.pymunk_body.angular_velocity) return State(pose, twist)

33.672131

84

0.717137

77e965477a37e700e6be14defbffca1b870271f9

2,341

py

Python

binary_search_tree.py

ValtersJZ/LearningDataStructuresAndAlgorithms

d608494cc6a3bba4b3a91a9469c59de077fcf36f

[ "MIT" ]

1

2019-07-10T10:44:09.000Z

binary_search_tree.py

ValtersJZ/LearningDataStructuresAndAlgorithms

d608494cc6a3bba4b3a91a9469c59de077fcf36f

[ "MIT" ]

null

binary_search_tree.py

ValtersJZ/LearningDataStructuresAndAlgorithms

d608494cc6a3bba4b3a91a9469c59de077fcf36f

[ "MIT" ]

null

class Node(object): def __init__(self, value): self.value = value self.left = None self.right = None class BST(object): """Binary search tree""" def __init__(self, root): self.root = Node(root) def insert(self, new_val): self._insert_node(self.root, new_val) def search(self, find_val): return self._search_node(self.root, find_val) def print_tree(self): return self._preorder_print(self.root, "")[:-1] # Remove a "- from the end def _insert_node(self, start, new_val): if start: if new_val < start.value: if start.left: self._insert_node(start.left, new_val) else: start.left = Node(new_val) elif new_val > start.value: if start.right: self._insert_node(start.right, new_val) else: start.right = Node(new_val) else: # This practice implementation assumes no duplicate values raise Exception("Error: tried to insert that is already present in the binary search tree") else: self.root = Node(new_val) def _search_node(self, start, find_val): if start: if find_val == start.value: return True elif find_val < start.value: return self._search_node(start.left, find_val) else: # find_val > start.value: return self._search_node(start.right, find_val) else: return False def _preorder_print(self, start, traversal): if start: traversal += "{}-".format(start.value) traversal = self._preorder_print(start.left, traversal) traversal = self._preorder_print(start.right, traversal) return traversal if __name__ == '__main__': # Set up tree """ 4 / \ 2 5 / \ 1 3 """ tree = BST(4) # Insert elements tree.insert(2) tree.insert(1) tree.insert(3) tree.insert(5) # Check search # Should be True print(tree.search(4)) # Should be False print(tree.search(6)) # Pre order depth first search print print("pre-order DFS traverse:", tree.print_tree())

28.54878

107

0.554464

e6a178b62334da0eca930230515c01df69a7c81c

1,530

py

Python

ivy/functional/backends/tensorflow/data_type.py

saurbhc/ivy

20b327b4fab543b26ad5a18acf4deddd6e3c804b

[ "Apache-2.0" ]

null

ivy/functional/backends/tensorflow/data_type.py

saurbhc/ivy

20b327b4fab543b26ad5a18acf4deddd6e3c804b

[ "Apache-2.0" ]

1

2022-03-08T13:29:20.000Z

ivy/functional/backends/tensorflow/data_type.py

saurbhc/ivy

20b327b4fab543b26ad5a18acf4deddd6e3c804b

[ "Apache-2.0" ]

null

# global import numpy as np import tensorflow as tf from typing import Union, Tuple from tensorflow.python.types.core import Tensor from tensorflow.python.framework.dtypes import DType # local import ivy # noinspection PyShadowingBuiltins def iinfo(type: Union[DType, str, Tensor])\ -> np.iinfo: return tf.experimental.numpy.iinfo(ivy.dtype_to_str(type)) class Finfo: def __init__(self, tf_finfo): self._tf_finfo = tf_finfo @property def bits(self): return self._tf_finfo.bits @property def eps(self): return float(self._tf_finfo.eps) @property def max(self): return float(self._tf_finfo.max) @property def min(self): return float(self._tf_finfo.min) @property def smallest_normal(self): return float(self._tf_finfo.tiny) # noinspection PyShadowingBuiltins def finfo(type: Union[DType, str, Tensor])\ -> Finfo: return Finfo(tf.experimental.numpy.finfo(ivy.dtype_from_str(type))) def result_type(*arrays_and_dtypes: Union[Tensor, tf.DType]) -> tf.DType: if len(arrays_and_dtypes) <= 1: return tf.experimental.numpy.result_type(arrays_and_dtypes) result = tf.experimental.numpy.result_type(arrays_and_dtypes[0], arrays_and_dtypes[1]) for i in range(2, len(arrays_and_dtypes)): result = tf.experimental.numpy.result_type(result, arrays_and_dtypes[i]) return result def broadcast_to (x: Tensor, shape: Tuple[int, ...])-> Tensor: return tf.broadcast_to(x, shape)

24.677419

90

0.701307

c3a2875d3476b47c8f992f6d3b1cc47576384c26

1,722

py

Python

src/tuplenet/tools/rebuild-etcd.py

vipshop/Tuplenet

25701765b4dfd11ff1da1a126109cce04d987a4b

[ "Apache-2.0" ]

null

src/tuplenet/tools/rebuild-etcd.py

vipshop/Tuplenet

25701765b4dfd11ff1da1a126109cce04d987a4b

[ "Apache-2.0" ]

null

src/tuplenet/tools/rebuild-etcd.py

vipshop/Tuplenet

25701765b4dfd11ff1da1a126109cce04d987a4b

[ "Apache-2.0" ]

null

#!/usr/bin/env python import sys import os def redefine_sys_path(): system_site_packet64_path = '/usr/lib/python2.7/site-packages' system_site_packet_path = '/usr/lib64/python2.7/site-packages' file_path = os.path.realpath(__file__) file_path = file_path.split('/') file_path = '/'.join(file_path[0:-3]) file_path += '/py_third' if system_site_packet64_path in sys.path: sys.path.remove(system_site_packet64_path) if system_site_packet_path in sys.path: sys.path.remove(system_site_packet_path) sys.path = [file_path] + sys.path parent_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) sys.path.append(parent_dir) redefine_sys_path() import etcd3 from optparse import OptionParser if __name__ == "__main__": parser = OptionParser("") parser.add_option("-a", "--host", dest = "host", action = "store", type = "string", default = "localhost:2379", help = "etcd host address") parser.add_option("-f", "--file", dest = "file", action = "store", type = "string", default = "data.txt", help = "data file to construct tuplenet env") (options, args) = parser.parse_args() host_ip = options.host.split(':')[0] host_port = options.host.split(':')[1] etcd = etcd3.client(host_ip, host_port) fd = open(options.file, 'r') etcd_data = fd.readlines() fd.close() kv_array = [] for i in xrange(0, len(etcd_data), 2): key = etcd_data[i].rstrip('\n') value = etcd_data[i+1].rstrip('\n') kv_array.append((key, value)) for key,value in kv_array: etcd.put(key,value)

33.764706

77

0.618467

1f265ae9c13617d5af6ebaebb69f456c03cd9ba9

28,788

py

Python

tests/test_caching.py

AtilaSaraiva/devito

629181c89a43054b9710920e76af0eb14138c97c

[ "MIT" ]

199

2016-08-18T23:33:05.000Z

2019-12-24T07:08:48.000Z

tests/test_caching.py

AtilaSaraiva/devito

629181c89a43054b9710920e76af0eb14138c97c

[ "MIT" ]

949

2016-04-25T11:41:34.000Z

2019-12-27T10:43:40.000Z

tests/test_caching.py

AtilaSaraiva/devito

629181c89a43054b9710920e76af0eb14138c97c

[ "MIT" ]

78

2016-08-30T07:42:34.000Z

2019-12-13T20:34:45.000Z

from ctypes import byref, c_void_p import weakref import numpy as np import pytest from devito import (Grid, Function, TimeFunction, SparseFunction, SparseTimeFunction, ConditionalDimension, SubDimension, Constant, Operator, Eq, Dimension, DefaultDimension, _SymbolCache, clear_cache, solve, VectorFunction, TensorFunction, TensorTimeFunction, VectorTimeFunction) from devito.types import (DeviceID, NThreadsBase, NPThreads, Object, Scalar, Symbol, ThreadID) @pytest.fixture def operate_on_empty_cache(): """ To be used by tests that assert against the cache size. There are two reasons this is required: * Most symbolic objects embed further symbolic objects. For example, Function embeds Dimension, DerivedDimension embed a parent Dimension, and so on. The embedded objects require more than one call to `clear_cache` to be evicted (typically two -- the first call evicts the main object, then the children become unreferenced and so they are evicted upon the second call). So, depending on what tests were executed before, it is possible that one `clear_cache()` evicts more than expected, making it impossible to assert against cache sizes. * Due to some global symbols in `conftest.py`, it is possible that when for example a SparseFunction is instantiated, fewer symbolic object than expected are created, since some of them are available from the cache already. """ old_cache = _SymbolCache.copy() _SymbolCache.clear() yield _SymbolCache.update(old_cache) class TestHashing(object): """ Test hashing of symbolic objects. """ def test_constant(self): """Test that different Constants have different hash value.""" c0 = Constant(name='c') c1 = Constant(name='c') assert c0 is not c1 assert hash(c0) != hash(c1) def test_dimension(self): """Test that different Dimensions have different hash value.""" d0 = Dimension(name='d') s0 = Scalar(name='s') d1 = Dimension(name='d', spacing=s0) assert hash(d0) != hash(d1) s1 = Scalar(name='s', dtype=np.int32) d2 = Dimension(name='d', spacing=s1) assert hash(d1) != hash(d2) d3 = Dimension(name='d', spacing=Constant(name='s1')) assert hash(d3) != hash(d0) assert hash(d3) != hash(d1) def test_sub_dimension(self): """Test that different SubDimensions have different hash value.""" d0 = Dimension(name='d') d1 = Dimension(name='d', spacing=Scalar(name='s')) di0 = SubDimension.middle('di', d0, 1, 1) di1 = SubDimension.middle('di', d1, 1, 1) assert hash(di0) != hash(d0) assert hash(di0) != hash(di1) dl0 = SubDimension.left('dl', d0, 2) assert hash(dl0) != hash(di0) def test_conditional_dimension(self): """Test that different ConditionalDimensions have different hash value.""" d0 = Dimension(name='d') s0 = Scalar(name='s') d1 = Dimension(name='d', spacing=s0) cd0 = ConditionalDimension(name='cd', parent=d0, factor=4) cd1 = ConditionalDimension(name='cd', parent=d0, factor=5) assert cd0 is not cd1 assert hash(cd0) != hash(cd1) cd2 = ConditionalDimension(name='cd', parent=d0, factor=4, indirect=True) assert hash(cd0) != hash(cd2) cd3 = ConditionalDimension(name='cd', parent=d1, factor=4) assert hash(cd0) != hash(cd3) s1 = Scalar(name='s', dtype=np.int32) cd4 = ConditionalDimension(name='cd', parent=d0, factor=4, condition=s0 > 3) assert hash(cd0) != hash(cd4) cd5 = ConditionalDimension(name='cd', parent=d0, factor=4, condition=s1 > 3) assert hash(cd0) != hash(cd5) assert hash(cd4) != hash(cd5) def test_default_dimension(self): """Test that different DefaultDimensions have different hash value.""" dd0 = DefaultDimension(name='dd') dd1 = DefaultDimension(name='dd') assert hash(dd0) != hash(dd1) @pytest.mark.parametrize('FunctionType', [Function, TimeFunction]) def test_function(self, FunctionType): """Test that different Functions have different hash value.""" grid0 = Grid(shape=(3, 3)) u0 = FunctionType(name='u', grid=grid0) grid1 = Grid(shape=(4, 4)) u1 = FunctionType(name='u', grid=grid1) assert u0 is not u1 assert hash(u0) != hash(u1) # Now with the same grid u2 = FunctionType(name='u', grid=grid0) assert u0 is not u2 assert hash(u0) != hash(u2) @pytest.mark.parametrize('FunctionType', [SparseFunction, SparseTimeFunction]) def test_sparse_function(self, FunctionType): """Test that different SparseFunctions have different hash value.""" grid0 = Grid(shape=(3, 3)) u0 = FunctionType(name='u', grid=grid0, npoint=1, nt=10) grid1 = Grid(shape=(4, 4)) u1 = FunctionType(name='u', grid=grid1, npoint=1, nt=10) assert u0 is not u1 assert hash(u0) != hash(u1) # Now with the same grid u2 = FunctionType(name='u', grid=grid0, npoint=1, nt=10) assert u0 is not u2 assert hash(u0) != hash(u2) # Now with different number of sparse points u3 = FunctionType(name='u', grid=grid0, npoint=2, nt=10) assert u0 is not u3 assert hash(u0) != hash(u3) # Now with different number of timesteps stored u4 = FunctionType(name='u', grid=grid0, npoint=1, nt=14) assert u0 is not u4 assert hash(u0) != hash(u4) @pytest.mark.parametrize('FunctionType', [TensorFunction, TensorTimeFunction, VectorTimeFunction, VectorFunction]) def test_tensor_hash(self, FunctionType): """Test that different Functions have different hash value.""" grid0 = Grid(shape=(3, 3)) u0 = FunctionType(name='u', grid=grid0) grid1 = Grid(shape=(4, 4)) u1 = FunctionType(name='u', grid=grid1) assert u0 is not u1 assert hash(u0) != hash(u1) # Now with the same grid u2 = FunctionType(name='u', grid=grid0) assert u0 is not u2 assert hash(u0) != hash(u2) def test_bound_symbol(self): grid = Grid(shape=(4, 4)) u0 = TimeFunction(name='u', grid=grid) u1 = TimeFunction(name='u', grid=grid) assert u0._C_symbol is not u1._C_symbol # Obviously assert hash(u0._C_symbol) != hash(u1._C_symbol) assert u0._C_symbol != u1._C_symbol def test_objects(self): v0 = byref(c_void_p(3)) v1 = byref(c_void_p(4)) dtype = type('Bar', (c_void_p,), {}) foo0 = Object('foo', dtype, v0) foo1 = Object('foo', dtype, v0) foo2 = Object('foo', dtype, v1) # Obviously: assert foo0 is not foo1 assert foo0 is not foo2 assert foo1 is not foo2 # Carried value doesn't matter -- an Object is always unique assert hash(foo0) != hash(foo1) # And obviously: assert hash(foo0) != hash(foo2) assert hash(foo1) != hash(foo2) class TestCaching(object): """ Test the symbol cache infrastructure. """ @pytest.mark.parametrize('FunctionType', [Function, TimeFunction]) def test_function(self, FunctionType): """Test that new u[x, y] instances don't cache""" grid = Grid(shape=(3, 4)) u0 = FunctionType(name='u', grid=grid) u0.data[:] = 6. u1 = FunctionType(name='u', grid=grid) u1.data[:] = 2. assert np.allclose(u0.data, 6.) assert np.allclose(u1.data, 2.) @pytest.mark.parametrize('FunctionType', [Function, TimeFunction]) def test_function_same_indices(self, FunctionType): """Test caching of derived u[x, y] instance from derivative""" grid = Grid(shape=(3, 4)) u0 = FunctionType(name='u', grid=grid) u0.data[:] = 6. # Pick u(x, y) and u(x + h_x, y) from derivative u1 = u0.dx.evaluate.args[1].args[2] u2 = u0.dx.evaluate.args[0].args[1] assert np.allclose(u1.data, 6.) assert np.allclose(u2.data, 6.) @pytest.mark.parametrize('FunctionType', [Function, TimeFunction]) def test_function_different_indices(self, FunctionType): """Test caching of u[x + h, y] instance from derivative""" grid = Grid(shape=(3, 4)) u0 = FunctionType(name='u', grid=grid) u0.data[:] = 6. # Pick u[x + h, y] (different indices) from derivative u = u0.dx.evaluate.args[0].args[1] assert np.allclose(u.data, u0.data) @pytest.mark.parametrize('FunctionType', [Function, TimeFunction]) def test_function_duplicates(self, FunctionType): """Test caching of u[x + h, y] instance from derivative""" grid = Grid(shape=(3, 4)) _cache_size = len(_SymbolCache) x = grid.dimensions[0] u0 = FunctionType(name='u', grid=grid) # u[x + h_x] uf = u0.subs({x: x + x.spacing}) # u[x] shifting back from u[x + h_x] ub = uf.subs({x: x - x.spacing}) # Make sure ub is u0 assert ub is u0 assert hash(ub) == hash(u0) # Three new cache entries: u, u(t,x,y), u(t, x+h_x, y) ncreated = 3 assert len(_SymbolCache) == _cache_size + ncreated # shift again, no new entry should be created uf2 = ub.subs({x: x + x.spacing}) assert uf is uf2 assert len(_SymbolCache) == _cache_size + ncreated def test_symbols(self): """ Test that ``Symbol(name='s') != Scalar(name='s') != Dimension(name='s')``. They all: * rely on the same caching mechanism * boil down to creating a sympy.Symbol * created with the same args/kwargs (``name='s'``) """ sy = Symbol(name='s') sc = Scalar(name='s') d = Dimension(name='s') assert sy is not sc assert sc is not d assert sy is not d assert isinstance(sy, Symbol) assert isinstance(sc, Scalar) assert isinstance(d, Dimension) def test_symbols_args_vs_kwargs(self): """ Unlike Functions, Symbols don't require the use of a kwarg to specify the name. This test basically checks that `Symbol('s') is Symbol(name='s')`, i.e. that we don't make any silly mistakes when it gets to compute the cache key. """ v_arg = Symbol('v') v_kwarg = Symbol(name='v') assert v_arg is v_kwarg d_arg = Dimension('d100') d_kwarg = Dimension(name='d100') assert d_arg is d_kwarg def test_scalar(self): """ Test that Scalars with same name but different attributes do not alias to the same Scalar. Conversely, if the name and the attributes are the same, they must alias to the same Scalar. """ s0 = Scalar(name='s0') s1 = Scalar(name='s0') assert s0 is s1 s2 = Scalar(name='s0', dtype=np.int32) assert s2 is not s1 s3 = Scalar(name='s0', is_const=True) assert s3 is not s1 def test_dimension(self): """ Test that Dimensions with same name but different attributes do not alias to the same Dimension. Conversely, if the name and the attributes are the same, they must alias to the same Dimension. """ d0 = Dimension(name='d') d1 = Dimension(name='d') assert d0 is d1 s0 = Scalar(name='s0') s1 = Scalar(name='s1') d2 = Dimension(name='d', spacing=s0) d3 = Dimension(name='d', spacing=s1) assert d2 is not d3 d4 = Dimension(name='d', spacing=s1) assert d3 is d4 d5 = Dimension(name='d', spacing=Constant(name='s1')) assert d2 is not d5 def test_conditional_dimension(self): """ Test that ConditionalDimensions with same name but different attributes do not alias to the same ConditionalDimension. Conversely, if the name and the attributes are the same, they must alias to the same ConditionalDimension. """ i = Dimension(name='i') ci0 = ConditionalDimension(name='ci', parent=i, factor=4) ci1 = ConditionalDimension(name='ci', parent=i, factor=4) assert ci0 is ci1 ci2 = ConditionalDimension(name='ci', parent=i, factor=8) assert ci2 is not ci1 ci3 = ConditionalDimension(name='ci', parent=i, factor=4, indirect=True) assert ci3 is not ci1 s = Scalar(name='s') ci4 = ConditionalDimension(name='ci', parent=i, factor=4, condition=s > 3) assert ci4 is not ci1 ci5 = ConditionalDimension(name='ci', parent=i, factor=4, condition=s > 3) assert ci5 is ci4 def test_sub_dimension(self): """ Test that SubDimensions with same name but different attributes do not alias to the same SubDimension. Conversely, if the name and the attributes are the same, they must alias to the same SubDimension. """ x = Dimension('x') xi0 = SubDimension.middle('xi', x, 1, 1) xi1 = SubDimension.middle('xi', x, 1, 1) assert xi0 is xi1 xl0 = SubDimension.left('xl', x, 2) xl1 = SubDimension.left('xl', x, 2) assert xl0 is xl1 xl2asxi = SubDimension.left('xi', x, 2) assert xl2asxi is not xl1 assert xl2asxi is not xi1 xr0 = SubDimension.right('xr', x, 1) xr1 = SubDimension.right('xr', x, 1) assert xr0 is xr1 def test_default_dimension(self): d = Dimension(name='d') dd0 = DefaultDimension(name='d') assert d is not dd0 dd1 = DefaultDimension(name='d') assert dd0 is not dd1 def test_constant_new(self): """Test that new Constant instances don't cache.""" u0 = Constant(name='u') u0.data = 6. u1 = Constant(name='u') u1.data = 2. assert u0.data == 6. assert u1.data == 2. def test_grid_objs(self): """ Test that two different Grids use the same Symbols/Dimensions if possible (i.e., if already in cache). This is because objects such as spacing and origin are Scalars, which carry no value. """ grid0 = Grid(shape=(4, 4)) x0, y0 = grid0.dimensions ox0, oy0 = grid0.origin grid1 = Grid(shape=(8, 8)) x1, y1 = grid1.dimensions ox1, oy1 = grid1.origin assert x0 is x1 assert y0 is y1 assert x0.spacing is x1.spacing assert y0.spacing is y1.spacing assert ox0 is ox1 assert oy0 is oy1 def test_special_symbols(self): """ This test checks the singletonization, through the caching infrastructure, of the special symbols that an Operator may generate (e.g., `nthreads`). """ grid = Grid(shape=(4, 4, 4)) f = TimeFunction(name='f', grid=grid) sf = SparseTimeFunction(name='sf', grid=grid, npoint=1, nt=10) eqns = [Eq(f.forward, f + 1.)] + sf.inject(field=f.forward, expr=sf) opt = ('advanced', {'par-nested': 0, 'openmp': True}) op0 = Operator(eqns, opt=opt) op1 = Operator(eqns, opt=opt) nthreads0, nthreads_nested0, nthreads_nonaffine0 =\ [i for i in op0.input if isinstance(i, NThreadsBase)] nthreads1, nthreads_nested1, nthreads_nonaffine1 =\ [i for i in op1.input if isinstance(i, NThreadsBase)] assert nthreads0 is nthreads1 assert nthreads_nested0 is nthreads_nested1 assert nthreads_nonaffine0 is nthreads_nonaffine1 tid0 = ThreadID(op0.nthreads) tid1 = ThreadID(op0.nthreads) assert tid0 is tid1 did0 = DeviceID() did1 = DeviceID() assert did0 is did1 npt0 = NPThreads(name='npt', size=3) npt1 = NPThreads(name='npt', size=3) npt2 = NPThreads(name='npt', size=4) assert npt0 is npt1 assert npt0 is not npt2 def test_symbol_aliasing(self): """Test to assert that our aliasing cache isn't defeated by sympys non-aliasing symbol cache. For further explanation consider the symbol u[x, y] and it's first derivative in x, which includes the symbols u[x, y] and u[x + h, y]. The two functions are aliased in devito's caching mechanism to allow multiple stencil indices pointing at the same data object u, but SymPy treats these two instances as separate functions and thus is allowed to delete one or the other when the cache is cleared. The test below asserts that u[x + h, y] is deleted, the data on u is still intact through our own caching mechanism.""" # Ensure a clean cache to start with clear_cache() # FIXME: Currently not working, presumably due to our # failure to cache new instances? # assert(len(_SymbolCache) == 0) # Create first instance of u and fill its data grid = Grid(shape=(3, 4)) u = Function(name='u', grid=grid) u.data[:] = 6. u_ref = weakref.ref(u.data) # Create u[x + h, y] and delete it again dx = u.dx # Contains two u symbols: u[x, y] and u[x + h, y] del dx clear_cache() # FIXME: Unreliable cache sizes # assert len(_SymbolCache) == 1 # We still have a reference to u assert np.allclose(u.data, 6.) # u.data is alive and well # Remove the final instance and ensure u.data got deallocated del u clear_cache() assert u_ref() is None def test_symbol_aliasing_reverse(self): """Test to assert that removing he original u[x, y] instance does not impede our alisaing cache or leaks memory. """ # Ensure a clean cache to start with clear_cache() # FIXME: Currently not working, presumably due to our # failure to cache new instances? # assert(len(_SymbolCache) == 0) # Create first instance of u and fill its data grid = Grid(shape=(3, 4)) u = Function(name='u', grid=grid) u.data[:] = 6. u_ref = weakref.ref(u.data) # Create derivative and delete orignal u[x, y] dx = u.dx del u clear_cache() # We still have a references to u # FIXME: Unreliable cache sizes # assert len(_SymbolCache) == 1 # Ensure u[x + h, y] still holds valid data assert np.allclose(dx.evaluate.args[0].args[1].data, 6.) del dx clear_cache() # FIXME: Unreliable cache sizes # assert len(_SymbolCache) == 0 # We still have a reference to u_h assert u_ref() is None def test_clear_cache(self, operate_on_empty_cache, nx=1000, ny=1000): grid = Grid(shape=(nx, ny), dtype=np.float64) cache_size = len(_SymbolCache) for i in range(10): assert(len(_SymbolCache) == cache_size) Function(name='u', grid=grid, space_order=2) # Both u and u(inds) added to cache assert(len(_SymbolCache) == cache_size + 2) clear_cache() def test_clear_cache_with_Csymbol(self, operate_on_empty_cache, nx=1000, ny=1000): grid = Grid(shape=(nx, ny), dtype=np.float64) cache_size = len(_SymbolCache) u = Function(name='u', grid=grid, space_order=2) # Both u and u(inds) added to cache assert(len(_SymbolCache) == cache_size + 2) u._C_symbol # Cache size won't change since _C_symbol isn't cached by devito to # avoid circular references in the cache assert(len(_SymbolCache) == cache_size + 2) def test_clear_cache_with_alive_symbols(self, operate_on_empty_cache, nx=1000, ny=1000): """ Test that `clear_cache` doesn't affect caching if an object is still alive. """ grid = Grid(shape=(nx, ny), dtype=np.float64) f0 = Function(name='f', grid=grid, space_order=2) f1 = Function(name='f', grid=grid, space_order=2) # Obviously: assert f0 is not f1 # And clearly, both still alive after a `clear_cache` clear_cache() assert f0 is not f1 assert f0.grid.dimensions[0] is grid.dimensions[0] # Now we try with symbols s0 = Scalar(name='s') s1 = Scalar(name='s') # Clearly: assert s1 is s0 clear_cache() s2 = Scalar(name='s') # s2 must still be s1/so, even after a clear_cache, as so/s1 are both alive! assert s2 is s1 del s0 del s1 s3 = Scalar(name='s') # And obviously, still: assert s3 is s2 cache_size = len(_SymbolCache) del s2 del s3 clear_cache() assert len(_SymbolCache) == cache_size - 1 def test_sparse_function(self, operate_on_empty_cache): """Test caching of SparseFunctions and children objects.""" grid = Grid(shape=(3, 3)) init_cache_size = len(_SymbolCache) cur_cache_size = len(_SymbolCache) u = SparseFunction(name='u', grid=grid, npoint=1, nt=10) # created: u, u(inds), p_u, h_p_u, u_coords, u_coords(inds), d, h_d ncreated = 8 assert len(_SymbolCache) == cur_cache_size + ncreated cur_cache_size = len(_SymbolCache) i = u.inject(expr=u, field=u) # created: ii_u_0*2 (Symbol and ConditionalDimension), ii_u_1*2, ii_u_2*2, # ii_u_3*2, px, py, posx, posy, u_coords (as indexified), ncreated = 2+2+2+2+2+1+1+1 # Note that injection is now lazy so no new symbols should be created assert len(_SymbolCache) == cur_cache_size i.evaluate assert len(_SymbolCache) == cur_cache_size + ncreated # No new symbolic obejcts are created u.inject(expr=u, field=u) assert len(_SymbolCache) == cur_cache_size + ncreated # Let's look at clear_cache now del u del i clear_cache() # At this point, not all children objects have been cleared. In particular, the # ii_u_* Symbols are still alive, as well as p_u and h_p_u. This is because # in the first clear_cache they were still referenced by their "parent" objects # (e.g., ii_u_* by ConditionalDimensions, through `condition`) assert len(_SymbolCache) == init_cache_size + 8 clear_cache() # Now we should be back to the original state assert len(_SymbolCache) == init_cache_size def test_after_indexification(self): """ Test to assert that the SymPy cache retrieves the right Devito data object after indexification. """ grid = Grid(shape=(4, 4, 4)) u0 = Function(name='u', grid=grid, space_order=0) u1 = Function(name='u', grid=grid, space_order=1) u2 = Function(name='u', grid=grid, space_order=2) for i in [u0, u1, u2]: assert i.indexify().base.function.space_order ==\ (i.indexify() + 1.).args[1].base.function.space_order def test_reinsertion_after_deletion(self, operate_on_empty_cache): """ Test that dead weakrefs in the symbol cache do not cause any issues when objects with the same key/hash are reinserted. """ d = Dimension(name='d') del d # `d` has just been deleted, but a weakref pointing to a dead object is still # in the symbol cache at this point; `h_d` is still in the cache too, dead too assert len(_SymbolCache) == 2 assert all(i() is None for i in _SymbolCache.values()) d = Dimension(name='d') # noqa assert len(_SymbolCache) == 2 assert all(i() is not None for i in _SymbolCache.values()) @pytest.mark.parametrize('FunctionType', [VectorFunction, TensorFunction, VectorTimeFunction, TensorTimeFunction]) def test_tensor_different_indices(self, FunctionType): """Test caching of u[x + h, y] instance from derivative""" grid = Grid(shape=(3, 4)) u0 = FunctionType(name='u', grid=grid) for s in u0: s.data[:] = 6. # Pick u[x + h, y] (different indices) from derivative u = u0.dx.evaluate[0].args[0].args[1] assert np.allclose(u.data, u0[0].data) @pytest.mark.parametrize('FunctionType', [VectorFunction, TensorFunction, VectorTimeFunction, TensorTimeFunction]) def test_tensor_same_indices(self, FunctionType): """Test caching of derived u[x, y] instance from derivative""" grid = Grid(shape=(3, 4)) u0 = FunctionType(name='u', grid=grid) for s in u0: s.data[:] = 6. # Pick u(x, y) and u(x + h_x, y) from derivative u1 = u0.dx.evaluate[0].args[1].args[2] u2 = u0.dx.evaluate[1].args[0].args[1] assert np.allclose(u1.data, 6.) assert np.allclose(u2.data, 6.) @pytest.mark.parametrize('FunctionType', [VectorFunction, TensorFunction, VectorTimeFunction, TensorTimeFunction]) def test_tensor_new(self, FunctionType): """Test that new u[x, y] instances don't cache""" grid = Grid(shape=(3, 4)) u0 = FunctionType(name='u', grid=grid) for s in u0: s.data[:] = 6. u1 = FunctionType(name='u', grid=grid) for s in u1: s.data[:] = 2. assert np.all(np.allclose(s.data, 6.) for s in u0) class TestMemoryLeaks(object): """ Tests ensuring there are no memory leaks. """ def test_operator_leakage_function(self): """ Test to ensure that Operator creation does not cause memory leaks for (Time)Functions. """ grid = Grid(shape=(5, 6)) f = Function(name='f', grid=grid) g = TimeFunction(name='g', grid=grid) # Take weakrefs to test whether symbols are dead or alive w_f = weakref.ref(f) w_g = weakref.ref(g) # Create operator and delete everything again op = Operator(Eq(f, 2 * g)) w_op = weakref.ref(op) del op del f del g clear_cache() # Test whether things are still hanging around assert w_f() is None assert w_g() is None assert w_op() is None def test_operator_leakage_sparse(self): """ Test to ensure that Operator creation does not cause memory leaks for SparseTimeFunctions. """ grid = Grid(shape=(5, 6)) a = Function(name='a', grid=grid) s = SparseTimeFunction(name='s', grid=grid, npoint=1, nt=1) w_a = weakref.ref(a) w_s = weakref.ref(s) # Create operator and delete everything again op = Operator(s.interpolate(a)) w_op = weakref.ref(op) del op del s del a clear_cache() # Test whether things are still hanging around assert w_a() is None assert w_s() is None assert w_op() is None def test_solve(self, operate_on_empty_cache): """ Test to ensure clear_cache wipes out *all of* sympy caches. ``sympy.solve``, in particular, relies on a series of private caches that must be purged too (calling sympy's clear_cache() API function isn't enough). """ grid = Grid(shape=(4,)) u = TimeFunction(name='u', grid=grid, time_order=1, space_order=2) eqn = Eq(u.dt, u.dx2) solve(eqn, u.forward) del u del eqn del grid # We only deleted `u`, however we also cache shifted version created by the # finite difference (u.dt, u.dx2). In this case we have three extra references # to u(t + dt), u(x - h_x) and u(x + h_x) that have to be cleared. # Then `u` points to the various Dimensions, the Dimensions point to the various # spacing symbols, hence, we need four sweeps to clear up the cache. assert len(_SymbolCache) == 14 clear_cache() assert len(_SymbolCache) == 9 clear_cache() assert len(_SymbolCache) == 3 clear_cache() assert len(_SymbolCache) == 1 clear_cache() assert len(_SymbolCache) == 0

36.211321

90

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4f48626ac19168d4fab97edd1ec3c65e3f412a79

1,124

py

Python

src/odcprovider/constants.py

52North/pygeoapi-odc-provider

81d6b6aa35836a4b6efa634153557b6db667a099

[ "Apache-2.0" ]

5

2021-11-09T13:36:41.000Z

2022-02-11T15:54:27.000Z

src/odcprovider/constants.py

MartinPontius/pygeoapi-odc-provider

81d6b6aa35836a4b6efa634153557b6db667a099

[ "Apache-2.0" ]

5

2021-07-07T15:45:04.000Z

2021-09-08T13:04:05.000Z

src/odcprovider/constants.py

52North/pygeoapi-odc-provider

81d6b6aa35836a4b6efa634153557b6db667a099

[ "Apache-2.0" ]

1

2021-07-21T22:23:09.000Z

# ================================================================= # Copyright (C) 2021-2021 52°North Spatial Information Research GmbH # # 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. # ================================================================= DEFAULT_APP = "pygeoapi_provider" """ Used for datacube object initialization to trigger datacube.conf find feature """ BBOX_COORD_PRECISION = "{:.4f}" """ Format string for BoundBox coordinates. Limits float output to four decimal places """ CACHE_PICKLE = 'ogc-tb-17/DATA/odc_cache.pickle' """ Path to ODC cache pickle file Defaults to /ogc-tb-17/DATA/odc_cache.pickle """

37.466667

82

0.672598

d5e20bf27f2c4773e7cc28400446dc7b91e03aac

1,676

py

Python

{{cookiecutter.out_dir}}/src/{{cookiecutter.django_project_name}}/settings/dev.py

corpusops/cookiecutter-django

71df2fc679535554a503aed8c170db42c5a02b29

[ "BSD-3-Clause" ]

1

2020-11-30T08:21:17.000Z

{{cookiecutter.out_dir}}/src/{{cookiecutter.django_project_name}}/settings/dev.py

corpusops/cookiecutter-django

71df2fc679535554a503aed8c170db42c5a02b29

[ "BSD-3-Clause" ]

4

2019-08-19T15:39:45.000Z

2021-02-16T10:20:28.000Z

{{cookiecutter.out_dir}}/src/{{cookiecutter.django_project_name}}/settings/dev.py

corpusops/cookiecutter-django

71df2fc679535554a503aed8c170db42c5a02b29

[ "BSD-3-Clause" ]

2

2019-08-19T14:52:06.000Z

2020-07-31T12:34:21.000Z

# -*- coding: utf-8 -*- from __future__ import absolute_import, division, print_function import logging import os try: from django.utils import six except ImportError: import six from .base import * # noqa os.environ['RELATIVE_SETTINGS_MODULE'] = '.dev' INSTALLED_APPS += tuple([ # noqa {%- if cookiecutter.with_toolbar %} 'debug_toolbar', {%- endif%} {%- if cookiecutter.with_djextensions %} 'django_extensions', {%- endif%} ]) SECRET_KEY = os.environ.get('SECRET_KEY', 'secretkey-superhot-12345678') ALLOWED_HOSTS = ['*'] CORS_ORIGIN_ALLOW_ALL = True # INTERNAL_IPS = ('127.0.0.1',) # Used by app debug_toolbar DEBUG = True # Force every loggers to use console handler only. Note that using 'root' # logger is not enough if children don't propage. for logger in six.itervalues(LOGGING['loggers']): # noqa logger['handlers'] = ['console'] # Log every level. LOGGING['handlers']['console']['level'] = logging.NOTSET # noqa MIDDLEWARE += tuple([ {% if cookiecutter.with_toolbar %}'debug_toolbar.middleware.DebugToolbarMiddleware',{%endif %} ]) DEBUG_TOOLBAR_CONFIG = { 'SHOW_TOOLBAR_CALLBACK': lambda x: True, } {% if cookiecutter.cache_only_in_prod %} # deactivate cache except if we have set either: # - ENABLE_CACHE_IN_DEV # - DJANGO__ENABLE_CACHE_IN_DEV=true (envvar) try: ENABLE_CACHE_IN_DEV # noqa except NameError: CACHES = { 'default': { 'BACKEND': 'django.core.cache.backends.dummy.DummyCache', } } {% endif %} USE_DJANGO_EXTENSIONS = True locs_, globs_, env = post_process_settings(locals()) globals().update(globs_) try: from .local import * # noqa except ImportError: pass

25.784615

98

0.701074

4b174e8bd238f7beeda0c733c705f96991215ad0

1,199

py

Python

dynamic_linear_nets.py

Srujan35007/TensorFlow

1fdacd1b0190fd08e7946a15210bbc553e001fea

[ "MIT" ]

null

dynamic_linear_nets.py

Srujan35007/TensorFlow

1fdacd1b0190fd08e7946a15210bbc553e001fea

[ "MIT" ]

null

dynamic_linear_nets.py

Srujan35007/TensorFlow

1fdacd1b0190fd08e7946a15210bbc553e001fea

[ "MIT" ]

null

import time b = time.time() import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' import tensorflow as tf from tensorflow.keras import layers, models, datasets a = time.time() print(f'Imports complete in {a-b} seconds') def get_flat_shape(foo_shape): prod = 1 for i in range(len(foo_shape)): prod = prod * foo_shape[i] return prod # __________TWEAKABLES____________ raw_input_shape = (28,28,1) # Height Width Channels flat_input_shape = get_flat_shape(raw_input_shape) hidden_layer_units = [200,100,30] n_out_classes = 10 classification = True # ________________________________ model = models.Sequential() model.add(layers.Flatten(input_shape = raw_input_shape)) model.add(layers.Dense(flat_input_shape, activation='relu')) for i in range(len(hidden_layer_units)): model.add(layers.Dense(hidden_layer_units[i], activation='relu')) if classification: model.add(layers.Dense(n_out_classes, activation='softmax')) else: model.add(layers.Dense(n_out_classes, activation='sigmoid')) model.summary() loss_fn = 'sparse_categorical_crossentropy' if classification else 'mse' model.compile(loss = loss_fn, optimizer='adam', metrics=['acciracy']) print('Model compiled')

30.74359

72

0.761468

2307e329bbc9e0e7669f2a0a7640081c32809989

2,395

py

Python

mangaloid_instance/sync/utils.py

zhet1c/instance

301e28c47bb4d18b26329dea81b7f45705d18670

[ "MIT" ]

null

mangaloid_instance/sync/utils.py

zhet1c/instance

301e28c47bb4d18b26329dea81b7f45705d18670

[ "MIT" ]

null

mangaloid_instance/sync/utils.py

zhet1c/instance

301e28c47bb4d18b26329dea81b7f45705d18670

[ "MIT" ]

null

from json import dumps from cryptography.hazmat.backends import default_backend from cryptography.hazmat.primitives import serialization, hashes from cryptography.hazmat.primitives.asymmetric import rsa from cryptography.hazmat.primitives.asymmetric import padding from base64 import b64encode, b64decode from asyncio import get_event_loop from concurrent.futures.process import ProcessPoolExecutor async def _run_async(call, *args): with ProcessPoolExecutor() as pool: return await get_event_loop().run_in_executor(pool, call, *args) def _get_key_pair(): private = rsa.generate_private_key( public_exponent=65537, key_size=4096, backend=default_backend()) public = private.public_key() private_string = private.private_bytes(encoding=serialization.Encoding.PEM, format=serialization.PrivateFormat.PKCS8, encryption_algorithm=serialization.NoEncryption()).decode("utf-8") public_string = public.public_bytes(encoding=serialization.Encoding.PEM, format=serialization.PublicFormat.SubjectPublicKeyInfo).decode("utf-8") return private_string, public_string def _create_sync_payload(dc, key): string = dumps(dc).encode("utf-8") pkey = serialization.load_pem_public_key(key.encode("utf-8"), backend=default_backend()) return b64encode(pkey.encrypt(string, padding.OAEP( mgf=padding.MGF1(algorithm=hashes.SHA256()), algorithm=hashes.SHA256(), label=None ))).decode("ascii") def _get_sync_payload(b64, key): byte = b64decode(b64) pkey = serialization.load_pem_private_key(key.encode("utf-8"), None ,backend=default_backend()) return loads(pkey.decrypt(byte, padding.OAEP( mgf=padding.MGF1(algorithm=hashes.SHA256()), algorithm=hashes.SHA256(), label=None )).decode("utf-8")) async def get_key_pair(): """Creates a public/private key pair and returns them in PEM format as strings""" return await _run_async(_get_key_pair) async def create_sync_payload(dc, key): """Converts dictionary to JSON, encrypts it with provided public key (PEM) and returns it as a Base64-encoded string. """ return await _run_async(_create_sync_payload, dc, key) async def get_sync_payload(b64, key): """Converts Base64-encoded string created with 'create_sync_payload' to dictionary""" return await _run_async(_get_sync_payload, b64, key)

42.017544

99

0.749896

eb0831f7d6d54bb15d2aa59ba4d21808afbd62d5

4,606

py

Python

Lib/test/test_importlib/import_/test_packages.py

oleksandr-pavlyk/cpython

eb002dbe0da9622245a355db5f0cd5aa2fc70b40

[ "0BSD" ]

5

2021-12-03T23:11:53.000Z

2022-01-08T21:02:50.000Z

Lib/test/test_importlib/import_/test_packages.py

dalakatt/cpython

2f49b97cc5426087b46515254b9a97a22ee8c807

[ "0BSD" ]

8

2022-01-07T11:31:11.000Z

2022-03-04T00:07:16.000Z

Lib/test/test_importlib/import_/test_packages.py

dalakatt/cpython

2f49b97cc5426087b46515254b9a97a22ee8c807

[ "0BSD" ]

3

2017-10-18T09:35:14.000Z

2018-09-09T16:40:13.000Z

from test.test_importlib import util import sys import unittest from test import support from test.support import import_helper class ParentModuleTests: """Importing a submodule should import the parent modules.""" def test_import_parent(self): with util.mock_spec('pkg.__init__', 'pkg.module') as mock: with util.import_state(meta_path=[mock]): module = self.__import__('pkg.module') self.assertIn('pkg', sys.modules) def test_bad_parent(self): with util.mock_spec('pkg.module') as mock: with util.import_state(meta_path=[mock]): with self.assertRaises(ImportError) as cm: self.__import__('pkg.module') self.assertEqual(cm.exception.name, 'pkg') def test_raising_parent_after_importing_child(self): def __init__(): import pkg.module 1/0 mock = util.mock_spec('pkg.__init__', 'pkg.module', module_code={'pkg': __init__}) with mock: with util.import_state(meta_path=[mock]): with self.assertRaises(ZeroDivisionError): self.__import__('pkg') self.assertNotIn('pkg', sys.modules) self.assertIn('pkg.module', sys.modules) with self.assertRaises(ZeroDivisionError): self.__import__('pkg.module') self.assertNotIn('pkg', sys.modules) self.assertIn('pkg.module', sys.modules) def test_raising_parent_after_relative_importing_child(self): def __init__(): from . import module 1/0 mock = util.mock_spec('pkg.__init__', 'pkg.module', module_code={'pkg': __init__}) with mock: with util.import_state(meta_path=[mock]): with self.assertRaises((ZeroDivisionError, ImportError)): # This raises ImportError on the "from . import module" # line, not sure why. self.__import__('pkg') self.assertNotIn('pkg', sys.modules) with self.assertRaises((ZeroDivisionError, ImportError)): self.__import__('pkg.module') self.assertNotIn('pkg', sys.modules) # XXX False #self.assertIn('pkg.module', sys.modules) def test_raising_parent_after_double_relative_importing_child(self): def __init__(): from ..subpkg import module 1/0 mock = util.mock_spec('pkg.__init__', 'pkg.subpkg.__init__', 'pkg.subpkg.module', module_code={'pkg.subpkg': __init__}) with mock: with util.import_state(meta_path=[mock]): with self.assertRaises((ZeroDivisionError, ImportError)): # This raises ImportError on the "from ..subpkg import module" # line, not sure why. self.__import__('pkg.subpkg') self.assertNotIn('pkg.subpkg', sys.modules) with self.assertRaises((ZeroDivisionError, ImportError)): self.__import__('pkg.subpkg.module') self.assertNotIn('pkg.subpkg', sys.modules) # XXX False #self.assertIn('pkg.subpkg.module', sys.modules) def test_module_not_package(self): # Try to import a submodule from a non-package should raise ImportError. assert not hasattr(sys, '__path__') with self.assertRaises(ImportError) as cm: self.__import__('sys.no_submodules_here') self.assertEqual(cm.exception.name, 'sys.no_submodules_here') def test_module_not_package_but_side_effects(self): # If a module injects something into sys.modules as a side-effect, then # pick up on that fact. name = 'mod' subname = name + '.b' def module_injection(): sys.modules[subname] = 'total bunk' mock_spec = util.mock_spec('mod', module_code={'mod': module_injection}) with mock_spec as mock: with util.import_state(meta_path=[mock]): try: submodule = self.__import__(subname) finally: import_helper.unload(subname) (Frozen_ParentTests, Source_ParentTests ) = util.test_both(ParentModuleTests, __import__=util.__import__) if __name__ == '__main__': unittest.main()

41.125

82

0.576639

75a06865c335831bfde26f0e43be77d0d61f7a3b

4,845

py

Python

Kaggle/Playgroud/RiskPrediction/Home-Credit-Default-Risk-master/py_prev/trash/903_for_f704.py

hehuanlin123/DeepLearning

6b7feabbbde9ac9489f76da4c06eeb6703fb165a

[ "MIT" ]

1

2020-02-28T12:03:39.000Z

Kaggle/Playgroud/RiskPrediction/Home-Credit-Default-Risk-master/py_prev/trash/903_for_f704.py

hehuanlin123/DeepLearning

6b7feabbbde9ac9489f76da4c06eeb6703fb165a

[ "MIT" ]

null

Kaggle/Playgroud/RiskPrediction/Home-Credit-Default-Risk-master/py_prev/trash/903_for_f704.py

hehuanlin123/DeepLearning

6b7feabbbde9ac9489f76da4c06eeb6703fb165a

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Sat Jul 28 22:20:21 2018 @author: Kazuki """ import gc, os from tqdm import tqdm import pandas as pd import sys sys.path.append(f'/home/{os.environ.get("USER")}/PythonLibrary') import lgbextension as ex import lightgbm as lgb from multiprocessing import cpu_count #from glob import glob from sklearn.model_selection import GroupKFold import count import utils_cat import utils utils.start(__file__) #============================================================================== HEAD = 300 NFOLD = 5 SEED = 71 day_start = -365*1 # min: -2922 day_end = -365*0 # min: -2922 param = { 'objective': 'binary', 'metric': 'auc', 'learning_rate': 0.02, 'max_depth': 6, 'num_leaves': 63, 'max_bin': 255, 'min_child_weight': 10, 'min_data_in_leaf': 150, 'reg_lambda': 0.5, # L2 regularization term on weights. 'reg_alpha': 0.5, # L1 regularization term on weights. 'colsample_bytree': 0.9, 'subsample': 0.9, 'nthread': 32, # 'nthread': cpu_count(), 'bagging_freq': 1, 'verbose':-1, 'seed': SEED } imp = pd.read_csv('LOG/imp_0728.py.csv').sort_values('total', ascending=False) imp = imp[~imp.feature.str.startswith('f101_app_')].reset_index(drop=True) val = utils.read_pickles('../data/prev_train', ['DAYS_DECISION']) tr_ind = val[val['DAYS_DECISION'].between(day_start, day_end)].index val = utils.read_pickles('../data/prev_test', ['DAYS_DECISION']) te_ind = val[val['DAYS_DECISION'].between(day_start, day_end)].index # ============================================================================= # load train # ============================================================================= files = ('../feature_prev/train_' + imp.head(HEAD).feature + '.f').tolist() X_train = pd.concat([ pd.read_feather(f) for f in tqdm(files, mininterval=60) ], axis=1).iloc[tr_ind] y_train = utils.read_pickles('../data/prev_label').iloc[tr_ind].TARGET if X_train.columns.duplicated().sum()>0: raise Exception(f'duplicated!: { X_train.columns[X_train.columns.duplicated()] }') print('no dup :) ') print(f'X_train.shape {X_train.shape}') gc.collect() sub_train = utils.read_pickles('../data/prev_train', ['SK_ID_CURR', 'SK_ID_PREV']).set_index('SK_ID_CURR').iloc[tr_ind] sub_train['y'] = y_train.values sub_train['cnt'] = sub_train.index.value_counts() sub_train['w'] = 1 / sub_train.cnt.values group_kfold = GroupKFold(n_splits=NFOLD) sub_train['g'] = sub_train.index % NFOLD CAT = list( set(X_train.columns)&set(utils_cat.ALL)) # ============================================================================= # load test # ============================================================================= files = ('../feature_prev/test_' + imp.head(HEAD).feature + '.f').tolist() X_test = pd.concat([ pd.read_feather(f) for f in tqdm(files, mininterval=60) ], axis=1).iloc[te_ind] sub_test = utils.read_pickles('../data/prev_test', ['SK_ID_CURR', 'SK_ID_PREV']).set_index('SK_ID_CURR').iloc[te_ind] # ============================================================================= # predict with eraly stopping # ============================================================================= sub_train['y_pred'] = 0 sub_test['y_pred'] = 0 sub_train.reset_index(inplace=True) sub_test.reset_index(inplace=True) for train_index, valid_index in group_kfold.split(X_train, sub_train.y, sub_train.g): dtrain = lgb.Dataset(X_train.iloc[train_index], sub_train.iloc[train_index].y, categorical_feature=CAT) dvalid = lgb.Dataset(X_train.iloc[valid_index], sub_train.iloc[valid_index].y, categorical_feature=CAT) model = lgb.train(params=param, train_set=dtrain, num_boost_round=9999, valid_sets=[dtrain, dvalid], valid_names=['train','valid'], early_stopping_rounds=100, #evals_result=evals_result, verbose_eval=50 ) sub_train.iloc[valid_index, -1] = model.predict(X_train.iloc[valid_index]) sub_test['y_pred'] += model.predict(X_test) sub_test['y_pred'] /= NFOLD print('train:', sub_train.y_pred.describe()) print('test:', sub_test.y_pred.describe()) # ============================================================================= # save # ============================================================================= sub_train.to_feather('../data/prev_train_imputation_f704.f') sub_test.to_feather('../data/prev_test_imputation_f704.f') #============================================================================== utils.end(__file__)

32.086093

119

0.544272

b2238d16f3b3d4036d9a6a373e4d9f8995654082

5,172

py

Python

models/5-Deepflash2/Labels.py

cns-iu/HuBMAP---Hacking-the-Kidney

1a41c887f8edb0b52f5afade384a17dc3d3efec4

[ "MIT" ]

null

models/5-Deepflash2/Labels.py

cns-iu/HuBMAP---Hacking-the-Kidney

1a41c887f8edb0b52f5afade384a17dc3d3efec4

[ "MIT" ]

null

models/5-Deepflash2/Labels.py

cns-iu/HuBMAP---Hacking-the-Kidney

1a41c887f8edb0b52f5afade384a17dc3d3efec4

[ "MIT" ]

null

# <h1> HubMap - Hacking the Kidney </h1> # <h3> Goal - Mapping the human body at function tissue unit level - detect crypts FTUs in colon </h3> # # Implementation of Kaggle Notebook - Innovation Prize Winner - Deep Flash2 <br> # Description - Create segmentations masks from RLE and probability density function (PDF) for efficient sampling from mask and anatmical structure. <br> # Input - train.csv (csv file containing rle format mask), HuBMAP-20-dataset_information.csv (csv containing meta data about the images), downscaled images <br> # - Sampling weight for cortex regions (`cortex_value`): 0.5 # - Sampling weight for medulla regions (`medulla_value`): 0.25 # - Sampling weight for other regions (`fbr`, foreground-background-ratio): 0.01 # # Output - downscaled masks, roi-stats.csv (csv containing pdfs for each image) <br> # # <b>How to use?</b><br> # Change the basepath to where your data lives and you're good to go. <br> # # Link to the original notebook - https://www.kaggle.com/matjes/hubmap-efficient-sampling-ii-deepflash2#HuBMAP-masks-and-probability-density-function <hr> # # <h6> Step 1 - Import useful libraries<h6> import cv2, zarr, gc import matplotlib.pyplot as plt, numpy as np, pandas as pd from pathlib import Path gc.enable() import time start = time.time() def elapsed_time(start_time): return time.time() - start_time # from https://www.kaggle.com/paulorzp/rle-functions-run-lenght-encode-decode def rle2mask(mask_rle, shape): ''' mask_rle: run-length as string formated (start length) shape: (width,height) of array to return Returns numpy array, 1 - mask, 0 - background ''' s = mask_rle.split() starts, lengths = [ np.asarray(x, dtype=int) for x in (s[0:][::2], s[1:][::2]) ] starts -= 1 ends = starts + lengths img = np.zeros(shape[0] * shape[1], dtype=np.uint8) for lo, hi in zip(starts, ends): img[lo : hi] = 1 return img.reshape(shape).T # <h6> Step 2 - Set paths and configuration </h6> BASE_PATH = r'/N/slate/soodn/' dataset = "colon" # dataset = "kidney" INPUT_PATH = BASE_PATH+'hubmap-'+dataset+'-segmentation' df_train = pd.read_csv(INPUT_PATH+"/train.csv") df_info = pd.read_csv(INPUT_PATH+"/HuBMAP-20-dataset_information.csv") grp_pdf = zarr.open_group(f'output_{dataset}/images_scale2/') class CONFIG: scale = 2 # Downscale final mask by factor 2 cdf_size = 512 # Downscale CDF for memory efficient loading during training bg_p = 0.1 # Background Probability cortex_p = 0.7 # Cortex Probability medulla_p = 0.2 # Medulla Probability cfg = CONFIG() # Output root = zarr.group(f'output_{dataset}/masks_scale{cfg.scale}') # Saving cdf in 'pdfs' due to naming conventions for sampling during training in deepflash2 g_msk, g_pdf, g_cdf = root.create_groups('labels', 'pdfs', 'cdfs', overwrite=True) if dataset == "colon": df_train = df_train.rename(columns={"predicted":"encoding"}) df_train = df_train[df_train.id != 'HandE_B005_CL_b_RGB_topright'] df_train.index = df_train.id # <h6> Step 3 - Resize the masks and find the pdf for each image </h6> df_list = [] for idx, row in df_train.iterrows(): # Get image info filename = idx img_info = df_info[df_info.image_file==filename] shape = (img_info.height_pixels.values[0], img_info.width_pixels.values[0]) msk = rle2mask(row.encoding, (shape[1], shape[0])).astype('uint8') # Plot fig, ax = plt.subplots(ncols=2, figsize=(15,15)) resize_w = int((msk.shape[1]/msk.shape[0])*cfg.cdf_size) ax[0].imshow(cv2.resize(msk, dsize=(resize_w, cfg.cdf_size))) ax[0].set_title('Mask') ax[0].set_axis_off() pdf = grp_pdf[idx][:] if cfg.scale!=1: new_size = (msk.shape[1] // cfg.scale, msk.shape[0] // cfg.scale) print('Scaling to', new_size) msk = cv2.resize(msk, new_size) pdf = cv2.resize(pdf, new_size) pdf = pdf.astype('float32') pdf[pdf==0] = cfg.bg_p/np.sum(pdf==0) pdf[msk>0] = 0 pdf[pdf==1] = cfg.cortex_p/np.sum(pdf==1) pdf[pdf==2] = cfg.medulla_p/np.sum(pdf==2) print('Getting glomeruli stats') nb_components, output, stats, centroids = cv2.connectedComponentsWithStats(msk, connectivity=4) print(f'Found {nb_components} glomeruli') df_centroids = pd.DataFrame(centroids[1:], columns=['cy', 'cx']) df_centroids = df_centroids.join(pd.DataFrame(stats[1:], columns=['left', 'top', 'width', 'height', 'area'])) df_centroids['idx'] = idx df_centroids.reset_index(inplace=True) df_centroids.set_index(['idx', 'index'], inplace=True) df_list.append(df_centroids) # Saving g_msk[idx] = msk g_pdf[idx] = pdf # Saving cdf pdf = cv2.resize(pdf, dsize=(resize_w, cfg.cdf_size)) g_cdf[idx] = np.cumsum(pdf/np.sum(pdf)) ax[1].imshow(pdf) ax[1].set_title('Probability density function for sampling') ax[1].set_axis_off() plt.show() df_stats = pd.concat(df_list) df_stats.to_csv(f'output_{dataset}/masks_scale{cfg.scale}/roi_stats.csv') print ("Run time = ", elapsed_time(start))

37.751825

160

0.671694

2855488f7315098aa90afd9884695e5338ec6899

1,940

py

Python

mac/google-cloud-sdk/lib/surface/dataproc/autoscaling_policies/delete.py

bopopescu/cndw

ee432efef88a4351b355f3d6d5350defc7f4246b

[ "Apache-2.0" ]

2

2019-11-10T09:17:07.000Z

2019-12-18T13:44:08.000Z

mac/google-cloud-sdk/lib/surface/dataproc/autoscaling_policies/delete.py

bopopescu/cndw

ee432efef88a4351b355f3d6d5350defc7f4246b

[ "Apache-2.0" ]

4

2020-07-21T12:51:46.000Z

2022-01-22T10:29:25.000Z

mac/google-cloud-sdk/lib/surface/dataproc/autoscaling_policies/delete.py

bopopescu/cndw

ee432efef88a4351b355f3d6d5350defc7f4246b

[ "Apache-2.0" ]

1

2020-07-25T18:17:57.000Z

# -*- coding: utf-8 -*- # # Copyright 2019 Google LLC. 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. """Delete autoscaling policy command.""" from __future__ import absolute_import from __future__ import division from __future__ import unicode_literals from googlecloudsdk.api_lib.dataproc import dataproc as dp from googlecloudsdk.calliope import base from googlecloudsdk.command_lib.dataproc import flags from googlecloudsdk.core.console import console_io class Delete(base.DeleteCommand): """Delete an autoscaling policy. ## EXAMPLES The following command deletes the autoscaling policy `example-autoscaling-policy`: $ {command} example-autoscaling-policy """ @classmethod def Args(cls, parser): dataproc = dp.Dataproc(cls.ReleaseTrack()) flags.AddAutoscalingPolicyResourceArg(parser, 'delete', dataproc.api_version) def Run(self, args): dataproc = dp.Dataproc(self.ReleaseTrack()) messages = dataproc.messages policy_ref = args.CONCEPTS.autoscaling_policy.Parse() request = messages.DataprocProjectsRegionsAutoscalingPoliciesDeleteRequest( name=policy_ref.RelativeName()) console_io.PromptContinue( message="The autoscaling policy '[{0}]' will be deleted.".format( policy_ref.Name()), cancel_on_no=True) dataproc.client.projects_regions_autoscalingPolicies.Delete(request)

32.881356

79

0.742268

db947203a27e69a6e56668dc63431f0e4f5599a2

5,639

py

Python

initialize.py

T-002/pyproject

06112f3f4b5c54d4789da3cb1eb007a33f00960b

[ "MIT" ]

null

initialize.py

T-002/pyproject

06112f3f4b5c54d4789da3cb1eb007a33f00960b

[ "MIT" ]

null

initialize.py

T-002/pyproject

06112f3f4b5c54d4789da3cb1eb007a33f00960b

[ "MIT" ]

null

# !/usr/bin/env python # -*- coding: UTF-8 -*- # Copyright (c) 2016-2017 Christian Schwarz # # Permission is hereby granted, free of charge, to any person obtaining # a copy of this software and associated documentation files (the # "Software"), to deal in the Software without restriction, including # without limitation the rights to use, copy, modify, merge, publish, # distribute, sublicense, and/or sell copies of the Software, and to # permit persons to whom the Software is furnished to do so, subject to # the following conditions: # # The above copyright notice and this permission notice shall be # included in all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND # NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE # LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION # OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION # WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """This module can be used to adapt the template to your project needs.""" import os def delete_dummy_files(): """Delete dummy test files.""" os.remove("package/dummy.py") os.remove("tests/dummy_test.py") def rename_project(project): """Rename the package directory.""" os.rename("package", project) def update_test_config(project): """Update the test configuration.""" original = open("tests/__init__.py", "r").read() open("tests/__init__.py", "w").write( original.replace("package", project)) def update_linter_test(project): """Update the linter test.""" original = open("tests/pylint_test.py", "r").read() open("tests/pylint_test.py", "w").write( original.replace('PROJECT_NAME="package"', 'PROJECT_NAME="%s"' % project)) def update_service_test(project): """Update the tests for the service submodule.""" original = open("tests/service_test.py", "r").read() open("tests/service_test.py", "w").write( original.replace("from package import", "from %s import" % project)) def update_builder_test(project): """Update the tests for the builder submodule.""" original = open("tests/builder_test.py", "r").read() open("tests/builder_test.py", "w").write( original.replace("from package import", "from %s import" % project)) def update_noseconfig(project): """Update the test configuration to match with the projects package name.""" original = open("nose.cfg", "r").read() open("nose.cfg", "w").write( original.replace("cover-package=package,tests", "cover-package=%s,tests" % project)) def update_pylintrc(project): """Update the init-hook for pylint.""" original = open("nose.cfg", "r").read() open("nose.cfg", "w").write(original.replace( """init-hook='import sys, os; sys.path.insert[0]("."); sys.path.insert[0]("./package");'""", """init-hook='import sys, os; sys.path.insert[0]("."); sys.path.insert[0]("./%s");'""" % project)) def update_main(project, is_flask_service): """Remove the not required code from __main__.py""" original = open("%s/__main__.py" % project, "r").read() original = original.replace("package", project) start_ms = "#### START MICROSERVICE CODE" end_ms = "#### END MICROSERVICE CODE" start_creation = "#### START MICROSERVICE INSTANCE CREATION" end_creation = "#### END MICROSERVICE INSTANCE CREATION" if not is_flask_service: current_index = 0 new = original[current_index : original.find(start_ms)] current_index = original.find(end_ms) + len(end_ms) new += original[current_index : original.find(start_creation)] current_index = original.find(end_creation) + len(end_creation) new += original[current_index:] original = new else: original = original.replace( 'make_app("package")', 'make_app("%s")' % project) original = original.replace(start_ms, "").replace(end_ms, "") original = original.replace(start_creation, "").replace(end_creation, "") open("%s/__main__.py" % project, "w").write(original) def delete_flask_service_files(project): """Delete flask related files.""" os.remove("%s/service.py" % project) os.remove("tests/service_test.py") os.remove("%s/builder.py" % project) os.remove("tests/builder_test.py") def get_user_config(): """Reads the project configuration from the user. Returns: tuple: Returns a tuple, containing (project_name, is_flask_service) """ project = str(input("""Please give your project name: """)) flask_service = str(input( """Should "%s" contain a Flask service? (y/n) """ % project) ).lower().strip() if flask_service: flask_service = flask_service[0] == "y" return project, flask_service def main(): """Run the initializtion and execute all steps to transform the template into a usable project. """ project, is_flask_service = get_user_config() delete_dummy_files() rename_project(project) update_test_config(project) update_linter_test(project) update_noseconfig(project) update_pylintrc(project) update_builder_test(project) update_service_test(project) update_main(project, is_flask_service) if not is_flask_service: delete_flask_service_files(project) if __name__=="__main__": main() print("You can now delete initialize.py")

36.380645

100

0.679021

6b94256496fbe4578b3f7da35a7b64fddc31a658

493

py

Python

42.py

Bascil/python-data-structures-code-challenge

d3ec548b828746ec1dfab0236666be543d27c35c

[ "MIT" ]

null

42.py

Bascil/python-data-structures-code-challenge

d3ec548b828746ec1dfab0236666be543d27c35c

[ "MIT" ]

null

42.py

Bascil/python-data-structures-code-challenge

d3ec548b828746ec1dfab0236666be543d27c35c

[ "MIT" ]

null

# Classes - Vertex and Graph classes ''' Vertex class Has a constructor that sets the name of the vertex and creates a new empty set to store neighbours The add neighbour method adds the name of a neighbouring vertex to the neighbours set. This set automatically eliminates duplicates. If a neighbour is already created, it cannot be added ''' class Vertex: def __init__(self, n): self.name = n self.neighbours = set() def add_neighbours(self, v): self.neighbours.add(v)

24.65

65

0.742394

e76ca647bed087802a782c174f2d9ccbe0445d5a

5,534

py

Python

jmetal/util/density_estimator.py

badchild0912/jMetalPy

46ec02d143e1db97d97cc6b5826136558cb382c1

[ "MIT" ]

null

jmetal/util/density_estimator.py

badchild0912/jMetalPy

46ec02d143e1db97d97cc6b5826136558cb382c1

[ "MIT" ]

null

jmetal/util/density_estimator.py

badchild0912/jMetalPy

46ec02d143e1db97d97cc6b5826136558cb382c1

[ "MIT" ]

null

import logging import numpy from abc import ABCMeta, abstractmethod from typing import TypeVar, List from functools import cmp_to_key from scipy.spatial.distance import euclidean from jmetal.util.solutions.comparator import SolutionAttributeComparator, Comparator LOGGER = logging.getLogger('jmetal') S = TypeVar('S') """ .. module:: crowding_distance :platform: Unix, Windows :synopsis: Crowding distance implementation. .. moduleauthor:: Antonio J. Nebro <ajnebro@uma.es> """ class DensityEstimator(List[S]): """This is the interface of any density estimator algorithm. """ __metaclass__ = ABCMeta @abstractmethod def compute_density_estimator(self, solution_list): pass @abstractmethod def sort(self, solution_list): pass @classmethod def get_comparator(cls): pass class CrowdingDistance(DensityEstimator[List[S]]): """This class implements a DensityEstimator based on the crowding distance of algorithm NSGA-II. """ def compute_density_estimator(self, front): """This function performs the computation of the crowding density estimation over the solution list. .. note:: This method assign the distance in the inner elements of the solution list. :param front: The list of solutions. """ size = len(front) if size is 0: return elif size is 1: front[0].attributes['crowding_distance'] = float("inf") return elif size is 2: front[0].attributes['crowding_distance'] = float("inf") front[1].attributes['crowding_distance'] = float("inf") return for i in range(len(front)): front[i].attributes['crowding_distance'] = 0.0 number_of_objectives = front[0].number_of_objectives for i in range(number_of_objectives): # Sort the population by Obj n front = sorted(front, key=lambda x: x.objectives[i]) objective_minn = front[0].objectives[i] objective_maxn = front[len(front) - 1].objectives[i] # Set de crowding distance front[0].attributes['crowding_distance'] = float('inf') front[size - 1].attributes['crowding_distance'] = float('inf') for j in range(1, size - 1): distance = front[j + 1].objectives[i] - front[j - 1].objectives[i] # Check if minimum and maximum are the same (in which case do nothing) if objective_maxn - objective_minn == 0: pass; # LOGGER.warning('Minimum and maximum are the same!') else: distance = distance / (objective_maxn - objective_minn) distance += front[j].attributes['crowding_distance'] front[j].attributes['crowding_distance'] = distance def sort(self, solutions): solutions.sort(key=cmp_to_key(self.get_comparator().compare)) @classmethod def get_comparator(cls): return SolutionAttributeComparator("crowding_distance", lowest_is_best=False) class KNearestNeighborDensityEstimator(DensityEstimator[List[S]]): """This class implements a density estimator based on the distance to the k-th nearest solution. """ def __init__(self, k=1): self.k = k self.distance_matrix = [] def compute_density_estimator(self, solutions): solutions_size = len(solutions) if solutions_size <= self.k: return points = [] for i in range(solutions_size): points.append(solutions[i].objectives) # Compute distance matrix self.distance_matrix = numpy.zeros(shape=(solutions_size, solutions_size)) for i in range(solutions_size): for j in range(solutions_size): self.distance_matrix[i, j] = self.distance_matrix[j, i] = euclidean(solutions[i].objectives, solutions[j].objectives) # Gets the k-nearest distance of all the solutions for i in range(solutions_size): distances = [] for j in range(solutions_size): distances.append(self.distance_matrix[i, j]) distances.sort() solutions[i].attributes['knn_density'] = distances[self.k] def sort(self, solutions): def compare(solution1, solution2): distances1 = solution1.attributes["distances_"] distances2 = solution2.attributes["distances_"] tmp_k = self.k if distances1[tmp_k] > distances2[tmp_k]: return -1 elif distances1[tmp_k] < distances2[tmp_k]: return 1 else: while tmp_k < (len(distances1) - 1): tmp_k += 1 if distances1[tmp_k] > distances2[tmp_k]: return -1 elif distances1[tmp_k] < distances2[tmp_k]: return 1 return 0 for i in range(len(solutions)): distances = [] for j in range(len(solutions)): distances.append(self.distance_matrix[i, j]) distances.sort() solutions[i].attributes["distances_"] = distances solutions.sort(key=cmp_to_key(compare)) @classmethod def get_comparator(cls): return SolutionAttributeComparator("knn_density", lowest_is_best=False)

34.160494

108

0.606072

37fd0cc0f2537388644cfc17fa309b5d56f1ece5

556

py

Python

python-leetcode/100.py

MDGSF/interviews

9faa9aacdb0cfbb777d4d3d4d1b14b55ca2c9f76

[ "MIT" ]

12

2020-01-16T08:55:27.000Z

2021-12-02T14:52:39.000Z

python-leetcode/100.py

MDGSF/interviews

9faa9aacdb0cfbb777d4d3d4d1b14b55ca2c9f76

[ "MIT" ]

null

python-leetcode/100.py

MDGSF/interviews

9faa9aacdb0cfbb777d4d3d4d1b14b55ca2c9f76

[ "MIT" ]

1

2019-12-11T12:00:38.000Z

# Definition for a binary tree node. # class TreeNode: # def __init__(self, x): # self.val = x # self.left = None # self.right = None class Solution: def isSameTree(self, p: TreeNode, q: TreeNode) -> bool: if p is None and q is None: return True elif p is not None and q is not None: if p.val == q.val: return self.isSameTree(p.left, q.left) and self.isSameTree(p.right, q.right) else: return False else: return False

27.8

92

0.534173

fa49397c56fd2663d9742f9d402375d944661917

22,046

py

Python

tests/master/test_pypi.py

bennuttall/piwheels

4331b6491ea661f9bc387ee6c0802e623dff6fe4

[ "BSD-3-Clause" ]

100

2017-06-15T21:04:07.000Z

2019-07-10T09:21:41.000Z

tests/master/test_pypi.py

bennuttall/piwheels

4331b6491ea661f9bc387ee6c0802e623dff6fe4

[ "BSD-3-Clause" ]

172

2017-04-13T18:50:36.000Z

2019-07-13T11:38:41.000Z

tests/master/test_pypi.py

bennuttall/piwheels

4331b6491ea661f9bc387ee6c0802e623dff6fe4

[ "BSD-3-Clause" ]

12

2017-07-13T12:54:35.000Z

2019-03-02T12:28:45.000Z

# The piwheels project # Copyright (c) 2017 Ben Nuttall <https://github.com/bennuttall> # Copyright (c) 2017 Dave Jones <dave@waveform.org.uk> # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # * Neither the name of the copyright holder nor the # names of its contributors may be used to endorse or promote products # derived from this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. from time import sleep from unittest import mock from random import randint from datetime import datetime, timezone from threading import Thread from socketserver import ThreadingMixIn from http.server import HTTPServer, BaseHTTPRequestHandler from xmlrpc.server import SimpleXMLRPCServer, SimpleXMLRPCRequestHandler from xmlrpc.client import ProtocolError from queue import Queue import pytest import requests import http.client import xmlrpc.client from piwheels.pypi import * UTC = timezone.utc def dt(s): return datetime.strptime(s, '%Y-%m-%d %H:%M:%S').replace(tzinfo=UTC) @pytest.fixture() def xml_server(request): q = Queue() def changelog_since_serial(n): return [ (pkg, ver, ts, msg, index) for index, (pkg, ver, ts, msg) in enumerate(q.get(), start=n + 1) ] class ThreadedXMLRPCServer(ThreadingMixIn, SimpleXMLRPCServer): pass xml_server = ThreadedXMLRPCServer(("127.0.0.1", 8000)) xml_server.register_introspection_functions() xml_server.register_function(changelog_since_serial) xml_server_thread = Thread(target=xml_server.serve_forever) xml_server_thread.daemon = True xml_server_thread.start() yield "http://127.0.0.1:8000/", q xml_server.shutdown() xml_server.server_close() @pytest.fixture() def mock_buffer(request): with mock.patch('piwheels.pypi.PyPIBuffer') as buffer_proxy: events = [] buffer_proxy().__iter__.return_value = events yield events def test_pypi_buf_talks_to_servers(xml_server): xml_url, xml_queue = xml_server # NOTE: Must use a serial after PYPI_EPOCH here to permit events thru, # and we must include at least 5 minutes worth of events buf = PyPIBuffer(pypi_xmlrpc=xml_url, serial=PYPI_EPOCH + 1000) buf._transport.use_https = False xml_queue.put([ ('bla', '0.0', 1531320000, 'create'), ] * PYPI_MARGIN + [ ('foo', '0.1', 1531327388, 'create'), ('foo', '0.1', 1531327389, 'add source file foo-0.1.tar.gz'), ('bar', '1.0', 1531328389, 'create'), ('bar', '1.0', 1531328390, 'add py2.py3 file bar-1.0-py2.py3-none-any.whl'), ('baz', '2.0', 1531329389, 'create'), ('baz', '2.0', 1531329390, 'add py2.py3 file baz-1.0-py2.py3-none-any.whl'), ]) # baz events aren't included in output because they've not "aged" for # 5 minutes assert list(buf) == [ ('bla', '0.0', 1531320000, 'create', PYPI_EPOCH + 1000), ('foo', '0.1', 1531327388, 'create', PYPI_EPOCH + 1001), ('foo', '0.1', 1531327389, 'add source file foo-0.1.tar.gz', PYPI_EPOCH + 1002), ('bar', '1.0', 1531328389, 'create', PYPI_EPOCH + 1003), ('bar', '1.0', 1531328390, 'add py2.py3 file bar-1.0-py2.py3-none-any.whl', PYPI_EPOCH + 1004), ] def test_pypi_buf_returns_empty_before_epoch(xml_server): # See notes in prior test xml_url, xml_queue = xml_server buf = PyPIBuffer(pypi_xmlrpc=xml_url, serial=0) buf._transport.use_https = False xml_queue.put([ ('bla', '0.0', ts, 'create') for ts in range(1531320000, 1531320000 + 1000) ]) # Nothing returned because it's all before the PYPI_EPOCH assert list(buf) == [] def test_pypi_buf_returns_empty_before_serial(xml_server): xml_url, xml_queue = xml_server # Make sure we're beyond the epoch, even accounting for the amount # PyPIBuffer jumps back by (the margin) i = PYPI_EPOCH + PYPI_MARGIN + 1000 buf = PyPIBuffer(pypi_xmlrpc=xml_url, serial=i) buf._transport.use_https = False xml_queue.put([ ('bla', '0.0', 1531320000, 'create'), ] * (PYPI_MARGIN - 1)) # Nothing returned yet because PyPIBuffer has jumped backwards PYPI_MARGIN # events assert list(buf) == [] xml_queue.put([ ('foo', '0.1', 1531327388, 'create'), ('foo', '0.1', 1531327389, 'add source file foo-0.1.tar.gz'), ('bar', '1.0', 1531328389, 'create'), ('bar', '1.0', 1531328390, 'add py2.py3 file bar-1.0-py2.py3-none-any.whl'), ('baz', '2.0', 1531329389, 'create'), ('baz', '2.0', 1531329390, 'add py2.py3 file baz-1.0-py2.py3-none-any.whl'), ]) assert list(buf) == [ ('foo', '0.1', 1531327388, 'create', i), ('foo', '0.1', 1531327389, 'add source file foo-0.1.tar.gz', i + 1), ('bar', '1.0', 1531328389, 'create', i + 2), ('bar', '1.0', 1531328390, 'add py2.py3 file bar-1.0-py2.py3-none-any.whl', i + 3), ] def test_pypi_buf_waits_for_more_events(xml_server): xml_url, xml_queue = xml_server # Make sure we're beyond the epoch, even accounting for the amount # PyPIBuffer jumps back by (the margin) i = PYPI_EPOCH + PYPI_MARGIN + 1000 buf = PyPIBuffer(pypi_xmlrpc=xml_url, serial=i) buf._transport.use_https = False xml_queue.put([ ('bla', '0.0', 1531320000, 'create'), ] * (PYPI_MARGIN - 1)) # Nothing yet because of PYPI_MARGIN (see prior test) assert list(buf) == [] xml_queue.put([ ('foo', '0.1', 1531327388, 'create'), ('foo', '0.1', 1531327389, 'add source file foo-0.1.tar.gz'), ]) # Nothing yet because even though we've pushed the event it's waiting for, # it's not 5 minutes "old" yet assert list(buf) == [] xml_queue.put([ ('bar', '1.0', 1531328389, 'create'), ('bar', '1.0', 1531328390, 'add py2.py3 file bar-1.0-py2.py3-none-any.whl'), ('baz', '2.0', 1531329389, 'create'), ('baz', '2.0', 1531329390, 'add py2.py3 file baz-1.0-py2.py3-none-any.whl'), ]) assert list(buf) == [ ('foo', '0.1', 1531327388, 'create', i), ('foo', '0.1', 1531327389, 'add source file foo-0.1.tar.gz', i + 1), ('bar', '1.0', 1531328389, 'create', i + 2), ('bar', '1.0', 1531328390, 'add py2.py3 file bar-1.0-py2.py3-none-any.whl', i + 3), ] def test_pypi_buf_raises_errors(): class BadXMLHandler(BaseHTTPRequestHandler): def do_POST(self): self.send_error(404, 'Function not found') class BadXMLRPCServer(ThreadingMixIn, HTTPServer): pass server = BadXMLRPCServer(("127.0.0.1", 8000), BadXMLHandler) server_thread = Thread(target=server.serve_forever) server_thread.daemon = True server_thread.start() try: buf = PyPIBuffer(pypi_xmlrpc='http://127.0.0.1:8000/') buf._transport.use_https = False with pytest.raises(ProtocolError): list(buf) finally: server.shutdown() server.server_close() def test_pypi_read_normal(mock_buffer, mock_json_server): mock_buffer[:] = [ ('foo', '0.1', 1531327388, 'create', 0), ('foo', '0.1', 1531327388, 'add source file foo-0.1.tar.gz', 1), ('bar', '1.0', 1531327389, 'create', 2), ('bar', '1.0', 1531327389, 'add py2.py3 file bar-1.0-py2.py3-none-any.whl', 3), ('baz', '1.0', 1531327390, 'create', 4), ('baz', '1.0', 1531327390, 'add py2.py3 file baz-1.0-py2.py3-none-any.whl', 5), ] mock_json_server['foo'] = 'package foo' mock_json_server['bar'] = 'package bar' mock_json_server['baz'] = None events = PyPIEvents() assert list(events) == [ ('foo', None, dt('2018-07-11 16:43:08'), 'create', 'package foo'), ('foo', '0.1', dt('2018-07-11 16:43:08'), 'source', 'package foo'), ('bar', None, dt('2018-07-11 16:43:09'), 'create', 'package bar'), ('bar', '1.0', dt('2018-07-11 16:43:09'), 'create', 'package bar'), ('baz', None, dt('2018-07-11 16:43:10'), 'create', ''), ('baz', '1.0', dt('2018-07-11 16:43:10'), 'create', ''), ] def test_pypi_read_json_err(mock_buffer, mock_json_server): mock_buffer[:] = [ ('foo', '0.1', 1531327388, 'create', 0), ('foo', '0.1', 1531327388, 'add source file foo-0.1.tar.gz', 1), ('pypi-http-err-503', '1.0', 1531327389, 'create', 2), ('pypi-http-err-503', '1.0', 1531327389, 'add py2.py3 file bar-1.0-py2.py3-none-any.whl', 3), ] mock_json_server['foo'] = 'package foo' events = PyPIEvents() assert list(events) == [ ('foo', None, dt('2018-07-11 16:43:08'), 'create', 'package foo'), ('foo', '0.1', dt('2018-07-11 16:43:08'), 'source', 'package foo'), ('pypi-http-err-503', None, dt('2018-07-11 16:43:09'), 'create', None), ('pypi-http-err-503', '1.0', dt('2018-07-11 16:43:09'), 'create', None), ] def test_pypi_read_json_bad(mock_buffer, mock_json_server): mock_buffer[:] = [ ('foo', '0.1', 1531327388, 'create', 0), ('foo', '0.1', 1531327388, 'add source file foo-0.1.tar.gz', 1), ('pypi-bad', '1.0', 1531327389, 'create', 2), ('pypi-bad', '1.0', 1531327389, 'add py2.py3 file bar-1.0-py2.py3-none-any.whl', 3), ] mock_json_server['foo'] = 'package foo' mock_json_server['pypi-bad'] = 'pypi broke' events = PyPIEvents() assert list(events) == [ ('foo', None, dt('2018-07-11 16:43:08'), 'create', 'package foo'), ('foo', '0.1', dt('2018-07-11 16:43:08'), 'source', 'package foo'), ('pypi-bad', None, dt('2018-07-11 16:43:09'), 'create', None), ('pypi-bad', '1.0', dt('2018-07-11 16:43:09'), 'create', None), ] def test_pypi_read_missing_description(mock_buffer, mock_json_server): mock_buffer[:] = [ ('foo', '0.1', 1531327388, 'create', 0), ('foo', '0.1', 1531327388, 'add source file foo-0.1.tar.gz', 1), ('bar', '1.0', 1531327389, 'create', 2), ('bar', '1.0', 1531327389, 'add py2.py3 file bar-1.0-py2.py3-none-any.whl', 3), ] mock_json_server['foo'] = 'package foo' events = PyPIEvents() assert list(events) == [ ('foo', None, dt('2018-07-11 16:43:08'), 'create', 'package foo'), ('foo', '0.1', dt('2018-07-11 16:43:08'), 'source', 'package foo'), ('bar', None, dt('2018-07-11 16:43:09'), 'create', None), ('bar', '1.0', dt('2018-07-11 16:43:09'), 'create', None), ] def test_pypi_read_huge_description(mock_buffer, mock_json_server): mock_buffer[:] = [ ('foo', '0.1', 1531327388, 'create', 0), ('foo', '0.1', 1531327388, 'add source file foo-0.1.tar.gz', 1), ('bar', '1.0', 1531327389, 'create', 2), ('bar', '1.0', 1531327389, 'add py2.py3 file bar-1.0-py2.py3-none-any.whl', 3), ] mock_json_server['foo'] = 'package foo' mock_json_server['bar'] = 'bar' * 1000 expected = ('bar' * 1000)[:199] + '…' events = PyPIEvents() assert list(events) == [ ('foo', None, dt('2018-07-11 16:43:08'), 'create', 'package foo'), ('foo', '0.1', dt('2018-07-11 16:43:08'), 'source', 'package foo'), ('bar', None, dt('2018-07-11 16:43:09'), 'create', expected), ('bar', '1.0', dt('2018-07-11 16:43:09'), 'create', expected), ] def test_pypi_ignore_other_events(mock_buffer, mock_json_server): mock_buffer[:] = [ ('foo', '0.1', 1531327388, 'create', 0), ('foo', '0.1', 1531327388, 'add source file foo-0.1.tar.gz', 1), ('bar', '1.0', 1531327389, 'create', 2), ('bar', '1.0', 1531327389, 'add py2.py3 file bar-1.0-py2.py3-none-any.whl', 3), ('bar', '1.0', 1531327392, 'foo', 4), ('bar', '1.0', 1531327392, 'foo bar baz', 5), ] mock_json_server['foo'] = 'package foo' mock_json_server['bar'] = 'package bar' events = PyPIEvents() assert list(events) == [ ('foo', None, dt('2018-07-11 16:43:08'), 'create', 'package foo'), ('foo', '0.1', dt('2018-07-11 16:43:08'), 'source', 'package foo'), ('bar', None, dt('2018-07-11 16:43:09'), 'create', 'package bar'), ('bar', '1.0', dt('2018-07-11 16:43:09'), 'create', 'package bar'), ] def test_pypi_cache_expunge(mock_buffer, mock_json_server): mock_buffer[:] = [ ('foo', '0.1', 1531327388, 'create', 0), ('foo', '0.1', 1531327388, 'add source file foo-0.1.tar.gz', 1), ('bar', '1.0', 1531327389, 'create', 2), ('bar', '1.0', 1531327389, 'add py2.py3 file bar-1.0-py2.py3-none-any.whl', 3), ] mock_json_server['foo'] = 'package foo' mock_json_server['bar'] = 'package bar' events = PyPIEvents(cache_size=1) assert list(events) == [ ('foo', None, dt('2018-07-11 16:43:08'), 'create', 'package foo'), ('foo', '0.1', dt('2018-07-11 16:43:08'), 'source', 'package foo'), ('bar', None, dt('2018-07-11 16:43:09'), 'create', 'package bar'), ('bar', '1.0', dt('2018-07-11 16:43:09'), 'create', 'package bar'), ] assert ('foo', '0.1') not in events._versions assert ('bar', '1.0') in events._versions def test_pypi_ignore_dupes(mock_buffer, mock_json_server): mock_buffer[:] = [ ('foo', '0.1', 1531327388, 'create', 0), ('foo', '0.1', 1531327388, 'add source file foo-0.1.tar.gz', 1), ('bar', '1.0', 1531327389, 'create', 2), ('bar', '1.0', 1531327389, 'add source file bar-1.0.tar.gz', 3), ('bar', '1.0', 1531327389, 'add source file bar-1.0.zip', 4), ('bar', '1.0', 1531327392, 'add cp34 file bar-0.1-cp34-cp34-manylinux1_x86_64.whl', 5), ('bar', '1.0', 1531327392, 'add cp35 file bar-0.1-cp35-cp35-manylinux1_x86_64.whl', 6), ] mock_json_server['foo'] = 'package foo' mock_json_server['bar'] = 'package bar' events = PyPIEvents() assert list(events) == [ ('foo', None, dt('2018-07-11 16:43:08'), 'create', 'package foo'), ('foo', '0.1', dt('2018-07-11 16:43:08'), 'source', 'package foo'), ('bar', None, dt('2018-07-11 16:43:09'), 'create', 'package bar'), ('bar', '1.0', dt('2018-07-11 16:43:09'), 'source', 'package bar'), ] def test_pypi_promote_binary_to_source(mock_buffer, mock_json_server): mock_buffer[:] = [ ('foo', '0.1', 1531327388, 'create', 0), ('foo', '0.1', 1531327388, 'add source file foo-0.1.tar.gz', 1), ('bar', '1.0', 1531327389, 'create', 2), ('bar', '1.0', 1531327390, 'add cp34 file bar-0.1-cp34-cp34-manylinux1_x86_64.whl', 3), ('bar', '1.0', 1531327390, 'add cp35 file bar-0.1-cp35-cp35-manylinux1_x86_64.whl', 4), ('bar', '1.0', 1531327392, 'add source file bar-1.0.tar.gz', 5), ('bar', '1.0', 1531327392, 'add source file bar-1.0.zip', 6), ] mock_json_server['foo'] = 'package foo' mock_json_server['bar'] = '' events = PyPIEvents() assert list(events) == [ ('foo', None, dt('2018-07-11 16:43:08'), 'create', 'package foo'), ('foo', '0.1', dt('2018-07-11 16:43:08'), 'source', 'package foo'), ('bar', None, dt('2018-07-11 16:43:09'), 'create', ''), ('bar', '1.0', dt('2018-07-11 16:43:10'), 'create', ''), # Note the timestamp doesn't alter as the release time is the # earliest release ('bar', '1.0', dt('2018-07-11 16:43:10'), 'source', ''), ] def test_pypi_ignore_removes(mock_buffer, mock_json_server): mock_buffer[:] = [ ('foo', '0.1', 1531327388, 'create', 0), ('foo', '0.1', 1531327388, 'add source file foo-0.1.tar.gz', 1), ('foo', '0.1', 1531327388, 'remove Owner foo', 2), ] mock_json_server['foo'] = 'package foo' events = PyPIEvents() assert list(events) == [ ('foo', None, dt('2018-07-11 16:43:08'), 'create', 'package foo'), ('foo', '0.1', dt('2018-07-11 16:43:08'), 'source', 'package foo'), ] def test_pypi_remove_version(mock_buffer, mock_json_server): mock_buffer[:] = [ ('foo', '0.1', 1531327388, 'create', 0), ('foo', '0.1', 1531327388, 'add source file foo-0.1.tar.gz', 1), ('foo', '0.1', 1531327388, 'remove project', 2), ] mock_json_server['foo'] = 'package foo' events = PyPIEvents() assert list(events) == [ ('foo', None, dt('2018-07-11 16:43:08'), 'create', 'package foo'), ('foo', '0.1', dt('2018-07-11 16:43:08'), 'source', 'package foo'), ('foo', '0.1', dt('2018-07-11 16:43:08'), 'remove', None), ] def test_pypi_remove_package(mock_buffer, mock_json_server): mock_buffer[:] = [ ('foo', '0.1', 1531327388, 'create', 0), ('foo', '0.1', 1531327388, 'add source file foo-0.1.tar.gz', 1), ('foo', None, 1531327388, 'remove release', 2), ] mock_json_server['foo'] = 'package foo' events = PyPIEvents() assert list(events) == [ ('foo', None, dt('2018-07-11 16:43:08'), 'create', 'package foo'), ('foo', '0.1', dt('2018-07-11 16:43:08'), 'source', 'package foo'), ('foo', None, dt('2018-07-11 16:43:08'), 'remove', None), ] def test_pypi_yank_version(mock_buffer, mock_json_server): mock_buffer[:] = [ ('foo', '0.1', 1531327388, 'yank release', 0), ] events = PyPIEvents() assert list(events) == [ ('foo', '0.1', dt('2018-07-11 16:43:08'), 'yank', None), ] def test_pypi_unyank_version(mock_buffer, mock_json_server): mock_buffer[:] = [ ('foo', '0.1', 1531327388, 'unyank release', 0), ] events = PyPIEvents() assert list(events) == [ ('foo', '0.1', dt('2018-07-11 16:43:08'), 'unyank', None), ] def test_pypi_backoff(mock_buffer, mock_json_server): mock_buffer[:] = [ ('foo', '0.1', 1531327388, 'create', 0), ('foo', '0.1', 1531327388, 'add source file foo-0.1.tar.gz', 1), ('bar', '1.0', 1531327389, 'create', 2), ('bar', '1.0', 1531327389, 'add py2.py3 file bar-1.0-py2.py3-none-any.whl', 3), ] mock_json_server['foo'] = 'package foo' mock_json_server['bar'] = '' events = PyPIEvents() assert list(events) == [ ('foo', None, dt('2018-07-11 16:43:08'), 'create', 'package foo'), ('foo', '0.1', dt('2018-07-11 16:43:08'), 'source', 'package foo'), ('bar', None, dt('2018-07-11 16:43:09'), 'create', ''), ('bar', '1.0', dt('2018-07-11 16:43:09'), 'create', ''), ] mock_buffer[:] = [] assert list(events) == [] mock_buffer[:] = [ ('bar', '1.1', 1531327392, 'create', 4), ('bar', '1.1', 1531327393, 'add source file bar-1.1.tar.gz', 5), ] # Because 10 seconds haven't elapsed... assert list(events) == [] def test_pypi_read_improper_state(): with mock.patch('xmlrpc.client.ServerProxy') as proxy: proxy().changelog_since_serial.side_effect = ( http.client.ImproperConnectionState('Something went horribly wrong') ) events = PyPIEvents() assert list(events) == [] def test_pypi_read_server_protocol_error(): with mock.patch('xmlrpc.client.ServerProxy') as proxy: proxy().changelog_since_serial.side_effect = ( xmlrpc.client.ProtocolError('Something else went wrong', 500, '', '') ) events = PyPIEvents() assert list(events) == [] def test_pypi_read_server_other_fault(): with mock.patch('xmlrpc.client.ServerProxy') as proxy: proxy().changelog_since_serial.side_effect = ( xmlrpc.client.Fault(-1000, 'Something went horribly wrong!') ) events = PyPIEvents() with pytest.raises(xmlrpc.client.Fault): list(events) def test_pypi_read_server_backoff(): with mock.patch('xmlrpc.client.ServerProxy') as proxy: proxy().changelog_since_serial.side_effect = ( xmlrpc.client.Fault(-32500, 'Too many requests') ) events = PyPIEvents() assert list(events) == [] def test_pypi_read_client_error(): with mock.patch('xmlrpc.client.ServerProxy') as proxy: proxy().changelog_since_serial.side_effect = ( xmlrpc.client.ProtocolError('Client did something stupid', 400, '', '') ) events = PyPIEvents() with pytest.raises(xmlrpc.client.ProtocolError): list(events) def test_pypi_json_timeouts(mock_json_server): assert pypi_package_description('pypi-timeout-err') is None assert pypi_package_description('pypi-http-err-408') is None def test_pypi_json_connection_error(mock_json_server): assert pypi_package_description('pypi-connect-err') is None def test_pypi_json_client_error(mock_json_server): with pytest.raises(requests.HTTPError): pypi_package_description('pypi-http-err-418') # I'm a teapot

41.054004

103

0.599383

91ab73d4ad4c5fbffceb1820e99aec81013fde36

10,256

py

Python

src/sentry/api/endpoints/organization_health.py

withrocks/commonlims

d8a925c917aa26e8205fefb3966a9f49f8f2e2f8

[ "BSD-3-Clause" ]

4

2019-05-27T13:55:07.000Z

2021-03-30T07:05:09.000Z

src/sentry/api/endpoints/organization_health.py

withrocks/commonlims

d8a925c917aa26e8205fefb3966a9f49f8f2e2f8

[ "BSD-3-Clause" ]

99

2019-05-20T14:16:33.000Z

2021-01-19T09:25:15.000Z

src/sentry/api/endpoints/organization_health.py

withrocks/commonlims

d8a925c917aa26e8205fefb3966a9f49f8f2e2f8

[ "BSD-3-Clause" ]

1

2020-08-10T07:55:40.000Z

from __future__ import absolute_import import six from collections import namedtuple, defaultdict from datetime import timedelta from rest_framework.exceptions import PermissionDenied from rest_framework.response import Response from django.utils import timezone from sentry.api.bases import OrganizationEndpoint, EnvironmentMixin from sentry.api.utils import get_date_range_from_params, InvalidParams from sentry.api.exceptions import ResourceDoesNotExist from sentry.models import ( Project, ProjectStatus, OrganizationMemberTeam, Environment, ) from sentry.api.serializers.snuba import ( SnubaResultSerializer, SnubaTSResultSerializer, value_from_row, SnubaLookup, ) from sentry.utils import snuba from sentry.utils.dates import parse_stats_period SnubaResultSet = namedtuple('SnubaResultSet', ('current', 'previous')) SnubaTSResult = namedtuple('SnubaTSResult', ('data', 'start', 'end', 'rollup')) def query(**kwargs): kwargs['referrer'] = 'health' kwargs['totals'] = True return snuba.raw_query(**kwargs) class OrganizationHealthEndpointBase(OrganizationEndpoint, EnvironmentMixin): def empty(self): return Response({'data': []}) def get_project_ids(self, request, organization): project_ids = set(map(int, request.GET.getlist('project'))) before = project_ids.copy() if request.user.is_superuser: # Superusers can query any projects within the organization qs = Project.objects.filter( organization=organization, status=ProjectStatus.VISIBLE, ) else: # Anyone else needs membership of the project qs = Project.objects.filter( organization=organization, teams__in=OrganizationMemberTeam.objects.filter( organizationmember__user=request.user, organizationmember__organization=organization, ).values_list('team'), status=ProjectStatus.VISIBLE, ) # If no project's are passed through querystring, we want to # return all projects, otherwise, limit to the passed in ones if project_ids: qs = qs.filter(id__in=project_ids) project_ids = set(qs.values_list('id', flat=True)) if before and project_ids != before: raise PermissionDenied if not project_ids: return # Make sure project_ids is now a list, otherwise # snuba isn't happy with it being a set return list(project_ids) def get_environment(self, request, organization): try: environment = self._get_environment_from_request( request, organization.id, ) except Environment.DoesNotExist: raise ResourceDoesNotExist if environment is None: return [] if environment.name == '': return [['tags[environment]', 'IS NULL', None]] return [['tags[environment]', '=', environment.name]] def get_query_condition(self, request, organization): qs = request.GET.getlist('q') if not qs: return [[]] conditions = defaultdict(list) for q in qs: try: tag, value = q.split(':', 1) except ValueError: # Malformed query continue try: lookup = SnubaLookup.get(tag) except KeyError: # Not a valid lookup tag continue conditions[lookup.filter_key].append(value) return [[k, 'IN', v] for k, v in conditions.items()] class OrganizationHealthTopEndpoint(OrganizationHealthEndpointBase): MIN_STATS_PERIOD = timedelta(hours=1) MAX_STATS_PERIOD = timedelta(days=45) MAX_LIMIT = 50 def get(self, request, organization): """ Returns a top-N view based on queryset over time period, as well as previous period. """ try: lookup = SnubaLookup.get(request.GET['tag']) except KeyError: raise ResourceDoesNotExist stats_period = parse_stats_period(request.GET.get('statsPeriod', '24h')) if stats_period is None or stats_period < self.MIN_STATS_PERIOD or stats_period >= self.MAX_STATS_PERIOD: return Response({'detail': 'Invalid statsPeriod'}, status=400) try: limit = int(request.GET.get('limit', '5')) except ValueError: return Response({'detail': 'Invalid limit'}, status=400) if limit > self.MAX_LIMIT: return Response({'detail': 'Invalid limit: max %d' % self.MAX_LIMIT}, status=400) if limit <= 0: return self.empty() try: project_ids = self.get_project_ids(request, organization) except ValueError: return Response({'detail': 'Invalid project ids'}, status=400) if not project_ids: return self.empty() environment = self.get_environment(request, organization) query_condition = self.get_query_condition(request, organization) aggregations = [('count()', '', 'count')] # If we pass `?topk` this means we also are # layering on top_projects and total_projects for each value. if 'topk' in request.GET: try: topk = int(request.GET['topk']) except ValueError: return Response({'detail': 'Invalid topk'}, status=400) aggregations += [ ('topK(%d)' % topk, 'project_id', 'top_projects'), ('uniq', 'project_id', 'total_projects'), ] now = timezone.now() data = query( end=now, start=now - stats_period, selected_columns=lookup.selected_columns, aggregations=aggregations, filter_keys={ 'project_id': project_ids, }, conditions=lookup.conditions + query_condition + environment, groupby=lookup.columns, orderby='-count', limit=limit, ) if not data['data']: return self.empty() # Convert our results from current period into a condition # to be used in the next query for the previous period. # This way our values overlap to be able to deduce a delta. values = [] is_null = False for row in data['data']: value = lookup.encoder(value_from_row(row, lookup.columns)) if value is None: is_null = True else: values.append(value) previous = query( end=now - stats_period, start=now - (stats_period * 2), selected_columns=lookup.selected_columns, aggregations=[ ('count()', '', 'count'), ], filter_keys={ 'project_id': project_ids, }, conditions=lookup.conditions + query_condition + environment + [ [lookup.filter_key, 'IN', values] if values else [], [lookup.tagkey, 'IS NULL', None] if is_null else [], ], groupby=lookup.columns, ) serializer = SnubaResultSerializer(organization, lookup, request.user) return Response( serializer.serialize( SnubaResultSet(data, previous), ), status=200, ) class OrganizationHealthGraphEndpoint(OrganizationHealthEndpointBase): def get_environments(self, request, organization): requested_environments = set(request.GET.getlist('environment')) if not requested_environments: return [] environments = set( Environment.objects.filter( organization_id=organization.id, name__in=requested_environments, ).values_list('name', flat=True), ) if requested_environments != environments: raise ResourceDoesNotExist conditions = [] # the "no environment" environment is null in snuba if '' in environments: environments.remove('') conditions.append(['tags[environment]', 'IS NULL', None]) if environments: conditions.append(['tags[environment]', 'IN', list(environments)]) return [conditions] def get(self, request, organization): """ Returns a time series view over statsPeriod over interval. """ try: lookup = SnubaLookup.get(request.GET['tag']) except KeyError: raise ResourceDoesNotExist try: start, end = get_date_range_from_params(request.GET) except InvalidParams as exc: return Response({'detail': six.text_type(exc)}, status=400) interval = parse_stats_period(request.GET.get('interval', '1h')) if interval is None: interval = timedelta(hours=1) try: project_ids = self.get_project_ids(request, organization) except ValueError: return Response({'detail': 'Invalid project ids'}, status=400) if not project_ids: return self.empty() environment_conditions = self.get_environments(request, organization) query_condition = self.get_query_condition(request, organization) rollup = int(interval.total_seconds()) data = query( end=end, start=start, rollup=rollup, selected_columns=lookup.selected_columns, aggregations=[ ('count()', '', 'count'), ], filter_keys={'project_id': project_ids}, conditions=lookup.conditions + query_condition + environment_conditions, groupby=['time'] + lookup.columns, orderby='time', ) serializer = SnubaTSResultSerializer(organization, lookup, request.user) return Response( serializer.serialize( SnubaTSResult(data, start, end, rollup), ), status=200, )

33.298701

113

0.593799

50b4d8e6aca744c590e2a5e46c011c1b68bdcc93

14,839

py

Python

code/constituents_utils.py

nateGeorge/beat_market_analysis

6c54b4aba4049d4e4a12b2d064d5d5d537ad8520

[ "Apache-2.0" ]

null

code/constituents_utils.py

nateGeorge/beat_market_analysis

6c54b4aba4049d4e4a12b2d064d5d5d537ad8520

[ "Apache-2.0" ]

null

code/constituents_utils.py

nateGeorge/beat_market_analysis

6c54b4aba4049d4e4a12b2d064d5d5d537ad8520

[ "Apache-2.0" ]

null

import os import glob import datetime from collections import OrderedDict import pandas as pd import numpy as np import pandas_market_calendars as mcal import matplotlib.pyplot as plt FILEPATH = '/home/nate/Dropbox/data/sp600/' CONSTITUENT_FILEPATH = '/home/nate/Dropbox/data/barchart.com/' WRDS_FILEPATH = '/home/nate/Dropbox/data/wrds/compustat_north_america/tsv/' def get_home_dir(repo_name='beat_market_analysis'): cwd = os.getcwd() cwd_list = cwd.split('/') repo_position = [i for i, s in enumerate(cwd_list) if s == repo_name] if len(repo_position) > 1: print("error! more than one intance of repo name in path") return None home_dir = '/'.join(cwd_list[:repo_position[0] + 1]) + '/' return home_dir def get_historical_constituents_wrds(): """ gets historical constituents from WRDS file """ # TODO: get latest file df = pd.read_csv(WRDS_FILEPATH + 'index_constituents_9-12-2018.txt', parse_dates=['from', 'thru'], infer_datetime_format=True, sep='\t') if df['from'][0].tzinfo is None: df['from'] = df['from'].dt.tz_localize('US/Eastern') if df['thru'][0].tzinfo is None: df['thru'] = df['thru'].dt.tz_localize('US/Eastern') # only use s&p600 for now sp600_df = df[df['conm'] == 'S&P Smallcap 600 Index'] # create dataframe with list of constituents for each day start = sp600_df['from'].min() # get todays date and reset hour, min, sec to 0s end = pd.Timestamp.today(tz='US/Eastern').replace(hour=0, minute=0, second=0, microsecond=0) # replace NaT with tomorrow's date # gives copy warning but can't get rid of it... sp600_df['thru'].fillna(end + pd.DateOffset(days=1), inplace=True) nyse = mcal.get_calendar('NYSE') # gets all dates # date_range = mcal.date_range(start=start, end=end) # gets only dates valid for NYSE date_range = nyse.valid_days(start_date=start.date(), end_date=end.date()) constituent_companies = OrderedDict() constituent_tickers = OrderedDict() lengths = [] # TODO: multiprocessing to speed up for d in date_range: # if date is within stock's from and thru, add to list # stocks were removed on 'thru', so if it is the 'thru' date, then shouldn't be included # but stocks were added on 'from' date, so include stocks on 'from' date # use dataframe masking date_string = d.strftime('%Y-%m-%d') current_stocks = sp600_df[(sp600_df['from'] <= d) & (sp600_df['thru'] > d)] current_companies = current_stocks['co_conm'] # company names current_tickers = current_stocks['co_tic'] # company tickers constituent_companies[date_string] = current_companies constituent_tickers[date_string] = current_tickers lengths.append(current_tickers.shape[0]) # look at number of constituents as a histogram; mostly 600 but a few above and below # pd.value_counts(lengths) # plt.hist(lengths) # plt.show() # TODO: # need to check that no tickers are used for multiple companies # get unique dates where changes were made unique_dates = set(sp600_df['from'].unique()) | set(sp600_df['thru'].unique()) return constituent_companies, constituent_tickers, unique_dates def get_latest_daily_date(source='barchart.com'): # get latest date from daily scrapes daily_files = glob.glob(FILEPATH + '{}/*.csv'.format(source)) if len(daily_files) == 0: return None daily_dates = [pd.to_datetime(f.split('/')[-1].split('_')[-1].split('.')[0]) for f in daily_files] last_daily = max(daily_dates) return last_daily def get_latest_daily_date_constituents(index='QQQ'): # get latest date from daily scrapes daily_files = glob.glob(CONSTITUENT_FILEPATH + '{}/*.csv'.format(index)) if len(daily_files) == 0: return None daily_dates = [pd.to_datetime(f.split('/')[-1].split('_')[-1].split('.')[0]) for f in daily_files] last_daily = max(daily_dates) return last_daily def get_latest_index_date(ticker='IJR'): # get latest date from daily scrapes extension = 'csv' if ticker == 'SLY': extension = 'xls' daily_files = glob.glob(FILEPATH + 'index_funds/{}/*.{}'.format(ticker, extension)) if len(daily_files) == 0: return None daily_dates = [pd.to_datetime(f.split('/')[-1].split('_')[-1].split('.')[0]) for f in daily_files] last_daily = max(daily_dates) return last_daily def load_sp600_files(date='latest', source='barchart.com'): """ loads data from files from investing.com https://www.investing.com/indices/s-p-600-components date should be a string, either 'latest' to use the latest available date, or a specific date like YYYY-mm-dd """ # TODO: deal with 0 bytes files folder = FILEPATH + '{}/'.format(source) dfs = [] labels = ['price', 'performance', 'technical', 'fundamental'] if date == 'latest': file_date = get_latest_daily_date(source=source).strftime('%Y-%m-%d') if file_date is None: print('no files to load!') return None else: file_date = date for l in labels: filename = 'sp600_{}_{}.csv'.format(l, file_date) print(filename) if source == 'barchart.com': # catch errors with 0 bytes filesize if os.path.getsize(folder + filename) == 0: print('filesize is 0 for', filename, 'returning None') return None dfs.append(pd.read_csv(folder + filename, skipfooter=1)) elif source == 'investing.com': dfs.append(pd.read_csv(folder + filename)) # ensure the names and symbols are identical eq01 = dfs[0][['Name', 'Symbol']].equals(dfs[1][['Name', 'Symbol']]) eq12 = dfs[1][['Name', 'Symbol']].equals(dfs[2][['Name', 'Symbol']]) eq23 = dfs[2][['Name', 'Symbol']].equals(dfs[3][['Name', 'Symbol']]) if eq01 and eq12 and eq23: print('all names/symbols are equal') else: print('WARNING: some names/symbols not equal') for d in dfs: d.set_index('Symbol', inplace=True) if source == 'barchart.com': d = d[:-2] # the last row has some info about barchart.com # remove 'Name' column from all but first df for d in dfs[1:]: d.drop('Name', axis=1, inplace=True) if source == 'barchart.com': if 'Last' in d.columns: d.drop('Last', axis=1, inplace=True) if source == 'investing.com': # add prefixes so 'Daily' is different for performance and technical dfs dfs[1].columns = ['perf ' + c for c in dfs[1].columns] dfs[2].columns = ['tech ' + c for c in dfs[2].columns] df = pd.concat(dfs, axis=1) # 'Time' column seems to be just year/month df.drop('Time', axis=1, inplace=True) # convert k to 1000, M to 1e6, and B to 1.9 if source == 'barchart.com': # just need to rename the column, the data is not $K, just $ df['Market Cap'] = df['Market Cap, $K'] df.drop('Market Cap, $K', axis=1, inplace=True) elif source == 'investing.com': for c in ['Vol.', 'Average Vol. (3m)', 'Market Cap', 'Revenue']: df[c] = df[c].apply(lambda x: clean_abbreviations(x)) # clean up % columns if source == 'barchart.com': if 'Div Yield(a)' in df.columns: cols = ['%Chg', 'Wtd Alpha', 'YTD %Chg', '1M %Chg', '3M %Chg', '52W %Chg', '20D Rel Str', '20D His Vol', 'Div Yield(a)'] elif 'Div Yield(ttm)' in df.columns: cols = ['%Chg', 'Wtd Alpha', 'YTD %Chg', '1M %Chg', '3M %Chg', '52W %Chg', '20D Rel Str', '20D His Vol', 'Div Yield(ttm)'] else: yield_col = [c for c in df.columns if 'Div Yield' in c] cols = ['%Chg', 'Wtd Alpha', 'YTD %Chg', '1M %Chg', '3M %Chg', '52W %Chg', '20D Rel Str', '20D His Vol'] + yield_col elif source == 'investing.com': cols = ['Chg. %', 'perf Daily', 'perf 1 Week', 'perf 1 Month', 'perf YTD', 'perf 1 Year', 'perf 3 Years'] for c in cols: df[c] = df[c].apply(lambda x: clean_pcts(x)) if source == 'investing.com': # maps technical indicators to numbers for sorting conversion_dict = {'Strong Buy': 2, 'Buy': 1, 'Neutral': 0, 'Sell': -1, 'Strong Sell': -2} for k, v in conversion_dict.items(): for c in dfs[2].columns: df.at[df[c] == k, c] = v return df def clean_pcts(x): """ the 'Chg. %' column and others have entries like +1.24% """ # if not enough data, will be '-' with investing.com if x == '-' or pd.isnull(x): return np.nan elif x == 'unch': return float(0) elif type(x) == float: return x new_x = x.replace('+', '') new_x = new_x.replace('%', '') new_x = float(new_x) / 100 return new_x def clean_abbreviations(x): """ replaces K with 000, M with 000000, B with 000000000 """ # a few entries in Revenue were nan if pd.isnull(x): return np.nan elif 'K' in x: return int(float(x[:-1]) * 1e3) elif 'M' in x: return int(float(x[:-1]) * 1e6) elif 'B' in x: return int(float(x[:-1]) * 1e9) else: return int(x) def get_current_smallest_mkt_cap(df, n=20): """ using df from investing.com and the load_sp600_files function, gets the n number of smallest market-cap stocks should use barchart.com or wrds as source of constituents """ sorted_df = df.sort_values(by='Market Cap') return sorted_df.iloc[:n].index def load_ijr_holdings(): latest_date = get_latest_index_date(ticker='IJR') if latest_date is None: print('no files') return filename = FILEPATH + 'index_funds/IJR/IJR_holdings_' + latest_date.strftime('%Y-%m-%d') + '.csv' df = pd.read_csv(filename, skiprows=10) df = df[df['Asset Class'] == 'Equity'] for c in ['Shares', 'Market Value', 'Notional Value']: df[c] = df[c].apply(lambda x: x.replace(',', '')).astype(float) df['Price'] = df['Price'].astype(float) df.set_index('Ticker', inplace=True) return df def load_sly_holdings(): latest_date = get_latest_index_date(ticker='SLY') if latest_date is None: print('no files') return filename = FILEPATH + 'index_funds/SLY/SLY_holdings_' + latest_date.strftime('%Y-%m-%d') + '.xls' df = pd.read_excel(filename, skiprows=3, skipfooter=11) # remove non-equities df = df[df['Identifier'] != 'Unassigned'] df.set_index('Identifier', inplace=True) return df def load_vioo_holdings(): latest_date = get_latest_index_date(ticker='VIOO') if latest_date is None: print('no files') return filename = FILEPATH + 'index_funds/VIOO/VIOO_holdings_' + latest_date.strftime('%Y-%m-%d') + '.csv' df = pd.read_csv(filename, skiprows=4) df.drop(['Unnamed: 0', 'Unnamed: 10'], axis=1, inplace=True) missing_row_idx = np.where(df.isna().sum(axis=1) == df.shape[1])[0][0] df = df.iloc[:missing_row_idx] df.drop('Security depository receipt type', axis=1, inplace=True) df['Shares'] = df['Shares'].apply(lambda x: x.replace(',', '')).astype(float) df['Market value'] = df['Market value'].apply(lambda x: x.replace('$', '').replace(',', '')).astype(float) df['% of fund*'] = df['% of fund*'].astype(float) df['SEDOL'] = df['SEDOL'].apply(lambda x: x.replace('=', '').replace('"', '')) df['Ticker'] = df['Ticker'].apply(lambda x: x.strip()) df.set_index('Ticker', inplace=True) return df # import sys # sys.path.append('../stock_prediction/code') # import dl_quandl_EOD as dlq # # all_stocks_dfs = dlq.load_stocks() # # # # get market cap of each stock in index for each unique date # # need to get more historical data from wrds # market_caps = OrderedDict() # unique_dates = sorted(pd.concat([sp600_df['from'], sp600_df['thru']]).unique())[:-1] # for d in unique_dates: # mcaps = [] # for ticker in current_tickers[d]: # mcaps.append(all_stocks_dfs[ticker]['']) # market_caps[d] = def load_barchart_constituents(date='latest', index='QQQ'): if date == 'latest': file_date = get_latest_daily_date_constituents(index=index).strftime('%Y-%m-%d') if file_date is None: print('no files to load!') return None else: file_date = date filename = CONSTITUENT_FILEPATH + 'QQQ/qqq_constituents_' + file_date + '.csv' df = pd.read_csv(filename) # last column says 'downloaded from...' df.drop(df.index[-1], inplace=True) df.set_index('Symbol', inplace=True) df['% Holding'] = df['% Holding'].apply(lambda x: x.replace('%', '')) df['% Holding'] = df['% Holding'].astype('float') return df if __name__ == '__main__': pass def qqq_analysis(): """ looking at the correlation in daily movements between the stocks and the qqq index """ df = load_barchart_constituents() def check_wrds_differences(): """ checks which tickers are missing from various ETFs and the barchart sp600 list """ constituent_companies, constituent_tickers, unique_dates = get_historical_constituents_wrds() # # get list of tickers from latest WRDS data # 6-26-2018 was last time it was downloaded wrds_tickers = constituent_tickers['2018-06-26'] wrds_set = set(wrds_tickers) ijr = load_ijr_holdings() sly = load_sly_holdings() vioo = load_vioo_holdings() ijr_set = set(ijr.index) sly_set = set(sly.index) vioo_set = set(vioo.index) # all currently have at least 3 differences -- ijr seems to differ the most ijr_set.difference(wrds_set) sly_set.difference(wrds_set) vioo_set.difference(wrds_set) df = load_sp600_files() current_set = set(df.index) current_set.difference(wrds_set) wrds_set.difference(current_set) print('latest constituents:') print(get_current_smallest_mkt_cap(df)) # TODO: # see how often the bottom 20 companies have changed # need to get historical market caps first # for d in unique_dates: # get_current_smallest_mkt_cap(df) # VIOO seems to be slower to remove companies that are not in the index; # companies that are not in the current set from barchart.com are only in vioo # overall, barchart seems to be a pretty good source # sorted_df = df.sort_values(by='Market Cap') # smallest = get_current_smallest_mkt_cap(df) # print(smallest)

35.163507

140

0.617157

aa818db0e7e9f7b792ae5dfcd2b8fcd61ec69b7f

2,628

py

Python

src/utils.py

a-lucic/focus

26fba5f60c9f71a6b1287a4a557cd8316ad12527

[ "MIT" ]

12

2020-12-26T11:48:34.000Z

2022-02-27T07:28:10.000Z

src/utils.py

a-lucic/focus

26fba5f60c9f71a6b1287a4a557cd8316ad12527

[ "MIT" ]

2

2020-11-02T05:29:10.000Z

2021-01-04T21:10:07.000Z

src/utils.py

a-lucic/focus

26fba5f60c9f71a6b1287a4a557cd8316ad12527

[ "MIT" ]

4

2020-12-22T15:48:21.000Z

2021-06-28T08:50:42.000Z

import tensorflow as tf import numpy as np from tensorflow.losses import Reduction import os import errno def filter_hinge_loss(n_class, mask_vector, feat_input, sigma, temperature, model_fn): n_input = feat_input.shape[0] if not np.any(mask_vector): return np.zeros((n_input, n_class)) filtered_input = tf.boolean_mask(feat_input, mask_vector) if type(sigma) != float or type(sigma) != int: sigma = tf.boolean_mask(sigma, mask_vector) if type(temperature) != float or type(temperature) != int: temperature = tf.boolean_mask(temperature, mask_vector) filtered_loss = model_fn(filtered_input, sigma, temperature) indices = np.where(mask_vector)[0] zero_loss = np.zeros((n_input, n_class)) hinge_loss = tf.tensor_scatter_nd_add( zero_loss, indices[:, None], filtered_loss, ) return hinge_loss def safe_euclidean(x, epsilon=10. ** -10, axis=-1): return (tf.reduce_sum(x ** 2, axis=axis) + epsilon) ** 0.5 def true_euclidean(x, axis=-1): return (tf.reduce_sum(x ** 2, axis=axis)) ** 0.5 def safe_cosine(x1, x2, epsilon=10. ** -10): normalize_x1 = tf.nn.l2_normalize(x1, dim=1) normalize_x2 = tf.nn.l2_normalize(x2, dim=1) dist = tf.losses.cosine_distance(normalize_x1, normalize_x2, axis=-1, reduction=Reduction.NONE) + epsilon dist = tf.squeeze(dist) dist = tf.cast(dist, tf.float64) return dist def true_cosine(x1: object, x2: object, axis=-1) -> object: normalize_x1 = tf.nn.l2_normalize(x1, dim=1) normalize_x2 = tf.nn.l2_normalize(x2, dim=1) dist = tf.losses.cosine_distance(normalize_x1, normalize_x2, axis=axis, reduction=Reduction.NONE) dist = tf.squeeze(dist) dist = tf.cast(dist, tf.float64) return dist def safe_l1(x, epsilon=10. ** -10, axis=1): return tf.reduce_sum(tf.abs(x), axis=axis) + epsilon def true_l1(x, axis=1): return tf.reduce_sum(tf.abs(x), axis=axis) def tf_cov(x): mean_x = tf.reduce_mean(x, axis=0, keep_dims=True) mx = tf.matmul(tf.transpose(mean_x), mean_x) vx = tf.matmul(tf.transpose(x), x) / tf.cast(tf.shape(x)[0], tf.float64) cov_xx = vx - mx return cov_xx def safe_mahal(x, inv_covar, epsilon=10. ** -10): return tf.reduce_sum(tf.multiply(tf.matmul(x + epsilon, inv_covar), x + epsilon), axis=1) def true_mahal(x, inv_covar): return tf.reduce_sum(tf.multiply(tf.matmul(x, inv_covar), x), axis=1) def mkdir_p(path): try: os.makedirs(path) except OSError as exc: # Python >2.5 if exc.errno == errno.EEXIST and os.path.isdir(path): pass else: raise def safe_open(path, w): ''' Open "path" for writing, creating any parent directories as needed.''' mkdir_p(os.path.dirname(path)) return open(path, w)

27.375

106

0.714612

3b86c0864cb27f827ac86839e34da296d0342b92

58,706

py

Python

nova/virt/driver.py

zaina/nova

181358c172d606b23c9cc14b58d677d911013c02

[ "Apache-2.0" ]

null

nova/virt/driver.py

zaina/nova

181358c172d606b23c9cc14b58d677d911013c02

[ "Apache-2.0" ]

null

nova/virt/driver.py

zaina/nova

181358c172d606b23c9cc14b58d677d911013c02

[ "Apache-2.0" ]

null

# Copyright 2011 Justin Santa Barbara # 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. """ Driver base-classes: (Beginning of) the contract that compute drivers must follow, and shared types that support that contract """ import sys from oslo_config import cfg from oslo_log import log as logging from oslo_utils import importutils from nova.i18n import _, _LE, _LI from nova import utils from nova.virt import event as virtevent driver_opts = [ cfg.StrOpt('compute_driver', help='Driver to use for controlling virtualization. Options ' 'include: libvirt.LibvirtDriver, xenapi.XenAPIDriver, ' 'fake.FakeDriver, ironic.IronicDriver, ' 'vmwareapi.VMwareVCDriver, hyperv.HyperVDriver'), cfg.StrOpt('default_ephemeral_format', help='The default format an ephemeral_volume will be ' 'formatted with on creation.'), cfg.StrOpt('preallocate_images', default='none', choices=('none', 'space'), help='VM image preallocation mode: ' '"none" => no storage provisioning is done up front, ' '"space" => storage is fully allocated at instance start'), cfg.BoolOpt('use_cow_images', default=True, help='Whether to use cow images'), cfg.BoolOpt('vif_plugging_is_fatal', default=True, help="Fail instance boot if vif plugging fails"), cfg.IntOpt('vif_plugging_timeout', default=300, help='Number of seconds to wait for neutron vif plugging ' 'events to arrive before continuing or failing (see ' 'vif_plugging_is_fatal). If this is set to zero and ' 'vif_plugging_is_fatal is False, events should not ' 'be expected to arrive at all.'), ] CONF = cfg.CONF CONF.register_opts(driver_opts) LOG = logging.getLogger(__name__) def driver_dict_from_config(named_driver_config, *args, **kwargs): driver_registry = dict() for driver_str in named_driver_config: driver_type, _sep, driver = driver_str.partition('=') driver_class = importutils.import_class(driver) driver_registry[driver_type] = driver_class(*args, **kwargs) return driver_registry def get_block_device_info(instance, block_device_mapping): """Converts block device mappings for an instance to driver format. Virt drivers expect block device mapping to be presented in the format of a dict containing the following keys: root_device_name: device name of the root disk ephemerals: a (potentially empty) list of DriverEphemeralBlockDevice instances swap: An instance of DriverSwapBlockDevice or None block_device_mapping: a (potentially empty) list of DriverVolumeBlockDevice or any of it's more specialized subclasses. """ from nova.virt import block_device as virt_block_device block_device_info = { 'root_device_name': instance.root_device_name, 'ephemerals': virt_block_device.convert_ephemerals( block_device_mapping), 'block_device_mapping': virt_block_device.convert_all_volumes(*block_device_mapping) } swap_list = virt_block_device.convert_swap(block_device_mapping) block_device_info['swap'] = virt_block_device.get_swap(swap_list) return block_device_info def block_device_info_get_root(block_device_info): block_device_info = block_device_info or {} return block_device_info.get('root_device_name') def block_device_info_get_swap(block_device_info): block_device_info = block_device_info or {} return block_device_info.get('swap') or {'device_name': None, 'swap_size': 0} def swap_is_usable(swap): return swap and swap['device_name'] and swap['swap_size'] > 0 def block_device_info_get_ephemerals(block_device_info): block_device_info = block_device_info or {} ephemerals = block_device_info.get('ephemerals') or [] return ephemerals def block_device_info_get_mapping(block_device_info): block_device_info = block_device_info or {} block_device_mapping = block_device_info.get('block_device_mapping') or [] return block_device_mapping class ComputeDriver(object): """Base class for compute drivers. The interface to this class talks in terms of 'instances' (Amazon EC2 and internal Nova terminology), by which we mean 'running virtual machine' (XenAPI terminology) or domain (Xen or libvirt terminology). An instance has an ID, which is the identifier chosen by Nova to represent the instance further up the stack. This is unfortunately also called a 'name' elsewhere. As far as this layer is concerned, 'instance ID' and 'instance name' are synonyms. Note that the instance ID or name is not human-readable or customer-controlled -- it's an internal ID chosen by Nova. At the nova.virt layer, instances do not have human-readable names at all -- such things are only known higher up the stack. Most virtualization platforms will also have their own identity schemes, to uniquely identify a VM or domain. These IDs must stay internal to the platform-specific layer, and never escape the connection interface. The platform-specific layer is responsible for keeping track of which instance ID maps to which platform-specific ID, and vice versa. Some methods here take an instance of nova.compute.service.Instance. This is the data structure used by nova.compute to store details regarding an instance, and pass them into this layer. This layer is responsible for translating that generic data structure into terms that are specific to the virtualization platform. """ capabilities = { "has_imagecache": False, "supports_recreate": False, "supports_migrate_to_same_host": False } def __init__(self, virtapi): self.virtapi = virtapi self._compute_event_callback = None def init_host(self, host): """Initialize anything that is necessary for the driver to function, including catching up with currently running VM's on the given host. """ # TODO(Vek): Need to pass context in for access to auth_token raise NotImplementedError() def cleanup_host(self, host): """Clean up anything that is necessary for the driver gracefully stop, including ending remote sessions. This is optional. """ pass def get_info(self, instance): """Get the current status of an instance, by name (not ID!) :param instance: nova.objects.instance.Instance object Returns a InstanceInfo object """ # TODO(Vek): Need to pass context in for access to auth_token raise NotImplementedError() def get_num_instances(self): """Return the total number of virtual machines. Return the number of virtual machines that the hypervisor knows about. .. note:: This implementation works for all drivers, but it is not particularly efficient. Maintainers of the virt drivers are encouraged to override this method with something more efficient. """ return len(self.list_instances()) def instance_exists(self, instance): """Checks existence of an instance on the host. :param instance: The instance to lookup Returns True if an instance with the supplied ID exists on the host, False otherwise. .. note:: This implementation works for all drivers, but it is not particularly efficient. Maintainers of the virt drivers are encouraged to override this method with something more efficient. """ try: return instance.uuid in self.list_instance_uuids() except NotImplementedError: return instance.name in self.list_instances() def estimate_instance_overhead(self, instance_info): """Estimate the virtualization overhead required to build an instance of the given flavor. Defaults to zero, drivers should override if per-instance overhead calculations are desired. :param instance_info: Instance/flavor to calculate overhead for. :returns: Dict of estimated overhead values. """ return {'memory_mb': 0} def list_instances(self): """Return the names of all the instances known to the virtualization layer, as a list. """ # TODO(Vek): Need to pass context in for access to auth_token raise NotImplementedError() def list_instance_uuids(self): """Return the UUIDS of all the instances known to the virtualization layer, as a list. """ raise NotImplementedError() def rebuild(self, context, instance, image_meta, injected_files, admin_password, bdms, detach_block_devices, attach_block_devices, network_info=None, recreate=False, block_device_info=None, preserve_ephemeral=False): """Destroy and re-make this instance. A 'rebuild' effectively purges all existing data from the system and remakes the VM with given 'metadata' and 'personalities'. This base class method shuts down the VM, detaches all block devices, then spins up the new VM afterwards. It may be overridden by hypervisors that need to - e.g. for optimisations, or when the 'VM' is actually proxied and needs to be held across the shutdown + spin up steps. :param context: security context :param instance: nova.objects.instance.Instance This function should use the data there to guide the creation of the new instance. :param image_meta: image object returned by nova.image.glance that defines the image from which to boot this instance :param injected_files: User files to inject into instance. :param admin_password: Administrator password to set in instance. :param bdms: block-device-mappings to use for rebuild :param detach_block_devices: function to detach block devices. See nova.compute.manager.ComputeManager:_rebuild_default_impl for usage. :param attach_block_devices: function to attach block devices. See nova.compute.manager.ComputeManager:_rebuild_default_impl for usage. :param network_info: :py:meth:`~nova.network.manager.NetworkManager.get_instance_nw_info` :param recreate: True if the instance is being recreated on a new hypervisor - all the cleanup of old state is skipped. :param block_device_info: Information about block devices to be attached to the instance. :param preserve_ephemeral: True if the default ephemeral storage partition must be preserved on rebuild """ raise NotImplementedError() def spawn(self, context, instance, image_meta, injected_files, admin_password, network_info=None, block_device_info=None): """Create a new instance/VM/domain on the virtualization platform. Once this successfully completes, the instance should be running (power_state.RUNNING). If this fails, any partial instance should be completely cleaned up, and the virtualization platform should be in the state that it was before this call began. :param context: security context :param instance: nova.objects.instance.Instance This function should use the data there to guide the creation of the new instance. :param image_meta: image object returned by nova.image.glance that defines the image from which to boot this instance :param injected_files: User files to inject into instance. :param admin_password: Administrator password to set in instance. :param network_info: :py:meth:`~nova.network.manager.NetworkManager.get_instance_nw_info` :param block_device_info: Information about block devices to be attached to the instance. """ raise NotImplementedError() def destroy(self, context, instance, network_info, block_device_info=None, destroy_disks=True, migrate_data=None): """Destroy the specified instance from the Hypervisor. If the instance is not found (for example if networking failed), this function should still succeed. It's probably a good idea to log a warning in that case. :param context: security context :param instance: Instance object as returned by DB layer. :param network_info: :py:meth:`~nova.network.manager.NetworkManager.get_instance_nw_info` :param block_device_info: Information about block devices that should be detached from the instance. :param destroy_disks: Indicates if disks should be destroyed :param migrate_data: implementation specific params """ raise NotImplementedError() def cleanup(self, context, instance, network_info, block_device_info=None, destroy_disks=True, migrate_data=None, destroy_vifs=True): """Cleanup the instance resources . Instance should have been destroyed from the Hypervisor before calling this method. :param context: security context :param instance: Instance object as returned by DB layer. :param network_info: :py:meth:`~nova.network.manager.NetworkManager.get_instance_nw_info` :param block_device_info: Information about block devices that should be detached from the instance. :param destroy_disks: Indicates if disks should be destroyed :param migrate_data: implementation specific params """ raise NotImplementedError() def reboot(self, context, instance, network_info, reboot_type, block_device_info=None, bad_volumes_callback=None): """Reboot the specified instance. After this is called successfully, the instance's state goes back to power_state.RUNNING. The virtualization platform should ensure that the reboot action has completed successfully even in cases in which the underlying domain/vm is paused or halted/stopped. :param instance: nova.objects.instance.Instance :param network_info: :py:meth:`~nova.network.manager.NetworkManager.get_instance_nw_info` :param reboot_type: Either a HARD or SOFT reboot :param block_device_info: Info pertaining to attached volumes :param bad_volumes_callback: Function to handle any bad volumes encountered """ raise NotImplementedError() def get_console_pool_info(self, console_type): # TODO(Vek): Need to pass context in for access to auth_token raise NotImplementedError() def get_console_output(self, context, instance): """Get console output for an instance :param context: security context :param instance: nova.objects.instance.Instance """ raise NotImplementedError() def get_vnc_console(self, context, instance): """Get connection info for a vnc console. :param context: security context :param instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleVNC """ raise NotImplementedError() def get_spice_console(self, context, instance): """Get connection info for a spice console. :param context: security context :param instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleSpice """ raise NotImplementedError() def get_rdp_console(self, context, instance): """Get connection info for a rdp console. :param context: security context :param instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleRDP """ raise NotImplementedError() def get_serial_console(self, context, instance): """Get connection info for a serial console. :param context: security context :param instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleSerial """ raise NotImplementedError() def get_mks_console(self, context, instance): """Get connection info for a MKS console. :param context: security context :param instance: nova.objects.instance.Instance :returns an instance of console.type.ConsoleMKS """ raise NotImplementedError() def get_diagnostics(self, instance): """Return data about VM diagnostics. :param instance: nova.objects.instance.Instance """ # TODO(Vek): Need to pass context in for access to auth_token raise NotImplementedError() def get_instance_diagnostics(self, instance): """Return data about VM diagnostics. :param instance: nova.objects.instance.Instance """ raise NotImplementedError() def get_all_bw_counters(self, instances): """Return bandwidth usage counters for each interface on each running VM. :param instances: nova.objects.instance.InstanceList """ raise NotImplementedError() def get_all_volume_usage(self, context, compute_host_bdms): """Return usage info for volumes attached to vms on a given host.- """ raise NotImplementedError() def get_host_ip_addr(self): """Retrieves the IP address of the dom0 """ # TODO(Vek): Need to pass context in for access to auth_token raise NotImplementedError() def attach_volume(self, context, connection_info, instance, mountpoint, disk_bus=None, device_type=None, encryption=None): """Attach the disk to the instance at mountpoint using info.""" raise NotImplementedError() def detach_volume(self, connection_info, instance, mountpoint, encryption=None): """Detach the disk attached to the instance.""" raise NotImplementedError() def swap_volume(self, old_connection_info, new_connection_info, instance, mountpoint, resize_to): """Replace the disk attached to the instance. :param instance: nova.objects.instance.Instance :param resize_to: This parameter is used to indicate the new volume size when the new volume lager than old volume. And the units is Gigabyte. """ raise NotImplementedError() def attach_interface(self, instance, image_meta, vif): """Attach an interface to the instance. :param instance: nova.objects.instance.Instance """ raise NotImplementedError() def detach_interface(self, instance, vif): """Detach an interface from the instance. :param instance: nova.objects.instance.Instance """ raise NotImplementedError() def migrate_disk_and_power_off(self, context, instance, dest, flavor, network_info, block_device_info=None, timeout=0, retry_interval=0): """Transfers the disk of a running instance in multiple phases, turning off the instance before the end. :param instance: nova.objects.instance.Instance :param timeout: time to wait for GuestOS to shutdown :param retry_interval: How often to signal guest while waiting for it to shutdown """ raise NotImplementedError() def snapshot(self, context, instance, image_id, update_task_state): """Snapshots the specified instance. :param context: security context :param instance: nova.objects.instance.Instance :param image_id: Reference to a pre-created image that will hold the snapshot. """ raise NotImplementedError() def post_interrupted_snapshot_cleanup(self, context, instance): """Cleans up any resources left after an interrupted snapshot. :param context: security context :param instance: nova.objects.instance.Instance """ pass def finish_migration(self, context, migration, instance, disk_info, network_info, image_meta, resize_instance, block_device_info=None, power_on=True): """Completes a resize. :param context: the context for the migration/resize :param migration: the migrate/resize information :param instance: nova.objects.instance.Instance being migrated/resized :param disk_info: the newly transferred disk information :param network_info: :py:meth:`~nova.network.manager.NetworkManager.get_instance_nw_info` :param image_meta: image object returned by nova.image.glance that defines the image from which this instance was created :param resize_instance: True if the instance is being resized, False otherwise :param block_device_info: instance volume block device info :param power_on: True if the instance should be powered on, False otherwise """ raise NotImplementedError() def confirm_migration(self, migration, instance, network_info): """Confirms a resize, destroying the source VM. :param instance: nova.objects.instance.Instance """ # TODO(Vek): Need to pass context in for access to auth_token raise NotImplementedError() def finish_revert_migration(self, context, instance, network_info, block_device_info=None, power_on=True): """Finish reverting a resize. :param context: the context for the finish_revert_migration :param instance: nova.objects.instance.Instance being migrated/resized :param network_info: :py:meth:`~nova.network.manager.NetworkManager.get_instance_nw_info` :param block_device_info: instance volume block device info :param power_on: True if the instance should be powered on, False otherwise """ raise NotImplementedError() def pause(self, instance): """Pause the specified instance. :param instance: nova.objects.instance.Instance """ # TODO(Vek): Need to pass context in for access to auth_token raise NotImplementedError() def unpause(self, instance): """Unpause paused VM instance. :param instance: nova.objects.instance.Instance """ # TODO(Vek): Need to pass context in for access to auth_token raise NotImplementedError() def suspend(self, context, instance): """suspend the specified instance. :param context: the context for the suspend :param instance: nova.objects.instance.Instance """ raise NotImplementedError() def resume(self, context, instance, network_info, block_device_info=None): """resume the specified instance. :param context: the context for the resume :param instance: nova.objects.instance.Instance being resumed :param network_info: :py:meth:`~nova.network.manager.NetworkManager.get_instance_nw_info` :param block_device_info: instance volume block device info """ raise NotImplementedError() def resume_state_on_host_boot(self, context, instance, network_info, block_device_info=None): """resume guest state when a host is booted. :param instance: nova.objects.instance.Instance """ raise NotImplementedError() def rescue(self, context, instance, network_info, image_meta, rescue_password): """Rescue the specified instance. :param instance: nova.objects.instance.Instance """ raise NotImplementedError() def set_bootable(self, instance, is_bootable): """Set the ability to power on/off an instance. :param instance: nova.objects.instance.Instance """ raise NotImplementedError() def unrescue(self, instance, network_info): """Unrescue the specified instance. :param instance: nova.objects.instance.Instance """ # TODO(Vek): Need to pass context in for access to auth_token raise NotImplementedError() def power_off(self, instance, timeout=0, retry_interval=0): """Power off the specified instance. :param instance: nova.objects.instance.Instance :param timeout: time to wait for GuestOS to shutdown :param retry_interval: How often to signal guest while waiting for it to shutdown """ raise NotImplementedError() def power_on(self, context, instance, network_info, block_device_info=None): """Power on the specified instance. :param instance: nova.objects.instance.Instance """ raise NotImplementedError() def soft_delete(self, instance): """Soft delete the specified instance. :param instance: nova.objects.instance.Instance """ raise NotImplementedError() def restore(self, instance): """Restore the specified instance. :param instance: nova.objects.instance.Instance """ raise NotImplementedError() def get_available_resource(self, nodename): """Retrieve resource information. This method is called when nova-compute launches, and as part of a periodic task that records the results in the DB. :param nodename: node which the caller want to get resources from a driver that manages only one node can safely ignore this :returns: Dictionary describing resources """ raise NotImplementedError() def pre_live_migration(self, context, instance, block_device_info, network_info, disk_info, migrate_data=None): """Prepare an instance for live migration :param context: security context :param instance: nova.objects.instance.Instance object :param block_device_info: instance block device information :param network_info: instance network information :param disk_info: instance disk information :param migrate_data: implementation specific data dict. """ raise NotImplementedError() def live_migration(self, context, instance, dest, post_method, recover_method, block_migration=False, migrate_data=None): """Live migration of an instance to another host. :param context: security context :param instance: nova.db.sqlalchemy.models.Instance object instance object that is migrated. :param dest: destination host :param post_method: post operation method. expected nova.compute.manager._post_live_migration. :param recover_method: recovery method when any exception occurs. expected nova.compute.manager._rollback_live_migration. :param block_migration: if true, migrate VM disk. :param migrate_data: implementation specific params. """ raise NotImplementedError() def rollback_live_migration_at_destination(self, context, instance, network_info, block_device_info, destroy_disks=True, migrate_data=None): """Clean up destination node after a failed live migration. :param context: security context :param instance: instance object that was being migrated :param network_info: instance network information :param block_device_info: instance block device information :param destroy_disks: if true, destroy disks at destination during cleanup :param migrate_data: implementation specific params """ raise NotImplementedError() def post_live_migration(self, context, instance, block_device_info, migrate_data=None): """Post operation of live migration at source host. :param context: security context :instance: instance object that was migrated :block_device_info: instance block device information :param migrate_data: if not None, it is a dict which has data """ pass def post_live_migration_at_source(self, context, instance, network_info): """Unplug VIFs from networks at source. :param context: security context :param instance: instance object reference :param network_info: instance network information """ raise NotImplementedError(_("Hypervisor driver does not support " "post_live_migration_at_source method")) def post_live_migration_at_destination(self, context, instance, network_info, block_migration=False, block_device_info=None): """Post operation of live migration at destination host. :param context: security context :param instance: instance object that is migrated :param network_info: instance network information :param block_migration: if true, post operation of block_migration. """ raise NotImplementedError() def check_instance_shared_storage_local(self, context, instance): """Check if instance files located on shared storage. This runs check on the destination host, and then calls back to the source host to check the results. :param context: security context :param instance: nova.objects.instance.Instance object """ raise NotImplementedError() def check_instance_shared_storage_remote(self, context, data): """Check if instance files located on shared storage. :param context: security context :param data: result of check_instance_shared_storage_local """ raise NotImplementedError() def check_instance_shared_storage_cleanup(self, context, data): """Do cleanup on host after check_instance_shared_storage calls :param context: security context :param data: result of check_instance_shared_storage_local """ pass def check_can_live_migrate_destination(self, context, instance, src_compute_info, dst_compute_info, block_migration=False, disk_over_commit=False): """Check if it is possible to execute live migration. This runs checks on the destination host, and then calls back to the source host to check the results. :param context: security context :param instance: nova.db.sqlalchemy.models.Instance :param src_compute_info: Info about the sending machine :param dst_compute_info: Info about the receiving machine :param block_migration: if true, prepare for block migration :param disk_over_commit: if true, allow disk over commit :returns: a dict containing migration info (hypervisor-dependent) """ raise NotImplementedError() def check_can_live_migrate_destination_cleanup(self, context, dest_check_data): """Do required cleanup on dest host after check_can_live_migrate calls :param context: security context :param dest_check_data: result of check_can_live_migrate_destination """ raise NotImplementedError() def check_can_live_migrate_source(self, context, instance, dest_check_data, block_device_info=None): """Check if it is possible to execute live migration. This checks if the live migration can succeed, based on the results from check_can_live_migrate_destination. :param context: security context :param instance: nova.db.sqlalchemy.models.Instance :param dest_check_data: result of check_can_live_migrate_destination :param block_device_info: result of _get_instance_block_device_info :returns: a dict containing migration info (hypervisor-dependent) """ raise NotImplementedError() def get_instance_disk_info(self, instance, block_device_info=None): """Retrieve information about actual disk sizes of an instance. :param instance: nova.objects.Instance :param block_device_info: Optional; Can be used to filter out devices which are actually volumes. :return: json strings with below format:: "[{'path':'disk', 'type':'raw', 'virt_disk_size':'10737418240', 'backing_file':'backing_file', 'disk_size':'83886080' 'over_committed_disk_size':'10737418240'}, ...]" """ raise NotImplementedError() def refresh_security_group_rules(self, security_group_id): """This method is called after a change to security groups. All security groups and their associated rules live in the datastore, and calling this method should apply the updated rules to instances running the specified security group. An error should be raised if the operation cannot complete. """ # TODO(Vek): Need to pass context in for access to auth_token raise NotImplementedError() def refresh_security_group_members(self, security_group_id): """This method is called when a security group is added to an instance. This message is sent to the virtualization drivers on hosts that are running an instance that belongs to a security group that has a rule that references the security group identified by `security_group_id`. It is the responsibility of this method to make sure any rules that authorize traffic flow with members of the security group are updated and any new members can communicate, and any removed members cannot. Scenario: * we are running on host 'H0' and we have an instance 'i-0'. * instance 'i-0' is a member of security group 'speaks-b' * group 'speaks-b' has an ingress rule that authorizes group 'b' * another host 'H1' runs an instance 'i-1' * instance 'i-1' is a member of security group 'b' When 'i-1' launches or terminates we will receive the message to update members of group 'b', at which time we will make any changes needed to the rules for instance 'i-0' to allow or deny traffic coming from 'i-1', depending on if it is being added or removed from the group. In this scenario, 'i-1' could just as easily have been running on our host 'H0' and this method would still have been called. The point was that this method isn't called on the host where instances of that group are running (as is the case with :py:meth:`refresh_security_group_rules`) but is called where references are made to authorizing those instances. An error should be raised if the operation cannot complete. """ # TODO(Vek): Need to pass context in for access to auth_token raise NotImplementedError() def refresh_provider_fw_rules(self): """This triggers a firewall update based on database changes. When this is called, rules have either been added or removed from the datastore. You can retrieve rules with :py:meth:`nova.db.provider_fw_rule_get_all`. Provider rules take precedence over security group rules. If an IP would be allowed by a security group ingress rule, but blocked by a provider rule, then packets from the IP are dropped. This includes intra-project traffic in the case of the allow_project_net_traffic flag for the libvirt-derived classes. """ # TODO(Vek): Need to pass context in for access to auth_token raise NotImplementedError() def refresh_instance_security_rules(self, instance): """Refresh security group rules Gets called when an instance gets added to or removed from the security group the instance is a member of or if the group gains or loses a rule. """ raise NotImplementedError() def reset_network(self, instance): """reset networking for specified instance.""" # TODO(Vek): Need to pass context in for access to auth_token pass def ensure_filtering_rules_for_instance(self, instance, network_info): """Setting up filtering rules and waiting for its completion. To migrate an instance, filtering rules to hypervisors and firewalls are inevitable on destination host. ( Waiting only for filtering rules to hypervisor, since filtering rules to firewall rules can be set faster). Concretely, the below method must be called. - setup_basic_filtering (for nova-basic, etc.) - prepare_instance_filter(for nova-instance-instance-xxx, etc.) to_xml may have to be called since it defines PROJNET, PROJMASK. but libvirt migrates those value through migrateToURI(), so , no need to be called. Don't use thread for this method since migration should not be started when setting-up filtering rules operations are not completed. :param instance: nova.objects.instance.Instance object """ # TODO(Vek): Need to pass context in for access to auth_token raise NotImplementedError() def filter_defer_apply_on(self): """Defer application of IPTables rules.""" pass def filter_defer_apply_off(self): """Turn off deferral of IPTables rules and apply the rules now.""" pass def unfilter_instance(self, instance, network_info): """Stop filtering instance.""" # TODO(Vek): Need to pass context in for access to auth_token raise NotImplementedError() def set_admin_password(self, instance, new_pass): """Set the root password on the specified instance. :param instance: nova.objects.instance.Instance :param new_pass: the new password """ raise NotImplementedError() def inject_file(self, instance, b64_path, b64_contents): """Writes a file on the specified instance. The first parameter is an instance of nova.compute.service.Instance, and so the instance is being specified as instance.name. The second parameter is the base64-encoded path to which the file is to be written on the instance; the third is the contents of the file, also base64-encoded. NOTE(russellb) This method is deprecated and will be removed once it can be removed from nova.compute.manager. """ # TODO(Vek): Need to pass context in for access to auth_token raise NotImplementedError() def change_instance_metadata(self, context, instance, diff): """Applies a diff to the instance metadata. This is an optional driver method which is used to publish changes to the instance's metadata to the hypervisor. If the hypervisor has no means of publishing the instance metadata to the instance, then this method should not be implemented. :param context: security context :param instance: nova.objects.instance.Instance """ pass def inject_network_info(self, instance, nw_info): """inject network info for specified instance.""" # TODO(Vek): Need to pass context in for access to auth_token pass def poll_rebooting_instances(self, timeout, instances): """Poll for rebooting instances :param timeout: the currently configured timeout for considering rebooting instances to be stuck :param instances: instances that have been in rebooting state longer than the configured timeout """ # TODO(Vek): Need to pass context in for access to auth_token raise NotImplementedError() def host_power_action(self, action): """Reboots, shuts down or powers up the host.""" raise NotImplementedError() def host_maintenance_mode(self, host, mode): """Start/Stop host maintenance window. On start, it triggers guest VMs evacuation. """ raise NotImplementedError() def set_host_enabled(self, enabled): """Sets the specified host's ability to accept new instances.""" # TODO(Vek): Need to pass context in for access to auth_token raise NotImplementedError() def get_host_uptime(self): """Returns the result of calling "uptime" on the target host.""" # TODO(Vek): Need to pass context in for access to auth_token raise NotImplementedError() def plug_vifs(self, instance, network_info): """Plug VIFs into networks. :param instance: nova.objects.instance.Instance """ # TODO(Vek): Need to pass context in for access to auth_token raise NotImplementedError() def unplug_vifs(self, instance, network_info): """Unplug VIFs from networks. :param instance: nova.objects.instance.Instance """ raise NotImplementedError() def get_host_cpu_stats(self): """Get the currently known host CPU stats. :returns: a dict containing the CPU stat info, eg: | {'kernel': kern, | 'idle': idle, | 'user': user, | 'iowait': wait, | 'frequency': freq}, where kern and user indicate the cumulative CPU time (nanoseconds) spent by kernel and user processes respectively, idle indicates the cumulative idle CPU time (nanoseconds), wait indicates the cumulative I/O wait CPU time (nanoseconds), since the host is booting up; freq indicates the current CPU frequency (MHz). All values are long integers. """ raise NotImplementedError() def block_stats(self, instance, disk_id): """Return performance counters associated with the given disk_id on the given instance. These are returned as [rd_req, rd_bytes, wr_req, wr_bytes, errs], where rd indicates read, wr indicates write, req is the total number of I/O requests made, bytes is the total number of bytes transferred, and errs is the number of requests held up due to a full pipeline. All counters are long integers. This method is optional. On some platforms (e.g. XenAPI) performance statistics can be retrieved directly in aggregate form, without Nova having to do the aggregation. On those platforms, this method is unused. Note that this function takes an instance ID. """ raise NotImplementedError() def deallocate_networks_on_reschedule(self, instance): """Does the driver want networks deallocated on reschedule?""" return False def macs_for_instance(self, instance): """What MAC addresses must this instance have? Some hypervisors (such as bare metal) cannot do freeform virtualisation of MAC addresses. This method allows drivers to return a set of MAC addresses that the instance is to have. allocate_for_instance will take this into consideration when provisioning networking for the instance. Mapping of MAC addresses to actual networks (or permitting them to be freeform) is up to the network implementation layer. For instance, with openflow switches, fixed MAC addresses can still be virtualised onto any L2 domain, with arbitrary VLANs etc, but regular switches require pre-configured MAC->network mappings that will match the actual configuration. Most hypervisors can use the default implementation which returns None. Hypervisors with MAC limits should return a set of MAC addresses, which will be supplied to the allocate_for_instance call by the compute manager, and it is up to that call to ensure that all assigned network details are compatible with the set of MAC addresses. This is called during spawn_instance by the compute manager. :return: None, or a set of MAC ids (e.g. set(['12:34:56:78:90:ab'])). None means 'no constraints', a set means 'these and only these MAC addresses'. """ return None def dhcp_options_for_instance(self, instance): """Get DHCP options for this instance. Some hypervisors (such as bare metal) require that instances boot from the network, and manage their own TFTP service. This requires passing the appropriate options out to the DHCP service. Most hypervisors can use the default implementation which returns None. This is called during spawn_instance by the compute manager. Note that the format of the return value is specific to Quantum client API. :return: None, or a set of DHCP options, eg: | [{'opt_name': 'bootfile-name', | 'opt_value': '/tftpboot/path/to/config'}, | {'opt_name': 'server-ip-address', | 'opt_value': '1.2.3.4'}, | {'opt_name': 'tftp-server', | 'opt_value': '1.2.3.4'} | ] """ return None def manage_image_cache(self, context, all_instances): """Manage the driver's local image cache. Some drivers chose to cache images for instances on disk. This method is an opportunity to do management of that cache which isn't directly related to other calls into the driver. The prime example is to clean the cache and remove images which are no longer of interest. :param all_instances: nova.objects.instance.InstanceList """ pass def add_to_aggregate(self, context, aggregate, host, **kwargs): """Add a compute host to an aggregate.""" # NOTE(jogo) Currently only used for XenAPI-Pool raise NotImplementedError() def remove_from_aggregate(self, context, aggregate, host, **kwargs): """Remove a compute host from an aggregate.""" raise NotImplementedError() def undo_aggregate_operation(self, context, op, aggregate, host, set_error=True): """Undo for Resource Pools.""" raise NotImplementedError() def get_volume_connector(self, instance): """Get connector information for the instance for attaching to volumes. Connector information is a dictionary representing the ip of the machine that will be making the connection, the name of the iscsi initiator and the hostname of the machine as follows:: { 'ip': ip, 'initiator': initiator, 'host': hostname } """ raise NotImplementedError() def get_available_nodes(self, refresh=False): """Returns nodenames of all nodes managed by the compute service. This method is for multi compute-nodes support. If a driver supports multi compute-nodes, this method returns a list of nodenames managed by the service. Otherwise, this method should return [hypervisor_hostname]. """ raise NotImplementedError() def node_is_available(self, nodename): """Return whether this compute service manages a particular node.""" if nodename in self.get_available_nodes(): return True # Refresh and check again. return nodename in self.get_available_nodes(refresh=True) def get_per_instance_usage(self): """Get information about instance resource usage. :returns: dict of nova uuid => dict of usage info """ return {} def instance_on_disk(self, instance): """Checks access of instance files on the host. :param instance: nova.objects.instance.Instance to lookup Returns True if files of an instance with the supplied ID accessible on the host, False otherwise. .. note:: Used in rebuild for HA implementation and required for validation of access to instance shared disk files """ return False def register_event_listener(self, callback): """Register a callback to receive events. Register a callback to receive asynchronous event notifications from hypervisors. The callback will be invoked with a single parameter, which will be an instance of the nova.virt.event.Event class. """ self._compute_event_callback = callback def emit_event(self, event): """Dispatches an event to the compute manager. Invokes the event callback registered by the compute manager to dispatch the event. This must only be invoked from a green thread. """ if not self._compute_event_callback: LOG.debug("Discarding event %s", str(event)) return if not isinstance(event, virtevent.Event): raise ValueError( _("Event must be an instance of nova.virt.event.Event")) try: LOG.debug("Emitting event %s", str(event)) self._compute_event_callback(event) except Exception as ex: LOG.error(_LE("Exception dispatching event %(event)s: %(ex)s"), {'event': event, 'ex': ex}) def delete_instance_files(self, instance): """Delete any lingering instance files for an instance. :param instance: nova.objects.instance.Instance :returns: True if the instance was deleted from disk, False otherwise. """ return True @property def need_legacy_block_device_info(self): """Tell the caller if the driver requires legacy block device info. Tell the caller whether we expect the legacy format of block device info to be passed in to methods that expect it. """ return True def volume_snapshot_create(self, context, instance, volume_id, create_info): """Snapshots volumes attached to a specified instance. :param context: request context :param instance: nova.objects.instance.Instance that has the volume attached :param volume_id: Volume to be snapshotted :param create_info: The data needed for nova to be able to attach to the volume. This is the same data format returned by Cinder's initialize_connection() API call. In the case of doing a snapshot, it is the image file Cinder expects to be used as the active disk after the snapshot operation has completed. There may be other data included as well that is needed for creating the snapshot. """ raise NotImplementedError() def volume_snapshot_delete(self, context, instance, volume_id, snapshot_id, delete_info): """Snapshots volumes attached to a specified instance. :param context: request context :param instance: nova.objects.instance.Instance that has the volume attached :param volume_id: Attached volume associated with the snapshot :param snapshot_id: The snapshot to delete. :param delete_info: Volume backend technology specific data needed to be able to complete the snapshot. For example, in the case of qcow2 backed snapshots, this would include the file being merged, and the file being merged into (if appropriate). """ raise NotImplementedError() def default_root_device_name(self, instance, image_meta, root_bdm): """Provide a default root device name for the driver.""" raise NotImplementedError() def default_device_names_for_instance(self, instance, root_device_name, *block_device_lists): """Default the missing device names in the block device mapping.""" raise NotImplementedError() def get_device_name_for_instance(self, instance, bdms, block_device_obj): """Get the next device name based on the block device mapping. :param instance: nova.objects.instance.Instance that volume is requesting a device name :param bdms: a nova.objects.BlockDeviceMappingList for the instance :param block_device_obj: A nova.objects.BlockDeviceMapping instance with all info about the requested block device. device_name does not need to be set, and should be decided by the driver implementation if not set. :returns: The chosen device name. """ raise NotImplementedError() def is_supported_fs_format(self, fs_type): """Check whether the file format is supported by this driver :param fs_type: the file system type to be checked, the validate values are defined at disk API module. """ # NOTE(jichenjc): Return False here so that every hypervisor # need to define their supported file system # type and implement this function at their # virt layer. return False def quiesce(self, context, instance, image_meta): """Quiesce the specified instance to prepare for snapshots. If the specified instance doesn't support quiescing, InstanceQuiesceNotSupported is raised. When it fails to quiesce by other errors (e.g. agent timeout), NovaException is raised. :param context: request context :param instance: nova.objects.instance.Instance to be quiesced :param image_meta: image object returned by nova.image.glance that defines the image from which this instance was created """ raise NotImplementedError() def unquiesce(self, context, instance, image_meta): """Unquiesce the specified instance after snapshots. If the specified instance doesn't support quiescing, InstanceQuiesceNotSupported is raised. When it fails to quiesce by other errors (e.g. agent timeout), NovaException is raised. :param context: request context :param instance: nova.objects.instance.Instance to be unquiesced :param image_meta: image object returned by nova.image.glance that defines the image from which this instance was created """ raise NotImplementedError() def load_compute_driver(virtapi, compute_driver=None): """Load a compute driver module. Load the compute driver module specified by the compute_driver configuration option or, if supplied, the driver name supplied as an argument. Compute drivers constructors take a VirtAPI object as their first object and this must be supplied. :param virtapi: a VirtAPI instance :param compute_driver: a compute driver name to override the config opt :returns: a ComputeDriver instance """ if not compute_driver: compute_driver = CONF.compute_driver if not compute_driver: LOG.error(_LE("Compute driver option required, but not specified")) sys.exit(1) LOG.info(_LI("Loading compute driver '%s'"), compute_driver) try: driver = importutils.import_object_ns('nova.virt', compute_driver, virtapi) return utils.check_isinstance(driver, ComputeDriver) except ImportError: LOG.exception(_LE("Unable to load the virtualization driver")) sys.exit(1) def compute_driver_matches(match): return CONF.compute_driver and CONF.compute_driver.endswith(match)

40.320055

79

0.650053

a44f16f7d0b3af87d8c60d3cec76e42d02884bab

7,499

py

Python

pymatgen/analysis/structure_prediction/dopant_predictor.py

hpatel1567/pymatgen

8304b25464206c74305214e45935df90bab95500

[ "MIT" ]

2

2017-10-02T03:11:47.000Z

2018-12-02T12:56:12.000Z

pymatgen/analysis/structure_prediction/dopant_predictor.py

darnoceloc/pymatgen

5cc42912a12a265a603df7e34c856561f76edc1f

[ "MIT" ]

3

2017-07-18T01:13:41.000Z

2019-04-29T18:17:30.000Z

pymatgen/analysis/structure_prediction/dopant_predictor.py

darnoceloc/pymatgen

5cc42912a12a265a603df7e34c856561f76edc1f

[ "MIT" ]

2

2016-06-15T00:12:59.000Z

2018-12-02T12:56:47.000Z

import warnings import numpy as np from pymatgen.analysis.structure_prediction.substitution_probability import \ SubstitutionPredictor from pymatgen.core.periodic_table import Specie, Element def get_dopants_from_substitution_probabilities(structure, num_dopants=5, threshold=0.001, match_oxi_sign=False): """ Get dopant suggestions based on substitution probabilities. Args: structure (Structure): A pymatgen structure decorated with oxidation states. num_dopants (int): The number of suggestions to return for n- and p-type dopants. threshold (float): Probability threshold for substitutions. match_oxi_sign (bool): Whether to force the dopant and original species to have the same sign of oxidation state. E.g. If the original site is in a negative charge state, then only negative dopants will be returned. Returns: (dict): Dopant suggestions, given as a dictionary with keys "n_type" and "p_type". The suggestions for each doping type are given as a list of dictionaries, each with they keys: - "probability": The probability of substitution. - "dopant_species": The dopant species. - "original_species": The substituted species. """ els_have_oxi_states = [hasattr(s, "oxi_state") for s in structure.species] if not all(els_have_oxi_states): raise ValueError("All sites in structure must have oxidation states to " "predict dopants.") sp = SubstitutionPredictor(threshold=threshold) subs = [sp.list_prediction([s]) for s in set(structure.species)] subs = [{'probability': pred['probability'], 'dopant_species': list(pred['substitutions'].keys())[0], 'original_species': list(pred['substitutions'].values())[0]} for species_preds in subs for pred in species_preds] subs.sort(key=lambda x: x['probability'], reverse=True) return _get_dopants(subs, num_dopants, match_oxi_sign) def get_dopants_from_shannon_radii(bonded_structure, num_dopants=5, match_oxi_sign=False): """ Get dopant suggestions based on Shannon radii differences. Args: bonded_structure (StructureGraph): A pymatgen structure graph decorated with oxidation states. For example, generated using the CrystalNN.get_bonded_structure() method. num_dopants (int): The nummber of suggestions to return for n- and p-type dopants. match_oxi_sign (bool): Whether to force the dopant and original species to have the same sign of oxidation state. E.g. If the original site is in a negative charge state, then only negative dopants will be returned. Returns: (dict): Dopant suggestions, given as a dictionary with keys "n_type" and "p_type". The suggestions for each doping type are given as a list of dictionaries, each with they keys: - "radii_diff": The difference between the Shannon radii of the species. - "dopant_spcies": The dopant species. - "original_species": The substituted species. """ # get a list of all Specie for all elements in all their common oxid states all_species = [Specie(el, oxi) for el in Element for oxi in el.common_oxidation_states] # get a series of tuples with (coordination number, specie) cn_and_species = set((bonded_structure.get_coordination_of_site(i), bonded_structure.structure[i].specie) for i in range(bonded_structure.structure.num_sites)) cn_to_radii_map = {} possible_dopants = [] for cn, species in cn_and_species: cn_roman = _int_to_roman(cn) try: species_radius = species.get_shannon_radius(cn_roman) except KeyError: warnings.warn("Shannon radius not found for {} with coordination " "number {}.\nSkipping...".format(species, cn)) continue if cn not in cn_to_radii_map: cn_to_radii_map[cn] = _shannon_radii_from_cn( all_species, cn_roman, radius_to_compare=species_radius) shannon_radii = cn_to_radii_map[cn] possible_dopants += [{'radii_diff': p['radii_diff'], 'dopant_species': p['species'], 'original_species': species} for p in shannon_radii] possible_dopants.sort(key=lambda x: abs(x['radii_diff'])) return _get_dopants(possible_dopants, num_dopants, match_oxi_sign) def _get_dopants(substitutions, num_dopants, match_oxi_sign): """ Utility method to get n- and p-type dopants from a list of substitutions. """ n_type = [pred for pred in substitutions if pred['dopant_species'].oxi_state > pred['original_species'].oxi_state and (not match_oxi_sign or np.sign(pred['dopant_species'].oxi_state) == np.sign(pred['original_species'].oxi_state))] p_type = [pred for pred in substitutions if pred['dopant_species'].oxi_state < pred['original_species'].oxi_state and (not match_oxi_sign or np.sign(pred['dopant_species'].oxi_state) == np.sign(pred['original_species'].oxi_state))] return {'n_type': n_type[:num_dopants], 'p_type': p_type[:num_dopants]} def _shannon_radii_from_cn(species_list, cn_roman, radius_to_compare=0): """ Utility func to get Shannon radii for a particular coordination number. As the Shannon radii depends on charge state and coordination number, species without an entry for a particular coordination number will be skipped. Args: species_list (list): A list of Species to get the Shannon radii for. cn_roman (str): The coordination number as a roman numeral. See Specie.get_shannon_radius for more details. radius_to_compare (float, optional): If set, the data will be returned with a "radii_diff" key, containing the difference between the shannon radii and this radius. Returns: (list of dict): The Shannon radii for all Species in species. Formatted as a list of dictionaries, with the keys: - "species": The species with charge state. - "radius": The Shannon radius for the species. - "radius_diff": The difference between the Shannon radius and the radius_to_compare optional argument. """ shannon_radii = [] for s in species_list: try: radius = s.get_shannon_radius(cn_roman) shannon_radii.append({ 'species': s, 'radius': radius, 'radii_diff': radius - radius_to_compare}) except KeyError: pass return shannon_radii def _int_to_roman(number): """Utility method to convert an int (less than 20) to a roman numeral.""" roman_conv = [(10, "X"), (9, "IX"), (5, "V"), (4, "IV"), (1, "I")] result = [] for (arabic, roman) in roman_conv: (factor, number) = divmod(number, arabic) result.append(roman * factor) if number == 0: break return "".join(result)

40.101604

80

0.636352

10fe4b7ad0b3581fac8aa462c812dcaa2656f3df

1,271

py

Python

.history/Classiles/ice_cream_20210614181315.py

minefarmer/Coding101-OOP

d5655977559e3bd1acf6a4f185a6121cc3b05ce4

[ "Unlicense" ]

null

.history/Classiles/ice_cream_20210614181315.py

minefarmer/Coding101-OOP

d5655977559e3bd1acf6a4f185a6121cc3b05ce4

[ "Unlicense" ]

null

.history/Classiles/ice_cream_20210614181315.py

minefarmer/Coding101-OOP

d5655977559e3bd1acf6a4f185a6121cc3b05ce4

[ "Unlicense" ]

null

"""[Practice: Ice Cream] Class keyword class name class Ice: Instantiation variable class name name ("instance") ice = Ice() Method an action or behavior ==== to add a method, I simply define a function inside the class | method name is (eat) def eat(self): print("hi") # this line is the method content Dot Expression # To test the method instance method name IceCream . eat () Assigning an attribute is very similar to defining a variable self attaches the attribute attribute value to the current instance self . cubes = 3 """ class IceCream: def __init__(self): print("Created ice cream") def eat(self, scoops): if self.scoops < scoops: print("Not enough bites left!") self.scoops -= scoops def add(self, scoops): self.scoops += scoops IceCream.eat() # Traceback (most recent call last): # File "/home/rich/Desktop/CarlsHub/Coding101-OOP/Classiles/ice_cream.py", line 37, in <module> # IceCream.eat() # TypeError: eat() missing 2 required positional arguments: 'self' and 'scoops'

27.042553

107

0.578285

ef8287e4a2a88f22bcb6dbda7c5cd7f8f95e04fa

1,673

py

Python

cpo/commands/fyre/info/get_quick_burn_max_hours.py

IBM/cloud-pak-operations-cli

45ddcefb4302801c9a833d1359ea4d740c384556

[ "Apache-2.0" ]

7

2021-12-07T09:16:24.000Z

2022-03-08T12:38:54.000Z

cpo/commands/fyre/info/get_quick_burn_max_hours.py

IBM/cloud-pak-operations-cli

45ddcefb4302801c9a833d1359ea4d740c384556

[ "Apache-2.0" ]

3

2021-11-26T09:43:03.000Z

2021-12-14T08:04:53.000Z

cpo/commands/fyre/info/get_quick_burn_max_hours.py

IBM/cloud-pak-operations-cli

45ddcefb4302801c9a833d1359ea4d740c384556

[ "Apache-2.0" ]

1

2022-03-10T07:14:49.000Z

# Copyright 2021 IBM Corporation # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Optional import click import cpo.config import cpo.lib.click.utils import cpo.utils.network from cpo.lib.fyre.ocp_plus_api_manager import OCPPlusAPIManager from cpo.lib.fyre.utils.click import fyre_command_options from cpo.utils.logging import loglevel_command @loglevel_command( context_settings=cpo.lib.click.utils.create_default_map_from_json_file( cpo.config.configuration_manager.get_credentials_file_path() ) ) @fyre_command_options @click.option("--json", help="Prints the command output in JSON format", is_flag=True) @click.option("--site", help="OCP+ site", type=click.Choice(["rtp", "svl"])) def get_quick_burn_max_hours( fyre_api_user_name: str, fyre_api_key: str, disable_strict_response_schema_check: bool, json: bool, site: Optional[str], ): """Get the maxmimum hours for a quick burn deployment""" cpo.utils.network.disable_insecure_request_warning() OCPPlusAPIManager(fyre_api_user_name, fyre_api_key, disable_strict_response_schema_check).get_quickburn_max_hours( site ).format(json)

34.142857

118

0.768679

8c76c329e71fd547affa7ba27d5a273330991966

2,433

py

Python

vol2/vol2-python-examples/examples/example_crossover.py

Sun-Joong/aifh

1b6363d26f54b77348020ce88ced0670568ed736

[ "Apache-2.0" ]

777

2015-01-17T22:48:26.000Z

2022-03-31T01:10:07.000Z

vol2/vol2-python-examples/examples/example_crossover.py

Sun-Joong/aifh

1b6363d26f54b77348020ce88ced0670568ed736

[ "Apache-2.0" ]

17

2015-01-02T14:41:24.000Z

2017-09-02T02:57:09.000Z

vol2/vol2-python-examples/examples/example_crossover.py

Sun-Joong/aifh

1b6363d26f54b77348020ce88ced0670568ed736

[ "Apache-2.0" ]

445

2015-01-26T17:01:49.000Z

2022-03-24T07:16:58.000Z

#!/usr/bin/env python """ Artificial Intelligence for Humans Volume 2: Nature-Inspired Algorithms Python Version http://www.aifh.org http://www.jeffheaton.com Code repository: https://github.com/jeffheaton/aifh Copyright 2014 by Jeff Heaton 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. For more information on Heaton Research copyrights, licenses and trademarks visit: http://www.heatonresearch.com/copyright ============================================================================================================ This example uses crossover to combine two parent genomes to produce two children. Both repeating and non-repeating splice are used. Crossover Splice Parent 1: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] Parent 2: [10, 9, 8, 7, 6, 5, 4, 3, 2, 1] Offspring 1: [1, 2, 3, 4, 5, 6, 4, 3, 2, 1] Offspring 2: [10, 9, 8, 7, 6, 5, 7, 8, 9, 10] Crossover Splice No Repeat Parent 1: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] Parent 2: [10, 9, 8, 7, 6, 5, 4, 3, 2, 1] Offspring 1: [10, 3, 2, 4, 5, 6, 7, 8, 9, 1] Offspring 2: [1, 8, 9, 7, 6, 5, 4, 3, 2, 10] """ import sys import os # Find the AIFH core files aifh_dir = os.path.dirname(os.path.abspath(__file__)) aifh_dir = os.path.abspath(aifh_dir + os.sep + ".." + os.sep + "lib" + os.sep + "aifh") sys.path.append(aifh_dir) from genetic import * from genetic import Genome from genetic import Species p1 = [ 1,2,3,4,5,6,7,8,9,10 ] p2 = [ 10,9,8,7,6,5,4,3,2,1 ] off = [[],[]] pop = Population() print("Crossover Splice") crossover_splice(pop, p1,p2,off) print("Parent 1: " + str(p1)) print("Parent 2: " + str(p2)) print("Offspring 1: " + str(off[0])) print("Offspring 2: " + str(off[1])) print() print("Crossover Splice No Repeat") crossover_splice_no_repeat(pop, p1,p2,off) print("Parent 1: " + str(p1)) print("Parent 2: " + str(p2)) print("Offspring 1: " + str(off[0])) print("Offspring 2: " + str(off[1]))

30.4125

112

0.633785

30401032ac9f894d9f8172bf845bf9bf99338c8c

1,628

py

Python

case/example/webdriver_savefile_test07.py

w718328952/webdriver_test

66d0ad70899981a9968717525b14a7dc239ef963

[ "Apache-2.0" ]

null

case/example/webdriver_savefile_test07.py

w718328952/webdriver_test

66d0ad70899981a9968717525b14a7dc239ef963

[ "Apache-2.0" ]

null

case/example/webdriver_savefile_test07.py

w718328952/webdriver_test

66d0ad70899981a9968717525b14a7dc239ef963

[ "Apache-2.0" ]

null

from selenium import webdriver from selenium.webdriver.common.keys import Keys from time import sleep # time。sleep()实现延时 # 火狐浏览器专用文件配置方法 profile = webdriver.FirefoxProfile() # 指定下载路径 profile.set_preference('browser.download.dir', 'd:\\') # 设置成 2 表示使用自定义下载路径；设置成 0 表示下载到桌面；设置成 1 表示下载到默认路径 profile.set_preference('browser.download.folderList', 2) # 在开始下载时是否显示下载管理器 profile.set_preference('browser.download.manager.showWhenStarting', False) # 对所给出文件类型不再弹出框进行询问 profile.set_preference('browser.helperApps.neverAsk.saveToDisk', 'application/zip') browser = webdriver.Firefox(firefox_profile=profile) # 配置浏览器时，必须定义配置对象profile browser.get('http://www.xxxxxxx.com') # 这里地址用的是查询页面，如果没有登录就会跳转到登录页面，登录之后会自动跳到查询页面 # assert 'Yahoo!' in browser 这行不要了 username = browser.find_element_by_name('uid') # 获取username输入框 username.clear() # 先清空输入框 username.send_keys(username) # 输入用户名 password = browser.find_element_by_name('password') # 获取username输入框 password.clear() # 先清空输入框 password.send_keys(password) # 输入密码 password.send_keys(Keys.RETURN) # 输入密码之后输入RETURN特殊键实现登录，不用再定位登录按钮 browser.implicitly_wait(5) # 延时5秒等待页面跳转 browser.find_element_by_name('项目').send_keys(ID) # 定位到项目ID输入框并输入项目ID browser.find_element_by_id('search').click() # 定位到搜索按钮，并点击 browser.implicitly_wait(5) # 延时等待搜索结果 browser.find_element_by_xpath('\\').click() # 定位到导出按钮，并点击 sleep(3) # 延时弹出导出提示框，提示用户到另外一个页面下载导出数据 browser.find_element_by_xpath('\\').click() # 定位到弹出框上的确定按钮，点击确定隐藏提示框，以方便导出下一个项目的bug列表 browser.get('http://www.yyyyyy.com') # 跳转到下载页面 filelist = browser.find_elements_by_xpath('\\') # 定位到文件列表中所有的文件 for file in filelist: file.click() # 点击保存文件

40.7

83

0.789926

3ca01dc134a15a595c20114e53b9194f50a12e6c

1,478

py

Python

articles/models.py

varkon/eparhiaodua

f2a2d6fd3c89748712e38f4035a720c6a7ad7fe3

[ "Apache-2.0" ]

2

2018-04-24T10:41:11.000Z

2018-04-24T10:41:15.000Z

articles/models.py

varkon/eparhia

f2a2d6fd3c89748712e38f4035a720c6a7ad7fe3

[ "Apache-2.0" ]

null

articles/models.py

varkon/eparhia

f2a2d6fd3c89748712e38f4035a720c6a7ad7fe3

[ "Apache-2.0" ]

null

from django.db import models from django.utils import timezone from tinymce import models as tinymce_model from filebrowser.fields import FileBrowseField from eparhiapp.apps import transliterate # Create your models here. class Article(models.Model) : title = models.CharField(max_length = 255, verbose_name='Заголовок') annonce = tinymce_model.HTMLField(verbose_name='Анонс новини') body = tinymce_model.HTMLField(verbose_name='Повний текст новини') link = models.CharField(max_length = 255, verbose_name='Посилання', unique=True, blank=True) author = models.ForeignKey('auth.User', on_delete=models.CASCADE) created_date = models.DateTimeField( default=timezone.now) published_date = models.DateTimeField( blank=True, null=True) icon = FileBrowseField("Зображення", max_length=250, directory="uploads/", extensions=[".jpg","jpeg","png"], null=True) class Meta: verbose_name = 'Стаття' verbose_name_plural = 'Статті' # this is not needed for create link def save(self, *args, **kwargs): self.createlink() super(Article, self).save(*args, **kwargs) def publish(self): self.published_date = timezone.now() if (self.link == ""): self.link = transliterate(self.title) self.save() def createlink(self): if (self.link == ""): self.link = transliterate(self.title) def __str__(self): return self.title

36.04878

123

0.679296

a9df85b1cc2c4548e7e4c02f5e7bd9d2737f3455

3,356

py

Python

library/apt-upgrade-list.py

cmurphy/ardana-ansible

f3fb1573cfd6adff24c122c85ae877582a24ab55

[ "Apache-2.0" ]

4

2018-12-21T13:30:46.000Z

2020-04-06T10:39:18.000Z

library/apt-upgrade-list.py

cmurphy/ardana-ansible

f3fb1573cfd6adff24c122c85ae877582a24ab55

[ "Apache-2.0" ]

null

library/apt-upgrade-list.py

cmurphy/ardana-ansible

f3fb1573cfd6adff24c122c85ae877582a24ab55

[ "Apache-2.0" ]

8

2018-03-09T19:50:19.000Z

2019-08-13T09:49:44.000Z

#!/usr/bin/env python # # An Ansible module to query apt for the list of packages # available for update. # # (c) Copyright 2015 Hewlett Packard Enterprise Development LP # (c) Copyright 2017 SUSE LLC # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # 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. DOCUMENTATION = ''' --- module: apt-upgrade-list author: Tom Howley short_description: Queries apt for list of packages available for upgrade. description: - Updates the local apt cache. - Queries the apt cache to get the list of packages avalailable for upgrade. That list of packages is written to the fact: list_pkg_upgrades options: timeout: description: - Timeout the module operation after specified number of seconds. required: false default: 30 ''' EXAMPLES = ''' - apt-upgrade-list: timeout: 30 ''' import datetime import json import os import signal from threading import Timer def kill_procgroup(proc): os.killpg(os.getpgid(proc.pid), signal.SIGKILL) def get_list_pkg_upgrades(module, timeout): cmd = "sudo aptitude -s -y upgrade" p = subprocess.Popen(cmd, stdout=subprocess.PIPE, shell=True, preexec_fn=os.setsid) t = Timer(timeout, kill_procgroup, [p]) t.start() output, err = p.communicate() if t.is_alive(): t.cancel() else: error_msg = "Timeout on cmd %s output %s" % (cmd, output) module.fail_json(msg=error_msg) if p.returncode != 0: error_msg = "Failed to run %s" % (cmd) module.fail_json(msg=error_msg) output = output.splitlines() list_pkg_upgrades = [] UPGRADE_STR="The following packages will be upgraded:" RECOMMEND_STR="The following packages are RECOMMENDED but will NOT be installed:" idx_start_match = next((i for i, v in enumerate(output) if v == UPGRADE_STR), -1) if idx_start_match == -1: return list_pkg_upgrades idx_end_match = next((i for i, v in enumerate(output) if v == RECOMMEND_STR), -1) if idx_end_match == -1: idx_end_match = next((i for i, v in enumerate(output) if re.match('^\d*\s*packages upgraded.*not upgraded.$',v)), -1) if idx_end_match == -1: return list_pkg_upgrades for line in output[idx_start_match+1:idx_end_match]: list_pkg_upgrades.extend(line.split()) for pkg in list_pkg_upgrades: print "Pkg: %s" % pkg return list_pkg_upgrades def main(): module = AnsibleModule( argument_spec = dict( timeout=dict(required=False, type='int', default=30) )) timeout = module.params['timeout'] list_pkg_upgrades = get_list_pkg_upgrades(module, timeout) changed = (len(list_pkg_upgrades) > 0) ansible_facts_dict = dict(list_pkg_upgrades=list_pkg_upgrades) result = dict(changed=changed, ansible_facts=ansible_facts_dict) module.exit_json(**result) from ansible.module_utils.basic import * main()

31.074074

125

0.701132

2c77f791d430bf98e3c705ae313dc99d7d4c301e

1,307

py

Python

pytket_cirq/setup.py

Travis-S/pytket

ba1b63b5b22533d9366c431b91d69bf9cf77b0d7

[ "Apache-2.0" ]

null

pytket_cirq/setup.py

Travis-S/pytket

ba1b63b5b22533d9366c431b91d69bf9cf77b0d7

[ "Apache-2.0" ]

null

pytket_cirq/setup.py

Travis-S/pytket

ba1b63b5b22533d9366c431b91d69bf9cf77b0d7

[ "Apache-2.0" ]

null

import setuptools from setuptools import setup def find_pytket_subpackages(): locations = [('pytket', 'pytket')] pkg_list = [] for location, prefix in locations: pkg_list += list( map(lambda package_name: '{}.{}'.format(prefix, package_name), setuptools.find_packages(where=location)) ) return pkg_list setup( name='pytket_cirq', version='0.1', author='Will Simmons', author_email='will.simmons@cambridgequantum.com', python_requires='>=3.6', url='https://github.com/CQCL/pytket', description='Extension for pytket, providing translation to and from the Cirq framework', license='Apache 2.0', packages = find_pytket_subpackages(), install_requires = [ 'pytket >=0.2.0', 'cirq ~=0.5.0', 'matplotlib ~=2.2' ], classifiers=[ "Environment :: Console", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "License :: OSI Approved :: Apache Software License", "Operating System :: MacOS :: MacOS X", "Operating System :: POSIX :: Linux", "Intended Audience :: Developers", "Intended Audience :: Science/Research", "Topic :: Scientific/Engineering" ], zip_safe=False )

30.395349

93

0.609028

064e54a074da849bece5a11954121930000b807b

1,344

py

Python

Lista-05/exercicio-03.py

joaofelipecsantos/ser-347

cfaebf7dcba8366b8e8b922c8c7f7e4d270892b8

[ "MIT" ]

null

Lista-05/exercicio-03.py

joaofelipecsantos/ser-347

cfaebf7dcba8366b8e8b922c8c7f7e4d270892b8

[ "MIT" ]

null

Lista-05/exercicio-03.py

joaofelipecsantos/ser-347

cfaebf7dcba8366b8e8b922c8c7f7e4d270892b8

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ SER-347 - Joao Felipe Lista-05 Exercício 03. Tomando como base os operadores disponíveis em Python documentation - String Methods, apresente as operações para converter os elementos da coluna string de entrada nos resultados apresentados na coluna string de saída. """ # 1 entrada = 'Gilberto' print(f'++{entrada}++') # saida = '++Gilberto++' # 2 entrada = 'sensoriamento remoto' print(entrada.capitalize()) # saida = 'Sensoriamento remoto' # 3 entrada = 'sensoriamento remoto' print(entrada.title()) # saida = 'Sensoriamento Remoto' # 4 entrada = 'GilberTo' print(entrada.lower()) # saida = 'gilberto' # 5 entrada = 'Gilberto' print(entrada + '**') # saida = 'Gilberto**' # 6 entrada = 'Gilberto' print('**' + entrada) # saida = '**Gilberto' # 7 entrada = ' Gilberto' print(entrada.strip()) # saida = 'Gilberto' # 8 entrada = 'ser347@dpi.inpe.br' print("('%s', '@', '%s')" %(entrada.split('@')[0], entrada.split('@')[1])) # saida = ('ser347', '@', 'dpi.inpe.br') # 9 entrada = 'CBERS_4_PAN5M_20180308' print(entrada.split('_')) # saida = ['CBERS', '4', 'PAN5M', '20180308'] # 10 entrada = 'Gilberto@@@' print(entrada.replace('@', '')) # saida = 'Gilberto' # 11 entrada = '@@Gilberto@@@' print(entrada.replace('@', '')) # saida = 'Gilberto' # END -----------------------

24.436364

116

0.633185

02482ffbc3c39476658b620e20dfe02e03f977ad

4,170

py

Python

setup.py

ziodave/opentapioca

e4d5d41c1fdb199a49745c3efc2a02c6d74be315

[ "Apache-2.0" ]

191

2019-04-12T11:28:02.000Z

2022-03-13T16:05:31.000Z

setup.py

ziodave/opentapioca

e4d5d41c1fdb199a49745c3efc2a02c6d74be315

[ "Apache-2.0" ]

38

2019-04-23T16:47:50.000Z

2022-03-22T09:29:21.000Z

setup.py

ziodave/opentapioca

e4d5d41c1fdb199a49745c3efc2a02c6d74be315

[ "Apache-2.0" ]

26

2019-04-23T13:49:05.000Z

2022-02-10T07:43:15.000Z

"""A setuptools based setup module. See: https://packaging.python.org/en/latest/distributing.html https://github.com/pypa/sampleproject """ # Always prefer setuptools over distutils from setuptools import setup, find_packages # To use a consistent encoding from codecs import open from os import path here = path.abspath(path.dirname(__file__)) # Get the long description from the README file with open(path.join(here, 'README.rst'), encoding='utf-8') as f: long_description = f.read() setup( name='opentapioca', # Versions should comply with PEP440. For a discussion on single-sourcing # the version across setup.py and the project code, see # https://packaging.python.org/en/latest/single_source_version.html version='0.1.1', description='Simple entity linking service for Wikidata', long_description=long_description, # The project's main homepage. url='https://github.com/wetneb/opentapioca', # Author details author='Antonin Delpeuch', author_email='antonin@delpeuch.eu', # Choose your license license='MIT', # See https://pypi.python.org/pypi?%3Aaction=list_classifiers classifiers=[ # How mature is this project? Common values are # 3 - Alpha # 4 - Beta # 5 - Production/Stable 'Development Status :: 3 - Alpha', # Indicate who your project is intended for 'Intended Audience :: Developers', 'Topic :: Scientific/Engineering :: Information Analysis', # Pick your license as you wish (should match "license" above) 'License :: OSI Approved :: MIT License', # Specify the Python versions you support here. In particular, ensure # that you indicate whether you support Python 2, Python 3 or both. 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', ], # What does your project relate to? keywords='NIF NLP Wikidata NERD', # You can just specify the packages manually here if your project is # simple. Or you can use find_packages(). packages=find_packages(exclude=['contrib', 'docs', 'tests']), # Alternatively, if you want to distribute just a my_module.py, uncomment # this: # py_modules=["my_module"], # List run-time dependencies here. These will be installed by pip when # your project is installed. For an analysis of "install_requires" vs pip's # requirements files see: # https://packaging.python.org/en/latest/requirements.html install_requires=['numpy', 'scipy', 'scikit-learn', 'bottle', 'requests-cache', 'requests_mock', 'unidecode', 'pynif', 'requests', 'click'], # List additional groups of dependencies here (e.g. development # dependencies). You can install these using the following syntax, # for example: # $ pip install -e .[dev,test] extras_require={ 'dev': ['check-manifest'], 'test': ['coverage', 'pytest'], }, # If there are data files included in your packages that need to be # installed, specify them here. If using Python 2.6 or less, then these # have to be included in MANIFEST.in as well. package_data={ }, # Although 'package_data' is the preferred approach, in some case you may # need to place data files outside of your packages. See: # http://docs.python.org/3.4/distutils/setupscript.html#installing-additional-files # noqa # In this case, 'data_file' will be installed into '<sys.prefix>/my_data' data_files=[], # To provide executable scripts, use entry points in preference to the # "scripts" keyword. Entry points provide cross-platform support and allow # pip to create the appropriate form of executable for the target platform. entry_points={ 'console_scripts': [ 'tapioca = opentapioca.cli:cli', ], }, )

36.26087

144

0.668345

2537bf7400f1054d01421fb8cd16307926b2c4df

2,716

py

Python

Birnn_Transformer/ncc/criterions/retrieval/triplet.py

code-backdoor/code-backdoor

1eeb3d79aa8a54c8f08e8d0156b569de5edd974e

[ "MIT" ]

71

2020-12-04T02:18:13.000Z

2022-03-30T15:19:50.000Z

Birnn_Transformer/ncc/criterions/retrieval/triplet.py

code-backdoor/code-backdoor

1eeb3d79aa8a54c8f08e8d0156b569de5edd974e

[ "MIT" ]

4

2021-03-10T17:48:50.000Z

2022-03-13T10:42:22.000Z

Birnn_Transformer/ncc/criterions/retrieval/triplet.py

code-backdoor/code-backdoor

1eeb3d79aa8a54c8f08e8d0156b569de5edd974e

[ "MIT" ]

11

2020-12-09T12:17:44.000Z

2022-03-30T09:02:13.000Z

# -*- coding: utf-8 -*- import math import torch.nn.functional as F from ncc.criterions import NccCriterion, register_criterion from ncc.data.constants import EPS from ncc.utils import utils from ncc.utils.logging import metrics @register_criterion('triplet') class TripletCriterion(NccCriterion): def __init__(self, task, sentence_avg): super().__init__(task) self.sentence_avg = sentence_avg self.margin = self.task.args['optimization']['margin'] def forward(self, model, sample, reduce=True): """Compute the loss for the given sample. Returns a tuple with three elements: 1) the loss 2) the sample size, which is used as the denominator for the gradient 3) logging outputs to display while training """ net_output = model(**sample['net_input']) loss, _ = self.compute_loss(model, net_output, reduce=reduce) sample_size = sample['nsentences'] logging_output = { 'loss': loss.data, 'nsentences': sample['nsentences'], 'sample_size': sample_size, } return loss, sample_size, logging_output def cos_similarity(self, src_repr, tgt_repr): return F.cosine_similarity(src_repr, tgt_repr) def compute_loss(self, model, net_output, reduce=True): src_repr, pos_repr, neg_repr = net_output pos_dist = self.cos_similarity(src_repr, pos_repr) # B X 1 neg_dist = self.cos_similarity(src_repr, neg_repr) # B X 1 loss = (self.margin - pos_dist + neg_dist).clamp(EPS).sum() return loss, loss @staticmethod def reduce_metrics(logging_outputs) -> None: """Aggregate logging outputs from data parallel training.""" loss_sum = sum(log.get('loss', 0) for log in logging_outputs) ntokens = sum(log.get('ntokens', 0) for log in logging_outputs) sample_size = sum(log.get('sample_size', 0) for log in logging_outputs) metrics.log_scalar('loss', loss_sum / sample_size / math.log(2), sample_size, round=6) if sample_size != ntokens: metrics.log_scalar('nll_loss', loss_sum / ntokens / math.log(2), ntokens, round=6) metrics.log_derived('ppl', lambda meters: utils.get_perplexity(meters['nll_loss'].avg)) else: metrics.log_derived('ppl', lambda meters: utils.get_perplexity(meters['loss'].avg)) @staticmethod def logging_outputs_can_be_summed() -> bool: """ Whether the logging outputs returned by `forward` can be summed across workers prior to calling `reduce_metrics`. Setting this to True will improves distributed training speed. """ return True

38.8

99

0.661635

e97d6ebeb53cc0b1023600963f736c5c2b22ed6f

579

py

Python

pacote-Download/pythontestes/desafio037.py

FernandoFalcone-dev/exercicios-Python

a867cf5cd08ae7325f2f7916b4116b5f31fe83ef

[ "MIT" ]

null

pacote-Download/pythontestes/desafio037.py

FernandoFalcone-dev/exercicios-Python

a867cf5cd08ae7325f2f7916b4116b5f31fe83ef

[ "MIT" ]

null

pacote-Download/pythontestes/desafio037.py

FernandoFalcone-dev/exercicios-Python

a867cf5cd08ae7325f2f7916b4116b5f31fe83ef

[ "MIT" ]

null

num = int(input('Digite um número: ')) print('''Escolha uma das bases para conversão: [ 1 ] converter para \033[1;31mBINÁRIO\033[m [ 2 ] converter para \033[1;32mOCTAL\033[m [ 3 ] converter para \033[1;33mHEXADECIMAL\033[m''') escolha = int(input('Sua opção é: ')) if escolha == 1: print(f'{num} convertido para BINÁRIO é igual a {bin(num)[2:]}') elif escolha == 2: print(f'{num} convertido para OCTAL é igual a {oct(num)[2:]}') elif escolha == 3: print(f'{num} convertido para HEXADECIMAL é igual a {hex(num)[2:]}') else: print('Opção inválida. Tente de novo.')

38.6

72

0.663212

85f1ccf43946d110972b4e1943e2803363d749d5

3,670

py

Python

python/GafferSceneUI/FilteredSceneProcessorUI.py

cwmartin/gaffer

1f8a0f75522105c9d5efefac6d55cb61c1038909

[ "BSD-3-Clause" ]

null

python/GafferSceneUI/FilteredSceneProcessorUI.py

cwmartin/gaffer

1f8a0f75522105c9d5efefac6d55cb61c1038909

[ "BSD-3-Clause" ]

null

python/GafferSceneUI/FilteredSceneProcessorUI.py

cwmartin/gaffer

1f8a0f75522105c9d5efefac6d55cb61c1038909

[ "BSD-3-Clause" ]

null

########################################################################## # # Copyright (c) 2013, Image Engine Design Inc. All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are # met: # # * Redistributions of source code must retain the above # copyright notice, this list of conditions and the following # disclaimer. # # * Redistributions in binary form must reproduce the above # copyright notice, this list of conditions and the following # disclaimer in the documentation and/or other materials provided with # the distribution. # # * Neither the name of John Haddon nor the names of # any other contributors to this software may be used to endorse or # promote products derived from this software without specific prior # written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS # IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, # THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR # PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR # CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, # EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, # PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR # PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF # LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING # NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # ########################################################################## import IECore import Gaffer import GafferUI import GafferScene import GafferSceneUI ########################################################################## # Metadata ########################################################################## Gaffer.Metadata.registerNodeDescription( GafferScene.FilteredSceneProcessor, """The base type for scene processors which use a Filter node to control which part of the scene is affected.""", "filter", { "description" : """The filter used to control which parts of the scene are processed. A Filter node should be connected here.""", "nodeUI:section" : "Filter", "nodeGadget:nodulePosition" : "right", } ) ########################################################################## # Widgets and Gadgets ########################################################################## GafferUI.PlugValueWidget.registerCreator( GafferScene.FilteredSceneProcessor, "filter", GafferSceneUI.FilterPlugValueWidget, ) GafferUI.Nodule.registerNodule( GafferScene.FilteredSceneProcessor, "filter", GafferUI.StandardNodule ) ########################################################################## # NodeGraph context menu ########################################################################## def __selectAffected( node, context ) : with context : pathMatcher = GafferScene.PathMatcher() GafferScene.matchingPaths( node["filter"], node["in"], pathMatcher ) context["ui:scene:selectedPaths"] = IECore.StringVectorData( pathMatcher.paths() ) def appendNodeContextMenuDefinitions( nodeGraph, node, menuDefinition ) : if not isinstance( node, GafferScene.FilteredSceneProcessor ) : return menuDefinition.append( "/FilteredSceneProcessorDivider", { "divider" : True } ) menuDefinition.append( "/Select Affected Objects", { "command" : IECore.curry( __selectAffected, node, nodeGraph.getContext() ) } )

39.042553

132

0.632698

1b2056d98311c91c13d5b48faed495f4a50c6d74

30,829

py

Python

agents/GFootball_with_Memory_Patterns_v47.py

sazas/football

a762fdedf7367ef08cb3a4c6b44713b93aa69d37

[ "Apache-2.0" ]

null

agents/GFootball_with_Memory_Patterns_v47.py

sazas/football

a762fdedf7367ef08cb3a4c6b44713b93aa69d37

[ "Apache-2.0" ]

null

agents/GFootball_with_Memory_Patterns_v47.py

sazas/football

a762fdedf7367ef08cb3a4c6b44713b93aa69d37

[ "Apache-2.0" ]

null

# start executing cells from here to rewrite submission.py from kaggle_environments.envs.football.helpers import * import math import random def find_patterns(obs, player_x, player_y): """ find list of appropriate patterns in groups of memory patterns """ for get_group in groups_of_memory_patterns: group = get_group(obs, player_x, player_y) if group["environment_fits"](obs, player_x, player_y): return group["get_memory_patterns"](obs, player_x, player_y) def get_action_of_agent(obs, player_x, player_y): """ get action of appropriate pattern in agent's memory """ memory_patterns = find_patterns(obs, player_x, player_y) # find appropriate pattern in list of memory patterns for get_pattern in memory_patterns: pattern = get_pattern(obs, player_x, player_y) if pattern["environment_fits"](obs, player_x, player_y): return pattern["get_action"](obs, player_x, player_y) def get_active_sticky_action(obs, exceptions): """ get release action of the first active sticky action, except those in exceptions list """ release_action = None for k in sticky_actions: if k not in exceptions and sticky_actions[k] in obs["sticky_actions"]: if k == "sprint": release_action = Action.ReleaseSprint elif k == "dribble": release_action = Action.ReleaseDribble else: release_action = Action.ReleaseDirection break return release_action def get_average_distance_to_opponents(obs, player_x, player_y): """ get average distance to closest opponents """ distances_sum = 0 distances_amount = 0 for i in range(1, len(obs["right_team"])): # if opponent is ahead of player if obs["right_team"][i][0] > (player_x - 0.03): distance_to_opponent = get_distance(player_x, player_y, obs["right_team"][i][0], obs["right_team"][i][1]) if distance_to_opponent < 0.07: distances_sum += distance_to_opponent distances_amount += 1 # if there is no opponents close around if distances_amount == 0: return 2, distances_amount return distances_sum / distances_amount, distances_amount def get_distance(x1, y1, x2, y2): """ get two-dimensional Euclidean distance, considering y size of the field """ return math.sqrt((x1 - x2) ** 2 + (y1 * 2.38 - y2 * 2.38) ** 2) # "%%writefile -a submission.py" will append the code below to submission.py, # it WILL NOT rewrite submission.py def bad_angle_short_pass(obs, player_x, player_y): """ perform a short pass, if player is at bad angle to opponent's goal """ def environment_fits(obs, player_x, player_y): """ environment fits constraints """ # player have the ball and is at bad angle to opponent's goal if (obs["ball_owned_player"] == obs["active"] and obs["ball_owned_team"] == 0 and ( (abs(player_y) > 0.2 and player_x > 0.9) or (player_x > 0.7 and player_y > 0.07 and obs["left_team_direction"][obs["active"]][1] > 0) or (player_x > 0.7 and player_y < -0.07 and obs["left_team_direction"][obs["active"]][1] < 0) )): return True return False def get_action(obs, player_x, player_y): """ get action of this memory pattern """ action_to_release = get_active_sticky_action(obs, ["top", "bottom"]) if action_to_release != None: return action_to_release if Action.Top not in obs["sticky_actions"] and Action.Bottom not in obs["sticky_actions"]: if player_y > 0: return Action.Top else: return Action.Bottom return Action.ShortPass return {"environment_fits": environment_fits, "get_action": get_action} def close_to_goalkeeper_shot(obs, player_x, player_y): """ shot if close to the goalkeeper """ def environment_fits(obs, player_x, player_y): """ environment fits constraints """ goalkeeper_x = obs["right_team"][0][0] + obs["right_team_direction"][0][0] * 13 goalkeeper_y = obs["right_team"][0][1] + obs["right_team_direction"][0][1] * 13 # player have the ball and located close to the goalkeeper if (obs["ball_owned_player"] == obs["active"] and obs["ball_owned_team"] == 0 and get_distance(player_x, player_y, goalkeeper_x, goalkeeper_y) < 0.3): return True return False def get_action(obs, player_x, player_y): """ get action of this memory pattern """ if player_y <= -0.03 or (player_y > 0 and player_y < 0.03): action_to_release = get_active_sticky_action(obs, ["bottom_right", "sprint"]) if action_to_release != None: return action_to_release if Action.BottomRight not in obs["sticky_actions"]: return Action.BottomRight else: action_to_release = get_active_sticky_action(obs, ["top_right", "sprint"]) if action_to_release != None: return action_to_release if Action.TopRight not in obs["sticky_actions"]: return Action.TopRight return Action.Shot return {"environment_fits": environment_fits, "get_action": get_action} def far_from_goal_shot(obs, player_x, player_y): """ perform a shot, if far from opponent's goal """ def environment_fits(obs, player_x, player_y): """ environment fits constraints """ # player have the ball and is far from opponent's goal if (obs["ball_owned_player"] == obs["active"] and obs["ball_owned_team"] == 0 and (player_x < -0.6 or obs["ball_owned_player"] == 0)): return True return False def get_action(obs, player_x, player_y): """ get action of this memory pattern """ action_to_release = get_active_sticky_action(obs, ["right", "sprint"]) if action_to_release != None: return action_to_release if Action.Right not in obs["sticky_actions"]: return Action.Right return Action.Shot return {"environment_fits": environment_fits, "get_action": get_action} def far_from_goal_high_pass(obs, player_x, player_y): """ perform a high pass, if far from opponent's goal """ def environment_fits(obs, player_x, player_y): """ environment fits constraints """ # player have the ball and is far from opponent's goal if (obs["ball_owned_player"] == obs["active"] and obs["ball_owned_team"] == 0 and (player_x < -0.3 or obs["ball_owned_player"] == 0)): return True return False def get_action(obs, player_x, player_y): """ get action of this memory pattern """ action_to_release = get_active_sticky_action(obs, ["right", "sprint"]) if action_to_release != None: return action_to_release if Action.Right not in obs["sticky_actions"]: return Action.Right return Action.HighPass return {"environment_fits": environment_fits, "get_action": get_action} def go_through_opponents(obs, player_x, player_y): """ avoid closest opponents by going around them """ def environment_fits(obs, player_x, player_y): """ environment fits constraints """ # right direction is safest biggest_distance, final_opponents_amount = get_average_distance_to_opponents(obs, player_x + 0.01, player_y) obs["memory_patterns"]["go_around_opponent"] = Action.Right # if top right direction is safest top_right, opponents_amount = get_average_distance_to_opponents(obs, player_x + 0.01, player_y - 0.007) if (top_right > biggest_distance and player_y > -0.15) or (top_right == 2 and player_y > 0.07): biggest_distance = top_right final_opponents_amount = opponents_amount obs["memory_patterns"]["go_around_opponent"] = Action.TopRight # if bottom right direction is safest bottom_right, opponents_amount = get_average_distance_to_opponents(obs, player_x + 0.01, player_y + 0.007) if (bottom_right > biggest_distance and player_y < 0.15) or (bottom_right == 2 and player_y < -0.07): biggest_distance = bottom_right final_opponents_amount = opponents_amount obs["memory_patterns"]["go_around_opponent"] = Action.BottomRight # is player surrounded? if final_opponents_amount >= 3: obs["memory_patterns"]["go_around_opponent_surrounded"] = True else: obs["memory_patterns"]["go_around_opponent_surrounded"] = False return True def get_action(obs, player_x, player_y): """ get action of this memory pattern """ # if player is surrounded if obs["memory_patterns"]["go_around_opponent_surrounded"]: return Action.HighPass if obs["memory_patterns"]["go_around_opponent"] not in obs["sticky_actions"]: action_to_release = get_active_sticky_action(obs, ["sprint"]) if action_to_release != None: return action_to_release return obs["memory_patterns"]["go_around_opponent"] if Action.Sprint not in obs["sticky_actions"]: return Action.Sprint return obs["memory_patterns"]["go_around_opponent"] return {"environment_fits": environment_fits, "get_action": get_action} # "%%writefile -a submission.py" will append the code below to submission.py, # it WILL NOT rewrite submission.py def khorne_slide(obs, berzerker_x, berzerker_y): """ BLOOD FOR THE BLOOD GOD!!! SKULLS FOR THE SKULL THRONE!!! """ def environment_fits(obs, berzerker_x, berzerker_y): """ environment fits constraints """ # if prey has the ball if obs["ball_owned_team"] == 1: prey_x = obs["right_team"][obs["ball_owned_player"]][0] prey_y = obs["right_team"][obs["ball_owned_player"]][1] # by x position, amount of berzerker's team players between prey and goal of berzerker's team players_amount = 0 for i in range(1, len(obs["left_team"])): if obs["left_team"][i][0] < prey_x: players_amount += 1 prey_x_direction = obs["right_team_direction"][obs["ball_owned_player"]][0] future_prey_x = prey_x + obs["right_team_direction"][obs["ball_owned_player"]][0] future_prey_y = prey_y + obs["right_team_direction"][obs["ball_owned_player"]][1] future_berzerker_x = berzerker_x + obs["left_team_direction"][obs["active"]][0] future_berzerker_y = berzerker_y + obs["left_team_direction"][obs["active"]][1] distance_to_prey = get_distance(berzerker_x, berzerker_y, prey_x, prey_y) future_distance_to_prey = get_distance(future_berzerker_x, future_berzerker_y, future_prey_x, future_prey_y) # if berzerker is not close to his own penalty zone # and prey is beyond x position of the most of berzerker's team # and berzerker is close enough to prey # and berzerker is running in direction of prey if ((berzerker_x > -0.65 or abs(berzerker_y) > 0.3) and players_amount <= 5 and future_distance_to_prey < 0.015 and distance_to_prey > future_distance_to_prey): return True return False def get_action(obs, berzerker_x, berzerker_y): """ get action of this memory pattern """ return Action.Slide return {"environment_fits": environment_fits, "get_action": get_action} def run_to_ball_bottom(obs, player_x, player_y): """ run to the ball if it is to the bottom from player's position """ def environment_fits(obs, player_x, player_y): """ environment fits constraints """ # ball is to the bottom from player's position if (obs["ball"][1] > player_y and abs(obs["ball"][0] - player_x) < 0.01): return True return False def get_action(obs, player_x, player_y): """ get action of this memory pattern """ if Action.Sprint not in obs["sticky_actions"]: return Action.Sprint return Action.Bottom return {"environment_fits": environment_fits, "get_action": get_action} def run_to_ball_bottom_left(obs, player_x, player_y): """ run to the ball if it is to the bottom left from player's position """ def environment_fits(obs, player_x, player_y): """ environment fits constraints """ # ball is to the bottom left from player's position if (obs["ball"][0] < player_x and obs["ball"][1] > player_y): return True return False def get_action(obs, player_x, player_y): """ get action of this memory pattern """ if Action.Sprint not in obs["sticky_actions"]: return Action.Sprint return Action.BottomLeft return {"environment_fits": environment_fits, "get_action": get_action} def run_to_ball_bottom_right(obs, player_x, player_y): """ run to the ball if it is to the bottom right from player's position """ def environment_fits(obs, player_x, player_y): """ environment fits constraints """ # ball is to the bottom right from player's position if (obs["ball"][0] > player_x and obs["ball"][1] > player_y): return True return False def get_action(obs, player_x, player_y): """ get action of this memory pattern """ if Action.Sprint not in obs["sticky_actions"]: return Action.Sprint return Action.BottomRight return {"environment_fits": environment_fits, "get_action": get_action} def run_to_ball_left(obs, player_x, player_y): """ run to the ball if it is to the left from player's position """ def environment_fits(obs, player_x, player_y): """ environment fits constraints """ # ball is to the left from player's position if (obs["ball"][0] < player_x and abs(obs["ball"][1] - player_y) < 0.01): return True return False def get_action(obs, player_x, player_y): """ get action of this memory pattern """ if Action.Sprint not in obs["sticky_actions"]: return Action.Sprint return Action.Left return {"environment_fits": environment_fits, "get_action": get_action} def run_to_ball_right(obs, player_x, player_y): """ run to the ball if it is to the right from player's position """ def environment_fits(obs, player_x, player_y): """ environment fits constraints """ # ball is to the right from player's position if (obs["ball"][0] > player_x and abs(obs["ball"][1] - player_y) < 0.01): return True return False def get_action(obs, player_x, player_y): """ get action of this memory pattern """ if Action.Sprint not in obs["sticky_actions"]: return Action.Sprint return Action.Right return {"environment_fits": environment_fits, "get_action": get_action} def run_to_ball_top(obs, player_x, player_y): """ run to the ball if it is to the top from player's position """ def environment_fits(obs, player_x, player_y): """ environment fits constraints """ # ball is to the top from player's position if (obs["ball"][1] < player_y and abs(obs["ball"][0] - player_x) < 0.01): return True return False def get_action(obs, player_x, player_y): """ get action of this memory pattern """ if Action.Sprint not in obs["sticky_actions"]: return Action.Sprint return Action.Top return {"environment_fits": environment_fits, "get_action": get_action} def run_to_ball_top_left(obs, player_x, player_y): """ run to the ball if it is to the top left from player's position """ def environment_fits(obs, player_x, player_y): """ environment fits constraints """ # ball is to the top left from player's position if (obs["ball"][0] < player_x and obs["ball"][1] < player_y): return True return False def get_action(obs, player_x, player_y): """ get action of this memory pattern """ if Action.Sprint not in obs["sticky_actions"]: return Action.Sprint return Action.TopLeft return {"environment_fits": environment_fits, "get_action": get_action} def run_to_ball_top_right(obs, player_x, player_y): """ run to the ball if it is to the top right from player's position """ def environment_fits(obs, player_x, player_y): """ environment fits constraints """ # ball is to the top right from player's position if (obs["ball"][0] > player_x and obs["ball"][1] < player_y): return True return False def get_action(obs, player_x, player_y): """ get action of this memory pattern """ if Action.Sprint not in obs["sticky_actions"]: return Action.Sprint return Action.TopRight return {"environment_fits": environment_fits, "get_action": get_action} # "%%writefile -a submission.py" will append the code below to submission.py, # it WILL NOT rewrite submission.py def idle(obs, player_x, player_y): """ do nothing, release all sticky actions """ def environment_fits(obs, player_x, player_y): """ environment fits constraints """ return True def get_action(obs, player_x, player_y): """ get action of this memory pattern """ action_to_release = get_active_sticky_action(obs, []) if action_to_release != None: return action_to_release return Action.Idle return {"environment_fits": environment_fits, "get_action": get_action} # "%%writefile -a submission.py" will append the code below to submission.py, # it WILL NOT rewrite submission.py def corner(obs, player_x, player_y): """ perform a high pass in corner game mode """ def environment_fits(obs, player_x, player_y): """ environment fits constraints """ # it is corner game mode if obs['game_mode'] == GameMode.Corner: return True return False def get_action(obs, player_x, player_y): """ get action of this memory pattern """ action_to_release = get_active_sticky_action(obs, ["top", "bottom"]) if action_to_release != None: return action_to_release if Action.Top not in obs["sticky_actions"] and Action.Bottom not in obs["sticky_actions"]: if player_y > 0: return Action.Top else: return Action.Bottom return Action.HighPass return {"environment_fits": environment_fits, "get_action": get_action} def free_kick(obs, player_x, player_y): """ perform a high pass or a shot in free kick game mode """ def environment_fits(obs, player_x, player_y): """ environment fits constraints """ # it is free kick game mode if obs['game_mode'] == GameMode.FreeKick: return True return False def get_action(obs, player_x, player_y): """ get action of this memory pattern """ # shot if player close to goal if player_x > 0.5: action_to_release = get_active_sticky_action(obs, ["top_right", "bottom_right"]) if action_to_release != None: return action_to_release if Action.TopRight not in obs["sticky_actions"] and Action.BottomRight not in obs["sticky_actions"]: if player_y > 0: return Action.TopRight else: return Action.BottomRight return Action.Shot # high pass if player far from goal else: action_to_release = get_active_sticky_action(obs, ["right"]) if action_to_release != None: return action_to_release if Action.Right not in obs["sticky_actions"]: return Action.Right return Action.HighPass return {"environment_fits": environment_fits, "get_action": get_action} def goal_kick(obs, player_x, player_y): """ perform a short pass in goal kick game mode """ def environment_fits(obs, player_x, player_y): """ environment fits constraints """ # it is goal kick game mode if obs['game_mode'] == GameMode.GoalKick: return True return False def get_action(obs, player_x, player_y): """ get action of this memory pattern """ action_to_release = get_active_sticky_action(obs, ["top_right", "bottom_right"]) if action_to_release != None: return action_to_release # randomly choose direction if Action.TopRight not in obs["sticky_actions"] and Action.BottomRight not in obs["sticky_actions"]: if random.random() < 0.5: return Action.TopRight else: return Action.BottomRight return Action.ShortPass return {"environment_fits": environment_fits, "get_action": get_action} def kick_off(obs, player_x, player_y): """ perform a short pass in kick off game mode """ def environment_fits(obs, player_x, player_y): """ environment fits constraints """ # it is kick off game mode if obs['game_mode'] == GameMode.KickOff: return True return False def get_action(obs, player_x, player_y): """ get action of this memory pattern """ action_to_release = get_active_sticky_action(obs, ["top", "bottom"]) if action_to_release != None: return action_to_release if Action.Top not in obs["sticky_actions"] and Action.Bottom not in obs["sticky_actions"]: if player_y > 0: return Action.Top else: return Action.Bottom return Action.ShortPass return {"environment_fits": environment_fits, "get_action": get_action} def penalty(obs, player_x, player_y): """ perform a shot in penalty game mode """ def environment_fits(obs, player_x, player_y): """ environment fits constraints """ # it is penalty game mode if obs['game_mode'] == GameMode.Penalty: return True return False def get_action(obs, player_x, player_y): """ get action of this memory pattern """ action_to_release = get_active_sticky_action(obs, ["top_right", "bottom_right"]) if action_to_release != None: return action_to_release # randomly choose direction if Action.TopRight not in obs["sticky_actions"] and Action.BottomRight not in obs["sticky_actions"]: if random.random() < 0.5: return Action.TopRight else: return Action.BottomRight return Action.Shot return {"environment_fits": environment_fits, "get_action": get_action} def throw_in(obs, player_x, player_y): """ perform a short pass in throw in game mode """ def environment_fits(obs, player_x, player_y): """ environment fits constraints """ # it is throw in game mode if obs['game_mode'] == GameMode.ThrowIn: return True return False def get_action(obs, player_x, player_y): """ get action of this memory pattern """ action_to_release = get_active_sticky_action(obs, ["right"]) if action_to_release != None: return action_to_release if Action.Right not in obs["sticky_actions"]: return Action.Right return Action.ShortPass return {"environment_fits": environment_fits, "get_action": get_action} # "%%writefile -a submission.py" will append the code below to submission.py, # it WILL NOT rewrite submission.py def defence_memory_patterns(obs, player_x, player_y): """ group of memory patterns for environments in which opponent's team has the ball """ def environment_fits(obs, player_x, player_y): """ environment fits constraints """ # player don't have the ball if obs["ball_owned_team"] != 0: return True return False def get_memory_patterns(obs, player_x, player_y): """ get list of memory patterns """ # shift ball position obs["ball"][0] += obs["ball_direction"][0] * 5 # if opponent has the ball and is far from y axis center if abs(obs["ball"][1]) > 0.07 and obs["ball_owned_team"] == 1: if obs["ball"][1] > 0: obs["ball"][1] -= 0.01 else: obs["ball"][1] += 0.01 memory_patterns = [ khorne_slide, run_to_ball_right, run_to_ball_left, run_to_ball_bottom, run_to_ball_top, run_to_ball_top_right, run_to_ball_top_left, run_to_ball_bottom_right, run_to_ball_bottom_left, idle ] return memory_patterns return {"environment_fits": environment_fits, "get_memory_patterns": get_memory_patterns} def goalkeeper_memory_patterns(obs, player_x, player_y): """ group of memory patterns for goalkeeper """ def environment_fits(obs, player_x, player_y): """ environment fits constraints """ # player is a goalkeeper have the ball if (obs["ball_owned_player"] == obs["active"] and obs["ball_owned_team"] == 0 and obs["ball_owned_player"] == 0): return True return False def get_memory_patterns(obs, player_x, player_y): """ get list of memory patterns """ memory_patterns = [ far_from_goal_shot, idle ] return memory_patterns return {"environment_fits": environment_fits, "get_memory_patterns": get_memory_patterns} def offence_memory_patterns(obs, player_x, player_y): """ group of memory patterns for environments in which player's team has the ball """ def environment_fits(obs, player_x, player_y): """ environment fits constraints """ # player have the ball if obs["ball_owned_player"] == obs["active"] and obs["ball_owned_team"] == 0: return True return False def get_memory_patterns(obs, player_x, player_y): """ get list of memory patterns """ memory_patterns = [ far_from_goal_shot, far_from_goal_high_pass, bad_angle_short_pass, close_to_goalkeeper_shot, go_through_opponents, idle ] return memory_patterns return {"environment_fits": environment_fits, "get_memory_patterns": get_memory_patterns} def other_memory_patterns(obs, player_x, player_y): """ group of memory patterns for all other environments """ def environment_fits(obs, player_x, player_y): """ environment fits constraints """ return True def get_memory_patterns(obs, player_x, player_y): """ get list of memory patterns """ memory_patterns = [ idle ] return memory_patterns return {"environment_fits": environment_fits, "get_memory_patterns": get_memory_patterns} def special_game_modes_memory_patterns(obs, player_x, player_y): """ group of memory patterns for special game mode environments """ def environment_fits(obs, player_x, player_y): """ environment fits constraints """ # if game mode is not normal if obs['game_mode'] != GameMode.Normal: return True return False def get_memory_patterns(obs, player_x, player_y): """ get list of memory patterns """ memory_patterns = [ corner, free_kick, goal_kick, kick_off, penalty, throw_in, idle ] return memory_patterns return {"environment_fits": environment_fits, "get_memory_patterns": get_memory_patterns} # "%%writefile -a submission.py" will append the code below to submission.py, # it WILL NOT rewrite submission.py # list of groups of memory patterns groups_of_memory_patterns = [ special_game_modes_memory_patterns, goalkeeper_memory_patterns, offence_memory_patterns, defence_memory_patterns, other_memory_patterns ] # dictionary of sticky actions sticky_actions = { "left": Action.Left, "top_left": Action.TopLeft, "top": Action.Top, "top_right": Action.TopRight, "right": Action.Right, "bottom_right": Action.BottomRight, "bottom": Action.Bottom, "bottom_left": Action.BottomLeft, "sprint": Action.Sprint, "dribble": Action.Dribble } # "%%writefile -a submission.py" will append the code below to submission.py, # it WILL NOT rewrite submission.py # @human_readable_agent wrapper modifies raw observations # provided by the environment: # https://github.com/google-research/football/blob/master/gfootball/doc/observation.md#raw-observations # into a form easier to work with by humans. # Following modifications are applied: # - Action, PlayerRole and GameMode enums are introduced. # - 'sticky_actions' are turned into a set of active actions (Action enum) # see usage example below. # - 'game_mode' is turned into GameMode enum. # - 'designated' field is removed, as it always equals to 'active' # when a single player is controlled on the team. # - 'left_team_roles'/'right_team_roles' are turned into PlayerRole enums. # - Action enum is to be returned by the agent function. @human_readable_agent def agent(obs): """ Ole ole ole ole """ # dictionary for Memory Patterns data obs["memory_patterns"] = {} # We always control left team (observations and actions # are mirrored appropriately by the environment). controlled_player_pos = obs["left_team"][obs["active"]] # get action of appropriate pattern in agent's memory action = get_action_of_agent(obs, controlled_player_pos[0], controlled_player_pos[1]) # return action return action

42.347527

120

0.63171

4481dc9ab16eaa9a759504f2f52023fe96dcab30

126

py

Python

python/code/docstrint.py

fishhello/learn

b87eaa9908e895a917c05c883a744533b38984cc

[ "MIT" ]

null

python/code/docstrint.py

fishhello/learn

b87eaa9908e895a917c05c883a744533b38984cc

[ "MIT" ]

null

python/code/docstrint.py

fishhello/learn

b87eaa9908e895a917c05c883a744533b38984cc

[ "MIT" ]

null

def add(x, y): """ 计算两个输入参数之和 :param x: int. :param y: int. :return: int,x 和 y之和 """ return x + y

14

24

0.460317

0865f6b32bd688b89e6c832866587822081fca82

1,665

py

Python

apps/accounts/admin.py

switzersc-usds/bluebutton-web-server

176b118fb3467fe6dc424f40bc117d814714d429

[ "Apache-2.0" ]

null

apps/accounts/admin.py

switzersc-usds/bluebutton-web-server

176b118fb3467fe6dc424f40bc117d814714d429

[ "Apache-2.0" ]

null

apps/accounts/admin.py

switzersc-usds/bluebutton-web-server

176b118fb3467fe6dc424f40bc117d814714d429

[ "Apache-2.0" ]

null

from django.contrib import admin from django.contrib.auth.models import User from django.contrib.auth.admin import UserAdmin from .models import ( ValidPasswordResetKey, UserProfile, ActivationKey, UserIdentificationLabel) admin.site.register(ActivationKey) admin.site.register(ValidPasswordResetKey) ua = UserAdmin ua.list_display = ('username', 'email', 'first_name', 'last_name', 'is_staff', 'is_active', 'date_joined') admin.site.unregister(User) admin.site.register(User, ua) class UserProfileAdmin(admin.ModelAdmin): def get_user_email(self, obj): return obj.user.email get_user_email.admin_order_field = "user__email" get_user_email.short_description = "Email Address" def get_user_joined(self, obj): return obj.user.date_joined get_user_joined.admin_order_field = "user__date_joined" get_user_joined.short_description = "Date Joined" list_display = ('user', 'name', 'user_type', 'organization_name', 'get_user_email', 'get_user_joined') search_fields = ('user__username', 'user__email', 'user__first_name', 'user__last_name', 'user_type', 'organization_name', 'user__date_joined') raw_id_fields = ("user", ) admin.site.register(UserProfile, UserProfileAdmin) class UserIdentificationLabelAdmin(admin.ModelAdmin): model = UserIdentificationLabel filter_horizontal = ('users',) list_display = ("name", "slug", "weight") list_filter = ("name", "slug") ordering = ("weight", ) admin.site.register(UserIdentificationLabel, UserIdentificationLabelAdmin)

27.75

74

0.6997

74bd83349961eb43ea7fcac9d1f84aa392773d13

231,686

py

Python

h2o-py/tests/pyunit_utils/utilsPY.py

JannisBush/h2o-3

30aa2a86e6bfa1febb5f95f3cb43811337895f7f

[ "Apache-2.0" ]

null

h2o-py/tests/pyunit_utils/utilsPY.py

JannisBush/h2o-3

30aa2a86e6bfa1febb5f95f3cb43811337895f7f

[ "Apache-2.0" ]

null

h2o-py/tests/pyunit_utils/utilsPY.py

JannisBush/h2o-3

30aa2a86e6bfa1febb5f95f3cb43811337895f7f

[ "Apache-2.0" ]

1

2021-09-09T03:47:11.000Z

# Py2 compat from __future__ import print_function from future import standard_library standard_library.install_aliases() from past.builtins import basestring # standard lib import copy import datetime from decimal import * from functools import reduce import imp import json import math import os import random import re import shutil import string import subprocess from subprocess import STDOUT,PIPE import sys import time # needed to randomly generate time import threading import urllib.request, urllib.error, urllib.parse import uuid # call uuid.uuid4() to generate unique uuid numbers try: from StringIO import StringIO # py2 (first as py2 also has io.StringIO, but without string support, only unicode) except: from io import StringIO # py3 try: from tempfile import TemporaryDirectory except ImportError: import tempfile class TemporaryDirectory: def __init__(self): self.tmp_dir = None def __enter__(self): self.tmp_dir = tempfile.mkdtemp() return self.tmp_dir def __exit__(self, *args): shutil.rmtree(self.tmp_dir) # 3rd parties import numpy as np import pandas as pd from scipy.sparse import csr_matrix import scipy.special # h2o sys.path.insert(1, "../../") import h2o from h2o.model.binomial import H2OBinomialModel from h2o.model.clustering import H2OClusteringModel from h2o.model.multinomial import H2OMultinomialModel from h2o.model.ordinal import H2OOrdinalModel from h2o.model.regression import H2ORegressionModel from h2o.estimators import H2OGradientBoostingEstimator, H2ODeepLearningEstimator, H2OGeneralizedLinearEstimator, \ H2OGeneralizedAdditiveEstimator, H2OKMeansEstimator, H2ONaiveBayesEstimator, H2ORandomForestEstimator, \ H2OPrincipalComponentAnalysisEstimator from h2o.utils.typechecks import is_type from h2o.utils.shared_utils import temp_ctr # unused in this file but exposed here for symmetry with rest_ctr class Timeout: def __init__(self, timeout_secs, on_timeout=None): enabled = timeout_secs is not None and timeout_secs >= 0 self.timer = threading.Timer(timeout_secs, on_timeout) if enabled else None def __enter__(self): if self.timer: self.timer.start() return self def __exit__(self, *args): if self.timer: self.timer.cancel() class Namespace: """ simplistic namespace class allowing to create bag/namespace objects that are easily extendable in a functional way """ @staticmethod def add(namespace, **kwargs): namespace.__dict__.update(kwargs) return namespace def __init__(self, **kwargs): self.__dict__.update(**kwargs) def __str__(self): return str(self.__dict__) def __repr__(self): return repr(self.__dict__) def extend(self, **kwargs): """ :param kwargs: attributes extending the current namespace :return: a new namespace containing same attributes as the original + the extended ones """ clone = Namespace(**self.__dict__) clone.__dict__.update(**kwargs) return clone def ns(**kwargs): return Namespace(**kwargs) def gen_random_uuid(numberUUID): uuidVec = numberUUID*[None] for uindex in range(numberUUID): uuidVec[uindex] = uuid.uuid4() return uuidVec def gen_random_time(numberTimes, maxtime= datetime.datetime(2080, 8,6,8,14,59), mintime=datetime.datetime(1980, 8,6,6,14,59)): ''' Simple method that I shameless copied from the internet. :param numberTimes: :param maxtime: :param mintime: :return: ''' mintime_ts = int(time.mktime(mintime.timetuple())) maxtime_ts = int(time.mktime(maxtime.timetuple())) randomTimes = numberTimes*[None] for tindex in range(numberTimes): temptime = random.randint(mintime_ts, maxtime_ts) randomTimes[tindex] = datetime.datetime.fromtimestamp(temptimes) return randomTimes def check_models(model1, model2, use_cross_validation=False, op='e'): """ Check that the given models are equivalent. :param model1: :param model2: :param use_cross_validation: boolean. if True, use validation metrics to determine model equality. Otherwise, use training metrics. :param op: comparison operator to use. 'e':==, 'g':>, 'ge':>= :return: None. Throw meaningful error messages if the check fails """ # 1. Check model types model1_type = model1.__class__.__name__ model2_type = model1.__class__.__name__ assert model1_type is model2_type, "The model types differ. The first model is of type {0} and the second " \ "models is of type {1}.".format(model1_type, model2_type) # 2. Check model metrics if isinstance(model1,H2OBinomialModel): # 2a. Binomial # F1 f1_1 = model1.F1(xval=use_cross_validation) f1_2 = model2.F1(xval=use_cross_validation) if op == 'e': assert f1_1[0][1] == f1_2[0][1], "The first model has an F1 of {0} and the second model has an F1 of " \ "{1}. Expected the first to be == to the second.".format(f1_1[0][1], f1_2[0][1]) elif op == 'g': assert f1_1[0][1] > f1_2[0][1], "The first model has an F1 of {0} and the second model has an F1 of " \ "{1}. Expected the first to be > than the second.".format(f1_1[0][1], f1_2[0][1]) elif op == 'ge': assert f1_1[0][1] >= f1_2[0][1], "The first model has an F1 of {0} and the second model has an F1 of " \ "{1}. Expected the first to be >= than the second.".format(f1_1[0][1], f1_2[0][1]) elif isinstance(model1,H2ORegressionModel): # 2b. Regression # MSE mse1 = model1.mse(xval=use_cross_validation) mse2 = model2.mse(xval=use_cross_validation) if op == 'e': assert mse1 == mse2, "The first model has an MSE of {0} and the second model has an MSE of " \ "{1}. Expected the first to be == to the second.".format(mse1, mse2) elif op == 'g': assert mse1 > mse2, "The first model has an MSE of {0} and the second model has an MSE of " \ "{1}. Expected the first to be > than the second.".format(mse1, mse2) elif op == 'ge': assert mse1 >= mse2, "The first model has an MSE of {0} and the second model has an MSE of " \ "{1}. Expected the first to be >= than the second.".format(mse1, mse2) elif isinstance(model1,H2OMultinomialModel) or isinstance(model1,H2OOrdinalModel): # 2c. Multinomial # hit-ratio pass elif isinstance(model1,H2OClusteringModel): # 2d. Clustering # totss totss1 = model1.totss(xval=use_cross_validation) totss2 = model2.totss(xval=use_cross_validation) if op == 'e': assert totss1 == totss2, "The first model has an TOTSS of {0} and the second model has an " \ "TOTSS of {1}. Expected the first to be == to the second.".format(totss1, totss2) elif op == 'g': assert totss1 > totss2, "The first model has an TOTSS of {0} and the second model has an " \ "TOTSS of {1}. Expected the first to be > than the second.".format(totss1, totss2) elif op == 'ge': assert totss1 >= totss2, "The first model has an TOTSS of {0} and the second model has an " \ "TOTSS of {1}. Expected the first to be >= than the second." \ "".format(totss1, totss2) def check_dims_values(python_obj, h2o_frame, rows, cols, dim_only=False): """ Check that the dimensions and values of the python object and H2OFrame are equivalent. Assumes that the python object conforms to the rules specified in the h2o frame documentation. :param python_obj: a (nested) list, tuple, dictionary, numpy.ndarray, ,or pandas.DataFrame :param h2o_frame: an H2OFrame :param rows: number of rows :param cols: number of columns :param dim_only: check the dimensions only :return: None """ h2o_rows, h2o_cols = h2o_frame.dim assert h2o_rows == rows and h2o_cols == cols, "failed dim check! h2o_rows:{0} rows:{1} h2o_cols:{2} cols:{3}" \ "".format(h2o_rows, rows, h2o_cols, cols) if not dim_only: if isinstance(python_obj, (list, tuple)): for c in range(cols): for r in range(rows): pval = python_obj[r] if isinstance(pval, (list, tuple)): pval = pval[c] hval = h2o_frame[r, c] assert pval == hval or abs(pval - hval) < 1e-10, \ "expected H2OFrame to have the same values as the python object for row {0} " \ "and column {1}, but h2o got {2} and python got {3}.".format(r, c, hval, pval) elif isinstance(python_obj, dict): for r in range(rows): for k in list(python_obj.keys()): pval = python_obj[k][r] if hasattr(python_obj[k],'__iter__') else python_obj[k] hval = h2o_frame[r,k] assert pval == hval, "expected H2OFrame to have the same values as the python object for row {0} " \ "and column {1}, but h2o got {2} and python got {3}.".format(r, k, hval, pval) def np_comparison_check(h2o_data, np_data, num_elements): """ Check values achieved by h2o against values achieved by numpy :param h2o_data: an H2OFrame or H2OVec :param np_data: a numpy array :param num_elements: number of elements to compare :return: None """ # Check for numpy try: imp.find_module('numpy') except ImportError: assert False, "failed comparison check because unable to import numpy" import numpy as np rows, cols = h2o_data.dim for i in range(num_elements): r = random.randint(0,rows-1) c = random.randint(0,cols-1) h2o_val = h2o_data[r,c] np_val = np_data[r,c] if len(np_data.shape) > 1 else np_data[r] if isinstance(np_val, np.bool_): np_val = bool(np_val) # numpy haz special bool type :( assert np.absolute(h2o_val - np_val) < 1e-5, \ "failed comparison check! h2o computed {0} and numpy computed {1}".format(h2o_val, np_val) # perform h2o predict and mojo predict. Frames containing h2o prediction is returned and mojo predict are # returned. def mojo_predict(model, tmpdir, mojoname, glrmReconstruct=False, get_leaf_node_assignment=False, glrmIterNumber=-1, zipFilePath=None): """ perform h2o predict and mojo predict. Frames containing h2o prediction is returned and mojo predict are returned. It is assumed that the input data set is saved as in.csv in tmpdir directory. :param model: h2o model where you want to use to perform prediction :param tmpdir: directory where your mojo zip files are stired :param mojoname: name of your mojo zip file. :param glrmReconstruct: True to return reconstructed dataset, else return the x factor. :return: the h2o prediction frame and the mojo prediction frame """ newTest = h2o.import_file(os.path.join(tmpdir, 'in.csv'), header=1) # Make sure h2o and mojo use same in.csv predict_h2o = model.predict(newTest) # load mojo and have it do predict outFileName = os.path.join(tmpdir, 'out_mojo.csv') mojoZip = os.path.join(tmpdir, mojoname) + ".zip" if not(zipFilePath==None): mojoZip = zipFilePath genJarDir = str.split(os.path.realpath("__file__"),'/') genJarDir = '/'.join(genJarDir[0:genJarDir.index('h2o-py')]) # locate directory of genmodel.jar java_cmd = ["java", "-ea", "-cp", os.path.join(genJarDir, "h2o-assemblies/genmodel/build/libs/genmodel.jar"), "-Xmx12g", "-XX:MaxPermSize=2g", "-XX:ReservedCodeCacheSize=256m", "hex.genmodel.tools.PredictCsv", "--input", os.path.join(tmpdir, 'in.csv'), "--output", outFileName, "--mojo", mojoZip, "--decimal"] if get_leaf_node_assignment: java_cmd.append("--leafNodeAssignment") predict_h2o = model.predict_leaf_node_assignment(newTest) if glrmReconstruct: # used for GLRM to grab the x coefficients (factors) instead of the predicted values java_cmd.append("--glrmReconstruct") if glrmIterNumber > 0: java_cmd.append("--glrmIterNumber") java_cmd.append(str(glrmIterNumber)) p = subprocess.Popen(java_cmd, stdout=PIPE, stderr=STDOUT) o, e = p.communicate() files = os.listdir(tmpdir) print("listing files {1} in directory {0}".format(tmpdir, files)) outfile = os.path.join(tmpdir, 'out_mojo.csv') if not os.path.exists(outfile) or os.stat(outfile).st_size == 0: print("MOJO SCORING FAILED:") print("--------------------") print(o.decode("utf-8")) print("***** importing file {0}".format(outfile)) pred_mojo = h2o.import_file(outfile, header=1) # load mojo prediction in # to a frame and compare if glrmReconstruct or ('glrm' not in model.algo): return predict_h2o, pred_mojo else: return newTest.frame_id, pred_mojo # perform pojo predict. Frame containing pojo predict is returned. def pojo_predict(model, tmpdir, pojoname): h2o.download_pojo(model, path=tmpdir) h2o_genmodel_jar = os.path.join(tmpdir, "h2o-genmodel.jar") java_file = os.path.join(tmpdir, pojoname + ".java") in_csv = (os.path.join(tmpdir, 'in.csv')) # import the test dataset print("Compiling Java Pojo") javac_cmd = ["javac", "-cp", h2o_genmodel_jar, "-J-Xmx12g", java_file] subprocess.check_call(javac_cmd) out_pojo_csv = os.path.join(tmpdir, "out_pojo.csv") cp_sep = ";" if sys.platform == "win32" else ":" java_cmd = ["java", "-ea", "-cp", h2o_genmodel_jar + cp_sep + tmpdir, "-Xmx12g", "-XX:ReservedCodeCacheSize=256m", "hex.genmodel.tools.PredictCsv", "--pojo", pojoname, "--input", in_csv, "--output", out_pojo_csv, "--decimal"] p = subprocess.Popen(java_cmd, stdout=PIPE, stderr=STDOUT) o, e = p.communicate() print("Java output: {0}".format(o)) assert os.path.exists(out_pojo_csv), "Expected file {0} to exist, but it does not.".format(out_pojo_csv) predict_pojo = h2o.import_file(out_pojo_csv, header=1) return predict_pojo def javapredict(algo, equality, train, test, x, y, compile_only=False, separator=",", setInvNumNA=False,**kwargs): print("Creating model in H2O") if algo == "gbm": model = H2OGradientBoostingEstimator(**kwargs) elif algo == "random_forest": model = H2ORandomForestEstimator(**kwargs) elif algo == "deeplearning": model = H2ODeepLearningEstimator(**kwargs) elif algo == "glm": model = H2OGeneralizedLinearEstimator(**kwargs) elif algo == "gam": model = H2OGeneralizedAdditiveEstimator(**kwargs) elif algo == "naive_bayes": model = H2ONaiveBayesEstimator(**kwargs) elif algo == "kmeans": model = H2OKMeansEstimator(**kwargs) elif algo == "pca": model = H2OPrincipalComponentAnalysisEstimator(**kwargs) else: raise ValueError if algo == "kmeans" or algo == "pca": model.train(x=x, training_frame=train) else: model.train(x=x, y=y, training_frame=train) print(model) # HACK: munge model._id so that it conforms to Java class name. For example, change K-means to K_means. # TODO: clients should extract Java class name from header. regex = re.compile("[+\\-* !@#$%^&()={}\\[\\]|;:'\"<>,.?/]") pojoname = regex.sub("_", model._id) print("Downloading Java prediction model code from H2O") tmpdir = os.path.normpath(os.path.join(os.path.dirname(os.path.realpath(__file__)), "..", "results", pojoname)) os.makedirs(tmpdir) h2o.download_pojo(model, path=tmpdir) h2o_genmodel_jar = os.path.join(tmpdir, "h2o-genmodel.jar") assert os.path.exists(h2o_genmodel_jar), "Expected file {0} to exist, but it does not.".format(h2o_genmodel_jar) print("h2o-genmodel.jar saved in {0}".format(h2o_genmodel_jar)) java_file = os.path.join(tmpdir, pojoname + ".java") assert os.path.exists(java_file), "Expected file {0} to exist, but it does not.".format(java_file) print("java code saved in {0}".format(java_file)) print("Compiling Java Pojo") javac_cmd = ["javac", "-cp", h2o_genmodel_jar, "-J-Xmx12g", "-J-XX:MaxPermSize=256m", java_file] subprocess.check_call(javac_cmd) if not compile_only: print("Predicting in H2O") predictions = model.predict(test) predictions.summary() predictions.head() out_h2o_csv = os.path.join(tmpdir, "out_h2o.csv") h2o.download_csv(predictions, out_h2o_csv) assert os.path.exists(out_h2o_csv), "Expected file {0} to exist, but it does not.".format(out_h2o_csv) print("H2O Predictions saved in {0}".format(out_h2o_csv)) print("Setting up for Java POJO") in_csv = os.path.join(tmpdir, "in.csv") h2o.download_csv(test[x], in_csv) # hack: the PredictCsv driver can't handle quoted strings, so remove them f = open(in_csv, "r+") csv = f.read() csv = re.sub('\"', "", csv) csv = re.sub(",", separator, csv) # replace with arbitrary separator for input dataset f.seek(0) f.write(csv) f.truncate() f.close() assert os.path.exists(in_csv), "Expected file {0} to exist, but it does not.".format(in_csv) print("Input CSV to PredictCsv saved in {0}".format(in_csv)) print("Running PredictCsv Java Program") out_pojo_csv = os.path.join(tmpdir, "out_pojo.csv") cp_sep = ";" if sys.platform == "win32" else ":" java_cmd = ["java", "-ea", "-cp", h2o_genmodel_jar + cp_sep + tmpdir, "-Xmx12g", "-XX:MaxPermSize=2g", "-XX:ReservedCodeCacheSize=256m", "hex.genmodel.tools.PredictCsv", "--decimal", "--pojo", pojoname, "--input", in_csv, "--output", out_pojo_csv, "--separator", separator] if setInvNumNA: java_cmd.append("--setConvertInvalidNum") p = subprocess.Popen(java_cmd, stdout=PIPE, stderr=STDOUT) o, e = p.communicate() print("Java output: {0}".format(o)) assert os.path.exists(out_pojo_csv), "Expected file {0} to exist, but it does not.".format(out_pojo_csv) predictions2 = h2o.upload_file(path=out_pojo_csv) print("Pojo predictions saved in {0}".format(out_pojo_csv)) print("Comparing predictions between H2O and Java POJO") # Dimensions hr, hc = predictions.dim pr, pc = predictions2.dim assert hr == pr, "Expected the same number of rows, but got {0} and {1}".format(hr, pr) assert hc == pc, "Expected the same number of cols, but got {0} and {1}".format(hc, pc) # Value if not(equality == "class"or equality == "numeric"): raise ValueError compare_frames_local(predictions, predictions2, prob=1, tol=1e-4) # faster frame compare def javamunge(assembly, pojoname, test, compile_only=False): """ Here's how to use: assembly is an already fit H2OAssembly; The test set should be used to compare the output here and the output of the POJO. """ print("Downloading munging POJO code from H2O") tmpdir = os.path.normpath(os.path.join(os.path.dirname(os.path.realpath(__file__)), "..", "results", pojoname)) os.makedirs(tmpdir) assembly.to_pojo(pojoname, path=tmpdir, get_jar=True) h2o_genmodel_jar = os.path.join(tmpdir, "h2o-genmodel.jar") assert os.path.exists(h2o_genmodel_jar), "Expected file {0} to exist, but it does not.".format(h2o_genmodel_jar) print("h2o-genmodel.jar saved in {0}".format(h2o_genmodel_jar)) java_file = os.path.join(tmpdir, pojoname + ".java") assert os.path.exists(java_file), "Expected file {0} to exist, but it does not.".format(java_file) print("java code saved in {0}".format(java_file)) print("Compiling Java Pojo") javac_cmd = ["javac", "-cp", h2o_genmodel_jar, "-J-Xmx12g", "-J-XX:MaxPermSize=256m", java_file] subprocess.check_call(javac_cmd) if not compile_only: print("Setting up for Java POJO") in_csv = os.path.join(tmpdir, "in.csv") h2o.download_csv(test, in_csv) assert os.path.exists(in_csv), "Expected file {0} to exist, but it does not.".format(in_csv) print("Input CSV to mungedCSV saved in {0}".format(in_csv)) print("Predicting in H2O") munged = assembly.fit(test) munged.head() out_h2o_csv = os.path.join(tmpdir, "out_h2o.csv") h2o.download_csv(munged, out_h2o_csv) assert os.path.exists(out_h2o_csv), "Expected file {0} to exist, but it does not.".format(out_h2o_csv) print("Munged frame saved in {0}".format(out_h2o_csv)) print("Running PredictCsv Java Program") out_pojo_csv = os.path.join(tmpdir, "out_pojo.csv") cp_sep = ";" if sys.platform == "win32" else ":" java_cmd = ["java", "-ea", "-cp", h2o_genmodel_jar + cp_sep + tmpdir, "-Xmx12g", "-XX:MaxPermSize=2g", "-XX:ReservedCodeCacheSize=256m", "hex.genmodel.tools.MungeCsv", "--header", "--munger", pojoname, "--input", in_csv, "--output", out_pojo_csv] print("JAVA COMMAND: " + " ".join(java_cmd)) p = subprocess.Popen(java_cmd, stdout=PIPE, stderr=STDOUT) o, e = p.communicate() print("Java output: {0}".format(o)) assert os.path.exists(out_pojo_csv), "Expected file {0} to exist, but it does not.".format(out_pojo_csv) munged2 = h2o.upload_file(path=out_pojo_csv, col_types=test.types) print("Pojo predictions saved in {0}".format(out_pojo_csv)) print("Comparing predictions between H2O and Java POJO") # Dimensions hr, hc = munged.dim pr, pc = munged2.dim assert hr == pr, "Expected the same number of rows, but got {0} and {1}".format(hr, pr) assert hc == pc, "Expected the same number of cols, but got {0} and {1}".format(hc, pc) # Value import math import numbers munged.show() munged2.show() for r in range(hr): for c in range(hc): hp = munged[r,c] pp = munged2[r,c] if isinstance(hp, numbers.Number): assert isinstance(pp, numbers.Number) assert (math.fabs(hp-pp) < 1e-8) or (math.isnan(hp) and math.isnan(pp)), "Expected munged rows to be the same for row {0}, but got {1}, and {2}".format(r, hp, pp) else: assert hp==pp, "Expected munged rows to be the same for row {0}, but got {1}, and {2}".format(r, hp, pp) def locate(path): """ Search for a relative path and turn it into an absolute path. This is handy when hunting for data files to be passed into h2o and used by import file. Note: This function is for unit testing purposes only. Parameters ---------- path : str Path to search for :return: Absolute path if it is found. None otherwise. """ if (test_is_on_hadoop()): # Jenkins jobs create symbolic links to smalldata and bigdata on the machine that starts the test. However, # in an h2o multinode hadoop cluster scenario, the clustered machines don't know about the symbolic link. # Consequently, `locate` needs to return the actual path to the data on the clustered machines. ALL jenkins # machines store smalldata and bigdata in /home/0xdiag/. If ON.HADOOP is set by the run.py, the path arg MUST # be an immediate subdirectory of /home/0xdiag/. Moreover, the only guaranteed subdirectories of /home/0xdiag/ # are smalldata and bigdata. p = os.path.realpath(os.path.join("/home/0xdiag/", path)) if not os.path.exists(p): raise ValueError("File not found: " + path) return p else: tmp_dir = os.path.realpath(os.getcwd()) possible_result = os.path.join(tmp_dir, path) try: while (True): if (os.path.exists(possible_result)): return possible_result next_tmp_dir = os.path.dirname(tmp_dir) if (next_tmp_dir == tmp_dir): raise ValueError("File not found: " + path) tmp_dir = next_tmp_dir possible_result = os.path.join(tmp_dir, path) except ValueError as e: url = "https://h2o-public-test-data.s3.amazonaws.com/{}".format(path) if url_exists(url): return url raise def url_exists(url): head_req = urllib.request.Request(url, method='HEAD') try: with urllib.request.urlopen(head_req) as test: return test.status == 200 except urllib.error.URLError: return False def hadoop_namenode_is_accessible(): url = "http://{0}:50070".format(hadoop_namenode()) try: urllib.urlopen(url) internal = True except: internal = False return internal def test_is_on_hadoop(): if hasattr(sys.modules["tests.pyunit_utils"], '__on_hadoop__'): return sys.modules["tests.pyunit_utils"].__on_hadoop__ return False def hadoop_namenode(): if os.getenv("NAME_NODE"): return os.getenv("NAME_NODE").split(".")[0] elif hasattr(sys.modules["tests.pyunit_utils"], '__hadoop_namenode__'): return sys.modules["tests.pyunit_utils"].__hadoop_namenode__ return None def pyunit_exec(test_name): with open(test_name, "r") as t: pyunit = t.read() test_path = os.path.abspath(test_name) pyunit_c = compile(pyunit, test_path, 'exec') exec(pyunit_c, dict(__name__='__main__', __file__=test_path)) # forcing module name to ensure that the test behaves the same way as when executed using `python my_test.py` def standalone_test(test): if not h2o.connection() or not h2o.connection().connected: print("Creating connection for test %s" % test.__name__) h2o.init(strict_version_check=False) print("New session: %s" % h2o.connection().session_id) h2o.remove_all() h2o.log_and_echo("------------------------------------------------------------") h2o.log_and_echo("") h2o.log_and_echo("STARTING TEST "+test.__name__) h2o.log_and_echo("") h2o.log_and_echo("------------------------------------------------------------") test() def run_tests(tests, run_in_isolation=True): #flatten in case of nested tests/test suites all_tests = reduce(lambda l, r: (l.extend(r) if isinstance(r, (list, tuple)) else l.append(r)) or l, tests, []) for test in all_tests: header = "Running {}{}".format(test.__name__, "" if not hasattr(test, 'tag') else " [{}]".format(test.tag)) print("\n"+('='*len(header))+"\n"+header) if run_in_isolation: standalone_test(test) else: test() def tag_test(test, tag): if tag is not None: test.tag = tag return test def assert_warn(predicate, message): try: assert predicate, message except AssertionError as e: print("WARN: {}".format(str(e))) def make_random_grid_space(algo, ncols=None, nrows=None): """ Construct a dictionary of the form {gbm_parameter:list_of_values, ...}, which will eventually be passed to H2OGridSearch to build a grid object. The gbm parameters, and their associated values, are randomly selected. :param algo: a string {"gbm", "rf", "dl", "km", "glm"} representing the algo dimension of the grid space :param ncols: Used for mtries selection or k (pca) :param nrows: Used for k (pca) :return: a dictionary of parameter_name:list_of_values """ grid_space = {} if algo in ["gbm", "rf"]: if random.randint(0,1): grid_space['ntrees'] = random.sample(list(range(1,6)),random.randint(2,3)) if random.randint(0,1): grid_space['max_depth'] = random.sample(list(range(1,6)),random.randint(2,3)) if random.randint(0,1): grid_space['min_rows'] = random.sample(list(range(1,11)),random.randint(2,3)) if random.randint(0,1): grid_space['nbins'] = random.sample(list(range(2,21)),random.randint(2,3)) if random.randint(0,1): grid_space['nbins_cats'] = random.sample(list(range(2,1025)),random.randint(2,3)) if algo == "gbm": if random.randint(0,1): grid_space['learn_rate'] = [random.random() for _ in range(random.randint(2,3))] grid_space['distribution'] = random.sample(['bernoulli', 'multinomial', 'gaussian', 'poisson', 'tweedie', 'gamma'], 1) if algo == "rf": if random.randint(0,1): grid_space['mtries'] = random.sample(list(range(1,ncols+1)),random.randint(2,3)) if random.randint(0,1): grid_space['sample_rate'] = [random.random() for r in range(random.randint(2,3))] elif algo == "km": grid_space['k'] = random.sample(list(range(1,10)),random.randint(2,3)) if random.randint(0,1): grid_space['max_iterations'] = random.sample(list(range(1,1000)),random.randint(2,3)) if random.randint(0,1): grid_space['standardize'] = [True, False] if random.randint(0,1): grid_space['seed'] = random.sample(list(range(1,1000)),random.randint(2,3)) if random.randint(0,1): grid_space['init'] = random.sample(['Random','PlusPlus','Furthest'],random.randint(2,3)) elif algo == "glm": if random.randint(0,1): grid_space['alpha'] = [random.random() for r in range(random.randint(2,3))] grid_space['family'] = random.sample(['binomial','gaussian','poisson','tweedie','gamma'], 1) if grid_space['family'] == "tweedie": if random.randint(0,1): grid_space['tweedie_variance_power'] = [round(random.random()+1,6) for r in range(random.randint(2,3))] grid_space['tweedie_link_power'] = 1 - grid_space['tweedie_variance_power'] elif algo == "dl": if random.randint(0,1): grid_space['activation'] = \ random.sample(["Rectifier", "Tanh", "TanhWithDropout", "RectifierWithDropout", "MaxoutWithDropout"], random.randint(2,3)) if random.randint(0,1): grid_space['l2'] = [0.001*random.random() for r in range(random.randint(2,3))] grid_space['distribution'] = random.sample(['bernoulli','multinomial','gaussian','poisson','tweedie','gamma'],1) return grid_space elif algo == "naiveBayes": grid_space['laplace'] = 0 if random.randint(0,1): grid_space['laplace'] = [round(random.random() + r, 6) for r in random.sample(list(range(0,11)), random.randint(2,3))] if random.randint(0,1): grid_space['min_sdev'] = [round(random.random(),6) for r in range(random.randint(2,3))] if random.randint(0,1): grid_space['eps_sdev'] = [round(random.random(),6) for r in range(random.randint(2,3))] elif algo == "pca": if random.randint(0,1): grid_space['max_iterations'] = random.sample(list(range(1,1000)),random.randint(2,3)) if random.randint(0,1): grid_space['transform'] = random.sample(["NONE","STANDARDIZE","NORMALIZE","DEMEAN","DESCALE"], random.randint(2,3)) grid_space['k'] = random.sample(list(range(1,min(ncols,nrows))),random.randint(2,3)) else: raise ValueError return grid_space # Validate given models' parameters against expected values def expect_model_param(models, attribute_name, expected_values): print("param: {0}".format(attribute_name)) actual_values = list(set([m.params[attribute_name]['actual'] \ if type(m.params[attribute_name]['actual']) != list else m.params[attribute_name]['actual'][0] for m in models.models])) # possible for actual to be a list (GLM) if type(expected_values) != list: expected_values = [expected_values] # limit precision. Rounding happens in some models like RF actual_values = [x if isinstance(x,basestring) else round(float(x),5) for x in actual_values] expected_values = [x if isinstance(x,basestring) else round(float(x),5) for x in expected_values] print("actual values: {0}".format(actual_values)) print("expected values: {0}".format(expected_values)) actual_values_len = len(actual_values) expected_values_len = len(expected_values) assert actual_values_len == expected_values_len, "Expected values len: {0}. Actual values len: " \ "{1}".format(expected_values_len, actual_values_len) actual_values = sorted(actual_values) expected_values = sorted(expected_values) for i in range(len(actual_values)): if isinstance(actual_values[i], float): assert abs(actual_values[i]-expected_values[i]) < 1.1e-5, "Too large of a difference betewen actual and " \ "expected value. Actual value: {}. Expected value: {}"\ .format(actual_values[i], expected_values[i]) else: assert actual_values[i] == expected_values[i], "Expected: {}. Actual: {}"\ .format(expected_values[i], actual_values[i]) def rest_ctr(): return h2o.connection().requests_count def write_syn_floating_point_dataset_glm(csv_training_data_filename, csv_validation_data_filename, csv_test_data_filename, csv_weight_name, row_count, col_count, data_type, max_p_value, min_p_value, max_w_value, min_w_value, noise_std, family_type, valid_row_count, test_row_count, class_number=2, class_method=('probability', 'probability', 'probability'), class_margin=[0.0, 0.0, 0.0]): """ Generate random data sets to test the GLM algo using the following steps: 1. randomly generate the intercept and weight vector; 2. generate a set of predictors X; 3. generate the corresponding response y using the formula: y = w^T x+b+e where T is transpose, e is a random Gaussian noise added. For the Binomial family, the relationship between the response Y and predictor vector X is assumed to be Prob(Y = 1|X) = exp(W^T * X + e)/(1+exp(W^T * X + e)). For the Multinomial family, the relationship between the response Y (K possible classes) and predictor vector X is assumed to be Prob(Y = c|X) = exp(Wc^T * X + e)/(sum k=0 to K-1 (ep(Wk^T *X+e)) :param csv_training_data_filename: string representing full path filename to store training data set. Set to null string if no training data set is to be generated. :param csv_validation_data_filename: string representing full path filename to store validation data set. Set to null string if no validation data set is to be generated. :param csv_test_data_filename: string representing full path filename to store test data set. Set to null string if no test data set is to be generated. :param csv_weight_name: string representing full path filename to store intercept and weight used to generate all data sets. :param row_count: integer representing number of samples (predictor, response) in training data set :param col_count: integer representing the number of predictors in the data set :param data_type: integer representing the type of predictors or weights (1: integers, 2: real) :param max_p_value: integer representing maximum predictor values :param min_p_value: integer representing minimum predictor values :param max_w_value: integer representing maximum intercept/weight values :param min_w_value: integer representing minimum intercept/weight values :param noise_std: Gaussian noise standard deviation used to generate noise e to add to response :param family_type: string represents the various distribution families (gaussian, multinomial, binomial) supported by our GLM algo :param valid_row_count: integer representing number of samples (predictor, response) in validation data set :param test_row_count: integer representing number of samples (predictor, response) in test data set :param class_number: integer, optional, representing number of classes for binomial and multinomial :param class_method: string tuple, optional, describing how we derive the final response from the class probabilities generated for binomial and multinomial family_type for training/validation/test data set respectively. If set to 'probability', response y is generated randomly according to the class probabilities calculated. If set to 'threshold', response y is set to the class with the maximum class probability if the maximum class probability exceeds the second highest class probability by the value set in margin. If the maximum class probability fails to be greater by the margin than the second highest class probability, the data sample is discarded. :param class_margin: float tuple, optional, denotes the threshold by how much the maximum class probability has to exceed the second highest class probability in order for us to keep the data sample for training/validation/test data set respectively. This field is only meaningful if class_method is set to 'threshold' :return: None """ # generate bias b and weight as a column vector weights = generate_weights_glm(csv_weight_name, col_count, data_type, min_w_value, max_w_value, family_type=family_type, class_number=class_number) # generate training data set if len(csv_training_data_filename) > 0: generate_training_set_glm(csv_training_data_filename, row_count, col_count, min_p_value, max_p_value, data_type, family_type, noise_std, weights, class_method=class_method[0], class_margin=class_margin[0], weightChange=True) # generate validation data set if len(csv_validation_data_filename) > 0: generate_training_set_glm(csv_validation_data_filename, valid_row_count, col_count, min_p_value, max_p_value, data_type, family_type, noise_std, weights, class_method=class_method[1], class_margin=class_margin[1]) # generate test data set if len(csv_test_data_filename) > 0: generate_training_set_glm(csv_test_data_filename, test_row_count, col_count, min_p_value, max_p_value, data_type, family_type, noise_std, weights, class_method=class_method[2], class_margin=class_margin[2]) def write_syn_mixed_dataset_glm(csv_training_data_filename, csv_training_data_filename_true_one_hot, csv_validation_data_filename, csv_validation_filename_true_one_hot, csv_test_data_filename, csv_test_filename_true_one_hot, csv_weight_filename, row_count, col_count, max_p_value, min_p_value, max_w_value, min_w_value, noise_std, family_type, valid_row_count, test_row_count, enum_col, enum_level_vec, class_number=2, class_method=['probability', 'probability', 'probability'], class_margin=[0.0, 0.0, 0.0]): """ This function differs from write_syn_floating_point_dataset_glm in one small point. The predictors in this case contains categorical data as well as real data. Generate random data sets to test the GLM algo using the following steps: 1. randomly generate the intercept and weight vector; 2. generate a set of predictors X; 3. generate the corresponding response y using the formula: y = w^T x+b+e where T is transpose, e is a random Gaussian noise added. For the Binomial family, the relationship between the response Y and predictor vector X is assumed to be Prob(Y = 1|X) = exp(W^T * X + e)/(1+exp(W^T * X + e)). For the Multinomial family, the relationship between the response Y (K possible classes) and predictor vector X is assumed to be Prob(Y = c|X) = exp(Wc^T * X + e)/(sum k=0 to K-1 (ep(Wk^T *X+e)) :param csv_training_data_filename: string representing full path filename to store training data set. Set to null string if no training data set is to be generated. :param csv_training_data_filename_true_one_hot: string representing full path filename to store training data set with true one-hot encoding. Set to null string if no training data set is to be generated. :param csv_validation_data_filename: string representing full path filename to store validation data set. Set to null string if no validation data set is to be generated. :param csv_validation_filename_true_one_hot: string representing full path filename to store validation data set with true one-hot. Set to null string if no validation data set is to be generated. :param csv_test_data_filename: string representing full path filename to store test data set. Set to null string if no test data set is to be generated. :param csv_test_filename_true_one_hot: string representing full path filename to store test data set with true one-hot encoding. Set to null string if no test data set is to be generated. :param csv_weight_filename: string representing full path filename to store intercept and weight used to generate all data sets. :param row_count: integer representing number of samples (predictor, response) in training data set :param col_count: integer representing the number of predictors in the data set :param max_p_value: integer representing maximum predictor values :param min_p_value: integer representing minimum predictor values :param max_w_value: integer representing maximum intercept/weight values :param min_w_value: integer representing minimum intercept/weight values :param noise_std: Gaussian noise standard deviation used to generate noise e to add to response :param family_type: string represents the various distribution families (gaussian, multinomial, binomial) supported by our GLM algo :param valid_row_count: integer representing number of samples (predictor, response) in validation data set :param test_row_count: integer representing number of samples (predictor, response) in test data set :param enum_col: integer representing actual number of categorical columns in data set :param enum_level_vec: vector containing maximum integer value for each categorical column :param class_number: integer, optional, representing number classes for binomial and multinomial :param class_method: string tuple, optional, describing how we derive the final response from the class probabilities generated for binomial and multinomial family_type for training/validation/test data set respectively. If set to 'probability', response y is generated randomly according to the class probabilities calculated. If set to 'threshold', response y is set to the class with the maximum class probability if the maximum class probability exceeds the second highest class probability by the value set in margin. If the maximum class probability fails to be greater by margin than the second highest class probability, the data sample is discarded. :param class_margin: float tuple, optional, denotes the threshold by how much the maximum class probability has to exceed the second highest class probability by in order for us to keep the data sample for training/validation/test data set respectively. This field is only meaningful if class_method is set to 'threshold' :return: None """ # add column count of encoded categorical predictors, if maximum value for enum is 3, it has 4 levels. # hence 4 bits are used to encode it with true one hot encoding. That is why we are adding 1 bit per # categorical columns added to our predictors new_col_count = col_count - enum_col + sum(enum_level_vec)+len(enum_level_vec) # generate the weights to be applied to the training/validation/test data sets # this is for true one hot encoding. For reference+one hot encoding, will skip # few extra weights weights = generate_weights_glm(csv_weight_filename, new_col_count, 2, min_w_value, max_w_value, family_type=family_type, class_number=class_number) # generate training data set if len(csv_training_data_filename) > 0: generate_training_set_mixed_glm(csv_training_data_filename, csv_training_data_filename_true_one_hot, row_count, col_count, min_p_value, max_p_value, family_type, noise_std, weights, enum_col, enum_level_vec, class_number=class_number, class_method=class_method[0], class_margin=class_margin[0], weightChange=True) # generate validation data set if len(csv_validation_data_filename) > 0: generate_training_set_mixed_glm(csv_validation_data_filename, csv_validation_filename_true_one_hot, valid_row_count, col_count, min_p_value, max_p_value, family_type, noise_std, weights, enum_col, enum_level_vec, class_number=class_number, class_method=class_method[1], class_margin=class_margin[1]) # generate test data set if len(csv_test_data_filename) > 0: generate_training_set_mixed_glm(csv_test_data_filename, csv_test_filename_true_one_hot, test_row_count, col_count, min_p_value, max_p_value, family_type, noise_std, weights, enum_col, enum_level_vec, class_number=class_number, class_method=class_method[2], class_margin=class_margin[2]) def generate_weights_glm(csv_weight_filename, col_count, data_type, min_w_value, max_w_value, family_type='gaussian', class_number=2): """ Generate random intercept and weight vectors (integer or real) for GLM algo and save the values in a file specified by csv_weight_filename. :param csv_weight_filename: string representing full path filename to store intercept and weight used to generate all data set :param col_count: integer representing the number of predictors in the data set :param data_type: integer representing the type of predictors or weights (1: integers, 2: real) :param max_w_value: integer representing maximum intercept/weight values :param min_w_value: integer representing minimum intercept/weight values :param family_type: string ,optional, represents the various distribution families (gaussian, multinomial, binomial) supported by our GLM algo :param class_number: integer, optional, representing number classes for binomial and multinomial :return: column vector of size 1+colCount representing intercept and weight or matrix of size 1+colCount by class_number """ # first generate random intercept and weight if 'gaussian' in family_type.lower(): if data_type == 1: # generate random integer intercept/weight weight = np.random.random_integers(min_w_value, max_w_value, [col_count+1, 1]) elif data_type == 2: # generate real intercept/weights weight = np.random.uniform(min_w_value, max_w_value, [col_count+1, 1]) else: assert False, "dataType must be 1 or 2 for now." elif ('binomial' in family_type.lower()) or ('multinomial' in family_type.lower() or ('ordinal' in family_type.lower())): if 'binomial' in family_type.lower(): # for binomial, only need 1 set of weight class_number -= 1 if class_number <= 0: assert False, "class_number must be >= 2!" if isinstance(col_count, np.ndarray): temp_col_count = col_count[0] else: temp_col_count = col_count if data_type == 1: # generate random integer intercept/weight weight = np.random.random_integers(min_w_value, max_w_value, [temp_col_count+1, class_number]) elif data_type == 2: # generate real intercept/weights weight = np.random.uniform(min_w_value, max_w_value, [temp_col_count+1, class_number]) else: assert False, "dataType must be 1 or 2 for now." # special treatment for ordinal weights if 'ordinal' in family_type.lower(): num_pred = len(weight) for index in range(class_number): weight[0,index] = 0 for indP in range(1,num_pred): weight[indP,index] = weight[indP,0] # make sure betas for all classes are the same np.savetxt(csv_weight_filename, weight.transpose(), delimiter=",") return weight def generate_training_set_glm(csv_filename, row_count, col_count, min_p_value, max_p_value, data_type, family_type, noise_std, weight, class_method='probability', class_margin=0.0, weightChange=False): """ Generate supervised data set given weights for the GLM algo. First randomly generate the predictors, then call function generate_response_glm to generate the corresponding response y using the formula: y = w^T x+b+e where T is transpose, e is a random Gaussian noise added. For the Binomial family, the relationship between the response Y and predictor vector X is assumed to be Prob(Y = 1|X) = exp(W^T * X + e)/(1+exp(W^T * X + e)). For the Multinomial family, the relationship between the response Y (K possible classes) and predictor vector X is assumed to be Prob(Y = c|X) = exp(Wc^T * X + e)/(sum k=0 to K-1 (ep(Wk^T *X+e)). The predictors and responses are saved in a file specified by csv_filename. :param csv_filename: string representing full path filename to store supervised data set :param row_count: integer representing the number of training samples in the data set :param col_count: integer representing the number of predictors in the data set :param max_p_value: integer representing maximum predictor values :param min_p_value: integer representing minimum predictor values :param data_type: integer representing the type of predictors or weights (1: integers, 2: real) :param family_type: string represents the various distribution families (gaussian, multinomial, binomial) supported by our GLM algo :param noise_std: Gaussian noise standard deviation used to generate noise e to add to response :param weight: vector representing w in our formula to generate the response. :param class_method: string tuple, optional, describing how we derive the final response from the class probabilities generated for binomial and multinomial family-type for training/validation/test data set respectively. If set to 'probability', response y is generated randomly according to the class probabilities calculated. If set to 'threshold', response y is set to the class with the maximum class probability if the maximum class probability exceeds the second highest class probability by the value set in the margin. If the maximum class probability fails to be greater by the margin than the second highest class probability, the data sample is discarded. :param class_margin: float tuple, optional, denotes the threshold by how much the maximum class probability has to exceed the second highest class probability in order for us to keep the data sample for training/validation/test data set respectively. This field is only meaningful if class_method is set to 'threshold' :return: None """ if data_type == 1: # generate random integers x_mat = np.random.random_integers(min_p_value, max_p_value, [row_count, col_count]) elif data_type == 2: # generate random real numbers x_mat = np.random.uniform(min_p_value, max_p_value, [row_count, col_count]) else: assert False, "dataType must be 1 or 2 for now. " # generate the response vector to the input predictors response_y = generate_response_glm(weight, x_mat, noise_std, family_type, class_method=class_method, class_margin=class_margin, weightChange=weightChange) # for family_type = 'multinomial' or 'binomial', response_y can be -ve to indicate bad sample data. # need to delete this data sample before proceeding # if ('multinomial' in family_type.lower()) or ('binomial' in family_type.lower()) or ('ordinal' in family_type.lower()): # if 'threshold' in class_method.lower(): # if np.any(response_y < 0): # remove negative entries out of data set # (x_mat, response_y) = remove_negative_response(x_mat, response_y) # write to file in csv format np.savetxt(csv_filename, np.concatenate((x_mat, response_y), axis=1), delimiter=",") def generate_clusters(cluster_center_list, cluster_pt_number_list, cluster_radius_list): """ This function is used to generate clusters of points around cluster_centers listed in cluster_center_list. The radius of the cluster of points are specified by cluster_pt_number_list. The size of each cluster could be different and it is specified in cluster_radius_list. :param cluster_center_list: list of coordinates of cluster centers :param cluster_pt_number_list: number of points to generate for each cluster center :param cluster_radius_list: list of size of each cluster :return: list of sample points that belong to various clusters """ k = len(cluster_pt_number_list) # number of clusters to generate clusters for if (not(k == len(cluster_center_list))) or (not(k == len(cluster_radius_list))): assert False, "Length of list cluster_center_list, cluster_pt_number_list, cluster_radius_list must be the same!" training_sets = [] for k_ind in range(k): new_cluster_data = generate_one_cluster(cluster_center_list[k_ind], cluster_pt_number_list[k_ind], cluster_radius_list[k_ind]) if k_ind > 0: training_sets = np.concatenate((training_sets, new_cluster_data), axis=0) else: training_sets = new_cluster_data # want to shuffle the data samples so that the clusters are all mixed up map(np.random.shuffle, training_sets) return training_sets def generate_one_cluster(cluster_center, cluster_number, cluster_size): """ This function will generate a full cluster wither cluster_number points centered on cluster_center with maximum radius cluster_size :param cluster_center: python list denoting coordinates of cluster center :param cluster_number: integer denoting number of points to generate for this cluster :param cluster_size: float denoting radius of cluster :return: np matrix denoting a cluster """ pt_dists = np.random.uniform(0, cluster_size, [cluster_number, 1]) coord_pts = len(cluster_center) # dimension of each cluster point one_cluster_data = np.zeros((cluster_number, coord_pts), dtype=np.float) for p_ind in range(cluster_number): coord_indices = list(range(coord_pts)) random.shuffle(coord_indices) # randomly determine which coordinate to generate left_radius = pt_dists[p_ind] for c_ind in range(coord_pts): coord_index = coord_indices[c_ind] one_cluster_data[p_ind, coord_index] = random.uniform(-1*left_radius+cluster_center[coord_index], left_radius+cluster_center[coord_index]) left_radius = math.sqrt(pow(left_radius, 2)-pow((one_cluster_data[p_ind, coord_index]- cluster_center[coord_index]), 2)) return one_cluster_data def remove_negative_response(x_mat, response_y): """ Recall that when the user chooses to generate a data set for multinomial or binomial using the 'threshold' method, response y is set to the class with the maximum class probability if the maximum class probability exceeds the second highest class probability by the value set in margin. If the maximum class probability fails to be greater by margin than the second highest class probability, the data sample is discarded. However, when we generate the data set, we keep all samples. For data sample with maximum class probability that fails to be greater by margin than the second highest class probability, the response is set to be -1. This function will remove all data samples (predictors and responses) with response set to -1. :param x_mat: predictor matrix containing all predictor values :param response_y: response that can be negative if that data sample is to be removed :return: tuple containing x_mat, response_y with negative data samples removed. """ y_response_negative = np.where(response_y < 0) # matrix of True or False x_mat = np.delete(x_mat,y_response_negative[0].transpose(),axis=0) # remove predictor row with negative response # remove rows with negative response response_y = response_y[response_y >= 0] return x_mat,response_y.transpose() def generate_training_set_mixed_glm(csv_filename, csv_filename_true_one_hot, row_count, col_count, min_p_value, max_p_value, family_type, noise_std, weight, enum_col, enum_level_vec, class_number=2, class_method='probability', class_margin=0.0, weightChange=False): """ Generate supervised data set given weights for the GLM algo with mixed categorical and real value predictors. First randomly generate the predictors, then call function generate_response_glm to generate the corresponding response y using the formula: y = w^T x+b+e where T is transpose, e is a random Gaussian noise added. For the Binomial family, the relationship between the response Y and predictor vector X is assumed to be Prob(Y = 1|X) = exp(W^T * X + e)/(1+exp(W^T * X + e)). For the Multinomial family, the relationship between the response Y (K possible classes) and predictor vector X is assumed to be Prob(Y = c|X) = exp(Wc^T * X + e)/(sum k=0 to K-1 (ep(Wk^T *X+e)) e is the random Gaussian noise added to the response. The predictors and responses are saved in a file specified by csv_filename. :param csv_filename: string representing full path filename to store supervised data set :param csv_filename_true_one_hot: string representing full path filename to store data set with true one-hot encoding. :param row_count: integer representing the number of training samples in the data set :param col_count: integer representing the number of predictors in the data set :param max_p_value: integer representing maximum predictor values :param min_p_value: integer representing minimum predictor values :param family_type: string represents the various distribution families (gaussian, multinomial, binomial) supported by our GLM algo :param noise_std: Gaussian noise standard deviation used to generate noise e to add to response :param weight: vector representing w in our formula to generate the response. :param enum_col: integer representing actual number of categorical columns in data set :param enum_level_vec: vector containing maximum integer value for each categorical column :param class_number: integer, optional, representing number classes for binomial and multinomial :param class_method: string, optional, describing how we derive the final response from the class probabilities generated for binomial and multinomial family_type. If set to 'probability', response y is generated randomly according to the class probabilities calculated. If set to 'threshold', response y is set to the class with the maximum class probability if the maximum class probability exceeds the second highest class probability by the value set in margin. If the maximum class probability fails to be greater by margin than the second highest class probability, the data sample is discarded. :param class_margin: float, optional, denotes the threshold by how much the maximum class probability has to exceed the second highest class probability by in order for us to keep the data set sample. This field is only meaningful if class_method is set to 'threshold' :return: None """ # generate the random training data sets enum_dataset = np.zeros((row_count, enum_col), dtype=np.int) # generate the categorical predictors # generate categorical data columns for indc in range(enum_col): enum_dataset[:, indc] = np.random.random_integers(0, enum_level_vec[indc], row_count) # generate real data columns x_mat = np.random.uniform(min_p_value, max_p_value, [row_count, col_count-enum_col]) x_mat = np.concatenate((enum_dataset, x_mat), axis=1) # concatenate categorical and real predictor columns if len(csv_filename_true_one_hot) > 0: generate_and_save_mixed_glm(csv_filename_true_one_hot, x_mat, enum_level_vec, enum_col, True, weight, noise_std, family_type, class_method=class_method, class_margin=class_margin, weightChange=weightChange) if len(csv_filename) > 0: generate_and_save_mixed_glm(csv_filename, x_mat, enum_level_vec, enum_col, False, weight, noise_std, family_type, class_method=class_method, class_margin=class_margin, weightChange=False) def generate_and_save_mixed_glm(csv_filename, x_mat, enum_level_vec, enum_col, true_one_hot, weight, noise_std, family_type, class_method='probability', class_margin=0.0, weightChange=False): """ Given the weights and input data matrix with mixed categorical and real value predictors, this function will generate a supervised data set and save the input data and response in a csv format file specified by csv_filename. It will first encode the enums without using one hot encoding with or without a reference level first before generating a response Y. :param csv_filename: string representing full path filename to store supervised data set with reference level plus true one-hot encoding. :param x_mat: predictor matrix with mixed columns (categorical/real values) :param enum_level_vec: vector containing maximum integer value for each categorical column :param enum_col: integer representing actual number of categorical columns in data set :param true_one_hot: bool indicating whether we are using true one hot encoding or reference level plus one hot encoding :param weight: vector representing w in our formula to generate the response :param noise_std: Gaussian noise standard deviation used to generate noise e to add to response :param family_type: string represents the various distribution families (gaussian, multinomial, binomial) supported by our GLM algo :param class_method: string, optional, describing how we derive the final response from the class probabilities generated for binomial and multinomial family_type. If set to 'probability', response y is generated randomly according to the class probabilities calculated. If set to 'threshold', response y is set to the class with the maximum class probability if the maximum class probability exceeds the second highest class probability by the value set in the margin. If the maximum class probability fails to be greater by margin than the second highest class probability, the data sample is discarded. :param class_margin: float, optional, denotes the threshold by how much the maximum class probability has to exceed the second highest class probability in order for us to keep the data sample. This field is only meaningful if class_method is set to 'threshold' :return: None """ # encode the enums x_mat_encoded = encode_enum_dataset(x_mat, enum_level_vec, enum_col, true_one_hot, False) # extract the correct weight dimension for the data set if not true_one_hot: (num_row, num_col) = x_mat_encoded.shape weight = weight[0:num_col+1] # +1 to take care of the intercept term # generate the corresponding response vector given the weight and encoded input predictors response_y = generate_response_glm(weight, x_mat_encoded, noise_std, family_type, class_method=class_method, class_margin=class_margin, weightChange=weightChange) # for familyType = 'multinomial' or 'binomial', response_y can be -ve to indicate bad sample data. # need to delete this before proceeding if ('multinomial' in family_type.lower()) or ('binomial' in family_type.lower()): if 'threshold' in class_method.lower(): (x_mat,response_y) = remove_negative_response(x_mat, response_y) # write generated data set to file in csv format np.savetxt(csv_filename, np.concatenate((x_mat, response_y), axis=1), delimiter=",") def encode_enum_dataset(dataset, enum_level_vec, enum_col, true_one_hot, include_nans): """ Given 2-d numpy array of predictors with categorical and real columns, this function will encode the enum columns with 1-hot encoding or with reference plus one hot encoding :param dataset: 2-d numpy array of predictors with both categorical and real columns :param enum_level_vec: vector containing maximum level for each categorical column :param enum_col: number of categorical columns in the data set :param true_one_hot: bool indicating if we are using true one hot encoding or with one reference level + one hot encoding :param include_nans: bool indicating if we have nans in categorical columns :return: data set with categorical columns encoded with 1-hot encoding or 1-hot encoding plus reference """ (num_row, num_col) = dataset.shape # split the data set into categorical and real parts enum_arrays = dataset[:, 0:enum_col] new_enum_arrays = [] # perform the encoding for each element of categorical part for indc in range(enum_col): enum_col_num = enum_level_vec[indc]+1 if not true_one_hot: enum_col_num -= 1 if include_nans and np.any(enum_arrays[:, indc]): enum_col_num += 1 new_temp_enum = np.zeros((num_row, enum_col_num)) one_hot_matrix = one_hot_encoding(enum_col_num) last_col_index = enum_col_num-1 # encode each enum using 1-hot encoding or plus reference value for indr in range(num_row): enum_val = enum_arrays[indr, indc] if true_one_hot: # not using true one hot new_temp_enum[indr, :] = replace_with_encoded_bits(one_hot_matrix, enum_val, 0, last_col_index) else: if enum_val: new_temp_enum[indr, :] = replace_with_encoded_bits(one_hot_matrix, enum_val, 1, last_col_index) if indc == 0: new_enum_arrays = new_temp_enum else: new_enum_arrays = np.concatenate((new_enum_arrays, new_temp_enum), axis=1) return np.concatenate((new_enum_arrays, dataset[:, enum_col:num_col]), axis=1) def replace_with_encoded_bits(one_hot_matrix, enum_val, add_value, last_col_index): """ Generate encoded bits for a categorical data value using one hot encoding. :param one_hot_matrix: matrix representing the encoding of categorical data value to 1-hot encoding :param enum_val: categorical data value, could be np.nan :param add_value: set to 1 if a reference value is needed in addition to 1-hot encoding :param last_col_index: index into encoding for np.nan if exists :return: vector representing the encoded values for a enum value """ if np.isnan(enum_val): # if data value is np.nan return one_hot_matrix[last_col_index] else: return one_hot_matrix[int(enum_val-add_value)] def one_hot_encoding(enum_level): """ Generate the one_hot_encoding matrix given the number of enum_level. :param enum_level: generate the actual one-hot encoding matrix :return: numpy array for the enum_level specified. Note, enum_level <= 6 """ if enum_level >= 2: base_array = np.array([[0, 1], [1, 0]]) # for 2 enum levels for enum_index in range(3, enum_level+1): # loop to build encoding for enum levels > 2 (num_row, num_col) = base_array.shape col_zeros = np.asmatrix(np.zeros(num_row)).transpose() # column of zero matrix base_array = np.concatenate((col_zeros, base_array), axis=1) # add column of zero row_zeros = np.asmatrix(np.zeros(num_row+1)) # add row of zeros row_zeros[0, 0] = 1 # set first element to 1 base_array = np.concatenate((base_array, row_zeros), axis=0) return base_array else: assert False, "enum_level must be >= 2." def generate_response_glm(weight, x_mat, noise_std, family_type, class_method='probability', class_margin=0.0, weightChange=False, even_distribution=True): """ Generate response vector given weight matrix, predictors matrix for the GLM algo. :param weight: vector representing w in our formula to generate the response :param x_mat: random numpy matrix (2-D ndarray) containing the predictors :param noise_std: Gaussian noise standard deviation used to generate noise e to add to response :param family_type: string represents the various distribution families (Gaussian, multinomial, binomial) supported by our GLM algo :param class_method: string, optional, describing how we derive the final response from the class probabilities generated for binomial and multinomial familyType. If set to 'probability', response y is generated randomly according to the class probabilities calculated. If set to 'threshold', response y is set to the class with the maximum class probability if the maximum class probability exceeds the second highest class probability by the value set in the margin. If the maximum class probability fails to be greater by margin than the second highest class probability, the data sample is discarded. :param class_margin: float, optional, denotes the threshold by how much the maximum class probability has to exceed the second highest class probability in order for us to keep the data set sample. This field is only meaningful if class_method is set to 'threshold' :return: vector representing the response """ (num_row, num_col) = x_mat.shape temp_ones_col = np.asmatrix(np.ones(num_row)).transpose() x_mat = np.concatenate((temp_ones_col, x_mat), axis=1) response_y = x_mat * weight + noise_std * np.random.standard_normal([num_row, 1]) if 'ordinal' in family_type.lower(): (num_sample, num_class) = response_y.shape lastClass = num_class - 1 if weightChange: tresp = [] # generate the new y threshold for indP in range(num_sample): tresp.append(-response_y[indP,0]) tresp.sort() num_per_class = int(len(tresp)/num_class) if (even_distribution): for indC in range(lastClass): weight[0,indC] = tresp[(indC+1)*num_per_class] else: # do not generate evenly distributed class, generate randomly distributed classes splitInd = [] lowV = 0.1 highV = 1 v1 = 0 acc = 0 for indC in range(lastClass): tempf = random.uniform(lowV, highV) splitInd.append(v1+int(tempf*num_per_class)) v1 = splitInd[indC] # from last class acc += 1-tempf highV = 1+acc for indC in range(lastClass): # put in threshold weight[0,indC] = tresp[splitInd[indC]] response_y = x_mat * weight + noise_std * np.random.standard_normal([num_row, 1]) discrete_y = np.zeros((num_sample, 1), dtype=np.int) for indR in range(num_sample): discrete_y[indR, 0] = lastClass for indC in range(lastClass): if (response_y[indR, indC] >= 0): discrete_y[indR, 0] = indC break return discrete_y # added more to form Multinomial response if ('multinomial' in family_type.lower()) or ('binomial' in family_type.lower()): temp_mat = np.exp(response_y) # matrix of n by K where K = 1 for binomials if 'binomial' in family_type.lower(): ntemp_mat = temp_mat + 1 btemp_mat = temp_mat / ntemp_mat temp_mat = np.concatenate((1-btemp_mat, btemp_mat), axis=1) # inflate temp_mat to 2 classes response_y = derive_discrete_response(temp_mat, class_method, class_margin, family_type) return response_y def derive_discrete_response(prob_mat, class_method, class_margin, family_type='binomial'): """ This function is written to generate the final class response given the probabilities (Prob(y=k)). There are two methods that we use and is specified by the class_method. If class_method is set to 'probability', response y is generated randomly according to the class probabilities calculated. If set to 'threshold', response y is set to the class with the maximum class probability if the maximum class probability exceeds the second highest class probability by the value set in margin. If the maximum class probability fails to be greater by margin than the second highest class probability, the data sample will be discarded later by marking the final response as -1. :param prob_mat: probability matrix specifying the probability that y=k where k is a class :param class_method: string set to 'probability' or 'threshold' :param class_margin: if class_method='threshold', class_margin is the margin used to determine if a response is to be kept or discarded. :return: response vector representing class of y or -1 if an data sample is to be discarded. """ (num_sample, num_class) = prob_mat.shape discrete_y = np.argmax(prob_mat, axis=1) return discrete_y def normalize_matrix(mat): """ This function will normalize a matrix across each row such that the row sum is 1. :param mat: matrix containing prob(y=k) :return: normalized matrix containing prob(y=k) """ (n, K) = mat.shape kronmat = np.ones((1, K), dtype=float) row_sum = np.sum(mat, axis=1) row_sum_mat = np.kron(row_sum, kronmat) return mat/row_sum_mat def move_files(dir_path, old_name, new_file, action='move'): """ Simple function to move or copy a data set (old_name) to a special directory (dir_path) with new name (new_file) so that we will be able to re-run the tests if we have found something wrong with the algorithm under test with the data set. This is done to avoid losing the data set. :param dir_path: string representing full directory path where a file is to be moved to :param old_name: string representing file (filename with full directory path) to be moved to new directory. :param new_file: string representing the file name of the moved in the new directory :param action: string, optional, represent the action 'move' or 'copy' file :return: None """ new_name = os.path.join(dir_path, new_file) # generate new filename including directory path if os.path.isfile(old_name): # only move/copy file old_name if it actually exists if 'move' in action: motion = 'mv ' elif 'copy' in action: motion = 'cp ' else: assert False, "Illegal action setting. It can only be 'move' or 'copy'!" cmd = motion+old_name+' '+new_name # generate cmd line string to move the file subprocess.call(cmd, shell=True) def remove_files(filename): """ Simple function to remove data set saved in filename if the dynamic test is completed with no error. Some data sets we use can be rather big. This is performed to save space. :param filename: string representing the file to be removed. Full path is included. :return: None """ cmd = 'rm ' + filename subprocess.call(cmd, shell=True) def random_col_duplication(num_cols, duplication_threshold, max_number, to_scale, max_scale_factor): """ This function will randomly determine for each column if it should be duplicated. If it is to be duplicated, how many times, the duplication should be. In addition, a scaling factor will be randomly applied to each duplicated column if enabled. :param num_cols: integer representing number of predictors used :param duplication_threshold: threshold to determine if a column is to be duplicated. Set this number to be low if you want to encourage column duplication and vice versa :param max_number: maximum number of times a column is to be duplicated :param to_scale: bool indicating if a duplicated column is to be scaled :param max_scale_factor: real representing maximum scale value for repeated columns :return: a tuple containing two vectors: col_return, col_scale_return. col_return: vector indicating the column indices of the original data matrix that will be included in the new data matrix with duplicated columns col_scale_return: vector indicating for each new column in the new data matrix with duplicated columns, what scale should be applied to that column. """ col_indices = list(range(num_cols)) # contains column indices of predictors in original data set col_scales = [1]*num_cols # scaling factor for original data set, all ones. for ind in range(num_cols): # determine for each column if to duplicate it temp = random.uniform(0, 1) # generate random number from 0 to 1 if temp > duplication_threshold: # duplicate column if random number generated exceeds duplication_threshold rep_num = random.randint(1, max_number) # randomly determine how many times to repeat a column more_col_indices = [ind]*rep_num col_indices.extend(more_col_indices) temp_scale = [] for ind in range(rep_num): if to_scale: # for each duplicated column, determine a scaling factor to multiply the column with temp_scale.append(random.uniform(0, max_scale_factor)) else: temp_scale.append(1) col_scales.extend(temp_scale) # randomly shuffle the predictor column orders and the corresponding scaling factors new_col_indices = list(range(len(col_indices))) random.shuffle(new_col_indices) col_return = [col_indices[i] for i in new_col_indices] col_scale_return = [col_scales[i] for i in new_col_indices] return col_return, col_scale_return def duplicate_scale_cols(col_indices, col_scale, old_filename, new_filename): """ This function actually performs the column duplication with scaling giving the column indices and scaling factors for each column. It will first load the original data set from old_filename. After performing column duplication and scaling, the new data set will be written to file with new_filename. :param col_indices: vector indicating the column indices of the original data matrix that will be included in the new data matrix with duplicated columns :param col_scale: vector indicating for each new column in the new data matrix with duplicated columns, what scale should be applied to that column :param old_filename: string representing full directory path and filename where data set is stored :param new_filename: string representing full directory path and filename where new data set is to be stored :return: None """ # pd_frame = pd.read_csv(old_filename, header=None) # read in original data set # # pd_frame_new = pd.DataFrame() # new empty data frame # # for ind in range(len(col_indices)): # for each column # tempc = pd_frame.ix[:, col_indices[ind]]*col_scale[ind] # extract a column from old data frame and scale it # pd_frame_new = pd.concat([pd_frame_new, tempc], axis=1) # add it to the new data frame np_frame = np.asmatrix(np.genfromtxt(old_filename, delimiter=',', dtype=None)) (num_row, num_col) = np_frame.shape np_frame_new = np.asmatrix(np.zeros((num_row, len(col_indices)), dtype=np.float)) for ind in range(len(col_indices)): np_frame_new[:, ind] = np_frame[:, col_indices[ind]]*col_scale[ind] # done changing the data frame. Save it in a new file np.savetxt(new_filename, np_frame_new, delimiter=",") def insert_nan_in_data(old_filename, new_filename, missing_fraction): """ Give the filename of a data set stored in old_filename, this function will randomly determine for each predictor to replace its value with nan or not with probability missing_frac. The new data set will be stored in filename new_filename. :param old_filename: string representing full directory path and filename where data set is stored :param new_filename: string representing full directory path and filename where new data set with missing values is to be stored :param missing_fraction: real value representing the probability of replacing a predictor with nan. :return: None """ # pd_frame = pd.read_csv(old_filename, header=None) # read in a dataset np_frame = np.asmatrix(np.genfromtxt(old_filename, delimiter=',', dtype=None)) (row_count, col_count) = np_frame.shape random_matrix = np.random.uniform(0, 1, [row_count, col_count-1]) for indr in range(row_count): # for each predictor value, determine if to replace value with nan for indc in range(col_count-1): if random_matrix[indr, indc] < missing_fraction: np_frame[indr, indc] = np.nan # save new data set with missing values to new file np.savetxt(new_filename, np_frame, delimiter=",") # pd_frame.to_csv(new_filename, sep=',', header=False, index=False, na_rep='nan') def print_message_values(start_string, nump_array): """ This function prints the value of a nump_array with a string message in front of it. :param start_string: string representing message to be printed :param nump_array: array storing something :return: None """ print(start_string) print(nump_array) def show_test_results(test_name, curr_test_val, new_test_val): """ This function prints the test execution results which can be passed or failed. A message will be printed on screen to warn user of the test result. :param test_name: string representing test name :param curr_test_val: integer representing number of tests failed so far before the test specified in test_name is executed :param new_test_val: integer representing number of tests failed after the test specified in test_name is executed :return: integer: 0 if test passed and 1 if test faild. """ failed_string = "Ooops, " + test_name + " failed. I am sorry..." pass_string = "Yeah, " + test_name + " passed!" if (curr_test_val < new_test_val): # this test has failed print(failed_string) return 1 else: print(pass_string) return 0 def assert_H2OTwoDimTable_equal_upto(table1, table2, col_header_list, tolerance=1e-6): ''' This method will compare two H2OTwoDimTables that are almost of the same size. table1 can be shorter than table2. However, for whatever part of table2 table1 has, they must be the same. :param table1: :param table2: :param col_header_list: :param tolerance: :return: ''' size1 = len(table1.cell_values) for cname in col_header_list: colindex = table1.col_header.index(cname) for cellind in range(size1): val1 = table1.cell_values[cellind][colindex] val2 = table2.cell_values[cellind][colindex] if isinstance(val1, float) and isinstance(val2, float): assert abs(val1-val2) < tolerance, \ "table 1 value {0} and table 2 value {1} in {2} differ more than tolerance of " \ "{3}".format(val1, val2, cname, tolerance) else: assert val1==val2, "table 1 value {0} and table 2 value {1} in {2} differ more than tolerance of " \ "{3}".format(val1, val2, cname, tolerance) print("******* Congrats! Test passed. ") def extract_col_value_H2OTwoDimTable(table, col_name): ''' This function given the column name will extract a list containing the value used for the column name from the H2OTwoDimTable. :param table: :param col_name: :return: ''' tableList = [] col_header = table.col_header colIndex = col_header.index(col_name) for ind in range(len(table.cell_values)): temp = table.cell_values[ind] tableList.append(temp[colIndex]) return tableList def assert_H2OTwoDimTable_equal_upto(table1, table2, col_header_list, tolerance=1e-6): ''' This method will compare two H2OTwoDimTables that are almost of the same size. table1 can be shorter than table2. However, for whatever part of table2 table1 has, they must be the same. :param table1: :param table2: :param col_header_list: :param tolerance: :return: ''' size1 = len(table1.cell_values) for cname in col_header_list: colindex = table1.col_header.index(cname) for cellind in range(size1): val1 = table1.cell_values[cellind][colindex] val2 = table2.cell_values[cellind][colindex] if isinstance(val1, float) and isinstance(val2, float) and not(math.isnan(val1) and math.isnan(val2)): assert abs(val1-val2) < tolerance, \ "table 1 value {0} and table 2 value {1} in {2} differ more than tolerance of " \ "{3}".format(val1, val2, cname, tolerance) elif not(isinstance(val1, float) and isinstance(val2, float)) : assert val1==val2, "table 1 value {0} and table 2 value {1} in {2} differ more than tolerance of " \ "{3}".format(val1, val2, cname, tolerance) print("******* Congrats! Test passed. ") def assert_equal_scoring_history(model1, model2, col_compare_list, tolerance=1e-6): scoring_hist1 = model1._model_json["output"]["scoring_history"] scoring_hist2 = model2._model_json["output"]["scoring_history"] assert_H2OTwoDimTable_equal_upto(scoring_hist1, scoring_hist2, col_compare_list, tolerance=tolerance) def assert_H2OTwoDimTable_equal(table1, table2, col_header_list, tolerance=1e-6, check_sign=False, check_all=True, num_per_dim=10): """ This method compares two H2OTwoDimTables and verify that their difference is less than value set in tolerance. It is probably an overkill for I have assumed that the order of col_header_list may not be in the same order as the values in the table.cell_values[ind][0]. In addition, I do not assume an order for the names in the table.cell_values[ind][0] either for there is no reason for an order to exist. To limit the test run time, we can test a randomly sampled of points instead of all points :param table1: H2OTwoDimTable to be compared :param table2: the other H2OTwoDimTable to be compared :param col_header_list: list of strings denote names that we want the comparison to be performed :param tolerance: default to 1e-6 :param check_sign: bool, determine if the sign of values are important or not. For eigenvectors, they are not. :param check_all: bool, determine if we need to compare every single element :param num_per_dim: integer, number of elements to sample per dimension. We have 3 here. :return: None if comparison succeed and raise an error if comparison failed for whatever reason """ num_comparison = len(set(col_header_list)) size1 = len(table1.cell_values) size2 = len(table2.cell_values) worst_error = 0 assert size1==size2, "The two H2OTwoDimTables are of different size!" assert num_comparison<=size1, "H2OTwoDimTable do not have all the attributes specified in col_header_list." flip_sign_vec = generate_sign_vec(table1, table2) if check_sign else [1]*len(table1.cell_values[0]) # correct for sign change for eigenvector comparisons randRange1 = generate_for_indices(len(table1.cell_values), check_all, num_per_dim, 0) randRange2 = generate_for_indices(len(table2.cell_values), check_all, num_per_dim, 0) for ind in range(num_comparison): col_name = col_header_list[ind] next_name=False for name_ind1 in randRange1: if col_name!=str(table1.cell_values[name_ind1][0]): continue for name_ind2 in randRange2: if not(col_name==str(table2.cell_values[name_ind2][0])): continue # now we have the col header names, do the actual comparison if str(table1.cell_values[name_ind1][0])==str(table2.cell_values[name_ind2][0]): randRange3 = generate_for_indices(min(len(table2.cell_values[name_ind2]), len(table1.cell_values[name_ind1])), check_all, num_per_dim,1) for indC in randRange3: val1 = table1.cell_values[name_ind1][indC] val2 = table2.cell_values[name_ind2][indC]*flip_sign_vec[indC] if isinstance(val1, float) and isinstance(val2, float): compare_val_ratio = abs(val1-val2)/max(1, abs(val1), abs(val2)) if compare_val_ratio > tolerance: print("Table entry difference is {0} at dimension {1} and eigenvector number " "{2}".format(compare_val_ratio, name_ind1, indC)) print("The first vector is {0} and the second vector is {1}".format(table1.cell_values[name_ind1], table2.cell_values[name_ind2])) assert False, "Table entries are not equal within tolerance." worst_error = max(worst_error, compare_val_ratio) else: assert False, "Tables contains non-numerical values. Comparison is for numericals only!" next_name=True break else: assert False, "Unknown metric names found in col_header_list." if next_name: # ready to go to the next name in col_header_list break print("******* Congrats! Test passed. Maximum difference of your comparison is {0}".format(worst_error)) def generate_for_indices(list_size, check_all, num_per_dim, start_val): if check_all: return list(range(start_val, list_size)) else: randomList = list(range(start_val, list_size)) random.shuffle(randomList) return randomList[0:min(list_size, num_per_dim)] def generate_sign_vec(table1, table2): sign_vec = [1]*len(table1.cell_values[0]) for indC in range(1, len(table2.cell_values[0])): # may need to look at other elements since some may be zero for indR in range(0, len(table2.cell_values)): if (abs(table1.cell_values[indR][indC]) > 0) and (abs(table2.cell_values[indR][indC]) > 0): sign_vec[indC] = int(np.sign(table1.cell_values[indR][indC]) * np.sign(table2.cell_values[indR][indC])) # if (np.sign(table1.cell_values[indR][indC])!=np.sign(table2.cell_values[indR][indC])): # sign_vec[indC] = -1 # else: # sign_vec[indC] = 1 break # found what we need. Goto next column return sign_vec def equal_two_dicts(dict1, dict2, tolerance=1e-6, throwError=True): size1 = len(dict1) if (size1 == len(dict2)): # only proceed if lengths are the same for key1 in dict1.keys(): diff = abs(dict1[key1]-dict2[key1]) if (diff > tolerance): if throwError: assert False, "Dict 1 value {0} and Dict 2 value {1} do not agree.".format(dict1[key1], dict2[key1]) else: return False def equal_two_arrays(array1, array2, eps=1e-6, tolerance=1e-6, throw_error=True): """ This function will compare the values of two python tuples. First, if the values are below eps which denotes the significance level that we care, no comparison is performed. Next, False is returned if the different between any elements of the two array exceeds some tolerance. :param array1: numpy array containing some values of interest :param array2: numpy array containing some values of interest that we would like to compare it with array1 :param eps: significance level that we care about in order to perform the comparison :param tolerance: threshold for which we allow the two array elements to be different by :param throw_error: throws error when two arrays are not equal :return: True if elements in array1 and array2 are close and False otherwise """ size1 = len(array1) if size1 == len(array2): # arrays must be the same size # compare two arrays for ind in range(size1): if not ((array1[ind] < eps) and (array2[ind] < eps)): # values to be compared are not too small, perform comparison # look at differences between elements of array1 and array2 compare_val_h2o_py = abs(array1[ind] - array2[ind]) if compare_val_h2o_py > tolerance: # difference is too high, return false if throw_error: assert False, "Array 1 value {0} and array 2 value {1} do not agree.".format(array1[ind], array2[ind]) else: return False return True # return True, elements of two arrays are close enough else: if throw_error: assert False, "The two arrays are of different size!" else: return False def equal_2d_tables(table1, table2, tolerance=1e-6): """ This function will compare the values of two python tuples. False is returned if the different between any elements of the two array exceeds some tolerance. :param table1: numpy array containing some values of interest :param table2: numpy array containing some values of interest that we would like to compare it with array1 :param tolerance: threshold for which we allow the two array elements to be different by :return: True if elements in array1 and array2 are close and False otherwise """ size1 = len(table1) if size1 == len(table2): # arrays must be the same size # compare two arrays for ind in range(size1): if len(table1[ind]) == len(table2[ind]): for ind2 in range(len(table1[ind])): if type(table1[ind][ind2]) == float: if abs(table1[ind][ind2]-table2[ind][ind2]) > tolerance: return False else: assert False, "The two arrays are of different size!" return True else: assert False, "The two arrays are of different size!" def compare_two_arrays(array1, array2, eps, tolerance, comparison_string, array1_string, array2_string, error_string, success_string, template_is_better, just_print=False): """ This function is written to print out the performance comparison results for various values that we care about. It will return 1 if the values of the two arrays exceed threshold specified in tolerance. The actual comparison is performed by calling function equal_two_array. :param array1: numpy array containing some values of interest :param array2: numpy array containing some values of interest that we would like to compare it with array1 :param eps: significance level that we care about in order to perform the comparison :param tolerance: threshold for which we allow the two array elements to be different by :param comparison_string: string stating what the comparison is about, e.g. "Comparing p-values ...." :param array1_string: string stating what is the array1 attribute of interest, e.g. "H2O p-values: " :param array2_string: string stating what is the array2 attribute of interest, e.g. "Theoretical p-values: " :param error_string: string stating what you want to say if the difference between array1 and array2 exceeds tolerance, e.g "P-values are not equal!" :param success_string: string stating what you want to say if the difference between array1 and array2 does not exceed tolerance "P-values are close enough!" :param template_is_better: bool, True, will return 1 if difference among elements of array1 and array2 exceeds tolerance. False, will always return 0 even if difference among elements of array1 and array2 exceeds tolerance. In this case, the system under test actually performs better than the template. :param just_print: bool if True will print attribute values without doing comparison. False will print attribute values and perform comparison :return: if template_is_better = True, return 0 if elements in array1 and array2 are close and 1 otherwise; if template_is_better = False, will always return 0 since system under tests performs better than template system. """ # display array1, array2 with proper description print(comparison_string) print(array1_string, array1) print(array2_string, array2) if just_print: # just print the two values and do no comparison return 0 else: # may need to actually perform comparison if template_is_better: try: assert equal_two_arrays(array1, array2, eps, tolerance), error_string print(success_string) sys.stdout.flush() return 0 except: sys.stdout.flush() return 1 else: print("Test result is actually better than comparison template!") return 0 def make_Rsandbox_dir(base_dir, test_name, make_dir): """ This function will remove directory "Rsandbox/test_name" off directory base_dir and contents if it exists. If make_dir is True, it will create a clean directory "Rsandbox/test_name" off directory base_dir. :param base_dir: string contains directory path where we want to build our Rsandbox/test_name off from :param test_name: string contains unit test name that the Rsandbox is created for :param make_dir: bool, True: will create directory baseDir/Rsandbox/test_name, False: will not create directory. :return: syndatasets_dir: string containing the full path of the directory name specified by base_dir, test_name """ # create the Rsandbox directory path for the test. syndatasets_dir = os.path.join(base_dir, "Rsandbox_" + test_name) if os.path.exists(syndatasets_dir): # remove Rsandbox directory if it exists shutil.rmtree(syndatasets_dir) if make_dir: # create Rsandbox directory if make_dir is True os.makedirs(syndatasets_dir) return syndatasets_dir def get_train_glm_params(model, what_param, family_type='gaussian'): """ This function will grab the various attributes (like coefficients, p-values, and others) off a GLM model that has been built. :param model: GLM model that we want to extract information from :param what_param: string indicating the model attribute of interest like 'p-value','weights',... :param family_type: string, optional, represents the various distribution families (gaussian, multinomial, binomial) supported by our GLM algo :return: attribute value of interest """ coeff_pvalues = model._model_json["output"]["coefficients_table"].cell_values if what_param == 'p-values': if 'gaussian' in family_type.lower(): p_value_h2o = [] for ind in range(len(coeff_pvalues)): p_value_h2o.append(coeff_pvalues[ind][-1]) return p_value_h2o else: assert False, "P-values are only available to Gaussian family." elif what_param == 'weights': if 'gaussian' in family_type.lower(): weights = [] for ind in range(len(coeff_pvalues)): weights.append(coeff_pvalues[ind][1]) return weights elif ('multinomial' in family_type.lower()) or ('binomial' in family_type.lower()): # for multinomial, the coefficients are organized as features by number of classes for # nonstandardized and then standardized weights. Need to grab the correct matrix as # number of classes by n_features matrix num_feature = len(coeff_pvalues) num_class = (len(coeff_pvalues[0])-1)/2 coeffs = np.zeros((num_class,num_feature), dtype=np.float) end_index = int(num_class+1) for col_index in range(len(coeff_pvalues)): coeffs[:, col_index] = coeff_pvalues[col_index][1:end_index] return coeffs elif what_param == 'best_lambda': lambda_str = model._model_json["output"]["model_summary"].cell_values[0][4].split('=') return float(str(lambda_str[-2]).split(',')[0]) elif what_param == 'confusion_matrix': if 'multinomial' in family_type.lower(): return model._model_json["output"]["training_metrics"]._metric_json["cm"]["table"] elif 'binomial' in family_type.lower(): return model.confusion_matrix().table else: assert False, "parameter value not found in GLM model" def less_than(val1, val2): """ Simple function that returns True if val1 <= val2 and False otherwise. :param val1: first value of interest :param val2: second value of interest :return: bool: True if val1 <= val2 and False otherwise """ if round(val1, 3) <= round(val2, 3): # only care to the 3rd position after decimal point return True else: return False def replace_nan_with_mean(data_with_nans, nans_row_col_indices, col_means): """ Given a data set with nans, row and column indices of where the nans are and the col_means, this function will replace the nans with the corresponding col_means. :param data_with_nans: data set matrix with nans :param nans_row_col_indices: matrix containing the row and column indices of where the nans are :param col_means: vector containing the column means of data_with_NAs :return: data_with_NAs: data set with nans replaced with column means """ num_NAs = len(nans_row_col_indices[0]) for ind in range(num_NAs): data_with_nans[nans_row_col_indices[0][ind], nans_row_col_indices[1][ind]] = \ col_means[nans_row_col_indices[1][ind]] return data_with_nans def remove_csv_files(dir_path, suffix=".csv", action='remove', new_dir_path=""): """ Given a directory, this function will gather all function ending with string specified in suffix. Next, it is going to delete those files if action is set to 'remove'. If action is set to 'copy', a new_dir_path must be specified where the files ending with suffix will be moved to this new directory instead. :param dir_path: string representing full path to directory of interest :param suffix: string representing suffix of filename that are to be found and deleted :param action: string, optional, denote the action to perform on files, 'remove' or 'move' :param new_dir_path: string, optional, representing full path to new directory :return: None """ filenames = os.listdir(dir_path) # list all files in directory # only collect files with filename ending with suffix to_remove = [filename for filename in filenames if filename.endswith(suffix)] # delete files ending with suffix for fn in to_remove: temp_fn = os.path.join(dir_path, fn) # only remove if file actually exists. if os.path.isfile(temp_fn): if 'remove' in action: remove_files(temp_fn) elif 'copy' in action: move_files(new_dir_path, temp_fn, fn, action=action) else: assert False, "action string can only be 'remove' or 'copy." def extract_comparison_attributes_and_print(model_h2o, h2o_model_test_metrics, end_test_str, want_p_values, attr1_bool, attr2_bool, att1_template, att2_template, att3_template, att4_template, compare_att1_str, h2o_att1_str, template_att1_str, att1_str_fail, att1_str_success, compare_att2_str, h2o_att2_str, template_att2_str, att2_str_fail, att2_str_success, compare_att3_str, h2o_att3_str, template_att3_str, att3_str_fail, att3_str_success, compare_att4_str, h2o_att4_str, template_att4_str, att4_str_fail, att4_str_success, failed_test_number, ignored_eps, allowed_diff, noise_var, template_must_be_better, attr3_bool=True, attr4_bool=True): """ This function basically will compare four attributes (weight, p-values, training data MSE, test data MSE) of a test with a template model. If the difference of comparison exceeds a certain threshold, the test will be determined as failed and vice versa. There are times when we do not care about p-values and/or weight comparisons but mainly concerned with MSEs. We can set the input parameters to indicate if this is the case. :param model_h2o: H2O model that we want to evaluate :param h2o_model_test_metrics: test performance of H2O model under evaluation :param end_test_str: string representing end test banner to be printed :param want_p_values: bool True if we want to care about p-values and False if we don't :param attr1_bool: bool True if we want to compare weight difference between H2O model and template model and False otherwise. :param attr2_bool: bool True if we want to compare p-value difference between H2O model and template model and False otherwise. :param att1_template: value of first template attribute, the weight vector :param att2_template: value of second template attribute, the p-value vector :param att3_template: value of third template attribute, the training data set MSE :param att4_template: value of fourth template attribute, the test data set MSE :param compare_att1_str: string describing the comparison of first attribute, e.g. "Comparing intercept and weights ...." :param h2o_att1_str: string describing H2O model first attribute values, e.g. "H2O intercept and weights: " :param template_att1_str: string describing template first attribute values, e.g. "Theoretical intercept and weights: " :param att1_str_fail: string describing message to print out if difference exceeds threshold, e.g. "Intercept and weights are not equal!" :param att1_str_success: string describing message to print out if difference < threshold, e.g. "Intercept and weights are close enough!" :param compare_att2_str: string describing the comparison of first attribute, e.g. "Comparing p-values ...." :param h2o_att2_str: string describing H2O model first attribute values, e.g. "H2O p-values: " :param template_att2_str: string describing template first attribute values, e.g. "Theoretical p-values: " :param att2_str_fail: string describing message to print out if difference exceeds threshold, e.g. "P-values are not equal!" :param att2_str_success: string describing message to print out if difference < threshold, e.g. "P-values are close enough!" :param compare_att3_str: string describing the comparison of first attribute, e.g. "Comparing training MSEs ...." :param h2o_att3_str: string describing H2O model first attribute values, e.g. "H2O training MSE: " :param template_att3_str: string describing template first attribute values, e.g. "Theoretical train MSE: " :param att3_str_fail: string describing message to print out if difference exceeds threshold, e.g. "Training MSEs are not equal!" :param att3_str_success: string describing message to print out if difference < threshold, e.g. "Training MSEs are close enough!" :param compare_att4_str: string describing the comparison of first attribute, e.g. "Comparing test MSEs ...." :param h2o_att4_str: string describing H2O model first attribute values, e.g. "H2O test MSE: " :param template_att4_str: string describing template first attribute values, e.g. "Theoretical test MSE: " :param att4_str_fail: string describing message to print out if difference exceeds threshold, e.g. "Test MSEs are not equal!" :param att4_str_success: string describing message to print out if difference < threshold, e.g. "Test MSEs are close enough!" :param failed_test_number: integer denote the number of tests failed :param ignored_eps: if value < than this value, no comparison is performed :param allowed_diff: threshold if exceeded will fail a test :param noise_var: Gaussian noise variance used to generate data set :param template_must_be_better: bool: True: template value must be lower, False: don't care :param attr3_bool: bool denoting if we should compare attribute 3 values :param attr4_bool: bool denoting if we should compare attribute 4 values :return: a tuple containing test h2o model training and test performance metrics that include: weight, pValues, mse_train, r2_train, mse_test, r2_test """ # grab weight from h2o model test1_weight = get_train_glm_params(model_h2o, 'weights') # grab p-values from h2o model test1_p_values = [] if want_p_values: test1_p_values = get_train_glm_params(model_h2o, 'p-values') # grab other performance metrics test1_mse_train = model_h2o.mse() test1_r2_train = model_h2o.r2() test1_mse_test = h2o_model_test_metrics.mse() test1_r2_test = h2o_model_test_metrics.r2() # compare performances of template and h2o model weights failed_test_number += compare_two_arrays(test1_weight, att1_template, ignored_eps, allowed_diff*100, compare_att1_str, h2o_att1_str, template_att1_str, att1_str_fail, att1_str_success, attr1_bool) # p-values if want_p_values: if np.isnan(np.asarray(test1_p_values)).any(): # p-values contain nan failed_test_number += 1 failed_test_number += compare_two_arrays(test1_p_values, att2_template, ignored_eps, allowed_diff, compare_att2_str, h2o_att2_str, template_att2_str, att2_str_fail, att2_str_success, attr2_bool) # Training MSE need_to_compare = less_than(att3_template, test1_mse_train) # in some cases, template value should always be better. Training data MSE should always # be better without regularization than with regularization if (not need_to_compare) and template_must_be_better: failed_test_number += 1 failed_test_number += compare_two_arrays([test1_mse_train], [att3_template], ignored_eps, noise_var, compare_att3_str, h2o_att3_str, template_att3_str, att3_str_fail, att3_str_success, attr3_bool) # Test MSE need_to_compare = less_than(att4_template, test1_mse_test) failed_test_number += compare_two_arrays([test1_mse_test], [att4_template], ignored_eps, noise_var, compare_att4_str, h2o_att4_str, template_att4_str, att4_str_fail, att4_str_success, need_to_compare, attr4_bool) # print end test banner print(end_test_str) print("*******************************************************************************************") sys.stdout.flush() return test1_weight, test1_p_values, test1_mse_train, test1_r2_train, test1_mse_test,\ test1_r2_test, failed_test_number def extract_comparison_attributes_and_print_multinomial(model_h2o, h2o_model_test_metrics, family_type, end_test_str, compare_att_str=["", "", "", "", "", "", ""], h2o_att_str=["", "", "", "", "", "", ""], template_att_str=["", "", "", "", "", "", ""], att_str_fail=["", "", "", "", "", "", ""], att_str_success=["", "", "", "", "", "", ""], test_model=None, test_model_metric=None, template_params=None, can_be_better_than_template=[ False, False, False, False, False, False], just_print=[True, True, True, True, True, True], ignored_eps=1e-15, allowed_diff=1e-5, failed_test_number=0): """ This function basically will compare and print out six performance metrics of a test with a template model. If the difference of comparison exceeds a certain threshold, the test will be determined as failed and vice versa. There are times when we do not care about comparisons but mainly concerned with logloss/prediction accuracy in determining if a test shall fail. We can set the input parameters to indicate if this is the case. :param model_h2o: H2O model that we want to evaluate :param h2o_model_test_metrics: test performance of H2O model under evaluation :param family_type: string represents the various distribution families (gaussian, multinomial, binomial) supported by our GLM algo :param end_test_str: string to be printed at the end of a test :param compare_att_str: array of strings describing what we are trying to compare :param h2o_att_str: array of strings describing each H2O attribute of interest :param template_att_str: array of strings describing template attribute of interest :param att_str_fail: array of strings to be printed if the comparison failed :param att_str_success: array of strings to be printed if comparison succeeded :param test_model: template model whose attributes we want to compare our H2O model with :param test_model_metric: performance on test data set of template model :param template_params: array containing template attribute values that we want to compare our H2O model with :param can_be_better_than_template: array of bool: True: template value must be lower, False: don't care :param just_print: array of bool for each attribute if True, no comparison is performed, just print the attributes and if False, will compare the attributes and print the attributes as well :param ignored_eps: if value < than this value, no comparison is performed :param allowed_diff: threshold if exceeded will fail a test :param failed_test_number: integer denote the number of tests failed so far :return: accumulated number of tests that have failed so far """ # grab performance metrics from h2o model (h2o_weight, h2o_logloss_train, h2o_confusion_matrix_train, h2o_accuracy_train, h2o_logloss_test, h2o_confusion_matrix_test, h2o_accuracy_test) = grab_model_params_metrics(model_h2o, h2o_model_test_metrics, family_type) # grab performance metrics from template model if test_model and test_model_metric: (template_weight, template_logloss_train, template_confusion_matrix_train, template_accuracy_train, template_logloss_test, template_confusion_matrix_test, template_accuracy_test) = \ grab_model_params_metrics(test_model, test_model_metric, family_type) elif template_params: # grab template comparison values from somewhere else (template_weight, template_logloss_train, template_confusion_matrix_train, template_accuracy_train, template_logloss_test, template_confusion_matrix_test, template_accuracy_test) = template_params else: assert False, "No valid template parameters are given for comparison." # print and/or compare the weights between template and H2O compare_index = 0 failed_test_number += compare_two_arrays(h2o_weight, template_weight, ignored_eps, allowed_diff, compare_att_str[compare_index], h2o_att_str[compare_index], template_att_str[compare_index], att_str_fail[compare_index], att_str_success[compare_index], True, just_print[compare_index]) compare_index += 1 # this is logloss from training data set, if not(just_print[compare_index]) and not(can_be_better_than_template[compare_index]): if (h2o_logloss_train < template_logloss_train) and \ (abs(h2o_logloss_train-template_logloss_train) > 1e-5): # H2O performed better than template which is not allowed failed_test_number += 1 # increment failed_test_number and just print the results compare_two_arrays([h2o_logloss_train], [template_logloss_train], ignored_eps, allowed_diff, compare_att_str[compare_index], h2o_att_str[compare_index], template_att_str[compare_index], att_str_fail[compare_index], att_str_success[compare_index], True, True) else: failed_test_number += compare_two_arrays([h2o_logloss_train], [template_logloss_train], ignored_eps, allowed_diff, compare_att_str[compare_index], h2o_att_str[compare_index], template_att_str[compare_index], att_str_fail[compare_index], att_str_success[compare_index], True, False) else: template_better = is_template_better(just_print[compare_index], can_be_better_than_template[compare_index], h2o_logloss_train, template_logloss_train, False) # print and compare the logloss between template and H2O for training data failed_test_number += compare_two_arrays([h2o_logloss_train], [template_logloss_train], ignored_eps, allowed_diff, compare_att_str[compare_index], h2o_att_str[compare_index], template_att_str[compare_index], att_str_fail[compare_index], att_str_success[compare_index], template_better, just_print[compare_index]) compare_index += 1 template_better = is_template_better(just_print[compare_index], can_be_better_than_template[compare_index], h2o_logloss_test, template_logloss_test, False) # print and compare the logloss between template and H2O for test data failed_test_number += compare_two_arrays([h2o_logloss_test], [template_logloss_test], ignored_eps, allowed_diff, compare_att_str[compare_index], h2o_att_str[compare_index], template_att_str[compare_index], att_str_fail[compare_index], att_str_success[compare_index], template_better, just_print[compare_index]) compare_index += 1 # print the confusion matrix from training data failed_test_number += compare_two_arrays(h2o_confusion_matrix_train, template_confusion_matrix_train, ignored_eps, allowed_diff, compare_att_str[compare_index], h2o_att_str[compare_index], template_att_str[compare_index], att_str_fail[compare_index], att_str_success[compare_index], True, just_print[compare_index]) compare_index += 1 # print the confusion matrix from test data failed_test_number += compare_two_arrays(h2o_confusion_matrix_test, template_confusion_matrix_test, ignored_eps, allowed_diff, compare_att_str[compare_index], h2o_att_str[compare_index], template_att_str[compare_index], att_str_fail[compare_index], att_str_success[compare_index], True, just_print[compare_index]) compare_index += 1 template_better = is_template_better(just_print[compare_index], can_be_better_than_template[compare_index], h2o_accuracy_train, template_accuracy_train, True) # print accuracy from training dataset failed_test_number += compare_two_arrays([h2o_accuracy_train], [template_accuracy_train], ignored_eps, allowed_diff, compare_att_str[compare_index], h2o_att_str[compare_index], template_att_str[compare_index], att_str_fail[compare_index], att_str_success[compare_index], template_better, just_print[compare_index]) compare_index += 1 # print accuracy from test dataset template_better = is_template_better(just_print[compare_index], can_be_better_than_template[compare_index], h2o_accuracy_test, template_accuracy_test, True) failed_test_number += compare_two_arrays([h2o_accuracy_test], [template_accuracy_test], ignored_eps, allowed_diff, compare_att_str[compare_index], h2o_att_str[compare_index], template_att_str[compare_index], att_str_fail[compare_index], att_str_success[compare_index], template_better, just_print[compare_index]) # print end test banner print(end_test_str) print("*******************************************************************************************") sys.stdout.flush() return failed_test_number def is_template_better(just_print, can_be_better_than_template, h2o_att, template_att, bigger_is_better): """ This function is written to determine if the system under test performs better than the template model performance. :param just_print: bool representing if we are just interested in printing the attribute values :param can_be_better_than_template: bool stating that it is okay in this case for the system under test to perform better than the template system. :param h2o_att: number representing the h2o attribute under test :param template_att: number representing the template attribute :param bigger_is_better: bool representing if metric is perceived to be better if its value is higher :return: bool indicating if the template attribute is better. """ if just_print: # not interested in comparison, just want to print attribute values return True # does not matter what we return here else: if bigger_is_better: # metric is better if it is greater return not(h2o_att > template_att) else: # metric is better if it is less return not(h2o_att < template_att) def grab_model_params_metrics(model_h2o, h2o_model_test_metrics, family_type): """ This function will extract and return the various metrics from a H2O GLM model and the corresponding H2O model test metrics. :param model_h2o: GLM H2O model :param h2o_model_test_metrics: performance on test data set from H2O GLM model :param family_type: string representing 'gaussian', 'binomial' or 'multinomial' :return: tuple containing weight, logloss/confusion matrix/prediction accuracy calculated from training data set and test data set respectively """ # grab weight from h2o model h2o_weight = get_train_glm_params(model_h2o, 'weights', family_type=family_type) # grab other performance metrics h2o_logloss_train = model_h2o.logloss() h2o_confusion_matrix_train = get_train_glm_params(model_h2o, 'confusion_matrix', family_type=family_type) last_index = len(h2o_confusion_matrix_train.cell_values)-1 h2o_logloss_test = h2o_model_test_metrics.logloss() if 'multinomial' in family_type.lower(): h2o_confusion_matrix_test = h2o_model_test_metrics.confusion_matrix() h2o_accuracy_train = 1-h2o_confusion_matrix_train.cell_values[last_index][last_index] h2o_accuracy_test = 1-h2o_confusion_matrix_test.cell_values[last_index][last_index] elif 'binomial' in family_type.lower(): h2o_confusion_matrix_test = h2o_model_test_metrics.confusion_matrix().table real_last_index = last_index+1 h2o_accuracy_train = 1-float(h2o_confusion_matrix_train.cell_values[last_index][real_last_index]) h2o_accuracy_test = 1-float(h2o_confusion_matrix_test.cell_values[last_index][real_last_index]) else: assert False, "Only 'multinomial' and 'binomial' distribution families are supported for " \ "grab_model_params_metrics function!" return h2o_weight, h2o_logloss_train, h2o_confusion_matrix_train, h2o_accuracy_train, h2o_logloss_test,\ h2o_confusion_matrix_test, h2o_accuracy_test def prepare_data_sklearn_multinomial(training_data_xy): """ Sklearn model requires that the input matrix should contain a column of ones in order for it to generate the intercept term. In addition, it wants the response vector to be in a certain format as well. :param training_data_xy: matrix containing both the predictors and response column :return: tuple containing the predictor columns with a column of ones as the first column and the response vector in the format that Sklearn wants. """ (num_row, num_col) = training_data_xy.shape # change response to be enum and not real y_ind = num_col-1 training_data_xy[y_ind] = training_data_xy[y_ind].astype(int) # prepare response column for sklearn logistic regression response_y = training_data_xy[:, y_ind] response_y = np.ravel(response_y) training_data = training_data_xy[:, range(0, y_ind)] # added column of ones into data matrix X_MAT temp_ones = np.asmatrix(np.ones(num_row)).transpose() x_mat = np.concatenate((temp_ones, training_data), axis=1) return response_y, x_mat def get_gridables(params_in_json): """ This function is written to walk through all parameters of a model and grab the parameters, its type and its default values as three lists of all the gridable parameters. :param params_in_json: a list of parameters associated with a H2O model. Each list is a dict containing fields of interest like name, type, gridable, default values, .... :return: three lists: gridable_params, gridable_types and gridable_defaults containing the names of the parameter, its associated type like int, float, unicode, bool and default parameter values """ # grab all gridable parameters and its type gridable_parameters = [] gridable_types = [] gridable_defaults = [] for each_param in params_in_json: if each_param['gridable']: gridable_parameters.append(str(each_param["name"])) gridable_types.append(each_param["type"]) if type(each_param["default_value"]) == 'unicode': # hyper-parameters cannot be unicode gridable_defaults.append(str(each_param["default_value"])) else: gridable_defaults.append(each_param["default_value"]) return gridable_parameters, gridable_types, gridable_defaults def add_fold_weights_offset_columns(h2o_frame, nfold_max_weight_offset, column_names, column_type='fold_assignment'): """ Add fold_columns to H2O training frame specified in h2o_frame according to nfold. The new added columns should use the names in column_names. Returns a h2o_frame with newly added fold_columns. Copied from Eric's code. :param h2o_frame: H2O frame containing training data :param nfold_max_weight_offset: integer, number of fold in the cross-validation or maximum weight scale or offset :param column_names: list of strings denoting the column names for the new fold columns :param column_type: optional string denoting whether we are trying to generate fold_assignment or weights_column or offset_column :return: H2O frame with added fold column assignments """ number_row = h2o_frame.nrow # copied this part from Eric's code for index in range(len(column_names)): if 'fold_assignment' in column_type: temp_a = np.random.random_integers(0, nfold_max_weight_offset - 1, [number_row, 1]) # inclusive elif 'weights_column' in column_type: temp_a = np.random.uniform(0, nfold_max_weight_offset, [number_row, 1]) elif 'offset_column' in column_type: temp_a = random.uniform(0, nfold_max_weight_offset)*np.asmatrix(np.ones(number_row)).transpose() else: assert False, "column_type must be either 'fold_assignment' or 'weights_column'!" fold_assignments = h2o.H2OFrame(temp_a) fold_assignments.set_names([column_names[index]]) h2o_frame = h2o_frame.cbind(fold_assignments) return h2o_frame def gen_grid_search(model_params, hyper_params, exclude_parameters, gridable_parameters, gridable_types, gridable_defaults, max_int_number, max_int_val, min_int_val, max_real_number, max_real_val, min_real_val, quantize_level='1.00000000'): """ This function is written to randomly generate griddable parameters for a gridsearch. For parameters already found in hyper_params, no random list will be generated. In addition, we will check to make sure that the griddable parameters are actually used by the model before adding them to the hyper_params dict. :param model_params: list of string containing names of argument to the model :param hyper_params: dict structure containing a list of gridable parameters names with their list :param exclude_parameters: list containing parameter names not to be added to hyper_params :param gridable_parameters: list of gridable parameter names :param gridable_types: list of gridable parameter types :param gridable_defaults: list of gridable parameter default values :param max_int_number: integer, size of integer gridable parameter list :param max_int_val: integer, maximum integer value for integer gridable parameter :param min_int_val: integer, minimum integer value for integer gridable parameter :param max_real_number: integer, size of real gridable parameter list :param max_real_val: float, maximum real value for real gridable parameter :param min_real_val: float, minimum real value for real gridable parameter :param quantize_level: string representing the quantization level of floating point values generated randomly. :return: a tuple of hyper_params: dict of hyper parameters for gridsearch, true_gridable_parameters: a list of string containing names of truely gridable parameters, true_gridable_types: a list of string denoting parameter types and true_gridable_defaults: default values of those truly gridable parameters """ count_index = 0 true_gridable_parameters = [] true_gridable_types = [] true_gridable_defaults = [] for para_name in gridable_parameters: # parameter must not in exclusion list if (para_name in model_params) and (para_name not in exclude_parameters): true_gridable_parameters.append(para_name) true_gridable_types.append(gridable_types[count_index]) true_gridable_defaults.append(gridable_defaults[count_index]) if para_name not in hyper_params.keys(): # add default value to user defined parameter list # gridable parameter not seen before. Randomly generate values for it if ('int' in gridable_types[count_index]) or ('long' in gridable_types[count_index]): # make sure integer values are not duplicated, using set action to remove duplicates hyper_params[para_name] = list(set([random.randint(min_int_val, max_int_val) for p in range(0, max_int_number)])) elif ('double' in gridable_types[count_index]) or ('float' in gridable_types[count_index]): hyper_params[para_name] = fix_float_precision(list(np.random.uniform(min_real_val, max_real_val, max_real_number)), quantize_level=quantize_level) count_index += 1 return hyper_params, true_gridable_parameters, true_gridable_types, true_gridable_defaults def fix_float_precision(float_list, quantize_level='1.00000000'): """ This function takes in a floating point tuple and attempt to change it to floating point number with fixed precision. :param float_list: tuple/list of floating point numbers :param quantize_level: string, optional, represent the number of fix points we care :return: tuple of floats to the exact precision specified in quantize_level """ fixed_float = [] for num in float_list: fixed_float.append(float(Decimal(num).quantize(Decimal(quantize_level)))) return list(set(fixed_float)) def extract_used_params_xval(a_grid_model, model_param_names, params_dict, algo="GBM"): """ This function performs similar functions to function extract_used_params. However, for max_runtime_secs, we need to go into each cross-valudation model and grab the max_runtime_secs and add them up in order to get the correct value. In addition, we put your algo model specific parameters into params_dict. :param a_grid_model: list of models generated by gridsearch :param model_param_names: hyper-parameter names that are specified for the gridsearch. :param params_dict: dict containing name/value pairs specified to an algo. :param algo: string, optional, denoting the algo we are looking at. :return: params_used: a dict structure containing parameters that take on values as name/value pairs which will be used to build a model by hand using the same parameter setting as the model built by gridsearch. """ params_used = dict() # need to extract the max_runtime_secs ONE cross-validation model or the base model if a_grid_model._is_xvalidated: xv_keys = a_grid_model._xval_keys for id in xv_keys: # only need to get info from one model each_xv_model = h2o.get_model(id) # get each model params_used = extract_used_params(model_param_names, each_xv_model.params, params_dict, algo) break else: params_used = extract_used_params(model_param_names, a_grid_model.params, params_dict, algo) return params_used def extract_used_params(model_param_names, grid_model_params, params_dict, algo="GLM"): """ This function is used to build a dict out of parameters used by our gridsearch to build a H2O model given the dict structure that describes the parameters and their values used by gridsearch to build that particular mode. :param model_param_names: list contains parameter names that we are interested in extracting :param grid_model_params: dict contains key as names of parameter and values as list of two values: default and actual. :param params_dict: dict containing extra parameters to add to params_used like family, e.g. 'gaussian', 'binomial', ... :return: params_used: a dict structure containing parameters that take on values as name/value pairs which will be used to build a model by hand using the same parameter setting as the model built by gridsearch. """ params_used = dict() grid_model_params_keys = grid_model_params.keys() for each_parameter in model_param_names: parameter_name = str(each_parameter) if parameter_name in grid_model_params_keys: params_used[parameter_name] = grid_model_params[each_parameter]['actual'] if params_dict: for key, value in params_dict.items(): params_used[key] = value # add distribution family to parameters used list # only for GLM, change lambda to Lambda if algo =="GLM": if 'lambda' in params_used.keys(): params_used['Lambda'] = params_used['lambda'] del params_used['lambda'] return params_used def insert_error_grid_search(hyper_params, gridable_parameters, gridable_types, error_number): """ This function will randomly introduce errors into a copy of hyper_params. Depending on the random number error_number generated, the following errors can be introduced: error_number = 0: randomly alter the name of a hyper-parameter name; error_number = 1: randomly choose a hyper-parameter and remove all elements in its list error_number = 2: add randomly generated new hyper-parameter names with random list error_number other: randomly choose a hyper-parameter and insert an illegal type into it :param hyper_params: dict containing all legal hyper-parameters for our grid search :param gridable_parameters: name of griddable parameters (some may not be griddable) :param gridable_types: type of griddable parameters :param error_number: integer representing which errors to introduce into the gridsearch hyper-parameters :return: new dict with errors in either parameter names or parameter values """ error_hyper_params = copy.deepcopy(hyper_params) # error_hyper_params = {k : v for k, v in hyper_params.items()} param_index = random.randint(0, len(hyper_params)-1) param_name = list(hyper_params)[param_index] param_type = gridable_types[gridable_parameters.index(param_name)] if error_number == 0: # grab a hyper-param randomly and copy its name twice new_name = param_name+param_name error_hyper_params[new_name] = error_hyper_params[param_name] del error_hyper_params[param_name] elif error_number == 1: error_hyper_params[param_name] = [] elif error_number == 2: new_param = generate_random_words(random.randint(20,100)) error_hyper_params[new_param] = error_hyper_params[param_name] else: error_hyper_params = insert_bad_value(error_hyper_params, param_name, param_type) return error_hyper_params def insert_bad_value(error_hyper_params, param_name, param_type): """ This function is written to insert a value that is of a different type into an array than the one its other elements are for. :param error_hyper_params: dict containing all hyper-parameters for a grid search :param param_name: string denoting the hyper-parameter we want to insert bad element to :param param_type: string denoting hyper-parameter type :return: dict containing new inserted error value """ if 'int' in param_type: # insert a real number into integer error_hyper_params[param_name].append(random.uniform(-10,10)) elif 'enum' in param_type: # insert an float into enums error_hyper_params[param_name].append(random.uniform(-10,10)) elif 'double' in param_type: # insert an enum into float error_hyper_params[param_name].append(random.uniform(0,1) > 0.5) else: # insert a random string for all other cases error_hyper_params[param_name].append(generate_random_words(random.randint(20,100))) return error_hyper_params def generate_random_words(word_length): """ This function will generate a random word consisting of letters, numbers and punctuation given the word_length. :param word_length: integer denoting length of the word :return: string representing the random word """ if word_length > 0: all_chars = string.ascii_letters + string.digits + string.punctuation return ''.join((random.choice(all_chars)) for index in range(int(word_length))) else: assert False, "word_length must be an integer greater than 0." def generate_redundant_parameters(hyper_params, gridable_parameters, gridable_defaults, error_number): """ This function will randomly choose a set of hyper_params and make a dict out of it so we can duplicate the parameter specification in both the model and grid search. :param hyper_params: dict containing all griddable parameters as hyper_param to grid search :param gridable_parameters: list of gridable parameters (not truly) :param gridable_defaults: list of default values for gridable parameters :param error_number: int, indicate ways to change the model parameter and the hyper-parameter Here are the actions performed on the model parameter and hyper-parameters. error_number = 0: set model parameter to be a value out of the hyper-parameter value list, should not generate error; error_number = 1: set model parameter to be default value, should not generate error in this case; error_number = 3: make sure model parameter is not set to default and choose a value not in the hyper-parameter value list. :return: 2 dicts containing duplicated parameters with specification, new hyperparameter specification """ error_hyper_params = copy.deepcopy(hyper_params) # error_hyper_params = {k : v for k, v in hyper_params.items()} params_dict = {} num_params = random.randint(1, len(error_hyper_params)) params_list = list(error_hyper_params) # remove default values out of hyper_params for key in params_list: default_value = gridable_defaults[gridable_parameters.index(key )] if default_value in error_hyper_params[key]: error_hyper_params[key].remove(default_value) for index in range(num_params): param_name = params_list[index] hyper_params_len = len(error_hyper_params[param_name]) if error_number == 0: # randomly assigned the parameter to take one value out of the list param_value_index = random.randint(0, len(error_hyper_params[param_name])-1) params_dict[param_name] = error_hyper_params[param_name][param_value_index] elif error_number == 1: param_value_index = gridable_parameters.index(param_name) params_dict[param_name] = gridable_defaults[param_value_index] else: # randomly assign model parameter to one of the hyper-parameter values, should create error condition here param_value_index = random.randint(0, hyper_params_len-1) params_dict[param_name] = error_hyper_params[param_name][param_value_index] # final check to make sure lambda is Lambda if 'lambda' in list(params_dict): params_dict["Lambda"] = params_dict['lambda'] del params_dict["lambda"] return params_dict, error_hyper_params def count_models(hyper_params): """ Given a hyper_params dict, this function will return the maximum number of models that can be built out of all the combination of hyper-parameters. :param hyper_params: dict containing parameter name and a list of values to iterate over :return: max_model_number: int representing maximum number of models built """ max_model_number = 1 for key in list(hyper_params): max_model_number *= len(hyper_params[key]) return max_model_number def error_diff_2_models(grid_table1, grid_table2, metric_name): """ This function will take two models generated by gridsearch and calculate the mean absolute differences of the metric values specified by the metric_name in the two model. It will return the mean differences. :param grid_table1: first H2OTwoDimTable generated by gridsearch :param grid_table2: second H2OTwoDimTable generated by gridsearch :param metric_name: string, name of the metric of interest :return: real number which is the mean absolute metric difference between the two models """ num_model = len(grid_table1.cell_values) metric_diff = 0 for model_index in range(num_model): metric_diff += abs(grid_table1.cell_values[model_index][-1] - grid_table2.cell_values[model_index][-1]) if (num_model > 0): return metric_diff/num_model else: assert False, "error_diff_2_models: your table contains zero models." def find_grid_runtime(model_list): """ This function given a grid_model built by gridsearch will go into the model and calculate the total amount of time it took to actually build all the models in second :param model_list: list of model built by gridsearch, cartesian or randomized with cross-validation enabled. :return: total_time_sec: total number of time in seconds in building all the models """ total_time_sec = 0 for each_model in model_list: total_time_sec += each_model._model_json["output"]["run_time"] # time in ms # if cross validation is used, need to add those run time in here too if each_model._is_xvalidated: xv_keys = each_model._xval_keys for id in xv_keys: each_xv_model = h2o.get_model(id) total_time_sec += each_xv_model._model_json["output"]["run_time"] return total_time_sec/1000.0 # return total run time in seconds def evaluate_metrics_stopping(model_list, metric_name, bigger_is_better, search_criteria, possible_model_number): """ This function given a list of dict that contains the value of metric_name will manually go through the early stopping condition and see if the randomized grid search will give us the correct number of models generated. Note that you cannot assume the model_list is in the order of when a model is built. It actually already come sorted which we do not want.... :param model_list: list of models built sequentially that contains metric of interest among other fields :param metric_name: string representing name of metric that we want to based our stopping condition on :param bigger_is_better: bool indicating if the metric is optimized by getting bigger if True and vice versa :param search_criteria: dict structure containing the search criteria for randomized gridsearch :param possible_model_number: integer, represent the absolute possible number of models built based on the hyper-parameter size :return: bool indicating if the early topping condition is justified """ tolerance = search_criteria["stopping_tolerance"] stop_round = search_criteria["stopping_rounds"] min_list_len = 2*stop_round # minimum length of metrics needed before we start early stopping evaluation metric_list = [] # store metric of optimization stop_now = False # provide metric list sorted by time. Oldest model appear first. metric_list_time_ordered = sort_model_by_time(model_list, metric_name) for metric_value in metric_list_time_ordered: metric_list.append(metric_value) if len(metric_list) > min_list_len: # start early stopping evaluation now stop_now = evaluate_early_stopping(metric_list, stop_round, tolerance, bigger_is_better) if stop_now: if len(metric_list) < len(model_list): # could have stopped early in randomized gridsearch return False else: # randomized gridsearch stopped at the correct condition return True if len(metric_list) == possible_model_number: # never meet early stopping condition at end of random gridsearch return True # if max number of model built, still ok else: return False # early stopping condition never met but random gridsearch did not build all models, bad! def sort_model_by_time(model_list, metric_name): """ This function is written to sort the metrics that we care in the order of when the model was built. The oldest model metric will be the first element. :param model_list: list of models built sequentially that contains metric of interest among other fields :param metric_name: string representing name of metric that we want to based our stopping condition on :return: model_metric_list sorted by time """ model_num = len(model_list) model_metric_list = [None] * model_num for index in range(model_num): model_index = int(model_list[index]._id.split('_')[-1]) model_metric_list[model_index] = \ model_list[index]._model_json["output"]["cross_validation_metrics"]._metric_json[metric_name] return model_metric_list def evaluate_early_stopping(metric_list, stop_round, tolerance, bigger_is_better): """ This function mimics the early stopping function as implemented in ScoreKeeper.java. Please see the Java file comment to see the explanation of how the early stopping works. :param metric_list: list containing the optimization metric under consideration for gridsearch model :param stop_round: integer, determine averaging length :param tolerance: real, tolerance to see if the grid search model has improved enough to keep going :param bigger_is_better: bool: True if metric is optimized as it gets bigger and vice versa :return: bool indicating if we should stop early and sorted metric_list """ if (bigger_is_better): metric_list.reverse() shortest_len = 2*stop_round if (isinstance(metric_list[0], float)): startIdx = 0 else: startIdx = 1 bestInLastK = 1.0*sum(metric_list[startIdx:stop_round])/stop_round lastBeforeK = 1.0*sum(metric_list[stop_round:shortest_len])/stop_round if not(np.sign(bestInLastK) == np.sign(lastBeforeK)): return False ratio = bestInLastK/lastBeforeK if math.isnan(ratio): return False if bigger_is_better: return not (ratio > 1+tolerance) else: return not (ratio < 1-tolerance) def check_and_count_models(hyper_params, params_zero_one, params_more_than_zero, params_more_than_one, params_zero_positive, max_grid_model): """ This function will look at the hyper-parameter space set in hyper_params, generate a new hyper_param space that will contain a smaller number of grid_models. It will determine how many models will be built from this new hyper_param space. In order to arrive at the correct answer, it must discount parameter settings that are illegal. :param hyper_params: dict containing model parameter names and list of values to set it to :param params_zero_one: list containing model parameter names whose values must be between 0 and 1 :param params_more_than_zero: list containing model parameter names whose values must exceed zero :param params_more_than_one: list containing model parameter names whose values must exceed one :param params_zero_positive: list containing model parameter names whose values must equal to or exceed zero :param max_grid_model: maximum number of grid_model that can be generated from the new hyper_params space :return: total model: integer denoting number of grid models that can be built from all legal parameter settings in new hyper_parameter space final_hyper_params: dict of new hyper parameter space derived from the original hyper_params """ total_model = 1 hyper_keys = list(hyper_params) random.shuffle(hyper_keys) # get all hyper_parameter names in random order final_hyper_params = dict() for param in hyper_keys: # this param should be > 0 and <= 2 if param == "col_sample_rate_change_per_level": param_len = len([x for x in hyper_params["col_sample_rate_change_per_level"] if (x > 0) and (x <= 2)]) elif param in params_zero_one: param_len = len([x for x in hyper_params[param] if (x >= 0) and (x <= 1)]) elif param in params_more_than_zero: param_len = len([x for x in hyper_params[param] if (x > 0)]) elif param in params_more_than_one: param_len = len([x for x in hyper_params[param] if (x > 1)]) elif param in params_zero_positive: param_len = len([x for x in hyper_params[param] if (x >= 0)]) else: param_len = len(hyper_params[param]) if (param_len >= 0) and ((total_model*param_len) <= max_grid_model): total_model *= param_len final_hyper_params[param] = hyper_params[param] elif (total_model*param_len) > max_grid_model: break return total_model, final_hyper_params def write_hyper_parameters_json(dir1, dir2, json_filename, hyper_parameters): """ Write a json file of the hyper_parameters in directories dir1 and dir2 for debugging purposes. :param dir1: String containing first directory where you want to write the json file to :param dir2: String containing second directory where you want to write the json file to :param json_filename: String containing json file name :param hyper_parameters: dict containing hyper-parameters used """ # save hyper-parameter file in test directory with open(os.path.join(dir1, json_filename), 'w') as test_file: json.dump(hyper_parameters, test_file) # save hyper-parameter file in sandbox with open(os.path.join(dir2, json_filename), 'w') as test_file: json.dump(hyper_parameters, test_file) def compare_frames(frame1, frame2, numElements, tol_time=0, tol_numeric=0, strict=False, compare_NA=True, custom_comparators=None): """ This function will compare two H2O frames to make sure their dimension, and values in all cells are the same. It will not compare the column names though. :param frame1: H2O frame to be compared :param frame2: H2O frame to be compared :param numElements: integer to denote number of rows to compare. Done to reduce compare time. Set to 0 or negative number if you want to compare all elements. :param tol_time: optional parameter to limit time value difference. :param tol_numeric: optional parameter to limit numeric value difference. :param strict: optional parameter to enforce strict comparison or not. If True, column type must match in order to pass the test. :param compare_NA: optional parameter to compare NA or not. For csv file generated from orc file, the NAs are represented as some other symbol but our CSV will not be able to parse it correctly as NA. In this case, do not compare the number of NAs. :param custom_comparators: dictionary specifying custom comparators for some columns. :return: boolean: True, the two frames are equal and False otherwise. """ # check frame dimensions rows1, cols1 = frame1.dim rows2, cols2 = frame2.dim assert rows1 == rows2 and cols1 == cols2, "failed dim check! frame 1 rows:{0} frame 2 rows:{1} frame 1 cols:{2} " \ "frame2 cols:{3}".format(rows1, rows2, cols1, cols2) na_frame1 = frame1.isna().sum().sum(axis=1)[:,0] na_frame2 = frame2.isna().sum().sum(axis=1)[:,0] probVal = numElements/rows1 if numElements > 0 else 1 if compare_NA: # check number of missing values assert na_frame1.flatten() == na_frame2.flatten(), "failed numbers of NA check! Frame 1 NA number: {0}, frame 2 " \ "NA number: {1}".format(na_frame1.flatten(), na_frame2.flatten()) # check column types are the same before proceeding to check each row content. for col_ind in range(cols1): c1_key = frame1.columns[col_ind] c2_key = frame2.columns[col_ind] c2_type = frame2.types[c2_key] c1_type = frame1.types[c1_key] print("###### Comparing column: {0} and column type is {1}.".format(col_ind, c1_type)) if strict: # every column type must match assert c1_type == c2_type, "failed column type check! frame1 col type: {0}, frame2 col type: " \ "{1}".format(c1_type, c2_type) else: if str(c2_type) == 'enum': # orc files do not have enum column type. We convert it here frame1[col_ind].asfactor() if custom_comparators and c1_key in custom_comparators: custom_comparators[c1_key](frame1, frame2, col_ind, rows1, numElements) elif (str(c1_type) == 'string') or (str(c1_type) == 'enum'): # compare string compare_frames_local_onecolumn_NA_string(frame1[col_ind], frame2[col_ind], prob=probVal) else: if str(c2_type) == 'time': # compare time columns compare_frames_local_onecolumn_NA(frame1[col_ind], frame2[col_ind], prob=probVal, tol=tol_time) else: compare_frames_local_onecolumn_NA(frame1[col_ind], frame2[col_ind], prob=probVal, tol=tol_numeric) return True def catch_warnings(): import warnings warnings.simplefilter("always", RuntimeWarning) for v in sys.modules.values(): if getattr(v, '__warningregistry__', None): v.__warningregistry__ = {} return warnings.catch_warnings(record=True) def contains_warning(ws, message): return any(issubclass(w.category, RuntimeWarning) and message in str(w.message) for w in ws) def no_warnings(ws): return len(ws) == 0 def expect_warnings(filewithpath, warn_phrase="warn", warn_string_of_interest="warn", number_of_times=1, in_hdfs=False): """ This function will execute a command to run and analyze the print outs of running the command. The goal here is to capture any warnings that we may expect out of running those commands. :param filewithpath: name of file to be parsed with path :param warn_phrase: capture the warning header, sometimes it is warn or userwarn. :param warn_string_of_interest: specific warning message string :param number_of_times: number of warning lines we are expecting. :return: True if warning was found and False otherwise """ number_warngings = 0 buffer = StringIO() # redirect warning messages to string buffer for later analysis sys.stderr = buffer frame = None if in_hdfs: frame = h2o.import_file(filewithpath) else: frame = h2o.import_file(path=locate(filewithpath)) sys.stderr = sys.__stderr__ # redirect it back to stdout. try: # for python 2.7 if len(buffer.buflist) > 0: for index in range(len(buffer.buflist)): print("*** captured warning message: {0}".format(buffer.buflist[index])) if (warn_phrase in buffer.buflist[index]) and (warn_string_of_interest in buffer.buflist[index]): number_warngings = number_warngings+1 except: # for python 3. warns = buffer.getvalue() print("*** captured warning message: {0}".format(warns)) if (warn_phrase in warns) and (warn_string_of_interest in warns): number_warngings = number_warngings+1 print("Number of warnings found: {0} and number of times that warnings should appear {1}.".format(number_warngings, number_of_times)) if number_warngings >= number_of_times: return True else: return False def compare_frame_summary(frame1_summary, frame2_summary, compareNames=False, compareTypes=False): """ This method is written to compare the frame summary between two frames. :param frame1_summary: :param frame2_summary: :param compareNames: :param compareTypes: :return: """ frame1_column_number = len(frame1_summary) frame2_column_number = len(frame2_summary) assert frame1_column_number == frame2_column_number, "failed column number check! Frame 1 column number: {0}," \ "frame 2 column number: {1}".format(frame1_column_number, frame2_column_number) for col_index in range(frame1_column_number): # check summary for each column for key_val in list(frame1_summary[col_index]): if not(compareNames) and (str(key_val) == 'label'): continue if not(compareTypes) and (str(key_val) == 'type'): continue if str(key_val) == 'precision': # skip comparing precision continue val1 = frame1_summary[col_index][key_val] val2 = frame2_summary[col_index][key_val] if isinstance(val1, list) or isinstance(val1, dict): if isinstance(val1, dict): assert val1 == val2, "failed column summary comparison for column {0} and summary " \ "type {1}, frame 1 value is {2}, frame 2 value is " \ "{3}".format(col_index, str(key_val), val1, val2) else: if len(val1) > 0: # find if elements are float float_found = False for ind in range(len(val1)): if isinstance(val1[ind], float): float_found = True break if float_found: for ind in range(len(val1)): if not(str(val1[ind] == 'NaN')): assert abs(val1[ind]-val2[ind]) < 1e-5, "failed column summary comparison for " \ "column {0} and summary type {1}, frame 1" \ " value is {2}, frame 2 value is " \ "{3}".format(col_index, str(key_val), val1[ind], val2[ind]) else: assert val1 == val2, "failed column summary comparison for column {0} and summary" \ " type {1}, frame 1 value is {2}, frame 2 value is " \ "{3}".format(col_index, str(key_val), val1, val2) else: if isinstance(val1, float): assert abs(val1-val2) < 1e-5, "failed column summary comparison for column {0} and summary type " \ "{1}, frame 1 value is {2}, frame 2 value is " \ "{3}".format(col_index, str(key_val), val1, val2) else: assert val1 == val2, "failed column summary comparison for column {0} and summary type " \ "{1}, frame 1 value is {2}, frame 2 value is " \ "{3}".format(col_index, str(key_val), val1, val2) def cannaryHDFSTest(hdfs_name_node, file_name): """ This function is written to detect if the hive-exec version is too old. It will return True if it is too old and false otherwise. :param hdfs_name_node: :param file_name: :return: """ url_orc = "hdfs://{0}{1}".format(hdfs_name_node, file_name) try: tempFrame = h2o.import_file(url_orc) h2o.remove(tempFrame) print("Your hive-exec version is good. Parsing success for {0}.".format(url_orc)) return False except Exception as e: print("Error exception is {0}".format(str(e))) if "NoSuchFieldError: vector" in str(e): return True else: # exception is caused by other reasons. return False def extract_scoring_history_field(aModel, fieldOfInterest, takeFirst=False): """ Given a fieldOfInterest that are found in the model scoring history, this function will extract the list of field values for you from the model. :param aModel: H2O model where you want to extract a list of fields from the scoring history :param fieldOfInterest: string representing a field of interest. :return: List of field values or None if it cannot be found """ return extract_from_twoDimTable(aModel._model_json["output"]["scoring_history"], fieldOfInterest, takeFirst) def extract_from_twoDimTable(metricOfInterest, fieldOfInterest, takeFirst=False): """ Given a fieldOfInterest that are found in the model scoring history, this function will extract the list of field values for you from the model. :param aModel: H2O model where you want to extract a list of fields from the scoring history :param fieldOfInterest: string representing a field of interest. :return: List of field values or None if it cannot be found """ allFields = metricOfInterest._col_header return extract_field_from_twoDimTable(allFields, metricOfInterest.cell_values, fieldOfInterest, takeFirst=False) def extract_field_from_twoDimTable(allFields, cell_values, fieldOfInterest, takeFirst=False): if fieldOfInterest in allFields: cellValues = [] fieldIndex = allFields.index(fieldOfInterest) for eachCell in cell_values: cellValues.append(eachCell[fieldIndex]) if takeFirst: # only grab the result from the first iteration. break return cellValues else: return None def model_run_time_sorted_by_time(model_list): """ This function is written to sort the metrics that we care in the order of when the model was built. The oldest model metric will be the first element. :param model_list: list of models built sequentially that contains metric of interest among other fields :return: model run time in secs sorted by order of building """ model_num = len(model_list) model_runtime_sec_list = [None] * model_num for index in range(model_num): model_index = int(model_list[index]._id.split('_')[-1]) - 1 # model names start at 1 model_runtime_sec_list[model_index] = \ (model_list[index]._model_json["output"]["run_time"]/1000.0) return model_runtime_sec_list def model_seed_sorted(model_list): """ This function is written to find the seed used by each model in the order of when the model was built. The oldest model metric will be the first element. :param model_list: list of models built sequentially that contains metric of interest among other fields :return: model seed sorted by order of building """ model_num = len(model_list) model_seed_list = [None] * model_num for index in range(model_num): for pIndex in range(len(model_list.models[0]._model_json["parameters"])): if model_list.models[index]._model_json["parameters"][pIndex]["name"]=="seed": model_seed_list[index]=model_list.models[index]._model_json["parameters"][pIndex]["actual_value"] break model_seed_list.sort() return model_seed_list def check_ignore_cols_automl(models,names,x,y): models = sum(models.as_data_frame().values.tolist(),[]) for model in models: if "StackedEnsemble" in model: continue else: assert set(h2o.get_model(model).params["ignored_columns"]["actual"]) == set(names) - {y} - set(x), \ "ignored columns are not honored for model " + model # This method is not changed to local method using as_data_frame because the frame size is too big. def check_sorted_2_columns(frame1, sorted_column_indices, prob=0.5, ascending=[True, True]): for colInd in sorted_column_indices: for rowInd in range(0, frame1.nrow-1): if (random.uniform(0.0,1.0) < prob): if colInd == sorted_column_indices[0]: if not(math.isnan(frame1[rowInd, colInd])) and not(math.isnan(frame1[rowInd+1,colInd])): if ascending[colInd]: assert frame1[rowInd,colInd] <= frame1[rowInd+1,colInd], "Wrong sort order: value at row {0}: {1}, value at " \ "row {2}: {3}".format(rowInd, frame1[rowInd,colInd], rowInd+1, frame1[rowInd+1,colInd]) else: assert frame1[rowInd,colInd] >= frame1[rowInd+1,colInd], "Wrong sort order: value at row {0}: {1}, value at " \ "row {2}: {3}".format(rowInd, frame1[rowInd,colInd], rowInd+1, frame1[rowInd+1,colInd]) else: # for second column if not(math.isnan(frame1[rowInd, sorted_column_indices[0]])) and not(math.isnan(frame1[rowInd+1,sorted_column_indices[0]])): if (frame1[rowInd,sorted_column_indices[0]]==frame1[rowInd+1, sorted_column_indices[0]]): # meaningful to compare row entries then if not(math.isnan(frame1[rowInd, colInd])) and not(math.isnan(frame1[rowInd+1,colInd])): if ascending[colInd]: assert frame1[rowInd,colInd] <= frame1[rowInd+1,colInd], "Wrong sort order: value at row {0}: {1}, value at " \ "row {2}: {3}".format(rowInd, frame1[rowInd,colInd], rowInd+1, frame1[rowInd+1,colInd]) else: assert frame1[rowInd,colInd] >= frame1[rowInd+1,colInd], "Wrong sort order: value at row {0}: {1}, value at " \ "row {2}: {3}".format(rowInd, frame1[rowInd,colInd], rowInd+1, frame1[rowInd+1,colInd]) # This method is not changed to local method using as_data_frame because the frame size is too big. def check_sorted_1_column(frame1, sorted_column_index, prob=0.5, ascending=True): totRow = frame1.nrow * prob skipRow = int(frame1.nrow/totRow) for rowInd in range(0, frame1.nrow-1, skipRow): if not (math.isnan(frame1[rowInd, sorted_column_index])) and not ( math.isnan(frame1[rowInd + 1, sorted_column_index])): if ascending: assert frame1[rowInd, sorted_column_index] <= frame1[ rowInd + 1, sorted_column_index], "Wrong sort order: value at row {0}: {1}, value at " \ "row {2}: {3}".format(rowInd, frame1[rowInd, sorted_column_index], rowInd + 1, frame1[rowInd + 1, sorted_column_index]) else: assert frame1[rowInd, sorted_column_index] >= frame1[ rowInd + 1, sorted_column_index], "Wrong sort order: value at row {0}: {1}, value at " \ "row {2}: {3}".format(rowInd, frame1[rowInd, sorted_column_index], rowInd + 1, frame1[rowInd + 1, sorted_column_index]) def assert_correct_frame_operation(sourceFrame, h2oResultFrame, operString): """ This method checks each element of a numeric H2OFrame and throw an assert error if its value does not equal to the same operation carried out by python. :param sourceFrame: original H2OFrame. :param h2oResultFrame: H2OFrame after operation on original H2OFrame is carried out. :param operString: str representing one of 'abs', 'acos', 'acosh', 'asin', 'asinh', 'atan', 'atanh', 'ceil', 'cos', 'cosh', 'cospi', 'cumprod', 'cumsum', 'digamma', 'exp', 'expm1', 'floor', 'round', 'sin', 'sign', 'round', 'sinh', 'tan', 'tanh' :return: None. """ validStrings = ['acos', 'acosh', 'asin', 'asinh', 'atan', 'atanh', 'ceil', 'cos', 'cosh', 'exp', 'floor', 'gamma', 'lgamma', 'log', 'log10', 'sin', 'sinh', 'sqrt', 'tan', 'tanh', 'trigamma', 'expm1'] npValidStrings = ['log2', 'sign'] nativeStrings = ['round', 'abs', 'cumsum'] multpi = ['cospi', 'sinpi', 'tanpi'] others = ['log1p', 'signif', 'trigamma', 'digamma', 'cumprod'] # check for valid operString assert operString in validStrings+npValidStrings+nativeStrings+multpi+others, "Illegal operator " \ "{0} specified.".format(operString) result_comp = lambda x:x # default method if operString == "log1p": result_comp = lambda x:math.log(x+1) elif operString == 'signif': result_comp = lambda x:round(x, 7) elif operString == 'trigamma': result_comp = lambda x:scipy.special.polygamma(1, x) elif operString == 'digamma': result_comp = lambda x:scipy.special.polygamma(0, x) elif operString=='cumprod': result_comp = lambda x:factorial(x) # stringOperations = 'result_val = factorial(sourceFrame[row_ind, col_ind])' elif operString in validStrings: result_comp = lambda x:getattr(math, operString)(x) elif operString in nativeStrings: result_comp =lambda x:__builtins__.get(operString)(x) stringOperations = 'result_val = '+operString+'(sourceFrame[row_ind, col_ind])' elif operString in npValidStrings: result_comp = lambda x:getattr(np, operString)(x) # stringOperations = 'result_val = np.'+operString+'(sourceFrame[row_ind, col_ind])' elif operString in multpi: result_comp = lambda x:getattr(math, operString.split('p')[0])(x*math.pi) #stringOperations = 'result_val = math.'+operString.split('p')[0]+'(sourceFrame[row_ind, col_ind]*math.pi)' for col_ind in range(sourceFrame.ncols): for row_ind in range(sourceFrame.nrows): result_val = result_comp(sourceFrame[row_ind, col_ind]) assert abs(h2oResultFrame[row_ind, col_ind]-result_val) <= 1e-6, \ " command {0}({3}) is not working. Expected: {1}. Received: {2}".format(operString, result_val, h2oResultFrame[row_ind, col_ind], sourceFrame[row_ind, col_ind]) def factorial(n): """ Defined my own factorial just in case using python2.5 or less. :param n: :return: """ if n>0 and n<2: return 1 if n>=2: return n*factorial(n-1) def cumop(items, op, colInd=0): # take in one column only res = [None]*len(items) for index in range(len(items)): res[index] = op(res[index-1], items[index, colInd]) if index > 0 else items[index, colInd] return res def compare_string_frames_local(f1, f2, prob=0.5): temp1 = f1.as_data_frame(use_pandas=False) temp2 = f2.as_data_frame(use_pandas=False) cname1 = temp1[0] cname2 = temp2[0] assert (f1.nrow==f2.nrow) and (f1.ncol==f2.ncol), "The two frames are of different sizes." for colInd in range(f1.ncol): name1 = cname1[colInd] for rowInd in range(1, f2.nrow): if random.uniform(0,1) < prob: assert temp1[rowInd][colInd]==temp2[rowInd][cname2.index(name1)], "Failed frame values check at row {2} and column {3}! " \ "frame1 value: {0}, frame2 value: " \ "{1}".format(temp1[rowInd][colInd], temp2[rowInd][colInd], rowInd, colInd) def check_data_rows(f1, f2, index_list=[], num_rows=10): ''' This method will compare the relationships of the data rows within each frames. In particular, we are interested in the relative direction of each row vectors and the relative distances. No assertions will be thrown. :param f1: :param f2: :param index_list: :param num_rows: :return: ''' temp1 = f1.as_data_frame(use_pandas=True).as_matrix() temp2 = f2.as_data_frame(use_pandas=True).as_matrix() if len(index_list)==0: index_list = random.sample(range(f1.nrow), num_rows) maxInnerProduct = 0 maxDistance = 0 for row_index in range(1, len(index_list)): r1 = np.inner(temp1[index_list[row_index-1]], temp1[index_list[row_index]]) r2 = np.inner(temp2[index_list[row_index-1]], temp2[index_list[row_index]]) d1 = np.linalg.norm(temp1[index_list[row_index-1]]-temp1[index_list[row_index]]) d2 = np.linalg.norm(temp2[index_list[row_index-1]]-temp2[index_list[row_index]]) diff1 = min(abs(r1-r2), abs(r1-r2)/max(abs(r1), abs(r2))) maxInnerProduct = max(maxInnerProduct, diff1) diff2 = min(abs(d1-d2), abs(d1-d2)/max(abs(d1), abs(d2))) maxDistance = max(maxDistance, diff2) print("Maximum inner product different is {0}. Maximum distance difference is " "{1}".format(maxInnerProduct, maxDistance)) def compare_data_rows(f1, f2, index_list=[], num_rows=10, tol=1e-3): ''' This method will compare the relationships of the data rows within each frames. In particular, we are interested in the relative direction of each row vectors and the relative distances. An assertion will be thrown if they are different beyond a tolerance. :param f1: :param f2: :param index_list: :param num_rows: :return: ''' temp1 = f1.as_data_frame(use_pandas=True).as_matrix() temp2 = f2.as_data_frame(use_pandas=True).as_matrix() if len(index_list)==0: index_list = random.sample(range(f1.nrow), num_rows) maxInnerProduct = 0 maxDistance = 0 for row_index in range(1, len(index_list)): r1 = np.inner(temp1[index_list[row_index-1]], temp1[index_list[row_index]]) r2 = np.inner(temp2[index_list[row_index-1]], temp2[index_list[row_index]]) d1 = np.linalg.norm(temp1[index_list[row_index-1]]-temp1[index_list[row_index]]) d2 = np.linalg.norm(temp2[index_list[row_index-1]]-temp2[index_list[row_index]]) diff1 = min(abs(r1-r2), abs(r1-r2)/max(abs(r1), abs(r2))) maxInnerProduct = max(maxInnerProduct, diff1) diff2 = min(abs(d1-d2), abs(d1-d2)/max(abs(d1), abs(d2))) maxDistance = max(maxDistance, diff2) assert diff1 < tol, \ "relationship between data row {0} and data row {1} are different among the two dataframes. Inner " \ "product from frame 1 is {2}. Inner product from frame 2 is {3}. The difference between the two is" \ " {4}".format(index_list[row_index-1], index_list[row_index], r1, r2, diff1) assert diff2 < tol, \ "distance betwee data row {0} and data row {1} are different among the two dataframes. Distance " \ "between 2 rows from frame 1 is {2}. Distance between 2 rows from frame 2 is {3}. The difference" \ " between the two is {4}".format(index_list[row_index-1], index_list[row_index], d1, d2, diff2) print("Maximum inner product different is {0}. Maximum distance difference is " "{1}".format(maxInnerProduct, maxDistance)) def compute_frame_diff(f1, f2): ''' This method will take the absolute difference two frames and sum across all elements :param f1: :param f2: :return: ''' frameDiff = h2o.H2OFrame.sum(h2o.H2OFrame.sum(h2o.H2OFrame.abs(f1-f2)), axis=1)[0,0] return frameDiff def compare_frames_local(f1, f2, prob=0.5, tol=1e-6, returnResult=False): ''' Compare two h2o frames and make sure they are equal. However, we do not compare uuid column at this point :param f1: :param f2: :param prob: :param tol: :param returnResult: :return: ''' assert (f1.nrow==f2.nrow) and (f1.ncol==f2.ncol), "Frame 1 row {0}, col {1}. Frame 2 row {2}, col {3}. They are " \ "different.".format(f1.nrow, f1.ncol, f2.nrow, f2.ncol) typeDict = f1.types frameNames = f1.names for colInd in range(f1.ncol): if (typeDict[frameNames[colInd]]==u'enum'): if returnResult: result = compare_frames_local_onecolumn_NA_enum(f1[colInd], f2[colInd], prob=prob, tol=tol, returnResult=returnResult) if not(result) and returnResult: return False else: result = compare_frames_local_onecolumn_NA_enum(f1[colInd], f2[colInd], prob=prob, tol=tol, returnResult=returnResult) if not(result) and returnResult: return False elif (typeDict[frameNames[colInd]]==u'string'): if returnResult: result = compare_frames_local_onecolumn_NA_string(f1[colInd], f2[colInd], prob=prob, returnResult=returnResult) if not(result) and returnResult: return False else: compare_frames_local_onecolumn_NA_string(f1[colInd], f2[colInd], prob=prob, returnResult=returnResult) elif (typeDict[frameNames[colInd]]==u'uuid'): continue # do nothing here else: if returnResult: result = compare_frames_local_onecolumn_NA(f1[colInd], f2[colInd], prob=prob, tol=tol, returnResult=returnResult) if not(result) and returnResult: return False else: compare_frames_local_onecolumn_NA(f1[colInd], f2[colInd], prob=prob, tol=tol, returnResult=returnResult) if returnResult: return True def compare_frames_local_svm(f1, f2, prob=0.5, tol=1e-6, returnResult=False): ''' compare f1 and f2 but with f2 parsed from svmlight parser. Here, the na's should be replaced with 0.0 :param f1: normal h2oFrame :param f2: h2oFrame parsed from a svmlight parser. :param prob: :param tol: :param returnResult: :return: ''' assert (f1.nrow==f2.nrow) and (f1.ncol==f2.ncol), "The two frames are of different sizes." temp1 = f1.as_data_frame(use_pandas=False) temp2 = f2.as_data_frame(use_pandas=False) for rowInd in range(1, f1.nrow): for colInd in range(f1.ncol): if (len(temp1[rowInd][colInd]))==0: # encounter NAs if returnResult: if (abs(float(temp2[rowInd][colInd]))) > tol: return False assert (abs(float(temp2[rowInd][colInd]))) <= tol, \ "Expected: 0.0 but received: {0} for row: {1}, col: " \ "{2}".format(temp2[rowInd][colInd], rowInd, colInd) else: if returnResult: if abs(float(temp1[rowInd][colInd])-float(temp2[rowInd][colInd]))>tol: return False assert abs(float(temp1[rowInd][colInd])-float(temp2[rowInd][colInd]))<=tol, \ "Expected: {1} but received: {0} for row: {2}, col: " \ "{3}".format(temp2[rowInd][colInd], temp1[rowInd][colInd], rowInd, colInd) if returnResult: return True # frame compare with NAs in column def compare_frames_local_onecolumn_NA(f1, f2, prob=0.5, tol=1e-6, returnResult=False, oneLessRow=False): if (f1.types[f1.names[0]] == u'time'): # we have to divide by 1000 before converting back and forth between ms and time format tol = 10 temp1 = f1.as_data_frame(use_pandas=False) temp2 = f2.as_data_frame(use_pandas=False) assert (f1.nrow==f2.nrow) and (f1.ncol==f2.ncol), "The two frames are of different sizes." if oneLessRow: lastF2Row = f2.nrow else: lastF2Row = f2.nrow+1 for colInd in range(f1.ncol): for rowInd in range(1,lastF2Row): if (random.uniform(0,1) < prob): if len(temp1[rowInd]) == 0 or len(temp2[rowInd]) == 0: if returnResult: if not(len(temp1[rowInd]) == len(temp2[rowInd])): return False else: assert len(temp1[rowInd]) == len(temp2[rowInd]), "Failed frame values check at row {2} ! " \ "frame1 value: {0}, frame2 value: " \ "{1}".format(temp1[rowInd], temp2[rowInd], rowInd) else: v1 = float(temp1[rowInd][colInd]) v2 = float(temp2[rowInd][colInd]) diff = abs(v1-v2)/max(1.0, abs(v1), abs(v2)) if returnResult: if (diff > tol): return False else: assert diff<=tol, "Failed frame values check at row {2} and column {3}! frame1 value: {0}, column name: {4}. frame2 value: " \ "{1}, column name:{5}".format(temp1[rowInd][colInd], temp2[rowInd][colInd], rowInd, colInd, f1.names[0], f2.names[0]) if returnResult: return True # frame compare with NAs in column def compare_frames_local_onecolumn_NA_enum(f1, f2, prob=0.5, tol=1e-6, returnResult=False): temp1 = f1.as_data_frame(use_pandas=False) temp2 = f2.as_data_frame(use_pandas=False) assert (f1.nrow==f2.nrow) and (f1.ncol==f2.ncol), "The two frames are of different sizes." for colInd in range(f1.ncol): for rowInd in range(1,f2.nrow+1): if (random.uniform(0,1) < prob): if len(temp1[rowInd]) == 0 or len(temp2[rowInd]) == 0: if returnResult: if not(len(temp1[rowInd]) == len(temp2[rowInd])): return False else: assert len(temp1[rowInd]) == len(temp2[rowInd]), "Failed frame values check at row {2} ! " \ "frame1 value: {0}, frame2 value: " \ "{1}".format(temp1[rowInd], temp2[rowInd], rowInd) else: if returnResult: if not(temp1[rowInd][colInd]==temp2[rowInd][colInd]): return False else: assert temp1[rowInd][colInd]==temp2[rowInd][colInd], "Failed frame values check at row {2} and column {3}! frame1 value: {0}, column name: {4}. frame2 value: " \ "{1}, column name:{5}".format(temp1[rowInd][colInd], temp2[rowInd][colInd], rowInd, colInd, f1.names[0], f2.names[0]) if returnResult: return True # frame compare with NAs in column def compare_frames_local_onecolumn_NA_string(f1, f2, prob=0.5, returnResult=False): temp1 = f1.as_data_frame(use_pandas=False) temp2 = f2.as_data_frame(use_pandas=False) assert (f1.nrow==f2.nrow) and (f1.ncol==f2.ncol), "The two frames are of different sizes." for colInd in range(f1.ncol): for rowInd in range(1,f2.nrow+1): if (random.uniform(0,1) < prob): if len(temp1[rowInd]) == 0 or len(temp2[rowInd]) == 0: if returnResult: if not(len(temp1[rowInd]) == len(temp2[rowInd])): return False else: assert len(temp1[rowInd]) == len(temp2[rowInd]), "Failed frame values check at row {2} ! " \ "frame1 value: {0}, frame2 value: " \ "{1}".format(temp1[rowInd], temp2[rowInd], rowInd) else: if returnResult: if not(temp1[rowInd][colInd]==temp2[rowInd][colInd]): return False else: assert temp1[rowInd][colInd]==temp2[rowInd][colInd], "Failed frame values check at row {2} and column {3}! frame1 value: {0}, column name: {4}. frame2 value: " \ "{1}, column name:{5}".format(temp1[rowInd][colInd], temp2[rowInd][colInd], rowInd, colInd, f1.names[0], f2.names[0]) if returnResult: return True def build_save_model_generic(params, x, train, respName, algoName, tmpdir): if algoName.lower() == "gam": model = H2OGeneralizedAdditiveEstimator(**params) elif algoName.lower() == "glm": model = H2OGeneralizedLinearEstimator(**params) elif algoName.lower() == "gbm": model = H2OGradientBoostingEstimator(**params) elif algoName.lower() == "drf": model = H2ORandomForestEstimator(**params) else: raise Exception("build_save_model does not support algo "+algoName+". Please add this to build_save_model.") model.train(x=x, y=respName, training_frame=train) model.download_mojo(path=tmpdir) return model # generate random dataset, copied from Pasha def random_dataset(response_type, verbose=True, ncol_upper=25000, ncol_lower=15000, NTESTROWS=200, missing_fraction=0.0, seed=None): """Create and return a random dataset.""" if verbose: print("\nCreating a dataset for a %s problem:" % response_type) random.seed(seed) fractions = {k + "_fraction": random.random() for k in "real categorical integer time string binary".split()} fractions["string_fraction"] = 0 # Right now we are dropping string columns, so no point in having them. fractions["binary_fraction"] /= 3 fractions["time_fraction"] /= 2 sum_fractions = sum(fractions.values()) for k in fractions: fractions[k] /= sum_fractions if response_type == 'binomial': response_factors = 2 elif response_type == 'gaussian': response_factors = 1 else: response_factors = random.randint(3, 10) df = h2o.create_frame(rows=random.randint(ncol_lower, ncol_upper) + NTESTROWS, cols=random.randint(3, 20), missing_fraction=missing_fraction, has_response=True, response_factors=response_factors, positive_response=True, factors=10, seed=seed, **fractions) if verbose: print() df.show() return df # generate random dataset of ncolumns of Strings, copied from Pasha def random_dataset_strings_only(nrow, ncol, seed=None): """Create and return a random dataset.""" fractions = dict() fractions["real_fraction"] = 0 # Right now we are dropping string columns, so no point in having them. fractions["categorical_fraction"] = 0 fractions["integer_fraction"] = 0 fractions["time_fraction"] = 0 fractions["string_fraction"] = 1 # Right now we are dropping string columns, so no point in having them. fractions["binary_fraction"] = 0 return h2o.create_frame(rows=nrow, cols=ncol, missing_fraction=0, has_response=False, seed=seed, **fractions) def random_dataset_all_types(nrow, ncol, seed=None): fractions=dict() fractions['real_fraction']=0.16, fractions['categorical_fraction']=0.16, fractions['integer_fraction']=0.16, fractions['binary_fraction']=0.16, fractions['time_fraction']=0.16, fractions['string_fraction']=0.2 return h2o.create_frame(rows=nrow, cols=ncol, missing_fraction=0.1, has_response=False, seed=seed) # generate random dataset of ncolumns of enums only, copied from Pasha def random_dataset_enums_only(nrow, ncol, factorL=10, misFrac=0.01, randSeed=None): """Create and return a random dataset.""" fractions = dict() fractions["real_fraction"] = 0 # Right now we are dropping string columns, so no point in having them. fractions["categorical_fraction"] = 1 fractions["integer_fraction"] = 0 fractions["time_fraction"] = 0 fractions["string_fraction"] = 0 # Right now we are dropping string columns, so no point in having them. fractions["binary_fraction"] = 0 df = h2o.create_frame(rows=nrow, cols=ncol, missing_fraction=misFrac, has_response=False, factors=factorL, seed=randSeed, **fractions) return df # generate random dataset of ncolumns of enums only, copied from Pasha def random_dataset_int_only(nrow, ncol, rangeR=10, misFrac=0.01, randSeed=None): """Create and return a random dataset.""" fractions = dict() fractions["real_fraction"] = 0 # Right now we are dropping string columns, so no point in having them. fractions["categorical_fraction"] = 0 fractions["integer_fraction"] = 1 fractions["time_fraction"] = 0 fractions["string_fraction"] = 0 # Right now we are dropping string columns, so no point in having them. fractions["binary_fraction"] = 0 df = h2o.create_frame(rows=nrow, cols=ncol, missing_fraction=misFrac, has_response=False, integer_range=rangeR, seed=randSeed, **fractions) return df # generate random dataset of ncolumns of integer and reals, copied from Pasha def random_dataset_numeric_only(nrow, ncol, integerR=100, misFrac=0.01, randSeed=None): """Create and return a random dataset.""" fractions = dict() fractions["real_fraction"] = 0.25 # Right now we are dropping string columns, so no point in having them. fractions["categorical_fraction"] = 0 fractions["integer_fraction"] = 0.75 fractions["time_fraction"] = 0 fractions["string_fraction"] = 0 # Right now we are dropping string columns, so no point in having them. fractions["binary_fraction"] = 0 df = h2o.create_frame(rows=nrow, cols=ncol, missing_fraction=misFrac, has_response=False, integer_range=integerR, seed=randSeed, **fractions) return df # generate random dataset of ncolumns of integer and reals, copied from Pasha def random_dataset_real_only(nrow, ncol, realR=100, misFrac=0.01, randSeed=None): """Create and return a random dataset.""" fractions = dict() fractions["real_fraction"] = 1 # Right now we are dropping string columns, so no point in having them. fractions["categorical_fraction"] = 0 fractions["integer_fraction"] = 0 fractions["time_fraction"] = 0 fractions["string_fraction"] = 0 # Right now we are dropping string columns, so no point in having them. fractions["binary_fraction"] = 0 df = h2o.create_frame(rows=nrow, cols=ncol, missing_fraction=misFrac, has_response=False, integer_range=realR, seed=randSeed, **fractions) return df def getMojoName(modelID): regex = re.compile("[+\\-* !@#$%^&()={}\\[\\]|;:'\"<>,.?/]") return regex.sub("_", modelID) def convertH2OFrameToDMatrix(h2oFrame, yresp, enumCols=[]): """ This method will convert a H2OFrame containing to a DMatrix that is can be used by native XGBoost. The H2OFrame can contain numerical and enum columns. Note that H2O one-hot-encoding introduces a missing(NA) column. There can be NAs in any columns. :param h2oFrame: H2OFrame to be converted to DMatrix :param yresp: string denoting the response column name :param enumCols: list of enum column names in the H2OFrame :return: DMatrix """ import xgboost as xgb pandas = __convertH2OFrameToPandas__(h2oFrame, yresp, enumCols); return xgb.DMatrix(data=pandas[0], label=pandas[1]) def convertH2OFrameToDMatrixSparse(h2oFrame, yresp, enumCols=[]): """ This method will convert a H2OFrame containing to a DMatrix that is can be used by native XGBoost. The H2OFrame can contain numerical and enum columns. Note that H2O one-hot-encoding introduces a missing(NA) column. There can be NAs in any columns. :param h2oFrame: H2OFrame to be converted to DMatrix :param yresp: string denoting the response column name :param enumCols: list of enum column names in the H2OFrame :return: DMatrix """ import xgboost as xgb pandas = __convertH2OFrameToPandas__(h2oFrame, yresp, enumCols); return xgb.DMatrix(data=csr_matrix(pandas[0]), label=pandas[1]) def __convertH2OFrameToPandas__(h2oFrame, yresp, enumCols=[]): """ This method will convert a H2OFrame containing to a DMatrix that is can be used by native XGBoost. The H2OFrame can contain numerical and enum columns. Note that H2O one-hot-encoding introduces a missing(NA) column. There can be NAs in any columns. :param h2oFrame: H2OFrame to be converted to DMatrix :param yresp: string denoting the response column name :param enumCols: list of enum column names in the H2OFrame :return: DMatrix """ import xgboost as xgb pandaFtrain = h2oFrame.as_data_frame(use_pandas=True, header=True) nrows = h2oFrame.nrow if len(enumCols) > 0: # start with first enum column pandaTrainPart = generatePandaEnumCols(pandaFtrain, enumCols[0], nrows) pandaFtrain.drop([enumCols[0]], axis=1, inplace=True) for colInd in range(1, len(enumCols)): cname=enumCols[colInd] ctemp = generatePandaEnumCols(pandaFtrain, cname, nrows) pandaTrainPart=pd.concat([pandaTrainPart, ctemp], axis=1) pandaFtrain.drop([cname], axis=1, inplace=True) pandaFtrain = pd.concat([pandaTrainPart, pandaFtrain], axis=1) c0= h2oFrame[yresp].asnumeric().as_data_frame(use_pandas=True, header=True) pandaFtrain.drop([yresp], axis=1, inplace=True) pandaF = pd.concat([c0, pandaFtrain], axis=1) pandaF.rename(columns={c0.columns[0]:yresp}, inplace=True) newX = list(pandaFtrain.columns.values) data = pandaF.as_matrix(newX) label = pandaF.as_matrix([yresp]) return (data,label) def generatePandaEnumCols(pandaFtrain, cname, nrows): """ For a H2O Enum column, we perform one-hot-encoding here and added one more column "missing(NA)" to it. :param pandaFtrain: :param cname: :param nrows: :return: """ cmissingNames=[cname+".missing(NA)"] tempnp = np.zeros((nrows,1), dtype=np.int) # check for nan and assign it correct value colVals = pandaFtrain[cname] for ind in range(nrows): try: float(colVals[ind]) if math.isnan(colVals[ind]): tempnp[ind]=1 except ValueError: pass zeroFrame = pd.DataFrame(tempnp) zeroFrame.columns=cmissingNames temp = pd.get_dummies(pandaFtrain[cname], prefix=cname, drop_first=False) tempNames = list(temp) # get column names colLength = len(tempNames) newNames = ['a']*colLength newIndics = [0]*colLength if "." in tempNames[0]: header = tempNames[0].split('.')[0] for ind in range(colLength): newIndics[ind] = int(tempNames[ind].split('.')[1][1:]) newIndics.sort() for ind in range(colLength): newNames[ind] = header+'.l'+str(newIndics[ind]) # generate correct order of names ftemp = temp[newNames] else: ftemp = temp ctemp = pd.concat([ftemp, zeroFrame], axis=1) return ctemp def summarizeResult_binomial(h2oPredictD, nativePred, h2oTrainTimeD, nativeTrainTime, h2oPredictTimeD, nativeScoreTime, tolerance=1e-6): ''' This method will summarize and compare H2OXGBoost and native XGBoost results for binomial classifiers. This method will summarize and compare H2OXGBoost and native XGBoost results for binomial classifiers. :param h2oPredictD: :param nativePred: :param h2oTrainTimeD: :param nativeTrainTime: :param h2oPredictTimeD: :param nativeScoreTime: :return: ''' # Result comparison in terms of time print("H2OXGBoost train time is {0}s. Native XGBoost train time is {1}s.\n H2OXGBoost scoring time is {2}s." " Native XGBoost scoring time is {3}s.".format(h2oTrainTimeD/1000.0, nativeTrainTime, h2oPredictTimeD, nativeScoreTime)) # Result comparison in terms of actual prediction value between the two colnames = h2oPredictD.names h2oPredictD['predict'] = h2oPredictD['predict'].asnumeric() h2oPredictLocalD = h2oPredictD.as_data_frame(use_pandas=True, header=True) # compare prediction probability and they should agree if they use the same seed for ind in range(h2oPredictD.nrow): assert abs(h2oPredictLocalD[colnames[2]][ind]-nativePred[ind])<tolerance, "H2O prediction prob: {0} and native " \ "XGBoost prediction prob: {1}. They are " \ "very different.".format(h2oPredictLocalD[colnames[2]][ind], nativePred[ind]) def summarize_metrics_binomial(h2o_metrics, xgboost_metrics, names, tolerance=1e-4): for i in range(len(h2o_metrics)): difference = abs(h2o_metrics[i] - xgboost_metrics[i]) print("H2O {0} metric: {1} and native " \ "XGBoost {0} metric: {2}. " \ "Difference is {3}".format(names[i], h2o_metrics[i], xgboost_metrics[i], difference)) assert difference < tolerance, "H2O {0} metric: {1} and native " \ "XGBoost {0} metric: {2}. They are " \ "very different.".format(names[i], h2o_metrics[i], xgboost_metrics[i]) def summarizeResult_multinomial(h2oPredictD, nativePred, h2oTrainTimeD, nativeTrainTime, h2oPredictTimeD, nativeScoreTime, tolerance=1e-6): # Result comparison in terms of time print("H2OXGBoost train time is {0}s. Native XGBoost train time is {1}s.\n H2OGBoost scoring time is {2}s." " Native XGBoost scoring time is {3}s.".format(h2oTrainTimeD/1000.0, nativeTrainTime, h2oPredictTimeD, nativeScoreTime)) # Result comparison in terms of actual prediction value between the two h2oPredictD['predict'] = h2oPredictD['predict'].asnumeric() h2oPredictLocalD = h2oPredictD.as_data_frame(use_pandas=True, header=True) nclass = len(nativePred[0]) colnames = h2oPredictD.names # compare prediction probability and they should agree if they use the same seed for ind in range(h2oPredictD.nrow): for col in range(nclass): assert abs(h2oPredictLocalD[colnames[col+1]][ind]-nativePred[ind][col])<tolerance, \ "H2O prediction prob: {0} and native XGBoost prediction prob: {1}. They are very " \ "different.".format(h2oPredictLocalD[colnames[col+1]][ind], nativePred[ind][col]) def genTrainFrame(nrow, ncol, enumCols=0, enumFactors=2, responseLevel=2, miscfrac=0, randseed=None): if ncol>0: trainFrameNumerics = random_dataset_numeric_only(nrow, ncol, integerR = 1000000, misFrac=miscfrac, randSeed=randseed) if enumCols > 0: trainFrameEnums = random_dataset_enums_only(nrow, enumCols, factorL=enumFactors, misFrac=miscfrac, randSeed=randseed) yresponse = random_dataset_enums_only(nrow, 1, factorL=responseLevel, misFrac=0, randSeed=randseed) yresponse.set_name(0,'response') if enumCols > 0: if ncol > 0: # mixed datasets trainFrame = trainFrameEnums.cbind(trainFrameNumerics.cbind(yresponse)) else: # contains enum datasets trainFrame = trainFrameEnums.cbind(yresponse) else: # contains numerical datasets trainFrame = trainFrameNumerics.cbind(yresponse) return trainFrame def check_xgb_var_imp(h2o_train, h2o_model, xgb_train, xgb_model, tolerance=1e-6): column_map = dict(zip(h2o_train.names, xgb_train.feature_names)) h2o_var_imps = h2o_model.varimp() h2o_var_frequencies = h2o_model._model_json["output"]["variable_importances_frequency"].cell_values freq_map = dict(map(lambda t: (t[0], t[1]), h2o_var_frequencies)) # XGBoost reports average gain of a split xgb_var_imps = xgb_model.get_score(importance_type="gain") for h2o_var_imp in h2o_var_imps: frequency = freq_map[h2o_var_imp[0]] xgb_var_imp = xgb_var_imps[column_map[h2o_var_imp[0]]] abs_diff = abs(h2o_var_imp[1]/frequency - xgb_var_imp) norm = max(1, abs(h2o_var_imp[1]/frequency), abs(xgb_var_imp)) assert abs_diff/norm < tolerance, "Variable importance of feature {0} is different. H2O: {1}, XGB {2}"\ .format(h2o_var_imp[0], h2o_var_imp[1], xgb_var_imp) def summarizeResult_regression(h2oPredictD, nativePred, h2oTrainTimeD, nativeTrainTime, h2oPredictTimeD, nativeScoreTime, tolerance=1e-6): # Result comparison in terms of time print("H2OXGBoost train time is {0}ms. Native XGBoost train time is {1}s.\n H2OGBoost scoring time is {2}s." " Native XGBoost scoring time is {3}s.".format(h2oTrainTimeD, nativeTrainTime, h2oPredictTimeD, nativeScoreTime)) # Result comparison in terms of actual prediction value between the two h2oPredictD['predict'] = h2oPredictD['predict'].asnumeric() h2oPredictLocalD = h2oPredictD.as_data_frame(use_pandas=True, header=True) # compare prediction probability and they should agree if they use the same seed for ind in range(h2oPredictD.nrow): assert abs((h2oPredictLocalD['predict'][ind]-nativePred[ind])/max(1, abs(h2oPredictLocalD['predict'][ind]), abs(nativePred[ind])))<tolerance, \ "H2O prediction: {0} and native XGBoost prediction: {1}. They are very " \ "different.".format(h2oPredictLocalD['predict'][ind], nativePred[ind]) def summarizeResult_binomial_DS(h2oPredictD, nativePred, h2oTrainTimeD, nativeTrainTime, h2oPredictTimeD, nativeScoreTime, h2oPredictS, tolerance=1e-6): # Result comparison in terms of time print("H2OXGBoost train time with sparse DMatrix is {0}s. Native XGBoost train time with dense DMtraix is {1}s.\n H2OGBoost scoring time is {2}s." " Native XGBoost scoring time with dense DMatrix is {3}s.".format(h2oTrainTimeD/1000.0, nativeTrainTime, h2oPredictTimeD, nativeScoreTime)) # Result comparison in terms of actual prediction value between the two h2oPredictD['predict'] = h2oPredictD['predict'].asnumeric() h2oPredictLocalD = h2oPredictD.as_data_frame(use_pandas=True, header=True) h2oPredictS['predict'] = h2oPredictS['predict'].asnumeric() h2oPredictLocalS = h2oPredictS.as_data_frame(use_pandas=True, header=True) # compare prediction probability and they should agree if they use the same seed for ind in range(h2oPredictD.nrow): assert abs(h2oPredictLocalD['c0.l1'][ind]-nativePred[ind])<tolerance or \ abs(h2oPredictLocalS['c0.l1'][ind]-nativePred[ind])<tolerance, \ "H2O prediction prob: {0} and native XGBoost prediction prob: {1}. They are very " \ "different.".format(h2oPredictLocalD['c0.l1'][ind], nativePred[ind]) def compare_weightedStats(model, dataframe, xlist, xname, weightV, pdpTDTable, tol=1e-6): ''' This method is used to test the partial dependency plots and is not meant for any other functions. :param model: :param dataframe: :param xlist: :param xname: :param weightV: :param pdpTDTable: :param tol: :return: ''' weightStat = manual_partial_dependence(model, dataframe, xlist, xname, weightV) # calculate theoretical weighted sts wMean = extract_col_value_H2OTwoDimTable(pdpTDTable, "mean_response") # stats for age predictor wStd = extract_col_value_H2OTwoDimTable(pdpTDTable, "stddev_response") wStdErr = extract_col_value_H2OTwoDimTable(pdpTDTable, "std_error_mean_response") equal_two_arrays(weightStat[0], wMean, tol, tol, throw_error=True) equal_two_arrays(weightStat[1], wStd, tol, tol, throw_error=True) equal_two_arrays(weightStat[2], wStdErr, tol, tol, throw_error=True) def manual_partial_dependence(model, dataframe, xlist, xname, weightV): meanV = [] stdV = [] stderrV = [] nRows = dataframe.nrow nCols = dataframe.ncol-1 for xval in xlist: cons = [xval]*nRows if xname in dataframe.names: dataframe=dataframe.drop(xname) if not((is_type(xval, str) and xval=='NA') or (isinstance(xval, float) and math.isnan(xval))): dataframe = dataframe.cbind(h2o.H2OFrame(cons)) dataframe.set_name(nCols, xname) pred = model.predict(dataframe).as_data_frame(use_pandas=False, header=False) pIndex = len(pred[0])-1 sumEle = 0.0 sumEleSq = 0.0 sumWeight = 0.0 numNonZeroWeightCount = 0.0 m = 1.0/math.sqrt(dataframe.nrow*1.0) for rindex in range(len(pred)): val = float(pred[rindex][pIndex]); weight = float(weightV[rindex][0]) if (abs(weight) > 0) and isinstance(val, float) and not(math.isnan(val)): temp = val*weight sumEle = sumEle+temp sumEleSq = sumEleSq+temp*val sumWeight = sumWeight+weight numNonZeroWeightCount = numNonZeroWeightCount+1 wMean = sumEle/sumWeight scale = numNonZeroWeightCount*1.0/(numNonZeroWeightCount-1) wSTD = math.sqrt((sumEleSq/sumWeight-wMean*wMean)*scale) meanV.append(wMean) stdV.append(wSTD) stderrV.append(wSTD*m) return meanV, stdV, stderrV def compare_frames_equal_names(frame1, frame2): ''' This method will compare two frames with same column names and column types. The current accepted column types are enum, int and string. :param frame1: :param frame2: :return: ''' cnames = frame1.names ctypes = frame1.types for cind in range(0, frame1.ncol): name1 = cnames[cind] type = str(ctypes[name1]) if (type=="enum"): compare_frames_local_onecolumn_NA_enum(frame1[name1], frame2[name1], prob=1, tol=0) elif (type=='string'): compare_frames_local_onecolumn_NA_string(frame1[name1], frame2[name1], prob=1) else: compare_frames_local_onecolumn_NA(frame1[name1], frame2[name1], prob=1, tol=1e-10) def write_H2OFrame_2_SVMLight(filename, h2oFrame): ''' The function will write a h2oFrame into svmlight format and save it to a file. However, it only supports column types of real/integer and nothing else :param filename: :param h2oFrame: :return: ''' fwriteFile = open(filename, 'w') ncol = h2oFrame.ncol nrow = h2oFrame.nrow fdataframe = h2oFrame.as_data_frame(use_pandas=False) for rowindex in range(1, nrow+1): if len(fdataframe[rowindex][0])==0: # special treatment for response column writeWords = "" # convert na response to 0.0 else: writeWords = fdataframe[rowindex][0] for colindex in range(1, ncol): if not(len(fdataframe[rowindex][colindex])==0): writeWords = writeWords + " "+str(colindex) + ":"+fdataframe[rowindex][colindex] fwriteFile.write(writeWords) fwriteFile.write('\n') fwriteFile.close() def write_H2OFrame_2_ARFF(filenameWithPath, filename, h2oFrame, uuidVecs, uuidNames): ''' This function will write a H2OFrame into arff format and save it to a text file in ARFF format. :param filename: :param h2oFrame: :return: ''' fwriteFile = open(filenameWithPath, 'w') nrow = h2oFrame.nrow # write the arff headers here writeWords = "@RELATION "+filename+'\n\n' fwriteFile.write(writeWords) typesDict = h2oFrame.types colnames = h2oFrame.names uuidtypes = len(uuidNames)*["UUID"] for cname in colnames: writeWords = "@ATTRIBUTE "+cname if typesDict[cname]==u'int': writeWords = writeWords + " integer" elif typesDict[cname]==u'time': writeWords = writeWords + " date" else: writeWords = writeWords + " "+typesDict[cname] fwriteFile.write(writeWords) fwriteFile.write('\n') for cindex in range(len(uuidNames)): writeWords = "@ATTRIBUTE " +uuidNames[cindex]+" uuid" fwriteFile.write(writeWords) fwriteFile.write('\n') fwriteFile.write("\n@DATA\n") # write the arff body as csv fdataframe = h2oFrame.as_data_frame(use_pandas=False) for rowindex in range(1,nrow+1): writeWords = "" for cindex in range(h2oFrame.ncol): if len(fdataframe[rowindex][cindex])>0: if typesDict[colnames[cindex]]==u'time': writeWords = writeWords+\ str(datetime.datetime.fromtimestamp(float(fdataframe[rowindex][cindex])/1000.0))+"," elif typesDict[colnames[cindex]] in [u'enum', u'string']: writeWords=writeWords+fdataframe[rowindex][cindex]+"," else: writeWords=writeWords+fdataframe[rowindex][cindex]+"," else: writeWords = writeWords + "," # process the uuid ones for cindex in range(len(uuidVecs)-1): writeWords=writeWords+str(uuidVecs[cindex][rowindex-1])+"," writeWords=writeWords+str(uuidVecs[-1][rowindex-1])+'\n' fwriteFile.write(writeWords) fwriteFile.close() def checkCorrectSkips(originalFullFrame, csvfile, skipped_columns): skippedFrameUF = h2o.upload_file(csvfile, skipped_columns=skipped_columns) skippedFrameIF = h2o.import_file(csvfile, skipped_columns=skipped_columns) # this two frames should be the same compare_frames_local(skippedFrameUF, skippedFrameIF, prob=0.5) skipCounter = 0 typeDict = originalFullFrame.types frameNames = originalFullFrame.names for cindex in range(len(frameNames)): if cindex not in skipped_columns: print("Checking column {0}...".format(cindex)) if typeDict[frameNames[cindex]] == u'enum': compare_frames_local_onecolumn_NA_enum(originalFullFrame[cindex], skippedFrameIF[skipCounter], prob=1, tol=1e-10, returnResult=False) elif typeDict[frameNames[cindex]] == u'string': compare_frames_local_onecolumn_NA_string(originalFullFrame[cindex], skippedFrameIF[skipCounter], prob=1, returnResult=False) else: compare_frames_local_onecolumn_NA(originalFullFrame[cindex], skippedFrameIF[skipCounter], prob=1, tol=1e-10, returnResult=False) skipCounter = skipCounter + 1 def checkCorrectSkipsFolder(originalFullFrame, csvfile, skipped_columns): skippedFrameIF = h2o.import_file(csvfile, skipped_columns=skipped_columns) # this two frames should be the same skipCounter = 0 typeDict = originalFullFrame.types frameNames = originalFullFrame.names for cindex in range(len(frameNames)): if cindex not in skipped_columns: print("Checking column {0}...".format(cindex)) if typeDict[frameNames[cindex]] == u'enum': compare_frames_local_onecolumn_NA_enum(originalFullFrame[cindex], skippedFrameIF[skipCounter], prob=1, tol=1e-10, returnResult=False) elif typeDict[frameNames[cindex]] == u'string': compare_frames_local_onecolumn_NA_string(originalFullFrame[cindex], skippedFrameIF[skipCounter], prob=1, returnResult=False) else: compare_frames_local_onecolumn_NA(originalFullFrame[cindex], skippedFrameIF[skipCounter], prob=1, tol=1e-10, returnResult=False) skipCounter = skipCounter + 1 def assertModelColNamesTypesCorrect(modelNames, modelTypes, frameNames, frameTypesDict): fName = list(frameNames) mName = list(modelNames) assert fName.sort() == mName.sort(), "Expected column names {0}, actual column names {1} and they" \ " are different".format(frameNames, modelNames) for ind in range(len(frameNames)): if modelTypes[modelNames.index(frameNames[ind])].lower()=="numeric": assert (frameTypesDict[frameNames[ind]].lower()=='real') or \ (frameTypesDict[frameNames[ind]].lower()=='int'), \ "Expected training data types for column {0} is {1}. Actual training data types for column {2} from " \ "model output is {3}".format(frameNames[ind], frameTypesDict[frameNames[ind]], frameNames[ind], modelTypes[modelNames.index(frameNames[ind])]) else: assert modelTypes[modelNames.index(frameNames[ind])].lower()==frameTypesDict[frameNames[ind]].lower(), \ "Expected training data types for column {0} is {1}. Actual training data types for column {2} from " \ "model output is {3}".format(frameNames[ind], frameTypesDict[frameNames[ind]], frameNames[ind], modelTypes[modelNames.index(frameNames[ind])]) def saveModelMojo(model): ''' Given a H2O model, this function will save it in a directory off the results directory. In addition, it will return the absolute path of where the mojo file is. :param model: :return: ''' # save model regex = re.compile("[+\\-* !@#$%^&()={}\\[\\]|;:'\"<>,.?/]") MOJONAME = regex.sub("_", model._id) print("Downloading Java prediction model code from H2O") tmpdir = os.path.normpath(os.path.join(os.path.dirname(os.path.realpath('__file__')), "..", "results", MOJONAME)) os.makedirs(tmpdir) model.download_mojo(path=tmpdir) # save mojo return tmpdir # This file will contain functions used by GLM test only. def assertEqualRegPaths(keys, pathList, index, onePath, tol=1e-6): for oneKey in keys: if (pathList[oneKey] != None): assert abs(pathList[oneKey][index]-onePath[oneKey][0]) < tol, \ "Expected value: {0}, Actual: {1}".format(pathList[oneKey][index], onePath[oneKey][0]) def assertEqualCoeffDicts(coef1Dict, coef2Dict, tol = 1e-6): assert len(coef1Dict) == len(coef2Dict), "Length of first coefficient dict: {0}, length of second coefficient " \ "dict: {1} and they are different.".format(len(coef1Dict, len(coef2Dict))) for key in coef1Dict: val1 = coef1Dict[key] val2 = coef2Dict[key] if (math.isnan(val1)): assert math.isnan(val2), "Coefficient for {0} from first dict: {1}, from second dict: {2} are different." \ "".format(key, coef1Dict[key], coef2Dict[key]) elif (math.isinf(val1)): assert math.isinf(val2), "Coefficient for {0} from first dict: {1}, from second dict: {2} are different." \ "".format(key, coef1Dict[key], coef2Dict[key]) else: assert abs(coef1Dict[key] - coef2Dict[key]) < tol, "Coefficient for {0} from first dict: {1}, from second" \ " dict: {2} and they are different.".format(key, coef1Dict[ key], coef2Dict[ key]) def assertEqualModelMetrics(metrics1, metrics2, tol = 1e-6, keySet=["MSE", "AUC", "Gini", "null_deviance", "logloss", "RMSE", "pr_auc", "r2"]): # 1. Check model types model1_type = metrics1.__class__.__name__ model2_type = metrics2.__class__.__name__ assert model1_type is model2_type, "The model types differ. The first model metric is of type {0} and the second " \ "model metric is of type {1}.".format(model1_type, model2_type) metricDict1 = metrics1._metric_json metricDict2 = metrics2._metric_json for key in keySet: if key in metricDict1.keys() and (isinstance(metricDict1[key], float)): # only compare floating point metrics assert abs(metricDict1[key]-metricDict2[key])/max(1,max(metricDict1[key],metricDict2[key])) < tol, \ "ModelMetric {0} from model 1, {1} from model 2 are different.".format(metricDict1[key],metricDict2[key]) # When an array of alpha and/or lambdas are given, a list of submodels are also built. For each submodel built, only # the coefficients, lambda/alpha/deviance values are returned. The model metrics is calculated from the submodel # with the best deviance. # # In this test, in addition, we build separate models using just one lambda and one alpha values as when building one # submodel. In theory, the coefficients obtained from the separate models should equal to the submodels. We check # and compare the followings: # 1. coefficients from submodels and individual model should match when they are using the same alpha/lambda value; # 2. training metrics from alpha array should equal to the individual model matching the alpha/lambda value; def compareSubmodelsNindividualModels(modelWithArray, trainingData, xarray, yindex): best_submodel_index = modelWithArray._model_json["output"]["best_submodel_index"] r = H2OGeneralizedLinearEstimator.getGLMRegularizationPath(modelWithArray) # contains all lambda/alpha values of submodels trained. submodel_num = len(r["lambdas"]) regKeys = ["alphas", "lambdas", "explained_deviance_valid", "explained_deviance_train"] for submodIndx in range(submodel_num): # manually build glm model and compare to those built before modelGLM = H2OGeneralizedLinearEstimator(family='binomial', alpha=[r["alphas"][submodIndx]], Lambda=[r["lambdas"][submodIndx]]) modelGLM.train(training_frame=trainingData, x=xarray, y=yindex) # check coefficients between submodels and model trained with same parameters assertEqualCoeffDicts(r["coefficients"][submodIndx], modelGLM.coef()) modelGLMr = H2OGeneralizedLinearEstimator.getGLMRegularizationPath(modelGLM) # contains one item only assertEqualRegPaths(regKeys, r, submodIndx, modelGLMr) if (best_submodel_index == submodIndx): # check training metrics of modelGLM should equal that of m since it is the best subModel assertEqualModelMetrics(modelWithArray._model_json["output"]["training_metrics"], modelGLM._model_json["output"]["training_metrics"]) assertEqualCoeffDicts(modelWithArray.coef(), modelGLM.coef()) # model coefficient should come from best submodel else: # check and make sure best_submodel_index has lowest deviance assert modelGLM.residual_deviance() - modelWithArray.residual_deviance() >= 0, \ "Individual model has better residual_deviance than best submodel!" def extractNextCoeff(cs_norm, orderedCoeffNames, startVal): for ind in range(0, len(startVal)): startVal[ind] = cs_norm[orderedCoeffNames[ind]] return startVal def assertEqualScoringHistoryIteration(model_long, model_short, col_list_compare, tolerance=1e-6): scoring_history_long = model_long._model_json["output"]["scoring_history"] scoring_history_short = model_short._model_json["output"]["scoring_history"] cv_4th_len = len(scoring_history_short.cell_values) - 1 # ignore last iteration, scoring is performed at different spots cv_len = len(scoring_history_long.cell_values) col_2D = scoring_history_short.col_header iterInd = col_2D.index('iterations') count = 0 for index in range(cv_4th_len): iterInd4th = scoring_history_short.cell_values[index][iterInd] iterIndlong = scoring_history_long.cell_values[count][iterInd] while not(iterInd4th == None) and (iterInd4th > iterIndlong): count = count+1 if count >= cv_len: break iterIndlong = scoring_history_long.cell_values[count][iterInd] if not(iterInd4th == None) and not(iterInd4th == '') and (iterInd4th == iterIndlong): for col_header in col_list_compare: ind = col_2D.index(col_header) val_short = scoring_history_short.cell_values[index][ind] val_long = scoring_history_long.cell_values[count][ind] if not(val_short == '' or math.isnan(val_short) or val_long == '' or math.isnan(val_long)): assert abs(scoring_history_short.cell_values[index][ind]- scoring_history_long.cell_values[count][ind]) < tolerance, \ "{0} expected: {1}, actual: {2}".format(col_header, scoring_history_short.cell_values[index][ind], scoring_history_long.cell_values[count][ind]) count = count+1 def assertCoefEqual(regCoeff, coeff, coeffClassSet, tol=1e-6): for key in regCoeff: temp = key.split('_') classInd = int(temp[1]) val1 = regCoeff[key] val2 = coeff[coeffClassSet[classInd]][temp[0]] assert type(val1)==type(val2), "type of coeff1: {0}, type of coeff2: {1}".format(type(val1), type(val2)) diff = abs(val1-val2) print("val1: {0}, val2: {1}, tol: {2}".format(val1, val2, tol)) assert diff < tol, "diff {0} exceeds tolerance {1}.".format(diff, tol) def assertCoefDictEqual(regCoeff, coeff, tol=1e-6): for key in regCoeff: val1 = regCoeff[key] val2 = coeff[key] assert type(val1)==type(val2), "type of coeff1: {0}, type of coeff2: {1}".format(type(val1), type(val2)) diff = abs(val1-val2) assert diff < tol, "diff {0} exceeds tolerance {1}.".format(diff, tol) def assert_equals(expected, actual, message=""): assert expected == actual, ("{0}\nexpected:{1}\nactual\t:{2}".format(message, expected, actual))

51.900986

194

0.659487

fd2736302d493bfef3216488385e0129dc44b446

3,669

py

Python

kuwala/pipelines/google-poi/src/pipeline/search_string_generator.py

arifluthfi16/kuwala

12fda181195acd06d369e05a976e7c9b917af0fd

[ "Apache-2.0" ]

null

kuwala/pipelines/google-poi/src/pipeline/search_string_generator.py

arifluthfi16/kuwala

12fda181195acd06d369e05a976e7c9b917af0fd

[ "Apache-2.0" ]

4

2021-11-30T15:44:58.000Z

2021-12-20T10:40:46.000Z

kuwala/pipelines/google-poi/src/pipeline/search_string_generator.py

arifluthfi16/kuwala

12fda181195acd06d369e05a976e7c9b917af0fd

[ "Apache-2.0" ]

1

2022-02-09T09:18:45.000Z

import json import moment import os from geojson import Polygon from python_utils.src.h3_utils import polyfill_polygon from pyspark.sql import SparkSession from pyspark.sql.functions import array, array_contains, col, concat_ws, lit, udf from pyspark.sql.types import StringType from python_utils.src.spark_udfs import h3_to_parent def generate_search_strings(continent, country, country_region, polygon_coords=None, polygon_res=None, limit=None): memory = os.getenv('SPARK_MEMORY') or '16g' spark = SparkSession.builder.appName('google-poi').config('spark.driver.memory', memory).getOrCreate() script_dir = os.path.dirname(__file__) file_path = os.path.join(script_dir, f'../../../../tmp/kuwala/osm_files/{continent}/{country}' f'{f"/{country_region}" if country_region else ""}/parquet/kuwala.parquet') df = spark.read.parquet(file_path) if polygon_coords: polygon_coords = json.loads(polygon_coords) polygon = Polygon(polygon_coords) polygon_resolution = 9 if polygon_res: polygon_resolution = int(polygon_res) polygon_cells = polyfill_polygon(polygon, resolution=polygon_resolution) df = df.withColumn('h3_polygon', h3_to_parent(col('h3_index'), lit(polygon_resolution))) df = df.filter(df.h3_polygon.isin(polygon_cells)) df = df \ .filter( df.address_street.isNotNull() | df.address_house_nr.isNotNull() | df.address_zip_code.isNotNull() | df.address_city.isNotNull() | df.address_full.isNotNull() ) \ .select('osm_id', 'osm_type', 'name', 'h3_index', 'address_street', 'address_house_nr', 'address_zip_code', 'address_city', 'address_full', 'categories') @udf(returnType=StringType()) def concat_search_strings(strings): strings = list(filter(lambda s: s, strings)) return ', '.join(strings) with_public_transport = df \ .filter(array_contains('categories', 'public_transportation')) \ .withColumn('station', concat_ws(' ', col('name'), lit('station'))) \ .withColumn( 'query', concat_search_strings(array(col('station'), concat_ws(' ', col('address_street'), col('address_house_nr')), col('address_zip_code'), col('address_city'))) ) \ .select('osm_id', 'osm_type', 'h3_index', 'name', 'query') with_address = df \ .filter(~array_contains('categories', 'public_transportation') & col('address_full').isNull()) \ .withColumn( 'query', concat_search_strings(array(col('name'), concat_ws(' ', col('address_street'), col('address_house_nr')), col('address_zip_code'), col('address_city'))) ) \ .select('osm_id', 'osm_type', 'h3_index', 'name', 'query') with_address_full = df \ .filter(~array_contains('categories', 'public_transportation') & col('address_full').isNotNull()) \ .withColumn('query', concat_search_strings(array(col('name'), col('address_full')))) \ .select('osm_id', 'osm_type', 'h3_index', 'name', 'query') union = with_public_transport.union(with_address).union(with_address_full) if limit is not None: union = union.limit(limit) union.write.parquet(f'../../../../tmp/kuwala/google_files/{continent}/{country}' f'{f"/{country_region}" if country_region else ""}/search_strings/osm_search_strings_' f'{moment.now().format("YYYY-MM-DDTHH-mm-ss")}.parquet')

45.8625

119

0.634233

fe4ae19dea405007a8472683506ce9e7c15c791a

5,764

py

Python

plugins/teedoc-plugin-jupyter-notebook-parser/teedoc_plugin_jupyter_notebook_parser/__init__.py

tyhdefu/teedoc

4a4b4f29e4db73a1d03365ce68b7594917f13fc6

[ "MIT" ]

null

plugins/teedoc-plugin-jupyter-notebook-parser/teedoc_plugin_jupyter_notebook_parser/__init__.py

tyhdefu/teedoc

4a4b4f29e4db73a1d03365ce68b7594917f13fc6

[ "MIT" ]

null

plugins/teedoc-plugin-jupyter-notebook-parser/teedoc_plugin_jupyter_notebook_parser/__init__.py

tyhdefu/teedoc

4a4b4f29e4db73a1d03365ce68b7594917f13fc6

[ "MIT" ]

null

import os, sys import re from collections import OrderedDict from datetime import datetime try: curr_path = os.path.dirname(os.path.abspath(__file__)) teedoc_project_path = os.path.abspath(os.path.join(curr_path, "..", "..", "..")) if os.path.basename(teedoc_project_path) == "teedoc": sys.path.insert(0, teedoc_project_path) except Exception: pass from teedoc import Plugin_Base from teedoc import Fake_Logger try: from .jupyter_convert import convert_ipynb_to_html except Exception: from jupyter_convert import convert_ipynb_to_html __version__ = "1.2.1" class Plugin(Plugin_Base): name = "teedoc-plugin-jupyter-notebook-parser" desc = "jupyter notebook parser plugin for teedoc" defautl_config = { "parse_files": ["ipynb"] } def on_init(self, config, doc_src_path, site_config, logger = None, multiprocess = True, **kw_args): ''' @config a dict object @logger teedoc.logger.Logger object ''' self.logger = Fake_Logger() if not logger else logger self.doc_src_path = doc_src_path self.site_config = site_config self.config = Plugin.defautl_config self.config.update(config) self.logger.i("-- plugin <{}> init".format(self.name)) self.logger.i("-- plugin <{}> config: {}".format(self.name, self.config)) def on_parse_files(self, files): # result, format must be this result = { "ok": False, "msg": "", "htmls": OrderedDict() } # function parse md file is disabled if not "ipynb" in self.config["parse_files"]: result["msg"] = "disabled notebook parse, but only support notebook" return result self.logger.d("-- plugin <{}> parse {} files".format(self.name, len(files))) # self.logger.d("files: {}".format(files)) for file in files: name = os.path.basename(file) # ignore temp file if name.startswith(".~"): result["htmls"][file] = None continue ext = os.path.splitext(file)[1].lower() if ext.endswith("ipynb"): html = convert_ipynb_to_html(file) html.body = self._update_link_html(html.body) metadata = html.metadata date = None ts = int(os.stat(file).st_mtime) if "date" in metadata: date = metadata["date"].strip().lower() # set date to false to disable date display if date and (date == "false" or date == "none"): date = "" else: GMT_FORMAT = '%Y-%m-%d' try: date_obj = datetime.strptime(date, GMT_FORMAT) ts = int(date_obj.timestamp()) except Exception as e: pass if "author" in metadata: author = metadata["author"] else: author = "" result["htmls"][file] = { "title": html.title, "desc": html.desc, "keywords": html.keywords, "tags": html.tags, "body": html.body, "author": author, "date": date, "ts": ts, "toc": html.toc, "metadata": metadata, "raw": html.raw } else: result["htmls"][file] = None result['ok'] = True return result def on_parse_pages(self, files): result = self.on_parse_files(files) return result def on_add_html_header_items(self, type_name): items = [] items.append('<meta name="html-generator" content="teedoc-plugin-jupyter-notebook-parser">') return items def _update_link_html(self, content): def re_del(c): ret = c[0] links = re.findall('href="(.*?)"', c[0]) if len(links) > 0: for link in links: if link.startswith(".") or os.path.isabs(link): ret = re.sub("README.md", "index.html", c[0], flags=re.I) ret = re.sub(r".md", ".html", ret, re.I) return ret return ret def re_del_ipynb(c): ret = c[0] links = re.findall('href="(.*?)"', c[0]) if len(links) > 0: for link in links: if link.startswith(".") or os.path.isabs(link): ret = re.sub("README.ipynb", "index.html", c[0], flags=re.I) ret = re.sub(r".ipynb", ".html", ret, re.I) return ret return ret # <a class="anchor-link" href="#链接"> </a></h2><p><a href="./syntax_markdown.md">markdown 语法</a> content = re.sub(r'\<a.*?href=.*?\.md.*?\</a\>', re_del, content, flags=re.I) content = re.sub(r'\<a.*?href=.*?\.ipynb.*?\</a\>', re_del_ipynb, content, flags=re.I) return content if __name__ == "__main__": config = { } plug = Plugin(config, "./", {}) res = plug.on_parse_files(["tests/test.ipynb"]) if not os.path.exists("out"): os.makedirs("out") for file, html in res["htmls"].items(): if html: file = "{}.html".format(os.path.splitext(os.path.basename(file))[0]) with open(os.path.join("out", file), "w") as f: f.write(html['body'])

37.921053

117

0.501214

50d1a83ac87a93488d61e1129daca6a4c2455731

115

py

Python

mmdet/version.py

nguyenquangduc2000/SKU110K-DenseDet

ba2027cb654d77867a65204a3ca83acefc7615ee

[ "Apache-2.0" ]

78

2020-07-17T09:56:12.000Z

2022-03-09T06:22:50.000Z

mmdet/version.py

nguyenquangduc2000/SKU110K-DenseDet

ba2027cb654d77867a65204a3ca83acefc7615ee

[ "Apache-2.0" ]

12

2020-10-21T13:05:46.000Z

2022-03-24T09:33:52.000Z

mmdet/version.py

nguyenquangduc2000/SKU110K-DenseDet

ba2027cb654d77867a65204a3ca83acefc7615ee

[ "Apache-2.0" ]

21

2020-08-16T13:21:42.000Z

2022-03-22T12:16:41.000Z

# GENERATED VERSION FILE # TIME: Fri Jul 3 10:58:01 2020 __version__ = '1.0rc1+fb983fe' short_version = '1.0rc1'

19.166667

32

0.713043

4e45ccf3ca2fdfaaa47b528d37bf71a67225c7ce

11,317

py

Python

task/grovertrainer.py

adamoyoung/grover

393afbd69482ed1fca3056fa0274626fab3ae789

[ "MIT" ]

155

2021-01-18T09:29:07.000Z

2022-03-30T20:50:15.000Z

task/grovertrainer.py

adamoyoung/grover

393afbd69482ed1fca3056fa0274626fab3ae789

[ "MIT" ]

13

2021-01-22T09:49:49.000Z

2022-03-07T12:25:18.000Z

task/grovertrainer.py

adamoyoung/grover

393afbd69482ed1fca3056fa0274626fab3ae789

[ "MIT" ]

38

2021-01-22T03:25:15.000Z

2022-03-09T02:45:24.000Z

""" The GROVER trainer. """ import os import time from logging import Logger from typing import List, Tuple from collections.abc import Callable import torch from torch.nn import Module from torch.utils.data import DataLoader from grover.model.models import GroverTask from grover.util.multi_gpu_wrapper import MultiGpuWrapper as mgw class GROVERTrainer: def __init__(self, args, embedding_model: Module, atom_vocab_size: int, # atom vocab size bond_vocab_size: int, fg_szie: int, train_dataloader: DataLoader, test_dataloader: DataLoader, optimizer_builder: Callable, scheduler_builder: Callable, logger: Logger = None, with_cuda: bool = False, enable_multi_gpu: bool = False): """ The init function of GROVERTrainer :param args: the input arguments. :param embedding_model: the model to generate atom/bond embeddings. :param atom_vocab_size: the vocabulary size of atoms. :param bond_vocab_size: the vocabulary size of bonds. :param fg_szie: the size of semantic motifs (functional groups) :param train_dataloader: the data loader of train data. :param test_dataloader: the data loader of validation data. :param optimizer_builder: the function of building the optimizer. :param scheduler_builder: the function of building the scheduler. :param logger: the logger :param with_cuda: enable gpu training. :param enable_multi_gpu: enable multi_gpu traning. """ self.args = args self.with_cuda = with_cuda self.grover = embedding_model self.model = GroverTask(args, embedding_model, atom_vocab_size, bond_vocab_size, fg_szie) self.loss_func = self.model.get_loss_func(args) self.enable_multi_gpu = enable_multi_gpu self.atom_vocab_size = atom_vocab_size self.bond_vocab_size = bond_vocab_size self.debug = logger.debug if logger is not None else print if self.with_cuda: # print("Using %d GPUs for training." % (torch.cuda.device_count())) self.model = self.model.cuda() self.train_data = train_dataloader self.test_data = test_dataloader self.optimizer = optimizer_builder(self.model, self.args) self.scheduler = scheduler_builder(self.optimizer, self.args) if self.enable_multi_gpu: self.optimizer = mgw.DistributedOptimizer(self.optimizer, named_parameters=self.model.named_parameters()) self.args = args self.n_iter = 0 def broadcast_parameters(self) -> None: """ Broadcast parameters before training. :return: no return. """ if self.enable_multi_gpu: # broadcast parameters & optimizer state. mgw.broadcast_parameters(self.model.state_dict(), root_rank=0) mgw.broadcast_optimizer_state(self.optimizer, root_rank=0) def train(self, epoch: int) -> List: """ The training iteration :param epoch: the current epoch number. :return: the loss terms of current epoch. """ # return self.mock_iter(epoch, self.train_data, train=True) return self.iter(epoch, self.train_data, train=True) def test(self, epoch: int) -> List: """ The test/validaiion iteration :param epoch: the current epoch number. :return: the loss terms as a list """ # return self.mock_iter(epoch, self.test_data, train=False) return self.iter(epoch, self.test_data, train=False) def mock_iter(self, epoch: int, data_loader: DataLoader, train: bool = True) -> List: """ Perform a mock iteration. For test only. :param epoch: the current epoch number. :param data_loader: the data loader. :param train: True: train model, False: validation model. :return: the loss terms as a list """ for _, _ in enumerate(data_loader): self.scheduler.step() cum_loss_sum = 0.0 self.n_iter += self.args.batch_size return self.n_iter, cum_loss_sum, (0, 0, 0, 0, 0, 0) def iter(self, epoch, data_loader, train=True) -> List: """ Perform a training / validation iteration. :param epoch: the current epoch number. :param data_loader: the data loader. :param train: True: train model, False: validation model. :return: the loss terms as a list """ if train: self.model.train() else: self.model.eval() loss_sum, iter_count = 0, 0 cum_loss_sum, cum_iter_count = 0, 0 av_loss_sum, bv_loss_sum, fg_loss_sum, av_dist_loss_sum, bv_dist_loss_sum, fg_dist_loss_sum = 0, 0, 0, 0, 0, 0 # loss_func = self.model.get_loss_func(self.args) for _, item in enumerate(data_loader): batch_graph = item["graph_input"] targets = item["targets"] if next(self.model.parameters()).is_cuda: targets["av_task"] = targets["av_task"].cuda() targets["bv_task"] = targets["bv_task"].cuda() targets["fg_task"] = targets["fg_task"].cuda() preds = self.model(batch_graph) # # ad-hoc code, for visualizing a model, comment this block when it is not needed # import dglt.contrib.grover.vis_model as vis_model # for task in ['av_task', 'bv_task', 'fg_task']: # vis_graph = vis_model.make_dot(self.model(batch_graph)[task], # params=dict(self.model.named_parameters())) # # vis_graph.view() # vis_graph.render(f"{self.args.backbone}_model_{task}_vis.png", format="png") # exit() loss, av_loss, bv_loss, fg_loss, av_dist_loss, bv_dist_loss, fg_dist_loss = self.loss_func(preds, targets) loss_sum += loss.item() iter_count += self.args.batch_size if train: cum_loss_sum += loss.item() # Run model self.model.zero_grad() self.optimizer.zero_grad() loss.backward() self.optimizer.step() self.scheduler.step() else: # For eval model, only consider the loss of three task. cum_loss_sum += av_loss.item() cum_loss_sum += bv_loss.item() cum_loss_sum += fg_loss.item() av_loss_sum += av_loss.item() bv_loss_sum += bv_loss.item() fg_loss_sum += fg_loss.item() av_dist_loss_sum += av_dist_loss.item() if type(av_dist_loss) != float else av_dist_loss bv_dist_loss_sum += bv_dist_loss.item() if type(bv_dist_loss) != float else bv_dist_loss fg_dist_loss_sum += fg_dist_loss.item() if type(fg_dist_loss) != float else fg_dist_loss cum_iter_count += 1 self.n_iter += self.args.batch_size # Debug only. # if i % 50 == 0: # print(f"epoch: {epoch}, batch_id: {i}, av_loss: {av_loss}, bv_loss: {bv_loss}, " # f"fg_loss: {fg_loss}, av_dist_loss: {av_dist_loss}, bv_dist_loss: {bv_dist_loss}, " # f"fg_dist_loss: {fg_dist_loss}") cum_loss_sum /= cum_iter_count av_loss_sum /= cum_iter_count bv_loss_sum /= cum_iter_count fg_loss_sum /= cum_iter_count av_dist_loss_sum /= cum_iter_count bv_dist_loss_sum /= cum_iter_count fg_dist_loss_sum /= cum_iter_count return self.n_iter, cum_loss_sum, (av_loss_sum, bv_loss_sum, fg_loss_sum, av_dist_loss_sum, bv_dist_loss_sum, fg_dist_loss_sum) def save(self, epoch, file_path, name=None) -> str: """ Save the intermediate models during training. :param epoch: the epoch number. :param file_path: the file_path to save the model. :return: the output path. """ # add specific time in model fine name, in order to distinguish different saved models now = time.localtime() if name is None: name = "_%04d_%02d_%02d_%02d_%02d_%02d" % ( now.tm_year, now.tm_mon, now.tm_mday, now.tm_hour, now.tm_min, now.tm_sec) output_path = file_path + name + ".ep%d" % epoch scaler = None features_scaler = None state = { 'args': self.args, 'state_dict': self.model.state_dict(), 'optimizer': self.optimizer.state_dict(), 'scheduler_step': self.scheduler.current_step, "epoch": epoch, 'data_scaler': { 'means': scaler.means, 'stds': scaler.stds } if scaler is not None else None, 'features_scaler': { 'means': features_scaler.means, 'stds': features_scaler.stds } if features_scaler is not None else None } torch.save(state, output_path) # Is this necessary? # if self.with_cuda: # self.model = self.model.cuda() print("EP:%d Model Saved on:" % epoch, output_path) return output_path def save_tmp(self, epoch, file_path, rank=0): """ Save the models for auto-restore during training. The model are stored in file_path/tmp folder and will replaced on each epoch. :param epoch: the epoch number. :param file_path: the file_path to store the model. :param rank: the current rank (decrypted). :return: """ store_path = os.path.join(file_path, "tmp") if not os.path.exists(store_path): os.makedirs(store_path, exist_ok=True) store_path = os.path.join(store_path, "model.%d" % rank) state = { 'args': self.args, 'state_dict': self.model.state_dict(), 'optimizer': self.optimizer.state_dict(), 'scheduler_step': self.scheduler.current_step, "epoch": epoch } torch.save(state, store_path) def restore(self, file_path, rank=0) -> Tuple[int, int]: """ Restore the training state saved by save_tmp. :param file_path: the file_path to store the model. :param rank: the current rank (decrypted). :return: the restored epoch number and the scheduler_step in scheduler. """ cpt_path = os.path.join(file_path, "tmp", "model.%d" % rank) if not os.path.exists(cpt_path): print("No checkpoint found %d") return 0, 0 cpt = torch.load(cpt_path) self.model.load_state_dict(cpt["state_dict"]) self.optimizer.load_state_dict(cpt["optimizer"]) epoch = cpt["epoch"] scheduler_step = cpt["scheduler_step"] self.scheduler.current_step = scheduler_step print("Restore checkpoint, current epoch: %d" % (epoch)) return epoch, scheduler_step

40.417857

118

0.596183

2325a7a4697dd349c33b99c59c43e630fb1581b7

20,530

py

Python

materials/code/python/planet_client.py

agroimpacts/nmeo

6cf657aa2e47223eff5b90ba175db14e03a07b43

[ "Apache-2.0" ]

null

materials/code/python/planet_client.py

agroimpacts/nmeo

6cf657aa2e47223eff5b90ba175db14e03a07b43

[ "Apache-2.0" ]

null

materials/code/python/planet_client.py

agroimpacts/nmeo

6cf657aa2e47223eff5b90ba175db14e03a07b43

[ "Apache-2.0" ]

1

2022-01-25T22:47:37.000Z

from planet import api from planet.api import filters from geo_utils import GeoUtils from pprint import pprint from requests.auth import HTTPBasicAuth from fixed_thread_pool_executor import FixedThreadPoolExecutor import os import ssl import requests import time import urllib.request import shutil import boto3 from boto3.s3.transfer import S3Transfer, TransferConfig import botocore import concurrent import logging import configparser import json import multiprocessing from retry import retry # PClientV1, class to simplify querying & downloading planet scenes using planet API V1 # We need to consider usage of a new API class PClientV1(): # def __init__(self, api_key): # self.api_key = api_key # self.max_clouds = 0.25 # self.max_bad_pixels = 0.25 # self.max_nodata = 0.25 # self.maximgs = 1 # self.catalog_path = "catalog/" # self.s3_catalog_bucket = "azavea-africa-test" # self.s3_catalog_prefix = "planet/images" # self.products = { # 'analytic_sr': { # 'item_type': 'PSScene4Band', # 'asset_type': 'analytic_sr', # 'ext': 'tif' # }, # 'analytic': { # 'item_type': 'PSScene4Band', # 'asset_type': 'analytic', # 'ext': 'tif' # }, # 'analytic_xml': { # 'item_type': 'PSScene4Band', # 'asset_type': 'analytic_xml', # 'ext': 'xml' # }, # 'visual': { # 'item_type': 'PSScene3Band', # 'asset_type': 'visual', # 'ext': 'tif' # } # } # self.client = api.ClientV1(api_key = api_key) # self.output_filename = "output.csv" # self.output_encoding = "utf-8" # self.s3client = boto3.client('s3') # self.with_analytic = False # self.with_analytic_xml = False # self.with_visual = False # self.local_mode = False # self.s3_only = False # self.transfer = S3Transfer(self.s3client, TransferConfig(use_threads = False)) # self.transfer_config = TransferConfig(use_threads = False) # self.logger = logging.getLogger(__name__) # self.logger.setLevel(logging.INFO) # self.secondary_uploads_executor = FixedThreadPoolExecutor(size = 5) # self.with_immediate_cleanup = False def __init__(self, api_key, config): imagery_config = config['imagery'] self.api_key = api_key self.max_clouds_initial = float(imagery_config['max_clouds_initial']) self.max_clouds = float(imagery_config['max_clouds']) # max proportion of pixels that are clouds self.max_bad_pixels = float(imagery_config['max_bad_pixels']) # max proportion of bad pixels (transmission errors, etc.) self.max_nodata = float(imagery_config['max_nodata']) # max nodata values per cellgrid self.maximgs = int(imagery_config['maximgs']) # 15 #10 #20 self.output_encoding = imagery_config['output_encoding'] # self.output_filename = imagery_config['output_filename'] # self.output_filename_csv = imagery_config['output_filename_csv'] self.catalog_path = imagery_config['catalog_path'] # self.s3_catalog_bucket = imagery_config['s3_catalog_bucket'] # self.s3_catalog_prefix = imagery_config['s3_catalog_prefix'] self.products = { 'analytic_sr': { 'item_type': 'PSScene4Band', 'asset_type': 'analytic_sr', 'ext': 'tif' }, 'analytic': { 'item_type': 'PSScene4Band', 'asset_type': 'analytic', 'ext': 'tif' }, 'analytic_xml': { 'item_type': 'PSScene4Band', 'asset_type': 'analytic_xml', 'ext': 'xml' }, 'visual': { 'item_type': 'PSScene3Band', 'asset_type': 'visual', 'ext': 'tif' } } self.client = api.ClientV1(api_key = self.api_key) self.s3client = boto3.client('s3') # self.with_analytic = json.loads(imagery_config['with_analytic'].lower()) # self.with_analytic_xml = json.loads(imagery_config['with_analytic_xml'].lower()) # self.with_visual = json.loads(imagery_config['with_visual'].lower()) # self.with_immediate_cleanup = json.loads(imagery_config['with_immediate_cleanup'].lower()) # self.local_mode = json.loads(imagery_config['local_mode'].lower()) # self.s3_only = json.loads(imagery_config['s3_only'].lower()) # self.transfer = S3Transfer(self.s3client, TransferConfig(use_threads = False)) self.logger = logging.getLogger(__name__) self.logger.setLevel(logging.INFO) # planet has limitation 5 sec per key (search queries) threads_number = imagery_config['threads'] if threads_number == 'default': threads_number = multiprocessing.cpu_count() * 2 + 1 else: threads_number = int(threads_number) self.secondary_uploads_executor = FixedThreadPoolExecutor(size = threads_number) # there are start_date and end_date present as it should be the part of a row retrieved from psql / tiff file def set_filters_sr(self, aoi, start_date='2017-12-15T00:00:00.000Z', end_date = '2018-03-15T00:00:00.000Z', id=''): # add an asset_filter for only those scenes that have an analytic_sr asset available date_filter = { 'type': 'DateRangeFilter', 'field_name': 'acquired', 'config': { 'gte': start_date, 'lte': end_date } } cloud_filter = { 'type': 'RangeFilter', 'field_name': 'cloud_cover', 'config': { 'lte': self.max_clouds_initial } } bad_pixel_filter = { 'type': 'RangeFilter', 'field_name': 'anomalous_pixels', 'config': { 'lte': self.max_bad_pixels } } location_filter = api.filters.geom_filter(aoi) geometry_filter = { "type": "GeometryFilter", "field_name": "geometry", "config": aoi } asset_filter = { "type": "PermissionFilter", "config": ["assets.analytic_sr:download"] } string_filter = { "type": "StringInFilter", "field_name": "id", "config": [id] } filters_list = [date_filter, cloud_filter,geometry_filter, bad_pixel_filter, asset_filter] if (id != ''): filters_list.append(string_filter) # combine filters: query = { 'type': 'AndFilter', 'config': filters_list } return query # there are start_date and end_date present as it should be the part of a row retrieved from psql / tiff file def set_filters_id(self, id=''): asset_filter = { "type": "PermissionFilter", "config": ["assets.analytic_sr:download"] } string_filter = { "type": "StringInFilter", "field_name": "id", "config": [id] } filters_list = [asset_filter, string_filter] # combine filters: query = { 'type': 'AndFilter', 'config': filters_list } return query @retry(tries = 10, delay = 2, backoff = 2) def request_intersecting_scenes(self, query): # build the request item_types = ['PSScene4Band'] # params["lst_item_types"] request = api.filters.build_search_request(query, item_types) # post the request results = self.client.quick_search(request) return results # returns a full URI here def download_localfs_generic(self, scene_id, season = '', asset_type = 'analytic_sr', ext = 'tif', item_type='PSScene4Band'): output_file = "{}{}/{}/{}.{}".format(self.catalog_path, asset_type, season, scene_id, ext) if not os.path.exists(output_file): # activation & download session = requests.Session() session.auth = (self.api_key, '') assets_uri = ("https://api.planet.com/data/v1/item-types/{}/items/{}/assets/").format(item_type, scene_id) assets_query_result = session.get(assets_uri) self.logger.info(assets_query_result.status_code) item_activation_json = assets_query_result.json() # self.logger.info(item_activation_json) item_activation_url = item_activation_json[asset_type]["_links"]["activate"] response = session.post(item_activation_url) self.logger.info(response.status_code) while response.status_code!=204: time.sleep(30) response = session.post(item_activation_url) response.status_code = response.status_code self.logger.info(response.status_code) item_url = 'https://api.planet.com/data/v1/item-types/{}/items/{}/assets/'.format(item_type, scene_id) result = requests.get(item_url, auth = HTTPBasicAuth(self.api_key, '')) if result.status_code != 200: self.logger.info(result.content.decode('utf-8')) download_url = result.json()[asset_type]['location'] # download with urllib.request.urlopen(download_url) as response, open(output_file, 'wb') as out_file: shutil.copyfileobj(response, out_file) return output_file # TODO: lots of copy pasting happens there, abstract over it? # returns a full S3 URI here def download_s3_generic(self, scene_id, season = '', asset_type = 'analytic_sr', ext = 'tif', item_type='PSScene4Band'): output_key = "{}/{}/{}/{}.{}".format(self.s3_catalog_prefix, asset_type, season, scene_id, ext) result_path = 's3://{}/{}'.format(self.s3_catalog_bucket, output_key) try: self.s3client.head_object(Bucket = self.s3_catalog_bucket, Key = output_key) except botocore.exceptions.ClientError: self.logger.exception('Error Encountered') self.logger.info("Downloading {}...".format(scene_id)) # activation & download session = requests.Session() session.auth = (self.api_key, '') assets_uri = ("https://api.planet.com/data/v1/item-types/{}/items/{}/assets/").format(item_type, scene_id) assets_query_result = session.get(assets_uri) self.logger.info(assets_query_result.status_code) item_activation_json = assets_query_result.json() # self.logger.info(item_activation_json) item_activation_url = item_activation_json[asset_type]["_links"]["activate"] response = session.post(item_activation_url) self.logger.info(response.status_code) while response.status_code!=204: time.sleep(30) response = session.post(item_activation_url) response.status_code = response.status_code self.logger.info(response.status_code) item_url = 'https://api.planet.com/data/v1/item-types/{}/items/{}/assets/'.format(item_type, scene_id) result = requests.get(item_url, auth = HTTPBasicAuth(self.api_key, '')) if result.status_code != 200: self.logger.info(result.content.decode('utf-8')) download_url = result.json()[asset_type]['location'] # upload on s3 directly from the response with urllib.request.urlopen(download_url) as response: self.s3client.put_object(Body = response.read(), Bucket = self.s3_catalog_bucket, Key = output_key) # finished self.logger.info("Downloaded {}".format(scene_id)) return result_path # returns a full URI here def download_localfs_product(self, product_type, scene_id, season = ''): cfg = self.products[product_type] return self.download_localfs_generic( scene_id = scene_id, season = season, asset_type = cfg['asset_type'], ext = cfg['ext'], item_type= cfg['item_type'] ) # returns a full URI here def download_s3_product(self, product_type, scene_id, season = ''): cfg = self.products[product_type] return self.download_s3_generic( scene_id = scene_id, season = season, asset_type = cfg['asset_type'], ext = cfg['ext'], item_type= cfg['item_type'] ) def download_localfs_analytic_sr(self, scene_id, season = ''): return self.download_localfs_product('analytic_sr', scene_id, season) def download_s3_analytic_sr(self, scene_id, season = ''): return self.download_s3_product('analytic_sr', scene_id, season) def download_localfs_analytic(self, scene_id, season = ''): return self.download_localfs_product('analytic', scene_id, season) def download_s3_analytic(self, scene_id, season = ''): return self.download_s3_product('analytic', scene_id, season) def download_localfs_analytic_xml(self, scene_id, season = ''): return self.download_localfs_product('analytic_xml', scene_id, season) def download_s3_analytic_xml(self, scene_id, season = ''): return self.download_s3_product('analytic_xml', scene_id, season) def download_localfs_visual(self, scene_id, season = ''): return self.download_localfs_product('visual', scene_id, season) def download_s3_visual(self, scene_id, season = ''): return self.download_s3_product('visual', scene_id, season) def upload_s3_csv(self): result = '' if not self.local_mode: output_key = "{}/{}".format(self.s3_catalog_prefix, self.output_filename.split('/')[-1]) result = 's3://{}/{}'.format(self.s3_catalog_bucket, output_key) self.transfer.upload_file(self.output_filename, self.s3_catalog_bucket, output_key) else: result = self.output_filename return result def upload_s3_csv_csv(self): output_key = "{}/{}".format(self.s3_catalog_prefix, self.output_filename_csv.split('/')[-1]) result = 's3://{}/{}'.format(self.s3_catalog_bucket, output_key) self.transfer.upload_file(self.output_filename_csv, self.s3_catalog_bucket, output_key) return result def download_localfs_s3_product(self, scene_id, season = '', product_type = 'analytic_sr'): cfg = self.products[product_type] asset_type = cfg['asset_type'] ext = cfg['ext'] item_type = cfg['item_type'] filepath = '' output_key = "{}/{}/{}/{}.{}".format(self.s3_catalog_prefix, asset_type, season, scene_id, ext) s3_result = 's3://{}/{}'.format(self.s3_catalog_bucket, output_key) local_result = "{}{}/{}/{}.{}".format(self.catalog_path, asset_type, season, scene_id, ext) if not self.s3_only: if not os.path.exists(local_result): if not self.local_mode: try: # if we have file in our s3 bucket, let's pull it down from the S3 (faster) self.s3client.head_object(Bucket = self.s3_catalog_bucket, Key = output_key) filepath = s3_result # self.logger.info("Downloading {} from the internal S3 storage...".format(scene_id)) # self.transfer.download_file(self.s3_catalog_bucket, output_key, local_result) # filepath = local_result # filepath = s3_result except botocore.exceptions.ClientError: self.logger.exception('Error Encountered') filepath = self.download_localfs_product(product_type, scene_id, season) self.logger.info("Uploading {}...".format(scene_id)) self.s3client.put_object(Bucket = self.s3_catalog_bucket, Key = output_key, Body = open(filepath, 'rb')) # self.transfer.upload_file(filepath, self.s3_catalog_bucket, output_key) else: filepath = self.download_localfs_product(product_type, scene_id, season) s3_result = local_result else: filepath = local_result if self.local_mode: s3_result = local_result else: try: self.s3client.head_object(Bucket = self.s3_catalog_bucket, Key = output_key) except botocore.exceptions.ClientError: self.logger.exception('Error Encountered') self.logger.info("Uploading {}...".format(scene_id)) self.s3client.put_object(Bucket = self.s3_catalog_bucket, Key = output_key, Body = open(filepath, 'rb')) # self.transfer.upload_file(filepath, self.s3_catalog_bucket, output_key) else: s3_result = self.download_s3_product('analytic_sr', scene_id, season) filepath = s3_result return filepath, s3_result def download_localfs_s3(self, scene_id, season = ''): sub_products = [] if self.with_analytic: sub_products.append('with_analytic') if self.with_analytic_xml: sub_products.append('with_analytic_xml') if self.with_visual: sub_products.append('with_visual') for sub_product in sub_products: self.secondary_uploads_executor.submit(self.download_localfs_s3_product, scene_id, season, sub_product) return self.download_localfs_s3_product(scene_id, season) def drain(self): self.secondary_uploads_executor.drain() # def cleanup_catalog(self): # self.logger.info("Catalog cleanup...") # if self.with_immediate_cleanup & (not self.s3_only): # for product_type in ['analytic', 'analytic_sr', 'analytic_xml', 'visual']: # for season in ['OS', 'GS']: # lpath = "{}{}/{}".format(self.catalog_path, product_type, season) # try: # shutil.rmtree(lpath, ignore_errors = False) # os.makedirs(lpath) # except: # self.logger.exception('Error Encountered') # self.logger.info("Could not remove a folder: {}".format(lpath)) def close(self): self.secondary_uploads_executor.close() self.cleanup_catalog() if __name__ == "__main__": # disable ssl ssl._create_default_https_context = ssl._create_unverified_context # read config config = configparser.ConfigParser() config.read('cfg/config.ini') planet_config = config['planet'] imagery_config = config['imagery'] # logger logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) logging.basicConfig(format = '%(message)s', datefmt = '%m-%d %H:%M') api_key = planet_config['api_key'] # pclient init pclient = PClientV1(api_key, config) # planet_filters = pclient.set_filters_id('20170828_092138_0f4b') # res = pclient.request_intersecting_scenes(planet_filters) # pick up scene id and its geometry # for item in res.items_iter(1): # each item is a GeoJSON feature # scene_id = item["id"] # print(item) # activation & download # it should be sync, to allow async check of neighbours # output_localfile = pclient.download_localfsV2(scene_id, asset_type = 'visual', item_type='PSScene3Band') # output_localfile = pclient.download_localfsV2(scene_id, asset_type = 'visual_xml', item_type='PSScene3Band', ext = 'xml') # output_localfile = pclient.download_localfsV2(scene_id, asset_type = 'analytic') # output_localfile = pclient.download_localfsV2(scene_id, asset_type = 'analytic_xml', ext = 'xml') # use custom cloud detection function to calculate clouds and shadows

41.558704

131

0.602874

b9d5a48e01de306f7a57a1d019b6ff94c0ef3701

2,965

py

Python

examples/mnist/mnist_mlp_candle.py

vgutta/Benchmarks

f739c1fb2b02dd8fb310e2182fa8c4baaaea7caf

[ "MIT" ]

51

2017-01-24T20:57:27.000Z

2022-02-15T00:33:45.000Z

examples/mnist/mnist_mlp_candle.py

vgutta/Benchmarks

f739c1fb2b02dd8fb310e2182fa8c4baaaea7caf

[ "MIT" ]

59

2017-08-21T22:19:44.000Z

2021-11-01T16:05:35.000Z

examples/mnist/mnist_mlp_candle.py

vgutta/Benchmarks

f739c1fb2b02dd8fb310e2182fa8c4baaaea7caf

[ "MIT" ]

90

2016-11-22T03:57:07.000Z

2022-01-11T04:43:23.000Z

# from tensorflow.keras.callbacks import CSVLogger from tensorflow.keras import backend as K import mnist import candle def initialize_parameters(): mnist_common = mnist.MNIST( mnist.file_path, 'mnist_params.txt', 'keras', prog='mnist_mlp', desc='MNIST example' ) # Initialize parameters gParameters = candle.finalize_parameters(mnist_common) # csv_logger = CSVLogger('{}/params.log'.format(gParameters)) return gParameters def run(gParameters): ########################################## # Your DL start here. See mnist_mlp.py # ########################################## '''Trains a simple deep NN on the MNIST dataset. Gets to 98.40% test accuracy after 20 epochs (there is *a lot* of margin for parameter tuning). 2 seconds per epoch on a K520 GPU. ''' # from __future__ import print_function from tensorflow import keras from tensorflow.keras.datasets import mnist from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Dropout batch_size = gParameters['batch_size'] num_classes = 10 epochs = gParameters['epochs'] activation = gParameters['activation'] optimizer = gParameters['optimizer'] # the data, split between train and test sets (x_train, y_train), (x_test, y_test) = mnist.load_data() x_train = x_train.reshape(60000, 784) x_test = x_test.reshape(10000, 784) x_train = x_train.astype('float32') x_test = x_test.astype('float32') x_train /= 255 x_test /= 255 print(x_train.shape[0], 'train samples') print(x_test.shape[0], 'test samples') # convert class vectors to binary class matrices y_train = keras.utils.to_categorical(y_train, num_classes) y_test = keras.utils.to_categorical(y_test, num_classes) model = Sequential() model.add(Dense(512, activation=activation, input_shape=(784,))) model.add(Dropout(0.2)) model.add(Dense(512, activation=activation)) model.add(Dropout(0.2)) model.add(Dense(num_classes, activation='softmax')) model.summary() model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy']) history = model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(x_test, y_test)) score = model.evaluate(x_test, y_test, verbose=0) print('Test loss:', score[0]) print('Test accuracy:', score[1]) ########################################## # End of mnist_mlp.py #################### ########################################## return history def main(): gParameters = initialize_parameters() run(gParameters) if __name__ == '__main__': main() try: K.clear_session() except AttributeError: pass

28.786408

68

0.609106

4f3daf9c48e96e2148c76b3072bd3ca82daeb14e

1,355

py

Python

app/core/tests/test_models.py

devmansurov/recipe-app-api

7adb702ae0b75981359d3f598742a1375eee7fde

[ "MIT" ]

null

app/core/tests/test_models.py

devmansurov/recipe-app-api

7adb702ae0b75981359d3f598742a1375eee7fde

[ "MIT" ]

null

app/core/tests/test_models.py

devmansurov/recipe-app-api

7adb702ae0b75981359d3f598742a1375eee7fde

[ "MIT" ]

null

#!/usr/bin/python # -*- coding: utf-8 -*- from django.test import TestCase from django.contrib.auth import get_user_model class ModelTest(TestCase): def test_create_user_with_email_successful(self): """Test creating a new user with an email is successful""" email = 'test@gmail.com' password = 'secret' user = get_user_model().objects.create_user( email=email, password=password ) self.assertEqual(user.email, email) self.assertTrue(user.check_password(password)) def test_new_user_email_normalized(self): """Test the email for a new user is normalized""" email = 'test@GMAIL.COM' user = get_user_model().objects.create_user(email, 'test456') self.assertEqual(user.email, email.lower()) def test_new_user_invalid_email(self): """Test creating user with no email raises error""" with self.assertRaises(ValueError): get_user_model().objects.create_user(None, 'test456') def test_create_new_superuser(self): """Test creating a new superuser""" user = \ get_user_model().objects.create_superuser( 'test_superuser@gmail.com', 'secret' ) self.assertTrue(user.is_superuser) self.assertTrue(user.is_staff)

28.829787

69

0.635424

1b0279bb4fd9a9661da48a5261787f6c594fdc8a

4,066

py

Python

official/mnist/dataset.py

jieming2002/models-quiz8

421dc407a10444cab4bd88c25599077acca96bdb

[ "Apache-2.0" ]

7

2018-04-29T03:54:00.000Z

2021-08-17T13:27:28.000Z

official/mnist/dataset.py

jieming2002/models-quiz8

421dc407a10444cab4bd88c25599077acca96bdb

[ "Apache-2.0" ]

null

official/mnist/dataset.py

jieming2002/models-quiz8

421dc407a10444cab4bd88c25599077acca96bdb

[ "Apache-2.0" ]

3

2018-10-23T01:46:50.000Z

2019-03-19T02:51:51.000Z

# 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. """tf.data.Dataset interface to the MNIST dataset.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import gzip import os import shutil import numpy as np from six.moves import urllib import tensorflow as tf def read32(bytestream): """Read 4 bytes from bytestream as an unsigned 32-bit integer.""" dt = np.dtype(np.uint32).newbyteorder('>') return np.frombuffer(bytestream.read(4), dtype=dt)[0] def check_image_file_header(filename): """Validate that filename corresponds to images for the MNIST dataset.""" with tf.gfile.Open(filename, 'rb') as f: magic = read32(f) read32(f) # num_images, unused rows = read32(f) cols = read32(f) if magic != 2051: raise ValueError('Invalid magic number %d in MNIST file %s' % (magic, f.name)) if rows != 28 or cols != 28: raise ValueError( 'Invalid MNIST file %s: Expected 28x28 images, found %dx%d' % (f.name, rows, cols)) def check_labels_file_header(filename): """Validate that filename corresponds to labels for the MNIST dataset.""" with tf.gfile.Open(filename, 'rb') as f: magic = read32(f) read32(f) # num_items, unused if magic != 2049: raise ValueError('Invalid magic number %d in MNIST file %s' % (magic, f.name)) def download(directory, filename): """Download (and unzip) a file from the MNIST dataset if not already done.""" filepath = os.path.join(directory, filename) if tf.gfile.Exists(filepath): return filepath if not tf.gfile.Exists(directory): tf.gfile.MakeDirs(directory) # CVDF mirror of http://yann.lecun.com/exdb/mnist/ url = 'https://storage.googleapis.com/cvdf-datasets/mnist/' + filename + '.gz' zipped_filepath = filepath + '.gz' print('Downloading %s to %s' % (url, zipped_filepath)) urllib.request.urlretrieve(url, zipped_filepath) with gzip.open(zipped_filepath, 'rb') as f_in, open(filepath, 'wb') as f_out: shutil.copyfileobj(f_in, f_out) os.remove(zipped_filepath) return filepath def dataset(directory, images_file, labels_file): """Download and parse MNIST dataset.""" images_file = download(directory, images_file) labels_file = download(directory, labels_file) check_image_file_header(images_file) check_labels_file_header(labels_file) def decode_image(image): # Normalize from [0, 255] to [0.0, 1.0] image = tf.decode_raw(image, tf.uint8) image = tf.cast(image, tf.float32) image = tf.reshape(image, [784]) return image / 255.0 def decode_label(label): label = tf.decode_raw(label, tf.uint8) # tf.string -> [tf.uint8] label = tf.reshape(label, []) # label is a scalar return tf.to_int32(label) images = tf.data.FixedLengthRecordDataset( images_file, 28 * 28, header_bytes=16).map(decode_image) labels = tf.data.FixedLengthRecordDataset( labels_file, 1, header_bytes=8).map(decode_label) return tf.data.Dataset.zip((images, labels)) def train(directory): """tf.data.Dataset object for MNIST training data.""" return dataset(directory, 'train-images-idx3-ubyte', 'train-labels-idx1-ubyte') def test(directory): """tf.data.Dataset object for MNIST test data.""" return dataset(directory, 't10k-images-idx3-ubyte', 't10k-labels-idx1-ubyte')

35.051724

80

0.686178

c328caacc8465f0095141c79008ed9f9f82e2122

3,948

py

Python

PyInquirer/prompts/editor.py

e3rd/PyInquirer

99a6d28d937c456526d93387fac706158f99775f

[ "MIT" ]

null

PyInquirer/prompts/editor.py

e3rd/PyInquirer

99a6d28d937c456526d93387fac706158f99775f

[ "MIT" ]

null

PyInquirer/prompts/editor.py

e3rd/PyInquirer

99a6d28d937c456526d93387fac706158f99775f

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ `input` type question """ import os import sys from __future__ import print_function, unicode_literals from prompt_toolkit.token import Token from prompt_toolkit.shortcuts import create_prompt_application from prompt_toolkit.validation import Validator, ValidationError from prompt_toolkit.layout.lexers import SimpleLexer from .common import default_style # use std prompt-toolkit control WIN = sys.platform.startswith('win') class Editor(object): def __init__(self, editor=None, env=None, require_save=True, extension='.txt'): self.editor = editor self.env = env self.require_save = require_save self.extension = extension def get_editor(self): if self.editor is not None: return self.editor for key in 'VISUAL', 'EDITOR': rv = os.environ.get(key) if rv: return rv if WIN: return 'notepad' for editor in 'vim', 'nano': if os.system('which %s >/dev/null 2>&1' % editor) == 0: return editor return 'vi' def edit_file(self, filename): import subprocess editor = self.get_editor() if self.env: environ = os.environ.copy() environ.update(self.env) else: environ = None try: c = subprocess.Popen('%s "%s"' % (editor, filename), env=environ, shell=True) exit_code = c.wait() if exit_code != 0: raise Exception('%s: Editing failed!' % editor) except OSError as e: raise Exception('%s: Editing failed: %s' % (editor, e)) def edit(self, text): import tempfile text = text or '' if text and not text.endswith('\n'): text += '\n' fd, name = tempfile.mkstemp(prefix='editor-', suffix=self.extension) try: if WIN: encoding = 'utf-8-sig' text = text.replace('\n', '\r\n') else: encoding = 'utf-8' text = text.encode(encoding) f = os.fdopen(fd, 'wb') f.write(text) f.close() timestamp = os.path.getmtime(name) self.edit_file(name) if self.require_save \ and os.path.getmtime(name) == timestamp: return None f = open(name, 'rb') try: rv = f.read() finally: f.close() return rv.decode('utf-8-sig').replace('\r\n', '\n') finally: os.unlink(name) def question(message, **kwargs): default = kwargs.pop('default', '') validate_prompt = kwargs.pop('validate', None) if validate_prompt: if issubclass(validate_prompt, Validator): kwargs['validator'] = validate_prompt() elif callable(validate_prompt): class _InputValidator(Validator): def validate(self, document): verdict = validate_prompt(document.text) if not verdict == True: if verdict == False: verdict = 'invalid input' raise ValidationError( message=verdict, cursor_position=len(document.text)) kwargs['validator'] = _InputValidator() # TODO style defaults on detail level kwargs['style'] = kwargs.pop('style', default_style) qmark = kwargs.pop('qmark', '?') def _get_prompt_tokens(cli): return [ (Token.QuestionMark, qmark), (Token.Question, ' %s ' % message) ] return create_prompt_application( get_prompt_tokens=_get_prompt_tokens, lexer=SimpleLexer(Token.Answer), default=default, **kwargs )

29.909091

76

0.536221

cb1d42d54dff017d017d58ae2e983e58a574e1d8

794

py

Python

mail_multi_website/wizard/mail_compose_message.py

brain-tec/mail-addons

92efb62ad5c4d9843654ae3e49b120a8759ff2bf

[ "MIT" ]

null

mail_multi_website/wizard/mail_compose_message.py

brain-tec/mail-addons

92efb62ad5c4d9843654ae3e49b120a8759ff2bf

[ "MIT" ]

1

2019-03-15T14:45:46.000Z

mail_multi_website/wizard/mail_compose_message.py

brain-tec/mail-addons

92efb62ad5c4d9843654ae3e49b120a8759ff2bf

[ "MIT" ]

1

2021-08-28T11:18:33.000Z

# Copyright 2018 Ivan Yelizariev <https://it-projects.info/team/yelizariev> # License MIT (https://opensource.org/licenses/MIT). from odoo import api, models from odoo.http import request class MailComposer(models.TransientModel): _inherit = "mail.compose.message" @api.model def create(self, vals): """Workaround for https://github.com/odoo/odoo/pull/26589""" if "website_id" not in self.env.context: website = ( request and hasattr(request, "website") and request.website or None ) if not website: website = self.env["website"].get_current_website() if website: self = self.with_context(website_id=website.id) return super(MailComposer, self).create(vals)

34.521739

83

0.639798

e854a95db23e8f335ecab2141cb6cb668d593f0a

1,870

py

Python

hl/hlist.py

DmitryOlshansky/hl

5f652ee16dbd3813ad997d860b2a0a60869d5d30

[ "MIT" ]

null

hl/hlist.py

DmitryOlshansky/hl

5f652ee16dbd3813ad997d860b2a0a60869d5d30

[ "MIT" ]

null

hl/hlist.py

DmitryOlshansky/hl

5f652ee16dbd3813ad997d860b2a0a60869d5d30

[ "MIT" ]

null

import json from hl import search def ensure(cond, err): if not cond: raise Exception(err) def check_host(h): ensure(type(h['host']) is str, "host name must be string") ensure(type(h['tags']) is list, "tags is a list") ensure(type(h['kv']) is dict, "key-value pairs is object") class HList(object): def __init__(self, nd_json=None): if not nd_json: self.hosts = [] else: self.hosts = [json.loads(line) for line in nd_json.split("\n") if line.strip() != ''] for h in self.hosts: check_host(h) def __str__(self): return "\n".join([json.dumps(h) for h in self.hosts]) """ Adds or replace one properly structured host entry """ def add(self, host): check_host(host) for i in range(len(self.hosts)): if self.hosts[i] == host: self.hosts[i] = host return self.hosts.append(host) """ Remove hosts matching query """ def remove_by_query(self, query): to_remove = set(self.select(query)) to_keep = [self.hosts[i] for i in range(len(self.hosts)) if i not in to_remove] self.hosts = to_keep """ List hosts matching query """ def list(self, query): return [self.hosts[i] for i in self.select(query)] """ Return indices of best matching host entries """ def select(self, query): if query == "": return range(len(self.hosts)) if len(self.hosts) == 0: return [] qt = search.terms(query) hts = [search.terms(h['host']) for h in self.hosts] scores = [search.score(qt, ht) for ht in hts] best = max(scores) if best == 0: return [] else: return [pair[0] for pair in enumerate(scores) if pair[1] == best]

28.769231

97

0.553476

2711ae5601ccf6278ba86408420b2431f45ea3ad

4,096

py

Python

src/modelling/LoNGAE/utils_gcn.py

1997alireza/Movie-Casting-Problems

df555e57401ec1b120d8e9d3c2d51b1d3a070f21

[ "MIT" ]

3

2021-04-20T06:02:34.000Z

2021-04-24T04:16:45.000Z

src/modelling/LoNGAE/utils_gcn.py

1997alireza/Movie-Casting-Problems

df555e57401ec1b120d8e9d3c2d51b1d3a070f21

[ "MIT" ]

null

src/modelling/LoNGAE/utils_gcn.py

1997alireza/Movie-Casting-Problems

df555e57401ec1b120d8e9d3c2d51b1d3a070f21

[ "MIT" ]

null

""" The MIT License Copyright (c) 2017 Thomas Kipf Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. Modified from https://github.com/tkipf/gae to work with citation network data. """ import numpy as np import scipy.sparse as sp np.random.seed(1982) def sparse_to_tuple(sparse_mx): if not sp.isspmatrix_coo(sparse_mx): sparse_mx = sparse_mx.tocoo() coords = np.vstack((sparse_mx.row, sparse_mx.col)).transpose() values = sparse_mx.data shape = sparse_mx.shape return coords, values, shape def split_adjacency_data(adj): """ Function to build test set with 10% positive links and the same number of randomly sampled negative links. NOTE: Splits are randomized and results might slightly deviate from reported numbers in the paper. """ # Remove diagonal elements adj = sp.csr_matrix(adj) adj = adj - sp.dia_matrix((adj.diagonal()[np.newaxis, :], [0]), shape=adj.shape) # Check that diag is zero: assert np.diag(adj.todense()).sum() == 0 adj_triu = sp.triu(adj) adj_tuple = sparse_to_tuple(adj_triu) edges = adj_tuple[0] edges_all = sparse_to_tuple(adj)[0] num_test = int(np.floor(edges.shape[0] / 10.)) num_val = int(np.floor(edges.shape[0] / 20.)) all_edge_idx = list(range(edges.shape[0])) np.random.shuffle(all_edge_idx) val_edge_idx = all_edge_idx[:num_val] test_edge_idx = all_edge_idx[num_val:(num_val + num_test)] test_edges = edges[test_edge_idx] val_edges = edges[val_edge_idx] train_edges = np.delete(edges, np.hstack([test_edge_idx, val_edge_idx]), axis=0) def ismember(a, b, tol=5): rows_close = np.all(np.round(a - b[:, None], tol) == 0, axis=-1) return (np.all(np.any(rows_close, axis=-1), axis=-1) and np.all(np.any(rows_close, axis=0), axis=0)) test_edges_false = [] while len(test_edges_false) < len(test_edges): idx_i = np.random.randint(0, adj.shape[0]) idx_j = np.random.randint(0, adj.shape[0]) if idx_i == idx_j: continue if ismember([idx_i, idx_j], edges_all): continue if test_edges_false: if ismember([idx_j, idx_i], np.array(test_edges_false)): continue if ismember([idx_i, idx_j], np.array(test_edges_false)): continue test_edges_false.append([idx_i, idx_j]) val_edges_false = [] while len(val_edges_false) < len(val_edges): idx_i = np.random.randint(0, adj.shape[0]) idx_j = np.random.randint(0, adj.shape[0]) if idx_i == idx_j: continue if ismember([idx_i, idx_j], train_edges): continue if ismember([idx_j, idx_i], train_edges): continue if ismember([idx_i, idx_j], val_edges): continue if ismember([idx_j, idx_i], val_edges): continue if val_edges_false: if ismember([idx_j, idx_i], np.array(val_edges_false)): continue if ismember([idx_i, idx_j], np.array(val_edges_false)): continue val_edges_false.append([idx_i, idx_j]) assert ~ismember(test_edges_false, edges_all) assert ~ismember(val_edges_false, edges_all) assert ~ismember(val_edges, train_edges) assert ~ismember(test_edges, train_edges) assert ~ismember(val_edges, test_edges) # data = np.ones(train_edges.shape[0]) # NOTE: the edge list only contains single direction of edge! return np.concatenate([test_edges, np.asarray(test_edges_false)], axis=0)

35.617391

84

0.664063

6c78b646b05fca7225c4bdd2844f7e13e0034b50

919

py

Python

first_app/views/unequip.py

CONSOLNY/rglk

bc3df20f32a29ecdd66534d4cd698c1601669bbd

[ "MIT" ]

null

first_app/views/unequip.py

CONSOLNY/rglk

bc3df20f32a29ecdd66534d4cd698c1601669bbd

[ "MIT" ]

1

2016-06-02T04:50:48.000Z

2017-02-06T03:16:27.000Z

first_app/views/unequip.py

CONSOLNY/rglk

bc3df20f32a29ecdd66534d4cd698c1601669bbd

[ "MIT" ]

null

from django.shortcuts import render from django.utils.http import urlquote from django.utils.http import urlencode from django.http import HttpResponse from django.db.models import Max from math import sqrt from first_app.models import * from first_app.utils import * from first_app.views.draw_world import draw_world def unequip(request): item_id = int(request.POST.get("Equipped", -1)) if item_id == -1: return draw_world(request) char = Character.objects.first() slot = SlotCharacter.objects.get(slot=InventoryCharacter.objects.get(id=item_id)) if slot.slot_type.item == 'Weapon': attack_stats = slot.slot.content.item_stats char.attack -= attack_stats elif slot.slot_type.item == 'Armor': defense_stats = slot.slot.content.item_stats char.defense -= defense_stats slot.slot = None slot.save() char.save() return draw_world(request)

34.037037

85

0.725789

4d46e1c65c380acb4123345dc4a12942b32d5ce3

6,733

py

Python

erdantic/base.py

adsharma/erdantic

722a98eda6c7f3f69189407a949d071467a2db97

[ "MIT" ]

null

erdantic/base.py

adsharma/erdantic

722a98eda6c7f3f69189407a949d071467a2db97

[ "MIT" ]

null

erdantic/base.py

adsharma/erdantic

722a98eda6c7f3f69189407a949d071467a2db97

[ "MIT" ]

null

import inspect from abc import ABC, abstractmethod from typing import Any, Callable, Dict, Generic, List, Type, TypeVar, Union from erdantic.etyping import Final, GenericAlias, repr_type, repr_type_with_mro _row_template = """<tr><td>{name}</td><td port="{name}">{type_name}</td></tr>""" FT = TypeVar("FT", bound=Any, covariant=True) """Type variable for a field object adapted by adapter class [`Field`][erdantic.base.Field].""" class Field(ABC, Generic[FT]): """Abstract base class that adapts a field object of a data model class to work with erdantic. Concrete implementations should subclass and implement abstract methods. Attributes: field (FT): Field object on a data model class associated with this adapter """ @abstractmethod def __init__(self, field: FT): """Initialize Field adapter instance. Args: field: Field object to associate with this adapter instance """ self.field: Final[FT] = field @property @abstractmethod def name(self) -> str: # pragma: no cover """Name of this field on the parent data model.""" @property @abstractmethod def type_obj(self) -> Union[type, GenericAlias]: """Python type object for this field.""" pass @abstractmethod def is_many(self) -> bool: # pragma: no cover """Check whether this field represents a one-to-one or one-to-many relationship. Returns: bool: True if one-to-many relationship, else False. """ pass @abstractmethod def is_nullable(self) -> bool: # pragma: no cover """Check whether this field is nullable, i.e., can be `None`. Returns: bool: True if nullable, else False. """ pass @property def type_name(self) -> str: # pragma: no cover """String representation of the Python type annotation for this field.""" return repr_type(self.type_obj) def dot_row(self) -> str: """Returns the DOT language "HTML-like" syntax specification of a row detailing this field that is part of a table describing the field's parent data model. It is used as part the `label` attribute of data model's node in the graph's DOT representation. Returns: str: DOT language for table row """ return _row_template.format(name=self.name, type_name=self.type_name) def __eq__(self, other: Any) -> bool: return isinstance(other, type(self)) and hash(self) == hash(other) def __hash__(self) -> int: return id(self.field) def __repr__(self) -> str: return f"<{type(self).__name__}: '{self.name}', {self.type_name}>" _table_template = """ <<table border="0" cellborder="1" cellspacing="0"> <tr><td port="_root" colspan="2"><b>{name}</b></td></tr> {rows} </table>> """ MT = TypeVar("MT", bound=type, covariant=True) """Type variable for a data model class adapted by adapter class [`Model`][erdantic.base.Model]. Bounded by `type`.""" class Model(ABC, Generic[MT]): """Abstract base class that adapts a data model class to work with erdantic. Instances represent a node in our entity relationship diagram graph. Concrete implementations should subclass and implement abstract methods. Attributes: model (MT): Data model class associated with this adapter """ @abstractmethod def __init__(self, model: MT): """Initialize model adapter instance. Args: model: Data model class to associate with this adapter instance """ self.model: Final[MT] = model @property @abstractmethod def fields(self) -> List[Field]: # pragma: no cover """List of fields defined on this data model.""" pass @staticmethod @abstractmethod def is_model_type(obj: Any) -> bool: # pragma: no cover """Check if object is the type of data model class that this model adapter works with.""" pass @property def name(self) -> str: # pragma: no cover """Name of this data model.""" return self.model.__name__ @property def docstring(self) -> str: """Docstring for this data model.""" out = f"{self.model.__module__}.{self.model.__qualname__}" docstring = inspect.getdoc(self.model) if docstring: out += "\n\n" + docstring return out @property def key(self) -> str: """Human-readable unique identifier for this data model. Should be stable across sessions.""" return f"{self.model.__module__}.{self.model.__qualname__}" def dot_label(self) -> str: """Returns the DOT language "HTML-like" syntax specification of a table for this data model. It is used as the `label` attribute of data model's node in the graph's DOT representation. Returns: str: DOT language for table """ rows = "\n".join(field.dot_row() for field in self.fields) return _table_template.format(name=self.name, rows=rows).replace("\n", "") def __eq__(self, other) -> bool: return isinstance(other, type(self)) and hash(self) == hash(other) def __hash__(self) -> int: return hash(self.key) def __lt__(self, other) -> bool: if not isinstance(other, Model): raise ValueError( f"Can only compare between instances of Model. Given: {repr_type_with_mro(other)}" ) return self.key < other.key def __repr__(self) -> str: return f"{type(self).__name__}({self.name})" model_adapter_registry: Dict[str, Type[Model]] = {} """Registry of concrete [`Model`][erdantic.base.Model] adapter subclasses. A concrete `Model` subclass must be registered for it to be available to the diagram creation workflow.""" def register_model_adapter(type_name: str) -> Callable[[Type[Model]], Type[Model]]: """Create decorator to register a concrete [`Model`][erdantic.base.Model] adapter subclass that will be identified under the key `type_name`. A concrete `Model` subclass must be registered for it to be available to the diagram creation workflow. Args: type_name (str): Key used to identify concrete `Model` adapter subclass Returns: Callable[[Type[Model]], Type[Model]]: A registration decorator for a concrete `Model` adapter subclass """ def decorator(cls: type) -> type: global model_adapter_registry if not issubclass(cls, Model): raise ValueError("Only subclasses of Model can be registered.") model_adapter_registry[type_name] = cls return cls return decorator

33.167488

98

0.643844

8d5f344c5c4f4bf52110ad4fd6e014af6edb2d68

1,277

py

Python

handlers/gcast.py

BROKENCOBRA/VOICE-CHAT-DIRECT-PLAY

f549495e8ad070636df29a6f80f4adffdd9fb036

[ "Apache-2.0" ]

null

handlers/gcast.py

BROKENCOBRA/VOICE-CHAT-DIRECT-PLAY

f549495e8ad070636df29a6f80f4adffdd9fb036

[ "Apache-2.0" ]

null

handlers/gcast.py

BROKENCOBRA/VOICE-CHAT-DIRECT-PLAY

f549495e8ad070636df29a6f80f4adffdd9fb036

[ "Apache-2.0" ]

null

import asyncio from pyrogram import Client, filters from pyrogram.types import Message from config import SUDO_USERS from callsmusic.callsmusic import client as USER @Client.on_message(filters.command(["broadcast"])) async def broadcast(_, message: Message): sent = 0 failed = 0 if message.from_user.id not in SUDO_USERS: return else: wtf = await message.reply("`Starting a broadcast...`") if not message.reply_to_message: await wtf.edit("Please Reply to a Message to broadcast!") return lmao = message.reply_to_message.text async for dialog in USER.iter_dialogs(): try: await USER.send_message(dialog.chat.id, lmao) sent = sent + 1 await wtf.edit( f"`broadcasting...` \n\n**Sent to:** `{sent}` Chats \n**Failed in:** {failed} Chats" ) await asyncio.sleep(3) except: failed = failed + 1 # await wtf.edit(f"`broadcasting...` \n\n**Sent to:** `{sent}` Chats \n**Failed in:** {failed} Chats") await message.reply_text( f"`Broadcast Finished ` \n\n**Sent to:** `{sent}` Chats \n**Failed in:** {failed} Chats" )

33.605263

118

0.576351

0fdaf5f6da13800f3292a9594607cb058f70e077

2,958

py

Python

code_pack/geometry/obj_processor.py

DedaoLiu/Postprocessing-3D

e89822a1110f20f02b652d5a8a2ef4ec08697aac

[ "MIT" ]

null

code_pack/geometry/obj_processor.py

DedaoLiu/Postprocessing-3D

e89822a1110f20f02b652d5a8a2ef4ec08697aac

[ "MIT" ]

null

code_pack/geometry/obj_processor.py

DedaoLiu/Postprocessing-3D

e89822a1110f20f02b652d5a8a2ef4ec08697aac

[ "MIT" ]

1

2020-08-27T17:07:27.000Z

from shutil import copyfile from math import ceil import numpy as np import time def vertex_on_surface(vertex,nodes,ap): """ This function tries to locate the vertex to its closest node. nodes is a list of coordinates: [[x0,y0,z0],[x1,y1,z1],[x2,y2,z2]] nodes is sorted in an ascending order. vertex is a point coordinates: [xa,ya,za] need to find the closest node to the vertex :param vertex: :param nodes: :return: """ # time0 = time.time() print("checking vertex",vertex) # nodes.sort() # matching_point = [None,None,None] # time1 = time.time() gap = ap['element_size']*0.001 # time2 = time.time() # nodes = np.array(nodes) # time3 = time.time() vertex = np.array(vertex) # time4 = time.time() dst = np.linalg.norm(np.subtract(nodes,vertex),axis=1) # time5 = time.time() dst_min = np.amin(dst) # time6 = time.time() # dst_min_size = np.linalg.norm(dst_min) # timers = [time1-time0,time2-time1,time3-time2,time4-time3,time5-time4,time6-time5] # with open(ap["test_dir_path"]+"timer.txt","a+") as f: # f.write(str(timers).strip("[").strip("]")+"\n") if dst_min < gap: return True else: return False def vertex_on_surface_1(vertex,nodes,ap): """ This function tries to locate the vertex to its closest node. nodes is a list of coordinates: [[x0,y0,z0],[x1,y1,z1],[x2,y2,z2]] nodes is sorted in an ascending order. vertex is a point coordinates: [xa,ya,za] need to find the closest node to the vertex :param vertex: :param nodes: :return: """ print("checking vertex",vertex) # nodes.sort() # matching_point = [None,None,None] gap = ap['element_size']*0.001 nodes = np.array(nodes) vertex = np.array(vertex) dst = np.linalg.norm(np.subtract(nodes,vertex),axis=1) dst_min = np.amin(dst) # dst_min_size = np.linalg.norm(dst_min) if dst_min < gap: return True else: return False def add_color(ap,surf_nodes): surf_nodes = np.array(surf_nodes) # obj_color_path = ap['test_dir_path']+ap['inp_name']+'c.obj' # copyfile(ap['obj_path'], obj_color_path) test_path = ap['test_dir_path'] with open(test_path+ap['inp_name']+'.obj','r') as f: lines = f.readlines() f.close() with open(test_path+ap['inp_name']+'_c.obj','w') as f: for idx in range(len(lines)): line = lines[idx] if line[:2] == "v ": vertex = line.strip("v").strip().split() vertex = [float(vertex[0]), float(vertex[1]), float(vertex[2])] if vertex_on_surface(vertex, surf_nodes, ap): lines[idx]=line.strip()+" 1 0 0"+"\n" else: lines[idx]=line.strip()+" 0 0 0"+"\n" else: pass for line in lines: f.write(line) f.close()

30.494845

88

0.588911

72479158c4169991579fa9823273482c564c1e60

1,929

py

Python

assignment2.py

seraphinatarrant/edinburgh_coursework

4d2ddc10204d6605abf755df263c4821bd3344cc

[ "CC0-1.0" ]

null

assignment2.py

seraphinatarrant/edinburgh_coursework

4d2ddc10204d6605abf755df263c4821bd3344cc

[ "CC0-1.0" ]

null

assignment2.py

seraphinatarrant/edinburgh_coursework

4d2ddc10204d6605abf755df263c4821bd3344cc

[ "CC0-1.0" ]

null

import argparse import numpy as np import spacy from spacy.language import Language from spacy.pipeline import Pipe ### GLOBALS ### # Choices of combination function: average, sum, first, last, maxpool COMBINATION_FUNCTION = "average" class ContextualVectors(Pipe): def __init__(self, nlp): self._nlp = nlp self.combination_function = COMBINATION_FUNCTION ### modify this here for different versions of part 3 def __call__(self, doc): if type(doc) == str: doc = self._nlp(doc) self.lengths = doc._.trf_data.align.lengths self.tensors = doc._.trf_data.tensors doc.user_token_hooks["vector"] = self.vector return doc ### HERE is where vectors are set def vector(self, token): trf_vector = [] for len_idx in range(self.lengths[token.i]): try: trf_vector.append(self.tensors[0][0][token.i+len_idx]) except IndexError: print("Error") print(token) return None trf_vector = np.array(trf_vector) return self.combine_vectors(trf_vector) def combine_vectors(self, trf_vector): if self.combination_function == "first": return trf_vector[0] if self.combination_function == "last": return trf_vector[-1] if self.combination_function == "maxpool": return np.maximum(trf_vector, axis=0) if self.combination_function == "average": return np.average(trf_vector, axis=0) if self.combination_function == "sum": return np.sum(trf_vector, axis=0) @Language.factory("trf_vector_hook", assigns=["doc.user_token_hooks"]) def create_contextual_hook(nlp, name): return ContextualVectors(nlp) def setup_argparse(): p = argparse.ArgumentParser() return p.parse_args() if __name__ == "__main__": args = setup_argparse()

29.676923

110

0.639191

4c3d911ece42b44ade148f99eb3aa2e58eb57f71

1,088

py

Python

cava/nightwatch/indent.py

arthurp/lapis2

1c0644cf1eeb2ddc5a735ca002d561e308ed6014

[ "BSD-2-Clause" ]

null

cava/nightwatch/indent.py

arthurp/lapis2

1c0644cf1eeb2ddc5a735ca002d561e308ed6014

[ "BSD-2-Clause" ]

null

cava/nightwatch/indent.py

arthurp/lapis2

1c0644cf1eeb2ddc5a735ca002d561e308ed6014

[ "BSD-2-Clause" ]

null

import subprocess _indent_options = ("-nbad -bap -bc -bbo -hnl -br -brs -c50 -cd50 -ncdb -ce -ci4 -cli0 -d0 -di1 -nfc1 " "-i4 -ip0 -l120 -nlp -npcs -nprs -psl -sai -saf -saw -ncs -nsc -sob -nfca -cp50 -ss " "-ts8 -il1 -cbi0 -nut".strip().split()) def indent_c(code): try: with subprocess.Popen(["indent"] + _indent_options, encoding="utf-8" if hasattr(code, "encode") else None, stdout=subprocess.PIPE, stdin=subprocess.PIPE) as proc: stdout, _ = proc.communicate(code) return stdout except FileNotFoundError: # Couldn't find indent, to just continue with raw code return code def write_file_c(filename, data, indent=True, filename_prefix=""): with open(filename_prefix + filename, "w" if hasattr(data, "encode") else "wb") as fi: fi.write(indent_c(data) if indent else data) def write_file_py(filename, data, filename_prefix=""): with open(filename_prefix + filename, "w" if hasattr(data, "encode") else "wb") as fi: fi.write(data)

40.296296

114

0.617647

133aad08134d7a9512553a9292d46e1c2d7253e7

260,797

py

Python

instances/passenger_demand/pas-20210422-1717-int14000000000000001e/39.py

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

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

instances/passenger_demand/pas-20210422-1717-int14000000000000001e/39.py

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

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

instances/passenger_demand/pas-20210422-1717-int14000000000000001e/39.py

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

bba1e6af5bc8d9deaa2dc3b83f6fe9ddf15d2a11

[ "BSD-3-Clause" ]

null

""" PASSENGERS """ numPassengers = 26581 passenger_arriving = ( (7, 9, 6, 6, 5, 3, 4, 0, 7, 0, 0, 2, 0, 10, 9, 1, 7, 8, 3, 1, 2, 1, 3, 2, 1, 0), # 0 (8, 10, 11, 8, 8, 2, 1, 1, 4, 1, 1, 1, 0, 5, 7, 3, 6, 5, 4, 4, 2, 1, 1, 1, 2, 0), # 1 (6, 7, 5, 8, 6, 4, 3, 3, 3, 1, 0, 0, 0, 13, 4, 3, 4, 3, 6, 3, 2, 4, 0, 0, 0, 0), # 2 (9, 9, 8, 8, 8, 1, 4, 4, 4, 3, 2, 2, 0, 5, 3, 9, 6, 7, 9, 8, 4, 4, 0, 0, 0, 0), # 3 (15, 12, 6, 13, 7, 1, 3, 3, 6, 3, 1, 0, 0, 15, 13, 6, 8, 6, 3, 2, 4, 7, 5, 3, 0, 0), # 4 (12, 9, 5, 5, 12, 3, 3, 2, 0, 1, 1, 0, 0, 8, 7, 3, 2, 6, 3, 7, 1, 5, 2, 2, 1, 0), # 5 (10, 6, 4, 11, 8, 3, 2, 2, 3, 3, 0, 0, 0, 12, 10, 9, 4, 9, 3, 5, 5, 4, 3, 3, 1, 0), # 6 (8, 9, 5, 6, 7, 4, 0, 0, 4, 3, 2, 0, 0, 11, 9, 10, 6, 4, 1, 4, 2, 3, 3, 0, 2, 0), # 7 (8, 14, 6, 14, 8, 8, 5, 4, 7, 2, 0, 0, 0, 13, 19, 8, 6, 6, 4, 3, 1, 3, 2, 3, 1, 0), # 8 (9, 8, 8, 12, 6, 2, 4, 5, 5, 1, 3, 1, 0, 10, 16, 9, 6, 13, 4, 1, 3, 3, 4, 1, 0, 0), # 9 (11, 5, 8, 18, 9, 4, 2, 3, 4, 2, 0, 0, 0, 10, 9, 5, 3, 6, 7, 6, 2, 6, 4, 0, 3, 0), # 10 (6, 7, 13, 8, 7, 10, 4, 3, 3, 2, 2, 0, 0, 9, 10, 4, 6, 7, 3, 3, 5, 6, 2, 2, 2, 0), # 11 (6, 11, 14, 9, 9, 1, 1, 2, 5, 5, 2, 0, 0, 9, 11, 8, 10, 9, 9, 6, 7, 5, 2, 2, 2, 0), # 12 (11, 11, 16, 13, 8, 4, 6, 4, 4, 2, 2, 0, 0, 13, 20, 13, 5, 17, 6, 10, 3, 7, 4, 0, 0, 0), # 13 (16, 11, 13, 9, 10, 4, 5, 7, 7, 3, 1, 1, 0, 18, 11, 11, 2, 6, 12, 4, 6, 8, 2, 4, 2, 0), # 14 (11, 13, 12, 20, 13, 2, 6, 6, 5, 4, 2, 0, 0, 15, 14, 12, 5, 15, 10, 6, 7, 5, 1, 0, 1, 0), # 15 (8, 16, 11, 12, 11, 4, 3, 9, 11, 3, 2, 0, 0, 10, 13, 6, 9, 16, 7, 10, 2, 6, 3, 0, 4, 0), # 16 (11, 8, 14, 10, 11, 3, 8, 7, 6, 1, 0, 1, 0, 12, 11, 9, 9, 7, 8, 4, 8, 7, 5, 0, 0, 0), # 17 (20, 13, 9, 6, 12, 5, 9, 3, 12, 7, 3, 0, 0, 15, 10, 12, 11, 4, 9, 6, 2, 5, 6, 3, 2, 0), # 18 (9, 11, 8, 13, 6, 8, 3, 2, 5, 1, 0, 1, 0, 18, 11, 11, 5, 12, 6, 8, 2, 7, 0, 2, 1, 0), # 19 (16, 12, 8, 12, 2, 4, 3, 7, 5, 1, 2, 0, 0, 14, 7, 5, 6, 10, 5, 8, 3, 3, 4, 0, 0, 0), # 20 (17, 17, 16, 12, 8, 4, 4, 7, 4, 3, 2, 2, 0, 12, 19, 11, 15, 9, 4, 4, 5, 9, 3, 3, 1, 0), # 21 (18, 18, 13, 7, 8, 6, 3, 4, 6, 0, 4, 1, 0, 19, 13, 14, 8, 5, 7, 6, 7, 8, 3, 2, 1, 0), # 22 (7, 15, 10, 19, 10, 5, 4, 6, 2, 0, 3, 0, 0, 19, 16, 12, 7, 13, 9, 5, 5, 2, 5, 4, 1, 0), # 23 (16, 10, 13, 12, 10, 6, 4, 2, 5, 0, 0, 1, 0, 11, 15, 16, 12, 14, 11, 7, 3, 4, 5, 3, 1, 0), # 24 (12, 15, 15, 9, 9, 6, 6, 12, 4, 0, 3, 0, 0, 10, 11, 11, 8, 8, 5, 2, 6, 6, 4, 5, 3, 0), # 25 (16, 19, 16, 13, 12, 6, 6, 4, 6, 6, 3, 0, 0, 17, 12, 9, 6, 13, 7, 5, 6, 5, 3, 2, 0, 0), # 26 (16, 17, 8, 6, 14, 10, 3, 15, 5, 2, 1, 1, 0, 7, 10, 10, 7, 13, 7, 5, 5, 4, 3, 4, 1, 0), # 27 (12, 25, 14, 10, 15, 5, 8, 2, 8, 3, 1, 0, 0, 13, 8, 10, 5, 12, 8, 4, 5, 3, 2, 3, 1, 0), # 28 (15, 16, 15, 10, 10, 2, 8, 3, 9, 1, 3, 2, 0, 9, 17, 10, 3, 11, 8, 4, 2, 9, 1, 5, 1, 0), # 29 (23, 13, 16, 15, 9, 0, 4, 3, 3, 2, 0, 2, 0, 23, 12, 5, 8, 6, 10, 11, 4, 7, 6, 3, 1, 0), # 30 (16, 13, 19, 15, 8, 3, 10, 7, 12, 3, 0, 0, 0, 12, 18, 8, 7, 9, 11, 7, 4, 4, 5, 1, 0, 0), # 31 (18, 15, 7, 9, 12, 4, 3, 4, 5, 7, 1, 1, 0, 11, 13, 4, 10, 8, 9, 6, 4, 1, 4, 3, 0, 0), # 32 (17, 12, 17, 16, 10, 7, 8, 7, 3, 2, 2, 1, 0, 8, 15, 9, 5, 13, 10, 4, 2, 8, 4, 5, 1, 0), # 33 (18, 9, 18, 10, 8, 6, 11, 9, 4, 3, 0, 1, 0, 9, 18, 11, 12, 11, 9, 6, 1, 1, 1, 1, 1, 0), # 34 (15, 8, 16, 14, 14, 9, 3, 6, 5, 2, 2, 1, 0, 12, 14, 10, 7, 16, 5, 3, 5, 8, 5, 1, 1, 0), # 35 (9, 9, 11, 9, 15, 10, 3, 4, 4, 3, 1, 0, 0, 12, 15, 10, 8, 9, 3, 6, 1, 4, 4, 2, 0, 0), # 36 (25, 7, 12, 14, 5, 7, 5, 8, 7, 2, 1, 4, 0, 14, 12, 7, 6, 11, 5, 6, 2, 5, 7, 2, 2, 0), # 37 (14, 13, 13, 14, 9, 4, 5, 6, 2, 0, 2, 1, 0, 15, 9, 14, 7, 9, 2, 3, 7, 5, 5, 1, 1, 0), # 38 (7, 11, 16, 17, 13, 11, 5, 6, 7, 3, 1, 1, 0, 25, 8, 13, 9, 4, 3, 2, 1, 6, 2, 3, 2, 0), # 39 (21, 15, 13, 13, 8, 4, 5, 7, 3, 2, 3, 1, 0, 9, 11, 9, 7, 11, 8, 5, 5, 5, 4, 3, 1, 0), # 40 (20, 15, 8, 21, 7, 5, 3, 6, 5, 2, 1, 3, 0, 17, 5, 12, 9, 9, 4, 5, 2, 5, 3, 1, 5, 0), # 41 (11, 13, 11, 15, 11, 4, 5, 4, 3, 1, 2, 0, 0, 17, 13, 7, 9, 7, 4, 4, 5, 3, 6, 4, 0, 0), # 42 (13, 17, 13, 15, 6, 5, 7, 7, 5, 4, 5, 2, 0, 20, 8, 15, 6, 6, 7, 6, 4, 3, 6, 2, 3, 0), # 43 (17, 14, 18, 11, 15, 4, 5, 3, 14, 0, 1, 1, 0, 14, 10, 11, 6, 8, 15, 7, 3, 7, 4, 1, 3, 0), # 44 (20, 14, 10, 14, 9, 4, 8, 5, 4, 1, 1, 1, 0, 14, 13, 14, 3, 13, 8, 6, 1, 6, 3, 2, 1, 0), # 45 (15, 14, 12, 17, 13, 3, 3, 4, 6, 3, 1, 0, 0, 12, 14, 16, 9, 13, 6, 4, 4, 10, 3, 0, 1, 0), # 46 (20, 18, 9, 23, 14, 5, 10, 6, 12, 0, 0, 0, 0, 18, 11, 10, 6, 16, 5, 7, 5, 5, 3, 2, 0, 0), # 47 (14, 9, 11, 19, 11, 7, 7, 6, 6, 2, 1, 1, 0, 15, 13, 7, 8, 9, 9, 5, 5, 6, 3, 1, 5, 0), # 48 (12, 8, 13, 10, 13, 5, 10, 4, 2, 3, 2, 3, 0, 15, 14, 6, 9, 12, 9, 4, 5, 6, 3, 3, 2, 0), # 49 (15, 16, 7, 9, 11, 2, 3, 4, 7, 2, 1, 2, 0, 21, 14, 12, 6, 14, 4, 5, 1, 5, 8, 6, 2, 0), # 50 (18, 12, 12, 12, 10, 8, 4, 4, 2, 3, 5, 2, 0, 21, 16, 11, 10, 15, 9, 7, 5, 5, 3, 4, 1, 0), # 51 (15, 12, 14, 11, 12, 6, 10, 2, 2, 1, 0, 0, 0, 8, 7, 8, 7, 6, 5, 5, 2, 3, 2, 0, 1, 0), # 52 (10, 14, 12, 21, 8, 7, 4, 4, 6, 4, 1, 3, 0, 20, 10, 9, 11, 16, 13, 4, 6, 6, 6, 2, 0, 0), # 53 (17, 11, 12, 11, 12, 3, 6, 5, 4, 2, 1, 1, 0, 13, 17, 14, 7, 13, 3, 11, 3, 5, 3, 0, 1, 0), # 54 (18, 20, 18, 10, 13, 5, 4, 5, 3, 1, 3, 2, 0, 20, 14, 6, 9, 4, 7, 5, 2, 6, 3, 2, 0, 0), # 55 (16, 16, 3, 18, 17, 3, 7, 6, 8, 1, 1, 0, 0, 20, 12, 12, 13, 2, 7, 6, 8, 3, 6, 3, 0, 0), # 56 (14, 14, 14, 11, 10, 4, 7, 4, 7, 3, 0, 0, 0, 12, 16, 11, 8, 13, 4, 7, 4, 7, 3, 3, 2, 0), # 57 (20, 14, 15, 13, 10, 5, 6, 5, 3, 1, 3, 0, 0, 14, 11, 2, 3, 13, 6, 5, 0, 4, 4, 4, 0, 0), # 58 (14, 15, 9, 14, 13, 5, 3, 5, 3, 1, 2, 1, 0, 13, 11, 8, 9, 15, 8, 7, 4, 3, 3, 3, 3, 0), # 59 (11, 14, 8, 10, 12, 5, 9, 3, 8, 3, 2, 0, 0, 11, 9, 8, 6, 9, 5, 8, 4, 3, 1, 1, 1, 0), # 60 (15, 10, 8, 10, 7, 10, 8, 5, 2, 3, 2, 0, 0, 9, 10, 11, 4, 10, 4, 2, 2, 5, 5, 0, 3, 0), # 61 (14, 21, 12, 13, 6, 7, 3, 2, 7, 0, 2, 1, 0, 13, 13, 4, 12, 12, 6, 5, 3, 8, 4, 1, 2, 0), # 62 (12, 11, 7, 9, 7, 6, 12, 5, 4, 3, 2, 0, 0, 7, 11, 8, 9, 9, 1, 6, 4, 5, 5, 2, 1, 0), # 63 (20, 11, 13, 8, 13, 3, 5, 3, 3, 3, 2, 1, 0, 16, 13, 10, 5, 8, 3, 4, 1, 4, 2, 2, 0, 0), # 64 (11, 14, 16, 13, 9, 4, 7, 3, 8, 3, 2, 0, 0, 21, 14, 7, 7, 12, 5, 3, 2, 7, 3, 1, 1, 0), # 65 (18, 7, 19, 12, 7, 5, 8, 7, 5, 2, 6, 1, 0, 24, 13, 9, 10, 19, 3, 9, 4, 6, 6, 1, 0, 0), # 66 (15, 11, 16, 12, 5, 4, 4, 6, 10, 1, 4, 0, 0, 11, 10, 8, 6, 16, 5, 3, 3, 5, 5, 3, 0, 0), # 67 (8, 11, 12, 13, 7, 6, 3, 6, 5, 3, 0, 1, 0, 8, 10, 11, 10, 7, 5, 7, 1, 3, 5, 2, 2, 0), # 68 (19, 10, 15, 10, 14, 7, 7, 1, 4, 2, 3, 2, 0, 16, 16, 4, 12, 15, 3, 5, 2, 5, 7, 2, 1, 0), # 69 (22, 21, 7, 15, 10, 8, 6, 3, 3, 2, 2, 2, 0, 17, 16, 8, 3, 8, 8, 3, 4, 12, 7, 4, 3, 0), # 70 (15, 14, 8, 11, 9, 8, 4, 9, 7, 1, 2, 1, 0, 12, 14, 12, 10, 12, 4, 3, 0, 5, 6, 1, 0, 0), # 71 (16, 12, 9, 12, 12, 6, 5, 8, 6, 2, 1, 2, 0, 14, 13, 7, 10, 14, 4, 4, 5, 4, 2, 3, 3, 0), # 72 (16, 8, 19, 15, 13, 5, 5, 2, 10, 2, 6, 1, 0, 18, 8, 11, 6, 8, 4, 6, 4, 3, 4, 3, 4, 0), # 73 (18, 15, 12, 15, 11, 3, 6, 4, 2, 3, 2, 0, 0, 14, 12, 8, 6, 14, 4, 4, 2, 5, 3, 2, 1, 0), # 74 (17, 13, 15, 8, 7, 6, 11, 4, 6, 3, 3, 1, 0, 12, 13, 11, 11, 7, 3, 4, 1, 11, 2, 4, 1, 0), # 75 (6, 14, 9, 12, 9, 4, 2, 0, 4, 2, 1, 1, 0, 10, 17, 6, 7, 11, 7, 6, 3, 8, 5, 6, 0, 0), # 76 (20, 13, 11, 14, 15, 9, 9, 0, 4, 3, 0, 1, 0, 11, 10, 11, 7, 2, 4, 2, 4, 4, 4, 2, 1, 0), # 77 (12, 7, 17, 14, 13, 6, 5, 5, 5, 1, 2, 3, 0, 11, 16, 9, 6, 7, 4, 6, 2, 5, 6, 5, 0, 0), # 78 (21, 12, 6, 9, 13, 10, 5, 4, 6, 4, 2, 0, 0, 14, 11, 8, 9, 9, 5, 3, 2, 4, 3, 0, 1, 0), # 79 (16, 13, 10, 18, 14, 9, 2, 6, 7, 1, 0, 4, 0, 14, 13, 9, 9, 19, 2, 7, 4, 5, 3, 3, 0, 0), # 80 (13, 12, 11, 9, 13, 6, 3, 2, 4, 3, 0, 3, 0, 12, 14, 10, 5, 13, 5, 5, 7, 7, 3, 4, 0, 0), # 81 (19, 15, 10, 11, 12, 7, 5, 2, 3, 2, 2, 2, 0, 16, 11, 8, 4, 10, 7, 4, 2, 9, 1, 3, 3, 0), # 82 (20, 14, 9, 16, 10, 2, 6, 3, 5, 2, 5, 2, 0, 22, 10, 7, 9, 12, 4, 6, 3, 7, 5, 1, 1, 0), # 83 (12, 14, 15, 9, 5, 4, 6, 5, 7, 3, 1, 0, 0, 14, 11, 11, 7, 6, 5, 5, 4, 5, 3, 0, 2, 0), # 84 (14, 10, 10, 14, 12, 6, 7, 4, 6, 1, 0, 0, 0, 12, 9, 10, 8, 14, 4, 9, 4, 6, 1, 2, 1, 0), # 85 (15, 15, 11, 10, 10, 9, 7, 3, 4, 3, 3, 1, 0, 15, 10, 8, 3, 18, 7, 3, 7, 5, 3, 4, 3, 0), # 86 (9, 15, 13, 12, 9, 3, 5, 3, 8, 0, 1, 1, 0, 12, 12, 7, 10, 17, 8, 3, 1, 2, 3, 3, 0, 0), # 87 (10, 14, 16, 10, 14, 7, 3, 3, 3, 2, 1, 2, 0, 18, 9, 13, 3, 12, 3, 4, 2, 6, 6, 3, 2, 0), # 88 (26, 9, 19, 15, 7, 5, 7, 3, 2, 1, 1, 0, 0, 13, 14, 13, 8, 14, 6, 6, 4, 3, 4, 3, 1, 0), # 89 (11, 12, 13, 16, 9, 6, 4, 1, 6, 2, 0, 2, 0, 15, 9, 11, 2, 7, 8, 2, 2, 9, 5, 2, 3, 0), # 90 (11, 13, 12, 14, 9, 5, 4, 2, 1, 1, 1, 0, 0, 14, 10, 8, 4, 9, 6, 4, 4, 5, 5, 3, 2, 0), # 91 (6, 13, 3, 8, 11, 5, 4, 3, 5, 1, 3, 0, 0, 10, 16, 10, 7, 8, 8, 6, 4, 7, 3, 2, 3, 0), # 92 (15, 13, 11, 12, 5, 5, 5, 2, 3, 3, 1, 1, 0, 15, 13, 9, 9, 9, 6, 8, 7, 9, 3, 1, 2, 0), # 93 (12, 12, 5, 10, 6, 3, 6, 0, 5, 2, 0, 0, 0, 11, 11, 11, 5, 6, 9, 4, 1, 8, 4, 1, 0, 0), # 94 (16, 10, 10, 12, 12, 10, 6, 8, 2, 1, 0, 2, 0, 7, 11, 4, 5, 12, 5, 8, 2, 4, 6, 2, 1, 0), # 95 (11, 11, 11, 13, 6, 3, 6, 3, 5, 0, 2, 4, 0, 16, 13, 8, 7, 17, 6, 2, 4, 2, 6, 2, 1, 0), # 96 (11, 11, 11, 10, 11, 2, 7, 6, 8, 2, 1, 1, 0, 15, 7, 7, 5, 8, 4, 6, 2, 4, 8, 0, 0, 0), # 97 (21, 9, 10, 14, 12, 6, 2, 1, 5, 1, 0, 1, 0, 18, 10, 11, 2, 14, 4, 3, 4, 4, 2, 1, 0, 0), # 98 (11, 9, 8, 12, 10, 4, 4, 3, 4, 3, 1, 0, 0, 12, 10, 8, 7, 15, 10, 7, 2, 9, 6, 3, 3, 0), # 99 (11, 14, 11, 8, 4, 7, 3, 4, 9, 2, 3, 0, 0, 17, 12, 8, 10, 10, 2, 4, 2, 3, 4, 0, 0, 0), # 100 (11, 11, 10, 13, 13, 6, 9, 1, 8, 5, 1, 4, 0, 16, 11, 10, 6, 12, 10, 5, 1, 4, 4, 4, 2, 0), # 101 (16, 5, 14, 12, 21, 7, 8, 6, 4, 4, 1, 0, 0, 16, 9, 5, 5, 8, 4, 4, 1, 4, 3, 0, 0, 0), # 102 (17, 12, 16, 7, 16, 3, 3, 3, 9, 2, 1, 1, 0, 26, 10, 7, 3, 9, 2, 2, 3, 4, 6, 4, 2, 0), # 103 (12, 11, 17, 15, 11, 3, 2, 5, 5, 3, 2, 0, 0, 14, 13, 9, 8, 8, 4, 3, 3, 6, 2, 3, 3, 0), # 104 (12, 18, 8, 16, 12, 0, 4, 4, 2, 1, 1, 2, 0, 14, 9, 10, 6, 9, 8, 6, 8, 5, 4, 5, 0, 0), # 105 (13, 12, 9, 16, 5, 3, 9, 2, 5, 5, 0, 3, 0, 19, 8, 6, 5, 7, 3, 2, 10, 4, 3, 2, 2, 0), # 106 (12, 11, 15, 12, 8, 6, 6, 3, 1, 3, 2, 1, 0, 11, 11, 8, 6, 7, 2, 6, 5, 6, 1, 1, 0, 0), # 107 (9, 6, 7, 14, 10, 5, 3, 2, 12, 1, 1, 0, 0, 9, 5, 8, 5, 10, 7, 2, 5, 7, 5, 1, 0, 0), # 108 (11, 7, 10, 9, 13, 3, 5, 5, 8, 0, 2, 1, 0, 14, 12, 10, 5, 11, 9, 5, 5, 4, 2, 2, 1, 0), # 109 (11, 11, 14, 14, 6, 6, 6, 3, 3, 1, 1, 3, 0, 13, 10, 10, 4, 9, 2, 4, 4, 3, 6, 1, 1, 0), # 110 (14, 8, 13, 7, 12, 2, 2, 3, 3, 1, 1, 1, 0, 13, 13, 17, 6, 10, 4, 4, 5, 4, 5, 2, 0, 0), # 111 (12, 10, 15, 23, 12, 1, 6, 3, 5, 0, 1, 0, 0, 11, 6, 5, 11, 12, 5, 3, 2, 2, 4, 5, 0, 0), # 112 (11, 9, 9, 7, 10, 3, 2, 4, 4, 4, 1, 1, 0, 6, 10, 3, 12, 8, 6, 1, 7, 4, 4, 1, 2, 0), # 113 (10, 12, 9, 10, 7, 3, 4, 2, 4, 2, 0, 1, 0, 10, 9, 6, 8, 16, 4, 6, 2, 2, 4, 7, 0, 0), # 114 (10, 9, 5, 13, 15, 4, 2, 3, 2, 1, 4, 1, 0, 8, 10, 10, 6, 13, 4, 7, 3, 5, 2, 2, 1, 0), # 115 (16, 12, 4, 16, 5, 3, 1, 5, 6, 2, 2, 2, 0, 9, 12, 6, 7, 10, 6, 4, 5, 8, 6, 2, 2, 0), # 116 (17, 13, 12, 7, 7, 8, 4, 2, 4, 2, 2, 2, 0, 11, 11, 3, 6, 9, 4, 3, 1, 4, 5, 4, 0, 0), # 117 (12, 3, 8, 8, 6, 3, 1, 6, 6, 4, 0, 1, 0, 16, 11, 7, 8, 12, 2, 4, 3, 5, 2, 3, 2, 0), # 118 (13, 12, 10, 7, 12, 6, 8, 0, 3, 2, 2, 3, 0, 9, 13, 11, 6, 9, 2, 8, 4, 5, 3, 2, 1, 0), # 119 (8, 10, 7, 11, 16, 3, 3, 2, 2, 3, 3, 2, 0, 8, 8, 2, 6, 18, 6, 4, 5, 6, 6, 1, 1, 0), # 120 (16, 11, 16, 11, 7, 2, 3, 6, 3, 1, 1, 0, 0, 14, 14, 6, 6, 9, 5, 3, 4, 3, 3, 3, 4, 0), # 121 (14, 10, 9, 6, 14, 6, 4, 5, 6, 2, 2, 3, 0, 16, 10, 10, 8, 9, 3, 5, 4, 2, 5, 2, 1, 0), # 122 (17, 11, 18, 10, 6, 2, 3, 3, 1, 2, 1, 1, 0, 11, 9, 8, 3, 11, 5, 4, 3, 3, 5, 0, 1, 0), # 123 (14, 8, 6, 10, 6, 11, 6, 2, 6, 0, 0, 0, 0, 8, 9, 9, 4, 9, 2, 3, 5, 7, 2, 3, 0, 0), # 124 (7, 8, 10, 12, 9, 6, 2, 2, 4, 0, 3, 0, 0, 12, 12, 11, 8, 15, 4, 5, 7, 2, 3, 2, 2, 0), # 125 (10, 11, 6, 11, 4, 6, 5, 3, 4, 0, 4, 0, 0, 7, 13, 9, 8, 6, 4, 2, 5, 0, 4, 2, 1, 0), # 126 (9, 7, 10, 12, 11, 2, 2, 3, 5, 3, 2, 2, 0, 7, 9, 7, 6, 9, 6, 1, 3, 5, 2, 0, 1, 0), # 127 (14, 11, 16, 3, 5, 4, 5, 2, 6, 4, 1, 1, 0, 19, 15, 10, 1, 19, 5, 8, 4, 10, 2, 1, 0, 0), # 128 (8, 3, 16, 10, 3, 4, 8, 4, 4, 1, 3, 2, 0, 6, 11, 18, 3, 10, 5, 3, 3, 8, 2, 0, 0, 0), # 129 (11, 9, 11, 6, 11, 4, 4, 4, 4, 3, 2, 0, 0, 12, 8, 10, 10, 4, 4, 4, 3, 5, 7, 3, 0, 0), # 130 (14, 9, 10, 11, 9, 6, 2, 3, 5, 1, 3, 0, 0, 10, 8, 8, 11, 15, 4, 8, 4, 2, 5, 2, 0, 0), # 131 (11, 2, 12, 10, 11, 8, 2, 5, 7, 4, 1, 2, 0, 12, 10, 7, 6, 8, 2, 4, 4, 5, 1, 1, 1, 0), # 132 (9, 5, 10, 17, 13, 7, 3, 4, 7, 0, 2, 0, 0, 15, 14, 8, 6, 17, 4, 3, 2, 4, 1, 0, 1, 0), # 133 (11, 13, 11, 8, 7, 6, 6, 2, 3, 3, 1, 0, 0, 11, 10, 7, 7, 4, 6, 1, 2, 4, 5, 2, 2, 0), # 134 (8, 12, 21, 5, 11, 6, 6, 6, 8, 4, 4, 2, 0, 10, 6, 12, 4, 12, 4, 2, 5, 5, 4, 2, 0, 0), # 135 (16, 8, 8, 15, 5, 3, 8, 2, 7, 0, 0, 2, 0, 14, 6, 9, 4, 11, 1, 4, 3, 5, 4, 2, 1, 0), # 136 (11, 10, 12, 14, 11, 2, 3, 6, 4, 2, 0, 0, 0, 16, 10, 7, 5, 9, 2, 3, 2, 3, 4, 3, 0, 0), # 137 (17, 6, 7, 6, 9, 4, 2, 2, 5, 0, 3, 1, 0, 8, 12, 7, 7, 10, 4, 2, 3, 2, 2, 1, 2, 0), # 138 (9, 8, 10, 11, 9, 6, 1, 3, 10, 3, 1, 0, 0, 11, 14, 3, 4, 11, 6, 4, 2, 4, 3, 2, 0, 0), # 139 (12, 9, 8, 12, 7, 2, 6, 5, 10, 1, 1, 1, 0, 11, 11, 9, 3, 11, 2, 9, 3, 2, 1, 3, 1, 0), # 140 (9, 7, 11, 10, 7, 7, 2, 9, 5, 1, 1, 0, 0, 13, 5, 8, 6, 7, 7, 2, 5, 5, 6, 1, 2, 0), # 141 (7, 4, 12, 10, 7, 4, 2, 3, 3, 0, 2, 2, 0, 14, 12, 13, 7, 11, 4, 3, 3, 3, 7, 4, 1, 0), # 142 (9, 4, 5, 9, 10, 1, 4, 3, 3, 1, 1, 1, 0, 23, 5, 10, 6, 4, 6, 2, 7, 5, 1, 2, 0, 0), # 143 (11, 5, 9, 10, 8, 6, 5, 2, 5, 1, 2, 0, 0, 15, 12, 3, 9, 9, 5, 4, 3, 3, 2, 3, 0, 0), # 144 (13, 11, 5, 16, 8, 5, 3, 1, 5, 2, 0, 0, 0, 11, 7, 6, 4, 12, 1, 8, 0, 2, 0, 2, 0, 0), # 145 (10, 11, 3, 10, 12, 5, 2, 2, 6, 0, 2, 1, 0, 14, 5, 11, 2, 12, 6, 3, 1, 11, 5, 1, 1, 0), # 146 (12, 11, 7, 11, 7, 3, 2, 2, 3, 1, 0, 0, 0, 8, 17, 9, 3, 12, 7, 9, 0, 3, 6, 0, 2, 0), # 147 (8, 9, 10, 10, 12, 5, 3, 2, 3, 0, 1, 0, 0, 11, 14, 6, 4, 10, 5, 2, 2, 3, 2, 2, 1, 0), # 148 (8, 7, 10, 14, 14, 3, 4, 3, 5, 1, 0, 1, 0, 9, 7, 8, 8, 11, 8, 9, 1, 5, 3, 2, 1, 0), # 149 (11, 9, 6, 11, 6, 4, 4, 2, 4, 2, 1, 0, 0, 18, 12, 9, 3, 4, 4, 0, 4, 2, 4, 1, 1, 0), # 150 (14, 5, 11, 16, 7, 4, 4, 2, 4, 1, 2, 0, 0, 11, 11, 2, 5, 7, 8, 2, 5, 5, 2, 5, 1, 0), # 151 (10, 9, 6, 11, 10, 5, 1, 3, 2, 1, 1, 0, 0, 17, 5, 8, 6, 9, 6, 4, 2, 5, 5, 2, 0, 0), # 152 (15, 6, 12, 7, 10, 1, 5, 2, 7, 3, 0, 2, 0, 11, 13, 1, 4, 11, 6, 4, 5, 6, 3, 2, 0, 0), # 153 (14, 6, 10, 9, 8, 3, 3, 4, 1, 3, 1, 0, 0, 12, 13, 4, 3, 10, 4, 2, 0, 1, 4, 0, 0, 0), # 154 (11, 6, 8, 7, 8, 8, 1, 3, 8, 1, 2, 1, 0, 16, 12, 9, 2, 6, 4, 4, 2, 6, 5, 1, 0, 0), # 155 (13, 10, 16, 12, 11, 2, 4, 2, 7, 0, 1, 3, 0, 7, 9, 8, 9, 7, 5, 4, 3, 6, 2, 1, 0, 0), # 156 (13, 3, 9, 7, 11, 2, 6, 1, 5, 2, 1, 0, 0, 7, 10, 7, 4, 5, 4, 5, 1, 4, 4, 1, 1, 0), # 157 (9, 7, 6, 7, 3, 4, 1, 3, 4, 3, 0, 0, 0, 11, 2, 7, 7, 8, 6, 1, 3, 3, 1, 1, 3, 0), # 158 (14, 5, 12, 13, 8, 2, 4, 3, 0, 3, 2, 0, 0, 16, 8, 4, 4, 6, 2, 2, 2, 3, 4, 5, 0, 0), # 159 (8, 6, 11, 9, 13, 8, 5, 2, 2, 1, 0, 0, 0, 10, 5, 2, 6, 5, 5, 1, 1, 5, 5, 3, 0, 0), # 160 (9, 5, 10, 6, 10, 3, 3, 2, 5, 2, 0, 0, 0, 4, 6, 14, 6, 9, 7, 6, 2, 3, 4, 2, 2, 0), # 161 (9, 6, 9, 14, 5, 2, 6, 5, 5, 0, 1, 1, 0, 11, 6, 7, 4, 6, 7, 2, 0, 4, 0, 5, 3, 0), # 162 (10, 6, 3, 7, 7, 7, 6, 3, 5, 6, 0, 2, 0, 11, 8, 9, 3, 6, 5, 2, 3, 6, 4, 2, 0, 0), # 163 (4, 5, 4, 2, 13, 3, 6, 4, 4, 1, 0, 0, 0, 9, 12, 2, 3, 13, 4, 5, 4, 8, 2, 0, 0, 0), # 164 (9, 7, 10, 6, 11, 3, 3, 3, 7, 0, 1, 1, 0, 10, 10, 4, 3, 6, 1, 1, 1, 1, 3, 2, 0, 0), # 165 (4, 5, 10, 4, 7, 7, 3, 3, 6, 0, 1, 3, 0, 3, 9, 7, 4, 5, 3, 5, 2, 7, 4, 2, 0, 0), # 166 (5, 4, 6, 10, 5, 4, 4, 2, 4, 0, 2, 0, 0, 6, 6, 14, 5, 8, 4, 1, 3, 8, 3, 1, 2, 0), # 167 (11, 9, 6, 8, 8, 6, 2, 2, 3, 1, 2, 1, 0, 11, 12, 9, 2, 4, 3, 0, 5, 4, 0, 0, 0, 0), # 168 (11, 6, 5, 3, 11, 0, 1, 4, 4, 0, 0, 0, 0, 11, 10, 7, 2, 8, 3, 3, 3, 2, 5, 0, 0, 0), # 169 (6, 3, 7, 10, 9, 4, 3, 3, 5, 1, 0, 2, 0, 10, 6, 3, 4, 4, 5, 3, 2, 1, 1, 2, 1, 0), # 170 (9, 9, 10, 8, 3, 1, 1, 4, 0, 0, 1, 0, 0, 8, 10, 3, 3, 9, 4, 2, 5, 3, 3, 1, 0, 0), # 171 (4, 5, 5, 7, 8, 2, 4, 0, 6, 0, 0, 0, 0, 7, 8, 6, 2, 9, 4, 1, 1, 1, 0, 0, 0, 0), # 172 (4, 3, 4, 8, 4, 2, 4, 0, 3, 0, 0, 0, 0, 6, 7, 4, 0, 5, 2, 2, 4, 2, 1, 2, 2, 0), # 173 (11, 7, 2, 13, 7, 6, 1, 1, 2, 2, 2, 0, 0, 6, 4, 2, 6, 6, 6, 1, 2, 6, 2, 1, 0, 0), # 174 (5, 5, 10, 9, 1, 2, 0, 1, 4, 0, 3, 0, 0, 5, 3, 2, 4, 5, 6, 3, 0, 4, 4, 1, 1, 0), # 175 (7, 1, 3, 5, 7, 2, 0, 1, 3, 1, 1, 1, 0, 5, 1, 10, 5, 3, 5, 1, 2, 2, 0, 1, 0, 0), # 176 (6, 7, 3, 10, 1, 4, 3, 0, 3, 3, 2, 0, 0, 8, 3, 3, 5, 2, 3, 1, 3, 6, 1, 0, 1, 0), # 177 (4, 3, 5, 3, 5, 2, 0, 3, 6, 0, 0, 0, 0, 9, 4, 4, 3, 4, 1, 3, 3, 4, 1, 1, 1, 0), # 178 (0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), # 179 ) station_arriving_intensity = ( (7.029211809720476, 7.735403983570434, 7.29579652145751, 8.700534883408807, 7.776559850653457, 4.394116904852274, 5.804449861523481, 6.514446642171193, 8.52613868703521, 5.541221021731318, 5.887371229439844, 6.857081109628643, 7.117432297609708), # 0 (7.496058012827964, 8.246084971802663, 7.777485227862214, 9.275201954587263, 8.291486472463932, 4.684377017659578, 6.187256517769172, 6.943319212067992, 9.089143456866074, 5.90657296918801, 6.2763345903385845, 7.309703325140097, 7.587708306415797), # 1 (7.9614122125716245, 8.754739239247371, 8.257259199766379, 9.847582786530712, 8.804548163249642, 4.9734791603174235, 6.568545911144986, 7.370475347066188, 9.64990152962857, 6.270479285028765, 6.663752408286839, 7.760525712874277, 8.056110759493567), # 2 (8.423460910405188, 9.259348702711026, 8.733215217047796, 10.415406970544904, 9.313726346402664, 5.260276871619158, 6.946805098307138, 7.79422162049231, 10.206189225289531, 6.631495777796654, 7.0480877765583365, 8.207759958902646, 8.520781928755916), # 3 (8.880390607782374, 9.757895279000085, 9.203450059584252, 10.976404097935598, 9.81700244531509, 5.543623690358135, 7.320521135911843, 8.212864605672882, 10.75578286381579, 6.988178256034751, 7.4278037884268056, 8.64961774929667, 8.979864086115745), # 4 (9.330387806156915, 10.248360884921025, 9.666060507253526, 11.528303760008551, 10.312357883378994, 5.822373155327701, 7.688181080615314, 8.62471087593443, 11.296458765174183, 7.339082528286129, 7.801363537165986, 9.084310770127807, 9.43149950348596), # 5 (9.771639006982534, 10.728727437280302, 10.119143339933412, 12.068835548069513, 10.79777408398646, 6.09537880532121, 8.048271989073768, 9.028067004603484, 11.825993249331543, 7.682764403093862, 8.167230116049597, 9.510050707467531, 9.87383045277945), # 6 (10.202330711712957, 11.196976852884385, 10.56079533750169, 12.595729053424249, 11.271232470529577, 6.36149417913201, 8.39928091794342, 9.421239565006573, 12.342162636254702, 8.017779689001022, 8.523866618351377, 9.925049247387301, 10.304999205909127), # 7 (10.62064942180191, 11.651091048539739, 10.989113279836156, 13.1067138673785, 11.730714466400421, 6.619572815553446, 8.739694923880478, 9.802535130470215, 12.842743245910489, 8.342684194550685, 8.86973613734505, 10.327518075958585, 10.723148034787885), # 8 (11.02478163870312, 12.089051941052832, 11.402193946814586, 13.599519581238038, 12.174201494991074, 6.868468253378878, 9.068001063541168, 10.170260274320949, 13.325511398265744, 8.65603372828592, 9.20330176630435, 10.71566887925284, 11.126419211328628), # 9 (11.412913863870306, 12.508841447230123, 11.798134118314776, 14.071875786308604, 12.599674979693622, 7.107034031401651, 9.382686393581697, 10.522721569885295, 13.7882434132873, 8.956384098749801, 9.523026598503003, 11.087713343341534, 11.512955007444255), # 10 (11.783232598757209, 12.90844148387809, 12.175030574214501, 14.521512073895957, 13.005116343900148, 7.334123688415116, 9.682237970658283, 10.85822559048978, 14.228715610941991, 9.242291114485408, 9.82737372721475, 11.441863154296136, 11.880897695047656), # 11 (12.133924344817538, 13.285833967803178, 12.530980094391557, 14.946158035305858, 13.38850701100273, 7.5485907632126175, 9.965142851427137, 11.17507890946093, 14.644704311196652, 9.512310584035802, 10.114806245713309, 11.776329998188096, 12.22838954605175), # 12 (12.463175603505027, 13.639000815811869, 12.864079458723728, 15.343543261844063, 13.747828404393443, 7.749288794587514, 10.22988809254448, 11.471588100125276, 15.033985834018106, 9.764998315944066, 10.383787247272418, 12.08932556108889, 12.55357283236943), # 13 (12.769172876273403, 13.965923944710624, 13.172425447088806, 15.71139734481631, 14.081061947464386, 7.935071321333148, 10.474960750666526, 11.746059735809345, 15.39433649937319, 9.998910118753269, 10.6327798251658, 12.379061529069986, 12.85458982591359), # 14 (13.050102664576398, 14.264585271305906, 13.45411483936456, 16.047449875528383, 14.386189063607633, 8.104791882242878, 10.698847882449478, 11.99680038983966, 15.723532627228748, 10.212601801006487, 10.860247072667189, 12.64374958820284, 13.129582798597134), # 15 (13.30415146986772, 14.532966712404187, 13.707244415428796, 16.349430445286004, 14.661191176215267, 8.257304016110044, 10.900036544549568, 12.222116635542745, 16.019350537551603, 10.404629171246796, 11.06465208305032, 12.881601424558916, 13.376694022332964), # 16 (13.529505793601107, 14.769050184811926, 13.929910955159293, 16.61506864539496, 14.904049708679375, 8.391461261728, 11.077013793622996, 12.420315046245145, 16.27956655030858, 10.573548038017254, 11.24445794958892, 13.090828724209679, 13.594065769033982), # 17 (13.724352137230287, 14.970817605335585, 14.120211238433834, 16.842094067160993, 15.112746084392025, 8.506117157890104, 11.228266686325993, 12.589702195273366, 16.501956985466535, 10.717914209860952, 11.398127765556712, 13.269643173226603, 13.779840310613086), # 18 (13.88687700220898, 15.136250890781643, 14.27624204513021, 17.02823630188984, 15.285261726745313, 8.600125243389693, 11.352282279314753, 12.728584655953943, 16.68429816299229, 10.83628349532096, 11.52412462422743, 13.416256457681136, 13.932159918983176), # 19 (14.015266889990915, 15.263331957956549, 14.396100155126206, 17.171224940887296, 15.419578059131322, 8.672339057020126, 11.44754762924551, 12.835269001613405, 16.82436640285268, 10.927211702940342, 11.62091161887481, 13.528880263644748, 14.049166866057154), # 20 (14.107708302029813, 15.350042723666784, 14.477882348299607, 17.26878957545908, 15.513676504942126, 8.72161213757475, 11.512549792774463, 12.908061805578273, 16.91993802501453, 10.989254641262178, 11.686951842772585, 13.60572627718891, 14.12900342374791), # 21 (14.162387739779412, 15.394365104718803, 14.5196854045282, 17.31865979691097, 15.565538487569807, 8.746798023846914, 11.54577582655784, 12.945269641175082, 16.968789349444684, 11.02096811882954, 11.720708389194478, 13.645006184385087, 14.16981186396836), # 22 (14.182550708679697, 15.39961303155007, 14.524892455418383, 17.324903137860087, 15.578824878445637, 8.75, 11.549725603163076, 12.949291358024693, 16.974896728395063, 11.024709181527207, 11.724941252026436, 13.649856607224509, 14.175), # 23 (14.197417378247815, 15.396551851851854, 14.524040740740743, 17.324134722222226, 15.586350659060795, 8.75, 11.547555337690634, 12.943700000000002, 16.974078333333335, 11.02241086419753, 11.724474410774413, 13.648720987654322, 14.175), # 24 (14.211970122296213, 15.390517832647463, 14.522359396433473, 17.322614454732513, 15.593710923832306, 8.75, 11.543278463648836, 12.932716049382718, 16.97246141975309, 11.01788637402835, 11.723548759196907, 13.646479195244629, 14.175), # 25 (14.226207826667249, 15.381603155006863, 14.519871467764064, 17.320359619341563, 15.600905415789548, 8.75, 11.53696140563221, 12.916546913580248, 16.97006672839506, 11.011210992226795, 11.722172677391198, 13.643161957018751, 14.175), # 26 (14.240129377203292, 15.3699, 14.5166, 17.3173875, 15.607933877961901, 8.75, 11.528670588235297, 12.895400000000002, 16.966915, 11.00246, 11.720354545454546, 13.638800000000003, 14.175), # 27 (14.253733659746702, 15.355500548696845, 14.51256803840878, 17.313715380658437, 15.614796053378763, 8.75, 11.518472436052612, 12.869482716049385, 16.963026975308644, 10.9917086785551, 11.718102743484225, 13.633424051211708, 14.175), # 28 (14.26701956013985, 15.338496982167355, 14.50779862825789, 17.30936054526749, 15.62149168506951, 8.75, 11.506433373678693, 12.839002469135803, 16.95842339506173, 10.979032309099225, 11.715425651577503, 13.627064837677183, 14.175), # 29 (14.279985964225098, 15.318981481481483, 14.502314814814815, 17.30434027777778, 15.628020516063533, 8.75, 11.492619825708061, 12.804166666666665, 16.953125, 10.964506172839508, 11.71233164983165, 13.619753086419752, 14.175), # 30 (14.292631757844802, 15.297046227709194, 14.496139643347053, 17.29867186213992, 15.634382289390214, 8.75, 11.477098216735257, 12.765182716049384, 16.947152530864198, 10.948205550983083, 11.708829118343933, 13.611519524462738, 14.175), # 31 (14.304955826841338, 15.27278340192044, 14.489296159122084, 17.29237258230453, 15.640576748078935, 8.75, 11.4599349713548, 12.72225802469136, 16.940526728395064, 10.930205724737084, 11.704926437211622, 13.602394878829449, 14.175), # 32 (14.316957057057056, 15.246285185185185, 14.481807407407409, 17.28545972222222, 15.646603635159089, 8.75, 11.441196514161222, 12.675600000000001, 16.933268333333334, 10.910581975308643, 11.700631986531986, 13.59240987654321, 14.175), # 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152 (10.897033588275185, 8.61712435158296, 11.482251647627416, 13.159187033792707, 13.11470454602428, 7.318791048167222, 6.929063810741687, 7.841232219061167, 14.058309087801755, 7.062929369089481, 8.316815455699683, 9.92402828118809, 11.68464226121364), # 153 (10.81262003423102, 8.534185365897834, 11.422924477189063, 13.078280853413174, 13.042726487861813, 7.288854057067317, 6.87025313968732, 7.8124531118187726, 14.009925457519413, 7.013019423465095, 8.260930064044857, 9.861181374586256, 11.6191092156515), # 154 (10.72606825993309, 8.448916964658093, 11.361703047816906, 12.99488293561833, 12.968776807822776, 7.257796941777861, 6.809664942048866, 7.782239138911491, 13.95962058285218, 6.9614650283384565, 8.203154434992767, 9.796328772892876, 11.551739314998438), # 155 (10.637327353293314, 8.361234611710243, 11.298532183655539, 12.908919102893627, 12.892813332647707, 7.225581056931246, 6.74723555776578, 7.750525284055986, 13.907325442542877, 6.9082075819648825, 8.143420671591107, 9.729406117611353, 11.48249014823076), # 156 (10.546346402223609, 8.271053770900794, 11.233356708849547, 12.820315177724513, 12.81479388907716, 7.19216775715986, 6.6829013267775075, 7.717246530968915, 13.852971015334345, 6.853188482599679, 8.08166087688757, 9.660349050245092, 11.411319304324769), # 157 (10.450553324967336, 8.176634369081162, 11.163028735463298, 12.725677414311741, 12.731153548219398, 7.155434266843955, 6.615149409299001, 7.680115733289122, 13.792326928238738, 6.794712282807602, 8.01583405355452, 9.586639389872076, 11.335080203181485), # 158 (10.335201473769764, 8.06829144743927, 11.069432945764184, 12.605568022303835, 12.62126783369428, 7.103165507209945, 6.535497868740003, 7.626098945870136, 13.700998165711002, 6.723193391738244, 7.934383709866593, 9.493907533156353, 11.235598705688274), # 159 (10.198820932866035, 7.945135419957, 10.950689341138245, 12.458008514572404, 12.482988183885514, 7.034077814466758, 6.443141247737298, 7.553838865338286, 13.576395318120113, 6.637687912608051, 7.8361633120533565, 9.380702728442985, 11.110988852451014), # 160 (10.042510876420344, 7.8079692153126565, 10.808065760674433, 12.28440150525942, 12.317750373994958, 6.94900813819844, 6.338754024409627, 7.464240746353693, 13.420161673798626, 6.5389214704393135, 7.7220383164395905, 9.248074456470599, 10.962523662746737), # 161 (9.8673704785969, 7.657595762184535, 10.642830043461695, 12.086149608506858, 12.126990179224487, 6.848793427989039, 6.223010676875733, 7.358209843576484, 13.233940521079093, 6.427619690254325, 7.592874179350069, 9.09707219797781, 10.791476155852466), # 162 (9.674498913559898, 7.494817989250934, 10.456250028588983, 11.864655438456708, 11.912143374775964, 6.734270633422602, 6.096585683254362, 7.2366514116667755, 13.019375148294069, 6.304508197075376, 7.449536357109572, 8.928745433703247, 10.599119351045232), # 163 (9.464995355473539, 7.320438825190149, 10.249593555145248, 11.621321609250947, 11.674645735851264, 6.606276704083181, 5.960153521664253, 7.100470705284697, 12.778108843776113, 6.170312615924756, 7.292890306042875, 8.744143644385526, 10.386726267602059), # 164 (9.239958978502024, 7.135261198680485, 10.024128462219437, 11.357550735031554, 11.415933037652254, 6.465648589554821, 5.814388670224151, 6.950572979090365, 12.511784895857772, 6.02575857182476, 7.123801482474756, 8.544316310763268, 10.155569924799979), # 165 (9.000488956809557, 6.940088038400237, 9.7811225889005, 11.074745429940503, 11.137441055380801, 6.313223239421572, 5.659965607052801, 6.787863487743908, 12.222046592871603, 5.871571689797677, 6.943135342729992, 8.330312913575103, 9.906923341916015), # 166 (8.747684464560333, 6.735722273027703, 9.521843774277388, 10.774308308119782, 10.840605564238773, 6.149837603267482, 5.497558810268945, 6.613247485905448, 11.91053722315016, 5.7084775948658, 6.751757343133359, 8.103182933559642, 9.642059538227196), # 167 (8.482644675918554, 6.52296683124118, 9.247559857439049, 10.457641983711365, 10.526862339428039, 5.9763286306765995, 5.327842757991326, 6.427630228235103, 11.578900075025999, 5.5372019120514215, 6.550532940009634, 7.863975851455517, 9.362251533010546), # 168 (8.206468765048422, 6.302624641718972, 8.959538677474432, 10.126149070857236, 10.197647156150468, 5.793533271232973, 5.151491928338689, 6.231916969393004, 11.228778436831673, 5.358470266376831, 6.3403275896835956, 7.613741148001342, 9.0687723455431), # 169 (7.9202559061141375, 6.0754986331393726, 8.659048073472489, 9.781232183699368, 9.854395789607928, 5.60228847452065, 4.9691807994297745, 6.027012964039266, 10.861815596899735, 5.173008282864322, 6.122006748480023, 7.353528303935743, 8.762894995101878), # 170 (7.6251052732799005, 5.842391734180682, 8.34735588452217, 9.424293936379751, 9.498544015002288, 5.403431190123678, 4.781583849383328, 5.813823466834017, 10.47965484356274, 4.981541586536184, 5.896435872723688, 7.0843867999973416, 8.445892500963913), # 171 (7.322116040709912, 5.604106873521197, 8.025729949712423, 9.056736943040356, 9.131527607535416, 5.197798367626108, 4.5893755563180925, 5.593253732437379, 10.083939465153241, 4.784795802414712, 5.664480418739371, 6.80736611692476, 8.119037882406225), # 172 (7.012387382568372, 5.3614469798392195, 7.695438108132197, 8.679963817823166, 8.754782342409182, 4.9862269566119855, 4.39323039835281, 5.366209015509473, 9.676312750003792, 4.583496555522195, 5.427005842851849, 6.523515735456615, 7.783604158705848), # 173 (6.697018473019482, 5.115214981813045, 7.357748198870443, 8.295377174870158, 8.369743994825454, 4.76955390666536, 4.193822853606226, 5.133594570710425, 9.25841798644695, 4.3783694708809255, 5.1848776013858995, 6.233885136331535, 7.440864349139807), # 174 (6.377108486227438, 4.866213808120973, 7.013928061016112, 7.904379628323315, 7.977848339986097, 4.54861616737028, 3.9918274001970815, 4.896315652700355, 8.831898462815268, 4.170140173513194, 4.938961150666297, 5.939523800288141, 7.092091472985131), # 175 (6.053756596356447, 4.615246387441302, 6.66524553365815, 7.508373792324615, 7.580531153092983, 4.324250688310793, 3.787918516244121, 4.655277516139389, 8.3983974674413, 3.959534288441294, 4.690121947017822, 5.641481208065051, 6.738558549518844), # 176 (5.7280619775707065, 4.363115648452332, 6.3129684558855095, 7.108762281016037, 7.179228209347984, 4.097294419070949, 3.582770679866088, 4.411385415687646, 7.959558288657599, 3.7472774406875144, 4.43922544676525, 5.340806840400891, 6.381538598017975), # 177 (5.401123804034416, 4.11062451983236, 5.95836466678714, 6.7069477085395635, 6.775375283952959, 3.8685843092347962, 3.3770583691817246, 4.165544606005252, 7.51702421479672, 3.5340952552741505, 4.187137106233358, 5.038550178034279, 6.022304637759553), # 178 (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 179 ) passenger_arriving_acc = ( (7, 9, 6, 6, 5, 3, 4, 0, 7, 0, 0, 2, 0, 10, 9, 1, 7, 8, 3, 1, 2, 1, 3, 2, 1, 0), # 0 (15, 19, 17, 14, 13, 5, 5, 1, 11, 1, 1, 3, 0, 15, 16, 4, 13, 13, 7, 5, 4, 2, 4, 3, 3, 0), # 1 (21, 26, 22, 22, 19, 9, 8, 4, 14, 2, 1, 3, 0, 28, 20, 7, 17, 16, 13, 8, 6, 6, 4, 3, 3, 0), # 2 (30, 35, 30, 30, 27, 10, 12, 8, 18, 5, 3, 5, 0, 33, 23, 16, 23, 23, 22, 16, 10, 10, 4, 3, 3, 0), # 3 (45, 47, 36, 43, 34, 11, 15, 11, 24, 8, 4, 5, 0, 48, 36, 22, 31, 29, 25, 18, 14, 17, 9, 6, 3, 0), # 4 (57, 56, 41, 48, 46, 14, 18, 13, 24, 9, 5, 5, 0, 56, 43, 25, 33, 35, 28, 25, 15, 22, 11, 8, 4, 0), # 5 (67, 62, 45, 59, 54, 17, 20, 15, 27, 12, 5, 5, 0, 68, 53, 34, 37, 44, 31, 30, 20, 26, 14, 11, 5, 0), # 6 (75, 71, 50, 65, 61, 21, 20, 15, 31, 15, 7, 5, 0, 79, 62, 44, 43, 48, 32, 34, 22, 29, 17, 11, 7, 0), # 7 (83, 85, 56, 79, 69, 29, 25, 19, 38, 17, 7, 5, 0, 92, 81, 52, 49, 54, 36, 37, 23, 32, 19, 14, 8, 0), # 8 (92, 93, 64, 91, 75, 31, 29, 24, 43, 18, 10, 6, 0, 102, 97, 61, 55, 67, 40, 38, 26, 35, 23, 15, 8, 0), # 9 (103, 98, 72, 109, 84, 35, 31, 27, 47, 20, 10, 6, 0, 112, 106, 66, 58, 73, 47, 44, 28, 41, 27, 15, 11, 0), # 10 (109, 105, 85, 117, 91, 45, 35, 30, 50, 22, 12, 6, 0, 121, 116, 70, 64, 80, 50, 47, 33, 47, 29, 17, 13, 0), # 11 (115, 116, 99, 126, 100, 46, 36, 32, 55, 27, 14, 6, 0, 130, 127, 78, 74, 89, 59, 53, 40, 52, 31, 19, 15, 0), # 12 (126, 127, 115, 139, 108, 50, 42, 36, 59, 29, 16, 6, 0, 143, 147, 91, 79, 106, 65, 63, 43, 59, 35, 19, 15, 0), # 13 (142, 138, 128, 148, 118, 54, 47, 43, 66, 32, 17, 7, 0, 161, 158, 102, 81, 112, 77, 67, 49, 67, 37, 23, 17, 0), # 14 (153, 151, 140, 168, 131, 56, 53, 49, 71, 36, 19, 7, 0, 176, 172, 114, 86, 127, 87, 73, 56, 72, 38, 23, 18, 0), # 15 (161, 167, 151, 180, 142, 60, 56, 58, 82, 39, 21, 7, 0, 186, 185, 120, 95, 143, 94, 83, 58, 78, 41, 23, 22, 0), # 16 (172, 175, 165, 190, 153, 63, 64, 65, 88, 40, 21, 8, 0, 198, 196, 129, 104, 150, 102, 87, 66, 85, 46, 23, 22, 0), # 17 (192, 188, 174, 196, 165, 68, 73, 68, 100, 47, 24, 8, 0, 213, 206, 141, 115, 154, 111, 93, 68, 90, 52, 26, 24, 0), # 18 (201, 199, 182, 209, 171, 76, 76, 70, 105, 48, 24, 9, 0, 231, 217, 152, 120, 166, 117, 101, 70, 97, 52, 28, 25, 0), # 19 (217, 211, 190, 221, 173, 80, 79, 77, 110, 49, 26, 9, 0, 245, 224, 157, 126, 176, 122, 109, 73, 100, 56, 28, 25, 0), # 20 (234, 228, 206, 233, 181, 84, 83, 84, 114, 52, 28, 11, 0, 257, 243, 168, 141, 185, 126, 113, 78, 109, 59, 31, 26, 0), # 21 (252, 246, 219, 240, 189, 90, 86, 88, 120, 52, 32, 12, 0, 276, 256, 182, 149, 190, 133, 119, 85, 117, 62, 33, 27, 0), # 22 (259, 261, 229, 259, 199, 95, 90, 94, 122, 52, 35, 12, 0, 295, 272, 194, 156, 203, 142, 124, 90, 119, 67, 37, 28, 0), # 23 (275, 271, 242, 271, 209, 101, 94, 96, 127, 52, 35, 13, 0, 306, 287, 210, 168, 217, 153, 131, 93, 123, 72, 40, 29, 0), # 24 (287, 286, 257, 280, 218, 107, 100, 108, 131, 52, 38, 13, 0, 316, 298, 221, 176, 225, 158, 133, 99, 129, 76, 45, 32, 0), # 25 (303, 305, 273, 293, 230, 113, 106, 112, 137, 58, 41, 13, 0, 333, 310, 230, 182, 238, 165, 138, 105, 134, 79, 47, 32, 0), # 26 (319, 322, 281, 299, 244, 123, 109, 127, 142, 60, 42, 14, 0, 340, 320, 240, 189, 251, 172, 143, 110, 138, 82, 51, 33, 0), # 27 (331, 347, 295, 309, 259, 128, 117, 129, 150, 63, 43, 14, 0, 353, 328, 250, 194, 263, 180, 147, 115, 141, 84, 54, 34, 0), # 28 (346, 363, 310, 319, 269, 130, 125, 132, 159, 64, 46, 16, 0, 362, 345, 260, 197, 274, 188, 151, 117, 150, 85, 59, 35, 0), # 29 (369, 376, 326, 334, 278, 130, 129, 135, 162, 66, 46, 18, 0, 385, 357, 265, 205, 280, 198, 162, 121, 157, 91, 62, 36, 0), # 30 (385, 389, 345, 349, 286, 133, 139, 142, 174, 69, 46, 18, 0, 397, 375, 273, 212, 289, 209, 169, 125, 161, 96, 63, 36, 0), # 31 (403, 404, 352, 358, 298, 137, 142, 146, 179, 76, 47, 19, 0, 408, 388, 277, 222, 297, 218, 175, 129, 162, 100, 66, 36, 0), # 32 (420, 416, 369, 374, 308, 144, 150, 153, 182, 78, 49, 20, 0, 416, 403, 286, 227, 310, 228, 179, 131, 170, 104, 71, 37, 0), # 33 (438, 425, 387, 384, 316, 150, 161, 162, 186, 81, 49, 21, 0, 425, 421, 297, 239, 321, 237, 185, 132, 171, 105, 72, 38, 0), # 34 (453, 433, 403, 398, 330, 159, 164, 168, 191, 83, 51, 22, 0, 437, 435, 307, 246, 337, 242, 188, 137, 179, 110, 73, 39, 0), # 35 (462, 442, 414, 407, 345, 169, 167, 172, 195, 86, 52, 22, 0, 449, 450, 317, 254, 346, 245, 194, 138, 183, 114, 75, 39, 0), # 36 (487, 449, 426, 421, 350, 176, 172, 180, 202, 88, 53, 26, 0, 463, 462, 324, 260, 357, 250, 200, 140, 188, 121, 77, 41, 0), # 37 (501, 462, 439, 435, 359, 180, 177, 186, 204, 88, 55, 27, 0, 478, 471, 338, 267, 366, 252, 203, 147, 193, 126, 78, 42, 0), # 38 (508, 473, 455, 452, 372, 191, 182, 192, 211, 91, 56, 28, 0, 503, 479, 351, 276, 370, 255, 205, 148, 199, 128, 81, 44, 0), # 39 (529, 488, 468, 465, 380, 195, 187, 199, 214, 93, 59, 29, 0, 512, 490, 360, 283, 381, 263, 210, 153, 204, 132, 84, 45, 0), # 40 (549, 503, 476, 486, 387, 200, 190, 205, 219, 95, 60, 32, 0, 529, 495, 372, 292, 390, 267, 215, 155, 209, 135, 85, 50, 0), # 41 (560, 516, 487, 501, 398, 204, 195, 209, 222, 96, 62, 32, 0, 546, 508, 379, 301, 397, 271, 219, 160, 212, 141, 89, 50, 0), # 42 (573, 533, 500, 516, 404, 209, 202, 216, 227, 100, 67, 34, 0, 566, 516, 394, 307, 403, 278, 225, 164, 215, 147, 91, 53, 0), # 43 (590, 547, 518, 527, 419, 213, 207, 219, 241, 100, 68, 35, 0, 580, 526, 405, 313, 411, 293, 232, 167, 222, 151, 92, 56, 0), # 44 (610, 561, 528, 541, 428, 217, 215, 224, 245, 101, 69, 36, 0, 594, 539, 419, 316, 424, 301, 238, 168, 228, 154, 94, 57, 0), # 45 (625, 575, 540, 558, 441, 220, 218, 228, 251, 104, 70, 36, 0, 606, 553, 435, 325, 437, 307, 242, 172, 238, 157, 94, 58, 0), # 46 (645, 593, 549, 581, 455, 225, 228, 234, 263, 104, 70, 36, 0, 624, 564, 445, 331, 453, 312, 249, 177, 243, 160, 96, 58, 0), # 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159 (10.198820932866035, 7.28304080162725, 9.125574450948537, 9.343506385929302, 8.321992122590341, 4.103212058438943, 3.221570623868649, 3.147432860557619, 4.525465106040038, 1.6594219781520132, 1.3060272186755595, 0.7817252273702489, 0.0, 11.110988852451014, 8.598977501072737, 6.530136093377798, 4.978265934456038, 9.050930212080075, 4.406406004780667, 3.221570623868649, 2.9308657560278157, 4.160996061295171, 3.114502128643102, 1.8251148901897079, 0.6620946183297501, 0.0), # 160 (10.042510876420344, 7.1573051140366015, 9.006721467228694, 9.213301128944565, 8.211833582663305, 4.053588080615757, 3.1693770122048135, 3.1101003109807053, 4.473387224599541, 1.6347303676098288, 1.2870063860732652, 0.77067287137255, 0.0, 10.962523662746737, 8.477401585098049, 6.435031930366326, 4.904191102829485, 8.946774449199083, 4.354140435372988, 3.1693770122048135, 2.8954200575826836, 4.105916791331652, 3.071100376314856, 1.801344293445739, 0.6506641012760548, 0.0), # 161 (9.8673704785969, 7.01946278200249, 8.86902503621808, 9.064612206380144, 8.08466011948299, 3.9951294996602726, 3.1115053384378664, 3.0659207681568685, 4.411313507026364, 1.6069049225635816, 1.2654790298916783, 0.7580893498314843, 0.0, 10.791476155852466, 8.338982848146326, 6.3273951494583915, 4.820714767690744, 8.822627014052728, 4.292289075419616, 3.1115053384378664, 2.8536639283287664, 4.042330059741495, 3.0215374021267154, 1.773805007243616, 0.6381329801820447, 0.0), # 162 (9.674498913559898, 6.870249823480022, 8.71354169049082, 8.898491578842531, 7.941428916517308, 3.928324536163185, 3.048292841627181, 3.015271421527823, 4.339791716098023, 1.5761270492688444, 1.2415893928515955, 0.7440621194752707, 0.0, 10.599119351045232, 8.184683314227977, 6.207946964257977, 4.728381147806532, 8.679583432196045, 4.221379990138953, 3.048292841627181, 2.8059460972594175, 3.970714458258654, 2.9661638596141775, 1.742708338098164, 0.6245681657709112, 0.0), # 163 (9.464995355473539, 6.710402256424303, 8.54132796262104, 8.71599120693821, 7.783097157234176, 3.853661410715189, 2.9800767608321266, 2.9585294605352903, 4.259369614592037, 1.5425781539811894, 1.2154817176738126, 0.7286786370321272, 0.0, 10.386726267602059, 8.015465007353399, 6.077408588369063, 4.627734461943566, 8.518739229184074, 4.141941244749407, 2.9800767608321266, 2.752615293367992, 3.891548578617088, 2.905330402312737, 1.7082655925242083, 0.6100365687658459, 0.0), # 164 (9.239958978502024, 6.5406560987904445, 8.353440385182864, 8.518163051273666, 7.610622025101502, 3.771628343906979, 2.9071943351120755, 2.8960720746209856, 4.1705949652859235, 1.5064396429561904, 1.1873002470791263, 0.7120263592302724, 0.0, 10.155569924799979, 7.832289951532995, 5.936501235395631, 4.51931892886857, 8.341189930571847, 4.05450090446938, 2.9071943351120755, 2.694020245647842, 3.805311012550751, 2.839387683757889, 1.670688077036573, 0.5946050998900405, 0.0), # 165 (9.000488956809557, 6.361747368533551, 8.150935490750417, 8.306059072455376, 7.4249607035872005, 3.682713556329251, 2.8299828035264003, 2.8282764532266285, 4.074015530957201, 1.4678929224494195, 1.157189223788332, 0.6941927427979253, 0.0, 9.906923341916015, 7.636120170777177, 5.78594611894166, 4.403678767348258, 8.148031061914402, 3.95958703451728, 2.8299828035264003, 2.630509683092322, 3.7124803517936003, 2.768686357485126, 1.6301870981500834, 0.5783406698666865, 0.0), # 166 (8.747684464560333, 6.174412083608727, 7.934869811897824, 8.080731231089835, 7.2270703761591815, 3.5874052685726983, 2.7487794051344725, 2.7555197857939366, 3.9701790743833865, 1.4271193987164503, 1.1252928905222266, 0.6752652444633036, 0.0, 9.642059538227196, 7.427917689096338, 5.626464452611132, 4.28135819614935, 7.940358148766773, 3.8577277001115116, 2.7487794051344725, 2.562432334694784, 3.6135351880795907, 2.693577077029946, 1.5869739623795647, 0.5613101894189753, 0.0), # 167 (8.482644675918554, 5.979386261971081, 7.706299881199207, 7.843231487783524, 7.017908226285359, 3.4861917012280164, 2.663921378995663, 2.6781792617646265, 3.8596333583419993, 1.3843004780128556, 1.0917554900016058, 0.6553313209546264, 0.0, 9.362251533010546, 7.20864453050089, 5.458777450008029, 4.152901434038566, 7.7192667166839986, 3.7494509664704774, 2.663921378995663, 2.490136929448583, 3.5089541131426794, 2.614410495927842, 1.5412599762398416, 0.5435805692700985, 0.0), # 168 (8.206468765048422, 5.777405921575724, 7.466282231228694, 7.594611803142927, 6.798431437433646, 3.3795610748859013, 2.5757459641693443, 2.5966320705804184, 3.7429261456105576, 1.339617566594208, 1.0567212649472661, 0.6344784290001119, 0.0, 9.0687723455431, 6.9792627190012295, 5.28360632473633, 4.018852699782624, 7.485852291221115, 3.635284898812586, 2.5757459641693443, 2.413972196347072, 3.399215718716823, 2.5315372677143095, 1.493256446245739, 0.5252187201432478, 0.0), # 169 (7.9202559061141375, 5.569207080377758, 7.215873394560408, 7.335924137774526, 6.569597193071951, 3.268001610137046, 2.4845903997148873, 2.5112554016830275, 3.620605198966578, 1.2932520707160806, 1.020334458080004, 0.6127940253279787, 0.0, 8.762894995101878, 6.740734278607764, 5.101672290400019, 3.879756212148241, 7.241210397933156, 3.5157575623562387, 2.4845903997148873, 2.3342868643836043, 3.2847985965359756, 2.4453080459248424, 1.4431746789120816, 0.5062915527616144, 0.0), # 170 (7.6251052732799005, 5.355525756332291, 6.956129903768475, 7.068220452284813, 6.3323626766681915, 3.152001527572146, 2.390791924691664, 2.4224264445141737, 3.4932182811875796, 1.2453853966340462, 0.9827393121206148, 0.5903655666664452, 0.0, 8.445892500963913, 6.494021233330896, 4.913696560603074, 3.736156189902138, 6.986436562375159, 3.3913970223198433, 2.390791924691664, 2.2514296625515327, 3.1661813383340958, 2.356073484094938, 1.391225980753695, 0.4868659778483902, 0.0), # 171 (7.322116040709912, 5.137097967394431, 6.688108291427019, 6.792552707280267, 6.087685071690277, 3.0320490477818964, 2.2946877781590462, 2.3305223885155746, 3.3613131550510804, 1.1961989506036783, 0.9440800697898953, 0.56728050974373, 0.0, 8.119037882406225, 6.24008560718103, 4.720400348949476, 3.588596851811034, 6.722626310102161, 3.2627313439218044, 2.2946877781590462, 2.165749319844212, 3.0438425358451386, 2.2641842357600894, 1.337621658285404, 0.4670089061267665, 0.0), # 172 (7.012387382568372, 4.914659731519285, 6.412865090110164, 6.509972863367375, 5.836521561606121, 2.9086323913569916, 2.196615199176405, 2.235920423128947, 3.225437583334597, 1.145874138880549, 0.9045009738086416, 0.5436263112880514, 0.0, 7.783604158705848, 5.979889424168563, 4.522504869043208, 3.437622416641646, 6.450875166669194, 3.130288592380526, 2.196615199176405, 2.077594565254994, 2.9182607808030605, 2.169990954455792, 1.282573018022033, 0.446787248319935, 0.0), # 173 (6.697018473019482, 4.6889470666619575, 6.131456832392036, 6.221532881152618, 5.579829329883635, 2.7822397788881266, 2.096911426803113, 2.1389977377960108, 3.08613932881565, 1.0945923677202316, 0.8641462668976501, 0.519490428027628, 0.0, 7.440864349139807, 5.7143947083039075, 4.32073133448825, 3.283777103160694, 6.1722786576313, 2.994596832914415, 2.096911426803113, 1.9873141277772333, 2.7899146649418176, 2.07384429371754, 1.2262913664784072, 0.42626791515108714, 0.0), # 174 (6.377108486227438, 4.460695990777558, 5.84494005084676, 5.928284721242486, 5.318565559990731, 2.653359430965997, 1.9959137000985407, 2.040131521958481, 2.943966154271756, 1.0425350433782987, 0.8231601917777163, 0.49496031669067847, 0.0, 7.092091472985131, 5.444563483597462, 4.115800958888581, 3.1276051301348957, 5.887932308543512, 2.8561841307418736, 1.9959137000985407, 1.8952567364042836, 2.6592827799953653, 1.9760949070808291, 1.1689880101693522, 0.40551781734341447, 0.0), # 175 (6.053756596356447, 4.230642521821194, 5.554371278048459, 5.631280344243462, 5.053687435395322, 2.5224795681812964, 1.8939592581220606, 1.9396989650580787, 2.7994658224804327, 0.9898835721103237, 0.781686991169637, 0.470123434005421, 0.0, 6.738558549518844, 5.17135777405963, 3.9084349558481852, 2.9696507163309707, 5.5989316449608655, 2.71557855108131, 1.8939592581220606, 1.8017711201294973, 2.526843717697661, 1.8770934480811543, 1.1108742556096918, 0.38460386562010856, 0.0), # 176 (5.7280619775707065, 3.9995226777479713, 5.260807046571258, 5.331571710762027, 4.786152139565322, 2.3900884111247205, 1.791385339933044, 1.8380772565365193, 2.6531860962191995, 0.9368193601718788, 0.7398709077942084, 0.4450672367000743, 0.0, 6.381538598017975, 4.895739603700816, 3.699354538971042, 2.8104580805156356, 5.306372192438399, 2.5733081591511273, 1.791385339933044, 1.707206007946229, 2.393076069782661, 1.7771905702540096, 1.0521614093142517, 0.3635929707043611, 0.0), # 177 (5.401123804034416, 3.7680724765129963, 4.9653038889892835, 5.030210781404673, 4.516916855968639, 2.2566741803869648, 1.6885291845908623, 1.7356435858355217, 2.505674738265573, 0.8835238138185378, 0.6978561843722264, 0.41987918150285664, 0.0, 6.022304637759553, 4.618670996531422, 3.489280921861132, 2.6505714414556127, 5.011349476531146, 2.4299010201697304, 1.6885291845908623, 1.611910128847832, 2.2584584279843196, 1.6767369271348913, 0.9930607777978567, 0.34255204331936334, 0.0), # 178 (0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0), # 179 ) passenger_allighting_rate = ( (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 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3 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 4 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 5 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 6 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 7 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 8 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 9 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 10 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 11 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 12 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 13 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 14 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 15 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 16 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 17 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 18 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 19 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 20 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 21 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 22 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 23 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 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168 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 169 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 170 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 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174 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 175 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 176 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 177 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 178 (0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1, 0, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 0.07692307692307693, 1), # 179 ) """ parameters for reproducibiliy. More information: https://numpy.org/doc/stable/reference/random/parallel.html """ #initial entropy entropy = 8991598675325360468762009371570610170 #index for seed sequence child child_seed_index = ( 1, # 0 38, # 1 )

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