xszheng2020/the_stack_dedup_python_hits_6 · Datasets at Hugging Face

0cd3f8ef4188bc3e708721caf09a4b04d214fa3a

82

py

Python

src/__init__.py

TNCT-Mechatech/SerialBridgePython

65fee93efb7ce3b37c21ca473eabf1b00b024fb8

[ "Apache-2.0" ]

1

2021-06-19T05:59:55.000Z

src/__init__.py

TNCT-Mechatech/SerialBridgePython

65fee93efb7ce3b37c21ca473eabf1b00b024fb8

[ "Apache-2.0" ]

9

2021-04-05T07:32:15.000Z

2021-07-08T01:40:30.000Z

src/__init__.py

TNCT-Mechatech/SerialBridgePython

65fee93efb7ce3b37c21ca473eabf1b00b024fb8

[ "Apache-2.0" ]

1

2021-07-08T07:56:36.000Z

#! /usr/bin/env python from src.message import * from src.serial_bridge import *

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py

Python

app/modules/auth/models.py

UnoYakshi/DebtCollector

ae101301c0f3e20e24368607c501eca5b319ad98

[ "MIT" ]

null

app/modules/auth/models.py

UnoYakshi/DebtCollector

ae101301c0f3e20e24368607c501eca5b319ad98

[ "MIT" ]

null

app/modules/auth/models.py

UnoYakshi/DebtCollector

ae101301c0f3e20e24368607c501eca5b319ad98

[ "MIT" ]

null

#! ~DebtCollector/app/modules/auth/models.py #from flask_sqlalchemy import SQLAlchemy from werkzeug import generate_password_hash, check_password_hash import json from datetime import datetime, date from app import db, bcrypt class Users(db.Model): __tablename__ = 'users' id = db.Column('id', db.Integer, primary_key=True, autoincrement=True) login = db.Column('login', db.String(32), unique=True, index=True) first_name = db.Column('first_name', db.String(128)) last_name = db.Column('last_name', db.String(128)) email = db.Column('email', db.String(128), unique=True, index=True) pwdhash = db.Column('pwdhash', db.String(128), unique=True) birthdate = db.Column('birthdate', db.Date()) def __init__(self, login, first_name, last_name, email, password, birthdate): self.login = login self.first_name = first_name self.last_name = last_name self.email = email self.pwdhash = bcrypt.generate_password_hash(password) self.birthdate = birthdate def is_authenticated(self): return True def is_active(self): return True def is_anonymous(self): return False def get_id(self): return str(self.id) def __repr__(self): return '<User %r>' % (self.login) def check_password(self, password): return check_password_hash(self.pwdhash, password) # TODO :: Make to_json/as_dict automated, but considering datetime and hash... def to_json(self): return { 'id': self.id, 'login': self.login, 'first_name': self.first_name, 'last_name': self.last_name, 'email': self.email, 'birthdate': self.birthdate } def as_dict(self): return json.dumps({c.name: (c.name.isoformat()) if (type(c) in (datetime, date)) else (getattr(self, c.name)) for c in self.__table__.columns})

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0b47d70f44921f04a904fb3a7842bcbebca33837

657

py

Python

tests/algorithm_tests.py

SergeyKonnov/walbot

28923523299bd18b47074915c8209833683d0b8c

[ "MIT" ]

2

2021-01-14T22:17:59.000Z

2021-12-31T11:18:21.000Z

tests/algorithm_tests.py

SergeyKonnov/walbot

28923523299bd18b47074915c8209833683d0b8c

[ "MIT" ]

221

2020-01-31T15:04:48.000Z

2022-01-15T12:03:13.000Z

tests/algorithm_tests.py

aobolensk/walbot

f11ee6971b232cdb177284933528730b70ec67ca

[ "MIT" ]

1

2019-11-26T18:18:46.000Z

from src.algorithms import levenshtein_distance def test_levenshtein_distance_from_equal_strings(): assert levenshtein_distance("abc", "abc") == 0 def test_levenshtein_distance_difference_1(): assert levenshtein_distance("aac", "abc") == 1 assert levenshtein_distance("abc", "aac") == 1 def test_levenshtein_distance_different_length(): assert levenshtein_distance("ab", "abc") == 1 assert levenshtein_distance("abc", "ab") == 1 assert levenshtein_distance("a", "abc") == 2 assert levenshtein_distance("abc", "a") == 2 def test_levenshtein_distance_swapped_letters(): assert levenshtein_distance("help", "hepl") == 2

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0b4a11ab18a3d74885fae4e8989f83e0be9328eb

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py

Python

tests/bootstrap_tests.py

reddit/cabot-alert-pagerduty

459c2bff53d4b947ab14178eae9d6b6c5068a145

[ "MIT" ]

1

2020-07-31T12:59:34.000Z

tests/bootstrap_tests.py

reddit/cabot-alert-pagerduty

459c2bff53d4b947ab14178eae9d6b6c5068a145

[ "MIT" ]

null

tests/bootstrap_tests.py

reddit/cabot-alert-pagerduty

459c2bff53d4b947ab14178eae9d6b6c5068a145

[ "MIT" ]

4

2017-06-02T00:34:38.000Z

2021-04-08T10:57:21.000Z

# -*- coding: utf-8 -*- from django.conf import settings settings.configure() settings.JENKINS_USER = 'Test' settings.JENKINS_PASS = 'Test' settings.GRAPHITE_API = 'Test' settings.GRAPHITE_USER = 'Test' settings.GRAPHITE_PASS = 'Test' settings.GRAPHITE_FROM = 'Test'

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0b793a3bfbf15bb23c8cabf6ead46fe07580c775

92

py

Python

scrapers/Library/__init__.py

arifer612/MDLPackage

7f5b3d66fe4dd1eaf0ee7b2f054707af428109a9

[ "MIT" ]

1

2021-06-15T08:52:01.000Z

scrapers/Library/__init__.py

arifer612/MDLPackage

7f5b3d66fe4dd1eaf0ee7b2f054707af428109a9

[ "MIT" ]

1

2022-01-31T06:33:30.000Z

2022-02-03T09:58:54.000Z

scrapers/Library/__init__.py

arifer612/MDLPackage

7f5b3d66fe4dd1eaf0ee7b2f054707af428109a9

[ "MIT" ]

1

2021-08-12T22:35:09.000Z

from . import database from . import tvTokyo from . import tvOsaka from . import YouTubeAPI

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0ba03e51c93e1b0bf5831873e619c7ed83295674

191

py

Python

budget/tests/test_budget.py

seanbreckenridge/mint

d19cb5cf28c584f3c3efe322fe154bea243a7eed

[ "MIT" ]

null

budget/tests/test_budget.py

seanbreckenridge/mint

d19cb5cf28c584f3c3efe322fe154bea243a7eed

[ "MIT" ]

3

2021-03-15T09:48:30.000Z

2022-02-14T06:02:01.000Z

budget/tests/test_budget.py

seanbreckenridge/mint

d19cb5cf28c584f3c3efe322fe154bea243a7eed

[ "MIT" ]

null

# just import stuff to make sure nothing is broken def test_budget() -> None: import budget.load.balances import budget.load.transactions import budget.analyze assert True

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e7e67f85af2fa5fe68fe9e15a41f9020e2759ee8

2,157

py

Python

shadowsocks.py

yingxuanxuan/fabric_script

7768c038b561ea5cd826edd24c5e2d53bc3a9cd0

[ "Apache-2.0" ]

1

2016-05-14T04:40:46.000Z

shadowsocks.py

yingxuanxuan/fabric_vultr

7768c038b561ea5cd826edd24c5e2d53bc3a9cd0

[ "Apache-2.0" ]

null

shadowsocks.py

yingxuanxuan/fabric_vultr

7768c038b561ea5cd826edd24c5e2d53bc3a9cd0

[ "Apache-2.0" ]

1

2021-06-18T04:10:33.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- import logging from fabric.api import reboot, sudo, settings logging.basicConfig(level=logging.INFO) def ssserver(port, password, method): try: sudo('hash yum') sudo('hash python') sudo('yum -y update 1>/dev/null') sudo('yum -y install python-setuptools 1>/dev/null') sudo('yum -y install m2crypto 1>/dev/null') sudo('easy_install pip 1>/dev/null') sudo('pip install shadowsocks 1>/dev/null') sudo('hash ssserver') sudo("sed -i '/ssserver/d' /etc/rc.d/rc.local") cmd = '/usr/bin/python /usr/bin/ssserver -p %s -k %s -m %s --user nobody -d start' % \ (port, password, method) sudo("sed -i '$a %s' /etc/rc.d/rc.local" % cmd) sudo('chmod +x /etc/rc.d/rc.local') sudo('firewall-cmd --zone=public --add-port=%s/tcp --permanent' % port) with settings(warn_only=True): reboot() sudo('ps -ef | grep ssserver') return True except BaseException as e: logging.error(e) return False def sslocal(server_addr, server_port, server_password, method, local_port): try: sudo('hash yum') sudo('hash python') sudo('yum -y update 1>/dev/null') sudo('yum -y install python-setuptools 1>/dev/null') sudo('yum -y install m2crypto 1>/dev/null') sudo('easy_install pip 1>/dev/null') sudo('pip install shadowsocks 1>/dev/null') sudo('hash sslocal') sudo("sed -i '/sslocal /d' /etc/rc.d/rc.local") cmd = '/usr/bin/python /usr/bin/sslocal -s %s -p %s -k %s -m %s -b 0.0.0.0 -l %s --user nobody -d start' % \ (server_addr, server_port, server_password, method, local_port) sudo("sed -i '$a %s' /etc/rc.d/rc.local" % cmd) sudo('chmod +x /etc/rc.d/rc.local') sudo('firewall-cmd --zone=public --add-port=%s/tcp --permanent' % local_port) with settings(warn_only=True): reboot() sudo('ps -ef | grep sslocal') return True except BaseException as e: logging.error(e) return False

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f00da12a4b433dc22423bcc4f137a7f9c47129a6

80,788

py

Python

newbomb.py

Cyber-Hades/sms_streak

352e829e4dd15e94589465e464d11d189b211d55

[ "Apache-2.0" ]

9

2021-06-07T17:02:10.000Z

2021-12-08T07:51:14.000Z

newbomb.py

Cyber-Hades/sms_streak

352e829e4dd15e94589465e464d11d189b211d55

[ "Apache-2.0" ]

null

newbomb.py

Cyber-Hades/sms_streak

352e829e4dd15e94589465e464d11d189b211d55

[ "Apache-2.0" ]

null

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py

Python

netbox/secrets/exceptions.py

0xAalaoui/netbox

07364abf9e9ff193bad49b790e657382cf186f0c

[ "Apache-2.0" ]

1

2020-07-16T17:50:31.000Z

netbox/secrets/exceptions.py

0xAalaoui/netbox

07364abf9e9ff193bad49b790e657382cf186f0c

[ "Apache-2.0" ]

9

2019-01-20T08:35:13.000Z

2022-03-12T00:50:13.000Z

netbox/secrets/exceptions.py

0xAalaoui/netbox

07364abf9e9ff193bad49b790e657382cf186f0c

[ "Apache-2.0" ]

1

2021-04-09T06:08:21.000Z

from __future__ import unicode_literals class InvalidKey(Exception): """ Raised when a provided key is invalid. """ pass

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f0425e21c51effdff92c4f7dd7740bd5681c68f7

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py

Python

examples/property_prediction/MTL/model/attentivefp.py

siboehm/dgl-lifesci

f8a176414b21b72c5ca1f8c7eb8d64702432ae24

[ "Apache-2.0" ]

390

2020-06-05T13:16:18.000Z

2022-03-31T07:36:34.000Z

examples/property_prediction/MTL/model/attentivefp.py

siboehm/dgl-lifesci

f8a176414b21b72c5ca1f8c7eb8d64702432ae24

[ "Apache-2.0" ]

71

2020-06-12T05:26:56.000Z

2022-03-29T06:26:39.000Z

examples/property_prediction/MTL/model/attentivefp.py

siboehm/dgl-lifesci

f8a176414b21b72c5ca1f8c7eb8d64702432ae24

[ "Apache-2.0" ]

113

2020-06-08T18:48:18.000Z

2022-03-22T01:16:26.000Z

import torch.nn as nn from dgllife.model import AttentiveFPGNN, AttentiveFPReadout from .regressor import BaseGNNRegressor, BaseGNNRegressorBypass class AttentiveFPRegressor(BaseGNNRegressor): """AttentiveFP-based model for multitask molecular property prediction. We assume all tasks are regression problems. Parameters ---------- in_node_feats : int Number of input node features in_edge_feats : int Number of input edge features gnn_out_feats : int The GNN output size num_layers : int Number of GNN layers num_timesteps : int Number of timesteps for updating molecular representations with GRU during readout n_tasks : int Number of prediction tasks regressor_hidden_feats : int Hidden size in MLP regressor dropout : float The probability for dropout. Default to 0, i.e. no dropout is performed. """ def __init__(self, in_node_feats, in_edge_feats, gnn_out_feats, num_layers, num_timesteps, n_tasks, regressor_hidden_feats=128, dropout=0.): super(AttentiveFPRegressor, self).__init__(readout_feats=gnn_out_feats, n_tasks=n_tasks, regressor_hidden_feats=regressor_hidden_feats, dropout=dropout) self.gnn = AttentiveFPGNN(in_node_feats, in_edge_feats, num_layers, gnn_out_feats, dropout) self.readout = AttentiveFPReadout(gnn_out_feats, num_timesteps, dropout) class AttentiveFPRegressorBypass(BaseGNNRegressorBypass): """AttentiveFP-based model for bypass multitask molecular property prediction. We assume all tasks are regression problems. Parameters ---------- in_node_feats : int Number of input node features in_edge_feats : int Number of input edge features gnn_out_feats : int The GNN output size num_layers : int Number of GNN layers num_timesteps : int Number of timesteps for updating molecular representations with GRU during readout n_tasks : int Number of prediction tasks regressor_hidden_feats : int Hidden size in MLP regressor dropout : float The probability for dropout. Default to 0, i.e. no dropout is performed. """ def __init__(self, in_node_feats, in_edge_feats, gnn_out_feats, num_layers, num_timesteps, n_tasks, regressor_hidden_feats=128, dropout=0.): super(AttentiveFPRegressorBypass, self).__init__( readout_feats= 2 * gnn_out_feats, n_tasks=n_tasks, regressor_hidden_feats=regressor_hidden_feats, dropout=dropout) self.shared_gnn = AttentiveFPGNN(in_node_feats, in_edge_feats, num_layers, gnn_out_feats, dropout) for _ in range(n_tasks): self.task_gnns.append(AttentiveFPGNN(in_node_feats, in_edge_feats, num_layers, gnn_out_feats, dropout)) self.readouts.append(AttentiveFPReadout(2 * gnn_out_feats, num_timesteps, dropout))

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py

Python

authors/apps/articles/serializers.py

andela/ah-backend-odin

0e9ef1a10c8a3f6736999a5111736f7bd7236689

[ "BSD-3-Clause" ]

null

authors/apps/articles/serializers.py

andela/ah-backend-odin

0e9ef1a10c8a3f6736999a5111736f7bd7236689

[ "BSD-3-Clause" ]

43

2018-10-25T10:14:52.000Z

2022-03-11T23:33:46.000Z

authors/apps/articles/serializers.py

andela/ah-backend-odin

0e9ef1a10c8a3f6736999a5111736f7bd7236689

[ "BSD-3-Clause" ]

4

2018-10-29T07:04:58.000Z

2020-04-02T14:15:10.000Z

from rest_framework import serializers from ..authentication.models import User from .models import (Article, ArticleLikes, Thread, Comment, FavoriteArticle, Rating, LikeComment, BookmarkingArticles,) from rest_framework.validators import UniqueTogetherValidator from ..authentication.serializers import UserSerializer from taggit_serializer.serializers import (TagListSerializerField, TaggitSerializer) class CreateArticleAPIViewSerializer(TaggitSerializer, serializers.ModelSerializer): tagList = TagListSerializerField() author = serializers.SerializerMethodField() def get_author(self, obj): user = { "username": obj.author.username, "email": obj.author.email } return user class Meta: model = Article fields = ['title', 'description', 'body', 'author', 'created_at', 'updated_at', 'tagList', 'slug', 'published', 'image', 'likescount', 'dislikescount', 'read_time', 'average_rating'] def validate_title(self, value): if len(value) > 50: raise serializers.ValidationError( 'The title should not be more than 50 characters' ) return value def validate_description(self, value): if len(value) > 200: raise serializers.ValidationError( 'The article should not be more than 200 characters' ) return value class ArticleDetailSerializer(serializers.ModelSerializer): tagList = TagListSerializerField() author = serializers.SerializerMethodField() def get_author(self, obj): user = { "username": obj.author.username, "email": obj.author.email } return user class Meta: model = Article fields = ['title', 'description', 'body', 'author', 'created_at', 'updated_at', 'tagList', 'slug', 'published', 'image', 'likescount', 'dislikescount', 'read_time', 'comments', 'average_rating'] class UpdateArticleAPIVIEWSerializer(serializers.ModelSerializer): class Meta: model = Article fields = ['title', 'description', 'body', 'author', 'created_at', 'updated_at', 'tagList', 'slug', 'published', 'image', 'read_time', 'average_rating'] def validate_title(self, value): if len(value) > 50: raise serializers.ValidationError( 'The title should not be more than 50 characters' ) return value def validate_description(self, value): if len(value) > 200: raise serializers.ValidationError( 'The article should not be more than 200 characters' ) return value def update_article(self, validated_data, article_instance): article_instance.title = validated_data.get('title') article_instance.body = validated_data.get('body') article_instance.description = validated_data.get('description') article_instance.image = validated_data.get('image') article_instance.tagList = validated_data.get('tagList') article_instance.save() return article_instance class LikeArticleAPIViewSerializer(serializers.ModelSerializer): action_performed = "created" class Meta: model = ArticleLikes fields = ['author', 'article', 'article_like'] def create(self, validated_data): try: # pragma: no cover self.instance = ArticleLikes.objects.filter(author=validated_data["author"].id)[ 0:1].get() except ArticleLikes.DoesNotExist: # pragma: no cover return ArticleLikes.objects.create(**validated_data) self.perform_update(validated_data) return self.instance def perform_update(self, validated_data): if self.instance.article_like == validated_data["article_like"]: self.instance.delete() self.action_performed = "deleted" else: self.instance.article_like = validated_data["article_like"] self.instance.save() self.action_performed = "updated" class FavoriteArticlesSerializer(serializers.ModelSerializer): class Meta: model = FavoriteArticle fields = ('article', 'favorite_status', 'author', 'favorited_at', 'last_updated_at') class CreateCommentAPIViewSerializer(serializers.ModelSerializer): author = UserSerializer(read_only=True) class Meta: model = Comment fields = ('id', 'body', 'article', 'createdAt', 'updatedAt', 'author', ) read_only_fields = ('article', ) def validate(self, data): comment = data.get('body', None) if len(comment) < 2: raise serializers.ValidationError( "Comment should have atlest 2 characters" ) else: return { 'body': comment, } def create(self, validated_data): author = self.context["author"] article = self.context["article"] body = validated_data.get('body') # return Comment.objects.create(body=body, article=article) return Comment.objects.create(body=body, author=author, article=article) class CreateThreadAPIViewSerializer(serializers.ModelSerializer): class Meta: model = Thread fields = ('id', 'body', 'author', 'comment', 'createdAt', 'updatedAt') read_only_fields = ('author', 'comment', ) def validate(self, data): comment_thread = data.get('body', None) if len(comment_thread) < 2: raise serializers.ValidationError( "Comment should have atlest 2 characters" ) else: return { 'body': comment_thread, } def create(self, validated_data): author = self.context["author"] comment = self.context["comment"] body = validated_data.get('body') return Thread.objects.create(body=body, author=author, comment=comment) class RatingsSerializer(serializers.ModelSerializer): class Meta: model = Rating fields = ['id', 'article', 'article_rate', 'author'] validators = [UniqueTogetherValidator( queryset=Rating.objects.all(), fields=('article', 'author',), message=("You cannot rate this article more than once") )] class LikeArticleAPIViewSerializer(serializers.ModelSerializer): action_performed = "created" class Meta: model = ArticleLikes fields = ['author', 'article', 'article_like'] def create(self, validated_data): try: self.instance = ArticleLikes.objects.filter(author=validated_data["author"].id)[ 0:1].get() except ArticleLikes.DoesNotExist: return ArticleLikes.objects.create(**validated_data) self.perform_update(validated_data) return self.instance def perform_update(self, validated_data): if self.instance.article_like == validated_data["article_like"]: self.instance.delete() self.action_performed = "deleted" else: self.instance.article_like = validated_data["article_like"] self.instance.save() self.action_performed = "updated" class FavoriteArticlesSerializer(serializers.ModelSerializer): class Meta: model = FavoriteArticle fields = ('article', 'favorite_status', 'author', 'favorited_at', 'last_updated_at') class CreateCommentAPIViewSerializer(serializers.ModelSerializer): author = UserSerializer(read_only=True) class Meta: model = Comment fields = ('id', 'body', 'article', 'createdAt', 'updatedAt', 'author', 'commentlikescount', 'commentdislikescount') read_only_fields = ('article', ) def validate(self, data): comment = data.get('body', None) if len(comment) < 2: raise serializers.ValidationError( "Comment should have atlest 2 characters" ) else: return { 'body': comment, } def create(self, validated_data): author = self.context["author"] article = self.context["article"] body = validated_data.get('body') return Comment.objects.create(body=body, author=author, article=article) class CreateThreadAPIViewSerializer(serializers.ModelSerializer): class Meta: model = Thread fields = ('id', 'body', 'author', 'comment', 'createdAt', 'updatedAt') read_only_fields = ('author', 'comment', ) def validate(self, data): comment_thread = data.get('body', None) if len(comment_thread) < 2: raise serializers.ValidationError( "Comment should have atlest 2 characters" ) else: return { 'body': comment_thread, } def create(self, validated_data): author = self.context["author"] comment = self.context["comment"] body = validated_data.get('body') return Thread.objects.create(body=body, author=author, comment=comment) class CommentLikeSerializer(serializers.ModelSerializer): class Meta: model = LikeComment fields = ['author', 'comment', 'like_status'] def create(self, validated_data): try: self.instance = LikeComment.objects.filter(author=validated_data["author"], comment=validated_data["comment"])[0:1].get() except LikeComment.DoesNotExist: return LikeComment.objects.create(**validated_data) self.perform_update(validated_data) return self.instance def perform_update(self, validated_data): if self.instance.like_status == validated_data["like_status"]: self.instance.delete() else: self.instance.like_status = validated_data["like_status"] self.instance.save() class BookmarkSerializer(serializers.ModelSerializer): user = serializers.ReadOnlyField(source="user.username") article_id = serializers.ReadOnlyField(source="article_id.title") class Meta: model = BookmarkingArticles fields = ['user', 'article_id', 'bookmarked_at']

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0.612712

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0.073079

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0.762336

0.756402

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0

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0

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1

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null

0

6

f07e35f8cb9915996c7d9dec8221534d9613bbc1

129

py

Python

app/app/calc.py

david-alejandro-reyes-milian/recipe-app-api

12caaaceff0ed13cfea6e25c57773d3afe24cf68

[ "MIT" ]

null

app/app/calc.py

david-alejandro-reyes-milian/recipe-app-api

12caaaceff0ed13cfea6e25c57773d3afe24cf68

[ "MIT" ]

null

app/app/calc.py

david-alejandro-reyes-milian/recipe-app-api

12caaaceff0ed13cfea6e25c57773d3afe24cf68

[ "MIT" ]

null

def add(x: int, y: int) -> int: """Add 2 numbers""" return x + y def subtract(x: int, y: int) -> int: return x - y

16.125

36

0.511628

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129

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0.121212

0.151515

0.242424

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b2b2ae644b9294c2c8008a759e447d9c369ab88b

24,048

py

Python

tests/app/clients/test_zendesk_sell.py

cds-snc/notifier-api

90b385ec49efbaee7e607516fc7d9f08991af813

[ "MIT" ]

41

2019-11-28T16:58:41.000Z

2022-01-28T21:11:16.000Z

tests/app/clients/test_zendesk_sell.py

cds-snc/notification-api

b1c1064f291eb860b494c3fa65ac256ad70bf47c

[ "MIT" ]

1,083

2019-07-08T12:57:24.000Z

2022-03-08T18:53:40.000Z

tests/app/clients/test_zendesk_sell.py

cds-snc/notifier-api

90b385ec49efbaee7e607516fc7d9f08991af813

[ "MIT" ]

9

2020-01-24T19:56:43.000Z

2022-01-27T21:36:53.000Z

import json from typing import Any, Dict, Optional, Union import pytest import requests_mock from flask import Flask from pytest_mock import MockFixture from app.clients.zendesk_sell import ZenDeskSell from app.models import Service from app.user.contact_request import ContactRequest def test_create_lead(notify_api: Flask): def match_json(request): expected = { "data": { "last_name": "User", "first_name": "Test", "organization_name": "", "email": "test@email.com", "description": "Program: \n: ", "tags": ["", "en"], "status": "New", "source_id": 2085874, "owner_id": ZenDeskSell.OWNER_ID, "custom_fields": { "Product": ["Notify"], "Intended recipients": "No value", }, } } json_matches = request.json() == expected basic_auth_header = request.headers.get("Authorization") == "Bearer zendesksell-api-key" return json_matches and basic_auth_header with requests_mock.mock() as rmock: rmock.request( "POST", url="https://zendesksell-test.com/v2/leads/upsert?email=test@email.com", headers={"Accept": "application/json", "Content-Type": "application/json"}, additional_matcher=match_json, status_code=201, ) with notify_api.app_context(): response = ZenDeskSell().upsert_lead(ContactRequest(email_address="test@email.com", name="Test User")) assert response == 201 def test_create_lead_missing_name(notify_api: Flask): # Name field is a requirement for the zendesk sell API interface with notify_api.app_context(): with pytest.raises(AssertionError): ZenDeskSell().upsert_lead(ContactRequest(email_address="test@email.com")) def generate_contact_url(existing_contact_id: Optional[str], service: Service) -> str: if existing_contact_id: return f"https://zendesksell-test.com/v2/contacts/{existing_contact_id}" else: return f"https://zendesksell-test.com/v2/contacts/upsert?" f"custom_fields[notify_user_id]={str(service.users[0].id)}" def contact_http_method(existing_contact_id: Optional[str]): return "PUT" if existing_contact_id else "POST" @pytest.mark.parametrize( "existing_contact_id,created_at,updated_at,expected_created", [ (None, "2021-03-24T14:49:38Z", "2021-03-24T14:49:38Z", True), (None, "2021-03-24T14:49:38Z", "2021-04-24T14:49:38Z", False), ("1", "2021-03-24T14:49:38Z", "2021-04-24T14:49:38Z", False), ], ) def test_create_or_upsert_contact( existing_contact_id: Optional[str], created_at: str, updated_at: str, expected_created: bool, notify_api: Flask, sample_service: Service, ): def match_json(request): expected = { "data": { "last_name": "User", "first_name": "Test", "email": "notify@digital.cabinet-office.gov.uk", "mobile": "+16502532222", "owner_id": ZenDeskSell.OWNER_ID, "custom_fields": {"notify_user_id": str(sample_service.users[0].id)}, } } json_matches = request.json() == expected basic_auth_header = request.headers.get("Authorization") == "Bearer zendesksell-api-key" return json_matches and basic_auth_header with requests_mock.mock() as rmock: expected_contact_id = existing_contact_id or "123456789" resp_data = { "data": { "id": expected_contact_id, "created_at": created_at, "updated_at": updated_at, } } rmock.request( contact_http_method(existing_contact_id), url=generate_contact_url(existing_contact_id, sample_service), headers={"Accept": "application/json", "Content-Type": "application/json"}, additional_matcher=match_json, status_code=200, text=json.dumps(resp_data), ) with notify_api.app_context(): contact_id, is_created = ZenDeskSell().upsert_contact(sample_service.users[0], existing_contact_id) assert expected_contact_id == contact_id assert is_created == expected_created @pytest.mark.parametrize( "existing_contact_id,expected_resp_data", [ (None, {"blank": "blank"}), ( None, { "data": { "created_at": "2021-02-24T14:49:38Z", "updated_at": "2021-03-24T14:49:38Z", } }, ), (None, {"data": {"id": "123456789", "created_at": "2021-02-24T14:49:38Z"}}), (None, {"data": {"id": "123456789", "updated_at": "2021-02-24T14:49:38Z"}}), (1, {"blank": "blank"}), ( 1, { "data": { "created_at": "2021-02-24T14:49:38Z", "updated_at": "2021-03-24T14:49:38Z", } }, ), (1, {"data": {"id": "123456789", "created_at": "2021-02-24T14:49:38Z"}}), (1, {"data": {"id": "123456789", "updated_at": "2021-02-24T14:49:38Z"}}), ], ) def test_create_contact_invalid_response( notify_api: Flask, sample_service: Service, existing_contact_id: Optional[str], expected_resp_data: Dict[str, Dict[str, Union[int, str]]], ): def match_json(request): expected = { "data": { "last_name": "User", "first_name": "Test", "email": "notify@digital.cabinet-office.gov.uk", "mobile": "+16502532222", "owner_id": ZenDeskSell.OWNER_ID, "custom_fields": {"notify_user_id": str(sample_service.users[0].id)}, } } json_matches = request.json() == expected basic_auth_header = request.headers.get("Authorization") == "Bearer zendesksell-api-key" return json_matches and basic_auth_header with requests_mock.mock() as rmock: rmock.request( contact_http_method(existing_contact_id), url=generate_contact_url(existing_contact_id, sample_service), headers={"Accept": "application/json", "Content-Type": "application/json"}, additional_matcher=match_json, status_code=200, text=json.dumps(expected_resp_data), ) with notify_api.app_context(): contact_id, _ = ZenDeskSell().upsert_contact(sample_service.users[0], existing_contact_id) assert not contact_id def test_convert_lead_to_contact(notify_api: Flask, sample_service: Service): lead_id = "123456789" def match_json(request): expected = { "data": { "lead_id": lead_id, "owner_id": ZenDeskSell.OWNER_ID, "create_deal": False, } } json_matches = request.json() == expected basic_auth_header = request.headers.get("Authorization") == "Bearer zendesksell-api-key" return json_matches and basic_auth_header with requests_mock.mock() as rmock: expected_contact_id = "1234567890" rmock.request( "GET", url=f"https://zendesksell-test.com/v2/leads?email={sample_service.users[0].email_address}", headers={"Accept": "application/json", "Content-Type": "application/json"}, status_code=200, text=json.dumps({"items": [{"data": {"id": lead_id}}]}), ) rmock.request( "POST", url="https://zendesksell-test.com/v2/lead_conversions", headers={"Accept": "application/json", "Content-Type": "application/json"}, additional_matcher=match_json, status_code=200, text=json.dumps({"data": {"individual_id": expected_contact_id}}), ) with notify_api.app_context(): contact_id = ZenDeskSell().convert_lead_to_contact(sample_service.users[0]) assert contact_id == expected_contact_id def test_convert_lead_to_contact_search_fails(notify_api: Flask, sample_service: Service, mocker: MockFixture): with notify_api.app_context(): search_lead_id_mock = mocker.patch("app.user.rest.ZenDeskSell.search_lead_id", return_value=None) contact_id = ZenDeskSell().convert_lead_to_contact(sample_service.users[0]) search_lead_id_mock.assert_called_once_with(sample_service.users[0]) assert not contact_id def test_delete_contact(notify_api: Flask): def match_header(request): return request.headers.get("Authorization") == "Bearer zendesksell-api-key" with requests_mock.mock() as rmock: contact_id = "123456789" rmock.request( "DELETE", url=f"https://zendesksell-test.com/v2/contacts/{contact_id}", headers={"Accept": "application/json", "Content-Type": "application/json"}, additional_matcher=match_header, status_code=200, ) with notify_api.app_context(): # as long as it doesn't throw we are OK as this is a best effort method ZenDeskSell().delete_contact(contact_id) def test_create_deal(notify_api: Flask, sample_service: Service): def match_json(request): expected = { "data": { "contact_id": "123456789", "name": "Sample service", "stage_id": 123456789, "owner_id": ZenDeskSell.OWNER_ID, "custom_fields": {"notify_service_id": str(sample_service.id)}, } } json_matches = request.json() == expected basic_auth_header = request.headers.get("Authorization") == "Bearer zendesksell-api-key" return json_matches and basic_auth_header with requests_mock.mock() as rmock: contact_id = "123456789" expected_deal_id = "987654321" resp_data = {"data": {"id": expected_deal_id, "contact_id": contact_id}} rmock.request( "POST", url=f"https://zendesksell-test.com/v2/deals/upsert?" f"custom_fields[notify_service_id]={str(sample_service.id)}", headers={"Accept": "application/json", "Content-Type": "application/json"}, additional_matcher=match_json, status_code=200, text=json.dumps(resp_data), ) with notify_api.app_context(): deal_id = ZenDeskSell().upsert_deal(contact_id, sample_service, 123456789) assert expected_deal_id == deal_id @pytest.mark.parametrize( "expected_resp_data", [ {"blank": "blank"}, {"data": {"blank": "blank"}}, ], ) def test_create_deal_invalid_response( notify_api: Flask, sample_service: Service, expected_resp_data: Dict[str, Dict[str, Union[int, str]]], ): def match_json(request): expected = { "data": { "contact_id": "123456789", "name": "Sample service", "stage_id": 123456789, "owner_id": ZenDeskSell.OWNER_ID, "custom_fields": {"notify_service_id": str(sample_service.id)}, } } json_matches = request.json() == expected basic_auth_header = request.headers.get("Authorization") == "Bearer zendesksell-api-key" return json_matches and basic_auth_header with requests_mock.mock() as rmock: contact_id = "123456789" rmock.request( "POST", url=f"https://zendesksell-test.com/v2/deals/upsert?" f"custom_fields[notify_service_id]={str(sample_service.id)}", headers={"Accept": "application/json", "Content-Type": "application/json"}, additional_matcher=match_json, status_code=200, text=json.dumps(expected_resp_data), ) with notify_api.app_context(): deal_id = ZenDeskSell().upsert_deal(contact_id, sample_service, 123456789) assert not deal_id def test_create_note(notify_api: Flask): resource_id = "1" def match_json(request): expected = { "data": { "resource_type": "deal", "resource_id": resource_id, "content": "\n".join( [ "Live Notes", "service_name just requested to go live.", "", "- Department/org: department_org_name", "- Intended recipients: intended_recipients", "- Purpose: main_use_case", "- Notification types: notification_types", "- Expected monthly volume: expected_volume", "---", "service_url", ] ), } } json_matches = request.json() == expected basic_auth_header = request.headers.get("Authorization") == "Bearer zendesksell-api-key" return json_matches and basic_auth_header with requests_mock.mock() as rmock: expected_note_id = "1" resp_data = {"data": {"id": expected_note_id}} rmock.request( "POST", url="https://zendesksell-test.com/v2/notes", headers={"Accept": "application/json", "Content-Type": "application/json"}, additional_matcher=match_json, status_code=200, text=json.dumps(resp_data), ) data: Dict[str, Any] = { "email_address": "test@email.com", "service_name": "service_name", "department_org_name": "department_org_name", "intended_recipients": "intended_recipients", "main_use_case": "main_use_case", "notification_types": "notification_types", "expected_volume": "expected_volume", "service_url": "service_url", "support_type": "go_live_request", } with notify_api.app_context(): note_id = ZenDeskSell().create_note(ZenDeskSell.NoteResourceType.DEAL, resource_id, ContactRequest(**data)) assert expected_note_id == note_id @pytest.mark.parametrize( "expected_resp_data", [ {"blank": "blank"}, {"data": {"blank": "blank"}}, ], ) def test_create_note_invalid_response( notify_api: Flask, sample_service: Service, expected_resp_data: Dict[str, Dict[str, Union[int, str]]], ): with requests_mock.mock() as rmock: rmock.request( "POST", url="https://zendesksell-test.com/v2/notes", headers={"Accept": "application/json", "Content-Type": "application/json"}, status_code=200, text=json.dumps(expected_resp_data), ) data: Dict[str, Any] = { "email_address": "test@email.com", "service_name": "service_name", "department_org_name": "department_org_name", "intended_recipients": "intended_recipients", "main_use_case": "main_use_case", "notification_types": "notification_types", "expected_volume": "expected_volume", "service_url": "service_url", "support_type": "go_live_request", } with notify_api.app_context(): note_id = ZenDeskSell().create_note(ZenDeskSell.NoteResourceType.DEAL, "1", ContactRequest(**data)) assert not note_id @pytest.mark.parametrize("is_go_live,existing_contact_id", [(False, None), (False, "1"), (True, None)]) def test_create_service_or_go_live_contact_fail( notify_api: Flask, sample_service: Service, mocker: MockFixture, is_go_live: bool, existing_contact_id: Optional[str], ): upsert_contact_mock = mocker.patch("app.user.rest.ZenDeskSell.upsert_contact", return_value=(None, False)) convert_lead_to_contact_mock = mocker.patch( "app.user.rest.ZenDeskSell.convert_lead_to_contact", return_value=existing_contact_id, ) with notify_api.app_context(): if is_go_live: assert not ZenDeskSell().send_go_live_service(sample_service, sample_service.users[0]) upsert_contact_mock.assert_called_once_with(sample_service.users[0], existing_contact_id) else: assert not ZenDeskSell().send_create_service(sample_service, sample_service.users[0]) convert_lead_to_contact_mock.assert_called_once_with(sample_service.users[0]) upsert_contact_mock.assert_called_once_with(sample_service.users[0], existing_contact_id) @pytest.mark.parametrize("is_go_live,existing_contact_id", [(False, None), (False, "2"), (True, None)]) def test_create_service_or_go_live_deal_fail( notify_api: Flask, sample_service: Service, mocker: MockFixture, is_go_live: bool, existing_contact_id: Optional[str], ): with requests_mock.mock() as rmock: contact_id = existing_contact_id or "1" rmock.request( contact_http_method(existing_contact_id), url=generate_contact_url(existing_contact_id, sample_service), headers={"Accept": "application/json", "Content-Type": "application/json"}, status_code=200, text=json.dumps({"data": {"id": contact_id, "created_at": "1", "updated_at": "1"}}), ) mocker.patch("app.user.rest.ZenDeskSell.upsert_deal", return_value=None) mocker.patch( "app.user.rest.ZenDeskSell.convert_lead_to_contact", return_value=existing_contact_id, ) contact_delete_mock = mocker.patch("app.user.rest.ZenDeskSell.delete_contact") with notify_api.app_context(): if is_go_live: assert not ZenDeskSell().send_go_live_service(sample_service, sample_service.users[0]) else: assert not ZenDeskSell().send_create_service(sample_service, sample_service.users[0]) contact_delete_mock.assert_called_once_with(contact_id) @pytest.mark.parametrize("is_go_live,existing_contact_id", [(False, None), (False, "1"), (True, None)]) def test_create_service_or_go_live_deal_fail_contact_exists( notify_api: Flask, sample_service: Service, mocker: MockFixture, is_go_live: bool, existing_contact_id: Optional[str], ): with requests_mock.mock() as rmock: contact_id = existing_contact_id or "1" rmock.request( contact_http_method(existing_contact_id), url=generate_contact_url(existing_contact_id, sample_service), headers={"Accept": "application/json", "Content-Type": "application/json"}, status_code=200, text=json.dumps({"data": {"id": contact_id, "created_at": "1", "updated_at": "2"}}), ) mocker.patch("app.user.rest.ZenDeskSell.upsert_deal", return_value=None) mocker.patch( "app.user.rest.ZenDeskSell.convert_lead_to_contact", return_value=existing_contact_id, ) contact_delete_mock = mocker.patch("app.user.rest.ZenDeskSell.delete_contact") with notify_api.app_context(): if is_go_live: assert not ZenDeskSell().send_go_live_service(sample_service, sample_service.users[0]) else: assert not ZenDeskSell().send_create_service(sample_service, sample_service.users[0]) contact_delete_mock.assert_not_called() @pytest.mark.parametrize("existing_contact_id", [None, "2"]) def test_send_create_service( notify_api: Flask, sample_service: Service, mocker: MockFixture, existing_contact_id: Optional[str], ): contact_id = existing_contact_id or "1" upsert_contact_mock = mocker.patch("app.user.rest.ZenDeskSell.upsert_contact", return_value=(contact_id, True)) convert_lead_to_contact_mock = mocker.patch( "app.user.rest.ZenDeskSell.convert_lead_to_contact", return_value=existing_contact_id, ) upsert_deal_mock = mocker.patch("app.user.rest.ZenDeskSell.upsert_deal", return_value=1) with notify_api.app_context(): assert ZenDeskSell().send_create_service(sample_service, sample_service.users[0]) convert_lead_to_contact_mock.assert_called_once_with(sample_service.users[0]) upsert_contact_mock.assert_called_once_with(sample_service.users[0], existing_contact_id) upsert_deal_mock.assert_called_once_with(contact_id, sample_service, ZenDeskSell.STATUS_CREATE_TRIAL) def test_send_go_live_request(notify_api: Flask, sample_service: Service, mocker: MockFixture): deal_id = "1" search_deal_id_mock = mocker.patch("app.user.rest.ZenDeskSell.search_deal_id", return_value=deal_id) send_create_service_mock = mocker.patch("app.user.rest.ZenDeskSell.send_create_service", return_value="1") create_note_mock = mocker.patch("app.user.rest.ZenDeskSell.create_note", return_value="2") data: Dict[str, Any] = { "email_address": "test@email.com", "service_name": "service_name", "department_org_name": "department_org_name", "intended_recipients": "intended_recipients", "main_use_case": "main_use_case", "notification_types": "notification_types", "expected_volume": "expected_volume", "service_url": "service_url", "support_type": "go_live_request", } contact = ContactRequest(**data) with notify_api.app_context(): assert ZenDeskSell().send_go_live_request(sample_service, sample_service.users[0], contact) search_deal_id_mock.assert_called_once_with(sample_service) send_create_service_mock.assert_not_called() create_note_mock.assert_called_once_with(ZenDeskSell.NoteResourceType.DEAL, deal_id, contact) def test_send_go_live_request_search_failed(notify_api: Flask, sample_service: Service, mocker: MockFixture): deal_id = "1" search_deal_id_mock = mocker.patch("app.user.rest.ZenDeskSell.search_deal_id", return_value=None) send_create_service_mock = mocker.patch("app.user.rest.ZenDeskSell.send_create_service", return_value=deal_id) create_note_mock = mocker.patch("app.user.rest.ZenDeskSell.create_note", return_value="1") data: Dict[str, Any] = { "email_address": "test@email.com", "service_name": "service_name", "department_org_name": "department_org_name", "intended_recipients": "intended_recipients", "main_use_case": "main_use_case", "notification_types": "notification_types", "expected_volume": "expected_volume", "service_url": "service_url", "support_type": "go_live_request", } contact = ContactRequest(**data) with notify_api.app_context(): assert ZenDeskSell().send_go_live_request(sample_service, sample_service.users[0], contact) search_deal_id_mock.assert_called_once_with(sample_service) send_create_service_mock.assert_called_once_with(sample_service, sample_service.users[0]) create_note_mock.assert_called_once_with(ZenDeskSell.NoteResourceType.DEAL, deal_id, contact) def test_send_go_live_service(notify_api: Flask, sample_service: Service, mocker: MockFixture): contact_id = 1 upsert_contact_mock = mocker.patch("app.user.rest.ZenDeskSell.upsert_contact", return_value=(contact_id, True)) upsert_deal_mock = mocker.patch("app.user.rest.ZenDeskSell.upsert_deal", return_value=1) with notify_api.app_context(): assert ZenDeskSell().send_go_live_service(sample_service, sample_service.users[0]) upsert_contact_mock.assert_called_once_with(sample_service.users[0], None) upsert_deal_mock.assert_called_once_with(contact_id, sample_service, ZenDeskSell.STATUS_CLOSE_LIVE)

39.230016

126

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null

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null

0

6

b2cc68b36f74374f809c38b4a5341aa5d5152a70

6,193

py

Python

HWs/CS464-Spring19-HWs/HW-1/code/tester.py

metehkaya/Bilkent-BSc-CS-Projects-and-HWs

bbc04b5207cf0b091d48cc2c26dfc75d6174505a

[ "MIT" ]

null

HWs/CS464-Spring19-HWs/HW-1/code/tester.py

metehkaya/Bilkent-BSc-CS-Projects-and-HWs

bbc04b5207cf0b091d48cc2c26dfc75d6174505a

[ "MIT" ]

null

HWs/CS464-Spring19-HWs/HW-1/code/tester.py

metehkaya/Bilkent-BSc-CS-Projects-and-HWs

bbc04b5207cf0b091d48cc2c26dfc75d6174505a

[ "MIT" ]

null

import math def multinomial_naive_bayes(train_pis, test_features_matrix, train_theta_t): n_tweet = len(test_features_matrix) n_feature = len(test_features_matrix[0]) test_result = [] for tweet in range(n_tweet): score = [0, 0, 0] for k in range(3): score[k] = math.log(train_pis[k]) for feature in range(n_feature): theta = train_theta_t[feature][k] test_feature = test_features_matrix[tweet][feature] if theta == 0: if test_feature == 0: score[k] += 0 else: score[k] += -math.inf else: score[k] += test_feature * math.log(theta) best_label = -1 best_score = -1 for k in range(3): if score[k] > best_score or best_label == -1: best_score = score[k] best_label = k if math.fabs(score[1] - score[2]) < 0.000001: best_label = 0 if best_label == 0: test_result.append('neutral') elif best_label == 1: test_result.append('positive') elif best_label == 2: test_result.append('negative') return test_result ''' def naive_bayes(train_pis, test_features_matrix, train_theta_t): n_tweet = len(test_features_matrix) n_feature = len(test_features_matrix[0]) test_result = [] for tweet in range(n_tweet): score = [0, 0, 0] minf = [0, 0, 0] for k in range(3): score[k] = math.log(train_pis[k]) for feature in range(n_feature): theta = train_theta_t[feature][k] test_feature = test_features_matrix[tweet][feature] if theta == 0: if test_feature == 0: score[k] += 0 else: minf[k] += 1 else: score[k] += test_feature * math.log(theta) best_label = -1 best_score = -1 best_minf = n_feature + 5 for k in range(3): if best_label == -1 or (minf[k] < best_minf) or (minf[k] == best_minf and score[k] > best_score): best_minf = minf[k] best_score = score[k] best_label = k if minf[1] == minf[2] and math.fabs(score[1] - score[2]) < 0.000001: best_label = 0 if best_label == 0: test_result.append('neutral') elif best_label == 1: test_result.append('positive') elif best_label == 2: test_result.append('negative') return test_result ''' def bernoulli_naive_bayes(train_pis, test_features_matrix, train_theta_s): n_tweet = len(test_features_matrix) n_feature = len(test_features_matrix[0]) test_result = [] for tweet in range(n_tweet): score = [0, 0, 0] for k in range(3): score[k] = math.log(train_pis[k]) for feature in range(n_feature): theta = train_theta_s[feature][k] test_feature = min(test_features_matrix[tweet][feature], 1) value = test_feature * theta + (1-test_feature) * (1-theta) if value == 0: score[k] += -math.inf else: score[k] += math.log(value) best_label = -1 best_score = -1 for k in range(3): if best_label == -1 or score[k] > best_score: best_score = score[k] best_label = k if math.fabs(score[1] - score[2]) < 0.000001: best_label = 0 if best_label == 0: test_result.append('neutral') elif best_label == 1: test_result.append('positive') elif best_label == 2: test_result.append('negative') return test_result ''' def bernoulli_naive_bayes(train_pis, test_features_matrix, train_theta_s): n_tweet = len(test_features_matrix) n_feature = len(test_features_matrix[0]) test_result = [] for tweet in range(n_tweet): score = [0, 0, 0] score_log = [0, 0, 0] minf = [0, 0, 0] for k in range(3): score[k] = math.log(train_pis[k]) for feature in range(n_feature): theta = train_theta_s[feature][k] test_feature = min(test_features_matrix[tweet][feature], 1) value = test_feature * theta + (1-test_feature) * (1-theta) if value == 0: minf[k] += 1 else: score_log[k] += math.log(value) score[k] += score_log[k] best_label = -1 best_score = -1 best_minf = n_feature + 5 for k in range(3): if best_label == -1 or (minf[k] < best_minf) or (minf[k] == best_minf and score[k] > best_score): best_minf = minf[k] best_score = score[k] best_label = k if minf[1] == minf[2] and math.fabs(score[1] - score[2]) < 0.000001: best_label = 0 if best_label == 0: test_result.append('neutral') elif best_label == 1: test_result.append('positive') elif best_label == 2: test_result.append('negative') return test_result ''' def get_class_id(label): if label == 'neutral': return 0 elif label == 'positive': return 1 elif label == 'negative': return 2 return -1 def find_accuracy(test_result, test_labels): correct = 0 failure = 0 counter = [[0, 0, 0], [0, 0, 0], [0, 0, 0]] n_tweet = len(test_result) for tweet in range(n_tweet): prediction = get_class_id(test_result[tweet]) actual = get_class_id(test_labels[tweet]) counter[prediction][actual] += 1 if test_result[tweet] == test_labels[tweet]: correct += 1 else: failure += 1 print('correct: ', correct) print('failure: ', failure) print('accuracy: ', float(correct) / (correct + failure)) print('counter: ', counter)

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6,193

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null

0

1

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null

0

6

b2e3b07bcfb8632ecae6a12475b580ccf0420bd0

144

py

Python

transcription_compare/tokenizer/__init__.py

HannaHUp/transcription-compare

e25d9651e604a854acba9659602ae1ea5497169e

[ "MIT" ]

2

2019-09-03T13:26:55.000Z

2020-08-04T20:32:35.000Z

transcription_compare/tokenizer/__init__.py

HannaHUp/transcription-compare

e25d9651e604a854acba9659602ae1ea5497169e

[ "MIT" ]

null

transcription_compare/tokenizer/__init__.py

HannaHUp/transcription-compare

e25d9651e604a854acba9659602ae1ea5497169e

[ "MIT" ]

null

from .abstract_tokenizer import AbstractTokenizer from .character_tokenizer import CharacterTokenizer from .word_tokenizer import WordTokenizer

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null

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1

0

1

0

1

0

6

654a721337574b3ca73fe38985f4aa469a26af02

8,981

py

Python

UstvarjanjeSQL/UstvarjanjeSQL rezultatov.py

MartinDolenc/Skoki

23877d02d8fbbd80defbb6276adfe636a543530a

[ "MIT" ]

null

UstvarjanjeSQL/UstvarjanjeSQL rezultatov.py

MartinDolenc/Skoki

23877d02d8fbbd80defbb6276adfe636a543530a

[ "MIT" ]

null

UstvarjanjeSQL/UstvarjanjeSQL rezultatov.py

MartinDolenc/Skoki

23877d02d8fbbd80defbb6276adfe636a543530a

[ "MIT" ]

null

#!/usr/bin/env python3 # Uvozimo potrebne knjižnice from lxml import html import requests import csv import re import pandas as pd import sqlalchemy import sqlite3 import itertools def text(tag): parts = [tag.text] + [text(t) for t in tag] + [tag.tail] if tag.tag == 'br': parts.insert(0, ' ') return re.sub(r'\s+', ' ', ''.join(filter(None, parts))) # za posamezne tekme link = "https://www.fis-ski.com/DB/general/results.html?sectorcode=JP&raceid=2832" stran = html.fromstring(requests.get(link).content) if len([text(r).replace('*', '').strip() for r in stran.xpath("//div[@class='g-lg-2 g-md-2 g-sm-2 justify-right hidden-xs pale']")]) > 2: ranki = [text(r).replace('*', '').strip() for r in stran.xpath("//div[@class='g-lg-1 g-md-1 g-sm-1 g-xs-2 justify-right pr-1 gray bold']")] startnaStevilka = [text(r).replace('*', '').strip() for r in stran.xpath( "//div[@class='g-lg-1 g-md-1 g-sm-1 justify-right hidden-xs pr-1 gray']")] fisCode = [text(r).replace('*', '').strip() for r in stran.xpath("//div[@class='g-lg-2 g-md-2 g-sm-2 hidden-xs justify-right gray pr-1']")] drzava = [text(r).replace('*', '').strip() for r in stran.xpath("//div[@id='events-info-results']//span[@class='country__name-short']")] skokiInRezultati = [text(r).replace('*', '').strip() for r in stran.xpath("//div[@class='g-lg-2 g-md-2 g-sm-2 justify-right bold hidden-xs']")] skoki = skokiInRezultati[0:][::2] rezultati = skokiInRezultati[1:][::2] # skoki = skoki[:len(drzava)] # rezultati = rezultati[:len(drzava)] ranki = ranki[:len(drzava)] fisCode = fisCode[:len(drzava)] startnaStevilka = startnaStevilka[:len(drzava)] ranki = list(itertools.chain(*zip(ranki, ranki))) startnaStevilka = list(itertools.chain(*zip(startnaStevilka, startnaStevilka))) fisCode = list(itertools.chain(*zip(fisCode, fisCode))) serija = list(itertools.chain(*zip(['1'] * (len(ranki) // 2), ['2'] * (len(ranki) // 2)))) mesto_v_ekipi = [''] * len(serija) drzava = list(itertools.chain(*zip(drzava, drzava))) skoki = skoki[:len(drzava)] rezultati = rezultati[:len(drzava)] else: ranki = [text(r).replace('*', '').strip() for r in stran.xpath("//div[@class='g-lg-1 g-md-1 g-sm-1 g-xs-2 justify-right pr-1 gray bold']")] startnaStevilka = [text(r).replace('*', '').strip() for r in stran.xpath( "//div[@class='g-lg-1 g-md-1 g-sm-1 justify-right hidden-xs pr-1 gray']")] fisCode = [text(r).replace('*', '').strip() for r in stran.xpath("//div[@class='g-lg-2 g-md-2 g-sm-2 hidden-xs justify-right gray pr-1']")] drzava = [text(r).replace('*', '').strip() for r in stran.xpath("//div[@id='events-info-results']//span[@class='country__name-short']")] skokiInRezultati = [text(r).replace('*', '').strip() for r in stran.xpath("//div[@class='g-lg-2 g-md-2 g-sm-2 justify-right bold hidden-xs']")] skoki = skokiInRezultati[0:][::2] rezultati = skokiInRezultati[1:][::2] skoki = skoki[:len(drzava)] rezultati = rezultati[:len(drzava)] ranki = ranki[:len(drzava)] fisCode = fisCode[:len(drzava)] startnaStevilka = startnaStevilka[:len(drzava)] serija = ['1'] * len(ranki) mesto_v_ekipi = [''] * len(serija) if (len(skoki) != 0) or (len(rezultati) != 0): print('različno od 0') else: print('enako 0') serija = ['3'] * len(ranki) rez = [text(r).replace('*', '').strip() for r in stran.xpath("//div[@class='g-lg-2 g-md-2 g-sm-3 g-xs-5 justify-right blue bold ']")] rezultati = rez[:len(serija)] skoki = [''] * len(serija) print(ranki) print(startnaStevilka) print(fisCode) print(drzava) print(skoki) print(rezultati) print(serija) print(mesto_v_ekipi) print([len(ranki), len(startnaStevilka), len(fisCode), len(drzava), len(skoki), len(rezultati), len(serija), len(mesto_v_ekipi)]) ''' # za ekipno link = "https://www.fis-ski.com/DB/general/results.html?sectorcode=JP&raceid=5276" stran = html.fromstring(requests.get(link).content) if len([text(r).replace('*', '').strip() for r in stran.xpath("//div[@class='g-lg-2 g-md-2 g-sm-2 justify-right hidden-xs text-right pale']")]) > 2: ranki = [text(r).replace('*', '').strip() for r in stran.xpath("//div[@class='g-lg-1 g-md-1 g-sm-1 g-xs-2 justify-right bold pr-1']")] fisCode = [text(r).replace('*', '').strip() for r in stran.xpath("//div[@class='g-lg-2 g-md-2 g-sm-3 hidden-xs justify-right gray pr-1']")] drzava1 = [text(r).replace('*', '').strip() for r in stran.xpath("//a[@class='table-row table-row_theme_main']")] skokiInRezultati = [text(r).replace('*', '').strip() for r in stran.xpath("//a[@class='table-row table-row_theme_additional']//div[@class='g-lg-2 g-md-2 g-sm-2 justify-right bold hidden-xs']")] ranki = list(itertools.chain(*zip(ranki,ranki))) ranki = list(itertools.chain(*zip(ranki,ranki))) ranki = list(itertools.chain(*zip(ranki,ranki))) print(len(skokiInRezultati)) skoki = skokiInRezultati[0:][::2] rezultati = skokiInRezultati[1:][::2] fisCodeReal = [] for str in fisCode: if len(str) == 4: fisCodeReal.append(str) fisCodeReal = list(itertools.chain(*zip(fisCodeReal,fisCodeReal))) serija = list(itertools.chain(*zip(['1']*(len(ranki)//2),['2']*(len(ranki)//2)))) mesto_v_ekipi = ['1', '2', '3', '4']*(len(ranki)//8) mesto_v_ekipi = list(itertools.chain(*zip(mesto_v_ekipi,mesto_v_ekipi))) #rezultati = list(filter(None, rezultati)) #skoki = list(filter(None, skoki)) startnaStevilka = ['']*len(ranki) drzava = [] for str in drzava1: drzava.append(str.split()[-2]) drzava = list(itertools.chain(*zip(drzava,drzava))) drzava = list(itertools.chain(*zip(drzava,drzava))) drzava = list(itertools.chain(*zip(drzava,drzava))) fisCode = fisCodeReal else: ranki = [text(r).replace('*', '').strip() for r in stran.xpath("//div[@class='g-lg-1 g-md-1 g-sm-1 g-xs-2 justify-right bold pr-1']")] fisCode = [text(r).replace('*', '').strip() for r in stran.xpath("//div[@class='g-lg-2 g-md-2 g-sm-3 hidden-xs justify-right gray pr-1']")] drzava1 = [text(r).replace('*', '').strip() for r in stran.xpath("//a[@class='table-row table-row_theme_main']")] skokiInRezultati = [text(r).replace('*', '').strip() for r in stran.xpath("//a[@class='table-row table-row_theme_additional']//div[@class='g-lg-2 g-md-2 g-sm-2 justify-right bold hidden-xs']")] ranki = list(itertools.chain(*zip(ranki, ranki))) ranki = list(itertools.chain(*zip(ranki, ranki))) print(len(skokiInRezultati)) skoki = skokiInRezultati[0:][::2] rezultati = skokiInRezultati[1:][::2] fisCodeReal = [] for str in fisCode: if len(str) == 4: fisCodeReal.append(str) serija = ['1'] * len(ranki) mesto_v_ekipi = ['1', '2', '3', '4'] * (len(ranki) // 4) # rezultati = list(filter(None, rezultati)) # skoki = list(filter(None, skoki)) startnaStevilka = [''] * len(ranki) drzava = [] for str in drzava1: drzava.append(str.split()[-2]) drzava = list(itertools.chain(*zip(drzava, drzava))) drzava = list(itertools.chain(*zip(drzava, drzava))) fisCode = fisCodeReal print(ranki) print(startnaStevilka) print(fisCode) print(drzava) print(skoki) print(rezultati) print(serija) print(mesto_v_ekipi) print([len(ranki), len(startnaStevilka), len(fisCode), len(drzava), len(skoki), len(rezultati), len(serija), len(mesto_v_ekipi)]) #link = "https://www.fis-ski.com/DB/general/results.html?sectorcode=JP&raceid=4862" #stran = html.fromstring(requests.get(link).content) #print(len([text(r).replace('*', '').strip() for r in stran.xpath("//div[@class='g-lg-2 g-md-2 g-sm-2 justify-right hidden-xs pale']")])) #link = "https://www.fis-ski.com/DB/general/results.html?sectorcode=JP&raceid=4861" #stran = html.fromstring(requests.get(link).content) #print('Team' in [text(r).replace('*', '').strip() for r in stran.xpath("//div[@class='g-lg-2 g-md-2 g-sm-2 justify-right hidden-xs pale']")][0]) # Naslov, od koder pobiramo podatke #link = "https://www.fis-ski.com/DB/ski-jumping/biographies.html?lastname=&firstname=&sectorcode=JP&gendercode=M&birthyear=&skiclub=&skis=&nationcode=&fiscode=&status=&search=true&limit=1000&offset=5000" #stran = html.fromstring(requests.get(link).content) #drzava = [text(r).replace('*', '').strip() for r in stran.xpath("//div[@class='table__body']")[0] # .xpath("//span[@class='country__name-short']")] '''

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3334b3ed93786359ecb56e59674d5ac4b3092a9b

158

py

Python

eds/openmtc-gevent/server/openmtc-ngsi/openmtc-ngsi-pylibs/flask/testsuite/test_apps/moduleapp/apps/frontend/__init__.py

piyush82/elastest-device-emulator-service

b4d6b393d6042c54a7b3dfb5f58cad5efd00f0e7

[ "Apache-2.0" ]

null

eds/openmtc-gevent/server/openmtc-ngsi/openmtc-ngsi-pylibs/flask/testsuite/test_apps/moduleapp/apps/frontend/__init__.py

piyush82/elastest-device-emulator-service

b4d6b393d6042c54a7b3dfb5f58cad5efd00f0e7

[ "Apache-2.0" ]

null

eds/openmtc-gevent/server/openmtc-ngsi/openmtc-ngsi-pylibs/flask/testsuite/test_apps/moduleapp/apps/frontend/__init__.py

piyush82/elastest-device-emulator-service

b4d6b393d6042c54a7b3dfb5f58cad5efd00f0e7

[ "Apache-2.0" ]

null

from flask import Module, render_template frontend = Module(__name__) @frontend.route('/') def index(): return render_template('FrontEnd/motor.html')

15.8

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null

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686fc4e18637b31019062d7f770fcfac86f4e090

24

py

Python

wendigo/application/__init__.py

medmsyk/wendigopy

36e0759bf8b065548fd638063768522704506236

[ "Apache-2.0" ]

null

wendigo/application/__init__.py

medmsyk/wendigopy

36e0759bf8b065548fd638063768522704506236

[ "Apache-2.0" ]

1

2022-01-05T10:28:49.000Z

2022-03-20T09:17:04.000Z

wendigo/application/__init__.py

medmsyk/wendigopy

36e0759bf8b065548fd638063768522704506236

[ "Apache-2.0" ]

null

from .dll import Wendigo

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0

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6895684e92084ac85c5d46c4b6ddedc1bfa8cad4

60

py

Python

theory/slides-2-0/topic-2-interactive-programs/python/integers.py

dgrafov/redi-python-intro

bbb29e58cd75602b720be5f7695fa4a66db521dd

[ "MIT" ]

8

2018-01-30T10:40:32.000Z

2018-09-08T21:08:03.000Z

theory/slides-2-0/topic-2-interactive-programs/python/integers.py

dgrafov/redi-python-intro

bbb29e58cd75602b720be5f7695fa4a66db521dd

[ "MIT" ]

null

theory/slides-2-0/topic-2-interactive-programs/python/integers.py

dgrafov/redi-python-intro

bbb29e58cd75602b720be5f7695fa4a66db521dd

[ "MIT" ]

5

2018-02-08T18:02:34.000Z

2019-10-05T17:51:23.000Z

num = 3 print(num, type(num)) Num = -5 print(Num, type(Num))

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null

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6

68a010e9103b38ea63b73bace8837d878b86dc77

8,111

py

Python

raw_packet/Tests/Unit_tests/Scripts/ARP/test_arp_spoof.py

4ekin/raw-packet

40322ec2f6c3ce0647ba69283df40fa8da4817e2

[ "MIT" ]

null

raw_packet/Tests/Unit_tests/Scripts/ARP/test_arp_spoof.py

4ekin/raw-packet

40322ec2f6c3ce0647ba69283df40fa8da4817e2

[ "MIT" ]

null

raw_packet/Tests/Unit_tests/Scripts/ARP/test_arp_spoof.py

4ekin/raw-packet

40322ec2f6c3ce0647ba69283df40fa8da4817e2

[ "MIT" ]

null

# region Description """ test_arp_spoof.py: Unit tests for Raw-packet script: arp_spoof.py Author: Vladimir Ivanov License: MIT Copyright 2020, Raw-packet Project """ # endregion # region Import from sys import path from os.path import dirname, abspath, isfile from os import remove, kill from signal import SIGTERM from time import sleep from subprocess import run, PIPE, Popen from scapy.all import rdpcap, ARP from typing import IO import unittest # endregion # region Authorship information __author__ = 'Vladimir Ivanov' __copyright__ = 'Copyright 2020, Raw-packet Project' __credits__ = [''] __license__ = 'MIT' __version__ = '0.2.1' __maintainer__ = 'Vladimir Ivanov' __email__ = 'ivanov.vladimir.mail@gmail.com' __status__ = 'Development' # endregion # region Main class - ScriptArpScanTest class ScriptArpSpoofTest(unittest.TestCase): # region Properties root_path = dirname(dirname(dirname(dirname(dirname(dirname(abspath(__file__))))))) path.append(root_path) from raw_packet.Utils.base import Base from raw_packet.Tests.Unit_tests.variables import Variables base: Base = Base() tshark_pcap_filename: str = '/tmp/arp_spoof_test.pcap' # endregion def test01_main_responses(self): find_spoof_packet: bool = False arp_spoof_command: str = 'python3 ' + self.root_path + '/Scripts/ARP/arp_spoof.py -i ' + \ ScriptArpSpoofTest.Variables.test_network_interface + ' -t ' + \ ScriptArpSpoofTest.Variables.apple_device_ipv4_address Popen(arp_spoof_command, shell=True) tshark_command: str = 'tshark -i ' + ScriptArpSpoofTest.Variables.test_network_interface + \ ' -f "ether src ' + ScriptArpSpoofTest.Variables.your_mac_address + \ ' and ether dst ' + ScriptArpSpoofTest.Variables.apple_device_mac_address + \ ' and arp" -B 65535 -w ' + self.tshark_pcap_filename + \ ' 1>/dev/null 2>&1' Popen(tshark_command, shell=True) sleep(5) while self.base.get_process_pid('/arp_spoof.py') != -1: kill(self.base.get_process_pid('/arp_spoof.py'), SIGTERM) sleep(0.5) while self.base.get_process_pid('tshark') != -1: kill(self.base.get_process_pid('tshark'), SIGTERM) sleep(0.5) try: packets = rdpcap(self.tshark_pcap_filename) for packet in packets: if packet.haslayer(ARP): arp_packet = packet[ARP] self.base.print_info('ARP opcode: ', str(arp_packet.op)) self.base.print_info('ARP sender MAC: ', arp_packet.hwsrc) self.base.print_info('ARP target MAC: ', arp_packet.hwdst) self.base.print_info('ARP sender IP: ', arp_packet.psrc) self.base.print_info('ARP target IP: ', arp_packet.pdst) if arp_packet.hwsrc == ScriptArpSpoofTest.Variables.your_mac_address and \ arp_packet.hwdst == ScriptArpSpoofTest.Variables.apple_device_mac_address and \ arp_packet.psrc == ScriptArpSpoofTest.Variables.router_ipv4_address and \ arp_packet.pdst == ScriptArpSpoofTest.Variables.apple_device_ipv4_address and \ arp_packet.op == 2: find_spoof_packet = True break except ValueError: pass if isfile(self.tshark_pcap_filename): remove(self.tshark_pcap_filename) self.assertTrue(find_spoof_packet) def test02_main_requests(self): find_spoof_packet: bool = False arp_spoof_command: str = 'python3 ' + self.root_path + '/Scripts/ARP/arp_spoof.py -i ' + \ ScriptArpSpoofTest.Variables.test_network_interface + ' -t ' + \ ScriptArpSpoofTest.Variables.apple_device_ipv4_address + ' -r' Popen(arp_spoof_command, shell=True) tshark_command: str = 'tshark -i ' + ScriptArpSpoofTest.Variables.test_network_interface + \ ' -f "ether src ' + ScriptArpSpoofTest.Variables.your_mac_address + \ ' and ether dst ' + ScriptArpSpoofTest.Variables.apple_device_mac_address + \ ' and arp" -B 65535 -w ' + self.tshark_pcap_filename + \ ' 1>/dev/null 2>&1' Popen(tshark_command, shell=True) sleep(5) while self.base.get_process_pid('/arp_spoof.py') != -1: kill(self.base.get_process_pid('/arp_spoof.py'), SIGTERM) sleep(0.5) while self.base.get_process_pid('tshark') != -1: kill(self.base.get_process_pid('tshark'), SIGTERM) sleep(0.5) try: packets = rdpcap(self.tshark_pcap_filename) for packet in packets: if packet.haslayer(ARP): arp_packet = packet[ARP] self.base.print_info('ARP opcode: ', str(arp_packet.op)) self.base.print_info('ARP sender MAC: ', arp_packet.hwsrc) self.base.print_info('ARP target MAC: ', arp_packet.hwdst) self.base.print_info('ARP sender IP: ', arp_packet.psrc) self.base.print_info('ARP target IP: ', arp_packet.pdst) if arp_packet.hwsrc == ScriptArpSpoofTest.Variables.your_mac_address and \ arp_packet.hwdst == '00:00:00:00:00:00' and \ arp_packet.psrc == ScriptArpSpoofTest.Variables.router_ipv4_address and \ arp_packet.op == 1: find_spoof_packet = True break except ValueError: pass if isfile(self.tshark_pcap_filename): remove(self.tshark_pcap_filename) self.assertTrue(find_spoof_packet) def test03_main_bad_interface(self): arp_spoof = run(['python3 ' + self.root_path + '/Scripts/ARP/arp_spoof.py -i ' + ScriptArpSpoofTest.Variables.bad_network_interface], shell=True, stdout=PIPE) arp_spoof_output: str = arp_spoof.stdout.decode('utf-8') print(arp_spoof_output) self.assertIn(ScriptArpSpoofTest.Variables.bad_network_interface, arp_spoof_output) def test04_main_bad_gateway_ip(self): arp_spoof = run(['python3 ' + self.root_path + '/Scripts/ARP/arp_spoof.py -i ' + ScriptArpSpoofTest.Variables.test_network_interface + ' -g ' + ScriptArpSpoofTest.Variables.bad_ipv4_address], shell=True, stdout=PIPE) arp_spoof_output: str = arp_spoof.stdout.decode('utf-8') print(arp_spoof_output) self.assertIn(ScriptArpSpoofTest.Variables.bad_ipv4_address, arp_spoof_output) def test05_main_bad_target_ip(self): arp_spoof = run(['python3 ' + self.root_path + '/Scripts/ARP/arp_spoof.py -i ' + ScriptArpSpoofTest.Variables.test_network_interface + ' -t ' + ScriptArpSpoofTest.Variables.bad_ipv4_address], shell=True, stdout=PIPE) arp_spoof_output: str = arp_spoof.stdout.decode('utf-8') print(arp_spoof_output) self.assertIn(ScriptArpSpoofTest.Variables.bad_ipv4_address, arp_spoof_output) def test06_main_bad_target_mac(self): arp_spoof = run(['python3 ' + self.root_path + '/Scripts/ARP/arp_spoof.py -i ' + ScriptArpSpoofTest.Variables.test_network_interface + ' -t ' + ScriptArpSpoofTest.Variables.apple_device_ipv4_address + ' -m ' + ScriptArpSpoofTest.Variables.bad_mac_address], shell=True, stdout=PIPE) arp_spoof_output: str = arp_spoof.stdout.decode('utf-8') print(arp_spoof_output) self.assertIn(ScriptArpSpoofTest.Variables.bad_mac_address, arp_spoof_output) # endregion

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null

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null

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6

cc1efc92565700f28a1e214f9336fcbf363f2ca3

2,259

py

Python

epytope/Data/pssms/smm/mat/B_42_01_9.py

christopher-mohr/epytope

8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd

[ "BSD-3-Clause" ]

7

2021-02-01T18:11:28.000Z

2022-01-31T19:14:07.000Z

epytope/Data/pssms/smm/mat/B_42_01_9.py

christopher-mohr/epytope

8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd

[ "BSD-3-Clause" ]

22

2021-01-02T15:25:23.000Z

2022-03-14T11:32:53.000Z

epytope/Data/pssms/smm/mat/B_42_01_9.py

christopher-mohr/epytope

8ac9fe52c0b263bdb03235a5a6dffcb72012a4fd

[ "BSD-3-Clause" ]

4

2021-05-28T08:50:38.000Z

2022-03-14T11:45:32.000Z

B_42_01_9 = {0: {'A': 0.055, 'C': 0.0, 'E': -0.236, 'D': 0.334, 'G': 0.124, 'F': -0.444, 'I': 0.01, 'H': 0.243, 'K': 0.016, 'M': 0.0, 'L': -0.297, 'N': -0.122, 'Q': -0.004, 'P': 0.065, 'S': 0.158, 'R': 0.092, 'T': 0.185, 'W': 0.181, 'V': -0.162, 'Y': -0.198}, 1: {'A': 0.306, 'C': 0.0, 'E': 0.632, 'D': 0.05, 'G': 0.139, 'F': -0.603, 'I': -0.341, 'H': 0.125, 'K': 0.011, 'M': 0.0, 'L': 0.289, 'N': 0.0, 'Q': 0.357, 'P': -1.512, 'S': 0.402, 'R': 0.303, 'T': 0.058, 'W': 0.0, 'V': -0.031, 'Y': -0.186}, 2: {'A': 0.037, 'C': 0.0, 'E': 0.181, 'D': 0.094, 'G': 0.227, 'F': -0.015, 'I': 0.073, 'H': 0.054, 'K': -0.058, 'M': -0.29, 'L': 0.156, 'N': 0.043, 'Q': 0.041, 'P': -0.027, 'S': -0.116, 'R': -0.305, 'T': -0.039, 'W': -0.0, 'V': -0.042, 'Y': -0.014}, 3: {'A': 0.0, 'C': -0.045, 'E': 0.073, 'D': -0.094, 'G': 0.238, 'F': -0.078, 'I': -0.084, 'H': -0.018, 'K': 0.14, 'M': 0.0, 'L': 0.152, 'N': 0.04, 'Q': 0.075, 'P': 0.013, 'S': -0.037, 'R': -0.116, 'T': -0.192, 'W': -0.065, 'V': -0.104, 'Y': 0.102}, 4: {'A': -0.16, 'C': -0.017, 'E': 0.404, 'D': 0.253, 'G': 0.092, 'F': 0.089, 'I': -0.081, 'H': 0.0, 'K': 0.322, 'M': -0.331, 'L': -0.526, 'N': 0.356, 'Q': 0.26, 'P': -0.171, 'S': -0.295, 'R': 0.017, 'T': 0.148, 'W': -0.244, 'V': -0.17, 'Y': 0.054}, 5: {'A': -0.057, 'C': 0.0, 'E': 0.466, 'D': 0.323, 'G': 0.053, 'F': 0.164, 'I': 0.185, 'H': -0.201, 'K': -0.357, 'M': -0.027, 'L': 0.048, 'N': -0.024, 'Q': -0.073, 'P': -0.024, 'S': 0.118, 'R': -0.479, 'T': 0.153, 'W': 0.0, 'V': -0.349, 'Y': 0.082}, 6: {'A': -0.109, 'C': 0.103, 'E': -0.025, 'D': 0.477, 'G': -0.065, 'F': -0.301, 'I': -0.083, 'H': -0.065, 'K': -0.15, 'M': -0.113, 'L': 0.013, 'N': -0.003, 'Q': 0.282, 'P': -0.185, 'S': 0.136, 'R': -0.315, 'T': 0.267, 'W': 0.134, 'V': -0.104, 'Y': 0.107}, 7: {'A': -0.393, 'C': 0.009, 'E': -0.147, 'D': 0.107, 'G': -0.136, 'F': 0.0, 'I': -0.269, 'H': 0.104, 'K': 0.229, 'M': 0.238, 'L': 0.19, 'N': 0.196, 'Q': -0.087, 'P': -0.264, 'S': -0.253, 'R': 0.101, 'T': 0.304, 'W': -0.248, 'V': 0.381, 'Y': -0.062}, 8: {'A': -0.47, 'C': 0.0, 'E': 0.0, 'D': 0.0, 'G': 0.0, 'F': -0.464, 'I': -0.404, 'H': 0.0, 'K': 1.278, 'M': -1.578, 'L': -0.605, 'N': 0.0, 'Q': 0.0, 'P': 0.0, 'S': 0.0, 'R': 1.152, 'T': -0.166, 'W': 0.341, 'V': -0.21, 'Y': 1.126}, -1: {'con': 3.85875}}

2,259

0.385126

557

2,259

1.556553

0.292639

0.053057

0.017301

0.023068

0.107266

0

0.362142

0.165117

2,259

1

2,259

0.097561

0

0.080973

0

1

0

false

0

1

null

0

1

0

1

0

null

0

6

cc21d22a0df477b9364223be687fc78b0c687565

6,794

py

Python

install/freqdb/common.py

mzimandl/wag

f9309bb50d21731304354d69f2d9347dcdcbe7a8

[ "Apache-2.0" ]

3

2020-06-01T08:59:25.000Z

2020-08-28T07:08:52.000Z

install/freqdb/common.py

czcorpus/wdglance

817607b29136394a4641cf1abc303db66cff6500

[ "Apache-2.0" ]

129

2019-02-05T08:04:33.000Z

2020-05-11T14:24:23.000Z

install/freqdb/common.py

czcorpus/wdglance

817607b29136394a4641cf1abc303db66cff6500

[ "Apache-2.0" ]

2

2019-09-13T14:52:24.000Z

2019-10-03T08:17:07.000Z

import re upcase_regex = re.compile(u"^[A-Z\u00C0-\u00D6\u00D8-\u00DE\u0100\u0102\u0104\u0106\u0108\u010A\u010C\u010E\u0110\u0112\u0114\u0116\u0118\u011A\u011C\u011E\u0120\u0122\u0124\u0126\u0128\u012A\u012C\u012E\u0130\u0132\u0134\u0136\u0139\u013B\u013D\u013F\u0141\u0143\u0145\u0147\u014A\u014C\u014E\u0150\u0152\u0154\u0156\u0158\u015A\u015C\u015E\u0160\u0162\u0164\u0166\u0168\u016A\u016C\u016E\u0170\u0172\u0174\u0176\u0178\u0179\u017B\u017D\u0181\u0182\u0184\u0186\u0187\u0189-\u018B\u018E-\u0191\u0193\u0194\u0196-\u0198\u019C\u019D\u019F\u01A0\u01A2\u01A4\u01A6\u01A7\u01A9\u01AC\u01AE\u01AF\u01B1-\u01B3\u01B5\u01B7\u01B8\u01BC\u01C4\u01C7\u01CA\u01CD\u01CF\u01D1\u01D3\u01D5\u01D7\u01D9\u01DB\u01DE\u01E0\u01E2\u01E4\u01E6\u01E8\u01EA\u01EC\u01EE\u01F1\u01F4\u01F6-\u01F8\u01FA\u01FC\u01FE\u0200\u0202\u0204\u0206\u0208\u020A\u020C\u020E\u0210\u0212\u0214\u0216\u0218\u021A\u021C\u021E\u0220\u0222\u0224\u0226\u0228\u022A\u022C\u022E\u0230\u0232\u023A\u023B\u023D\u023E\u0241\u0243-\u0246\u0248\u024A\u024C\u024E\u0370\u0372\u0376\u037F\u0386\u0388-\u038A\u038C\u038E\u038F\u0391-\u03A1\u03A3-\u03AB\u03CF\u03D2-\u03D4\u03D8\u03DA\u03DC\u03DE\u03E0\u03E2\u03E4\u03E6\u03E8\u03EA\u03EC\u03EE\u03F4\u03F7\u03F9\u03FA\u03FD-\u042F\u0460\u0462\u0464\u0466\u0468\u046A\u046C\u046E\u0470\u0472\u0474\u0476\u0478\u047A\u047C\u047E\u0480\u048A\u048C\u048E\u0490\u0492\u0494\u0496\u0498\u049A\u049C\u049E\u04A0\u04A2\u04A4\u04A6\u04A8\u04AA\u04AC\u04AE\u04B0\u04B2\u04B4\u04B6\u04B8\u04BA\u04BC\u04BE\u04C0\u04C1\u04C3\u04C5\u04C7\u04C9\u04CB\u04CD\u04D0\u04D2\u04D4\u04D6\u04D8\u04DA\u04DC\u04DE\u04E0\u04E2\u04E4\u04E6\u04E8\u04EA\u04EC\u04EE\u04F0\u04F2\u04F4\u04F6\u04F8\u04FA\u04FC\u04FE\u0500\u0502\u0504\u0506\u0508\u050A\u050C\u050E\u0510\u0512\u0514\u0516\u0518\u051A\u051C\u051E\u0520\u0522\u0524\u0526\u0528\u052A\u052C\u052E\u0531-\u0556\u10A0-\u10C5\u10C7\u10CD\u13A0-\u13F5\u1E00\u1E02\u1E04\u1E06\u1E08\u1E0A\u1E0C\u1E0E\u1E10\u1E12\u1E14\u1E16\u1E18\u1E1A\u1E1C\u1E1E\u1E20\u1E22\u1E24\u1E26\u1E28\u1E2A\u1E2C\u1E2E\u1E30\u1E32\u1E34\u1E36\u1E38\u1E3A\u1E3C\u1E3E\u1E40\u1E42\u1E44\u1E46\u1E48\u1E4A\u1E4C\u1E4E\u1E50\u1E52\u1E54\u1E56\u1E58\u1E5A\u1E5C\u1E5E\u1E60\u1E62\u1E64\u1E66\u1E68\u1E6A\u1E6C\u1E6E\u1E70\u1E72\u1E74\u1E76\u1E78\u1E7A\u1E7C\u1E7E\u1E80\u1E82\u1E84\u1E86\u1E88\u1E8A\u1E8C\u1E8E\u1E90\u1E92\u1E94\u1E9E\u1EA0\u1EA2\u1EA4\u1EA6\u1EA8\u1EAA\u1EAC\u1EAE\u1EB0\u1EB2\u1EB4\u1EB6\u1EB8\u1EBA\u1EBC\u1EBE\u1EC0\u1EC2\u1EC4\u1EC6\u1EC8\u1ECA\u1ECC\u1ECE\u1ED0\u1ED2\u1ED4\u1ED6\u1ED8\u1EDA\u1EDC\u1EDE\u1EE0\u1EE2\u1EE4\u1EE6\u1EE8\u1EEA\u1EEC\u1EEE\u1EF0\u1EF2\u1EF4\u1EF6\u1EF8\u1EFA\u1EFC\u1EFE\u1F08-\u1F0F\u1F18-\u1F1D\u1F28-\u1F2F\u1F38-\u1F3F\u1F48-\u1F4D\u1F59\u1F5B\u1F5D\u1F5F\u1F68-\u1F6F\u1FB8-\u1FBB\u1FC8-\u1FCB\u1FD8-\u1FDB\u1FE8-\u1FEC\u1FF8-\u1FFB\u2102\u2107\u210B-\u210D\u2110-\u2112\u2115\u2119-\u211D\u2124\u2126\u2128\u212A-\u212D\u2130-\u2133\u213E\u213F\u2145\u2160-\u216F\u2183\u24B6-\u24CF\u2C00-\u2C2E\u2C60\u2C62-\u2C64\u2C67\u2C69\u2C6B\u2C6D-\u2C70\u2C72\u2C75\u2C7E-\u2C80\u2C82\u2C84\u2C86\u2C88\u2C8A\u2C8C\u2C8E\u2C90\u2C92\u2C94\u2C96\u2C98\u2C9A\u2C9C\u2C9E\u2CA0\u2CA2\u2CA4\u2CA6\u2CA8\u2CAA\u2CAC\u2CAE\u2CB0\u2CB2\u2CB4\u2CB6\u2CB8\u2CBA\u2CBC\u2CBE\u2CC0\u2CC2\u2CC4\u2CC6\u2CC8\u2CCA\u2CCC\u2CCE\u2CD0\u2CD2\u2CD4\u2CD6\u2CD8\u2CDA\u2CDC\u2CDE\u2CE0\u2CE2\u2CEB\u2CED\u2CF2\uA640\uA642\uA644\uA646\uA648\uA64A\uA64C\uA64E\uA650\uA652\uA654\uA656\uA658\uA65A\uA65C\uA65E\uA660\uA662\uA664\uA666\uA668\uA66A\uA66C\uA680\uA682\uA684\uA686\uA688\uA68A\uA68C\uA68E\uA690\uA692\uA694\uA696\uA698\uA69A\uA722\uA724\uA726\uA728\uA72A\uA72C\uA72E\uA732\uA734\uA736\uA738\uA73A\uA73C\uA73E\uA740\uA742\uA744\uA746\uA748\uA74A\uA74C\uA74E\uA750\uA752\uA754\uA756\uA758\uA75A\uA75C\uA75E\uA760\uA762\uA764\uA766\uA768\uA76A\uA76C\uA76E\uA779\uA77B\uA77D\uA77E\uA780\uA782\uA784\uA786\uA78B\uA78D\uA790\uA792\uA796\uA798\uA79A\uA79C\uA79E\uA7A0\uA7A2\uA7A4\uA7A6\uA7A8\uA7AA-\uA7AE\uA7B0-\uA7B4\uA7B6\uFF21-\uFF3A\U00010400-\U00010427\U000104B0-\U000104D3\U00010C80-\U00010CB2\U000118A0-\U000118BF\U0001D400-\U0001D419\U0001D434-\U0001D44D\U0001D468-\U0001D481\U0001D49C\U0001D49E\U0001D49F\U0001D4A2\U0001D4A5\U0001D4A6\U0001D4A9-\U0001D4AC\U0001D4AE-\U0001D4B5\U0001D4D0-\U0001D4E9\U0001D504\U0001D505\U0001D507-\U0001D50A\U0001D50D-\U0001D514\U0001D516-\U0001D51C\U0001D538\U0001D539\U0001D53B-\U0001D53E\U0001D540-\U0001D544\U0001D546\U0001D54A-\U0001D550\U0001D56C-\U0001D585\U0001D5A0-\U0001D5B9\U0001D5D4-\U0001D5ED\U0001D608-\U0001D621\U0001D63C-\U0001D655\U0001D670-\U0001D689\U0001D6A8-\U0001D6C0\U0001D6E2-\U0001D6FA\U0001D71C-\U0001D734\U0001D756-\U0001D76E\U0001D790-\U0001D7A8\U0001D7CA\U0001E900-\U0001E921\U0001F130-\U0001F149\U0001F150-\U0001F169\U0001F170-\U0001F189].+") def is_tag(t): return re.match(r'[a-zA-Z$]', 'J$') def pos2pos(s): return s[0] def penn2pos(s): try: return { 'CC': 'J', # Coordinating conjunction 'CD': 'C', # Cardinal number 'DT': 'X', # Determiner 'EX': 'X', # Existential there 'FW': 'X', # Foreign word 'IN': 'R', # Preposition or subordinating conjunction 'JJ': 'A', # Adjective 'JJR': 'A', # Adjective, comparative 'JJS': 'A', # Adjective, superlative 'LS': 'X', # List item marker 'MD': 'X', # Modal 'NN': 'N', # Noun, singular or mass 'NNS': 'N', # Noun, plural 'NNP': 'X', # Proper noun, singular 'NNPS': 'X', # Proper noun, plural 'PDT': 'X', # Predeterminer 'POS': 'X', # Possessive ending 'PRP': 'P', # Personal pronoun 'PRP$': 'P', # Possessive pronoun 'RB': 'D', # Adverb 'RBR': 'D', # Adverb, comparative 'RBS': 'D', # Adverb, superlative 'RP': 'T', # Particle 'SYM': 'X', # Symbol 'TO': 'X', # to 'UH': 'I', # Interjection 'VB': 'V', # Verb, base form 'VBD': 'V', # Verb, past tense 'VBG': 'V', # Verb, gerund or present participle 'VBN': 'V', # Verb, past participle 'VBP': 'V', # Verb, non-3rd person singular present 'VBZ': 'V', # Verb, 3rd person singular present 'WDT': 'V', # Wh-determiner 'WP': 'P', # Wh-pronoun 'WP$': 'P', # Possessive wh-pronoun 'WRB': 'D' # Wh-adverb }[s] except KeyError: #print('Unrecognized tag {0}'.format(s)) return 'X' def is_stop_word(w): return w is None or re.match(r'^[\d\.,\:;\!\?%\$\[\]=\*\-\+\{\}/\|"\'_<>"&#@~\^§]+$', w) def is_stop_ngram(w): items = w.split(' ') return any([is_stop_word(x) for x in items])

111.377049

4,744

0.712688

956

6,794

5.056485

0.883891

0.006206

0.00331

0.00993

0

0.378021

0.110686

6,794

60

4,745

113.233333

0.42188

0.103915

0

0.019231

0.809721

0.78608

0

1

0.096154

false

0

0.019231

0.057692

0.211538

0

null

0

1

0

1

0

1

null

0

6

0bdbc35a6b7c1a54cfcff26d6405a80bcf772b9f

3,820

py

Python

problem8.py

Yusufpek/project-euler-with-python-csharp-tr

16bd0730e71882738a981b14a465172262e369f0

[ "CC0-1.0" ]

2

2021-05-01T01:13:41.000Z

2021-06-28T18:21:17.000Z

problem8.py

Yusufpek/project-euler-with-python-csharp-tr

16bd0730e71882738a981b14a465172262e369f0

[ "CC0-1.0" ]

null

problem8.py

Yusufpek/project-euler-with-python-csharp-tr

16bd0730e71882738a981b14a465172262e369f0

[ "CC0-1.0" ]

2

2021-03-12T21:44:35.000Z

2021-06-18T06:27:23.000Z

""" Project Euler Problem 8 EN:The four adjacent digits in the 1000-digit number that have the greatest product are 9 × 9 × 8 × 9 = 5832. 73167176531330624919225119674426574742355349194934 96983520312774506326239578318016984801869478851843 85861560789112949495459501737958331952853208805511 12540698747158523863050715693290963295227443043557 66896648950445244523161731856403098711121722383113 62229893423380308135336276614282806444486645238749 30358907296290491560440772390713810515859307960866 70172427121883998797908792274921901699720888093776 65727333001053367881220235421809751254540594752243 52584907711670556013604839586446706324415722155397 53697817977846174064955149290862569321978468622482 83972241375657056057490261407972968652414535100474 82166370484403199890008895243450658541227588666881 16427171479924442928230863465674813919123162824586 17866458359124566529476545682848912883142607690042 24219022671055626321111109370544217506941658960408 07198403850962455444362981230987879927244284909188 84580156166097919133875499200524063689912560717606 05886116467109405077541002256983155200055935729725 71636269561882670428252483600823257530420752963450 Find the thirteen adjacent digits in the 1000-digit number that have the greatest product. What is the value of this product? TUR: 1000 basamaklı sayının içinde çarpımı en yüksek olan 4 komşu sayının çarpımı 9x9x8x9 = 5832. Bu sayının içinde çarpımı en büyük olan komşu 13 sayının çarpımını bulun. Bu sayının değeri nedir? """ # Sayıyı kopyaladım lakin şu an bu sayıda alt satır kaçış karakterleri var number ="""73167176531330624919225119674426574742355349194934 96983520312774506326239578318016984801869478851843 85861560789112949495459501737958331952853208805511 12540698747158523863050715693290963295227443043557 66896648950445244523161731856403098711121722383113 62229893423380308135336276614282806444486645238749 30358907296290491560440772390713810515859307960866 70172427121883998797908792274921901699720888093776 65727333001053367881220235421809751254540594752243 52584907711670556013604839586446706324415722155397 53697817977846174064955149290862569321978468622482 83972241375657056057490261407972968652414535100474 82166370484403199890008895243450658541227588666881 16427171479924442928230863465674813919123162824586 17866458359124566529476545682848912883142607690042 24219022671055626321111109370544217506941658960408 07198403850962455444362981230987879927244284909188 84580156166097919133875499200524063689912560717606 05886116467109405077541002256983155200055935729725 71636269561882670428252483600823257530420752963450""" # Sayıdaki karakterleri temizledim number = number.replace("\n","") # Sayıları 13'lü paketlere ayıracağız ve bu listeye koyacağız digits_13_packs = [] # başlangıç ve bitiş bir bir kayacak ve biz de paketleyeceğiz for start,end in zip(range(0,len(number)-13),range(13,len(number))): digits_13_packs.append(number[start:end]) # Bu listeye bu sayıları tek tek ayırıp stringden integere çevirdiklerimizi ekleyeceğiz # "321" yerine [3,2,1] şeklinde digits_13_int = [list(pack) for pack in digits_13_packs] # integere dönüştürüyoruz for pack_index,pack in enumerate(digits_13_int): for digit_index,digit in enumerate(pack): digits_13_int[pack_index][digit_index] = int(digit) # paketlerin içindeki sayıların çarpımını bu listeye ekleyeceğiz product_list = [] # her paketi tek tek dönüyoruz for pack in digits_13_int: result = 1 # her paketteki rakamı tek tek dönüyoruz for digit in pack: # rakamları çarpıp listeye ekliyoruz result = result * digit product_list.append(result) # en büyük değeri ekrana yazdırıyoruz print(max(product_list))

39.791667

126

0.850524

291

3,820

11.103093

0.47079

0.017332

0.013618

0.011761

0.666667

0.656144

0

0.612002

0.118848

3,820

96

127

39.791667

0.346999

0.536649

0

0.616174

0.6035

0

1

0

false

0

0.028571

0

1

null

0

1

0

1

0

1

0

1

null

0

6

0bde8e55cd7e619f9b899b1d177cd79ffbc9ca47

38

py

Python

django_grpc_bus/__init__.py

rameezarshad/django-grpc-framework

6f9cea9828343b23c60656b561bca5d750fe6b5f

[ "Apache-2.0" ]

2

2020-12-19T18:56:49.000Z

2022-01-13T07:01:37.000Z

django_grpc_bus/__init__.py

rameezarshad/django-grpc-framework

6f9cea9828343b23c60656b561bca5d750fe6b5f

[ "Apache-2.0" ]

null

django_grpc_bus/__init__.py

rameezarshad/django-grpc-framework

6f9cea9828343b23c60656b561bca5d750fe6b5f

[ "Apache-2.0" ]

null

from .client.registry import registry

19

37

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38

6.4

0.8

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1

38

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0

1

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null

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1

0

1

0

1

0

6

0bf85ba6f810558cbed2ab6f477cf7b1c27fc89d

63

py

Python

models/utils/__init__.py

JackPlayer/nhl-salary-prediction

a32d9bc1468e671f60b81076df69a47f3910ae6c

[ "MIT" ]

null

models/utils/__init__.py

JackPlayer/nhl-salary-prediction

a32d9bc1468e671f60b81076df69a47f3910ae6c

[ "MIT" ]

null

models/utils/__init__.py

JackPlayer/nhl-salary-prediction

a32d9bc1468e671f60b81076df69a47f3910ae6c

[ "MIT" ]

null

""" utils module """ from .feature_list import feature_list

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0.174603

63

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38

15.75

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null

0

1

0

1

0

1

0

6

f099905ba86043f73a6df3a4319e7e15837e8887

3,556

py

Python

metric.py

Yukei7/Multimodal-Segmentation-Network

0a38aa8bbd2eb87e28209c810438248c0464a240

[ "MIT" ]

null

metric.py

Yukei7/Multimodal-Segmentation-Network

0a38aa8bbd2eb87e28209c810438248c0464a240

[ "MIT" ]

null

metric.py

Yukei7/Multimodal-Segmentation-Network

0a38aa8bbd2eb87e28209c810438248c0464a240

[ "MIT" ]

null

import numpy as np import torch def dice_coef(y_pred: torch.Tensor, y_true: torch.Tensor, ts: float = 0.5, eps: float = 1e-9): assert y_pred.shape == y_true.shape scores = [] n_samples = y_pred.shape[0] preds = (y_pred > ts).float() for i in range(n_samples): pred = preds[i] true = y_true[i] intersect = 2.0 * (true * pred).sum() union = true.sum() + pred.sum() if true.sum() == 0 and pred.sum() == 0: scores.append(1.0) else: scores.append((intersect + eps) / union) return np.mean(scores) def jaccard_coef(y_pred: torch.Tensor, y_true: torch.Tensor, ts: float = 0.5, eps: float = 1e-9): assert y_pred.shape == y_true.shape scores = [] n_samples = y_pred.shape[0] preds = (y_pred > ts).float() for i in range(n_samples): pred = preds[i] true = y_true[i] intersect = (pred * true).sum() union = (true.sum() + pred.sum()) - intersect + eps if true.sum() == 0 and pred.sum() == 0: scores.append(1.0) else: scores.append((intersect + eps) / union) return np.mean(scores) def dice_coef_pc(probabilities: np.ndarray, truth: np.ndarray, treshold: float = 0.5, eps: float = 1e-9, classes: list = ['WT', 'TC', 'ET']) -> dict: scores = {key: list() for key in classes} num = probabilities.shape[0] num_classes = probabilities.shape[1] predictions = (probabilities >= treshold).astype(np.float32) assert (predictions.shape == truth.shape) for i in range(num): for class_ in range(num_classes): prediction = predictions[i][class_] truth_ = truth[i][class_] intersection = 2.0 * (truth_ * prediction).sum() union = truth_.sum() + prediction.sum() if truth_.sum() == 0 and prediction.sum() == 0: scores[classes[class_]].append(1.0) else: scores[classes[class_]].append((intersection + eps) / union) return scores def jaccard_coef_pc(probabilities: np.ndarray, truth: np.ndarray, treshold: float = 0.5, eps: float = 1e-9, classes: list = ['WT', 'TC', 'ET']) -> dict: """ Calculate Jaccard index for data batch and for each class. Params: probobilities: model outputs after activation function. truth: model targets. threshold: threshold for probabilities. eps: additive to refine the estimate. classes: list with name classes. Returns: dict with jaccard scores for each class." """ scores = {key: list() for key in classes} num = probabilities.shape[0] num_classes = probabilities.shape[1] predictions = (probabilities >= treshold).astype(np.float32) assert (predictions.shape == truth.shape) for i in range(num): for class_ in range(num_classes): prediction = predictions[i][class_] truth_ = truth[i][class_] intersection = (prediction * truth_).sum() union = (prediction.sum() + truth_.sum()) - intersection + eps if truth_.sum() == 0 and prediction.sum() == 0: scores[classes[class_]].append(1.0) else: scores[classes[class_]].append((intersection + eps) / union) return scores

34.862745

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0.551181

427

3,556

4.482436

0.192037

0.020899

0.014629

0.020899

0.77116

0.748171

0

0.019127

0.323678

3,556

101

77

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1

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1

0

null

0

6

f0be8f77f423547c26af9fd70d752da70a0c2036

186

py

Python

test.py

vivekraghu17/Twitter-Sentiment-Analysis-

7583f6cf9db848830e82f0885755b98a2dd21c2d

[ "Unlicense" ]

1

2018-11-19T14:14:24.000Z

test.py

vivekraghu17/Twitter-Sentiment-Analysis-

7583f6cf9db848830e82f0885755b98a2dd21c2d

[ "Unlicense" ]

null

test.py

vivekraghu17/Twitter-Sentiment-Analysis-

7583f6cf9db848830e82f0885755b98a2dd21c2d

[ "Unlicense" ]

null

import sentiment_mod as s print(s.sentiment("This is utter flop movie and not worth the money ")) print(s.sentiment("One of the best sentimental and thriller movies i have ever seen"))

37.2

86

0.774194

33

186

4.333333

0.787879

0.083916

0.20979

0

0.150538

186

4

87

46.5

0.905063

0

0.607527

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1

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true

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null

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1

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null

0

1

0

1

0

1

0

6

9bd539dc90a283fab2a48056854fbe17837d92e6

124

py

Python

flask_images/__init__.py

Club-Alpin-Annecy/Flask-Images

5c0d4028d3e6e04769ab7bb68258c02cd4769406

[ "BSD-3-Clause" ]

75

2015-01-07T20:25:53.000Z

2021-11-01T17:49:00.000Z

flask_images/__init__.py

Club-Alpin-Annecy/Flask-Images

5c0d4028d3e6e04769ab7bb68258c02cd4769406

[ "BSD-3-Clause" ]

30

2015-01-07T19:57:58.000Z

2021-08-31T09:14:33.000Z

flask_images/__init__.py

Club-Alpin-Annecy/Flask-Images

5c0d4028d3e6e04769ab7bb68258c02cd4769406

[ "BSD-3-Clause" ]

46

2015-01-14T03:09:03.000Z

2022-02-01T20:18:50.000Z

from .core import Images, resized_img_src, resized_img_size, resized_img_attrs, resized_img_tag from .size import ImageSize

41.333333

95

0.854839

20

124

4.9

0.55

0.408163

0

0.096774

124

2

96

62

0.875

0

1

0

true

0

1

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1

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null

1

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1

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null

0

1

0

1

0

1

0

6

501956d5f6d3fecc12a6a351e24e95f90a3f027d

1,437

py

Python

examples/memtest.py

tescalada/npyscreen-restructure

0833bbbdec18439182f102d2147f3756fa98aadd

[ "BSD-2-Clause" ]

2

2015-01-12T14:47:19.000Z

2018-10-03T09:27:22.000Z

examples/memtest.py

tescalada/npyscreen-restructure

0833bbbdec18439182f102d2147f3756fa98aadd

[ "BSD-2-Clause" ]

null

examples/memtest.py

tescalada/npyscreen-restructure

0833bbbdec18439182f102d2147f3756fa98aadd

[ "BSD-2-Clause" ]

1

2020-03-20T20:19:33.000Z

#!/usr/bin/env python import npyscreen import EXAMPLE #def Mainloop(scr): # while 1: # sampleform() # #def sampleform(): # F = npyscreen.Form(name = "Welcome to Npyscreen") # t = F.add(npyscreen.TitleText, name = "Text:") # p = F.add(npyscreen.TitlePassword, name = "Password:") # fn = F.add(npyscreen.TitleFilename, name = "Filename:") # s = F.add(npyscreen.TitleSlider, out_of=12, name = "Slider") # ml= F.add(npyscreen.MultiLineEdit, value = "try typing here! Mutiline text, press ^R to reformat.", max_height=4) # ms= F.add(npyscreen.MultiSelect, max_height=4, value = [1,], values = ["Option1","Option2","Option3"], scroll_exit=True) class TestMem(npyscreen.NPSApp): def main(self): F = npyscreen.Form(name = "Welcome to Npyscreen") t = F.add(npyscreen.TitleText, name = "Text:") p = F.add(npyscreen.TitlePassword, name = "Password:") fn = F.add(npyscreen.TitleFilename, name = "Filename:") s = F.add(npyscreen.TitleSlider, out_of=12, name = "Slider") ml= F.add(npyscreen.MultiLineEdit, value = "try typing here! Mutiline text, press ^R to reformat.", max_height=3, rely=7) ms= F.add(npyscreen.MultiSelect, max_height=4, value = [1,], values = ["Option1","Option2","Option3"], scroll_exit=True) F.display() if __name__ == "__main__": Test = TestMem() Test.run() while 1: Test = TestMem() Test.main()

38.837838

129

0.637439

186

1,437

4.83871

0.365591

0.053333

0.173333

0.04

0.786667

0

0.016522

0.199722

1,437

36

130

39.916667

0.766087

0.425887

0

0.111111

0

0.161529

0

1

0.055556

false

0.055556

0.111111

0

0.222222

0

null

0

1

0

null

0

1

0

6

501d0d7b7cd1729dbcb7bc36c20fcd11b26d5097

95

py

Python

fortniteversus/bin/db_create.py

oliverbarreto/FortniteVS

c09f0514558912d9d058552dc1295ddfa266132c

[ "Apache-2.0" ]

1

2019-07-02T19:53:04.000Z

fortniteversus/bin/db_create.py

oliverbarreto/FortniteVS

c09f0514558912d9d058552dc1295ddfa266132c

[ "Apache-2.0" ]

null

fortniteversus/bin/db_create.py

oliverbarreto/FortniteVS

c09f0514558912d9d058552dc1295ddfa266132c

[ "Apache-2.0" ]

null

from fortniteversus import db from fortniteversus.models import StoreItem db.create_all()

10.555556

43

0.810526

12

95

6.333333

0.666667

0.473684

0

0.147368

95

8

44

11.875

0.938272

0

1

0

true

0

0.666667

0

0.666667

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

acdb42a18331e099bd1333fa1de92a7c4a7bfe04

25,613

py

Python

tests/integration/standard/test_udts.py

HackerEarth/cassandra-python-driver

65fca43785046f88d7aabac3a567c52f8a3e24cd

[ "Apache-2.0" ]

1

2017-10-17T11:30:52.000Z

tests/integration/standard/test_udts.py

HackerEarth/cassandra-python-driver

65fca43785046f88d7aabac3a567c52f8a3e24cd

[ "Apache-2.0" ]

null

tests/integration/standard/test_udts.py

HackerEarth/cassandra-python-driver

65fca43785046f88d7aabac3a567c52f8a3e24cd

[ "Apache-2.0" ]

null

# Copyright 2013-2015 DataStax, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from cassandra.query import dict_factory try: import unittest2 as unittest except ImportError: import unittest # noqa import logging log = logging.getLogger(__name__) from collections import namedtuple from cassandra.cluster import Cluster, UserTypeDoesNotExist from tests.integration import get_server_versions, use_singledc, PROTOCOL_VERSION from tests.integration.datatype_utils import get_sample, get_nonprim_sample,\ DATA_TYPE_PRIMITIVES, DATA_TYPE_NON_PRIMITIVE_NAMES def setup_module(): use_singledc() class TypeTests(unittest.TestCase): def setUp(self): if PROTOCOL_VERSION < 3: raise unittest.SkipTest("v3 protocol is required for UDT tests") self._cass_version, self._cql_version = get_server_versions() def test_unprepared_registered_udts(self): c = Cluster(protocol_version=PROTOCOL_VERSION) s = c.connect() s.execute(""" CREATE KEYSPACE udt_test_unprepared_registered WITH replication = { 'class' : 'SimpleStrategy', 'replication_factor': '1' } """) s.set_keyspace("udt_test_unprepared_registered") s.execute("CREATE TYPE user (age int, name text)") s.execute("CREATE TABLE mytable (a int PRIMARY KEY, b frozen<user>)") User = namedtuple('user', ('age', 'name')) c.register_user_type("udt_test_unprepared_registered", "user", User) s.execute("INSERT INTO mytable (a, b) VALUES (%s, %s)", (0, User(42, 'bob'))) result = s.execute("SELECT b FROM mytable WHERE a=0") self.assertEqual(1, len(result)) row = result[0] self.assertEqual(42, row.b.age) self.assertEqual('bob', row.b.name) self.assertTrue(type(row.b) is User) # use the same UDT name in a different keyspace s.execute(""" CREATE KEYSPACE udt_test_unprepared_registered2 WITH replication = { 'class' : 'SimpleStrategy', 'replication_factor': '1' } """) s.set_keyspace("udt_test_unprepared_registered2") s.execute("CREATE TYPE user (state text, is_cool boolean)") s.execute("CREATE TABLE mytable (a int PRIMARY KEY, b frozen<user>)") User = namedtuple('user', ('state', 'is_cool')) c.register_user_type("udt_test_unprepared_registered2", "user", User) s.execute("INSERT INTO mytable (a, b) VALUES (%s, %s)", (0, User('Texas', True))) result = s.execute("SELECT b FROM mytable WHERE a=0") self.assertEqual(1, len(result)) row = result[0] self.assertEqual('Texas', row.b.state) self.assertEqual(True, row.b.is_cool) self.assertTrue(type(row.b) is User) c.shutdown() def test_register_before_connecting(self): User1 = namedtuple('user', ('age', 'name')) User2 = namedtuple('user', ('state', 'is_cool')) c = Cluster(protocol_version=PROTOCOL_VERSION) s = c.connect() s.execute(""" CREATE KEYSPACE udt_test_register_before_connecting WITH replication = { 'class' : 'SimpleStrategy', 'replication_factor': '1' } """) s.set_keyspace("udt_test_register_before_connecting") s.execute("CREATE TYPE user (age int, name text)") s.execute("CREATE TABLE mytable (a int PRIMARY KEY, b frozen<user>)") s.execute(""" CREATE KEYSPACE udt_test_register_before_connecting2 WITH replication = { 'class' : 'SimpleStrategy', 'replication_factor': '1' } """) s.set_keyspace("udt_test_register_before_connecting2") s.execute("CREATE TYPE user (state text, is_cool boolean)") s.execute("CREATE TABLE mytable (a int PRIMARY KEY, b frozen<user>)") # now that types are defined, shutdown and re-create Cluster c.shutdown() c = Cluster(protocol_version=PROTOCOL_VERSION) c.register_user_type("udt_test_register_before_connecting", "user", User1) c.register_user_type("udt_test_register_before_connecting2", "user", User2) s = c.connect() s.set_keyspace("udt_test_register_before_connecting") s.execute("INSERT INTO mytable (a, b) VALUES (%s, %s)", (0, User1(42, 'bob'))) result = s.execute("SELECT b FROM mytable WHERE a=0") self.assertEqual(1, len(result)) row = result[0] self.assertEqual(42, row.b.age) self.assertEqual('bob', row.b.name) self.assertTrue(type(row.b) is User1) # use the same UDT name in a different keyspace s.set_keyspace("udt_test_register_before_connecting2") s.execute("INSERT INTO mytable (a, b) VALUES (%s, %s)", (0, User2('Texas', True))) result = s.execute("SELECT b FROM mytable WHERE a=0") self.assertEqual(1, len(result)) row = result[0] self.assertEqual('Texas', row.b.state) self.assertEqual(True, row.b.is_cool) self.assertTrue(type(row.b) is User2) c.shutdown() def test_prepared_unregistered_udts(self): c = Cluster(protocol_version=PROTOCOL_VERSION) s = c.connect() s.execute(""" CREATE KEYSPACE udt_test_prepared_unregistered WITH replication = { 'class' : 'SimpleStrategy', 'replication_factor': '1' } """) s.set_keyspace("udt_test_prepared_unregistered") s.execute("CREATE TYPE user (age int, name text)") s.execute("CREATE TABLE mytable (a int PRIMARY KEY, b frozen<user>)") User = namedtuple('user', ('age', 'name')) insert = s.prepare("INSERT INTO mytable (a, b) VALUES (?, ?)") s.execute(insert, (0, User(42, 'bob'))) select = s.prepare("SELECT b FROM mytable WHERE a=?") result = s.execute(select, (0,)) self.assertEqual(1, len(result)) row = result[0] self.assertEqual(42, row.b.age) self.assertEqual('bob', row.b.name) # use the same UDT name in a different keyspace s.execute(""" CREATE KEYSPACE udt_test_prepared_unregistered2 WITH replication = { 'class' : 'SimpleStrategy', 'replication_factor': '1' } """) s.set_keyspace("udt_test_prepared_unregistered2") s.execute("CREATE TYPE user (state text, is_cool boolean)") s.execute("CREATE TABLE mytable (a int PRIMARY KEY, b frozen<user>)") User = namedtuple('user', ('state', 'is_cool')) insert = s.prepare("INSERT INTO mytable (a, b) VALUES (?, ?)") s.execute(insert, (0, User('Texas', True))) select = s.prepare("SELECT b FROM mytable WHERE a=?") result = s.execute(select, (0,)) self.assertEqual(1, len(result)) row = result[0] self.assertEqual('Texas', row.b.state) self.assertEqual(True, row.b.is_cool) c.shutdown() def test_prepared_registered_udts(self): c = Cluster(protocol_version=PROTOCOL_VERSION) s = c.connect() s.execute(""" CREATE KEYSPACE udt_test_prepared_registered WITH replication = { 'class' : 'SimpleStrategy', 'replication_factor': '1' } """) s.set_keyspace("udt_test_prepared_registered") s.execute("CREATE TYPE user (age int, name text)") User = namedtuple('user', ('age', 'name')) c.register_user_type("udt_test_prepared_registered", "user", User) s.execute("CREATE TABLE mytable (a int PRIMARY KEY, b frozen<user>)") insert = s.prepare("INSERT INTO mytable (a, b) VALUES (?, ?)") s.execute(insert, (0, User(42, 'bob'))) select = s.prepare("SELECT b FROM mytable WHERE a=?") result = s.execute(select, (0,)) self.assertEqual(1, len(result)) row = result[0] self.assertEqual(42, row.b.age) self.assertEqual('bob', row.b.name) self.assertTrue(type(row.b) is User) # use the same UDT name in a different keyspace s.execute(""" CREATE KEYSPACE udt_test_prepared_registered2 WITH replication = { 'class' : 'SimpleStrategy', 'replication_factor': '1' } """) s.set_keyspace("udt_test_prepared_registered2") s.execute("CREATE TYPE user (state text, is_cool boolean)") User = namedtuple('user', ('state', 'is_cool')) c.register_user_type("udt_test_prepared_registered2", "user", User) s.execute("CREATE TABLE mytable (a int PRIMARY KEY, b frozen<user>)") insert = s.prepare("INSERT INTO mytable (a, b) VALUES (?, ?)") s.execute(insert, (0, User('Texas', True))) select = s.prepare("SELECT b FROM mytable WHERE a=?") result = s.execute(select, (0,)) self.assertEqual(1, len(result)) row = result[0] self.assertEqual('Texas', row.b.state) self.assertEqual(True, row.b.is_cool) self.assertTrue(type(row.b) is User) c.shutdown() def test_udts_with_nulls(self): """ Test UDTs with null and empty string fields. """ c = Cluster(protocol_version=PROTOCOL_VERSION) s = c.connect() s.execute(""" CREATE KEYSPACE test_udts_with_nulls WITH replication = { 'class' : 'SimpleStrategy', 'replication_factor': '1' } """) s.set_keyspace("test_udts_with_nulls") s.execute("CREATE TYPE user (a text, b int, c uuid, d blob)") User = namedtuple('user', ('a', 'b', 'c', 'd')) c.register_user_type("test_udts_with_nulls", "user", User) s.execute("CREATE TABLE mytable (a int PRIMARY KEY, b frozen<user>)") insert = s.prepare("INSERT INTO mytable (a, b) VALUES (0, ?)") s.execute(insert, [User(None, None, None, None)]) results = s.execute("SELECT b FROM mytable WHERE a=0") self.assertEqual((None, None, None, None), results[0].b) select = s.prepare("SELECT b FROM mytable WHERE a=0") self.assertEqual((None, None, None, None), s.execute(select)[0].b) # also test empty strings s.execute(insert, [User('', None, None, '')]) results = s.execute("SELECT b FROM mytable WHERE a=0") self.assertEqual(('', None, None, ''), results[0].b) self.assertEqual(('', None, None, ''), s.execute(select)[0].b) c.shutdown() def test_udt_sizes(self): """ Test for ensuring extra-lengthy udts are handled correctly. """ if self._cass_version < (2, 1, 0): raise unittest.SkipTest("The tuple type was introduced in Cassandra 2.1") MAX_TEST_LENGTH = 16384 EXTENDED_QUERY_TIMEOUT = 60 c = Cluster(protocol_version=PROTOCOL_VERSION) s = c.connect() s.execute("""CREATE KEYSPACE test_udt_sizes WITH replication = { 'class' : 'SimpleStrategy', 'replication_factor': '1'}""") s.set_keyspace("test_udt_sizes") # create the seed udt, increase timeout to avoid the query failure on slow systems s.execute("CREATE TYPE lengthy_udt ({})" .format(', '.join(['v_{} int'.format(i) for i in range(MAX_TEST_LENGTH)])), timeout=EXTENDED_QUERY_TIMEOUT) # create a table with multiple sizes of nested udts # no need for all nested types, only a spot checked few and the largest one s.execute("CREATE TABLE mytable (" "k int PRIMARY KEY, " "v frozen<lengthy_udt>)", timeout=EXTENDED_QUERY_TIMEOUT) # create and register the seed udt type udt = namedtuple('lengthy_udt', tuple(['v_{}'.format(i) for i in range(MAX_TEST_LENGTH)])) c.register_user_type("test_udt_sizes", "lengthy_udt", udt) # verify inserts and reads for i in (0, 1, 2, 3, MAX_TEST_LENGTH): # create udt params = [j for j in range(i)] + [None for j in range(MAX_TEST_LENGTH - i)] created_udt = udt(*params) # write udt s.execute("INSERT INTO mytable (k, v) VALUES (0, %s)", (created_udt,)) # verify udt was written and read correctly, increase timeout to avoid the query failure on slow systems result = s.execute("SELECT v FROM mytable WHERE k=0", timeout=EXTENDED_QUERY_TIMEOUT)[0] self.assertEqual(created_udt, result.v) c.shutdown() def nested_udt_helper(self, udts, i): """ Helper for creating nested udts. """ if i == 0: return udts[0](42, 'Bob') else: return udts[i](self.nested_udt_helper(udts, i - 1)) def test_nested_registered_udts(self): """ Test for ensuring nested udts are handled correctly. """ if self._cass_version < (2, 1, 0): raise unittest.SkipTest("The tuple type was introduced in Cassandra 2.1") MAX_NESTING_DEPTH = 16 c = Cluster(protocol_version=PROTOCOL_VERSION) s = c.connect() # set the row_factory to dict_factory for programmatically accessing values s.row_factory = dict_factory s.execute("""CREATE KEYSPACE test_nested_registered_udts WITH replication = { 'class' : 'SimpleStrategy', 'replication_factor': '1'}""") s.set_keyspace("test_nested_registered_udts") # create the seed udt s.execute("CREATE TYPE depth_0 (age int, name text)") # create the nested udts for i in range(MAX_NESTING_DEPTH): s.execute("CREATE TYPE depth_{} (value frozen<depth_{}>)".format(i + 1, i)) # create a table with multiple sizes of nested udts # no need for all nested types, only a spot checked few and the largest one s.execute("CREATE TABLE mytable (" "k int PRIMARY KEY, " "v_0 frozen<depth_0>, " "v_1 frozen<depth_1>, " "v_2 frozen<depth_2>, " "v_3 frozen<depth_3>, " "v_{0} frozen<depth_{0}>)".format(MAX_NESTING_DEPTH)) # create the udt container udts = [] # create and register the seed udt type udt = namedtuple('depth_0', ('age', 'name')) udts.append(udt) c.register_user_type("test_nested_registered_udts", "depth_0", udts[0]) # create and register the nested udt types for i in range(MAX_NESTING_DEPTH): udt = namedtuple('depth_{}'.format(i + 1), ('value')) udts.append(udt) c.register_user_type("test_nested_registered_udts", "depth_{}".format(i + 1), udts[i + 1]) # verify inserts and reads for i in (0, 1, 2, 3, MAX_NESTING_DEPTH): # create udt udt = self.nested_udt_helper(udts, i) # write udt s.execute("INSERT INTO mytable (k, v_%s) VALUES (0, %s)", (i, udt)) # verify udt was written and read correctly result = s.execute("SELECT v_%s FROM mytable WHERE k=0", (i,))[0] self.assertEqual(udt, result['v_%s' % i]) c.shutdown() def test_nested_unregistered_udts(self): """ Test for ensuring nested unregistered udts are handled correctly. """ if self._cass_version < (2, 1, 0): raise unittest.SkipTest("The tuple type was introduced in Cassandra 2.1") MAX_NESTING_DEPTH = 16 c = Cluster(protocol_version=PROTOCOL_VERSION) s = c.connect() # set the row_factory to dict_factory for programmatically accessing values s.row_factory = dict_factory s.execute("""CREATE KEYSPACE test_nested_unregistered_udts WITH replication = { 'class' : 'SimpleStrategy', 'replication_factor': '1'}""") s.set_keyspace("test_nested_unregistered_udts") # create the seed udt s.execute("CREATE TYPE depth_0 (age int, name text)") # create the nested udts for i in range(MAX_NESTING_DEPTH): s.execute("CREATE TYPE depth_{} (value frozen<depth_{}>)".format(i + 1, i)) # create a table with multiple sizes of nested udts # no need for all nested types, only a spot checked few and the largest one s.execute("CREATE TABLE mytable (" "k int PRIMARY KEY, " "v_0 frozen<depth_0>, " "v_1 frozen<depth_1>, " "v_2 frozen<depth_2>, " "v_3 frozen<depth_3>, " "v_{0} frozen<depth_{0}>)".format(MAX_NESTING_DEPTH)) # create the udt container udts = [] # create and register the seed udt type udt = namedtuple('depth_0', ('age', 'name')) udts.append(udt) # create and register the nested udt types for i in range(MAX_NESTING_DEPTH): udt = namedtuple('depth_{}'.format(i + 1), ('value')) udts.append(udt) # verify inserts and reads for i in (0, 1, 2, 3, MAX_NESTING_DEPTH): # create udt udt = self.nested_udt_helper(udts, i) # write udt insert = s.prepare("INSERT INTO mytable (k, v_{0}) VALUES (0, ?)".format(i)) s.execute(insert, (udt,)) # verify udt was written and read correctly result = s.execute("SELECT v_%s FROM mytable WHERE k=0", (i,))[0] self.assertEqual(udt, result['v_%s' % i]) c.shutdown() def test_nested_registered_udts_with_different_namedtuples(self): """ Test for ensuring nested udts are handled correctly when the created namedtuples are use names that are different the cql type. Future improvement: optimize these three related tests using a single helper method to cut down on code repetition. """ if self._cass_version < (2, 1, 0): raise unittest.SkipTest("The tuple type was introduced in Cassandra 2.1") MAX_NESTING_DEPTH = 16 c = Cluster(protocol_version=PROTOCOL_VERSION) s = c.connect() # set the row_factory to dict_factory for programmatically accessing values s.row_factory = dict_factory s.execute("""CREATE KEYSPACE different_namedtuples WITH replication = { 'class' : 'SimpleStrategy', 'replication_factor': '1'}""") s.set_keyspace("different_namedtuples") # create the seed udt s.execute("CREATE TYPE depth_0 (age int, name text)") # create the nested udts for i in range(MAX_NESTING_DEPTH): s.execute("CREATE TYPE depth_{} (value frozen<depth_{}>)".format(i + 1, i)) # create a table with multiple sizes of nested udts # no need for all nested types, only a spot checked few and the largest one s.execute("CREATE TABLE mytable (" "k int PRIMARY KEY, " "v_0 frozen<depth_0>, " "v_1 frozen<depth_1>, " "v_2 frozen<depth_2>, " "v_3 frozen<depth_3>, " "v_{0} frozen<depth_{0}>)".format(MAX_NESTING_DEPTH)) # create the udt container udts = [] # create and register the seed udt type udt = namedtuple('level_0', ('age', 'name')) udts.append(udt) c.register_user_type("different_namedtuples", "depth_0", udts[0]) # create and register the nested udt types for i in range(MAX_NESTING_DEPTH): udt = namedtuple('level_{}'.format(i + 1), ('value')) udts.append(udt) c.register_user_type("different_namedtuples", "depth_{}".format(i + 1), udts[i + 1]) # verify inserts and reads for i in (0, 1, 2, 3, MAX_NESTING_DEPTH): # create udt udt = self.nested_udt_helper(udts, i) # write udt s.execute("INSERT INTO mytable (k, v_%s) VALUES (0, %s)", (i, udt)) # verify udt was written and read correctly result = s.execute("SELECT v_%s FROM mytable WHERE k=0", (i,))[0] self.assertEqual(udt, result['v_%s' % i]) c.shutdown() def test_non_existing_types(self): c = Cluster(protocol_version=PROTOCOL_VERSION) c.connect() User = namedtuple('user', ('age', 'name')) self.assertRaises(UserTypeDoesNotExist, c.register_user_type, "some_bad_keyspace", "user", User) self.assertRaises(UserTypeDoesNotExist, c.register_user_type, "system", "user", User) c.shutdown() def test_primitive_datatypes(self): """ Test for inserting various types of DATA_TYPE_PRIMITIVES into UDT's """ c = Cluster(protocol_version=PROTOCOL_VERSION) s = c.connect() # create keyspace s.execute(""" CREATE KEYSPACE test_primitive_datatypes WITH replication = { 'class' : 'SimpleStrategy', 'replication_factor': '1' } """) s.set_keyspace("test_primitive_datatypes") # create UDT alpha_type_list = [] start_index = ord('a') for i, datatype in enumerate(DATA_TYPE_PRIMITIVES): alpha_type_list.append("{0} {1}".format(chr(start_index + i), datatype)) s.execute(""" CREATE TYPE alldatatypes ({0}) """.format(', '.join(alpha_type_list)) ) s.execute("CREATE TABLE mytable (a int PRIMARY KEY, b frozen<alldatatypes>)") # register UDT alphabet_list = [] for i in range(ord('a'), ord('a') + len(DATA_TYPE_PRIMITIVES)): alphabet_list.append('{}'.format(chr(i))) Alldatatypes = namedtuple("alldatatypes", alphabet_list) c.register_user_type("test_primitive_datatypes", "alldatatypes", Alldatatypes) # insert UDT data params = [] for datatype in DATA_TYPE_PRIMITIVES: params.append((get_sample(datatype))) insert = s.prepare("INSERT INTO mytable (a, b) VALUES (?, ?)") s.execute(insert, (0, Alldatatypes(*params))) # retrieve and verify data results = s.execute("SELECT * FROM mytable") self.assertEqual(1, len(results)) row = results[0].b for expected, actual in zip(params, row): self.assertEqual(expected, actual) c.shutdown() def test_nonprimitive_datatypes(self): """ Test for inserting various types of DATA_TYPE_NON_PRIMITIVE into UDT's """ c = Cluster(protocol_version=PROTOCOL_VERSION) s = c.connect() # create keyspace s.execute(""" CREATE KEYSPACE test_nonprimitive_datatypes WITH replication = { 'class' : 'SimpleStrategy', 'replication_factor': '1' } """) s.set_keyspace("test_nonprimitive_datatypes") # create UDT alpha_type_list = [] start_index = ord('a') for i, nonprim_datatype in enumerate(DATA_TYPE_NON_PRIMITIVE_NAMES): for j, datatype in enumerate(DATA_TYPE_PRIMITIVES): if nonprim_datatype == "map": type_string = "{0}_{1} {2}<{3}, {3}>".format(chr(start_index + i), chr(start_index + j), nonprim_datatype, datatype) elif nonprim_datatype == "tuple": type_string = "{0}_{1} frozen<{2}<{3}>>".format(chr(start_index + i), chr(start_index + j), nonprim_datatype, datatype) else: type_string = "{0}_{1} {2}<{3}>".format(chr(start_index + i), chr(start_index + j), nonprim_datatype, datatype) alpha_type_list.append(type_string) s.execute(""" CREATE TYPE alldatatypes ({0}) """.format(', '.join(alpha_type_list)) ) s.execute("CREATE TABLE mytable (a int PRIMARY KEY, b frozen<alldatatypes>)") # register UDT alphabet_list = [] for i in range(ord('a'), ord('a') + len(DATA_TYPE_NON_PRIMITIVE_NAMES)): for j in range(ord('a'), ord('a') + len(DATA_TYPE_PRIMITIVES)): alphabet_list.append('{0}_{1}'.format(chr(i), chr(j))) Alldatatypes = namedtuple("alldatatypes", alphabet_list) c.register_user_type("test_nonprimitive_datatypes", "alldatatypes", Alldatatypes) # insert UDT data params = [] for nonprim_datatype in DATA_TYPE_NON_PRIMITIVE_NAMES: for datatype in DATA_TYPE_PRIMITIVES: params.append((get_nonprim_sample(nonprim_datatype, datatype))) insert = s.prepare("INSERT INTO mytable (a, b) VALUES (?, ?)") s.execute(insert, (0, Alldatatypes(*params))) # retrieve and verify data results = s.execute("SELECT * FROM mytable") self.assertEqual(1, len(results)) row = results[0].b for expected, actual in zip(params, row): self.assertEqual(expected, actual) c.shutdown()

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0.601335

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25,613

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0.089916

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0.045146

0.021767

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0

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25,613

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117

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0

0.065693

0

null

0

1

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null

0

6

acdc402b742f117467d8cefb45af5f6c10e1024c

102

py

Python

build/lib/federal_holiday/__init__.py

mmcelhan/federalholiday

0b9667991a1045c00abe2e272af0e3eef7f39d45

[ "MIT" ]

null

build/lib/federal_holiday/__init__.py

mmcelhan/federalholiday

0b9667991a1045c00abe2e272af0e3eef7f39d45

[ "MIT" ]

null

build/lib/federal_holiday/__init__.py

mmcelhan/federalholiday

0b9667991a1045c00abe2e272af0e3eef7f39d45

[ "MIT" ]

null

from .federal_holiday import holiday_name, is_federal_holiday, is_weekend, is_working_day, is_off_day

51

101

0.872549

17

102

4.705882

0.588235

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0

null

0

1

0

1

0

6

c5f49c765d6d5e809273e131d8b22f26a06b7e8d

3,638

py

Python

plugins/calculadora.py

gorpo/manicomio_bot_heroku

aa8dc217468d076f26604a209b5798642217c789

[ "MIT" ]

null

plugins/calculadora.py

gorpo/manicomio_bot_heroku

aa8dc217468d076f26604a209b5798642217c789

[ "MIT" ]

null

plugins/calculadora.py

gorpo/manicomio_bot_heroku

aa8dc217468d076f26604a209b5798642217c789

[ "MIT" ]

null

import html import re import random import amanobot import aiohttp from amanobot.exception import TelegramError import time from config import bot, sudoers, logs, bot_username from utils import send_to_dogbin, send_to_hastebin async def calculadora(msg): if msg.get('text'): if '+' in msg['text']: n1 = int(msg['text'].split('+')[0]) n2 = int(msg['text'].split('+')[1]) calc = n1 + n2 print('Usuario {} solicitou a calculadora {}+{}={} '.format(msg['from']['first_name'],n1,n2,calc)) log = '\nUsuario {} solicitou a calculadora {}+{}={} --> Grupo: {} --> Data/hora:{}'.format(msg['from']['first_name'],n1,n2,calc,msg['chat']['title'],time.ctime()) arquivo = open('logs/calc.txt','a') arquivo.write(log) arquivo.close() await bot.sendMessage(msg['chat']['id'],'`Sua soma {}+{}={} {} seu pau no cu!`'.format(n1,n2,calc,msg['from']['first_name']), 'markdown', reply_to_message_id=msg['message_id']) return True if '-' in msg['text']: n1 = int(msg['text'].split('-')[0]) n2 = int(msg['text'].split('-')[1]) calc = n1 - n2 print('Usuario {} solicitou a calculadora {}-{}={}'.format(msg['from']['first_name'],n1,n2,calc)) log = '\nUsuario {} solicitou a calculadora {}-{}={} --> Grupo: {} --> Data/hora:{}'.format(msg['from']['first_name'],n1,n2,calc,msg['chat']['title'],time.ctime()) arquivo = open('logs/calc.txt','a') arquivo.write(log) arquivo.close() await bot.sendMessage(msg['chat']['id'],'`Sua subtração {}-{}={} {}seu filho da puta!`'.format(n1,n2,calc,msg['from']['first_name']), 'markdown', reply_to_message_id=msg['message_id']) return True if '*' in msg['text']: n1 = int(msg['text'].split('*')[0]) n2 = int(msg['text'].split('*')[1]) calc = n1 * n2 print('Usuario {} solicitou a calculadora {}*{}={}'.format(msg['from']['first_name'],n1,n2,calc)) log = '\nUsuario {} solicitou a calculadora {}*{}={} --> Grupo: {} --> Data/hora:{}'.format(msg['from']['first_name'],n1,n2,calc,msg['chat']['title'],time.ctime()) arquivo = open('logs/calc.txt','a') arquivo.write(log) arquivo.close() await bot.sendMessage(msg['chat']['id'],'`Sua multiplicação {}*{}={} {} seu arrombado do caralho!`'.format(n1,n2,calc,msg['from']['first_name']), 'markdown', reply_to_message_id=msg['message_id']) return True if 'div' in msg['text']: n1 = int(msg['text'].split('/')[0]) n2 = int(msg['text'].split('/')[1]) calc = n1 / n2 print('Usuario {} solicitou a calculadora {}/{}={}'.format(msg['from']['first_name'],n1,n2,calc)) log = '\nUsuario {} solicitou a calculadora {}/{}={} --> Grupo: {} --> Data/hora:{}'.format(msg['from']['first_name'],n1,n2,calc,msg['chat']['title'],time.ctime()) arquivo = open('logs/calc.txt','a') arquivo.write(log) arquivo.close() await bot.sendMessage(msg['chat']['id'],'`Sua divisão {}/{}={} {} seu lixo`'.format(n1,n2,calc,msg['from']['first_name']), 'markdown', reply_to_message_id=msg['message_id']) return True

53.5

177

0.502474

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3,638

4.304556

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3,638

68

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53.5

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0

null

0

1

0

null

0

6

a8628e6e505fa0da43afb17b135825241d5e63bc

2,373

py

Python

client.py

a-r-i/nokiahealth_py

8e78ed94051c7463ff1491b50fd572be724041ff

[ "MIT" ]

null

client.py

a-r-i/nokiahealth_py

8e78ed94051c7463ff1491b50fd572be724041ff

[ "MIT" ]

null

client.py

a-r-i/nokiahealth_py

8e78ed94051c7463ff1491b50fd572be724041ff

[ "MIT" ]

null

import requests def get_accesstoken(client_id, client_secret, code, redirect_uri): url = "https://account.health.nokia.com/oauth2/token" payload = { "grant_type": "authorization_code", "client_id": client_id, "client_secret": client_secret, "code": code, "redirect_uri": redirect_uri } r = requests.post(url, data=payload) return r.json() def refresh_accesstoken(client_id, client_secret, refresh_token): url = "https://account.health.nokia.com/oauth2/token" payload = { "grant_type": "refresh_token", "client_id": client_id, "client_secret": client_secret, "refresh_token": refresh_token } r = requests.post(url, data=payload) return r.json() def refresh_accesstoken(client_id, client_secret, refresh_token): url = "https://account.health.nokia.com/oauth2/token" payload = { "grant_type": "refresh_token", "client_id": client_id, "client_secret": client_secret, "refresh_token": refresh_token } r = requests.post(url, data=payload) return r.json() def get_sleep(access_token, startdate, enddate): url = "https://api.health.nokia.com/v2/sleep?action=get" payload = { "access_token": access_token, "startdate": startdate, # UNIXタイムスタンプで指定 "enddate": enddate # UNIXタイムスタンプで指定 } r = requests.get(url, params=payload) return r.json() def get_summary(access_token, startdateymd, enddateymd): url = "https://api.health.nokia.com/v2/sleep?action=getsummary" payload = { "access_token": access_token, "startdateymd": startdateymd, # YYYY-MM-DD形式で指定 "enddateymd": enddateymd # YYYY-MM-DD形式で指定 } r = requests.get(url, params=payload) return r.json() def get_meas(access_token, startdate, enddate): url = "https://api.health.nokia.com/measure?action=getmeas" payload = { "access_token": access_token, "startdate": startdate, # UNIXタイムスタンプで指定 "enddate": enddate # UNIXタイムスタンプで指定 } r = requests.get(url, params=payload) return r.json()

28.25

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248

2,373

5.41129

0.193548

0.053651

0.09389

0.089419

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0.748882

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0

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0.299621

2,373

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0

0.220339

0

null

0

1

0

null

0

6

a89930681ef324360c60437758352c93d81548ee

46

py

Python

pxolly/models/callback/__init__.py

lordralinc/pxolly_api

8b55d299c67332cca0cfe19d4091e73ab5a028a6

[ "MIT" ]

null

pxolly/models/callback/__init__.py

lordralinc/pxolly_api

8b55d299c67332cca0cfe19d4091e73ab5a028a6

[ "MIT" ]

null

pxolly/models/callback/__init__.py

lordralinc/pxolly_api

8b55d299c67332cca0cfe19d4091e73ab5a028a6

[ "MIT" ]

null

from .get_settings import CallbackGetSettings

23

45

0.891304

5

46

8

1

0

0.086957

46

1

46

0.952381

0

1

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true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

a8d98c3b214fb887604ebc62fd1cd3f52a8ff4d7

9,584

py

Python

boml/setup_model/meta_feat_v2.py

bmlsoc/PyBML

cc5dc8c3689a144776a5c77e9efcf8cdb6328e51

[ "MIT" ]

172

2020-08-15T01:56:30.000Z

2022-03-19T16:49:14.000Z

boml/setup_model/meta_feat_v2.py

bmlsoc/PyBML

cc5dc8c3689a144776a5c77e9efcf8cdb6328e51

[ "MIT" ]

4

2020-09-07T14:58:04.000Z

2020-12-20T11:53:32.000Z

boml/setup_model/meta_feat_v2.py

bmlsoc/PyBML

cc5dc8c3689a144776a5c77e9efcf8cdb6328e51

[ "MIT" ]

32

2020-09-05T02:13:32.000Z

2022-03-19T16:49:35.000Z

# MIT License # Copyright (c) 2020 Yaohua Liu # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. """ The base class in setup_model to encapsulate Residual Block for meta-feature-based methods. """ from collections import OrderedDict import tensorflow as tf from tensorflow.contrib import layers as tcl import boml.extension from boml.setup_model import network_utils from boml.setup_model.network import BOMLNet class BOMLNetMiniMetaFeatV2(BOMLNet): def __init__( self, _input, name="BOMLNetMiniMetaFeatV2", outer_param_dict=OrderedDict(), dim_output=-1, model_param_dict=OrderedDict(), task_parameter=OrderedDict(), use_t=False, use_warp=False, reuse=False, outer_method="Reverse", ): """ :param _input: original input :param dim_output: dimension of output :param name: scope of meta-learner :param outer_param_dict: dictionary of outer parameters :param model_param_dict:dictonary of model parameters for specific algorithms such t-layer or warp-layer :param task_parameter: dictionary of task-specific parameters or temporary values of task-specific parameters :param use_t: Boolean, whether to use t-layer for neural network construction :param use_warp: Boolean, whether to use warp-layer for neural network construction :param outer_method: the name of outer method :param reuse: Boolean, whether to reuse the parameters """ self.var_coll = boml.extension.METAPARAMETERS_COLLECTIONS self.task_paramter = task_parameter self.outer_method = outer_method self.dim_output = dim_output self.use_t = use_t self.use_warp = use_warp super().__init__( _input=_input, outer_param_dict=outer_param_dict, model_param_dict=model_param_dict, name=name, reuse=reuse, ) self.betas = self.filter_vars("beta") self.moving_means = self.filter_vars("moving_mean") self.moving_variances = self.filter_vars("moving_variance") if not reuse: boml.extension.remove_from_collection( boml.extension.GraphKeys.MODEL_VARIABLES, *self.moving_means, *self.moving_variances ) boml.extension.remove_from_collection( boml.extension.GraphKeys.METAPARAMETERS, *self.moving_means, *self.moving_variances ) print(name, "MODEL CREATED") def _forward(self): """ _forward() uses defined convolutional neural networks with initial input :return: """ def residual_block(x, n_filters): skip_c = tcl.conv2d(x, n_filters, 1, activation_fn=None) def conv_block(xx): out = tcl.conv2d( xx, n_filters, 3, activation_fn=None, normalizer_fn=tcl.batch_norm, variables_collections=self.var_coll, ) return network_utils.leaky_relu(out, 0.1) out = x for _ in range(3): out = conv_block(out) add = tf.add(skip_c, out) return tf.nn.max_pool(add, [1, 2, 2, 1], [1, 2, 2, 1], "SAME") self + residual_block(self.out, 64) self + residual_block(self.out, 96) self + residual_block(self.out, 128) self + residual_block(self.out, 256) self + tcl.conv2d(self.out, 2048, 1, variables_collections=self.var_coll) self + tf.nn.avg_pool(self.out, [1, 6, 6, 1], [1, 6, 6, 1], "VALID") self + tcl.conv2d(self.out, 512, 1, variables_collections=self.var_coll) self + tf.reshape(self.out, (-1, 512)) def re_forward(self, new_input=None): """ reuses defined convolutional networks with new input and update the output results :param new_input: new input with same shape as the old one :param task_parameter: the dictionary of task-specific :return: updated instance of BOMLNet """ return BOMLNetMiniMetaFeatV2( _input=new_input if new_input is not None else self.layers[0], model_param_dict=self.model_param_dict, name=self.name, dim_output=self.dim_output, outer_param_dict=self.outer_param_dict, reuse=tf.AUTO_REUSE, outer_method=self.outer_method, use_t=self.use_t, ) class BOMLNetOmniglotMetaFeatV2(BOMLNet): def __init__( self, _input, name="BOMLNetOmniglotMetaFeatV2", outer_param_dict=OrderedDict(), dim_output=-1, model_param_dict=OrderedDict(), use_t=False, use_warp=False, reuse=False, outer_method="Reverse", ): """ :param _input: original input :param dim_output: dimension of output :param name: scope of meta-learner :param outer_param_dict: dictionary of outer parameters :param model_param_dict:dictonary of model parameters for specific algorithms such t-layer or warp-layer :param use_t: Boolean, whether to use t-layer for neural network construction :param use_warp: Boolean, whether to use warp-layer for neural network construction :param outer_method: the name of outer method :param reuse: Boolean, whether to reuse the parameters """ self.var_coll = boml.extension.METAPARAMETERS_COLLECTIONS self.outer_method = outer_method self.dim_output = dim_output self.use_t = use_t self.use_warp = use_warp super().__init__( _input=_input, outer_param_dict=outer_param_dict, model_param_dict=model_param_dict, name=name, reuse=reuse, ) self.betas = self.filter_vars("beta") self.moving_means = self.filter_vars("moving_mean") self.moving_variances = self.filter_vars("moving_variance") if not reuse: boml.extension.remove_from_collection( boml.extension.GraphKeys.MODEL_VARIABLES, *self.moving_means, *self.moving_variances ) boml.extension.remove_from_collection( boml.extension.GraphKeys.METAPARAMETERS, *self.moving_means, *self.moving_variances ) print(name, "MODEL CREATED") def _forward(self): """ _forward() uses defined convolutional neural networks with initial input :return: """ def residual_block(x, n_filters): skip_c = tcl.conv2d(x, n_filters, 1, activation_fn=None) def conv_block(xx): out = tcl.conv2d( xx, n_filters, 3, activation_fn=None, normalizer_fn=tcl.batch_norm, variables_collections=self.var_coll, ) return network_utils.leaky_relu(out, 0.1) out = x for _ in range(3): out = conv_block(out) add = tf.add(skip_c, out) return tf.nn.max_pool(add, [1, 2, 2, 1], [1, 2, 2, 1], "SAME") self + residual_block(self.out, 64) self + residual_block(self.out, 96) self + tcl.conv2d(self.out, 2048, 1, variables_collections=self.var_coll) self + tf.nn.avg_pool(self.out, [1, 6, 6, 1], [1, 6, 6, 1], "VALID") self + tcl.conv2d(self.out, 512, 1, variables_collections=self.var_coll) self + tf.reshape(self.out, (-1, 512)) def re_forward(self, new_input=None, task_parameter=OrderedDict()): """ reuses defined convolutional networks with new input and update the output results :param new_input: new input with same shape as the old one :param task_parameter: the dictionary of task-specific :return: updated instance of BOMLNet """ return BOMLNetOmniglotMetaFeatV2( new_input if new_input is not None else self.layers[0], model_param_dict=self.model_param_dict, name=self.name, dim_output=self.dim_output, outer_param_dict=self.outer_param_dict, reuse=tf.AUTO_REUSE, use_t=self.use_t, outer_method=self.outer_method, )

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null

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6

763da60785b68db90621f072b34dc3cffcd9ae8f

47

py

Python

test_project/tests/fixtures/__init__.py

wishmaestro/drf-fat-models

09b8c8a15140044e570db4e9af3354c42768ec5c

[ "MIT" ]

null

test_project/tests/fixtures/__init__.py

wishmaestro/drf-fat-models

09b8c8a15140044e570db4e9af3354c42768ec5c

[ "MIT" ]

null

test_project/tests/fixtures/__init__.py

wishmaestro/drf-fat-models

09b8c8a15140044e570db4e9af3354c42768ec5c

[ "MIT" ]

null

from .customers import * from .orders import *

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1

0

6

763fde9d67a0b1d98cec20b941f6e3efb64cdcb3

521

py

Python

flow/scenarios/traffic_light_grid.py

SHITIANYU-hue/flow

6fb5697868517fea7098a81b78c1be8e925f09f7

[ "MIT" ]

805

2018-08-16T22:30:51.000Z

2022-03-31T09:25:50.000Z

flow/scenarios/traffic_light_grid.py

SHITIANYU-hue/flow

6fb5697868517fea7098a81b78c1be8e925f09f7

[ "MIT" ]

879

2018-08-22T17:37:06.000Z

2022-03-29T01:06:11.000Z

flow/scenarios/traffic_light_grid.py

SHITIANYU-hue/flow

6fb5697868517fea7098a81b78c1be8e925f09f7

[ "MIT" ]

325

2018-08-22T06:48:00.000Z

2022-03-21T15:09:04.000Z

"""Pending deprecation file. To view the actual content, go to: flow/networks/traffic_light_grid.py """ from flow.utils.flow_warnings import deprecated from flow.networks.traffic_light_grid import TrafficLightGridNetwork from flow.networks.traffic_light_grid import ADDITIONAL_NET_PARAMS # noqa: F401 @deprecated('flow.scenarios.traffic_light_grid', 'flow.networks.traffic_light_grid.TrafficLightGridNetwork') class TrafficLightGridScenario(TrafficLightGridNetwork): """See parent class.""" pass

32.5625

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521

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true

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null

0

1

0

null

0

1

0

1

0

6

76633d401362f455c662bf00a01b0c66c0125e68

48,981

py

Python

src/datafactory/azext_datafactory/generated/_params.py

haroonf/azure-cli-extensions

61c044d34c224372f186934fa7c9313f1cd3a525

[ "MIT" ]

207

2017-11-29T06:59:41.000Z

2022-03-31T10:00:53.000Z

src/datafactory/azext_datafactory/generated/_params.py

haroonf/azure-cli-extensions

61c044d34c224372f186934fa7c9313f1cd3a525

[ "MIT" ]

4,061

2017-10-27T23:19:56.000Z

2022-03-31T23:18:30.000Z

src/datafactory/azext_datafactory/generated/_params.py

haroonf/azure-cli-extensions

61c044d34c224372f186934fa7c9313f1cd3a525

[ "MIT" ]

802

2017-10-11T17:36:26.000Z

2022-03-31T22:24:32.000Z

# -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for # license information. # # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is # regenerated. # -------------------------------------------------------------------------- # pylint: disable=too-many-lines # pylint: disable=too-many-statements from azure.cli.core.commands.parameters import ( tags_type, get_three_state_flag, get_enum_type, resource_group_name_type, get_location_type ) from azure.cli.core.commands.validators import ( get_default_location_from_resource_group, validate_file_or_dict ) from azext_datafactory.action import ( AddFactoryVstsConfiguration, AddFactoryGitHubConfiguration, AddFolder, AddFilters, AddOrderBy ) def load_arguments(self, _): with self.argument_context('datafactory list') as c: c.argument('resource_group_name', resource_group_name_type) with self.argument_context('datafactory show') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', options_list=['--name', '-n', '--factory-name'], type=str, help='The factory name.', id_part='name') c.argument('if_none_match', type=str, help='ETag of the factory entity. Should only be specified for get. If ' 'the ETag matches the existing entity tag, or if * was provided, then no content will be returned.') with self.argument_context('datafactory create') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', options_list=['--name', '-n', '--factory-name'], type=str, help='The factory name.') c.argument('if_match', type=str, help='ETag of the factory entity. Should only be specified for update, for ' 'which it should match existing entity or can be * for unconditional update.') c.argument('location', arg_type=get_location_type(self.cli_ctx), required=False, validator=get_default_location_from_resource_group) c.argument('tags', tags_type) c.argument('factory_vsts_configuration', action=AddFactoryVstsConfiguration, nargs='+', help='Factory\'s VSTS ' 'repo information.', arg_group='RepoConfiguration') c.argument('factory_git_hub_configuration', action=AddFactoryGitHubConfiguration, nargs='+', help='Factory\'s ' 'GitHub repo information.', arg_group='RepoConfiguration') c.argument('global_parameters', type=validate_file_or_dict, help='List of parameters for factory. Expected ' 'value: json-string/json-file/@json-file.') with self.argument_context('datafactory update') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', options_list=['--name', '-n', '--factory-name'], type=str, help='The factory name.', id_part='name') c.argument('tags', tags_type) with self.argument_context('datafactory delete') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', options_list=['--name', '-n', '--factory-name'], type=str, help='The factory name.', id_part='name') with self.argument_context('datafactory configure-factory-repo') as c: c.argument('location', arg_type=get_location_type(self.cli_ctx), id_part='name') c.argument('factory_resource_id', type=str, help='The factory resource id.') c.argument('factory_vsts_configuration', action=AddFactoryVstsConfiguration, nargs='+', help='Factory\'s VSTS ' 'repo information.', arg_group='RepoConfiguration') c.argument('factory_git_hub_configuration', action=AddFactoryGitHubConfiguration, nargs='+', help='Factory\'s ' 'GitHub repo information.', arg_group='RepoConfiguration') with self.argument_context('datafactory get-data-plane-access') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', options_list=['--name', '-n', '--factory-name'], type=str, help='The factory name.', id_part='name') c.argument('permissions', type=str, help='The string with permissions for Data Plane access. Currently only ' '\'r\' is supported which grants read only access.') c.argument('access_resource_path', type=str, help='The resource path to get access relative to factory. ' 'Currently only empty string is supported which corresponds to the factory resource.') c.argument('profile_name', type=str, help='The name of the profile. Currently only the default is supported. ' 'The default value is DefaultProfile.') c.argument('start_time', type=str, help='Start time for the token. If not specified the current time will be ' 'used.') c.argument('expire_time', type=str, help='Expiration time for the token. Maximum duration for the token is ' 'eight hours and by default the token will expire in eight hours.') with self.argument_context('datafactory get-git-hub-access-token') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', options_list=['--name', '-n', '--factory-name'], type=str, help='The factory name.', id_part='name') c.argument('git_hub_access_code', type=str, help='GitHub access code.') c.argument('git_hub_client_id', type=str, help='GitHub application client ID.') c.argument('git_hub_access_token_base_url', type=str, help='GitHub access token base URL.') with self.argument_context('datafactory integration-runtime list') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.') with self.argument_context('datafactory integration-runtime show') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('integration_runtime_name', options_list=['--name', '-n', '--integration-runtime-name'], type=str, help='The integration runtime name.', id_part='child_name_1') c.argument('if_none_match', type=str, help='ETag of the integration runtime entity. Should only be specified ' 'for get. If the ETag matches the existing entity tag, or if * was provided, then no content will ' 'be returned.') with self.argument_context('datafactory integration-runtime linked-integration-runtime create') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.') c.argument('integration_runtime_name', type=str, help='The integration runtime name.') c.argument('name', type=str, help='The name of the linked integration runtime.') c.argument('subscription_id', type=str, help='The ID of the subscription that the linked integration runtime ' 'belongs to.') c.argument('data_factory_name', type=str, help='The name of the data factory that the linked integration ' 'runtime belongs to.') c.argument('location', arg_type=get_location_type(self.cli_ctx), required=False, validator=get_default_location_from_resource_group) with self.argument_context('datafactory integration-runtime managed create') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.') c.argument('integration_runtime_name', options_list=['--name', '-n', '--integration-runtime-name'], type=str, help='The integration runtime name.') c.argument('if_match', type=str, help='ETag of the integration runtime entity. Should only be specified for ' 'update, for which it should match existing entity or can be * for unconditional update.') c.argument('description', type=str, help='Integration runtime description.') c.argument('compute_properties', type=validate_file_or_dict, help='The compute resource for managed ' 'integration runtime. Expected value: json-string/json-file/@json-file.', arg_group='Type ' 'Properties') c.argument('ssis_properties', type=validate_file_or_dict, help='SSIS properties for managed integration ' 'runtime. Expected value: json-string/json-file/@json-file.', arg_group='Type Properties') with self.argument_context('datafactory integration-runtime self-hosted create') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.') c.argument('integration_runtime_name', options_list=['--name', '-n', '--integration-runtime-name'], type=str, help='The integration runtime name.') c.argument('if_match', type=str, help='ETag of the integration runtime entity. Should only be specified for ' 'update, for which it should match existing entity or can be * for unconditional update.') c.argument('description', type=str, help='Integration runtime description.') c.argument('linked_info', type=validate_file_or_dict, help='The base definition of a linked integration ' 'runtime. Expected value: json-string/json-file/@json-file.', arg_group='Type Properties') with self.argument_context('datafactory integration-runtime update') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('integration_runtime_name', options_list=['--name', '-n', '--integration-runtime-name'], type=str, help='The integration runtime name.', id_part='child_name_1') c.argument('auto_update', arg_type=get_enum_type(['On', 'Off']), help='Enables or disables the auto-update ' 'feature of the self-hosted integration runtime. See https://go.microsoft.com/fwlink/?linkid=854189.' '') c.argument('update_delay_offset', type=str, help='The time offset (in hours) in the day, e.g., PT03H is 3 ' 'hours. The integration runtime auto update will happen on that time.') with self.argument_context('datafactory integration-runtime delete') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('integration_runtime_name', options_list=['--name', '-n', '--integration-runtime-name'], type=str, help='The integration runtime name.', id_part='child_name_1') with self.argument_context('datafactory integration-runtime get-connection-info') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('integration_runtime_name', options_list=['--name', '-n', '--integration-runtime-name'], type=str, help='The integration runtime name.', id_part='child_name_1') with self.argument_context('datafactory integration-runtime get-monitoring-data') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('integration_runtime_name', options_list=['--name', '-n', '--integration-runtime-name'], type=str, help='The integration runtime name.', id_part='child_name_1') with self.argument_context('datafactory integration-runtime get-status') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('integration_runtime_name', options_list=['--name', '-n', '--integration-runtime-name'], type=str, help='The integration runtime name.', id_part='child_name_1') with self.argument_context('datafactory integration-runtime list-auth-key') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.') c.argument('integration_runtime_name', options_list=['--name', '-n', '--integration-runtime-name'], type=str, help='The integration runtime name.') with self.argument_context('datafactory integration-runtime regenerate-auth-key') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('integration_runtime_name', options_list=['--name', '-n', '--integration-runtime-name'], type=str, help='The integration runtime name.', id_part='child_name_1') c.argument('key_name', arg_type=get_enum_type(['authKey1', 'authKey2']), help='The name of the authentication ' 'key to regenerate.') with self.argument_context('datafactory integration-runtime remove-link') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('integration_runtime_name', options_list=['--name', '-n', '--integration-runtime-name'], type=str, help='The integration runtime name.', id_part='child_name_1') c.argument('linked_factory_name', type=str, help='The data factory name for linked integration runtime.') with self.argument_context('datafactory integration-runtime start') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('integration_runtime_name', options_list=['--name', '-n', '--integration-runtime-name'], type=str, help='The integration runtime name.', id_part='child_name_1') with self.argument_context('datafactory integration-runtime stop') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('integration_runtime_name', options_list=['--name', '-n', '--integration-runtime-name'], type=str, help='The integration runtime name.', id_part='child_name_1') with self.argument_context('datafactory integration-runtime sync-credentials') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('integration_runtime_name', options_list=['--name', '-n', '--integration-runtime-name'], type=str, help='The integration runtime name.', id_part='child_name_1') with self.argument_context('datafactory integration-runtime upgrade') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('integration_runtime_name', options_list=['--name', '-n', '--integration-runtime-name'], type=str, help='The integration runtime name.', id_part='child_name_1') with self.argument_context('datafactory integration-runtime wait') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('integration_runtime_name', options_list=['--name', '-n', '--integration-runtime-name'], type=str, help='The integration runtime name.', id_part='child_name_1') c.argument('if_none_match', type=str, help='ETag of the integration runtime entity. Should only be specified ' 'for get. If the ETag matches the existing entity tag, or if * was provided, then no content will ' 'be returned.') with self.argument_context('datafactory integration-runtime-node show') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('integration_runtime_name', type=str, help='The integration runtime name.', id_part='child_name_1') c.argument('node_name', type=str, help='The integration runtime node name.', id_part='child_name_2') with self.argument_context('datafactory integration-runtime-node update') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('integration_runtime_name', type=str, help='The integration runtime name.', id_part='child_name_1') c.argument('node_name', type=str, help='The integration runtime node name.', id_part='child_name_2') c.argument('concurrent_jobs_limit', type=int, help='The number of concurrent jobs permitted to run on the ' 'integration runtime node. Values between 1 and maxConcurrentJobs(inclusive) are allowed.') with self.argument_context('datafactory integration-runtime-node delete') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('integration_runtime_name', type=str, help='The integration runtime name.', id_part='child_name_1') c.argument('node_name', type=str, help='The integration runtime node name.', id_part='child_name_2') with self.argument_context('datafactory integration-runtime-node get-ip-address') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('integration_runtime_name', type=str, help='The integration runtime name.', id_part='child_name_1') c.argument('node_name', type=str, help='The integration runtime node name.', id_part='child_name_2') with self.argument_context('datafactory linked-service list') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.') with self.argument_context('datafactory linked-service show') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('linked_service_name', options_list=['--name', '-n', '--linked-service-name'], type=str, help='The ' 'linked service name.', id_part='child_name_1') c.argument('if_none_match', type=str, help='ETag of the linked service entity. Should only be specified for ' 'get. If the ETag matches the existing entity tag, or if * was provided, then no content will be ' 'returned.') with self.argument_context('datafactory linked-service create') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.') c.argument('linked_service_name', options_list=['--name', '-n', '--linked-service-name'], type=str, help='The ' 'linked service name.') c.argument('if_match', type=str, help='ETag of the linkedService entity. Should only be specified for update, ' 'for which it should match existing entity or can be * for unconditional update.') c.argument('properties', type=validate_file_or_dict, help='Properties of linked service. Expected value: ' 'json-string/json-file/@json-file.') with self.argument_context('datafactory linked-service update') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('linked_service_name', options_list=['--name', '-n', '--linked-service-name'], type=str, help='The ' 'linked service name.', id_part='child_name_1') c.argument('if_match', type=str, help='ETag of the linkedService entity. Should only be specified for update, ' 'for which it should match existing entity or can be * for unconditional update.') c.argument('connect_via', type=validate_file_or_dict, help='The integration runtime reference. Expected value: ' 'json-string/json-file/@json-file.') c.argument('description', type=str, help='Linked service description.') c.argument('parameters', type=validate_file_or_dict, help='Parameters for linked service. Expected value: ' 'json-string/json-file/@json-file.') c.argument('annotations', type=validate_file_or_dict, help='List of tags that can be used for describing the ' 'linked service. Expected value: json-string/json-file/@json-file.') c.ignore('linked_service') with self.argument_context('datafactory linked-service delete') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('linked_service_name', options_list=['--name', '-n', '--linked-service-name'], type=str, help='The ' 'linked service name.', id_part='child_name_1') with self.argument_context('datafactory dataset list') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.') with self.argument_context('datafactory dataset show') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('dataset_name', options_list=['--name', '-n', '--dataset-name'], type=str, help='The dataset name.', id_part='child_name_1') c.argument('if_none_match', type=str, help='ETag of the dataset entity. Should only be specified for get. If ' 'the ETag matches the existing entity tag, or if * was provided, then no content will be returned.') with self.argument_context('datafactory dataset create') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.') c.argument('dataset_name', options_list=['--name', '-n', '--dataset-name'], type=str, help='The dataset name.') c.argument('if_match', type=str, help='ETag of the dataset entity. Should only be specified for update, for ' 'which it should match existing entity or can be * for unconditional update.') c.argument('properties', type=validate_file_or_dict, help='Dataset properties. Expected value: ' 'json-string/json-file/@json-file.') with self.argument_context('datafactory dataset update') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('dataset_name', options_list=['--name', '-n', '--dataset-name'], type=str, help='The dataset name.', id_part='child_name_1') c.argument('if_match', type=str, help='ETag of the dataset entity. Should only be specified for update, for ' 'which it should match existing entity or can be * for unconditional update.') c.argument('description', type=str, help='Dataset description.') c.argument('structure', type=validate_file_or_dict, help='Columns that define the structure of the dataset. ' 'Type: array (or Expression with resultType array), itemType: DatasetDataElement. Expected value: ' 'json-string/json-file/@json-file.') c.argument('schema', type=validate_file_or_dict, help='Columns that define the physical type schema of the ' 'dataset. Type: array (or Expression with resultType array), itemType: DatasetSchemaDataElement. ' 'Expected value: json-string/json-file/@json-file.') c.argument('linked_service_name', type=validate_file_or_dict, help='Linked service reference. Expected value: ' 'json-string/json-file/@json-file.') c.argument('parameters', type=validate_file_or_dict, help='Parameters for dataset. Expected value: ' 'json-string/json-file/@json-file.') c.argument('annotations', type=validate_file_or_dict, help='List of tags that can be used for describing the ' 'Dataset. Expected value: json-string/json-file/@json-file.') c.argument('folder', action=AddFolder, nargs='+', help='The folder that this Dataset is in. If not specified, ' 'Dataset will appear at the root level.') c.ignore('dataset') with self.argument_context('datafactory dataset delete') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('dataset_name', options_list=['--name', '-n', '--dataset-name'], type=str, help='The dataset name.', id_part='child_name_1') with self.argument_context('datafactory pipeline list') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.') with self.argument_context('datafactory pipeline show') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('pipeline_name', options_list=['--name', '-n', '--pipeline-name'], type=str, help='The pipeline ' 'name.', id_part='child_name_1') c.argument('if_none_match', type=str, help='ETag of the pipeline entity. Should only be specified for get. If ' 'the ETag matches the existing entity tag, or if * was provided, then no content will be returned.') with self.argument_context('datafactory pipeline create') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.') c.argument('pipeline_name', options_list=['--name', '-n', '--pipeline-name'], type=str, help='The pipeline ' 'name.') c.argument('if_match', type=str, help='ETag of the pipeline entity. Should only be specified for update, for ' 'which it should match existing entity or can be * for unconditional update.') c.argument('pipeline', type=validate_file_or_dict, help='Pipeline resource definition. Expected value: ' 'json-string/json-file/@json-file.') with self.argument_context('datafactory pipeline update') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('pipeline_name', options_list=['--name', '-n', '--pipeline-name'], type=str, help='The pipeline ' 'name.', id_part='child_name_1') c.argument('if_match', type=str, help='ETag of the pipeline entity. Should only be specified for update, for ' 'which it should match existing entity or can be * for unconditional update.') c.argument('description', type=str, help='The description of the pipeline.') c.argument('activities', type=validate_file_or_dict, help='List of activities in pipeline. Expected value: ' 'json-string/json-file/@json-file.') c.argument('parameters', type=validate_file_or_dict, help='List of parameters for pipeline. Expected value: ' 'json-string/json-file/@json-file.') c.argument('variables', type=validate_file_or_dict, help='List of variables for pipeline. Expected value: ' 'json-string/json-file/@json-file.') c.argument('concurrency', type=int, help='The max number of concurrent runs for the pipeline.') c.argument('annotations', type=validate_file_or_dict, help='List of tags that can be used for describing the ' 'Pipeline. Expected value: json-string/json-file/@json-file.') c.argument('run_dimensions', type=validate_file_or_dict, help='Dimensions emitted by Pipeline. Expected value: ' 'json-string/json-file/@json-file.') c.argument('duration', type=validate_file_or_dict, help='TimeSpan value, after which an Azure Monitoring ' 'Metric is fired. Expected value: json-string/json-file/@json-file.', arg_group='Policy Elapsed ' 'Time Metric') c.argument('folder_name', type=str, help='The name of the folder that this Pipeline is in.', arg_group='Folder') c.ignore('pipeline') with self.argument_context('datafactory pipeline delete') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('pipeline_name', options_list=['--name', '-n', '--pipeline-name'], type=str, help='The pipeline ' 'name.', id_part='child_name_1') with self.argument_context('datafactory pipeline create-run') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.') c.argument('pipeline_name', options_list=['--name', '-n', '--pipeline-name'], type=str, help='The pipeline ' 'name.') c.argument('reference_pipeline_run_id', type=str, help='The pipeline run identifier. If run ID is specified ' 'the parameters of the specified run will be used to create a new run.') c.argument('is_recovery', arg_type=get_three_state_flag(), help='Recovery mode flag. If recovery mode is set ' 'to true, the specified referenced pipeline run and the new run will be grouped under the same ' 'groupId.') c.argument('start_activity_name', type=str, help='In recovery mode, the rerun will start from this activity. ' 'If not specified, all activities will run.') c.argument('start_from_failure', arg_type=get_three_state_flag(), help='In recovery mode, if set to true, the ' 'rerun will start from failed activities. The property will be used only if startActivityName is ' 'not specified.') c.argument('parameters', type=validate_file_or_dict, help='Parameters of the pipeline run. These parameters ' 'will be used only if the runId is not specified. Expected value: json-string/json-file/@json-file.') with self.argument_context('datafactory pipeline-run show') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('run_id', type=str, help='The pipeline run identifier.', id_part='child_name_1') with self.argument_context('datafactory pipeline-run cancel') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('run_id', type=str, help='The pipeline run identifier.', id_part='child_name_1') c.argument('is_recursive', arg_type=get_three_state_flag(), help='If true, cancel all the Child pipelines that ' 'are triggered by the current pipeline.') with self.argument_context('datafactory pipeline-run query-by-factory') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('continuation_token', type=str, help='The continuation token for getting the next page of results. ' 'Null for first page.') c.argument('last_updated_after', help='The time at or after which the run event was updated in \'ISO 8601\' ' 'format.') c.argument('last_updated_before', help='The time at or before which the run event was updated in \'ISO 8601\' ' 'format.') c.argument('filters', action=AddFilters, nargs='+', help='List of filters.') c.argument('order_by', action=AddOrderBy, nargs='+', help='List of OrderBy option.') with self.argument_context('datafactory activity-run query-by-pipeline-run') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('run_id', type=str, help='The pipeline run identifier.', id_part='child_name_1') c.argument('continuation_token', type=str, help='The continuation token for getting the next page of results. ' 'Null for first page.') c.argument('last_updated_after', help='The time at or after which the run event was updated in \'ISO 8601\' ' 'format.') c.argument('last_updated_before', help='The time at or before which the run event was updated in \'ISO 8601\' ' 'format.') c.argument('filters', action=AddFilters, nargs='+', help='List of filters.') c.argument('order_by', action=AddOrderBy, nargs='+', help='List of OrderBy option.') with self.argument_context('datafactory trigger list') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.') with self.argument_context('datafactory trigger show') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('trigger_name', options_list=['--name', '-n', '--trigger-name'], type=str, help='The trigger name.', id_part='child_name_1') c.argument('if_none_match', type=str, help='ETag of the trigger entity. Should only be specified for get. If ' 'the ETag matches the existing entity tag, or if * was provided, then no content will be returned.') with self.argument_context('datafactory trigger create') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.') c.argument('trigger_name', options_list=['--name', '-n', '--trigger-name'], type=str, help='The trigger name.') c.argument('if_match', type=str, help='ETag of the trigger entity. Should only be specified for update, for ' 'which it should match existing entity or can be * for unconditional update.') c.argument('properties', type=validate_file_or_dict, help='Properties of the trigger. Expected value: ' 'json-string/json-file/@json-file.') with self.argument_context('datafactory trigger update') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('trigger_name', options_list=['--name', '-n', '--trigger-name'], type=str, help='The trigger name.', id_part='child_name_1') c.argument('if_match', type=str, help='ETag of the trigger entity. Should only be specified for update, for ' 'which it should match existing entity or can be * for unconditional update.') c.argument('description', type=str, help='Trigger description.') c.argument('annotations', type=validate_file_or_dict, help='List of tags that can be used for describing the ' 'trigger. Expected value: json-string/json-file/@json-file.') c.ignore('trigger') with self.argument_context('datafactory trigger delete') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('trigger_name', options_list=['--name', '-n', '--trigger-name'], type=str, help='The trigger name.', id_part='child_name_1') with self.argument_context('datafactory trigger get-event-subscription-status') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('trigger_name', options_list=['--name', '-n', '--trigger-name'], type=str, help='The trigger name.', id_part='child_name_1') with self.argument_context('datafactory trigger query-by-factory') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('continuation_token', type=str, help='The continuation token for getting the next page of results. ' 'Null for first page.') c.argument('parent_trigger_name', type=str, help='The name of the parent TumblingWindowTrigger to get the ' 'child rerun triggers') with self.argument_context('datafactory trigger start') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('trigger_name', options_list=['--name', '-n', '--trigger-name'], type=str, help='The trigger name.', id_part='child_name_1') with self.argument_context('datafactory trigger stop') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('trigger_name', options_list=['--name', '-n', '--trigger-name'], type=str, help='The trigger name.', id_part='child_name_1') with self.argument_context('datafactory trigger subscribe-to-event') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('trigger_name', options_list=['--name', '-n', '--trigger-name'], type=str, help='The trigger name.', id_part='child_name_1') with self.argument_context('datafactory trigger unsubscribe-from-event') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('trigger_name', options_list=['--name', '-n', '--trigger-name'], type=str, help='The trigger name.', id_part='child_name_1') with self.argument_context('datafactory trigger wait') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('trigger_name', options_list=['--name', '-n', '--trigger-name'], type=str, help='The trigger name.', id_part='child_name_1') c.argument('if_none_match', type=str, help='ETag of the trigger entity. Should only be specified for get. If ' 'the ETag matches the existing entity tag, or if * was provided, then no content will be returned.') with self.argument_context('datafactory trigger-run cancel') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('trigger_name', type=str, help='The trigger name.', id_part='child_name_1') c.argument('run_id', type=str, help='The pipeline run identifier.', id_part='child_name_2') with self.argument_context('datafactory trigger-run query-by-factory') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('continuation_token', type=str, help='The continuation token for getting the next page of results. ' 'Null for first page.') c.argument('last_updated_after', help='The time at or after which the run event was updated in \'ISO 8601\' ' 'format.') c.argument('last_updated_before', help='The time at or before which the run event was updated in \'ISO 8601\' ' 'format.') c.argument('filters', action=AddFilters, nargs='+', help='List of filters.') c.argument('order_by', action=AddOrderBy, nargs='+', help='List of OrderBy option.') with self.argument_context('datafactory trigger-run rerun') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('trigger_name', type=str, help='The trigger name.', id_part='child_name_1') c.argument('run_id', type=str, help='The pipeline run identifier.', id_part='child_name_2') with self.argument_context('datafactory managed-virtual-network list') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.') with self.argument_context('datafactory managed-virtual-network show') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('managed_virtual_network_name', options_list=['--name', '-n', '--managed-virtual-network-name'], type=str, help='Managed virtual network name', id_part='child_name_1') c.argument('if_none_match', type=str, help='ETag of the managed Virtual Network entity. Should only be ' 'specified for get. If the ETag matches the existing entity tag, or if * was provided, then no ' 'content will be returned.') with self.argument_context('datafactory managed-virtual-network create') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.') c.argument('managed_virtual_network_name', options_list=['--name', '-n', '--managed-virtual-network-name'], type=str, help='Managed virtual network name') c.argument('if_match', type=str, help='ETag of the managed Virtual Network entity. Should only be specified ' 'for update, for which it should match existing entity or can be * for unconditional update.') with self.argument_context('datafactory managed-virtual-network update') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('managed_virtual_network_name', options_list=['--name', '-n', '--managed-virtual-network-name'], type=str, help='Managed virtual network name', id_part='child_name_1') c.argument('if_match', type=str, help='ETag of the managed Virtual Network entity. Should only be specified ' 'for update, for which it should match existing entity or can be * for unconditional update.') c.ignore('managed_virtual_network') with self.argument_context('datafactory managed-private-endpoint list') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.') c.argument('managed_virtual_network_name', options_list=['--managed-virtual-network-name', '--mvnet-name'], type=str, help='Managed virtual network name') with self.argument_context('datafactory managed-private-endpoint show') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('managed_virtual_network_name', options_list=['--managed-virtual-network-name', '--mvnet-name'], type=str, help='Managed virtual network name', id_part='child_name_1') c.argument('managed_private_endpoint_name', options_list=['--name', '-n', '--managed-private-endpoint-name'], type=str, help='Managed private endpoint name', id_part='child_name_2') c.argument('if_none_match', type=str, help='ETag of the managed private endpoint entity. Should only be ' 'specified for get. If the ETag matches the existing entity tag, or if * was provided, then no ' 'content will be returned.') with self.argument_context('datafactory managed-private-endpoint create') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.') c.argument('managed_virtual_network_name', options_list=['--managed-virtual-network-name', '--mvnet-name'], type=str, help='Managed virtual network name') c.argument('managed_private_endpoint_name', options_list=['--name', '-n', '--managed-private-endpoint-name'], type=str, help='Managed private endpoint name') c.argument('if_match', type=str, help='ETag of the managed private endpoint entity. Should only be specified ' 'for update, for which it should match existing entity or can be * for unconditional update.') c.argument('fqdns', nargs='+', help='Fully qualified domain names') c.argument('group_id', type=str, help='The groupId to which the managed private endpoint is created') c.argument('private_link_resource_id', options_list=['--private-link-resource-id', '--private-link'], type=str, help='The ARM resource ID of the resource to which the managed private endpoint is created') with self.argument_context('datafactory managed-private-endpoint update') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('managed_virtual_network_name', options_list=['--managed-virtual-network-name', '--mvnet-name'], type=str, help='Managed virtual network name', id_part='child_name_1') c.argument('managed_private_endpoint_name', options_list=['--name', '-n', '--managed-private-endpoint-name'], type=str, help='Managed private endpoint name', id_part='child_name_2') c.argument('if_match', type=str, help='ETag of the managed private endpoint entity. Should only be specified ' 'for update, for which it should match existing entity or can be * for unconditional update.') c.argument('fqdns', nargs='+', help='Fully qualified domain names') c.argument('group_id', type=str, help='The groupId to which the managed private endpoint is created') c.argument('private_link_resource_id', options_list=['--private-link-resource-id', '--private-link'], type=str, help='The ARM resource ID of the resource to which the managed private endpoint is created') c.ignore('managed_private_endpoint') with self.argument_context('datafactory managed-private-endpoint delete') as c: c.argument('resource_group_name', resource_group_name_type) c.argument('factory_name', type=str, help='The factory name.', id_part='name') c.argument('managed_virtual_network_name', options_list=['--managed-virtual-network-name', '--mvnet-name'], type=str, help='Managed virtual network name', id_part='child_name_1') c.argument('managed_private_endpoint_name', options_list=['--name', '-n', '--managed-private-endpoint-name'], type=str, help='Managed private endpoint name', id_part='child_name_2')

73.434783

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4.889299

0.051732

0.091825

0.067893

0.065248

0.888462

0.875488

0.858074

0.837291

0.813862

0.809768

0

0.002288

0.19677

48,981

666

121

73.545045

0.80487

0.010331

0

0.648789

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0.020761

0.481563

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0

0.00692

0

null

0

1

0

null

0

6

7663a99c630bf2660be058a4631d5ed9bc3e5862

95

py

Python

QuantTorch/DorefaNet.py

Enderdead/BinaryConnect_PyTorch

990e970b1fbd299ff88200db21a9cc3fe44706d3

[ "MIT" ]

75

2019-03-19T07:36:56.000Z

2021-12-23T02:34:59.000Z

QuantTorch/DorefaNet.py

Enderdead/BinaryConnect_PyTorch

990e970b1fbd299ff88200db21a9cc3fe44706d3

[ "MIT" ]

10

2019-03-19T21:16:56.000Z

2019-04-16T15:05:37.000Z

QuantTorch/DorefaNet.py

Enderdead/BinaryConnect_PyTorch

990e970b1fbd299ff88200db21a9cc3fe44706d3

[ "MIT" ]

9

2019-08-12T10:33:55.000Z

2021-07-23T02:10:06.000Z

from QuantTorch.functions.dorefa_connect import * from QuantTorch.layers.dorefa_layers import *

47.5

49

0.863158

12

95

6.666667

0.583333

0.35

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0.073684

95

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47.5

0.909091

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true

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null

0

1

0

1

0

1

0

6

7664aaba072ca257952878cf91d39bb85d019333

46

py

Python

MousePosition.py

PratyushRanjanTiwari/Automatic-Form-Filler

4347dcd9a9d7ef3cf71a28587c7871300ac431cd

[ "MIT" ]

2

2017-05-06T16:35:42.000Z

2018-05-18T11:04:46.000Z

MousePosition.py

PratyushRanjanTiwari/Automatic-Form-Filler

4347dcd9a9d7ef3cf71a28587c7871300ac431cd

[ "MIT" ]

null

MousePosition.py

PratyushRanjanTiwari/Automatic-Form-Filler

4347dcd9a9d7ef3cf71a28587c7871300ac431cd

[ "MIT" ]

null

import pyautogui print pyautogui.position()

15.333333

27

0.804348

5

46

7.4

0.8

0

0.130435

46

2

28

23

0.925

0

null

0

0.5

null

0.5

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

768dc8920b91817efc4bf4a9bcbb3c85a1e54171

151

py

Python

python/datamongo/text/dmo/__init__.py

jiportilla/ontology

8a66bb7f76f805c64fc76cfc40ab7dfbc1146f40

[ "MIT" ]

null

python/datamongo/text/dmo/__init__.py

jiportilla/ontology

8a66bb7f76f805c64fc76cfc40ab7dfbc1146f40

[ "MIT" ]

null

python/datamongo/text/dmo/__init__.py

jiportilla/ontology

8a66bb7f76f805c64fc76cfc40ab7dfbc1146f40

[ "MIT" ]

null

from .text_query_filter import TextQueryFilter from .text_query_generator import TextQueryGenerator from .text_query_windower import TextQueryWindower

37.75

52

0.900662

18

151

7.222222

0.555556

0.184615

0.3

0

0.07947

151

3

53

50.333333

0.935252

0

1

0

true

0

1

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1

0

1

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null

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1

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1

0

null

0

1

0

1

0

1

0

6

76c2c9b293269833686e2bb6e6e8c4aaa224f47f

42

py

Python

python/graphiler/utils/__init__.py

xiezhq-hermann/graphiler

4d3dd2d2e0a095e3e834be3a34f402bf43e4b4fa

[ "Apache-2.0" ]

17

2022-02-09T16:43:15.000Z

2022-03-30T07:05:40.000Z

python/graphiler/utils/__init__.py

xiezhq-hermann/graphiler

4d3dd2d2e0a095e3e834be3a34f402bf43e4b4fa

[ "Apache-2.0" ]

1

2022-02-12T08:24:21.000Z

python/graphiler/utils/__init__.py

xiezhq-hermann/graphiler

4d3dd2d2e0a095e3e834be3a34f402bf43e4b4fa

[ "Apache-2.0" ]

2

2022-02-09T23:51:42.000Z

2022-03-10T16:21:27.000Z

from .setup import * from .bench import *

14

20

0.714286

6

42

5

0.666667

0

0.190476

42

2

21

0.882353

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

4f3b63470bbcc2944b797569bd44662cc7ac9e3c

62

py

Python

multilingual_t5/r_ic_all_ta/__init__.py

sumanthd17/mt5

c99b4e3ad1c69908c852c730a1323ccb52d48f58

[ "Apache-2.0" ]

null

multilingual_t5/r_ic_all_ta/__init__.py

sumanthd17/mt5

c99b4e3ad1c69908c852c730a1323ccb52d48f58

[ "Apache-2.0" ]

null

multilingual_t5/r_ic_all_ta/__init__.py

sumanthd17/mt5

c99b4e3ad1c69908c852c730a1323ccb52d48f58

[ "Apache-2.0" ]

null

"""r_ic_all_ta dataset.""" from .r_ic_all_ta import RIcAllTa

15.5

33

0.758065

12

62

3.416667

0.666667

0.146341

0.292683

0.390244

0

0.112903

62

3

34

20.666667

0.745455

0.322581

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

4f954beeec51facce69cd04b6acd2750c11f2029

579

py

Python

tests/conftest.py

Enselic/git-repo-language-trend

b701138a85f7c7b4e3cde5f6cd29b6d006b493cf

[ "MIT" ]

1

2021-07-27T12:08:52.000Z

tests/conftest.py

Enselic/git-repo-language-trend

b701138a85f7c7b4e3cde5f6cd29b6d006b493cf

[ "MIT" ]

5

2021-01-24T10:18:26.000Z

2021-07-02T09:48:00.000Z

tests/conftest.py

Enselic/git-repo-language-trends

b701138a85f7c7b4e3cde5f6cd29b6d006b493cf

[ "MIT" ]

null

from random import randint import pytest @pytest.fixture def random_output_basename(tmp_path): return str(tmp_path / f"output-{randint(1,100000)}") @pytest.fixture def tsv_output_path(random_output_basename): return f"{random_output_basename}.tsv" @pytest.fixture def csv_output_path(random_output_basename): return f"{random_output_basename}.csv" @pytest.fixture def svg_output_path(random_output_basename): return f"{random_output_basename}.svg" @pytest.fixture def png_output_path(random_output_basename): return f"{random_output_basename}.png"

19.965517

56

0.792746

83

579

5.192771

0.240964

0.25058

0.417633

0.204176

0.529002

0

0.013592

0.110535

579

28

57

20.678571

0.823301

0

0.294118

0

0.238342

0

1

0.294118

false

0

0.117647

0.294118

0.705882

0

null

1

0

null

0

1

0

1

0

6

4fffea1408e201dad439a35be9406858e0d67c01

46

py

Python

fastmsa/logging.py

abhan00/fastana

46f90832d8e24185619292f76cbd1b3ce73f2dae

[ "Apache-2.0" ]

1

2021-05-01T13:44:44.000Z

fastmsa/logging.py

abhan00/fastana

46f90832d8e24185619292f76cbd1b3ce73f2dae

[ "Apache-2.0" ]

9

2021-04-17T03:22:56.000Z

2021-05-12T16:40:53.000Z

fastmsa/logging.py

abhan00/fastana

46f90832d8e24185619292f76cbd1b3ce73f2dae

[ "Apache-2.0" ]

null

from .core._logging import get_logger # noqa

23

45

0.782609

7

46

4.857143

1

0

0.152174

46

1

46

0.871795

0.086957

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

8b0b681a807652f33b42f6ca5c781e602255191d

72

py

Python

src/huggingmolecules/models/__init__.py

chrislybaer/huggingmolecules

210239ac46b467e900a47e8f4520054636744ca6

[ "Apache-2.0" ]

60

2021-05-07T16:07:26.000Z

2022-03-26T19:23:54.000Z

src/huggingmolecules/models/__init__.py

gabegomes/huggingmolecules

adc581c97fbc21d9967dd9334afa94b22fb77651

[ "Apache-2.0" ]

11

2021-05-07T16:01:35.000Z

2022-03-09T13:06:05.000Z

src/huggingmolecules/models/__init__.py

gabegomes/huggingmolecules

adc581c97fbc21d9967dd9334afa94b22fb77651

[ "Apache-2.0" ]

12

2021-05-20T08:02:25.000Z

2022-03-10T14:11:36.000Z

from .models_grover import GroverModel from .models_mat import MatModel

24

38

0.861111

10

72

6

0.7

0.333333

0

0.111111

72

2

39

36

0.9375

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

8b0c0923f230a5329da990775c88734ee944169b

1,143

py

Python

dns_check/datadog_checks/dns_check/config_models/defaults.py

tdimnet/integrations-core

a78133a3b71a1b8377fa214d121a98647031ab06

[ "BSD-3-Clause" ]

1

2021-12-15T22:45:14.000Z

dns_check/datadog_checks/dns_check/config_models/defaults.py

tdimnet/integrations-core

a78133a3b71a1b8377fa214d121a98647031ab06

[ "BSD-3-Clause" ]

null

dns_check/datadog_checks/dns_check/config_models/defaults.py

tdimnet/integrations-core

a78133a3b71a1b8377fa214d121a98647031ab06

[ "BSD-3-Clause" ]

null

# (C) Datadog, Inc. 2021-present # All rights reserved # Licensed under a 3-clause BSD style license (see LICENSE) from datadog_checks.base.utils.models.fields import get_default_field_value def shared_default_timeout(field, value): return 5 def shared_service(field, value): return get_default_field_value(field, value) def instance_disable_generic_tags(field, value): return False def instance_empty_default_hostname(field, value): return False def instance_min_collection_interval(field, value): return 15 def instance_name(field, value): return get_default_field_value(field, value) def instance_nameserver(field, value): return get_default_field_value(field, value) def instance_nameserver_port(field, value): return 53 def instance_record_type(field, value): return 'A' def instance_resolves_as(field, value): return get_default_field_value(field, value) def instance_service(field, value): return get_default_field_value(field, value) def instance_tags(field, value): return get_default_field_value(field, value) def instance_timeout(field, value): return 5

20.052632

75

0.773403

161

1,143

5.21118

0.329193

0.309893

0.247914

0.166865

0.581645

0.524434

0.448153

0

0.011329

0.150481

1,143

56

76

20.410714

0.852729

0.094488

0

0.37037

0

0.00097

0

1

0.481481

false

0

0.037037

0.481481

1

0

null

1

0

null

0

1

0

1

0

6

8b1d4064a5e19e9b59d8953a84cdda0366402380

25

py

Python

modules/dirsearch/lib/core/controller/__init__.py

Farz7/Darkness

4f3eb5fee3d8a476d001ad319ca22bca274eeac9

[ "MIT" ]

655

2017-09-11T19:35:20.000Z

2022-03-31T08:01:10.000Z

Module/dirsearch/lib/controller/__init__.py

bemonolit/Yuki-Chan-The-Auto-Pentest

bea1af4e1d544eadc166f728be2f543ea10af191

[ "MIT" ]

10

2017-09-29T18:45:39.000Z

2022-03-30T15:34:29.000Z

Module/dirsearch/lib/controller/__init__.py

bemonolit/Yuki-Chan-The-Auto-Pentest

bea1af4e1d544eadc166f728be2f543ea10af191

[ "MIT" ]

262

2017-09-16T22:15:50.000Z

2022-03-31T00:38:42.000Z

from .Controller import *

25

0.8

3

25

6.666667

1

0

0.12

25

1

25

0.909091

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

8b4aa1ec412e1720eb36920fc5d5c00b07f7f147

10,196

py

Python

convolution_lstm.py

braraki/Convolution_LSTM_PyTorch

e30502568f28380f1a5eb355d38590f403478a10

[ "MIT" ]

null

convolution_lstm.py

braraki/Convolution_LSTM_PyTorch

e30502568f28380f1a5eb355d38590f403478a10

[ "MIT" ]

null

convolution_lstm.py

braraki/Convolution_LSTM_PyTorch

e30502568f28380f1a5eb355d38590f403478a10

[ "MIT" ]

null

import torch import torch.nn as nn from torch.autograd import Variable import pdb ''' NOTES - steps are how many times you recurse over the data! (eg the length of the trajectory) - effective_steps is a list of which step outputs you want to save - the format of the data as-is is [batch size, input channels, h, w, d] - you need to change it to accept [batch size, trajectory, input channels, h, w, d] and then have 'step' index the trajectory ''' class ConvLSTMCell(nn.Module): def __init__(self, input_channels, hidden_channels, kernel_size): super(ConvLSTMCell, self).__init__() assert hidden_channels % 2 == 0 self.input_channels = input_channels self.hidden_channels = hidden_channels self.kernel_size = kernel_size self.num_features = 4 self.padding = int((kernel_size - 1) / 2) self.Wxi = nn.Conv2d(self.input_channels, self.hidden_channels, self.kernel_size, 1, self.padding, bias=True) self.Whi = nn.Conv2d(self.hidden_channels, self.hidden_channels, self.kernel_size, 1, self.padding, bias=False) self.Wxf = nn.Conv2d(self.input_channels, self.hidden_channels, self.kernel_size, 1, self.padding, bias=True) self.Whf = nn.Conv2d(self.hidden_channels, self.hidden_channels, self.kernel_size, 1, self.padding, bias=False) self.Wxc = nn.Conv2d(self.input_channels, self.hidden_channels, self.kernel_size, 1, self.padding, bias=True) self.Whc = nn.Conv2d(self.hidden_channels, self.hidden_channels, self.kernel_size, 1, self.padding, bias=False) self.Wxo = nn.Conv2d(self.input_channels, self.hidden_channels, self.kernel_size, 1, self.padding, bias=True) self.Who = nn.Conv2d(self.hidden_channels, self.hidden_channels, self.kernel_size, 1, self.padding, bias=False) self.Wci = None self.Wcf = None self.Wco = None def forward(self, x, h, c): ci = torch.sigmoid(self.Wxi(x) + self.Whi(h) + c * self.Wci) cf = torch.sigmoid(self.Wxf(x) + self.Whf(h) + c * self.Wcf) cc = cf * c + ci * torch.tanh(self.Wxc(x) + self.Whc(h)) co = torch.sigmoid(self.Wxo(x) + self.Who(h) + cc * self.Wco) ch = co * torch.tanh(cc) return ch, cc def init_hidden(self, batch_size, hidden, shape): if self.Wci is None: self.Wci = Variable(torch.zeros(1, hidden, shape[0], shape[1])).cuda() self.Wcf = Variable(torch.zeros(1, hidden, shape[0], shape[1])).cuda() self.Wco = Variable(torch.zeros(1, hidden, shape[0], shape[1])).cuda() else: assert shape[0] == self.Wci.size()[2], 'Input Height Mismatched!' assert shape[1] == self.Wci.size()[3], 'Input Width Mismatched!' return (Variable(torch.zeros(batch_size, hidden, shape[0], shape[1])).cuda(), Variable(torch.zeros(batch_size, hidden, shape[0], shape[1])).cuda()) class ConvLSTM(nn.Module): # input_channels corresponds to the first input feature map # hidden state is a list of succeeding lstm layers. def __init__(self, input_channels, hidden_channels, kernel_size, step=1, effective_step=[0]): super(ConvLSTM, self).__init__() self.input_channels = [input_channels] + hidden_channels self.hidden_channels = hidden_channels self.kernel_size = kernel_size self.num_layers = len(hidden_channels) self.step = step self.effective_step = effective_step self._all_layers = [] for i in range(self.num_layers): name = 'cell{}'.format(i) cell = ConvLSTMCell(self.input_channels[i], self.hidden_channels[i], self.kernel_size) setattr(self, name, cell) self._all_layers.append(cell) def forward(self, input): internal_state = [] outputs = [] for step in range(self.step): x = input for i in range(self.num_layers): # all cells are initialized in the first step name = 'cell{}'.format(i) if step == 0: bsize, _, height, width = x.size() (h, c) = getattr(self, name).init_hidden(batch_size=bsize, hidden=self.hidden_channels[i], shape=(height, width)) internal_state.append((h, c)) # do forward (h, c) = internal_state[i] x, new_c = getattr(self, name)(x, h, c) internal_state[i] = (x, new_c) # only record effective steps if step in self.effective_step: outputs.append(x) return outputs, (x, new_c) class ConvLSTMCell3d(nn.Module): def __init__(self, input_channels, hidden_channels, kernel_size): super(ConvLSTMCell3d, self).__init__() assert hidden_channels % 2 == 0 self.input_channels = input_channels self.hidden_channels = hidden_channels self.kernel_size = kernel_size self.num_features = 4 self.padding = 0 # int((kernel_size - 1) / 2) self.Wxi = nn.Conv3d(in_channels=self.input_channels, out_channels=self.hidden_channels, kernel_size=self.kernel_size, stride=1, padding=self.padding, bias=True) self.Whi = nn.Conv3d(self.hidden_channels, self.hidden_channels, self.kernel_size, 1, self.padding, bias=False) self.Wxf = nn.Conv3d(self.input_channels, self.hidden_channels, self.kernel_size, 1, self.padding, bias=True) self.Whf = nn.Conv3d(self.hidden_channels, self.hidden_channels, self.kernel_size, 1, self.padding, bias=False) self.Wxc = nn.Conv3d(self.input_channels, self.hidden_channels, self.kernel_size, 1, self.padding, bias=True) self.Whc = nn.Conv3d(self.hidden_channels, self.hidden_channels, self.kernel_size, 1, self.padding, bias=False) self.Wxo = nn.Conv3d(self.input_channels, self.hidden_channels, self.kernel_size, 1, self.padding, bias=True) self.Who = nn.Conv3d(self.hidden_channels, self.hidden_channels, self.kernel_size, 1, self.padding, bias=False) self.Wci = None self.Wcf = None self.Wco = None def forward(self, x, h, c): ci = torch.sigmoid(self.Wxi(x) + self.Whi(h) + c * self.Wci) cf = torch.sigmoid(self.Wxf(x) + self.Whf(h) + c * self.Wcf) cc = cf * c + ci * torch.tanh(self.Wxc(x) + self.Whc(h)) co = torch.sigmoid(self.Wxo(x) + self.Who(h) + cc * self.Wco) ch = co * torch.tanh(cc) return ch, cc def init_hidden(self, batch_size, hidden, shape): if self.Wci is None: self.Wci = Variable(torch.zeros(1, hidden, shape[0], shape[1], shape[2])).cuda() self.Wcf = Variable(torch.zeros(1, hidden, shape[0], shape[1], shape[2])).cuda() self.Wco = Variable(torch.zeros(1, hidden, shape[0], shape[1], shape[2])).cuda() else: assert shape[0] == self.Wci.size()[2], 'Input Height Mismatched!' assert shape[1] == self.Wci.size()[3], 'Input Width Mismatched!' assert shape[2] == self.Wci.size()[4], 'Input Depth Mismatched!' return (Variable(torch.zeros(batch_size, hidden, shape[0], shape[1], shape[2])).cuda(), Variable(torch.zeros(batch_size, hidden, shape[0], shape[1], shape[2])).cuda()) # CHANGES from the original code: # step now indexes the trajectory # got rid of effective_step because I need to save every step # the 3D conv needs only 1 input channel so I got rid of the # requirement you need an even # of input channels (what's up with that anyway?) class ConvLSTM3d(nn.Module): # input_channels corresponds to the first input feature map # hidden state is a list of succeeding lstm layers. def __init__(self, input_channels, hidden_channels, kernel_size, step=1, effective_step=[0]): super(ConvLSTM3d, self).__init__() self.input_channels = [input_channels] + hidden_channels self.hidden_channels = hidden_channels self.kernel_size = kernel_size self.num_layers = len(hidden_channels) self.step = step self.effective_step = effective_step self._all_layers = [] for i in range(self.num_layers): name = 'cell{}'.format(i) cell = ConvLSTMCell3d(self.input_channels[i], self.hidden_channels[i], self.kernel_size) setattr(self, name, cell) self._all_layers.append(cell) def forward(self, input): internal_state = [] outputs = [] for step in range(self.step): x = input for i in range(self.num_layers): # all cells are initialized in the first step name = 'cell{}'.format(i) if step == 0: bsize, _, height, width, depth = x.size() (h, c) = getattr(self, name).init_hidden(batch_size=bsize, hidden=self.hidden_channels[i], shape=(height, width, depth)) internal_state.append((h, c)) # do forward (h, c) = internal_state[i] x, new_c = getattr(self, name)(x, h, c) internal_state[i] = (x, new_c) # only record effective steps if step in self.effective_step: outputs.append(x) return outputs, (x, new_c) if __name__ == '__main__': # gradient check convlstm = ConvLSTM(input_channels=512, hidden_channels=[128, 64, 64, 32, 32], kernel_size=3, step=5, effective_step=[4]).cuda() loss_fn = torch.nn.MSELoss() input = Variable(torch.randn(1, 512, 64, 32)).cuda() target = Variable(torch.randn(1, 32, 64, 32)).double().cuda() output = convlstm(input) output2 = output[0][0].double() res = torch.autograd.gradcheck(loss_fn, (output2, target), eps=1e-6, raise_exception=True) print(res) pdb.set_trace() print('hi')

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0.610043

1,377

10,196

4.366739

0.135802

0.10943

0.095792

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0.811908

0.806586

0.801763

0.794113

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0.26991

10,196

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120

47.423256

0.789629

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0.012346

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null

0

1

0

null

0

6

8c9b9b3bfe9a283884709dc4112979036d85f802

46

py

Python

Part_1_beginner/04_print_function/special_separator_3.py

Mikma03/InfoShareacademy_Python_Courses

3df1008c8c92831bebf1625f960f25b39d6987e6

[ "MIT" ]

null

Part_1_beginner/04_print_function/special_separator_3.py

Mikma03/InfoShareacademy_Python_Courses

3df1008c8c92831bebf1625f960f25b39d6987e6

[ "MIT" ]

null

Part_1_beginner/04_print_function/special_separator_3.py

Mikma03/InfoShareacademy_Python_Courses

3df1008c8c92831bebf1625f960f25b39d6987e6

[ "MIT" ]

1

2021-02-20T08:30:56.000Z

print("Mój ulubiony sport to \\triathlon\ ")

15.333333

44

0.695652

6

46

5.333333

1

0

0.152174

46

2

45

23

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0.777778

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true

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null

0

1

0

1

0

6

8ca7caaa0a55affaa1c1a80379fc6d3e4bbd965a

12,158

py

Python

tests/components/homeassistant/triggers/test_event.py

MrDelik/core

93a66cc357b226389967668441000498a10453bb

[ "Apache-2.0" ]

30,023

2016-04-13T10:17:53.000Z

2020-03-02T12:56:31.000Z

tests/components/homeassistant/triggers/test_event.py

jagadeeshvenkatesh/core

1bd982668449815fee2105478569f8e4b5670add

[ "Apache-2.0" ]

31,101

2020-03-02T13:00:16.000Z

2022-03-31T23:57:36.000Z

tests/components/homeassistant/triggers/test_event.py

jagadeeshvenkatesh/core

1bd982668449815fee2105478569f8e4b5670add

[ "Apache-2.0" ]

11,956

2016-04-13T18:42:31.000Z

2020-03-02T09:32:12.000Z

"""The tests for the Event automation.""" import pytest import homeassistant.components.automation as automation from homeassistant.const import ATTR_ENTITY_ID, ENTITY_MATCH_ALL, SERVICE_TURN_OFF from homeassistant.core import Context from homeassistant.setup import async_setup_component from tests.common import async_mock_service, mock_component @pytest.fixture def calls(hass): """Track calls to a mock service.""" return async_mock_service(hass, "test", "automation") @pytest.fixture def context_with_user(): """Create a context with default user_id.""" return Context(user_id="test_user_id") @pytest.fixture(autouse=True) def setup_comp(hass): """Initialize components.""" mock_component(hass, "group") async def test_if_fires_on_event(hass, calls): """Test the firing of events.""" context = Context() assert await async_setup_component( hass, automation.DOMAIN, { automation.DOMAIN: { "trigger": {"platform": "event", "event_type": "test_event"}, "action": { "service": "test.automation", "data_template": {"id": "{{ trigger.id}}"}, }, } }, ) hass.bus.async_fire("test_event", context=context) await hass.async_block_till_done() assert len(calls) == 1 assert calls[0].context.parent_id == context.id await hass.services.async_call( automation.DOMAIN, SERVICE_TURN_OFF, {ATTR_ENTITY_ID: ENTITY_MATCH_ALL}, blocking=True, ) hass.bus.async_fire("test_event") await hass.async_block_till_done() assert len(calls) == 1 assert calls[0].data["id"] == 0 async def test_if_fires_on_templated_event(hass, calls): """Test the firing of events.""" context = Context() assert await async_setup_component( hass, automation.DOMAIN, { automation.DOMAIN: { "trigger_variables": {"event_type": "test_event"}, "trigger": {"platform": "event", "event_type": "{{event_type}}"}, "action": {"service": "test.automation"}, } }, ) hass.bus.async_fire("test_event", context=context) await hass.async_block_till_done() assert len(calls) == 1 assert calls[0].context.parent_id == context.id await hass.services.async_call( automation.DOMAIN, SERVICE_TURN_OFF, {ATTR_ENTITY_ID: ENTITY_MATCH_ALL}, blocking=True, ) hass.bus.async_fire("test_event") await hass.async_block_till_done() assert len(calls) == 1 async def test_if_fires_on_multiple_events(hass, calls): """Test the firing of events.""" context = Context() assert await async_setup_component( hass, automation.DOMAIN, { automation.DOMAIN: { "trigger": { "platform": "event", "event_type": ["test_event", "test2_event"], }, "action": {"service": "test.automation"}, } }, ) hass.bus.async_fire("test_event", context=context) await hass.async_block_till_done() hass.bus.async_fire("test2_event", context=context) await hass.async_block_till_done() assert len(calls) == 2 assert calls[0].context.parent_id == context.id assert calls[1].context.parent_id == context.id async def test_if_fires_on_event_extra_data(hass, calls, context_with_user): """Test the firing of events still matches with event data and context.""" assert await async_setup_component( hass, automation.DOMAIN, { automation.DOMAIN: { "trigger": {"platform": "event", "event_type": "test_event"}, "action": {"service": "test.automation"}, } }, ) hass.bus.async_fire( "test_event", {"extra_key": "extra_data"}, context=context_with_user ) await hass.async_block_till_done() assert len(calls) == 1 await hass.services.async_call( automation.DOMAIN, SERVICE_TURN_OFF, {ATTR_ENTITY_ID: ENTITY_MATCH_ALL}, blocking=True, ) hass.bus.async_fire("test_event") await hass.async_block_till_done() assert len(calls) == 1 async def test_if_fires_on_event_with_data_and_context(hass, calls, context_with_user): """Test the firing of events with data and context.""" assert await async_setup_component( hass, automation.DOMAIN, { automation.DOMAIN: { "trigger": { "platform": "event", "event_type": "test_event", "event_data": { "some_attr": "some_value", "second_attr": "second_value", }, "context": {"user_id": context_with_user.user_id}, }, "action": {"service": "test.automation"}, } }, ) hass.bus.async_fire( "test_event", {"some_attr": "some_value", "another": "value", "second_attr": "second_value"}, context=context_with_user, ) await hass.async_block_till_done() assert len(calls) == 1 hass.bus.async_fire( "test_event", {"some_attr": "some_value", "another": "value"}, context=context_with_user, ) await hass.async_block_till_done() assert len(calls) == 1 # No new call hass.bus.async_fire( "test_event", {"some_attr": "some_value", "another": "value", "second_attr": "second_value"}, ) await hass.async_block_till_done() assert len(calls) == 1 async def test_if_fires_on_event_with_templated_data_and_context( hass, calls, context_with_user ): """Test the firing of events with templated data and context.""" assert await async_setup_component( hass, automation.DOMAIN, { automation.DOMAIN: { "trigger_variables": { "attr_1_val": "milk", "attr_2_val": "beer", "user_id": context_with_user.user_id, }, "trigger": { "platform": "event", "event_type": "test_event", "event_data": { "attr_1": "{{attr_1_val}}", "attr_2": "{{attr_2_val}}", }, "context": {"user_id": "{{user_id}}"}, }, "action": {"service": "test.automation"}, } }, ) hass.bus.async_fire( "test_event", {"attr_1": "milk", "another": "value", "attr_2": "beer"}, context=context_with_user, ) await hass.async_block_till_done() assert len(calls) == 1 hass.bus.async_fire( "test_event", {"attr_1": "milk", "another": "value"}, context=context_with_user, ) await hass.async_block_till_done() assert len(calls) == 1 # No new call hass.bus.async_fire( "test_event", {"attr_1": "milk", "another": "value", "attr_2": "beer"}, ) await hass.async_block_till_done() assert len(calls) == 1 async def test_if_fires_on_event_with_empty_data_and_context_config( hass, calls, context_with_user ): """Test the firing of events with empty data and context config. The frontend automation editor can produce configurations with an empty dict for event_data instead of no key. """ assert await async_setup_component( hass, automation.DOMAIN, { automation.DOMAIN: { "trigger": { "platform": "event", "event_type": "test_event", "event_data": {}, "context": {}, }, "action": {"service": "test.automation"}, } }, ) hass.bus.async_fire( "test_event", {"some_attr": "some_value", "another": "value"}, context=context_with_user, ) await hass.async_block_till_done() assert len(calls) == 1 async def test_if_fires_on_event_with_nested_data(hass, calls): """Test the firing of events with nested data.""" assert await async_setup_component( hass, automation.DOMAIN, { automation.DOMAIN: { "trigger": { "platform": "event", "event_type": "test_event", "event_data": {"parent_attr": {"some_attr": "some_value"}}, }, "action": {"service": "test.automation"}, } }, ) hass.bus.async_fire( "test_event", {"parent_attr": {"some_attr": "some_value", "another": "value"}} ) await hass.async_block_till_done() assert len(calls) == 1 async def test_if_not_fires_if_event_data_not_matches(hass, calls): """Test firing of event if no data match.""" assert await async_setup_component( hass, automation.DOMAIN, { automation.DOMAIN: { "trigger": { "platform": "event", "event_type": "test_event", "event_data": {"some_attr": "some_value"}, }, "action": {"service": "test.automation"}, } }, ) hass.bus.async_fire("test_event", {"some_attr": "some_other_value"}) await hass.async_block_till_done() assert len(calls) == 0 async def test_if_not_fires_if_event_context_not_matches( hass, calls, context_with_user ): """Test firing of event if no context match.""" assert await async_setup_component( hass, automation.DOMAIN, { automation.DOMAIN: { "trigger": { "platform": "event", "event_type": "test_event", "context": {"user_id": "some_user"}, }, "action": {"service": "test.automation"}, } }, ) hass.bus.async_fire("test_event", {}, context=context_with_user) await hass.async_block_till_done() assert len(calls) == 0 async def test_if_fires_on_multiple_user_ids(hass, calls, context_with_user): """Test the firing of event when the trigger has multiple user ids.""" assert await async_setup_component( hass, automation.DOMAIN, { automation.DOMAIN: { "trigger": { "platform": "event", "event_type": "test_event", "event_data": {}, "context": {"user_id": [context_with_user.user_id, "another id"]}, }, "action": {"service": "test.automation"}, } }, ) hass.bus.async_fire("test_event", {}, context=context_with_user) await hass.async_block_till_done() assert len(calls) == 1 async def test_event_data_with_list(hass, calls): """Test the (non)firing of event when the data schema has lists.""" assert await async_setup_component( hass, automation.DOMAIN, { automation.DOMAIN: { "trigger": { "platform": "event", "event_type": "test_event", "event_data": {"some_attr": [1, 2]}, "context": {}, }, "action": {"service": "test.automation"}, } }, ) hass.bus.async_fire("test_event", {"some_attr": [1, 2]}) await hass.async_block_till_done() assert len(calls) == 1 # don't match a single value hass.bus.async_fire("test_event", {"some_attr": 1}) await hass.async_block_till_done() assert len(calls) == 1 # don't match a containing list hass.bus.async_fire("test_event", {"some_attr": [1, 2, 3]}) await hass.async_block_till_done() assert len(calls) == 1

29.653659

87

0.556917

1,325

12,158

4.815849

0.087547

0.047955

0.041373

0.055164

0.834352

0.81821

0.785143

0.761793

0.74283

0.724024

0

0.005786

0.317651

12,158

409

88

29.726161

0.76338

0.017273

0

0.604863

0

0.168622

0

0.115502

1

0.009119

false

0

0.018237

0

0.033435

0

null

0

1

0

null

0

6

8ca95d533d0d15abc6108952f1d65ecf31857894

1,672

py

Python

tests/search/SearchQuery.py

Gawaboumga/PyMatex

3ccc0aa23211a064aa31a9b509b108cd606a4992

[ "MIT" ]

1

2019-03-05T09:45:04.000Z

tests/search/SearchQuery.py

Gawaboumga/PyMatex

3ccc0aa23211a064aa31a9b509b108cd606a4992

[ "MIT" ]

null

tests/search/SearchQuery.py

Gawaboumga/PyMatex

3ccc0aa23211a064aa31a9b509b108cd606a4992

[ "MIT" ]

null

from tests import BaseTest from pymatex.search import SearchQuery class SearchQueryTests(BaseTest.BaseTest): def test_read(self): s = SearchQuery(path='tests/search/resources/search-content-simple-tests.txt') def test_search_simple(self): s = SearchQuery(path='tests/search/resources/search-content-simple-tests.txt') results = s.search(r'(ky+o)') self.assertListEqual(list(map(lambda x: x[0], results)), [2, 1]) def test_search_less_simple(self): s = SearchQuery(path='tests/search/resources/search-content-simple-tests.txt') results = s.search(r'(ky+o) * (uy^{2} + vy + n)') self.assertListEqual(list(map(lambda x: x[0], results)), [1, 2]) def test_search_summation_and_bound_variables(self): s = SearchQuery(path='tests/search/resources/search-content-summation-tests.txt') results = s.search(r'\sum_{k=0}^{\infty} k') self.assertListEqual(list(map(lambda x: x[0], results)), [2, 1]) def test_search_weird_summation_and_fraction(self): s = SearchQuery(path='tests/search/resources/search-content-weird-summation-tests.txt') results = s.search(r'\sum_{k=0}^{\infty} k') self.assertListEqual(list(map(lambda x: x[0], results)), [1, 3, 2]) def test_remove(self): s = SearchQuery(path='tests/search/resources/search-content-simple-tests.txt') results = s.search(r'(ky+o) * (uy^{2} + vy + n)') self.assertListEqual(list(map(lambda x: x[0], results)), [1, 2]) s.remove(1) results = s.search(r'(ky+o) * (uy^{2} + vy + n)') self.assertListEqual(list(map(lambda x: x[0], results)), [2])

38

95

0.647727

238

1,672

4.470588

0.193277

0.039474

0.090226

0.112782

0.798872

0.699248

0

0.01757

0.183014

1,672

43

96

38.883721

0.761347

0

0.464286

0

0.276316

0.200957

0

0.214286

1

0.214286

false

0

0.071429

0

0.321429

0

null

0

1

0

null

0

1

0

6

8ced868760db7844739902b77e80163d14804aa4

39

py

Python

tests/test_so.py

lazyxu/pythonvm

8c25acc6ee1e01a0bb65bb35aae987264d6876aa

[ "MIT" ]

null

tests/test_so.py

lazyxu/pythonvm

8c25acc6ee1e01a0bb65bb35aae987264d6876aa

[ "MIT" ]

null

tests/test_so.py

lazyxu/pythonvm

8c25acc6ee1e01a0bb65bb35aae987264d6876aa

[ "MIT" ]

null

import libmath print libmath.add(1, 2)

13

23

0.769231

7

39

4.285714

0.857143

0

0.058824

0.128205

39

3

23

13

0.823529

0

null

0

0.5

null

0.5

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

50b01881feff7ab77d41b1e18ec713bff005d8ee

20

py

Python

vigobusapi/vigobus_getters/http/__init__.py

David-Lor/Python_VigoBusAPI

2ba327654908b761b3086be2fec09bfebc6e3029

[ "Apache-2.0" ]

4

2019-07-18T22:25:31.000Z

2021-03-09T19:01:14.000Z

vigobusapi/vigobus_getters/http/__init__.py

David-Lor/Python_VigoBusAPI

2ba327654908b761b3086be2fec09bfebc6e3029

[ "Apache-2.0" ]

3

2021-09-12T20:15:38.000Z

2021-09-18T16:35:27.000Z

vigobusapi/vigobus_getters/http/__init__.py

David-Lor/VigoBusAPI

40db5a644f43a8f98cb40a9e5519a028fe18db14

[ "Apache-2.0" ]

3

2020-10-03T21:45:39.000Z

2021-05-06T21:27:03.000Z

from .http import *

10

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3

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1

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6

50bb05784e1ea3caad1078dec01819d01b8b82e3

2,770

py

Python

pycdo/asa_services.py

aaronhackney/pycdo

0c0b938b74dbc0ab45a1ed7926c2d5e2f8651e7a

[ "MIT" ]

null

pycdo/asa_services.py

aaronhackney/pycdo

0c0b938b74dbc0ab45a1ed7926c2d5e2f8651e7a

[ "MIT" ]

1

2022-03-17T22:38:44.000Z

pycdo/asa_services.py

aaronhackney/pycdo

0c0b938b74dbc0ab45a1ed7926c2d5e2f8651e7a

[ "MIT" ]

null

from .base import CDOBaseClient import logging logger = logging.getLogger(__name__) class CDOASAServices(CDOBaseClient): """Class for performing CDO ASA operations""" # TODO: Full CRUD operations where available # TODO: packettracer method(s) def get_asa_config_summary_list(self): return self.get_operation(f"{self.PREFIX_LIST['SERVICES']}/asa/configs") def get_asa_config_summary(self, device_uid): return self.get_operation(f"{self.PREFIX_LIST['SERVICES']}/asa/configs/{device_uid}") def get_asa_nats_list(self): return self.get_operation(f"{self.PREFIX_LIST['SERVICES']}/asa/nats") def get_asa_nat(self, nat_uid): return self.get_operation(f"{self.PREFIX_LIST['SERVICES']}/asa/nats/{nat_uid}") def get_asa_twice_nat_events_list(self): return self.get_operation(f"{self.PREFIX_LIST['SERVICES']}/asa/twicenatevents") def get_asa_twice_nat_events(self, twice_nat_uid): return self.get_operation(f"{self.PREFIX_LIST['SERVICES']}/asa/twicenatevents/{twice_nat_uid}") def get_asa_exports_list(self): return self.get_operation(f"{self.PREFIX_LIST['SERVICES']}/asa/exports") def get_asa_exports(self, export_uid): return self.get_operation(f"{self.PREFIX_LIST['SERVICES']}/asa/exports/{export_uid}") def get_asa_templates_list(self): return self.get_operation(f"{self.PREFIX_LIST['SERVICES']}/asa/templates") def get_asa_templates(self, template_uid): return self.get_operation(f"{self.PREFIX_LIST['SERVICES']}/asa/templates/{template_uid}") def get_asa_debug_events_list(self): return self.get_operation(f"{self.PREFIX_LIST['SERVICES']}/asa/debugevents") def get_asa_debug_events(self, events_uid): return self.get_operation(f"{self.PREFIX_LIST['SERVICES']}/asa/debugevents/{events_uid}") def get_asa_ordered_nats_list(self, params): return self.get_operation(f"{self.PREFIX_LIST['SERVICES']}/asa/orderednats", params=params) def get_asa_ordered_nats(self, ordered_nats_uid, params): return self.get_operation(f"{self.PREFIX_LIST['SERVICES']}/asa/orderednats/{ordered_nats_uid}", params=params) def get_asa_configs_exports_list(self): return self.get_operation(f"{self.PREFIX_LIST['SERVICES']}/asa/configs-exports") def get_asa_configs_exports(self, configs_exports_uid): return self.get_operation(f"{self.PREFIX_LIST['SERVICES']}/asa/configs-exports/{configs_exports_uid}") def get_asa_nat_events_list(self): return self.get_operation(f"{self.PREFIX_LIST['SERVICES']}/asa/natevents") def get_asa_nat_events(self, nat_events_uid): return self.get_operation(f"{self.PREFIX_LIST['SERVICES']}/asa/natevents/{nat_events_uid}")

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6

0f9c39f1bddc9494f522a489f297ea357d75b02c

128

py

Python

mirtorch/__init__.py

guanhuaw/MIRTorch

535690946d1f105c6d3532c9fbc68cd78307e945

[ "BSD-3-Clause" ]

38

2021-03-07T21:51:51.000Z

2022-03-29T09:32:52.000Z

mirtorch/__init__.py

guanhuaw/MIRTorch

535690946d1f105c6d3532c9fbc68cd78307e945

[ "BSD-3-Clause" ]

2

2021-09-17T19:52:50.000Z

2022-03-29T09:32:42.000Z

mirtorch/__init__.py

guanhuaw/MIRTorch

535690946d1f105c6d3532c9fbc68cd78307e945

[ "BSD-3-Clause" ]

null

from mirtorch import prox from mirtorch import linear from mirtorch import nn __all__ = ['linear', 'prox', 'alg', 'nn', 'dic']

21.333333

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6

e8556b026e8027d295b775f9d851a8a54cfd4cec

46

py

Python

packages/compile-vyper/test/sources/VyperContract3.vyper.py

wbt/truffle

94e5ce757ef61302288088fbde83e1889385413c

[ "MIT" ]

24

2020-06-19T07:41:25.000Z

2022-02-15T08:51:10.000Z

packages/compile-vyper/test/sources/VyperContract3.vyper.py

wbt/truffle

94e5ce757ef61302288088fbde83e1889385413c

[ "MIT" ]

11

2019-04-26T04:05:42.000Z

2019-08-23T17:27:10.000Z

packages/compile-vyper/test/sources/VyperContract3.vyper.py

wbt/truffle

94e5ce757ef61302288088fbde83e1889385413c

[ "MIT" ]

8

2020-06-16T11:32:51.000Z

2022-02-11T09:10:45.000Z

@public @payable def vyper_action(): pass

9.2

19

0.695652

6

46

5.166667

1

0

0.195652

46

4

20

11.5

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0.25

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null

0

1

0

null

0

1

0

6

e89f97b35437e7dd8581422c9fcc71f80a91716f

31

py

Python

cosmosdb_sdk/__init__.py

jxjia/cosmos_sdk

be74647f65bed29641c119f842c56510eacbb82a

[ "MIT" ]

null

cosmosdb_sdk/__init__.py

jxjia/cosmos_sdk

be74647f65bed29641c119f842c56510eacbb82a

[ "MIT" ]

null

cosmosdb_sdk/__init__.py

jxjia/cosmos_sdk

be74647f65bed29641c119f842c56510eacbb82a

[ "MIT" ]

null

from .cosmosdb import CosmosDB

15.5

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null

0

1

0

1

0

1

0

6

e8ad77148ce51329d1bd38a91fe48c206e378393

106

py

Python

Code/quote_user.py

UPstartDeveloper/CS-1.2-Intro-Data-Structures

d36ee5ff4496315f07deb748c06afb043bd97c41

[ "MIT" ]

1

2020-06-05T19:08:45.000Z

Code/quote_user.py

UPstartDeveloper/CS-1.2-Intro-Data-Structures

d36ee5ff4496315f07deb748c06afb043bd97c41

[ "MIT" ]

7

2019-11-07T03:13:52.000Z

2019-12-16T16:49:00.000Z

Code/quote_user.py

UPstartDeveloper/adam-smith-tweet-generator

d36ee5ff4496315f07deb748c06afb043bd97c41

[ "MIT" ]

null

from python_quote import random_python_quote if __name__ == "__main__": print(random_python_quote())

21.2

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106

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null

0

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0

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6

e8b67eeb1e2a34a29fedf28d5f6887297faa596f

2,620

py

Python

tests/test_time_mask.py

jeongyoonlee/audiomentations

7f0112ae310989430e0ef7eb32c4116114810966

[ "MIT" ]

930

2019-02-14T10:21:22.000Z

2022-03-31T03:49:48.000Z

tests/test_time_mask.py

jeongyoonlee/audiomentations

7f0112ae310989430e0ef7eb32c4116114810966

[ "MIT" ]

169

2019-02-12T21:16:14.000Z

2022-03-18T07:53:43.000Z

tests/test_time_mask.py

jeongyoonlee/audiomentations

7f0112ae310989430e0ef7eb32c4116114810966

[ "MIT" ]

122

2019-02-26T05:12:45.000Z

2022-03-24T08:45:51.000Z

import unittest import numpy as np from audiomentations.augmentations.transforms import TimeMask from audiomentations.core.composition import Compose class TestTimeMask(unittest.TestCase): def test_apply_time_mask(self): sample_len = 1024 samples_in = np.random.normal(0, 1, size=sample_len).astype(np.float32) sample_rate = 16000 augmenter = Compose([TimeMask(min_band_part=0.2, max_band_part=0.5, p=1.0)]) samples_out = augmenter(samples=samples_in, sample_rate=sample_rate) self.assertEqual(samples_out.dtype, np.float32) self.assertEqual(len(samples_out), sample_len) std_in = np.mean(np.abs(samples_in)) std_out = np.mean(np.abs(samples_out)) self.assertLess(std_out, std_in) def test_apply_time_mask_multichannel(self): sample_len = 1024 samples_in = np.random.normal(0, 1, size=(2, sample_len)).astype(np.float32) sample_rate = 16000 augmenter = TimeMask(min_band_part=0.2, max_band_part=0.5, p=1.0) samples_out = augmenter(samples=samples_in, sample_rate=sample_rate) self.assertEqual(samples_out.dtype, np.float32) self.assertEqual(samples_out.shape, samples_in.shape) std_in = np.mean(np.abs(samples_in)) std_out = np.mean(np.abs(samples_out)) self.assertLess(std_out, std_in) def test_apply_time_mask_with_fade(self): sample_len = 1024 samples_in = np.random.normal(0, 1, size=sample_len).astype(np.float32) sample_rate = 16000 augmenter = Compose( [TimeMask(min_band_part=0.2, max_band_part=0.5, fade=True, p=1.0)] ) samples_out = augmenter(samples=samples_in, sample_rate=sample_rate) self.assertEqual(samples_out.dtype, np.float32) self.assertEqual(len(samples_out), sample_len) std_in = np.mean(np.abs(samples_in)) std_out = np.mean(np.abs(samples_out)) self.assertLess(std_out, std_in) def test_apply_time_mask_with_fade_short_signal(self): sample_len = 100 samples_in = np.random.normal(0, 1, size=sample_len).astype(np.float32) sample_rate = 16000 augmenter = Compose( [TimeMask(min_band_part=0.2, max_band_part=0.5, fade=True, p=1.0)] ) samples_out = augmenter(samples=samples_in, sample_rate=sample_rate) self.assertEqual(samples_out.dtype, np.float32) self.assertEqual(len(samples_out), sample_len) std_in = np.mean(np.abs(samples_in)) std_out = np.mean(np.abs(samples_out)) self.assertLess(std_out, std_in)

37.971014

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6

e8c643284e5eb0048db5e44deafdd8c3f6ec0e42

184

py

Python

vendor/packages/pylint/test/input/func_w0704.py

jgmize/kitsune

8f23727a9c7fcdd05afc86886f0134fb08d9a2f0

[ "BSD-3-Clause" ]

2

2019-08-19T17:08:47.000Z

2019-10-05T11:37:02.000Z

vendor/packages/pylint/test/input/func_w0704.py

jgmize/kitsune

8f23727a9c7fcdd05afc86886f0134fb08d9a2f0

[ "BSD-3-Clause" ]

null

vendor/packages/pylint/test/input/func_w0704.py

jgmize/kitsune

8f23727a9c7fcdd05afc86886f0134fb08d9a2f0

[ "BSD-3-Clause" ]

null

"""test empty except """ __revision__ = 1 try: __revision__ += 1 except TypeError: pass try: __revision__ += 1 except TypeError: pass else: __revision__ = None

10.823529

24

0.641304

20

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184

16

25

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1

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0

null

0

1

0

6

2cde165a01180fdf8a58607038b0867e0a8f76b5

187

py

Python

pyclopedia/p02_ref/p03_objective_oriented/p04_magic_method/__init__.py

MacHu-GWU/pyclopedia-project

c6ee156eb40bc5a4ac5f51aa735b6fd004cb68ee

[ "MIT" ]

null

pyclopedia/p02_ref/p03_objective_oriented/p04_magic_method/__init__.py

MacHu-GWU/pyclopedia-project

c6ee156eb40bc5a4ac5f51aa735b6fd004cb68ee

[ "MIT" ]

null

pyclopedia/p02_ref/p03_objective_oriented/p04_magic_method/__init__.py

MacHu-GWU/pyclopedia-project

c6ee156eb40bc5a4ac5f51aa735b6fd004cb68ee

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- """ Python2 Doc: https://docs.python.org/2/reference/datamodel.html Python3.3 Doc: https://docs.python.org/3.3/reference/datamodel.html """

23.375

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6

fa173302ebc414485b9a480ac26c512ed16b1bb6

98

py

Python

tensortrade/features/stationarity/__init__.py

Kukunin/tensortrade

c5b5c40232a334d9b38359eab0c0ce0e4c9e43ed

[ "Apache-2.0" ]

7

2020-09-28T23:36:40.000Z

2022-02-22T02:00:32.000Z

tensortrade/features/stationarity/__init__.py

Kukunin/tensortrade

c5b5c40232a334d9b38359eab0c0ce0e4c9e43ed

[ "Apache-2.0" ]

4

2020-11-13T18:48:52.000Z

2022-02-10T01:29:47.000Z

tensortrade/features/stationarity/__init__.py

Kukunin/tensortrade

c5b5c40232a334d9b38359eab0c0ce0e4c9e43ed

[ "Apache-2.0" ]

3

2020-11-23T17:31:59.000Z

2021-04-08T10:55:03.000Z

from .log_difference import LogDifference from .fractional_difference import FractionalDifference

32.666667

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fa33f54f0859d7a3c7f734437589b77eefe5d3b4

135

py

Python

kaptos/config.py

VA7EXE/kaptos

88de0969796b0467970dd553158e7b9fc5698070

[ "MIT" ]

1

2019-11-29T22:53:44.000Z

kaptos/config.py

VA7EXE/kaptos

88de0969796b0467970dd553158e7b9fc5698070

[ "MIT" ]

2

2020-03-07T21:49:29.000Z

2021-03-10T10:39:20.000Z

kaptos/config.py

VA7EXE/kaptos

88de0969796b0467970dd553158e7b9fc5698070

[ "MIT" ]

2

2019-01-27T10:53:26.000Z

2019-11-29T22:27:01.000Z

"""Kaptos configuration module.""" import appdirs import toml config = toml.load(f"{appdirs.user_config_dir('kaptos')}/config.toml")

19.285714

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6

fa4ccf64bbdebfd6cf3110ff89c3f92ea08a841e

2,247

py

Python

tests/unit/qm/propagators/poppropagator_test.py

slamavl/quantarhei

d822bc2db86152c418e330a9152e7866869776f7

[ "MIT" ]

14

2016-10-16T13:26:05.000Z

2021-11-09T11:40:52.000Z

tests/unit/qm/propagators/poppropagator_test.py

slamavl/quantarhei

d822bc2db86152c418e330a9152e7866869776f7

[ "MIT" ]

61

2016-09-19T10:45:56.000Z

2021-11-10T13:53:06.000Z

tests/unit/qm/propagators/poppropagator_test.py

slamavl/quantarhei

d822bc2db86152c418e330a9152e7866869776f7

[ "MIT" ]

21

2016-08-30T09:09:28.000Z

2022-03-30T03:16:35.000Z

# -*- coding: utf-8 -*- import unittest import numpy """ ******************************************************************************* Tests of the quantarhei.qm.propagators.poppropagator module ******************************************************************************* """ from quantarhei import PopulationPropagator from quantarhei import TimeAxis class TestPopulationPropagator(unittest.TestCase): """Tests population propagator module """ def test_of_population_evolution_1(self): """Testing population evolution matrix 2x2 starting from t = 0""" KK = numpy.array([[-1.0/100.0, 1.0/100.0], [ 1.0/100.0, -1.0/100.0]]) U0 = numpy.eye(2) Ntd = 10 t = TimeAxis(0.0, 1000, 1.0) prop = PopulationPropagator(t, rate_matrix=KK) td = TimeAxis(0.0, Ntd, 10.0) U = prop.get_PropagationMatrix(td) # analytical result Ucheck = numpy.zeros((2,2,Ntd)) Ucheck[0,0,:] = 0.5*(1.0+numpy.exp(2.0*KK[0,0]*td.data)) Ucheck[1,1,:] = Ucheck[0,0,:] Ucheck[1,0,:] = 0.5*(1.0-numpy.exp(2.0*KK[0,0]*td.data)) Ucheck[0,1,:] = Ucheck[1,0,:] for n in range(Ntd): numpy.testing.assert_allclose(U[:,:,n],Ucheck[:,:,n]) def test_of_population_evolution_2(self): """Testing population evolution matrix 2x2 starting from t > 0""" KK = numpy.array([[-1.0/100.0, 1.0/100.0], [ 1.0/100.0, -1.0/100.0]]) U0 = numpy.eye(2) Ntd = 10 t = TimeAxis(0.0, 1000, 1.0) prop = PopulationPropagator(t, rate_matrix=KK) td = TimeAxis(2.0, Ntd, 10.0) U = prop.get_PropagationMatrix(td) # analytical result Ucheck = numpy.zeros((2,2,Ntd)) Ucheck[0,0,:] = 0.5*(1.0+numpy.exp(2.0*KK[0,0]*td.data)) Ucheck[1,1,:] = Ucheck[0,0,:] Ucheck[1,0,:] = 0.5*(1.0-numpy.exp(2.0*KK[0,0]*td.data)) Ucheck[0,1,:] = Ucheck[1,0,:] for n in range(Ntd): numpy.testing.assert_allclose(U[:,:,n],Ucheck[:,:,n])

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fa679d2ad36473d072be4237fbf6535b28a6f9e8

165

py

Python

docs/cookbook/defaults/test.py

BlameJohnny/redo

b08b5efcef8ab9cf9d532fdd50994e1092144924

[ "Apache-2.0" ]

1,002

2015-01-03T04:53:27.000Z

2022-03-30T11:25:06.000Z

docs/cookbook/defaults/test.py

BlameJohnny/redo

b08b5efcef8ab9cf9d532fdd50994e1092144924

[ "Apache-2.0" ]

24

2015-08-27T20:32:56.000Z

2021-11-30T16:59:59.000Z

docs/cookbook/defaults/test.py

BlameJohnny/redo

b08b5efcef8ab9cf9d532fdd50994e1092144924

[ "Apache-2.0" ]

84

2015-01-05T14:43:51.000Z

2022-02-11T13:19:28.000Z

#!/usr/bin/env python """Test program for auto-generated version.py""" import version print('Version %r has build date %r' % (version.VERSION, version.DATE))

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d75b0646fb60d0d48af2feeab63ce12f68da8787

114

py

Python

webomics/pipelines/views.py

silva1jos/webomics-tbd

f719fe0c51086fd6c67aa53d04fdea269f049d9a

[ "MIT" ]

null

webomics/pipelines/views.py

silva1jos/webomics-tbd

f719fe0c51086fd6c67aa53d04fdea269f049d9a

[ "MIT" ]

null

webomics/pipelines/views.py

silva1jos/webomics-tbd

f719fe0c51086fd6c67aa53d04fdea269f049d9a

[ "MIT" ]

null

from django.shortcuts import render def index_view(request): return render(request, 'pipelines/index.html')

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d7930c6605e170b51f9c5e57950d9b9d4962245f

76

py

Python

phosphoproteomics/models.py

naderm/django_rest_omics

c6314f96f157d95cd95e03a5fdea4a1176d6d05a

[ "BSD-2-Clause" ]

null

phosphoproteomics/models.py

naderm/django_rest_omics

c6314f96f157d95cd95e03a5fdea4a1176d6d05a

[ "BSD-2-Clause" ]

null

phosphoproteomics/models.py

naderm/django_rest_omics

c6314f96f157d95cd95e03a5fdea4a1176d6d05a

[ "BSD-2-Clause" ]

null

from django.db import models class Phosphopeptide(models.Model): pass

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ad02341f4ca99c17a7537966a6caf9f324b5f023

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py

Python

project/apps/keller/migrations/0001_initial.py

bhs-contests/barberscore-api

7bd06b074c99903f031220f41b15a22474724044

[ "BSD-2-Clause" ]

null

project/apps/keller/migrations/0001_initial.py

bhs-contests/barberscore-api

7bd06b074c99903f031220f41b15a22474724044

[ "BSD-2-Clause" ]

9

2020-06-05T22:17:17.000Z

2022-03-12T00:04:00.000Z

project/apps/keller/migrations/0001_initial.py

bhs-contests/barberscore-api

7bd06b074c99903f031220f41b15a22474724044

[ "BSD-2-Clause" ]

null

# Generated by Django 2.1.9 on 2019-06-24 22:49 import django.contrib.postgres.fields import django.contrib.postgres.fields.jsonb from django.db import migrations, models import django.db.models.deletion import uuid class Migration(migrations.Migration): initial = True dependencies = [ ('rmanager', '0001_initial'), ('bhs', '0001_initial'), ] operations = [ migrations.CreateModel( name='CleanFlat', fields=[ ('id', models.UUIDField(default=uuid.uuid4, editable=False, primary_key=True, serialize=False)), ('points', models.IntegerField()), ], ), migrations.CreateModel( name='CleanPanelist', fields=[ ('id', models.IntegerField(editable=False, primary_key=True, serialize=False)), ('year', models.IntegerField()), ('season', models.IntegerField(choices=[(1, 'Summer'), (2, 'Midwinter'), (3, 'Fall'), (4, 'Spring')])), ('district', models.CharField(max_length=255)), ('convention', models.CharField(max_length=255)), ('session', models.IntegerField(choices=[(32, 'Chorus'), (41, 'Quartet'), (42, 'Mixed'), (43, 'Senior'), (44, 'Youth'), (45, 'Unknown'), (46, 'VLQ')])), ('round', models.IntegerField(choices=[(1, 'Finals'), (2, 'Semi-Finals'), (3, 'Quarter-Finals')])), ('category', models.IntegerField(choices=[(30, 'Music'), (40, 'Performance'), (50, 'Singing')])), ('num', models.IntegerField()), ('legacy_person', models.CharField(max_length=255)), ('scores', django.contrib.postgres.fields.jsonb.JSONField()), ('panelist', models.OneToOneField(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to='rmanager.Panelist')), ], ), migrations.CreateModel( name='CleanSong', fields=[ ('id', models.IntegerField(editable=False, primary_key=True, serialize=False)), ('year', models.IntegerField()), ('season', models.IntegerField(choices=[(1, 'Summer'), (2, 'Midwinter'), (3, 'Fall'), (4, 'Spring')])), ('district', models.CharField(max_length=255)), ('convention', models.CharField(max_length=255)), ('session', models.IntegerField(choices=[(32, 'Chorus'), (41, 'Quartet'), (42, 'Mixed'), (43, 'Senior'), (44, 'Youth'), (45, 'Unknown'), (46, 'VLQ')])), ('round', models.IntegerField(choices=[(1, 'Finals'), (2, 'Semi-Finals'), (3, 'Quarter-Finals')])), ('appearance_num', models.IntegerField()), ('song_num', models.IntegerField()), ('legacy_group', models.CharField(max_length=255)), ('legacy_chart', models.CharField(max_length=255)), ('scores', django.contrib.postgres.fields.jsonb.JSONField()), ('appearance', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to='rmanager.Appearance')), ('song', models.OneToOneField(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to='rmanager.Song')), ], ), migrations.CreateModel( name='Complete', fields=[ ('id', models.UUIDField(default=uuid.uuid4, editable=False, primary_key=True, serialize=False)), ('row_id', models.IntegerField(blank=True, null=True)), ('year', models.IntegerField(blank=True, null=True)), ('season_kind', models.IntegerField(blank=True, choices=[(1, 'Summer'), (2, 'Midwinter'), (3, 'Fall'), (4, 'Spring')], null=True)), ('district_code', models.CharField(blank=True, max_length=255)), ('convention_name', models.CharField(blank=True, max_length=255)), ('session_kind', models.IntegerField(blank=True, choices=[(32, 'Chorus'), (41, 'Quartet'), (42, 'Mixed'), (43, 'Senior'), (44, 'Youth'), (45, 'Unknown'), (46, 'VLQ')], null=True)), ('round_kind', models.IntegerField(blank=True, choices=[(1, 'Finals'), (2, 'Semi-Finals'), (3, 'Quarter-Finals')], null=True)), ('category_kind', models.IntegerField(blank=True, choices=[(30, 'Music'), (40, 'Performance'), (50, 'Singing')], null=True)), ('points', django.contrib.postgres.fields.ArrayField(base_field=models.IntegerField(blank=True, null=True), blank=True, null=True, size=None)), ('panelist', models.OneToOneField(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to='rmanager.Panelist')), ('person', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='completes', to='bhs.Person')), ], ), migrations.CreateModel( name='Flat', fields=[ ('id', models.UUIDField(default=uuid.uuid4, editable=False, primary_key=True, serialize=False)), ('complete', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='flats', to='keller.Complete')), ('score', models.OneToOneField(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to='rmanager.Score')), ], ), migrations.CreateModel( name='RawPanelist', fields=[ ('id', models.IntegerField(editable=False, primary_key=True, serialize=False)), ('year', models.IntegerField()), ('season', models.CharField(max_length=255)), ('district', models.CharField(max_length=255)), ('convention', models.CharField(max_length=255)), ('session', models.CharField(max_length=255)), ('round', models.CharField(max_length=255)), ('category', models.CharField(max_length=255)), ('num', models.IntegerField(blank=True, null=True)), ('judge', models.CharField(max_length=255)), ('scores', django.contrib.postgres.fields.jsonb.JSONField()), ], ), migrations.CreateModel( name='RawSong', fields=[ ('id', models.IntegerField(editable=False, primary_key=True, serialize=False)), ('season', models.CharField(max_length=255)), ('year', models.IntegerField()), ('district', models.CharField(max_length=255)), ('event', models.CharField(max_length=255)), ('session', models.CharField(max_length=255)), ('group_name', models.CharField(max_length=255)), ('appearance_num', models.IntegerField()), ('song_num', models.IntegerField()), ('song_title', models.CharField(max_length=255)), ('totals', models.IntegerField()), ('scores', django.contrib.postgres.fields.jsonb.JSONField()), ], ), migrations.CreateModel( name='Selection', fields=[ ('id', models.UUIDField(default=uuid.uuid4, editable=False, primary_key=True, serialize=False)), ('mark', models.BooleanField(default=False)), ('row_id', models.IntegerField(blank=True, null=True)), ('year', models.IntegerField(blank=True, null=True)), ('season_kind', models.IntegerField(blank=True, choices=[(1, 'Summer'), (2, 'Midwinter'), (3, 'Fall'), (4, 'Spring')], null=True)), ('district_code', models.CharField(blank=True, max_length=255)), ('convention_name', models.CharField(blank=True, max_length=255)), ('session_kind', models.IntegerField(blank=True, choices=[(32, 'Chorus'), (41, 'Quartet'), (42, 'Mixed'), (43, 'Senior'), (44, 'Youth'), (45, 'Unknown'), (46, 'VLQ')], null=True)), ('round_kind', models.IntegerField(blank=True, choices=[(1, 'Finals'), (2, 'Semi-Finals'), (3, 'Quarter-Finals')], null=True)), ('group_name', models.CharField(blank=True, max_length=255)), ('appearance_num', models.IntegerField(blank=True, null=True)), ('song_num', models.IntegerField(blank=True, null=True)), ('song_title', models.CharField(blank=True, max_length=255)), ('totals', models.IntegerField(blank=True, null=True)), ('points', django.contrib.postgres.fields.ArrayField(base_field=models.IntegerField(blank=True, null=True), blank=True, null=True, size=None)), ('song', models.OneToOneField(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to='rmanager.Song')), ], ), migrations.AddField( model_name='flat', name='selection', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='flats', to='keller.Selection'), ), migrations.AddField( model_name='cleanflat', name='cleanpanelist', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='cleanflats', to='keller.CleanPanelist'), ), migrations.AddField( model_name='cleanflat', name='cleansong', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='cleanflats', to='keller.CleanSong'), ), migrations.AddField( model_name='cleanflat', name='score', field=models.OneToOneField(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, to='rmanager.Score'), ), migrations.AlterUniqueTogether( name='flat', unique_together={('complete', 'selection', 'score')}, ), migrations.AlterUniqueTogether( name='cleanflat', unique_together={('cleanpanelist', 'cleansong')}, ), ]

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null

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6

ad0d3000ed7a4634033b1ce00bdd993cdd7ea6e4

47

py

Python

scripts/portal/dublportal100.py

G00dBye/YYMS

1de816fc842b6598d5b4b7896b6ab0ee8f7cdcfb

[ "MIT" ]

54

2019-04-16T23:24:48.000Z

2021-12-18T11:41:50.000Z

scripts/portal/dublportal100.py

G00dBye/YYMS

1de816fc842b6598d5b4b7896b6ab0ee8f7cdcfb

[ "MIT" ]

3

2019-05-19T15:19:41.000Z

2020-04-27T16:29:16.000Z

scripts/portal/dublportal100.py

G00dBye/YYMS

1de816fc842b6598d5b4b7896b6ab0ee8f7cdcfb

[ "MIT" ]

49

2020-11-25T23:29:16.000Z

2022-03-26T16:20:24.000Z

# 103050100 sm.warp(103050200, 4) sm.dispose()

11.75

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3

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null

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1

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6

ad59bc15df66a5d6f6a17682c953d0484283946e

45

py

Python

catkin_ws/src/00-infrastructure/duckieteam/include/duckieteam/__init__.py

yxiao1996/dev

e2181233aaa3d16c472b792b58fc4863983825bd

[ "CC-BY-2.0" ]

2

2018-06-25T02:51:25.000Z

2018-06-25T02:51:27.000Z

catkin_ws/src/00-infrastructure/duckieteam/include/duckieteam/__init__.py

yxiao1996/dev

e2181233aaa3d16c472b792b58fc4863983825bd

[ "CC-BY-2.0" ]

null

catkin_ws/src/00-infrastructure/duckieteam/include/duckieteam/__init__.py

yxiao1996/dev

e2181233aaa3d16c472b792b58fc4863983825bd

[ "CC-BY-2.0" ]

2

2018-09-04T06:44:21.000Z

2018-10-15T02:30:50.000Z

from .persons import * from .robots import *

15

22

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5.5

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23

22.5

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true

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null

0

1

0

1

0

1

0

6

ad6a31cb2085aa5bd0305d46eb55ea7c71a04f08

27

py

Python

jammit/jammit/__init__.py

granatumx/gbox-py

b3e264a22bc6a041f2dd631d952eae29c0ecae21

[ "MIT" ]

1

2021-03-04T13:04:28.000Z

g_packages/official_py_docker/docker/jammit/jammit/__init__.py

lanagarmire/granatumx

3dee3a8fb2ba851c31a9f6338aef1817217769f9

[ "MIT" ]

16

2020-01-28T23:03:40.000Z

2022-02-10T00:30:16.000Z

g_packages/official_py_docker/docker/jammit/jammit/__init__.py

lanagarmire/granatumx

3dee3a8fb2ba851c31a9f6338aef1817217769f9

[ "MIT" ]

2

2020-06-16T16:42:40.000Z

2020-08-28T16:59:42.000Z

from .jammit import JAMMIT

13.5

26

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27

5.5

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1

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0.956522

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true

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null

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1

0

1

0

6

ad7b706a45500d6856a11d91d70bdd1d19d2a3c5

38

py

Python

bayesian_irl/src/utils/__init__.py

clear-nus/BOIRL

cc872111fda3c7b8118e1a864831013c30f63948

[ "MIT" ]

1

2021-02-26T10:09:15.000Z

bayesian_irl/src/utils/__init__.py

clear-nus/BOIRL

cc872111fda3c7b8118e1a864831013c30f63948

[ "MIT" ]

null

bayesian_irl/src/utils/__init__.py

clear-nus/BOIRL

cc872111fda3c7b8118e1a864831013c30f63948

[ "MIT" ]

null

from .sample_demos import sample_demos

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6

ad8b2ae891c777a11dd0d08281170aecd58b4675

15,903

py

Python

multiagant_task_manager/maGraphEngines.py

Benson516/multiagant_task_manager

a70eacbb47a7c7243811fbaadb5b3c4ab4c6d703

[ "MIT" ]

null

multiagant_task_manager/maGraphEngines.py

Benson516/multiagant_task_manager

a70eacbb47a7c7243811fbaadb5b3c4ab4c6d703

[ "MIT" ]

null

multiagant_task_manager/maGraphEngines.py

Benson516/multiagant_task_manager

a70eacbb47a7c7243811fbaadb5b3c4ab4c6d703

[ "MIT" ]

null

""" Graph engines These function are graph engins apecifically for useing with the graph data structure defined by GEOMETRY_TASK_GRAPH and its following sub-class. """ import Queue # import sys # Kernel function for finding reachability def Explore(nid, adj, visited): """ Recursive function for depth-first search. """ visited[nid] = True for to_nid, eid in adj[nid]: if not visited[to_nid]: Explore(to_nid, adj, visited) # the end # Kernel function for finding reachability def Explore_capacity(nid, adj, edges, visited, T_zone, only_count_activated_agent=False): """ Recursive function for depth-first search. """ visited[nid] = True for to_nid, eid in adj[nid]: if not visited[to_nid] and edges[eid].is_available_for_T_zone(T_zone, only_count_activated_agent): Explore_capacity(to_nid, adj, edges, visited, T_zone, only_count_activated_agent) # the end # Kernel function for finding connected components def Explore_cc(nid, adj, visited, cc, CCnum): """ Recursive function for depth-first search. """ visited[nid] = True CCnum[nid] = cc for to_nid, eid in adj[nid]: if not visited[to_nid]: Explore_cc(to_nid, adj, visited, cc, CCnum) # the end # Kernel function for finding connected components def Explore_cc_capcity(nid, adj, edges, visited, cc, CCnum, T_zone, only_count_activated_agent=False): """ Recursive function for depth-first search. """ visited[nid] = True CCnum[nid] = cc for to_nid, eid in adj[nid]: if not visited[to_nid] and edges[eid].is_available_for_T_zone(T_zone, only_count_activated_agent): Explore_cc_capcity(to_nid, adj, edges, visited, cc, CCnum, T_zone, only_count_activated_agent) # the end #------------------------------------------------# def reachability(x, y, adj, edges=None, count_capacity=True, T_zone=(0,None), only_count_activated_agent=False): """ Finding the reachiability from node_id:x to node_id:y Important: This method only consider the current (a specific time instant) topological state. """ visited = [False for _ in range(len(adj))] if count_capacity: Explore_capacity(x, adj, edges, visited, T_zone, only_count_activated_agent) else: # Simply traverse through the topology of graph, # not counting capacity of edges Explore(x, adj, visited) return visited[y] def number_of_connected_components(adj, edges=None, count_capacity=True, T_zone=(0,None), only_count_activated_agent=False): """ Find the total number of connected components Important: This method only consider the current (a specific time instant) topological state. """ visited = [False for _ in range(len(adj))] CCnum = [0 for _ in range(len(adj))] cc = 1 if count_capacity: for nid in range(len(adj)): if not visited[nid]: Explore_cc_capcity(nid, adj, edges, visited, cc, CCnum, T_zone, only_count_activated_agent) cc += 1 else: # Simply traverse through the topology of graph, # not counting capacity of edges for nid in range(len(adj)): if not visited[nid]: Explore_cc(nid, adj, visited, cc, CCnum) cc += 1 return (cc-1) # Graph traversal #--------------------------------# def get_path(prev, last_nid, is_reversing_path=True): """ This utility function help generate the path from parent list. inputs - prev - last_nid: the node_id to start backtracking with - is_reversing_path (default: True): Decide if we are going to return the path generated by following parent list outputs - path """ nid_i = last_nid; path_inv = list() path_inv.append(nid_i) while True: nid_prev = prev[nid_i] if not nid_prev is None: nid_i = nid_prev path_inv.append(nid_prev) else: # No parent, not able to continue break # if is_reversing_path: # Reverse the path, make it from start_id to end_id path = path_inv[::-1] return path else: return path_inv def dijkstras(adj, edges, T_zone_start, start_id, end_id, top_priority_for_activated_agent=False, agent_id=None): """ This method ues dijkstra alogorithm to find out the best path or find out that there is no path at all. Optimization problem: Given a graph, the state of graph, the time-zone at start_id, find a path with "valid edges" that minimize the "duration_max" at reaching the end_id (after passing through the last edge) inputs - adj: adjacent graph - T_zone_start = (T_min, T_max) - start_id - end_id - top_priority_for_activated_agent - agent_id (default: None): If agent_id is given, ignore this agent in this edge. outputs - path/None: a sequence (list) of node_id from start_id to end_id or "None" means no valid path """ # Decide that if we only see the activated agent!! only_count_activated_agent = top_priority_for_activated_agent # We minimize the T_max id_opt_target = 1 # minimize the total duration_max max_value = float('inf') # sys.maxsize # Get sizes num_nodes = len(adj) num_edges = len(edges) # Initialize the dist and prev dist = [max_value for _ in range(num_nodes)] # distance list, "None" stand for infinity prev = [None for _ in range(num_nodes)] # Parents list, "None" stands for no parent T_zone_nodes = [(max_value, max_value) for _ in range(num_nodes)] # (T_min, T_max) of each node / "Key" for passing edges! #-------------------------------# # dist[s] = 0 <-- acturally, minimum distance in graph, not actually need to be zero dist[start_id] = 0 # Count for the duration, minimum or maximum (or maybe the difference??) T_zone_nodes[start_id] = T_zone_start # Make a min-heap heap = Queue.PriorityQueue() for u in range(num_nodes): heap.put_nowait( (dist[u], u) ) # Iteration while (not heap.empty()): # Get a node_id from heap (currently smallest distance) #----------------------------------# uh = heap.get_nowait() # Filter out some trash in heap while uh[0] != dist[uh[1]] and (not heap.empty()): # Pop out old one and try new one uh = heap.get_nowait() if heap.empty(): # This means that the heap actually has no valuable things # in this iteration, just leave break #----------------------------------# # for all (u,v) in E # We have to find nodes through "valid" edges # that is, it is "possible to pass", it is activated (if we want to check) nid_u = uh[1] # print("uh[0] = " + str(uh[0]) + ", uh[1] = " + str(uh[1])) for nid_v, eid in adj[nid_u]: # Check if the edge is "valid" # nid_u --eid--> nid_v if edges[eid].is_possible_to_pass(T_zone_nodes[nid_u], only_count_activated_agent, agent_id): # Relax if id_opt_target == 2: weight_uv = edges[eid].duration[1] - edges[eid].duration[0] # Minimoze the difference of duration_max and duration_min, for minimizing the uncertainties else: weight_uv = edges[eid].duration[id_opt_target] # Minimize the total duration with specified id_opt_target # if dist[nid_v] > (dist[nid_u] + weight_uv): dist[nid_v] = (dist[nid_u] + weight_uv) T_zone_nodes[nid_v] = edges[eid].get_T_zone_end_from_start(T_zone_nodes[nid_u]) # Update time_zone of the node prev[nid_v] = nid_u """ print("update (u, v) = (%d, %d)" % (nid_u, nid_v)) print("dist = " + str(dist)) print("prev = " + str(prev)) print("\n") """ heap.put_nowait( (dist[nid_v], nid_v) ) # # end for # end while """ # Post proccesing: replace max_value with "None" for i in range(len(dist)): if dist[i] == max_value: dist[i] = None """ # test try: delta_T_max = dist[end_id] - dist[start_id] except: delta_T_max = None # print("\n") print("INFO: Dijkstra finished") print("INFO: Distance from start_id <%d> to end_id <%d> = %s" % (start_id, end_id, (str(delta_T_max) if not delta_T_max is None else "None" ) ) ) print("dist = " + str(dist)) print("prev = " + str(prev)) print("T_zone_nodes[end_id] = " + str(T_zone_nodes[end_id])) # # Generate the path if (dist[end_id] is None) or (dist[end_id] == max_value): # The end_id is not reachable from start_id # in the sense of "valid" edge traversal print('INFO: The end_id is not reachable from start_id in the sense of "valid" edge traversal.') return None # If the goal is reachable path = get_path(prev, end_id, is_reversing_path=True) if path[0] != start_id: print("ERROR: path[0] != start_id, something wrong in dijkstra.") else: # print("INFO: The path generated correctly in dijkstra.") pass # test print("path = " + str(path) ) print("\n") # return path #--------------------------------# # Reversed traversal #--------------------------------------# def generate_reverse_graph(adj): """ This function is for used with the adj_graph defined in GEOMETRY_TASK_GRAPH outputs - adj_reversed: a reversed version of adj (All directed edges will be reversed.) """ adj_reversed = [[] for _ in range(len(adj))] for nid_u in range(len(adj)): for nid_v, eid_uv in adj[nid_u]: adj_reversed[nid_v].append( (nid_u,eid_uv) ) # return adj_reversed def dijkstras_backtrack(adj_in, edges, T_zone_end, start_id, end_id, top_priority_for_activated_agent=False, agent_id=None): """ This method ues dijkstra alogorithm to find out the best path or find out that there is no path at all. Optimization problem: Given a graph, the state of graph, the time-zone at end_id, find a path with "valid edges" that minimize the "duration_max" at reaching the start_id (before passing through the first edge) inputs - adj: original adjacent graph - T_zone_end = (T_min, T_max) - start_id - end_id - top_priority_for_activated_agent - agent_id (default: None): If agent_id is given, ignore this agent in this edge. outputs - path/None: a sequence (list) of node_id from start_id to end_id or "None" means no valid path """ # Get a reversed graph adj = generate_reverse_graph(adj_in) # Decide that if we only see the activated agent!! only_count_activated_agent = top_priority_for_activated_agent # We minimize the T_max id_opt_target = 1 # minimize the total duration_max max_value = float('inf') # sys.maxsize # Get sizes num_nodes = len(adj) num_edges = len(edges) # Initialize the dist and prev dist = [max_value for _ in range(num_nodes)] # distance list, "None" stand for infinity prev = [None for _ in range(num_nodes)] # Parents list, "None" stands for no parent T_zone_nodes = [(max_value, max_value) for _ in range(num_nodes)] # (T_min, T_max) of each node / "Key" for passing edges! #-------------------------------# # dist[s] = 0 <-- acturally, minimum distance in graph, not actually need to be zero dist[end_id] = 0 # Count for the duration, minimum or maximum (or maybe the difference??) T_zone_nodes[end_id] = T_zone_end # Make a min-heap heap = Queue.PriorityQueue() for u in range(num_nodes): heap.put_nowait( (dist[u], u) ) # Iteration while (not heap.empty()): # Get a node_id from heap (currently smallest distance) #----------------------------------# uh = heap.get_nowait() # Filter out some trash in heap while uh[0] != dist[uh[1]] and (not heap.empty()): # Pop out old one and try new one uh = heap.get_nowait() if heap.empty(): # This means that the heap actually has no valuable things # in this iteration, just leave break #----------------------------------# # for all (u,v) in E # We have to find nodes through "valid" edges # that is, it is "possible to pass", it is activated (if we want to check) nid_u = uh[1] # print("uh[0] = " + str(uh[0]) + ", uh[1] = " + str(uh[1])) for nid_v, eid in adj[nid_u]: # Check if the edge is "valid" # nid_u --eid--> nid_v if edges[eid].is_possible_to_pass_backtrack(T_zone_nodes[nid_u], only_count_activated_agent, agent_id): T_v_tmp = edges[eid].get_T_zone_start_from_end(T_zone_nodes[nid_u]) # Update time_zone of the node if T_v_tmp[1] < T_v_tmp[0]: # This edge is closed, unable to be backtracked continue # Relax if id_opt_target == 2: weight_uv = edges[eid].duration[1] - edges[eid].duration[0] # Minimoze the difference of duration_max and duration_min, for minimizing the uncertainties else: weight_uv = edges[eid].duration[id_opt_target] # Minimize the total duration with specified id_opt_target # if dist[nid_v] > (dist[nid_u] + weight_uv): dist[nid_v] = (dist[nid_u] + weight_uv) T_zone_nodes[nid_v] = T_v_tmp # Update time_zone of the node prev[nid_v] = nid_u """ print("update (u, v) = (%d, %d)" % (nid_u, nid_v)) print("dist = " + str(dist)) print("prev = " + str(prev)) print("\n") """ heap.put_nowait( (dist[nid_v], nid_v) ) # # end for # end while """ # Post proccesing: replace max_value with "None" for i in range(len(dist)): if dist[i] == max_value: dist[i] = None """ # test try: delta_T_max = dist[start_id] - dist[end_id] except: delta_T_max = None # print("\n") print("INFO: Dijkstra (backtrack) finished") print("INFO: Distance from start_id <%d> to end_id <%d> = %s" % (start_id, end_id, (str(delta_T_max) if not delta_T_max is None else "None" ) ) ) print("dist = " + str(dist)) print("prev = " + str(prev)) print("T_zone_nodes[start_id] = " + str(T_zone_nodes[start_id])) # # Generate the path if (dist[start_id] is None) or (dist[start_id] == max_value): # The end_id is not reachable from start_id # in the sense of "valid" edge traversal print('INFO: The start_id is not reachable from end_id in the sense of "valid" edge backward traversal.') return None # If the goal is reachable path = get_path(prev, start_id, is_reversing_path=False) # No need to reverse the path if path[-1] != end_id: print("ERROR: path[-1] != end_id, something wrong in dijkstra.") else: # print("INFO: The path generated correctly in dijkstra.") pass # test print("path = " + str(path) ) print("\n") # return path #--------------------------------------#

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ad995fe6d8f4abb4d8706874ba0cc1b03fd061c7

48

py

Python

timeline/__init__.py

meinert/timesatpoi

1a82fb4c9950e7636f74fd651d55fde45adca961

[ "BSD-2-Clause" ]

null

timeline/__init__.py

meinert/timesatpoi

1a82fb4c9950e7636f74fd651d55fde45adca961

[ "BSD-2-Clause" ]

null

timeline/__init__.py

meinert/timesatpoi

1a82fb4c9950e7636f74fd651d55fde45adca961

[ "BSD-2-Clause" ]

null

from .core import hmm from .timesatpoi import *

16

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1

0

1

0

6

ad9c335db07dd60bf2d59fd98c04fc2fe5fcc21c

11,649

py

Python

model/mobile_ssd_v1/net.py

BoChenUIUC/realtime-action-detection

98f6d0b3f5faa4da6a1395daf07771d5f157ba8f

[ "MIT" ]

null

model/mobile_ssd_v1/net.py

BoChenUIUC/realtime-action-detection

98f6d0b3f5faa4da6a1395daf07771d5f157ba8f

[ "MIT" ]

null

model/mobile_ssd_v1/net.py

BoChenUIUC/realtime-action-detection

98f6d0b3f5faa4da6a1395daf07771d5f157ba8f

[ "MIT" ]

null

'''SSD model with VGG16 as feature extractor.''' import torch import torch.nn as nn import torch.nn.functional as F import math from torch.autograd import Variable import sys sys.path.insert(0, '/home/bo/research/realtime-action-detection') from layers.functions.sph_prior_box import SphPriorBox from data import v1,v2,v3,v4,v5 class MobileNetExtractor512(nn.Module): def __init__(self): super(MobileNetExtractor512, self).__init__() def conv_bn(inp, oup, stride): return nn.Sequential( nn.Conv2d(inp, oup, 3, stride, 1, bias=False), nn.BatchNorm2d(oup), nn.ReLU(inplace=True) ) def conv_dw(inp, oup, stride): return nn.Sequential( nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False), nn.BatchNorm2d(inp), nn.ReLU(inplace=True), nn.Conv2d(inp, oup, 1, 1, 0, bias=False), nn.BatchNorm2d(oup), nn.ReLU(inplace=True), ) self.model = nn.Sequential( conv_bn(3, 64, 2), conv_dw(64, 64, 1), conv_dw(64, 128, 2), conv_dw(128, 128, 1), conv_dw(128, 256, 2), conv_dw(256, 256, 1), conv_dw(256, 256, 1), conv_dw(256, 512, 1), conv_dw(512, 512, 1), conv_dw(512, 512, 1), conv_dw(512, 512, 2), conv_dw(512, 512, 1), conv_dw(512, 512, 1), conv_dw(512, 1024, 1), conv_dw(1024, 1024, 1), ) self.extras = nn.ModuleList([ nn.Sequential( nn.Conv2d(in_channels=1024, out_channels=256, kernel_size=1), nn.ReLU(), nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, stride=2, padding=1), nn.ReLU() ), nn.Sequential( nn.Conv2d(in_channels=512, out_channels=128, kernel_size=1), nn.ReLU(), nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=2, padding=1), nn.ReLU() ), nn.Sequential( nn.Conv2d(in_channels=256, out_channels=128, kernel_size=1), nn.ReLU(), nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=2, padding=1), nn.ReLU() ), nn.Sequential( nn.Conv2d(in_channels=256, out_channels=128, kernel_size=1), nn.ReLU(), nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=2, padding=1), nn.ReLU() ), nn.Sequential( nn.Conv2d(in_channels=256, out_channels=128, kernel_size=1), nn.ReLU(), nn.Conv2d(in_channels=128, out_channels=256, kernel_size=4, padding=1), nn.ReLU() ) ]) def forward(self, x): hs = [] for i,l in enumerate(self.model): x = l(x) if i == 9 or i == 14: hs.append(x) for l in self.extras: x = l(x) hs.append(x) return hs # def SeperableConv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0): # """Replace Conv2d with a depthwise Conv2d and Pointwise Conv2d. # """ # return Sequential( # Conv2d(in_channels=in_channels, out_channels=in_channels, kernel_size=kernel_size, # groups=in_channels, stride=stride, padding=padding), # ReLU(), # Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1), # ) # class MobileNetExtractorLite512(nn.Module): # def __init__(self): # super(MobileNetExtractor512, self).__init__() # def conv_bn(inp, oup, stride): # return nn.Sequential( # nn.Conv2d(inp, oup, 3, stride, 1, bias=False), # nn.BatchNorm2d(oup), # nn.ReLU(inplace=True) # ) # def conv_dw(inp, oup, stride): # return nn.Sequential( # nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False), # nn.BatchNorm2d(inp), # nn.ReLU(inplace=True), # nn.Conv2d(inp, oup, 1, 1, 0, bias=False), # nn.BatchNorm2d(oup), # nn.ReLU(inplace=True), # ) # self.model = nn.Sequential( # conv_bn(3, 64, 2), # conv_dw(64, 64, 1), # conv_dw(64, 128, 2), # conv_dw(128, 128, 1), # conv_dw(128, 256, 2), # conv_dw(256, 256, 1), # conv_dw(256, 256, 1), # conv_dw(256, 512, 1), # conv_dw(512, 512, 1), # conv_dw(512, 512, 1), # conv_dw(512, 512, 2), # conv_dw(512, 512, 1), # conv_dw(512, 512, 1), # conv_dw(512, 1024, 1), # conv_dw(1024, 1024, 1), # ) # self.extras = nn.ModuleList([ # nn.Sequential( # nn.Conv2d(in_channels=1024, out_channels=256, kernel_size=1), # nn.ReLU(), # SeperableConv2d(in_channels=256, out_channels=512, kernel_size=3, stride=2, padding=1), # ), # nn.Sequential( # nn.Conv2d(in_channels=512, out_channels=128, kernel_size=1), # nn.ReLU(), # SeperableConv2d(in_channels=128, out_channels=256, kernel_size=3, stride=2, padding=1), # ), # nn.Sequential( # nn.Conv2d(in_channels=256, out_channels=128, kernel_size=1), # nn.ReLU(), # SeperableConv2d(in_channels=128, out_channels=256, kernel_size=3, stride=2, padding=1), # ), # nn.Sequential( # nn.Conv2d(in_channels=256, out_channels=128, kernel_size=1), # nn.ReLU(), # SeperableConv2d(in_channels=128, out_channels=256, kernel_size=3, stride=2, padding=1), # ), # nn.Sequential( # nn.Conv2d(in_channels=256, out_channels=128, kernel_size=1), # nn.ReLU(), # SeperableConv2d(in_channels=128, out_channels=256, kernel_size=3, stride=2, padding=1), # ) # ]) # def forward(self, x): # hs = [] # for i,l in enumerate(self.model): # x = l(x) # if i == 9 or i == 14: # hs.append(x) # for l in self.extras: # x = l(x) # hs.append(x) # return hs class MobileSSD512(nn.Module): steps = (8, 16, 32, 64, 128, 256, 512) box_sizes = (35.84, 76.8, 153.6, 230.4, 307.2, 384.0, 460.8, 537.6) # default bounding box sizes for each feature map. aspect_ratios = ((2,), (2, 3), (2, 3), (2, 3), (2, 3), (2,), (2,)) fm_sizes = (64, 32, 16, 8, 4, 2, 1) def __init__(self, num_classes, cfg): super(MobileSSD512, self).__init__() self.num_classes = num_classes self.num_anchors = (4, 6, 6, 6, 6, 4, 4) self.in_channels = (512, 1024, 512, 256, 256, 256, 256) self.extractor = MobileNetExtractor512() priorbox = SphPriorBox(cfg) with torch.no_grad(): self.priors = priorbox.forward().cuda() self.softmax = nn.Softmax(dim=1).cuda() self.loc_layers = nn.ModuleList() self.cls_layers = nn.ModuleList() for i in range(len(self.in_channels)): self.loc_layers += [nn.Conv2d(self.in_channels[i], self.num_anchors[i]*4, kernel_size=3, padding=1)] self.cls_layers += [nn.Conv2d(self.in_channels[i], self.num_anchors[i]*self.num_classes, kernel_size=3, padding=1)] self._initialize_weights() def forward(self, x): loc_preds = [] cls_preds = [] xs = self.extractor(x) for i, x in enumerate(xs): loc_pred = self.loc_layers[i](x) loc_pred = loc_pred.permute(0,2,3,1).contiguous() loc_preds.append(loc_pred.view(loc_pred.size(0),-1,4)) cls_pred = self.cls_layers[i](x) cls_pred = cls_pred.permute(0,2,3,1).contiguous() cls_preds.append(cls_pred.view(cls_pred.size(0),-1,self.num_classes)) loc_preds = torch.cat(loc_preds, 1) cls_preds = torch.cat(cls_preds, 1) return loc_preds, cls_preds, self.priors def _initialize_weights(self): for m in self.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) if m.bias is not None: m.bias.data.zero_() elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() elif isinstance(m, nn.Linear): n = m.weight.size(1) m.weight.data.normal_(0, 0.01) m.bias.data.zero_() # class MobileSSDLite512(nn.Module): # steps = (8, 16, 32, 64, 128, 256, 512) # box_sizes = (35.84, 76.8, 153.6, 230.4, 307.2, 384.0, 460.8, 537.6) # default bounding box sizes for each feature map. # aspect_ratios = ((2,), (2, 3), (2, 3), (2, 3), (2, 3), (2,), (2,)) # fm_sizes = (64, 32, 16, 8, 4, 2, 1) # def __init__(self, num_classes, cfg): # super(MobileSSDLite512, self).__init__() # self.num_classes = num_classes # self.num_anchors = (4, 6, 6, 6, 6, 4, 4) # self.in_channels = (512, 1024, 512, 256, 256, 256, 256) # self.extractor = MobileNetExtractorLite512() # priorbox = SphPriorBox(cfg) # with torch.no_grad(): # self.priors = priorbox.forward().cuda() # self.softmax = nn.Softmax(dim=1).cuda() # self.loc_layers = nn.ModuleList() # self.cls_layers = nn.ModuleList() # for i in range(len(self.in_channels)): # self.loc_layers += [nn.Conv2d(self.in_channels[i], self.num_anchors[i]*4, kernel_size=3, padding=1)] # self.cls_layers += [nn.Conv2d(self.in_channels[i], self.num_anchors[i]*self.num_classes, kernel_size=3, padding=1)] # self._initialize_weights() # def forward(self, x): # loc_preds = [] # cls_preds = [] # xs = self.extractor(x) # for i, x in enumerate(xs): # loc_pred = self.loc_layers[i](x) # loc_pred = loc_pred.permute(0,2,3,1).contiguous() # loc_preds.append(loc_pred.view(loc_pred.size(0),-1,4)) # cls_pred = self.cls_layers[i](x) # cls_pred = cls_pred.permute(0,2,3,1).contiguous() # cls_preds.append(cls_pred.view(cls_pred.size(0),-1,self.num_classes)) # loc_preds = torch.cat(loc_preds, 1) # cls_preds = torch.cat(cls_preds, 1) # return loc_preds, cls_preds, self.priors # def _initialize_weights(self): # for m in self.modules(): # if isinstance(m, nn.Conv2d): # n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels # m.weight.data.normal_(0, math.sqrt(2. / n)) # if m.bias is not None: # m.bias.data.zero_() # elif isinstance(m, nn.BatchNorm2d): # m.weight.data.fill_(1) # m.bias.data.zero_() # elif isinstance(m, nn.Linear): # n = m.weight.size(1) # m.weight.data.normal_(0, 0.01) # m.bias.data.zero_() def test(): net = MobileSSD512(25,v3) loc_preds, cls_preds, priors = net(Variable(torch.randn(16,3,512,1024))) print(loc_preds.size(), cls_preds.size(), priors.size()) if __name__ == '__main__': test()

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129

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11,649

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null

0

1

0

null

0

6

a8e02c138e0376d6a6c4f6ae67818b95bdc22961

100

py

Python

astruct/__init__.py

misterfifths/nis_mods

9d460414cb88d10f2737e9be90babe85c9856001

[ "MIT" ]

1

2021-10-18T13:42:09.000Z

astruct/__init__.py

misterfifths/nis-mods

9d460414cb88d10f2737e9be90babe85c9856001

[ "MIT" ]

null

astruct/__init__.py

misterfifths/nis-mods

9d460414cb88d10f2737e9be90babe85c9856001

[ "MIT" ]

null

# pyright: reportUnusedImport=none from . import type_hints from .typed_struct import typed_struct

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a8e712eac3d4723bb96269e4cb4432a469174b16

261

py

Python

ariadne_jwt/__init__.py

abaumg/ariadne-jwt

edb9d63b27cf072da4f9940ed81db9d21d95307a

[ "MIT" ]

24

2020-09-05T17:34:16.000Z

2022-03-17T11:45:55.000Z

ariadne_jwt/__init__.py

abaumg/ariadne-jwt

edb9d63b27cf072da4f9940ed81db9d21d95307a

[ "MIT" ]

16

2020-09-05T16:55:49.000Z

2022-03-20T16:44:25.000Z

ariadne_jwt/__init__.py

abaumg/ariadne-jwt

edb9d63b27cf072da4f9940ed81db9d21d95307a

[ "MIT" ]

12

2020-09-15T21:53:48.000Z

2022-03-20T15:07:43.000Z

from .mutations import (resolve_verify, resolve_refresh, resolve_revoke, resolve_token_auth, jwt_schema) from .scalar import GenericScalar __all__ = ['resolve_verify', 'resolve_refresh', 'resolve_revoke', 'resolve_token_auth', 'jwt_schema', 'GenericScalar', ]

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d12c530460a7203a43389fdac34d227de92c6e1a

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py

Python

pyspj/__init__.py

HansBug/pyspj

ed776cf7d2d1766ee4c2152221d1d3dbdd18d93a

[ "Apache-2.0" ]

null

pyspj/__init__.py

HansBug/pyspj

ed776cf7d2d1766ee4c2152221d1d3dbdd18d93a

[ "Apache-2.0" ]

null

pyspj/__init__.py

HansBug/pyspj

ed776cf7d2d1766ee4c2152221d1d3dbdd18d93a

[ "Apache-2.0" ]

null

from .entry import * from .models import *

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6

d14efa4a299788bb79d2226bc2a3b4538aed7f41

64

py

Python

acq4/devices/DAQGeneric/__init__.py

aleonlein/acq4

4b1fcb9ad2c5e8d4595a2b9cf99d50ece0c0f555

[ "MIT" ]

47

2015-01-05T16:18:10.000Z

2022-03-16T13:09:30.000Z

acq4/devices/DAQGeneric/__init__.py

aleonlein/acq4

4b1fcb9ad2c5e8d4595a2b9cf99d50ece0c0f555

[ "MIT" ]

48

2015-04-19T16:51:41.000Z

2022-03-31T14:48:16.000Z

acq4/devices/DAQGeneric/__init__.py

sensapex/acq4

9561ba73caff42c609bd02270527858433862ad8

[ "MIT" ]

32

2015-01-15T14:11:49.000Z

2021-07-15T13:44:52.000Z

from __future__ import print_function from .DAQGeneric import *

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6

d1742f28a056bc38a289926917fc66fcd6ecbec5

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py

Python

face_utils/cropping/__init__.py

HermasTV/mmfu

dc14f0c06dbff3f1c92606ff11fc30d782ea23ef

[ "MIT" ]

1

2021-04-28T02:32:28.000Z

face_utils/cropping/__init__.py

HermasTV/mmfu

dc14f0c06dbff3f1c92606ff11fc30d782ea23ef

[ "MIT" ]

null

face_utils/cropping/__init__.py

HermasTV/mmfu

dc14f0c06dbff3f1c92606ff11fc30d782ea23ef

[ "MIT" ]

null

from face_utils.cropping import cropping

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6

0f0e2e41b3f5b5b70d0f8865580a3b4982061faa

15,954

py

Python

tests/services/test_afc_shell.py

iOSForensics/pymobiledevice3

6b148f4e58cc51cb44c18935913a3e6cec5b60d5

[ "MIT" ]

1

2022-01-20T16:53:15.000Z

tests/services/test_afc_shell.py

iOSForensics/pymobiledevice3

6b148f4e58cc51cb44c18935913a3e6cec5b60d5

[ "MIT" ]

null

tests/services/test_afc_shell.py

iOSForensics/pymobiledevice3

6b148f4e58cc51cb44c18935913a3e6cec5b60d5

[ "MIT" ]

null

from pathlib import Path from unittest import mock import pytest from cmd2_ext_test import ExternalTestMixin from cmd2 import CommandResult import gnureadline from pymobiledevice3.services.afc import AfcShell SINGLE_PARAM_COMMANDS = ['edit', 'cd', 'walk', 'cat', 'rm', 'head', 'hexdump', 'stat'] class AfcShellTester(ExternalTestMixin, AfcShell): def __init__(self, *args, **kwargs): # gotta have this or neither the plugin or cmd2 will initialize super().__init__(*args, **kwargs) @pytest.fixture(scope='function') def afc_shell(lockdown): app = AfcShellTester(lockdown) try: yield app finally: app.afc.service.close() def get_completions(line, part, app): def get_line(): return line def get_begidx(): return len(line) - len(part) def get_endidx(): return len(line) with mock.patch.object(gnureadline, 'get_line_buffer', get_line): with mock.patch.object(gnureadline, 'get_begidx', get_begidx): with mock.patch.object(gnureadline, 'get_endidx', get_endidx): app.complete(part, 0) return app.completion_matches @pytest.mark.parametrize('command', SINGLE_PARAM_COMMANDS) def test_completion(command, afc_shell): filenames = get_completions(f'{command} D', 'D', afc_shell) assert 'DCIM' in filenames assert 'Downloads' in filenames assert 'Books' not in filenames @pytest.mark.parametrize('command', SINGLE_PARAM_COMMANDS) def test_completion_empty(command, afc_shell): filenames = get_completions(f'{command} ', '', afc_shell) assert 'DCIM' in filenames assert 'Downloads' in filenames assert 'Books' in filenames @pytest.mark.parametrize('command', SINGLE_PARAM_COMMANDS) def test_completion_with_space(command, afc_shell): afc_shell.afc.makedirs('aa bb cc/dd ee ff') try: assert ['"aa bb cc" '] == get_completions(f'{command} aa ', 'aa ', afc_shell) assert ['aa bb cc" '] == get_completions(f'{command} "aa ', 'aa ', afc_shell) assert ['"aa bb cc/dd ee ff" '] == get_completions(f'{command} aa bb cc/dd ee', 'aa bb cc/dd ee', afc_shell) finally: afc_shell.afc.rm('aa bb cc') @pytest.mark.parametrize('command', SINGLE_PARAM_COMMANDS) def test_in_folder_completion(command, afc_shell): afc_shell.afc.makedirs('temp1/temp2') afc_shell.afc.makedirs('temp1/temp4') try: assert ['temp1 '] == get_completions(f'{command} temp', 'temp', afc_shell) assert ['temp1/temp2', 'temp1/temp4'] == get_completions(f'{command} temp1/', 'temp1/', afc_shell) assert ['temp1/temp2', 'temp1/temp4'] == get_completions(f'{command} temp1/temp', 'temp1/temp', afc_shell) finally: afc_shell.afc.rm('temp1') @pytest.mark.parametrize('command', SINGLE_PARAM_COMMANDS) def test_completion_after_cd(command, afc_shell): afc_shell.afc.makedirs('temp1/temp2') afc_shell.afc.makedirs('temp1/temp4') afc_shell.app_cmd('cd temp1') completions = get_completions(f'{command} temp', 'temp', afc_shell) afc_shell.afc.rm('temp1') assert ['temp2', 'temp4'] == completions @pytest.mark.parametrize('command', SINGLE_PARAM_COMMANDS) def test_not_over_completing(command, afc_shell): assert not get_completions(f'{command} DCIM ', '', afc_shell) def test_mv_completion(afc_shell): afc_shell.afc.makedirs('temp1') afc_shell.afc.set_file_contents('temp1/temp.txt', b'data') try: assert get_completions('mv temp1/t', 'temp1/t', afc_shell) == ['temp1/temp.txt '] assert get_completions('mv temp1/temp.txt tem', 'tem', afc_shell) == ['temp1 '] finally: afc_shell.afc.rm('temp1') def test_push_completion(afc_shell, tmp_path: Path): (tmp_path / 'temp1.txt').write_text('hey1') (tmp_path / 'temp2.txt').write_text('hey2') assert get_completions(f'push {tmp_path}', str(tmp_path), afc_shell) == [f'{tmp_path} '] assert get_completions(f'push {tmp_path}/', f'{tmp_path}/', afc_shell) == [f'{tmp_path}/temp1.txt', f'{tmp_path}/temp2.txt'] second_completion = get_completions(f'push {tmp_path} D', 'D', afc_shell) assert 'DCIM' in second_completion assert 'Downloads' in second_completion assert 'Books' not in second_completion def test_pull_completion(afc_shell, tmp_path: Path): completions = get_completions('pull D', 'D', afc_shell) assert 'DCIM' in completions assert 'Downloads' in completions assert 'Books' not in completions (tmp_path / 'temp1.txt').write_text('hey1') (tmp_path / 'temp2.txt').write_text('hey2') assert get_completions(f'pull DCIM {tmp_path}', str(tmp_path), afc_shell) == [f'{tmp_path} '] assert get_completions(f'pull DCIM {tmp_path}/', f'{tmp_path}/', afc_shell) == [f'{tmp_path}/temp1.txt', f'{tmp_path}/temp2.txt'] def test_ls(afc_shell): out = afc_shell.app_cmd('ls') assert isinstance(out, CommandResult) filenames = str(out.stdout).strip().splitlines() assert 'DCIM' in filenames assert 'Downloads' in filenames assert 'Books' in filenames def test_pull_after_cd_single_file(afc_shell, tmp_path: Path): """ source: temp1 └── temp.txt cd temp1 pull temp.txt target target └── temp.txt """ file_name = 'temp.txt' file_data = b'data' afc_shell.afc.makedirs('temp1') afc_shell.afc.set_file_contents(f'temp1/{file_name}', file_data) out = afc_shell.app_cmd(f'pull {file_name} {tmp_path.absolute()}') try: assert 'AfcFileNotFoundError' in out.stderr afc_shell.app_cmd('cd temp1') out = afc_shell.app_cmd(f'pull {file_name} {tmp_path.absolute()}') assert not out.stderr assert (tmp_path / file_name).read_bytes() == file_data finally: afc_shell.afc.rm('temp1') def test_pull_after_cd_single_file_with_prefix(afc_shell, tmp_path: Path): """ source: temp1 └── temp2 └── temp3 └── temp.txt cd temp1 pull temp2/temp3/temp.txt target target └── temp.txt """ file_name = 'temp.txt' file_data = b'data' afc_shell.afc.makedirs('temp1/temp2/temp3') afc_shell.afc.set_file_contents(f'temp1/temp2/temp3/{file_name}', file_data) try: afc_shell.app_cmd('cd temp1') out = afc_shell.app_cmd(f'pull temp2/temp3/{file_name} {tmp_path.absolute()}') assert not out.stderr assert (tmp_path / file_name).read_bytes() == file_data finally: afc_shell.afc.rm('temp1') def test_pull_after_cd_single_file_with_rename(afc_shell, tmp_path: Path): """ source: temp1 └── temp.txt cd temp1 pull temp.txt target/temp1.txt target └── temp1.txt """ file_name = 'temp.txt' file_rename = 'temp1.txt' file_data = b'data' afc_shell.afc.makedirs('temp1') afc_shell.afc.set_file_contents(f'temp1/{file_name}', file_data) try: afc_shell.app_cmd('cd temp1') out = afc_shell.app_cmd(f'pull {file_name} {(tmp_path / file_rename).absolute()}') assert not out.stderr assert (tmp_path / file_rename).read_bytes() == file_data finally: afc_shell.afc.rm('temp1') def test_pull_after_cd_recursive(afc_shell, tmp_path: Path): """ source: temp1 └── temp2 └── temp3 ├── temp4 │ └── temp1.txt └── temp.txt cd temp1/temp2 pull temp3 target target └── temp3 ├── temp4 │ └── temp1.txt └── temp.txt """ file_name = 'temp.txt' file_name1 = 'temp.txt' file_data = b'data' file_data1 = b'data1' afc_shell.afc.makedirs('temp1/temp2/temp3/temp4') afc_shell.afc.set_file_contents(f'temp1/temp2/temp3/{file_name}', file_data) afc_shell.afc.set_file_contents(f'temp1/temp2/temp3/temp4/{file_name1}', file_data1) try: afc_shell.app_cmd('cd temp1/temp2') out = afc_shell.app_cmd(f'pull temp3 {tmp_path.absolute()}') assert not out.stderr assert (tmp_path / 'temp3' / file_name).read_bytes() == file_data assert len(list((tmp_path / 'temp3').iterdir())) == 2 assert (tmp_path / 'temp3' / 'temp4' / file_name1).read_bytes() == file_data1 assert len(list((tmp_path / 'temp3' / 'temp4').iterdir())) == 1 finally: afc_shell.afc.rm('temp1') def test_pull_after_cd_recursive_current(afc_shell, tmp_path: Path): """ source: temp1 └── temp2 └── temp3 ├── temp4 │ └── temp1.txt └── temp.txt cd temp1/temp2/temp3 pull . target target ├── temp4 │ └── temp1.txt └── temp.txt """ file_name = 'temp.txt' file_name1 = 'temp1.txt' file_data = b'data' file_data1 = b'data1' afc_shell.afc.makedirs('temp1/temp2/temp3/temp4') afc_shell.afc.set_file_contents(f'temp1/temp2/temp3/{file_name}', file_data) afc_shell.afc.set_file_contents(f'temp1/temp2/temp3/temp4/{file_name1}', file_data1) try: afc_shell.app_cmd('cd temp1/temp2/temp3') out = afc_shell.app_cmd(f'pull . {tmp_path.absolute()}') assert not out.stderr assert (tmp_path / file_name).read_bytes() == file_data assert len(list(tmp_path.iterdir())) == 2 assert (tmp_path / 'temp4' / file_name1).read_bytes() == file_data1 assert len(list((tmp_path / 'temp4').iterdir())) == 1 finally: afc_shell.afc.rm('temp1') def test_pull_after_cd_recursive_with_prefix(afc_shell, tmp_path: Path): """ source: temp1 └── temp2 └── temp3 ├── temp4 │ └── temp1.txt └── temp.txt cd temp1 pull temp2/temp3 target target └── temp3 ├── temp4 │ └── temp1.txt └── temp.txt """ file_name = 'temp.txt' file_name1 = 'temp.txt' file_data = b'data' file_data1 = b'data1' afc_shell.afc.makedirs('temp1/temp2/temp3/temp4') afc_shell.afc.set_file_contents(f'temp1/temp2/temp3/{file_name}', file_data) afc_shell.afc.set_file_contents(f'temp1/temp2/temp3/temp4/{file_name1}', file_data1) try: afc_shell.app_cmd('cd temp1') out = afc_shell.app_cmd(f'pull temp2/temp3 {tmp_path.absolute()}') assert not out.stderr assert (tmp_path / 'temp3' / file_name).read_bytes() == file_data assert len(list((tmp_path / 'temp3').iterdir())) == 2 assert (tmp_path / 'temp3' / 'temp4' / file_name1).read_bytes() == file_data1 assert len(list((tmp_path / 'temp3' / 'temp4').iterdir())) == 1 finally: afc_shell.afc.rm('temp1') def test_push_after_cd_single_file_current(afc_shell, tmp_path: Path): """ source: temp.txt cd target push source/temp.txt . target └── temp.txt """ file_name = 'temp.txt' file_data = b'data' source_file = (tmp_path / file_name) source_file.write_bytes(file_data) afc_shell.afc.makedirs('temp1') afc_shell.app_cmd('cd temp1') try: out = afc_shell.app_cmd(f'push {source_file} .') assert not out.stderr assert afc_shell.afc.get_file_contents(f'temp1/{file_name}') == file_data finally: afc_shell.afc.rm('temp1') def test_push_after_cd_single_file_with_prefix(afc_shell, tmp_path: Path): """ source: temp.txt cd temp1/temp2 push source/temp.txt temp3/temp.txt target temp1 └── temp2 └── temp3 └── temp.txt """ file_name = 'temp.txt' file_data = b'data' source_file = (tmp_path / file_name) source_file.write_bytes(file_data) afc_shell.afc.makedirs('temp1/temp2/temp3') afc_shell.app_cmd('cd temp1/temp2') try: out = afc_shell.app_cmd(f'push {source_file} temp3/{file_name}') assert not out.stderr assert afc_shell.afc.get_file_contents(f'temp1/temp2/temp3/{file_name}') == file_data finally: afc_shell.afc.rm('temp1') def test_push_after_cd_single_file_with_rename(afc_shell, tmp_path: Path): """ source: temp.txt cd temp1 push source/temp.txt temp1.txt target temp1 └── temp1.txt """ file_name = 'temp.txt' file_rename = 'temp1.txt' file_data = b'data' source_file = (tmp_path / file_name) source_file.write_bytes(file_data) afc_shell.afc.makedirs('temp1') afc_shell.app_cmd('cd temp1') try: out = afc_shell.app_cmd(f'push {source_file} {file_rename}') assert not out.stderr assert afc_shell.afc.get_file_contents(f'temp1/{file_rename}') == file_data finally: afc_shell.afc.rm('temp1') def test_push_after_cd_recursive_current(afc_shell, tmp_path: Path): """ source: temp2 └── temp3 ├── temp4 │ └── temp1.txt └── temp.txt mkdir temp1 cd temp1 push temp2 . temp1 └── temp2 └── temp3 ├── temp4 │ └── temp1.txt └── temp.txt """ (tmp_path / 'temp2' / 'temp3' / 'temp4').mkdir(parents=True) (tmp_path / 'temp2' / 'temp3' / 'temp.txt').write_bytes(b'data') (tmp_path / 'temp2' / 'temp3' / 'temp4' / 'temp1.txt').write_bytes(b'data1') source_file = tmp_path / 'temp2' afc_shell.afc.makedirs('temp1') afc_shell.app_cmd('cd temp1') try: out = afc_shell.app_cmd(f'push {source_file} .') assert not out.stderr assert afc_shell.afc.get_file_contents('temp1/temp2/temp3/temp.txt') == b'data' assert afc_shell.afc.get_file_contents('temp1/temp2/temp3/temp4/temp1.txt') == b'data1' finally: afc_shell.afc.rm('temp1') def test_push_after_cd_recursive_with_slash_current(afc_shell, tmp_path: Path): """ source: temp2 └── temp3 ├── temp4 │ └── temp1.txt └── temp.txt mkdir temp1 cd temp1 push temp2/ . temp1 └── temp3 ├── temp4 │ └── temp1.txt └── temp.txt """ (tmp_path / 'temp2' / 'temp3' / 'temp4').mkdir(parents=True) (tmp_path / 'temp2' / 'temp3' / 'temp.txt').write_bytes(b'data') (tmp_path / 'temp2' / 'temp3' / 'temp4' / 'temp1.txt').write_bytes(b'data1') source_file = tmp_path / 'temp2' afc_shell.afc.makedirs('temp1') afc_shell.app_cmd('cd temp1') try: out = afc_shell.app_cmd(f'push {source_file}/ .') assert not out.stderr assert afc_shell.afc.get_file_contents('temp1/temp3/temp.txt') == b'data' assert afc_shell.afc.get_file_contents('temp1/temp3/temp4/temp1.txt') == b'data1' finally: afc_shell.afc.rm('temp1') def test_push_after_cd_recursive_with_prefix(afc_shell, tmp_path: Path): """ source: temp2 └── temp3 ├── temp4 │ └── temp1.txt └── temp.txt mkdir temp1/temp1a/temp1b cd temp1 push temp2 temp1a/temp1b temp1 └── temp1a └── temp1b └── temp2 └── temp3 ├── temp4 │ └── temp1.txt └── temp.txt """ (tmp_path / 'temp2' / 'temp3' / 'temp4').mkdir(parents=True) (tmp_path / 'temp2' / 'temp3' / 'temp.txt').write_bytes(b'data') (tmp_path / 'temp2' / 'temp3' / 'temp4' / 'temp1.txt').write_bytes(b'data1') source_file = tmp_path / 'temp2' afc_shell.afc.makedirs('temp1/temp1a/temp1b') afc_shell.app_cmd('cd temp1') try: out = afc_shell.app_cmd(f'push {source_file} temp1a/temp1b') assert not out.stderr assert afc_shell.afc.get_file_contents('temp1/temp1a/temp1b/temp2/temp3/temp.txt') == b'data' assert afc_shell.afc.get_file_contents('temp1/temp1a/temp1b/temp2/temp3/temp4/temp1.txt') == b'data1' finally: afc_shell.afc.rm('temp1')

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0.624358

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0.039179

0.839345

0.823072

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0.772388

0.747823

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0

null

0

1

0

null

0

6

0f2ce616a7e0014dd25f90385d1da17c560cab72

14,833

py

Python

model/group_op.py

LingshenHe/Efficient-Equivariant-Network

50eab7727907655df8932d905595cf9af504be2a

[ "MIT" ]

8

2022-01-10T04:01:14.000Z

2022-03-25T08:56:42.000Z

model/group_op.py

LingshenHe/Efficient-Equivariant-Network

50eab7727907655df8932d905595cf9af504be2a

[ "MIT" ]

1

2022-01-10T04:07:33.000Z

model/group_op.py

LingshenHe/Efficient-Equivariant-Network

50eab7727907655df8932d905595cf9af504be2a

[ "MIT" ]

1

2022-03-25T03:00:16.000Z

import torch.nn as nn import torch import math class C_4_1x1(nn.Module): def __init__(self, in_channels, out_channels, stride=1): super(C_4_1x1, self).__init__() self.in_channels = in_channels self.out_channels = out_channels weight = torch.randn(out_channels, in_channels, 4) / math.sqrt(4 * in_channels / 2) self.weight = torch.nn.Parameter(weight) self.stride = stride self.pool = nn.MaxPool2d(2, 2) def forward(self, x): weight = torch.zeros(self.out_channels, 4, self.in_channels, 4).to(x.device) weight[::, 0, ...] = self.weight weight[::, 1, ...] = self.weight[..., [3, 0, 1, 2]] weight[::, 2, ...] = self.weight[..., [2, 3, 0, 1]] weight[::, 3, ...] = self.weight[..., [1, 2, 3, 0]] x = torch.nn.functional.conv2d(x, weight.reshape(self.out_channels * 4, self.in_channels * 4, 1, 1), stride=1, padding=0) if (self.stride != 1): x = self.pool(x) return x class C_4_3x3(nn.Module): def __init__(self, in_channels, out_channels, bias=False, stride=1): super(C_4_3x3, self).__init__() self.in_channels = in_channels self.out_channels = out_channels weight = torch.randn(out_channels, in_channels, 4, 3, 3) / math.sqrt(4 * in_channels * 9 / 2) self.weight = torch.nn.Parameter(weight) self.pool = nn.MaxPool2d(2, 2) self.stride = stride self.bias = None if (bias): self.bias = torch.nn.Parameter(torch.randn(out_channels)) def forward(self, x): weight = torch.zeros(self.out_channels, 4, self.in_channels, 4, 3, 3).to(x.device) weight[::, 0, ...] = self.weight weight[::, 1, ...] = torch.rot90(self.weight[..., [3, 0, 1, 2], ::, ::], 1, [3, 4]) weight[::, 2, ...] = torch.rot90(self.weight[..., [2, 3, 0, 1], ::, ::], 2, [3, 4]) weight[::, 3, ...] = torch.rot90(self.weight[..., [1, 2, 3, 0], ::, ::], 3, [3, 4]) x = torch.nn.functional.conv2d(x, weight.reshape(self.out_channels * 4, self.in_channels * 4, 3, 3), padding=1) if (self.stride != 1): x = self.pool(x) if (self.bias is not None): b, c, w, h = x.shape x = (x.reshape(b, c // 4, 4, h, w) + self.bias.reshape(1, -1, 1, 1, 1)).reshape(x.shape) return x class C_4_BN(nn.Module): def __init__(self, in_channels): super(C_4_BN, self).__init__() self.bn = nn.BatchNorm3d(in_channels) def forward(self, x): b, c, h, w = x.shape return self.bn(x.reshape(b, c // 4, 4, h, w)).reshape(x.size()) class D_4_BN(nn.Module): def __init__(self, in_channels, momentum=0.1): super(D_4_BN, self).__init__() self.bn = nn.BatchNorm3d(in_channels, momentum=momentum) def forward(self, x): b, c, h, w = x.shape return self.bn(x.reshape(b, c // 8, 8, h, w)).reshape(x.size()) class C_4_1x1_(nn.Module): def __init__(self, in_channels, out_channels): super(C_4_1x1_, self).__init__() self.net = nn.Conv3d(in_channels, out_channels, 1, bias=True) self.in_channels = in_channels self.out_channels = out_channels def forward(self, x): b, c, h, w = x.shape x = self.net(x.view(b, c // 4, 4, h, w)).reshape(b, self.out_channels * 4, h, w) return x class E4_C4(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, reduction_ratio=2, groups=1, stride=1 ): super(E4_C4, self).__init__() self.kernel_size = kernel_size self.stride = stride self.in_channels = in_channels self.out_channels = out_channels self.reduction_ratio = reduction_ratio self.group_channels = groups self.groups = self.out_channels // self.group_channels self.dim_g = 4 self.v = nn.Sequential(C_4_1x1(in_channels, out_channels)) self.conv1 = nn.Sequential(C_4_1x1(in_channels, int(in_channels // reduction_ratio)), nn.GroupNorm(int(in_channels // reduction_ratio),int(in_channels // reduction_ratio)*4), nn.ReLU() ) self.conv2 = nn.Sequential(C_4_1x1_(int(in_channels // reduction_ratio), kernel_size ** 2 * self.groups), ) if stride > 1: self.avgpool = nn.AvgPool2d(stride, stride) self.unfold = nn.Unfold(kernel_size, 1, (kernel_size - 1) // 2, stride) def forward(self, x): weight = self.conv2(self.conv1(x if self.stride == 1 else self.avgpool(x))) b, c, h, w = weight.shape weight = weight.view(b, self.groups, self.kernel_size, self.kernel_size, 4, h, w) weight[::, ::, ::, ::, 1, ::, ::] = torch.rot90(weight[::, ::, ::, ::, 1, ::, ::], 1, [2, 3]) weight[::, ::, ::, ::, 2, ::, ::] = torch.rot90(weight[::, ::, ::, ::, 2, ::, ::], 2, [2, 3]) weight[::, ::, ::, ::, 3, ::, ::] = torch.rot90(weight[::, ::, ::, ::, 3, ::, ::], 3, [2, 3]) weight = weight.reshape(b, self.groups, self.kernel_size ** 2, 4, h, w).unsqueeze(2).transpose(3, 4) x = self.v(x) out = self.unfold(x).view(b, self.groups, self.group_channels, 4, self.kernel_size ** 2, h, w) out = (weight * out).sum(dim=4).view(b, self.out_channels * 4, h, w) return out class D_4_1x1(nn.Module): def __init__(self, in_channels, out_channels, stride=1): super(D_4_1x1, self).__init__() self.in_channels = in_channels self.out_channels = out_channels weight = torch.randn(out_channels, in_channels, 8) / math.sqrt(8 * in_channels / 2) self.weight = torch.nn.Parameter(weight) self.stride = stride self.pool = nn.MaxPool2d(2, 2) def forward(self, x): weight = torch.zeros(self.out_channels, 8, self.in_channels, 8).to(x.device) weight[::, 0, ...] = self.weight weight[::, 1, ...] = self.weight[..., [3, 0, 1, 2, 5, 6, 7, 4]] weight[::, 2, ...] = self.weight[..., [2, 3, 0, 1, 6, 7, 4, 5]] weight[::, 3, ...] = self.weight[..., [1, 2, 3, 0, 7, 4, 5, 6]] weight[::, 4, ...] = self.weight[..., [4, 5, 6, 7, 0, 1, 2, 3]] weight[::, 5, ...] = self.weight[..., [5, 6, 7, 4, 3, 0, 1, 2]] weight[::, 6, ...] = self.weight[..., [6, 7, 4, 5, 2, 3, 0, 1]] weight[::, 7, ...] = self.weight[..., [7, 4, 5, 6, 1, 2, 3, 0]] x = torch.nn.functional.conv2d(x, weight.reshape(self.out_channels * 8, self.in_channels * 8, 1, 1), stride=1, padding=0) if (self.stride != 1): x = self.pool(x) return x class D_4_3x3(nn.Module): def __init__(self, in_channels, out_channels, bias=False, stride=1): super(D_4_3x3, self).__init__() self.in_channels = in_channels self.out_channels = out_channels weight = torch.randn(out_channels, in_channels, 8, 3, 3) / math.sqrt(8 * in_channels * 9 / 2) self.weight = torch.nn.Parameter(weight) self.pool = nn.MaxPool2d(2, 2) self.stride = stride self.bias = None if (bias): self.bias = torch.nn.Parameter(torch.randn(out_channels)) def forward(self, x): weight = torch.zeros(self.out_channels, 8, self.in_channels, 8, 3, 3).to(x.device) weight[::, 0, ...] = self.weight weight[::, 1, ...] = torch.rot90(self.weight[..., [3, 0, 1, 2, 5, 6, 7, 4], ::, ::], 1, [3, 4]) weight[::, 2, ...] = torch.rot90(self.weight[..., [2, 3, 0, 1, 6, 7, 4, 5], ::, ::], 2, [3, 4]) weight[::, 3, ...] = torch.rot90(self.weight[..., [1, 2, 3, 0, 7, 4, 5, 6], ::, ::], 3, [3, 4]) weight[::, 4, ...] = torch.rot90(self.weight[..., [4, 5, 6, 7, 0, 1, 2, 3], ::, ::].transpose(3, 4), 3, [3, 4]) weight[::, 5, ...] = torch.rot90(self.weight[..., [5, 6, 7, 4, 3, 0, 1, 2], ::, ::].transpose(3, 4), 2, [3, 4]) weight[::, 6, ...] = torch.rot90(self.weight[..., [6, 7, 4, 5, 2, 3, 0, 1], ::, ::].transpose(3, 4), 1, [3, 4]) weight[::, 7, ...] = torch.rot90(self.weight[..., [7, 4, 5, 6, 1, 2, 3, 0], ::, ::].transpose(3, 4), 0, [3, 4]) x = torch.nn.functional.conv2d(x, weight.reshape(self.out_channels * 8, self.in_channels * 8, 3, 3), padding=1) if (self.stride != 1): x = self.pool(x) if (self.bias is not None): b, c, w, h = x.shape x = (x.reshape(b, c // 8, 8, h, w) + self.bias.reshape(1, -1, 1, 1, 1)).reshape(x.shape) return x class D_4_1x1_(nn.Module): def __init__(self, in_channels, out_channels): super(D_4_1x1_, self).__init__() self.net = nn.Conv3d(in_channels, out_channels, 1, bias=True) self.in_channels = in_channels self.out_channels = out_channels def forward(self, x): b, c, h, w = x.shape x = self.net(x.view(b, c // 8, 8, h, w)).reshape(b, self.out_channels * 8, h, w) return x class E4_D4(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, reduction_ratio=2, groups=1, stride=1): super(E4_D4, self).__init__() self.kernel_size = kernel_size self.stride = stride self.in_channels = in_channels self.out_channels = out_channels self.reduction_ratio = reduction_ratio self.group_channels = groups self.groups = self.out_channels // self.group_channels self.dim_g = 8 self.v = nn.Sequential(D_4_1x1(in_channels, out_channels)) self.conv1 = nn.Sequential(D_4_1x1(in_channels, int(in_channels // reduction_ratio)), nn.GroupNorm(int(in_channels // reduction_ratio), int(in_channels // reduction_ratio)*8), nn.ReLU() ) self.conv2 = nn.Sequential(D_4_1x1_(int(in_channels // reduction_ratio), kernel_size ** 2 * self.groups), ) if stride > 1: self.avgpool = nn.AvgPool2d(stride, stride) self.unfold = nn.Unfold(kernel_size, 1, (kernel_size - 1) // 2, stride) def forward(self, x): weight = self.conv2(self.conv1(x if self.stride == 1 else self.avgpool(x))) b, c, h, w = weight.shape weight = weight.view(b, self.groups, self.kernel_size, self.kernel_size, 8, h, w) weight[::, ::, ::, ::, 1, ::, ::] = torch.rot90(weight[::, ::, ::, ::, 1, ::, ::], 1, [2, 3]) weight[::, ::, ::, ::, 2, ::, ::] = torch.rot90(weight[::, ::, ::, ::, 2, ::, ::], 2, [2, 3]) weight[::, ::, ::, ::, 3, ::, ::] = torch.rot90(weight[::, ::, ::, ::, 3, ::, ::], 3, [2, 3]) weight[::, ::, ::, ::, 4, ::, ::] = torch.rot90(weight[::, ::, ::, ::, 4, ::, ::].transpose(2, 3), 3, [2, 3]) weight[::, ::, ::, ::, 5, ::, ::] = torch.rot90(weight[::, ::, ::, ::, 5, ::, ::].transpose(2, 3), 2, [2, 3]) weight[::, ::, ::, ::, 6, ::, ::] = torch.rot90(weight[::, ::, ::, ::, 6, ::, ::].transpose(2, 3), 1, [2, 3]) weight[::, ::, ::, ::, 7, ::, ::] = torch.rot90(weight[::, ::, ::, ::, 7, ::, ::].transpose(2, 3), 0, [2, 3]) weight = weight.view(b, self.groups, self.kernel_size ** 2, 8, h, w).unsqueeze(2).transpose(3, 4) x = self.v(x) out = self.unfold(x).view(b, self.groups, self.group_channels, 8, self.kernel_size ** 2, h, w) out = (weight * out).sum(dim=4).view(b, self.out_channels * 8, h, w) return out def C_4_rot(x): b, c, h, w = x.shape return torch.rot90(x.view(b, c // 4, 4, h, w)[::, ::, [3, 0, 1, 2]], 1, [3, 4]).reshape(x.shape) def D_4_rot(x): b, c, h, w = x.shape return torch.rot90(x.view(b, c // 8, 8, h, w)[::, ::, [3, 0, 1, 2, 5, 6, 7, 4]], 1, [3, 4]).reshape(x.shape) def D_4_m(x): b, c, h, w = x.shape return torch.rot90(x.view(b, c // 8, 8, h, w)[::, ::, [4, 5, 6, 7, 0, 1, 2, 3]].transpose(3, 4), 3, [3, 4]).reshape( x.shape) class C_4_Conv(nn.Module): def __init__(self, in_channels, out_channels): super(C_4_Conv, self).__init__() self.in_channels = in_channels self.out_channels = out_channels weight = torch.randn(out_channels, in_channels, 3, 3) / math.sqrt(9 * in_channels / 2) self.weight = torch.nn.Parameter(weight) def forward(self, input): weight = torch.zeros(self.out_channels, 4, self.in_channels, 3, 3).to(input.device) weight[::, 0] = self.weight weight[::, 1] = torch.rot90(self.weight[::], 1, [2, 3]) weight[::, 2] = torch.rot90(self.weight[::], 2, [2, 3]) weight[::, 3] = torch.rot90(self.weight[::], 3, [2, 3]) out = nn.functional.conv2d(input, weight.reshape(self.out_channels * 4, self.in_channels, 3, 3), padding=1) return out class D_4_Conv(nn.Module): def __init__(self, in_channels, out_channels): super(D_4_Conv, self).__init__() self.in_channels = in_channels self.out_channels = out_channels weight = torch.randn(out_channels, in_channels, 3, 3) / math.sqrt(9 * in_channels / 2) self.weight = torch.nn.Parameter(weight) def forward(self, input): weight = torch.zeros(self.out_channels, 8, self.in_channels, 3, 3).to(input.device) weight[::, 0] = self.weight weight[::, 1] = torch.rot90(self.weight[::], 1, [2, 3]) weight[::, 2] = torch.rot90(self.weight[::], 2, [2, 3]) weight[::, 3] = torch.rot90(self.weight[::], 3, [2, 3]) weight[::, 4] = torch.rot90(self.weight[::].transpose(2, 3), 3, [2, 3]) weight[::, 5] = torch.rot90(self.weight[::].transpose(2, 3), 2, [2, 3]) weight[::, 6] = torch.rot90(self.weight[::].transpose(2, 3), 1, [2, 3]) weight[::, 7] = torch.rot90(self.weight[::].transpose(2, 3), 0, [2, 3]) out = nn.functional.conv2d(input, weight.reshape(self.out_channels * 8, self.in_channels, 3, 3), padding=1) return out class C_4_Pool(nn.Module): def __init__(self): super(C_4_Pool, self).__init__() self.pool = nn.MaxPool3d((4, 1, 1), (4, 1, 1)) def forward(self, x): b, c, h, w = x.shape return self.pool(x.reshape(b, c // 4, 4, h, w)).squeeze(2) class D_4_Pool(nn.Module): def __init__(self): super(D_4_Pool, self).__init__() self.pool = nn.MaxPool3d((8, 1, 1), (8, 1, 1)) def forward(self, x): b, c, h, w = x.shape return self.pool(x.reshape(b, c // 8, 8, h, w)).squeeze(2) #Right

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0.529697

2,156

14,833

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0.044063

0.096064

0.063509

0.053369

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null

0

6

0f34744602c541a7088ad50a990d81dddfce1057

467

py

Python

rivr/http/__init__.py

rivrproject/rivr

b4f7eb481cc28ae48169f1d3982b896b7cfd5c91

[ "BSD-2-Clause-FreeBSD" ]

3

2015-02-23T12:14:54.000Z

2015-11-08T13:25:02.000Z

rivr/http/__init__.py

rivrproject/rivr

b4f7eb481cc28ae48169f1d3982b896b7cfd5c91

[ "BSD-2-Clause-FreeBSD" ]

8

2015-01-10T09:37:13.000Z

2020-10-10T15:32:27.000Z

rivr/http/__init__.py

rivrproject/rivr

b4f7eb481cc28ae48169f1d3982b896b7cfd5c91

[ "BSD-2-Clause-FreeBSD" ]

null

from rivr.http.request import Request from rivr.http.response import ( Http404, Response, ResponseNoContent, ResponseNotAllowed, ResponseNotFound, ResponseNotModified, ResponsePermanentRedirect, ResponseRedirect, ) __all__ = [ 'Request', 'Http404', 'Response', 'ResponseNoContent', 'ResponseNotAllowed', 'ResponseNotFound', 'ResponseNotModified', 'ResponsePermanentRedirect', 'ResponseRedirect', ]

19.458333

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0.69379

29

467

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null

0

1

0

1

0

null

0

6

0f562eebdfd9bace25c6e277a65468e7c8ff98b3

146

py

Python

clases/admin.py

Etxea/gestion_eide_web

8a59be1ddb59a4713cb3346534fd01f643d8f924

[ "MIT" ]

null

clases/admin.py

Etxea/gestion_eide_web

8a59be1ddb59a4713cb3346534fd01f643d8f924

[ "MIT" ]

null

clases/admin.py

Etxea/gestion_eide_web

8a59be1ddb59a4713cb3346534fd01f643d8f924

[ "MIT" ]

null

from django.contrib import admin from models import * class ClaseAdmin(admin.ModelAdmin): pass admin.site.register(Clase, ClaseAdmin)

14.6

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0.753425

18

146

6.111111

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146

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true

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1

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null

0

1

0

1

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6

7e599e1f8c1ef3378d64da16dab5091afdb27255

59

py

Python

data/micro-benchmark/imports/chained_import/from_import.py

vitsalis/pycg-evaluation

ce37eb5668465b0c17371914e863d699826447ee

[ "Apache-2.0" ]

121

2020-12-16T20:31:37.000Z

2022-03-21T20:32:43.000Z

data/micro-benchmark/imports/chained_import/from_import.py

vitsalis/pycg-evaluation

ce37eb5668465b0c17371914e863d699826447ee

[ "Apache-2.0" ]

24

2021-03-13T00:04:00.000Z

2022-03-21T17:28:11.000Z

data/micro-benchmark/imports/chained_import/from_import.py

vitsalis/pycg-evaluation

ce37eb5668465b0c17371914e863d699826447ee

[ "Apache-2.0" ]

19

2021-03-23T10:58:47.000Z

2022-03-24T19:46:50.000Z

from chained_import import func1 def func2(): func1()

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59

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1

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1

0

1

0

6

0e495fa6d3896d9dfdabeba77021e2a589e8e61b

29,510

py

Python

tests/test_utils.py

KarrLab/model_generator

b2735391545bcd5f21faaa1ceaa1949e53497378

[ "MIT" ]

6

2018-12-24T16:20:11.000Z

2022-01-26T23:38:25.000Z

tests/test_utils.py

KarrLab/model_generator

b2735391545bcd5f21faaa1ceaa1949e53497378

[ "MIT" ]

15

2018-08-08T20:34:40.000Z

2021-10-31T20:08:40.000Z

tests/test_utils.py

KarrLab/model_generator

b2735391545bcd5f21faaa1ceaa1949e53497378

[ "MIT" ]

2

2019-04-05T16:11:57.000Z

2020-04-29T14:14:30.000Z

""" Tests for utility methods :Author: Yin Hoon Chew <yinhoon.chew@mssm.edu> :Date: 2019-02-13 :Copyright: 2019, Karr Lab :License: MIT """ from wc_onto import onto as wc_ontology from wc_utils.util.units import unit_registry import wc_model_gen.utils as utils import math import scipy.constants import unittest import wc_lang class TestCase(unittest.TestCase): def test_calc_avg_syn_rate(self): test_rate = utils.calc_avg_syn_rate(0.5, 300., 36000.) self.assertAlmostEqual(test_rate, 0.001164872, places=9) def test_calc_avg_deg_rate(self): test_rate = utils.calc_avg_deg_rate(0.5, 300.) self.assertAlmostEqual(test_rate, 0.0011552453009332421, places=16) def test_test_metabolite_production(self): model = wc_lang.Model() submodel = wc_lang.Submodel(model=model, id='metabolism') compartment = model.compartments.create(id='c') for i in ['o2', 'h2o', 'atp']: st = model.species_types.create(id=i) species = model.species.create(species_type=st, compartment=compartment) species.id = species.gen_id() R1 = model.reactions.create(submodel=submodel, id='Ex_o2') R1.participants.add(model.species.get_one( id='o2[c]').species_coefficients.get_or_create(coefficient=1.0)) R2 = model.reactions.create(submodel=submodel, id='Ex_h2o') R2.participants.add(model.species.get_one( id='h2o[c]').species_coefficients.get_or_create(coefficient=1.0)) R3 = model.reactions.create(submodel=submodel, id='Ex_atp') R3.participants.add(model.species.get_one( id='atp[c]').species_coefficients.get_or_create(coefficient=-1.0)) biomass_reaction = model.reactions.create(submodel=submodel, id='biomass_reaction') biomass_reaction.participants.add(model.species.get_one( id='o2[c]').species_coefficients.get_or_create(coefficient=-1.0)) biomass_reaction.participants.add(model.species.get_one( id='h2o[c]').species_coefficients.get_or_create(coefficient=-1.0)) biomass_reaction.participants.add(model.species.get_one( id='atp[c]').species_coefficients.get_or_create(coefficient=1.0)) submodel.dfba_obj = wc_lang.DfbaObjective(model=model) submodel.dfba_obj.id = submodel.dfba_obj.gen_id() obj_expression = biomass_reaction.id dfba_obj_expression, error = wc_lang.DfbaObjectiveExpression.deserialize( obj_expression, {wc_lang.Reaction: {biomass_reaction.id: biomass_reaction}}) assert error is None, str(error) submodel.dfba_obj.expression = dfba_obj_expression reaction_bounds = {i.id:(0., 1000.) for i in model.reactions} unproducibles, unrecyclables = utils.test_metabolite_production(submodel, reaction_bounds, pseudo_reactions=['biomass_reaction']) self.assertEqual(unproducibles, []) self.assertEqual(unrecyclables, []) unproducibles, unrecyclables = utils.test_metabolite_production(submodel, reaction_bounds) self.assertEqual(unproducibles, []) self.assertEqual(unrecyclables, []) mock1 = model.species.create(id='mock1') mock2 = model.species.create(id='mock2') biomass_reaction.participants.add(mock1.species_coefficients.get_or_create(coefficient=1.0)) biomass_reaction.participants.add(mock2.species_coefficients.get_or_create(coefficient=-1.0)) unproducibles, unrecyclables = utils.test_metabolite_production(submodel, reaction_bounds) self.assertEqual(unproducibles, ['mock2']) self.assertEqual(unrecyclables, ['mock1']) unproducibles, unrecyclables = utils.test_metabolite_production(submodel, reaction_bounds, pseudo_reactions=['biomass_reaction'], test_producibles=['mock1'], test_recyclables=['mock2']) self.assertEqual(unproducibles, ['mock1']) self.assertEqual(unrecyclables, ['mock2']) unproducibles, unrecyclables = utils.test_metabolite_production(submodel, reaction_bounds, test_producibles=['mock1'], test_recyclables=['mock2']) self.assertEqual(unproducibles, []) self.assertEqual(unrecyclables, []) R4 = model.reactions.create(submodel=submodel, id='Ex_mock1') R4.participants.add(mock1.species_coefficients.get_or_create(coefficient=-1.0)) R5 = model.reactions.create(submodel=submodel, id='Ex_mock2') R5.participants.add(mock2.species_coefficients.get_or_create(coefficient=1.0)) reaction_bounds = {i.id:(0., 1000.) for i in model.reactions} unproducibles, unrecyclables = utils.test_metabolite_production(submodel, reaction_bounds) self.assertEqual(unproducibles, []) self.assertEqual(unrecyclables, []) def test_simple_repressor(self): model = wc_lang.Model() init_volume = wc_lang.core.InitVolume(distribution=wc_ontology['WC:normal_distribution'], mean=0.5, std=0) c = wc_lang.Compartment(id='c', init_volume=init_volume) c.init_density = wc_lang.Parameter(id='density_' + c.id, value=1.) volume = wc_lang.Function(id='volume_' + c.id) volume.expression, error = wc_lang.FunctionExpression.deserialize(f'{c.id} / {c.init_density.id}', { wc_lang.Compartment: {c.id: c}, wc_lang.Parameter: {c.init_density.id: c.init_density}, }) assert error is None, str(error) tf_species_type = wc_lang.SpeciesType(id='Repressor') tf_species = wc_lang.Species(species_type=tf_species_type, compartment=c) tf_species.id = tf_species.gen_id() wc_lang.DistributionInitConcentration(species=tf_species, mean=0.5) F_rep, species, parameters, functions = utils.simple_repressor(model, 'transcription_rna1', tf_species) self.assertEqual(F_rep, '(1 / (1 + Repressor[c] / (Kr_transcription_rna1_Repressor * Avogadro * volume_c)))') self.assertEqual(species, {'Repressor[c]': tf_species}) self.assertEqual(functions, {'volume_c': volume}) self.assertEqual(set(model.parameters), set(parameters.values())) self.assertEqual(sorted(list(parameters.keys())), sorted(['Avogadro', 'Kr_transcription_rna1_Repressor'])) self.assertEqual(model.parameters.get_one(id='Kr_transcription_rna1_Repressor').type, None) self.assertEqual(model.parameters.get_one(id='Kr_transcription_rna1_Repressor').units, unit_registry.parse_units('M')) def test_simple_activator(self): model = wc_lang.Model() init_volume = wc_lang.core.InitVolume(distribution=wc_ontology['WC:normal_distribution'], mean=0.5, std=0) c = wc_lang.Compartment(id='c', init_volume=init_volume) c.init_density = wc_lang.Parameter(id='density_' + c.id, value=1.) volume = wc_lang.Function(id='volume_' + c.id) volume.expression, error = wc_lang.FunctionExpression.deserialize(f'{c.id} / {c.init_density.id}', { wc_lang.Compartment: {c.id: c}, wc_lang.Parameter: {c.init_density.id: c.init_density}, }) assert error is None, str(error) tf_species_type = wc_lang.SpeciesType(id='Activator') tf_species = wc_lang.Species(species_type=tf_species_type, compartment=c) tf_species.id = tf_species.gen_id() wc_lang.DistributionInitConcentration(species=tf_species, mean=0.5) F_act, species, parameters, functions = utils.simple_activator(model, 'transcription_rna1', tf_species) self.assertEqual(F_act, '((1 + Activator[c] / (Ka_transcription_rna1_Activator * Avogadro * volume_c) * f_transcription_rna1_Activator) / ' '(1 + Activator[c] / (Ka_transcription_rna1_Activator * Avogadro * volume_c)))') self.assertEqual(species, {'Activator[c]': tf_species}) self.assertEqual(functions, {'volume_c': volume}) self.assertEqual(set(model.parameters), set(parameters.values())) self.assertEqual(sorted(list(parameters.keys())), sorted(['Avogadro', 'f_transcription_rna1_Activator', 'Ka_transcription_rna1_Activator'])) self.assertEqual(model.parameters.get_one(id='Ka_transcription_rna1_Activator').type, None) self.assertEqual(model.parameters.get_one(id='Ka_transcription_rna1_Activator').units, unit_registry.parse_units('M')) def test_gen_michaelis_menten_like_rate_law(self): model = wc_lang.Model() init_volume = wc_lang.core.InitVolume(distribution=wc_ontology['WC:normal_distribution'], mean=0.5, std=0) c = wc_lang.Compartment(id='c', init_volume=init_volume) c.init_density = wc_lang.Parameter(id='density_' + c.id, value=1.) volume = wc_lang.Function(id='volume_' + c.id) volume.expression, error = wc_lang.FunctionExpression.deserialize(f'{c.id} / {c.init_density.id}', { wc_lang.Compartment: {c.id: c}, wc_lang.Parameter: {c.init_density.id: c.init_density}, }) assert error is None, str(error) species_types = {} species = {} for i in range(1,7): Id = 's' + str(i) species_types[Id] = wc_lang.SpeciesType(id=Id) species[Id + '[c]'] = wc_lang.Species(species_type=species_types[Id], compartment=c) species[Id + '[c]'].id = species[Id + '[c]'].gen_id() wc_lang.DistributionInitConcentration(species=species[Id + '[c]'], mean=0.5) ob_exp1, error = wc_lang.ObservableExpression.deserialize('s4[c] + s5[c]', { wc_lang.Species:{species['s4[c]'].gen_id(): species['s4[c]'], species['s5[c]'].gen_id(): species['s5[c]']}}) assert error is None, str(error) modifier1 = wc_lang.Observable(id='e1', expression=ob_exp1) ob_exp2, error = wc_lang.ObservableExpression.deserialize('2 * s6[c]', { wc_lang.Species:{species['s6[c]'].gen_id(): species['s6[c]']}}) assert error is None, str(error) modifier2 = wc_lang.Observable(id='e2', expression=ob_exp2) participant1 = wc_lang.SpeciesCoefficient(species=species['s1[c]'], coefficient=-1) participant2 = wc_lang.SpeciesCoefficient(species=species['s2[c]'], coefficient=-1) participant3 = wc_lang.SpeciesCoefficient(species=species['s3[c]'], coefficient=1) participant4 = wc_lang.SpeciesCoefficient(species=species['s4[c]'], coefficient=-1) participant5 = wc_lang.SpeciesCoefficient(species=species['s4[c]'], coefficient=1) participant6 = wc_lang.SpeciesCoefficient(species=species['s6[c]'], coefficient=-1) participant7 = wc_lang.SpeciesCoefficient(species=species['s6[c]'], coefficient=-1) participant8 = wc_lang.SpeciesCoefficient(species=species['s6[c]'], coefficient=1) reaction = wc_lang.Reaction(id='r1', participants=[participant1, participant2, participant3, participant4, participant5, participant6, participant7, participant8]) rate_law, parameters = utils.gen_michaelis_menten_like_rate_law( model, reaction, modifiers=[modifier1, modifier2], modifier_reactants=[species['s6[c]']]) self.assertEqual(rate_law.expression, 'k_cat_r1 * e1 * e2 * ' '(s1[c] / (s1[c] + K_m_r1_s1 * Avogadro * volume_c)) * ' '(s2[c] / (s2[c] + K_m_r1_s2 * Avogadro * volume_c)) * ' '(s6[c] / (s6[c] + K_m_r1_s6 * Avogadro * volume_c))') self.assertEqual(set([i.gen_id() for i in rate_law.species]), set(['s1[c]', 's2[c]', 's6[c]'])) self.assertEqual(set(rate_law.observables), set([modifier1, modifier2])) self.assertEqual(set(rate_law.parameters), set(parameters)) self.assertEqual(rate_law.parameters.get_one(id='k_cat_r1').type, wc_ontology['WC:k_cat']) self.assertEqual(rate_law.parameters.get_one(id='k_cat_r1').units, unit_registry.parse_units('s^-1 molecule^-2')) self.assertEqual(rate_law.parameters.get_one(id='K_m_r1_s2').type, wc_ontology['WC:K_m']) self.assertEqual(rate_law.parameters.get_one(id='K_m_r1_s2').units, unit_registry.parse_units('M')) reaction = wc_lang.Reaction(id='r1', participants=[participant1, participant2, participant4, participant8]) rate_law, parameters = utils.gen_michaelis_menten_like_rate_law( model, reaction) self.assertEqual(rate_law.expression, 'k_cat_r1 * ' '(s1[c] / (s1[c] + K_m_r1_s1 * Avogadro * volume_c)) * ' '(s2[c] / (s2[c] + K_m_r1_s2 * Avogadro * volume_c)) * ' '(s4[c] / (s4[c] + K_m_r1_s4 * Avogadro * volume_c))') reaction = wc_lang.Reaction(id='r1', participants=[participant3]) rate_law, parameters = utils.gen_michaelis_menten_like_rate_law( model, reaction) self.assertEqual(rate_law.expression, 'k_cat_r1') reaction = wc_lang.Reaction(id='r1', participants=[participant3, participant6]) rate_law, parameters = utils.gen_michaelis_menten_like_rate_law( model, reaction, modifiers=[modifier1, species['s6[c]']]) self.assertEqual(rate_law.expression, 'k_cat_r1 * e1 * s6[c]') reaction = wc_lang.Reaction(id='r1', participants=[participant1, participant2, participant4, participant8]) rate_law, parameters = utils.gen_michaelis_menten_like_rate_law( model, reaction, exclude_substrates=[species['s1[c]']]) self.assertEqual(rate_law.expression, 'k_cat_r1 * ' '(s2[c] / (s2[c] + K_m_r1_s2 * Avogadro * volume_c)) * ' '(s4[c] / (s4[c] + K_m_r1_s4 * Avogadro * volume_c))') with self.assertRaises(TypeError) as ctx: rate_law, parameters = utils.gen_michaelis_menten_like_rate_law( model, reaction, modifiers=['s6[c]']) self.assertEqual('The modifiers contain element(s) that is not an observable or a species', str(ctx.exception)) def test_gen_michaelis_menten_like_propensity_function(self): model = wc_lang.Model() init_volume = wc_lang.core.InitVolume(distribution=wc_ontology['WC:normal_distribution'], mean=0.5, std=0) c = wc_lang.Compartment(id='c', init_volume=init_volume) c.init_density = wc_lang.Parameter(id='density_' + c.id, value=1.) volume = wc_lang.Function(id='volume_' + c.id) volume.expression, error = wc_lang.FunctionExpression.deserialize(f'{c.id} / {c.init_density.id}', { wc_lang.Compartment: {c.id: c}, wc_lang.Parameter: {c.init_density.id: c.init_density}, }) assert error is None, str(error) species_types = {} species = {} for i in range(1,7): Id = 's' + str(i) species_types[Id] = wc_lang.SpeciesType(id=Id) model_species = wc_lang.Species(species_type=species_types[Id], compartment=c) model_species.id = model_species.gen_id() species[Id + '_c'] = model_species wc_lang.DistributionInitConcentration(species=species[Id + '_c'], mean=0.5) participant1 = wc_lang.SpeciesCoefficient(species=species['s1_c'], coefficient=-1) participant2 = wc_lang.SpeciesCoefficient(species=species['s2_c'], coefficient=-1) participant3 = wc_lang.SpeciesCoefficient(species=species['s3_c'], coefficient=-1) participant4 = wc_lang.SpeciesCoefficient(species=species['s4_c'], coefficient=-1) participant5 = wc_lang.SpeciesCoefficient(species=species['s5_c'], coefficient=1) participant6 = wc_lang.SpeciesCoefficient(species=species['s6_c'], coefficient=1) reaction = wc_lang.Reaction(id='r1', participants=[participant1, participant2, participant3, participant4, participant5, participant6]) with self.assertRaises(ValueError): rate_law1, parameters = utils.gen_michaelis_menten_like_propensity_function( model, reaction) rate_law2, parameters = utils.gen_michaelis_menten_like_propensity_function( model, reaction, substrates_as_modifiers=[species['s3_c']]) self.assertEqual(rate_law2.expression, 'k_cat_r1 * s3[c] * ' '(s1[c] / (s1[c] + K_m_r1_s1 * Avogadro * volume_c)) * ' '(s2[c] / (s2[c] + K_m_r1_s2 * Avogadro * volume_c)) * ' '(s4[c] / (s4[c] + K_m_r1_s4 * Avogadro * volume_c))') self.assertEqual(set([i.gen_id() for i in rate_law2.species]), set(['s1[c]', 's2[c]', 's3[c]', 's4[c]'])) self.assertEqual(set(rate_law2.parameters), set(parameters)) self.assertEqual(rate_law2.parameters.get_one(id='k_cat_r1').type, wc_ontology['WC:k_cat']) self.assertEqual(rate_law2.parameters.get_one(id='k_cat_r1').units, unit_registry.parse_units('s^-1 molecule^-1')) self.assertEqual(rate_law2.parameters.get_one(id='K_m_r1_s2').type, wc_ontology['WC:K_m']) self.assertEqual(rate_law2.parameters.get_one(id='K_m_r1_s2').units, unit_registry.parse_units('M')) rate_law3, parameters = utils.gen_michaelis_menten_like_propensity_function( model, reaction, substrates_as_modifiers=[species['s3_c']], exclude_substrates=[species['s1_c']]) self.assertEqual(rate_law3.expression, 'k_cat_r1 * s3[c] * ' '(s2[c] / (s2[c] + K_m_r1_s2 * Avogadro * volume_c)) * ' '(s4[c] / (s4[c] + K_m_r1_s4 * Avogadro * volume_c))') self.assertEqual(set([i.gen_id() for i in rate_law3.species]), set(['s2[c]', 's3[c]', 's4[c]'])) self.assertEqual(set(rate_law3.parameters), set(parameters)) def test_gen_response_functions(self): model = wc_lang.Model() beta = 2 init_volume = wc_lang.core.InitVolume(distribution=wc_ontology['WC:normal_distribution'], mean=0.5, std=0) c = wc_lang.Compartment(id='c', name='cytosol', init_volume=init_volume) c.init_density = wc_lang.Parameter(id='density_' + c.id, value=1.) volume = wc_lang.Function(id='volume_' + c.id) volume.expression, error = wc_lang.FunctionExpression.deserialize(f'{c.id} / {c.init_density.id}', { wc_lang.Compartment: {c.id: c}, wc_lang.Parameter: {c.init_density.id: c.init_density}, }) assert error is None, str(error) reaction = wc_lang.Reaction() species_types = {} species = {} for i in range(1,5): Id = 's' + str(i) species_types[Id] = wc_lang.SpeciesType(model=model, id=Id, name='species_type_{}'.format(i)) model_species = wc_lang.Species(model=model, species_type=species_types[Id], compartment=c) model_species.id = model_species.gen_id() species[Id + '_c'] = model_species wc_lang.DistributionInitConcentration(species=species[Id + '_c'], mean=0.5) factors = [['s1', 'species_type_2'], ['s3'], ['species_type_4']] factor_exp, all_species, all_parameters, all_volumes, all_observables = utils.gen_response_functions( model, beta, 'reaction_id', 'reaction_class', c, factors) self.assertEqual(factor_exp, [ '(reaction_class_factors_c_1 / (reaction_class_factors_c_1 + K_m_reaction_class_reaction_class_factors_c_1 * Avogadro * volume_c))', '(s3[c] / (s3[c] + K_m_reaction_id_s3 * Avogadro * volume_c))', '(s4[c] / (s4[c] + K_m_reaction_id_s4 * Avogadro * volume_c))']) self.assertEqual(all_species, {'s1[c]': species['s1_c'], 's2[c]': species['s2_c'], 's3[c]': species['s3_c'], 's4[c]': species['s4_c']}) self.assertEqual(len(all_parameters), 4) self.assertEqual(all_parameters['Avogadro'].value, scipy.constants.Avogadro) self.assertEqual(all_parameters['Avogadro'].units, unit_registry.parse_units('molecule mol^-1')) self.assertEqual(all_parameters['K_m_reaction_class_reaction_class_factors_c_1'].value, beta * 1. / scipy.constants.Avogadro / c.init_volume.mean) self.assertEqual(all_parameters['K_m_reaction_class_reaction_class_factors_c_1'].comments, 'The value was assumed to be 2 times the value of reaction_class_factors_c_1') self.assertEqual(all_parameters['K_m_reaction_id_s3'].value, beta * 0.5 / scipy.constants.Avogadro / c.init_volume.mean) self.assertEqual(all_parameters['K_m_reaction_id_s4'].type, wc_ontology['WC:K_m']) self.assertEqual(all_parameters['K_m_reaction_id_s4'].units, unit_registry.parse_units('M')) self.assertEqual(all_parameters['K_m_reaction_id_s4'].comments, 'The value was assumed to be 2 times the concentration of s4 in cytosol') self.assertEqual(all_volumes, {'volume_c': volume}) self.assertEqual(len(all_observables), 1) self.assertEqual(len(model.observables), 1) self.assertEqual(all_observables['reaction_class_factors_c_1'].name, 'factor for reaction_class in cytosol') self.assertEqual(all_observables['reaction_class_factors_c_1'].units, unit_registry.parse_units('molecule')) self.assertEqual(all_observables['reaction_class_factors_c_1'].expression.expression, 's1[c] + s2[c]') for i in range(5,9): Id = 's' + str(i) species_types[Id] = wc_lang.SpeciesType(model=model, id=Id, name='species_type_{}'.format(i)) model_species = wc_lang.Species(model=model, species_type=species_types[Id], compartment=c) model_species.id = model_species.gen_id() species[Id + '_c'] = model_species wc_lang.DistributionInitConcentration(species=species[Id + '_c'], mean=0.) factors = [['s5', 'species_type_6'], ['s7'], ['species_type_8']] factor_exp, all_species, all_parameters, all_volumes, all_observables = utils.gen_response_functions( model, beta, 'reaction_id', 'reaction_class', c, factors) self.assertEqual(len(model.observables), 2) self.assertEqual(all_parameters['K_m_reaction_class_reaction_class_factors_c_2'].value, 1e-05) self.assertEqual(all_parameters['K_m_reaction_class_reaction_class_factors_c_2'].comments, 'The value was assigned to 1e-05 because the value of reaction_class_factors_c_2 was zero') self.assertEqual(all_parameters['K_m_reaction_id_s7'].value, 1e-05) self.assertEqual(all_parameters['K_m_reaction_id_s8'].comments, 'The value was assigned to 1e-05 because the concentration of s8 in cytosol was zero') factors = [['s5', 'species_type_6']] factor_exp, all_species, all_parameters, all_volumes, all_observables = utils.gen_response_functions( model, beta, 'reaction_id2', 'reaction_class2', c, factors) self.assertEqual(len(model.observables), 2) self.assertEqual(all_parameters['K_m_reaction_class2_reaction_class_factors_c_2'].value, 1e-05) def test_gen_mass_action_rate_law(self): model = wc_lang.Model() c = wc_lang.Compartment(id='c', init_volume=wc_lang.InitVolume(mean=0.5)) c.init_density = wc_lang.Parameter(id='density_' + c.id, value=1.) kinetic_parameter = wc_lang.Parameter(id='this_parameter', value=1.) volume = wc_lang.Function(id='volume_' + c.id) volume.expression, error = wc_lang.FunctionExpression.deserialize(f'{c.id} / {c.init_density.id}', { wc_lang.Compartment: {c.id: c}, wc_lang.Parameter: {c.init_density.id: c.init_density}, }) assert error is None, str(error) species_types = {} species = {} for i in range(1,7): Id = 's' + str(i) species_types[Id] = wc_lang.SpeciesType(id=Id) species[Id + '_c'] = wc_lang.Species(species_type=species_types[Id], compartment=c) wc_lang.DistributionInitConcentration(species=species[Id + '_c'], mean=0.5) Id = 'e' species_types[Id] = wc_lang.SpeciesType(id=Id) species[Id + '_c'] = wc_lang.Species(species_type=species_types[Id], compartment=c) wc_lang.DistributionInitConcentration(species=species[Id + '_c'], mean=0.5) # ob_exp1, error = wc_lang.ObservableExpression.deserialize('s4[c] + s5[c]', { # wc_lang.Species:{species['s4_c'].gen_id(): species['s4_c'], # species['s5_c'].gen_id(): species['s5_c']}}) # assert error is None, str(error) # modifier1 = wc_lang.Observable(id='e1', expression=ob_exp1) # ob_exp2, error = wc_lang.ObservableExpression.deserialize('2 * s6[c]', { # wc_lang.Species:{species['s6_c'].gen_id(): species['s6_c']}}) # assert error is None, str(error) # modifier2 = wc_lang.Observable(id='e2', expression=ob_exp2) participant1 = wc_lang.SpeciesCoefficient(species=species['s1_c'], coefficient=-1) participant2 = wc_lang.SpeciesCoefficient(species=species['s2_c'], coefficient=-1) participant3 = wc_lang.SpeciesCoefficient(species=species['s3_c'], coefficient=1) participant4 = wc_lang.SpeciesCoefficient(species=species['s4_c'], coefficient=1) enzyme_lhs = wc_lang.SpeciesCoefficient(species=species['e_c'], coefficient=-1) enzyme_rhs = wc_lang.SpeciesCoefficient(species=species['e_c'], coefficient=1) reaction = wc_lang.Reaction(id='Assosication', participants=[participant1, participant2, participant3]) rate_law, parameters = utils.gen_mass_action_rate_law(model, reaction, kinetic_parameter) self.assertTrue(rate_law.expression == 'this_parameter * s1[c] * s2[c]' or rate_law.expression == 'this_parameter * s2[c] * s1[c]') self.assertEqual(set([i.gen_id() for i in rate_law.species]), set(['s1[c]', 's2[c]'])) # self.assertEqual(set(rate_law.observables), set([modifier1, modifier2])) self.assertEqual(set(rate_law.parameters), set(parameters)) # self.assertEqual(rate_law.parameters.get_one(id='k_r1').type, wc_ontology['WC:k_cat']) self.assertEqual(rate_law.parameters.get_one(id='this_parameter').units, unit_registry.parse_units('s^-1 * molecule^-1')) # self.assertEqual(rate_law.parameters.get_one(id='K_m_r1_s2').type, wc_ontology['WC:K_m']) # self.assertEqual(rate_law.parameters.get_one(id='K_m_r1_s2').units, unit_registry.parse_units('M')) reaction = wc_lang.Reaction(id='Dissociation', participants=[participant1, participant3, participant4]) rate_law, parameters = utils.gen_mass_action_rate_law(model, reaction, kinetic_parameter) self.assertEqual(rate_law.expression, 'this_parameter * s1[c]') self.assertEqual(set([i.gen_id() for i in rate_law.species]), set(['s1[c]'])) self.assertEqual(rate_law.parameters.get_one(id='this_parameter').units, unit_registry.parse_units('s^-1')) reaction = wc_lang.Reaction(id='Degradation1', participants=[participant1]) rate_law, parameters = utils.gen_mass_action_rate_law(model, reaction, kinetic_parameter) self.assertEqual(rate_law.expression, 'this_parameter * s1[c]') self.assertEqual(set([i.gen_id() for i in rate_law.species]), set(['s1[c]'])) self.assertEqual(rate_law.parameters.get_one(id='this_parameter').units, unit_registry.parse_units('s^-1')) reaction = wc_lang.Reaction(id='Degradation2', participants=[participant1, enzyme_lhs, enzyme_rhs]) rate_law, parameters = utils.gen_mass_action_rate_law(model, reaction, kinetic_parameter) self.assertTrue(rate_law.expression, 'this_parameter * s1[c] * e[c]' or 'this_parameter * e[c] * s1[c]') self.assertEqual(set([i.gen_id() for i in rate_law.species]), set(['s1[c]', 'e[c]'])) self.assertEqual(rate_law.parameters.get_one(id='this_parameter').units, unit_registry.parse_units('s^-1 * molecule^-1')) reaction = wc_lang.Reaction(id='Synthesis1', participants=[participant3]) rate_law, parameters = utils.gen_mass_action_rate_law(model, reaction, kinetic_parameter) self.assertEqual(rate_law.expression, 'this_parameter') self.assertEqual(set([i.gen_id() for i in rate_law.species]), set([])) self.assertEqual(rate_law.parameters.get_one(id='this_parameter').units, unit_registry.parse_units('s^-1 * molecule')) reaction = wc_lang.Reaction(id='Synthesis2', participants=[enzyme_lhs, enzyme_rhs, participant3]) rate_law, parameters = utils.gen_mass_action_rate_law(model, reaction, kinetic_parameter) self.assertEqual(rate_law.expression, 'this_parameter * e[c]') self.assertEqual(set([i.gen_id() for i in rate_law.species]), set(['e[c]'])) self.assertEqual(rate_law.parameters.get_one(id='this_parameter').units, unit_registry.parse_units('s^-1')) reaction = wc_lang.Reaction(id='Conversion', participants=[participant1, enzyme_lhs, enzyme_rhs, participant3]) # Ask Yin Hoon why I can add as many copies of participant2 as I want. rate_law, parameters = utils.gen_mass_action_rate_law(model, reaction, kinetic_parameter) self.assertTrue(rate_law.expression == 'this_parameter * s1[c] * e[c]' or rate_law.expression == 'this_parameter * e[c] * s1[c]') self.assertEqual(set([i.gen_id() for i in rate_law.species]), set(['s1[c]', 'e[c]'])) self.assertEqual(rate_law.parameters.get_one(id='this_parameter').units, unit_registry.parse_units('s^-1 * molecule^-1'))

59.736842

190

0.67428

3,852

29,510

4.90135

0.068017

0.039725

0.026112

0.026801

0.865413

0.837341

0.80572

0.784057

0.754661

0.719756

0

0.023302

0.188546

29,510

494

191

59.736842

0.765138

0.036733

0

0.484293

0

0.151658

0.031965

0

0.282723

1

0.02356

false

0

0.018325

0

0.044503

0

null

0

1

0

null

0

6

0e6a69372d51e5edb892ba9530a96041d9ac1696

82

py

Python

regression/__init__.py

george-j-zhu/timeseriesprocessing

5d774a438c5e835a8d5c802009f4d5303388b69d

[ "CC-BY-4.0" ]

1

2018-06-26T05:27:55.000Z

regression/__init__.py

george-j-zhu/timeseriesprocessing

5d774a438c5e835a8d5c802009f4d5303388b69d

[ "CC-BY-4.0" ]

null

regression/__init__.py

george-j-zhu/timeseriesprocessing

5d774a438c5e835a8d5c802009f4d5303388b69d

[ "CC-BY-4.0" ]

null

from . import timeSeriesDataFrame from . import constants from . import utilities

20.5

33

0.817073

9

82

7.444444

0.555556

0.447761

0

0.146341

82

3

34

27.333333

0.957143

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

0e755c3e59cfea99b2c1bb3b5c46310bfdfa3f04

163

py

Python

ABC/180/A.py

yu9824/AtCoder

50a209059c005efadc1c912e443ec41365381c16

[ "MIT" ]

null

ABC/180/A.py

yu9824/AtCoder

50a209059c005efadc1c912e443ec41365381c16

[ "MIT" ]

null

ABC/180/A.py

yu9824/AtCoder

50a209059c005efadc1c912e443ec41365381c16

[ "MIT" ]

null

# list(map(int, input().split())) # int(input()) def main(): N, A, B = list(map(int, input().split())) print(N-A+B) if __name__ == '__main__': main()

18.111111

45

0.539877

25

163

3.2

0.52

0.3

0.25

0.375

0.5

0

0.196319

163

9

46

18.111111

0.610687

0.269939

0

0.068376

0

1

0.2

true

0

0.2

1

0

null

1

0

1

0

null

0

1

0

6

7d05b1e5b65fc151ebf19a8dc46105621faeb9d1

76

py

Python

torchlib/datasets/__init__.py

CarlosPena00/kaggle-datasciencebowl-2018

c234f03483142f618825812d5fa310375a7eb6fa

[ "MIT" ]

null

torchlib/datasets/__init__.py

CarlosPena00/kaggle-datasciencebowl-2018

c234f03483142f618825812d5fa310375a7eb6fa

[ "MIT" ]

null

torchlib/datasets/__init__.py

CarlosPena00/kaggle-datasciencebowl-2018

c234f03483142f618825812d5fa310375a7eb6fa

[ "MIT" ]

2

2018-12-16T00:17:40.000Z

2019-11-18T09:47:23.000Z

from .dsxbdata import * from .ctechdata import * from .imageutl import *

10.857143

24

0.723684

9

76

6.111111

0.555556

0.363636

0

0.197368

76

6

25

12.666667

0.901639

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

7d405b30bc0bef9d0fe269e4ce56869f39aa23f3

119

py

Python

short_lived_tokens/endec/__init__.py

FriedBotStudio/short_lived_tokens

dd823cfd81ae6e211f9281826bb367a8fcb6fd5a

[ "MIT" ]

null

short_lived_tokens/endec/__init__.py

FriedBotStudio/short_lived_tokens

dd823cfd81ae6e211f9281826bb367a8fcb6fd5a

[ "MIT" ]

null

short_lived_tokens/endec/__init__.py

FriedBotStudio/short_lived_tokens

dd823cfd81ae6e211f9281826bb367a8fcb6fd5a

[ "MIT" ]

null

from short_lived_tokens.endec.engine import EndecEngine from short_lived_tokens.endec.rsa_engine import RSAEndecEngine

39.666667

62

0.89916

17

119

6

0.588235

0.176471

0.27451

0.392157

0.490196

0

0.067227

119

2

63

59.5

0.918919

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

addb111c0815305675f44504a409da95bcbd1d1c

28

py

Python

xrsigproc/__init__.py

kaipak/xrsigproc

109cab137ea7e9e61a3cdffe6b8cbac8bd52fc3f

[ "MIT" ]

null

xrsigproc/__init__.py

kaipak/xrsigproc

109cab137ea7e9e61a3cdffe6b8cbac8bd52fc3f

[ "MIT" ]

null

xrsigproc/__init__.py

kaipak/xrsigproc

109cab137ea7e9e61a3cdffe6b8cbac8bd52fc3f

[ "MIT" ]

null

from .xrsigproc import *

14

27

0.678571

3

28

6.333333

1

0

0.25

28

1

28

0.904762

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

adeeb83881cf0b29cee8848279015e85dd9622e2

71

py

Python

gpgLabs/EM/__init__.py

victortocantins/gpgLabs

310b69c681dd1ebf91ba8be2b5ac27adf5fc0f12

[ "MIT" ]

null

gpgLabs/EM/__init__.py

victortocantins/gpgLabs

310b69c681dd1ebf91ba8be2b5ac27adf5fc0f12

[ "MIT" ]

null

gpgLabs/EM/__init__.py

victortocantins/gpgLabs

310b69c681dd1ebf91ba8be2b5ac27adf5fc0f12

[ "MIT" ]

null

from . import FEM3loop from . import FEMpipe from . import ResponseFct

17.75

25

0.788732

9

71

6.222222

0.555556

0.535714

0

0.016949

0.169014

71

3

26

23.666667

0.932203

0

1

0

true

0

1

0

1

0

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

bc1b34a776cfb4ef22883529b144cf5750666882

96

py

Python

venv/lib/python3.8/site-packages/setuptools/__init__.py

GiulianaPola/select_repeats

17a0d053d4f874e42cf654dd142168c2ec8fbd11

[ "MIT" ]

2

2022-03-13T01:58:52.000Z

2022-03-31T06:07:54.000Z

venv/lib/python3.8/site-packages/setuptools/__init__.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

19

2021-11-20T04:09:18.000Z

2022-03-23T15:05:55.000Z

venv/lib/python3.8/site-packages/setuptools/__init__.py

DesmoSearch/Desmobot

b70b45df3485351f471080deb5c785c4bc5c4beb

[ "MIT" ]

null

/home/runner/.cache/pip/pool/21/e4/69/64abd25b8c299895c989a21f571e044f02e365df9ae7b460d42f2e3b1b

96

0.895833

9

96

9.555556

1

0

0.4375

0

96

1

96

0.458333

0

1

0

null

0

null

0

1

0

null

0

1

0

1

0

1

0

null

1

0

1

0

6

bc213c8197e7bdffa409e407d93312481e196b5f

77

py

Python

snippets/super_simple/sys.py

fpiantini/python_snippets

3d7ad42c2e3a77f46c8e373bb51ea3227801a239

[ "MIT" ]

null

snippets/super_simple/sys.py

fpiantini/python_snippets

3d7ad42c2e3a77f46c8e373bb51ea3227801a239

[ "MIT" ]

null

snippets/super_simple/sys.py

fpiantini/python_snippets

3d7ad42c2e3a77f46c8e373bb51ea3227801a239

[ "MIT" ]

null

#!/usr/bin/env python # import sys print(sys.version) print(sys.float_info)

11

21

0.74026

13

77

4.307692

0.769231

0.285714

0

0.103896

77

6

22

12.833333

0.811594

0.25974

0

1

0

true

0

0.333333

0

0.333333

0.666667

1

0

null

1

0

1

0

null

0

1

0

1

0

1

0

6

bc28113660044d65cef0416f1e88f3f44fcb6fdc

29

py

Python

scuttle/__init__.py

scuttle/python-scuttle

273e793b15b4f4390b3991ba66192d27b392ed3a

[ "MIT" ]

1

2021-03-30T05:31:19.000Z

scuttle/__init__.py

scuttle/python-scuttle

273e793b15b4f4390b3991ba66192d27b392ed3a

[ "MIT" ]

9

2020-05-23T07:33:00.000Z

2020-09-27T03:32:51.000Z

scuttle/__init__.py

scuttle/python-scuttle

273e793b15b4f4390b3991ba66192d27b392ed3a

[ "MIT" ]

null

from .wrapper import scuttle

14.5

28

0.827586

4

29

6

1

0

0.137931

29

1

29

0.96

0

1

0

true

0

1

0

1

0

1

0

null

0

1

0

null

0

1

0

1

0

1

0

6

bc2f2035ec6f7f2e3ab05b873bace145372a009a

1,759

py

Python

retired/old_version/original/tests/test_echo_topic.py

gecko-robotics/pygecko

a809593a894d8e591e992455a01aa73d8f7b7981

[ "MIT" ]

3

2019-06-13T07:52:12.000Z

2020-07-05T13:28:43.000Z

retired/old_version/original/tests/test_echo_topic.py

walchko/pygecko

a809593a894d8e591e992455a01aa73d8f7b7981

[ "MIT" ]

23

2017-07-07T01:29:33.000Z

2018-11-23T18:41:08.000Z

retired/old_version/original/tests/test_echo_topic.py

MomsFriendlyRobotCompany/pygecko

a809593a894d8e591e992455a01aa73d8f7b7981

[ "MIT" ]

null

from __future__ import print_function # import sys import threading import time from pygecko import ZmqClass as Zmq from pygecko import Messages as Msg # from pygecko.Topic import TopicPub # import random # import zmq def subscriber(msg): # Subscribe to everything sub = Zmq.Sub('test', connect_to=('localhost', 9000)) # Get and process messages for i in range(7): tp, ret = sub.recv() if ret: assert ret == msg print('found:', ret == msg) return time.sleep(0.1) def publisher(msg): # Prepare publisher pub = Zmq.Pub(bind_to=('localhost', 9000)) for i in range(7): pub.pub('test', msg) time.sleep(0.1) def test(): msg = Msg.Vector() msg.set(1, 2, 3) pub_thread = threading.Thread(target=publisher, args=(msg,)) pub_thread.daemon = True pub_thread.start() sub_thread = threading.Thread(target=subscriber, args=(msg,)) sub_thread.daemon = True sub_thread.start() pub_thread.join() sub_thread.join() time.sleep(0.1) # def topic(msg): # # Subscribe to everything # sub = Zmq.Sub('test', connect_to=('localhost', 9000)) # # # Get and process messages # for i in range(7): # tp, ret = sub.recv() # if ret: # assert ret == msg # print 'found:', ret == msg # return # time.sleep(0.1) # # # def echo(msg): # # Prepare publisher # pub = Zmq.Pub(bind_to=('localhost', 9000)) # # for i in range(7): # pub.pub('test', msg) # time.sleep(0.1) # # # def test(): # msg = Msg.Vector() # msg.set(1, 2, 3) # # pub_thread = threading.Thread(target=publisher, args=(msg,)) # pub_thread.daemon = True # pub_thread.start() # sub_thread = threading.Thread(target=subscriber, args=(msg,)) # sub_thread.daemon = True # sub_thread.start() # # pub_thread.join() # sub_thread.join() # time.sleep(0.1)

19.544444

64

0.660034

264

1,759

4.30303

0.231061

0.06338

0.052817

0.058099

0.819542

0.816901

0

0.026371

0.180785

1,759

89

65

19.764045

0.761971

0.486072

0

0.16129

0

0.037471

0

0.032258

1

0.096774

false

0

0.16129

0

0.290323

0.064516

0

null

0

1

0

null

0

6

bc3010ebef6a73224f62b6c4ac588d33a3fe7866

34

py

Python

script1-master.py

julie-rudolph/ktbyers-pynet

f8f9f126e44a10bcd5f4886b6923693a70e69534

[ "Apache-2.0" ]

null

script1-master.py

julie-rudolph/ktbyers-pynet

f8f9f126e44a10bcd5f4886b6923693a70e69534

[ "Apache-2.0" ]

null

script1-master.py

julie-rudolph/ktbyers-pynet

f8f9f126e44a10bcd5f4886b6923693a70e69534

[ "Apache-2.0" ]

null

print "script1 in master branch."

17

33

0.764706

5

34

5.2

1

0

0.034483

0.147059

34

1

34

0.862069

0

0.735294

0

null

0

null

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6

70ee17fb156c0e558ae9a41381bbf54da6eea839

233

py

Python

python_modules/libraries/dagster-papertrail/dagster_papertrail/__init__.py

dbatten5/dagster

d76e50295054ffe5a72f9b292ef57febae499528

[ "Apache-2.0" ]

4,606

2018-06-21T17:45:20.000Z

2022-03-31T23:39:42.000Z

python_modules/libraries/dagster-papertrail/dagster_papertrail/__init__.py

dbatten5/dagster

d76e50295054ffe5a72f9b292ef57febae499528

[ "Apache-2.0" ]

6,221

2018-06-12T04:36:01.000Z

2022-03-31T21:43:05.000Z

python_modules/libraries/dagster-papertrail/dagster_papertrail/__init__.py

dbatten5/dagster

d76e50295054ffe5a72f9b292ef57febae499528

[ "Apache-2.0" ]

619

2018-08-22T22:43:09.000Z

2022-03-31T22:48:06.000Z

from dagster.core.utils import check_dagster_package_version from .loggers import papertrail_logger from .version import __version__ check_dagster_package_version("dagster-papertrail", __version__) __all__ = ["papertrail_logger"]

25.888889

64

0.849785

28

233

6.357143

0.428571

0.134831

0.213483

0.292135

0

0.085837

233

8

65

29.125

0.835681

0

0.150215

0

1

0

false

0

0.6

0

0.6

0

1

0

null

0

1

0

1

0

null

0

1

0

1

0

6